From e1f666abbc7ed8841ef153efb9f415f1c49fd9e9 Mon Sep 17 00:00:00 2001
From: Christiane Reinert <christiane.reinert@ltt.rwth-aachen.de>
Date: Mon, 9 May 2022 10:57:00 +0200
Subject: [PATCH] Initial commit of public branch of the SecMOD framework
---
.gitignore | 8 +
.gitlab-ci.yml | 31 +
.gitmodules | 1 +
CONTRIBUTING.md | 26 +
LICENSE.txt | 12 +
MANIFEST.in | 8 +
README.md | 53 +
SecMOD_Documentation_PDFversion.pdf | Bin 0 -> 643988 bytes
docs/conf.py | 192 +
docs/img/.gitkeep | 0
docs/img/GUI_SecMOD.png | Bin 0 -> 359226 bytes
docs/img/secmod_scheme.svg | 2842 ++++++
docs/index.rst | 27 +
docs/usage/data.rst | 30 +
docs/usage/developer_reference.rst | 69 +
docs/usage/installation.rst | 60 +
docs/usage/packages.rst | 22 +
docs/usage/quickstart.rst | 37 +
docs/usage/troubleshooting.rst | 7 +
sampledata/00-EXTERNAL/README.md | 10 +
.../external_data_packages/README.md | 21 +
.../external_data_packages/datapackage.json | 30 +
.../external_data_packages.csv | 3 +
.../scripts/conventional_power_plants.py | 147 +
.../scripts/renewable_power_plants.py | 125 +
sampledata/00-UNITS-DEFINITIONS.txt | 25 +
sampledata/01-GRID/DE/README.md | 6 +
sampledata/01-GRID/DE/connections.csv | 130 +
sampledata/01-GRID/DE/datapackage.json | 85 +
sampledata/01-GRID/DE/nodes.csv | 19 +
sampledata/01-GRID/README.md | 9 +
sampledata/01-UNITS-TARGET-OPTIMIZATION.json | 15 +
.../01-ECOINVENT-BASED/README.md | 4 +
.../natural_gas_combined_cycle/DE/README.md | 4 +
.../DE/availability_timeseries.csv | 8761 +++++++++++++++++
.../DE/existing_capacity.csv | 19 +
.../DE/maximum_production_share.csv | 19 +
.../DE/potential_capacity.csv | 19 +
.../natural_gas_combined_cycle/README.md | 6 +
.../natural_gas_combined_cycle/costs.csv | 5 +
.../ecoinvent_invest.csv | 2 +
.../ecoinvent_operation.csv | 3 +
.../lifetime_duration.csv | 2 +
.../natural_gas_combined_cycle/properties.csv | 2 +
.../secured_capacity_factor.csv | 2 +
.../technologymatrix.csv | 3 +
.../technologymatrix_timeseries.csv | 8761 +++++++++++++++++
.../wind_offshore/DE/README.md | 4 +
.../DE/availability_timeseries.csv | 8761 +++++++++++++++++
.../wind_offshore/DE/existing_capacity.csv | 19 +
.../DE/maximum_production_share.csv | 19 +
.../wind_offshore/DE/potential_capacity.csv | 19 +
.../wind_offshore/README.md | 6 +
.../wind_offshore/costs.csv | 5 +
.../wind_offshore/ecoinvent_invest.csv | 3 +
.../wind_offshore/ecoinvent_operation.csv | 4 +
.../wind_offshore/lifetime_duration.csv | 2 +
.../wind_offshore/properties.csv | 2 +
.../wind_offshore/secured_capacity_factor.csv | 2 +
.../wind_offshore/technologymatrix.csv | 2 +
.../technologymatrix_timeseries.csv | 8761 +++++++++++++++++
.../wind_onshore/DE/README.md | 4 +
.../DE/availability_timeseries.csv | 8761 +++++++++++++++++
.../wind_onshore/DE/existing_capacity.csv | 19 +
.../DE/maximum_production_share.csv | 19 +
.../wind_onshore/DE/potential_capacity.csv | 19 +
.../01-ECOINVENT-BASED/wind_onshore/README.md | 6 +
.../01-ECOINVENT-BASED/wind_onshore/costs.csv | 5 +
.../wind_onshore/ecoinvent_invest.csv | 4 +
.../wind_onshore/ecoinvent_operation.csv | 3 +
.../wind_onshore/lifetime_duration.csv | 2 +
.../wind_onshore/properties.csv | 2 +
.../wind_onshore/secured_capacity_factor.csv | 2 +
.../wind_onshore/technologymatrix.csv | 2 +
.../technologymatrix_timeseries.csv | 8761 +++++++++++++++++
.../02-PROCESSES/01-PRODUCTION/README.md | 4 +
.../02-STORAGE/01-ECOINVENT-BASED/README.md | 4 +
.../pumped_storage_hydro/DE/README.md | 4 +
.../DE/existing_capacity.csv | 19 +
.../DE/maximum_production_share.csv | 19 +
.../DE/potential_capacity.csv | 19 +
.../pumped_storage_hydro/README.md | 6 +
.../pumped_storage_hydro/costs.csv | 5 +
.../pumped_storage_hydro/ecoinvent_invest.csv | 2 +
.../ecoinvent_operation.csv | 3 +
.../flow_to_storage_capacity_factor.csv | 2 +
.../lifetime_duration.csv | 2 +
.../pumped_storage_hydro/properties.csv | 2 +
.../secured_capacity_factor.csv | 2 +
.../pumped_storage_hydro/technologymatrix.csv | 3 +
sampledata/02-PROCESSES/02-STORAGE/README.md | 5 +
.../01-ECOINVENT-BASED/README.md | 4 +
.../diesel_transshipment/DE/README.md | 4 +
.../DE/existing_capacity.csv | 130 +
.../DE/potential_capacity.csv | 130 +
.../diesel_transshipment/README.md | 4 +
.../diesel_transshipment/costs.csv | 5 +
.../diesel_transshipment/ecoinvent_invest.csv | 1 +
.../ecoinvent_operation.csv | 1 +
.../lifetime_duration.csv | 2 +
.../diesel_transshipment/properties.csv | 4 +
.../02-PROCESSES/03-TRANSSHIPMENT/README.md | 4 +
.../01-ECOINVENT-BASED/README.md | 4 +
.../power_line_220_kV/DE/README.md | 4 +
.../DE/existing_capacity.csv | 130 +
.../DE/potential_capacity.csv | 130 +
.../power_line_220_kV/README.md | 6 +
.../power_line_220_kV/costs.csv | 5 +
.../power_line_220_kV/ecoinvent_invest.csv | 15 +
.../power_line_220_kV/ecoinvent_operation.csv | 1 +
.../power_line_220_kV/lifetime_duration.csv | 2 +
.../power_line_220_kV/properties.csv | 8 +
.../power_line_380_kV/DE/README.md | 4 +
.../DE/existing_capacity.csv | 130 +
.../DE/potential_capacity.csv | 130 +
.../power_line_380_kV/README.md | 6 +
.../power_line_380_kV/costs.csv | 5 +
.../power_line_380_kV/ecoinvent_invest.csv | 15 +
.../power_line_380_kV/ecoinvent_operation.csv | 1 +
.../power_line_380_kV/lifetime_duration.csv | 2 +
.../power_line_380_kV/properties.csv | 8 +
.../DE/README.md | 4 +
.../DE/existing_capacity.csv | 130 +
.../DE/potential_capacity.csv | 130 +
.../switching_from_220_kV_to_380_kV/README.md | 6 +
.../switching_from_220_kV_to_380_kV/costs.csv | 5 +
.../ecoinvent_invest.csv | 2 +
.../ecoinvent_operation.csv | 1 +
.../lifetime_duration.csv | 2 +
.../properties.csv | 6 +
.../02-PROCESSES/04-TRANSMISSION/README.md | 4 +
sampledata/02-PROCESSES/README.md | 4 +
sampledata/03-PRODUCTS/README.md | 4 +
.../03-PRODUCTS/electricity/DE/README.md | 4 +
.../electricity/DE/demand_nodal_average.csv | 2 +
.../DE/demand_nodal_timeseries.csv | 8761 +++++++++++++++++
...equired_average_nodal_secured_capacity.csv | 2 +
...quired_relative_nodal_secured_capacity.csv | 19 +
.../DE/required_total_secured_capacity.csv | 2 +
sampledata/03-PRODUCTS/electricity/README.md | 4 +
.../electricity/costs_yearly_average.csv | 2 +
.../electricity/costs_yearly_timeseries.csv | 8761 +++++++++++++++++
.../electricity/ecoinvent_operation.csv | 2 +
.../03-PRODUCTS/natural_gas/DE/README.md | 4 +
.../natural_gas/DE/demand_nodal_average.csv | 2 +
.../DE/demand_nodal_timeseries.csv | 8761 +++++++++++++++++
...equired_average_nodal_secured_capacity.csv | 2 +
...quired_relative_nodal_secured_capacity.csv | 19 +
.../DE/required_total_secured_capacity.csv | 2 +
sampledata/03-PRODUCTS/natural_gas/README.md | 4 +
.../natural_gas/costs_yearly_average.csv | 2 +
.../natural_gas/costs_yearly_timeseries.csv | 8761 +++++++++++++++++
.../natural_gas/ecoinvent_operation.csv | 2 +
.../00-ECOINVENT-SUBASSEMBLIES/README.md | 8 +
.../insulators_150kV.csv | 3 +
.../insulators_and_conductors_400kV.csv | 6 +
sampledata/04-ECOINVENT/README.md | 5 +
sampledata/04-ECOINVENT/ecoinvent.csv | 21 +
.../ecoinvent_units_to_change.json | 23 +
.../DE/README.md | 5 +
.../DE/invest_impact_limits.csv | 2 +
.../DE/invest_nodal_impact_limits.csv | 417 +
.../DE/operational_impact_limits.csv | 2 +
.../DE/operational_nodal_impact_limits.csv | 417 +
.../DE/total_impact_limits.csv | 2 +
.../DE/total_nodal_impact_limits.csv | 417 +
.../README.md | 5 +
.../objective_factor_impact.csv | 3 +
.../objective_factor_impact_overshot.csv | 2 +
.../properties.csv | 2 +
sampledata/05-IMPACT-CATEGORIES/README.md | 6 +
.../05-IMPACT-CATEGORIES/cost/DE/README.md | 4 +
.../cost/DE/invest_impact_limits.csv | 2 +
.../cost/DE/invest_nodal_impact_limits.csv | 19 +
.../cost/DE/operational_impact_limits.csv | 2 +
.../DE/operational_nodal_impact_limits.csv | 19 +
.../cost/DE/total_impact_limits.csv | 2 +
.../cost/DE/total_nodal_impact_limits.csv | 19 +
.../05-IMPACT-CATEGORIES/cost/README.md | 5 +
.../cost/objective_factor_impact.csv | 3 +
.../cost/objective_factor_impact_overshot.csv | 2 +
.../05-IMPACT-CATEGORIES/cost/properties.csv | 2 +
sampledata/README.md | 7 +
sampledata/config.py | 72 +
secmod/__main__.py | 107 +
secmod/classes.py | 3347 +++++++
secmod/data_preprocessing.py | 164 +
secmod/data_processing.py | 327 +
secmod/evaluation.py | 1082 ++
secmod/helpers.py | 126 +
secmod/optimization.py | 2706 +++++
secmod/result_processing.py | 929 ++
secmod/scaling.py | 35 +
secmod/setup.py | 103 +
secmod/time_series_aggregation.py | 228 +
setup.cfg | 13 +
setup.py | 193 +
197 files changed, 104156 insertions(+)
create mode 100644 .gitignore
create mode 100644 .gitlab-ci.yml
create mode 100644 .gitmodules
create mode 100644 CONTRIBUTING.md
create mode 100644 LICENSE.txt
create mode 100644 MANIFEST.in
create mode 100644 SecMOD_Documentation_PDFversion.pdf
create mode 100644 docs/conf.py
create mode 100644 docs/img/.gitkeep
create mode 100644 docs/img/GUI_SecMOD.png
create mode 100644 docs/img/secmod_scheme.svg
create mode 100644 docs/index.rst
create mode 100644 docs/usage/data.rst
create mode 100644 docs/usage/developer_reference.rst
create mode 100644 docs/usage/installation.rst
create mode 100644 docs/usage/packages.rst
create mode 100644 docs/usage/quickstart.rst
create mode 100644 docs/usage/troubleshooting.rst
create mode 100644 sampledata/00-EXTERNAL/README.md
create mode 100644 sampledata/00-EXTERNAL/external_data_packages/README.md
create mode 100644 sampledata/00-EXTERNAL/external_data_packages/datapackage.json
create mode 100644 sampledata/00-EXTERNAL/external_data_packages/external_data_packages.csv
create mode 100644 sampledata/00-EXTERNAL/external_data_packages/scripts/conventional_power_plants.py
create mode 100644 sampledata/00-EXTERNAL/external_data_packages/scripts/renewable_power_plants.py
create mode 100644 sampledata/00-UNITS-DEFINITIONS.txt
create mode 100644 sampledata/01-GRID/DE/README.md
create mode 100644 sampledata/01-GRID/DE/connections.csv
create mode 100644 sampledata/01-GRID/DE/datapackage.json
create mode 100644 sampledata/01-GRID/DE/nodes.csv
create mode 100644 sampledata/01-GRID/README.md
create mode 100644 sampledata/01-UNITS-TARGET-OPTIMIZATION.json
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/README.md
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/README.md
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/availability_timeseries.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/maximum_production_share.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/README.md
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/costs.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_invest.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_operation.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/lifetime_duration.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/properties.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/secured_capacity_factor.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix_timeseries.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/README.md
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/availability_timeseries.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/maximum_production_share.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/README.md
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/costs.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_invest.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_operation.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/lifetime_duration.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/properties.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/secured_capacity_factor.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix_timeseries.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/README.md
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/availability_timeseries.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/maximum_production_share.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/README.md
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/costs.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_invest.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_operation.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/lifetime_duration.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/properties.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/secured_capacity_factor.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix_timeseries.csv
create mode 100644 sampledata/02-PROCESSES/01-PRODUCTION/README.md
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/README.md
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/README.md
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/maximum_production_share.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/README.md
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/costs.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_invest.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_operation.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/flow_to_storage_capacity_factor.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/lifetime_duration.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/properties.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/secured_capacity_factor.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/technologymatrix.csv
create mode 100644 sampledata/02-PROCESSES/02-STORAGE/README.md
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/README.md
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/README.md
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/README.md
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/costs.csv
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_invest.csv
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_operation.csv
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/lifetime_duration.csv
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/properties.csv
create mode 100644 sampledata/02-PROCESSES/03-TRANSSHIPMENT/README.md
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/README.md
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/README.md
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/README.md
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/costs.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_invest.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_operation.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/lifetime_duration.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/properties.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/README.md
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/README.md
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/costs.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_invest.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_operation.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/lifetime_duration.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/properties.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/README.md
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/existing_capacity.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/potential_capacity.csv
create mode 100644 sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/README.md
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create mode 100644 sampledata/04-ECOINVENT/README.md
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create mode 100644 sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_impact_limits.csv
create mode 100644 sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_nodal_impact_limits.csv
create mode 100644 sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_impact_limits.csv
create mode 100644 sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_nodal_impact_limits.csv
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create mode 100644 sampledata/05-IMPACT-CATEGORIES/cost/README.md
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create mode 100644 sampledata/05-IMPACT-CATEGORIES/cost/properties.csv
create mode 100644 sampledata/README.md
create mode 100644 sampledata/config.py
create mode 100644 secmod/__main__.py
create mode 100644 secmod/classes.py
create mode 100644 secmod/data_preprocessing.py
create mode 100644 secmod/data_processing.py
create mode 100644 secmod/evaluation.py
create mode 100644 secmod/helpers.py
create mode 100644 secmod/optimization.py
create mode 100644 secmod/result_processing.py
create mode 100644 secmod/scaling.py
create mode 100644 secmod/setup.py
create mode 100644 secmod/time_series_aggregation.py
create mode 100644 setup.cfg
create mode 100644 setup.py
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..cc2bca2
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,8 @@
+.vscode
+.vscode/tags
+SecMOD.egg-info
+secmod/__pycache__
+doc/source/_build
+tests/downloads
+docs/_build/
+logs
\ No newline at end of file
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
new file mode 100644
index 0000000..a911bee
--- /dev/null
+++ b/.gitlab-ci.yml
@@ -0,0 +1,31 @@
+pages:
+ stage: deploy
+ tags:
+ - anaconda
+ - sphinx
+ - sphinx-rtd-theme
+ - windows
+ variables:
+ GIT_SUBMODULE_STRATEGY: recursive
+ before_script:
+ - git submodule sync --recursive
+ - git submodule update --init --recursive --remote
+ script:
+ - C:\Anaconda3\shell\condabin\conda-hook.ps1
+ - conda activate
+ - python setup.py install_egg_info
+ - pip install --user -e .
+ - $dir = Get-Location
+ - $pubdir = ($dir.ToString() + "\public")
+ - $docsdir = ($dir.ToString() + "\docs")
+ - Rename-Item -Path "SecMOD" -NewName "SecMODTemp"
+ - Rename-Item -Path "SecMODTemp" -NewName "secmod"
+ - New-Item -ItemType Directory -Force -Path "WD"
+ - cd "WD"
+ - python -m secmod.setup
+ - sphinx-build -b html $docsdir $pubdir
+ artifacts:
+ paths:
+ - public
+ only:
+ - master
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1 @@
+
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..6f773a3
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,26 @@
+# Contributing
+
+You are interested in contributing to SecMOD? We appreciate any contributions!
+
+## Reporting issues
+
+Please note that the issue tracker is not for questions. Please use other platforms, such as Stack Overflow
+instead. Make sure to tag your question with SecMOD tag.
+
+If possible, before submitting an issue report, try to verify that the issue
+hasen't already been fixed and is not a duplicate.
+
+## Submitting code
+
+If you contribute code to SecMOD, you agree to license your code under the MIT.
+
+The new code should follow PEP8 coding style (except the line length limit,
+which is 90) and adhere to the style of the surrounding code.
+
+You should preferably use atomic (very small) commits for your changes. Read more
+about why and how [here](https://www.freshconsulting.com/atomic-commits/).
+
+You must follow some simple formatting rules for your commit messages, which can
+be found [here](https://chris.beams.io/posts/git-commit/)
+
+
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..739a177
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,12 @@
+Copyright © 2022 Christiane Reinert (a), Lars Schellhas (a), Jacob Mannhardt (a), David Yang Shu (b), Andreas Kämper (b), Nils Baumgärtner (a), Sarah Deutz (a), and André Bardow (b,c).
+(a) Institute of Technical Thermodynamics, RWTH Aachen University, Aachen, Germany.
+(b) Energy & Process Systems Engineering, Department of Mechanical and Process Engi-
+neering, ETH Zurich, Zürich, Switzerland.
+(c) Institute of Energy and Climate Research – Energy Systems Engineering (IEK-10),
+Forschungszentrum Jülich, Jülich, Germany.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..c066bbd
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,8 @@
+# Include the README
+include *.md
+
+# Include the license file
+include LICENSE.txt
+
+# Include the data files
+recursive-include data *
diff --git a/README.md b/README.md
index 8b13789..ba9777e 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,54 @@
+# SecMOD - A framework for multi-energy system optimization and life-cycle assessment
+The SecMOD framework is a flexible framework for the optimization and life-cycle assessment of linear multi-sector systems.
+
+## Referencing
+
+If you use our software or any part of it, please cite:
+
+Reinert, C.; Schellhas, L.; Mannhardt, J.; Shu, D.; Kämper, A.; Baumgärtner, N.; Deutz, S., and Bardow, A. (2022): "SecMOD: An open-source modular framework
+combining multi-sector system optimization and life-cycle assessment". Frontiers in Energy Research. DOI: 10.3389/fenrg.2022.884525.
+
+## License
+This project is licensed under the MIT license, for more information please refer to the license file.
+
+## Documentation and Support
+**Please find the full documentation of the SecMOD framework [here](https://ltt.pages.git-ce.rwth-aachen.de/opt/secmod/secmod/).**
+
+If you need help with Git, please refer to the git documentation [here](https://git-scm.com/docs).
+
+[](https://git-ce.rwth-aachen.de/ltt/secmod/secmod/commits/master)
+ <a href="https://git-ce.rwth-aachen.de/ltt/opt/secmod/secmod/-/commits/master"><img alt="pipeline status" src="https://git-ce.rwth-aachen.de/ltt/opt/secmod/secmod/badges/master/pipeline.svg" /></a>
+
+## Installation
+A brief instruction to install SecMOD can also be found below:
+Clone a copy of the whole repository to your computer:
+```
+git clone git@git-ce.rwth-aachen.de:ltt/secmod/secmod.git
+```
+
+Open a terminal with a python enviroment (e.g. Anaconda promt) and install the secmod package with:
+```shell
+pip install --user -e '<Path\to\cloned\repository>'
+```
+The path should point to the directory where secmod is saved (repository folder).
+Make sure that the setup.py file is located in your repository folder. You can use this installation for several projects in multiple working directories.
+
+For further installation instruction please go [here](https://ltt.pages.git-ce.rwth-aachen.de/opt/secmod/secmod/usage/installation.html). The code was tested with Python 3.7.5.
+## First Steps
+
+Create a working directory for the optimization framework. Next, cd to that directory and enter
+```shell
+python -m secmod.setup
+```
+in the terminal. This sets up the right folder structure.
+Start the optimization with double clicking the start.bat file, where you can choose your enviroment or
+alternatively use
+
+```shell
+python -m secmod
+```
+to start.
+
+
+
diff --git a/SecMOD_Documentation_PDFversion.pdf b/SecMOD_Documentation_PDFversion.pdf
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diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 0000000..7a7d6f6
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,192 @@
+# -*- coding: utf-8 -*-
+#
+# Configuration file for the Sphinx documentation builder.
+#
+# This file does only contain a selection of the most common options. For a
+# full list see the documentation:
+# http://www.sphinx-doc.org/en/master/config
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+sys.path.insert(0, os.path.abspath('..'))
+
+
+# -- Project information -----------------------------------------------------
+
+project = 'SecMOD'
+copyright = '2022, Institute of Technical Thermodynamics, RWTH Aachen University'
+author = 'Institute of Technical Thermodynamics, RWTH Aachen University'
+
+# The short X.Y version
+version = '2.0'
+# The full version, including alpha/beta/rc tags
+release = ''
+
+
+# -- General configuration ---------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+ 'sphinx.ext.autodoc',
+ 'sphinx.ext.doctest',
+ 'sphinx.ext.coverage',
+ 'sphinx.ext.mathjax',
+ 'sphinx.ext.viewcode',
+ 'sphinx.ext.napoleon',
+ 'sphinx.ext.todo',
+]
+
+# Add private methods to autodoc
+autodoc_default_options = {
+ 'members': None,
+ 'member-order': 'bysource',
+ 'private-members': None,
+ 'undoc-members': None
+}
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The master toctree document.
+master_doc = 'index'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = None
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages. See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further. For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# The default sidebars (for documents that don't match any pattern) are
+# defined by theme itself. Builtin themes are using these templates by
+# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
+# 'searchbox.html']``.
+#
+# html_sidebars = {}
+
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'SecMODdoc'
+todo_include_todos = True
+
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_elements = {
+ # The paper size ('letterpaper' or 'a4paper').
+ #
+ # 'papersize': 'letterpaper',
+
+ # The font size ('10pt', '11pt' or '12pt').
+ #
+ # 'pointsize': '10pt',
+
+ # Additional stuff for the LaTeX preamble.
+ #
+ # 'preamble': '',
+
+ # Latex figure (float) alignment
+ #
+ # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+# author, documentclass [howto, manual, or own class]).
+latex_documents = [
+ (master_doc, 'SecMOD.tex', 'SecMOD Documentation',
+ 'Institute of Technical Thermodynamics, RWTH Aachen University', 'manual'),
+]
+
+
+# -- Options for manual page output ------------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+ (master_doc, 'secmod', 'SecMOD Documentation',
+ [author], 1)
+]
+
+
+# -- Options for Texinfo output ----------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+# dir menu entry, description, category)
+texinfo_documents = [
+ (master_doc, 'SecMOD', 'SecMOD Documentation',
+ author, 'SecMOD', 'One line description of project.',
+ 'Miscellaneous'),
+]
+
+
+# -- Options for Epub output -------------------------------------------------
+
+# Bibliographic Dublin Core info.
+epub_title = project
+
+# The unique identifier of the text. This can be a ISBN number
+# or the project homepage.
+#
+# epub_identifier = ''
+
+# A unique identification for the text.
+#
+# epub_uid = ''
+
+# A list of files that should not be packed into the epub file.
+epub_exclude_files = ['search.html']
+
+
+# -- Extension configuration -------------------------------------------------
diff --git a/docs/img/.gitkeep b/docs/img/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/docs/img/GUI_SecMOD.png b/docs/img/GUI_SecMOD.png
new file mode 100644
index 0000000000000000000000000000000000000000..193ea54de69f348c5a89e80154142920658b3a58
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diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..f4e32af
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,27 @@
+.. SecMOD documentation master file, created by
+ sphinx-quickstart on Thu Apr 25 15:42:33 2019.
+ You can adapt this file completely to your liking, but it should at least
+ contain the root `toctree` directive.
+
+Welcome to SecMOD's documentation!
+==================================
+
+.. toctree::
+ :maxdepth: 2
+ :caption: Contents
+
+ usage/quickstart
+ usage/installation
+ usage/data
+ usage/developer_reference
+ usage/packages
+ usage/troubleshooting
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/docs/usage/data.rst b/docs/usage/data.rst
new file mode 100644
index 0000000..bb008fc
--- /dev/null
+++ b/docs/usage/data.rst
@@ -0,0 +1,30 @@
+==============
+Data in SecMOD
+==============
+Plese refer to our publication for a detailled description of required data:
+Reinert, C.; Schellhas, L.; Mannhardt, J.; Shu, D.; Kämper, A.; Baumgärtner, N.; Deutz, S., and Bardow, A. (2022): "SecMOD: An open-source modular framework
+combining multi-sector system optimization and life-cycle assessment". Frontiers in Energy Research. DOI: 10.3389/fenrg.2022.884525 .
+
+Input data
+==================================
+Input data is used to model a given multi-sector system with the desired spatial, technical and temporal resolution. Input data can be added manually in the sampledata folder or in some cases be obtained automatically from open webpages, such as the open power system database by the scripts in sampledata/00-EXTERNAL.
+
+.. image:: ../img/secmod_scheme.svg
+
+Units in SecMOD
+==================================
+SecMOD employs unitizing to automatically convert units. All units used in the model must be defined in the sample data file.
+
+Output data and evaluation
+==================================
+The objective of the optimization is to determine an optimal design and operation of the multi-sector system according to the given objective and constraints.
+The output data of the optimization is saved in the working directory in `01-MODEL-RESULTS/InvestmentModel_year.pickle`.
+Usually, the evaluation will start automatically. To start the evaluation without the optimization, type::
+
+ python -m secmod.evaluation
+
+This will open a graphical user interface (GUI).
+In the user interface, the capacity, product flows, and impacts can be shown for all products in different plot types. Clicking on a capacity will open a second layer to investigate the construction years. Further, all raw results are shown as a table next to the plot.
+The data can be exported in several formats including tikz, png, xlsx and pdf.
+
+
diff --git a/docs/usage/developer_reference.rst b/docs/usage/developer_reference.rst
new file mode 100644
index 0000000..8c0ee4e
--- /dev/null
+++ b/docs/usage/developer_reference.rst
@@ -0,0 +1,69 @@
+===================
+Developer reference
+===================
+
+
+secmod.classes module
+=====================
+
+.. automodule:: secmod.classes
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+secmod.data\_preprocessing module
+=================================
+
+.. automodule:: secmod.data_preprocessing
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+secmod.data\_processing module
+==============================
+
+.. automodule:: secmod.data_processing
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+secmod.evaluation module
+========================
+
+.. automodule:: secmod.evaluation
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+secmod.helpers module
+=====================
+
+.. automodule:: secmod.helpers
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+secmod.optimization module
+==========================
+
+.. automodule:: secmod.optimization
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+secmod.setup module
+===================
+
+.. automodule:: secmod.setup
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+
+Module contents
+===============
+
+.. automodule:: secmod
+ :members:
+ :undoc-members:
+ :show-inheritance:
diff --git a/docs/usage/installation.rst b/docs/usage/installation.rst
new file mode 100644
index 0000000..968595f
--- /dev/null
+++ b/docs/usage/installation.rst
@@ -0,0 +1,60 @@
+============
+Installation
+============
+
+The installation of SecMOD is easily done.
+If you want to edit its source code, you need to
+follow :ref:`editable-installation`.
+If you just want to use the published package,
+follow :ref:`package-installation`.
+
+
+.. _editable-installation:
+
+Editable Installation
+=====================
+
+To edit the source code and therefore contribute to the development of SecMOD,
+you need to clone the `repository <https://git.rwth-aachen.de/ltt/secmod/secmod>`_
+to your local machine **recursively**. Using SSH as authentication to the git server
+the following command needs to be used::
+
+ git clone --recurse-submodules -j8 git@git-ce.rwth-aachen.de:ltt/opt/secmod/secmod.git 01-SecMOD
+
+.. NOTE:: If you don´t know yet how to clone a repository have a
+ look `here <https://git.rwth-aachen.de/help/#new-to-git-and-gitlab>`_.
+ Make sure that you have Python already installed on your PC.
+
+After the clone process is completed, open a python terminal e.g. in your
+IDE (Visual Studio Code, Spyder, ...) or the Anaconda prompt.
+Afterwards you can install SecMOD in form of your local repository by using the
+following install command::
+
+ pip install --user -e PATH
+
+.. NOTE:: The PATH at the end of the command stands for the directory
+ of your local repository. You can either use an absolute path or
+ a path relative to the currently active directory of the terminal.
+
+Your local repository should now be installed, if no errors occurred during the process.
+You can now proceed to the :doc:`quickstart <quickstart>` guide to complete your setup.
+
+.. _package-installation:
+
+Package Installation
+====================
+
+.. error:: SecMOD is not yet packaged and therefore not available through PyPI.
+ Use the :ref:`editable-installation` instead.
+
+ See `Issue 13 <https://git.rwth-aachen.de/ltt/secmod/secmod/issues/13>`_
+ for further information about the packaged distribution of SecMOD.
+
+If you don´t need to edit the source code of the SecMOD package,
+but just want to use the modules, you can install the package directly
+from PyPi using the following command::
+
+ pip install secmod
+
+SecMOD should now be installed, if no errors occurred during the process.
+You can now proceed to the :doc:`quickstart <quickstart>` guide to complete your setup.
diff --git a/docs/usage/packages.rst b/docs/usage/packages.rst
new file mode 100644
index 0000000..4dd32fe
--- /dev/null
+++ b/docs/usage/packages.rst
@@ -0,0 +1,22 @@
+====================
+Packages in SecMOD
+====================
+
+.. csv-table::
+ :widths: auto
+ :header: Package name, Usage, Link
+
+ Pyomo, Optimization, https://www.pyomo.org/
+ Numpy, Scientific Computing, https://numpy.org/
+ Pandas, Scientific Computing, https://pandas.pydata.org/
+ Scipy, Scientific Computing, https://www.scipy.org/
+ datapackage, Datamanagement, https://frictionlessdata.io/docs/using-data-packages-in-python/
+ geopy, Geocoordinates/distances, https://github.com/geopy/geopy
+ pint, Unit-Handling, https://pint.readthedocs.io/en/latest/
+ pathlib, , https://docs.python.org/3/library/pathlib.html
+ requests, Network, https://3.python-requests.org/
+ clint, Terminal-Visualation, https://github.com/kennethreitz/clint
+ tikzplotlib, Visualization, https://github.com/nschloe/tikzplotlib
+ sympy, , https://www.sympy.org/en/index.html
+ tsam, Scientific Computing, https://github.com/FZJ-IEK3-VSA/tsam
+ pandastable, Visualization, https://pandastable.readthedocs.io/en/latest/description.html
diff --git a/docs/usage/quickstart.rst b/docs/usage/quickstart.rst
new file mode 100644
index 0000000..0775e06
--- /dev/null
+++ b/docs/usage/quickstart.rst
@@ -0,0 +1,37 @@
+==========
+Quickstart
+==========
+
+In order to improve the distributability of SecMOD the package code is
+separated from the handled input data. This allows to use a single installation
+of SecMOD for several projects or scenarios in multiple working directories.
+
+To get started with SecMOD, the following two steps must be completed:
+
+1. :doc:`Installation of SecMOD <installation>`
+2. :ref:`Set up a working directory <setupWD>`
+
+For the first step follow the instructions for the :doc:`installation of SecMOD <installation>`.
+Afterwards return to :ref:`Step 2 <setupWD>` to set up your first working directory.
+
+.. _setupWD:
+
+Set up a working directory
+==========================
+
+Create a new directory where you want. Note that this directory will contain all
+input and results data and can become large in size, depending on the size of your
+examined model.
+
+1. Open a terminal with an activated Python environment (e.g. Anaconda prompt)!
+2. Navigate to your newly created directory in the terminal (e.g. "cd D:/WorkingDirectory")!
+3. Run the command "python -m secmod.setup" in the terminal!
+
+SecMOD will now create the necessary folders for the input data and furthermore copy the
+sample data provided in the SecMOD repository.
+Afterwards all necessary data to run SecMOD is available in the working directory. Furthermore,
+a file called "start.bat" is created at the top level of your working directory.
+This file allows you to start SecMOD with a simple double click. Depending on your Python
+installation, you might need to edit the file (right click > edit) according to the instructions.
+
+If you encounter problems during the automatic setup, please refer to :doc:`Troubleshooting <troubleshooting>`.
diff --git a/docs/usage/troubleshooting.rst b/docs/usage/troubleshooting.rst
new file mode 100644
index 0000000..ea63bd9
--- /dev/null
+++ b/docs/usage/troubleshooting.rst
@@ -0,0 +1,7 @@
+===============
+Troubleshooting
+===============
+
+SecMOD is programmed to handle most things internally, if possible.
+If you still encounter an error, SecMOD mostly provides you with an error message
+which should help to resolve the issue.
diff --git a/sampledata/00-EXTERNAL/README.md b/sampledata/00-EXTERNAL/README.md
new file mode 100644
index 0000000..e40c4c3
--- /dev/null
+++ b/sampledata/00-EXTERNAL/README.md
@@ -0,0 +1,10 @@
+# README - 00-EXTERNAL
+
+This directory contains a data package "external_data_packages", which has links
+to the sources of some useful external data: the open power system data (OPSD) platform. In our current work, we included their data on existing conventional and renewable power plants in Germany. Please refer to the folder "external data packages" for the script we used.
+
+Please note that in some cases, updates on the script might be necessary if the data format on the OPSD platform changes.
+
+Furthermore all external data packages are downloaded to this directory. The downloaded
+data packages should not be modified, since the modifications will be overwritten,
+if an update of the external data packages is available from the source.
diff --git a/sampledata/00-EXTERNAL/external_data_packages/README.md b/sampledata/00-EXTERNAL/external_data_packages/README.md
new file mode 100644
index 0000000..b65e307
--- /dev/null
+++ b/sampledata/00-EXTERNAL/external_data_packages/README.md
@@ -0,0 +1,21 @@
+
+EXTERNAL DATA PACKAGES
+===========================================================================
+SecMOD can include external data from the OPSD-database. The folder "scripts" contains the code we used to include data about existing conventional and renewable plants. Please note that existing infrastructure is not included automatically in the current settings.
+
+
+We follow the Data Package standard by the [Frictionless Data project](http://frictionlessdata.io/),
+a part of the Open Knowledge Foundation.
+
+Field documentation
+===========================================================================
+
+external_data_packages.csv
+---------------------------------------------------------------------------
+
+* internal_name
+ * Type: string
+ * Description: unique and fixed name for the internal use of the external data package
+* source_url
+ * Type: string
+ * Description: unique and fixed name for the internal use of the external data package
diff --git a/sampledata/00-EXTERNAL/external_data_packages/datapackage.json b/sampledata/00-EXTERNAL/external_data_packages/datapackage.json
new file mode 100644
index 0000000..5cff650
--- /dev/null
+++ b/sampledata/00-EXTERNAL/external_data_packages/datapackage.json
@@ -0,0 +1,30 @@
+{
+ "profile": "tabular-data-package",
+ "resources": [
+ {
+ "path": "external_data_packages.csv",
+ "profile": "tabular-data-resource",
+ "name": "external_data_packages",
+ "format": "csv",
+ "mediatype": "text/csv",
+ "encoding": "utf-8",
+ "schema": {
+ "fields": [
+ {
+ "name": "internal_name",
+ "type": "string",
+ "format": "default"
+ },
+ {
+ "name": "source_url",
+ "type": "string",
+ "format": "default"
+ }
+ ],
+ "missingValues": [
+ ""
+ ]
+ }
+ }
+ ]
+}
\ No newline at end of file
diff --git a/sampledata/00-EXTERNAL/external_data_packages/external_data_packages.csv b/sampledata/00-EXTERNAL/external_data_packages/external_data_packages.csv
new file mode 100644
index 0000000..e0b009d
--- /dev/null
+++ b/sampledata/00-EXTERNAL/external_data_packages/external_data_packages.csv
@@ -0,0 +1,3 @@
+internal_name,source_url
+renewable_power_plants,https://data.open-power-system-data.org/renewable_power_plants/2016-10-21
+conventional_power_plants,https://data.open-power-system-data.org/conventional_power_plants/2016-10-27
diff --git a/sampledata/00-EXTERNAL/external_data_packages/scripts/conventional_power_plants.py b/sampledata/00-EXTERNAL/external_data_packages/scripts/conventional_power_plants.py
new file mode 100644
index 0000000..1a957c7
--- /dev/null
+++ b/sampledata/00-EXTERNAL/external_data_packages/scripts/conventional_power_plants.py
@@ -0,0 +1,147 @@
+# Important columns of the file renewable_power_plants_DE.csv:
+# - commissioned_original
+# - shutdown
+# - fuel
+# - capacity_net_bnetza
+# - lat
+# - lon
+
+import pandas as pd
+import numpy as np
+from geopy import distance
+from pathlib import Path
+from tqdm import tqdm
+import secmod.helpers as helpers
+
+def setup(working_directory: Path, data_package_name: str, grid: str = "DE", unit: str = "MW", years: list =[1900,1905,1910,1915,1920,1925,1930,1935,1940,1945,1950,1955,1960,1965,1970,1975,1980,1985,1990,1995,2000,2005,2010,2016,2020,2025,2030,2035,2040,2045,2050], debug=False):
+ """This method is called in order to import the data from this data package to the input data."""
+
+ tqdm.pandas()
+
+ used_processes = {"Natural gas", "Hard coal", "Hydro", "Waste", "Biomass and biogas", "Lignite", "Nuclear"}
+ process_to_limit_potential = {"Hydro", "Waste", "Biomass and biogas"}
+ internal_process_name = {"Natural gas": "natural_gas_turbine", "Hard coal": "hard_coal", "Hydro": "pumped_storage_hydro", "Waste": "waste_incineration", "Biomass and biogas": "biogas-to-power", "Lignite": "lignite", "Nuclear": "nuclear"}
+ process_classes = {"Natural gas": "production", "Hard coal": "production", "Hydro": "storage", "Waste": "production", "Biomass and biogas": "production", "Lignite": "production", "Nuclear": "production"}
+
+ fuel = "fuel"
+ columns = ["commissioned_original", "shutdown", fuel, "technology", "capacity_net_bnetza", "lat", "lon"]
+ print("Load data")
+ plants = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / data_package_name / "conventional_power_plants_{0}.csv".format(grid), float_precision="high", usecols=(lambda column: column in columns), parse_dates=["commissioned_original", "shutdown"])
+ # check if all columns are present and replace columns that at some point in time change their names
+ if fuel not in plants.columns: # check if fuels were loaded, i.e., if the fuel column is missing
+ fuel = 'energy_source' # staring in 2020, 'fuel' has been renamed to 'energy_source'
+ plants = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / data_package_name / "conventional_power_plants_{0}.csv".format(grid), float_precision="high", usecols=(lambda column: column in columns), parse_dates=["commissioned_original", "shutdown"])
+ assert (plants.columns.isin(["commissioned_original", "shutdown", fuel, "technology", "capacity_net_bnetza", "lat", "lon"]).all()), 'At least one of the columns could not be loaded. Check if any of the column names has been updated.'
+
+ if debug:
+ plants = plants.sample(500)
+ nodes = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "01-GRID" / grid / "nodes.csv", index_col="node", usecols=lambda x: "Unnamed" not in x, float_precision="high")
+ print("Select used technologies")
+ plants = plants[plants[fuel].isin(used_processes)]
+ print("Determine commissioning year")
+ plants["commissioning_year"] = plants["commissioned_original"].progress_apply(lambda date: parse_year(date))
+ print("Determine decommissioning year")
+ plants["decommissioning_year"] = plants["shutdown"].progress_apply(lambda date: parse_year(date))
+ plants = plants[[fuel, "capacity_net_bnetza", "lat", "lon", "commissioning_year", "decommissioning_year", "technology"]]
+ print("Determine actual construction year")
+ plants["construction_year"] = plants.progress_apply(lambda plant: get_construction_year(plant, working_directory, internal_process_name[plant.fuel], process_classes[plant.fuel]), axis=1)
+ print("Get used construction year")
+ plants["construction_year"] = plants["construction_year"].progress_apply(lambda year: get_closest_year(years, year))
+ plants = plants[[fuel, "capacity_net_bnetza", "lat", "lon", "construction_year", "technology"]]
+ plants = plants.dropna()
+ print("Determine closest nodes")
+ plants["node"] = plants[["lat", "lon"]].progress_apply(lambda plant: find_closest_node(nodes, plant.lon, plant.lat), axis=1)
+ plants = plants.loc[plants["technology"] != "Run-of-river"]
+ plants = plants[[fuel, "capacity_net_bnetza", "node", "construction_year"]]
+ plants.set_index([fuel, "node", "construction_year"], inplace=True)
+ plants.sort_index(inplace=True)
+ print("Aggregate capacities by fuel, nodes and year of construction")
+ plants = plants["capacity_net_bnetza"].groupby(level=[0,1,2]).sum()
+
+ print("Write existing capacity for {0}".format(list(plants.index.get_level_values(fuel).unique())))
+ for process in tqdm(list(plants.index.get_level_values(fuel).unique())):
+ capacity = plants.loc[process].unstack("construction_year").reindex(index=nodes.index).fillna(0)
+ capacity_columns = list(capacity.columns)
+ capacity_columns.sort()
+ if process != "Hydro":
+ capacity["unit"] = unit
+ else:
+ capacity = capacity / 0.164332428589893
+ capacity["unit"] = unit + "h"
+ capacity["comments"] = ""
+ capacity.reset_index(inplace=True)
+ capacity = capacity[["node", "unit"] + capacity_columns + ["comments"]]
+ export_existing_capacity(capacity, working_directory, internal_process_name[process], grid, process_classes[process])
+ if process in process_to_limit_potential:
+ potential = get_nodal_capacity_by_latest_year(capacity, working_directory, internal_process_name[process], process_classes[process], years)
+ export_potential_capacity(potential, working_directory, internal_process_name[process], grid, process_classes[process])
+ print("Done!")
+
+def parse_year(date):
+ try:
+ return int(pd.to_datetime(date, format="%d.%m.%Y").year)
+ except:
+ try:
+ return int(pd.to_datetime(date, format="%Y").year)
+ except:
+ return np.nan
+
+def get_nodal_capacity_by_latest_year(capacity: pd.DataFrame, working_directory: Path, process: str, process_class: str, years: list):
+ relevant_years = [int(column) for column in capacity.columns if helpers.isInteger(column)]
+ reference_year = max(relevant_years)
+ lifetime_duration = get_lifetime_duration(working_directory, reference_year, process, process_class)
+ relevant_years = [year for year in relevant_years if (reference_year - year) <= lifetime_duration]
+ pd.options.mode.chained_assignment = None # default='warn'
+ potential = capacity[relevant_years]
+ potential["total"] = potential.sum(axis=1)
+ potential[["node", "unit"]] = capacity[["node", "unit"]]
+ potential["comments"] = "Based on the total existing capacity in {0}".format(reference_year)
+ for year in years:
+ potential[year] = potential["total"]
+ potential = potential[["node", "unit"] + years + ["comments"]]
+ pd.options.mode.chained_assignment = "warn" # default='warn'
+ return potential
+
+def get_construction_year(plant, working_directory: Path, process: str, process_class: str = "production"):
+ """Returns the construction year, to be used"""
+ if np.isnan(plant.decommissioning_year):
+ return plant.commissioning_year
+ elif not np.isnan(plant.commissioning_year):
+ return (plant.decommissioning_year - get_lifetime_duration(working_directory, plant.commissioning_year, process, process_class))
+ else:
+ return np.nan
+
+def find_closest_node(nodes: pd.DataFrame, longitude, latitude):
+ """This method return the ID of the node, which is closest to the plant."""
+ nodes["distance"] = nodes.apply(lambda node: distance.great_circle((node.latitude, node.longitude), (latitude, longitude)).km, axis=1)
+ return nodes["distance"].idxmin()
+
+def get_lifetime_duration(working_directory: Path, construction_year: int, process: str, process_class: str = "production"):
+ """Return the lifetime duration of a process from lifetime_duration.csv"""
+ process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+ target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / "lifetime_duration.csv"
+ lifetime_duration = pd.read_csv(target_directory, float_precision="high", usecols=(lambda column: (column != "comments") and (column != "unit")))
+ years_of_data = [int(column) for column in lifetime_duration.columns]
+ lifetime_duration.columns = years_of_data
+ closest_year = get_closest_year(years_of_data, construction_year)
+ return lifetime_duration.at[0,get_closest_year(years_of_data, construction_year)]
+
+def get_closest_year(years: list, year_of_construction: int):
+ """Return the closest year"""
+ difference = [abs(year - year_of_construction) for year in years]
+ return years[difference.index(min(difference))]
+
+def export_existing_capacity(capacity: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str = "production"):
+ """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+ process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+ target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "existing_capacity.csv"
+ capacity.to_csv(path_or_buf=target_directory, index=False)
+
+def export_potential_capacity(potential: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str):
+ """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+ process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+ target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "potential_capacity.csv"
+ potential.to_csv(path_or_buf=target_directory)
+
+if __name__ == "__main__":
+ setup(Path().cwd(), "conventional_power_plants", debug=False)
\ No newline at end of file
diff --git a/sampledata/00-EXTERNAL/external_data_packages/scripts/renewable_power_plants.py b/sampledata/00-EXTERNAL/external_data_packages/scripts/renewable_power_plants.py
new file mode 100644
index 0000000..abea69b
--- /dev/null
+++ b/sampledata/00-EXTERNAL/external_data_packages/scripts/renewable_power_plants.py
@@ -0,0 +1,125 @@
+# Important columns of the file renewable_power_plants_DE.csv:
+# - commissioning_date
+# - decommissioning_date
+# - technology
+# - electrical_capacity
+# - lat
+# - lon
+
+import pandas as pd
+import numpy as np
+from geopy import distance
+from pathlib import Path
+from tqdm import tqdm
+import secmod.helpers as helpers
+
+def setup(working_directory: Path, data_package_name: str, grid: str = "DE", unit: str = "MW", years: list =[1900,1905,1910,1915,1920,1925,1930,1935,1940,1945,1950,1955,1960,1965,1970,1975,1980,1985,1990,1995,2000,2005,2010,2016,2020,2025,2030,2035,2040,2045,2050], debug=False):
+ """This method is called in order to import the data from this data package to the input data."""
+
+ tqdm.pandas()
+ used_processes = {"Run-of-river", "Onshore", "Photovoltaics", "Geothermal", "Offshore"}
+ process_to_limit_potential = {"Run-of-river", "Geothermal"}
+ internal_process_name = {"Run-of-river": "run-of-river_hydro", "Onshore": "wind_onshore", "Photovoltaics": "photovoltaics", "Geothermal": "geothermal", "Offshore": "wind_offshore"}
+ process_classes = {"Run-of-river": "production", "Biomass and biogas": "production", "Sewage and landfill gas": "production", "Onshore": "production", "Photovoltaics": "production", "Other fossil fuels": "production", "Geothermal": "production", "Offshore": "production", "Photovoltaics ground": "production", "Storage": "storage"}
+
+ columns = ["commissioning_date", "decommissioning_date", "technology", "electrical_capacity", "lat", "lon"]
+ print("Load data")
+ plants = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / data_package_name / "renewable_power_plants_{0}.csv".format(grid), float_precision="high", usecols=(lambda column: column in columns), parse_dates=["commissioning_date", "decommissioning_date"])
+ if debug:
+ plants = plants.sample(500)
+ nodes = pd.read_csv(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "01-GRID" / grid / "nodes.csv", index_col="node", usecols=lambda x: "Unnamed" not in x, float_precision="high")
+ print("Select used technologies")
+ plants = plants[plants["technology"].isin(used_processes)]
+ print("Determine commissioning year")
+ plants["commissioning_year"] = plants["commissioning_date"].progress_apply(lambda date: date.year)
+ print("Determine decommissioning year")
+ plants["decommissioning_year"] = plants["decommissioning_date"].progress_apply(lambda date: date.year)
+ plants = plants[["technology", "electrical_capacity", "lat", "lon", "commissioning_year", "decommissioning_year"]]
+ print("Determine actual construction year")
+ plants["construction_year"] = plants.progress_apply(lambda plant: get_construction_year(plant, working_directory, internal_process_name[plant.technology], process_classes[plant.technology]), axis=1)
+ print("Get used construction year")
+ plants["construction_year"] = plants["construction_year"].progress_apply(lambda year: get_closest_year(years, year))
+ plants = plants[["technology", "electrical_capacity", "lat", "lon", "construction_year"]]
+ plants = plants.dropna()
+ print("Determine closest nodes")
+ plants["node"] = plants[["lat", "lon"]].progress_apply(lambda plant: find_closest_node(nodes, plant.lon, plant.lat), axis=1)
+ plants = plants[["technology", "electrical_capacity", "node", "construction_year"]]
+ plants.set_index(["technology", "node", "construction_year"], inplace=True)
+ plants.sort_index(inplace=True)
+ print("Aggregate capacities by technology, nodes and year of construction")
+ plants = plants["electrical_capacity"].groupby(level=[0,1,2]).sum()
+
+ print("Write existing capacity for {0}".format(list(plants.index.get_level_values("technology").unique())))
+ for process in tqdm(list(plants.index.get_level_values("technology").unique())):
+ capacity = plants.loc[process].unstack("construction_year").reindex(index=nodes.index).fillna(0)
+ capacity_columns = list(capacity.columns)
+ capacity_columns.sort()
+ capacity["unit"] = unit
+ capacity["comments"] = ""
+ capacity.reset_index(inplace=True)
+ capacity = capacity[["node", "unit"] + capacity_columns + ["comments"]]
+ export_existing_capacity(capacity, working_directory, internal_process_name[process], grid, process_classes[process])
+ if process in process_to_limit_potential:
+ potential = get_nodal_capacity_by_latest_year(capacity, working_directory, internal_process_name[process], process_classes[process], years)
+ export_potential_capacity(potential, working_directory, internal_process_name[process], grid, process_classes[process])
+ print("Done!")
+
+def get_nodal_capacity_by_latest_year(capacity: pd.DataFrame, working_directory: Path, process: str, process_class: str, years: list):
+ relevant_years = [int(column) for column in capacity.columns if helpers.isInteger(column)]
+ reference_year = max(relevant_years)
+ lifetime_duration = get_lifetime_duration(working_directory, reference_year, process, process_class)
+ relevant_years = [year for year in relevant_years if (reference_year - year) <= lifetime_duration]
+ pd.options.mode.chained_assignment = None # default='warn'
+ potential = capacity[relevant_years]
+ potential["total"] = potential.sum(axis=1)
+ potential[["node", "unit"]] = capacity[["node", "unit"]]
+ potential["comments"] = "Based on the total existing capacity in {0}".format(reference_year)
+ for year in years:
+ potential[year] = potential["total"]
+ potential = potential[["node", "unit"] + years + ["comments"]]
+ pd.options.mode.chained_assignment = "warn" # default='warn'
+ return potential
+
+def get_construction_year(plant, working_directory: Path, process: str, process_class: str = "production"):
+ """Returns the construction year, to be used"""
+ if np.isnan(plant.decommissioning_year):
+ return plant.commissioning_year
+ elif not np.isnan(plant.commissioning_year):
+ return (plant.decommissioning_year - get_lifetime_duration(working_directory, plant.commissioning_year, process, process_class))
+ else:
+ return np.nan
+
+def find_closest_node(nodes: pd.DataFrame, longitude, latitude):
+ """This method return the ID of the node, which is closest to the plant."""
+ nodes["distance"] = nodes.apply(lambda node: distance.great_circle((node.latitude, node.longitude), (latitude, longitude)).km, axis=1)
+ return nodes["distance"].idxmin()
+
+def get_lifetime_duration(working_directory: Path, construction_year: int, process: str, process_class: str = "production"):
+ """Return the lifetime duration of a process from lifetime_duration.csv"""
+ process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+ target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / "lifetime_duration.csv"
+ lifetime_duration = pd.read_csv(target_directory, float_precision="high", usecols=(lambda column: (column != "comments") and (column != "unit")))
+ years_of_data = [int(column) for column in lifetime_duration.columns]
+ lifetime_duration.columns = years_of_data
+ closest_year = get_closest_year(years_of_data, construction_year)
+ return lifetime_duration.at[0,get_closest_year(years_of_data, construction_year)]
+
+def get_closest_year(years: list, year_of_construction: int):
+ """Return the closest year"""
+ difference = [abs(year - year_of_construction) for year in years]
+ return years[difference.index(min(difference))]
+
+def export_existing_capacity(capacity: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str = "production"):
+ """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+ process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+ target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "existing_capacity.csv"
+ capacity.to_csv(path_or_buf=target_directory, index=False)
+
+def export_potential_capacity(potential: pd.DataFrame, working_directory: Path, process: str, grid: str, process_class: str):
+ """This method saves a dataframe to the correct position in a process and grid-subfolder."""
+ process_class_directories = {"production": "01-PRODUCTION", "storage": "02-STORAGE", "transshipment": "03-TRANSSHIPMENT", "transmission": "04-TRANSMISSION"}
+ target_directory = working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "02-PROCESSES" / process_class_directories[process_class] / "01-ECOINVENT-BASED" / process / grid / "potential_capacity.csv"
+ potential.to_csv(path_or_buf=target_directory)
+
+if __name__ == "__main__":
+ setup(Path().cwd(), "renewable_power_plants", debug=False)
\ No newline at end of file
diff --git a/sampledata/00-UNITS-DEFINITIONS.txt b/sampledata/00-UNITS-DEFINITIONS.txt
new file mode 100644
index 0000000..4d0cd28
--- /dev/null
+++ b/sampledata/00-UNITS-DEFINITIONS.txt
@@ -0,0 +1,25 @@
+m3 = m ** 3 = cubic_meter
+m2 = m ** 2 = square_meter
+mass_CO2_equivalent = [Global_Warming_Potential_100a] = CO2_eq = CO2_Eq = CO2_EQ
+megajoule_equivalent = [cumulative_energy_demand] = MJ_Eq = MJ_EQ = MJ_eq
+mole_h_plus_equivalent = [acidification] = H_plus_eq = H_plus_Eq = H_plus_EQ
+comparative_toxic_unit_for_humans = [ecotoxicity] = CTUh
+phosphor_equivalent = [freshwater_eutrophication] = P_Eq = P_EQ = P_eq
+nitrogen_equivalent = [marine_eutrophication] = N_Eq = N_EQ = N_eq
+mass_uranium_235_equivalent = [ionising_radiation] = U235_Eq = U235_EQ = U235_eq
+mass_CFC_11_equivalent = [ozone_layer_depletion] = CFC_11_Eq = CFC_11_EQ = CFC_11_eq
+mass_ethylen_equivalent = [photochemical_ozone_creation] = ethylen_Eq = ethylen_EQ = ethylen_eq = C2H4_Eq = C2H4_EQ = C2H4_eq
+mass_particulate_matter_2_5_equivalent = [respiratory_effects] = PM2_5_Eq = PM2_5_EQ = PM2_5_eq = PM2_5
+mass_deficit_of_soil_organic_carbon = [land_use] = soil_organic_carbon = SOC
+mass_antimony_equivalent = [mineral] = Sb_Eq = Sb_EQ = Sb_eq
+oil_equivalent = [FDP] = oil_Eq = oil_EQ = oil_eq
+mass_1_4_DCB_equivalent = [FETPinf] = DCB_Eq = DCB_EQ = DCB_eq
+iron_equivalent = [MDP] = Fe_Eq = Fe_EQ = Fe_eq
+particulate_matter_10_equivalent = [PMFP] = PM10_Eq = PM10_EQ = PM10_eq = PM10
+NMVOC_equivalent = [POFP] = NMVOC_Eq = NMVOC_EQ = NMVOC_eq = NMVOC
+mass_sulfur_dioxide = [TAP100] = SO2_Eq = SO2_EQ = SO2_eq
+
+Euro = [money] = EURO
+car = [car]
+meter_vehicle = [mobility] = meter * car
+circuit = [power_circuit]
\ No newline at end of file
diff --git a/sampledata/01-GRID/DE/README.md b/sampledata/01-GRID/DE/README.md
new file mode 100644
index 0000000..7a82cb4
--- /dev/null
+++ b/sampledata/01-GRID/DE/README.md
@@ -0,0 +1,6 @@
+# README - GRID DE
+
+This data package contains all the data about the German Electricity
+grid. It consists of nodes and connections. Nodes have an integer
+ID and geo coordinates. Connections have an integer ID as well
+and are defined by the two nodes they connect.
diff --git a/sampledata/01-GRID/DE/connections.csv b/sampledata/01-GRID/DE/connections.csv
new file mode 100644
index 0000000..1c518f3
--- /dev/null
+++ b/sampledata/01-GRID/DE/connections.csv
@@ -0,0 +1,130 @@
+connection,node1,node2,manual_distance,manual_distance_unit
+1,1,16,48.9379,km
+2,1,16,52.1117,km
+3,1,2,57.3021,km
+4,1,16,49.3466,km
+5,1,16,52.0148,km
+6,1,16,57.3962,km
+7,1,16,62.8176,km
+8,2,3,131.201,km
+9,2,9,46.991,km
+10,2,9,47.2844,km
+11,2,16,28.7351,km
+12,2,3,34.6007,km
+13,2,3,104.834,km
+14,2,9,105.559,km
+15,2,16,24.004,km
+16,3,11,27.5512,km
+17,3,11,34.2476,km
+18,3,11,45.6801,km
+19,3,4,15.4317,km
+20,3,4,29.0022,km
+21,3,4,56.892,km
+22,3,9,79.2796,km
+23,3,11,32.0209,km
+24,3,11,60.8896,km
+25,3,15,48.9509,km
+26,3,16,28.5293,km
+27,4,5,29.9327,km
+28,4,5,116.183,km
+29,4,11,86.3902,km
+30,4,12,37.9364,km
+31,4,17,58.4932,km
+32,4,17,130.04,km
+33,4,5,29.5558,km
+34,4,11,33.4769,km
+35,4,11,54.2218,km
+36,4,12,54.4385,km
+37,4,13,20.7582,km
+38,4,13,23.5401,km
+39,5,6,73.9456,km
+40,5,6,95,km
+41,5,6,89.7931,km
+42,5,6,93.1178,km
+43,5,6,100.562,km
+44,5,6,112.844,km
+45,5,7,92.4486,km
+46,5,7,101.55,km
+47,5,17,56.3032,km
+48,6,14,50.1904,km
+49,7,8,13.3963,km
+50,7,8,20.279,km
+51,7,8,104.812,km
+52,7,13,21.0193,km
+53,7,13,26.7976,km
+54,7,8,14.1789,km
+55,7,8,23.5505,km
+56,7,8,45.7653,km
+57,7,8,55.481,km
+58,7,8,120.971,km
+59,7,8,204.077,km
+60,7,13,47.3946,km
+61,7,13,61.5578,km
+62,7,13,74.1112,km
+63,7,13,152.519,km
+64,8,13,111.813,km
+65,8,13,86.4628,km
+66,8,13,138.549,km
+67,8,14,16.9606,km
+68,8,14,19.9738,km
+69,8,14,26.6516,km
+70,8,14,40.8788,km
+71,8,14,41.1006,km
+72,8,14,46.9505,km
+73,9,10,64.6059,km
+74,9,10,64.6962,km
+75,9,10,110.981,km
+76,9,11,42.3182,km
+77,9,11,69.992,km
+78,9,11,115.669,km
+79,9,11,59.2155,km
+80,10,11,10.0014,km
+81,10,11,21.97,km
+82,10,11,22.4985,km
+83,10,11,24.2453,km
+84,10,11,26.707,km
+85,10,11,28.8168,km
+86,10,11,77.1936,km
+87,10,12,3.98041,km
+88,10,12,5.3362,km
+89,10,12,11.3042,km
+90,10,12,18.9789,km
+91,10,12,18.9865,km
+92,10,12,22.6509,km
+93,10,12,22.764,km
+94,10,12,24.5185,km
+95,10,12,27.1188,km
+96,10,12,31.2371,km
+97,10,12,31.3148,km
+98,10,12,43.979,km
+99,10,11,25.743,km
+100,10,11,34.8071,km
+101,10,11,37.6526,km
+102,10,11,57.8004,km
+103,10,12,10.1819,km
+104,10,12,30.9199,km
+105,10,12,74.0115,km
+106,10,12,83.9702,km
+107,11,12,51.9124,km
+108,12,13,56.9823,km
+109,12,13,58.7159,km
+110,12,13,73.8307,km
+111,12,13,95.3573,km
+112,12,13,118.637,km
+113,12,13,124.115,km
+114,15,16,87.3598,km
+115,15,17,119.15,km
+116,15,18,62.4058,km
+117,15,18,94.9249,km
+118,15,18,125.677,km
+119,15,18,130.612,km
+120,15,18,208.282,km
+121,17,18,61.8101,km
+122,17,18,98.654,km
+123,17,18,109.47,km
+124,17,18,131.693,km
+125,17,18,157.592,km
+126,17,18,169.377,km
+127,17,18,67.4574,km
+128,17,18,103.507,km
+129,17,18,104.592,km
diff --git a/sampledata/01-GRID/DE/datapackage.json b/sampledata/01-GRID/DE/datapackage.json
new file mode 100644
index 0000000..d170a30
--- /dev/null
+++ b/sampledata/01-GRID/DE/datapackage.json
@@ -0,0 +1,85 @@
+{
+ "profile": "tabular-data-package",
+ "resources": [
+ {
+ "path": "connections.csv",
+ "profile": "tabular-data-resource",
+ "name": "connections",
+ "format": "csv",
+ "mediatype": "text/csv",
+ "encoding": "utf-8",
+ "schema": {
+ "fields": [
+ {
+ "name": "connection",
+ "type": "integer",
+ "format": "default"
+ },
+ {
+ "name": "node1",
+ "type": "integer",
+ "format": "default"
+ },
+ {
+ "name": "node2",
+ "type": "integer",
+ "format": "default"
+ }
+ ],
+ "missingValues": [
+ ""
+ ]
+ }
+ },
+ {
+ "path": "nodes.csv",
+ "profile": "tabular-data-resource",
+ "name": "nodes",
+ "format": "csv",
+ "mediatype": "text/csv",
+ "encoding": "utf-8",
+ "schema": {
+ "fields": [
+ {
+ "name": "node",
+ "type": "integer",
+ "format": "default"
+ },
+ {
+ "name": "country",
+ "type": "string",
+ "format": "default"
+ },
+ {
+ "name": "state",
+ "type": "string",
+ "format": "default"
+ },
+ {
+ "name": "dena_region",
+ "type": "string",
+ "format": "default"
+ },
+ {
+ "name": "z6_region",
+ "type": "string",
+ "format": "default"
+ },
+ {
+ "name": "latitude",
+ "type": "number",
+ "format": "default"
+ },
+ {
+ "name": "longitude",
+ "type": "number",
+ "format": "default"
+ }
+ ],
+ "missingValues": [
+ ""
+ ]
+ }
+ }
+ ]
+}
\ No newline at end of file
diff --git a/sampledata/01-GRID/DE/nodes.csv b/sampledata/01-GRID/DE/nodes.csv
new file mode 100644
index 0000000..4f5f6f1
--- /dev/null
+++ b/sampledata/01-GRID/DE/nodes.csv
@@ -0,0 +1,19 @@
+node,latitude,longitude,country,state,dena_region,z6_region
+1,54.29,9.725,DE,DE_SH,dena21,DE_NW
+2,53.235,8.406,DE,DE_NI,dena22,DE_NW
+3,52.278,9.917,DE,DE_NI,dena23,DE_NW
+4,51.059,9.258,DE,DE_HE,dena24,DE_WE
+5,49.929,10.836,DE,DE_BY,dena25,DE_SE
+6,48.207,11.868,DE,DE_BY,dena26,DE_SE
+7,49.29,8.674,DE,DE_BW,dena41,DE_SW
+8,48.31,8.873,DE,DE_BW,dena42,DE_SW
+9,52.288,7.748,DE,DE_NRW,dena71,DE_NW
+10,51.445,6.948,DE,DE_NRW,dena72,DE_WE
+11,51.684,7.992,DE,DE_NRW,dena73,DE_WE
+12,50.795,6.973,DE,DE_NRW,dena74,DE_WE
+13,49.483,7.729,DE,DE_RP,dena75,DE_SW
+14,48.027,10.642,DE,DE_BY,dena76,DE_SE
+15,52.562,12.941,DE,DE_BB,dena81,DE_NE
+16,53.555,10.158,DE,DE_HH,dena82,DE_NW
+17,50.93,11.471,DE,DE_TH,dena83,DE_EA
+18,51.356,13.382,DE,DE_SN,dena84,DE_EA
diff --git a/sampledata/01-GRID/README.md b/sampledata/01-GRID/README.md
new file mode 100644
index 0000000..90dc71b
--- /dev/null
+++ b/sampledata/01-GRID/README.md
@@ -0,0 +1,9 @@
+# README - 01-GRID
+
+This directory contains all data packages which define
+a grid model. A grid definition contains the information
+about the nodes of the grid, their geo coordinates
+and connections between the nodes. A connection is only
+defined by the two nodes it connects.
+
+SecMOD can handle multiple grid folders at once. The grid which should be used in the optimization must be indicated in the config.py.
diff --git a/sampledata/01-UNITS-TARGET-OPTIMIZATION.json b/sampledata/01-UNITS-TARGET-OPTIMIZATION.json
new file mode 100644
index 0000000..04cf4f6
--- /dev/null
+++ b/sampledata/01-UNITS-TARGET-OPTIMIZATION.json
@@ -0,0 +1,15 @@
+[
+ "megameter",
+ "kilocar",
+ "MW",
+ "MWh",
+ "circuit",
+ "hour",
+ "cubic_meter",
+ "",
+ "gigagram",
+ "mass_CO2_equivalent",
+ "gigaEuro",
+ "ohm",
+ "kV"
+ ]
\ No newline at end of file
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/README.md
new file mode 100644
index 0000000..82349d0
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/README.md
@@ -0,0 +1,4 @@
+# README - 01-ECOINVENT-BASED
+
+This directory contains all data packages which define
+production processes using ecoinvent.
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/README.md
new file mode 100644
index 0000000..caeb424
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/README.md
@@ -0,0 +1,4 @@
+# README - NATURAL GAS COMBINED CYCLE - DE
+
+This data package contains all necessary information
+about the production process **natural gas combined cycle** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/availability_timeseries.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/availability_timeseries.csv
new file mode 100644
index 0000000..089ad8b
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/availability_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18
+01-Jan-2019 00:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 01:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 02:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 03:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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+01-Jan-2019 06:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 07:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 08:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 09:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 10:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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+27-Dec-2019 17:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+27-Dec-2019 18:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+27-Dec-2019 19:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+27-Dec-2019 20:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+27-Dec-2019 21:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+27-Dec-2019 22:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+27-Dec-2019 23:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 00:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 01:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 02:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 03:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 04:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 05:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 06:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 07:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 08:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 09:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 10:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 11:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 12:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 13:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 14:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 15:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 16:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 17:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 18:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 19:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 20:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 21:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 22:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+28-Dec-2019 23:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 00:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 01:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 02:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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+29-Dec-2019 05:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 06:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 07:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 08:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 09:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 10:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 11:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 12:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 13:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 14:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 15:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 16:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 17:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 18:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 19:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 20:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 21:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 22:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+29-Dec-2019 23:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 00:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 01:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 02:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 03:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 04:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 05:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 06:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 07:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 08:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 09:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 10:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 11:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 12:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 13:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 14:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 15:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 16:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 17:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 18:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 19:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 20:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 21:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 22:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+30-Dec-2019 23:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 00:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 01:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 02:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 03:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 04:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 05:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 06:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 07:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 08:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 09:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 10:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 11:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 12:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 13:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 14:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 15:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 16:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 17:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 18:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 19:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 20:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 21:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 22:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+31-Dec-2019 23:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/existing_capacity.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/existing_capacity.csv
new file mode 100644
index 0000000..2bb3610
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/existing_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,2016,comments
+1,MW ,,
+2,MW ,,
+3,MW ,,
+4,MW ,,
+5,MW ,,
+6,MW ,,
+7,MW ,,
+8,MW ,,
+9,MW ,,
+10,MW ,,
+11,MW ,,
+12,MW ,,
+13,MW ,,
+14,MW ,,
+15,MW ,,
+16,MW ,,
+17,MW ,,
+18,MW ,,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/maximum_production_share.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/maximum_production_share.csv
new file mode 100644
index 0000000..5333c85
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/maximum_production_share.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,1,,,,,,,,,
+2,1,,,,,,,,,
+3,1,,,,,,,,,
+4,1,,,,,,,,,
+5,1,,,,,,,,,
+6,1,,,,,,,,,
+7,1,,,,,,,,,
+8,1,,,,,,,,,
+9,1,,,,,,,,,
+10,1,,,,,,,,,
+11,1,,,,,,,,,
+12,1,,,,,,,,,
+13,1,,,,,,,,,
+14,1,,,,,,,,,
+15,1,,,,,,,,,
+16,1,,,,,,,,,
+17,1,,,,,,,,,
+18,1,,,,,,,,,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/potential_capacity.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/potential_capacity.csv
new file mode 100644
index 0000000..c714446
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/DE/potential_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,MW ,,,,,,,,,
+2,MW ,,,,,,,,,
+3,MW ,,,,,,,,,
+4,MW ,,,,,,,,,
+5,MW ,,,,,,,,,
+6,MW ,,,,,,,,,
+7,MW ,,,,,,,,,
+8,MW ,,,,,,,,,
+9,MW ,,,,,,,,,
+10,MW ,,,,,,,,,
+11,MW ,,,,,,,,,
+12,MW ,,,,,,,,,
+13,MW ,,,,,,,,,
+14,MW ,,,,,,,,,
+15,MW ,,,,,,,,,
+16,MW ,,,,,,,,,
+17,MW ,,,,,,,,,
+18,MW ,,,,,,,,,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/README.md
new file mode 100644
index 0000000..bddc9fd
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/README.md
@@ -0,0 +1,6 @@
+# README - NATURAL GAS COMBINED CYCLE
+
+This data package contains all necessary information
+about the production process **natural gas combined cycle**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/costs.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/costs.csv
new file mode 100644
index 0000000..4b2701a
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,kiloEuro * MW ** (-1),700,700,700,700,700,700,700,700,
+operation,kiloEuro * MWh ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * MW ** (-1),24,24,24,24,24,24,24,24,
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_invest.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_invest.csv
new file mode 100644
index 0000000..32eb583
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_invest.csv
@@ -0,0 +1,2 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+gas_power_plant_combined_cycle_400MW_electrical_RER_gas_power_plant_construction_combined_cycle_400MW_electrical,MW ** (-1),0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,MW,,,gas power plant. combined cycle. 400MW electrical,RER,gas power plant construction. combined cycle. 400MW electrical,1,0,1,0.0025,1
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_operation.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_operation.csv
new file mode 100644
index 0000000..704e335
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/ecoinvent_operation.csv
@@ -0,0 +1,3 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+electricity_high_voltage_DE_electricity_production_natural_gas_combined_cycle_power_plant,MWh ** (-1),1000,1000,1000,1000,1000,1000,1000,1000,,,electricity. high voltage,DE,electricity production. natural gas. combined cycle power plant,1,0,1,1000,,1
+gas_power_plant_combined_cycle_400MW_electrical_RER_gas_power_plant_construction_combined_cycle_400MW_electrical,MWh ** (-1),-1e-07,-1e-07,-1e-07,-1e-07,-1e-07,-1e-07,-1e-07,-1e-07,,,gas power plant. combined cycle. 400MW electrical,RER,gas power plant construction. combined cycle. 400MW electrical,-1e-07,0,1,1000,,1
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/lifetime_duration.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/lifetime_duration.csv
new file mode 100644
index 0000000..d9d255b
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,30,30,30,30,30,30,30,30,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/properties.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/properties.csv
new file mode 100644
index 0000000..0f9dc45
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/properties.csv
@@ -0,0 +1,2 @@
+property,value,unit
+reference product,electricity,MW
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/secured_capacity_factor.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/secured_capacity_factor.csv
new file mode 100644
index 0000000..472adb8
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/secured_capacity_factor.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,0.86,0.86,0.86,0.86,0.86,0.86,0.86,0.86,0.86
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix.csv
new file mode 100644
index 0000000..d8d2325
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix.csv
@@ -0,0 +1,3 @@
+product,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+electricity,MW,1,1,1,1,1,1,1,1,
+natural gas,MW,-1.6667,-1.6667,-1.6667,-1.6667,-1.6667,-1.6667,-1.6667,-1.6667,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix_timeseries.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix_timeseries.csv
new file mode 100644
index 0000000..fe37eb2
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/natural_gas_combined_cycle/technologymatrix_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,electricity,natural gas
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diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/README.md
new file mode 100644
index 0000000..e585115
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/README.md
@@ -0,0 +1,4 @@
+# README - WIND OFFSHORE - DE
+
+This data package contains all necessary information
+about the production process **wind offshore** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/availability_timeseries.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/availability_timeseries.csv
new file mode 100644
index 0000000..81df610
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/availability_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18
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+23-Jan-2019 20:00:00,1,0.12519,0.12519,0.12519,0.26157,0.046597,0.046597,0.046597,0.046597,0.26157,0.26157,0.26157,0.26157,0.26157,0.046597,0.071944,0.12519,0.071944,0.071944
+23-Jan-2019 21:00:00,1,0.1293,0.1293,0.1293,0.23511,0.079603,0.079603,0.079603,0.079603,0.23511,0.23511,0.23511,0.23511,0.23511,0.079603,0.073643,0.1293,0.073643,0.073643
+23-Jan-2019 22:00:00,1,0.11458,0.11458,0.11458,0.2097,0.079603,0.079603,0.079603,0.079603,0.2097,0.2097,0.2097,0.2097,0.2097,0.079603,0.072133,0.11458,0.072133,0.072133
+23-Jan-2019 23:00:00,1,0.11774,0.11774,0.11774,0.20123,0.11067,0.11067,0.11067,0.11067,0.20123,0.20123,0.20123,0.20123,0.20123,0.11067,0.070293,0.11774,0.070293,0.070293
+24-Jan-2019 00:00:00,1,0.12466,0.12466,0.12466,0.19694,0.099019,0.099019,0.099019,0.099019,0.19694,0.19694,0.19694,0.19694,0.19694,0.099019,0.060453,0.12466,0.060453,0.060453
+24-Jan-2019 01:00:00,1,0.12839,0.12839,0.12839,0.17411,0.10678,0.10678,0.10678,0.10678,0.17411,0.17411,0.17411,0.17411,0.17411,0.10678,0.051841,0.12839,0.051841,0.051841
+24-Jan-2019 02:00:00,1,0.12643,0.12643,0.12643,0.16547,0.056305,0.056305,0.056305,0.056305,0.16547,0.16547,0.16547,0.16547,0.16547,0.056305,0.059698,0.12643,0.059698,0.059698
+24-Jan-2019 03:00:00,1,0.11974,0.11974,0.11974,0.18082,0.07572,0.07572,0.07572,0.07572,0.18082,0.18082,0.18082,0.18082,0.18082,0.07572,0.067414,0.11974,0.067414,0.067414
+24-Jan-2019 04:00:00,1,0.11876,0.11876,0.11876,0.16288,0.079603,0.079603,0.079603,0.079603,0.16288,0.16288,0.16288,0.16288,0.16288,0.079603,0.065691,0.11876,0.065691,0.065691
+24-Jan-2019 05:00:00,1,0.11974,0.11974,0.11974,0.17466,0.083486,0.083486,0.083486,0.083486,0.17466,0.17466,0.17466,0.17466,0.17466,0.083486,0.051982,0.11974,0.051982,0.051982
+24-Jan-2019 06:00:00,1,0.11138,0.11138,0.11138,0.17312,0.10096,0.10096,0.10096,0.10096,0.17312,0.17312,0.17312,0.17312,0.17312,0.10096,0.047239,0.11138,0.047239,0.047239
+24-Jan-2019 07:00:00,1,0.098485,0.098485,0.098485,0.1636,0.038831,0.038831,0.038831,0.038831,0.1636,0.1636,0.1636,0.1636,0.1636,0.038831,0.04042,0.098485,0.04042,0.04042
+24-Jan-2019 08:00:00,1,0.085705,0.085705,0.085705,0.10809,0.027182,0.027182,0.027182,0.027182,0.10809,0.10809,0.10809,0.10809,0.10809,0.027182,0.037117,0.085705,0.037117,0.037117
+24-Jan-2019 09:00:00,1,0.082481,0.082481,0.082481,0.096432,0.033006,0.033006,0.033006,0.033006,0.096432,0.096432,0.096432,0.096432,0.096432,0.033006,0.030321,0.082481,0.030321,0.030321
+24-Jan-2019 10:00:00,1,0.060529,0.060529,0.060529,0.086035,0.031065,0.031065,0.031065,0.031065,0.086035,0.086035,0.086035,0.086035,0.086035,0.031065,0.028197,0.060529,0.028197,0.028197
+24-Jan-2019 11:00:00,1,0.05193,0.05193,0.05193,0.060511,0.031065,0.031065,0.031065,0.031065,0.060511,0.060511,0.060511,0.060511,0.060511,0.031065,0.021897,0.05193,0.021897,0.021897
+24-Jan-2019 12:00:00,1,0.04517,0.04517,0.04517,0.046758,0.015532,0.015532,0.015532,0.015532,0.046758,0.046758,0.046758,0.046758,0.046758,0.015532,0.022062,0.04517,0.022062,0.022062
+24-Jan-2019 13:00:00,1,0.040536,0.040536,0.040536,0.057045,0.023299,0.023299,0.023299,0.023299,0.057045,0.057045,0.057045,0.057045,0.057045,0.023299,0.018523,0.040536,0.018523,0.018523
+24-Jan-2019 14:00:00,1,0.037407,0.037407,0.037407,0.049509,0.021357,0.021357,0.021357,0.021357,0.049509,0.049509,0.049509,0.049509,0.049509,0.021357,0.014016,0.037407,0.014016,0.014016
+24-Jan-2019 15:00:00,1,0.035042,0.035042,0.035042,0.02943,0.015532,0.015532,0.015532,0.015532,0.02943,0.02943,0.02943,0.02943,0.02943,0.015532,0.011633,0.035042,0.011633,0.011633
+24-Jan-2019 16:00:00,1,0.033274,0.033274,0.033274,0.031191,0.015532,0.015532,0.015532,0.015532,0.031191,0.031191,0.031191,0.031191,0.031191,0.015532,0.011562,0.033274,0.011562,0.011562
+24-Jan-2019 17:00:00,1,0.034588,0.034588,0.034588,0.041147,0.015532,0.015532,0.015532,0.015532,0.041147,0.041147,0.041147,0.041147,0.041147,0.015532,0.010028,0.034588,0.010028,0.010028
+24-Jan-2019 18:00:00,1,0.035352,0.035352,0.035352,0.035701,0.0097077,0.0097077,0.0097077,0.0097077,0.035701,0.035701,0.035701,0.035701,0.035701,0.0097077,0.0077631,0.035352,0.0077631,0.0077631
+24-Jan-2019 19:00:00,1,0.036188,0.036188,0.036188,0.034766,0.0058246,0.0058246,0.0058246,0.0058246,0.034766,0.034766,0.034766,0.034766,0.034766,0.0058246,0.008589,0.036188,0.008589,0.008589
+24-Jan-2019 20:00:00,1,0.034588,0.034588,0.034588,0.026735,0.0038831,0.0038831,0.0038831,0.0038831,0.026735,0.026735,0.026735,0.026735,0.026735,0.0038831,0.0098632,0.034588,0.0098632,0.0098632
+24-Jan-2019 21:00:00,1,0.028807,0.028807,0.028807,0.021289,0.0058246,0.0058246,0.0058246,0.0058246,0.021289,0.021289,0.021289,0.021289,0.021289,0.0058246,0.0059698,0.028807,0.0059698,0.0059698
+24-Jan-2019 22:00:00,1,0.023337,0.023337,0.023337,0.014468,0.0019415,0.0019415,0.0019415,0.0019415,0.014468,0.014468,0.014468,0.014468,0.014468,0.0019415,0.0048844,0.023337,0.0048844,0.0048844
+24-Jan-2019 23:00:00,1,0.024914,0.024914,0.024914,0.018043,0,0,0,0,0.018043,0.018043,0.018043,0.018043,0.018043,0,0.0051911,0.024914,0.0051911,0.0051911
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+25-Jan-2019 03:00:00,1,0.018679,0.018679,0.018679,0.039552,0,0,0,0,0.039552,0.039552,0.039552,0.039552,0.039552,0,0.010972,0.018679,0.010972,0.010972
+25-Jan-2019 04:00:00,1,0.022143,0.022143,0.022143,0.047033,0,0,0,0,0.047033,0.047033,0.047033,0.047033,0.047033,0,0.011161,0.022143,0.011161,0.011161
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+25-Jan-2019 13:00:00,1,0.080068,0.080068,0.080068,0.058145,0.0019415,0.0019415,0.0019415,0.0019415,0.058145,0.058145,0.058145,0.058145,0.058145,0.0019415,0.0020765,0.080068,0.0020765,0.0020765
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+29-Jan-2019 20:00:00,1,0.23294,0.23294,0.23294,0.087521,0.011649,0.011649,0.011649,0.011649,0.087521,0.087521,0.087521,0.087521,0.087521,0.011649,0.28804,0.23294,0.28804,0.28804
+29-Jan-2019 21:00:00,1,0.21653,0.21653,0.21653,0.094287,0.015532,0.015532,0.015532,0.015532,0.094287,0.094287,0.094287,0.094287,0.094287,0.015532,0.28672,0.21653,0.28672,0.28672
+29-Jan-2019 22:00:00,1,0.2091,0.2091,0.2091,0.089831,0.013591,0.013591,0.013591,0.013591,0.089831,0.089831,0.089831,0.089831,0.089831,0.013591,0.27782,0.2091,0.27782,0.27782
+29-Jan-2019 23:00:00,1,0.21964,0.21964,0.21964,0.078719,0.011649,0.011649,0.011649,0.011649,0.078719,0.078719,0.078719,0.078719,0.078719,0.011649,0.2714,0.21964,0.2714,0.2714
+30-Jan-2019 00:00:00,1,0.22131,0.22131,0.22131,0.080314,0.015532,0.015532,0.015532,0.015532,0.080314,0.080314,0.080314,0.080314,0.080314,0.015532,0.27008,0.22131,0.27008,0.27008
+30-Jan-2019 01:00:00,1,0.23923,0.23923,0.23923,0.083835,0.0097077,0.0097077,0.0097077,0.0097077,0.083835,0.083835,0.083835,0.083835,0.083835,0.0097077,0.29344,0.23923,0.29344,0.29344
+30-Jan-2019 02:00:00,1,0.25559,0.25559,0.25559,0.091426,0.0097077,0.0097077,0.0097077,0.0097077,0.091426,0.091426,0.091426,0.091426,0.091426,0.0097077,0.32015,0.25559,0.32015,0.32015
+30-Jan-2019 03:00:00,1,0.26927,0.26927,0.26927,0.087246,0.0097077,0.0097077,0.0097077,0.0097077,0.087246,0.087246,0.087246,0.087246,0.087246,0.0097077,0.32548,0.26927,0.32548,0.32548
+30-Jan-2019 04:00:00,1,0.29402,0.29402,0.29402,0.08279,0.0058246,0.0058246,0.0058246,0.0058246,0.08279,0.08279,0.08279,0.08279,0.08279,0.0058246,0.33254,0.29402,0.33254,0.33254
+30-Jan-2019 05:00:00,1,0.30797,0.30797,0.30797,0.074318,0.0058246,0.0058246,0.0058246,0.0058246,0.074318,0.074318,0.074318,0.074318,0.074318,0.0058246,0.34917,0.30797,0.34917,0.34917
+30-Jan-2019 06:00:00,1,0.31182,0.31182,0.31182,0.079819,0.015532,0.015532,0.015532,0.015532,0.079819,0.079819,0.079819,0.079819,0.079819,0.015532,0.33131,0.31182,0.33131,0.33131
+30-Jan-2019 07:00:00,1,0.32027,0.32027,0.32027,0.08455,0.0077662,0.0077662,0.0077662,0.0077662,0.08455,0.08455,0.08455,0.08455,0.08455,0.0077662,0.3092,0.32027,0.3092,0.3092
+30-Jan-2019 08:00:00,1,0.32063,0.32063,0.32063,0.068597,0.017474,0.017474,0.017474,0.017474,0.068597,0.068597,0.068597,0.068597,0.068597,0.017474,0.30701,0.32063,0.30701,0.30701
+30-Jan-2019 09:00:00,1,0.33482,0.33482,0.33482,0.067827,0.0097077,0.0097077,0.0097077,0.0097077,0.067827,0.067827,0.067827,0.067827,0.067827,0.0097077,0.29051,0.33482,0.29051,0.29051
+30-Jan-2019 10:00:00,1,0.33776,0.33776,0.33776,0.068267,0.0038831,0.0038831,0.0038831,0.0038831,0.068267,0.068267,0.068267,0.068267,0.068267,0.0038831,0.26654,0.33776,0.26654,0.26654
+30-Jan-2019 11:00:00,1,0.339,0.339,0.339,0.066727,0.0038831,0.0038831,0.0038831,0.0038831,0.066727,0.066727,0.066727,0.066727,0.066727,0.0038831,0.24894,0.339,0.24894,0.24894
+30-Jan-2019 12:00:00,1,0.33673,0.33673,0.33673,0.066397,0.0038831,0.0038831,0.0038831,0.0038831,0.066397,0.066397,0.066397,0.066397,0.066397,0.0038831,0.25208,0.33673,0.25208,0.25208
+30-Jan-2019 13:00:00,1,0.35691,0.35691,0.35691,0.068762,0.0058246,0.0058246,0.0058246,0.0058246,0.068762,0.068762,0.068762,0.068762,0.068762,0.0058246,0.27626,0.35691,0.27626,0.27626
+30-Jan-2019 14:00:00,1,0.36322,0.36322,0.36322,0.070358,0.0058246,0.0058246,0.0058246,0.0058246,0.070358,0.070358,0.070358,0.070358,0.070358,0.0058246,0.30092,0.36322,0.30092,0.30092
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+30-Jan-2019 16:00:00,1,0.37461,0.37461,0.37461,0.084825,0.0038831,0.0038831,0.0038831,0.0038831,0.084825,0.084825,0.084825,0.084825,0.084825,0.0038831,0.37327,0.37461,0.37327,0.37327
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+30-Jan-2019 21:00:00,1,0.38171,0.38171,0.38171,0.1636,0.0038831,0.0038831,0.0038831,0.0038831,0.1636,0.1636,0.1636,0.1636,0.1636,0.0038831,0.4051,0.38171,0.4051,0.4051
+30-Jan-2019 22:00:00,1,0.3523,0.3523,0.3523,0.15804,0.0019415,0.0019415,0.0019415,0.0019415,0.15804,0.15804,0.15804,0.15804,0.15804,0.0019415,0.39472,0.3523,0.39472,0.39472
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+31-Jan-2019 03:00:00,1,0.3802,0.3802,0.3802,0.14638,0.019415,0.019415,0.019415,0.019415,0.14638,0.14638,0.14638,0.14638,0.14638,0.019415,0.35158,0.3802,0.35158,0.35158
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+31-Jan-2019 07:00:00,1,0.39838,0.39838,0.39838,0.21206,0.02524,0.02524,0.02524,0.02524,0.21206,0.21206,0.21206,0.21206,0.21206,0.02524,0.31475,0.39838,0.31475,0.31475
+31-Jan-2019 08:00:00,1,0.40025,0.40025,0.40025,0.22345,0.013591,0.013591,0.013591,0.013591,0.22345,0.22345,0.22345,0.22345,0.22345,0.013591,0.3068,0.40025,0.3068,0.3068
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+31-Jan-2019 10:00:00,1,0.42537,0.42537,0.42537,0.21404,0.023299,0.023299,0.023299,0.023299,0.21404,0.21404,0.21404,0.21404,0.21404,0.023299,0.27133,0.42537,0.27133,0.27133
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+31-Jan-2019 12:00:00,1,0.36222,0.36222,0.36222,0.20937,0.052422,0.052422,0.052422,0.052422,0.20937,0.20937,0.20937,0.20937,0.20937,0.052422,0.26189,0.36222,0.26189,0.26189
+31-Jan-2019 13:00:00,1,0.35426,0.35426,0.35426,0.22356,0.058246,0.058246,0.058246,0.058246,0.22356,0.22356,0.22356,0.22356,0.22356,0.058246,0.28832,0.35426,0.28832,0.28832
+31-Jan-2019 14:00:00,1,0.35918,0.35918,0.35918,0.24518,0.062129,0.062129,0.062129,0.062129,0.24518,0.24518,0.24518,0.24518,0.24518,0.062129,0.32305,0.35918,0.32305,0.32305
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+31-Jan-2019 18:00:00,1,0.4345,0.4345,0.4345,0.32203,0.067954,0.067954,0.067954,0.067954,0.32203,0.32203,0.32203,0.32203,0.32203,0.067954,0.39391,0.4345,0.39391,0.39391
+31-Jan-2019 19:00:00,1,0.43713,0.43713,0.43713,0.33528,0.060188,0.060188,0.060188,0.060188,0.33528,0.33528,0.33528,0.33528,0.33528,0.060188,0.36956,0.43713,0.36956,0.36956
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+01-Feb-2019 01:00:00,1,0.30205,0.30205,0.30205,0.22375,0.069544,0.069544,0.069544,0.069544,0.22375,0.22375,0.22375,0.22375,0.22375,0.069544,0.32485,0.30205,0.32485,0.32485
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+01-Feb-2019 03:00:00,1,0.27737,0.27737,0.27737,0.39147,0.08693,0.08693,0.08693,0.08693,0.39147,0.39147,0.39147,0.39147,0.39147,0.08693,0.28903,0.27737,0.28903,0.28903
+01-Feb-2019 04:00:00,1,0.27775,0.27775,0.27775,0.3327,0.06568,0.06568,0.06568,0.06568,0.3327,0.3327,0.3327,0.3327,0.3327,0.06568,0.26484,0.27775,0.26484,0.26484
+01-Feb-2019 05:00:00,1,0.28038,0.28038,0.28038,0.28147,0.069544,0.069544,0.069544,0.069544,0.28147,0.28147,0.28147,0.28147,0.28147,0.069544,0.25482,0.28038,0.25482,0.25482
+01-Feb-2019 06:00:00,1,0.2848,0.2848,0.2848,0.268,0.067612,0.067612,0.067612,0.067612,0.268,0.268,0.268,0.268,0.268,0.067612,0.24742,0.2848,0.24742,0.24742
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+01-Feb-2019 08:00:00,1,0.25268,0.25268,0.25268,0.26631,0.052158,0.052158,0.052158,0.052158,0.26631,0.26631,0.26631,0.26631,0.26631,0.052158,0.23422,0.25268,0.23422,0.23422
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+11-Mar-2019 05:00:00,1,0.38832,0.38832,0.38832,0.1163,0.044208,0.044208,0.044208,0.044208,0.1163,0.1163,0.1163,0.1163,0.1163,0.044208,0.57627,0.38832,0.57627,0.57627
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+11-Mar-2019 10:00:00,1,0.25524,0.25524,0.25524,0.062613,0.023065,0.023065,0.023065,0.023065,0.062613,0.062613,0.062613,0.062613,0.062613,0.023065,0.47351,0.25524,0.47351,0.47351
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+12-Mar-2019 09:00:00,1,0.40637,0.40637,0.40637,0.062451,0.015377,0.015377,0.015377,0.015377,0.062451,0.062451,0.062451,0.062451,0.062451,0.015377,0.41271,0.40637,0.41271,0.41271
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+13-Mar-2019 09:00:00,1,0.1534,0.1534,0.1534,0.013259,0.0019221,0.0019221,0.0019221,0.0019221,0.013259,0.013259,0.013259,0.013259,0.013259,0.0019221,0.24677,0.1534,0.24677,0.24677
+13-Mar-2019 10:00:00,1,0.1721,0.1721,0.1721,0.013421,0.0038442,0.0038442,0.0038442,0.0038442,0.013421,0.013421,0.013421,0.013421,0.013421,0.0038442,0.24146,0.1721,0.24146,0.24146
+13-Mar-2019 11:00:00,1,0.20339,0.20339,0.20339,0.016235,0.0038442,0.0038442,0.0038442,0.0038442,0.016235,0.016235,0.016235,0.016235,0.016235,0.0038442,0.27715,0.20339,0.27715,0.27715
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+13-Mar-2019 13:00:00,1,0.25427,0.25427,0.25427,0.016452,0.0019221,0.0019221,0.0019221,0.0019221,0.016452,0.016452,0.016452,0.016452,0.016452,0.0019221,0.29779,0.25427,0.29779,0.29779
+13-Mar-2019 14:00:00,1,0.26758,0.26758,0.26758,0.017642,0.0019221,0.0019221,0.0019221,0.0019221,0.017642,0.017642,0.017642,0.017642,0.017642,0.0019221,0.30231,0.26758,0.30231,0.30231
+13-Mar-2019 15:00:00,1,0.25595,0.25595,0.25595,0.017913,0.0076884,0.0076884,0.0076884,0.0076884,0.017913,0.017913,0.017913,0.017913,0.017913,0.0076884,0.32412,0.25595,0.32412,0.32412
+13-Mar-2019 16:00:00,1,0.23009,0.23009,0.23009,0.020781,0.0076884,0.0076884,0.0076884,0.0076884,0.020781,0.020781,0.020781,0.020781,0.020781,0.0076884,0.30662,0.23009,0.30662,0.30662
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+13-Mar-2019 18:00:00,1,0.20641,0.20641,0.20641,0.029331,0.015377,0.015377,0.015377,0.015377,0.029331,0.029331,0.029331,0.029331,0.029331,0.015377,0.29665,0.20641,0.29665,0.29665
+13-Mar-2019 19:00:00,1,0.18547,0.18547,0.18547,0.021647,0.0096105,0.0096105,0.0096105,0.0096105,0.021647,0.021647,0.021647,0.021647,0.021647,0.0096105,0.30033,0.18547,0.30033,0.30033
+13-Mar-2019 20:00:00,1,0.16728,0.16728,0.16728,0.02143,0.013455,0.013455,0.013455,0.013455,0.02143,0.02143,0.02143,0.02143,0.02143,0.013455,0.30825,0.16728,0.30825,0.30825
+13-Mar-2019 21:00:00,1,0.15388,0.15388,0.15388,0.020023,0.0057663,0.0057663,0.0057663,0.0057663,0.020023,0.020023,0.020023,0.020023,0.020023,0.0057663,0.29409,0.15388,0.29409,0.29409
+13-Mar-2019 22:00:00,1,0.13202,0.13202,0.13202,0.020673,0.0076884,0.0076884,0.0076884,0.0076884,0.020673,0.020673,0.020673,0.020673,0.020673,0.0076884,0.29854,0.13202,0.29854,0.29854
+13-Mar-2019 23:00:00,1,0.11776,0.11776,0.11776,0.023487,0.0057663,0.0057663,0.0057663,0.0057663,0.023487,0.023487,0.023487,0.023487,0.023487,0.0057663,0.28968,0.11776,0.28968,0.28968
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+14-Mar-2019 01:00:00,1,0.11859,0.11859,0.11859,0.021051,0.0076884,0.0076884,0.0076884,0.0076884,0.021051,0.021051,0.021051,0.021051,0.021051,0.0076884,0.30469,0.11859,0.30469,0.30469
+14-Mar-2019 02:00:00,1,0.12478,0.12478,0.12478,0.013421,0.0057663,0.0057663,0.0057663,0.0057663,0.013421,0.013421,0.013421,0.013421,0.013421,0.0057663,0.29483,0.12478,0.29483,0.29483
+14-Mar-2019 03:00:00,1,0.13525,0.13525,0.13525,0.011148,0.0019221,0.0019221,0.0019221,0.0019221,0.011148,0.011148,0.011148,0.011148,0.011148,0.0019221,0.29975,0.13525,0.29975,0.29975
+14-Mar-2019 04:00:00,1,0.13856,0.13856,0.13856,0.010012,0.0019221,0.0019221,0.0019221,0.0019221,0.010012,0.010012,0.010012,0.010012,0.010012,0.0019221,0.2769,0.13856,0.2769,0.2769
+14-Mar-2019 05:00:00,1,0.14603,0.14603,0.14603,0.010769,0,0,0,0,0.010769,0.010769,0.010769,0.010769,0.010769,0,0.27454,0.14603,0.27454,0.27454
+14-Mar-2019 06:00:00,1,0.14194,0.14194,0.14194,0.0094163,0,0,0,0,0.0094163,0.0094163,0.0094163,0.0094163,0.0094163,0,0.28696,0.14194,0.28696,0.28696
+14-Mar-2019 07:00:00,1,0.12698,0.12698,0.12698,0.0093622,0,0,0,0,0.0093622,0.0093622,0.0093622,0.0093622,0.0093622,0,0.29201,0.12698,0.29201,0.29201
+14-Mar-2019 08:00:00,1,0.11535,0.11535,0.11535,0.0085505,0,0,0,0,0.0085505,0.0085505,0.0085505,0.0085505,0.0085505,0,0.24983,0.11535,0.24983,0.24983
+14-Mar-2019 09:00:00,1,0.094391,0.094391,0.094391,0.0083881,0,0,0,0,0.0083881,0.0083881,0.0083881,0.0083881,0.0083881,0,0.21966,0.094391,0.21966,0.21966
+14-Mar-2019 10:00:00,1,0.084134,0.084134,0.084134,0.0073599,0,0,0,0,0.0073599,0.0073599,0.0073599,0.0073599,0.0073599,0,0.21262,0.084134,0.21262,0.21262
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+14-Mar-2019 19:00:00,1,0.030841,0.030841,0.030841,0.010282,0.013455,0.013455,0.013455,0.013455,0.010282,0.010282,0.010282,0.010282,0.010282,0.013455,0.047034,0.030841,0.047034,0.047034
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+14-Mar-2019 22:00:00,1,0.015952,0.015952,0.015952,0.026247,0.017299,0.017299,0.017299,0.017299,0.026247,0.026247,0.026247,0.026247,0.026247,0.017299,0.033313,0.015952,0.033313,0.033313
+14-Mar-2019 23:00:00,1,0.017347,0.017347,0.017347,0.026301,0.019221,0.019221,0.019221,0.019221,0.026301,0.026301,0.026301,0.026301,0.026301,0.019221,0.03541,0.017347,0.03541,0.03541
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+15-Mar-2019 01:00:00,1,0.017701,0.017701,0.017701,0.049679,0.024987,0.024987,0.024987,0.024987,0.049679,0.049679,0.049679,0.049679,0.049679,0.024987,0.027303,0.017701,0.027303,0.027303
+15-Mar-2019 02:00:00,1,0.020537,0.020537,0.020537,0.056119,0.015377,0.015377,0.015377,0.015377,0.056119,0.056119,0.056119,0.056119,0.056119,0.015377,0.028887,0.020537,0.028887,0.028887
+15-Mar-2019 03:00:00,1,0.024437,0.024437,0.024437,0.058013,0.019221,0.019221,0.019221,0.019221,0.058013,0.058013,0.058013,0.058013,0.058013,0.019221,0.023971,0.024437,0.023971,0.023971
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+15-Mar-2019 08:00:00,1,0.025406,0.025406,0.025406,0.02879,0.0038442,0.0038442,0.0038442,0.0038442,0.02879,0.02879,0.02879,0.02879,0.02879,0.0038442,0.013651,0.025406,0.013651,0.013651
+15-Mar-2019 09:00:00,1,0.024011,0.024011,0.024011,0.012934,0,0,0,0,0.012934,0.012934,0.012934,0.012934,0.012934,0,0.011135,0.024011,0.011135,0.011135
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+19-Mar-2019 14:00:00,1,0.30286,0.30286,0.30286,0.085883,0.015377,0.015377,0.015377,0.015377,0.085883,0.085883,0.085883,0.085883,0.085883,0.015377,0.40346,0.30286,0.40346,0.40346
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+19-Mar-2019 16:00:00,1,0.25262,0.25262,0.25262,0.087886,0.011533,0.011533,0.011533,0.011533,0.087886,0.087886,0.087886,0.087886,0.087886,0.011533,0.33777,0.25262,0.33777,0.33777
+19-Mar-2019 17:00:00,1,0.21234,0.21234,0.21234,0.073491,0.0096105,0.0096105,0.0096105,0.0096105,0.073491,0.073491,0.073491,0.073491,0.073491,0.0096105,0.27589,0.21234,0.27589,0.27589
+19-Mar-2019 18:00:00,1,0.14974,0.14974,0.14974,0.069919,0.024987,0.024987,0.024987,0.024987,0.069919,0.069919,0.069919,0.069919,0.069919,0.024987,0.2167,0.14974,0.2167,0.2167
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+19-Mar-2019 23:00:00,1,0.19034,0.19034,0.19034,0.09898,0.051897,0.051897,0.051897,0.051897,0.09898,0.09898,0.09898,0.09898,0.09898,0.051897,0.24204,0.19034,0.24204,0.24204
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+20-Mar-2019 01:00:00,1,0.21579,0.21579,0.21579,0.091945,0.024987,0.024987,0.024987,0.024987,0.091945,0.091945,0.091945,0.091945,0.091945,0.024987,0.23489,0.21579,0.23489,0.23489
+20-Mar-2019 02:00:00,1,0.23829,0.23829,0.23829,0.11435,0.048053,0.048053,0.048053,0.048053,0.11435,0.11435,0.11435,0.11435,0.11435,0.048053,0.23564,0.23829,0.23564,0.23564
+20-Mar-2019 03:00:00,1,0.2602,0.2602,0.2602,0.14763,0.055741,0.055741,0.055741,0.055741,0.14763,0.14763,0.14763,0.14763,0.14763,0.055741,0.25295,0.2602,0.25295,0.25295
+20-Mar-2019 04:00:00,1,0.28417,0.28417,0.28417,0.15153,0.049975,0.049975,0.049975,0.049975,0.15153,0.15153,0.15153,0.15153,0.15153,0.049975,0.26471,0.28417,0.26471,0.26471
+20-Mar-2019 05:00:00,1,0.31045,0.31045,0.31045,0.16127,0.038442,0.038442,0.038442,0.038442,0.16127,0.16127,0.16127,0.16127,0.16127,0.038442,0.26981,0.31045,0.26981,0.26981
+20-Mar-2019 06:00:00,1,0.33268,0.33268,0.33268,0.16587,0.028832,0.028832,0.028832,0.028832,0.16587,0.16587,0.16587,0.16587,0.16587,0.028832,0.27254,0.33268,0.27254,0.27254
+20-Mar-2019 07:00:00,1,0.32415,0.32415,0.32415,0.15927,0.038442,0.038442,0.038442,0.038442,0.15927,0.15927,0.15927,0.15927,0.15927,0.038442,0.22655,0.32415,0.22655,0.22655
+20-Mar-2019 08:00:00,1,0.28617,0.28617,0.28617,0.10618,0.015377,0.015377,0.015377,0.015377,0.10618,0.10618,0.10618,0.10618,0.10618,0.015377,0.20312,0.28617,0.20312,0.20312
+20-Mar-2019 09:00:00,1,0.27526,0.27526,0.27526,0.073653,0.0096105,0.0096105,0.0096105,0.0096105,0.073653,0.073653,0.073653,0.073653,0.073653,0.0096105,0.21679,0.27526,0.21679,0.21679
+20-Mar-2019 10:00:00,1,0.33727,0.33727,0.33727,0.079876,0.0096105,0.0096105,0.0096105,0.0096105,0.079876,0.079876,0.079876,0.079876,0.079876,0.0096105,0.25476,0.33727,0.25476,0.25476
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+20-Mar-2019 13:00:00,1,0.3778,0.3778,0.3778,0.11251,0.0057663,0.0057663,0.0057663,0.0057663,0.11251,0.11251,0.11251,0.11251,0.11251,0.0057663,0.33222,0.3778,0.33222,0.33222
+20-Mar-2019 14:00:00,1,0.34691,0.34691,0.34691,0.084152,0.0019221,0.0019221,0.0019221,0.0019221,0.084152,0.084152,0.084152,0.084152,0.084152,0.0019221,0.3288,0.34691,0.3288,0.3288
+20-Mar-2019 15:00:00,1,0.31366,0.31366,0.31366,0.06927,0,0,0,0,0.06927,0.06927,0.06927,0.06927,0.06927,0,0.29712,0.31366,0.29712,0.29712
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+20-Mar-2019 20:00:00,1,0.17054,0.17054,0.17054,0.061314,0.021143,0.021143,0.021143,0.021143,0.061314,0.061314,0.061314,0.061314,0.061314,0.021143,0.22748,0.17054,0.22748,0.22748
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+20-Mar-2019 22:00:00,1,0.14631,0.14631,0.14631,0.058013,0.019221,0.019221,0.019221,0.019221,0.058013,0.058013,0.058013,0.058013,0.058013,0.019221,0.23396,0.14631,0.23396,0.23396
+20-Mar-2019 23:00:00,1,0.13308,0.13308,0.13308,0.061369,0.028832,0.028832,0.028832,0.028832,0.061369,0.061369,0.061369,0.061369,0.061369,0.028832,0.21295,0.13308,0.21295,0.21295
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+21-Mar-2019 01:00:00,1,0.13367,0.13367,0.13367,0.051086,0.038442,0.038442,0.038442,0.038442,0.051086,0.051086,0.051086,0.051086,0.051086,0.038442,0.1969,0.13367,0.1969,0.1969
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+21-Mar-2019 03:00:00,1,0.10488,0.10488,0.10488,0.036799,0.03652,0.03652,0.03652,0.03652,0.036799,0.036799,0.036799,0.036799,0.036799,0.03652,0.1821,0.10488,0.1821,0.1821
+21-Mar-2019 04:00:00,1,0.098007,0.098007,0.098007,0.031983,0.030754,0.030754,0.030754,0.030754,0.031983,0.031983,0.031983,0.031983,0.031983,0.030754,0.15715,0.098007,0.15715,0.15715
+21-Mar-2019 05:00:00,1,0.079526,0.079526,0.079526,0.032145,0.032676,0.032676,0.032676,0.032676,0.032145,0.032145,0.032145,0.032145,0.032145,0.032676,0.13705,0.079526,0.13705,0.13705
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+21-Mar-2019 07:00:00,1,0.060099,0.060099,0.060099,0.057418,0.053819,0.053819,0.053819,0.053819,0.057418,0.057418,0.057418,0.057418,0.057418,0.053819,0.10513,0.060099,0.10513,0.10513
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+21-Mar-2019 10:00:00,1,0.028431,0.028431,0.028431,0.029115,0.034598,0.034598,0.034598,0.034598,0.029115,0.029115,0.029115,0.029115,0.029115,0.034598,0.095071,0.028431,0.095071,0.095071
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+21-Mar-2019 13:00:00,1,0.066244,0.066244,0.066244,0.035392,0.017299,0.017299,0.017299,0.017299,0.035392,0.035392,0.035392,0.035392,0.035392,0.017299,0.11958,0.066244,0.11958,0.11958
+21-Mar-2019 14:00:00,1,0.079502,0.079502,0.079502,0.045566,0.026909,0.026909,0.026909,0.026909,0.045566,0.045566,0.045566,0.045566,0.045566,0.026909,0.11264,0.079502,0.11264,0.11264
+21-Mar-2019 15:00:00,1,0.076241,0.076241,0.076241,0.055849,0.024987,0.024987,0.024987,0.024987,0.055849,0.055849,0.055849,0.055849,0.055849,0.024987,0.11499,0.076241,0.11499,0.11499
+21-Mar-2019 16:00:00,1,0.059012,0.059012,0.059012,0.071651,0.017299,0.017299,0.017299,0.017299,0.071651,0.071651,0.071651,0.071651,0.071651,0.017299,0.10905,0.059012,0.10905,0.10905
+21-Mar-2019 17:00:00,1,0.044052,0.044052,0.044052,0.061747,0.034598,0.034598,0.034598,0.034598,0.061747,0.061747,0.061747,0.061747,0.061747,0.034598,0.086451,0.044052,0.086451,0.086451
+21-Mar-2019 18:00:00,1,0.040436,0.040436,0.040436,0.074844,0.034598,0.034598,0.034598,0.034598,0.074844,0.074844,0.074844,0.074844,0.074844,0.034598,0.079672,0.040436,0.079672,0.079672
+21-Mar-2019 19:00:00,1,0.043911,0.043911,0.043911,0.1242,0.034598,0.034598,0.034598,0.034598,0.1242,0.1242,0.1242,0.1242,0.1242,0.034598,0.07874,0.043911,0.07874,0.07874
+21-Mar-2019 20:00:00,1,0.045612,0.045612,0.045612,0.14595,0.049975,0.049975,0.049975,0.049975,0.14595,0.14595,0.14595,0.14595,0.14595,0.049975,0.08093,0.045612,0.08093,0.08093
+21-Mar-2019 21:00:00,1,0.041075,0.041075,0.041075,0.15975,0.028832,0.028832,0.028832,0.028832,0.15975,0.15975,0.15975,0.15975,0.15975,0.028832,0.076038,0.041075,0.076038,0.076038
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+21-Mar-2019 23:00:00,1,0.040389,0.040389,0.040389,0.17999,0.08265,0.08265,0.08265,0.08265,0.17999,0.17999,0.17999,0.17999,0.17999,0.08265,0.063854,0.040389,0.063854,0.063854
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+22-Mar-2019 01:00:00,1,0.047503,0.047503,0.047503,0.16035,0.08265,0.08265,0.08265,0.08265,0.16035,0.16035,0.16035,0.16035,0.16035,0.08265,0.064367,0.047503,0.064367,0.064367
+22-Mar-2019 02:00:00,1,0.046746,0.046746,0.046746,0.18465,0.098027,0.098027,0.098027,0.098027,0.18465,0.18465,0.18465,0.18465,0.18465,0.098027,0.056097,0.046746,0.056097,0.056097
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+22-Mar-2019 12:00:00,1,0.024271,0.024271,0.024271,0.15894,0.030754,0.030754,0.030754,0.030754,0.15894,0.15894,0.15894,0.15894,0.15894,0.030754,0.022085,0.024271,0.022085,0.022085
+22-Mar-2019 13:00:00,1,0.029967,0.029967,0.029967,0.13957,0.044208,0.044208,0.044208,0.044208,0.13957,0.13957,0.13957,0.13957,0.13957,0.044208,0.021991,0.029967,0.021991,0.021991
+22-Mar-2019 14:00:00,1,0.036561,0.036561,0.036561,0.11738,0.053819,0.053819,0.053819,0.053819,0.11738,0.11738,0.11738,0.11738,0.11738,0.053819,0.023715,0.036561,0.023715,0.023715
+22-Mar-2019 15:00:00,1,0.042374,0.042374,0.042374,0.13242,0.028832,0.028832,0.028832,0.028832,0.13242,0.13242,0.13242,0.13242,0.13242,0.028832,0.027256,0.042374,0.027256,0.027256
+22-Mar-2019 16:00:00,1,0.05334,0.05334,0.05334,0.13729,0.042286,0.042286,0.042286,0.042286,0.13729,0.13729,0.13729,0.13729,0.13729,0.042286,0.026883,0.05334,0.026883,0.026883
+22-Mar-2019 17:00:00,1,0.059272,0.059272,0.059272,0.14081,0.055741,0.055741,0.055741,0.055741,0.14081,0.14081,0.14081,0.14081,0.14081,0.055741,0.024647,0.059272,0.024647,0.024647
+22-Mar-2019 18:00:00,1,0.063313,0.063313,0.063313,0.15429,0.048053,0.048053,0.048053,0.048053,0.15429,0.15429,0.15429,0.15429,0.15429,0.048053,0.029143,0.063313,0.029143,0.029143
+22-Mar-2019 19:00:00,1,0.087821,0.087821,0.087821,0.22302,0.051897,0.051897,0.051897,0.051897,0.22302,0.22302,0.22302,0.22302,0.22302,0.051897,0.036435,0.087821,0.036435,0.036435
+22-Mar-2019 20:00:00,1,0.10954,0.10954,0.10954,0.22908,0.078806,0.078806,0.078806,0.078806,0.22908,0.22908,0.22908,0.22908,0.22908,0.078806,0.046196,0.10954,0.046196,0.046196
+22-Mar-2019 21:00:00,1,0.11609,0.11609,0.11609,0.24547,0.080728,0.080728,0.080728,0.080728,0.24547,0.24547,0.24547,0.24547,0.24547,0.080728,0.040372,0.11609,0.040372,0.040372
+22-Mar-2019 22:00:00,1,0.11649,0.11649,0.11649,0.20007,0.059585,0.059585,0.059585,0.059585,0.20007,0.20007,0.20007,0.20007,0.20007,0.059585,0.02891,0.11649,0.02891,0.02891
+22-Mar-2019 23:00:00,1,0.1107,0.1107,0.1107,0.14525,0.048053,0.048053,0.048053,0.048053,0.14525,0.14525,0.14525,0.14525,0.14525,0.048053,0.02297,0.1107,0.02297,0.02297
+23-Mar-2019 00:00:00,1,0.095904,0.095904,0.095904,0.17377,0.034598,0.034598,0.034598,0.034598,0.17377,0.17377,0.17377,0.17377,0.17377,0.034598,0.018963,0.095904,0.018963,0.018963
+23-Mar-2019 01:00:00,1,0.074657,0.074657,0.074657,0.17014,0.03652,0.03652,0.03652,0.03652,0.17014,0.17014,0.17014,0.17014,0.17014,0.03652,0.016936,0.074657,0.016936,0.016936
+23-Mar-2019 02:00:00,1,0.057051,0.057051,0.057051,0.16857,0.024987,0.024987,0.024987,0.024987,0.16857,0.16857,0.16857,0.16857,0.16857,0.024987,0.016284,0.057051,0.016284,0.016284
+23-Mar-2019 03:00:00,1,0.045069,0.045069,0.045069,0.13919,0.017299,0.017299,0.017299,0.017299,0.13919,0.13919,0.13919,0.13919,0.13919,0.017299,0.011438,0.045069,0.011438,0.011438
+23-Mar-2019 04:00:00,1,0.041476,0.041476,0.041476,0.098655,0.015377,0.015377,0.015377,0.015377,0.098655,0.098655,0.098655,0.098655,0.098655,0.015377,0.009132,0.041476,0.009132,0.009132
+23-Mar-2019 05:00:00,1,0.043272,0.043272,0.043272,0.072895,0.015377,0.015377,0.015377,0.015377,0.072895,0.072895,0.072895,0.072895,0.072895,0.015377,0.0073615,0.043272,0.0073615,0.0073615
+23-Mar-2019 06:00:00,1,0.04228,0.04228,0.04228,0.074357,0.011533,0.011533,0.011533,0.011533,0.074357,0.074357,0.074357,0.074357,0.074357,0.011533,0.0074547,0.04228,0.0074547,0.0074547
+23-Mar-2019 07:00:00,1,0.039302,0.039302,0.039302,0.06743,0.013455,0.013455,0.013455,0.013455,0.06743,0.06743,0.06743,0.06743,0.06743,0.013455,0.0069189,0.039302,0.0069189,0.0069189
+23-Mar-2019 08:00:00,1,0.028124,0.028124,0.028124,0.043131,0.0038442,0.0038442,0.0038442,0.0038442,0.043131,0.043131,0.043131,0.043131,0.043131,0.0038442,0.0049853,0.028124,0.0049853,0.0049853
+23-Mar-2019 09:00:00,1,0.015622,0.015622,0.015622,0.019699,0,0,0,0,0.019699,0.019699,0.019699,0.019699,0.019699,0,0.0050319,0.015622,0.0050319,0.0050319
+23-Mar-2019 10:00:00,1,0.012856,0.012856,0.012856,0.021809,0.0019221,0.0019221,0.0019221,0.0019221,0.021809,0.021809,0.021809,0.021809,0.021809,0.0019221,0.0061035,0.012856,0.0061035,0.0061035
+23-Mar-2019 11:00:00,1,0.012573,0.012573,0.012573,0.021863,0.0038442,0.0038442,0.0038442,0.0038442,0.021863,0.021863,0.021863,0.021863,0.021863,0.0038442,0.008503,0.012573,0.008503,0.008503
+23-Mar-2019 12:00:00,1,0.013636,0.013636,0.013636,0.023703,0.0057663,0.0057663,0.0057663,0.0057663,0.023703,0.023703,0.023703,0.023703,0.023703,0.0057663,0.0082933,0.013636,0.0082933,0.0082933
+23-Mar-2019 13:00:00,1,0.014369,0.014369,0.014369,0.023,0.0019221,0.0019221,0.0019221,0.0019221,0.023,0.023,0.023,0.023,0.023,0.0019221,0.0096911,0.014369,0.0096911,0.0096911
+23-Mar-2019 14:00:00,1,0.016236,0.016236,0.016236,0.02511,0.0019221,0.0019221,0.0019221,0.0019221,0.02511,0.02511,0.02511,0.02511,0.02511,0.0019221,0.0095746,0.016236,0.0095746,0.0095746
+23-Mar-2019 15:00:00,1,0.019096,0.019096,0.019096,0.024461,0.0019221,0.0019221,0.0019221,0.0019221,0.024461,0.024461,0.024461,0.024461,0.024461,0.0019221,0.01011,0.019096,0.01011,0.01011
+23-Mar-2019 16:00:00,1,0.02075,0.02075,0.02075,0.02668,0.0038442,0.0038442,0.0038442,0.0038442,0.02668,0.02668,0.02668,0.02668,0.02668,0.0038442,0.012277,0.02075,0.012277,0.012277
+23-Mar-2019 17:00:00,1,0.0242,0.0242,0.0242,0.025164,0.0038442,0.0038442,0.0038442,0.0038442,0.025164,0.025164,0.025164,0.025164,0.025164,0.0038442,0.013582,0.0242,0.013582,0.013582
+23-Mar-2019 18:00:00,1,0.037884,0.037884,0.037884,0.026301,0.017299,0.017299,0.017299,0.017299,0.026301,0.026301,0.026301,0.026301,0.026301,0.017299,0.021362,0.037884,0.021362,0.021362
+23-Mar-2019 19:00:00,1,0.057618,0.057618,0.057618,0.051519,0.023065,0.023065,0.023065,0.023065,0.051519,0.051519,0.051519,0.051519,0.051519,0.023065,0.026604,0.057618,0.026604,0.026604
+23-Mar-2019 20:00:00,1,0.070545,0.070545,0.070545,0.083178,0.026909,0.026909,0.026909,0.026909,0.083178,0.083178,0.083178,0.083178,0.083178,0.026909,0.031752,0.070545,0.031752,0.031752
+23-Mar-2019 21:00:00,1,0.072199,0.072199,0.072199,0.079552,0.030754,0.030754,0.030754,0.030754,0.079552,0.079552,0.079552,0.079552,0.079552,0.030754,0.030075,0.072199,0.030075,0.030075
+23-Mar-2019 22:00:00,1,0.058894,0.058894,0.058894,0.069324,0.030754,0.030754,0.030754,0.030754,0.069324,0.069324,0.069324,0.069324,0.069324,0.030754,0.019592,0.058894,0.019592,0.019592
+23-Mar-2019 23:00:00,1,0.059154,0.059154,0.059154,0.056606,0.038442,0.038442,0.038442,0.038442,0.056606,0.056606,0.056606,0.056606,0.056606,0.038442,0.015771,0.059154,0.015771,0.015771
+24-Mar-2019 00:00:00,1,0.050291,0.050291,0.050291,0.053955,0.023065,0.023065,0.023065,0.023065,0.053955,0.053955,0.053955,0.053955,0.053955,0.023065,0.014653,0.050291,0.014653,0.014653
+24-Mar-2019 01:00:00,1,0.039798,0.039798,0.039798,0.053197,0.017299,0.017299,0.017299,0.017299,0.053197,0.053197,0.053197,0.053197,0.053197,0.017299,0.01067,0.039798,0.01067,0.01067
+24-Mar-2019 02:00:00,1,0.031834,0.031834,0.031834,0.058392,0.0057663,0.0057663,0.0057663,0.0057663,0.058392,0.058392,0.058392,0.058392,0.058392,0.0057663,0.0066626,0.031834,0.0066626,0.0066626
+24-Mar-2019 03:00:00,1,0.026824,0.026824,0.026824,0.042806,0.013455,0.013455,0.013455,0.013455,0.042806,0.042806,0.042806,0.042806,0.042806,0.013455,0.0065927,0.026824,0.0065927,0.0065927
+24-Mar-2019 04:00:00,1,0.019025,0.019025,0.019025,0.038477,0.015377,0.015377,0.015377,0.015377,0.038477,0.038477,0.038477,0.038477,0.038477,0.015377,0.0053348,0.019025,0.0053348,0.0053348
+24-Mar-2019 05:00:00,1,0.018056,0.018056,0.018056,0.031767,0.0096105,0.0096105,0.0096105,0.0096105,0.031767,0.031767,0.031767,0.031767,0.031767,0.0096105,0.0054047,0.018056,0.0054047,0.0054047
+24-Mar-2019 06:00:00,1,0.021364,0.021364,0.021364,0.030847,0.011533,0.011533,0.011533,0.011533,0.030847,0.030847,0.030847,0.030847,0.030847,0.011533,0.0067092,0.021364,0.0067092,0.0067092
+24-Mar-2019 07:00:00,1,0.022026,0.022026,0.022026,0.027816,0.0096105,0.0096105,0.0096105,0.0096105,0.027816,0.027816,0.027816,0.027816,0.027816,0.0096105,0.01025,0.022026,0.01025,0.01025
+24-Mar-2019 08:00:00,1,0.015952,0.015952,0.015952,0.016343,0.0038442,0.0038442,0.0038442,0.0038442,0.016343,0.016343,0.016343,0.016343,0.016343,0.0038442,0.010739,0.015952,0.010739,0.010739
+24-Mar-2019 09:00:00,1,0.01737,0.01737,0.01737,0.0080093,0,0,0,0,0.0080093,0.0080093,0.0080093,0.0080093,0.0080093,0,0.007082,0.01737,0.007082,0.007082
+24-Mar-2019 10:00:00,1,0.017867,0.017867,0.017867,0.010986,0.0019221,0.0019221,0.0019221,0.0019221,0.010986,0.010986,0.010986,0.010986,0.010986,0.0019221,0.011928,0.017867,0.011928,0.011928
+24-Mar-2019 11:00:00,1,0.027273,0.027273,0.027273,0.012501,0.0057663,0.0057663,0.0057663,0.0057663,0.012501,0.012501,0.012501,0.012501,0.012501,0.0057663,0.020384,0.027273,0.020384,0.020384
+24-Mar-2019 12:00:00,1,0.029801,0.029801,0.029801,0.018454,0.0038442,0.0038442,0.0038442,0.0038442,0.018454,0.018454,0.018454,0.018454,0.018454,0.0038442,0.029982,0.029801,0.029982,0.029982
+24-Mar-2019 13:00:00,1,0.034245,0.034245,0.034245,0.015369,0.0076884,0.0076884,0.0076884,0.0076884,0.015369,0.015369,0.015369,0.015369,0.015369,0.0076884,0.041094,0.034245,0.041094,0.041094
+24-Mar-2019 14:00:00,1,0.041311,0.041311,0.041311,0.021106,0.015377,0.015377,0.015377,0.015377,0.021106,0.021106,0.021106,0.021106,0.021106,0.015377,0.054652,0.041311,0.054652,0.054652
+24-Mar-2019 15:00:00,1,0.048188,0.048188,0.048188,0.031658,0.019221,0.019221,0.019221,0.019221,0.031658,0.031658,0.031658,0.031658,0.031658,0.019221,0.065112,0.048188,0.065112,0.065112
+24-Mar-2019 16:00:00,1,0.048803,0.048803,0.048803,0.042806,0.017299,0.017299,0.017299,0.017299,0.042806,0.042806,0.042806,0.042806,0.042806,0.017299,0.07676,0.048803,0.07676,0.07676
+24-Mar-2019 17:00:00,1,0.045541,0.045541,0.045541,0.05547,0.011533,0.011533,0.011533,0.011533,0.05547,0.05547,0.05547,0.05547,0.05547,0.011533,0.087919,0.045541,0.087919,0.087919
+24-Mar-2019 18:00:00,1,0.045943,0.045943,0.045943,0.068079,0.017299,0.017299,0.017299,0.017299,0.068079,0.068079,0.068079,0.068079,0.068079,0.017299,0.084867,0.045943,0.084867,0.084867
+24-Mar-2019 19:00:00,1,0.058776,0.058776,0.058776,0.089293,0.017299,0.017299,0.017299,0.017299,0.089293,0.089293,0.089293,0.089293,0.089293,0.017299,0.097144,0.058776,0.097144,0.097144
+24-Mar-2019 20:00:00,1,0.066598,0.066598,0.066598,0.089293,0.040364,0.040364,0.040364,0.040364,0.089293,0.089293,0.089293,0.089293,0.089293,0.040364,0.10972,0.066598,0.10972,0.10972
+24-Mar-2019 21:00:00,1,0.069623,0.069623,0.069623,0.081662,0.0096105,0.0096105,0.0096105,0.0096105,0.081662,0.081662,0.081662,0.081662,0.081662,0.0096105,0.10479,0.069623,0.10479,0.10479
+24-Mar-2019 22:00:00,1,0.05438,0.05438,0.05438,0.061369,0.013455,0.013455,0.013455,0.013455,0.061369,0.061369,0.061369,0.061369,0.061369,0.013455,0.081512,0.05438,0.081512,0.081512
+24-Mar-2019 23:00:00,1,0.045329,0.045329,0.045329,0.049246,0.0076884,0.0076884,0.0076884,0.0076884,0.049246,0.049246,0.049246,0.049246,0.049246,0.0076884,0.067745,0.045329,0.067745,0.067745
+25-Mar-2019 00:00:00,1,0.036797,0.036797,0.036797,0.039614,0.0076884,0.0076884,0.0076884,0.0076884,0.039614,0.039614,0.039614,0.039614,0.039614,0.0076884,0.063202,0.036797,0.063202,0.063202
+25-Mar-2019 01:00:00,1,0.037175,0.037175,0.037175,0.033823,0.0057663,0.0057663,0.0057663,0.0057663,0.033823,0.033823,0.033823,0.033823,0.033823,0.0057663,0.064693,0.037175,0.064693,0.064693
+25-Mar-2019 02:00:00,1,0.040046,0.040046,0.040046,0.030495,0.0057663,0.0057663,0.0057663,0.0057663,0.030495,0.030495,0.030495,0.030495,0.030495,0.0057663,0.067523,0.040046,0.067523,0.067523
+25-Mar-2019 03:00:00,1,0.042918,0.042918,0.042918,0.027167,0.0096105,0.0096105,0.0096105,0.0096105,0.027167,0.027167,0.027167,0.027167,0.027167,0.0096105,0.070354,0.042918,0.070354,0.070354
+25-Mar-2019 04:00:00,1,0.050244,0.050244,0.050244,0.025651,0.017299,0.017299,0.017299,0.017299,0.025651,0.025651,0.025651,0.025651,0.025651,0.017299,0.081955,0.050244,0.081955,0.081955
+25-Mar-2019 05:00:00,1,0.056223,0.056223,0.056223,0.023541,0.019221,0.019221,0.019221,0.019221,0.023541,0.023541,0.023541,0.023541,0.023541,0.019221,0.088152,0.056223,0.088152,0.088152
+25-Mar-2019 06:00:00,1,0.058799,0.058799,0.058799,0.021809,0.024987,0.024987,0.024987,0.024987,0.021809,0.021809,0.021809,0.021809,0.021809,0.024987,0.093813,0.058799,0.093813,0.093813
+25-Mar-2019 07:00:00,1,0.063408,0.063408,0.063408,0.031225,0.011533,0.011533,0.011533,0.011533,0.031225,0.031225,0.031225,0.031225,0.031225,0.011533,0.095746,0.063408,0.095746,0.095746
+25-Mar-2019 08:00:00,1,0.056791,0.056791,0.056791,0.027654,0.0076884,0.0076884,0.0076884,0.0076884,0.027654,0.027654,0.027654,0.027654,0.027654,0.0076884,0.086218,0.056791,0.086218,0.086218
+25-Mar-2019 09:00:00,1,0.049772,0.049772,0.049772,0.021322,0.0038442,0.0038442,0.0038442,0.0038442,0.021322,0.021322,0.021322,0.021322,0.021322,0.0038442,0.072637,0.049772,0.072637,0.072637
+25-Mar-2019 10:00:00,1,0.046581,0.046581,0.046581,0.013313,0.015377,0.015377,0.015377,0.015377,0.013313,0.013313,0.013313,0.013313,0.013313,0.015377,0.073289,0.046581,0.073289,0.073289
+25-Mar-2019 11:00:00,1,0.046959,0.046959,0.046959,0.022675,0.021143,0.021143,0.021143,0.021143,0.022675,0.022675,0.022675,0.022675,0.022675,0.021143,0.061455,0.046959,0.061455,0.061455
+25-Mar-2019 12:00:00,1,0.050764,0.050764,0.050764,0.039505,0.023065,0.023065,0.023065,0.023065,0.039505,0.039505,0.039505,0.039505,0.039505,0.023065,0.061198,0.050764,0.061198,0.061198
+25-Mar-2019 13:00:00,1,0.070522,0.070522,0.070522,0.040696,0.019221,0.019221,0.019221,0.019221,0.040696,0.040696,0.040696,0.040696,0.040696,0.019221,0.075875,0.070522,0.075875,0.075875
+25-Mar-2019 14:00:00,1,0.090964,0.090964,0.090964,0.043456,0.019221,0.019221,0.019221,0.019221,0.043456,0.043456,0.043456,0.043456,0.043456,0.019221,0.10267,0.090964,0.10267,0.10267
+25-Mar-2019 15:00:00,1,0.10013,0.10013,0.10013,0.050437,0.026909,0.026909,0.026909,0.026909,0.050437,0.050437,0.050437,0.050437,0.050437,0.026909,0.12028,0.10013,0.12028,0.12028
+25-Mar-2019 16:00:00,1,0.11576,0.11576,0.11576,0.066726,0.030754,0.030754,0.030754,0.030754,0.066726,0.066726,0.066726,0.066726,0.066726,0.030754,0.15261,0.11576,0.15261,0.15261
+25-Mar-2019 17:00:00,1,0.11883,0.11883,0.11883,0.086316,0.044208,0.044208,0.044208,0.044208,0.086316,0.086316,0.086316,0.086316,0.086316,0.044208,0.15599,0.11883,0.15599,0.15599
+25-Mar-2019 18:00:00,1,0.10663,0.10663,0.10663,0.075114,0.028832,0.028832,0.028832,0.028832,0.075114,0.075114,0.075114,0.075114,0.075114,0.028832,0.14211,0.10663,0.14211,0.14211
+25-Mar-2019 19:00:00,1,0.10685,0.10685,0.10685,0.079173,0.080728,0.080728,0.080728,0.080728,0.079173,0.079173,0.079173,0.079173,0.079173,0.080728,0.11592,0.10685,0.11592,0.11592
+25-Mar-2019 20:00:00,1,0.11661,0.11661,0.11661,0.12133,0.048053,0.048053,0.048053,0.048053,0.12133,0.12133,0.12133,0.12133,0.12133,0.048053,0.12212,0.11661,0.12212,0.12212
+25-Mar-2019 21:00:00,1,0.12308,0.12308,0.12308,0.13935,0.03652,0.03652,0.03652,0.03652,0.13935,0.13935,0.13935,0.13935,0.13935,0.03652,0.12321,0.12308,0.12321,0.12321
+25-Mar-2019 22:00:00,1,0.11708,0.11708,0.11708,0.17907,0.067274,0.067274,0.067274,0.067274,0.17907,0.17907,0.17907,0.17907,0.17907,0.067274,0.10089,0.11708,0.10089,0.10089
+25-Mar-2019 23:00:00,1,0.11098,0.11098,0.11098,0.14314,0.055741,0.055741,0.055741,0.055741,0.14314,0.14314,0.14314,0.14314,0.14314,0.055741,0.07047,0.11098,0.07047,0.07047
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+26-Mar-2019 01:00:00,1,0.06251,0.06251,0.06251,0.13226,0.059585,0.059585,0.059585,0.059585,0.13226,0.13226,0.13226,0.13226,0.13226,0.059585,0.033639,0.06251,0.033639,0.033639
+26-Mar-2019 02:00:00,1,0.039326,0.039326,0.039326,0.13448,0.069196,0.069196,0.069196,0.069196,0.13448,0.13448,0.13448,0.13448,0.13448,0.069196,0.017565,0.039326,0.017565,0.017565
+26-Mar-2019 03:00:00,1,0.034055,0.034055,0.034055,0.12966,0.065351,0.065351,0.065351,0.065351,0.12966,0.12966,0.12966,0.12966,0.12966,0.065351,0.014187,0.034055,0.014187,0.014187
+26-Mar-2019 04:00:00,1,0.023066,0.023066,0.023066,0.12382,0.04613,0.04613,0.04613,0.04613,0.12382,0.12382,0.12382,0.12382,0.12382,0.04613,0.018171,0.023066,0.018171,0.018171
+26-Mar-2019 05:00:00,1,0.020466,0.020466,0.020466,0.086858,0.063429,0.063429,0.063429,0.063429,0.086858,0.086858,0.086858,0.086858,0.086858,0.063429,0.014374,0.020466,0.014374,0.014374
+26-Mar-2019 06:00:00,1,0.02049,0.02049,0.02049,0.061747,0.028832,0.028832,0.028832,0.028832,0.061747,0.061747,0.061747,0.061747,0.061747,0.028832,0.014443,0.02049,0.014443,0.014443
+26-Mar-2019 07:00:00,1,0.018694,0.018694,0.018694,0.054279,0.019221,0.019221,0.019221,0.019221,0.054279,0.054279,0.054279,0.054279,0.054279,0.019221,0.013512,0.018694,0.013512,0.013512
+26-Mar-2019 08:00:00,1,0.013778,0.013778,0.013778,0.023541,0.0057663,0.0057663,0.0057663,0.0057663,0.023541,0.023541,0.023541,0.023541,0.023541,0.0057663,0.018567,0.013778,0.018567,0.018567
+26-Mar-2019 09:00:00,1,0.012526,0.012526,0.012526,0.012068,0.0019221,0.0019221,0.0019221,0.0019221,0.012068,0.012068,0.012068,0.012068,0.012068,0.0019221,0.013838,0.012526,0.013838,0.013838
+26-Mar-2019 10:00:00,1,0.013022,0.013022,0.013022,0.0087128,0.013455,0.013455,0.013455,0.013455,0.0087128,0.0087128,0.0087128,0.0087128,0.0087128,0.013455,0.0086428,0.013022,0.0086428,0.0086428
+26-Mar-2019 11:00:00,1,0.016071,0.016071,0.016071,0.013205,0.013455,0.013455,0.013455,0.013455,0.013205,0.013205,0.013205,0.013205,0.013205,0.013455,0.014583,0.016071,0.014583,0.014583
+26-Mar-2019 12:00:00,1,0.019356,0.019356,0.019356,0.025922,0.024987,0.024987,0.024987,0.024987,0.025922,0.025922,0.025922,0.025922,0.025922,0.024987,0.023412,0.019356,0.023412,0.023412
+26-Mar-2019 13:00:00,1,0.023421,0.023421,0.023421,0.075493,0.024987,0.024987,0.024987,0.024987,0.075493,0.075493,0.075493,0.075493,0.075493,0.024987,0.023971,0.023421,0.023971,0.023971
+26-Mar-2019 14:00:00,1,0.029163,0.029163,0.029163,0.072084,0.024987,0.024987,0.024987,0.024987,0.072084,0.072084,0.072084,0.072084,0.072084,0.024987,0.018241,0.029163,0.018241,0.018241
+26-Mar-2019 15:00:00,1,0.035497,0.035497,0.035497,0.072787,0.017299,0.017299,0.017299,0.017299,0.072787,0.072787,0.072787,0.072787,0.072787,0.017299,0.017449,0.035497,0.017449,0.017449
+26-Mar-2019 16:00:00,1,0.042445,0.042445,0.042445,0.060611,0.019221,0.019221,0.019221,0.019221,0.060611,0.060611,0.060611,0.060611,0.060611,0.019221,0.015911,0.042445,0.015911,0.015911
+26-Mar-2019 17:00:00,1,0.057311,0.057311,0.057311,0.050329,0.015377,0.015377,0.015377,0.015377,0.050329,0.050329,0.050329,0.050329,0.050329,0.015377,0.016284,0.057311,0.016284,0.016284
+26-Mar-2019 18:00:00,1,0.064637,0.064637,0.064637,0.060178,0.019221,0.019221,0.019221,0.019221,0.060178,0.060178,0.060178,0.060178,0.060178,0.019221,0.014164,0.064637,0.014164,0.014164
+26-Mar-2019 19:00:00,1,0.07565,0.07565,0.07565,0.065536,0.024987,0.024987,0.024987,0.024987,0.065536,0.065536,0.065536,0.065536,0.065536,0.024987,0.023203,0.07565,0.023203,0.023203
+26-Mar-2019 20:00:00,1,0.090232,0.090232,0.090232,0.10677,0.055741,0.055741,0.055741,0.055741,0.10677,0.10677,0.10677,0.10677,0.10677,0.055741,0.038811,0.090232,0.038811,0.038811
+26-Mar-2019 21:00:00,1,0.097558,0.097558,0.097558,0.1275,0.023065,0.023065,0.023065,0.023065,0.1275,0.1275,0.1275,0.1275,0.1275,0.023065,0.061967,0.097558,0.061967,0.061967
+26-Mar-2019 22:00:00,1,0.094107,0.094107,0.094107,0.11283,0.028832,0.028832,0.028832,0.028832,0.11283,0.11283,0.11283,0.11283,0.11283,0.028832,0.079206,0.094107,0.079206,0.079206
+26-Mar-2019 23:00:00,1,0.10335,0.10335,0.10335,0.11213,0.023065,0.023065,0.023065,0.023065,0.11213,0.11213,0.11213,0.11213,0.11213,0.023065,0.10262,0.10335,0.10262,0.10262
+27-Mar-2019 00:00:00,1,0.11264,0.11264,0.11264,0.088643,0.015377,0.015377,0.015377,0.015377,0.088643,0.088643,0.088643,0.088643,0.088643,0.015377,0.13828,0.11264,0.13828,0.13828
+27-Mar-2019 01:00:00,1,0.12251,0.12251,0.12251,0.087128,0.030754,0.030754,0.030754,0.030754,0.087128,0.087128,0.087128,0.087128,0.087128,0.030754,0.16358,0.12251,0.16358,0.16358
+27-Mar-2019 02:00:00,1,0.13237,0.13237,0.13237,0.086208,0.042286,0.042286,0.042286,0.042286,0.086208,0.086208,0.086208,0.086208,0.086208,0.042286,0.1968,0.13237,0.1968,0.1968
+27-Mar-2019 03:00:00,1,0.13691,0.13691,0.13691,0.071488,0.021143,0.021143,0.021143,0.021143,0.071488,0.071488,0.071488,0.071488,0.071488,0.021143,0.22693,0.13691,0.22693,0.22693
+27-Mar-2019 04:00:00,1,0.15454,0.15454,0.15454,0.046162,0.017299,0.017299,0.017299,0.017299,0.046162,0.046162,0.046162,0.046162,0.046162,0.017299,0.2529,0.15454,0.2529,0.2529
+27-Mar-2019 05:00:00,1,0.17089,0.17089,0.17089,0.031334,0.013455,0.013455,0.013455,0.013455,0.031334,0.031334,0.031334,0.031334,0.031334,0.013455,0.27631,0.17089,0.27631,0.27631
+27-Mar-2019 06:00:00,1,0.18921,0.18921,0.18921,0.03404,0.011533,0.011533,0.011533,0.011533,0.03404,0.03404,0.03404,0.03404,0.03404,0.011533,0.28747,0.18921,0.28747,0.28747
+27-Mar-2019 07:00:00,1,0.20225,0.20225,0.20225,0.037719,0.013455,0.013455,0.013455,0.013455,0.037719,0.037719,0.037719,0.037719,0.037719,0.013455,0.29882,0.20225,0.29882,0.29882
+27-Mar-2019 08:00:00,1,0.18616,0.18616,0.18616,0.042969,0.0076884,0.0076884,0.0076884,0.0076884,0.042969,0.042969,0.042969,0.042969,0.042969,0.0076884,0.26485,0.18616,0.26485,0.26485
+27-Mar-2019 09:00:00,1,0.14976,0.14976,0.14976,0.026788,0.011533,0.011533,0.011533,0.011533,0.026788,0.026788,0.026788,0.026788,0.026788,0.011533,0.22129,0.14976,0.22129,0.22129
+27-Mar-2019 10:00:00,1,0.13383,0.13383,0.13383,0.020997,0.019221,0.019221,0.019221,0.019221,0.020997,0.020997,0.020997,0.020997,0.020997,0.019221,0.19058,0.13383,0.19058,0.19058
+27-Mar-2019 11:00:00,1,0.14409,0.14409,0.14409,0.023378,0.015377,0.015377,0.015377,0.015377,0.023378,0.023378,0.023378,0.023378,0.023378,0.015377,0.17148,0.14409,0.17148,0.17148
+27-Mar-2019 12:00:00,1,0.15156,0.15156,0.15156,0.039451,0.015377,0.015377,0.015377,0.015377,0.039451,0.039451,0.039451,0.039451,0.039451,0.015377,0.16084,0.15156,0.16084,0.16084
+27-Mar-2019 13:00:00,1,0.1763,0.1763,0.1763,0.07874,0.015377,0.015377,0.015377,0.015377,0.07874,0.07874,0.07874,0.07874,0.07874,0.015377,0.17791,0.1763,0.17791,0.17791
+27-Mar-2019 14:00:00,1,0.20164,0.20164,0.20164,0.12625,0.013455,0.013455,0.013455,0.013455,0.12625,0.12625,0.12625,0.12625,0.12625,0.013455,0.19242,0.20164,0.19242,0.19242
+27-Mar-2019 15:00:00,1,0.21057,0.21057,0.21057,0.12663,0.013455,0.013455,0.013455,0.013455,0.12663,0.12663,0.12663,0.12663,0.12663,0.013455,0.18949,0.21057,0.18949,0.18949
+27-Mar-2019 16:00:00,1,0.20119,0.20119,0.20119,0.12366,0.0057663,0.0057663,0.0057663,0.0057663,0.12366,0.12366,0.12366,0.12366,0.12366,0.0057663,0.18315,0.20119,0.18315,0.18315
+27-Mar-2019 17:00:00,1,0.18514,0.18514,0.18514,0.11765,0.028832,0.028832,0.028832,0.028832,0.11765,0.11765,0.11765,0.11765,0.11765,0.028832,0.16372,0.18514,0.16372,0.16372
+27-Mar-2019 18:00:00,1,0.16808,0.16808,0.16808,0.10585,0.019221,0.019221,0.019221,0.019221,0.10585,0.10585,0.10585,0.10585,0.10585,0.019221,0.13952,0.16808,0.13952,0.13952
+27-Mar-2019 19:00:00,1,0.1595,0.1595,0.1595,0.089293,0.032676,0.032676,0.032676,0.032676,0.089293,0.089293,0.089293,0.089293,0.089293,0.032676,0.13572,0.1595,0.13572,0.13572
+27-Mar-2019 20:00:00,1,0.17571,0.17571,0.17571,0.10564,0.032676,0.032676,0.032676,0.032676,0.10564,0.10564,0.10564,0.10564,0.10564,0.032676,0.17442,0.17571,0.17442,0.17442
+27-Mar-2019 21:00:00,1,0.19041,0.19041,0.19041,0.098926,0.044208,0.044208,0.044208,0.044208,0.098926,0.098926,0.098926,0.098926,0.098926,0.044208,0.21362,0.19041,0.21362,0.21362
+27-Mar-2019 22:00:00,1,0.1932,0.1932,0.1932,0.080364,0.038442,0.038442,0.038442,0.038442,0.080364,0.080364,0.080364,0.080364,0.080364,0.038442,0.20451,0.1932,0.20451,0.20451
+27-Mar-2019 23:00:00,1,0.18079,0.18079,0.18079,0.076521,0.032676,0.032676,0.032676,0.032676,0.076521,0.076521,0.076521,0.076521,0.076521,0.032676,0.18625,0.18079,0.18625,0.18625
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+28-Mar-2019 01:00:00,1,0.17548,0.17548,0.17548,0.084476,0.024987,0.024987,0.024987,0.024987,0.084476,0.084476,0.084476,0.084476,0.084476,0.024987,0.17689,0.17548,0.17689,0.17689
+28-Mar-2019 02:00:00,1,0.19323,0.19323,0.19323,0.07203,0.0096105,0.0096105,0.0096105,0.0096105,0.07203,0.07203,0.07203,0.07203,0.07203,0.0096105,0.18674,0.19323,0.18674,0.18674
+28-Mar-2019 03:00:00,1,0.18294,0.18294,0.18294,0.055957,0.03652,0.03652,0.03652,0.03652,0.055957,0.055957,0.055957,0.055957,0.055957,0.03652,0.1945,0.18294,0.1945,0.1945
+28-Mar-2019 04:00:00,1,0.19781,0.19781,0.19781,0.073112,0.023065,0.023065,0.023065,0.023065,0.073112,0.073112,0.073112,0.073112,0.073112,0.023065,0.19969,0.19781,0.19969,0.19969
+28-Mar-2019 05:00:00,1,0.20608,0.20608,0.20608,0.080147,0.017299,0.017299,0.017299,0.017299,0.080147,0.080147,0.080147,0.080147,0.080147,0.017299,0.22693,0.20608,0.22693,0.22693
+28-Mar-2019 06:00:00,1,0.20745,0.20745,0.20745,0.082095,0.019221,0.019221,0.019221,0.019221,0.082095,0.082095,0.082095,0.082095,0.082095,0.019221,0.24712,0.20745,0.24712,0.24712
+28-Mar-2019 07:00:00,1,0.21818,0.21818,0.21818,0.093243,0.0076884,0.0076884,0.0076884,0.0076884,0.093243,0.093243,0.093243,0.093243,0.093243,0.0076884,0.25525,0.21818,0.25525,0.25525
+28-Mar-2019 08:00:00,1,0.19564,0.19564,0.19564,0.084585,0.0019221,0.0019221,0.0019221,0.0019221,0.084585,0.084585,0.084585,0.084585,0.084585,0.0019221,0.25111,0.19564,0.25111,0.25111
+28-Mar-2019 09:00:00,1,0.14913,0.14913,0.14913,0.053197,0,0,0,0,0.053197,0.053197,0.053197,0.053197,0.053197,0,0.24528,0.14913,0.24528,0.24528
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+28-Mar-2019 11:00:00,1,0.16964,0.16964,0.16964,0.036042,0.0038442,0.0038442,0.0038442,0.0038442,0.036042,0.036042,0.036042,0.036042,0.036042,0.0038442,0.26553,0.16964,0.26553,0.26553
+28-Mar-2019 12:00:00,1,0.20504,0.20504,0.20504,0.059096,0.0076884,0.0076884,0.0076884,0.0076884,0.059096,0.059096,0.059096,0.059096,0.059096,0.0076884,0.32146,0.20504,0.32146,0.32146
+28-Mar-2019 13:00:00,1,0.26034,0.26034,0.26034,0.073978,0.0096105,0.0096105,0.0096105,0.0096105,0.073978,0.073978,0.073978,0.073978,0.073978,0.0096105,0.3787,0.26034,0.3787,0.3787
+28-Mar-2019 14:00:00,1,0.3501,0.3501,0.3501,0.078415,0.011533,0.011533,0.011533,0.011533,0.078415,0.078415,0.078415,0.078415,0.078415,0.011533,0.45287,0.3501,0.45287,0.45287
+28-Mar-2019 15:00:00,1,0.4418,0.4418,0.4418,0.083448,0.013455,0.013455,0.013455,0.013455,0.083448,0.083448,0.083448,0.083448,0.083448,0.013455,0.54403,0.4418,0.54403,0.54403
+28-Mar-2019 16:00:00,1,0.49939,0.49939,0.49939,0.11889,0.017299,0.017299,0.017299,0.017299,0.11889,0.11889,0.11889,0.11889,0.11889,0.017299,0.58785,0.49939,0.58785,0.58785
+28-Mar-2019 17:00:00,1,0.47923,0.47923,0.47923,0.13773,0.019221,0.019221,0.019221,0.019221,0.13773,0.13773,0.13773,0.13773,0.13773,0.019221,0.63153,0.47923,0.63153,0.63153
+28-Mar-2019 18:00:00,1,0.44761,0.44761,0.44761,0.12993,0.019221,0.019221,0.019221,0.019221,0.12993,0.12993,0.12993,0.12993,0.12993,0.019221,0.60176,0.44761,0.60176,0.60176
+28-Mar-2019 19:00:00,1,0.43294,0.43294,0.43294,0.12425,0.019221,0.019221,0.019221,0.019221,0.12425,0.12425,0.12425,0.12425,0.12425,0.019221,0.59365,0.43294,0.59365,0.59365
+28-Mar-2019 20:00:00,1,0.43726,0.43726,0.43726,0.13123,0.023065,0.023065,0.023065,0.023065,0.13123,0.13123,0.13123,0.13123,0.13123,0.023065,0.65739,0.43726,0.65739,0.65739
+28-Mar-2019 21:00:00,1,0.48722,0.48722,0.48722,0.15975,0.028832,0.028832,0.028832,0.028832,0.15975,0.15975,0.15975,0.15975,0.15975,0.028832,0.60844,0.48722,0.60844,0.60844
+28-Mar-2019 22:00:00,1,0.51116,0.51116,0.51116,0.12847,0.030754,0.030754,0.030754,0.030754,0.12847,0.12847,0.12847,0.12847,0.12847,0.030754,0.5773,0.51116,0.5773,0.5773
+28-Mar-2019 23:00:00,1,0.52102,0.52102,0.52102,0.10585,0.026909,0.026909,0.026909,0.026909,0.10585,0.10585,0.10585,0.10585,0.10585,0.026909,0.58023,0.52102,0.58023,0.58023
+29-Mar-2019 00:00:00,1,0.54337,0.54337,0.54337,0.089022,0.024987,0.024987,0.024987,0.024987,0.089022,0.089022,0.089022,0.089022,0.089022,0.024987,0.57897,0.54337,0.57897,0.57897
+29-Mar-2019 01:00:00,1,0.51398,0.51398,0.51398,0.085342,0.026909,0.026909,0.026909,0.026909,0.085342,0.085342,0.085342,0.085342,0.085342,0.026909,0.64546,0.51398,0.64546,0.64546
+29-Mar-2019 02:00:00,1,0.50195,0.50195,0.50195,0.08426,0.028832,0.028832,0.028832,0.028832,0.08426,0.08426,0.08426,0.08426,0.08426,0.028832,0.77256,0.50195,0.77256,0.77256
+29-Mar-2019 03:00:00,1,0.49289,0.49289,0.49289,0.078307,0.038442,0.038442,0.038442,0.038442,0.078307,0.078307,0.078307,0.078307,0.078307,0.038442,0.72569,0.49289,0.72569,0.72569
+29-Mar-2019 04:00:00,1,0.51012,0.51012,0.51012,0.091512,0.044208,0.044208,0.044208,0.044208,0.091512,0.091512,0.091512,0.091512,0.091512,0.044208,0.69336,0.51012,0.69336,0.69336
+29-Mar-2019 05:00:00,1,0.51721,0.51721,0.51721,0.10028,0.048053,0.048053,0.048053,0.048053,0.10028,0.10028,0.10028,0.10028,0.10028,0.048053,0.65564,0.51721,0.65564,0.65564
+29-Mar-2019 06:00:00,1,0.54215,0.54215,0.54215,0.090267,0.051897,0.051897,0.051897,0.051897,0.090267,0.090267,0.090267,0.090267,0.090267,0.051897,0.57956,0.54215,0.57956,0.57956
+29-Mar-2019 07:00:00,1,0.5603,0.5603,0.5603,0.11451,0.055741,0.055741,0.055741,0.055741,0.11451,0.11451,0.11451,0.11451,0.11451,0.055741,0.53646,0.5603,0.53646,0.53646
+29-Mar-2019 08:00:00,1,0.54586,0.54586,0.54586,0.12577,0.055741,0.055741,0.055741,0.055741,0.12577,0.12577,0.12577,0.12577,0.12577,0.055741,0.60837,0.54586,0.60837,0.60837
+29-Mar-2019 09:00:00,1,0.5754,0.5754,0.5754,0.18394,0.061507,0.061507,0.061507,0.061507,0.18394,0.18394,0.18394,0.18394,0.18394,0.061507,0.68679,0.5754,0.68679,0.68679
+29-Mar-2019 10:00:00,1,0.60915,0.60915,0.60915,0.18064,0.07304,0.07304,0.07304,0.07304,0.18064,0.18064,0.18064,0.18064,0.18064,0.07304,0.70661,0.60915,0.70661,0.70661
+29-Mar-2019 11:00:00,1,0.65804,0.65804,0.65804,0.21587,0.090339,0.090339,0.090339,0.090339,0.21587,0.21587,0.21587,0.21587,0.21587,0.090339,0.72117,0.65804,0.72117,0.72117
+29-Mar-2019 12:00:00,1,0.68983,0.68983,0.68983,0.30554,0.11148,0.11148,0.11148,0.11148,0.30554,0.30554,0.30554,0.30554,0.30554,0.11148,0.71467,0.68983,0.71467,0.71467
+29-Mar-2019 13:00:00,1,0.72344,0.72344,0.72344,0.38666,0.1307,0.1307,0.1307,0.1307,0.38666,0.38666,0.38666,0.38666,0.38666,0.1307,0.71078,0.72344,0.71078,0.71078
+29-Mar-2019 14:00:00,1,0.73986,0.73986,0.73986,0.47872,0.15377,0.15377,0.15377,0.15377,0.47872,0.47872,0.47872,0.47872,0.47872,0.15377,0.71716,0.73986,0.71716,0.71716
+29-Mar-2019 15:00:00,1,0.75151,0.75151,0.75151,0.54485,0.16722,0.16722,0.16722,0.16722,0.54485,0.54485,0.54485,0.54485,0.54485,0.16722,0.74656,0.75151,0.74656,0.74656
+29-Mar-2019 16:00:00,1,0.75421,0.75421,0.75421,0.5212,0.17876,0.17876,0.17876,0.17876,0.5212,0.5212,0.5212,0.5212,0.5212,0.17876,0.7594,0.75421,0.7594,0.7594
+29-Mar-2019 17:00:00,1,0.77032,0.77032,0.77032,0.5015,0.16722,0.16722,0.16722,0.16722,0.5015,0.5015,0.5015,0.5015,0.5015,0.16722,0.72986,0.77032,0.72986,0.72986
+29-Mar-2019 18:00:00,1,0.77524,0.77524,0.77524,0.44673,0.14608,0.14608,0.14608,0.14608,0.44673,0.44673,0.44673,0.44673,0.44673,0.14608,0.75994,0.77524,0.75994,0.75994
+29-Mar-2019 19:00:00,1,0.74272,0.74272,0.74272,0.42574,0.13262,0.13262,0.13262,0.13262,0.42574,0.42574,0.42574,0.42574,0.42574,0.13262,0.75004,0.74272,0.75004,0.75004
+29-Mar-2019 20:00:00,1,0.7106,0.7106,0.7106,0.36767,0.12686,0.12686,0.12686,0.12686,0.36767,0.36767,0.36767,0.36767,0.36767,0.12686,0.74945,0.7106,0.74945,0.74945
+29-Mar-2019 21:00:00,1,0.67818,0.67818,0.67818,0.37211,0.12109,0.12109,0.12109,0.12109,0.37211,0.37211,0.37211,0.37211,0.37211,0.12109,0.76571,0.67818,0.76571,0.76571
+29-Mar-2019 22:00:00,1,0.61427,0.61427,0.61427,0.34104,0.11533,0.11533,0.11533,0.11533,0.34104,0.34104,0.34104,0.34104,0.34104,0.11533,0.76001,0.61427,0.76001,0.76001
+29-Mar-2019 23:00:00,1,0.59485,0.59485,0.59485,0.24185,0.10379,0.10379,0.10379,0.10379,0.24185,0.24185,0.24185,0.24185,0.24185,0.10379,0.70107,0.59485,0.70107,0.70107
+30-Mar-2019 00:00:00,1,0.55037,0.55037,0.55037,0.30213,0.10572,0.10572,0.10572,0.10572,0.30213,0.30213,0.30213,0.30213,0.30213,0.10572,0.66307,0.55037,0.66307,0.66307
+30-Mar-2019 01:00:00,1,0.52834,0.52834,0.52834,0.30673,0.10572,0.10572,0.10572,0.10572,0.30673,0.30673,0.30673,0.30673,0.30673,0.10572,0.61832,0.52834,0.61832,0.61832
+30-Mar-2019 02:00:00,1,0.53066,0.53066,0.53066,0.30408,0.10379,0.10379,0.10379,0.10379,0.30408,0.30408,0.30408,0.30408,0.30408,0.10379,0.55607,0.53066,0.55607,0.55607
+30-Mar-2019 03:00:00,1,0.55647,0.55647,0.55647,0.29651,0.098027,0.098027,0.098027,0.098027,0.29651,0.29651,0.29651,0.29651,0.29651,0.098027,0.50433,0.55647,0.50433,0.50433
+30-Mar-2019 04:00:00,1,0.5459,0.5459,0.5459,0.31561,0.092261,0.092261,0.092261,0.092261,0.31561,0.31561,0.31561,0.31561,0.31561,0.092261,0.47857,0.5459,0.47857,0.47857
+30-Mar-2019 05:00:00,1,0.54411,0.54411,0.54411,0.3162,0.088417,0.088417,0.088417,0.088417,0.3162,0.3162,0.3162,0.3162,0.3162,0.088417,0.43261,0.54411,0.43261,0.43261
+30-Mar-2019 06:00:00,1,0.58532,0.58532,0.58532,0.30976,0.086495,0.086495,0.086495,0.086495,0.30976,0.30976,0.30976,0.30976,0.30976,0.086495,0.41092,0.58532,0.41092,0.41092
+30-Mar-2019 07:00:00,1,0.59381,0.59381,0.59381,0.34272,0.08265,0.08265,0.08265,0.08265,0.34272,0.34272,0.34272,0.34272,0.34272,0.08265,0.42194,0.59381,0.42194,0.42194
+30-Mar-2019 08:00:00,1,0.61855,0.61855,0.61855,0.33439,0.08265,0.08265,0.08265,0.08265,0.33439,0.33439,0.33439,0.33439,0.33439,0.08265,0.43678,0.61855,0.43678,0.43678
+30-Mar-2019 09:00:00,1,0.65136,0.65136,0.65136,0.31182,0.053819,0.053819,0.053819,0.053819,0.31182,0.31182,0.31182,0.31182,0.31182,0.053819,0.45241,0.65136,0.45241,0.45241
+30-Mar-2019 10:00:00,1,0.69449,0.69449,0.69449,0.28574,0.12301,0.12301,0.12301,0.12301,0.28574,0.28574,0.28574,0.28574,0.28574,0.12301,0.53217,0.69449,0.53217,0.53217
+30-Mar-2019 11:00:00,1,0.70746,0.70746,0.70746,0.28752,0.11148,0.11148,0.11148,0.11148,0.28752,0.28752,0.28752,0.28752,0.28752,0.11148,0.55614,0.70746,0.55614,0.55614
+30-Mar-2019 12:00:00,1,0.72374,0.72374,0.72374,0.32405,0.08265,0.08265,0.08265,0.08265,0.32405,0.32405,0.32405,0.32405,0.32405,0.08265,0.6285,0.72374,0.6285,0.6285
+30-Mar-2019 13:00:00,1,0.75279,0.75279,0.75279,0.35739,0.074962,0.074962,0.074962,0.074962,0.35739,0.35739,0.35739,0.35739,0.35739,0.074962,0.70591,0.75279,0.70591,0.70591
+30-Mar-2019 14:00:00,1,0.77184,0.77184,0.77184,0.3319,0.12878,0.12878,0.12878,0.12878,0.3319,0.3319,0.3319,0.3319,0.3319,0.12878,0.79516,0.77184,0.79516,0.79516
+30-Mar-2019 15:00:00,1,0.77689,0.77689,0.77689,0.328,0.10764,0.10764,0.10764,0.10764,0.328,0.328,0.328,0.328,0.328,0.10764,0.84646,0.77689,0.84646,0.84646
+30-Mar-2019 16:00:00,1,0.7599,0.7599,0.7599,0.35338,0.069196,0.069196,0.069196,0.069196,0.35338,0.35338,0.35338,0.35338,0.35338,0.069196,0.8814,0.7599,0.8814,0.8814
+30-Mar-2019 17:00:00,1,0.7305,0.7305,0.7305,0.32973,0.10572,0.10572,0.10572,0.10572,0.32973,0.32973,0.32973,0.32973,0.32973,0.10572,0.87162,0.7305,0.87162,0.87162
+30-Mar-2019 18:00:00,1,0.68555,0.68555,0.68555,0.27345,0.051897,0.051897,0.051897,0.051897,0.27345,0.27345,0.27345,0.27345,0.27345,0.051897,0.83197,0.68555,0.83197,0.83197
+30-Mar-2019 19:00:00,1,0.66402,0.66402,0.66402,0.23498,0.048053,0.048053,0.048053,0.048053,0.23498,0.23498,0.23498,0.23498,0.23498,0.048053,0.76094,0.66402,0.76094,0.76094
+30-Mar-2019 20:00:00,1,0.6337,0.6337,0.6337,0.23898,0.032676,0.032676,0.032676,0.032676,0.23898,0.23898,0.23898,0.23898,0.23898,0.032676,0.75635,0.6337,0.75635,0.75635
+30-Mar-2019 21:00:00,1,0.65819,0.65819,0.65819,0.26014,0.024987,0.024987,0.024987,0.024987,0.26014,0.26014,0.26014,0.26014,0.26014,0.024987,0.77694,0.65819,0.77694,0.77694
+30-Mar-2019 22:00:00,1,0.6756,0.6756,0.6756,0.24596,0.032676,0.032676,0.032676,0.032676,0.24596,0.24596,0.24596,0.24596,0.24596,0.032676,0.77298,0.6756,0.77298,0.77298
+30-Mar-2019 23:00:00,1,0.67142,0.67142,0.67142,0.17593,0.0096105,0.0096105,0.0096105,0.0096105,0.17593,0.17593,0.17593,0.17593,0.17593,0.0096105,0.81172,0.67142,0.81172,0.81172
+31-Mar-2019 00:00:00,1,0.70807,0.70807,0.70807,0.18167,0.028832,0.028832,0.028832,0.028832,0.18167,0.18167,0.18167,0.18167,0.18167,0.028832,0.81722,0.70807,0.81722,0.81722
+31-Mar-2019 01:00:00,1,0.72353,0.72353,0.72353,0.19515,0.030754,0.030754,0.030754,0.030754,0.19515,0.19515,0.19515,0.19515,0.19515,0.030754,0.87301,0.72353,0.87301,0.87301
+31-Mar-2019 02:00:00,1,0.73294,0.73294,0.73294,0.21149,0.021143,0.021143,0.021143,0.021143,0.21149,0.21149,0.21149,0.21149,0.21149,0.021143,0.91618,0.73294,0.91618,0.91618
+31-Mar-2019 03:00:00,1,0.741,0.741,0.741,0.27205,0.026909,0.026909,0.026909,0.026909,0.27205,0.27205,0.27205,0.27205,0.27205,0.026909,0.94614,0.741,0.94614,0.94614
+31-Mar-2019 04:00:00,1,0.73428,0.73428,0.73428,0.30922,0.034598,0.034598,0.034598,0.034598,0.30922,0.30922,0.30922,0.30922,0.30922,0.034598,0.93284,0.73428,0.93284,0.93284
+31-Mar-2019 05:00:00,1,0.73204,0.73204,0.73204,0.31886,0.057663,0.057663,0.057663,0.057663,0.31886,0.31886,0.31886,0.31886,0.31886,0.057663,0.91867,0.73204,0.91867,0.91867
+31-Mar-2019 06:00:00,1,0.7539,0.7539,0.7539,0.33628,0.074962,0.074962,0.074962,0.074962,0.33628,0.33628,0.33628,0.33628,0.33628,0.074962,0.93149,0.7539,0.93149,0.93149
+31-Mar-2019 07:00:00,1,0.75614,0.75614,0.75614,0.36567,0.067274,0.067274,0.067274,0.067274,0.36567,0.36567,0.36567,0.36567,0.36567,0.067274,0.92177,0.75614,0.92177,0.92177
+31-Mar-2019 08:00:00,1,0.78673,0.78673,0.78673,0.33222,0.11148,0.11148,0.11148,0.11148,0.33222,0.33222,0.33222,0.33222,0.33222,0.11148,0.92517,0.78673,0.92517,0.92517
+31-Mar-2019 09:00:00,1,0.8019,0.8019,0.8019,0.35084,0.10764,0.10764,0.10764,0.10764,0.35084,0.35084,0.35084,0.35084,0.35084,0.10764,0.90665,0.8019,0.90665,0.90665
+31-Mar-2019 10:00:00,1,0.80842,0.80842,0.80842,0.40198,0.063429,0.063429,0.063429,0.063429,0.40198,0.40198,0.40198,0.40198,0.40198,0.063429,0.86295,0.80842,0.86295,0.86295
+31-Mar-2019 11:00:00,1,0.78758,0.78758,0.78758,0.44295,0.10956,0.10956,0.10956,0.10956,0.44295,0.44295,0.44295,0.44295,0.44295,0.10956,0.85482,0.78758,0.85482,0.85482
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+12-May-2019 22:00:00,1,0.24281,0.24281,0.24281,0.070932,0.020933,0.020933,0.020933,0.020933,0.070932,0.070932,0.070932,0.070932,0.070932,0.020933,0.31257,0.24281,0.31257,0.31257
+12-May-2019 23:00:00,1,0.21933,0.21933,0.21933,0.059217,0.034255,0.034255,0.034255,0.034255,0.059217,0.059217,0.059217,0.059217,0.059217,0.034255,0.2897,0.21933,0.2897,0.2897
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+13-May-2019 01:00:00,1,0.18579,0.18579,0.18579,0.075778,0.032352,0.032352,0.032352,0.032352,0.075778,0.075778,0.075778,0.075778,0.075778,0.032352,0.24809,0.18579,0.24809,0.24809
+13-May-2019 02:00:00,1,0.17181,0.17181,0.17181,0.079772,0.030449,0.030449,0.030449,0.030449,0.079772,0.079772,0.079772,0.079772,0.079772,0.030449,0.22532,0.17181,0.22532,0.22532
+13-May-2019 03:00:00,1,0.15804,0.15804,0.15804,0.063956,0.039964,0.039964,0.039964,0.039964,0.063956,0.063956,0.063956,0.063956,0.063956,0.039964,0.20556,0.15804,0.20556,0.20556
+13-May-2019 04:00:00,1,0.14286,0.14286,0.14286,0.067045,0.049479,0.049479,0.049479,0.049479,0.067045,0.067045,0.067045,0.067045,0.067045,0.049479,0.18644,0.14286,0.18644,0.18644
+13-May-2019 05:00:00,1,0.1234,0.1234,0.1234,0.056714,0.047576,0.047576,0.047576,0.047576,0.056714,0.056714,0.056714,0.056714,0.056714,0.047576,0.17377,0.1234,0.17377,0.17377
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+13-May-2019 08:00:00,1,0.072868,0.072868,0.072868,0.022686,0.011418,0.011418,0.011418,0.011418,0.022686,0.022686,0.022686,0.022686,0.022686,0.011418,0.11412,0.072868,0.11412,0.11412
+13-May-2019 09:00:00,1,0.073944,0.073944,0.073944,0.031792,0.017127,0.017127,0.017127,0.017127,0.031792,0.031792,0.031792,0.031792,0.031792,0.017127,0.11021,0.073944,0.11021,0.11021
+13-May-2019 10:00:00,1,0.063748,0.063748,0.063748,0.040632,0.017127,0.017127,0.017127,0.017127,0.040632,0.040632,0.040632,0.040632,0.040632,0.017127,0.091898,0.063748,0.091898,0.091898
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+13-May-2019 12:00:00,1,0.051424,0.051424,0.051424,0.034987,0.02474,0.02474,0.02474,0.02474,0.034987,0.034987,0.034987,0.034987,0.034987,0.02474,0.063788,0.051424,0.063788,0.063788
+13-May-2019 13:00:00,1,0.050138,0.050138,0.050138,0.031632,0.020933,0.020933,0.020933,0.020933,0.031632,0.031632,0.031632,0.031632,0.031632,0.020933,0.053966,0.050138,0.053966,0.053966
+13-May-2019 14:00:00,1,0.051962,0.051962,0.051962,0.022792,0.026643,0.026643,0.026643,0.026643,0.022792,0.022792,0.022792,0.022792,0.022792,0.026643,0.044488,0.051962,0.044488,0.044488
+13-May-2019 15:00:00,1,0.05437,0.05437,0.05437,0.02423,0.011418,0.011418,0.011418,0.011418,0.02423,0.02423,0.02423,0.02423,0.02423,0.011418,0.036161,0.05437,0.036161,0.036161
+13-May-2019 16:00:00,1,0.04953,0.04953,0.04953,0.02162,0.015224,0.015224,0.015224,0.015224,0.02162,0.02162,0.02162,0.02162,0.02162,0.015224,0.022106,0.04953,0.022106,0.022106
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+13-May-2019 21:00:00,1,0.052804,0.052804,0.052804,0.028064,0.039964,0.039964,0.039964,0.039964,0.028064,0.028064,0.028064,0.028064,0.028064,0.039964,0.0018863,0.052804,0.0018863,0.0018863
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+13-May-2019 23:00:00,1,0.15154,0.15154,0.15154,0.081263,0.028546,0.028546,0.028546,0.028546,0.081263,0.081263,0.081263,0.081263,0.081263,0.028546,0.0078901,0.15154,0.0078901,0.0078901
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+14-May-2019 01:00:00,1,0.23525,0.23525,0.23525,0.14746,0.032352,0.032352,0.032352,0.032352,0.14746,0.14746,0.14746,0.14746,0.14746,0.032352,0.03462,0.23525,0.03462,0.03462
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+14-May-2019 07:00:00,1,0.2075,0.2075,0.2075,0.11886,0,0,0,0,0.11886,0.11886,0.11886,0.11886,0.11886,0,0.078878,0.2075,0.078878,0.078878
+14-May-2019 08:00:00,1,0.19615,0.19615,0.19615,0.080305,0,0,0,0,0.080305,0.080305,0.080305,0.080305,0.080305,0,0.059855,0.19615,0.059855,0.059855
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+14-May-2019 10:00:00,1,0.23083,0.23083,0.23083,0.11236,0,0,0,0,0.11236,0.11236,0.11236,0.11236,0.11236,0,0.12992,0.23083,0.12992,0.12992
+14-May-2019 11:00:00,1,0.25686,0.25686,0.25686,0.13175,0,0,0,0,0.13175,0.13175,0.13175,0.13175,0.13175,0,0.12383,0.25686,0.12383,0.12383
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+14-May-2019 13:00:00,1,0.24197,0.24197,0.24197,0.11865,0,0,0,0,0.11865,0.11865,0.11865,0.11865,0.11865,0,0.13738,0.24197,0.13738,0.13738
+14-May-2019 14:00:00,1,0.22106,0.22106,0.22106,0.10943,0,0,0,0,0.10943,0.10943,0.10943,0.10943,0.10943,0,0.1446,0.22106,0.1446,0.1446
+14-May-2019 15:00:00,1,0.19718,0.19718,0.19718,0.1064,0,0,0,0,0.1064,0.1064,0.1064,0.1064,0.1064,0,0.15601,0.19718,0.15601,0.15601
+14-May-2019 16:00:00,1,0.14489,0.14489,0.14489,0.0966,0.001903,0.001903,0.001903,0.001903,0.0966,0.0966,0.0966,0.0966,0.0966,0.001903,0.14632,0.14489,0.14632,0.14632
+14-May-2019 17:00:00,1,0.11204,0.11204,0.11204,0.080944,0,0,0,0,0.080944,0.080944,0.080944,0.080944,0.080944,0,0.13123,0.11204,0.13123,0.13123
+14-May-2019 18:00:00,1,0.077147,0.077147,0.077147,0.060175,0.001903,0.001903,0.001903,0.001903,0.060175,0.060175,0.060175,0.060175,0.060175,0.001903,0.10947,0.077147,0.10947,0.10947
+14-May-2019 19:00:00,1,0.051026,0.051026,0.051026,0.051389,0.011418,0.011418,0.011418,0.011418,0.051389,0.051389,0.051389,0.051389,0.051389,0.011418,0.078533,0.051026,0.078533,0.078533
+14-May-2019 20:00:00,1,0.036668,0.036668,0.036668,0.036904,0.01903,0.01903,0.01903,0.01903,0.036904,0.036904,0.036904,0.036904,0.036904,0.01903,0.084169,0.036668,0.084169,0.084169
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+14-May-2019 22:00:00,1,0.06784,0.06784,0.06784,0.054317,0.036158,0.036158,0.036158,0.036158,0.054317,0.054317,0.054317,0.054317,0.054317,0.036158,0.17683,0.06784,0.17683,0.17683
+14-May-2019 23:00:00,1,0.083695,0.083695,0.083695,0.063051,0.038061,0.038061,0.038061,0.038061,0.063051,0.063051,0.063051,0.063051,0.063051,0.038061,0.22235,0.083695,0.22235,0.22235
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+15-May-2019 01:00:00,1,0.087694,0.087694,0.087694,0.049685,0.030449,0.030449,0.030449,0.030449,0.049685,0.049685,0.049685,0.049685,0.049685,0.030449,0.24526,0.087694,0.24526,0.24526
+15-May-2019 02:00:00,1,0.07965,0.07965,0.07965,0.027798,0.038061,0.038061,0.038061,0.038061,0.027798,0.027798,0.027798,0.027798,0.027798,0.038061,0.22304,0.07965,0.22304,0.22304
+15-May-2019 03:00:00,1,0.088185,0.088185,0.088185,0.03062,0.041867,0.041867,0.041867,0.041867,0.03062,0.03062,0.03062,0.03062,0.03062,0.041867,0.21614,0.088185,0.21614,0.21614
+15-May-2019 04:00:00,1,0.085215,0.085215,0.085215,0.045424,0.057091,0.057091,0.057091,0.057091,0.045424,0.045424,0.045424,0.045424,0.045424,0.057091,0.21869,0.085215,0.21869,0.21869
+15-May-2019 05:00:00,1,0.079673,0.079673,0.079673,0.060335,0.034255,0.034255,0.034255,0.034255,0.060335,0.060335,0.060335,0.060335,0.060335,0.034255,0.22594,0.079673,0.22594,0.22594
+15-May-2019 06:00:00,1,0.085005,0.085005,0.085005,0.0704,0.026643,0.026643,0.026643,0.026643,0.0704,0.0704,0.0704,0.0704,0.0704,0.026643,0.20737,0.085005,0.20737,0.20737
+15-May-2019 07:00:00,1,0.083882,0.083882,0.083882,0.088559,0.022837,0.022837,0.022837,0.022837,0.088559,0.088559,0.088559,0.088559,0.088559,0.022837,0.14984,0.083882,0.14984,0.14984
+15-May-2019 08:00:00,1,0.07827,0.07827,0.07827,0.089997,0.039964,0.039964,0.039964,0.039964,0.089997,0.089997,0.089997,0.089997,0.089997,0.039964,0.11683,0.07827,0.11683,0.11683
+15-May-2019 09:00:00,1,0.083298,0.083298,0.083298,0.14149,0.066607,0.066607,0.066607,0.066607,0.14149,0.14149,0.14149,0.14149,0.14149,0.066607,0.14336,0.083298,0.14336,0.14336
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+15-May-2019 12:00:00,1,0.091178,0.091178,0.091178,0.26584,0.055188,0.055188,0.055188,0.055188,0.26584,0.26584,0.26584,0.26584,0.26584,0.055188,0.24975,0.091178,0.24975,0.24975
+15-May-2019 13:00:00,1,0.089354,0.089354,0.089354,0.31872,0.070413,0.070413,0.070413,0.070413,0.31872,0.31872,0.31872,0.31872,0.31872,0.070413,0.26833,0.089354,0.26833,0.26833
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+15-May-2019 19:00:00,1,0.14169,0.14169,0.14169,0.10294,0.079928,0.079928,0.079928,0.079928,0.10294,0.10294,0.10294,0.10294,0.10294,0.079928,0.095234,0.14169,0.095234,0.095234
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+19-May-2019 01:00:00,1,0.10722,0.10722,0.10722,0.082967,0.0057091,0.0057091,0.0057091,0.0057091,0.082967,0.082967,0.082967,0.082967,0.082967,0.0057091,0.18389,0.10722,0.18389,0.18389
+19-May-2019 02:00:00,1,0.092324,0.092324,0.092324,0.091381,0.0038061,0.0038061,0.0038061,0.0038061,0.091381,0.091381,0.091381,0.091381,0.091381,0.0038061,0.16567,0.092324,0.16567,0.16567
+19-May-2019 03:00:00,1,0.077966,0.077966,0.077966,0.077695,0.0038061,0.0038061,0.0038061,0.0038061,0.077695,0.077695,0.077695,0.077695,0.077695,0.0038061,0.14444,0.077966,0.14444,0.14444
+19-May-2019 04:00:00,1,0.077288,0.077288,0.077288,0.067844,0.001903,0.001903,0.001903,0.001903,0.067844,0.067844,0.067844,0.067844,0.067844,0.001903,0.12647,0.077288,0.12647,0.12647
+19-May-2019 05:00:00,1,0.072377,0.072377,0.072377,0.074127,0.0038061,0.0038061,0.0038061,0.0038061,0.074127,0.074127,0.074127,0.074127,0.074127,0.0038061,0.10492,0.072377,0.10492,0.10492
+19-May-2019 06:00:00,1,0.06777,0.06777,0.06777,0.073222,0.001903,0.001903,0.001903,0.001903,0.073222,0.073222,0.073222,0.073222,0.073222,0.001903,0.089736,0.06777,0.089736,0.089736
+19-May-2019 07:00:00,1,0.05568,0.05568,0.05568,0.061933,0,0,0,0,0.061933,0.061933,0.061933,0.061933,0.061933,0,0.060637,0.05568,0.060637,0.060637
+19-May-2019 08:00:00,1,0.035007,0.035007,0.035007,0.044306,0,0,0,0,0.044306,0.044306,0.044306,0.044306,0.044306,0,0.029996,0.035007,0.029996,0.029996
+19-May-2019 09:00:00,1,0.030541,0.030541,0.030541,0.041963,0,0,0,0,0.041963,0.041963,0.041963,0.041963,0.041963,0,0.023072,0.030541,0.023072,0.023072
+19-May-2019 10:00:00,1,0.025934,0.025934,0.025934,0.047608,0.001903,0.001903,0.001903,0.001903,0.047608,0.047608,0.047608,0.047608,0.047608,0.001903,0.017966,0.025934,0.017966,0.017966
+19-May-2019 11:00:00,1,0.028623,0.028623,0.028623,0.048247,0.0057091,0.0057091,0.0057091,0.0057091,0.048247,0.048247,0.048247,0.048247,0.048247,0.0057091,0.030479,0.028623,0.030479,0.030479
+19-May-2019 12:00:00,1,0.032388,0.032388,0.032388,0.035892,0.0095152,0.0095152,0.0095152,0.0095152,0.035892,0.035892,0.035892,0.035892,0.035892,0.0095152,0.039865,0.032388,0.039865,0.039865
+19-May-2019 13:00:00,1,0.031593,0.031593,0.031593,0.0352,0.013321,0.013321,0.013321,0.013321,0.0352,0.0352,0.0352,0.0352,0.0352,0.013321,0.037771,0.031593,0.037771,0.037771
+19-May-2019 14:00:00,1,0.027852,0.027852,0.027852,0.035839,0.0095152,0.0095152,0.0095152,0.0095152,0.035839,0.035839,0.035839,0.035839,0.035839,0.0095152,0.032918,0.027852,0.032918,0.032918
+19-May-2019 15:00:00,1,0.028436,0.028436,0.028436,0.019757,0.0076122,0.0076122,0.0076122,0.0076122,0.019757,0.019757,0.019757,0.019757,0.019757,0.0076122,0.025833,0.028436,0.025833,0.025833
+19-May-2019 16:00:00,1,0.0286,0.0286,0.0286,0.016135,0.0076122,0.0076122,0.0076122,0.0076122,0.016135,0.016135,0.016135,0.016135,0.016135,0.0076122,0.022635,0.0286,0.022635,0.022635
+19-May-2019 17:00:00,1,0.029956,0.029956,0.029956,0.015603,0.0057091,0.0057091,0.0057091,0.0057091,0.015603,0.015603,0.015603,0.015603,0.015603,0.0057091,0.023555,0.029956,0.023555,0.023555
+19-May-2019 18:00:00,1,0.028249,0.028249,0.028249,0.013313,0.0095152,0.0095152,0.0095152,0.0095152,0.013313,0.013313,0.013313,0.013313,0.013313,0.0095152,0.029858,0.028249,0.029858,0.029858
+19-May-2019 19:00:00,1,0.037276,0.037276,0.037276,0.022792,0.0095152,0.0095152,0.0095152,0.0095152,0.022792,0.022792,0.022792,0.022792,0.022792,0.0095152,0.048008,0.037276,0.048008,0.048008
+19-May-2019 20:00:00,1,0.057761,0.057761,0.057761,0.0352,0.020933,0.020933,0.020933,0.020933,0.0352,0.0352,0.0352,0.0352,0.0352,0.020933,0.06648,0.057761,0.06648,0.06648
+19-May-2019 21:00:00,1,0.083625,0.083625,0.083625,0.076044,0.049479,0.049479,0.049479,0.049479,0.076044,0.076044,0.076044,0.076044,0.076044,0.049479,0.12176,0.083625,0.12176,0.12176
+19-May-2019 22:00:00,1,0.10079,0.10079,0.10079,0.10608,0.036158,0.036158,0.036158,0.036158,0.10608,0.10608,0.10608,0.10608,0.10608,0.036158,0.17618,0.10079,0.17618,0.17618
+19-May-2019 23:00:00,1,0.10388,0.10388,0.10388,0.11689,0.017127,0.017127,0.017127,0.017127,0.11689,0.11689,0.11689,0.11689,0.11689,0.017127,0.20172,0.10388,0.20172,0.20172
+20-May-2019 00:00:00,1,0.10521,0.10521,0.10521,0.11641,0.02474,0.02474,0.02474,0.02474,0.11641,0.11641,0.11641,0.11641,0.11641,0.02474,0.19679,0.10521,0.19679,0.19679
+20-May-2019 01:00:00,1,0.098638,0.098638,0.098638,0.10288,0.039964,0.039964,0.039964,0.039964,0.10288,0.10288,0.10288,0.10288,0.10288,0.039964,0.17229,0.098638,0.17229,0.17229
+20-May-2019 02:00:00,1,0.093657,0.093657,0.093657,0.096493,0.022837,0.022837,0.022837,0.022837,0.096493,0.096493,0.096493,0.096493,0.096493,0.022837,0.14165,0.093657,0.14165,0.14165
+20-May-2019 03:00:00,1,0.087928,0.087928,0.087928,0.11385,0.01903,0.01903,0.01903,0.01903,0.11385,0.11385,0.11385,0.11385,0.11385,0.01903,0.11867,0.087928,0.11867,0.11867
+20-May-2019 04:00:00,1,0.078246,0.078246,0.078246,0.098943,0.013321,0.013321,0.013321,0.013321,0.098943,0.098943,0.098943,0.098943,0.098943,0.013321,0.10754,0.078246,0.10754,0.10754
+20-May-2019 05:00:00,1,0.075487,0.075487,0.075487,0.078015,0.0076122,0.0076122,0.0076122,0.0076122,0.078015,0.078015,0.078015,0.078015,0.078015,0.0076122,0.094682,0.075487,0.094682,0.094682
+20-May-2019 06:00:00,1,0.076352,0.076352,0.076352,0.049631,0.001903,0.001903,0.001903,0.001903,0.049631,0.049631,0.049631,0.049631,0.049631,0.001903,0.094613,0.076352,0.094613,0.094613
+20-May-2019 07:00:00,1,0.059702,0.059702,0.059702,0.062625,0.0057091,0.0057091,0.0057091,0.0057091,0.062625,0.062625,0.062625,0.062625,0.062625,0.0057091,0.055622,0.059702,0.055622,0.055622
+20-May-2019 08:00:00,1,0.044525,0.044525,0.044525,0.040312,0.0057091,0.0057091,0.0057091,0.0057091,0.040312,0.040312,0.040312,0.040312,0.040312,0.0057091,0.024314,0.044525,0.024314,0.024314
+20-May-2019 09:00:00,1,0.03903,0.03903,0.03903,0.025135,0.001903,0.001903,0.001903,0.001903,0.025135,0.025135,0.025135,0.025135,0.025135,0.001903,0.041222,0.03903,0.041222,0.041222
+20-May-2019 10:00:00,1,0.051938,0.051938,0.051938,0.02439,0.001903,0.001903,0.001903,0.001903,0.02439,0.02439,0.02439,0.02439,0.02439,0.001903,0.076348,0.051938,0.076348,0.076348
+20-May-2019 11:00:00,1,0.070108,0.070108,0.070108,0.018372,0.0057091,0.0057091,0.0057091,0.0057091,0.018372,0.018372,0.018372,0.018372,0.018372,0.0057091,0.14271,0.070108,0.14271,0.14271
+20-May-2019 12:00:00,1,0.096089,0.096089,0.096089,0.017999,0.001903,0.001903,0.001903,0.001903,0.017999,0.017999,0.017999,0.017999,0.017999,0.001903,0.1642,0.096089,0.1642,0.1642
+20-May-2019 13:00:00,1,0.10161,0.10161,0.10161,0.021407,0.0038061,0.0038061,0.0038061,0.0038061,0.021407,0.021407,0.021407,0.021407,0.021407,0.0038061,0.15918,0.10161,0.15918,0.15918
+20-May-2019 14:00:00,1,0.11566,0.11566,0.11566,0.021727,0.0038061,0.0038061,0.0038061,0.0038061,0.021727,0.021727,0.021727,0.021727,0.021727,0.0038061,0.14839,0.11566,0.14839,0.14839
+20-May-2019 15:00:00,1,0.12113,0.12113,0.12113,0.022313,0.0095152,0.0095152,0.0095152,0.0095152,0.022313,0.022313,0.022313,0.022313,0.022313,0.0095152,0.14435,0.12113,0.14435,0.14435
+20-May-2019 16:00:00,1,0.11903,0.11903,0.11903,0.036638,0.020933,0.020933,0.020933,0.020933,0.036638,0.036638,0.036638,0.036638,0.036638,0.020933,0.15086,0.11903,0.15086,0.15086
+20-May-2019 17:00:00,1,0.11777,0.11777,0.11777,0.035945,0.01903,0.01903,0.01903,0.01903,0.035945,0.035945,0.035945,0.035945,0.035945,0.01903,0.17236,0.11777,0.17236,0.17236
+20-May-2019 18:00:00,1,0.12743,0.12743,0.12743,0.035466,0.028546,0.028546,0.028546,0.028546,0.035466,0.035466,0.035466,0.035466,0.035466,0.028546,0.17719,0.12743,0.17719,0.17719
+20-May-2019 19:00:00,1,0.13865,0.13865,0.13865,0.035732,0.04377,0.04377,0.04377,0.04377,0.035732,0.035732,0.035732,0.035732,0.035732,0.04377,0.17324,0.13865,0.17324,0.17324
+20-May-2019 20:00:00,1,0.15745,0.15745,0.15745,0.049365,0.060897,0.060897,0.060897,0.060897,0.049365,0.049365,0.049365,0.049365,0.049365,0.060897,0.19286,0.15745,0.19286,0.19286
+20-May-2019 21:00:00,1,0.18804,0.18804,0.18804,0.084991,0.026643,0.026643,0.026643,0.026643,0.084991,0.084991,0.084991,0.084991,0.084991,0.026643,0.23284,0.18804,0.23284,0.23284
+20-May-2019 22:00:00,1,0.19711,0.19711,0.19711,0.13382,0.026643,0.026643,0.026643,0.026643,0.13382,0.13382,0.13382,0.13382,0.13382,0.026643,0.23606,0.19711,0.23606,0.23606
+20-May-2019 23:00:00,1,0.18989,0.18989,0.18989,0.15257,0.02474,0.02474,0.02474,0.02474,0.15257,0.15257,0.15257,0.15257,0.15257,0.02474,0.23519,0.18989,0.23519,0.23519
+21-May-2019 00:00:00,1,0.18374,0.18374,0.18374,0.17648,0.011418,0.011418,0.011418,0.011418,0.17648,0.17648,0.17648,0.17648,0.17648,0.011418,0.23433,0.18374,0.23433,0.23433
+21-May-2019 01:00:00,1,0.16568,0.16568,0.16568,0.2031,0.026643,0.026643,0.026643,0.026643,0.2031,0.2031,0.2031,0.2031,0.2031,0.026643,0.21441,0.16568,0.21441,0.21441
+21-May-2019 02:00:00,1,0.15759,0.15759,0.15759,0.1548,0.020933,0.020933,0.020933,0.020933,0.1548,0.1548,0.1548,0.1548,0.1548,0.020933,0.19484,0.15759,0.19484,0.19484
+21-May-2019 03:00:00,1,0.15373,0.15373,0.15373,0.11556,0.036158,0.036158,0.036158,0.036158,0.11556,0.11556,0.11556,0.11556,0.11556,0.036158,0.18198,0.15373,0.18198,0.18198
+21-May-2019 04:00:00,1,0.16653,0.16653,0.16653,0.089198,0.01903,0.01903,0.01903,0.01903,0.089198,0.089198,0.089198,0.089198,0.089198,0.01903,0.17966,0.16653,0.17966,0.17966
+21-May-2019 05:00:00,1,0.17349,0.17349,0.17349,0.096813,0.0095152,0.0095152,0.0095152,0.0095152,0.096813,0.096813,0.096813,0.096813,0.096813,0.0095152,0.17437,0.17349,0.17437,0.17437
+21-May-2019 06:00:00,1,0.1597,0.1597,0.1597,0.11146,0.0076122,0.0076122,0.0076122,0.0076122,0.11146,0.11146,0.11146,0.11146,0.11146,0.0076122,0.1864,0.1597,0.1864,0.1864
+21-May-2019 07:00:00,1,0.10907,0.10907,0.10907,0.11758,0.001903,0.001903,0.001903,0.001903,0.11758,0.11758,0.11758,0.11758,0.11758,0.001903,0.14699,0.10907,0.14699,0.14699
+21-May-2019 08:00:00,1,0.075113,0.075113,0.075113,0.082967,0.001903,0.001903,0.001903,0.001903,0.082967,0.082967,0.082967,0.082967,0.082967,0.001903,0.10476,0.075113,0.10476,0.10476
+21-May-2019 09:00:00,1,0.061596,0.061596,0.061596,0.068749,0.0057091,0.0057091,0.0057091,0.0057091,0.068749,0.068749,0.068749,0.068749,0.068749,0.0057091,0.10851,0.061596,0.10851,0.10851
+21-May-2019 10:00:00,1,0.072611,0.072611,0.072611,0.059163,0.0038061,0.0038061,0.0038061,0.0038061,0.059163,0.059163,0.059163,0.059163,0.059163,0.0038061,0.14623,0.072611,0.14623,0.14623
+21-May-2019 11:00:00,1,0.10126,0.10126,0.10126,0.075192,0.0057091,0.0057091,0.0057091,0.0057091,0.075192,0.075192,0.075192,0.075192,0.075192,0.0057091,0.17025,0.10126,0.17025,0.17025
+21-May-2019 12:00:00,1,0.14464,0.14464,0.14464,0.12142,0.0057091,0.0057091,0.0057091,0.0057091,0.12142,0.12142,0.12142,0.12142,0.12142,0.0057091,0.22385,0.14464,0.22385,0.22385
+21-May-2019 13:00:00,1,0.17932,0.17932,0.17932,0.14447,0.0095152,0.0095152,0.0095152,0.0095152,0.14447,0.14447,0.14447,0.14447,0.14447,0.0095152,0.24604,0.17932,0.24604,0.24604
+21-May-2019 14:00:00,1,0.21114,0.21114,0.21114,0.13382,0.013321,0.013321,0.013321,0.013321,0.13382,0.13382,0.13382,0.13382,0.13382,0.013321,0.28717,0.21114,0.28717,0.28717
+21-May-2019 15:00:00,1,0.26145,0.26145,0.26145,0.12647,0.0095152,0.0095152,0.0095152,0.0095152,0.12647,0.12647,0.12647,0.12647,0.12647,0.0095152,0.29191,0.26145,0.29191,0.29191
+21-May-2019 16:00:00,1,0.28216,0.28216,0.28216,0.14565,0.022837,0.022837,0.022837,0.022837,0.14565,0.14565,0.14565,0.14565,0.14565,0.022837,0.29725,0.28216,0.29725,0.29725
+21-May-2019 17:00:00,1,0.26336,0.26336,0.26336,0.13297,0.055188,0.055188,0.055188,0.055188,0.13297,0.13297,0.13297,0.13297,0.13297,0.055188,0.28697,0.26336,0.28697,0.28697
+21-May-2019 18:00:00,1,0.23509,0.23509,0.23509,0.13271,0.064704,0.064704,0.064704,0.064704,0.13271,0.13271,0.13271,0.13271,0.13271,0.064704,0.29311,0.23509,0.29311,0.29311
+21-May-2019 19:00:00,1,0.20607,0.20607,0.20607,0.09905,0.060897,0.060897,0.060897,0.060897,0.09905,0.09905,0.09905,0.09905,0.09905,0.060897,0.25113,0.20607,0.25113,0.25113
+21-May-2019 20:00:00,1,0.19199,0.19199,0.19199,0.086375,0.051382,0.051382,0.051382,0.051382,0.086375,0.086375,0.086375,0.086375,0.086375,0.051382,0.2566,0.19199,0.2566,0.2566
+21-May-2019 21:00:00,1,0.2122,0.2122,0.2122,0.10666,0.072316,0.072316,0.072316,0.072316,0.10666,0.10666,0.10666,0.10666,0.10666,0.072316,0.28998,0.2122,0.28998,0.28998
+21-May-2019 22:00:00,1,0.23144,0.23144,0.23144,0.16599,0.058994,0.058994,0.058994,0.058994,0.16599,0.16599,0.16599,0.16599,0.16599,0.058994,0.3015,0.23144,0.3015,0.3015
+21-May-2019 23:00:00,1,0.24601,0.24601,0.24601,0.19048,0.039964,0.039964,0.039964,0.039964,0.19048,0.19048,0.19048,0.19048,0.19048,0.039964,0.2822,0.24601,0.2822,0.2822
+22-May-2019 00:00:00,1,0.25359,0.25359,0.25359,0.17004,0.017127,0.017127,0.017127,0.017127,0.17004,0.17004,0.17004,0.17004,0.17004,0.017127,0.25196,0.25359,0.25196,0.25196
+22-May-2019 01:00:00,1,0.24054,0.24054,0.24054,0.14533,0.011418,0.011418,0.011418,0.011418,0.14533,0.14533,0.14533,0.14533,0.14533,0.011418,0.23374,0.24054,0.23374,0.23374
+22-May-2019 02:00:00,1,0.21788,0.21788,0.21788,0.14495,0.015224,0.015224,0.015224,0.015224,0.14495,0.14495,0.14495,0.14495,0.14495,0.015224,0.24176,0.21788,0.24176,0.24176
+22-May-2019 03:00:00,1,0.19863,0.19863,0.19863,0.11614,0.0095152,0.0095152,0.0095152,0.0095152,0.11614,0.11614,0.11614,0.11614,0.11614,0.0095152,0.22872,0.19863,0.22872,0.22872
+22-May-2019 04:00:00,1,0.19489,0.19489,0.19489,0.11412,0.0076122,0.0076122,0.0076122,0.0076122,0.11412,0.11412,0.11412,0.11412,0.11412,0.0076122,0.22474,0.19489,0.22474,0.22474
+22-May-2019 05:00:00,1,0.18392,0.18392,0.18392,0.12546,0.001903,0.001903,0.001903,0.001903,0.12546,0.12546,0.12546,0.12546,0.12546,0.001903,0.19981,0.18392,0.19981,0.19981
+22-May-2019 06:00:00,1,0.16419,0.16419,0.16419,0.11811,0.001903,0.001903,0.001903,0.001903,0.11811,0.11811,0.11811,0.11811,0.11811,0.001903,0.16843,0.16419,0.16843,0.16843
+22-May-2019 07:00:00,1,0.13222,0.13222,0.13222,0.095269,0,0,0,0,0.095269,0.095269,0.095269,0.095269,0.095269,0,0.10876,0.13222,0.10876,0.10876
+22-May-2019 08:00:00,1,0.099387,0.099387,0.099387,0.049312,0.001903,0.001903,0.001903,0.001903,0.049312,0.049312,0.049312,0.049312,0.049312,0.001903,0.062983,0.099387,0.062983,0.062983
+22-May-2019 09:00:00,1,0.088232,0.088232,0.088232,0.029289,0,0,0,0,0.029289,0.029289,0.029289,0.029289,0.029289,0,0.069171,0.088232,0.069171,0.069171
+22-May-2019 10:00:00,1,0.091412,0.091412,0.091412,0.039726,0,0,0,0,0.039726,0.039726,0.039726,0.039726,0.039726,0,0.08111,0.091412,0.08111,0.08111
+22-May-2019 11:00:00,1,0.10914,0.10914,0.10914,0.065447,0.001903,0.001903,0.001903,0.001903,0.065447,0.065447,0.065447,0.065447,0.065447,0.001903,0.097856,0.10914,0.097856,0.097856
+22-May-2019 12:00:00,1,0.15214,0.15214,0.15214,0.055436,0.001903,0.001903,0.001903,0.001903,0.055436,0.055436,0.055436,0.055436,0.055436,0.001903,0.11419,0.15214,0.11419,0.11419
+22-May-2019 13:00:00,1,0.17087,0.17087,0.17087,0.052187,0.001903,0.001903,0.001903,0.001903,0.052187,0.052187,0.052187,0.052187,0.052187,0.001903,0.11801,0.17087,0.11801,0.11801
+22-May-2019 14:00:00,1,0.17092,0.17092,0.17092,0.064116,0.0038061,0.0038061,0.0038061,0.0038061,0.064116,0.064116,0.064116,0.064116,0.064116,0.0038061,0.10554,0.17092,0.10554,0.10554
+22-May-2019 15:00:00,1,0.1669,0.1669,0.1669,0.074979,0.0057091,0.0057091,0.0057091,0.0057091,0.074979,0.074979,0.074979,0.074979,0.074979,0.0057091,0.090311,0.1669,0.090311,0.090311
+22-May-2019 16:00:00,1,0.15394,0.15394,0.15394,0.051708,0.0057091,0.0057091,0.0057091,0.0057091,0.051708,0.051708,0.051708,0.051708,0.051708,0.0057091,0.091484,0.15394,0.091484,0.091484
+22-May-2019 17:00:00,1,0.14875,0.14875,0.14875,0.037703,0.0076122,0.0076122,0.0076122,0.0076122,0.037703,0.037703,0.037703,0.037703,0.037703,0.0076122,0.098247,0.14875,0.098247,0.098247
+22-May-2019 18:00:00,1,0.15661,0.15661,0.15661,0.028863,0.015224,0.015224,0.015224,0.015224,0.028863,0.028863,0.028863,0.028863,0.028863,0.015224,0.10563,0.15661,0.10563,0.10563
+22-May-2019 19:00:00,1,0.16346,0.16346,0.16346,0.02636,0.020933,0.020933,0.020933,0.020933,0.02636,0.02636,0.02636,0.02636,0.02636,0.020933,0.098454,0.16346,0.098454,0.098454
+22-May-2019 20:00:00,1,0.18479,0.18479,0.18479,0.020768,0.045673,0.045673,0.045673,0.045673,0.020768,0.020768,0.020768,0.020768,0.020768,0.045673,0.11718,0.18479,0.11718,0.11718
+22-May-2019 21:00:00,1,0.19559,0.19559,0.19559,0.039673,0.053285,0.053285,0.053285,0.053285,0.039673,0.039673,0.039673,0.039673,0.039673,0.053285,0.13915,0.19559,0.13915,0.13915
+22-May-2019 22:00:00,1,0.20195,0.20195,0.20195,0.06795,0.028546,0.028546,0.028546,0.028546,0.06795,0.06795,0.06795,0.06795,0.06795,0.028546,0.15829,0.20195,0.15829,0.15829
+22-May-2019 23:00:00,1,0.19576,0.19576,0.19576,0.08531,0.02474,0.02474,0.02474,0.02474,0.08531,0.08531,0.08531,0.08531,0.08531,0.02474,0.15309,0.19576,0.15309,0.15309
+23-May-2019 00:00:00,1,0.17565,0.17565,0.17565,0.08366,0.017127,0.017127,0.017127,0.017127,0.08366,0.08366,0.08366,0.08366,0.08366,0.017127,0.13459,0.17565,0.13459,0.13459
+23-May-2019 01:00:00,1,0.16613,0.16613,0.16613,0.074926,0.020933,0.020933,0.020933,0.020933,0.074926,0.074926,0.074926,0.074926,0.074926,0.020933,0.12123,0.16613,0.12123,0.12123
+23-May-2019 02:00:00,1,0.15348,0.15348,0.15348,0.060069,0.026643,0.026643,0.026643,0.026643,0.060069,0.060069,0.060069,0.060069,0.060069,0.026643,0.11085,0.15348,0.11085,0.11085
+23-May-2019 03:00:00,1,0.13989,0.13989,0.13989,0.055116,0.036158,0.036158,0.036158,0.036158,0.055116,0.055116,0.055116,0.055116,0.055116,0.036158,0.096568,0.13989,0.096568,0.096568
+23-May-2019 04:00:00,1,0.12824,0.12824,0.12824,0.046383,0.032352,0.032352,0.032352,0.032352,0.046383,0.046383,0.046383,0.046383,0.046383,0.032352,0.093278,0.12824,0.093278,0.093278
+23-May-2019 05:00:00,1,0.13746,0.13746,0.13746,0.03701,0.011418,0.011418,0.011418,0.011418,0.03701,0.03701,0.03701,0.03701,0.03701,0.011418,0.095878,0.13746,0.095878,0.095878
+23-May-2019 06:00:00,1,0.13479,0.13479,0.13479,0.029023,0.0095152,0.0095152,0.0095152,0.0095152,0.029023,0.029023,0.029023,0.029023,0.029023,0.0095152,0.07644,0.13479,0.07644,0.07644
+23-May-2019 07:00:00,1,0.11241,0.11241,0.11241,0.016349,0.0057091,0.0057091,0.0057091,0.0057091,0.016349,0.016349,0.016349,0.016349,0.016349,0.0057091,0.040233,0.11241,0.040233,0.040233
+23-May-2019 08:00:00,1,0.10402,0.10402,0.10402,0.0085204,0.0038061,0.0038061,0.0038061,0.0038061,0.0085204,0.0085204,0.0085204,0.0085204,0.0085204,0.0038061,0.03416,0.10402,0.03416,0.03416
+23-May-2019 09:00:00,1,0.092652,0.092652,0.092652,0.0077749,0,0,0,0,0.0077749,0.0077749,0.0077749,0.0077749,0.0077749,0,0.038347,0.092652,0.038347,0.038347
+23-May-2019 10:00:00,1,0.10163,0.10163,0.10163,0.010757,0,0,0,0,0.010757,0.010757,0.010757,0.010757,0.010757,0,0.046674,0.10163,0.046674,0.046674
+23-May-2019 11:00:00,1,0.10879,0.10879,0.10879,0.019384,0.001903,0.001903,0.001903,0.001903,0.019384,0.019384,0.019384,0.019384,0.019384,0.001903,0.072897,0.10879,0.072897,0.072897
+23-May-2019 12:00:00,1,0.12214,0.12214,0.12214,0.024177,0.0038061,0.0038061,0.0038061,0.0038061,0.024177,0.024177,0.024177,0.024177,0.024177,0.0038061,0.10726,0.12214,0.10726,0.10726
+23-May-2019 13:00:00,1,0.12467,0.12467,0.12467,0.043774,0.0038061,0.0038061,0.0038061,0.0038061,0.043774,0.043774,0.043774,0.043774,0.043774,0.0038061,0.14745,0.12467,0.14745,0.14745
+23-May-2019 14:00:00,1,0.14922,0.14922,0.14922,0.031099,0.001903,0.001903,0.001903,0.001903,0.031099,0.031099,0.031099,0.031099,0.031099,0.001903,0.17466,0.14922,0.17466,0.17466
+23-May-2019 15:00:00,1,0.16035,0.16035,0.16035,0.022153,0.0076122,0.0076122,0.0076122,0.0076122,0.022153,0.022153,0.022153,0.022153,0.022153,0.0076122,0.19472,0.16035,0.19472,0.19472
+23-May-2019 16:00:00,1,0.18212,0.18212,0.18212,0.075459,0.039964,0.039964,0.039964,0.039964,0.075459,0.075459,0.075459,0.075459,0.075459,0.039964,0.2342,0.18212,0.2342,0.2342
+23-May-2019 17:00:00,1,0.20235,0.20235,0.20235,0.11513,0.020933,0.020933,0.020933,0.020933,0.11513,0.11513,0.11513,0.11513,0.11513,0.020933,0.27234,0.20235,0.27234,0.27234
+23-May-2019 18:00:00,1,0.22924,0.22924,0.22924,0.098198,0.02474,0.02474,0.02474,0.02474,0.098198,0.098198,0.098198,0.098198,0.098198,0.02474,0.29624,0.22924,0.29624,0.29624
+23-May-2019 19:00:00,1,0.25041,0.25041,0.25041,0.078973,0.039964,0.039964,0.039964,0.039964,0.078973,0.078973,0.078973,0.078973,0.078973,0.039964,0.30728,0.25041,0.30728,0.30728
+23-May-2019 20:00:00,1,0.28962,0.28962,0.28962,0.088932,0.026643,0.026643,0.026643,0.026643,0.088932,0.088932,0.088932,0.088932,0.088932,0.026643,0.31659,0.28962,0.31659,0.31659
+23-May-2019 21:00:00,1,0.29196,0.29196,0.29196,0.060282,0.030449,0.030449,0.030449,0.030449,0.060282,0.060282,0.060282,0.060282,0.060282,0.030449,0.31137,0.29196,0.31137,0.31137
+23-May-2019 22:00:00,1,0.26734,0.26734,0.26734,0.098996,0.045673,0.045673,0.045673,0.045673,0.098996,0.098996,0.098996,0.098996,0.098996,0.045673,0.31899,0.26734,0.31899,0.31899
+23-May-2019 23:00:00,1,0.23387,0.23387,0.23387,0.091008,0.013321,0.013321,0.013321,0.013321,0.091008,0.091008,0.091008,0.091008,0.091008,0.013321,0.28639,0.23387,0.28639,0.28639
+24-May-2019 00:00:00,1,0.21421,0.21421,0.21421,0.12605,0.0076122,0.0076122,0.0076122,0.0076122,0.12605,0.12605,0.12605,0.12605,0.12605,0.0076122,0.25881,0.21421,0.25881,0.25881
+24-May-2019 01:00:00,1,0.22482,0.22482,0.22482,0.1203,0.017127,0.017127,0.017127,0.017127,0.1203,0.1203,0.1203,0.1203,0.1203,0.017127,0.2255,0.22482,0.2255,0.2255
+24-May-2019 02:00:00,1,0.23598,0.23598,0.23598,0.12237,0.011418,0.011418,0.011418,0.011418,0.12237,0.12237,0.12237,0.12237,0.12237,0.011418,0.22102,0.23598,0.22102,0.22102
+24-May-2019 03:00:00,1,0.22181,0.22181,0.22181,0.13649,0.013321,0.013321,0.013321,0.013321,0.13649,0.13649,0.13649,0.13649,0.13649,0.013321,0.20908,0.22181,0.20908,0.20908
+24-May-2019 04:00:00,1,0.20745,0.20745,0.20745,0.1392,0.0095152,0.0095152,0.0095152,0.0095152,0.1392,0.1392,0.1392,0.1392,0.1392,0.0095152,0.19518,0.20745,0.19518,0.19518
+24-May-2019 05:00:00,1,0.20464,0.20464,0.20464,0.13814,0.0095152,0.0095152,0.0095152,0.0095152,0.13814,0.13814,0.13814,0.13814,0.13814,0.0095152,0.17402,0.20464,0.17402,0.17402
+24-May-2019 06:00:00,1,0.18282,0.18282,0.18282,0.11918,0.0095152,0.0095152,0.0095152,0.0095152,0.11918,0.11918,0.11918,0.11918,0.11918,0.0095152,0.1307,0.18282,0.1307,0.1307
+24-May-2019 07:00:00,1,0.14945,0.14945,0.14945,0.097665,0.013321,0.013321,0.013321,0.013321,0.097665,0.097665,0.097665,0.097665,0.097665,0.013321,0.085296,0.14945,0.085296,0.085296
+24-May-2019 08:00:00,1,0.14192,0.14192,0.14192,0.046489,0.0057091,0.0057091,0.0057091,0.0057091,0.046489,0.046489,0.046489,0.046489,0.046489,0.0057091,0.067768,0.14192,0.067768,0.067768
+24-May-2019 09:00:00,1,0.16557,0.16557,0.16557,0.031632,0.0057091,0.0057091,0.0057091,0.0057091,0.031632,0.031632,0.031632,0.031632,0.031632,0.0057091,0.078096,0.16557,0.078096,0.078096
+24-May-2019 10:00:00,1,0.19491,0.19491,0.19491,0.0352,0.01903,0.01903,0.01903,0.01903,0.0352,0.0352,0.0352,0.0352,0.0352,0.01903,0.14517,0.19491,0.14517,0.14517
+24-May-2019 11:00:00,1,0.21163,0.21163,0.21163,0.056607,0.020933,0.020933,0.020933,0.020933,0.056607,0.056607,0.056607,0.056607,0.056607,0.020933,0.20206,0.21163,0.20206,0.20206
+24-May-2019 12:00:00,1,0.22595,0.22595,0.22595,0.092073,0.034255,0.034255,0.034255,0.034255,0.092073,0.092073,0.092073,0.092073,0.092073,0.034255,0.23958,0.22595,0.23958,0.23958
+24-May-2019 13:00:00,1,0.23949,0.23949,0.23949,0.13563,0.051382,0.051382,0.051382,0.051382,0.13563,0.13563,0.13563,0.13563,0.13563,0.051382,0.26615,0.23949,0.26615,0.26615
+24-May-2019 14:00:00,1,0.27089,0.27089,0.27089,0.14948,0.0628,0.0628,0.0628,0.0628,0.14948,0.14948,0.14948,0.14948,0.14948,0.0628,0.30118,0.27089,0.30118,0.30118
+24-May-2019 15:00:00,1,0.31315,0.31315,0.31315,0.15294,0.053285,0.053285,0.053285,0.053285,0.15294,0.15294,0.15294,0.15294,0.15294,0.053285,0.3396,0.31315,0.3396,0.3396
+24-May-2019 16:00:00,1,0.3248,0.3248,0.3248,0.19032,0.066607,0.066607,0.066607,0.066607,0.19032,0.19032,0.19032,0.19032,0.19032,0.066607,0.37109,0.3248,0.37109,0.37109
+24-May-2019 17:00:00,1,0.33382,0.33382,0.33382,0.19277,0.034255,0.034255,0.034255,0.034255,0.19277,0.19277,0.19277,0.19277,0.19277,0.034255,0.39432,0.33382,0.39432,0.39432
+24-May-2019 18:00:00,1,0.34112,0.34112,0.34112,0.20444,0.026643,0.026643,0.026643,0.026643,0.20444,0.20444,0.20444,0.20444,0.20444,0.026643,0.39375,0.34112,0.39375,0.39375
+24-May-2019 19:00:00,1,0.33717,0.33717,0.33717,0.19405,0.049479,0.049479,0.049479,0.049479,0.19405,0.19405,0.19405,0.19405,0.19405,0.049479,0.37456,0.33717,0.37456,0.37456
+24-May-2019 20:00:00,1,0.28867,0.28867,0.28867,0.14682,0.070413,0.070413,0.070413,0.070413,0.14682,0.14682,0.14682,0.14682,0.14682,0.070413,0.33099,0.28867,0.33099,0.33099
+24-May-2019 21:00:00,1,0.28705,0.28705,0.28705,0.091221,0.11989,0.11989,0.11989,0.11989,0.091221,0.091221,0.091221,0.091221,0.091221,0.11989,0.31094,0.28705,0.31094,0.31094
+24-May-2019 22:00:00,1,0.28712,0.28712,0.28712,0,0.039964,0.039964,0.039964,0.039964,0,0,0,0,0,0.039964,0.31091,0.28712,0.31091,0.31091
+24-May-2019 23:00:00,1,0.25307,0.25307,0.25307,0,0.076122,0.076122,0.076122,0.076122,0,0,0,0,0,0.076122,0.30167,0.25307,0.30167,0.30167
+25-May-2019 00:00:00,1,0.22931,0.22931,0.22931,0.3258,0.079928,0.079928,0.079928,0.079928,0.3258,0.3258,0.3258,0.3258,0.3258,0.079928,0.25653,0.22931,0.25653,0.25653
+25-May-2019 01:00:00,1,0.21058,0.21058,0.21058,0.31392,0.091346,0.091346,0.091346,0.091346,0.31392,0.31392,0.31392,0.31392,0.31392,0.091346,0.22226,0.21058,0.22226,0.22226
+25-May-2019 02:00:00,1,0.17555,0.17555,0.17555,0.36238,0.089443,0.089443,0.089443,0.089443,0.36238,0.36238,0.36238,0.36238,0.36238,0.089443,0.17834,0.17555,0.17834,0.17834
+25-May-2019 03:00:00,1,0.16905,0.16905,0.16905,0.31067,0.06851,0.06851,0.06851,0.06851,0.31067,0.31067,0.31067,0.31067,0.31067,0.06851,0.15656,0.16905,0.15656,0.15656
+25-May-2019 04:00:00,1,0.16007,0.16007,0.16007,0.26775,0.10086,0.10086,0.10086,0.10086,0.26775,0.26775,0.26775,0.26775,0.26775,0.10086,0.13832,0.16007,0.13832,0.13832
+25-May-2019 05:00:00,1,0.143,0.143,0.143,0.25609,0.06851,0.06851,0.06851,0.06851,0.25609,0.25609,0.25609,0.25609,0.25609,0.06851,0.13052,0.143,0.13052,0.13052
+25-May-2019 06:00:00,1,0.13004,0.13004,0.13004,0.22329,0.055188,0.055188,0.055188,0.055188,0.22329,0.22329,0.22329,0.22329,0.22329,0.055188,0.098339,0.13004,0.098339,0.098339
+25-May-2019 07:00:00,1,0.085075,0.085075,0.085075,0.15763,0.036158,0.036158,0.036158,0.036158,0.15763,0.15763,0.15763,0.15763,0.15763,0.036158,0.035195,0.085075,0.035195,0.035195
+25-May-2019 08:00:00,1,0.052757,0.052757,0.052757,0.13143,0.045673,0.045673,0.045673,0.045673,0.13143,0.13143,0.13143,0.13143,0.13143,0.045673,0.017114,0.052757,0.017114,0.017114
+25-May-2019 09:00:00,1,0.045811,0.045811,0.045811,0.18527,0.045673,0.045673,0.045673,0.045673,0.18527,0.18527,0.18527,0.18527,0.18527,0.045673,0.028685,0.045811,0.028685,0.028685
+25-May-2019 10:00:00,1,0.064403,0.064403,0.064403,0.21402,0.058994,0.058994,0.058994,0.058994,0.21402,0.21402,0.21402,0.21402,0.21402,0.058994,0.060936,0.064403,0.060936,0.060936
+25-May-2019 11:00:00,1,0.089261,0.089261,0.089261,0.30077,0.083734,0.083734,0.083734,0.083734,0.30077,0.30077,0.30077,0.30077,0.30077,0.083734,0.099006,0.089261,0.099006,0.099006
+25-May-2019 12:00:00,1,0.10825,0.10825,0.10825,0.35605,0.076122,0.076122,0.076122,0.076122,0.35605,0.35605,0.35605,0.35605,0.35605,0.076122,0.11122,0.10825,0.11122,0.11122
+25-May-2019 13:00:00,1,0.12955,0.12955,0.12955,0.33869,0.072316,0.072316,0.072316,0.072316,0.33869,0.33869,0.33869,0.33869,0.33869,0.072316,0.12422,0.12955,0.12422,0.12422
+25-May-2019 14:00:00,1,0.13795,0.13795,0.13795,0.38134,0.083734,0.083734,0.083734,0.083734,0.38134,0.38134,0.38134,0.38134,0.38134,0.083734,0.13972,0.13795,0.13972,0.13972
+25-May-2019 15:00:00,1,0.14408,0.14408,0.14408,0.37042,0.098958,0.098958,0.098958,0.098958,0.37042,0.37042,0.37042,0.37042,0.37042,0.098958,0.16075,0.14408,0.16075,0.16075
+25-May-2019 16:00:00,1,0.15097,0.15097,0.15097,0.35658,0.049479,0.049479,0.049479,0.049479,0.35658,0.35658,0.35658,0.35658,0.35658,0.049479,0.1774,0.15097,0.1774,0.1774
+25-May-2019 17:00:00,1,0.17045,0.17045,0.17045,0.36957,0.032352,0.032352,0.032352,0.032352,0.36957,0.36957,0.36957,0.36957,0.36957,0.032352,0.19689,0.17045,0.19689,0.19689
+25-May-2019 18:00:00,1,0.18975,0.18975,0.18975,0.35211,0.072316,0.072316,0.072316,0.072316,0.35211,0.35211,0.35211,0.35211,0.35211,0.072316,0.20022,0.18975,0.20022,0.20022
+25-May-2019 19:00:00,1,0.173,0.173,0.173,0.30109,0.051382,0.051382,0.051382,0.051382,0.30109,0.30109,0.30109,0.30109,0.30109,0.051382,0.17823,0.173,0.17823,0.17823
+25-May-2019 20:00:00,1,0.15193,0.15193,0.15193,0.2341,0.081831,0.081831,0.081831,0.081831,0.2341,0.2341,0.2341,0.2341,0.2341,0.081831,0.14674,0.15193,0.14674,0.14674
+25-May-2019 21:00:00,1,0.15871,0.15871,0.15871,0.23564,0.0628,0.0628,0.0628,0.0628,0.23564,0.23564,0.23564,0.23564,0.23564,0.0628,0.16921,0.15871,0.16921,0.16921
+25-May-2019 22:00:00,1,0.18767,0.18767,0.18767,0.24139,0.039964,0.039964,0.039964,0.039964,0.24139,0.24139,0.24139,0.24139,0.24139,0.039964,0.19587,0.18767,0.19587,0.19587
+25-May-2019 23:00:00,1,0.19971,0.19971,0.19971,0.19267,0.074219,0.074219,0.074219,0.074219,0.19267,0.19267,0.19267,0.19267,0.19267,0.074219,0.19677,0.19971,0.19677,0.19677
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+26-May-2019 01:00:00,1,0.1955,0.1955,0.1955,0.31115,0.039964,0.039964,0.039964,0.039964,0.31115,0.31115,0.31115,0.31115,0.31115,0.039964,0.15932,0.1955,0.15932,0.15932
+26-May-2019 02:00:00,1,0.17373,0.17373,0.17373,0.26653,0.08754,0.08754,0.08754,0.08754,0.26653,0.26653,0.26653,0.26653,0.26653,0.08754,0.13574,0.17373,0.13574,0.13574
+26-May-2019 03:00:00,1,0.16016,0.16016,0.16016,0.25167,0.093249,0.093249,0.093249,0.093249,0.25167,0.25167,0.25167,0.25167,0.25167,0.093249,0.12054,0.16016,0.12054,0.12054
+26-May-2019 04:00:00,1,0.15025,0.15025,0.15025,0.26695,0.06851,0.06851,0.06851,0.06851,0.26695,0.26695,0.26695,0.26695,0.26695,0.06851,0.10667,0.15025,0.10667,0.10667
+26-May-2019 05:00:00,1,0.14054,0.14054,0.14054,0.2522,0.030449,0.030449,0.030449,0.030449,0.2522,0.2522,0.2522,0.2522,0.2522,0.030449,0.078096,0.14054,0.078096,0.078096
+26-May-2019 06:00:00,1,0.11405,0.11405,0.11405,0.20987,0.041867,0.041867,0.041867,0.041867,0.20987,0.20987,0.20987,0.20987,0.20987,0.041867,0.052701,0.11405,0.052701,0.052701
+26-May-2019 07:00:00,1,0.065361,0.065361,0.065361,0.12935,0.034255,0.034255,0.034255,0.034255,0.12935,0.12935,0.12935,0.12935,0.12935,0.034255,0.014906,0.065361,0.014906,0.014906
+26-May-2019 08:00:00,1,0.031219,0.031219,0.031219,0.080251,0.038061,0.038061,0.038061,0.038061,0.080251,0.080251,0.080251,0.080251,0.080251,0.038061,0.0026914,0.031219,0.0026914,0.0026914
+26-May-2019 09:00:00,1,0.027033,0.027033,0.027033,0.16599,0.049479,0.049479,0.049479,0.049479,0.16599,0.16599,0.16599,0.16599,0.16599,0.049479,0.0034505,0.027033,0.0034505,0.0034505
+26-May-2019 10:00:00,1,0.039404,0.039404,0.039404,0.21418,0.079928,0.079928,0.079928,0.079928,0.21418,0.21418,0.21418,0.21418,0.21418,0.079928,0.017575,0.039404,0.017575,0.017575
+26-May-2019 11:00:00,1,0.058673,0.058673,0.058673,0.22707,0.079928,0.079928,0.079928,0.079928,0.22707,0.22707,0.22707,0.22707,0.22707,0.079928,0.025419,0.058673,0.025419,0.025419
+26-May-2019 12:00:00,1,0.081637,0.081637,0.081637,0.23372,0.04377,0.04377,0.04377,0.04377,0.23372,0.23372,0.23372,0.23372,0.23372,0.04377,0.030732,0.081637,0.030732,0.030732
+26-May-2019 13:00:00,1,0.090804,0.090804,0.090804,0.23799,0.051382,0.051382,0.051382,0.051382,0.23799,0.23799,0.23799,0.23799,0.23799,0.051382,0.029145,0.090804,0.029145,0.029145
+26-May-2019 14:00:00,1,0.10357,0.10357,0.10357,0.2333,0.030449,0.030449,0.030449,0.030449,0.2333,0.2333,0.2333,0.2333,0.2333,0.030449,0.036115,0.10357,0.036115,0.036115
+26-May-2019 15:00:00,1,0.11134,0.11134,0.11134,0.23106,0.026643,0.026643,0.026643,0.026643,0.23106,0.23106,0.23106,0.23106,0.23106,0.026643,0.042096,0.11134,0.042096,0.042096
+26-May-2019 16:00:00,1,0.11283,0.11283,0.11283,0.2439,0.032352,0.032352,0.032352,0.032352,0.2439,0.2439,0.2439,0.2439,0.2439,0.032352,0.052517,0.11283,0.052517,0.052517
+26-May-2019 17:00:00,1,0.12483,0.12483,0.12483,0.25902,0.045673,0.045673,0.045673,0.045673,0.25902,0.25902,0.25902,0.25902,0.25902,0.045673,0.063351,0.12483,0.063351,0.063351
+26-May-2019 18:00:00,1,0.12064,0.12064,0.12064,0.22329,0.026643,0.026643,0.026643,0.026643,0.22329,0.22329,0.22329,0.22329,0.22329,0.026643,0.069585,0.12064,0.069585,0.069585
+26-May-2019 19:00:00,1,0.10961,0.10961,0.10961,0.20183,0.034255,0.034255,0.034255,0.034255,0.20183,0.20183,0.20183,0.20183,0.20183,0.034255,0.066687,0.10961,0.066687,0.066687
+26-May-2019 20:00:00,1,0.097773,0.097773,0.097773,0.12285,0.0628,0.0628,0.0628,0.0628,0.12285,0.12285,0.12285,0.12285,0.12285,0.0628,0.066457,0.097773,0.066457,0.066457
+26-May-2019 21:00:00,1,0.1072,0.1072,0.1072,0.14224,0.038061,0.038061,0.038061,0.038061,0.14224,0.14224,0.14224,0.14224,0.14224,0.038061,0.070919,0.1072,0.070919,0.070919
+26-May-2019 22:00:00,1,0.11068,0.11068,0.11068,0.18649,0.053285,0.053285,0.053285,0.053285,0.18649,0.18649,0.18649,0.18649,0.18649,0.053285,0.07713,0.11068,0.07713,0.07713
+26-May-2019 23:00:00,1,0.11309,0.11309,0.11309,0.18681,0.051382,0.051382,0.051382,0.051382,0.18681,0.18681,0.18681,0.18681,0.18681,0.051382,0.071701,0.11309,0.071701,0.071701
+27-May-2019 00:00:00,1,0.11664,0.11664,0.11664,0.20454,0.01903,0.01903,0.01903,0.01903,0.20454,0.20454,0.20454,0.20454,0.20454,0.01903,0.070551,0.11664,0.070551,0.070551
+27-May-2019 01:00:00,1,0.09775,0.09775,0.09775,0.18351,0.032352,0.032352,0.032352,0.032352,0.18351,0.18351,0.18351,0.18351,0.18351,0.032352,0.068987,0.09775,0.068987,0.068987
+27-May-2019 02:00:00,1,0.087413,0.087413,0.087413,0.17616,0.020933,0.020933,0.020933,0.020933,0.17616,0.17616,0.17616,0.17616,0.17616,0.020933,0.051596,0.087413,0.051596,0.051596
+27-May-2019 03:00:00,1,0.074248,0.074248,0.074248,0.15145,0.01903,0.01903,0.01903,0.01903,0.15145,0.15145,0.15145,0.15145,0.15145,0.01903,0.037242,0.074248,0.037242,0.037242
+27-May-2019 04:00:00,1,0.054628,0.054628,0.054628,0.12179,0.017127,0.017127,0.017127,0.017127,0.12179,0.12179,0.12179,0.12179,0.12179,0.017127,0.034919,0.054628,0.034919,0.034919
+27-May-2019 05:00:00,1,0.050863,0.050863,0.050863,0.073542,0.013321,0.013321,0.013321,0.013321,0.073542,0.073542,0.073542,0.073542,0.073542,0.013321,0.028133,0.050863,0.028133,0.028133
+27-May-2019 06:00:00,1,0.043403,0.043403,0.043403,0.044093,0.0076122,0.0076122,0.0076122,0.0076122,0.044093,0.044093,0.044093,0.044093,0.044093,0.0076122,0.020013,0.043403,0.020013,0.020013
+27-May-2019 07:00:00,1,0.033417,0.033417,0.033417,0.025348,0.0038061,0.0038061,0.0038061,0.0038061,0.025348,0.025348,0.025348,0.025348,0.025348,0.0038061,0.007154,0.033417,0.007154,0.007154
+27-May-2019 08:00:00,1,0.029231,0.029231,0.029231,0.028969,0.0038061,0.0038061,0.0038061,0.0038061,0.028969,0.028969,0.028969,0.028969,0.028969,0.0038061,0.0064409,0.029231,0.0064409,0.0064409
+27-May-2019 09:00:00,1,0.031897,0.031897,0.031897,0.012621,0.0076122,0.0076122,0.0076122,0.0076122,0.012621,0.012621,0.012621,0.012621,0.012621,0.0076122,0.0058428,0.031897,0.0058428,0.0058428
+27-May-2019 10:00:00,1,0.026121,0.026121,0.026121,0.021727,0.0076122,0.0076122,0.0076122,0.0076122,0.021727,0.021727,0.021727,0.021727,0.021727,0.0076122,0.010029,0.026121,0.010029,0.010029
+27-May-2019 11:00:00,1,0.026215,0.026215,0.026215,0.033549,0.0095152,0.0095152,0.0095152,0.0095152,0.033549,0.033549,0.033549,0.033549,0.033549,0.0095152,0.011364,0.026215,0.011364,0.011364
+27-May-2019 12:00:00,1,0.028156,0.028156,0.028156,0.043667,0.0076122,0.0076122,0.0076122,0.0076122,0.043667,0.043667,0.043667,0.043667,0.043667,0.0076122,0.010167,0.028156,0.010167,0.010167
+27-May-2019 13:00:00,1,0.030868,0.030868,0.030868,0.037649,0.0076122,0.0076122,0.0076122,0.0076122,0.037649,0.037649,0.037649,0.037649,0.037649,0.0076122,0.011801,0.030868,0.011801,0.011801
+27-May-2019 14:00:00,1,0.029442,0.029442,0.029442,0.04143,0.0076122,0.0076122,0.0076122,0.0076122,0.04143,0.04143,0.04143,0.04143,0.04143,0.0076122,0.014975,0.029442,0.014975,0.014975
+27-May-2019 15:00:00,1,0.031289,0.031289,0.031289,0.028597,0.0057091,0.0057091,0.0057091,0.0057091,0.028597,0.028597,0.028597,0.028597,0.028597,0.0057091,0.015964,0.031289,0.015964,0.015964
+27-May-2019 16:00:00,1,0.034025,0.034025,0.034025,0.026679,0.0038061,0.0038061,0.0038061,0.0038061,0.026679,0.026679,0.026679,0.026679,0.026679,0.0038061,0.019438,0.034025,0.019438,0.019438
+27-May-2019 17:00:00,1,0.038445,0.038445,0.038445,0.029449,0.0038061,0.0038061,0.0038061,0.0038061,0.029449,0.029449,0.029449,0.029449,0.029449,0.0038061,0.020496,0.038445,0.020496,0.020496
+27-May-2019 18:00:00,1,0.036715,0.036715,0.036715,0.022632,0.0038061,0.0038061,0.0038061,0.0038061,0.022632,0.022632,0.022632,0.022632,0.022632,0.0038061,0.023509,0.036715,0.023509,0.023509
+27-May-2019 19:00:00,1,0.037603,0.037603,0.037603,0.026679,0.0038061,0.0038061,0.0038061,0.0038061,0.026679,0.026679,0.026679,0.026679,0.026679,0.0038061,0.025143,0.037603,0.025143,0.025143
+27-May-2019 20:00:00,1,0.041953,0.041953,0.041953,0.03749,0.0057091,0.0057091,0.0057091,0.0057091,0.03749,0.03749,0.03749,0.03749,0.03749,0.0057091,0.036713,0.041953,0.036713,0.036713
+27-May-2019 21:00:00,1,0.049015,0.049015,0.049015,0.026999,0.0038061,0.0038061,0.0038061,0.0038061,0.026999,0.026999,0.026999,0.026999,0.026999,0.0038061,0.044097,0.049015,0.044097,0.044097
+27-May-2019 22:00:00,1,0.057153,0.057153,0.057153,0.023538,0.001903,0.001903,0.001903,0.001903,0.023538,0.023538,0.023538,0.023538,0.023538,0.001903,0.054058,0.057153,0.054058,0.054058
+27-May-2019 23:00:00,1,0.064099,0.064099,0.064099,0.021461,0.001903,0.001903,0.001903,0.001903,0.021461,0.021461,0.021461,0.021461,0.021461,0.001903,0.05178,0.064099,0.05178,0.05178
+28-May-2019 00:00:00,1,0.062485,0.062485,0.062485,0.013313,0.0038061,0.0038061,0.0038061,0.0038061,0.013313,0.013313,0.013313,0.013313,0.013313,0.0038061,0.045109,0.062485,0.045109,0.045109
+28-May-2019 01:00:00,1,0.052453,0.052453,0.052453,0.0095854,0.001903,0.001903,0.001903,0.001903,0.0095854,0.0095854,0.0095854,0.0095854,0.0095854,0.001903,0.037426,0.052453,0.037426,0.037426
+28-May-2019 02:00:00,1,0.047846,0.047846,0.047846,0.0092659,0.001903,0.001903,0.001903,0.001903,0.0092659,0.0092659,0.0092659,0.0092659,0.0092659,0.001903,0.025189,0.047846,0.025189,0.025189
+28-May-2019 03:00:00,1,0.035405,0.035405,0.035405,0.0089464,0,0,0,0,0.0089464,0.0089464,0.0089464,0.0089464,0.0089464,0,0.023578,0.035405,0.023578,0.023578
+28-May-2019 04:00:00,1,0.02563,0.02563,0.02563,0.0083074,0,0,0,0,0.0083074,0.0083074,0.0083074,0.0083074,0.0083074,0,0.023808,0.02563,0.023808,0.023808
+28-May-2019 05:00:00,1,0.021304,0.021304,0.021304,0.0082009,0,0,0,0,0.0082009,0.0082009,0.0082009,0.0082009,0.0082009,0,0.023187,0.021304,0.023187,0.023187
+28-May-2019 06:00:00,1,0.017936,0.017936,0.017936,0.0089464,0,0,0,0,0.0089464,0.0089464,0.0089464,0.0089464,0.0089464,0,0.025396,0.017936,0.025396,0.025396
+28-May-2019 07:00:00,1,0.017094,0.017094,0.017094,0.010331,0,0,0,0,0.010331,0.010331,0.010331,0.010331,0.010331,0,0.01953,0.017094,0.01953,0.01953
+28-May-2019 08:00:00,1,0.015434,0.015434,0.015434,0.0092127,0,0,0,0,0.0092127,0.0092127,0.0092127,0.0092127,0.0092127,0,0.012054,0.015434,0.012054,0.012054
+28-May-2019 09:00:00,1,0.016112,0.016112,0.016112,0.012727,0,0,0,0,0.012727,0.012727,0.012727,0.012727,0.012727,0,0.012951,0.016112,0.012951,0.012951
+28-May-2019 10:00:00,1,0.021655,0.021655,0.021655,0.016242,0.001903,0.001903,0.001903,0.001903,0.016242,0.016242,0.016242,0.016242,0.016242,0.001903,0.021853,0.021655,0.021853,0.021853
+28-May-2019 11:00:00,1,0.028249,0.028249,0.028249,0.017201,0.001903,0.001903,0.001903,0.001903,0.017201,0.017201,0.017201,0.017201,0.017201,0.001903,0.029582,0.028249,0.029582,0.029582
+28-May-2019 12:00:00,1,0.038632,0.038632,0.038632,0.022792,0.0057091,0.0057091,0.0057091,0.0057091,0.022792,0.022792,0.022792,0.022792,0.022792,0.0057091,0.037127,0.038632,0.037127,0.037127
+28-May-2019 13:00:00,1,0.050255,0.050255,0.050255,0.029076,0.017127,0.017127,0.017127,0.017127,0.029076,0.029076,0.029076,0.029076,0.029076,0.017127,0.04251,0.050255,0.04251,0.04251
+28-May-2019 14:00:00,1,0.064613,0.064613,0.064613,0.041963,0.0095152,0.0095152,0.0095152,0.0095152,0.041963,0.041963,0.041963,0.041963,0.041963,0.0095152,0.050239,0.064613,0.050239,0.050239
+28-May-2019 15:00:00,1,0.081357,0.081357,0.081357,0.072583,0.022837,0.022837,0.022837,0.022837,0.072583,0.072583,0.072583,0.072583,0.072583,0.022837,0.066065,0.081357,0.066065,0.066065
+28-May-2019 16:00:00,1,0.10404,0.10404,0.10404,0.097079,0.02474,0.02474,0.02474,0.02474,0.097079,0.097079,0.097079,0.097079,0.097079,0.02474,0.082766,0.10404,0.082766,0.082766
+28-May-2019 17:00:00,1,0.11534,0.11534,0.11534,0.12328,0.013321,0.013321,0.013321,0.013321,0.12328,0.12328,0.12328,0.12328,0.12328,0.013321,0.084146,0.11534,0.084146,0.084146
+28-May-2019 18:00:00,1,0.12569,0.12569,0.12569,0.16333,0.017127,0.017127,0.017127,0.017127,0.16333,0.16333,0.16333,0.16333,0.16333,0.017127,0.077659,0.12569,0.077659,0.077659
+28-May-2019 19:00:00,1,0.12137,0.12137,0.12137,0.16519,0.015224,0.015224,0.015224,0.015224,0.16519,0.16519,0.16519,0.16519,0.16519,0.015224,0.063972,0.12137,0.063972,0.063972
+28-May-2019 20:00:00,1,0.10187,0.10187,0.10187,0.15662,0.026643,0.026643,0.026643,0.026643,0.15662,0.15662,0.15662,0.15662,0.15662,0.026643,0.050929,0.10187,0.050929,0.050929
+28-May-2019 21:00:00,1,0.096978,0.096978,0.096978,0.13047,0.01903,0.01903,0.01903,0.01903,0.13047,0.13047,0.13047,0.13047,0.13047,0.01903,0.053391,0.096978,0.053391,0.053391
+28-May-2019 22:00:00,1,0.10795,0.10795,0.10795,0.10203,0.039964,0.039964,0.039964,0.039964,0.10203,0.10203,0.10203,0.10203,0.10203,0.039964,0.053023,0.10795,0.053023,0.053023
+28-May-2019 23:00:00,1,0.11206,0.11206,0.11206,0.075192,0.036158,0.036158,0.036158,0.036158,0.075192,0.075192,0.075192,0.075192,0.075192,0.036158,0.05829,0.11206,0.05829,0.05829
+29-May-2019 00:00:00,1,0.11929,0.11929,0.11929,0.09005,0.032352,0.032352,0.032352,0.032352,0.09005,0.09005,0.09005,0.09005,0.09005,0.032352,0.06595,0.11929,0.06595,0.06595
+29-May-2019 01:00:00,1,0.13145,0.13145,0.13145,0.078494,0.051382,0.051382,0.051382,0.051382,0.078494,0.078494,0.078494,0.078494,0.078494,0.051382,0.082122,0.13145,0.082122,0.082122
+29-May-2019 02:00:00,1,0.14903,0.14903,0.14903,0.056714,0.026643,0.026643,0.026643,0.026643,0.056714,0.056714,0.056714,0.056714,0.056714,0.026643,0.1155,0.14903,0.1155,0.1155
+29-May-2019 03:00:00,1,0.16774,0.16774,0.16774,0.068057,0.013321,0.013321,0.013321,0.013321,0.068057,0.068057,0.068057,0.068057,0.068057,0.013321,0.1572,0.16774,0.1572,0.1572
+29-May-2019 04:00:00,1,0.19194,0.19194,0.19194,0.06337,0.036158,0.036158,0.036158,0.036158,0.06337,0.06337,0.06337,0.06337,0.06337,0.036158,0.17457,0.19194,0.17457,0.17457
+29-May-2019 05:00:00,1,0.22756,0.22756,0.22756,0.051708,0.032352,0.032352,0.032352,0.032352,0.051708,0.051708,0.051708,0.051708,0.051708,0.032352,0.19314,0.22756,0.19314,0.19314
+29-May-2019 06:00:00,1,0.25403,0.25403,0.25403,0.051229,0.01903,0.01903,0.01903,0.01903,0.051229,0.051229,0.051229,0.051229,0.051229,0.01903,0.18479,0.25403,0.18479,0.18479
+29-May-2019 07:00:00,1,0.26341,0.26341,0.26341,0.063637,0.011418,0.011418,0.011418,0.011418,0.063637,0.063637,0.063637,0.063637,0.063637,0.011418,0.18446,0.26341,0.18446,0.18446
+29-May-2019 08:00:00,1,0.26979,0.26979,0.26979,0.051815,0.017127,0.017127,0.017127,0.017127,0.051815,0.051815,0.051815,0.051815,0.051815,0.017127,0.24013,0.26979,0.24013,0.24013
+29-May-2019 09:00:00,1,0.25623,0.25623,0.25623,0.048992,0.017127,0.017127,0.017127,0.017127,0.048992,0.048992,0.048992,0.048992,0.048992,0.017127,0.29805,0.25623,0.29805,0.29805
+29-May-2019 10:00:00,1,0.22768,0.22768,0.22768,0.0876,0.02474,0.02474,0.02474,0.02474,0.0876,0.0876,0.0876,0.0876,0.0876,0.02474,0.31411,0.22768,0.31411,0.31411
+29-May-2019 11:00:00,1,0.20555,0.20555,0.20555,0.096067,0.02474,0.02474,0.02474,0.02474,0.096067,0.096067,0.096067,0.096067,0.096067,0.02474,0.34691,0.20555,0.34691,0.34691
+29-May-2019 12:00:00,1,0.22726,0.22726,0.22726,0.080145,0.028546,0.028546,0.028546,0.028546,0.080145,0.080145,0.080145,0.080145,0.080145,0.028546,0.38406,0.22726,0.38406,0.38406
+29-May-2019 13:00:00,1,0.23701,0.23701,0.23701,0.084192,0.038061,0.038061,0.038061,0.038061,0.084192,0.084192,0.084192,0.084192,0.084192,0.038061,0.41114,0.23701,0.41114,0.41114
+29-May-2019 14:00:00,1,0.2328,0.2328,0.2328,0.083021,0.039964,0.039964,0.039964,0.039964,0.083021,0.083021,0.083021,0.083021,0.083021,0.039964,0.44544,0.2328,0.44544,0.44544
+29-May-2019 15:00:00,1,0.23596,0.23596,0.23596,0.070986,0.028546,0.028546,0.028546,0.028546,0.070986,0.070986,0.070986,0.070986,0.070986,0.028546,0.44419,0.23596,0.44419,0.44419
+29-May-2019 16:00:00,1,0.23696,0.23696,0.23696,0.080677,0.034255,0.034255,0.034255,0.034255,0.080677,0.080677,0.080677,0.080677,0.080677,0.034255,0.44348,0.23696,0.44348,0.44348
+29-May-2019 17:00:00,1,0.21315,0.21315,0.21315,0.070293,0.020933,0.020933,0.020933,0.020933,0.070293,0.070293,0.070293,0.070293,0.070293,0.020933,0.39418,0.21315,0.39418,0.39418
+29-May-2019 18:00:00,1,0.18014,0.18014,0.18014,0.057619,0.02474,0.02474,0.02474,0.02474,0.057619,0.057619,0.057619,0.057619,0.057619,0.02474,0.31666,0.18014,0.31666,0.31666
+29-May-2019 19:00:00,1,0.1401,0.1401,0.1401,0.071784,0.0095152,0.0095152,0.0095152,0.0095152,0.071784,0.071784,0.071784,0.071784,0.071784,0.0095152,0.21766,0.1401,0.21766,0.21766
+29-May-2019 20:00:00,1,0.096884,0.096884,0.096884,0.048247,0.011418,0.011418,0.011418,0.011418,0.048247,0.048247,0.048247,0.048247,0.048247,0.011418,0.13294,0.096884,0.13294,0.13294
+29-May-2019 21:00:00,1,0.068401,0.068401,0.068401,0.04585,0.026643,0.026643,0.026643,0.026643,0.04585,0.04585,0.04585,0.04585,0.04585,0.026643,0.083916,0.068401,0.083916,0.083916
+29-May-2019 22:00:00,1,0.059188,0.059188,0.059188,0.042176,0.017127,0.017127,0.017127,0.017127,0.042176,0.042176,0.042176,0.042176,0.042176,0.017127,0.054426,0.059188,0.054426,0.054426
+29-May-2019 23:00:00,1,0.06176,0.06176,0.06176,0.036478,0.017127,0.017127,0.017127,0.017127,0.036478,0.036478,0.036478,0.036478,0.036478,0.017127,0.028892,0.06176,0.028892,0.028892
+30-May-2019 00:00:00,1,0.062555,0.062555,0.062555,0.02849,0.015224,0.015224,0.015224,0.015224,0.02849,0.02849,0.02849,0.02849,0.02849,0.015224,0.014837,0.062555,0.014837,0.014837
+30-May-2019 01:00:00,1,0.06162,0.06162,0.06162,0.033336,0.017127,0.017127,0.017127,0.017127,0.033336,0.033336,0.033336,0.033336,0.033336,0.017127,0.0092243,0.06162,0.0092243,0.0092243
+30-May-2019 02:00:00,1,0.065081,0.065081,0.065081,0.024549,0.0057091,0.0057091,0.0057091,0.0057091,0.024549,0.024549,0.024549,0.024549,0.024549,0.0057091,0.01118,0.065081,0.01118,0.01118
+30-May-2019 03:00:00,1,0.072611,0.072611,0.072611,0.012674,0.001903,0.001903,0.001903,0.001903,0.012674,0.012674,0.012674,0.012674,0.012674,0.001903,0.014561,0.072611,0.014561,0.014561
+30-May-2019 04:00:00,1,0.087998,0.087998,0.087998,0.014112,0.001903,0.001903,0.001903,0.001903,0.014112,0.014112,0.014112,0.014112,0.014112,0.001903,0.022152,0.087998,0.022152,0.022152
+30-May-2019 05:00:00,1,0.10675,0.10675,0.10675,0.021727,0.0038061,0.0038061,0.0038061,0.0038061,0.021727,0.021727,0.021727,0.021727,0.021727,0.0038061,0.031791,0.10675,0.031791,0.031791
+30-May-2019 06:00:00,1,0.1046,0.1046,0.1046,0.015496,0.0057091,0.0057091,0.0057091,0.0057091,0.015496,0.015496,0.015496,0.015496,0.015496,0.0057091,0.043591,0.1046,0.043591,0.043591
+30-May-2019 07:00:00,1,0.10731,0.10731,0.10731,0.0082541,0.0038061,0.0038061,0.0038061,0.0038061,0.0082541,0.0082541,0.0082541,0.0082541,0.0082541,0.0038061,0.044925,0.10731,0.044925,0.044925
+30-May-2019 08:00:00,1,0.12623,0.12623,0.12623,0.011077,0.0076122,0.0076122,0.0076122,0.0076122,0.011077,0.011077,0.011077,0.011077,0.011077,0.0076122,0.067469,0.12623,0.067469,0.067469
+30-May-2019 09:00:00,1,0.13456,0.13456,0.13456,0.012674,0.0057091,0.0057091,0.0057091,0.0057091,0.012674,0.012674,0.012674,0.012674,0.012674,0.0057091,0.1112,0.13456,0.1112,0.1112
+30-May-2019 10:00:00,1,0.12085,0.12085,0.12085,0.012887,0.017127,0.017127,0.017127,0.017127,0.012887,0.012887,0.012887,0.012887,0.012887,0.017127,0.15056,0.12085,0.15056,0.15056
+30-May-2019 11:00:00,1,0.11774,0.11774,0.11774,0.025242,0.015224,0.015224,0.015224,0.015224,0.025242,0.025242,0.025242,0.025242,0.025242,0.015224,0.19373,0.11774,0.19373,0.19373
+30-May-2019 12:00:00,1,0.12705,0.12705,0.12705,0.04143,0.020933,0.020933,0.020933,0.020933,0.04143,0.04143,0.04143,0.04143,0.04143,0.020933,0.20933,0.12705,0.20933,0.20933
+30-May-2019 13:00:00,1,0.13056,0.13056,0.13056,0.050909,0.036158,0.036158,0.036158,0.036158,0.050909,0.050909,0.050909,0.050909,0.050909,0.036158,0.23864,0.13056,0.23864,0.23864
+30-May-2019 14:00:00,1,0.14036,0.14036,0.14036,0.046915,0.036158,0.036158,0.036158,0.036158,0.046915,0.046915,0.046915,0.046915,0.046915,0.036158,0.25281,0.14036,0.25281,0.25281
+30-May-2019 15:00:00,1,0.13355,0.13355,0.13355,0.051069,0.039964,0.039964,0.039964,0.039964,0.051069,0.051069,0.051069,0.051069,0.051069,0.039964,0.26953,0.13355,0.26953,0.26953
+30-May-2019 16:00:00,1,0.12389,0.12389,0.12389,0.06598,0.02474,0.02474,0.02474,0.02474,0.06598,0.06598,0.06598,0.06598,0.06598,0.02474,0.24975,0.12389,0.24975,0.24975
+30-May-2019 17:00:00,1,0.11283,0.11283,0.11283,0.074926,0.01903,0.01903,0.01903,0.01903,0.074926,0.074926,0.074926,0.074926,0.074926,0.01903,0.22272,0.11283,0.22272,0.22272
+30-May-2019 18:00:00,1,0.08573,0.08573,0.08573,0.06795,0.0076122,0.0076122,0.0076122,0.0076122,0.06795,0.06795,0.06795,0.06795,0.06795,0.0076122,0.176,0.08573,0.176,0.176
+30-May-2019 19:00:00,1,0.064052,0.064052,0.064052,0.043347,0.013321,0.013321,0.013321,0.013321,0.043347,0.043347,0.043347,0.043347,0.043347,0.013321,0.11911,0.064052,0.11911,0.11911
+30-May-2019 20:00:00,1,0.040667,0.040667,0.040667,0.027478,0.0095152,0.0095152,0.0095152,0.0095152,0.027478,0.027478,0.027478,0.027478,0.027478,0.0095152,0.073565,0.040667,0.073565,0.073565
+30-May-2019 21:00:00,1,0.026589,0.026589,0.026589,0.021514,0.013321,0.013321,0.013321,0.013321,0.021514,0.021514,0.021514,0.021514,0.021514,0.013321,0.04833,0.026589,0.04833,0.04833
+30-May-2019 22:00:00,1,0.023408,0.023408,0.023408,0.013526,0.0057091,0.0057091,0.0057091,0.0057091,0.013526,0.013526,0.013526,0.013526,0.013526,0.0057091,0.041613,0.023408,0.041613,0.041613
+30-May-2019 23:00:00,1,0.024905,0.024905,0.024905,0.013686,0.0095152,0.0095152,0.0095152,0.0095152,0.013686,0.013686,0.013686,0.013686,0.013686,0.0095152,0.038347,0.024905,0.038347,0.038347
+31-May-2019 00:00:00,1,0.021959,0.021959,0.021959,0.027052,0.0057091,0.0057091,0.0057091,0.0057091,0.027052,0.027052,0.027052,0.027052,0.027052,0.0057091,0.033631,0.021959,0.033631,0.033631
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+21-Jun-2019 04:00:00,1,0.10087,0.10087,0.10087,0.043532,0.005681,0.005681,0.005681,0.005681,0.043532,0.043532,0.043532,0.043532,0.043532,0.005681,0.11615,0.10087,0.11615,0.11615
+21-Jun-2019 05:00:00,1,0.10494,0.10494,0.10494,0.047442,0.0018937,0.0018937,0.0018937,0.0018937,0.047442,0.047442,0.047442,0.047442,0.047442,0.0018937,0.10952,0.10494,0.10952,0.10952
+21-Jun-2019 06:00:00,1,0.086189,0.086189,0.086189,0.043902,0,0,0,0,0.043902,0.043902,0.043902,0.043902,0.043902,0,0.093176,0.086189,0.093176,0.093176
+21-Jun-2019 07:00:00,1,0.092284,0.092284,0.092284,0.041102,0,0,0,0,0.041102,0.041102,0.041102,0.041102,0.041102,0,0.073585,0.092284,0.073585,0.073585
+21-Jun-2019 08:00:00,1,0.11566,0.11566,0.11566,0.040257,0,0,0,0,0.040257,0.040257,0.040257,0.040257,0.040257,0,0.057401,0.11566,0.057401,0.057401
+21-Jun-2019 09:00:00,1,0.13707,0.13707,0.13707,0.047547,0,0,0,0,0.047547,0.047547,0.047547,0.047547,0.047547,0,0.053195,0.13707,0.053195,0.053195
+21-Jun-2019 10:00:00,1,0.12208,0.12208,0.12208,0.062551,0,0,0,0,0.062551,0.062551,0.062551,0.062551,0.062551,0,0.059572,0.12208,0.059572,0.059572
+21-Jun-2019 11:00:00,1,0.11406,0.11406,0.11406,0.061548,0.0018937,0.0018937,0.0018937,0.0018937,0.061548,0.061548,0.061548,0.061548,0.061548,0.0018937,0.066133,0.11406,0.066133,0.066133
+21-Jun-2019 12:00:00,1,0.11317,0.11317,0.11317,0.058431,0.0018937,0.0018937,0.0018937,0.0018937,0.058431,0.058431,0.058431,0.058431,0.058431,0.0018937,0.055481,0.11317,0.055481,0.055481
+21-Jun-2019 13:00:00,1,0.12388,0.12388,0.12388,0.053834,0.0037873,0.0037873,0.0037873,0.0037873,0.053834,0.053834,0.053834,0.053834,0.053834,0.0037873,0.061813,0.12388,0.061813,0.061813
+21-Jun-2019 14:00:00,1,0.13423,0.13423,0.13423,0.048129,0.0018937,0.0018937,0.0018937,0.0018937,0.048129,0.048129,0.048129,0.048129,0.048129,0.0018937,0.065905,0.13423,0.065905,0.065905
+21-Jun-2019 15:00:00,1,0.145,0.145,0.145,0.051774,0.0018937,0.0018937,0.0018937,0.0018937,0.051774,0.051774,0.051774,0.051774,0.051774,0.0018937,0.046931,0.145,0.046931,0.046931
+21-Jun-2019 16:00:00,1,0.14653,0.14653,0.14653,0.0588,0,0,0,0,0.0588,0.0588,0.0588,0.0588,0.0588,0,0.048028,0.14653,0.048028,0.048028
+21-Jun-2019 17:00:00,1,0.14642,0.14642,0.14642,0.088121,0,0,0,0,0.088121,0.088121,0.088121,0.088121,0.088121,0,0.056601,0.14642,0.056601,0.056601
+21-Jun-2019 18:00:00,1,0.17889,0.17889,0.17889,0.096733,0.0018937,0.0018937,0.0018937,0.0018937,0.096733,0.096733,0.096733,0.096733,0.096733,0.0018937,0.040965,0.17889,0.040965,0.040965
+21-Jun-2019 19:00:00,1,0.18931,0.18931,0.18931,0.089653,0.047341,0.047341,0.047341,0.047341,0.089653,0.089653,0.089653,0.089653,0.089653,0.047341,0.031135,0.18931,0.031135,0.031135
+21-Jun-2019 20:00:00,1,0.18971,0.18971,0.18971,0.15992,0.05681,0.05681,0.05681,0.05681,0.15992,0.15992,0.15992,0.15992,0.15992,0.05681,0.025694,0.18971,0.025694,0.025694
+21-Jun-2019 21:00:00,1,0.18555,0.18555,0.18555,0.14338,0.085215,0.085215,0.085215,0.085215,0.14338,0.14338,0.14338,0.14338,0.14338,0.085215,0.016619,0.18555,0.016619,0.016619
+21-Jun-2019 22:00:00,1,0.1764,0.1764,0.1764,0.20202,0.037873,0.037873,0.037873,0.037873,0.20202,0.20202,0.20202,0.20202,0.20202,0.037873,0.01607,0.1764,0.01607,0.01607
+21-Jun-2019 23:00:00,1,0.15123,0.15123,0.15123,0.13768,0.028405,0.028405,0.028405,0.028405,0.13768,0.13768,0.13768,0.13768,0.13768,0.028405,0.01191,0.15123,0.01191,0.01191
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+22-Jun-2019 01:00:00,1,0.11357,0.11357,0.11357,0.18031,0.04166,0.04166,0.04166,0.04166,0.18031,0.18031,0.18031,0.18031,0.18031,0.04166,0.0087324,0.11357,0.0087324,0.0087324
+22-Jun-2019 02:00:00,1,0.13381,0.13381,0.13381,0.21227,0.049235,0.049235,0.049235,0.049235,0.21227,0.21227,0.21227,0.21227,0.21227,0.049235,0.010173,0.13381,0.010173,0.010173
+22-Jun-2019 03:00:00,1,0.18296,0.18296,0.18296,0.17075,0.030299,0.030299,0.030299,0.030299,0.17075,0.17075,0.17075,0.17075,0.17075,0.030299,0.024231,0.18296,0.024231,0.024231
+22-Jun-2019 04:00:00,1,0.18964,0.18964,0.18964,0.16441,0.047341,0.047341,0.047341,0.047341,0.16441,0.16441,0.16441,0.16441,0.16441,0.047341,0.045628,0.18964,0.045628,0.045628
+22-Jun-2019 05:00:00,1,0.21455,0.21455,0.21455,0.19172,0.037873,0.037873,0.037873,0.037873,0.19172,0.19172,0.19172,0.19172,0.19172,0.037873,0.059298,0.21455,0.059298,0.059298
+22-Jun-2019 06:00:00,1,0.2227,0.2227,0.2227,0.24107,0.024618,0.024618,0.024618,0.024618,0.24107,0.24107,0.24107,0.24107,0.24107,0.024618,0.092399,0.2227,0.092399,0.092399
+22-Jun-2019 07:00:00,1,0.21528,0.21528,0.21528,0.23082,0.045448,0.045448,0.045448,0.045448,0.23082,0.23082,0.23082,0.23082,0.23082,0.045448,0.099577,0.21528,0.099577,0.099577
+22-Jun-2019 08:00:00,1,0.23,0.23,0.23,0.29664,0.079534,0.079534,0.079534,0.079534,0.29664,0.29664,0.29664,0.29664,0.29664,0.079534,0.13679,0.23,0.13679,0.13679
+22-Jun-2019 09:00:00,1,0.27348,0.27348,0.27348,0.35978,0.064384,0.064384,0.064384,0.064384,0.35978,0.35978,0.35978,0.35978,0.35978,0.064384,0.14406,0.27348,0.14406,0.14406
+22-Jun-2019 10:00:00,1,0.27518,0.27518,0.27518,0.44441,0.045448,0.045448,0.045448,0.045448,0.44441,0.44441,0.44441,0.44441,0.44441,0.045448,0.15437,0.27518,0.15437,0.15437
+22-Jun-2019 11:00:00,1,0.28323,0.28323,0.28323,0.46597,0.051129,0.051129,0.051129,0.051129,0.46597,0.46597,0.46597,0.46597,0.46597,0.051129,0.19154,0.28323,0.19154,0.19154
+22-Jun-2019 12:00:00,1,0.29946,0.29946,0.29946,0.51092,0.060597,0.060597,0.060597,0.060597,0.51092,0.51092,0.51092,0.51092,0.51092,0.060597,0.22933,0.29946,0.22933,0.22933
+22-Jun-2019 13:00:00,1,0.32559,0.32559,0.32559,0.47426,0.054916,0.054916,0.054916,0.054916,0.47426,0.47426,0.47426,0.47426,0.47426,0.054916,0.23015,0.32559,0.23015,0.23015
+22-Jun-2019 14:00:00,1,0.34001,0.34001,0.34001,0.45455,0.062491,0.062491,0.062491,0.062491,0.45455,0.45455,0.45455,0.45455,0.45455,0.062491,0.23728,0.34001,0.23728,0.23728
+22-Jun-2019 15:00:00,1,0.33566,0.33566,0.33566,0.45487,0.043554,0.043554,0.043554,0.043554,0.45487,0.45487,0.45487,0.45487,0.45487,0.043554,0.23941,0.33566,0.23941,0.23941
+22-Jun-2019 16:00:00,1,0.33434,0.33434,0.33434,0.50443,0.053022,0.053022,0.053022,0.053022,0.50443,0.50443,0.50443,0.50443,0.50443,0.053022,0.25644,0.33434,0.25644,0.25644
+22-Jun-2019 17:00:00,1,0.29153,0.29153,0.29153,0.45635,0.032192,0.032192,0.032192,0.032192,0.45635,0.45635,0.45635,0.45635,0.45635,0.032192,0.24828,0.29153,0.24828,0.24828
+22-Jun-2019 18:00:00,1,0.26438,0.26438,0.26438,0.31778,0.030299,0.030299,0.030299,0.030299,0.31778,0.31778,0.31778,0.31778,0.31778,0.030299,0.22789,0.26438,0.22789,0.22789
+22-Jun-2019 19:00:00,1,0.21769,0.21769,0.21769,0.27039,0.0075746,0.0075746,0.0075746,0.0075746,0.27039,0.27039,0.27039,0.27039,0.27039,0.0075746,0.1725,0.21769,0.1725,0.1725
+22-Jun-2019 20:00:00,1,0.14825,0.14825,0.14825,0.24878,0.0075746,0.0075746,0.0075746,0.0075746,0.24878,0.24878,0.24878,0.24878,0.24878,0.0075746,0.13099,0.14825,0.13099,0.13099
+22-Jun-2019 21:00:00,1,0.16112,0.16112,0.16112,0.20805,0.0094683,0.0094683,0.0094683,0.0094683,0.20805,0.20805,0.20805,0.20805,0.20805,0.0094683,0.10815,0.16112,0.10815,0.10815
+22-Jun-2019 22:00:00,1,0.18157,0.18157,0.18157,0.20202,0.015149,0.015149,0.015149,0.015149,0.20202,0.20202,0.20202,0.20202,0.20202,0.015149,0.11862,0.18157,0.11862,0.11862
+22-Jun-2019 23:00:00,1,0.21586,0.21586,0.21586,0.14919,0.017043,0.017043,0.017043,0.017043,0.14919,0.14919,0.14919,0.14919,0.14919,0.017043,0.12191,0.21586,0.12191,0.12191
+23-Jun-2019 00:00:00,1,0.23237,0.23237,0.23237,0.20968,0.005681,0.005681,0.005681,0.005681,0.20968,0.20968,0.20968,0.20968,0.20968,0.005681,0.13432,0.23237,0.13432,0.13432
+23-Jun-2019 01:00:00,1,0.24501,0.24501,0.24501,0.23008,0.0075746,0.0075746,0.0075746,0.0075746,0.23008,0.23008,0.23008,0.23008,0.23008,0.0075746,0.14612,0.24501,0.14612,0.14612
+23-Jun-2019 02:00:00,1,0.24533,0.24533,0.24533,0.23219,0.0075746,0.0075746,0.0075746,0.0075746,0.23219,0.23219,0.23219,0.23219,0.23219,0.0075746,0.15641,0.24533,0.15641,0.15641
+23-Jun-2019 03:00:00,1,0.2337,0.2337,0.2337,0.21127,0.0037873,0.0037873,0.0037873,0.0037873,0.21127,0.21127,0.21127,0.21127,0.21127,0.0037873,0.15398,0.2337,0.15398,0.15398
+23-Jun-2019 04:00:00,1,0.22579,0.22579,0.22579,0.1792,0.005681,0.005681,0.005681,0.005681,0.1792,0.1792,0.1792,0.1792,0.1792,0.005681,0.1581,0.22579,0.1581,0.1581
+23-Jun-2019 05:00:00,1,0.22663,0.22663,0.22663,0.18374,0.0037873,0.0037873,0.0037873,0.0037873,0.18374,0.18374,0.18374,0.18374,0.18374,0.0037873,0.16811,0.22663,0.16811,0.16811
+23-Jun-2019 06:00:00,1,0.21604,0.21604,0.21604,0.17614,0.005681,0.005681,0.005681,0.005681,0.17614,0.17614,0.17614,0.17614,0.17614,0.005681,0.1327,0.21604,0.1327,0.1327
+23-Jun-2019 07:00:00,1,0.22914,0.22914,0.22914,0.15009,0.0037873,0.0037873,0.0037873,0.0037873,0.15009,0.15009,0.15009,0.15009,0.15009,0.0037873,0.078546,0.22914,0.078546,0.078546
+23-Jun-2019 08:00:00,1,0.27078,0.27078,0.27078,0.16689,0.0018937,0.0018937,0.0018937,0.0018937,0.16689,0.16689,0.16689,0.16689,0.16689,0.0018937,0.080215,0.27078,0.080215,0.080215
+23-Jun-2019 09:00:00,1,0.30933,0.30933,0.30933,0.18089,0.0075746,0.0075746,0.0075746,0.0075746,0.18089,0.18089,0.18089,0.18089,0.18089,0.0075746,0.15124,0.30933,0.15124,0.15124
+23-Jun-2019 10:00:00,1,0.36595,0.36595,0.36595,0.2156,0.0037873,0.0037873,0.0037873,0.0037873,0.2156,0.2156,0.2156,0.2156,0.2156,0.0037873,0.24096,0.36595,0.24096,0.24096
+23-Jun-2019 11:00:00,1,0.39249,0.39249,0.39249,0.23066,0.0037873,0.0037873,0.0037873,0.0037873,0.23066,0.23066,0.23066,0.23066,0.23066,0.0037873,0.26677,0.39249,0.26677,0.26677
+23-Jun-2019 12:00:00,1,0.38179,0.38179,0.38179,0.2483,0.011362,0.011362,0.011362,0.011362,0.2483,0.2483,0.2483,0.2483,0.2483,0.011362,0.26266,0.38179,0.26266,0.26266
+23-Jun-2019 13:00:00,1,0.38558,0.38558,0.38558,0.29363,0.011362,0.011362,0.011362,0.011362,0.29363,0.29363,0.29363,0.29363,0.29363,0.011362,0.24163,0.38558,0.24163,0.24163
+23-Jun-2019 14:00:00,1,0.3757,0.3757,0.3757,0.25845,0.0075746,0.0075746,0.0075746,0.0075746,0.25845,0.25845,0.25845,0.25845,0.25845,0.0075746,0.22803,0.3757,0.22803,0.22803
+23-Jun-2019 15:00:00,1,0.39352,0.39352,0.39352,0.24038,0.011362,0.011362,0.011362,0.011362,0.24038,0.24038,0.24038,0.24038,0.24038,0.011362,0.22304,0.39352,0.22304,0.22304
+23-Jun-2019 16:00:00,1,0.37304,0.37304,0.37304,0.20562,0.0075746,0.0075746,0.0075746,0.0075746,0.20562,0.20562,0.20562,0.20562,0.20562,0.0075746,0.18455,0.37304,0.18455,0.18455
+23-Jun-2019 17:00:00,1,0.32957,0.32957,0.32957,0.17608,0.0075746,0.0075746,0.0075746,0.0075746,0.17608,0.17608,0.17608,0.17608,0.17608,0.0075746,0.16969,0.32957,0.16969,0.16969
+23-Jun-2019 18:00:00,1,0.27678,0.27678,0.27678,0.14407,0.005681,0.005681,0.005681,0.005681,0.14407,0.14407,0.14407,0.14407,0.14407,0.005681,0.12884,0.27678,0.12884,0.12884
+23-Jun-2019 19:00:00,1,0.19392,0.19392,0.19392,0.086167,0.005681,0.005681,0.005681,0.005681,0.086167,0.086167,0.086167,0.086167,0.086167,0.005681,0.097702,0.19392,0.097702,0.097702
+23-Jun-2019 20:00:00,1,0.1135,0.1135,0.1135,0.054151,0.0075746,0.0075746,0.0075746,0.0075746,0.054151,0.054151,0.054151,0.054151,0.054151,0.0075746,0.05068,0.1135,0.05068,0.05068
+23-Jun-2019 21:00:00,1,0.077884,0.077884,0.077884,0.041366,0.0037873,0.0037873,0.0037873,0.0037873,0.041366,0.041366,0.041366,0.041366,0.041366,0.0037873,0.028872,0.077884,0.028872,0.028872
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+23-Jun-2019 23:00:00,1,0.069417,0.069417,0.069417,0.030272,0.0018937,0.0018937,0.0018937,0.0018937,0.030272,0.030272,0.030272,0.030272,0.030272,0.0018937,0.030358,0.069417,0.030358,0.030358
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+24-Jun-2019 01:00:00,1,0.08149,0.08149,0.08149,0.052778,0.0018937,0.0018937,0.0018937,0.0018937,0.052778,0.052778,0.052778,0.052778,0.052778,0.0018937,0.01063,0.08149,0.01063,0.01063
+24-Jun-2019 02:00:00,1,0.093215,0.093215,0.093215,0.048234,0.0018937,0.0018937,0.0018937,0.0018937,0.048234,0.048234,0.048234,0.048234,0.048234,0.0018937,0.0096925,0.093215,0.0096925,0.0096925
+24-Jun-2019 03:00:00,1,0.096309,0.096309,0.096309,0.055472,0,0,0,0,0.055472,0.055472,0.055472,0.055472,0.055472,0,0.018699,0.096309,0.018699,0.018699
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+24-Jun-2019 05:00:00,1,0.11578,0.11578,0.11578,0.095729,0.005681,0.005681,0.005681,0.005681,0.095729,0.095729,0.095729,0.095729,0.095729,0.005681,0.040667,0.11578,0.040667,0.040667
+24-Jun-2019 06:00:00,1,0.1101,0.1101,0.1101,0.12722,0.0094683,0.0094683,0.0094683,0.0094683,0.12722,0.12722,0.12722,0.12722,0.12722,0.0094683,0.050657,0.1101,0.050657,0.050657
+24-Jun-2019 07:00:00,1,0.0914,0.0914,0.0914,0.1549,0.0037873,0.0037873,0.0037873,0.0037873,0.1549,0.1549,0.1549,0.1549,0.1549,0.0037873,0.056646,0.0914,0.056646,0.056646
+24-Jun-2019 08:00:00,1,0.072929,0.072929,0.072929,0.195,0.024618,0.024618,0.024618,0.024618,0.195,0.195,0.195,0.195,0.195,0.024618,0.034495,0.072929,0.034495,0.034495
+24-Jun-2019 09:00:00,1,0.082165,0.082165,0.082165,0.32274,0.060597,0.060597,0.060597,0.060597,0.32274,0.32274,0.32274,0.32274,0.32274,0.060597,0.061516,0.082165,0.061516,0.061516
+24-Jun-2019 10:00:00,1,0.12346,0.12346,0.12346,0.43934,0.066278,0.066278,0.066278,0.066278,0.43934,0.43934,0.43934,0.43934,0.43934,0.066278,0.15718,0.12346,0.15718,0.15718
+24-Jun-2019 11:00:00,1,0.18727,0.18727,0.18727,0.55847,0.071959,0.071959,0.071959,0.071959,0.55847,0.55847,0.55847,0.55847,0.55847,0.071959,0.2641,0.18727,0.2641,0.2641
+24-Jun-2019 12:00:00,1,0.25177,0.25177,0.25177,0.57749,0.11362,0.11362,0.11362,0.11362,0.57749,0.57749,0.57749,0.57749,0.57749,0.11362,0.29059,0.25177,0.29059,0.29059
+24-Jun-2019 13:00:00,1,0.29923,0.29923,0.29923,0.62351,0.10983,0.10983,0.10983,0.10983,0.62351,0.62351,0.62351,0.62351,0.62351,0.10983,0.27205,0.29923,0.27205,0.27205
+24-Jun-2019 14:00:00,1,0.32215,0.32215,0.32215,0.63323,0.13824,0.13824,0.13824,0.13824,0.63323,0.63323,0.63323,0.63323,0.63323,0.13824,0.29596,0.32215,0.29596,0.29596
+24-Jun-2019 15:00:00,1,0.391,0.391,0.391,0.55398,0.11741,0.11741,0.11741,0.11741,0.55398,0.55398,0.55398,0.55398,0.55398,0.11741,0.32452,0.391,0.32452,0.32452
+24-Jun-2019 16:00:00,1,0.45353,0.45353,0.45353,0.54225,0.054916,0.054916,0.054916,0.054916,0.54225,0.54225,0.54225,0.54225,0.54225,0.054916,0.32623,0.45353,0.32623,0.32623
+24-Jun-2019 17:00:00,1,0.44879,0.44879,0.44879,0.62514,0.13634,0.13634,0.13634,0.13634,0.62514,0.62514,0.62514,0.62514,0.62514,0.13634,0.36144,0.44879,0.36144,0.36144
+24-Jun-2019 18:00:00,1,0.38461,0.38461,0.38461,0.67945,0.07764,0.07764,0.07764,0.07764,0.67945,0.67945,0.67945,0.67945,0.67945,0.07764,0.44833,0.38461,0.44833,0.44833
+24-Jun-2019 19:00:00,1,0.39852,0.39852,0.39852,0.62879,0.07764,0.07764,0.07764,0.07764,0.62879,0.62879,0.62879,0.62879,0.62879,0.07764,0.51245,0.39852,0.51245,0.51245
+24-Jun-2019 20:00:00,1,0.48943,0.48943,0.48943,0.51029,0.10226,0.10226,0.10226,0.10226,0.51029,0.51029,0.51029,0.51029,0.51029,0.10226,0.49807,0.48943,0.49807,0.49807
+24-Jun-2019 21:00:00,1,0.55159,0.55159,0.55159,0.48129,0.10604,0.10604,0.10604,0.10604,0.48129,0.48129,0.48129,0.48129,0.48129,0.10604,0.44304,0.55159,0.44304,0.44304
+24-Jun-2019 22:00:00,1,0.58374,0.58374,0.58374,0.33584,0.083321,0.083321,0.083321,0.083321,0.33584,0.33584,0.33584,0.33584,0.33584,0.083321,0.46961,0.58374,0.46961,0.46961
+24-Jun-2019 23:00:00,1,0.55591,0.55591,0.55591,0.25866,0.075746,0.075746,0.075746,0.075746,0.25866,0.25866,0.25866,0.25866,0.25866,0.075746,0.50696,0.55591,0.50696,0.50696
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+25-Jun-2019 01:00:00,1,0.44709,0.44709,0.44709,0.21423,0.070065,0.070065,0.070065,0.070065,0.21423,0.21423,0.21423,0.21423,0.21423,0.070065,0.58846,0.44709,0.58846,0.58846
+25-Jun-2019 02:00:00,1,0.39594,0.39594,0.39594,0.20271,0.047341,0.047341,0.047341,0.047341,0.20271,0.20271,0.20271,0.20271,0.20271,0.047341,0.53755,0.39594,0.53755,0.53755
+25-Jun-2019 03:00:00,1,0.33485,0.33485,0.33485,0.22284,0.037873,0.037873,0.037873,0.037873,0.22284,0.22284,0.22284,0.22284,0.22284,0.037873,0.51043,0.33485,0.51043,0.51043
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+25-Jun-2019 08:00:00,1,0.38703,0.38703,0.38703,0.20303,0.02083,0.02083,0.02083,0.02083,0.20303,0.20303,0.20303,0.20303,0.20303,0.02083,0.3517,0.38703,0.3517,0.3517
+25-Jun-2019 09:00:00,1,0.43048,0.43048,0.43048,0.27678,0.013256,0.013256,0.013256,0.013256,0.27678,0.27678,0.27678,0.27678,0.27678,0.013256,0.38932,0.43048,0.38932,0.38932
+25-Jun-2019 10:00:00,1,0.48575,0.48575,0.48575,0.32195,0.026511,0.026511,0.026511,0.026511,0.32195,0.32195,0.32195,0.32195,0.32195,0.026511,0.43079,0.48575,0.43079,0.43079
+25-Jun-2019 11:00:00,1,0.53512,0.53512,0.53512,0.34815,0.068172,0.068172,0.068172,0.068172,0.34815,0.34815,0.34815,0.34815,0.34815,0.068172,0.39058,0.53512,0.39058,0.39058
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+25-Jun-2019 13:00:00,1,0.61316,0.61316,0.61316,0.40547,0.070065,0.070065,0.070065,0.070065,0.40547,0.40547,0.40547,0.40547,0.40547,0.070065,0.42272,0.61316,0.42272,0.42272
+25-Jun-2019 14:00:00,1,0.65997,0.65997,0.65997,0.43654,0.10604,0.10604,0.10604,0.10604,0.43654,0.43654,0.43654,0.43654,0.43654,0.10604,0.467,0.65997,0.467,0.467
+25-Jun-2019 15:00:00,1,0.64159,0.64159,0.64159,0.41942,0.09847,0.09847,0.09847,0.09847,0.41942,0.41942,0.41942,0.41942,0.41942,0.09847,0.48979,0.64159,0.48979,0.48979
+25-Jun-2019 16:00:00,1,0.6606,0.6606,0.6606,0.37721,0.11173,0.11173,0.11173,0.11173,0.37721,0.37721,0.37721,0.37721,0.37721,0.11173,0.52045,0.6606,0.52045,0.52045
+25-Jun-2019 17:00:00,1,0.6506,0.6506,0.6506,0.41615,0.10415,0.10415,0.10415,0.10415,0.41615,0.41615,0.41615,0.41615,0.41615,0.10415,0.54683,0.6506,0.54683,0.54683
+25-Jun-2019 18:00:00,1,0.61284,0.61284,0.61284,0.37589,0.10036,0.10036,0.10036,0.10036,0.37589,0.37589,0.37589,0.37589,0.37589,0.10036,0.57917,0.61284,0.57917,0.57917
+25-Jun-2019 19:00:00,1,0.57346,0.57346,0.57346,0.29136,0.045448,0.045448,0.045448,0.045448,0.29136,0.29136,0.29136,0.29136,0.29136,0.045448,0.55983,0.57346,0.55983,0.55983
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+25-Jun-2019 21:00:00,1,0.50662,0.50662,0.50662,0.17313,0.024618,0.024618,0.024618,0.024618,0.17313,0.17313,0.17313,0.17313,0.17313,0.024618,0.57261,0.50662,0.57261,0.57261
+25-Jun-2019 22:00:00,1,0.43983,0.43983,0.43983,0.12146,0.028405,0.028405,0.028405,0.028405,0.12146,0.12146,0.12146,0.12146,0.12146,0.028405,0.5656,0.43983,0.5656,0.5656
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+26-Jun-2019 01:00:00,1,0.40789,0.40789,0.40789,0.079404,0.011362,0.011362,0.011362,0.011362,0.079404,0.079404,0.079404,0.079404,0.079404,0.011362,0.52479,0.40789,0.52479,0.52479
+26-Jun-2019 02:00:00,1,0.40254,0.40254,0.40254,0.084317,0.022724,0.022724,0.022724,0.022724,0.084317,0.084317,0.084317,0.084317,0.084317,0.022724,0.53366,0.40254,0.53366,0.53366
+26-Jun-2019 03:00:00,1,0.39205,0.39205,0.39205,0.07354,0.015149,0.015149,0.015149,0.015149,0.07354,0.07354,0.07354,0.07354,0.07354,0.015149,0.52909,0.39205,0.52909,0.52909
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+26-Jun-2019 07:00:00,1,0.27101,0.27101,0.27101,0.04443,0.011362,0.011362,0.011362,0.011362,0.04443,0.04443,0.04443,0.04443,0.04443,0.011362,0.40238,0.27101,0.40238,0.40238
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+26-Jun-2019 09:00:00,1,0.28167,0.28167,0.28167,0.043638,0.017043,0.017043,0.017043,0.017043,0.043638,0.043638,0.043638,0.043638,0.043638,0.017043,0.36198,0.28167,0.36198,0.36198
+26-Jun-2019 10:00:00,1,0.309,0.309,0.309,0.064823,0.017043,0.017043,0.017043,0.017043,0.064823,0.064823,0.064823,0.064823,0.064823,0.017043,0.36994,0.309,0.36994,0.36994
+26-Jun-2019 11:00:00,1,0.33147,0.33147,0.33147,0.063238,0.015149,0.015149,0.015149,0.015149,0.063238,0.063238,0.063238,0.063238,0.063238,0.015149,0.36352,0.33147,0.36352,0.36352
+26-Jun-2019 12:00:00,1,0.35074,0.35074,0.35074,0.070582,0.028405,0.028405,0.028405,0.028405,0.070582,0.070582,0.070582,0.070582,0.070582,0.028405,0.36838,0.35074,0.36838,0.36838
+26-Jun-2019 13:00:00,1,0.37667,0.37667,0.37667,0.081306,0.022724,0.022724,0.022724,0.022724,0.081306,0.081306,0.081306,0.081306,0.081306,0.022724,0.36251,0.37667,0.36251,0.36251
+26-Jun-2019 14:00:00,1,0.37407,0.37407,0.37407,0.083314,0.02083,0.02083,0.02083,0.02083,0.083314,0.083314,0.083314,0.083314,0.083314,0.02083,0.36297,0.37407,0.36297,0.36297
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+26-Jun-2019 16:00:00,1,0.36411,0.36411,0.36411,0.087223,0.017043,0.017043,0.017043,0.017043,0.087223,0.087223,0.087223,0.087223,0.087223,0.017043,0.36902,0.36411,0.36902,0.36902
+26-Jun-2019 17:00:00,1,0.33929,0.33929,0.33929,0.05917,0.013256,0.013256,0.013256,0.013256,0.05917,0.05917,0.05917,0.05917,0.05917,0.013256,0.39486,0.33929,0.39486,0.39486
+26-Jun-2019 18:00:00,1,0.28202,0.28202,0.28202,0.044219,0.0094683,0.0094683,0.0094683,0.0094683,0.044219,0.044219,0.044219,0.044219,0.044219,0.0094683,0.38768,0.28202,0.38768,0.38768
+26-Jun-2019 19:00:00,1,0.23207,0.23207,0.23207,0.031645,0.0037873,0.0037873,0.0037873,0.0037873,0.031645,0.031645,0.031645,0.031645,0.031645,0.0037873,0.3258,0.23207,0.3258,0.3258
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+27-Jun-2019 01:00:00,1,0.068928,0.068928,0.068928,0.023668,0.0037873,0.0037873,0.0037873,0.0037873,0.023668,0.023668,0.023668,0.023668,0.023668,0.0037873,0.20711,0.068928,0.20711,0.20711
+27-Jun-2019 02:00:00,1,0.06844,0.06844,0.06844,0.029268,0.0018937,0.0018937,0.0018937,0.0018937,0.029268,0.029268,0.029268,0.029268,0.029268,0.0018937,0.18777,0.06844,0.18777,0.18777
+27-Jun-2019 03:00:00,1,0.058809,0.058809,0.058809,0.051457,0.0075746,0.0075746,0.0075746,0.0075746,0.051457,0.051457,0.051457,0.051457,0.051457,0.0075746,0.1717,0.058809,0.1717,0.1717
+27-Jun-2019 04:00:00,1,0.056901,0.056901,0.056901,0.080778,0.011362,0.011362,0.011362,0.011362,0.080778,0.080778,0.080778,0.080778,0.080778,0.011362,0.15254,0.056901,0.15254,0.15254
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+20-Aug-2019 14:00:00,1,0.15156,0.15156,0.15156,0.12778,0.10126,0.10126,0.10126,0.10126,0.12778,0.12778,0.12778,0.12778,0.12778,0.10126,0.073627,0.15156,0.073627,0.073627
+20-Aug-2019 15:00:00,1,0.20932,0.20932,0.20932,0.14307,0.075006,0.075006,0.075006,0.075006,0.14307,0.14307,0.14307,0.14307,0.14307,0.075006,0.08487,0.20932,0.08487,0.08487
+20-Aug-2019 16:00:00,1,0.2579,0.2579,0.2579,0.12091,0.065631,0.065631,0.065631,0.065631,0.12091,0.12091,0.12091,0.12091,0.12091,0.065631,0.14032,0.2579,0.14032,0.14032
+20-Aug-2019 17:00:00,1,0.26188,0.26188,0.26188,0.085238,0.011251,0.011251,0.011251,0.011251,0.085238,0.085238,0.085238,0.085238,0.085238,0.011251,0.17548,0.26188,0.17548,0.17548
+20-Aug-2019 18:00:00,1,0.21498,0.21498,0.21498,0.055647,0.026252,0.026252,0.026252,0.026252,0.055647,0.055647,0.055647,0.055647,0.055647,0.026252,0.22641,0.21498,0.22641,0.22641
+20-Aug-2019 19:00:00,1,0.16014,0.16014,0.16014,0.03526,0.026252,0.026252,0.026252,0.026252,0.03526,0.03526,0.03526,0.03526,0.03526,0.026252,0.19981,0.16014,0.19981,0.19981
+20-Aug-2019 20:00:00,1,0.11456,0.11456,0.11456,0.039785,0.026252,0.026252,0.026252,0.026252,0.039785,0.039785,0.039785,0.039785,0.039785,0.026252,0.17913,0.11456,0.17913,0.17913
+20-Aug-2019 21:00:00,1,0.089949,0.089949,0.089949,0.024235,0.020627,0.020627,0.020627,0.020627,0.024235,0.024235,0.024235,0.024235,0.024235,0.020627,0.1251,0.089949,0.1251,0.1251
+20-Aug-2019 22:00:00,1,0.078875,0.078875,0.078875,0.019086,0.018752,0.018752,0.018752,0.018752,0.019086,0.019086,0.019086,0.019086,0.019086,0.018752,0.093314,0.078875,0.093314,0.093314
+20-Aug-2019 23:00:00,1,0.067824,0.067824,0.067824,0.078218,0.020627,0.020627,0.020627,0.020627,0.078218,0.078218,0.078218,0.078218,0.078218,0.020627,0.063218,0.067824,0.063218,0.063218
+21-Aug-2019 00:00:00,1,0.04616,0.04616,0.04616,0.072237,0.013126,0.013126,0.013126,0.013126,0.072237,0.072237,0.072237,0.072237,0.072237,0.013126,0.064821,0.04616,0.064821,0.064821
+21-Aug-2019 01:00:00,1,0.030459,0.030459,0.030459,0.029488,0.016876,0.016876,0.016876,0.016876,0.029488,0.029488,0.029488,0.029488,0.029488,0.016876,0.064753,0.030459,0.064753,0.064753
+21-Aug-2019 02:00:00,1,0.020697,0.020697,0.020697,0.025275,0.024377,0.024377,0.024377,0.024377,0.025275,0.025275,0.025275,0.025275,0.025275,0.024377,0.072091,0.020697,0.072091,0.072091
+21-Aug-2019 03:00:00,1,0.02003,0.02003,0.02003,0.023507,0.015001,0.015001,0.015001,0.015001,0.023507,0.023507,0.023507,0.023507,0.023507,0.015001,0.077691,0.02003,0.077691,0.077691
+21-Aug-2019 04:00:00,1,0.02148,0.02148,0.02148,0.032088,0.024377,0.024377,0.024377,0.024377,0.032088,0.032088,0.032088,0.032088,0.032088,0.024377,0.077194,0.02148,0.077194,0.077194
+21-Aug-2019 05:00:00,1,0.018648,0.018648,0.018648,0.029332,0.024377,0.024377,0.024377,0.024377,0.029332,0.029332,0.029332,0.029332,0.029332,0.024377,0.06674,0.018648,0.06674,0.06674
+21-Aug-2019 06:00:00,1,0.016001,0.016001,0.016001,0.025327,0.022502,0.022502,0.022502,0.022502,0.025327,0.025327,0.025327,0.025327,0.025327,0.022502,0.053645,0.016001,0.053645,0.053645
+21-Aug-2019 07:00:00,1,0.012409,0.012409,0.012409,0.034584,0.016876,0.016876,0.016876,0.016876,0.034584,0.034584,0.034584,0.034584,0.034584,0.016876,0.030503,0.012409,0.030503,0.030503
+21-Aug-2019 08:00:00,1,0.011281,0.011281,0.011281,0.025119,0.0037503,0.0037503,0.0037503,0.0037503,0.025119,0.025119,0.025119,0.025119,0.025119,0.0037503,0.017678,0.011281,0.017678,0.017678
+21-Aug-2019 09:00:00,1,0.010959,0.010959,0.010959,0.0086331,0.0056255,0.0056255,0.0056255,0.0056255,0.0086331,0.0086331,0.0086331,0.0086331,0.0086331,0.0056255,0.01025,0.010959,0.01025,0.01025
+21-Aug-2019 10:00:00,1,0.011764,0.011764,0.011764,0.007853,0.0056255,0.0056255,0.0056255,0.0056255,0.007853,0.007853,0.007853,0.007853,0.007853,0.0056255,0.006096,0.011764,0.006096,0.006096
+21-Aug-2019 11:00:00,1,0.010682,0.010682,0.010682,0.014302,0.015001,0.015001,0.015001,0.015001,0.014302,0.014302,0.014302,0.014302,0.014302,0.015001,0.0036576,0.010682,0.0036576,0.0036576
+21-Aug-2019 12:00:00,1,0.012133,0.012133,0.012133,0.008425,0.020627,0.020627,0.020627,0.020627,0.008425,0.008425,0.008425,0.008425,0.008425,0.020627,0.0049446,0.012133,0.0049446,0.0049446
+21-Aug-2019 13:00:00,1,0.014205,0.014205,0.014205,0.0090491,0.028127,0.028127,0.028127,0.028127,0.0090491,0.0090491,0.0090491,0.0090491,0.0090491,0.028127,0.0040414,0.014205,0.0040414,0.0040414
+21-Aug-2019 14:00:00,1,0.015425,0.015425,0.015425,0.0075409,0.028127,0.028127,0.028127,0.028127,0.0075409,0.0075409,0.0075409,0.0075409,0.0075409,0.028127,0.0025739,0.015425,0.0025739,0.0025739
+21-Aug-2019 15:00:00,1,0.017129,0.017129,0.017129,0.0098812,0.030003,0.030003,0.030003,0.030003,0.0098812,0.0098812,0.0098812,0.0098812,0.0098812,0.030003,0.0032286,0.017129,0.0032286,0.0032286
+21-Aug-2019 16:00:00,1,0.018948,0.018948,0.018948,0.0073329,0.013126,0.013126,0.013126,0.013126,0.0073329,0.0073329,0.0073329,0.0073329,0.0073329,0.013126,0.0061186,0.018948,0.0061186,0.0061186
+21-Aug-2019 17:00:00,1,0.019914,0.019914,0.019914,0.0089971,0.011251,0.011251,0.011251,0.011251,0.0089971,0.0089971,0.0089971,0.0089971,0.0089971,0.011251,0.013434,0.019914,0.013434,0.013434
+21-Aug-2019 18:00:00,1,0.020191,0.020191,0.020191,0.014666,0.011251,0.011251,0.011251,0.011251,0.014666,0.014666,0.014666,0.014666,0.014666,0.011251,0.019056,0.020191,0.019056,0.019056
+21-Aug-2019 19:00:00,1,0.024082,0.024082,0.024082,0.013002,0.0075006,0.0075006,0.0075006,0.0075006,0.013002,0.013002,0.013002,0.013002,0.013002,0.0075006,0.025739,0.024082,0.025739,0.025739
+21-Aug-2019 20:00:00,1,0.033475,0.033475,0.033475,0.014562,0.020627,0.020627,0.020627,0.020627,0.014562,0.014562,0.014562,0.014562,0.014562,0.020627,0.047188,0.033475,0.047188,0.047188
+21-Aug-2019 21:00:00,1,0.050465,0.050465,0.050465,0.022207,0.020627,0.020627,0.020627,0.020627,0.022207,0.022207,0.022207,0.022207,0.022207,0.020627,0.083674,0.050465,0.083674,0.083674
+21-Aug-2019 22:00:00,1,0.057856,0.057856,0.057856,0.044882,0.018752,0.018752,0.018752,0.018752,0.044882,0.044882,0.044882,0.044882,0.044882,0.018752,0.092637,0.057856,0.092637,0.092637
+21-Aug-2019 23:00:00,1,0.057326,0.057326,0.057326,0.051122,0.035628,0.035628,0.035628,0.035628,0.051122,0.051122,0.051122,0.051122,0.051122,0.035628,0.08189,0.057326,0.08189,0.08189
+22-Aug-2019 00:00:00,1,0.052883,0.052883,0.052883,0.06584,0.046879,0.046879,0.046879,0.046879,0.06584,0.06584,0.06584,0.06584,0.06584,0.046879,0.0804,0.052883,0.0804,0.0804
+22-Aug-2019 01:00:00,1,0.06817,0.06817,0.06817,0.085759,0.05813,0.05813,0.05813,0.05813,0.085759,0.085759,0.085759,0.085759,0.085759,0.05813,0.08688,0.06817,0.08688,0.08688
+22-Aug-2019 02:00:00,1,0.099273,0.099273,0.099273,0.11753,0.048754,0.048754,0.048754,0.048754,0.11753,0.11753,0.11753,0.11753,0.11753,0.048754,0.058386,0.099273,0.058386,0.058386
+22-Aug-2019 03:00:00,1,0.13132,0.13132,0.13132,0.12669,0.018752,0.018752,0.018752,0.018752,0.12669,0.12669,0.12669,0.12669,0.12669,0.018752,0.04852,0.13132,0.04852,0.04852
+22-Aug-2019 04:00:00,1,0.13887,0.13887,0.13887,0.12861,0.046879,0.046879,0.046879,0.046879,0.12861,0.12861,0.12861,0.12861,0.12861,0.046879,0.065566,0.13887,0.065566,0.065566
+22-Aug-2019 05:00:00,1,0.15874,0.15874,0.15874,0.12081,0.039378,0.039378,0.039378,0.039378,0.12081,0.12081,0.12081,0.12081,0.12081,0.039378,0.10521,0.15874,0.10521,0.10521
+22-Aug-2019 06:00:00,1,0.14935,0.14935,0.14935,0.15399,0.026252,0.026252,0.026252,0.026252,0.15399,0.15399,0.15399,0.15399,0.15399,0.026252,0.1412,0.14935,0.1412,0.1412
+22-Aug-2019 07:00:00,1,0.1522,0.1522,0.1522,0.14302,0.045004,0.045004,0.045004,0.045004,0.14302,0.14302,0.14302,0.14302,0.14302,0.045004,0.17417,0.1522,0.17417,0.17417
+22-Aug-2019 08:00:00,1,0.17122,0.17122,0.17122,0.13433,0.05438,0.05438,0.05438,0.05438,0.13433,0.13433,0.13433,0.13433,0.13433,0.05438,0.24592,0.17122,0.24592,0.24592
+22-Aug-2019 09:00:00,1,0.22382,0.22382,0.22382,0.14806,0.033753,0.033753,0.033753,0.033753,0.14806,0.14806,0.14806,0.14806,0.14806,0.033753,0.29317,0.22382,0.29317,0.29317
+22-Aug-2019 10:00:00,1,0.30275,0.30275,0.30275,0.17188,0.046879,0.046879,0.046879,0.046879,0.17188,0.17188,0.17188,0.17188,0.17188,0.046879,0.32912,0.30275,0.32912,0.32912
+22-Aug-2019 11:00:00,1,0.3786,0.3786,0.3786,0.2255,0.095633,0.095633,0.095633,0.095633,0.2255,0.2255,0.2255,0.2255,0.2255,0.095633,0.36551,0.3786,0.36551,0.36551
+22-Aug-2019 12:00:00,1,0.431,0.431,0.431,0.25421,0.086257,0.086257,0.086257,0.086257,0.25421,0.25421,0.25421,0.25421,0.25421,0.086257,0.36432,0.431,0.36432,0.36432
+22-Aug-2019 13:00:00,1,0.47083,0.47083,0.47083,0.23762,0.10876,0.10876,0.10876,0.10876,0.23762,0.23762,0.23762,0.23762,0.23762,0.10876,0.39952,0.47083,0.39952,0.39952
+22-Aug-2019 14:00:00,1,0.49468,0.49468,0.49468,0.26887,0.073131,0.073131,0.073131,0.073131,0.26887,0.26887,0.26887,0.26887,0.26887,0.073131,0.39471,0.49468,0.39471,0.39471
+22-Aug-2019 15:00:00,1,0.46043,0.46043,0.46043,0.26029,0.050629,0.050629,0.050629,0.050629,0.26029,0.26029,0.26029,0.26029,0.26029,0.050629,0.37254,0.46043,0.37254,0.37254
+22-Aug-2019 16:00:00,1,0.44588,0.44588,0.44588,0.2241,0.041253,0.041253,0.041253,0.041253,0.2241,0.2241,0.2241,0.2241,0.2241,0.041253,0.3216,0.44588,0.3216,0.3216
+22-Aug-2019 17:00:00,1,0.41056,0.41056,0.41056,0.17006,0.026252,0.026252,0.026252,0.026252,0.17006,0.17006,0.17006,0.17006,0.17006,0.026252,0.27717,0.41056,0.27717,0.27717
+22-Aug-2019 18:00:00,1,0.33891,0.33891,0.33891,0.12632,0.020627,0.020627,0.020627,0.020627,0.12632,0.12632,0.12632,0.12632,0.12632,0.020627,0.21952,0.33891,0.21952,0.21952
+22-Aug-2019 19:00:00,1,0.24268,0.24268,0.24268,0.095484,0.022502,0.022502,0.022502,0.022502,0.095484,0.095484,0.095484,0.095484,0.095484,0.022502,0.15236,0.24268,0.15236,0.15236
+22-Aug-2019 20:00:00,1,0.18278,0.18278,0.18278,0.058715,0.015001,0.015001,0.015001,0.015001,0.058715,0.058715,0.058715,0.058715,0.058715,0.015001,0.11018,0.18278,0.11018,0.11018
+22-Aug-2019 21:00:00,1,0.15326,0.15326,0.15326,0.061576,0.0093758,0.0093758,0.0093758,0.0093758,0.061576,0.061576,0.061576,0.061576,0.061576,0.0093758,0.12226,0.15326,0.12226,0.12226
+22-Aug-2019 22:00:00,1,0.15593,0.15593,0.15593,0.095172,0.0093758,0.0093758,0.0093758,0.0093758,0.095172,0.095172,0.095172,0.095172,0.095172,0.0093758,0.12382,0.15593,0.12382,0.12382
+22-Aug-2019 23:00:00,1,0.17007,0.17007,0.17007,0.10126,0.0075006,0.0075006,0.0075006,0.0075006,0.10126,0.10126,0.10126,0.10126,0.10126,0.0075006,0.097627,0.17007,0.097627,0.097627
+23-Aug-2019 00:00:00,1,0.18183,0.18183,0.18183,0.097928,0.0056255,0.0056255,0.0056255,0.0056255,0.097928,0.097928,0.097928,0.097928,0.097928,0.0056255,0.089205,0.18183,0.089205,0.089205
+23-Aug-2019 01:00:00,1,0.1895,0.1895,0.1895,0.10588,0.0056255,0.0056255,0.0056255,0.0056255,0.10588,0.10588,0.10588,0.10588,0.10588,0.0056255,0.095234,0.1895,0.095234,0.095234
+23-Aug-2019 02:00:00,1,0.18839,0.18839,0.18839,0.10713,0.0037503,0.0037503,0.0037503,0.0037503,0.10713,0.10713,0.10713,0.10713,0.10713,0.0037503,0.096566,0.18839,0.096566,0.096566
+23-Aug-2019 03:00:00,1,0.17527,0.17527,0.17527,0.1154,0.0037503,0.0037503,0.0037503,0.0037503,0.1154,0.1154,0.1154,0.1154,0.1154,0.0037503,0.094918,0.17527,0.094918,0.094918
+23-Aug-2019 04:00:00,1,0.16433,0.16433,0.16433,0.10656,0,0,0,0,0.10656,0.10656,0.10656,0.10656,0.10656,0,0.10415,0.16433,0.10415,0.10415
+23-Aug-2019 05:00:00,1,0.16482,0.16482,0.16482,0.09408,0.0037503,0.0037503,0.0037503,0.0037503,0.09408,0.09408,0.09408,0.09408,0.09408,0.0037503,0.1277,0.16482,0.1277,0.1277
+23-Aug-2019 06:00:00,1,0.16456,0.16456,0.16456,0.094704,0,0,0,0,0.094704,0.094704,0.094704,0.094704,0.094704,0,0.15168,0.16456,0.15168,0.15168
+23-Aug-2019 07:00:00,1,0.14444,0.14444,0.14444,0.070573,0,0,0,0,0.070573,0.070573,0.070573,0.070573,0.070573,0,0.13526,0.14444,0.13526,0.13526
+23-Aug-2019 08:00:00,1,0.11916,0.11916,0.11916,0.042229,0,0,0,0,0.042229,0.042229,0.042229,0.042229,0.042229,0,0.079203,0.11916,0.079203,0.079203
+23-Aug-2019 09:00:00,1,0.12073,0.12073,0.12073,0.028968,0,0,0,0,0.028968,0.028968,0.028968,0.028968,0.028968,0,0.084532,0.12073,0.084532,0.084532
+23-Aug-2019 10:00:00,1,0.13238,0.13238,0.13238,0.032036,0,0,0,0,0.032036,0.032036,0.032036,0.032036,0.032036,0,0.11912,0.13238,0.11912,0.11912
+23-Aug-2019 11:00:00,1,0.16219,0.16219,0.16219,0.034168,0,0,0,0,0.034168,0.034168,0.034168,0.034168,0.034168,0,0.14224,0.16219,0.14224,0.14224
+23-Aug-2019 12:00:00,1,0.17239,0.17239,0.17239,0.03526,0,0,0,0,0.03526,0.03526,0.03526,0.03526,0.03526,0,0.14486,0.17239,0.14486,0.14486
+23-Aug-2019 13:00:00,1,0.15616,0.15616,0.15616,0.035728,0,0,0,0,0.035728,0.035728,0.035728,0.035728,0.035728,0,0.12723,0.15616,0.12723,0.12723
+23-Aug-2019 14:00:00,1,0.14085,0.14085,0.14085,0.034688,0.0018752,0.0018752,0.0018752,0.0018752,0.034688,0.034688,0.034688,0.034688,0.034688,0.0018752,0.11458,0.14085,0.11458,0.11458
+23-Aug-2019 15:00:00,1,0.13804,0.13804,0.13804,0.028239,0.0037503,0.0037503,0.0037503,0.0037503,0.028239,0.028239,0.028239,0.028239,0.028239,0.0037503,0.10399,0.13804,0.10399,0.10399
+23-Aug-2019 16:00:00,1,0.12064,0.12064,0.12064,0.020543,0.011251,0.011251,0.011251,0.011251,0.020543,0.020543,0.020543,0.020543,0.020543,0.011251,0.090763,0.12064,0.090763,0.090763
+23-Aug-2019 17:00:00,1,0.094139,0.094139,0.094139,0.029592,0.0018752,0.0018752,0.0018752,0.0018752,0.029592,0.029592,0.029592,0.029592,0.029592,0.0018752,0.0762,0.094139,0.0762,0.0762
+23-Aug-2019 18:00:00,1,0.07206,0.07206,0.07206,0.064384,0.0037503,0.0037503,0.0037503,0.0037503,0.064384,0.064384,0.064384,0.064384,0.064384,0.0037503,0.061457,0.07206,0.061457,0.061457
+23-Aug-2019 19:00:00,1,0.039368,0.039368,0.039368,0.021167,0.024377,0.024377,0.024377,0.024377,0.021167,0.021167,0.021167,0.021167,0.021167,0.024377,0.035312,0.039368,0.035312,0.035312
+23-Aug-2019 20:00:00,1,0.027857,0.027857,0.027857,0.0094652,0.020627,0.020627,0.020627,0.020627,0.0094652,0.0094652,0.0094652,0.0094652,0.0094652,0.020627,0.023933,0.027857,0.023933,0.023933
+23-Aug-2019 21:00:00,1,0.032278,0.032278,0.032278,0.039421,0.013126,0.013126,0.013126,0.013126,0.039421,0.039421,0.039421,0.039421,0.039421,0.013126,0.024384,0.032278,0.024384,0.024384
+23-Aug-2019 22:00:00,1,0.037412,0.037412,0.037412,0.070313,0.086257,0.086257,0.086257,0.086257,0.070313,0.070313,0.070313,0.070313,0.070313,0.086257,0.025784,0.037412,0.025784,0.025784
+23-Aug-2019 23:00:00,1,0.048163,0.048163,0.048163,0.10302,0.086257,0.086257,0.086257,0.086257,0.10302,0.10302,0.10302,0.10302,0.10302,0.086257,0.023684,0.048163,0.023684,0.023684
+24-Aug-2019 00:00:00,1,0.067594,0.067594,0.067594,0.11015,0.097508,0.097508,0.097508,0.097508,0.11015,0.11015,0.11015,0.11015,0.11015,0.097508,0.028922,0.067594,0.028922,0.028922
+24-Aug-2019 01:00:00,1,0.065085,0.065085,0.065085,0.171,0.073131,0.073131,0.073131,0.073131,0.171,0.171,0.171,0.171,0.171,0.073131,0.04423,0.065085,0.04423,0.04423
+24-Aug-2019 02:00:00,1,0.077609,0.077609,0.077609,0.14635,0.12001,0.12001,0.12001,0.12001,0.14635,0.14635,0.14635,0.14635,0.14635,0.12001,0.047143,0.077609,0.047143,0.047143
+24-Aug-2019 03:00:00,1,0.076435,0.076435,0.076435,0.16314,0.082507,0.082507,0.082507,0.082507,0.16314,0.16314,0.16314,0.16314,0.16314,0.082507,0.046059,0.076435,0.046059,0.046059
+24-Aug-2019 04:00:00,1,0.080118,0.080118,0.080118,0.12378,0.088132,0.088132,0.088132,0.088132,0.12378,0.12378,0.12378,0.12378,0.12378,0.088132,0.046194,0.080118,0.046194,0.046194
+24-Aug-2019 05:00:00,1,0.063473,0.063473,0.063473,0.087475,0.15564,0.15564,0.15564,0.15564,0.087475,0.087475,0.087475,0.087475,0.087475,0.15564,0.064641,0.063473,0.064641,0.064641
+24-Aug-2019 06:00:00,1,0.052399,0.052399,0.052399,0.045454,0.20252,0.20252,0.20252,0.20252,0.045454,0.045454,0.045454,0.045454,0.045454,0.20252,0.060622,0.052399,0.060622,0.060622
+24-Aug-2019 07:00:00,1,0.051225,0.051225,0.051225,0.040617,0.2869,0.2869,0.2869,0.2869,0.040617,0.040617,0.040617,0.040617,0.040617,0.2869,0.045043,0.051225,0.045043,0.045043
+24-Aug-2019 08:00:00,1,0.050373,0.050373,0.050373,0.068908,0.17064,0.17064,0.17064,0.17064,0.068908,0.068908,0.068908,0.068908,0.068908,0.17064,0.037728,0.050373,0.037728,0.037728
+24-Aug-2019 09:00:00,1,0.041763,0.041763,0.041763,0.044517,0.17251,0.17251,0.17251,0.17251,0.044517,0.044517,0.044517,0.044517,0.044517,0.17251,0.021449,0.041763,0.021449,0.021449
+24-Aug-2019 10:00:00,1,0.030252,0.030252,0.030252,0.060587,0.18939,0.18939,0.18939,0.18939,0.060587,0.060587,0.060587,0.060587,0.060587,0.18939,0.01585,0.030252,0.01585,0.01585
+24-Aug-2019 11:00:00,1,0.026177,0.026177,0.026177,0.0324,0.14814,0.14814,0.14814,0.14814,0.0324,0.0324,0.0324,0.0324,0.0324,0.14814,0.01874,0.026177,0.01874,0.01874
+24-Aug-2019 12:00:00,1,0.027282,0.027282,0.027282,0.049874,0.080632,0.080632,0.080632,0.080632,0.049874,0.049874,0.049874,0.049874,0.049874,0.080632,0.016414,0.027282,0.016414,0.016414
+24-Aug-2019 13:00:00,1,0.030758,0.030758,0.030758,0.06766,0.091883,0.091883,0.091883,0.091883,0.06766,0.06766,0.06766,0.06766,0.06766,0.091883,0.024429,0.030758,0.024429,0.024429
+24-Aug-2019 14:00:00,1,0.025716,0.025716,0.025716,0.043321,0.13126,0.13126,0.13126,0.13126,0.043321,0.043321,0.043321,0.043321,0.043321,0.13126,0.032648,0.025716,0.032648,0.032648
+24-Aug-2019 15:00:00,1,0.018924,0.018924,0.018924,0.024131,0.031878,0.031878,0.031878,0.031878,0.024131,0.024131,0.024131,0.024131,0.024131,0.031878,0.035628,0.018924,0.035628,0.035628
+24-Aug-2019 16:00:00,1,0.019132,0.019132,0.019132,0.034064,0.093758,0.093758,0.093758,0.093758,0.034064,0.034064,0.034064,0.034064,0.034064,0.093758,0.037073,0.019132,0.037073,0.037073
+24-Aug-2019 17:00:00,1,0.019868,0.019868,0.019868,0.027147,0.05438,0.05438,0.05438,0.05438,0.027147,0.027147,0.027147,0.027147,0.027147,0.05438,0.031925,0.019868,0.031925,0.031925
+24-Aug-2019 18:00:00,1,0.027097,0.027097,0.027097,0.029904,0.076881,0.076881,0.076881,0.076881,0.029904,0.029904,0.029904,0.029904,0.029904,0.076881,0.013027,0.027097,0.013027,0.013027
+24-Aug-2019 19:00:00,1,0.038079,0.038079,0.038079,0.016174,0.05438,0.05438,0.05438,0.05438,0.016174,0.016174,0.016174,0.016174,0.016174,0.05438,0.0048543,0.038079,0.0048543,0.0048543
+24-Aug-2019 20:00:00,1,0.040796,0.040796,0.040796,0.028499,0.06188,0.06188,0.06188,0.06188,0.028499,0.028499,0.028499,0.028499,0.028499,0.06188,0.0027771,0.040796,0.0027771,0.0027771
+24-Aug-2019 21:00:00,1,0.074109,0.074109,0.074109,0.053151,0.078757,0.078757,0.078757,0.078757,0.053151,0.053151,0.053151,0.053151,0.053151,0.078757,0.0061863,0.074109,0.0061863,0.0061863
+24-Aug-2019 22:00:00,1,0.095912,0.095912,0.095912,0.078426,0.093758,0.093758,0.093758,0.093758,0.078426,0.078426,0.078426,0.078426,0.078426,0.093758,0.0065927,0.095912,0.0065927,0.0065927
+24-Aug-2019 23:00:00,1,0.11067,0.11067,0.11067,0.13797,0.14814,0.14814,0.14814,0.14814,0.13797,0.13797,0.13797,0.13797,0.13797,0.14814,0.0025287,0.11067,0.0025287,0.0025287
+25-Aug-2019 00:00:00,1,0.10809,0.10809,0.10809,0.13074,0.12751,0.12751,0.12751,0.12751,0.13074,0.13074,0.13074,0.13074,0.13074,0.12751,0.0055542,0.10809,0.0055542,0.0055542
+25-Aug-2019 01:00:00,1,0.1124,0.1124,0.1124,0.10131,0.10501,0.10501,0.10501,0.10501,0.10131,0.10131,0.10131,0.10131,0.10131,0.10501,0.0077894,0.1124,0.0077894,0.0077894
+25-Aug-2019 02:00:00,1,0.11276,0.11276,0.11276,0.1606,0.086257,0.086257,0.086257,0.086257,0.1606,0.1606,0.1606,0.1606,0.1606,0.086257,0.012102,0.11276,0.012102,0.012102
+25-Aug-2019 03:00:00,1,0.1039,0.1039,0.1039,0.17318,0.033753,0.033753,0.033753,0.033753,0.17318,0.17318,0.17318,0.17318,0.17318,0.033753,0.029058,0.1039,0.029058,0.029058
+25-Aug-2019 04:00:00,1,0.1048,0.1048,0.1048,0.18993,0.037503,0.037503,0.037503,0.037503,0.18993,0.18993,0.18993,0.18993,0.18993,0.037503,0.043621,0.1048,0.043621,0.043621
+25-Aug-2019 05:00:00,1,0.11468,0.11468,0.11468,0.24625,0.031878,0.031878,0.031878,0.031878,0.24625,0.24625,0.24625,0.24625,0.24625,0.031878,0.058635,0.11468,0.058635,0.058635
+25-Aug-2019 06:00:00,1,0.12098,0.12098,0.12098,0.2178,0.033753,0.033753,0.033753,0.033753,0.2178,0.2178,0.2178,0.2178,0.2178,0.033753,0.08548,0.12098,0.08548,0.08548
+25-Aug-2019 07:00:00,1,0.13042,0.13042,0.13042,0.20054,0.043129,0.043129,0.043129,0.043129,0.20054,0.20054,0.20054,0.20054,0.20054,0.043129,0.080287,0.13042,0.080287,0.080287
+25-Aug-2019 08:00:00,1,0.11587,0.11587,0.11587,0.21733,0.10126,0.10126,0.10126,0.10126,0.21733,0.21733,0.21733,0.21733,0.21733,0.10126,0.070353,0.11587,0.070353,0.070353
+25-Aug-2019 09:00:00,1,0.12856,0.12856,0.12856,0.26466,0.11813,0.11813,0.11813,0.11813,0.26466,0.26466,0.26466,0.26466,0.26466,0.11813,0.08189,0.12856,0.08189,0.08189
+25-Aug-2019 10:00:00,1,0.16452,0.16452,0.16452,0.35224,0.2419,0.2419,0.2419,0.2419,0.35224,0.35224,0.35224,0.35224,0.35224,0.2419,0.11433,0.16452,0.11433,0.11433
+25-Aug-2019 11:00:00,1,0.2003,0.2003,0.2003,0.39426,0.12751,0.12751,0.12751,0.12751,0.39426,0.39426,0.39426,0.39426,0.39426,0.12751,0.1536,0.2003,0.1536,0.1536
+25-Aug-2019 12:00:00,1,0.23244,0.23244,0.23244,0.41506,0.12001,0.12001,0.12001,0.12001,0.41506,0.41506,0.41506,0.41506,0.41506,0.12001,0.17274,0.23244,0.17274,0.17274
+25-Aug-2019 13:00:00,1,0.25341,0.25341,0.25341,0.4563,0.11438,0.11438,0.11438,0.11438,0.4563,0.4563,0.4563,0.4563,0.4563,0.11438,0.18311,0.25341,0.18311,0.18311
+25-Aug-2019 14:00:00,1,0.28663,0.28663,0.28663,0.43893,0.15376,0.15376,0.15376,0.15376,0.43893,0.43893,0.43893,0.43893,0.43893,0.15376,0.18266,0.28663,0.18266,0.18266
+25-Aug-2019 15:00:00,1,0.27192,0.27192,0.27192,0.43181,0.11813,0.11813,0.11813,0.11813,0.43181,0.43181,0.43181,0.43181,0.43181,0.11813,0.1997,0.27192,0.1997,0.1997
+25-Aug-2019 16:00:00,1,0.25543,0.25543,0.25543,0.42869,0.084382,0.084382,0.084382,0.084382,0.42869,0.42869,0.42869,0.42869,0.42869,0.084382,0.17304,0.25543,0.17304,0.17304
+25-Aug-2019 17:00:00,1,0.24139,0.24139,0.24139,0.43108,0.045004,0.045004,0.045004,0.045004,0.43108,0.43108,0.43108,0.43108,0.43108,0.045004,0.17631,0.24139,0.17631,0.17631
+25-Aug-2019 18:00:00,1,0.18146,0.18146,0.18146,0.37913,0.039378,0.039378,0.039378,0.039378,0.37913,0.37913,0.37913,0.37913,0.37913,0.039378,0.15238,0.18146,0.15238,0.15238
+25-Aug-2019 19:00:00,1,0.12269,0.12269,0.12269,0.22888,0.097508,0.097508,0.097508,0.097508,0.22888,0.22888,0.22888,0.22888,0.22888,0.097508,0.13181,0.12269,0.13181,0.13181
+25-Aug-2019 20:00:00,1,0.11253,0.11253,0.11253,0.1529,0.11251,0.11251,0.11251,0.11251,0.1529,0.1529,0.1529,0.1529,0.1529,0.11251,0.12786,0.11253,0.12786,0.12786
+25-Aug-2019 21:00:00,1,0.14553,0.14553,0.14553,0.17651,0.076881,0.076881,0.076881,0.076881,0.17651,0.17651,0.17651,0.17651,0.17651,0.076881,0.11235,0.14553,0.11235,0.11235
+25-Aug-2019 22:00:00,1,0.18402,0.18402,0.18402,0.21843,0.10126,0.10126,0.10126,0.10126,0.21843,0.21843,0.21843,0.21843,0.21843,0.10126,0.11623,0.18402,0.11623,0.11623
+25-Aug-2019 23:00:00,1,0.18179,0.18179,0.18179,0.30809,0.099383,0.099383,0.099383,0.099383,0.30809,0.30809,0.30809,0.30809,0.30809,0.099383,0.14583,0.18179,0.14583,0.14583
+26-Aug-2019 00:00:00,1,0.23494,0.23494,0.23494,0.29664,0.12001,0.12001,0.12001,0.12001,0.29664,0.29664,0.29664,0.29664,0.29664,0.12001,0.13992,0.23494,0.13992,0.13992
+26-Aug-2019 01:00:00,1,0.27385,0.27385,0.27385,0.32681,0.11438,0.11438,0.11438,0.11438,0.32681,0.32681,0.32681,0.32681,0.32681,0.11438,0.15877,0.27385,0.15877,0.15877
+26-Aug-2019 02:00:00,1,0.29635,0.29635,0.29635,0.35723,0.11813,0.11813,0.11813,0.11813,0.35723,0.35723,0.35723,0.35723,0.35723,0.11813,0.17058,0.29635,0.17058,0.17058
+26-Aug-2019 03:00:00,1,0.30737,0.30737,0.30737,0.39301,0.082507,0.082507,0.082507,0.082507,0.39301,0.39301,0.39301,0.39301,0.39301,0.082507,0.1778,0.30737,0.1778,0.1778
+26-Aug-2019 04:00:00,1,0.31163,0.31163,0.31163,0.43639,0.11063,0.11063,0.11063,0.11063,0.43639,0.43639,0.43639,0.43639,0.43639,0.11063,0.18627,0.31163,0.18627,0.18627
+26-Aug-2019 05:00:00,1,0.32123,0.32123,0.32123,0.43207,0.11063,0.11063,0.11063,0.11063,0.43207,0.43207,0.43207,0.43207,0.43207,0.11063,0.224,0.32123,0.224,0.224
+26-Aug-2019 06:00:00,1,0.32231,0.32231,0.32231,0.46395,0.18189,0.18189,0.18189,0.18189,0.46395,0.46395,0.46395,0.46395,0.46395,0.18189,0.244,0.32231,0.244,0.244
+26-Aug-2019 07:00:00,1,0.31578,0.31578,0.31578,0.48335,0.21377,0.21377,0.21377,0.21377,0.48335,0.48335,0.48335,0.48335,0.48335,0.21377,0.26956,0.31578,0.26956,0.26956
+26-Aug-2019 08:00:00,1,0.35059,0.35059,0.35059,0.50311,0.19314,0.19314,0.19314,0.19314,0.50311,0.50311,0.50311,0.50311,0.50311,0.19314,0.25816,0.35059,0.25816,0.25816
+26-Aug-2019 09:00:00,1,0.39606,0.39606,0.39606,0.51684,0.18752,0.18752,0.18752,0.18752,0.51684,0.51684,0.51684,0.51684,0.51684,0.18752,0.33108,0.39606,0.33108,0.33108
+26-Aug-2019 10:00:00,1,0.39454,0.39454,0.39454,0.48023,0.20627,0.20627,0.20627,0.20627,0.48023,0.48023,0.48023,0.48023,0.48023,0.20627,0.42796,0.39454,0.42796,0.42796
+26-Aug-2019 11:00:00,1,0.37395,0.37395,0.37395,0.51741,0.22127,0.22127,0.22127,0.22127,0.51741,0.51741,0.51741,0.51741,0.51741,0.22127,0.49294,0.37395,0.49294,0.49294
+26-Aug-2019 12:00:00,1,0.36562,0.36562,0.36562,0.48298,0.18377,0.18377,0.18377,0.18377,0.48298,0.48298,0.48298,0.48298,0.48298,0.18377,0.45158,0.36562,0.45158,0.45158
+26-Aug-2019 13:00:00,1,0.38245,0.38245,0.38245,0.43945,0.22502,0.22502,0.22502,0.22502,0.43945,0.43945,0.43945,0.43945,0.43945,0.22502,0.47066,0.38245,0.47066,0.47066
+26-Aug-2019 14:00:00,1,0.40826,0.40826,0.40826,0.39795,0.21002,0.21002,0.21002,0.21002,0.39795,0.39795,0.39795,0.39795,0.39795,0.21002,0.5206,0.40826,0.5206,0.5206
+26-Aug-2019 15:00:00,1,0.42677,0.42677,0.42677,0.30533,0.17626,0.17626,0.17626,0.17626,0.30533,0.30533,0.30533,0.30533,0.30533,0.17626,0.55521,0.42677,0.55521,0.55521
+26-Aug-2019 16:00:00,1,0.40333,0.40333,0.40333,0.328,0.19502,0.19502,0.19502,0.19502,0.328,0.328,0.328,0.328,0.328,0.19502,0.56235,0.40333,0.56235,0.56235
+26-Aug-2019 17:00:00,1,0.37863,0.37863,0.37863,0.31916,0.17814,0.17814,0.17814,0.17814,0.31916,0.31916,0.31916,0.31916,0.31916,0.17814,0.53361,0.37863,0.53361,0.53361
+26-Aug-2019 18:00:00,1,0.38535,0.38535,0.38535,0.315,0.10126,0.10126,0.10126,0.10126,0.315,0.315,0.315,0.315,0.315,0.10126,0.43038,0.38535,0.43038,0.43038
+26-Aug-2019 19:00:00,1,0.34681,0.34681,0.34681,0.22987,0.05813,0.05813,0.05813,0.05813,0.22987,0.22987,0.22987,0.22987,0.22987,0.05813,0.36174,0.34681,0.36174,0.36174
+26-Aug-2019 20:00:00,1,0.3053,0.3053,0.3053,0.20974,0.05813,0.05813,0.05813,0.05813,0.20974,0.20974,0.20974,0.20974,0.20974,0.05813,0.33871,0.3053,0.33871,0.33871
+26-Aug-2019 21:00:00,1,0.29167,0.29167,0.29167,0.18629,0.069381,0.069381,0.069381,0.069381,0.18629,0.18629,0.18629,0.18629,0.18629,0.069381,0.35014,0.29167,0.35014,0.35014
+26-Aug-2019 22:00:00,1,0.27394,0.27394,0.27394,0.14468,0.076881,0.076881,0.076881,0.076881,0.14468,0.14468,0.14468,0.14468,0.14468,0.076881,0.36545,0.27394,0.36545,0.36545
+26-Aug-2019 23:00:00,1,0.23812,0.23812,0.23812,0.074681,0.088132,0.088132,0.088132,0.088132,0.074681,0.074681,0.074681,0.074681,0.074681,0.088132,0.37073,0.23812,0.37073,0.37073
+27-Aug-2019 00:00:00,1,0.21333,0.21333,0.21333,0.054139,0.05813,0.05813,0.05813,0.05813,0.054139,0.054139,0.054139,0.054139,0.054139,0.05813,0.3626,0.21333,0.3626,0.3626
+27-Aug-2019 01:00:00,1,0.20184,0.20184,0.20184,0.051694,0.039378,0.039378,0.039378,0.039378,0.051694,0.051694,0.051694,0.051694,0.051694,0.039378,0.33171,0.20184,0.33171,0.33171
+27-Aug-2019 02:00:00,1,0.21013,0.21013,0.21013,0.039109,0.031878,0.031878,0.031878,0.031878,0.039109,0.039109,0.039109,0.039109,0.039109,0.031878,0.30729,0.21013,0.30729,0.30729
+27-Aug-2019 03:00:00,1,0.19343,0.19343,0.19343,0.039005,0.026252,0.026252,0.026252,0.026252,0.039005,0.039005,0.039005,0.039005,0.039005,0.026252,0.2689,0.19343,0.2689,0.2689
+27-Aug-2019 04:00:00,1,0.16809,0.16809,0.16809,0.033076,0.0093758,0.0093758,0.0093758,0.0093758,0.033076,0.033076,0.033076,0.033076,0.033076,0.0093758,0.25269,0.16809,0.25269,0.25269
+27-Aug-2019 05:00:00,1,0.16067,0.16067,0.16067,0.021791,0.0075006,0.0075006,0.0075006,0.0075006,0.021791,0.021791,0.021791,0.021791,0.021791,0.0075006,0.23445,0.16067,0.23445,0.23445
+27-Aug-2019 06:00:00,1,0.14474,0.14474,0.14474,0.020126,0.0075006,0.0075006,0.0075006,0.0075006,0.020126,0.020126,0.020126,0.020126,0.020126,0.0075006,0.20918,0.14474,0.20918,0.20918
+27-Aug-2019 07:00:00,1,0.12306,0.12306,0.12306,0.025483,0,0,0,0,0.025483,0.025483,0.025483,0.025483,0.025483,0,0.17773,0.12306,0.17773,0.17773
+27-Aug-2019 08:00:00,1,0.07952,0.07952,0.07952,0.03136,0,0,0,0,0.03136,0.03136,0.03136,0.03136,0.03136,0,0.16518,0.07952,0.16518,0.16518
+27-Aug-2019 09:00:00,1,0.071255,0.071255,0.071255,0.028708,0,0,0,0,0.028708,0.028708,0.028708,0.028708,0.028708,0,0.18613,0.071255,0.18613,0.18613
+27-Aug-2019 10:00:00,1,0.084285,0.084285,0.084285,0.026679,0.0018752,0.0018752,0.0018752,0.0018752,0.026679,0.026679,0.026679,0.026679,0.026679,0.0018752,0.19701,0.084285,0.19701,0.19701
+27-Aug-2019 11:00:00,1,0.07701,0.07701,0.07701,0.031672,0.0018752,0.0018752,0.0018752,0.0018752,0.031672,0.031672,0.031672,0.031672,0.031672,0.0018752,0.19146,0.07701,0.19146,0.19146
+27-Aug-2019 12:00:00,1,0.066374,0.066374,0.066374,0.027979,0.0018752,0.0018752,0.0018752,0.0018752,0.027979,0.027979,0.027979,0.027979,0.027979,0.0018752,0.16672,0.066374,0.16672,0.16672
+27-Aug-2019 13:00:00,1,0.05187,0.05187,0.05187,0.035052,0.0018752,0.0018752,0.0018752,0.0018752,0.035052,0.035052,0.035052,0.035052,0.035052,0.0018752,0.15604,0.05187,0.15604,0.15604
+27-Aug-2019 14:00:00,1,0.040566,0.040566,0.040566,0.038017,0.0018752,0.0018752,0.0018752,0.0018752,0.038017,0.038017,0.038017,0.038017,0.038017,0.0018752,0.13867,0.040566,0.13867,0.13867
+27-Aug-2019 15:00:00,1,0.036122,0.036122,0.036122,0.050654,0.0018752,0.0018752,0.0018752,0.0018752,0.050654,0.050654,0.050654,0.050654,0.050654,0.0018752,0.10925,0.036122,0.10925,0.10925
+27-Aug-2019 16:00:00,1,0.033429,0.033429,0.033429,0.069377,0.0037503,0.0037503,0.0037503,0.0037503,0.069377,0.069377,0.069377,0.069377,0.069377,0.0037503,0.069066,0.033429,0.069066,0.069066
+27-Aug-2019 17:00:00,1,0.031357,0.031357,0.031357,0.078374,0.0075006,0.0075006,0.0075006,0.0075006,0.078374,0.078374,0.078374,0.078374,0.078374,0.0075006,0.040392,0.031357,0.040392,0.040392
+27-Aug-2019 18:00:00,1,0.028249,0.028249,0.028249,0.069012,0.020627,0.020627,0.020627,0.020627,0.069012,0.069012,0.069012,0.069012,0.069012,0.020627,0.016234,0.028249,0.016234,0.016234
+27-Aug-2019 19:00:00,1,0.033314,0.033314,0.033314,0.064904,0.039378,0.039378,0.039378,0.039378,0.064904,0.064904,0.064904,0.064904,0.064904,0.039378,0.0017159,0.033314,0.0017159,0.0017159
+27-Aug-2019 20:00:00,1,0.056037,0.056037,0.056037,0.099072,0.05813,0.05813,0.05813,0.05813,0.099072,0.099072,0.099072,0.099072,0.099072,0.05813,0.00051929,0.056037,0.00051929,0.00051929
+27-Aug-2019 21:00:00,1,0.099181,0.099181,0.099181,0.15155,0.063755,0.063755,0.063755,0.063755,0.15155,0.15155,0.15155,0.15155,0.15155,0.063755,0.0019643,0.099181,0.0019643,0.0019643
+27-Aug-2019 22:00:00,1,0.14437,0.14437,0.14437,0.19222,0.045004,0.045004,0.045004,0.045004,0.19222,0.19222,0.19222,0.19222,0.19222,0.045004,0.0074959,0.14437,0.0074959,0.0074959
+27-Aug-2019 23:00:00,1,0.1709,0.1709,0.1709,0.22389,0.056255,0.056255,0.056255,0.056255,0.22389,0.22389,0.22389,0.22389,0.22389,0.056255,0.023007,0.1709,0.023007,0.023007
+28-Aug-2019 00:00:00,1,0.1872,0.1872,0.1872,0.21489,0.063755,0.063755,0.063755,0.063755,0.21489,0.21489,0.21489,0.21489,0.21489,0.063755,0.049197,0.1872,0.049197,0.049197
+28-Aug-2019 01:00:00,1,0.20918,0.20918,0.20918,0.19534,0.063755,0.063755,0.063755,0.063755,0.19534,0.19534,0.19534,0.19534,0.19534,0.063755,0.08548,0.20918,0.08548,0.08548
+28-Aug-2019 02:00:00,1,0.22907,0.22907,0.22907,0.17349,0.065631,0.065631,0.065631,0.065631,0.17349,0.17349,0.17349,0.17349,0.17349,0.065631,0.13355,0.22907,0.13355,0.13355
+28-Aug-2019 03:00:00,1,0.23324,0.23324,0.23324,0.18582,0.088132,0.088132,0.088132,0.088132,0.18582,0.18582,0.18582,0.18582,0.18582,0.088132,0.1715,0.23324,0.1715,0.1715
+28-Aug-2019 04:00:00,1,0.22818,0.22818,0.22818,0.15633,0.084382,0.084382,0.084382,0.084382,0.15633,0.15633,0.15633,0.15633,0.15633,0.084382,0.20487,0.22818,0.20487,0.20487
+28-Aug-2019 05:00:00,1,0.21715,0.21715,0.21715,0.13386,0.045004,0.045004,0.045004,0.045004,0.13386,0.13386,0.13386,0.13386,0.13386,0.045004,0.20022,0.21715,0.20022,0.20022
+28-Aug-2019 06:00:00,1,0.18651,0.18651,0.18651,0.14453,0.033753,0.033753,0.033753,0.033753,0.14453,0.14453,0.14453,0.14453,0.14453,0.033753,0.19645,0.18651,0.19645,0.19645
+28-Aug-2019 07:00:00,1,0.16869,0.16869,0.16869,0.14619,0.028127,0.028127,0.028127,0.028127,0.14619,0.14619,0.14619,0.14619,0.14619,0.028127,0.17545,0.16869,0.17545,0.17545
+28-Aug-2019 08:00:00,1,0.14035,0.14035,0.14035,0.16106,0.020627,0.020627,0.020627,0.020627,0.16106,0.16106,0.16106,0.16106,0.16106,0.020627,0.10641,0.14035,0.10641,0.10641
+28-Aug-2019 09:00:00,1,0.10966,0.10966,0.10966,0.1166,0.024377,0.024377,0.024377,0.024377,0.1166,0.1166,0.1166,0.1166,0.1166,0.024377,0.061593,0.10966,0.061593,0.061593
+28-Aug-2019 10:00:00,1,0.09453,0.09453,0.09453,0.099176,0.050629,0.050629,0.050629,0.050629,0.099176,0.099176,0.099176,0.099176,0.099176,0.050629,0.060531,0.09453,0.060531,0.060531
+28-Aug-2019 11:00:00,1,0.081891,0.081891,0.081891,0.13345,0.06188,0.06188,0.06188,0.06188,0.13345,0.13345,0.13345,0.13345,0.13345,0.06188,0.070353,0.081891,0.070353,0.070353
+28-Aug-2019 12:00:00,1,0.0716,0.0716,0.0716,0.14775,0.05438,0.05438,0.05438,0.05438,0.14775,0.14775,0.14775,0.14775,0.14775,0.05438,0.072859,0.0716,0.072859,0.072859
+28-Aug-2019 13:00:00,1,0.059789,0.059789,0.059789,0.1621,0.056255,0.056255,0.056255,0.056255,0.1621,0.1621,0.1621,0.1621,0.1621,0.056255,0.063376,0.059789,0.063376,0.063376
+28-Aug-2019 14:00:00,1,0.05926,0.05926,0.05926,0.21177,0.022502,0.022502,0.022502,0.022502,0.21177,0.21177,0.21177,0.21177,0.21177,0.022502,0.048836,0.05926,0.048836,0.048836
+28-Aug-2019 15:00:00,1,0.073511,0.073511,0.073511,0.17708,0.048754,0.048754,0.048754,0.048754,0.17708,0.17708,0.17708,0.17708,0.17708,0.048754,0.052042,0.073511,0.052042,0.052042
+28-Aug-2019 16:00:00,1,0.084838,0.084838,0.084838,0.16372,0.045004,0.045004,0.045004,0.045004,0.16372,0.16372,0.16372,0.16372,0.16372,0.045004,0.054593,0.084838,0.054593,0.054593
+28-Aug-2019 17:00:00,1,0.081799,0.081799,0.081799,0.14666,0.030003,0.030003,0.030003,0.030003,0.14666,0.14666,0.14666,0.14666,0.14666,0.030003,0.053329,0.081799,0.053329,0.053329
+28-Aug-2019 18:00:00,1,0.09064,0.09064,0.09064,0.093195,0.015001,0.015001,0.015001,0.015001,0.093195,0.093195,0.093195,0.093195,0.093195,0.015001,0.048994,0.09064,0.048994,0.048994
+28-Aug-2019 19:00:00,1,0.058984,0.058984,0.058984,0.070105,0.0075006,0.0075006,0.0075006,0.0075006,0.070105,0.070105,0.070105,0.070105,0.070105,0.0075006,0.045923,0.058984,0.045923,0.045923
+28-Aug-2019 20:00:00,1,0.048439,0.048439,0.048439,0.042021,0.0075006,0.0075006,0.0075006,0.0075006,0.042021,0.042021,0.042021,0.042021,0.042021,0.0075006,0.070082,0.048439,0.070082,0.070082
+28-Aug-2019 21:00:00,1,0.049613,0.049613,0.049613,0.039213,0.018752,0.018752,0.018752,0.018752,0.039213,0.039213,0.039213,0.039213,0.039213,0.018752,0.095437,0.049613,0.095437,0.095437
+28-Aug-2019 22:00:00,1,0.056106,0.056106,0.056106,0.039785,0.016876,0.016876,0.016876,0.016876,0.039785,0.039785,0.039785,0.039785,0.039785,0.016876,0.082048,0.056106,0.082048,0.082048
+28-Aug-2019 23:00:00,1,0.050557,0.050557,0.050557,0.048106,0.035628,0.035628,0.035628,0.035628,0.048106,0.048106,0.048106,0.048106,0.048106,0.035628,0.092366,0.050557,0.092366,0.092366
+29-Aug-2019 00:00:00,1,0.033452,0.033452,0.033452,0.037185,0.022502,0.022502,0.022502,0.022502,0.037185,0.037185,0.037185,0.037185,0.037185,0.022502,0.090944,0.033452,0.090944,0.090944
+29-Aug-2019 01:00:00,1,0.02003,0.02003,0.02003,0.026159,0.018752,0.018752,0.018752,0.018752,0.026159,0.026159,0.026159,0.026159,0.026159,0.018752,0.073333,0.02003,0.073333,0.073333
+29-Aug-2019 02:00:00,1,0.018119,0.018119,0.018119,0.021791,0.031878,0.031878,0.031878,0.031878,0.021791,0.021791,0.021791,0.021791,0.021791,0.031878,0.060306,0.018119,0.060306,0.060306
+29-Aug-2019 03:00:00,1,0.023667,0.023667,0.023667,0.023195,0.030003,0.030003,0.030003,0.030003,0.023195,0.023195,0.023195,0.023195,0.023195,0.030003,0.045427,0.023667,0.045427,0.045427
+29-Aug-2019 04:00:00,1,0.024035,0.024035,0.024035,0.044569,0.020627,0.020627,0.020627,0.020627,0.044569,0.044569,0.044569,0.044569,0.044569,0.020627,0.032986,0.024035,0.032986,0.032986
+29-Aug-2019 05:00:00,1,0.031126,0.031126,0.031126,0.052214,0.033753,0.033753,0.033753,0.033753,0.052214,0.052214,0.052214,0.052214,0.052214,0.033753,0.030232,0.031126,0.030232,0.030232
+29-Aug-2019 06:00:00,1,0.034096,0.034096,0.034096,0.050342,0.031878,0.031878,0.031878,0.031878,0.050342,0.050342,0.050342,0.050342,0.050342,0.031878,0.023571,0.034096,0.023571,0.023571
+29-Aug-2019 07:00:00,1,0.039737,0.039737,0.039737,0.04769,0.028127,0.028127,0.028127,0.028127,0.04769,0.04769,0.04769,0.04769,0.04769,0.028127,0.01585,0.039737,0.01585,0.01585
+29-Aug-2019 08:00:00,1,0.034188,0.034188,0.034188,0.036665,0.0056255,0.0056255,0.0056255,0.0056255,0.036665,0.036665,0.036665,0.036665,0.036665,0.0056255,0.0034544,0.034188,0.0034544,0.0034544
+29-Aug-2019 09:00:00,1,0.020053,0.020053,0.020053,0.028499,0.0037503,0.0037503,0.0037503,0.0037503,0.028499,0.028499,0.028499,0.028499,0.028499,0.0037503,6.77e-05,0.020053,6.77e-05,6.77e-05
+29-Aug-2019 10:00:00,1,0.018349,0.018349,0.018349,0.024911,0.0075006,0.0075006,0.0075006,0.0075006,0.024911,0.024911,0.024911,0.024911,0.024911,0.0075006,0.00069991,0.018349,0.00069991,0.00069991
+29-Aug-2019 11:00:00,1,0.023207,0.023207,0.023207,0.038849,0.0075006,0.0075006,0.0075006,0.0075006,0.038849,0.038849,0.038849,0.038849,0.038849,0.0075006,0.0012644,0.023207,0.0012644,0.0012644
+29-Aug-2019 12:00:00,1,0.040681,0.040681,0.040681,0.075357,0.015001,0.015001,0.015001,0.015001,0.075357,0.075357,0.075357,0.075357,0.075357,0.015001,0.0027997,0.040681,0.0027997,0.0027997
+29-Aug-2019 13:00:00,1,0.054678,0.054678,0.054678,0.1282,0.0093758,0.0093758,0.0093758,0.0093758,0.1282,0.1282,0.1282,0.1282,0.1282,0.0093758,0.0057348,0.054678,0.0057348,0.0057348
+29-Aug-2019 14:00:00,1,0.06467,0.06467,0.06467,0.15404,0.045004,0.045004,0.045004,0.045004,0.15404,0.15404,0.15404,0.15404,0.15404,0.045004,0.01296,0.06467,0.01296,0.01296
+29-Aug-2019 15:00:00,1,0.070311,0.070311,0.070311,0.19409,0.011251,0.011251,0.011251,0.011251,0.19409,0.19409,0.19409,0.19409,0.19409,0.011251,0.014947,0.070311,0.014947,0.014947
+29-Aug-2019 16:00:00,1,0.078207,0.078207,0.078207,0.15784,0.0093758,0.0093758,0.0093758,0.0093758,0.15784,0.15784,0.15784,0.15784,0.15784,0.0093758,0.015985,0.078207,0.015985,0.015985
+29-Aug-2019 17:00:00,1,0.074547,0.074547,0.074547,0.15534,0.0018752,0.0018752,0.0018752,0.0018752,0.15534,0.15534,0.15534,0.15534,0.15534,0.0018752,0.016685,0.074547,0.016685,0.016685
+29-Aug-2019 18:00:00,1,0.05827,0.05827,0.05827,0.10354,0.011251,0.011251,0.011251,0.011251,0.10354,0.10354,0.10354,0.10354,0.10354,0.011251,0.015285,0.05827,0.015285,0.015285
+29-Aug-2019 19:00:00,1,0.053965,0.053965,0.053965,0.060067,0.028127,0.028127,0.028127,0.028127,0.060067,0.060067,0.060067,0.060067,0.060067,0.028127,0.027658,0.053965,0.027658,0.027658
+29-Aug-2019 20:00:00,1,0.078875,0.078875,0.078875,0.063864,0.022502,0.022502,0.022502,0.022502,0.063864,0.063864,0.063864,0.063864,0.063864,0.022502,0.072565,0.078875,0.072565,0.072565
+29-Aug-2019 21:00:00,1,0.089097,0.089097,0.089097,0.047742,0.028127,0.028127,0.028127,0.028127,0.047742,0.047742,0.047742,0.047742,0.047742,0.028127,0.11718,0.089097,0.11718,0.11718
+29-Aug-2019 22:00:00,1,0.091883,0.091883,0.091883,0.051694,0.052504,0.052504,0.052504,0.052504,0.051694,0.051694,0.051694,0.051694,0.051694,0.052504,0.15522,0.091883,0.15522,0.15522
+29-Aug-2019 23:00:00,1,0.091722,0.091722,0.091722,0.062408,0.048754,0.048754,0.048754,0.048754,0.062408,0.062408,0.062408,0.062408,0.062408,0.048754,0.19221,0.091722,0.19221,0.19221
+30-Aug-2019 00:00:00,1,0.086772,0.086772,0.086772,0.059183,0.063755,0.063755,0.063755,0.063755,0.059183,0.059183,0.059183,0.059183,0.059183,0.063755,0.21316,0.086772,0.21316,0.21316
+30-Aug-2019 01:00:00,1,0.07777,0.07777,0.07777,0.069116,0.065631,0.065631,0.065631,0.065631,0.069116,0.069116,0.069116,0.069116,0.069116,0.065631,0.20361,0.07777,0.20361,0.20361
+30-Aug-2019 02:00:00,1,0.064463,0.064463,0.064463,0.053099,0.046879,0.046879,0.046879,0.046879,0.053099,0.053099,0.053099,0.053099,0.053099,0.046879,0.18674,0.064463,0.18674,0.18674
+30-Aug-2019 03:00:00,1,0.072521,0.072521,0.072521,0.08373,0.056255,0.056255,0.056255,0.056255,0.08373,0.08373,0.08373,0.08373,0.08373,0.056255,0.18069,0.072521,0.18069,0.18069
+30-Aug-2019 04:00:00,1,0.060181,0.060181,0.060181,0.091427,0.043129,0.043129,0.043129,0.043129,0.091427,0.091427,0.091427,0.091427,0.091427,0.043129,0.15985,0.060181,0.15985,0.15985
+30-Aug-2019 05:00:00,1,0.059582,0.059582,0.059582,0.08477,0.030003,0.030003,0.030003,0.030003,0.08477,0.08477,0.08477,0.08477,0.08477,0.030003,0.14037,0.059582,0.14037,0.14037
+30-Aug-2019 06:00:00,1,0.059997,0.059997,0.059997,0.061576,0.011251,0.011251,0.011251,0.011251,0.061576,0.061576,0.061576,0.061576,0.061576,0.011251,0.14305,0.059997,0.14305,0.14305
+30-Aug-2019 07:00:00,1,0.054241,0.054241,0.054241,0.045558,0.011251,0.011251,0.011251,0.011251,0.045558,0.045558,0.045558,0.045558,0.045558,0.011251,0.10472,0.054241,0.10472,0.10472
+30-Aug-2019 08:00:00,1,0.040335,0.040335,0.040335,0.026783,0.0018752,0.0018752,0.0018752,0.0018752,0.026783,0.026783,0.026783,0.026783,0.026783,0.0018752,0.068524,0.040335,0.068524,0.068524
+30-Aug-2019 09:00:00,1,0.023437,0.023437,0.023437,0.022155,0,0,0,0,0.022155,0.022155,0.022155,0.022155,0.022155,0,0.04554,0.023437,0.04554,0.04554
+30-Aug-2019 10:00:00,1,0.031771,0.031771,0.031771,0.026731,0,0,0,0,0.026731,0.026731,0.026731,0.026731,0.026731,0,0.054751,0.031771,0.054751,0.054751
+30-Aug-2019 11:00:00,1,0.03435,0.03435,0.03435,0.030996,0.0093758,0.0093758,0.0093758,0.0093758,0.030996,0.030996,0.030996,0.030996,0.030996,0.0093758,0.055451,0.03435,0.055451,0.055451
+30-Aug-2019 12:00:00,1,0.028456,0.028456,0.028456,0.038173,0.0093758,0.0093758,0.0093758,0.0093758,0.038173,0.038173,0.038173,0.038173,0.038173,0.0093758,0.032602,0.028456,0.032602,0.032602
+30-Aug-2019 13:00:00,1,0.028226,0.028226,0.028226,0.049198,0.026252,0.026252,0.026252,0.026252,0.049198,0.049198,0.049198,0.049198,0.049198,0.026252,0.020749,0.028226,0.020749,0.020749
+30-Aug-2019 14:00:00,1,0.030965,0.030965,0.030965,0.057623,0.031878,0.031878,0.031878,0.031878,0.057623,0.057623,0.057623,0.057623,0.057623,0.031878,0.016708,0.030965,0.016708,0.016708
+30-Aug-2019 15:00:00,1,0.0291,0.0291,0.0291,0.069064,0.052504,0.052504,0.052504,0.052504,0.069064,0.069064,0.069064,0.069064,0.069064,0.052504,0.010725,0.0291,0.010725,0.010725
+30-Aug-2019 16:00:00,1,0.026591,0.026591,0.026591,0.086331,0.028127,0.028127,0.028127,0.028127,0.086331,0.086331,0.086331,0.086331,0.086331,0.028127,0.015285,0.026591,0.015285,0.015285
+30-Aug-2019 17:00:00,1,0.028087,0.028087,0.028087,0.096368,0.026252,0.026252,0.026252,0.026252,0.096368,0.096368,0.096368,0.096368,0.096368,0.026252,0.013524,0.028087,0.013524,0.013524
+30-Aug-2019 18:00:00,1,0.023529,0.023529,0.023529,0.071665,0.016876,0.016876,0.016876,0.016876,0.071665,0.071665,0.071665,0.071665,0.071665,0.016876,0.0078571,0.023529,0.0078571,0.0078571
+30-Aug-2019 19:00:00,1,0.020697,0.020697,0.020697,0.060379,0.022502,0.022502,0.022502,0.022502,0.060379,0.060379,0.060379,0.060379,0.060379,0.022502,0.015285,0.020697,0.015285,0.015285
+30-Aug-2019 20:00:00,1,0.02003,0.02003,0.02003,0.072653,0.037503,0.037503,0.037503,0.037503,0.072653,0.072653,0.072653,0.072653,0.072653,0.037503,0.015172,0.02003,0.015172,0.015172
+30-Aug-2019 21:00:00,1,0.015886,0.015886,0.015886,0.075097,0.052504,0.052504,0.052504,0.052504,0.075097,0.075097,0.075097,0.075097,0.075097,0.052504,0.023684,0.015886,0.023684,0.023684
+30-Aug-2019 22:00:00,1,0.015886,0.015886,0.015886,0.060119,0.041253,0.041253,0.041253,0.041253,0.060119,0.060119,0.060119,0.060119,0.060119,0.041253,0.038811,0.015886,0.038811,0.038811
+30-Aug-2019 23:00:00,1,0.018878,0.018878,0.018878,0.062044,0.045004,0.045004,0.045004,0.045004,0.062044,0.062044,0.062044,0.062044,0.062044,0.045004,0.050371,0.018878,0.050371,0.050371
+31-Aug-2019 00:00:00,1,0.021779,0.021779,0.021779,0.053723,0.050629,0.050629,0.050629,0.050629,0.053723,0.053723,0.053723,0.053723,0.053723,0.050629,0.04861,0.021779,0.04861,0.04861
+31-Aug-2019 01:00:00,1,0.023805,0.023805,0.023805,0.080558,0.037503,0.037503,0.037503,0.037503,0.080558,0.080558,0.080558,0.080558,0.080558,0.037503,0.044501,0.023805,0.044501,0.044501
+31-Aug-2019 02:00:00,1,0.025025,0.025025,0.025025,0.073017,0.018752,0.018752,0.018752,0.018752,0.073017,0.073017,0.073017,0.073017,0.073017,0.018752,0.034612,0.025025,0.034612,0.034612
+31-Aug-2019 03:00:00,1,0.026131,0.026131,0.026131,0.074889,0.016876,0.016876,0.016876,0.016876,0.074889,0.074889,0.074889,0.074889,0.074889,0.016876,0.033348,0.026131,0.033348,0.033348
+31-Aug-2019 04:00:00,1,0.02689,0.02689,0.02689,0.07749,0.037503,0.037503,0.037503,0.037503,0.07749,0.07749,0.07749,0.07749,0.07749,0.037503,0.03407,0.02689,0.03407,0.03407
+31-Aug-2019 05:00:00,1,0.043812,0.043812,0.043812,0.06506,0.030003,0.030003,0.030003,0.030003,0.06506,0.06506,0.06506,0.06506,0.06506,0.030003,0.042446,0.043812,0.042446,0.042446
+31-Aug-2019 06:00:00,1,0.05431,0.05431,0.05431,0.08477,0.035628,0.035628,0.035628,0.035628,0.08477,0.08477,0.08477,0.08477,0.08477,0.035628,0.027116,0.05431,0.027116,0.027116
+31-Aug-2019 07:00:00,1,0.053228,0.053228,0.053228,0.10282,0.045004,0.045004,0.045004,0.045004,0.10282,0.10282,0.10282,0.10282,0.10282,0.045004,0.04064,0.053228,0.04064,0.04064
+31-Aug-2019 08:00:00,1,0.066259,0.066259,0.066259,0.077229,0.041253,0.041253,0.041253,0.041253,0.077229,0.077229,0.077229,0.077229,0.077229,0.041253,0.041182,0.066259,0.041182,0.041182
+31-Aug-2019 09:00:00,1,0.078069,0.078069,0.078069,0.091271,0.045004,0.045004,0.045004,0.045004,0.091271,0.091271,0.091271,0.091271,0.091271,0.045004,0.028719,0.078069,0.028719,0.028719
+31-Aug-2019 10:00:00,1,0.10779,0.10779,0.10779,0.10583,0.039378,0.039378,0.039378,0.039378,0.10583,0.10583,0.10583,0.10583,0.10583,0.039378,0.02102,0.10779,0.02102,0.02102
+31-Aug-2019 11:00:00,1,0.15688,0.15688,0.15688,0.171,0.031878,0.031878,0.031878,0.031878,0.171,0.171,0.171,0.171,0.171,0.031878,0.021675,0.15688,0.021675,0.021675
+31-Aug-2019 12:00:00,1,0.18595,0.18595,0.18595,0.25031,0.026252,0.026252,0.026252,0.026252,0.25031,0.25031,0.25031,0.25031,0.25031,0.026252,0.016753,0.18595,0.016753,0.016753
+31-Aug-2019 13:00:00,1,0.19196,0.19196,0.19196,0.27012,0.037503,0.037503,0.037503,0.037503,0.27012,0.27012,0.27012,0.27012,0.27012,0.037503,0.016211,0.19196,0.016211,0.016211
+31-Aug-2019 14:00:00,1,0.21798,0.21798,0.21798,0.37164,0.063755,0.063755,0.063755,0.063755,0.37164,0.37164,0.37164,0.37164,0.37164,0.063755,0.022397,0.21798,0.022397,0.022397
+31-Aug-2019 15:00:00,1,0.22159,0.22159,0.22159,0.40331,0.050629,0.050629,0.050629,0.050629,0.40331,0.40331,0.40331,0.40331,0.40331,0.050629,0.022217,0.22159,0.022217,0.022217
+31-Aug-2019 16:00:00,1,0.2203,0.2203,0.2203,0.42375,0.020627,0.020627,0.020627,0.020627,0.42375,0.42375,0.42375,0.42375,0.42375,0.020627,0.027748,0.2203,0.027748,0.027748
+31-Aug-2019 17:00:00,1,0.19965,0.19965,0.19965,0.42271,0.013126,0.013126,0.013126,0.013126,0.42271,0.42271,0.42271,0.42271,0.42271,0.013126,0.025468,0.19965,0.025468,0.025468
+31-Aug-2019 18:00:00,1,0.17615,0.17615,0.17615,0.33747,0.039378,0.039378,0.039378,0.039378,0.33747,0.33747,0.33747,0.33747,0.33747,0.039378,0.039873,0.17615,0.039873,0.039873
+31-Aug-2019 19:00:00,1,0.1443,0.1443,0.1443,0.26346,0.031878,0.031878,0.031878,0.031878,0.26346,0.26346,0.26346,0.26346,0.26346,0.031878,0.046217,0.1443,0.046217,0.046217
+31-Aug-2019 20:00:00,1,0.1342,0.1342,0.1342,0.21837,0.016876,0.016876,0.016876,0.016876,0.21837,0.21837,0.21837,0.21837,0.21837,0.016876,0.051997,0.1342,0.051997,0.051997
+31-Aug-2019 21:00:00,1,0.14081,0.14081,0.14081,0.21655,0.0093758,0.0093758,0.0093758,0.0093758,0.21655,0.21655,0.21655,0.21655,0.21655,0.0093758,0.063015,0.14081,0.063015,0.063015
+31-Aug-2019 22:00:00,1,0.1283,0.1283,0.1283,0.18509,0.015001,0.015001,0.015001,0.015001,0.18509,0.18509,0.18509,0.18509,0.18509,0.015001,0.074417,0.1283,0.074417,0.074417
+31-Aug-2019 23:00:00,1,0.13206,0.13206,0.13206,0.16548,0.026252,0.026252,0.026252,0.026252,0.16548,0.16548,0.16548,0.16548,0.16548,0.026252,0.07392,0.13206,0.07392,0.07392
+01-Sep-2019 00:00:00,1,0.12604,0.12604,0.12604,0.135,0.031723,0.031723,0.031723,0.031723,0.135,0.135,0.135,0.135,0.135,0.031723,0.080782,0.12604,0.080782,0.080782
+01-Sep-2019 01:00:00,1,0.10476,0.10476,0.10476,0.10636,0.035455,0.035455,0.035455,0.035455,0.10636,0.10636,0.10636,0.10636,0.10636,0.035455,0.096333,0.10476,0.096333,0.096333
+01-Sep-2019 02:00:00,1,0.10646,0.10646,0.10646,0.09975,0.041053,0.041053,0.041053,0.041053,0.09975,0.09975,0.09975,0.09975,0.09975,0.041053,0.091127,0.10646,0.091127,0.091127
+01-Sep-2019 03:00:00,1,0.11289,0.11289,0.11289,0.096293,0.044785,0.044785,0.044785,0.044785,0.096293,0.096293,0.096293,0.096293,0.096293,0.044785,0.096692,0.11289,0.096692,0.096692
+01-Sep-2019 04:00:00,1,0.11115,0.11115,0.11115,0.10723,0.054115,0.054115,0.054115,0.054115,0.10723,0.10723,0.10723,0.10723,0.10723,0.054115,0.093461,0.11115,0.093461,0.093461
+01-Sep-2019 05:00:00,1,0.097411,0.097411,0.097411,0.11585,0.044785,0.044785,0.044785,0.044785,0.11585,0.11585,0.11585,0.11585,0.11585,0.044785,0.07892,0.097411,0.07892,0.07892
+01-Sep-2019 06:00:00,1,0.086669,0.086669,0.086669,0.11048,0.063445,0.063445,0.063445,0.063445,0.11048,0.11048,0.11048,0.11048,0.11048,0.063445,0.072974,0.086669,0.072974,0.072974
+01-Sep-2019 07:00:00,1,0.072469,0.072469,0.072469,0.092165,0.055981,0.055981,0.055981,0.055981,0.092165,0.092165,0.092165,0.092165,0.092165,0.055981,0.066466,0.072469,0.066466,0.066466
+01-Sep-2019 08:00:00,1,0.046221,0.046221,0.046221,0.082979,0.048517,0.048517,0.048517,0.048517,0.082979,0.082979,0.082979,0.082979,0.082979,0.048517,0.036734,0.046221,0.036734,0.036734
+01-Sep-2019 09:00:00,1,0.038021,0.038021,0.038021,0.068943,0.052249,0.052249,0.052249,0.052249,0.068943,0.068943,0.068943,0.068943,0.068943,0.052249,0.022754,0.038021,0.022754,0.022754
+01-Sep-2019 10:00:00,1,0.045236,0.045236,0.045236,0.056455,0.050383,0.050383,0.050383,0.050383,0.056455,0.056455,0.056455,0.056455,0.056455,0.050383,0.02769,0.045236,0.02769,0.02769
+01-Sep-2019 11:00:00,1,0.052405,0.052405,0.052405,0.064247,0.046651,0.046651,0.046651,0.046651,0.064247,0.064247,0.064247,0.064247,0.064247,0.046651,0.036734,0.052405,0.036734,0.036734
+01-Sep-2019 12:00:00,1,0.059642,0.059642,0.059642,0.051139,0.08024,0.08024,0.08024,0.08024,0.051139,0.051139,0.051139,0.051139,0.051139,0.08024,0.041468,0.059642,0.041468,0.041468
+01-Sep-2019 13:00:00,1,0.061841,0.061841,0.061841,0.06048,0.069044,0.069044,0.069044,0.069044,0.06048,0.06048,0.06048,0.06048,0.06048,0.069044,0.040055,0.061841,0.040055,0.040055
+01-Sep-2019 14:00:00,1,0.05978,0.05978,0.05978,0.058467,0.065312,0.065312,0.065312,0.065312,0.058467,0.058467,0.058467,0.058467,0.058467,0.065312,0.042299,0.05978,0.042299,0.042299
+01-Sep-2019 15:00:00,1,0.050756,0.050756,0.050756,0.048869,0.082106,0.082106,0.082106,0.082106,0.048869,0.048869,0.048869,0.048869,0.048869,0.082106,0.039269,0.050756,0.039269,0.039269
+01-Sep-2019 16:00:00,1,0.044961,0.044961,0.044961,0.045102,0.08024,0.08024,0.08024,0.08024,0.045102,0.045102,0.045102,0.045102,0.045102,0.08024,0.041468,0.044961,0.041468,0.041468
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+13-Sep-2019 20:00:00,1,0.040632,0.040632,0.040632,0.0193,0,0,0,0,0.0193,0.0193,0.0193,0.0193,0.0193,0,0.01315,0.040632,0.01315,0.01315
+13-Sep-2019 21:00:00,1,0.060581,0.060581,0.060581,0.024821,0.001866,0.001866,0.001866,0.001866,0.024821,0.024821,0.024821,0.024821,0.024821,0.001866,0.017234,0.060581,0.017234,0.017234
+13-Sep-2019 22:00:00,1,0.08502,0.08502,0.08502,0.034626,0.001866,0.001866,0.001866,0.001866,0.034626,0.034626,0.034626,0.034626,0.034626,0.001866,0.033749,0.08502,0.033749,0.033749
+13-Sep-2019 23:00:00,1,0.13195,0.13195,0.13195,0.051397,0.001866,0.001866,0.001866,0.001866,0.051397,0.051397,0.051397,0.051397,0.051397,0.001866,0.039045,0.13195,0.039045,0.039045
+14-Sep-2019 00:00:00,1,0.16615,0.16615,0.16615,0.090204,0.0037321,0.0037321,0.0037321,0.0037321,0.090204,0.090204,0.090204,0.090204,0.090204,0.0037321,0.052374,0.16615,0.052374,0.052374
+14-Sep-2019 01:00:00,1,0.21056,0.21056,0.21056,0.10837,0.0055981,0.0055981,0.0055981,0.0055981,0.10837,0.10837,0.10837,0.10837,0.10837,0.0055981,0.082017,0.21056,0.082017,0.082017
+14-Sep-2019 02:00:00,1,0.24711,0.24711,0.24711,0.1191,0.0055981,0.0055981,0.0055981,0.0055981,0.1191,0.1191,0.1191,0.1191,0.1191,0.0055981,0.11788,0.24711,0.11788,0.11788
+14-Sep-2019 03:00:00,1,0.26113,0.26113,0.26113,0.16606,0.0093302,0.0093302,0.0093302,0.0093302,0.16606,0.16606,0.16606,0.16606,0.16606,0.0093302,0.16538,0.26113,0.16538,0.16538
+14-Sep-2019 04:00:00,1,0.30854,0.30854,0.30854,0.21214,0.016794,0.016794,0.016794,0.016794,0.21214,0.21214,0.21214,0.21214,0.21214,0.016794,0.21524,0.30854,0.21524,0.21524
+14-Sep-2019 05:00:00,1,0.37801,0.37801,0.37801,0.25528,0.029857,0.029857,0.029857,0.029857,0.25528,0.25528,0.25528,0.25528,0.25528,0.029857,0.24933,0.37801,0.24933,0.24933
+14-Sep-2019 06:00:00,1,0.42627,0.42627,0.42627,0.32541,0.044785,0.044785,0.044785,0.044785,0.32541,0.32541,0.32541,0.32541,0.32541,0.044785,0.2695,0.42627,0.2695,0.2695
+14-Sep-2019 07:00:00,1,0.46005,0.46005,0.46005,0.39033,0.052249,0.052249,0.052249,0.052249,0.39033,0.39033,0.39033,0.39033,0.39033,0.052249,0.28355,0.46005,0.28355,0.28355
+14-Sep-2019 08:00:00,1,0.49177,0.49177,0.49177,0.4071,0.054115,0.054115,0.054115,0.054115,0.4071,0.4071,0.4071,0.4071,0.4071,0.054115,0.2588,0.49177,0.2588,0.2588
+14-Sep-2019 09:00:00,1,0.53509,0.53509,0.53509,0.40297,0.024259,0.024259,0.024259,0.024259,0.40297,0.40297,0.40297,0.40297,0.40297,0.024259,0.28568,0.53509,0.28568,0.28568
+14-Sep-2019 10:00:00,1,0.56062,0.56062,0.56062,0.50804,0.057847,0.057847,0.057847,0.057847,0.50804,0.50804,0.50804,0.50804,0.50804,0.057847,0.39999,0.56062,0.39999,0.39999
+14-Sep-2019 11:00:00,1,0.58296,0.58296,0.58296,0.52466,0.055981,0.055981,0.055981,0.055981,0.52466,0.52466,0.52466,0.52466,0.52466,0.055981,0.51048,0.58296,0.51048,0.51048
+14-Sep-2019 12:00:00,1,0.61479,0.61479,0.61479,0.48869,0.074642,0.074642,0.074642,0.074642,0.48869,0.48869,0.48869,0.48869,0.48869,0.074642,0.56925,0.61479,0.56925,0.56925
+14-Sep-2019 13:00:00,1,0.606,0.606,0.606,0.48296,0.085838,0.085838,0.085838,0.085838,0.48296,0.48296,0.48296,0.48296,0.48296,0.085838,0.56819,0.606,0.56819,0.56819
+14-Sep-2019 14:00:00,1,0.58142,0.58142,0.58142,0.43801,0.11756,0.11756,0.11756,0.11756,0.43801,0.43801,0.43801,0.43801,0.43801,0.11756,0.5705,0.58142,0.5705,0.5705
+14-Sep-2019 15:00:00,1,0.56392,0.56392,0.56392,0.42305,0.13995,0.13995,0.13995,0.13995,0.42305,0.42305,0.42305,0.42305,0.42305,0.13995,0.55379,0.56392,0.55379,0.55379
+14-Sep-2019 16:00:00,1,0.50467,0.50467,0.50467,0.36917,0.15675,0.15675,0.15675,0.15675,0.36917,0.36917,0.36917,0.36917,0.36917,0.15675,0.60223,0.50467,0.60223,0.60223
+14-Sep-2019 17:00:00,1,0.4707,0.4707,0.4707,0.32449,0.13249,0.13249,0.13249,0.13249,0.32449,0.32449,0.32449,0.32449,0.32449,0.13249,0.61285,0.4707,0.61285,0.61285
+14-Sep-2019 18:00:00,1,0.42904,0.42904,0.42904,0.25126,0.11756,0.11756,0.11756,0.11756,0.25126,0.25126,0.25126,0.25126,0.25126,0.11756,0.53797,0.42904,0.53797,0.53797
+14-Sep-2019 19:00:00,1,0.39441,0.39441,0.39441,0.21725,0.12316,0.12316,0.12316,0.12316,0.21725,0.21725,0.21725,0.21725,0.21725,0.12316,0.48615,0.39441,0.48615,0.48615
+14-Sep-2019 20:00:00,1,0.35611,0.35611,0.35611,0.2559,0.12316,0.12316,0.12316,0.12316,0.2559,0.2559,0.2559,0.2559,0.2559,0.12316,0.44399,0.35611,0.44399,0.44399
+14-Sep-2019 21:00:00,1,0.33165,0.33165,0.33165,0.24378,0.061579,0.061579,0.061579,0.061579,0.24378,0.24378,0.24378,0.24378,0.24378,0.061579,0.42034,0.33165,0.42034,0.42034
+14-Sep-2019 22:00:00,1,0.30685,0.30685,0.30685,0.22654,0.093302,0.093302,0.093302,0.093302,0.22654,0.22654,0.22654,0.22654,0.22654,0.093302,0.42512,0.30685,0.42512,0.42512
+14-Sep-2019 23:00:00,1,0.30323,0.30323,0.30323,0.19202,0.08024,0.08024,0.08024,0.08024,0.19202,0.19202,0.19202,0.19202,0.19202,0.08024,0.39193,0.30323,0.39193,0.39193
+15-Sep-2019 00:00:00,1,0.30701,0.30701,0.30701,0.17721,0.082106,0.082106,0.082106,0.082106,0.17721,0.17721,0.17721,0.17721,0.17721,0.082106,0.36879,0.30701,0.36879,0.36879
+15-Sep-2019 01:00:00,1,0.31603,0.31603,0.31603,0.15455,0.072776,0.072776,0.072776,0.072776,0.15455,0.15455,0.15455,0.15455,0.15455,0.072776,0.36354,0.31603,0.36354,0.36354
+15-Sep-2019 02:00:00,1,0.3035,0.3035,0.3035,0.12375,0.035455,0.035455,0.035455,0.035455,0.12375,0.12375,0.12375,0.12375,0.12375,0.035455,0.34577,0.3035,0.34577,0.34577
+15-Sep-2019 03:00:00,1,0.28962,0.28962,0.28962,0.12679,0.033589,0.033589,0.033589,0.033589,0.12679,0.12679,0.12679,0.12679,0.12679,0.033589,0.33769,0.28962,0.33769,0.33769
+15-Sep-2019 04:00:00,1,0.28074,0.28074,0.28074,0.1254,0.020526,0.020526,0.020526,0.020526,0.1254,0.1254,0.1254,0.1254,0.1254,0.020526,0.32959,0.28074,0.32959,0.32959
+15-Sep-2019 05:00:00,1,0.24665,0.24665,0.24665,0.11023,0.024259,0.024259,0.024259,0.024259,0.11023,0.11023,0.11023,0.11023,0.11023,0.024259,0.33422,0.24665,0.33422,0.33422
+15-Sep-2019 06:00:00,1,0.20671,0.20671,0.20671,0.10501,0.016794,0.016794,0.016794,0.016794,0.10501,0.10501,0.10501,0.10501,0.10501,0.016794,0.33668,0.20671,0.33668,0.33668
+15-Sep-2019 07:00:00,1,0.20611,0.20611,0.20611,0.086385,0.033589,0.033589,0.033589,0.033589,0.086385,0.086385,0.086385,0.086385,0.086385,0.033589,0.30035,0.20611,0.30035,0.30035
+15-Sep-2019 08:00:00,1,0.17977,0.17977,0.17977,0.082721,0.01866,0.01866,0.01866,0.01866,0.082721,0.082721,0.082721,0.082721,0.082721,0.01866,0.29836,0.17977,0.29836,0.29836
+15-Sep-2019 09:00:00,1,0.18891,0.18891,0.18891,0.052636,0.0074642,0.0074642,0.0074642,0.0074642,0.052636,0.052636,0.052636,0.052636,0.052636,0.0074642,0.30069,0.18891,0.30069,0.30069
+15-Sep-2019 10:00:00,1,0.2167,0.2167,0.2167,0.044173,0.0037321,0.0037321,0.0037321,0.0037321,0.044173,0.044173,0.044173,0.044173,0.044173,0.0037321,0.32795,0.2167,0.32795,0.32795
+15-Sep-2019 11:00:00,1,0.24693,0.24693,0.24693,0.068582,0.0055981,0.0055981,0.0055981,0.0055981,0.068582,0.068582,0.068582,0.068582,0.068582,0.0055981,0.3633,0.24693,0.3633,0.3633
+15-Sep-2019 12:00:00,1,0.24894,0.24894,0.24894,0.069304,0.0037321,0.0037321,0.0037321,0.0037321,0.069304,0.069304,0.069304,0.069304,0.069304,0.0037321,0.35091,0.24894,0.35091,0.35091
+15-Sep-2019 13:00:00,1,0.24061,0.24061,0.24061,0.06404,0.001866,0.001866,0.001866,0.001866,0.06404,0.06404,0.06404,0.06404,0.06404,0.001866,0.35757,0.24061,0.35757,0.35757
+15-Sep-2019 14:00:00,1,0.23894,0.23894,0.23894,0.072555,0.001866,0.001866,0.001866,0.001866,0.072555,0.072555,0.072555,0.072555,0.072555,0.001866,0.33033,0.23894,0.33033,0.33033
+15-Sep-2019 15:00:00,1,0.2281,0.2281,0.2281,0.082773,0.001866,0.001866,0.001866,0.001866,0.082773,0.082773,0.082773,0.082773,0.082773,0.001866,0.29643,0.2281,0.29643,0.29643
+15-Sep-2019 16:00:00,1,0.21255,0.21255,0.21255,0.062183,0.001866,0.001866,0.001866,0.001866,0.062183,0.062183,0.062183,0.062183,0.062183,0.001866,0.25619,0.21255,0.25619,0.25619
+15-Sep-2019 17:00:00,1,0.17748,0.17748,0.17748,0.035968,0.001866,0.001866,0.001866,0.001866,0.035968,0.035968,0.035968,0.035968,0.035968,0.001866,0.1722,0.17748,0.1722,0.1722
+15-Sep-2019 18:00:00,1,0.12904,0.12904,0.12904,0.027402,0.001866,0.001866,0.001866,0.001866,0.027402,0.027402,0.027402,0.027402,0.027402,0.001866,0.11175,0.12904,0.11175,0.11175
+15-Sep-2019 19:00:00,1,0.10195,0.10195,0.10195,0.03122,0.022393,0.022393,0.022393,0.022393,0.03122,0.03122,0.03122,0.03122,0.03122,0.022393,0.10152,0.10195,0.10152,0.10152
+15-Sep-2019 20:00:00,1,0.087723,0.087723,0.087723,0.042109,0.020526,0.020526,0.020526,0.020526,0.042109,0.042109,0.042109,0.042109,0.042109,0.020526,0.10174,0.087723,0.10174,0.10174
+15-Sep-2019 21:00:00,1,0.098259,0.098259,0.098259,0.045669,0.020526,0.020526,0.020526,0.020526,0.045669,0.045669,0.045669,0.045669,0.045669,0.020526,0.10816,0.098259,0.10816,0.10816
+15-Sep-2019 22:00:00,1,0.09551,0.09551,0.09551,0.060893,0.020526,0.020526,0.020526,0.020526,0.060893,0.060893,0.060893,0.060893,0.060893,0.020526,0.09842,0.09551,0.09842,0.09842
+15-Sep-2019 23:00:00,1,0.08928,0.08928,0.08928,0.083289,0.016794,0.016794,0.016794,0.016794,0.083289,0.083289,0.083289,0.083289,0.083289,0.016794,0.093147,0.08928,0.093147,0.093147
+16-Sep-2019 00:00:00,1,0.084402,0.084402,0.084402,0.10718,0.0093302,0.0093302,0.0093302,0.0093302,0.10718,0.10718,0.10718,0.10718,0.10718,0.0093302,0.076788,0.084402,0.076788,0.076788
+16-Sep-2019 01:00:00,1,0.087494,0.087494,0.087494,0.12266,0.0093302,0.0093302,0.0093302,0.0093302,0.12266,0.12266,0.12266,0.12266,0.12266,0.0093302,0.078651,0.087494,0.078651,0.078651
+16-Sep-2019 02:00:00,1,0.095121,0.095121,0.095121,0.14929,0.011196,0.011196,0.011196,0.011196,0.14929,0.14929,0.14929,0.14929,0.14929,0.011196,0.076945,0.095121,0.076945,0.076945
+16-Sep-2019 03:00:00,1,0.10508,0.10508,0.10508,0.15073,0.013062,0.013062,0.013062,0.013062,0.15073,0.15073,0.15073,0.15073,0.15073,0.013062,0.075217,0.10508,0.075217,0.075217
+16-Sep-2019 04:00:00,1,0.12476,0.12476,0.12476,0.15223,0.0093302,0.0093302,0.0093302,0.0093302,0.15223,0.15223,0.15223,0.15223,0.15223,0.0093302,0.068732,0.12476,0.068732,0.068732
+16-Sep-2019 05:00:00,1,0.14688,0.14688,0.14688,0.16426,0.013062,0.013062,0.013062,0.013062,0.16426,0.16426,0.16426,0.16426,0.16426,0.013062,0.069585,0.14688,0.069585,0.069585
+16-Sep-2019 06:00:00,1,0.16104,0.16104,0.16104,0.17427,0.0093302,0.0093302,0.0093302,0.0093302,0.17427,0.17427,0.17427,0.17427,0.17427,0.0093302,0.06862,0.16104,0.06862,0.06862
+16-Sep-2019 07:00:00,1,0.16922,0.16922,0.16922,0.1594,0.0093302,0.0093302,0.0093302,0.0093302,0.1594,0.1594,0.1594,0.1594,0.1594,0.0093302,0.072233,0.16922,0.072233,0.072233
+16-Sep-2019 08:00:00,1,0.14849,0.14849,0.14849,0.12782,0.0037321,0.0037321,0.0037321,0.0037321,0.12782,0.12782,0.12782,0.12782,0.12782,0.0037321,0.061238,0.14849,0.061238,0.061238
+16-Sep-2019 09:00:00,1,0.11122,0.11122,0.11122,0.071471,0.0055981,0.0055981,0.0055981,0.0055981,0.071471,0.071471,0.071471,0.071471,0.071471,0.0055981,0.03532,0.11122,0.03532,0.03532
+16-Sep-2019 10:00:00,1,0.10918,0.10918,0.10918,0.065795,0.0037321,0.0037321,0.0037321,0.0037321,0.065795,0.065795,0.065795,0.065795,0.065795,0.0037321,0.042029,0.10918,0.042029,0.042029
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+16-Sep-2019 12:00:00,1,0.19326,0.19326,0.19326,0.088914,0.001866,0.001866,0.001866,0.001866,0.088914,0.088914,0.088914,0.088914,0.088914,0.001866,0.13118,0.19326,0.13118,0.13118
+16-Sep-2019 13:00:00,1,0.18765,0.18765,0.18765,0.10249,0,0,0,0,0.10249,0.10249,0.10249,0.10249,0.10249,0,0.1369,0.18765,0.1369,0.1369
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+16-Sep-2019 15:00:00,1,0.17238,0.17238,0.17238,0.095674,0.0037321,0.0037321,0.0037321,0.0037321,0.095674,0.095674,0.095674,0.095674,0.095674,0.0037321,0.12959,0.17238,0.12959,0.12959
+16-Sep-2019 16:00:00,1,0.15188,0.15188,0.15188,0.083753,0.0037321,0.0037321,0.0037321,0.0037321,0.083753,0.083753,0.083753,0.083753,0.083753,0.0037321,0.11989,0.15188,0.11989,0.11989
+16-Sep-2019 17:00:00,1,0.12045,0.12045,0.12045,0.052946,0.0074642,0.0074642,0.0074642,0.0074642,0.052946,0.052946,0.052946,0.052946,0.052946,0.0074642,0.096759,0.12045,0.096759,0.096759
+16-Sep-2019 18:00:00,1,0.078767,0.078767,0.078767,0.023944,0.0093302,0.0093302,0.0093302,0.0093302,0.023944,0.023944,0.023944,0.023944,0.023944,0.0093302,0.061484,0.078767,0.061484,0.061484
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+16-Sep-2019 22:00:00,1,0.11287,0.11287,0.11287,0.063215,0.033589,0.033589,0.033589,0.033589,0.063215,0.063215,0.063215,0.063215,0.063215,0.033589,0.16953,0.11287,0.16953,0.16953
+16-Sep-2019 23:00:00,1,0.11301,0.11301,0.11301,0.11146,0.022393,0.022393,0.022393,0.022393,0.11146,0.11146,0.11146,0.11146,0.11146,0.022393,0.16525,0.11301,0.16525,0.16525
+17-Sep-2019 00:00:00,1,0.13926,0.13926,0.13926,0.14568,0.020526,0.020526,0.020526,0.020526,0.14568,0.14568,0.14568,0.14568,0.14568,0.020526,0.17173,0.13926,0.17173,0.17173
+17-Sep-2019 01:00:00,1,0.16356,0.16356,0.16356,0.16353,0.033589,0.033589,0.033589,0.033589,0.16353,0.16353,0.16353,0.16353,0.16353,0.033589,0.18762,0.16356,0.18762,0.18762
+17-Sep-2019 02:00:00,1,0.20676,0.20676,0.20676,0.12849,0.041053,0.041053,0.041053,0.041053,0.12849,0.12849,0.12849,0.12849,0.12849,0.041053,0.19345,0.20676,0.19345,0.19345
+17-Sep-2019 03:00:00,1,0.23635,0.23635,0.23635,0.15765,0.044785,0.044785,0.044785,0.044785,0.15765,0.15765,0.15765,0.15765,0.15765,0.044785,0.1959,0.23635,0.1959,0.1959
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+17-Sep-2019 08:00:00,1,0.13328,0.13328,0.13328,0.084424,0.014928,0.014928,0.014928,0.014928,0.084424,0.084424,0.084424,0.084424,0.084424,0.014928,0.15023,0.13328,0.15023,0.15023
+17-Sep-2019 09:00:00,1,0.1224,0.1224,0.1224,0.075032,0.013062,0.013062,0.013062,0.013062,0.075032,0.075032,0.075032,0.075032,0.075032,0.013062,0.088075,0.1224,0.088075,0.088075
+17-Sep-2019 10:00:00,1,0.13898,0.13898,0.13898,0.057022,0.011196,0.011196,0.011196,0.011196,0.057022,0.057022,0.057022,0.057022,0.057022,0.011196,0.067476,0.13898,0.067476,0.067476
+17-Sep-2019 11:00:00,1,0.15332,0.15332,0.15332,0.066414,0.0074642,0.0074642,0.0074642,0.0074642,0.066414,0.066414,0.066414,0.066414,0.066414,0.0074642,0.064559,0.15332,0.064559,0.064559
+17-Sep-2019 12:00:00,1,0.14693,0.14693,0.14693,0.086282,0.014928,0.014928,0.014928,0.014928,0.086282,0.086282,0.086282,0.086282,0.086282,0.014928,0.07607,0.14693,0.07607,0.07607
+17-Sep-2019 13:00:00,1,0.14645,0.14645,0.14645,0.088294,0.0074642,0.0074642,0.0074642,0.0074642,0.088294,0.088294,0.088294,0.088294,0.088294,0.0074642,0.095032,0.14645,0.095032,0.095032
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+18-Sep-2019 01:00:00,1,0.15124,0.15124,0.15124,0.091958,0.016794,0.016794,0.016794,0.016794,0.091958,0.091958,0.091958,0.091958,0.091958,0.016794,0.040683,0.15124,0.040683,0.040683
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+18-Sep-2019 06:00:00,1,0.17627,0.17627,0.17627,0.10212,0.042919,0.042919,0.042919,0.042919,0.10212,0.10212,0.10212,0.10212,0.10212,0.042919,0.17487,0.17627,0.17487,0.17487
+18-Sep-2019 07:00:00,1,0.16395,0.16395,0.16395,0.10176,0.01866,0.01866,0.01866,0.01866,0.10176,0.10176,0.10176,0.10176,0.10176,0.01866,0.18483,0.16395,0.18483,0.18483
+18-Sep-2019 08:00:00,1,0.14622,0.14622,0.14622,0.085043,0.0093302,0.0093302,0.0093302,0.0093302,0.085043,0.085043,0.085043,0.085043,0.085043,0.0093302,0.14272,0.14622,0.14272,0.14272
+18-Sep-2019 09:00:00,1,0.1628,0.1628,0.1628,0.092732,0.0037321,0.0037321,0.0037321,0.0037321,0.092732,0.092732,0.092732,0.092732,0.092732,0.0037321,0.073041,0.1628,0.073041,0.073041
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+18-Sep-2019 11:00:00,1,0.23506,0.23506,0.23506,0.14779,0.020526,0.020526,0.020526,0.020526,0.14779,0.14779,0.14779,0.14779,0.14779,0.020526,0.083879,0.23506,0.083879,0.083879
+18-Sep-2019 12:00:00,1,0.26507,0.26507,0.26507,0.16059,0.026125,0.026125,0.026125,0.026125,0.16059,0.16059,0.16059,0.16059,0.16059,0.026125,0.12254,0.26507,0.12254,0.12254
+18-Sep-2019 13:00:00,1,0.28818,0.28818,0.28818,0.15559,0.031723,0.031723,0.031723,0.031723,0.15559,0.15559,0.15559,0.15559,0.15559,0.031723,0.14718,0.28818,0.14718,0.14718
+18-Sep-2019 14:00:00,1,0.2803,0.2803,0.2803,0.15362,0.046651,0.046651,0.046651,0.046651,0.15362,0.15362,0.15362,0.15362,0.15362,0.046651,0.17287,0.2803,0.17287,0.17287
+18-Sep-2019 15:00:00,1,0.27496,0.27496,0.27496,0.15904,0.033589,0.033589,0.033589,0.033589,0.15904,0.15904,0.15904,0.15904,0.15904,0.033589,0.18957,0.27496,0.18957,0.18957
+18-Sep-2019 16:00:00,1,0.23889,0.23889,0.23889,0.19124,0.061579,0.061579,0.061579,0.061579,0.19124,0.19124,0.19124,0.19124,0.19124,0.061579,0.17198,0.23889,0.17198,0.17198
+18-Sep-2019 17:00:00,1,0.24617,0.24617,0.24617,0.17824,0.10263,0.10263,0.10263,0.10263,0.17824,0.17824,0.17824,0.17824,0.17824,0.10263,0.14247,0.24617,0.14247,0.14247
+18-Sep-2019 18:00:00,1,0.21956,0.21956,0.21956,0.12437,0.15115,0.15115,0.15115,0.15115,0.12437,0.12437,0.12437,0.12437,0.12437,0.15115,0.13188,0.21956,0.13188,0.13188
+18-Sep-2019 19:00:00,1,0.20481,0.20481,0.20481,0.13071,0.15675,0.15675,0.15675,0.15675,0.13071,0.13071,0.13071,0.13071,0.13071,0.15675,0.15187,0.20481,0.15187,0.15187
+18-Sep-2019 20:00:00,1,0.19592,0.19592,0.19592,0.13448,0.087704,0.087704,0.087704,0.087704,0.13448,0.13448,0.13448,0.13448,0.13448,0.087704,0.17243,0.19592,0.17243,0.17243
+18-Sep-2019 21:00:00,1,0.20066,0.20066,0.20066,0.13954,0.072776,0.072776,0.072776,0.072776,0.13954,0.13954,0.13954,0.13954,0.13954,0.072776,0.17229,0.20066,0.17229,0.17229
+18-Sep-2019 22:00:00,1,0.20055,0.20055,0.20055,0.12777,0.063445,0.063445,0.063445,0.063445,0.12777,0.12777,0.12777,0.12777,0.12777,0.063445,0.16282,0.20055,0.16282,0.16282
+18-Sep-2019 23:00:00,1,0.20094,0.20094,0.20094,0.13691,0.027991,0.027991,0.027991,0.027991,0.13691,0.13691,0.13691,0.13691,0.13691,0.027991,0.16982,0.20094,0.16982,0.16982
+19-Sep-2019 00:00:00,1,0.19125,0.19125,0.19125,0.12277,0.065312,0.065312,0.065312,0.065312,0.12277,0.12277,0.12277,0.12277,0.12277,0.065312,0.14415,0.19125,0.14415,0.14415
+19-Sep-2019 01:00:00,1,0.18999,0.18999,0.18999,0.12075,0.055981,0.055981,0.055981,0.055981,0.12075,0.12075,0.12075,0.12075,0.12075,0.055981,0.11792,0.18999,0.11792,0.11792
+19-Sep-2019 02:00:00,1,0.1803,0.1803,0.1803,0.11497,0.01866,0.01866,0.01866,0.01866,0.11497,0.11497,0.11497,0.11497,0.11497,0.01866,0.11823,0.1803,0.11823,0.11823
+19-Sep-2019 03:00:00,1,0.18616,0.18616,0.18616,0.092939,0.054115,0.054115,0.054115,0.054115,0.092939,0.092939,0.092939,0.092939,0.092939,0.054115,0.14132,0.18616,0.14132,0.14132
+19-Sep-2019 04:00:00,1,0.19317,0.19317,0.19317,0.084321,0.042919,0.042919,0.042919,0.042919,0.084321,0.084321,0.084321,0.084321,0.084321,0.042919,0.14597,0.19317,0.14597,0.14597
+19-Sep-2019 05:00:00,1,0.20856,0.20856,0.20856,0.1125,0.020526,0.020526,0.020526,0.020526,0.1125,0.1125,0.1125,0.1125,0.1125,0.020526,0.15872,0.20856,0.15872,0.15872
+19-Sep-2019 06:00:00,1,0.21528,0.21528,0.21528,0.11575,0.029857,0.029857,0.029857,0.029857,0.11575,0.11575,0.11575,0.11575,0.11575,0.029857,0.16269,0.21528,0.16269,0.16269
+19-Sep-2019 07:00:00,1,0.21285,0.21285,0.21285,0.10568,0.026125,0.026125,0.026125,0.026125,0.10568,0.10568,0.10568,0.10568,0.10568,0.026125,0.17171,0.21285,0.17171,0.17171
+19-Sep-2019 08:00:00,1,0.18314,0.18314,0.18314,0.08334,0.0093302,0.0093302,0.0093302,0.0093302,0.08334,0.08334,0.08334,0.08334,0.08334,0.0093302,0.15447,0.18314,0.15447,0.15447
+19-Sep-2019 09:00:00,1,0.18488,0.18488,0.18488,0.076838,0.016794,0.016794,0.016794,0.016794,0.076838,0.076838,0.076838,0.076838,0.076838,0.016794,0.15463,0.18488,0.15463,0.15463
+19-Sep-2019 10:00:00,1,0.22741,0.22741,0.22741,0.11399,0.041053,0.041053,0.041053,0.041053,0.11399,0.11399,0.11399,0.11399,0.11399,0.041053,0.20144,0.22741,0.20144,0.20144
+19-Sep-2019 11:00:00,1,0.26005,0.26005,0.26005,0.15698,0.039187,0.039187,0.039187,0.039187,0.15698,0.15698,0.15698,0.15698,0.15698,0.039187,0.27309,0.26005,0.27309,0.27309
+19-Sep-2019 12:00:00,1,0.29558,0.29558,0.29558,0.19816,0.050383,0.050383,0.050383,0.050383,0.19816,0.19816,0.19816,0.19816,0.19816,0.050383,0.2962,0.29558,0.2962,0.2962
+19-Sep-2019 13:00:00,1,0.27703,0.27703,0.27703,0.18082,0.11569,0.11569,0.11569,0.11569,0.18082,0.18082,0.18082,0.18082,0.18082,0.11569,0.27002,0.27703,0.27002,0.27002
+19-Sep-2019 14:00:00,1,0.26431,0.26431,0.26431,0.23165,0.13249,0.13249,0.13249,0.13249,0.23165,0.23165,0.23165,0.23165,0.23165,0.13249,0.26079,0.26431,0.26079,0.26079
+19-Sep-2019 15:00:00,1,0.29482,0.29482,0.29482,0.16018,0.078374,0.078374,0.078374,0.078374,0.16018,0.16018,0.16018,0.16018,0.16018,0.078374,0.23335,0.29482,0.23335,0.23335
+19-Sep-2019 16:00:00,1,0.2819,0.2819,0.2819,0.13897,0.050383,0.050383,0.050383,0.050383,0.13897,0.13897,0.13897,0.13897,0.13897,0.050383,0.21674,0.2819,0.21674,0.21674
+19-Sep-2019 17:00:00,1,0.22824,0.22824,0.22824,0.10682,0.020526,0.020526,0.020526,0.020526,0.10682,0.10682,0.10682,0.10682,0.10682,0.020526,0.18095,0.22824,0.18095,0.18095
+19-Sep-2019 18:00:00,1,0.1667,0.1667,0.1667,0.094693,0.020526,0.020526,0.020526,0.020526,0.094693,0.094693,0.094693,0.094693,0.094693,0.020526,0.15613,0.1667,0.15613,0.15613
+19-Sep-2019 19:00:00,1,0.11949,0.11949,0.11949,0.11079,0.026125,0.026125,0.026125,0.026125,0.11079,0.11079,0.11079,0.11079,0.11079,0.026125,0.13825,0.11949,0.13825,0.13825
+19-Sep-2019 20:00:00,1,0.13314,0.13314,0.13314,0.12602,0.037321,0.037321,0.037321,0.037321,0.12602,0.12602,0.12602,0.12602,0.12602,0.037321,0.13854,0.13314,0.13854,0.13854
+19-Sep-2019 21:00:00,1,0.14501,0.14501,0.14501,0.12313,0.031723,0.031723,0.031723,0.031723,0.12313,0.12313,0.12313,0.12313,0.12313,0.031723,0.15187,0.14501,0.15187,0.15187
+19-Sep-2019 22:00:00,1,0.14448,0.14448,0.14448,0.10527,0.035455,0.035455,0.035455,0.035455,0.10527,0.10527,0.10527,0.10527,0.10527,0.035455,0.15546,0.14448,0.15546,0.15546
+19-Sep-2019 23:00:00,1,0.15827,0.15827,0.15827,0.10935,0.033589,0.033589,0.033589,0.033589,0.10935,0.10935,0.10935,0.10935,0.10935,0.033589,0.17079,0.15827,0.17079,0.17079
+20-Sep-2019 00:00:00,1,0.16147,0.16147,0.16147,0.11699,0.031723,0.031723,0.031723,0.031723,0.11699,0.11699,0.11699,0.11699,0.11699,0.031723,0.19134,0.16147,0.19134,0.19134
+20-Sep-2019 01:00:00,1,0.15435,0.15435,0.15435,0.11828,0.013062,0.013062,0.013062,0.013062,0.11828,0.11828,0.11828,0.11828,0.11828,0.013062,0.19175,0.15435,0.19175,0.19175
+20-Sep-2019 02:00:00,1,0.16173,0.16173,0.16173,0.12111,0.0093302,0.0093302,0.0093302,0.0093302,0.12111,0.12111,0.12111,0.12111,0.12111,0.0093302,0.17685,0.16173,0.17685,0.17685
+20-Sep-2019 03:00:00,1,0.1511,0.1511,0.1511,0.11461,0.001866,0.001866,0.001866,0.001866,0.11461,0.11461,0.11461,0.11461,0.11461,0.001866,0.15925,0.1511,0.15925,0.15925
+20-Sep-2019 04:00:00,1,0.16207,0.16207,0.16207,0.10713,0.001866,0.001866,0.001866,0.001866,0.10713,0.10713,0.10713,0.10713,0.10713,0.001866,0.15524,0.16207,0.15524,0.15524
+20-Sep-2019 05:00:00,1,0.16924,0.16924,0.16924,0.10997,0.001866,0.001866,0.001866,0.001866,0.10997,0.10997,0.10997,0.10997,0.10997,0.001866,0.15131,0.16924,0.15131,0.15131
+20-Sep-2019 06:00:00,1,0.16901,0.16901,0.16901,0.11322,0,0,0,0,0.11322,0.11322,0.11322,0.11322,0.11322,0,0.14126,0.16901,0.14126,0.14126
+20-Sep-2019 07:00:00,1,0.17705,0.17705,0.17705,0.11322,0,0,0,0,0.11322,0.11322,0.11322,0.11322,0.11322,0,0.14653,0.17705,0.14653,0.14653
+20-Sep-2019 08:00:00,1,0.15728,0.15728,0.15728,0.093816,0,0,0,0,0.093816,0.093816,0.093816,0.093816,0.093816,0,0.10082,0.15728,0.10082,0.10082
+20-Sep-2019 09:00:00,1,0.1369,0.1369,0.1369,0.061305,0,0,0,0,0.061305,0.061305,0.061305,0.061305,0.061305,0,0.059555,0.1369,0.059555,0.059555
+20-Sep-2019 10:00:00,1,0.15082,0.15082,0.15082,0.052791,0,0,0,0,0.052791,0.052791,0.052791,0.052791,0.052791,0,0.057378,0.15082,0.057378,0.057378
+20-Sep-2019 11:00:00,1,0.18353,0.18353,0.18353,0.054132,0,0,0,0,0.054132,0.054132,0.054132,0.054132,0.054132,0,0.09272,0.18353,0.09272,0.09272
+20-Sep-2019 12:00:00,1,0.21709,0.21709,0.21709,0.074722,0,0,0,0,0.074722,0.074722,0.074722,0.074722,0.074722,0,0.12375,0.21709,0.12375,0.12375
+20-Sep-2019 13:00:00,1,0.26422,0.26422,0.26422,0.097428,0,0,0,0,0.097428,0.097428,0.097428,0.097428,0.097428,0,0.13147,0.26422,0.13147,0.13147
+20-Sep-2019 14:00:00,1,0.26376,0.26376,0.26376,0.1078,0,0,0,0,0.1078,0.1078,0.1078,0.1078,0.1078,0,0.14153,0.26376,0.14153,0.14153
+20-Sep-2019 15:00:00,1,0.25236,0.25236,0.25236,0.099957,0,0,0,0,0.099957,0.099957,0.099957,0.099957,0.099957,0,0.14144,0.25236,0.14144,0.14144
+20-Sep-2019 16:00:00,1,0.20206,0.20206,0.20206,0.079676,0,0,0,0,0.079676,0.079676,0.079676,0.079676,0.079676,0,0.1286,0.20206,0.1286,0.1286
+20-Sep-2019 17:00:00,1,0.16145,0.16145,0.16145,0.066569,0.001866,0.001866,0.001866,0.001866,0.066569,0.066569,0.066569,0.066569,0.066569,0.001866,0.091666,0.16145,0.091666,0.091666
+20-Sep-2019 18:00:00,1,0.11393,0.11393,0.11393,0.052068,0.0074642,0.0074642,0.0074642,0.0074642,0.052068,0.052068,0.052068,0.052068,0.052068,0.0074642,0.0688,0.11393,0.0688,0.0688
+20-Sep-2019 19:00:00,1,0.11054,0.11054,0.11054,0.063008,0.0074642,0.0074642,0.0074642,0.0074642,0.063008,0.063008,0.063008,0.063008,0.063008,0.0074642,0.074791,0.11054,0.074791,0.074791
+20-Sep-2019 20:00:00,1,0.13733,0.13733,0.13733,0.075187,0.011196,0.011196,0.011196,0.011196,0.075187,0.075187,0.075187,0.075187,0.075187,0.011196,0.090499,0.13733,0.090499,0.090499
+20-Sep-2019 21:00:00,1,0.16995,0.16995,0.16995,0.093042,0.011196,0.011196,0.011196,0.011196,0.093042,0.093042,0.093042,0.093042,0.093042,0.011196,0.10939,0.16995,0.10939,0.10939
+20-Sep-2019 22:00:00,1,0.17769,0.17769,0.17769,0.10264,0.013062,0.013062,0.013062,0.013062,0.10264,0.10264,0.10264,0.10264,0.10264,0.013062,0.11877,0.17769,0.11877,0.11877
+20-Sep-2019 23:00:00,1,0.19109,0.19109,0.19109,0.11141,0.052249,0.052249,0.052249,0.052249,0.11141,0.11141,0.11141,0.11141,0.11141,0.052249,0.12429,0.19109,0.12429,0.12429
+21-Sep-2019 00:00:00,1,0.20284,0.20284,0.20284,0.1256,0.052249,0.052249,0.052249,0.052249,0.1256,0.1256,0.1256,0.1256,0.1256,0.052249,0.13646,0.20284,0.13646,0.13646
+21-Sep-2019 01:00:00,1,0.21528,0.21528,0.21528,0.13696,0.052249,0.052249,0.052249,0.052249,0.13696,0.13696,0.13696,0.13696,0.13696,0.052249,0.13347,0.21528,0.13347,0.13347
+21-Sep-2019 02:00:00,1,0.21619,0.21619,0.21619,0.14557,0.054115,0.054115,0.054115,0.054115,0.14557,0.14557,0.14557,0.14557,0.14557,0.054115,0.12741,0.21619,0.12741,0.12741
+21-Sep-2019 03:00:00,1,0.21741,0.21741,0.21741,0.15615,0.055981,0.055981,0.055981,0.055981,0.15615,0.15615,0.15615,0.15615,0.15615,0.055981,0.12048,0.21741,0.12048,0.12048
+21-Sep-2019 04:00:00,1,0.21589,0.21589,0.21589,0.14986,0.059713,0.059713,0.059713,0.059713,0.14986,0.14986,0.14986,0.14986,0.14986,0.059713,0.117,0.21589,0.117,0.117
+21-Sep-2019 05:00:00,1,0.21995,0.21995,0.21995,0.13907,0.055981,0.055981,0.055981,0.055981,0.13907,0.13907,0.13907,0.13907,0.13907,0.055981,0.11034,0.21995,0.11034,0.11034
+21-Sep-2019 06:00:00,1,0.22551,0.22551,0.22551,0.14898,0.048517,0.048517,0.048517,0.048517,0.14898,0.14898,0.14898,0.14898,0.14898,0.048517,0.1106,0.22551,0.1106,0.1106
+21-Sep-2019 07:00:00,1,0.22386,0.22386,0.22386,0.14702,0.033589,0.033589,0.033589,0.033589,0.14702,0.14702,0.14702,0.14702,0.14702,0.033589,0.10551,0.22386,0.10551,0.10551
+21-Sep-2019 08:00:00,1,0.20973,0.20973,0.20973,0.1223,0.0055981,0.0055981,0.0055981,0.0055981,0.1223,0.1223,0.1223,0.1223,0.1223,0.0055981,0.095682,0.20973,0.095682,0.095682
+21-Sep-2019 09:00:00,1,0.18071,0.18071,0.18071,0.084321,0.011196,0.011196,0.011196,0.011196,0.084321,0.084321,0.084321,0.084321,0.084321,0.011196,0.06126,0.18071,0.06126,0.06126
+21-Sep-2019 10:00:00,1,0.16413,0.16413,0.16413,0.045463,0.029857,0.029857,0.029857,0.029857,0.045463,0.045463,0.045463,0.045463,0.045463,0.029857,0.041401,0.16413,0.041401,0.041401
+21-Sep-2019 11:00:00,1,0.17155,0.17155,0.17155,0.025647,0.046651,0.046651,0.046651,0.046651,0.025647,0.025647,0.025647,0.025647,0.025647,0.046651,0.075554,0.17155,0.075554,0.075554
+21-Sep-2019 12:00:00,1,0.19317,0.19317,0.19317,0.037052,0.057847,0.057847,0.057847,0.057847,0.037052,0.037052,0.037052,0.037052,0.037052,0.057847,0.1308,0.19317,0.1308,0.1308
+21-Sep-2019 13:00:00,1,0.20799,0.20799,0.20799,0.034936,0.076508,0.076508,0.076508,0.076508,0.034936,0.034936,0.034936,0.034936,0.034936,0.076508,0.16717,0.20799,0.16717,0.16717
+21-Sep-2019 14:00:00,1,0.20854,0.20854,0.20854,0.026886,0.046651,0.046651,0.046651,0.046651,0.026886,0.026886,0.026886,0.026886,0.026886,0.046651,0.17689,0.20854,0.17689,0.17689
+21-Sep-2019 15:00:00,1,0.18184,0.18184,0.18184,0.025286,0.08024,0.08024,0.08024,0.08024,0.025286,0.025286,0.025286,0.025286,0.025286,0.08024,0.17032,0.18184,0.17032,0.17032
+21-Sep-2019 16:00:00,1,0.14301,0.14301,0.14301,0.015791,0.026125,0.026125,0.026125,0.026125,0.015791,0.015791,0.015791,0.015791,0.015791,0.026125,0.14083,0.14301,0.14083,0.14083
+21-Sep-2019 17:00:00,1,0.10302,0.10302,0.10302,0.010063,0.048517,0.048517,0.048517,0.048517,0.010063,0.010063,0.010063,0.010063,0.010063,0.048517,0.11,0.10302,0.11,0.11
+21-Sep-2019 18:00:00,1,0.078561,0.078561,0.078561,0.0099079,0.061579,0.061579,0.061579,0.061579,0.0099079,0.0099079,0.0099079,0.0099079,0.0099079,0.061579,0.10048,0.078561,0.10048,0.10048
+21-Sep-2019 19:00:00,1,0.073293,0.073293,0.073293,0.064144,0.044785,0.044785,0.044785,0.044785,0.064144,0.064144,0.064144,0.064144,0.064144,0.044785,0.13796,0.073293,0.13796,0.13796
+21-Sep-2019 20:00:00,1,0.073018,0.073018,0.073018,0.039322,0.054115,0.054115,0.054115,0.054115,0.039322,0.039322,0.039322,0.039322,0.039322,0.054115,0.16798,0.073018,0.16798,0.16798
+21-Sep-2019 21:00:00,1,0.072904,0.072904,0.072904,0.033955,0.054115,0.054115,0.054115,0.054115,0.033955,0.033955,0.033955,0.033955,0.033955,0.054115,0.17054,0.072904,0.17054,0.17054
+21-Sep-2019 22:00:00,1,0.056207,0.056207,0.056207,0.049953,0.046651,0.046651,0.046651,0.046651,0.049953,0.049953,0.049953,0.049953,0.049953,0.046651,0.15636,0.056207,0.15636,0.15636
+21-Sep-2019 23:00:00,1,0.044159,0.044159,0.044159,0.082927,0.035455,0.035455,0.035455,0.035455,0.082927,0.082927,0.082927,0.082927,0.082927,0.035455,0.15533,0.044159,0.15533,0.15533
+22-Sep-2019 00:00:00,1,0.034379,0.034379,0.034379,0.072916,0.046651,0.046651,0.046651,0.046651,0.072916,0.072916,0.072916,0.072916,0.072916,0.046651,0.15459,0.034379,0.15459,0.15459
+22-Sep-2019 01:00:00,1,0.041502,0.041502,0.041502,0.11074,0.052249,0.052249,0.052249,0.052249,0.11074,0.11074,0.11074,0.11074,0.11074,0.052249,0.11211,0.041502,0.11211,0.11211
+22-Sep-2019 02:00:00,1,0.06404,0.06404,0.06404,0.18242,0.091436,0.091436,0.091436,0.091436,0.18242,0.18242,0.18242,0.18242,0.18242,0.091436,0.093708,0.06404,0.093708,0.093708
+22-Sep-2019 03:00:00,1,0.064269,0.064269,0.064269,0.17602,0.10636,0.10636,0.10636,0.10636,0.17602,0.17602,0.17602,0.17602,0.17602,0.10636,0.1041,0.064269,0.1041,0.1041
+22-Sep-2019 04:00:00,1,0.074919,0.074919,0.074919,0.12689,0.097034,0.097034,0.097034,0.097034,0.12689,0.12689,0.12689,0.12689,0.12689,0.097034,0.10286,0.074919,0.10286,0.10286
+22-Sep-2019 05:00:00,1,0.10236,0.10236,0.10236,0.11157,0.11383,0.11383,0.11383,0.11383,0.11157,0.11157,0.11157,0.11157,0.11157,0.11383,0.10888,0.10236,0.10888,0.10888
+22-Sep-2019 06:00:00,1,0.13351,0.13351,0.13351,0.14041,0.095168,0.095168,0.095168,0.095168,0.14041,0.14041,0.14041,0.14041,0.14041,0.095168,0.13623,0.13351,0.13623,0.13623
+22-Sep-2019 07:00:00,1,0.16837,0.16837,0.16837,0.13035,0.12689,0.12689,0.12689,0.12689,0.13035,0.13035,0.13035,0.13035,0.13035,0.12689,0.15239,0.16837,0.15239,0.15239
+22-Sep-2019 08:00:00,1,0.1926,0.1926,0.1926,0.13484,0.095168,0.095168,0.095168,0.095168,0.13484,0.13484,0.13484,0.13484,0.13484,0.095168,0.14438,0.1926,0.14438,0.14438
+22-Sep-2019 09:00:00,1,0.22856,0.22856,0.22856,0.11415,0.091436,0.091436,0.091436,0.091436,0.11415,0.11415,0.11415,0.11415,0.11415,0.091436,0.17413,0.22856,0.17413,0.17413
+22-Sep-2019 10:00:00,1,0.25884,0.25884,0.25884,0.11146,0.067178,0.067178,0.067178,0.067178,0.11146,0.11146,0.11146,0.11146,0.11146,0.067178,0.28945,0.25884,0.28945,0.28945
+22-Sep-2019 11:00:00,1,0.32267,0.32267,0.32267,0.15332,0.072776,0.072776,0.072776,0.072776,0.15332,0.15332,0.15332,0.15332,0.15332,0.072776,0.41767,0.32267,0.41767,0.41767
+22-Sep-2019 12:00:00,1,0.36502,0.36502,0.36502,0.15595,0.069044,0.069044,0.069044,0.069044,0.15595,0.15595,0.15595,0.15595,0.15595,0.069044,0.53298,0.36502,0.53298,0.53298
+22-Sep-2019 13:00:00,1,0.36976,0.36976,0.36976,0.15719,0.055981,0.055981,0.055981,0.055981,0.15719,0.15719,0.15719,0.15719,0.15719,0.055981,0.63383,0.36976,0.63383,0.63383
+22-Sep-2019 14:00:00,1,0.36525,0.36525,0.36525,0.10218,0.054115,0.054115,0.054115,0.054115,0.10218,0.10218,0.10218,0.10218,0.10218,0.054115,0.63724,0.36525,0.63724,0.63724
+22-Sep-2019 15:00:00,1,0.34123,0.34123,0.34123,0.11152,0.046651,0.046651,0.046651,0.046651,0.11152,0.11152,0.11152,0.11152,0.11152,0.046651,0.66661,0.34123,0.66661,0.66661
+22-Sep-2019 16:00:00,1,0.29862,0.29862,0.29862,0.08463,0.020526,0.020526,0.020526,0.020526,0.08463,0.08463,0.08463,0.08463,0.08463,0.020526,0.65806,0.29862,0.65806,0.65806
+22-Sep-2019 17:00:00,1,0.26457,0.26457,0.26457,0.073742,0.0093302,0.0093302,0.0093302,0.0093302,0.073742,0.073742,0.073742,0.073742,0.073742,0.0093302,0.55852,0.26457,0.55852,0.55852
+22-Sep-2019 18:00:00,1,0.17183,0.17183,0.17183,0.050211,0.0037321,0.0037321,0.0037321,0.0037321,0.050211,0.050211,0.050211,0.050211,0.050211,0.0037321,0.43706,0.17183,0.43706,0.43706
+22-Sep-2019 19:00:00,1,0.1442,0.1442,0.1442,0.037206,0.026125,0.026125,0.026125,0.026125,0.037206,0.037206,0.037206,0.037206,0.037206,0.026125,0.32048,0.1442,0.32048,0.32048
+22-Sep-2019 20:00:00,1,0.13271,0.13271,0.13271,0.044276,0.014928,0.014928,0.014928,0.014928,0.044276,0.044276,0.044276,0.044276,0.044276,0.014928,0.28092,0.13271,0.28092,0.28092
+22-Sep-2019 21:00:00,1,0.12712,0.12712,0.12712,0.056351,0.020526,0.020526,0.020526,0.020526,0.056351,0.056351,0.056351,0.056351,0.056351,0.020526,0.31023,0.12712,0.31023,0.31023
+22-Sep-2019 22:00:00,1,0.11988,0.11988,0.11988,0.064505,0.020526,0.020526,0.020526,0.020526,0.064505,0.064505,0.064505,0.064505,0.064505,0.020526,0.30305,0.11988,0.30305,0.30305
+22-Sep-2019 23:00:00,1,0.11608,0.11608,0.11608,0.070491,0.026125,0.026125,0.026125,0.026125,0.070491,0.070491,0.070491,0.070491,0.070491,0.026125,0.29443,0.11608,0.29443,0.29443
+23-Sep-2019 00:00:00,1,0.1022,0.1022,0.1022,0.072658,0.026125,0.026125,0.026125,0.026125,0.072658,0.072658,0.072658,0.072658,0.072658,0.026125,0.26723,0.1022,0.26723,0.26723
+23-Sep-2019 01:00:00,1,0.093357,0.093357,0.093357,0.074774,0.026125,0.026125,0.026125,0.026125,0.074774,0.074774,0.074774,0.074774,0.074774,0.026125,0.23885,0.093357,0.23885,0.23885
+23-Sep-2019 02:00:00,1,0.091296,0.091296,0.091296,0.053513,0.041053,0.041053,0.041053,0.041053,0.053513,0.053513,0.053513,0.053513,0.053513,0.041053,0.21158,0.091296,0.21158,0.21158
+23-Sep-2019 03:00:00,1,0.09764,0.09764,0.09764,0.060738,0.042919,0.042919,0.042919,0.042919,0.060738,0.060738,0.060738,0.060738,0.060738,0.042919,0.20308,0.09764,0.20308,0.20308
+23-Sep-2019 04:00:00,1,0.07989,0.07989,0.07989,0.065795,0.037321,0.037321,0.037321,0.037321,0.065795,0.065795,0.065795,0.065795,0.065795,0.037321,0.19617,0.07989,0.19617,0.19617
+23-Sep-2019 05:00:00,1,0.06956,0.06956,0.06956,0.066517,0.054115,0.054115,0.054115,0.054115,0.066517,0.066517,0.066517,0.066517,0.066517,0.054115,0.17572,0.06956,0.17572,0.17572
+23-Sep-2019 06:00:00,1,0.066468,0.066468,0.066468,0.074774,0.046651,0.046651,0.046651,0.046651,0.074774,0.074774,0.074774,0.074774,0.074774,0.046651,0.16893,0.066468,0.16893,0.16893
+23-Sep-2019 07:00:00,1,0.062345,0.062345,0.062345,0.088965,0.041053,0.041053,0.041053,0.041053,0.088965,0.088965,0.088965,0.088965,0.088965,0.041053,0.1694,0.062345,0.1694,0.1694
+23-Sep-2019 08:00:00,1,0.052611,0.052611,0.052611,0.08236,0.01866,0.01866,0.01866,0.01866,0.08236,0.08236,0.08236,0.08236,0.08236,0.01866,0.11507,0.052611,0.11507,0.11507
+23-Sep-2019 09:00:00,1,0.030325,0.030325,0.030325,0.069252,0.016794,0.016794,0.016794,0.016794,0.069252,0.069252,0.069252,0.069252,0.069252,0.016794,0.068014,0.030325,0.068014,0.068014
+23-Sep-2019 10:00:00,1,0.019285,0.019285,0.019285,0.059912,0.026125,0.026125,0.026125,0.026125,0.059912,0.059912,0.059912,0.059912,0.059912,0.026125,0.068373,0.019285,0.068373,0.068373
+23-Sep-2019 11:00:00,1,0.020545,0.020545,0.020545,0.074671,0.029857,0.029857,0.029857,0.029857,0.074671,0.074671,0.074671,0.074671,0.074671,0.029857,0.090319,0.020545,0.090319,0.090319
+23-Sep-2019 12:00:00,1,0.022286,0.022286,0.022286,0.0949,0.027991,0.027991,0.027991,0.027991,0.0949,0.0949,0.0949,0.0949,0.0949,0.027991,0.07699,0.022286,0.07699,0.07699
+23-Sep-2019 13:00:00,1,0.020637,0.020637,0.020637,0.089378,0.029857,0.029857,0.029857,0.029857,0.089378,0.089378,0.089378,0.089378,0.089378,0.029857,0.064245,0.020637,0.064245,0.064245
+23-Sep-2019 14:00:00,1,0.020522,0.020522,0.020522,0.084011,0.035455,0.035455,0.035455,0.035455,0.084011,0.084011,0.084011,0.084011,0.084011,0.035455,0.04811,0.020522,0.04811,0.04811
+23-Sep-2019 15:00:00,1,0.024324,0.024324,0.024324,0.10723,0.033589,0.033589,0.033589,0.033589,0.10723,0.10723,0.10723,0.10723,0.10723,0.033589,0.031572,0.024324,0.031572,0.031572
+23-Sep-2019 16:00:00,1,0.031745,0.031745,0.031745,0.15497,0.037321,0.037321,0.037321,0.037321,0.15497,0.15497,0.15497,0.15497,0.15497,0.037321,0.023023,0.031745,0.023023,0.023023
+23-Sep-2019 17:00:00,1,0.048511,0.048511,0.048511,0.1834,0.063445,0.063445,0.063445,0.063445,0.1834,0.1834,0.1834,0.1834,0.1834,0.063445,0.018737,0.048511,0.018737,0.018737
+23-Sep-2019 18:00:00,1,0.067269,0.067269,0.067269,0.23722,0.061579,0.061579,0.061579,0.061579,0.23722,0.23722,0.23722,0.23722,0.23722,0.061579,0.021901,0.067269,0.021901,0.021901
+23-Sep-2019 19:00:00,1,0.10893,0.10893,0.10893,0.29935,0.091436,0.091436,0.091436,0.091436,0.29935,0.29935,0.29935,0.29935,0.29935,0.091436,0.030495,0.10893,0.030495,0.030495
+23-Sep-2019 20:00:00,1,0.14503,0.14503,0.14503,0.25766,0.091436,0.091436,0.091436,0.091436,0.25766,0.25766,0.25766,0.25766,0.25766,0.091436,0.057243,0.14503,0.057243,0.057243
+23-Sep-2019 21:00:00,1,0.18197,0.18197,0.18197,0.41866,0.085838,0.085838,0.085838,0.085838,0.41866,0.41866,0.41866,0.41866,0.41866,0.085838,0.089152,0.18197,0.089152,0.089152
+23-Sep-2019 22:00:00,1,0.22011,0.22011,0.22011,0.38264,0.093302,0.093302,0.093302,0.093302,0.38264,0.38264,0.38264,0.38264,0.38264,0.093302,0.10345,0.22011,0.10345,0.10345
+23-Sep-2019 23:00:00,1,0.22817,0.22817,0.22817,0.37418,0.12689,0.12689,0.12689,0.12689,0.37418,0.37418,0.37418,0.37418,0.37418,0.12689,0.1089,0.22817,0.1089,0.1089
+24-Sep-2019 00:00:00,1,0.26512,0.26512,0.26512,0.39441,0.095168,0.095168,0.095168,0.095168,0.39441,0.39441,0.39441,0.39441,0.39441,0.095168,0.12957,0.26512,0.12957,0.12957
+24-Sep-2019 01:00:00,1,0.3213,0.3213,0.3213,0.38785,0.08024,0.08024,0.08024,0.08024,0.38785,0.38785,0.38785,0.38785,0.38785,0.08024,0.1547,0.3213,0.1547,0.1547
+24-Sep-2019 02:00:00,1,0.38577,0.38577,0.38577,0.35075,0.059713,0.059713,0.059713,0.059713,0.35075,0.35075,0.35075,0.35075,0.35075,0.059713,0.19482,0.38577,0.19482,0.19482
+24-Sep-2019 03:00:00,1,0.42673,0.42673,0.42673,0.33099,0.072776,0.072776,0.072776,0.072776,0.33099,0.33099,0.33099,0.33099,0.33099,0.072776,0.21538,0.42673,0.21538,0.21538
+24-Sep-2019 04:00:00,1,0.45295,0.45295,0.45295,0.30235,0.037321,0.037321,0.037321,0.037321,0.30235,0.30235,0.30235,0.30235,0.30235,0.037321,0.22004,0.45295,0.22004,0.22004
+24-Sep-2019 05:00:00,1,0.4778,0.4778,0.4778,0.27092,0.041053,0.041053,0.041053,0.041053,0.27092,0.27092,0.27092,0.27092,0.27092,0.041053,0.25577,0.4778,0.25577,0.25577
+24-Sep-2019 06:00:00,1,0.48373,0.48373,0.48373,0.32113,0.050383,0.050383,0.050383,0.050383,0.32113,0.32113,0.32113,0.32113,0.32113,0.050383,0.27814,0.48373,0.27814,0.27814
+24-Sep-2019 07:00:00,1,0.52164,0.52164,0.52164,0.30978,0.12689,0.12689,0.12689,0.12689,0.30978,0.30978,0.30978,0.30978,0.30978,0.12689,0.29997,0.52164,0.29997,0.29997
+24-Sep-2019 08:00:00,1,0.54819,0.54819,0.54819,0.37139,0.52996,0.52996,0.52996,0.52996,0.37139,0.37139,0.37139,0.37139,0.37139,0.52996,0.2883,0.54819,0.2883,0.2883
+24-Sep-2019 09:00:00,1,0.55522,0.55522,0.55522,0.42873,0.33589,0.33589,0.33589,0.33589,0.42873,0.42873,0.42873,0.42873,0.42873,0.33589,0.24724,0.55522,0.24724,0.24724
+24-Sep-2019 10:00:00,1,0.52773,0.52773,0.52773,0.46485,0.21273,0.21273,0.21273,0.21273,0.46485,0.46485,0.46485,0.46485,0.46485,0.21273,0.25384,0.52773,0.25384,0.25384
+24-Sep-2019 11:00:00,1,0.4565,0.4565,0.4565,0.49963,0.16048,0.16048,0.16048,0.16048,0.49963,0.49963,0.49963,0.49963,0.49963,0.16048,0.24172,0.4565,0.24172,0.24172
+24-Sep-2019 12:00:00,1,0.40144,0.40144,0.40144,0.49075,0.24632,0.24632,0.24632,0.24632,0.49075,0.49075,0.49075,0.49075,0.49075,0.24632,0.24239,0.40144,0.24239,0.24239
+24-Sep-2019 13:00:00,1,0.38264,0.38264,0.38264,0.52213,0.30976,0.30976,0.30976,0.30976,0.52213,0.52213,0.52213,0.52213,0.52213,0.30976,0.2653,0.38264,0.2653,0.2653
+24-Sep-2019 14:00:00,1,0.36885,0.36885,0.36885,0.52383,0.39,0.39,0.39,0.39,0.52383,0.52383,0.52383,0.52383,0.52383,0.39,0.27444,0.36885,0.27444,0.27444
+24-Sep-2019 15:00:00,1,0.37547,0.37547,0.37547,0.62456,0.43479,0.43479,0.43479,0.43479,0.62456,0.62456,0.62456,0.62456,0.62456,0.43479,0.26254,0.37547,0.26254,0.26254
+24-Sep-2019 16:00:00,1,0.39377,0.39377,0.39377,0.66455,0.4124,0.4124,0.4124,0.4124,0.66455,0.66455,0.66455,0.66455,0.66455,0.4124,0.26387,0.39377,0.26387,0.26387
+24-Sep-2019 17:00:00,1,0.42499,0.42499,0.42499,0.70718,0.35268,0.35268,0.35268,0.35268,0.70718,0.70718,0.70718,0.70718,0.70718,0.35268,0.26099,0.42499,0.26099,0.26099
+24-Sep-2019 18:00:00,1,0.47769,0.47769,0.47769,0.73603,0.27431,0.27431,0.27431,0.27431,0.73603,0.73603,0.73603,0.73603,0.73603,0.27431,0.27311,0.47769,0.27311,0.27311
+24-Sep-2019 19:00:00,1,0.57176,0.57176,0.57176,0.70161,0.27058,0.27058,0.27058,0.27058,0.70161,0.70161,0.70161,0.70161,0.70161,0.27058,0.35196,0.57176,0.35196,0.35196
+24-Sep-2019 20:00:00,1,0.57441,0.57441,0.57441,0.69939,0.23512,0.23512,0.23512,0.23512,0.69939,0.69939,0.69939,0.69939,0.69939,0.23512,0.42247,0.57441,0.42247,0.42247
+24-Sep-2019 21:00:00,1,0.54425,0.54425,0.54425,0.68571,0.1922,0.1922,0.1922,0.1922,0.68571,0.68571,0.68571,0.68571,0.68571,0.1922,0.43118,0.54425,0.43118,0.43118
+24-Sep-2019 22:00:00,1,0.43406,0.43406,0.43406,0.68127,0.18101,0.18101,0.18101,0.18101,0.68127,0.68127,0.68127,0.68127,0.68127,0.18101,0.39687,0.43406,0.39687,0.39687
+24-Sep-2019 23:00:00,1,0.41198,0.41198,0.41198,0.60985,0.18101,0.18101,0.18101,0.18101,0.60985,0.60985,0.60985,0.60985,0.60985,0.18101,0.33071,0.41198,0.33071,0.33071
+25-Sep-2019 00:00:00,1,0.39894,0.39894,0.39894,0.56279,0.25378,0.25378,0.25378,0.25378,0.56279,0.56279,0.56279,0.56279,0.56279,0.25378,0.29618,0.39894,0.29618,0.29618
+25-Sep-2019 01:00:00,1,0.37625,0.37625,0.37625,0.50928,0.42359,0.42359,0.42359,0.42359,0.50928,0.50928,0.50928,0.50928,0.50928,0.42359,0.27271,0.37625,0.27271,0.27271
+25-Sep-2019 02:00:00,1,0.36298,0.36298,0.36298,0.50056,0.22206,0.22206,0.22206,0.22206,0.50056,0.50056,0.50056,0.50056,0.50056,0.22206,0.23871,0.36298,0.23871,0.23871
+25-Sep-2019 03:00:00,1,0.36285,0.36285,0.36285,0.48348,0.19034,0.19034,0.19034,0.19034,0.48348,0.48348,0.48348,0.48348,0.48348,0.19034,0.25072,0.36285,0.25072,0.25072
+25-Sep-2019 04:00:00,1,0.341,0.341,0.341,0.48755,0.16235,0.16235,0.16235,0.16235,0.48755,0.48755,0.48755,0.48755,0.48755,0.16235,0.24556,0.341,0.24556,0.24556
+25-Sep-2019 05:00:00,1,0.34626,0.34626,0.34626,0.44591,0.22206,0.22206,0.22206,0.22206,0.44591,0.44591,0.44591,0.44591,0.44591,0.22206,0.24701,0.34626,0.24701,0.24701
+25-Sep-2019 06:00:00,1,0.3437,0.3437,0.3437,0.36468,0.13622,0.13622,0.13622,0.13622,0.36468,0.36468,0.36468,0.36468,0.36468,0.13622,0.29421,0.3437,0.29421,0.29421
+25-Sep-2019 07:00:00,1,0.32423,0.32423,0.32423,0.33424,0.13436,0.13436,0.13436,0.13436,0.33424,0.33424,0.33424,0.33424,0.33424,0.13436,0.27354,0.32423,0.27354,0.27354
+25-Sep-2019 08:00:00,1,0.30007,0.30007,0.30007,0.3251,0.12876,0.12876,0.12876,0.12876,0.3251,0.3251,0.3251,0.3251,0.3251,0.12876,0.24403,0.30007,0.24403,0.24403
+25-Sep-2019 09:00:00,1,0.27451,0.27451,0.27451,0.31876,0.15488,0.15488,0.15488,0.15488,0.31876,0.31876,0.31876,0.31876,0.31876,0.15488,0.18865,0.27451,0.18865,0.18865
+25-Sep-2019 10:00:00,1,0.27622,0.27622,0.27622,0.27639,0.22766,0.22766,0.22766,0.22766,0.27639,0.27639,0.27639,0.27639,0.27639,0.22766,0.20862,0.27622,0.20862,0.20862
+25-Sep-2019 11:00:00,1,0.26415,0.26415,0.26415,0.31066,0.13622,0.13622,0.13622,0.13622,0.31066,0.31066,0.31066,0.31066,0.31066,0.13622,0.19657,0.26415,0.19657,0.19657
+25-Sep-2019 12:00:00,1,0.26631,0.26631,0.26631,0.33981,0.20713,0.20713,0.20713,0.20713,0.33981,0.33981,0.33981,0.33981,0.33981,0.20713,0.21389,0.26631,0.21389,0.21389
+25-Sep-2019 13:00:00,1,0.23985,0.23985,0.23985,0.43925,0.14182,0.14182,0.14182,0.14182,0.43925,0.43925,0.43925,0.43925,0.43925,0.14182,0.2557,0.23985,0.2557,0.2557
+25-Sep-2019 14:00:00,1,0.24269,0.24269,0.24269,0.35736,0.20153,0.20153,0.20153,0.20153,0.35736,0.35736,0.35736,0.35736,0.35736,0.20153,0.24571,0.24269,0.24571,0.24571
+25-Sep-2019 15:00:00,1,0.24759,0.24759,0.24759,0.37877,0.054115,0.054115,0.054115,0.054115,0.37877,0.37877,0.37877,0.37877,0.37877,0.054115,0.22271,0.24759,0.22271,0.22271
+25-Sep-2019 16:00:00,1,0.21919,0.21919,0.21919,0.38558,0.035455,0.035455,0.035455,0.035455,0.38558,0.38558,0.38558,0.38558,0.38558,0.035455,0.19969,0.21919,0.19969,0.19969
+25-Sep-2019 17:00:00,1,0.20721,0.20721,0.20721,0.29517,0.026125,0.026125,0.026125,0.026125,0.29517,0.29517,0.29517,0.29517,0.29517,0.026125,0.18371,0.20721,0.18371,0.18371
+25-Sep-2019 18:00:00,1,0.17396,0.17396,0.17396,0.317,0.01866,0.01866,0.01866,0.01866,0.317,0.317,0.317,0.317,0.317,0.01866,0.18044,0.17396,0.18044,0.18044
+25-Sep-2019 19:00:00,1,0.13612,0.13612,0.13612,0.28217,0.013062,0.013062,0.013062,0.013062,0.28217,0.28217,0.28217,0.28217,0.28217,0.013062,0.22682,0.13612,0.22682,0.22682
+25-Sep-2019 20:00:00,1,0.16658,0.16658,0.16658,0.24842,0.013062,0.013062,0.013062,0.013062,0.24842,0.24842,0.24842,0.24842,0.24842,0.013062,0.18858,0.16658,0.18858,0.18858
+25-Sep-2019 21:00:00,1,0.17941,0.17941,0.17941,0.24073,0.016794,0.016794,0.016794,0.016794,0.24073,0.24073,0.24073,0.24073,0.24073,0.016794,0.1363,0.17941,0.1363,0.1363
+25-Sep-2019 22:00:00,1,0.18802,0.18802,0.18802,0.2187,0.022393,0.022393,0.022393,0.022393,0.2187,0.2187,0.2187,0.2187,0.2187,0.022393,0.1052,0.18802,0.1052,0.1052
+25-Sep-2019 23:00:00,1,0.17233,0.17233,0.17233,0.22711,0.031723,0.031723,0.031723,0.031723,0.22711,0.22711,0.22711,0.22711,0.22711,0.031723,0.099048,0.17233,0.099048,0.099048
+26-Sep-2019 00:00:00,1,0.16395,0.16395,0.16395,0.24244,0.065312,0.065312,0.065312,0.065312,0.24244,0.24244,0.24244,0.24244,0.24244,0.065312,0.090185,0.16395,0.090185,0.090185
+26-Sep-2019 01:00:00,1,0.16305,0.16305,0.16305,0.21725,0.08957,0.08957,0.08957,0.08957,0.21725,0.21725,0.21725,0.21725,0.21725,0.08957,0.074567,0.16305,0.074567,0.074567
+26-Sep-2019 02:00:00,1,0.16537,0.16537,0.16537,0.16931,0.10077,0.10077,0.10077,0.10077,0.16931,0.16931,0.16931,0.16931,0.16931,0.10077,0.070303,0.16537,0.070303,0.070303
+26-Sep-2019 03:00:00,1,0.16177,0.16177,0.16177,0.16405,0.069044,0.069044,0.069044,0.069044,0.16405,0.16405,0.16405,0.16405,0.16405,0.069044,0.057804,0.16177,0.057804,0.057804
+26-Sep-2019 04:00:00,1,0.13878,0.13878,0.13878,0.1561,0.07091,0.07091,0.07091,0.07091,0.1561,0.1561,0.1561,0.1561,0.1561,0.07091,0.061417,0.13878,0.061417,0.061417
+26-Sep-2019 05:00:00,1,0.11606,0.11606,0.11606,0.1543,0.054115,0.054115,0.054115,0.054115,0.1543,0.1543,0.1543,0.1543,0.1543,0.054115,0.096176,0.11606,0.096176,0.096176
+26-Sep-2019 06:00:00,1,0.12691,0.12691,0.12691,0.18732,0.055981,0.055981,0.055981,0.055981,0.18732,0.18732,0.18732,0.18732,0.18732,0.055981,0.14743,0.12691,0.14743,0.14743
+26-Sep-2019 07:00:00,1,0.1344,0.1344,0.1344,0.17695,0.041053,0.041053,0.041053,0.041053,0.17695,0.17695,0.17695,0.17695,0.17695,0.041053,0.16565,0.1344,0.16565,0.16565
+26-Sep-2019 08:00:00,1,0.12588,0.12588,0.12588,0.18485,0.024259,0.024259,0.024259,0.024259,0.18485,0.18485,0.18485,0.18485,0.18485,0.024259,0.17041,0.12588,0.17041,0.17041
+26-Sep-2019 09:00:00,1,0.11727,0.11727,0.11727,0.18392,0.057847,0.057847,0.057847,0.057847,0.18392,0.18392,0.18392,0.18392,0.18392,0.057847,0.13587,0.11727,0.13587,0.13587
+26-Sep-2019 10:00:00,1,0.1054,0.1054,0.1054,0.15961,0.029857,0.029857,0.029857,0.029857,0.15961,0.15961,0.15961,0.15961,0.15961,0.029857,0.084575,0.1054,0.084575,0.084575
+26-Sep-2019 11:00:00,1,0.093747,0.093747,0.093747,0.19207,0.027991,0.027991,0.027991,0.027991,0.19207,0.19207,0.19207,0.19207,0.19207,0.027991,0.089511,0.093747,0.089511,0.089511
+26-Sep-2019 12:00:00,1,0.095075,0.095075,0.095075,0.18686,0.063445,0.063445,0.063445,0.063445,0.18686,0.18686,0.18686,0.18686,0.18686,0.063445,0.080715,0.095075,0.080715,0.080715
+26-Sep-2019 13:00:00,1,0.088066,0.088066,0.088066,0.21209,0.031723,0.031723,0.031723,0.031723,0.21209,0.21209,0.21209,0.21209,0.21209,0.031723,0.082982,0.088066,0.082982,0.082982
+26-Sep-2019 14:00:00,1,0.09109,0.09109,0.09109,0.22912,0.041053,0.041053,0.041053,0.041053,0.22912,0.22912,0.22912,0.22912,0.22912,0.041053,0.075936,0.09109,0.075936,0.075936
+26-Sep-2019 15:00:00,1,0.10712,0.10712,0.10712,0.214,0.042919,0.042919,0.042919,0.042919,0.214,0.214,0.214,0.214,0.214,0.042919,0.069473,0.10712,0.069473,0.069473
+26-Sep-2019 16:00:00,1,0.09464,0.09464,0.09464,0.21674,0.07091,0.07091,0.07091,0.07091,0.21674,0.21674,0.21674,0.21674,0.21674,0.07091,0.077641,0.09464,0.077641,0.077641
+26-Sep-2019 17:00:00,1,0.08486,0.08486,0.08486,0.22174,0.10077,0.10077,0.10077,0.10077,0.22174,0.22174,0.22174,0.22174,0.22174,0.10077,0.078539,0.08486,0.078539,0.078539
+26-Sep-2019 18:00:00,1,0.091296,0.091296,0.091296,0.26081,0.08957,0.08957,0.08957,0.08957,0.26081,0.26081,0.26081,0.26081,0.26081,0.08957,0.071358,0.091296,0.071358,0.071358
+26-Sep-2019 19:00:00,1,0.096793,0.096793,0.096793,0.28506,0.057847,0.057847,0.057847,0.057847,0.28506,0.28506,0.28506,0.28506,0.28506,0.057847,0.062045,0.096793,0.062045,0.062045
+26-Sep-2019 20:00:00,1,0.12945,0.12945,0.12945,0.25823,0.027991,0.027991,0.027991,0.027991,0.25823,0.25823,0.25823,0.25823,0.25823,0.027991,0.069383,0.12945,0.069383,0.069383
+26-Sep-2019 21:00:00,1,0.13486,0.13486,0.13486,0.21999,0.037321,0.037321,0.037321,0.037321,0.21999,0.21999,0.21999,0.21999,0.21999,0.037321,0.065254,0.13486,0.065254,0.065254
+26-Sep-2019 22:00:00,1,0.13195,0.13195,0.13195,0.2123,0.027991,0.027991,0.027991,0.027991,0.2123,0.2123,0.2123,0.2123,0.2123,0.027991,0.066892,0.13195,0.066892,0.066892
+26-Sep-2019 23:00:00,1,0.15527,0.15527,0.15527,0.20058,0.057847,0.057847,0.057847,0.057847,0.20058,0.20058,0.20058,0.20058,0.20058,0.057847,0.0596,0.15527,0.0596,0.0596
+27-Sep-2019 00:00:00,1,0.13763,0.13763,0.13763,0.20012,0.12129,0.12129,0.12129,0.12129,0.20012,0.20012,0.20012,0.20012,0.20012,0.12129,0.056413,0.13763,0.056413,0.056413
+27-Sep-2019 01:00:00,1,0.12945,0.12945,0.12945,0.24925,0.17168,0.17168,0.17168,0.17168,0.24925,0.24925,0.24925,0.24925,0.24925,0.17168,0.053518,0.12945,0.053518,0.053518
+27-Sep-2019 02:00:00,1,0.15412,0.15412,0.15412,0.32242,0.11196,0.11196,0.11196,0.11196,0.32242,0.32242,0.32242,0.32242,0.32242,0.11196,0.045665,0.15412,0.045665,0.045665
+27-Sep-2019 03:00:00,1,0.15721,0.15721,0.15721,0.32841,0.1101,0.1101,0.1101,0.1101,0.32841,0.32841,0.32841,0.32841,0.32841,0.1101,0.033255,0.15721,0.033255,0.033255
+27-Sep-2019 04:00:00,1,0.18701,0.18701,0.18701,0.34079,0.10823,0.10823,0.10823,0.10823,0.34079,0.34079,0.34079,0.34079,0.34079,0.10823,0.04452,0.18701,0.04452,0.04452
+27-Sep-2019 05:00:00,1,0.19796,0.19796,0.19796,0.34157,0.10263,0.10263,0.10263,0.10263,0.34157,0.34157,0.34157,0.34157,0.34157,0.10263,0.081725,0.19796,0.081725,0.081725
+27-Sep-2019 06:00:00,1,0.16679,0.16679,0.16679,0.35256,0.11383,0.11383,0.11383,0.11383,0.35256,0.35256,0.35256,0.35256,0.35256,0.11383,0.12277,0.16679,0.12277,0.12277
+27-Sep-2019 07:00:00,1,0.15737,0.15737,0.15737,0.29512,0.093302,0.093302,0.093302,0.093302,0.29512,0.29512,0.29512,0.29512,0.29512,0.093302,0.14361,0.15737,0.14361,0.14361
+27-Sep-2019 08:00:00,1,0.15799,0.15799,0.15799,0.25492,0.13436,0.13436,0.13436,0.13436,0.25492,0.25492,0.25492,0.25492,0.25492,0.13436,0.13944,0.15799,0.13944,0.13944
+27-Sep-2019 09:00:00,1,0.17318,0.17318,0.17318,0.22948,0.14555,0.14555,0.14555,0.14555,0.22948,0.22948,0.22948,0.22948,0.22948,0.14555,0.13374,0.17318,0.13374,0.13374
+27-Sep-2019 10:00:00,1,0.20939,0.20939,0.20939,0.23377,0.27058,0.27058,0.27058,0.27058,0.23377,0.23377,0.23377,0.23377,0.23377,0.27058,0.17622,0.20939,0.17622,0.17622
+27-Sep-2019 11:00:00,1,0.22185,0.22185,0.22185,0.26132,0.43665,0.43665,0.43665,0.43665,0.26132,0.26132,0.26132,0.26132,0.26132,0.43665,0.23137,0.22185,0.23137,0.23137
+27-Sep-2019 12:00:00,1,0.25339,0.25339,0.25339,0.28434,0.18474,0.18474,0.18474,0.18474,0.28434,0.28434,0.28434,0.28434,0.28434,0.18474,0.29649,0.25339,0.29649,0.29649
+27-Sep-2019 13:00:00,1,0.25511,0.25511,0.25511,0.26148,0.27058,0.27058,0.27058,0.27058,0.26148,0.26148,0.26148,0.26148,0.26148,0.27058,0.3142,0.25511,0.3142,0.3142
+27-Sep-2019 14:00:00,1,0.2669,0.2669,0.2669,0.26746,0.21273,0.21273,0.21273,0.21273,0.26746,0.26746,0.26746,0.26746,0.26746,0.21273,0.33706,0.2669,0.33706,0.33706
+27-Sep-2019 15:00:00,1,0.25245,0.25245,0.25245,0.28011,0.17168,0.17168,0.17168,0.17168,0.28011,0.28011,0.28011,0.28011,0.28011,0.17168,0.29589,0.25245,0.29589,0.29589
+27-Sep-2019 16:00:00,1,0.24139,0.24139,0.24139,0.25033,0.24818,0.24818,0.24818,0.24818,0.25033,0.25033,0.25033,0.25033,0.25033,0.24818,0.29607,0.24139,0.29607,0.29607
+27-Sep-2019 17:00:00,1,0.21713,0.21713,0.21713,0.1801,0.3135,0.3135,0.3135,0.3135,0.1801,0.1801,0.1801,0.1801,0.1801,0.3135,0.27518,0.21713,0.27518,0.27518
+27-Sep-2019 18:00:00,1,0.18048,0.18048,0.18048,0.17695,0.38627,0.38627,0.38627,0.38627,0.17695,0.17695,0.17695,0.17695,0.17695,0.38627,0.24859,0.18048,0.24859,0.24859
+27-Sep-2019 19:00:00,1,0.18685,0.18685,0.18685,0.16426,0.2034,0.2034,0.2034,0.2034,0.16426,0.16426,0.16426,0.16426,0.16426,0.2034,0.27138,0.18685,0.27138,0.27138
+27-Sep-2019 20:00:00,1,0.19865,0.19865,0.19865,0.23299,0.27617,0.27617,0.27617,0.27617,0.23299,0.23299,0.23299,0.23299,0.23299,0.27617,0.29308,0.19865,0.29308,0.29308
+27-Sep-2019 21:00:00,1,0.20891,0.20891,0.20891,0.21447,0.18101,0.18101,0.18101,0.18101,0.21447,0.21447,0.21447,0.21447,0.21447,0.18101,0.33451,0.20891,0.33451,0.33451
+27-Sep-2019 22:00:00,1,0.21658,0.21658,0.21658,0.24378,0.16608,0.16608,0.16608,0.16608,0.24378,0.24378,0.24378,0.24378,0.24378,0.16608,0.32865,0.21658,0.32865,0.32865
+27-Sep-2019 23:00:00,1,0.21209,0.21209,0.21209,0.24491,0.15488,0.15488,0.15488,0.15488,0.24491,0.24491,0.24491,0.24491,0.24491,0.15488,0.34725,0.21209,0.34725,0.34725
+28-Sep-2019 00:00:00,1,0.16157,0.16157,0.16157,0.20224,0.18474,0.18474,0.18474,0.18474,0.20224,0.20224,0.20224,0.20224,0.20224,0.18474,0.36913,0.16157,0.36913,0.36913
+28-Sep-2019 01:00:00,1,0.21012,0.21012,0.21012,0.17587,0.097034,0.097034,0.097034,0.097034,0.17587,0.17587,0.17587,0.17587,0.17587,0.097034,0.34748,0.21012,0.34748,0.34748
+28-Sep-2019 02:00:00,1,0.21074,0.21074,0.21074,0.14315,0.065312,0.065312,0.065312,0.065312,0.14315,0.14315,0.14315,0.14315,0.14315,0.065312,0.32661,0.21074,0.32661,0.32661
+28-Sep-2019 03:00:00,1,0.22515,0.22515,0.22515,0.1577,0.069044,0.069044,0.069044,0.069044,0.1577,0.1577,0.1577,0.1577,0.1577,0.069044,0.30576,0.22515,0.30576,0.30576
+28-Sep-2019 04:00:00,1,0.21546,0.21546,0.21546,0.1881,0.063445,0.063445,0.063445,0.063445,0.1881,0.1881,0.1881,0.1881,0.1881,0.063445,0.29171,0.21546,0.29171,0.29171
+28-Sep-2019 05:00:00,1,0.19881,0.19881,0.19881,0.1624,0.042919,0.042919,0.042919,0.042919,0.1624,0.1624,0.1624,0.1624,0.1624,0.042919,0.24612,0.19881,0.24612,0.24612
+28-Sep-2019 06:00:00,1,0.18376,0.18376,0.18376,0.17396,0.029857,0.029857,0.029857,0.029857,0.17396,0.17396,0.17396,0.17396,0.17396,0.029857,0.2233,0.18376,0.2233,0.2233
+28-Sep-2019 07:00:00,1,0.19091,0.19091,0.19091,0.17628,0.0074642,0.0074642,0.0074642,0.0074642,0.17628,0.17628,0.17628,0.17628,0.17628,0.0074642,0.21807,0.19091,0.21807,0.21807
+28-Sep-2019 08:00:00,1,0.20046,0.20046,0.20046,0.18624,0.0055981,0.0055981,0.0055981,0.0055981,0.18624,0.18624,0.18624,0.18624,0.18624,0.0055981,0.18297,0.20046,0.18297,0.18297
+28-Sep-2019 09:00:00,1,0.20346,0.20346,0.20346,0.19842,0.0093302,0.0093302,0.0093302,0.0093302,0.19842,0.19842,0.19842,0.19842,0.19842,0.0093302,0.19325,0.20346,0.19325,0.19325
+28-Sep-2019 10:00:00,1,0.23841,0.23841,0.23841,0.19176,0.0093302,0.0093302,0.0093302,0.0093302,0.19176,0.19176,0.19176,0.19176,0.19176,0.0093302,0.22617,0.23841,0.22617,0.22617
+28-Sep-2019 11:00:00,1,0.27609,0.27609,0.27609,0.21472,0.011196,0.011196,0.011196,0.011196,0.21472,0.21472,0.21472,0.21472,0.21472,0.011196,0.22309,0.27609,0.22309,0.22309
+28-Sep-2019 12:00:00,1,0.28353,0.28353,0.28353,0.22742,0.0093302,0.0093302,0.0093302,0.0093302,0.22742,0.22742,0.22742,0.22742,0.22742,0.0093302,0.21892,0.28353,0.21892,0.21892
+28-Sep-2019 13:00:00,1,0.27144,0.27144,0.27144,0.22603,0.0055981,0.0055981,0.0055981,0.0055981,0.22603,0.22603,0.22603,0.22603,0.22603,0.0055981,0.19807,0.27144,0.19807,0.19807
+28-Sep-2019 14:00:00,1,0.25556,0.25556,0.25556,0.2123,0.0055981,0.0055981,0.0055981,0.0055981,0.2123,0.2123,0.2123,0.2123,0.2123,0.0055981,0.18338,0.25556,0.18338,0.18338
+28-Sep-2019 15:00:00,1,0.23843,0.23843,0.23843,0.1929,0.001866,0.001866,0.001866,0.001866,0.1929,0.1929,0.1929,0.1929,0.1929,0.001866,0.17312,0.23843,0.17312,0.17312
+28-Sep-2019 16:00:00,1,0.21388,0.21388,0.21388,0.16235,0,0,0,0,0.16235,0.16235,0.16235,0.16235,0.16235,0,0.13049,0.21388,0.13049,0.13049
+28-Sep-2019 17:00:00,1,0.1866,0.1866,0.1866,0.12942,0.001866,0.001866,0.001866,0.001866,0.12942,0.12942,0.12942,0.12942,0.12942,0.001866,0.10345,0.1866,0.10345,0.10345
+28-Sep-2019 18:00:00,1,0.15575,0.15575,0.15575,0.1094,0.011196,0.011196,0.011196,0.011196,0.1094,0.1094,0.1094,0.1094,0.1094,0.011196,0.086146,0.15575,0.086146,0.086146
+28-Sep-2019 19:00:00,1,0.14521,0.14521,0.14521,0.13794,0.0055981,0.0055981,0.0055981,0.0055981,0.13794,0.13794,0.13794,0.13794,0.13794,0.0055981,0.10051,0.14521,0.10051,0.10051
+28-Sep-2019 20:00:00,1,0.18362,0.18362,0.18362,0.17143,0.013062,0.013062,0.013062,0.013062,0.17143,0.17143,0.17143,0.17143,0.17143,0.013062,0.1472,0.18362,0.1472,0.1472
+28-Sep-2019 21:00:00,1,0.20673,0.20673,0.20673,0.19661,0.0093302,0.0093302,0.0093302,0.0093302,0.19661,0.19661,0.19661,0.19661,0.19661,0.0093302,0.18937,0.20673,0.18937,0.18937
+28-Sep-2019 22:00:00,1,0.22927,0.22927,0.22927,0.22086,0.013062,0.013062,0.013062,0.013062,0.22086,0.22086,0.22086,0.22086,0.22086,0.013062,0.20465,0.22927,0.20465,0.20465
+28-Sep-2019 23:00:00,1,0.2584,0.2584,0.2584,0.22019,0.022393,0.022393,0.022393,0.022393,0.22019,0.22019,0.22019,0.22019,0.22019,0.022393,0.20909,0.2584,0.20909,0.20909
+29-Sep-2019 00:00:00,1,0.28793,0.28793,0.28793,0.24703,0.014928,0.014928,0.014928,0.014928,0.24703,0.24703,0.24703,0.24703,0.24703,0.014928,0.20157,0.28793,0.20157,0.20157
+29-Sep-2019 01:00:00,1,0.29336,0.29336,0.29336,0.25621,0.014928,0.014928,0.014928,0.014928,0.25621,0.25621,0.25621,0.25621,0.25621,0.014928,0.18308,0.29336,0.18308,0.18308
+29-Sep-2019 02:00:00,1,0.29063,0.29063,0.29063,0.22086,0.014928,0.014928,0.014928,0.014928,0.22086,0.22086,0.22086,0.22086,0.22086,0.014928,0.1924,0.29063,0.1924,0.1924
+29-Sep-2019 03:00:00,1,0.28328,0.28328,0.28328,0.19831,0.0093302,0.0093302,0.0093302,0.0093302,0.19831,0.19831,0.19831,0.19831,0.19831,0.0093302,0.20007,0.28328,0.20007,0.20007
+29-Sep-2019 04:00:00,1,0.25208,0.25208,0.25208,0.22737,0.013062,0.013062,0.013062,0.013062,0.22737,0.22737,0.22737,0.22737,0.22737,0.013062,0.18955,0.25208,0.18955,0.18955
+29-Sep-2019 05:00:00,1,0.24292,0.24292,0.24292,0.16704,0.016794,0.016794,0.016794,0.016794,0.16704,0.16704,0.16704,0.16704,0.16704,0.016794,0.18349,0.24292,0.18349,0.18349
+29-Sep-2019 06:00:00,1,0.23685,0.23685,0.23685,0.14212,0.016794,0.016794,0.016794,0.016794,0.14212,0.14212,0.14212,0.14212,0.14212,0.016794,0.18055,0.23685,0.18055,0.18055
+29-Sep-2019 07:00:00,1,0.23213,0.23213,0.23213,0.13505,0.0093302,0.0093302,0.0093302,0.0093302,0.13505,0.13505,0.13505,0.13505,0.13505,0.0093302,0.19906,0.23213,0.19906,0.19906
+29-Sep-2019 08:00:00,1,0.22819,0.22819,0.22819,0.12612,0.0074642,0.0074642,0.0074642,0.0074642,0.12612,0.12612,0.12612,0.12612,0.12612,0.0074642,0.19031,0.22819,0.19031,0.19031
+29-Sep-2019 09:00:00,1,0.23177,0.23177,0.23177,0.10981,0.001866,0.001866,0.001866,0.001866,0.10981,0.10981,0.10981,0.10981,0.10981,0.001866,0.1534,0.23177,0.1534,0.1534
+29-Sep-2019 10:00:00,1,0.22322,0.22322,0.22322,0.12824,0.001866,0.001866,0.001866,0.001866,0.12824,0.12824,0.12824,0.12824,0.12824,0.001866,0.13046,0.22322,0.13046,0.13046
+29-Sep-2019 11:00:00,1,0.25321,0.25321,0.25321,0.14552,0.0074642,0.0074642,0.0074642,0.0074642,0.14552,0.14552,0.14552,0.14552,0.14552,0.0074642,0.15481,0.25321,0.15481,0.15481
+29-Sep-2019 12:00:00,1,0.27398,0.27398,0.27398,0.18531,0.0055981,0.0055981,0.0055981,0.0055981,0.18531,0.18531,0.18531,0.18531,0.18531,0.0055981,0.21237,0.27398,0.21237,0.21237
+29-Sep-2019 13:00:00,1,0.29714,0.29714,0.29714,0.16813,0.016794,0.016794,0.016794,0.016794,0.16813,0.16813,0.16813,0.16813,0.16813,0.016794,0.23321,0.29714,0.23321,0.23321
+29-Sep-2019 14:00:00,1,0.30744,0.30744,0.30744,0.17494,0.024259,0.024259,0.024259,0.024259,0.17494,0.17494,0.17494,0.17494,0.17494,0.024259,0.23158,0.30744,0.23158,0.23158
+29-Sep-2019 15:00:00,1,0.3238,0.3238,0.3238,0.17458,0.013062,0.013062,0.013062,0.013062,0.17458,0.17458,0.17458,0.17458,0.17458,0.013062,0.2329,0.3238,0.2329,0.2329
+29-Sep-2019 16:00:00,1,0.35364,0.35364,0.35364,0.22463,0.0055981,0.0055981,0.0055981,0.0055981,0.22463,0.22463,0.22463,0.22463,0.22463,0.0055981,0.20797,0.35364,0.20797,0.20797
+29-Sep-2019 17:00:00,1,0.33543,0.33543,0.33543,0.19295,0.0093302,0.0093302,0.0093302,0.0093302,0.19295,0.19295,0.19295,0.19295,0.19295,0.0093302,0.2329,0.33543,0.2329,0.2329
+29-Sep-2019 18:00:00,1,0.30426,0.30426,0.30426,0.13582,0.0055981,0.0055981,0.0055981,0.0055981,0.13582,0.13582,0.13582,0.13582,0.13582,0.0055981,0.22841,0.30426,0.22841,0.22841
+29-Sep-2019 19:00:00,1,0.25387,0.25387,0.25387,0.12039,0.022393,0.022393,0.022393,0.022393,0.12039,0.12039,0.12039,0.12039,0.12039,0.022393,0.20734,0.25387,0.20734,0.20734
+29-Sep-2019 20:00:00,1,0.26017,0.26017,0.26017,0.15027,0.0093302,0.0093302,0.0093302,0.0093302,0.15027,0.15027,0.15027,0.15027,0.15027,0.0093302,0.21914,0.26017,0.21914,0.21914
+29-Sep-2019 21:00:00,1,0.29484,0.29484,0.29484,0.17757,0.013062,0.013062,0.013062,0.013062,0.17757,0.17757,0.17757,0.17757,0.17757,0.013062,0.21324,0.29484,0.21324,0.21324
+29-Sep-2019 22:00:00,1,0.30442,0.30442,0.30442,0.193,0.01866,0.01866,0.01866,0.01866,0.193,0.193,0.193,0.193,0.193,0.01866,0.24172,0.30442,0.24172,0.24172
+29-Sep-2019 23:00:00,1,0.31083,0.31083,0.31083,0.18237,0.022393,0.022393,0.022393,0.022393,0.18237,0.18237,0.18237,0.18237,0.18237,0.022393,0.25763,0.31083,0.25763,0.25763
+30-Sep-2019 00:00:00,1,0.30607,0.30607,0.30607,0.18247,0.033589,0.033589,0.033589,0.033589,0.18247,0.18247,0.18247,0.18247,0.18247,0.033589,0.28794,0.30607,0.28794,0.28794
+30-Sep-2019 01:00:00,1,0.28619,0.28619,0.28619,0.15497,0.046651,0.046651,0.046651,0.046651,0.15497,0.15497,0.15497,0.15497,0.15497,0.046651,0.28996,0.28619,0.28996,0.28996
+30-Sep-2019 02:00:00,1,0.27632,0.27632,0.27632,0.13629,0.065312,0.065312,0.065312,0.065312,0.13629,0.13629,0.13629,0.13629,0.13629,0.065312,0.28402,0.27632,0.28402,0.28402
+30-Sep-2019 03:00:00,1,0.2535,0.2535,0.2535,0.12576,0.069044,0.069044,0.069044,0.069044,0.12576,0.12576,0.12576,0.12576,0.12576,0.069044,0.29504,0.2535,0.29504,0.29504
+30-Sep-2019 04:00:00,1,0.25524,0.25524,0.25524,0.11688,0.063445,0.063445,0.063445,0.063445,0.11688,0.11688,0.11688,0.11688,0.11688,0.063445,0.27984,0.25524,0.27984,0.27984
+30-Sep-2019 05:00:00,1,0.23669,0.23669,0.23669,0.11533,0.061579,0.061579,0.061579,0.061579,0.11533,0.11533,0.11533,0.11533,0.11533,0.061579,0.2734,0.23669,0.2734,0.2734
+30-Sep-2019 06:00:00,1,0.2142,0.2142,0.2142,0.12406,0.069044,0.069044,0.069044,0.069044,0.12406,0.12406,0.12406,0.12406,0.12406,0.069044,0.26948,0.2142,0.26948,0.26948
+30-Sep-2019 07:00:00,1,0.21692,0.21692,0.21692,0.12555,0.054115,0.054115,0.054115,0.054115,0.12555,0.12555,0.12555,0.12555,0.12555,0.054115,0.25305,0.21692,0.25305,0.25305
+30-Sep-2019 08:00:00,1,0.20653,0.20653,0.20653,0.13649,0.024259,0.024259,0.024259,0.024259,0.13649,0.13649,0.13649,0.13649,0.13649,0.024259,0.21217,0.20653,0.21217,0.21217
+30-Sep-2019 09:00:00,1,0.17758,0.17758,0.17758,0.097119,0.011196,0.011196,0.011196,0.011196,0.097119,0.097119,0.097119,0.097119,0.097119,0.011196,0.1466,0.17758,0.1466,0.1466
+30-Sep-2019 10:00:00,1,0.18261,0.18261,0.18261,0.078283,0.011196,0.011196,0.011196,0.011196,0.078283,0.078283,0.078283,0.078283,0.078283,0.011196,0.11242,0.18261,0.11242,0.11242
+30-Sep-2019 11:00:00,1,0.20527,0.20527,0.20527,0.093713,0.024259,0.024259,0.024259,0.024259,0.093713,0.093713,0.093713,0.093713,0.093713,0.024259,0.11274,0.20527,0.11274,0.11274
+30-Sep-2019 12:00:00,1,0.28871,0.28871,0.28871,0.15595,0.01866,0.01866,0.01866,0.01866,0.15595,0.15595,0.15595,0.15595,0.15595,0.01866,0.12629,0.28871,0.12629,0.12629
+30-Sep-2019 13:00:00,1,0.34356,0.34356,0.34356,0.17752,0.014928,0.014928,0.014928,0.014928,0.17752,0.17752,0.17752,0.17752,0.17752,0.014928,0.12407,0.34356,0.12407,0.12407
+30-Sep-2019 14:00:00,1,0.31468,0.31468,0.31468,0.15347,0.013062,0.013062,0.013062,0.013062,0.15347,0.15347,0.15347,0.15347,0.15347,0.013062,0.10179,0.31468,0.10179,0.10179
+30-Sep-2019 15:00:00,1,0.26461,0.26461,0.26461,0.14717,0.0037321,0.0037321,0.0037321,0.0037321,0.14717,0.14717,0.14717,0.14717,0.14717,0.0037321,0.08444,0.26461,0.08444,0.08444
+30-Sep-2019 16:00:00,1,0.23589,0.23589,0.23589,0.14062,0.001866,0.001866,0.001866,0.001866,0.14062,0.14062,0.14062,0.14062,0.14062,0.001866,0.063751,0.23589,0.063751,0.063751
+30-Sep-2019 17:00:00,1,0.20233,0.20233,0.20233,0.098873,0.0037321,0.0037321,0.0037321,0.0037321,0.098873,0.098873,0.098873,0.098873,0.098873,0.0037321,0.041872,0.20233,0.041872,0.041872
+30-Sep-2019 18:00:00,1,0.1467,0.1467,0.1467,0.081483,0.0074642,0.0074642,0.0074642,0.0074642,0.081483,0.081483,0.081483,0.081483,0.081483,0.0074642,0.041266,0.1467,0.041266,0.041266
+30-Sep-2019 19:00:00,1,0.15245,0.15245,0.15245,0.10522,0.0093302,0.0093302,0.0093302,0.0093302,0.10522,0.10522,0.10522,0.10522,0.10522,0.0093302,0.057288,0.15245,0.057288,0.057288
+30-Sep-2019 20:00:00,1,0.18181,0.18181,0.18181,0.13479,0.013062,0.013062,0.013062,0.013062,0.13479,0.13479,0.13479,0.13479,0.13479,0.013062,0.077551,0.18181,0.077551,0.077551
+30-Sep-2019 21:00:00,1,0.19913,0.19913,0.19913,0.1463,0.013062,0.013062,0.013062,0.013062,0.1463,0.1463,0.1463,0.1463,0.1463,0.013062,0.092675,0.19913,0.092675,0.092675
+30-Sep-2019 22:00:00,1,0.20144,0.20144,0.20144,0.15755,0.0055981,0.0055981,0.0055981,0.0055981,0.15755,0.15755,0.15755,0.15755,0.15755,0.0055981,0.087223,0.20144,0.087223,0.087223
+30-Sep-2019 23:00:00,1,0.20272,0.20272,0.20272,0.14129,0.0055981,0.0055981,0.0055981,0.0055981,0.14129,0.14129,0.14129,0.14129,0.14129,0.0055981,0.085899,0.20272,0.085899,0.085899
+01-Oct-2019 00:00:00,1,0.19177,0.19177,0.19177,0.15746,0.0055711,0.0055711,0.0055711,0.0055711,0.15746,0.15746,0.15746,0.15746,0.15746,0.0055711,0.079443,0.19177,0.079443,0.079443
+01-Oct-2019 01:00:00,1,0.18802,0.18802,0.18802,0.19018,0.003714,0.003714,0.003714,0.003714,0.19018,0.19018,0.19018,0.19018,0.19018,0.003714,0.085019,0.18802,0.085019,0.085019
+01-Oct-2019 02:00:00,1,0.19699,0.19699,0.19699,0.20304,0.001857,0.001857,0.001857,0.001857,0.20304,0.20304,0.20304,0.20304,0.20304,0.001857,0.090215,0.19699,0.090215,0.090215
+01-Oct-2019 03:00:00,1,0.18715,0.18715,0.18715,0.16468,0,0,0,0,0.16468,0.16468,0.16468,0.16468,0.16468,0,0.075852,0.18715,0.075852,0.075852
+01-Oct-2019 04:00:00,1,0.18941,0.18941,0.18941,0.15413,0.001857,0.001857,0.001857,0.001857,0.15413,0.15413,0.15413,0.15413,0.15413,0.001857,0.070723,0.18941,0.070723,0.070723
+01-Oct-2019 05:00:00,1,0.17653,0.17653,0.17653,0.14492,0,0,0,0,0.14492,0.14492,0.14492,0.14492,0.14492,0,0.06934,0.17653,0.06934,0.06934
+01-Oct-2019 06:00:00,1,0.17049,0.17049,0.17049,0.17825,0,0,0,0,0.17825,0.17825,0.17825,0.17825,0.17825,0,0.061891,0.17049,0.061891,0.061891
+01-Oct-2019 07:00:00,1,0.17687,0.17687,0.17687,0.21261,0,0,0,0,0.21261,0.21261,0.21261,0.21261,0.21261,0,0.048464,0.17687,0.048464,0.048464
+01-Oct-2019 08:00:00,1,0.1704,0.1704,0.1704,0.19551,0,0,0,0,0.19551,0.19551,0.19551,0.19551,0.19551,0,0.039967,0.1704,0.039967,0.039967
+01-Oct-2019 09:00:00,1,0.15249,0.15249,0.15249,0.13488,0,0,0,0,0.13488,0.13488,0.13488,0.13488,0.13488,0,0.028637,0.15249,0.028637,0.028637
+01-Oct-2019 10:00:00,1,0.12426,0.12426,0.12426,0.10912,0,0,0,0,0.10912,0.10912,0.10912,0.10912,0.10912,0,0.014542,0.12426,0.014542,0.014542
+01-Oct-2019 11:00:00,1,0.14625,0.14625,0.14625,0.13043,0,0,0,0,0.13043,0.13043,0.13043,0.13043,0.13043,0,0.01356,0.14625,0.01356,0.01356
+01-Oct-2019 12:00:00,1,0.16995,0.16995,0.16995,0.14169,0,0,0,0,0.14169,0.14169,0.14169,0.14169,0.14169,0,0.021589,0.16995,0.021589,0.021589
+01-Oct-2019 13:00:00,1,0.17175,0.17175,0.17175,0.1399,0,0,0,0,0.1399,0.1399,0.1399,0.1399,0.1399,0,0.032941,0.17175,0.032941,0.032941
+01-Oct-2019 14:00:00,1,0.15185,0.15185,0.15185,0.12643,0,0,0,0,0.12643,0.12643,0.12643,0.12643,0.12643,0,0.032005,0.15185,0.032005,0.032005
+01-Oct-2019 15:00:00,1,0.14041,0.14041,0.14041,0.12546,0.001857,0.001857,0.001857,0.001857,0.12546,0.12546,0.12546,0.12546,0.12546,0.001857,0.026875,0.14041,0.026875,0.026875
+01-Oct-2019 16:00:00,1,0.11893,0.11893,0.11893,0.12648,0.003714,0.003714,0.003714,0.003714,0.12648,0.12648,0.12648,0.12648,0.12648,0.003714,0.020273,0.11893,0.020273,0.020273
+01-Oct-2019 17:00:00,1,0.10295,0.10295,0.10295,0.12131,0.0055711,0.0055711,0.0055711,0.0055711,0.12131,0.12131,0.12131,0.12131,0.12131,0.0055711,0.015344,0.10295,0.015344,0.015344
+01-Oct-2019 18:00:00,1,0.10147,0.10147,0.10147,0.13478,0.0055711,0.0055711,0.0055711,0.0055711,0.13478,0.13478,0.13478,0.13478,0.13478,0.0055711,0.021545,0.10147,0.021545,0.021545
+01-Oct-2019 19:00:00,1,0.13339,0.13339,0.13339,0.19648,0.01857,0.01857,0.01857,0.01857,0.19648,0.19648,0.19648,0.19648,0.19648,0.01857,0.043067,0.13339,0.043067,0.043067
+01-Oct-2019 20:00:00,1,0.16452,0.16452,0.16452,0.24477,0.022284,0.022284,0.022284,0.022284,0.24477,0.24477,0.24477,0.24477,0.24477,0.022284,0.076878,0.16452,0.076878,0.076878
+01-Oct-2019 21:00:00,1,0.19936,0.19936,0.19936,0.25128,0.027855,0.027855,0.027855,0.027855,0.25128,0.25128,0.25128,0.25128,0.25128,0.027855,0.10186,0.19936,0.10186,0.10186
+01-Oct-2019 22:00:00,1,0.22716,0.22716,0.22716,0.25363,0.042711,0.042711,0.042711,0.042711,0.25363,0.25363,0.25363,0.25363,0.25363,0.042711,0.12291,0.22716,0.12291,0.12291
+01-Oct-2019 23:00:00,1,0.24478,0.24478,0.24478,0.23468,0.048282,0.048282,0.048282,0.048282,0.23468,0.23468,0.23468,0.23468,0.23468,0.048282,0.14147,0.24478,0.14147,0.14147
+02-Oct-2019 00:00:00,1,0.24027,0.24027,0.24027,0.2183,0.059425,0.059425,0.059425,0.059425,0.2183,0.2183,0.2183,0.2183,0.2183,0.059425,0.16328,0.24027,0.16328,0.16328
+02-Oct-2019 01:00:00,1,0.24129,0.24129,0.24129,0.1889,0.064996,0.064996,0.064996,0.064996,0.1889,0.1889,0.1889,0.1889,0.1889,0.064996,0.15438,0.24129,0.15438,0.15438
+02-Oct-2019 02:00:00,1,0.23516,0.23516,0.23516,0.18962,0.074281,0.074281,0.074281,0.074281,0.18962,0.18962,0.18962,0.18962,0.18962,0.074281,0.14535,0.23516,0.14535,0.14535
+02-Oct-2019 03:00:00,1,0.23163,0.23163,0.23163,0.17508,0.063139,0.063139,0.063139,0.063139,0.17508,0.17508,0.17508,0.17508,0.17508,0.063139,0.12686,0.23163,0.12686,0.12686
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+20-Oct-2019 23:00:00,1,0.028552,0.028552,0.028552,0.096629,0.063139,0.063139,0.063139,0.063139,0.096629,0.096629,0.096629,0.096629,0.096629,0.063139,0.024556,0.028552,0.024556,0.024556
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+21-Oct-2019 01:00:00,1,0.043341,0.043341,0.043341,0.088845,0.020427,0.020427,0.020427,0.020427,0.088845,0.088845,0.088845,0.088845,0.088845,0.020427,0.046702,0.043341,0.046702,0.046702
+21-Oct-2019 02:00:00,1,0.051863,0.051863,0.051863,0.12423,0.012999,0.012999,0.012999,0.012999,0.12423,0.12423,0.12423,0.12423,0.12423,0.012999,0.047305,0.051863,0.047305,0.047305
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+21-Oct-2019 04:00:00,1,0.072303,0.072303,0.072303,0.22214,0.01857,0.01857,0.01857,0.01857,0.22214,0.22214,0.22214,0.22214,0.22214,0.01857,0.059214,0.072303,0.059214,0.059214
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+21-Oct-2019 07:00:00,1,0.076929,0.076929,0.076929,0.062934,0.0074281,0.0074281,0.0074281,0.0074281,0.062934,0.062934,0.062934,0.062934,0.062934,0.0074281,0.075362,0.076929,0.075362,0.075362
+21-Oct-2019 08:00:00,1,0.077635,0.077635,0.077635,0.050286,0.011142,0.011142,0.011142,0.011142,0.050286,0.050286,0.050286,0.050286,0.050286,0.011142,0.070054,0.077635,0.070054,0.070054
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+21-Oct-2019 10:00:00,1,0.081486,0.081486,0.081486,0.030212,0,0,0,0,0.030212,0.030212,0.030212,0.030212,0.030212,0,0.017664,0.081486,0.017664,0.017664
+21-Oct-2019 11:00:00,1,0.066948,0.066948,0.066948,0.026167,0,0,0,0,0.026167,0.026167,0.026167,0.026167,0.026167,0,0.0065125,0.066948,0.0065125,0.0065125
+21-Oct-2019 12:00:00,1,0.067199,0.067199,0.067199,0.024016,0,0,0,0,0.024016,0.024016,0.024016,0.024016,0.024016,0,0.0027656,0.067199,0.0027656,0.0027656
+21-Oct-2019 13:00:00,1,0.07547,0.07547,0.07547,0.018025,0,0,0,0,0.018025,0.018025,0.018025,0.018025,0.018025,0,0.0024533,0.07547,0.0024533,0.0024533
+21-Oct-2019 14:00:00,1,0.090031,0.090031,0.090031,0.018435,0,0,0,0,0.018435,0.018435,0.018435,0.018435,0.018435,0,0.0036131,0.090031,0.0036131,0.0036131
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+21-Oct-2019 16:00:00,1,0.094087,0.094087,0.094087,0.029086,0.0092851,0.0092851,0.0092851,0.0092851,0.029086,0.029086,0.029086,0.029086,0.029086,0.0092851,0.01608,0.094087,0.01608,0.01608
+21-Oct-2019 17:00:00,1,0.11938,0.11938,0.11938,0.038815,0.01857,0.01857,0.01857,0.01857,0.038815,0.038815,0.038815,0.038815,0.038815,0.01857,0.037112,0.11938,0.037112,0.037112
+21-Oct-2019 18:00:00,1,0.15873,0.15873,0.15873,0.059554,0.012999,0.012999,0.012999,0.012999,0.059554,0.059554,0.059554,0.059554,0.059554,0.012999,0.078863,0.15873,0.078863,0.078863
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+22-Oct-2019 01:00:00,1,0.17425,0.17425,0.17425,0.038764,0.01857,0.01857,0.01857,0.01857,0.038764,0.038764,0.038764,0.038764,0.038764,0.01857,0.054285,0.17425,0.054285,0.054285
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+23-Oct-2019 02:00:00,1,0.055577,0.055577,0.055577,0.099189,0.024141,0.024141,0.024141,0.024141,0.099189,0.099189,0.099189,0.099189,0.099189,0.024141,0.012735,0.055577,0.012735,0.012735
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+23-Oct-2019 18:00:00,1,0.041609,0.041609,0.041609,0.059145,0.012999,0.012999,0.012999,0.012999,0.059145,0.059145,0.059145,0.059145,0.059145,0.012999,0.010772,0.041609,0.010772,0.010772
+23-Oct-2019 19:00:00,1,0.049903,0.049903,0.049903,0.079679,0.012999,0.012999,0.012999,0.012999,0.079679,0.079679,0.079679,0.079679,0.079679,0.012999,0.01307,0.049903,0.01307,0.01307
+23-Oct-2019 20:00:00,1,0.045141,0.045141,0.045141,0.076811,0.012999,0.012999,0.012999,0.012999,0.076811,0.076811,0.076811,0.076811,0.076811,0.012999,0.013828,0.045141,0.013828,0.013828
+23-Oct-2019 21:00:00,1,0.037804,0.037804,0.037804,0.065597,0.0092851,0.0092851,0.0092851,0.0092851,0.065597,0.065597,0.065597,0.065597,0.065597,0.0092851,0.013203,0.037804,0.013203,0.013203
+23-Oct-2019 22:00:00,1,0.03475,0.03475,0.03475,0.068669,0.0055711,0.0055711,0.0055711,0.0055711,0.068669,0.068669,0.068669,0.068669,0.068669,0.0055711,0.014385,0.03475,0.014385,0.014385
+23-Oct-2019 23:00:00,1,0.034317,0.034317,0.034317,0.067594,0.0092851,0.0092851,0.0092851,0.0092851,0.067594,0.067594,0.067594,0.067594,0.067594,0.0092851,0.0068693,0.034317,0.0068693,0.0068693
+24-Oct-2019 00:00:00,1,0.022263,0.022263,0.022263,0.05341,0.0074281,0.0074281,0.0074281,0.0074281,0.05341,0.05341,0.05341,0.05341,0.05341,0.0074281,0.004639,0.022263,0.004639,0.004639
+24-Oct-2019 01:00:00,1,0.021534,0.021534,0.021534,0.05259,0.003714,0.003714,0.003714,0.003714,0.05259,0.05259,0.05259,0.05259,0.05259,0.003714,0.0029217,0.021534,0.0029217,0.0029217
+24-Oct-2019 02:00:00,1,0.016999,0.016999,0.016999,0.052846,0.001857,0.001857,0.001857,0.001857,0.052846,0.052846,0.052846,0.052846,0.052846,0.001857,0.0007583,0.016999,0.0007583,0.0007583
+24-Oct-2019 03:00:00,1,0.016703,0.016703,0.016703,0.031697,0.001857,0.001857,0.001857,0.001857,0.031697,0.031697,0.031697,0.031697,0.031697,0.001857,0.00028994,0.016703,0.00028994,0.00028994
+24-Oct-2019 04:00:00,1,0.018685,0.018685,0.018685,0.022531,0.0055711,0.0055711,0.0055711,0.0055711,0.022531,0.022531,0.022531,0.022531,0.022531,0.0055711,0.00035685,0.018685,0.00035685,0.00035685
+24-Oct-2019 05:00:00,1,0.01472,0.01472,0.01472,0.01992,0.011142,0.011142,0.011142,0.011142,0.01992,0.01992,0.01992,0.01992,0.01992,0.011142,0.00053527,0.01472,0.00053527,0.00053527
+24-Oct-2019 06:00:00,1,0.0091148,0.0091148,0.0091148,0.031544,0.012999,0.012999,0.012999,0.012999,0.031544,0.031544,0.031544,0.031544,0.031544,0.012999,0.00020073,0.0091148,0.00020073,0.00020073
+24-Oct-2019 07:00:00,1,0.0091604,0.0091604,0.0091604,0.042861,0.011142,0.011142,0.011142,0.011142,0.042861,0.042861,0.042861,0.042861,0.042861,0.011142,0.00015612,0.0091604,0.00015612,0.00015612
+24-Oct-2019 08:00:00,1,0.0085451,0.0085451,0.0085451,0.041273,0.003714,0.003714,0.003714,0.003714,0.041273,0.041273,0.041273,0.041273,0.041273,0.003714,0.00089212,0.0085451,0.00089212,0.00089212
+24-Oct-2019 09:00:00,1,0.0083628,0.0083628,0.0083628,0.029649,0.003714,0.003714,0.003714,0.003714,0.029649,0.029649,0.029649,0.029649,0.029649,0.003714,0.000736,0.0083628,0.000736,0.000736
+24-Oct-2019 10:00:00,1,0.0071323,0.0071323,0.0071323,0.02079,0.001857,0.001857,0.001857,0.001857,0.02079,0.02079,0.02079,0.02079,0.02079,0.001857,0.00053527,0.0071323,0.00053527,0.00053527
+24-Oct-2019 11:00:00,1,0.0085223,0.0085223,0.0085223,0.022327,0.003714,0.003714,0.003714,0.003714,0.022327,0.022327,0.022327,0.022327,0.022327,0.003714,0.00026764,0.0085223,0.00026764,0.00026764
+24-Oct-2019 12:00:00,1,0.0073602,0.0073602,0.0073602,0.016386,0.001857,0.001857,0.001857,0.001857,0.016386,0.016386,0.016386,0.016386,0.016386,0.001857,4.46e-05,0.0073602,4.46e-05,4.46e-05
+24-Oct-2019 13:00:00,1,0.0073374,0.0073374,0.0073374,0.016745,0,0,0,0,0.016745,0.016745,0.016745,0.016745,0.016745,0,0,0.0073374,0,0
+24-Oct-2019 14:00:00,1,0.0061753,0.0061753,0.0061753,0.016028,0,0,0,0,0.016028,0.016028,0.016028,0.016028,0.016028,0,0,0.0061753,0,0
+24-Oct-2019 15:00:00,1,0.0067905,0.0067905,0.0067905,0.01229,0,0,0,0,0.01229,0.01229,0.01229,0.01229,0.01229,0,0,0.0067905,0,0
+24-Oct-2019 16:00:00,1,0.0070184,0.0070184,0.0070184,0.009627,0.001857,0.001857,0.001857,0.001857,0.009627,0.009627,0.009627,0.009627,0.009627,0.001857,8.92e-05,0.0070184,8.92e-05,8.92e-05
+24-Oct-2019 17:00:00,1,0.012077,0.012077,0.012077,0.013621,0.003714,0.003714,0.003714,0.003714,0.013621,0.013621,0.013621,0.013621,0.013621,0.003714,0.00033454,0.012077,0.00033454,0.00033454
+24-Oct-2019 18:00:00,1,0.011804,0.011804,0.011804,0.022019,0.003714,0.003714,0.003714,0.003714,0.022019,0.022019,0.022019,0.022019,0.022019,0.003714,4.46e-05,0.011804,4.46e-05,4.46e-05
+24-Oct-2019 19:00:00,1,0.0099123,0.0099123,0.0099123,0.030571,0.0074281,0.0074281,0.0074281,0.0074281,0.030571,0.030571,0.030571,0.030571,0.030571,0.0074281,0.00033454,0.0099123,0.00033454,0.00033454
+24-Oct-2019 20:00:00,1,0.010938,0.010938,0.010938,0.024887,0.003714,0.003714,0.003714,0.003714,0.024887,0.024887,0.024887,0.024887,0.024887,0.003714,0.0021411,0.010938,0.0021411,0.0021411
+24-Oct-2019 21:00:00,1,0.014219,0.014219,0.014219,0.018332,0.003714,0.003714,0.003714,0.003714,0.018332,0.018332,0.018332,0.018332,0.018332,0.003714,0.0049513,0.014219,0.0049513,0.0049513
+24-Oct-2019 22:00:00,1,0.013786,0.013786,0.013786,0.023197,0.003714,0.003714,0.003714,0.003714,0.023197,0.023197,0.023197,0.023197,0.023197,0.003714,0.0095234,0.013786,0.0095234,0.0095234
+24-Oct-2019 23:00:00,1,0.012259,0.012259,0.012259,0.015772,0.003714,0.003714,0.003714,0.003714,0.015772,0.015772,0.015772,0.015772,0.015772,0.003714,0.011107,0.012259,0.011107,0.011107
+25-Oct-2019 00:00:00,1,0.011302,0.011302,0.011302,0.011829,0.0055711,0.0055711,0.0055711,0.0055711,0.011829,0.011829,0.011829,0.011829,0.011829,0.0055711,0.012869,0.011302,0.012869,0.012869
+25-Oct-2019 01:00:00,1,0.012806,0.012806,0.012806,0.0069642,0.001857,0.001857,0.001857,0.001857,0.0069642,0.0069642,0.0069642,0.0069642,0.0069642,0.001857,0.021567,0.012806,0.021567,0.021567
+25-Oct-2019 02:00:00,1,0.01807,0.01807,0.01807,0.0064009,0.001857,0.001857,0.001857,0.001857,0.0064009,0.0064009,0.0064009,0.0064009,0.0064009,0.001857,0.028213,0.01807,0.028213,0.028213
+25-Oct-2019 03:00:00,1,0.026797,0.026797,0.026797,0.0077836,0.001857,0.001857,0.001857,0.001857,0.0077836,0.0077836,0.0077836,0.0077836,0.0077836,0.001857,0.045141,0.026797,0.045141,0.045141
+25-Oct-2019 04:00:00,1,0.034773,0.034773,0.034773,0.0098831,0.001857,0.001857,0.001857,0.001857,0.0098831,0.0098831,0.0098831,0.0098831,0.0098831,0.001857,0.066708,0.034773,0.066708,0.066708
+25-Oct-2019 05:00:00,1,0.045437,0.045437,0.045437,0.0091662,0.001857,0.001857,0.001857,0.001857,0.0091662,0.0091662,0.0091662,0.0091662,0.0091662,0.001857,0.075875,0.045437,0.075875,0.075875
+25-Oct-2019 06:00:00,1,0.062938,0.062938,0.062938,0.0084493,0.001857,0.001857,0.001857,0.001857,0.0084493,0.0084493,0.0084493,0.0084493,0.0084493,0.001857,0.096326,0.062938,0.096326,0.096326
+25-Oct-2019 07:00:00,1,0.079868,0.079868,0.079868,0.0086029,0,0,0,0,0.0086029,0.0086029,0.0086029,0.0086029,0.0086029,0,0.096549,0.079868,0.096549,0.096549
+25-Oct-2019 08:00:00,1,0.090897,0.090897,0.090897,0.0082444,0,0,0,0,0.0082444,0.0082444,0.0082444,0.0082444,0.0082444,0,0.10491,0.090897,0.10491,0.10491
+25-Oct-2019 09:00:00,1,0.11517,0.11517,0.11517,0.011727,0,0,0,0,0.011727,0.011727,0.011727,0.011727,0.011727,0,0.11183,0.11517,0.11183,0.11183
+25-Oct-2019 10:00:00,1,0.1413,0.1413,0.1413,0.010958,0,0,0,0,0.010958,0.010958,0.010958,0.010958,0.010958,0,0.12737,0.1413,0.12737,0.12737
+25-Oct-2019 11:00:00,1,0.18446,0.18446,0.18446,0.01147,0,0,0,0,0.01147,0.01147,0.01147,0.01147,0.01147,0,0.15822,0.18446,0.15822,0.15822
+25-Oct-2019 12:00:00,1,0.24125,0.24125,0.24125,0.018742,0,0,0,0,0.018742,0.018742,0.018742,0.018742,0.018742,0,0.21056,0.24125,0.21056,0.21056
+25-Oct-2019 13:00:00,1,0.31624,0.31624,0.31624,0.032619,0,0,0,0,0.032619,0.032619,0.032619,0.032619,0.032619,0,0.26028,0.31624,0.26028,0.26028
+25-Oct-2019 14:00:00,1,0.34493,0.34493,0.34493,0.049825,0,0,0,0,0.049825,0.049825,0.049825,0.049825,0.049825,0,0.27392,0.34493,0.27392,0.27392
+25-Oct-2019 15:00:00,1,0.37448,0.37448,0.37448,0.063907,0.001857,0.001857,0.001857,0.001857,0.063907,0.063907,0.063907,0.063907,0.063907,0.001857,0.31726,0.37448,0.31726,0.31726
+25-Oct-2019 16:00:00,1,0.36815,0.36815,0.36815,0.060886,0,0,0,0,0.060886,0.060886,0.060886,0.060886,0.060886,0,0.3401,0.36815,0.3401,0.3401
+25-Oct-2019 17:00:00,1,0.35921,0.35921,0.35921,0.075275,0,0,0,0,0.075275,0.075275,0.075275,0.075275,0.075275,0,0.35071,0.35921,0.35071,0.35071
+25-Oct-2019 18:00:00,1,0.32136,0.32136,0.32136,0.070564,0,0,0,0,0.070564,0.070564,0.070564,0.070564,0.070564,0,0.39924,0.32136,0.39924,0.39924
+25-Oct-2019 19:00:00,1,0.30008,0.30008,0.30008,0.074046,0.001857,0.001857,0.001857,0.001857,0.074046,0.074046,0.074046,0.074046,0.074046,0.001857,0.41341,0.30008,0.41341,0.41341
+25-Oct-2019 20:00:00,1,0.27258,0.27258,0.27258,0.077682,0.001857,0.001857,0.001857,0.001857,0.077682,0.077682,0.077682,0.077682,0.077682,0.001857,0.40647,0.27258,0.40647,0.40647
+25-Oct-2019 21:00:00,1,0.29297,0.29297,0.29297,0.065136,0.001857,0.001857,0.001857,0.001857,0.065136,0.065136,0.065136,0.065136,0.065136,0.001857,0.40988,0.29297,0.40988,0.40988
+25-Oct-2019 22:00:00,1,0.31564,0.31564,0.31564,0.05556,0.0055711,0.0055711,0.0055711,0.0055711,0.05556,0.05556,0.05556,0.05556,0.05556,0.0055711,0.43671,0.31564,0.43671,0.43671
+25-Oct-2019 23:00:00,1,0.35388,0.35388,0.35388,0.061654,0.0074281,0.0074281,0.0074281,0.0074281,0.061654,0.061654,0.061654,0.061654,0.061654,0.0074281,0.40774,0.35388,0.40774,0.40774
+26-Oct-2019 00:00:00,1,0.32316,0.32316,0.32316,0.086592,0.012999,0.012999,0.012999,0.012999,0.086592,0.086592,0.086592,0.086592,0.086592,0.012999,0.40264,0.32316,0.40264,0.40264
+26-Oct-2019 01:00:00,1,0.30687,0.30687,0.30687,0.10277,0.029712,0.029712,0.029712,0.029712,0.10277,0.10277,0.10277,0.10277,0.10277,0.029712,0.39175,0.30687,0.39175,0.39175
+26-Oct-2019 02:00:00,1,0.23252,0.23252,0.23252,0.11107,0.042711,0.042711,0.042711,0.042711,0.11107,0.11107,0.11107,0.11107,0.11107,0.042711,0.35803,0.23252,0.35803,0.35803
+26-Oct-2019 03:00:00,1,0.16108,0.16108,0.16108,0.10697,0.046425,0.046425,0.046425,0.046425,0.10697,0.10697,0.10697,0.10697,0.10697,0.046425,0.30044,0.16108,0.30044,0.30044
+26-Oct-2019 04:00:00,1,0.13196,0.13196,0.13196,0.10994,0.050139,0.050139,0.050139,0.050139,0.10994,0.10994,0.10994,0.10994,0.10994,0.050139,0.25642,0.13196,0.25642,0.25642
+26-Oct-2019 05:00:00,1,0.10377,0.10377,0.10377,0.13427,0.083566,0.083566,0.083566,0.083566,0.13427,0.13427,0.13427,0.13427,0.13427,0.083566,0.21021,0.10377,0.21021,0.21021
+26-Oct-2019 06:00:00,1,0.086682,0.086682,0.086682,0.16847,0.059425,0.059425,0.059425,0.059425,0.16847,0.16847,0.16847,0.16847,0.16847,0.059425,0.14153,0.086682,0.14153,0.14153
+26-Oct-2019 07:00:00,1,0.085975,0.085975,0.085975,0.18035,0.055711,0.055711,0.055711,0.055711,0.18035,0.18035,0.18035,0.18035,0.18035,0.055711,0.10545,0.085975,0.10545,0.10545
+26-Oct-2019 08:00:00,1,0.073716,0.073716,0.073716,0.19551,0.042711,0.042711,0.042711,0.042711,0.19551,0.19551,0.19551,0.19551,0.19551,0.042711,0.080692,0.073716,0.080692,0.080692
+26-Oct-2019 09:00:00,1,0.061342,0.061342,0.061342,0.191,0.046425,0.046425,0.046425,0.046425,0.191,0.191,0.191,0.191,0.191,0.046425,0.047639,0.061342,0.047639,0.047639
+26-Oct-2019 10:00:00,1,0.056352,0.056352,0.056352,0.15173,0.044568,0.044568,0.044568,0.044568,0.15173,0.15173,0.15173,0.15173,0.15173,0.044568,0.038896,0.056352,0.038896,0.038896
+26-Oct-2019 11:00:00,1,0.050519,0.050519,0.050519,0.17779,0.044568,0.044568,0.044568,0.044568,0.17779,0.17779,0.17779,0.17779,0.17779,0.044568,0.04755,0.050519,0.04755,0.04755
+26-Oct-2019 12:00:00,1,0.053549,0.053549,0.053549,0.14087,0.046425,0.046425,0.046425,0.046425,0.14087,0.14087,0.14087,0.14087,0.14087,0.046425,0.053549,0.053549,0.053549,0.053549
+26-Oct-2019 13:00:00,1,0.054324,0.054324,0.054324,0.11542,0.031569,0.031569,0.031569,0.031569,0.11542,0.11542,0.11542,0.11542,0.11542,0.031569,0.052746,0.054324,0.052746,0.052746
+26-Oct-2019 14:00:00,1,0.042931,0.042931,0.042931,0.089255,0.031569,0.031569,0.031569,0.031569,0.089255,0.089255,0.089255,0.089255,0.089255,0.031569,0.047884,0.042931,0.047884,0.047884
+26-Oct-2019 15:00:00,1,0.039604,0.039604,0.039604,0.10636,0.024141,0.024141,0.024141,0.024141,0.10636,0.10636,0.10636,0.10636,0.10636,0.024141,0.042666,0.039604,0.042666,0.042666
+26-Oct-2019 16:00:00,1,0.042657,0.042657,0.042657,0.1083,0.027855,0.027855,0.027855,0.027855,0.1083,0.1083,0.1083,0.1083,0.1083,0.027855,0.042777,0.042657,0.042777,0.042777
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+26-Oct-2019 18:00:00,1,0.048673,0.048673,0.048673,0.10876,0.038997,0.038997,0.038997,0.038997,0.10876,0.10876,0.10876,0.10876,0.10876,0.038997,0.062158,0.048673,0.062158,0.062158
+26-Oct-2019 19:00:00,1,0.051681,0.051681,0.051681,0.13073,0.024141,0.024141,0.024141,0.024141,0.13073,0.13073,0.13073,0.13073,0.13073,0.024141,0.076834,0.051681,0.076834,0.076834
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+27-Oct-2019 01:00:00,1,0.048605,0.048605,0.048605,0.11168,0.055711,0.055711,0.055711,0.055711,0.11168,0.11168,0.11168,0.11168,0.11168,0.055711,0.069853,0.048605,0.069853,0.069853
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+27-Oct-2019 08:00:00,1,0.14561,0.14561,0.14561,0.25696,0.070567,0.070567,0.070567,0.070567,0.25696,0.25696,0.25696,0.25696,0.25696,0.070567,0.22878,0.14561,0.22878,0.22878
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+27-Oct-2019 13:00:00,1,0.13927,0.13927,0.13927,0.13242,0.094708,0.094708,0.094708,0.094708,0.13242,0.13242,0.13242,0.13242,0.13242,0.094708,0.19595,0.13927,0.19595,0.19595
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+29-Oct-2019 21:00:00,1,0.3735,0.3735,0.3735,0.34913,0.051996,0.051996,0.051996,0.051996,0.34913,0.34913,0.34913,0.34913,0.34913,0.051996,0.31552,0.3735,0.31552,0.31552
+29-Oct-2019 22:00:00,1,0.34399,0.34399,0.34399,0.36291,0.055711,0.055711,0.055711,0.055711,0.36291,0.36291,0.36291,0.36291,0.36291,0.055711,0.30481,0.34399,0.30481,0.30481
+29-Oct-2019 23:00:00,1,0.32141,0.32141,0.32141,0.33572,0.06871,0.06871,0.06871,0.06871,0.33572,0.33572,0.33572,0.33572,0.33572,0.06871,0.28791,0.32141,0.28791,0.28791
+30-Oct-2019 00:00:00,1,0.26971,0.26971,0.26971,0.27094,0.050139,0.050139,0.050139,0.050139,0.27094,0.27094,0.27094,0.27094,0.27094,0.050139,0.26108,0.26971,0.26108,0.26108
+30-Oct-2019 01:00:00,1,0.20098,0.20098,0.20098,0.2861,0.022284,0.022284,0.022284,0.022284,0.2861,0.2861,0.2861,0.2861,0.2861,0.022284,0.25802,0.20098,0.25802,0.25802
+30-Oct-2019 02:00:00,1,0.14622,0.14622,0.14622,0.24984,0.038997,0.038997,0.038997,0.038997,0.24984,0.24984,0.24984,0.24984,0.24984,0.038997,0.22876,0.14622,0.22876,0.22876
+30-Oct-2019 03:00:00,1,0.13877,0.13877,0.13877,0.21732,0.029712,0.029712,0.029712,0.029712,0.21732,0.21732,0.21732,0.21732,0.21732,0.029712,0.22901,0.13877,0.22901,0.22901
+30-Oct-2019 04:00:00,1,0.12578,0.12578,0.12578,0.15588,0.050139,0.050139,0.050139,0.050139,0.15588,0.15588,0.15588,0.15588,0.15588,0.050139,0.24159,0.12578,0.24159,0.24159
+30-Oct-2019 05:00:00,1,0.12913,0.12913,0.12913,0.13959,0.033426,0.033426,0.033426,0.033426,0.13959,0.13959,0.13959,0.13959,0.13959,0.033426,0.23398,0.12913,0.23398,0.23398
+30-Oct-2019 06:00:00,1,0.1164,0.1164,0.1164,0.142,0.064996,0.064996,0.064996,0.064996,0.142,0.142,0.142,0.142,0.142,0.064996,0.22455,0.1164,0.22455,0.22455
+30-Oct-2019 07:00:00,1,0.10865,0.10865,0.10865,0.13703,0.070567,0.070567,0.070567,0.070567,0.13703,0.13703,0.13703,0.13703,0.13703,0.070567,0.214,0.10865,0.214,0.214
+30-Oct-2019 08:00:00,1,0.094475,0.094475,0.094475,0.09668,0.059425,0.059425,0.059425,0.059425,0.09668,0.09668,0.09668,0.09668,0.09668,0.059425,0.21924,0.094475,0.21924,0.21924
+30-Oct-2019 09:00:00,1,0.087775,0.087775,0.087775,0.072766,0.094708,0.094708,0.094708,0.094708,0.072766,0.072766,0.072766,0.072766,0.072766,0.094708,0.20708,0.087775,0.20708,0.20708
+30-Oct-2019 10:00:00,1,0.07203,0.07203,0.07203,0.086336,0.094708,0.094708,0.094708,0.094708,0.086336,0.086336,0.086336,0.086336,0.086336,0.094708,0.17151,0.07203,0.17151,0.17151
+30-Oct-2019 11:00:00,1,0.068498,0.068498,0.068498,0.074712,0.079852,0.079852,0.079852,0.079852,0.074712,0.074712,0.074712,0.074712,0.074712,0.079852,0.1358,0.068498,0.1358,0.1358
+30-Oct-2019 12:00:00,1,0.073374,0.073374,0.073374,0.072305,0.072424,0.072424,0.072424,0.072424,0.072305,0.072305,0.072305,0.072305,0.072305,0.072424,0.13451,0.073374,0.13451,0.13451
+30-Oct-2019 13:00:00,1,0.081646,0.081646,0.081646,0.083059,0.064996,0.064996,0.064996,0.064996,0.083059,0.083059,0.083059,0.083059,0.083059,0.064996,0.12021,0.081646,0.12021,0.12021
+30-Oct-2019 14:00:00,1,0.078752,0.078752,0.078752,0.079986,0.055711,0.055711,0.055711,0.055711,0.079986,0.079986,0.079986,0.079986,0.079986,0.055711,0.093583,0.078752,0.093583,0.093583
+30-Oct-2019 15:00:00,1,0.064122,0.064122,0.064122,0.045984,0.033426,0.033426,0.033426,0.033426,0.045984,0.045984,0.045984,0.045984,0.045984,0.033426,0.076566,0.064122,0.076566,0.076566
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+30-Oct-2019 17:00:00,1,0.067814,0.067814,0.067814,0.047316,0.035283,0.035283,0.035283,0.035283,0.047316,0.047316,0.047316,0.047316,0.047316,0.035283,0.086379,0.067814,0.086379,0.086379
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+30-Oct-2019 19:00:00,1,0.096115,0.096115,0.096115,0.072971,0.046425,0.046425,0.046425,0.046425,0.072971,0.072971,0.072971,0.072971,0.072971,0.046425,0.10775,0.096115,0.10775,0.10775
+30-Oct-2019 20:00:00,1,0.11801,0.11801,0.11801,0.073483,0.03714,0.03714,0.03714,0.03714,0.073483,0.073483,0.073483,0.073483,0.073483,0.03714,0.12851,0.11801,0.12851,0.12851
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+31-Oct-2019 13:00:00,1,0.25435,0.25435,0.25435,0.17779,0.0055711,0.0055711,0.0055711,0.0055711,0.17779,0.17779,0.17779,0.17779,0.17779,0.0055711,0.14078,0.25435,0.14078,0.14078
+31-Oct-2019 14:00:00,1,0.23769,0.23769,0.23769,0.21246,0.0092851,0.0092851,0.0092851,0.0092851,0.21246,0.21246,0.21246,0.21246,0.21246,0.0092851,0.15338,0.23769,0.15338,0.15338
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+31-Oct-2019 18:00:00,1,0.33681,0.33681,0.33681,0.31078,0.089137,0.089137,0.089137,0.089137,0.31078,0.31078,0.31078,0.31078,0.31078,0.089137,0.24787,0.33681,0.24787,0.24787
+31-Oct-2019 19:00:00,1,0.37136,0.37136,0.37136,0.27447,0.079852,0.079852,0.079852,0.079852,0.27447,0.27447,0.27447,0.27447,0.27447,0.079852,0.2758,0.37136,0.2758,0.2758
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+31-Oct-2019 21:00:00,1,0.36587,0.36587,0.36587,0.2864,0.029712,0.029712,0.029712,0.029712,0.2864,0.2864,0.2864,0.2864,0.2864,0.029712,0.30553,0.36587,0.30553,0.30553
+31-Oct-2019 22:00:00,1,0.36325,0.36325,0.36325,0.29132,0.029712,0.029712,0.029712,0.029712,0.29132,0.29132,0.29132,0.29132,0.29132,0.029712,0.29745,0.36325,0.29745,0.29745
+31-Oct-2019 23:00:00,1,0.32939,0.32939,0.32939,0.063702,0.012999,0.012999,0.012999,0.012999,0.063702,0.063702,0.063702,0.063702,0.063702,0.012999,0.3062,0.32939,0.3062,0.3062
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+01-Nov-2019 03:00:00,1,0.28511,0.28511,0.28511,0.21247,0.0018481,0.0018481,0.0018481,0.0018481,0.21247,0.21247,0.21247,0.21247,0.21247,0.0018481,0.25442,0.28511,0.25442,0.25442
+01-Nov-2019 04:00:00,1,0.25643,0.25643,0.25643,0.052037,0.0018481,0.0018481,0.0018481,0.0018481,0.052037,0.052037,0.052037,0.052037,0.052037,0.0018481,0.25724,0.25643,0.25724,0.25724
+01-Nov-2019 05:00:00,1,0.24172,0.24172,0.24172,0.20744,0.012937,0.012937,0.012937,0.012937,0.20744,0.20744,0.20744,0.20744,0.20744,0.012937,0.26415,0.24172,0.26415,0.26415
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+01-Nov-2019 07:00:00,1,0.23315,0.23315,0.23315,0.071093,0.03881,0.03881,0.03881,0.03881,0.071093,0.071093,0.071093,0.071093,0.071093,0.03881,0.28422,0.23315,0.28422,0.28422
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+01-Nov-2019 10:00:00,1,0.2134,0.2134,0.2134,0.43423,0.03881,0.03881,0.03881,0.03881,0.43423,0.43423,0.43423,0.43423,0.43423,0.03881,0.25376,0.2134,0.25376,0.25376
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+20-Nov-2019 07:00:00,1,0.14521,0.14521,0.14521,0.13045,0.0018481,0.0018481,0.0018481,0.0018481,0.13045,0.13045,0.13045,0.13045,0.13045,0.0018481,0.052073,0.14521,0.052073,0.052073
+20-Nov-2019 08:00:00,1,0.15237,0.15237,0.15237,0.11698,0.0018481,0.0018481,0.0018481,0.0018481,0.11698,0.11698,0.11698,0.11698,0.11698,0.0018481,0.047572,0.15237,0.047572,0.047572
+20-Nov-2019 09:00:00,1,0.12444,0.12444,0.12444,0.12699,0,0,0,0,0.12699,0.12699,0.12699,0.12699,0.12699,0,0.037464,0.12444,0.037464,0.037464
+20-Nov-2019 10:00:00,1,0.10256,0.10256,0.10256,0.086542,0,0,0,0,0.086542,0.086542,0.086542,0.086542,0.086542,0,0.032919,0.10256,0.032919,0.032919
+20-Nov-2019 11:00:00,1,0.090707,0.090707,0.090707,0.084865,0,0,0,0,0.084865,0.084865,0.084865,0.084865,0.084865,0,0.029306,0.090707,0.029306,0.029306
+20-Nov-2019 12:00:00,1,0.083429,0.083429,0.083429,0.043754,0,0,0,0,0.043754,0.043754,0.043754,0.043754,0.043754,0,0.022678,0.083429,0.022678,0.022678
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+20-Nov-2019 16:00:00,1,0.14745,0.14745,0.14745,0.077801,0.0018481,0.0018481,0.0018481,0.0018481,0.077801,0.077801,0.077801,0.077801,0.077801,0.0018481,0.037486,0.14745,0.037486,0.037486
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+20-Nov-2019 18:00:00,1,0.23054,0.23054,0.23054,0.14153,0.0036962,0.0036962,0.0036962,0.0036962,0.14153,0.14153,0.14153,0.14153,0.14153,0.0036962,0.040035,0.23054,0.040035,0.040035
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+20-Nov-2019 22:00:00,1,0.20848,0.20848,0.20848,0.10296,0.0073923,0.0073923,0.0073923,0.0073923,0.10296,0.10296,0.10296,0.10296,0.10296,0.0073923,0.03988,0.20848,0.03988,0.03988
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+21-Nov-2019 01:00:00,1,0.15412,0.15412,0.15412,0.12826,0.011088,0.011088,0.011088,0.011088,0.12826,0.12826,0.12826,0.12826,0.12826,0.011088,0.054511,0.15412,0.054511,0.054511
+21-Nov-2019 02:00:00,1,0.14376,0.14376,0.14376,0.10692,0.016633,0.016633,0.016633,0.016633,0.10692,0.10692,0.10692,0.10692,0.10692,0.016633,0.058856,0.14376,0.058856,0.058856
+21-Nov-2019 03:00:00,1,0.13646,0.13646,0.13646,0.095435,0.022177,0.022177,0.022177,0.022177,0.095435,0.095435,0.095435,0.095435,0.095435,0.022177,0.060097,0.13646,0.060097,0.060097
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+21-Nov-2019 05:00:00,1,0.12936,0.12936,0.12936,0.11007,0.025873,0.025873,0.025873,0.025873,0.11007,0.11007,0.11007,0.11007,0.11007,0.025873,0.088517,0.12936,0.088517,0.088517
+21-Nov-2019 06:00:00,1,0.13566,0.13566,0.13566,0.10433,0.020329,0.020329,0.020329,0.020329,0.10433,0.10433,0.10433,0.10433,0.10433,0.020329,0.09315,0.13566,0.09315,0.09315
+21-Nov-2019 07:00:00,1,0.14976,0.14976,0.14976,0.09635,0.025873,0.025873,0.025873,0.025873,0.09635,0.09635,0.09635,0.09635,0.09635,0.025873,0.099822,0.14976,0.099822,0.099822
+21-Nov-2019 08:00:00,1,0.16854,0.16854,0.16854,0.14295,0.03881,0.03881,0.03881,0.03881,0.14295,0.14295,0.14295,0.14295,0.14295,0.03881,0.10696,0.16854,0.10696,0.10696
+21-Nov-2019 09:00:00,1,0.1835,0.1835,0.1835,0.14391,0.014785,0.014785,0.014785,0.014785,0.14391,0.14391,0.14391,0.14391,0.14391,0.014785,0.11906,0.1835,0.11906,0.11906
+21-Nov-2019 10:00:00,1,0.2027,0.2027,0.2027,0.16058,0.0092404,0.0092404,0.0092404,0.0092404,0.16058,0.16058,0.16058,0.16058,0.16058,0.0092404,0.12614,0.2027,0.12614,0.12614
+21-Nov-2019 11:00:00,1,0.23682,0.23682,0.23682,0.17847,0.016633,0.016633,0.016633,0.016633,0.17847,0.17847,0.17847,0.17847,0.17847,0.016633,0.14363,0.23682,0.14363,0.14363
+21-Nov-2019 12:00:00,1,0.24684,0.24684,0.24684,0.17364,0.016633,0.016633,0.016633,0.016633,0.17364,0.17364,0.17364,0.17364,0.17364,0.016633,0.15573,0.24684,0.15573,0.15573
+21-Nov-2019 13:00:00,1,0.24736,0.24736,0.24736,0.16353,0.016633,0.016633,0.016633,0.016633,0.16353,0.16353,0.16353,0.16353,0.16353,0.016633,0.1681,0.24736,0.1681,0.1681
+21-Nov-2019 14:00:00,1,0.23464,0.23464,0.23464,0.18513,0.031417,0.031417,0.031417,0.031417,0.18513,0.18513,0.18513,0.18513,0.18513,0.031417,0.19707,0.23464,0.19707,0.19707
+21-Nov-2019 15:00:00,1,0.22369,0.22369,0.22369,0.18655,0.027721,0.027721,0.027721,0.027721,0.18655,0.18655,0.18655,0.18655,0.18655,0.027721,0.22893,0.22369,0.22893,0.22893
+21-Nov-2019 16:00:00,1,0.21803,0.21803,0.21803,0.25678,0.029569,0.029569,0.029569,0.029569,0.25678,0.25678,0.25678,0.25678,0.25678,0.029569,0.25296,0.21803,0.25296,0.25296
+21-Nov-2019 17:00:00,1,0.24675,0.24675,0.24675,0.34022,0.027721,0.027721,0.027721,0.027721,0.34022,0.34022,0.34022,0.34022,0.34022,0.027721,0.2855,0.24675,0.2855,0.2855
+21-Nov-2019 18:00:00,1,0.28092,0.28092,0.28092,0.39373,0.027721,0.027721,0.027721,0.027721,0.39373,0.39373,0.39373,0.39373,0.39373,0.027721,0.27991,0.28092,0.27991,0.27991
+21-Nov-2019 19:00:00,1,0.31966,0.31966,0.31966,0.42773,0.025873,0.025873,0.025873,0.025873,0.42773,0.42773,0.42773,0.42773,0.42773,0.025873,0.2552,0.31966,0.2552,0.2552
+21-Nov-2019 20:00:00,1,0.32714,0.32714,0.32714,0.45553,0.031417,0.031417,0.031417,0.031417,0.45553,0.45553,0.45553,0.45553,0.45553,0.031417,0.23245,0.32714,0.23245,0.23245
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+21-Nov-2019 22:00:00,1,0.35816,0.35816,0.35816,0.44221,0.060987,0.060987,0.060987,0.060987,0.44221,0.44221,0.44221,0.44221,0.44221,0.060987,0.20667,0.35816,0.20667,0.20667
+21-Nov-2019 23:00:00,1,0.40846,0.40846,0.40846,0.44983,0.029569,0.029569,0.029569,0.029569,0.44983,0.44983,0.44983,0.44983,0.44983,0.029569,0.21798,0.40846,0.21798,0.21798
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+22-Nov-2019 01:00:00,1,0.48575,0.48575,0.48575,0.46193,0.024025,0.024025,0.024025,0.024025,0.46193,0.46193,0.46193,0.46193,0.46193,0.024025,0.217,0.48575,0.217,0.217
+22-Nov-2019 02:00:00,1,0.49824,0.49824,0.49824,0.34886,0.014785,0.014785,0.014785,0.014785,0.34886,0.34886,0.34886,0.34886,0.34886,0.014785,0.20097,0.49824,0.20097,0.20097
+22-Nov-2019 03:00:00,1,0.53404,0.53404,0.53404,0.2487,0.016633,0.016633,0.016633,0.016633,0.2487,0.2487,0.2487,0.2487,0.2487,0.016633,0.22314,0.53404,0.22314,0.22314
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+22-Nov-2019 05:00:00,1,0.50878,0.50878,0.50878,0.1648,0.0073923,0.0073923,0.0073923,0.0073923,0.1648,0.1648,0.1648,0.1648,0.1648,0.0073923,0.26679,0.50878,0.26679,0.26679
+22-Nov-2019 06:00:00,1,0.4641,0.4641,0.4641,0.16195,0.0036962,0.0036962,0.0036962,0.0036962,0.16195,0.16195,0.16195,0.16195,0.16195,0.0036962,0.25859,0.4641,0.25859,0.25859
+22-Nov-2019 07:00:00,1,0.41499,0.41499,0.41499,0.15748,0.0036962,0.0036962,0.0036962,0.0036962,0.15748,0.15748,0.15748,0.15748,0.15748,0.0036962,0.27435,0.41499,0.27435,0.27435
+22-Nov-2019 08:00:00,1,0.34818,0.34818,0.34818,0.16074,0,0,0,0,0.16074,0.16074,0.16074,0.16074,0.16074,0,0.24912,0.34818,0.24912,0.24912
+22-Nov-2019 09:00:00,1,0.26527,0.26527,0.26527,0.15804,0,0,0,0,0.15804,0.15804,0.15804,0.15804,0.15804,0,0.22749,0.26527,0.22749,0.22749
+22-Nov-2019 10:00:00,1,0.23102,0.23102,0.23102,0.14188,0,0,0,0,0.14188,0.14188,0.14188,0.14188,0.14188,0,0.20889,0.23102,0.20889,0.20889
+22-Nov-2019 11:00:00,1,0.21331,0.21331,0.21331,0.13355,0,0,0,0,0.13355,0.13355,0.13355,0.13355,0.13355,0,0.17847,0.21331,0.17847,0.17847
+22-Nov-2019 12:00:00,1,0.201,0.201,0.201,0.15032,0,0,0,0,0.15032,0.15032,0.15032,0.15032,0.15032,0,0.14079,0.201,0.14079,0.14079
+22-Nov-2019 13:00:00,1,0.19184,0.19184,0.19184,0.1711,0,0,0,0,0.1711,0.1711,0.1711,0.1711,0.1711,0,0.10882,0.19184,0.10882,0.10882
+22-Nov-2019 14:00:00,1,0.17708,0.17708,0.17708,0.17674,0,0,0,0,0.17674,0.17674,0.17674,0.17674,0.17674,0,0.077876,0.17708,0.077876,0.077876
+22-Nov-2019 15:00:00,1,0.15264,0.15264,0.15264,0.15911,0.0018481,0.0018481,0.0018481,0.0018481,0.15911,0.15911,0.15911,0.15911,0.15911,0.0018481,0.066836,0.15264,0.066836,0.066836
+22-Nov-2019 16:00:00,1,0.1896,0.1896,0.1896,0.1898,0.0073923,0.0073923,0.0073923,0.0073923,0.1898,0.1898,0.1898,0.1898,0.1898,0.0073923,0.086832,0.1896,0.086832,0.086832
+22-Nov-2019 17:00:00,1,0.24589,0.24589,0.24589,0.21348,0.012937,0.012937,0.012937,0.012937,0.21348,0.21348,0.21348,0.21348,0.21348,0.012937,0.12503,0.24589,0.12503,0.12503
+22-Nov-2019 18:00:00,1,0.28815,0.28815,0.28815,0.25515,0.029569,0.029569,0.029569,0.029569,0.25515,0.25515,0.25515,0.25515,0.25515,0.029569,0.14682,0.28815,0.14682,0.14682
+22-Nov-2019 19:00:00,1,0.31318,0.31318,0.31318,0.26328,0.011088,0.011088,0.011088,0.011088,0.26328,0.26328,0.26328,0.26328,0.26328,0.011088,0.15604,0.31318,0.15604,0.15604
+22-Nov-2019 20:00:00,1,0.33419,0.33419,0.33419,0.2582,0.016633,0.016633,0.016633,0.016633,0.2582,0.2582,0.2582,0.2582,0.2582,0.016633,0.16163,0.33419,0.16163,0.16163
+22-Nov-2019 21:00:00,1,0.33682,0.33682,0.33682,0.28641,0.016633,0.016633,0.016633,0.016633,0.28641,0.28641,0.28641,0.28641,0.28641,0.016633,0.15143,0.33682,0.15143,0.15143
+22-Nov-2019 22:00:00,1,0.3404,0.3404,0.3404,0.26765,0.016633,0.016633,0.016633,0.016633,0.26765,0.26765,0.26765,0.26765,0.26765,0.016633,0.13984,0.3404,0.13984,0.13984
+22-Nov-2019 23:00:00,1,0.33993,0.33993,0.33993,0.25221,0.0092404,0.0092404,0.0092404,0.0092404,0.25221,0.25221,0.25221,0.25221,0.25221,0.0092404,0.12578,0.33993,0.12578,0.12578
+23-Nov-2019 00:00:00,1,0.35031,0.35031,0.35031,0.29052,0.0073923,0.0073923,0.0073923,0.0073923,0.29052,0.29052,0.29052,0.29052,0.29052,0.0073923,0.11392,0.35031,0.11392,0.11392
+23-Nov-2019 01:00:00,1,0.35793,0.35793,0.35793,0.32894,0.016633,0.016633,0.016633,0.016633,0.32894,0.32894,0.32894,0.32894,0.32894,0.016633,0.10916,0.35793,0.10916,0.10916
+23-Nov-2019 02:00:00,1,0.36446,0.36446,0.36446,0.34668,0.018481,0.018481,0.018481,0.018481,0.34668,0.34668,0.34668,0.34668,0.34668,0.018481,0.11146,0.36446,0.11146,0.11146
+23-Nov-2019 03:00:00,1,0.37759,0.37759,0.37759,0.36792,0.035113,0.035113,0.035113,0.035113,0.36792,0.36792,0.36792,0.36792,0.36792,0.035113,0.13549,0.37759,0.13549,0.13549
+23-Nov-2019 04:00:00,1,0.37965,0.37965,0.37965,0.35582,0.036962,0.036962,0.036962,0.036962,0.35582,0.35582,0.35582,0.35582,0.35582,0.036962,0.17621,0.37965,0.17621,0.17621
+23-Nov-2019 05:00:00,1,0.39033,0.39033,0.39033,0.40776,0.042506,0.042506,0.042506,0.042506,0.40776,0.40776,0.40776,0.40776,0.40776,0.042506,0.20135,0.39033,0.20135,0.20135
+23-Nov-2019 06:00:00,1,0.4079,0.4079,0.4079,0.39668,0.03881,0.03881,0.03881,0.03881,0.39668,0.39668,0.39668,0.39668,0.39668,0.03881,0.23919,0.4079,0.23919,0.23919
+23-Nov-2019 07:00:00,1,0.43868,0.43868,0.43868,0.39998,0.042506,0.042506,0.042506,0.042506,0.39998,0.39998,0.39998,0.39998,0.39998,0.042506,0.23427,0.43868,0.23427,0.23427
+23-Nov-2019 08:00:00,1,0.43351,0.43351,0.43351,0.41228,0.031417,0.031417,0.031417,0.031417,0.41228,0.41228,0.41228,0.41228,0.41228,0.031417,0.23414,0.43351,0.23414,0.23414
+23-Nov-2019 09:00:00,1,0.41148,0.41148,0.41148,0.40954,0.031417,0.031417,0.031417,0.031417,0.40954,0.40954,0.40954,0.40954,0.40954,0.031417,0.20949,0.41148,0.20949,0.20949
+23-Nov-2019 10:00:00,1,0.38026,0.38026,0.38026,0.37955,0.029569,0.029569,0.029569,0.029569,0.37955,0.37955,0.37955,0.37955,0.37955,0.029569,0.18351,0.38026,0.18351,0.18351
+23-Nov-2019 11:00:00,1,0.36074,0.36074,0.36074,0.31532,0.036962,0.036962,0.036962,0.036962,0.31532,0.31532,0.31532,0.31532,0.31532,0.036962,0.14203,0.36074,0.14203,0.14203
+23-Nov-2019 12:00:00,1,0.35743,0.35743,0.35743,0.29835,0.035113,0.035113,0.035113,0.035113,0.29835,0.29835,0.29835,0.29835,0.29835,0.035113,0.13591,0.35743,0.13591,0.13591
+23-Nov-2019 13:00:00,1,0.31263,0.31263,0.31263,0.2302,0.024025,0.024025,0.024025,0.024025,0.2302,0.2302,0.2302,0.2302,0.2302,0.024025,0.13961,0.31263,0.13961,0.13961
+23-Nov-2019 14:00:00,1,0.26983,0.26983,0.26983,0.20215,0.012937,0.012937,0.012937,0.012937,0.20215,0.20215,0.20215,0.20215,0.20215,0.012937,0.15888,0.26983,0.15888,0.15888
+23-Nov-2019 15:00:00,1,0.22762,0.22762,0.22762,0.19717,0.016633,0.016633,0.016633,0.016633,0.19717,0.19717,0.19717,0.19717,0.19717,0.016633,0.19401,0.22762,0.19401,0.19401
+23-Nov-2019 16:00:00,1,0.18876,0.18876,0.18876,0.14925,0.024025,0.024025,0.024025,0.024025,0.14925,0.14925,0.14925,0.14925,0.14925,0.024025,0.22017,0.18876,0.22017,0.22017
+23-Nov-2019 17:00:00,1,0.16822,0.16822,0.16822,0.11185,0.022177,0.022177,0.022177,0.022177,0.11185,0.11185,0.11185,0.11185,0.11185,0.022177,0.21893,0.16822,0.21893,0.21893
+23-Nov-2019 18:00:00,1,0.1441,0.1441,0.1441,0.13065,0.031417,0.031417,0.031417,0.031417,0.13065,0.13065,0.13065,0.13065,0.13065,0.031417,0.20627,0.1441,0.20627,0.20627
+23-Nov-2019 19:00:00,1,0.13564,0.13564,0.13564,0.12003,0.036962,0.036962,0.036962,0.036962,0.12003,0.12003,0.12003,0.12003,0.12003,0.036962,0.19962,0.13564,0.19962,0.19962
+23-Nov-2019 20:00:00,1,0.13949,0.13949,0.13949,0.1055,0.035113,0.035113,0.035113,0.035113,0.1055,0.1055,0.1055,0.1055,0.1055,0.035113,0.17956,0.13949,0.17956,0.17956
+23-Nov-2019 21:00:00,1,0.12034,0.12034,0.12034,0.10738,0.059138,0.059138,0.059138,0.059138,0.10738,0.10738,0.10738,0.10738,0.10738,0.059138,0.14921,0.12034,0.14921,0.14921
+23-Nov-2019 22:00:00,1,0.10077,0.10077,0.10077,0.094723,0.068379,0.068379,0.068379,0.068379,0.094723,0.094723,0.094723,0.094723,0.094723,0.068379,0.14939,0.10077,0.14939,0.14939
+23-Nov-2019 23:00:00,1,0.085968,0.085968,0.085968,0.10448,0.077619,0.077619,0.077619,0.077619,0.10448,0.10448,0.10448,0.10448,0.10448,0.077619,0.12212,0.085968,0.12212,0.12212
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+24-Nov-2019 01:00:00,1,0.040354,0.040354,0.040354,0.070941,0.046202,0.046202,0.046202,0.046202,0.070941,0.070941,0.070941,0.070941,0.070941,0.046202,0.088428,0.040354,0.088428,0.088428
+24-Nov-2019 02:00:00,1,0.037861,0.037861,0.037861,0.064843,0.020329,0.020329,0.020329,0.020329,0.064843,0.064843,0.064843,0.064843,0.064843,0.020329,0.072378,0.037861,0.072378,0.072378
+24-Nov-2019 03:00:00,1,0.038269,0.038269,0.038269,0.042534,0.024025,0.024025,0.024025,0.024025,0.042534,0.042534,0.042534,0.042534,0.042534,0.024025,0.057216,0.038269,0.057216,0.057216
+24-Nov-2019 04:00:00,1,0.031989,0.031989,0.031989,0.019006,0.014785,0.014785,0.014785,0.014785,0.019006,0.019006,0.019006,0.019006,0.019006,0.014785,0.039658,0.031989,0.039658,0.039658
+24-Nov-2019 05:00:00,1,0.028543,0.028543,0.028543,0.017532,0.018481,0.018481,0.018481,0.018481,0.017532,0.017532,0.017532,0.017532,0.017532,0.018481,0.028175,0.028543,0.028175,0.028175
+24-Nov-2019 06:00:00,1,0.028543,0.028543,0.028543,0.013111,0.029569,0.029569,0.029569,0.029569,0.013111,0.013111,0.013111,0.013111,0.013111,0.029569,0.016958,0.028543,0.016958,0.016958
+24-Nov-2019 07:00:00,1,0.025686,0.025686,0.025686,0.013517,0.022177,0.022177,0.022177,0.022177,0.013517,0.013517,0.013517,0.013517,0.013517,0.022177,0.011173,0.025686,0.011173,0.011173
+24-Nov-2019 08:00:00,1,0.026888,0.026888,0.026888,0.016973,0.018481,0.018481,0.018481,0.018481,0.016973,0.016973,0.016973,0.016973,0.016973,0.018481,0.0093992,0.026888,0.0093992,0.0093992
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+24-Nov-2019 10:00:00,1,0.020812,0.020812,0.020812,0.02236,0.012937,0.012937,0.012937,0.012937,0.02236,0.02236,0.02236,0.02236,0.02236,0.012937,0.0074484,0.020812,0.0074484,0.0074484
+24-Nov-2019 11:00:00,1,0.016913,0.016913,0.016913,0.032371,0.0036962,0.0036962,0.0036962,0.0036962,0.032371,0.032371,0.032371,0.032371,0.032371,0.0036962,0.0061849,0.016913,0.0061849,0.0061849
+24-Nov-2019 12:00:00,1,0.010678,0.010678,0.010678,0.040806,0.0036962,0.0036962,0.0036962,0.0036962,0.040806,0.040806,0.040806,0.040806,0.040806,0.0036962,0.0046996,0.010678,0.0046996,0.0046996
+24-Nov-2019 13:00:00,1,0.010089,0.010089,0.010089,0.041264,0.0055442,0.0055442,0.0055442,0.0055442,0.041264,0.041264,0.041264,0.041264,0.041264,0.0055442,0.0023498,0.010089,0.0023498,0.0023498
+24-Nov-2019 14:00:00,1,0.014918,0.014918,0.014918,0.051884,0.0036962,0.0036962,0.0036962,0.0036962,0.051884,0.051884,0.051884,0.051884,0.051884,0.0036962,0.0024163,0.014918,0.0024163,0.0024163
+24-Nov-2019 15:00:00,1,0.0258,0.0258,0.0258,0.070992,0.0092404,0.0092404,0.0092404,0.0092404,0.070992,0.070992,0.070992,0.070992,0.070992,0.0092404,0.0044336,0.0258,0.0044336,0.0044336
+24-Nov-2019 16:00:00,1,0.044367,0.044367,0.044367,0.12018,0.018481,0.018481,0.018481,0.018481,0.12018,0.12018,0.12018,0.12018,0.12018,0.018481,0.0098647,0.044367,0.0098647,0.0098647
+24-Nov-2019 17:00:00,1,0.074293,0.074293,0.074293,0.11368,0.027721,0.027721,0.027721,0.027721,0.11368,0.11368,0.11368,0.11368,0.11368,0.027721,0.021902,0.074293,0.021902,0.021902
+24-Nov-2019 18:00:00,1,0.10098,0.10098,0.10098,0.11942,0.033265,0.033265,0.033265,0.033265,0.11942,0.11942,0.11942,0.11942,0.11942,0.033265,0.044779,0.10098,0.044779,0.044779
+24-Nov-2019 19:00:00,1,0.13641,0.13641,0.13641,0.15291,0.05729,0.05729,0.05729,0.05729,0.15291,0.15291,0.15291,0.15291,0.15291,0.05729,0.058945,0.13641,0.058945,0.058945
+24-Nov-2019 20:00:00,1,0.18175,0.18175,0.18175,0.19082,0.060987,0.060987,0.060987,0.060987,0.19082,0.19082,0.19082,0.19082,0.19082,0.060987,0.068588,0.18175,0.068588,0.068588
+24-Nov-2019 21:00:00,1,0.20352,0.20352,0.20352,0.23813,0.024025,0.024025,0.024025,0.024025,0.23813,0.23813,0.23813,0.23813,0.23813,0.024025,0.084283,0.20352,0.084283,0.084283
+24-Nov-2019 22:00:00,1,0.22363,0.22363,0.22363,0.25007,0.018481,0.018481,0.018481,0.018481,0.25007,0.25007,0.25007,0.25007,0.25007,0.018481,0.083307,0.22363,0.083307,0.083307
+24-Nov-2019 23:00:00,1,0.26797,0.26797,0.26797,0.34652,0.064683,0.064683,0.064683,0.064683,0.34652,0.34652,0.34652,0.34652,0.34652,0.064683,0.092174,0.26797,0.092174,0.092174
+25-Nov-2019 00:00:00,1,0.32082,0.32082,0.32082,0.4105,0.068379,0.068379,0.068379,0.068379,0.4105,0.4105,0.4105,0.4105,0.4105,0.068379,0.10142,0.32082,0.10142,0.10142
+25-Nov-2019 01:00:00,1,0.3682,0.3682,0.3682,0.4909,0.10904,0.10904,0.10904,0.10904,0.4909,0.4909,0.4909,0.4909,0.4909,0.10904,0.12414,0.3682,0.12414,0.12414
+25-Nov-2019 02:00:00,1,0.4276,0.4276,0.4276,0.53861,0.10534,0.10534,0.10534,0.10534,0.53861,0.53861,0.53861,0.53861,0.53861,0.10534,0.15223,0.4276,0.15223,0.15223
+25-Nov-2019 03:00:00,1,0.47346,0.47346,0.47346,0.57256,0.12937,0.12937,0.12937,0.12937,0.57256,0.57256,0.57256,0.57256,0.57256,0.12937,0.17655,0.47346,0.17655,0.17655
+25-Nov-2019 04:00:00,1,0.51134,0.51134,0.51134,0.62393,0.11088,0.11088,0.11088,0.11088,0.62393,0.62393,0.62393,0.62393,0.62393,0.11088,0.22048,0.51134,0.22048,0.22048
+25-Nov-2019 05:00:00,1,0.5327,0.5327,0.5327,0.65072,0.12567,0.12567,0.12567,0.12567,0.65072,0.65072,0.65072,0.65072,0.65072,0.12567,0.28546,0.5327,0.28546,0.28546
+25-Nov-2019 06:00:00,1,0.54952,0.54952,0.54952,0.71383,0.16818,0.16818,0.16818,0.16818,0.71383,0.71383,0.71383,0.71383,0.71383,0.16818,0.35028,0.54952,0.35028,0.35028
+25-Nov-2019 07:00:00,1,0.53456,0.53456,0.53456,0.78421,0.14415,0.14415,0.14415,0.14415,0.78421,0.78421,0.78421,0.78421,0.78421,0.14415,0.4225,0.53456,0.4225,0.4225
+25-Nov-2019 08:00:00,1,0.53225,0.53225,0.53225,0.79163,0.092404,0.092404,0.092404,0.092404,0.79163,0.79163,0.79163,0.79163,0.79163,0.092404,0.4491,0.53225,0.4491,0.4491
+25-Nov-2019 09:00:00,1,0.58575,0.58575,0.58575,0.81115,0.13121,0.13121,0.13121,0.13121,0.81115,0.81115,0.81115,0.81115,0.81115,0.13121,0.4837,0.58575,0.4837,0.4837
+25-Nov-2019 10:00:00,1,0.58786,0.58786,0.58786,0.86013,0.090556,0.090556,0.090556,0.090556,0.86013,0.86013,0.86013,0.86013,0.86013,0.090556,0.49676,0.58786,0.49676,0.49676
+25-Nov-2019 11:00:00,1,0.60593,0.60593,0.60593,0.852,0.15524,0.15524,0.15524,0.15524,0.852,0.852,0.852,0.852,0.852,0.15524,0.44147,0.60593,0.44147,0.44147
+25-Nov-2019 12:00:00,1,0.63853,0.63853,0.63853,0.86262,0.18481,0.18481,0.18481,0.18481,0.86262,0.86262,0.86262,0.86262,0.86262,0.18481,0.42061,0.63853,0.42061,0.42061
+25-Nov-2019 13:00:00,1,0.72663,0.72663,0.72663,0.84047,0.097948,0.097948,0.097948,0.097948,0.84047,0.84047,0.84047,0.84047,0.84047,0.097948,0.43103,0.72663,0.43103,0.43103
+25-Nov-2019 14:00:00,1,0.73334,0.73334,0.73334,0.80215,0.18481,0.18481,0.18481,0.18481,0.80215,0.80215,0.80215,0.80215,0.80215,0.18481,0.44968,0.73334,0.44968,0.44968
+25-Nov-2019 15:00:00,1,0.74479,0.74479,0.74479,0.75596,0.12752,0.12752,0.12752,0.12752,0.75596,0.75596,0.75596,0.75596,0.75596,0.12752,0.46899,0.74479,0.46899,0.46899
+25-Nov-2019 16:00:00,1,0.7449,0.7449,0.7449,0.65935,0.079467,0.079467,0.079467,0.079467,0.65935,0.65935,0.65935,0.65935,0.65935,0.079467,0.48113,0.7449,0.48113,0.48113
+25-Nov-2019 17:00:00,1,0.74016,0.74016,0.74016,0.59939,0.040658,0.040658,0.040658,0.040658,0.59939,0.59939,0.59939,0.59939,0.59939,0.040658,0.49106,0.74016,0.49106,0.49106
+25-Nov-2019 18:00:00,1,0.74331,0.74331,0.74331,0.60757,0.035113,0.035113,0.035113,0.035113,0.60757,0.60757,0.60757,0.60757,0.60757,0.035113,0.51598,0.74331,0.51598,0.51598
+25-Nov-2019 19:00:00,1,0.74928,0.74928,0.74928,0.6029,0.033265,0.033265,0.033265,0.033265,0.6029,0.6029,0.6029,0.6029,0.6029,0.033265,0.53372,0.74928,0.53372,0.53372
+25-Nov-2019 20:00:00,1,0.76517,0.76517,0.76517,0.55823,0.025873,0.025873,0.025873,0.025873,0.55823,0.55823,0.55823,0.55823,0.55823,0.025873,0.54161,0.76517,0.54161,0.54161
+25-Nov-2019 21:00:00,1,0.7544,0.7544,0.7544,0.51915,0.011088,0.011088,0.011088,0.011088,0.51915,0.51915,0.51915,0.51915,0.51915,0.011088,0.56462,0.7544,0.56462,0.56462
+25-Nov-2019 22:00:00,1,0.70425,0.70425,0.70425,0.4573,0.012937,0.012937,0.012937,0.012937,0.4573,0.4573,0.4573,0.4573,0.4573,0.012937,0.562,0.70425,0.562,0.562
+25-Nov-2019 23:00:00,1,0.68122,0.68122,0.68122,0.44013,0.024025,0.024025,0.024025,0.024025,0.44013,0.44013,0.44013,0.44013,0.44013,0.024025,0.53797,0.68122,0.53797,0.53797
+26-Nov-2019 00:00:00,1,0.62676,0.62676,0.62676,0.34906,0.025873,0.025873,0.025873,0.025873,0.34906,0.34906,0.34906,0.34906,0.34906,0.025873,0.48147,0.62676,0.48147,0.48147
+26-Nov-2019 01:00:00,1,0.56002,0.56002,0.56002,0.28351,0.029569,0.029569,0.029569,0.029569,0.28351,0.28351,0.28351,0.28351,0.28351,0.029569,0.48284,0.56002,0.48284,0.48284
+26-Nov-2019 02:00:00,1,0.48237,0.48237,0.48237,0.2268,0.051746,0.051746,0.051746,0.051746,0.2268,0.2268,0.2268,0.2268,0.2268,0.051746,0.46486,0.48237,0.46486,0.46486
+26-Nov-2019 03:00:00,1,0.42937,0.42937,0.42937,0.20479,0.044354,0.044354,0.044354,0.044354,0.20479,0.20479,0.20479,0.20479,0.20479,0.044354,0.39264,0.42937,0.39264,0.39264
+26-Nov-2019 04:00:00,1,0.39237,0.39237,0.39237,0.19336,0.029569,0.029569,0.029569,0.029569,0.19336,0.19336,0.19336,0.19336,0.19336,0.029569,0.34934,0.39237,0.34934,0.34934
+26-Nov-2019 05:00:00,1,0.3388,0.3388,0.3388,0.2178,0.025873,0.025873,0.025873,0.025873,0.2178,0.2178,0.2178,0.2178,0.2178,0.025873,0.30095,0.3388,0.30095,0.30095
+26-Nov-2019 06:00:00,1,0.32803,0.32803,0.32803,0.20317,0.025873,0.025873,0.025873,0.025873,0.20317,0.20317,0.20317,0.20317,0.20317,0.025873,0.27493,0.32803,0.27493,0.27493
+26-Nov-2019 07:00:00,1,0.30241,0.30241,0.30241,0.25058,0.025873,0.025873,0.025873,0.025873,0.25058,0.25058,0.25058,0.25058,0.25058,0.025873,0.23338,0.30241,0.23338,0.23338
+26-Nov-2019 08:00:00,1,0.26015,0.26015,0.26015,0.24575,0.04805,0.04805,0.04805,0.04805,0.24575,0.24575,0.24575,0.24575,0.24575,0.04805,0.22208,0.26015,0.22208,0.22208
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+26-Nov-2019 10:00:00,1,0.156,0.156,0.156,0.19737,0.036962,0.036962,0.036962,0.036962,0.19737,0.19737,0.19737,0.19737,0.19737,0.036962,0.12483,0.156,0.12483,0.12483
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+27-Nov-2019 01:00:00,1,0.11116,0.11116,0.11116,0.24179,0.016633,0.016633,0.016633,0.016633,0.24179,0.24179,0.24179,0.24179,0.24179,0.016633,0.14172,0.11116,0.14172,0.14172
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+27-Nov-2019 13:00:00,1,0.031671,0.031671,0.031671,0.063013,0,0,0,0,0.063013,0.063013,0.063013,0.063013,0.063013,0,0.01933,0.031671,0.01933,0.01933
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+28-Nov-2019 01:00:00,1,0.013807,0.013807,0.013807,0.007775,0.0055442,0.0055442,0.0055442,0.0055442,0.007775,0.007775,0.007775,0.007775,0.007775,0.0055442,0.0044336,0.013807,0.0044336,0.0044336
+28-Nov-2019 02:00:00,1,0.011744,0.011744,0.011744,0.0065046,0.0073923,0.0073923,0.0073923,0.0073923,0.0065046,0.0065046,0.0065046,0.0065046,0.0065046,0.0073923,0.0050765,0.011744,0.0050765,0.0050765
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+28-Nov-2019 10:00:00,1,0.016686,0.016686,0.016686,0.051224,0.031417,0.031417,0.031417,0.031417,0.051224,0.051224,0.051224,0.051224,0.051224,0.031417,0.022478,0.016686,0.022478,0.022478
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+28-Nov-2019 20:00:00,1,0.096578,0.096578,0.096578,0.081054,0.072075,0.072075,0.072075,0.072075,0.081054,0.081054,0.081054,0.081054,0.081054,0.072075,0.093194,0.096578,0.093194,0.093194
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+28-Nov-2019 23:00:00,1,0.14355,0.14355,0.14355,0,0.11643,0.11643,0.11643,0.11643,0,0,0,0,0,0.11643,0.12467,0.14355,0.12467,0.12467
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+29-Nov-2019 01:00:00,1,0.14294,0.14294,0.14294,0.25144,0.14415,0.14415,0.14415,0.14415,0.25144,0.25144,0.25144,0.25144,0.25144,0.14415,0.16344,0.14294,0.16344,0.16344
+29-Nov-2019 02:00:00,1,0.16425,0.16425,0.16425,0.25048,0.11273,0.11273,0.11273,0.11273,0.25048,0.25048,0.25048,0.25048,0.25048,0.11273,0.19306,0.16425,0.19306,0.19306
+29-Nov-2019 03:00:00,1,0.1808,0.1808,0.1808,0.21582,0.11828,0.11828,0.11828,0.11828,0.21582,0.21582,0.21582,0.21582,0.21582,0.11828,0.22312,0.1808,0.22312,0.22312
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+29-Nov-2019 07:00:00,1,0.257,0.257,0.257,0.22141,0.094252,0.094252,0.094252,0.094252,0.22141,0.22141,0.22141,0.22141,0.22141,0.094252,0.31891,0.257,0.31891,0.31891
+29-Nov-2019 08:00:00,1,0.24773,0.24773,0.24773,0.14402,0.083163,0.083163,0.083163,0.083163,0.14402,0.14402,0.14402,0.14402,0.14402,0.083163,0.34983,0.24773,0.34983,0.34983
+29-Nov-2019 09:00:00,1,0.23251,0.23251,0.23251,0.14539,0.062835,0.062835,0.062835,0.062835,0.14539,0.14539,0.14539,0.14539,0.14539,0.062835,0.37659,0.23251,0.37659,0.37659
+29-Nov-2019 10:00:00,1,0.23446,0.23446,0.23446,0.16094,0.12013,0.12013,0.12013,0.12013,0.16094,0.16094,0.16094,0.16094,0.16094,0.12013,0.37847,0.23446,0.37847,0.37847
+29-Nov-2019 11:00:00,1,0.25514,0.25514,0.25514,0.1585,0.12013,0.12013,0.12013,0.12013,0.1585,0.1585,0.1585,0.1585,0.1585,0.12013,0.43547,0.25514,0.43547,0.43547
+29-Nov-2019 12:00:00,1,0.28259,0.28259,0.28259,0.15398,0.092404,0.092404,0.092404,0.092404,0.15398,0.15398,0.15398,0.15398,0.15398,0.092404,0.44059,0.28259,0.44059,0.44059
+29-Nov-2019 13:00:00,1,0.30542,0.30542,0.30542,0.17954,0.10534,0.10534,0.10534,0.10534,0.17954,0.17954,0.17954,0.17954,0.17954,0.10534,0.43281,0.30542,0.43281,0.43281
+29-Nov-2019 14:00:00,1,0.29275,0.29275,0.29275,0.18457,0.046202,0.046202,0.046202,0.046202,0.18457,0.18457,0.18457,0.18457,0.18457,0.046202,0.42489,0.29275,0.42489,0.42489
+29-Nov-2019 15:00:00,1,0.27226,0.27226,0.27226,0.17364,0.051746,0.051746,0.051746,0.051746,0.17364,0.17364,0.17364,0.17364,0.17364,0.051746,0.42977,0.27226,0.42977,0.42977
+29-Nov-2019 16:00:00,1,0.24904,0.24904,0.24904,0.15052,0.035113,0.035113,0.035113,0.035113,0.15052,0.15052,0.15052,0.15052,0.15052,0.035113,0.43551,0.24904,0.43551,0.43551
+29-Nov-2019 17:00:00,1,0.22793,0.22793,0.22793,0.13263,0.024025,0.024025,0.024025,0.024025,0.13263,0.13263,0.13263,0.13263,0.13263,0.024025,0.44254,0.22793,0.44254,0.44254
+29-Nov-2019 18:00:00,1,0.20361,0.20361,0.20361,0.11713,0.022177,0.022177,0.022177,0.022177,0.11713,0.11713,0.11713,0.11713,0.11713,0.022177,0.43988,0.20361,0.43988,0.43988
+29-Nov-2019 19:00:00,1,0.18332,0.18332,0.18332,0.10763,0.012937,0.012937,0.012937,0.012937,0.10763,0.10763,0.10763,0.10763,0.10763,0.012937,0.43804,0.18332,0.43804,0.43804
+29-Nov-2019 20:00:00,1,0.16008,0.16008,0.16008,0.095892,0.011088,0.011088,0.011088,0.011088,0.095892,0.095892,0.095892,0.095892,0.095892,0.011088,0.41995,0.16008,0.41995,0.41995
+29-Nov-2019 21:00:00,1,0.14523,0.14523,0.14523,0.079427,0.0092404,0.0092404,0.0092404,0.0092404,0.079427,0.079427,0.079427,0.079427,0.079427,0.0092404,0.37666,0.14523,0.37666,0.37666
+29-Nov-2019 22:00:00,1,0.13321,0.13321,0.13321,0.061641,0.0055442,0.0055442,0.0055442,0.0055442,0.061641,0.061641,0.061641,0.061641,0.061641,0.0055442,0.34739,0.13321,0.34739,0.34739
+29-Nov-2019 23:00:00,1,0.12508,0.12508,0.12508,0.055797,0.0036962,0.0036962,0.0036962,0.0036962,0.055797,0.055797,0.055797,0.055797,0.055797,0.0036962,0.33183,0.12508,0.33183,0.33183
+30-Nov-2019 00:00:00,1,0.1139,0.1139,0.1139,0.05158,0.0073923,0.0073923,0.0073923,0.0073923,0.05158,0.05158,0.05158,0.05158,0.05158,0.0073923,0.31412,0.1139,0.31412,0.31412
+30-Nov-2019 01:00:00,1,0.1105,0.1105,0.1105,0.048785,0.0092404,0.0092404,0.0092404,0.0092404,0.048785,0.048785,0.048785,0.048785,0.048785,0.0092404,0.29508,0.1105,0.29508,0.29508
+30-Nov-2019 02:00:00,1,0.10138,0.10138,0.10138,0.043042,0.0073923,0.0073923,0.0073923,0.0073923,0.043042,0.043042,0.043042,0.043042,0.043042,0.0073923,0.26313,0.10138,0.26313,0.26313
+30-Nov-2019 03:00:00,1,0.10621,0.10621,0.10621,0.046447,0.0055442,0.0055442,0.0055442,0.0055442,0.046447,0.046447,0.046447,0.046447,0.046447,0.0055442,0.2184,0.10621,0.2184,0.2184
+30-Nov-2019 04:00:00,1,0.11469,0.11469,0.11469,0.042026,0.0055442,0.0055442,0.0055442,0.0055442,0.042026,0.042026,0.042026,0.042026,0.042026,0.0055442,0.1843,0.11469,0.1843,0.1843
+30-Nov-2019 05:00:00,1,0.10973,0.10973,0.10973,0.033082,0.0073923,0.0073923,0.0073923,0.0073923,0.033082,0.033082,0.033082,0.033082,0.033082,0.0073923,0.16699,0.10973,0.16699,0.16699
+30-Nov-2019 06:00:00,1,0.10424,0.10424,0.10424,0.032167,0.0073923,0.0073923,0.0073923,0.0073923,0.032167,0.032167,0.032167,0.032167,0.032167,0.0073923,0.16466,0.10424,0.16466,0.16466
+30-Nov-2019 07:00:00,1,0.087238,0.087238,0.087238,0.026171,0.0073923,0.0073923,0.0073923,0.0073923,0.026171,0.026171,0.026171,0.026171,0.026171,0.0073923,0.15212,0.087238,0.15212,0.15212
+30-Nov-2019 08:00:00,1,0.078147,0.078147,0.078147,0.02363,0.0055442,0.0055442,0.0055442,0.0055442,0.02363,0.02363,0.02363,0.02363,0.02363,0.0055442,0.13662,0.078147,0.13662,0.13662
+30-Nov-2019 09:00:00,1,0.065202,0.065202,0.065202,0.017278,0.0036962,0.0036962,0.0036962,0.0036962,0.017278,0.017278,0.017278,0.017278,0.017278,0.0036962,0.11126,0.065202,0.11126,0.11126
+30-Nov-2019 10:00:00,1,0.056428,0.056428,0.056428,0.0082832,0.0036962,0.0036962,0.0036962,0.0036962,0.0082832,0.0082832,0.0082832,0.0082832,0.0082832,0.0036962,0.094569,0.056428,0.094569,0.094569
+30-Nov-2019 11:00:00,1,0.043302,0.043302,0.043302,0.0070636,0.0036962,0.0036962,0.0036962,0.0036962,0.0070636,0.0070636,0.0070636,0.0070636,0.0070636,0.0036962,0.092219,0.043302,0.092219,0.092219
+30-Nov-2019 12:00:00,1,0.036274,0.036274,0.036274,0.008334,0.0036962,0.0036962,0.0036962,0.0036962,0.008334,0.008334,0.008334,0.008334,0.008334,0.0036962,0.10457,0.036274,0.10457,0.10457
+30-Nov-2019 13:00:00,1,0.043302,0.043302,0.043302,0.010468,0.0018481,0.0018481,0.0018481,0.0018481,0.010468,0.010468,0.010468,0.010468,0.010468,0.0018481,0.11277,0.043302,0.11277,0.11277
+30-Nov-2019 14:00:00,1,0.049967,0.049967,0.049967,0.014026,0.0036962,0.0036962,0.0036962,0.0036962,0.014026,0.014026,0.014026,0.014026,0.014026,0.0036962,0.11113,0.049967,0.11113,0.11113
+30-Nov-2019 15:00:00,1,0.042553,0.042553,0.042553,0.01743,0.0018481,0.0018481,0.0018481,0.0018481,0.01743,0.01743,0.01743,0.01743,0.01743,0.0018481,0.099357,0.042553,0.099357,0.099357
+30-Nov-2019 16:00:00,1,0.045501,0.045501,0.045501,0.020022,0.0036962,0.0036962,0.0036962,0.0036962,0.020022,0.020022,0.020022,0.020022,0.020022,0.0036962,0.09315,0.045501,0.09315,0.09315
+30-Nov-2019 17:00:00,1,0.059557,0.059557,0.059557,0.023173,0.0073923,0.0073923,0.0073923,0.0073923,0.023173,0.023173,0.023173,0.023173,0.023173,0.0073923,0.088361,0.059557,0.088361,0.088361
+30-Nov-2019 18:00:00,1,0.075154,0.075154,0.075154,0.028458,0.0055442,0.0055442,0.0055442,0.0055442,0.028458,0.028458,0.028458,0.028458,0.028458,0.0055442,0.091731,0.075154,0.091731,0.091731
+30-Nov-2019 19:00:00,1,0.086853,0.086853,0.086853,0.03481,0.0092404,0.0092404,0.0092404,0.0092404,0.03481,0.03481,0.03481,0.03481,0.03481,0.0092404,0.10126,0.086853,0.10126,0.10126
+30-Nov-2019 20:00:00,1,0.084789,0.084789,0.084789,0.057271,0.0036962,0.0036962,0.0036962,0.0036962,0.057271,0.057271,0.057271,0.057271,0.057271,0.0036962,0.11208,0.084789,0.11208,0.11208
+30-Nov-2019 21:00:00,1,0.091636,0.091636,0.091636,0.07399,0.0055442,0.0055442,0.0055442,0.0055442,0.07399,0.07399,0.07399,0.07399,0.07399,0.0055442,0.099246,0.091636,0.099246,0.099246
+30-Nov-2019 22:00:00,1,0.096941,0.096941,0.096941,0.086796,0.0036962,0.0036962,0.0036962,0.0036962,0.086796,0.086796,0.086796,0.086796,0.086796,0.0036962,0.10109,0.096941,0.10109,0.10109
+30-Nov-2019 23:00:00,1,0.1068,0.1068,0.1068,0.075616,0.0036962,0.0036962,0.0036962,0.0036962,0.075616,0.075616,0.075616,0.075616,0.075616,0.0036962,0.098359,0.1068,0.098359,0.098359
+01-Dec-2019 00:00:00,1,0.11402,0.11402,0.11402,0.069345,0.0055177,0.0055177,0.0055177,0.0055177,0.069345,0.069345,0.069345,0.069345,0.069345,0.0055177,0.099376,0.11402,0.099376,0.099376
+01-Dec-2019 01:00:00,1,0.12119,0.12119,0.12119,0.07575,0.0055177,0.0055177,0.0055177,0.0055177,0.07575,0.07575,0.07575,0.07575,0.07575,0.0055177,0.10894,0.12119,0.10894,0.10894
+01-Dec-2019 02:00:00,1,0.14102,0.14102,0.14102,0.079533,0.0091961,0.0091961,0.0091961,0.0091961,0.079533,0.079533,0.079533,0.079533,0.079533,0.0091961,0.11581,0.14102,0.11581,0.11581
+01-Dec-2019 03:00:00,1,0.13863,0.13863,0.13863,0.075448,0.0073569,0.0073569,0.0073569,0.0073569,0.075448,0.075448,0.075448,0.075448,0.075448,0.0073569,0.11866,0.13863,0.11866,0.11866
+01-Dec-2019 04:00:00,1,0.13949,0.13949,0.13949,0.074086,0.0091961,0.0091961,0.0091961,0.0091961,0.074086,0.074086,0.074086,0.074086,0.074086,0.0091961,0.12289,0.13949,0.12289,0.12289
+01-Dec-2019 05:00:00,1,0.13861,0.13861,0.13861,0.069749,0.012875,0.012875,0.012875,0.012875,0.069749,0.069749,0.069749,0.069749,0.069749,0.012875,0.12751,0.13861,0.12751,0.12751
+01-Dec-2019 06:00:00,1,0.12345,0.12345,0.12345,0.070707,0.014714,0.014714,0.014714,0.014714,0.070707,0.070707,0.070707,0.070707,0.070707,0.014714,0.14142,0.12345,0.14142,0.14142
+01-Dec-2019 07:00:00,1,0.12981,0.12981,0.12981,0.068084,0.014714,0.014714,0.014714,0.014714,0.068084,0.068084,0.068084,0.068084,0.068084,0.014714,0.16105,0.12981,0.16105,0.16105
+01-Dec-2019 08:00:00,1,0.13563,0.13563,0.13563,0.072724,0.0091961,0.0091961,0.0091961,0.0091961,0.072724,0.072724,0.072724,0.072724,0.072724,0.0091961,0.16552,0.13563,0.16552,0.16552
+01-Dec-2019 09:00:00,1,0.12537,0.12537,0.12537,0.085837,0.0055177,0.0055177,0.0055177,0.0055177,0.085837,0.085837,0.085837,0.085837,0.085837,0.0055177,0.15999,0.12537,0.15999,0.15999
+01-Dec-2019 10:00:00,1,0.12453,0.12453,0.12453,0.072119,0.0036784,0.0036784,0.0036784,0.0036784,0.072119,0.072119,0.072119,0.072119,0.072119,0.0036784,0.15197,0.12453,0.15197,0.15197
+01-Dec-2019 11:00:00,1,0.11847,0.11847,0.11847,0.079987,0,0,0,0,0.079987,0.079987,0.079987,0.079987,0.079987,0,0.12899,0.11847,0.12899,0.12899
+01-Dec-2019 12:00:00,1,0.12077,0.12077,0.12077,0.097487,0,0,0,0,0.097487,0.097487,0.097487,0.097487,0.097487,0,0.11374,0.12077,0.11374,0.11374
+01-Dec-2019 13:00:00,1,0.11698,0.11698,0.11698,0.099958,0,0,0,0,0.099958,0.099958,0.099958,0.099958,0.099958,0,0.097635,0.11698,0.097635,0.097635
+01-Dec-2019 14:00:00,1,0.12679,0.12679,0.12679,0.10813,0,0,0,0,0.10813,0.10813,0.10813,0.10813,0.10813,0,0.097459,0.12679,0.097459,0.097459
+01-Dec-2019 15:00:00,1,0.11819,0.11819,0.11819,0.12956,0.0018392,0.0018392,0.0018392,0.0018392,0.12956,0.12956,0.12956,0.12956,0.12956,0.0018392,0.095256,0.11819,0.095256,0.095256
+01-Dec-2019 16:00:00,1,0.11847,0.11847,0.11847,0.15856,0.0036784,0.0036784,0.0036784,0.0036784,0.15856,0.15856,0.15856,0.15856,0.15856,0.0036784,0.11456,0.11847,0.11456,0.11456
+01-Dec-2019 17:00:00,1,0.15618,0.15618,0.15618,0.21424,0.0036784,0.0036784,0.0036784,0.0036784,0.21424,0.21424,0.21424,0.21424,0.21424,0.0036784,0.1154,0.15618,0.1154,0.1154
+01-Dec-2019 18:00:00,1,0.21167,0.21167,0.21167,0.26008,0.0055177,0.0055177,0.0055177,0.0055177,0.26008,0.26008,0.26008,0.26008,0.26008,0.0055177,0.13044,0.21167,0.13044,0.13044
+01-Dec-2019 19:00:00,1,0.24927,0.24927,0.24927,0.25514,0.0073569,0.0073569,0.0073569,0.0073569,0.25514,0.25514,0.25514,0.25514,0.25514,0.0073569,0.14232,0.24927,0.14232,0.14232
+01-Dec-2019 20:00:00,1,0.2637,0.2637,0.2637,0.26724,0.012875,0.012875,0.012875,0.012875,0.26724,0.26724,0.26724,0.26724,0.26724,0.012875,0.1408,0.2637,0.1408,0.1408
+01-Dec-2019 21:00:00,1,0.25748,0.25748,0.25748,0.28898,0.016553,0.016553,0.016553,0.016553,0.28898,0.28898,0.28898,0.28898,0.28898,0.016553,0.15556,0.25748,0.15556,0.15556
+01-Dec-2019 22:00:00,1,0.25107,0.25107,0.25107,0.29559,0.018392,0.018392,0.018392,0.018392,0.29559,0.29559,0.29559,0.29559,0.29559,0.018392,0.16112,0.25107,0.16112,0.16112
+01-Dec-2019 23:00:00,1,0.24185,0.24185,0.24185,0.26482,0.029428,0.029428,0.029428,0.029428,0.26482,0.26482,0.26482,0.26482,0.26482,0.029428,0.16154,0.24185,0.16154,0.16154
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+11-Dec-2019 17:00:00,1,0.14145,0.14145,0.14145,0.066925,0.012875,0.012875,0.012875,0.012875,0.066925,0.066925,0.066925,0.066925,0.066925,0.012875,0.34945,0.14145,0.34945,0.34945
+11-Dec-2019 18:00:00,1,0.13831,0.13831,0.13831,0.065865,0.018392,0.018392,0.018392,0.018392,0.065865,0.065865,0.065865,0.065865,0.065865,0.018392,0.33307,0.13831,0.33307,0.33307
+11-Dec-2019 19:00:00,1,0.12823,0.12823,0.12823,0.084526,0.018392,0.018392,0.018392,0.018392,0.084526,0.084526,0.084526,0.084526,0.084526,0.018392,0.30943,0.12823,0.30943,0.30943
+11-Dec-2019 20:00:00,1,0.11727,0.11727,0.11727,0.12195,0.016553,0.016553,0.016553,0.016553,0.12195,0.12195,0.12195,0.12195,0.12195,0.016553,0.28718,0.11727,0.28718,0.28718
+11-Dec-2019 21:00:00,1,0.098977,0.098977,0.098977,0.11468,0.018392,0.018392,0.018392,0.018392,0.11468,0.11468,0.11468,0.11468,0.11468,0.018392,0.26034,0.098977,0.26034,0.26034
+11-Dec-2019 22:00:00,1,0.09248,0.09248,0.09248,0.11569,0.022071,0.022071,0.022071,0.022071,0.11569,0.11569,0.11569,0.11569,0.11569,0.022071,0.2263,0.09248,0.2263,0.2263
+11-Dec-2019 23:00:00,1,0.078067,0.078067,0.078067,0.10863,0.016553,0.016553,0.016553,0.016553,0.10863,0.10863,0.10863,0.10863,0.10863,0.016553,0.19853,0.078067,0.19853,0.19853
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+12-Dec-2019 01:00:00,1,0.074075,0.074075,0.074075,0.12154,0.036784,0.036784,0.036784,0.036784,0.12154,0.12154,0.12154,0.12154,0.12154,0.036784,0.15775,0.074075,0.15775,0.15775
+12-Dec-2019 02:00:00,1,0.068751,0.068751,0.068751,0.1162,0.022071,0.022071,0.022071,0.022071,0.1162,0.1162,0.1162,0.1162,0.1162,0.022071,0.13655,0.068751,0.13655,0.13655
+12-Dec-2019 03:00:00,1,0.065368,0.065368,0.065368,0.10324,0.0091961,0.0091961,0.0091961,0.0091961,0.10324,0.10324,0.10324,0.10324,0.10324,0.0091961,0.12117,0.065368,0.12117,0.12117
+12-Dec-2019 04:00:00,1,0.061443,0.061443,0.061443,0.10677,0.012875,0.012875,0.012875,0.012875,0.10677,0.10677,0.10677,0.10677,0.10677,0.012875,0.12009,0.061443,0.12009,0.12009
+12-Dec-2019 05:00:00,1,0.050729,0.050729,0.050729,0.096831,0.02391,0.02391,0.02391,0.02391,0.096831,0.096831,0.096831,0.096831,0.096831,0.02391,0.10116,0.050729,0.10116,0.10116
+12-Dec-2019 06:00:00,1,0.043962,0.043962,0.043962,0.10127,0.0073569,0.0073569,0.0073569,0.0073569,0.10127,0.10127,0.10127,0.10127,0.10127,0.0073569,0.091884,0.043962,0.091884,0.091884
+12-Dec-2019 07:00:00,1,0.048,0.048,0.048,0.094057,0.025749,0.025749,0.025749,0.025749,0.094057,0.094057,0.094057,0.094057,0.094057,0.025749,0.079435,0.048,0.079435,0.079435
+12-Dec-2019 08:00:00,1,0.043218,0.043218,0.043218,0.10359,0.04782,0.04782,0.04782,0.04782,0.10359,0.10359,0.10359,0.10359,0.10359,0.04782,0.065487,0.043218,0.065487,0.065487
+12-Dec-2019 09:00:00,1,0.039721,0.039721,0.039721,0.095369,0.027588,0.027588,0.027588,0.027588,0.095369,0.095369,0.095369,0.095369,0.095369,0.027588,0.048322,0.039721,0.048322,0.048322
+12-Dec-2019 10:00:00,1,0.041548,0.041548,0.041548,0.095823,0.014714,0.014714,0.014714,0.014714,0.095823,0.095823,0.095823,0.095823,0.095823,0.014714,0.044223,0.041548,0.044223,0.044223
+12-Dec-2019 11:00:00,1,0.051834,0.051834,0.051834,0.081802,0.020231,0.020231,0.020231,0.020231,0.081802,0.081802,0.081802,0.081802,0.081802,0.020231,0.042373,0.051834,0.042373,0.042373
+12-Dec-2019 12:00:00,1,0.052624,0.052624,0.052624,0.068286,0.018392,0.018392,0.018392,0.018392,0.068286,0.068286,0.068286,0.068286,0.068286,0.018392,0.038494,0.052624,0.038494,0.038494
+12-Dec-2019 13:00:00,1,0.051609,0.051609,0.051609,0.076255,0.014714,0.014714,0.014714,0.014714,0.076255,0.076255,0.076255,0.076255,0.076255,0.014714,0.034793,0.051609,0.034793,0.034793
+12-Dec-2019 14:00:00,1,0.049556,0.049556,0.049556,0.072724,0.0073569,0.0073569,0.0073569,0.0073569,0.072724,0.072724,0.072724,0.072724,0.072724,0.0073569,0.035894,0.049556,0.035894,0.035894
+12-Dec-2019 15:00:00,1,0.06309,0.06309,0.06309,0.08276,0.012875,0.012875,0.012875,0.012875,0.08276,0.08276,0.08276,0.08276,0.08276,0.012875,0.026287,0.06309,0.026287,0.026287
+12-Dec-2019 16:00:00,1,0.065774,0.065774,0.065774,0.095016,0.0091961,0.0091961,0.0091961,0.0091961,0.095016,0.095016,0.095016,0.095016,0.095016,0.0091961,0.024723,0.065774,0.024723,0.024723
+12-Dec-2019 17:00:00,1,0.068165,0.068165,0.068165,0.11927,0.0073569,0.0073569,0.0073569,0.0073569,0.11927,0.11927,0.11927,0.11927,0.11927,0.0073569,0.031928,0.068165,0.031928,0.031928
+12-Dec-2019 18:00:00,1,0.070939,0.070939,0.070939,0.15236,0.018392,0.018392,0.018392,0.018392,0.15236,0.15236,0.15236,0.15236,0.15236,0.018392,0.034947,0.070939,0.034947,0.034947
+12-Dec-2019 19:00:00,1,0.070037,0.070037,0.070037,0.14565,0.018392,0.018392,0.018392,0.018392,0.14565,0.14565,0.14565,0.14565,0.14565,0.018392,0.031532,0.070037,0.031532,0.031532
+12-Dec-2019 20:00:00,1,0.068435,0.068435,0.068435,0.13748,0.012875,0.012875,0.012875,0.012875,0.13748,0.13748,0.13748,0.13748,0.13748,0.012875,0.028425,0.068435,0.028425,0.028425
+12-Dec-2019 21:00:00,1,0.0683,0.0683,0.0683,0.12391,0.044141,0.044141,0.044141,0.044141,0.12391,0.12391,0.12391,0.12391,0.12391,0.044141,0.027764,0.0683,0.027764,0.027764
+12-Dec-2019 22:00:00,1,0.067781,0.067781,0.067781,0.11443,0.034945,0.034945,0.034945,0.034945,0.11443,0.11443,0.11443,0.11443,0.11443,0.034945,0.028689,0.067781,0.028689,0.028689
+12-Dec-2019 23:00:00,1,0.072766,0.072766,0.072766,0.10662,0.049659,0.049659,0.049659,0.049659,0.10662,0.10662,0.10662,0.10662,0.10662,0.049659,0.031532,0.072766,0.031532,0.031532
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+13-Dec-2019 01:00:00,1,0.068503,0.068503,0.068503,0.086442,0.068051,0.068051,0.068051,0.068051,0.086442,0.086442,0.086442,0.086442,0.086442,0.068051,0.031928,0.068503,0.031928,0.031928
+13-Dec-2019 02:00:00,1,0.070984,0.070984,0.070984,0.090275,0.058855,0.058855,0.058855,0.058855,0.090275,0.090275,0.090275,0.090275,0.090275,0.058855,0.033823,0.070984,0.033823,0.033823
+13-Dec-2019 03:00:00,1,0.074278,0.074278,0.074278,0.096075,0.091961,0.091961,0.091961,0.091961,0.096075,0.096075,0.096075,0.096075,0.096075,0.091961,0.029725,0.074278,0.029725,0.029725
+13-Dec-2019 04:00:00,1,0.08048,0.08048,0.08048,0.077415,0.079087,0.079087,0.079087,0.079087,0.077415,0.077415,0.077415,0.077415,0.077415,0.079087,0.027081,0.08048,0.027081,0.027081
+13-Dec-2019 05:00:00,1,0.082623,0.082623,0.082623,0.079432,0.082765,0.082765,0.082765,0.082765,0.079432,0.079432,0.079432,0.079432,0.079432,0.082765,0.024502,0.082623,0.024502,0.024502
+13-Dec-2019 06:00:00,1,0.089503,0.089503,0.089503,0.099706,0.099318,0.099318,0.099318,0.099318,0.099706,0.099706,0.099706,0.099706,0.099706,0.099318,0.022806,0.089503,0.022806,0.022806
+13-Dec-2019 07:00:00,1,0.099563,0.099563,0.099563,0.1742,0.082765,0.082765,0.082765,0.082765,0.1742,0.1742,0.1742,0.1742,0.1742,0.082765,0.027499,0.099563,0.027499,0.027499
+13-Dec-2019 08:00:00,1,0.097104,0.097104,0.097104,0.19255,0.053337,0.053337,0.053337,0.053337,0.19255,0.19255,0.19255,0.19255,0.19255,0.053337,0.030276,0.097104,0.030276,0.030276
+13-Dec-2019 09:00:00,1,0.092683,0.092683,0.092683,0.21444,0.086443,0.086443,0.086443,0.086443,0.21444,0.21444,0.21444,0.21444,0.21444,0.086443,0.026265,0.092683,0.026265,0.026265
+13-Dec-2019 10:00:00,1,0.088759,0.088759,0.088759,0.15972,0.053337,0.053337,0.053337,0.053337,0.15972,0.15972,0.15972,0.15972,0.15972,0.053337,0.02534,0.088759,0.02534,0.02534
+13-Dec-2019 11:00:00,1,0.080548,0.080548,0.080548,0.15105,0.077247,0.077247,0.077247,0.077247,0.15105,0.15105,0.15105,0.15105,0.15105,0.077247,0.032633,0.080548,0.032633,0.032633
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+13-Dec-2019 14:00:00,1,0.092909,0.092909,0.092909,0.065966,0.04782,0.04782,0.04782,0.04782,0.065966,0.065966,0.065966,0.065966,0.065966,0.04782,0.048851,0.092909,0.048851,0.048851
+13-Dec-2019 15:00:00,1,0.13319,0.13319,0.13319,0.088762,0.051498,0.051498,0.051498,0.051498,0.088762,0.088762,0.088762,0.088762,0.088762,0.051498,0.071855,0.13319,0.071855,0.071855
+13-Dec-2019 16:00:00,1,0.16529,0.16529,0.16529,0.1219,0.064373,0.064373,0.064373,0.064373,0.1219,0.1219,0.1219,0.1219,0.1219,0.064373,0.10889,0.16529,0.10889,0.10889
+13-Dec-2019 17:00:00,1,0.19398,0.19398,0.19398,0.11907,0.051498,0.051498,0.051498,0.051498,0.11907,0.11907,0.11907,0.11907,0.11907,0.051498,0.14062,0.19398,0.14062,0.14062
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+13-Dec-2019 19:00:00,1,0.19247,0.19247,0.19247,0.17298,0.088283,0.088283,0.088283,0.088283,0.17298,0.17298,0.17298,0.17298,0.17298,0.088283,0.16618,0.19247,0.16618,0.16618
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+13-Dec-2019 21:00:00,1,0.18264,0.18264,0.18264,0.17122,0.053337,0.053337,0.053337,0.053337,0.17122,0.17122,0.17122,0.17122,0.17122,0.053337,0.17079,0.18264,0.17079,0.17079
+13-Dec-2019 22:00:00,1,0.17382,0.17382,0.17382,0.14091,0.086443,0.086443,0.086443,0.086443,0.14091,0.14091,0.14091,0.14091,0.14091,0.086443,0.151,0.17382,0.151,0.151
+13-Dec-2019 23:00:00,1,0.15934,0.15934,0.15934,0.12452,0.06989,0.06989,0.06989,0.06989,0.12452,0.12452,0.12452,0.12452,0.12452,0.06989,0.15918,0.15934,0.15918,0.15918
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+14-Dec-2019 01:00:00,1,0.15977,0.15977,0.15977,0.0815,0.042302,0.042302,0.042302,0.042302,0.0815,0.0815,0.0815,0.0815,0.0815,0.042302,0.14622,0.15977,0.14622,0.14622
+14-Dec-2019 02:00:00,1,0.17357,0.17357,0.17357,0.081298,0.057016,0.057016,0.057016,0.057016,0.081298,0.081298,0.081298,0.081298,0.081298,0.057016,0.13981,0.17357,0.13981,0.13981
+14-Dec-2019 03:00:00,1,0.17436,0.17436,0.17436,0.097638,0.068051,0.068051,0.068051,0.068051,0.097638,0.097638,0.097638,0.097638,0.097638,0.068051,0.13661,0.17436,0.13661,0.13661
+14-Dec-2019 04:00:00,1,0.17086,0.17086,0.17086,0.11201,0.040463,0.040463,0.040463,0.040463,0.11201,0.11201,0.11201,0.11201,0.11201,0.040463,0.14364,0.17086,0.14364,0.14364
+14-Dec-2019 05:00:00,1,0.15219,0.15219,0.15219,0.10525,0.058855,0.058855,0.058855,0.058855,0.10525,0.10525,0.10525,0.10525,0.10525,0.058855,0.14406,0.15219,0.14406,0.14406
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+14-Dec-2019 07:00:00,1,0.12532,0.12532,0.12532,0.1578,0.034945,0.034945,0.034945,0.034945,0.1578,0.1578,0.1578,0.1578,0.1578,0.034945,0.1167,0.12532,0.1167,0.1167
+14-Dec-2019 08:00:00,1,0.13755,0.13755,0.13755,0.22523,0.053337,0.053337,0.053337,0.053337,0.22523,0.22523,0.22523,0.22523,0.22523,0.053337,0.11672,0.13755,0.11672,0.11672
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+14-Dec-2019 11:00:00,1,0.20621,0.20621,0.20621,0.37956,0.073569,0.073569,0.073569,0.073569,0.37956,0.37956,0.37956,0.37956,0.37956,0.073569,0.11921,0.20621,0.11921,0.11921
+14-Dec-2019 12:00:00,1,0.22739,0.22739,0.22739,0.3906,0.084604,0.084604,0.084604,0.084604,0.3906,0.3906,0.3906,0.3906,0.3906,0.084604,0.11912,0.22739,0.11912,0.11912
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+14-Dec-2019 17:00:00,1,0.63038,0.63038,0.63038,0.6754,0.10116,0.10116,0.10116,0.10116,0.6754,0.6754,0.6754,0.6754,0.6754,0.10116,0.32713,0.63038,0.32713,0.32713
+14-Dec-2019 18:00:00,1,0.69365,0.69365,0.69365,0.72144,0.091961,0.091961,0.091961,0.091961,0.72144,0.72144,0.72144,0.72144,0.72144,0.091961,0.39356,0.69365,0.39356,0.39356
+14-Dec-2019 19:00:00,1,0.73429,0.73429,0.73429,0.75049,0.090122,0.090122,0.090122,0.090122,0.75049,0.75049,0.75049,0.75049,0.75049,0.090122,0.43501,0.73429,0.43501,0.43501
+14-Dec-2019 20:00:00,1,0.76612,0.76612,0.76612,0.71887,0.14346,0.14346,0.14346,0.14346,0.71887,0.71887,0.71887,0.71887,0.71887,0.14346,0.47136,0.76612,0.47136,0.47136
+14-Dec-2019 21:00:00,1,0.74187,0.74187,0.74187,0.67444,0.12507,0.12507,0.12507,0.12507,0.67444,0.67444,0.67444,0.67444,0.67444,0.12507,0.53114,0.74187,0.53114,0.53114
+14-Dec-2019 22:00:00,1,0.68819,0.68819,0.68819,0.57317,0.088283,0.088283,0.088283,0.088283,0.57317,0.57317,0.57317,0.57317,0.57317,0.088283,0.54225,0.68819,0.54225,0.54225
+14-Dec-2019 23:00:00,1,0.61878,0.61878,0.61878,0.6061,0.14714,0.14714,0.14714,0.14714,0.6061,0.6061,0.6061,0.6061,0.6061,0.14714,0.54778,0.61878,0.54778,0.54778
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+15-Dec-2019 01:00:00,1,0.52696,0.52696,0.52696,0.51795,0.10484,0.10484,0.10484,0.10484,0.51795,0.51795,0.51795,0.51795,0.51795,0.10484,0.46178,0.52696,0.46178,0.46178
+15-Dec-2019 02:00:00,1,0.5088,0.5088,0.5088,0.47074,0.11035,0.11035,0.11035,0.11035,0.47074,0.47074,0.47074,0.47074,0.47074,0.11035,0.49296,0.5088,0.49296,0.49296
+15-Dec-2019 03:00:00,1,0.4975,0.4975,0.4975,0.43645,0.068051,0.068051,0.068051,0.068051,0.43645,0.43645,0.43645,0.43645,0.43645,0.068051,0.50902,0.4975,0.50902,0.50902
+15-Dec-2019 04:00:00,1,0.45721,0.45721,0.45721,0.39302,0.12507,0.12507,0.12507,0.12507,0.39302,0.39302,0.39302,0.39302,0.39302,0.12507,0.47994,0.45721,0.47994,0.47994
+15-Dec-2019 05:00:00,1,0.40536,0.40536,0.40536,0.3963,0.049659,0.049659,0.049659,0.049659,0.3963,0.3963,0.3963,0.3963,0.3963,0.049659,0.39482,0.40536,0.39482,0.39482
+15-Dec-2019 06:00:00,1,0.39324,0.39324,0.39324,0.38455,0.10851,0.10851,0.10851,0.10851,0.38455,0.38455,0.38455,0.38455,0.38455,0.10851,0.35557,0.39324,0.35557,0.35557
+15-Dec-2019 07:00:00,1,0.36507,0.36507,0.36507,0.45188,0.14898,0.14898,0.14898,0.14898,0.45188,0.45188,0.45188,0.45188,0.45188,0.14898,0.32649,0.36507,0.32649,0.32649
+15-Dec-2019 08:00:00,1,0.34883,0.34883,0.34883,0.44043,0.11587,0.11587,0.11587,0.11587,0.44043,0.44043,0.44043,0.44043,0.44043,0.11587,0.30703,0.34883,0.30703,0.30703
+15-Dec-2019 09:00:00,1,0.33442,0.33442,0.33442,0.39716,0.1876,0.1876,0.1876,0.1876,0.39716,0.39716,0.39716,0.39716,0.39716,0.1876,0.29502,0.33442,0.29502,0.29502
+15-Dec-2019 10:00:00,1,0.3339,0.3339,0.3339,0.44497,0.103,0.103,0.103,0.103,0.44497,0.44497,0.44497,0.44497,0.44497,0.103,0.26642,0.3339,0.26642,0.26642
+15-Dec-2019 11:00:00,1,0.31382,0.31382,0.31382,0.41683,0.12691,0.12691,0.12691,0.12691,0.41683,0.41683,0.41683,0.41683,0.41683,0.12691,0.21429,0.31382,0.21429,0.21429
+15-Dec-2019 12:00:00,1,0.30122,0.30122,0.30122,0.40518,0.09564,0.09564,0.09564,0.09564,0.40518,0.40518,0.40518,0.40518,0.40518,0.09564,0.18939,0.30122,0.18939,0.18939
+15-Dec-2019 13:00:00,1,0.26855,0.26855,0.26855,0.38944,0.062534,0.062534,0.062534,0.062534,0.38944,0.38944,0.38944,0.38944,0.38944,0.062534,0.18313,0.26855,0.18313,0.18313
+15-Dec-2019 14:00:00,1,0.24101,0.24101,0.24101,0.34027,0.049659,0.049659,0.049659,0.049659,0.34027,0.34027,0.34027,0.34027,0.34027,0.049659,0.18562,0.24101,0.18562,0.18562
+15-Dec-2019 15:00:00,1,0.2125,0.2125,0.2125,0.32549,0.073569,0.073569,0.073569,0.073569,0.32549,0.32549,0.32549,0.32549,0.32549,0.073569,0.18677,0.2125,0.18677,0.18677
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+15-Dec-2019 17:00:00,1,0.23111,0.23111,0.23111,0.3675,0.088283,0.088283,0.088283,0.088283,0.3675,0.3675,0.3675,0.3675,0.3675,0.088283,0.19144,0.23111,0.19144,0.19144
+15-Dec-2019 18:00:00,1,0.2545,0.2545,0.2545,0.35116,0.060694,0.060694,0.060694,0.060694,0.35116,0.35116,0.35116,0.35116,0.35116,0.060694,0.17359,0.2545,0.17359,0.17359
+15-Dec-2019 19:00:00,1,0.24801,0.24801,0.24801,0.36009,0.0938,0.0938,0.0938,0.0938,0.36009,0.36009,0.36009,0.36009,0.36009,0.0938,0.17136,0.24801,0.17136,0.17136
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+15-Dec-2019 21:00:00,1,0.24575,0.24575,0.24575,0.41254,0.09564,0.09564,0.09564,0.09564,0.41254,0.41254,0.41254,0.41254,0.41254,0.09564,0.17059,0.24575,0.17059,0.17059
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+15-Dec-2019 23:00:00,1,0.22811,0.22811,0.22811,0.34244,0.1361,0.1361,0.1361,0.1361,0.34244,0.34244,0.34244,0.34244,0.34244,0.1361,0.15418,0.22811,0.15418,0.15418
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+16-Dec-2019 01:00:00,1,0.20616,0.20616,0.20616,0.35535,0.10484,0.10484,0.10484,0.10484,0.35535,0.35535,0.35535,0.35535,0.35535,0.10484,0.12941,0.20616,0.12941,0.12941
+16-Dec-2019 02:00:00,1,0.20366,0.20366,0.20366,0.3374,0.1453,0.1453,0.1453,0.1453,0.3374,0.3374,0.3374,0.3374,0.3374,0.1453,0.13161,0.20366,0.13161,0.13161
+16-Dec-2019 03:00:00,1,0.19407,0.19407,0.19407,0.35817,0.15817,0.15817,0.15817,0.15817,0.35817,0.35817,0.35817,0.35817,0.35817,0.15817,0.14016,0.19407,0.14016,0.14016
+16-Dec-2019 04:00:00,1,0.18981,0.18981,0.18981,0.40457,0.17473,0.17473,0.17473,0.17473,0.40457,0.40457,0.40457,0.40457,0.40457,0.17473,0.17418,0.18981,0.17418,0.17418
+16-Dec-2019 05:00:00,1,0.18388,0.18388,0.18388,0.37875,0.091961,0.091961,0.091961,0.091961,0.37875,0.37875,0.37875,0.37875,0.37875,0.091961,0.19049,0.18388,0.19049,0.19049
+16-Dec-2019 06:00:00,1,0.18079,0.18079,0.18079,0.35631,0.11587,0.11587,0.11587,0.11587,0.35631,0.35631,0.35631,0.35631,0.35631,0.11587,0.19069,0.18079,0.19069,0.19069
+16-Dec-2019 07:00:00,1,0.18189,0.18189,0.18189,0.35974,0.088283,0.088283,0.088283,0.088283,0.35974,0.35974,0.35974,0.35974,0.35974,0.088283,0.20891,0.18189,0.20891,0.20891
+16-Dec-2019 08:00:00,1,0.18399,0.18399,0.18399,0.32433,0.10851,0.10851,0.10851,0.10851,0.32433,0.32433,0.32433,0.32433,0.32433,0.10851,0.20997,0.18399,0.20997,0.20997
+16-Dec-2019 09:00:00,1,0.18437,0.18437,0.18437,0.28137,0.11035,0.11035,0.11035,0.11035,0.28137,0.28137,0.28137,0.28137,0.28137,0.11035,0.19487,0.18437,0.19487,0.19487
+16-Dec-2019 10:00:00,1,0.18706,0.18706,0.18706,0.30527,0.11219,0.11219,0.11219,0.11219,0.30527,0.30527,0.30527,0.30527,0.30527,0.11219,0.18569,0.18706,0.18569,0.18569
+16-Dec-2019 11:00:00,1,0.19107,0.19107,0.19107,0.3316,0.099318,0.099318,0.099318,0.099318,0.3316,0.3316,0.3316,0.3316,0.3316,0.099318,0.18439,0.19107,0.18439,0.18439
+16-Dec-2019 12:00:00,1,0.19094,0.19094,0.19094,0.25786,0.10116,0.10116,0.10116,0.10116,0.25786,0.25786,0.25786,0.25786,0.25786,0.10116,0.15076,0.19094,0.15076,0.15076
+16-Dec-2019 13:00:00,1,0.17386,0.17386,0.17386,0.34289,0.045981,0.045981,0.045981,0.045981,0.34289,0.34289,0.34289,0.34289,0.34289,0.045981,0.1313,0.17386,0.1313,0.1313
+16-Dec-2019 14:00:00,1,0.15325,0.15325,0.15325,0.33104,0.079087,0.079087,0.079087,0.079087,0.33104,0.33104,0.33104,0.33104,0.33104,0.079087,0.11773,0.15325,0.11773,0.11773
+16-Dec-2019 15:00:00,1,0.14244,0.14244,0.14244,0.30789,0.080926,0.080926,0.080926,0.080926,0.30789,0.30789,0.30789,0.30789,0.30789,0.080926,0.11526,0.14244,0.11526,0.11526
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+16-Dec-2019 18:00:00,1,0.13198,0.13198,0.13198,0.20864,0.12875,0.12875,0.12875,0.12875,0.20864,0.20864,0.20864,0.20864,0.20864,0.12875,0.15054,0.13198,0.15054,0.15054
+16-Dec-2019 19:00:00,1,0.11966,0.11966,0.11966,0.19296,0.09564,0.09564,0.09564,0.09564,0.19296,0.19296,0.19296,0.19296,0.19296,0.09564,0.13935,0.11966,0.13935,0.13935
+16-Dec-2019 20:00:00,1,0.11702,0.11702,0.11702,0.15725,0.10667,0.10667,0.10667,0.10667,0.15725,0.15725,0.15725,0.15725,0.15725,0.10667,0.14512,0.11702,0.14512,0.14512
+16-Dec-2019 21:00:00,1,0.114,0.114,0.114,0.14948,0.097479,0.097479,0.097479,0.097479,0.14948,0.14948,0.14948,0.14948,0.14948,0.097479,0.13188,0.114,0.13188,0.13188
+16-Dec-2019 22:00:00,1,0.10405,0.10405,0.10405,0.14126,0.07173,0.07173,0.07173,0.07173,0.14126,0.14126,0.14126,0.14126,0.14126,0.07173,0.11429,0.10405,0.11429,0.11429
+16-Dec-2019 23:00:00,1,0.09151,0.09151,0.09151,0.11156,0.042302,0.042302,0.042302,0.042302,0.11156,0.11156,0.11156,0.11156,0.11156,0.042302,0.09021,0.09151,0.09021,0.09021
+17-Dec-2019 00:00:00,1,0.081586,0.081586,0.081586,0.076608,0.075408,0.075408,0.075408,0.075408,0.076608,0.076608,0.076608,0.076608,0.076608,0.075408,0.08556,0.081586,0.08556,0.08556
+17-Dec-2019 01:00:00,1,0.076849,0.076849,0.076849,0.076658,0.068051,0.068051,0.068051,0.068051,0.076658,0.076658,0.076658,0.076658,0.076658,0.068051,0.074918,0.076849,0.074918,0.074918
+17-Dec-2019 02:00:00,1,0.069293,0.069293,0.069293,0.099454,0.027588,0.027588,0.027588,0.027588,0.099454,0.099454,0.099454,0.099454,0.099454,0.027588,0.064826,0.069293,0.064826,0.064826
+17-Dec-2019 03:00:00,1,0.059684,0.059684,0.059684,0.15084,0.04782,0.04782,0.04782,0.04782,0.15084,0.15084,0.15084,0.15084,0.15084,0.04782,0.058237,0.059684,0.058237,0.058237
+17-Dec-2019 04:00:00,1,0.05833,0.05833,0.05833,0.1228,0.058855,0.058855,0.058855,0.058855,0.1228,0.1228,0.1228,0.1228,0.1228,0.058855,0.049313,0.05833,0.049313,0.049313
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+17-Dec-2019 06:00:00,1,0.055037,0.055037,0.055037,0.14383,0.038624,0.038624,0.038624,0.038624,0.14383,0.14383,0.14383,0.14383,0.14383,0.038624,0.040918,0.055037,0.040918,0.040918
+17-Dec-2019 07:00:00,1,0.057248,0.057248,0.057248,0.14318,0.058855,0.058855,0.058855,0.058855,0.14318,0.14318,0.14318,0.14318,0.14318,0.058855,0.048983,0.057248,0.048983,0.048983
+17-Dec-2019 08:00:00,1,0.059842,0.059842,0.059842,0.13809,0.053337,0.053337,0.053337,0.053337,0.13809,0.13809,0.13809,0.13809,0.13809,0.053337,0.036093,0.059842,0.036093,0.036093
+17-Dec-2019 09:00:00,1,0.061488,0.061488,0.061488,0.18443,0.036784,0.036784,0.036784,0.036784,0.18443,0.18443,0.18443,0.18443,0.18443,0.036784,0.032104,0.061488,0.032104,0.032104
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+17-Dec-2019 11:00:00,1,0.048631,0.048631,0.048631,0.15296,0.075408,0.075408,0.075408,0.075408,0.15296,0.15296,0.15296,0.15296,0.15296,0.075408,0.022277,0.048631,0.022277,0.022277
+17-Dec-2019 12:00:00,1,0.05021,0.05021,0.05021,0.1514,0.064373,0.064373,0.064373,0.064373,0.1514,0.1514,0.1514,0.1514,0.1514,0.064373,0.020955,0.05021,0.020955,0.020955
+17-Dec-2019 13:00:00,1,0.046218,0.046218,0.046218,0.16113,0.04782,0.04782,0.04782,0.04782,0.16113,0.16113,0.16113,0.16113,0.16113,0.04782,0.016129,0.046218,0.016129,0.016129
+17-Dec-2019 14:00:00,1,0.049939,0.049939,0.049939,0.16703,0.049659,0.049659,0.049659,0.049659,0.16703,0.16703,0.16703,0.16703,0.16703,0.049659,0.018509,0.049939,0.018509,0.018509
+17-Dec-2019 15:00:00,1,0.052894,0.052894,0.052894,0.15402,0.066212,0.066212,0.066212,0.066212,0.15402,0.15402,0.15402,0.15402,0.15402,0.066212,0.025186,0.052894,0.025186,0.025186
+17-Dec-2019 16:00:00,1,0.067578,0.067578,0.067578,0.13995,0.11035,0.11035,0.11035,0.11035,0.13995,0.13995,0.13995,0.13995,0.13995,0.11035,0.034616,0.067578,0.034616,0.034616
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+17-Dec-2019 18:00:00,1,0.083187,0.083187,0.083187,0.16048,0.082765,0.082765,0.082765,0.082765,0.16048,0.16048,0.16048,0.16048,0.16048,0.082765,0.056915,0.083187,0.056915,0.056915
+17-Dec-2019 19:00:00,1,0.088601,0.088601,0.088601,0.16603,0.0938,0.0938,0.0938,0.0938,0.16603,0.16603,0.16603,0.16603,0.16603,0.0938,0.058259,0.088601,0.058259,0.058259
+17-Dec-2019 20:00:00,1,0.081428,0.081428,0.081428,0.1342,0.090122,0.090122,0.090122,0.090122,0.1342,0.1342,0.1342,0.1342,0.1342,0.090122,0.053875,0.081428,0.053875,0.053875
+17-Dec-2019 21:00:00,1,0.072856,0.072856,0.072856,0.13778,0.080926,0.080926,0.080926,0.080926,0.13778,0.13778,0.13778,0.13778,0.13778,0.080926,0.049512,0.072856,0.049512,0.049512
+17-Dec-2019 22:00:00,1,0.067263,0.067263,0.067263,0.13481,0.053337,0.053337,0.053337,0.053337,0.13481,0.13481,0.13481,0.13481,0.13481,0.053337,0.03682,0.067263,0.03682,0.03682
+17-Dec-2019 23:00:00,1,0.062729,0.062729,0.062729,0.12976,0.060694,0.060694,0.060694,0.060694,0.12976,0.12976,0.12976,0.12976,0.12976,0.060694,0.046427,0.062729,0.046427,0.046427
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+18-Dec-2019 01:00:00,1,0.059819,0.059819,0.059819,0.10253,0.064373,0.064373,0.064373,0.064373,0.10253,0.10253,0.10253,0.10253,0.10253,0.064373,0.041689,0.059819,0.041689,0.041689
+18-Dec-2019 02:00:00,1,0.062233,0.062233,0.062233,0.099908,0.068051,0.068051,0.068051,0.068051,0.099908,0.099908,0.099908,0.099908,0.099908,0.068051,0.034132,0.062233,0.034132,0.034132
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+18-Dec-2019 05:00:00,1,0.052736,0.052736,0.052736,0.071665,0.058855,0.058855,0.058855,0.058855,0.071665,0.071665,0.071665,0.071665,0.071665,0.058855,0.027896,0.052736,0.027896,0.027896
+18-Dec-2019 06:00:00,1,0.048203,0.048203,0.048203,0.060822,0.051498,0.051498,0.051498,0.051498,0.060822,0.060822,0.060822,0.060822,0.060822,0.051498,0.030848,0.048203,0.030848,0.030848
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+18-Dec-2019 08:00:00,1,0.044458,0.044458,0.044458,0.073128,0.042302,0.042302,0.042302,0.042302,0.073128,0.073128,0.073128,0.073128,0.073128,0.042302,0.030584,0.044458,0.030584,0.030584
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+18-Dec-2019 10:00:00,1,0.036315,0.036315,0.036315,0.042565,0.012875,0.012875,0.012875,0.012875,0.042565,0.042565,0.042565,0.042565,0.042565,0.012875,0.02144,0.036315,0.02144,0.02144
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+18-Dec-2019 12:00:00,1,0.024767,0.024767,0.024767,0.026175,0.0091961,0.0091961,0.0091961,0.0091961,0.026175,0.026175,0.026175,0.026175,0.026175,0.0091961,0.020206,0.024767,0.020206,0.020206
+18-Dec-2019 13:00:00,1,0.022985,0.022985,0.022985,0.01629,0.0018392,0.0018392,0.0018392,0.0018392,0.01629,0.01629,0.01629,0.01629,0.01629,0.0018392,0.01884,0.022985,0.01884,0.01884
+18-Dec-2019 14:00:00,1,0.020977,0.020977,0.020977,0.013113,0.0018392,0.0018392,0.0018392,0.0018392,0.013113,0.013113,0.013113,0.013113,0.013113,0.0018392,0.012934,0.020977,0.012934,0.012934
+18-Dec-2019 15:00:00,1,0.020977,0.020977,0.020977,0.010187,0,0,0,0,0.010187,0.010187,0.010187,0.010187,0.010187,0,0.012339,0.020977,0.012339,0.012339
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+18-Dec-2019 20:00:00,1,0.028714,0.028714,0.028714,0.010187,0.0018392,0.0018392,0.0018392,0.0018392,0.010187,0.010187,0.010187,0.010187,0.010187,0.0018392,0.015094,0.028714,0.015094,0.015094
+18-Dec-2019 21:00:00,1,0.027496,0.027496,0.027496,0.011146,0,0,0,0,0.011146,0.011146,0.011146,0.011146,0.011146,0,0.010511,0.027496,0.010511,0.010511
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+19-Dec-2019 01:00:00,1,0.035594,0.035594,0.035594,0.0063041,0.0018392,0.0018392,0.0018392,0.0018392,0.0063041,0.0063041,0.0063041,0.0063041,0.0063041,0.0018392,0.0079986,0.035594,0.0079986,0.0079986
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+19-Dec-2019 03:00:00,1,0.03309,0.03309,0.03309,0.0072623,0.0018392,0.0018392,0.0018392,0.0018392,0.0072623,0.0072623,0.0072623,0.0072623,0.0072623,0.0018392,0.011061,0.03309,0.011061,0.011061
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+19-Dec-2019 06:00:00,1,0.021406,0.021406,0.021406,0.0070102,0,0,0,0,0.0070102,0.0070102,0.0070102,0.0070102,0.0070102,0,0.0092986,0.021406,0.0092986,0.0092986
+19-Dec-2019 07:00:00,1,0.024113,0.024113,0.024113,0.0070102,0.0018392,0.0018392,0.0018392,0.0018392,0.0070102,0.0070102,0.0070102,0.0070102,0.0070102,0.0018392,0.011634,0.024113,0.011634,0.011634
+19-Dec-2019 08:00:00,1,0.024338,0.024338,0.024338,0.0098849,0,0,0,0,0.0098849,0.0098849,0.0098849,0.0098849,0.0098849,0,0.011039,0.024338,0.011039,0.011039
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+19-Dec-2019 10:00:00,1,0.027496,0.027496,0.027496,0.0095318,0,0,0,0,0.0095318,0.0095318,0.0095318,0.0095318,0.0095318,0,0.0067867,0.027496,0.0067867,0.0067867
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+19-Dec-2019 12:00:00,1,0.018361,0.018361,0.018361,0.0075649,0,0,0,0,0.0075649,0.0075649,0.0075649,0.0075649,0.0075649,0,0.0027984,0.018361,0.0027984,0.0027984
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+19-Dec-2019 14:00:00,1,0.02303,0.02303,0.02303,0.0072119,0,0,0,0,0.0072119,0.0072119,0.0072119,0.0072119,0.0072119,0,0.0081308,0.02303,0.0081308,0.0081308
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+19-Dec-2019 16:00:00,1,0.02312,0.02312,0.02312,0.015735,0.0036784,0.0036784,0.0036784,0.0036784,0.015735,0.015735,0.015735,0.015735,0.015735,0.0036784,0.012207,0.02312,0.012207,0.012207
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+19-Dec-2019 23:00:00,1,0.075225,0.075225,0.075225,0.11685,0.033106,0.033106,0.033106,0.033106,0.11685,0.11685,0.11685,0.11685,0.11685,0.033106,0.016614,0.075225,0.016614,0.016614
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+20-Dec-2019 03:00:00,1,0.16464,0.16464,0.16464,0.20672,0.038624,0.038624,0.038624,0.038624,0.20672,0.20672,0.20672,0.20672,0.20672,0.038624,0.029218,0.16464,0.029218,0.029218
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+20-Dec-2019 13:00:00,1,0.38185,0.38185,0.38185,0.32212,0.029428,0.029428,0.029428,0.029428,0.32212,0.32212,0.32212,0.32212,0.32212,0.029428,0.14356,0.38185,0.14356,0.14356
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+20-Dec-2019 16:00:00,1,0.50557,0.50557,0.50557,0.39761,0.034945,0.034945,0.034945,0.034945,0.39761,0.39761,0.39761,0.39761,0.39761,0.034945,0.24987,0.50557,0.24987,0.24987
+20-Dec-2019 17:00:00,1,0.572,0.572,0.572,0.42333,0.034945,0.034945,0.034945,0.034945,0.42333,0.42333,0.42333,0.42333,0.42333,0.034945,0.27942,0.572,0.27942,0.27942
+20-Dec-2019 18:00:00,1,0.60051,0.60051,0.60051,0.40432,0.025749,0.025749,0.025749,0.025749,0.40432,0.40432,0.40432,0.40432,0.40432,0.025749,0.2961,0.60051,0.2961,0.2961
+20-Dec-2019 19:00:00,1,0.62359,0.62359,0.62359,0.34209,0.034945,0.034945,0.034945,0.034945,0.34209,0.34209,0.34209,0.34209,0.34209,0.034945,0.30818,0.62359,0.30818,0.30818
+20-Dec-2019 20:00:00,1,0.60266,0.60266,0.60266,0.29307,0.036784,0.036784,0.036784,0.036784,0.29307,0.29307,0.29307,0.29307,0.29307,0.036784,0.31051,0.60266,0.31051,0.31051
+20-Dec-2019 21:00:00,1,0.57541,0.57541,0.57541,0.29982,0.036784,0.036784,0.036784,0.036784,0.29982,0.29982,0.29982,0.29982,0.29982,0.036784,0.2762,0.57541,0.2762,0.2762
+20-Dec-2019 22:00:00,1,0.53431,0.53431,0.53431,0.29478,0.040463,0.040463,0.040463,0.040463,0.29478,0.29478,0.29478,0.29478,0.29478,0.040463,0.27391,0.53431,0.27391,0.27391
+20-Dec-2019 23:00:00,1,0.52795,0.52795,0.52795,0.28656,0.034945,0.034945,0.034945,0.034945,0.28656,0.28656,0.28656,0.28656,0.28656,0.034945,0.25452,0.52795,0.25452,0.25452
+21-Dec-2019 00:00:00,1,0.50147,0.50147,0.50147,0.28212,0.038624,0.038624,0.038624,0.038624,0.28212,0.28212,0.28212,0.28212,0.28212,0.038624,0.24095,0.50147,0.24095,0.24095
+21-Dec-2019 01:00:00,1,0.49441,0.49441,0.49441,0.26099,0.029428,0.029428,0.029428,0.029428,0.26099,0.26099,0.26099,0.26099,0.26099,0.029428,0.25432,0.49441,0.25432,0.25432
+21-Dec-2019 02:00:00,1,0.50607,0.50607,0.50607,0.24642,0.053337,0.053337,0.053337,0.053337,0.24642,0.24642,0.24642,0.24642,0.24642,0.053337,0.23883,0.50607,0.23883,0.23883
+21-Dec-2019 03:00:00,1,0.49495,0.49495,0.49495,0.23436,0.055177,0.055177,0.055177,0.055177,0.23436,0.23436,0.23436,0.23436,0.23436,0.055177,0.21821,0.49495,0.21821,0.21821
+21-Dec-2019 04:00:00,1,0.48322,0.48322,0.48322,0.20425,0.04782,0.04782,0.04782,0.04782,0.20425,0.20425,0.20425,0.20425,0.20425,0.04782,0.21151,0.48322,0.21151,0.21151
+21-Dec-2019 05:00:00,1,0.47878,0.47878,0.47878,0.18832,0.040463,0.040463,0.040463,0.040463,0.18832,0.18832,0.18832,0.18832,0.18832,0.040463,0.18784,0.47878,0.18784,0.18784
+21-Dec-2019 06:00:00,1,0.46933,0.46933,0.46933,0.17601,0.057016,0.057016,0.057016,0.057016,0.17601,0.17601,0.17601,0.17601,0.17601,0.057016,0.18789,0.46933,0.18789,0.18789
+21-Dec-2019 07:00:00,1,0.44857,0.44857,0.44857,0.18176,0.049659,0.049659,0.049659,0.049659,0.18176,0.18176,0.18176,0.18176,0.18176,0.049659,0.17689,0.44857,0.17689,0.17689
+21-Dec-2019 08:00:00,1,0.44936,0.44936,0.44936,0.18348,0.060694,0.060694,0.060694,0.060694,0.18348,0.18348,0.18348,0.18348,0.18348,0.060694,0.16389,0.44936,0.16389,0.16389
+21-Dec-2019 09:00:00,1,0.43416,0.43416,0.43416,0.17278,0.066212,0.066212,0.066212,0.066212,0.17278,0.17278,0.17278,0.17278,0.17278,0.066212,0.14869,0.43416,0.14869,0.14869
+21-Dec-2019 10:00:00,1,0.41524,0.41524,0.41524,0.15231,0.075408,0.075408,0.075408,0.075408,0.15231,0.15231,0.15231,0.15231,0.15231,0.075408,0.14272,0.41524,0.14272,0.14272
+21-Dec-2019 11:00:00,1,0.38569,0.38569,0.38569,0.1278,0.084604,0.084604,0.084604,0.084604,0.1278,0.1278,0.1278,0.1278,0.1278,0.084604,0.12776,0.38569,0.12776,0.12776
+21-Dec-2019 12:00:00,1,0.32215,0.32215,0.32215,0.13551,0.086443,0.086443,0.086443,0.086443,0.13551,0.13551,0.13551,0.13551,0.13551,0.086443,0.11484,0.32215,0.11484,0.11484
+21-Dec-2019 13:00:00,1,0.29761,0.29761,0.29761,0.11837,0.091961,0.091961,0.091961,0.091961,0.11837,0.11837,0.11837,0.11837,0.11837,0.091961,0.11599,0.29761,0.11599,0.11599
+21-Dec-2019 14:00:00,1,0.26312,0.26312,0.26312,0.11468,0.044141,0.044141,0.044141,0.044141,0.11468,0.11468,0.11468,0.11468,0.11468,0.044141,0.10442,0.26312,0.10442,0.10442
+21-Dec-2019 15:00:00,1,0.24602,0.24602,0.24602,0.090628,0.053337,0.053337,0.053337,0.053337,0.090628,0.090628,0.090628,0.090628,0.090628,0.053337,0.09563,0.24602,0.09563,0.09563
+21-Dec-2019 16:00:00,1,0.23188,0.23188,0.23188,0.078121,0.049659,0.049659,0.049659,0.049659,0.078121,0.078121,0.078121,0.078121,0.078121,0.049659,0.10026,0.23188,0.10026,0.10026
+21-Dec-2019 17:00:00,1,0.21787,0.21787,0.21787,0.083013,0.040463,0.040463,0.040463,0.040463,0.083013,0.083013,0.083013,0.083013,0.083013,0.040463,0.095917,0.21787,0.095917,0.095917
+21-Dec-2019 18:00:00,1,0.22687,0.22687,0.22687,0.079482,0.045981,0.045981,0.045981,0.045981,0.079482,0.079482,0.079482,0.079482,0.079482,0.045981,0.091598,0.22687,0.091598,0.091598
+21-Dec-2019 19:00:00,1,0.23413,0.23413,0.23413,0.092141,0.051498,0.051498,0.051498,0.051498,0.092141,0.092141,0.092141,0.092141,0.092141,0.051498,0.091135,0.23413,0.091135,0.091135
+21-Dec-2019 20:00:00,1,0.23107,0.23107,0.23107,0.09663,0.038624,0.038624,0.038624,0.038624,0.09663,0.09663,0.09663,0.09663,0.09663,0.038624,0.092215,0.23107,0.092215,0.092215
+21-Dec-2019 21:00:00,1,0.23409,0.23409,0.23409,0.09673,0.038624,0.038624,0.038624,0.038624,0.09673,0.09673,0.09673,0.09673,0.09673,0.038624,0.090122,0.23409,0.090122,0.090122
+21-Dec-2019 22:00:00,1,0.22281,0.22281,0.22281,0.088964,0.033106,0.033106,0.033106,0.033106,0.088964,0.088964,0.088964,0.088964,0.088964,0.033106,0.072141,0.22281,0.072141,0.072141
+21-Dec-2019 23:00:00,1,0.20513,0.20513,0.20513,0.091435,0.033106,0.033106,0.033106,0.033106,0.091435,0.091435,0.091435,0.091435,0.091435,0.033106,0.063702,0.20513,0.063702,0.063702
+22-Dec-2019 00:00:00,1,0.20163,0.20163,0.20163,0.1051,0.027588,0.027588,0.027588,0.027588,0.1051,0.1051,0.1051,0.1051,0.1051,0.027588,0.059626,0.20163,0.059626,0.059626
+22-Dec-2019 01:00:00,1,0.2088,0.2088,0.2088,0.1043,0.034945,0.034945,0.034945,0.034945,0.1043,0.1043,0.1043,0.1043,0.1043,0.034945,0.058722,0.2088,0.058722,0.058722
+22-Dec-2019 02:00:00,1,0.20064,0.20064,0.20064,0.11448,0.040463,0.040463,0.040463,0.040463,0.11448,0.11448,0.11448,0.11448,0.11448,0.040463,0.06227,0.20064,0.06227,0.06227
+22-Dec-2019 03:00:00,1,0.19529,0.19529,0.19529,0.10964,0.027588,0.027588,0.027588,0.027588,0.10964,0.10964,0.10964,0.10964,0.10964,0.027588,0.061388,0.19529,0.061388,0.061388
+22-Dec-2019 04:00:00,1,0.1871,0.1871,0.1871,0.10954,0.040463,0.040463,0.040463,0.040463,0.10954,0.10954,0.10954,0.10954,0.10954,0.040463,0.06509,0.1871,0.06509,0.06509
+22-Dec-2019 05:00:00,1,0.19374,0.19374,0.19374,0.11211,0.033106,0.033106,0.033106,0.033106,0.11211,0.11211,0.11211,0.11211,0.11211,0.033106,0.067955,0.19374,0.067955,0.067955
+22-Dec-2019 06:00:00,1,0.18922,0.18922,0.18922,0.10576,0.033106,0.033106,0.033106,0.033106,0.10576,0.10576,0.10576,0.10576,0.10576,0.033106,0.079677,0.18922,0.079677,0.079677
+22-Dec-2019 07:00:00,1,0.1691,0.1691,0.1691,0.10228,0.02391,0.02391,0.02391,0.02391,0.10228,0.10228,0.10228,0.10228,0.10228,0.02391,0.09151,0.1691,0.09151,0.09151
+22-Dec-2019 08:00:00,1,0.15647,0.15647,0.15647,0.11821,0.027588,0.027588,0.027588,0.027588,0.11821,0.11821,0.11821,0.11821,0.11821,0.027588,0.085693,0.15647,0.085693,0.085693
+22-Dec-2019 09:00:00,1,0.16502,0.16502,0.16502,0.14383,0.02391,0.02391,0.02391,0.02391,0.14383,0.14383,0.14383,0.14383,0.14383,0.02391,0.089835,0.16502,0.089835,0.089835
+22-Dec-2019 10:00:00,1,0.16676,0.16676,0.16676,0.1517,0.018392,0.018392,0.018392,0.018392,0.1517,0.1517,0.1517,0.1517,0.1517,0.018392,0.097371,0.16676,0.097371,0.097371
+22-Dec-2019 11:00:00,1,0.15649,0.15649,0.15649,0.16441,0.018392,0.018392,0.018392,0.018392,0.16441,0.16441,0.16441,0.16441,0.16441,0.018392,0.10451,0.15649,0.10451,0.10451
+22-Dec-2019 12:00:00,1,0.14474,0.14474,0.14474,0.17389,0.02391,0.02391,0.02391,0.02391,0.17389,0.17389,0.17389,0.17389,0.17389,0.02391,0.1038,0.14474,0.1038,0.1038
+22-Dec-2019 13:00:00,1,0.1352,0.1352,0.1352,0.16895,0.025749,0.025749,0.025749,0.025749,0.16895,0.16895,0.16895,0.16895,0.16895,0.025749,0.10281,0.1352,0.10281,0.10281
+22-Dec-2019 14:00:00,1,0.13218,0.13218,0.13218,0.20622,0.018392,0.018392,0.018392,0.018392,0.20622,0.20622,0.20622,0.20622,0.20622,0.018392,0.1048,0.13218,0.1048,0.1048
+22-Dec-2019 15:00:00,1,0.12918,0.12918,0.12918,0.22448,0.038624,0.038624,0.038624,0.038624,0.22448,0.22448,0.22448,0.22448,0.22448,0.038624,0.11659,0.12918,0.11659,0.11659
+22-Dec-2019 16:00:00,1,0.14729,0.14729,0.14729,0.26094,0.04782,0.04782,0.04782,0.04782,0.26094,0.26094,0.26094,0.26094,0.26094,0.04782,0.12236,0.14729,0.12236,0.12236
+22-Dec-2019 17:00:00,1,0.17835,0.17835,0.17835,0.28288,0.057016,0.057016,0.057016,0.057016,0.28288,0.28288,0.28288,0.28288,0.28288,0.057016,0.12399,0.17835,0.12399,0.12399
+22-Dec-2019 18:00:00,1,0.19407,0.19407,0.19407,0.31884,0.079087,0.079087,0.079087,0.079087,0.31884,0.31884,0.31884,0.31884,0.31884,0.079087,0.14444,0.19407,0.14444,0.14444
+22-Dec-2019 19:00:00,1,0.2271,0.2271,0.2271,0.34441,0.079087,0.079087,0.079087,0.079087,0.34441,0.34441,0.34441,0.34441,0.34441,0.079087,0.16638,0.2271,0.16638,0.16638
+22-Dec-2019 20:00:00,1,0.24722,0.24722,0.24722,0.33664,0.10667,0.10667,0.10667,0.10667,0.33664,0.33664,0.33664,0.33664,0.33664,0.10667,0.18575,0.24722,0.18575,0.18575
+22-Dec-2019 21:00:00,1,0.26048,0.26048,0.26048,0.32176,0.11955,0.11955,0.11955,0.11955,0.32176,0.32176,0.32176,0.32176,0.32176,0.11955,0.20181,0.26048,0.20181,0.20181
+22-Dec-2019 22:00:00,1,0.29291,0.29291,0.29291,0.37159,0.11955,0.11955,0.11955,0.11955,0.37159,0.37159,0.37159,0.37159,0.37159,0.11955,0.21107,0.29291,0.21107,0.21107
+22-Dec-2019 23:00:00,1,0.33288,0.33288,0.33288,0.39237,0.17289,0.17289,0.17289,0.17289,0.39237,0.39237,0.39237,0.39237,0.39237,0.17289,0.22376,0.33288,0.22376,0.22376
+23-Dec-2019 00:00:00,1,0.34391,0.34391,0.34391,0.46141,0.16369,0.16369,0.16369,0.16369,0.46141,0.46141,0.46141,0.46141,0.46141,0.16369,0.24613,0.34391,0.24613,0.24613
+23-Dec-2019 01:00:00,1,0.32984,0.32984,0.32984,0.46353,0.1968,0.1968,0.1968,0.1968,0.46353,0.46353,0.46353,0.46353,0.46353,0.1968,0.24743,0.32984,0.24743,0.24743
+23-Dec-2019 02:00:00,1,0.35093,0.35093,0.35093,0.47674,0.20783,0.20783,0.20783,0.20783,0.47674,0.47674,0.47674,0.47674,0.47674,0.20783,0.22896,0.35093,0.22896,0.22896
+23-Dec-2019 03:00:00,1,0.35979,0.35979,0.35979,0.48189,0.25565,0.25565,0.25565,0.25565,0.48189,0.48189,0.48189,0.48189,0.48189,0.25565,0.25067,0.35979,0.25067,0.25067
+23-Dec-2019 04:00:00,1,0.36176,0.36176,0.36176,0.48804,0.28692,0.28692,0.28692,0.28692,0.48804,0.48804,0.48804,0.48804,0.48804,0.28692,0.26532,0.36176,0.26532,0.26532
+23-Dec-2019 05:00:00,1,0.34033,0.34033,0.34033,0.49898,0.28876,0.28876,0.28876,0.28876,0.49898,0.49898,0.49898,0.49898,0.49898,0.28876,0.27907,0.34033,0.27907,0.27907
+23-Dec-2019 06:00:00,1,0.35594,0.35594,0.35594,0.55935,0.27772,0.27772,0.27772,0.27772,0.55935,0.55935,0.55935,0.55935,0.55935,0.27772,0.29522,0.35594,0.29522,0.29522
+23-Dec-2019 07:00:00,1,0.37755,0.37755,0.37755,0.64519,0.24278,0.24278,0.24278,0.24278,0.64519,0.64519,0.64519,0.64519,0.64519,0.24278,0.31045,0.37755,0.31045,0.31045
+23-Dec-2019 08:00:00,1,0.37229,0.37229,0.37229,0.6757,0.25749,0.25749,0.25749,0.25749,0.6757,0.6757,0.6757,0.6757,0.6757,0.25749,0.29401,0.37229,0.29401,0.29401
+23-Dec-2019 09:00:00,1,0.36022,0.36022,0.36022,0.73587,0.27772,0.27772,0.27772,0.27772,0.73587,0.73587,0.73587,0.73587,0.73587,0.27772,0.29855,0.36022,0.29855,0.29855
+23-Dec-2019 10:00:00,1,0.353,0.353,0.353,0.78237,0.27221,0.27221,0.27221,0.27221,0.78237,0.78237,0.78237,0.78237,0.78237,0.27221,0.29795,0.353,0.29795,0.29795
+23-Dec-2019 11:00:00,1,0.36852,0.36852,0.36852,0.76335,0.25013,0.25013,0.25013,0.25013,0.76335,0.76335,0.76335,0.76335,0.76335,0.25013,0.31459,0.36852,0.31459,0.31459
+23-Dec-2019 12:00:00,1,0.38055,0.38055,0.38055,0.78938,0.2391,0.2391,0.2391,0.2391,0.78938,0.78938,0.78938,0.78938,0.78938,0.2391,0.33466,0.38055,0.33466,0.33466
+23-Dec-2019 13:00:00,1,0.40242,0.40242,0.40242,0.75639,0.23174,0.23174,0.23174,0.23174,0.75639,0.75639,0.75639,0.75639,0.75639,0.23174,0.34887,0.40242,0.34887,0.34887
+23-Dec-2019 14:00:00,1,0.41488,0.41488,0.41488,0.70117,0.22071,0.22071,0.22071,0.22071,0.70117,0.70117,0.70117,0.70117,0.70117,0.22071,0.33594,0.41488,0.33594,0.33594
+23-Dec-2019 15:00:00,1,0.42726,0.42726,0.42726,0.64852,0.21151,0.21151,0.21151,0.21151,0.64852,0.64852,0.64852,0.64852,0.64852,0.21151,0.32177,0.42726,0.32177,0.32177
+23-Dec-2019 16:00:00,1,0.41449,0.41449,0.41449,0.61548,0.19496,0.19496,0.19496,0.19496,0.61548,0.61548,0.61548,0.61548,0.61548,0.19496,0.27528,0.41449,0.27528,0.27528
+23-Dec-2019 17:00:00,1,0.43044,0.43044,0.43044,0.58966,0.17289,0.17289,0.17289,0.17289,0.58966,0.58966,0.58966,0.58966,0.58966,0.17289,0.25838,0.43044,0.25838,0.25838
+23-Dec-2019 18:00:00,1,0.42293,0.42293,0.42293,0.5071,0.19128,0.19128,0.19128,0.19128,0.5071,0.5071,0.5071,0.5071,0.5071,0.19128,0.25179,0.42293,0.25179,0.25179
+23-Dec-2019 19:00:00,1,0.43179,0.43179,0.43179,0.42545,0.20048,0.20048,0.20048,0.20048,0.42545,0.42545,0.42545,0.42545,0.42545,0.20048,0.27579,0.43179,0.27579,0.27579
+23-Dec-2019 20:00:00,1,0.41045,0.41045,0.41045,0.41996,0.18944,0.18944,0.18944,0.18944,0.41996,0.41996,0.41996,0.41996,0.41996,0.18944,0.28081,0.41045,0.28081,0.28081
+23-Dec-2019 21:00:00,1,0.4093,0.4093,0.4093,0.47059,0.18392,0.18392,0.18392,0.18392,0.47059,0.47059,0.47059,0.47059,0.47059,0.18392,0.28647,0.4093,0.28647,0.28647
+23-Dec-2019 22:00:00,1,0.40854,0.40854,0.40854,0.4425,0.17105,0.17105,0.17105,0.17105,0.4425,0.4425,0.4425,0.4425,0.4425,0.17105,0.29086,0.40854,0.29086,0.29086
+23-Dec-2019 23:00:00,1,0.40177,0.40177,0.40177,0.45349,0.14714,0.14714,0.14714,0.14714,0.45349,0.45349,0.45349,0.45349,0.45349,0.14714,0.29559,0.40177,0.29559,0.29559
+24-Dec-2019 00:00:00,1,0.40197,0.40197,0.40197,0.44119,0.14346,0.14346,0.14346,0.14346,0.44119,0.44119,0.44119,0.44119,0.44119,0.14346,0.30258,0.40197,0.30258,0.30258
+24-Dec-2019 01:00:00,1,0.4024,0.4024,0.4024,0.48098,0.15266,0.15266,0.15266,0.15266,0.48098,0.48098,0.48098,0.48098,0.48098,0.15266,0.31948,0.4024,0.31948,0.31948
+24-Dec-2019 02:00:00,1,0.36931,0.36931,0.36931,0.44905,0.15633,0.15633,0.15633,0.15633,0.44905,0.44905,0.44905,0.44905,0.44905,0.15633,0.31254,0.36931,0.31254,0.31254
+24-Dec-2019 03:00:00,1,0.33879,0.33879,0.33879,0.44825,0.13978,0.13978,0.13978,0.13978,0.44825,0.44825,0.44825,0.44825,0.44825,0.13978,0.30469,0.33879,0.30469,0.30469
+24-Dec-2019 04:00:00,1,0.31362,0.31362,0.31362,0.40155,0.10484,0.10484,0.10484,0.10484,0.40155,0.40155,0.40155,0.40155,0.40155,0.10484,0.29498,0.31362,0.29498,0.29498
+24-Dec-2019 05:00:00,1,0.29041,0.29041,0.29041,0.36841,0.082765,0.082765,0.082765,0.082765,0.36841,0.36841,0.36841,0.36841,0.36841,0.082765,0.28308,0.29041,0.28308,0.28308
+24-Dec-2019 06:00:00,1,0.27343,0.27343,0.27343,0.29428,0.057016,0.057016,0.057016,0.057016,0.29428,0.29428,0.29428,0.29428,0.29428,0.057016,0.29088,0.27343,0.29088,0.29088
+24-Dec-2019 07:00:00,1,0.25105,0.25105,0.25105,0.24954,0.075408,0.075408,0.075408,0.075408,0.24954,0.24954,0.24954,0.24954,0.24954,0.075408,0.28665,0.25105,0.28665,0.28665
+24-Dec-2019 08:00:00,1,0.21431,0.21431,0.21431,0.24944,0.057016,0.057016,0.057016,0.057016,0.24944,0.24944,0.24944,0.24944,0.24944,0.057016,0.27352,0.21431,0.27352,0.27352
+24-Dec-2019 09:00:00,1,0.20542,0.20542,0.20542,0.15135,0.060694,0.060694,0.060694,0.060694,0.15135,0.15135,0.15135,0.15135,0.15135,0.060694,0.24743,0.20542,0.24743,0.24743
+24-Dec-2019 10:00:00,1,0.18261,0.18261,0.18261,0.12129,0.060694,0.060694,0.060694,0.060694,0.12129,0.12129,0.12129,0.12129,0.12129,0.060694,0.22665,0.18261,0.22665,0.22665
+24-Dec-2019 11:00:00,1,0.15519,0.15519,0.15519,0.215,0.038624,0.038624,0.038624,0.038624,0.215,0.215,0.215,0.215,0.215,0.038624,0.17643,0.15519,0.17643,0.17643
+24-Dec-2019 12:00:00,1,0.14332,0.14332,0.14332,0.2681,0.034945,0.034945,0.034945,0.034945,0.2681,0.2681,0.2681,0.2681,0.2681,0.034945,0.13948,0.14332,0.13948,0.13948
+24-Dec-2019 13:00:00,1,0.13301,0.13301,0.13301,0.22776,0.034945,0.034945,0.034945,0.034945,0.22776,0.22776,0.22776,0.22776,0.22776,0.034945,0.13093,0.13301,0.13093,0.13093
+24-Dec-2019 14:00:00,1,0.13209,0.13209,0.13209,0.28192,0.044141,0.044141,0.044141,0.044141,0.28192,0.28192,0.28192,0.28192,0.28192,0.044141,0.12723,0.13209,0.12723,0.12723
+24-Dec-2019 15:00:00,1,0.18625,0.18625,0.18625,0.34884,0.103,0.103,0.103,0.103,0.34884,0.34884,0.34884,0.34884,0.34884,0.103,0.14818,0.18625,0.14818,0.14818
+24-Dec-2019 16:00:00,1,0.25836,0.25836,0.25836,0.44991,0.103,0.103,0.103,0.103,0.44991,0.44991,0.44991,0.44991,0.44991,0.103,0.17927,0.25836,0.17927,0.17927
+24-Dec-2019 17:00:00,1,0.32429,0.32429,0.32429,0.55043,0.10667,0.10667,0.10667,0.10667,0.55043,0.55043,0.55043,0.55043,0.55043,0.10667,0.24165,0.32429,0.24165,0.24165
+24-Dec-2019 18:00:00,1,0.41235,0.41235,0.41235,0.71499,0.10851,0.10851,0.10851,0.10851,0.71499,0.71499,0.71499,0.71499,0.71499,0.10851,0.29509,0.41235,0.29509,0.29509
+24-Dec-2019 19:00:00,1,0.50914,0.50914,0.50914,0.76925,0.103,0.103,0.103,0.103,0.76925,0.76925,0.76925,0.76925,0.76925,0.103,0.36818,0.50914,0.36818,0.36818
+24-Dec-2019 20:00:00,1,0.64154,0.64154,0.64154,0.757,0.06989,0.06989,0.06989,0.06989,0.757,0.757,0.757,0.757,0.757,0.06989,0.49053,0.64154,0.49053,0.49053
+24-Dec-2019 21:00:00,1,0.74323,0.74323,0.74323,0.74817,0.11771,0.11771,0.11771,0.11771,0.74817,0.74817,0.74817,0.74817,0.74817,0.11771,0.55342,0.74323,0.55342,0.55342
+24-Dec-2019 22:00:00,1,0.78389,0.78389,0.78389,0.64811,0.075408,0.075408,0.075408,0.075408,0.64811,0.64811,0.64811,0.64811,0.64811,0.075408,0.59416,0.78389,0.59416,0.59416
+24-Dec-2019 23:00:00,1,0.81658,0.81658,0.81658,0.53318,0.088283,0.088283,0.088283,0.088283,0.53318,0.53318,0.53318,0.53318,0.53318,0.088283,0.66163,0.81658,0.66163,0.66163
+25-Dec-2019 00:00:00,1,0.75728,0.75728,0.75728,0.37124,0.075408,0.075408,0.075408,0.075408,0.37124,0.37124,0.37124,0.37124,0.37124,0.075408,0.65077,0.75728,0.65077,0.65077
+25-Dec-2019 01:00:00,1,0.68467,0.68467,0.68467,0.25564,0.103,0.103,0.103,0.103,0.25564,0.25564,0.25564,0.25564,0.25564,0.103,0.6344,0.68467,0.6344,0.6344
+25-Dec-2019 02:00:00,1,0.57484,0.57484,0.57484,0.23885,0.11219,0.11219,0.11219,0.11219,0.23885,0.23885,0.23885,0.23885,0.23885,0.11219,0.56867,0.57484,0.56867,0.56867
+25-Dec-2019 03:00:00,1,0.43351,0.43351,0.43351,0.30865,0.10484,0.10484,0.10484,0.10484,0.30865,0.30865,0.30865,0.30865,0.30865,0.10484,0.51444,0.43351,0.51444,0.51444
+25-Dec-2019 04:00:00,1,0.32188,0.32188,0.32188,0.38173,0.14898,0.14898,0.14898,0.14898,0.38173,0.38173,0.38173,0.38173,0.38173,0.14898,0.47427,0.32188,0.47427,0.47427
+25-Dec-2019 05:00:00,1,0.24724,0.24724,0.24724,0.49979,0.16001,0.16001,0.16001,0.16001,0.49979,0.49979,0.49979,0.49979,0.49979,0.16001,0.44331,0.24724,0.44331,0.44331
+25-Dec-2019 06:00:00,1,0.21834,0.21834,0.21834,0.66289,0.19312,0.19312,0.19312,0.19312,0.66289,0.66289,0.66289,0.66289,0.66289,0.19312,0.4787,0.21834,0.4787,0.4787
+25-Dec-2019 07:00:00,1,0.25096,0.25096,0.25096,0.53081,0.2391,0.2391,0.2391,0.2391,0.53081,0.53081,0.53081,0.53081,0.53081,0.2391,0.50221,0.25096,0.50221,0.50221
+25-Dec-2019 08:00:00,1,0.27352,0.27352,0.27352,0.48824,0.14162,0.14162,0.14162,0.14162,0.48824,0.48824,0.48824,0.48824,0.48824,0.14162,0.51634,0.27352,0.51634,0.51634
+25-Dec-2019 09:00:00,1,0.27155,0.27155,0.27155,0.48799,0.19496,0.19496,0.19496,0.19496,0.48799,0.48799,0.48799,0.48799,0.48799,0.19496,0.53363,0.27155,0.53363,0.53363
+25-Dec-2019 10:00:00,1,0.26409,0.26409,0.26409,0.37305,0.17657,0.17657,0.17657,0.17657,0.37305,0.37305,0.37305,0.37305,0.37305,0.17657,0.56177,0.26409,0.56177,0.56177
+25-Dec-2019 11:00:00,1,0.26391,0.26391,0.26391,0.3555,0.22806,0.22806,0.22806,0.22806,0.3555,0.3555,0.3555,0.3555,0.3555,0.22806,0.57711,0.26391,0.57711,0.57711
+25-Dec-2019 12:00:00,1,0.26397,0.26397,0.26397,0.35379,0.24278,0.24278,0.24278,0.24278,0.35379,0.35379,0.35379,0.35379,0.35379,0.24278,0.58343,0.26397,0.58343,0.58343
+25-Dec-2019 13:00:00,1,0.27158,0.27158,0.27158,0.38874,0.22806,0.22806,0.22806,0.22806,0.38874,0.38874,0.38874,0.38874,0.38874,0.22806,0.5606,0.27158,0.5606,0.5606
+25-Dec-2019 14:00:00,1,0.27401,0.27401,0.27401,0.53524,0.20599,0.20599,0.20599,0.20599,0.53524,0.53524,0.53524,0.53524,0.53524,0.20599,0.50085,0.27401,0.50085,0.50085
+25-Dec-2019 15:00:00,1,0.28434,0.28434,0.28434,0.58104,0.17657,0.17657,0.17657,0.17657,0.58104,0.58104,0.58104,0.58104,0.58104,0.17657,0.43523,0.28434,0.43523,0.43523
+25-Dec-2019 16:00:00,1,0.30909,0.30909,0.30909,0.62804,0.15817,0.15817,0.15817,0.15817,0.62804,0.62804,0.62804,0.62804,0.62804,0.15817,0.35612,0.30909,0.35612,0.35612
+25-Dec-2019 17:00:00,1,0.35864,0.35864,0.35864,0.67716,0.13978,0.13978,0.13978,0.13978,0.67716,0.67716,0.67716,0.67716,0.67716,0.13978,0.2967,0.35864,0.2967,0.2967
+25-Dec-2019 18:00:00,1,0.41003,0.41003,0.41003,0.73329,0.22439,0.22439,0.22439,0.22439,0.73329,0.73329,0.73329,0.73329,0.73329,0.22439,0.29013,0.41003,0.29013,0.29013
+25-Dec-2019 19:00:00,1,0.4728,0.4728,0.4728,0.77193,0.23174,0.23174,0.23174,0.23174,0.77193,0.77193,0.77193,0.77193,0.77193,0.23174,0.34486,0.4728,0.34486,0.34486
+25-Dec-2019 20:00:00,1,0.53009,0.53009,0.53009,0.74187,0.24646,0.24646,0.24646,0.24646,0.74187,0.74187,0.74187,0.74187,0.74187,0.24646,0.40467,0.53009,0.40467,0.40467
+25-Dec-2019 21:00:00,1,0.60487,0.60487,0.60487,0.74288,0.22622,0.22622,0.22622,0.22622,0.74288,0.74288,0.74288,0.74288,0.74288,0.22622,0.49739,0.60487,0.49739,0.49739
+25-Dec-2019 22:00:00,1,0.68327,0.68327,0.68327,0.66183,0.20783,0.20783,0.20783,0.20783,0.66183,0.66183,0.66183,0.66183,0.66183,0.20783,0.60774,0.68327,0.60774,0.60774
+25-Dec-2019 23:00:00,1,0.6812,0.6812,0.6812,0.62128,0.18392,0.18392,0.18392,0.18392,0.62128,0.62128,0.62128,0.62128,0.62128,0.18392,0.66681,0.6812,0.66681,0.66681
+26-Dec-2019 00:00:00,1,0.64118,0.64118,0.64118,0.58321,0.14898,0.14898,0.14898,0.14898,0.58321,0.58321,0.58321,0.58321,0.58321,0.14898,0.66329,0.64118,0.66329,0.66329
+26-Dec-2019 01:00:00,1,0.6131,0.6131,0.6131,0.54992,0.13058,0.13058,0.13058,0.13058,0.54992,0.54992,0.54992,0.54992,0.54992,0.13058,0.65652,0.6131,0.65652,0.65652
+26-Dec-2019 02:00:00,1,0.59542,0.59542,0.59542,0.55713,0.11955,0.11955,0.11955,0.11955,0.55713,0.55713,0.55713,0.55713,0.55713,0.11955,0.63211,0.59542,0.63211,0.63211
+26-Dec-2019 03:00:00,1,0.5764,0.5764,0.5764,0.51159,0.12139,0.12139,0.12139,0.12139,0.51159,0.51159,0.51159,0.51159,0.51159,0.12139,0.61336,0.5764,0.61336,0.61336
+26-Dec-2019 04:00:00,1,0.55093,0.55093,0.55093,0.46565,0.12875,0.12875,0.12875,0.12875,0.46565,0.46565,0.46565,0.46565,0.46565,0.12875,0.58383,0.55093,0.58383,0.58383
+26-Dec-2019 05:00:00,1,0.50309,0.50309,0.50309,0.43856,0.14346,0.14346,0.14346,0.14346,0.43856,0.43856,0.43856,0.43856,0.43856,0.14346,0.56259,0.50309,0.56259,0.56259
+26-Dec-2019 06:00:00,1,0.48859,0.48859,0.48859,0.42338,0.1453,0.1453,0.1453,0.1453,0.42338,0.42338,0.42338,0.42338,0.42338,0.1453,0.53315,0.48859,0.53315,0.53315
+26-Dec-2019 07:00:00,1,0.46888,0.46888,0.46888,0.44905,0.13426,0.13426,0.13426,0.13426,0.44905,0.44905,0.44905,0.44905,0.44905,0.13426,0.53339,0.46888,0.53339,0.53339
+26-Dec-2019 08:00:00,1,0.43222,0.43222,0.43222,0.41567,0.18944,0.18944,0.18944,0.18944,0.41567,0.41567,0.41567,0.41567,0.41567,0.18944,0.51746,0.43222,0.51746,0.51746
+26-Dec-2019 09:00:00,1,0.38711,0.38711,0.38711,0.4774,0.23358,0.23358,0.23358,0.23358,0.4774,0.4774,0.4774,0.4774,0.4774,0.23358,0.44664,0.38711,0.44664,0.44664
+26-Dec-2019 10:00:00,1,0.38176,0.38176,0.38176,0.51553,0.21335,0.21335,0.21335,0.21335,0.51553,0.51553,0.51553,0.51553,0.51553,0.21335,0.38854,0.38176,0.38854,0.38854
+26-Dec-2019 11:00:00,1,0.39473,0.39473,0.39473,0.49777,0.25013,0.25013,0.25013,0.25013,0.49777,0.49777,0.49777,0.49777,0.49777,0.25013,0.36934,0.39473,0.36934,0.36934
+26-Dec-2019 12:00:00,1,0.44903,0.44903,0.44903,0.59491,0.25565,0.25565,0.25565,0.25565,0.59491,0.59491,0.59491,0.59491,0.59491,0.25565,0.33601,0.44903,0.33601,0.33601
+26-Dec-2019 13:00:00,1,0.48173,0.48173,0.48173,0.55774,0.22255,0.22255,0.22255,0.22255,0.55774,0.55774,0.55774,0.55774,0.55774,0.22255,0.32305,0.48173,0.32305,0.32305
+26-Dec-2019 14:00:00,1,0.50769,0.50769,0.50769,0.5712,0.23174,0.23174,0.23174,0.23174,0.5712,0.5712,0.5712,0.5712,0.5712,0.23174,0.32267,0.50769,0.32267,0.32267
+26-Dec-2019 15:00:00,1,0.49867,0.49867,0.49867,0.58316,0.17105,0.17105,0.17105,0.17105,0.58316,0.58316,0.58316,0.58316,0.58316,0.17105,0.32801,0.49867,0.32801,0.32801
+26-Dec-2019 16:00:00,1,0.5049,0.5049,0.5049,0.54689,0.15817,0.15817,0.15817,0.15817,0.54689,0.54689,0.54689,0.54689,0.54689,0.15817,0.35667,0.5049,0.35667,0.35667
+26-Dec-2019 17:00:00,1,0.48545,0.48545,0.48545,0.52107,0.15266,0.15266,0.15266,0.15266,0.52107,0.52107,0.52107,0.52107,0.52107,0.15266,0.41667,0.48545,0.41667,0.41667
+26-Dec-2019 18:00:00,1,0.48755,0.48755,0.48755,0.55083,0.16553,0.16553,0.16553,0.16553,0.55083,0.55083,0.55083,0.55083,0.55083,0.16553,0.45338,0.48755,0.45338,0.45338
+26-Dec-2019 19:00:00,1,0.48097,0.48097,0.48097,0.63535,0.17289,0.17289,0.17289,0.17289,0.63535,0.63535,0.63535,0.63535,0.63535,0.17289,0.46107,0.48097,0.46107,0.46107
+26-Dec-2019 20:00:00,1,0.4774,0.4774,0.4774,0.72724,0.17289,0.17289,0.17289,0.17289,0.72724,0.72724,0.72724,0.72724,0.72724,0.17289,0.49036,0.4774,0.49036,0.49036
+26-Dec-2019 21:00:00,1,0.46112,0.46112,0.46112,0.81005,0.19312,0.19312,0.19312,0.19312,0.81005,0.81005,0.81005,0.81005,0.81005,0.19312,0.51471,0.46112,0.51471,0.51471
+26-Dec-2019 22:00:00,1,0.46211,0.46211,0.46211,0.80047,0.18576,0.18576,0.18576,0.18576,0.80047,0.80047,0.80047,0.80047,0.80047,0.18576,0.46592,0.46211,0.46592,0.46592
+26-Dec-2019 23:00:00,1,0.4931,0.4931,0.4931,0.8101,0.19496,0.19496,0.19496,0.19496,0.8101,0.8101,0.8101,0.8101,0.8101,0.19496,0.47326,0.4931,0.47326,0.47326
+27-Dec-2019 00:00:00,1,0.53039,0.53039,0.53039,0.79134,0.22439,0.22439,0.22439,0.22439,0.79134,0.79134,0.79134,0.79134,0.79134,0.22439,0.49344,0.53039,0.49344,0.49344
+27-Dec-2019 01:00:00,1,0.59154,0.59154,0.59154,0.79482,0.23542,0.23542,0.23542,0.23542,0.79482,0.79482,0.79482,0.79482,0.79482,0.23542,0.49274,0.59154,0.49274,0.49274
+27-Dec-2019 02:00:00,1,0.66572,0.66572,0.66572,0.70929,0.2814,0.2814,0.2814,0.2814,0.70929,0.70929,0.70929,0.70929,0.70929,0.2814,0.5302,0.66572,0.5302,0.5302
+27-Dec-2019 03:00:00,1,0.70456,0.70456,0.70456,0.76517,0.27037,0.27037,0.27037,0.27037,0.76517,0.76517,0.76517,0.76517,0.76517,0.27037,0.6273,0.70456,0.6273,0.6273
+27-Dec-2019 04:00:00,1,0.74539,0.74539,0.74539,0.74141,0.21887,0.21887,0.21887,0.21887,0.74141,0.74141,0.74141,0.74141,0.74141,0.21887,0.70777,0.74539,0.70777,0.70777
+27-Dec-2019 05:00:00,1,0.7543,0.7543,0.7543,0.67777,0.26117,0.26117,0.26117,0.26117,0.67777,0.67777,0.67777,0.67777,0.67777,0.26117,0.73217,0.7543,0.73217,0.73217
+27-Dec-2019 06:00:00,1,0.77334,0.77334,0.77334,0.6288,0.21519,0.21519,0.21519,0.21519,0.6288,0.6288,0.6288,0.6288,0.6288,0.21519,0.74285,0.77334,0.74285,0.74285
+27-Dec-2019 07:00:00,1,0.79008,0.79008,0.79008,0.5532,0.1968,0.1968,0.1968,0.1968,0.5532,0.5532,0.5532,0.5532,0.5532,0.1968,0.70947,0.79008,0.70947,0.70947
+27-Dec-2019 08:00:00,1,0.7656,0.7656,0.7656,0.48688,0.24462,0.24462,0.24462,0.24462,0.48688,0.48688,0.48688,0.48688,0.48688,0.24462,0.68043,0.7656,0.68043,0.68043
+27-Dec-2019 09:00:00,1,0.73242,0.73242,0.73242,0.45828,0.15082,0.15082,0.15082,0.15082,0.45828,0.45828,0.45828,0.45828,0.45828,0.15082,0.65394,0.73242,0.65394,0.65394
+27-Dec-2019 10:00:00,1,0.68431,0.68431,0.68431,0.39978,0.2299,0.2299,0.2299,0.2299,0.39978,0.39978,0.39978,0.39978,0.39978,0.2299,0.65443,0.68431,0.65443,0.65443
+27-Dec-2019 11:00:00,1,0.62133,0.62133,0.62133,0.39877,0.25933,0.25933,0.25933,0.25933,0.39877,0.39877,0.39877,0.39877,0.39877,0.25933,0.67944,0.62133,0.67944,0.67944
+27-Dec-2019 12:00:00,1,0.52181,0.52181,0.52181,0.25807,0.15633,0.15633,0.15633,0.15633,0.25807,0.25807,0.25807,0.25807,0.25807,0.15633,0.68384,0.52181,0.68384,0.68384
+27-Dec-2019 13:00:00,1,0.40159,0.40159,0.40159,0.20042,0.16921,0.16921,0.16921,0.16921,0.20042,0.20042,0.20042,0.20042,0.20042,0.16921,0.60027,0.40159,0.60027,0.60027
+27-Dec-2019 14:00:00,1,0.26393,0.26393,0.26393,0.21631,0.16921,0.16921,0.16921,0.16921,0.21631,0.21631,0.21631,0.21631,0.21631,0.16921,0.49369,0.26393,0.49369,0.49369
+27-Dec-2019 15:00:00,1,0.16299,0.16299,0.16299,0.21969,0.13978,0.13978,0.13978,0.13978,0.21969,0.21969,0.21969,0.21969,0.21969,0.13978,0.40222,0.16299,0.40222,0.40222
+27-Dec-2019 16:00:00,1,0.11116,0.11116,0.11116,0.31536,0.13794,0.13794,0.13794,0.13794,0.31536,0.31536,0.31536,0.31536,0.31536,0.13794,0.29689,0.11116,0.29689,0.29689
+27-Dec-2019 17:00:00,1,0.11644,0.11644,0.11644,0.30618,0.17473,0.17473,0.17473,0.17473,0.30618,0.30618,0.30618,0.30618,0.30618,0.17473,0.21991,0.11644,0.21991,0.21991
+27-Dec-2019 18:00:00,1,0.1474,0.1474,0.1474,0.2966,0.23358,0.23358,0.23358,0.23358,0.2966,0.2966,0.2966,0.2966,0.2966,0.23358,0.17586,0.1474,0.17586,0.17586
+27-Dec-2019 19:00:00,1,0.19509,0.19509,0.19509,0.2794,0.26117,0.26117,0.26117,0.26117,0.2794,0.2794,0.2794,0.2794,0.2794,0.26117,0.14754,0.19509,0.14754,0.14754
+27-Dec-2019 20:00:00,1,0.231,0.231,0.231,0.32166,0.22071,0.22071,0.22071,0.22071,0.32166,0.32166,0.32166,0.32166,0.32166,0.22071,0.15061,0.231,0.15061,0.15061
+27-Dec-2019 21:00:00,1,0.24458,0.24458,0.24458,0.28666,0.26669,0.26669,0.26669,0.26669,0.28666,0.28666,0.28666,0.28666,0.28666,0.26669,0.1676,0.24458,0.1676,0.1676
+27-Dec-2019 22:00:00,1,0.24611,0.24611,0.24611,0.27163,0.22255,0.22255,0.22255,0.22255,0.27163,0.27163,0.27163,0.27163,0.27163,0.22255,0.21437,0.24611,0.21437,0.21437
+27-Dec-2019 23:00:00,1,0.21031,0.21031,0.21031,0.28585,0.27588,0.27588,0.27588,0.27588,0.28585,0.28585,0.28585,0.28585,0.28585,0.27588,0.26382,0.21031,0.26382,0.26382
+28-Dec-2019 00:00:00,1,0.15422,0.15422,0.15422,0.21701,0.19496,0.19496,0.19496,0.19496,0.21701,0.21701,0.21701,0.21701,0.21701,0.19496,0.26765,0.15422,0.26765,0.26765
+28-Dec-2019 01:00:00,1,0.13883,0.13883,0.13883,0.23048,0.22806,0.22806,0.22806,0.22806,0.23048,0.23048,0.23048,0.23048,0.23048,0.22806,0.25877,0.13883,0.25877,0.25877
+28-Dec-2019 02:00:00,1,0.12113,0.12113,0.12113,0.2096,0.26853,0.26853,0.26853,0.26853,0.2096,0.2096,0.2096,0.2096,0.2096,0.26853,0.22793,0.12113,0.22793,0.22793
+28-Dec-2019 03:00:00,1,0.10236,0.10236,0.10236,0.22654,0.16737,0.16737,0.16737,0.16737,0.22654,0.22654,0.22654,0.22654,0.22654,0.16737,0.24157,0.10236,0.24157,0.24157
+28-Dec-2019 04:00:00,1,0.063654,0.063654,0.063654,0.18211,0.14162,0.14162,0.14162,0.14162,0.18211,0.18211,0.18211,0.18211,0.18211,0.14162,0.23509,0.063654,0.23509,0.23509
+28-Dec-2019 05:00:00,1,0.04209,0.04209,0.04209,0.12265,0.10484,0.10484,0.10484,0.10484,0.12265,0.12265,0.12265,0.12265,0.12265,0.10484,0.19214,0.04209,0.19214,0.19214
+28-Dec-2019 06:00:00,1,0.038639,0.038639,0.038639,0.11176,0.084604,0.084604,0.084604,0.084604,0.11176,0.11176,0.11176,0.11176,0.11176,0.084604,0.12974,0.038639,0.12974,0.12974
+28-Dec-2019 07:00:00,1,0.050052,0.050052,0.050052,0.13113,0.077247,0.077247,0.077247,0.077247,0.13113,0.13113,0.13113,0.13113,0.13113,0.077247,0.074609,0.050052,0.074609,0.074609
+28-Dec-2019 08:00:00,1,0.066383,0.066383,0.066383,0.14752,0.079087,0.079087,0.079087,0.079087,0.14752,0.14752,0.14752,0.14752,0.14752,0.079087,0.045744,0.066383,0.045744,0.045744
+28-Dec-2019 09:00:00,1,0.10491,0.10491,0.10491,0.16981,0.06989,0.06989,0.06989,0.06989,0.16981,0.16981,0.16981,0.16981,0.16981,0.06989,0.039552,0.10491,0.039552,0.039552
+28-Dec-2019 10:00:00,1,0.12313,0.12313,0.12313,0.17803,0.057016,0.057016,0.057016,0.057016,0.17803,0.17803,0.17803,0.17803,0.17803,0.057016,0.032171,0.12313,0.032171,0.032171
+28-Dec-2019 11:00:00,1,0.1165,0.1165,0.1165,0.16149,0.060694,0.060694,0.060694,0.060694,0.16149,0.16149,0.16149,0.16149,0.16149,0.060694,0.024943,0.1165,0.024943,0.024943
+28-Dec-2019 12:00:00,1,0.12927,0.12927,0.12927,0.19664,0.051498,0.051498,0.051498,0.051498,0.19664,0.19664,0.19664,0.19664,0.19664,0.051498,0.042681,0.12927,0.042681,0.042681
+28-Dec-2019 13:00:00,1,0.14071,0.14071,0.14071,0.23436,0.053337,0.053337,0.053337,0.053337,0.23436,0.23436,0.23436,0.23436,0.23436,0.053337,0.068153,0.14071,0.068153,0.068153
+28-Dec-2019 14:00:00,1,0.17804,0.17804,0.17804,0.27652,0.040463,0.040463,0.040463,0.040463,0.27652,0.27652,0.27652,0.27652,0.27652,0.040463,0.069475,0.17804,0.069475,0.069475
+28-Dec-2019 15:00:00,1,0.22292,0.22292,0.22292,0.29579,0.031267,0.031267,0.031267,0.031267,0.29579,0.29579,0.29579,0.29579,0.29579,0.031267,0.083092,0.22292,0.083092,0.083092
+28-Dec-2019 16:00:00,1,0.29948,0.29948,0.29948,0.34194,0.031267,0.031267,0.031267,0.031267,0.34194,0.34194,0.34194,0.34194,0.34194,0.031267,0.14721,0.29948,0.14721,0.14721
+28-Dec-2019 17:00:00,1,0.36915,0.36915,0.36915,0.38647,0.031267,0.031267,0.031267,0.031267,0.38647,0.38647,0.38647,0.38647,0.38647,0.031267,0.22484,0.36915,0.22484,0.22484
+28-Dec-2019 18:00:00,1,0.4151,0.4151,0.4151,0.42016,0.029428,0.029428,0.029428,0.029428,0.42016,0.42016,0.42016,0.42016,0.42016,0.029428,0.28768,0.4151,0.28768,0.28768
+28-Dec-2019 19:00:00,1,0.44111,0.44111,0.44111,0.41854,0.051498,0.051498,0.051498,0.051498,0.41854,0.41854,0.41854,0.41854,0.41854,0.051498,0.31051,0.44111,0.31051,0.31051
+28-Dec-2019 20:00:00,1,0.48437,0.48437,0.48437,0.42807,0.057016,0.057016,0.057016,0.057016,0.42807,0.42807,0.42807,0.42807,0.42807,0.057016,0.31172,0.48437,0.31172,0.31172
+28-Dec-2019 21:00:00,1,0.52549,0.52549,0.52549,0.44442,0.055177,0.055177,0.055177,0.055177,0.44442,0.44442,0.44442,0.44442,0.44442,0.055177,0.33955,0.52549,0.33955,0.33955
+28-Dec-2019 22:00:00,1,0.54378,0.54378,0.54378,0.41219,0.075408,0.075408,0.075408,0.075408,0.41219,0.41219,0.41219,0.41219,0.41219,0.075408,0.37805,0.54378,0.37805,0.37805
+28-Dec-2019 23:00:00,1,0.55028,0.55028,0.55028,0.37305,0.053337,0.053337,0.053337,0.053337,0.37305,0.37305,0.37305,0.37305,0.37305,0.053337,0.38653,0.55028,0.38653,0.38653
+29-Dec-2019 00:00:00,1,0.52538,0.52538,0.52538,0.37789,0.053337,0.053337,0.053337,0.053337,0.37789,0.37789,0.37789,0.37789,0.37789,0.053337,0.41399,0.52538,0.41399,0.41399
+29-Dec-2019 01:00:00,1,0.53118,0.53118,0.53118,0.40165,0.045981,0.045981,0.045981,0.045981,0.40165,0.40165,0.40165,0.40165,0.40165,0.045981,0.43159,0.53118,0.43159,0.43159
+29-Dec-2019 02:00:00,1,0.50747,0.50747,0.50747,0.33059,0.04782,0.04782,0.04782,0.04782,0.33059,0.33059,0.33059,0.33059,0.33059,0.04782,0.43963,0.50747,0.43963,0.43963
+29-Dec-2019 03:00:00,1,0.4901,0.4901,0.4901,0.35212,0.036784,0.036784,0.036784,0.036784,0.35212,0.35212,0.35212,0.35212,0.35212,0.036784,0.4419,0.4901,0.4419,0.4419
+29-Dec-2019 04:00:00,1,0.46511,0.46511,0.46511,0.36216,0.033106,0.033106,0.033106,0.033106,0.36216,0.36216,0.36216,0.36216,0.36216,0.033106,0.4369,0.46511,0.4369,0.4369
+29-Dec-2019 05:00:00,1,0.44537,0.44537,0.44537,0.37704,0.040463,0.040463,0.040463,0.040463,0.37704,0.37704,0.37704,0.37704,0.37704,0.040463,0.38776,0.44537,0.38776,0.38776
+29-Dec-2019 06:00:00,1,0.45004,0.45004,0.45004,0.39247,0.029428,0.029428,0.029428,0.029428,0.39247,0.39247,0.39247,0.39247,0.39247,0.029428,0.3784,0.45004,0.3784,0.3784
+29-Dec-2019 07:00:00,1,0.44711,0.44711,0.44711,0.42066,0.027588,0.027588,0.027588,0.027588,0.42066,0.42066,0.42066,0.42066,0.42066,0.027588,0.38735,0.44711,0.38735,0.38735
+29-Dec-2019 08:00:00,1,0.44138,0.44138,0.44138,0.38042,0.055177,0.055177,0.055177,0.055177,0.38042,0.38042,0.38042,0.38042,0.38042,0.055177,0.4031,0.44138,0.4031,0.4031
+29-Dec-2019 09:00:00,1,0.4527,0.4527,0.4527,0.38183,0.062534,0.062534,0.062534,0.062534,0.38183,0.38183,0.38183,0.38183,0.38183,0.062534,0.37569,0.4527,0.37569,0.37569
+29-Dec-2019 10:00:00,1,0.43064,0.43064,0.43064,0.39075,0.051498,0.051498,0.051498,0.051498,0.39075,0.39075,0.39075,0.39075,0.39075,0.051498,0.33764,0.43064,0.33764,0.33764
+29-Dec-2019 11:00:00,1,0.41878,0.41878,0.41878,0.32797,0.053337,0.053337,0.053337,0.053337,0.32797,0.32797,0.32797,0.32797,0.32797,0.053337,0.33691,0.41878,0.33691,0.33691
+29-Dec-2019 12:00:00,1,0.41659,0.41659,0.41659,0.35187,0.04782,0.04782,0.04782,0.04782,0.35187,0.35187,0.35187,0.35187,0.35187,0.04782,0.30267,0.41659,0.30267,0.30267
+29-Dec-2019 13:00:00,1,0.4209,0.4209,0.4209,0.35177,0.045981,0.045981,0.045981,0.045981,0.35177,0.35177,0.35177,0.35177,0.35177,0.045981,0.26071,0.4209,0.26071,0.26071
+29-Dec-2019 14:00:00,1,0.44386,0.44386,0.44386,0.45294,0.027588,0.027588,0.027588,0.027588,0.45294,0.45294,0.45294,0.45294,0.45294,0.027588,0.2545,0.44386,0.2545,0.2545
+29-Dec-2019 15:00:00,1,0.45286,0.45286,0.45286,0.45425,0.042302,0.042302,0.042302,0.042302,0.45425,0.45425,0.45425,0.45425,0.45425,0.042302,0.26871,0.45286,0.26871,0.26871
+29-Dec-2019 16:00:00,1,0.50422,0.50422,0.50422,0.42853,0.068051,0.068051,0.068051,0.068051,0.42853,0.42853,0.42853,0.42853,0.42853,0.068051,0.31699,0.50422,0.31699,0.31699
+29-Dec-2019 17:00:00,1,0.56648,0.56648,0.56648,0.48108,0.084604,0.084604,0.084604,0.084604,0.48108,0.48108,0.48108,0.48108,0.48108,0.084604,0.36212,0.56648,0.36212,0.36212
+29-Dec-2019 18:00:00,1,0.59384,0.59384,0.59384,0.44653,0.051498,0.051498,0.051498,0.051498,0.44653,0.44653,0.44653,0.44653,0.44653,0.051498,0.39808,0.59384,0.39808,0.39808
+29-Dec-2019 19:00:00,1,0.60678,0.60678,0.60678,0.4596,0.079087,0.079087,0.079087,0.079087,0.4596,0.4596,0.4596,0.4596,0.4596,0.079087,0.44799,0.60678,0.44799,0.44799
+29-Dec-2019 20:00:00,1,0.59117,0.59117,0.59117,0.48975,0.09564,0.09564,0.09564,0.09564,0.48975,0.48975,0.48975,0.48975,0.48975,0.09564,0.49084,0.59117,0.49084,0.49084
+29-Dec-2019 21:00:00,1,0.61342,0.61342,0.61342,0.56545,0.11035,0.11035,0.11035,0.11035,0.56545,0.56545,0.56545,0.56545,0.56545,0.11035,0.46544,0.61342,0.46544,0.46544
+29-Dec-2019 22:00:00,1,0.62079,0.62079,0.62079,0.57388,0.12139,0.12139,0.12139,0.12139,0.57388,0.57388,0.57388,0.57388,0.57388,0.12139,0.46414,0.62079,0.46414,0.46414
+29-Dec-2019 23:00:00,1,0.57586,0.57586,0.57586,0.60898,0.16553,0.16553,0.16553,0.16553,0.60898,0.60898,0.60898,0.60898,0.60898,0.16553,0.47009,0.57586,0.47009,0.47009
+30-Dec-2019 00:00:00,1,0.55678,0.55678,0.55678,0.59022,0.1361,0.1361,0.1361,0.1361,0.59022,0.59022,0.59022,0.59022,0.59022,0.1361,0.45587,0.55678,0.45587,0.45587
+30-Dec-2019 01:00:00,1,0.53014,0.53014,0.53014,0.60686,0.12875,0.12875,0.12875,0.12875,0.60686,0.60686,0.60686,0.60686,0.60686,0.12875,0.45281,0.53014,0.45281,0.45281
+30-Dec-2019 02:00:00,1,0.51622,0.51622,0.51622,0.55355,0.14898,0.14898,0.14898,0.14898,0.55355,0.55355,0.55355,0.55355,0.55355,0.14898,0.48888,0.51622,0.48888,0.48888
+30-Dec-2019 03:00:00,1,0.51656,0.51656,0.51656,0.55809,0.18208,0.18208,0.18208,0.18208,0.55809,0.55809,0.55809,0.55809,0.55809,0.18208,0.50475,0.51656,0.50475,0.50475
+30-Dec-2019 04:00:00,1,0.51097,0.51097,0.51097,0.57524,0.13794,0.13794,0.13794,0.13794,0.57524,0.57524,0.57524,0.57524,0.57524,0.13794,0.52337,0.51097,0.52337,0.52337
+30-Dec-2019 05:00:00,1,0.51997,0.51997,0.51997,0.5994,0.13978,0.13978,0.13978,0.13978,0.5994,0.5994,0.5994,0.5994,0.5994,0.13978,0.51865,0.51997,0.51865,0.51865
+30-Dec-2019 06:00:00,1,0.53636,0.53636,0.53636,0.65336,0.15082,0.15082,0.15082,0.15082,0.65336,0.65336,0.65336,0.65336,0.65336,0.15082,0.51933,0.53636,0.51933,0.51933
+30-Dec-2019 07:00:00,1,0.54442,0.54442,0.54442,0.67378,0.16185,0.16185,0.16185,0.16185,0.67378,0.67378,0.67378,0.67378,0.67378,0.16185,0.53092,0.54442,0.53092,0.53092
+30-Dec-2019 08:00:00,1,0.60293,0.60293,0.60293,0.7101,0.17289,0.17289,0.17289,0.17289,0.7101,0.7101,0.7101,0.7101,0.7101,0.17289,0.50796,0.60293,0.50796,0.50796
+30-Dec-2019 09:00:00,1,0.64554,0.64554,0.64554,0.65094,0.2299,0.2299,0.2299,0.2299,0.65094,0.65094,0.65094,0.65094,0.65094,0.2299,0.54278,0.64554,0.54278,0.54278
+30-Dec-2019 10:00:00,1,0.69175,0.69175,0.69175,0.71373,0.19128,0.19128,0.19128,0.19128,0.71373,0.71373,0.71373,0.71373,0.71373,0.19128,0.56243,0.69175,0.56243,0.56243
+30-Dec-2019 11:00:00,1,0.73937,0.73937,0.73937,0.76421,0.22439,0.22439,0.22439,0.22439,0.76421,0.76421,0.76421,0.76421,0.76421,0.22439,0.58923,0.73937,0.58923,0.58923
+30-Dec-2019 12:00:00,1,0.77077,0.77077,0.77077,0.77742,0.17473,0.17473,0.17473,0.17473,0.77742,0.77742,0.77742,0.77742,0.77742,0.17473,0.6325,0.77077,0.6325,0.6325
+30-Dec-2019 13:00:00,1,0.78191,0.78191,0.78191,0.77137,0.15266,0.15266,0.15266,0.15266,0.77137,0.77137,0.77137,0.77137,0.77137,0.15266,0.62799,0.78191,0.62799,0.62799
+30-Dec-2019 14:00:00,1,0.77733,0.77733,0.77733,0.7628,0.15082,0.15082,0.15082,0.15082,0.7628,0.7628,0.7628,0.7628,0.7628,0.15082,0.6071,0.77733,0.6071,0.6071
+30-Dec-2019 15:00:00,1,0.74553,0.74553,0.74553,0.71458,0.1968,0.1968,0.1968,0.1968,0.71458,0.71458,0.71458,0.71458,0.71458,0.1968,0.57222,0.74553,0.57222,0.57222
+30-Dec-2019 16:00:00,1,0.74634,0.74634,0.74634,0.68654,0.15817,0.15817,0.15817,0.15817,0.68654,0.68654,0.68654,0.68654,0.68654,0.15817,0.50316,0.74634,0.50316,0.50316
+30-Dec-2019 17:00:00,1,0.70953,0.70953,0.70953,0.74772,0.15266,0.15266,0.15266,0.15266,0.74772,0.74772,0.74772,0.74772,0.74772,0.15266,0.54322,0.70953,0.54322,0.54322
+30-Dec-2019 18:00:00,1,0.68704,0.68704,0.68704,0.73985,0.15449,0.15449,0.15449,0.15449,0.73985,0.73985,0.73985,0.73985,0.73985,0.15449,0.5855,0.68704,0.5855,0.5855
+30-Dec-2019 19:00:00,1,0.71253,0.71253,0.71253,0.70677,0.16553,0.16553,0.16553,0.16553,0.70677,0.70677,0.70677,0.70677,0.70677,0.16553,0.58176,0.71253,0.58176,0.58176
+30-Dec-2019 20:00:00,1,0.69859,0.69859,0.69859,0.70026,0.14346,0.14346,0.14346,0.14346,0.70026,0.70026,0.70026,0.70026,0.70026,0.14346,0.60417,0.69859,0.60417,0.60417
+30-Dec-2019 21:00:00,1,0.68659,0.68659,0.68659,0.7393,0.15082,0.15082,0.15082,0.15082,0.7393,0.7393,0.7393,0.7393,0.7393,0.15082,0.65284,0.68659,0.65284,0.65284
+30-Dec-2019 22:00:00,1,0.65219,0.65219,0.65219,0.65815,0.13242,0.13242,0.13242,0.13242,0.65815,0.65815,0.65815,0.65815,0.65815,0.13242,0.62404,0.65219,0.62404,0.62404
+30-Dec-2019 23:00:00,1,0.66085,0.66085,0.66085,0.6582,0.12507,0.12507,0.12507,0.12507,0.6582,0.6582,0.6582,0.6582,0.6582,0.12507,0.59529,0.66085,0.59529,0.59529
+31-Dec-2019 00:00:00,1,0.67569,0.67569,0.67569,0.64544,0.11955,0.11955,0.11955,0.11955,0.64544,0.64544,0.64544,0.64544,0.64544,0.11955,0.60062,0.67569,0.60062,0.60062
+31-Dec-2019 01:00:00,1,0.68185,0.68185,0.68185,0.66713,0.13426,0.13426,0.13426,0.13426,0.66713,0.66713,0.66713,0.66713,0.66713,0.13426,0.57288,0.68185,0.57288,0.57288
+31-Dec-2019 02:00:00,1,0.69191,0.69191,0.69191,0.67368,0.14346,0.14346,0.14346,0.14346,0.67368,0.67368,0.67368,0.67368,0.67368,0.14346,0.58354,0.69191,0.58354,0.58354
+31-Dec-2019 03:00:00,1,0.7054,0.7054,0.7054,0.68599,0.13978,0.13978,0.13978,0.13978,0.68599,0.68599,0.68599,0.68599,0.68599,0.13978,0.6069,0.7054,0.6069,0.6069
+31-Dec-2019 04:00:00,1,0.74596,0.74596,0.74596,0.73219,0.090122,0.090122,0.090122,0.090122,0.73219,0.73219,0.73219,0.73219,0.73219,0.090122,0.61166,0.74596,0.61166,0.61166
+31-Dec-2019 05:00:00,1,0.79465,0.79465,0.79465,0.71983,0.12139,0.12139,0.12139,0.12139,0.71983,0.71983,0.71983,0.71983,0.71983,0.12139,0.64709,0.79465,0.64709,0.64709
+31-Dec-2019 06:00:00,1,0.80325,0.80325,0.80325,0.72109,0.099318,0.099318,0.099318,0.099318,0.72109,0.72109,0.72109,0.72109,0.72109,0.099318,0.69497,0.80325,0.69497,0.69497
+31-Dec-2019 07:00:00,1,0.77956,0.77956,0.77956,0.71388,0.103,0.103,0.103,0.103,0.71388,0.71388,0.71388,0.71388,0.71388,0.103,0.74058,0.77956,0.74058,0.74058
+31-Dec-2019 08:00:00,1,0.76736,0.76736,0.76736,0.66491,0.099318,0.099318,0.099318,0.099318,0.66491,0.66491,0.66491,0.66491,0.66491,0.099318,0.7312,0.76736,0.7312,0.7312
+31-Dec-2019 09:00:00,1,0.77665,0.77665,0.77665,0.67918,0.091961,0.091961,0.091961,0.091961,0.67918,0.67918,0.67918,0.67918,0.67918,0.091961,0.72456,0.77665,0.72456,0.72456
+31-Dec-2019 10:00:00,1,0.76763,0.76763,0.76763,0.66748,0.068051,0.068051,0.068051,0.068051,0.66748,0.66748,0.66748,0.66748,0.66748,0.068051,0.70456,0.76763,0.70456,0.70456
+31-Dec-2019 11:00:00,1,0.75121,0.75121,0.75121,0.69915,0.066212,0.066212,0.066212,0.066212,0.69915,0.69915,0.69915,0.69915,0.69915,0.066212,0.67911,0.75121,0.67911,0.67911
+31-Dec-2019 12:00:00,1,0.7552,0.7552,0.7552,0.7573,0.053337,0.053337,0.053337,0.053337,0.7573,0.7573,0.7573,0.7573,0.7573,0.053337,0.64068,0.7552,0.64068,0.64068
+31-Dec-2019 13:00:00,1,0.75611,0.75611,0.75611,0.71519,0.066212,0.066212,0.066212,0.066212,0.71519,0.71519,0.71519,0.71519,0.71519,0.066212,0.58821,0.75611,0.58821,0.58821
+31-Dec-2019 14:00:00,1,0.73826,0.73826,0.73826,0.66183,0.062534,0.062534,0.062534,0.062534,0.66183,0.66183,0.66183,0.66183,0.66183,0.062534,0.53489,0.73826,0.53489,0.53489
+31-Dec-2019 15:00:00,1,0.71336,0.71336,0.71336,0.60847,0.04782,0.04782,0.04782,0.04782,0.60847,0.60847,0.60847,0.60847,0.60847,0.04782,0.50387,0.71336,0.50387,0.50387
+31-Dec-2019 16:00:00,1,0.73161,0.73161,0.73161,0.58865,0.04782,0.04782,0.04782,0.04782,0.58865,0.58865,0.58865,0.58865,0.58865,0.04782,0.48201,0.73161,0.48201,0.48201
+31-Dec-2019 17:00:00,1,0.72879,0.72879,0.72879,0.6352,0.062534,0.062534,0.062534,0.062534,0.6352,0.6352,0.6352,0.6352,0.6352,0.062534,0.49547,0.72879,0.49547,0.49547
+31-Dec-2019 18:00:00,1,0.7442,0.7442,0.7442,0.65109,0.068051,0.068051,0.068051,0.068051,0.65109,0.65109,0.65109,0.65109,0.65109,0.068051,0.51724,0.7442,0.51724,0.51724
+31-Dec-2019 19:00:00,1,0.74823,0.74823,0.74823,0.69517,0.064373,0.064373,0.064373,0.064373,0.69517,0.69517,0.69517,0.69517,0.69517,0.064373,0.53295,0.74823,0.53295,0.53295
+31-Dec-2019 20:00:00,1,0.73538,0.73538,0.73538,0.65366,0.073569,0.073569,0.073569,0.073569,0.65366,0.65366,0.65366,0.65366,0.65366,0.073569,0.54084,0.73538,0.54084,0.54084
+31-Dec-2019 21:00:00,1,0.74097,0.74097,0.74097,0.66188,0.07173,0.07173,0.07173,0.07173,0.66188,0.66188,0.66188,0.66188,0.66188,0.07173,0.57076,0.74097,0.57076,0.57076
+31-Dec-2019 22:00:00,1,0.72482,0.72482,0.72482,0.59329,0.09564,0.09564,0.09564,0.09564,0.59329,0.59329,0.59329,0.59329,0.59329,0.09564,0.59456,0.72482,0.59456,0.59456
+31-Dec-2019 23:00:00,1,0.7252,0.7252,0.7252,0.56803,0.11035,0.11035,0.11035,0.11035,0.56803,0.56803,0.56803,0.56803,0.56803,0.11035,0.59456,0.7252,0.59456,0.59456
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/existing_capacity.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/existing_capacity.csv
new file mode 100644
index 0000000..b1a1f62
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/existing_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,1900,2010,2016,comments
+1,MW,353.50000000000045,0.0,2942.5140000000088,
+2,MW,0.0,0.0,0.0,
+3,MW,0.0,0.0,0.0,
+4,MW,0.0,0.0,0.0,
+5,MW,0.0,0.0,0.0,
+6,MW,0.0,0.0,0.0,
+7,MW,0.0,0.0,0.0,
+8,MW,0.0,0.0,0.0,
+9,MW,0.0,0.0,0.0,
+10,MW,0.0,0.0,0.0,
+11,MW,0.0,0.0,0.0,
+12,MW,0.0,0.0,0.0,
+13,MW,0.0,0.0,0.0,
+14,MW,0.0,0.0,0.0,
+15,MW,0.0,0.0,0.0,
+16,MW,0.0,48.29999999999998,287.9999999999999,
+17,MW,0.0,0.0,0.0,
+18,MW,0.0,0.0,0.0,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/maximum_production_share.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/maximum_production_share.csv
new file mode 100644
index 0000000..5333c85
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/maximum_production_share.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,1,,,,,,,,,
+2,1,,,,,,,,,
+3,1,,,,,,,,,
+4,1,,,,,,,,,
+5,1,,,,,,,,,
+6,1,,,,,,,,,
+7,1,,,,,,,,,
+8,1,,,,,,,,,
+9,1,,,,,,,,,
+10,1,,,,,,,,,
+11,1,,,,,,,,,
+12,1,,,,,,,,,
+13,1,,,,,,,,,
+14,1,,,,,,,,,
+15,1,,,,,,,,,
+16,1,,,,,,,,,
+17,1,,,,,,,,,
+18,1,,,,,,,,,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/potential_capacity.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/potential_capacity.csv
new file mode 100644
index 0000000..627de35
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/DE/potential_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,MW ,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,
+2,MW ,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,
+3,MW ,0,0,0,0,0,0,0,0,
+4,MW ,0,0,0,0,0,0,0,0,
+5,MW ,0,0,0,0,0,0,0,0,
+6,MW ,0,0,0,0,0,0,0,0,
+7,MW ,0,0,0,0,0,0,0,0,
+8,MW ,0,0,0,0,0,0,0,0,
+9,MW ,0,0,0,0,0,0,0,0,
+10,MW ,0,0,0,0,0,0,0,0,
+11,MW ,0,0,0,0,0,0,0,0,
+12,MW ,0,0,0,0,0,0,0,0,
+13,MW ,0,0,0,0,0,0,0,0,
+14,MW ,0,0,0,0,0,0,0,0,
+15,MW ,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,12666.6667,
+16,MW ,0,0,0,0,0,0,0,0,
+17,MW ,0,0,0,0,0,0,0,0,
+18,MW ,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/README.md
new file mode 100644
index 0000000..a9279ab
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/README.md
@@ -0,0 +1,6 @@
+# README - WIND OFFSHORE
+
+This data package contains all necessary information
+about the production process **wind offshore**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/costs.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/costs.csv
new file mode 100644
index 0000000..f31d1fe
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,kiloEuro * MW ** (-1),2590,2590,2373,2155,1938,1720,1503,1285,
+operation,kiloEuro * MWh ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * MW ** (-1),93,93,93,93,93,93,93,93,k€/(MW*a)
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_invest.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_invest.csv
new file mode 100644
index 0000000..97b1018
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_invest.csv
@@ -0,0 +1,3 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+wind_power_plant_2MW_offshore_fixed_parts_GLO_market_for_wind_power_plant_2MW_offshore_fixed_parts,MW ** (-1),0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,MW,,,wind power plant. 2MW. offshore. fixed parts,GLO,market for wind power plant. 2MW. offshore. fixed parts,1,0,1,0.5,1
+wind_power_plant_2MW_offshore_moving_parts_GLO_market_for_wind_power_plant_2MW_offshore_moving_parts,MW ** (-1),0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,MW,,,wind power plant. 2MW. offshore. moving parts,GLO,market for wind power plant. 2MW. offshore. moving parts,1,0,1,0.5,1
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_operation.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_operation.csv
new file mode 100644
index 0000000..986fda6
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/ecoinvent_operation.csv
@@ -0,0 +1,4 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+electricity_high_voltage_DE_electricity_production_wind_1_3MW_turbine_offshore,MWh ** (-1),1000,1000,1000,1000,1000,1000,1000,1000,MWh,,,electricity. high voltage,DE,electricity production. wind. 1-3MW turbine. offshore,1,0,1,1000,1
+wind_power_plant_2MW_offshore_fixed_parts_GLO_market_for_wind_power_plant_2MW_offshore_fixed_parts,MWh ** (-1),-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,MW,,,wind power plant. 2MW. offshore. fixed parts,GLO,market for wind power plant. 2MW. offshore. fixed parts,-1e-06,0,1,1000,1
+wind_power_plant_2MW_offshore_moving_parts_GLO_market_for_wind_power_plant_2MW_offshore_moving_parts,MWh ** (-1),-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,-1e-06,MW,,,wind power plant. 2MW. offshore. moving parts,GLO,market for wind power plant. 2MW. offshore. moving parts,-1e-06,0,1,1000,1
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/lifetime_duration.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/lifetime_duration.csv
new file mode 100644
index 0000000..3439ce1
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,25,25,25,25,25,25,25,25,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/properties.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/properties.csv
new file mode 100644
index 0000000..0f9dc45
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/properties.csv
@@ -0,0 +1,2 @@
+property,value,unit
+reference product,electricity,MW
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/secured_capacity_factor.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/secured_capacity_factor.csv
new file mode 100644
index 0000000..202835f
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/secured_capacity_factor.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix.csv
new file mode 100644
index 0000000..30026d9
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix.csv
@@ -0,0 +1,2 @@
+product,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+electricity,MW,1,1,1,1,1,1,1,1,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix_timeseries.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix_timeseries.csv
new file mode 100644
index 0000000..7ae20c3
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_offshore/technologymatrix_timeseries.csv
@@ -0,0 +1,8761 @@
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diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/README.md
new file mode 100644
index 0000000..899c002
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/README.md
@@ -0,0 +1,4 @@
+# README - WIND ONSHORE - DE
+
+This data package contains all necessary information
+about the production process **wind onshore** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/availability_timeseries.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/availability_timeseries.csv
new file mode 100644
index 0000000..81df610
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/availability_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18
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+24-Jan-2019 01:00:00,1,0.12839,0.12839,0.12839,0.17411,0.10678,0.10678,0.10678,0.10678,0.17411,0.17411,0.17411,0.17411,0.17411,0.10678,0.051841,0.12839,0.051841,0.051841
+24-Jan-2019 02:00:00,1,0.12643,0.12643,0.12643,0.16547,0.056305,0.056305,0.056305,0.056305,0.16547,0.16547,0.16547,0.16547,0.16547,0.056305,0.059698,0.12643,0.059698,0.059698
+24-Jan-2019 03:00:00,1,0.11974,0.11974,0.11974,0.18082,0.07572,0.07572,0.07572,0.07572,0.18082,0.18082,0.18082,0.18082,0.18082,0.07572,0.067414,0.11974,0.067414,0.067414
+24-Jan-2019 04:00:00,1,0.11876,0.11876,0.11876,0.16288,0.079603,0.079603,0.079603,0.079603,0.16288,0.16288,0.16288,0.16288,0.16288,0.079603,0.065691,0.11876,0.065691,0.065691
+24-Jan-2019 05:00:00,1,0.11974,0.11974,0.11974,0.17466,0.083486,0.083486,0.083486,0.083486,0.17466,0.17466,0.17466,0.17466,0.17466,0.083486,0.051982,0.11974,0.051982,0.051982
+24-Jan-2019 06:00:00,1,0.11138,0.11138,0.11138,0.17312,0.10096,0.10096,0.10096,0.10096,0.17312,0.17312,0.17312,0.17312,0.17312,0.10096,0.047239,0.11138,0.047239,0.047239
+24-Jan-2019 07:00:00,1,0.098485,0.098485,0.098485,0.1636,0.038831,0.038831,0.038831,0.038831,0.1636,0.1636,0.1636,0.1636,0.1636,0.038831,0.04042,0.098485,0.04042,0.04042
+24-Jan-2019 08:00:00,1,0.085705,0.085705,0.085705,0.10809,0.027182,0.027182,0.027182,0.027182,0.10809,0.10809,0.10809,0.10809,0.10809,0.027182,0.037117,0.085705,0.037117,0.037117
+24-Jan-2019 09:00:00,1,0.082481,0.082481,0.082481,0.096432,0.033006,0.033006,0.033006,0.033006,0.096432,0.096432,0.096432,0.096432,0.096432,0.033006,0.030321,0.082481,0.030321,0.030321
+24-Jan-2019 10:00:00,1,0.060529,0.060529,0.060529,0.086035,0.031065,0.031065,0.031065,0.031065,0.086035,0.086035,0.086035,0.086035,0.086035,0.031065,0.028197,0.060529,0.028197,0.028197
+24-Jan-2019 11:00:00,1,0.05193,0.05193,0.05193,0.060511,0.031065,0.031065,0.031065,0.031065,0.060511,0.060511,0.060511,0.060511,0.060511,0.031065,0.021897,0.05193,0.021897,0.021897
+24-Jan-2019 12:00:00,1,0.04517,0.04517,0.04517,0.046758,0.015532,0.015532,0.015532,0.015532,0.046758,0.046758,0.046758,0.046758,0.046758,0.015532,0.022062,0.04517,0.022062,0.022062
+24-Jan-2019 13:00:00,1,0.040536,0.040536,0.040536,0.057045,0.023299,0.023299,0.023299,0.023299,0.057045,0.057045,0.057045,0.057045,0.057045,0.023299,0.018523,0.040536,0.018523,0.018523
+24-Jan-2019 14:00:00,1,0.037407,0.037407,0.037407,0.049509,0.021357,0.021357,0.021357,0.021357,0.049509,0.049509,0.049509,0.049509,0.049509,0.021357,0.014016,0.037407,0.014016,0.014016
+24-Jan-2019 15:00:00,1,0.035042,0.035042,0.035042,0.02943,0.015532,0.015532,0.015532,0.015532,0.02943,0.02943,0.02943,0.02943,0.02943,0.015532,0.011633,0.035042,0.011633,0.011633
+24-Jan-2019 16:00:00,1,0.033274,0.033274,0.033274,0.031191,0.015532,0.015532,0.015532,0.015532,0.031191,0.031191,0.031191,0.031191,0.031191,0.015532,0.011562,0.033274,0.011562,0.011562
+24-Jan-2019 17:00:00,1,0.034588,0.034588,0.034588,0.041147,0.015532,0.015532,0.015532,0.015532,0.041147,0.041147,0.041147,0.041147,0.041147,0.015532,0.010028,0.034588,0.010028,0.010028
+24-Jan-2019 18:00:00,1,0.035352,0.035352,0.035352,0.035701,0.0097077,0.0097077,0.0097077,0.0097077,0.035701,0.035701,0.035701,0.035701,0.035701,0.0097077,0.0077631,0.035352,0.0077631,0.0077631
+24-Jan-2019 19:00:00,1,0.036188,0.036188,0.036188,0.034766,0.0058246,0.0058246,0.0058246,0.0058246,0.034766,0.034766,0.034766,0.034766,0.034766,0.0058246,0.008589,0.036188,0.008589,0.008589
+24-Jan-2019 20:00:00,1,0.034588,0.034588,0.034588,0.026735,0.0038831,0.0038831,0.0038831,0.0038831,0.026735,0.026735,0.026735,0.026735,0.026735,0.0038831,0.0098632,0.034588,0.0098632,0.0098632
+24-Jan-2019 21:00:00,1,0.028807,0.028807,0.028807,0.021289,0.0058246,0.0058246,0.0058246,0.0058246,0.021289,0.021289,0.021289,0.021289,0.021289,0.0058246,0.0059698,0.028807,0.0059698,0.0059698
+24-Jan-2019 22:00:00,1,0.023337,0.023337,0.023337,0.014468,0.0019415,0.0019415,0.0019415,0.0019415,0.014468,0.014468,0.014468,0.014468,0.014468,0.0019415,0.0048844,0.023337,0.0048844,0.0048844
+24-Jan-2019 23:00:00,1,0.024914,0.024914,0.024914,0.018043,0,0,0,0,0.018043,0.018043,0.018043,0.018043,0.018043,0,0.0051911,0.024914,0.0051911,0.0051911
+25-Jan-2019 00:00:00,1,0.021259,0.021259,0.021259,0.021289,0,0,0,0,0.021289,0.021289,0.021289,0.021289,0.021289,0,0.0037046,0.021259,0.0037046,0.0037046
+25-Jan-2019 01:00:00,1,0.017461,0.017461,0.017461,0.023929,0,0,0,0,0.023929,0.023929,0.023929,0.023929,0.023929,0,0.0056867,0.017461,0.0056867,0.0056867
+25-Jan-2019 02:00:00,1,0.017581,0.017581,0.017581,0.02899,0,0,0,0,0.02899,0.02899,0.02899,0.02899,0.02899,0,0.0096036,0.017581,0.0096036,0.0096036
+25-Jan-2019 03:00:00,1,0.018679,0.018679,0.018679,0.039552,0,0,0,0,0.039552,0.039552,0.039552,0.039552,0.039552,0,0.010972,0.018679,0.010972,0.010972
+25-Jan-2019 04:00:00,1,0.022143,0.022143,0.022143,0.047033,0,0,0,0,0.047033,0.047033,0.047033,0.047033,0.047033,0,0.011161,0.022143,0.011161,0.011161
+25-Jan-2019 05:00:00,1,0.022716,0.022716,0.022716,0.067937,0,0,0,0,0.067937,0.067937,0.067937,0.067937,0.067937,0,0.0097216,0.022716,0.0097216,0.0097216
+25-Jan-2019 06:00:00,1,0.034875,0.034875,0.034875,0.083395,0.0019415,0.0019415,0.0019415,0.0019415,0.083395,0.083395,0.083395,0.083395,0.083395,0.0019415,0.0072204,0.034875,0.0072204,0.0072204
+25-Jan-2019 07:00:00,1,0.040751,0.040751,0.040751,0.097697,0.0019415,0.0019415,0.0019415,0.0019415,0.097697,0.097697,0.097697,0.097697,0.097697,0.0019415,0.0041057,0.040751,0.0041057,0.0041057
+25-Jan-2019 08:00:00,1,0.042614,0.042614,0.042614,0.11156,0.0038831,0.0038831,0.0038831,0.0038831,0.11156,0.11156,0.11156,0.11156,0.11156,0.0038831,0.0031383,0.042614,0.0031383,0.0031383
+25-Jan-2019 09:00:00,1,0.04966,0.04966,0.04966,0.11068,0.0097077,0.0097077,0.0097077,0.0097077,0.11068,0.11068,0.11068,0.11068,0.11068,0.0097077,0.0029259,0.04966,0.0029259,0.0029259
+25-Jan-2019 10:00:00,1,0.066357,0.066357,0.066357,0.10171,0.011649,0.011649,0.011649,0.011649,0.10171,0.10171,0.10171,0.10171,0.10171,0.011649,0.0022416,0.066357,0.0022416,0.0022416
+25-Jan-2019 11:00:00,1,0.069654,0.069654,0.069654,0.078334,0.0097077,0.0097077,0.0097077,0.0097077,0.078334,0.078334,0.078334,0.078334,0.078334,0.0097077,0.0016281,0.069654,0.0016281,0.0016281
+25-Jan-2019 12:00:00,1,0.072401,0.072401,0.072401,0.060126,0.0038831,0.0038831,0.0038831,0.0038831,0.060126,0.060126,0.060126,0.060126,0.060126,0.0038831,0.0019349,0.072401,0.0019349,0.0019349
+25-Jan-2019 13:00:00,1,0.080068,0.080068,0.080068,0.058145,0.0019415,0.0019415,0.0019415,0.0019415,0.058145,0.058145,0.058145,0.058145,0.058145,0.0019415,0.0020765,0.080068,0.0020765,0.0020765
+25-Jan-2019 14:00:00,1,0.095953,0.095953,0.095953,0.068872,0.0058246,0.0058246,0.0058246,0.0058246,0.068872,0.068872,0.068872,0.068872,0.068872,0.0058246,0.0032091,0.095953,0.0032091,0.0032091
+25-Jan-2019 15:00:00,1,0.1088,0.1088,0.1088,0.08609,0.0077662,0.0077662,0.0077662,0.0077662,0.08609,0.08609,0.08609,0.08609,0.08609,0.0077662,0.003799,0.1088,0.003799,0.003799
+25-Jan-2019 16:00:00,1,0.14174,0.14174,0.14174,0.11415,0.0097077,0.0097077,0.0097077,0.0097077,0.11415,0.11415,0.11415,0.11415,0.11415,0.0097077,0.0066305,0.14174,0.0066305,0.0066305
+25-Jan-2019 17:00:00,1,0.178,0.178,0.178,0.14022,0.015532,0.015532,0.015532,0.015532,0.14022,0.14022,0.14022,0.14022,0.14022,0.015532,0.010052,0.178,0.010052,0.010052
+25-Jan-2019 18:00:00,1,0.20282,0.20282,0.20282,0.15034,0.023299,0.023299,0.023299,0.023299,0.15034,0.15034,0.15034,0.15034,0.15034,0.023299,0.018523,0.20282,0.018523,0.018523
+25-Jan-2019 19:00:00,1,0.2268,0.2268,0.2268,0.17389,0.023299,0.023299,0.023299,0.023299,0.17389,0.17389,0.17389,0.17389,0.17389,0.023299,0.02303,0.2268,0.02303,0.02303
+25-Jan-2019 20:00:00,1,0.23977,0.23977,0.23977,0.18483,0.0097077,0.0097077,0.0097077,0.0097077,0.18483,0.18483,0.18483,0.18483,0.18483,0.0097077,0.028764,0.23977,0.028764,0.028764
+25-Jan-2019 21:00:00,1,0.26488,0.26488,0.26488,0.18923,0.013591,0.013591,0.013591,0.013591,0.18923,0.18923,0.18923,0.18923,0.18923,0.013591,0.037541,0.26488,0.037541,0.037541
+25-Jan-2019 22:00:00,1,0.28368,0.28368,0.28368,0.19594,0.011649,0.011649,0.011649,0.011649,0.19594,0.19594,0.19594,0.19594,0.19594,0.011649,0.060972,0.28368,0.060972,0.060972
+25-Jan-2019 23:00:00,1,0.29643,0.29643,0.29643,0.19407,0.017474,0.017474,0.017474,0.017474,0.19407,0.19407,0.19407,0.19407,0.19407,0.017474,0.077159,0.29643,0.077159,0.077159
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+26-Jan-2019 01:00:00,1,0.30828,0.30828,0.30828,0.1691,0.011649,0.011649,0.011649,0.011649,0.1691,0.1691,0.1691,0.1691,0.1691,0.011649,0.086999,0.30828,0.086999,0.086999
+26-Jan-2019 02:00:00,1,0.32386,0.32386,0.32386,0.17152,0.027182,0.027182,0.027182,0.027182,0.17152,0.17152,0.17152,0.17152,0.17152,0.027182,0.1114,0.32386,0.1114,0.1114
+26-Jan-2019 03:00:00,1,0.35369,0.35369,0.35369,0.17856,0.036889,0.036889,0.036889,0.036889,0.17856,0.17856,0.17856,0.17856,0.17856,0.036889,0.11076,0.35369,0.11076,0.11076
+26-Jan-2019 04:00:00,1,0.36413,0.36413,0.36413,0.1801,0.027182,0.027182,0.027182,0.027182,0.1801,0.1801,0.1801,0.1801,0.1801,0.027182,0.12473,0.36413,0.12473,0.12473
+26-Jan-2019 05:00:00,1,0.39829,0.39829,0.39829,0.18549,0.019415,0.019415,0.019415,0.019415,0.18549,0.18549,0.18549,0.18549,0.18549,0.019415,0.13185,0.39829,0.13185,0.13185
+26-Jan-2019 06:00:00,1,0.42222,0.42222,0.42222,0.18742,0.017474,0.017474,0.017474,0.017474,0.18742,0.18742,0.18742,0.18742,0.18742,0.017474,0.15205,0.42222,0.15205,0.15205
+26-Jan-2019 07:00:00,1,0.43612,0.43612,0.43612,0.20656,0.027182,0.027182,0.027182,0.027182,0.20656,0.20656,0.20656,0.20656,0.20656,0.027182,0.17669,0.43612,0.17669,0.17669
+26-Jan-2019 08:00:00,1,0.4516,0.4516,0.4516,0.22466,0.031065,0.031065,0.031065,0.031065,0.22466,0.22466,0.22466,0.22466,0.22466,0.031065,0.19167,0.4516,0.19167,0.19167
+26-Jan-2019 09:00:00,1,0.48079,0.48079,0.48079,0.25717,0.031065,0.031065,0.031065,0.031065,0.25717,0.25717,0.25717,0.25717,0.25717,0.031065,0.18006,0.48079,0.18006,0.18006
+26-Jan-2019 10:00:00,1,0.50941,0.50941,0.50941,0.24727,0.017474,0.017474,0.017474,0.017474,0.24727,0.24727,0.24727,0.24727,0.24727,0.017474,0.13931,0.50941,0.13931,0.13931
+26-Jan-2019 11:00:00,1,0.52723,0.52723,0.52723,0.22378,0.027182,0.027182,0.027182,0.027182,0.22378,0.22378,0.22378,0.22378,0.22378,0.027182,0.12487,0.52723,0.12487,0.12487
+26-Jan-2019 12:00:00,1,0.55329,0.55329,0.55329,0.21052,0.027182,0.027182,0.027182,0.027182,0.21052,0.21052,0.21052,0.21052,0.21052,0.027182,0.1411,0.55329,0.1411,0.1411
+26-Jan-2019 13:00:00,1,0.56934,0.56934,0.56934,0.23467,0.038831,0.038831,0.038831,0.038831,0.23467,0.23467,0.23467,0.23467,0.23467,0.038831,0.1597,0.56934,0.1597,0.1597
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+26-Jan-2019 16:00:00,1,0.58353,0.58353,0.58353,0.32395,0.023299,0.023299,0.023299,0.023299,0.32395,0.32395,0.32395,0.32395,0.32395,0.023299,0.26069,0.58353,0.26069,0.26069
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+26-Jan-2019 18:00:00,1,0.65712,0.65712,0.65712,0.24149,0.027182,0.027182,0.027182,0.027182,0.24149,0.24149,0.24149,0.24149,0.24149,0.027182,0.33245,0.65712,0.33245,0.33245
+26-Jan-2019 19:00:00,1,0.64265,0.64265,0.64265,0.19611,0.023299,0.023299,0.023299,0.023299,0.19611,0.19611,0.19611,0.19611,0.19611,0.023299,0.34257,0.64265,0.34257,0.34257
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+26-Jan-2019 21:00:00,1,0.51504,0.51504,0.51504,0.12718,0.05048,0.05048,0.05048,0.05048,0.12718,0.12718,0.12718,0.12718,0.12718,0.05048,0.40271,0.51504,0.40271,0.40271
+26-Jan-2019 22:00:00,1,0.44728,0.44728,0.44728,0.089721,0.027182,0.027182,0.027182,0.027182,0.089721,0.089721,0.089721,0.089721,0.089721,0.027182,0.39441,0.44728,0.39441,0.39441
+26-Jan-2019 23:00:00,1,0.40543,0.40543,0.40543,0,0.02524,0.02524,0.02524,0.02524,0,0,0,0,0,0.02524,0.38421,0.40543,0.38421,0.38421
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+27-Jan-2019 01:00:00,1,0.31148,0.31148,0.31148,0.087521,0.011649,0.011649,0.011649,0.011649,0.087521,0.087521,0.087521,0.087521,0.087521,0.011649,0.38891,0.31148,0.38891,0.38891
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+30-Jan-2019 06:00:00,1,0.31182,0.31182,0.31182,0.079819,0.015532,0.015532,0.015532,0.015532,0.079819,0.079819,0.079819,0.079819,0.079819,0.015532,0.33131,0.31182,0.33131,0.33131
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+30-Jan-2019 09:00:00,1,0.33482,0.33482,0.33482,0.067827,0.0097077,0.0097077,0.0097077,0.0097077,0.067827,0.067827,0.067827,0.067827,0.067827,0.0097077,0.29051,0.33482,0.29051,0.29051
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+10-Mar-2019 14:00:00,1,0.44565,0.44565,0.44565,0.086912,0.0057663,0.0057663,0.0057663,0.0057663,0.086912,0.086912,0.086912,0.086912,0.086912,0.0057663,0.41641,0.44565,0.41641,0.41641
+10-Mar-2019 15:00:00,1,0.45416,0.45416,0.45416,0.079876,0.0096105,0.0096105,0.0096105,0.0096105,0.079876,0.079876,0.079876,0.079876,0.079876,0.0096105,0.46587,0.45416,0.46587,0.46587
+10-Mar-2019 16:00:00,1,0.42228,0.42228,0.42228,0.07046,0.013455,0.013455,0.013455,0.013455,0.07046,0.07046,0.07046,0.07046,0.07046,0.013455,0.49047,0.42228,0.49047,0.49047
+10-Mar-2019 17:00:00,1,0.33968,0.33968,0.33968,0.071326,0.0096105,0.0096105,0.0096105,0.0096105,0.071326,0.071326,0.071326,0.071326,0.071326,0.0096105,0.4928,0.33968,0.4928,0.4928
+10-Mar-2019 18:00:00,1,0.29903,0.29903,0.29903,0.10039,0.0038442,0.0038442,0.0038442,0.0038442,0.10039,0.10039,0.10039,0.10039,0.10039,0.0038442,0.49958,0.29903,0.49958,0.49958
+10-Mar-2019 19:00:00,1,0.29976,0.29976,0.29976,0.11164,0.0096105,0.0096105,0.0096105,0.0096105,0.11164,0.11164,0.11164,0.11164,0.11164,0.0096105,0.51708,0.29976,0.51708,0.51708
+10-Mar-2019 20:00:00,1,0.29414,0.29414,0.29414,0.11251,0.011533,0.011533,0.011533,0.011533,0.11251,0.11251,0.11251,0.11251,0.11251,0.011533,0.52017,0.29414,0.52017,0.52017
+10-Mar-2019 21:00:00,1,0.30562,0.30562,0.30562,0.098222,0.017299,0.017299,0.017299,0.017299,0.098222,0.098222,0.098222,0.098222,0.098222,0.017299,0.53676,0.30562,0.53676,0.53676
+10-Mar-2019 22:00:00,1,0.32975,0.32975,0.32975,0.086803,0.015377,0.015377,0.015377,0.015377,0.086803,0.086803,0.086803,0.086803,0.086803,0.015377,0.55738,0.32975,0.55738,0.55738
+10-Mar-2019 23:00:00,1,0.35114,0.35114,0.35114,0.070568,0.019221,0.019221,0.019221,0.019221,0.070568,0.070568,0.070568,0.070568,0.070568,0.019221,0.55179,0.35114,0.55179,0.55179
+11-Mar-2019 00:00:00,1,0.38569,0.38569,0.38569,0.068458,0.021143,0.021143,0.021143,0.021143,0.068458,0.068458,0.068458,0.068458,0.068458,0.021143,0.55218,0.38569,0.55218,0.55218
+11-Mar-2019 01:00:00,1,0.38383,0.38383,0.38383,0.067105,0.023065,0.023065,0.023065,0.023065,0.067105,0.067105,0.067105,0.067105,0.067105,0.023065,0.5848,0.38383,0.5848,0.5848
+11-Mar-2019 02:00:00,1,0.36175,0.36175,0.36175,0.076197,0.0096105,0.0096105,0.0096105,0.0096105,0.076197,0.076197,0.076197,0.076197,0.076197,0.0096105,0.59898,0.36175,0.59898,0.59898
+11-Mar-2019 03:00:00,1,0.35776,0.35776,0.35776,0.085451,0.015377,0.015377,0.015377,0.015377,0.085451,0.085451,0.085451,0.085451,0.085451,0.015377,0.60565,0.35776,0.60565,0.60565
+11-Mar-2019 04:00:00,1,0.37506,0.37506,0.37506,0.092377,0.017299,0.017299,0.017299,0.017299,0.092377,0.092377,0.092377,0.092377,0.092377,0.017299,0.59915,0.37506,0.59915,0.59915
+11-Mar-2019 05:00:00,1,0.38832,0.38832,0.38832,0.1163,0.044208,0.044208,0.044208,0.044208,0.1163,0.1163,0.1163,0.1163,0.1163,0.044208,0.57627,0.38832,0.57627,0.57627
+11-Mar-2019 06:00:00,1,0.36537,0.36537,0.36537,0.12073,0.023065,0.023065,0.023065,0.023065,0.12073,0.12073,0.12073,0.12073,0.12073,0.023065,0.56854,0.36537,0.56854,0.56854
+11-Mar-2019 07:00:00,1,0.3151,0.3151,0.3151,0.10629,0.021143,0.021143,0.021143,0.021143,0.10629,0.10629,0.10629,0.10629,0.10629,0.021143,0.51628,0.3151,0.51628,0.51628
+11-Mar-2019 08:00:00,1,0.29922,0.29922,0.29922,0.11235,0.028832,0.028832,0.028832,0.028832,0.11235,0.11235,0.11235,0.11235,0.11235,0.028832,0.4822,0.29922,0.4822,0.4822
+11-Mar-2019 09:00:00,1,0.27682,0.27682,0.27682,0.10039,0.013455,0.013455,0.013455,0.013455,0.10039,0.10039,0.10039,0.10039,0.10039,0.013455,0.47957,0.27682,0.47957,0.47957
+11-Mar-2019 10:00:00,1,0.25524,0.25524,0.25524,0.062613,0.023065,0.023065,0.023065,0.023065,0.062613,0.062613,0.062613,0.062613,0.062613,0.023065,0.47351,0.25524,0.47351,0.47351
+11-Mar-2019 11:00:00,1,0.24916,0.24916,0.24916,0.078957,0.023065,0.023065,0.023065,0.023065,0.078957,0.078957,0.078957,0.078957,0.078957,0.023065,0.49171,0.24916,0.49171,0.49171
+11-Mar-2019 12:00:00,1,0.2672,0.2672,0.2672,0.1176,0.0096105,0.0096105,0.0096105,0.0096105,0.1176,0.1176,0.1176,0.1176,0.1176,0.0096105,0.46601,0.2672,0.46601,0.46601
+11-Mar-2019 13:00:00,1,0.28929,0.28929,0.28929,0.12274,0.013455,0.013455,0.013455,0.013455,0.12274,0.12274,0.12274,0.12274,0.12274,0.013455,0.43857,0.28929,0.43857,0.43857
+11-Mar-2019 14:00:00,1,0.29442,0.29442,0.29442,0.12041,0.024987,0.024987,0.024987,0.024987,0.12041,0.12041,0.12041,0.12041,0.12041,0.024987,0.37527,0.29442,0.37527,0.37527
+11-Mar-2019 15:00:00,1,0.29832,0.29832,0.29832,0.15499,0.021143,0.021143,0.021143,0.021143,0.15499,0.15499,0.15499,0.15499,0.15499,0.021143,0.3373,0.29832,0.3373,0.3373
+11-Mar-2019 16:00:00,1,0.30444,0.30444,0.30444,0.15136,0.0057663,0.0057663,0.0057663,0.0057663,0.15136,0.15136,0.15136,0.15136,0.15136,0.0057663,0.32202,0.30444,0.32202,0.32202
+11-Mar-2019 17:00:00,1,0.29749,0.29749,0.29749,0.12241,0.0076884,0.0076884,0.0076884,0.0076884,0.12241,0.12241,0.12241,0.12241,0.12241,0.0076884,0.2991,0.29749,0.2991,0.2991
+11-Mar-2019 18:00:00,1,0.27244,0.27244,0.27244,0.097789,0.015377,0.015377,0.015377,0.015377,0.097789,0.097789,0.097789,0.097789,0.097789,0.015377,0.29721,0.27244,0.29721,0.29721
+11-Mar-2019 19:00:00,1,0.23484,0.23484,0.23484,0.087399,0.0076884,0.0076884,0.0076884,0.0076884,0.087399,0.087399,0.087399,0.087399,0.087399,0.0076884,0.26632,0.23484,0.26632,0.26632
+11-Mar-2019 20:00:00,1,0.26119,0.26119,0.26119,0.079281,0.011533,0.011533,0.011533,0.011533,0.079281,0.079281,0.079281,0.079281,0.079281,0.011533,0.27715,0.26119,0.27715,0.27715
+11-Mar-2019 21:00:00,1,0.27615,0.27615,0.27615,0.09373,0.011533,0.011533,0.011533,0.011533,0.09373,0.09373,0.09373,0.09373,0.09373,0.011533,0.27314,0.27615,0.27314,0.27314
+11-Mar-2019 22:00:00,1,0.28272,0.28272,0.28272,0.1177,0.023065,0.023065,0.023065,0.023065,0.1177,0.1177,0.1177,0.1177,0.1177,0.023065,0.30678,0.28272,0.30678,0.30678
+11-Mar-2019 23:00:00,1,0.2871,0.2871,0.2871,0.10888,0.019221,0.019221,0.019221,0.019221,0.10888,0.10888,0.10888,0.10888,0.10888,0.019221,0.30115,0.2871,0.30115,0.30115
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+12-Mar-2019 01:00:00,1,0.31295,0.31295,0.31295,0.068837,0.03652,0.03652,0.03652,0.03652,0.068837,0.068837,0.068837,0.068837,0.068837,0.03652,0.32477,0.31295,0.32477,0.32477
+12-Mar-2019 02:00:00,1,0.33268,0.33268,0.33268,0.054658,0.03652,0.03652,0.03652,0.03652,0.054658,0.054658,0.054658,0.054658,0.054658,0.03652,0.34156,0.33268,0.34156,0.34156
+12-Mar-2019 03:00:00,1,0.35866,0.35866,0.35866,0.054658,0.024987,0.024987,0.024987,0.024987,0.054658,0.054658,0.054658,0.054658,0.054658,0.024987,0.38441,0.35866,0.38441,0.38441
+12-Mar-2019 04:00:00,1,0.35334,0.35334,0.35334,0.054117,0.032676,0.032676,0.032676,0.032676,0.054117,0.054117,0.054117,0.054117,0.054117,0.032676,0.39694,0.35334,0.39694,0.39694
+12-Mar-2019 05:00:00,1,0.36662,0.36662,0.36662,0.059853,0.026909,0.026909,0.026909,0.026909,0.059853,0.059853,0.059853,0.059853,0.059853,0.026909,0.418,0.36662,0.418,0.418
+12-Mar-2019 06:00:00,1,0.39285,0.39285,0.39285,0.070839,0.026909,0.026909,0.026909,0.026909,0.070839,0.070839,0.070839,0.070839,0.070839,0.026909,0.40989,0.39285,0.40989,0.40989
+12-Mar-2019 07:00:00,1,0.38491,0.38491,0.38491,0.073815,0.021143,0.021143,0.021143,0.021143,0.073815,0.073815,0.073815,0.073815,0.073815,0.021143,0.4268,0.38491,0.4268,0.4268
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+12-Mar-2019 09:00:00,1,0.40637,0.40637,0.40637,0.062451,0.015377,0.015377,0.015377,0.015377,0.062451,0.062451,0.062451,0.062451,0.062451,0.015377,0.41271,0.40637,0.41271,0.41271
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+12-Mar-2019 11:00:00,1,0.42015,0.42015,0.42015,0.050816,0.023065,0.023065,0.023065,0.023065,0.050816,0.050816,0.050816,0.050816,0.050816,0.023065,0.43591,0.42015,0.43591,0.43591
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+12-Mar-2019 13:00:00,1,0.4168,0.4168,0.4168,0.071813,0.026909,0.026909,0.026909,0.026909,0.071813,0.071813,0.071813,0.071813,0.071813,0.026909,0.43945,0.4168,0.43945,0.43945
+12-Mar-2019 14:00:00,1,0.41942,0.41942,0.41942,0.072462,0.023065,0.023065,0.023065,0.023065,0.072462,0.072462,0.072462,0.072462,0.072462,0.023065,0.42997,0.41942,0.42997,0.42997
+12-Mar-2019 15:00:00,1,0.41093,0.41093,0.41093,0.08242,0.026909,0.026909,0.026909,0.026909,0.08242,0.08242,0.08242,0.08242,0.08242,0.026909,0.42552,0.41093,0.42552,0.42552
+12-Mar-2019 16:00:00,1,0.40819,0.40819,0.40819,0.08794,0.021143,0.021143,0.021143,0.021143,0.08794,0.08794,0.08794,0.08794,0.08794,0.021143,0.40803,0.40819,0.40803,0.40803
+12-Mar-2019 17:00:00,1,0.35076,0.35076,0.35076,0.071218,0.019221,0.019221,0.019221,0.019221,0.071218,0.071218,0.071218,0.071218,0.071218,0.019221,0.37928,0.35076,0.37928,0.37928
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+12-Mar-2019 19:00:00,1,0.27443,0.27443,0.27443,0.076738,0.011533,0.011533,0.011533,0.011533,0.076738,0.076738,0.076738,0.076738,0.076738,0.011533,0.37504,0.27443,0.37504,0.37504
+12-Mar-2019 20:00:00,1,0.2429,0.2429,0.2429,0.10158,0.013455,0.013455,0.013455,0.013455,0.10158,0.10158,0.10158,0.10158,0.10158,0.013455,0.37229,0.2429,0.37229,0.37229
+12-Mar-2019 21:00:00,1,0.21936,0.21936,0.21936,0.056173,0.011533,0.011533,0.011533,0.011533,0.056173,0.056173,0.056173,0.056173,0.056173,0.011533,0.32512,0.21936,0.32512,0.32512
+12-Mar-2019 22:00:00,1,0.19455,0.19455,0.19455,0.040804,0.0096105,0.0096105,0.0096105,0.0096105,0.040804,0.040804,0.040804,0.040804,0.040804,0.0096105,0.31654,0.19455,0.31654,0.31654
+12-Mar-2019 23:00:00,1,0.16489,0.16489,0.16489,0.022729,0.0038442,0.0038442,0.0038442,0.0038442,0.022729,0.022729,0.022729,0.022729,0.022729,0.0038442,0.32048,0.16489,0.32048,0.32048
+13-Mar-2019 00:00:00,1,0.14811,0.14811,0.14811,0.021214,0.0076884,0.0076884,0.0076884,0.0076884,0.021214,0.021214,0.021214,0.021214,0.021214,0.0076884,0.28046,0.14811,0.28046,0.28046
+13-Mar-2019 01:00:00,1,0.16394,0.16394,0.16394,0.031171,0.0038442,0.0038442,0.0038442,0.0038442,0.031171,0.031171,0.031171,0.031171,0.031171,0.0038442,0.26823,0.16394,0.26823,0.26823
+13-Mar-2019 02:00:00,1,0.18762,0.18762,0.18762,0.046216,0.0038442,0.0038442,0.0038442,0.0038442,0.046216,0.046216,0.046216,0.046216,0.046216,0.0038442,0.26122,0.18762,0.26122,0.26122
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+13-Mar-2019 05:00:00,1,0.18604,0.18604,0.18604,0.028574,0.0057663,0.0057663,0.0057663,0.0057663,0.028574,0.028574,0.028574,0.028574,0.028574,0.0057663,0.20479,0.18604,0.20479,0.20479
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+13-Mar-2019 16:00:00,1,0.23009,0.23009,0.23009,0.020781,0.0076884,0.0076884,0.0076884,0.0076884,0.020781,0.020781,0.020781,0.020781,0.020781,0.0076884,0.30662,0.23009,0.30662,0.30662
+13-Mar-2019 17:00:00,1,0.22114,0.22114,0.22114,0.020348,0.013455,0.013455,0.013455,0.013455,0.020348,0.020348,0.020348,0.020348,0.020348,0.013455,0.28493,0.22114,0.28493,0.28493
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+13-Mar-2019 19:00:00,1,0.18547,0.18547,0.18547,0.021647,0.0096105,0.0096105,0.0096105,0.0096105,0.021647,0.021647,0.021647,0.021647,0.021647,0.0096105,0.30033,0.18547,0.30033,0.30033
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+19-Mar-2019 18:00:00,1,0.14974,0.14974,0.14974,0.069919,0.024987,0.024987,0.024987,0.024987,0.069919,0.069919,0.069919,0.069919,0.069919,0.024987,0.2167,0.14974,0.2167,0.2167
+19-Mar-2019 19:00:00,1,0.13532,0.13532,0.13532,0.075655,0.034598,0.034598,0.034598,0.034598,0.075655,0.075655,0.075655,0.075655,0.075655,0.034598,0.22858,0.13532,0.22858,0.22858
+19-Mar-2019 20:00:00,1,0.15132,0.15132,0.15132,0.068187,0.042286,0.042286,0.042286,0.042286,0.068187,0.068187,0.068187,0.068187,0.068187,0.042286,0.23548,0.15132,0.23548,0.23548
+19-Mar-2019 21:00:00,1,0.1686,0.1686,0.1686,0.07782,0.051897,0.051897,0.051897,0.051897,0.07782,0.07782,0.07782,0.07782,0.07782,0.051897,0.24854,0.1686,0.24854,0.24854
+19-Mar-2019 22:00:00,1,0.18328,0.18328,0.18328,0.09898,0.042286,0.042286,0.042286,0.042286,0.09898,0.09898,0.09898,0.09898,0.09898,0.042286,0.24484,0.18328,0.24484,0.24484
+19-Mar-2019 23:00:00,1,0.19034,0.19034,0.19034,0.09898,0.051897,0.051897,0.051897,0.051897,0.09898,0.09898,0.09898,0.09898,0.09898,0.051897,0.24204,0.19034,0.24204,0.24204
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+20-Mar-2019 01:00:00,1,0.21579,0.21579,0.21579,0.091945,0.024987,0.024987,0.024987,0.024987,0.091945,0.091945,0.091945,0.091945,0.091945,0.024987,0.23489,0.21579,0.23489,0.23489
+20-Mar-2019 02:00:00,1,0.23829,0.23829,0.23829,0.11435,0.048053,0.048053,0.048053,0.048053,0.11435,0.11435,0.11435,0.11435,0.11435,0.048053,0.23564,0.23829,0.23564,0.23564
+20-Mar-2019 03:00:00,1,0.2602,0.2602,0.2602,0.14763,0.055741,0.055741,0.055741,0.055741,0.14763,0.14763,0.14763,0.14763,0.14763,0.055741,0.25295,0.2602,0.25295,0.25295
+20-Mar-2019 04:00:00,1,0.28417,0.28417,0.28417,0.15153,0.049975,0.049975,0.049975,0.049975,0.15153,0.15153,0.15153,0.15153,0.15153,0.049975,0.26471,0.28417,0.26471,0.26471
+20-Mar-2019 05:00:00,1,0.31045,0.31045,0.31045,0.16127,0.038442,0.038442,0.038442,0.038442,0.16127,0.16127,0.16127,0.16127,0.16127,0.038442,0.26981,0.31045,0.26981,0.26981
+20-Mar-2019 06:00:00,1,0.33268,0.33268,0.33268,0.16587,0.028832,0.028832,0.028832,0.028832,0.16587,0.16587,0.16587,0.16587,0.16587,0.028832,0.27254,0.33268,0.27254,0.27254
+20-Mar-2019 07:00:00,1,0.32415,0.32415,0.32415,0.15927,0.038442,0.038442,0.038442,0.038442,0.15927,0.15927,0.15927,0.15927,0.15927,0.038442,0.22655,0.32415,0.22655,0.22655
+20-Mar-2019 08:00:00,1,0.28617,0.28617,0.28617,0.10618,0.015377,0.015377,0.015377,0.015377,0.10618,0.10618,0.10618,0.10618,0.10618,0.015377,0.20312,0.28617,0.20312,0.20312
+20-Mar-2019 09:00:00,1,0.27526,0.27526,0.27526,0.073653,0.0096105,0.0096105,0.0096105,0.0096105,0.073653,0.073653,0.073653,0.073653,0.073653,0.0096105,0.21679,0.27526,0.21679,0.21679
+20-Mar-2019 10:00:00,1,0.33727,0.33727,0.33727,0.079876,0.0096105,0.0096105,0.0096105,0.0096105,0.079876,0.079876,0.079876,0.079876,0.079876,0.0096105,0.25476,0.33727,0.25476,0.25476
+20-Mar-2019 11:00:00,1,0.4042,0.4042,0.4042,0.11034,0.0057663,0.0057663,0.0057663,0.0057663,0.11034,0.11034,0.11034,0.11034,0.11034,0.0057663,0.31,0.4042,0.31,0.31
+20-Mar-2019 12:00:00,1,0.41325,0.41325,0.41325,0.13865,0.0096105,0.0096105,0.0096105,0.0096105,0.13865,0.13865,0.13865,0.13865,0.13865,0.0096105,0.323,0.41325,0.323,0.323
+20-Mar-2019 13:00:00,1,0.3778,0.3778,0.3778,0.11251,0.0057663,0.0057663,0.0057663,0.0057663,0.11251,0.11251,0.11251,0.11251,0.11251,0.0057663,0.33222,0.3778,0.33222,0.33222
+20-Mar-2019 14:00:00,1,0.34691,0.34691,0.34691,0.084152,0.0019221,0.0019221,0.0019221,0.0019221,0.084152,0.084152,0.084152,0.084152,0.084152,0.0019221,0.3288,0.34691,0.3288,0.3288
+20-Mar-2019 15:00:00,1,0.31366,0.31366,0.31366,0.06927,0,0,0,0,0.06927,0.06927,0.06927,0.06927,0.06927,0,0.29712,0.31366,0.29712,0.29712
+20-Mar-2019 16:00:00,1,0.27731,0.27731,0.27731,0.054387,0,0,0,0,0.054387,0.054387,0.054387,0.054387,0.054387,0,0.27205,0.27731,0.27205,0.27205
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+20-Mar-2019 18:00:00,1,0.20131,0.20131,0.20131,0.042698,0.0019221,0.0019221,0.0019221,0.0019221,0.042698,0.042698,0.042698,0.042698,0.042698,0.0019221,0.20028,0.20131,0.20028,0.20028
+20-Mar-2019 19:00:00,1,0.18963,0.18963,0.18963,0.05022,0.011533,0.011533,0.011533,0.011533,0.05022,0.05022,0.05022,0.05022,0.05022,0.011533,0.21409,0.18963,0.21409,0.21409
+20-Mar-2019 20:00:00,1,0.17054,0.17054,0.17054,0.061314,0.021143,0.021143,0.021143,0.021143,0.061314,0.061314,0.061314,0.061314,0.061314,0.021143,0.22748,0.17054,0.22748,0.22748
+20-Mar-2019 21:00:00,1,0.15298,0.15298,0.15298,0.066293,0.024987,0.024987,0.024987,0.024987,0.066293,0.066293,0.066293,0.066293,0.066293,0.024987,0.23263,0.15298,0.23263,0.23263
+20-Mar-2019 22:00:00,1,0.14631,0.14631,0.14631,0.058013,0.019221,0.019221,0.019221,0.019221,0.058013,0.058013,0.058013,0.058013,0.058013,0.019221,0.23396,0.14631,0.23396,0.23396
+20-Mar-2019 23:00:00,1,0.13308,0.13308,0.13308,0.061369,0.028832,0.028832,0.028832,0.028832,0.061369,0.061369,0.061369,0.061369,0.061369,0.028832,0.21295,0.13308,0.21295,0.21295
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+21-Mar-2019 01:00:00,1,0.13367,0.13367,0.13367,0.051086,0.038442,0.038442,0.038442,0.038442,0.051086,0.051086,0.051086,0.051086,0.051086,0.038442,0.1969,0.13367,0.1969,0.1969
+21-Mar-2019 02:00:00,1,0.11479,0.11479,0.11479,0.034148,0.032676,0.032676,0.032676,0.032676,0.034148,0.034148,0.034148,0.034148,0.034148,0.032676,0.18392,0.11479,0.18392,0.18392
+21-Mar-2019 03:00:00,1,0.10488,0.10488,0.10488,0.036799,0.03652,0.03652,0.03652,0.03652,0.036799,0.036799,0.036799,0.036799,0.036799,0.03652,0.1821,0.10488,0.1821,0.1821
+21-Mar-2019 04:00:00,1,0.098007,0.098007,0.098007,0.031983,0.030754,0.030754,0.030754,0.030754,0.031983,0.031983,0.031983,0.031983,0.031983,0.030754,0.15715,0.098007,0.15715,0.15715
+21-Mar-2019 05:00:00,1,0.079526,0.079526,0.079526,0.032145,0.032676,0.032676,0.032676,0.032676,0.032145,0.032145,0.032145,0.032145,0.032145,0.032676,0.13705,0.079526,0.13705,0.13705
+21-Mar-2019 06:00:00,1,0.075319,0.075319,0.075319,0.041616,0.03652,0.03652,0.03652,0.03652,0.041616,0.041616,0.041616,0.041616,0.041616,0.03652,0.12403,0.075319,0.12403,0.12403
+21-Mar-2019 07:00:00,1,0.060099,0.060099,0.060099,0.057418,0.053819,0.053819,0.053819,0.053819,0.057418,0.057418,0.057418,0.057418,0.057418,0.053819,0.10513,0.060099,0.10513,0.10513
+21-Mar-2019 08:00:00,1,0.042611,0.042611,0.042611,0.044376,0.032676,0.032676,0.032676,0.032676,0.044376,0.044376,0.044376,0.044376,0.044376,0.032676,0.10525,0.042611,0.10525,0.10525
+21-Mar-2019 09:00:00,1,0.030983,0.030983,0.030983,0.030576,0.024987,0.024987,0.024987,0.024987,0.030576,0.030576,0.030576,0.030576,0.030576,0.024987,0.10215,0.030983,0.10215,0.10215
+21-Mar-2019 10:00:00,1,0.028431,0.028431,0.028431,0.029115,0.034598,0.034598,0.034598,0.034598,0.029115,0.029115,0.029115,0.029115,0.029115,0.034598,0.095071,0.028431,0.095071,0.095071
+21-Mar-2019 11:00:00,1,0.042705,0.042705,0.042705,0.033336,0.019221,0.019221,0.019221,0.019221,0.033336,0.033336,0.033336,0.033336,0.033336,0.019221,0.096818,0.042705,0.096818,0.096818
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+21-Mar-2019 16:00:00,1,0.059012,0.059012,0.059012,0.071651,0.017299,0.017299,0.017299,0.017299,0.071651,0.071651,0.071651,0.071651,0.071651,0.017299,0.10905,0.059012,0.10905,0.10905
+21-Mar-2019 17:00:00,1,0.044052,0.044052,0.044052,0.061747,0.034598,0.034598,0.034598,0.034598,0.061747,0.061747,0.061747,0.061747,0.061747,0.034598,0.086451,0.044052,0.086451,0.086451
+21-Mar-2019 18:00:00,1,0.040436,0.040436,0.040436,0.074844,0.034598,0.034598,0.034598,0.034598,0.074844,0.074844,0.074844,0.074844,0.074844,0.034598,0.079672,0.040436,0.079672,0.079672
+21-Mar-2019 19:00:00,1,0.043911,0.043911,0.043911,0.1242,0.034598,0.034598,0.034598,0.034598,0.1242,0.1242,0.1242,0.1242,0.1242,0.034598,0.07874,0.043911,0.07874,0.07874
+21-Mar-2019 20:00:00,1,0.045612,0.045612,0.045612,0.14595,0.049975,0.049975,0.049975,0.049975,0.14595,0.14595,0.14595,0.14595,0.14595,0.049975,0.08093,0.045612,0.08093,0.08093
+21-Mar-2019 21:00:00,1,0.041075,0.041075,0.041075,0.15975,0.028832,0.028832,0.028832,0.028832,0.15975,0.15975,0.15975,0.15975,0.15975,0.028832,0.076038,0.041075,0.076038,0.076038
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+21-Mar-2019 23:00:00,1,0.040389,0.040389,0.040389,0.17999,0.08265,0.08265,0.08265,0.08265,0.17999,0.17999,0.17999,0.17999,0.17999,0.08265,0.063854,0.040389,0.063854,0.063854
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+22-Mar-2019 01:00:00,1,0.047503,0.047503,0.047503,0.16035,0.08265,0.08265,0.08265,0.08265,0.16035,0.16035,0.16035,0.16035,0.16035,0.08265,0.064367,0.047503,0.064367,0.064367
+22-Mar-2019 02:00:00,1,0.046746,0.046746,0.046746,0.18465,0.098027,0.098027,0.098027,0.098027,0.18465,0.18465,0.18465,0.18465,0.18465,0.098027,0.056097,0.046746,0.056097,0.056097
+22-Mar-2019 03:00:00,1,0.040484,0.040484,0.040484,0.19812,0.078806,0.078806,0.078806,0.078806,0.19812,0.19812,0.19812,0.19812,0.19812,0.078806,0.050016,0.040484,0.050016,0.050016
+22-Mar-2019 04:00:00,1,0.039775,0.039775,0.039775,0.15277,0.08265,0.08265,0.08265,0.08265,0.15277,0.15277,0.15277,0.15277,0.15277,0.08265,0.039021,0.039775,0.039021,0.039021
+22-Mar-2019 05:00:00,1,0.043012,0.043012,0.043012,0.16392,0.10956,0.10956,0.10956,0.10956,0.16392,0.16392,0.16392,0.16392,0.16392,0.10956,0.034781,0.043012,0.034781,0.034781
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+22-Mar-2019 07:00:00,1,0.037577,0.037577,0.037577,0.21365,0.078806,0.078806,0.078806,0.078806,0.21365,0.21365,0.21365,0.21365,0.21365,0.078806,0.026651,0.037577,0.026651,0.026651
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+22-Mar-2019 16:00:00,1,0.05334,0.05334,0.05334,0.13729,0.042286,0.042286,0.042286,0.042286,0.13729,0.13729,0.13729,0.13729,0.13729,0.042286,0.026883,0.05334,0.026883,0.026883
+22-Mar-2019 17:00:00,1,0.059272,0.059272,0.059272,0.14081,0.055741,0.055741,0.055741,0.055741,0.14081,0.14081,0.14081,0.14081,0.14081,0.055741,0.024647,0.059272,0.024647,0.024647
+22-Mar-2019 18:00:00,1,0.063313,0.063313,0.063313,0.15429,0.048053,0.048053,0.048053,0.048053,0.15429,0.15429,0.15429,0.15429,0.15429,0.048053,0.029143,0.063313,0.029143,0.029143
+22-Mar-2019 19:00:00,1,0.087821,0.087821,0.087821,0.22302,0.051897,0.051897,0.051897,0.051897,0.22302,0.22302,0.22302,0.22302,0.22302,0.051897,0.036435,0.087821,0.036435,0.036435
+22-Mar-2019 20:00:00,1,0.10954,0.10954,0.10954,0.22908,0.078806,0.078806,0.078806,0.078806,0.22908,0.22908,0.22908,0.22908,0.22908,0.078806,0.046196,0.10954,0.046196,0.046196
+22-Mar-2019 21:00:00,1,0.11609,0.11609,0.11609,0.24547,0.080728,0.080728,0.080728,0.080728,0.24547,0.24547,0.24547,0.24547,0.24547,0.080728,0.040372,0.11609,0.040372,0.040372
+22-Mar-2019 22:00:00,1,0.11649,0.11649,0.11649,0.20007,0.059585,0.059585,0.059585,0.059585,0.20007,0.20007,0.20007,0.20007,0.20007,0.059585,0.02891,0.11649,0.02891,0.02891
+22-Mar-2019 23:00:00,1,0.1107,0.1107,0.1107,0.14525,0.048053,0.048053,0.048053,0.048053,0.14525,0.14525,0.14525,0.14525,0.14525,0.048053,0.02297,0.1107,0.02297,0.02297
+23-Mar-2019 00:00:00,1,0.095904,0.095904,0.095904,0.17377,0.034598,0.034598,0.034598,0.034598,0.17377,0.17377,0.17377,0.17377,0.17377,0.034598,0.018963,0.095904,0.018963,0.018963
+23-Mar-2019 01:00:00,1,0.074657,0.074657,0.074657,0.17014,0.03652,0.03652,0.03652,0.03652,0.17014,0.17014,0.17014,0.17014,0.17014,0.03652,0.016936,0.074657,0.016936,0.016936
+23-Mar-2019 02:00:00,1,0.057051,0.057051,0.057051,0.16857,0.024987,0.024987,0.024987,0.024987,0.16857,0.16857,0.16857,0.16857,0.16857,0.024987,0.016284,0.057051,0.016284,0.016284
+23-Mar-2019 03:00:00,1,0.045069,0.045069,0.045069,0.13919,0.017299,0.017299,0.017299,0.017299,0.13919,0.13919,0.13919,0.13919,0.13919,0.017299,0.011438,0.045069,0.011438,0.011438
+23-Mar-2019 04:00:00,1,0.041476,0.041476,0.041476,0.098655,0.015377,0.015377,0.015377,0.015377,0.098655,0.098655,0.098655,0.098655,0.098655,0.015377,0.009132,0.041476,0.009132,0.009132
+23-Mar-2019 05:00:00,1,0.043272,0.043272,0.043272,0.072895,0.015377,0.015377,0.015377,0.015377,0.072895,0.072895,0.072895,0.072895,0.072895,0.015377,0.0073615,0.043272,0.0073615,0.0073615
+23-Mar-2019 06:00:00,1,0.04228,0.04228,0.04228,0.074357,0.011533,0.011533,0.011533,0.011533,0.074357,0.074357,0.074357,0.074357,0.074357,0.011533,0.0074547,0.04228,0.0074547,0.0074547
+23-Mar-2019 07:00:00,1,0.039302,0.039302,0.039302,0.06743,0.013455,0.013455,0.013455,0.013455,0.06743,0.06743,0.06743,0.06743,0.06743,0.013455,0.0069189,0.039302,0.0069189,0.0069189
+23-Mar-2019 08:00:00,1,0.028124,0.028124,0.028124,0.043131,0.0038442,0.0038442,0.0038442,0.0038442,0.043131,0.043131,0.043131,0.043131,0.043131,0.0038442,0.0049853,0.028124,0.0049853,0.0049853
+23-Mar-2019 09:00:00,1,0.015622,0.015622,0.015622,0.019699,0,0,0,0,0.019699,0.019699,0.019699,0.019699,0.019699,0,0.0050319,0.015622,0.0050319,0.0050319
+23-Mar-2019 10:00:00,1,0.012856,0.012856,0.012856,0.021809,0.0019221,0.0019221,0.0019221,0.0019221,0.021809,0.021809,0.021809,0.021809,0.021809,0.0019221,0.0061035,0.012856,0.0061035,0.0061035
+23-Mar-2019 11:00:00,1,0.012573,0.012573,0.012573,0.021863,0.0038442,0.0038442,0.0038442,0.0038442,0.021863,0.021863,0.021863,0.021863,0.021863,0.0038442,0.008503,0.012573,0.008503,0.008503
+23-Mar-2019 12:00:00,1,0.013636,0.013636,0.013636,0.023703,0.0057663,0.0057663,0.0057663,0.0057663,0.023703,0.023703,0.023703,0.023703,0.023703,0.0057663,0.0082933,0.013636,0.0082933,0.0082933
+23-Mar-2019 13:00:00,1,0.014369,0.014369,0.014369,0.023,0.0019221,0.0019221,0.0019221,0.0019221,0.023,0.023,0.023,0.023,0.023,0.0019221,0.0096911,0.014369,0.0096911,0.0096911
+23-Mar-2019 14:00:00,1,0.016236,0.016236,0.016236,0.02511,0.0019221,0.0019221,0.0019221,0.0019221,0.02511,0.02511,0.02511,0.02511,0.02511,0.0019221,0.0095746,0.016236,0.0095746,0.0095746
+23-Mar-2019 15:00:00,1,0.019096,0.019096,0.019096,0.024461,0.0019221,0.0019221,0.0019221,0.0019221,0.024461,0.024461,0.024461,0.024461,0.024461,0.0019221,0.01011,0.019096,0.01011,0.01011
+23-Mar-2019 16:00:00,1,0.02075,0.02075,0.02075,0.02668,0.0038442,0.0038442,0.0038442,0.0038442,0.02668,0.02668,0.02668,0.02668,0.02668,0.0038442,0.012277,0.02075,0.012277,0.012277
+23-Mar-2019 17:00:00,1,0.0242,0.0242,0.0242,0.025164,0.0038442,0.0038442,0.0038442,0.0038442,0.025164,0.025164,0.025164,0.025164,0.025164,0.0038442,0.013582,0.0242,0.013582,0.013582
+23-Mar-2019 18:00:00,1,0.037884,0.037884,0.037884,0.026301,0.017299,0.017299,0.017299,0.017299,0.026301,0.026301,0.026301,0.026301,0.026301,0.017299,0.021362,0.037884,0.021362,0.021362
+23-Mar-2019 19:00:00,1,0.057618,0.057618,0.057618,0.051519,0.023065,0.023065,0.023065,0.023065,0.051519,0.051519,0.051519,0.051519,0.051519,0.023065,0.026604,0.057618,0.026604,0.026604
+23-Mar-2019 20:00:00,1,0.070545,0.070545,0.070545,0.083178,0.026909,0.026909,0.026909,0.026909,0.083178,0.083178,0.083178,0.083178,0.083178,0.026909,0.031752,0.070545,0.031752,0.031752
+23-Mar-2019 21:00:00,1,0.072199,0.072199,0.072199,0.079552,0.030754,0.030754,0.030754,0.030754,0.079552,0.079552,0.079552,0.079552,0.079552,0.030754,0.030075,0.072199,0.030075,0.030075
+23-Mar-2019 22:00:00,1,0.058894,0.058894,0.058894,0.069324,0.030754,0.030754,0.030754,0.030754,0.069324,0.069324,0.069324,0.069324,0.069324,0.030754,0.019592,0.058894,0.019592,0.019592
+23-Mar-2019 23:00:00,1,0.059154,0.059154,0.059154,0.056606,0.038442,0.038442,0.038442,0.038442,0.056606,0.056606,0.056606,0.056606,0.056606,0.038442,0.015771,0.059154,0.015771,0.015771
+24-Mar-2019 00:00:00,1,0.050291,0.050291,0.050291,0.053955,0.023065,0.023065,0.023065,0.023065,0.053955,0.053955,0.053955,0.053955,0.053955,0.023065,0.014653,0.050291,0.014653,0.014653
+24-Mar-2019 01:00:00,1,0.039798,0.039798,0.039798,0.053197,0.017299,0.017299,0.017299,0.017299,0.053197,0.053197,0.053197,0.053197,0.053197,0.017299,0.01067,0.039798,0.01067,0.01067
+24-Mar-2019 02:00:00,1,0.031834,0.031834,0.031834,0.058392,0.0057663,0.0057663,0.0057663,0.0057663,0.058392,0.058392,0.058392,0.058392,0.058392,0.0057663,0.0066626,0.031834,0.0066626,0.0066626
+24-Mar-2019 03:00:00,1,0.026824,0.026824,0.026824,0.042806,0.013455,0.013455,0.013455,0.013455,0.042806,0.042806,0.042806,0.042806,0.042806,0.013455,0.0065927,0.026824,0.0065927,0.0065927
+24-Mar-2019 04:00:00,1,0.019025,0.019025,0.019025,0.038477,0.015377,0.015377,0.015377,0.015377,0.038477,0.038477,0.038477,0.038477,0.038477,0.015377,0.0053348,0.019025,0.0053348,0.0053348
+24-Mar-2019 05:00:00,1,0.018056,0.018056,0.018056,0.031767,0.0096105,0.0096105,0.0096105,0.0096105,0.031767,0.031767,0.031767,0.031767,0.031767,0.0096105,0.0054047,0.018056,0.0054047,0.0054047
+24-Mar-2019 06:00:00,1,0.021364,0.021364,0.021364,0.030847,0.011533,0.011533,0.011533,0.011533,0.030847,0.030847,0.030847,0.030847,0.030847,0.011533,0.0067092,0.021364,0.0067092,0.0067092
+24-Mar-2019 07:00:00,1,0.022026,0.022026,0.022026,0.027816,0.0096105,0.0096105,0.0096105,0.0096105,0.027816,0.027816,0.027816,0.027816,0.027816,0.0096105,0.01025,0.022026,0.01025,0.01025
+24-Mar-2019 08:00:00,1,0.015952,0.015952,0.015952,0.016343,0.0038442,0.0038442,0.0038442,0.0038442,0.016343,0.016343,0.016343,0.016343,0.016343,0.0038442,0.010739,0.015952,0.010739,0.010739
+24-Mar-2019 09:00:00,1,0.01737,0.01737,0.01737,0.0080093,0,0,0,0,0.0080093,0.0080093,0.0080093,0.0080093,0.0080093,0,0.007082,0.01737,0.007082,0.007082
+24-Mar-2019 10:00:00,1,0.017867,0.017867,0.017867,0.010986,0.0019221,0.0019221,0.0019221,0.0019221,0.010986,0.010986,0.010986,0.010986,0.010986,0.0019221,0.011928,0.017867,0.011928,0.011928
+24-Mar-2019 11:00:00,1,0.027273,0.027273,0.027273,0.012501,0.0057663,0.0057663,0.0057663,0.0057663,0.012501,0.012501,0.012501,0.012501,0.012501,0.0057663,0.020384,0.027273,0.020384,0.020384
+24-Mar-2019 12:00:00,1,0.029801,0.029801,0.029801,0.018454,0.0038442,0.0038442,0.0038442,0.0038442,0.018454,0.018454,0.018454,0.018454,0.018454,0.0038442,0.029982,0.029801,0.029982,0.029982
+24-Mar-2019 13:00:00,1,0.034245,0.034245,0.034245,0.015369,0.0076884,0.0076884,0.0076884,0.0076884,0.015369,0.015369,0.015369,0.015369,0.015369,0.0076884,0.041094,0.034245,0.041094,0.041094
+24-Mar-2019 14:00:00,1,0.041311,0.041311,0.041311,0.021106,0.015377,0.015377,0.015377,0.015377,0.021106,0.021106,0.021106,0.021106,0.021106,0.015377,0.054652,0.041311,0.054652,0.054652
+24-Mar-2019 15:00:00,1,0.048188,0.048188,0.048188,0.031658,0.019221,0.019221,0.019221,0.019221,0.031658,0.031658,0.031658,0.031658,0.031658,0.019221,0.065112,0.048188,0.065112,0.065112
+24-Mar-2019 16:00:00,1,0.048803,0.048803,0.048803,0.042806,0.017299,0.017299,0.017299,0.017299,0.042806,0.042806,0.042806,0.042806,0.042806,0.017299,0.07676,0.048803,0.07676,0.07676
+24-Mar-2019 17:00:00,1,0.045541,0.045541,0.045541,0.05547,0.011533,0.011533,0.011533,0.011533,0.05547,0.05547,0.05547,0.05547,0.05547,0.011533,0.087919,0.045541,0.087919,0.087919
+24-Mar-2019 18:00:00,1,0.045943,0.045943,0.045943,0.068079,0.017299,0.017299,0.017299,0.017299,0.068079,0.068079,0.068079,0.068079,0.068079,0.017299,0.084867,0.045943,0.084867,0.084867
+24-Mar-2019 19:00:00,1,0.058776,0.058776,0.058776,0.089293,0.017299,0.017299,0.017299,0.017299,0.089293,0.089293,0.089293,0.089293,0.089293,0.017299,0.097144,0.058776,0.097144,0.097144
+24-Mar-2019 20:00:00,1,0.066598,0.066598,0.066598,0.089293,0.040364,0.040364,0.040364,0.040364,0.089293,0.089293,0.089293,0.089293,0.089293,0.040364,0.10972,0.066598,0.10972,0.10972
+24-Mar-2019 21:00:00,1,0.069623,0.069623,0.069623,0.081662,0.0096105,0.0096105,0.0096105,0.0096105,0.081662,0.081662,0.081662,0.081662,0.081662,0.0096105,0.10479,0.069623,0.10479,0.10479
+24-Mar-2019 22:00:00,1,0.05438,0.05438,0.05438,0.061369,0.013455,0.013455,0.013455,0.013455,0.061369,0.061369,0.061369,0.061369,0.061369,0.013455,0.081512,0.05438,0.081512,0.081512
+24-Mar-2019 23:00:00,1,0.045329,0.045329,0.045329,0.049246,0.0076884,0.0076884,0.0076884,0.0076884,0.049246,0.049246,0.049246,0.049246,0.049246,0.0076884,0.067745,0.045329,0.067745,0.067745
+25-Mar-2019 00:00:00,1,0.036797,0.036797,0.036797,0.039614,0.0076884,0.0076884,0.0076884,0.0076884,0.039614,0.039614,0.039614,0.039614,0.039614,0.0076884,0.063202,0.036797,0.063202,0.063202
+25-Mar-2019 01:00:00,1,0.037175,0.037175,0.037175,0.033823,0.0057663,0.0057663,0.0057663,0.0057663,0.033823,0.033823,0.033823,0.033823,0.033823,0.0057663,0.064693,0.037175,0.064693,0.064693
+25-Mar-2019 02:00:00,1,0.040046,0.040046,0.040046,0.030495,0.0057663,0.0057663,0.0057663,0.0057663,0.030495,0.030495,0.030495,0.030495,0.030495,0.0057663,0.067523,0.040046,0.067523,0.067523
+25-Mar-2019 03:00:00,1,0.042918,0.042918,0.042918,0.027167,0.0096105,0.0096105,0.0096105,0.0096105,0.027167,0.027167,0.027167,0.027167,0.027167,0.0096105,0.070354,0.042918,0.070354,0.070354
+25-Mar-2019 04:00:00,1,0.050244,0.050244,0.050244,0.025651,0.017299,0.017299,0.017299,0.017299,0.025651,0.025651,0.025651,0.025651,0.025651,0.017299,0.081955,0.050244,0.081955,0.081955
+25-Mar-2019 05:00:00,1,0.056223,0.056223,0.056223,0.023541,0.019221,0.019221,0.019221,0.019221,0.023541,0.023541,0.023541,0.023541,0.023541,0.019221,0.088152,0.056223,0.088152,0.088152
+25-Mar-2019 06:00:00,1,0.058799,0.058799,0.058799,0.021809,0.024987,0.024987,0.024987,0.024987,0.021809,0.021809,0.021809,0.021809,0.021809,0.024987,0.093813,0.058799,0.093813,0.093813
+25-Mar-2019 07:00:00,1,0.063408,0.063408,0.063408,0.031225,0.011533,0.011533,0.011533,0.011533,0.031225,0.031225,0.031225,0.031225,0.031225,0.011533,0.095746,0.063408,0.095746,0.095746
+25-Mar-2019 08:00:00,1,0.056791,0.056791,0.056791,0.027654,0.0076884,0.0076884,0.0076884,0.0076884,0.027654,0.027654,0.027654,0.027654,0.027654,0.0076884,0.086218,0.056791,0.086218,0.086218
+25-Mar-2019 09:00:00,1,0.049772,0.049772,0.049772,0.021322,0.0038442,0.0038442,0.0038442,0.0038442,0.021322,0.021322,0.021322,0.021322,0.021322,0.0038442,0.072637,0.049772,0.072637,0.072637
+25-Mar-2019 10:00:00,1,0.046581,0.046581,0.046581,0.013313,0.015377,0.015377,0.015377,0.015377,0.013313,0.013313,0.013313,0.013313,0.013313,0.015377,0.073289,0.046581,0.073289,0.073289
+25-Mar-2019 11:00:00,1,0.046959,0.046959,0.046959,0.022675,0.021143,0.021143,0.021143,0.021143,0.022675,0.022675,0.022675,0.022675,0.022675,0.021143,0.061455,0.046959,0.061455,0.061455
+25-Mar-2019 12:00:00,1,0.050764,0.050764,0.050764,0.039505,0.023065,0.023065,0.023065,0.023065,0.039505,0.039505,0.039505,0.039505,0.039505,0.023065,0.061198,0.050764,0.061198,0.061198
+25-Mar-2019 13:00:00,1,0.070522,0.070522,0.070522,0.040696,0.019221,0.019221,0.019221,0.019221,0.040696,0.040696,0.040696,0.040696,0.040696,0.019221,0.075875,0.070522,0.075875,0.075875
+25-Mar-2019 14:00:00,1,0.090964,0.090964,0.090964,0.043456,0.019221,0.019221,0.019221,0.019221,0.043456,0.043456,0.043456,0.043456,0.043456,0.019221,0.10267,0.090964,0.10267,0.10267
+25-Mar-2019 15:00:00,1,0.10013,0.10013,0.10013,0.050437,0.026909,0.026909,0.026909,0.026909,0.050437,0.050437,0.050437,0.050437,0.050437,0.026909,0.12028,0.10013,0.12028,0.12028
+25-Mar-2019 16:00:00,1,0.11576,0.11576,0.11576,0.066726,0.030754,0.030754,0.030754,0.030754,0.066726,0.066726,0.066726,0.066726,0.066726,0.030754,0.15261,0.11576,0.15261,0.15261
+25-Mar-2019 17:00:00,1,0.11883,0.11883,0.11883,0.086316,0.044208,0.044208,0.044208,0.044208,0.086316,0.086316,0.086316,0.086316,0.086316,0.044208,0.15599,0.11883,0.15599,0.15599
+25-Mar-2019 18:00:00,1,0.10663,0.10663,0.10663,0.075114,0.028832,0.028832,0.028832,0.028832,0.075114,0.075114,0.075114,0.075114,0.075114,0.028832,0.14211,0.10663,0.14211,0.14211
+25-Mar-2019 19:00:00,1,0.10685,0.10685,0.10685,0.079173,0.080728,0.080728,0.080728,0.080728,0.079173,0.079173,0.079173,0.079173,0.079173,0.080728,0.11592,0.10685,0.11592,0.11592
+25-Mar-2019 20:00:00,1,0.11661,0.11661,0.11661,0.12133,0.048053,0.048053,0.048053,0.048053,0.12133,0.12133,0.12133,0.12133,0.12133,0.048053,0.12212,0.11661,0.12212,0.12212
+25-Mar-2019 21:00:00,1,0.12308,0.12308,0.12308,0.13935,0.03652,0.03652,0.03652,0.03652,0.13935,0.13935,0.13935,0.13935,0.13935,0.03652,0.12321,0.12308,0.12321,0.12321
+25-Mar-2019 22:00:00,1,0.11708,0.11708,0.11708,0.17907,0.067274,0.067274,0.067274,0.067274,0.17907,0.17907,0.17907,0.17907,0.17907,0.067274,0.10089,0.11708,0.10089,0.10089
+25-Mar-2019 23:00:00,1,0.11098,0.11098,0.11098,0.14314,0.055741,0.055741,0.055741,0.055741,0.14314,0.14314,0.14314,0.14314,0.14314,0.055741,0.07047,0.11098,0.07047,0.07047
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+26-Mar-2019 02:00:00,1,0.039326,0.039326,0.039326,0.13448,0.069196,0.069196,0.069196,0.069196,0.13448,0.13448,0.13448,0.13448,0.13448,0.069196,0.017565,0.039326,0.017565,0.017565
+26-Mar-2019 03:00:00,1,0.034055,0.034055,0.034055,0.12966,0.065351,0.065351,0.065351,0.065351,0.12966,0.12966,0.12966,0.12966,0.12966,0.065351,0.014187,0.034055,0.014187,0.014187
+26-Mar-2019 04:00:00,1,0.023066,0.023066,0.023066,0.12382,0.04613,0.04613,0.04613,0.04613,0.12382,0.12382,0.12382,0.12382,0.12382,0.04613,0.018171,0.023066,0.018171,0.018171
+26-Mar-2019 05:00:00,1,0.020466,0.020466,0.020466,0.086858,0.063429,0.063429,0.063429,0.063429,0.086858,0.086858,0.086858,0.086858,0.086858,0.063429,0.014374,0.020466,0.014374,0.014374
+26-Mar-2019 06:00:00,1,0.02049,0.02049,0.02049,0.061747,0.028832,0.028832,0.028832,0.028832,0.061747,0.061747,0.061747,0.061747,0.061747,0.028832,0.014443,0.02049,0.014443,0.014443
+26-Mar-2019 07:00:00,1,0.018694,0.018694,0.018694,0.054279,0.019221,0.019221,0.019221,0.019221,0.054279,0.054279,0.054279,0.054279,0.054279,0.019221,0.013512,0.018694,0.013512,0.013512
+26-Mar-2019 08:00:00,1,0.013778,0.013778,0.013778,0.023541,0.0057663,0.0057663,0.0057663,0.0057663,0.023541,0.023541,0.023541,0.023541,0.023541,0.0057663,0.018567,0.013778,0.018567,0.018567
+26-Mar-2019 09:00:00,1,0.012526,0.012526,0.012526,0.012068,0.0019221,0.0019221,0.0019221,0.0019221,0.012068,0.012068,0.012068,0.012068,0.012068,0.0019221,0.013838,0.012526,0.013838,0.013838
+26-Mar-2019 10:00:00,1,0.013022,0.013022,0.013022,0.0087128,0.013455,0.013455,0.013455,0.013455,0.0087128,0.0087128,0.0087128,0.0087128,0.0087128,0.013455,0.0086428,0.013022,0.0086428,0.0086428
+26-Mar-2019 11:00:00,1,0.016071,0.016071,0.016071,0.013205,0.013455,0.013455,0.013455,0.013455,0.013205,0.013205,0.013205,0.013205,0.013205,0.013455,0.014583,0.016071,0.014583,0.014583
+26-Mar-2019 12:00:00,1,0.019356,0.019356,0.019356,0.025922,0.024987,0.024987,0.024987,0.024987,0.025922,0.025922,0.025922,0.025922,0.025922,0.024987,0.023412,0.019356,0.023412,0.023412
+26-Mar-2019 13:00:00,1,0.023421,0.023421,0.023421,0.075493,0.024987,0.024987,0.024987,0.024987,0.075493,0.075493,0.075493,0.075493,0.075493,0.024987,0.023971,0.023421,0.023971,0.023971
+26-Mar-2019 14:00:00,1,0.029163,0.029163,0.029163,0.072084,0.024987,0.024987,0.024987,0.024987,0.072084,0.072084,0.072084,0.072084,0.072084,0.024987,0.018241,0.029163,0.018241,0.018241
+26-Mar-2019 15:00:00,1,0.035497,0.035497,0.035497,0.072787,0.017299,0.017299,0.017299,0.017299,0.072787,0.072787,0.072787,0.072787,0.072787,0.017299,0.017449,0.035497,0.017449,0.017449
+26-Mar-2019 16:00:00,1,0.042445,0.042445,0.042445,0.060611,0.019221,0.019221,0.019221,0.019221,0.060611,0.060611,0.060611,0.060611,0.060611,0.019221,0.015911,0.042445,0.015911,0.015911
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+26-Mar-2019 18:00:00,1,0.064637,0.064637,0.064637,0.060178,0.019221,0.019221,0.019221,0.019221,0.060178,0.060178,0.060178,0.060178,0.060178,0.019221,0.014164,0.064637,0.014164,0.014164
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+26-Mar-2019 23:00:00,1,0.10335,0.10335,0.10335,0.11213,0.023065,0.023065,0.023065,0.023065,0.11213,0.11213,0.11213,0.11213,0.11213,0.023065,0.10262,0.10335,0.10262,0.10262
+27-Mar-2019 00:00:00,1,0.11264,0.11264,0.11264,0.088643,0.015377,0.015377,0.015377,0.015377,0.088643,0.088643,0.088643,0.088643,0.088643,0.015377,0.13828,0.11264,0.13828,0.13828
+27-Mar-2019 01:00:00,1,0.12251,0.12251,0.12251,0.087128,0.030754,0.030754,0.030754,0.030754,0.087128,0.087128,0.087128,0.087128,0.087128,0.030754,0.16358,0.12251,0.16358,0.16358
+27-Mar-2019 02:00:00,1,0.13237,0.13237,0.13237,0.086208,0.042286,0.042286,0.042286,0.042286,0.086208,0.086208,0.086208,0.086208,0.086208,0.042286,0.1968,0.13237,0.1968,0.1968
+27-Mar-2019 03:00:00,1,0.13691,0.13691,0.13691,0.071488,0.021143,0.021143,0.021143,0.021143,0.071488,0.071488,0.071488,0.071488,0.071488,0.021143,0.22693,0.13691,0.22693,0.22693
+27-Mar-2019 04:00:00,1,0.15454,0.15454,0.15454,0.046162,0.017299,0.017299,0.017299,0.017299,0.046162,0.046162,0.046162,0.046162,0.046162,0.017299,0.2529,0.15454,0.2529,0.2529
+27-Mar-2019 05:00:00,1,0.17089,0.17089,0.17089,0.031334,0.013455,0.013455,0.013455,0.013455,0.031334,0.031334,0.031334,0.031334,0.031334,0.013455,0.27631,0.17089,0.27631,0.27631
+27-Mar-2019 06:00:00,1,0.18921,0.18921,0.18921,0.03404,0.011533,0.011533,0.011533,0.011533,0.03404,0.03404,0.03404,0.03404,0.03404,0.011533,0.28747,0.18921,0.28747,0.28747
+27-Mar-2019 07:00:00,1,0.20225,0.20225,0.20225,0.037719,0.013455,0.013455,0.013455,0.013455,0.037719,0.037719,0.037719,0.037719,0.037719,0.013455,0.29882,0.20225,0.29882,0.29882
+27-Mar-2019 08:00:00,1,0.18616,0.18616,0.18616,0.042969,0.0076884,0.0076884,0.0076884,0.0076884,0.042969,0.042969,0.042969,0.042969,0.042969,0.0076884,0.26485,0.18616,0.26485,0.26485
+27-Mar-2019 09:00:00,1,0.14976,0.14976,0.14976,0.026788,0.011533,0.011533,0.011533,0.011533,0.026788,0.026788,0.026788,0.026788,0.026788,0.011533,0.22129,0.14976,0.22129,0.22129
+27-Mar-2019 10:00:00,1,0.13383,0.13383,0.13383,0.020997,0.019221,0.019221,0.019221,0.019221,0.020997,0.020997,0.020997,0.020997,0.020997,0.019221,0.19058,0.13383,0.19058,0.19058
+27-Mar-2019 11:00:00,1,0.14409,0.14409,0.14409,0.023378,0.015377,0.015377,0.015377,0.015377,0.023378,0.023378,0.023378,0.023378,0.023378,0.015377,0.17148,0.14409,0.17148,0.17148
+27-Mar-2019 12:00:00,1,0.15156,0.15156,0.15156,0.039451,0.015377,0.015377,0.015377,0.015377,0.039451,0.039451,0.039451,0.039451,0.039451,0.015377,0.16084,0.15156,0.16084,0.16084
+27-Mar-2019 13:00:00,1,0.1763,0.1763,0.1763,0.07874,0.015377,0.015377,0.015377,0.015377,0.07874,0.07874,0.07874,0.07874,0.07874,0.015377,0.17791,0.1763,0.17791,0.17791
+27-Mar-2019 14:00:00,1,0.20164,0.20164,0.20164,0.12625,0.013455,0.013455,0.013455,0.013455,0.12625,0.12625,0.12625,0.12625,0.12625,0.013455,0.19242,0.20164,0.19242,0.19242
+27-Mar-2019 15:00:00,1,0.21057,0.21057,0.21057,0.12663,0.013455,0.013455,0.013455,0.013455,0.12663,0.12663,0.12663,0.12663,0.12663,0.013455,0.18949,0.21057,0.18949,0.18949
+27-Mar-2019 16:00:00,1,0.20119,0.20119,0.20119,0.12366,0.0057663,0.0057663,0.0057663,0.0057663,0.12366,0.12366,0.12366,0.12366,0.12366,0.0057663,0.18315,0.20119,0.18315,0.18315
+27-Mar-2019 17:00:00,1,0.18514,0.18514,0.18514,0.11765,0.028832,0.028832,0.028832,0.028832,0.11765,0.11765,0.11765,0.11765,0.11765,0.028832,0.16372,0.18514,0.16372,0.16372
+27-Mar-2019 18:00:00,1,0.16808,0.16808,0.16808,0.10585,0.019221,0.019221,0.019221,0.019221,0.10585,0.10585,0.10585,0.10585,0.10585,0.019221,0.13952,0.16808,0.13952,0.13952
+27-Mar-2019 19:00:00,1,0.1595,0.1595,0.1595,0.089293,0.032676,0.032676,0.032676,0.032676,0.089293,0.089293,0.089293,0.089293,0.089293,0.032676,0.13572,0.1595,0.13572,0.13572
+27-Mar-2019 20:00:00,1,0.17571,0.17571,0.17571,0.10564,0.032676,0.032676,0.032676,0.032676,0.10564,0.10564,0.10564,0.10564,0.10564,0.032676,0.17442,0.17571,0.17442,0.17442
+27-Mar-2019 21:00:00,1,0.19041,0.19041,0.19041,0.098926,0.044208,0.044208,0.044208,0.044208,0.098926,0.098926,0.098926,0.098926,0.098926,0.044208,0.21362,0.19041,0.21362,0.21362
+27-Mar-2019 22:00:00,1,0.1932,0.1932,0.1932,0.080364,0.038442,0.038442,0.038442,0.038442,0.080364,0.080364,0.080364,0.080364,0.080364,0.038442,0.20451,0.1932,0.20451,0.20451
+27-Mar-2019 23:00:00,1,0.18079,0.18079,0.18079,0.076521,0.032676,0.032676,0.032676,0.032676,0.076521,0.076521,0.076521,0.076521,0.076521,0.032676,0.18625,0.18079,0.18625,0.18625
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+28-Mar-2019 01:00:00,1,0.17548,0.17548,0.17548,0.084476,0.024987,0.024987,0.024987,0.024987,0.084476,0.084476,0.084476,0.084476,0.084476,0.024987,0.17689,0.17548,0.17689,0.17689
+28-Mar-2019 02:00:00,1,0.19323,0.19323,0.19323,0.07203,0.0096105,0.0096105,0.0096105,0.0096105,0.07203,0.07203,0.07203,0.07203,0.07203,0.0096105,0.18674,0.19323,0.18674,0.18674
+28-Mar-2019 03:00:00,1,0.18294,0.18294,0.18294,0.055957,0.03652,0.03652,0.03652,0.03652,0.055957,0.055957,0.055957,0.055957,0.055957,0.03652,0.1945,0.18294,0.1945,0.1945
+28-Mar-2019 04:00:00,1,0.19781,0.19781,0.19781,0.073112,0.023065,0.023065,0.023065,0.023065,0.073112,0.073112,0.073112,0.073112,0.073112,0.023065,0.19969,0.19781,0.19969,0.19969
+28-Mar-2019 05:00:00,1,0.20608,0.20608,0.20608,0.080147,0.017299,0.017299,0.017299,0.017299,0.080147,0.080147,0.080147,0.080147,0.080147,0.017299,0.22693,0.20608,0.22693,0.22693
+28-Mar-2019 06:00:00,1,0.20745,0.20745,0.20745,0.082095,0.019221,0.019221,0.019221,0.019221,0.082095,0.082095,0.082095,0.082095,0.082095,0.019221,0.24712,0.20745,0.24712,0.24712
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+28-Mar-2019 08:00:00,1,0.19564,0.19564,0.19564,0.084585,0.0019221,0.0019221,0.0019221,0.0019221,0.084585,0.084585,0.084585,0.084585,0.084585,0.0019221,0.25111,0.19564,0.25111,0.25111
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+28-Mar-2019 12:00:00,1,0.20504,0.20504,0.20504,0.059096,0.0076884,0.0076884,0.0076884,0.0076884,0.059096,0.059096,0.059096,0.059096,0.059096,0.0076884,0.32146,0.20504,0.32146,0.32146
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+28-Mar-2019 15:00:00,1,0.4418,0.4418,0.4418,0.083448,0.013455,0.013455,0.013455,0.013455,0.083448,0.083448,0.083448,0.083448,0.083448,0.013455,0.54403,0.4418,0.54403,0.54403
+28-Mar-2019 16:00:00,1,0.49939,0.49939,0.49939,0.11889,0.017299,0.017299,0.017299,0.017299,0.11889,0.11889,0.11889,0.11889,0.11889,0.017299,0.58785,0.49939,0.58785,0.58785
+28-Mar-2019 17:00:00,1,0.47923,0.47923,0.47923,0.13773,0.019221,0.019221,0.019221,0.019221,0.13773,0.13773,0.13773,0.13773,0.13773,0.019221,0.63153,0.47923,0.63153,0.63153
+28-Mar-2019 18:00:00,1,0.44761,0.44761,0.44761,0.12993,0.019221,0.019221,0.019221,0.019221,0.12993,0.12993,0.12993,0.12993,0.12993,0.019221,0.60176,0.44761,0.60176,0.60176
+28-Mar-2019 19:00:00,1,0.43294,0.43294,0.43294,0.12425,0.019221,0.019221,0.019221,0.019221,0.12425,0.12425,0.12425,0.12425,0.12425,0.019221,0.59365,0.43294,0.59365,0.59365
+28-Mar-2019 20:00:00,1,0.43726,0.43726,0.43726,0.13123,0.023065,0.023065,0.023065,0.023065,0.13123,0.13123,0.13123,0.13123,0.13123,0.023065,0.65739,0.43726,0.65739,0.65739
+28-Mar-2019 21:00:00,1,0.48722,0.48722,0.48722,0.15975,0.028832,0.028832,0.028832,0.028832,0.15975,0.15975,0.15975,0.15975,0.15975,0.028832,0.60844,0.48722,0.60844,0.60844
+28-Mar-2019 22:00:00,1,0.51116,0.51116,0.51116,0.12847,0.030754,0.030754,0.030754,0.030754,0.12847,0.12847,0.12847,0.12847,0.12847,0.030754,0.5773,0.51116,0.5773,0.5773
+28-Mar-2019 23:00:00,1,0.52102,0.52102,0.52102,0.10585,0.026909,0.026909,0.026909,0.026909,0.10585,0.10585,0.10585,0.10585,0.10585,0.026909,0.58023,0.52102,0.58023,0.58023
+29-Mar-2019 00:00:00,1,0.54337,0.54337,0.54337,0.089022,0.024987,0.024987,0.024987,0.024987,0.089022,0.089022,0.089022,0.089022,0.089022,0.024987,0.57897,0.54337,0.57897,0.57897
+29-Mar-2019 01:00:00,1,0.51398,0.51398,0.51398,0.085342,0.026909,0.026909,0.026909,0.026909,0.085342,0.085342,0.085342,0.085342,0.085342,0.026909,0.64546,0.51398,0.64546,0.64546
+29-Mar-2019 02:00:00,1,0.50195,0.50195,0.50195,0.08426,0.028832,0.028832,0.028832,0.028832,0.08426,0.08426,0.08426,0.08426,0.08426,0.028832,0.77256,0.50195,0.77256,0.77256
+29-Mar-2019 03:00:00,1,0.49289,0.49289,0.49289,0.078307,0.038442,0.038442,0.038442,0.038442,0.078307,0.078307,0.078307,0.078307,0.078307,0.038442,0.72569,0.49289,0.72569,0.72569
+29-Mar-2019 04:00:00,1,0.51012,0.51012,0.51012,0.091512,0.044208,0.044208,0.044208,0.044208,0.091512,0.091512,0.091512,0.091512,0.091512,0.044208,0.69336,0.51012,0.69336,0.69336
+29-Mar-2019 05:00:00,1,0.51721,0.51721,0.51721,0.10028,0.048053,0.048053,0.048053,0.048053,0.10028,0.10028,0.10028,0.10028,0.10028,0.048053,0.65564,0.51721,0.65564,0.65564
+29-Mar-2019 06:00:00,1,0.54215,0.54215,0.54215,0.090267,0.051897,0.051897,0.051897,0.051897,0.090267,0.090267,0.090267,0.090267,0.090267,0.051897,0.57956,0.54215,0.57956,0.57956
+29-Mar-2019 07:00:00,1,0.5603,0.5603,0.5603,0.11451,0.055741,0.055741,0.055741,0.055741,0.11451,0.11451,0.11451,0.11451,0.11451,0.055741,0.53646,0.5603,0.53646,0.53646
+29-Mar-2019 08:00:00,1,0.54586,0.54586,0.54586,0.12577,0.055741,0.055741,0.055741,0.055741,0.12577,0.12577,0.12577,0.12577,0.12577,0.055741,0.60837,0.54586,0.60837,0.60837
+29-Mar-2019 09:00:00,1,0.5754,0.5754,0.5754,0.18394,0.061507,0.061507,0.061507,0.061507,0.18394,0.18394,0.18394,0.18394,0.18394,0.061507,0.68679,0.5754,0.68679,0.68679
+29-Mar-2019 10:00:00,1,0.60915,0.60915,0.60915,0.18064,0.07304,0.07304,0.07304,0.07304,0.18064,0.18064,0.18064,0.18064,0.18064,0.07304,0.70661,0.60915,0.70661,0.70661
+29-Mar-2019 11:00:00,1,0.65804,0.65804,0.65804,0.21587,0.090339,0.090339,0.090339,0.090339,0.21587,0.21587,0.21587,0.21587,0.21587,0.090339,0.72117,0.65804,0.72117,0.72117
+29-Mar-2019 12:00:00,1,0.68983,0.68983,0.68983,0.30554,0.11148,0.11148,0.11148,0.11148,0.30554,0.30554,0.30554,0.30554,0.30554,0.11148,0.71467,0.68983,0.71467,0.71467
+29-Mar-2019 13:00:00,1,0.72344,0.72344,0.72344,0.38666,0.1307,0.1307,0.1307,0.1307,0.38666,0.38666,0.38666,0.38666,0.38666,0.1307,0.71078,0.72344,0.71078,0.71078
+29-Mar-2019 14:00:00,1,0.73986,0.73986,0.73986,0.47872,0.15377,0.15377,0.15377,0.15377,0.47872,0.47872,0.47872,0.47872,0.47872,0.15377,0.71716,0.73986,0.71716,0.71716
+29-Mar-2019 15:00:00,1,0.75151,0.75151,0.75151,0.54485,0.16722,0.16722,0.16722,0.16722,0.54485,0.54485,0.54485,0.54485,0.54485,0.16722,0.74656,0.75151,0.74656,0.74656
+29-Mar-2019 16:00:00,1,0.75421,0.75421,0.75421,0.5212,0.17876,0.17876,0.17876,0.17876,0.5212,0.5212,0.5212,0.5212,0.5212,0.17876,0.7594,0.75421,0.7594,0.7594
+29-Mar-2019 17:00:00,1,0.77032,0.77032,0.77032,0.5015,0.16722,0.16722,0.16722,0.16722,0.5015,0.5015,0.5015,0.5015,0.5015,0.16722,0.72986,0.77032,0.72986,0.72986
+29-Mar-2019 18:00:00,1,0.77524,0.77524,0.77524,0.44673,0.14608,0.14608,0.14608,0.14608,0.44673,0.44673,0.44673,0.44673,0.44673,0.14608,0.75994,0.77524,0.75994,0.75994
+29-Mar-2019 19:00:00,1,0.74272,0.74272,0.74272,0.42574,0.13262,0.13262,0.13262,0.13262,0.42574,0.42574,0.42574,0.42574,0.42574,0.13262,0.75004,0.74272,0.75004,0.75004
+29-Mar-2019 20:00:00,1,0.7106,0.7106,0.7106,0.36767,0.12686,0.12686,0.12686,0.12686,0.36767,0.36767,0.36767,0.36767,0.36767,0.12686,0.74945,0.7106,0.74945,0.74945
+29-Mar-2019 21:00:00,1,0.67818,0.67818,0.67818,0.37211,0.12109,0.12109,0.12109,0.12109,0.37211,0.37211,0.37211,0.37211,0.37211,0.12109,0.76571,0.67818,0.76571,0.76571
+29-Mar-2019 22:00:00,1,0.61427,0.61427,0.61427,0.34104,0.11533,0.11533,0.11533,0.11533,0.34104,0.34104,0.34104,0.34104,0.34104,0.11533,0.76001,0.61427,0.76001,0.76001
+29-Mar-2019 23:00:00,1,0.59485,0.59485,0.59485,0.24185,0.10379,0.10379,0.10379,0.10379,0.24185,0.24185,0.24185,0.24185,0.24185,0.10379,0.70107,0.59485,0.70107,0.70107
+30-Mar-2019 00:00:00,1,0.55037,0.55037,0.55037,0.30213,0.10572,0.10572,0.10572,0.10572,0.30213,0.30213,0.30213,0.30213,0.30213,0.10572,0.66307,0.55037,0.66307,0.66307
+30-Mar-2019 01:00:00,1,0.52834,0.52834,0.52834,0.30673,0.10572,0.10572,0.10572,0.10572,0.30673,0.30673,0.30673,0.30673,0.30673,0.10572,0.61832,0.52834,0.61832,0.61832
+30-Mar-2019 02:00:00,1,0.53066,0.53066,0.53066,0.30408,0.10379,0.10379,0.10379,0.10379,0.30408,0.30408,0.30408,0.30408,0.30408,0.10379,0.55607,0.53066,0.55607,0.55607
+30-Mar-2019 03:00:00,1,0.55647,0.55647,0.55647,0.29651,0.098027,0.098027,0.098027,0.098027,0.29651,0.29651,0.29651,0.29651,0.29651,0.098027,0.50433,0.55647,0.50433,0.50433
+30-Mar-2019 04:00:00,1,0.5459,0.5459,0.5459,0.31561,0.092261,0.092261,0.092261,0.092261,0.31561,0.31561,0.31561,0.31561,0.31561,0.092261,0.47857,0.5459,0.47857,0.47857
+30-Mar-2019 05:00:00,1,0.54411,0.54411,0.54411,0.3162,0.088417,0.088417,0.088417,0.088417,0.3162,0.3162,0.3162,0.3162,0.3162,0.088417,0.43261,0.54411,0.43261,0.43261
+30-Mar-2019 06:00:00,1,0.58532,0.58532,0.58532,0.30976,0.086495,0.086495,0.086495,0.086495,0.30976,0.30976,0.30976,0.30976,0.30976,0.086495,0.41092,0.58532,0.41092,0.41092
+30-Mar-2019 07:00:00,1,0.59381,0.59381,0.59381,0.34272,0.08265,0.08265,0.08265,0.08265,0.34272,0.34272,0.34272,0.34272,0.34272,0.08265,0.42194,0.59381,0.42194,0.42194
+30-Mar-2019 08:00:00,1,0.61855,0.61855,0.61855,0.33439,0.08265,0.08265,0.08265,0.08265,0.33439,0.33439,0.33439,0.33439,0.33439,0.08265,0.43678,0.61855,0.43678,0.43678
+30-Mar-2019 09:00:00,1,0.65136,0.65136,0.65136,0.31182,0.053819,0.053819,0.053819,0.053819,0.31182,0.31182,0.31182,0.31182,0.31182,0.053819,0.45241,0.65136,0.45241,0.45241
+30-Mar-2019 10:00:00,1,0.69449,0.69449,0.69449,0.28574,0.12301,0.12301,0.12301,0.12301,0.28574,0.28574,0.28574,0.28574,0.28574,0.12301,0.53217,0.69449,0.53217,0.53217
+30-Mar-2019 11:00:00,1,0.70746,0.70746,0.70746,0.28752,0.11148,0.11148,0.11148,0.11148,0.28752,0.28752,0.28752,0.28752,0.28752,0.11148,0.55614,0.70746,0.55614,0.55614
+30-Mar-2019 12:00:00,1,0.72374,0.72374,0.72374,0.32405,0.08265,0.08265,0.08265,0.08265,0.32405,0.32405,0.32405,0.32405,0.32405,0.08265,0.6285,0.72374,0.6285,0.6285
+30-Mar-2019 13:00:00,1,0.75279,0.75279,0.75279,0.35739,0.074962,0.074962,0.074962,0.074962,0.35739,0.35739,0.35739,0.35739,0.35739,0.074962,0.70591,0.75279,0.70591,0.70591
+30-Mar-2019 14:00:00,1,0.77184,0.77184,0.77184,0.3319,0.12878,0.12878,0.12878,0.12878,0.3319,0.3319,0.3319,0.3319,0.3319,0.12878,0.79516,0.77184,0.79516,0.79516
+30-Mar-2019 15:00:00,1,0.77689,0.77689,0.77689,0.328,0.10764,0.10764,0.10764,0.10764,0.328,0.328,0.328,0.328,0.328,0.10764,0.84646,0.77689,0.84646,0.84646
+30-Mar-2019 16:00:00,1,0.7599,0.7599,0.7599,0.35338,0.069196,0.069196,0.069196,0.069196,0.35338,0.35338,0.35338,0.35338,0.35338,0.069196,0.8814,0.7599,0.8814,0.8814
+30-Mar-2019 17:00:00,1,0.7305,0.7305,0.7305,0.32973,0.10572,0.10572,0.10572,0.10572,0.32973,0.32973,0.32973,0.32973,0.32973,0.10572,0.87162,0.7305,0.87162,0.87162
+30-Mar-2019 18:00:00,1,0.68555,0.68555,0.68555,0.27345,0.051897,0.051897,0.051897,0.051897,0.27345,0.27345,0.27345,0.27345,0.27345,0.051897,0.83197,0.68555,0.83197,0.83197
+30-Mar-2019 19:00:00,1,0.66402,0.66402,0.66402,0.23498,0.048053,0.048053,0.048053,0.048053,0.23498,0.23498,0.23498,0.23498,0.23498,0.048053,0.76094,0.66402,0.76094,0.76094
+30-Mar-2019 20:00:00,1,0.6337,0.6337,0.6337,0.23898,0.032676,0.032676,0.032676,0.032676,0.23898,0.23898,0.23898,0.23898,0.23898,0.032676,0.75635,0.6337,0.75635,0.75635
+30-Mar-2019 21:00:00,1,0.65819,0.65819,0.65819,0.26014,0.024987,0.024987,0.024987,0.024987,0.26014,0.26014,0.26014,0.26014,0.26014,0.024987,0.77694,0.65819,0.77694,0.77694
+30-Mar-2019 22:00:00,1,0.6756,0.6756,0.6756,0.24596,0.032676,0.032676,0.032676,0.032676,0.24596,0.24596,0.24596,0.24596,0.24596,0.032676,0.77298,0.6756,0.77298,0.77298
+30-Mar-2019 23:00:00,1,0.67142,0.67142,0.67142,0.17593,0.0096105,0.0096105,0.0096105,0.0096105,0.17593,0.17593,0.17593,0.17593,0.17593,0.0096105,0.81172,0.67142,0.81172,0.81172
+31-Mar-2019 00:00:00,1,0.70807,0.70807,0.70807,0.18167,0.028832,0.028832,0.028832,0.028832,0.18167,0.18167,0.18167,0.18167,0.18167,0.028832,0.81722,0.70807,0.81722,0.81722
+31-Mar-2019 01:00:00,1,0.72353,0.72353,0.72353,0.19515,0.030754,0.030754,0.030754,0.030754,0.19515,0.19515,0.19515,0.19515,0.19515,0.030754,0.87301,0.72353,0.87301,0.87301
+31-Mar-2019 02:00:00,1,0.73294,0.73294,0.73294,0.21149,0.021143,0.021143,0.021143,0.021143,0.21149,0.21149,0.21149,0.21149,0.21149,0.021143,0.91618,0.73294,0.91618,0.91618
+31-Mar-2019 03:00:00,1,0.741,0.741,0.741,0.27205,0.026909,0.026909,0.026909,0.026909,0.27205,0.27205,0.27205,0.27205,0.27205,0.026909,0.94614,0.741,0.94614,0.94614
+31-Mar-2019 04:00:00,1,0.73428,0.73428,0.73428,0.30922,0.034598,0.034598,0.034598,0.034598,0.30922,0.30922,0.30922,0.30922,0.30922,0.034598,0.93284,0.73428,0.93284,0.93284
+31-Mar-2019 05:00:00,1,0.73204,0.73204,0.73204,0.31886,0.057663,0.057663,0.057663,0.057663,0.31886,0.31886,0.31886,0.31886,0.31886,0.057663,0.91867,0.73204,0.91867,0.91867
+31-Mar-2019 06:00:00,1,0.7539,0.7539,0.7539,0.33628,0.074962,0.074962,0.074962,0.074962,0.33628,0.33628,0.33628,0.33628,0.33628,0.074962,0.93149,0.7539,0.93149,0.93149
+31-Mar-2019 07:00:00,1,0.75614,0.75614,0.75614,0.36567,0.067274,0.067274,0.067274,0.067274,0.36567,0.36567,0.36567,0.36567,0.36567,0.067274,0.92177,0.75614,0.92177,0.92177
+31-Mar-2019 08:00:00,1,0.78673,0.78673,0.78673,0.33222,0.11148,0.11148,0.11148,0.11148,0.33222,0.33222,0.33222,0.33222,0.33222,0.11148,0.92517,0.78673,0.92517,0.92517
+31-Mar-2019 09:00:00,1,0.8019,0.8019,0.8019,0.35084,0.10764,0.10764,0.10764,0.10764,0.35084,0.35084,0.35084,0.35084,0.35084,0.10764,0.90665,0.8019,0.90665,0.90665
+31-Mar-2019 10:00:00,1,0.80842,0.80842,0.80842,0.40198,0.063429,0.063429,0.063429,0.063429,0.40198,0.40198,0.40198,0.40198,0.40198,0.063429,0.86295,0.80842,0.86295,0.86295
+31-Mar-2019 11:00:00,1,0.78758,0.78758,0.78758,0.44295,0.10956,0.10956,0.10956,0.10956,0.44295,0.44295,0.44295,0.44295,0.44295,0.10956,0.85482,0.78758,0.85482,0.85482
+31-Mar-2019 12:00:00,1,0.77548,0.77548,0.77548,0.44701,0.13455,0.13455,0.13455,0.13455,0.44701,0.44701,0.44701,0.44701,0.44701,0.13455,0.83274,0.77548,0.83274,0.83274
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+31-Mar-2019 14:00:00,1,0.69267,0.69267,0.69267,0.46676,0.1307,0.1307,0.1307,0.1307,0.46676,0.46676,0.46676,0.46676,0.46676,0.1307,0.82868,0.69267,0.82868,0.82868
+31-Mar-2019 15:00:00,1,0.66078,0.66078,0.66078,0.47184,0.15761,0.15761,0.15761,0.15761,0.47184,0.47184,0.47184,0.47184,0.47184,0.15761,0.80629,0.66078,0.80629,0.80629
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+11-May-2019 05:00:00,1,0.3172,0.3172,0.3172,0.1793,0.04377,0.04377,0.04377,0.04377,0.1793,0.1793,0.1793,0.1793,0.1793,0.04377,0.28227,0.3172,0.28227,0.28227
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+11-May-2019 08:00:00,1,0.34116,0.34116,0.34116,0.19437,0.011418,0.011418,0.011418,0.011418,0.19437,0.19437,0.19437,0.19437,0.19437,0.011418,0.22631,0.34116,0.22631,0.22631
+11-May-2019 09:00:00,1,0.39158,0.39158,0.39158,0.18053,0.01903,0.01903,0.01903,0.01903,0.18053,0.18053,0.18053,0.18053,0.18053,0.01903,0.21425,0.39158,0.21425,0.21425
+11-May-2019 10:00:00,1,0.44128,0.44128,0.44128,0.28533,0.022837,0.022837,0.022837,0.022837,0.28533,0.28533,0.28533,0.28533,0.28533,0.022837,0.25221,0.44128,0.25221,0.25221
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+11-May-2019 13:00:00,1,0.58156,0.58156,0.58156,0.3569,0.01903,0.01903,0.01903,0.01903,0.3569,0.3569,0.3569,0.3569,0.3569,0.01903,0.40168,0.58156,0.40168,0.40168
+11-May-2019 14:00:00,1,0.56302,0.56302,0.56302,0.30306,0.034255,0.034255,0.034255,0.034255,0.30306,0.30306,0.30306,0.30306,0.30306,0.034255,0.3957,0.56302,0.3957,0.3957
+11-May-2019 15:00:00,1,0.55797,0.55797,0.55797,0.26408,0.034255,0.034255,0.034255,0.034255,0.26408,0.26408,0.26408,0.26408,0.26408,0.034255,0.39071,0.55797,0.39071,0.39071
+11-May-2019 16:00:00,1,0.51676,0.51676,0.51676,0.29449,0.04377,0.04377,0.04377,0.04377,0.29449,0.29449,0.29449,0.29449,0.29449,0.04377,0.35848,0.51676,0.35848,0.35848
+11-May-2019 17:00:00,1,0.4482,0.4482,0.4482,0.28416,0.022837,0.022837,0.022837,0.022837,0.28416,0.28416,0.28416,0.28416,0.28416,0.022837,0.35147,0.4482,0.35147,0.35147
+11-May-2019 18:00:00,1,0.43578,0.43578,0.43578,0.24645,0.045673,0.045673,0.045673,0.045673,0.24645,0.24645,0.24645,0.24645,0.24645,0.045673,0.30079,0.43578,0.30079,0.30079
+11-May-2019 19:00:00,1,0.43508,0.43508,0.43508,0.19283,0.051382,0.051382,0.051382,0.051382,0.19283,0.19283,0.19283,0.19283,0.19283,0.051382,0.25646,0.43508,0.25646,0.25646
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+11-May-2019 23:00:00,1,0.37164,0.37164,0.37164,0.16625,0.032352,0.032352,0.032352,0.032352,0.16625,0.16625,0.16625,0.16625,0.16625,0.032352,0.26599,0.37164,0.26599,0.26599
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+12-May-2019 01:00:00,1,0.34872,0.34872,0.34872,0.12211,0.036158,0.036158,0.036158,0.036158,0.12211,0.12211,0.12211,0.12211,0.12211,0.036158,0.25232,0.34872,0.25232,0.25232
+12-May-2019 02:00:00,1,0.32589,0.32589,0.32589,0.10911,0.055188,0.055188,0.055188,0.055188,0.10911,0.10911,0.10911,0.10911,0.10911,0.055188,0.27004,0.32589,0.27004,0.27004
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+12-May-2019 09:00:00,1,0.52179,0.52179,0.52179,0.10469,0.038061,0.038061,0.038061,0.038061,0.10469,0.10469,0.10469,0.10469,0.10469,0.038061,0.50718,0.52179,0.50718,0.50718
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+13-May-2019 01:00:00,1,0.18579,0.18579,0.18579,0.075778,0.032352,0.032352,0.032352,0.032352,0.075778,0.075778,0.075778,0.075778,0.075778,0.032352,0.24809,0.18579,0.24809,0.24809
+13-May-2019 02:00:00,1,0.17181,0.17181,0.17181,0.079772,0.030449,0.030449,0.030449,0.030449,0.079772,0.079772,0.079772,0.079772,0.079772,0.030449,0.22532,0.17181,0.22532,0.22532
+13-May-2019 03:00:00,1,0.15804,0.15804,0.15804,0.063956,0.039964,0.039964,0.039964,0.039964,0.063956,0.063956,0.063956,0.063956,0.063956,0.039964,0.20556,0.15804,0.20556,0.20556
+13-May-2019 04:00:00,1,0.14286,0.14286,0.14286,0.067045,0.049479,0.049479,0.049479,0.049479,0.067045,0.067045,0.067045,0.067045,0.067045,0.049479,0.18644,0.14286,0.18644,0.18644
+13-May-2019 05:00:00,1,0.1234,0.1234,0.1234,0.056714,0.047576,0.047576,0.047576,0.047576,0.056714,0.056714,0.056714,0.056714,0.056714,0.047576,0.17377,0.1234,0.17377,0.17377
+13-May-2019 06:00:00,1,0.099012,0.099012,0.099012,0.049259,0.030449,0.030449,0.030449,0.030449,0.049259,0.049259,0.049259,0.049259,0.049259,0.030449,0.144,0.099012,0.144,0.144
+13-May-2019 07:00:00,1,0.068986,0.068986,0.068986,0.036105,0.036158,0.036158,0.036158,0.036158,0.036105,0.036105,0.036105,0.036105,0.036105,0.036158,0.11423,0.068986,0.11423,0.11423
+13-May-2019 08:00:00,1,0.072868,0.072868,0.072868,0.022686,0.011418,0.011418,0.011418,0.011418,0.022686,0.022686,0.022686,0.022686,0.022686,0.011418,0.11412,0.072868,0.11412,0.11412
+13-May-2019 09:00:00,1,0.073944,0.073944,0.073944,0.031792,0.017127,0.017127,0.017127,0.017127,0.031792,0.031792,0.031792,0.031792,0.031792,0.017127,0.11021,0.073944,0.11021,0.11021
+13-May-2019 10:00:00,1,0.063748,0.063748,0.063748,0.040632,0.017127,0.017127,0.017127,0.017127,0.040632,0.040632,0.040632,0.040632,0.040632,0.017127,0.091898,0.063748,0.091898,0.091898
+13-May-2019 11:00:00,1,0.05409,0.05409,0.05409,0.041164,0.026643,0.026643,0.026643,0.026643,0.041164,0.041164,0.041164,0.041164,0.041164,0.026643,0.072759,0.05409,0.072759,0.072759
+13-May-2019 12:00:00,1,0.051424,0.051424,0.051424,0.034987,0.02474,0.02474,0.02474,0.02474,0.034987,0.034987,0.034987,0.034987,0.034987,0.02474,0.063788,0.051424,0.063788,0.063788
+13-May-2019 13:00:00,1,0.050138,0.050138,0.050138,0.031632,0.020933,0.020933,0.020933,0.020933,0.031632,0.031632,0.031632,0.031632,0.031632,0.020933,0.053966,0.050138,0.053966,0.053966
+13-May-2019 14:00:00,1,0.051962,0.051962,0.051962,0.022792,0.026643,0.026643,0.026643,0.026643,0.022792,0.022792,0.022792,0.022792,0.022792,0.026643,0.044488,0.051962,0.044488,0.044488
+13-May-2019 15:00:00,1,0.05437,0.05437,0.05437,0.02423,0.011418,0.011418,0.011418,0.011418,0.02423,0.02423,0.02423,0.02423,0.02423,0.011418,0.036161,0.05437,0.036161,0.036161
+13-May-2019 16:00:00,1,0.04953,0.04953,0.04953,0.02162,0.015224,0.015224,0.015224,0.015224,0.02162,0.02162,0.02162,0.02162,0.02162,0.015224,0.022106,0.04953,0.022106,0.022106
+13-May-2019 17:00:00,1,0.048945,0.048945,0.048945,0.018106,0.01903,0.01903,0.01903,0.01903,0.018106,0.018106,0.018106,0.018106,0.018106,0.01903,0.015734,0.048945,0.015734,0.015734
+13-May-2019 18:00:00,1,0.042818,0.042818,0.042818,0.014059,0.0076122,0.0076122,0.0076122,0.0076122,0.014059,0.014059,0.014059,0.014059,0.014059,0.0076122,0.0083272,0.042818,0.0083272,0.0083272
+13-May-2019 19:00:00,1,0.034774,0.034774,0.034774,0.015709,0.01903,0.01903,0.01903,0.01903,0.015709,0.015709,0.015709,0.015709,0.015709,0.01903,0.0048307,0.034774,0.0048307,0.0048307
+13-May-2019 20:00:00,1,0.033534,0.033534,0.033534,0.024283,0.026643,0.026643,0.026643,0.026643,0.024283,0.024283,0.024283,0.024283,0.024283,0.026643,0.0025994,0.033534,0.0025994,0.0025994
+13-May-2019 21:00:00,1,0.052804,0.052804,0.052804,0.028064,0.039964,0.039964,0.039964,0.039964,0.028064,0.028064,0.028064,0.028064,0.028064,0.039964,0.0018863,0.052804,0.0018863,0.0018863
+13-May-2019 22:00:00,1,0.095247,0.095247,0.095247,0.045584,0.022837,0.022837,0.022837,0.022837,0.045584,0.045584,0.045584,0.045584,0.045584,0.022837,0.0034045,0.095247,0.0034045,0.0034045
+13-May-2019 23:00:00,1,0.15154,0.15154,0.15154,0.081263,0.028546,0.028546,0.028546,0.028546,0.081263,0.081263,0.081263,0.081263,0.081263,0.028546,0.0078901,0.15154,0.0078901,0.0078901
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+14-May-2019 01:00:00,1,0.23525,0.23525,0.23525,0.14746,0.032352,0.032352,0.032352,0.032352,0.14746,0.14746,0.14746,0.14746,0.14746,0.032352,0.03462,0.23525,0.03462,0.03462
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+14-May-2019 03:00:00,1,0.26689,0.26689,0.26689,0.17461,0.015224,0.015224,0.015224,0.015224,0.17461,0.17461,0.17461,0.17461,0.17461,0.015224,0.057163,0.26689,0.057163,0.057163
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+14-May-2019 05:00:00,1,0.27419,0.27419,0.27419,0.17973,0.0057091,0.0057091,0.0057091,0.0057091,0.17973,0.17973,0.17973,0.17973,0.17973,0.0057091,0.093462,0.27419,0.093462,0.093462
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+14-May-2019 07:00:00,1,0.2075,0.2075,0.2075,0.11886,0,0,0,0,0.11886,0.11886,0.11886,0.11886,0.11886,0,0.078878,0.2075,0.078878,0.078878
+14-May-2019 08:00:00,1,0.19615,0.19615,0.19615,0.080305,0,0,0,0,0.080305,0.080305,0.080305,0.080305,0.080305,0,0.059855,0.19615,0.059855,0.059855
+14-May-2019 09:00:00,1,0.20836,0.20836,0.20836,0.084405,0,0,0,0,0.084405,0.084405,0.084405,0.084405,0.084405,0,0.12148,0.20836,0.12148,0.12148
+14-May-2019 10:00:00,1,0.23083,0.23083,0.23083,0.11236,0,0,0,0,0.11236,0.11236,0.11236,0.11236,0.11236,0,0.12992,0.23083,0.12992,0.12992
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+14-May-2019 13:00:00,1,0.24197,0.24197,0.24197,0.11865,0,0,0,0,0.11865,0.11865,0.11865,0.11865,0.11865,0,0.13738,0.24197,0.13738,0.13738
+14-May-2019 14:00:00,1,0.22106,0.22106,0.22106,0.10943,0,0,0,0,0.10943,0.10943,0.10943,0.10943,0.10943,0,0.1446,0.22106,0.1446,0.1446
+14-May-2019 15:00:00,1,0.19718,0.19718,0.19718,0.1064,0,0,0,0,0.1064,0.1064,0.1064,0.1064,0.1064,0,0.15601,0.19718,0.15601,0.15601
+14-May-2019 16:00:00,1,0.14489,0.14489,0.14489,0.0966,0.001903,0.001903,0.001903,0.001903,0.0966,0.0966,0.0966,0.0966,0.0966,0.001903,0.14632,0.14489,0.14632,0.14632
+14-May-2019 17:00:00,1,0.11204,0.11204,0.11204,0.080944,0,0,0,0,0.080944,0.080944,0.080944,0.080944,0.080944,0,0.13123,0.11204,0.13123,0.13123
+14-May-2019 18:00:00,1,0.077147,0.077147,0.077147,0.060175,0.001903,0.001903,0.001903,0.001903,0.060175,0.060175,0.060175,0.060175,0.060175,0.001903,0.10947,0.077147,0.10947,0.10947
+14-May-2019 19:00:00,1,0.051026,0.051026,0.051026,0.051389,0.011418,0.011418,0.011418,0.011418,0.051389,0.051389,0.051389,0.051389,0.051389,0.011418,0.078533,0.051026,0.078533,0.078533
+14-May-2019 20:00:00,1,0.036668,0.036668,0.036668,0.036904,0.01903,0.01903,0.01903,0.01903,0.036904,0.036904,0.036904,0.036904,0.036904,0.01903,0.084169,0.036668,0.084169,0.084169
+14-May-2019 21:00:00,1,0.04677,0.04677,0.04677,0.043241,0.026643,0.026643,0.026643,0.026643,0.043241,0.043241,0.043241,0.043241,0.043241,0.026643,0.12799,0.04677,0.12799,0.12799
+14-May-2019 22:00:00,1,0.06784,0.06784,0.06784,0.054317,0.036158,0.036158,0.036158,0.036158,0.054317,0.054317,0.054317,0.054317,0.054317,0.036158,0.17683,0.06784,0.17683,0.17683
+14-May-2019 23:00:00,1,0.083695,0.083695,0.083695,0.063051,0.038061,0.038061,0.038061,0.038061,0.063051,0.063051,0.063051,0.063051,0.063051,0.038061,0.22235,0.083695,0.22235,0.22235
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+15-May-2019 01:00:00,1,0.087694,0.087694,0.087694,0.049685,0.030449,0.030449,0.030449,0.030449,0.049685,0.049685,0.049685,0.049685,0.049685,0.030449,0.24526,0.087694,0.24526,0.24526
+15-May-2019 02:00:00,1,0.07965,0.07965,0.07965,0.027798,0.038061,0.038061,0.038061,0.038061,0.027798,0.027798,0.027798,0.027798,0.027798,0.038061,0.22304,0.07965,0.22304,0.22304
+15-May-2019 03:00:00,1,0.088185,0.088185,0.088185,0.03062,0.041867,0.041867,0.041867,0.041867,0.03062,0.03062,0.03062,0.03062,0.03062,0.041867,0.21614,0.088185,0.21614,0.21614
+15-May-2019 04:00:00,1,0.085215,0.085215,0.085215,0.045424,0.057091,0.057091,0.057091,0.057091,0.045424,0.045424,0.045424,0.045424,0.045424,0.057091,0.21869,0.085215,0.21869,0.21869
+15-May-2019 05:00:00,1,0.079673,0.079673,0.079673,0.060335,0.034255,0.034255,0.034255,0.034255,0.060335,0.060335,0.060335,0.060335,0.060335,0.034255,0.22594,0.079673,0.22594,0.22594
+15-May-2019 06:00:00,1,0.085005,0.085005,0.085005,0.0704,0.026643,0.026643,0.026643,0.026643,0.0704,0.0704,0.0704,0.0704,0.0704,0.026643,0.20737,0.085005,0.20737,0.20737
+15-May-2019 07:00:00,1,0.083882,0.083882,0.083882,0.088559,0.022837,0.022837,0.022837,0.022837,0.088559,0.088559,0.088559,0.088559,0.088559,0.022837,0.14984,0.083882,0.14984,0.14984
+15-May-2019 08:00:00,1,0.07827,0.07827,0.07827,0.089997,0.039964,0.039964,0.039964,0.039964,0.089997,0.089997,0.089997,0.089997,0.089997,0.039964,0.11683,0.07827,0.11683,0.11683
+15-May-2019 09:00:00,1,0.083298,0.083298,0.083298,0.14149,0.066607,0.066607,0.066607,0.066607,0.14149,0.14149,0.14149,0.14149,0.14149,0.066607,0.14336,0.083298,0.14336,0.14336
+15-May-2019 10:00:00,1,0.09644,0.09644,0.09644,0.15997,0.08754,0.08754,0.08754,0.08754,0.15997,0.15997,0.15997,0.15997,0.15997,0.08754,0.18232,0.09644,0.18232,0.18232
+15-May-2019 11:00:00,1,0.095341,0.095341,0.095341,0.15379,0.11038,0.11038,0.11038,0.11038,0.15379,0.15379,0.15379,0.15379,0.15379,0.11038,0.23551,0.095341,0.23551,0.23551
+15-May-2019 12:00:00,1,0.091178,0.091178,0.091178,0.26584,0.055188,0.055188,0.055188,0.055188,0.26584,0.26584,0.26584,0.26584,0.26584,0.055188,0.24975,0.091178,0.24975,0.24975
+15-May-2019 13:00:00,1,0.089354,0.089354,0.089354,0.31872,0.070413,0.070413,0.070413,0.070413,0.31872,0.31872,0.31872,0.31872,0.31872,0.070413,0.26833,0.089354,0.26833,0.26833
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+15-May-2019 18:00:00,1,0.11924,0.11924,0.11924,0.15683,0.089443,0.089443,0.089443,0.089443,0.15683,0.15683,0.15683,0.15683,0.15683,0.089443,0.14071,0.11924,0.14071,0.14071
+15-May-2019 19:00:00,1,0.14169,0.14169,0.14169,0.10294,0.079928,0.079928,0.079928,0.079928,0.10294,0.10294,0.10294,0.10294,0.10294,0.079928,0.095234,0.14169,0.095234,0.095234
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+15-May-2019 21:00:00,1,0.15677,0.15677,0.15677,0.1293,0.08754,0.08754,0.08754,0.08754,0.1293,0.1293,0.1293,0.1293,0.1293,0.08754,0.057002,0.15677,0.057002,0.057002
+15-May-2019 22:00:00,1,0.15301,0.15301,0.15301,0.090423,0.085637,0.085637,0.085637,0.085637,0.090423,0.090423,0.090423,0.090423,0.090423,0.085637,0.061465,0.15301,0.061465,0.061465
+15-May-2019 23:00:00,1,0.17761,0.17761,0.17761,0.13143,0.049479,0.049479,0.049479,0.049479,0.13143,0.13143,0.13143,0.13143,0.13143,0.049479,0.062201,0.17761,0.062201,0.062201
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+16-May-2019 01:00:00,1,0.25331,0.25331,0.25331,0.19389,0.078025,0.078025,0.078025,0.078025,0.19389,0.19389,0.19389,0.19389,0.19389,0.078025,0.13105,0.25331,0.13105,0.13105
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+19-May-2019 01:00:00,1,0.10722,0.10722,0.10722,0.082967,0.0057091,0.0057091,0.0057091,0.0057091,0.082967,0.082967,0.082967,0.082967,0.082967,0.0057091,0.18389,0.10722,0.18389,0.18389
+19-May-2019 02:00:00,1,0.092324,0.092324,0.092324,0.091381,0.0038061,0.0038061,0.0038061,0.0038061,0.091381,0.091381,0.091381,0.091381,0.091381,0.0038061,0.16567,0.092324,0.16567,0.16567
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+19-May-2019 04:00:00,1,0.077288,0.077288,0.077288,0.067844,0.001903,0.001903,0.001903,0.001903,0.067844,0.067844,0.067844,0.067844,0.067844,0.001903,0.12647,0.077288,0.12647,0.12647
+19-May-2019 05:00:00,1,0.072377,0.072377,0.072377,0.074127,0.0038061,0.0038061,0.0038061,0.0038061,0.074127,0.074127,0.074127,0.074127,0.074127,0.0038061,0.10492,0.072377,0.10492,0.10492
+19-May-2019 06:00:00,1,0.06777,0.06777,0.06777,0.073222,0.001903,0.001903,0.001903,0.001903,0.073222,0.073222,0.073222,0.073222,0.073222,0.001903,0.089736,0.06777,0.089736,0.089736
+19-May-2019 07:00:00,1,0.05568,0.05568,0.05568,0.061933,0,0,0,0,0.061933,0.061933,0.061933,0.061933,0.061933,0,0.060637,0.05568,0.060637,0.060637
+19-May-2019 08:00:00,1,0.035007,0.035007,0.035007,0.044306,0,0,0,0,0.044306,0.044306,0.044306,0.044306,0.044306,0,0.029996,0.035007,0.029996,0.029996
+19-May-2019 09:00:00,1,0.030541,0.030541,0.030541,0.041963,0,0,0,0,0.041963,0.041963,0.041963,0.041963,0.041963,0,0.023072,0.030541,0.023072,0.023072
+19-May-2019 10:00:00,1,0.025934,0.025934,0.025934,0.047608,0.001903,0.001903,0.001903,0.001903,0.047608,0.047608,0.047608,0.047608,0.047608,0.001903,0.017966,0.025934,0.017966,0.017966
+19-May-2019 11:00:00,1,0.028623,0.028623,0.028623,0.048247,0.0057091,0.0057091,0.0057091,0.0057091,0.048247,0.048247,0.048247,0.048247,0.048247,0.0057091,0.030479,0.028623,0.030479,0.030479
+19-May-2019 12:00:00,1,0.032388,0.032388,0.032388,0.035892,0.0095152,0.0095152,0.0095152,0.0095152,0.035892,0.035892,0.035892,0.035892,0.035892,0.0095152,0.039865,0.032388,0.039865,0.039865
+19-May-2019 13:00:00,1,0.031593,0.031593,0.031593,0.0352,0.013321,0.013321,0.013321,0.013321,0.0352,0.0352,0.0352,0.0352,0.0352,0.013321,0.037771,0.031593,0.037771,0.037771
+19-May-2019 14:00:00,1,0.027852,0.027852,0.027852,0.035839,0.0095152,0.0095152,0.0095152,0.0095152,0.035839,0.035839,0.035839,0.035839,0.035839,0.0095152,0.032918,0.027852,0.032918,0.032918
+19-May-2019 15:00:00,1,0.028436,0.028436,0.028436,0.019757,0.0076122,0.0076122,0.0076122,0.0076122,0.019757,0.019757,0.019757,0.019757,0.019757,0.0076122,0.025833,0.028436,0.025833,0.025833
+19-May-2019 16:00:00,1,0.0286,0.0286,0.0286,0.016135,0.0076122,0.0076122,0.0076122,0.0076122,0.016135,0.016135,0.016135,0.016135,0.016135,0.0076122,0.022635,0.0286,0.022635,0.022635
+19-May-2019 17:00:00,1,0.029956,0.029956,0.029956,0.015603,0.0057091,0.0057091,0.0057091,0.0057091,0.015603,0.015603,0.015603,0.015603,0.015603,0.0057091,0.023555,0.029956,0.023555,0.023555
+19-May-2019 18:00:00,1,0.028249,0.028249,0.028249,0.013313,0.0095152,0.0095152,0.0095152,0.0095152,0.013313,0.013313,0.013313,0.013313,0.013313,0.0095152,0.029858,0.028249,0.029858,0.029858
+19-May-2019 19:00:00,1,0.037276,0.037276,0.037276,0.022792,0.0095152,0.0095152,0.0095152,0.0095152,0.022792,0.022792,0.022792,0.022792,0.022792,0.0095152,0.048008,0.037276,0.048008,0.048008
+19-May-2019 20:00:00,1,0.057761,0.057761,0.057761,0.0352,0.020933,0.020933,0.020933,0.020933,0.0352,0.0352,0.0352,0.0352,0.0352,0.020933,0.06648,0.057761,0.06648,0.06648
+19-May-2019 21:00:00,1,0.083625,0.083625,0.083625,0.076044,0.049479,0.049479,0.049479,0.049479,0.076044,0.076044,0.076044,0.076044,0.076044,0.049479,0.12176,0.083625,0.12176,0.12176
+19-May-2019 22:00:00,1,0.10079,0.10079,0.10079,0.10608,0.036158,0.036158,0.036158,0.036158,0.10608,0.10608,0.10608,0.10608,0.10608,0.036158,0.17618,0.10079,0.17618,0.17618
+19-May-2019 23:00:00,1,0.10388,0.10388,0.10388,0.11689,0.017127,0.017127,0.017127,0.017127,0.11689,0.11689,0.11689,0.11689,0.11689,0.017127,0.20172,0.10388,0.20172,0.20172
+20-May-2019 00:00:00,1,0.10521,0.10521,0.10521,0.11641,0.02474,0.02474,0.02474,0.02474,0.11641,0.11641,0.11641,0.11641,0.11641,0.02474,0.19679,0.10521,0.19679,0.19679
+20-May-2019 01:00:00,1,0.098638,0.098638,0.098638,0.10288,0.039964,0.039964,0.039964,0.039964,0.10288,0.10288,0.10288,0.10288,0.10288,0.039964,0.17229,0.098638,0.17229,0.17229
+20-May-2019 02:00:00,1,0.093657,0.093657,0.093657,0.096493,0.022837,0.022837,0.022837,0.022837,0.096493,0.096493,0.096493,0.096493,0.096493,0.022837,0.14165,0.093657,0.14165,0.14165
+20-May-2019 03:00:00,1,0.087928,0.087928,0.087928,0.11385,0.01903,0.01903,0.01903,0.01903,0.11385,0.11385,0.11385,0.11385,0.11385,0.01903,0.11867,0.087928,0.11867,0.11867
+20-May-2019 04:00:00,1,0.078246,0.078246,0.078246,0.098943,0.013321,0.013321,0.013321,0.013321,0.098943,0.098943,0.098943,0.098943,0.098943,0.013321,0.10754,0.078246,0.10754,0.10754
+20-May-2019 05:00:00,1,0.075487,0.075487,0.075487,0.078015,0.0076122,0.0076122,0.0076122,0.0076122,0.078015,0.078015,0.078015,0.078015,0.078015,0.0076122,0.094682,0.075487,0.094682,0.094682
+20-May-2019 06:00:00,1,0.076352,0.076352,0.076352,0.049631,0.001903,0.001903,0.001903,0.001903,0.049631,0.049631,0.049631,0.049631,0.049631,0.001903,0.094613,0.076352,0.094613,0.094613
+20-May-2019 07:00:00,1,0.059702,0.059702,0.059702,0.062625,0.0057091,0.0057091,0.0057091,0.0057091,0.062625,0.062625,0.062625,0.062625,0.062625,0.0057091,0.055622,0.059702,0.055622,0.055622
+20-May-2019 08:00:00,1,0.044525,0.044525,0.044525,0.040312,0.0057091,0.0057091,0.0057091,0.0057091,0.040312,0.040312,0.040312,0.040312,0.040312,0.0057091,0.024314,0.044525,0.024314,0.024314
+20-May-2019 09:00:00,1,0.03903,0.03903,0.03903,0.025135,0.001903,0.001903,0.001903,0.001903,0.025135,0.025135,0.025135,0.025135,0.025135,0.001903,0.041222,0.03903,0.041222,0.041222
+20-May-2019 10:00:00,1,0.051938,0.051938,0.051938,0.02439,0.001903,0.001903,0.001903,0.001903,0.02439,0.02439,0.02439,0.02439,0.02439,0.001903,0.076348,0.051938,0.076348,0.076348
+20-May-2019 11:00:00,1,0.070108,0.070108,0.070108,0.018372,0.0057091,0.0057091,0.0057091,0.0057091,0.018372,0.018372,0.018372,0.018372,0.018372,0.0057091,0.14271,0.070108,0.14271,0.14271
+20-May-2019 12:00:00,1,0.096089,0.096089,0.096089,0.017999,0.001903,0.001903,0.001903,0.001903,0.017999,0.017999,0.017999,0.017999,0.017999,0.001903,0.1642,0.096089,0.1642,0.1642
+20-May-2019 13:00:00,1,0.10161,0.10161,0.10161,0.021407,0.0038061,0.0038061,0.0038061,0.0038061,0.021407,0.021407,0.021407,0.021407,0.021407,0.0038061,0.15918,0.10161,0.15918,0.15918
+20-May-2019 14:00:00,1,0.11566,0.11566,0.11566,0.021727,0.0038061,0.0038061,0.0038061,0.0038061,0.021727,0.021727,0.021727,0.021727,0.021727,0.0038061,0.14839,0.11566,0.14839,0.14839
+20-May-2019 15:00:00,1,0.12113,0.12113,0.12113,0.022313,0.0095152,0.0095152,0.0095152,0.0095152,0.022313,0.022313,0.022313,0.022313,0.022313,0.0095152,0.14435,0.12113,0.14435,0.14435
+20-May-2019 16:00:00,1,0.11903,0.11903,0.11903,0.036638,0.020933,0.020933,0.020933,0.020933,0.036638,0.036638,0.036638,0.036638,0.036638,0.020933,0.15086,0.11903,0.15086,0.15086
+20-May-2019 17:00:00,1,0.11777,0.11777,0.11777,0.035945,0.01903,0.01903,0.01903,0.01903,0.035945,0.035945,0.035945,0.035945,0.035945,0.01903,0.17236,0.11777,0.17236,0.17236
+20-May-2019 18:00:00,1,0.12743,0.12743,0.12743,0.035466,0.028546,0.028546,0.028546,0.028546,0.035466,0.035466,0.035466,0.035466,0.035466,0.028546,0.17719,0.12743,0.17719,0.17719
+20-May-2019 19:00:00,1,0.13865,0.13865,0.13865,0.035732,0.04377,0.04377,0.04377,0.04377,0.035732,0.035732,0.035732,0.035732,0.035732,0.04377,0.17324,0.13865,0.17324,0.17324
+20-May-2019 20:00:00,1,0.15745,0.15745,0.15745,0.049365,0.060897,0.060897,0.060897,0.060897,0.049365,0.049365,0.049365,0.049365,0.049365,0.060897,0.19286,0.15745,0.19286,0.19286
+20-May-2019 21:00:00,1,0.18804,0.18804,0.18804,0.084991,0.026643,0.026643,0.026643,0.026643,0.084991,0.084991,0.084991,0.084991,0.084991,0.026643,0.23284,0.18804,0.23284,0.23284
+20-May-2019 22:00:00,1,0.19711,0.19711,0.19711,0.13382,0.026643,0.026643,0.026643,0.026643,0.13382,0.13382,0.13382,0.13382,0.13382,0.026643,0.23606,0.19711,0.23606,0.23606
+20-May-2019 23:00:00,1,0.18989,0.18989,0.18989,0.15257,0.02474,0.02474,0.02474,0.02474,0.15257,0.15257,0.15257,0.15257,0.15257,0.02474,0.23519,0.18989,0.23519,0.23519
+21-May-2019 00:00:00,1,0.18374,0.18374,0.18374,0.17648,0.011418,0.011418,0.011418,0.011418,0.17648,0.17648,0.17648,0.17648,0.17648,0.011418,0.23433,0.18374,0.23433,0.23433
+21-May-2019 01:00:00,1,0.16568,0.16568,0.16568,0.2031,0.026643,0.026643,0.026643,0.026643,0.2031,0.2031,0.2031,0.2031,0.2031,0.026643,0.21441,0.16568,0.21441,0.21441
+21-May-2019 02:00:00,1,0.15759,0.15759,0.15759,0.1548,0.020933,0.020933,0.020933,0.020933,0.1548,0.1548,0.1548,0.1548,0.1548,0.020933,0.19484,0.15759,0.19484,0.19484
+21-May-2019 03:00:00,1,0.15373,0.15373,0.15373,0.11556,0.036158,0.036158,0.036158,0.036158,0.11556,0.11556,0.11556,0.11556,0.11556,0.036158,0.18198,0.15373,0.18198,0.18198
+21-May-2019 04:00:00,1,0.16653,0.16653,0.16653,0.089198,0.01903,0.01903,0.01903,0.01903,0.089198,0.089198,0.089198,0.089198,0.089198,0.01903,0.17966,0.16653,0.17966,0.17966
+21-May-2019 05:00:00,1,0.17349,0.17349,0.17349,0.096813,0.0095152,0.0095152,0.0095152,0.0095152,0.096813,0.096813,0.096813,0.096813,0.096813,0.0095152,0.17437,0.17349,0.17437,0.17437
+21-May-2019 06:00:00,1,0.1597,0.1597,0.1597,0.11146,0.0076122,0.0076122,0.0076122,0.0076122,0.11146,0.11146,0.11146,0.11146,0.11146,0.0076122,0.1864,0.1597,0.1864,0.1864
+21-May-2019 07:00:00,1,0.10907,0.10907,0.10907,0.11758,0.001903,0.001903,0.001903,0.001903,0.11758,0.11758,0.11758,0.11758,0.11758,0.001903,0.14699,0.10907,0.14699,0.14699
+21-May-2019 08:00:00,1,0.075113,0.075113,0.075113,0.082967,0.001903,0.001903,0.001903,0.001903,0.082967,0.082967,0.082967,0.082967,0.082967,0.001903,0.10476,0.075113,0.10476,0.10476
+21-May-2019 09:00:00,1,0.061596,0.061596,0.061596,0.068749,0.0057091,0.0057091,0.0057091,0.0057091,0.068749,0.068749,0.068749,0.068749,0.068749,0.0057091,0.10851,0.061596,0.10851,0.10851
+21-May-2019 10:00:00,1,0.072611,0.072611,0.072611,0.059163,0.0038061,0.0038061,0.0038061,0.0038061,0.059163,0.059163,0.059163,0.059163,0.059163,0.0038061,0.14623,0.072611,0.14623,0.14623
+21-May-2019 11:00:00,1,0.10126,0.10126,0.10126,0.075192,0.0057091,0.0057091,0.0057091,0.0057091,0.075192,0.075192,0.075192,0.075192,0.075192,0.0057091,0.17025,0.10126,0.17025,0.17025
+21-May-2019 12:00:00,1,0.14464,0.14464,0.14464,0.12142,0.0057091,0.0057091,0.0057091,0.0057091,0.12142,0.12142,0.12142,0.12142,0.12142,0.0057091,0.22385,0.14464,0.22385,0.22385
+21-May-2019 13:00:00,1,0.17932,0.17932,0.17932,0.14447,0.0095152,0.0095152,0.0095152,0.0095152,0.14447,0.14447,0.14447,0.14447,0.14447,0.0095152,0.24604,0.17932,0.24604,0.24604
+21-May-2019 14:00:00,1,0.21114,0.21114,0.21114,0.13382,0.013321,0.013321,0.013321,0.013321,0.13382,0.13382,0.13382,0.13382,0.13382,0.013321,0.28717,0.21114,0.28717,0.28717
+21-May-2019 15:00:00,1,0.26145,0.26145,0.26145,0.12647,0.0095152,0.0095152,0.0095152,0.0095152,0.12647,0.12647,0.12647,0.12647,0.12647,0.0095152,0.29191,0.26145,0.29191,0.29191
+21-May-2019 16:00:00,1,0.28216,0.28216,0.28216,0.14565,0.022837,0.022837,0.022837,0.022837,0.14565,0.14565,0.14565,0.14565,0.14565,0.022837,0.29725,0.28216,0.29725,0.29725
+21-May-2019 17:00:00,1,0.26336,0.26336,0.26336,0.13297,0.055188,0.055188,0.055188,0.055188,0.13297,0.13297,0.13297,0.13297,0.13297,0.055188,0.28697,0.26336,0.28697,0.28697
+21-May-2019 18:00:00,1,0.23509,0.23509,0.23509,0.13271,0.064704,0.064704,0.064704,0.064704,0.13271,0.13271,0.13271,0.13271,0.13271,0.064704,0.29311,0.23509,0.29311,0.29311
+21-May-2019 19:00:00,1,0.20607,0.20607,0.20607,0.09905,0.060897,0.060897,0.060897,0.060897,0.09905,0.09905,0.09905,0.09905,0.09905,0.060897,0.25113,0.20607,0.25113,0.25113
+21-May-2019 20:00:00,1,0.19199,0.19199,0.19199,0.086375,0.051382,0.051382,0.051382,0.051382,0.086375,0.086375,0.086375,0.086375,0.086375,0.051382,0.2566,0.19199,0.2566,0.2566
+21-May-2019 21:00:00,1,0.2122,0.2122,0.2122,0.10666,0.072316,0.072316,0.072316,0.072316,0.10666,0.10666,0.10666,0.10666,0.10666,0.072316,0.28998,0.2122,0.28998,0.28998
+21-May-2019 22:00:00,1,0.23144,0.23144,0.23144,0.16599,0.058994,0.058994,0.058994,0.058994,0.16599,0.16599,0.16599,0.16599,0.16599,0.058994,0.3015,0.23144,0.3015,0.3015
+21-May-2019 23:00:00,1,0.24601,0.24601,0.24601,0.19048,0.039964,0.039964,0.039964,0.039964,0.19048,0.19048,0.19048,0.19048,0.19048,0.039964,0.2822,0.24601,0.2822,0.2822
+22-May-2019 00:00:00,1,0.25359,0.25359,0.25359,0.17004,0.017127,0.017127,0.017127,0.017127,0.17004,0.17004,0.17004,0.17004,0.17004,0.017127,0.25196,0.25359,0.25196,0.25196
+22-May-2019 01:00:00,1,0.24054,0.24054,0.24054,0.14533,0.011418,0.011418,0.011418,0.011418,0.14533,0.14533,0.14533,0.14533,0.14533,0.011418,0.23374,0.24054,0.23374,0.23374
+22-May-2019 02:00:00,1,0.21788,0.21788,0.21788,0.14495,0.015224,0.015224,0.015224,0.015224,0.14495,0.14495,0.14495,0.14495,0.14495,0.015224,0.24176,0.21788,0.24176,0.24176
+22-May-2019 03:00:00,1,0.19863,0.19863,0.19863,0.11614,0.0095152,0.0095152,0.0095152,0.0095152,0.11614,0.11614,0.11614,0.11614,0.11614,0.0095152,0.22872,0.19863,0.22872,0.22872
+22-May-2019 04:00:00,1,0.19489,0.19489,0.19489,0.11412,0.0076122,0.0076122,0.0076122,0.0076122,0.11412,0.11412,0.11412,0.11412,0.11412,0.0076122,0.22474,0.19489,0.22474,0.22474
+22-May-2019 05:00:00,1,0.18392,0.18392,0.18392,0.12546,0.001903,0.001903,0.001903,0.001903,0.12546,0.12546,0.12546,0.12546,0.12546,0.001903,0.19981,0.18392,0.19981,0.19981
+22-May-2019 06:00:00,1,0.16419,0.16419,0.16419,0.11811,0.001903,0.001903,0.001903,0.001903,0.11811,0.11811,0.11811,0.11811,0.11811,0.001903,0.16843,0.16419,0.16843,0.16843
+22-May-2019 07:00:00,1,0.13222,0.13222,0.13222,0.095269,0,0,0,0,0.095269,0.095269,0.095269,0.095269,0.095269,0,0.10876,0.13222,0.10876,0.10876
+22-May-2019 08:00:00,1,0.099387,0.099387,0.099387,0.049312,0.001903,0.001903,0.001903,0.001903,0.049312,0.049312,0.049312,0.049312,0.049312,0.001903,0.062983,0.099387,0.062983,0.062983
+22-May-2019 09:00:00,1,0.088232,0.088232,0.088232,0.029289,0,0,0,0,0.029289,0.029289,0.029289,0.029289,0.029289,0,0.069171,0.088232,0.069171,0.069171
+22-May-2019 10:00:00,1,0.091412,0.091412,0.091412,0.039726,0,0,0,0,0.039726,0.039726,0.039726,0.039726,0.039726,0,0.08111,0.091412,0.08111,0.08111
+22-May-2019 11:00:00,1,0.10914,0.10914,0.10914,0.065447,0.001903,0.001903,0.001903,0.001903,0.065447,0.065447,0.065447,0.065447,0.065447,0.001903,0.097856,0.10914,0.097856,0.097856
+22-May-2019 12:00:00,1,0.15214,0.15214,0.15214,0.055436,0.001903,0.001903,0.001903,0.001903,0.055436,0.055436,0.055436,0.055436,0.055436,0.001903,0.11419,0.15214,0.11419,0.11419
+22-May-2019 13:00:00,1,0.17087,0.17087,0.17087,0.052187,0.001903,0.001903,0.001903,0.001903,0.052187,0.052187,0.052187,0.052187,0.052187,0.001903,0.11801,0.17087,0.11801,0.11801
+22-May-2019 14:00:00,1,0.17092,0.17092,0.17092,0.064116,0.0038061,0.0038061,0.0038061,0.0038061,0.064116,0.064116,0.064116,0.064116,0.064116,0.0038061,0.10554,0.17092,0.10554,0.10554
+22-May-2019 15:00:00,1,0.1669,0.1669,0.1669,0.074979,0.0057091,0.0057091,0.0057091,0.0057091,0.074979,0.074979,0.074979,0.074979,0.074979,0.0057091,0.090311,0.1669,0.090311,0.090311
+22-May-2019 16:00:00,1,0.15394,0.15394,0.15394,0.051708,0.0057091,0.0057091,0.0057091,0.0057091,0.051708,0.051708,0.051708,0.051708,0.051708,0.0057091,0.091484,0.15394,0.091484,0.091484
+22-May-2019 17:00:00,1,0.14875,0.14875,0.14875,0.037703,0.0076122,0.0076122,0.0076122,0.0076122,0.037703,0.037703,0.037703,0.037703,0.037703,0.0076122,0.098247,0.14875,0.098247,0.098247
+22-May-2019 18:00:00,1,0.15661,0.15661,0.15661,0.028863,0.015224,0.015224,0.015224,0.015224,0.028863,0.028863,0.028863,0.028863,0.028863,0.015224,0.10563,0.15661,0.10563,0.10563
+22-May-2019 19:00:00,1,0.16346,0.16346,0.16346,0.02636,0.020933,0.020933,0.020933,0.020933,0.02636,0.02636,0.02636,0.02636,0.02636,0.020933,0.098454,0.16346,0.098454,0.098454
+22-May-2019 20:00:00,1,0.18479,0.18479,0.18479,0.020768,0.045673,0.045673,0.045673,0.045673,0.020768,0.020768,0.020768,0.020768,0.020768,0.045673,0.11718,0.18479,0.11718,0.11718
+22-May-2019 21:00:00,1,0.19559,0.19559,0.19559,0.039673,0.053285,0.053285,0.053285,0.053285,0.039673,0.039673,0.039673,0.039673,0.039673,0.053285,0.13915,0.19559,0.13915,0.13915
+22-May-2019 22:00:00,1,0.20195,0.20195,0.20195,0.06795,0.028546,0.028546,0.028546,0.028546,0.06795,0.06795,0.06795,0.06795,0.06795,0.028546,0.15829,0.20195,0.15829,0.15829
+22-May-2019 23:00:00,1,0.19576,0.19576,0.19576,0.08531,0.02474,0.02474,0.02474,0.02474,0.08531,0.08531,0.08531,0.08531,0.08531,0.02474,0.15309,0.19576,0.15309,0.15309
+23-May-2019 00:00:00,1,0.17565,0.17565,0.17565,0.08366,0.017127,0.017127,0.017127,0.017127,0.08366,0.08366,0.08366,0.08366,0.08366,0.017127,0.13459,0.17565,0.13459,0.13459
+23-May-2019 01:00:00,1,0.16613,0.16613,0.16613,0.074926,0.020933,0.020933,0.020933,0.020933,0.074926,0.074926,0.074926,0.074926,0.074926,0.020933,0.12123,0.16613,0.12123,0.12123
+23-May-2019 02:00:00,1,0.15348,0.15348,0.15348,0.060069,0.026643,0.026643,0.026643,0.026643,0.060069,0.060069,0.060069,0.060069,0.060069,0.026643,0.11085,0.15348,0.11085,0.11085
+23-May-2019 03:00:00,1,0.13989,0.13989,0.13989,0.055116,0.036158,0.036158,0.036158,0.036158,0.055116,0.055116,0.055116,0.055116,0.055116,0.036158,0.096568,0.13989,0.096568,0.096568
+23-May-2019 04:00:00,1,0.12824,0.12824,0.12824,0.046383,0.032352,0.032352,0.032352,0.032352,0.046383,0.046383,0.046383,0.046383,0.046383,0.032352,0.093278,0.12824,0.093278,0.093278
+23-May-2019 05:00:00,1,0.13746,0.13746,0.13746,0.03701,0.011418,0.011418,0.011418,0.011418,0.03701,0.03701,0.03701,0.03701,0.03701,0.011418,0.095878,0.13746,0.095878,0.095878
+23-May-2019 06:00:00,1,0.13479,0.13479,0.13479,0.029023,0.0095152,0.0095152,0.0095152,0.0095152,0.029023,0.029023,0.029023,0.029023,0.029023,0.0095152,0.07644,0.13479,0.07644,0.07644
+23-May-2019 07:00:00,1,0.11241,0.11241,0.11241,0.016349,0.0057091,0.0057091,0.0057091,0.0057091,0.016349,0.016349,0.016349,0.016349,0.016349,0.0057091,0.040233,0.11241,0.040233,0.040233
+23-May-2019 08:00:00,1,0.10402,0.10402,0.10402,0.0085204,0.0038061,0.0038061,0.0038061,0.0038061,0.0085204,0.0085204,0.0085204,0.0085204,0.0085204,0.0038061,0.03416,0.10402,0.03416,0.03416
+23-May-2019 09:00:00,1,0.092652,0.092652,0.092652,0.0077749,0,0,0,0,0.0077749,0.0077749,0.0077749,0.0077749,0.0077749,0,0.038347,0.092652,0.038347,0.038347
+23-May-2019 10:00:00,1,0.10163,0.10163,0.10163,0.010757,0,0,0,0,0.010757,0.010757,0.010757,0.010757,0.010757,0,0.046674,0.10163,0.046674,0.046674
+23-May-2019 11:00:00,1,0.10879,0.10879,0.10879,0.019384,0.001903,0.001903,0.001903,0.001903,0.019384,0.019384,0.019384,0.019384,0.019384,0.001903,0.072897,0.10879,0.072897,0.072897
+23-May-2019 12:00:00,1,0.12214,0.12214,0.12214,0.024177,0.0038061,0.0038061,0.0038061,0.0038061,0.024177,0.024177,0.024177,0.024177,0.024177,0.0038061,0.10726,0.12214,0.10726,0.10726
+23-May-2019 13:00:00,1,0.12467,0.12467,0.12467,0.043774,0.0038061,0.0038061,0.0038061,0.0038061,0.043774,0.043774,0.043774,0.043774,0.043774,0.0038061,0.14745,0.12467,0.14745,0.14745
+23-May-2019 14:00:00,1,0.14922,0.14922,0.14922,0.031099,0.001903,0.001903,0.001903,0.001903,0.031099,0.031099,0.031099,0.031099,0.031099,0.001903,0.17466,0.14922,0.17466,0.17466
+23-May-2019 15:00:00,1,0.16035,0.16035,0.16035,0.022153,0.0076122,0.0076122,0.0076122,0.0076122,0.022153,0.022153,0.022153,0.022153,0.022153,0.0076122,0.19472,0.16035,0.19472,0.19472
+23-May-2019 16:00:00,1,0.18212,0.18212,0.18212,0.075459,0.039964,0.039964,0.039964,0.039964,0.075459,0.075459,0.075459,0.075459,0.075459,0.039964,0.2342,0.18212,0.2342,0.2342
+23-May-2019 17:00:00,1,0.20235,0.20235,0.20235,0.11513,0.020933,0.020933,0.020933,0.020933,0.11513,0.11513,0.11513,0.11513,0.11513,0.020933,0.27234,0.20235,0.27234,0.27234
+23-May-2019 18:00:00,1,0.22924,0.22924,0.22924,0.098198,0.02474,0.02474,0.02474,0.02474,0.098198,0.098198,0.098198,0.098198,0.098198,0.02474,0.29624,0.22924,0.29624,0.29624
+23-May-2019 19:00:00,1,0.25041,0.25041,0.25041,0.078973,0.039964,0.039964,0.039964,0.039964,0.078973,0.078973,0.078973,0.078973,0.078973,0.039964,0.30728,0.25041,0.30728,0.30728
+23-May-2019 20:00:00,1,0.28962,0.28962,0.28962,0.088932,0.026643,0.026643,0.026643,0.026643,0.088932,0.088932,0.088932,0.088932,0.088932,0.026643,0.31659,0.28962,0.31659,0.31659
+23-May-2019 21:00:00,1,0.29196,0.29196,0.29196,0.060282,0.030449,0.030449,0.030449,0.030449,0.060282,0.060282,0.060282,0.060282,0.060282,0.030449,0.31137,0.29196,0.31137,0.31137
+23-May-2019 22:00:00,1,0.26734,0.26734,0.26734,0.098996,0.045673,0.045673,0.045673,0.045673,0.098996,0.098996,0.098996,0.098996,0.098996,0.045673,0.31899,0.26734,0.31899,0.31899
+23-May-2019 23:00:00,1,0.23387,0.23387,0.23387,0.091008,0.013321,0.013321,0.013321,0.013321,0.091008,0.091008,0.091008,0.091008,0.091008,0.013321,0.28639,0.23387,0.28639,0.28639
+24-May-2019 00:00:00,1,0.21421,0.21421,0.21421,0.12605,0.0076122,0.0076122,0.0076122,0.0076122,0.12605,0.12605,0.12605,0.12605,0.12605,0.0076122,0.25881,0.21421,0.25881,0.25881
+24-May-2019 01:00:00,1,0.22482,0.22482,0.22482,0.1203,0.017127,0.017127,0.017127,0.017127,0.1203,0.1203,0.1203,0.1203,0.1203,0.017127,0.2255,0.22482,0.2255,0.2255
+24-May-2019 02:00:00,1,0.23598,0.23598,0.23598,0.12237,0.011418,0.011418,0.011418,0.011418,0.12237,0.12237,0.12237,0.12237,0.12237,0.011418,0.22102,0.23598,0.22102,0.22102
+24-May-2019 03:00:00,1,0.22181,0.22181,0.22181,0.13649,0.013321,0.013321,0.013321,0.013321,0.13649,0.13649,0.13649,0.13649,0.13649,0.013321,0.20908,0.22181,0.20908,0.20908
+24-May-2019 04:00:00,1,0.20745,0.20745,0.20745,0.1392,0.0095152,0.0095152,0.0095152,0.0095152,0.1392,0.1392,0.1392,0.1392,0.1392,0.0095152,0.19518,0.20745,0.19518,0.19518
+24-May-2019 05:00:00,1,0.20464,0.20464,0.20464,0.13814,0.0095152,0.0095152,0.0095152,0.0095152,0.13814,0.13814,0.13814,0.13814,0.13814,0.0095152,0.17402,0.20464,0.17402,0.17402
+24-May-2019 06:00:00,1,0.18282,0.18282,0.18282,0.11918,0.0095152,0.0095152,0.0095152,0.0095152,0.11918,0.11918,0.11918,0.11918,0.11918,0.0095152,0.1307,0.18282,0.1307,0.1307
+24-May-2019 07:00:00,1,0.14945,0.14945,0.14945,0.097665,0.013321,0.013321,0.013321,0.013321,0.097665,0.097665,0.097665,0.097665,0.097665,0.013321,0.085296,0.14945,0.085296,0.085296
+24-May-2019 08:00:00,1,0.14192,0.14192,0.14192,0.046489,0.0057091,0.0057091,0.0057091,0.0057091,0.046489,0.046489,0.046489,0.046489,0.046489,0.0057091,0.067768,0.14192,0.067768,0.067768
+24-May-2019 09:00:00,1,0.16557,0.16557,0.16557,0.031632,0.0057091,0.0057091,0.0057091,0.0057091,0.031632,0.031632,0.031632,0.031632,0.031632,0.0057091,0.078096,0.16557,0.078096,0.078096
+24-May-2019 10:00:00,1,0.19491,0.19491,0.19491,0.0352,0.01903,0.01903,0.01903,0.01903,0.0352,0.0352,0.0352,0.0352,0.0352,0.01903,0.14517,0.19491,0.14517,0.14517
+24-May-2019 11:00:00,1,0.21163,0.21163,0.21163,0.056607,0.020933,0.020933,0.020933,0.020933,0.056607,0.056607,0.056607,0.056607,0.056607,0.020933,0.20206,0.21163,0.20206,0.20206
+24-May-2019 12:00:00,1,0.22595,0.22595,0.22595,0.092073,0.034255,0.034255,0.034255,0.034255,0.092073,0.092073,0.092073,0.092073,0.092073,0.034255,0.23958,0.22595,0.23958,0.23958
+24-May-2019 13:00:00,1,0.23949,0.23949,0.23949,0.13563,0.051382,0.051382,0.051382,0.051382,0.13563,0.13563,0.13563,0.13563,0.13563,0.051382,0.26615,0.23949,0.26615,0.26615
+24-May-2019 14:00:00,1,0.27089,0.27089,0.27089,0.14948,0.0628,0.0628,0.0628,0.0628,0.14948,0.14948,0.14948,0.14948,0.14948,0.0628,0.30118,0.27089,0.30118,0.30118
+24-May-2019 15:00:00,1,0.31315,0.31315,0.31315,0.15294,0.053285,0.053285,0.053285,0.053285,0.15294,0.15294,0.15294,0.15294,0.15294,0.053285,0.3396,0.31315,0.3396,0.3396
+24-May-2019 16:00:00,1,0.3248,0.3248,0.3248,0.19032,0.066607,0.066607,0.066607,0.066607,0.19032,0.19032,0.19032,0.19032,0.19032,0.066607,0.37109,0.3248,0.37109,0.37109
+24-May-2019 17:00:00,1,0.33382,0.33382,0.33382,0.19277,0.034255,0.034255,0.034255,0.034255,0.19277,0.19277,0.19277,0.19277,0.19277,0.034255,0.39432,0.33382,0.39432,0.39432
+24-May-2019 18:00:00,1,0.34112,0.34112,0.34112,0.20444,0.026643,0.026643,0.026643,0.026643,0.20444,0.20444,0.20444,0.20444,0.20444,0.026643,0.39375,0.34112,0.39375,0.39375
+24-May-2019 19:00:00,1,0.33717,0.33717,0.33717,0.19405,0.049479,0.049479,0.049479,0.049479,0.19405,0.19405,0.19405,0.19405,0.19405,0.049479,0.37456,0.33717,0.37456,0.37456
+24-May-2019 20:00:00,1,0.28867,0.28867,0.28867,0.14682,0.070413,0.070413,0.070413,0.070413,0.14682,0.14682,0.14682,0.14682,0.14682,0.070413,0.33099,0.28867,0.33099,0.33099
+24-May-2019 21:00:00,1,0.28705,0.28705,0.28705,0.091221,0.11989,0.11989,0.11989,0.11989,0.091221,0.091221,0.091221,0.091221,0.091221,0.11989,0.31094,0.28705,0.31094,0.31094
+24-May-2019 22:00:00,1,0.28712,0.28712,0.28712,0,0.039964,0.039964,0.039964,0.039964,0,0,0,0,0,0.039964,0.31091,0.28712,0.31091,0.31091
+24-May-2019 23:00:00,1,0.25307,0.25307,0.25307,0,0.076122,0.076122,0.076122,0.076122,0,0,0,0,0,0.076122,0.30167,0.25307,0.30167,0.30167
+25-May-2019 00:00:00,1,0.22931,0.22931,0.22931,0.3258,0.079928,0.079928,0.079928,0.079928,0.3258,0.3258,0.3258,0.3258,0.3258,0.079928,0.25653,0.22931,0.25653,0.25653
+25-May-2019 01:00:00,1,0.21058,0.21058,0.21058,0.31392,0.091346,0.091346,0.091346,0.091346,0.31392,0.31392,0.31392,0.31392,0.31392,0.091346,0.22226,0.21058,0.22226,0.22226
+25-May-2019 02:00:00,1,0.17555,0.17555,0.17555,0.36238,0.089443,0.089443,0.089443,0.089443,0.36238,0.36238,0.36238,0.36238,0.36238,0.089443,0.17834,0.17555,0.17834,0.17834
+25-May-2019 03:00:00,1,0.16905,0.16905,0.16905,0.31067,0.06851,0.06851,0.06851,0.06851,0.31067,0.31067,0.31067,0.31067,0.31067,0.06851,0.15656,0.16905,0.15656,0.15656
+25-May-2019 04:00:00,1,0.16007,0.16007,0.16007,0.26775,0.10086,0.10086,0.10086,0.10086,0.26775,0.26775,0.26775,0.26775,0.26775,0.10086,0.13832,0.16007,0.13832,0.13832
+25-May-2019 05:00:00,1,0.143,0.143,0.143,0.25609,0.06851,0.06851,0.06851,0.06851,0.25609,0.25609,0.25609,0.25609,0.25609,0.06851,0.13052,0.143,0.13052,0.13052
+25-May-2019 06:00:00,1,0.13004,0.13004,0.13004,0.22329,0.055188,0.055188,0.055188,0.055188,0.22329,0.22329,0.22329,0.22329,0.22329,0.055188,0.098339,0.13004,0.098339,0.098339
+25-May-2019 07:00:00,1,0.085075,0.085075,0.085075,0.15763,0.036158,0.036158,0.036158,0.036158,0.15763,0.15763,0.15763,0.15763,0.15763,0.036158,0.035195,0.085075,0.035195,0.035195
+25-May-2019 08:00:00,1,0.052757,0.052757,0.052757,0.13143,0.045673,0.045673,0.045673,0.045673,0.13143,0.13143,0.13143,0.13143,0.13143,0.045673,0.017114,0.052757,0.017114,0.017114
+25-May-2019 09:00:00,1,0.045811,0.045811,0.045811,0.18527,0.045673,0.045673,0.045673,0.045673,0.18527,0.18527,0.18527,0.18527,0.18527,0.045673,0.028685,0.045811,0.028685,0.028685
+25-May-2019 10:00:00,1,0.064403,0.064403,0.064403,0.21402,0.058994,0.058994,0.058994,0.058994,0.21402,0.21402,0.21402,0.21402,0.21402,0.058994,0.060936,0.064403,0.060936,0.060936
+25-May-2019 11:00:00,1,0.089261,0.089261,0.089261,0.30077,0.083734,0.083734,0.083734,0.083734,0.30077,0.30077,0.30077,0.30077,0.30077,0.083734,0.099006,0.089261,0.099006,0.099006
+25-May-2019 12:00:00,1,0.10825,0.10825,0.10825,0.35605,0.076122,0.076122,0.076122,0.076122,0.35605,0.35605,0.35605,0.35605,0.35605,0.076122,0.11122,0.10825,0.11122,0.11122
+25-May-2019 13:00:00,1,0.12955,0.12955,0.12955,0.33869,0.072316,0.072316,0.072316,0.072316,0.33869,0.33869,0.33869,0.33869,0.33869,0.072316,0.12422,0.12955,0.12422,0.12422
+25-May-2019 14:00:00,1,0.13795,0.13795,0.13795,0.38134,0.083734,0.083734,0.083734,0.083734,0.38134,0.38134,0.38134,0.38134,0.38134,0.083734,0.13972,0.13795,0.13972,0.13972
+25-May-2019 15:00:00,1,0.14408,0.14408,0.14408,0.37042,0.098958,0.098958,0.098958,0.098958,0.37042,0.37042,0.37042,0.37042,0.37042,0.098958,0.16075,0.14408,0.16075,0.16075
+25-May-2019 16:00:00,1,0.15097,0.15097,0.15097,0.35658,0.049479,0.049479,0.049479,0.049479,0.35658,0.35658,0.35658,0.35658,0.35658,0.049479,0.1774,0.15097,0.1774,0.1774
+25-May-2019 17:00:00,1,0.17045,0.17045,0.17045,0.36957,0.032352,0.032352,0.032352,0.032352,0.36957,0.36957,0.36957,0.36957,0.36957,0.032352,0.19689,0.17045,0.19689,0.19689
+25-May-2019 18:00:00,1,0.18975,0.18975,0.18975,0.35211,0.072316,0.072316,0.072316,0.072316,0.35211,0.35211,0.35211,0.35211,0.35211,0.072316,0.20022,0.18975,0.20022,0.20022
+25-May-2019 19:00:00,1,0.173,0.173,0.173,0.30109,0.051382,0.051382,0.051382,0.051382,0.30109,0.30109,0.30109,0.30109,0.30109,0.051382,0.17823,0.173,0.17823,0.17823
+25-May-2019 20:00:00,1,0.15193,0.15193,0.15193,0.2341,0.081831,0.081831,0.081831,0.081831,0.2341,0.2341,0.2341,0.2341,0.2341,0.081831,0.14674,0.15193,0.14674,0.14674
+25-May-2019 21:00:00,1,0.15871,0.15871,0.15871,0.23564,0.0628,0.0628,0.0628,0.0628,0.23564,0.23564,0.23564,0.23564,0.23564,0.0628,0.16921,0.15871,0.16921,0.16921
+25-May-2019 22:00:00,1,0.18767,0.18767,0.18767,0.24139,0.039964,0.039964,0.039964,0.039964,0.24139,0.24139,0.24139,0.24139,0.24139,0.039964,0.19587,0.18767,0.19587,0.19587
+25-May-2019 23:00:00,1,0.19971,0.19971,0.19971,0.19267,0.074219,0.074219,0.074219,0.074219,0.19267,0.19267,0.19267,0.19267,0.19267,0.074219,0.19677,0.19971,0.19677,0.19677
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+26-May-2019 01:00:00,1,0.1955,0.1955,0.1955,0.31115,0.039964,0.039964,0.039964,0.039964,0.31115,0.31115,0.31115,0.31115,0.31115,0.039964,0.15932,0.1955,0.15932,0.15932
+26-May-2019 02:00:00,1,0.17373,0.17373,0.17373,0.26653,0.08754,0.08754,0.08754,0.08754,0.26653,0.26653,0.26653,0.26653,0.26653,0.08754,0.13574,0.17373,0.13574,0.13574
+26-May-2019 03:00:00,1,0.16016,0.16016,0.16016,0.25167,0.093249,0.093249,0.093249,0.093249,0.25167,0.25167,0.25167,0.25167,0.25167,0.093249,0.12054,0.16016,0.12054,0.12054
+26-May-2019 04:00:00,1,0.15025,0.15025,0.15025,0.26695,0.06851,0.06851,0.06851,0.06851,0.26695,0.26695,0.26695,0.26695,0.26695,0.06851,0.10667,0.15025,0.10667,0.10667
+26-May-2019 05:00:00,1,0.14054,0.14054,0.14054,0.2522,0.030449,0.030449,0.030449,0.030449,0.2522,0.2522,0.2522,0.2522,0.2522,0.030449,0.078096,0.14054,0.078096,0.078096
+26-May-2019 06:00:00,1,0.11405,0.11405,0.11405,0.20987,0.041867,0.041867,0.041867,0.041867,0.20987,0.20987,0.20987,0.20987,0.20987,0.041867,0.052701,0.11405,0.052701,0.052701
+26-May-2019 07:00:00,1,0.065361,0.065361,0.065361,0.12935,0.034255,0.034255,0.034255,0.034255,0.12935,0.12935,0.12935,0.12935,0.12935,0.034255,0.014906,0.065361,0.014906,0.014906
+26-May-2019 08:00:00,1,0.031219,0.031219,0.031219,0.080251,0.038061,0.038061,0.038061,0.038061,0.080251,0.080251,0.080251,0.080251,0.080251,0.038061,0.0026914,0.031219,0.0026914,0.0026914
+26-May-2019 09:00:00,1,0.027033,0.027033,0.027033,0.16599,0.049479,0.049479,0.049479,0.049479,0.16599,0.16599,0.16599,0.16599,0.16599,0.049479,0.0034505,0.027033,0.0034505,0.0034505
+26-May-2019 10:00:00,1,0.039404,0.039404,0.039404,0.21418,0.079928,0.079928,0.079928,0.079928,0.21418,0.21418,0.21418,0.21418,0.21418,0.079928,0.017575,0.039404,0.017575,0.017575
+26-May-2019 11:00:00,1,0.058673,0.058673,0.058673,0.22707,0.079928,0.079928,0.079928,0.079928,0.22707,0.22707,0.22707,0.22707,0.22707,0.079928,0.025419,0.058673,0.025419,0.025419
+26-May-2019 12:00:00,1,0.081637,0.081637,0.081637,0.23372,0.04377,0.04377,0.04377,0.04377,0.23372,0.23372,0.23372,0.23372,0.23372,0.04377,0.030732,0.081637,0.030732,0.030732
+26-May-2019 13:00:00,1,0.090804,0.090804,0.090804,0.23799,0.051382,0.051382,0.051382,0.051382,0.23799,0.23799,0.23799,0.23799,0.23799,0.051382,0.029145,0.090804,0.029145,0.029145
+26-May-2019 14:00:00,1,0.10357,0.10357,0.10357,0.2333,0.030449,0.030449,0.030449,0.030449,0.2333,0.2333,0.2333,0.2333,0.2333,0.030449,0.036115,0.10357,0.036115,0.036115
+26-May-2019 15:00:00,1,0.11134,0.11134,0.11134,0.23106,0.026643,0.026643,0.026643,0.026643,0.23106,0.23106,0.23106,0.23106,0.23106,0.026643,0.042096,0.11134,0.042096,0.042096
+26-May-2019 16:00:00,1,0.11283,0.11283,0.11283,0.2439,0.032352,0.032352,0.032352,0.032352,0.2439,0.2439,0.2439,0.2439,0.2439,0.032352,0.052517,0.11283,0.052517,0.052517
+26-May-2019 17:00:00,1,0.12483,0.12483,0.12483,0.25902,0.045673,0.045673,0.045673,0.045673,0.25902,0.25902,0.25902,0.25902,0.25902,0.045673,0.063351,0.12483,0.063351,0.063351
+26-May-2019 18:00:00,1,0.12064,0.12064,0.12064,0.22329,0.026643,0.026643,0.026643,0.026643,0.22329,0.22329,0.22329,0.22329,0.22329,0.026643,0.069585,0.12064,0.069585,0.069585
+26-May-2019 19:00:00,1,0.10961,0.10961,0.10961,0.20183,0.034255,0.034255,0.034255,0.034255,0.20183,0.20183,0.20183,0.20183,0.20183,0.034255,0.066687,0.10961,0.066687,0.066687
+26-May-2019 20:00:00,1,0.097773,0.097773,0.097773,0.12285,0.0628,0.0628,0.0628,0.0628,0.12285,0.12285,0.12285,0.12285,0.12285,0.0628,0.066457,0.097773,0.066457,0.066457
+26-May-2019 21:00:00,1,0.1072,0.1072,0.1072,0.14224,0.038061,0.038061,0.038061,0.038061,0.14224,0.14224,0.14224,0.14224,0.14224,0.038061,0.070919,0.1072,0.070919,0.070919
+26-May-2019 22:00:00,1,0.11068,0.11068,0.11068,0.18649,0.053285,0.053285,0.053285,0.053285,0.18649,0.18649,0.18649,0.18649,0.18649,0.053285,0.07713,0.11068,0.07713,0.07713
+26-May-2019 23:00:00,1,0.11309,0.11309,0.11309,0.18681,0.051382,0.051382,0.051382,0.051382,0.18681,0.18681,0.18681,0.18681,0.18681,0.051382,0.071701,0.11309,0.071701,0.071701
+27-May-2019 00:00:00,1,0.11664,0.11664,0.11664,0.20454,0.01903,0.01903,0.01903,0.01903,0.20454,0.20454,0.20454,0.20454,0.20454,0.01903,0.070551,0.11664,0.070551,0.070551
+27-May-2019 01:00:00,1,0.09775,0.09775,0.09775,0.18351,0.032352,0.032352,0.032352,0.032352,0.18351,0.18351,0.18351,0.18351,0.18351,0.032352,0.068987,0.09775,0.068987,0.068987
+27-May-2019 02:00:00,1,0.087413,0.087413,0.087413,0.17616,0.020933,0.020933,0.020933,0.020933,0.17616,0.17616,0.17616,0.17616,0.17616,0.020933,0.051596,0.087413,0.051596,0.051596
+27-May-2019 03:00:00,1,0.074248,0.074248,0.074248,0.15145,0.01903,0.01903,0.01903,0.01903,0.15145,0.15145,0.15145,0.15145,0.15145,0.01903,0.037242,0.074248,0.037242,0.037242
+27-May-2019 04:00:00,1,0.054628,0.054628,0.054628,0.12179,0.017127,0.017127,0.017127,0.017127,0.12179,0.12179,0.12179,0.12179,0.12179,0.017127,0.034919,0.054628,0.034919,0.034919
+27-May-2019 05:00:00,1,0.050863,0.050863,0.050863,0.073542,0.013321,0.013321,0.013321,0.013321,0.073542,0.073542,0.073542,0.073542,0.073542,0.013321,0.028133,0.050863,0.028133,0.028133
+27-May-2019 06:00:00,1,0.043403,0.043403,0.043403,0.044093,0.0076122,0.0076122,0.0076122,0.0076122,0.044093,0.044093,0.044093,0.044093,0.044093,0.0076122,0.020013,0.043403,0.020013,0.020013
+27-May-2019 07:00:00,1,0.033417,0.033417,0.033417,0.025348,0.0038061,0.0038061,0.0038061,0.0038061,0.025348,0.025348,0.025348,0.025348,0.025348,0.0038061,0.007154,0.033417,0.007154,0.007154
+27-May-2019 08:00:00,1,0.029231,0.029231,0.029231,0.028969,0.0038061,0.0038061,0.0038061,0.0038061,0.028969,0.028969,0.028969,0.028969,0.028969,0.0038061,0.0064409,0.029231,0.0064409,0.0064409
+27-May-2019 09:00:00,1,0.031897,0.031897,0.031897,0.012621,0.0076122,0.0076122,0.0076122,0.0076122,0.012621,0.012621,0.012621,0.012621,0.012621,0.0076122,0.0058428,0.031897,0.0058428,0.0058428
+27-May-2019 10:00:00,1,0.026121,0.026121,0.026121,0.021727,0.0076122,0.0076122,0.0076122,0.0076122,0.021727,0.021727,0.021727,0.021727,0.021727,0.0076122,0.010029,0.026121,0.010029,0.010029
+27-May-2019 11:00:00,1,0.026215,0.026215,0.026215,0.033549,0.0095152,0.0095152,0.0095152,0.0095152,0.033549,0.033549,0.033549,0.033549,0.033549,0.0095152,0.011364,0.026215,0.011364,0.011364
+27-May-2019 12:00:00,1,0.028156,0.028156,0.028156,0.043667,0.0076122,0.0076122,0.0076122,0.0076122,0.043667,0.043667,0.043667,0.043667,0.043667,0.0076122,0.010167,0.028156,0.010167,0.010167
+27-May-2019 13:00:00,1,0.030868,0.030868,0.030868,0.037649,0.0076122,0.0076122,0.0076122,0.0076122,0.037649,0.037649,0.037649,0.037649,0.037649,0.0076122,0.011801,0.030868,0.011801,0.011801
+27-May-2019 14:00:00,1,0.029442,0.029442,0.029442,0.04143,0.0076122,0.0076122,0.0076122,0.0076122,0.04143,0.04143,0.04143,0.04143,0.04143,0.0076122,0.014975,0.029442,0.014975,0.014975
+27-May-2019 15:00:00,1,0.031289,0.031289,0.031289,0.028597,0.0057091,0.0057091,0.0057091,0.0057091,0.028597,0.028597,0.028597,0.028597,0.028597,0.0057091,0.015964,0.031289,0.015964,0.015964
+27-May-2019 16:00:00,1,0.034025,0.034025,0.034025,0.026679,0.0038061,0.0038061,0.0038061,0.0038061,0.026679,0.026679,0.026679,0.026679,0.026679,0.0038061,0.019438,0.034025,0.019438,0.019438
+27-May-2019 17:00:00,1,0.038445,0.038445,0.038445,0.029449,0.0038061,0.0038061,0.0038061,0.0038061,0.029449,0.029449,0.029449,0.029449,0.029449,0.0038061,0.020496,0.038445,0.020496,0.020496
+27-May-2019 18:00:00,1,0.036715,0.036715,0.036715,0.022632,0.0038061,0.0038061,0.0038061,0.0038061,0.022632,0.022632,0.022632,0.022632,0.022632,0.0038061,0.023509,0.036715,0.023509,0.023509
+27-May-2019 19:00:00,1,0.037603,0.037603,0.037603,0.026679,0.0038061,0.0038061,0.0038061,0.0038061,0.026679,0.026679,0.026679,0.026679,0.026679,0.0038061,0.025143,0.037603,0.025143,0.025143
+27-May-2019 20:00:00,1,0.041953,0.041953,0.041953,0.03749,0.0057091,0.0057091,0.0057091,0.0057091,0.03749,0.03749,0.03749,0.03749,0.03749,0.0057091,0.036713,0.041953,0.036713,0.036713
+27-May-2019 21:00:00,1,0.049015,0.049015,0.049015,0.026999,0.0038061,0.0038061,0.0038061,0.0038061,0.026999,0.026999,0.026999,0.026999,0.026999,0.0038061,0.044097,0.049015,0.044097,0.044097
+27-May-2019 22:00:00,1,0.057153,0.057153,0.057153,0.023538,0.001903,0.001903,0.001903,0.001903,0.023538,0.023538,0.023538,0.023538,0.023538,0.001903,0.054058,0.057153,0.054058,0.054058
+27-May-2019 23:00:00,1,0.064099,0.064099,0.064099,0.021461,0.001903,0.001903,0.001903,0.001903,0.021461,0.021461,0.021461,0.021461,0.021461,0.001903,0.05178,0.064099,0.05178,0.05178
+28-May-2019 00:00:00,1,0.062485,0.062485,0.062485,0.013313,0.0038061,0.0038061,0.0038061,0.0038061,0.013313,0.013313,0.013313,0.013313,0.013313,0.0038061,0.045109,0.062485,0.045109,0.045109
+28-May-2019 01:00:00,1,0.052453,0.052453,0.052453,0.0095854,0.001903,0.001903,0.001903,0.001903,0.0095854,0.0095854,0.0095854,0.0095854,0.0095854,0.001903,0.037426,0.052453,0.037426,0.037426
+28-May-2019 02:00:00,1,0.047846,0.047846,0.047846,0.0092659,0.001903,0.001903,0.001903,0.001903,0.0092659,0.0092659,0.0092659,0.0092659,0.0092659,0.001903,0.025189,0.047846,0.025189,0.025189
+28-May-2019 03:00:00,1,0.035405,0.035405,0.035405,0.0089464,0,0,0,0,0.0089464,0.0089464,0.0089464,0.0089464,0.0089464,0,0.023578,0.035405,0.023578,0.023578
+28-May-2019 04:00:00,1,0.02563,0.02563,0.02563,0.0083074,0,0,0,0,0.0083074,0.0083074,0.0083074,0.0083074,0.0083074,0,0.023808,0.02563,0.023808,0.023808
+28-May-2019 05:00:00,1,0.021304,0.021304,0.021304,0.0082009,0,0,0,0,0.0082009,0.0082009,0.0082009,0.0082009,0.0082009,0,0.023187,0.021304,0.023187,0.023187
+28-May-2019 06:00:00,1,0.017936,0.017936,0.017936,0.0089464,0,0,0,0,0.0089464,0.0089464,0.0089464,0.0089464,0.0089464,0,0.025396,0.017936,0.025396,0.025396
+28-May-2019 07:00:00,1,0.017094,0.017094,0.017094,0.010331,0,0,0,0,0.010331,0.010331,0.010331,0.010331,0.010331,0,0.01953,0.017094,0.01953,0.01953
+28-May-2019 08:00:00,1,0.015434,0.015434,0.015434,0.0092127,0,0,0,0,0.0092127,0.0092127,0.0092127,0.0092127,0.0092127,0,0.012054,0.015434,0.012054,0.012054
+28-May-2019 09:00:00,1,0.016112,0.016112,0.016112,0.012727,0,0,0,0,0.012727,0.012727,0.012727,0.012727,0.012727,0,0.012951,0.016112,0.012951,0.012951
+28-May-2019 10:00:00,1,0.021655,0.021655,0.021655,0.016242,0.001903,0.001903,0.001903,0.001903,0.016242,0.016242,0.016242,0.016242,0.016242,0.001903,0.021853,0.021655,0.021853,0.021853
+28-May-2019 11:00:00,1,0.028249,0.028249,0.028249,0.017201,0.001903,0.001903,0.001903,0.001903,0.017201,0.017201,0.017201,0.017201,0.017201,0.001903,0.029582,0.028249,0.029582,0.029582
+28-May-2019 12:00:00,1,0.038632,0.038632,0.038632,0.022792,0.0057091,0.0057091,0.0057091,0.0057091,0.022792,0.022792,0.022792,0.022792,0.022792,0.0057091,0.037127,0.038632,0.037127,0.037127
+28-May-2019 13:00:00,1,0.050255,0.050255,0.050255,0.029076,0.017127,0.017127,0.017127,0.017127,0.029076,0.029076,0.029076,0.029076,0.029076,0.017127,0.04251,0.050255,0.04251,0.04251
+28-May-2019 14:00:00,1,0.064613,0.064613,0.064613,0.041963,0.0095152,0.0095152,0.0095152,0.0095152,0.041963,0.041963,0.041963,0.041963,0.041963,0.0095152,0.050239,0.064613,0.050239,0.050239
+28-May-2019 15:00:00,1,0.081357,0.081357,0.081357,0.072583,0.022837,0.022837,0.022837,0.022837,0.072583,0.072583,0.072583,0.072583,0.072583,0.022837,0.066065,0.081357,0.066065,0.066065
+28-May-2019 16:00:00,1,0.10404,0.10404,0.10404,0.097079,0.02474,0.02474,0.02474,0.02474,0.097079,0.097079,0.097079,0.097079,0.097079,0.02474,0.082766,0.10404,0.082766,0.082766
+28-May-2019 17:00:00,1,0.11534,0.11534,0.11534,0.12328,0.013321,0.013321,0.013321,0.013321,0.12328,0.12328,0.12328,0.12328,0.12328,0.013321,0.084146,0.11534,0.084146,0.084146
+28-May-2019 18:00:00,1,0.12569,0.12569,0.12569,0.16333,0.017127,0.017127,0.017127,0.017127,0.16333,0.16333,0.16333,0.16333,0.16333,0.017127,0.077659,0.12569,0.077659,0.077659
+28-May-2019 19:00:00,1,0.12137,0.12137,0.12137,0.16519,0.015224,0.015224,0.015224,0.015224,0.16519,0.16519,0.16519,0.16519,0.16519,0.015224,0.063972,0.12137,0.063972,0.063972
+28-May-2019 20:00:00,1,0.10187,0.10187,0.10187,0.15662,0.026643,0.026643,0.026643,0.026643,0.15662,0.15662,0.15662,0.15662,0.15662,0.026643,0.050929,0.10187,0.050929,0.050929
+28-May-2019 21:00:00,1,0.096978,0.096978,0.096978,0.13047,0.01903,0.01903,0.01903,0.01903,0.13047,0.13047,0.13047,0.13047,0.13047,0.01903,0.053391,0.096978,0.053391,0.053391
+28-May-2019 22:00:00,1,0.10795,0.10795,0.10795,0.10203,0.039964,0.039964,0.039964,0.039964,0.10203,0.10203,0.10203,0.10203,0.10203,0.039964,0.053023,0.10795,0.053023,0.053023
+28-May-2019 23:00:00,1,0.11206,0.11206,0.11206,0.075192,0.036158,0.036158,0.036158,0.036158,0.075192,0.075192,0.075192,0.075192,0.075192,0.036158,0.05829,0.11206,0.05829,0.05829
+29-May-2019 00:00:00,1,0.11929,0.11929,0.11929,0.09005,0.032352,0.032352,0.032352,0.032352,0.09005,0.09005,0.09005,0.09005,0.09005,0.032352,0.06595,0.11929,0.06595,0.06595
+29-May-2019 01:00:00,1,0.13145,0.13145,0.13145,0.078494,0.051382,0.051382,0.051382,0.051382,0.078494,0.078494,0.078494,0.078494,0.078494,0.051382,0.082122,0.13145,0.082122,0.082122
+29-May-2019 02:00:00,1,0.14903,0.14903,0.14903,0.056714,0.026643,0.026643,0.026643,0.026643,0.056714,0.056714,0.056714,0.056714,0.056714,0.026643,0.1155,0.14903,0.1155,0.1155
+29-May-2019 03:00:00,1,0.16774,0.16774,0.16774,0.068057,0.013321,0.013321,0.013321,0.013321,0.068057,0.068057,0.068057,0.068057,0.068057,0.013321,0.1572,0.16774,0.1572,0.1572
+29-May-2019 04:00:00,1,0.19194,0.19194,0.19194,0.06337,0.036158,0.036158,0.036158,0.036158,0.06337,0.06337,0.06337,0.06337,0.06337,0.036158,0.17457,0.19194,0.17457,0.17457
+29-May-2019 05:00:00,1,0.22756,0.22756,0.22756,0.051708,0.032352,0.032352,0.032352,0.032352,0.051708,0.051708,0.051708,0.051708,0.051708,0.032352,0.19314,0.22756,0.19314,0.19314
+29-May-2019 06:00:00,1,0.25403,0.25403,0.25403,0.051229,0.01903,0.01903,0.01903,0.01903,0.051229,0.051229,0.051229,0.051229,0.051229,0.01903,0.18479,0.25403,0.18479,0.18479
+29-May-2019 07:00:00,1,0.26341,0.26341,0.26341,0.063637,0.011418,0.011418,0.011418,0.011418,0.063637,0.063637,0.063637,0.063637,0.063637,0.011418,0.18446,0.26341,0.18446,0.18446
+29-May-2019 08:00:00,1,0.26979,0.26979,0.26979,0.051815,0.017127,0.017127,0.017127,0.017127,0.051815,0.051815,0.051815,0.051815,0.051815,0.017127,0.24013,0.26979,0.24013,0.24013
+29-May-2019 09:00:00,1,0.25623,0.25623,0.25623,0.048992,0.017127,0.017127,0.017127,0.017127,0.048992,0.048992,0.048992,0.048992,0.048992,0.017127,0.29805,0.25623,0.29805,0.29805
+29-May-2019 10:00:00,1,0.22768,0.22768,0.22768,0.0876,0.02474,0.02474,0.02474,0.02474,0.0876,0.0876,0.0876,0.0876,0.0876,0.02474,0.31411,0.22768,0.31411,0.31411
+29-May-2019 11:00:00,1,0.20555,0.20555,0.20555,0.096067,0.02474,0.02474,0.02474,0.02474,0.096067,0.096067,0.096067,0.096067,0.096067,0.02474,0.34691,0.20555,0.34691,0.34691
+29-May-2019 12:00:00,1,0.22726,0.22726,0.22726,0.080145,0.028546,0.028546,0.028546,0.028546,0.080145,0.080145,0.080145,0.080145,0.080145,0.028546,0.38406,0.22726,0.38406,0.38406
+29-May-2019 13:00:00,1,0.23701,0.23701,0.23701,0.084192,0.038061,0.038061,0.038061,0.038061,0.084192,0.084192,0.084192,0.084192,0.084192,0.038061,0.41114,0.23701,0.41114,0.41114
+29-May-2019 14:00:00,1,0.2328,0.2328,0.2328,0.083021,0.039964,0.039964,0.039964,0.039964,0.083021,0.083021,0.083021,0.083021,0.083021,0.039964,0.44544,0.2328,0.44544,0.44544
+29-May-2019 15:00:00,1,0.23596,0.23596,0.23596,0.070986,0.028546,0.028546,0.028546,0.028546,0.070986,0.070986,0.070986,0.070986,0.070986,0.028546,0.44419,0.23596,0.44419,0.44419
+29-May-2019 16:00:00,1,0.23696,0.23696,0.23696,0.080677,0.034255,0.034255,0.034255,0.034255,0.080677,0.080677,0.080677,0.080677,0.080677,0.034255,0.44348,0.23696,0.44348,0.44348
+29-May-2019 17:00:00,1,0.21315,0.21315,0.21315,0.070293,0.020933,0.020933,0.020933,0.020933,0.070293,0.070293,0.070293,0.070293,0.070293,0.020933,0.39418,0.21315,0.39418,0.39418
+29-May-2019 18:00:00,1,0.18014,0.18014,0.18014,0.057619,0.02474,0.02474,0.02474,0.02474,0.057619,0.057619,0.057619,0.057619,0.057619,0.02474,0.31666,0.18014,0.31666,0.31666
+29-May-2019 19:00:00,1,0.1401,0.1401,0.1401,0.071784,0.0095152,0.0095152,0.0095152,0.0095152,0.071784,0.071784,0.071784,0.071784,0.071784,0.0095152,0.21766,0.1401,0.21766,0.21766
+29-May-2019 20:00:00,1,0.096884,0.096884,0.096884,0.048247,0.011418,0.011418,0.011418,0.011418,0.048247,0.048247,0.048247,0.048247,0.048247,0.011418,0.13294,0.096884,0.13294,0.13294
+29-May-2019 21:00:00,1,0.068401,0.068401,0.068401,0.04585,0.026643,0.026643,0.026643,0.026643,0.04585,0.04585,0.04585,0.04585,0.04585,0.026643,0.083916,0.068401,0.083916,0.083916
+29-May-2019 22:00:00,1,0.059188,0.059188,0.059188,0.042176,0.017127,0.017127,0.017127,0.017127,0.042176,0.042176,0.042176,0.042176,0.042176,0.017127,0.054426,0.059188,0.054426,0.054426
+29-May-2019 23:00:00,1,0.06176,0.06176,0.06176,0.036478,0.017127,0.017127,0.017127,0.017127,0.036478,0.036478,0.036478,0.036478,0.036478,0.017127,0.028892,0.06176,0.028892,0.028892
+30-May-2019 00:00:00,1,0.062555,0.062555,0.062555,0.02849,0.015224,0.015224,0.015224,0.015224,0.02849,0.02849,0.02849,0.02849,0.02849,0.015224,0.014837,0.062555,0.014837,0.014837
+30-May-2019 01:00:00,1,0.06162,0.06162,0.06162,0.033336,0.017127,0.017127,0.017127,0.017127,0.033336,0.033336,0.033336,0.033336,0.033336,0.017127,0.0092243,0.06162,0.0092243,0.0092243
+30-May-2019 02:00:00,1,0.065081,0.065081,0.065081,0.024549,0.0057091,0.0057091,0.0057091,0.0057091,0.024549,0.024549,0.024549,0.024549,0.024549,0.0057091,0.01118,0.065081,0.01118,0.01118
+30-May-2019 03:00:00,1,0.072611,0.072611,0.072611,0.012674,0.001903,0.001903,0.001903,0.001903,0.012674,0.012674,0.012674,0.012674,0.012674,0.001903,0.014561,0.072611,0.014561,0.014561
+30-May-2019 04:00:00,1,0.087998,0.087998,0.087998,0.014112,0.001903,0.001903,0.001903,0.001903,0.014112,0.014112,0.014112,0.014112,0.014112,0.001903,0.022152,0.087998,0.022152,0.022152
+30-May-2019 05:00:00,1,0.10675,0.10675,0.10675,0.021727,0.0038061,0.0038061,0.0038061,0.0038061,0.021727,0.021727,0.021727,0.021727,0.021727,0.0038061,0.031791,0.10675,0.031791,0.031791
+30-May-2019 06:00:00,1,0.1046,0.1046,0.1046,0.015496,0.0057091,0.0057091,0.0057091,0.0057091,0.015496,0.015496,0.015496,0.015496,0.015496,0.0057091,0.043591,0.1046,0.043591,0.043591
+30-May-2019 07:00:00,1,0.10731,0.10731,0.10731,0.0082541,0.0038061,0.0038061,0.0038061,0.0038061,0.0082541,0.0082541,0.0082541,0.0082541,0.0082541,0.0038061,0.044925,0.10731,0.044925,0.044925
+30-May-2019 08:00:00,1,0.12623,0.12623,0.12623,0.011077,0.0076122,0.0076122,0.0076122,0.0076122,0.011077,0.011077,0.011077,0.011077,0.011077,0.0076122,0.067469,0.12623,0.067469,0.067469
+30-May-2019 09:00:00,1,0.13456,0.13456,0.13456,0.012674,0.0057091,0.0057091,0.0057091,0.0057091,0.012674,0.012674,0.012674,0.012674,0.012674,0.0057091,0.1112,0.13456,0.1112,0.1112
+30-May-2019 10:00:00,1,0.12085,0.12085,0.12085,0.012887,0.017127,0.017127,0.017127,0.017127,0.012887,0.012887,0.012887,0.012887,0.012887,0.017127,0.15056,0.12085,0.15056,0.15056
+30-May-2019 11:00:00,1,0.11774,0.11774,0.11774,0.025242,0.015224,0.015224,0.015224,0.015224,0.025242,0.025242,0.025242,0.025242,0.025242,0.015224,0.19373,0.11774,0.19373,0.19373
+30-May-2019 12:00:00,1,0.12705,0.12705,0.12705,0.04143,0.020933,0.020933,0.020933,0.020933,0.04143,0.04143,0.04143,0.04143,0.04143,0.020933,0.20933,0.12705,0.20933,0.20933
+30-May-2019 13:00:00,1,0.13056,0.13056,0.13056,0.050909,0.036158,0.036158,0.036158,0.036158,0.050909,0.050909,0.050909,0.050909,0.050909,0.036158,0.23864,0.13056,0.23864,0.23864
+30-May-2019 14:00:00,1,0.14036,0.14036,0.14036,0.046915,0.036158,0.036158,0.036158,0.036158,0.046915,0.046915,0.046915,0.046915,0.046915,0.036158,0.25281,0.14036,0.25281,0.25281
+30-May-2019 15:00:00,1,0.13355,0.13355,0.13355,0.051069,0.039964,0.039964,0.039964,0.039964,0.051069,0.051069,0.051069,0.051069,0.051069,0.039964,0.26953,0.13355,0.26953,0.26953
+30-May-2019 16:00:00,1,0.12389,0.12389,0.12389,0.06598,0.02474,0.02474,0.02474,0.02474,0.06598,0.06598,0.06598,0.06598,0.06598,0.02474,0.24975,0.12389,0.24975,0.24975
+30-May-2019 17:00:00,1,0.11283,0.11283,0.11283,0.074926,0.01903,0.01903,0.01903,0.01903,0.074926,0.074926,0.074926,0.074926,0.074926,0.01903,0.22272,0.11283,0.22272,0.22272
+30-May-2019 18:00:00,1,0.08573,0.08573,0.08573,0.06795,0.0076122,0.0076122,0.0076122,0.0076122,0.06795,0.06795,0.06795,0.06795,0.06795,0.0076122,0.176,0.08573,0.176,0.176
+30-May-2019 19:00:00,1,0.064052,0.064052,0.064052,0.043347,0.013321,0.013321,0.013321,0.013321,0.043347,0.043347,0.043347,0.043347,0.043347,0.013321,0.11911,0.064052,0.11911,0.11911
+30-May-2019 20:00:00,1,0.040667,0.040667,0.040667,0.027478,0.0095152,0.0095152,0.0095152,0.0095152,0.027478,0.027478,0.027478,0.027478,0.027478,0.0095152,0.073565,0.040667,0.073565,0.073565
+30-May-2019 21:00:00,1,0.026589,0.026589,0.026589,0.021514,0.013321,0.013321,0.013321,0.013321,0.021514,0.021514,0.021514,0.021514,0.021514,0.013321,0.04833,0.026589,0.04833,0.04833
+30-May-2019 22:00:00,1,0.023408,0.023408,0.023408,0.013526,0.0057091,0.0057091,0.0057091,0.0057091,0.013526,0.013526,0.013526,0.013526,0.013526,0.0057091,0.041613,0.023408,0.041613,0.041613
+30-May-2019 23:00:00,1,0.024905,0.024905,0.024905,0.013686,0.0095152,0.0095152,0.0095152,0.0095152,0.013686,0.013686,0.013686,0.013686,0.013686,0.0095152,0.038347,0.024905,0.038347,0.038347
+31-May-2019 00:00:00,1,0.021959,0.021959,0.021959,0.027052,0.0057091,0.0057091,0.0057091,0.0057091,0.027052,0.027052,0.027052,0.027052,0.027052,0.0057091,0.033631,0.021959,0.033631,0.033631
+31-May-2019 01:00:00,1,0.023198,0.023198,0.023198,0.032377,0.01903,0.01903,0.01903,0.01903,0.032377,0.032377,0.032377,0.032377,0.032377,0.01903,0.035517,0.023198,0.035517,0.035517
+31-May-2019 02:00:00,1,0.022543,0.022543,0.022543,0.023751,0.017127,0.017127,0.017127,0.017127,0.023751,0.023751,0.023751,0.023751,0.023751,0.017127,0.035149,0.022543,0.035149,0.035149
+31-May-2019 03:00:00,1,0.024227,0.024227,0.024227,0.041963,0.011418,0.011418,0.011418,0.011418,0.041963,0.041963,0.041963,0.041963,0.041963,0.011418,0.034413,0.024227,0.034413,0.034413
+31-May-2019 04:00:00,1,0.023058,0.023058,0.023058,0.056075,0.011418,0.011418,0.011418,0.011418,0.056075,0.056075,0.056075,0.056075,0.056075,0.011418,0.04297,0.023058,0.04297,0.04297
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+21-Jun-2019 07:00:00,1,0.092284,0.092284,0.092284,0.041102,0,0,0,0,0.041102,0.041102,0.041102,0.041102,0.041102,0,0.073585,0.092284,0.073585,0.073585
+21-Jun-2019 08:00:00,1,0.11566,0.11566,0.11566,0.040257,0,0,0,0,0.040257,0.040257,0.040257,0.040257,0.040257,0,0.057401,0.11566,0.057401,0.057401
+21-Jun-2019 09:00:00,1,0.13707,0.13707,0.13707,0.047547,0,0,0,0,0.047547,0.047547,0.047547,0.047547,0.047547,0,0.053195,0.13707,0.053195,0.053195
+21-Jun-2019 10:00:00,1,0.12208,0.12208,0.12208,0.062551,0,0,0,0,0.062551,0.062551,0.062551,0.062551,0.062551,0,0.059572,0.12208,0.059572,0.059572
+21-Jun-2019 11:00:00,1,0.11406,0.11406,0.11406,0.061548,0.0018937,0.0018937,0.0018937,0.0018937,0.061548,0.061548,0.061548,0.061548,0.061548,0.0018937,0.066133,0.11406,0.066133,0.066133
+21-Jun-2019 12:00:00,1,0.11317,0.11317,0.11317,0.058431,0.0018937,0.0018937,0.0018937,0.0018937,0.058431,0.058431,0.058431,0.058431,0.058431,0.0018937,0.055481,0.11317,0.055481,0.055481
+21-Jun-2019 13:00:00,1,0.12388,0.12388,0.12388,0.053834,0.0037873,0.0037873,0.0037873,0.0037873,0.053834,0.053834,0.053834,0.053834,0.053834,0.0037873,0.061813,0.12388,0.061813,0.061813
+21-Jun-2019 14:00:00,1,0.13423,0.13423,0.13423,0.048129,0.0018937,0.0018937,0.0018937,0.0018937,0.048129,0.048129,0.048129,0.048129,0.048129,0.0018937,0.065905,0.13423,0.065905,0.065905
+21-Jun-2019 15:00:00,1,0.145,0.145,0.145,0.051774,0.0018937,0.0018937,0.0018937,0.0018937,0.051774,0.051774,0.051774,0.051774,0.051774,0.0018937,0.046931,0.145,0.046931,0.046931
+21-Jun-2019 16:00:00,1,0.14653,0.14653,0.14653,0.0588,0,0,0,0,0.0588,0.0588,0.0588,0.0588,0.0588,0,0.048028,0.14653,0.048028,0.048028
+21-Jun-2019 17:00:00,1,0.14642,0.14642,0.14642,0.088121,0,0,0,0,0.088121,0.088121,0.088121,0.088121,0.088121,0,0.056601,0.14642,0.056601,0.056601
+21-Jun-2019 18:00:00,1,0.17889,0.17889,0.17889,0.096733,0.0018937,0.0018937,0.0018937,0.0018937,0.096733,0.096733,0.096733,0.096733,0.096733,0.0018937,0.040965,0.17889,0.040965,0.040965
+21-Jun-2019 19:00:00,1,0.18931,0.18931,0.18931,0.089653,0.047341,0.047341,0.047341,0.047341,0.089653,0.089653,0.089653,0.089653,0.089653,0.047341,0.031135,0.18931,0.031135,0.031135
+21-Jun-2019 20:00:00,1,0.18971,0.18971,0.18971,0.15992,0.05681,0.05681,0.05681,0.05681,0.15992,0.15992,0.15992,0.15992,0.15992,0.05681,0.025694,0.18971,0.025694,0.025694
+21-Jun-2019 21:00:00,1,0.18555,0.18555,0.18555,0.14338,0.085215,0.085215,0.085215,0.085215,0.14338,0.14338,0.14338,0.14338,0.14338,0.085215,0.016619,0.18555,0.016619,0.016619
+21-Jun-2019 22:00:00,1,0.1764,0.1764,0.1764,0.20202,0.037873,0.037873,0.037873,0.037873,0.20202,0.20202,0.20202,0.20202,0.20202,0.037873,0.01607,0.1764,0.01607,0.01607
+21-Jun-2019 23:00:00,1,0.15123,0.15123,0.15123,0.13768,0.028405,0.028405,0.028405,0.028405,0.13768,0.13768,0.13768,0.13768,0.13768,0.028405,0.01191,0.15123,0.01191,0.01191
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+22-Jun-2019 01:00:00,1,0.11357,0.11357,0.11357,0.18031,0.04166,0.04166,0.04166,0.04166,0.18031,0.18031,0.18031,0.18031,0.18031,0.04166,0.0087324,0.11357,0.0087324,0.0087324
+22-Jun-2019 02:00:00,1,0.13381,0.13381,0.13381,0.21227,0.049235,0.049235,0.049235,0.049235,0.21227,0.21227,0.21227,0.21227,0.21227,0.049235,0.010173,0.13381,0.010173,0.010173
+22-Jun-2019 03:00:00,1,0.18296,0.18296,0.18296,0.17075,0.030299,0.030299,0.030299,0.030299,0.17075,0.17075,0.17075,0.17075,0.17075,0.030299,0.024231,0.18296,0.024231,0.024231
+22-Jun-2019 04:00:00,1,0.18964,0.18964,0.18964,0.16441,0.047341,0.047341,0.047341,0.047341,0.16441,0.16441,0.16441,0.16441,0.16441,0.047341,0.045628,0.18964,0.045628,0.045628
+22-Jun-2019 05:00:00,1,0.21455,0.21455,0.21455,0.19172,0.037873,0.037873,0.037873,0.037873,0.19172,0.19172,0.19172,0.19172,0.19172,0.037873,0.059298,0.21455,0.059298,0.059298
+22-Jun-2019 06:00:00,1,0.2227,0.2227,0.2227,0.24107,0.024618,0.024618,0.024618,0.024618,0.24107,0.24107,0.24107,0.24107,0.24107,0.024618,0.092399,0.2227,0.092399,0.092399
+22-Jun-2019 07:00:00,1,0.21528,0.21528,0.21528,0.23082,0.045448,0.045448,0.045448,0.045448,0.23082,0.23082,0.23082,0.23082,0.23082,0.045448,0.099577,0.21528,0.099577,0.099577
+22-Jun-2019 08:00:00,1,0.23,0.23,0.23,0.29664,0.079534,0.079534,0.079534,0.079534,0.29664,0.29664,0.29664,0.29664,0.29664,0.079534,0.13679,0.23,0.13679,0.13679
+22-Jun-2019 09:00:00,1,0.27348,0.27348,0.27348,0.35978,0.064384,0.064384,0.064384,0.064384,0.35978,0.35978,0.35978,0.35978,0.35978,0.064384,0.14406,0.27348,0.14406,0.14406
+22-Jun-2019 10:00:00,1,0.27518,0.27518,0.27518,0.44441,0.045448,0.045448,0.045448,0.045448,0.44441,0.44441,0.44441,0.44441,0.44441,0.045448,0.15437,0.27518,0.15437,0.15437
+22-Jun-2019 11:00:00,1,0.28323,0.28323,0.28323,0.46597,0.051129,0.051129,0.051129,0.051129,0.46597,0.46597,0.46597,0.46597,0.46597,0.051129,0.19154,0.28323,0.19154,0.19154
+22-Jun-2019 12:00:00,1,0.29946,0.29946,0.29946,0.51092,0.060597,0.060597,0.060597,0.060597,0.51092,0.51092,0.51092,0.51092,0.51092,0.060597,0.22933,0.29946,0.22933,0.22933
+22-Jun-2019 13:00:00,1,0.32559,0.32559,0.32559,0.47426,0.054916,0.054916,0.054916,0.054916,0.47426,0.47426,0.47426,0.47426,0.47426,0.054916,0.23015,0.32559,0.23015,0.23015
+22-Jun-2019 14:00:00,1,0.34001,0.34001,0.34001,0.45455,0.062491,0.062491,0.062491,0.062491,0.45455,0.45455,0.45455,0.45455,0.45455,0.062491,0.23728,0.34001,0.23728,0.23728
+22-Jun-2019 15:00:00,1,0.33566,0.33566,0.33566,0.45487,0.043554,0.043554,0.043554,0.043554,0.45487,0.45487,0.45487,0.45487,0.45487,0.043554,0.23941,0.33566,0.23941,0.23941
+22-Jun-2019 16:00:00,1,0.33434,0.33434,0.33434,0.50443,0.053022,0.053022,0.053022,0.053022,0.50443,0.50443,0.50443,0.50443,0.50443,0.053022,0.25644,0.33434,0.25644,0.25644
+22-Jun-2019 17:00:00,1,0.29153,0.29153,0.29153,0.45635,0.032192,0.032192,0.032192,0.032192,0.45635,0.45635,0.45635,0.45635,0.45635,0.032192,0.24828,0.29153,0.24828,0.24828
+22-Jun-2019 18:00:00,1,0.26438,0.26438,0.26438,0.31778,0.030299,0.030299,0.030299,0.030299,0.31778,0.31778,0.31778,0.31778,0.31778,0.030299,0.22789,0.26438,0.22789,0.22789
+22-Jun-2019 19:00:00,1,0.21769,0.21769,0.21769,0.27039,0.0075746,0.0075746,0.0075746,0.0075746,0.27039,0.27039,0.27039,0.27039,0.27039,0.0075746,0.1725,0.21769,0.1725,0.1725
+22-Jun-2019 20:00:00,1,0.14825,0.14825,0.14825,0.24878,0.0075746,0.0075746,0.0075746,0.0075746,0.24878,0.24878,0.24878,0.24878,0.24878,0.0075746,0.13099,0.14825,0.13099,0.13099
+22-Jun-2019 21:00:00,1,0.16112,0.16112,0.16112,0.20805,0.0094683,0.0094683,0.0094683,0.0094683,0.20805,0.20805,0.20805,0.20805,0.20805,0.0094683,0.10815,0.16112,0.10815,0.10815
+22-Jun-2019 22:00:00,1,0.18157,0.18157,0.18157,0.20202,0.015149,0.015149,0.015149,0.015149,0.20202,0.20202,0.20202,0.20202,0.20202,0.015149,0.11862,0.18157,0.11862,0.11862
+22-Jun-2019 23:00:00,1,0.21586,0.21586,0.21586,0.14919,0.017043,0.017043,0.017043,0.017043,0.14919,0.14919,0.14919,0.14919,0.14919,0.017043,0.12191,0.21586,0.12191,0.12191
+23-Jun-2019 00:00:00,1,0.23237,0.23237,0.23237,0.20968,0.005681,0.005681,0.005681,0.005681,0.20968,0.20968,0.20968,0.20968,0.20968,0.005681,0.13432,0.23237,0.13432,0.13432
+23-Jun-2019 01:00:00,1,0.24501,0.24501,0.24501,0.23008,0.0075746,0.0075746,0.0075746,0.0075746,0.23008,0.23008,0.23008,0.23008,0.23008,0.0075746,0.14612,0.24501,0.14612,0.14612
+23-Jun-2019 02:00:00,1,0.24533,0.24533,0.24533,0.23219,0.0075746,0.0075746,0.0075746,0.0075746,0.23219,0.23219,0.23219,0.23219,0.23219,0.0075746,0.15641,0.24533,0.15641,0.15641
+23-Jun-2019 03:00:00,1,0.2337,0.2337,0.2337,0.21127,0.0037873,0.0037873,0.0037873,0.0037873,0.21127,0.21127,0.21127,0.21127,0.21127,0.0037873,0.15398,0.2337,0.15398,0.15398
+23-Jun-2019 04:00:00,1,0.22579,0.22579,0.22579,0.1792,0.005681,0.005681,0.005681,0.005681,0.1792,0.1792,0.1792,0.1792,0.1792,0.005681,0.1581,0.22579,0.1581,0.1581
+23-Jun-2019 05:00:00,1,0.22663,0.22663,0.22663,0.18374,0.0037873,0.0037873,0.0037873,0.0037873,0.18374,0.18374,0.18374,0.18374,0.18374,0.0037873,0.16811,0.22663,0.16811,0.16811
+23-Jun-2019 06:00:00,1,0.21604,0.21604,0.21604,0.17614,0.005681,0.005681,0.005681,0.005681,0.17614,0.17614,0.17614,0.17614,0.17614,0.005681,0.1327,0.21604,0.1327,0.1327
+23-Jun-2019 07:00:00,1,0.22914,0.22914,0.22914,0.15009,0.0037873,0.0037873,0.0037873,0.0037873,0.15009,0.15009,0.15009,0.15009,0.15009,0.0037873,0.078546,0.22914,0.078546,0.078546
+23-Jun-2019 08:00:00,1,0.27078,0.27078,0.27078,0.16689,0.0018937,0.0018937,0.0018937,0.0018937,0.16689,0.16689,0.16689,0.16689,0.16689,0.0018937,0.080215,0.27078,0.080215,0.080215
+23-Jun-2019 09:00:00,1,0.30933,0.30933,0.30933,0.18089,0.0075746,0.0075746,0.0075746,0.0075746,0.18089,0.18089,0.18089,0.18089,0.18089,0.0075746,0.15124,0.30933,0.15124,0.15124
+23-Jun-2019 10:00:00,1,0.36595,0.36595,0.36595,0.2156,0.0037873,0.0037873,0.0037873,0.0037873,0.2156,0.2156,0.2156,0.2156,0.2156,0.0037873,0.24096,0.36595,0.24096,0.24096
+23-Jun-2019 11:00:00,1,0.39249,0.39249,0.39249,0.23066,0.0037873,0.0037873,0.0037873,0.0037873,0.23066,0.23066,0.23066,0.23066,0.23066,0.0037873,0.26677,0.39249,0.26677,0.26677
+23-Jun-2019 12:00:00,1,0.38179,0.38179,0.38179,0.2483,0.011362,0.011362,0.011362,0.011362,0.2483,0.2483,0.2483,0.2483,0.2483,0.011362,0.26266,0.38179,0.26266,0.26266
+23-Jun-2019 13:00:00,1,0.38558,0.38558,0.38558,0.29363,0.011362,0.011362,0.011362,0.011362,0.29363,0.29363,0.29363,0.29363,0.29363,0.011362,0.24163,0.38558,0.24163,0.24163
+23-Jun-2019 14:00:00,1,0.3757,0.3757,0.3757,0.25845,0.0075746,0.0075746,0.0075746,0.0075746,0.25845,0.25845,0.25845,0.25845,0.25845,0.0075746,0.22803,0.3757,0.22803,0.22803
+23-Jun-2019 15:00:00,1,0.39352,0.39352,0.39352,0.24038,0.011362,0.011362,0.011362,0.011362,0.24038,0.24038,0.24038,0.24038,0.24038,0.011362,0.22304,0.39352,0.22304,0.22304
+23-Jun-2019 16:00:00,1,0.37304,0.37304,0.37304,0.20562,0.0075746,0.0075746,0.0075746,0.0075746,0.20562,0.20562,0.20562,0.20562,0.20562,0.0075746,0.18455,0.37304,0.18455,0.18455
+23-Jun-2019 17:00:00,1,0.32957,0.32957,0.32957,0.17608,0.0075746,0.0075746,0.0075746,0.0075746,0.17608,0.17608,0.17608,0.17608,0.17608,0.0075746,0.16969,0.32957,0.16969,0.16969
+23-Jun-2019 18:00:00,1,0.27678,0.27678,0.27678,0.14407,0.005681,0.005681,0.005681,0.005681,0.14407,0.14407,0.14407,0.14407,0.14407,0.005681,0.12884,0.27678,0.12884,0.12884
+23-Jun-2019 19:00:00,1,0.19392,0.19392,0.19392,0.086167,0.005681,0.005681,0.005681,0.005681,0.086167,0.086167,0.086167,0.086167,0.086167,0.005681,0.097702,0.19392,0.097702,0.097702
+23-Jun-2019 20:00:00,1,0.1135,0.1135,0.1135,0.054151,0.0075746,0.0075746,0.0075746,0.0075746,0.054151,0.054151,0.054151,0.054151,0.054151,0.0075746,0.05068,0.1135,0.05068,0.05068
+23-Jun-2019 21:00:00,1,0.077884,0.077884,0.077884,0.041366,0.0037873,0.0037873,0.0037873,0.0037873,0.041366,0.041366,0.041366,0.041366,0.041366,0.0037873,0.028872,0.077884,0.028872,0.028872
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+23-Jun-2019 23:00:00,1,0.069417,0.069417,0.069417,0.030272,0.0018937,0.0018937,0.0018937,0.0018937,0.030272,0.030272,0.030272,0.030272,0.030272,0.0018937,0.030358,0.069417,0.030358,0.030358
+24-Jun-2019 00:00:00,1,0.074162,0.074162,0.074162,0.047283,0.0018937,0.0018937,0.0018937,0.0018937,0.047283,0.047283,0.047283,0.047283,0.047283,0.0018937,0.014379,0.074162,0.014379,0.014379
+24-Jun-2019 01:00:00,1,0.08149,0.08149,0.08149,0.052778,0.0018937,0.0018937,0.0018937,0.0018937,0.052778,0.052778,0.052778,0.052778,0.052778,0.0018937,0.01063,0.08149,0.01063,0.01063
+24-Jun-2019 02:00:00,1,0.093215,0.093215,0.093215,0.048234,0.0018937,0.0018937,0.0018937,0.0018937,0.048234,0.048234,0.048234,0.048234,0.048234,0.0018937,0.0096925,0.093215,0.0096925,0.0096925
+24-Jun-2019 03:00:00,1,0.096309,0.096309,0.096309,0.055472,0,0,0,0,0.055472,0.055472,0.055472,0.055472,0.055472,0,0.018699,0.096309,0.018699,0.018699
+24-Jun-2019 04:00:00,1,0.10375,0.10375,0.10375,0.069366,0.0018937,0.0018937,0.0018937,0.0018937,0.069366,0.069366,0.069366,0.069366,0.069366,0.0018937,0.031981,0.10375,0.031981,0.031981
+24-Jun-2019 05:00:00,1,0.11578,0.11578,0.11578,0.095729,0.005681,0.005681,0.005681,0.005681,0.095729,0.095729,0.095729,0.095729,0.095729,0.005681,0.040667,0.11578,0.040667,0.040667
+24-Jun-2019 06:00:00,1,0.1101,0.1101,0.1101,0.12722,0.0094683,0.0094683,0.0094683,0.0094683,0.12722,0.12722,0.12722,0.12722,0.12722,0.0094683,0.050657,0.1101,0.050657,0.050657
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+24-Jun-2019 08:00:00,1,0.072929,0.072929,0.072929,0.195,0.024618,0.024618,0.024618,0.024618,0.195,0.195,0.195,0.195,0.195,0.024618,0.034495,0.072929,0.034495,0.034495
+24-Jun-2019 09:00:00,1,0.082165,0.082165,0.082165,0.32274,0.060597,0.060597,0.060597,0.060597,0.32274,0.32274,0.32274,0.32274,0.32274,0.060597,0.061516,0.082165,0.061516,0.061516
+24-Jun-2019 10:00:00,1,0.12346,0.12346,0.12346,0.43934,0.066278,0.066278,0.066278,0.066278,0.43934,0.43934,0.43934,0.43934,0.43934,0.066278,0.15718,0.12346,0.15718,0.15718
+24-Jun-2019 11:00:00,1,0.18727,0.18727,0.18727,0.55847,0.071959,0.071959,0.071959,0.071959,0.55847,0.55847,0.55847,0.55847,0.55847,0.071959,0.2641,0.18727,0.2641,0.2641
+24-Jun-2019 12:00:00,1,0.25177,0.25177,0.25177,0.57749,0.11362,0.11362,0.11362,0.11362,0.57749,0.57749,0.57749,0.57749,0.57749,0.11362,0.29059,0.25177,0.29059,0.29059
+24-Jun-2019 13:00:00,1,0.29923,0.29923,0.29923,0.62351,0.10983,0.10983,0.10983,0.10983,0.62351,0.62351,0.62351,0.62351,0.62351,0.10983,0.27205,0.29923,0.27205,0.27205
+24-Jun-2019 14:00:00,1,0.32215,0.32215,0.32215,0.63323,0.13824,0.13824,0.13824,0.13824,0.63323,0.63323,0.63323,0.63323,0.63323,0.13824,0.29596,0.32215,0.29596,0.29596
+24-Jun-2019 15:00:00,1,0.391,0.391,0.391,0.55398,0.11741,0.11741,0.11741,0.11741,0.55398,0.55398,0.55398,0.55398,0.55398,0.11741,0.32452,0.391,0.32452,0.32452
+24-Jun-2019 16:00:00,1,0.45353,0.45353,0.45353,0.54225,0.054916,0.054916,0.054916,0.054916,0.54225,0.54225,0.54225,0.54225,0.54225,0.054916,0.32623,0.45353,0.32623,0.32623
+24-Jun-2019 17:00:00,1,0.44879,0.44879,0.44879,0.62514,0.13634,0.13634,0.13634,0.13634,0.62514,0.62514,0.62514,0.62514,0.62514,0.13634,0.36144,0.44879,0.36144,0.36144
+24-Jun-2019 18:00:00,1,0.38461,0.38461,0.38461,0.67945,0.07764,0.07764,0.07764,0.07764,0.67945,0.67945,0.67945,0.67945,0.67945,0.07764,0.44833,0.38461,0.44833,0.44833
+24-Jun-2019 19:00:00,1,0.39852,0.39852,0.39852,0.62879,0.07764,0.07764,0.07764,0.07764,0.62879,0.62879,0.62879,0.62879,0.62879,0.07764,0.51245,0.39852,0.51245,0.51245
+24-Jun-2019 20:00:00,1,0.48943,0.48943,0.48943,0.51029,0.10226,0.10226,0.10226,0.10226,0.51029,0.51029,0.51029,0.51029,0.51029,0.10226,0.49807,0.48943,0.49807,0.49807
+24-Jun-2019 21:00:00,1,0.55159,0.55159,0.55159,0.48129,0.10604,0.10604,0.10604,0.10604,0.48129,0.48129,0.48129,0.48129,0.48129,0.10604,0.44304,0.55159,0.44304,0.44304
+24-Jun-2019 22:00:00,1,0.58374,0.58374,0.58374,0.33584,0.083321,0.083321,0.083321,0.083321,0.33584,0.33584,0.33584,0.33584,0.33584,0.083321,0.46961,0.58374,0.46961,0.46961
+24-Jun-2019 23:00:00,1,0.55591,0.55591,0.55591,0.25866,0.075746,0.075746,0.075746,0.075746,0.25866,0.25866,0.25866,0.25866,0.25866,0.075746,0.50696,0.55591,0.50696,0.50696
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+25-Jun-2019 01:00:00,1,0.44709,0.44709,0.44709,0.21423,0.070065,0.070065,0.070065,0.070065,0.21423,0.21423,0.21423,0.21423,0.21423,0.070065,0.58846,0.44709,0.58846,0.58846
+25-Jun-2019 02:00:00,1,0.39594,0.39594,0.39594,0.20271,0.047341,0.047341,0.047341,0.047341,0.20271,0.20271,0.20271,0.20271,0.20271,0.047341,0.53755,0.39594,0.53755,0.53755
+25-Jun-2019 03:00:00,1,0.33485,0.33485,0.33485,0.22284,0.037873,0.037873,0.037873,0.037873,0.22284,0.22284,0.22284,0.22284,0.22284,0.037873,0.51043,0.33485,0.51043,0.51043
+25-Jun-2019 04:00:00,1,0.31959,0.31959,0.31959,0.22083,0.032192,0.032192,0.032192,0.032192,0.22083,0.22083,0.22083,0.22083,0.22083,0.032192,0.43904,0.31959,0.43904,0.43904
+25-Jun-2019 05:00:00,1,0.31942,0.31942,0.31942,0.21296,0.030299,0.030299,0.030299,0.030299,0.21296,0.21296,0.21296,0.21296,0.21296,0.030299,0.38507,0.31942,0.38507,0.38507
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+25-Jun-2019 07:00:00,1,0.34187,0.34187,0.34187,0.18998,0.017043,0.017043,0.017043,0.017043,0.18998,0.18998,0.18998,0.18998,0.18998,0.017043,0.34077,0.34187,0.34077,0.34077
+25-Jun-2019 08:00:00,1,0.38703,0.38703,0.38703,0.20303,0.02083,0.02083,0.02083,0.02083,0.20303,0.20303,0.20303,0.20303,0.20303,0.02083,0.3517,0.38703,0.3517,0.3517
+25-Jun-2019 09:00:00,1,0.43048,0.43048,0.43048,0.27678,0.013256,0.013256,0.013256,0.013256,0.27678,0.27678,0.27678,0.27678,0.27678,0.013256,0.38932,0.43048,0.38932,0.38932
+25-Jun-2019 10:00:00,1,0.48575,0.48575,0.48575,0.32195,0.026511,0.026511,0.026511,0.026511,0.32195,0.32195,0.32195,0.32195,0.32195,0.026511,0.43079,0.48575,0.43079,0.43079
+25-Jun-2019 11:00:00,1,0.53512,0.53512,0.53512,0.34815,0.068172,0.068172,0.068172,0.068172,0.34815,0.34815,0.34815,0.34815,0.34815,0.068172,0.39058,0.53512,0.39058,0.39058
+25-Jun-2019 12:00:00,1,0.57501,0.57501,0.57501,0.36432,0.034086,0.034086,0.034086,0.034086,0.36432,0.36432,0.36432,0.36432,0.36432,0.034086,0.4235,0.57501,0.4235,0.4235
+25-Jun-2019 13:00:00,1,0.61316,0.61316,0.61316,0.40547,0.070065,0.070065,0.070065,0.070065,0.40547,0.40547,0.40547,0.40547,0.40547,0.070065,0.42272,0.61316,0.42272,0.42272
+25-Jun-2019 14:00:00,1,0.65997,0.65997,0.65997,0.43654,0.10604,0.10604,0.10604,0.10604,0.43654,0.43654,0.43654,0.43654,0.43654,0.10604,0.467,0.65997,0.467,0.467
+25-Jun-2019 15:00:00,1,0.64159,0.64159,0.64159,0.41942,0.09847,0.09847,0.09847,0.09847,0.41942,0.41942,0.41942,0.41942,0.41942,0.09847,0.48979,0.64159,0.48979,0.48979
+25-Jun-2019 16:00:00,1,0.6606,0.6606,0.6606,0.37721,0.11173,0.11173,0.11173,0.11173,0.37721,0.37721,0.37721,0.37721,0.37721,0.11173,0.52045,0.6606,0.52045,0.52045
+25-Jun-2019 17:00:00,1,0.6506,0.6506,0.6506,0.41615,0.10415,0.10415,0.10415,0.10415,0.41615,0.41615,0.41615,0.41615,0.41615,0.10415,0.54683,0.6506,0.54683,0.54683
+25-Jun-2019 18:00:00,1,0.61284,0.61284,0.61284,0.37589,0.10036,0.10036,0.10036,0.10036,0.37589,0.37589,0.37589,0.37589,0.37589,0.10036,0.57917,0.61284,0.57917,0.57917
+25-Jun-2019 19:00:00,1,0.57346,0.57346,0.57346,0.29136,0.045448,0.045448,0.045448,0.045448,0.29136,0.29136,0.29136,0.29136,0.29136,0.045448,0.55983,0.57346,0.55983,0.55983
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+25-Jun-2019 21:00:00,1,0.50662,0.50662,0.50662,0.17313,0.024618,0.024618,0.024618,0.024618,0.17313,0.17313,0.17313,0.17313,0.17313,0.024618,0.57261,0.50662,0.57261,0.57261
+25-Jun-2019 22:00:00,1,0.43983,0.43983,0.43983,0.12146,0.028405,0.028405,0.028405,0.028405,0.12146,0.12146,0.12146,0.12146,0.12146,0.028405,0.5656,0.43983,0.5656,0.5656
+25-Jun-2019 23:00:00,1,0.42711,0.42711,0.42711,0.10418,0.024618,0.024618,0.024618,0.024618,0.10418,0.10418,0.10418,0.10418,0.10418,0.024618,0.56713,0.42711,0.56713,0.56713
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+26-Jun-2019 01:00:00,1,0.40789,0.40789,0.40789,0.079404,0.011362,0.011362,0.011362,0.011362,0.079404,0.079404,0.079404,0.079404,0.079404,0.011362,0.52479,0.40789,0.52479,0.52479
+26-Jun-2019 02:00:00,1,0.40254,0.40254,0.40254,0.084317,0.022724,0.022724,0.022724,0.022724,0.084317,0.084317,0.084317,0.084317,0.084317,0.022724,0.53366,0.40254,0.53366,0.53366
+26-Jun-2019 03:00:00,1,0.39205,0.39205,0.39205,0.07354,0.015149,0.015149,0.015149,0.015149,0.07354,0.07354,0.07354,0.07354,0.07354,0.015149,0.52909,0.39205,0.52909,0.52909
+26-Jun-2019 04:00:00,1,0.36581,0.36581,0.36581,0.070053,0.013256,0.013256,0.013256,0.013256,0.070053,0.070053,0.070053,0.070053,0.070053,0.013256,0.51172,0.36581,0.51172,0.51172
+26-Jun-2019 05:00:00,1,0.32826,0.32826,0.32826,0.073804,0.0075746,0.0075746,0.0075746,0.0075746,0.073804,0.073804,0.073804,0.073804,0.073804,0.0075746,0.475,0.32826,0.475,0.475
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+26-Jun-2019 08:00:00,1,0.26497,0.26497,0.26497,0.038408,0.02083,0.02083,0.02083,0.02083,0.038408,0.038408,0.038408,0.038408,0.038408,0.02083,0.34911,0.26497,0.34911,0.34911
+26-Jun-2019 09:00:00,1,0.28167,0.28167,0.28167,0.043638,0.017043,0.017043,0.017043,0.017043,0.043638,0.043638,0.043638,0.043638,0.043638,0.017043,0.36198,0.28167,0.36198,0.36198
+26-Jun-2019 10:00:00,1,0.309,0.309,0.309,0.064823,0.017043,0.017043,0.017043,0.017043,0.064823,0.064823,0.064823,0.064823,0.064823,0.017043,0.36994,0.309,0.36994,0.36994
+26-Jun-2019 11:00:00,1,0.33147,0.33147,0.33147,0.063238,0.015149,0.015149,0.015149,0.015149,0.063238,0.063238,0.063238,0.063238,0.063238,0.015149,0.36352,0.33147,0.36352,0.36352
+26-Jun-2019 12:00:00,1,0.35074,0.35074,0.35074,0.070582,0.028405,0.028405,0.028405,0.028405,0.070582,0.070582,0.070582,0.070582,0.070582,0.028405,0.36838,0.35074,0.36838,0.36838
+26-Jun-2019 13:00:00,1,0.37667,0.37667,0.37667,0.081306,0.022724,0.022724,0.022724,0.022724,0.081306,0.081306,0.081306,0.081306,0.081306,0.022724,0.36251,0.37667,0.36251,0.36251
+26-Jun-2019 14:00:00,1,0.37407,0.37407,0.37407,0.083314,0.02083,0.02083,0.02083,0.02083,0.083314,0.083314,0.083314,0.083314,0.083314,0.02083,0.36297,0.37407,0.36297,0.36297
+26-Jun-2019 15:00:00,1,0.38426,0.38426,0.38426,0.079668,0.013256,0.013256,0.013256,0.013256,0.079668,0.079668,0.079668,0.079668,0.079668,0.013256,0.35524,0.38426,0.35524,0.35524
+26-Jun-2019 16:00:00,1,0.36411,0.36411,0.36411,0.087223,0.017043,0.017043,0.017043,0.017043,0.087223,0.087223,0.087223,0.087223,0.087223,0.017043,0.36902,0.36411,0.36902,0.36902
+26-Jun-2019 17:00:00,1,0.33929,0.33929,0.33929,0.05917,0.013256,0.013256,0.013256,0.013256,0.05917,0.05917,0.05917,0.05917,0.05917,0.013256,0.39486,0.33929,0.39486,0.39486
+26-Jun-2019 18:00:00,1,0.28202,0.28202,0.28202,0.044219,0.0094683,0.0094683,0.0094683,0.0094683,0.044219,0.044219,0.044219,0.044219,0.044219,0.0094683,0.38768,0.28202,0.38768,0.38768
+26-Jun-2019 19:00:00,1,0.23207,0.23207,0.23207,0.031645,0.0037873,0.0037873,0.0037873,0.0037873,0.031645,0.031645,0.031645,0.031645,0.031645,0.0037873,0.3258,0.23207,0.3258,0.3258
+26-Jun-2019 20:00:00,1,0.16984,0.16984,0.16984,0.022189,0.0018937,0.0018937,0.0018937,0.0018937,0.022189,0.022189,0.022189,0.022189,0.022189,0.0018937,0.27279,0.16984,0.27279,0.27279
+26-Jun-2019 21:00:00,1,0.13921,0.13921,0.13921,0.018649,0.0018937,0.0018937,0.0018937,0.0018937,0.018649,0.018649,0.018649,0.018649,0.018649,0.0018937,0.24949,0.13921,0.24949,0.24949
+26-Jun-2019 22:00:00,1,0.11764,0.11764,0.11764,0.02071,0,0,0,0,0.02071,0.02071,0.02071,0.02071,0.02071,0,0.22912,0.11764,0.22912,0.22912
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+27-Jun-2019 00:00:00,1,0.081606,0.081606,0.081606,0.017804,0.0018937,0.0018937,0.0018937,0.0018937,0.017804,0.017804,0.017804,0.017804,0.017804,0.0018937,0.22519,0.081606,0.22519,0.22519
+27-Jun-2019 01:00:00,1,0.068928,0.068928,0.068928,0.023668,0.0037873,0.0037873,0.0037873,0.0037873,0.023668,0.023668,0.023668,0.023668,0.023668,0.0037873,0.20711,0.068928,0.20711,0.20711
+27-Jun-2019 02:00:00,1,0.06844,0.06844,0.06844,0.029268,0.0018937,0.0018937,0.0018937,0.0018937,0.029268,0.029268,0.029268,0.029268,0.029268,0.0018937,0.18777,0.06844,0.18777,0.18777
+27-Jun-2019 03:00:00,1,0.058809,0.058809,0.058809,0.051457,0.0075746,0.0075746,0.0075746,0.0075746,0.051457,0.051457,0.051457,0.051457,0.051457,0.0075746,0.1717,0.058809,0.1717,0.1717
+27-Jun-2019 04:00:00,1,0.056901,0.056901,0.056901,0.080778,0.011362,0.011362,0.011362,0.011362,0.080778,0.080778,0.080778,0.080778,0.080778,0.011362,0.15254,0.056901,0.15254,0.15254
+27-Jun-2019 05:00:00,1,0.058483,0.058483,0.058483,0.090446,0.0075746,0.0075746,0.0075746,0.0075746,0.090446,0.090446,0.090446,0.090446,0.090446,0.0075746,0.12491,0.058483,0.12491,0.12491
+27-Jun-2019 06:00:00,1,0.055319,0.055319,0.055319,0.085057,0.0037873,0.0037873,0.0037873,0.0037873,0.085057,0.085057,0.085057,0.085057,0.085057,0.0037873,0.11364,0.055319,0.11364,0.11364
+27-Jun-2019 07:00:00,1,0.041873,0.041873,0.041873,0.082204,0.005681,0.005681,0.005681,0.005681,0.082204,0.082204,0.082204,0.082204,0.082204,0.005681,0.083575,0.041873,0.083575,0.083575
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+29-Jun-2019 03:00:00,1,0.20732,0.20732,0.20732,0.17223,0.02083,0.02083,0.02083,0.02083,0.17223,0.17223,0.17223,0.17223,0.17223,0.02083,0.17559,0.20732,0.17559,0.17559
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+20-Aug-2019 15:00:00,1,0.20932,0.20932,0.20932,0.14307,0.075006,0.075006,0.075006,0.075006,0.14307,0.14307,0.14307,0.14307,0.14307,0.075006,0.08487,0.20932,0.08487,0.08487
+20-Aug-2019 16:00:00,1,0.2579,0.2579,0.2579,0.12091,0.065631,0.065631,0.065631,0.065631,0.12091,0.12091,0.12091,0.12091,0.12091,0.065631,0.14032,0.2579,0.14032,0.14032
+20-Aug-2019 17:00:00,1,0.26188,0.26188,0.26188,0.085238,0.011251,0.011251,0.011251,0.011251,0.085238,0.085238,0.085238,0.085238,0.085238,0.011251,0.17548,0.26188,0.17548,0.17548
+20-Aug-2019 18:00:00,1,0.21498,0.21498,0.21498,0.055647,0.026252,0.026252,0.026252,0.026252,0.055647,0.055647,0.055647,0.055647,0.055647,0.026252,0.22641,0.21498,0.22641,0.22641
+20-Aug-2019 19:00:00,1,0.16014,0.16014,0.16014,0.03526,0.026252,0.026252,0.026252,0.026252,0.03526,0.03526,0.03526,0.03526,0.03526,0.026252,0.19981,0.16014,0.19981,0.19981
+20-Aug-2019 20:00:00,1,0.11456,0.11456,0.11456,0.039785,0.026252,0.026252,0.026252,0.026252,0.039785,0.039785,0.039785,0.039785,0.039785,0.026252,0.17913,0.11456,0.17913,0.17913
+20-Aug-2019 21:00:00,1,0.089949,0.089949,0.089949,0.024235,0.020627,0.020627,0.020627,0.020627,0.024235,0.024235,0.024235,0.024235,0.024235,0.020627,0.1251,0.089949,0.1251,0.1251
+20-Aug-2019 22:00:00,1,0.078875,0.078875,0.078875,0.019086,0.018752,0.018752,0.018752,0.018752,0.019086,0.019086,0.019086,0.019086,0.019086,0.018752,0.093314,0.078875,0.093314,0.093314
+20-Aug-2019 23:00:00,1,0.067824,0.067824,0.067824,0.078218,0.020627,0.020627,0.020627,0.020627,0.078218,0.078218,0.078218,0.078218,0.078218,0.020627,0.063218,0.067824,0.063218,0.063218
+21-Aug-2019 00:00:00,1,0.04616,0.04616,0.04616,0.072237,0.013126,0.013126,0.013126,0.013126,0.072237,0.072237,0.072237,0.072237,0.072237,0.013126,0.064821,0.04616,0.064821,0.064821
+21-Aug-2019 01:00:00,1,0.030459,0.030459,0.030459,0.029488,0.016876,0.016876,0.016876,0.016876,0.029488,0.029488,0.029488,0.029488,0.029488,0.016876,0.064753,0.030459,0.064753,0.064753
+21-Aug-2019 02:00:00,1,0.020697,0.020697,0.020697,0.025275,0.024377,0.024377,0.024377,0.024377,0.025275,0.025275,0.025275,0.025275,0.025275,0.024377,0.072091,0.020697,0.072091,0.072091
+21-Aug-2019 03:00:00,1,0.02003,0.02003,0.02003,0.023507,0.015001,0.015001,0.015001,0.015001,0.023507,0.023507,0.023507,0.023507,0.023507,0.015001,0.077691,0.02003,0.077691,0.077691
+21-Aug-2019 04:00:00,1,0.02148,0.02148,0.02148,0.032088,0.024377,0.024377,0.024377,0.024377,0.032088,0.032088,0.032088,0.032088,0.032088,0.024377,0.077194,0.02148,0.077194,0.077194
+21-Aug-2019 05:00:00,1,0.018648,0.018648,0.018648,0.029332,0.024377,0.024377,0.024377,0.024377,0.029332,0.029332,0.029332,0.029332,0.029332,0.024377,0.06674,0.018648,0.06674,0.06674
+21-Aug-2019 06:00:00,1,0.016001,0.016001,0.016001,0.025327,0.022502,0.022502,0.022502,0.022502,0.025327,0.025327,0.025327,0.025327,0.025327,0.022502,0.053645,0.016001,0.053645,0.053645
+21-Aug-2019 07:00:00,1,0.012409,0.012409,0.012409,0.034584,0.016876,0.016876,0.016876,0.016876,0.034584,0.034584,0.034584,0.034584,0.034584,0.016876,0.030503,0.012409,0.030503,0.030503
+21-Aug-2019 08:00:00,1,0.011281,0.011281,0.011281,0.025119,0.0037503,0.0037503,0.0037503,0.0037503,0.025119,0.025119,0.025119,0.025119,0.025119,0.0037503,0.017678,0.011281,0.017678,0.017678
+21-Aug-2019 09:00:00,1,0.010959,0.010959,0.010959,0.0086331,0.0056255,0.0056255,0.0056255,0.0056255,0.0086331,0.0086331,0.0086331,0.0086331,0.0086331,0.0056255,0.01025,0.010959,0.01025,0.01025
+21-Aug-2019 10:00:00,1,0.011764,0.011764,0.011764,0.007853,0.0056255,0.0056255,0.0056255,0.0056255,0.007853,0.007853,0.007853,0.007853,0.007853,0.0056255,0.006096,0.011764,0.006096,0.006096
+21-Aug-2019 11:00:00,1,0.010682,0.010682,0.010682,0.014302,0.015001,0.015001,0.015001,0.015001,0.014302,0.014302,0.014302,0.014302,0.014302,0.015001,0.0036576,0.010682,0.0036576,0.0036576
+21-Aug-2019 12:00:00,1,0.012133,0.012133,0.012133,0.008425,0.020627,0.020627,0.020627,0.020627,0.008425,0.008425,0.008425,0.008425,0.008425,0.020627,0.0049446,0.012133,0.0049446,0.0049446
+21-Aug-2019 13:00:00,1,0.014205,0.014205,0.014205,0.0090491,0.028127,0.028127,0.028127,0.028127,0.0090491,0.0090491,0.0090491,0.0090491,0.0090491,0.028127,0.0040414,0.014205,0.0040414,0.0040414
+21-Aug-2019 14:00:00,1,0.015425,0.015425,0.015425,0.0075409,0.028127,0.028127,0.028127,0.028127,0.0075409,0.0075409,0.0075409,0.0075409,0.0075409,0.028127,0.0025739,0.015425,0.0025739,0.0025739
+21-Aug-2019 15:00:00,1,0.017129,0.017129,0.017129,0.0098812,0.030003,0.030003,0.030003,0.030003,0.0098812,0.0098812,0.0098812,0.0098812,0.0098812,0.030003,0.0032286,0.017129,0.0032286,0.0032286
+21-Aug-2019 16:00:00,1,0.018948,0.018948,0.018948,0.0073329,0.013126,0.013126,0.013126,0.013126,0.0073329,0.0073329,0.0073329,0.0073329,0.0073329,0.013126,0.0061186,0.018948,0.0061186,0.0061186
+21-Aug-2019 17:00:00,1,0.019914,0.019914,0.019914,0.0089971,0.011251,0.011251,0.011251,0.011251,0.0089971,0.0089971,0.0089971,0.0089971,0.0089971,0.011251,0.013434,0.019914,0.013434,0.013434
+21-Aug-2019 18:00:00,1,0.020191,0.020191,0.020191,0.014666,0.011251,0.011251,0.011251,0.011251,0.014666,0.014666,0.014666,0.014666,0.014666,0.011251,0.019056,0.020191,0.019056,0.019056
+21-Aug-2019 19:00:00,1,0.024082,0.024082,0.024082,0.013002,0.0075006,0.0075006,0.0075006,0.0075006,0.013002,0.013002,0.013002,0.013002,0.013002,0.0075006,0.025739,0.024082,0.025739,0.025739
+21-Aug-2019 20:00:00,1,0.033475,0.033475,0.033475,0.014562,0.020627,0.020627,0.020627,0.020627,0.014562,0.014562,0.014562,0.014562,0.014562,0.020627,0.047188,0.033475,0.047188,0.047188
+21-Aug-2019 21:00:00,1,0.050465,0.050465,0.050465,0.022207,0.020627,0.020627,0.020627,0.020627,0.022207,0.022207,0.022207,0.022207,0.022207,0.020627,0.083674,0.050465,0.083674,0.083674
+21-Aug-2019 22:00:00,1,0.057856,0.057856,0.057856,0.044882,0.018752,0.018752,0.018752,0.018752,0.044882,0.044882,0.044882,0.044882,0.044882,0.018752,0.092637,0.057856,0.092637,0.092637
+21-Aug-2019 23:00:00,1,0.057326,0.057326,0.057326,0.051122,0.035628,0.035628,0.035628,0.035628,0.051122,0.051122,0.051122,0.051122,0.051122,0.035628,0.08189,0.057326,0.08189,0.08189
+22-Aug-2019 00:00:00,1,0.052883,0.052883,0.052883,0.06584,0.046879,0.046879,0.046879,0.046879,0.06584,0.06584,0.06584,0.06584,0.06584,0.046879,0.0804,0.052883,0.0804,0.0804
+22-Aug-2019 01:00:00,1,0.06817,0.06817,0.06817,0.085759,0.05813,0.05813,0.05813,0.05813,0.085759,0.085759,0.085759,0.085759,0.085759,0.05813,0.08688,0.06817,0.08688,0.08688
+22-Aug-2019 02:00:00,1,0.099273,0.099273,0.099273,0.11753,0.048754,0.048754,0.048754,0.048754,0.11753,0.11753,0.11753,0.11753,0.11753,0.048754,0.058386,0.099273,0.058386,0.058386
+22-Aug-2019 03:00:00,1,0.13132,0.13132,0.13132,0.12669,0.018752,0.018752,0.018752,0.018752,0.12669,0.12669,0.12669,0.12669,0.12669,0.018752,0.04852,0.13132,0.04852,0.04852
+22-Aug-2019 04:00:00,1,0.13887,0.13887,0.13887,0.12861,0.046879,0.046879,0.046879,0.046879,0.12861,0.12861,0.12861,0.12861,0.12861,0.046879,0.065566,0.13887,0.065566,0.065566
+22-Aug-2019 05:00:00,1,0.15874,0.15874,0.15874,0.12081,0.039378,0.039378,0.039378,0.039378,0.12081,0.12081,0.12081,0.12081,0.12081,0.039378,0.10521,0.15874,0.10521,0.10521
+22-Aug-2019 06:00:00,1,0.14935,0.14935,0.14935,0.15399,0.026252,0.026252,0.026252,0.026252,0.15399,0.15399,0.15399,0.15399,0.15399,0.026252,0.1412,0.14935,0.1412,0.1412
+22-Aug-2019 07:00:00,1,0.1522,0.1522,0.1522,0.14302,0.045004,0.045004,0.045004,0.045004,0.14302,0.14302,0.14302,0.14302,0.14302,0.045004,0.17417,0.1522,0.17417,0.17417
+22-Aug-2019 08:00:00,1,0.17122,0.17122,0.17122,0.13433,0.05438,0.05438,0.05438,0.05438,0.13433,0.13433,0.13433,0.13433,0.13433,0.05438,0.24592,0.17122,0.24592,0.24592
+22-Aug-2019 09:00:00,1,0.22382,0.22382,0.22382,0.14806,0.033753,0.033753,0.033753,0.033753,0.14806,0.14806,0.14806,0.14806,0.14806,0.033753,0.29317,0.22382,0.29317,0.29317
+22-Aug-2019 10:00:00,1,0.30275,0.30275,0.30275,0.17188,0.046879,0.046879,0.046879,0.046879,0.17188,0.17188,0.17188,0.17188,0.17188,0.046879,0.32912,0.30275,0.32912,0.32912
+22-Aug-2019 11:00:00,1,0.3786,0.3786,0.3786,0.2255,0.095633,0.095633,0.095633,0.095633,0.2255,0.2255,0.2255,0.2255,0.2255,0.095633,0.36551,0.3786,0.36551,0.36551
+22-Aug-2019 12:00:00,1,0.431,0.431,0.431,0.25421,0.086257,0.086257,0.086257,0.086257,0.25421,0.25421,0.25421,0.25421,0.25421,0.086257,0.36432,0.431,0.36432,0.36432
+22-Aug-2019 13:00:00,1,0.47083,0.47083,0.47083,0.23762,0.10876,0.10876,0.10876,0.10876,0.23762,0.23762,0.23762,0.23762,0.23762,0.10876,0.39952,0.47083,0.39952,0.39952
+22-Aug-2019 14:00:00,1,0.49468,0.49468,0.49468,0.26887,0.073131,0.073131,0.073131,0.073131,0.26887,0.26887,0.26887,0.26887,0.26887,0.073131,0.39471,0.49468,0.39471,0.39471
+22-Aug-2019 15:00:00,1,0.46043,0.46043,0.46043,0.26029,0.050629,0.050629,0.050629,0.050629,0.26029,0.26029,0.26029,0.26029,0.26029,0.050629,0.37254,0.46043,0.37254,0.37254
+22-Aug-2019 16:00:00,1,0.44588,0.44588,0.44588,0.2241,0.041253,0.041253,0.041253,0.041253,0.2241,0.2241,0.2241,0.2241,0.2241,0.041253,0.3216,0.44588,0.3216,0.3216
+22-Aug-2019 17:00:00,1,0.41056,0.41056,0.41056,0.17006,0.026252,0.026252,0.026252,0.026252,0.17006,0.17006,0.17006,0.17006,0.17006,0.026252,0.27717,0.41056,0.27717,0.27717
+22-Aug-2019 18:00:00,1,0.33891,0.33891,0.33891,0.12632,0.020627,0.020627,0.020627,0.020627,0.12632,0.12632,0.12632,0.12632,0.12632,0.020627,0.21952,0.33891,0.21952,0.21952
+22-Aug-2019 19:00:00,1,0.24268,0.24268,0.24268,0.095484,0.022502,0.022502,0.022502,0.022502,0.095484,0.095484,0.095484,0.095484,0.095484,0.022502,0.15236,0.24268,0.15236,0.15236
+22-Aug-2019 20:00:00,1,0.18278,0.18278,0.18278,0.058715,0.015001,0.015001,0.015001,0.015001,0.058715,0.058715,0.058715,0.058715,0.058715,0.015001,0.11018,0.18278,0.11018,0.11018
+22-Aug-2019 21:00:00,1,0.15326,0.15326,0.15326,0.061576,0.0093758,0.0093758,0.0093758,0.0093758,0.061576,0.061576,0.061576,0.061576,0.061576,0.0093758,0.12226,0.15326,0.12226,0.12226
+22-Aug-2019 22:00:00,1,0.15593,0.15593,0.15593,0.095172,0.0093758,0.0093758,0.0093758,0.0093758,0.095172,0.095172,0.095172,0.095172,0.095172,0.0093758,0.12382,0.15593,0.12382,0.12382
+22-Aug-2019 23:00:00,1,0.17007,0.17007,0.17007,0.10126,0.0075006,0.0075006,0.0075006,0.0075006,0.10126,0.10126,0.10126,0.10126,0.10126,0.0075006,0.097627,0.17007,0.097627,0.097627
+23-Aug-2019 00:00:00,1,0.18183,0.18183,0.18183,0.097928,0.0056255,0.0056255,0.0056255,0.0056255,0.097928,0.097928,0.097928,0.097928,0.097928,0.0056255,0.089205,0.18183,0.089205,0.089205
+23-Aug-2019 01:00:00,1,0.1895,0.1895,0.1895,0.10588,0.0056255,0.0056255,0.0056255,0.0056255,0.10588,0.10588,0.10588,0.10588,0.10588,0.0056255,0.095234,0.1895,0.095234,0.095234
+23-Aug-2019 02:00:00,1,0.18839,0.18839,0.18839,0.10713,0.0037503,0.0037503,0.0037503,0.0037503,0.10713,0.10713,0.10713,0.10713,0.10713,0.0037503,0.096566,0.18839,0.096566,0.096566
+23-Aug-2019 03:00:00,1,0.17527,0.17527,0.17527,0.1154,0.0037503,0.0037503,0.0037503,0.0037503,0.1154,0.1154,0.1154,0.1154,0.1154,0.0037503,0.094918,0.17527,0.094918,0.094918
+23-Aug-2019 04:00:00,1,0.16433,0.16433,0.16433,0.10656,0,0,0,0,0.10656,0.10656,0.10656,0.10656,0.10656,0,0.10415,0.16433,0.10415,0.10415
+23-Aug-2019 05:00:00,1,0.16482,0.16482,0.16482,0.09408,0.0037503,0.0037503,0.0037503,0.0037503,0.09408,0.09408,0.09408,0.09408,0.09408,0.0037503,0.1277,0.16482,0.1277,0.1277
+23-Aug-2019 06:00:00,1,0.16456,0.16456,0.16456,0.094704,0,0,0,0,0.094704,0.094704,0.094704,0.094704,0.094704,0,0.15168,0.16456,0.15168,0.15168
+23-Aug-2019 07:00:00,1,0.14444,0.14444,0.14444,0.070573,0,0,0,0,0.070573,0.070573,0.070573,0.070573,0.070573,0,0.13526,0.14444,0.13526,0.13526
+23-Aug-2019 08:00:00,1,0.11916,0.11916,0.11916,0.042229,0,0,0,0,0.042229,0.042229,0.042229,0.042229,0.042229,0,0.079203,0.11916,0.079203,0.079203
+23-Aug-2019 09:00:00,1,0.12073,0.12073,0.12073,0.028968,0,0,0,0,0.028968,0.028968,0.028968,0.028968,0.028968,0,0.084532,0.12073,0.084532,0.084532
+23-Aug-2019 10:00:00,1,0.13238,0.13238,0.13238,0.032036,0,0,0,0,0.032036,0.032036,0.032036,0.032036,0.032036,0,0.11912,0.13238,0.11912,0.11912
+23-Aug-2019 11:00:00,1,0.16219,0.16219,0.16219,0.034168,0,0,0,0,0.034168,0.034168,0.034168,0.034168,0.034168,0,0.14224,0.16219,0.14224,0.14224
+23-Aug-2019 12:00:00,1,0.17239,0.17239,0.17239,0.03526,0,0,0,0,0.03526,0.03526,0.03526,0.03526,0.03526,0,0.14486,0.17239,0.14486,0.14486
+23-Aug-2019 13:00:00,1,0.15616,0.15616,0.15616,0.035728,0,0,0,0,0.035728,0.035728,0.035728,0.035728,0.035728,0,0.12723,0.15616,0.12723,0.12723
+23-Aug-2019 14:00:00,1,0.14085,0.14085,0.14085,0.034688,0.0018752,0.0018752,0.0018752,0.0018752,0.034688,0.034688,0.034688,0.034688,0.034688,0.0018752,0.11458,0.14085,0.11458,0.11458
+23-Aug-2019 15:00:00,1,0.13804,0.13804,0.13804,0.028239,0.0037503,0.0037503,0.0037503,0.0037503,0.028239,0.028239,0.028239,0.028239,0.028239,0.0037503,0.10399,0.13804,0.10399,0.10399
+23-Aug-2019 16:00:00,1,0.12064,0.12064,0.12064,0.020543,0.011251,0.011251,0.011251,0.011251,0.020543,0.020543,0.020543,0.020543,0.020543,0.011251,0.090763,0.12064,0.090763,0.090763
+23-Aug-2019 17:00:00,1,0.094139,0.094139,0.094139,0.029592,0.0018752,0.0018752,0.0018752,0.0018752,0.029592,0.029592,0.029592,0.029592,0.029592,0.0018752,0.0762,0.094139,0.0762,0.0762
+23-Aug-2019 18:00:00,1,0.07206,0.07206,0.07206,0.064384,0.0037503,0.0037503,0.0037503,0.0037503,0.064384,0.064384,0.064384,0.064384,0.064384,0.0037503,0.061457,0.07206,0.061457,0.061457
+23-Aug-2019 19:00:00,1,0.039368,0.039368,0.039368,0.021167,0.024377,0.024377,0.024377,0.024377,0.021167,0.021167,0.021167,0.021167,0.021167,0.024377,0.035312,0.039368,0.035312,0.035312
+23-Aug-2019 20:00:00,1,0.027857,0.027857,0.027857,0.0094652,0.020627,0.020627,0.020627,0.020627,0.0094652,0.0094652,0.0094652,0.0094652,0.0094652,0.020627,0.023933,0.027857,0.023933,0.023933
+23-Aug-2019 21:00:00,1,0.032278,0.032278,0.032278,0.039421,0.013126,0.013126,0.013126,0.013126,0.039421,0.039421,0.039421,0.039421,0.039421,0.013126,0.024384,0.032278,0.024384,0.024384
+23-Aug-2019 22:00:00,1,0.037412,0.037412,0.037412,0.070313,0.086257,0.086257,0.086257,0.086257,0.070313,0.070313,0.070313,0.070313,0.070313,0.086257,0.025784,0.037412,0.025784,0.025784
+23-Aug-2019 23:00:00,1,0.048163,0.048163,0.048163,0.10302,0.086257,0.086257,0.086257,0.086257,0.10302,0.10302,0.10302,0.10302,0.10302,0.086257,0.023684,0.048163,0.023684,0.023684
+24-Aug-2019 00:00:00,1,0.067594,0.067594,0.067594,0.11015,0.097508,0.097508,0.097508,0.097508,0.11015,0.11015,0.11015,0.11015,0.11015,0.097508,0.028922,0.067594,0.028922,0.028922
+24-Aug-2019 01:00:00,1,0.065085,0.065085,0.065085,0.171,0.073131,0.073131,0.073131,0.073131,0.171,0.171,0.171,0.171,0.171,0.073131,0.04423,0.065085,0.04423,0.04423
+24-Aug-2019 02:00:00,1,0.077609,0.077609,0.077609,0.14635,0.12001,0.12001,0.12001,0.12001,0.14635,0.14635,0.14635,0.14635,0.14635,0.12001,0.047143,0.077609,0.047143,0.047143
+24-Aug-2019 03:00:00,1,0.076435,0.076435,0.076435,0.16314,0.082507,0.082507,0.082507,0.082507,0.16314,0.16314,0.16314,0.16314,0.16314,0.082507,0.046059,0.076435,0.046059,0.046059
+24-Aug-2019 04:00:00,1,0.080118,0.080118,0.080118,0.12378,0.088132,0.088132,0.088132,0.088132,0.12378,0.12378,0.12378,0.12378,0.12378,0.088132,0.046194,0.080118,0.046194,0.046194
+24-Aug-2019 05:00:00,1,0.063473,0.063473,0.063473,0.087475,0.15564,0.15564,0.15564,0.15564,0.087475,0.087475,0.087475,0.087475,0.087475,0.15564,0.064641,0.063473,0.064641,0.064641
+24-Aug-2019 06:00:00,1,0.052399,0.052399,0.052399,0.045454,0.20252,0.20252,0.20252,0.20252,0.045454,0.045454,0.045454,0.045454,0.045454,0.20252,0.060622,0.052399,0.060622,0.060622
+24-Aug-2019 07:00:00,1,0.051225,0.051225,0.051225,0.040617,0.2869,0.2869,0.2869,0.2869,0.040617,0.040617,0.040617,0.040617,0.040617,0.2869,0.045043,0.051225,0.045043,0.045043
+24-Aug-2019 08:00:00,1,0.050373,0.050373,0.050373,0.068908,0.17064,0.17064,0.17064,0.17064,0.068908,0.068908,0.068908,0.068908,0.068908,0.17064,0.037728,0.050373,0.037728,0.037728
+24-Aug-2019 09:00:00,1,0.041763,0.041763,0.041763,0.044517,0.17251,0.17251,0.17251,0.17251,0.044517,0.044517,0.044517,0.044517,0.044517,0.17251,0.021449,0.041763,0.021449,0.021449
+24-Aug-2019 10:00:00,1,0.030252,0.030252,0.030252,0.060587,0.18939,0.18939,0.18939,0.18939,0.060587,0.060587,0.060587,0.060587,0.060587,0.18939,0.01585,0.030252,0.01585,0.01585
+24-Aug-2019 11:00:00,1,0.026177,0.026177,0.026177,0.0324,0.14814,0.14814,0.14814,0.14814,0.0324,0.0324,0.0324,0.0324,0.0324,0.14814,0.01874,0.026177,0.01874,0.01874
+24-Aug-2019 12:00:00,1,0.027282,0.027282,0.027282,0.049874,0.080632,0.080632,0.080632,0.080632,0.049874,0.049874,0.049874,0.049874,0.049874,0.080632,0.016414,0.027282,0.016414,0.016414
+24-Aug-2019 13:00:00,1,0.030758,0.030758,0.030758,0.06766,0.091883,0.091883,0.091883,0.091883,0.06766,0.06766,0.06766,0.06766,0.06766,0.091883,0.024429,0.030758,0.024429,0.024429
+24-Aug-2019 14:00:00,1,0.025716,0.025716,0.025716,0.043321,0.13126,0.13126,0.13126,0.13126,0.043321,0.043321,0.043321,0.043321,0.043321,0.13126,0.032648,0.025716,0.032648,0.032648
+24-Aug-2019 15:00:00,1,0.018924,0.018924,0.018924,0.024131,0.031878,0.031878,0.031878,0.031878,0.024131,0.024131,0.024131,0.024131,0.024131,0.031878,0.035628,0.018924,0.035628,0.035628
+24-Aug-2019 16:00:00,1,0.019132,0.019132,0.019132,0.034064,0.093758,0.093758,0.093758,0.093758,0.034064,0.034064,0.034064,0.034064,0.034064,0.093758,0.037073,0.019132,0.037073,0.037073
+24-Aug-2019 17:00:00,1,0.019868,0.019868,0.019868,0.027147,0.05438,0.05438,0.05438,0.05438,0.027147,0.027147,0.027147,0.027147,0.027147,0.05438,0.031925,0.019868,0.031925,0.031925
+24-Aug-2019 18:00:00,1,0.027097,0.027097,0.027097,0.029904,0.076881,0.076881,0.076881,0.076881,0.029904,0.029904,0.029904,0.029904,0.029904,0.076881,0.013027,0.027097,0.013027,0.013027
+24-Aug-2019 19:00:00,1,0.038079,0.038079,0.038079,0.016174,0.05438,0.05438,0.05438,0.05438,0.016174,0.016174,0.016174,0.016174,0.016174,0.05438,0.0048543,0.038079,0.0048543,0.0048543
+24-Aug-2019 20:00:00,1,0.040796,0.040796,0.040796,0.028499,0.06188,0.06188,0.06188,0.06188,0.028499,0.028499,0.028499,0.028499,0.028499,0.06188,0.0027771,0.040796,0.0027771,0.0027771
+24-Aug-2019 21:00:00,1,0.074109,0.074109,0.074109,0.053151,0.078757,0.078757,0.078757,0.078757,0.053151,0.053151,0.053151,0.053151,0.053151,0.078757,0.0061863,0.074109,0.0061863,0.0061863
+24-Aug-2019 22:00:00,1,0.095912,0.095912,0.095912,0.078426,0.093758,0.093758,0.093758,0.093758,0.078426,0.078426,0.078426,0.078426,0.078426,0.093758,0.0065927,0.095912,0.0065927,0.0065927
+24-Aug-2019 23:00:00,1,0.11067,0.11067,0.11067,0.13797,0.14814,0.14814,0.14814,0.14814,0.13797,0.13797,0.13797,0.13797,0.13797,0.14814,0.0025287,0.11067,0.0025287,0.0025287
+25-Aug-2019 00:00:00,1,0.10809,0.10809,0.10809,0.13074,0.12751,0.12751,0.12751,0.12751,0.13074,0.13074,0.13074,0.13074,0.13074,0.12751,0.0055542,0.10809,0.0055542,0.0055542
+25-Aug-2019 01:00:00,1,0.1124,0.1124,0.1124,0.10131,0.10501,0.10501,0.10501,0.10501,0.10131,0.10131,0.10131,0.10131,0.10131,0.10501,0.0077894,0.1124,0.0077894,0.0077894
+25-Aug-2019 02:00:00,1,0.11276,0.11276,0.11276,0.1606,0.086257,0.086257,0.086257,0.086257,0.1606,0.1606,0.1606,0.1606,0.1606,0.086257,0.012102,0.11276,0.012102,0.012102
+25-Aug-2019 03:00:00,1,0.1039,0.1039,0.1039,0.17318,0.033753,0.033753,0.033753,0.033753,0.17318,0.17318,0.17318,0.17318,0.17318,0.033753,0.029058,0.1039,0.029058,0.029058
+25-Aug-2019 04:00:00,1,0.1048,0.1048,0.1048,0.18993,0.037503,0.037503,0.037503,0.037503,0.18993,0.18993,0.18993,0.18993,0.18993,0.037503,0.043621,0.1048,0.043621,0.043621
+25-Aug-2019 05:00:00,1,0.11468,0.11468,0.11468,0.24625,0.031878,0.031878,0.031878,0.031878,0.24625,0.24625,0.24625,0.24625,0.24625,0.031878,0.058635,0.11468,0.058635,0.058635
+25-Aug-2019 06:00:00,1,0.12098,0.12098,0.12098,0.2178,0.033753,0.033753,0.033753,0.033753,0.2178,0.2178,0.2178,0.2178,0.2178,0.033753,0.08548,0.12098,0.08548,0.08548
+25-Aug-2019 07:00:00,1,0.13042,0.13042,0.13042,0.20054,0.043129,0.043129,0.043129,0.043129,0.20054,0.20054,0.20054,0.20054,0.20054,0.043129,0.080287,0.13042,0.080287,0.080287
+25-Aug-2019 08:00:00,1,0.11587,0.11587,0.11587,0.21733,0.10126,0.10126,0.10126,0.10126,0.21733,0.21733,0.21733,0.21733,0.21733,0.10126,0.070353,0.11587,0.070353,0.070353
+25-Aug-2019 09:00:00,1,0.12856,0.12856,0.12856,0.26466,0.11813,0.11813,0.11813,0.11813,0.26466,0.26466,0.26466,0.26466,0.26466,0.11813,0.08189,0.12856,0.08189,0.08189
+25-Aug-2019 10:00:00,1,0.16452,0.16452,0.16452,0.35224,0.2419,0.2419,0.2419,0.2419,0.35224,0.35224,0.35224,0.35224,0.35224,0.2419,0.11433,0.16452,0.11433,0.11433
+25-Aug-2019 11:00:00,1,0.2003,0.2003,0.2003,0.39426,0.12751,0.12751,0.12751,0.12751,0.39426,0.39426,0.39426,0.39426,0.39426,0.12751,0.1536,0.2003,0.1536,0.1536
+25-Aug-2019 12:00:00,1,0.23244,0.23244,0.23244,0.41506,0.12001,0.12001,0.12001,0.12001,0.41506,0.41506,0.41506,0.41506,0.41506,0.12001,0.17274,0.23244,0.17274,0.17274
+25-Aug-2019 13:00:00,1,0.25341,0.25341,0.25341,0.4563,0.11438,0.11438,0.11438,0.11438,0.4563,0.4563,0.4563,0.4563,0.4563,0.11438,0.18311,0.25341,0.18311,0.18311
+25-Aug-2019 14:00:00,1,0.28663,0.28663,0.28663,0.43893,0.15376,0.15376,0.15376,0.15376,0.43893,0.43893,0.43893,0.43893,0.43893,0.15376,0.18266,0.28663,0.18266,0.18266
+25-Aug-2019 15:00:00,1,0.27192,0.27192,0.27192,0.43181,0.11813,0.11813,0.11813,0.11813,0.43181,0.43181,0.43181,0.43181,0.43181,0.11813,0.1997,0.27192,0.1997,0.1997
+25-Aug-2019 16:00:00,1,0.25543,0.25543,0.25543,0.42869,0.084382,0.084382,0.084382,0.084382,0.42869,0.42869,0.42869,0.42869,0.42869,0.084382,0.17304,0.25543,0.17304,0.17304
+25-Aug-2019 17:00:00,1,0.24139,0.24139,0.24139,0.43108,0.045004,0.045004,0.045004,0.045004,0.43108,0.43108,0.43108,0.43108,0.43108,0.045004,0.17631,0.24139,0.17631,0.17631
+25-Aug-2019 18:00:00,1,0.18146,0.18146,0.18146,0.37913,0.039378,0.039378,0.039378,0.039378,0.37913,0.37913,0.37913,0.37913,0.37913,0.039378,0.15238,0.18146,0.15238,0.15238
+25-Aug-2019 19:00:00,1,0.12269,0.12269,0.12269,0.22888,0.097508,0.097508,0.097508,0.097508,0.22888,0.22888,0.22888,0.22888,0.22888,0.097508,0.13181,0.12269,0.13181,0.13181
+25-Aug-2019 20:00:00,1,0.11253,0.11253,0.11253,0.1529,0.11251,0.11251,0.11251,0.11251,0.1529,0.1529,0.1529,0.1529,0.1529,0.11251,0.12786,0.11253,0.12786,0.12786
+25-Aug-2019 21:00:00,1,0.14553,0.14553,0.14553,0.17651,0.076881,0.076881,0.076881,0.076881,0.17651,0.17651,0.17651,0.17651,0.17651,0.076881,0.11235,0.14553,0.11235,0.11235
+25-Aug-2019 22:00:00,1,0.18402,0.18402,0.18402,0.21843,0.10126,0.10126,0.10126,0.10126,0.21843,0.21843,0.21843,0.21843,0.21843,0.10126,0.11623,0.18402,0.11623,0.11623
+25-Aug-2019 23:00:00,1,0.18179,0.18179,0.18179,0.30809,0.099383,0.099383,0.099383,0.099383,0.30809,0.30809,0.30809,0.30809,0.30809,0.099383,0.14583,0.18179,0.14583,0.14583
+26-Aug-2019 00:00:00,1,0.23494,0.23494,0.23494,0.29664,0.12001,0.12001,0.12001,0.12001,0.29664,0.29664,0.29664,0.29664,0.29664,0.12001,0.13992,0.23494,0.13992,0.13992
+26-Aug-2019 01:00:00,1,0.27385,0.27385,0.27385,0.32681,0.11438,0.11438,0.11438,0.11438,0.32681,0.32681,0.32681,0.32681,0.32681,0.11438,0.15877,0.27385,0.15877,0.15877
+26-Aug-2019 02:00:00,1,0.29635,0.29635,0.29635,0.35723,0.11813,0.11813,0.11813,0.11813,0.35723,0.35723,0.35723,0.35723,0.35723,0.11813,0.17058,0.29635,0.17058,0.17058
+26-Aug-2019 03:00:00,1,0.30737,0.30737,0.30737,0.39301,0.082507,0.082507,0.082507,0.082507,0.39301,0.39301,0.39301,0.39301,0.39301,0.082507,0.1778,0.30737,0.1778,0.1778
+26-Aug-2019 04:00:00,1,0.31163,0.31163,0.31163,0.43639,0.11063,0.11063,0.11063,0.11063,0.43639,0.43639,0.43639,0.43639,0.43639,0.11063,0.18627,0.31163,0.18627,0.18627
+26-Aug-2019 05:00:00,1,0.32123,0.32123,0.32123,0.43207,0.11063,0.11063,0.11063,0.11063,0.43207,0.43207,0.43207,0.43207,0.43207,0.11063,0.224,0.32123,0.224,0.224
+26-Aug-2019 06:00:00,1,0.32231,0.32231,0.32231,0.46395,0.18189,0.18189,0.18189,0.18189,0.46395,0.46395,0.46395,0.46395,0.46395,0.18189,0.244,0.32231,0.244,0.244
+26-Aug-2019 07:00:00,1,0.31578,0.31578,0.31578,0.48335,0.21377,0.21377,0.21377,0.21377,0.48335,0.48335,0.48335,0.48335,0.48335,0.21377,0.26956,0.31578,0.26956,0.26956
+26-Aug-2019 08:00:00,1,0.35059,0.35059,0.35059,0.50311,0.19314,0.19314,0.19314,0.19314,0.50311,0.50311,0.50311,0.50311,0.50311,0.19314,0.25816,0.35059,0.25816,0.25816
+26-Aug-2019 09:00:00,1,0.39606,0.39606,0.39606,0.51684,0.18752,0.18752,0.18752,0.18752,0.51684,0.51684,0.51684,0.51684,0.51684,0.18752,0.33108,0.39606,0.33108,0.33108
+26-Aug-2019 10:00:00,1,0.39454,0.39454,0.39454,0.48023,0.20627,0.20627,0.20627,0.20627,0.48023,0.48023,0.48023,0.48023,0.48023,0.20627,0.42796,0.39454,0.42796,0.42796
+26-Aug-2019 11:00:00,1,0.37395,0.37395,0.37395,0.51741,0.22127,0.22127,0.22127,0.22127,0.51741,0.51741,0.51741,0.51741,0.51741,0.22127,0.49294,0.37395,0.49294,0.49294
+26-Aug-2019 12:00:00,1,0.36562,0.36562,0.36562,0.48298,0.18377,0.18377,0.18377,0.18377,0.48298,0.48298,0.48298,0.48298,0.48298,0.18377,0.45158,0.36562,0.45158,0.45158
+26-Aug-2019 13:00:00,1,0.38245,0.38245,0.38245,0.43945,0.22502,0.22502,0.22502,0.22502,0.43945,0.43945,0.43945,0.43945,0.43945,0.22502,0.47066,0.38245,0.47066,0.47066
+26-Aug-2019 14:00:00,1,0.40826,0.40826,0.40826,0.39795,0.21002,0.21002,0.21002,0.21002,0.39795,0.39795,0.39795,0.39795,0.39795,0.21002,0.5206,0.40826,0.5206,0.5206
+26-Aug-2019 15:00:00,1,0.42677,0.42677,0.42677,0.30533,0.17626,0.17626,0.17626,0.17626,0.30533,0.30533,0.30533,0.30533,0.30533,0.17626,0.55521,0.42677,0.55521,0.55521
+26-Aug-2019 16:00:00,1,0.40333,0.40333,0.40333,0.328,0.19502,0.19502,0.19502,0.19502,0.328,0.328,0.328,0.328,0.328,0.19502,0.56235,0.40333,0.56235,0.56235
+26-Aug-2019 17:00:00,1,0.37863,0.37863,0.37863,0.31916,0.17814,0.17814,0.17814,0.17814,0.31916,0.31916,0.31916,0.31916,0.31916,0.17814,0.53361,0.37863,0.53361,0.53361
+26-Aug-2019 18:00:00,1,0.38535,0.38535,0.38535,0.315,0.10126,0.10126,0.10126,0.10126,0.315,0.315,0.315,0.315,0.315,0.10126,0.43038,0.38535,0.43038,0.43038
+26-Aug-2019 19:00:00,1,0.34681,0.34681,0.34681,0.22987,0.05813,0.05813,0.05813,0.05813,0.22987,0.22987,0.22987,0.22987,0.22987,0.05813,0.36174,0.34681,0.36174,0.36174
+26-Aug-2019 20:00:00,1,0.3053,0.3053,0.3053,0.20974,0.05813,0.05813,0.05813,0.05813,0.20974,0.20974,0.20974,0.20974,0.20974,0.05813,0.33871,0.3053,0.33871,0.33871
+26-Aug-2019 21:00:00,1,0.29167,0.29167,0.29167,0.18629,0.069381,0.069381,0.069381,0.069381,0.18629,0.18629,0.18629,0.18629,0.18629,0.069381,0.35014,0.29167,0.35014,0.35014
+26-Aug-2019 22:00:00,1,0.27394,0.27394,0.27394,0.14468,0.076881,0.076881,0.076881,0.076881,0.14468,0.14468,0.14468,0.14468,0.14468,0.076881,0.36545,0.27394,0.36545,0.36545
+26-Aug-2019 23:00:00,1,0.23812,0.23812,0.23812,0.074681,0.088132,0.088132,0.088132,0.088132,0.074681,0.074681,0.074681,0.074681,0.074681,0.088132,0.37073,0.23812,0.37073,0.37073
+27-Aug-2019 00:00:00,1,0.21333,0.21333,0.21333,0.054139,0.05813,0.05813,0.05813,0.05813,0.054139,0.054139,0.054139,0.054139,0.054139,0.05813,0.3626,0.21333,0.3626,0.3626
+27-Aug-2019 01:00:00,1,0.20184,0.20184,0.20184,0.051694,0.039378,0.039378,0.039378,0.039378,0.051694,0.051694,0.051694,0.051694,0.051694,0.039378,0.33171,0.20184,0.33171,0.33171
+27-Aug-2019 02:00:00,1,0.21013,0.21013,0.21013,0.039109,0.031878,0.031878,0.031878,0.031878,0.039109,0.039109,0.039109,0.039109,0.039109,0.031878,0.30729,0.21013,0.30729,0.30729
+27-Aug-2019 03:00:00,1,0.19343,0.19343,0.19343,0.039005,0.026252,0.026252,0.026252,0.026252,0.039005,0.039005,0.039005,0.039005,0.039005,0.026252,0.2689,0.19343,0.2689,0.2689
+27-Aug-2019 04:00:00,1,0.16809,0.16809,0.16809,0.033076,0.0093758,0.0093758,0.0093758,0.0093758,0.033076,0.033076,0.033076,0.033076,0.033076,0.0093758,0.25269,0.16809,0.25269,0.25269
+27-Aug-2019 05:00:00,1,0.16067,0.16067,0.16067,0.021791,0.0075006,0.0075006,0.0075006,0.0075006,0.021791,0.021791,0.021791,0.021791,0.021791,0.0075006,0.23445,0.16067,0.23445,0.23445
+27-Aug-2019 06:00:00,1,0.14474,0.14474,0.14474,0.020126,0.0075006,0.0075006,0.0075006,0.0075006,0.020126,0.020126,0.020126,0.020126,0.020126,0.0075006,0.20918,0.14474,0.20918,0.20918
+27-Aug-2019 07:00:00,1,0.12306,0.12306,0.12306,0.025483,0,0,0,0,0.025483,0.025483,0.025483,0.025483,0.025483,0,0.17773,0.12306,0.17773,0.17773
+27-Aug-2019 08:00:00,1,0.07952,0.07952,0.07952,0.03136,0,0,0,0,0.03136,0.03136,0.03136,0.03136,0.03136,0,0.16518,0.07952,0.16518,0.16518
+27-Aug-2019 09:00:00,1,0.071255,0.071255,0.071255,0.028708,0,0,0,0,0.028708,0.028708,0.028708,0.028708,0.028708,0,0.18613,0.071255,0.18613,0.18613
+27-Aug-2019 10:00:00,1,0.084285,0.084285,0.084285,0.026679,0.0018752,0.0018752,0.0018752,0.0018752,0.026679,0.026679,0.026679,0.026679,0.026679,0.0018752,0.19701,0.084285,0.19701,0.19701
+27-Aug-2019 11:00:00,1,0.07701,0.07701,0.07701,0.031672,0.0018752,0.0018752,0.0018752,0.0018752,0.031672,0.031672,0.031672,0.031672,0.031672,0.0018752,0.19146,0.07701,0.19146,0.19146
+27-Aug-2019 12:00:00,1,0.066374,0.066374,0.066374,0.027979,0.0018752,0.0018752,0.0018752,0.0018752,0.027979,0.027979,0.027979,0.027979,0.027979,0.0018752,0.16672,0.066374,0.16672,0.16672
+27-Aug-2019 13:00:00,1,0.05187,0.05187,0.05187,0.035052,0.0018752,0.0018752,0.0018752,0.0018752,0.035052,0.035052,0.035052,0.035052,0.035052,0.0018752,0.15604,0.05187,0.15604,0.15604
+27-Aug-2019 14:00:00,1,0.040566,0.040566,0.040566,0.038017,0.0018752,0.0018752,0.0018752,0.0018752,0.038017,0.038017,0.038017,0.038017,0.038017,0.0018752,0.13867,0.040566,0.13867,0.13867
+27-Aug-2019 15:00:00,1,0.036122,0.036122,0.036122,0.050654,0.0018752,0.0018752,0.0018752,0.0018752,0.050654,0.050654,0.050654,0.050654,0.050654,0.0018752,0.10925,0.036122,0.10925,0.10925
+27-Aug-2019 16:00:00,1,0.033429,0.033429,0.033429,0.069377,0.0037503,0.0037503,0.0037503,0.0037503,0.069377,0.069377,0.069377,0.069377,0.069377,0.0037503,0.069066,0.033429,0.069066,0.069066
+27-Aug-2019 17:00:00,1,0.031357,0.031357,0.031357,0.078374,0.0075006,0.0075006,0.0075006,0.0075006,0.078374,0.078374,0.078374,0.078374,0.078374,0.0075006,0.040392,0.031357,0.040392,0.040392
+27-Aug-2019 18:00:00,1,0.028249,0.028249,0.028249,0.069012,0.020627,0.020627,0.020627,0.020627,0.069012,0.069012,0.069012,0.069012,0.069012,0.020627,0.016234,0.028249,0.016234,0.016234
+27-Aug-2019 19:00:00,1,0.033314,0.033314,0.033314,0.064904,0.039378,0.039378,0.039378,0.039378,0.064904,0.064904,0.064904,0.064904,0.064904,0.039378,0.0017159,0.033314,0.0017159,0.0017159
+27-Aug-2019 20:00:00,1,0.056037,0.056037,0.056037,0.099072,0.05813,0.05813,0.05813,0.05813,0.099072,0.099072,0.099072,0.099072,0.099072,0.05813,0.00051929,0.056037,0.00051929,0.00051929
+27-Aug-2019 21:00:00,1,0.099181,0.099181,0.099181,0.15155,0.063755,0.063755,0.063755,0.063755,0.15155,0.15155,0.15155,0.15155,0.15155,0.063755,0.0019643,0.099181,0.0019643,0.0019643
+27-Aug-2019 22:00:00,1,0.14437,0.14437,0.14437,0.19222,0.045004,0.045004,0.045004,0.045004,0.19222,0.19222,0.19222,0.19222,0.19222,0.045004,0.0074959,0.14437,0.0074959,0.0074959
+27-Aug-2019 23:00:00,1,0.1709,0.1709,0.1709,0.22389,0.056255,0.056255,0.056255,0.056255,0.22389,0.22389,0.22389,0.22389,0.22389,0.056255,0.023007,0.1709,0.023007,0.023007
+28-Aug-2019 00:00:00,1,0.1872,0.1872,0.1872,0.21489,0.063755,0.063755,0.063755,0.063755,0.21489,0.21489,0.21489,0.21489,0.21489,0.063755,0.049197,0.1872,0.049197,0.049197
+28-Aug-2019 01:00:00,1,0.20918,0.20918,0.20918,0.19534,0.063755,0.063755,0.063755,0.063755,0.19534,0.19534,0.19534,0.19534,0.19534,0.063755,0.08548,0.20918,0.08548,0.08548
+28-Aug-2019 02:00:00,1,0.22907,0.22907,0.22907,0.17349,0.065631,0.065631,0.065631,0.065631,0.17349,0.17349,0.17349,0.17349,0.17349,0.065631,0.13355,0.22907,0.13355,0.13355
+28-Aug-2019 03:00:00,1,0.23324,0.23324,0.23324,0.18582,0.088132,0.088132,0.088132,0.088132,0.18582,0.18582,0.18582,0.18582,0.18582,0.088132,0.1715,0.23324,0.1715,0.1715
+28-Aug-2019 04:00:00,1,0.22818,0.22818,0.22818,0.15633,0.084382,0.084382,0.084382,0.084382,0.15633,0.15633,0.15633,0.15633,0.15633,0.084382,0.20487,0.22818,0.20487,0.20487
+28-Aug-2019 05:00:00,1,0.21715,0.21715,0.21715,0.13386,0.045004,0.045004,0.045004,0.045004,0.13386,0.13386,0.13386,0.13386,0.13386,0.045004,0.20022,0.21715,0.20022,0.20022
+28-Aug-2019 06:00:00,1,0.18651,0.18651,0.18651,0.14453,0.033753,0.033753,0.033753,0.033753,0.14453,0.14453,0.14453,0.14453,0.14453,0.033753,0.19645,0.18651,0.19645,0.19645
+28-Aug-2019 07:00:00,1,0.16869,0.16869,0.16869,0.14619,0.028127,0.028127,0.028127,0.028127,0.14619,0.14619,0.14619,0.14619,0.14619,0.028127,0.17545,0.16869,0.17545,0.17545
+28-Aug-2019 08:00:00,1,0.14035,0.14035,0.14035,0.16106,0.020627,0.020627,0.020627,0.020627,0.16106,0.16106,0.16106,0.16106,0.16106,0.020627,0.10641,0.14035,0.10641,0.10641
+28-Aug-2019 09:00:00,1,0.10966,0.10966,0.10966,0.1166,0.024377,0.024377,0.024377,0.024377,0.1166,0.1166,0.1166,0.1166,0.1166,0.024377,0.061593,0.10966,0.061593,0.061593
+28-Aug-2019 10:00:00,1,0.09453,0.09453,0.09453,0.099176,0.050629,0.050629,0.050629,0.050629,0.099176,0.099176,0.099176,0.099176,0.099176,0.050629,0.060531,0.09453,0.060531,0.060531
+28-Aug-2019 11:00:00,1,0.081891,0.081891,0.081891,0.13345,0.06188,0.06188,0.06188,0.06188,0.13345,0.13345,0.13345,0.13345,0.13345,0.06188,0.070353,0.081891,0.070353,0.070353
+28-Aug-2019 12:00:00,1,0.0716,0.0716,0.0716,0.14775,0.05438,0.05438,0.05438,0.05438,0.14775,0.14775,0.14775,0.14775,0.14775,0.05438,0.072859,0.0716,0.072859,0.072859
+28-Aug-2019 13:00:00,1,0.059789,0.059789,0.059789,0.1621,0.056255,0.056255,0.056255,0.056255,0.1621,0.1621,0.1621,0.1621,0.1621,0.056255,0.063376,0.059789,0.063376,0.063376
+28-Aug-2019 14:00:00,1,0.05926,0.05926,0.05926,0.21177,0.022502,0.022502,0.022502,0.022502,0.21177,0.21177,0.21177,0.21177,0.21177,0.022502,0.048836,0.05926,0.048836,0.048836
+28-Aug-2019 15:00:00,1,0.073511,0.073511,0.073511,0.17708,0.048754,0.048754,0.048754,0.048754,0.17708,0.17708,0.17708,0.17708,0.17708,0.048754,0.052042,0.073511,0.052042,0.052042
+28-Aug-2019 16:00:00,1,0.084838,0.084838,0.084838,0.16372,0.045004,0.045004,0.045004,0.045004,0.16372,0.16372,0.16372,0.16372,0.16372,0.045004,0.054593,0.084838,0.054593,0.054593
+28-Aug-2019 17:00:00,1,0.081799,0.081799,0.081799,0.14666,0.030003,0.030003,0.030003,0.030003,0.14666,0.14666,0.14666,0.14666,0.14666,0.030003,0.053329,0.081799,0.053329,0.053329
+28-Aug-2019 18:00:00,1,0.09064,0.09064,0.09064,0.093195,0.015001,0.015001,0.015001,0.015001,0.093195,0.093195,0.093195,0.093195,0.093195,0.015001,0.048994,0.09064,0.048994,0.048994
+28-Aug-2019 19:00:00,1,0.058984,0.058984,0.058984,0.070105,0.0075006,0.0075006,0.0075006,0.0075006,0.070105,0.070105,0.070105,0.070105,0.070105,0.0075006,0.045923,0.058984,0.045923,0.045923
+28-Aug-2019 20:00:00,1,0.048439,0.048439,0.048439,0.042021,0.0075006,0.0075006,0.0075006,0.0075006,0.042021,0.042021,0.042021,0.042021,0.042021,0.0075006,0.070082,0.048439,0.070082,0.070082
+28-Aug-2019 21:00:00,1,0.049613,0.049613,0.049613,0.039213,0.018752,0.018752,0.018752,0.018752,0.039213,0.039213,0.039213,0.039213,0.039213,0.018752,0.095437,0.049613,0.095437,0.095437
+28-Aug-2019 22:00:00,1,0.056106,0.056106,0.056106,0.039785,0.016876,0.016876,0.016876,0.016876,0.039785,0.039785,0.039785,0.039785,0.039785,0.016876,0.082048,0.056106,0.082048,0.082048
+28-Aug-2019 23:00:00,1,0.050557,0.050557,0.050557,0.048106,0.035628,0.035628,0.035628,0.035628,0.048106,0.048106,0.048106,0.048106,0.048106,0.035628,0.092366,0.050557,0.092366,0.092366
+29-Aug-2019 00:00:00,1,0.033452,0.033452,0.033452,0.037185,0.022502,0.022502,0.022502,0.022502,0.037185,0.037185,0.037185,0.037185,0.037185,0.022502,0.090944,0.033452,0.090944,0.090944
+29-Aug-2019 01:00:00,1,0.02003,0.02003,0.02003,0.026159,0.018752,0.018752,0.018752,0.018752,0.026159,0.026159,0.026159,0.026159,0.026159,0.018752,0.073333,0.02003,0.073333,0.073333
+29-Aug-2019 02:00:00,1,0.018119,0.018119,0.018119,0.021791,0.031878,0.031878,0.031878,0.031878,0.021791,0.021791,0.021791,0.021791,0.021791,0.031878,0.060306,0.018119,0.060306,0.060306
+29-Aug-2019 03:00:00,1,0.023667,0.023667,0.023667,0.023195,0.030003,0.030003,0.030003,0.030003,0.023195,0.023195,0.023195,0.023195,0.023195,0.030003,0.045427,0.023667,0.045427,0.045427
+29-Aug-2019 04:00:00,1,0.024035,0.024035,0.024035,0.044569,0.020627,0.020627,0.020627,0.020627,0.044569,0.044569,0.044569,0.044569,0.044569,0.020627,0.032986,0.024035,0.032986,0.032986
+29-Aug-2019 05:00:00,1,0.031126,0.031126,0.031126,0.052214,0.033753,0.033753,0.033753,0.033753,0.052214,0.052214,0.052214,0.052214,0.052214,0.033753,0.030232,0.031126,0.030232,0.030232
+29-Aug-2019 06:00:00,1,0.034096,0.034096,0.034096,0.050342,0.031878,0.031878,0.031878,0.031878,0.050342,0.050342,0.050342,0.050342,0.050342,0.031878,0.023571,0.034096,0.023571,0.023571
+29-Aug-2019 07:00:00,1,0.039737,0.039737,0.039737,0.04769,0.028127,0.028127,0.028127,0.028127,0.04769,0.04769,0.04769,0.04769,0.04769,0.028127,0.01585,0.039737,0.01585,0.01585
+29-Aug-2019 08:00:00,1,0.034188,0.034188,0.034188,0.036665,0.0056255,0.0056255,0.0056255,0.0056255,0.036665,0.036665,0.036665,0.036665,0.036665,0.0056255,0.0034544,0.034188,0.0034544,0.0034544
+29-Aug-2019 09:00:00,1,0.020053,0.020053,0.020053,0.028499,0.0037503,0.0037503,0.0037503,0.0037503,0.028499,0.028499,0.028499,0.028499,0.028499,0.0037503,6.77e-05,0.020053,6.77e-05,6.77e-05
+29-Aug-2019 10:00:00,1,0.018349,0.018349,0.018349,0.024911,0.0075006,0.0075006,0.0075006,0.0075006,0.024911,0.024911,0.024911,0.024911,0.024911,0.0075006,0.00069991,0.018349,0.00069991,0.00069991
+29-Aug-2019 11:00:00,1,0.023207,0.023207,0.023207,0.038849,0.0075006,0.0075006,0.0075006,0.0075006,0.038849,0.038849,0.038849,0.038849,0.038849,0.0075006,0.0012644,0.023207,0.0012644,0.0012644
+29-Aug-2019 12:00:00,1,0.040681,0.040681,0.040681,0.075357,0.015001,0.015001,0.015001,0.015001,0.075357,0.075357,0.075357,0.075357,0.075357,0.015001,0.0027997,0.040681,0.0027997,0.0027997
+29-Aug-2019 13:00:00,1,0.054678,0.054678,0.054678,0.1282,0.0093758,0.0093758,0.0093758,0.0093758,0.1282,0.1282,0.1282,0.1282,0.1282,0.0093758,0.0057348,0.054678,0.0057348,0.0057348
+29-Aug-2019 14:00:00,1,0.06467,0.06467,0.06467,0.15404,0.045004,0.045004,0.045004,0.045004,0.15404,0.15404,0.15404,0.15404,0.15404,0.045004,0.01296,0.06467,0.01296,0.01296
+29-Aug-2019 15:00:00,1,0.070311,0.070311,0.070311,0.19409,0.011251,0.011251,0.011251,0.011251,0.19409,0.19409,0.19409,0.19409,0.19409,0.011251,0.014947,0.070311,0.014947,0.014947
+29-Aug-2019 16:00:00,1,0.078207,0.078207,0.078207,0.15784,0.0093758,0.0093758,0.0093758,0.0093758,0.15784,0.15784,0.15784,0.15784,0.15784,0.0093758,0.015985,0.078207,0.015985,0.015985
+29-Aug-2019 17:00:00,1,0.074547,0.074547,0.074547,0.15534,0.0018752,0.0018752,0.0018752,0.0018752,0.15534,0.15534,0.15534,0.15534,0.15534,0.0018752,0.016685,0.074547,0.016685,0.016685
+29-Aug-2019 18:00:00,1,0.05827,0.05827,0.05827,0.10354,0.011251,0.011251,0.011251,0.011251,0.10354,0.10354,0.10354,0.10354,0.10354,0.011251,0.015285,0.05827,0.015285,0.015285
+29-Aug-2019 19:00:00,1,0.053965,0.053965,0.053965,0.060067,0.028127,0.028127,0.028127,0.028127,0.060067,0.060067,0.060067,0.060067,0.060067,0.028127,0.027658,0.053965,0.027658,0.027658
+29-Aug-2019 20:00:00,1,0.078875,0.078875,0.078875,0.063864,0.022502,0.022502,0.022502,0.022502,0.063864,0.063864,0.063864,0.063864,0.063864,0.022502,0.072565,0.078875,0.072565,0.072565
+29-Aug-2019 21:00:00,1,0.089097,0.089097,0.089097,0.047742,0.028127,0.028127,0.028127,0.028127,0.047742,0.047742,0.047742,0.047742,0.047742,0.028127,0.11718,0.089097,0.11718,0.11718
+29-Aug-2019 22:00:00,1,0.091883,0.091883,0.091883,0.051694,0.052504,0.052504,0.052504,0.052504,0.051694,0.051694,0.051694,0.051694,0.051694,0.052504,0.15522,0.091883,0.15522,0.15522
+29-Aug-2019 23:00:00,1,0.091722,0.091722,0.091722,0.062408,0.048754,0.048754,0.048754,0.048754,0.062408,0.062408,0.062408,0.062408,0.062408,0.048754,0.19221,0.091722,0.19221,0.19221
+30-Aug-2019 00:00:00,1,0.086772,0.086772,0.086772,0.059183,0.063755,0.063755,0.063755,0.063755,0.059183,0.059183,0.059183,0.059183,0.059183,0.063755,0.21316,0.086772,0.21316,0.21316
+30-Aug-2019 01:00:00,1,0.07777,0.07777,0.07777,0.069116,0.065631,0.065631,0.065631,0.065631,0.069116,0.069116,0.069116,0.069116,0.069116,0.065631,0.20361,0.07777,0.20361,0.20361
+30-Aug-2019 02:00:00,1,0.064463,0.064463,0.064463,0.053099,0.046879,0.046879,0.046879,0.046879,0.053099,0.053099,0.053099,0.053099,0.053099,0.046879,0.18674,0.064463,0.18674,0.18674
+30-Aug-2019 03:00:00,1,0.072521,0.072521,0.072521,0.08373,0.056255,0.056255,0.056255,0.056255,0.08373,0.08373,0.08373,0.08373,0.08373,0.056255,0.18069,0.072521,0.18069,0.18069
+30-Aug-2019 04:00:00,1,0.060181,0.060181,0.060181,0.091427,0.043129,0.043129,0.043129,0.043129,0.091427,0.091427,0.091427,0.091427,0.091427,0.043129,0.15985,0.060181,0.15985,0.15985
+30-Aug-2019 05:00:00,1,0.059582,0.059582,0.059582,0.08477,0.030003,0.030003,0.030003,0.030003,0.08477,0.08477,0.08477,0.08477,0.08477,0.030003,0.14037,0.059582,0.14037,0.14037
+30-Aug-2019 06:00:00,1,0.059997,0.059997,0.059997,0.061576,0.011251,0.011251,0.011251,0.011251,0.061576,0.061576,0.061576,0.061576,0.061576,0.011251,0.14305,0.059997,0.14305,0.14305
+30-Aug-2019 07:00:00,1,0.054241,0.054241,0.054241,0.045558,0.011251,0.011251,0.011251,0.011251,0.045558,0.045558,0.045558,0.045558,0.045558,0.011251,0.10472,0.054241,0.10472,0.10472
+30-Aug-2019 08:00:00,1,0.040335,0.040335,0.040335,0.026783,0.0018752,0.0018752,0.0018752,0.0018752,0.026783,0.026783,0.026783,0.026783,0.026783,0.0018752,0.068524,0.040335,0.068524,0.068524
+30-Aug-2019 09:00:00,1,0.023437,0.023437,0.023437,0.022155,0,0,0,0,0.022155,0.022155,0.022155,0.022155,0.022155,0,0.04554,0.023437,0.04554,0.04554
+30-Aug-2019 10:00:00,1,0.031771,0.031771,0.031771,0.026731,0,0,0,0,0.026731,0.026731,0.026731,0.026731,0.026731,0,0.054751,0.031771,0.054751,0.054751
+30-Aug-2019 11:00:00,1,0.03435,0.03435,0.03435,0.030996,0.0093758,0.0093758,0.0093758,0.0093758,0.030996,0.030996,0.030996,0.030996,0.030996,0.0093758,0.055451,0.03435,0.055451,0.055451
+30-Aug-2019 12:00:00,1,0.028456,0.028456,0.028456,0.038173,0.0093758,0.0093758,0.0093758,0.0093758,0.038173,0.038173,0.038173,0.038173,0.038173,0.0093758,0.032602,0.028456,0.032602,0.032602
+30-Aug-2019 13:00:00,1,0.028226,0.028226,0.028226,0.049198,0.026252,0.026252,0.026252,0.026252,0.049198,0.049198,0.049198,0.049198,0.049198,0.026252,0.020749,0.028226,0.020749,0.020749
+30-Aug-2019 14:00:00,1,0.030965,0.030965,0.030965,0.057623,0.031878,0.031878,0.031878,0.031878,0.057623,0.057623,0.057623,0.057623,0.057623,0.031878,0.016708,0.030965,0.016708,0.016708
+30-Aug-2019 15:00:00,1,0.0291,0.0291,0.0291,0.069064,0.052504,0.052504,0.052504,0.052504,0.069064,0.069064,0.069064,0.069064,0.069064,0.052504,0.010725,0.0291,0.010725,0.010725
+30-Aug-2019 16:00:00,1,0.026591,0.026591,0.026591,0.086331,0.028127,0.028127,0.028127,0.028127,0.086331,0.086331,0.086331,0.086331,0.086331,0.028127,0.015285,0.026591,0.015285,0.015285
+30-Aug-2019 17:00:00,1,0.028087,0.028087,0.028087,0.096368,0.026252,0.026252,0.026252,0.026252,0.096368,0.096368,0.096368,0.096368,0.096368,0.026252,0.013524,0.028087,0.013524,0.013524
+30-Aug-2019 18:00:00,1,0.023529,0.023529,0.023529,0.071665,0.016876,0.016876,0.016876,0.016876,0.071665,0.071665,0.071665,0.071665,0.071665,0.016876,0.0078571,0.023529,0.0078571,0.0078571
+30-Aug-2019 19:00:00,1,0.020697,0.020697,0.020697,0.060379,0.022502,0.022502,0.022502,0.022502,0.060379,0.060379,0.060379,0.060379,0.060379,0.022502,0.015285,0.020697,0.015285,0.015285
+30-Aug-2019 20:00:00,1,0.02003,0.02003,0.02003,0.072653,0.037503,0.037503,0.037503,0.037503,0.072653,0.072653,0.072653,0.072653,0.072653,0.037503,0.015172,0.02003,0.015172,0.015172
+30-Aug-2019 21:00:00,1,0.015886,0.015886,0.015886,0.075097,0.052504,0.052504,0.052504,0.052504,0.075097,0.075097,0.075097,0.075097,0.075097,0.052504,0.023684,0.015886,0.023684,0.023684
+30-Aug-2019 22:00:00,1,0.015886,0.015886,0.015886,0.060119,0.041253,0.041253,0.041253,0.041253,0.060119,0.060119,0.060119,0.060119,0.060119,0.041253,0.038811,0.015886,0.038811,0.038811
+30-Aug-2019 23:00:00,1,0.018878,0.018878,0.018878,0.062044,0.045004,0.045004,0.045004,0.045004,0.062044,0.062044,0.062044,0.062044,0.062044,0.045004,0.050371,0.018878,0.050371,0.050371
+31-Aug-2019 00:00:00,1,0.021779,0.021779,0.021779,0.053723,0.050629,0.050629,0.050629,0.050629,0.053723,0.053723,0.053723,0.053723,0.053723,0.050629,0.04861,0.021779,0.04861,0.04861
+31-Aug-2019 01:00:00,1,0.023805,0.023805,0.023805,0.080558,0.037503,0.037503,0.037503,0.037503,0.080558,0.080558,0.080558,0.080558,0.080558,0.037503,0.044501,0.023805,0.044501,0.044501
+31-Aug-2019 02:00:00,1,0.025025,0.025025,0.025025,0.073017,0.018752,0.018752,0.018752,0.018752,0.073017,0.073017,0.073017,0.073017,0.073017,0.018752,0.034612,0.025025,0.034612,0.034612
+31-Aug-2019 03:00:00,1,0.026131,0.026131,0.026131,0.074889,0.016876,0.016876,0.016876,0.016876,0.074889,0.074889,0.074889,0.074889,0.074889,0.016876,0.033348,0.026131,0.033348,0.033348
+31-Aug-2019 04:00:00,1,0.02689,0.02689,0.02689,0.07749,0.037503,0.037503,0.037503,0.037503,0.07749,0.07749,0.07749,0.07749,0.07749,0.037503,0.03407,0.02689,0.03407,0.03407
+31-Aug-2019 05:00:00,1,0.043812,0.043812,0.043812,0.06506,0.030003,0.030003,0.030003,0.030003,0.06506,0.06506,0.06506,0.06506,0.06506,0.030003,0.042446,0.043812,0.042446,0.042446
+31-Aug-2019 06:00:00,1,0.05431,0.05431,0.05431,0.08477,0.035628,0.035628,0.035628,0.035628,0.08477,0.08477,0.08477,0.08477,0.08477,0.035628,0.027116,0.05431,0.027116,0.027116
+31-Aug-2019 07:00:00,1,0.053228,0.053228,0.053228,0.10282,0.045004,0.045004,0.045004,0.045004,0.10282,0.10282,0.10282,0.10282,0.10282,0.045004,0.04064,0.053228,0.04064,0.04064
+31-Aug-2019 08:00:00,1,0.066259,0.066259,0.066259,0.077229,0.041253,0.041253,0.041253,0.041253,0.077229,0.077229,0.077229,0.077229,0.077229,0.041253,0.041182,0.066259,0.041182,0.041182
+31-Aug-2019 09:00:00,1,0.078069,0.078069,0.078069,0.091271,0.045004,0.045004,0.045004,0.045004,0.091271,0.091271,0.091271,0.091271,0.091271,0.045004,0.028719,0.078069,0.028719,0.028719
+31-Aug-2019 10:00:00,1,0.10779,0.10779,0.10779,0.10583,0.039378,0.039378,0.039378,0.039378,0.10583,0.10583,0.10583,0.10583,0.10583,0.039378,0.02102,0.10779,0.02102,0.02102
+31-Aug-2019 11:00:00,1,0.15688,0.15688,0.15688,0.171,0.031878,0.031878,0.031878,0.031878,0.171,0.171,0.171,0.171,0.171,0.031878,0.021675,0.15688,0.021675,0.021675
+31-Aug-2019 12:00:00,1,0.18595,0.18595,0.18595,0.25031,0.026252,0.026252,0.026252,0.026252,0.25031,0.25031,0.25031,0.25031,0.25031,0.026252,0.016753,0.18595,0.016753,0.016753
+31-Aug-2019 13:00:00,1,0.19196,0.19196,0.19196,0.27012,0.037503,0.037503,0.037503,0.037503,0.27012,0.27012,0.27012,0.27012,0.27012,0.037503,0.016211,0.19196,0.016211,0.016211
+31-Aug-2019 14:00:00,1,0.21798,0.21798,0.21798,0.37164,0.063755,0.063755,0.063755,0.063755,0.37164,0.37164,0.37164,0.37164,0.37164,0.063755,0.022397,0.21798,0.022397,0.022397
+31-Aug-2019 15:00:00,1,0.22159,0.22159,0.22159,0.40331,0.050629,0.050629,0.050629,0.050629,0.40331,0.40331,0.40331,0.40331,0.40331,0.050629,0.022217,0.22159,0.022217,0.022217
+31-Aug-2019 16:00:00,1,0.2203,0.2203,0.2203,0.42375,0.020627,0.020627,0.020627,0.020627,0.42375,0.42375,0.42375,0.42375,0.42375,0.020627,0.027748,0.2203,0.027748,0.027748
+31-Aug-2019 17:00:00,1,0.19965,0.19965,0.19965,0.42271,0.013126,0.013126,0.013126,0.013126,0.42271,0.42271,0.42271,0.42271,0.42271,0.013126,0.025468,0.19965,0.025468,0.025468
+31-Aug-2019 18:00:00,1,0.17615,0.17615,0.17615,0.33747,0.039378,0.039378,0.039378,0.039378,0.33747,0.33747,0.33747,0.33747,0.33747,0.039378,0.039873,0.17615,0.039873,0.039873
+31-Aug-2019 19:00:00,1,0.1443,0.1443,0.1443,0.26346,0.031878,0.031878,0.031878,0.031878,0.26346,0.26346,0.26346,0.26346,0.26346,0.031878,0.046217,0.1443,0.046217,0.046217
+31-Aug-2019 20:00:00,1,0.1342,0.1342,0.1342,0.21837,0.016876,0.016876,0.016876,0.016876,0.21837,0.21837,0.21837,0.21837,0.21837,0.016876,0.051997,0.1342,0.051997,0.051997
+31-Aug-2019 21:00:00,1,0.14081,0.14081,0.14081,0.21655,0.0093758,0.0093758,0.0093758,0.0093758,0.21655,0.21655,0.21655,0.21655,0.21655,0.0093758,0.063015,0.14081,0.063015,0.063015
+31-Aug-2019 22:00:00,1,0.1283,0.1283,0.1283,0.18509,0.015001,0.015001,0.015001,0.015001,0.18509,0.18509,0.18509,0.18509,0.18509,0.015001,0.074417,0.1283,0.074417,0.074417
+31-Aug-2019 23:00:00,1,0.13206,0.13206,0.13206,0.16548,0.026252,0.026252,0.026252,0.026252,0.16548,0.16548,0.16548,0.16548,0.16548,0.026252,0.07392,0.13206,0.07392,0.07392
+01-Sep-2019 00:00:00,1,0.12604,0.12604,0.12604,0.135,0.031723,0.031723,0.031723,0.031723,0.135,0.135,0.135,0.135,0.135,0.031723,0.080782,0.12604,0.080782,0.080782
+01-Sep-2019 01:00:00,1,0.10476,0.10476,0.10476,0.10636,0.035455,0.035455,0.035455,0.035455,0.10636,0.10636,0.10636,0.10636,0.10636,0.035455,0.096333,0.10476,0.096333,0.096333
+01-Sep-2019 02:00:00,1,0.10646,0.10646,0.10646,0.09975,0.041053,0.041053,0.041053,0.041053,0.09975,0.09975,0.09975,0.09975,0.09975,0.041053,0.091127,0.10646,0.091127,0.091127
+01-Sep-2019 03:00:00,1,0.11289,0.11289,0.11289,0.096293,0.044785,0.044785,0.044785,0.044785,0.096293,0.096293,0.096293,0.096293,0.096293,0.044785,0.096692,0.11289,0.096692,0.096692
+01-Sep-2019 04:00:00,1,0.11115,0.11115,0.11115,0.10723,0.054115,0.054115,0.054115,0.054115,0.10723,0.10723,0.10723,0.10723,0.10723,0.054115,0.093461,0.11115,0.093461,0.093461
+01-Sep-2019 05:00:00,1,0.097411,0.097411,0.097411,0.11585,0.044785,0.044785,0.044785,0.044785,0.11585,0.11585,0.11585,0.11585,0.11585,0.044785,0.07892,0.097411,0.07892,0.07892
+01-Sep-2019 06:00:00,1,0.086669,0.086669,0.086669,0.11048,0.063445,0.063445,0.063445,0.063445,0.11048,0.11048,0.11048,0.11048,0.11048,0.063445,0.072974,0.086669,0.072974,0.072974
+01-Sep-2019 07:00:00,1,0.072469,0.072469,0.072469,0.092165,0.055981,0.055981,0.055981,0.055981,0.092165,0.092165,0.092165,0.092165,0.092165,0.055981,0.066466,0.072469,0.066466,0.066466
+01-Sep-2019 08:00:00,1,0.046221,0.046221,0.046221,0.082979,0.048517,0.048517,0.048517,0.048517,0.082979,0.082979,0.082979,0.082979,0.082979,0.048517,0.036734,0.046221,0.036734,0.036734
+01-Sep-2019 09:00:00,1,0.038021,0.038021,0.038021,0.068943,0.052249,0.052249,0.052249,0.052249,0.068943,0.068943,0.068943,0.068943,0.068943,0.052249,0.022754,0.038021,0.022754,0.022754
+01-Sep-2019 10:00:00,1,0.045236,0.045236,0.045236,0.056455,0.050383,0.050383,0.050383,0.050383,0.056455,0.056455,0.056455,0.056455,0.056455,0.050383,0.02769,0.045236,0.02769,0.02769
+01-Sep-2019 11:00:00,1,0.052405,0.052405,0.052405,0.064247,0.046651,0.046651,0.046651,0.046651,0.064247,0.064247,0.064247,0.064247,0.064247,0.046651,0.036734,0.052405,0.036734,0.036734
+01-Sep-2019 12:00:00,1,0.059642,0.059642,0.059642,0.051139,0.08024,0.08024,0.08024,0.08024,0.051139,0.051139,0.051139,0.051139,0.051139,0.08024,0.041468,0.059642,0.041468,0.041468
+01-Sep-2019 13:00:00,1,0.061841,0.061841,0.061841,0.06048,0.069044,0.069044,0.069044,0.069044,0.06048,0.06048,0.06048,0.06048,0.06048,0.069044,0.040055,0.061841,0.040055,0.040055
+01-Sep-2019 14:00:00,1,0.05978,0.05978,0.05978,0.058467,0.065312,0.065312,0.065312,0.065312,0.058467,0.058467,0.058467,0.058467,0.058467,0.065312,0.042299,0.05978,0.042299,0.042299
+01-Sep-2019 15:00:00,1,0.050756,0.050756,0.050756,0.048869,0.082106,0.082106,0.082106,0.082106,0.048869,0.048869,0.048869,0.048869,0.048869,0.082106,0.039269,0.050756,0.039269,0.039269
+01-Sep-2019 16:00:00,1,0.044961,0.044961,0.044961,0.045102,0.08024,0.08024,0.08024,0.08024,0.045102,0.045102,0.045102,0.045102,0.045102,0.08024,0.041468,0.044961,0.041468,0.041468
+01-Sep-2019 17:00:00,1,0.039029,0.039029,0.039029,0.045308,0.055981,0.055981,0.055981,0.055981,0.045308,0.045308,0.045308,0.045308,0.045308,0.055981,0.030271,0.039029,0.030271,0.030271
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+11-Sep-2019 01:00:00,1,0.10137,0.10137,0.10137,0.094177,0.027991,0.027991,0.027991,0.027991,0.094177,0.094177,0.094177,0.094177,0.094177,0.027991,0.079257,0.10137,0.079257,0.079257
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+13-Sep-2019 21:00:00,1,0.060581,0.060581,0.060581,0.024821,0.001866,0.001866,0.001866,0.001866,0.024821,0.024821,0.024821,0.024821,0.024821,0.001866,0.017234,0.060581,0.017234,0.017234
+13-Sep-2019 22:00:00,1,0.08502,0.08502,0.08502,0.034626,0.001866,0.001866,0.001866,0.001866,0.034626,0.034626,0.034626,0.034626,0.034626,0.001866,0.033749,0.08502,0.033749,0.033749
+13-Sep-2019 23:00:00,1,0.13195,0.13195,0.13195,0.051397,0.001866,0.001866,0.001866,0.001866,0.051397,0.051397,0.051397,0.051397,0.051397,0.001866,0.039045,0.13195,0.039045,0.039045
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+14-Sep-2019 01:00:00,1,0.21056,0.21056,0.21056,0.10837,0.0055981,0.0055981,0.0055981,0.0055981,0.10837,0.10837,0.10837,0.10837,0.10837,0.0055981,0.082017,0.21056,0.082017,0.082017
+14-Sep-2019 02:00:00,1,0.24711,0.24711,0.24711,0.1191,0.0055981,0.0055981,0.0055981,0.0055981,0.1191,0.1191,0.1191,0.1191,0.1191,0.0055981,0.11788,0.24711,0.11788,0.11788
+14-Sep-2019 03:00:00,1,0.26113,0.26113,0.26113,0.16606,0.0093302,0.0093302,0.0093302,0.0093302,0.16606,0.16606,0.16606,0.16606,0.16606,0.0093302,0.16538,0.26113,0.16538,0.16538
+14-Sep-2019 04:00:00,1,0.30854,0.30854,0.30854,0.21214,0.016794,0.016794,0.016794,0.016794,0.21214,0.21214,0.21214,0.21214,0.21214,0.016794,0.21524,0.30854,0.21524,0.21524
+14-Sep-2019 05:00:00,1,0.37801,0.37801,0.37801,0.25528,0.029857,0.029857,0.029857,0.029857,0.25528,0.25528,0.25528,0.25528,0.25528,0.029857,0.24933,0.37801,0.24933,0.24933
+14-Sep-2019 06:00:00,1,0.42627,0.42627,0.42627,0.32541,0.044785,0.044785,0.044785,0.044785,0.32541,0.32541,0.32541,0.32541,0.32541,0.044785,0.2695,0.42627,0.2695,0.2695
+14-Sep-2019 07:00:00,1,0.46005,0.46005,0.46005,0.39033,0.052249,0.052249,0.052249,0.052249,0.39033,0.39033,0.39033,0.39033,0.39033,0.052249,0.28355,0.46005,0.28355,0.28355
+14-Sep-2019 08:00:00,1,0.49177,0.49177,0.49177,0.4071,0.054115,0.054115,0.054115,0.054115,0.4071,0.4071,0.4071,0.4071,0.4071,0.054115,0.2588,0.49177,0.2588,0.2588
+14-Sep-2019 09:00:00,1,0.53509,0.53509,0.53509,0.40297,0.024259,0.024259,0.024259,0.024259,0.40297,0.40297,0.40297,0.40297,0.40297,0.024259,0.28568,0.53509,0.28568,0.28568
+14-Sep-2019 10:00:00,1,0.56062,0.56062,0.56062,0.50804,0.057847,0.057847,0.057847,0.057847,0.50804,0.50804,0.50804,0.50804,0.50804,0.057847,0.39999,0.56062,0.39999,0.39999
+14-Sep-2019 11:00:00,1,0.58296,0.58296,0.58296,0.52466,0.055981,0.055981,0.055981,0.055981,0.52466,0.52466,0.52466,0.52466,0.52466,0.055981,0.51048,0.58296,0.51048,0.51048
+14-Sep-2019 12:00:00,1,0.61479,0.61479,0.61479,0.48869,0.074642,0.074642,0.074642,0.074642,0.48869,0.48869,0.48869,0.48869,0.48869,0.074642,0.56925,0.61479,0.56925,0.56925
+14-Sep-2019 13:00:00,1,0.606,0.606,0.606,0.48296,0.085838,0.085838,0.085838,0.085838,0.48296,0.48296,0.48296,0.48296,0.48296,0.085838,0.56819,0.606,0.56819,0.56819
+14-Sep-2019 14:00:00,1,0.58142,0.58142,0.58142,0.43801,0.11756,0.11756,0.11756,0.11756,0.43801,0.43801,0.43801,0.43801,0.43801,0.11756,0.5705,0.58142,0.5705,0.5705
+14-Sep-2019 15:00:00,1,0.56392,0.56392,0.56392,0.42305,0.13995,0.13995,0.13995,0.13995,0.42305,0.42305,0.42305,0.42305,0.42305,0.13995,0.55379,0.56392,0.55379,0.55379
+14-Sep-2019 16:00:00,1,0.50467,0.50467,0.50467,0.36917,0.15675,0.15675,0.15675,0.15675,0.36917,0.36917,0.36917,0.36917,0.36917,0.15675,0.60223,0.50467,0.60223,0.60223
+14-Sep-2019 17:00:00,1,0.4707,0.4707,0.4707,0.32449,0.13249,0.13249,0.13249,0.13249,0.32449,0.32449,0.32449,0.32449,0.32449,0.13249,0.61285,0.4707,0.61285,0.61285
+14-Sep-2019 18:00:00,1,0.42904,0.42904,0.42904,0.25126,0.11756,0.11756,0.11756,0.11756,0.25126,0.25126,0.25126,0.25126,0.25126,0.11756,0.53797,0.42904,0.53797,0.53797
+14-Sep-2019 19:00:00,1,0.39441,0.39441,0.39441,0.21725,0.12316,0.12316,0.12316,0.12316,0.21725,0.21725,0.21725,0.21725,0.21725,0.12316,0.48615,0.39441,0.48615,0.48615
+14-Sep-2019 20:00:00,1,0.35611,0.35611,0.35611,0.2559,0.12316,0.12316,0.12316,0.12316,0.2559,0.2559,0.2559,0.2559,0.2559,0.12316,0.44399,0.35611,0.44399,0.44399
+14-Sep-2019 21:00:00,1,0.33165,0.33165,0.33165,0.24378,0.061579,0.061579,0.061579,0.061579,0.24378,0.24378,0.24378,0.24378,0.24378,0.061579,0.42034,0.33165,0.42034,0.42034
+14-Sep-2019 22:00:00,1,0.30685,0.30685,0.30685,0.22654,0.093302,0.093302,0.093302,0.093302,0.22654,0.22654,0.22654,0.22654,0.22654,0.093302,0.42512,0.30685,0.42512,0.42512
+14-Sep-2019 23:00:00,1,0.30323,0.30323,0.30323,0.19202,0.08024,0.08024,0.08024,0.08024,0.19202,0.19202,0.19202,0.19202,0.19202,0.08024,0.39193,0.30323,0.39193,0.39193
+15-Sep-2019 00:00:00,1,0.30701,0.30701,0.30701,0.17721,0.082106,0.082106,0.082106,0.082106,0.17721,0.17721,0.17721,0.17721,0.17721,0.082106,0.36879,0.30701,0.36879,0.36879
+15-Sep-2019 01:00:00,1,0.31603,0.31603,0.31603,0.15455,0.072776,0.072776,0.072776,0.072776,0.15455,0.15455,0.15455,0.15455,0.15455,0.072776,0.36354,0.31603,0.36354,0.36354
+15-Sep-2019 02:00:00,1,0.3035,0.3035,0.3035,0.12375,0.035455,0.035455,0.035455,0.035455,0.12375,0.12375,0.12375,0.12375,0.12375,0.035455,0.34577,0.3035,0.34577,0.34577
+15-Sep-2019 03:00:00,1,0.28962,0.28962,0.28962,0.12679,0.033589,0.033589,0.033589,0.033589,0.12679,0.12679,0.12679,0.12679,0.12679,0.033589,0.33769,0.28962,0.33769,0.33769
+15-Sep-2019 04:00:00,1,0.28074,0.28074,0.28074,0.1254,0.020526,0.020526,0.020526,0.020526,0.1254,0.1254,0.1254,0.1254,0.1254,0.020526,0.32959,0.28074,0.32959,0.32959
+15-Sep-2019 05:00:00,1,0.24665,0.24665,0.24665,0.11023,0.024259,0.024259,0.024259,0.024259,0.11023,0.11023,0.11023,0.11023,0.11023,0.024259,0.33422,0.24665,0.33422,0.33422
+15-Sep-2019 06:00:00,1,0.20671,0.20671,0.20671,0.10501,0.016794,0.016794,0.016794,0.016794,0.10501,0.10501,0.10501,0.10501,0.10501,0.016794,0.33668,0.20671,0.33668,0.33668
+15-Sep-2019 07:00:00,1,0.20611,0.20611,0.20611,0.086385,0.033589,0.033589,0.033589,0.033589,0.086385,0.086385,0.086385,0.086385,0.086385,0.033589,0.30035,0.20611,0.30035,0.30035
+15-Sep-2019 08:00:00,1,0.17977,0.17977,0.17977,0.082721,0.01866,0.01866,0.01866,0.01866,0.082721,0.082721,0.082721,0.082721,0.082721,0.01866,0.29836,0.17977,0.29836,0.29836
+15-Sep-2019 09:00:00,1,0.18891,0.18891,0.18891,0.052636,0.0074642,0.0074642,0.0074642,0.0074642,0.052636,0.052636,0.052636,0.052636,0.052636,0.0074642,0.30069,0.18891,0.30069,0.30069
+15-Sep-2019 10:00:00,1,0.2167,0.2167,0.2167,0.044173,0.0037321,0.0037321,0.0037321,0.0037321,0.044173,0.044173,0.044173,0.044173,0.044173,0.0037321,0.32795,0.2167,0.32795,0.32795
+15-Sep-2019 11:00:00,1,0.24693,0.24693,0.24693,0.068582,0.0055981,0.0055981,0.0055981,0.0055981,0.068582,0.068582,0.068582,0.068582,0.068582,0.0055981,0.3633,0.24693,0.3633,0.3633
+15-Sep-2019 12:00:00,1,0.24894,0.24894,0.24894,0.069304,0.0037321,0.0037321,0.0037321,0.0037321,0.069304,0.069304,0.069304,0.069304,0.069304,0.0037321,0.35091,0.24894,0.35091,0.35091
+15-Sep-2019 13:00:00,1,0.24061,0.24061,0.24061,0.06404,0.001866,0.001866,0.001866,0.001866,0.06404,0.06404,0.06404,0.06404,0.06404,0.001866,0.35757,0.24061,0.35757,0.35757
+15-Sep-2019 14:00:00,1,0.23894,0.23894,0.23894,0.072555,0.001866,0.001866,0.001866,0.001866,0.072555,0.072555,0.072555,0.072555,0.072555,0.001866,0.33033,0.23894,0.33033,0.33033
+15-Sep-2019 15:00:00,1,0.2281,0.2281,0.2281,0.082773,0.001866,0.001866,0.001866,0.001866,0.082773,0.082773,0.082773,0.082773,0.082773,0.001866,0.29643,0.2281,0.29643,0.29643
+15-Sep-2019 16:00:00,1,0.21255,0.21255,0.21255,0.062183,0.001866,0.001866,0.001866,0.001866,0.062183,0.062183,0.062183,0.062183,0.062183,0.001866,0.25619,0.21255,0.25619,0.25619
+15-Sep-2019 17:00:00,1,0.17748,0.17748,0.17748,0.035968,0.001866,0.001866,0.001866,0.001866,0.035968,0.035968,0.035968,0.035968,0.035968,0.001866,0.1722,0.17748,0.1722,0.1722
+15-Sep-2019 18:00:00,1,0.12904,0.12904,0.12904,0.027402,0.001866,0.001866,0.001866,0.001866,0.027402,0.027402,0.027402,0.027402,0.027402,0.001866,0.11175,0.12904,0.11175,0.11175
+15-Sep-2019 19:00:00,1,0.10195,0.10195,0.10195,0.03122,0.022393,0.022393,0.022393,0.022393,0.03122,0.03122,0.03122,0.03122,0.03122,0.022393,0.10152,0.10195,0.10152,0.10152
+15-Sep-2019 20:00:00,1,0.087723,0.087723,0.087723,0.042109,0.020526,0.020526,0.020526,0.020526,0.042109,0.042109,0.042109,0.042109,0.042109,0.020526,0.10174,0.087723,0.10174,0.10174
+15-Sep-2019 21:00:00,1,0.098259,0.098259,0.098259,0.045669,0.020526,0.020526,0.020526,0.020526,0.045669,0.045669,0.045669,0.045669,0.045669,0.020526,0.10816,0.098259,0.10816,0.10816
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+15-Sep-2019 23:00:00,1,0.08928,0.08928,0.08928,0.083289,0.016794,0.016794,0.016794,0.016794,0.083289,0.083289,0.083289,0.083289,0.083289,0.016794,0.093147,0.08928,0.093147,0.093147
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+16-Sep-2019 01:00:00,1,0.087494,0.087494,0.087494,0.12266,0.0093302,0.0093302,0.0093302,0.0093302,0.12266,0.12266,0.12266,0.12266,0.12266,0.0093302,0.078651,0.087494,0.078651,0.078651
+16-Sep-2019 02:00:00,1,0.095121,0.095121,0.095121,0.14929,0.011196,0.011196,0.011196,0.011196,0.14929,0.14929,0.14929,0.14929,0.14929,0.011196,0.076945,0.095121,0.076945,0.076945
+16-Sep-2019 03:00:00,1,0.10508,0.10508,0.10508,0.15073,0.013062,0.013062,0.013062,0.013062,0.15073,0.15073,0.15073,0.15073,0.15073,0.013062,0.075217,0.10508,0.075217,0.075217
+16-Sep-2019 04:00:00,1,0.12476,0.12476,0.12476,0.15223,0.0093302,0.0093302,0.0093302,0.0093302,0.15223,0.15223,0.15223,0.15223,0.15223,0.0093302,0.068732,0.12476,0.068732,0.068732
+16-Sep-2019 05:00:00,1,0.14688,0.14688,0.14688,0.16426,0.013062,0.013062,0.013062,0.013062,0.16426,0.16426,0.16426,0.16426,0.16426,0.013062,0.069585,0.14688,0.069585,0.069585
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+16-Sep-2019 07:00:00,1,0.16922,0.16922,0.16922,0.1594,0.0093302,0.0093302,0.0093302,0.0093302,0.1594,0.1594,0.1594,0.1594,0.1594,0.0093302,0.072233,0.16922,0.072233,0.072233
+16-Sep-2019 08:00:00,1,0.14849,0.14849,0.14849,0.12782,0.0037321,0.0037321,0.0037321,0.0037321,0.12782,0.12782,0.12782,0.12782,0.12782,0.0037321,0.061238,0.14849,0.061238,0.061238
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+16-Sep-2019 12:00:00,1,0.19326,0.19326,0.19326,0.088914,0.001866,0.001866,0.001866,0.001866,0.088914,0.088914,0.088914,0.088914,0.088914,0.001866,0.13118,0.19326,0.13118,0.13118
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+16-Sep-2019 17:00:00,1,0.12045,0.12045,0.12045,0.052946,0.0074642,0.0074642,0.0074642,0.0074642,0.052946,0.052946,0.052946,0.052946,0.052946,0.0074642,0.096759,0.12045,0.096759,0.096759
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+16-Sep-2019 22:00:00,1,0.11287,0.11287,0.11287,0.063215,0.033589,0.033589,0.033589,0.033589,0.063215,0.063215,0.063215,0.063215,0.063215,0.033589,0.16953,0.11287,0.16953,0.16953
+16-Sep-2019 23:00:00,1,0.11301,0.11301,0.11301,0.11146,0.022393,0.022393,0.022393,0.022393,0.11146,0.11146,0.11146,0.11146,0.11146,0.022393,0.16525,0.11301,0.16525,0.16525
+17-Sep-2019 00:00:00,1,0.13926,0.13926,0.13926,0.14568,0.020526,0.020526,0.020526,0.020526,0.14568,0.14568,0.14568,0.14568,0.14568,0.020526,0.17173,0.13926,0.17173,0.17173
+17-Sep-2019 01:00:00,1,0.16356,0.16356,0.16356,0.16353,0.033589,0.033589,0.033589,0.033589,0.16353,0.16353,0.16353,0.16353,0.16353,0.033589,0.18762,0.16356,0.18762,0.18762
+17-Sep-2019 02:00:00,1,0.20676,0.20676,0.20676,0.12849,0.041053,0.041053,0.041053,0.041053,0.12849,0.12849,0.12849,0.12849,0.12849,0.041053,0.19345,0.20676,0.19345,0.19345
+17-Sep-2019 03:00:00,1,0.23635,0.23635,0.23635,0.15765,0.044785,0.044785,0.044785,0.044785,0.15765,0.15765,0.15765,0.15765,0.15765,0.044785,0.1959,0.23635,0.1959,0.1959
+17-Sep-2019 04:00:00,1,0.23181,0.23181,0.23181,0.14537,0.042919,0.042919,0.042919,0.042919,0.14537,0.14537,0.14537,0.14537,0.14537,0.042919,0.20386,0.23181,0.20386,0.20386
+17-Sep-2019 05:00:00,1,0.22281,0.22281,0.22281,0.13396,0.039187,0.039187,0.039187,0.039187,0.13396,0.13396,0.13396,0.13396,0.13396,0.039187,0.20501,0.22281,0.20501,0.20501
+17-Sep-2019 06:00:00,1,0.19769,0.19769,0.19769,0.10687,0.041053,0.041053,0.041053,0.041053,0.10687,0.10687,0.10687,0.10687,0.10687,0.041053,0.19581,0.19769,0.19581,0.19581
+17-Sep-2019 07:00:00,1,0.16141,0.16141,0.16141,0.10187,0.037321,0.037321,0.037321,0.037321,0.10187,0.10187,0.10187,0.10187,0.10187,0.037321,0.19168,0.16141,0.19168,0.19168
+17-Sep-2019 08:00:00,1,0.13328,0.13328,0.13328,0.084424,0.014928,0.014928,0.014928,0.014928,0.084424,0.084424,0.084424,0.084424,0.084424,0.014928,0.15023,0.13328,0.15023,0.15023
+17-Sep-2019 09:00:00,1,0.1224,0.1224,0.1224,0.075032,0.013062,0.013062,0.013062,0.013062,0.075032,0.075032,0.075032,0.075032,0.075032,0.013062,0.088075,0.1224,0.088075,0.088075
+17-Sep-2019 10:00:00,1,0.13898,0.13898,0.13898,0.057022,0.011196,0.011196,0.011196,0.011196,0.057022,0.057022,0.057022,0.057022,0.057022,0.011196,0.067476,0.13898,0.067476,0.067476
+17-Sep-2019 11:00:00,1,0.15332,0.15332,0.15332,0.066414,0.0074642,0.0074642,0.0074642,0.0074642,0.066414,0.066414,0.066414,0.066414,0.066414,0.0074642,0.064559,0.15332,0.064559,0.064559
+17-Sep-2019 12:00:00,1,0.14693,0.14693,0.14693,0.086282,0.014928,0.014928,0.014928,0.014928,0.086282,0.086282,0.086282,0.086282,0.086282,0.014928,0.07607,0.14693,0.07607,0.07607
+17-Sep-2019 13:00:00,1,0.14645,0.14645,0.14645,0.088294,0.0074642,0.0074642,0.0074642,0.0074642,0.088294,0.088294,0.088294,0.088294,0.088294,0.0074642,0.095032,0.14645,0.095032,0.095032
+17-Sep-2019 14:00:00,1,0.13978,0.13978,0.13978,0.07689,0.011196,0.011196,0.011196,0.011196,0.07689,0.07689,0.07689,0.07689,0.07689,0.011196,0.085966,0.13978,0.085966,0.085966
+17-Sep-2019 15:00:00,1,0.11903,0.11903,0.11903,0.088862,0.0074642,0.0074642,0.0074642,0.0074642,0.088862,0.088862,0.088862,0.088862,0.088862,0.0074642,0.076384,0.11903,0.076384,0.076384
+17-Sep-2019 16:00:00,1,0.091227,0.091227,0.091227,0.070336,0.001866,0.001866,0.001866,0.001866,0.070336,0.070336,0.070336,0.070336,0.070336,0.001866,0.058163,0.091227,0.058163,0.058163
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+17-Sep-2019 18:00:00,1,0.044755,0.044755,0.044755,0.052791,0.0037321,0.0037321,0.0037321,0.0037321,0.052791,0.052791,0.052791,0.052791,0.052791,0.0037321,0.014877,0.044755,0.014877,0.014877
+17-Sep-2019 19:00:00,1,0.036509,0.036509,0.036509,0.058312,0,0,0,0,0.058312,0.058312,0.058312,0.058312,0.058312,0,0.016628,0.036509,0.016628,0.016628
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+17-Sep-2019 22:00:00,1,0.08234,0.08234,0.08234,0.097944,0.001866,0.001866,0.001866,0.001866,0.097944,0.097944,0.097944,0.097944,0.097944,0.001866,0.024818,0.08234,0.024818,0.024818
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+18-Sep-2019 00:00:00,1,0.12984,0.12984,0.12984,0.10207,0.011196,0.011196,0.011196,0.011196,0.10207,0.10207,0.10207,0.10207,0.10207,0.011196,0.0333,0.12984,0.0333,0.0333
+18-Sep-2019 01:00:00,1,0.15124,0.15124,0.15124,0.091958,0.016794,0.016794,0.016794,0.016794,0.091958,0.091958,0.091958,0.091958,0.091958,0.016794,0.040683,0.15124,0.040683,0.040683
+18-Sep-2019 02:00:00,1,0.15224,0.15224,0.15224,0.077303,0.027991,0.027991,0.027991,0.027991,0.077303,0.077303,0.077303,0.077303,0.077303,0.027991,0.054259,0.15224,0.054259,0.054259
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+18-Sep-2019 05:00:00,1,0.17561,0.17561,0.17561,0.088346,0.037321,0.037321,0.037321,0.037321,0.088346,0.088346,0.088346,0.088346,0.088346,0.037321,0.14716,0.17561,0.14716,0.14716
+18-Sep-2019 06:00:00,1,0.17627,0.17627,0.17627,0.10212,0.042919,0.042919,0.042919,0.042919,0.10212,0.10212,0.10212,0.10212,0.10212,0.042919,0.17487,0.17627,0.17487,0.17487
+18-Sep-2019 07:00:00,1,0.16395,0.16395,0.16395,0.10176,0.01866,0.01866,0.01866,0.01866,0.10176,0.10176,0.10176,0.10176,0.10176,0.01866,0.18483,0.16395,0.18483,0.18483
+18-Sep-2019 08:00:00,1,0.14622,0.14622,0.14622,0.085043,0.0093302,0.0093302,0.0093302,0.0093302,0.085043,0.085043,0.085043,0.085043,0.085043,0.0093302,0.14272,0.14622,0.14272,0.14272
+18-Sep-2019 09:00:00,1,0.1628,0.1628,0.1628,0.092732,0.0037321,0.0037321,0.0037321,0.0037321,0.092732,0.092732,0.092732,0.092732,0.092732,0.0037321,0.073041,0.1628,0.073041,0.073041
+18-Sep-2019 10:00:00,1,0.19682,0.19682,0.19682,0.11616,0.0055981,0.0055981,0.0055981,0.0055981,0.11616,0.11616,0.11616,0.11616,0.11616,0.0055981,0.072727,0.19682,0.072727,0.072727
+18-Sep-2019 11:00:00,1,0.23506,0.23506,0.23506,0.14779,0.020526,0.020526,0.020526,0.020526,0.14779,0.14779,0.14779,0.14779,0.14779,0.020526,0.083879,0.23506,0.083879,0.083879
+18-Sep-2019 12:00:00,1,0.26507,0.26507,0.26507,0.16059,0.026125,0.026125,0.026125,0.026125,0.16059,0.16059,0.16059,0.16059,0.16059,0.026125,0.12254,0.26507,0.12254,0.12254
+18-Sep-2019 13:00:00,1,0.28818,0.28818,0.28818,0.15559,0.031723,0.031723,0.031723,0.031723,0.15559,0.15559,0.15559,0.15559,0.15559,0.031723,0.14718,0.28818,0.14718,0.14718
+18-Sep-2019 14:00:00,1,0.2803,0.2803,0.2803,0.15362,0.046651,0.046651,0.046651,0.046651,0.15362,0.15362,0.15362,0.15362,0.15362,0.046651,0.17287,0.2803,0.17287,0.17287
+18-Sep-2019 15:00:00,1,0.27496,0.27496,0.27496,0.15904,0.033589,0.033589,0.033589,0.033589,0.15904,0.15904,0.15904,0.15904,0.15904,0.033589,0.18957,0.27496,0.18957,0.18957
+18-Sep-2019 16:00:00,1,0.23889,0.23889,0.23889,0.19124,0.061579,0.061579,0.061579,0.061579,0.19124,0.19124,0.19124,0.19124,0.19124,0.061579,0.17198,0.23889,0.17198,0.17198
+18-Sep-2019 17:00:00,1,0.24617,0.24617,0.24617,0.17824,0.10263,0.10263,0.10263,0.10263,0.17824,0.17824,0.17824,0.17824,0.17824,0.10263,0.14247,0.24617,0.14247,0.14247
+18-Sep-2019 18:00:00,1,0.21956,0.21956,0.21956,0.12437,0.15115,0.15115,0.15115,0.15115,0.12437,0.12437,0.12437,0.12437,0.12437,0.15115,0.13188,0.21956,0.13188,0.13188
+18-Sep-2019 19:00:00,1,0.20481,0.20481,0.20481,0.13071,0.15675,0.15675,0.15675,0.15675,0.13071,0.13071,0.13071,0.13071,0.13071,0.15675,0.15187,0.20481,0.15187,0.15187
+18-Sep-2019 20:00:00,1,0.19592,0.19592,0.19592,0.13448,0.087704,0.087704,0.087704,0.087704,0.13448,0.13448,0.13448,0.13448,0.13448,0.087704,0.17243,0.19592,0.17243,0.17243
+18-Sep-2019 21:00:00,1,0.20066,0.20066,0.20066,0.13954,0.072776,0.072776,0.072776,0.072776,0.13954,0.13954,0.13954,0.13954,0.13954,0.072776,0.17229,0.20066,0.17229,0.17229
+18-Sep-2019 22:00:00,1,0.20055,0.20055,0.20055,0.12777,0.063445,0.063445,0.063445,0.063445,0.12777,0.12777,0.12777,0.12777,0.12777,0.063445,0.16282,0.20055,0.16282,0.16282
+18-Sep-2019 23:00:00,1,0.20094,0.20094,0.20094,0.13691,0.027991,0.027991,0.027991,0.027991,0.13691,0.13691,0.13691,0.13691,0.13691,0.027991,0.16982,0.20094,0.16982,0.16982
+19-Sep-2019 00:00:00,1,0.19125,0.19125,0.19125,0.12277,0.065312,0.065312,0.065312,0.065312,0.12277,0.12277,0.12277,0.12277,0.12277,0.065312,0.14415,0.19125,0.14415,0.14415
+19-Sep-2019 01:00:00,1,0.18999,0.18999,0.18999,0.12075,0.055981,0.055981,0.055981,0.055981,0.12075,0.12075,0.12075,0.12075,0.12075,0.055981,0.11792,0.18999,0.11792,0.11792
+19-Sep-2019 02:00:00,1,0.1803,0.1803,0.1803,0.11497,0.01866,0.01866,0.01866,0.01866,0.11497,0.11497,0.11497,0.11497,0.11497,0.01866,0.11823,0.1803,0.11823,0.11823
+19-Sep-2019 03:00:00,1,0.18616,0.18616,0.18616,0.092939,0.054115,0.054115,0.054115,0.054115,0.092939,0.092939,0.092939,0.092939,0.092939,0.054115,0.14132,0.18616,0.14132,0.14132
+19-Sep-2019 04:00:00,1,0.19317,0.19317,0.19317,0.084321,0.042919,0.042919,0.042919,0.042919,0.084321,0.084321,0.084321,0.084321,0.084321,0.042919,0.14597,0.19317,0.14597,0.14597
+19-Sep-2019 05:00:00,1,0.20856,0.20856,0.20856,0.1125,0.020526,0.020526,0.020526,0.020526,0.1125,0.1125,0.1125,0.1125,0.1125,0.020526,0.15872,0.20856,0.15872,0.15872
+19-Sep-2019 06:00:00,1,0.21528,0.21528,0.21528,0.11575,0.029857,0.029857,0.029857,0.029857,0.11575,0.11575,0.11575,0.11575,0.11575,0.029857,0.16269,0.21528,0.16269,0.16269
+19-Sep-2019 07:00:00,1,0.21285,0.21285,0.21285,0.10568,0.026125,0.026125,0.026125,0.026125,0.10568,0.10568,0.10568,0.10568,0.10568,0.026125,0.17171,0.21285,0.17171,0.17171
+19-Sep-2019 08:00:00,1,0.18314,0.18314,0.18314,0.08334,0.0093302,0.0093302,0.0093302,0.0093302,0.08334,0.08334,0.08334,0.08334,0.08334,0.0093302,0.15447,0.18314,0.15447,0.15447
+19-Sep-2019 09:00:00,1,0.18488,0.18488,0.18488,0.076838,0.016794,0.016794,0.016794,0.016794,0.076838,0.076838,0.076838,0.076838,0.076838,0.016794,0.15463,0.18488,0.15463,0.15463
+19-Sep-2019 10:00:00,1,0.22741,0.22741,0.22741,0.11399,0.041053,0.041053,0.041053,0.041053,0.11399,0.11399,0.11399,0.11399,0.11399,0.041053,0.20144,0.22741,0.20144,0.20144
+19-Sep-2019 11:00:00,1,0.26005,0.26005,0.26005,0.15698,0.039187,0.039187,0.039187,0.039187,0.15698,0.15698,0.15698,0.15698,0.15698,0.039187,0.27309,0.26005,0.27309,0.27309
+19-Sep-2019 12:00:00,1,0.29558,0.29558,0.29558,0.19816,0.050383,0.050383,0.050383,0.050383,0.19816,0.19816,0.19816,0.19816,0.19816,0.050383,0.2962,0.29558,0.2962,0.2962
+19-Sep-2019 13:00:00,1,0.27703,0.27703,0.27703,0.18082,0.11569,0.11569,0.11569,0.11569,0.18082,0.18082,0.18082,0.18082,0.18082,0.11569,0.27002,0.27703,0.27002,0.27002
+19-Sep-2019 14:00:00,1,0.26431,0.26431,0.26431,0.23165,0.13249,0.13249,0.13249,0.13249,0.23165,0.23165,0.23165,0.23165,0.23165,0.13249,0.26079,0.26431,0.26079,0.26079
+19-Sep-2019 15:00:00,1,0.29482,0.29482,0.29482,0.16018,0.078374,0.078374,0.078374,0.078374,0.16018,0.16018,0.16018,0.16018,0.16018,0.078374,0.23335,0.29482,0.23335,0.23335
+19-Sep-2019 16:00:00,1,0.2819,0.2819,0.2819,0.13897,0.050383,0.050383,0.050383,0.050383,0.13897,0.13897,0.13897,0.13897,0.13897,0.050383,0.21674,0.2819,0.21674,0.21674
+19-Sep-2019 17:00:00,1,0.22824,0.22824,0.22824,0.10682,0.020526,0.020526,0.020526,0.020526,0.10682,0.10682,0.10682,0.10682,0.10682,0.020526,0.18095,0.22824,0.18095,0.18095
+19-Sep-2019 18:00:00,1,0.1667,0.1667,0.1667,0.094693,0.020526,0.020526,0.020526,0.020526,0.094693,0.094693,0.094693,0.094693,0.094693,0.020526,0.15613,0.1667,0.15613,0.15613
+19-Sep-2019 19:00:00,1,0.11949,0.11949,0.11949,0.11079,0.026125,0.026125,0.026125,0.026125,0.11079,0.11079,0.11079,0.11079,0.11079,0.026125,0.13825,0.11949,0.13825,0.13825
+19-Sep-2019 20:00:00,1,0.13314,0.13314,0.13314,0.12602,0.037321,0.037321,0.037321,0.037321,0.12602,0.12602,0.12602,0.12602,0.12602,0.037321,0.13854,0.13314,0.13854,0.13854
+19-Sep-2019 21:00:00,1,0.14501,0.14501,0.14501,0.12313,0.031723,0.031723,0.031723,0.031723,0.12313,0.12313,0.12313,0.12313,0.12313,0.031723,0.15187,0.14501,0.15187,0.15187
+19-Sep-2019 22:00:00,1,0.14448,0.14448,0.14448,0.10527,0.035455,0.035455,0.035455,0.035455,0.10527,0.10527,0.10527,0.10527,0.10527,0.035455,0.15546,0.14448,0.15546,0.15546
+19-Sep-2019 23:00:00,1,0.15827,0.15827,0.15827,0.10935,0.033589,0.033589,0.033589,0.033589,0.10935,0.10935,0.10935,0.10935,0.10935,0.033589,0.17079,0.15827,0.17079,0.17079
+20-Sep-2019 00:00:00,1,0.16147,0.16147,0.16147,0.11699,0.031723,0.031723,0.031723,0.031723,0.11699,0.11699,0.11699,0.11699,0.11699,0.031723,0.19134,0.16147,0.19134,0.19134
+20-Sep-2019 01:00:00,1,0.15435,0.15435,0.15435,0.11828,0.013062,0.013062,0.013062,0.013062,0.11828,0.11828,0.11828,0.11828,0.11828,0.013062,0.19175,0.15435,0.19175,0.19175
+20-Sep-2019 02:00:00,1,0.16173,0.16173,0.16173,0.12111,0.0093302,0.0093302,0.0093302,0.0093302,0.12111,0.12111,0.12111,0.12111,0.12111,0.0093302,0.17685,0.16173,0.17685,0.17685
+20-Sep-2019 03:00:00,1,0.1511,0.1511,0.1511,0.11461,0.001866,0.001866,0.001866,0.001866,0.11461,0.11461,0.11461,0.11461,0.11461,0.001866,0.15925,0.1511,0.15925,0.15925
+20-Sep-2019 04:00:00,1,0.16207,0.16207,0.16207,0.10713,0.001866,0.001866,0.001866,0.001866,0.10713,0.10713,0.10713,0.10713,0.10713,0.001866,0.15524,0.16207,0.15524,0.15524
+20-Sep-2019 05:00:00,1,0.16924,0.16924,0.16924,0.10997,0.001866,0.001866,0.001866,0.001866,0.10997,0.10997,0.10997,0.10997,0.10997,0.001866,0.15131,0.16924,0.15131,0.15131
+20-Sep-2019 06:00:00,1,0.16901,0.16901,0.16901,0.11322,0,0,0,0,0.11322,0.11322,0.11322,0.11322,0.11322,0,0.14126,0.16901,0.14126,0.14126
+20-Sep-2019 07:00:00,1,0.17705,0.17705,0.17705,0.11322,0,0,0,0,0.11322,0.11322,0.11322,0.11322,0.11322,0,0.14653,0.17705,0.14653,0.14653
+20-Sep-2019 08:00:00,1,0.15728,0.15728,0.15728,0.093816,0,0,0,0,0.093816,0.093816,0.093816,0.093816,0.093816,0,0.10082,0.15728,0.10082,0.10082
+20-Sep-2019 09:00:00,1,0.1369,0.1369,0.1369,0.061305,0,0,0,0,0.061305,0.061305,0.061305,0.061305,0.061305,0,0.059555,0.1369,0.059555,0.059555
+20-Sep-2019 10:00:00,1,0.15082,0.15082,0.15082,0.052791,0,0,0,0,0.052791,0.052791,0.052791,0.052791,0.052791,0,0.057378,0.15082,0.057378,0.057378
+20-Sep-2019 11:00:00,1,0.18353,0.18353,0.18353,0.054132,0,0,0,0,0.054132,0.054132,0.054132,0.054132,0.054132,0,0.09272,0.18353,0.09272,0.09272
+20-Sep-2019 12:00:00,1,0.21709,0.21709,0.21709,0.074722,0,0,0,0,0.074722,0.074722,0.074722,0.074722,0.074722,0,0.12375,0.21709,0.12375,0.12375
+20-Sep-2019 13:00:00,1,0.26422,0.26422,0.26422,0.097428,0,0,0,0,0.097428,0.097428,0.097428,0.097428,0.097428,0,0.13147,0.26422,0.13147,0.13147
+20-Sep-2019 14:00:00,1,0.26376,0.26376,0.26376,0.1078,0,0,0,0,0.1078,0.1078,0.1078,0.1078,0.1078,0,0.14153,0.26376,0.14153,0.14153
+20-Sep-2019 15:00:00,1,0.25236,0.25236,0.25236,0.099957,0,0,0,0,0.099957,0.099957,0.099957,0.099957,0.099957,0,0.14144,0.25236,0.14144,0.14144
+20-Sep-2019 16:00:00,1,0.20206,0.20206,0.20206,0.079676,0,0,0,0,0.079676,0.079676,0.079676,0.079676,0.079676,0,0.1286,0.20206,0.1286,0.1286
+20-Sep-2019 17:00:00,1,0.16145,0.16145,0.16145,0.066569,0.001866,0.001866,0.001866,0.001866,0.066569,0.066569,0.066569,0.066569,0.066569,0.001866,0.091666,0.16145,0.091666,0.091666
+20-Sep-2019 18:00:00,1,0.11393,0.11393,0.11393,0.052068,0.0074642,0.0074642,0.0074642,0.0074642,0.052068,0.052068,0.052068,0.052068,0.052068,0.0074642,0.0688,0.11393,0.0688,0.0688
+20-Sep-2019 19:00:00,1,0.11054,0.11054,0.11054,0.063008,0.0074642,0.0074642,0.0074642,0.0074642,0.063008,0.063008,0.063008,0.063008,0.063008,0.0074642,0.074791,0.11054,0.074791,0.074791
+20-Sep-2019 20:00:00,1,0.13733,0.13733,0.13733,0.075187,0.011196,0.011196,0.011196,0.011196,0.075187,0.075187,0.075187,0.075187,0.075187,0.011196,0.090499,0.13733,0.090499,0.090499
+20-Sep-2019 21:00:00,1,0.16995,0.16995,0.16995,0.093042,0.011196,0.011196,0.011196,0.011196,0.093042,0.093042,0.093042,0.093042,0.093042,0.011196,0.10939,0.16995,0.10939,0.10939
+20-Sep-2019 22:00:00,1,0.17769,0.17769,0.17769,0.10264,0.013062,0.013062,0.013062,0.013062,0.10264,0.10264,0.10264,0.10264,0.10264,0.013062,0.11877,0.17769,0.11877,0.11877
+20-Sep-2019 23:00:00,1,0.19109,0.19109,0.19109,0.11141,0.052249,0.052249,0.052249,0.052249,0.11141,0.11141,0.11141,0.11141,0.11141,0.052249,0.12429,0.19109,0.12429,0.12429
+21-Sep-2019 00:00:00,1,0.20284,0.20284,0.20284,0.1256,0.052249,0.052249,0.052249,0.052249,0.1256,0.1256,0.1256,0.1256,0.1256,0.052249,0.13646,0.20284,0.13646,0.13646
+21-Sep-2019 01:00:00,1,0.21528,0.21528,0.21528,0.13696,0.052249,0.052249,0.052249,0.052249,0.13696,0.13696,0.13696,0.13696,0.13696,0.052249,0.13347,0.21528,0.13347,0.13347
+21-Sep-2019 02:00:00,1,0.21619,0.21619,0.21619,0.14557,0.054115,0.054115,0.054115,0.054115,0.14557,0.14557,0.14557,0.14557,0.14557,0.054115,0.12741,0.21619,0.12741,0.12741
+21-Sep-2019 03:00:00,1,0.21741,0.21741,0.21741,0.15615,0.055981,0.055981,0.055981,0.055981,0.15615,0.15615,0.15615,0.15615,0.15615,0.055981,0.12048,0.21741,0.12048,0.12048
+21-Sep-2019 04:00:00,1,0.21589,0.21589,0.21589,0.14986,0.059713,0.059713,0.059713,0.059713,0.14986,0.14986,0.14986,0.14986,0.14986,0.059713,0.117,0.21589,0.117,0.117
+21-Sep-2019 05:00:00,1,0.21995,0.21995,0.21995,0.13907,0.055981,0.055981,0.055981,0.055981,0.13907,0.13907,0.13907,0.13907,0.13907,0.055981,0.11034,0.21995,0.11034,0.11034
+21-Sep-2019 06:00:00,1,0.22551,0.22551,0.22551,0.14898,0.048517,0.048517,0.048517,0.048517,0.14898,0.14898,0.14898,0.14898,0.14898,0.048517,0.1106,0.22551,0.1106,0.1106
+21-Sep-2019 07:00:00,1,0.22386,0.22386,0.22386,0.14702,0.033589,0.033589,0.033589,0.033589,0.14702,0.14702,0.14702,0.14702,0.14702,0.033589,0.10551,0.22386,0.10551,0.10551
+21-Sep-2019 08:00:00,1,0.20973,0.20973,0.20973,0.1223,0.0055981,0.0055981,0.0055981,0.0055981,0.1223,0.1223,0.1223,0.1223,0.1223,0.0055981,0.095682,0.20973,0.095682,0.095682
+21-Sep-2019 09:00:00,1,0.18071,0.18071,0.18071,0.084321,0.011196,0.011196,0.011196,0.011196,0.084321,0.084321,0.084321,0.084321,0.084321,0.011196,0.06126,0.18071,0.06126,0.06126
+21-Sep-2019 10:00:00,1,0.16413,0.16413,0.16413,0.045463,0.029857,0.029857,0.029857,0.029857,0.045463,0.045463,0.045463,0.045463,0.045463,0.029857,0.041401,0.16413,0.041401,0.041401
+21-Sep-2019 11:00:00,1,0.17155,0.17155,0.17155,0.025647,0.046651,0.046651,0.046651,0.046651,0.025647,0.025647,0.025647,0.025647,0.025647,0.046651,0.075554,0.17155,0.075554,0.075554
+21-Sep-2019 12:00:00,1,0.19317,0.19317,0.19317,0.037052,0.057847,0.057847,0.057847,0.057847,0.037052,0.037052,0.037052,0.037052,0.037052,0.057847,0.1308,0.19317,0.1308,0.1308
+21-Sep-2019 13:00:00,1,0.20799,0.20799,0.20799,0.034936,0.076508,0.076508,0.076508,0.076508,0.034936,0.034936,0.034936,0.034936,0.034936,0.076508,0.16717,0.20799,0.16717,0.16717
+21-Sep-2019 14:00:00,1,0.20854,0.20854,0.20854,0.026886,0.046651,0.046651,0.046651,0.046651,0.026886,0.026886,0.026886,0.026886,0.026886,0.046651,0.17689,0.20854,0.17689,0.17689
+21-Sep-2019 15:00:00,1,0.18184,0.18184,0.18184,0.025286,0.08024,0.08024,0.08024,0.08024,0.025286,0.025286,0.025286,0.025286,0.025286,0.08024,0.17032,0.18184,0.17032,0.17032
+21-Sep-2019 16:00:00,1,0.14301,0.14301,0.14301,0.015791,0.026125,0.026125,0.026125,0.026125,0.015791,0.015791,0.015791,0.015791,0.015791,0.026125,0.14083,0.14301,0.14083,0.14083
+21-Sep-2019 17:00:00,1,0.10302,0.10302,0.10302,0.010063,0.048517,0.048517,0.048517,0.048517,0.010063,0.010063,0.010063,0.010063,0.010063,0.048517,0.11,0.10302,0.11,0.11
+21-Sep-2019 18:00:00,1,0.078561,0.078561,0.078561,0.0099079,0.061579,0.061579,0.061579,0.061579,0.0099079,0.0099079,0.0099079,0.0099079,0.0099079,0.061579,0.10048,0.078561,0.10048,0.10048
+21-Sep-2019 19:00:00,1,0.073293,0.073293,0.073293,0.064144,0.044785,0.044785,0.044785,0.044785,0.064144,0.064144,0.064144,0.064144,0.064144,0.044785,0.13796,0.073293,0.13796,0.13796
+21-Sep-2019 20:00:00,1,0.073018,0.073018,0.073018,0.039322,0.054115,0.054115,0.054115,0.054115,0.039322,0.039322,0.039322,0.039322,0.039322,0.054115,0.16798,0.073018,0.16798,0.16798
+21-Sep-2019 21:00:00,1,0.072904,0.072904,0.072904,0.033955,0.054115,0.054115,0.054115,0.054115,0.033955,0.033955,0.033955,0.033955,0.033955,0.054115,0.17054,0.072904,0.17054,0.17054
+21-Sep-2019 22:00:00,1,0.056207,0.056207,0.056207,0.049953,0.046651,0.046651,0.046651,0.046651,0.049953,0.049953,0.049953,0.049953,0.049953,0.046651,0.15636,0.056207,0.15636,0.15636
+21-Sep-2019 23:00:00,1,0.044159,0.044159,0.044159,0.082927,0.035455,0.035455,0.035455,0.035455,0.082927,0.082927,0.082927,0.082927,0.082927,0.035455,0.15533,0.044159,0.15533,0.15533
+22-Sep-2019 00:00:00,1,0.034379,0.034379,0.034379,0.072916,0.046651,0.046651,0.046651,0.046651,0.072916,0.072916,0.072916,0.072916,0.072916,0.046651,0.15459,0.034379,0.15459,0.15459
+22-Sep-2019 01:00:00,1,0.041502,0.041502,0.041502,0.11074,0.052249,0.052249,0.052249,0.052249,0.11074,0.11074,0.11074,0.11074,0.11074,0.052249,0.11211,0.041502,0.11211,0.11211
+22-Sep-2019 02:00:00,1,0.06404,0.06404,0.06404,0.18242,0.091436,0.091436,0.091436,0.091436,0.18242,0.18242,0.18242,0.18242,0.18242,0.091436,0.093708,0.06404,0.093708,0.093708
+22-Sep-2019 03:00:00,1,0.064269,0.064269,0.064269,0.17602,0.10636,0.10636,0.10636,0.10636,0.17602,0.17602,0.17602,0.17602,0.17602,0.10636,0.1041,0.064269,0.1041,0.1041
+22-Sep-2019 04:00:00,1,0.074919,0.074919,0.074919,0.12689,0.097034,0.097034,0.097034,0.097034,0.12689,0.12689,0.12689,0.12689,0.12689,0.097034,0.10286,0.074919,0.10286,0.10286
+22-Sep-2019 05:00:00,1,0.10236,0.10236,0.10236,0.11157,0.11383,0.11383,0.11383,0.11383,0.11157,0.11157,0.11157,0.11157,0.11157,0.11383,0.10888,0.10236,0.10888,0.10888
+22-Sep-2019 06:00:00,1,0.13351,0.13351,0.13351,0.14041,0.095168,0.095168,0.095168,0.095168,0.14041,0.14041,0.14041,0.14041,0.14041,0.095168,0.13623,0.13351,0.13623,0.13623
+22-Sep-2019 07:00:00,1,0.16837,0.16837,0.16837,0.13035,0.12689,0.12689,0.12689,0.12689,0.13035,0.13035,0.13035,0.13035,0.13035,0.12689,0.15239,0.16837,0.15239,0.15239
+22-Sep-2019 08:00:00,1,0.1926,0.1926,0.1926,0.13484,0.095168,0.095168,0.095168,0.095168,0.13484,0.13484,0.13484,0.13484,0.13484,0.095168,0.14438,0.1926,0.14438,0.14438
+22-Sep-2019 09:00:00,1,0.22856,0.22856,0.22856,0.11415,0.091436,0.091436,0.091436,0.091436,0.11415,0.11415,0.11415,0.11415,0.11415,0.091436,0.17413,0.22856,0.17413,0.17413
+22-Sep-2019 10:00:00,1,0.25884,0.25884,0.25884,0.11146,0.067178,0.067178,0.067178,0.067178,0.11146,0.11146,0.11146,0.11146,0.11146,0.067178,0.28945,0.25884,0.28945,0.28945
+22-Sep-2019 11:00:00,1,0.32267,0.32267,0.32267,0.15332,0.072776,0.072776,0.072776,0.072776,0.15332,0.15332,0.15332,0.15332,0.15332,0.072776,0.41767,0.32267,0.41767,0.41767
+22-Sep-2019 12:00:00,1,0.36502,0.36502,0.36502,0.15595,0.069044,0.069044,0.069044,0.069044,0.15595,0.15595,0.15595,0.15595,0.15595,0.069044,0.53298,0.36502,0.53298,0.53298
+22-Sep-2019 13:00:00,1,0.36976,0.36976,0.36976,0.15719,0.055981,0.055981,0.055981,0.055981,0.15719,0.15719,0.15719,0.15719,0.15719,0.055981,0.63383,0.36976,0.63383,0.63383
+22-Sep-2019 14:00:00,1,0.36525,0.36525,0.36525,0.10218,0.054115,0.054115,0.054115,0.054115,0.10218,0.10218,0.10218,0.10218,0.10218,0.054115,0.63724,0.36525,0.63724,0.63724
+22-Sep-2019 15:00:00,1,0.34123,0.34123,0.34123,0.11152,0.046651,0.046651,0.046651,0.046651,0.11152,0.11152,0.11152,0.11152,0.11152,0.046651,0.66661,0.34123,0.66661,0.66661
+22-Sep-2019 16:00:00,1,0.29862,0.29862,0.29862,0.08463,0.020526,0.020526,0.020526,0.020526,0.08463,0.08463,0.08463,0.08463,0.08463,0.020526,0.65806,0.29862,0.65806,0.65806
+22-Sep-2019 17:00:00,1,0.26457,0.26457,0.26457,0.073742,0.0093302,0.0093302,0.0093302,0.0093302,0.073742,0.073742,0.073742,0.073742,0.073742,0.0093302,0.55852,0.26457,0.55852,0.55852
+22-Sep-2019 18:00:00,1,0.17183,0.17183,0.17183,0.050211,0.0037321,0.0037321,0.0037321,0.0037321,0.050211,0.050211,0.050211,0.050211,0.050211,0.0037321,0.43706,0.17183,0.43706,0.43706
+22-Sep-2019 19:00:00,1,0.1442,0.1442,0.1442,0.037206,0.026125,0.026125,0.026125,0.026125,0.037206,0.037206,0.037206,0.037206,0.037206,0.026125,0.32048,0.1442,0.32048,0.32048
+22-Sep-2019 20:00:00,1,0.13271,0.13271,0.13271,0.044276,0.014928,0.014928,0.014928,0.014928,0.044276,0.044276,0.044276,0.044276,0.044276,0.014928,0.28092,0.13271,0.28092,0.28092
+22-Sep-2019 21:00:00,1,0.12712,0.12712,0.12712,0.056351,0.020526,0.020526,0.020526,0.020526,0.056351,0.056351,0.056351,0.056351,0.056351,0.020526,0.31023,0.12712,0.31023,0.31023
+22-Sep-2019 22:00:00,1,0.11988,0.11988,0.11988,0.064505,0.020526,0.020526,0.020526,0.020526,0.064505,0.064505,0.064505,0.064505,0.064505,0.020526,0.30305,0.11988,0.30305,0.30305
+22-Sep-2019 23:00:00,1,0.11608,0.11608,0.11608,0.070491,0.026125,0.026125,0.026125,0.026125,0.070491,0.070491,0.070491,0.070491,0.070491,0.026125,0.29443,0.11608,0.29443,0.29443
+23-Sep-2019 00:00:00,1,0.1022,0.1022,0.1022,0.072658,0.026125,0.026125,0.026125,0.026125,0.072658,0.072658,0.072658,0.072658,0.072658,0.026125,0.26723,0.1022,0.26723,0.26723
+23-Sep-2019 01:00:00,1,0.093357,0.093357,0.093357,0.074774,0.026125,0.026125,0.026125,0.026125,0.074774,0.074774,0.074774,0.074774,0.074774,0.026125,0.23885,0.093357,0.23885,0.23885
+23-Sep-2019 02:00:00,1,0.091296,0.091296,0.091296,0.053513,0.041053,0.041053,0.041053,0.041053,0.053513,0.053513,0.053513,0.053513,0.053513,0.041053,0.21158,0.091296,0.21158,0.21158
+23-Sep-2019 03:00:00,1,0.09764,0.09764,0.09764,0.060738,0.042919,0.042919,0.042919,0.042919,0.060738,0.060738,0.060738,0.060738,0.060738,0.042919,0.20308,0.09764,0.20308,0.20308
+23-Sep-2019 04:00:00,1,0.07989,0.07989,0.07989,0.065795,0.037321,0.037321,0.037321,0.037321,0.065795,0.065795,0.065795,0.065795,0.065795,0.037321,0.19617,0.07989,0.19617,0.19617
+23-Sep-2019 05:00:00,1,0.06956,0.06956,0.06956,0.066517,0.054115,0.054115,0.054115,0.054115,0.066517,0.066517,0.066517,0.066517,0.066517,0.054115,0.17572,0.06956,0.17572,0.17572
+23-Sep-2019 06:00:00,1,0.066468,0.066468,0.066468,0.074774,0.046651,0.046651,0.046651,0.046651,0.074774,0.074774,0.074774,0.074774,0.074774,0.046651,0.16893,0.066468,0.16893,0.16893
+23-Sep-2019 07:00:00,1,0.062345,0.062345,0.062345,0.088965,0.041053,0.041053,0.041053,0.041053,0.088965,0.088965,0.088965,0.088965,0.088965,0.041053,0.1694,0.062345,0.1694,0.1694
+23-Sep-2019 08:00:00,1,0.052611,0.052611,0.052611,0.08236,0.01866,0.01866,0.01866,0.01866,0.08236,0.08236,0.08236,0.08236,0.08236,0.01866,0.11507,0.052611,0.11507,0.11507
+23-Sep-2019 09:00:00,1,0.030325,0.030325,0.030325,0.069252,0.016794,0.016794,0.016794,0.016794,0.069252,0.069252,0.069252,0.069252,0.069252,0.016794,0.068014,0.030325,0.068014,0.068014
+23-Sep-2019 10:00:00,1,0.019285,0.019285,0.019285,0.059912,0.026125,0.026125,0.026125,0.026125,0.059912,0.059912,0.059912,0.059912,0.059912,0.026125,0.068373,0.019285,0.068373,0.068373
+23-Sep-2019 11:00:00,1,0.020545,0.020545,0.020545,0.074671,0.029857,0.029857,0.029857,0.029857,0.074671,0.074671,0.074671,0.074671,0.074671,0.029857,0.090319,0.020545,0.090319,0.090319
+23-Sep-2019 12:00:00,1,0.022286,0.022286,0.022286,0.0949,0.027991,0.027991,0.027991,0.027991,0.0949,0.0949,0.0949,0.0949,0.0949,0.027991,0.07699,0.022286,0.07699,0.07699
+23-Sep-2019 13:00:00,1,0.020637,0.020637,0.020637,0.089378,0.029857,0.029857,0.029857,0.029857,0.089378,0.089378,0.089378,0.089378,0.089378,0.029857,0.064245,0.020637,0.064245,0.064245
+23-Sep-2019 14:00:00,1,0.020522,0.020522,0.020522,0.084011,0.035455,0.035455,0.035455,0.035455,0.084011,0.084011,0.084011,0.084011,0.084011,0.035455,0.04811,0.020522,0.04811,0.04811
+23-Sep-2019 15:00:00,1,0.024324,0.024324,0.024324,0.10723,0.033589,0.033589,0.033589,0.033589,0.10723,0.10723,0.10723,0.10723,0.10723,0.033589,0.031572,0.024324,0.031572,0.031572
+23-Sep-2019 16:00:00,1,0.031745,0.031745,0.031745,0.15497,0.037321,0.037321,0.037321,0.037321,0.15497,0.15497,0.15497,0.15497,0.15497,0.037321,0.023023,0.031745,0.023023,0.023023
+23-Sep-2019 17:00:00,1,0.048511,0.048511,0.048511,0.1834,0.063445,0.063445,0.063445,0.063445,0.1834,0.1834,0.1834,0.1834,0.1834,0.063445,0.018737,0.048511,0.018737,0.018737
+23-Sep-2019 18:00:00,1,0.067269,0.067269,0.067269,0.23722,0.061579,0.061579,0.061579,0.061579,0.23722,0.23722,0.23722,0.23722,0.23722,0.061579,0.021901,0.067269,0.021901,0.021901
+23-Sep-2019 19:00:00,1,0.10893,0.10893,0.10893,0.29935,0.091436,0.091436,0.091436,0.091436,0.29935,0.29935,0.29935,0.29935,0.29935,0.091436,0.030495,0.10893,0.030495,0.030495
+23-Sep-2019 20:00:00,1,0.14503,0.14503,0.14503,0.25766,0.091436,0.091436,0.091436,0.091436,0.25766,0.25766,0.25766,0.25766,0.25766,0.091436,0.057243,0.14503,0.057243,0.057243
+23-Sep-2019 21:00:00,1,0.18197,0.18197,0.18197,0.41866,0.085838,0.085838,0.085838,0.085838,0.41866,0.41866,0.41866,0.41866,0.41866,0.085838,0.089152,0.18197,0.089152,0.089152
+23-Sep-2019 22:00:00,1,0.22011,0.22011,0.22011,0.38264,0.093302,0.093302,0.093302,0.093302,0.38264,0.38264,0.38264,0.38264,0.38264,0.093302,0.10345,0.22011,0.10345,0.10345
+23-Sep-2019 23:00:00,1,0.22817,0.22817,0.22817,0.37418,0.12689,0.12689,0.12689,0.12689,0.37418,0.37418,0.37418,0.37418,0.37418,0.12689,0.1089,0.22817,0.1089,0.1089
+24-Sep-2019 00:00:00,1,0.26512,0.26512,0.26512,0.39441,0.095168,0.095168,0.095168,0.095168,0.39441,0.39441,0.39441,0.39441,0.39441,0.095168,0.12957,0.26512,0.12957,0.12957
+24-Sep-2019 01:00:00,1,0.3213,0.3213,0.3213,0.38785,0.08024,0.08024,0.08024,0.08024,0.38785,0.38785,0.38785,0.38785,0.38785,0.08024,0.1547,0.3213,0.1547,0.1547
+24-Sep-2019 02:00:00,1,0.38577,0.38577,0.38577,0.35075,0.059713,0.059713,0.059713,0.059713,0.35075,0.35075,0.35075,0.35075,0.35075,0.059713,0.19482,0.38577,0.19482,0.19482
+24-Sep-2019 03:00:00,1,0.42673,0.42673,0.42673,0.33099,0.072776,0.072776,0.072776,0.072776,0.33099,0.33099,0.33099,0.33099,0.33099,0.072776,0.21538,0.42673,0.21538,0.21538
+24-Sep-2019 04:00:00,1,0.45295,0.45295,0.45295,0.30235,0.037321,0.037321,0.037321,0.037321,0.30235,0.30235,0.30235,0.30235,0.30235,0.037321,0.22004,0.45295,0.22004,0.22004
+24-Sep-2019 05:00:00,1,0.4778,0.4778,0.4778,0.27092,0.041053,0.041053,0.041053,0.041053,0.27092,0.27092,0.27092,0.27092,0.27092,0.041053,0.25577,0.4778,0.25577,0.25577
+24-Sep-2019 06:00:00,1,0.48373,0.48373,0.48373,0.32113,0.050383,0.050383,0.050383,0.050383,0.32113,0.32113,0.32113,0.32113,0.32113,0.050383,0.27814,0.48373,0.27814,0.27814
+24-Sep-2019 07:00:00,1,0.52164,0.52164,0.52164,0.30978,0.12689,0.12689,0.12689,0.12689,0.30978,0.30978,0.30978,0.30978,0.30978,0.12689,0.29997,0.52164,0.29997,0.29997
+24-Sep-2019 08:00:00,1,0.54819,0.54819,0.54819,0.37139,0.52996,0.52996,0.52996,0.52996,0.37139,0.37139,0.37139,0.37139,0.37139,0.52996,0.2883,0.54819,0.2883,0.2883
+24-Sep-2019 09:00:00,1,0.55522,0.55522,0.55522,0.42873,0.33589,0.33589,0.33589,0.33589,0.42873,0.42873,0.42873,0.42873,0.42873,0.33589,0.24724,0.55522,0.24724,0.24724
+24-Sep-2019 10:00:00,1,0.52773,0.52773,0.52773,0.46485,0.21273,0.21273,0.21273,0.21273,0.46485,0.46485,0.46485,0.46485,0.46485,0.21273,0.25384,0.52773,0.25384,0.25384
+24-Sep-2019 11:00:00,1,0.4565,0.4565,0.4565,0.49963,0.16048,0.16048,0.16048,0.16048,0.49963,0.49963,0.49963,0.49963,0.49963,0.16048,0.24172,0.4565,0.24172,0.24172
+24-Sep-2019 12:00:00,1,0.40144,0.40144,0.40144,0.49075,0.24632,0.24632,0.24632,0.24632,0.49075,0.49075,0.49075,0.49075,0.49075,0.24632,0.24239,0.40144,0.24239,0.24239
+24-Sep-2019 13:00:00,1,0.38264,0.38264,0.38264,0.52213,0.30976,0.30976,0.30976,0.30976,0.52213,0.52213,0.52213,0.52213,0.52213,0.30976,0.2653,0.38264,0.2653,0.2653
+24-Sep-2019 14:00:00,1,0.36885,0.36885,0.36885,0.52383,0.39,0.39,0.39,0.39,0.52383,0.52383,0.52383,0.52383,0.52383,0.39,0.27444,0.36885,0.27444,0.27444
+24-Sep-2019 15:00:00,1,0.37547,0.37547,0.37547,0.62456,0.43479,0.43479,0.43479,0.43479,0.62456,0.62456,0.62456,0.62456,0.62456,0.43479,0.26254,0.37547,0.26254,0.26254
+24-Sep-2019 16:00:00,1,0.39377,0.39377,0.39377,0.66455,0.4124,0.4124,0.4124,0.4124,0.66455,0.66455,0.66455,0.66455,0.66455,0.4124,0.26387,0.39377,0.26387,0.26387
+24-Sep-2019 17:00:00,1,0.42499,0.42499,0.42499,0.70718,0.35268,0.35268,0.35268,0.35268,0.70718,0.70718,0.70718,0.70718,0.70718,0.35268,0.26099,0.42499,0.26099,0.26099
+24-Sep-2019 18:00:00,1,0.47769,0.47769,0.47769,0.73603,0.27431,0.27431,0.27431,0.27431,0.73603,0.73603,0.73603,0.73603,0.73603,0.27431,0.27311,0.47769,0.27311,0.27311
+24-Sep-2019 19:00:00,1,0.57176,0.57176,0.57176,0.70161,0.27058,0.27058,0.27058,0.27058,0.70161,0.70161,0.70161,0.70161,0.70161,0.27058,0.35196,0.57176,0.35196,0.35196
+24-Sep-2019 20:00:00,1,0.57441,0.57441,0.57441,0.69939,0.23512,0.23512,0.23512,0.23512,0.69939,0.69939,0.69939,0.69939,0.69939,0.23512,0.42247,0.57441,0.42247,0.42247
+24-Sep-2019 21:00:00,1,0.54425,0.54425,0.54425,0.68571,0.1922,0.1922,0.1922,0.1922,0.68571,0.68571,0.68571,0.68571,0.68571,0.1922,0.43118,0.54425,0.43118,0.43118
+24-Sep-2019 22:00:00,1,0.43406,0.43406,0.43406,0.68127,0.18101,0.18101,0.18101,0.18101,0.68127,0.68127,0.68127,0.68127,0.68127,0.18101,0.39687,0.43406,0.39687,0.39687
+24-Sep-2019 23:00:00,1,0.41198,0.41198,0.41198,0.60985,0.18101,0.18101,0.18101,0.18101,0.60985,0.60985,0.60985,0.60985,0.60985,0.18101,0.33071,0.41198,0.33071,0.33071
+25-Sep-2019 00:00:00,1,0.39894,0.39894,0.39894,0.56279,0.25378,0.25378,0.25378,0.25378,0.56279,0.56279,0.56279,0.56279,0.56279,0.25378,0.29618,0.39894,0.29618,0.29618
+25-Sep-2019 01:00:00,1,0.37625,0.37625,0.37625,0.50928,0.42359,0.42359,0.42359,0.42359,0.50928,0.50928,0.50928,0.50928,0.50928,0.42359,0.27271,0.37625,0.27271,0.27271
+25-Sep-2019 02:00:00,1,0.36298,0.36298,0.36298,0.50056,0.22206,0.22206,0.22206,0.22206,0.50056,0.50056,0.50056,0.50056,0.50056,0.22206,0.23871,0.36298,0.23871,0.23871
+25-Sep-2019 03:00:00,1,0.36285,0.36285,0.36285,0.48348,0.19034,0.19034,0.19034,0.19034,0.48348,0.48348,0.48348,0.48348,0.48348,0.19034,0.25072,0.36285,0.25072,0.25072
+25-Sep-2019 04:00:00,1,0.341,0.341,0.341,0.48755,0.16235,0.16235,0.16235,0.16235,0.48755,0.48755,0.48755,0.48755,0.48755,0.16235,0.24556,0.341,0.24556,0.24556
+25-Sep-2019 05:00:00,1,0.34626,0.34626,0.34626,0.44591,0.22206,0.22206,0.22206,0.22206,0.44591,0.44591,0.44591,0.44591,0.44591,0.22206,0.24701,0.34626,0.24701,0.24701
+25-Sep-2019 06:00:00,1,0.3437,0.3437,0.3437,0.36468,0.13622,0.13622,0.13622,0.13622,0.36468,0.36468,0.36468,0.36468,0.36468,0.13622,0.29421,0.3437,0.29421,0.29421
+25-Sep-2019 07:00:00,1,0.32423,0.32423,0.32423,0.33424,0.13436,0.13436,0.13436,0.13436,0.33424,0.33424,0.33424,0.33424,0.33424,0.13436,0.27354,0.32423,0.27354,0.27354
+25-Sep-2019 08:00:00,1,0.30007,0.30007,0.30007,0.3251,0.12876,0.12876,0.12876,0.12876,0.3251,0.3251,0.3251,0.3251,0.3251,0.12876,0.24403,0.30007,0.24403,0.24403
+25-Sep-2019 09:00:00,1,0.27451,0.27451,0.27451,0.31876,0.15488,0.15488,0.15488,0.15488,0.31876,0.31876,0.31876,0.31876,0.31876,0.15488,0.18865,0.27451,0.18865,0.18865
+25-Sep-2019 10:00:00,1,0.27622,0.27622,0.27622,0.27639,0.22766,0.22766,0.22766,0.22766,0.27639,0.27639,0.27639,0.27639,0.27639,0.22766,0.20862,0.27622,0.20862,0.20862
+25-Sep-2019 11:00:00,1,0.26415,0.26415,0.26415,0.31066,0.13622,0.13622,0.13622,0.13622,0.31066,0.31066,0.31066,0.31066,0.31066,0.13622,0.19657,0.26415,0.19657,0.19657
+25-Sep-2019 12:00:00,1,0.26631,0.26631,0.26631,0.33981,0.20713,0.20713,0.20713,0.20713,0.33981,0.33981,0.33981,0.33981,0.33981,0.20713,0.21389,0.26631,0.21389,0.21389
+25-Sep-2019 13:00:00,1,0.23985,0.23985,0.23985,0.43925,0.14182,0.14182,0.14182,0.14182,0.43925,0.43925,0.43925,0.43925,0.43925,0.14182,0.2557,0.23985,0.2557,0.2557
+25-Sep-2019 14:00:00,1,0.24269,0.24269,0.24269,0.35736,0.20153,0.20153,0.20153,0.20153,0.35736,0.35736,0.35736,0.35736,0.35736,0.20153,0.24571,0.24269,0.24571,0.24571
+25-Sep-2019 15:00:00,1,0.24759,0.24759,0.24759,0.37877,0.054115,0.054115,0.054115,0.054115,0.37877,0.37877,0.37877,0.37877,0.37877,0.054115,0.22271,0.24759,0.22271,0.22271
+25-Sep-2019 16:00:00,1,0.21919,0.21919,0.21919,0.38558,0.035455,0.035455,0.035455,0.035455,0.38558,0.38558,0.38558,0.38558,0.38558,0.035455,0.19969,0.21919,0.19969,0.19969
+25-Sep-2019 17:00:00,1,0.20721,0.20721,0.20721,0.29517,0.026125,0.026125,0.026125,0.026125,0.29517,0.29517,0.29517,0.29517,0.29517,0.026125,0.18371,0.20721,0.18371,0.18371
+25-Sep-2019 18:00:00,1,0.17396,0.17396,0.17396,0.317,0.01866,0.01866,0.01866,0.01866,0.317,0.317,0.317,0.317,0.317,0.01866,0.18044,0.17396,0.18044,0.18044
+25-Sep-2019 19:00:00,1,0.13612,0.13612,0.13612,0.28217,0.013062,0.013062,0.013062,0.013062,0.28217,0.28217,0.28217,0.28217,0.28217,0.013062,0.22682,0.13612,0.22682,0.22682
+25-Sep-2019 20:00:00,1,0.16658,0.16658,0.16658,0.24842,0.013062,0.013062,0.013062,0.013062,0.24842,0.24842,0.24842,0.24842,0.24842,0.013062,0.18858,0.16658,0.18858,0.18858
+25-Sep-2019 21:00:00,1,0.17941,0.17941,0.17941,0.24073,0.016794,0.016794,0.016794,0.016794,0.24073,0.24073,0.24073,0.24073,0.24073,0.016794,0.1363,0.17941,0.1363,0.1363
+25-Sep-2019 22:00:00,1,0.18802,0.18802,0.18802,0.2187,0.022393,0.022393,0.022393,0.022393,0.2187,0.2187,0.2187,0.2187,0.2187,0.022393,0.1052,0.18802,0.1052,0.1052
+25-Sep-2019 23:00:00,1,0.17233,0.17233,0.17233,0.22711,0.031723,0.031723,0.031723,0.031723,0.22711,0.22711,0.22711,0.22711,0.22711,0.031723,0.099048,0.17233,0.099048,0.099048
+26-Sep-2019 00:00:00,1,0.16395,0.16395,0.16395,0.24244,0.065312,0.065312,0.065312,0.065312,0.24244,0.24244,0.24244,0.24244,0.24244,0.065312,0.090185,0.16395,0.090185,0.090185
+26-Sep-2019 01:00:00,1,0.16305,0.16305,0.16305,0.21725,0.08957,0.08957,0.08957,0.08957,0.21725,0.21725,0.21725,0.21725,0.21725,0.08957,0.074567,0.16305,0.074567,0.074567
+26-Sep-2019 02:00:00,1,0.16537,0.16537,0.16537,0.16931,0.10077,0.10077,0.10077,0.10077,0.16931,0.16931,0.16931,0.16931,0.16931,0.10077,0.070303,0.16537,0.070303,0.070303
+26-Sep-2019 03:00:00,1,0.16177,0.16177,0.16177,0.16405,0.069044,0.069044,0.069044,0.069044,0.16405,0.16405,0.16405,0.16405,0.16405,0.069044,0.057804,0.16177,0.057804,0.057804
+26-Sep-2019 04:00:00,1,0.13878,0.13878,0.13878,0.1561,0.07091,0.07091,0.07091,0.07091,0.1561,0.1561,0.1561,0.1561,0.1561,0.07091,0.061417,0.13878,0.061417,0.061417
+26-Sep-2019 05:00:00,1,0.11606,0.11606,0.11606,0.1543,0.054115,0.054115,0.054115,0.054115,0.1543,0.1543,0.1543,0.1543,0.1543,0.054115,0.096176,0.11606,0.096176,0.096176
+26-Sep-2019 06:00:00,1,0.12691,0.12691,0.12691,0.18732,0.055981,0.055981,0.055981,0.055981,0.18732,0.18732,0.18732,0.18732,0.18732,0.055981,0.14743,0.12691,0.14743,0.14743
+26-Sep-2019 07:00:00,1,0.1344,0.1344,0.1344,0.17695,0.041053,0.041053,0.041053,0.041053,0.17695,0.17695,0.17695,0.17695,0.17695,0.041053,0.16565,0.1344,0.16565,0.16565
+26-Sep-2019 08:00:00,1,0.12588,0.12588,0.12588,0.18485,0.024259,0.024259,0.024259,0.024259,0.18485,0.18485,0.18485,0.18485,0.18485,0.024259,0.17041,0.12588,0.17041,0.17041
+26-Sep-2019 09:00:00,1,0.11727,0.11727,0.11727,0.18392,0.057847,0.057847,0.057847,0.057847,0.18392,0.18392,0.18392,0.18392,0.18392,0.057847,0.13587,0.11727,0.13587,0.13587
+26-Sep-2019 10:00:00,1,0.1054,0.1054,0.1054,0.15961,0.029857,0.029857,0.029857,0.029857,0.15961,0.15961,0.15961,0.15961,0.15961,0.029857,0.084575,0.1054,0.084575,0.084575
+26-Sep-2019 11:00:00,1,0.093747,0.093747,0.093747,0.19207,0.027991,0.027991,0.027991,0.027991,0.19207,0.19207,0.19207,0.19207,0.19207,0.027991,0.089511,0.093747,0.089511,0.089511
+26-Sep-2019 12:00:00,1,0.095075,0.095075,0.095075,0.18686,0.063445,0.063445,0.063445,0.063445,0.18686,0.18686,0.18686,0.18686,0.18686,0.063445,0.080715,0.095075,0.080715,0.080715
+26-Sep-2019 13:00:00,1,0.088066,0.088066,0.088066,0.21209,0.031723,0.031723,0.031723,0.031723,0.21209,0.21209,0.21209,0.21209,0.21209,0.031723,0.082982,0.088066,0.082982,0.082982
+26-Sep-2019 14:00:00,1,0.09109,0.09109,0.09109,0.22912,0.041053,0.041053,0.041053,0.041053,0.22912,0.22912,0.22912,0.22912,0.22912,0.041053,0.075936,0.09109,0.075936,0.075936
+26-Sep-2019 15:00:00,1,0.10712,0.10712,0.10712,0.214,0.042919,0.042919,0.042919,0.042919,0.214,0.214,0.214,0.214,0.214,0.042919,0.069473,0.10712,0.069473,0.069473
+26-Sep-2019 16:00:00,1,0.09464,0.09464,0.09464,0.21674,0.07091,0.07091,0.07091,0.07091,0.21674,0.21674,0.21674,0.21674,0.21674,0.07091,0.077641,0.09464,0.077641,0.077641
+26-Sep-2019 17:00:00,1,0.08486,0.08486,0.08486,0.22174,0.10077,0.10077,0.10077,0.10077,0.22174,0.22174,0.22174,0.22174,0.22174,0.10077,0.078539,0.08486,0.078539,0.078539
+26-Sep-2019 18:00:00,1,0.091296,0.091296,0.091296,0.26081,0.08957,0.08957,0.08957,0.08957,0.26081,0.26081,0.26081,0.26081,0.26081,0.08957,0.071358,0.091296,0.071358,0.071358
+26-Sep-2019 19:00:00,1,0.096793,0.096793,0.096793,0.28506,0.057847,0.057847,0.057847,0.057847,0.28506,0.28506,0.28506,0.28506,0.28506,0.057847,0.062045,0.096793,0.062045,0.062045
+26-Sep-2019 20:00:00,1,0.12945,0.12945,0.12945,0.25823,0.027991,0.027991,0.027991,0.027991,0.25823,0.25823,0.25823,0.25823,0.25823,0.027991,0.069383,0.12945,0.069383,0.069383
+26-Sep-2019 21:00:00,1,0.13486,0.13486,0.13486,0.21999,0.037321,0.037321,0.037321,0.037321,0.21999,0.21999,0.21999,0.21999,0.21999,0.037321,0.065254,0.13486,0.065254,0.065254
+26-Sep-2019 22:00:00,1,0.13195,0.13195,0.13195,0.2123,0.027991,0.027991,0.027991,0.027991,0.2123,0.2123,0.2123,0.2123,0.2123,0.027991,0.066892,0.13195,0.066892,0.066892
+26-Sep-2019 23:00:00,1,0.15527,0.15527,0.15527,0.20058,0.057847,0.057847,0.057847,0.057847,0.20058,0.20058,0.20058,0.20058,0.20058,0.057847,0.0596,0.15527,0.0596,0.0596
+27-Sep-2019 00:00:00,1,0.13763,0.13763,0.13763,0.20012,0.12129,0.12129,0.12129,0.12129,0.20012,0.20012,0.20012,0.20012,0.20012,0.12129,0.056413,0.13763,0.056413,0.056413
+27-Sep-2019 01:00:00,1,0.12945,0.12945,0.12945,0.24925,0.17168,0.17168,0.17168,0.17168,0.24925,0.24925,0.24925,0.24925,0.24925,0.17168,0.053518,0.12945,0.053518,0.053518
+27-Sep-2019 02:00:00,1,0.15412,0.15412,0.15412,0.32242,0.11196,0.11196,0.11196,0.11196,0.32242,0.32242,0.32242,0.32242,0.32242,0.11196,0.045665,0.15412,0.045665,0.045665
+27-Sep-2019 03:00:00,1,0.15721,0.15721,0.15721,0.32841,0.1101,0.1101,0.1101,0.1101,0.32841,0.32841,0.32841,0.32841,0.32841,0.1101,0.033255,0.15721,0.033255,0.033255
+27-Sep-2019 04:00:00,1,0.18701,0.18701,0.18701,0.34079,0.10823,0.10823,0.10823,0.10823,0.34079,0.34079,0.34079,0.34079,0.34079,0.10823,0.04452,0.18701,0.04452,0.04452
+27-Sep-2019 05:00:00,1,0.19796,0.19796,0.19796,0.34157,0.10263,0.10263,0.10263,0.10263,0.34157,0.34157,0.34157,0.34157,0.34157,0.10263,0.081725,0.19796,0.081725,0.081725
+27-Sep-2019 06:00:00,1,0.16679,0.16679,0.16679,0.35256,0.11383,0.11383,0.11383,0.11383,0.35256,0.35256,0.35256,0.35256,0.35256,0.11383,0.12277,0.16679,0.12277,0.12277
+27-Sep-2019 07:00:00,1,0.15737,0.15737,0.15737,0.29512,0.093302,0.093302,0.093302,0.093302,0.29512,0.29512,0.29512,0.29512,0.29512,0.093302,0.14361,0.15737,0.14361,0.14361
+27-Sep-2019 08:00:00,1,0.15799,0.15799,0.15799,0.25492,0.13436,0.13436,0.13436,0.13436,0.25492,0.25492,0.25492,0.25492,0.25492,0.13436,0.13944,0.15799,0.13944,0.13944
+27-Sep-2019 09:00:00,1,0.17318,0.17318,0.17318,0.22948,0.14555,0.14555,0.14555,0.14555,0.22948,0.22948,0.22948,0.22948,0.22948,0.14555,0.13374,0.17318,0.13374,0.13374
+27-Sep-2019 10:00:00,1,0.20939,0.20939,0.20939,0.23377,0.27058,0.27058,0.27058,0.27058,0.23377,0.23377,0.23377,0.23377,0.23377,0.27058,0.17622,0.20939,0.17622,0.17622
+27-Sep-2019 11:00:00,1,0.22185,0.22185,0.22185,0.26132,0.43665,0.43665,0.43665,0.43665,0.26132,0.26132,0.26132,0.26132,0.26132,0.43665,0.23137,0.22185,0.23137,0.23137
+27-Sep-2019 12:00:00,1,0.25339,0.25339,0.25339,0.28434,0.18474,0.18474,0.18474,0.18474,0.28434,0.28434,0.28434,0.28434,0.28434,0.18474,0.29649,0.25339,0.29649,0.29649
+27-Sep-2019 13:00:00,1,0.25511,0.25511,0.25511,0.26148,0.27058,0.27058,0.27058,0.27058,0.26148,0.26148,0.26148,0.26148,0.26148,0.27058,0.3142,0.25511,0.3142,0.3142
+27-Sep-2019 14:00:00,1,0.2669,0.2669,0.2669,0.26746,0.21273,0.21273,0.21273,0.21273,0.26746,0.26746,0.26746,0.26746,0.26746,0.21273,0.33706,0.2669,0.33706,0.33706
+27-Sep-2019 15:00:00,1,0.25245,0.25245,0.25245,0.28011,0.17168,0.17168,0.17168,0.17168,0.28011,0.28011,0.28011,0.28011,0.28011,0.17168,0.29589,0.25245,0.29589,0.29589
+27-Sep-2019 16:00:00,1,0.24139,0.24139,0.24139,0.25033,0.24818,0.24818,0.24818,0.24818,0.25033,0.25033,0.25033,0.25033,0.25033,0.24818,0.29607,0.24139,0.29607,0.29607
+27-Sep-2019 17:00:00,1,0.21713,0.21713,0.21713,0.1801,0.3135,0.3135,0.3135,0.3135,0.1801,0.1801,0.1801,0.1801,0.1801,0.3135,0.27518,0.21713,0.27518,0.27518
+27-Sep-2019 18:00:00,1,0.18048,0.18048,0.18048,0.17695,0.38627,0.38627,0.38627,0.38627,0.17695,0.17695,0.17695,0.17695,0.17695,0.38627,0.24859,0.18048,0.24859,0.24859
+27-Sep-2019 19:00:00,1,0.18685,0.18685,0.18685,0.16426,0.2034,0.2034,0.2034,0.2034,0.16426,0.16426,0.16426,0.16426,0.16426,0.2034,0.27138,0.18685,0.27138,0.27138
+27-Sep-2019 20:00:00,1,0.19865,0.19865,0.19865,0.23299,0.27617,0.27617,0.27617,0.27617,0.23299,0.23299,0.23299,0.23299,0.23299,0.27617,0.29308,0.19865,0.29308,0.29308
+27-Sep-2019 21:00:00,1,0.20891,0.20891,0.20891,0.21447,0.18101,0.18101,0.18101,0.18101,0.21447,0.21447,0.21447,0.21447,0.21447,0.18101,0.33451,0.20891,0.33451,0.33451
+27-Sep-2019 22:00:00,1,0.21658,0.21658,0.21658,0.24378,0.16608,0.16608,0.16608,0.16608,0.24378,0.24378,0.24378,0.24378,0.24378,0.16608,0.32865,0.21658,0.32865,0.32865
+27-Sep-2019 23:00:00,1,0.21209,0.21209,0.21209,0.24491,0.15488,0.15488,0.15488,0.15488,0.24491,0.24491,0.24491,0.24491,0.24491,0.15488,0.34725,0.21209,0.34725,0.34725
+28-Sep-2019 00:00:00,1,0.16157,0.16157,0.16157,0.20224,0.18474,0.18474,0.18474,0.18474,0.20224,0.20224,0.20224,0.20224,0.20224,0.18474,0.36913,0.16157,0.36913,0.36913
+28-Sep-2019 01:00:00,1,0.21012,0.21012,0.21012,0.17587,0.097034,0.097034,0.097034,0.097034,0.17587,0.17587,0.17587,0.17587,0.17587,0.097034,0.34748,0.21012,0.34748,0.34748
+28-Sep-2019 02:00:00,1,0.21074,0.21074,0.21074,0.14315,0.065312,0.065312,0.065312,0.065312,0.14315,0.14315,0.14315,0.14315,0.14315,0.065312,0.32661,0.21074,0.32661,0.32661
+28-Sep-2019 03:00:00,1,0.22515,0.22515,0.22515,0.1577,0.069044,0.069044,0.069044,0.069044,0.1577,0.1577,0.1577,0.1577,0.1577,0.069044,0.30576,0.22515,0.30576,0.30576
+28-Sep-2019 04:00:00,1,0.21546,0.21546,0.21546,0.1881,0.063445,0.063445,0.063445,0.063445,0.1881,0.1881,0.1881,0.1881,0.1881,0.063445,0.29171,0.21546,0.29171,0.29171
+28-Sep-2019 05:00:00,1,0.19881,0.19881,0.19881,0.1624,0.042919,0.042919,0.042919,0.042919,0.1624,0.1624,0.1624,0.1624,0.1624,0.042919,0.24612,0.19881,0.24612,0.24612
+28-Sep-2019 06:00:00,1,0.18376,0.18376,0.18376,0.17396,0.029857,0.029857,0.029857,0.029857,0.17396,0.17396,0.17396,0.17396,0.17396,0.029857,0.2233,0.18376,0.2233,0.2233
+28-Sep-2019 07:00:00,1,0.19091,0.19091,0.19091,0.17628,0.0074642,0.0074642,0.0074642,0.0074642,0.17628,0.17628,0.17628,0.17628,0.17628,0.0074642,0.21807,0.19091,0.21807,0.21807
+28-Sep-2019 08:00:00,1,0.20046,0.20046,0.20046,0.18624,0.0055981,0.0055981,0.0055981,0.0055981,0.18624,0.18624,0.18624,0.18624,0.18624,0.0055981,0.18297,0.20046,0.18297,0.18297
+28-Sep-2019 09:00:00,1,0.20346,0.20346,0.20346,0.19842,0.0093302,0.0093302,0.0093302,0.0093302,0.19842,0.19842,0.19842,0.19842,0.19842,0.0093302,0.19325,0.20346,0.19325,0.19325
+28-Sep-2019 10:00:00,1,0.23841,0.23841,0.23841,0.19176,0.0093302,0.0093302,0.0093302,0.0093302,0.19176,0.19176,0.19176,0.19176,0.19176,0.0093302,0.22617,0.23841,0.22617,0.22617
+28-Sep-2019 11:00:00,1,0.27609,0.27609,0.27609,0.21472,0.011196,0.011196,0.011196,0.011196,0.21472,0.21472,0.21472,0.21472,0.21472,0.011196,0.22309,0.27609,0.22309,0.22309
+28-Sep-2019 12:00:00,1,0.28353,0.28353,0.28353,0.22742,0.0093302,0.0093302,0.0093302,0.0093302,0.22742,0.22742,0.22742,0.22742,0.22742,0.0093302,0.21892,0.28353,0.21892,0.21892
+28-Sep-2019 13:00:00,1,0.27144,0.27144,0.27144,0.22603,0.0055981,0.0055981,0.0055981,0.0055981,0.22603,0.22603,0.22603,0.22603,0.22603,0.0055981,0.19807,0.27144,0.19807,0.19807
+28-Sep-2019 14:00:00,1,0.25556,0.25556,0.25556,0.2123,0.0055981,0.0055981,0.0055981,0.0055981,0.2123,0.2123,0.2123,0.2123,0.2123,0.0055981,0.18338,0.25556,0.18338,0.18338
+28-Sep-2019 15:00:00,1,0.23843,0.23843,0.23843,0.1929,0.001866,0.001866,0.001866,0.001866,0.1929,0.1929,0.1929,0.1929,0.1929,0.001866,0.17312,0.23843,0.17312,0.17312
+28-Sep-2019 16:00:00,1,0.21388,0.21388,0.21388,0.16235,0,0,0,0,0.16235,0.16235,0.16235,0.16235,0.16235,0,0.13049,0.21388,0.13049,0.13049
+28-Sep-2019 17:00:00,1,0.1866,0.1866,0.1866,0.12942,0.001866,0.001866,0.001866,0.001866,0.12942,0.12942,0.12942,0.12942,0.12942,0.001866,0.10345,0.1866,0.10345,0.10345
+28-Sep-2019 18:00:00,1,0.15575,0.15575,0.15575,0.1094,0.011196,0.011196,0.011196,0.011196,0.1094,0.1094,0.1094,0.1094,0.1094,0.011196,0.086146,0.15575,0.086146,0.086146
+28-Sep-2019 19:00:00,1,0.14521,0.14521,0.14521,0.13794,0.0055981,0.0055981,0.0055981,0.0055981,0.13794,0.13794,0.13794,0.13794,0.13794,0.0055981,0.10051,0.14521,0.10051,0.10051
+28-Sep-2019 20:00:00,1,0.18362,0.18362,0.18362,0.17143,0.013062,0.013062,0.013062,0.013062,0.17143,0.17143,0.17143,0.17143,0.17143,0.013062,0.1472,0.18362,0.1472,0.1472
+28-Sep-2019 21:00:00,1,0.20673,0.20673,0.20673,0.19661,0.0093302,0.0093302,0.0093302,0.0093302,0.19661,0.19661,0.19661,0.19661,0.19661,0.0093302,0.18937,0.20673,0.18937,0.18937
+28-Sep-2019 22:00:00,1,0.22927,0.22927,0.22927,0.22086,0.013062,0.013062,0.013062,0.013062,0.22086,0.22086,0.22086,0.22086,0.22086,0.013062,0.20465,0.22927,0.20465,0.20465
+28-Sep-2019 23:00:00,1,0.2584,0.2584,0.2584,0.22019,0.022393,0.022393,0.022393,0.022393,0.22019,0.22019,0.22019,0.22019,0.22019,0.022393,0.20909,0.2584,0.20909,0.20909
+29-Sep-2019 00:00:00,1,0.28793,0.28793,0.28793,0.24703,0.014928,0.014928,0.014928,0.014928,0.24703,0.24703,0.24703,0.24703,0.24703,0.014928,0.20157,0.28793,0.20157,0.20157
+29-Sep-2019 01:00:00,1,0.29336,0.29336,0.29336,0.25621,0.014928,0.014928,0.014928,0.014928,0.25621,0.25621,0.25621,0.25621,0.25621,0.014928,0.18308,0.29336,0.18308,0.18308
+29-Sep-2019 02:00:00,1,0.29063,0.29063,0.29063,0.22086,0.014928,0.014928,0.014928,0.014928,0.22086,0.22086,0.22086,0.22086,0.22086,0.014928,0.1924,0.29063,0.1924,0.1924
+29-Sep-2019 03:00:00,1,0.28328,0.28328,0.28328,0.19831,0.0093302,0.0093302,0.0093302,0.0093302,0.19831,0.19831,0.19831,0.19831,0.19831,0.0093302,0.20007,0.28328,0.20007,0.20007
+29-Sep-2019 04:00:00,1,0.25208,0.25208,0.25208,0.22737,0.013062,0.013062,0.013062,0.013062,0.22737,0.22737,0.22737,0.22737,0.22737,0.013062,0.18955,0.25208,0.18955,0.18955
+29-Sep-2019 05:00:00,1,0.24292,0.24292,0.24292,0.16704,0.016794,0.016794,0.016794,0.016794,0.16704,0.16704,0.16704,0.16704,0.16704,0.016794,0.18349,0.24292,0.18349,0.18349
+29-Sep-2019 06:00:00,1,0.23685,0.23685,0.23685,0.14212,0.016794,0.016794,0.016794,0.016794,0.14212,0.14212,0.14212,0.14212,0.14212,0.016794,0.18055,0.23685,0.18055,0.18055
+29-Sep-2019 07:00:00,1,0.23213,0.23213,0.23213,0.13505,0.0093302,0.0093302,0.0093302,0.0093302,0.13505,0.13505,0.13505,0.13505,0.13505,0.0093302,0.19906,0.23213,0.19906,0.19906
+29-Sep-2019 08:00:00,1,0.22819,0.22819,0.22819,0.12612,0.0074642,0.0074642,0.0074642,0.0074642,0.12612,0.12612,0.12612,0.12612,0.12612,0.0074642,0.19031,0.22819,0.19031,0.19031
+29-Sep-2019 09:00:00,1,0.23177,0.23177,0.23177,0.10981,0.001866,0.001866,0.001866,0.001866,0.10981,0.10981,0.10981,0.10981,0.10981,0.001866,0.1534,0.23177,0.1534,0.1534
+29-Sep-2019 10:00:00,1,0.22322,0.22322,0.22322,0.12824,0.001866,0.001866,0.001866,0.001866,0.12824,0.12824,0.12824,0.12824,0.12824,0.001866,0.13046,0.22322,0.13046,0.13046
+29-Sep-2019 11:00:00,1,0.25321,0.25321,0.25321,0.14552,0.0074642,0.0074642,0.0074642,0.0074642,0.14552,0.14552,0.14552,0.14552,0.14552,0.0074642,0.15481,0.25321,0.15481,0.15481
+29-Sep-2019 12:00:00,1,0.27398,0.27398,0.27398,0.18531,0.0055981,0.0055981,0.0055981,0.0055981,0.18531,0.18531,0.18531,0.18531,0.18531,0.0055981,0.21237,0.27398,0.21237,0.21237
+29-Sep-2019 13:00:00,1,0.29714,0.29714,0.29714,0.16813,0.016794,0.016794,0.016794,0.016794,0.16813,0.16813,0.16813,0.16813,0.16813,0.016794,0.23321,0.29714,0.23321,0.23321
+29-Sep-2019 14:00:00,1,0.30744,0.30744,0.30744,0.17494,0.024259,0.024259,0.024259,0.024259,0.17494,0.17494,0.17494,0.17494,0.17494,0.024259,0.23158,0.30744,0.23158,0.23158
+29-Sep-2019 15:00:00,1,0.3238,0.3238,0.3238,0.17458,0.013062,0.013062,0.013062,0.013062,0.17458,0.17458,0.17458,0.17458,0.17458,0.013062,0.2329,0.3238,0.2329,0.2329
+29-Sep-2019 16:00:00,1,0.35364,0.35364,0.35364,0.22463,0.0055981,0.0055981,0.0055981,0.0055981,0.22463,0.22463,0.22463,0.22463,0.22463,0.0055981,0.20797,0.35364,0.20797,0.20797
+29-Sep-2019 17:00:00,1,0.33543,0.33543,0.33543,0.19295,0.0093302,0.0093302,0.0093302,0.0093302,0.19295,0.19295,0.19295,0.19295,0.19295,0.0093302,0.2329,0.33543,0.2329,0.2329
+29-Sep-2019 18:00:00,1,0.30426,0.30426,0.30426,0.13582,0.0055981,0.0055981,0.0055981,0.0055981,0.13582,0.13582,0.13582,0.13582,0.13582,0.0055981,0.22841,0.30426,0.22841,0.22841
+29-Sep-2019 19:00:00,1,0.25387,0.25387,0.25387,0.12039,0.022393,0.022393,0.022393,0.022393,0.12039,0.12039,0.12039,0.12039,0.12039,0.022393,0.20734,0.25387,0.20734,0.20734
+29-Sep-2019 20:00:00,1,0.26017,0.26017,0.26017,0.15027,0.0093302,0.0093302,0.0093302,0.0093302,0.15027,0.15027,0.15027,0.15027,0.15027,0.0093302,0.21914,0.26017,0.21914,0.21914
+29-Sep-2019 21:00:00,1,0.29484,0.29484,0.29484,0.17757,0.013062,0.013062,0.013062,0.013062,0.17757,0.17757,0.17757,0.17757,0.17757,0.013062,0.21324,0.29484,0.21324,0.21324
+29-Sep-2019 22:00:00,1,0.30442,0.30442,0.30442,0.193,0.01866,0.01866,0.01866,0.01866,0.193,0.193,0.193,0.193,0.193,0.01866,0.24172,0.30442,0.24172,0.24172
+29-Sep-2019 23:00:00,1,0.31083,0.31083,0.31083,0.18237,0.022393,0.022393,0.022393,0.022393,0.18237,0.18237,0.18237,0.18237,0.18237,0.022393,0.25763,0.31083,0.25763,0.25763
+30-Sep-2019 00:00:00,1,0.30607,0.30607,0.30607,0.18247,0.033589,0.033589,0.033589,0.033589,0.18247,0.18247,0.18247,0.18247,0.18247,0.033589,0.28794,0.30607,0.28794,0.28794
+30-Sep-2019 01:00:00,1,0.28619,0.28619,0.28619,0.15497,0.046651,0.046651,0.046651,0.046651,0.15497,0.15497,0.15497,0.15497,0.15497,0.046651,0.28996,0.28619,0.28996,0.28996
+30-Sep-2019 02:00:00,1,0.27632,0.27632,0.27632,0.13629,0.065312,0.065312,0.065312,0.065312,0.13629,0.13629,0.13629,0.13629,0.13629,0.065312,0.28402,0.27632,0.28402,0.28402
+30-Sep-2019 03:00:00,1,0.2535,0.2535,0.2535,0.12576,0.069044,0.069044,0.069044,0.069044,0.12576,0.12576,0.12576,0.12576,0.12576,0.069044,0.29504,0.2535,0.29504,0.29504
+30-Sep-2019 04:00:00,1,0.25524,0.25524,0.25524,0.11688,0.063445,0.063445,0.063445,0.063445,0.11688,0.11688,0.11688,0.11688,0.11688,0.063445,0.27984,0.25524,0.27984,0.27984
+30-Sep-2019 05:00:00,1,0.23669,0.23669,0.23669,0.11533,0.061579,0.061579,0.061579,0.061579,0.11533,0.11533,0.11533,0.11533,0.11533,0.061579,0.2734,0.23669,0.2734,0.2734
+30-Sep-2019 06:00:00,1,0.2142,0.2142,0.2142,0.12406,0.069044,0.069044,0.069044,0.069044,0.12406,0.12406,0.12406,0.12406,0.12406,0.069044,0.26948,0.2142,0.26948,0.26948
+30-Sep-2019 07:00:00,1,0.21692,0.21692,0.21692,0.12555,0.054115,0.054115,0.054115,0.054115,0.12555,0.12555,0.12555,0.12555,0.12555,0.054115,0.25305,0.21692,0.25305,0.25305
+30-Sep-2019 08:00:00,1,0.20653,0.20653,0.20653,0.13649,0.024259,0.024259,0.024259,0.024259,0.13649,0.13649,0.13649,0.13649,0.13649,0.024259,0.21217,0.20653,0.21217,0.21217
+30-Sep-2019 09:00:00,1,0.17758,0.17758,0.17758,0.097119,0.011196,0.011196,0.011196,0.011196,0.097119,0.097119,0.097119,0.097119,0.097119,0.011196,0.1466,0.17758,0.1466,0.1466
+30-Sep-2019 10:00:00,1,0.18261,0.18261,0.18261,0.078283,0.011196,0.011196,0.011196,0.011196,0.078283,0.078283,0.078283,0.078283,0.078283,0.011196,0.11242,0.18261,0.11242,0.11242
+30-Sep-2019 11:00:00,1,0.20527,0.20527,0.20527,0.093713,0.024259,0.024259,0.024259,0.024259,0.093713,0.093713,0.093713,0.093713,0.093713,0.024259,0.11274,0.20527,0.11274,0.11274
+30-Sep-2019 12:00:00,1,0.28871,0.28871,0.28871,0.15595,0.01866,0.01866,0.01866,0.01866,0.15595,0.15595,0.15595,0.15595,0.15595,0.01866,0.12629,0.28871,0.12629,0.12629
+30-Sep-2019 13:00:00,1,0.34356,0.34356,0.34356,0.17752,0.014928,0.014928,0.014928,0.014928,0.17752,0.17752,0.17752,0.17752,0.17752,0.014928,0.12407,0.34356,0.12407,0.12407
+30-Sep-2019 14:00:00,1,0.31468,0.31468,0.31468,0.15347,0.013062,0.013062,0.013062,0.013062,0.15347,0.15347,0.15347,0.15347,0.15347,0.013062,0.10179,0.31468,0.10179,0.10179
+30-Sep-2019 15:00:00,1,0.26461,0.26461,0.26461,0.14717,0.0037321,0.0037321,0.0037321,0.0037321,0.14717,0.14717,0.14717,0.14717,0.14717,0.0037321,0.08444,0.26461,0.08444,0.08444
+30-Sep-2019 16:00:00,1,0.23589,0.23589,0.23589,0.14062,0.001866,0.001866,0.001866,0.001866,0.14062,0.14062,0.14062,0.14062,0.14062,0.001866,0.063751,0.23589,0.063751,0.063751
+30-Sep-2019 17:00:00,1,0.20233,0.20233,0.20233,0.098873,0.0037321,0.0037321,0.0037321,0.0037321,0.098873,0.098873,0.098873,0.098873,0.098873,0.0037321,0.041872,0.20233,0.041872,0.041872
+30-Sep-2019 18:00:00,1,0.1467,0.1467,0.1467,0.081483,0.0074642,0.0074642,0.0074642,0.0074642,0.081483,0.081483,0.081483,0.081483,0.081483,0.0074642,0.041266,0.1467,0.041266,0.041266
+30-Sep-2019 19:00:00,1,0.15245,0.15245,0.15245,0.10522,0.0093302,0.0093302,0.0093302,0.0093302,0.10522,0.10522,0.10522,0.10522,0.10522,0.0093302,0.057288,0.15245,0.057288,0.057288
+30-Sep-2019 20:00:00,1,0.18181,0.18181,0.18181,0.13479,0.013062,0.013062,0.013062,0.013062,0.13479,0.13479,0.13479,0.13479,0.13479,0.013062,0.077551,0.18181,0.077551,0.077551
+30-Sep-2019 21:00:00,1,0.19913,0.19913,0.19913,0.1463,0.013062,0.013062,0.013062,0.013062,0.1463,0.1463,0.1463,0.1463,0.1463,0.013062,0.092675,0.19913,0.092675,0.092675
+30-Sep-2019 22:00:00,1,0.20144,0.20144,0.20144,0.15755,0.0055981,0.0055981,0.0055981,0.0055981,0.15755,0.15755,0.15755,0.15755,0.15755,0.0055981,0.087223,0.20144,0.087223,0.087223
+30-Sep-2019 23:00:00,1,0.20272,0.20272,0.20272,0.14129,0.0055981,0.0055981,0.0055981,0.0055981,0.14129,0.14129,0.14129,0.14129,0.14129,0.0055981,0.085899,0.20272,0.085899,0.085899
+01-Oct-2019 00:00:00,1,0.19177,0.19177,0.19177,0.15746,0.0055711,0.0055711,0.0055711,0.0055711,0.15746,0.15746,0.15746,0.15746,0.15746,0.0055711,0.079443,0.19177,0.079443,0.079443
+01-Oct-2019 01:00:00,1,0.18802,0.18802,0.18802,0.19018,0.003714,0.003714,0.003714,0.003714,0.19018,0.19018,0.19018,0.19018,0.19018,0.003714,0.085019,0.18802,0.085019,0.085019
+01-Oct-2019 02:00:00,1,0.19699,0.19699,0.19699,0.20304,0.001857,0.001857,0.001857,0.001857,0.20304,0.20304,0.20304,0.20304,0.20304,0.001857,0.090215,0.19699,0.090215,0.090215
+01-Oct-2019 03:00:00,1,0.18715,0.18715,0.18715,0.16468,0,0,0,0,0.16468,0.16468,0.16468,0.16468,0.16468,0,0.075852,0.18715,0.075852,0.075852
+01-Oct-2019 04:00:00,1,0.18941,0.18941,0.18941,0.15413,0.001857,0.001857,0.001857,0.001857,0.15413,0.15413,0.15413,0.15413,0.15413,0.001857,0.070723,0.18941,0.070723,0.070723
+01-Oct-2019 05:00:00,1,0.17653,0.17653,0.17653,0.14492,0,0,0,0,0.14492,0.14492,0.14492,0.14492,0.14492,0,0.06934,0.17653,0.06934,0.06934
+01-Oct-2019 06:00:00,1,0.17049,0.17049,0.17049,0.17825,0,0,0,0,0.17825,0.17825,0.17825,0.17825,0.17825,0,0.061891,0.17049,0.061891,0.061891
+01-Oct-2019 07:00:00,1,0.17687,0.17687,0.17687,0.21261,0,0,0,0,0.21261,0.21261,0.21261,0.21261,0.21261,0,0.048464,0.17687,0.048464,0.048464
+01-Oct-2019 08:00:00,1,0.1704,0.1704,0.1704,0.19551,0,0,0,0,0.19551,0.19551,0.19551,0.19551,0.19551,0,0.039967,0.1704,0.039967,0.039967
+01-Oct-2019 09:00:00,1,0.15249,0.15249,0.15249,0.13488,0,0,0,0,0.13488,0.13488,0.13488,0.13488,0.13488,0,0.028637,0.15249,0.028637,0.028637
+01-Oct-2019 10:00:00,1,0.12426,0.12426,0.12426,0.10912,0,0,0,0,0.10912,0.10912,0.10912,0.10912,0.10912,0,0.014542,0.12426,0.014542,0.014542
+01-Oct-2019 11:00:00,1,0.14625,0.14625,0.14625,0.13043,0,0,0,0,0.13043,0.13043,0.13043,0.13043,0.13043,0,0.01356,0.14625,0.01356,0.01356
+01-Oct-2019 12:00:00,1,0.16995,0.16995,0.16995,0.14169,0,0,0,0,0.14169,0.14169,0.14169,0.14169,0.14169,0,0.021589,0.16995,0.021589,0.021589
+01-Oct-2019 13:00:00,1,0.17175,0.17175,0.17175,0.1399,0,0,0,0,0.1399,0.1399,0.1399,0.1399,0.1399,0,0.032941,0.17175,0.032941,0.032941
+01-Oct-2019 14:00:00,1,0.15185,0.15185,0.15185,0.12643,0,0,0,0,0.12643,0.12643,0.12643,0.12643,0.12643,0,0.032005,0.15185,0.032005,0.032005
+01-Oct-2019 15:00:00,1,0.14041,0.14041,0.14041,0.12546,0.001857,0.001857,0.001857,0.001857,0.12546,0.12546,0.12546,0.12546,0.12546,0.001857,0.026875,0.14041,0.026875,0.026875
+01-Oct-2019 16:00:00,1,0.11893,0.11893,0.11893,0.12648,0.003714,0.003714,0.003714,0.003714,0.12648,0.12648,0.12648,0.12648,0.12648,0.003714,0.020273,0.11893,0.020273,0.020273
+01-Oct-2019 17:00:00,1,0.10295,0.10295,0.10295,0.12131,0.0055711,0.0055711,0.0055711,0.0055711,0.12131,0.12131,0.12131,0.12131,0.12131,0.0055711,0.015344,0.10295,0.015344,0.015344
+01-Oct-2019 18:00:00,1,0.10147,0.10147,0.10147,0.13478,0.0055711,0.0055711,0.0055711,0.0055711,0.13478,0.13478,0.13478,0.13478,0.13478,0.0055711,0.021545,0.10147,0.021545,0.021545
+01-Oct-2019 19:00:00,1,0.13339,0.13339,0.13339,0.19648,0.01857,0.01857,0.01857,0.01857,0.19648,0.19648,0.19648,0.19648,0.19648,0.01857,0.043067,0.13339,0.043067,0.043067
+01-Oct-2019 20:00:00,1,0.16452,0.16452,0.16452,0.24477,0.022284,0.022284,0.022284,0.022284,0.24477,0.24477,0.24477,0.24477,0.24477,0.022284,0.076878,0.16452,0.076878,0.076878
+01-Oct-2019 21:00:00,1,0.19936,0.19936,0.19936,0.25128,0.027855,0.027855,0.027855,0.027855,0.25128,0.25128,0.25128,0.25128,0.25128,0.027855,0.10186,0.19936,0.10186,0.10186
+01-Oct-2019 22:00:00,1,0.22716,0.22716,0.22716,0.25363,0.042711,0.042711,0.042711,0.042711,0.25363,0.25363,0.25363,0.25363,0.25363,0.042711,0.12291,0.22716,0.12291,0.12291
+01-Oct-2019 23:00:00,1,0.24478,0.24478,0.24478,0.23468,0.048282,0.048282,0.048282,0.048282,0.23468,0.23468,0.23468,0.23468,0.23468,0.048282,0.14147,0.24478,0.14147,0.14147
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+20-Oct-2019 23:00:00,1,0.028552,0.028552,0.028552,0.096629,0.063139,0.063139,0.063139,0.063139,0.096629,0.096629,0.096629,0.096629,0.096629,0.063139,0.024556,0.028552,0.024556,0.024556
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+21-Oct-2019 01:00:00,1,0.043341,0.043341,0.043341,0.088845,0.020427,0.020427,0.020427,0.020427,0.088845,0.088845,0.088845,0.088845,0.088845,0.020427,0.046702,0.043341,0.046702,0.046702
+21-Oct-2019 02:00:00,1,0.051863,0.051863,0.051863,0.12423,0.012999,0.012999,0.012999,0.012999,0.12423,0.12423,0.12423,0.12423,0.12423,0.012999,0.047305,0.051863,0.047305,0.047305
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+21-Oct-2019 04:00:00,1,0.072303,0.072303,0.072303,0.22214,0.01857,0.01857,0.01857,0.01857,0.22214,0.22214,0.22214,0.22214,0.22214,0.01857,0.059214,0.072303,0.059214,0.059214
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+21-Oct-2019 07:00:00,1,0.076929,0.076929,0.076929,0.062934,0.0074281,0.0074281,0.0074281,0.0074281,0.062934,0.062934,0.062934,0.062934,0.062934,0.0074281,0.075362,0.076929,0.075362,0.075362
+21-Oct-2019 08:00:00,1,0.077635,0.077635,0.077635,0.050286,0.011142,0.011142,0.011142,0.011142,0.050286,0.050286,0.050286,0.050286,0.050286,0.011142,0.070054,0.077635,0.070054,0.070054
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+21-Oct-2019 10:00:00,1,0.081486,0.081486,0.081486,0.030212,0,0,0,0,0.030212,0.030212,0.030212,0.030212,0.030212,0,0.017664,0.081486,0.017664,0.017664
+21-Oct-2019 11:00:00,1,0.066948,0.066948,0.066948,0.026167,0,0,0,0,0.026167,0.026167,0.026167,0.026167,0.026167,0,0.0065125,0.066948,0.0065125,0.0065125
+21-Oct-2019 12:00:00,1,0.067199,0.067199,0.067199,0.024016,0,0,0,0,0.024016,0.024016,0.024016,0.024016,0.024016,0,0.0027656,0.067199,0.0027656,0.0027656
+21-Oct-2019 13:00:00,1,0.07547,0.07547,0.07547,0.018025,0,0,0,0,0.018025,0.018025,0.018025,0.018025,0.018025,0,0.0024533,0.07547,0.0024533,0.0024533
+21-Oct-2019 14:00:00,1,0.090031,0.090031,0.090031,0.018435,0,0,0,0,0.018435,0.018435,0.018435,0.018435,0.018435,0,0.0036131,0.090031,0.0036131,0.0036131
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+21-Oct-2019 16:00:00,1,0.094087,0.094087,0.094087,0.029086,0.0092851,0.0092851,0.0092851,0.0092851,0.029086,0.029086,0.029086,0.029086,0.029086,0.0092851,0.01608,0.094087,0.01608,0.01608
+21-Oct-2019 17:00:00,1,0.11938,0.11938,0.11938,0.038815,0.01857,0.01857,0.01857,0.01857,0.038815,0.038815,0.038815,0.038815,0.038815,0.01857,0.037112,0.11938,0.037112,0.037112
+21-Oct-2019 18:00:00,1,0.15873,0.15873,0.15873,0.059554,0.012999,0.012999,0.012999,0.012999,0.059554,0.059554,0.059554,0.059554,0.059554,0.012999,0.078863,0.15873,0.078863,0.078863
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+22-Oct-2019 01:00:00,1,0.17425,0.17425,0.17425,0.038764,0.01857,0.01857,0.01857,0.01857,0.038764,0.038764,0.038764,0.038764,0.038764,0.01857,0.054285,0.17425,0.054285,0.054285
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+23-Oct-2019 02:00:00,1,0.055577,0.055577,0.055577,0.099189,0.024141,0.024141,0.024141,0.024141,0.099189,0.099189,0.099189,0.099189,0.099189,0.024141,0.012735,0.055577,0.012735,0.012735
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+23-Oct-2019 18:00:00,1,0.041609,0.041609,0.041609,0.059145,0.012999,0.012999,0.012999,0.012999,0.059145,0.059145,0.059145,0.059145,0.059145,0.012999,0.010772,0.041609,0.010772,0.010772
+23-Oct-2019 19:00:00,1,0.049903,0.049903,0.049903,0.079679,0.012999,0.012999,0.012999,0.012999,0.079679,0.079679,0.079679,0.079679,0.079679,0.012999,0.01307,0.049903,0.01307,0.01307
+23-Oct-2019 20:00:00,1,0.045141,0.045141,0.045141,0.076811,0.012999,0.012999,0.012999,0.012999,0.076811,0.076811,0.076811,0.076811,0.076811,0.012999,0.013828,0.045141,0.013828,0.013828
+23-Oct-2019 21:00:00,1,0.037804,0.037804,0.037804,0.065597,0.0092851,0.0092851,0.0092851,0.0092851,0.065597,0.065597,0.065597,0.065597,0.065597,0.0092851,0.013203,0.037804,0.013203,0.013203
+23-Oct-2019 22:00:00,1,0.03475,0.03475,0.03475,0.068669,0.0055711,0.0055711,0.0055711,0.0055711,0.068669,0.068669,0.068669,0.068669,0.068669,0.0055711,0.014385,0.03475,0.014385,0.014385
+23-Oct-2019 23:00:00,1,0.034317,0.034317,0.034317,0.067594,0.0092851,0.0092851,0.0092851,0.0092851,0.067594,0.067594,0.067594,0.067594,0.067594,0.0092851,0.0068693,0.034317,0.0068693,0.0068693
+24-Oct-2019 00:00:00,1,0.022263,0.022263,0.022263,0.05341,0.0074281,0.0074281,0.0074281,0.0074281,0.05341,0.05341,0.05341,0.05341,0.05341,0.0074281,0.004639,0.022263,0.004639,0.004639
+24-Oct-2019 01:00:00,1,0.021534,0.021534,0.021534,0.05259,0.003714,0.003714,0.003714,0.003714,0.05259,0.05259,0.05259,0.05259,0.05259,0.003714,0.0029217,0.021534,0.0029217,0.0029217
+24-Oct-2019 02:00:00,1,0.016999,0.016999,0.016999,0.052846,0.001857,0.001857,0.001857,0.001857,0.052846,0.052846,0.052846,0.052846,0.052846,0.001857,0.0007583,0.016999,0.0007583,0.0007583
+24-Oct-2019 03:00:00,1,0.016703,0.016703,0.016703,0.031697,0.001857,0.001857,0.001857,0.001857,0.031697,0.031697,0.031697,0.031697,0.031697,0.001857,0.00028994,0.016703,0.00028994,0.00028994
+24-Oct-2019 04:00:00,1,0.018685,0.018685,0.018685,0.022531,0.0055711,0.0055711,0.0055711,0.0055711,0.022531,0.022531,0.022531,0.022531,0.022531,0.0055711,0.00035685,0.018685,0.00035685,0.00035685
+24-Oct-2019 05:00:00,1,0.01472,0.01472,0.01472,0.01992,0.011142,0.011142,0.011142,0.011142,0.01992,0.01992,0.01992,0.01992,0.01992,0.011142,0.00053527,0.01472,0.00053527,0.00053527
+24-Oct-2019 06:00:00,1,0.0091148,0.0091148,0.0091148,0.031544,0.012999,0.012999,0.012999,0.012999,0.031544,0.031544,0.031544,0.031544,0.031544,0.012999,0.00020073,0.0091148,0.00020073,0.00020073
+24-Oct-2019 07:00:00,1,0.0091604,0.0091604,0.0091604,0.042861,0.011142,0.011142,0.011142,0.011142,0.042861,0.042861,0.042861,0.042861,0.042861,0.011142,0.00015612,0.0091604,0.00015612,0.00015612
+24-Oct-2019 08:00:00,1,0.0085451,0.0085451,0.0085451,0.041273,0.003714,0.003714,0.003714,0.003714,0.041273,0.041273,0.041273,0.041273,0.041273,0.003714,0.00089212,0.0085451,0.00089212,0.00089212
+24-Oct-2019 09:00:00,1,0.0083628,0.0083628,0.0083628,0.029649,0.003714,0.003714,0.003714,0.003714,0.029649,0.029649,0.029649,0.029649,0.029649,0.003714,0.000736,0.0083628,0.000736,0.000736
+24-Oct-2019 10:00:00,1,0.0071323,0.0071323,0.0071323,0.02079,0.001857,0.001857,0.001857,0.001857,0.02079,0.02079,0.02079,0.02079,0.02079,0.001857,0.00053527,0.0071323,0.00053527,0.00053527
+24-Oct-2019 11:00:00,1,0.0085223,0.0085223,0.0085223,0.022327,0.003714,0.003714,0.003714,0.003714,0.022327,0.022327,0.022327,0.022327,0.022327,0.003714,0.00026764,0.0085223,0.00026764,0.00026764
+24-Oct-2019 12:00:00,1,0.0073602,0.0073602,0.0073602,0.016386,0.001857,0.001857,0.001857,0.001857,0.016386,0.016386,0.016386,0.016386,0.016386,0.001857,4.46e-05,0.0073602,4.46e-05,4.46e-05
+24-Oct-2019 13:00:00,1,0.0073374,0.0073374,0.0073374,0.016745,0,0,0,0,0.016745,0.016745,0.016745,0.016745,0.016745,0,0,0.0073374,0,0
+24-Oct-2019 14:00:00,1,0.0061753,0.0061753,0.0061753,0.016028,0,0,0,0,0.016028,0.016028,0.016028,0.016028,0.016028,0,0,0.0061753,0,0
+24-Oct-2019 15:00:00,1,0.0067905,0.0067905,0.0067905,0.01229,0,0,0,0,0.01229,0.01229,0.01229,0.01229,0.01229,0,0,0.0067905,0,0
+24-Oct-2019 16:00:00,1,0.0070184,0.0070184,0.0070184,0.009627,0.001857,0.001857,0.001857,0.001857,0.009627,0.009627,0.009627,0.009627,0.009627,0.001857,8.92e-05,0.0070184,8.92e-05,8.92e-05
+24-Oct-2019 17:00:00,1,0.012077,0.012077,0.012077,0.013621,0.003714,0.003714,0.003714,0.003714,0.013621,0.013621,0.013621,0.013621,0.013621,0.003714,0.00033454,0.012077,0.00033454,0.00033454
+24-Oct-2019 18:00:00,1,0.011804,0.011804,0.011804,0.022019,0.003714,0.003714,0.003714,0.003714,0.022019,0.022019,0.022019,0.022019,0.022019,0.003714,4.46e-05,0.011804,4.46e-05,4.46e-05
+24-Oct-2019 19:00:00,1,0.0099123,0.0099123,0.0099123,0.030571,0.0074281,0.0074281,0.0074281,0.0074281,0.030571,0.030571,0.030571,0.030571,0.030571,0.0074281,0.00033454,0.0099123,0.00033454,0.00033454
+24-Oct-2019 20:00:00,1,0.010938,0.010938,0.010938,0.024887,0.003714,0.003714,0.003714,0.003714,0.024887,0.024887,0.024887,0.024887,0.024887,0.003714,0.0021411,0.010938,0.0021411,0.0021411
+24-Oct-2019 21:00:00,1,0.014219,0.014219,0.014219,0.018332,0.003714,0.003714,0.003714,0.003714,0.018332,0.018332,0.018332,0.018332,0.018332,0.003714,0.0049513,0.014219,0.0049513,0.0049513
+24-Oct-2019 22:00:00,1,0.013786,0.013786,0.013786,0.023197,0.003714,0.003714,0.003714,0.003714,0.023197,0.023197,0.023197,0.023197,0.023197,0.003714,0.0095234,0.013786,0.0095234,0.0095234
+24-Oct-2019 23:00:00,1,0.012259,0.012259,0.012259,0.015772,0.003714,0.003714,0.003714,0.003714,0.015772,0.015772,0.015772,0.015772,0.015772,0.003714,0.011107,0.012259,0.011107,0.011107
+25-Oct-2019 00:00:00,1,0.011302,0.011302,0.011302,0.011829,0.0055711,0.0055711,0.0055711,0.0055711,0.011829,0.011829,0.011829,0.011829,0.011829,0.0055711,0.012869,0.011302,0.012869,0.012869
+25-Oct-2019 01:00:00,1,0.012806,0.012806,0.012806,0.0069642,0.001857,0.001857,0.001857,0.001857,0.0069642,0.0069642,0.0069642,0.0069642,0.0069642,0.001857,0.021567,0.012806,0.021567,0.021567
+25-Oct-2019 02:00:00,1,0.01807,0.01807,0.01807,0.0064009,0.001857,0.001857,0.001857,0.001857,0.0064009,0.0064009,0.0064009,0.0064009,0.0064009,0.001857,0.028213,0.01807,0.028213,0.028213
+25-Oct-2019 03:00:00,1,0.026797,0.026797,0.026797,0.0077836,0.001857,0.001857,0.001857,0.001857,0.0077836,0.0077836,0.0077836,0.0077836,0.0077836,0.001857,0.045141,0.026797,0.045141,0.045141
+25-Oct-2019 04:00:00,1,0.034773,0.034773,0.034773,0.0098831,0.001857,0.001857,0.001857,0.001857,0.0098831,0.0098831,0.0098831,0.0098831,0.0098831,0.001857,0.066708,0.034773,0.066708,0.066708
+25-Oct-2019 05:00:00,1,0.045437,0.045437,0.045437,0.0091662,0.001857,0.001857,0.001857,0.001857,0.0091662,0.0091662,0.0091662,0.0091662,0.0091662,0.001857,0.075875,0.045437,0.075875,0.075875
+25-Oct-2019 06:00:00,1,0.062938,0.062938,0.062938,0.0084493,0.001857,0.001857,0.001857,0.001857,0.0084493,0.0084493,0.0084493,0.0084493,0.0084493,0.001857,0.096326,0.062938,0.096326,0.096326
+25-Oct-2019 07:00:00,1,0.079868,0.079868,0.079868,0.0086029,0,0,0,0,0.0086029,0.0086029,0.0086029,0.0086029,0.0086029,0,0.096549,0.079868,0.096549,0.096549
+25-Oct-2019 08:00:00,1,0.090897,0.090897,0.090897,0.0082444,0,0,0,0,0.0082444,0.0082444,0.0082444,0.0082444,0.0082444,0,0.10491,0.090897,0.10491,0.10491
+25-Oct-2019 09:00:00,1,0.11517,0.11517,0.11517,0.011727,0,0,0,0,0.011727,0.011727,0.011727,0.011727,0.011727,0,0.11183,0.11517,0.11183,0.11183
+25-Oct-2019 10:00:00,1,0.1413,0.1413,0.1413,0.010958,0,0,0,0,0.010958,0.010958,0.010958,0.010958,0.010958,0,0.12737,0.1413,0.12737,0.12737
+25-Oct-2019 11:00:00,1,0.18446,0.18446,0.18446,0.01147,0,0,0,0,0.01147,0.01147,0.01147,0.01147,0.01147,0,0.15822,0.18446,0.15822,0.15822
+25-Oct-2019 12:00:00,1,0.24125,0.24125,0.24125,0.018742,0,0,0,0,0.018742,0.018742,0.018742,0.018742,0.018742,0,0.21056,0.24125,0.21056,0.21056
+25-Oct-2019 13:00:00,1,0.31624,0.31624,0.31624,0.032619,0,0,0,0,0.032619,0.032619,0.032619,0.032619,0.032619,0,0.26028,0.31624,0.26028,0.26028
+25-Oct-2019 14:00:00,1,0.34493,0.34493,0.34493,0.049825,0,0,0,0,0.049825,0.049825,0.049825,0.049825,0.049825,0,0.27392,0.34493,0.27392,0.27392
+25-Oct-2019 15:00:00,1,0.37448,0.37448,0.37448,0.063907,0.001857,0.001857,0.001857,0.001857,0.063907,0.063907,0.063907,0.063907,0.063907,0.001857,0.31726,0.37448,0.31726,0.31726
+25-Oct-2019 16:00:00,1,0.36815,0.36815,0.36815,0.060886,0,0,0,0,0.060886,0.060886,0.060886,0.060886,0.060886,0,0.3401,0.36815,0.3401,0.3401
+25-Oct-2019 17:00:00,1,0.35921,0.35921,0.35921,0.075275,0,0,0,0,0.075275,0.075275,0.075275,0.075275,0.075275,0,0.35071,0.35921,0.35071,0.35071
+25-Oct-2019 18:00:00,1,0.32136,0.32136,0.32136,0.070564,0,0,0,0,0.070564,0.070564,0.070564,0.070564,0.070564,0,0.39924,0.32136,0.39924,0.39924
+25-Oct-2019 19:00:00,1,0.30008,0.30008,0.30008,0.074046,0.001857,0.001857,0.001857,0.001857,0.074046,0.074046,0.074046,0.074046,0.074046,0.001857,0.41341,0.30008,0.41341,0.41341
+25-Oct-2019 20:00:00,1,0.27258,0.27258,0.27258,0.077682,0.001857,0.001857,0.001857,0.001857,0.077682,0.077682,0.077682,0.077682,0.077682,0.001857,0.40647,0.27258,0.40647,0.40647
+25-Oct-2019 21:00:00,1,0.29297,0.29297,0.29297,0.065136,0.001857,0.001857,0.001857,0.001857,0.065136,0.065136,0.065136,0.065136,0.065136,0.001857,0.40988,0.29297,0.40988,0.40988
+25-Oct-2019 22:00:00,1,0.31564,0.31564,0.31564,0.05556,0.0055711,0.0055711,0.0055711,0.0055711,0.05556,0.05556,0.05556,0.05556,0.05556,0.0055711,0.43671,0.31564,0.43671,0.43671
+25-Oct-2019 23:00:00,1,0.35388,0.35388,0.35388,0.061654,0.0074281,0.0074281,0.0074281,0.0074281,0.061654,0.061654,0.061654,0.061654,0.061654,0.0074281,0.40774,0.35388,0.40774,0.40774
+26-Oct-2019 00:00:00,1,0.32316,0.32316,0.32316,0.086592,0.012999,0.012999,0.012999,0.012999,0.086592,0.086592,0.086592,0.086592,0.086592,0.012999,0.40264,0.32316,0.40264,0.40264
+26-Oct-2019 01:00:00,1,0.30687,0.30687,0.30687,0.10277,0.029712,0.029712,0.029712,0.029712,0.10277,0.10277,0.10277,0.10277,0.10277,0.029712,0.39175,0.30687,0.39175,0.39175
+26-Oct-2019 02:00:00,1,0.23252,0.23252,0.23252,0.11107,0.042711,0.042711,0.042711,0.042711,0.11107,0.11107,0.11107,0.11107,0.11107,0.042711,0.35803,0.23252,0.35803,0.35803
+26-Oct-2019 03:00:00,1,0.16108,0.16108,0.16108,0.10697,0.046425,0.046425,0.046425,0.046425,0.10697,0.10697,0.10697,0.10697,0.10697,0.046425,0.30044,0.16108,0.30044,0.30044
+26-Oct-2019 04:00:00,1,0.13196,0.13196,0.13196,0.10994,0.050139,0.050139,0.050139,0.050139,0.10994,0.10994,0.10994,0.10994,0.10994,0.050139,0.25642,0.13196,0.25642,0.25642
+26-Oct-2019 05:00:00,1,0.10377,0.10377,0.10377,0.13427,0.083566,0.083566,0.083566,0.083566,0.13427,0.13427,0.13427,0.13427,0.13427,0.083566,0.21021,0.10377,0.21021,0.21021
+26-Oct-2019 06:00:00,1,0.086682,0.086682,0.086682,0.16847,0.059425,0.059425,0.059425,0.059425,0.16847,0.16847,0.16847,0.16847,0.16847,0.059425,0.14153,0.086682,0.14153,0.14153
+26-Oct-2019 07:00:00,1,0.085975,0.085975,0.085975,0.18035,0.055711,0.055711,0.055711,0.055711,0.18035,0.18035,0.18035,0.18035,0.18035,0.055711,0.10545,0.085975,0.10545,0.10545
+26-Oct-2019 08:00:00,1,0.073716,0.073716,0.073716,0.19551,0.042711,0.042711,0.042711,0.042711,0.19551,0.19551,0.19551,0.19551,0.19551,0.042711,0.080692,0.073716,0.080692,0.080692
+26-Oct-2019 09:00:00,1,0.061342,0.061342,0.061342,0.191,0.046425,0.046425,0.046425,0.046425,0.191,0.191,0.191,0.191,0.191,0.046425,0.047639,0.061342,0.047639,0.047639
+26-Oct-2019 10:00:00,1,0.056352,0.056352,0.056352,0.15173,0.044568,0.044568,0.044568,0.044568,0.15173,0.15173,0.15173,0.15173,0.15173,0.044568,0.038896,0.056352,0.038896,0.038896
+26-Oct-2019 11:00:00,1,0.050519,0.050519,0.050519,0.17779,0.044568,0.044568,0.044568,0.044568,0.17779,0.17779,0.17779,0.17779,0.17779,0.044568,0.04755,0.050519,0.04755,0.04755
+26-Oct-2019 12:00:00,1,0.053549,0.053549,0.053549,0.14087,0.046425,0.046425,0.046425,0.046425,0.14087,0.14087,0.14087,0.14087,0.14087,0.046425,0.053549,0.053549,0.053549,0.053549
+26-Oct-2019 13:00:00,1,0.054324,0.054324,0.054324,0.11542,0.031569,0.031569,0.031569,0.031569,0.11542,0.11542,0.11542,0.11542,0.11542,0.031569,0.052746,0.054324,0.052746,0.052746
+26-Oct-2019 14:00:00,1,0.042931,0.042931,0.042931,0.089255,0.031569,0.031569,0.031569,0.031569,0.089255,0.089255,0.089255,0.089255,0.089255,0.031569,0.047884,0.042931,0.047884,0.047884
+26-Oct-2019 15:00:00,1,0.039604,0.039604,0.039604,0.10636,0.024141,0.024141,0.024141,0.024141,0.10636,0.10636,0.10636,0.10636,0.10636,0.024141,0.042666,0.039604,0.042666,0.042666
+26-Oct-2019 16:00:00,1,0.042657,0.042657,0.042657,0.1083,0.027855,0.027855,0.027855,0.027855,0.1083,0.1083,0.1083,0.1083,0.1083,0.027855,0.042777,0.042657,0.042777,0.042777
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+26-Oct-2019 18:00:00,1,0.048673,0.048673,0.048673,0.10876,0.038997,0.038997,0.038997,0.038997,0.10876,0.10876,0.10876,0.10876,0.10876,0.038997,0.062158,0.048673,0.062158,0.062158
+26-Oct-2019 19:00:00,1,0.051681,0.051681,0.051681,0.13073,0.024141,0.024141,0.024141,0.024141,0.13073,0.13073,0.13073,0.13073,0.13073,0.024141,0.076834,0.051681,0.076834,0.076834
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+27-Oct-2019 01:00:00,1,0.048605,0.048605,0.048605,0.11168,0.055711,0.055711,0.055711,0.055711,0.11168,0.11168,0.11168,0.11168,0.11168,0.055711,0.069853,0.048605,0.069853,0.069853
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+27-Oct-2019 08:00:00,1,0.14561,0.14561,0.14561,0.25696,0.070567,0.070567,0.070567,0.070567,0.25696,0.25696,0.25696,0.25696,0.25696,0.070567,0.22878,0.14561,0.22878,0.22878
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+27-Oct-2019 13:00:00,1,0.13927,0.13927,0.13927,0.13242,0.094708,0.094708,0.094708,0.094708,0.13242,0.13242,0.13242,0.13242,0.13242,0.094708,0.19595,0.13927,0.19595,0.19595
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+29-Oct-2019 21:00:00,1,0.3735,0.3735,0.3735,0.34913,0.051996,0.051996,0.051996,0.051996,0.34913,0.34913,0.34913,0.34913,0.34913,0.051996,0.31552,0.3735,0.31552,0.31552
+29-Oct-2019 22:00:00,1,0.34399,0.34399,0.34399,0.36291,0.055711,0.055711,0.055711,0.055711,0.36291,0.36291,0.36291,0.36291,0.36291,0.055711,0.30481,0.34399,0.30481,0.30481
+29-Oct-2019 23:00:00,1,0.32141,0.32141,0.32141,0.33572,0.06871,0.06871,0.06871,0.06871,0.33572,0.33572,0.33572,0.33572,0.33572,0.06871,0.28791,0.32141,0.28791,0.28791
+30-Oct-2019 00:00:00,1,0.26971,0.26971,0.26971,0.27094,0.050139,0.050139,0.050139,0.050139,0.27094,0.27094,0.27094,0.27094,0.27094,0.050139,0.26108,0.26971,0.26108,0.26108
+30-Oct-2019 01:00:00,1,0.20098,0.20098,0.20098,0.2861,0.022284,0.022284,0.022284,0.022284,0.2861,0.2861,0.2861,0.2861,0.2861,0.022284,0.25802,0.20098,0.25802,0.25802
+30-Oct-2019 02:00:00,1,0.14622,0.14622,0.14622,0.24984,0.038997,0.038997,0.038997,0.038997,0.24984,0.24984,0.24984,0.24984,0.24984,0.038997,0.22876,0.14622,0.22876,0.22876
+30-Oct-2019 03:00:00,1,0.13877,0.13877,0.13877,0.21732,0.029712,0.029712,0.029712,0.029712,0.21732,0.21732,0.21732,0.21732,0.21732,0.029712,0.22901,0.13877,0.22901,0.22901
+30-Oct-2019 04:00:00,1,0.12578,0.12578,0.12578,0.15588,0.050139,0.050139,0.050139,0.050139,0.15588,0.15588,0.15588,0.15588,0.15588,0.050139,0.24159,0.12578,0.24159,0.24159
+30-Oct-2019 05:00:00,1,0.12913,0.12913,0.12913,0.13959,0.033426,0.033426,0.033426,0.033426,0.13959,0.13959,0.13959,0.13959,0.13959,0.033426,0.23398,0.12913,0.23398,0.23398
+30-Oct-2019 06:00:00,1,0.1164,0.1164,0.1164,0.142,0.064996,0.064996,0.064996,0.064996,0.142,0.142,0.142,0.142,0.142,0.064996,0.22455,0.1164,0.22455,0.22455
+30-Oct-2019 07:00:00,1,0.10865,0.10865,0.10865,0.13703,0.070567,0.070567,0.070567,0.070567,0.13703,0.13703,0.13703,0.13703,0.13703,0.070567,0.214,0.10865,0.214,0.214
+30-Oct-2019 08:00:00,1,0.094475,0.094475,0.094475,0.09668,0.059425,0.059425,0.059425,0.059425,0.09668,0.09668,0.09668,0.09668,0.09668,0.059425,0.21924,0.094475,0.21924,0.21924
+30-Oct-2019 09:00:00,1,0.087775,0.087775,0.087775,0.072766,0.094708,0.094708,0.094708,0.094708,0.072766,0.072766,0.072766,0.072766,0.072766,0.094708,0.20708,0.087775,0.20708,0.20708
+30-Oct-2019 10:00:00,1,0.07203,0.07203,0.07203,0.086336,0.094708,0.094708,0.094708,0.094708,0.086336,0.086336,0.086336,0.086336,0.086336,0.094708,0.17151,0.07203,0.17151,0.17151
+30-Oct-2019 11:00:00,1,0.068498,0.068498,0.068498,0.074712,0.079852,0.079852,0.079852,0.079852,0.074712,0.074712,0.074712,0.074712,0.074712,0.079852,0.1358,0.068498,0.1358,0.1358
+30-Oct-2019 12:00:00,1,0.073374,0.073374,0.073374,0.072305,0.072424,0.072424,0.072424,0.072424,0.072305,0.072305,0.072305,0.072305,0.072305,0.072424,0.13451,0.073374,0.13451,0.13451
+30-Oct-2019 13:00:00,1,0.081646,0.081646,0.081646,0.083059,0.064996,0.064996,0.064996,0.064996,0.083059,0.083059,0.083059,0.083059,0.083059,0.064996,0.12021,0.081646,0.12021,0.12021
+30-Oct-2019 14:00:00,1,0.078752,0.078752,0.078752,0.079986,0.055711,0.055711,0.055711,0.055711,0.079986,0.079986,0.079986,0.079986,0.079986,0.055711,0.093583,0.078752,0.093583,0.093583
+30-Oct-2019 15:00:00,1,0.064122,0.064122,0.064122,0.045984,0.033426,0.033426,0.033426,0.033426,0.045984,0.045984,0.045984,0.045984,0.045984,0.033426,0.076566,0.064122,0.076566,0.076566
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+30-Oct-2019 17:00:00,1,0.067814,0.067814,0.067814,0.047316,0.035283,0.035283,0.035283,0.035283,0.047316,0.047316,0.047316,0.047316,0.047316,0.035283,0.086379,0.067814,0.086379,0.086379
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+30-Oct-2019 19:00:00,1,0.096115,0.096115,0.096115,0.072971,0.046425,0.046425,0.046425,0.046425,0.072971,0.072971,0.072971,0.072971,0.072971,0.046425,0.10775,0.096115,0.10775,0.10775
+30-Oct-2019 20:00:00,1,0.11801,0.11801,0.11801,0.073483,0.03714,0.03714,0.03714,0.03714,0.073483,0.073483,0.073483,0.073483,0.073483,0.03714,0.12851,0.11801,0.12851,0.12851
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+31-Oct-2019 13:00:00,1,0.25435,0.25435,0.25435,0.17779,0.0055711,0.0055711,0.0055711,0.0055711,0.17779,0.17779,0.17779,0.17779,0.17779,0.0055711,0.14078,0.25435,0.14078,0.14078
+31-Oct-2019 14:00:00,1,0.23769,0.23769,0.23769,0.21246,0.0092851,0.0092851,0.0092851,0.0092851,0.21246,0.21246,0.21246,0.21246,0.21246,0.0092851,0.15338,0.23769,0.15338,0.15338
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+31-Oct-2019 18:00:00,1,0.33681,0.33681,0.33681,0.31078,0.089137,0.089137,0.089137,0.089137,0.31078,0.31078,0.31078,0.31078,0.31078,0.089137,0.24787,0.33681,0.24787,0.24787
+31-Oct-2019 19:00:00,1,0.37136,0.37136,0.37136,0.27447,0.079852,0.079852,0.079852,0.079852,0.27447,0.27447,0.27447,0.27447,0.27447,0.079852,0.2758,0.37136,0.2758,0.2758
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+31-Oct-2019 21:00:00,1,0.36587,0.36587,0.36587,0.2864,0.029712,0.029712,0.029712,0.029712,0.2864,0.2864,0.2864,0.2864,0.2864,0.029712,0.30553,0.36587,0.30553,0.30553
+31-Oct-2019 22:00:00,1,0.36325,0.36325,0.36325,0.29132,0.029712,0.029712,0.029712,0.029712,0.29132,0.29132,0.29132,0.29132,0.29132,0.029712,0.29745,0.36325,0.29745,0.29745
+31-Oct-2019 23:00:00,1,0.32939,0.32939,0.32939,0.063702,0.012999,0.012999,0.012999,0.012999,0.063702,0.063702,0.063702,0.063702,0.063702,0.012999,0.3062,0.32939,0.3062,0.3062
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+01-Nov-2019 03:00:00,1,0.28511,0.28511,0.28511,0.21247,0.0018481,0.0018481,0.0018481,0.0018481,0.21247,0.21247,0.21247,0.21247,0.21247,0.0018481,0.25442,0.28511,0.25442,0.25442
+01-Nov-2019 04:00:00,1,0.25643,0.25643,0.25643,0.052037,0.0018481,0.0018481,0.0018481,0.0018481,0.052037,0.052037,0.052037,0.052037,0.052037,0.0018481,0.25724,0.25643,0.25724,0.25724
+01-Nov-2019 05:00:00,1,0.24172,0.24172,0.24172,0.20744,0.012937,0.012937,0.012937,0.012937,0.20744,0.20744,0.20744,0.20744,0.20744,0.012937,0.26415,0.24172,0.26415,0.26415
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+01-Nov-2019 07:00:00,1,0.23315,0.23315,0.23315,0.071093,0.03881,0.03881,0.03881,0.03881,0.071093,0.071093,0.071093,0.071093,0.071093,0.03881,0.28422,0.23315,0.28422,0.28422
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+01-Nov-2019 10:00:00,1,0.2134,0.2134,0.2134,0.43423,0.03881,0.03881,0.03881,0.03881,0.43423,0.43423,0.43423,0.43423,0.43423,0.03881,0.25376,0.2134,0.25376,0.25376
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+20-Nov-2019 07:00:00,1,0.14521,0.14521,0.14521,0.13045,0.0018481,0.0018481,0.0018481,0.0018481,0.13045,0.13045,0.13045,0.13045,0.13045,0.0018481,0.052073,0.14521,0.052073,0.052073
+20-Nov-2019 08:00:00,1,0.15237,0.15237,0.15237,0.11698,0.0018481,0.0018481,0.0018481,0.0018481,0.11698,0.11698,0.11698,0.11698,0.11698,0.0018481,0.047572,0.15237,0.047572,0.047572
+20-Nov-2019 09:00:00,1,0.12444,0.12444,0.12444,0.12699,0,0,0,0,0.12699,0.12699,0.12699,0.12699,0.12699,0,0.037464,0.12444,0.037464,0.037464
+20-Nov-2019 10:00:00,1,0.10256,0.10256,0.10256,0.086542,0,0,0,0,0.086542,0.086542,0.086542,0.086542,0.086542,0,0.032919,0.10256,0.032919,0.032919
+20-Nov-2019 11:00:00,1,0.090707,0.090707,0.090707,0.084865,0,0,0,0,0.084865,0.084865,0.084865,0.084865,0.084865,0,0.029306,0.090707,0.029306,0.029306
+20-Nov-2019 12:00:00,1,0.083429,0.083429,0.083429,0.043754,0,0,0,0,0.043754,0.043754,0.043754,0.043754,0.043754,0,0.022678,0.083429,0.022678,0.022678
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+20-Nov-2019 16:00:00,1,0.14745,0.14745,0.14745,0.077801,0.0018481,0.0018481,0.0018481,0.0018481,0.077801,0.077801,0.077801,0.077801,0.077801,0.0018481,0.037486,0.14745,0.037486,0.037486
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+20-Nov-2019 18:00:00,1,0.23054,0.23054,0.23054,0.14153,0.0036962,0.0036962,0.0036962,0.0036962,0.14153,0.14153,0.14153,0.14153,0.14153,0.0036962,0.040035,0.23054,0.040035,0.040035
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+20-Nov-2019 22:00:00,1,0.20848,0.20848,0.20848,0.10296,0.0073923,0.0073923,0.0073923,0.0073923,0.10296,0.10296,0.10296,0.10296,0.10296,0.0073923,0.03988,0.20848,0.03988,0.03988
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+21-Nov-2019 01:00:00,1,0.15412,0.15412,0.15412,0.12826,0.011088,0.011088,0.011088,0.011088,0.12826,0.12826,0.12826,0.12826,0.12826,0.011088,0.054511,0.15412,0.054511,0.054511
+21-Nov-2019 02:00:00,1,0.14376,0.14376,0.14376,0.10692,0.016633,0.016633,0.016633,0.016633,0.10692,0.10692,0.10692,0.10692,0.10692,0.016633,0.058856,0.14376,0.058856,0.058856
+21-Nov-2019 03:00:00,1,0.13646,0.13646,0.13646,0.095435,0.022177,0.022177,0.022177,0.022177,0.095435,0.095435,0.095435,0.095435,0.095435,0.022177,0.060097,0.13646,0.060097,0.060097
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+21-Nov-2019 05:00:00,1,0.12936,0.12936,0.12936,0.11007,0.025873,0.025873,0.025873,0.025873,0.11007,0.11007,0.11007,0.11007,0.11007,0.025873,0.088517,0.12936,0.088517,0.088517
+21-Nov-2019 06:00:00,1,0.13566,0.13566,0.13566,0.10433,0.020329,0.020329,0.020329,0.020329,0.10433,0.10433,0.10433,0.10433,0.10433,0.020329,0.09315,0.13566,0.09315,0.09315
+21-Nov-2019 07:00:00,1,0.14976,0.14976,0.14976,0.09635,0.025873,0.025873,0.025873,0.025873,0.09635,0.09635,0.09635,0.09635,0.09635,0.025873,0.099822,0.14976,0.099822,0.099822
+21-Nov-2019 08:00:00,1,0.16854,0.16854,0.16854,0.14295,0.03881,0.03881,0.03881,0.03881,0.14295,0.14295,0.14295,0.14295,0.14295,0.03881,0.10696,0.16854,0.10696,0.10696
+21-Nov-2019 09:00:00,1,0.1835,0.1835,0.1835,0.14391,0.014785,0.014785,0.014785,0.014785,0.14391,0.14391,0.14391,0.14391,0.14391,0.014785,0.11906,0.1835,0.11906,0.11906
+21-Nov-2019 10:00:00,1,0.2027,0.2027,0.2027,0.16058,0.0092404,0.0092404,0.0092404,0.0092404,0.16058,0.16058,0.16058,0.16058,0.16058,0.0092404,0.12614,0.2027,0.12614,0.12614
+21-Nov-2019 11:00:00,1,0.23682,0.23682,0.23682,0.17847,0.016633,0.016633,0.016633,0.016633,0.17847,0.17847,0.17847,0.17847,0.17847,0.016633,0.14363,0.23682,0.14363,0.14363
+21-Nov-2019 12:00:00,1,0.24684,0.24684,0.24684,0.17364,0.016633,0.016633,0.016633,0.016633,0.17364,0.17364,0.17364,0.17364,0.17364,0.016633,0.15573,0.24684,0.15573,0.15573
+21-Nov-2019 13:00:00,1,0.24736,0.24736,0.24736,0.16353,0.016633,0.016633,0.016633,0.016633,0.16353,0.16353,0.16353,0.16353,0.16353,0.016633,0.1681,0.24736,0.1681,0.1681
+21-Nov-2019 14:00:00,1,0.23464,0.23464,0.23464,0.18513,0.031417,0.031417,0.031417,0.031417,0.18513,0.18513,0.18513,0.18513,0.18513,0.031417,0.19707,0.23464,0.19707,0.19707
+21-Nov-2019 15:00:00,1,0.22369,0.22369,0.22369,0.18655,0.027721,0.027721,0.027721,0.027721,0.18655,0.18655,0.18655,0.18655,0.18655,0.027721,0.22893,0.22369,0.22893,0.22893
+21-Nov-2019 16:00:00,1,0.21803,0.21803,0.21803,0.25678,0.029569,0.029569,0.029569,0.029569,0.25678,0.25678,0.25678,0.25678,0.25678,0.029569,0.25296,0.21803,0.25296,0.25296
+21-Nov-2019 17:00:00,1,0.24675,0.24675,0.24675,0.34022,0.027721,0.027721,0.027721,0.027721,0.34022,0.34022,0.34022,0.34022,0.34022,0.027721,0.2855,0.24675,0.2855,0.2855
+21-Nov-2019 18:00:00,1,0.28092,0.28092,0.28092,0.39373,0.027721,0.027721,0.027721,0.027721,0.39373,0.39373,0.39373,0.39373,0.39373,0.027721,0.27991,0.28092,0.27991,0.27991
+21-Nov-2019 19:00:00,1,0.31966,0.31966,0.31966,0.42773,0.025873,0.025873,0.025873,0.025873,0.42773,0.42773,0.42773,0.42773,0.42773,0.025873,0.2552,0.31966,0.2552,0.2552
+21-Nov-2019 20:00:00,1,0.32714,0.32714,0.32714,0.45553,0.031417,0.031417,0.031417,0.031417,0.45553,0.45553,0.45553,0.45553,0.45553,0.031417,0.23245,0.32714,0.23245,0.23245
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+21-Nov-2019 22:00:00,1,0.35816,0.35816,0.35816,0.44221,0.060987,0.060987,0.060987,0.060987,0.44221,0.44221,0.44221,0.44221,0.44221,0.060987,0.20667,0.35816,0.20667,0.20667
+21-Nov-2019 23:00:00,1,0.40846,0.40846,0.40846,0.44983,0.029569,0.029569,0.029569,0.029569,0.44983,0.44983,0.44983,0.44983,0.44983,0.029569,0.21798,0.40846,0.21798,0.21798
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+22-Nov-2019 01:00:00,1,0.48575,0.48575,0.48575,0.46193,0.024025,0.024025,0.024025,0.024025,0.46193,0.46193,0.46193,0.46193,0.46193,0.024025,0.217,0.48575,0.217,0.217
+22-Nov-2019 02:00:00,1,0.49824,0.49824,0.49824,0.34886,0.014785,0.014785,0.014785,0.014785,0.34886,0.34886,0.34886,0.34886,0.34886,0.014785,0.20097,0.49824,0.20097,0.20097
+22-Nov-2019 03:00:00,1,0.53404,0.53404,0.53404,0.2487,0.016633,0.016633,0.016633,0.016633,0.2487,0.2487,0.2487,0.2487,0.2487,0.016633,0.22314,0.53404,0.22314,0.22314
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+22-Nov-2019 05:00:00,1,0.50878,0.50878,0.50878,0.1648,0.0073923,0.0073923,0.0073923,0.0073923,0.1648,0.1648,0.1648,0.1648,0.1648,0.0073923,0.26679,0.50878,0.26679,0.26679
+22-Nov-2019 06:00:00,1,0.4641,0.4641,0.4641,0.16195,0.0036962,0.0036962,0.0036962,0.0036962,0.16195,0.16195,0.16195,0.16195,0.16195,0.0036962,0.25859,0.4641,0.25859,0.25859
+22-Nov-2019 07:00:00,1,0.41499,0.41499,0.41499,0.15748,0.0036962,0.0036962,0.0036962,0.0036962,0.15748,0.15748,0.15748,0.15748,0.15748,0.0036962,0.27435,0.41499,0.27435,0.27435
+22-Nov-2019 08:00:00,1,0.34818,0.34818,0.34818,0.16074,0,0,0,0,0.16074,0.16074,0.16074,0.16074,0.16074,0,0.24912,0.34818,0.24912,0.24912
+22-Nov-2019 09:00:00,1,0.26527,0.26527,0.26527,0.15804,0,0,0,0,0.15804,0.15804,0.15804,0.15804,0.15804,0,0.22749,0.26527,0.22749,0.22749
+22-Nov-2019 10:00:00,1,0.23102,0.23102,0.23102,0.14188,0,0,0,0,0.14188,0.14188,0.14188,0.14188,0.14188,0,0.20889,0.23102,0.20889,0.20889
+22-Nov-2019 11:00:00,1,0.21331,0.21331,0.21331,0.13355,0,0,0,0,0.13355,0.13355,0.13355,0.13355,0.13355,0,0.17847,0.21331,0.17847,0.17847
+22-Nov-2019 12:00:00,1,0.201,0.201,0.201,0.15032,0,0,0,0,0.15032,0.15032,0.15032,0.15032,0.15032,0,0.14079,0.201,0.14079,0.14079
+22-Nov-2019 13:00:00,1,0.19184,0.19184,0.19184,0.1711,0,0,0,0,0.1711,0.1711,0.1711,0.1711,0.1711,0,0.10882,0.19184,0.10882,0.10882
+22-Nov-2019 14:00:00,1,0.17708,0.17708,0.17708,0.17674,0,0,0,0,0.17674,0.17674,0.17674,0.17674,0.17674,0,0.077876,0.17708,0.077876,0.077876
+22-Nov-2019 15:00:00,1,0.15264,0.15264,0.15264,0.15911,0.0018481,0.0018481,0.0018481,0.0018481,0.15911,0.15911,0.15911,0.15911,0.15911,0.0018481,0.066836,0.15264,0.066836,0.066836
+22-Nov-2019 16:00:00,1,0.1896,0.1896,0.1896,0.1898,0.0073923,0.0073923,0.0073923,0.0073923,0.1898,0.1898,0.1898,0.1898,0.1898,0.0073923,0.086832,0.1896,0.086832,0.086832
+22-Nov-2019 17:00:00,1,0.24589,0.24589,0.24589,0.21348,0.012937,0.012937,0.012937,0.012937,0.21348,0.21348,0.21348,0.21348,0.21348,0.012937,0.12503,0.24589,0.12503,0.12503
+22-Nov-2019 18:00:00,1,0.28815,0.28815,0.28815,0.25515,0.029569,0.029569,0.029569,0.029569,0.25515,0.25515,0.25515,0.25515,0.25515,0.029569,0.14682,0.28815,0.14682,0.14682
+22-Nov-2019 19:00:00,1,0.31318,0.31318,0.31318,0.26328,0.011088,0.011088,0.011088,0.011088,0.26328,0.26328,0.26328,0.26328,0.26328,0.011088,0.15604,0.31318,0.15604,0.15604
+22-Nov-2019 20:00:00,1,0.33419,0.33419,0.33419,0.2582,0.016633,0.016633,0.016633,0.016633,0.2582,0.2582,0.2582,0.2582,0.2582,0.016633,0.16163,0.33419,0.16163,0.16163
+22-Nov-2019 21:00:00,1,0.33682,0.33682,0.33682,0.28641,0.016633,0.016633,0.016633,0.016633,0.28641,0.28641,0.28641,0.28641,0.28641,0.016633,0.15143,0.33682,0.15143,0.15143
+22-Nov-2019 22:00:00,1,0.3404,0.3404,0.3404,0.26765,0.016633,0.016633,0.016633,0.016633,0.26765,0.26765,0.26765,0.26765,0.26765,0.016633,0.13984,0.3404,0.13984,0.13984
+22-Nov-2019 23:00:00,1,0.33993,0.33993,0.33993,0.25221,0.0092404,0.0092404,0.0092404,0.0092404,0.25221,0.25221,0.25221,0.25221,0.25221,0.0092404,0.12578,0.33993,0.12578,0.12578
+23-Nov-2019 00:00:00,1,0.35031,0.35031,0.35031,0.29052,0.0073923,0.0073923,0.0073923,0.0073923,0.29052,0.29052,0.29052,0.29052,0.29052,0.0073923,0.11392,0.35031,0.11392,0.11392
+23-Nov-2019 01:00:00,1,0.35793,0.35793,0.35793,0.32894,0.016633,0.016633,0.016633,0.016633,0.32894,0.32894,0.32894,0.32894,0.32894,0.016633,0.10916,0.35793,0.10916,0.10916
+23-Nov-2019 02:00:00,1,0.36446,0.36446,0.36446,0.34668,0.018481,0.018481,0.018481,0.018481,0.34668,0.34668,0.34668,0.34668,0.34668,0.018481,0.11146,0.36446,0.11146,0.11146
+23-Nov-2019 03:00:00,1,0.37759,0.37759,0.37759,0.36792,0.035113,0.035113,0.035113,0.035113,0.36792,0.36792,0.36792,0.36792,0.36792,0.035113,0.13549,0.37759,0.13549,0.13549
+23-Nov-2019 04:00:00,1,0.37965,0.37965,0.37965,0.35582,0.036962,0.036962,0.036962,0.036962,0.35582,0.35582,0.35582,0.35582,0.35582,0.036962,0.17621,0.37965,0.17621,0.17621
+23-Nov-2019 05:00:00,1,0.39033,0.39033,0.39033,0.40776,0.042506,0.042506,0.042506,0.042506,0.40776,0.40776,0.40776,0.40776,0.40776,0.042506,0.20135,0.39033,0.20135,0.20135
+23-Nov-2019 06:00:00,1,0.4079,0.4079,0.4079,0.39668,0.03881,0.03881,0.03881,0.03881,0.39668,0.39668,0.39668,0.39668,0.39668,0.03881,0.23919,0.4079,0.23919,0.23919
+23-Nov-2019 07:00:00,1,0.43868,0.43868,0.43868,0.39998,0.042506,0.042506,0.042506,0.042506,0.39998,0.39998,0.39998,0.39998,0.39998,0.042506,0.23427,0.43868,0.23427,0.23427
+23-Nov-2019 08:00:00,1,0.43351,0.43351,0.43351,0.41228,0.031417,0.031417,0.031417,0.031417,0.41228,0.41228,0.41228,0.41228,0.41228,0.031417,0.23414,0.43351,0.23414,0.23414
+23-Nov-2019 09:00:00,1,0.41148,0.41148,0.41148,0.40954,0.031417,0.031417,0.031417,0.031417,0.40954,0.40954,0.40954,0.40954,0.40954,0.031417,0.20949,0.41148,0.20949,0.20949
+23-Nov-2019 10:00:00,1,0.38026,0.38026,0.38026,0.37955,0.029569,0.029569,0.029569,0.029569,0.37955,0.37955,0.37955,0.37955,0.37955,0.029569,0.18351,0.38026,0.18351,0.18351
+23-Nov-2019 11:00:00,1,0.36074,0.36074,0.36074,0.31532,0.036962,0.036962,0.036962,0.036962,0.31532,0.31532,0.31532,0.31532,0.31532,0.036962,0.14203,0.36074,0.14203,0.14203
+23-Nov-2019 12:00:00,1,0.35743,0.35743,0.35743,0.29835,0.035113,0.035113,0.035113,0.035113,0.29835,0.29835,0.29835,0.29835,0.29835,0.035113,0.13591,0.35743,0.13591,0.13591
+23-Nov-2019 13:00:00,1,0.31263,0.31263,0.31263,0.2302,0.024025,0.024025,0.024025,0.024025,0.2302,0.2302,0.2302,0.2302,0.2302,0.024025,0.13961,0.31263,0.13961,0.13961
+23-Nov-2019 14:00:00,1,0.26983,0.26983,0.26983,0.20215,0.012937,0.012937,0.012937,0.012937,0.20215,0.20215,0.20215,0.20215,0.20215,0.012937,0.15888,0.26983,0.15888,0.15888
+23-Nov-2019 15:00:00,1,0.22762,0.22762,0.22762,0.19717,0.016633,0.016633,0.016633,0.016633,0.19717,0.19717,0.19717,0.19717,0.19717,0.016633,0.19401,0.22762,0.19401,0.19401
+23-Nov-2019 16:00:00,1,0.18876,0.18876,0.18876,0.14925,0.024025,0.024025,0.024025,0.024025,0.14925,0.14925,0.14925,0.14925,0.14925,0.024025,0.22017,0.18876,0.22017,0.22017
+23-Nov-2019 17:00:00,1,0.16822,0.16822,0.16822,0.11185,0.022177,0.022177,0.022177,0.022177,0.11185,0.11185,0.11185,0.11185,0.11185,0.022177,0.21893,0.16822,0.21893,0.21893
+23-Nov-2019 18:00:00,1,0.1441,0.1441,0.1441,0.13065,0.031417,0.031417,0.031417,0.031417,0.13065,0.13065,0.13065,0.13065,0.13065,0.031417,0.20627,0.1441,0.20627,0.20627
+23-Nov-2019 19:00:00,1,0.13564,0.13564,0.13564,0.12003,0.036962,0.036962,0.036962,0.036962,0.12003,0.12003,0.12003,0.12003,0.12003,0.036962,0.19962,0.13564,0.19962,0.19962
+23-Nov-2019 20:00:00,1,0.13949,0.13949,0.13949,0.1055,0.035113,0.035113,0.035113,0.035113,0.1055,0.1055,0.1055,0.1055,0.1055,0.035113,0.17956,0.13949,0.17956,0.17956
+23-Nov-2019 21:00:00,1,0.12034,0.12034,0.12034,0.10738,0.059138,0.059138,0.059138,0.059138,0.10738,0.10738,0.10738,0.10738,0.10738,0.059138,0.14921,0.12034,0.14921,0.14921
+23-Nov-2019 22:00:00,1,0.10077,0.10077,0.10077,0.094723,0.068379,0.068379,0.068379,0.068379,0.094723,0.094723,0.094723,0.094723,0.094723,0.068379,0.14939,0.10077,0.14939,0.14939
+23-Nov-2019 23:00:00,1,0.085968,0.085968,0.085968,0.10448,0.077619,0.077619,0.077619,0.077619,0.10448,0.10448,0.10448,0.10448,0.10448,0.077619,0.12212,0.085968,0.12212,0.12212
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+24-Nov-2019 01:00:00,1,0.040354,0.040354,0.040354,0.070941,0.046202,0.046202,0.046202,0.046202,0.070941,0.070941,0.070941,0.070941,0.070941,0.046202,0.088428,0.040354,0.088428,0.088428
+24-Nov-2019 02:00:00,1,0.037861,0.037861,0.037861,0.064843,0.020329,0.020329,0.020329,0.020329,0.064843,0.064843,0.064843,0.064843,0.064843,0.020329,0.072378,0.037861,0.072378,0.072378
+24-Nov-2019 03:00:00,1,0.038269,0.038269,0.038269,0.042534,0.024025,0.024025,0.024025,0.024025,0.042534,0.042534,0.042534,0.042534,0.042534,0.024025,0.057216,0.038269,0.057216,0.057216
+24-Nov-2019 04:00:00,1,0.031989,0.031989,0.031989,0.019006,0.014785,0.014785,0.014785,0.014785,0.019006,0.019006,0.019006,0.019006,0.019006,0.014785,0.039658,0.031989,0.039658,0.039658
+24-Nov-2019 05:00:00,1,0.028543,0.028543,0.028543,0.017532,0.018481,0.018481,0.018481,0.018481,0.017532,0.017532,0.017532,0.017532,0.017532,0.018481,0.028175,0.028543,0.028175,0.028175
+24-Nov-2019 06:00:00,1,0.028543,0.028543,0.028543,0.013111,0.029569,0.029569,0.029569,0.029569,0.013111,0.013111,0.013111,0.013111,0.013111,0.029569,0.016958,0.028543,0.016958,0.016958
+24-Nov-2019 07:00:00,1,0.025686,0.025686,0.025686,0.013517,0.022177,0.022177,0.022177,0.022177,0.013517,0.013517,0.013517,0.013517,0.013517,0.022177,0.011173,0.025686,0.011173,0.011173
+24-Nov-2019 08:00:00,1,0.026888,0.026888,0.026888,0.016973,0.018481,0.018481,0.018481,0.018481,0.016973,0.016973,0.016973,0.016973,0.016973,0.018481,0.0093992,0.026888,0.0093992,0.0093992
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+24-Nov-2019 10:00:00,1,0.020812,0.020812,0.020812,0.02236,0.012937,0.012937,0.012937,0.012937,0.02236,0.02236,0.02236,0.02236,0.02236,0.012937,0.0074484,0.020812,0.0074484,0.0074484
+24-Nov-2019 11:00:00,1,0.016913,0.016913,0.016913,0.032371,0.0036962,0.0036962,0.0036962,0.0036962,0.032371,0.032371,0.032371,0.032371,0.032371,0.0036962,0.0061849,0.016913,0.0061849,0.0061849
+24-Nov-2019 12:00:00,1,0.010678,0.010678,0.010678,0.040806,0.0036962,0.0036962,0.0036962,0.0036962,0.040806,0.040806,0.040806,0.040806,0.040806,0.0036962,0.0046996,0.010678,0.0046996,0.0046996
+24-Nov-2019 13:00:00,1,0.010089,0.010089,0.010089,0.041264,0.0055442,0.0055442,0.0055442,0.0055442,0.041264,0.041264,0.041264,0.041264,0.041264,0.0055442,0.0023498,0.010089,0.0023498,0.0023498
+24-Nov-2019 14:00:00,1,0.014918,0.014918,0.014918,0.051884,0.0036962,0.0036962,0.0036962,0.0036962,0.051884,0.051884,0.051884,0.051884,0.051884,0.0036962,0.0024163,0.014918,0.0024163,0.0024163
+24-Nov-2019 15:00:00,1,0.0258,0.0258,0.0258,0.070992,0.0092404,0.0092404,0.0092404,0.0092404,0.070992,0.070992,0.070992,0.070992,0.070992,0.0092404,0.0044336,0.0258,0.0044336,0.0044336
+24-Nov-2019 16:00:00,1,0.044367,0.044367,0.044367,0.12018,0.018481,0.018481,0.018481,0.018481,0.12018,0.12018,0.12018,0.12018,0.12018,0.018481,0.0098647,0.044367,0.0098647,0.0098647
+24-Nov-2019 17:00:00,1,0.074293,0.074293,0.074293,0.11368,0.027721,0.027721,0.027721,0.027721,0.11368,0.11368,0.11368,0.11368,0.11368,0.027721,0.021902,0.074293,0.021902,0.021902
+24-Nov-2019 18:00:00,1,0.10098,0.10098,0.10098,0.11942,0.033265,0.033265,0.033265,0.033265,0.11942,0.11942,0.11942,0.11942,0.11942,0.033265,0.044779,0.10098,0.044779,0.044779
+24-Nov-2019 19:00:00,1,0.13641,0.13641,0.13641,0.15291,0.05729,0.05729,0.05729,0.05729,0.15291,0.15291,0.15291,0.15291,0.15291,0.05729,0.058945,0.13641,0.058945,0.058945
+24-Nov-2019 20:00:00,1,0.18175,0.18175,0.18175,0.19082,0.060987,0.060987,0.060987,0.060987,0.19082,0.19082,0.19082,0.19082,0.19082,0.060987,0.068588,0.18175,0.068588,0.068588
+24-Nov-2019 21:00:00,1,0.20352,0.20352,0.20352,0.23813,0.024025,0.024025,0.024025,0.024025,0.23813,0.23813,0.23813,0.23813,0.23813,0.024025,0.084283,0.20352,0.084283,0.084283
+24-Nov-2019 22:00:00,1,0.22363,0.22363,0.22363,0.25007,0.018481,0.018481,0.018481,0.018481,0.25007,0.25007,0.25007,0.25007,0.25007,0.018481,0.083307,0.22363,0.083307,0.083307
+24-Nov-2019 23:00:00,1,0.26797,0.26797,0.26797,0.34652,0.064683,0.064683,0.064683,0.064683,0.34652,0.34652,0.34652,0.34652,0.34652,0.064683,0.092174,0.26797,0.092174,0.092174
+25-Nov-2019 00:00:00,1,0.32082,0.32082,0.32082,0.4105,0.068379,0.068379,0.068379,0.068379,0.4105,0.4105,0.4105,0.4105,0.4105,0.068379,0.10142,0.32082,0.10142,0.10142
+25-Nov-2019 01:00:00,1,0.3682,0.3682,0.3682,0.4909,0.10904,0.10904,0.10904,0.10904,0.4909,0.4909,0.4909,0.4909,0.4909,0.10904,0.12414,0.3682,0.12414,0.12414
+25-Nov-2019 02:00:00,1,0.4276,0.4276,0.4276,0.53861,0.10534,0.10534,0.10534,0.10534,0.53861,0.53861,0.53861,0.53861,0.53861,0.10534,0.15223,0.4276,0.15223,0.15223
+25-Nov-2019 03:00:00,1,0.47346,0.47346,0.47346,0.57256,0.12937,0.12937,0.12937,0.12937,0.57256,0.57256,0.57256,0.57256,0.57256,0.12937,0.17655,0.47346,0.17655,0.17655
+25-Nov-2019 04:00:00,1,0.51134,0.51134,0.51134,0.62393,0.11088,0.11088,0.11088,0.11088,0.62393,0.62393,0.62393,0.62393,0.62393,0.11088,0.22048,0.51134,0.22048,0.22048
+25-Nov-2019 05:00:00,1,0.5327,0.5327,0.5327,0.65072,0.12567,0.12567,0.12567,0.12567,0.65072,0.65072,0.65072,0.65072,0.65072,0.12567,0.28546,0.5327,0.28546,0.28546
+25-Nov-2019 06:00:00,1,0.54952,0.54952,0.54952,0.71383,0.16818,0.16818,0.16818,0.16818,0.71383,0.71383,0.71383,0.71383,0.71383,0.16818,0.35028,0.54952,0.35028,0.35028
+25-Nov-2019 07:00:00,1,0.53456,0.53456,0.53456,0.78421,0.14415,0.14415,0.14415,0.14415,0.78421,0.78421,0.78421,0.78421,0.78421,0.14415,0.4225,0.53456,0.4225,0.4225
+25-Nov-2019 08:00:00,1,0.53225,0.53225,0.53225,0.79163,0.092404,0.092404,0.092404,0.092404,0.79163,0.79163,0.79163,0.79163,0.79163,0.092404,0.4491,0.53225,0.4491,0.4491
+25-Nov-2019 09:00:00,1,0.58575,0.58575,0.58575,0.81115,0.13121,0.13121,0.13121,0.13121,0.81115,0.81115,0.81115,0.81115,0.81115,0.13121,0.4837,0.58575,0.4837,0.4837
+25-Nov-2019 10:00:00,1,0.58786,0.58786,0.58786,0.86013,0.090556,0.090556,0.090556,0.090556,0.86013,0.86013,0.86013,0.86013,0.86013,0.090556,0.49676,0.58786,0.49676,0.49676
+25-Nov-2019 11:00:00,1,0.60593,0.60593,0.60593,0.852,0.15524,0.15524,0.15524,0.15524,0.852,0.852,0.852,0.852,0.852,0.15524,0.44147,0.60593,0.44147,0.44147
+25-Nov-2019 12:00:00,1,0.63853,0.63853,0.63853,0.86262,0.18481,0.18481,0.18481,0.18481,0.86262,0.86262,0.86262,0.86262,0.86262,0.18481,0.42061,0.63853,0.42061,0.42061
+25-Nov-2019 13:00:00,1,0.72663,0.72663,0.72663,0.84047,0.097948,0.097948,0.097948,0.097948,0.84047,0.84047,0.84047,0.84047,0.84047,0.097948,0.43103,0.72663,0.43103,0.43103
+25-Nov-2019 14:00:00,1,0.73334,0.73334,0.73334,0.80215,0.18481,0.18481,0.18481,0.18481,0.80215,0.80215,0.80215,0.80215,0.80215,0.18481,0.44968,0.73334,0.44968,0.44968
+25-Nov-2019 15:00:00,1,0.74479,0.74479,0.74479,0.75596,0.12752,0.12752,0.12752,0.12752,0.75596,0.75596,0.75596,0.75596,0.75596,0.12752,0.46899,0.74479,0.46899,0.46899
+25-Nov-2019 16:00:00,1,0.7449,0.7449,0.7449,0.65935,0.079467,0.079467,0.079467,0.079467,0.65935,0.65935,0.65935,0.65935,0.65935,0.079467,0.48113,0.7449,0.48113,0.48113
+25-Nov-2019 17:00:00,1,0.74016,0.74016,0.74016,0.59939,0.040658,0.040658,0.040658,0.040658,0.59939,0.59939,0.59939,0.59939,0.59939,0.040658,0.49106,0.74016,0.49106,0.49106
+25-Nov-2019 18:00:00,1,0.74331,0.74331,0.74331,0.60757,0.035113,0.035113,0.035113,0.035113,0.60757,0.60757,0.60757,0.60757,0.60757,0.035113,0.51598,0.74331,0.51598,0.51598
+25-Nov-2019 19:00:00,1,0.74928,0.74928,0.74928,0.6029,0.033265,0.033265,0.033265,0.033265,0.6029,0.6029,0.6029,0.6029,0.6029,0.033265,0.53372,0.74928,0.53372,0.53372
+25-Nov-2019 20:00:00,1,0.76517,0.76517,0.76517,0.55823,0.025873,0.025873,0.025873,0.025873,0.55823,0.55823,0.55823,0.55823,0.55823,0.025873,0.54161,0.76517,0.54161,0.54161
+25-Nov-2019 21:00:00,1,0.7544,0.7544,0.7544,0.51915,0.011088,0.011088,0.011088,0.011088,0.51915,0.51915,0.51915,0.51915,0.51915,0.011088,0.56462,0.7544,0.56462,0.56462
+25-Nov-2019 22:00:00,1,0.70425,0.70425,0.70425,0.4573,0.012937,0.012937,0.012937,0.012937,0.4573,0.4573,0.4573,0.4573,0.4573,0.012937,0.562,0.70425,0.562,0.562
+25-Nov-2019 23:00:00,1,0.68122,0.68122,0.68122,0.44013,0.024025,0.024025,0.024025,0.024025,0.44013,0.44013,0.44013,0.44013,0.44013,0.024025,0.53797,0.68122,0.53797,0.53797
+26-Nov-2019 00:00:00,1,0.62676,0.62676,0.62676,0.34906,0.025873,0.025873,0.025873,0.025873,0.34906,0.34906,0.34906,0.34906,0.34906,0.025873,0.48147,0.62676,0.48147,0.48147
+26-Nov-2019 01:00:00,1,0.56002,0.56002,0.56002,0.28351,0.029569,0.029569,0.029569,0.029569,0.28351,0.28351,0.28351,0.28351,0.28351,0.029569,0.48284,0.56002,0.48284,0.48284
+26-Nov-2019 02:00:00,1,0.48237,0.48237,0.48237,0.2268,0.051746,0.051746,0.051746,0.051746,0.2268,0.2268,0.2268,0.2268,0.2268,0.051746,0.46486,0.48237,0.46486,0.46486
+26-Nov-2019 03:00:00,1,0.42937,0.42937,0.42937,0.20479,0.044354,0.044354,0.044354,0.044354,0.20479,0.20479,0.20479,0.20479,0.20479,0.044354,0.39264,0.42937,0.39264,0.39264
+26-Nov-2019 04:00:00,1,0.39237,0.39237,0.39237,0.19336,0.029569,0.029569,0.029569,0.029569,0.19336,0.19336,0.19336,0.19336,0.19336,0.029569,0.34934,0.39237,0.34934,0.34934
+26-Nov-2019 05:00:00,1,0.3388,0.3388,0.3388,0.2178,0.025873,0.025873,0.025873,0.025873,0.2178,0.2178,0.2178,0.2178,0.2178,0.025873,0.30095,0.3388,0.30095,0.30095
+26-Nov-2019 06:00:00,1,0.32803,0.32803,0.32803,0.20317,0.025873,0.025873,0.025873,0.025873,0.20317,0.20317,0.20317,0.20317,0.20317,0.025873,0.27493,0.32803,0.27493,0.27493
+26-Nov-2019 07:00:00,1,0.30241,0.30241,0.30241,0.25058,0.025873,0.025873,0.025873,0.025873,0.25058,0.25058,0.25058,0.25058,0.25058,0.025873,0.23338,0.30241,0.23338,0.23338
+26-Nov-2019 08:00:00,1,0.26015,0.26015,0.26015,0.24575,0.04805,0.04805,0.04805,0.04805,0.24575,0.24575,0.24575,0.24575,0.24575,0.04805,0.22208,0.26015,0.22208,0.22208
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+26-Nov-2019 10:00:00,1,0.156,0.156,0.156,0.19737,0.036962,0.036962,0.036962,0.036962,0.19737,0.19737,0.19737,0.19737,0.19737,0.036962,0.12483,0.156,0.12483,0.12483
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+27-Nov-2019 01:00:00,1,0.11116,0.11116,0.11116,0.24179,0.016633,0.016633,0.016633,0.016633,0.24179,0.24179,0.24179,0.24179,0.24179,0.016633,0.14172,0.11116,0.14172,0.14172
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+27-Nov-2019 13:00:00,1,0.031671,0.031671,0.031671,0.063013,0,0,0,0,0.063013,0.063013,0.063013,0.063013,0.063013,0,0.01933,0.031671,0.01933,0.01933
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+28-Nov-2019 01:00:00,1,0.013807,0.013807,0.013807,0.007775,0.0055442,0.0055442,0.0055442,0.0055442,0.007775,0.007775,0.007775,0.007775,0.007775,0.0055442,0.0044336,0.013807,0.0044336,0.0044336
+28-Nov-2019 02:00:00,1,0.011744,0.011744,0.011744,0.0065046,0.0073923,0.0073923,0.0073923,0.0073923,0.0065046,0.0065046,0.0065046,0.0065046,0.0065046,0.0073923,0.0050765,0.011744,0.0050765,0.0050765
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+28-Nov-2019 10:00:00,1,0.016686,0.016686,0.016686,0.051224,0.031417,0.031417,0.031417,0.031417,0.051224,0.051224,0.051224,0.051224,0.051224,0.031417,0.022478,0.016686,0.022478,0.022478
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+28-Nov-2019 20:00:00,1,0.096578,0.096578,0.096578,0.081054,0.072075,0.072075,0.072075,0.072075,0.081054,0.081054,0.081054,0.081054,0.081054,0.072075,0.093194,0.096578,0.093194,0.093194
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+28-Nov-2019 23:00:00,1,0.14355,0.14355,0.14355,0,0.11643,0.11643,0.11643,0.11643,0,0,0,0,0,0.11643,0.12467,0.14355,0.12467,0.12467
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+29-Nov-2019 01:00:00,1,0.14294,0.14294,0.14294,0.25144,0.14415,0.14415,0.14415,0.14415,0.25144,0.25144,0.25144,0.25144,0.25144,0.14415,0.16344,0.14294,0.16344,0.16344
+29-Nov-2019 02:00:00,1,0.16425,0.16425,0.16425,0.25048,0.11273,0.11273,0.11273,0.11273,0.25048,0.25048,0.25048,0.25048,0.25048,0.11273,0.19306,0.16425,0.19306,0.19306
+29-Nov-2019 03:00:00,1,0.1808,0.1808,0.1808,0.21582,0.11828,0.11828,0.11828,0.11828,0.21582,0.21582,0.21582,0.21582,0.21582,0.11828,0.22312,0.1808,0.22312,0.22312
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+29-Nov-2019 07:00:00,1,0.257,0.257,0.257,0.22141,0.094252,0.094252,0.094252,0.094252,0.22141,0.22141,0.22141,0.22141,0.22141,0.094252,0.31891,0.257,0.31891,0.31891
+29-Nov-2019 08:00:00,1,0.24773,0.24773,0.24773,0.14402,0.083163,0.083163,0.083163,0.083163,0.14402,0.14402,0.14402,0.14402,0.14402,0.083163,0.34983,0.24773,0.34983,0.34983
+29-Nov-2019 09:00:00,1,0.23251,0.23251,0.23251,0.14539,0.062835,0.062835,0.062835,0.062835,0.14539,0.14539,0.14539,0.14539,0.14539,0.062835,0.37659,0.23251,0.37659,0.37659
+29-Nov-2019 10:00:00,1,0.23446,0.23446,0.23446,0.16094,0.12013,0.12013,0.12013,0.12013,0.16094,0.16094,0.16094,0.16094,0.16094,0.12013,0.37847,0.23446,0.37847,0.37847
+29-Nov-2019 11:00:00,1,0.25514,0.25514,0.25514,0.1585,0.12013,0.12013,0.12013,0.12013,0.1585,0.1585,0.1585,0.1585,0.1585,0.12013,0.43547,0.25514,0.43547,0.43547
+29-Nov-2019 12:00:00,1,0.28259,0.28259,0.28259,0.15398,0.092404,0.092404,0.092404,0.092404,0.15398,0.15398,0.15398,0.15398,0.15398,0.092404,0.44059,0.28259,0.44059,0.44059
+29-Nov-2019 13:00:00,1,0.30542,0.30542,0.30542,0.17954,0.10534,0.10534,0.10534,0.10534,0.17954,0.17954,0.17954,0.17954,0.17954,0.10534,0.43281,0.30542,0.43281,0.43281
+29-Nov-2019 14:00:00,1,0.29275,0.29275,0.29275,0.18457,0.046202,0.046202,0.046202,0.046202,0.18457,0.18457,0.18457,0.18457,0.18457,0.046202,0.42489,0.29275,0.42489,0.42489
+29-Nov-2019 15:00:00,1,0.27226,0.27226,0.27226,0.17364,0.051746,0.051746,0.051746,0.051746,0.17364,0.17364,0.17364,0.17364,0.17364,0.051746,0.42977,0.27226,0.42977,0.42977
+29-Nov-2019 16:00:00,1,0.24904,0.24904,0.24904,0.15052,0.035113,0.035113,0.035113,0.035113,0.15052,0.15052,0.15052,0.15052,0.15052,0.035113,0.43551,0.24904,0.43551,0.43551
+29-Nov-2019 17:00:00,1,0.22793,0.22793,0.22793,0.13263,0.024025,0.024025,0.024025,0.024025,0.13263,0.13263,0.13263,0.13263,0.13263,0.024025,0.44254,0.22793,0.44254,0.44254
+29-Nov-2019 18:00:00,1,0.20361,0.20361,0.20361,0.11713,0.022177,0.022177,0.022177,0.022177,0.11713,0.11713,0.11713,0.11713,0.11713,0.022177,0.43988,0.20361,0.43988,0.43988
+29-Nov-2019 19:00:00,1,0.18332,0.18332,0.18332,0.10763,0.012937,0.012937,0.012937,0.012937,0.10763,0.10763,0.10763,0.10763,0.10763,0.012937,0.43804,0.18332,0.43804,0.43804
+29-Nov-2019 20:00:00,1,0.16008,0.16008,0.16008,0.095892,0.011088,0.011088,0.011088,0.011088,0.095892,0.095892,0.095892,0.095892,0.095892,0.011088,0.41995,0.16008,0.41995,0.41995
+29-Nov-2019 21:00:00,1,0.14523,0.14523,0.14523,0.079427,0.0092404,0.0092404,0.0092404,0.0092404,0.079427,0.079427,0.079427,0.079427,0.079427,0.0092404,0.37666,0.14523,0.37666,0.37666
+29-Nov-2019 22:00:00,1,0.13321,0.13321,0.13321,0.061641,0.0055442,0.0055442,0.0055442,0.0055442,0.061641,0.061641,0.061641,0.061641,0.061641,0.0055442,0.34739,0.13321,0.34739,0.34739
+29-Nov-2019 23:00:00,1,0.12508,0.12508,0.12508,0.055797,0.0036962,0.0036962,0.0036962,0.0036962,0.055797,0.055797,0.055797,0.055797,0.055797,0.0036962,0.33183,0.12508,0.33183,0.33183
+30-Nov-2019 00:00:00,1,0.1139,0.1139,0.1139,0.05158,0.0073923,0.0073923,0.0073923,0.0073923,0.05158,0.05158,0.05158,0.05158,0.05158,0.0073923,0.31412,0.1139,0.31412,0.31412
+30-Nov-2019 01:00:00,1,0.1105,0.1105,0.1105,0.048785,0.0092404,0.0092404,0.0092404,0.0092404,0.048785,0.048785,0.048785,0.048785,0.048785,0.0092404,0.29508,0.1105,0.29508,0.29508
+30-Nov-2019 02:00:00,1,0.10138,0.10138,0.10138,0.043042,0.0073923,0.0073923,0.0073923,0.0073923,0.043042,0.043042,0.043042,0.043042,0.043042,0.0073923,0.26313,0.10138,0.26313,0.26313
+30-Nov-2019 03:00:00,1,0.10621,0.10621,0.10621,0.046447,0.0055442,0.0055442,0.0055442,0.0055442,0.046447,0.046447,0.046447,0.046447,0.046447,0.0055442,0.2184,0.10621,0.2184,0.2184
+30-Nov-2019 04:00:00,1,0.11469,0.11469,0.11469,0.042026,0.0055442,0.0055442,0.0055442,0.0055442,0.042026,0.042026,0.042026,0.042026,0.042026,0.0055442,0.1843,0.11469,0.1843,0.1843
+30-Nov-2019 05:00:00,1,0.10973,0.10973,0.10973,0.033082,0.0073923,0.0073923,0.0073923,0.0073923,0.033082,0.033082,0.033082,0.033082,0.033082,0.0073923,0.16699,0.10973,0.16699,0.16699
+30-Nov-2019 06:00:00,1,0.10424,0.10424,0.10424,0.032167,0.0073923,0.0073923,0.0073923,0.0073923,0.032167,0.032167,0.032167,0.032167,0.032167,0.0073923,0.16466,0.10424,0.16466,0.16466
+30-Nov-2019 07:00:00,1,0.087238,0.087238,0.087238,0.026171,0.0073923,0.0073923,0.0073923,0.0073923,0.026171,0.026171,0.026171,0.026171,0.026171,0.0073923,0.15212,0.087238,0.15212,0.15212
+30-Nov-2019 08:00:00,1,0.078147,0.078147,0.078147,0.02363,0.0055442,0.0055442,0.0055442,0.0055442,0.02363,0.02363,0.02363,0.02363,0.02363,0.0055442,0.13662,0.078147,0.13662,0.13662
+30-Nov-2019 09:00:00,1,0.065202,0.065202,0.065202,0.017278,0.0036962,0.0036962,0.0036962,0.0036962,0.017278,0.017278,0.017278,0.017278,0.017278,0.0036962,0.11126,0.065202,0.11126,0.11126
+30-Nov-2019 10:00:00,1,0.056428,0.056428,0.056428,0.0082832,0.0036962,0.0036962,0.0036962,0.0036962,0.0082832,0.0082832,0.0082832,0.0082832,0.0082832,0.0036962,0.094569,0.056428,0.094569,0.094569
+30-Nov-2019 11:00:00,1,0.043302,0.043302,0.043302,0.0070636,0.0036962,0.0036962,0.0036962,0.0036962,0.0070636,0.0070636,0.0070636,0.0070636,0.0070636,0.0036962,0.092219,0.043302,0.092219,0.092219
+30-Nov-2019 12:00:00,1,0.036274,0.036274,0.036274,0.008334,0.0036962,0.0036962,0.0036962,0.0036962,0.008334,0.008334,0.008334,0.008334,0.008334,0.0036962,0.10457,0.036274,0.10457,0.10457
+30-Nov-2019 13:00:00,1,0.043302,0.043302,0.043302,0.010468,0.0018481,0.0018481,0.0018481,0.0018481,0.010468,0.010468,0.010468,0.010468,0.010468,0.0018481,0.11277,0.043302,0.11277,0.11277
+30-Nov-2019 14:00:00,1,0.049967,0.049967,0.049967,0.014026,0.0036962,0.0036962,0.0036962,0.0036962,0.014026,0.014026,0.014026,0.014026,0.014026,0.0036962,0.11113,0.049967,0.11113,0.11113
+30-Nov-2019 15:00:00,1,0.042553,0.042553,0.042553,0.01743,0.0018481,0.0018481,0.0018481,0.0018481,0.01743,0.01743,0.01743,0.01743,0.01743,0.0018481,0.099357,0.042553,0.099357,0.099357
+30-Nov-2019 16:00:00,1,0.045501,0.045501,0.045501,0.020022,0.0036962,0.0036962,0.0036962,0.0036962,0.020022,0.020022,0.020022,0.020022,0.020022,0.0036962,0.09315,0.045501,0.09315,0.09315
+30-Nov-2019 17:00:00,1,0.059557,0.059557,0.059557,0.023173,0.0073923,0.0073923,0.0073923,0.0073923,0.023173,0.023173,0.023173,0.023173,0.023173,0.0073923,0.088361,0.059557,0.088361,0.088361
+30-Nov-2019 18:00:00,1,0.075154,0.075154,0.075154,0.028458,0.0055442,0.0055442,0.0055442,0.0055442,0.028458,0.028458,0.028458,0.028458,0.028458,0.0055442,0.091731,0.075154,0.091731,0.091731
+30-Nov-2019 19:00:00,1,0.086853,0.086853,0.086853,0.03481,0.0092404,0.0092404,0.0092404,0.0092404,0.03481,0.03481,0.03481,0.03481,0.03481,0.0092404,0.10126,0.086853,0.10126,0.10126
+30-Nov-2019 20:00:00,1,0.084789,0.084789,0.084789,0.057271,0.0036962,0.0036962,0.0036962,0.0036962,0.057271,0.057271,0.057271,0.057271,0.057271,0.0036962,0.11208,0.084789,0.11208,0.11208
+30-Nov-2019 21:00:00,1,0.091636,0.091636,0.091636,0.07399,0.0055442,0.0055442,0.0055442,0.0055442,0.07399,0.07399,0.07399,0.07399,0.07399,0.0055442,0.099246,0.091636,0.099246,0.099246
+30-Nov-2019 22:00:00,1,0.096941,0.096941,0.096941,0.086796,0.0036962,0.0036962,0.0036962,0.0036962,0.086796,0.086796,0.086796,0.086796,0.086796,0.0036962,0.10109,0.096941,0.10109,0.10109
+30-Nov-2019 23:00:00,1,0.1068,0.1068,0.1068,0.075616,0.0036962,0.0036962,0.0036962,0.0036962,0.075616,0.075616,0.075616,0.075616,0.075616,0.0036962,0.098359,0.1068,0.098359,0.098359
+01-Dec-2019 00:00:00,1,0.11402,0.11402,0.11402,0.069345,0.0055177,0.0055177,0.0055177,0.0055177,0.069345,0.069345,0.069345,0.069345,0.069345,0.0055177,0.099376,0.11402,0.099376,0.099376
+01-Dec-2019 01:00:00,1,0.12119,0.12119,0.12119,0.07575,0.0055177,0.0055177,0.0055177,0.0055177,0.07575,0.07575,0.07575,0.07575,0.07575,0.0055177,0.10894,0.12119,0.10894,0.10894
+01-Dec-2019 02:00:00,1,0.14102,0.14102,0.14102,0.079533,0.0091961,0.0091961,0.0091961,0.0091961,0.079533,0.079533,0.079533,0.079533,0.079533,0.0091961,0.11581,0.14102,0.11581,0.11581
+01-Dec-2019 03:00:00,1,0.13863,0.13863,0.13863,0.075448,0.0073569,0.0073569,0.0073569,0.0073569,0.075448,0.075448,0.075448,0.075448,0.075448,0.0073569,0.11866,0.13863,0.11866,0.11866
+01-Dec-2019 04:00:00,1,0.13949,0.13949,0.13949,0.074086,0.0091961,0.0091961,0.0091961,0.0091961,0.074086,0.074086,0.074086,0.074086,0.074086,0.0091961,0.12289,0.13949,0.12289,0.12289
+01-Dec-2019 05:00:00,1,0.13861,0.13861,0.13861,0.069749,0.012875,0.012875,0.012875,0.012875,0.069749,0.069749,0.069749,0.069749,0.069749,0.012875,0.12751,0.13861,0.12751,0.12751
+01-Dec-2019 06:00:00,1,0.12345,0.12345,0.12345,0.070707,0.014714,0.014714,0.014714,0.014714,0.070707,0.070707,0.070707,0.070707,0.070707,0.014714,0.14142,0.12345,0.14142,0.14142
+01-Dec-2019 07:00:00,1,0.12981,0.12981,0.12981,0.068084,0.014714,0.014714,0.014714,0.014714,0.068084,0.068084,0.068084,0.068084,0.068084,0.014714,0.16105,0.12981,0.16105,0.16105
+01-Dec-2019 08:00:00,1,0.13563,0.13563,0.13563,0.072724,0.0091961,0.0091961,0.0091961,0.0091961,0.072724,0.072724,0.072724,0.072724,0.072724,0.0091961,0.16552,0.13563,0.16552,0.16552
+01-Dec-2019 09:00:00,1,0.12537,0.12537,0.12537,0.085837,0.0055177,0.0055177,0.0055177,0.0055177,0.085837,0.085837,0.085837,0.085837,0.085837,0.0055177,0.15999,0.12537,0.15999,0.15999
+01-Dec-2019 10:00:00,1,0.12453,0.12453,0.12453,0.072119,0.0036784,0.0036784,0.0036784,0.0036784,0.072119,0.072119,0.072119,0.072119,0.072119,0.0036784,0.15197,0.12453,0.15197,0.15197
+01-Dec-2019 11:00:00,1,0.11847,0.11847,0.11847,0.079987,0,0,0,0,0.079987,0.079987,0.079987,0.079987,0.079987,0,0.12899,0.11847,0.12899,0.12899
+01-Dec-2019 12:00:00,1,0.12077,0.12077,0.12077,0.097487,0,0,0,0,0.097487,0.097487,0.097487,0.097487,0.097487,0,0.11374,0.12077,0.11374,0.11374
+01-Dec-2019 13:00:00,1,0.11698,0.11698,0.11698,0.099958,0,0,0,0,0.099958,0.099958,0.099958,0.099958,0.099958,0,0.097635,0.11698,0.097635,0.097635
+01-Dec-2019 14:00:00,1,0.12679,0.12679,0.12679,0.10813,0,0,0,0,0.10813,0.10813,0.10813,0.10813,0.10813,0,0.097459,0.12679,0.097459,0.097459
+01-Dec-2019 15:00:00,1,0.11819,0.11819,0.11819,0.12956,0.0018392,0.0018392,0.0018392,0.0018392,0.12956,0.12956,0.12956,0.12956,0.12956,0.0018392,0.095256,0.11819,0.095256,0.095256
+01-Dec-2019 16:00:00,1,0.11847,0.11847,0.11847,0.15856,0.0036784,0.0036784,0.0036784,0.0036784,0.15856,0.15856,0.15856,0.15856,0.15856,0.0036784,0.11456,0.11847,0.11456,0.11456
+01-Dec-2019 17:00:00,1,0.15618,0.15618,0.15618,0.21424,0.0036784,0.0036784,0.0036784,0.0036784,0.21424,0.21424,0.21424,0.21424,0.21424,0.0036784,0.1154,0.15618,0.1154,0.1154
+01-Dec-2019 18:00:00,1,0.21167,0.21167,0.21167,0.26008,0.0055177,0.0055177,0.0055177,0.0055177,0.26008,0.26008,0.26008,0.26008,0.26008,0.0055177,0.13044,0.21167,0.13044,0.13044
+01-Dec-2019 19:00:00,1,0.24927,0.24927,0.24927,0.25514,0.0073569,0.0073569,0.0073569,0.0073569,0.25514,0.25514,0.25514,0.25514,0.25514,0.0073569,0.14232,0.24927,0.14232,0.14232
+01-Dec-2019 20:00:00,1,0.2637,0.2637,0.2637,0.26724,0.012875,0.012875,0.012875,0.012875,0.26724,0.26724,0.26724,0.26724,0.26724,0.012875,0.1408,0.2637,0.1408,0.1408
+01-Dec-2019 21:00:00,1,0.25748,0.25748,0.25748,0.28898,0.016553,0.016553,0.016553,0.016553,0.28898,0.28898,0.28898,0.28898,0.28898,0.016553,0.15556,0.25748,0.15556,0.15556
+01-Dec-2019 22:00:00,1,0.25107,0.25107,0.25107,0.29559,0.018392,0.018392,0.018392,0.018392,0.29559,0.29559,0.29559,0.29559,0.29559,0.018392,0.16112,0.25107,0.16112,0.16112
+01-Dec-2019 23:00:00,1,0.24185,0.24185,0.24185,0.26482,0.029428,0.029428,0.029428,0.029428,0.26482,0.26482,0.26482,0.26482,0.26482,0.029428,0.16154,0.24185,0.16154,0.16154
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+11-Dec-2019 17:00:00,1,0.14145,0.14145,0.14145,0.066925,0.012875,0.012875,0.012875,0.012875,0.066925,0.066925,0.066925,0.066925,0.066925,0.012875,0.34945,0.14145,0.34945,0.34945
+11-Dec-2019 18:00:00,1,0.13831,0.13831,0.13831,0.065865,0.018392,0.018392,0.018392,0.018392,0.065865,0.065865,0.065865,0.065865,0.065865,0.018392,0.33307,0.13831,0.33307,0.33307
+11-Dec-2019 19:00:00,1,0.12823,0.12823,0.12823,0.084526,0.018392,0.018392,0.018392,0.018392,0.084526,0.084526,0.084526,0.084526,0.084526,0.018392,0.30943,0.12823,0.30943,0.30943
+11-Dec-2019 20:00:00,1,0.11727,0.11727,0.11727,0.12195,0.016553,0.016553,0.016553,0.016553,0.12195,0.12195,0.12195,0.12195,0.12195,0.016553,0.28718,0.11727,0.28718,0.28718
+11-Dec-2019 21:00:00,1,0.098977,0.098977,0.098977,0.11468,0.018392,0.018392,0.018392,0.018392,0.11468,0.11468,0.11468,0.11468,0.11468,0.018392,0.26034,0.098977,0.26034,0.26034
+11-Dec-2019 22:00:00,1,0.09248,0.09248,0.09248,0.11569,0.022071,0.022071,0.022071,0.022071,0.11569,0.11569,0.11569,0.11569,0.11569,0.022071,0.2263,0.09248,0.2263,0.2263
+11-Dec-2019 23:00:00,1,0.078067,0.078067,0.078067,0.10863,0.016553,0.016553,0.016553,0.016553,0.10863,0.10863,0.10863,0.10863,0.10863,0.016553,0.19853,0.078067,0.19853,0.19853
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+12-Dec-2019 01:00:00,1,0.074075,0.074075,0.074075,0.12154,0.036784,0.036784,0.036784,0.036784,0.12154,0.12154,0.12154,0.12154,0.12154,0.036784,0.15775,0.074075,0.15775,0.15775
+12-Dec-2019 02:00:00,1,0.068751,0.068751,0.068751,0.1162,0.022071,0.022071,0.022071,0.022071,0.1162,0.1162,0.1162,0.1162,0.1162,0.022071,0.13655,0.068751,0.13655,0.13655
+12-Dec-2019 03:00:00,1,0.065368,0.065368,0.065368,0.10324,0.0091961,0.0091961,0.0091961,0.0091961,0.10324,0.10324,0.10324,0.10324,0.10324,0.0091961,0.12117,0.065368,0.12117,0.12117
+12-Dec-2019 04:00:00,1,0.061443,0.061443,0.061443,0.10677,0.012875,0.012875,0.012875,0.012875,0.10677,0.10677,0.10677,0.10677,0.10677,0.012875,0.12009,0.061443,0.12009,0.12009
+12-Dec-2019 05:00:00,1,0.050729,0.050729,0.050729,0.096831,0.02391,0.02391,0.02391,0.02391,0.096831,0.096831,0.096831,0.096831,0.096831,0.02391,0.10116,0.050729,0.10116,0.10116
+12-Dec-2019 06:00:00,1,0.043962,0.043962,0.043962,0.10127,0.0073569,0.0073569,0.0073569,0.0073569,0.10127,0.10127,0.10127,0.10127,0.10127,0.0073569,0.091884,0.043962,0.091884,0.091884
+12-Dec-2019 07:00:00,1,0.048,0.048,0.048,0.094057,0.025749,0.025749,0.025749,0.025749,0.094057,0.094057,0.094057,0.094057,0.094057,0.025749,0.079435,0.048,0.079435,0.079435
+12-Dec-2019 08:00:00,1,0.043218,0.043218,0.043218,0.10359,0.04782,0.04782,0.04782,0.04782,0.10359,0.10359,0.10359,0.10359,0.10359,0.04782,0.065487,0.043218,0.065487,0.065487
+12-Dec-2019 09:00:00,1,0.039721,0.039721,0.039721,0.095369,0.027588,0.027588,0.027588,0.027588,0.095369,0.095369,0.095369,0.095369,0.095369,0.027588,0.048322,0.039721,0.048322,0.048322
+12-Dec-2019 10:00:00,1,0.041548,0.041548,0.041548,0.095823,0.014714,0.014714,0.014714,0.014714,0.095823,0.095823,0.095823,0.095823,0.095823,0.014714,0.044223,0.041548,0.044223,0.044223
+12-Dec-2019 11:00:00,1,0.051834,0.051834,0.051834,0.081802,0.020231,0.020231,0.020231,0.020231,0.081802,0.081802,0.081802,0.081802,0.081802,0.020231,0.042373,0.051834,0.042373,0.042373
+12-Dec-2019 12:00:00,1,0.052624,0.052624,0.052624,0.068286,0.018392,0.018392,0.018392,0.018392,0.068286,0.068286,0.068286,0.068286,0.068286,0.018392,0.038494,0.052624,0.038494,0.038494
+12-Dec-2019 13:00:00,1,0.051609,0.051609,0.051609,0.076255,0.014714,0.014714,0.014714,0.014714,0.076255,0.076255,0.076255,0.076255,0.076255,0.014714,0.034793,0.051609,0.034793,0.034793
+12-Dec-2019 14:00:00,1,0.049556,0.049556,0.049556,0.072724,0.0073569,0.0073569,0.0073569,0.0073569,0.072724,0.072724,0.072724,0.072724,0.072724,0.0073569,0.035894,0.049556,0.035894,0.035894
+12-Dec-2019 15:00:00,1,0.06309,0.06309,0.06309,0.08276,0.012875,0.012875,0.012875,0.012875,0.08276,0.08276,0.08276,0.08276,0.08276,0.012875,0.026287,0.06309,0.026287,0.026287
+12-Dec-2019 16:00:00,1,0.065774,0.065774,0.065774,0.095016,0.0091961,0.0091961,0.0091961,0.0091961,0.095016,0.095016,0.095016,0.095016,0.095016,0.0091961,0.024723,0.065774,0.024723,0.024723
+12-Dec-2019 17:00:00,1,0.068165,0.068165,0.068165,0.11927,0.0073569,0.0073569,0.0073569,0.0073569,0.11927,0.11927,0.11927,0.11927,0.11927,0.0073569,0.031928,0.068165,0.031928,0.031928
+12-Dec-2019 18:00:00,1,0.070939,0.070939,0.070939,0.15236,0.018392,0.018392,0.018392,0.018392,0.15236,0.15236,0.15236,0.15236,0.15236,0.018392,0.034947,0.070939,0.034947,0.034947
+12-Dec-2019 19:00:00,1,0.070037,0.070037,0.070037,0.14565,0.018392,0.018392,0.018392,0.018392,0.14565,0.14565,0.14565,0.14565,0.14565,0.018392,0.031532,0.070037,0.031532,0.031532
+12-Dec-2019 20:00:00,1,0.068435,0.068435,0.068435,0.13748,0.012875,0.012875,0.012875,0.012875,0.13748,0.13748,0.13748,0.13748,0.13748,0.012875,0.028425,0.068435,0.028425,0.028425
+12-Dec-2019 21:00:00,1,0.0683,0.0683,0.0683,0.12391,0.044141,0.044141,0.044141,0.044141,0.12391,0.12391,0.12391,0.12391,0.12391,0.044141,0.027764,0.0683,0.027764,0.027764
+12-Dec-2019 22:00:00,1,0.067781,0.067781,0.067781,0.11443,0.034945,0.034945,0.034945,0.034945,0.11443,0.11443,0.11443,0.11443,0.11443,0.034945,0.028689,0.067781,0.028689,0.028689
+12-Dec-2019 23:00:00,1,0.072766,0.072766,0.072766,0.10662,0.049659,0.049659,0.049659,0.049659,0.10662,0.10662,0.10662,0.10662,0.10662,0.049659,0.031532,0.072766,0.031532,0.031532
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+13-Dec-2019 01:00:00,1,0.068503,0.068503,0.068503,0.086442,0.068051,0.068051,0.068051,0.068051,0.086442,0.086442,0.086442,0.086442,0.086442,0.068051,0.031928,0.068503,0.031928,0.031928
+13-Dec-2019 02:00:00,1,0.070984,0.070984,0.070984,0.090275,0.058855,0.058855,0.058855,0.058855,0.090275,0.090275,0.090275,0.090275,0.090275,0.058855,0.033823,0.070984,0.033823,0.033823
+13-Dec-2019 03:00:00,1,0.074278,0.074278,0.074278,0.096075,0.091961,0.091961,0.091961,0.091961,0.096075,0.096075,0.096075,0.096075,0.096075,0.091961,0.029725,0.074278,0.029725,0.029725
+13-Dec-2019 04:00:00,1,0.08048,0.08048,0.08048,0.077415,0.079087,0.079087,0.079087,0.079087,0.077415,0.077415,0.077415,0.077415,0.077415,0.079087,0.027081,0.08048,0.027081,0.027081
+13-Dec-2019 05:00:00,1,0.082623,0.082623,0.082623,0.079432,0.082765,0.082765,0.082765,0.082765,0.079432,0.079432,0.079432,0.079432,0.079432,0.082765,0.024502,0.082623,0.024502,0.024502
+13-Dec-2019 06:00:00,1,0.089503,0.089503,0.089503,0.099706,0.099318,0.099318,0.099318,0.099318,0.099706,0.099706,0.099706,0.099706,0.099706,0.099318,0.022806,0.089503,0.022806,0.022806
+13-Dec-2019 07:00:00,1,0.099563,0.099563,0.099563,0.1742,0.082765,0.082765,0.082765,0.082765,0.1742,0.1742,0.1742,0.1742,0.1742,0.082765,0.027499,0.099563,0.027499,0.027499
+13-Dec-2019 08:00:00,1,0.097104,0.097104,0.097104,0.19255,0.053337,0.053337,0.053337,0.053337,0.19255,0.19255,0.19255,0.19255,0.19255,0.053337,0.030276,0.097104,0.030276,0.030276
+13-Dec-2019 09:00:00,1,0.092683,0.092683,0.092683,0.21444,0.086443,0.086443,0.086443,0.086443,0.21444,0.21444,0.21444,0.21444,0.21444,0.086443,0.026265,0.092683,0.026265,0.026265
+13-Dec-2019 10:00:00,1,0.088759,0.088759,0.088759,0.15972,0.053337,0.053337,0.053337,0.053337,0.15972,0.15972,0.15972,0.15972,0.15972,0.053337,0.02534,0.088759,0.02534,0.02534
+13-Dec-2019 11:00:00,1,0.080548,0.080548,0.080548,0.15105,0.077247,0.077247,0.077247,0.077247,0.15105,0.15105,0.15105,0.15105,0.15105,0.077247,0.032633,0.080548,0.032633,0.032633
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+13-Dec-2019 14:00:00,1,0.092909,0.092909,0.092909,0.065966,0.04782,0.04782,0.04782,0.04782,0.065966,0.065966,0.065966,0.065966,0.065966,0.04782,0.048851,0.092909,0.048851,0.048851
+13-Dec-2019 15:00:00,1,0.13319,0.13319,0.13319,0.088762,0.051498,0.051498,0.051498,0.051498,0.088762,0.088762,0.088762,0.088762,0.088762,0.051498,0.071855,0.13319,0.071855,0.071855
+13-Dec-2019 16:00:00,1,0.16529,0.16529,0.16529,0.1219,0.064373,0.064373,0.064373,0.064373,0.1219,0.1219,0.1219,0.1219,0.1219,0.064373,0.10889,0.16529,0.10889,0.10889
+13-Dec-2019 17:00:00,1,0.19398,0.19398,0.19398,0.11907,0.051498,0.051498,0.051498,0.051498,0.11907,0.11907,0.11907,0.11907,0.11907,0.051498,0.14062,0.19398,0.14062,0.14062
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+13-Dec-2019 19:00:00,1,0.19247,0.19247,0.19247,0.17298,0.088283,0.088283,0.088283,0.088283,0.17298,0.17298,0.17298,0.17298,0.17298,0.088283,0.16618,0.19247,0.16618,0.16618
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+13-Dec-2019 21:00:00,1,0.18264,0.18264,0.18264,0.17122,0.053337,0.053337,0.053337,0.053337,0.17122,0.17122,0.17122,0.17122,0.17122,0.053337,0.17079,0.18264,0.17079,0.17079
+13-Dec-2019 22:00:00,1,0.17382,0.17382,0.17382,0.14091,0.086443,0.086443,0.086443,0.086443,0.14091,0.14091,0.14091,0.14091,0.14091,0.086443,0.151,0.17382,0.151,0.151
+13-Dec-2019 23:00:00,1,0.15934,0.15934,0.15934,0.12452,0.06989,0.06989,0.06989,0.06989,0.12452,0.12452,0.12452,0.12452,0.12452,0.06989,0.15918,0.15934,0.15918,0.15918
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+14-Dec-2019 01:00:00,1,0.15977,0.15977,0.15977,0.0815,0.042302,0.042302,0.042302,0.042302,0.0815,0.0815,0.0815,0.0815,0.0815,0.042302,0.14622,0.15977,0.14622,0.14622
+14-Dec-2019 02:00:00,1,0.17357,0.17357,0.17357,0.081298,0.057016,0.057016,0.057016,0.057016,0.081298,0.081298,0.081298,0.081298,0.081298,0.057016,0.13981,0.17357,0.13981,0.13981
+14-Dec-2019 03:00:00,1,0.17436,0.17436,0.17436,0.097638,0.068051,0.068051,0.068051,0.068051,0.097638,0.097638,0.097638,0.097638,0.097638,0.068051,0.13661,0.17436,0.13661,0.13661
+14-Dec-2019 04:00:00,1,0.17086,0.17086,0.17086,0.11201,0.040463,0.040463,0.040463,0.040463,0.11201,0.11201,0.11201,0.11201,0.11201,0.040463,0.14364,0.17086,0.14364,0.14364
+14-Dec-2019 05:00:00,1,0.15219,0.15219,0.15219,0.10525,0.058855,0.058855,0.058855,0.058855,0.10525,0.10525,0.10525,0.10525,0.10525,0.058855,0.14406,0.15219,0.14406,0.14406
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+14-Dec-2019 07:00:00,1,0.12532,0.12532,0.12532,0.1578,0.034945,0.034945,0.034945,0.034945,0.1578,0.1578,0.1578,0.1578,0.1578,0.034945,0.1167,0.12532,0.1167,0.1167
+14-Dec-2019 08:00:00,1,0.13755,0.13755,0.13755,0.22523,0.053337,0.053337,0.053337,0.053337,0.22523,0.22523,0.22523,0.22523,0.22523,0.053337,0.11672,0.13755,0.11672,0.11672
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+14-Dec-2019 11:00:00,1,0.20621,0.20621,0.20621,0.37956,0.073569,0.073569,0.073569,0.073569,0.37956,0.37956,0.37956,0.37956,0.37956,0.073569,0.11921,0.20621,0.11921,0.11921
+14-Dec-2019 12:00:00,1,0.22739,0.22739,0.22739,0.3906,0.084604,0.084604,0.084604,0.084604,0.3906,0.3906,0.3906,0.3906,0.3906,0.084604,0.11912,0.22739,0.11912,0.11912
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+14-Dec-2019 17:00:00,1,0.63038,0.63038,0.63038,0.6754,0.10116,0.10116,0.10116,0.10116,0.6754,0.6754,0.6754,0.6754,0.6754,0.10116,0.32713,0.63038,0.32713,0.32713
+14-Dec-2019 18:00:00,1,0.69365,0.69365,0.69365,0.72144,0.091961,0.091961,0.091961,0.091961,0.72144,0.72144,0.72144,0.72144,0.72144,0.091961,0.39356,0.69365,0.39356,0.39356
+14-Dec-2019 19:00:00,1,0.73429,0.73429,0.73429,0.75049,0.090122,0.090122,0.090122,0.090122,0.75049,0.75049,0.75049,0.75049,0.75049,0.090122,0.43501,0.73429,0.43501,0.43501
+14-Dec-2019 20:00:00,1,0.76612,0.76612,0.76612,0.71887,0.14346,0.14346,0.14346,0.14346,0.71887,0.71887,0.71887,0.71887,0.71887,0.14346,0.47136,0.76612,0.47136,0.47136
+14-Dec-2019 21:00:00,1,0.74187,0.74187,0.74187,0.67444,0.12507,0.12507,0.12507,0.12507,0.67444,0.67444,0.67444,0.67444,0.67444,0.12507,0.53114,0.74187,0.53114,0.53114
+14-Dec-2019 22:00:00,1,0.68819,0.68819,0.68819,0.57317,0.088283,0.088283,0.088283,0.088283,0.57317,0.57317,0.57317,0.57317,0.57317,0.088283,0.54225,0.68819,0.54225,0.54225
+14-Dec-2019 23:00:00,1,0.61878,0.61878,0.61878,0.6061,0.14714,0.14714,0.14714,0.14714,0.6061,0.6061,0.6061,0.6061,0.6061,0.14714,0.54778,0.61878,0.54778,0.54778
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+15-Dec-2019 01:00:00,1,0.52696,0.52696,0.52696,0.51795,0.10484,0.10484,0.10484,0.10484,0.51795,0.51795,0.51795,0.51795,0.51795,0.10484,0.46178,0.52696,0.46178,0.46178
+15-Dec-2019 02:00:00,1,0.5088,0.5088,0.5088,0.47074,0.11035,0.11035,0.11035,0.11035,0.47074,0.47074,0.47074,0.47074,0.47074,0.11035,0.49296,0.5088,0.49296,0.49296
+15-Dec-2019 03:00:00,1,0.4975,0.4975,0.4975,0.43645,0.068051,0.068051,0.068051,0.068051,0.43645,0.43645,0.43645,0.43645,0.43645,0.068051,0.50902,0.4975,0.50902,0.50902
+15-Dec-2019 04:00:00,1,0.45721,0.45721,0.45721,0.39302,0.12507,0.12507,0.12507,0.12507,0.39302,0.39302,0.39302,0.39302,0.39302,0.12507,0.47994,0.45721,0.47994,0.47994
+15-Dec-2019 05:00:00,1,0.40536,0.40536,0.40536,0.3963,0.049659,0.049659,0.049659,0.049659,0.3963,0.3963,0.3963,0.3963,0.3963,0.049659,0.39482,0.40536,0.39482,0.39482
+15-Dec-2019 06:00:00,1,0.39324,0.39324,0.39324,0.38455,0.10851,0.10851,0.10851,0.10851,0.38455,0.38455,0.38455,0.38455,0.38455,0.10851,0.35557,0.39324,0.35557,0.35557
+15-Dec-2019 07:00:00,1,0.36507,0.36507,0.36507,0.45188,0.14898,0.14898,0.14898,0.14898,0.45188,0.45188,0.45188,0.45188,0.45188,0.14898,0.32649,0.36507,0.32649,0.32649
+15-Dec-2019 08:00:00,1,0.34883,0.34883,0.34883,0.44043,0.11587,0.11587,0.11587,0.11587,0.44043,0.44043,0.44043,0.44043,0.44043,0.11587,0.30703,0.34883,0.30703,0.30703
+15-Dec-2019 09:00:00,1,0.33442,0.33442,0.33442,0.39716,0.1876,0.1876,0.1876,0.1876,0.39716,0.39716,0.39716,0.39716,0.39716,0.1876,0.29502,0.33442,0.29502,0.29502
+15-Dec-2019 10:00:00,1,0.3339,0.3339,0.3339,0.44497,0.103,0.103,0.103,0.103,0.44497,0.44497,0.44497,0.44497,0.44497,0.103,0.26642,0.3339,0.26642,0.26642
+15-Dec-2019 11:00:00,1,0.31382,0.31382,0.31382,0.41683,0.12691,0.12691,0.12691,0.12691,0.41683,0.41683,0.41683,0.41683,0.41683,0.12691,0.21429,0.31382,0.21429,0.21429
+15-Dec-2019 12:00:00,1,0.30122,0.30122,0.30122,0.40518,0.09564,0.09564,0.09564,0.09564,0.40518,0.40518,0.40518,0.40518,0.40518,0.09564,0.18939,0.30122,0.18939,0.18939
+15-Dec-2019 13:00:00,1,0.26855,0.26855,0.26855,0.38944,0.062534,0.062534,0.062534,0.062534,0.38944,0.38944,0.38944,0.38944,0.38944,0.062534,0.18313,0.26855,0.18313,0.18313
+15-Dec-2019 14:00:00,1,0.24101,0.24101,0.24101,0.34027,0.049659,0.049659,0.049659,0.049659,0.34027,0.34027,0.34027,0.34027,0.34027,0.049659,0.18562,0.24101,0.18562,0.18562
+15-Dec-2019 15:00:00,1,0.2125,0.2125,0.2125,0.32549,0.073569,0.073569,0.073569,0.073569,0.32549,0.32549,0.32549,0.32549,0.32549,0.073569,0.18677,0.2125,0.18677,0.18677
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+15-Dec-2019 17:00:00,1,0.23111,0.23111,0.23111,0.3675,0.088283,0.088283,0.088283,0.088283,0.3675,0.3675,0.3675,0.3675,0.3675,0.088283,0.19144,0.23111,0.19144,0.19144
+15-Dec-2019 18:00:00,1,0.2545,0.2545,0.2545,0.35116,0.060694,0.060694,0.060694,0.060694,0.35116,0.35116,0.35116,0.35116,0.35116,0.060694,0.17359,0.2545,0.17359,0.17359
+15-Dec-2019 19:00:00,1,0.24801,0.24801,0.24801,0.36009,0.0938,0.0938,0.0938,0.0938,0.36009,0.36009,0.36009,0.36009,0.36009,0.0938,0.17136,0.24801,0.17136,0.17136
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+15-Dec-2019 21:00:00,1,0.24575,0.24575,0.24575,0.41254,0.09564,0.09564,0.09564,0.09564,0.41254,0.41254,0.41254,0.41254,0.41254,0.09564,0.17059,0.24575,0.17059,0.17059
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+15-Dec-2019 23:00:00,1,0.22811,0.22811,0.22811,0.34244,0.1361,0.1361,0.1361,0.1361,0.34244,0.34244,0.34244,0.34244,0.34244,0.1361,0.15418,0.22811,0.15418,0.15418
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+16-Dec-2019 01:00:00,1,0.20616,0.20616,0.20616,0.35535,0.10484,0.10484,0.10484,0.10484,0.35535,0.35535,0.35535,0.35535,0.35535,0.10484,0.12941,0.20616,0.12941,0.12941
+16-Dec-2019 02:00:00,1,0.20366,0.20366,0.20366,0.3374,0.1453,0.1453,0.1453,0.1453,0.3374,0.3374,0.3374,0.3374,0.3374,0.1453,0.13161,0.20366,0.13161,0.13161
+16-Dec-2019 03:00:00,1,0.19407,0.19407,0.19407,0.35817,0.15817,0.15817,0.15817,0.15817,0.35817,0.35817,0.35817,0.35817,0.35817,0.15817,0.14016,0.19407,0.14016,0.14016
+16-Dec-2019 04:00:00,1,0.18981,0.18981,0.18981,0.40457,0.17473,0.17473,0.17473,0.17473,0.40457,0.40457,0.40457,0.40457,0.40457,0.17473,0.17418,0.18981,0.17418,0.17418
+16-Dec-2019 05:00:00,1,0.18388,0.18388,0.18388,0.37875,0.091961,0.091961,0.091961,0.091961,0.37875,0.37875,0.37875,0.37875,0.37875,0.091961,0.19049,0.18388,0.19049,0.19049
+16-Dec-2019 06:00:00,1,0.18079,0.18079,0.18079,0.35631,0.11587,0.11587,0.11587,0.11587,0.35631,0.35631,0.35631,0.35631,0.35631,0.11587,0.19069,0.18079,0.19069,0.19069
+16-Dec-2019 07:00:00,1,0.18189,0.18189,0.18189,0.35974,0.088283,0.088283,0.088283,0.088283,0.35974,0.35974,0.35974,0.35974,0.35974,0.088283,0.20891,0.18189,0.20891,0.20891
+16-Dec-2019 08:00:00,1,0.18399,0.18399,0.18399,0.32433,0.10851,0.10851,0.10851,0.10851,0.32433,0.32433,0.32433,0.32433,0.32433,0.10851,0.20997,0.18399,0.20997,0.20997
+16-Dec-2019 09:00:00,1,0.18437,0.18437,0.18437,0.28137,0.11035,0.11035,0.11035,0.11035,0.28137,0.28137,0.28137,0.28137,0.28137,0.11035,0.19487,0.18437,0.19487,0.19487
+16-Dec-2019 10:00:00,1,0.18706,0.18706,0.18706,0.30527,0.11219,0.11219,0.11219,0.11219,0.30527,0.30527,0.30527,0.30527,0.30527,0.11219,0.18569,0.18706,0.18569,0.18569
+16-Dec-2019 11:00:00,1,0.19107,0.19107,0.19107,0.3316,0.099318,0.099318,0.099318,0.099318,0.3316,0.3316,0.3316,0.3316,0.3316,0.099318,0.18439,0.19107,0.18439,0.18439
+16-Dec-2019 12:00:00,1,0.19094,0.19094,0.19094,0.25786,0.10116,0.10116,0.10116,0.10116,0.25786,0.25786,0.25786,0.25786,0.25786,0.10116,0.15076,0.19094,0.15076,0.15076
+16-Dec-2019 13:00:00,1,0.17386,0.17386,0.17386,0.34289,0.045981,0.045981,0.045981,0.045981,0.34289,0.34289,0.34289,0.34289,0.34289,0.045981,0.1313,0.17386,0.1313,0.1313
+16-Dec-2019 14:00:00,1,0.15325,0.15325,0.15325,0.33104,0.079087,0.079087,0.079087,0.079087,0.33104,0.33104,0.33104,0.33104,0.33104,0.079087,0.11773,0.15325,0.11773,0.11773
+16-Dec-2019 15:00:00,1,0.14244,0.14244,0.14244,0.30789,0.080926,0.080926,0.080926,0.080926,0.30789,0.30789,0.30789,0.30789,0.30789,0.080926,0.11526,0.14244,0.11526,0.11526
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+16-Dec-2019 18:00:00,1,0.13198,0.13198,0.13198,0.20864,0.12875,0.12875,0.12875,0.12875,0.20864,0.20864,0.20864,0.20864,0.20864,0.12875,0.15054,0.13198,0.15054,0.15054
+16-Dec-2019 19:00:00,1,0.11966,0.11966,0.11966,0.19296,0.09564,0.09564,0.09564,0.09564,0.19296,0.19296,0.19296,0.19296,0.19296,0.09564,0.13935,0.11966,0.13935,0.13935
+16-Dec-2019 20:00:00,1,0.11702,0.11702,0.11702,0.15725,0.10667,0.10667,0.10667,0.10667,0.15725,0.15725,0.15725,0.15725,0.15725,0.10667,0.14512,0.11702,0.14512,0.14512
+16-Dec-2019 21:00:00,1,0.114,0.114,0.114,0.14948,0.097479,0.097479,0.097479,0.097479,0.14948,0.14948,0.14948,0.14948,0.14948,0.097479,0.13188,0.114,0.13188,0.13188
+16-Dec-2019 22:00:00,1,0.10405,0.10405,0.10405,0.14126,0.07173,0.07173,0.07173,0.07173,0.14126,0.14126,0.14126,0.14126,0.14126,0.07173,0.11429,0.10405,0.11429,0.11429
+16-Dec-2019 23:00:00,1,0.09151,0.09151,0.09151,0.11156,0.042302,0.042302,0.042302,0.042302,0.11156,0.11156,0.11156,0.11156,0.11156,0.042302,0.09021,0.09151,0.09021,0.09021
+17-Dec-2019 00:00:00,1,0.081586,0.081586,0.081586,0.076608,0.075408,0.075408,0.075408,0.075408,0.076608,0.076608,0.076608,0.076608,0.076608,0.075408,0.08556,0.081586,0.08556,0.08556
+17-Dec-2019 01:00:00,1,0.076849,0.076849,0.076849,0.076658,0.068051,0.068051,0.068051,0.068051,0.076658,0.076658,0.076658,0.076658,0.076658,0.068051,0.074918,0.076849,0.074918,0.074918
+17-Dec-2019 02:00:00,1,0.069293,0.069293,0.069293,0.099454,0.027588,0.027588,0.027588,0.027588,0.099454,0.099454,0.099454,0.099454,0.099454,0.027588,0.064826,0.069293,0.064826,0.064826
+17-Dec-2019 03:00:00,1,0.059684,0.059684,0.059684,0.15084,0.04782,0.04782,0.04782,0.04782,0.15084,0.15084,0.15084,0.15084,0.15084,0.04782,0.058237,0.059684,0.058237,0.058237
+17-Dec-2019 04:00:00,1,0.05833,0.05833,0.05833,0.1228,0.058855,0.058855,0.058855,0.058855,0.1228,0.1228,0.1228,0.1228,0.1228,0.058855,0.049313,0.05833,0.049313,0.049313
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+17-Dec-2019 06:00:00,1,0.055037,0.055037,0.055037,0.14383,0.038624,0.038624,0.038624,0.038624,0.14383,0.14383,0.14383,0.14383,0.14383,0.038624,0.040918,0.055037,0.040918,0.040918
+17-Dec-2019 07:00:00,1,0.057248,0.057248,0.057248,0.14318,0.058855,0.058855,0.058855,0.058855,0.14318,0.14318,0.14318,0.14318,0.14318,0.058855,0.048983,0.057248,0.048983,0.048983
+17-Dec-2019 08:00:00,1,0.059842,0.059842,0.059842,0.13809,0.053337,0.053337,0.053337,0.053337,0.13809,0.13809,0.13809,0.13809,0.13809,0.053337,0.036093,0.059842,0.036093,0.036093
+17-Dec-2019 09:00:00,1,0.061488,0.061488,0.061488,0.18443,0.036784,0.036784,0.036784,0.036784,0.18443,0.18443,0.18443,0.18443,0.18443,0.036784,0.032104,0.061488,0.032104,0.032104
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+17-Dec-2019 11:00:00,1,0.048631,0.048631,0.048631,0.15296,0.075408,0.075408,0.075408,0.075408,0.15296,0.15296,0.15296,0.15296,0.15296,0.075408,0.022277,0.048631,0.022277,0.022277
+17-Dec-2019 12:00:00,1,0.05021,0.05021,0.05021,0.1514,0.064373,0.064373,0.064373,0.064373,0.1514,0.1514,0.1514,0.1514,0.1514,0.064373,0.020955,0.05021,0.020955,0.020955
+17-Dec-2019 13:00:00,1,0.046218,0.046218,0.046218,0.16113,0.04782,0.04782,0.04782,0.04782,0.16113,0.16113,0.16113,0.16113,0.16113,0.04782,0.016129,0.046218,0.016129,0.016129
+17-Dec-2019 14:00:00,1,0.049939,0.049939,0.049939,0.16703,0.049659,0.049659,0.049659,0.049659,0.16703,0.16703,0.16703,0.16703,0.16703,0.049659,0.018509,0.049939,0.018509,0.018509
+17-Dec-2019 15:00:00,1,0.052894,0.052894,0.052894,0.15402,0.066212,0.066212,0.066212,0.066212,0.15402,0.15402,0.15402,0.15402,0.15402,0.066212,0.025186,0.052894,0.025186,0.025186
+17-Dec-2019 16:00:00,1,0.067578,0.067578,0.067578,0.13995,0.11035,0.11035,0.11035,0.11035,0.13995,0.13995,0.13995,0.13995,0.13995,0.11035,0.034616,0.067578,0.034616,0.034616
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+17-Dec-2019 18:00:00,1,0.083187,0.083187,0.083187,0.16048,0.082765,0.082765,0.082765,0.082765,0.16048,0.16048,0.16048,0.16048,0.16048,0.082765,0.056915,0.083187,0.056915,0.056915
+17-Dec-2019 19:00:00,1,0.088601,0.088601,0.088601,0.16603,0.0938,0.0938,0.0938,0.0938,0.16603,0.16603,0.16603,0.16603,0.16603,0.0938,0.058259,0.088601,0.058259,0.058259
+17-Dec-2019 20:00:00,1,0.081428,0.081428,0.081428,0.1342,0.090122,0.090122,0.090122,0.090122,0.1342,0.1342,0.1342,0.1342,0.1342,0.090122,0.053875,0.081428,0.053875,0.053875
+17-Dec-2019 21:00:00,1,0.072856,0.072856,0.072856,0.13778,0.080926,0.080926,0.080926,0.080926,0.13778,0.13778,0.13778,0.13778,0.13778,0.080926,0.049512,0.072856,0.049512,0.049512
+17-Dec-2019 22:00:00,1,0.067263,0.067263,0.067263,0.13481,0.053337,0.053337,0.053337,0.053337,0.13481,0.13481,0.13481,0.13481,0.13481,0.053337,0.03682,0.067263,0.03682,0.03682
+17-Dec-2019 23:00:00,1,0.062729,0.062729,0.062729,0.12976,0.060694,0.060694,0.060694,0.060694,0.12976,0.12976,0.12976,0.12976,0.12976,0.060694,0.046427,0.062729,0.046427,0.046427
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+18-Dec-2019 01:00:00,1,0.059819,0.059819,0.059819,0.10253,0.064373,0.064373,0.064373,0.064373,0.10253,0.10253,0.10253,0.10253,0.10253,0.064373,0.041689,0.059819,0.041689,0.041689
+18-Dec-2019 02:00:00,1,0.062233,0.062233,0.062233,0.099908,0.068051,0.068051,0.068051,0.068051,0.099908,0.099908,0.099908,0.099908,0.099908,0.068051,0.034132,0.062233,0.034132,0.034132
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+18-Dec-2019 05:00:00,1,0.052736,0.052736,0.052736,0.071665,0.058855,0.058855,0.058855,0.058855,0.071665,0.071665,0.071665,0.071665,0.071665,0.058855,0.027896,0.052736,0.027896,0.027896
+18-Dec-2019 06:00:00,1,0.048203,0.048203,0.048203,0.060822,0.051498,0.051498,0.051498,0.051498,0.060822,0.060822,0.060822,0.060822,0.060822,0.051498,0.030848,0.048203,0.030848,0.030848
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+18-Dec-2019 08:00:00,1,0.044458,0.044458,0.044458,0.073128,0.042302,0.042302,0.042302,0.042302,0.073128,0.073128,0.073128,0.073128,0.073128,0.042302,0.030584,0.044458,0.030584,0.030584
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+18-Dec-2019 10:00:00,1,0.036315,0.036315,0.036315,0.042565,0.012875,0.012875,0.012875,0.012875,0.042565,0.042565,0.042565,0.042565,0.042565,0.012875,0.02144,0.036315,0.02144,0.02144
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+18-Dec-2019 12:00:00,1,0.024767,0.024767,0.024767,0.026175,0.0091961,0.0091961,0.0091961,0.0091961,0.026175,0.026175,0.026175,0.026175,0.026175,0.0091961,0.020206,0.024767,0.020206,0.020206
+18-Dec-2019 13:00:00,1,0.022985,0.022985,0.022985,0.01629,0.0018392,0.0018392,0.0018392,0.0018392,0.01629,0.01629,0.01629,0.01629,0.01629,0.0018392,0.01884,0.022985,0.01884,0.01884
+18-Dec-2019 14:00:00,1,0.020977,0.020977,0.020977,0.013113,0.0018392,0.0018392,0.0018392,0.0018392,0.013113,0.013113,0.013113,0.013113,0.013113,0.0018392,0.012934,0.020977,0.012934,0.012934
+18-Dec-2019 15:00:00,1,0.020977,0.020977,0.020977,0.010187,0,0,0,0,0.010187,0.010187,0.010187,0.010187,0.010187,0,0.012339,0.020977,0.012339,0.012339
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+18-Dec-2019 20:00:00,1,0.028714,0.028714,0.028714,0.010187,0.0018392,0.0018392,0.0018392,0.0018392,0.010187,0.010187,0.010187,0.010187,0.010187,0.0018392,0.015094,0.028714,0.015094,0.015094
+18-Dec-2019 21:00:00,1,0.027496,0.027496,0.027496,0.011146,0,0,0,0,0.011146,0.011146,0.011146,0.011146,0.011146,0,0.010511,0.027496,0.010511,0.010511
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+19-Dec-2019 01:00:00,1,0.035594,0.035594,0.035594,0.0063041,0.0018392,0.0018392,0.0018392,0.0018392,0.0063041,0.0063041,0.0063041,0.0063041,0.0063041,0.0018392,0.0079986,0.035594,0.0079986,0.0079986
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+19-Dec-2019 03:00:00,1,0.03309,0.03309,0.03309,0.0072623,0.0018392,0.0018392,0.0018392,0.0018392,0.0072623,0.0072623,0.0072623,0.0072623,0.0072623,0.0018392,0.011061,0.03309,0.011061,0.011061
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+19-Dec-2019 06:00:00,1,0.021406,0.021406,0.021406,0.0070102,0,0,0,0,0.0070102,0.0070102,0.0070102,0.0070102,0.0070102,0,0.0092986,0.021406,0.0092986,0.0092986
+19-Dec-2019 07:00:00,1,0.024113,0.024113,0.024113,0.0070102,0.0018392,0.0018392,0.0018392,0.0018392,0.0070102,0.0070102,0.0070102,0.0070102,0.0070102,0.0018392,0.011634,0.024113,0.011634,0.011634
+19-Dec-2019 08:00:00,1,0.024338,0.024338,0.024338,0.0098849,0,0,0,0,0.0098849,0.0098849,0.0098849,0.0098849,0.0098849,0,0.011039,0.024338,0.011039,0.011039
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+19-Dec-2019 10:00:00,1,0.027496,0.027496,0.027496,0.0095318,0,0,0,0,0.0095318,0.0095318,0.0095318,0.0095318,0.0095318,0,0.0067867,0.027496,0.0067867,0.0067867
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+19-Dec-2019 12:00:00,1,0.018361,0.018361,0.018361,0.0075649,0,0,0,0,0.0075649,0.0075649,0.0075649,0.0075649,0.0075649,0,0.0027984,0.018361,0.0027984,0.0027984
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+19-Dec-2019 14:00:00,1,0.02303,0.02303,0.02303,0.0072119,0,0,0,0,0.0072119,0.0072119,0.0072119,0.0072119,0.0072119,0,0.0081308,0.02303,0.0081308,0.0081308
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+19-Dec-2019 16:00:00,1,0.02312,0.02312,0.02312,0.015735,0.0036784,0.0036784,0.0036784,0.0036784,0.015735,0.015735,0.015735,0.015735,0.015735,0.0036784,0.012207,0.02312,0.012207,0.012207
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+19-Dec-2019 23:00:00,1,0.075225,0.075225,0.075225,0.11685,0.033106,0.033106,0.033106,0.033106,0.11685,0.11685,0.11685,0.11685,0.11685,0.033106,0.016614,0.075225,0.016614,0.016614
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+20-Dec-2019 03:00:00,1,0.16464,0.16464,0.16464,0.20672,0.038624,0.038624,0.038624,0.038624,0.20672,0.20672,0.20672,0.20672,0.20672,0.038624,0.029218,0.16464,0.029218,0.029218
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+20-Dec-2019 13:00:00,1,0.38185,0.38185,0.38185,0.32212,0.029428,0.029428,0.029428,0.029428,0.32212,0.32212,0.32212,0.32212,0.32212,0.029428,0.14356,0.38185,0.14356,0.14356
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+20-Dec-2019 16:00:00,1,0.50557,0.50557,0.50557,0.39761,0.034945,0.034945,0.034945,0.034945,0.39761,0.39761,0.39761,0.39761,0.39761,0.034945,0.24987,0.50557,0.24987,0.24987
+20-Dec-2019 17:00:00,1,0.572,0.572,0.572,0.42333,0.034945,0.034945,0.034945,0.034945,0.42333,0.42333,0.42333,0.42333,0.42333,0.034945,0.27942,0.572,0.27942,0.27942
+20-Dec-2019 18:00:00,1,0.60051,0.60051,0.60051,0.40432,0.025749,0.025749,0.025749,0.025749,0.40432,0.40432,0.40432,0.40432,0.40432,0.025749,0.2961,0.60051,0.2961,0.2961
+20-Dec-2019 19:00:00,1,0.62359,0.62359,0.62359,0.34209,0.034945,0.034945,0.034945,0.034945,0.34209,0.34209,0.34209,0.34209,0.34209,0.034945,0.30818,0.62359,0.30818,0.30818
+20-Dec-2019 20:00:00,1,0.60266,0.60266,0.60266,0.29307,0.036784,0.036784,0.036784,0.036784,0.29307,0.29307,0.29307,0.29307,0.29307,0.036784,0.31051,0.60266,0.31051,0.31051
+20-Dec-2019 21:00:00,1,0.57541,0.57541,0.57541,0.29982,0.036784,0.036784,0.036784,0.036784,0.29982,0.29982,0.29982,0.29982,0.29982,0.036784,0.2762,0.57541,0.2762,0.2762
+20-Dec-2019 22:00:00,1,0.53431,0.53431,0.53431,0.29478,0.040463,0.040463,0.040463,0.040463,0.29478,0.29478,0.29478,0.29478,0.29478,0.040463,0.27391,0.53431,0.27391,0.27391
+20-Dec-2019 23:00:00,1,0.52795,0.52795,0.52795,0.28656,0.034945,0.034945,0.034945,0.034945,0.28656,0.28656,0.28656,0.28656,0.28656,0.034945,0.25452,0.52795,0.25452,0.25452
+21-Dec-2019 00:00:00,1,0.50147,0.50147,0.50147,0.28212,0.038624,0.038624,0.038624,0.038624,0.28212,0.28212,0.28212,0.28212,0.28212,0.038624,0.24095,0.50147,0.24095,0.24095
+21-Dec-2019 01:00:00,1,0.49441,0.49441,0.49441,0.26099,0.029428,0.029428,0.029428,0.029428,0.26099,0.26099,0.26099,0.26099,0.26099,0.029428,0.25432,0.49441,0.25432,0.25432
+21-Dec-2019 02:00:00,1,0.50607,0.50607,0.50607,0.24642,0.053337,0.053337,0.053337,0.053337,0.24642,0.24642,0.24642,0.24642,0.24642,0.053337,0.23883,0.50607,0.23883,0.23883
+21-Dec-2019 03:00:00,1,0.49495,0.49495,0.49495,0.23436,0.055177,0.055177,0.055177,0.055177,0.23436,0.23436,0.23436,0.23436,0.23436,0.055177,0.21821,0.49495,0.21821,0.21821
+21-Dec-2019 04:00:00,1,0.48322,0.48322,0.48322,0.20425,0.04782,0.04782,0.04782,0.04782,0.20425,0.20425,0.20425,0.20425,0.20425,0.04782,0.21151,0.48322,0.21151,0.21151
+21-Dec-2019 05:00:00,1,0.47878,0.47878,0.47878,0.18832,0.040463,0.040463,0.040463,0.040463,0.18832,0.18832,0.18832,0.18832,0.18832,0.040463,0.18784,0.47878,0.18784,0.18784
+21-Dec-2019 06:00:00,1,0.46933,0.46933,0.46933,0.17601,0.057016,0.057016,0.057016,0.057016,0.17601,0.17601,0.17601,0.17601,0.17601,0.057016,0.18789,0.46933,0.18789,0.18789
+21-Dec-2019 07:00:00,1,0.44857,0.44857,0.44857,0.18176,0.049659,0.049659,0.049659,0.049659,0.18176,0.18176,0.18176,0.18176,0.18176,0.049659,0.17689,0.44857,0.17689,0.17689
+21-Dec-2019 08:00:00,1,0.44936,0.44936,0.44936,0.18348,0.060694,0.060694,0.060694,0.060694,0.18348,0.18348,0.18348,0.18348,0.18348,0.060694,0.16389,0.44936,0.16389,0.16389
+21-Dec-2019 09:00:00,1,0.43416,0.43416,0.43416,0.17278,0.066212,0.066212,0.066212,0.066212,0.17278,0.17278,0.17278,0.17278,0.17278,0.066212,0.14869,0.43416,0.14869,0.14869
+21-Dec-2019 10:00:00,1,0.41524,0.41524,0.41524,0.15231,0.075408,0.075408,0.075408,0.075408,0.15231,0.15231,0.15231,0.15231,0.15231,0.075408,0.14272,0.41524,0.14272,0.14272
+21-Dec-2019 11:00:00,1,0.38569,0.38569,0.38569,0.1278,0.084604,0.084604,0.084604,0.084604,0.1278,0.1278,0.1278,0.1278,0.1278,0.084604,0.12776,0.38569,0.12776,0.12776
+21-Dec-2019 12:00:00,1,0.32215,0.32215,0.32215,0.13551,0.086443,0.086443,0.086443,0.086443,0.13551,0.13551,0.13551,0.13551,0.13551,0.086443,0.11484,0.32215,0.11484,0.11484
+21-Dec-2019 13:00:00,1,0.29761,0.29761,0.29761,0.11837,0.091961,0.091961,0.091961,0.091961,0.11837,0.11837,0.11837,0.11837,0.11837,0.091961,0.11599,0.29761,0.11599,0.11599
+21-Dec-2019 14:00:00,1,0.26312,0.26312,0.26312,0.11468,0.044141,0.044141,0.044141,0.044141,0.11468,0.11468,0.11468,0.11468,0.11468,0.044141,0.10442,0.26312,0.10442,0.10442
+21-Dec-2019 15:00:00,1,0.24602,0.24602,0.24602,0.090628,0.053337,0.053337,0.053337,0.053337,0.090628,0.090628,0.090628,0.090628,0.090628,0.053337,0.09563,0.24602,0.09563,0.09563
+21-Dec-2019 16:00:00,1,0.23188,0.23188,0.23188,0.078121,0.049659,0.049659,0.049659,0.049659,0.078121,0.078121,0.078121,0.078121,0.078121,0.049659,0.10026,0.23188,0.10026,0.10026
+21-Dec-2019 17:00:00,1,0.21787,0.21787,0.21787,0.083013,0.040463,0.040463,0.040463,0.040463,0.083013,0.083013,0.083013,0.083013,0.083013,0.040463,0.095917,0.21787,0.095917,0.095917
+21-Dec-2019 18:00:00,1,0.22687,0.22687,0.22687,0.079482,0.045981,0.045981,0.045981,0.045981,0.079482,0.079482,0.079482,0.079482,0.079482,0.045981,0.091598,0.22687,0.091598,0.091598
+21-Dec-2019 19:00:00,1,0.23413,0.23413,0.23413,0.092141,0.051498,0.051498,0.051498,0.051498,0.092141,0.092141,0.092141,0.092141,0.092141,0.051498,0.091135,0.23413,0.091135,0.091135
+21-Dec-2019 20:00:00,1,0.23107,0.23107,0.23107,0.09663,0.038624,0.038624,0.038624,0.038624,0.09663,0.09663,0.09663,0.09663,0.09663,0.038624,0.092215,0.23107,0.092215,0.092215
+21-Dec-2019 21:00:00,1,0.23409,0.23409,0.23409,0.09673,0.038624,0.038624,0.038624,0.038624,0.09673,0.09673,0.09673,0.09673,0.09673,0.038624,0.090122,0.23409,0.090122,0.090122
+21-Dec-2019 22:00:00,1,0.22281,0.22281,0.22281,0.088964,0.033106,0.033106,0.033106,0.033106,0.088964,0.088964,0.088964,0.088964,0.088964,0.033106,0.072141,0.22281,0.072141,0.072141
+21-Dec-2019 23:00:00,1,0.20513,0.20513,0.20513,0.091435,0.033106,0.033106,0.033106,0.033106,0.091435,0.091435,0.091435,0.091435,0.091435,0.033106,0.063702,0.20513,0.063702,0.063702
+22-Dec-2019 00:00:00,1,0.20163,0.20163,0.20163,0.1051,0.027588,0.027588,0.027588,0.027588,0.1051,0.1051,0.1051,0.1051,0.1051,0.027588,0.059626,0.20163,0.059626,0.059626
+22-Dec-2019 01:00:00,1,0.2088,0.2088,0.2088,0.1043,0.034945,0.034945,0.034945,0.034945,0.1043,0.1043,0.1043,0.1043,0.1043,0.034945,0.058722,0.2088,0.058722,0.058722
+22-Dec-2019 02:00:00,1,0.20064,0.20064,0.20064,0.11448,0.040463,0.040463,0.040463,0.040463,0.11448,0.11448,0.11448,0.11448,0.11448,0.040463,0.06227,0.20064,0.06227,0.06227
+22-Dec-2019 03:00:00,1,0.19529,0.19529,0.19529,0.10964,0.027588,0.027588,0.027588,0.027588,0.10964,0.10964,0.10964,0.10964,0.10964,0.027588,0.061388,0.19529,0.061388,0.061388
+22-Dec-2019 04:00:00,1,0.1871,0.1871,0.1871,0.10954,0.040463,0.040463,0.040463,0.040463,0.10954,0.10954,0.10954,0.10954,0.10954,0.040463,0.06509,0.1871,0.06509,0.06509
+22-Dec-2019 05:00:00,1,0.19374,0.19374,0.19374,0.11211,0.033106,0.033106,0.033106,0.033106,0.11211,0.11211,0.11211,0.11211,0.11211,0.033106,0.067955,0.19374,0.067955,0.067955
+22-Dec-2019 06:00:00,1,0.18922,0.18922,0.18922,0.10576,0.033106,0.033106,0.033106,0.033106,0.10576,0.10576,0.10576,0.10576,0.10576,0.033106,0.079677,0.18922,0.079677,0.079677
+22-Dec-2019 07:00:00,1,0.1691,0.1691,0.1691,0.10228,0.02391,0.02391,0.02391,0.02391,0.10228,0.10228,0.10228,0.10228,0.10228,0.02391,0.09151,0.1691,0.09151,0.09151
+22-Dec-2019 08:00:00,1,0.15647,0.15647,0.15647,0.11821,0.027588,0.027588,0.027588,0.027588,0.11821,0.11821,0.11821,0.11821,0.11821,0.027588,0.085693,0.15647,0.085693,0.085693
+22-Dec-2019 09:00:00,1,0.16502,0.16502,0.16502,0.14383,0.02391,0.02391,0.02391,0.02391,0.14383,0.14383,0.14383,0.14383,0.14383,0.02391,0.089835,0.16502,0.089835,0.089835
+22-Dec-2019 10:00:00,1,0.16676,0.16676,0.16676,0.1517,0.018392,0.018392,0.018392,0.018392,0.1517,0.1517,0.1517,0.1517,0.1517,0.018392,0.097371,0.16676,0.097371,0.097371
+22-Dec-2019 11:00:00,1,0.15649,0.15649,0.15649,0.16441,0.018392,0.018392,0.018392,0.018392,0.16441,0.16441,0.16441,0.16441,0.16441,0.018392,0.10451,0.15649,0.10451,0.10451
+22-Dec-2019 12:00:00,1,0.14474,0.14474,0.14474,0.17389,0.02391,0.02391,0.02391,0.02391,0.17389,0.17389,0.17389,0.17389,0.17389,0.02391,0.1038,0.14474,0.1038,0.1038
+22-Dec-2019 13:00:00,1,0.1352,0.1352,0.1352,0.16895,0.025749,0.025749,0.025749,0.025749,0.16895,0.16895,0.16895,0.16895,0.16895,0.025749,0.10281,0.1352,0.10281,0.10281
+22-Dec-2019 14:00:00,1,0.13218,0.13218,0.13218,0.20622,0.018392,0.018392,0.018392,0.018392,0.20622,0.20622,0.20622,0.20622,0.20622,0.018392,0.1048,0.13218,0.1048,0.1048
+22-Dec-2019 15:00:00,1,0.12918,0.12918,0.12918,0.22448,0.038624,0.038624,0.038624,0.038624,0.22448,0.22448,0.22448,0.22448,0.22448,0.038624,0.11659,0.12918,0.11659,0.11659
+22-Dec-2019 16:00:00,1,0.14729,0.14729,0.14729,0.26094,0.04782,0.04782,0.04782,0.04782,0.26094,0.26094,0.26094,0.26094,0.26094,0.04782,0.12236,0.14729,0.12236,0.12236
+22-Dec-2019 17:00:00,1,0.17835,0.17835,0.17835,0.28288,0.057016,0.057016,0.057016,0.057016,0.28288,0.28288,0.28288,0.28288,0.28288,0.057016,0.12399,0.17835,0.12399,0.12399
+22-Dec-2019 18:00:00,1,0.19407,0.19407,0.19407,0.31884,0.079087,0.079087,0.079087,0.079087,0.31884,0.31884,0.31884,0.31884,0.31884,0.079087,0.14444,0.19407,0.14444,0.14444
+22-Dec-2019 19:00:00,1,0.2271,0.2271,0.2271,0.34441,0.079087,0.079087,0.079087,0.079087,0.34441,0.34441,0.34441,0.34441,0.34441,0.079087,0.16638,0.2271,0.16638,0.16638
+22-Dec-2019 20:00:00,1,0.24722,0.24722,0.24722,0.33664,0.10667,0.10667,0.10667,0.10667,0.33664,0.33664,0.33664,0.33664,0.33664,0.10667,0.18575,0.24722,0.18575,0.18575
+22-Dec-2019 21:00:00,1,0.26048,0.26048,0.26048,0.32176,0.11955,0.11955,0.11955,0.11955,0.32176,0.32176,0.32176,0.32176,0.32176,0.11955,0.20181,0.26048,0.20181,0.20181
+22-Dec-2019 22:00:00,1,0.29291,0.29291,0.29291,0.37159,0.11955,0.11955,0.11955,0.11955,0.37159,0.37159,0.37159,0.37159,0.37159,0.11955,0.21107,0.29291,0.21107,0.21107
+22-Dec-2019 23:00:00,1,0.33288,0.33288,0.33288,0.39237,0.17289,0.17289,0.17289,0.17289,0.39237,0.39237,0.39237,0.39237,0.39237,0.17289,0.22376,0.33288,0.22376,0.22376
+23-Dec-2019 00:00:00,1,0.34391,0.34391,0.34391,0.46141,0.16369,0.16369,0.16369,0.16369,0.46141,0.46141,0.46141,0.46141,0.46141,0.16369,0.24613,0.34391,0.24613,0.24613
+23-Dec-2019 01:00:00,1,0.32984,0.32984,0.32984,0.46353,0.1968,0.1968,0.1968,0.1968,0.46353,0.46353,0.46353,0.46353,0.46353,0.1968,0.24743,0.32984,0.24743,0.24743
+23-Dec-2019 02:00:00,1,0.35093,0.35093,0.35093,0.47674,0.20783,0.20783,0.20783,0.20783,0.47674,0.47674,0.47674,0.47674,0.47674,0.20783,0.22896,0.35093,0.22896,0.22896
+23-Dec-2019 03:00:00,1,0.35979,0.35979,0.35979,0.48189,0.25565,0.25565,0.25565,0.25565,0.48189,0.48189,0.48189,0.48189,0.48189,0.25565,0.25067,0.35979,0.25067,0.25067
+23-Dec-2019 04:00:00,1,0.36176,0.36176,0.36176,0.48804,0.28692,0.28692,0.28692,0.28692,0.48804,0.48804,0.48804,0.48804,0.48804,0.28692,0.26532,0.36176,0.26532,0.26532
+23-Dec-2019 05:00:00,1,0.34033,0.34033,0.34033,0.49898,0.28876,0.28876,0.28876,0.28876,0.49898,0.49898,0.49898,0.49898,0.49898,0.28876,0.27907,0.34033,0.27907,0.27907
+23-Dec-2019 06:00:00,1,0.35594,0.35594,0.35594,0.55935,0.27772,0.27772,0.27772,0.27772,0.55935,0.55935,0.55935,0.55935,0.55935,0.27772,0.29522,0.35594,0.29522,0.29522
+23-Dec-2019 07:00:00,1,0.37755,0.37755,0.37755,0.64519,0.24278,0.24278,0.24278,0.24278,0.64519,0.64519,0.64519,0.64519,0.64519,0.24278,0.31045,0.37755,0.31045,0.31045
+23-Dec-2019 08:00:00,1,0.37229,0.37229,0.37229,0.6757,0.25749,0.25749,0.25749,0.25749,0.6757,0.6757,0.6757,0.6757,0.6757,0.25749,0.29401,0.37229,0.29401,0.29401
+23-Dec-2019 09:00:00,1,0.36022,0.36022,0.36022,0.73587,0.27772,0.27772,0.27772,0.27772,0.73587,0.73587,0.73587,0.73587,0.73587,0.27772,0.29855,0.36022,0.29855,0.29855
+23-Dec-2019 10:00:00,1,0.353,0.353,0.353,0.78237,0.27221,0.27221,0.27221,0.27221,0.78237,0.78237,0.78237,0.78237,0.78237,0.27221,0.29795,0.353,0.29795,0.29795
+23-Dec-2019 11:00:00,1,0.36852,0.36852,0.36852,0.76335,0.25013,0.25013,0.25013,0.25013,0.76335,0.76335,0.76335,0.76335,0.76335,0.25013,0.31459,0.36852,0.31459,0.31459
+23-Dec-2019 12:00:00,1,0.38055,0.38055,0.38055,0.78938,0.2391,0.2391,0.2391,0.2391,0.78938,0.78938,0.78938,0.78938,0.78938,0.2391,0.33466,0.38055,0.33466,0.33466
+23-Dec-2019 13:00:00,1,0.40242,0.40242,0.40242,0.75639,0.23174,0.23174,0.23174,0.23174,0.75639,0.75639,0.75639,0.75639,0.75639,0.23174,0.34887,0.40242,0.34887,0.34887
+23-Dec-2019 14:00:00,1,0.41488,0.41488,0.41488,0.70117,0.22071,0.22071,0.22071,0.22071,0.70117,0.70117,0.70117,0.70117,0.70117,0.22071,0.33594,0.41488,0.33594,0.33594
+23-Dec-2019 15:00:00,1,0.42726,0.42726,0.42726,0.64852,0.21151,0.21151,0.21151,0.21151,0.64852,0.64852,0.64852,0.64852,0.64852,0.21151,0.32177,0.42726,0.32177,0.32177
+23-Dec-2019 16:00:00,1,0.41449,0.41449,0.41449,0.61548,0.19496,0.19496,0.19496,0.19496,0.61548,0.61548,0.61548,0.61548,0.61548,0.19496,0.27528,0.41449,0.27528,0.27528
+23-Dec-2019 17:00:00,1,0.43044,0.43044,0.43044,0.58966,0.17289,0.17289,0.17289,0.17289,0.58966,0.58966,0.58966,0.58966,0.58966,0.17289,0.25838,0.43044,0.25838,0.25838
+23-Dec-2019 18:00:00,1,0.42293,0.42293,0.42293,0.5071,0.19128,0.19128,0.19128,0.19128,0.5071,0.5071,0.5071,0.5071,0.5071,0.19128,0.25179,0.42293,0.25179,0.25179
+23-Dec-2019 19:00:00,1,0.43179,0.43179,0.43179,0.42545,0.20048,0.20048,0.20048,0.20048,0.42545,0.42545,0.42545,0.42545,0.42545,0.20048,0.27579,0.43179,0.27579,0.27579
+23-Dec-2019 20:00:00,1,0.41045,0.41045,0.41045,0.41996,0.18944,0.18944,0.18944,0.18944,0.41996,0.41996,0.41996,0.41996,0.41996,0.18944,0.28081,0.41045,0.28081,0.28081
+23-Dec-2019 21:00:00,1,0.4093,0.4093,0.4093,0.47059,0.18392,0.18392,0.18392,0.18392,0.47059,0.47059,0.47059,0.47059,0.47059,0.18392,0.28647,0.4093,0.28647,0.28647
+23-Dec-2019 22:00:00,1,0.40854,0.40854,0.40854,0.4425,0.17105,0.17105,0.17105,0.17105,0.4425,0.4425,0.4425,0.4425,0.4425,0.17105,0.29086,0.40854,0.29086,0.29086
+23-Dec-2019 23:00:00,1,0.40177,0.40177,0.40177,0.45349,0.14714,0.14714,0.14714,0.14714,0.45349,0.45349,0.45349,0.45349,0.45349,0.14714,0.29559,0.40177,0.29559,0.29559
+24-Dec-2019 00:00:00,1,0.40197,0.40197,0.40197,0.44119,0.14346,0.14346,0.14346,0.14346,0.44119,0.44119,0.44119,0.44119,0.44119,0.14346,0.30258,0.40197,0.30258,0.30258
+24-Dec-2019 01:00:00,1,0.4024,0.4024,0.4024,0.48098,0.15266,0.15266,0.15266,0.15266,0.48098,0.48098,0.48098,0.48098,0.48098,0.15266,0.31948,0.4024,0.31948,0.31948
+24-Dec-2019 02:00:00,1,0.36931,0.36931,0.36931,0.44905,0.15633,0.15633,0.15633,0.15633,0.44905,0.44905,0.44905,0.44905,0.44905,0.15633,0.31254,0.36931,0.31254,0.31254
+24-Dec-2019 03:00:00,1,0.33879,0.33879,0.33879,0.44825,0.13978,0.13978,0.13978,0.13978,0.44825,0.44825,0.44825,0.44825,0.44825,0.13978,0.30469,0.33879,0.30469,0.30469
+24-Dec-2019 04:00:00,1,0.31362,0.31362,0.31362,0.40155,0.10484,0.10484,0.10484,0.10484,0.40155,0.40155,0.40155,0.40155,0.40155,0.10484,0.29498,0.31362,0.29498,0.29498
+24-Dec-2019 05:00:00,1,0.29041,0.29041,0.29041,0.36841,0.082765,0.082765,0.082765,0.082765,0.36841,0.36841,0.36841,0.36841,0.36841,0.082765,0.28308,0.29041,0.28308,0.28308
+24-Dec-2019 06:00:00,1,0.27343,0.27343,0.27343,0.29428,0.057016,0.057016,0.057016,0.057016,0.29428,0.29428,0.29428,0.29428,0.29428,0.057016,0.29088,0.27343,0.29088,0.29088
+24-Dec-2019 07:00:00,1,0.25105,0.25105,0.25105,0.24954,0.075408,0.075408,0.075408,0.075408,0.24954,0.24954,0.24954,0.24954,0.24954,0.075408,0.28665,0.25105,0.28665,0.28665
+24-Dec-2019 08:00:00,1,0.21431,0.21431,0.21431,0.24944,0.057016,0.057016,0.057016,0.057016,0.24944,0.24944,0.24944,0.24944,0.24944,0.057016,0.27352,0.21431,0.27352,0.27352
+24-Dec-2019 09:00:00,1,0.20542,0.20542,0.20542,0.15135,0.060694,0.060694,0.060694,0.060694,0.15135,0.15135,0.15135,0.15135,0.15135,0.060694,0.24743,0.20542,0.24743,0.24743
+24-Dec-2019 10:00:00,1,0.18261,0.18261,0.18261,0.12129,0.060694,0.060694,0.060694,0.060694,0.12129,0.12129,0.12129,0.12129,0.12129,0.060694,0.22665,0.18261,0.22665,0.22665
+24-Dec-2019 11:00:00,1,0.15519,0.15519,0.15519,0.215,0.038624,0.038624,0.038624,0.038624,0.215,0.215,0.215,0.215,0.215,0.038624,0.17643,0.15519,0.17643,0.17643
+24-Dec-2019 12:00:00,1,0.14332,0.14332,0.14332,0.2681,0.034945,0.034945,0.034945,0.034945,0.2681,0.2681,0.2681,0.2681,0.2681,0.034945,0.13948,0.14332,0.13948,0.13948
+24-Dec-2019 13:00:00,1,0.13301,0.13301,0.13301,0.22776,0.034945,0.034945,0.034945,0.034945,0.22776,0.22776,0.22776,0.22776,0.22776,0.034945,0.13093,0.13301,0.13093,0.13093
+24-Dec-2019 14:00:00,1,0.13209,0.13209,0.13209,0.28192,0.044141,0.044141,0.044141,0.044141,0.28192,0.28192,0.28192,0.28192,0.28192,0.044141,0.12723,0.13209,0.12723,0.12723
+24-Dec-2019 15:00:00,1,0.18625,0.18625,0.18625,0.34884,0.103,0.103,0.103,0.103,0.34884,0.34884,0.34884,0.34884,0.34884,0.103,0.14818,0.18625,0.14818,0.14818
+24-Dec-2019 16:00:00,1,0.25836,0.25836,0.25836,0.44991,0.103,0.103,0.103,0.103,0.44991,0.44991,0.44991,0.44991,0.44991,0.103,0.17927,0.25836,0.17927,0.17927
+24-Dec-2019 17:00:00,1,0.32429,0.32429,0.32429,0.55043,0.10667,0.10667,0.10667,0.10667,0.55043,0.55043,0.55043,0.55043,0.55043,0.10667,0.24165,0.32429,0.24165,0.24165
+24-Dec-2019 18:00:00,1,0.41235,0.41235,0.41235,0.71499,0.10851,0.10851,0.10851,0.10851,0.71499,0.71499,0.71499,0.71499,0.71499,0.10851,0.29509,0.41235,0.29509,0.29509
+24-Dec-2019 19:00:00,1,0.50914,0.50914,0.50914,0.76925,0.103,0.103,0.103,0.103,0.76925,0.76925,0.76925,0.76925,0.76925,0.103,0.36818,0.50914,0.36818,0.36818
+24-Dec-2019 20:00:00,1,0.64154,0.64154,0.64154,0.757,0.06989,0.06989,0.06989,0.06989,0.757,0.757,0.757,0.757,0.757,0.06989,0.49053,0.64154,0.49053,0.49053
+24-Dec-2019 21:00:00,1,0.74323,0.74323,0.74323,0.74817,0.11771,0.11771,0.11771,0.11771,0.74817,0.74817,0.74817,0.74817,0.74817,0.11771,0.55342,0.74323,0.55342,0.55342
+24-Dec-2019 22:00:00,1,0.78389,0.78389,0.78389,0.64811,0.075408,0.075408,0.075408,0.075408,0.64811,0.64811,0.64811,0.64811,0.64811,0.075408,0.59416,0.78389,0.59416,0.59416
+24-Dec-2019 23:00:00,1,0.81658,0.81658,0.81658,0.53318,0.088283,0.088283,0.088283,0.088283,0.53318,0.53318,0.53318,0.53318,0.53318,0.088283,0.66163,0.81658,0.66163,0.66163
+25-Dec-2019 00:00:00,1,0.75728,0.75728,0.75728,0.37124,0.075408,0.075408,0.075408,0.075408,0.37124,0.37124,0.37124,0.37124,0.37124,0.075408,0.65077,0.75728,0.65077,0.65077
+25-Dec-2019 01:00:00,1,0.68467,0.68467,0.68467,0.25564,0.103,0.103,0.103,0.103,0.25564,0.25564,0.25564,0.25564,0.25564,0.103,0.6344,0.68467,0.6344,0.6344
+25-Dec-2019 02:00:00,1,0.57484,0.57484,0.57484,0.23885,0.11219,0.11219,0.11219,0.11219,0.23885,0.23885,0.23885,0.23885,0.23885,0.11219,0.56867,0.57484,0.56867,0.56867
+25-Dec-2019 03:00:00,1,0.43351,0.43351,0.43351,0.30865,0.10484,0.10484,0.10484,0.10484,0.30865,0.30865,0.30865,0.30865,0.30865,0.10484,0.51444,0.43351,0.51444,0.51444
+25-Dec-2019 04:00:00,1,0.32188,0.32188,0.32188,0.38173,0.14898,0.14898,0.14898,0.14898,0.38173,0.38173,0.38173,0.38173,0.38173,0.14898,0.47427,0.32188,0.47427,0.47427
+25-Dec-2019 05:00:00,1,0.24724,0.24724,0.24724,0.49979,0.16001,0.16001,0.16001,0.16001,0.49979,0.49979,0.49979,0.49979,0.49979,0.16001,0.44331,0.24724,0.44331,0.44331
+25-Dec-2019 06:00:00,1,0.21834,0.21834,0.21834,0.66289,0.19312,0.19312,0.19312,0.19312,0.66289,0.66289,0.66289,0.66289,0.66289,0.19312,0.4787,0.21834,0.4787,0.4787
+25-Dec-2019 07:00:00,1,0.25096,0.25096,0.25096,0.53081,0.2391,0.2391,0.2391,0.2391,0.53081,0.53081,0.53081,0.53081,0.53081,0.2391,0.50221,0.25096,0.50221,0.50221
+25-Dec-2019 08:00:00,1,0.27352,0.27352,0.27352,0.48824,0.14162,0.14162,0.14162,0.14162,0.48824,0.48824,0.48824,0.48824,0.48824,0.14162,0.51634,0.27352,0.51634,0.51634
+25-Dec-2019 09:00:00,1,0.27155,0.27155,0.27155,0.48799,0.19496,0.19496,0.19496,0.19496,0.48799,0.48799,0.48799,0.48799,0.48799,0.19496,0.53363,0.27155,0.53363,0.53363
+25-Dec-2019 10:00:00,1,0.26409,0.26409,0.26409,0.37305,0.17657,0.17657,0.17657,0.17657,0.37305,0.37305,0.37305,0.37305,0.37305,0.17657,0.56177,0.26409,0.56177,0.56177
+25-Dec-2019 11:00:00,1,0.26391,0.26391,0.26391,0.3555,0.22806,0.22806,0.22806,0.22806,0.3555,0.3555,0.3555,0.3555,0.3555,0.22806,0.57711,0.26391,0.57711,0.57711
+25-Dec-2019 12:00:00,1,0.26397,0.26397,0.26397,0.35379,0.24278,0.24278,0.24278,0.24278,0.35379,0.35379,0.35379,0.35379,0.35379,0.24278,0.58343,0.26397,0.58343,0.58343
+25-Dec-2019 13:00:00,1,0.27158,0.27158,0.27158,0.38874,0.22806,0.22806,0.22806,0.22806,0.38874,0.38874,0.38874,0.38874,0.38874,0.22806,0.5606,0.27158,0.5606,0.5606
+25-Dec-2019 14:00:00,1,0.27401,0.27401,0.27401,0.53524,0.20599,0.20599,0.20599,0.20599,0.53524,0.53524,0.53524,0.53524,0.53524,0.20599,0.50085,0.27401,0.50085,0.50085
+25-Dec-2019 15:00:00,1,0.28434,0.28434,0.28434,0.58104,0.17657,0.17657,0.17657,0.17657,0.58104,0.58104,0.58104,0.58104,0.58104,0.17657,0.43523,0.28434,0.43523,0.43523
+25-Dec-2019 16:00:00,1,0.30909,0.30909,0.30909,0.62804,0.15817,0.15817,0.15817,0.15817,0.62804,0.62804,0.62804,0.62804,0.62804,0.15817,0.35612,0.30909,0.35612,0.35612
+25-Dec-2019 17:00:00,1,0.35864,0.35864,0.35864,0.67716,0.13978,0.13978,0.13978,0.13978,0.67716,0.67716,0.67716,0.67716,0.67716,0.13978,0.2967,0.35864,0.2967,0.2967
+25-Dec-2019 18:00:00,1,0.41003,0.41003,0.41003,0.73329,0.22439,0.22439,0.22439,0.22439,0.73329,0.73329,0.73329,0.73329,0.73329,0.22439,0.29013,0.41003,0.29013,0.29013
+25-Dec-2019 19:00:00,1,0.4728,0.4728,0.4728,0.77193,0.23174,0.23174,0.23174,0.23174,0.77193,0.77193,0.77193,0.77193,0.77193,0.23174,0.34486,0.4728,0.34486,0.34486
+25-Dec-2019 20:00:00,1,0.53009,0.53009,0.53009,0.74187,0.24646,0.24646,0.24646,0.24646,0.74187,0.74187,0.74187,0.74187,0.74187,0.24646,0.40467,0.53009,0.40467,0.40467
+25-Dec-2019 21:00:00,1,0.60487,0.60487,0.60487,0.74288,0.22622,0.22622,0.22622,0.22622,0.74288,0.74288,0.74288,0.74288,0.74288,0.22622,0.49739,0.60487,0.49739,0.49739
+25-Dec-2019 22:00:00,1,0.68327,0.68327,0.68327,0.66183,0.20783,0.20783,0.20783,0.20783,0.66183,0.66183,0.66183,0.66183,0.66183,0.20783,0.60774,0.68327,0.60774,0.60774
+25-Dec-2019 23:00:00,1,0.6812,0.6812,0.6812,0.62128,0.18392,0.18392,0.18392,0.18392,0.62128,0.62128,0.62128,0.62128,0.62128,0.18392,0.66681,0.6812,0.66681,0.66681
+26-Dec-2019 00:00:00,1,0.64118,0.64118,0.64118,0.58321,0.14898,0.14898,0.14898,0.14898,0.58321,0.58321,0.58321,0.58321,0.58321,0.14898,0.66329,0.64118,0.66329,0.66329
+26-Dec-2019 01:00:00,1,0.6131,0.6131,0.6131,0.54992,0.13058,0.13058,0.13058,0.13058,0.54992,0.54992,0.54992,0.54992,0.54992,0.13058,0.65652,0.6131,0.65652,0.65652
+26-Dec-2019 02:00:00,1,0.59542,0.59542,0.59542,0.55713,0.11955,0.11955,0.11955,0.11955,0.55713,0.55713,0.55713,0.55713,0.55713,0.11955,0.63211,0.59542,0.63211,0.63211
+26-Dec-2019 03:00:00,1,0.5764,0.5764,0.5764,0.51159,0.12139,0.12139,0.12139,0.12139,0.51159,0.51159,0.51159,0.51159,0.51159,0.12139,0.61336,0.5764,0.61336,0.61336
+26-Dec-2019 04:00:00,1,0.55093,0.55093,0.55093,0.46565,0.12875,0.12875,0.12875,0.12875,0.46565,0.46565,0.46565,0.46565,0.46565,0.12875,0.58383,0.55093,0.58383,0.58383
+26-Dec-2019 05:00:00,1,0.50309,0.50309,0.50309,0.43856,0.14346,0.14346,0.14346,0.14346,0.43856,0.43856,0.43856,0.43856,0.43856,0.14346,0.56259,0.50309,0.56259,0.56259
+26-Dec-2019 06:00:00,1,0.48859,0.48859,0.48859,0.42338,0.1453,0.1453,0.1453,0.1453,0.42338,0.42338,0.42338,0.42338,0.42338,0.1453,0.53315,0.48859,0.53315,0.53315
+26-Dec-2019 07:00:00,1,0.46888,0.46888,0.46888,0.44905,0.13426,0.13426,0.13426,0.13426,0.44905,0.44905,0.44905,0.44905,0.44905,0.13426,0.53339,0.46888,0.53339,0.53339
+26-Dec-2019 08:00:00,1,0.43222,0.43222,0.43222,0.41567,0.18944,0.18944,0.18944,0.18944,0.41567,0.41567,0.41567,0.41567,0.41567,0.18944,0.51746,0.43222,0.51746,0.51746
+26-Dec-2019 09:00:00,1,0.38711,0.38711,0.38711,0.4774,0.23358,0.23358,0.23358,0.23358,0.4774,0.4774,0.4774,0.4774,0.4774,0.23358,0.44664,0.38711,0.44664,0.44664
+26-Dec-2019 10:00:00,1,0.38176,0.38176,0.38176,0.51553,0.21335,0.21335,0.21335,0.21335,0.51553,0.51553,0.51553,0.51553,0.51553,0.21335,0.38854,0.38176,0.38854,0.38854
+26-Dec-2019 11:00:00,1,0.39473,0.39473,0.39473,0.49777,0.25013,0.25013,0.25013,0.25013,0.49777,0.49777,0.49777,0.49777,0.49777,0.25013,0.36934,0.39473,0.36934,0.36934
+26-Dec-2019 12:00:00,1,0.44903,0.44903,0.44903,0.59491,0.25565,0.25565,0.25565,0.25565,0.59491,0.59491,0.59491,0.59491,0.59491,0.25565,0.33601,0.44903,0.33601,0.33601
+26-Dec-2019 13:00:00,1,0.48173,0.48173,0.48173,0.55774,0.22255,0.22255,0.22255,0.22255,0.55774,0.55774,0.55774,0.55774,0.55774,0.22255,0.32305,0.48173,0.32305,0.32305
+26-Dec-2019 14:00:00,1,0.50769,0.50769,0.50769,0.5712,0.23174,0.23174,0.23174,0.23174,0.5712,0.5712,0.5712,0.5712,0.5712,0.23174,0.32267,0.50769,0.32267,0.32267
+26-Dec-2019 15:00:00,1,0.49867,0.49867,0.49867,0.58316,0.17105,0.17105,0.17105,0.17105,0.58316,0.58316,0.58316,0.58316,0.58316,0.17105,0.32801,0.49867,0.32801,0.32801
+26-Dec-2019 16:00:00,1,0.5049,0.5049,0.5049,0.54689,0.15817,0.15817,0.15817,0.15817,0.54689,0.54689,0.54689,0.54689,0.54689,0.15817,0.35667,0.5049,0.35667,0.35667
+26-Dec-2019 17:00:00,1,0.48545,0.48545,0.48545,0.52107,0.15266,0.15266,0.15266,0.15266,0.52107,0.52107,0.52107,0.52107,0.52107,0.15266,0.41667,0.48545,0.41667,0.41667
+26-Dec-2019 18:00:00,1,0.48755,0.48755,0.48755,0.55083,0.16553,0.16553,0.16553,0.16553,0.55083,0.55083,0.55083,0.55083,0.55083,0.16553,0.45338,0.48755,0.45338,0.45338
+26-Dec-2019 19:00:00,1,0.48097,0.48097,0.48097,0.63535,0.17289,0.17289,0.17289,0.17289,0.63535,0.63535,0.63535,0.63535,0.63535,0.17289,0.46107,0.48097,0.46107,0.46107
+26-Dec-2019 20:00:00,1,0.4774,0.4774,0.4774,0.72724,0.17289,0.17289,0.17289,0.17289,0.72724,0.72724,0.72724,0.72724,0.72724,0.17289,0.49036,0.4774,0.49036,0.49036
+26-Dec-2019 21:00:00,1,0.46112,0.46112,0.46112,0.81005,0.19312,0.19312,0.19312,0.19312,0.81005,0.81005,0.81005,0.81005,0.81005,0.19312,0.51471,0.46112,0.51471,0.51471
+26-Dec-2019 22:00:00,1,0.46211,0.46211,0.46211,0.80047,0.18576,0.18576,0.18576,0.18576,0.80047,0.80047,0.80047,0.80047,0.80047,0.18576,0.46592,0.46211,0.46592,0.46592
+26-Dec-2019 23:00:00,1,0.4931,0.4931,0.4931,0.8101,0.19496,0.19496,0.19496,0.19496,0.8101,0.8101,0.8101,0.8101,0.8101,0.19496,0.47326,0.4931,0.47326,0.47326
+27-Dec-2019 00:00:00,1,0.53039,0.53039,0.53039,0.79134,0.22439,0.22439,0.22439,0.22439,0.79134,0.79134,0.79134,0.79134,0.79134,0.22439,0.49344,0.53039,0.49344,0.49344
+27-Dec-2019 01:00:00,1,0.59154,0.59154,0.59154,0.79482,0.23542,0.23542,0.23542,0.23542,0.79482,0.79482,0.79482,0.79482,0.79482,0.23542,0.49274,0.59154,0.49274,0.49274
+27-Dec-2019 02:00:00,1,0.66572,0.66572,0.66572,0.70929,0.2814,0.2814,0.2814,0.2814,0.70929,0.70929,0.70929,0.70929,0.70929,0.2814,0.5302,0.66572,0.5302,0.5302
+27-Dec-2019 03:00:00,1,0.70456,0.70456,0.70456,0.76517,0.27037,0.27037,0.27037,0.27037,0.76517,0.76517,0.76517,0.76517,0.76517,0.27037,0.6273,0.70456,0.6273,0.6273
+27-Dec-2019 04:00:00,1,0.74539,0.74539,0.74539,0.74141,0.21887,0.21887,0.21887,0.21887,0.74141,0.74141,0.74141,0.74141,0.74141,0.21887,0.70777,0.74539,0.70777,0.70777
+27-Dec-2019 05:00:00,1,0.7543,0.7543,0.7543,0.67777,0.26117,0.26117,0.26117,0.26117,0.67777,0.67777,0.67777,0.67777,0.67777,0.26117,0.73217,0.7543,0.73217,0.73217
+27-Dec-2019 06:00:00,1,0.77334,0.77334,0.77334,0.6288,0.21519,0.21519,0.21519,0.21519,0.6288,0.6288,0.6288,0.6288,0.6288,0.21519,0.74285,0.77334,0.74285,0.74285
+27-Dec-2019 07:00:00,1,0.79008,0.79008,0.79008,0.5532,0.1968,0.1968,0.1968,0.1968,0.5532,0.5532,0.5532,0.5532,0.5532,0.1968,0.70947,0.79008,0.70947,0.70947
+27-Dec-2019 08:00:00,1,0.7656,0.7656,0.7656,0.48688,0.24462,0.24462,0.24462,0.24462,0.48688,0.48688,0.48688,0.48688,0.48688,0.24462,0.68043,0.7656,0.68043,0.68043
+27-Dec-2019 09:00:00,1,0.73242,0.73242,0.73242,0.45828,0.15082,0.15082,0.15082,0.15082,0.45828,0.45828,0.45828,0.45828,0.45828,0.15082,0.65394,0.73242,0.65394,0.65394
+27-Dec-2019 10:00:00,1,0.68431,0.68431,0.68431,0.39978,0.2299,0.2299,0.2299,0.2299,0.39978,0.39978,0.39978,0.39978,0.39978,0.2299,0.65443,0.68431,0.65443,0.65443
+27-Dec-2019 11:00:00,1,0.62133,0.62133,0.62133,0.39877,0.25933,0.25933,0.25933,0.25933,0.39877,0.39877,0.39877,0.39877,0.39877,0.25933,0.67944,0.62133,0.67944,0.67944
+27-Dec-2019 12:00:00,1,0.52181,0.52181,0.52181,0.25807,0.15633,0.15633,0.15633,0.15633,0.25807,0.25807,0.25807,0.25807,0.25807,0.15633,0.68384,0.52181,0.68384,0.68384
+27-Dec-2019 13:00:00,1,0.40159,0.40159,0.40159,0.20042,0.16921,0.16921,0.16921,0.16921,0.20042,0.20042,0.20042,0.20042,0.20042,0.16921,0.60027,0.40159,0.60027,0.60027
+27-Dec-2019 14:00:00,1,0.26393,0.26393,0.26393,0.21631,0.16921,0.16921,0.16921,0.16921,0.21631,0.21631,0.21631,0.21631,0.21631,0.16921,0.49369,0.26393,0.49369,0.49369
+27-Dec-2019 15:00:00,1,0.16299,0.16299,0.16299,0.21969,0.13978,0.13978,0.13978,0.13978,0.21969,0.21969,0.21969,0.21969,0.21969,0.13978,0.40222,0.16299,0.40222,0.40222
+27-Dec-2019 16:00:00,1,0.11116,0.11116,0.11116,0.31536,0.13794,0.13794,0.13794,0.13794,0.31536,0.31536,0.31536,0.31536,0.31536,0.13794,0.29689,0.11116,0.29689,0.29689
+27-Dec-2019 17:00:00,1,0.11644,0.11644,0.11644,0.30618,0.17473,0.17473,0.17473,0.17473,0.30618,0.30618,0.30618,0.30618,0.30618,0.17473,0.21991,0.11644,0.21991,0.21991
+27-Dec-2019 18:00:00,1,0.1474,0.1474,0.1474,0.2966,0.23358,0.23358,0.23358,0.23358,0.2966,0.2966,0.2966,0.2966,0.2966,0.23358,0.17586,0.1474,0.17586,0.17586
+27-Dec-2019 19:00:00,1,0.19509,0.19509,0.19509,0.2794,0.26117,0.26117,0.26117,0.26117,0.2794,0.2794,0.2794,0.2794,0.2794,0.26117,0.14754,0.19509,0.14754,0.14754
+27-Dec-2019 20:00:00,1,0.231,0.231,0.231,0.32166,0.22071,0.22071,0.22071,0.22071,0.32166,0.32166,0.32166,0.32166,0.32166,0.22071,0.15061,0.231,0.15061,0.15061
+27-Dec-2019 21:00:00,1,0.24458,0.24458,0.24458,0.28666,0.26669,0.26669,0.26669,0.26669,0.28666,0.28666,0.28666,0.28666,0.28666,0.26669,0.1676,0.24458,0.1676,0.1676
+27-Dec-2019 22:00:00,1,0.24611,0.24611,0.24611,0.27163,0.22255,0.22255,0.22255,0.22255,0.27163,0.27163,0.27163,0.27163,0.27163,0.22255,0.21437,0.24611,0.21437,0.21437
+27-Dec-2019 23:00:00,1,0.21031,0.21031,0.21031,0.28585,0.27588,0.27588,0.27588,0.27588,0.28585,0.28585,0.28585,0.28585,0.28585,0.27588,0.26382,0.21031,0.26382,0.26382
+28-Dec-2019 00:00:00,1,0.15422,0.15422,0.15422,0.21701,0.19496,0.19496,0.19496,0.19496,0.21701,0.21701,0.21701,0.21701,0.21701,0.19496,0.26765,0.15422,0.26765,0.26765
+28-Dec-2019 01:00:00,1,0.13883,0.13883,0.13883,0.23048,0.22806,0.22806,0.22806,0.22806,0.23048,0.23048,0.23048,0.23048,0.23048,0.22806,0.25877,0.13883,0.25877,0.25877
+28-Dec-2019 02:00:00,1,0.12113,0.12113,0.12113,0.2096,0.26853,0.26853,0.26853,0.26853,0.2096,0.2096,0.2096,0.2096,0.2096,0.26853,0.22793,0.12113,0.22793,0.22793
+28-Dec-2019 03:00:00,1,0.10236,0.10236,0.10236,0.22654,0.16737,0.16737,0.16737,0.16737,0.22654,0.22654,0.22654,0.22654,0.22654,0.16737,0.24157,0.10236,0.24157,0.24157
+28-Dec-2019 04:00:00,1,0.063654,0.063654,0.063654,0.18211,0.14162,0.14162,0.14162,0.14162,0.18211,0.18211,0.18211,0.18211,0.18211,0.14162,0.23509,0.063654,0.23509,0.23509
+28-Dec-2019 05:00:00,1,0.04209,0.04209,0.04209,0.12265,0.10484,0.10484,0.10484,0.10484,0.12265,0.12265,0.12265,0.12265,0.12265,0.10484,0.19214,0.04209,0.19214,0.19214
+28-Dec-2019 06:00:00,1,0.038639,0.038639,0.038639,0.11176,0.084604,0.084604,0.084604,0.084604,0.11176,0.11176,0.11176,0.11176,0.11176,0.084604,0.12974,0.038639,0.12974,0.12974
+28-Dec-2019 07:00:00,1,0.050052,0.050052,0.050052,0.13113,0.077247,0.077247,0.077247,0.077247,0.13113,0.13113,0.13113,0.13113,0.13113,0.077247,0.074609,0.050052,0.074609,0.074609
+28-Dec-2019 08:00:00,1,0.066383,0.066383,0.066383,0.14752,0.079087,0.079087,0.079087,0.079087,0.14752,0.14752,0.14752,0.14752,0.14752,0.079087,0.045744,0.066383,0.045744,0.045744
+28-Dec-2019 09:00:00,1,0.10491,0.10491,0.10491,0.16981,0.06989,0.06989,0.06989,0.06989,0.16981,0.16981,0.16981,0.16981,0.16981,0.06989,0.039552,0.10491,0.039552,0.039552
+28-Dec-2019 10:00:00,1,0.12313,0.12313,0.12313,0.17803,0.057016,0.057016,0.057016,0.057016,0.17803,0.17803,0.17803,0.17803,0.17803,0.057016,0.032171,0.12313,0.032171,0.032171
+28-Dec-2019 11:00:00,1,0.1165,0.1165,0.1165,0.16149,0.060694,0.060694,0.060694,0.060694,0.16149,0.16149,0.16149,0.16149,0.16149,0.060694,0.024943,0.1165,0.024943,0.024943
+28-Dec-2019 12:00:00,1,0.12927,0.12927,0.12927,0.19664,0.051498,0.051498,0.051498,0.051498,0.19664,0.19664,0.19664,0.19664,0.19664,0.051498,0.042681,0.12927,0.042681,0.042681
+28-Dec-2019 13:00:00,1,0.14071,0.14071,0.14071,0.23436,0.053337,0.053337,0.053337,0.053337,0.23436,0.23436,0.23436,0.23436,0.23436,0.053337,0.068153,0.14071,0.068153,0.068153
+28-Dec-2019 14:00:00,1,0.17804,0.17804,0.17804,0.27652,0.040463,0.040463,0.040463,0.040463,0.27652,0.27652,0.27652,0.27652,0.27652,0.040463,0.069475,0.17804,0.069475,0.069475
+28-Dec-2019 15:00:00,1,0.22292,0.22292,0.22292,0.29579,0.031267,0.031267,0.031267,0.031267,0.29579,0.29579,0.29579,0.29579,0.29579,0.031267,0.083092,0.22292,0.083092,0.083092
+28-Dec-2019 16:00:00,1,0.29948,0.29948,0.29948,0.34194,0.031267,0.031267,0.031267,0.031267,0.34194,0.34194,0.34194,0.34194,0.34194,0.031267,0.14721,0.29948,0.14721,0.14721
+28-Dec-2019 17:00:00,1,0.36915,0.36915,0.36915,0.38647,0.031267,0.031267,0.031267,0.031267,0.38647,0.38647,0.38647,0.38647,0.38647,0.031267,0.22484,0.36915,0.22484,0.22484
+28-Dec-2019 18:00:00,1,0.4151,0.4151,0.4151,0.42016,0.029428,0.029428,0.029428,0.029428,0.42016,0.42016,0.42016,0.42016,0.42016,0.029428,0.28768,0.4151,0.28768,0.28768
+28-Dec-2019 19:00:00,1,0.44111,0.44111,0.44111,0.41854,0.051498,0.051498,0.051498,0.051498,0.41854,0.41854,0.41854,0.41854,0.41854,0.051498,0.31051,0.44111,0.31051,0.31051
+28-Dec-2019 20:00:00,1,0.48437,0.48437,0.48437,0.42807,0.057016,0.057016,0.057016,0.057016,0.42807,0.42807,0.42807,0.42807,0.42807,0.057016,0.31172,0.48437,0.31172,0.31172
+28-Dec-2019 21:00:00,1,0.52549,0.52549,0.52549,0.44442,0.055177,0.055177,0.055177,0.055177,0.44442,0.44442,0.44442,0.44442,0.44442,0.055177,0.33955,0.52549,0.33955,0.33955
+28-Dec-2019 22:00:00,1,0.54378,0.54378,0.54378,0.41219,0.075408,0.075408,0.075408,0.075408,0.41219,0.41219,0.41219,0.41219,0.41219,0.075408,0.37805,0.54378,0.37805,0.37805
+28-Dec-2019 23:00:00,1,0.55028,0.55028,0.55028,0.37305,0.053337,0.053337,0.053337,0.053337,0.37305,0.37305,0.37305,0.37305,0.37305,0.053337,0.38653,0.55028,0.38653,0.38653
+29-Dec-2019 00:00:00,1,0.52538,0.52538,0.52538,0.37789,0.053337,0.053337,0.053337,0.053337,0.37789,0.37789,0.37789,0.37789,0.37789,0.053337,0.41399,0.52538,0.41399,0.41399
+29-Dec-2019 01:00:00,1,0.53118,0.53118,0.53118,0.40165,0.045981,0.045981,0.045981,0.045981,0.40165,0.40165,0.40165,0.40165,0.40165,0.045981,0.43159,0.53118,0.43159,0.43159
+29-Dec-2019 02:00:00,1,0.50747,0.50747,0.50747,0.33059,0.04782,0.04782,0.04782,0.04782,0.33059,0.33059,0.33059,0.33059,0.33059,0.04782,0.43963,0.50747,0.43963,0.43963
+29-Dec-2019 03:00:00,1,0.4901,0.4901,0.4901,0.35212,0.036784,0.036784,0.036784,0.036784,0.35212,0.35212,0.35212,0.35212,0.35212,0.036784,0.4419,0.4901,0.4419,0.4419
+29-Dec-2019 04:00:00,1,0.46511,0.46511,0.46511,0.36216,0.033106,0.033106,0.033106,0.033106,0.36216,0.36216,0.36216,0.36216,0.36216,0.033106,0.4369,0.46511,0.4369,0.4369
+29-Dec-2019 05:00:00,1,0.44537,0.44537,0.44537,0.37704,0.040463,0.040463,0.040463,0.040463,0.37704,0.37704,0.37704,0.37704,0.37704,0.040463,0.38776,0.44537,0.38776,0.38776
+29-Dec-2019 06:00:00,1,0.45004,0.45004,0.45004,0.39247,0.029428,0.029428,0.029428,0.029428,0.39247,0.39247,0.39247,0.39247,0.39247,0.029428,0.3784,0.45004,0.3784,0.3784
+29-Dec-2019 07:00:00,1,0.44711,0.44711,0.44711,0.42066,0.027588,0.027588,0.027588,0.027588,0.42066,0.42066,0.42066,0.42066,0.42066,0.027588,0.38735,0.44711,0.38735,0.38735
+29-Dec-2019 08:00:00,1,0.44138,0.44138,0.44138,0.38042,0.055177,0.055177,0.055177,0.055177,0.38042,0.38042,0.38042,0.38042,0.38042,0.055177,0.4031,0.44138,0.4031,0.4031
+29-Dec-2019 09:00:00,1,0.4527,0.4527,0.4527,0.38183,0.062534,0.062534,0.062534,0.062534,0.38183,0.38183,0.38183,0.38183,0.38183,0.062534,0.37569,0.4527,0.37569,0.37569
+29-Dec-2019 10:00:00,1,0.43064,0.43064,0.43064,0.39075,0.051498,0.051498,0.051498,0.051498,0.39075,0.39075,0.39075,0.39075,0.39075,0.051498,0.33764,0.43064,0.33764,0.33764
+29-Dec-2019 11:00:00,1,0.41878,0.41878,0.41878,0.32797,0.053337,0.053337,0.053337,0.053337,0.32797,0.32797,0.32797,0.32797,0.32797,0.053337,0.33691,0.41878,0.33691,0.33691
+29-Dec-2019 12:00:00,1,0.41659,0.41659,0.41659,0.35187,0.04782,0.04782,0.04782,0.04782,0.35187,0.35187,0.35187,0.35187,0.35187,0.04782,0.30267,0.41659,0.30267,0.30267
+29-Dec-2019 13:00:00,1,0.4209,0.4209,0.4209,0.35177,0.045981,0.045981,0.045981,0.045981,0.35177,0.35177,0.35177,0.35177,0.35177,0.045981,0.26071,0.4209,0.26071,0.26071
+29-Dec-2019 14:00:00,1,0.44386,0.44386,0.44386,0.45294,0.027588,0.027588,0.027588,0.027588,0.45294,0.45294,0.45294,0.45294,0.45294,0.027588,0.2545,0.44386,0.2545,0.2545
+29-Dec-2019 15:00:00,1,0.45286,0.45286,0.45286,0.45425,0.042302,0.042302,0.042302,0.042302,0.45425,0.45425,0.45425,0.45425,0.45425,0.042302,0.26871,0.45286,0.26871,0.26871
+29-Dec-2019 16:00:00,1,0.50422,0.50422,0.50422,0.42853,0.068051,0.068051,0.068051,0.068051,0.42853,0.42853,0.42853,0.42853,0.42853,0.068051,0.31699,0.50422,0.31699,0.31699
+29-Dec-2019 17:00:00,1,0.56648,0.56648,0.56648,0.48108,0.084604,0.084604,0.084604,0.084604,0.48108,0.48108,0.48108,0.48108,0.48108,0.084604,0.36212,0.56648,0.36212,0.36212
+29-Dec-2019 18:00:00,1,0.59384,0.59384,0.59384,0.44653,0.051498,0.051498,0.051498,0.051498,0.44653,0.44653,0.44653,0.44653,0.44653,0.051498,0.39808,0.59384,0.39808,0.39808
+29-Dec-2019 19:00:00,1,0.60678,0.60678,0.60678,0.4596,0.079087,0.079087,0.079087,0.079087,0.4596,0.4596,0.4596,0.4596,0.4596,0.079087,0.44799,0.60678,0.44799,0.44799
+29-Dec-2019 20:00:00,1,0.59117,0.59117,0.59117,0.48975,0.09564,0.09564,0.09564,0.09564,0.48975,0.48975,0.48975,0.48975,0.48975,0.09564,0.49084,0.59117,0.49084,0.49084
+29-Dec-2019 21:00:00,1,0.61342,0.61342,0.61342,0.56545,0.11035,0.11035,0.11035,0.11035,0.56545,0.56545,0.56545,0.56545,0.56545,0.11035,0.46544,0.61342,0.46544,0.46544
+29-Dec-2019 22:00:00,1,0.62079,0.62079,0.62079,0.57388,0.12139,0.12139,0.12139,0.12139,0.57388,0.57388,0.57388,0.57388,0.57388,0.12139,0.46414,0.62079,0.46414,0.46414
+29-Dec-2019 23:00:00,1,0.57586,0.57586,0.57586,0.60898,0.16553,0.16553,0.16553,0.16553,0.60898,0.60898,0.60898,0.60898,0.60898,0.16553,0.47009,0.57586,0.47009,0.47009
+30-Dec-2019 00:00:00,1,0.55678,0.55678,0.55678,0.59022,0.1361,0.1361,0.1361,0.1361,0.59022,0.59022,0.59022,0.59022,0.59022,0.1361,0.45587,0.55678,0.45587,0.45587
+30-Dec-2019 01:00:00,1,0.53014,0.53014,0.53014,0.60686,0.12875,0.12875,0.12875,0.12875,0.60686,0.60686,0.60686,0.60686,0.60686,0.12875,0.45281,0.53014,0.45281,0.45281
+30-Dec-2019 02:00:00,1,0.51622,0.51622,0.51622,0.55355,0.14898,0.14898,0.14898,0.14898,0.55355,0.55355,0.55355,0.55355,0.55355,0.14898,0.48888,0.51622,0.48888,0.48888
+30-Dec-2019 03:00:00,1,0.51656,0.51656,0.51656,0.55809,0.18208,0.18208,0.18208,0.18208,0.55809,0.55809,0.55809,0.55809,0.55809,0.18208,0.50475,0.51656,0.50475,0.50475
+30-Dec-2019 04:00:00,1,0.51097,0.51097,0.51097,0.57524,0.13794,0.13794,0.13794,0.13794,0.57524,0.57524,0.57524,0.57524,0.57524,0.13794,0.52337,0.51097,0.52337,0.52337
+30-Dec-2019 05:00:00,1,0.51997,0.51997,0.51997,0.5994,0.13978,0.13978,0.13978,0.13978,0.5994,0.5994,0.5994,0.5994,0.5994,0.13978,0.51865,0.51997,0.51865,0.51865
+30-Dec-2019 06:00:00,1,0.53636,0.53636,0.53636,0.65336,0.15082,0.15082,0.15082,0.15082,0.65336,0.65336,0.65336,0.65336,0.65336,0.15082,0.51933,0.53636,0.51933,0.51933
+30-Dec-2019 07:00:00,1,0.54442,0.54442,0.54442,0.67378,0.16185,0.16185,0.16185,0.16185,0.67378,0.67378,0.67378,0.67378,0.67378,0.16185,0.53092,0.54442,0.53092,0.53092
+30-Dec-2019 08:00:00,1,0.60293,0.60293,0.60293,0.7101,0.17289,0.17289,0.17289,0.17289,0.7101,0.7101,0.7101,0.7101,0.7101,0.17289,0.50796,0.60293,0.50796,0.50796
+30-Dec-2019 09:00:00,1,0.64554,0.64554,0.64554,0.65094,0.2299,0.2299,0.2299,0.2299,0.65094,0.65094,0.65094,0.65094,0.65094,0.2299,0.54278,0.64554,0.54278,0.54278
+30-Dec-2019 10:00:00,1,0.69175,0.69175,0.69175,0.71373,0.19128,0.19128,0.19128,0.19128,0.71373,0.71373,0.71373,0.71373,0.71373,0.19128,0.56243,0.69175,0.56243,0.56243
+30-Dec-2019 11:00:00,1,0.73937,0.73937,0.73937,0.76421,0.22439,0.22439,0.22439,0.22439,0.76421,0.76421,0.76421,0.76421,0.76421,0.22439,0.58923,0.73937,0.58923,0.58923
+30-Dec-2019 12:00:00,1,0.77077,0.77077,0.77077,0.77742,0.17473,0.17473,0.17473,0.17473,0.77742,0.77742,0.77742,0.77742,0.77742,0.17473,0.6325,0.77077,0.6325,0.6325
+30-Dec-2019 13:00:00,1,0.78191,0.78191,0.78191,0.77137,0.15266,0.15266,0.15266,0.15266,0.77137,0.77137,0.77137,0.77137,0.77137,0.15266,0.62799,0.78191,0.62799,0.62799
+30-Dec-2019 14:00:00,1,0.77733,0.77733,0.77733,0.7628,0.15082,0.15082,0.15082,0.15082,0.7628,0.7628,0.7628,0.7628,0.7628,0.15082,0.6071,0.77733,0.6071,0.6071
+30-Dec-2019 15:00:00,1,0.74553,0.74553,0.74553,0.71458,0.1968,0.1968,0.1968,0.1968,0.71458,0.71458,0.71458,0.71458,0.71458,0.1968,0.57222,0.74553,0.57222,0.57222
+30-Dec-2019 16:00:00,1,0.74634,0.74634,0.74634,0.68654,0.15817,0.15817,0.15817,0.15817,0.68654,0.68654,0.68654,0.68654,0.68654,0.15817,0.50316,0.74634,0.50316,0.50316
+30-Dec-2019 17:00:00,1,0.70953,0.70953,0.70953,0.74772,0.15266,0.15266,0.15266,0.15266,0.74772,0.74772,0.74772,0.74772,0.74772,0.15266,0.54322,0.70953,0.54322,0.54322
+30-Dec-2019 18:00:00,1,0.68704,0.68704,0.68704,0.73985,0.15449,0.15449,0.15449,0.15449,0.73985,0.73985,0.73985,0.73985,0.73985,0.15449,0.5855,0.68704,0.5855,0.5855
+30-Dec-2019 19:00:00,1,0.71253,0.71253,0.71253,0.70677,0.16553,0.16553,0.16553,0.16553,0.70677,0.70677,0.70677,0.70677,0.70677,0.16553,0.58176,0.71253,0.58176,0.58176
+30-Dec-2019 20:00:00,1,0.69859,0.69859,0.69859,0.70026,0.14346,0.14346,0.14346,0.14346,0.70026,0.70026,0.70026,0.70026,0.70026,0.14346,0.60417,0.69859,0.60417,0.60417
+30-Dec-2019 21:00:00,1,0.68659,0.68659,0.68659,0.7393,0.15082,0.15082,0.15082,0.15082,0.7393,0.7393,0.7393,0.7393,0.7393,0.15082,0.65284,0.68659,0.65284,0.65284
+30-Dec-2019 22:00:00,1,0.65219,0.65219,0.65219,0.65815,0.13242,0.13242,0.13242,0.13242,0.65815,0.65815,0.65815,0.65815,0.65815,0.13242,0.62404,0.65219,0.62404,0.62404
+30-Dec-2019 23:00:00,1,0.66085,0.66085,0.66085,0.6582,0.12507,0.12507,0.12507,0.12507,0.6582,0.6582,0.6582,0.6582,0.6582,0.12507,0.59529,0.66085,0.59529,0.59529
+31-Dec-2019 00:00:00,1,0.67569,0.67569,0.67569,0.64544,0.11955,0.11955,0.11955,0.11955,0.64544,0.64544,0.64544,0.64544,0.64544,0.11955,0.60062,0.67569,0.60062,0.60062
+31-Dec-2019 01:00:00,1,0.68185,0.68185,0.68185,0.66713,0.13426,0.13426,0.13426,0.13426,0.66713,0.66713,0.66713,0.66713,0.66713,0.13426,0.57288,0.68185,0.57288,0.57288
+31-Dec-2019 02:00:00,1,0.69191,0.69191,0.69191,0.67368,0.14346,0.14346,0.14346,0.14346,0.67368,0.67368,0.67368,0.67368,0.67368,0.14346,0.58354,0.69191,0.58354,0.58354
+31-Dec-2019 03:00:00,1,0.7054,0.7054,0.7054,0.68599,0.13978,0.13978,0.13978,0.13978,0.68599,0.68599,0.68599,0.68599,0.68599,0.13978,0.6069,0.7054,0.6069,0.6069
+31-Dec-2019 04:00:00,1,0.74596,0.74596,0.74596,0.73219,0.090122,0.090122,0.090122,0.090122,0.73219,0.73219,0.73219,0.73219,0.73219,0.090122,0.61166,0.74596,0.61166,0.61166
+31-Dec-2019 05:00:00,1,0.79465,0.79465,0.79465,0.71983,0.12139,0.12139,0.12139,0.12139,0.71983,0.71983,0.71983,0.71983,0.71983,0.12139,0.64709,0.79465,0.64709,0.64709
+31-Dec-2019 06:00:00,1,0.80325,0.80325,0.80325,0.72109,0.099318,0.099318,0.099318,0.099318,0.72109,0.72109,0.72109,0.72109,0.72109,0.099318,0.69497,0.80325,0.69497,0.69497
+31-Dec-2019 07:00:00,1,0.77956,0.77956,0.77956,0.71388,0.103,0.103,0.103,0.103,0.71388,0.71388,0.71388,0.71388,0.71388,0.103,0.74058,0.77956,0.74058,0.74058
+31-Dec-2019 08:00:00,1,0.76736,0.76736,0.76736,0.66491,0.099318,0.099318,0.099318,0.099318,0.66491,0.66491,0.66491,0.66491,0.66491,0.099318,0.7312,0.76736,0.7312,0.7312
+31-Dec-2019 09:00:00,1,0.77665,0.77665,0.77665,0.67918,0.091961,0.091961,0.091961,0.091961,0.67918,0.67918,0.67918,0.67918,0.67918,0.091961,0.72456,0.77665,0.72456,0.72456
+31-Dec-2019 10:00:00,1,0.76763,0.76763,0.76763,0.66748,0.068051,0.068051,0.068051,0.068051,0.66748,0.66748,0.66748,0.66748,0.66748,0.068051,0.70456,0.76763,0.70456,0.70456
+31-Dec-2019 11:00:00,1,0.75121,0.75121,0.75121,0.69915,0.066212,0.066212,0.066212,0.066212,0.69915,0.69915,0.69915,0.69915,0.69915,0.066212,0.67911,0.75121,0.67911,0.67911
+31-Dec-2019 12:00:00,1,0.7552,0.7552,0.7552,0.7573,0.053337,0.053337,0.053337,0.053337,0.7573,0.7573,0.7573,0.7573,0.7573,0.053337,0.64068,0.7552,0.64068,0.64068
+31-Dec-2019 13:00:00,1,0.75611,0.75611,0.75611,0.71519,0.066212,0.066212,0.066212,0.066212,0.71519,0.71519,0.71519,0.71519,0.71519,0.066212,0.58821,0.75611,0.58821,0.58821
+31-Dec-2019 14:00:00,1,0.73826,0.73826,0.73826,0.66183,0.062534,0.062534,0.062534,0.062534,0.66183,0.66183,0.66183,0.66183,0.66183,0.062534,0.53489,0.73826,0.53489,0.53489
+31-Dec-2019 15:00:00,1,0.71336,0.71336,0.71336,0.60847,0.04782,0.04782,0.04782,0.04782,0.60847,0.60847,0.60847,0.60847,0.60847,0.04782,0.50387,0.71336,0.50387,0.50387
+31-Dec-2019 16:00:00,1,0.73161,0.73161,0.73161,0.58865,0.04782,0.04782,0.04782,0.04782,0.58865,0.58865,0.58865,0.58865,0.58865,0.04782,0.48201,0.73161,0.48201,0.48201
+31-Dec-2019 17:00:00,1,0.72879,0.72879,0.72879,0.6352,0.062534,0.062534,0.062534,0.062534,0.6352,0.6352,0.6352,0.6352,0.6352,0.062534,0.49547,0.72879,0.49547,0.49547
+31-Dec-2019 18:00:00,1,0.7442,0.7442,0.7442,0.65109,0.068051,0.068051,0.068051,0.068051,0.65109,0.65109,0.65109,0.65109,0.65109,0.068051,0.51724,0.7442,0.51724,0.51724
+31-Dec-2019 19:00:00,1,0.74823,0.74823,0.74823,0.69517,0.064373,0.064373,0.064373,0.064373,0.69517,0.69517,0.69517,0.69517,0.69517,0.064373,0.53295,0.74823,0.53295,0.53295
+31-Dec-2019 20:00:00,1,0.73538,0.73538,0.73538,0.65366,0.073569,0.073569,0.073569,0.073569,0.65366,0.65366,0.65366,0.65366,0.65366,0.073569,0.54084,0.73538,0.54084,0.54084
+31-Dec-2019 21:00:00,1,0.74097,0.74097,0.74097,0.66188,0.07173,0.07173,0.07173,0.07173,0.66188,0.66188,0.66188,0.66188,0.66188,0.07173,0.57076,0.74097,0.57076,0.57076
+31-Dec-2019 22:00:00,1,0.72482,0.72482,0.72482,0.59329,0.09564,0.09564,0.09564,0.09564,0.59329,0.59329,0.59329,0.59329,0.59329,0.09564,0.59456,0.72482,0.59456,0.59456
+31-Dec-2019 23:00:00,1,0.7252,0.7252,0.7252,0.56803,0.11035,0.11035,0.11035,0.11035,0.56803,0.56803,0.56803,0.56803,0.56803,0.11035,0.59456,0.7252,0.59456,0.59456
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/existing_capacity.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/existing_capacity.csv
new file mode 100644
index 0000000..4847361
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/existing_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,1900,1985,1990,1995,2000,2005,2010,2016,comments
+1,MW,443.62600000000026,0.215,237.75000000000026,155.64099999999962,822.1509999999987,721.7464999999991,1929.4257699999891,4082.910800000045,
+2,MW,294.1700000000003,0.45999999999999996,143.16999999999996,226.50999999999945,1173.1449999999916,1174.2899999999968,1393.1727999999882,1350.8893999999902,
+3,MW,48.65,0.0,16.617,48.28400000000001,710.0400000000005,683.4550000000004,554.6850000000007,438.4270000000003,
+4,MW,287.6499999999998,0.0,42.480000000000004,92.70999999999988,361.7100000000005,246.20000000000024,569.8922,690.2614999999976,
+5,MW,296.76000000000033,0.0,4.133000000000001,2.58,65.69940000000004,144.0786,576.9646699999995,1218.1350600000003,
+6,MW,18.445,0.0,0.036,3.5391000000000004,13.308500000000002,12.4135,43.87894000000001,132.58426,
+7,MW,68.28,0.0,0.00175,0.0,68.28649999999996,111.1,162.14749999999987,258.9724000000003,
+8,MW,54.059999999999995,0.0,0.3755,0.957,92.42999999999992,90.5,48.04485,61.34610000000001,
+9,MW,104.61000000000003,0.745,36.907,49.012000000000015,684.7450000000003,682.6549999999994,507.97810000000095,498.3811000000004,
+10,MW,45.3,0.415,2.5415000000000005,8.285,134.67583999999994,226.52599999999998,129.65189999999987,181.7883000000002,
+11,MW,101.75,5.6,27.175000000000008,91.83895999999993,256.3417999999999,203.9900000000002,407.46820000000116,545.9460000000013,
+12,MW,105.335,0.9,57.96,30.050000000000008,348.9104000000005,607.5499999999997,601.5930000000005,771.045499999999,
+13,MW,142.27999999999992,0.0,18.099999999999998,9.09,230.55349999999987,348.70000000000016,1184.4993999999983,1113.472499999996,
+14,MW,59.64999999999999,0.0,4.0046,0.12,55.20575000000001,99.83,78.09629999999999,272.1563400000001,
+15,MW,460.7839999999999,0.0,70.19999999999995,127.40399999999971,1578.5369999999914,2690.797199999998,2775.078750000006,2242.3689999999942,
+16,MW,178.6700000000001,0.0,35.196999999999996,79.28999999999996,829.1804999999979,621.6220000000009,721.0532999999988,821.0592999999981,
+17,MW,208.2300000000001,0.0,10.482499999999998,58.99700000000006,804.8849999999999,936.1829999999981,1332.76225,698.3703999999993,
+18,MW,94.20000000000002,0.0,63.17719999999997,99.1319999999998,691.8960000000012,946.8329999999985,945.8748999999978,1097.0015799999967,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/maximum_production_share.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/maximum_production_share.csv
new file mode 100644
index 0000000..5333c85
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/maximum_production_share.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,1,,,,,,,,,
+2,1,,,,,,,,,
+3,1,,,,,,,,,
+4,1,,,,,,,,,
+5,1,,,,,,,,,
+6,1,,,,,,,,,
+7,1,,,,,,,,,
+8,1,,,,,,,,,
+9,1,,,,,,,,,
+10,1,,,,,,,,,
+11,1,,,,,,,,,
+12,1,,,,,,,,,
+13,1,,,,,,,,,
+14,1,,,,,,,,,
+15,1,,,,,,,,,
+16,1,,,,,,,,,
+17,1,,,,,,,,,
+18,1,,,,,,,,,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/potential_capacity.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/potential_capacity.csv
new file mode 100644
index 0000000..1e693c6
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/DE/potential_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,MW ,34783.7181,34783.7181,34783.7181,34783.7181,34783.7181,34783.7181,34783.7181,34783.7181,
+2,MW ,42408.4227,42408.4227,42408.4227,42408.4227,42408.4227,42408.4227,42408.4227,42408.4227,
+3,MW ,33710.5193,33710.5193,33710.5193,33710.5193,33710.5193,33710.5193,33710.5193,33710.5193,
+4,MW ,18833.9555,18833.9555,18833.9555,18833.9555,18833.9555,18833.9555,18833.9555,18833.9555,
+5,MW ,88760.7458,88760.7458,88760.7458,88760.7458,88760.7458,88760.7458,88760.7458,88760.7458,
+6,MW ,80174.2054,80174.2054,80174.2054,80174.2054,80174.2054,80174.2054,80174.2054,80174.2054,
+7,MW ,27222.9159,27222.9159,27222.9159,27222.9159,27222.9159,27222.9159,27222.9159,27222.9159,
+8,MW ,89910.8042,89910.8042,89910.8042,89910.8042,89910.8042,89910.8042,89910.8042,89910.8042,
+9,MW ,13249.7869,13249.7869,13249.7869,13249.7869,13249.7869,13249.7869,13249.7869,13249.7869,
+10,MW ,4193.23506,4193.23506,4193.23506,4193.23506,4193.23506,4193.23506,4193.23506,4193.23506,
+11,MW ,8148.536,8148.536,8148.536,8148.536,8148.536,8148.536,8148.536,8148.536,
+12,MW ,14171.1396,14171.1396,14171.1396,14171.1396,14171.1396,14171.1396,14171.1396,14171.1396,
+13,MW ,19404.3534,19404.3534,19404.3534,19404.3534,19404.3534,19404.3534,19404.3534,19404.3534,
+14,MW ,59553.356,59553.356,59553.356,59553.356,59553.356,59553.356,59553.356,59553.356,
+15,MW ,83113.9903,83113.9903,83113.9903,83113.9903,83113.9903,83113.9903,83113.9903,83113.9903,
+16,MW ,6041.26523,6041.26523,6041.26523,6041.26523,6041.26523,6041.26523,6041.26523,6041.26523,
+17,MW ,43077.4437,43077.4437,43077.4437,43077.4437,43077.4437,43077.4437,43077.4437,43077.4437,
+18,MW ,32860.8657,32860.8657,32860.8657,32860.8657,32860.8657,32860.8657,32860.8657,32860.8657,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/README.md
new file mode 100644
index 0000000..59c9ddb
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/README.md
@@ -0,0 +1,6 @@
+# README - WIND ONSHORE
+
+This data package contains all necessary information
+about the production process **wind onshore**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/costs.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/costs.csv
new file mode 100644
index 0000000..3d089d1
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,kiloEuro * MW ** (-1),1113,1113,1084,1055,1026,996,967,938,
+operation,kiloEuro * MWh ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * MW ** (-1),13,13,13,13,13,13,13,13,k€/(MW*a)
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_invest.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_invest.csv
new file mode 100644
index 0000000..6373f30
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_invest.csv
@@ -0,0 +1,4 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+wind_turbine_2MW_onshore_GLO_wind_turbine_construction_2MW_onshore,MW ** (-1),0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,MW,,"Kapazität: 2 MW. Scaling factor = 1MW/2MW = 0.5",wind turbine. 2MW. onshore,GLO,wind turbine construction. 2MW. onshore,1,0,1,0.5,1
+wind_turbine_network_connection_2MW_onshore_RoW_wind_turbine_network_connection_construction_2MW_onshore,MW ** (-1),0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,MW,,"verwendete Prozesse nach report ""electricity production, wind, 1-3MW turbine, onshore - DE""",wind turbine network connection. 2MW. onshore,RoW,wind turbine network connection construction. 2MW. onshore,1,0,1,0.5,1
+transport_freight_lorry_7_5_16_metric_ton_EURO3_RER_transport_freight_lorry_7_5_16_metric_ton_EURO3,MW ** (-1),2.5e-05,2.5e-05,2.5e-05,2.5e-05,2.5e-05,2.5e-05,2.5e-05,2.5e-05,t*km,,"Menge transport: nach report",transport. freight. lorry 7.5-16 metric ton. EURO3,RER,transport. freight. lorry 7.5-16 metric ton. EURO3,0.0006,0,1,0.5,1
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_operation.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_operation.csv
new file mode 100644
index 0000000..ff45d91
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/ecoinvent_operation.csv
@@ -0,0 +1,3 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,"comments",product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+lubricating_oil_RER_lubricating_oil_production,MWh ** (-1),0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,kg,,"FU: 1MWh - siehe report ""electricity production, wind, 1-3MW turbine, onshore - DE""",lubricating oil,RER,lubricating oil production,1e-01,0,1,1000,1
+waste_mineral_oil_Europe_without_Switzerland_treatment_of_waste_mineral_oil_hazardous_waste_incineration,MWh ** (-1),0.0491,0.0491,0.0491,0.0491,0.0491,0.0491,0.0491,0.0491,kg,,"",waste mineral oil,Europe without Switzerland,treatment of waste mineral oil. hazardous waste incineration,4.91e-02,0,1,1000,1
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/lifetime_duration.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/lifetime_duration.csv
new file mode 100644
index 0000000..3439ce1
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,25,25,25,25,25,25,25,25,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/properties.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/properties.csv
new file mode 100644
index 0000000..0f9dc45
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/properties.csv
@@ -0,0 +1,2 @@
+property,value,unit
+reference product,electricity,MW
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/secured_capacity_factor.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/secured_capacity_factor.csv
new file mode 100644
index 0000000..202835f
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/secured_capacity_factor.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix.csv
new file mode 100644
index 0000000..30026d9
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix.csv
@@ -0,0 +1,2 @@
+product,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+electricity,MW,1,1,1,1,1,1,1,1,
diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix_timeseries.csv b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix_timeseries.csv
new file mode 100644
index 0000000..7ae20c3
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/01-ECOINVENT-BASED/wind_onshore/technologymatrix_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,electricity
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diff --git a/sampledata/02-PROCESSES/01-PRODUCTION/README.md b/sampledata/02-PROCESSES/01-PRODUCTION/README.md
new file mode 100644
index 0000000..a739772
--- /dev/null
+++ b/sampledata/02-PROCESSES/01-PRODUCTION/README.md
@@ -0,0 +1,4 @@
+# README - 01-PRODUCTION
+
+This directory contains all data packages which define
+the production processes used.
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/README.md b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/README.md
new file mode 100644
index 0000000..1bcbede
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/README.md
@@ -0,0 +1,4 @@
+# README - 01-ECOINVENT-BASED
+
+This directory contains all data packages which define
+storage processes using ecoinvent.
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/README.md b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/README.md
new file mode 100644
index 0000000..a51d635
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/README.md
@@ -0,0 +1,4 @@
+# README - PUMPED STORAGE HYDRO - DE
+
+This data package contains all necessary information
+about the storage process **pumped storage hydro** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/existing_capacity.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/existing_capacity.csv
new file mode 100644
index 0000000..0f8f666
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/existing_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,1925,1930,1945,1950,1955,1960,1965,1970,1975,1980,1990,2005,comments
+1,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+2,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+3,MWh,0.0,0.0,0.0,0.0,0.0,0.0,1338.7497640470624,0.0,0.0,0.0,0.0,0.0,
+4,MWh,0.0,870.1873466305906,0.0,0.0,0.0,0.0,0.0,0.0,2920.9085761026818,0.0,0.0,0.0,
+5,MWh,0.0,0.0,0.0,0.0,602.4373938211781,973.6361920342273,0.0,0.0,997.977096835083,0.0,0.0,0.0,
+6,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+7,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+8,MWh,0.0,608.5226200213921,1338.7497640470624,912.783930032088,0.0,0.0,2190.6814320770113,0.0,5537.555842194668,0.0,0.0,0.0,
+9,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+10,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,931.0396086327298,0.0,
+11,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,839.7612156295211,0.0,0.0,0.0,0.0,
+12,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+13,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+14,MWh,754.5680488265261,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+15,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
+16,MWh,0.0,0.0,0.0,0.0,0.0,724.7504404454779,0.0,0.0,0.0,0.0,0.0,0.0,
+17,MWh,0.0,0.0,0.0,0.0,0.0,0.0,1933.884886427984,0.0,0.0,0.0,0.0,6401.657962625044,
+18,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,6360.2784244635895,0.0,0.0,
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/maximum_production_share.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/maximum_production_share.csv
new file mode 100644
index 0000000..5333c85
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/maximum_production_share.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,1,,,,,,,,,
+2,1,,,,,,,,,
+3,1,,,,,,,,,
+4,1,,,,,,,,,
+5,1,,,,,,,,,
+6,1,,,,,,,,,
+7,1,,,,,,,,,
+8,1,,,,,,,,,
+9,1,,,,,,,,,
+10,1,,,,,,,,,
+11,1,,,,,,,,,
+12,1,,,,,,,,,
+13,1,,,,,,,,,
+14,1,,,,,,,,,
+15,1,,,,,,,,,
+16,1,,,,,,,,,
+17,1,,,,,,,,,
+18,1,,,,,,,,,
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/potential_capacity.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/potential_capacity.csv
new file mode 100644
index 0000000..0f55fbc
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/DE/potential_capacity.csv
@@ -0,0 +1,19 @@
+,node,unit,1900,1905,1910,1915,1920,1925,1930,1935,1940,1945,1950,1955,1960,1965,1970,1975,1980,1985,1990,1995,2000,2005,2010,2016,2020,2025,2030,2035,2040,2045,2050,comments
+0,1,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+1,2,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+2,3,MWh,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,1338.7497640470624,Based on the total existing capacity in 2005
+3,4,MWh,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,3791.0959227332723,Based on the total existing capacity in 2005
+4,5,MWh,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,2574.0506826904884,Based on the total existing capacity in 2005
+5,6,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+6,7,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+7,8,MWh,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,10588.293588372222,Based on the total existing capacity in 2005
+8,9,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+9,10,MWh,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,931.0396086327298,Based on the total existing capacity in 2005
+10,11,MWh,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,839.7612156295211,Based on the total existing capacity in 2005
+11,12,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+12,13,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+13,14,MWh,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,754.5680488265261,Based on the total existing capacity in 2005
+14,15,MWh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Based on the total existing capacity in 2005
+15,16,MWh,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,724.7504404454779,Based on the total existing capacity in 2005
+16,17,MWh,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,8335.542849053028,Based on the total existing capacity in 2005
+17,18,MWh,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,6360.2784244635895,Based on the total existing capacity in 2005
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/README.md b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/README.md
new file mode 100644
index 0000000..b43ed52
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/README.md
@@ -0,0 +1,6 @@
+# README - PUMPED STORAGE HYDRO
+
+This data package contains all necessary information
+about the storage process **pumped storage hydro**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/costs.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/costs.csv
new file mode 100644
index 0000000..cc0786c
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,kiloEuro * MWh ** (-1),1218,1218,1218,1218,1218,1218,1218,1218,
+operation,kiloEuro * MWh ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * MWh ** (-1),12,12,12,12,12,12,12,12,k€/(MW*a)
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_invest.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_invest.csv
new file mode 100644
index 0000000..aa9a45b
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_invest.csv
@@ -0,0 +1,2 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+hydropower_plant_reservoir_non_alpine_regions_RER_hydropower_plant_construction_reservoir_non_alpine_regions,MWh ** (-1),0.0006,0.0006,0.0006,0.0006,0.0006,0.0006,0.0006,0.0006,MW,,,hydropower plant. reservoir. non-alpine regions,RER,hydropower plant construction. reservoir. non-alpine regions,1,0,1,0.0009,1
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_operation.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_operation.csv
new file mode 100644
index 0000000..12d8b44
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/ecoinvent_operation.csv
@@ -0,0 +1,3 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+lubricating_oil_RER_lubricating_oil_production,MWh ** (-1),0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,kg,,,lubricating oil,RER,lubricating oil production,2.00E-05,0,1,1000,1
+waste_mineral_oil_Europe_without_Switzerland_treatment_of_waste_mineral_oil_hazardous_waste_incineration,MWh ** (-1),0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001,kg,,,waste mineral oil,Europe without Switzerland,treatment of waste mineral oil. hazardous waste incineration,1.00E-06,0,1,1000,1
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/flow_to_storage_capacity_factor.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/flow_to_storage_capacity_factor.csv
new file mode 100644
index 0000000..ce85d2a
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/flow_to_storage_capacity_factor.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+MW / MWh,0.164332428589893,0.164332428589893,0.164332428589893,0.164332428589893,0.164332428589893,0.164332428589893,0.164332428589893,0.164332428589893,
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/lifetime_duration.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/lifetime_duration.csv
new file mode 100644
index 0000000..69dbc7e
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,100,100,100,100,100,100,100,100,
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/properties.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/properties.csv
new file mode 100644
index 0000000..a611c08
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/properties.csv
@@ -0,0 +1,2 @@
+property,value,unit
+reference product,electricity,MWh
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/secured_capacity_factor.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/secured_capacity_factor.csv
new file mode 100644
index 0000000..c9b7aff
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/secured_capacity_factor.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,0.9,0.9,0.9,0.9,0.9,0.9,0.9,0.9,0.9
diff --git a/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/technologymatrix.csv b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/technologymatrix.csv
new file mode 100644
index 0000000..96ac22a
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/01-ECOINVENT-BASED/pumped_storage_hydro/technologymatrix.csv
@@ -0,0 +1,3 @@
+direction,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+deposit,1,0.8661,0.8661,0.8661,0.8661,0.8661,0.8661,0.8661,0.8661,
+withdraw,1,0.8661,0.8661,0.8661,0.8661,0.8661,0.8661,0.8661,0.8661,
diff --git a/sampledata/02-PROCESSES/02-STORAGE/README.md b/sampledata/02-PROCESSES/02-STORAGE/README.md
new file mode 100644
index 0000000..21505b6
--- /dev/null
+++ b/sampledata/02-PROCESSES/02-STORAGE/README.md
@@ -0,0 +1,5 @@
+# README - 01-ECOINVENT-BASED
+
+This directory contains all data packages which define
+the storage processes used.
+.
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/README.md b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/README.md
new file mode 100644
index 0000000..0440c70
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/README.md
@@ -0,0 +1,4 @@
+# README - 01-ECOINVENT-BASED
+
+This directory contains all data packages which define
+transshipment processes using ecoinvent.
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/README.md b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/README.md
new file mode 100644
index 0000000..3369010
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/README.md
@@ -0,0 +1,4 @@
+# README - POWER LINE 220 KV - DE
+
+This data package contains all necessary information
+about the transmission process **power line 220 kV** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/existing_capacity.csv b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/existing_capacity.csv
new file mode 100644
index 0000000..befc6d1
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/existing_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,unit,comments
+1,,cubic_meter / hour,
+2,,cubic_meter / hour,
+3,,cubic_meter / hour,
+4,,cubic_meter / hour,
+5,,cubic_meter / hour,
+6,,cubic_meter / hour,
+7,,cubic_meter / hour,
+8,,cubic_meter / hour,
+9,,cubic_meter / hour,
+10,,cubic_meter / hour,
+11,,cubic_meter / hour,
+12,,cubic_meter / hour,
+13,,cubic_meter / hour,
+14,,cubic_meter / hour,
+15,,cubic_meter / hour,
+16,,cubic_meter / hour,
+17,,cubic_meter / hour,
+18,,cubic_meter / hour,
+19,,cubic_meter / hour,
+20,,cubic_meter / hour,
+21,,cubic_meter / hour,
+22,,cubic_meter / hour,
+23,,cubic_meter / hour,
+24,,cubic_meter / hour,
+25,,cubic_meter / hour,
+26,,cubic_meter / hour,
+27,,cubic_meter / hour,
+28,,cubic_meter / hour,
+29,,cubic_meter / hour,
+30,,cubic_meter / hour,
+31,,cubic_meter / hour,
+32,,cubic_meter / hour,
+33,,cubic_meter / hour,
+34,,cubic_meter / hour,
+35,,cubic_meter / hour,
+36,,cubic_meter / hour,
+37,,cubic_meter / hour,
+38,,cubic_meter / hour,
+39,,cubic_meter / hour,
+40,,cubic_meter / hour,
+41,,cubic_meter / hour,
+42,,cubic_meter / hour,
+43,,cubic_meter / hour,
+44,,cubic_meter / hour,
+45,,cubic_meter / hour,
+46,,cubic_meter / hour,
+47,,cubic_meter / hour,
+48,,cubic_meter / hour,
+49,,cubic_meter / hour,
+50,,cubic_meter / hour,
+51,,cubic_meter / hour,
+52,,cubic_meter / hour,
+53,,cubic_meter / hour,
+54,,cubic_meter / hour,
+55,,cubic_meter / hour,
+56,,cubic_meter / hour,
+57,,cubic_meter / hour,
+58,,cubic_meter / hour,
+59,,cubic_meter / hour,
+60,,cubic_meter / hour,
+61,,cubic_meter / hour,
+62,,cubic_meter / hour,
+63,,cubic_meter / hour,
+64,,cubic_meter / hour,
+65,,cubic_meter / hour,
+66,,cubic_meter / hour,
+67,,cubic_meter / hour,
+68,,cubic_meter / hour,
+69,,cubic_meter / hour,
+70,,cubic_meter / hour,
+71,,cubic_meter / hour,
+72,,cubic_meter / hour,
+73,,cubic_meter / hour,
+74,,cubic_meter / hour,
+75,,cubic_meter / hour,
+76,,cubic_meter / hour,
+77,,cubic_meter / hour,
+78,,cubic_meter / hour,
+79,,cubic_meter / hour,
+80,,cubic_meter / hour,
+81,,cubic_meter / hour,
+82,,cubic_meter / hour,
+83,,cubic_meter / hour,
+84,,cubic_meter / hour,
+85,,cubic_meter / hour,
+86,,cubic_meter / hour,
+87,,cubic_meter / hour,
+88,,cubic_meter / hour,
+89,,cubic_meter / hour,
+90,,cubic_meter / hour,
+91,,cubic_meter / hour,
+92,,cubic_meter / hour,
+93,,cubic_meter / hour,
+94,,cubic_meter / hour,
+95,,cubic_meter / hour,
+96,,cubic_meter / hour,
+97,,cubic_meter / hour,
+98,,cubic_meter / hour,
+99,,cubic_meter / hour,
+100,,cubic_meter / hour,
+101,,cubic_meter / hour,
+102,,cubic_meter / hour,
+103,,cubic_meter / hour,
+104,,cubic_meter / hour,
+105,,cubic_meter / hour,
+106,,cubic_meter / hour,
+107,,cubic_meter / hour,
+108,,cubic_meter / hour,
+109,,cubic_meter / hour,
+110,,cubic_meter / hour,
+111,,cubic_meter / hour,
+112,,cubic_meter / hour,
+113,,cubic_meter / hour,
+114,,cubic_meter / hour,
+115,,cubic_meter / hour,
+116,,cubic_meter / hour,
+117,,cubic_meter / hour,
+118,,cubic_meter / hour,
+119,,cubic_meter / hour,
+120,,cubic_meter / hour,
+121,,cubic_meter / hour,
+122,,cubic_meter / hour,
+123,,cubic_meter / hour,
+124,,cubic_meter / hour,
+125,,cubic_meter / hour,
+126,,cubic_meter / hour,
+127,,cubic_meter / hour,
+128,,cubic_meter / hour,
+129,,cubic_meter / hour,
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/potential_capacity.csv b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/potential_capacity.csv
new file mode 100644
index 0000000..6efdf6d
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/DE/potential_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,2020,2025,2030,2035,2040,2045,2050,unit,comments
+1,,,,,,,,,cubic_meter / hour,
+2,,,,,,,,,cubic_meter / hour,
+3,,,,,,,,,cubic_meter / hour,
+4,,,,,,,,,cubic_meter / hour,
+5,,,,,,,,,cubic_meter / hour,
+6,,,,,,,,,cubic_meter / hour,
+7,,,,,,,,,cubic_meter / hour,
+8,,,,,,,,,cubic_meter / hour,
+9,,,,,,,,,cubic_meter / hour,
+10,,,,,,,,,cubic_meter / hour,
+11,,,,,,,,,cubic_meter / hour,
+12,,,,,,,,,cubic_meter / hour,
+13,,,,,,,,,cubic_meter / hour,
+14,,,,,,,,,cubic_meter / hour,
+15,,,,,,,,,cubic_meter / hour,
+16,,,,,,,,,cubic_meter / hour,
+17,,,,,,,,,cubic_meter / hour,
+18,,,,,,,,,cubic_meter / hour,
+19,,,,,,,,,cubic_meter / hour,
+20,,,,,,,,,cubic_meter / hour,
+21,,,,,,,,,cubic_meter / hour,
+22,,,,,,,,,cubic_meter / hour,
+23,,,,,,,,,cubic_meter / hour,
+24,,,,,,,,,cubic_meter / hour,
+25,,,,,,,,,cubic_meter / hour,
+26,,,,,,,,,cubic_meter / hour,
+27,,,,,,,,,cubic_meter / hour,
+28,,,,,,,,,cubic_meter / hour,
+29,,,,,,,,,cubic_meter / hour,
+30,,,,,,,,,cubic_meter / hour,
+31,,,,,,,,,cubic_meter / hour,
+32,,,,,,,,,cubic_meter / hour,
+33,,,,,,,,,cubic_meter / hour,
+34,,,,,,,,,cubic_meter / hour,
+35,,,,,,,,,cubic_meter / hour,
+36,,,,,,,,,cubic_meter / hour,
+37,,,,,,,,,cubic_meter / hour,
+38,,,,,,,,,cubic_meter / hour,
+39,,,,,,,,,cubic_meter / hour,
+40,,,,,,,,,cubic_meter / hour,
+41,,,,,,,,,cubic_meter / hour,
+42,,,,,,,,,cubic_meter / hour,
+43,,,,,,,,,cubic_meter / hour,
+44,,,,,,,,,cubic_meter / hour,
+45,,,,,,,,,cubic_meter / hour,
+46,,,,,,,,,cubic_meter / hour,
+47,,,,,,,,,cubic_meter / hour,
+48,,,,,,,,,cubic_meter / hour,
+49,,,,,,,,,cubic_meter / hour,
+50,,,,,,,,,cubic_meter / hour,
+51,,,,,,,,,cubic_meter / hour,
+52,,,,,,,,,cubic_meter / hour,
+53,,,,,,,,,cubic_meter / hour,
+54,,,,,,,,,cubic_meter / hour,
+55,,,,,,,,,cubic_meter / hour,
+56,,,,,,,,,cubic_meter / hour,
+57,,,,,,,,,cubic_meter / hour,
+58,,,,,,,,,cubic_meter / hour,
+59,,,,,,,,,cubic_meter / hour,
+60,,,,,,,,,cubic_meter / hour,
+61,,,,,,,,,cubic_meter / hour,
+62,,,,,,,,,cubic_meter / hour,
+63,,,,,,,,,cubic_meter / hour,
+64,,,,,,,,,cubic_meter / hour,
+65,,,,,,,,,cubic_meter / hour,
+66,,,,,,,,,cubic_meter / hour,
+67,,,,,,,,,cubic_meter / hour,
+68,,,,,,,,,cubic_meter / hour,
+69,,,,,,,,,cubic_meter / hour,
+70,,,,,,,,,cubic_meter / hour,
+71,,,,,,,,,cubic_meter / hour,
+72,,,,,,,,,cubic_meter / hour,
+73,,,,,,,,,cubic_meter / hour,
+74,,,,,,,,,cubic_meter / hour,
+75,,,,,,,,,cubic_meter / hour,
+76,,,,,,,,,cubic_meter / hour,
+77,,,,,,,,,cubic_meter / hour,
+78,,,,,,,,,cubic_meter / hour,
+79,,,,,,,,,cubic_meter / hour,
+80,,,,,,,,,cubic_meter / hour,
+81,,,,,,,,,cubic_meter / hour,
+82,,,,,,,,,cubic_meter / hour,
+83,,,,,,,,,cubic_meter / hour,
+84,,,,,,,,,cubic_meter / hour,
+85,,,,,,,,,cubic_meter / hour,
+86,,,,,,,,,cubic_meter / hour,
+87,,,,,,,,,cubic_meter / hour,
+88,,,,,,,,,cubic_meter / hour,
+89,,,,,,,,,cubic_meter / hour,
+90,,,,,,,,,cubic_meter / hour,
+91,,,,,,,,,cubic_meter / hour,
+92,,,,,,,,,cubic_meter / hour,
+93,,,,,,,,,cubic_meter / hour,
+94,,,,,,,,,cubic_meter / hour,
+95,,,,,,,,,cubic_meter / hour,
+96,,,,,,,,,cubic_meter / hour,
+97,,,,,,,,,cubic_meter / hour,
+98,,,,,,,,,cubic_meter / hour,
+99,,,,,,,,,cubic_meter / hour,
+100,,,,,,,,,cubic_meter / hour,
+101,,,,,,,,,cubic_meter / hour,
+102,,,,,,,,,cubic_meter / hour,
+103,,,,,,,,,cubic_meter / hour,
+104,,,,,,,,,cubic_meter / hour,
+105,,,,,,,,,cubic_meter / hour,
+106,,,,,,,,,cubic_meter / hour,
+107,,,,,,,,,cubic_meter / hour,
+108,,,,,,,,,cubic_meter / hour,
+109,,,,,,,,,cubic_meter / hour,
+110,,,,,,,,,cubic_meter / hour,
+111,,,,,,,,,cubic_meter / hour,
+112,,,,,,,,,cubic_meter / hour,
+113,,,,,,,,,cubic_meter / hour,
+114,,,,,,,,,cubic_meter / hour,
+115,,,,,,,,,cubic_meter / hour,
+116,,,,,,,,,cubic_meter / hour,
+117,,,,,,,,,cubic_meter / hour,
+118,,,,,,,,,cubic_meter / hour,
+119,,,,,,,,,cubic_meter / hour,
+120,,,,,,,,,cubic_meter / hour,
+121,,,,,,,,,cubic_meter / hour,
+122,,,,,,,,,cubic_meter / hour,
+123,,,,,,,,,cubic_meter / hour,
+124,,,,,,,,,cubic_meter / hour,
+125,,,,,,,,,cubic_meter / hour,
+126,,,,,,,,,cubic_meter / hour,
+127,,,,,,,,,cubic_meter / hour,
+128,,,,,,,,,cubic_meter / hour,
+129,,,,,,,,,cubic_meter / hour,
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/README.md b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/README.md
new file mode 100644
index 0000000..d63d78c
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/README.md
@@ -0,0 +1,4 @@
+# README - POWER LINE 220 KV
+
+This data package contains all necessary information
+about the transmission process **power line 220 kV**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/costs.csv b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/costs.csv
new file mode 100644
index 0000000..70593b1
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,kiloEuro * (cubic_meter/hour * 100 km) ** (-1),1,1,1,1,1,1,1,1,
+operation,kiloEuro * (cubic_meter * 100 km) ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * (cubic_meter/hour * 100 km) ** (-1),0,0,0,0,0,0,0,0,
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_invest.csv b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_invest.csv
new file mode 100644
index 0000000..17b9a2b
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_invest.csv
@@ -0,0 +1 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_operation.csv b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_operation.csv
new file mode 100644
index 0000000..17b9a2b
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/ecoinvent_operation.csv
@@ -0,0 +1 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/lifetime_duration.csv b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/lifetime_duration.csv
new file mode 100644
index 0000000..e1f63ca
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,80,80,80,80,80,80,80,80,
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/properties.csv b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/properties.csv
new file mode 100644
index 0000000..572150b
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/01-ECOINVENT-BASED/diesel_transshipment/properties.csv
@@ -0,0 +1,4 @@
+property,value,unit
+product,diesel,(cubic_meter/hour)
+transshipment efficiency,1,1
+efficiency exponent,1,(1 / (100 km))
\ No newline at end of file
diff --git a/sampledata/02-PROCESSES/03-TRANSSHIPMENT/README.md b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/README.md
new file mode 100644
index 0000000..d8b537e
--- /dev/null
+++ b/sampledata/02-PROCESSES/03-TRANSSHIPMENT/README.md
@@ -0,0 +1,4 @@
+# README - 03-TRANSSHIPMENT
+
+This directory contains all data packages which define
+the transshipment processes used.
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/README.md
new file mode 100644
index 0000000..07f2baf
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/README.md
@@ -0,0 +1,4 @@
+# README - 01-ECOINVENT-BASED
+
+This directory contains all data packages which define
+transmission processes using ecoinvent.
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/README.md
new file mode 100644
index 0000000..3369010
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/README.md
@@ -0,0 +1,4 @@
+# README - POWER LINE 220 KV - DE
+
+This data package contains all necessary information
+about the transmission process **power line 220 kV** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/existing_capacity.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/existing_capacity.csv
new file mode 100644
index 0000000..4945219
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/existing_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,unit,comments
+1,1,circuits,
+2,1,circuits,
+3,0,circuits,
+4,1,circuits,
+5,1,circuits,
+6,0,circuits,
+7,2,circuits,
+8,0,circuits,
+9,0,circuits,
+10,0,circuits,
+11,0,circuits,
+12,0,circuits,
+13,2,circuits,
+14,0,circuits,
+15,2,circuits,
+16,1,circuits,
+17,0,circuits,
+18,1,circuits,
+19,0,circuits,
+20,2,circuits,
+21,0,circuits,
+22,0,circuits,
+23,1,circuits,
+24,1,circuits,
+25,0,circuits,
+26,0,circuits,
+27,0,circuits,
+28,0,circuits,
+29,0,circuits,
+30,0,circuits,
+31,0,circuits,
+32,0,circuits,
+33,2,circuits,
+34,0,circuits,
+35,0,circuits,
+36,0,circuits,
+37,0,circuits,
+38,0,circuits,
+39,2,circuits,
+40,0,circuits,
+41,0,circuits,
+42,0,circuits,
+43,2,circuits,
+44,1,circuits,
+45,0,circuits,
+46,0,circuits,
+47,0,circuits,
+48,0,circuits,
+49,1,circuits,
+50,0,circuits,
+51,2,circuits,
+52,1,circuits,
+53,1,circuits,
+54,0,circuits,
+55,1,circuits,
+56,0,circuits,
+57,0,circuits,
+58,0,circuits,
+59,0,circuits,
+60,1,circuits,
+61,1,circuits,
+62,0,circuits,
+63,1,circuits,
+64,0,circuits,
+65,1,circuits,
+66,1,circuits,
+67,1,circuits,
+68,1,circuits,
+69,1,circuits,
+70,0,circuits,
+71,0,circuits,
+72,0,circuits,
+73,0,circuits,
+74,1,circuits,
+75,0,circuits,
+76,1,circuits,
+77,0,circuits,
+78,0,circuits,
+79,1,circuits,
+80,1,circuits,
+81,1,circuits,
+82,0,circuits,
+83,1,circuits,
+84,1,circuits,
+85,2,circuits,
+86,1,circuits,
+87,1,circuits,
+88,2,circuits,
+89,0,circuits,
+90,0,circuits,
+91,0,circuits,
+92,0,circuits,
+93,1,circuits,
+94,2,circuits,
+95,1,circuits,
+96,1,circuits,
+97,1,circuits,
+98,1,circuits,
+99,0,circuits,
+100,0,circuits,
+101,2,circuits,
+102,2,circuits,
+103,0,circuits,
+104,1,circuits,
+105,0,circuits,
+106,2,circuits,
+107,2,circuits,
+108,0,circuits,
+109,0,circuits,
+110,0,circuits,
+111,0,circuits,
+112,1,circuits,
+113,1,circuits,
+114,0,circuits,
+115,0,circuits,
+116,0,circuits,
+117,0,circuits,
+118,0,circuits,
+119,0,circuits,
+120,1,circuits,
+121,0,circuits,
+122,1,circuits,
+123,1,circuits,
+124,0,circuits,
+125,0,circuits,
+126,1,circuits,
+127,1,circuits,
+128,0,circuits,
+129,0,circuits,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/potential_capacity.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/potential_capacity.csv
new file mode 100644
index 0000000..8fdfb4e
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/DE/potential_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,2020,2025,2030,2035,2040,2045,2050,unit,comments
+1,1,1,1,1,1,1,1,1,circuits,
+2,1,1,1,1,1,1,1,1,circuits,
+3,0,0,0,0,0,0,0,0,circuits,
+4,1,1,1,1,1,1,1,1,circuits,
+5,1,1,1,1,1,1,1,1,circuits,
+6,0,0,0,0,0,0,0,0,circuits,
+7,2,2,2,2,2,2,2,2,circuits,
+8,0,0,0,0,0,0,0,0,circuits,
+9,0,0,0,0,0,0,0,0,circuits,
+10,0,0,0,0,0,0,0,0,circuits,
+11,0,0,0,0,0,0,0,0,circuits,
+12,0,0,0,0,0,0,0,0,circuits,
+13,2,2,2,2,2,2,2,2,circuits,
+14,0,0,0,0,0,0,0,0,circuits,
+15,2,2,2,2,2,2,2,2,circuits,
+16,1,1,1,1,1,1,1,1,circuits,
+17,0,0,0,0,0,0,0,0,circuits,
+18,1,1,1,1,1,1,1,1,circuits,
+19,0,0,0,0,0,0,0,0,circuits,
+20,2,2,2,2,2,2,2,2,circuits,
+21,0,0,0,0,0,0,0,0,circuits,
+22,0,0,0,0,0,0,0,0,circuits,
+23,1,1,1,1,1,1,1,1,circuits,
+24,1,1,1,1,1,1,1,1,circuits,
+25,0,0,0,0,0,0,0,0,circuits,
+26,0,0,0,0,0,0,0,0,circuits,
+27,0,0,0,0,0,0,0,0,circuits,
+28,0,0,0,0,0,0,0,0,circuits,
+29,0,0,0,0,0,0,0,0,circuits,
+30,0,0,0,0,0,0,0,0,circuits,
+31,0,0,0,0,0,0,0,0,circuits,
+32,0,0,0,0,0,0,0,0,circuits,
+33,2,2,2,2,2,2,2,2,circuits,
+34,0,0,0,0,0,0,0,0,circuits,
+35,0,0,0,0,0,0,0,0,circuits,
+36,0,0,0,0,0,0,0,0,circuits,
+37,0,0,0,0,0,0,0,0,circuits,
+38,0,0,0,0,0,0,0,0,circuits,
+39,2,2,2,2,2,2,2,2,circuits,
+40,0,0,0,0,0,0,0,0,circuits,
+41,0,0,0,0,0,0,0,0,circuits,
+42,0,0,0,0,0,0,0,0,circuits,
+43,2,2,2,2,2,2,2,2,circuits,
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+45,0,0,0,0,0,0,0,0,circuits,
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+48,0,0,0,0,0,0,0,0,circuits,
+49,1,1,1,1,1,1,1,1,circuits,
+50,0,0,0,0,0,0,0,0,circuits,
+51,2,2,2,2,2,2,2,2,circuits,
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+53,1,1,1,1,1,1,1,1,circuits,
+54,0,0,0,0,0,0,0,0,circuits,
+55,1,1,1,1,1,1,1,1,circuits,
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+58,0,0,0,0,0,0,0,0,circuits,
+59,0,0,0,0,0,0,0,0,circuits,
+60,1,1,1,1,1,1,1,1,circuits,
+61,1,1,1,1,1,1,1,1,circuits,
+62,0,0,0,0,0,0,0,0,circuits,
+63,1,1,1,1,1,1,1,1,circuits,
+64,0,0,0,0,0,0,0,0,circuits,
+65,1,1,1,1,1,1,1,1,circuits,
+66,1,1,1,1,1,1,1,1,circuits,
+67,1,1,1,1,1,1,1,1,circuits,
+68,1,1,1,1,1,1,1,1,circuits,
+69,1,1,1,1,1,1,1,1,circuits,
+70,0,0,0,0,0,0,0,0,circuits,
+71,0,0,0,0,0,0,0,0,circuits,
+72,0,0,0,0,0,0,0,0,circuits,
+73,0,0,0,0,0,0,0,0,circuits,
+74,1,1,1,1,1,1,1,1,circuits,
+75,0,0,0,0,0,0,0,0,circuits,
+76,1,1,1,1,1,1,1,1,circuits,
+77,0,0,0,0,0,0,0,0,circuits,
+78,0,0,0,0,0,0,0,0,circuits,
+79,1,1,1,1,1,1,1,1,circuits,
+80,1,1,1,1,1,1,1,1,circuits,
+81,1,1,1,1,1,1,1,1,circuits,
+82,0,0,0,0,0,0,0,0,circuits,
+83,1,1,1,1,1,1,1,1,circuits,
+84,1,1,1,1,1,1,1,1,circuits,
+85,2,2,2,2,2,2,2,2,circuits,
+86,1,1,1,1,1,1,1,1,circuits,
+87,1,1,1,1,1,1,1,1,circuits,
+88,2,2,2,2,2,2,2,2,circuits,
+89,0,0,0,0,0,0,0,0,circuits,
+90,0,0,0,0,0,0,0,0,circuits,
+91,0,0,0,0,0,0,0,0,circuits,
+92,0,0,0,0,0,0,0,0,circuits,
+93,1,1,1,1,1,1,1,1,circuits,
+94,2,2,2,2,2,2,2,2,circuits,
+95,1,1,1,1,1,1,1,1,circuits,
+96,1,1,1,1,1,1,1,1,circuits,
+97,1,1,1,1,1,1,1,1,circuits,
+98,1,1,1,1,1,1,1,1,circuits,
+99,0,0,0,0,0,0,0,0,circuits,
+100,0,0,0,0,0,0,0,0,circuits,
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+122,1,1,1,1,1,1,1,1,circuits,
+123,1,1,1,1,1,1,1,1,circuits,
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+125,0,0,0,0,0,0,0,0,circuits,
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+129,0,0,0,0,0,0,0,0,circuits,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/README.md
new file mode 100644
index 0000000..ad20b0b
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/README.md
@@ -0,0 +1,6 @@
+# README - POWER LINE 220 KV
+
+This data package contains all necessary information
+about the transmission process **power line 220 kV**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/costs.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/costs.csv
new file mode 100644
index 0000000..e38e387
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,kiloEuro * (MW * 100 km) ** (-1),88.2,88.2,88.2,88.2,88.2,88.2,88.2,88.2,
+operation,kiloEuro * (MWh * 100 km) ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * (MW * 100 km) ** (-1),0,0,0,0,0,0,0,0,k€/(MW*a)
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_invest.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_invest.csv
new file mode 100644
index 0000000..2d1a3b7
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_invest.csv
@@ -0,0 +1,15 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+concrete_normal_CH_unreinforced_concrete_production_with_cement_CEM_II_A,(MW * 100 km) ** (-1),10,10,10,10,10,10,10,10,m3,Foundation,,concrete. normal,CH,unreinforced concrete production. with cement CEM II/A,50,0,1,0.15,1
+cast_iron_RER_cast_iron_production,(MW * 100 km) ** (-1),160,160,160,160,160,160,160,160,kg,Foundation,,cast iron,RER,cast iron production,6000,0.85,1,0.15,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,(MW * 100 km) ** (-1),600,600,600,600,600,600,600,600,kg,Masts,,steel. low-alloyed,RER,steel production. converter. low-alloyed,18000,0.85,1,0.15,1
+zinc_RoW_primary_zinc_production_from_concentrate,(MW * 100 km) ** (-1),30,30,30,30,30,30,30,30,kg,Masts,,zinc,RoW,primary zinc production from concentrate,200,0,1,0.15,1
+insulators_150kV,(MW * 100 km) ** (-1),0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8,unit,Insulators,,insulators_150kV,$,,2,0,1,0.15,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,(MW * 100 km) ** (-1),50,50,50,50,50,50,50,50,kg,Phase Conductors,,steel. low-alloyed,RER,steel production. converter. low-alloyed,2400,0.85,1,0.15,1
+aluminium_primary_cast_alloy_slab_from_continuous_casting_RoW_aluminium_production_primary_cast_alloy_slab_from_continuous_casting,(MW * 100 km) ** (-1),133,133,133,133,133,133,133,133,kg,Phase Conductors,,aluminium. primary. cast alloy slab from continuous casting,RoW,aluminium production. primary. cast alloy slab from continuous casting,7150,0.9,1,0.15,1
+lubricating_oil_RER_lubricating_oil_production,(MW * 100 km) ** (-1),78,78,78,78,78,78,78,78,kg,Phase Conductors,,lubricating oil,RER,lubricating oil production,480,0,1,0.15,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,(MW * 100 km) ** (-1),19,19,19,19,19,19,19,19,kg,Earth conductor,,steel. low-alloyed,RER,steel production. converter. low-alloyed,480,0.85,1,0.15,1
+aluminium_primary_cast_alloy_slab_from_continuous_casting_RoW_aluminium_production_primary_cast_alloy_slab_from_continuous_casting,(MW * 100 km) ** (-1),9.5,9.5,9.5,9.5,9.5,9.5,9.5,9.5,kg,Earth conductor,,aluminium. primary. cast alloy slab from continuous casting,RoW,aluminium production. primary. cast alloy slab from continuous casting,220,0.9,1,0.15,1
+lubricating_oil_RER_lubricating_oil_production,(MW * 100 km) ** (-1),1.7,1.7,1.7,1.7,1.7,1.7,1.7,1.7,kg,Earth conductor,,lubricating oil,RER,lubricating oil production,18,0,1,0.15,1
+waste_concrete_CH_treatment_of_waste_concrete_inert_material_landfill,(MW * 100 km) ** (-1),25400,25400,25400,25400,25400,25400,25400,25400,kg,waste treatment,,waste concrete,CH,treatment of waste concrete. inert material landfill,140000,0,1,0.15,1
+waste_mineral_oil_Europe_without_Switzerland_treatment_of_waste_mineral_oil_hazardous_waste_incineration,(MW * 100 km) ** (-1),114,114,114,114,114,114,114,114,kg,waste treatment,,waste mineral oil,Europe without Switzerland,treatment of waste mineral oil. hazardous waste incineration,500,0,1,0.15,1
+inert_waste_for_final_disposal_CH_treatment_of_inert_waste_inert_material_landfill,(MW * 100 km) ** (-1),48,48,48,48,48,48,48,48,kg,waste treatment,,inert waste. for final disposal,CH,treatment of inert waste. inert material landfill,200,0,1,0.15,1
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_operation.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_operation.csv
new file mode 100644
index 0000000..965d376
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/ecoinvent_operation.csv
@@ -0,0 +1 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/lifetime_duration.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/lifetime_duration.csv
new file mode 100644
index 0000000..e1f63ca
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,80,80,80,80,80,80,80,80,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/properties.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/properties.csv
new file mode 100644
index 0000000..c3183e4
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_220_kV/properties.csv
@@ -0,0 +1,8 @@
+property,value,unit
+reference product,electricity,(MW * 100 km)
+process type,power line,
+safety margin,0.2,1
+voltage,220,kV
+power limit,490,MW
+specific reactance,0.4,ohm / km
+specific resistance,0.075,ohm / km
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/README.md
new file mode 100644
index 0000000..8a32c8d
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/README.md
@@ -0,0 +1,4 @@
+# README - POWER LINE 380 KV - DE
+
+This data package contains all necessary information
+about the transmission process **power line 380 kV** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/existing_capacity.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/existing_capacity.csv
new file mode 100644
index 0000000..2a1a613
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/existing_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,unit,comments
+1,0,circuits,
+2,0,circuits,
+3,2,circuits,
+4,0,circuits,
+5,0,circuits,
+6,2,circuits,
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+10,1,circuits,
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+27,1,circuits,
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+32,1,circuits,
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+128,2,circuits,
+129,1,circuits,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/potential_capacity.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/potential_capacity.csv
new file mode 100644
index 0000000..37f5866
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/DE/potential_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,2020,2025,2030,2035,2040,2045,2050,unit,comments
+1,,,,,,,,,circuits,
+2,,,,,,,,,circuits,
+3,,,,,,,,,circuits,
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+100,,,,,,,,,circuits,
+101,,,,,,,,,circuits,
+102,,,,,,,,,circuits,
+103,,,,,,,,,circuits,
+104,,,,,,,,,circuits,
+105,,,,,,,,,circuits,
+106,,,,,,,,,circuits,
+107,,,,,,,,,circuits,
+108,,,,,,,,,circuits,
+109,,,,,,,,,circuits,
+110,,,,,,,,,circuits,
+111,,,,,,,,,circuits,
+112,,,,,,,,,circuits,
+113,,,,,,,,,circuits,
+114,,,,,,,,,circuits,
+115,,,,,,,,,circuits,
+116,,,,,,,,,circuits,
+117,,,,,,,,,circuits,
+118,,,,,,,,,circuits,
+119,,,,,,,,,circuits,
+120,,,,,,,,,circuits,
+121,,,,,,,,,circuits,
+122,,,,,,,,,circuits,
+123,,,,,,,,,circuits,
+124,,,,,,,,,circuits,
+125,,,,,,,,,circuits,
+126,,,,,,,,,circuits,
+127,,,,,,,,,circuits,
+128,,,,,,,,,circuits,
+129,,,,,,,,,circuits,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/README.md
new file mode 100644
index 0000000..de46310
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/README.md
@@ -0,0 +1,6 @@
+# README - POWER LINE 380 KV
+
+This data package contains all necessary information
+about the transmission process **power line 380 kV**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/costs.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/costs.csv
new file mode 100644
index 0000000..c79efb6
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,"comments"
+invest,kiloEuro * (MW * 100 km) ** (-1),73.53,73.53,73.53,73.53,73.53,73.53,73.53,73.53,"Quelle: Standardkosten NEP 2019; mit power limits aus Kiessling, siehe Doku"
+operation,kiloEuro * (MWh * 100 km) ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * (MW * 100 km) ** (-1),0,0,0,0,0,0,0,0,"k€/(MW*100km*a)"
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_invest.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_invest.csv
new file mode 100644
index 0000000..a493354
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_invest.csv
@@ -0,0 +1,15 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+concrete_normal_CH_unreinforced_concrete_production_with_cement_CEM_II_A,(MW * 100 km) ** (-1),6.2,6.2,6.2,6.2,6.2,6.2,6.2,6.2,m3,Foundation,,concrete. normal,CH,unreinforced concrete production. with cement CEM II/A,150,0,1,0.083,1
+cast_iron_RER_cast_iron_production,(MW * 100 km) ** (-1),144,144,144,144,144,144,144,144,kg,Foundation,,cast iron,RER,cast iron production,15000,0.85,1,0.083,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,(MW * 100 km) ** (-1),410,410,410,410,410,410,410,410,kg,Masts,,steel. low-alloyed,RER,steel production. converter. low-alloyed,50000,0.85,1,0.083,1
+zinc_RoW_primary_zinc_production_from_concentrate,(MW * 100 km) ** (-1),72,72,72,72,72,72,72,72,kg,Masts,,zinc,RoW,primary zinc production from concentrate,1400,0.42,1,0.083,1
+insulators_150kV,(MW * 100 km) ** (-1),0.023,0.023,0.023,0.023,0.023,0.023,0.023,0.023,,Insulators 150 kV,,insulators_150kV,$,,1,0,1,0.083,1
+insulators_and_conductors_400kV,(MW * 100 km) ** (-1),0.023,0.023,0.023,0.023,0.023,0.023,0.023,0.023,,Insulators and conductors 400 kV,,insulators_and_conductors_400kV,$,,1,0,1,0.083,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,(MW * 100 km) ** (-1),25,25,25,25,25,25,25,25,kg,Conductors 150kV,,steel. low-alloyed,RER,steel production. converter. low-alloyed,2400,0.85,1,0.083,1
+aluminium_primary_cast_alloy_slab_from_continuous_casting_RoW_aluminium_production_primary_cast_alloy_slab_from_continuous_casting,(MW * 100 km) ** (-1),42.7,42.7,42.7,42.7,42.7,42.7,42.7,42.7,kg,Conductors 150kV,,aluminium. primary. cast alloy slab from continuous casting,RoW,aluminium production. primary. cast alloy slab from continuous casting,5500,0.9,1,0.083,1
+lubricating_oil_RER_lubricating_oil_production,(MW * 100 km) ** (-1),8,8,8,8,8,8,8,8,kg,Conductors 150kV,,lubricating oil,RER,lubricating oil production,170,0,1,0.083,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,(MW * 100 km) ** (-1),10.5,10.5,10.5,10.5,10.5,10.5,10.5,10.5,kg,Earth conductor,,steel. low-alloyed,RER,steel production. converter. low-alloyed,1000,0.85,1,0.083,1
+aluminium_primary_cast_alloy_slab_from_continuous_casting_RoW_aluminium_production_primary_cast_alloy_slab_from_continuous_casting,(MW * 100 km) ** (-1),1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9,kg,Earth conductor,,aluminium. primary. cast alloy slab from continuous casting,RoW,aluminium production. primary. cast alloy slab from continuous casting,570,0.9,1,0.083,1
+lubricating_oil_RER_lubricating_oil_production,(MW * 100 km) ** (-1),3,3,3,3,3,3,3,3,kg,Earth conductor,,lubricating oil,RER,lubricating oil production,25,0,1,0.083,1
+waste_concrete_CH_treatment_of_waste_concrete_inert_material_landfill,(MW * 100 km) ** (-1),15890,15890,15890,15890,15890,15890,15890,15890,kg,waste treatment,,waste concrete,CH,treatment of waste concrete. inert material landfill,290000,0,1,0.083,1
+waste_mineral_oil_Europe_without_Switzerland_treatment_of_waste_mineral_oil_hazardous_waste_incineration,(MW * 100 km) ** (-1),10.2,10.2,10.2,10.2,10.2,10.2,10.2,10.2,kg,waste treatment,,waste mineral oil,Europe without Switzerland,treatment of waste mineral oil. hazardous waste incineration,200,0,1,0.083,1
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_operation.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_operation.csv
new file mode 100644
index 0000000..965d376
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/ecoinvent_operation.csv
@@ -0,0 +1 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/lifetime_duration.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/lifetime_duration.csv
new file mode 100644
index 0000000..e1f63ca
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,80,80,80,80,80,80,80,80,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/properties.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/properties.csv
new file mode 100644
index 0000000..b10f75f
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/power_line_380_kV/properties.csv
@@ -0,0 +1,8 @@
+property,value,unit
+reference product,electricity,(MW * 100 km)
+process type,power line,
+safety margin,0.2,1
+voltage,380,kV
+power limit,1700,MW
+specific reactance,0.33,ohm / km
+specific resistance,0.029,ohm / km
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/README.md
new file mode 100644
index 0000000..e32483e
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/README.md
@@ -0,0 +1,4 @@
+# README - SWITCHING FROM 220 KV TO 380 KV - DE
+
+This data package contains all necessary information
+about the transmission process **switching from 220 kV to 380 kV** in the grid: **DE**
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/existing_capacity.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/existing_capacity.csv
new file mode 100644
index 0000000..bcac17e
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/existing_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,unit,comments
+1,0,circuits,
+2,0,circuits,
+3,0,circuits,
+4,0,circuits,
+5,0,circuits,
+6,0,circuits,
+7,0,circuits,
+8,0,circuits,
+9,0,circuits,
+10,0,circuits,
+11,0,circuits,
+12,0,circuits,
+13,0,circuits,
+14,0,circuits,
+15,0,circuits,
+16,0,circuits,
+17,0,circuits,
+18,0,circuits,
+19,0,circuits,
+20,0,circuits,
+21,0,circuits,
+22,0,circuits,
+23,0,circuits,
+24,0,circuits,
+25,0,circuits,
+26,0,circuits,
+27,0,circuits,
+28,0,circuits,
+29,0,circuits,
+30,0,circuits,
+31,0,circuits,
+32,0,circuits,
+33,0,circuits,
+34,0,circuits,
+35,0,circuits,
+36,0,circuits,
+37,0,circuits,
+38,0,circuits,
+39,0,circuits,
+40,0,circuits,
+41,0,circuits,
+42,0,circuits,
+43,0,circuits,
+44,0,circuits,
+45,0,circuits,
+46,0,circuits,
+47,0,circuits,
+48,0,circuits,
+49,0,circuits,
+50,0,circuits,
+51,0,circuits,
+52,0,circuits,
+53,0,circuits,
+54,0,circuits,
+55,0,circuits,
+56,0,circuits,
+57,0,circuits,
+58,0,circuits,
+59,0,circuits,
+60,0,circuits,
+61,0,circuits,
+62,0,circuits,
+63,0,circuits,
+64,0,circuits,
+65,0,circuits,
+66,0,circuits,
+67,0,circuits,
+68,0,circuits,
+69,0,circuits,
+70,0,circuits,
+71,0,circuits,
+72,0,circuits,
+73,0,circuits,
+74,0,circuits,
+75,0,circuits,
+76,0,circuits,
+77,0,circuits,
+78,0,circuits,
+79,0,circuits,
+80,0,circuits,
+81,0,circuits,
+82,0,circuits,
+83,0,circuits,
+84,0,circuits,
+85,0,circuits,
+86,0,circuits,
+87,0,circuits,
+88,0,circuits,
+89,0,circuits,
+90,0,circuits,
+91,0,circuits,
+92,0,circuits,
+93,0,circuits,
+94,0,circuits,
+95,0,circuits,
+96,0,circuits,
+97,0,circuits,
+98,0,circuits,
+99,0,circuits,
+100,0,circuits,
+101,0,circuits,
+102,0,circuits,
+103,0,circuits,
+104,0,circuits,
+105,0,circuits,
+106,0,circuits,
+107,0,circuits,
+108,0,circuits,
+109,0,circuits,
+110,0,circuits,
+111,0,circuits,
+112,0,circuits,
+113,0,circuits,
+114,0,circuits,
+115,0,circuits,
+116,0,circuits,
+117,0,circuits,
+118,0,circuits,
+119,0,circuits,
+120,0,circuits,
+121,0,circuits,
+122,0,circuits,
+123,0,circuits,
+124,0,circuits,
+125,0,circuits,
+126,0,circuits,
+127,0,circuits,
+128,0,circuits,
+129,0,circuits,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/potential_capacity.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/potential_capacity.csv
new file mode 100644
index 0000000..37f5866
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/DE/potential_capacity.csv
@@ -0,0 +1,130 @@
+connection,2016,2020,2025,2030,2035,2040,2045,2050,unit,comments
+1,,,,,,,,,circuits,
+2,,,,,,,,,circuits,
+3,,,,,,,,,circuits,
+4,,,,,,,,,circuits,
+5,,,,,,,,,circuits,
+6,,,,,,,,,circuits,
+7,,,,,,,,,circuits,
+8,,,,,,,,,circuits,
+9,,,,,,,,,circuits,
+10,,,,,,,,,circuits,
+11,,,,,,,,,circuits,
+12,,,,,,,,,circuits,
+13,,,,,,,,,circuits,
+14,,,,,,,,,circuits,
+15,,,,,,,,,circuits,
+16,,,,,,,,,circuits,
+17,,,,,,,,,circuits,
+18,,,,,,,,,circuits,
+19,,,,,,,,,circuits,
+20,,,,,,,,,circuits,
+21,,,,,,,,,circuits,
+22,,,,,,,,,circuits,
+23,,,,,,,,,circuits,
+24,,,,,,,,,circuits,
+25,,,,,,,,,circuits,
+26,,,,,,,,,circuits,
+27,,,,,,,,,circuits,
+28,,,,,,,,,circuits,
+29,,,,,,,,,circuits,
+30,,,,,,,,,circuits,
+31,,,,,,,,,circuits,
+32,,,,,,,,,circuits,
+33,,,,,,,,,circuits,
+34,,,,,,,,,circuits,
+35,,,,,,,,,circuits,
+36,,,,,,,,,circuits,
+37,,,,,,,,,circuits,
+38,,,,,,,,,circuits,
+39,,,,,,,,,circuits,
+40,,,,,,,,,circuits,
+41,,,,,,,,,circuits,
+42,,,,,,,,,circuits,
+43,,,,,,,,,circuits,
+44,,,,,,,,,circuits,
+45,,,,,,,,,circuits,
+46,,,,,,,,,circuits,
+47,,,,,,,,,circuits,
+48,,,,,,,,,circuits,
+49,,,,,,,,,circuits,
+50,,,,,,,,,circuits,
+51,,,,,,,,,circuits,
+52,,,,,,,,,circuits,
+53,,,,,,,,,circuits,
+54,,,,,,,,,circuits,
+55,,,,,,,,,circuits,
+56,,,,,,,,,circuits,
+57,,,,,,,,,circuits,
+58,,,,,,,,,circuits,
+59,,,,,,,,,circuits,
+60,,,,,,,,,circuits,
+61,,,,,,,,,circuits,
+62,,,,,,,,,circuits,
+63,,,,,,,,,circuits,
+64,,,,,,,,,circuits,
+65,,,,,,,,,circuits,
+66,,,,,,,,,circuits,
+67,,,,,,,,,circuits,
+68,,,,,,,,,circuits,
+69,,,,,,,,,circuits,
+70,,,,,,,,,circuits,
+71,,,,,,,,,circuits,
+72,,,,,,,,,circuits,
+73,,,,,,,,,circuits,
+74,,,,,,,,,circuits,
+75,,,,,,,,,circuits,
+76,,,,,,,,,circuits,
+77,,,,,,,,,circuits,
+78,,,,,,,,,circuits,
+79,,,,,,,,,circuits,
+80,,,,,,,,,circuits,
+81,,,,,,,,,circuits,
+82,,,,,,,,,circuits,
+83,,,,,,,,,circuits,
+84,,,,,,,,,circuits,
+85,,,,,,,,,circuits,
+86,,,,,,,,,circuits,
+87,,,,,,,,,circuits,
+88,,,,,,,,,circuits,
+89,,,,,,,,,circuits,
+90,,,,,,,,,circuits,
+91,,,,,,,,,circuits,
+92,,,,,,,,,circuits,
+93,,,,,,,,,circuits,
+94,,,,,,,,,circuits,
+95,,,,,,,,,circuits,
+96,,,,,,,,,circuits,
+97,,,,,,,,,circuits,
+98,,,,,,,,,circuits,
+99,,,,,,,,,circuits,
+100,,,,,,,,,circuits,
+101,,,,,,,,,circuits,
+102,,,,,,,,,circuits,
+103,,,,,,,,,circuits,
+104,,,,,,,,,circuits,
+105,,,,,,,,,circuits,
+106,,,,,,,,,circuits,
+107,,,,,,,,,circuits,
+108,,,,,,,,,circuits,
+109,,,,,,,,,circuits,
+110,,,,,,,,,circuits,
+111,,,,,,,,,circuits,
+112,,,,,,,,,circuits,
+113,,,,,,,,,circuits,
+114,,,,,,,,,circuits,
+115,,,,,,,,,circuits,
+116,,,,,,,,,circuits,
+117,,,,,,,,,circuits,
+118,,,,,,,,,circuits,
+119,,,,,,,,,circuits,
+120,,,,,,,,,circuits,
+121,,,,,,,,,circuits,
+122,,,,,,,,,circuits,
+123,,,,,,,,,circuits,
+124,,,,,,,,,circuits,
+125,,,,,,,,,circuits,
+126,,,,,,,,,circuits,
+127,,,,,,,,,circuits,
+128,,,,,,,,,circuits,
+129,,,,,,,,,circuits,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/README.md
new file mode 100644
index 0000000..eb8e96a
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/README.md
@@ -0,0 +1,6 @@
+# README - SWITCHING FROM 220 KV TO 380 KV
+
+This data package contains all necessary information
+about the transmission process **switching from 220 kV to 380 kV**. Additional information for a specific grid can be modified in the folder named similar to the grid which is used.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/costs.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/costs.csv
new file mode 100644
index 0000000..a061131
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/costs.csv
@@ -0,0 +1,5 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,"comments"
+invest,kiloEuro * (MW * 100 km) ** (-1),41.32,41.32,41.32,41.32,41.32,41.32,41.32,41.32,"Quelle: Standardkosten NEP 2019; mit power limits aus Kiessling, siehe Doku"
+operation,kiloEuro * (MWh * 100 km) ** (-1),0,0,0,0,0,0,0,0,
+maintenance_absolute,kiloEuro * (MW * 100 km) ** (-1),0,0,0,0,0,0,0,0,
+maintenance_relative_invest,1,0,0,0,0,0,0,0,0,"100% Inv.-cost/a"
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/ecoinvent_invest.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/ecoinvent_invest.csv
new file mode 100644
index 0000000..1022c69
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/ecoinvent_invest.csv
@@ -0,0 +1,2 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+insulators_and_conductors_400kV,(MW * 100 km) ** (-1),0.083,0.083,0.083,0.083,0.083,0.083,0.083,0.083,unit,,,insulators_and_conductors_400kV,$,,1,0,1,0.083,1
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/ecoinvent_operation.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/ecoinvent_operation.csv
new file mode 100644
index 0000000..965d376
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/ecoinvent_operation.csv
@@ -0,0 +1 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/lifetime_duration.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/lifetime_duration.csv
new file mode 100644
index 0000000..e1f63ca
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/lifetime_duration.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+year,80,80,80,80,80,80,80,80,
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/properties.csv b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/properties.csv
new file mode 100644
index 0000000..1a487ef
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/01-ECOINVENT-BASED/switching_from_220_kV_to_380_kV/properties.csv
@@ -0,0 +1,6 @@
+property,value,unit
+reference product,electricity,(MW * 100 km)
+process type,voltage switch,
+safety margin,0.2,1
+base voltage,220,kV
+target voltage,380,kV
diff --git a/sampledata/02-PROCESSES/04-TRANSMISSION/README.md b/sampledata/02-PROCESSES/04-TRANSMISSION/README.md
new file mode 100644
index 0000000..2e9425e
--- /dev/null
+++ b/sampledata/02-PROCESSES/04-TRANSMISSION/README.md
@@ -0,0 +1,4 @@
+# README - 04-TRANSMISSION
+
+This directory contains all data packages which define
+the transmission processes used.
diff --git a/sampledata/02-PROCESSES/README.md b/sampledata/02-PROCESSES/README.md
new file mode 100644
index 0000000..15fc267
--- /dev/null
+++ b/sampledata/02-PROCESSES/README.md
@@ -0,0 +1,4 @@
+# README - 02-PROCESSES
+
+This directory contains all data packages which define
+the processes used.
diff --git a/sampledata/03-PRODUCTS/README.md b/sampledata/03-PRODUCTS/README.md
new file mode 100644
index 0000000..066ecad
--- /dev/null
+++ b/sampledata/03-PRODUCTS/README.md
@@ -0,0 +1,4 @@
+# README - PRODUCTS
+
+This directory contains all data packages which define the products
+used in SecMOD, containing the product name as well as nodal demand time series.
diff --git a/sampledata/03-PRODUCTS/electricity/DE/README.md b/sampledata/03-PRODUCTS/electricity/DE/README.md
new file mode 100644
index 0000000..87f90f8
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/DE/README.md
@@ -0,0 +1,4 @@
+# README - ELECTRICITY - DE
+
+This data package contains all necessary information
+about the product **electricity** in the grid: **DE**
diff --git a/sampledata/03-PRODUCTS/electricity/DE/demand_nodal_average.csv b/sampledata/03-PRODUCTS/electricity/DE/demand_nodal_average.csv
new file mode 100644
index 0000000..1443107
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/DE/demand_nodal_average.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+MW,2356.2247,2356.2247,2356.2247,2356.2247,2356.2247,2356.2247,2356.2247,2356.2247,
diff --git a/sampledata/03-PRODUCTS/electricity/DE/demand_nodal_timeseries.csv b/sampledata/03-PRODUCTS/electricity/DE/demand_nodal_timeseries.csv
new file mode 100644
index 0000000..003909e
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/DE/demand_nodal_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18
+01-Jan-2019 00:00:00,1,0.29078,0.67719,1.1056,1.0676,0.72321,1.4925,1.056,1.0469,0.29907,3.19,0.921621,2.14801,2.02671,0.353099,1.56092,0.491664,0.7947,1.18716
+01-Jan-2019 01:00:00,1,0.27757,0.63776,1.0466,1.0258,0.68115,1.4273,1.0092,0.97651,0.28351,3.0699,0.875393,2.06892,1.95391,0.331829,1.49571,0.465567,0.761864,1.14398
+01-Jan-2019 02:00:00,1,0.26452,0.59893,0.98844,0.98452,0.64001,1.3635,0.9626,0.90652,0.26797,2.9477,0.828478,1.98856,1.88126,0.310936,1.43153,0.439779,0.729623,1.1008
+01-Jan-2019 03:00:00,1,0.2564,0.57479,0.95217,0.9588,0.61458,1.3241,0.93335,0.86265,0.25821,2.8698,0.798599,1.93733,1.83561,0.297976,1.39166,0.423703,0.709632,1.07362
+01-Jan-2019 04:00:00,1,0.25141,0.55999,0.92989,0.943,0.59904,1.3,0.9153,0.83561,0.25218,2.8212,0.779997,1.90542,1.80743,0.290039,1.36722,0.413827,0.697392,1.05683
+01-Jan-2019 05:00:00,1,0.24422,0.53871,0.8978,0.92025,0.57677,1.2654,0.8892,0.79654,0.24345,2.7504,0.752868,1.85885,1.76664,0.27864,1.33208,0.3996,0.679815,1.0325
+01-Jan-2019 06:00:00,1,0.23184,0.50212,0.84247,0.88105,0.53868,1.2063,0.84389,0.72881,0.22827,2.6255,0.705149,1.77686,1.69576,0.259077,1.27169,0.375068,0.649663,0.990173
+01-Jan-2019 07:00:00,1,0.2349,0.51115,0.85615,0.89074,0.54806,1.2208,0.85512,0.74559,0.23204,2.6567,0.717055,1.79733,1.71335,0.263901,1.2866,0.381132,0.657097,1.00068
+01-Jan-2019 08:00:00,1,0.24073,0.52838,0.8822,0.9092,0.56599,1.2487,0.87647,0.77749,0.23919,2.7155,0.739539,1.83596,1.74673,0.273113,1.31503,0.392685,0.671296,1.02062
+01-Jan-2019 09:00:00,1,0.25475,0.56992,0.94483,0.9536,0.60946,1.3161,0.92741,0.85376,0.25623,2.8539,0.792496,1.92686,1.82635,0.295361,1.38361,0.420452,0.705599,1.0681
+01-Jan-2019 10:00:00,1,0.27278,0.6235,1.0253,1.0107,0.66601,1.4038,0.99215,0.95089,0.27783,3.0255,0.85833,2.03971,1.92735,0.324151,1.47214,0.456108,0.750014,1.1282
+01-Jan-2019 11:00:00,1,0.29393,0.68661,1.1196,1.0776,0.7333,1.5081,1.067,1.0636,0.30275,3.2182,0.932457,2.16653,2.04396,0.358187,1.57651,0.497885,0.802557,1.19738
+01-Jan-2019 12:00:00,1,0.29813,0.69916,1.1383,1.0908,0.74676,1.529,1.0818,1.0858,0.30764,3.2553,0.946773,2.19099,2.06686,0.36497,1.59727,0.506164,0.813033,1.21094
+01-Jan-2019 13:00:00,1,0.29492,0.68956,1.124,1.0807,0.73646,1.513,1.0705,1.0688,0.3039,3.2269,0.93583,2.17229,2.04934,0.359778,1.58138,0.499828,0.805015,1.20057
+01-Jan-2019 14:00:00,1,0.29258,0.68256,1.1136,1.0733,0.72896,1.5014,1.0623,1.0564,0.30117,3.2061,0.92781,2.15859,2.03655,0.356,1.56981,0.495212,0.79918,1.193
+01-Jan-2019 15:00:00,1,0.29458,0.68856,1.1225,1.0796,0.73539,1.5113,1.0693,1.067,0.30351,3.224,0.934692,2.17035,2.04753,0.359241,1.57973,0.499172,0.804185,1.19949
+01-Jan-2019 16:00:00,1,0.31329,0.7446,1.2059,1.1388,0.79573,1.6048,1.1346,1.1655,0.32517,3.3867,0.997476,2.27755,2.14899,0.389574,1.67249,0.53607,0.851043,1.25951
+01-Jan-2019 17:00:00,1,0.34273,0.83319,1.3369,1.2318,0.89214,1.754,1.2357,1.3186,0.3586,3.6292,1.0914,2.4375,2.30571,0.437713,1.81925,0.594045,0.925461,1.35188
+01-Jan-2019 18:00:00,1,0.35168,0.86022,1.3767,1.2601,0.92181,1.7998,1.2661,1.3647,0.36861,3.6998,1.11883,2.48411,2.35268,0.452449,1.86406,0.611657,0.948253,1.37949
+01-Jan-2019 19:00:00,1,0.34441,0.83827,1.3444,1.2372,0.89771,1.7626,1.2414,1.3273,0.36049,3.6426,1.0966,2.44634,2.31457,0.440481,1.82767,0.597358,0.929743,1.35709
+01-Jan-2019 20:00:00,1,0.32788,0.78845,1.2708,1.1849,0.8433,1.6784,1.185,1.2417,0.34184,3.5089,1.04476,2.35814,2.22711,0.413374,1.74511,0.564819,0.887828,1.3056
+01-Jan-2019 21:00:00,1,0.31915,0.76219,1.2319,1.1573,0.81477,1.6343,1.1549,1.1962,0.33189,3.4362,1.01663,2.3102,2.18044,0.399111,1.7016,0.54761,0.86578,1.27808
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+02-Jan-2019 14:00:00,1,0.44523,1.145,1.7918,1.5554,1.2405,2.2919,1.5733,1.834,0.46918,4.356,1.37601,2.9185,2.8257,0.60882,2.33702,0.795099,1.19044,1.65549
+02-Jan-2019 15:00:00,1,0.44241,1.1363,1.7793,1.5464,1.2306,2.2767,1.5643,1.8201,0.46624,4.3382,1.36897,2.9067,2.81186,0.604034,2.32261,0.78957,1.18302,1.64747
+02-Jan-2019 16:00:00,1,0.45155,1.1643,1.8198,1.5753,1.2625,2.3259,1.5934,1.8649,0.47573,4.3952,1.39156,2.94459,2.85654,0.619527,2.36924,0.80745,1.20704,1.67336
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+03-Jan-2019 01:00:00,1,0.311,0.73775,1.1957,1.1316,0.78832,1.5933,1.1267,1.1536,0.32255,3.3672,0.989938,2.26469,2.13667,0.385857,1.66114,0.531565,0.8453,1.25223
+03-Jan-2019 02:00:00,1,0.30612,0.72311,1.1739,1.1161,0.77252,1.5689,1.1097,1.1279,0.31691,3.3252,0.97371,2.23699,2.11029,0.377927,1.6369,0.521937,0.833047,1.23663
+03-Jan-2019 03:00:00,1,0.30875,0.73098,1.1856,1.1244,0.78101,1.582,1.1189,1.1417,0.31995,3.3478,0.982459,2.25192,2.12449,0.382191,1.64993,0.527116,0.839635,1.24503
+03-Jan-2019 04:00:00,1,0.31536,0.7508,1.2151,1.1453,0.80244,1.6152,1.1418,1.1764,0.32755,3.4043,1.00426,2.28912,2.1601,0.392936,1.68276,0.540142,0.856239,1.26607
+03-Jan-2019 05:00:00,1,0.33073,0.79701,1.2835,1.1939,0.85262,1.6928,1.1947,1.2565,0.34507,3.5323,1.05381,2.37354,2.24223,0.418027,1.75929,0.570419,0.89502,1.31451
+03-Jan-2019 06:00:00,1,0.36485,0.90003,1.4352,1.3017,0.96569,1.8677,1.3104,1.432,0.38319,3.801,1.15821,2.55095,2.42117,0.474186,1.93009,0.637526,0.981885,1.41968
+03-Jan-2019 07:00:00,1,0.40691,1.0278,1.6219,1.4345,1.1081,2.0876,1.4496,1.6443,0.42883,4.1045,1.27688,2.7517,2.63569,0.544246,2.14227,0.720067,1.09036,1.54507
+03-Jan-2019 08:00:00,1,0.43128,1.1022,1.73,1.5114,1.192,2.2171,1.5286,1.7653,0.45462,4.2671,1.34087,2.85949,2.7571,0.585233,2.26596,0.767801,1.15387,1.6157
+03-Jan-2019 09:00:00,1,0.44108,1.1322,1.7734,1.5423,1.226,2.2696,1.56,1.8136,0.46486,4.3298,1.36566,2.90114,2.80535,0.601791,2.31585,0.786976,1.17954,1.6437
+03-Jan-2019 10:00:00,1,0.4535,1.1703,1.8284,1.5814,1.2693,2.3364,1.5996,1.8744,0.47774,4.4072,1.39631,2.95256,2.86603,0.622835,2.37919,0.81126,1.21217,1.67886
+03-Jan-2019 11:00:00,1,0.46144,1.1947,1.8636,1.6065,1.2972,2.3794,1.6248,1.9132,0.48591,4.4555,1.41548,2.98467,2.90458,0.63633,2.41977,0.826784,1.2331,1.70116
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+09-Jan-2019 21:00:00,1,0.43104,1.1015,1.7289,1.5106,1.1911,2.2158,1.5278,1.7641,0.45436,4.2655,1.34024,2.85843,2.75589,0.584818,2.26471,0.76732,1.15323,1.61499
+09-Jan-2019 22:00:00,1,0.40647,1.0265,1.6199,1.4331,1.1065,2.0852,1.4481,1.6421,0.42836,4.1014,1.27568,2.74968,2.63346,0.543502,2.14002,0.719196,1.08921,1.54377
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+10-Jan-2019 01:00:00,1,0.31764,0.75767,1.2252,1.1525,0.80987,1.6267,1.1497,1.1883,0.33016,3.4236,1.01173,2.30185,2.17238,0.396659,1.69412,0.544646,0.861991,1.27332
+10-Jan-2019 02:00:00,1,0.31028,0.73556,1.1924,1.1293,0.78596,1.5897,1.1242,1.1497,0.32171,3.3609,0.987525,2.26057,2.13274,0.384672,1.65751,0.530127,0.843469,1.24991
+10-Jan-2019 03:00:00,1,0.31246,0.74213,1.2022,1.1362,0.79305,1.6006,1.1318,1.1612,0.32422,3.3797,0.994757,2.27291,2.14454,0.388231,1.66839,0.534442,0.848967,1.25688
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+10-Jan-2019 08:00:00,1,0.47613,1.2399,1.9286,1.6528,1.349,2.4592,1.671,1.9845,0.50092,4.5425,1.45007,3.0425,2.97536,0.661369,2.49499,0.855486,1.27194,1.74204
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+11-Jan-2019 01:00:00,1,0.33758,0.81765,1.314,1.2156,0.87515,1.7277,1.2182,1.292,0.35281,3.588,1.07538,2.41026,2.27853,0.429254,1.7935,0.583907,0.912379,1.33589
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+12-Jan-2019 22:00:00,1,0.40812,1.0315,1.6273,1.4383,1.1122,2.094,1.4535,1.6503,0.43013,4.1128,1.28014,2.75721,2.64178,0.546279,2.14841,0.722443,1.09351,1.54862
+12-Jan-2019 23:00:00,1,0.37346,0.92612,1.4734,1.3289,0.99457,1.9123,1.3392,1.4758,0.39266,3.8656,1.18337,2.59361,2.46561,0.488454,1.97338,0.654438,1.00397,1.44572
+13-Jan-2019 00:00:00,1,0.34423,0.83772,1.3436,1.2366,0.89711,1.7616,1.2408,1.3263,0.36028,3.6412,1.09604,2.44539,2.31361,0.440183,1.82676,0.597001,0.929281,1.35653
+13-Jan-2019 01:00:00,1,0.32955,0.79347,1.2782,1.1902,0.84877,1.6869,1.1907,1.2504,0.34373,3.5227,1.05008,2.36719,2.23598,0.416103,1.75343,0.568105,0.892046,1.31083
+13-Jan-2019 02:00:00,1,0.32251,0.7723,1.2469,1.1679,0.82574,1.6512,1.1665,1.2137,0.33573,3.4644,1.02752,2.32877,2.19845,0.404599,1.71835,0.554239,0.874263,1.2887
+13-Jan-2019 03:00:00,1,0.32435,0.77783,1.2551,1.1738,0.83175,1.6605,1.1729,1.2233,0.33783,3.4797,1.03346,2.33888,2.20829,0.407606,1.72752,0.557867,0.878911,1.29451
+13-Jan-2019 04:00:00,1,0.33305,0.80402,1.2938,1.2013,0.86026,1.7046,1.2027,1.2685,0.3477,3.5513,1.06117,2.38608,2.25459,0.421838,1.77091,0.575002,0.900913,1.32179
+13-Jan-2019 05:00:00,1,0.35189,0.86083,1.3776,1.2608,0.92248,1.8009,1.2668,1.3657,0.36883,3.7014,1.11944,2.48515,2.35374,0.452781,1.86508,0.612053,0.948768,1.38011
+13-Jan-2019 06:00:00,1,0.40435,1.02,1.6106,1.4264,1.0993,2.0741,1.4412,1.6315,0.4261,4.0869,1.26997,2.74004,2.62284,0.539963,2.12932,0.715055,1.08373,1.53758
+13-Jan-2019 07:00:00,1,0.45874,1.1864,1.8516,1.5979,1.2877,2.3648,1.6162,1.9,0.48314,4.4392,1.40901,2.97382,2.8915,0.631739,2.40597,0.821508,1.22598,1.69359
+13-Jan-2019 08:00:00,1,0.4778,1.245,1.936,1.658,1.3549,2.4683,1.6763,1.9926,0.50261,4.5521,1.45392,3.04893,2.98334,0.664216,2.50353,0.858741,1.27636,1.74665
+13-Jan-2019 09:00:00,1,0.47564,1.2384,1.9264,1.6512,1.3472,2.4565,1.6695,1.9821,0.50041,4.5396,1.44892,3.04058,2.97297,0.660521,2.49244,0.854515,1.27063,1.74067
+13-Jan-2019 10:00:00,1,0.48132,1.2558,1.9515,1.6691,1.3673,2.4875,1.6873,2.0095,0.50617,4.5724,1.462,3.06241,3.00015,0.670221,2.52155,0.865602,1.28568,1.75635
+13-Jan-2019 11:00:00,1,0.48489,1.2668,1.9673,1.6803,1.38,2.507,1.6984,2.0268,0.50978,4.5928,1.47014,3.07599,3.01719,0.676327,2.53987,0.872572,1.29515,1.76617
+13-Jan-2019 12:00:00,1,0.48311,1.2614,1.9595,1.6748,1.3737,2.4973,1.6929,2.0182,0.50799,4.5827,1.4661,3.06926,3.00874,0.673296,2.53078,0.869113,1.29045,1.7613
+13-Jan-2019 13:00:00,1,0.47327,1.2311,1.9159,1.6437,1.3388,2.4436,1.662,1.9706,0.498,4.5258,1.44341,3.03137,2.96159,0.656474,2.48029,0.849884,1.26435,1.7341
+13-Jan-2019 14:00:00,1,0.4659,1.2084,1.8833,1.6205,1.3129,2.4035,1.6389,1.9349,0.49048,4.4823,1.4261,3.00243,2.92612,0.643916,2.44257,0.835494,1.24487,1.71361
+13-Jan-2019 15:00:00,1,0.45987,1.1899,1.8566,1.6015,1.2917,2.3709,1.6198,1.9055,0.4843,4.4461,1.41172,2.97837,2.89698,0.633662,2.41175,0.823718,1.22896,1.69676
+13-Jan-2019 16:00:00,1,0.46363,1.2014,1.8733,1.6134,1.3049,2.3912,1.6317,1.9238,0.48816,4.4687,1.42071,2.99341,2.91517,0.640055,2.43097,0.831061,1.23888,1.70728
+13-Jan-2019 17:00:00,1,0.49015,1.2831,1.9906,1.6969,1.3987,2.5358,1.7148,2.0521,0.51509,4.6225,1.48203,3.09581,3.04227,0.685349,2.56693,0.882855,1.30915,1.78061
+13-Jan-2019 18:00:00,1,0.485,1.2672,1.9678,1.6807,1.3804,2.5076,1.6988,2.0273,0.50989,4.5934,1.47039,3.07641,3.01773,0.67652,2.54045,0.872791,1.29545,1.76648
+13-Jan-2019 19:00:00,1,0.46298,1.1994,1.8704,1.6113,1.3026,2.3877,1.6297,1.9207,0.4875,4.4648,1.41917,2.99084,2.91204,0.638954,2.42766,0.829798,1.23717,1.70547
+13-Jan-2019 20:00:00,1,0.42916,1.0958,1.7206,1.5047,1.1846,2.2058,1.5217,1.7548,0.45239,4.2533,1.33543,2.85034,2.74661,0.581652,2.25517,0.763646,1.14832,1.6096
+13-Jan-2019 21:00:00,1,0.40562,1.0239,1.6162,1.4304,1.1036,2.0808,1.4453,1.6378,0.42745,4.0956,1.27339,2.74582,2.6292,0.542081,2.13572,0.717534,1.08701,1.54128
+13-Jan-2019 22:00:00,1,0.39269,0.98452,1.5588,1.3896,1.0596,2.0127,1.4029,1.573,0.41356,4.0052,1.23795,2.68596,2.56388,0.52046,2.07034,0.692182,1.05352,1.50318
+13-Jan-2019 23:00:00,1,0.36387,0.89707,1.4308,1.2986,0.96242,1.8626,1.3071,1.427,0.38212,3.7936,1.15532,2.54605,2.4161,0.472568,1.92518,0.635605,0.979381,1.41671
+14-Jan-2019 00:00:00,1,0.33977,0.82427,1.3237,1.2225,0.88238,1.7389,1.2257,1.3033,0.35528,3.6056,1.08223,2.42191,2.29013,0.432857,1.80447,0.588228,0.917952,1.34271
+14-Jan-2019 01:00:00,1,0.32357,0.77547,1.2516,1.1713,0.82918,1.6566,1.1701,1.2192,0.33693,3.4732,1.03092,2.33457,2.20409,0.406321,1.7236,0.556317,0.876925,1.29203
+14-Jan-2019 02:00:00,1,0.31388,0.74638,1.2085,1.1407,0.79765,1.6078,1.1367,1.1686,0.32585,3.3917,0.999419,2.28086,2.15217,0.390535,1.67542,0.537234,0.852528,1.26139
+14-Jan-2019 03:00:00,1,0.3108,0.73714,1.1948,1.1309,0.78766,1.5923,1.126,1.1525,0.32231,3.3655,0.989267,2.26354,2.13558,0.385527,1.66013,0.531164,0.84479,1.25158
+14-Jan-2019 04:00:00,1,0.31135,0.73879,1.1972,1.1327,0.78945,1.5951,1.1279,1.1554,0.32295,3.3702,0.991091,2.26665,2.13855,0.386425,1.66287,0.532252,0.846177,1.25334
+14-Jan-2019 05:00:00,1,0.30732,0.72669,1.1793,1.1199,0.77638,1.5748,1.1139,1.1342,0.31829,3.3355,0.977699,2.2438,2.11675,0.379867,1.64283,0.524294,0.836045,1.24046
+14-Jan-2019 06:00:00,1,0.30565,0.72169,1.1718,1.1146,0.77099,1.5665,1.1081,1.1254,0.31636,3.3211,0.972126,2.23429,2.10772,0.377157,1.63455,0.521002,0.831859,1.23512
+14-Jan-2019 07:00:00,1,0.32992,0.79457,1.2799,1.1913,0.84997,1.6887,1.192,1.2523,0.34415,3.5257,1.05124,2.36917,2.23793,0.416702,1.75526,0.568826,0.892972,1.31198
+14-Jan-2019 08:00:00,1,0.35724,0.87702,1.4014,1.2777,0.9403,1.8284,1.2848,1.3932,0.37479,3.7429,1.13559,2.51257,2.38168,0.461616,1.89192,0.622583,0.962436,1.39652
+14-Jan-2019 09:00:00,1,0.38196,0.95192,1.5112,1.3557,1.0232,1.9566,1.3675,1.5189,0.40194,3.9281,1.20778,2.63494,2.50923,0.502582,2.01621,0.671131,1.02584,1.47124
+14-Jan-2019 10:00:00,1,0.39792,1.0004,1.582,1.4061,1.0774,2.0402,1.4201,1.5993,0.4192,4.0421,1.2524,2.71039,2.59037,0.529195,2.09677,0.702438,1.06705,1.51864
+14-Jan-2019 11:00:00,1,0.40423,1.0197,1.61,1.426,1.0989,2.0735,1.4408,1.6309,0.42597,4.086,1.26964,2.73949,2.62223,0.53976,2.12871,0.714818,1.08341,1.53722
+14-Jan-2019 12:00:00,1,0.39809,1.001,1.5828,1.4066,1.078,2.0411,1.4207,1.6001,0.41938,4.0433,1.25288,2.71119,2.59125,0.529486,2.09765,0.70278,1.0675,1.51916
+14-Jan-2019 13:00:00,1,0.38269,0.95413,1.5144,1.358,1.0257,1.9604,1.3699,1.5226,0.40273,3.9334,1.20986,2.63845,2.51296,0.503797,2.01989,0.672564,1.02772,1.47342
+14-Jan-2019 14:00:00,1,0.37079,0.91804,1.4616,1.3205,0.98562,1.8985,1.3303,1.4623,0.38973,3.8457,1.17564,2.5805,2.45189,0.484035,1.95998,0.649206,0.997128,1.43768
+14-Jan-2019 15:00:00,1,0.3683,0.91048,1.4505,1.3126,0.97725,1.8855,1.3219,1.4496,0.38699,3.8271,1.16835,2.56815,2.43901,0.479899,1.94743,0.644305,0.990727,1.43014
+14-Jan-2019 16:00:00,1,0.37843,0.9412,1.4955,1.3446,1.0113,1.9382,1.3557,1.501,0.39809,3.9023,1.1977,2.61787,2.49114,0.496709,1.99841,0.664198,1.01675,1.46066
+14-Jan-2019 17:00:00,1,0.4153,1.0534,1.6591,1.4609,1.1368,2.132,1.4769,1.6861,0.43776,4.1615,1.29929,2.7895,2.67772,0.558325,2.18479,0.736507,1.11217,1.56955
+14-Jan-2019 18:00:00,1,0.41989,1.0674,1.6795,1.4754,1.1526,2.1564,1.4918,1.7089,0.44262,4.1922,1.31138,2.80985,2.7006,0.566036,2.20807,0.745492,1.12412,1.58286
+14-Jan-2019 19:00:00,1,0.40513,1.0224,1.614,1.4289,1.102,2.0782,1.4438,1.6354,0.42693,4.0923,1.27208,2.74361,2.62676,0.541269,2.13327,0.716585,1.08575,1.53986
+14-Jan-2019 20:00:00,1,0.37808,0.94015,1.494,1.3435,1.0101,1.9364,1.3546,1.4993,0.39771,3.8997,1.19671,2.6162,2.48938,0.496137,1.99668,0.663522,1.01586,1.45963
+14-Jan-2019 21:00:00,1,0.36431,0.89842,1.4328,1.3,0.96391,1.8649,1.3086,1.4293,0.38261,3.797,1.15664,2.54829,2.41841,0.473306,1.92742,0.636482,0.980524,1.41806
+14-Jan-2019 22:00:00,1,0.36522,0.90116,1.4368,1.3029,0.96694,1.8696,1.3116,1.4339,0.38361,3.8039,1.15931,2.55282,2.4231,0.474805,1.93197,0.638262,0.982844,1.42081
+14-Jan-2019 23:00:00,1,0.34574,0.84228,1.3503,1.2414,0.90211,1.7694,1.246,1.3341,0.36197,3.6532,1.10069,2.45329,2.32155,0.442668,1.83432,0.599973,0.933124,1.3612
+15-Jan-2019 00:00:00,1,0.32549,0.78126,1.2602,1.1774,0.83548,1.6663,1.1768,1.2293,0.33912,3.4892,1.03711,2.34511,2.21437,0.409467,1.7332,0.560112,0.881788,1.29809
+15-Jan-2019 01:00:00,1,0.30801,0.72878,1.1824,1.1221,0.77864,1.5783,1.1163,1.1379,0.3191,3.3415,0.980017,2.24776,2.12052,0.380998,1.64629,0.525667,0.837792,1.24269
+15-Jan-2019 02:00:00,1,0.3009,0.70745,1.1506,1.0996,0.75567,1.5428,1.0915,1.1004,0.31086,3.2797,0.956155,2.20702,2.08193,0.369454,1.61099,0.511628,0.819959,1.21986
+15-Jan-2019 03:00:00,1,0.29554,0.69141,1.1268,1.0827,0.73845,1.5161,1.0727,1.0721,0.30462,3.2324,0.937954,2.17592,2.05274,0.360783,1.58445,0.501055,0.806566,1.20258
+15-Jan-2019 04:00:00,1,0.29305,0.68397,1.1157,1.0748,0.73047,1.5037,1.0639,1.0589,0.30172,3.2103,0.929425,2.16135,2.03913,0.356759,1.57213,0.49614,0.800352,1.19452
+15-Jan-2019 05:00:00,1,0.28871,0.67101,1.0963,1.0611,0.7166,1.4822,1.0487,1.0359,0.29664,3.1715,0.914464,2.13577,2.01536,0.34976,1.55069,0.487578,0.789545,1.18043
+15-Jan-2019 06:00:00,1,0.27829,0.63992,1.0499,1.0281,0.68345,1.4309,1.0118,0.98039,0.28437,3.0766,0.877966,2.07333,1.95793,0.332994,1.49928,0.467001,0.763663,1.14636
+15-Jan-2019 07:00:00,1,0.28852,0.67045,1.0955,1.0605,0.716,1.4813,1.048,1.0349,0.29642,3.1698,0.913814,2.13466,2.01433,0.349458,1.54977,0.487207,0.789078,1.17982
+15-Jan-2019 08:00:00,1,0.31072,0.73689,1.1944,1.1307,0.78739,1.5919,1.1257,1.1521,0.32222,3.3647,0.988994,2.26308,2.13513,0.385393,1.65972,0.531002,0.844583,1.25132
+15-Jan-2019 09:00:00,1,0.33504,0.81001,1.3027,1.2075,0.8668,1.7148,1.2095,1.2789,0.34995,3.5675,1.06744,2.39674,2.26513,0.425098,1.78084,0.578919,0.905954,1.328
+15-Jan-2019 10:00:00,1,0.35969,0.88442,1.4123,1.2854,0.94845,1.841,1.2931,1.4057,0.3775,3.7617,1.1429,2.52499,2.39441,0.465655,1.90419,0.627389,0.968686,1.40398
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+16-Jan-2019 01:00:00,1,0.33354,0.80549,1.296,1.2028,0.86187,1.7071,1.2044,1.2711,0.34825,3.5553,1.06272,2.38871,2.25718,0.422639,1.77335,0.575965,0.902152,1.32332
+16-Jan-2019 02:00:00,1,0.32707,0.786,1.2672,1.1823,0.84063,1.6743,1.1822,1.2375,0.34092,3.5022,1.04216,2.35371,2.22277,0.412043,1.74105,0.563216,0.88577,1.30305
+16-Jan-2019 03:00:00,1,0.32897,0.79173,1.2757,1.1884,0.84687,1.6839,1.1887,1.2474,0.34308,3.5179,1.04824,2.36406,2.23291,0.415158,1.75055,0.566967,0.890585,1.30902
+16-Jan-2019 04:00:00,1,0.33944,0.82328,1.3223,1.2215,0.8813,1.7372,1.2245,1.3016,0.35491,3.603,1.0812,2.42016,2.28839,0.432316,1.80282,0.587579,0.917115,1.34169
+16-Jan-2019 05:00:00,1,0.36337,0.89556,1.4286,1.297,0.96075,1.86,1.3054,1.4245,0.38157,3.7898,1.15384,2.54355,2.41352,0.471743,1.92268,0.634625,0.978105,1.41519
+16-Jan-2019 06:00:00,1,0.42673,1.0883,1.7098,1.497,1.1762,2.1928,1.5139,1.7428,0.44984,4.2374,1.32918,2.83983,2.7346,0.577562,2.24284,0.758896,1.14198,1.60262
+16-Jan-2019 07:00:00,1,0.48407,1.2643,1.9637,1.6778,1.3771,2.5025,1.6959,2.0228,0.50896,4.5881,1.46828,3.0729,3.0133,0.674931,2.53568,0.870979,1.29299,1.76393
+16-Jan-2019 08:00:00,1,0.50006,1.3137,2.0344,1.7282,1.434,2.5902,1.7456,2.0997,0.52503,4.6774,1.50401,3.13242,3.08919,0.702347,2.61787,0.902188,1.33554,1.80762
+16-Jan-2019 09:00:00,1,0.5004,1.3147,2.036,1.7292,1.4352,2.5921,1.7467,2.1013,0.52537,4.6793,1.50476,3.13366,3.0908,0.702932,2.61962,0.902852,1.33644,1.80854
+16-Jan-2019 10:00:00,1,0.50834,1.3392,2.071,1.7542,1.4636,2.6358,1.7711,2.1392,0.53327,4.7222,1.52199,3.1623,3.12814,0.716568,2.66046,0.91832,1.35761,1.83001
+16-Jan-2019 11:00:00,1,0.51271,1.3527,2.0903,1.768,1.4793,2.66,1.7846,2.1601,0.53761,4.7455,1.53136,3.17785,3.14864,0.724094,2.68298,0.926842,1.3693,1.84178
+16-Jan-2019 12:00:00,1,0.5081,1.3385,2.07,1.7535,1.4628,2.6345,1.7704,2.1381,0.53304,4.721,1.52149,3.16146,3.12703,0.716164,2.65925,0.917862,1.35698,1.82937
+16-Jan-2019 13:00:00,1,0.50284,1.3222,2.0467,1.7369,1.4439,2.6055,1.7542,2.113,0.5278,4.6926,1.51008,3.14251,3.10228,0.707114,2.63215,0.9076,1.34294,1.81514
+16-Jan-2019 14:00:00,1,0.49564,1.3,2.0149,1.7142,1.4182,2.5659,1.7319,2.0785,0.5206,4.6531,1.49427,3.1162,3.0683,0.69476,2.59514,0.893566,1.32376,1.7956
+16-Jan-2019 15:00:00,1,0.48934,1.2806,1.987,1.6944,1.3958,2.5313,1.7123,2.0482,0.51427,4.6179,1.48019,3.09275,3.03838,0.683948,2.56272,0.881259,1.30698,1.77837
+16-Jan-2019 16:00:00,1,0.48913,1.2799,1.9861,1.6937,1.395,2.5302,1.7116,2.0472,0.51405,4.6167,1.47972,3.09196,3.03737,0.683585,2.56164,0.880846,1.30642,1.7778
+16-Jan-2019 17:00:00,1,0.51861,1.371,2.1164,1.7866,1.5005,2.6926,1.8027,2.1881,0.54344,4.7766,1.54384,3.19857,3.17621,0.734262,2.7134,0.938338,1.38508,1.8576
+16-Jan-2019 18:00:00,1,0.51986,1.3749,2.1219,1.7905,1.505,2.6996,1.8065,2.1941,0.54468,4.7831,1.54648,3.20293,3.18205,0.736424,2.71987,0.940781,1.38844,1.86095
+16-Jan-2019 19:00:00,1,0.50708,1.3354,2.0655,1.7503,1.4591,2.6289,1.7673,2.1332,0.53203,4.7155,1.51929,3.15781,3.12225,0.714412,2.654,0.915877,1.35427,1.82662
+16-Jan-2019 20:00:00,1,0.47683,1.242,1.9317,1.6549,1.3514,2.463,1.6732,1.9879,0.50162,4.5465,1.45167,3.04518,2.97868,0.662553,2.49854,0.85684,1.27378,1.74396
+16-Jan-2019 21:00:00,1,0.45326,1.1696,1.8273,1.5807,1.2685,2.3351,1.5988,1.8733,0.47749,4.4058,1.39573,2.95157,2.86485,0.622424,2.37796,0.810787,1.21153,1.67817
+16-Jan-2019 22:00:00,1,0.43627,1.1175,1.7521,1.5271,1.2093,2.2438,1.5446,1.7899,0.45984,4.2992,1.35355,2.8808,2.78169,0.593651,2.29133,0.777557,1.16692,1.62997
+16-Jan-2019 23:00:00,1,0.40485,1.0215,1.6128,1.428,1.101,2.0767,1.4428,1.634,0.42663,4.0903,1.27132,2.74232,2.62534,0.540796,2.13184,0.716031,1.08502,1.53904
+17-Jan-2019 00:00:00,1,0.37864,0.94184,1.4964,1.3452,1.012,1.9393,1.3564,1.5021,0.39832,3.9038,1.1983,2.6189,2.49223,0.497061,1.99948,0.664614,1.01729,1.4613
+17-Jan-2019 01:00:00,1,0.36103,0.88848,1.4182,1.2896,0.95293,1.8479,1.2976,1.4125,0.37898,3.772,1.1469,2.53177,2.40138,0.467873,1.91093,0.630027,0.972118,1.40807
+17-Jan-2019 02:00:00,1,0.35174,0.8604,1.3769,1.2603,0.922,1.8001,1.2663,1.365,0.36867,3.7003,1.119,2.48441,2.35299,0.452545,1.86436,0.611772,0.948403,1.37967
+17-Jan-2019 03:00:00,1,0.35122,0.85882,1.3746,1.2587,0.92027,1.7975,1.2645,1.3623,0.36809,3.6962,1.11742,2.48173,2.35027,0.451687,1.86175,0.610748,0.947075,1.37807
+17-Jan-2019 04:00:00,1,0.35852,0.88087,1.4071,1.2817,0.94455,1.835,1.2891,1.3997,0.3762,3.7528,1.13941,2.51905,2.38832,0.463721,1.89832,0.625088,0.965692,1.40041
+17-Jan-2019 05:00:00,1,0.37872,0.94208,1.4968,1.3455,1.0123,1.9397,1.3567,1.5025,0.39841,3.9044,1.19853,2.61928,2.49263,0.497193,1.99988,0.66477,1.01749,1.46153
+17-Jan-2019 06:00:00,1,0.43784,1.1223,1.7591,1.532,1.2147,2.2522,1.5496,1.7976,0.46149,4.3092,1.35753,2.88748,2.78944,0.596314,2.29936,0.780641,1.17105,1.63447
+17-Jan-2019 07:00:00,1,0.49518,1.2986,2.0129,1.7128,1.4166,2.5634,1.7305,2.0763,0.52014,4.6506,1.49325,3.1145,3.06612,0.693971,2.59277,0.892669,1.32254,1.79435
+17-Jan-2019 08:00:00,1,0.50985,1.3439,2.0777,1.759,1.4691,2.6442,1.7758,2.1465,0.53478,4.7304,1.52526,3.16772,3.13526,0.71918,2.66828,0.921279,1.36167,1.8341
+17-Jan-2019 09:00:00,1,0.50555,1.3306,2.0587,1.7455,1.4536,2.6205,1.7626,2.1259,0.5305,4.7073,1.51598,3.15232,3.11506,0.711781,2.64612,0.912894,1.35018,1.82249
+17-Jan-2019 10:00:00,1,0.51094,1.3473,2.0825,1.7624,1.4729,2.6502,1.7792,2.1516,0.53585,4.7361,1.52758,3.17158,3.14034,0.721046,2.67386,0.923392,1.36456,1.83702
+17-Jan-2019 11:00:00,1,0.5173,1.3669,2.1106,1.7824,1.4958,2.6854,1.7987,2.1819,0.54215,4.7697,1.54109,3.19399,3.17009,0.732002,2.70664,0.935785,1.38158,1.85409
+17-Jan-2019 12:00:00,1,0.51103,1.3476,2.0829,1.7627,1.4733,2.6507,1.7794,2.1521,0.53595,4.7366,1.52778,3.17191,3.14078,0.721206,2.67434,0.923573,1.36481,1.83727
+17-Jan-2019 13:00:00,1,0.50549,1.3304,2.0584,1.7453,1.4534,2.6201,1.7624,2.1256,0.53044,4.7069,1.51584,3.15209,3.11476,0.711671,2.64579,0.912769,1.35001,1.82232
+17-Jan-2019 14:00:00,1,0.49737,1.3054,2.0225,1.7197,1.4244,2.5754,1.7373,2.0868,0.52233,4.6627,1.49809,3.12256,3.07647,0.697724,2.60402,0.896935,1.32836,1.8003
+17-Jan-2019 15:00:00,1,0.49121,1.2863,1.9953,1.7003,1.4024,2.5416,1.7181,2.0572,0.51615,4.6284,1.48438,3.09974,3.04726,0.687151,2.57233,0.884907,1.31195,1.78349
+17-Jan-2019 16:00:00,1,0.49135,1.2868,1.9959,1.7007,1.4029,2.5424,1.7186,2.0579,0.51629,4.6292,1.4847,3.10027,3.04793,0.687393,2.57305,0.885182,1.31232,1.78387
+17-Jan-2019 17:00:00,1,0.52372,1.3868,2.139,1.8027,1.5189,2.721,1.8183,2.2124,0.54848,4.8031,1.55453,3.21628,3.20002,0.743085,2.73979,0.9483,1.39879,1.87125
+17-Jan-2019 18:00:00,1,0.52632,1.3949,2.1505,1.8109,1.5283,2.7355,1.8263,2.2247,0.55103,4.8164,1.55992,3.2252,3.2121,0.747578,2.75322,0.953367,1.40576,1.87818
+17-Jan-2019 19:00:00,1,0.51339,1.3549,2.0934,1.7701,1.4818,2.6638,1.7867,2.1633,0.53829,4.7492,1.53282,3.18027,3.15184,0.725275,2.68651,0.928178,1.37113,1.84362
+17-Jan-2019 20:00:00,1,0.48573,1.2694,1.9711,1.683,1.383,2.5116,1.7011,2.0308,0.51063,4.5976,1.47205,3.07918,3.02121,0.677772,2.5442,0.87422,1.29739,1.76849
+17-Jan-2019 21:00:00,1,0.46186,1.196,1.8654,1.6078,1.2987,2.3817,1.6261,1.9152,0.48635,4.4581,1.4165,2.98637,2.90663,0.637051,2.42194,0.827612,1.23422,1.70234
+17-Jan-2019 22:00:00,1,0.44536,1.1454,1.7924,1.5558,1.2409,2.2926,1.5737,1.8346,0.46932,4.3568,1.37633,2.91905,2.82634,0.609042,2.33769,0.795355,1.19078,1.65587
+17-Jan-2019 23:00:00,1,0.41355,1.0481,1.6514,1.4554,1.1308,2.1227,1.4712,1.6774,0.4359,4.1497,1.29465,2.78167,2.66897,0.555383,2.17591,0.733075,1.10761,1.56445
+18-Jan-2019 00:00:00,1,0.38727,0.96805,1.5348,1.3725,1.0412,1.9843,1.385,1.5457,0.4077,3.9665,1.2228,2.66034,2.53633,0.511422,2.04298,0.68155,1.03952,1.48708
+18-Jan-2019 01:00:00,1,0.3712,0.91928,1.4634,1.3218,0.98699,1.9006,1.3317,1.4644,0.39018,3.8488,1.17683,2.58252,2.45399,0.484712,1.96204,0.650008,0.998177,1.43891
+18-Jan-2019 02:00:00,1,0.36228,0.89224,1.4238,1.2936,0.95709,1.8544,1.3018,1.4189,0.38036,3.7815,1.1506,2.53804,2.40784,0.469932,1.91718,0.632474,0.975302,1.41186
+18-Jan-2019 03:00:00,1,0.36222,0.89207,1.4235,1.2934,0.95689,1.8541,1.3016,1.4186,0.38029,3.7811,1.15042,2.53775,2.40753,0.469834,1.91688,0.632358,0.975151,1.41168
+18-Jan-2019 04:00:00,1,0.36801,0.9096,1.4492,1.3117,0.97628,1.884,1.321,1.4481,0.38668,3.8249,1.1675,2.56671,2.43752,0.47942,1.94598,0.643736,0.989985,1.42926
+18-Jan-2019 05:00:00,1,0.38917,0.97383,1.5432,1.3785,1.0476,1.9943,1.3913,1.5553,0.40977,3.9801,1.22814,2.66938,2.54602,0.514597,2.05259,0.685287,1.04444,1.49275
+18-Jan-2019 06:00:00,1,0.44182,1.1345,1.7767,1.5446,1.2286,2.2736,1.5624,1.8172,0.46564,4.3345,1.36752,2.90425,2.80899,0.603046,2.31963,0.788427,1.18149,1.64581
+18-Jan-2019 07:00:00,1,0.49389,1.2946,2.0071,1.7087,1.412,2.5563,1.7265,2.0701,0.51884,4.6434,1.49037,3.10972,3.05999,0.691752,2.58612,0.890144,1.31909,1.79082
+18-Jan-2019 08:00:00,1,0.50195,1.3195,2.0428,1.7341,1.4408,2.6006,1.7514,2.1087,0.52691,4.6877,1.50815,3.13929,3.09809,0.705589,2.62758,0.905869,1.34057,1.81274
+18-Jan-2019 09:00:00,1,0.49685,1.3038,2.0203,1.7181,1.4226,2.5726,1.7357,2.0843,0.52182,4.6598,1.49695,3.12066,3.07402,0.696837,2.60136,0.895927,1.32698,1.79889
+18-Jan-2019 10:00:00,1,0.50625,1.3328,2.0618,1.7477,1.4561,2.6243,1.7647,2.1293,0.5312,4.711,1.51749,3.15482,3.11834,0.71298,2.64971,0.914253,1.35204,1.82438
+18-Jan-2019 11:00:00,1,0.51258,1.3524,2.0898,1.7676,1.4788,2.6593,1.7842,2.1595,0.53748,4.7449,1.53108,3.1774,3.14803,0.723873,2.68232,0.926591,1.36895,1.84143
+18-Jan-2019 12:00:00,1,0.5081,1.3385,2.07,1.7535,1.4628,2.6345,1.7704,2.1381,0.53304,4.721,1.52149,3.16146,3.12703,0.716164,2.65925,0.917862,1.35698,1.82937
+18-Jan-2019 13:00:00,1,0.50223,1.3204,2.044,1.735,1.4417,2.6022,1.7523,2.11,0.52719,4.6893,1.50875,3.14029,3.0994,0.706065,2.629,0.906409,1.34131,1.81349
+18-Jan-2019 14:00:00,1,0.4945,1.2965,2.0098,1.7106,1.4142,2.5596,1.7283,2.073,0.51945,4.6468,1.49173,3.11197,3.06287,0.692795,2.58925,0.89133,1.32071,1.79248
+18-Jan-2019 15:00:00,1,0.49078,1.285,1.9934,1.6989,1.4009,2.5392,1.7168,2.0551,0.51572,4.626,1.48342,3.09813,3.04521,0.686413,2.57012,0.884067,1.3108,1.78231
+18-Jan-2019 16:00:00,1,0.49256,1.2905,2.0013,1.7045,1.4073,2.549,1.7223,2.0637,0.51751,4.636,1.48741,3.10479,3.05369,0.689474,2.57929,0.887552,1.31556,1.78719
+18-Jan-2019 17:00:00,1,0.52253,1.3831,2.1337,1.7989,1.5146,2.7144,1.8147,2.2067,0.54731,4.7969,1.55205,3.21217,3.19448,0.741028,2.73364,0.945978,1.39559,1.86807
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+18-Jan-2019 19:00:00,1,0.49831,1.3082,2.0267,1.7226,1.4277,2.5806,1.7402,2.0913,0.52327,4.6678,1.50015,3.12599,3.08089,0.699329,2.60882,0.898759,1.33085,1.80284
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+18-Jan-2019 21:00:00,1,0.43393,1.1103,1.7417,1.5197,1.2012,2.2312,1.5371,1.7784,0.45739,4.2841,1.34762,2.87082,2.77016,0.589697,2.27942,0.772977,1.16079,1.62328
+18-Jan-2019 22:00:00,1,0.41523,1.0532,1.6588,1.4607,1.1366,2.1317,1.4767,1.6858,0.43769,4.1611,1.29911,2.78919,2.67738,0.558211,2.18445,0.736374,1.112,1.56935
+18-Jan-2019 23:00:00,1,0.38128,0.94984,1.5082,1.3536,1.0209,1.953,1.3652,1.5154,0.40119,3.9231,1.20584,2.63165,2.50573,0.501446,2.01276,0.66979,1.02408,1.4692
+19-Jan-2019 00:00:00,1,0.3541,0.86752,1.3874,1.2678,0.92984,1.8123,1.2743,1.3771,0.3713,3.7187,1.12614,2.49654,2.36531,0.456435,1.87618,0.61641,0.95442,1.38691
+19-Jan-2019 01:00:00,1,0.33977,0.82427,1.3237,1.2225,0.88238,1.7389,1.2257,1.3033,0.35528,3.6056,1.08223,2.42191,2.29013,0.432857,1.80447,0.588228,0.917952,1.34271
+19-Jan-2019 02:00:00,1,0.33314,0.80429,1.2942,1.2015,0.86056,1.7051,1.203,1.269,0.3478,3.552,1.06146,2.38656,2.25506,0.421985,1.77136,0.575179,0.901141,1.32207
+19-Jan-2019 03:00:00,1,0.33695,0.81574,1.3111,1.2136,0.87306,1.7244,1.216,1.2887,0.35209,3.5829,1.0734,2.4069,2.27519,0.428216,1.79034,0.582662,0.910775,1.33392
+19-Jan-2019 04:00:00,1,0.34532,0.84101,1.3484,1.24,0.90071,1.7672,1.2445,1.3319,0.3615,3.6498,1.09939,2.45109,2.31934,0.441974,1.83221,0.599144,0.932051,1.35989
+19-Jan-2019 05:00:00,1,0.36634,0.90456,1.4418,1.3064,0.9707,1.8754,1.3154,1.4396,0.38484,3.8123,1.16262,2.55843,2.42892,0.476665,1.93762,0.640468,0.985722,1.42422
+19-Jan-2019 06:00:00,1,0.42216,1.0744,1.6896,1.4826,1.1605,2.1685,1.4991,1.7202,0.44503,4.2073,1.31732,2.81986,2.71191,0.569863,2.21961,0.749946,1.13005,1.58944
+19-Jan-2019 07:00:00,1,0.4784,1.2469,1.9386,1.6599,1.357,2.4715,1.6781,1.9954,0.50321,4.5556,1.4553,3.05123,2.9862,0.665234,2.50658,0.859904,1.27794,1.7483
+19-Jan-2019 08:00:00,1,0.49615,1.3016,2.0172,1.7158,1.4201,2.5687,1.7335,2.0809,0.52111,4.6559,1.4954,3.11808,3.07071,0.695634,2.59776,0.89456,1.32512,1.79699
+19-Jan-2019 09:00:00,1,0.4992,1.311,2.0306,1.7254,1.4309,2.5855,1.7429,2.0955,0.52416,4.6727,1.50211,3.12926,3.08511,0.700862,2.61342,0.900501,1.33323,1.80527
+19-Jan-2019 10:00:00,1,0.50393,1.3256,2.0516,1.7404,1.4478,2.6115,1.7576,2.1182,0.52889,4.6985,1.51246,3.14647,3.10743,0.708994,2.63778,0.909733,1.34585,1.81811
+19-Jan-2019 11:00:00,1,0.51181,1.35,2.0864,1.7652,1.4761,2.655,1.7818,2.1558,0.53672,4.7408,1.52944,3.17467,3.14442,0.722545,2.67835,0.925089,1.36689,1.83936
+19-Jan-2019 12:00:00,1,0.51179,1.3499,2.0863,1.7651,1.476,2.6549,1.7818,2.1557,0.5367,4.7407,1.52941,3.17461,3.14436,0.722521,2.67827,0.925061,1.36685,1.83932
+19-Jan-2019 13:00:00,1,0.50904,1.3414,2.0741,1.7564,1.4661,2.6397,1.7733,2.1426,0.53396,4.726,1.5235,3.1648,3.13142,0.71777,2.66405,0.919681,1.35948,1.83189
+19-Jan-2019 14:00:00,1,0.50394,1.3256,2.0516,1.7404,1.4479,2.6116,1.7576,2.1182,0.5289,4.6985,1.51248,3.14649,3.10746,0.709007,2.63782,0.909747,1.34587,1.81813
+19-Jan-2019 15:00:00,1,0.50263,1.3216,2.0458,1.7362,1.4432,2.6043,1.7535,2.1119,0.52759,4.6914,1.50962,3.14174,3.10127,0.706748,2.63105,0.907184,1.34237,1.81457
+19-Jan-2019 16:00:00,1,0.5018,1.319,2.0421,1.7336,1.4402,2.5998,1.751,2.108,0.52676,4.6869,1.50782,3.13875,3.09739,0.705333,2.62681,0.905578,1.34017,1.81234
+19-Jan-2019 17:00:00,1,0.51398,1.3567,2.096,1.772,1.4839,2.667,1.7885,2.1661,0.53887,4.7523,1.53406,3.18234,3.15459,0.726284,2.68953,0.929319,1.3727,1.84519
+19-Jan-2019 18:00:00,1,0.50274,1.3219,2.0463,1.7366,1.4436,2.605,1.7539,2.1125,0.5277,4.692,1.50987,3.14215,3.10181,0.706943,2.63164,0.907406,1.34267,1.81488
+19-Jan-2019 19:00:00,1,0.48357,1.2628,1.9615,1.6762,1.3753,2.4998,1.6943,2.0204,0.50845,4.5853,1.46715,3.071,3.01092,0.674078,2.53312,0.870005,1.29166,1.76255
+19-Jan-2019 20:00:00,1,0.45185,1.1653,1.8211,1.5762,1.2636,2.3275,1.5944,1.8664,0.47603,4.3971,1.39229,2.94581,2.85799,0.620031,2.37076,0.808031,1.20782,1.6742
+19-Jan-2019 21:00:00,1,0.4249,1.0827,1.7017,1.4912,1.1699,2.1831,1.508,1.7337,0.44791,4.2254,1.32445,2.83185,2.72552,0.574476,2.23353,0.755311,1.1372,1.59735
+19-Jan-2019 22:00:00,1,0.40717,1.0286,1.6231,1.4353,1.109,2.089,1.4504,1.6456,0.42911,4.1063,1.27759,2.7529,2.63701,0.544687,2.1436,0.720582,1.09104,1.54584
+19-Jan-2019 23:00:00,1,0.37358,0.92648,1.474,1.3293,0.99497,1.9129,1.3396,1.4764,0.39279,3.8665,1.18372,2.5942,2.46622,0.488651,1.97398,0.654671,1.00427,1.44607
+20-Jan-2019 00:00:00,1,0.34997,0.85503,1.369,1.2547,0.9161,1.791,1.2603,1.3558,0.36669,3.6864,1.1136,2.47524,2.34369,0.449618,1.85546,0.608278,0.943874,1.37421
+20-Jan-2019 01:00:00,1,0.33706,0.81609,1.3117,1.2139,0.87344,1.725,1.2164,1.2893,0.35222,3.5838,1.07377,2.40751,2.27581,0.428407,1.79092,0.582891,0.91107,1.33429
+20-Jan-2019 02:00:00,1,0.33151,0.79937,1.287,1.1964,0.85519,1.6968,1.1974,1.2605,0.34595,3.5387,1.05629,2.37777,2.2464,0.41931,1.76321,0.571963,0.897004,1.31697
+20-Jan-2019 03:00:00,1,0.33248,0.8023,1.2913,1.1994,0.85838,1.7017,1.2008,1.2656,0.34705,3.5466,1.05936,2.383,2.25155,0.4209,1.76805,0.573875,0.899463,1.32
+20-Jan-2019 04:00:00,1,0.34363,0.8359,1.3409,1.2347,0.89512,1.7586,1.2388,1.3232,0.35961,3.6364,1.09418,2.44223,2.31044,0.439192,1.82375,0.595816,0.927749,1.35466
+20-Jan-2019 05:00:00,1,0.36648,0.90498,1.4424,1.3069,0.97117,1.8761,1.3159,1.4403,0.385,3.8134,1.16302,2.55912,2.42963,0.476893,1.93831,0.640739,0.986075,1.42464
+20-Jan-2019 06:00:00,1,0.42175,1.0731,1.6877,1.4813,1.159,2.1663,1.4978,1.7181,0.44459,4.2046,1.31624,2.81804,2.70985,0.569165,2.21751,0.749134,1.12897,1.58824
+20-Jan-2019 07:00:00,1,0.4749,1.2361,1.9231,1.6489,1.3446,2.4525,1.6672,1.9785,0.49966,4.5353,1.44721,3.03773,2.96945,0.659266,2.48867,0.85308,1.26868,1.73863
+20-Jan-2019 08:00:00,1,0.48872,1.2787,1.9843,1.6924,1.3936,2.5279,1.7104,2.0452,0.51364,4.6144,1.4788,3.09043,3.03543,0.682885,2.55954,0.880048,1.30533,1.77668
+20-Jan-2019 09:00:00,1,0.48901,1.2795,1.9855,1.6933,1.3946,2.5295,1.7113,2.0466,0.51393,4.6161,1.47945,3.09151,3.0368,0.68338,2.56102,0.880612,1.3061,1.77747
+20-Jan-2019 10:00:00,1,0.49296,1.2918,2.0031,1.7058,1.4087,2.5512,1.7236,2.0656,0.51792,4.6382,1.48831,3.10628,3.0556,0.690165,2.58136,0.888338,1.31663,1.78829
+20-Jan-2019 11:00:00,1,0.49688,1.3038,2.0204,1.7181,1.4227,2.5727,1.7357,2.0844,0.52184,4.66,1.49701,3.12076,3.07416,0.696885,2.6015,0.895982,1.32706,1.79897
+20-Jan-2019 12:00:00,1,0.49246,1.2902,2.0008,1.7042,1.4069,2.5485,1.722,2.0632,0.51741,4.6354,1.48719,3.10442,3.05322,0.689305,2.57878,0.887359,1.31529,1.78692
+20-Jan-2019 13:00:00,1,0.48348,1.2625,1.9611,1.6759,1.375,2.4993,1.694,2.02,0.50836,4.5848,1.46694,3.07065,3.01048,0.673921,2.53265,0.869826,1.29142,1.7623
+20-Jan-2019 14:00:00,1,0.47531,1.2373,1.9249,1.6502,1.3461,2.4547,1.6685,1.9805,0.50008,4.5377,1.44815,3.0393,2.9714,0.659959,2.49075,0.853873,1.26976,1.73976
+20-Jan-2019 15:00:00,1,0.46993,1.2208,1.9011,1.6332,1.3271,2.4254,1.6515,1.9544,0.4946,4.5061,1.4356,3.01832,2.94554,0.650781,2.46319,0.843364,1.25552,1.72483
+20-Jan-2019 16:00:00,1,0.47484,1.2359,1.9229,1.6487,1.3444,2.4521,1.667,1.9782,0.4996,4.5349,1.44707,3.03748,2.96915,0.659159,2.48835,0.852957,1.26851,1.73846
+20-Jan-2019 17:00:00,1,0.50353,1.3244,2.0498,1.7391,1.4464,2.6093,1.7563,2.1163,0.52848,4.6963,1.51158,3.145,3.10552,0.708298,2.63569,0.908943,1.34477,1.81701
+20-Jan-2019 18:00:00,1,0.50307,1.323,2.0478,1.7376,1.4448,2.6068,1.7549,2.1141,0.52803,4.6938,1.51059,3.14336,3.10338,0.707517,2.63335,0.908057,1.34356,1.81578
+20-Jan-2019 19:00:00,1,0.48444,1.2655,1.9654,1.6789,1.3784,2.5045,1.697,2.0246,0.50933,4.5903,1.46913,3.07431,3.01508,0.675569,2.5376,0.871707,1.29398,1.76495
+20-Jan-2019 20:00:00,1,0.45024,1.1603,1.814,1.5712,1.258,2.3189,1.5893,1.8585,0.47438,4.3872,1.38836,2.93923,2.85018,0.617314,2.36258,0.804898,1.20361,1.66968
+20-Jan-2019 21:00:00,1,0.42646,1.0875,1.7086,1.4961,1.1753,2.1914,1.513,1.7415,0.44956,4.2356,1.32848,2.83864,2.73325,0.577102,2.24145,0.758363,1.14127,1.60184
+20-Jan-2019 22:00:00,1,0.41454,1.0511,1.6557,1.4585,1.1342,2.128,1.4744,1.6823,0.43695,4.1564,1.29727,2.78608,2.6739,0.557041,2.18091,0.735009,1.11018,1.56732
+20-Jan-2019 23:00:00,1,0.38666,0.96618,1.532,1.3706,1.0391,1.9811,1.383,1.5426,0.40704,3.9621,1.22107,2.65743,2.53321,0.510402,2.03989,0.680349,1.03795,1.48526
+21-Jan-2019 00:00:00,1,0.36007,0.88556,1.4139,1.2866,0.94972,1.843,1.2943,1.4076,0.37792,3.7646,1.14403,2.52691,2.39638,0.466283,1.9061,0.628136,0.969656,1.40514
+21-Jan-2019 01:00:00,1,0.34328,0.83485,1.3393,1.2336,0.89396,1.7568,1.2376,1.3214,0.35922,3.6336,1.0931,2.4404,2.3086,0.438617,1.822,0.595128,0.92686,1.35358
+21-Jan-2019 02:00:00,1,0.33392,0.80662,1.2977,1.204,0.8631,1.709,1.2057,1.273,0.34867,3.5583,1.06389,2.39071,2.25916,0.423251,1.77521,0.5767,0.903097,1.32448
+21-Jan-2019 03:00:00,1,0.32865,0.79077,1.2742,1.1873,0.84582,1.6823,1.1876,1.2457,0.34271,3.5153,1.04722,2.36232,2.23121,0.414633,1.74895,0.566335,0.889774,1.30802
+21-Jan-2019 04:00:00,1,0.32663,0.78469,1.2653,1.181,0.83921,1.6721,1.1807,1.2352,0.34042,3.4986,1.04077,2.35133,2.22045,0.41133,1.73888,0.562357,0.884668,1.30168
+21-Jan-2019 05:00:00,1,0.32324,0.77449,1.2502,1.1702,0.82812,1.6549,1.169,1.2175,0.33656,3.4705,1.02987,2.33278,2.20235,0.405788,1.72198,0.555675,0.876102,1.291
+21-Jan-2019 06:00:00,1,0.32344,0.77508,1.251,1.1709,0.82876,1.6559,1.1697,1.2186,0.33678,3.4721,1.03051,2.33387,2.20341,0.406112,1.72297,0.556065,0.876602,1.29163
+21-Jan-2019 07:00:00,1,0.34884,0.85161,1.364,1.2511,0.91235,1.7852,1.2564,1.35,0.36543,3.6775,1.11016,2.46938,2.33777,0.447754,1.84979,0.606053,0.940992,1.37073
+21-Jan-2019 08:00:00,1,0.3812,0.94962,1.5078,1.3533,1.0207,1.9526,1.3649,1.5151,0.40111,3.9226,1.20563,2.6313,2.50536,0.501324,2.0124,0.669647,1.02389,1.46898
+21-Jan-2019 09:00:00,1,0.40494,1.0218,1.6132,1.4283,1.1013,2.0772,1.4431,1.6344,0.42672,4.0909,1.27155,2.74272,2.62578,0.540943,2.13228,0.716202,1.08524,1.53929
+21-Jan-2019 10:00:00,1,0.42132,1.0718,1.6858,1.4799,1.1576,2.164,1.4964,1.716,0.44414,4.2017,1.31512,2.81616,2.70772,0.568445,2.21534,0.748296,1.12786,1.587
+21-Jan-2019 11:00:00,1,0.42711,1.0895,1.7115,1.4982,1.1775,2.1948,1.5151,1.7447,0.45024,4.2399,1.33015,2.84146,2.73645,0.578193,2.24474,0.75963,1.14296,1.6037
+21-Jan-2019 12:00:00,1,0.41695,1.0585,1.6665,1.4662,1.1425,2.1408,1.4822,1.6943,0.43951,4.1726,1.30366,2.79685,2.68597,0.5611,2.19317,0.739743,1.11647,1.57435
+21-Jan-2019 13:00:00,1,0.39946,1.0051,1.5889,1.411,1.0826,2.0483,1.4252,1.607,0.42085,4.0529,1.25663,2.71753,2.59816,0.531772,2.10456,0.70546,1.07104,1.52319
+21-Jan-2019 14:00:00,1,0.38861,0.97213,1.5407,1.3767,1.0457,1.9914,1.3895,1.5525,0.40916,3.9761,1.22657,2.66673,2.54317,0.513664,2.04977,0.684189,1.043,1.49109
+21-Jan-2019 15:00:00,1,0.3851,0.96146,1.5251,1.3656,1.0338,1.973,1.3779,1.5348,0.40535,3.9509,1.21668,2.65001,2.52527,0.50781,2.03204,0.677295,1.03393,1.48062
+21-Jan-2019 16:00:00,1,0.38855,0.97193,1.5404,1.3765,1.0455,1.991,1.3893,1.5521,0.40909,3.9757,1.22639,2.66642,2.54284,0.513553,2.04943,0.684058,1.04282,1.49089
+21-Jan-2019 17:00:00,1,0.42165,1.0728,1.6873,1.481,1.1587,2.1657,1.4975,1.7176,0.44448,4.2039,1.31598,2.81759,2.70934,0.568994,2.21699,0.748935,1.12871,1.58794
+21-Jan-2019 18:00:00,1,0.42992,1.0981,1.7239,1.5071,1.1873,2.2098,1.5242,1.7586,0.45319,4.2582,1.33738,2.85361,2.75036,0.58293,2.25902,0.765129,1.1503,1.61178
+21-Jan-2019 19:00:00,1,0.4103,1.0382,1.637,1.4452,1.1197,2.1055,1.4606,1.6612,0.43245,4.1277,1.28599,2.76706,2.65271,0.549931,2.15944,0.726711,1.09917,1.55498
+21-Jan-2019 20:00:00,1,0.38171,0.95117,1.5101,1.3549,1.0224,1.9553,1.3666,1.5177,0.40167,3.9263,1.20708,2.63376,2.50797,0.502174,2.01497,0.670649,1.02521,1.47051
+21-Jan-2019 21:00:00,1,0.36106,0.88856,1.4183,1.2897,0.95302,1.8481,1.2977,1.4127,0.37901,3.7722,1.14698,2.5319,2.40151,0.467917,1.91106,0.630079,0.972185,1.40815
+21-Jan-2019 22:00:00,1,0.35646,0.87467,1.3979,1.2752,0.93771,1.8244,1.2822,1.3892,0.37392,3.737,1.13326,2.50861,2.37763,0.460333,1.88803,0.621054,0.96045,1.39414
+21-Jan-2019 23:00:00,1,0.33364,0.80578,1.2964,1.2031,0.86219,1.7076,1.2047,1.2716,0.34836,3.5561,1.06302,2.38922,2.25769,0.422797,1.77383,0.576155,0.902396,1.32362
+22-Jan-2019 00:00:00,1,0.31133,0.73874,1.1971,1.1326,0.78939,1.595,1.1278,1.1553,0.32292,3.37,0.991029,2.26655,2.13845,0.386394,1.66277,0.532215,0.846129,1.25328
+22-Jan-2019 01:00:00,1,0.29382,0.68629,1.1191,1.0772,0.73296,1.5076,1.0667,1.063,0.30263,3.2172,0.932092,2.16591,2.04338,0.358015,1.57598,0.497674,0.802291,1.19704
+22-Jan-2019 02:00:00,1,0.28584,0.66243,1.0835,1.052,0.70744,1.468,1.0385,1.0206,0.29327,3.1455,0.904482,2.1187,1.99957,0.345132,1.53651,0.481907,0.782399,1.17108
+22-Jan-2019 03:00:00,1,0.28356,0.65564,1.0734,1.0448,0.70019,1.4568,1.0305,1.0085,0.29059,3.1249,0.896533,2.1051,1.98705,0.341469,1.52528,0.477414,0.776745,1.16364
+22-Jan-2019 04:00:00,1,0.28254,0.6526,1.0688,1.0415,0.69695,1.4518,1.0269,1.0031,0.28939,3.1155,0.892953,2.09898,1.98142,0.339826,1.52024,0.475398,0.774209,1.1603
+22-Jan-2019 05:00:00,1,0.28074,0.64722,1.0608,1.0358,0.69122,1.4429,1.0205,0.99345,0.28726,3.0991,0.88661,2.08812,1.97146,0.336926,1.51135,0.471835,0.769731,1.15439
+22-Jan-2019 06:00:00,1,0.27155,0.61984,1.0198,1.0068,0.66213,1.3978,0.98776,0.9443,0.27637,3.014,0.853919,2.03215,1.9205,0.322181,1.46609,0.453678,0.746974,1.12413
+22-Jan-2019 07:00:00,1,0.28213,0.65135,1.067,1.0402,0.69562,1.4497,1.0254,1.0008,0.2889,3.1117,0.891489,2.09647,1.97912,0.339156,1.51819,0.474574,0.773173,1.15894
+22-Jan-2019 08:00:00,1,0.30052,0.70634,1.149,1.0984,0.75447,1.5409,1.0902,1.0984,0.31043,3.2764,0.954899,2.20487,2.07991,0.368852,1.60915,0.510895,0.819029,1.21867
+22-Jan-2019 09:00:00,1,0.32682,0.78527,1.2661,1.1816,0.83984,1.673,1.1814,1.2362,0.34064,3.5002,1.04138,2.35238,2.22147,0.411645,1.73984,0.562736,0.885154,1.30228
+22-Jan-2019 10:00:00,1,0.34893,0.85189,1.3644,1.2514,0.91265,1.7857,1.2568,1.3505,0.36553,3.6783,1.11043,2.46985,2.33824,0.447904,1.85025,0.606232,0.941224,1.37101
+22-Jan-2019 11:00:00,1,0.37424,0.92848,1.4769,1.3313,0.99719,1.9163,1.3418,1.4798,0.39351,3.8714,1.18563,2.59743,2.46962,0.489747,1.9773,0.655968,1.00597,1.44806
+22-Jan-2019 12:00:00,1,0.37118,0.91922,1.4633,1.3217,0.98693,1.9005,1.3316,1.4643,0.39016,3.8486,1.17677,2.58242,2.45389,0.48468,1.96194,0.64997,0.998126,1.43885
+22-Jan-2019 13:00:00,1,0.36169,0.89048,1.4212,1.2917,0.95514,1.8514,1.2998,1.4159,0.37971,3.7771,1.14887,2.53511,2.40481,0.468967,1.91425,0.631327,0.97381,1.41009
+22-Jan-2019 14:00:00,1,0.35246,0.86256,1.3801,1.2626,0.92438,1.8038,1.2687,1.3686,0.36947,3.7059,1.12118,2.4881,2.35673,0.453726,1.86795,0.61318,0.95023,1.38187
+22-Jan-2019 15:00:00,1,0.3498,0.85452,1.3683,1.2542,0.91554,1.7901,1.2597,1.355,0.3665,3.6851,1.11309,2.47437,2.34281,0.449339,1.85461,0.607945,0.943443,1.37369
+22-Jan-2019 16:00:00,1,0.35551,0.87176,1.3937,1.2722,0.93451,1.8195,1.279,1.3843,0.37286,3.7295,1.13037,2.50372,2.37263,0.458749,1.88321,0.619168,0.958,1.3912
+22-Jan-2019 17:00:00,1,0.38625,0.96495,1.5302,1.3693,1.0377,1.979,1.3817,1.5406,0.4066,3.9592,1.21993,2.6555,2.53114,0.509726,2.03785,0.679553,1.0369,1.48405
+22-Jan-2019 18:00:00,1,0.40152,1.0114,1.598,1.4175,1.0896,2.0591,1.4319,1.6173,0.42306,4.0672,1.26226,2.72703,2.60855,0.535217,2.11498,0.709498,1.07638,1.52925
+22-Jan-2019 19:00:00,1,0.39461,0.99037,1.5674,1.3957,1.0661,2.0228,1.4092,1.5827,0.41564,4.0188,1.24329,2.69498,2.57364,0.523673,2.08006,0.695956,1.0585,1.50888
+22-Jan-2019 20:00:00,1,0.37692,0.93662,1.4888,1.3398,1.0062,1.9303,1.3507,1.4934,0.39644,3.8912,1.19337,2.61054,2.4834,0.494202,1.99081,0.661236,1.01287,1.45613
+22-Jan-2019 21:00:00,1,0.3639,0.89717,1.431,1.2987,0.96253,1.8628,1.3072,1.4272,0.38215,3.7939,1.15541,2.54621,2.41627,0.472622,1.92535,0.635669,0.979465,1.41681
+22-Jan-2019 22:00:00,1,0.36792,0.90932,1.4488,1.3114,0.97597,1.8836,1.3207,1.4476,0.38657,3.8242,1.16723,2.56625,2.43704,0.479267,1.94552,0.643555,0.989749,1.42898
+22-Jan-2019 23:00:00,1,0.3393,0.82285,1.3216,1.221,0.88083,1.7365,1.2241,1.3009,0.35475,3.6018,1.08076,2.41941,2.28764,0.432083,1.80211,0.5873,0.916754,1.34125
+23-Jan-2019 00:00:00,1,0.31531,0.75065,1.2148,1.1452,0.80227,1.6149,1.1416,1.1761,0.32749,3.4038,1.00409,2.28883,2.15983,0.392853,1.6825,0.540042,0.856111,1.26591
+23-Jan-2019 01:00:00,1,0.30303,0.71383,1.1601,1.1063,0.76253,1.5534,1.0989,1.1116,0.31333,3.2983,0.963337,2.21928,2.09351,0.372907,1.62155,0.515832,0.825293,1.22671
+23-Jan-2019 02:00:00,1,0.30228,0.7116,1.1568,1.104,0.76014,1.5497,1.0963,1.1077,0.31247,3.2918,0.960832,2.21501,2.08947,0.371701,1.61786,0.514364,0.823429,1.22432
+23-Jan-2019 03:00:00,1,0.30706,0.72591,1.1781,1.1191,0.77555,1.5735,1.113,1.1328,0.31799,3.3333,0.976834,2.24232,2.11535,0.379446,1.64154,0.523783,0.835395,1.23963
+23-Jan-2019 04:00:00,1,0.31971,0.76389,1.2345,1.1591,0.81662,1.6371,1.1569,1.1991,0.33253,3.441,1.01847,2.31334,2.18349,0.400036,1.70443,0.548729,0.867211,1.27987
+23-Jan-2019 05:00:00,1,0.34978,0.85448,1.3682,1.2541,0.9155,1.7901,1.2597,1.3549,0.36649,3.685,1.11305,2.4743,2.34274,0.449318,1.85455,0.60792,0.94341,1.37365
+23-Jan-2019 06:00:00,1,0.41494,1.0523,1.6575,1.4598,1.1356,2.1301,1.4757,1.6843,0.43738,4.1591,1.29834,2.7879,2.67593,0.557722,2.18297,0.735805,1.11124,1.5685
+23-Jan-2019 07:00:00,1,0.4762,1.2401,1.9289,1.653,1.3492,2.4596,1.6713,1.9848,0.50099,4.5429,1.45023,3.04277,2.97569,0.661489,2.49534,0.855623,1.27213,1.74224
+23-Jan-2019 08:00:00,1,0.49212,1.2892,1.9993,1.7031,1.4057,2.5466,1.721,2.0616,0.51707,4.6336,1.48644,3.10316,3.05161,0.688724,2.57704,0.886698,1.31439,1.786
+23-Jan-2019 09:00:00,1,0.49512,1.2984,2.0126,1.7126,1.4164,2.5631,1.7303,2.076,0.52008,4.6502,1.49311,3.11427,3.06582,0.693862,2.59244,0.892544,1.32237,1.79417
+23-Jan-2019 10:00:00,1,0.50285,1.3223,2.0467,1.7369,1.444,2.6056,1.7542,2.113,0.5278,4.6926,1.5101,3.14253,3.10231,0.707126,2.63218,0.907613,1.34295,1.81516
+23-Jan-2019 11:00:00,1,0.50931,1.3422,2.0753,1.7573,1.4671,2.6412,1.7742,2.1439,0.53424,4.7275,1.52409,3.16579,3.13272,0.718248,2.66548,0.920223,1.36022,1.83264
+23-Jan-2019 12:00:00,1,0.50598,1.3319,2.0606,1.7468,1.4552,2.6228,1.7639,2.128,0.53093,4.7096,1.51691,3.15385,3.11707,0.712515,2.64832,0.913726,1.35132,1.82364
+23-Jan-2019 13:00:00,1,0.50379,1.3252,2.0509,1.7399,1.4473,2.6108,1.7571,2.1175,0.52875,4.6977,1.51216,3.14596,3.10676,0.70875,2.63705,0.909456,1.34548,1.81772
+23-Jan-2019 14:00:00,1,0.49521,1.2987,2.013,1.7129,1.4167,2.5636,1.7306,2.0764,0.52017,4.6507,1.49331,3.11461,3.06625,0.69402,2.59292,0.892724,1.32261,1.79442
+23-Jan-2019 15:00:00,1,0.48839,1.2777,1.9828,1.6914,1.3924,2.5262,1.7094,2.0437,0.51332,4.6126,1.47807,3.08921,3.03389,0.68233,2.55787,0.879415,1.30447,1.77579
+23-Jan-2019 16:00:00,1,0.48502,1.2673,1.9679,1.6808,1.3804,2.5077,1.6988,2.0274,0.50992,4.5935,1.47044,3.07649,3.01783,0.676556,2.54056,0.872832,1.29551,1.76653
+23-Jan-2019 17:00:00,1,0.5096,1.3431,2.0766,1.7582,1.4681,2.6428,1.775,2.1452,0.53452,4.729,1.5247,3.16681,3.13406,0.718739,2.66695,0.920779,1.36098,1.83341
+23-Jan-2019 18:00:00,1,0.51344,1.355,2.0936,1.7703,1.4819,2.664,1.7868,2.1635,0.53833,4.7494,1.53291,3.18042,3.15204,0.725349,2.68674,0.928261,1.37124,1.84373
+23-Jan-2019 19:00:00,1,0.49792,1.3071,2.025,1.7214,1.4264,2.5785,1.739,2.0894,0.52289,4.6657,1.49931,3.12458,3.07908,0.698672,2.60686,0.898013,1.32983,1.8018
+23-Jan-2019 20:00:00,1,0.46586,1.2083,1.8831,1.6204,1.3127,2.4033,1.6387,1.9347,0.49044,4.482,1.426,3.00226,2.92592,0.643845,2.44236,0.835412,1.24476,1.71349
+23-Jan-2019 21:00:00,1,0.44141,1.1333,1.7749,1.5433,1.2272,2.2714,1.5611,1.8152,0.46521,4.3319,1.36649,2.90252,2.80697,0.602349,2.31753,0.787621,1.18041,1.64464
+23-Jan-2019 22:00:00,1,0.42405,1.0801,1.6979,1.4885,1.167,2.1785,1.5052,1.7295,0.44702,4.2198,1.32224,2.82814,2.7213,0.573044,2.22921,0.753646,1.13498,1.59489
+23-Jan-2019 23:00:00,1,0.3899,0.97604,1.5464,1.3808,1.0501,1.9981,1.3937,1.559,0.41055,3.9853,1.23017,2.67282,2.54972,0.515808,2.05626,0.686712,1.04632,1.49491
+24-Jan-2019 00:00:00,1,0.36549,0.90198,1.438,1.3037,0.96784,1.871,1.3126,1.4353,0.3839,3.8059,1.1601,2.55417,2.4245,0.475251,1.93333,0.638791,0.983533,1.42163
+24-Jan-2019 01:00:00,1,0.35243,0.86246,1.38,1.2625,0.92427,1.8036,1.2686,1.3685,0.36943,3.7056,1.12108,2.48794,2.35656,0.453672,1.86779,0.613116,0.950146,1.38177
+24-Jan-2019 02:00:00,1,0.34492,0.83979,1.3466,1.2388,0.89938,1.7652,1.2432,1.3299,0.36105,3.6466,1.09816,2.44899,2.31723,0.441313,1.8302,0.598353,0.931029,1.35865
+24-Jan-2019 03:00:00,1,0.34781,0.84853,1.3595,1.2479,0.90897,1.78,1.253,1.3448,0.36429,3.6695,1.10704,2.46408,2.33242,0.446075,1.84469,0.604047,0.938394,1.36758
+24-Jan-2019 04:00:00,1,0.3553,0.87113,1.3927,1.2715,0.93382,1.8184,1.2783,1.3832,0.37263,3.7279,1.12974,2.50265,2.37154,0.458404,1.88217,0.618757,0.957466,1.39056
+24-Jan-2019 05:00:00,1,0.3754,0.93201,1.4821,1.335,1.0011,1.9224,1.3456,1.4857,0.39478,3.88,1.18899,2.60312,2.47559,0.491677,1.98316,0.658251,1.00896,1.45156
+24-Jan-2019 06:00:00,1,0.43089,1.101,1.7282,1.5101,1.1906,2.215,1.5273,1.7633,0.45421,4.2645,1.33986,2.85778,2.75515,0.584565,2.26395,0.767026,1.15284,1.61456
+24-Jan-2019 07:00:00,1,0.48361,1.2629,1.9617,1.6763,1.3755,2.5,1.6945,2.0206,0.50849,4.5855,1.46724,3.07116,3.01112,0.67415,2.53334,0.870087,1.29177,1.76267
+24-Jan-2019 08:00:00,1,0.497,1.3042,2.0209,1.7185,1.4231,2.5734,1.7361,2.085,0.52196,4.6606,1.49726,3.12118,3.07469,0.69708,2.60209,0.896203,1.32736,1.79928
+24-Jan-2019 09:00:00,1,0.49464,1.2969,2.0105,1.7111,1.4147,2.5604,1.7288,2.0737,0.5196,4.6476,1.49204,3.11249,3.06354,0.693037,2.58997,0.891606,1.32109,1.79286
+24-Jan-2019 10:00:00,1,0.50113,1.317,2.0392,1.7315,1.4378,2.5961,1.7489,2.1048,0.52609,4.6833,1.50635,3.13629,3.09421,0.704175,2.62334,0.904263,1.33837,1.81051
+24-Jan-2019 11:00:00,1,0.50849,1.3397,2.0717,1.7547,1.4642,2.6367,1.7716,2.1399,0.53342,4.723,1.52232,3.16284,3.12884,0.716826,2.66123,0.918612,1.35801,1.83041
+24-Jan-2019 12:00:00,1,0.50513,1.3293,2.0569,1.7441,1.4521,2.6182,1.7613,2.1239,0.53009,4.705,1.51507,3.15081,3.11309,0.71106,2.64396,0.912076,1.34906,1.82136
+24-Jan-2019 13:00:00,1,0.50296,1.3226,2.0472,1.7373,1.4444,2.6062,1.7546,2.1135,0.52792,4.6932,1.51035,3.14295,3.10285,0.707322,2.63277,0.907835,1.34326,1.81547
+24-Jan-2019 14:00:00,1,0.49634,1.3022,2.018,1.7164,1.4207,2.5698,1.7341,2.0818,0.5213,4.657,1.4958,3.11875,3.07158,0.69595,2.5987,0.894919,1.32561,1.79749
+24-Jan-2019 15:00:00,1,0.49375,1.2942,2.0065,1.7083,1.4115,2.5555,1.726,2.0694,0.5187,4.6426,1.49006,3.10919,3.05932,0.69151,2.58539,0.889868,1.31871,1.79043
+24-Jan-2019 16:00:00,1,0.48796,1.2763,1.9809,1.69,1.3909,2.5238,1.708,2.0415,0.51288,4.6102,1.47708,3.08757,3.03181,0.681581,2.55563,0.878563,1.30331,1.77459
+24-Jan-2019 17:00:00,1,0.5125,1.3521,2.0894,1.7673,1.4786,2.6588,1.784,2.1591,0.5374,4.7444,1.53092,3.17712,3.14767,0.723737,2.68191,0.926438,1.36874,1.84122
+24-Jan-2019 18:00:00,1,0.51807,1.3693,2.114,1.7849,1.4986,2.6897,1.8011,2.1856,0.54291,4.7738,1.54272,3.19669,3.1737,0.733335,2.71063,0.937292,1.38365,1.85616
+24-Jan-2019 19:00:00,1,0.50625,1.3328,2.0618,1.7477,1.4561,2.6243,1.7647,2.1293,0.5312,4.711,1.51749,3.15482,3.11834,0.71298,2.64971,0.914253,1.35204,1.82438
+24-Jan-2019 20:00:00,1,0.47597,1.2394,1.9279,1.6522,1.3484,2.4583,1.6705,1.9837,0.50075,4.5415,1.44968,3.04185,2.97455,0.661082,2.49412,0.855158,1.2715,1.74158
+24-Jan-2019 21:00:00,1,0.45061,1.1615,1.8156,1.5723,1.2592,2.3208,1.5904,1.8603,0.47475,4.3894,1.38926,2.94073,2.85196,0.617934,2.36445,0.805614,1.20457,1.67071
+24-Jan-2019 22:00:00,1,0.43087,1.101,1.7282,1.5101,1.1906,2.2149,1.5272,1.7633,0.45419,4.2644,1.33983,2.85773,2.75508,0.584542,2.26388,0.766999,1.1528,1.61452
+24-Jan-2019 23:00:00,1,0.39873,1.0029,1.5856,1.4087,1.0801,2.0445,1.4228,1.6033,0.42007,4.0478,1.25463,2.71415,2.59447,0.530551,2.10087,0.704028,1.06915,1.52103
+25-Jan-2019 00:00:00,1,0.3757,0.93293,1.4834,1.336,1.0021,1.924,1.3467,1.4872,0.39511,3.8822,1.18986,2.6046,2.47715,0.492182,1.98469,0.658847,1.00974,1.45248
+25-Jan-2019 01:00:00,1,0.36085,0.88792,1.4174,1.2891,0.95232,1.847,1.297,1.4116,0.37878,3.7706,1.14635,2.53085,2.40043,0.46757,1.91001,0.629667,0.971649,1.40751
+25-Jan-2019 02:00:00,1,0.3519,0.86087,1.3776,1.2608,0.92252,1.8009,1.2668,1.3658,0.36885,3.7015,1.11948,2.48522,2.35381,0.452803,1.86514,0.612079,0.948801,1.38015
+25-Jan-2019 03:00:00,1,0.35282,0.86364,1.3817,1.2637,0.92557,1.8057,1.2699,1.3705,0.36987,3.7087,1.12226,2.48995,2.35861,0.454317,1.86974,0.613885,0.951143,1.38297
+25-Jan-2019 04:00:00,1,0.36158,0.89014,1.4207,1.2914,0.95477,1.8508,1.2994,1.4153,0.37959,3.7762,1.14854,2.53455,2.40423,0.468783,1.91369,0.631108,0.973525,1.40975
+25-Jan-2019 05:00:00,1,0.38158,0.95077,1.5095,1.3545,1.022,1.9546,1.3662,1.517,0.40153,3.9253,1.20671,2.63312,2.50729,0.501953,2.0143,0.670389,1.02486,1.47011
+25-Jan-2019 06:00:00,1,0.43719,1.1203,1.7561,1.53,1.2125,2.2487,1.5475,1.7944,0.4608,4.305,1.35587,2.88469,2.78621,0.595202,2.29601,0.779354,1.16933,1.63259
+25-Jan-2019 07:00:00,1,0.48776,1.2757,1.9801,1.6894,1.3902,2.5227,1.7074,2.0406,0.51268,4.6091,1.47665,3.08685,3.0309,0.681256,2.55465,0.878191,1.3028,1.77407
+25-Jan-2019 08:00:00,1,0.49898,1.3103,2.0297,1.7247,1.4301,2.5843,1.7422,2.0945,0.52394,4.6715,1.50163,3.12845,3.08407,0.700484,2.61229,0.900072,1.33264,1.80467
+25-Jan-2019 09:00:00,1,0.49812,1.3077,2.0258,1.722,1.4271,2.5795,1.7396,2.0903,0.52308,4.6667,1.49973,3.12529,3.07998,0.699,2.60784,0.898386,1.33034,1.80232
+25-Jan-2019 10:00:00,1,0.50553,1.3305,2.0586,1.7454,1.4535,2.6203,1.7625,2.1258,0.53047,4.7071,1.51592,3.15221,3.11493,0.711732,2.64598,0.912838,1.3501,1.82241
+25-Jan-2019 11:00:00,1,0.50799,1.3382,2.0695,1.7531,1.4624,2.6339,1.7701,2.1376,0.53292,4.7204,1.52124,3.16105,3.1265,0.715968,2.65866,0.91764,1.35668,1.82906
+25-Jan-2019 12:00:00,1,0.50607,1.3322,2.061,1.7471,1.4555,2.6233,1.7642,2.1284,0.53102,4.7101,1.51711,3.15419,3.1175,0.712674,2.6488,0.913906,1.35157,1.82389
+25-Jan-2019 13:00:00,1,0.50131,1.3175,2.04,1.7321,1.4385,2.5971,1.7495,2.1057,0.52627,4.6843,1.50675,3.13697,3.09508,0.704491,2.62429,0.904623,1.33886,1.81101
+25-Jan-2019 14:00:00,1,0.49148,1.2872,1.9965,1.7011,1.4034,2.5431,1.719,2.0585,0.51643,4.63,1.485,3.10076,3.04857,0.687623,2.57374,0.885444,1.31268,1.78424
+25-Jan-2019 15:00:00,1,0.4836,1.2629,1.9616,1.6763,1.3754,2.4999,1.6944,2.0206,0.50848,4.5854,1.46721,3.07111,3.01105,0.674126,2.53327,0.87006,1.29174,1.76263
+25-Jan-2019 16:00:00,1,0.48086,1.2544,1.9495,1.6677,1.3657,2.485,1.6858,2.0073,0.50571,4.5698,1.46095,3.06067,2.99797,0.669441,2.51921,0.864712,1.28447,1.75509
+25-Jan-2019 17:00:00,1,0.50838,1.3394,2.0712,1.7544,1.4638,2.6361,1.7713,2.1394,0.53331,4.7225,1.52209,3.16246,3.12834,0.716642,2.66068,0.918403,1.35773,1.83012
+25-Jan-2019 18:00:00,1,0.51745,1.3674,2.1113,1.7829,1.4963,2.6862,1.7991,2.1826,0.5423,4.7705,1.5414,3.19452,3.17079,0.732261,2.70742,0.936078,1.38198,1.85449
+25-Jan-2019 19:00:00,1,0.50399,1.3258,2.0518,1.7405,1.448,2.6119,1.7577,2.1185,0.52895,4.6988,1.51259,3.14667,3.1077,0.709092,2.63807,0.909844,1.34601,1.81826
+25-Jan-2019 20:00:00,1,0.47528,1.2373,1.9248,1.6501,1.346,2.4545,1.6684,1.9804,0.50005,4.5375,1.44809,3.03919,2.97126,0.659911,2.49061,0.853818,1.26968,1.73968
+25-Jan-2019 21:00:00,1,0.45052,1.1612,1.8152,1.572,1.2589,2.3204,1.5901,1.8599,0.47466,4.3889,1.38904,2.94037,2.85153,0.617782,2.36399,0.805438,1.20434,1.67046
+25-Jan-2019 22:00:00,1,0.43538,1.1148,1.7481,1.5243,1.2062,2.239,1.5417,1.7855,0.45891,4.2935,1.3513,2.87701,2.77731,0.592147,2.2868,0.775816,1.16459,1.62743
+25-Jan-2019 23:00:00,1,0.40201,1.0129,1.6002,1.419,1.0913,2.0617,1.4335,1.6198,0.42359,4.0706,1.2636,2.72929,2.61102,0.536037,2.11746,0.710459,1.07765,1.53069
+26-Jan-2019 00:00:00,1,0.37716,0.93736,1.4899,1.3406,1.007,1.9316,1.3515,1.4946,0.39671,3.893,1.19407,2.61173,2.48466,0.494609,1.99204,0.661716,1.01349,1.45687
+26-Jan-2019 01:00:00,1,0.36242,0.89268,1.4244,1.294,0.95757,1.8551,1.3023,1.4196,0.38052,3.7826,1.15103,2.53877,2.40859,0.47017,1.9179,0.632757,0.975671,1.4123
+26-Jan-2019 02:00:00,1,0.35364,0.86613,1.3854,1.2663,0.9283,1.8099,1.2727,1.3747,0.37078,3.7151,1.12474,2.49416,2.3629,0.455671,1.87386,0.6155,0.953238,1.38549
+26-Jan-2019 03:00:00,1,0.35469,0.8693,1.39,1.2696,0.9318,1.8153,1.2762,1.3801,0.37195,3.7232,1.12791,2.49954,2.36838,0.457403,1.87912,0.617564,0.955918,1.38871
+26-Jan-2019 04:00:00,1,0.36013,0.88576,1.4142,1.2868,0.94994,1.8433,1.2946,1.4079,0.37799,3.7651,1.14423,2.52724,2.39672,0.466391,1.90643,0.628264,0.969823,1.40534
+26-Jan-2019 05:00:00,1,0.37978,0.9453,1.5015,1.3488,1.0159,1.9452,1.3602,1.5079,0.39956,3.9122,1.20156,2.62441,2.49806,0.498954,2.00521,0.66685,1.02022,1.46471
+26-Jan-2019 06:00:00,1,0.42975,1.0976,1.7232,1.5065,1.1867,2.2089,1.5236,1.7577,0.45302,4.2571,1.33696,2.8529,2.74955,0.582653,2.25819,0.764808,1.14987,1.61131
+26-Jan-2019 07:00:00,1,0.47972,1.2509,1.9444,1.664,1.3616,2.4787,1.6823,2.0018,0.50455,4.5632,1.45833,3.05629,2.9925,0.667486,2.51334,0.862478,1.28143,1.75193
+26-Jan-2019 08:00:00,1,0.49172,1.2879,1.9976,1.7019,1.4043,2.5444,1.7197,2.0597,0.51667,4.6313,1.48554,3.10166,3.0497,0.688034,2.57497,0.885913,1.31332,1.7849
+26-Jan-2019 09:00:00,1,0.49349,1.2934,2.0054,1.7075,1.4106,2.5541,1.7252,2.0682,0.51845,4.6412,1.48949,3.10825,3.05811,0.691073,2.58409,0.889372,1.31804,1.78974
+26-Jan-2019 10:00:00,1,0.50292,1.3225,2.0471,1.7372,1.4442,2.606,1.7544,2.1134,0.52788,4.693,1.51027,3.14282,3.10268,0.707261,2.63259,0.907766,1.34316,1.81538
+26-Jan-2019 11:00:00,1,0.50959,1.3431,2.0766,1.7582,1.4681,2.6428,1.775,2.1452,0.53452,4.7289,1.52469,3.16678,3.13402,0.718727,2.66692,0.920765,1.36096,1.83339
+26-Jan-2019 12:00:00,1,0.50406,1.326,2.0521,1.7408,1.4483,2.6123,1.758,2.1188,0.52902,4.6992,1.51274,3.14693,3.10803,0.709214,2.63844,0.909983,1.3462,1.81845
+26-Jan-2019 13:00:00,1,0.49855,1.309,2.0278,1.7234,1.4286,2.5819,1.7409,2.0925,0.52352,4.6692,1.5007,3.1269,3.08206,0.699754,2.6101,0.899243,1.33151,1.80352
+26-Jan-2019 14:00:00,1,0.49324,1.2926,2.0043,1.7067,1.4097,2.5527,1.7244,2.067,0.51819,4.6398,1.48893,3.1073,3.05691,0.690637,2.58278,0.888876,1.31736,1.78904
+26-Jan-2019 15:00:00,1,0.48631,1.2713,1.9736,1.6848,1.385,2.5148,1.7029,2.0336,0.51122,4.6009,1.47337,3.08139,3.024,0.678772,2.5472,0.87536,1.29895,1.77009
+26-Jan-2019 16:00:00,1,0.4815,1.2564,1.9524,1.6697,1.368,2.4885,1.6879,2.0105,0.50636,4.5735,1.46243,3.06313,3.00106,0.670545,2.52252,0.865973,1.28618,1.75687
+26-Jan-2019 17:00:00,1,0.50592,1.3318,2.0603,1.7466,1.4549,2.6225,1.7637,2.1277,0.53086,4.7092,1.51677,3.15362,3.11677,0.712405,2.64799,0.913601,1.35115,1.82347
+26-Jan-2019 18:00:00,1,0.50997,1.3443,2.0782,1.7594,1.4695,2.6449,1.7762,2.147,0.53489,4.731,1.5255,3.16813,3.1358,0.719377,2.66886,0.921501,1.36197,1.8344
+26-Jan-2019 19:00:00,1,0.49445,1.2963,2.0096,1.7105,1.414,2.5594,1.7282,2.0728,0.51941,4.6465,1.49162,3.11178,3.06264,0.69271,2.58899,0.891234,1.32058,1.79234
+26-Jan-2019 20:00:00,1,0.46023,1.191,1.8582,1.6026,1.2929,2.3728,1.6209,1.9073,0.48467,4.4482,1.41257,2.9798,2.89869,0.634264,2.41356,0.824409,1.2299,1.69775
+26-Jan-2019 21:00:00,1,0.43457,1.1123,1.7445,1.5217,1.2034,2.2347,1.5391,1.7815,0.45807,4.2883,1.34925,2.87357,2.77333,0.590783,2.28269,0.774236,1.16248,1.62512
+26-Jan-2019 22:00:00,1,0.42023,1.0685,1.681,1.4765,1.1538,2.1582,1.4929,1.7106,0.44299,4.1945,1.31228,2.81138,2.70232,0.566618,2.20982,0.746169,1.12502,1.58386
+26-Jan-2019 23:00:00,1,0.39249,0.98393,1.558,1.389,1.0589,2.0117,1.4023,1.572,0.41336,4.0038,1.23741,2.68505,2.5629,0.520137,2.06936,0.691802,1.05302,1.50261
+27-Jan-2019 00:00:00,1,0.36508,0.90072,1.4362,1.3024,0.96646,1.8689,1.3112,1.4332,0.38345,3.8028,1.15888,2.5521,2.42236,0.474566,1.93125,0.637978,0.982474,1.42038
+27-Jan-2019 01:00:00,1,0.34866,0.85108,1.3632,1.2506,0.91177,1.7843,1.2558,1.3491,0.36523,3.6762,1.10962,2.46847,2.33685,0.447466,1.84891,0.605708,0.940545,1.37019
+27-Jan-2019 02:00:00,1,0.3417,0.83008,1.3323,1.2286,0.88874,1.7487,1.2322,1.3133,0.35744,3.621,1.08821,2.43209,2.30029,0.436022,1.8141,0.59202,0.922846,1.34869
+27-Jan-2019 03:00:00,1,0.34315,0.83446,1.3387,1.2332,0.89353,1.7561,1.2372,1.3208,0.35907,3.6326,1.0927,2.43972,2.30792,0.438405,1.82135,0.594873,0.926531,1.35318
+27-Jan-2019 04:00:00,1,0.35059,0.85691,1.3718,1.2567,0.91818,1.7942,1.2624,1.359,0.36739,3.6913,1.11551,2.47847,2.34696,0.450647,1.85859,0.609506,0.945466,1.37613
+27-Jan-2019 05:00:00,1,0.36913,0.91299,1.4542,1.3152,0.98003,1.8898,1.3247,1.4538,0.3879,3.8333,1.17077,2.57226,2.44329,0.481273,1.9516,0.645933,0.992853,1.43265
+27-Jan-2019 06:00:00,1,0.42032,1.0687,1.6814,1.4768,1.1541,2.1587,1.4932,1.711,0.44308,4.1951,1.31251,2.81177,2.70276,0.566766,2.21027,0.746342,1.12525,1.58411
+27-Jan-2019 07:00:00,1,0.4742,1.2339,1.9201,1.6467,1.3421,2.4487,1.665,1.9752,0.49895,4.5312,1.44559,3.03501,2.96609,0.658073,2.48509,0.851714,1.26683,1.73669
+27-Jan-2019 08:00:00,1,0.49364,1.2939,2.0061,1.7079,1.4111,2.555,1.7257,2.0689,0.5186,4.642,1.48982,3.1088,3.05882,0.691328,2.58485,0.889661,1.31843,1.79014
+27-Jan-2019 09:00:00,1,0.49248,1.2903,2.0009,1.7043,1.407,2.5486,1.7221,2.0633,0.51743,4.6355,1.48723,3.10447,3.05329,0.689329,2.57886,0.887387,1.31533,1.78696
+27-Jan-2019 10:00:00,1,0.50062,1.3154,2.0369,1.7299,1.436,2.5933,1.7473,2.1024,0.52559,4.6805,1.50524,3.13446,3.09183,0.703309,2.62075,0.903281,1.33703,1.80914
+27-Jan-2019 11:00:00,1,0.50495,1.3288,2.056,1.7436,1.4515,2.6172,1.7607,2.1231,0.5299,4.704,1.51467,3.15014,3.11222,0.710742,2.64301,0.911715,1.34857,1.82086
+27-Jan-2019 12:00:00,1,0.49989,1.3131,2.0337,1.7276,1.4334,2.5893,1.7451,2.0988,0.52485,4.6765,1.50363,3.13177,3.08835,0.702042,2.61696,0.901842,1.33506,1.80714
+27-Jan-2019 13:00:00,1,0.48667,1.2723,1.9752,1.686,1.3863,2.5167,1.704,2.0353,0.51158,4.6029,1.47417,3.08272,3.02567,0.679375,2.54901,0.876047,1.29988,1.77106
+27-Jan-2019 14:00:00,1,0.4759,1.2392,1.9275,1.652,1.3481,2.4579,1.6703,1.9834,0.50067,4.5411,1.44952,3.04158,2.97421,0.660963,2.49377,0.855021,1.27131,1.74138
+27-Jan-2019 15:00:00,1,0.47136,1.2252,1.9075,1.6377,1.3321,2.4332,1.6561,1.9614,0.49606,4.5146,1.43895,3.02392,2.95243,0.65322,2.47052,0.846159,1.2593,1.72881
+27-Jan-2019 16:00:00,1,0.471,1.2241,1.9059,1.6366,1.3308,2.4312,1.6549,1.9596,0.49569,4.5124,1.4381,3.0225,2.95068,0.652601,2.46866,0.84545,1.25834,1.7278
+27-Jan-2019 17:00:00,1,0.49567,1.3001,2.015,1.7143,1.4183,2.5661,1.732,2.0786,0.52063,4.6533,1.49433,3.1163,3.06843,0.694809,2.59528,0.893621,1.32383,1.79568
+27-Jan-2019 18:00:00,1,0.49901,1.3104,2.0298,1.7248,1.4302,2.5844,1.7423,2.0946,0.52397,4.6717,1.50169,3.12856,3.0842,0.700533,2.61243,0.900127,1.33272,1.80475
+27-Jan-2019 19:00:00,1,0.48336,1.2621,1.9606,1.6755,1.3746,2.4986,1.6937,2.0194,0.50824,4.5841,1.46667,3.0702,3.00991,0.673717,2.53204,0.869593,1.2911,1.76197
+27-Jan-2019 20:00:00,1,0.45189,1.1654,1.8213,1.5763,1.2637,2.3277,1.5945,1.8666,0.47608,4.3973,1.39239,2.94598,2.85819,0.620102,2.37097,0.808112,1.20793,1.67432
+27-Jan-2019 21:00:00,1,0.42907,1.0955,1.7202,1.5044,1.1843,2.2053,1.5214,1.7544,0.4523,4.2527,1.33521,2.84996,2.74617,0.581503,2.25472,0.763473,1.14809,1.60935
+27-Jan-2019 22:00:00,1,0.41705,1.0587,1.6669,1.4665,1.1428,2.1413,1.4826,1.6948,0.43961,4.1732,1.30391,2.79727,2.68644,0.56126,2.19365,0.739928,1.11672,1.57462
+27-Jan-2019 23:00:00,1,0.3909,0.97908,1.5509,1.3839,1.0535,2.0033,1.397,1.564,0.41163,3.9925,1.23297,2.67754,2.5548,0.517476,2.06131,0.688674,1.0489,1.49788
+28-Jan-2019 00:00:00,1,0.36608,0.90377,1.4407,1.3056,0.96982,1.8741,1.3145,1.4383,0.38456,3.8104,1.16184,2.55712,2.42756,0.47623,1.9363,0.639952,0.985048,1.42342
+28-Jan-2019 01:00:00,1,0.3498,0.85452,1.3683,1.2542,0.91554,1.7901,1.2597,1.355,0.3665,3.6851,1.11309,2.47437,2.34281,0.449339,1.85461,0.607945,0.943443,1.37369
+28-Jan-2019 02:00:00,1,0.33912,0.82228,1.3208,1.2204,0.88021,1.7355,1.2234,1.2999,0.35454,3.6003,1.08018,2.41842,2.28665,0.431775,1.80117,0.586931,0.916279,1.34067
+28-Jan-2019 03:00:00,1,0.337,0.81592,1.3114,1.2137,0.87325,1.7247,1.2162,1.289,0.35216,3.5833,1.07359,2.4072,2.2755,0.428312,1.79063,0.582777,0.910923,1.3341
+28-Jan-2019 04:00:00,1,0.33572,0.81205,1.3057,1.2097,0.86903,1.7182,1.2118,1.2824,0.35071,3.573,1.06956,2.40036,2.26871,0.426207,1.78422,0.580251,0.907669,1.33011
+28-Jan-2019 05:00:00,1,0.32892,0.79158,1.2754,1.1882,0.8467,1.6837,1.1886,1.2471,0.34302,3.5175,1.04807,2.36378,2.23264,0.415074,1.75029,0.566866,0.890455,1.30886
+28-Jan-2019 06:00:00,1,0.32727,0.78662,1.2681,1.183,0.84131,1.6753,1.1829,1.2385,0.34115,3.5039,1.04282,2.35482,2.22386,0.412378,1.74208,0.56362,0.886288,1.30369
+28-Jan-2019 07:00:00,1,0.34885,0.85165,1.3641,1.2512,0.91239,1.7853,1.2565,1.3501,0.36544,3.6776,1.1102,2.46945,2.33784,0.447776,1.84986,0.606078,0.941025,1.37077
+28-Jan-2019 08:00:00,1,0.37652,0.93539,1.487,1.3385,1.0049,1.9282,1.3494,1.4913,0.396,3.8882,1.19221,2.60857,2.48133,0.493532,1.98878,0.660444,1.01183,1.45492
+28-Jan-2019 09:00:00,1,0.40181,1.0123,1.5993,1.4184,1.0906,2.0607,1.4329,1.6188,0.42337,4.0692,1.26305,2.72836,2.61001,0.5357,2.11644,0.710064,1.07712,1.5301
+28-Jan-2019 10:00:00,1,0.41482,1.0519,1.657,1.4594,1.1352,2.1295,1.4753,1.6837,0.43725,4.1583,1.29802,2.78735,2.67532,0.557518,2.18235,0.735566,1.11092,1.56815
+28-Jan-2019 11:00:00,1,0.42024,1.0685,1.681,1.4765,1.1538,2.1582,1.4929,1.7106,0.44299,4.1946,1.3123,2.81141,2.70235,0.566629,2.20986,0.746183,1.12504,1.58388
+28-Jan-2019 12:00:00,1,0.41403,1.0495,1.6535,1.4569,1.1325,2.1253,1.4728,1.6798,0.43641,4.1529,1.29592,2.78381,2.67137,0.556189,2.17834,0.734016,1.10886,1.56585
+28-Jan-2019 13:00:00,1,0.39996,1.0066,1.5911,1.4125,1.0843,2.0509,1.4268,1.6095,0.42139,4.0563,1.25799,2.71982,2.60067,0.532602,2.10707,0.706433,1.07232,1.52465
+28-Jan-2019 14:00:00,1,0.39123,0.9801,1.5524,1.385,1.0546,2.0051,1.3981,1.5657,0.41199,3.9948,1.2339,2.67912,2.5565,0.518032,2.06299,0.689328,1.04976,1.49887
+28-Jan-2019 15:00:00,1,0.38668,0.96627,1.5322,1.3706,1.0392,1.9813,1.3831,1.5427,0.40707,3.9623,1.22115,2.65756,2.53334,0.510446,2.04003,0.680401,1.03801,1.48534
+28-Jan-2019 16:00:00,1,0.38921,0.97396,1.5434,1.3786,1.0478,1.9945,1.3915,1.5555,0.40981,3.9804,1.22825,2.66957,2.54623,0.514663,2.05279,0.685365,1.04454,1.49287
+28-Jan-2019 17:00:00,1,0.42008,1.068,1.6803,1.476,1.1533,2.1574,1.4924,1.7098,0.44282,4.1935,1.31187,2.81069,2.70154,0.566355,2.20903,0.745864,1.12462,1.58341
+28-Jan-2019 18:00:00,1,0.42889,1.0949,1.7194,1.5038,1.1837,2.2043,1.5209,1.7535,0.45212,4.2515,1.33475,2.84919,2.7453,0.581204,2.25382,0.763126,1.14763,1.60884
+28-Jan-2019 19:00:00,1,0.41347,1.0478,1.651,1.4552,1.1305,2.1223,1.4709,1.677,0.43582,4.1492,1.29443,2.7813,2.66856,0.555247,2.1755,0.732917,1.1074,1.56422
+28-Jan-2019 20:00:00,1,0.38788,0.96991,1.5375,1.3744,1.0433,1.9875,1.3871,1.5488,0.40837,3.9709,1.22452,2.66326,2.53945,0.512443,2.04607,0.682752,1.04111,1.48891
+28-Jan-2019 21:00:00,1,0.37137,0.91978,1.4641,1.3223,0.98755,1.9014,1.3322,1.4652,0.39036,3.85,1.17731,2.58333,2.45484,0.484986,1.96286,0.650332,0.998599,1.43941
+28-Jan-2019 22:00:00,1,0.37177,0.921,1.4659,1.3235,0.9889,1.9035,1.3335,1.4672,0.3908,3.853,1.17847,2.58531,2.45691,0.485653,1.96489,0.651122,0.999632,1.44063
+28-Jan-2019 23:00:00,1,0.3553,0.87115,1.3928,1.2715,0.93384,1.8184,1.2783,1.3832,0.37263,3.728,1.12976,2.50268,2.37158,0.458415,1.8822,0.61877,0.957483,1.39058
+29-Jan-2019 00:00:00,1,0.3356,0.8117,1.3052,1.2093,0.86865,1.7176,1.2114,1.2818,0.35058,3.572,1.0692,2.39974,2.2681,0.426017,1.78364,0.580022,0.907375,1.32975
+29-Jan-2019 01:00:00,1,0.31804,0.75887,1.227,1.1538,0.81118,1.6287,1.1511,1.1904,0.33062,3.427,1.01304,2.30408,2.17453,0.397313,1.69612,0.545437,0.863002,1.27459
+29-Jan-2019 02:00:00,1,0.30907,0.73195,1.1871,1.1254,0.78206,1.5836,1.12,1.1434,0.32032,3.3506,0.983532,2.25376,2.12624,0.382715,1.65154,0.527753,0.840446,1.24606
+29-Jan-2019 03:00:00,1,0.30347,0.71516,1.1621,1.1077,0.76396,1.5556,1.1005,1.1139,0.31384,3.3021,0.964822,2.22182,2.0959,0.373623,1.62374,0.516704,0.826399,1.22813
+29-Jan-2019 04:00:00,1,0.29949,0.70325,1.1444,1.0952,0.75115,1.5358,1.0865,1.093,0.30923,3.2673,0.951405,2.1989,2.07429,0.36718,1.60403,0.508858,0.816446,1.21534
+29-Jan-2019 05:00:00,1,0.29293,0.68361,1.1151,1.0744,0.73009,1.5031,1.0635,1.0583,0.30158,3.2092,0.929016,2.16065,2.03847,0.356567,1.57154,0.495905,0.800055,1.19413
+29-Jan-2019 06:00:00,1,0.28018,0.64555,1.0583,1.0341,0.68944,1.4402,1.0185,0.99047,0.2866,3.0939,0.884638,2.08475,1.96837,0.336027,1.50859,0.47073,0.768344,1.15256
+29-Jan-2019 07:00:00,1,0.29065,0.67682,1.105,1.0672,0.72281,1.4919,1.0555,1.0462,0.29892,3.1889,0.921189,2.14727,2.02603,0.352897,1.5603,0.491417,0.794388,1.18676
+29-Jan-2019 08:00:00,1,0.31517,0.75025,1.2142,1.1447,0.80184,1.6142,1.1412,1.1754,0.32733,3.4027,1.00366,2.28808,2.15911,0.392636,1.68184,0.539778,0.855775,1.26549
+29-Jan-2019 09:00:00,1,0.3399,0.82464,1.3243,1.2229,0.88279,1.7395,1.2261,1.304,0.35542,3.6066,1.08261,2.42256,2.29078,0.433059,1.80508,0.58847,0.918263,1.3431
+29-Jan-2019 10:00:00,1,0.36136,0.88947,1.4197,1.2907,0.95403,1.8496,1.2987,1.4142,0.37934,3.7745,1.14787,2.53342,2.40308,0.468415,1.91257,0.630671,0.972955,1.40907
+29-Jan-2019 11:00:00,1,0.38021,0.9466,1.5034,1.3502,1.0173,1.9474,1.3616,1.51,0.40003,3.9153,1.20279,2.6265,2.50027,0.499671,2.00739,0.667695,1.02133,1.466
+29-Jan-2019 12:00:00,1,0.37824,0.94063,1.4947,1.344,1.0107,1.9372,1.3551,1.5001,0.39789,3.9009,1.19717,2.61697,2.49019,0.496401,1.99748,0.663834,1.01627,1.4601
+29-Jan-2019 13:00:00,1,0.36361,0.89629,1.4297,1.2978,0.96156,1.8613,1.3063,1.4257,0.38183,3.7917,1.15456,2.54477,2.41478,0.472144,1.9239,0.635102,0.978726,1.41593
+29-Jan-2019 14:00:00,1,0.35351,0.86573,1.3848,1.2659,0.92787,1.8092,1.2722,1.374,0.37064,3.7141,1.12435,2.49349,2.36221,0.455456,1.87321,0.615243,0.952906,1.38509
+29-Jan-2019 15:00:00,1,0.34843,0.85039,1.3622,1.2498,0.91101,1.7831,1.2551,1.3479,0.36498,3.6744,1.10892,2.46729,2.33566,0.447091,1.84778,0.60526,0.939966,1.36949
+29-Jan-2019 16:00:00,1,0.35053,0.85674,1.3716,1.2565,0.91798,1.7939,1.2622,1.3587,0.36732,3.6908,1.11533,2.47817,2.34666,0.45055,1.85829,0.609391,0.945317,1.37595
+29-Jan-2019 17:00:00,1,0.38377,0.9574,1.5192,1.3614,1.0293,1.966,1.3734,1.528,0.4039,3.9412,1.21291,2.64361,2.51845,0.505587,2.02531,0.674675,1.03049,1.47664
+29-Jan-2019 18:00:00,1,0.40116,1.0103,1.5964,1.4163,1.0884,2.0573,1.4308,1.6155,0.42268,4.0648,1.26129,2.72539,2.60676,0.534622,2.11318,0.708801,1.07545,1.5282
+29-Jan-2019 19:00:00,1,0.39206,0.98261,1.556,1.3876,1.0574,2.0094,1.4008,1.5698,0.41289,4.0007,1.2362,2.68301,2.5607,0.519413,2.06717,0.690951,1.0519,1.50132
+29-Jan-2019 20:00:00,1,0.37511,0.93114,1.4808,1.3341,1.0001,1.9209,1.3447,1.4842,0.39447,3.8779,1.18817,2.60173,2.47413,0.491205,1.98173,0.657693,1.00823,1.45071
+29-Jan-2019 21:00:00,1,0.36796,0.90946,1.449,1.3115,0.97612,1.8838,1.3208,1.4479,0.38662,3.8245,1.16736,2.56648,2.43728,0.479343,1.94575,0.643646,0.989867,1.42912
+29-Jan-2019 22:00:00,1,0.37395,0.9276,1.4756,1.3304,0.99621,1.9148,1.3408,1.4783,0.39319,3.8692,1.18479,2.59601,2.46812,0.489265,1.97584,0.655397,1.00522,1.44719
+29-Jan-2019 23:00:00,1,0.35549,0.87173,1.3936,1.2721,0.93447,1.8194,1.2789,1.3842,0.37284,3.7294,1.13033,2.50365,2.37256,0.458727,1.88315,0.619142,0.957966,1.39116
+30-Jan-2019 00:00:00,1,0.33678,0.81526,1.3104,1.213,0.87253,1.7236,1.2155,1.2879,0.35191,3.5816,1.0729,2.40604,2.27434,0.427952,1.78953,0.582345,0.910366,1.33342
+30-Jan-2019 01:00:00,1,0.3249,0.77949,1.2576,1.1755,0.83355,1.6633,1.1747,1.2262,0.33845,3.4843,1.03522,2.34189,2.21123,0.408505,1.73027,0.558951,0.880301,1.29624
+30-Jan-2019 02:00:00,1,0.31685,0.75528,1.2217,1.15,0.80728,1.6227,1.1469,1.1842,0.32925,3.4169,1.00913,2.29742,2.1681,0.395362,1.69016,0.543077,0.859987,1.27079
+30-Jan-2019 03:00:00,1,0.31726,0.75652,1.2235,1.1513,0.80863,1.6247,1.1484,1.1863,0.32973,3.4204,1.01048,2.29973,2.17032,0.396036,1.69222,0.543893,0.861029,1.27211
+30-Jan-2019 04:00:00,1,0.32593,0.78257,1.2621,1.1787,0.8369,1.6685,1.1783,1.2315,0.33962,3.4928,1.03851,2.34749,2.21669,0.410179,1.73537,0.560969,0.882888,1.29946
+30-Jan-2019 05:00:00,1,0.34905,0.85226,1.365,1.2518,0.91306,1.7863,1.2572,1.3511,0.36567,3.6792,1.11081,2.47049,2.33889,0.448108,1.85087,0.606475,0.941538,1.37139
+30-Jan-2019 06:00:00,1,0.40429,1.0198,1.6103,1.4262,1.0991,2.0737,1.441,1.6312,0.42603,4.0864,1.26979,2.73974,2.6225,0.53985,2.12898,0.714924,1.08355,1.53738
+30-Jan-2019 07:00:00,1,0.4613,1.1943,1.8629,1.606,1.2967,2.3786,1.6243,1.9125,0.48576,4.4547,1.41514,2.98409,2.90387,0.636082,2.41902,0.826499,1.23272,1.70075
+30-Jan-2019 08:00:00,1,0.48096,1.2547,1.9499,1.668,1.366,2.4855,1.6862,2.0078,0.50581,4.5703,1.46118,3.06104,2.99844,0.669609,2.51972,0.864903,1.28473,1.75536
+30-Jan-2019 09:00:00,1,0.48943,1.2809,1.9874,1.6947,1.3961,2.5318,1.7126,2.0486,0.51436,4.6185,1.4804,3.09309,3.03881,0.684105,2.56319,0.881438,1.30722,1.77863
+30-Jan-2019 10:00:00,1,0.49908,1.3106,2.0301,1.7251,1.4305,2.5848,1.7425,2.095,0.52404,4.672,1.50185,3.12882,3.08454,0.700655,2.6128,0.900266,1.33291,1.80494
+30-Jan-2019 11:00:00,1,0.50787,1.3378,2.0689,1.7527,1.4619,2.6332,1.7697,2.137,0.5328,4.7197,1.52098,3.16062,3.12593,0.71576,2.65804,0.917404,1.35636,1.82874
+30-Jan-2019 12:00:00,1,0.50574,1.3312,2.0595,1.746,1.4543,2.6215,1.7631,2.1268,0.53069,4.7083,1.51638,3.15298,3.11593,0.712099,2.64708,0.913255,1.35067,1.82299
+30-Jan-2019 13:00:00,1,0.50048,1.315,2.0363,1.7295,1.4355,2.5925,1.7469,2.1017,0.52545,4.6797,1.50493,3.13394,3.09117,0.703066,2.62002,0.903004,1.33665,1.80876
+30-Jan-2019 14:00:00,1,0.49044,1.284,1.9919,1.6978,1.3997,2.5374,1.7157,2.0535,0.51538,4.6241,1.48266,3.09687,3.04361,0.685833,2.56838,0.883406,1.3099,1.78139
+30-Jan-2019 15:00:00,1,0.48308,1.2613,1.9593,1.6746,1.3736,2.4971,1.6928,2.0181,0.50795,4.5825,1.46602,3.06913,3.00857,0.673236,2.5306,0.869044,1.29036,1.7612
+30-Jan-2019 16:00:00,1,0.4724,1.2284,1.9121,1.641,1.3358,2.4389,1.6593,1.9664,0.49712,4.5207,1.44138,3.02799,2.95743,0.654995,2.47585,0.848192,1.26205,1.73169
+30-Jan-2019 17:00:00,1,0.51317,1.3542,2.0924,1.7694,1.4809,2.6625,1.786,2.1622,0.53806,4.748,1.53233,3.17947,3.15077,0.724881,2.68534,0.927732,1.37052,1.843
+30-Jan-2019 18:00:00,1,0.50303,1.3228,2.0476,1.7375,1.4446,2.6066,1.7548,2.1139,0.52799,4.6936,1.5105,3.1432,3.10318,0.707444,2.63313,0.907974,1.34345,1.81566
+30-Jan-2019 19:00:00,1,0.49086,1.2853,1.9937,1.6992,1.4012,2.5397,1.717,2.0555,0.5158,4.6265,1.48361,3.09845,3.04562,0.686558,2.57055,0.884232,1.31103,1.78254
+30-Jan-2019 20:00:00,1,0.46485,1.2052,1.8786,1.6172,1.3092,2.3978,1.6355,1.9298,0.48941,4.476,1.4236,2.99826,2.92105,0.642127,2.43719,0.83344,1.24209,1.71068
+30-Jan-2019 21:00:00,1,0.44228,1.1359,1.7787,1.546,1.2302,2.276,1.5638,1.8194,0.46611,4.3374,1.36865,2.90615,2.81121,0.603813,2.32194,0.789315,1.18268,1.6471
+30-Jan-2019 22:00:00,1,0.42921,1.0959,1.7208,1.5048,1.1848,2.206,1.5219,1.7551,0.45244,4.2536,1.33556,2.85055,2.74685,0.581733,2.25541,0.76374,1.14845,1.60974
+30-Jan-2019 23:00:00,1,0.39847,1.0021,1.5845,1.4079,1.0793,2.0431,1.4219,1.602,0.41979,4.046,1.25393,2.71297,2.59318,0.530125,2.09958,0.703529,1.06849,1.52028
+31-Jan-2019 00:00:00,1,0.37855,0.94156,1.496,1.3449,1.0117,1.9388,1.3561,1.5016,0.39822,3.9031,1.19804,2.61845,2.49175,0.496907,1.99901,0.664432,1.01705,1.46102
+31-Jan-2019 01:00:00,1,0.36408,0.8977,1.4318,1.2993,0.96312,1.8637,1.3078,1.4281,0.38235,3.7952,1.15594,2.5471,2.41719,0.472915,1.92624,0.636017,0.979919,1.41735
+31-Jan-2019 02:00:00,1,0.35644,0.87459,1.3978,1.2751,0.93762,1.8243,1.2821,1.389,0.37389,3.7367,1.13318,2.50848,2.3775,0.46029,1.88789,0.621003,0.960384,1.39406
+31-Jan-2019 03:00:00,1,0.35604,0.87338,1.396,1.2739,0.93629,1.8222,1.2808,1.387,0.37345,3.7337,1.13198,2.50645,2.37542,0.459632,1.8859,0.62022,0.959366,1.39284
+31-Jan-2019 04:00:00,1,0.35941,0.88358,1.411,1.2845,0.94753,1.8396,1.2921,1.4043,0.37719,3.7596,1.14208,2.5236,2.39298,0.465201,1.90282,0.626849,0.967983,1.40314
+31-Jan-2019 05:00:00,1,0.37553,0.93241,1.4826,1.3354,1.0015,1.9231,1.3461,1.4863,0.39492,3.8809,1.18937,2.60377,2.47627,0.491896,1.98382,0.65851,1.0093,1.45196
+31-Jan-2019 06:00:00,1,0.42504,1.0832,1.7023,1.4917,1.1704,2.1838,1.5085,1.7345,0.44807,4.2263,1.32482,2.83248,2.72623,0.574717,2.23426,0.75559,1.13757,1.59776
+31-Jan-2019 07:00:00,1,0.47574,1.2387,1.9268,1.6515,1.3476,2.457,1.6698,1.9826,0.50051,4.5402,1.44914,3.04095,2.97344,0.660688,2.49294,0.854707,1.27089,1.74094
+31-Jan-2019 08:00:00,1,0.48805,1.2766,1.9813,1.6903,1.3912,2.5243,1.7083,2.042,0.51297,4.6107,1.47729,3.08791,3.03224,0.681738,2.5561,0.878741,1.30355,1.77484
+31-Jan-2019 09:00:00,1,0.49149,1.2872,1.9965,1.7011,1.4034,2.5431,1.719,2.0585,0.51643,4.63,1.48502,3.10079,3.0486,0.687635,2.57378,0.885458,1.3127,1.78426
+31-Jan-2019 10:00:00,1,0.49705,1.3044,2.0211,1.7187,1.4232,2.5736,1.7363,2.0852,0.52201,4.6609,1.49737,3.12136,3.07493,0.697165,2.60234,0.8963,1.32749,1.79941
+31-Jan-2019 11:00:00,1,0.50089,1.3162,2.0381,1.7308,1.437,2.5948,1.7481,2.1036,0.52585,4.6819,1.50582,3.13542,3.09307,0.70376,2.6221,0.903793,1.33773,1.80985
+31-Jan-2019 12:00:00,1,0.4972,1.3048,2.0218,1.7191,1.4238,2.5745,1.7367,2.086,0.52216,4.6617,1.49771,3.12193,3.07566,0.697432,2.60314,0.896603,1.32791,1.79984
+31-Jan-2019 13:00:00,1,0.49217,1.2893,1.9995,1.7033,1.4059,2.5469,1.7211,2.0618,0.51711,4.6338,1.48653,3.10332,3.05182,0.688796,2.57726,0.886781,1.3145,1.78611
+31-Jan-2019 14:00:00,1,0.48332,1.262,1.9604,1.6754,1.3744,2.4984,1.6935,2.0192,0.5082,4.5838,1.46657,3.07004,3.00971,0.673645,2.53182,0.869511,1.29099,1.76186
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+19-Feb-2019 13:00:00,1,0.34064,0.82688,1.3276,1.2252,0.88524,1.7433,1.2286,1.3078,0.35625,3.6126,1.08492,2.42649,2.2947,0.43428,1.8088,0.589933,0.920151,1.3454
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+19-Feb-2019 15:00:00,1,0.32934,0.79284,1.2773,1.1895,0.84807,1.6858,1.19,1.2493,0.34349,3.5209,1.0494,2.36604,2.23486,0.415756,1.75238,0.567688,0.891511,1.31017
+19-Feb-2019 16:00:00,1,0.32928,0.79266,1.277,1.1893,0.84788,1.6855,1.1898,1.249,0.34343,3.5205,1.04922,2.36573,2.23455,0.415662,1.75209,0.567574,0.891364,1.30999
+19-Feb-2019 17:00:00,1,0.34752,0.84765,1.3582,1.247,0.908,1.7785,1.252,1.3433,0.36396,3.6672,1.10614,2.46256,2.33089,0.445594,1.84322,0.603472,0.93765,1.36668
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+19-Feb-2019 22:00:00,1,0.37088,0.9183,1.462,1.3207,0.98591,1.8989,1.3306,1.4627,0.38983,3.8464,1.17589,2.58092,2.45233,0.484177,1.96041,0.649374,0.997348,1.43794
+19-Feb-2019 23:00:00,1,0.35185,0.86073,1.3774,1.2607,0.92237,1.8007,1.2667,1.3655,0.3688,3.7012,1.11934,2.48498,2.35357,0.452728,1.86491,0.611989,0.948685,1.38001
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+20-Feb-2019 01:00:00,1,0.31906,0.76192,1.2315,1.157,0.81448,1.6338,1.1546,1.1957,0.33178,3.4355,1.01634,2.30971,2.17996,0.398965,1.70116,0.547434,0.865556,1.2778
+20-Feb-2019 02:00:00,1,0.31304,0.74384,1.2047,1.138,0.7949,1.6035,1.1337,1.1642,0.32488,3.3845,0.99664,2.27612,2.14762,0.38916,1.67123,0.535569,0.850404,1.2587
+20-Feb-2019 03:00:00,1,0.31597,0.75266,1.2178,1.1473,0.80444,1.6183,1.1439,1.1796,0.32825,3.4095,1.00628,2.29256,2.16342,0.393941,1.68583,0.541358,0.857792,1.26803
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+20-Feb-2019 22:00:00,1,0.42365,1.0789,1.6962,1.4873,1.1656,2.1764,1.5039,1.7275,0.4466,4.2172,1.3212,2.82639,2.71931,0.572368,2.22717,0.75286,1.13393,1.59374
+20-Feb-2019 23:00:00,1,0.39751,0.99919,1.5802,1.4048,1.076,2.038,1.4188,1.5972,0.41876,4.0392,1.25128,2.70849,2.58831,0.528513,2.0947,0.701637,1.06599,1.51744
+21-Feb-2019 00:00:00,1,0.37402,0.92784,1.476,1.3307,0.99648,1.9152,1.3411,1.4787,0.39328,3.8698,1.18502,2.5964,2.46853,0.489396,1.97624,0.655553,1.00543,1.44743
+21-Feb-2019 01:00:00,1,0.35782,0.87876,1.4039,1.2795,0.94221,1.8314,1.2868,1.3961,0.37542,3.7474,1.13731,2.5155,2.38468,0.462566,1.89481,0.623713,0.963905,1.39827
+21-Feb-2019 02:00:00,1,0.34822,0.84977,1.3613,1.2492,0.91032,1.7821,1.2544,1.3469,0.36475,3.6727,1.10829,2.46621,2.33457,0.446749,1.84673,0.604851,0.939436,1.36885
+21-Feb-2019 03:00:00,1,0.34698,0.846,1.3558,1.2452,0.90619,1.7757,1.2501,1.3405,0.36335,3.6629,1.10447,2.45973,2.32803,0.444696,1.84049,0.602399,0.936262,1.365
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+21-Feb-2019 05:00:00,1,0.36737,0.90767,1.4464,1.3097,0.97414,1.8807,1.3188,1.4449,0.38597,3.8201,1.16563,2.56354,2.43422,0.478363,1.94277,0.642483,0.988349,1.42733
+21-Feb-2019 06:00:00,1,0.40555,1.0237,1.6159,1.4302,1.1034,2.0804,1.4451,1.6375,0.42738,4.0951,1.27321,2.74551,2.62886,0.541968,2.13538,0.717402,1.08683,1.54109
+21-Feb-2019 07:00:00,1,0.44242,1.1364,1.7793,1.5465,1.2307,2.2768,1.5643,1.8202,0.46626,4.3383,1.36901,2.90676,2.81192,0.604058,2.32268,0.789597,1.18306,1.64751
+21-Feb-2019 08:00:00,1,0.45846,1.1855,1.8504,1.597,1.2867,2.3632,1.6153,1.8986,0.48285,4.4375,1.40832,2.97268,2.89012,0.631256,2.40452,0.820952,1.22523,1.69279
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+22-Feb-2019 14:00:00,1,0.48108,1.2551,1.9505,1.6684,1.3665,2.4862,1.6866,2.0084,0.50594,4.5711,1.46147,3.06153,2.99905,0.669825,2.52036,0.86515,1.28506,1.75571
+22-Feb-2019 15:00:00,1,0.47129,1.225,1.9072,1.6375,1.3319,2.4328,1.6558,1.961,0.49599,4.5142,1.43879,3.02365,2.95209,0.653101,2.47016,0.846023,1.25912,1.72861
+22-Feb-2019 16:00:00,1,0.45934,1.1882,1.8543,1.5998,1.2898,2.368,1.6181,1.9029,0.48375,4.4428,1.41044,2.97623,2.89439,0.632753,2.40902,0.822673,1.22755,1.69526
+22-Feb-2019 17:00:00,1,0.46847,1.2163,1.8947,1.6286,1.3219,2.4175,1.647,1.9474,0.49311,4.4975,1.43217,3.01259,2.93852,0.648296,2.45573,0.840516,1.25166,1.72077
+22-Feb-2019 18:00:00,1,0.49925,1.3112,2.0308,1.7256,1.4311,2.5858,1.7431,2.0958,0.52421,4.673,1.50222,3.12944,3.08534,0.700947,2.61367,0.900597,1.33336,1.80541
+22-Feb-2019 19:00:00,1,0.48984,1.2821,1.9893,1.696,1.3976,2.5341,1.7139,2.0506,0.51478,4.6208,1.48133,3.09465,3.04079,0.684817,2.56533,0.88225,1.30833,1.77976
+22-Feb-2019 20:00:00,1,0.45773,1.1833,1.8471,1.5947,1.2842,2.3593,1.613,1.8951,0.4821,4.4331,1.40657,2.96974,2.88659,0.630019,2.4008,0.819529,1.22331,1.69075
+22-Feb-2019 21:00:00,1,0.4347,1.1127,1.7451,1.5221,1.2038,2.2354,1.5396,1.7822,0.4582,4.2891,1.34958,2.87412,2.77397,0.591003,2.28336,0.77449,1.16282,1.62549
+22-Feb-2019 22:00:00,1,0.41781,1.0611,1.6703,1.4689,1.1455,2.1453,1.485,1.6986,0.44043,4.1784,1.30593,2.80067,2.69026,0.562547,2.19753,0.741428,1.11871,1.57684
+22-Feb-2019 23:00:00,1,0.38079,0.94835,1.506,1.352,1.0193,1.9505,1.3636,1.513,0.40066,3.9195,1.20444,2.62928,2.50322,0.50063,2.01029,0.668827,1.02282,1.46773
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+23-Feb-2019 01:00:00,1,0.34251,0.83252,1.3359,1.2311,0.89142,1.7528,1.235,1.3174,0.35835,3.6275,1.09072,2.43635,2.30455,0.437351,1.81814,0.593612,0.924901,1.3512
+23-Feb-2019 02:00:00,1,0.33298,0.80381,1.2935,1.201,0.86003,1.7043,1.2025,1.2682,0.34762,3.5507,1.06095,2.3857,2.25421,0.421722,1.77055,0.574863,0.900733,1.32157
+23-Feb-2019 03:00:00,1,0.33282,0.8033,1.2928,1.2005,0.85948,1.7034,1.2019,1.2673,0.34743,3.5494,1.06042,2.3848,2.25332,0.421448,1.76972,0.574533,0.90031,1.32105
+23-Feb-2019 04:00:00,1,0.33792,0.81868,1.3155,1.2166,0.87627,1.7294,1.2193,1.2937,0.35319,3.5907,1.07645,2.41208,2.28034,0.429815,1.7952,0.58458,0.913247,1.33695
+23-Feb-2019 05:00:00,1,0.35676,0.87556,1.3992,1.2761,0.93869,1.8259,1.2832,1.3907,0.37425,3.7392,1.13414,2.51011,2.37916,0.460818,1.8895,0.621632,0.961201,1.39504
+23-Feb-2019 06:00:00,1,0.4019,1.0126,1.5997,1.4187,1.0909,2.0612,1.4332,1.6192,0.42347,4.0699,1.2633,2.72879,2.61048,0.535858,2.11691,0.710248,1.07737,1.53037
+23-Feb-2019 07:00:00,1,0.44499,1.1442,1.7907,1.5546,1.2396,2.2906,1.5725,1.8328,0.46892,4.3544,1.37539,2.91747,2.82449,0.608401,2.33576,0.794614,1.18979,1.65479
+23-Feb-2019 08:00:00,1,0.46366,1.2015,1.8734,1.6134,1.305,2.3914,1.6318,1.924,0.48819,4.4689,1.42078,2.99353,2.9153,0.640102,2.43111,0.831116,1.23895,1.70736
+23-Feb-2019 09:00:00,1,0.47218,1.2277,1.9111,1.6403,1.335,2.4376,1.6586,1.9653,0.49689,4.5194,1.44086,3.02712,2.95635,0.654614,2.4747,0.847755,1.26146,1.73107
+23-Feb-2019 10:00:00,1,0.48238,1.2591,1.9562,1.6725,1.3711,2.4933,1.6906,2.0147,0.50725,4.5785,1.46444,3.06648,3.00525,0.672046,2.52703,0.867686,1.28851,1.75929
+23-Feb-2019 11:00:00,1,0.4903,1.2835,1.9913,1.6974,1.3992,2.5366,1.7153,2.0528,0.51524,4.6234,1.48236,3.09637,3.04297,0.685603,2.56769,0.883144,1.30955,1.78102
+23-Feb-2019 12:00:00,1,0.48867,1.2785,1.9841,1.6923,1.3934,2.5277,1.7102,2.045,0.5136,4.6142,1.4787,3.09027,3.03523,0.682813,2.55932,0.879966,1.30522,1.77656
+23-Feb-2019 13:00:00,1,0.48585,1.2698,1.9716,1.6834,1.3834,2.5122,1.7014,2.0314,0.51075,4.5982,1.47232,3.07963,3.02178,0.677977,2.54482,0.874453,1.29771,1.76881
+23-Feb-2019 14:00:00,1,0.47887,1.2483,1.9407,1.6614,1.3587,2.4741,1.6796,1.9978,0.5037,4.5583,1.45639,3.05306,2.98848,0.666048,2.50903,0.860835,1.2792,1.74961
+23-Feb-2019 15:00:00,1,0.47144,1.2255,1.9078,1.638,1.3324,2.4336,1.6563,1.9618,0.49614,4.5151,1.43915,3.02425,2.95283,0.653363,2.47095,0.846323,1.25952,1.72904
+23-Feb-2019 16:00:00,1,0.46167,1.1954,1.8646,1.6072,1.298,2.3806,1.6255,1.9143,0.48615,4.4569,1.41603,2.98559,2.90569,0.63672,2.42094,0.827232,1.23371,1.7018
+23-Feb-2019 17:00:00,1,0.47427,1.2342,1.9204,1.6469,1.3424,2.4491,1.6652,1.9755,0.49902,4.5316,1.44575,3.03528,2.96643,0.658192,2.48545,0.851851,1.26701,1.73689
+23-Feb-2019 18:00:00,1,0.49501,1.2981,2.0121,1.7122,1.416,2.5624,1.7299,2.0754,0.51997,4.6496,1.49286,3.11385,3.06528,0.693668,2.59186,0.892324,1.32206,1.79386
+23-Feb-2019 19:00:00,1,0.48596,1.2701,1.972,1.6837,1.3838,2.5128,1.7018,2.0319,0.51086,4.5988,1.47256,3.08003,3.02229,0.678158,2.54536,0.874659,1.29799,1.7691
+23-Feb-2019 20:00:00,1,0.45597,1.1779,1.8393,1.5892,1.278,2.3498,1.6074,1.8865,0.48029,4.4224,1.40231,2.96261,2.87804,0.627029,2.39181,0.81609,1.21868,1.68581
+23-Feb-2019 21:00:00,1,0.43359,1.1093,1.7402,1.5186,1.2,2.2295,1.536,1.7767,0.45704,4.282,1.34676,2.86939,2.76851,0.589132,2.27772,0.772321,1.15992,1.62232
+23-Feb-2019 22:00:00,1,0.41832,1.0626,1.6725,1.4705,1.1472,2.148,1.4867,1.7011,0.44096,4.1818,1.30726,2.80292,2.69279,0.563401,2.20011,0.742424,1.12004,1.57832
+23-Feb-2019 23:00:00,1,0.38086,0.94858,1.5063,1.3522,1.0195,1.9508,1.3638,1.5133,0.40074,3.9201,1.20465,2.62963,2.50359,0.500751,2.01066,0.66897,1.023,1.46795
+24-Feb-2019 00:00:00,1,0.3491,0.8524,1.3652,1.2519,0.91321,1.7865,1.2573,1.3514,0.36572,3.6796,1.11095,2.47073,2.33913,0.448183,1.85109,0.606564,0.941654,1.37153
+24-Feb-2019 01:00:00,1,0.33308,0.8041,1.2939,1.2013,0.86035,1.7048,1.2028,1.2687,0.34773,3.5515,1.06125,2.38622,2.25472,0.42188,1.77104,0.575053,0.900978,1.32187
+24-Feb-2019 02:00:00,1,0.3226,0.77257,1.2473,1.1682,0.82603,1.6517,1.1668,1.2142,0.33583,3.4652,1.02781,2.32926,2.19893,0.404745,1.7188,0.554415,0.874489,1.28899
+24-Feb-2019 03:00:00,1,0.32523,0.78047,1.259,1.1765,0.83462,1.665,1.1759,1.2279,0.33882,3.487,1.03627,2.34368,2.21297,0.409038,1.73189,0.559594,0.881125,1.29727
+24-Feb-2019 04:00:00,1,0.332,0.80084,1.2891,1.1979,0.8568,1.6993,1.1991,1.2631,0.34651,3.5427,1.05784,2.38041,2.24899,0.42011,1.76564,0.572925,0.898241,1.31849
+24-Feb-2019 05:00:00,1,0.35384,0.86672,1.3862,1.2669,0.92895,1.8109,1.2734,1.3757,0.371,3.7166,1.12534,2.49517,2.36392,0.455994,1.87484,0.615884,0.953737,1.38609
+24-Feb-2019 06:00:00,1,0.40061,1.0086,1.594,1.4146,1.0865,2.0543,1.4289,1.6127,0.42208,4.0609,1.25977,2.72283,2.60395,0.53369,2.11036,0.707709,1.07401,1.52656
+24-Feb-2019 07:00:00,1,0.44112,1.1324,1.7736,1.5424,1.2261,2.2698,1.5601,1.8137,0.4649,4.33,1.36575,2.90128,2.80552,0.601849,2.31603,0.787043,1.17963,1.6438
+24-Feb-2019 08:00:00,1,0.46123,1.1941,1.8626,1.6058,1.2965,2.3782,1.6241,1.9122,0.4857,4.4543,1.41499,2.98384,2.90357,0.635976,2.41871,0.826377,1.23255,1.70057
+24-Feb-2019 09:00:00,1,0.46707,1.212,1.8885,1.6242,1.317,2.4099,1.6425,1.9406,0.49168,4.4892,1.42886,3.00706,2.93177,0.645909,2.44856,0.837779,1.24796,1.71687
+24-Feb-2019 10:00:00,1,0.4761,1.2398,1.9284,1.6527,1.3488,2.459,1.6709,1.9843,0.50088,4.5423,1.44999,3.04236,2.97519,0.66131,2.49481,0.855418,1.27185,1.74195
+24-Feb-2019 11:00:00,1,0.48378,1.2634,1.9624,1.6769,1.3761,2.5009,1.695,2.0214,0.50866,4.5865,1.46763,3.0718,3.01193,0.674438,2.5342,0.870416,1.29222,1.76313
+24-Feb-2019 12:00:00,1,0.47585,1.239,1.9273,1.6519,1.348,2.4576,1.6701,1.9831,0.50063,4.5408,1.4494,3.04139,2.97398,0.660879,2.49351,0.854925,1.27118,1.74125
+24-Feb-2019 13:00:00,1,0.4671,1.2121,1.8886,1.6243,1.3171,2.4101,1.6427,1.9407,0.49171,4.4894,1.42895,3.0072,2.93194,0.645969,2.44874,0.837848,1.24805,1.71697
+24-Feb-2019 14:00:00,1,0.45463,1.1738,1.8334,1.585,1.2733,2.3426,1.6032,1.88,0.47891,4.4142,1.39907,2.95718,2.87155,0.624761,2.38498,0.813478,1.21516,1.68205
+24-Feb-2019 15:00:00,1,0.44668,1.1494,1.7982,1.5599,1.2455,2.2997,1.5779,1.8411,0.47068,4.365,1.37959,2.92451,2.83277,0.61127,2.34439,0.797927,1.19424,1.65959
+24-Feb-2019 16:00:00,1,0.43766,1.1218,1.7582,1.5315,1.2141,2.2512,1.549,1.7967,0.46129,4.3081,1.35706,2.8867,2.78853,0.596002,2.29842,0.780279,1.17057,1.63394
+24-Feb-2019 17:00:00,1,0.44857,1.1552,1.8066,1.5659,1.2521,2.3098,1.5839,1.8503,0.47264,4.3768,1.38424,2.93232,2.842,0.614471,2.35403,0.80162,1.1992,1.66494
+24-Feb-2019 18:00:00,1,0.46872,1.2171,1.8958,1.6294,1.3228,2.4189,1.6477,1.9486,0.49337,4.499,1.43276,3.01357,2.93973,0.648724,2.45701,0.841007,1.25233,1.72147
+24-Feb-2019 19:00:00,1,0.45747,1.1825,1.846,1.5939,1.2833,2.3579,1.6122,1.8938,0.48183,4.4315,1.40595,2.9687,2.88534,0.629583,2.39949,0.819028,1.22264,1.69003
+24-Feb-2019 20:00:00,1,0.42266,1.0759,1.6918,1.4842,1.1622,2.1711,1.5008,1.7227,0.44556,4.2106,1.31863,2.82207,2.71441,0.570709,2.22217,0.75093,1.13136,1.59089
+24-Feb-2019 21:00:00,1,0.39917,1.0042,1.5876,1.41,1.0816,2.0468,1.4242,1.6055,0.42054,4.0509,1.25584,2.71619,2.5967,0.53129,2.1031,0.704895,1.07029,1.52234
+24-Feb-2019 22:00:00,1,0.38694,0.96705,1.5333,1.3715,1.0401,1.9826,1.3839,1.5441,0.40735,3.9641,1.22188,2.65879,2.53466,0.510879,2.04134,0.68091,1.03868,1.48611
+24-Feb-2019 23:00:00,1,0.35455,0.86887,1.3894,1.2692,0.93132,1.8145,1.2757,1.3793,0.37179,3.7221,1.12748,2.49881,2.36763,0.457166,1.8784,0.617282,0.955551,1.38827
+25-Feb-2019 00:00:00,1,0.31693,0.75554,1.2221,1.1503,0.80757,1.6231,1.1473,1.1846,0.32935,3.4176,1.00942,2.29792,2.16858,0.395507,1.69061,0.543253,0.860211,1.27108
+25-Feb-2019 01:00:00,1,0.30329,0.71461,1.1613,1.1072,0.76337,1.5547,1.0998,1.113,0.31363,3.3005,0.964207,2.22077,2.09491,0.373326,1.62283,0.516343,0.825941,1.22754
+25-Feb-2019 02:00:00,1,0.29694,0.69561,1.133,1.0871,0.74294,1.523,1.0776,1.0795,0.30626,3.2448,0.942731,2.18409,2.06038,0.363048,1.59139,0.503819,0.810064,1.20711
+25-Feb-2019 03:00:00,1,0.29696,0.69566,1.1331,1.0871,0.743,1.5231,1.0777,1.0796,0.30628,3.245,0.942795,2.1842,2.06048,0.363078,1.59148,0.503856,0.810111,1.20717
+25-Feb-2019 04:00:00,1,0.29928,0.70261,1.1434,1.0945,0.75046,1.5347,1.0858,1.0918,0.30898,3.2655,0.95068,2.19767,2.07313,0.366833,1.60297,0.508435,0.815911,1.21465
+25-Feb-2019 05:00:00,1,0.29693,0.69559,1.133,1.0871,0.74292,1.523,1.0776,1.0794,0.30625,3.2448,0.94271,2.18405,2.06034,0.363038,1.59135,0.503807,0.810049,1.20709
+25-Feb-2019 06:00:00,1,0.29554,0.69141,1.1268,1.0827,0.73845,1.5161,1.0727,1.0721,0.30462,3.2324,0.937954,2.17592,2.05274,0.360783,1.58445,0.501055,0.806566,1.20258
+25-Feb-2019 07:00:00,1,0.31567,0.75174,1.2164,1.1463,0.80345,1.6167,1.1429,1.178,0.3279,3.4069,1.00528,2.29086,2.16178,0.393444,1.68431,0.540756,0.857023,1.26706
+25-Feb-2019 08:00:00,1,0.34248,0.83244,1.3358,1.2311,0.89133,1.7527,1.2349,1.3173,0.35832,3.6273,1.09064,2.43621,2.30441,0.437309,1.81802,0.593561,0.924836,1.35112
+25-Feb-2019 09:00:00,1,0.36675,0.90578,1.4436,1.3077,0.97205,1.8775,1.3168,1.4417,0.38529,3.8154,1.1638,2.56043,2.43099,0.477328,1.93963,0.641256,0.986749,1.42544
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+25-Feb-2019 11:00:00,1,0.38851,0.97183,1.5403,1.3764,1.0454,1.9908,1.3891,1.552,0.40905,3.9754,1.22629,2.66626,2.54267,0.513497,2.04926,0.683993,1.04274,1.49079
+25-Feb-2019 12:00:00,1,0.38087,0.9486,1.5063,1.3523,1.0195,1.9509,1.3638,1.5134,0.40075,3.9201,1.20467,2.62967,2.50363,0.500762,2.01069,0.668983,1.02302,1.46797
+25-Feb-2019 13:00:00,1,0.36787,0.90918,1.4486,1.3112,0.97581,1.8833,1.3205,1.4474,0.38652,3.8238,1.16709,2.56602,2.4368,0.479191,1.94528,0.643465,0.989631,1.42884
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+25-Feb-2019 17:00:00,1,0.3537,0.86632,1.3857,1.2665,0.92852,1.8102,1.2729,1.375,0.37086,3.7156,1.12494,2.4945,2.36324,0.455779,1.87419,0.615628,0.953405,1.38569
+25-Feb-2019 18:00:00,1,0.38735,0.96829,1.5351,1.3727,1.0415,1.9847,1.3853,1.5461,0.40779,3.9671,1.22302,2.66072,2.53673,0.511555,2.04338,0.681707,1.03973,1.48732
+25-Feb-2019 19:00:00,1,0.38133,0.95,1.5084,1.3537,1.0211,1.9533,1.3654,1.5157,0.40125,3.9235,1.20599,2.6319,2.506,0.501534,2.01303,0.669894,1.02422,1.46936
+25-Feb-2019 20:00:00,1,0.3532,0.86481,1.3834,1.2649,0.92685,1.8076,1.2712,1.3724,0.3703,3.7117,1.12342,2.49192,2.36062,0.454951,1.87167,0.614641,0.952124,1.38415
+25-Feb-2019 21:00:00,1,0.34058,0.82669,1.3273,1.225,0.88503,1.743,1.2284,1.3075,0.35618,3.612,1.08472,2.42615,2.29436,0.434173,1.80847,0.589805,0.919987,1.3452
+25-Feb-2019 22:00:00,1,0.34112,0.82832,1.3297,1.2267,0.88682,1.7457,1.2302,1.3103,0.35679,3.6164,1.08641,2.42902,2.29722,0.435065,1.81119,0.590874,0.921367,1.34689
+25-Feb-2019 23:00:00,1,0.31887,0.76136,1.2307,1.1564,0.81387,1.6329,1.1539,1.1947,0.33157,3.4339,1.01574,2.30868,2.17897,0.398664,1.70024,0.54707,0.86509,1.27721
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+26-Feb-2019 01:00:00,1,0.27861,0.64088,1.0513,1.0291,0.68448,1.4325,1.0129,0.98211,0.28475,3.0795,0.879108,2.07528,1.95971,0.333513,1.50087,0.467638,0.764463,1.14742
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+26-Feb-2019 08:00:00,1,0.29668,0.69483,1.1319,1.0863,0.74212,1.5218,1.0767,1.0781,0.30596,3.2426,0.941853,2.18259,2.05897,0.362631,1.59011,0.503311,0.809421,1.20627
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+27-Feb-2019 01:00:00,1,0.32036,0.76583,1.2373,1.1611,0.81872,1.6404,1.1591,1.2025,0.33327,3.4464,1.02056,2.31691,2.18694,0.401087,1.70764,0.549999,0.868835,1.28191
+27-Feb-2019 02:00:00,1,0.31795,0.75861,1.2266,1.1535,0.81089,1.6282,1.1508,1.19,0.33052,3.4262,1.01275,2.30359,2.17405,0.397168,1.69567,0.545261,0.862778,1.27431
+27-Feb-2019 03:00:00,1,0.32036,0.76583,1.2373,1.1611,0.81872,1.6404,1.1591,1.2025,0.33327,3.4464,1.02056,2.31691,2.18694,0.401087,1.70764,0.549999,0.868835,1.28191
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+27-Feb-2019 07:00:00,1,0.45985,1.1898,1.8565,1.6014,1.2916,2.3708,1.6197,1.9054,0.48428,4.446,1.41167,2.97829,2.89688,0.633626,2.41164,0.823677,1.22891,1.6967
+27-Feb-2019 08:00:00,1,0.47865,1.2476,1.9397,1.6607,1.3579,2.4729,1.6789,1.9967,0.50347,4.557,1.45588,3.0522,2.9874,0.665665,2.50788,0.860397,1.27861,1.74899
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+27-Feb-2019 10:00:00,1,0.49632,1.3021,2.0179,1.7164,1.4206,2.5696,1.734,2.0817,0.52128,4.6568,1.49576,3.11868,3.07148,0.695913,2.59859,0.894877,1.32555,1.79743
+27-Feb-2019 11:00:00,1,0.50572,1.3312,2.0595,1.746,1.4543,2.6214,1.7631,2.1268,0.53067,4.7082,1.51635,3.15293,3.11586,0.712075,2.647,0.913227,1.35064,1.82295
+27-Feb-2019 12:00:00,1,0.49938,1.3115,2.0314,1.726,1.4316,2.5865,1.7435,2.0964,0.52434,4.6737,1.5025,3.1299,3.08594,0.701166,2.61433,0.900846,1.3337,1.80575
+27-Feb-2019 13:00:00,1,0.49224,1.2895,1.9999,1.7035,1.4061,2.5473,1.7213,2.0622,0.51719,4.6342,1.4867,3.1036,3.05218,0.68893,2.57766,0.886932,1.31471,1.78632
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+27-Feb-2019 16:00:00,1,0.46614,1.2091,1.8843,1.6212,1.3137,2.4048,1.6396,1.936,0.49072,4.4837,1.42666,3.00337,2.92726,0.64432,2.44378,0.835956,1.24549,1.71427
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+27-Feb-2019 18:00:00,1,0.49797,1.3072,2.0252,1.7216,1.4266,2.5787,1.7391,2.0897,0.52294,4.666,1.49942,3.12477,3.07931,0.698757,2.60711,0.898109,1.32996,1.80194
+27-Feb-2019 19:00:00,1,0.49155,1.2874,1.9968,1.7013,1.4036,2.5434,1.7192,2.0588,0.51649,4.6303,1.48514,3.101,3.04887,0.687731,2.57407,0.885568,1.31285,1.78442
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+28-Feb-2019 01:00:00,1,0.33687,0.81553,1.3108,1.2133,0.87283,1.7241,1.2158,1.2883,0.35201,3.5823,1.07318,2.40652,2.27482,0.4281,1.78998,0.582523,0.910595,1.3337
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+10-Mar-2019 05:00:00,1,0.30817,0.72925,1.1831,1.1226,0.77915,1.5791,1.1168,1.1387,0.31928,3.3429,0.980544,2.24866,2.12138,0.381255,1.64707,0.525979,0.838189,1.24319
+10-Mar-2019 06:00:00,1,0.30674,0.72496,1.1767,1.1181,0.77452,1.572,1.1119,1.1312,0.31763,3.3305,0.975779,2.24052,2.11364,0.378933,1.63997,0.523159,0.834601,1.23862
+10-Mar-2019 07:00:00,1,0.3244,0.77797,1.2553,1.1739,0.8319,1.6608,1.173,1.2236,0.33788,3.4801,1.0336,2.33913,2.20853,0.407679,1.72775,0.557955,0.879024,1.29465
+10-Mar-2019 08:00:00,1,0.3546,0.86902,1.3896,1.2693,0.93149,1.8148,1.2759,1.3796,0.37185,3.7225,1.12764,2.49908,2.3679,0.457252,1.87867,0.617385,0.955685,1.38843
+10-Mar-2019 09:00:00,1,0.37985,0.94552,1.5018,1.3491,1.0161,1.9456,1.3605,1.5082,0.39964,3.9127,1.20177,2.62477,2.49844,0.499076,2.00558,0.666993,1.02041,1.46493
+10-Mar-2019 10:00:00,1,0.39324,0.98619,1.5612,1.3913,1.0614,2.0156,1.4047,1.5757,0.41415,4.0091,1.23947,2.68853,2.56666,0.521374,2.07311,0.693256,1.05494,1.5048
+10-Mar-2019 11:00:00,1,0.40144,1.0112,1.5977,1.4172,1.0894,2.0588,1.4317,1.6169,0.42298,4.0667,1.26206,2.72669,2.60818,0.535094,2.1146,0.709353,1.07618,1.52903
+10-Mar-2019 12:00:00,1,0.39477,0.99086,1.5681,1.3962,1.0667,2.0237,1.4098,1.5835,0.41581,4.02,1.24373,2.69573,2.57445,0.523941,2.08087,0.696271,1.05891,1.50935
+10-Mar-2019 13:00:00,1,0.38331,0.95601,1.5172,1.36,1.0278,1.9636,1.3719,1.5257,0.4034,3.9379,1.21161,2.64142,2.51611,0.504824,2.023,0.673775,1.02931,1.47527
+10-Mar-2019 14:00:00,1,0.36638,0.90468,1.442,1.3065,0.97084,1.8756,1.3155,1.4398,0.38489,3.8126,1.16273,2.55863,2.42912,0.47673,1.93782,0.640546,0.985823,1.42434
+10-Mar-2019 15:00:00,1,0.3581,0.87961,1.4052,1.2804,0.94315,1.8328,1.2877,1.3975,0.37573,3.7495,1.13815,2.51693,2.38614,0.46303,1.89622,0.624266,0.964623,1.39913
+10-Mar-2019 16:00:00,1,0.35215,0.86164,1.3788,1.2616,0.92336,1.8022,1.2677,1.3671,0.36913,3.7035,1.12025,2.48653,2.35513,0.453221,1.86641,0.612579,0.949449,1.38093
+10-Mar-2019 17:00:00,1,0.35814,0.87975,1.4054,1.2805,0.9433,1.8331,1.2879,1.3978,0.37579,3.7499,1.13829,2.51716,2.38638,0.463105,1.89645,0.624355,0.96474,1.39927
+10-Mar-2019 18:00:00,1,0.38315,0.95553,1.5165,1.3595,1.0272,1.9628,1.3714,1.5249,0.40323,3.9367,1.21116,2.64065,2.5153,0.504559,2.0222,0.673463,1.0289,1.47479
+10-Mar-2019 19:00:00,1,0.38342,0.95635,1.5177,1.3603,1.0282,1.9642,1.3723,1.5263,0.40353,3.9387,1.21193,2.64196,2.51669,0.505012,2.02357,0.673997,1.0296,1.47561
+10-Mar-2019 20:00:00,1,0.3539,0.86691,1.3865,1.2671,0.92917,1.8112,1.2736,1.376,0.37107,3.7171,1.12553,2.4955,2.36426,0.456101,1.87517,0.616013,0.953904,1.38629
+10-Mar-2019 21:00:00,1,0.33739,0.81709,1.3131,1.215,0.87453,1.7267,1.2175,1.291,0.35259,3.5865,1.0748,2.40926,2.27754,0.428947,1.79256,0.583538,0.911905,1.33531
+10-Mar-2019 22:00:00,1,0.32981,0.79425,1.2794,1.191,0.84961,1.6882,1.1916,1.2517,0.34402,3.5248,1.05089,2.36858,2.23735,0.416523,1.75471,0.568611,0.892696,1.31164
+10-Mar-2019 23:00:00,1,0.30774,0.72796,1.1811,1.1212,0.77776,1.577,1.1153,1.1364,0.31878,3.3392,0.979111,2.24621,2.11905,0.380556,1.64494,0.52513,0.837109,1.24182
+11-Mar-2019 00:00:00,1,0.28372,0.65613,1.0741,1.0453,0.70071,1.4576,1.031,1.0094,0.29078,3.1263,0.8971,2.10607,1.98794,0.34173,1.52608,0.477734,0.777147,1.16417
+11-Mar-2019 01:00:00,1,0.26295,0.59425,0.98142,0.97954,0.63508,1.3559,0.95695,0.89805,0.26609,2.9327,0.822736,1.97872,1.87245,0.308425,1.42381,0.436668,0.725749,1.09556
+11-Mar-2019 02:00:00,1,0.25387,0.56731,0.94091,0.95082,0.60672,1.3119,0.92424,0.849,0.25517,2.8453,0.789223,1.92125,1.82139,0.293963,1.37931,0.418713,0.703443,1.06515
+11-Mar-2019 03:00:00,1,0.25447,0.56907,0.94355,0.95269,0.60856,1.3147,0.92638,0.85221,0.25588,2.8511,0.791428,1.92503,1.82473,0.294905,1.3822,0.419884,0.704895,1.06714
+11-Mar-2019 04:00:00,1,0.25872,0.58169,0.96254,0.96615,0.62184,1.3353,0.94173,0.87522,0.26101,2.8922,0.807196,1.95208,1.8487,0.301678,1.40305,0.428301,0.715342,1.08142
+11-Mar-2019 05:00:00,1,0.25778,0.57889,0.95834,0.96317,0.61889,1.3308,0.93833,0.87013,0.25987,2.8831,0.803715,1.94611,1.8434,0.300177,1.39843,0.426437,0.713026,1.07826
+11-Mar-2019 06:00:00,1,0.25009,0.5561,0.92402,0.93884,0.59496,1.2936,0.91053,0.82848,0.25059,2.8083,0.775064,1.89695,1.79998,0.287951,1.36079,0.411224,0.694172,1.05239
+11-Mar-2019 07:00:00,1,0.26121,0.58908,0.97365,0.97403,0.62962,1.3474,0.95068,0.88865,0.264,2.9161,0.816351,1.96778,1.86268,0.305644,1.41526,0.433222,0.72146,1.08974
+11-Mar-2019 08:00:00,1,0.28907,0.67209,1.098,1.0622,0.71776,1.484,1.0499,1.0378,0.29706,3.1747,0.91572,2.13792,2.01735,0.350344,1.55248,0.488293,0.790447,1.18161
+11-Mar-2019 09:00:00,1,0.31643,0.75401,1.2198,1.1487,0.80591,1.6205,1.1455,1.182,0.32877,3.4133,1.00776,2.29508,2.16585,0.394677,1.68807,0.542249,0.858929,1.26946
+11-Mar-2019 10:00:00,1,0.33397,0.80677,1.2979,1.2041,0.86327,1.7093,1.2058,1.2733,0.34873,3.5587,1.06405,2.39098,2.25943,0.423335,1.77547,0.576801,0.903228,1.32464
+11-Mar-2019 11:00:00,1,0.35331,0.86512,1.3839,1.2652,0.9272,1.8082,1.2716,1.373,0.37041,3.7125,1.12374,2.49246,2.36116,0.455123,1.87219,0.614846,0.95239,1.38447
+11-Mar-2019 12:00:00,1,0.35093,0.85794,1.3733,1.2577,0.9193,1.796,1.2635,1.3608,0.36777,3.6939,1.11653,2.48022,2.34873,0.451204,1.86028,0.610172,0.946328,1.37717
+11-Mar-2019 13:00:00,1,0.33849,0.82039,1.318,1.2184,0.87814,1.7323,1.2213,1.2967,0.35383,3.5953,1.07823,2.4151,2.28334,0.430747,1.79804,0.585698,0.914689,1.33872
+11-Mar-2019 14:00:00,1,0.32938,0.79297,1.2775,1.1897,0.84822,1.686,1.1901,1.2495,0.34354,3.5213,1.04954,2.36628,2.2351,0.41583,1.7526,0.567776,0.891624,1.31031
+11-Mar-2019 15:00:00,1,0.32438,0.77793,1.2552,1.1739,0.83186,1.6607,1.173,1.2235,0.33786,3.48,1.03356,2.33906,2.20846,0.407658,1.72768,0.55793,0.878992,1.29461
+11-Mar-2019 16:00:00,1,0.32528,0.78063,1.2592,1.1767,0.83479,1.6652,1.176,1.2282,0.33888,3.4874,1.03644,2.34396,2.21324,0.409122,1.73215,0.559695,0.881254,1.29743
+11-Mar-2019 17:00:00,1,0.34115,0.82842,1.3298,1.2268,0.88693,1.7459,1.2304,1.3104,0.35682,3.6166,1.08651,2.42919,2.29739,0.435119,1.81135,0.590938,0.921449,1.34699
+11-Mar-2019 18:00:00,1,0.36918,0.91315,1.4544,1.3154,0.9802,1.8901,1.3249,1.4541,0.38796,3.8337,1.17093,2.57252,2.44356,0.48136,1.95187,0.646037,0.992988,1.4328
+11-Mar-2019 19:00:00,1,0.3731,0.92504,1.4718,1.3278,0.99337,1.9104,1.338,1.474,0.39227,3.8629,1.18234,2.59186,2.46378,0.487863,1.97159,0.653738,1.00305,1.44464
+11-Mar-2019 20:00:00,1,0.35605,0.8734,1.3961,1.2739,0.93631,1.8223,1.2808,1.387,0.37346,3.7337,1.132,2.50648,2.37545,0.459643,1.88593,0.620233,0.959383,1.39286
+11-Mar-2019 21:00:00,1,0.34676,0.84535,1.3548,1.2446,0.90548,1.7746,1.2494,1.3394,0.36311,3.6612,1.10382,2.45861,2.3269,0.444344,1.83942,0.601977,0.935717,1.36434
+11-Mar-2019 22:00:00,1,0.34818,0.84963,1.3611,1.249,0.91017,1.7818,1.2542,1.3466,0.3647,3.6724,1.10815,2.46597,2.33433,0.446674,1.84651,0.604762,0.939321,1.36871
+11-Mar-2019 23:00:00,1,0.32161,0.76959,1.2429,1.1651,0.8228,1.6467,1.1634,1.209,0.3347,3.4569,1.02461,2.32381,2.19363,0.403129,1.71387,0.552464,0.871991,1.28586
+12-Mar-2019 00:00:00,1,0.29892,0.70153,1.1418,1.0933,0.74931,1.5329,1.0845,1.0899,0.30856,3.2623,0.949463,2.19559,2.07117,0.366253,1.60119,0.507728,0.815014,1.2135
+12-Mar-2019 01:00:00,1,0.28642,0.66416,1.0861,1.0538,0.70929,1.4709,1.0406,1.0237,0.29395,3.1508,0.906503,2.12216,2.00277,0.346066,1.53937,0.483052,0.783842,1.17297
+12-Mar-2019 02:00:00,1,0.28287,0.65356,1.0703,1.0426,0.69798,1.4534,1.028,1.0048,0.28977,3.1185,0.894089,2.10092,1.9832,0.340347,1.52184,0.476037,0.775012,1.16136
+12-Mar-2019 03:00:00,1,0.2869,0.66562,1.0883,1.0553,0.71084,1.4733,1.0423,1.0263,0.29452,3.1552,0.908198,2.12505,2.00544,0.34685,1.54178,0.484014,0.785053,1.17455
+12-Mar-2019 04:00:00,1,0.29831,0.69972,1.1391,1.0914,0.74737,1.5299,1.0824,1.0868,0.30786,3.257,0.947414,2.19209,2.06788,0.365275,1.5982,0.506536,0.813504,1.21155
+12-Mar-2019 05:00:00,1,0.32599,0.78276,1.2624,1.1789,0.83711,1.6688,1.1785,1.2319,0.33969,3.4933,1.03872,2.34784,2.21703,0.410283,1.73569,0.561095,0.88305,1.29966
+12-Mar-2019 06:00:00,1,0.38323,0.95577,1.5168,1.3597,1.0275,1.9632,1.3717,1.5253,0.40332,3.9373,1.21138,2.64103,2.51571,0.504691,2.0226,0.673619,1.0291,1.47503
+12-Mar-2019 07:00:00,1,0.42641,1.0874,1.7084,1.496,1.1751,2.1911,1.5129,1.7412,0.44951,4.2353,1.32836,2.83844,2.73301,0.577022,2.24121,0.758269,1.14115,1.6017
+12-Mar-2019 08:00:00,1,0.4506,1.1614,1.8156,1.5723,1.2592,2.3208,1.5904,1.8602,0.47474,4.3894,1.38923,2.94068,2.8519,0.617911,2.36438,0.805587,1.20454,1.67067
+12-Mar-2019 09:00:00,1,0.45671,1.1802,1.8426,1.5915,1.2806,2.3538,1.6098,1.8901,0.48105,4.4269,1.40411,2.96562,2.88164,0.628288,2.39559,0.817538,1.22063,1.68789
+12-Mar-2019 10:00:00,1,0.47029,1.2219,1.9027,1.6343,1.3283,2.4274,1.6527,1.9562,0.49497,4.5083,1.43645,3.01974,2.94729,0.651399,2.46505,0.844073,1.25648,1.72584
+12-Mar-2019 11:00:00,1,0.47625,1.2403,1.9291,1.6531,1.3494,2.4598,1.6714,1.9851,0.50104,4.5432,1.45034,3.04296,2.97593,0.661573,2.4956,0.855718,1.27226,1.74237
+12-Mar-2019 12:00:00,1,0.47518,1.237,1.9244,1.6498,1.3456,2.454,1.6681,1.9799,0.49995,4.5369,1.44786,3.03881,2.97079,0.659744,2.49011,0.853627,1.26942,1.73941
+12-Mar-2019 13:00:00,1,0.4708,1.2235,1.905,1.636,1.3301,2.4302,1.6543,1.9587,0.49549,4.5113,1.43764,3.02174,2.94974,0.652268,2.46766,0.845068,1.25782,1.72725
+12-Mar-2019 14:00:00,1,0.46056,1.192,1.8597,1.6037,1.2941,2.3746,1.622,1.9089,0.48501,4.4502,1.41337,2.98113,2.90031,0.63483,2.41526,0.82506,1.23077,1.69869
+12-Mar-2019 15:00:00,1,0.44951,1.1581,1.8107,1.5688,1.2554,2.3149,1.5869,1.8549,0.47361,4.3826,1.38655,2.93619,2.84658,0.616061,2.35881,0.803455,1.20167,1.66759
+12-Mar-2019 16:00:00,1,0.43984,1.1284,1.7679,1.5383,1.2217,2.2629,1.556,1.8075,0.46357,4.3219,1.36255,2.89591,2.79925,0.599689,2.30952,0.784546,1.17628,1.64016
+12-Mar-2019 17:00:00,1,0.44034,1.13,1.7701,1.5399,1.2234,2.2656,1.5576,1.8099,0.46409,4.3251,1.3638,2.89802,2.80171,0.600536,2.31208,0.785526,1.1776,1.64159
+12-Mar-2019 18:00:00,1,0.46225,1.1972,1.8672,1.609,1.3,2.3838,1.6273,1.9171,0.48675,4.4604,1.41743,2.98792,2.90851,0.637713,2.42393,0.828372,1.23525,1.70343
+12-Mar-2019 19:00:00,1,0.47126,1.2249,1.907,1.6374,1.3318,2.4327,1.6557,1.9609,0.49596,4.514,1.43872,3.02354,2.95196,0.653054,2.47002,0.845968,1.25904,1.72853
+12-Mar-2019 20:00:00,1,0.44134,1.133,1.7745,1.5431,1.2269,2.271,1.5608,1.8148,0.46513,4.3314,1.3663,2.90221,2.8066,0.602221,2.31715,0.787473,1.18021,1.64442
+12-Mar-2019 21:00:00,1,0.41357,1.0481,1.6515,1.4555,1.1309,2.1228,1.4713,1.6775,0.43592,4.1498,1.2947,2.78176,2.66907,0.555417,2.17601,0.733115,1.10767,1.56451
+12-Mar-2019 22:00:00,1,0.39335,0.98653,1.5618,1.3917,1.0618,2.0162,1.4051,1.5763,0.41428,4.0099,1.23979,2.68907,2.56724,0.521564,2.07368,0.693479,1.05523,1.50514
+12-Mar-2019 23:00:00,1,0.35524,0.87096,1.3925,1.2713,0.93362,1.8181,1.2781,1.3829,0.37256,3.7275,1.12957,2.50235,2.37123,0.458307,1.88187,0.618642,0.957317,1.39038
+13-Mar-2019 00:00:00,1,0.32962,0.79367,1.2785,1.1904,0.84898,1.6872,1.1909,1.2507,0.34381,3.5232,1.05028,2.36753,2.23633,0.416208,1.75375,0.568231,0.892209,1.31103
+13-Mar-2019 01:00:00,1,0.31685,0.7553,1.2217,1.15,0.8073,1.6227,1.147,1.1842,0.32926,3.4169,1.00915,2.29746,2.16814,0.395372,1.69019,0.54309,0.860003,1.27082
+13-Mar-2019 02:00:00,1,0.31334,0.74476,1.2061,1.1389,0.79589,1.605,1.1348,1.1658,0.32523,3.3871,0.997643,2.27783,2.14926,0.389656,1.67274,0.53617,0.851171,1.25967
+13-Mar-2019 03:00:00,1,0.32026,0.76552,1.2369,1.1608,0.81838,1.6398,1.1587,1.202,0.33315,3.4456,1.02023,2.31634,2.18639,0.400921,1.70713,0.549798,0.868578,1.28159
+13-Mar-2019 04:00:00,1,0.33249,0.80232,1.2913,1.1995,0.8584,1.7018,1.2008,1.2656,0.34706,3.5467,1.05938,2.38304,2.25158,0.420911,1.76808,0.573887,0.899479,1.32002
+13-Mar-2019 05:00:00,1,0.3537,0.8663,1.3856,1.2665,0.9285,1.8102,1.2729,1.375,0.37085,3.7155,1.12492,2.49446,2.3632,0.455768,1.87415,0.615615,0.953388,1.38567
+13-Mar-2019 06:00:00,1,0.40402,1.019,1.6091,1.4254,1.0982,2.0724,1.4401,1.6298,0.42575,4.0846,1.26908,2.73854,2.62118,0.539411,2.12766,0.71441,1.08287,1.53661
+13-Mar-2019 07:00:00,1,0.4441,1.1415,1.7868,1.5518,1.2365,2.2858,1.5697,1.8284,0.468,4.3488,1.37319,2.91377,2.82014,0.606898,2.33123,0.792878,1.18746,1.65227
+13-Mar-2019 08:00:00,1,0.4652,1.2062,1.8802,1.6183,1.3104,2.3997,1.6366,1.9315,0.48976,4.4781,1.42443,2.99964,2.92273,0.642719,2.43897,0.83412,1.24301,1.71165
+13-Mar-2019 09:00:00,1,0.47057,1.2228,1.904,1.6352,1.3293,2.4289,1.6536,1.9575,0.49525,4.5099,1.4371,3.02084,2.94863,0.651875,2.46648,0.844618,1.25721,1.72661
+13-Mar-2019 10:00:00,1,0.4799,1.2515,1.9453,1.6646,1.3623,2.4798,1.6829,2.0027,0.50474,4.5643,1.45876,3.05701,2.99341,0.66781,2.51432,0.862848,1.28194,1.75246
+13-Mar-2019 11:00:00,1,0.48983,1.2821,1.9892,1.6959,1.3975,2.534,1.7138,2.0506,0.51476,4.6207,1.4813,3.0946,3.04072,0.684793,2.56526,0.882222,1.30829,1.77973
+13-Mar-2019 12:00:00,1,0.48133,1.2559,1.9516,1.6691,1.3674,2.4875,1.6873,2.0096,0.50619,4.5725,1.46203,3.06246,3.00022,0.670245,2.52162,0.86563,1.28572,1.75638
+13-Mar-2019 13:00:00,1,0.47695,1.2424,1.9322,1.6553,1.3519,2.4637,1.6736,1.9885,0.50175,4.5472,1.45196,3.04566,2.97928,0.662768,2.49918,0.857086,1.27411,1.74431
+13-Mar-2019 14:00:00,1,0.46627,1.2096,1.885,1.6217,1.3142,2.4056,1.64,1.9367,0.49087,4.4845,1.42699,3.00392,2.92794,0.644557,2.44449,0.836228,1.24586,1.71466
+13-Mar-2019 15:00:00,1,0.45609,1.1783,1.8399,1.5896,1.2784,2.3504,1.6078,1.8871,0.48041,4.4231,1.40262,2.96312,2.87865,0.627241,2.39244,0.816333,1.219,1.68616
+13-Mar-2019 16:00:00,1,0.44534,1.1453,1.7923,1.5557,1.2408,2.2925,1.5736,1.8345,0.46929,4.3566,1.37626,2.91893,2.82621,0.608995,2.33755,0.795301,1.19071,1.65579
+13-Mar-2019 17:00:00,1,0.44824,1.1542,1.8051,1.5648,1.251,2.3081,1.5829,1.8487,0.4723,4.3748,1.38345,2.93098,2.84042,0.613921,2.35237,0.800986,1.19835,1.66402
+13-Mar-2019 18:00:00,1,0.46942,1.2192,1.8989,1.6316,1.3253,2.4227,1.65,1.952,0.49408,4.5032,1.43442,3.01634,2.94312,0.649924,2.46062,0.842383,1.25419,1.72343
+13-Mar-2019 19:00:00,1,0.47692,1.2423,1.9321,1.6552,1.3517,2.4635,1.6735,1.9883,0.50171,4.547,1.45188,3.04553,2.97912,0.662709,2.49901,0.857018,1.27402,1.74421
+13-Mar-2019 20:00:00,1,0.44625,1.1481,1.7963,1.5585,1.244,2.2973,1.5765,1.8389,0.47023,4.3623,1.37851,2.92271,2.83065,0.610535,2.34218,0.797078,1.1931,1.65836
+13-Mar-2019 21:00:00,1,0.41869,1.0638,1.6741,1.4716,1.1485,2.15,1.4879,1.7029,0.44135,4.1842,1.30823,2.80454,2.69462,0.564017,2.20197,0.743141,1.12099,1.57938
+13-Mar-2019 22:00:00,1,0.39673,0.99681,1.5767,1.4023,1.0733,2.0339,1.4162,1.5933,0.41792,4.0337,1.24912,2.70485,2.58434,0.527204,2.09074,0.700101,1.06396,1.51513
+13-Mar-2019 23:00:00,1,0.35985,0.88489,1.413,1.2859,0.94898,1.8418,1.2936,1.4065,0.37767,3.7629,1.14337,2.52578,2.39522,0.465915,1.90498,0.627698,0.969087,1.40446
+14-Mar-2019 00:00:00,1,0.33455,0.80852,1.3005,1.206,0.86517,1.7122,1.2078,1.2763,0.34939,3.5635,1.06588,2.39409,2.2625,0.424285,1.77836,0.577942,0.904696,1.32645
+14-Mar-2019 01:00:00,1,0.32061,0.7666,1.2385,1.1619,0.81955,1.6417,1.16,1.2038,0.33356,3.4486,1.02139,2.31832,2.1883,0.401504,1.70891,0.550502,0.869479,1.28272
+14-Mar-2019 02:00:00,1,0.31531,0.75067,1.2149,1.1452,0.80229,1.6149,1.1416,1.1761,0.32749,3.4039,1.00411,2.28887,2.15986,0.392864,1.68254,0.540054,0.856127,1.26593
+14-Mar-2019 03:00:00,1,0.3197,0.76385,1.2344,1.1591,0.81658,1.637,1.1568,1.1991,0.33252,3.4409,1.01843,2.31327,2.18342,0.400015,1.70437,0.548704,0.867179,1.27983
+14-Mar-2019 04:00:00,1,0.33165,0.79978,1.2876,1.1968,0.85564,1.6975,1.1979,1.2612,0.34611,3.5398,1.05672,2.3785,2.24711,0.419531,1.76388,0.572229,0.897346,1.31739
+14-Mar-2019 05:00:00,1,0.35491,0.86997,1.391,1.2703,0.93253,1.8164,1.277,1.3812,0.3722,3.7249,1.12858,2.50068,2.36953,0.457769,1.88024,0.618,0.956484,1.38938
+14-Mar-2019 06:00:00,1,0.40317,1.0164,1.6053,1.4227,1.0953,2.0679,1.4373,1.6256,0.42483,4.0787,1.26676,2.73462,2.61688,0.537982,2.12334,0.712737,1.08066,1.5341
+14-Mar-2019 07:00:00,1,0.44409,1.1415,1.7867,1.5518,1.2365,2.2858,1.5696,1.8284,0.468,4.3488,1.37317,2.91374,2.82011,0.606886,2.3312,0.792865,1.18744,1.65225
+14-Mar-2019 08:00:00,1,0.4656,1.2075,1.882,1.6196,1.3118,2.4019,1.6379,1.9334,0.49017,4.4805,1.42539,3.00124,2.92468,0.643407,2.44104,0.834909,1.24408,1.71278
+14-Mar-2019 09:00:00,1,0.47016,1.2215,1.9022,1.6339,1.3279,2.4267,1.6523,1.9555,0.49483,4.5075,1.43614,3.01922,2.94665,0.651173,2.46437,0.843814,1.25612,1.72547
+14-Mar-2019 10:00:00,1,0.47842,1.2469,1.9387,1.66,1.3571,2.4716,1.6782,1.9955,0.50323,4.5557,1.45534,3.05131,2.9863,0.66527,2.50669,0.859946,1.27799,1.74835
+14-Mar-2019 11:00:00,1,0.48999,1.2826,1.9899,1.6964,1.3981,2.5349,1.7143,2.0513,0.51492,4.6216,1.48165,3.09518,3.04146,0.685059,2.56606,0.882525,1.3087,1.78015
+14-Mar-2019 12:00:00,1,0.4861,1.2706,1.9727,1.6842,1.3843,2.5136,1.7022,2.0326,0.51101,4.5997,1.4729,3.08059,3.02299,0.678411,2.54612,0.874948,1.29839,1.76951
+14-Mar-2019 13:00:00,1,0.48153,1.2565,1.9525,1.6698,1.3681,2.4886,1.6879,2.0106,0.50638,4.5736,1.46248,3.06322,3.00116,0.670581,2.52263,0.866014,1.28624,1.75693
+14-Mar-2019 14:00:00,1,0.46991,1.2208,1.9011,1.6332,1.327,2.4253,1.6515,1.9544,0.49458,4.5061,1.43556,3.01826,2.94547,0.650757,2.46312,0.843337,1.25548,1.72479
+14-Mar-2019 15:00:00,1,0.45947,1.1887,1.8548,1.6002,1.2903,2.3687,1.6185,1.9036,0.48389,4.4436,1.41076,2.97676,2.89503,0.632978,2.40969,0.822931,1.2279,1.69563
+14-Mar-2019 16:00:00,1,0.44575,1.1466,1.7941,1.557,1.2423,2.2947,1.5749,1.8365,0.46972,4.3592,1.37728,2.92065,2.82823,0.609695,2.33965,0.796109,1.1918,1.65696
+14-Mar-2019 17:00:00,1,0.4485,1.155,1.8063,1.5657,1.2519,2.3095,1.5837,1.85,0.47257,4.3763,1.38407,2.93204,2.84166,0.614354,2.35367,0.801485,1.19902,1.66474
+14-Mar-2019 18:00:00,1,0.47104,1.2242,1.9061,1.6367,1.331,2.4315,1.6551,1.9598,0.49574,4.5127,1.43822,3.02269,2.95091,0.652685,2.46891,0.845545,1.25847,1.72793
+14-Mar-2019 19:00:00,1,0.48584,1.2698,1.9715,1.6833,1.3833,2.5122,1.7014,2.0313,0.51074,4.5982,1.47229,3.07958,3.02172,0.677953,2.54475,0.874426,1.29768,1.76878
+14-Mar-2019 20:00:00,1,0.45724,1.1818,1.845,1.5932,1.2825,2.3567,1.6115,1.8927,0.4816,4.4301,1.4054,2.96778,2.88423,0.629195,2.39832,0.818581,1.22203,1.68939
+14-Mar-2019 21:00:00,1,0.43158,1.1032,1.7313,1.5123,1.193,2.2187,1.5295,1.7668,0.45494,4.269,1.34164,2.86078,2.75859,0.58574,2.26749,0.768389,1.15466,1.61656
+14-Mar-2019 22:00:00,1,0.41028,1.0381,1.6369,1.4451,1.1196,2.1054,1.4605,1.6611,0.43243,4.1275,1.28593,2.76697,2.6526,0.549897,2.15934,0.726671,1.09912,1.55492
+14-Mar-2019 23:00:00,1,0.37402,0.92784,1.476,1.3307,0.99648,1.9152,1.3411,1.4787,0.39328,3.8698,1.18502,2.5964,2.46853,0.489396,1.97624,0.655553,1.00543,1.44743
+15-Mar-2019 00:00:00,1,0.3468,0.84547,1.355,1.2447,0.90561,1.7748,1.2496,1.3396,0.36316,3.6615,1.10394,2.45881,2.32711,0.444408,1.83961,0.602054,0.935816,1.36446
+15-Mar-2019 01:00:00,1,0.3347,0.80898,1.3012,1.2065,0.86568,1.713,1.2084,1.2771,0.34956,3.5647,1.06637,2.39492,2.26332,0.424538,1.77913,0.578247,0.905088,1.32693
+15-Mar-2019 02:00:00,1,0.33077,0.79715,1.2837,1.194,0.85277,1.6931,1.1949,1.2567,0.34512,3.5327,1.05395,2.37379,2.24247,0.4181,1.75952,0.570508,0.895134,1.31465
+15-Mar-2019 03:00:00,1,0.33806,0.81909,1.3161,1.2171,0.87672,1.7301,1.2198,1.2944,0.35334,3.5918,1.07687,2.4128,2.28106,0.430037,1.79588,0.584847,0.913591,1.33738
+15-Mar-2019 04:00:00,1,0.34861,0.85092,1.363,1.2504,0.9116,1.784,1.2557,1.3488,0.36518,3.6757,1.10946,2.4682,2.33658,0.44738,1.84865,0.605606,0.940413,1.37003
+15-Mar-2019 05:00:00,1,0.3705,0.91716,1.4603,1.3196,0.98465,1.897,1.3293,1.4608,0.38942,3.8436,1.17479,2.57907,2.45039,0.483554,1.95852,0.648637,0.996384,1.43681
+15-Mar-2019 06:00:00,1,0.41703,1.0587,1.6668,1.4664,1.1428,2.1412,1.4825,1.6947,0.4396,4.1732,1.30387,2.79721,2.68637,0.561237,2.19358,0.739902,1.11669,1.57458
+15-Mar-2019 07:00:00,1,0.45181,1.1651,1.8209,1.5761,1.2634,2.3273,1.5942,1.8662,0.47599,4.3968,1.39218,2.94564,2.85779,0.619961,2.37055,0.80795,1.20771,1.67408
+15-Mar-2019 08:00:00,1,0.46978,1.2203,1.9005,1.6327,1.3265,2.4246,1.6511,1.9537,0.49445,4.5053,1.43525,3.01774,2.94483,0.650531,2.46244,0.843078,1.25513,1.72442
+15-Mar-2019 09:00:00,1,0.47499,1.2364,1.9236,1.6492,1.3449,2.453,1.6675,1.979,0.49976,4.5358,1.44742,3.03808,2.96988,0.659422,2.48914,0.853258,1.26892,1.73888
+15-Mar-2019 10:00:00,1,0.48241,1.2592,1.9564,1.6725,1.3712,2.4934,1.6907,2.0148,0.50728,4.5787,1.4645,3.06659,3.00539,0.672095,2.52717,0.867741,1.28858,1.75936
+15-Mar-2019 11:00:00,1,0.48933,1.2805,1.987,1.6943,1.3958,2.5313,1.7123,2.0482,0.51426,4.6179,1.48018,3.09273,3.03834,0.683936,2.56269,0.881245,1.30696,1.77836
+15-Mar-2019 12:00:00,1,0.48316,1.2615,1.9597,1.6749,1.3738,2.4975,1.693,2.0184,0.50803,4.5829,1.4662,3.06942,3.00894,0.673368,2.53099,0.869195,1.29056,1.76141
+15-Mar-2019 13:00:00,1,0.4767,1.2416,1.9311,1.6545,1.351,2.4623,1.6728,1.9872,0.50149,4.5458,1.45138,3.04469,2.97807,0.662338,2.49789,0.856594,1.27345,1.74361
+15-Mar-2019 14:00:00,1,0.46596,1.2086,1.8836,1.6207,1.3131,2.4039,1.6391,1.9352,0.49055,4.4826,1.42624,3.00268,2.92642,0.644023,2.44289,0.835616,1.24503,1.71379
+15-Mar-2019 15:00:00,1,0.45147,1.1641,1.8194,1.575,1.2623,2.3255,1.5932,1.8645,0.47565,4.3948,1.39137,2.94428,2.85617,0.619398,2.36885,0.807301,1.20684,1.67315
+15-Mar-2019 16:00:00,1,0.43931,1.1268,1.7656,1.5367,1.2198,2.2601,1.5543,1.8049,0.46302,4.3186,1.36122,2.89368,2.79666,0.598795,2.30683,0.783512,1.1749,1.63865
+15-Mar-2019 17:00:00,1,0.44008,1.1292,1.769,1.5391,1.2225,2.2642,1.5568,1.8086,0.46382,4.3235,1.36315,2.89692,2.80043,0.600095,2.31075,0.785015,1.17691,1.64084
+15-Mar-2019 18:00:00,1,0.46449,1.2041,1.877,1.616,1.3079,2.3959,1.6344,1.928,0.48903,4.4738,1.42274,2.99682,2.9193,0.641511,2.43534,0.832733,1.24114,1.70967
+15-Mar-2019 19:00:00,1,0.48824,1.2772,1.9821,1.6909,1.3919,2.5253,1.7089,2.0429,0.51316,4.6117,1.47772,3.08863,3.03315,0.682064,2.55708,0.879113,1.30406,1.77536
+15-Mar-2019 20:00:00,1,0.46032,1.1913,1.8586,1.6029,1.2932,2.3733,1.6212,1.9077,0.48476,4.4488,1.41279,2.98016,2.89913,0.634417,2.41402,0.824586,1.23013,1.69801
+15-Mar-2019 21:00:00,1,0.43011,1.0987,1.7248,1.5077,1.1879,2.2108,1.5248,1.7595,0.45339,4.2594,1.33787,2.85443,2.7513,0.583252,2.25999,0.765503,1.1508,1.61233
+15-Mar-2019 22:00:00,1,0.40355,1.0176,1.607,1.4239,1.0966,2.0699,1.4386,1.6275,0.42524,4.0813,1.2678,2.73638,2.61881,0.538623,2.12527,0.713487,1.08165,1.53523
+15-Mar-2019 23:00:00,1,0.36742,0.90781,1.4466,1.3098,0.97429,1.881,1.319,1.4451,0.38602,3.8204,1.16576,2.56377,2.43446,0.478439,1.943,0.642573,0.988467,1.42747
+16-Mar-2019 00:00:00,1,0.33854,0.82055,1.3182,1.2186,0.87831,1.7326,1.2215,1.2969,0.35389,3.5957,1.07839,2.41537,2.28361,0.430832,1.7983,0.5858,0.91482,1.33888
+16-Mar-2019 01:00:00,1,0.32556,0.78145,1.2605,1.1776,0.83569,1.6666,1.177,1.2296,0.3392,3.4897,1.03732,2.34546,2.21471,0.409572,1.73352,0.560238,0.88195,1.29829
+16-Mar-2019 02:00:00,1,0.3206,0.76656,1.2384,1.1619,0.81951,1.6416,1.1599,1.2038,0.33355,3.4485,1.02135,2.31825,2.18824,0.401483,1.70884,0.550477,0.869447,1.28268
+16-Mar-2019 03:00:00,1,0.32435,0.77782,1.2551,1.1737,0.83173,1.6605,1.1728,1.2233,0.33782,3.4797,1.03344,2.33885,2.20826,0.407595,1.72749,0.557855,0.878895,1.29449
+16-Mar-2019 04:00:00,1,0.33635,0.81396,1.3085,1.2117,0.87111,1.7214,1.214,1.2856,0.35142,3.5781,1.07155,2.40373,2.27205,0.427243,1.78737,0.581494,0.909271,1.33208
+16-Mar-2019 05:00:00,1,0.35937,0.88345,1.4108,1.2844,0.94738,1.8394,1.292,1.404,0.37714,3.7593,1.14195,2.52336,2.39274,0.465126,1.90259,0.626759,0.967866,1.403
+16-Mar-2019 06:00:00,1,0.403,1.0159,1.6046,1.4221,1.0947,2.0669,1.4368,1.6247,0.42465,4.0775,1.26628,2.73382,2.616,0.53769,2.12245,0.712394,1.08021,1.53359
+16-Mar-2019 07:00:00,1,0.43843,1.1241,1.7616,1.5339,1.2168,2.2553,1.5515,1.8005,0.4621,4.313,1.359,2.88995,2.79231,0.597299,2.30233,0.781781,1.17258,1.63613
+16-Mar-2019 08:00:00,1,0.45642,1.1793,1.8413,1.5906,1.2796,2.3522,1.6089,1.8887,0.48075,4.4251,1.4034,2.96444,2.88023,0.627794,2.39411,0.816969,1.21986,1.68707
+16-Mar-2019 09:00:00,1,0.46859,1.2167,1.8952,1.629,1.3223,2.4181,1.6473,1.9479,0.49323,4.4982,1.43245,3.01305,2.93909,0.648498,2.45633,0.840748,1.25197,1.7211
+16-Mar-2019 10:00:00,1,0.48276,1.2603,1.9579,1.6736,1.3724,2.4953,1.6918,2.0165,0.50763,4.5806,1.46529,3.0679,3.00703,0.672683,2.52894,0.868413,1.2895,1.76031
+16-Mar-2019 11:00:00,1,0.48784,1.276,1.9804,1.6897,1.3905,2.5232,1.7076,2.041,0.51276,4.6095,1.47683,3.08714,3.03127,0.681388,2.55505,0.878343,1.30301,1.77428
+16-Mar-2019 12:00:00,1,0.48297,1.2609,1.9588,1.6743,1.3732,2.4965,1.6924,2.0175,0.50784,4.5818,1.46577,3.0687,3.00804,0.673044,2.53002,0.868825,1.29006,1.76089
+16-Mar-2019 13:00:00,1,0.46981,1.2204,1.9006,1.6328,1.3266,2.4248,1.6512,1.9539,0.49448,4.5054,1.43532,3.01785,2.94497,0.650578,2.46258,0.843132,1.2552,1.7245
+16-Mar-2019 14:00:00,1,0.45148,1.1641,1.8195,1.5751,1.2623,2.3255,1.5932,1.8646,0.47565,4.3948,1.39139,2.94431,2.8562,0.61941,2.36889,0.807315,1.20686,1.67317
+16-Mar-2019 15:00:00,1,0.43601,1.1167,1.7509,1.5263,1.2084,2.2424,1.5438,1.7886,0.45957,4.2975,1.35289,2.87969,2.78041,0.593211,2.29001,0.777048,1.16624,1.62923
+16-Mar-2019 16:00:00,1,0.42525,1.0838,1.7032,1.4923,1.1711,2.1849,1.5091,1.7355,0.44828,4.2277,1.32535,2.83337,2.72724,0.575061,2.23529,0.75599,1.13811,1.59835
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+16-Mar-2019 18:00:00,1,0.44353,1.1397,1.7842,1.55,1.2345,2.2827,1.5678,1.8256,0.46741,4.3453,1.37177,2.91139,2.81735,0.605931,2.32832,0.791762,1.18596,1.65065
+16-Mar-2019 19:00:00,1,0.46076,1.1926,1.8606,1.6043,1.2948,2.3757,1.6226,1.9099,0.48521,4.4514,1.41385,2.98194,2.90128,0.635173,2.41629,0.825454,1.23131,1.69925
+16-Mar-2019 20:00:00,1,0.42727,1.09,1.7122,1.4987,1.1781,2.1957,1.5156,1.7455,0.45041,4.241,1.33057,2.84216,2.73726,0.578469,2.24557,0.75995,1.14339,1.60417
+16-Mar-2019 21:00:00,1,0.39697,0.99754,1.5778,1.4031,1.0741,2.0352,1.417,1.5945,0.41817,4.0354,1.24979,2.70597,2.58556,0.527606,2.09196,0.700574,1.06459,1.51584
+16-Mar-2019 22:00:00,1,0.37615,0.93429,1.4854,1.3374,1.0036,1.9263,1.3482,1.4895,0.3956,3.8855,1.19116,2.6068,2.47946,0.492928,1.98695,0.65973,1.01089,1.45383
+16-Mar-2019 23:00:00,1,0.34777,0.84839,1.3593,1.2478,0.90882,1.7797,1.2528,1.3445,0.36424,3.6692,1.1069,2.46385,2.33218,0.446,1.84446,0.603957,0.938279,1.36744
+17-Mar-2019 00:00:00,1,0.32208,0.77099,1.245,1.1666,0.82432,1.649,1.165,1.2115,0.33523,3.4608,1.02612,2.32638,2.19613,0.40389,1.71619,0.553383,0.873167,1.28733
+17-Mar-2019 01:00:00,1,0.30619,0.7233,1.1742,1.1163,0.77273,1.5692,1.1099,1.1282,0.31699,3.3257,0.973921,2.23735,2.11063,0.378029,1.63721,0.522061,0.833206,1.23684
+17-Mar-2019 02:00:00,1,0.29852,0.70035,1.1401,1.0921,0.74803,1.5309,1.0832,1.0879,0.3081,3.2588,0.948118,2.19329,2.06901,0.365611,1.59923,0.506945,0.814023,1.21222
+17-Mar-2019 03:00:00,1,0.29961,0.7036,1.1449,1.0955,0.75153,1.5364,1.087,1.0936,0.30937,3.2684,0.95181,2.1996,2.07494,0.367373,1.60462,0.509094,0.816745,1.21573
+17-Mar-2019 04:00:00,1,0.30345,0.7151,1.162,1.1077,0.7639,1.5555,1.1004,1.1138,0.31382,3.302,0.964758,2.22171,2.0958,0.373593,1.62365,0.516666,0.826352,1.22807
+17-Mar-2019 05:00:00,1,0.30326,0.71453,1.1612,1.1071,0.76329,1.5546,1.0997,1.1129,0.3136,3.3003,0.964122,2.22062,2.09477,0.373286,1.62271,0.516293,0.825878,1.22746
+17-Mar-2019 06:00:00,1,0.30211,0.71109,1.1561,1.1034,0.75959,1.5488,1.0957,1.1068,0.31227,3.2903,0.960259,2.21403,2.08854,0.371425,1.61702,0.514028,0.823003,1.22377
+17-Mar-2019 07:00:00,1,0.32634,0.7838,1.2639,1.18,0.83824,1.6706,1.1797,1.2337,0.34009,3.4962,1.03982,2.34973,2.21888,0.410849,1.73741,0.561777,0.883924,1.30075
+17-Mar-2019 08:00:00,1,0.36204,0.89153,1.4227,1.2928,0.9563,1.8532,1.301,1.4177,0.3801,3.7797,1.1499,2.53686,2.40661,0.469541,1.916,0.63201,0.974699,1.41114
+17-Mar-2019 09:00:00,1,0.38628,0.96505,1.5304,1.3694,1.0379,1.9792,1.3818,1.5407,0.40664,3.9594,1.22002,2.65566,2.53131,0.509781,2.03801,0.679618,1.03698,1.48415
+17-Mar-2019 10:00:00,1,0.39917,1.0042,1.5876,1.41,1.0816,2.0468,1.4242,1.6055,0.42054,4.0509,1.25584,2.71619,2.5967,0.53129,2.1031,0.704895,1.07029,1.52234
+17-Mar-2019 11:00:00,1,0.40488,1.0217,1.6129,1.4281,1.1011,2.0769,1.4429,1.6341,0.42667,4.0905,1.27141,2.74247,2.62551,0.540853,2.13201,0.716097,1.0851,1.53913
+17-Mar-2019 12:00:00,1,0.39679,0.99701,1.577,1.4025,1.0735,2.0343,1.4164,1.5936,0.41799,4.0342,1.24931,2.70516,2.58468,0.527315,2.09108,0.700233,1.06414,1.51532
+17-Mar-2019 13:00:00,1,0.38044,0.94731,1.5044,1.3509,1.0181,1.9487,1.3624,1.5112,0.40028,3.917,1.20346,2.62762,2.50146,0.500056,2.00855,0.668151,1.02193,1.4667
+17-Mar-2019 14:00:00,1,0.36468,0.89951,1.4344,1.3012,0.96512,1.8668,1.3098,1.4311,0.38301,3.7997,1.1577,2.55009,2.42028,0.473903,1.92924,0.637191,0.981448,1.41916
+17-Mar-2019 15:00:00,1,0.35312,0.86455,1.3831,1.2646,0.92657,1.8072,1.2709,1.372,0.3702,3.711,1.12317,2.49149,2.36017,0.454811,1.87125,0.614474,0.951908,1.38389
+17-Mar-2019 16:00:00,1,0.34973,0.85432,1.368,1.254,0.91533,1.7898,1.2595,1.3546,0.36643,3.6846,1.11289,2.47403,2.34247,0.449232,1.85429,0.607817,0.943278,1.37349
+17-Mar-2019 17:00:00,1,0.35948,0.88378,1.4113,1.2847,0.94775,1.8399,1.2924,1.4046,0.37726,3.7601,1.14228,2.52393,2.39332,0.465309,1.90314,0.626978,0.96815,1.40334
+17-Mar-2019 18:00:00,1,0.38377,0.9574,1.5192,1.3614,1.0293,1.966,1.3734,1.528,0.4039,3.9412,1.21291,2.64361,2.51845,0.505587,2.02531,0.674675,1.03049,1.47664
+17-Mar-2019 19:00:00,1,0.39144,0.98073,1.5533,1.3857,1.0553,2.0062,1.3988,1.5667,0.41222,3.9963,1.23447,2.68009,2.55755,0.518378,2.06404,0.689734,1.0503,1.49948
+17-Mar-2019 20:00:00,1,0.36347,0.89586,1.4291,1.2973,0.96108,1.8605,1.3058,1.425,0.38167,3.7906,1.15413,2.54404,2.41403,0.471905,1.92317,0.634819,0.978357,1.41549
+17-Mar-2019 21:00:00,1,0.34551,0.84157,1.3492,1.2406,0.90134,1.7682,1.2452,1.3329,0.36171,3.6513,1.09997,2.45207,2.32033,0.442283,1.83315,0.599514,0.93253,1.36048
+17-Mar-2019 22:00:00,1,0.33756,0.81759,1.3139,1.2155,0.87508,1.7276,1.2181,1.2919,0.35278,3.5878,1.07532,2.41016,2.27843,0.429222,1.7934,0.583869,0.91233,1.33583
+17-Mar-2019 23:00:00,1,0.31414,0.74714,1.2096,1.1415,0.79847,1.609,1.1376,1.17,0.32614,3.3939,1.00025,2.28228,2.15354,0.390949,1.67669,0.537736,0.853168,1.26219
+18-Mar-2019 00:00:00,1,0.29221,0.68148,1.112,1.0721,0.7278,1.4996,1.061,1.0545,0.30074,3.2029,0.926561,2.15645,2.03457,0.355413,1.56801,0.494495,0.798274,1.19182
+18-Mar-2019 01:00:00,1,0.27705,0.63622,1.0443,1.0242,0.67952,1.4248,1.0074,0.97376,0.2829,3.0651,0.873566,2.0658,1.95106,0.331003,1.49317,0.46455,0.760588,1.14228
+18-Mar-2019 02:00:00,1,0.26975,0.61447,1.0118,1.0011,0.65645,1.389,0.98132,0.93463,0.27422,2.9971,0.847432,2.02104,1.91046,0.319295,1.45722,0.450115,0.742521,1.11817
+18-Mar-2019 03:00:00,1,0.26734,0.60731,1.001,0.99344,0.64887,1.3773,0.9727,0.92168,0.27134,2.9744,0.83872,2.00612,1.89702,0.315439,1.44537,0.44535,0.736571,1.11017
+18-Mar-2019 04:00:00,1,0.26937,0.61334,1.0101,0.99985,0.65525,1.3871,0.97995,0.93257,0.27376,2.9935,0.846052,2.01868,1.90833,0.318683,1.45534,0.449359,0.741576,1.1169
+18-Mar-2019 05:00:00,1,0.269,0.61224,1.0084,0.99868,0.65408,1.3853,0.97863,0.93059,0.27332,2.99,0.844717,2.01639,1.90627,0.318091,1.45352,0.448627,0.740663,1.11567
+18-Mar-2019 06:00:00,1,0.26212,0.59179,0.97773,0.97692,0.63248,1.3518,0.95397,0.89358,0.2651,2.9248,0.819703,1.97352,1.86781,0.307102,1.41974,0.43503,0.723709,1.09279
+18-Mar-2019 07:00:00,1,0.27825,0.63981,1.0497,1.028,0.68334,1.4307,1.0116,0.98019,0.28432,3.0762,0.877834,2.0731,1.95772,0.332935,1.4991,0.466927,0.76357,1.14624
+18-Mar-2019 08:00:00,1,0.30785,0.72828,1.1816,1.1216,0.7781,1.5775,1.1157,1.137,0.31891,3.3401,0.979469,2.24682,2.11963,0.38073,1.64547,0.525343,0.837379,1.24216
+18-Mar-2019 09:00:00,1,0.33601,0.81293,1.307,1.2106,0.86998,1.7197,1.2128,1.2839,0.35104,3.5753,1.07047,2.40191,2.27025,0.426683,1.78567,0.580822,0.908404,1.33101
+18-Mar-2019 10:00:00,1,0.35417,0.86774,1.3877,1.268,0.93008,1.8126,1.2745,1.3774,0.37138,3.7192,1.12636,2.49691,2.36569,0.456553,1.87654,0.616551,0.954603,1.38713
+18-Mar-2019 11:00:00,1,0.3712,0.91928,1.4634,1.3218,0.98699,1.9006,1.3317,1.4644,0.39018,3.8488,1.17683,2.58252,2.45399,0.484712,1.96204,0.650008,0.998177,1.43891
+18-Mar-2019 12:00:00,1,0.36072,0.88752,1.4168,1.2886,0.95188,1.8463,1.2965,1.4109,0.37863,3.7696,1.14597,2.53018,2.39975,0.467354,1.90935,0.629409,0.971314,1.40712
+18-Mar-2019 13:00:00,1,0.34889,0.85177,1.3642,1.2513,0.91252,1.7855,1.2566,1.3503,0.36549,3.6779,1.11031,2.46965,2.33804,0.44784,1.85005,0.606155,0.941125,1.37089
+18-Mar-2019 14:00:00,1,0.33327,0.80466,1.2948,1.2019,0.86096,1.7057,1.2034,1.2696,0.34794,3.553,1.06184,2.38722,2.25571,0.422186,1.77197,0.57542,0.901451,1.32245
+18-Mar-2019 15:00:00,1,0.32618,0.78332,1.2632,1.1795,0.83772,1.6698,1.1791,1.2328,0.3399,3.4949,1.03931,2.34885,2.21803,0.410587,1.73661,0.561461,0.883519,1.30025
+18-Mar-2019 16:00:00,1,0.32499,0.77974,1.2579,1.1758,0.83382,1.6637,1.175,1.2266,0.33855,3.485,1.03549,2.34235,2.21167,0.408641,1.73068,0.559115,0.880511,1.2965
+18-Mar-2019 17:00:00,1,0.33673,0.8151,1.3102,1.2129,0.87236,1.7234,1.2153,1.2876,0.35185,3.5811,1.07274,2.40576,2.27407,0.427867,1.78927,0.582243,0.910236,1.33326
+18-Mar-2019 18:00:00,1,0.36195,0.89127,1.4223,1.2926,0.95602,1.8527,1.3007,1.4172,0.38,3.7791,1.14965,2.53643,2.40617,0.469401,1.91557,0.631842,0.974481,1.41089
+18-Mar-2019 19:00:00,1,0.37421,0.9284,1.4768,1.3313,0.9971,1.9162,1.3417,1.4796,0.39348,3.8712,1.18555,2.5973,2.46948,0.489703,1.97717,0.655916,1.0059,1.44798
+18-Mar-2019 20:00:00,1,0.3548,0.86963,1.3905,1.27,0.93217,1.8158,1.2766,1.3806,0.37207,3.7241,1.12825,2.50011,2.36895,0.457586,1.87968,0.617782,0.956201,1.38905
+18-Mar-2019 21:00:00,1,0.34076,0.82723,1.3281,1.2256,0.88563,1.7439,1.229,1.3084,0.35638,3.6135,1.08529,2.4271,2.29531,0.434471,1.80938,0.590162,0.920447,1.34576
+18-Mar-2019 22:00:00,1,0.33328,0.8047,1.2948,1.202,0.861,1.7058,1.2035,1.2697,0.34795,3.5531,1.06188,2.38729,2.25578,0.422207,1.77203,0.575445,0.901483,1.32249
+18-Mar-2019 23:00:00,1,0.30919,0.73229,1.1876,1.1258,0.78243,1.5842,1.1204,1.144,0.32045,3.3516,0.983911,2.2544,2.12685,0.382901,1.6521,0.527977,0.840732,1.24643
+19-Mar-2019 00:00:00,1,0.28664,0.66483,1.0871,1.0545,0.71,1.472,1.0414,1.0249,0.29421,3.1528,0.907285,2.12349,2.004,0.346428,1.54048,0.483496,0.7844,1.1737
+19-Mar-2019 01:00:00,1,0.27347,0.62554,1.0284,1.0128,0.66818,1.4072,0.9946,0.95457,0.27864,3.0319,0.860787,2.04392,1.93116,0.325251,1.47552,0.457465,0.751712,1.13047
+19-Mar-2019 02:00:00,1,0.27127,0.61901,1.0186,1.0059,0.66126,1.3965,0.98677,0.94281,0.27604,3.0114,0.85292,2.03044,1.91896,0.321736,1.46472,0.453128,0.746287,1.12321
+19-Mar-2019 03:00:00,1,0.27817,0.63957,1.0494,1.0277,0.68308,1.4303,1.0114,0.97976,0.28423,3.0755,0.877548,2.07261,1.95727,0.332805,1.4987,0.466768,0.76337,1.14597
+19-Mar-2019 04:00:00,1,0.2941,0.68712,1.1204,1.0781,0.73384,1.5089,1.0676,1.0645,0.30295,3.2197,0.933038,2.16752,2.04489,0.35846,1.57734,0.498219,0.80298,1.19793
+19-Mar-2019 05:00:00,1,0.32277,0.77307,1.248,1.1687,0.82657,1.6525,1.1674,1.2151,0.33602,3.4665,1.02835,2.33018,2.19982,0.405016,1.71962,0.554743,0.874908,1.28951
+19-Mar-2019 06:00:00,1,0.37894,0.94276,1.4978,1.3462,1.013,1.9408,1.3574,1.5036,0.39865,3.9061,1.19918,2.62037,2.49379,0.497567,2.00101,0.665212,1.01807,1.46221
+19-Mar-2019 07:00:00,1,0.42294,1.0768,1.693,1.4851,1.1632,2.1726,1.5017,1.724,0.44585,4.2125,1.31936,2.82329,2.71579,0.571178,2.22358,0.751476,1.13209,1.59169
+19-Mar-2019 08:00:00,1,0.44196,1.1349,1.7773,1.545,1.2291,2.2743,1.5628,1.8179,0.46578,4.3354,1.36786,2.90483,2.80966,0.603279,2.32033,0.788696,1.18185,1.6462
+19-Mar-2019 09:00:00,1,0.4473,1.1513,1.8009,1.5619,1.2477,2.303,1.5799,1.8441,0.47133,4.3689,1.38112,2.92708,2.8358,0.612321,2.34756,0.79914,1.19587,1.66135
+19-Mar-2019 10:00:00,1,0.45912,1.1876,1.8533,1.5991,1.2891,2.3668,1.6174,1.9019,0.48353,4.4415,1.40992,2.97536,2.89335,0.632388,2.40792,0.822253,1.22699,1.69466
+19-Mar-2019 11:00:00,1,0.46571,1.2078,1.8825,1.6199,1.3122,2.4025,1.6383,1.934,0.49029,4.4811,1.42565,3.00168,2.92521,0.643596,2.44161,0.835126,1.24437,1.71309
+19-Mar-2019 12:00:00,1,0.46059,1.1921,1.8598,1.6038,1.2942,2.3747,1.6221,1.909,0.48504,4.4504,1.41344,2.98125,2.90044,0.634877,2.4154,0.825115,1.23085,1.69877
+19-Mar-2019 13:00:00,1,0.45678,1.1804,1.8429,1.5918,1.2808,2.3541,1.61,1.8905,0.48112,4.4273,1.40427,2.9659,2.88198,0.628406,2.39595,0.817674,1.22081,1.68809
+19-Mar-2019 14:00:00,1,0.45047,1.161,1.815,1.5719,1.2587,2.3201,1.59,1.8596,0.4746,4.3885,1.3889,2.94014,2.85126,0.617688,2.36371,0.80533,1.20419,1.6703
+19-Mar-2019 15:00:00,1,0.44233,1.1361,1.7789,1.5462,1.2304,2.2763,1.564,1.8197,0.46617,4.3377,1.36879,2.90638,2.81148,0.603906,2.32222,0.789422,1.18282,1.64725
+19-Mar-2019 16:00:00,1,0.42882,1.0947,1.7191,1.5036,1.1835,2.2039,1.5206,1.7531,0.45204,4.2511,1.33456,2.84887,2.74492,0.581077,2.25344,0.762979,1.14743,1.60862
+19-Mar-2019 17:00:00,1,0.4277,1.0913,1.7141,1.5001,1.1796,2.198,1.517,1.7476,0.45086,4.2438,1.33168,2.84403,2.73939,0.579192,2.24775,0.76079,1.14451,1.60541
+19-Mar-2019 18:00:00,1,0.44694,1.1502,1.7994,1.5607,1.2464,2.3011,1.5787,1.8423,0.47095,4.3666,1.38023,2.92559,2.83405,0.611714,2.34573,0.798439,1.19493,1.66033
+19-Mar-2019 19:00:00,1,0.46987,1.2206,1.9009,1.633,1.3269,2.4251,1.6514,1.9542,0.49454,4.5058,1.43547,3.0181,2.94527,0.650685,2.4629,0.843255,1.25537,1.72467
+19-Mar-2019 20:00:00,1,0.44184,1.1346,1.7767,1.5446,1.2286,2.2736,1.5624,1.8173,0.46565,4.3346,1.36755,2.90431,2.80906,0.603069,2.3197,0.788454,1.18152,1.64585
+19-Mar-2019 21:00:00,1,0.41212,1.0437,1.645,1.4509,1.1259,2.1151,1.4665,1.6702,0.43438,4.14,1.29084,2.77524,2.6618,0.552978,2.16865,0.730269,1.10389,1.56028
+19-Mar-2019 22:00:00,1,0.38913,0.97371,1.543,1.3784,1.0475,1.9941,1.3912,1.5551,0.40972,3.9799,1.22803,2.66919,2.54582,0.51453,2.05239,0.685208,1.04434,1.49263
+19-Mar-2019 23:00:00,1,0.35253,0.86278,1.3804,1.2628,0.92462,1.8042,1.2689,1.369,0.36955,3.7064,1.12139,2.48847,2.35711,0.453844,1.86831,0.613321,0.950412,1.38209
+20-Mar-2019 00:00:00,1,0.33122,0.7985,1.2857,1.1955,0.85424,1.6953,1.1964,1.259,0.34563,3.5364,1.05538,2.37621,2.24486,0.418836,1.76176,0.571394,0.896272,1.31606
+20-Mar-2019 01:00:00,1,0.32013,0.76516,1.2363,1.1604,0.81799,1.6392,1.1583,1.2013,0.33302,3.4445,1.01984,2.31567,2.18574,0.400723,1.70653,0.549559,0.868272,1.28121
+20-Mar-2019 02:00:00,1,0.31771,0.75786,1.2255,1.1527,0.81008,1.627,1.1499,1.1887,0.33024,3.4241,1.01194,2.30221,2.17272,0.396763,1.69444,0.544772,0.862152,1.27352
+20-Mar-2019 03:00:00,1,0.32139,0.76892,1.2419,1.1644,0.82207,1.6456,1.1626,1.2079,0.33445,3.455,1.02389,2.32258,2.19244,0.402764,1.71275,0.552024,0.871427,1.28516
+20-Mar-2019 04:00:00,1,0.33144,0.79916,1.2866,1.1962,0.85496,1.6964,1.1972,1.2602,0.34587,3.5381,1.05607,2.37739,2.24602,0.419194,1.76285,0.571824,0.896825,1.31675
+20-Mar-2019 05:00:00,1,0.35558,0.87198,1.394,1.2724,0.93475,1.8198,1.2792,1.3846,0.37294,3.7301,1.13059,2.50408,2.373,0.458867,1.88357,0.619309,0.958183,1.39142
+20-Mar-2019 06:00:00,1,0.39803,1.0008,1.5825,1.4064,1.0777,2.0408,1.4205,1.5998,0.41931,4.0428,1.2527,2.71088,2.59091,0.529374,2.09731,0.702649,1.06733,1.51896
+20-Mar-2019 07:00:00,1,0.43641,1.1179,1.7527,1.5275,1.2098,2.2445,1.545,1.7906,0.45999,4.3001,1.35391,2.88141,2.7824,0.593894,2.29207,0.777839,1.1673,1.63038
+20-Mar-2019 08:00:00,1,0.45704,1.1812,1.8441,1.5926,1.2817,2.3555,1.6108,1.8917,0.48138,4.4289,1.40489,2.96694,2.88322,0.628842,2.39726,0.818175,1.22149,1.68881
+20-Mar-2019 09:00:00,1,0.46358,1.2013,1.873,1.6132,1.3047,2.391,1.6315,1.9236,0.48811,4.4684,1.42059,2.99322,2.91493,0.639972,2.43072,0.830966,1.23875,1.70714
+20-Mar-2019 10:00:00,1,0.47403,1.2334,1.9193,1.6461,1.3415,2.4477,1.6644,1.9743,0.49878,4.5302,1.44518,3.03433,2.96525,0.657774,2.48419,0.851373,1.26637,1.73621
+20-Mar-2019 11:00:00,1,0.48217,1.2585,1.9553,1.6718,1.3703,2.4921,1.69,2.0137,0.50704,4.5773,1.46396,3.06568,3.00425,0.671686,2.52595,0.867275,1.28795,1.75871
+20-Mar-2019 12:00:00,1,0.47568,1.2385,1.9266,1.6513,1.3474,2.4567,1.6696,1.9823,0.50045,4.5398,1.44901,3.04074,2.97317,0.660592,2.49265,0.854597,1.27074,1.74078
+20-Mar-2019 13:00:00,1,0.46676,1.211,1.8871,1.6232,1.3159,2.4082,1.6416,1.939,0.49136,4.4874,1.42812,3.00582,2.93026,0.645375,2.44695,0.837167,1.24713,1.716
+20-Mar-2019 14:00:00,1,0.45677,1.1804,1.8429,1.5917,1.2808,2.3541,1.61,1.8904,0.48111,4.4273,1.40426,2.96587,2.88195,0.628394,2.39591,0.81766,1.22079,1.68807
+20-Mar-2019 15:00:00,1,0.44878,1.1558,1.8075,1.5665,1.2528,2.311,1.5846,1.8513,0.47285,4.3781,1.38475,2.93318,2.84301,0.614822,2.35508,0.802025,1.19974,1.66552
+20-Mar-2019 16:00:00,1,0.43647,1.1181,1.753,1.5277,1.21,2.2449,1.5452,1.7909,0.46006,4.3005,1.35407,2.88167,2.7827,0.593998,2.29238,0.777959,1.16746,1.63056
+20-Mar-2019 17:00:00,1,0.43579,1.116,1.7499,1.5256,1.2076,2.2412,1.543,1.7875,0.45934,4.2961,1.35234,2.87876,2.77933,0.592841,2.28889,0.77662,1.16567,1.6286
+20-Mar-2019 18:00:00,1,0.45417,1.1724,1.8314,1.5835,1.2717,2.34,1.6017,1.8777,0.47843,4.4114,1.39795,2.9553,2.8693,0.623974,2.38262,0.812572,1.21394,1.68074
+20-Mar-2019 19:00:00,1,0.47616,1.24,1.9287,1.6528,1.349,2.4593,1.6711,1.9846,0.50094,4.5426,1.45012,3.04258,2.97546,0.661405,2.49509,0.855527,1.272,1.7421
+20-Mar-2019 20:00:00,1,0.44799,1.1534,1.804,1.564,1.2501,2.3067,1.5821,1.8475,0.47204,4.3732,1.38282,2.92993,2.83917,0.613489,2.35107,0.800488,1.19768,1.6633
+20-Mar-2019 21:00:00,1,0.41887,1.0643,1.675,1.4722,1.1491,2.151,1.4885,1.7038,0.44155,4.1854,1.30871,2.80535,2.69553,0.564325,2.2029,0.743499,1.12147,1.57991
+20-Mar-2019 22:00:00,1,0.39502,0.99162,1.5692,1.397,1.0675,2.025,1.4106,1.5847,0.41608,4.0217,1.24441,2.69689,2.57571,0.524354,2.08212,0.696756,1.05955,1.51008
+20-Mar-2019 23:00:00,1,0.36109,0.88865,1.4185,1.2898,0.95313,1.8483,1.2978,1.4128,0.37905,3.7725,1.14708,2.53207,2.40168,0.467971,1.91123,0.630143,0.972268,1.40825
+21-Mar-2019 00:00:00,1,0.33365,0.8058,1.2965,1.2031,0.86221,1.7077,1.2047,1.2716,0.34837,3.5561,1.06304,2.38926,2.25772,0.422808,1.77386,0.576168,0.902412,1.32364
+21-Mar-2019 01:00:00,1,0.32124,0.76848,1.2412,1.1639,0.82159,1.6448,1.1621,1.2071,0.33428,3.4538,1.02342,2.32177,2.19165,0.402524,1.71202,0.551735,0.871056,1.28469
+21-Mar-2019 02:00:00,1,0.3144,0.74794,1.2108,1.1423,0.79934,1.6104,1.1385,1.1714,0.32645,3.3962,1.00113,2.28378,2.15497,0.391384,1.67802,0.538262,0.853839,1.26304
+21-Mar-2019 03:00:00,1,0.31989,0.76441,1.2352,1.1596,0.81718,1.638,1.1575,1.2,0.33273,3.4425,1.01903,2.3143,2.18441,0.400317,1.70529,0.549068,0.867645,1.28042
+21-Mar-2019 04:00:00,1,0.33291,0.80357,1.2932,1.2008,0.85978,1.7039,1.2022,1.2678,0.34753,3.5501,1.06071,2.38528,2.2538,0.421595,1.77017,0.574711,0.900538,1.32133
+21-Mar-2019 05:00:00,1,0.3563,0.87415,1.3972,1.2747,0.93714,1.8235,1.2816,1.3883,0.37373,3.7356,1.13275,2.50775,2.37675,0.460052,1.88717,0.620721,0.960017,1.39362
+21-Mar-2019 06:00:00,1,0.40292,1.0157,1.6042,1.4219,1.0944,2.0665,1.4365,1.6243,0.42457,4.077,1.26608,2.73348,2.61563,0.537566,2.12208,0.712249,1.08001,1.53337
+21-Mar-2019 07:00:00,1,0.44152,1.1336,1.7753,1.5436,1.2275,2.2719,1.5614,1.8157,0.46532,4.3326,1.36676,2.90298,2.80751,0.602535,2.31809,0.787836,1.18069,1.64495
+21-Mar-2019 08:00:00,1,0.45914,1.1876,1.8534,1.5992,1.2891,2.3669,1.6175,1.902,0.48355,4.4417,1.40997,2.97544,2.89345,0.632423,2.40803,0.822294,1.22704,1.69472
+21-Mar-2019 09:00:00,1,0.46427,1.2034,1.8761,1.6154,1.3071,2.3947,1.6337,1.927,0.48881,4.4725,1.42223,2.99596,2.91826,0.641144,2.43424,0.832312,1.24057,1.70907
+21-Mar-2019 10:00:00,1,0.47352,1.2319,1.9171,1.6445,1.3397,2.445,1.6629,1.9719,0.49826,4.5273,1.44401,3.03237,2.96283,0.656915,2.48161,0.85039,1.26503,1.73482
+21-Mar-2019 11:00:00,1,0.48303,1.2611,1.9591,1.6745,1.3734,2.4968,1.6926,2.0178,0.5079,4.5822,1.46591,3.06894,3.00834,0.673152,2.53034,0.868948,1.29023,1.76106
+21-Mar-2019 12:00:00,1,0.478,1.2456,1.9369,1.6587,1.3556,2.4694,1.6769,1.9935,0.50282,4.5533,1.45439,3.04971,2.98432,0.664563,2.50457,0.859138,1.2769,1.74721
+21-Mar-2019 13:00:00,1,0.47475,1.2356,1.9225,1.6484,1.3441,2.4516,1.6667,1.9778,0.49951,4.5344,1.44686,3.03713,2.96871,0.659004,2.48788,0.85278,1.26827,1.73821
+21-Mar-2019 14:00:00,1,0.46328,1.2003,1.8717,1.6122,1.3036,2.3893,1.6306,1.9221,0.48779,4.4666,1.41986,2.992,2.91345,0.639451,2.42915,0.830369,1.23794,1.70629
+21-Mar-2019 15:00:00,1,0.45055,1.1613,1.8153,1.5721,1.259,2.3205,1.5902,1.86,0.47469,4.3891,1.38911,2.94048,2.85166,0.617829,2.36413,0.805492,1.20441,1.67053
+21-Mar-2019 16:00:00,1,0.43262,1.1063,1.7359,1.5156,1.1966,2.2243,1.5329,1.7719,0.45603,4.2757,1.34429,2.86523,2.76372,0.587493,2.27278,0.770422,1.15738,1.61954
+21-Mar-2019 17:00:00,1,0.43199,1.1044,1.7331,1.5136,1.1944,2.2209,1.5308,1.7688,0.45536,4.2716,1.34267,2.86251,2.76058,0.58642,2.26954,0.769178,1.15571,1.61772
+21-Mar-2019 18:00:00,1,0.45078,1.162,1.8163,1.5728,1.2598,2.3217,1.5909,1.8611,0.47493,4.3905,1.38967,2.94142,2.85277,0.618215,2.36529,0.805938,1.20501,1.67118
+21-Mar-2019 19:00:00,1,0.47689,1.2422,1.9319,1.6551,1.3516,2.4633,1.6734,1.9882,0.50168,4.5469,1.45182,3.04542,2.97898,0.662661,2.49886,0.856963,1.27395,1.74413
+21-Mar-2019 20:00:00,1,0.4512,1.1633,1.8182,1.5742,1.2613,2.3241,1.5923,1.8632,0.47537,4.3931,1.39071,2.94317,2.85486,0.618941,2.36748,0.806775,1.20613,1.67239
+21-Mar-2019 21:00:00,1,0.42158,1.0726,1.687,1.4808,1.1585,2.1654,1.4973,1.7173,0.44442,4.2035,1.31582,2.81733,2.70904,0.568891,2.21668,0.748815,1.12855,1.58777
+21-Mar-2019 22:00:00,1,0.39818,1.0012,1.5832,1.4069,1.0783,2.0416,1.421,1.6006,0.41948,4.0439,1.25312,2.7116,2.59169,0.529632,2.09809,0.702951,1.06772,1.51941
+21-Mar-2019 23:00:00,1,0.36367,0.89647,1.43,1.298,0.96176,1.8616,1.3065,1.426,0.3819,3.7921,1.15474,2.54506,2.41508,0.472242,1.92419,0.635218,0.978877,1.41611
+22-Mar-2019 00:00:00,1,0.33583,0.81238,1.3062,1.21,0.86939,1.7188,1.2122,1.2829,0.35083,3.5738,1.06991,2.40095,2.26929,0.426387,1.78477,0.580466,0.907947,1.33045
+22-Mar-2019 01:00:00,1,0.32216,0.77124,1.2453,1.1668,0.82459,1.6495,1.1653,1.2119,0.33533,3.4615,1.02639,2.32684,2.19657,0.404025,1.7166,0.553547,0.873376,1.2876
+22-Mar-2019 02:00:00,1,0.3175,0.75725,1.2246,1.1521,0.80941,1.626,1.1492,1.1876,0.33,3.4224,1.01127,2.30107,2.17162,0.39643,1.69342,0.54437,0.861638,1.27287
+22-Mar-2019 03:00:00,1,0.3228,0.77318,1.2482,1.1689,0.8267,1.6527,1.1675,1.2153,0.33606,3.4669,1.02847,2.33039,2.20002,0.405079,1.71982,0.554818,0.875005,1.28963
+22-Mar-2019 04:00:00,1,0.33377,0.80617,1.297,1.2035,0.86261,1.7083,1.2052,1.2723,0.34851,3.5571,1.06343,2.38991,2.25837,0.423008,1.77447,0.576409,0.902722,1.32402
+22-Mar-2019 05:00:00,1,0.35909,0.88261,1.4096,1.2835,0.94646,1.838,1.2911,1.4026,0.37684,3.7572,1.14113,2.52197,2.39131,0.464672,1.90121,0.626219,0.967164,1.40217
+22-Mar-2019 06:00:00,1,0.40529,1.0229,1.6148,1.4294,1.1025,2.0791,1.4443,1.6362,0.42711,4.0934,1.27252,2.74435,2.62757,0.54154,2.13409,0.716901,1.08617,1.54034
+22-Mar-2019 07:00:00,1,0.44395,1.141,1.7861,1.5513,1.236,2.285,1.5692,1.8277,0.46785,4.3479,1.37281,2.91314,2.8194,0.606641,2.33046,0.792582,1.18706,1.65184
+22-Mar-2019 08:00:00,1,0.46168,1.1955,1.8646,1.6072,1.298,2.3807,1.6255,1.9144,0.48616,4.457,1.41607,2.98564,2.90575,0.636744,2.42101,0.827259,1.23374,1.70184
+22-Mar-2019 09:00:00,1,0.46549,1.2072,1.8815,1.6192,1.3114,2.4013,1.6376,1.9329,0.49007,4.4798,1.42514,3.00083,2.92417,0.643229,2.4405,0.834705,1.2438,1.71248
+22-Mar-2019 10:00:00,1,0.47807,1.2459,1.9372,1.6589,1.3558,2.4698,1.6771,1.9939,0.50289,4.5537,1.45455,3.04998,2.98465,0.664683,2.50493,0.859275,1.27708,1.74741
+22-Mar-2019 11:00:00,1,0.48982,1.2821,1.9892,1.6959,1.3975,2.534,1.7138,2.0505,0.51476,4.6207,1.48128,3.09457,3.04069,0.684781,2.56522,0.882208,1.30827,1.77971
+22-Mar-2019 12:00:00,1,0.48393,1.2639,1.9631,1.6773,1.3766,2.5017,1.6954,2.0222,0.50881,4.5873,1.46796,3.07236,3.01263,0.674691,2.53496,0.870705,1.29261,1.76354
+22-Mar-2019 13:00:00,1,0.47786,1.2452,1.9362,1.6582,1.3551,2.4686,1.6765,1.9928,0.50267,4.5525,1.45405,3.04915,2.98361,0.664312,2.50382,0.858851,1.27651,1.74681
+22-Mar-2019 14:00:00,1,0.46568,1.2077,1.8823,1.6198,1.3121,2.4024,1.6382,1.9338,0.49026,4.481,1.42558,3.00157,2.92508,0.643549,2.44147,0.835072,1.2443,1.71301
+22-Mar-2019 15:00:00,1,0.45426,1.1727,1.8318,1.5838,1.272,2.3405,1.602,1.8782,0.47852,4.4119,1.39817,2.95566,2.86973,0.624126,2.38308,0.812748,1.21417,1.681
+22-Mar-2019 16:00:00,1,0.43865,1.1248,1.7626,1.5346,1.2175,2.2565,1.5522,1.8016,0.46232,4.3144,1.35955,2.89087,2.79339,0.59767,2.30344,0.78221,1.17315,1.63676
+22-Mar-2019 17:00:00,1,0.43756,1.1215,1.7578,1.5312,1.2138,2.2507,1.5487,1.7963,0.46119,4.3075,1.35682,2.88629,2.78806,0.595839,2.29793,0.780091,1.17031,1.63367
+22-Mar-2019 18:00:00,1,0.45331,1.1697,1.8275,1.5808,1.2687,2.3354,1.599,1.8735,0.47754,4.4061,1.39584,2.95177,2.86509,0.622506,2.3782,0.810882,1.21166,1.67831
+22-Mar-2019 19:00:00,1,0.47919,1.2493,1.9421,1.6624,1.3598,2.4759,1.6806,1.9993,0.50402,4.5602,1.45712,3.05427,2.98999,0.666587,2.51065,0.861451,1.28004,1.75048
+22-Mar-2019 20:00:00,1,0.45396,1.1717,1.8305,1.5829,1.271,2.3389,1.6011,1.8767,0.47822,4.4101,1.39744,2.95445,2.86829,0.623621,2.38156,0.812166,1.21339,1.68016
+22-Mar-2019 21:00:00,1,0.42466,1.082,1.7006,1.4905,1.1691,2.1818,1.5072,1.7325,0.44766,4.2238,1.32381,2.83079,2.7243,0.574064,2.23229,0.754831,1.13656,1.59664
+22-Mar-2019 22:00:00,1,0.39669,0.99668,1.5766,1.4022,1.0732,2.0337,1.4161,1.5931,0.41787,4.0334,1.24901,2.70466,2.58414,0.527136,2.09054,0.700023,1.06386,1.51501
+22-Mar-2019 23:00:00,1,0.35863,0.88121,1.4075,1.2821,0.94492,1.8356,1.2895,1.4002,0.37632,3.7536,1.13974,2.51962,2.38889,0.463905,1.89888,0.625307,0.965977,1.40075
+23-Mar-2019 00:00:00,1,0.3356,0.8117,1.3052,1.2093,0.86865,1.7176,1.2114,1.2818,0.35058,3.572,1.0692,2.39974,2.2681,0.426017,1.78364,0.580022,0.907375,1.32975
+23-Mar-2019 01:00:00,1,0.32286,0.77333,1.2484,1.169,0.82686,1.653,1.1677,1.2155,0.33612,3.4673,1.02864,2.33067,2.2003,0.405162,1.72007,0.554919,0.875134,1.28979
+23-Mar-2019 02:00:00,1,0.31796,0.75862,1.2266,1.1536,0.81091,1.6283,1.1508,1.19,0.33053,3.4263,1.01277,2.30362,2.17409,0.397178,1.69571,0.545274,0.862794,1.27433
+23-Mar-2019 03:00:00,1,0.32242,0.77203,1.2465,1.1677,0.82545,1.6508,1.1662,1.2133,0.33563,3.4637,1.02723,2.32828,2.19797,0.404453,1.71791,0.554063,0.874037,1.28842
+23-Mar-2019 04:00:00,1,0.33312,0.80421,1.2941,1.2015,0.86047,1.705,1.2029,1.2689,0.34777,3.5518,1.06138,2.38643,2.25493,0.421943,1.77123,0.575129,0.901076,1.32199
+23-Mar-2019 05:00:00,1,0.35841,0.88056,1.4066,1.2814,0.9442,1.8344,1.2888,1.3991,0.37608,3.7519,1.13909,2.51852,2.38777,0.463548,1.89779,0.624882,0.965425,1.40009
+23-Mar-2019 06:00:00,1,0.403,1.0159,1.6046,1.4221,1.0947,2.0669,1.4368,1.6247,0.42465,4.0775,1.26628,2.73382,2.616,0.53769,2.12245,0.712394,1.08021,1.53359
+23-Mar-2019 07:00:00,1,0.44186,1.1346,1.7768,1.5447,1.2287,2.2738,1.5625,1.8174,0.46567,4.3347,1.3676,2.9044,2.80916,0.603104,2.31981,0.788495,1.18158,1.64591
+23-Mar-2019 08:00:00,1,0.46182,1.1959,1.8652,1.6076,1.2985,2.3814,1.626,1.915,0.4863,4.4578,1.41638,2.98617,2.90639,0.636968,2.42169,0.827517,1.23409,1.70221
+23-Mar-2019 09:00:00,1,0.46749,1.2133,1.8904,1.6255,1.3185,2.4122,1.6439,1.9426,0.49211,4.4917,1.42987,3.00874,2.93382,0.646633,2.45073,0.83861,1.24908,1.71806
+23-Mar-2019 10:00:00,1,0.47591,1.2392,1.9276,1.6521,1.3482,2.458,1.6703,1.9834,0.50069,4.5412,1.44955,3.04163,2.97428,0.660987,2.49384,0.855048,1.27135,1.74142
+23-Mar-2019 11:00:00,1,0.48237,1.2591,1.9562,1.6724,1.371,2.4932,1.6906,2.0146,0.50724,4.5784,1.46441,3.06643,3.00518,0.672022,2.52696,0.867659,1.28847,1.75925
+23-Mar-2019 12:00:00,1,0.47497,1.2363,1.9235,1.6491,1.3449,2.4529,1.6674,1.9789,0.49974,4.5357,1.44738,3.038,2.96978,0.659386,2.48903,0.853217,1.26887,1.73883
+23-Mar-2019 13:00:00,1,0.46366,1.2015,1.8734,1.6135,1.305,2.3914,1.6318,1.924,0.48819,4.4689,1.42079,2.99355,2.91533,0.640114,2.43114,0.831129,1.23897,1.70738
+23-Mar-2019 14:00:00,1,0.44713,1.1508,1.8002,1.5613,1.2471,2.3021,1.5793,1.8433,0.47115,4.3679,1.38071,2.92639,2.835,0.612041,2.34671,0.798816,1.19543,1.66088
+23-Mar-2019 15:00:00,1,0.43495,1.1135,1.7462,1.5229,1.2047,2.2367,1.5403,1.7834,0.45846,4.2907,1.3502,2.87517,2.77518,0.591419,2.28461,0.774972,1.16346,1.62619
+23-Mar-2019 16:00:00,1,0.42339,1.0781,1.695,1.4865,1.1647,2.175,1.5031,1.7263,0.44632,4.2155,1.32053,2.82526,2.71802,0.571933,2.22586,0.752354,1.13326,1.59299
+23-Mar-2019 17:00:00,1,0.42245,1.0752,1.6908,1.4835,1.1614,2.17,1.5001,1.7216,0.44533,4.2092,1.31807,2.82112,2.71333,0.570343,2.22106,0.750504,1.1308,1.59026
+23-Mar-2019 18:00:00,1,0.4335,1.109,1.7398,1.5184,1.1997,2.229,1.5357,1.7763,0.45695,4.2814,1.34654,2.86901,2.76807,0.588982,2.27726,0.772147,1.15968,1.62206
+23-Mar-2019 19:00:00,1,0.45641,1.1793,1.8413,1.5906,1.2795,2.3522,1.6089,1.8887,0.48074,4.4251,1.40339,2.96441,2.8802,0.627782,2.39407,0.816956,1.21984,1.68705
+23-Mar-2019 20:00:00,1,0.42953,1.0969,1.7222,1.5058,1.1859,2.2077,1.5229,1.7566,0.45278,4.2557,1.33638,2.85193,2.74843,0.582274,2.25704,0.764367,1.14929,1.61066
+23-Mar-2019 21:00:00,1,0.4014,1.011,1.5975,1.4171,1.0893,2.0585,1.4315,1.6167,0.42294,4.0664,1.26195,2.72651,2.60798,0.535026,2.1144,0.709274,1.07608,1.52891
+23-Mar-2019 22:00:00,1,0.37761,0.93871,1.4919,1.342,1.0085,1.9339,1.353,1.4969,0.39719,3.8962,1.19534,2.61388,2.48693,0.495345,1.99428,0.662587,1.01463,1.4582
+23-Mar-2019 23:00:00,1,0.34743,0.84737,1.3578,1.2467,0.9077,1.778,1.2517,1.3428,0.36386,3.6665,1.10587,2.46209,2.33041,0.445444,1.84277,0.603293,0.937419,1.3664
+24-Mar-2019 00:00:00,1,0.32263,0.77266,1.2474,1.1683,0.82613,1.6518,1.1669,1.2144,0.33587,3.4654,1.02792,2.32944,2.1991,0.404797,1.71896,0.554478,0.874569,1.28909
+24-Mar-2019 01:00:00,1,0.30867,0.73073,1.1853,1.1242,0.78075,1.5816,1.1186,1.1413,0.31985,3.3471,0.982186,2.25146,2.12405,0.382057,1.64952,0.526953,0.839428,1.24477
+24-Mar-2019 02:00:00,1,0.30102,0.70783,1.1512,1.1,0.75607,1.5434,1.0919,1.1011,0.31101,3.2808,0.956581,2.20775,2.08262,0.369658,1.61161,0.511877,0.820274,1.22027
+24-Mar-2019 03:00:00,1,0.30135,0.70881,1.1527,1.101,0.75713,1.545,1.093,1.1028,0.31139,3.2836,0.957687,2.20963,2.0844,0.370189,1.61324,0.512523,0.821094,1.22132
+24-Mar-2019 04:00:00,1,0.305,0.71973,1.1689,1.1126,0.76889,1.5632,1.1058,1.122,0.31561,3.3154,0.969947,2.23057,2.10419,0.376101,1.63132,0.519718,0.830228,1.23303
+24-Mar-2019 05:00:00,1,0.30769,0.72781,1.1809,1.1211,0.77759,1.5767,1.1152,1.1362,0.31873,3.3387,0.978943,2.24592,2.11877,0.380473,1.64468,0.525031,0.836982,1.24165
+24-Mar-2019 06:00:00,1,0.30847,0.73014,1.1844,1.1235,0.78011,1.5806,1.1179,1.1403,0.31962,3.3454,0.981533,2.25034,2.12298,0.381738,1.64855,0.526566,0.838936,1.24414
+24-Mar-2019 07:00:00,1,0.33679,0.81528,1.3105,1.2131,0.87255,1.7237,1.2155,1.2879,0.35192,3.5816,1.07292,2.40607,2.27437,0.427963,1.78956,0.582358,0.910383,1.33344
+24-Mar-2019 08:00:00,1,0.36824,0.91032,1.4503,1.3124,0.97707,1.8853,1.3218,1.4493,0.38694,3.8267,1.16819,2.56788,2.43874,0.479812,1.94717,0.644201,0.990592,1.42998
+24-Mar-2019 09:00:00,1,0.38982,0.9758,1.5461,1.3805,1.0498,1.9977,1.3935,1.5586,0.41046,3.9848,1.22995,2.67244,2.54931,0.515674,2.05586,0.686555,1.04611,1.49467
+24-Mar-2019 10:00:00,1,0.40077,1.0091,1.5947,1.4151,1.0871,2.0552,1.4295,1.6136,0.42226,4.062,1.26023,2.7236,2.6048,0.533971,2.11121,0.708038,1.07444,1.52706
+24-Mar-2019 11:00:00,1,0.40641,1.0263,1.6197,1.4329,1.1064,2.085,1.4479,1.6418,0.4283,4.1011,1.27554,2.74944,2.63319,0.543411,2.13974,0.71909,1.08907,1.54361
+24-Mar-2019 12:00:00,1,0.39587,0.9942,1.5729,1.3996,1.0704,2.0294,1.4134,1.589,0.41699,4.0277,1.24676,2.70086,2.58001,0.525772,2.08641,0.698422,1.06175,1.5126
+24-Mar-2019 13:00:00,1,0.37525,0.93156,1.4814,1.3346,1.0006,1.9216,1.3452,1.4849,0.39462,3.8789,1.18857,2.60241,2.47485,0.491435,1.98242,0.657965,1.00858,1.45112
+24-Mar-2019 14:00:00,1,0.36147,0.8898,1.4202,1.291,0.9544,1.8502,1.2991,1.4148,0.37947,3.7754,1.14821,2.53398,2.40366,0.468599,1.91313,0.63089,0.97324,1.40941
+24-Mar-2019 15:00:00,1,0.35291,0.86392,1.3821,1.264,0.92588,1.8061,1.2702,1.3709,0.36997,3.7094,1.12254,2.49041,2.35908,0.454467,1.8702,0.614064,0.951376,1.38325
+24-Mar-2019 16:00:00,1,0.34771,0.84822,1.359,1.2476,0.90862,1.7794,1.2526,1.3442,0.36417,3.6687,1.10672,2.46354,2.33187,0.445904,1.84417,0.603842,0.93813,1.36726
+24-Mar-2019 17:00:00,1,0.35092,0.8579,1.3733,1.2577,0.91926,1.7959,1.2635,1.3607,0.36775,3.6938,1.11649,2.48015,2.34866,0.451183,1.86022,0.610146,0.946295,1.37713
+24-Mar-2019 18:00:00,1,0.36612,0.90389,1.4408,1.3057,0.96995,1.8743,1.3147,1.4385,0.3846,3.8107,1.16196,2.55731,2.42776,0.476295,1.9365,0.640029,0.985149,1.42354
+24-Mar-2019 19:00:00,1,0.38604,0.96433,1.5293,1.3686,1.037,1.9779,1.381,1.5395,0.40638,3.9577,1.21935,2.65451,2.53009,0.509383,2.03681,0.679148,1.03637,1.48344
+24-Mar-2019 20:00:00,1,0.36146,0.88976,1.4201,1.291,0.95435,1.8501,1.299,1.4147,0.37945,3.7753,1.14817,2.53392,2.40359,0.468577,1.91307,0.630864,0.973207,1.40937
+24-Mar-2019 21:00:00,1,0.33981,0.82437,1.3239,1.2226,0.88249,1.739,1.2258,1.3035,0.35531,3.6059,1.08233,2.42208,2.2903,0.43291,1.80463,0.588292,0.918034,1.34281
+24-Mar-2019 22:00:00,1,0.32815,0.78926,1.272,1.1858,0.84418,1.6798,1.1859,1.2431,0.34215,3.5112,1.04562,2.3596,2.22854,0.413815,1.74646,0.56535,0.888509,1.30645
+24-Mar-2019 23:00:00,1,0.30304,0.71387,1.1602,1.1064,0.76257,1.5535,1.099,1.1117,0.31335,3.2984,0.96338,2.21936,2.09358,0.372928,1.62161,0.515857,0.825325,1.22675
+25-Mar-2019 00:00:00,1,0.27994,0.64483,1.0572,1.0333,0.68867,1.439,1.0176,0.98917,0.28631,3.0917,0.883783,2.08328,1.96703,0.335638,1.50739,0.470251,0.767743,1.15177
+25-Mar-2019 01:00:00,1,0.26589,0.603,0.99456,0.98886,0.64432,1.3702,0.96751,0.9139,0.26962,2.9607,0.83347,1.99712,1.88893,0.313127,1.43827,0.44249,0.733004,1.10536
+25-Mar-2019 02:00:00,1,0.26333,0.59539,0.98312,0.98075,0.63627,1.3577,0.95832,0.9001,0.26655,2.9364,0.824128,1.98111,1.87459,0.309033,1.42568,0.437421,0.726687,1.09683
+25-Mar-2019 03:00:00,1,0.26077,0.58777,0.97168,0.97263,0.62824,1.3453,0.9491,0.88627,0.26347,2.9119,0.81473,1.965,1.8602,0.30494,1.41309,0.432349,0.720373,1.08826
+25-Mar-2019 04:00:00,1,0.26045,0.58682,0.97025,0.97162,0.62724,1.3437,0.94795,0.88455,0.26309,2.9088,0.813558,1.96299,1.85841,0.304432,1.41153,0.431718,0.719589,1.0872
+25-Mar-2019 05:00:00,1,0.26272,0.59356,0.98038,0.9788,0.63435,1.3547,0.95611,0.89679,0.26581,2.9305,0.821883,1.97726,1.87114,0.308052,1.42266,0.436207,0.725175,1.09478
+25-Mar-2019 06:00:00,1,0.25982,0.58496,0.96746,0.96964,0.62528,1.3407,0.9457,0.88117,0.26234,2.9028,0.811259,1.95904,1.8549,0.303435,1.40846,0.430482,0.718052,1.08511
+25-Mar-2019 07:00:00,1,0.26315,0.59484,0.9823,0.98016,0.6357,1.3568,0.95766,0.89911,0.26633,2.9346,0.823455,1.97995,1.87355,0.308738,1.42477,0.437057,0.726233,1.09621
+25-Mar-2019 08:00:00,1,0.2878,0.66828,1.0923,1.0582,0.71369,1.4777,1.0454,1.031,0.29557,3.1632,0.9113,2.13036,2.01035,0.348289,1.54619,0.485777,0.787274,1.17746
+25-Mar-2019 09:00:00,1,0.31859,0.76052,1.2295,1.1555,0.81296,1.6315,1.153,1.1933,0.33125,3.4316,1.01482,2.30713,2.17747,0.398206,1.69885,0.546517,0.864383,1.27632
+25-Mar-2019 10:00:00,1,0.34157,0.82969,1.3317,1.2282,0.88832,1.748,1.2318,1.3126,0.3573,3.62,1.08781,2.4314,2.29961,0.435809,1.81345,0.591765,0.922517,1.34829
+25-Mar-2019 11:00:00,1,0.36031,0.8863,1.415,1.2874,0.95053,1.8442,1.2952,1.4088,0.37818,3.7665,1.14476,2.52813,2.39764,0.466683,1.90732,0.628612,0.970275,1.40588
+25-Mar-2019 12:00:00,1,0.35609,0.87352,1.3962,1.274,0.93645,1.8225,1.2809,1.3872,0.3735,3.734,1.13212,2.50668,2.37566,0.459708,1.88613,0.62031,0.959483,1.39298
+25-Mar-2019 13:00:00,1,0.33922,0.82259,1.3212,1.2207,0.88055,1.736,1.2238,1.3005,0.35465,3.6012,1.0805,2.41897,2.2872,0.431945,1.80169,0.587134,0.916541,1.34099
+25-Mar-2019 14:00:00,1,0.32199,0.77072,1.2446,1.1663,0.82403,1.6486,1.1647,1.211,0.33513,3.46,1.02583,2.32589,2.19565,0.403744,1.71574,0.553207,0.872941,1.28705
+25-Mar-2019 15:00:00,1,0.30946,0.73311,1.1888,1.1267,0.78331,1.5856,1.1213,1.1454,0.32076,3.3539,0.984815,2.25594,2.12832,0.383343,1.65345,0.528515,0.841416,1.2473
+25-Mar-2019 16:00:00,1,0.30199,0.71074,1.1555,1.1031,0.7592,1.5482,1.0953,1.1062,0.31213,3.2893,0.959855,2.21334,2.08789,0.371231,1.61642,0.513792,0.822703,1.22339
+25-Mar-2019 17:00:00,1,0.30902,0.7318,1.1868,1.1253,0.78189,1.5834,1.1198,1.1432,0.32026,3.3502,0.983364,2.25347,2.12596,0.382633,1.65128,0.527653,0.840318,1.2459
+25-Mar-2019 18:00:00,1,0.32142,0.76903,1.2421,1.1645,0.82219,1.6457,1.1628,1.2081,0.33449,3.4553,1.02401,2.32279,2.19264,0.402826,1.71294,0.552099,0.871523,1.28528
+25-Mar-2019 19:00:00,1,0.33583,0.81238,1.3062,1.21,0.86939,1.7188,1.2122,1.2829,0.35083,3.5738,1.06991,2.40095,2.26929,0.426387,1.78477,0.580466,0.907947,1.33045
+25-Mar-2019 20:00:00,1,0.35629,0.87413,1.3971,1.2747,0.93712,1.8235,1.2816,1.3883,0.37373,3.7356,1.13273,2.50772,2.37671,0.460042,1.88714,0.620708,0.96,1.3936
+25-Mar-2019 21:00:00,1,0.34411,0.83735,1.343,1.2362,0.8967,1.761,1.2404,1.3257,0.36014,3.6402,1.09566,2.44475,2.31297,0.439981,1.82615,0.596759,0.928968,1.35615
+25-Mar-2019 22:00:00,1,0.3381,0.81923,1.3163,1.2172,0.87687,1.7303,1.22,1.2947,0.35339,3.5922,1.07702,2.41304,2.2813,0.430112,1.79611,0.584936,0.913706,1.33752
+25-Mar-2019 23:00:00,1,0.31166,0.73973,1.1986,1.1336,0.79046,1.5966,1.129,1.157,0.3233,3.3728,0.992118,2.26841,2.14023,0.38693,1.66441,0.532865,0.846958,1.25433
+26-Mar-2019 00:00:00,1,0.29015,0.67532,1.1028,1.0656,0.72121,1.4894,1.0537,1.0435,0.29833,3.1844,0.919461,2.14432,2.02328,0.352089,1.55783,0.490429,0.793141,1.18513
+26-Mar-2019 01:00:00,1,0.28075,0.64725,1.0609,1.0359,0.69126,1.443,1.0205,0.99352,0.28727,3.0992,0.886653,2.0882,1.97153,0.336946,1.51141,0.471859,0.769762,1.15444
+26-Mar-2019 02:00:00,1,0.27817,0.63955,1.0493,1.0277,0.68306,1.4303,1.0113,0.97972,0.28422,3.0754,0.877526,2.07258,1.95724,0.332795,1.49867,0.466756,0.763355,1.14595
+26-Mar-2019 03:00:00,1,0.28596,0.6628,1.0841,1.0524,0.70784,1.4687,1.039,1.0213,0.29341,3.1466,0.904917,2.11944,2.00026,0.345333,1.53712,0.482153,0.782709,1.17148
+26-Mar-2019 04:00:00,1,0.30013,0.70515,1.1472,1.0972,0.7532,1.5389,1.0888,1.0963,0.30997,3.2729,0.953557,2.20258,2.07775,0.368209,1.60718,0.510112,0.818036,1.21739
+26-Mar-2019 05:00:00,1,0.32874,0.79104,1.2746,1.1876,0.84612,1.6828,1.1879,1.2462,0.34282,3.516,1.0475,2.3628,2.23168,0.41478,1.7494,0.566512,0.890001,1.3083
+26-Mar-2019 06:00:00,1,0.39307,0.98568,1.5605,1.3908,1.0609,2.0147,1.4042,1.5749,0.41397,4.0079,1.23901,2.68775,2.56581,0.521096,2.07226,0.692929,1.0545,1.50431
+26-Mar-2019 07:00:00,1,0.43007,1.0986,1.7246,1.5075,1.1878,2.2106,1.5247,1.7593,0.45335,4.2592,1.33778,2.85429,2.75113,0.583194,2.25982,0.765436,1.15071,1.61223
+26-Mar-2019 08:00:00,1,0.44628,1.1482,1.7964,1.5587,1.2441,2.2975,1.5766,1.8391,0.47027,4.3625,1.3786,2.92285,2.83082,0.610593,2.34236,0.797146,1.19319,1.65846
+26-Mar-2019 09:00:00,1,0.45237,1.1668,1.8234,1.5778,1.2654,2.3303,1.596,1.8689,0.47657,4.4003,1.39355,2.94793,2.86051,0.620911,2.3734,0.809044,1.20919,1.67566
+26-Mar-2019 10:00:00,1,0.46513,1.2061,1.8799,1.6181,1.3102,2.3994,1.6364,1.9311,0.4897,4.4777,1.42428,2.99939,2.92243,0.642613,2.43865,0.833997,1.24285,1.71147
+26-Mar-2019 11:00:00,1,0.47415,1.2338,1.9198,1.6465,1.3419,2.4484,1.6648,1.9749,0.4989,4.5309,1.44546,3.03479,2.96582,0.657977,2.4848,0.851605,1.26668,1.73654
+26-Mar-2019 12:00:00,1,0.4725,1.2287,1.9125,1.6413,1.3361,2.4394,1.6596,1.9669,0.49722,4.5213,1.44161,3.02837,2.9579,0.655162,2.47635,0.848383,1.26231,1.73197
+26-Mar-2019 13:00:00,1,0.46745,1.2132,1.8902,1.6254,1.3183,2.412,1.6438,1.9424,0.49207,4.4915,1.42977,3.00857,2.93361,0.646562,2.45052,0.838528,1.24897,1.71794
+26-Mar-2019 14:00:00,1,0.45738,1.1822,1.8456,1.5936,1.2829,2.3574,1.6119,1.8934,0.48173,4.4309,1.40571,2.96831,2.88487,0.629418,2.39899,0.818838,1.22238,1.68976
+26-Mar-2019 15:00:00,1,0.44371,1.1403,1.785,1.5505,1.2351,2.2837,1.5684,1.8265,0.4676,4.3464,1.37221,2.91213,2.81822,0.606234,2.32923,0.792111,1.18643,1.65116
+26-Mar-2019 16:00:00,1,0.42748,1.0906,1.7131,1.4994,1.1788,2.1968,1.5163,1.7465,0.45063,4.2423,1.33112,2.84308,2.73831,0.578825,2.24664,0.760363,1.14394,1.60478
+26-Mar-2019 17:00:00,1,0.42213,1.0743,1.6894,1.4825,1.1603,2.1683,1.499,1.72,0.44499,4.2071,1.31723,2.81971,2.71174,0.569805,2.21944,0.749879,1.12996,1.58934
+26-Mar-2019 18:00:00,1,0.42445,1.0813,1.6997,1.4898,1.1683,2.1806,1.5065,1.7315,0.44743,4.2224,1.32326,2.82987,2.72326,0.573708,2.23121,0.754418,1.13601,1.59603
+26-Mar-2019 19:00:00,1,0.432,1.1044,1.7332,1.5136,1.1945,2.2209,1.5309,1.7688,0.45537,4.2717,1.34271,2.86257,2.76065,0.586443,2.26961,0.769205,1.15575,1.61776
+26-Mar-2019 20:00:00,1,0.44323,1.1388,1.7829,1.549,1.2335,2.2811,1.5669,1.8241,0.46709,4.3434,1.37101,2.91012,2.81586,0.605419,2.32678,0.79117,1.18517,1.64979
+26-Mar-2019 21:00:00,1,0.41664,1.0575,1.6651,1.4652,1.1415,2.1391,1.4812,1.6928,0.43918,4.1705,1.30284,2.79547,2.68441,0.560577,2.19159,0.739133,1.11566,1.57344
+26-Mar-2019 22:00:00,1,0.38514,0.96158,1.5253,1.3658,1.034,1.9732,1.378,1.535,0.40539,3.9511,1.2168,2.6502,2.52548,0.507877,2.03224,0.677374,1.03404,1.48074
+26-Mar-2019 23:00:00,1,0.34641,0.84428,1.3532,1.2434,0.9043,1.7728,1.2482,1.3375,0.36272,3.6584,1.10272,2.45675,2.32503,0.443756,1.83763,0.601275,0.934808,1.36324
+27-Mar-2019 00:00:00,1,0.3205,0.76625,1.238,1.1616,0.81917,1.6411,1.1596,1.2032,0.33343,3.4476,1.02102,2.31768,2.18769,0.401316,1.70834,0.550275,0.869189,1.28235
+27-Mar-2019 01:00:00,1,0.30596,0.72261,1.1732,1.1156,0.77199,1.568,1.1091,1.1271,0.31672,3.3237,0.973161,2.23605,2.1094,0.37766,1.63608,0.521613,0.832635,1.23611
+27-Mar-2019 02:00:00,1,0.29977,0.70408,1.1456,1.096,0.75204,1.5371,1.0875,1.0944,0.30955,3.2698,0.952343,2.20051,2.0758,0.367628,1.6054,0.509404,0.817139,1.21623
+27-Mar-2019 03:00:00,1,0.30324,0.71446,1.1611,1.107,0.76321,1.5544,1.0996,1.1127,0.31357,3.3001,0.964037,2.22048,2.09464,0.373245,1.62258,0.516243,0.825814,1.22738
+27-Mar-2019 04:00:00,1,0.31307,0.74396,1.2049,1.1381,0.79503,1.6037,1.1339,1.1644,0.32493,3.3849,0.996765,2.27633,2.14782,0.389222,1.67142,0.535644,0.8505,1.25882
+27-Mar-2019 05:00:00,1,0.33836,0.82,1.3174,1.218,0.87772,1.7317,1.2208,1.296,0.35369,3.5943,1.07782,2.41441,2.28266,0.430535,1.7974,0.585444,0.914361,1.33832
+27-Mar-2019 06:00:00,1,0.39513,0.99196,1.5697,1.3973,1.0679,2.0256,1.411,1.5853,0.4162,4.0225,1.24473,2.69742,2.57628,0.524544,2.0827,0.696979,1.05984,1.51042
+27-Mar-2019 07:00:00,1,0.43061,1.1002,1.727,1.5092,1.1896,2.2135,1.5264,1.762,0.45391,4.2627,1.33914,2.85658,2.75376,0.584092,2.26253,0.766478,1.15211,1.61376
+27-Mar-2019 08:00:00,1,0.44576,1.1466,1.7941,1.557,1.2423,2.2948,1.575,1.8366,0.46973,4.3593,1.37732,2.92071,2.82829,0.609718,2.33972,0.796136,1.19183,1.657
+27-Mar-2019 09:00:00,1,0.4519,1.1654,1.8213,1.5764,1.2638,2.3278,1.5945,1.8666,0.47609,4.3974,1.39242,2.94603,2.85826,0.620125,2.37104,0.808139,1.20797,1.67435
+27-Mar-2019 10:00:00,1,0.46403,1.2027,1.875,1.6146,1.3063,2.3934,1.633,1.9258,0.48857,4.4711,1.42167,2.99502,2.91712,0.640741,2.43303,0.83185,1.23994,1.70841
+27-Mar-2019 11:00:00,1,0.47719,1.2431,1.9333,1.6561,1.3527,2.465,1.6744,1.9896,0.50199,4.5486,1.45251,3.04658,2.98042,0.663175,2.50041,0.857551,1.27474,1.74497
+27-Mar-2019 12:00:00,1,0.4724,1.2284,1.9121,1.641,1.3358,2.4389,1.6593,1.9664,0.49712,4.5207,1.44138,3.02799,2.95743,0.654995,2.47585,0.848192,1.26205,1.73169
+27-Mar-2019 13:00:00,1,0.46752,1.2134,1.8905,1.6256,1.3186,2.4123,1.644,1.9428,0.49214,4.4919,1.42993,3.00885,2.93395,0.646681,2.45087,0.838664,1.24916,1.71813
+27-Mar-2019 14:00:00,1,0.45742,1.1823,1.8458,1.5938,1.2831,2.3576,1.612,1.8936,0.48178,4.4312,1.40581,2.96848,2.88507,0.629489,2.3992,0.81892,1.22249,1.68988
+27-Mar-2019 15:00:00,1,0.44598,1.1473,1.7951,1.5577,1.2431,2.2959,1.5757,1.8376,0.46995,4.3606,1.37785,2.92159,2.82934,0.61008,2.34081,0.796553,1.19239,1.6576
+27-Mar-2019 16:00:00,1,0.42943,1.0966,1.7218,1.5055,1.1856,2.2072,1.5226,1.7562,0.45268,4.2551,1.33613,2.85152,2.74796,0.582112,2.25656,0.76418,1.14904,1.61039
+27-Mar-2019 17:00:00,1,0.42039,1.069,1.6817,1.477,1.1544,2.159,1.4934,1.7114,0.44315,4.1956,1.31269,2.81207,2.7031,0.56688,2.21061,0.746475,1.12543,1.58431
+27-Mar-2019 18:00:00,1,0.42105,1.071,1.6846,1.4791,1.1566,2.1625,1.4955,1.7146,0.44385,4.1999,1.31441,2.81497,2.70637,0.567988,2.21396,0.747765,1.12715,1.58622
+27-Mar-2019 19:00:00,1,0.42759,1.091,1.7136,1.4997,1.1792,2.1974,1.5167,1.7471,0.45075,4.243,1.3314,2.84355,2.73885,0.579008,2.2472,0.760577,1.14423,1.6051
+27-Mar-2019 20:00:00,1,0.43788,1.1224,1.7592,1.5322,1.2149,2.2524,1.5497,1.7978,0.46152,4.3095,1.35761,2.88762,2.78961,0.596372,2.29953,0.780708,1.17114,1.63457
+27-Mar-2019 21:00:00,1,0.41251,1.0449,1.6467,1.4521,1.1273,2.1172,1.4678,1.6722,0.4348,4.1427,1.29188,2.777,2.66376,0.553636,2.17063,0.731037,1.10491,1.56142
+27-Mar-2019 22:00:00,1,0.38287,0.95468,1.5152,1.3586,1.0263,1.9613,1.3705,1.5235,0.40293,3.9347,1.21036,2.63931,2.51387,0.504095,2.02079,0.672915,1.02818,1.47396
+27-Mar-2019 23:00:00,1,0.34437,0.83813,1.3442,1.237,0.89756,1.7623,1.2413,1.327,0.36044,3.6423,1.09646,2.4461,2.31433,0.440407,1.82744,0.597269,0.929627,1.35695
+28-Mar-2019 00:00:00,1,0.32073,0.76694,1.239,1.1623,0.81992,1.6422,1.1604,1.2044,0.33369,3.4495,1.02176,2.31895,2.18892,0.401691,1.70948,0.550728,0.869769,1.28308
+28-Mar-2019 01:00:00,1,0.30805,0.72889,1.1825,1.1222,0.77876,1.5785,1.1164,1.1381,0.31914,3.3418,0.980144,2.24797,2.12073,0.381059,1.64647,0.525742,0.837887,1.24281
+28-Mar-2019 02:00:00,1,0.30142,0.70902,1.153,1.1013,0.75735,1.5454,1.0933,1.1031,0.31147,3.2842,0.957921,2.21003,2.08477,0.370301,1.61358,0.51266,0.821268,1.22155
+28-Mar-2019 03:00:00,1,0.30253,0.71236,1.158,1.1048,0.76095,1.5509,1.0972,1.109,0.31276,3.294,0.961682,2.21646,2.09084,0.37211,1.61911,0.514862,0.824061,1.22513
+28-Mar-2019 04:00:00,1,0.31514,0.75016,1.2141,1.1446,0.80174,1.6141,1.141,1.1752,0.3273,3.4024,1.00355,2.28791,2.15894,0.392584,1.68168,0.539716,0.855695,1.26538
+28-Mar-2019 05:00:00,1,0.34184,0.83049,1.3329,1.229,0.88919,1.7494,1.2327,1.314,0.3576,3.6221,1.08864,2.4328,2.301,0.436245,1.81478,0.592287,0.923191,1.34911
+28-Mar-2019 06:00:00,1,0.39631,0.99554,1.5749,1.401,1.0719,2.0317,1.4148,1.5912,0.41747,4.0308,1.24798,2.70291,2.58224,0.52651,2.08864,0.699288,1.06289,1.5139
+28-Mar-2019 07:00:00,1,0.42995,1.0982,1.7241,1.5072,1.1874,2.21,1.5243,1.7587,0.45323,4.2584,1.33746,2.85376,2.75053,0.582987,2.25919,0.765195,1.15039,1.61188
+28-Mar-2019 08:00:00,1,0.44338,1.1393,1.7836,1.5495,1.234,2.2819,1.5674,1.8248,0.46725,4.3443,1.37139,2.91076,2.81661,0.605675,2.32755,0.791466,1.18556,1.65022
+28-Mar-2019 09:00:00,1,0.45049,1.1611,1.8151,1.5719,1.2588,2.3202,1.59,1.8597,0.47463,4.3887,1.38896,2.94022,2.85136,0.617723,2.36381,0.805371,1.20424,1.67036
+28-Mar-2019 10:00:00,1,0.46231,1.1974,1.8674,1.6092,1.3002,2.3841,1.6275,1.9174,0.4868,4.4608,1.41756,2.98814,2.90878,0.637807,2.42421,0.828481,1.23539,1.70359
+28-Mar-2019 11:00:00,1,0.47226,1.228,1.9115,1.6406,1.3353,2.4381,1.6589,1.9657,0.49698,4.5199,1.44106,3.02744,2.95676,0.654757,2.47513,0.847919,1.26168,1.73131
+28-Mar-2019 12:00:00,1,0.46733,1.2128,1.8896,1.625,1.3179,2.4113,1.6434,1.9418,0.49194,4.4907,1.42947,3.00808,2.93301,0.646348,2.44988,0.838283,1.24864,1.71759
+28-Mar-2019 13:00:00,1,0.46018,1.1908,1.858,1.6025,1.2928,2.3725,1.6208,1.907,0.48462,4.4479,1.41246,2.9796,2.89846,0.634181,2.41331,0.824314,1.22977,1.69762
+28-Mar-2019 14:00:00,1,0.44934,1.1576,1.81,1.5683,1.2548,2.314,1.5864,1.8541,0.47344,4.3816,1.38614,2.93551,2.84577,0.615781,2.35797,0.803131,1.20123,1.66712
+28-Mar-2019 15:00:00,1,0.43294,1.1073,1.7373,1.5166,1.1977,2.226,1.5339,1.7735,0.45636,4.2778,1.34511,2.86661,2.7653,0.588035,2.27441,0.77105,1.15822,1.62046
+28-Mar-2019 16:00:00,1,0.41686,1.0582,1.666,1.4659,1.1422,2.1403,1.4819,1.6938,0.43941,4.172,1.30341,2.79643,2.68549,0.560941,2.19269,0.739557,1.11623,1.57407
+28-Mar-2019 17:00:00,1,0.40713,1.0285,1.6229,1.4352,1.1088,2.0888,1.4503,1.6454,0.42907,4.106,1.27748,2.75271,2.63681,0.544619,2.14339,0.720502,1.09094,1.54572
+28-Mar-2019 18:00:00,1,0.40602,1.0251,1.618,1.4317,1.105,2.0829,1.4467,1.6398,0.42788,4.0984,1.27448,2.74766,2.63123,0.542757,2.13777,0.718325,1.08806,1.54247
+28-Mar-2019 19:00:00,1,0.40871,1.0333,1.6299,1.4401,1.1142,2.0971,1.4554,1.6533,0.43075,4.1168,1.28172,2.75987,2.64472,0.547262,2.15138,0.723592,1.09503,1.55033
+28-Mar-2019 20:00:00,1,0.41605,1.0557,1.6625,1.4633,1.1394,2.136,1.4793,1.6898,0.43856,4.1666,1.30128,2.79284,2.68147,0.559587,2.1886,0.737979,1.11413,1.57173
+28-Mar-2019 21:00:00,1,0.39497,0.99147,1.569,1.3968,1.0673,2.0247,1.4104,1.5845,0.41603,4.0214,1.24428,2.69667,2.57547,0.524276,2.08188,0.696664,1.05943,1.50995
+28-Mar-2019 22:00:00,1,0.36696,0.90641,1.4445,1.3084,0.97275,1.8786,1.3175,1.4428,0.38552,3.817,1.16441,2.56147,2.43208,0.477677,1.94069,0.641669,0.987288,1.42607
+28-Mar-2019 23:00:00,1,0.33045,0.79618,1.2822,1.193,0.85171,1.6914,1.1938,1.255,0.34475,3.53,1.05293,2.37205,2.24076,0.417574,1.75792,0.569875,0.894321,1.31365
+29-Mar-2019 00:00:00,1,0.30279,0.71312,1.1591,1.1056,0.76176,1.5522,1.0981,1.1104,0.31305,3.2962,0.962531,2.21791,2.09221,0.372518,1.62036,0.51536,0.824693,1.22594
+29-Mar-2019 01:00:00,1,0.28561,0.66176,1.0825,1.0513,0.70672,1.4669,1.0377,1.0194,0.293,3.1435,0.903699,2.11736,1.99834,0.34477,1.5354,0.481463,0.78184,1.17034
+29-Mar-2019 02:00:00,1,0.27841,0.64027,1.0504,1.0285,0.68383,1.4315,1.0122,0.98102,0.28451,3.0777,0.878383,2.07404,1.95858,0.333184,1.49987,0.467234,0.763955,1.14675
+29-Mar-2019 03:00:00,1,0.2841,0.65724,1.0758,1.0465,0.7019,1.4595,1.0324,1.0114,0.29122,3.1297,0.898408,2.10831,1.99,0.342331,1.52792,0.478472,0.778076,1.1654
+29-Mar-2019 04:00:00,1,0.29543,0.6911,1.1263,1.0823,0.73811,1.5156,1.0723,1.0715,0.3045,3.2315,0.937589,2.1753,2.05215,0.36061,1.58392,0.500844,0.8063,1.20224
+29-Mar-2019 05:00:00,1,0.32327,0.77458,1.2503,1.1703,0.82822,1.6551,1.1691,1.2177,0.3366,3.4708,1.02998,2.33295,2.20252,0.405841,1.72214,0.555738,0.876182,1.2911
+29-Mar-2019 06:00:00,1,0.379,0.94292,1.498,1.3464,1.0132,1.9411,1.3576,1.5039,0.39871,3.9064,1.19933,2.62063,2.49406,0.497655,2.00128,0.665316,1.01821,1.46237
+29-Mar-2019 07:00:00,1,0.41838,1.0628,1.6728,1.4706,1.1474,2.1483,1.4869,1.7014,0.44102,4.1821,1.30741,2.80316,2.69306,0.563492,2.20039,0.74253,1.12018,1.57847
+29-Mar-2019 08:00:00,1,0.4293,1.0962,1.7212,1.5051,1.1851,2.2065,1.5222,1.7555,0.45254,4.2542,1.33578,2.85093,2.74729,0.581882,2.25586,0.763913,1.14868,1.61
+29-Mar-2019 09:00:00,1,0.43605,1.1169,1.7511,1.5264,1.2085,2.2426,1.5439,1.7888,0.45962,4.2978,1.35301,2.87989,2.78064,0.593292,2.29025,0.777142,1.16637,1.62936
+29-Mar-2019 10:00:00,1,0.4466,1.1492,1.7979,1.5597,1.2452,2.2993,1.5777,1.8407,0.4706,4.3645,1.3794,2.92419,2.8324,0.611142,2.34401,0.797779,1.19404,1.65938
+29-Mar-2019 11:00:00,1,0.46032,1.1913,1.8586,1.6029,1.2932,2.3733,1.6212,1.9077,0.48476,4.4488,1.41279,2.98016,2.89913,0.634417,2.41402,0.824586,1.23013,1.69801
+29-Mar-2019 12:00:00,1,0.45928,1.1881,1.854,1.5996,1.2896,2.3677,1.6179,1.9027,0.4837,4.4425,1.41031,2.976,2.89412,0.632659,2.40874,0.822565,1.22741,1.69511
+29-Mar-2019 13:00:00,1,0.45446,1.1733,1.8327,1.5844,1.2727,2.3416,1.6027,1.8791,0.47873,4.4131,1.39865,2.95648,2.87071,0.624467,2.3841,0.81314,1.2147,1.68156
+29-Mar-2019 14:00:00,1,0.44008,1.1292,1.769,1.5391,1.2225,2.2642,1.5568,1.8086,0.46382,4.3235,1.36315,2.89692,2.80043,0.600095,2.31075,0.785015,1.17691,1.64084
+29-Mar-2019 15:00:00,1,0.42941,1.0965,1.7217,1.5055,1.1855,2.2071,1.5225,1.7561,0.45266,4.2549,1.33608,2.85143,2.74786,0.582078,2.25645,0.76414,1.14898,1.61033
+29-Mar-2019 16:00:00,1,0.41766,1.0606,1.6696,1.4684,1.145,2.1446,1.4846,1.6978,0.44027,4.1774,1.30553,2.80001,2.68952,0.562296,2.19678,0.741136,1.11833,1.57641
+29-Mar-2019 17:00:00,1,0.41421,1.0501,1.6543,1.4575,1.1331,2.1262,1.4733,1.6807,0.43661,4.1542,1.29641,2.78463,2.67228,0.556495,2.17927,0.734373,1.10934,1.56638
+29-Mar-2019 18:00:00,1,0.41267,1.0454,1.6475,1.4526,1.1278,2.1181,1.4683,1.673,0.43497,4.1438,1.29231,2.77773,2.66457,0.553908,2.17145,0.731354,1.10533,1.56189
+29-Mar-2019 19:00:00,1,0.4167,1.0577,1.6653,1.4654,1.1417,2.1394,1.4814,1.6931,0.43925,4.1709,1.303,2.79574,2.68472,0.560679,2.1919,0.739252,1.11582,1.57362
+29-Mar-2019 20:00:00,1,0.41692,1.0584,1.6663,1.466,1.1424,2.1406,1.4821,1.6941,0.43948,4.1724,1.30357,2.7967,2.6858,0.561043,2.193,0.739676,1.11639,1.57425
+29-Mar-2019 21:00:00,1,0.39469,0.9906,1.5677,1.3959,1.0664,2.0232,1.4095,1.583,0.41572,4.0193,1.24349,2.69533,2.57401,0.523795,2.08043,0.6961,1.05869,1.5091
+29-Mar-2019 22:00:00,1,0.36971,0.91475,1.4568,1.317,0.98197,1.8928,1.3267,1.4567,0.38854,3.8376,1.17247,2.57513,2.44628,0.482233,1.95452,0.647071,0.994339,1.4344
+29-Mar-2019 23:00:00,1,0.33873,0.82113,1.3191,1.2192,0.87895,1.7336,1.2221,1.2979,0.35411,3.5973,1.07899,2.4164,2.28464,0.43115,1.79927,0.586181,0.915311,1.33948
+30-Mar-2019 00:00:00,1,0.30881,0.73117,1.1859,1.1246,0.78122,1.5823,1.1191,1.1421,0.32002,3.3484,0.98267,2.25228,2.12483,0.382293,1.65025,0.52724,0.839794,1.24523
+30-Mar-2019 01:00:00,1,0.29263,0.68273,1.1138,1.0735,0.72914,1.5017,1.0625,1.0567,0.30123,3.2066,0.928004,2.15892,2.03686,0.356091,1.57009,0.495323,0.799321,1.19318
+30-Mar-2019 02:00:00,1,0.2871,0.66619,1.0892,1.056,0.71146,1.4743,1.043,1.0273,0.29475,3.1569,0.90887,2.12621,2.00651,0.347162,1.54273,0.484396,0.785534,1.17519
+30-Mar-2019 03:00:00,1,0.29426,0.68761,1.1211,1.0786,0.73436,1.5098,1.0682,1.0653,0.30314,3.2211,0.933596,2.16848,2.04578,0.358724,1.57815,0.498541,0.803387,1.19846
+30-Mar-2019 04:00:00,1,0.30474,0.71898,1.1678,1.1118,0.76807,1.562,1.1049,1.1207,0.31532,3.3132,0.969101,2.22912,2.10282,0.375692,1.63007,0.51922,0.829594,1.23222
+30-Mar-2019 05:00:00,1,0.32813,0.78919,1.2719,1.1857,0.8441,1.6796,1.1858,1.243,0.34212,3.511,1.04554,2.35946,2.22841,0.413773,1.74633,0.565299,0.888444,1.30637
+30-Mar-2019 06:00:00,1,0.38468,0.96019,1.5233,1.3643,1.0324,1.9708,1.3765,1.5326,0.4049,3.9478,1.2155,2.648,2.52313,0.507113,2.02993,0.676474,1.03285,1.47937
+30-Mar-2019 07:00:00,1,0.42391,1.0797,1.6973,1.4881,1.1665,2.1778,1.5048,1.7288,0.44687,4.2189,1.32187,2.82752,2.72059,0.572803,2.22848,0.753366,1.13461,1.59448
+30-Mar-2019 08:00:00,1,0.44175,1.1343,1.7764,1.5444,1.2283,2.2732,1.5621,1.8168,0.46556,4.334,1.36733,2.90394,2.80862,0.602918,2.31925,0.78828,1.18129,1.64559
+30-Mar-2019 09:00:00,1,0.44655,1.149,1.7976,1.5595,1.2451,2.299,1.5775,1.8404,0.47055,4.3642,1.37926,2.92397,2.83213,0.611048,2.34373,0.797671,1.19389,1.65922
+30-Mar-2019 10:00:00,1,0.4553,1.1759,1.8364,1.5871,1.2757,2.3462,1.6053,1.8833,0.4796,4.4183,1.4007,2.95991,2.87481,0.625901,2.38841,0.81479,1.21693,1.68394
+30-Mar-2019 11:00:00,1,0.46061,1.1922,1.8599,1.6038,1.2943,2.3749,1.6222,1.9092,0.48506,4.4506,1.41351,2.98136,2.90058,0.634925,2.41555,0.825169,1.23092,1.69884
+30-Mar-2019 12:00:00,1,0.45166,1.1647,1.8203,1.5756,1.2629,2.3265,1.5938,1.8654,0.47584,4.3959,1.39183,2.94504,2.85708,0.619715,2.36981,0.807666,1.20733,1.67367
+30-Mar-2019 13:00:00,1,0.43959,1.1277,1.7668,1.5375,1.2208,2.2616,1.5552,1.8062,0.4633,4.3203,1.36191,2.89484,2.79801,0.59926,2.30823,0.784049,1.17562,1.63944
+30-Mar-2019 14:00:00,1,0.42657,1.0878,1.7091,1.4965,1.1757,2.1919,1.5134,1.742,0.44967,4.2364,1.32876,2.83912,2.73379,0.577286,2.242,0.758576,1.14156,1.60215
+30-Mar-2019 15:00:00,1,0.4149,1.0522,1.6574,1.4597,1.1355,2.1299,1.4756,1.6841,0.43734,4.1588,1.29824,2.78771,2.67573,0.557654,2.18277,0.735725,1.11113,1.56839
+30-Mar-2019 16:00:00,1,0.4049,1.0217,1.613,1.4281,1.1012,2.077,1.443,1.6342,0.42668,4.0906,1.27144,2.74253,2.62558,0.540875,2.13208,0.716123,1.08514,1.53917
+30-Mar-2019 17:00:00,1,0.40059,1.0086,1.5939,1.4145,1.0865,2.0542,1.4289,1.6126,0.42206,4.0607,1.25971,2.72273,2.60385,0.533657,2.11026,0.707669,1.07396,1.5265
+30-Mar-2019 18:00:00,1,0.39694,0.99746,1.5777,1.403,1.074,2.035,1.4169,1.5944,0.41815,4.0352,1.24971,2.70584,2.58543,0.527561,2.09182,0.700521,1.06452,1.51576
+30-Mar-2019 19:00:00,1,0.40116,1.0103,1.5964,1.4163,1.0884,2.0572,1.4307,1.6155,0.42267,4.0647,1.26127,2.72536,2.60673,0.534611,2.11314,0.708788,1.07544,1.52818
+30-Mar-2019 20:00:00,1,0.40044,1.0081,1.5932,1.414,1.086,2.0535,1.4284,1.6119,0.4219,4.0597,1.25931,2.72205,2.6031,0.53341,2.10951,0.70738,1.07358,1.52607
+30-Mar-2019 21:00:00,1,0.37931,0.94389,1.4994,1.3474,1.0143,1.9428,1.3587,1.5055,0.39906,3.9088,1.20024,2.62217,2.49569,0.498184,2.00288,0.66594,1.01903,1.46332
+30-Mar-2019 22:00:00,1,0.36136,0.88949,1.4197,1.2907,0.95405,1.8497,1.2987,1.4142,0.37935,3.7746,1.14789,2.53346,2.40311,0.468426,1.91261,0.630684,0.972972,1.40909
+30-Mar-2019 23:00:00,1,0.32994,0.79465,1.28,1.1914,0.85005,1.6888,1.1921,1.2524,0.34418,3.5259,1.05132,2.36931,2.23807,0.416744,1.75538,0.568876,0.893037,1.31206
+31-Mar-2019 00:00:00,1,0.30041,0.706,1.1485,1.0981,0.75411,1.5403,1.0898,1.0978,0.3103,3.2754,0.954516,2.20422,2.07929,0.368668,1.60858,0.510671,0.818745,1.2183
+31-Mar-2019 01:00:00,1,0.28158,0.64972,1.0645,1.0385,0.69388,1.447,1.0234,0.99793,0.28825,3.1067,0.889565,2.09318,1.97609,0.338276,1.51549,0.473493,0.771815,1.15715
+31-Mar-2019 02:00:00,1,0.26972,0.61438,1.0116,1.001,0.65635,1.3889,0.98121,0.93446,0.27418,2.9968,0.847321,2.02085,1.91029,0.319246,1.45707,0.450054,0.742445,1.11806
+31-Mar-2019 03:00:00,1,0.27104,0.61833,1.0176,1.0052,0.66054,1.3954,0.98595,0.94158,0.27576,3.0092,0.852099,2.02904,1.91769,0.32137,1.4636,0.452677,0.745723,1.12246
+31-Mar-2019 04:00:00,1,0.27391,0.62685,1.0303,1.0142,0.66957,1.4094,0.99616,0.95692,0.27917,3.036,0.862358,2.04661,1.9336,0.325955,1.47768,0.458333,0.752799,1.13191
+31-Mar-2019 05:00:00,1,0.27678,0.63543,1.0432,1.0233,0.67868,1.4235,1.0064,0.97233,0.28258,3.0627,0.87262,2.06418,1.94958,0.330575,1.49186,0.464023,0.759928,1.14141
+31-Mar-2019 06:00:00,1,0.28305,0.65412,1.0711,1.0432,0.69857,1.4543,1.0287,1.0058,0.28999,3.1202,0.894744,2.10204,1.98423,0.340648,1.52276,0.476406,0.775476,1.16197
+31-Mar-2019 07:00:00,1,0.30195,0.7106,1.1553,1.1029,0.75906,1.548,1.0951,1.1059,0.31208,3.2889,0.959707,2.21308,2.08765,0.371159,1.6162,0.513705,0.822593,1.22325
+31-Mar-2019 08:00:00,1,0.32941,0.79305,1.2776,1.1897,0.8483,1.6861,1.1902,1.2496,0.34357,3.5215,1.04963,2.36642,2.23523,0.415872,1.75273,0.567827,0.891689,1.31039
+31-Mar-2019 09:00:00,1,0.35545,0.87159,1.3934,1.272,0.93432,1.8192,1.2788,1.384,0.37279,3.7291,1.13019,2.50342,2.37232,0.458652,1.88292,0.619052,0.95785,1.39102
+31-Mar-2019 10:00:00,1,0.372,0.9217,1.467,1.3243,0.98967,1.9047,1.3343,1.4684,0.39106,3.8547,1.17915,2.58645,2.4581,0.486035,1.96605,0.651575,1.00022,1.44132
+31-Mar-2019 11:00:00,1,0.37964,0.94487,1.5009,1.3484,1.0154,1.9445,1.3598,1.5072,0.39941,3.9111,1.20117,2.62374,2.49735,0.498723,2.00451,0.666577,1.01986,1.46429
+31-Mar-2019 12:00:00,1,0.37148,0.92012,1.4646,1.3226,0.98792,1.902,1.3326,1.4658,0.39049,3.8509,1.17763,2.58388,2.45542,0.485171,1.96343,0.650552,0.998887,1.43975
+31-Mar-2019 13:00:00,1,0.35948,0.88378,1.4113,1.2847,0.94775,1.8399,1.2924,1.4046,0.37726,3.7601,1.14228,2.52393,2.39332,0.465309,1.90314,0.626978,0.96815,1.40334
+31-Mar-2019 14:00:00,1,0.3439,0.83672,1.3421,1.2355,0.89602,1.76,1.2397,1.3246,0.35991,3.6386,1.09502,2.44366,2.31188,0.43964,1.82511,0.596351,0.928441,1.35551
+31-Mar-2019 15:00:00,1,0.33437,0.80798,1.2997,1.2054,0.86458,1.7113,1.2072,1.2754,0.34918,3.562,1.06531,2.39312,2.26155,0.42399,1.77746,0.577587,0.90424,1.32589
+31-Mar-2019 16:00:00,1,0.32872,0.79096,1.2745,1.1875,0.84603,1.6826,1.1878,1.246,0.34279,3.5158,1.04742,2.36267,2.23155,0.414738,1.74927,0.566462,0.889936,1.30822
+31-Mar-2019 17:00:00,1,0.33901,0.82197,1.3203,1.2201,0.87987,1.735,1.2231,1.2994,0.35442,3.5995,1.07986,2.41787,2.2861,0.431606,1.80066,0.586728,0.916016,1.34035
+31-Mar-2019 18:00:00,1,0.34941,0.85336,1.3666,1.2529,0.91427,1.7882,1.2584,1.353,0.36608,3.6821,1.11192,2.47238,2.3408,0.448707,1.85269,0.607191,0.942466,1.37251
+31-Mar-2019 19:00:00,1,0.3547,0.86932,1.3901,1.2696,0.93182,1.8153,1.2763,1.3801,0.37196,3.7233,1.12793,2.49958,2.36841,0.457414,1.87916,0.617577,0.955934,1.38873
+31-Mar-2019 20:00:00,1,0.35814,0.87975,1.4054,1.2805,0.9433,1.8331,1.2879,1.3978,0.37579,3.7499,1.13829,2.51716,2.38638,0.463105,1.89645,0.624355,0.96474,1.39927
+31-Mar-2019 21:00:00,1,0.34667,0.84508,1.3544,1.2443,0.90518,1.7741,1.2491,1.3389,0.36301,3.6605,1.10354,2.45814,2.32642,0.444194,1.83896,0.601799,0.935485,1.36406
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+29-Apr-2019 02:00:00,1,0.25958,0.58425,0.9664,0.96889,0.62454,1.3395,0.94484,0.87988,0.26205,2.9005,0.810379,1.95754,1.85355,0.303054,1.40729,0.430009,0.717465,1.08431
+29-Apr-2019 03:00:00,1,0.25881,0.58195,0.96293,0.96643,0.62211,1.3358,0.94204,0.87568,0.26111,2.893,0.807512,1.95262,1.84918,0.301815,1.40347,0.428471,0.715553,1.08171
+29-Apr-2019 04:00:00,1,0.26486,0.59993,0.98995,0.98559,0.64107,1.3652,0.96381,0.90834,0.26838,2.9509,0.82971,1.99068,1.88315,0.311476,1.43319,0.440447,0.730457,1.10192
+29-Apr-2019 05:00:00,1,0.272,0.62118,1.0218,1.0082,0.66356,1.4,0.98937,0.94672,0.2769,3.0182,0.855538,2.03493,1.92301,0.322904,1.46831,0.454569,0.748089,1.12562
+29-Apr-2019 06:00:00,1,0.28032,0.64597,1.0589,1.0345,0.68989,1.4409,1.019,0.99123,0.28677,3.0952,0.885142,2.08561,1.96916,0.336257,1.50929,0.471012,0.768698,1.15303
+29-Apr-2019 07:00:00,1,0.30166,0.70973,1.154,1.102,0.75812,1.5466,1.0941,1.1044,0.31174,3.2863,0.958729,2.21141,2.08608,0.370689,1.61477,0.513133,0.821867,1.22232
+29-Apr-2019 08:00:00,1,0.32881,0.79125,1.275,1.1879,0.84635,1.6831,1.1882,1.2465,0.3429,3.5166,1.04773,2.36319,2.23206,0.414896,1.74975,0.566651,0.89018,1.30852
+29-Apr-2019 09:00:00,1,0.35307,0.86439,1.3828,1.2645,0.9264,1.8069,1.2708,1.3717,0.37015,3.7106,1.12301,2.49122,2.3599,0.454725,1.87099,0.614372,0.951775,1.38373
+29-Apr-2019 10:00:00,1,0.36116,0.88887,1.4188,1.2901,0.95337,1.8486,1.298,1.4132,0.37913,3.773,1.14729,2.53243,2.40206,0.46809,1.91159,0.630285,0.972453,1.40847
+29-Apr-2019 11:00:00,1,0.36587,0.90313,1.4397,1.3049,0.96912,1.873,1.3138,1.4372,0.38432,3.8088,1.16122,2.55607,2.42647,0.475881,1.93524,0.639539,0.984509,1.42279
+29-Apr-2019 12:00:00,1,0.35804,0.87943,1.4049,1.2802,0.94295,1.8325,1.2875,1.3972,0.37567,3.7491,1.13798,2.51663,2.38583,0.462933,1.89592,0.62415,0.964473,1.39895
+29-Apr-2019 13:00:00,1,0.33444,0.80821,1.3,1.2057,0.86483,1.7117,1.2075,1.2758,0.34927,3.5626,1.06555,2.39354,2.26196,0.424116,1.77785,0.57774,0.904435,1.32613
+29-Apr-2019 14:00:00,1,0.32062,0.76661,1.2385,1.162,0.81957,1.6417,1.16,1.2039,0.33357,3.4486,1.02141,2.31835,2.18834,0.401514,1.70894,0.550514,0.869495,1.28274
+29-Apr-2019 15:00:00,1,0.31194,0.74056,1.1999,1.1345,0.79136,1.598,1.13,1.1585,0.32363,3.3752,0.99304,2.26998,2.14173,0.387384,1.6658,0.533416,0.847659,1.25522
+29-Apr-2019 16:00:00,1,0.30827,0.72954,1.1835,1.1229,0.77945,1.5796,1.1172,1.1392,0.31939,3.3437,0.98086,2.24919,2.12189,0.381409,1.64754,0.526167,0.838427,1.24349
+29-Apr-2019 17:00:00,1,0.3117,0.73982,1.1988,1.1337,0.79056,1.5968,1.1291,1.1572,0.32334,3.3731,0.992223,2.26858,2.1404,0.386982,1.66457,0.532928,0.847037,1.25443
+29-Apr-2019 18:00:00,1,0.31959,0.76353,1.2339,1.1587,0.81622,1.6365,1.1564,1.1985,0.3324,3.44,1.01808,2.31267,2.18284,0.399838,1.70383,0.54849,0.866906,1.27949
+29-Apr-2019 19:00:00,1,0.32259,0.77253,1.2472,1.1682,0.82599,1.6516,1.1668,1.2141,0.33582,3.4651,1.02777,2.32919,2.19886,0.404724,1.71873,0.55439,0.874456,1.28895
+29-Apr-2019 20:00:00,1,0.32188,0.7704,1.2441,1.1659,0.82367,1.648,1.1643,1.2104,0.33501,3.4591,1.02548,2.32529,2.19507,0.403566,1.7152,0.552993,0.872667,1.28671
+29-Apr-2019 21:00:00,1,0.32675,0.78504,1.2658,1.1813,0.83958,1.6727,1.1811,1.2358,0.34055,3.4996,1.04114,2.35196,2.22106,0.411519,1.73946,0.562585,0.88496,1.30204
+29-Apr-2019 22:00:00,1,0.31722,0.7564,1.2234,1.1512,0.8085,1.6246,1.1482,1.1861,0.32968,3.42,1.01036,2.29951,2.17012,0.395974,1.69203,0.543818,0.860933,1.27199
+29-Apr-2019 23:00:00,1,0.29059,0.67663,1.1047,1.067,0.72261,1.4916,1.0553,1.0459,0.29885,3.1884,0.920973,2.1469,2.02568,0.352796,1.55999,0.491294,0.794233,1.18655
+30-Apr-2019 00:00:00,1,0.27016,0.61569,1.0136,1.0024,0.65773,1.391,0.98278,0.93681,0.27471,3.0009,0.848899,2.02356,1.91273,0.319947,1.45922,0.45092,0.743527,1.11951
+30-Apr-2019 01:00:00,1,0.25657,0.57532,0.95296,0.95936,0.61513,1.3249,0.93399,0.86361,0.25842,2.8715,0.799256,1.93846,1.83661,0.298258,1.39253,0.424053,0.710067,1.07422
+30-Apr-2019 02:00:00,1,0.2514,0.55998,0.92986,0.94298,0.59902,1.2999,0.91528,0.83558,0.25217,2.8212,0.779975,1.90538,1.80739,0.29003,1.36719,0.413815,0.697377,1.05681
+30-Apr-2019 03:00:00,1,0.25393,0.56747,0.94116,0.95099,0.60689,1.3122,0.92443,0.8493,0.25523,2.8458,0.789428,1.9216,1.8217,0.294051,1.37957,0.418821,0.703578,1.06533
+30-Apr-2019 04:00:00,1,0.26507,0.60055,0.99088,0.98625,0.64173,1.3662,0.96456,0.90947,0.26863,2.9529,0.830472,1.99198,1.88432,0.31181,1.43422,0.44086,0.730972,1.10262
+30-Apr-2019 05:00:00,1,0.28147,0.64939,1.064,1.0381,0.69353,1.4465,1.023,0.99733,0.28812,3.1057,0.889171,2.09251,1.97548,0.338096,1.51493,0.473272,0.771537,1.15678
+30-Apr-2019 06:00:00,1,0.31377,0.74605,1.208,1.1403,0.7973,1.6072,1.1363,1.1681,0.32573,3.3908,0.999064,2.28025,2.15159,0.390359,1.67489,0.537021,0.852257,1.26104
+30-Apr-2019 07:00:00,1,0.35204,0.8613,1.3783,1.2613,0.923,1.8017,1.2673,1.3665,0.36901,3.7026,1.11991,2.48596,2.35456,0.453039,1.86586,0.612361,0.949166,1.38059
+30-Apr-2019 08:00:00,1,0.38687,0.96683,1.533,1.3712,1.0398,1.9822,1.3837,1.5437,0.40727,3.9636,1.22167,2.65844,2.53429,0.510757,2.04097,0.680767,1.03849,1.48589
+30-Apr-2019 09:00:00,1,0.40533,1.023,1.6149,1.4295,1.1027,2.0792,1.4444,1.6364,0.42714,4.0936,1.27261,2.7445,2.62774,0.541596,2.13426,0.716967,1.08626,1.54044
+30-Apr-2019 10:00:00,1,0.4171,1.0589,1.6671,1.4666,1.143,2.1416,1.4827,1.695,0.43967,4.1736,1.30405,2.79751,2.68671,0.561351,2.19393,0.740035,1.11686,1.57478
+30-Apr-2019 11:00:00,1,0.42698,1.0891,1.7109,1.4978,1.1771,2.1941,1.5147,1.744,0.4501,4.239,1.32982,2.84089,2.73581,0.577975,2.24408,0.759376,1.14262,1.60333
+30-Apr-2019 12:00:00,1,0.42027,1.0686,1.6812,1.4766,1.154,2.1584,1.493,1.7108,0.44303,4.1948,1.31239,2.81156,2.70252,0.566686,2.21003,0.746249,1.12513,1.58398
+30-Apr-2019 13:00:00,1,0.40849,1.0326,1.6289,1.4394,1.1135,2.0959,1.4547,1.6521,0.43051,4.1153,1.28112,2.75886,2.64361,0.546889,2.15025,0.723156,1.09446,1.54968
+30-Apr-2019 14:00:00,1,0.39564,0.99351,1.5719,1.3989,1.0696,2.0282,1.4126,1.5878,0.41675,4.0261,1.24613,2.69979,2.57886,0.525393,2.08526,0.697976,1.06116,1.51192
+30-Apr-2019 15:00:00,1,0.38667,0.96623,1.5321,1.3706,1.0392,1.9812,1.383,1.5427,0.40705,3.9622,1.22111,2.65749,2.53327,0.510424,2.03996,0.680375,1.03798,1.4853
+30-Apr-2019 16:00:00,1,0.37552,0.93239,1.4826,1.3354,1.0015,1.923,1.3461,1.4863,0.39492,3.8809,1.18935,2.60373,2.47624,0.491885,1.98379,0.658497,1.00928,1.45194
+30-Apr-2019 17:00:00,1,0.36779,0.90894,1.4483,1.311,0.97555,1.8829,1.3203,1.447,0.38644,3.8232,1.16686,2.56563,2.43639,0.47906,1.94489,0.64331,0.989428,1.4286
+30-Apr-2019 18:00:00,1,0.36205,0.89155,1.4227,1.2928,0.95632,1.8532,1.301,1.4177,0.3801,3.7798,1.14992,2.53689,2.40665,0.469552,1.91603,0.632023,0.974715,1.41116
+30-Apr-2019 19:00:00,1,0.35638,0.87441,1.3976,1.275,0.93742,1.824,1.2819,1.3887,0.37383,3.7363,1.133,2.50818,2.37719,0.460193,1.8876,0.620888,0.960233,1.39388
+30-Apr-2019 20:00:00,1,0.34531,0.84097,1.3483,1.24,0.90067,1.7671,1.2445,1.3319,0.36149,3.6497,1.09935,2.45102,2.31927,0.441953,1.83215,0.599119,0.932018,1.35985
+30-Apr-2019 21:00:00,1,0.34144,0.82928,1.3311,1.2277,0.88787,1.7473,1.2313,1.3119,0.35714,3.6189,1.08739,2.43069,2.29889,0.435586,1.81277,0.591498,0.922172,1.34787
+30-Apr-2019 22:00:00,1,0.32492,0.77953,1.2576,1.1755,0.83359,1.6634,1.1748,1.2263,0.33847,3.4844,1.03527,2.34196,2.2113,0.408526,1.73033,0.558977,0.880333,1.29628
+30-Apr-2019 23:00:00,1,0.30022,0.70541,1.1476,1.0974,0.75348,1.5394,1.0891,1.0968,0.31007,3.2737,0.953856,2.20309,2.07823,0.368352,1.60762,0.510286,0.818257,1.21767
+01-May-2019 00:00:00,1,0.28205,0.65113,1.0666,1.04,0.69538,1.4494,1.0251,1.0004,0.28881,3.1111,0.891226,2.09602,1.9787,0.339036,1.51782,0.474427,0.772988,1.15869
+01-May-2019 01:00:00,1,0.26723,0.60699,1.0005,0.9931,0.64854,1.3767,0.97232,0.92112,0.27122,2.9734,0.838341,2.00547,1.89643,0.315272,1.44486,0.445143,0.736313,1.10982
+01-May-2019 02:00:00,1,0.25721,0.57721,0.9558,0.96137,0.61712,1.328,0.93628,0.86705,0.25919,2.8776,0.801611,1.9425,1.84019,0.299271,1.39564,0.425311,0.711629,1.07635
+01-May-2019 03:00:00,1,0.25479,0.57003,0.945,0.95371,0.60957,1.3163,0.92754,0.85396,0.25627,2.8542,0.792632,1.9271,1.82655,0.295419,1.38379,0.420524,0.705689,1.06823
+01-May-2019 04:00:00,1,0.25701,0.57662,0.95492,0.96075,0.61651,1.3271,0.93557,0.86599,0.25895,2.8757,0.800887,1.94126,1.83909,0.298959,1.39469,0.424924,0.711148,1.0757
+01-May-2019 05:00:00,1,0.25394,0.56751,0.94121,0.95103,0.60693,1.3122,0.92448,0.84936,0.25525,2.846,0.789473,1.92168,1.82176,0.29407,1.37963,0.418845,0.703608,1.06537
+01-May-2019 06:00:00,1,0.2502,0.5564,0.92448,0.93917,0.59528,1.2941,0.91091,0.82904,0.25071,2.8094,0.775453,1.89762,1.80057,0.288115,1.36129,0.411429,0.694425,1.05274
+01-May-2019 07:00:00,1,0.26538,0.60149,0.99228,0.98724,0.64271,1.3677,0.96568,0.91115,0.269,2.9559,0.831613,1.99394,1.88608,0.312311,1.43576,0.441481,0.731745,1.10366
+01-May-2019 08:00:00,1,0.29392,0.68659,1.1196,1.0775,0.73328,1.5081,1.067,1.0635,0.30274,3.2181,0.932436,2.16649,2.04393,0.358177,1.57647,0.497872,0.802542,1.19736
+01-May-2019 09:00:00,1,0.31565,0.75168,1.2164,1.1462,0.80339,1.6166,1.1428,1.1779,0.32788,3.4067,1.00522,2.29075,2.16167,0.393413,1.68421,0.540719,0.856975,1.267
+01-May-2019 10:00:00,1,0.33233,0.80185,1.2906,1.199,0.8579,1.701,1.2002,1.2648,0.34688,3.5454,1.0589,2.38221,2.25076,0.420658,1.76731,0.573584,0.899088,1.31954
+01-May-2019 11:00:00,1,0.34699,0.84604,1.3558,1.2453,0.90624,1.7758,1.2502,1.3405,0.36337,3.663,1.10451,2.45979,2.32809,0.444718,1.84056,0.602424,0.936295,1.36504
+01-May-2019 12:00:00,1,0.34168,0.83,1.3322,1.2285,0.88866,1.7486,1.2321,1.3131,0.35741,3.6208,1.08813,2.43195,2.30015,0.435979,1.81397,0.591969,0.92278,1.34861
+01-May-2019 13:00:00,1,0.32258,0.77251,1.2472,1.1682,0.82597,1.6516,1.1667,1.2141,0.33581,3.465,1.02775,2.32916,2.19883,0.404714,1.7187,0.554378,0.87444,1.28893
+01-May-2019 14:00:00,1,0.31245,0.74209,1.2021,1.1361,0.79301,1.6006,1.1317,1.1612,0.32421,3.3796,0.994715,2.27284,2.14447,0.38821,1.66832,0.534417,0.848936,1.25684
+01-May-2019 15:00:00,1,0.30298,0.7137,1.1599,1.1062,0.76239,1.5532,1.0988,1.1114,0.31328,3.2979,0.963189,2.21903,2.09327,0.372835,1.62133,0.515745,0.825182,1.22657
+01-May-2019 16:00:00,1,0.29885,0.70133,1.1415,1.0931,0.74909,1.5326,1.0843,1.0896,0.30848,3.2617,0.949229,2.19519,2.0708,0.366141,1.60085,0.507591,0.814841,1.21327
+01-May-2019 17:00:00,1,0.30914,0.73216,1.1874,1.1257,0.78228,1.584,1.1202,1.1438,0.3204,3.3512,0.983764,2.25415,2.12661,0.382829,1.65188,0.52789,0.84062,1.24629
+01-May-2019 18:00:00,1,0.31844,0.76006,1.2288,1.1551,0.81246,1.6307,1.1525,1.1925,0.33108,3.4303,1.01432,2.30628,2.17665,0.397957,1.69808,0.546216,0.863998,1.27584
+01-May-2019 19:00:00,1,0.3203,0.76566,1.2371,1.161,0.81853,1.6401,1.1589,1.2022,0.33321,3.4459,1.02038,2.31659,2.18663,0.400993,1.70735,0.549886,0.868691,1.28173
+01-May-2019 20:00:00,1,0.32041,0.76598,1.2376,1.1613,0.81888,1.6406,1.1593,1.2028,0.33333,3.4468,1.02073,2.31719,2.18721,0.40117,1.70789,0.550099,0.868964,1.28207
+01-May-2019 21:00:00,1,0.32741,0.78704,1.2687,1.1834,0.84177,1.676,1.1834,1.2393,0.34131,3.5051,1.04327,2.35559,2.22462,0.412609,1.74278,0.563897,0.886645,1.30413
+01-May-2019 22:00:00,1,0.31336,0.74481,1.2062,1.139,0.79596,1.6051,1.1349,1.1659,0.32525,3.3873,0.997706,2.27794,2.14936,0.389687,1.67284,0.536207,0.851218,1.25973
+01-May-2019 23:00:00,1,0.28457,0.65865,1.0779,1.048,0.70341,1.4618,1.034,1.0139,0.29178,3.134,0.900064,2.11114,1.99261,0.343094,1.53026,0.479407,0.779252,1.16694
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+08-May-2019 09:00:00,1,0.4706,1.2229,1.9041,1.6353,1.3294,2.4291,1.6537,1.9577,0.49528,4.5101,1.43717,3.02094,2.94876,0.651923,2.46662,0.844673,1.25729,1.72669
+08-May-2019 10:00:00,1,0.48033,1.2528,1.9472,1.666,1.3638,2.4821,1.6842,2.0048,0.50518,4.5667,1.45975,3.05865,2.99546,0.668542,2.51651,0.863684,1.28307,1.75364
+08-May-2019 11:00:00,1,0.49249,1.2903,2.001,1.7043,1.407,2.5486,1.7221,2.0634,0.51744,4.6356,1.48726,3.10452,3.05336,0.689353,2.57893,0.887414,1.31537,1.787
+08-May-2019 12:00:00,1,0.48418,1.2647,1.9642,1.6781,1.3775,2.5031,1.6962,2.0234,0.50907,4.5888,1.46854,3.07332,3.01384,0.675124,2.53626,0.871199,1.29329,1.76423
+08-May-2019 13:00:00,1,0.47798,1.2456,1.9368,1.6586,1.3555,2.4692,1.6768,1.9934,0.50279,4.5532,1.45433,3.04961,2.98418,0.664515,2.50443,0.859083,1.27682,1.74713
+08-May-2019 14:00:00,1,0.46949,1.2195,1.8992,1.6318,1.3255,2.4231,1.6502,1.9523,0.49416,4.5036,1.43458,3.01662,2.94346,0.650043,2.46098,0.842519,1.25437,1.72363
+08-May-2019 15:00:00,1,0.46157,1.1951,1.8641,1.6068,1.2976,2.38,1.6252,1.9138,0.48604,4.4563,1.41578,2.98517,2.90518,0.636543,2.42041,0.827028,1.23343,1.70151
+08-May-2019 16:00:00,1,0.44845,1.1548,1.806,1.5655,1.2517,2.3092,1.5835,1.8497,0.47252,4.376,1.38396,2.93184,2.84143,0.614272,2.35343,0.801391,1.19889,1.66461
+08-May-2019 17:00:00,1,0.44422,1.1419,1.7873,1.5521,1.2369,2.2864,1.57,1.829,0.46812,4.3496,1.37348,2.91426,2.82072,0.607096,2.33183,0.793107,1.18777,1.6526
+08-May-2019 18:00:00,1,0.44055,1.1306,1.771,1.5406,1.2241,2.2667,1.5583,1.8109,0.46431,4.3264,1.36432,2.89889,2.80272,0.600885,2.31312,0.785929,1.17814,1.64217
+08-May-2019 19:00:00,1,0.43527,1.1144,1.7476,1.5239,1.2058,2.2384,1.5414,1.785,0.4588,4.2928,1.35102,2.87654,2.77677,0.591962,2.28625,0.775601,1.1643,1.62711
+08-May-2019 20:00:00,1,0.42115,1.0713,1.6851,1.4794,1.157,2.1631,1.4959,1.7152,0.44396,4.2006,1.31468,2.81541,2.70688,0.56816,2.21447,0.747964,1.12741,1.58651
+08-May-2019 21:00:00,1,0.41508,1.0528,1.6582,1.4603,1.1361,2.1309,1.4762,1.685,0.43753,4.1601,1.29872,2.78853,2.67664,0.557961,2.18369,0.736083,1.11161,1.56892
+08-May-2019 22:00:00,1,0.38167,0.95103,1.5099,1.3548,1.0222,1.9551,1.3665,1.5174,0.40162,3.926,1.20695,2.63353,2.50773,0.502097,2.01474,0.670558,1.02509,1.47037
+08-May-2019 23:00:00,1,0.34102,0.82803,1.3293,1.2264,0.8865,1.7452,1.2299,1.3098,0.35668,3.6156,1.08611,2.4285,2.29671,0.434906,1.8107,0.590683,0.92112,1.34659
+09-May-2019 00:00:00,1,0.31609,0.753,1.2183,1.1476,0.80482,1.6188,1.1443,1.1802,0.32838,3.4105,1.00666,2.2932,2.16403,0.394128,1.68639,0.541584,0.85808,1.26839
+09-May-2019 01:00:00,1,0.30228,0.7116,1.1568,1.104,0.76014,1.5497,1.0963,1.1077,0.31247,3.2918,0.960832,2.21501,2.08947,0.371701,1.61786,0.514364,0.823429,1.22432
+09-May-2019 02:00:00,1,0.29554,0.69143,1.1268,1.0827,0.73847,1.5161,1.0727,1.0721,0.30463,3.2325,0.937975,2.17596,2.05277,0.360793,1.58448,0.501067,0.806582,1.2026
+09-May-2019 03:00:00,1,0.2966,0.69461,1.1315,1.086,0.74187,1.5214,1.0764,1.0777,0.30587,3.2419,0.941597,2.18215,2.05856,0.362509,1.58974,0.503162,0.809232,1.20603
+09-May-2019 04:00:00,1,0.30581,0.72218,1.1726,1.1151,0.77152,1.5673,1.1086,1.1263,0.31655,3.3225,0.972675,2.23523,2.10861,0.377424,1.63536,0.521326,0.832271,1.23564
+09-May-2019 05:00:00,1,0.32919,0.79237,1.2766,1.189,0.84757,1.685,1.1895,1.2485,0.34332,3.5197,1.04891,2.36521,2.23404,0.415504,1.75161,0.567384,0.891121,1.30969
+09-May-2019 06:00:00,1,0.38646,0.9656,1.5312,1.3699,1.0385,1.9801,1.3824,1.5416,0.40683,3.9607,1.22053,2.65651,2.53222,0.510081,2.03892,0.67997,1.03745,1.48469
+09-May-2019 07:00:00,1,0.43955,1.1276,1.7666,1.5374,1.2207,2.2614,1.5551,1.806,0.46327,4.3201,1.36182,2.89469,2.79784,0.599202,2.30806,0.783982,1.17553,1.63934
+09-May-2019 08:00:00,1,0.46795,1.2147,1.8924,1.627,1.3201,2.4147,1.6453,1.9448,0.49258,4.4944,1.43094,3.01052,2.936,0.647405,2.45305,0.839495,1.25028,1.71932
+09-May-2019 09:00:00,1,0.47557,1.2381,1.9261,1.651,1.347,2.4561,1.6693,1.9818,0.50034,4.5392,1.44876,3.0403,2.97264,0.660401,2.49208,0.854379,1.27044,1.74047
+09-May-2019 10:00:00,1,0.48793,1.2762,1.9808,1.6899,1.3908,2.5237,1.7079,2.0414,0.51286,4.61,1.47703,3.08749,3.03171,0.681545,2.55552,0.878521,1.30325,1.77453
+09-May-2019 11:00:00,1,0.49829,1.3082,2.0266,1.7226,1.4277,2.5805,1.7401,2.0912,0.52326,4.6677,1.50012,3.12594,3.08082,0.699304,2.60875,0.898731,1.33081,1.80281
+09-May-2019 12:00:00,1,0.48737,1.2745,1.9783,1.6882,1.3888,2.5206,1.7062,2.0387,0.51229,4.6069,1.47576,3.08537,3.02903,0.68058,2.55263,0.877421,1.30175,1.77299
+09-May-2019 13:00:00,1,0.48536,1.2683,1.9694,1.6818,1.3816,2.5095,1.6999,2.029,0.51026,4.5955,1.47121,3.07777,3.01944,0.677134,2.54229,0.873492,1.2964,1.76746
+09-May-2019 14:00:00,1,0.47462,1.2352,1.9219,1.648,1.3436,2.4509,1.6663,1.9772,0.49937,4.5336,1.44655,3.03661,2.96807,0.658777,2.4872,0.85252,1.26792,1.73784
+09-May-2019 15:00:00,1,0.47086,1.2236,1.9052,1.6361,1.3303,2.4305,1.6545,1.9589,0.49554,4.5116,1.43777,3.02196,2.95001,0.652363,2.46794,0.845177,1.25797,1.72741
+09-May-2019 16:00:00,1,0.45836,1.1852,1.8499,1.5967,1.2864,2.3627,1.615,1.8982,0.48275,4.4369,1.40809,2.97228,2.88965,0.631091,2.40402,0.820762,1.22498,1.69252
+09-May-2019 17:00:00,1,0.45543,1.1762,1.837,1.5875,1.2761,2.3469,1.6057,1.8839,0.47973,4.4191,1.401,2.96042,2.87542,0.626112,2.38905,0.815034,1.21725,1.68429
+09-May-2019 18:00:00,1,0.45446,1.1733,1.8327,1.5844,1.2727,2.3416,1.6027,1.8791,0.47873,4.4131,1.39865,2.95648,2.87071,0.624467,2.3841,0.81314,1.2147,1.68156
+09-May-2019 19:00:00,1,0.44847,1.1549,1.8061,1.5655,1.2517,2.3093,1.5836,1.8498,0.47253,4.3761,1.38399,2.9319,2.84149,0.614295,2.3535,0.801418,1.19893,1.66465
+09-May-2019 20:00:00,1,0.43283,1.107,1.7369,1.5162,1.1974,2.2254,1.5336,1.773,0.45625,4.2771,1.34483,2.86614,2.76476,0.58785,2.27385,0.770836,1.15793,1.62014
+09-May-2019 21:00:00,1,0.42638,1.0873,1.7082,1.4959,1.175,2.1909,1.5128,1.7411,0.44947,4.2351,1.32827,2.83829,2.73284,0.576965,2.24104,0.758203,1.14106,1.6016
+09-May-2019 22:00:00,1,0.39945,1.0051,1.5888,1.4109,1.0826,2.0483,1.4251,1.607,0.42085,4.0528,1.25661,2.7175,2.59813,0.531761,2.10453,0.705447,1.07102,1.52317
+09-May-2019 23:00:00,1,0.3537,0.86632,1.3857,1.2665,0.92852,1.8102,1.2729,1.375,0.37086,3.7156,1.12494,2.4945,2.36324,0.455779,1.87419,0.615628,0.953405,1.38569
+10-May-2019 00:00:00,1,0.3219,0.77045,1.2442,1.166,0.82374,1.6481,1.1644,1.2105,0.33503,3.4593,1.02554,2.3254,2.19517,0.403598,1.7153,0.553031,0.872716,1.28677
+10-May-2019 01:00:00,1,0.30736,0.7268,1.1794,1.12,0.77651,1.575,1.114,1.1344,0.31834,3.3358,0.977825,2.24402,2.11696,0.379929,1.64302,0.524369,0.83614,1.24058
+10-May-2019 02:00:00,1,0.30266,0.71274,1.1585,1.1052,0.76135,1.5516,1.0976,1.1097,0.31291,3.2951,0.962106,2.21718,2.09152,0.372314,1.61974,0.515111,0.824377,1.22554
+10-May-2019 03:00:00,1,0.30647,0.72415,1.1755,1.1172,0.77364,1.5706,1.1109,1.1297,0.31731,3.3282,0.974872,2.23897,2.11217,0.378491,1.63862,0.522623,0.833919,1.23775
+10-May-2019 04:00:00,1,0.31467,0.74874,1.212,1.1431,0.80021,1.6117,1.1394,1.1728,0.32676,3.3984,1.00201,2.28527,2.15641,0.391818,1.67934,0.538788,0.854511,1.26389
+10-May-2019 05:00:00,1,0.33797,0.81884,1.3157,1.2168,0.87644,1.7297,1.2195,1.294,0.35325,3.5911,1.07661,2.41235,2.28061,0.4299,1.79546,0.584682,0.913378,1.33712
+10-May-2019 06:00:00,1,0.39123,0.9801,1.5524,1.385,1.0546,2.0051,1.3981,1.5657,0.41199,3.9948,1.2339,2.67912,2.5565,0.518032,2.06299,0.689328,1.04976,1.49887
+10-May-2019 07:00:00,1,0.44067,1.131,1.7716,1.541,1.2246,2.2674,1.5587,1.8115,0.46443,4.3272,1.36463,2.89941,2.80333,0.601094,2.31375,0.78617,1.17846,1.64252
+10-May-2019 08:00:00,1,0.47153,1.2257,1.9082,1.6383,1.3327,2.4341,1.6566,1.9622,0.49623,4.5156,1.43936,3.02461,2.95326,0.653518,2.47141,0.8465,1.25976,1.72929
+10-May-2019 09:00:00,1,0.48002,1.2519,1.9458,1.665,1.3627,2.4804,1.6832,2.0033,0.50486,4.565,1.45904,3.05747,2.99398,0.668014,2.51493,0.863081,1.28225,1.75279
+10-May-2019 10:00:00,1,0.48904,1.2797,1.9857,1.6934,1.3947,2.5297,1.7114,2.0468,0.51397,4.6163,1.47953,3.09164,3.03697,0.68344,2.5612,0.880681,1.30619,1.77756
+10-May-2019 11:00:00,1,0.50121,1.3172,2.0395,1.7318,1.4381,2.5966,1.7492,2.1052,0.52618,4.6837,1.50653,3.1366,3.09461,0.704321,2.62378,0.904429,1.3386,1.81074
+10-May-2019 12:00:00,1,0.49364,1.2939,2.0061,1.7079,1.4111,2.555,1.7257,2.0689,0.5186,4.642,1.48982,3.1088,3.05882,0.691328,2.58485,0.889661,1.31843,1.79014
+10-May-2019 13:00:00,1,0.48579,1.2696,1.9713,1.6832,1.3832,2.5119,1.7013,2.0311,0.5107,4.5979,1.47219,3.07942,3.02152,0.677881,2.54453,0.874343,1.29756,1.76866
+10-May-2019 14:00:00,1,0.48292,1.2608,1.9586,1.6742,1.373,2.4962,1.6923,2.0173,0.5078,4.5816,1.46567,3.06854,3.00783,0.672972,2.5298,0.868743,1.28995,1.76077
+10-May-2019 15:00:00,1,0.47836,1.2468,1.9385,1.6598,1.3569,2.4713,1.678,1.9953,0.50318,4.5554,1.45521,3.05109,2.98603,0.665174,2.50641,0.859836,1.27785,1.7482
+10-May-2019 16:00:00,1,0.46274,1.1987,1.8693,1.6105,1.3018,2.3864,1.6289,1.9195,0.48725,4.4634,1.41859,2.98987,2.91086,0.63854,2.42641,0.829323,1.23653,1.70479
+10-May-2019 17:00:00,1,0.45975,1.1895,1.8561,1.6011,1.2912,2.3702,1.6194,1.9049,0.48417,4.4453,1.41142,2.97787,2.89637,0.633449,2.41111,0.823473,1.22863,1.69641
+10-May-2019 18:00:00,1,0.4535,1.1703,1.8284,1.5814,1.2693,2.3364,1.5996,1.8744,0.47774,4.4072,1.39631,2.95256,2.86603,0.622835,2.37919,0.81126,1.21217,1.67886
+10-May-2019 19:00:00,1,0.44515,1.1447,1.7914,1.5551,1.2402,2.2915,1.573,1.8336,0.4691,4.3555,1.3758,2.91816,2.8253,0.608681,2.3366,0.794937,1.19022,1.65526
+10-May-2019 20:00:00,1,0.42432,1.081,1.6991,1.4894,1.1679,2.1799,1.5061,1.7309,0.4473,4.2216,1.32293,2.8293,2.72261,0.573491,2.23056,0.754165,1.13567,1.59566
+10-May-2019 21:00:00,1,0.41507,1.0527,1.6581,1.4602,1.1361,2.1308,1.4761,1.6849,0.43752,4.16,1.29868,2.78847,2.67657,0.557938,2.18362,0.736056,1.11157,1.56888
+10-May-2019 22:00:00,1,0.38107,0.94922,1.5072,1.3529,1.0202,1.9519,1.3645,1.5144,0.40097,3.9216,1.20525,2.63066,2.50468,0.501104,2.01173,0.669387,1.02355,1.46858
+10-May-2019 23:00:00,1,0.33808,0.81917,1.3162,1.2171,0.8768,1.7302,1.2199,1.2946,0.35337,3.592,1.07696,2.41294,2.28119,0.43008,1.79601,0.584898,0.913657,1.33746
+11-May-2019 00:00:00,1,0.31423,0.74743,1.21,1.1418,0.79878,1.6095,1.1379,1.1705,0.32625,3.3947,1.00057,2.28282,2.15405,0.391104,1.67716,0.537923,0.853408,1.2625
+11-May-2019 01:00:00,1,0.30153,0.70934,1.1535,1.1016,0.7577,1.5459,1.0937,1.1037,0.31159,3.2852,0.958282,2.21065,2.08536,0.370475,1.61411,0.512872,0.821536,1.22189
+11-May-2019 02:00:00,1,0.29383,0.68631,1.1192,1.0773,0.73298,1.5076,1.0667,1.063,0.30263,3.2173,0.932113,2.16594,2.04341,0.358025,1.57601,0.497686,0.802307,1.19706
+11-May-2019 03:00:00,1,0.29521,0.69044,1.1253,1.0816,0.7374,1.5145,1.0715,1.0703,0.30424,3.2295,0.936838,2.17402,2.05095,0.360255,1.58284,0.500411,0.805751,1.20152
+11-May-2019 04:00:00,1,0.30305,0.71391,1.1603,1.1064,0.76262,1.5535,1.099,1.1118,0.31336,3.2985,0.963422,2.21943,2.09364,0.372948,1.62168,0.515882,0.825356,1.22679
+11-May-2019 05:00:00,1,0.32456,0.77847,1.256,1.1744,0.83244,1.6616,1.1736,1.2244,0.33807,3.4815,1.03413,2.34004,2.20942,0.407951,1.72858,0.558283,0.879444,1.29517
+11-May-2019 06:00:00,1,0.37971,0.94507,1.5012,1.3486,1.0156,1.9448,1.36,1.5075,0.39948,3.9116,1.20135,2.62406,2.49769,0.498833,2.00485,0.666707,1.02003,1.46449
+11-May-2019 07:00:00,1,0.42988,1.098,1.7238,1.5069,1.1871,2.2096,1.5241,1.7584,0.45315,4.258,1.33729,2.85346,2.75019,0.582872,2.25885,0.765062,1.15021,1.61168
+11-May-2019 08:00:00,1,0.46342,1.2008,1.8723,1.6127,1.3042,2.3901,1.631,1.9228,0.48794,4.4674,1.42021,2.99258,2.91416,0.6397,2.4299,0.830654,1.23833,1.7067
+11-May-2019 09:00:00,1,0.4742,1.2339,1.9201,1.6467,1.3421,2.4487,1.665,1.9752,0.49895,4.5312,1.44559,3.03501,2.96609,0.658073,2.48509,0.851714,1.26683,1.73669
+11-May-2019 10:00:00,1,0.48865,1.2784,1.984,1.6922,1.3933,2.5276,1.7102,2.0449,0.51357,4.614,1.47864,3.09016,3.03509,0.682764,2.55918,0.87991,1.30514,1.77648
+11-May-2019 11:00:00,1,0.49928,1.3113,2.031,1.7257,1.4312,2.586,1.7432,2.0959,0.52425,4.6732,1.5023,3.12957,3.08551,0.701008,2.61386,0.900666,1.33346,1.8055
+11-May-2019 12:00:00,1,0.49289,1.2915,2.0027,1.7056,1.4084,2.5508,1.7234,2.0653,0.51784,4.6378,1.48816,3.10602,3.05527,0.690044,2.581,0.8882,1.31644,1.7881
+11-May-2019 13:00:00,1,0.48174,1.2571,1.9534,1.6704,1.3688,2.4898,1.6886,2.0116,0.5066,4.5748,1.46296,3.06402,3.00217,0.670942,2.52371,0.866425,1.2868,1.75751
+11-May-2019 14:00:00,1,0.46731,1.2128,1.8896,1.625,1.3178,2.4112,1.6433,1.9417,0.49193,4.4907,1.42944,3.00802,2.93294,0.646325,2.4498,0.838256,1.2486,1.71755
+11-May-2019 15:00:00,1,0.45803,1.1842,1.8485,1.5957,1.2852,2.3609,1.614,1.8966,0.48241,4.4349,1.4073,2.97097,2.88807,0.630537,2.40236,0.820125,1.22412,1.69161
+11-May-2019 16:00:00,1,0.44351,1.1397,1.7842,1.5499,1.2345,2.2827,1.5678,1.8255,0.4674,4.3452,1.37173,2.91133,2.81728,0.605908,2.32825,0.791735,1.18592,1.65061
+11-May-2019 17:00:00,1,0.43756,1.1214,1.7578,1.5311,1.2137,2.2507,1.5487,1.7962,0.46119,4.3074,1.3568,2.88626,2.78803,0.595828,2.29789,0.780078,1.1703,1.63365
+11-May-2019 18:00:00,1,0.43462,1.1125,1.7448,1.5219,1.2036,2.235,1.5393,1.7818,0.45812,4.2886,1.34939,2.8738,2.7736,0.590876,2.28297,0.774343,1.16262,1.62527
+11-May-2019 19:00:00,1,0.42148,1.0723,1.6865,1.4804,1.1581,2.1648,1.4969,1.7168,0.4443,4.2028,1.31553,2.81685,2.7085,0.568708,2.21613,0.748602,1.12826,1.58745
+11-May-2019 20:00:00,1,0.40163,1.0117,1.5985,1.4178,1.09,2.0597,1.4323,1.6179,0.42318,4.068,1.26257,2.72756,2.60913,0.535408,2.11555,0.709722,1.07667,1.52958
+11-May-2019 21:00:00,1,0.39043,0.97764,1.5488,1.3825,1.0519,2.0009,1.3955,1.5616,0.41112,3.9891,1.23165,2.67531,2.55239,0.516686,2.05892,0.687745,1.04768,1.49647
+11-May-2019 22:00:00,1,0.36854,0.91122,1.4516,1.3134,0.97806,1.8868,1.3228,1.4508,0.38726,3.8289,1.16906,2.56935,2.44027,0.480302,1.94866,0.644783,0.991351,1.43087
+11-May-2019 23:00:00,1,0.33697,0.8158,1.3112,1.2136,0.87313,1.7245,1.2161,1.2888,0.35211,3.583,1.07347,2.407,2.27529,0.428248,1.79043,0.5827,0.910825,1.33398
+12-May-2019 00:00:00,1,0.31097,0.73763,1.1955,1.1314,0.7882,1.5931,1.1266,1.1534,0.3225,3.3669,0.989812,2.26447,2.13647,0.385795,1.66095,0.53149,0.845205,1.25211
+12-May-2019 01:00:00,1,0.29511,0.69014,1.1249,1.0813,0.73708,1.514,1.0712,1.0698,0.30413,3.2287,0.936495,2.17343,2.0504,0.360093,1.58234,0.500212,0.805501,1.2012
+12-May-2019 02:00:00,1,0.28404,0.65707,1.0755,1.0463,0.70172,1.4592,1.0322,1.0111,0.29116,3.1292,0.898212,2.10797,1.98969,0.342241,1.52765,0.478361,0.777936,1.16521
+12-May-2019 03:00:00,1,0.28143,0.64929,1.0639,1.038,0.69343,1.4463,1.0229,0.99716,0.28808,3.1054,0.889061,2.09232,1.9753,0.338046,1.51478,0.47321,0.771459,1.15668
+12-May-2019 04:00:00,1,0.28158,0.64972,1.0645,1.0385,0.69388,1.447,1.0234,0.99793,0.28825,3.1067,0.889565,2.09318,1.97609,0.338276,1.51549,0.473493,0.771815,1.15715
+12-May-2019 05:00:00,1,0.28194,0.65081,1.0662,1.0397,0.69505,1.4488,1.0247,0.99988,0.28868,3.1101,0.890855,2.09539,1.97812,0.338866,1.5173,0.474218,0.772726,1.15835
+12-May-2019 06:00:00,1,0.29048,0.67629,1.1042,1.0667,0.72225,1.491,1.0549,1.0453,0.29871,3.1873,0.920585,2.14624,2.02507,0.352614,1.55944,0.491071,0.793952,1.18619
+12-May-2019 07:00:00,1,0.31615,0.75317,1.2186,1.1478,0.805,1.6191,1.1445,1.1805,0.32845,3.4109,1.00684,2.29352,2.16434,0.394221,1.68668,0.541697,0.858224,1.26857
+12-May-2019 08:00:00,1,0.34522,0.84069,1.3479,1.2397,0.90037,1.7667,1.2442,1.3314,0.36139,3.649,1.09907,2.45055,2.31879,0.441803,1.83169,0.59894,0.931787,1.35957
+12-May-2019 09:00:00,1,0.36317,0.89494,1.4277,1.2964,0.96007,1.859,1.3048,1.4234,0.38134,3.7883,1.15324,2.54253,2.41246,0.471406,1.92166,0.634226,0.977584,1.41457
+12-May-2019 10:00:00,1,0.3754,0.93203,1.4821,1.335,1.0011,1.9224,1.3457,1.4857,0.39479,3.88,1.18901,2.60315,2.47563,0.491688,1.98319,0.658264,1.00897,1.45158
+12-May-2019 11:00:00,1,0.38491,0.96089,1.5243,1.3651,1.0332,1.972,1.3772,1.5338,0.40515,3.9495,1.21616,2.64912,2.52432,0.5075,2.0311,0.67693,1.03345,1.48007
+12-May-2019 12:00:00,1,0.38264,0.95397,1.5142,1.3579,1.0255,1.9601,1.3697,1.5223,0.40267,3.933,1.2097,2.6382,2.51269,0.503708,2.01962,0.67246,1.02758,1.47326
+12-May-2019 13:00:00,1,0.36738,0.90771,1.4464,1.3097,0.97418,1.8808,1.3189,1.4449,0.38599,3.8202,1.16567,2.5636,2.43429,0.478385,1.94284,0.642509,0.988383,1.42737
+12-May-2019 14:00:00,1,0.35305,0.86435,1.3828,1.2644,0.92635,1.8069,1.2707,1.3717,0.37013,3.7105,1.12297,2.49115,2.35983,0.454704,1.87092,0.614346,0.951742,1.38369
+12-May-2019 15:00:00,1,0.34383,0.83651,1.3418,1.2353,0.89578,1.7596,1.2395,1.3243,0.35983,3.638,1.0948,2.44329,2.3115,0.439523,1.82475,0.596211,0.92826,1.35529
+12-May-2019 16:00:00,1,0.3392,0.82254,1.3212,1.2207,0.88049,1.7359,1.2237,1.3004,0.35463,3.601,1.08044,2.41886,2.28709,0.431913,1.80159,0.587096,0.916492,1.34093
+12-May-2019 17:00:00,1,0.34298,0.83395,1.338,1.2326,0.89298,1.7553,1.2366,1.3199,0.35888,3.6313,1.09218,2.43883,2.30704,0.438128,1.82051,0.594542,0.926103,1.35266
+12-May-2019 18:00:00,1,0.34489,0.8397,1.3465,1.2386,0.89928,1.765,1.2431,1.3297,0.36102,3.6464,1.09806,2.44882,2.31705,0.44126,1.83004,0.598289,0.930946,1.35855
+12-May-2019 19:00:00,1,0.34018,0.8255,1.3255,1.2238,0.88373,1.7409,1.227,1.3054,0.35573,3.6089,1.0835,2.42406,2.29228,0.433526,1.8065,0.589029,0.918986,1.34398
+12-May-2019 20:00:00,1,0.3275,0.78729,1.2691,1.1837,0.84204,1.6765,1.1837,1.2397,0.3414,3.5058,1.04353,2.35605,2.22506,0.412745,1.74319,0.564062,0.886855,1.30439
+12-May-2019 21:00:00,1,0.3259,0.78249,1.262,1.1787,0.83682,1.6684,1.1782,1.2314,0.33959,3.4926,1.03843,2.34735,2.21656,0.410137,1.73524,0.560919,0.882823,1.29938
+12-May-2019 22:00:00,1,0.32127,0.76857,1.2414,1.164,0.82169,1.645,1.1622,1.2073,0.33431,3.4541,1.02352,2.32195,2.19182,0.402576,1.71218,0.551797,0.871137,1.28479
+12-May-2019 23:00:00,1,0.29742,0.69705,1.1352,1.0886,0.7445,1.5255,1.0793,1.082,0.30682,3.2491,0.944379,2.1869,2.06302,0.36383,1.59378,0.504774,0.811273,1.20867
+13-May-2019 00:00:00,1,0.27196,0.62107,1.0217,1.0081,0.66344,1.3999,0.98924,0.94652,0.27686,3.0179,0.855405,2.0347,1.92281,0.322844,1.46812,0.454496,0.747997,1.1255
+13-May-2019 01:00:00,1,0.25879,0.58191,0.96287,0.96639,0.62207,1.3357,0.94199,0.87562,0.2611,2.8929,0.807467,1.95254,1.84911,0.301795,1.40341,0.428446,0.715522,1.08166
+13-May-2019 02:00:00,1,0.25308,0.56494,0.93734,0.94829,0.60423,1.308,0.92135,0.84467,0.2542,2.8375,0.786243,1.91614,1.81687,0.292693,1.37539,0.417131,0.701484,1.06246
+13-May-2019 03:00:00,1,0.25302,0.56478,0.9371,0.94811,0.60406,1.3078,0.92115,0.84437,0.25413,2.837,0.786038,1.91578,1.81656,0.292606,1.37512,0.417023,0.701349,1.06227
+13-May-2019 04:00:00,1,0.255,0.57066,0.94595,0.95439,0.61024,1.3173,0.92832,0.85512,0.25653,2.8563,0.793427,1.92846,1.82776,0.295759,1.38484,0.420947,0.706213,1.06895
+13-May-2019 05:00:00,1,0.24992,0.55557,0.92323,0.93828,0.59441,1.2928,0.90989,0.82752,0.25037,2.8066,0.774401,1.89582,1.79898,0.287671,1.35992,0.410875,0.69374,1.0518
+13-May-2019 06:00:00,1,0.24996,0.55572,0.92345,0.93844,0.59456,1.293,0.91007,0.82778,0.25043,2.8071,0.774584,1.89613,1.79926,0.287748,1.36016,0.410972,0.693859,1.05196
+13-May-2019 07:00:00,1,0.26896,0.61213,1.0082,0.99856,0.65397,1.3852,0.9785,0.93039,0.27328,2.9897,0.844583,2.01616,1.90606,0.318032,1.45334,0.448554,0.740571,1.11555
+13-May-2019 08:00:00,1,0.29818,0.69931,1.1385,1.091,0.74692,1.5292,1.0819,1.086,0.3077,3.2558,0.946944,2.19128,2.06713,0.365051,1.59752,0.506263,0.813159,1.2111
+13-May-2019 09:00:00,1,0.3216,0.76955,1.2428,1.165,0.82276,1.6466,1.1634,1.209,0.33469,3.4568,1.02457,2.32374,2.19357,0.403108,1.7138,0.552439,0.871958,1.28582
+13-May-2019 10:00:00,1,0.3425,0.83248,1.3358,1.2311,0.89137,1.7528,1.2349,1.3174,0.35834,3.6274,1.09068,2.43628,2.30448,0.43733,1.81808,0.593586,0.924868,1.35116
+13-May-2019 11:00:00,1,0.35969,0.88444,1.4123,1.2854,0.94847,1.8411,1.2931,1.4057,0.3775,3.7618,1.14292,2.52502,2.39444,0.465666,1.90423,0.627402,0.968702,1.404
+13-May-2019 12:00:00,1,0.35139,0.85933,1.3754,1.2592,0.92083,1.7983,1.2651,1.3632,0.36828,3.6976,1.11794,2.4826,2.35115,0.451966,1.8626,0.61108,0.947506,1.37859
+13-May-2019 13:00:00,1,0.33468,0.80893,1.3011,1.2064,0.86562,1.7129,1.2083,1.277,0.34954,3.5646,1.0663,2.39481,2.26322,0.424507,1.77904,0.578209,0.905039,1.32687
+13-May-2019 14:00:00,1,0.32494,0.77959,1.2577,1.1756,0.83366,1.6635,1.1749,1.2264,0.33849,3.4846,1.03533,2.34207,2.2114,0.408557,1.73042,0.559014,0.880381,1.29634
+13-May-2019 15:00:00,1,0.32012,0.76512,1.2363,1.1604,0.81795,1.6392,1.1583,1.2013,0.333,3.4444,1.0198,2.3156,2.18567,0.400702,1.70646,0.549533,0.86824,1.28116
+13-May-2019 16:00:00,1,0.31517,0.75023,1.2142,1.1447,0.80182,1.6142,1.1411,1.1754,0.32733,3.4026,1.00363,2.28805,2.15908,0.392626,1.68181,0.539766,0.855759,1.26547
+13-May-2019 17:00:00,1,0.32309,0.77403,1.2495,1.1698,0.82762,1.6541,1.1685,1.2167,0.33638,3.4692,1.02938,2.33193,2.20153,0.405538,1.72122,0.555372,0.875714,1.29052
+13-May-2019 18:00:00,1,0.33444,0.80821,1.3,1.2057,0.86483,1.7117,1.2075,1.2758,0.34927,3.5626,1.06555,2.39354,2.26196,0.424116,1.77785,0.57774,0.904435,1.32613
+13-May-2019 19:00:00,1,0.33654,0.81452,1.3093,1.2123,0.87172,1.7224,1.2146,1.2866,0.35163,3.5796,1.07213,2.40473,2.27304,0.42755,1.78831,0.581862,0.909745,1.33266
+13-May-2019 20:00:00,1,0.33426,0.80765,1.2992,1.2051,0.86422,1.7108,1.2068,1.2748,0.34906,3.5611,1.06497,2.39254,2.26097,0.42381,1.77692,0.577372,0.903962,1.32555
+13-May-2019 21:00:00,1,0.33938,0.82308,1.322,1.2212,0.88108,1.7369,1.2243,1.3013,0.35483,3.6025,1.081,2.41982,2.28805,0.43221,1.8025,0.587452,0.916951,1.34149
+13-May-2019 22:00:00,1,0.34268,0.83303,1.3366,1.2317,0.89197,1.7537,1.2355,1.3183,0.35854,3.6288,1.09124,2.43723,2.30543,0.437628,1.81899,0.593943,0.925329,1.35172
+13-May-2019 23:00:00,1,0.31612,0.7531,1.2185,1.1477,0.80492,1.619,1.1444,1.1804,0.32842,3.4107,1.00676,2.29338,2.1642,0.39418,1.68655,0.541647,0.85816,1.26849
+14-May-2019 00:00:00,1,0.29536,0.69087,1.126,1.0821,0.73786,1.5152,1.072,1.0711,0.30441,3.2308,0.937332,2.17486,2.05174,0.360489,1.58355,0.500696,0.806112,1.20199
+14-May-2019 01:00:00,1,0.28267,0.65299,1.0694,1.042,0.69736,1.4524,1.0273,1.0038,0.28954,3.1167,0.893412,2.09976,1.98214,0.340037,1.52089,0.475656,0.774533,1.16073
+14-May-2019 02:00:00,1,0.27818,0.63959,1.0494,1.0277,0.6831,1.4303,1.0114,0.97979,0.28423,3.0755,0.87757,2.07265,1.95731,0.332815,1.49873,0.46678,0.763386,1.146
+14-May-2019 03:00:00,1,0.28221,0.65161,1.0674,1.0405,0.6959,1.4502,1.0257,1.0013,0.289,3.1125,0.891795,2.097,1.9796,0.339296,1.51862,0.474746,0.77339,1.15922
+14-May-2019 04:00:00,1,0.29716,0.69626,1.134,1.0878,0.74365,1.5241,1.0784,1.0806,0.30651,3.2468,0.94348,2.18537,2.06158,0.363403,1.59247,0.504253,0.810614,1.20782
+14-May-2019 05:00:00,1,0.32151,0.76928,1.2424,1.1648,0.82246,1.6462,1.163,1.2085,0.33458,3.456,1.02428,2.32325,2.19309,0.402962,1.71336,0.552263,0.871733,1.28554
+14-May-2019 06:00:00,1,0.38315,0.95555,1.5165,1.3595,1.0273,1.9628,1.3714,1.5249,0.40324,3.9368,1.21117,2.64068,2.51533,0.50457,2.02223,0.673476,1.02892,1.47481
+14-May-2019 07:00:00,1,0.42951,1.0968,1.7221,1.5058,1.1859,2.2077,1.5229,1.7566,0.45277,4.2556,1.33634,2.85187,2.74837,0.582251,2.25697,0.764341,1.14925,1.61062
+14-May-2019 08:00:00,1,0.45695,1.1809,1.8437,1.5923,1.2814,2.355,1.6105,1.8913,0.48129,4.4283,1.40468,2.96657,2.88279,0.628689,2.3968,0.817999,1.22125,1.68855
+14-May-2019 09:00:00,1,0.46058,1.1921,1.8598,1.6037,1.2942,2.3747,1.6221,1.909,0.48503,4.4504,1.41342,2.98122,2.90041,0.634866,2.41537,0.825101,1.23083,1.69875
+14-May-2019 10:00:00,1,0.47245,1.2286,1.9123,1.6412,1.336,2.4392,1.6595,1.9667,0.49717,4.521,1.44152,3.02821,2.9577,0.655091,2.47613,0.848301,1.2622,1.73185
+14-May-2019 11:00:00,1,0.48327,1.2619,1.9602,1.6752,1.3742,2.4981,1.6934,2.019,0.50815,4.5836,1.46646,3.06985,3.00948,0.673561,2.53157,0.869415,1.29086,1.76172
+14-May-2019 12:00:00,1,0.47835,1.2467,1.9384,1.6597,1.3568,2.4713,1.678,1.9952,0.50316,4.5553,1.45518,3.05104,2.98596,0.66515,2.50633,0.859809,1.27781,1.74816
+14-May-2019 13:00:00,1,0.47494,1.2362,1.9233,1.649,1.3448,2.4527,1.6673,1.9787,0.49971,4.5356,1.44731,3.03789,2.96965,0.659338,2.48889,0.853162,1.26879,1.73875
+14-May-2019 14:00:00,1,0.46674,1.211,1.887,1.6232,1.3158,2.4081,1.6415,1.939,0.49134,4.4873,1.42809,3.00576,2.93019,0.645352,2.44688,0.83714,1.24709,1.71596
+14-May-2019 15:00:00,1,0.45708,1.1813,1.8442,1.5927,1.2819,2.3558,1.611,1.8919,0.48143,4.4291,1.40499,2.9671,2.88343,0.628912,2.39747,0.818256,1.2216,1.68892
+14-May-2019 16:00:00,1,0.44411,1.1415,1.7868,1.5518,1.2365,2.2858,1.5697,1.8284,0.46801,4.3489,1.3732,2.9138,2.82018,0.606909,2.33127,0.792892,1.18748,1.65229
+14-May-2019 17:00:00,1,0.43716,1.1202,1.756,1.5299,1.2124,2.2485,1.5474,1.7943,0.46077,4.3049,1.3558,2.88458,2.78607,0.595156,2.29587,0.7793,1.16926,1.63251
+14-May-2019 18:00:00,1,0.4351,1.1139,1.7469,1.5234,1.2052,2.2375,1.5408,1.7841,0.45862,4.2917,1.35059,2.87581,2.77592,0.591673,2.28538,0.775267,1.16386,1.62662
+14-May-2019 19:00:00,1,0.43102,1.1014,1.7288,1.5105,1.1911,2.2157,1.5277,1.764,0.45434,4.2653,1.34019,2.85834,2.75579,0.584784,2.26461,0.76728,1.15318,1.61493
+14-May-2019 20:00:00,1,0.41862,1.0635,1.6738,1.4714,1.1483,2.1496,1.4877,1.7026,0.44128,4.1838,1.30805,2.80424,2.69428,0.563903,2.20163,0.743008,1.12082,1.57918
+14-May-2019 21:00:00,1,0.41188,1.043,1.644,1.4502,1.1251,2.1139,1.4658,1.6691,0.43413,4.1384,1.29021,2.77418,2.66062,0.552581,2.16745,0.729806,1.10327,1.55959
+14-May-2019 22:00:00,1,0.38319,0.95567,1.5167,1.3596,1.0274,1.963,1.3715,1.5251,0.40328,3.937,1.21129,2.64087,2.51554,0.504636,2.02243,0.673554,1.02902,1.47493
+14-May-2019 23:00:00,1,0.33864,0.82084,1.3187,1.2189,0.87863,1.7331,1.2218,1.2974,0.354,3.5965,1.07869,2.41588,2.28413,0.430991,1.79878,0.58599,0.915066,1.33918
+15-May-2019 00:00:00,1,0.31294,0.74355,1.2043,1.1377,0.7946,1.603,1.1334,1.1637,0.32477,3.3837,0.996326,2.27558,2.14711,0.389005,1.67075,0.535381,0.850165,1.2584
+15-May-2019 01:00:00,1,0.29833,0.69978,1.1392,1.0915,0.74743,1.53,1.0825,1.0869,0.30788,3.2572,0.947478,2.1922,2.06799,0.365306,1.5983,0.506573,0.813552,1.21161
+15-May-2019 02:00:00,1,0.29112,0.67822,1.1071,1.0687,0.72431,1.4942,1.0572,1.0487,0.29947,3.1931,0.922808,2.15004,2.0286,0.353654,1.56262,0.492344,0.795558,1.18828
+15-May-2019 03:00:00,1,0.29499,0.68978,1.1243,1.0809,0.7367,1.5134,1.0708,1.0692,0.30399,3.2276,0.936087,2.17273,2.04975,0.3599,1.58175,0.499977,0.805203,1.20081
+15-May-2019 04:00:00,1,0.30481,0.71919,1.1681,1.112,0.7683,1.5623,1.1051,1.121,0.3154,3.3138,0.969334,2.22952,2.1032,0.375804,1.63041,0.519357,0.829768,1.23244
+15-May-2019 05:00:00,1,0.32633,0.78378,1.2639,1.18,0.83822,1.6705,1.1797,1.2336,0.34008,3.4961,1.0398,2.34969,2.21884,0.410838,1.73738,0.561764,0.883907,1.30073
+15-May-2019 06:00:00,1,0.38268,0.95409,1.5144,1.358,1.0256,1.9603,1.3698,1.5225,0.40272,3.9333,1.20982,2.63839,2.51289,0.503775,2.01982,0.672538,1.02768,1.47338
+15-May-2019 07:00:00,1,0.43456,1.1123,1.7445,1.5217,1.2033,2.2346,1.5391,1.7815,0.45805,4.2882,1.34921,2.87351,2.77326,0.59076,2.28262,0.774209,1.16244,1.62508
+15-May-2019 08:00:00,1,0.4629,1.1992,1.87,1.6111,1.3023,2.3873,1.6294,1.9203,0.48741,4.4643,1.41897,2.9905,2.91164,0.638812,2.42723,0.829635,1.23695,1.70524
+15-May-2019 09:00:00,1,0.47102,1.2242,1.906,1.6367,1.3309,2.4314,1.655,1.9597,0.49572,4.5126,1.43817,3.02261,2.95081,0.652649,2.4688,0.845504,1.25841,1.72787
+15-May-2019 10:00:00,1,0.48379,1.2635,1.9625,1.6769,1.3761,2.501,1.695,2.0215,0.50867,4.5865,1.46764,3.07183,3.01196,0.67445,2.53424,0.87043,1.29224,1.76315
+15-May-2019 11:00:00,1,0.49557,1.2998,2.0146,1.714,1.418,2.5655,1.7317,2.0781,0.52053,4.6527,1.4941,3.11591,3.06793,0.694627,2.59474,0.893414,1.32355,1.79539
+15-May-2019 12:00:00,1,0.49336,1.293,2.0048,1.707,1.4101,2.5534,1.7248,2.0675,0.51831,4.6404,1.48919,3.10775,3.05748,0.690843,2.5834,0.88911,1.31768,1.78937
+15-May-2019 13:00:00,1,0.49214,1.2892,1.9994,1.7032,1.4058,2.5467,1.721,2.0617,0.51709,4.6336,1.48647,3.10321,3.05168,0.688748,2.57712,0.886725,1.31443,1.78604
+15-May-2019 14:00:00,1,0.48446,1.2655,1.9655,1.679,1.3785,2.5047,1.6971,2.0247,0.50935,4.5904,1.46918,3.07439,3.01518,0.675605,2.5377,0.871748,1.29403,1.76501
+15-May-2019 15:00:00,1,0.47039,1.2222,1.9032,1.6347,1.3287,2.4279,1.653,1.9567,0.49507,4.5089,1.43668,3.02012,2.94776,0.651566,2.46555,0.844264,1.25673,1.72611
+15-May-2019 16:00:00,1,0.46129,1.1942,1.8629,1.606,1.2967,2.3785,1.6243,1.9124,0.48576,4.4546,1.41512,2.98406,2.90384,0.63607,2.41899,0.826485,1.2327,1.70073
+15-May-2019 17:00:00,1,0.45286,1.1684,1.8256,1.5794,1.2671,2.333,1.5976,1.8713,0.47708,4.4033,1.39476,2.94996,2.86294,0.621755,2.37594,0.810017,1.2105,1.67706
+15-May-2019 18:00:00,1,0.45052,1.1612,1.8152,1.572,1.2589,2.3204,1.5901,1.8599,0.47466,4.3889,1.38904,2.94037,2.85153,0.617782,2.36399,0.805438,1.20434,1.67046
+15-May-2019 19:00:00,1,0.44562,1.1462,1.7935,1.5566,1.2418,2.294,1.5745,1.8359,0.46958,4.3584,1.37696,2.9201,2.82759,0.609473,2.33898,0.795853,1.19145,1.65659
+15-May-2019 20:00:00,1,0.43086,1.101,1.7281,1.51,1.1905,2.2148,1.5272,1.7632,0.45418,4.2643,1.33979,2.85767,2.75501,0.584519,2.26381,0.766972,1.15277,1.61448
+15-May-2019 21:00:00,1,0.42234,1.0749,1.6903,1.4831,1.1611,2.1694,1.4997,1.721,0.44521,4.2085,1.31778,2.82064,2.71279,0.57016,2.22051,0.750292,1.13051,1.58995
+15-May-2019 22:00:00,1,0.39268,0.98448,1.5588,1.3895,1.0595,2.0126,1.4029,1.5729,0.41355,4.0051,1.23791,2.6859,2.56382,0.520438,2.07027,0.692156,1.05349,1.50314
+15-May-2019 23:00:00,1,0.35266,0.86315,1.381,1.2632,0.92503,1.8048,1.2694,1.3696,0.36969,3.7074,1.12177,2.48911,2.35776,0.454048,1.86893,0.613565,0.950728,1.38247
+16-May-2019 00:00:00,1,0.32334,0.7748,1.2506,1.1706,0.82845,1.6554,1.1694,1.2181,0.33668,3.4713,1.0302,2.33334,2.20289,0.405955,1.72249,0.555876,0.87636,1.29132
+16-May-2019 01:00:00,1,0.3099,0.73442,1.1907,1.1281,0.78472,1.5877,1.1228,1.1477,0.32127,3.3577,0.986265,2.25842,2.13068,0.384054,1.65563,0.529377,0.842514,1.24869
+16-May-2019 02:00:00,1,0.30757,0.72745,1.1804,1.1207,0.7772,1.5761,1.1148,1.1355,0.31859,3.3377,0.978542,2.24524,2.11812,0.380278,1.64409,0.524793,0.83668,1.24127
+16-May-2019 03:00:00,1,0.31062,0.73659,1.194,1.1303,0.78706,1.5914,1.1254,1.1515,0.3221,3.3639,0.988658,2.2625,2.13458,0.385228,1.65921,0.530802,0.844328,1.251
+16-May-2019 04:00:00,1,0.31932,0.7627,1.2327,1.1578,0.81533,1.6351,1.1555,1.1971,0.33208,3.4377,1.01719,2.31115,2.18137,0.399391,1.70246,0.54795,0.866214,1.27862
+16-May-2019 05:00:00,1,0.33803,0.81901,1.316,1.217,0.87663,1.73,1.2197,1.2943,0.35331,3.5916,1.07679,2.41266,2.28092,0.429995,1.79575,0.584796,0.913526,1.3373
+16-May-2019 06:00:00,1,0.3863,0.96511,1.5305,1.3694,1.0379,1.9793,1.3818,1.5408,0.40666,3.9595,1.22008,2.65575,2.53141,0.509815,2.03811,0.679657,1.03704,1.48421
+16-May-2019 07:00:00,1,0.42967,1.0973,1.7228,1.5063,1.1864,2.2085,1.5234,1.7573,0.45293,4.2566,1.33675,2.85255,2.74914,0.582515,2.25777,0.764648,1.14966,1.61107
+16-May-2019 08:00:00,1,0.45069,1.1617,1.816,1.5726,1.2595,2.3213,1.5907,1.8607,0.47484,4.39,1.38946,2.94107,2.85237,0.618075,2.36487,0.805776,1.20479,1.67094
+16-May-2019 09:00:00,1,0.46264,1.1984,1.8689,1.6102,1.3014,2.3858,1.6286,1.919,0.48714,4.4627,1.41834,2.98945,2.91036,0.638363,2.42588,0.829119,1.23625,1.7045
+16-May-2019 10:00:00,1,0.4725,1.2287,1.9125,1.6413,1.3361,2.4394,1.6596,1.9669,0.49722,4.5213,1.44161,3.02837,2.9579,0.655162,2.47635,0.848383,1.26231,1.73197
+16-May-2019 11:00:00,1,0.48417,1.2646,1.9641,1.6781,1.3774,2.503,1.6962,2.0233,0.50906,4.5887,1.46851,3.07327,3.01377,0.6751,2.53619,0.871171,1.29325,1.7642
+16-May-2019 12:00:00,1,0.47541,1.2377,1.9254,1.6505,1.3464,2.4552,1.6688,1.981,0.50018,4.5382,1.44838,3.03968,2.97187,0.660126,2.49125,0.854064,1.27002,1.74003
+16-May-2019 13:00:00,1,0.46908,1.2182,1.8974,1.6305,1.3241,2.4208,1.6489,1.9503,0.49374,4.5012,1.43361,3.015,2.94148,0.649342,2.45887,0.841715,1.25328,1.72248
+16-May-2019 14:00:00,1,0.46041,1.1916,1.859,1.6032,1.2936,2.3738,1.6215,1.9082,0.48486,4.4493,1.41302,2.98055,2.8996,0.634582,2.41452,0.824776,1.23039,1.69828
+16-May-2019 15:00:00,1,0.44825,1.1542,1.8052,1.5649,1.251,2.3081,1.5829,1.8488,0.47231,4.3748,1.38346,2.93101,2.84045,0.613933,2.35241,0.801,1.19837,1.66404
+16-May-2019 16:00:00,1,0.43426,1.1114,1.7432,1.5208,1.2023,2.233,1.5381,1.78,0.45774,4.2863,1.34847,2.87225,2.77181,0.590263,2.28113,0.773633,1.16167,1.62424
+16-May-2019 17:00:00,1,0.42827,1.093,1.7166,1.5018,1.1815,2.201,1.5188,1.7504,0.45146,4.2475,1.33314,2.84648,2.74219,0.580146,2.25063,0.761898,1.14599,1.60704
+16-May-2019 18:00:00,1,0.42466,1.082,1.7006,1.4905,1.1691,2.1818,1.5072,1.7325,0.44766,4.2238,1.32381,2.83079,2.7243,0.574064,2.23229,0.754831,1.13656,1.59664
+16-May-2019 19:00:00,1,0.4177,1.0608,1.6698,1.4685,1.1451,2.1448,1.4847,1.698,0.44031,4.1776,1.30564,2.80019,2.68972,0.562364,2.19698,0.741216,1.11843,1.57653
+16-May-2019 20:00:00,1,0.40104,1.0099,1.5959,1.416,1.088,2.0566,1.4304,1.6149,0.42255,4.0639,1.26096,2.72484,2.60615,0.53442,2.11257,0.708564,1.07514,1.52785
+16-May-2019 21:00:00,1,0.39194,0.98225,1.5555,1.3872,1.057,2.0088,1.4004,1.5692,0.41276,3.9999,1.23587,2.68245,2.56009,0.519213,2.06656,0.690716,1.05159,1.50096
+16-May-2019 22:00:00,1,0.3727,0.92384,1.4701,1.3265,0.99204,1.9084,1.3367,1.472,0.39183,3.86,1.18119,2.58992,2.46174,0.487206,1.9696,0.652961,1.00204,1.44345
+16-May-2019 23:00:00,1,0.33321,0.80449,1.2945,1.2017,0.86077,1.7054,1.2032,1.2693,0.34787,3.5526,1.06166,2.38691,2.2554,0.422091,1.77168,0.575306,0.901304,1.32227
+17-May-2019 00:00:00,1,0.30713,0.72614,1.1784,1.1193,0.77579,1.5739,1.1132,1.1332,0.31808,3.3339,0.977087,2.24276,2.11576,0.379569,1.64192,0.523932,0.835585,1.23987
+17-May-2019 01:00:00,1,0.29302,0.68389,1.1156,1.0747,0.73039,1.5036,1.0638,1.0588,0.30169,3.2101,0.929339,2.1612,2.03899,0.356718,1.57201,0.49609,0.80029,1.19444
+17-May-2019 02:00:00,1,0.28461,0.65877,1.0781,1.0481,0.70353,1.462,1.0342,1.0141,0.29182,3.1344,0.900195,2.11137,1.99281,0.343154,1.53044,0.479481,0.779345,1.16706
+17-May-2019 03:00:00,1,0.28298,0.6539,1.0708,1.0429,0.69833,1.4539,1.0284,1.0054,0.2899,3.1195,0.894482,2.10159,1.98382,0.340527,1.52239,0.476258,0.775291,1.16173
+17-May-2019 04:00:00,1,0.28209,0.65126,1.0668,1.0401,0.69552,1.4496,1.0253,1.0007,0.28886,3.1115,0.89138,2.09628,1.97894,0.339106,1.51803,0.474513,0.773096,1.15884
+17-May-2019 05:00:00,1,0.26969,0.61431,1.0115,1.0009,0.65628,1.3887,0.98112,0.93433,0.27415,2.9966,0.847232,2.0207,1.91016,0.319206,1.45695,0.450005,0.742384,1.11798
+17-May-2019 06:00:00,1,0.26381,0.59683,0.98529,0.98228,0.63779,1.3601,0.96006,0.90271,0.26713,2.941,0.8259,1.98415,1.8773,0.309807,1.42806,0.438381,0.727882,1.09844
+17-May-2019 07:00:00,1,0.27822,0.6397,1.0496,1.0279,0.68322,1.4305,1.0115,0.97999,0.28428,3.0759,0.877702,2.07288,1.95751,0.332875,1.49892,0.466854,0.763478,1.14612
+17-May-2019 08:00:00,1,0.30144,0.70907,1.1531,1.1013,0.75741,1.5455,1.0934,1.1032,0.31149,3.2844,0.957985,2.21014,2.08488,0.370332,1.61367,0.512697,0.821315,1.22161
+17-May-2019 09:00:00,1,0.32179,0.77013,1.2437,1.1657,0.82338,1.6476,1.164,1.21,0.3349,3.4584,1.02519,2.3248,2.19459,0.40342,1.71476,0.552817,0.872442,1.28643
+17-May-2019 10:00:00,1,0.33798,0.81888,1.3158,1.2168,0.87648,1.7297,1.2196,1.2941,0.35326,3.5912,1.07665,2.41242,2.28068,0.429921,1.79553,0.584707,0.913411,1.33716
+17-May-2019 11:00:00,1,0.3577,0.8784,1.4034,1.2791,0.94182,1.8308,1.2864,1.3955,0.37529,3.7465,1.13696,2.5149,2.38406,0.462371,1.89422,0.623482,0.963604,1.39791
+17-May-2019 12:00:00,1,0.352,0.86116,1.3781,1.2611,0.92285,1.8014,1.2671,1.3663,0.36896,3.7023,1.11977,2.48572,2.35432,0.452964,1.86563,0.612271,0.94905,1.38045
+17-May-2019 13:00:00,1,0.33664,0.81483,1.3098,1.2126,0.87206,1.7229,1.215,1.2871,0.35175,3.5804,1.07246,2.40528,2.27359,0.427719,1.78882,0.582066,0.910007,1.33298
+17-May-2019 14:00:00,1,0.32431,0.77772,1.2549,1.1736,0.83163,1.6603,1.1727,1.2231,0.33778,3.4794,1.03333,2.33867,2.20809,0.407543,1.72733,0.557792,0.878814,1.29439
+17-May-2019 15:00:00,1,0.31733,0.75673,1.2238,1.1516,0.80885,1.6251,1.1486,1.1867,0.32981,3.421,1.01071,2.30012,2.1707,0.39615,1.69257,0.544031,0.861205,1.27233
+17-May-2019 16:00:00,1,0.31213,0.74114,1.2007,1.1351,0.79198,1.599,1.1306,1.1595,0.32384,3.3768,0.993668,2.27105,2.14276,0.387694,1.66675,0.533791,0.848138,1.25583
+17-May-2019 17:00:00,1,0.31727,0.75654,1.2236,1.1514,0.80865,1.6248,1.1484,1.1864,0.32973,3.4204,1.0105,2.29976,2.17036,0.396046,1.69225,0.543905,0.861045,1.27213
+17-May-2019 18:00:00,1,0.3239,0.77647,1.2531,1.1723,0.83027,1.6582,1.1713,1.221,0.33731,3.476,1.03199,2.33639,2.20587,0.406864,1.72526,0.556972,0.877764,1.29308
+17-May-2019 19:00:00,1,0.32408,0.77701,1.2539,1.1729,0.83085,1.6591,1.1719,1.2219,0.33751,3.4775,1.03257,2.33737,2.20682,0.407156,1.72615,0.557325,0.878216,1.29364
+17-May-2019 20:00:00,1,0.31987,0.76437,1.2352,1.1596,0.81714,1.6379,1.1574,1.2,0.33272,3.4424,1.01899,2.31423,2.18434,0.400296,1.70522,0.549043,0.867613,1.28038
+17-May-2019 21:00:00,1,0.32584,0.78232,1.2617,1.1785,0.83663,1.6681,1.178,1.2311,0.33952,3.4921,1.03824,2.34704,2.21625,0.410043,1.73495,0.560805,0.882678,1.2992
+17-May-2019 22:00:00,1,0.32307,0.77399,1.2494,1.1697,0.82757,1.6541,1.1684,1.2167,0.33637,3.4691,1.02934,2.33186,2.20146,0.405517,1.72115,0.555347,0.875682,1.29048
+17-May-2019 23:00:00,1,0.29928,0.70261,1.1434,1.0945,0.75046,1.5347,1.0858,1.0918,0.30898,3.2655,0.95068,2.19767,2.07313,0.366833,1.60297,0.508435,0.815911,1.21465
+18-May-2019 00:00:00,1,0.27564,0.63201,1.038,1.0197,0.67505,1.4178,1.0023,0.9662,0.28122,3.0521,0.868542,2.0572,1.94322,0.328735,1.48621,0.461758,0.757089,1.13763
+18-May-2019 01:00:00,1,0.26224,0.59216,0.97827,0.97731,0.63287,1.3524,0.95441,0.89424,0.26524,2.926,0.820153,1.97429,1.86849,0.307298,1.42034,0.435272,0.724011,1.0932
+18-May-2019 02:00:00,1,0.25885,0.58207,0.96312,0.96656,0.62224,1.336,0.94219,0.87591,0.26116,2.8934,0.80767,1.95289,1.84942,0.301883,1.40368,0.428555,0.715658,1.08185
+18-May-2019 03:00:00,1,0.26024,0.5862,0.96932,0.97096,0.62659,1.3427,0.9472,0.88342,0.26284,2.9068,0.812792,1.96167,1.85724,0.304099,1.4105,0.431306,0.719077,1.0865
+18-May-2019 04:00:00,1,0.26761,0.60811,1.0022,0.99429,0.64972,1.3786,0.97367,0.92314,0.27167,2.977,0.839702,2.0078,1.89853,0.315873,1.44671,0.445886,0.73724,1.11107
+18-May-2019 05:00:00,1,0.28112,0.64835,1.0625,1.037,0.69242,1.4448,1.0218,0.99547,0.28771,3.1025,0.887945,2.09041,1.97355,0.337536,1.51322,0.472584,0.770672,1.15564
+18-May-2019 06:00:00,1,0.31666,0.75472,1.2209,1.1494,0.80668,1.6217,1.1463,1.1832,0.32904,3.4153,1.00853,2.29639,2.16711,0.395061,1.68924,0.542713,0.859522,1.27021
+18-May-2019 07:00:00,1,0.35449,0.86871,1.3892,1.269,0.93114,1.8143,1.2756,1.3791,0.37173,3.7217,1.12732,2.49854,2.36735,0.45708,1.87814,0.617179,0.955418,1.38811
+18-May-2019 08:00:00,1,0.3837,0.9572,1.5189,1.3612,1.0291,1.9657,1.3732,1.5277,0.40383,3.9407,1.21272,2.6433,2.51812,0.505476,2.02497,0.674544,1.03032,1.47644
+18-May-2019 09:00:00,1,0.39408,0.98877,1.565,1.394,1.0643,2.02,1.4075,1.58,0.41507,4.0151,1.24182,2.69251,2.57096,0.522791,2.07739,0.694921,1.05713,1.50732
+18-May-2019 10:00:00,1,0.40237,1.014,1.6018,1.4201,1.0926,2.0636,1.4347,1.6216,0.42398,4.0732,1.26458,2.73095,2.61285,0.536644,2.11929,0.71117,1.07859,1.53176
+18-May-2019 11:00:00,1,0.42095,1.0707,1.6842,1.4788,1.1563,2.162,1.4952,1.7141,0.44374,4.1993,1.31415,2.81452,2.70587,0.567817,2.21344,0.747565,1.12688,1.58592
+18-May-2019 12:00:00,1,0.42527,1.0839,1.7033,1.4924,1.1712,2.185,1.5092,1.7356,0.4483,4.2278,1.3254,2.83346,2.72734,0.575095,2.2354,0.75603,1.13816,1.59841
+18-May-2019 13:00:00,1,0.41265,1.0453,1.6474,1.4526,1.1277,2.118,1.4683,1.6729,0.43495,4.1436,1.29226,2.77764,2.66447,0.553874,2.17135,0.731314,1.10528,1.56183
+18-May-2019 14:00:00,1,0.40026,1.0076,1.5924,1.4135,1.0853,2.0525,1.4278,1.611,0.42171,4.0584,1.25882,2.72122,2.60219,0.533107,2.1086,0.707025,1.07311,1.52554
+18-May-2019 15:00:00,1,0.39509,0.99184,1.5695,1.3972,1.0678,2.0253,1.4108,1.5851,0.41616,4.0222,1.24462,2.69723,2.57608,0.524477,2.08249,0.6969,1.05974,1.5103
+18-May-2019 16:00:00,1,0.3861,0.96449,1.5296,1.3688,1.0372,1.9782,1.3812,1.5398,0.40643,3.9581,1.2195,2.65477,2.53036,0.509471,2.03707,0.679252,1.0365,1.4836
+18-May-2019 17:00:00,1,0.3863,0.96509,1.5304,1.3694,1.0379,1.9792,1.3818,1.5408,0.40665,3.9595,1.22006,2.65572,2.53138,0.509804,2.03808,0.679644,1.03702,1.48419
+18-May-2019 18:00:00,1,0.38417,0.95863,1.521,1.3627,1.0307,1.9681,1.3748,1.5301,0.40434,3.9441,1.21405,2.64556,2.52052,0.506261,2.02735,0.67547,1.03153,1.47785
+18-May-2019 19:00:00,1,0.37951,0.94447,1.5003,1.348,1.0149,1.9438,1.3593,1.5065,0.39927,3.9102,1.20079,2.6231,2.49667,0.498503,2.00385,0.666317,1.01952,1.4639
+18-May-2019 20:00:00,1,0.36482,0.89995,1.4351,1.3016,0.9656,1.8675,1.3103,1.4319,0.38317,3.8008,1.15813,2.55082,2.42103,0.474142,1.92996,0.637475,0.981818,1.4196
+18-May-2019 21:00:00,1,0.35678,0.87563,1.3994,1.2762,0.93877,1.8261,1.2833,1.3908,0.37428,3.7394,1.13422,2.51025,2.3793,0.460861,1.88963,0.621684,0.961268,1.39512
+18-May-2019 22:00:00,1,0.34212,0.83135,1.3342,1.2299,0.89013,1.7509,1.2337,1.3154,0.35791,3.6244,1.08952,2.4343,2.3025,0.436713,1.8162,0.592847,0.923914,1.34999
+18-May-2019 23:00:00,1,0.31215,0.74117,1.2008,1.1352,0.79202,1.599,1.1307,1.1596,0.32386,3.377,0.99371,2.27112,2.14283,0.387714,1.66681,0.533816,0.84817,1.25587
+19-May-2019 00:00:00,1,0.2898,0.67428,1.1012,1.0645,0.72009,1.4877,1.0525,1.0417,0.29792,3.1813,0.918251,2.14225,2.02136,0.351524,1.5561,0.489738,0.792269,1.18399
+19-May-2019 01:00:00,1,0.27573,0.63227,1.0384,1.02,0.67532,1.4183,1.0026,0.96666,0.28133,3.0529,0.868851,2.05772,1.9437,0.328874,1.48664,0.461929,0.757304,1.13792
+19-May-2019 02:00:00,1,0.27051,0.61673,1.0152,1.0035,0.65884,1.3927,0.98403,0.9387,0.27512,3.0042,0.850166,2.02573,1.91469,0.32051,1.46095,0.451615,0.744396,1.12068
+19-May-2019 03:00:00,1,0.26771,0.60842,1.0027,0.99462,0.65005,1.3791,0.97404,0.9237,0.27179,2.9779,0.840081,2.00845,1.89912,0.31604,1.44722,0.446093,0.737499,1.11142
+19-May-2019 04:00:00,1,0.27167,0.62021,1.0204,1.0072,0.66252,1.3984,0.9882,0.94496,0.27651,3.0151,0.854362,2.03291,1.92119,0.322379,1.46669,0.453922,0.74728,1.12454
+19-May-2019 05:00:00,1,0.27343,0.62543,1.0282,1.0127,0.66807,1.407,0.99446,0.95437,0.2786,3.0315,0.860654,2.04369,1.93095,0.325192,1.47533,0.457391,0.75162,1.13034
+19-May-2019 06:00:00,1,0.28659,0.66469,1.0869,1.0544,0.70984,1.4718,1.0412,1.0246,0.29415,3.1524,0.907112,2.1232,2.00373,0.346348,1.54024,0.483397,0.784276,1.17354
+19-May-2019 07:00:00,1,0.31394,0.74655,1.2087,1.1408,0.79783,1.608,1.1369,1.1689,0.32592,3.3922,0.999607,2.28118,2.15248,0.390628,1.67571,0.537347,0.852672,1.26157
+19-May-2019 08:00:00,1,0.34797,0.849,1.3602,1.2484,0.90948,1.7808,1.2535,1.3456,0.36446,3.6707,1.10751,2.46489,2.33324,0.446332,1.84547,0.604353,0.938791,1.36806
+19-May-2019 09:00:00,1,0.37307,0.92494,1.4717,1.3276,0.99326,1.9103,1.3379,1.4738,0.39223,3.8627,1.18225,2.5917,2.46361,0.487808,1.97142,0.653674,1.00297,1.44454
+19-May-2019 10:00:00,1,0.38659,0.96598,1.5317,1.3703,1.0389,1.9808,1.3828,1.5423,0.40697,3.9616,1.22089,2.65711,2.53287,0.510291,2.03956,0.680218,1.03777,1.48506
+19-May-2019 11:00:00,1,0.39467,0.99054,1.5676,1.3958,1.0663,2.0231,1.4094,1.5829,0.4157,4.0192,1.24343,2.69523,2.57391,0.523762,2.08033,0.696061,1.05863,1.50904
+19-May-2019 12:00:00,1,0.38875,0.97256,1.5414,1.3772,1.0462,1.9921,1.3899,1.5532,0.40931,3.9771,1.22697,2.66739,2.54389,0.513897,2.05047,0.684463,1.04336,1.4915
+19-May-2019 13:00:00,1,0.37643,0.93513,1.4866,1.3383,1.0046,1.9278,1.3491,1.4909,0.39591,3.8876,1.19196,2.60815,2.48089,0.493389,1.98835,0.660275,1.01161,1.45466
+19-May-2019 14:00:00,1,0.36392,0.89723,1.4311,1.2988,0.96259,1.8629,1.3073,1.4273,0.38217,3.794,1.15547,2.54631,2.41637,0.472654,1.92545,0.635708,0.979516,1.41687
+19-May-2019 15:00:00,1,0.35612,0.87362,1.3964,1.2741,0.93655,1.8226,1.2811,1.3874,0.37354,3.7343,1.13222,2.50685,2.37583,0.459761,1.88629,0.620374,0.959566,1.39308
+19-May-2019 16:00:00,1,0.34948,0.85356,1.3669,1.2532,0.91448,1.7885,1.2586,1.3533,0.36615,3.6826,1.11212,2.47272,2.34114,0.448814,1.85302,0.607319,0.942632,1.37271
+19-May-2019 17:00:00,1,0.35092,0.8579,1.3733,1.2577,0.91926,1.7959,1.2635,1.3607,0.36775,3.6938,1.11649,2.48015,2.34866,0.451183,1.86022,0.610146,0.946295,1.37713
+19-May-2019 18:00:00,1,0.35239,0.86234,1.3798,1.2623,0.92414,1.8034,1.2685,1.3683,0.36939,3.7053,1.12096,2.48773,2.35636,0.453608,1.86759,0.61304,0.950047,1.38165
+19-May-2019 19:00:00,1,0.34644,0.84438,1.3534,1.2435,0.90441,1.7729,1.2483,1.3377,0.36275,3.6587,1.10282,2.45692,2.3252,0.44381,1.8378,0.601339,0.934891,1.36334
+19-May-2019 20:00:00,1,0.33129,0.79869,1.286,1.1957,0.85446,1.6957,1.1967,1.2594,0.3457,3.5369,1.05558,2.37656,2.2452,0.418942,1.76208,0.57152,0.896435,1.31626
+19-May-2019 21:00:00,1,0.32556,0.78145,1.2605,1.1776,0.83569,1.6666,1.177,1.2296,0.3392,3.4897,1.03732,2.34546,2.21471,0.409572,1.73352,0.560238,0.88195,1.29829
+19-May-2019 22:00:00,1,0.31757,0.75744,1.2249,1.1523,0.80962,1.6263,1.1494,1.1879,0.33008,3.4229,1.01148,2.30143,2.17197,0.396534,1.69374,0.544495,0.861799,1.27308
+19-May-2019 23:00:00,1,0.29794,0.69861,1.1375,1.0903,0.74617,1.5281,1.0811,1.0848,0.30743,3.2537,0.946153,2.18993,2.06586,0.364675,1.59637,0.505804,0.812577,1.21035
+20-May-2019 00:00:00,1,0.2754,0.63131,1.037,1.019,0.67431,1.4167,1.0015,0.96494,0.28094,3.0499,0.867703,2.05576,1.94192,0.328358,1.48505,0.461293,0.756506,1.13686
+20-May-2019 01:00:00,1,0.26007,0.58571,0.96859,0.97044,0.62607,1.3419,0.9466,0.88253,0.26264,2.9052,0.812183,1.96063,1.85631,0.303836,1.40969,0.430979,0.71867,1.08595
+20-May-2019 02:00:00,1,0.25101,0.55882,0.92812,0.94175,0.59781,1.2981,0.91387,0.83347,0.2517,2.8174,0.778514,1.90287,1.80519,0.289411,1.36528,0.413044,0.696422,1.0555
+20-May-2019 03:00:00,1,0.25012,0.55617,0.92413,0.93892,0.59503,1.2938,0.91062,0.82861,0.25061,2.8086,0.775156,1.89711,1.80012,0.28799,1.3609,0.411273,0.694231,1.05248
+20-May-2019 04:00:00,1,0.25141,0.55999,0.92989,0.943,0.59904,1.3,0.9153,0.83561,0.25218,2.8212,0.779997,1.90542,1.80743,0.290039,1.36722,0.413827,0.697392,1.05683
+20-May-2019 05:00:00,1,0.24929,0.55372,0.92043,0.9363,0.59246,1.2898,0.90762,0.82412,0.24961,2.8005,0.772045,1.89177,1.79543,0.286676,1.35686,0.409635,0.692206,1.04968
+20-May-2019 06:00:00,1,0.25407,0.56789,0.94178,0.95143,0.60733,1.3128,0.92494,0.85006,0.2554,2.8472,0.789951,1.9225,1.82249,0.294274,1.38026,0.419099,0.703922,1.06581
+20-May-2019 07:00:00,1,0.27338,0.62528,1.028,1.0126,0.66791,1.4068,0.99429,0.9541,0.27854,3.0311,0.860477,2.04339,1.93068,0.325113,1.47509,0.457294,0.751498,1.13018
+20-May-2019 08:00:00,1,0.30233,0.71175,1.1571,1.1041,0.7603,1.5499,1.0965,1.108,0.31253,3.2922,0.961002,2.2153,2.08974,0.371782,1.61811,0.514464,0.823556,1.22448
+20-May-2019 09:00:00,1,0.3278,0.7882,1.2704,1.1847,0.84303,1.678,1.1847,1.2413,0.34175,3.5083,1.04449,2.35768,2.22666,0.413238,1.7447,0.564655,0.887617,1.30534
+20-May-2019 10:00:00,1,0.34739,0.84724,1.3576,1.2465,0.90755,1.7778,1.2515,1.3426,0.36381,3.6661,1.10573,2.46185,2.33017,0.445369,1.84254,0.603203,0.937303,1.36626
+20-May-2019 11:00:00,1,0.36539,0.90166,1.4376,1.3034,0.96749,1.8705,1.3122,1.4347,0.38379,3.8051,1.15979,2.55364,2.42395,0.475077,1.9328,0.638584,0.983264,1.42131
+20-May-2019 12:00:00,1,0.35977,0.88465,1.4126,1.2856,0.94871,1.8414,1.2933,1.4061,0.37758,3.7623,1.14314,2.52539,2.39481,0.465785,1.90459,0.627544,0.968886,1.40422
+20-May-2019 13:00:00,1,0.34212,0.83133,1.3341,1.2299,0.89011,1.7508,1.2336,1.3154,0.35791,3.6243,1.0895,2.43427,2.30247,0.436702,1.81617,0.592835,0.923898,1.34997
+20-May-2019 14:00:00,1,0.32561,0.78161,1.2607,1.1777,0.83585,1.6669,1.1772,1.2299,0.33925,3.4902,1.03748,2.34574,2.21499,0.409655,1.73377,0.560339,0.882079,1.29846
+20-May-2019 15:00:00,1,0.31927,0.76255,1.2325,1.1577,0.81516,1.6349,1.1553,1.1968,0.33202,3.4373,1.01702,2.31087,2.18109,0.399308,1.70221,0.547849,0.866086,1.27846
+20-May-2019 16:00:00,1,0.31302,0.74378,1.2046,1.1379,0.79484,1.6034,1.1337,1.1641,0.32486,3.3844,0.996577,2.27601,2.14752,0.389129,1.67113,0.535531,0.850356,1.25864
+20-May-2019 17:00:00,1,0.31903,0.76184,1.2314,1.1569,0.81439,1.6337,1.1545,1.1956,0.33175,3.4353,1.01625,2.30956,2.17983,0.398923,1.70103,0.547384,0.865491,1.27772
+20-May-2019 18:00:00,1,0.3287,0.7909,1.2744,1.1875,0.84597,1.6825,1.1878,1.2459,0.34276,3.5157,1.04736,2.36256,2.23144,0.414707,1.74917,0.566424,0.889888,1.30816
+20-May-2019 19:00:00,1,0.33194,0.80067,1.2889,1.1977,0.85661,1.699,1.1989,1.2628,0.34644,3.5422,1.05766,2.38009,2.24868,0.420015,1.76535,0.572811,0.898095,1.31831
+20-May-2019 20:00:00,1,0.32775,0.78807,1.2702,1.1845,0.84288,1.6778,1.1845,1.241,0.34169,3.5079,1.04435,2.35744,2.22643,0.413164,1.74447,0.564567,0.887504,1.3052
+20-May-2019 21:00:00,1,0.33105,0.798,1.2849,1.1949,0.8537,1.6945,1.1959,1.2582,0.34544,3.535,1.05485,2.37531,2.24397,0.418563,1.76093,0.571065,0.895849,1.31554
+20-May-2019 22:00:00,1,0.32894,0.79164,1.2755,1.1883,0.84677,1.6838,1.1886,1.2472,0.34304,3.5177,1.04814,2.36388,2.23274,0.415105,1.75039,0.566904,0.890504,1.30892
+20-May-2019 23:00:00,1,0.30195,0.7106,1.1553,1.1029,0.75906,1.548,1.0951,1.1059,0.31208,3.2889,0.959707,2.21308,2.08765,0.371159,1.6162,0.513705,0.822593,1.22325
+21-May-2019 00:00:00,1,0.28221,0.65161,1.0674,1.0405,0.6959,1.4502,1.0257,1.0013,0.289,3.1125,0.891795,2.097,1.9796,0.339296,1.51862,0.474746,0.77339,1.15922
+21-May-2019 01:00:00,1,0.2728,0.62355,1.0254,1.0107,0.66607,1.4039,0.99221,0.95099,0.27785,3.0256,0.858396,2.03982,1.92745,0.324181,1.47223,0.456145,0.75006,1.12826
+21-May-2019 02:00:00,1,0.26711,0.60663,1,0.99271,0.64815,1.3761,0.97188,0.92046,0.27107,2.9722,0.837894,2.0047,1.89574,0.315075,1.44425,0.4449,0.736009,1.10941
+21-May-2019 03:00:00,1,0.26752,0.60784,1.0018,0.994,0.64943,1.3781,0.97334,0.92264,0.27156,2.9761,0.839367,2.00723,1.89802,0.315725,1.44625,0.445703,0.737012,1.11076
+21-May-2019 04:00:00,1,0.27834,0.64007,1.0501,1.0283,0.68361,1.4311,1.0119,0.98065,0.28443,3.077,0.878141,2.07363,1.9582,0.333074,1.49953,0.467099,0.763786,1.14653
+21-May-2019 05:00:00,1,0.30405,0.7169,1.1647,1.1096,0.76583,1.5585,1.1025,1.117,0.31452,3.3072,0.966772,2.22515,2.09906,0.374565,1.62662,0.517849,0.827854,1.22999
+21-May-2019 06:00:00,1,0.36982,0.91509,1.4573,1.3174,0.98235,1.8934,1.327,1.4573,0.38866,3.8385,1.17279,2.57568,2.44686,0.482419,1.95508,0.647291,0.994627,1.43474
+21-May-2019 07:00:00,1,0.42105,1.071,1.6846,1.4791,1.1566,2.1626,1.4955,1.7147,0.44386,4.2,1.31443,2.815,2.70641,0.568,2.21399,0.747778,1.12716,1.58624
+21-May-2019 08:00:00,1,0.45241,1.167,1.8236,1.578,1.2655,2.3305,1.5961,1.8691,0.47661,4.4005,1.39365,2.9481,2.86071,0.620981,2.37361,0.809125,1.2093,1.67578
+21-May-2019 09:00:00,1,0.46853,1.2165,1.8949,1.6288,1.3221,2.4178,1.6471,1.9476,0.49317,4.4979,1.4323,3.0128,2.93879,0.648391,2.45601,0.840625,1.25181,1.72093
+21-May-2019 10:00:00,1,0.47871,1.2478,1.94,1.6609,1.3581,2.4733,1.6791,1.997,0.50353,4.5574,1.45602,3.05244,2.98771,0.665773,2.5082,0.86052,1.27878,1.74917
+21-May-2019 11:00:00,1,0.48963,1.2815,1.9883,1.6953,1.3968,2.5329,1.7132,2.0496,0.51456,4.6196,1.48084,3.09383,3.03975,0.684443,2.56421,0.881823,1.30775,1.77917
+21-May-2019 12:00:00,1,0.48498,1.2671,1.9677,1.6806,1.3803,2.5075,1.6987,2.0272,0.50987,4.5933,1.47034,3.07633,3.01763,0.676484,2.54034,0.87275,1.2954,1.76642
+21-May-2019 13:00:00,1,0.48235,1.259,1.9561,1.6724,1.371,2.4931,1.6905,2.0146,0.50722,4.5783,1.46437,3.06638,3.00512,0.671998,2.52688,0.867632,1.28844,1.75921
+21-May-2019 14:00:00,1,0.47215,1.2276,1.911,1.6402,1.3349,2.4375,1.6586,1.9652,0.49687,4.5192,1.44081,3.02704,2.95625,0.654578,2.4746,0.847714,1.26141,1.73102
+21-May-2019 15:00:00,1,0.46009,1.1905,1.8576,1.6022,1.2924,2.372,1.6205,1.9066,0.48452,4.4474,1.41224,2.97924,2.89802,0.634028,2.41285,0.824138,1.22953,1.69737
+21-May-2019 16:00:00,1,0.44945,1.1579,1.8105,1.5687,1.2552,2.3146,1.5867,1.8546,0.47355,4.3823,1.38641,2.93596,2.84631,0.615968,2.35853,0.803347,1.20152,1.66743
+21-May-2019 17:00:00,1,0.43908,1.1261,1.7646,1.536,1.2191,2.2589,1.5536,1.8038,0.46278,4.3171,1.36065,2.89273,2.79554,0.598413,2.30568,0.783069,1.1743,1.63801
+21-May-2019 18:00:00,1,0.43683,1.1192,1.7546,1.5289,1.2112,2.2468,1.5464,1.7927,0.46043,4.3028,1.35497,2.88318,2.78445,0.5946,2.29419,0.778656,1.16839,1.63158
+21-May-2019 19:00:00,1,0.43045,1.0997,1.7263,1.5087,1.1891,2.2127,1.5259,1.7612,0.45375,4.2617,1.33874,2.8559,2.75299,0.583828,2.26173,0.766171,1.15169,1.61331
+21-May-2019 20:00:00,1,0.41124,1.041,1.6411,1.4481,1.1229,2.1105,1.4637,1.6659,0.43345,4.134,1.28849,2.77129,2.65741,0.551505,2.1642,0.728549,1.10161,1.55772
+21-May-2019 21:00:00,1,0.40071,1.0089,1.5944,1.4149,1.0869,2.0549,1.4293,1.6133,0.4222,4.0616,1.26006,2.72332,2.60449,0.53387,2.1109,0.707919,1.07429,1.52688
+21-May-2019 22:00:00,1,0.37582,0.93329,1.4839,1.3363,1.0025,1.9246,1.3471,1.4878,0.39524,3.8831,1.19021,2.60518,2.47777,0.492379,1.98529,0.659081,1.01004,1.45283
+21-May-2019 23:00:00,1,0.34061,0.8268,1.3275,1.2251,0.88516,1.7432,1.2285,1.3077,0.35622,3.6124,1.08484,2.42635,2.29456,0.434237,1.80867,0.589882,0.920085,1.34532
+22-May-2019 00:00:00,1,0.3182,0.75935,1.2277,1.1543,0.81169,1.6295,1.1516,1.1912,0.33081,3.4283,1.01356,2.30497,2.17538,0.397573,1.69691,0.545751,0.863404,1.27509
+22-May-2019 01:00:00,1,0.3047,0.71885,1.1676,1.1116,0.76793,1.5617,1.1048,1.1204,0.31527,3.3128,0.968953,2.22887,2.10258,0.37562,1.62985,0.519132,0.829484,1.23208
+22-May-2019 02:00:00,1,0.29828,0.69963,1.139,1.0913,0.74727,1.5297,1.0823,1.0866,0.30782,3.2567,0.947307,2.1919,2.06771,0.365224,1.59805,0.506474,0.813426,1.21145
+22-May-2019 03:00:00,1,0.30023,0.70547,1.1477,1.0975,0.75354,1.5395,1.0891,1.0969,0.31009,3.2739,0.95392,2.2032,2.07833,0.368383,1.60771,0.510323,0.818304,1.21773
+22-May-2019 04:00:00,1,0.30926,0.7325,1.1879,1.126,0.78265,1.5845,1.1206,1.1444,0.32053,3.3522,0.984142,2.2548,2.12723,0.383014,1.65245,0.528115,0.840907,1.24665
+22-May-2019 05:00:00,1,0.33005,0.79498,1.2805,1.1918,0.85041,1.6894,1.1924,1.253,0.3443,3.5268,1.05167,2.3699,2.23865,0.416923,1.75593,0.569091,0.893313,1.3124
+22-May-2019 06:00:00,1,0.38691,0.96695,1.5332,1.3713,1.04,1.9824,1.3838,1.5439,0.40731,3.9639,1.22179,2.65863,2.5345,0.510823,2.04117,0.680845,1.0386,1.48601
+22-May-2019 07:00:00,1,0.43809,1.1231,1.7601,1.5328,1.2156,2.2535,1.5504,1.7989,0.46175,4.3108,1.35815,2.88852,2.79066,0.596732,2.30061,0.781124,1.1717,1.63517
+22-May-2019 08:00:00,1,0.46825,1.2156,1.8937,1.6279,1.3211,2.4163,1.6463,1.9463,0.49288,4.4962,1.43164,3.01171,2.93744,0.647916,2.45458,0.84008,1.25107,1.72015
+22-May-2019 09:00:00,1,0.4755,1.2379,1.9258,1.6508,1.3467,2.4557,1.6691,1.9814,0.50027,4.5388,1.44859,3.04003,2.9723,0.660282,2.49172,0.854242,1.27026,1.74028
+22-May-2019 10:00:00,1,0.486,1.2703,1.9722,1.6838,1.3839,2.5131,1.7019,2.0321,0.5109,4.5991,1.47266,3.08019,3.02249,0.67823,2.54558,0.874742,1.29811,1.76922
+22-May-2019 11:00:00,1,0.49676,1.3035,2.0198,1.7177,1.4222,2.572,1.7354,2.0838,0.52172,4.6593,1.49673,3.12029,3.07355,0.696667,2.60085,0.895733,1.32672,1.79863
+22-May-2019 12:00:00,1,0.48904,1.2797,1.9857,1.6934,1.3947,2.5297,1.7114,2.0468,0.51397,4.6163,1.47953,3.09164,3.03697,0.68344,2.5612,0.880681,1.30619,1.77756
+22-May-2019 13:00:00,1,0.48711,1.2737,1.9772,1.6873,1.3879,2.5191,1.7054,2.0375,0.51202,4.6054,1.47517,3.08439,3.02779,0.680134,2.55129,0.876913,1.30106,1.77227
+22-May-2019 14:00:00,1,0.48214,1.2584,1.9552,1.6717,1.3702,2.492,1.6899,2.0135,0.50701,4.5771,1.46389,3.06557,3.00411,0.671638,2.5258,0.86722,1.28788,1.75863
+22-May-2019 15:00:00,1,0.47377,1.2326,1.9181,1.6453,1.3406,2.4463,1.6636,1.9731,0.49851,4.5287,1.44458,3.03333,2.96401,0.657333,2.48287,0.850868,1.26568,1.73549
+22-May-2019 16:00:00,1,0.46502,1.2057,1.8794,1.6177,1.3098,2.3988,1.6361,1.9306,0.48958,4.477,1.42402,2.99895,2.92189,0.642423,2.43808,0.83378,1.24255,1.71116
+22-May-2019 17:00:00,1,0.45752,1.1827,1.8462,1.5941,1.2834,2.3582,1.6124,1.8941,0.48188,4.4318,1.40606,2.9689,2.88558,0.629666,2.39974,0.819123,1.22276,1.69017
+22-May-2019 18:00:00,1,0.45285,1.1683,1.8255,1.5794,1.2671,2.3329,1.5975,1.8713,0.47707,4.4032,1.39473,2.94991,2.86287,0.621732,2.37587,0.80999,1.21046,1.67702
+22-May-2019 19:00:00,1,0.4448,1.1436,1.7899,1.554,1.239,2.2896,1.5719,1.8318,0.46873,4.3533,1.37493,2.9167,2.82358,0.608086,2.33481,0.794251,1.1893,1.65426
+22-May-2019 20:00:00,1,0.42741,1.0904,1.7128,1.4991,1.1786,2.1964,1.5161,1.7462,0.45055,4.2418,1.33093,2.84276,2.73794,0.578698,2.24626,0.760216,1.14375,1.60457
+22-May-2019 21:00:00,1,0.41646,1.057,1.6643,1.4646,1.1408,2.1382,1.4807,1.6919,0.439,4.1693,1.30237,2.79468,2.68353,0.560281,2.1907,0.738788,1.1152,1.57293
+22-May-2019 22:00:00,1,0.3902,0.97695,1.5478,1.3817,1.0511,1.9997,1.3947,1.5605,0.41088,3.9875,1.23101,2.67424,2.55124,0.516308,2.05777,0.6873,1.04709,1.4958
+22-May-2019 23:00:00,1,0.35167,0.86016,1.3766,1.2601,0.92174,1.7997,1.266,1.3646,0.36859,3.6997,1.11877,2.48401,2.35258,0.452416,1.86397,0.611618,0.948203,1.37943
+23-May-2019 00:00:00,1,0.32951,0.79336,1.2781,1.1901,0.84864,1.6867,1.1906,1.2502,0.34369,3.5224,1.04995,2.36698,2.23578,0.41604,1.75324,0.568029,0.891949,1.31071
+23-May-2019 01:00:00,1,0.3152,0.75035,1.2144,1.1448,0.80194,1.6144,1.1413,1.1756,0.32737,3.403,1.00376,2.28826,2.15928,0.392688,1.682,0.539841,0.855855,1.26559
+23-May-2019 02:00:00,1,0.3069,0.72544,1.1774,1.1186,0.77503,1.5727,1.1124,1.132,0.31781,3.3319,0.976307,2.24142,2.1145,0.379189,1.64076,0.523471,0.834998,1.23912
+23-May-2019 03:00:00,1,0.30779,0.72809,1.1813,1.1214,0.7779,1.5772,1.1155,1.1367,0.31883,3.3395,0.979259,2.24646,2.11929,0.380628,1.64515,0.525218,0.83722,1.24196
+23-May-2019 04:00:00,1,0.31694,0.75556,1.2221,1.1503,0.80759,1.6231,1.1473,1.1847,0.32936,3.4177,1.00944,2.29795,2.16862,0.395517,1.69064,0.543265,0.860227,1.2711
+23-May-2019 05:00:00,1,0.33525,0.81063,1.3036,1.2082,0.86748,1.7158,1.2102,1.2799,0.35018,3.5691,1.06809,2.39785,2.26622,0.425436,1.78187,0.579325,0.906476,1.32864
+23-May-2019 06:00:00,1,0.39685,0.99719,1.5773,1.4027,1.0737,2.0346,1.4166,1.5939,0.41805,4.0346,1.24947,2.70544,2.58499,0.527416,2.09138,0.700351,1.06429,1.5155
+23-May-2019 07:00:00,1,0.44884,1.156,1.8078,1.5667,1.2531,2.3113,1.5848,1.8517,0.47293,4.3785,1.38492,2.93346,2.84335,0.614939,2.35543,0.80216,1.19993,1.66572
+23-May-2019 08:00:00,1,0.47568,1.2385,1.9266,1.6513,1.3474,2.4567,1.6696,1.9823,0.50045,4.5398,1.44901,3.04074,2.97317,0.660592,2.49265,0.854597,1.27074,1.74078
+23-May-2019 09:00:00,1,0.48374,1.2633,1.9622,1.6767,1.3759,2.5007,1.6948,2.0212,0.50862,4.5862,1.46753,3.07164,3.01172,0.674366,2.53399,0.870334,1.29211,1.76302
+23-May-2019 10:00:00,1,0.49528,1.2989,2.0133,1.7131,1.4169,2.5639,1.7308,2.0767,0.52023,4.6511,1.49345,3.11484,3.06655,0.694129,2.59324,0.892848,1.32278,1.7946
+23-May-2019 11:00:00,1,0.50618,1.3326,2.0615,1.7474,1.4559,2.6239,1.7645,2.1289,0.53113,4.7107,1.51734,3.15457,3.118,0.712858,2.64935,0.914115,1.35185,1.82418
+23-May-2019 12:00:00,1,0.50229,1.3206,2.0443,1.7352,1.442,2.6025,1.7525,2.1103,0.52725,4.6896,1.50889,3.14053,3.0997,0.706174,2.62933,0.906533,1.34148,1.81366
+23-May-2019 13:00:00,1,0.4962,1.3017,2.0173,1.716,1.4202,2.569,1.7336,2.0811,0.52116,4.6562,1.49549,3.11823,3.07091,0.695707,2.59797,0.894642,1.32523,1.7971
+23-May-2019 14:00:00,1,0.48648,1.2717,1.9744,1.6854,1.3856,2.5157,1.7034,2.0344,0.51139,4.6018,1.47374,3.082,3.02477,0.679049,2.54803,0.875676,1.29938,1.77053
+23-May-2019 15:00:00,1,0.47413,1.2337,1.9197,1.6465,1.3419,2.4483,1.6648,1.9748,0.49888,4.5308,1.44542,3.03474,2.96576,0.657953,2.48473,0.851578,1.26664,1.7365
+23-May-2019 16:00:00,1,0.46499,1.2056,1.8793,1.6176,1.3097,2.3986,1.636,1.9305,0.48955,4.4769,1.42395,2.99884,2.92175,0.642376,2.43794,0.833726,1.24248,1.71109
+23-May-2019 17:00:00,1,0.45959,1.189,1.8554,1.6006,1.2907,2.3694,1.6189,1.9042,0.48402,4.4444,1.41106,2.97726,2.89563,0.63319,2.41033,0.823175,1.22823,1.69598
+23-May-2019 18:00:00,1,0.45174,1.1649,1.8206,1.5759,1.2632,2.327,1.594,1.8659,0.47593,4.3964,1.39203,2.94538,2.85748,0.619856,2.37023,0.807828,1.20755,1.67391
+23-May-2019 19:00:00,1,0.43952,1.1275,1.7665,1.5373,1.2206,2.2612,1.555,1.8059,0.46324,4.3199,1.36176,2.89458,2.7977,0.599155,2.30792,0.783928,1.17545,1.63926
+23-May-2019 20:00:00,1,0.41901,1.0647,1.6756,1.4727,1.1496,2.1517,1.4889,1.7045,0.4417,4.1864,1.30908,2.80598,2.69624,0.564564,2.20362,0.743778,1.12184,1.58032
+23-May-2019 21:00:00,1,0.4095,1.0357,1.6334,1.4426,1.1169,2.1013,1.458,1.6572,0.43159,4.1222,1.28384,2.76344,2.64868,0.548585,2.15537,0.725138,1.09708,1.55264
+23-May-2019 22:00:00,1,0.39106,0.97957,1.5516,1.3845,1.054,2.0042,1.3975,1.5648,0.41181,3.9936,1.23341,2.6783,2.55561,0.517743,2.06212,0.688988,1.04931,1.49835
+23-May-2019 23:00:00,1,0.35361,0.86605,1.3853,1.2662,0.92822,1.8097,1.2726,1.3746,0.37075,3.7149,1.12467,2.49403,2.36276,0.455628,1.87373,0.615449,0.953172,1.38541
+24-May-2019 00:00:00,1,0.32604,0.7829,1.2626,1.1791,0.83726,1.6691,1.1786,1.2321,0.33974,3.4937,1.03886,2.34809,2.21727,0.410357,1.73591,0.561184,0.883163,1.2998
+24-May-2019 01:00:00,1,0.31299,0.74369,1.2045,1.1378,0.79474,1.6033,1.1336,1.1639,0.32482,3.3841,0.996473,2.27583,2.14734,0.389078,1.67097,0.535469,0.850276,1.25854
+24-May-2019 02:00:00,1,0.30622,0.72341,1.1744,1.1164,0.77285,1.5694,1.1101,1.1284,0.31703,3.326,0.974048,2.23757,2.11083,0.378091,1.6374,0.522136,0.833301,1.23696
+24-May-2019 03:00:00,1,0.30626,0.72352,1.1745,1.1166,0.77297,1.5695,1.1102,1.1286,0.31707,3.3264,0.974175,2.23779,2.11104,0.378153,1.63759,0.522211,0.833396,1.23708
+24-May-2019 04:00:00,1,0.31349,0.74521,1.2068,1.1394,0.79639,1.6058,1.1353,1.1666,0.32541,3.3884,0.998145,2.27869,2.15008,0.389905,1.6735,0.53647,0.851554,1.26015
+24-May-2019 05:00:00,1,0.33123,0.79852,1.2857,1.1955,0.85427,1.6954,1.1965,1.2591,0.34563,3.5364,1.0554,2.37625,2.24489,0.418847,1.76179,0.571406,0.896289,1.31608
+24-May-2019 06:00:00,1,0.38885,0.97286,1.5418,1.3775,1.0466,1.9926,1.3903,1.5537,0.40942,3.9779,1.22725,2.66787,2.5444,0.514063,2.05098,0.684659,1.04361,1.4918
+24-May-2019 07:00:00,1,0.43902,1.1259,1.7642,1.5357,1.2188,2.2585,1.5534,1.8034,0.46271,4.3167,1.36048,2.89244,2.79521,0.598297,2.30533,0.782935,1.17412,1.63781
+24-May-2019 08:00:00,1,0.46561,1.2075,1.882,1.6196,1.3119,2.402,1.638,1.9335,0.49019,4.4806,1.42542,3.0013,2.92474,0.64343,2.44111,0.834936,1.24411,1.71281
+24-May-2019 09:00:00,1,0.47294,1.2301,1.9145,1.6427,1.3377,2.4418,1.661,1.9691,0.49767,4.5239,1.44266,3.03012,2.96005,0.655925,2.47864,0.849256,1.2635,1.73321
+24-May-2019 10:00:00,1,0.4885,1.278,1.9833,1.6917,1.3928,2.5268,1.7097,2.0442,0.51343,4.6133,1.47832,3.08963,3.03442,0.682523,2.55845,0.879635,1.30477,1.7761
+24-May-2019 11:00:00,1,0.50067,1.3155,2.0371,1.7301,1.4362,2.5936,1.7475,2.1026,0.52563,4.6807,1.50534,3.13462,3.09204,0.703382,2.62097,0.903364,1.33714,1.80926
+24-May-2019 12:00:00,1,0.49613,1.3015,2.0171,1.7158,1.42,2.5686,1.7334,2.0808,0.52109,4.6558,1.49535,3.118,3.07061,0.695598,2.59765,0.894518,1.32506,1.79693
+24-May-2019 13:00:00,1,0.48982,1.2821,1.9892,1.6959,1.3975,2.534,1.7138,2.0505,0.51476,4.6207,1.48128,3.09457,3.04069,0.684781,2.56522,0.882208,1.30827,1.77971
+24-May-2019 14:00:00,1,0.48133,1.2559,1.9516,1.6691,1.3674,2.4875,1.6873,2.0096,0.50619,4.5725,1.46203,3.06246,3.00022,0.670245,2.52162,0.86563,1.28572,1.75638
+24-May-2019 15:00:00,1,0.47021,1.2217,1.9024,1.6341,1.3281,2.427,1.6524,1.9558,0.49489,4.5078,1.43627,3.01944,2.94692,0.651268,2.46465,0.843923,1.25627,1.72562
+24-May-2019 16:00:00,1,0.45252,1.1673,1.8241,1.5783,1.2659,2.3312,1.5965,1.8697,0.47673,4.4012,1.39394,2.94858,2.86129,0.62118,2.37421,0.809355,1.20961,1.67611
+24-May-2019 17:00:00,1,0.44371,1.1403,1.785,1.5505,1.2351,2.2837,1.5684,1.8265,0.4676,4.3464,1.37221,2.91213,2.81822,0.606234,2.32923,0.792111,1.18643,1.65116
+24-May-2019 18:00:00,1,0.43809,1.1231,1.7601,1.5328,1.2156,2.2535,1.5504,1.7989,0.46175,4.3108,1.35815,2.88852,2.79066,0.596732,2.30061,0.781124,1.1717,1.63517
+24-May-2019 19:00:00,1,0.43003,1.0984,1.7244,1.5074,1.1876,2.2104,1.5245,1.7591,0.4533,4.2589,1.33766,2.85408,2.7509,0.583114,2.25958,0.765342,1.15059,1.61209
+24-May-2019 20:00:00,1,0.41006,1.0374,1.6359,1.4444,1.1188,2.1042,1.4598,1.66,0.43219,4.126,1.28534,2.76597,2.65149,0.549524,2.15821,0.726235,1.09854,1.55427
+24-May-2019 21:00:00,1,0.39951,1.0053,1.5891,1.4111,1.0828,2.0486,1.4253,1.6073,0.42091,4.0532,1.25678,2.71778,2.59843,0.531862,2.10483,0.705566,1.07118,1.52334
+24-May-2019 22:00:00,1,0.39021,0.97697,1.5478,1.3818,1.0511,1.9997,1.3947,1.5605,0.41088,3.9875,1.23103,2.67427,2.55128,0.516319,2.05781,0.687313,1.04711,1.49582
+24-May-2019 23:00:00,1,0.3642,0.89808,1.4323,1.2997,0.96354,1.8643,1.3082,1.4287,0.38248,3.7962,1.15631,2.54773,2.41784,0.473121,1.92686,0.636262,0.980238,1.41772
+25-May-2019 00:00:00,1,0.3213,0.76865,1.2415,1.1641,0.82178,1.6451,1.1623,1.2074,0.33434,3.4543,1.0236,2.32209,2.19196,0.402618,1.71231,0.551848,0.871201,1.28487
+25-May-2019 01:00:00,1,0.30766,0.72771,1.1808,1.121,0.77749,1.5765,1.1151,1.136,0.31869,3.3384,0.978837,2.24574,2.1186,0.380422,1.64453,0.524968,0.836902,1.24155
+25-May-2019 02:00:00,1,0.30044,0.70609,1.1486,1.0982,0.75421,1.5405,1.0899,1.098,0.31033,3.2757,0.954622,2.2044,2.07946,0.368719,1.60874,0.510733,0.818824,1.2184
+25-May-2019 03:00:00,1,0.30353,0.71535,1.1624,1.1079,0.76416,1.5559,1.1007,1.1143,0.31392,3.3027,0.965034,2.22218,2.09625,0.373726,1.62405,0.516828,0.826557,1.22833
+25-May-2019 04:00:00,1,0.31259,0.74251,1.2027,1.1366,0.79346,1.6013,1.1322,1.1619,0.32437,3.3807,0.995176,2.27362,2.14522,0.388437,1.66902,0.534692,0.849287,1.25728
+25-May-2019 05:00:00,1,0.32999,0.79479,1.2802,1.1916,0.8502,1.6891,1.1922,1.2526,0.34423,3.5263,1.05146,2.36955,2.23831,0.416818,1.75561,0.568965,0.893151,1.3122
+25-May-2019 06:00:00,1,0.3865,0.96572,1.5314,1.3701,1.0386,1.9803,1.3825,1.5418,0.40687,3.961,1.22064,2.6567,2.53243,0.510147,2.03912,0.680049,1.03755,1.4848
+25-May-2019 07:00:00,1,0.43642,1.118,1.7528,1.5276,1.2098,2.2446,1.5451,1.7907,0.46001,4.3002,1.35395,2.88146,2.78247,0.593917,2.29214,0.777866,1.16733,1.63042
+25-May-2019 08:00:00,1,0.4664,1.2099,1.8855,1.6221,1.3146,2.4063,1.6404,1.9373,0.49099,4.4852,1.42728,3.00441,2.92854,0.64477,2.44513,0.836473,1.24619,1.71501
+25-May-2019 09:00:00,1,0.47847,1.2471,1.939,1.6601,1.3572,2.472,1.6784,1.9958,0.50329,4.556,1.45547,3.05152,2.98657,0.665366,2.50698,0.860055,1.27814,1.74851
+25-May-2019 10:00:00,1,0.48983,1.2821,1.9892,1.6959,1.3975,2.534,1.7138,2.0506,0.51476,4.6207,1.4813,3.0946,3.04072,0.684793,2.56526,0.882222,1.30829,1.77973
+25-May-2019 11:00:00,1,0.49673,1.3034,2.0197,1.7177,1.4221,2.5719,1.7353,2.0837,0.5217,4.6591,1.49668,3.12021,3.07345,0.69663,2.60074,0.895692,1.32666,1.79857
+25-May-2019 12:00:00,1,0.48974,1.2818,1.9888,1.6956,1.3972,2.5335,1.7136,2.0501,0.51467,4.6202,1.48109,3.09426,3.04029,0.684636,2.56479,0.882043,1.30805,1.77947
+25-May-2019 13:00:00,1,0.47683,1.242,1.9317,1.655,1.3514,2.463,1.6732,1.9879,0.50163,4.5465,1.45169,3.0452,2.97871,0.662565,2.49857,0.856853,1.2738,1.74398
+25-May-2019 14:00:00,1,0.4613,1.1943,1.8629,1.606,1.2967,2.3786,1.6243,1.9125,0.48576,4.4547,1.41514,2.98409,2.90387,0.636082,2.41902,0.826499,1.23272,1.70075
+25-May-2019 15:00:00,1,0.44841,1.1547,1.8059,1.5654,1.2516,2.309,1.5834,1.8495,0.47248,4.3758,1.38385,2.93167,2.84122,0.614202,2.35322,0.80131,1.19878,1.66449
+25-May-2019 16:00:00,1,0.43313,1.1079,1.7382,1.5172,1.1984,2.227,1.5345,1.7744,0.45656,4.279,1.34558,2.8674,2.76621,0.588347,2.27535,0.771412,1.1587,1.62099
+25-May-2019 17:00:00,1,0.42865,1.0942,1.7183,1.5031,1.1829,2.2031,1.5201,1.7523,0.45186,4.25,1.33414,2.84816,2.74411,0.580801,2.2526,0.762658,1.147,1.60815
+25-May-2019 18:00:00,1,0.42167,1.0729,1.6874,1.481,1.1588,2.1658,1.4975,1.7177,0.4445,4.204,1.31603,2.81768,2.70945,0.569028,2.21709,0.748975,1.12876,1.588
+25-May-2019 19:00:00,1,0.41083,1.0398,1.6393,1.4468,1.1215,2.1083,1.4623,1.6638,0.43301,4.1312,1.28739,2.76944,2.65534,0.550814,2.16211,0.727742,1.10054,1.55652
+25-May-2019 20:00:00,1,0.39079,0.97874,1.5504,1.3836,1.0531,2.0027,1.3966,1.5634,0.41151,3.9917,1.23265,2.67701,2.55422,0.517287,2.06074,0.688452,1.04861,1.49754
+25-May-2019 21:00:00,1,0.37616,0.93433,1.4855,1.3374,1.0037,1.9264,1.3482,1.4896,0.39562,3.8856,1.1912,2.60686,2.47953,0.49295,1.98702,0.659756,1.01093,1.45387
+25-May-2019 22:00:00,1,0.36344,0.89578,1.4289,1.2973,0.96099,1.8604,1.3057,1.4248,0.38165,3.7904,1.15406,2.54391,2.41389,0.471862,1.92304,0.634767,0.97829,1.41541
+25-May-2019 23:00:00,1,0.33115,0.79829,1.2854,1.1952,0.85401,1.695,1.1962,1.2587,0.34555,3.5358,1.05515,2.37583,2.24448,0.418721,1.76141,0.571254,0.896093,1.31584
+26-May-2019 00:00:00,1,0.31129,0.7386,1.1969,1.1325,0.78924,1.5947,1.1277,1.1551,0.32287,3.3696,0.990882,2.2663,2.13821,0.386321,1.66255,0.532127,0.846017,1.25314
+26-May-2019 01:00:00,1,0.29445,0.68815,1.1219,1.0792,0.73495,1.5107,1.0688,1.0663,0.30335,3.2227,0.934219,2.16954,2.04677,0.359018,1.57905,0.4989,0.803841,1.19905
+26-May-2019 02:00:00,1,0.28445,0.65828,1.0773,1.0476,0.70301,1.4612,1.0336,1.0132,0.29163,3.1329,0.899629,2.1104,1.99192,0.342893,1.52965,0.479161,0.778943,1.16654
+26-May-2019 03:00:00,1,0.28244,0.6523,1.0684,1.0412,0.69663,1.4513,1.0265,1.0025,0.28927,3.1146,0.892603,2.09838,1.98087,0.339666,1.51975,0.475201,0.773961,1.15998
+26-May-2019 04:00:00,1,0.28462,0.6588,1.0781,1.0481,0.70357,1.462,1.0342,1.0141,0.29184,3.1345,0.900239,2.11144,1.99288,0.343174,1.53051,0.479506,0.779376,1.16711
+26-May-2019 05:00:00,1,0.2815,0.64948,1.0642,1.0382,0.69363,1.4466,1.0232,0.9975,0.28815,3.106,0.88928,2.09269,1.97565,0.338146,1.51509,0.473333,0.771614,1.15688
+26-May-2019 06:00:00,1,0.29604,0.69292,1.129,1.0842,0.74006,1.5186,1.0744,1.0747,0.30521,3.2369,0.939669,2.17885,2.05548,0.361595,1.58694,0.502046,0.807821,1.2042
+26-May-2019 07:00:00,1,0.32611,0.78311,1.2629,1.1793,0.83749,1.6694,1.1789,1.2325,0.33982,3.4943,1.03908,2.34847,2.21765,0.410472,1.73626,0.561323,0.883341,1.30003
+26-May-2019 08:00:00,1,0.36112,0.88875,1.4186,1.2899,0.95324,1.8484,1.2979,1.413,0.37908,3.7727,1.14717,2.53223,2.40185,0.468025,1.91139,0.630207,0.972352,1.40835
+26-May-2019 09:00:00,1,0.38784,0.96978,1.5373,1.3743,1.0431,1.9873,1.3869,1.5486,0.40832,3.9706,1.22441,2.66307,2.53924,0.512376,2.04587,0.682673,1.041,1.48879
+26-May-2019 10:00:00,1,0.40208,1.0131,1.6005,1.4192,1.0916,2.0621,1.4338,1.6201,0.42367,4.0711,1.2638,2.72963,2.6114,0.536161,2.11783,0.710604,1.07784,1.53091
+26-May-2019 11:00:00,1,0.40602,1.0251,1.618,1.4317,1.105,2.0829,1.4467,1.6398,0.42788,4.0984,1.27448,2.74766,2.63123,0.542757,2.13777,0.718325,1.08806,1.54247
+26-May-2019 12:00:00,1,0.39717,0.99817,1.5787,1.4037,1.0748,2.0363,1.4177,1.5955,0.4184,4.0369,1.25036,2.70693,2.58661,0.527953,2.09301,0.700981,1.06512,1.51645
+26-May-2019 13:00:00,1,0.38432,0.9591,1.5217,1.3632,1.0312,1.9689,1.3753,1.5308,0.40451,3.9452,1.21449,2.64629,2.5213,0.506516,2.02812,0.67577,1.03193,1.4783
+26-May-2019 14:00:00,1,0.36988,0.91529,1.4576,1.3176,0.98257,1.8938,1.3273,1.4577,0.38874,3.8389,1.17299,2.57601,2.4472,0.482528,1.95541,0.647421,0.994796,1.43493
+26-May-2019 15:00:00,1,0.3589,0.88202,1.4087,1.2829,0.94581,1.8369,1.2904,1.4016,0.37662,3.7557,1.14054,2.52098,2.39029,0.464347,1.90022,0.625834,0.966662,1.40157
+26-May-2019 16:00:00,1,0.3513,0.85906,1.375,1.2589,0.92053,1.7979,1.2648,1.3627,0.36818,3.6968,1.11766,2.48213,2.35067,0.451816,1.86214,0.610901,0.947274,1.37831
+26-May-2019 17:00:00,1,0.3538,0.86662,1.3861,1.2668,0.92885,1.8107,1.2732,1.3755,0.37097,3.7163,1.12524,2.495,2.36375,0.45594,1.87468,0.61582,0.953654,1.38599
+26-May-2019 18:00:00,1,0.35307,0.86439,1.3828,1.2645,0.9264,1.8069,1.2708,1.3717,0.37015,3.7106,1.12301,2.49122,2.3599,0.454725,1.87099,0.614372,0.951775,1.38373
+26-May-2019 19:00:00,1,0.34382,0.83647,1.3417,1.2353,0.89574,1.7595,1.2394,1.3242,0.35982,3.6379,1.09476,2.44322,2.31143,0.439501,1.82469,0.596186,0.928227,1.35525
+26-May-2019 20:00:00,1,0.32656,0.78446,1.2649,1.1807,0.83896,1.6717,1.1804,1.2348,0.34033,3.498,1.04052,2.35092,2.22004,0.411205,1.7385,0.562206,0.884474,1.30144
+26-May-2019 21:00:00,1,0.32126,0.76853,1.2413,1.164,0.82165,1.6449,1.1622,1.2072,0.3343,3.454,1.02348,2.32188,2.19176,0.402555,1.71212,0.551772,0.871105,1.28475
+26-May-2019 22:00:00,1,0.31668,0.75478,1.2209,1.1495,0.80674,1.6218,1.1464,1.1833,0.32906,3.4155,1.00859,2.2965,2.16721,0.395092,1.68934,0.542751,0.85957,1.27027
+26-May-2019 23:00:00,1,0.29423,0.68749,1.1209,1.0785,0.73424,1.5096,1.0681,1.0651,0.30309,3.2208,0.933467,2.16826,2.04557,0.358663,1.57796,0.498466,0.803293,1.19834
+27-May-2019 00:00:00,1,0.27535,0.63115,1.0367,1.0188,0.67413,1.4164,1.0013,0.96464,0.28088,3.0494,0.867505,2.05542,1.94161,0.328268,1.48478,0.461183,0.756368,1.13667
+27-May-2019 01:00:00,1,0.25824,0.58027,0.96041,0.96464,0.62035,1.333,0.94001,0.87264,0.26043,2.8876,0.805434,1.94906,1.84601,0.300917,1.40071,0.427357,0.714169,1.07982
+27-May-2019 02:00:00,1,0.24879,0.55222,0.91818,0.9347,0.5909,1.2873,0.90579,0.82137,0.249,2.7955,0.770145,1.88851,1.79257,0.285875,1.35439,0.408636,0.690971,1.04797
+27-May-2019 03:00:00,1,0.24589,0.54365,0.90524,0.92553,0.58193,1.2734,0.89527,0.80562,0.24548,2.7669,0.759199,1.86972,1.77612,0.281281,1.34023,0.402902,0.683888,1.03816
+27-May-2019 04:00:00,1,0.24399,0.53801,0.89674,0.91951,0.57604,1.2643,0.88834,0.79525,0.24316,2.748,0.751969,1.85731,1.76529,0.278266,1.33093,0.399132,0.679238,1.0317
+27-May-2019 05:00:00,1,0.23866,0.52224,0.87292,0.90262,0.55959,1.2388,0.86887,0.76614,0.23664,2.6946,0.731561,1.82225,1.73486,0.269829,1.3049,0.38857,0.666234,1.01353
+27-May-2019 06:00:00,1,0.24449,0.5395,0.89899,0.9211,0.57759,1.2667,0.89017,0.79799,0.24377,2.753,0.753882,1.86059,1.76816,0.279062,1.33338,0.400128,0.680466,1.03341
+27-May-2019 07:00:00,1,0.26326,0.59518,0.98282,0.98053,0.63606,1.3574,0.95807,0.89973,0.26647,2.9357,0.823881,1.98068,1.87421,0.308925,1.42535,0.437288,0.72652,1.0966
+27-May-2019 08:00:00,1,0.28995,0.67471,1.1019,1.065,0.72055,1.4884,1.053,1.0425,0.29809,3.1826,0.918748,2.1431,2.02215,0.351756,1.55681,0.490022,0.792627,1.18446
+27-May-2019 09:00:00,1,0.31062,0.73661,1.194,1.1304,0.78709,1.5914,1.1254,1.1516,0.32211,3.3639,0.988679,2.26254,2.13462,0.385239,1.65925,0.530814,0.844344,1.25102
+27-May-2019 10:00:00,1,0.3266,0.78459,1.2651,1.1809,0.8391,1.6719,1.1806,1.235,0.34038,3.4984,1.04066,2.35116,2.22028,0.411278,1.73872,0.562294,0.884587,1.30158
+27-May-2019 11:00:00,1,0.34236,0.83207,1.3352,1.2307,0.89092,1.7521,1.2345,1.3167,0.35818,3.6263,1.09026,2.43556,2.30376,0.437106,1.8174,0.593319,0.924523,1.35074
+27-May-2019 12:00:00,1,0.33823,0.81962,1.3169,1.2176,0.87729,1.731,1.2204,1.2953,0.35354,3.5932,1.07742,2.41373,2.28198,0.430323,1.79675,0.58519,0.914033,1.33792
+27-May-2019 13:00:00,1,0.32118,0.76828,1.241,1.1637,0.82138,1.6445,1.1619,1.2068,0.3342,3.4533,1.02321,2.32142,2.19131,0.40242,1.7117,0.551609,0.870895,1.28449
+27-May-2019 14:00:00,1,0.30803,0.72883,1.1824,1.1222,0.7787,1.5784,1.1164,1.138,0.31912,3.3417,0.98008,2.24787,2.12062,0.381028,1.64638,0.525705,0.837839,1.24275
+27-May-2019 15:00:00,1,0.29987,0.70438,1.1461,1.0964,0.75236,1.5376,1.0879,1.095,0.30967,3.2707,0.952684,2.20109,2.07635,0.367791,1.6059,0.509603,0.817391,1.21656
+27-May-2019 16:00:00,1,0.29503,0.68991,1.1245,1.0811,0.73684,1.5136,1.0709,1.0694,0.30404,3.228,0.936237,2.17299,2.04999,0.359971,1.58197,0.500064,0.805313,1.20096
+27-May-2019 17:00:00,1,0.29886,0.70136,1.1416,1.0932,0.74913,1.5326,1.0843,1.0897,0.3085,3.2618,0.949271,2.19526,2.07087,0.366161,1.60091,0.507616,0.814873,1.21331
+27-May-2019 18:00:00,1,0.30774,0.72796,1.1811,1.1212,0.77776,1.577,1.1153,1.1364,0.31878,3.3392,0.979111,2.24621,2.11905,0.380556,1.64494,0.52513,0.837109,1.24182
+27-May-2019 19:00:00,1,0.3084,0.72993,1.1841,1.1233,0.77988,1.5803,1.1176,1.1399,0.31954,3.3448,0.981302,2.24995,2.12261,0.381625,1.6482,0.526429,0.838761,1.24392
+27-May-2019 20:00:00,1,0.30298,0.7137,1.1599,1.1062,0.76239,1.5532,1.0988,1.1114,0.31328,3.2979,0.963189,2.21903,2.09327,0.372835,1.62133,0.515745,0.825182,1.22657
+27-May-2019 21:00:00,1,0.30215,0.71121,1.1562,1.1036,0.75971,1.549,1.0958,1.107,0.31231,3.2906,0.960386,2.21424,2.08875,0.371486,1.6172,0.514103,0.823098,1.2239
+27-May-2019 22:00:00,1,0.30362,0.71561,1.1628,1.1082,0.76445,1.5563,1.101,1.1147,0.31402,3.3034,0.965331,2.22269,2.09673,0.373869,1.62449,0.517003,0.826779,1.22862
+27-May-2019 23:00:00,1,0.28316,0.65445,1.0716,1.0435,0.69893,1.4549,1.0291,1.0064,0.29012,3.1212,0.895136,2.10271,1.98485,0.340828,1.52331,0.476627,0.775755,1.16234
+28-May-2019 00:00:00,1,0.26278,0.59374,0.98066,0.979,0.63454,1.355,0.95633,0.89712,0.26588,2.9311,0.822107,1.97764,1.87149,0.30815,1.42296,0.436328,0.725326,1.09498
+28-May-2019 01:00:00,1,0.24815,0.55035,0.91536,0.9327,0.58894,1.2843,0.9035,0.81794,0.24823,2.7893,0.767762,1.88442,1.78899,0.284872,1.3513,0.407385,0.689425,1.04584
+28-May-2019 02:00:00,1,0.24536,0.54207,0.90286,0.92384,0.58027,1.2709,0.89332,0.80271,0.24483,2.7616,0.757175,1.86624,1.77309,0.280436,1.33762,0.401845,0.682584,1.03635
+28-May-2019 03:00:00,1,0.24334,0.53611,0.89387,0.91747,0.57406,1.2612,0.886,0.79175,0.24238,2.7416,0.749525,1.85311,1.76164,0.277249,1.32779,0.397861,0.677671,1.02952
+28-May-2019 04:00:00,1,0.24204,0.53225,0.88804,0.91333,0.57002,1.2549,0.88123,0.78462,0.24078,2.7286,0.744536,1.84454,1.75418,0.27518,1.32141,0.395272,0.674481,1.02507
+28-May-2019 05:00:00,1,0.23781,0.51973,0.86912,0.89993,0.55698,1.2347,0.86576,0.76149,0.2356,2.6861,0.728285,1.81663,1.72999,0.268485,1.30075,0.386885,0.664162,1.01062
+28-May-2019 06:00:00,1,0.24453,0.53963,0.89918,0.92123,0.57772,1.2669,0.89032,0.79822,0.24382,2.7534,0.754043,1.86087,1.7684,0.279129,1.33359,0.400212,0.68057,1.03355
+28-May-2019 07:00:00,1,0.26202,0.5915,0.97729,0.97661,0.63217,1.3514,0.95362,0.89305,0.26498,2.9239,0.819343,1.97291,1.86725,0.306946,1.41926,0.434835,0.723467,1.09246
+28-May-2019 08:00:00,1,0.29241,0.68208,1.1128,1.0728,0.72844,1.5006,1.0617,1.0555,0.30098,3.2047,0.92725,2.15763,2.03566,0.355737,1.569,0.49489,0.798774,1.19247
+28-May-2019 09:00:00,1,0.31522,0.75038,1.2144,1.1449,0.80198,1.6145,1.1413,1.1756,0.32738,3.4031,1.0038,2.28833,2.15935,0.392708,1.68206,0.539866,0.855887,1.26563
+28-May-2019 10:00:00,1,0.33034,0.79585,1.2818,1.1927,0.85136,1.6909,1.1934,1.2545,0.34463,3.5291,1.05258,2.37146,2.24018,0.417396,1.75737,0.56966,0.894045,1.31331
+28-May-2019 11:00:00,1,0.34547,0.84144,1.349,1.2405,0.90119,1.7679,1.245,1.3327,0.36166,3.651,1.09983,2.45184,2.32009,0.442209,1.83293,0.599425,0.932414,1.36034
+28-May-2019 12:00:00,1,0.34223,0.83168,1.3347,1.2303,0.8905,1.7514,1.234,1.316,0.35804,3.6253,1.08986,2.43488,2.30308,0.436894,1.81675,0.593064,0.924194,1.35033
+28-May-2019 13:00:00,1,0.32812,0.78917,1.2719,1.1857,0.84408,1.6796,1.1858,1.2429,0.34211,3.5109,1.04552,2.35943,2.22837,0.413762,1.7463,0.565287,0.888428,1.30635
+28-May-2019 14:00:00,1,0.31431,0.74767,1.2104,1.142,0.79905,1.6099,1.1382,1.1709,0.32635,3.3954,1.00084,2.28328,2.15449,0.391239,1.67757,0.538086,0.853615,1.26276
+28-May-2019 15:00:00,1,0.30589,0.72241,1.1729,1.1154,0.77177,1.5677,1.1089,1.1267,0.31664,3.3231,0.972929,2.23566,2.10902,0.377547,1.63574,0.521475,0.832461,1.23589
+28-May-2019 16:00:00,1,0.30458,0.71849,1.1671,1.1112,0.76754,1.5611,1.1043,1.1198,0.31513,3.3118,0.968551,2.22818,2.10193,0.375425,1.62925,0.518896,0.829183,1.23169
+28-May-2019 17:00:00,1,0.31332,0.74468,1.206,1.1389,0.79581,1.6049,1.1347,1.1657,0.3252,3.3869,0.99756,2.27769,2.14912,0.389615,1.67261,0.53612,0.851107,1.25959
+28-May-2019 18:00:00,1,0.3258,0.78219,1.2615,1.1783,0.83648,1.6679,1.1778,1.2309,0.33947,3.4917,1.0381,2.34679,2.21601,0.409969,1.73473,0.560717,0.882564,1.29906
+28-May-2019 19:00:00,1,0.3276,0.7876,1.2696,1.184,0.84238,1.677,1.184,1.2402,0.34152,3.5066,1.04386,2.3566,2.22561,0.412913,1.74371,0.564264,0.887114,1.30472
+28-May-2019 20:00:00,1,0.32343,0.77505,1.251,1.1708,0.82872,1.6558,1.1697,1.2185,0.33677,3.472,1.03047,2.33379,2.20334,0.406091,1.7229,0.55604,0.87657,1.29159
+28-May-2019 21:00:00,1,0.32393,0.77656,1.2532,1.1724,0.83037,1.6584,1.1714,1.2211,0.33735,3.4762,1.0321,2.33657,2.20604,0.406916,1.72542,0.557035,0.877845,1.29318
+28-May-2019 22:00:00,1,0.326,0.7828,1.2625,1.179,0.83715,1.6689,1.1785,1.2319,0.33971,3.4934,1.03876,2.34791,2.2171,0.410304,1.73575,0.561121,0.883082,1.2997
+28-May-2019 23:00:00,1,0.29927,0.70259,1.1434,1.0945,0.75044,1.5347,1.0858,1.0918,0.30897,3.2654,0.950658,2.19763,2.07309,0.366823,1.60294,0.508423,0.815895,1.21463
+29-May-2019 00:00:00,1,0.27817,0.63957,1.0494,1.0277,0.68308,1.4303,1.0114,0.97976,0.28423,3.0755,0.877548,2.07261,1.95727,0.332805,1.4987,0.466768,0.76337,1.14597
+29-May-2019 01:00:00,1,0.26503,0.60043,0.99069,0.98611,0.64159,1.366,0.9644,0.90923,0.26858,2.9525,0.830315,1.99171,1.88408,0.311741,1.43401,0.440775,0.730866,1.10248
+29-May-2019 02:00:00,1,0.2584,0.58073,0.9611,0.96513,0.62082,1.3338,0.94056,0.87347,0.26062,2.8891,0.805999,1.95002,1.84687,0.301161,1.40146,0.427659,0.714545,1.08033
+29-May-2019 03:00:00,1,0.25909,0.58278,0.96418,0.96732,0.62299,1.3371,0.94305,0.8772,0.26145,2.8957,0.808551,1.9544,1.85077,0.302264,1.40486,0.429028,0.716245,1.08265
+29-May-2019 04:00:00,1,0.26766,0.60826,1.0024,0.99445,0.64987,1.3788,0.97384,0.9234,0.27173,2.9774,0.839881,2.0081,1.89881,0.315952,1.44695,0.445984,0.737362,1.11123
+29-May-2019 05:00:00,1,0.29094,0.67766,1.1063,1.0681,0.72371,1.4933,1.0565,1.0477,0.29925,3.1914,0.922161,2.14893,2.02757,0.353351,1.5617,0.491973,0.79509,1.18767
+29-May-2019 06:00:00,1,0.35849,0.88079,1.4069,1.2816,0.94446,1.8348,1.289,1.3995,0.37617,3.7526,1.13933,2.51892,2.38818,0.463678,1.89819,0.625037,0.965626,1.40033
+29-May-2019 07:00:00,1,0.4089,1.0339,1.6307,1.4407,1.1149,2.0981,1.456,1.6542,0.43095,4.1181,1.28223,2.76072,2.64567,0.547578,2.15233,0.723962,1.09552,1.55088
+29-May-2019 08:00:00,1,0.44369,1.1402,1.7849,1.5505,1.2351,2.2836,1.5683,1.8264,0.46757,4.3463,1.37216,2.91205,2.81812,0.606199,2.32913,0.792071,1.18638,1.6511
+29-May-2019 09:00:00,1,0.45801,1.1842,1.8484,1.5956,1.2852,2.3608,1.6139,1.8965,0.48239,4.4348,1.40725,2.97089,2.88797,0.630502,2.40225,0.820085,1.22406,1.69155
+29-May-2019 10:00:00,1,0.47223,1.2279,1.9113,1.6405,1.3352,2.4379,1.6588,1.9656,0.49695,4.5197,1.44099,3.02734,2.95662,0.654709,2.47499,0.847864,1.26161,1.73123
+29-May-2019 11:00:00,1,0.48425,1.2649,1.9645,1.6783,1.3777,2.5035,1.6964,2.0237,0.50913,4.5891,1.46868,3.07356,3.01414,0.675232,2.53659,0.871322,1.29345,1.76441
+29-May-2019 12:00:00,1,0.47613,1.2399,1.9286,1.6528,1.349,2.4592,1.671,1.9845,0.50092,4.5425,1.45007,3.0425,2.97536,0.661369,2.49499,0.855486,1.27194,1.74204
+29-May-2019 13:00:00,1,0.46718,1.2123,1.889,1.6245,1.3174,2.4105,1.6429,1.9411,0.49179,4.4899,1.42913,3.0075,2.9323,0.646099,2.44913,0.837997,1.24825,1.71718
+29-May-2019 14:00:00,1,0.45757,1.1828,1.8464,1.5942,1.2836,2.3584,1.6125,1.8943,0.48193,4.4321,1.40618,2.96909,2.88581,0.629748,2.39998,0.819218,1.22289,1.6903
+29-May-2019 15:00:00,1,0.44684,1.1499,1.7989,1.5604,1.2461,2.3005,1.5784,1.8418,0.47085,4.366,1.37998,2.92517,2.83355,0.611539,2.3452,0.798237,1.19465,1.66004
+29-May-2019 16:00:00,1,0.43664,1.1186,1.7537,1.5282,1.2106,2.2458,1.5458,1.7917,0.46023,4.3015,1.35449,2.88237,2.78351,0.594276,2.29322,0.778281,1.16789,1.63103
+29-May-2019 17:00:00,1,0.43346,1.1089,1.7396,1.5182,1.1995,2.2288,1.5356,1.7761,0.45691,4.2812,1.34643,2.86883,2.76786,0.588912,2.27705,0.772067,1.15958,1.62195
+29-May-2019 18:00:00,1,0.43235,1.1055,1.7347,1.5147,1.1957,2.2228,1.532,1.7706,0.45575,4.274,1.34361,2.86409,2.7624,0.587043,2.27142,0.7699,1.15668,1.61877
+29-May-2019 19:00:00,1,0.42827,1.093,1.7166,1.5019,1.1816,2.201,1.5189,1.7504,0.45146,4.2475,1.33315,2.84651,2.74222,0.580158,2.25066,0.761911,1.14601,1.60706
+29-May-2019 20:00:00,1,0.41243,1.0447,1.6464,1.4519,1.127,2.1168,1.4676,1.6718,0.43472,4.1422,1.29168,2.77667,2.66339,0.553511,2.17025,0.730891,1.10471,1.5612
+29-May-2019 21:00:00,1,0.40112,1.0102,1.5962,1.4162,1.0883,2.0571,1.4306,1.6153,0.42264,4.0645,1.26118,2.72521,2.60656,0.534555,2.11297,0.708722,1.07535,1.52808
+29-May-2019 22:00:00,1,0.38563,0.96307,1.5275,1.3673,1.0356,1.9758,1.3796,1.5374,0.40593,3.9547,1.21819,2.65255,2.52798,0.508696,2.03473,0.678339,1.0353,1.48221
+29-May-2019 23:00:00,1,0.3493,0.85301,1.3661,1.2526,0.91388,1.7876,1.258,1.3524,0.36595,3.6812,1.11156,2.47177,2.34019,0.448515,1.8521,0.606961,0.942168,1.37215
+30-May-2019 00:00:00,1,0.32429,0.77764,1.2548,1.1736,0.83154,1.6602,1.1726,1.223,0.33775,3.4792,1.03325,2.33853,2.20795,0.407501,1.7272,0.557741,0.878749,1.29431
+30-May-2019 01:00:00,1,0.31278,0.74308,1.2036,1.1372,0.79408,1.6022,1.1329,1.1629,0.32459,3.3824,0.995803,2.27469,2.14625,0.388747,1.66996,0.535068,0.849765,1.25789
+30-May-2019 02:00:00,1,0.30414,0.71716,1.1651,1.1098,0.76612,1.5589,1.1028,1.1175,0.31462,3.3079,0.967068,2.22565,2.09953,0.374708,1.62706,0.518024,0.828075,1.23028
+30-May-2019 03:00:00,1,0.30386,0.71633,1.1639,1.109,0.76522,1.5575,1.1018,1.116,0.3143,3.3055,0.966136,2.22406,2.09803,0.374258,1.62568,0.517476,0.827379,1.22939
+30-May-2019 04:00:00,1,0.31127,0.73855,1.1969,1.1324,0.78918,1.5946,1.1276,1.155,0.32285,3.3695,0.990819,2.26619,2.13811,0.38629,1.66246,0.53209,0.845969,1.25308
+30-May-2019 05:00:00,1,0.33051,0.79635,1.2825,1.1932,0.8519,1.6917,1.194,1.2553,0.34482,3.5305,1.05311,2.37236,2.24107,0.417669,1.7582,0.569989,0.894467,1.31383
+30-May-2019 06:00:00,1,0.38646,0.9656,1.5312,1.3699,1.0385,1.9801,1.3824,1.5416,0.40683,3.9607,1.22053,2.65651,2.53222,0.510081,2.03892,0.67997,1.03745,1.48469
+30-May-2019 07:00:00,1,0.43321,1.1081,1.7385,1.5174,1.1987,2.2274,1.5348,1.7748,0.45664,4.2795,1.34579,2.86775,2.76662,0.588485,2.27577,0.771572,1.15891,1.62122
+30-May-2019 08:00:00,1,0.4621,1.1967,1.8665,1.6085,1.2995,2.3829,1.6269,1.9164,0.48659,4.4595,1.41706,2.98731,2.90777,0.637453,2.42314,0.828073,1.23484,1.703
+30-May-2019 09:00:00,1,0.46868,1.217,1.8956,1.6293,1.3227,2.4186,1.6476,1.9484,0.49332,4.4988,1.43266,3.01341,2.93953,0.648652,2.4568,0.840925,1.25221,1.72136
+30-May-2019 10:00:00,1,0.47794,1.2455,1.9366,1.6585,1.3554,2.469,1.6767,1.9932,0.50275,4.553,1.45425,3.04947,2.98401,0.664456,2.50425,0.859015,1.27673,1.74704
+30-May-2019 11:00:00,1,0.49162,1.2876,1.9971,1.7015,1.4039,2.5438,1.7194,2.0592,0.51656,4.6307,1.4853,3.10126,3.0492,0.687852,2.57443,0.885706,1.31304,1.78461
+30-May-2019 12:00:00,1,0.49003,1.2827,1.9901,1.6966,1.3983,2.5352,1.7145,2.0516,0.51497,4.6219,1.48176,3.09536,3.0417,0.685144,2.56631,0.882621,1.30883,1.78029
+30-May-2019 13:00:00,1,0.48378,1.2634,1.9624,1.6769,1.3761,2.5009,1.695,2.0214,0.50866,4.5865,1.46763,3.0718,3.01193,0.674438,2.5342,0.870416,1.29222,1.76313
+30-May-2019 14:00:00,1,0.47047,1.2225,1.9035,1.6349,1.329,2.4284,1.6533,1.9571,0.49515,4.5093,1.43687,3.02045,2.94816,0.651709,2.46598,0.844427,1.25696,1.72634
+30-May-2019 15:00:00,1,0.45463,1.1738,1.8334,1.585,1.2733,2.3426,1.6032,1.88,0.47891,4.4142,1.39907,2.95718,2.87155,0.624761,2.38498,0.813478,1.21516,1.68205
+30-May-2019 16:00:00,1,0.44488,1.1439,1.7902,1.5542,1.2392,2.29,1.5721,1.8322,0.46881,4.3537,1.37512,2.91701,2.82395,0.608214,2.33519,0.794399,1.1895,1.65448
+30-May-2019 17:00:00,1,0.44284,1.1376,1.7812,1.5478,1.2321,2.279,1.5656,1.8222,0.46669,4.3409,1.37005,2.90851,2.81398,0.604767,2.32482,0.790417,1.18416,1.6487
+30-May-2019 18:00:00,1,0.43934,1.1269,1.7657,1.5368,1.22,2.2603,1.5545,1.805,0.46305,4.3188,1.36131,2.89383,2.79683,0.598853,2.30701,0.783579,1.17499,1.63875
+30-May-2019 19:00:00,1,0.43388,1.1102,1.7415,1.5195,1.201,2.231,1.5369,1.7781,0.45734,4.2838,1.34749,2.87062,2.76992,0.589617,2.27918,0.772883,1.16067,1.62314
+30-May-2019 20:00:00,1,0.41671,1.0577,1.6654,1.4654,1.1417,2.1395,1.4815,1.6931,0.43926,4.171,1.30302,2.79577,2.68475,0.560691,2.19193,0.739265,1.11584,1.57364
+30-May-2019 21:00:00,1,0.40355,1.0176,1.607,1.4239,1.0966,2.0698,1.4386,1.6275,0.42524,4.0813,1.26778,2.73635,2.61878,0.538612,2.12524,0.713474,1.08163,1.53521
+30-May-2019 22:00:00,1,0.38865,0.97223,1.5409,1.3768,1.0459,1.9915,1.3896,1.5526,0.4092,3.9764,1.22667,2.66689,2.54334,0.513719,2.04994,0.684254,1.04308,1.49118
+30-May-2019 23:00:00,1,0.35402,0.86727,1.3871,1.2675,0.92956,1.8118,1.274,1.3766,0.3712,3.718,1.12589,2.4961,2.36487,0.456295,1.87576,0.616243,0.954203,1.38665
+31-May-2019 00:00:00,1,0.32958,0.79355,1.2784,1.1903,0.84885,1.687,1.1908,1.2505,0.34376,3.5229,1.05016,2.36733,2.23612,0.416145,1.75356,0.568155,0.892111,1.31091
+31-May-2019 01:00:00,1,0.31511,0.75008,1.214,1.1446,0.80165,1.614,1.141,1.1751,0.32727,3.4022,1.00347,2.28776,2.1588,0.392543,1.68156,0.539666,0.855631,1.2653
+31-May-2019 02:00:00,1,0.30707,0.72595,1.1782,1.1191,0.77559,1.5736,1.113,1.1329,0.31801,3.3334,0.976876,2.2424,2.11542,0.379467,1.64161,0.523808,0.835426,1.23967
+31-May-2019 03:00:00,1,0.30666,0.72472,1.1763,1.1178,0.77426,1.5715,1.1116,1.1307,0.31753,3.3298,0.975505,2.24006,2.1132,0.378799,1.63957,0.522997,0.834395,1.23836
+31-May-2019 04:00:00,1,0.31347,0.74514,1.2066,1.1394,0.79631,1.6057,1.1352,1.1665,0.32538,3.3882,0.998061,2.27854,2.14994,0.389863,1.67337,0.53642,0.85149,1.26007
+31-May-2019 05:00:00,1,0.33139,0.799,1.2864,1.196,0.85479,1.6962,1.197,1.2599,0.34582,3.5377,1.05591,2.37711,2.24575,0.41911,1.7626,0.571723,0.896695,1.31658
+31-May-2019 06:00:00,1,0.38474,0.96037,1.5235,1.3645,1.0326,1.9711,1.3767,1.5329,0.40496,3.9483,1.21567,2.64829,2.52344,0.507213,2.03023,0.676591,1.03301,1.47955
+31-May-2019 07:00:00,1,0.43156,1.1031,1.7312,1.5122,1.1929,2.2186,1.5294,1.7666,0.45491,4.2688,1.34157,2.86066,2.75845,0.585694,2.26735,0.768336,1.15459,1.61648
+31-May-2019 08:00:00,1,0.46271,1.1986,1.8692,1.6105,1.3017,2.3863,1.6288,1.9194,0.48722,4.4632,1.41852,2.98975,2.91073,0.638493,2.42627,0.829268,1.23646,1.70471
+31-May-2019 09:00:00,1,0.47605,1.2396,1.9282,1.6525,1.3487,2.4587,1.6708,1.9841,0.50083,4.542,1.44987,3.04217,2.97495,0.661226,2.49455,0.855322,1.27172,1.74181
+31-May-2019 10:00:00,1,0.4928,1.2913,2.0023,1.7053,1.4081,2.5503,1.7231,2.0649,0.51775,4.6373,1.48795,3.10568,3.05483,0.689886,2.58053,0.888021,1.31619,1.78785
+31-May-2019 11:00:00,1,0.50432,1.3268,2.0533,1.7416,1.4492,2.6137,1.7588,2.1201,0.52928,4.7006,1.51331,3.14788,3.10927,0.709666,2.63979,0.910495,1.3469,1.81916
+31-May-2019 12:00:00,1,0.49729,1.3051,2.0222,1.7194,1.4241,2.575,1.737,2.0864,0.52226,4.6622,1.49792,3.12227,3.0761,0.69759,2.60362,0.896783,1.32815,1.80009
+31-May-2019 13:00:00,1,0.48845,1.2778,1.9831,1.6916,1.3926,2.5265,1.7095,2.0439,0.51337,4.6129,1.47819,3.08942,3.03415,0.682426,2.55816,0.879525,1.30462,1.77594
+31-May-2019 14:00:00,1,0.48303,1.2611,1.9591,1.6745,1.3734,2.4968,1.6926,2.0178,0.5079,4.5822,1.46591,3.06894,3.00834,0.673152,2.53034,0.868948,1.29023,1.76106
+31-May-2019 15:00:00,1,0.47179,1.2265,1.9094,1.6391,1.3336,2.4355,1.6574,1.9635,0.4965,4.5171,1.43996,3.02562,2.95451,0.653959,2.47274,0.847005,1.26045,1.73001
+31-May-2019 16:00:00,1,0.4623,1.1974,1.8674,1.6092,1.3002,2.384,1.6275,1.9174,0.4868,4.4607,1.41754,2.98812,2.90875,0.637795,2.42418,0.828467,1.23537,1.70357
+31-May-2019 17:00:00,1,0.45817,1.1847,1.8491,1.5961,1.2857,2.3617,1.6144,1.8973,0.48255,4.4358,1.40764,2.97153,2.88874,0.630773,2.40306,0.820396,1.22448,1.692
+31-May-2019 18:00:00,1,0.45703,1.1812,1.844,1.5925,1.2817,2.3555,1.6108,1.8917,0.48138,4.4288,1.40488,2.96691,2.88319,0.62883,2.39722,0.818161,1.22147,1.68879
+31-May-2019 19:00:00,1,0.4484,1.1547,1.8058,1.5653,1.2515,2.3089,1.5834,1.8495,0.47246,4.3757,1.38382,2.93161,2.84116,0.614179,2.35315,0.801283,1.19875,1.66445
+31-May-2019 20:00:00,1,0.42741,1.0904,1.7128,1.4991,1.1786,2.1964,1.5161,1.7462,0.45055,4.2418,1.33093,2.84276,2.73794,0.578698,2.24626,0.760216,1.14375,1.60457
+31-May-2019 21:00:00,1,0.40915,1.0346,1.6318,1.4415,1.1157,2.0994,1.4569,1.6554,0.43122,4.1198,1.2829,2.76185,2.64692,0.547997,2.1536,0.724451,1.09617,1.55161
+31-May-2019 22:00:00,1,0.37978,0.9453,1.5015,1.3488,1.0159,1.9452,1.3602,1.5079,0.39956,3.9122,1.20156,2.62441,2.49806,0.498954,2.00521,0.66685,1.02022,1.46471
+31-May-2019 23:00:00,1,0.34077,0.82727,1.3281,1.2256,0.88567,1.7439,1.2291,1.3085,0.3564,3.6136,1.08533,2.42717,2.29538,0.434492,1.80944,0.590187,0.92048,1.3458
+01-Jun-2019 00:00:00,1,0.30746,0.72713,1.1799,1.1204,0.77685,1.5756,1.1144,1.135,0.31846,3.3367,0.978184,2.24463,2.11754,0.380103,1.64355,0.524581,0.83641,1.24093
+01-Jun-2019 01:00:00,1,0.29143,0.67914,1.1085,1.0697,0.72529,1.4957,1.0582,1.0503,0.29983,3.1959,0.923866,2.15185,2.03028,0.354149,1.56414,0.492949,0.796323,1.18928
+01-Jun-2019 02:00:00,1,0.27976,0.64429,1.0564,1.0327,0.6881,1.4381,1.017,0.98821,0.2861,3.09,0.883147,2.0822,1.96603,0.335348,1.50651,0.469896,0.767296,1.15117
+01-Jun-2019 03:00:00,1,0.28024,0.64573,1.0586,1.0343,0.68964,1.4405,1.0187,0.9908,0.28667,3.0945,0.884857,2.08512,1.96871,0.336127,1.50889,0.470853,0.768498,1.15276
+01-Jun-2019 04:00:00,1,0.29022,0.67553,1.1031,1.0658,0.72143,1.4897,1.054,1.0439,0.29841,3.185,0.919699,2.14472,2.02366,0.3522,1.55817,0.490565,0.793313,1.18535
+01-Jun-2019 05:00:00,1,0.30849,0.7302,1.1845,1.1236,0.78017,1.5807,1.1179,1.1404,0.31965,3.3456,0.981596,2.25045,2.12309,0.381769,1.64864,0.526604,0.838983,1.2442
+01-Jun-2019 06:00:00,1,0.36077,0.8877,1.4171,1.2888,0.95208,1.8466,1.2967,1.4112,0.3787,3.7701,1.14614,2.53048,2.40005,0.467451,1.90965,0.629525,0.971464,1.40729
+01-Jun-2019 07:00:00,1,0.4063,1.026,1.6192,1.4326,1.106,2.0844,1.4476,1.6412,0.42818,4.1003,1.27525,2.74895,2.63265,0.543231,2.1392,0.718879,1.08879,1.5433
+01-Jun-2019 08:00:00,1,0.43233,1.1054,1.7346,1.5147,1.1956,2.2227,1.5319,1.7705,0.45572,4.2738,1.34354,2.86398,2.76227,0.586997,2.27128,0.769846,1.15661,1.6187
+01-Jun-2019 09:00:00,1,0.44138,1.1332,1.7747,1.5432,1.227,2.2712,1.561,1.815,0.46517,4.3317,1.3664,2.90238,2.8068,0.60229,2.31736,0.787554,1.18032,1.64454
+01-Jun-2019 10:00:00,1,0.4506,1.1614,1.8156,1.5723,1.2592,2.3208,1.5904,1.8602,0.47474,4.3894,1.38923,2.94068,2.8519,0.617911,2.36438,0.805587,1.20454,1.67067
+01-Jun-2019 11:00:00,1,0.4586,1.186,1.851,1.5975,1.2872,2.364,1.6158,1.8994,0.483,4.4384,1.40867,2.97326,2.89082,0.631504,2.40526,0.821236,1.22561,1.6932
+01-Jun-2019 12:00:00,1,0.44615,1.1478,1.7959,1.5582,1.2437,2.2968,1.5762,1.8385,0.47013,4.3617,1.37827,2.92231,2.83018,0.610372,2.34169,0.79689,1.19284,1.65809
+01-Jun-2019 13:00:00,1,0.43477,1.1129,1.7455,1.5224,1.2041,2.2358,1.5398,1.7825,0.45828,4.2896,1.34977,2.87444,2.77434,0.59113,2.28374,0.774637,1.16301,1.6257
+01-Jun-2019 14:00:00,1,0.42366,1.0789,1.6962,1.4873,1.1656,2.1764,1.504,1.7276,0.4466,4.2172,1.32122,2.82642,2.71934,0.57238,2.22721,0.752874,1.13395,1.59376
+01-Jun-2019 15:00:00,1,0.41281,1.0458,1.6481,1.4531,1.1283,2.1188,1.4688,1.6737,0.43512,4.1447,1.29269,2.77836,2.66528,0.554146,2.17217,0.731632,1.1057,1.56231
+01-Jun-2019 16:00:00,1,0.40584,1.0246,1.6172,1.4311,1.1044,2.0819,1.4461,1.6389,0.42769,4.0971,1.27399,2.74683,2.63031,0.542453,2.13685,0.717969,1.08758,1.54194
+01-Jun-2019 17:00:00,1,0.40265,1.0148,1.603,1.421,1.0935,2.0651,1.4356,1.623,0.42427,4.0751,1.26533,2.73222,2.61424,0.537105,2.12068,0.711709,1.0793,1.53256
+01-Jun-2019 18:00:00,1,0.39428,0.98936,1.5659,1.3946,1.065,2.0211,1.4081,1.581,0.41528,4.0165,1.24236,2.69342,2.57195,0.523115,2.07837,0.695301,1.05763,1.50789
+01-Jun-2019 19:00:00,1,0.38602,0.96426,1.5292,1.3686,1.037,1.9778,1.3809,1.5394,0.40635,3.9575,1.21929,2.65442,2.52999,0.509349,2.0367,0.679109,1.03632,1.48338
+01-Jun-2019 20:00:00,1,0.36926,0.91339,1.4548,1.3156,0.98047,1.8905,1.3252,1.4545,0.38805,3.8343,1.17116,2.57291,2.44397,0.481491,1.95226,0.646192,0.993191,1.43304
+01-Jun-2019 21:00:00,1,0.35739,0.87747,1.4021,1.2781,0.9408,1.8292,1.2853,1.3939,0.37495,3.7441,1.13604,2.51334,2.38246,0.461864,1.89268,0.622878,0.96282,1.39698
+01-Jun-2019 22:00:00,1,0.34971,0.85424,1.3679,1.2539,0.91524,1.7897,1.2594,1.3545,0.3664,3.6844,1.11281,2.47389,2.34233,0.449189,1.85416,0.607766,0.943211,1.37341
+01-Jun-2019 23:00:00,1,0.319,0.76175,1.2313,1.1568,0.81429,1.6335,1.1544,1.1954,0.33172,3.435,1.01615,2.30939,2.17966,0.398871,1.70088,0.547321,0.865411,1.27762
+02-Jun-2019 00:00:00,1,0.2955,0.6913,1.1266,1.0825,0.73833,1.5159,1.0726,1.0719,0.30458,3.2321,0.937825,2.1757,2.05253,0.360722,1.58427,0.50098,0.806472,1.20246
+02-Jun-2019 01:00:00,1,0.27888,0.64166,1.0525,1.0299,0.6853,1.4337,1.0138,0.9835,0.28506,3.0819,0.880031,2.07686,1.96116,0.333931,1.50216,0.468154,0.765109,1.14828
+02-Jun-2019 02:00:00,1,0.26877,0.61156,1.0074,0.99796,0.65336,1.3842,0.97781,0.92936,0.27305,2.9879,0.843893,2.01498,1.905,0.317726,1.4524,0.448177,0.7401,1.11492
+02-Jun-2019 03:00:00,1,0.26756,0.60796,1.002,0.99414,0.64956,1.3783,0.97349,0.92287,0.27161,2.9765,0.839524,2.00749,1.89826,0.315794,1.44646,0.445789,0.737119,1.11091
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+11-Jun-2019 13:00:00,1,0.4801,1.2521,1.9462,1.6653,1.363,2.4808,1.6835,2.0037,0.50494,4.5654,1.45921,3.05777,2.99435,0.668146,2.51532,0.863232,1.28246,1.753
+11-Jun-2019 14:00:00,1,0.46993,1.2208,1.9011,1.6332,1.3271,2.4254,1.6515,1.9544,0.4946,4.5061,1.4356,3.01832,2.94554,0.650781,2.46319,0.843364,1.25552,1.72483
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+11-Jun-2019 17:00:00,1,0.44383,1.1407,1.7856,1.5509,1.2356,2.2844,1.5688,1.8271,0.46772,4.3472,1.37252,2.91265,2.81883,0.606443,2.32986,0.792354,1.18675,1.65151
+11-Jun-2019 18:00:00,1,0.44445,1.1426,1.7883,1.5529,1.2377,2.2877,1.5708,1.8301,0.46837,4.351,1.37406,2.91523,2.82186,0.607492,2.33302,0.793565,1.18838,1.65327
+11-Jun-2019 19:00:00,1,0.43261,1.1063,1.7359,1.5155,1.1966,2.2242,1.5328,1.7718,0.45601,4.2756,1.34426,2.86518,2.76365,0.58747,2.27271,0.770395,1.15734,1.6195
+11-Jun-2019 20:00:00,1,0.4143,1.0504,1.6547,1.4578,1.1334,2.1267,1.4736,1.6811,0.4367,4.1548,1.29664,2.78502,2.67272,0.556643,2.17971,0.734546,1.10957,1.56664
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+11-Jun-2019 23:00:00,1,0.34025,0.82569,1.3258,1.224,0.88394,1.7413,1.2273,1.3058,0.35581,3.6094,1.0837,2.42441,2.29262,0.433632,1.80682,0.589156,0.91915,1.34418
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+12-Jun-2019 01:00:00,1,0.29955,0.70344,1.1447,1.0954,0.75135,1.5361,1.0868,1.0933,0.3093,3.2679,0.951618,2.19927,2.07463,0.367282,1.60434,0.508982,0.816603,1.21554
+12-Jun-2019 02:00:00,1,0.29323,0.68451,1.1165,1.0753,0.73105,1.5046,1.0646,1.0599,0.30193,3.2119,0.930049,2.16241,2.04012,0.357052,1.57303,0.496498,0.800806,1.19511
+12-Jun-2019 03:00:00,1,0.29455,0.68847,1.1224,1.0795,0.73529,1.5112,1.0692,1.0669,0.30347,3.2237,0.934584,2.17017,2.04735,0.35919,1.57958,0.49911,0.804107,1.19939
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+12-Jun-2019 10:00:00,1,0.4794,1.2499,1.943,1.6631,1.3605,2.477,1.6813,2.0003,0.50423,4.5614,1.4576,3.05508,2.99099,0.666947,2.51173,0.861862,1.2806,1.75106
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+12-Jun-2019 22:00:00,1,0.37816,0.94037,1.4943,1.3437,1.0104,1.9367,1.3548,1.4996,0.39779,3.9003,1.19692,2.61655,2.48975,0.496258,1.99704,0.663665,1.01605,1.45985
+12-Jun-2019 23:00:00,1,0.3391,0.82224,1.3207,1.2204,0.88017,1.7354,1.2234,1.2999,0.35452,3.6002,1.08014,2.41835,2.28658,0.431754,1.80111,0.586906,0.916246,1.34063
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+13-Jun-2019 01:00:00,1,0.29845,0.70012,1.1397,1.0919,0.74779,1.5306,1.0829,1.0875,0.30801,3.2581,0.947862,2.19285,2.0686,0.365489,1.59886,0.506797,0.813835,1.21198
+13-Jun-2019 02:00:00,1,0.2905,0.67637,1.1043,1.0667,0.72233,1.4911,1.055,1.0454,0.29874,3.1876,0.920671,2.14638,2.0252,0.352655,1.55956,0.491121,0.794014,1.18627
+13-Jun-2019 03:00:00,1,0.29006,0.67504,1.1024,1.0653,0.72091,1.4889,1.0534,1.0431,0.29822,3.1836,0.919137,2.14376,2.02277,0.351938,1.55737,0.490244,0.792908,1.18483
+13-Jun-2019 04:00:00,1,0.29789,0.69845,1.1372,1.0901,0.74599,1.5278,1.0809,1.0845,0.30736,3.2532,0.945961,2.1896,2.06555,0.364583,1.59609,0.505692,0.812436,1.21017
+13-Jun-2019 05:00:00,1,0.31727,0.75654,1.2236,1.1514,0.80865,1.6248,1.1484,1.1864,0.32973,3.4204,1.0105,2.29976,2.17036,0.396046,1.69225,0.543905,0.861045,1.27213
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+13-Jun-2019 07:00:00,1,0.42308,1.0772,1.6936,1.4855,1.1636,2.1733,1.5021,1.7247,0.44599,4.2134,1.31971,2.82389,2.71647,0.571407,2.22427,0.751742,1.13244,1.59209
+13-Jun-2019 08:00:00,1,0.4521,1.166,1.8222,1.577,1.2644,2.3289,1.5951,1.8676,0.47629,4.3986,1.39289,2.94683,2.8592,0.620453,2.37203,0.808517,1.20848,1.6749
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+13-Jun-2019 15:00:00,1,0.45834,1.1852,1.8498,1.5967,1.2863,2.3626,1.615,1.8981,0.48273,4.4368,1.40804,2.9722,2.88955,0.631056,2.40391,0.820721,1.22492,1.69246
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+13-Jun-2019 17:00:00,1,0.43963,1.1278,1.767,1.5377,1.221,2.2618,1.5554,1.8065,0.46335,4.3206,1.36203,2.89504,2.79824,0.599341,2.30847,0.784143,1.17574,1.63957
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+13-Jun-2019 23:00:00,1,0.33916,0.82242,1.321,1.2206,0.88036,1.7357,1.2236,1.3002,0.35459,3.6007,1.08032,2.41866,2.28689,0.43185,1.8014,0.58702,0.916393,1.34081
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+14-Jun-2019 01:00:00,1,0.2994,0.70296,1.144,1.0949,0.75085,1.5353,1.0862,1.0925,0.30912,3.2665,0.951085,2.19836,2.07378,0.367027,1.60356,0.508671,0.81621,1.21504
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+14-Jun-2019 16:00:00,1,0.44191,1.1348,1.7771,1.5449,1.2289,2.2741,1.5627,1.8177,0.46573,4.3351,1.36774,2.90463,2.80943,0.603197,2.32009,0.788602,1.18172,1.64606
+14-Jun-2019 17:00:00,1,0.4388,1.1252,1.7633,1.5351,1.2181,2.2573,1.5527,1.8023,0.46248,4.3153,1.35993,2.89151,2.79413,0.597926,2.30421,0.782505,1.17355,1.63719
+14-Jun-2019 18:00:00,1,0.4337,1.1096,1.7407,1.519,1.2004,2.23,1.5363,1.7772,0.45716,4.2827,1.34704,2.86986,2.76904,0.589316,2.27827,0.772536,1.1602,1.62263
+14-Jun-2019 19:00:00,1,0.42639,1.0873,1.7083,1.4959,1.175,2.191,1.5128,1.7411,0.44948,4.2352,1.32829,2.83832,2.73288,0.576976,2.24107,0.758216,1.14108,1.60162
+14-Jun-2019 20:00:00,1,0.41011,1.0376,1.6361,1.4446,1.119,2.1045,1.46,1.6602,0.43225,4.1264,1.28548,2.76621,2.65176,0.549614,2.15848,0.726341,1.09868,1.55443
+14-Jun-2019 21:00:00,1,0.39544,0.9929,1.571,1.3983,1.0689,2.0272,1.412,1.5868,0.41653,4.0247,1.24558,2.69886,2.57784,0.525057,2.08425,0.697582,1.06064,1.51133
+14-Jun-2019 22:00:00,1,0.37989,0.94564,1.502,1.3492,1.0162,1.9458,1.3606,1.5084,0.39969,3.913,1.20188,2.62496,2.49864,0.499142,2.00578,0.667071,1.02051,1.46505
+14-Jun-2019 23:00:00,1,0.3421,0.83127,1.334,1.2298,0.89005,1.7507,1.2336,1.3153,0.35789,3.6242,1.08944,2.43417,2.30237,0.43667,1.81607,0.592797,0.923849,1.34991
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+15-Jun-2019 01:00:00,1,0.3004,0.70598,1.1485,1.098,0.75409,1.5403,1.0897,1.0978,0.31029,3.2754,0.954495,2.20418,2.07926,0.368658,1.60855,0.510659,0.81873,1.21828
+15-Jun-2019 02:00:00,1,0.29211,0.68116,1.1115,1.0718,0.72746,1.4991,1.0606,1.0539,0.30062,3.2019,0.926194,2.15583,2.03398,0.355241,1.56748,0.494284,0.798009,1.19147
+15-Jun-2019 03:00:00,1,0.29323,0.68453,1.1165,1.0754,0.73107,1.5047,1.0646,1.0599,0.30194,3.212,0.93007,2.16245,2.04015,0.357063,1.57306,0.496511,0.800821,1.19513
+15-Jun-2019 04:00:00,1,0.30107,0.70796,1.1514,1.1001,0.75622,1.5436,1.0921,1.1013,0.31106,3.2811,0.95673,2.208,2.08286,0.369729,1.61183,0.511964,0.820384,1.22041
+15-Jun-2019 05:00:00,1,0.31899,0.76171,1.2312,1.1568,0.81425,1.6334,1.1543,1.1953,0.3317,3.4349,1.01611,2.30932,2.17959,0.398851,1.70081,0.547296,0.865379,1.27757
+15-Jun-2019 06:00:00,1,0.37426,0.92854,1.477,1.3314,0.99726,1.9165,1.3418,1.4799,0.39353,3.8715,1.18569,2.59753,2.46972,0.48978,1.9774,0.656007,1.00602,1.44812
+15-Jun-2019 07:00:00,1,0.4281,1.0925,1.7159,1.5013,1.181,2.2001,1.5183,1.7496,0.45128,4.2464,1.33272,2.84577,2.74138,0.57987,2.2498,0.761577,1.14556,1.60657
+15-Jun-2019 08:00:00,1,0.45856,1.1859,1.8508,1.5974,1.2871,2.3638,1.6157,1.8991,0.48295,4.4381,1.40857,2.9731,2.89062,0.631433,2.40505,0.821155,1.22551,1.69309
+15-Jun-2019 09:00:00,1,0.47025,1.2218,1.9026,1.6342,1.3282,2.4272,1.6526,1.956,0.49493,4.508,1.43635,3.01958,2.94709,0.651328,2.46483,0.843991,1.25636,1.72572
+15-Jun-2019 10:00:00,1,0.48552,1.2688,1.9701,1.6823,1.3822,2.5104,1.7004,2.0298,0.51042,4.5964,1.47157,3.07838,3.02021,0.677411,2.54312,0.873808,1.29683,1.76791
+15-Jun-2019 11:00:00,1,0.49725,1.305,2.022,1.7193,1.424,2.5748,1.7369,2.0862,0.52221,4.662,1.49782,3.12211,3.0759,0.697517,2.6034,0.8967,1.32804,1.79997
+15-Jun-2019 12:00:00,1,0.48644,1.2716,1.9742,1.6852,1.3855,2.5155,1.7033,2.0343,0.51135,4.6016,1.47366,3.08187,3.0246,0.678989,2.54785,0.875607,1.29928,1.77044
+15-Jun-2019 13:00:00,1,0.47123,1.2248,1.9069,1.6373,1.3317,2.4325,1.6557,1.9608,0.49593,4.5138,1.43866,3.02343,2.95182,0.653006,2.46987,0.845913,1.25897,1.72846
+15-Jun-2019 14:00:00,1,0.45678,1.1804,1.8429,1.5918,1.2808,2.3541,1.61,1.8905,0.48112,4.4273,1.40427,2.9659,2.88198,0.628406,2.39595,0.817674,1.22081,1.68809
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+15-Jun-2019 17:00:00,1,0.43132,1.1024,1.7301,1.5115,1.1921,2.2173,1.5287,1.7655,0.45466,4.2673,1.34096,2.85963,2.75727,0.585291,2.26614,0.767868,1.15396,1.6158
+15-Jun-2019 18:00:00,1,0.42492,1.0828,1.7018,1.4913,1.17,2.1832,1.5081,1.7339,0.44794,4.2255,1.3245,2.83194,2.72562,0.57451,2.23363,0.755351,1.13725,1.59741
+15-Jun-2019 19:00:00,1,0.41399,1.0494,1.6533,1.4568,1.1323,2.1251,1.4726,1.6796,0.43637,4.1527,1.29582,2.78363,2.67116,0.556121,2.17814,0.733936,1.10876,1.56573
+15-Jun-2019 20:00:00,1,0.39211,0.98276,1.5562,1.3878,1.0576,2.0097,1.401,1.5701,0.41294,4.0011,1.23633,2.68323,2.56093,0.519491,2.06741,0.691043,1.05202,1.50146
+15-Jun-2019 21:00:00,1,0.37346,0.92614,1.4735,1.3289,0.99459,1.9123,1.3392,1.4758,0.39266,3.8656,1.18339,2.59365,2.46564,0.488465,1.97342,0.654451,1.00398,1.44574
+15-Jun-2019 22:00:00,1,0.36271,0.89357,1.4257,1.295,0.95856,1.8566,1.3032,1.4211,0.38084,3.7848,1.1519,2.54025,2.41012,0.470658,1.91939,0.633337,0.976426,1.4132
+15-Jun-2019 23:00:00,1,0.33503,0.80997,1.3026,1.2075,0.86676,1.7147,1.2095,1.2788,0.34993,3.5674,1.0674,2.39668,2.26506,0.425077,1.78077,0.578893,0.905921,1.32796
+16-Jun-2019 00:00:00,1,0.309,0.73172,1.1867,1.1252,0.78181,1.5832,1.1197,1.143,0.32023,3.3499,0.98328,2.25333,2.12582,0.382592,1.65116,0.527603,0.840255,1.24582
+16-Jun-2019 01:00:00,1,0.29183,0.68033,1.1103,1.0709,0.72658,1.4977,1.0597,1.0524,0.3003,3.1995,0.925246,2.1542,2.03247,0.354796,1.56612,0.493741,0.797322,1.19058
+16-Jun-2019 02:00:00,1,0.28229,0.65185,1.0677,1.0408,0.69616,1.4506,1.026,1.0017,0.28909,3.1133,0.892079,2.09748,1.98004,0.339426,1.51901,0.474906,0.773591,1.15949
+16-Jun-2019 03:00:00,1,0.28021,0.64564,1.0584,1.0342,0.68954,1.4403,1.0186,0.99063,0.28664,3.0942,0.884747,2.08494,1.96854,0.336077,1.50874,0.470791,0.768421,1.15266
+16-Jun-2019 04:00:00,1,0.27974,0.64425,1.0564,1.0327,0.68806,1.438,1.0169,0.98814,0.28608,3.0899,0.883103,2.08212,1.96596,0.335328,1.50644,0.469871,0.767265,1.15113
+16-Jun-2019 05:00:00,1,0.27678,0.63543,1.0432,1.0233,0.67868,1.4235,1.0064,0.97233,0.28258,3.0627,0.87262,2.06418,1.94958,0.330575,1.49186,0.464023,0.759928,1.14141
+16-Jun-2019 06:00:00,1,0.29289,0.6835,1.115,1.0743,0.72997,1.5029,1.0634,1.0581,0.30153,3.2089,0.928887,2.16043,2.03827,0.356506,1.57136,0.49583,0.799961,1.19401
+16-Jun-2019 07:00:00,1,0.32277,0.77308,1.2481,1.1688,0.82659,1.6526,1.1674,1.2151,0.33603,3.4666,1.02837,2.33021,2.19985,0.405026,1.71966,0.554755,0.874924,1.28953
+16-Jun-2019 08:00:00,1,0.35838,0.88046,1.4064,1.2813,0.94409,1.8343,1.2887,1.399,0.37605,3.7517,1.139,2.51836,2.3876,0.463494,1.89763,0.624818,0.965342,1.39999
+16-Jun-2019 09:00:00,1,0.38752,0.96879,1.5359,1.3733,1.042,1.9856,1.3858,1.5469,0.40797,3.9682,1.22349,2.66152,2.53758,0.511832,2.04422,0.682033,1.04016,1.48782
+16-Jun-2019 10:00:00,1,0.40309,1.0162,1.605,1.4224,1.095,2.0674,1.4371,1.6252,0.42475,4.0781,1.26654,2.73425,2.61647,0.537847,2.12293,0.712579,1.08045,1.53387
+16-Jun-2019 11:00:00,1,0.40773,1.0303,1.6256,1.4371,1.1109,2.0919,1.4522,1.6484,0.42971,4.1101,1.27909,2.75544,2.63982,0.545624,2.14643,0.721677,1.0925,1.54747
+16-Jun-2019 12:00:00,1,0.39857,1.0024,1.5849,1.4082,1.0796,2.0436,1.4223,1.6026,0.4199,4.0467,1.25421,2.71343,2.59369,0.530293,2.10009,0.703726,1.06875,1.52058
+16-Jun-2019 13:00:00,1,0.37929,0.94383,1.4994,1.3473,1.0142,1.9427,1.3586,1.5054,0.39904,3.9086,1.20018,2.62207,2.49559,0.498151,2.00278,0.665901,1.01898,1.46326
+16-Jun-2019 14:00:00,1,0.36436,0.89856,1.433,1.3002,0.96406,1.8652,1.3088,1.4295,0.38266,3.7974,1.15677,2.54852,2.41865,0.473382,1.92765,0.636572,0.980641,1.4182
+16-Jun-2019 15:00:00,1,0.35589,0.87293,1.3954,1.2734,0.93579,1.8215,1.2803,1.3862,0.37329,3.7325,1.13153,2.50568,2.37464,0.459384,1.88514,0.619925,0.958983,1.39238
+16-Jun-2019 16:00:00,1,0.35068,0.85717,1.3722,1.2569,0.91846,1.7947,1.2627,1.3595,0.36748,3.692,1.11576,2.47891,2.34741,0.450786,1.85901,0.609672,0.945681,1.37639
+16-Jun-2019 17:00:00,1,0.35405,0.86737,1.3872,1.2676,0.92967,1.812,1.2741,1.3768,0.37124,3.7183,1.12599,2.49627,2.36504,0.456349,1.87592,0.616307,0.954286,1.38675
+16-Jun-2019 18:00:00,1,0.35666,0.87526,1.3988,1.2758,0.93836,1.8254,1.2829,1.3902,0.37414,3.7385,1.13384,2.50961,2.37865,0.460656,1.88901,0.62144,0.960951,1.39474
+16-Jun-2019 19:00:00,1,0.34966,0.85411,1.3677,1.2537,0.91509,1.7894,1.2592,1.3543,0.36635,3.684,1.11267,2.47366,2.34209,0.449114,1.85393,0.607677,0.943095,1.37327
+16-Jun-2019 20:00:00,1,0.32992,0.79459,1.2799,1.1914,0.84999,1.6888,1.192,1.2523,0.34416,3.5257,1.05126,2.3692,2.23796,0.416712,1.75529,0.568838,0.892989,1.312
+16-Jun-2019 21:00:00,1,0.31564,0.75166,1.2163,1.1462,0.80337,1.6166,1.1428,1.1779,0.32787,3.4067,1.0052,2.29071,2.16164,0.393402,1.68418,0.540706,0.856959,1.26698
+16-Jun-2019 22:00:00,1,0.31085,0.73727,1.195,1.1311,0.7878,1.5925,1.1261,1.1527,0.32236,3.3658,0.989414,2.26379,2.13582,0.3856,1.66035,0.531252,0.844902,1.25173
+16-Jun-2019 23:00:00,1,0.28914,0.6723,1.0983,1.0624,0.71798,1.4844,1.0502,1.0382,0.29715,3.1753,0.915958,2.13833,2.01772,0.350455,1.55282,0.488429,0.790618,1.18184
+17-Jun-2019 00:00:00,1,0.26507,0.60057,0.99091,0.98627,0.64175,1.3662,0.96458,0.9095,0.26864,2.953,0.830494,1.99202,1.88436,0.31182,1.43425,0.440873,0.730987,1.10264
+17-Jun-2019 01:00:00,1,0.24947,0.55426,0.92125,0.93688,0.59303,1.2906,0.90828,0.82511,0.24983,2.8023,0.772731,1.89295,1.79647,0.286966,1.35775,0.409996,0.692653,1.0503
+17-Jun-2019 02:00:00,1,0.24016,0.52669,0.87964,0.90738,0.56422,1.2459,0.87437,0.77436,0.23848,2.7098,0.737338,1.83218,1.74345,0.272206,1.31223,0.391548,0.669897,1.01866
+17-Jun-2019 03:00:00,1,0.23599,0.51435,0.86099,0.89417,0.55138,1.226,0.8591,0.75153,0.23337,2.6677,0.721254,1.80455,1.71956,0.265611,1.29188,0.383279,0.659733,1.0044
+17-Jun-2019 04:00:00,1,0.23621,0.51501,0.86198,0.89487,0.55207,1.2271,0.85991,0.75275,0.23364,2.6699,0.722116,1.80603,1.72084,0.265963,1.29296,0.383721,0.660275,1.00516
+17-Jun-2019 05:00:00,1,0.23256,0.50424,0.84569,0.88333,0.54089,1.2097,0.84654,0.73276,0.22916,2.6329,0.70796,1.7817,1.69991,0.260213,1.2752,0.376496,0.651414,0.992651
+17-Jun-2019 06:00:00,1,0.23952,0.52479,0.87678,0.90536,0.56225,1.2429,0.87203,0.77086,0.2377,2.7033,0.73488,1.82796,1.73979,0.271193,1.30911,0.39028,0.668337,1.01648
+17-Jun-2019 07:00:00,1,0.25782,0.57902,0.95853,0.96331,0.61903,1.331,0.93849,0.87036,0.25993,2.8835,0.803874,1.94638,1.84364,0.300245,1.39864,0.426521,0.713131,1.0784
+17-Jun-2019 08:00:00,1,0.28633,0.6639,1.0857,1.0535,0.70901,1.4705,1.0403,1.0232,0.29385,3.15,0.906199,2.12164,2.00229,0.345925,1.53894,0.48288,0.783624,1.17268
+17-Jun-2019 09:00:00,1,0.30872,0.7309,1.1855,1.1243,0.78093,1.5819,1.1188,1.1416,0.31992,3.3476,0.982375,2.25178,2.12435,0.382149,1.64981,0.527066,0.839571,1.24495
+17-Jun-2019 10:00:00,1,0.32571,0.78192,1.2611,1.178,0.83619,1.6674,1.1775,1.2304,0.33937,3.491,1.03781,2.3463,2.21553,0.409823,1.73428,0.56054,0.882338,1.29878
+17-Jun-2019 11:00:00,1,0.34113,0.82834,1.3297,1.2268,0.88684,1.7458,1.2303,1.3103,0.3568,3.6164,1.08643,2.42905,2.29725,0.435076,1.81122,0.590887,0.921383,1.34691
+17-Jun-2019 12:00:00,1,0.33778,0.81827,1.3149,1.2162,0.87583,1.7287,1.2189,1.293,0.35304,3.5896,1.07603,2.41136,2.27962,0.429593,1.79453,0.584313,0.912903,1.33653
+17-Jun-2019 13:00:00,1,0.32279,0.77312,1.2481,1.1688,0.82663,1.6526,1.1675,1.2152,0.33604,3.4667,1.02841,2.33028,2.19992,0.405047,1.71972,0.554781,0.874956,1.28957
+17-Jun-2019 14:00:00,1,0.3086,0.73052,1.185,1.1239,0.78052,1.5812,1.1183,1.1409,0.31977,3.3465,0.981954,2.25106,2.12367,0.381944,1.64918,0.526816,0.839253,1.24455
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+17-Jun-2019 16:00:00,1,0.30522,0.72042,1.1699,1.1133,0.76962,1.5644,1.1066,1.1232,0.31587,3.3174,0.970709,2.23187,2.10542,0.37647,1.63245,0.520167,0.830798,1.23376
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+17-Jun-2019 23:00:00,1,0.3072,0.72635,1.1787,1.1195,0.77602,1.5743,1.1135,1.1336,0.31816,3.3345,0.977319,2.24315,2.11614,0.379682,1.64227,0.52407,0.83576,1.2401
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+18-Jun-2019 01:00:00,1,0.26779,0.60866,1.003,0.99488,0.6503,1.3795,0.97433,0.92413,0.27189,2.9787,0.840371,2.00895,1.89956,0.316168,1.44761,0.446252,0.737696,1.11168
+18-Jun-2019 02:00:00,1,0.26252,0.59298,0.97951,0.97818,0.63373,1.3538,0.95541,0.89573,0.26558,2.9286,0.821164,1.97603,1.87004,0.307739,1.4217,0.435819,0.724691,1.09412
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+18-Jun-2019 05:00:00,1,0.29534,0.69081,1.1259,1.082,0.7378,1.5151,1.072,1.071,0.30439,3.2307,0.937267,2.17475,2.05164,0.360458,1.58346,0.500658,0.806065,1.20193
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+20-Jun-2019 01:00:00,1,0.30529,0.72061,1.1702,1.1135,0.76983,1.5647,1.1068,1.1235,0.31595,3.3179,0.97092,2.23223,2.10577,0.376573,1.63276,0.520291,0.830956,1.23396
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+20-Jun-2019 21:00:00,1,0.39675,0.99687,1.5768,1.4024,1.0734,2.034,1.4163,1.5934,0.41794,4.0339,1.24918,2.70494,2.58445,0.527237,2.09084,0.700141,1.06402,1.51518
+20-Jun-2019 22:00:00,1,0.37807,0.94011,1.4939,1.3434,1.0101,1.9363,1.3545,1.4992,0.3977,3.8996,1.19667,2.61613,2.48931,0.496115,1.99661,0.663496,1.01583,1.45959
+20-Jun-2019 23:00:00,1,0.34436,0.83811,1.3441,1.237,0.89754,1.7623,1.2413,1.327,0.36043,3.6422,1.09644,2.44607,2.31429,0.440396,1.82741,0.597256,0.929611,1.35693
+21-Jun-2019 00:00:00,1,0.31739,0.7569,1.2241,1.1517,0.80904,1.6254,1.1488,1.187,0.32987,3.4214,1.0109,2.30044,2.17101,0.396244,1.69285,0.544144,0.86135,1.27251
+21-Jun-2019 01:00:00,1,0.30286,0.71332,1.1594,1.1058,0.76198,1.5525,1.0983,1.1107,0.31313,3.2968,0.962764,2.21831,2.09258,0.372631,1.62071,0.515496,0.824866,1.22617
+21-Jun-2019 02:00:00,1,0.29784,0.69831,1.137,1.0899,0.74585,1.5276,1.0808,1.0843,0.30731,3.2528,0.945811,2.18935,2.06531,0.364512,1.59587,0.505605,0.812326,1.21003
+21-Jun-2019 03:00:00,1,0.29836,0.69986,1.1393,1.0916,0.74751,1.5301,1.0826,1.087,0.30791,3.2574,0.947563,2.19234,2.06812,0.365346,1.59842,0.506623,0.813615,1.21169
+21-Jun-2019 04:00:00,1,0.30382,0.71622,1.1637,1.1089,0.7651,1.5574,1.1017,1.1158,0.31425,3.3052,0.966009,2.22385,2.09782,0.374196,1.62549,0.517401,0.827285,1.22926
+21-Jun-2019 05:00:00,1,0.32338,0.77491,1.2508,1.1707,0.82858,1.6556,1.1695,1.2183,0.33672,3.4717,1.03033,2.33355,2.2031,0.406018,1.72268,0.555952,0.876457,1.29145
+21-Jun-2019 06:00:00,1,0.3803,0.94689,1.5038,1.3505,1.0176,1.9479,1.362,1.5105,0.40013,3.916,1.20306,2.62695,2.50074,0.499825,2.00785,0.667877,1.02157,1.46628
+21-Jun-2019 07:00:00,1,0.43303,1.1076,1.7377,1.5169,1.198,2.2265,1.5342,1.7739,0.45646,4.2784,1.34534,2.86699,2.76574,0.588185,2.27486,0.771224,1.15845,1.62071
+21-Jun-2019 08:00:00,1,0.4622,1.197,1.8669,1.6088,1.2999,2.3835,1.6272,1.9169,0.48669,4.4601,1.41729,2.9877,2.90824,0.637618,2.42364,0.828263,1.2351,1.70328
+21-Jun-2019 09:00:00,1,0.47081,1.2235,1.9051,1.636,1.3302,2.4302,1.6543,1.9587,0.4955,4.5114,1.43768,3.02179,2.94981,0.652292,2.46773,0.845095,1.25786,1.72729
+21-Jun-2019 10:00:00,1,0.48222,1.2586,1.9555,1.6719,1.3705,2.4924,1.6901,2.0139,0.50709,4.5776,1.46407,3.06587,3.00448,0.67177,2.5262,0.867371,1.28808,1.75884
+21-Jun-2019 11:00:00,1,0.49385,1.2945,2.007,1.7086,1.4118,2.5561,1.7263,2.0699,0.5188,4.6432,1.49028,3.10956,3.05979,0.691679,2.5859,0.890061,1.31898,1.7907
+21-Jun-2019 12:00:00,1,0.49102,1.2857,1.9944,1.6997,1.4018,2.5405,1.7175,2.0563,0.51596,4.6274,1.48396,3.09903,3.04635,0.686824,2.57135,0.884535,1.31144,1.78297
+21-Jun-2019 13:00:00,1,0.48941,1.2808,1.9873,1.6946,1.396,2.5317,1.7125,2.0485,0.51434,4.6183,1.48035,3.09302,3.03871,0.684068,2.56309,0.881396,1.30717,1.77857
+21-Jun-2019 14:00:00,1,0.48341,1.2623,1.9608,1.6757,1.3747,2.4989,1.6938,2.0196,0.50829,4.5844,1.46678,3.07039,3.01015,0.673801,2.53229,0.869689,1.29123,1.76211
+21-Jun-2019 15:00:00,1,0.47561,1.2383,1.9263,1.6511,1.3471,2.4563,1.6694,1.982,0.50038,4.5394,1.44885,3.04047,2.97284,0.660473,2.49229,0.854461,1.27055,1.74059
+21-Jun-2019 16:00:00,1,0.46023,1.191,1.8582,1.6026,1.2929,2.3728,1.621,1.9073,0.48467,4.4483,1.41259,2.97982,2.89873,0.634275,2.41359,0.824423,1.22991,1.69777
+21-Jun-2019 17:00:00,1,0.45413,1.1723,1.8312,1.5834,1.2715,2.3398,1.6016,1.8775,0.47839,4.4111,1.39785,2.95513,2.86909,0.623903,2.3824,0.812491,1.21383,1.68063
+21-Jun-2019 18:00:00,1,0.44971,1.1587,1.8116,1.5695,1.2561,2.316,1.5876,1.8559,0.47383,4.3839,1.38706,2.93704,2.84759,0.616412,2.35987,0.803859,1.20221,1.66818
+21-Jun-2019 19:00:00,1,0.44143,1.1333,1.775,1.5434,1.2272,2.2715,1.5611,1.8153,0.46523,4.332,1.36654,2.90261,2.80707,0.602383,2.31764,0.787661,1.18046,1.64469
+21-Jun-2019 20:00:00,1,0.42251,1.0754,1.6911,1.4837,1.1617,2.1703,1.5003,1.7219,0.4454,4.2097,1.31824,2.82141,2.71367,0.570457,2.22141,0.750638,1.13097,1.59046
+21-Jun-2019 21:00:00,1,0.40916,1.0347,1.6319,1.4416,1.1158,2.0995,1.4569,1.6555,0.43123,4.1199,1.28293,2.76191,2.64699,0.548019,2.15367,0.724477,1.09621,1.55165
+21-Jun-2019 22:00:00,1,0.39123,0.98008,1.5523,1.385,1.0546,2.005,1.3981,1.5656,0.41199,3.9948,1.23388,2.67909,2.55646,0.518021,2.06296,0.689315,1.04974,1.49885
+21-Jun-2019 23:00:00,1,0.3503,0.85605,1.3705,1.2558,0.91723,1.7927,1.2614,1.3576,0.36707,3.6891,1.11463,2.47699,2.34546,0.450175,1.85715,0.608943,0.944736,1.37525
+22-Jun-2019 00:00:00,1,0.32264,0.7727,1.2475,1.1684,0.82618,1.6519,1.167,1.2144,0.33588,3.4655,1.02796,2.32951,2.19917,0.404818,1.71902,0.554504,0.874602,1.28913
+22-Jun-2019 01:00:00,1,0.3099,0.73442,1.1907,1.1281,0.78472,1.5877,1.1228,1.1477,0.32127,3.3577,0.986265,2.25842,2.13068,0.384054,1.65563,0.529377,0.842514,1.24869
+22-Jun-2019 02:00:00,1,0.3002,0.70536,1.1475,1.0974,0.75342,1.5393,1.089,1.0967,0.31005,3.2735,0.953792,2.20298,2.07813,0.368321,1.60752,0.510249,0.81821,1.21761
+22-Jun-2019 03:00:00,1,0.29961,0.7036,1.1449,1.0955,0.75153,1.5364,1.087,1.0936,0.30937,3.2684,0.95181,2.1996,2.07494,0.367373,1.60462,0.509094,0.816745,1.21573
+22-Jun-2019 04:00:00,1,0.30407,0.71695,1.1648,1.1096,0.76589,1.5586,1.1025,1.1171,0.31454,3.3073,0.966835,2.22526,2.09916,0.374596,1.62671,0.517887,0.827901,1.23005
+22-Jun-2019 05:00:00,1,0.32126,0.76853,1.2413,1.164,0.82165,1.6449,1.1622,1.2072,0.3343,3.454,1.02348,2.32188,2.19176,0.402555,1.71212,0.551772,0.871105,1.28475
+22-Jun-2019 06:00:00,1,0.37657,0.93557,1.4873,1.3387,1.0051,1.9285,1.3496,1.4916,0.39607,3.8886,1.19238,2.60886,2.48164,0.493631,1.98908,0.660561,1.01198,1.4551
+22-Jun-2019 07:00:00,1,0.42341,1.0782,1.6951,1.4865,1.1648,2.1751,1.5032,1.7263,0.44634,4.2155,1.32056,2.82532,2.71809,0.571956,2.22593,0.752381,1.1333,1.59303
+22-Jun-2019 08:00:00,1,0.44854,1.1551,1.8064,1.5658,1.252,2.3097,1.5838,1.8502,0.47261,4.3766,1.38418,2.93221,2.84186,0.614424,2.35389,0.801566,1.19913,1.66486
+22-Jun-2019 09:00:00,1,0.45793,1.1839,1.848,1.5954,1.2849,2.3604,1.6137,1.8961,0.4823,4.4343,1.40705,2.97055,2.88756,0.630361,2.40182,0.819922,1.22384,1.69132
+22-Jun-2019 10:00:00,1,0.46965,1.2199,1.8999,1.6323,1.3261,2.4239,1.6507,1.9531,0.49431,4.5045,1.43494,3.01722,2.9442,0.650305,2.46176,0.842819,1.25478,1.72405
+22-Jun-2019 11:00:00,1,0.47923,1.2494,1.9423,1.6625,1.3599,2.4761,1.6808,1.9995,0.50406,4.5604,1.45722,3.05443,2.99019,0.666659,2.51086,0.861534,1.28015,1.7506
+22-Jun-2019 12:00:00,1,0.47124,1.2248,1.9069,1.6373,1.3317,2.4325,1.6557,1.9608,0.49594,4.5139,1.43867,3.02346,2.95185,0.653018,2.46991,0.845927,1.25899,1.72848
+22-Jun-2019 13:00:00,1,0.46114,1.1938,1.8622,1.6055,1.2961,2.3777,1.6238,1.9117,0.4856,4.4537,1.41475,2.98345,2.9031,0.63581,2.41821,0.826187,1.23229,1.7003
+22-Jun-2019 14:00:00,1,0.44619,1.1479,1.796,1.5584,1.2438,2.297,1.5763,1.8387,0.47018,4.362,1.37838,2.92248,2.83038,0.610442,2.3419,0.796971,1.19295,1.65821
+22-Jun-2019 15:00:00,1,0.43266,1.1064,1.7361,1.5157,1.1967,2.2244,1.533,1.7721,0.45606,4.2759,1.34438,2.86538,2.76388,0.58755,2.27295,0.770488,1.15747,1.61964
+22-Jun-2019 16:00:00,1,0.42552,1.0846,1.7044,1.4932,1.1721,2.1864,1.51,1.7368,0.44857,4.2295,1.32605,2.83456,2.72859,0.575519,2.23668,0.756523,1.13882,1.59913
+22-Jun-2019 17:00:00,1,0.41936,1.0658,1.6771,1.4737,1.1508,2.1536,1.4901,1.7063,0.44206,4.1887,1.30999,2.80751,2.69796,0.565146,2.20538,0.744456,1.12274,1.58132
+22-Jun-2019 18:00:00,1,0.40948,1.0357,1.6333,1.4426,1.1169,2.1012,1.4579,1.6571,0.43157,4.1221,1.28378,2.76335,2.64858,0.548551,2.15527,0.725098,1.09703,1.55258
+22-Jun-2019 19:00:00,1,0.39558,0.9933,1.5716,1.3987,1.0694,2.0279,1.4124,1.5875,0.41668,4.0256,1.24595,2.69948,2.57852,0.525281,2.08493,0.697844,1.06099,1.51173
+22-Jun-2019 20:00:00,1,0.36892,0.91237,1.4533,1.3146,0.97934,1.8888,1.324,1.4528,0.38768,3.8317,1.17018,2.57125,2.44224,0.480935,1.95058,0.645533,0.99233,1.43203
+22-Jun-2019 21:00:00,1,0.34811,0.84943,1.3608,1.2488,0.90996,1.7815,1.254,1.3463,0.36462,3.6719,1.10795,2.46564,2.33399,0.446567,1.84618,0.604634,0.939155,1.36851
+22-Jun-2019 22:00:00,1,0.33959,0.82372,1.3229,1.2219,0.88179,1.7379,1.225,1.3024,0.35507,3.6042,1.08167,2.42095,2.28917,0.43256,1.80356,0.587872,0.917492,1.34215
+22-Jun-2019 23:00:00,1,0.32627,0.78359,1.2636,1.1798,0.83801,1.6702,1.1794,1.2333,0.34,3.4956,1.0396,2.34934,2.2185,0.410733,1.73706,0.561638,0.883746,1.30053
+23-Jun-2019 00:00:00,1,0.30146,0.70915,1.1532,1.1014,0.75749,1.5456,1.0934,1.1034,0.31152,3.2846,0.95807,2.21029,2.08501,0.370373,1.6138,0.512747,0.821378,1.22169
+23-Jun-2019 01:00:00,1,0.28378,0.65629,1.0744,1.0455,0.70089,1.4579,1.0312,1.0097,0.29085,3.1268,0.897296,2.10641,1.98825,0.34182,1.52636,0.477845,0.777286,1.16436
+23-Jun-2019 02:00:00,1,0.2746,0.62891,1.0334,1.0164,0.67176,1.4127,0.99863,0.96063,0.27999,3.0424,0.864833,2.05085,1.93745,0.327067,1.48109,0.459702,0.754514,1.1342
+23-Jun-2019 03:00:00,1,0.27195,0.62103,1.0216,1.008,0.6634,1.3998,0.98919,0.94645,0.27684,3.0177,0.85536,2.03462,1.92274,0.322824,1.46806,0.454471,0.747967,1.12546
+23-Jun-2019 04:00:00,1,0.26924,0.61295,1.0095,0.99944,0.65484,1.3865,0.97949,0.93188,0.27361,2.9923,0.845585,2.01788,1.90761,0.318476,1.4547,0.449103,0.741257,1.11647
+23-Jun-2019 05:00:00,1,0.26813,0.60965,1.0045,0.99593,0.65135,1.3811,0.97552,0.92592,0.27229,2.9818,0.841575,2.01101,1.90142,0.316701,1.44925,0.446909,0.738517,1.11279
+23-Jun-2019 06:00:00,1,0.28498,0.65988,1.0797,1.0493,0.70472,1.4638,1.0355,1.0161,0.29226,3.1378,0.901501,2.1136,1.99487,0.343756,1.53229,0.48022,0.780275,1.16829
+23-Jun-2019 07:00:00,1,0.31511,0.75008,1.214,1.1446,0.80165,1.614,1.141,1.1751,0.32727,3.4022,1.00347,2.28776,2.1588,0.392543,1.68156,0.539666,0.855631,1.2653
+23-Jun-2019 08:00:00,1,0.34945,0.85346,1.3667,1.253,0.91438,1.7883,1.2585,1.3532,0.36611,3.6823,1.11202,2.47255,2.34097,0.448761,1.85285,0.607255,0.942549,1.37261
+23-Jun-2019 09:00:00,1,0.36982,0.91511,1.4573,1.3174,0.98237,1.8934,1.3271,1.4574,0.38867,3.8385,1.17281,2.57571,2.44689,0.48243,1.95511,0.647304,0.994643,1.43476
+23-Jun-2019 10:00:00,1,0.38341,0.95631,1.5176,1.3603,1.0281,1.9641,1.3722,1.5262,0.40351,3.9386,1.21189,2.64189,2.51662,0.50499,2.0235,0.673971,1.02957,1.47557
+23-Jun-2019 11:00:00,1,0.38939,0.9745,1.5442,1.3792,1.0484,1.9954,1.392,1.5564,0.41,3.9817,1.22876,2.67042,2.54714,0.514963,2.0537,0.685718,1.04501,1.4934
+23-Jun-2019 12:00:00,1,0.37813,0.94029,1.4942,1.3436,1.0103,1.9366,1.3547,1.4995,0.39776,3.9001,1.19684,2.61642,2.48961,0.496214,1.99691,0.663613,1.01598,1.45977
+23-Jun-2019 13:00:00,1,0.36683,0.90604,1.444,1.308,0.97233,1.8779,1.317,1.4421,0.38538,3.816,1.16405,2.56085,2.43143,0.47747,1.94006,0.641424,0.986968,1.4257
+23-Jun-2019 14:00:00,1,0.35504,0.87034,1.3916,1.2707,0.93295,1.8171,1.2774,1.3818,0.37234,3.7259,1.12896,2.50131,2.37018,0.457973,1.88086,0.618244,0.9568,1.38976
+23-Jun-2019 15:00:00,1,0.34601,0.84308,1.3515,1.2422,0.90299,1.7707,1.2469,1.3355,0.36227,3.6553,1.10151,2.45468,2.32295,0.443105,1.83565,0.600497,0.933801,1.36202
+23-Jun-2019 16:00:00,1,0.34023,0.82563,1.3257,1.2239,0.88388,1.7412,1.2272,1.3057,0.35579,3.6092,1.08364,2.4243,2.29251,0.4336,1.80673,0.589118,0.9191,1.34412
+23-Jun-2019 17:00:00,1,0.34048,0.82639,1.3269,1.2247,0.88471,1.7425,1.2281,1.307,0.35607,3.6113,1.08442,2.42564,2.29384,0.434014,1.80799,0.589614,0.919741,1.3449
+23-Jun-2019 18:00:00,1,0.34331,0.83494,1.3395,1.2337,0.89407,1.7569,1.2377,1.3216,0.35925,3.6339,1.0932,2.44057,2.30877,0.438671,1.82216,0.595191,0.926942,1.35368
+23-Jun-2019 19:00:00,1,0.33582,0.81234,1.3061,1.21,0.86935,1.7187,1.2122,1.2829,0.35082,3.5737,1.06987,2.40088,2.26922,0.426366,1.7847,0.580441,0.907914,1.33041
+23-Jun-2019 20:00:00,1,0.32033,0.76573,1.2372,1.161,0.81861,1.6402,1.159,1.2023,0.33323,3.4462,1.02046,2.31673,2.18677,0.401035,1.70748,0.549936,0.868755,1.28181
+23-Jun-2019 21:00:00,1,0.31243,0.74203,1.202,1.1361,0.79295,1.6005,1.1317,1.1611,0.32419,3.3794,0.994652,2.27273,2.14437,0.388179,1.66823,0.534379,0.848888,1.25678
+23-Jun-2019 22:00:00,1,0.31144,0.73904,1.1976,1.1329,0.78972,1.5955,1.1282,1.1558,0.32304,3.3709,0.991364,2.26712,2.139,0.386559,1.66328,0.532415,0.846384,1.25361
+23-Jun-2019 23:00:00,1,0.28869,0.67095,1.0963,1.061,0.71654,1.4821,1.0486,1.0358,0.29662,3.1713,0.914399,2.13566,2.01525,0.34973,1.5506,0.487541,0.789498,1.18037
+24-Jun-2019 00:00:00,1,0.26952,0.6138,1.0108,1.0003,0.65573,1.3879,0.9805,0.9334,0.27395,2.9949,0.846609,2.01963,1.90919,0.31893,1.4561,0.449664,0.741957,1.11741
+24-Jun-2019 01:00:00,1,0.2559,0.57331,0.94993,0.95721,0.61302,1.3217,0.93154,0.85994,0.25761,2.8649,0.79674,1.93414,1.83279,0.297178,1.3892,0.422711,0.708402,1.07194
+24-Jun-2019 02:00:00,1,0.24702,0.54701,0.91031,0.92912,0.58544,1.2789,0.89939,0.81179,0.24686,2.7781,0.763495,1.8771,1.78257,0.283081,1.34577,0.405148,0.686662,1.04201
+24-Jun-2019 03:00:00,1,0.2435,0.53656,0.89455,0.91795,0.57452,1.2619,0.88655,0.79258,0.24256,2.7431,0.750101,1.8541,1.7625,0.277489,1.32853,0.398161,0.678041,1.03003
+24-Jun-2019 04:00:00,1,0.24142,0.5304,0.88525,0.91136,0.5681,1.252,0.87896,0.78122,0.24002,2.7223,0.742154,1.84045,1.75063,0.274194,1.31837,0.394038,0.672962,1.02295
+24-Jun-2019 05:00:00,1,0.23775,0.51957,0.86888,0.89976,0.55681,1.2344,0.86556,0.76119,0.23553,2.6855,0.728075,1.81627,1.72968,0.268399,1.30049,0.386778,0.66403,1.01044
+24-Jun-2019 06:00:00,1,0.24311,0.53543,0.89285,0.91675,0.57334,1.2601,0.88516,0.7905,0.2421,2.7393,0.748648,1.8516,1.76033,0.276885,1.32667,0.397406,0.67711,1.02874
+24-Jun-2019 07:00:00,1,0.25991,0.58524,0.96787,0.96993,0.62557,1.3411,0.94603,0.88167,0.26245,2.9037,0.811597,1.95962,1.85541,0.303582,1.40891,0.430664,0.718278,1.08542
+24-Jun-2019 08:00:00,1,0.28965,0.67381,1.1005,1.064,0.71959,1.4869,1.052,1.0409,0.29774,3.1799,0.91771,2.14132,2.0205,0.351272,1.55533,0.489429,0.79188,1.18348
+24-Jun-2019 09:00:00,1,0.31399,0.7467,1.209,1.141,0.798,1.6083,1.1371,1.1692,0.32598,3.3927,0.999775,2.28147,2.15275,0.390711,1.67596,0.537447,0.8528,1.26173
+24-Jun-2019 10:00:00,1,0.32945,0.79316,1.2778,1.1899,0.84843,1.6863,1.1904,1.2498,0.34362,3.5218,1.04975,2.36663,2.23544,0.415935,1.75292,0.567903,0.891787,1.31051
+24-Jun-2019 11:00:00,1,0.34375,0.83627,1.3414,1.2351,0.89553,1.7592,1.2392,1.3239,0.35975,3.6374,1.09456,2.44288,2.31109,0.439395,1.82436,0.596058,0.928062,1.35505
+24-Jun-2019 12:00:00,1,0.34409,0.83729,1.3429,1.2361,0.89664,1.7609,1.2403,1.3256,0.36012,3.6401,1.0956,2.44464,2.31286,0.439949,1.82605,0.596721,0.928919,1.35609
+24-Jun-2019 13:00:00,1,0.33214,0.80127,1.2898,1.1984,0.85726,1.7,1.1996,1.2638,0.34667,3.5439,1.05829,2.38117,2.24974,0.420342,1.76635,0.573204,0.8986,1.31894
+24-Jun-2019 14:00:00,1,0.31527,0.75056,1.2147,1.1451,0.80217,1.6148,1.1415,1.1759,0.32745,3.4036,1.00399,2.28865,2.15966,0.392802,1.68235,0.539979,0.856031,1.26581
+24-Jun-2019 15:00:00,1,0.30265,0.7127,1.1585,1.1051,0.76131,1.5515,1.0976,1.1096,0.31289,3.295,0.962064,2.21711,2.09145,0.372294,1.61967,0.515086,0.824345,1.2255
+24-Jun-2019 16:00:00,1,0.29924,0.70249,1.1433,1.0944,0.75034,1.5345,1.0857,1.0916,0.30894,3.2651,0.950552,2.19745,2.07292,0.366772,1.60278,0.508361,0.815817,1.21453
+24-Jun-2019 17:00:00,1,0.30814,0.72916,1.1829,1.1225,0.77905,1.579,1.1167,1.1385,0.31924,3.3426,0.980438,2.24848,2.1212,0.381203,1.64691,0.525917,0.838109,1.24309
+24-Jun-2019 18:00:00,1,0.31563,0.75163,1.2163,1.1462,0.80333,1.6165,1.1427,1.1778,0.32786,3.4066,1.00516,2.29064,2.16157,0.393382,1.68412,0.540681,0.856927,1.26694
+24-Jun-2019 19:00:00,1,0.30844,0.73005,1.1842,1.1234,0.78001,1.5804,1.1178,1.1401,0.31959,3.3452,0.981428,2.25016,2.12281,0.381687,1.64839,0.526504,0.838856,1.24404
+24-Jun-2019 20:00:00,1,0.30619,0.7233,1.1742,1.1163,0.77273,1.5692,1.1099,1.1282,0.31699,3.3257,0.973921,2.23735,2.11063,0.378029,1.63721,0.522061,0.833206,1.23684
+24-Jun-2019 21:00:00,1,0.30343,0.71504,1.1619,1.1076,0.76384,1.5554,1.1003,1.1137,0.3138,3.3018,0.964695,2.2216,2.0957,0.373562,1.62355,0.516629,0.826304,1.22801
+24-Jun-2019 22:00:00,1,0.30732,0.72669,1.1793,1.1199,0.77638,1.5748,1.1139,1.1342,0.31829,3.3355,0.977699,2.2438,2.11675,0.379867,1.64283,0.524294,0.836045,1.24046
+24-Jun-2019 23:00:00,1,0.28404,0.65705,1.0755,1.0463,0.7017,1.4592,1.0321,1.011,0.29115,3.1292,0.89819,2.10794,1.98966,0.342231,1.52762,0.478349,0.777921,1.16519
+25-Jun-2019 00:00:00,1,0.25691,0.57632,0.95446,0.96042,0.61618,1.3266,0.9352,0.86543,0.25883,2.8747,0.800502,1.9406,1.83851,0.298794,1.39418,0.424719,0.710893,1.07535
+25-Jun-2019 01:00:00,1,0.24608,0.5442,0.90608,0.92613,0.58251,1.2743,0.89595,0.80664,0.24571,2.7688,0.759911,1.87094,1.77719,0.28158,1.34115,0.403274,0.684348,1.0388
+25-Jun-2019 02:00:00,1,0.24241,0.53336,0.88971,0.91452,0.57118,1.2567,0.8826,0.78667,0.24124,2.7323,0.745969,1.847,1.75632,0.275773,1.32324,0.396015,0.675396,1.02635
+25-Jun-2019 03:00:00,1,0.24506,0.54119,0.90153,0.9229,0.57935,1.2694,0.89224,0.8011,0.24447,2.7587,0.756047,1.86431,1.7714,0.279965,1.33617,0.401257,0.681858,1.03534
+25-Jun-2019 04:00:00,1,0.26037,0.58658,0.9699,0.97137,0.62699,1.3433,0.94766,0.88412,0.26299,2.908,0.813265,1.96249,1.85796,0.304305,1.41114,0.431561,0.719393,1.08693
+25-Jun-2019 05:00:00,1,0.28624,0.66362,1.0853,1.0532,0.70871,1.47,1.0399,1.0227,0.29374,3.1491,0.905873,2.12108,2.00177,0.345775,1.53848,0.482695,0.783392,1.17238
+25-Jun-2019 06:00:00,1,0.34776,0.84837,1.3592,1.2477,0.9088,1.7797,1.2528,1.3445,0.36423,3.6691,1.10688,2.46381,2.33215,0.445989,1.84443,0.603944,0.938262,1.36742
+25-Jun-2019 07:00:00,1,0.3952,0.99216,1.57,1.3975,1.0681,2.0259,1.4112,1.5856,0.41627,4.023,1.24491,2.69773,2.57662,0.524655,2.08303,0.69711,1.06002,1.51062
+25-Jun-2019 08:00:00,1,0.42298,1.0769,1.6932,1.4852,1.1633,2.1728,1.5018,1.7242,0.44589,4.2128,1.31946,2.82347,2.716,0.571247,2.22379,0.751556,1.1322,1.59181
+25-Jun-2019 09:00:00,1,0.43469,1.1127,1.7451,1.5221,1.2038,2.2353,1.5395,1.7821,0.4582,4.2891,1.34956,2.87409,2.77393,0.590991,2.28332,0.774477,1.1628,1.62547
+25-Jun-2019 10:00:00,1,0.4491,1.1568,1.8089,1.5675,1.254,2.3127,1.5856,1.8529,0.47319,4.3801,1.38555,2.93451,2.84459,0.615371,2.35674,0.802659,1.2006,1.66644
+25-Jun-2019 11:00:00,1,0.46122,1.194,1.8626,1.6058,1.2964,2.3782,1.6241,1.9121,0.48568,4.4542,1.41495,2.98378,2.9035,0.635952,2.41863,0.826349,1.23251,1.70053
+25-Jun-2019 12:00:00,1,0.45636,1.1791,1.8411,1.5904,1.2794,2.3519,1.6087,1.8884,0.48069,4.4247,1.40325,2.96419,2.87993,0.627688,2.39379,0.816848,1.2197,1.6869
+25-Jun-2019 13:00:00,1,0.45627,1.1788,1.8407,1.5902,1.2791,2.3514,1.6084,1.888,0.4806,4.4242,1.40305,2.96385,2.87952,0.627547,2.39336,0.816685,1.21948,1.68666
+25-Jun-2019 14:00:00,1,0.45052,1.1612,1.8152,1.572,1.2589,2.3204,1.5901,1.8599,0.47466,4.3889,1.38904,2.94037,2.85153,0.617782,2.36399,0.805438,1.20434,1.67046
+25-Jun-2019 15:00:00,1,0.43701,1.1198,1.7553,1.5294,1.2118,2.2477,1.547,1.7935,0.46061,4.3039,1.35542,2.88394,2.78533,0.594901,2.2951,0.779005,1.16886,1.63208
+25-Jun-2019 16:00:00,1,0.42162,1.0727,1.6871,1.4809,1.1586,2.1656,1.4974,1.7175,0.44445,4.2037,1.3159,2.81748,2.70921,0.568948,2.21685,0.748882,1.12863,1.58787
+25-Jun-2019 17:00:00,1,0.41556,1.0542,1.6603,1.4618,1.1378,2.1334,1.4777,1.6874,0.43804,4.1633,1.29999,2.79067,2.67904,0.558768,2.18613,0.737024,1.11286,1.57031
+25-Jun-2019 18:00:00,1,0.40756,1.0298,1.6248,1.4365,1.1103,2.091,1.4517,1.6475,0.42953,4.109,1.27864,2.75467,2.63897,0.545341,2.14558,0.721347,1.09206,1.54698
+25-Jun-2019 19:00:00,1,0.3992,1.0043,1.5877,1.4101,1.0817,2.0469,1.4243,1.6057,0.42057,4.051,1.25592,2.71632,2.59684,0.531335,2.10324,0.704948,1.07036,1.52242
+25-Jun-2019 20:00:00,1,0.37926,0.94371,1.4992,1.3472,1.0141,1.9425,1.3585,1.5052,0.39899,3.9083,1.20007,2.62188,2.49538,0.498084,2.00258,0.665823,1.01888,1.46314
+25-Jun-2019 21:00:00,1,0.36253,0.89302,1.4249,1.2944,0.95794,1.8557,1.3026,1.4202,0.38064,3.7834,1.15136,2.53933,2.40917,0.470354,1.91846,0.632976,0.975957,1.41264
+25-Jun-2019 22:00:00,1,0.34747,0.84749,1.3579,1.2468,0.90783,1.7782,1.2518,1.343,0.36391,3.6668,1.10598,2.46229,2.33061,0.445508,1.84296,0.603369,0.937518,1.36652
+25-Jun-2019 23:00:00,1,0.31346,0.74512,1.2066,1.1393,0.79629,1.6056,1.1352,1.1664,0.32537,3.3882,0.998041,2.27851,2.14991,0.389853,1.67334,0.536408,0.851474,1.26005
+26-Jun-2019 00:00:00,1,0.28946,0.67325,1.0997,1.0634,0.71899,1.4859,1.0513,1.0399,0.29752,3.1782,0.917061,2.14021,2.01947,0.350969,1.5544,0.489059,0.791412,1.18287
+26-Jun-2019 01:00:00,1,0.27558,0.63185,1.0378,1.0195,0.67487,1.4176,1.0021,0.9659,0.28116,3.0515,0.868343,2.05686,1.94291,0.328646,1.48594,0.461648,0.756951,1.13745
+26-Jun-2019 02:00:00,1,0.26916,0.61273,1.0092,0.99921,0.65461,1.3862,0.97923,0.93148,0.27352,2.9916,0.845318,2.01742,1.9072,0.318357,1.45434,0.448957,0.741074,1.11622
+26-Jun-2019 03:00:00,1,0.27241,0.62239,1.0236,1.0095,0.66484,1.402,0.99082,0.9489,0.27739,3.022,0.857001,2.03743,1.92528,0.323557,1.47031,0.455375,0.749097,1.12697
+26-Jun-2019 04:00:00,1,0.28059,0.64677,1.0601,1.0354,0.69074,1.4422,1.0199,0.99265,0.28708,3.0977,0.886084,2.08722,1.97063,0.336686,1.51061,0.47154,0.769361,1.15391
+26-Jun-2019 05:00:00,1,0.30071,0.70689,1.1498,1.099,0.75506,1.5418,1.0908,1.0994,0.31064,3.278,0.955517,2.20593,2.0809,0.369148,1.61005,0.511255,0.819486,1.21925
+26-Jun-2019 06:00:00,1,0.35447,0.86863,1.3891,1.2689,0.93106,1.8141,1.2755,1.3789,0.3717,3.7215,1.12725,2.49841,2.36722,0.457037,1.87801,0.617128,0.955352,1.38803
+26-Jun-2019 07:00:00,1,0.40176,1.0121,1.5991,1.4182,1.0905,2.0604,1.4327,1.6185,0.42332,4.0689,1.26292,2.72814,2.60977,0.535622,2.1162,0.709972,1.077,1.52996
+26-Jun-2019 08:00:00,1,0.43222,1.1051,1.7342,1.5143,1.1952,2.2221,1.5316,1.77,0.45561,4.2732,1.34328,2.86354,2.76176,0.586824,2.27076,0.769646,1.15634,1.6184
+26-Jun-2019 09:00:00,1,0.44117,1.1325,1.7738,1.5425,1.2263,2.2701,1.5603,1.814,0.46496,4.3304,1.36588,2.90151,2.80579,0.601942,2.31631,0.787151,1.17978,1.64395
+26-Jun-2019 10:00:00,1,0.45574,1.1772,1.8383,1.5885,1.2772,2.3485,1.6067,1.8854,0.48005,4.421,1.40176,2.96169,2.87693,0.626641,2.39064,0.815643,1.21807,1.68517
+26-Jun-2019 11:00:00,1,0.46722,1.2125,1.8892,1.6247,1.3175,2.4107,1.643,1.9413,0.49183,4.4901,1.42923,3.00766,2.93251,0.64617,2.44934,0.838079,1.24836,1.7173
+26-Jun-2019 12:00:00,1,0.4647,1.2047,1.878,1.6167,1.3087,2.397,1.6351,1.929,0.48926,4.4751,1.42326,2.99768,2.92034,0.641878,2.43644,0.833155,1.24171,1.71027
+26-Jun-2019 13:00:00,1,0.4598,1.1897,1.8563,1.6013,1.2914,2.3705,1.6196,1.9052,0.48423,4.4457,1.41156,2.9781,2.89664,0.633544,2.41139,0.823582,1.22878,1.69657
+26-Jun-2019 14:00:00,1,0.44982,1.159,1.8121,1.5698,1.2565,2.3166,1.5879,1.8564,0.47393,4.3845,1.38731,2.93747,2.84809,0.616588,2.3604,0.804062,1.20248,1.66847
+26-Jun-2019 15:00:00,1,0.44279,1.1375,1.7809,1.5476,1.2319,2.2787,1.5655,1.8219,0.46664,4.3406,1.36992,2.90828,2.81371,0.604674,2.32454,0.790309,1.18401,1.64854
+26-Jun-2019 16:00:00,1,0.43004,1.0984,1.7245,1.5074,1.1877,2.2105,1.5246,1.7592,0.45332,4.259,1.33769,2.85414,2.75096,0.583137,2.25965,0.765369,1.15062,1.61213
+26-Jun-2019 17:00:00,1,0.4229,1.0766,1.6928,1.4849,1.163,2.1724,1.5015,1.7238,0.4458,4.2122,1.31923,2.82308,2.71556,0.571098,2.22334,0.751383,1.13197,1.59156
+26-Jun-2019 18:00:00,1,0.41713,1.059,1.6672,1.4667,1.1431,2.1417,1.4828,1.6952,0.4397,4.1738,1.30412,2.79763,2.68685,0.561396,2.19406,0.740088,1.11693,1.57486
+26-Jun-2019 19:00:00,1,0.41226,1.0442,1.6457,1.4514,1.1264,2.1159,1.467,1.671,0.43454,4.141,1.29123,2.77591,2.66254,0.553228,2.1694,0.73056,1.10428,1.56071
+26-Jun-2019 20:00:00,1,0.39525,0.99233,1.5702,1.3977,1.0683,2.0262,1.4114,1.5859,0.41633,4.0234,1.24506,2.69798,2.57689,0.524745,2.0833,0.697215,1.06016,1.51078
+26-Jun-2019 21:00:00,1,0.38165,0.95099,1.5098,1.3548,1.0222,1.955,1.3664,1.5174,0.40161,3.9259,1.20691,2.63347,2.50767,0.502074,2.01467,0.670532,1.02505,1.47033
+26-Jun-2019 22:00:00,1,0.36745,0.90791,1.4467,1.3099,0.9744,1.8811,1.3191,1.4453,0.38606,3.8207,1.16586,2.56393,2.43463,0.478493,1.94317,0.642638,0.988552,1.42757
+26-Jun-2019 23:00:00,1,0.3323,0.80175,1.2905,1.1989,0.85779,1.7008,1.2001,1.2646,0.34685,3.5452,1.0588,2.38203,2.25059,0.420605,1.76715,0.57352,0.899007,1.31944
+27-Jun-2019 00:00:00,1,0.30722,0.7264,1.1788,1.1196,0.77608,1.5744,1.1135,1.1337,0.31818,3.3347,0.977383,2.24326,2.11624,0.379713,1.64236,0.524107,0.835807,1.24016
+27-Jun-2019 01:00:00,1,0.29147,0.67925,1.1086,1.0698,0.72541,1.4959,1.0584,1.0505,0.29987,3.1962,0.923995,2.15207,2.03048,0.35421,1.56433,0.493024,0.796416,1.1894
+27-Jun-2019 02:00:00,1,0.28599,0.66288,1.0842,1.0524,0.70791,1.4688,1.039,1.0214,0.29344,3.1469,0.905004,2.11959,2.0004,0.345373,1.53725,0.482202,0.782771,1.17156
+27-Jun-2019 03:00:00,1,0.28682,0.66536,1.0879,1.0551,0.71056,1.4729,1.042,1.0258,0.29442,3.1544,0.907894,2.12453,2.00496,0.34671,1.54135,0.483841,0.784835,1.17427
+27-Jun-2019 04:00:00,1,0.29552,0.69136,1.1267,1.0826,0.73839,1.516,1.0726,1.072,0.3046,3.2323,0.937889,2.17581,2.05263,0.360752,1.58436,0.501018,0.806519,1.20252
+27-Jun-2019 05:00:00,1,0.31569,0.75182,1.2166,1.1464,0.80353,1.6169,1.143,1.1781,0.32793,3.4071,1.00536,2.291,2.16191,0.393485,1.68443,0.540807,0.857087,1.26714
+27-Jun-2019 06:00:00,1,0.3742,0.92836,1.4767,1.3312,0.99706,1.9161,1.3416,1.4796,0.39347,3.8711,1.18551,2.59724,2.46941,0.489681,1.9771,0.65589,1.00587,1.44794
+27-Jun-2019 07:00:00,1,0.42404,1.0801,1.6979,1.4885,1.167,2.1785,1.5052,1.7295,0.44701,4.2198,1.32222,2.82811,2.72126,0.573032,2.22918,0.753632,1.13496,1.59487
+27-Jun-2019 08:00:00,1,0.4548,1.1743,1.8342,1.5855,1.2739,2.3434,1.6037,1.8808,0.47908,4.4152,1.39948,2.95786,2.87236,0.625043,2.38583,0.813803,1.21559,1.68252
+27-Jun-2019 09:00:00,1,0.46491,1.2054,1.8789,1.6174,1.3094,2.3982,1.6357,1.9301,0.48947,4.4764,1.42375,2.99851,2.92135,0.642233,2.43751,0.833562,1.24226,1.71085
+27-Jun-2019 10:00:00,1,0.48393,1.2639,1.9631,1.6773,1.3766,2.5017,1.6954,2.0222,0.50881,4.5873,1.46796,3.07236,3.01263,0.674691,2.53496,0.870705,1.29261,1.76354
+27-Jun-2019 11:00:00,1,0.49748,1.3057,2.023,1.72,1.4248,2.576,1.7376,2.0873,0.52244,4.6632,1.49832,3.12295,3.07697,0.697906,2.60456,0.897142,1.32864,1.80059
+27-Jun-2019 12:00:00,1,0.48965,1.2815,1.9884,1.6953,1.3969,2.533,1.7133,2.0497,0.51458,4.6197,1.48089,3.09391,3.03985,0.684479,2.56432,0.881864,1.3078,1.77922
+27-Jun-2019 13:00:00,1,0.48615,1.2707,1.9729,1.6843,1.3844,2.5139,1.7024,2.0328,0.51105,4.5999,1.47299,3.08075,3.02319,0.678483,2.54634,0.87503,1.2985,1.76963
+27-Jun-2019 14:00:00,1,0.47758,1.2444,1.935,1.6573,1.3541,2.4671,1.6756,1.9915,0.50239,4.5509,1.45342,3.0481,2.9823,0.663845,2.50242,0.858317,1.27578,1.74605
+27-Jun-2019 15:00:00,1,0.46923,1.2186,1.898,1.631,1.3246,2.4216,1.6494,1.951,0.49389,4.502,1.43396,3.01558,2.94218,0.649591,2.45962,0.842001,1.25367,1.72289
+27-Jun-2019 16:00:00,1,0.45795,1.184,1.8481,1.5954,1.2849,2.3605,1.6137,1.8962,0.48233,4.4344,1.4071,2.97063,2.88766,0.630396,2.40193,0.819963,1.2239,1.69137
+27-Jun-2019 17:00:00,1,0.45367,1.1709,1.8292,1.582,1.2699,2.3374,1.6002,1.8753,0.47792,4.4083,1.39674,2.95327,2.86687,0.623128,2.38007,0.811598,1.21263,1.67934
+27-Jun-2019 18:00:00,1,0.44854,1.1551,1.8064,1.5658,1.252,2.3097,1.5838,1.8502,0.47261,4.3766,1.38418,2.93221,2.84186,0.614424,2.35389,0.801566,1.19913,1.66486
+27-Jun-2019 19:00:00,1,0.4416,1.1338,1.7757,1.5439,1.2278,2.2724,1.5617,1.8161,0.46541,4.3331,1.36697,2.90333,2.80791,0.602674,2.31851,0.787997,1.18091,1.64518
+27-Jun-2019 20:00:00,1,0.42064,1.0697,1.6828,1.4778,1.1552,2.1604,1.4942,1.7126,0.44342,4.1972,1.31335,2.81317,2.70435,0.567303,2.21189,0.746967,1.12609,1.58504
+27-Jun-2019 21:00:00,1,0.40402,1.019,1.6091,1.4254,1.0982,2.0724,1.4401,1.6298,0.42575,4.0846,1.26908,2.73854,2.62118,0.539411,2.12766,0.71441,1.08287,1.53661
+27-Jun-2019 22:00:00,1,0.38857,0.97199,1.5405,1.3766,1.0456,1.9911,1.3893,1.5522,0.40911,3.9758,1.22644,2.66651,2.54294,0.513586,2.04953,0.684097,1.04288,1.49095
+27-Jun-2019 23:00:00,1,0.3565,0.87477,1.3981,1.2753,0.93782,1.8246,1.2823,1.3893,0.37396,3.7372,1.13335,2.50878,2.3778,0.460387,1.88819,0.621119,0.960534,1.39424
+28-Jun-2019 00:00:00,1,0.32413,0.77716,1.2541,1.173,0.83102,1.6594,1.1721,1.2222,0.33757,3.4779,1.03274,2.33765,2.2071,0.40724,1.72641,0.557426,0.878346,1.2938
+28-Jun-2019 01:00:00,1,0.30692,0.72549,1.1775,1.1186,0.7751,1.5728,1.1125,1.1321,0.31783,3.3321,0.97637,2.24153,2.1146,0.37922,1.64085,0.523508,0.835046,1.23919
+28-Jun-2019 02:00:00,1,0.29809,0.69907,1.1382,1.0907,0.74666,1.5288,1.0817,1.0856,0.3076,3.255,0.946666,2.19081,2.06668,0.364919,1.59711,0.506102,0.812954,1.21084
+28-Jun-2019 03:00:00,1,0.29814,0.6992,1.1384,1.0909,0.7468,1.529,1.0818,1.0858,0.30765,3.2554,0.946815,2.19107,2.06692,0.36499,1.59733,0.506188,0.813064,1.21098
+28-Jun-2019 04:00:00,1,0.30429,0.71762,1.1658,1.1103,0.76661,1.5597,1.1033,1.1183,0.31479,3.3093,0.967577,2.22652,2.10036,0.374954,1.62781,0.518323,0.828455,1.23076
+28-Jun-2019 05:00:00,1,0.32268,0.77282,1.2477,1.1685,0.8263,1.6521,1.1671,1.2146,0.33592,3.4659,1.02808,2.32972,2.19937,0.40488,1.71921,0.554579,0.874698,1.28925
+28-Jun-2019 06:00:00,1,0.38167,0.95105,1.5099,1.3548,1.0223,1.9551,1.3665,1.5175,0.40163,3.926,1.20697,2.63357,2.50777,0.502108,2.01477,0.670571,1.0251,1.47039
+28-Jun-2019 07:00:00,1,0.43455,1.1122,1.7445,1.5217,1.2033,2.2346,1.5391,1.7814,0.45805,4.2882,1.3492,2.87348,2.77323,0.590749,2.28259,0.774195,1.16242,1.62506
+28-Jun-2019 08:00:00,1,0.46125,1.1941,1.8627,1.6058,1.2965,2.3783,1.6242,1.9122,0.48571,4.4544,1.41502,2.98389,2.90364,0.635999,2.41878,0.826404,1.23259,1.70061
+28-Jun-2019 09:00:00,1,0.47094,1.2239,1.9056,1.6364,1.3306,2.4309,1.6547,1.9593,0.49563,4.5121,1.43797,3.02228,2.95041,0.652506,2.46837,0.845341,1.25819,1.72764
+28-Jun-2019 10:00:00,1,0.48633,1.2713,1.9737,1.6849,1.3851,2.5149,1.7029,2.0337,0.51124,4.601,1.47341,3.08144,3.02406,0.678796,2.54728,0.875387,1.29898,1.77013
+28-Jun-2019 11:00:00,1,0.50224,1.3204,2.0441,1.735,1.4418,2.6022,1.7523,2.1101,0.5272,4.6893,1.50878,3.14035,3.09947,0.706089,2.62908,0.906436,1.34134,1.81353
+28-Jun-2019 12:00:00,1,0.50018,1.314,2.035,1.7285,1.4344,2.5909,1.746,2.1003,0.52515,4.6781,1.50428,3.13286,3.08976,0.702554,2.61849,0.902423,1.33586,1.80795
+28-Jun-2019 13:00:00,1,0.4977,1.3064,2.024,1.7207,1.4256,2.5772,1.7383,2.0883,0.52266,4.6644,1.49881,3.12375,3.07801,0.698283,2.60569,0.89757,1.32923,1.80119
+28-Jun-2019 14:00:00,1,0.48865,1.2784,1.984,1.6922,1.3933,2.5276,1.7102,2.0449,0.51357,4.614,1.47864,3.09016,3.03509,0.682764,2.55918,0.87991,1.30514,1.77648
+28-Jun-2019 15:00:00,1,0.47782,1.2451,1.9361,1.6581,1.3549,2.4684,1.6763,1.9927,0.50263,4.5523,1.45397,3.04901,2.98344,0.664252,2.50364,0.858782,1.27642,1.74671
+28-Jun-2019 16:00:00,1,0.46433,1.2036,1.8764,1.6156,1.3074,2.395,1.6339,1.9273,0.48888,4.4729,1.42238,2.99621,2.91856,0.64125,2.43456,0.832434,1.24073,1.70924
+28-Jun-2019 17:00:00,1,0.45573,1.1772,1.8383,1.5885,1.2772,2.3485,1.6067,1.8854,0.48004,4.4209,1.40174,2.96166,2.8769,0.62663,2.3906,0.815629,1.21806,1.68515
+28-Jun-2019 18:00:00,1,0.44716,1.1509,1.8003,1.5614,1.2472,2.3022,1.5794,1.8434,0.47118,4.368,1.38076,2.92648,2.8351,0.612076,2.34682,0.798857,1.19549,1.66094
+28-Jun-2019 19:00:00,1,0.43018,1.0989,1.7251,1.5079,1.1882,2.2112,1.525,1.7598,0.45346,4.2599,1.33804,2.85473,2.75164,0.583367,2.26034,0.765636,1.15098,1.61252
+28-Jun-2019 20:00:00,1,0.39145,0.98077,1.5533,1.3857,1.0554,2.0062,1.3988,1.5668,0.41223,3.9964,1.23451,2.68015,2.55761,0.5184,2.0641,0.68976,1.05033,1.49952
+28-Jun-2019 21:00:00,1,0.36684,0.90606,1.444,1.308,0.97235,1.878,1.3171,1.4421,0.38539,3.8161,1.16407,2.56089,2.43147,0.477481,1.9401,0.641437,0.986985,1.42572
+28-Jun-2019 22:00:00,1,0.35723,0.87698,1.4013,1.2776,0.94025,1.8283,1.2848,1.3931,0.37477,3.7428,1.13555,2.51251,2.38161,0.461594,1.89186,0.622557,0.962402,1.39648
+28-Jun-2019 23:00:00,1,0.33931,0.82287,1.3216,1.221,0.88085,1.7365,1.2241,1.3009,0.35475,3.6019,1.08078,2.41945,2.28767,0.432093,1.80214,0.587312,0.916771,1.34127
+29-Jun-2019 00:00:00,1,0.31265,0.74268,1.203,1.1368,0.79365,1.6016,1.1324,1.1622,0.32444,3.3812,0.995364,2.27394,2.14553,0.38853,1.6693,0.534805,0.84943,1.25747
+29-Jun-2019 01:00:00,1,0.29875,0.70102,1.1411,1.0928,0.74876,1.5321,1.0839,1.0891,0.30836,3.2608,0.948887,2.1946,2.07025,0.365978,1.60035,0.507392,0.814589,1.21295
+29-Jun-2019 02:00:00,1,0.29086,0.67743,1.1059,1.0679,0.72347,1.4929,1.0562,1.0473,0.29916,3.1908,0.921902,2.14849,2.02716,0.35323,1.56132,0.491825,0.794903,1.18743
+29-Jun-2019 03:00:00,1,0.29012,0.67523,1.1026,1.0655,0.72111,1.4892,1.0536,1.0434,0.2983,3.1842,0.919353,2.14413,2.02311,0.352039,1.55768,0.490367,0.793064,1.18503
+29-Jun-2019 04:00:00,1,0.29587,0.69241,1.1283,1.0837,0.73952,1.5177,1.0739,1.0738,0.30501,3.2354,0.93909,2.17787,2.05455,0.361321,1.5861,0.501712,0.807397,1.20366
+29-Jun-2019 05:00:00,1,0.31206,0.74093,1.2004,1.1349,0.79175,1.5986,1.1304,1.1591,0.32376,3.3763,0.993438,2.27066,2.14238,0.38758,1.6664,0.533654,0.847962,1.25561
+29-Jun-2019 06:00:00,1,0.36879,0.91197,1.4527,1.3141,0.9789,1.8881,1.3236,1.4521,0.38754,3.8308,1.16979,2.57059,2.44156,0.480717,1.94991,0.645274,0.991992,1.43163
+29-Jun-2019 07:00:00,1,0.42203,1.074,1.689,1.4822,1.16,2.1678,1.4987,1.7195,0.44489,4.2065,1.31699,2.8193,2.71127,0.569645,2.21896,0.749693,1.12971,1.58906
+29-Jun-2019 08:00:00,1,0.44811,1.1538,1.8045,1.5644,1.2505,2.3074,1.5824,1.848,0.47216,4.3739,1.38311,2.93041,2.83974,0.613688,2.35167,0.800717,1.19799,1.66363
+29-Jun-2019 09:00:00,1,0.46491,1.2054,1.8789,1.6174,1.3094,2.3982,1.6357,1.9301,0.48947,4.4764,1.42375,2.99851,2.92135,0.642233,2.43751,0.833562,1.24226,1.71085
+29-Jun-2019 10:00:00,1,0.48188,1.2576,1.954,1.6709,1.3693,2.4905,1.689,2.0123,0.50674,4.5756,1.46328,3.06455,3.00284,0.671182,2.52443,0.866699,1.28717,1.75789
+29-Jun-2019 11:00:00,1,0.50657,1.3338,2.0632,1.7487,1.4573,2.6261,1.7657,2.1308,0.53152,4.7128,1.51818,3.15598,3.11984,0.713531,2.65136,0.914878,1.3529,1.82524
+29-Jun-2019 12:00:00,1,0.50235,1.3207,2.0445,1.7354,1.4422,2.6028,1.7527,2.1106,0.52731,4.6899,1.50901,3.14073,3.09997,0.706272,2.62963,0.906644,1.34163,1.81382
+29-Jun-2019 13:00:00,1,0.49489,1.2977,2.0116,1.7119,1.4156,2.5618,1.7296,2.0749,0.51985,4.649,1.49261,3.11343,3.06475,0.693474,2.59128,0.892103,1.32176,1.79356
+29-Jun-2019 14:00:00,1,0.47576,1.2387,1.9269,1.6516,1.3476,2.4571,1.6699,1.9827,0.50053,4.5403,1.44919,3.04104,2.97354,0.660724,2.49305,0.854748,1.27094,1.741
+29-Jun-2019 15:00:00,1,0.45468,1.174,1.8336,1.5851,1.2735,2.3428,1.6034,1.8802,0.47896,4.4145,1.39919,2.95738,2.87179,0.624843,2.38523,0.813573,1.21529,1.68219
+29-Jun-2019 16:00:00,1,0.44437,1.1423,1.788,1.5526,1.2374,2.2872,1.5705,1.8297,0.46828,4.3505,1.37385,2.91489,2.82146,0.607352,2.3326,0.793403,1.18816,1.65303
+29-Jun-2019 17:00:00,1,0.44356,1.1398,1.7844,1.5501,1.2346,2.2829,1.5679,1.8257,0.46744,4.3454,1.37183,2.9115,2.81748,0.605978,2.32846,0.791816,1.18603,1.65073
+29-Jun-2019 18:00:00,1,0.43439,1.1118,1.7438,1.5212,1.2028,2.2337,1.5386,1.7806,0.45788,4.2871,1.3488,2.87281,2.77245,0.590483,2.28179,0.773887,1.16201,1.62461
+29-Jun-2019 19:00:00,1,0.41968,1.0668,1.6785,1.4748,1.1519,2.1553,1.4911,1.7078,0.4424,4.1908,1.31082,2.80892,2.69955,0.565682,2.207,0.74508,1.12357,1.58225
+29-Jun-2019 20:00:00,1,0.39346,0.98686,1.5622,1.392,1.0622,2.0167,1.4054,1.5769,0.41439,4.0106,1.24008,2.68957,2.56778,0.521742,2.07422,0.693689,1.05551,1.50546
+29-Jun-2019 21:00:00,1,0.37138,0.91982,1.4642,1.3223,0.98759,1.9015,1.3322,1.4653,0.39038,3.8501,1.17734,2.58339,2.45491,0.485008,1.96293,0.650358,0.998633,1.43945
+29-Jun-2019 22:00:00,1,0.36106,0.88856,1.4183,1.2897,0.95302,1.8481,1.2977,1.4127,0.37901,3.7722,1.14698,2.5319,2.40151,0.467917,1.91106,0.630079,0.972185,1.40815
+29-Jun-2019 23:00:00,1,0.33165,0.79978,1.2876,1.1968,0.85564,1.6975,1.1979,1.2612,0.34611,3.5398,1.05672,2.3785,2.24711,0.419531,1.76388,0.572229,0.897346,1.31739
+30-Jun-2019 00:00:00,1,0.30691,0.72546,1.1774,1.1186,0.77506,1.5728,1.1124,1.132,0.31782,3.3319,0.976328,2.24146,2.11453,0.3792,1.64079,0.523483,0.835014,1.23914
+30-Jun-2019 01:00:00,1,0.29381,0.68624,1.1191,1.0772,0.7329,1.5075,1.0666,1.0629,0.3026,3.2171,0.932027,2.1658,2.04327,0.357984,1.57588,0.497637,0.802244,1.19698
+30-Jun-2019 02:00:00,1,0.2831,0.65427,1.0713,1.0433,0.69873,1.4545,1.0288,1.0061,0.29005,3.1207,0.894918,2.10234,1.98451,0.340728,1.52301,0.476504,0.7756,1.16214
+30-Jun-2019 03:00:00,1,0.27983,0.64451,1.0567,1.033,0.68834,1.4384,1.0172,0.98861,0.28619,3.0907,0.88341,2.08265,1.96644,0.335468,1.50687,0.470043,0.767481,1.15142
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+12-Aug-2019 17:00:00,1,0.29949,0.70323,1.1444,1.0951,0.75113,1.5357,1.0865,1.0929,0.30922,3.2673,0.951383,2.19887,2.07426,0.367169,1.604,0.508845,0.81643,1.21532
+12-Aug-2019 18:00:00,1,0.30895,0.73159,1.1865,1.1251,0.78167,1.583,1.1196,1.1428,0.32018,3.3496,0.983133,2.25307,2.12559,0.38252,1.65094,0.527515,0.840143,1.24568
+12-Aug-2019 19:00:00,1,0.31036,0.73581,1.1928,1.1295,0.78622,1.5901,1.1244,1.1502,0.3218,3.3616,0.987798,2.26103,2.13318,0.384806,1.65792,0.530289,0.843676,1.25017
+12-Aug-2019 20:00:00,1,0.3071,0.72603,1.1783,1.1192,0.77567,1.5737,1.1131,1.133,0.31804,3.3336,0.976961,2.24254,2.11556,0.379508,1.64173,0.523858,0.83549,1.23975
+12-Aug-2019 21:00:00,1,0.31249,0.7422,1.2023,1.1363,0.79313,1.6008,1.1319,1.1614,0.32425,3.3799,0.994841,2.27305,2.14468,0.388272,1.66851,0.534492,0.849031,1.25696
+12-Aug-2019 22:00:00,1,0.30376,0.71603,1.1634,1.1087,0.76489,1.557,1.1015,1.1155,0.31418,3.3047,0.965797,2.22348,2.09748,0.374094,1.62518,0.517277,0.827126,1.22906
+12-Aug-2019 23:00:00,1,0.28203,0.65106,1.0665,1.0399,0.69531,1.4492,1.025,1.0003,0.28878,3.1108,0.891139,2.09587,1.97857,0.338996,1.51769,0.474378,0.772926,1.15861
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+13-Aug-2019 01:00:00,1,0.25644,0.57494,0.95239,0.95895,0.61473,1.3243,0.93352,0.86292,0.25827,2.8703,0.79878,1.93764,1.83589,0.298054,1.3919,0.423799,0.709752,1.07379
+13-Aug-2019 02:00:00,1,0.24995,0.55566,0.92337,0.93838,0.5945,1.2929,0.91001,0.82768,0.25041,2.8069,0.774515,1.89601,1.79916,0.287719,1.36007,0.410935,0.693814,1.0519
+13-Aug-2019 03:00:00,1,0.2518,0.56115,0.93163,0.94424,0.60025,1.3019,0.91671,0.83773,0.25265,2.825,0.781457,1.90793,1.80963,0.290659,1.36913,0.414598,0.698347,1.05815
+13-Aug-2019 04:00:00,1,0.26116,0.58893,0.97343,0.97387,0.62947,1.3472,0.95051,0.88839,0.26394,2.9156,0.816171,1.96747,1.8624,0.305566,1.41501,0.433125,0.721339,1.08958
+13-Aug-2019 05:00:00,1,0.28637,0.66403,1.0859,1.0537,0.70915,1.4707,1.0404,1.0235,0.2939,3.1504,0.906351,2.1219,2.00253,0.345996,1.53916,0.482966,0.783733,1.17283
+13-Aug-2019 06:00:00,1,0.33792,0.81868,1.3155,1.2166,0.87627,1.7294,1.2193,1.2937,0.35319,3.5907,1.07645,2.41208,2.28034,0.429815,1.7952,0.58458,0.913247,1.33695
+13-Aug-2019 07:00:00,1,0.38466,0.96012,1.5232,1.3643,1.0324,1.9707,1.3764,1.5325,0.40488,3.9477,1.21545,2.64791,2.52303,0.50708,2.02983,0.676435,1.0328,1.47931
+13-Aug-2019 08:00:00,1,0.4139,1.0491,1.6529,1.4565,1.132,2.1246,1.4723,1.6791,0.43628,4.1521,1.29558,2.78324,2.67073,0.555973,2.17769,0.733764,1.10853,1.56547
+13-Aug-2019 09:00:00,1,0.42765,1.0911,1.7139,1.4999,1.1794,2.1977,1.5168,1.7473,0.4508,4.2434,1.33154,2.84379,2.73912,0.5791,2.24748,0.760683,1.14437,1.60525
+13-Aug-2019 10:00:00,1,0.43955,1.1276,1.7666,1.5374,1.2207,2.2614,1.5551,1.806,0.46327,4.3201,1.36182,2.89469,2.79784,0.599202,2.30806,0.783982,1.17553,1.63934
+13-Aug-2019 11:00:00,1,0.45422,1.1725,1.8316,1.5837,1.2719,2.3403,1.6019,1.878,0.47848,4.4117,1.39806,2.95549,2.86953,0.624056,2.38286,0.812667,1.21406,1.68088
+13-Aug-2019 12:00:00,1,0.45181,1.1651,1.8209,1.5761,1.2634,2.3273,1.5942,1.8662,0.47599,4.3968,1.39218,2.94564,2.85779,0.619961,2.37055,0.80795,1.20771,1.67408
+13-Aug-2019 13:00:00,1,0.4466,1.1492,1.7978,1.5597,1.2452,2.2992,1.5776,1.8407,0.4706,4.3645,1.37938,2.92417,2.83237,0.61113,2.34397,0.797765,1.19402,1.65936
+13-Aug-2019 14:00:00,1,0.43766,1.1218,1.7583,1.5315,1.2141,2.2513,1.5491,1.7968,0.4613,4.3081,1.35708,2.88672,2.78857,0.596013,2.29845,0.780292,1.17058,1.63396
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+13-Aug-2019 18:00:00,1,0.41124,1.041,1.6411,1.4481,1.1229,2.1105,1.4637,1.6659,0.43345,4.134,1.28849,2.77129,2.65741,0.551505,2.1642,0.728549,1.10161,1.55772
+13-Aug-2019 19:00:00,1,0.40146,1.0112,1.5978,1.4173,1.0895,2.0589,1.4317,1.617,0.423,4.0669,1.26211,2.72679,2.60828,0.535127,2.1147,0.709393,1.07624,1.52909
+13-Aug-2019 20:00:00,1,0.38901,0.97335,1.5425,1.378,1.0471,1.9934,1.3908,1.5545,0.40959,3.979,1.22769,2.66863,2.54521,0.51433,2.05179,0.684973,1.04403,1.49227
+13-Aug-2019 21:00:00,1,0.37965,0.94491,1.5009,1.3484,1.0154,1.9445,1.3598,1.5072,0.39943,3.9112,1.2012,2.62381,2.49742,0.498745,2.00458,0.666603,1.0199,1.46433
+13-Aug-2019 22:00:00,1,0.34934,0.85312,1.3662,1.2527,0.91401,1.7878,1.2581,1.3526,0.36599,3.6815,1.11168,2.47198,2.34039,0.448579,1.8523,0.607037,0.942267,1.37227
+13-Aug-2019 23:00:00,1,0.31208,0.74096,1.2005,1.135,0.79179,1.5987,1.1304,1.1592,0.32378,3.3764,0.99348,2.27073,2.14245,0.387601,1.66646,0.533679,0.847994,1.25565
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+14-Aug-2019 01:00:00,1,0.27615,0.63355,1.0403,1.0213,0.67668,1.4204,1.0042,0.96895,0.28183,3.0568,0.870372,2.06033,1.94607,0.32956,1.48874,0.462774,0.758362,1.13932
+14-Aug-2019 02:00:00,1,0.27101,0.61822,1.0174,1.005,0.66042,1.3952,0.98582,0.94138,0.27572,3.0089,0.851965,2.02881,1.91748,0.321311,1.46341,0.452604,0.745631,1.12233
+14-Aug-2019 03:00:00,1,0.27466,0.6291,1.0337,1.0166,0.67196,1.4131,0.99885,0.96096,0.28006,3.043,0.865054,2.05122,1.93779,0.327166,1.48139,0.459825,0.754667,1.13441
+14-Aug-2019 04:00:00,1,0.28315,0.6544,1.0715,1.0435,0.69887,1.4548,1.029,1.0063,0.2901,3.1211,0.895071,2.1026,1.98475,0.340798,1.52322,0.47659,0.775708,1.16228
+14-Aug-2019 05:00:00,1,0.30612,0.72309,1.1739,1.1161,0.7725,1.5688,1.1097,1.1279,0.31691,3.3251,0.973689,2.23696,2.11025,0.377916,1.63687,0.521924,0.833032,1.23661
+14-Aug-2019 06:00:00,1,0.35314,0.86461,1.3831,1.2647,0.92663,1.8073,1.271,1.3721,0.37023,3.7112,1.12323,2.49159,2.36027,0.454843,1.87134,0.614513,0.951958,1.38395
+14-Aug-2019 07:00:00,1,0.39872,1.0029,1.5856,1.4086,1.0801,2.0444,1.4227,1.6033,0.42006,4.0477,1.25461,2.71411,2.59444,0.530539,2.10083,0.704015,1.06913,1.52101
+14-Aug-2019 08:00:00,1,0.42494,1.0829,1.7019,1.4914,1.1701,2.1833,1.5081,1.734,0.44796,4.2257,1.32455,2.83203,2.72572,0.574545,2.23374,0.755391,1.13731,1.59746
+14-Aug-2019 09:00:00,1,0.43532,1.1146,1.7479,1.5241,1.206,2.2387,1.5416,1.7852,0.45885,4.2931,1.35116,2.87678,2.77704,0.592055,2.28652,0.775709,1.16445,1.62727
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+14-Aug-2019 11:00:00,1,0.46575,1.208,1.8827,1.62,1.3123,2.4027,1.6384,1.9342,0.49033,4.4814,1.42575,3.00185,2.92542,0.643667,2.44182,0.835208,1.24448,1.7132
+14-Aug-2019 12:00:00,1,0.46669,1.2108,1.8868,1.623,1.3157,2.4078,1.6414,1.9387,0.49129,4.487,1.42798,3.00557,2.92995,0.645268,2.44663,0.837045,1.24697,1.71582
+14-Aug-2019 13:00:00,1,0.46234,1.1975,1.8675,1.6093,1.3003,2.3842,1.6276,1.9175,0.48683,4.4609,1.41763,2.98826,2.90891,0.637855,2.42435,0.828535,1.23547,1.70366
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+14-Aug-2019 22:00:00,1,0.34875,0.85136,1.3636,1.2509,0.91207,1.7848,1.2562,1.3496,0.36534,3.6769,1.1099,2.46894,2.33732,0.447615,1.84937,0.605887,0.940777,1.37047
+14-Aug-2019 23:00:00,1,0.31578,0.75207,1.2169,1.1466,0.8038,1.6173,1.1432,1.1786,0.32803,3.4078,1.00564,2.29146,2.16236,0.39362,1.68484,0.54097,0.857295,1.2674
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+15-Aug-2019 01:00:00,1,0.2838,0.65635,1.0744,1.0455,0.70095,1.458,1.0313,1.0098,0.29087,3.127,0.897362,2.10652,1.98835,0.34185,1.52645,0.477882,0.777333,1.16442
+15-Aug-2019 02:00:00,1,0.2776,0.63785,1.0468,1.0259,0.68125,1.4275,1.0093,0.97667,0.28354,3.0702,0.875503,2.06911,1.95408,0.331879,1.49586,0.465628,0.761941,1.14408
+15-Aug-2019 03:00:00,1,0.27747,0.63746,1.0462,1.0255,0.68084,1.4268,1.0088,0.97598,0.28339,3.069,0.875041,2.06832,1.95336,0.33167,1.49522,0.465371,0.761618,1.14365
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+15-Aug-2019 19:00:00,1,0.4032,1.0165,1.6055,1.4228,1.0954,2.068,1.4374,1.6257,0.42487,4.0789,1.26685,2.73478,2.61705,0.538038,2.12351,0.712803,1.08075,1.5342
+15-Aug-2019 20:00:00,1,0.39041,0.9776,1.5487,1.3824,1.0518,2.0008,1.3954,1.5615,0.41111,3.989,1.23161,2.67525,2.55233,0.516664,2.05885,0.687719,1.04764,1.49643
+15-Aug-2019 21:00:00,1,0.38274,0.9543,1.5147,1.3582,1.0259,1.9607,1.37,1.5229,0.40279,3.9338,1.21001,2.63871,2.51323,0.503885,2.02015,0.672668,1.02786,1.47358
+15-Aug-2019 22:00:00,1,0.35406,0.86741,1.3873,1.2676,0.92971,1.8121,1.2741,1.3769,0.37126,3.7184,1.12602,2.49634,2.36511,0.45637,1.87598,0.616333,0.95432,1.38679
+15-Aug-2019 23:00:00,1,0.31911,0.76207,1.2318,1.1572,0.81464,1.6341,1.1548,1.196,0.33184,3.4359,1.0165,2.30999,2.18024,0.399048,1.70141,0.547535,0.865684,1.27796
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+16-Aug-2019 01:00:00,1,0.28678,0.66524,1.0877,1.055,0.71044,1.4727,1.0418,1.0256,0.29437,3.154,0.907763,2.12431,2.00476,0.346649,1.54116,0.483767,0.784742,1.17415
+16-Aug-2019 02:00:00,1,0.28083,0.64749,1.0612,1.0361,0.69151,1.4434,1.0208,0.99395,0.28737,3.0999,0.886938,2.08868,1.97197,0.337076,1.51181,0.472019,0.769963,1.1547
+16-Aug-2019 03:00:00,1,0.28248,0.65241,1.0686,1.0413,0.69675,1.4515,1.0266,1.0027,0.28931,3.115,0.892734,2.0986,1.98107,0.339726,1.51994,0.475275,0.774054,1.1601
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+16-Aug-2019 05:00:00,1,0.31583,0.75224,1.2172,1.1468,0.80399,1.6176,1.1434,1.1789,0.32809,3.4083,1.00582,2.29178,2.16267,0.393713,1.68513,0.541082,0.857439,1.26759
+16-Aug-2019 06:00:00,1,0.36612,0.90389,1.4408,1.3057,0.96995,1.8743,1.3147,1.4385,0.3846,3.8107,1.16196,2.55731,2.42776,0.476295,1.9365,0.640029,0.985149,1.42354
+16-Aug-2019 07:00:00,1,0.41669,1.0577,1.6653,1.4653,1.1416,2.1394,1.4814,1.693,0.43924,4.1709,1.30298,2.79571,2.68468,0.560668,2.19186,0.739239,1.1158,1.5736
+16-Aug-2019 08:00:00,1,0.43712,1.1201,1.7558,1.5298,1.2122,2.2483,1.5473,1.7941,0.46073,4.3046,1.3557,2.8844,2.78587,0.595086,2.29566,0.779219,1.16915,1.6324
+16-Aug-2019 09:00:00,1,0.44474,1.1435,1.7896,1.5538,1.2387,2.2892,1.5717,1.8315,0.46867,4.3529,1.37478,2.91644,2.82328,0.607981,2.33449,0.79413,1.18914,1.65409
+16-Aug-2019 10:00:00,1,0.45164,1.1646,1.8202,1.5756,1.2628,2.3264,1.5937,1.8653,0.47582,4.3958,1.39178,2.94496,2.85698,0.61968,2.3697,0.807626,1.20728,1.67361
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+16-Aug-2019 12:00:00,1,0.45857,1.1859,1.8509,1.5974,1.2871,2.3639,1.6157,1.8992,0.48297,4.4382,1.4086,2.97315,2.89069,0.631457,2.40512,0.821182,1.22554,1.69312
+16-Aug-2019 13:00:00,1,0.45356,1.1705,1.8287,1.5816,1.2696,2.3368,1.5998,1.8747,0.4778,4.4076,1.39647,2.95282,2.86633,0.62294,2.37951,0.811382,1.21233,1.67903
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+16-Aug-2019 22:00:00,1,0.36287,0.89403,1.4264,1.2954,0.95906,1.8574,1.3037,1.4219,0.38101,3.786,1.15235,2.54101,2.4109,0.470907,1.92014,0.633633,0.976812,1.41366
+16-Aug-2019 23:00:00,1,0.33001,0.79485,1.2803,1.1916,0.85026,1.6892,1.1923,1.2527,0.34425,3.5264,1.05152,2.36965,2.23841,0.416849,1.75571,0.569003,0.8932,1.31226
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+17-Aug-2019 01:00:00,1,0.29235,0.68189,1.1126,1.0726,0.72824,1.5003,1.0615,1.0552,0.3009,3.2041,0.927035,2.15726,2.03532,0.355636,1.56869,0.494767,0.798618,1.19226
+17-Aug-2019 02:00:00,1,0.28572,0.66208,1.083,1.0516,0.70706,1.4675,1.0381,1.02,0.29313,3.1444,0.904069,2.11799,1.99892,0.344941,1.53592,0.481673,0.782104,1.17069
+17-Aug-2019 03:00:00,1,0.28486,0.65951,1.0792,1.0489,0.70432,1.4632,1.0351,1.0154,0.29212,3.1366,0.901066,2.11286,1.99419,0.343555,1.53168,0.479974,0.779965,1.16788
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+17-Aug-2019 05:00:00,1,0.31378,0.74607,1.208,1.1403,0.79732,1.6072,1.1363,1.1681,0.32574,3.3909,0.999085,2.28029,2.15162,0.39037,1.67492,0.537034,0.852273,1.26106
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+17-Aug-2019 08:00:00,1,0.43503,1.1137,1.7466,1.5232,1.205,2.2372,1.5406,1.7838,0.45855,4.2913,1.35043,2.87555,2.77562,0.591569,2.28506,0.775146,1.1637,1.62645
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+17-Aug-2019 10:00:00,1,0.45001,1.1596,1.813,1.5704,1.2571,2.3176,1.5885,1.8574,0.47413,4.3857,1.38779,2.93827,2.84904,0.616916,2.36138,0.80444,1.20299,1.66901
+17-Aug-2019 11:00:00,1,0.45989,1.1899,1.8567,1.6015,1.2917,2.3709,1.6199,1.9056,0.48432,4.4462,1.41176,2.97843,2.89705,0.633685,2.41182,0.823745,1.229,1.6968
+17-Aug-2019 12:00:00,1,0.46217,1.1969,1.8668,1.6088,1.2998,2.3833,1.6271,1.9167,0.48666,4.4599,1.41723,2.98759,2.90811,0.637571,2.4235,0.828209,1.23503,1.7032
+17-Aug-2019 13:00:00,1,0.45161,1.1645,1.82,1.5754,1.2627,2.3262,1.5936,1.8652,0.47578,4.3956,1.39169,2.94482,2.85681,0.619621,2.36952,0.807558,1.20719,1.67352
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+18-Aug-2019 21:00:00,1,0.32226,0.77155,1.2458,1.1671,0.82492,1.65,1.1656,1.2124,0.33544,3.4623,1.02672,2.3274,2.19712,0.404192,1.71711,0.553748,0.873634,1.28792
+18-Aug-2019 22:00:00,1,0.30392,0.71652,1.1641,1.1092,0.76542,1.5579,1.102,1.1163,0.31437,3.3061,0.966348,2.22442,2.09837,0.37436,1.62599,0.5176,0.827538,1.22959
+18-Aug-2019 23:00:00,1,0.28188,0.65061,1.0659,1.0394,0.69483,1.4485,1.0245,0.99952,0.2886,3.1095,0.890614,2.09498,1.97774,0.338756,1.51696,0.474083,0.772556,1.15812
+19-Aug-2019 00:00:00,1,0.2643,0.59827,0.98745,0.98382,0.63932,1.3624,0.9618,0.90533,0.26771,2.9456,0.827672,1.98718,1.88002,0.310583,1.43044,0.439341,0.729078,1.10006
+19-Aug-2019 01:00:00,1,0.25104,0.55891,0.92826,0.94185,0.59791,1.2982,0.91398,0.83363,0.25174,2.8177,0.778628,1.90307,1.80536,0.289459,1.36543,0.413104,0.696497,1.0556
+19-Aug-2019 02:00:00,1,0.24129,0.53003,0.88469,0.91096,0.56771,1.2513,0.8785,0.78053,0.23987,2.7211,0.741668,1.83962,1.74991,0.273993,1.31775,0.393787,0.672652,1.02252
+19-Aug-2019 03:00:00,1,0.23864,0.52221,0.87287,0.90258,0.55956,1.2387,0.86883,0.76607,0.23663,2.6945,0.731515,1.82217,1.73479,0.26981,1.30484,0.388546,0.666204,1.01349
+19-Aug-2019 04:00:00,1,0.23844,0.52162,0.87198,0.90195,0.55894,1.2377,0.8681,0.76499,0.23638,2.6925,0.730748,1.82086,1.73365,0.269495,1.30387,0.388152,0.665719,1.01281
+19-Aug-2019 05:00:00,1,0.24126,0.52994,0.88455,0.91087,0.56761,1.2512,0.87839,0.78036,0.23983,2.7208,0.741553,1.83942,1.74973,0.273945,1.3176,0.393727,0.672578,1.02241
+19-Aug-2019 06:00:00,1,0.24293,0.53488,0.89201,0.91615,0.57277,1.2592,0.88448,0.78947,0.24187,2.7375,0.747932,1.85037,1.75926,0.276588,1.32575,0.397034,0.676652,1.0281
+19-Aug-2019 07:00:00,1,0.26017,0.586,0.96902,0.97075,0.62638,1.3424,0.94696,0.88306,0.26276,2.9062,0.812544,1.96125,1.85686,0.303992,1.41017,0.431173,0.718911,1.08628
+19-Aug-2019 08:00:00,1,0.28846,0.67026,1.0952,1.0603,0.7158,1.481,1.0478,1.0346,0.29635,3.1692,0.913598,2.13429,2.01399,0.349357,1.54946,0.487084,0.788922,1.17962
+19-Aug-2019 09:00:00,1,0.31434,0.74775,1.2105,1.1421,0.79913,1.6101,1.1383,1.171,0.32638,3.3956,1.00092,2.28342,2.15463,0.39128,1.6777,0.538136,0.853679,1.26284
+19-Aug-2019 10:00:00,1,0.33006,0.795,1.2805,1.1918,0.85043,1.6894,1.1925,1.253,0.34431,3.5268,1.05169,2.36993,2.23868,0.416933,1.75596,0.569104,0.89333,1.31242
+19-Aug-2019 11:00:00,1,0.34428,0.83786,1.3438,1.2367,0.89726,1.7619,1.241,1.3266,0.36033,3.6416,1.09618,2.44563,2.31385,0.440258,1.82699,0.597091,0.929397,1.35667
+19-Aug-2019 12:00:00,1,0.33968,0.824,1.3233,1.2222,0.88208,1.7384,1.2254,1.3029,0.35518,3.6049,1.08195,2.42143,2.28965,0.432709,1.80401,0.58805,0.917722,1.34243
+19-Aug-2019 13:00:00,1,0.32629,0.78367,1.2637,1.1799,0.8381,1.6704,1.1795,1.2334,0.34003,3.4958,1.03968,2.34948,2.21864,0.410775,1.73719,0.561689,0.88381,1.30061
+19-Aug-2019 14:00:00,1,0.31771,0.75786,1.2255,1.1527,0.81008,1.627,1.1499,1.1887,0.33024,3.4241,1.01194,2.30221,2.17272,0.396763,1.69444,0.544772,0.862152,1.27352
+19-Aug-2019 15:00:00,1,0.3134,0.74495,1.2064,1.1391,0.7961,1.6054,1.135,1.1661,0.3253,3.3877,0.997852,2.27819,2.1496,0.38976,1.67306,0.536295,0.85133,1.25987
+19-Aug-2019 16:00:00,1,0.31248,0.74216,1.2022,1.1362,0.79309,1.6007,1.1318,1.1613,0.32424,3.3798,0.994799,2.27298,2.14461,0.388251,1.66845,0.534467,0.848999,1.25692
+19-Aug-2019 17:00:00,1,0.32043,0.76606,1.2377,1.1614,0.81897,1.6407,1.1593,1.2029,0.33336,3.4471,1.02081,2.31733,2.18735,0.401212,1.70802,0.55015,0.869028,1.28215
+19-Aug-2019 18:00:00,1,0.33024,0.79556,1.2813,1.1924,0.85104,1.6904,1.1931,1.254,0.34452,3.5284,1.05228,2.37094,2.23967,0.417238,1.75689,0.569471,0.893801,1.31301
+19-Aug-2019 19:00:00,1,0.33025,0.79558,1.2814,1.1924,0.85106,1.6904,1.1931,1.254,0.34453,3.5284,1.0523,2.37097,2.23971,0.417248,1.75692,0.569483,0.893817,1.31303
+19-Aug-2019 20:00:00,1,0.33156,0.79953,1.2872,1.1965,0.85536,1.6971,1.1976,1.2608,0.34601,3.5391,1.05646,2.37805,2.24667,0.419394,1.76346,0.572064,0.897135,1.31713
+19-Aug-2019 21:00:00,1,0.33557,0.81159,1.305,1.2092,0.86852,1.7174,1.2113,1.2816,0.35054,3.5717,1.06908,2.39953,2.26789,0.425954,1.78345,0.579946,0.907277,1.32963
+19-Aug-2019 22:00:00,1,0.32208,0.77099,1.245,1.1666,0.82432,1.649,1.165,1.2115,0.33523,3.4608,1.02612,2.32638,2.19613,0.40389,1.71619,0.553383,0.873167,1.28733
+19-Aug-2019 23:00:00,1,0.29626,0.69358,1.13,1.0849,0.74077,1.5197,1.0752,1.0759,0.30547,3.2388,0.940419,2.18014,2.05668,0.36195,1.58803,0.50248,0.80837,1.20491
+20-Aug-2019 00:00:00,1,0.27683,0.63558,1.0434,1.0235,0.67884,1.4237,1.0066,0.9726,0.28264,3.0631,0.872796,2.06448,1.94985,0.330654,1.4921,0.464121,0.760051,1.14157
+20-Aug-2019 01:00:00,1,0.26698,0.60622,0.99939,0.99229,0.64772,1.3755,0.9714,0.91973,0.27091,2.971,0.837403,2.00386,1.89499,0.314859,1.44359,0.444632,0.735675,1.10896
+20-Aug-2019 02:00:00,1,0.25968,0.58454,0.96684,0.9692,0.62484,1.34,0.94519,0.88041,0.26217,2.9015,0.81074,1.95816,1.8541,0.30321,1.40777,0.430203,0.717706,1.08464
+20-Aug-2019 03:00:00,1,0.26189,0.5911,0.97669,0.97618,0.63175,1.3507,0.95313,0.89232,0.26482,2.9226,0.818848,1.97206,1.8665,0.30673,1.4186,0.434568,0.723135,1.09201
+20-Aug-2019 04:00:00,1,0.27363,0.62604,1.0291,1.0134,0.66871,1.408,0.99519,0.95546,0.27884,3.0334,0.861384,2.04494,1.93209,0.325519,1.47634,0.457795,0.752125,1.13102
+20-Aug-2019 05:00:00,1,0.3009,0.70747,1.1507,1.0996,0.75569,1.5428,1.0915,1.1004,0.31087,3.2797,0.956176,2.20705,2.08197,0.369464,1.61102,0.511641,0.819975,1.21988
+20-Aug-2019 06:00:00,1,0.36105,0.88854,1.4183,1.2897,0.953,1.848,1.2977,1.4126,0.379,3.7722,1.14696,2.53187,2.40148,0.467906,1.91103,0.630066,0.972168,1.40813
+20-Aug-2019 07:00:00,1,0.40987,1.0368,1.635,1.4438,1.1182,2.1032,1.4592,1.659,0.43199,4.1247,1.28483,2.76511,2.65054,0.549207,2.15725,0.725865,1.09805,1.55372
+20-Aug-2019 08:00:00,1,0.44394,1.141,1.7861,1.5513,1.236,2.2849,1.5692,1.8276,0.46784,4.3479,1.37279,2.91311,2.81937,0.60663,2.33042,0.792569,1.18704,1.65182
+20-Aug-2019 09:00:00,1,0.46305,1.1996,1.8707,1.6115,1.3028,2.3881,1.6298,1.921,0.48756,4.4652,1.41932,2.99109,2.91234,0.639061,2.42798,0.82992,1.23734,1.70565
+20-Aug-2019 10:00:00,1,0.47841,1.2469,1.9387,1.6599,1.357,2.4716,1.6782,1.9955,0.50323,4.5557,1.45533,3.05128,2.98626,0.665258,2.50666,0.859932,1.27798,1.74833
+20-Aug-2019 11:00:00,1,0.48623,1.271,1.9733,1.6846,1.3847,2.5143,1.7026,2.0332,0.51114,4.6004,1.47318,3.08107,3.02359,0.678627,2.54677,0.875195,1.29872,1.76986
+20-Aug-2019 12:00:00,1,0.49027,1.2835,1.9912,1.6973,1.3991,2.5365,1.7152,2.0527,0.51521,4.6232,1.4823,3.09626,3.04284,0.685555,2.56754,0.883089,1.30947,1.78094
+20-Aug-2019 13:00:00,1,0.48579,1.2696,1.9713,1.6832,1.3832,2.5119,1.7013,2.0311,0.5107,4.5979,1.47219,3.07942,3.02152,0.677881,2.54453,0.874343,1.29756,1.76866
+20-Aug-2019 14:00:00,1,0.47786,1.2452,1.9363,1.6582,1.3551,2.4686,1.6765,1.9929,0.50267,4.5525,1.45407,3.04918,2.98365,0.664324,2.50385,0.858864,1.27653,1.74682
+20-Aug-2019 15:00:00,1,0.46186,1.196,1.8654,1.6078,1.2987,2.3817,1.6261,1.9152,0.48635,4.4581,1.4165,2.98637,2.90663,0.637051,2.42194,0.827612,1.23422,1.70234
+20-Aug-2019 16:00:00,1,0.45098,1.1626,1.8173,1.5735,1.2605,2.3229,1.5916,1.8621,0.47514,4.3917,1.39017,2.94227,2.85378,0.618567,2.36635,0.806343,1.20555,1.67176
+20-Aug-2019 17:00:00,1,0.44108,1.1322,1.7734,1.5423,1.226,2.2696,1.56,1.8136,0.46486,4.3298,1.36566,2.90114,2.80535,0.601791,2.31585,0.786976,1.17954,1.6437
+20-Aug-2019 18:00:00,1,0.4308,1.1008,1.7278,1.5098,1.1903,2.2145,1.527,1.7629,0.45411,4.2639,1.33963,2.8574,2.75471,0.584415,2.2635,0.766852,1.15261,1.61431
+20-Aug-2019 19:00:00,1,0.42258,1.0756,1.6914,1.4839,1.1619,2.1707,1.5005,1.7222,0.44546,4.2101,1.3184,2.82168,2.71397,0.57056,2.22172,0.750757,1.13113,1.59063
+20-Aug-2019 20:00:00,1,0.41509,1.0528,1.6582,1.4603,1.1361,2.1309,1.4762,1.6851,0.43754,4.1601,1.29874,2.78856,2.67667,0.557972,2.18373,0.736096,1.11163,1.56894
+20-Aug-2019 21:00:00,1,0.40409,1.0192,1.6094,1.4256,1.0984,2.0727,1.4403,1.6302,0.42582,4.0851,1.26926,2.73884,2.62152,0.539524,2.128,0.714541,1.08305,1.53681
+20-Aug-2019 22:00:00,1,0.37249,0.92318,1.4691,1.3258,0.99131,1.9073,1.3359,1.4709,0.39159,3.8584,1.18056,2.58885,2.46062,0.486845,1.9685,0.652533,1.00148,1.44279
+20-Aug-2019 23:00:00,1,0.34349,0.83547,1.3402,1.2342,0.89465,1.7578,1.2383,1.3225,0.35945,3.6353,1.09374,2.44148,2.30969,0.438958,1.82304,0.595536,0.927387,1.35422
+21-Aug-2019 00:00:00,1,0.32382,0.77624,1.2527,1.1721,0.83002,1.6579,1.171,1.2206,0.33722,3.4753,1.03175,2.33597,2.20546,0.406739,1.72488,0.556821,0.87757,1.29284
+21-Aug-2019 01:00:00,1,0.31276,0.74302,1.2035,1.1371,0.79402,1.6021,1.1328,1.1628,0.32457,3.3822,0.99574,2.27458,2.14615,0.388716,1.66987,0.53503,0.849718,1.25783
+21-Aug-2019 02:00:00,1,0.30443,0.71805,1.1664,1.1108,0.76707,1.5604,1.1038,1.119,0.31496,3.3105,0.968064,2.22735,2.10114,0.37519,1.62853,0.518609,0.828819,1.23123
+21-Aug-2019 03:00:00,1,0.30441,0.71797,1.1663,1.1107,0.76699,1.5603,1.1037,1.1189,0.31493,3.3103,0.967979,2.22721,2.10101,0.375149,1.62841,0.518559,0.828756,1.23115
+21-Aug-2019 04:00:00,1,0.3101,0.73503,1.1916,1.1287,0.78538,1.5888,1.1235,1.1488,0.3215,3.3594,0.986937,2.25957,2.13178,0.384383,1.65663,0.529777,0.843023,1.24934
+21-Aug-2019 05:00:00,1,0.33556,0.81157,1.305,1.2092,0.8685,1.7174,1.2113,1.2815,0.35053,3.5717,1.06906,2.3995,2.26786,0.425943,1.78341,0.579934,0.90726,1.32961
+21-Aug-2019 06:00:00,1,0.3854,0.96237,1.5265,1.3666,1.0349,1.9745,1.3788,1.5363,0.40568,3.953,1.21753,2.65144,2.5268,0.508308,2.03355,0.677882,1.0347,1.48151
+21-Aug-2019 07:00:00,1,0.42782,1.0917,1.7146,1.5004,1.18,2.1986,1.5174,1.7482,0.45099,4.2446,1.332,2.84456,2.74,0.579399,2.24838,0.76103,1.14483,1.60576
+21-Aug-2019 08:00:00,1,0.45502,1.175,1.8351,1.5862,1.2747,2.3446,1.6044,1.8819,0.47931,4.4166,1.40001,2.95876,2.87343,0.625419,2.38696,0.814236,1.21618,1.68314
+21-Aug-2019 09:00:00,1,0.47053,1.2226,1.9038,1.6351,1.3292,2.4287,1.6534,1.9573,0.49521,4.5097,1.43701,3.02067,2.94843,0.651804,2.46626,0.844536,1.2571,1.7265
+21-Aug-2019 10:00:00,1,0.48775,1.2757,1.98,1.6894,1.3901,2.5227,1.7074,2.0406,0.51267,4.609,1.47662,3.0868,3.03084,0.681232,2.55458,0.878164,1.30276,1.77403
+21-Aug-2019 11:00:00,1,0.50414,1.3263,2.0525,1.741,1.4486,2.6127,1.7582,2.1192,0.52909,4.6996,1.51291,3.14721,3.1084,0.709349,2.63884,0.910135,1.3464,1.81866
+21-Aug-2019 12:00:00,1,0.50037,1.3146,2.0358,1.7291,1.4351,2.5919,1.7465,2.1011,0.52533,4.6791,1.50468,3.13353,3.09063,0.702871,2.61944,0.902783,1.33635,1.80845
+21-Aug-2019 13:00:00,1,0.49294,1.2917,2.003,1.7057,1.4086,2.5511,1.7235,2.0655,0.51789,4.6381,1.48827,3.1062,3.0555,0.690128,2.58125,0.888297,1.31657,1.78823
+21-Aug-2019 14:00:00,1,0.4836,1.2629,1.9616,1.6763,1.3754,2.4999,1.6944,2.0206,0.50848,4.5854,1.46721,3.07111,3.01105,0.674126,2.53327,0.87006,1.29174,1.76263
+21-Aug-2019 15:00:00,1,0.47076,1.2233,1.9048,1.6358,1.33,2.4299,1.6542,1.9585,0.49544,4.511,1.43755,3.02157,2.94954,0.652196,2.46744,0.844986,1.25771,1.72714
+21-Aug-2019 16:00:00,1,0.4553,1.1758,1.8364,1.5871,1.2756,2.3461,1.6053,1.8832,0.47959,4.4183,1.40069,2.95989,2.87478,0.625889,2.38838,0.814777,1.21691,1.68392
+21-Aug-2019 17:00:00,1,0.44734,1.1514,1.8011,1.562,1.2478,2.3032,1.58,1.8443,0.47137,4.3691,1.38122,2.92725,2.83601,0.612391,2.34777,0.799221,1.19598,1.66147
+21-Aug-2019 18:00:00,1,0.44339,1.1393,1.7836,1.5495,1.234,2.282,1.5674,1.8249,0.46727,4.3444,1.37142,2.91081,2.81667,0.605698,2.32762,0.791493,1.1856,1.65026
+21-Aug-2019 19:00:00,1,0.43419,1.1111,1.7429,1.5205,1.2021,2.2326,1.5379,1.7796,0.45767,4.2858,1.34828,2.87193,2.77144,0.590136,2.28074,0.773486,1.16147,1.62402
+21-Aug-2019 20:00:00,1,0.42094,1.0706,1.6841,1.4787,1.1562,2.162,1.4952,1.7141,0.44373,4.1992,1.31413,2.81449,2.70583,0.567805,2.21341,0.747552,1.12686,1.5859
+21-Aug-2019 21:00:00,1,0.41119,1.0409,1.6409,1.448,1.1227,2.1102,1.4635,1.6656,0.4334,4.1337,1.28837,2.77108,2.65717,0.551426,2.16396,0.728456,1.10148,1.55758
+21-Aug-2019 22:00:00,1,0.37877,0.94224,1.497,1.3457,1.0125,1.94,1.3569,1.5028,0.39846,3.9048,1.19868,2.61954,2.49291,0.497281,2.00014,0.664874,1.01763,1.46169
+21-Aug-2019 23:00:00,1,0.34542,0.8413,1.3488,1.2403,0.90104,1.7677,1.2449,1.3324,0.36161,3.6506,1.09969,2.4516,2.31985,0.442134,1.8327,0.599335,0.932299,1.36019
+22-Aug-2019 00:00:00,1,0.31921,0.76238,1.2322,1.1575,0.81498,1.6346,1.1551,1.1965,0.33196,3.4368,1.01683,2.31055,2.18078,0.399215,1.70192,0.547736,0.865941,1.27828
+22-Aug-2019 01:00:00,1,0.30718,0.72629,1.1787,1.1195,0.77595,1.5742,1.1134,1.1335,0.31814,3.3343,0.977256,2.24304,2.11604,0.379651,1.64217,0.524032,0.835712,1.24004
+22-Aug-2019 02:00:00,1,0.30171,0.70989,1.1543,1.1022,0.75829,1.5468,1.0943,1.1047,0.3118,3.2868,0.958899,2.2117,2.08635,0.370771,1.61502,0.513232,0.821993,1.22248
+22-Aug-2019 03:00:00,1,0.30151,0.7093,1.1534,1.1016,0.75766,1.5458,1.0936,1.1036,0.31158,3.2851,0.95824,2.21058,2.08529,0.370454,1.61405,0.512847,0.821504,1.22185
+22-Aug-2019 04:00:00,1,0.30483,0.71922,1.1682,1.112,0.76834,1.5624,1.1052,1.1211,0.31541,3.3139,0.969376,2.22959,2.10327,0.375825,1.63047,0.519382,0.8298,1.23248
+22-Aug-2019 05:00:00,1,0.32981,0.79427,1.2794,1.191,0.84963,1.6882,1.1916,1.2517,0.34403,3.5248,1.05091,2.36861,2.23738,0.416534,1.75474,0.568623,0.892712,1.31166
+22-Aug-2019 06:00:00,1,0.378,0.93991,1.4936,1.3432,1.0099,1.936,1.3543,1.4989,0.39763,3.8992,1.19648,2.61581,2.48897,0.496005,1.99628,0.663366,1.01566,1.45939
+22-Aug-2019 07:00:00,1,0.41823,1.0624,1.6721,1.4702,1.1469,2.1476,1.4864,1.7007,0.44087,4.1812,1.30703,2.80253,2.69235,0.563253,2.19967,0.742251,1.11981,1.57806
+22-Aug-2019 08:00:00,1,0.43846,1.1242,1.7618,1.534,1.2169,2.2555,1.5516,1.8007,0.46213,4.3132,1.35908,2.89009,2.79248,0.597357,2.3025,0.781848,1.17267,1.63623
+22-Aug-2019 09:00:00,1,0.44967,1.1586,1.8115,1.5693,1.256,2.3158,1.5874,1.8557,0.47378,4.3836,1.38696,2.93687,2.84739,0.616342,2.35966,0.803778,1.2021,1.66806
+22-Aug-2019 10:00:00,1,0.46336,1.2006,1.8721,1.6125,1.3039,2.3898,1.6308,1.9225,0.48788,4.4671,1.42006,2.99233,2.91386,0.639593,2.42958,0.830532,1.23816,1.70652
+22-Aug-2019 11:00:00,1,0.47554,1.2381,1.926,1.6509,1.3469,2.4559,1.6692,1.9816,0.50031,4.539,1.44869,3.0402,2.9725,0.660353,2.49194,0.854324,1.27037,1.7404
+22-Aug-2019 12:00:00,1,0.46774,1.2141,1.8914,1.6263,1.3193,2.4135,1.6447,1.9438,0.49236,4.4932,1.43044,3.0097,2.93499,0.647049,2.45198,0.839086,1.24973,1.71874
+22-Aug-2019 13:00:00,1,0.46377,1.2019,1.8739,1.6138,1.3054,2.392,1.6321,1.9245,0.4883,4.4695,1.42104,2.99397,2.91584,0.640291,2.43168,0.831333,1.23924,1.70767
+22-Aug-2019 14:00:00,1,0.45401,1.1719,1.8307,1.583,1.2711,2.3392,1.6012,1.8769,0.47827,4.4104,1.39756,2.95465,2.86852,0.623704,2.3818,0.812261,1.21352,1.6803
+22-Aug-2019 15:00:00,1,0.44033,1.13,1.7701,1.5399,1.2234,2.2656,1.5576,1.8099,0.46408,4.3251,1.36379,2.89799,2.80168,0.600525,2.31204,0.785512,1.17758,1.64157
+22-Aug-2019 16:00:00,1,0.43209,1.1047,1.7336,1.5139,1.1948,2.2214,1.5312,1.7693,0.45547,4.2723,1.34295,2.86298,2.76112,0.586605,2.2701,0.769392,1.156,1.61803
+22-Aug-2019 17:00:00,1,0.42707,1.0894,1.7113,1.4981,1.1774,2.1946,1.515,1.7445,0.45019,4.2396,1.33005,2.84128,2.73625,0.578124,2.24453,0.75955,1.14286,1.60359
+22-Aug-2019 18:00:00,1,0.42305,1.0771,1.6935,1.4854,1.1635,2.1732,1.502,1.7246,0.44596,4.2132,1.31962,2.82374,2.7163,0.57135,2.2241,0.751676,1.13236,1.59199
+22-Aug-2019 19:00:00,1,0.4161,1.0559,1.6627,1.4635,1.1396,2.1362,1.4795,1.6901,0.43861,4.1669,1.30141,2.79306,2.68171,0.559667,2.18884,0.738071,1.11425,1.57187
+22-Aug-2019 20:00:00,1,0.40632,1.026,1.6193,1.4326,1.106,2.0845,1.4476,1.6413,0.4282,4.1004,1.27528,2.74901,2.63272,0.543254,2.13927,0.718906,1.08882,1.54333
+22-Aug-2019 21:00:00,1,0.39745,0.99901,1.5799,1.4046,1.0758,2.0377,1.4186,1.5969,0.41869,4.0388,1.25111,2.70821,2.588,0.528412,2.0944,0.701519,1.06583,1.51726
+22-Aug-2019 22:00:00,1,0.3665,0.90504,1.4425,1.3069,0.97123,1.8762,1.3159,1.4404,0.38502,3.8135,1.16308,2.55921,2.42973,0.476926,1.93841,0.640778,0.986126,1.4247
+22-Aug-2019 23:00:00,1,0.33193,0.80065,1.2888,1.1977,0.85659,1.699,1.1989,1.2627,0.34643,3.5422,1.05764,2.38006,2.24865,0.420005,1.76532,0.572799,0.898079,1.31829
+23-Aug-2019 00:00:00,1,0.31067,0.73674,1.1942,1.1305,0.78723,1.5916,1.1255,1.1518,0.32216,3.3643,0.988826,2.26279,2.13486,0.385311,1.65947,0.530902,0.844456,1.25116
+23-Aug-2019 01:00:00,1,0.29803,0.69888,1.1379,1.0905,0.74646,1.5285,1.0814,1.0853,0.30753,3.2545,0.946452,2.19044,2.06634,0.364817,1.5968,0.505977,0.812797,1.21064
+23-Aug-2019 02:00:00,1,0.29,0.67487,1.1021,1.0651,0.72073,1.4886,1.0532,1.0428,0.29816,3.1831,0.918942,2.14343,2.02246,0.351847,1.55709,0.490133,0.792768,1.18464
+23-Aug-2019 03:00:00,1,0.29056,0.67654,1.1046,1.0669,0.72251,1.4914,1.0552,1.0457,0.29881,3.1881,0.920865,2.14672,2.02551,0.352745,1.55984,0.491232,0.794155,1.18645
+23-Aug-2019 04:00:00,1,0.29617,0.69331,1.1296,1.0847,0.74048,1.5192,1.0749,1.0754,0.30536,3.2381,0.940119,2.17962,2.0562,0.361808,1.58759,0.502306,0.80815,1.20463
+23-Aug-2019 05:00:00,1,0.31894,0.76155,1.231,1.1566,0.81408,1.6332,1.1542,1.1951,0.33165,3.4345,1.01594,2.30903,2.17932,0.398767,1.70056,0.547196,0.86525,1.27741
+23-Aug-2019 06:00:00,1,0.37142,0.91994,1.4644,1.3224,0.98772,1.9017,1.3324,1.4655,0.39042,3.8504,1.17746,2.58359,2.45511,0.485073,1.96313,0.650436,0.998735,1.43957
+23-Aug-2019 07:00:00,1,0.41402,1.0495,1.6534,1.4569,1.1324,2.1252,1.4727,1.6797,0.4364,4.1529,1.29589,2.78375,2.6713,0.556166,2.17827,0.733989,1.10883,1.56581
+23-Aug-2019 08:00:00,1,0.43602,1.1167,1.751,1.5263,1.2084,2.2425,1.5438,1.7887,0.45958,4.2976,1.35293,2.87975,2.78048,0.593235,2.29008,0.777075,1.16628,1.62927
+23-Aug-2019 09:00:00,1,0.44781,1.1529,1.8032,1.5635,1.2495,2.3058,1.5815,1.8466,0.47185,4.3721,1.38238,2.92919,2.8383,0.613185,2.35016,0.800137,1.19721,1.66279
+23-Aug-2019 10:00:00,1,0.45973,1.1895,1.856,1.6011,1.2912,2.3701,1.6194,1.9049,0.48416,4.4452,1.41139,2.97782,2.89631,0.633426,2.41104,0.823446,1.2286,1.69637
+23-Aug-2019 11:00:00,1,0.47077,1.2234,1.9049,1.6359,1.33,2.43,1.6542,1.9585,0.49546,4.5111,1.43758,3.02163,2.9496,0.65222,2.46751,0.845013,1.25775,1.72718
+23-Aug-2019 12:00:00,1,0.46904,1.2181,1.8972,1.6304,1.3239,2.4206,1.6488,1.9501,0.49369,4.5009,1.43352,3.01484,2.94127,0.64927,2.45865,0.841633,1.25317,1.72237
+23-Aug-2019 13:00:00,1,0.46281,1.1989,1.8696,1.6108,1.302,2.3868,1.6291,1.9198,0.48732,4.4638,1.41875,2.99014,2.9112,0.638659,2.42677,0.829458,1.23671,1.70499
+23-Aug-2019 14:00:00,1,0.45616,1.1785,1.8402,1.5898,1.2786,2.3508,1.608,1.8874,0.48048,4.4235,1.40277,2.96337,2.87895,0.627347,2.39276,0.816455,1.21917,1.68633
+23-Aug-2019 15:00:00,1,0.44804,1.1536,1.8042,1.5642,1.2503,2.307,1.5822,1.8477,0.4721,4.3735,1.38295,2.93016,2.83944,0.613583,2.35135,0.800595,1.19782,1.66346
+23-Aug-2019 16:00:00,1,0.43484,1.1131,1.7457,1.5226,1.2043,2.2361,1.54,1.7828,0.45835,4.29,1.34993,2.87471,2.77464,0.591234,2.28405,0.774758,1.16318,1.62588
+23-Aug-2019 17:00:00,1,0.43067,1.1004,1.7273,1.5094,1.1899,2.2139,1.5266,1.7623,0.45398,4.2631,1.33932,2.85687,2.7541,0.584208,2.26287,0.766611,1.15228,1.61396
+23-Aug-2019 18:00:00,1,0.4279,1.0919,1.715,1.5007,1.1803,2.199,1.5177,1.7486,0.45107,4.245,1.33219,2.84488,2.74037,0.579525,2.24876,0.761177,1.14503,1.60598
+23-Aug-2019 19:00:00,1,0.42039,1.069,1.6817,1.477,1.1544,2.159,1.4934,1.7114,0.44315,4.1956,1.31269,2.81207,2.7031,0.56688,2.21061,0.746475,1.12543,1.58431
+23-Aug-2019 20:00:00,1,0.41456,1.0511,1.6558,1.4586,1.1343,2.1281,1.4745,1.6824,0.43697,4.1565,1.29732,2.78617,2.674,0.557075,2.18102,0.735049,1.11024,1.56738
+23-Aug-2019 21:00:00,1,0.40676,1.0274,1.6213,1.434,1.1076,2.0868,1.4491,1.6435,0.42867,4.1035,1.27648,2.75103,2.63495,0.543998,2.14152,0.719777,1.08998,1.54464
+23-Aug-2019 22:00:00,1,0.37283,0.92422,1.4706,1.3269,0.99246,1.909,1.3371,1.4726,0.39197,3.8609,1.18156,2.59053,2.46238,0.487414,1.97023,0.653207,1.00236,1.44383
+23-Aug-2019 23:00:00,1,0.33829,0.81981,1.3171,1.2178,0.87751,1.7313,1.2206,1.2957,0.35361,3.5937,1.07762,2.41407,2.28232,0.430429,1.79707,0.585317,0.914197,1.33812
+24-Aug-2019 00:00:00,1,0.31497,0.74964,1.2133,1.1441,0.80118,1.6132,1.1404,1.1743,0.3271,3.401,1.00299,2.28695,2.15802,0.392305,1.68083,0.539377,0.855263,1.26484
+24-Aug-2019 01:00:00,1,0.30073,0.70694,1.1499,1.0991,0.75512,1.5419,1.0909,1.0995,0.31066,3.2782,0.955581,2.20604,2.08101,0.369178,1.61015,0.511293,0.819533,1.21932
+24-Aug-2019 02:00:00,1,0.29168,0.67989,1.1096,1.0705,0.7261,1.4969,1.0591,1.0517,0.30012,3.1981,0.924728,2.15332,2.03165,0.354554,1.56538,0.493444,0.796947,1.19009
+24-Aug-2019 03:00:00,1,0.29345,0.68519,1.1175,1.0761,0.73177,1.5057,1.0654,1.061,0.30219,3.2139,0.930823,2.16374,2.04135,0.357417,1.57415,0.496944,0.801368,1.19584
+24-Aug-2019 04:00:00,1,0.30125,0.70853,1.1522,1.1007,0.75683,1.5445,1.0927,1.1023,0.31128,3.2828,0.957368,2.20909,2.08388,0.370036,1.61277,0.512337,0.820857,1.22102
+24-Aug-2019 05:00:00,1,0.32369,0.77585,1.2522,1.1717,0.8296,1.6572,1.1706,1.2199,0.33708,3.4743,1.03134,2.33527,2.20477,0.40653,1.72424,0.556569,0.877248,1.29243
+24-Aug-2019 06:00:00,1,0.37763,0.93879,1.492,1.3421,1.0086,1.934,1.3531,1.497,0.39722,3.8964,1.19542,2.61401,2.48707,0.495389,1.99441,0.662639,1.0147,1.45828
+24-Aug-2019 07:00:00,1,0.42494,1.0828,1.7019,1.4913,1.17,2.1832,1.5081,1.7339,0.44795,4.2256,1.32453,2.832,2.72569,0.574533,2.2337,0.755377,1.13729,1.59745
+24-Aug-2019 08:00:00,1,0.44998,1.1595,1.8128,1.5703,1.257,2.3174,1.5884,1.8572,0.4741,4.3855,1.3877,2.93812,2.84887,0.616857,2.36121,0.804372,1.2029,1.66892
+24-Aug-2019 09:00:00,1,0.45832,1.1851,1.8497,1.5966,1.2862,2.3625,1.6149,1.898,0.4827,4.4367,1.40799,2.97212,2.88945,0.63102,2.40381,0.820681,1.22487,1.69241
+24-Aug-2019 10:00:00,1,0.47157,1.2258,1.9084,1.6384,1.3328,2.4343,1.6567,1.9624,0.49627,4.5158,1.43944,3.02474,2.95343,0.653578,2.47159,0.846568,1.25986,1.72939
+24-Aug-2019 11:00:00,1,0.48007,1.252,1.946,1.6652,1.3629,2.4807,1.6834,2.0035,0.50491,4.5653,1.45915,3.05766,2.99422,0.668098,2.51518,0.863177,1.28238,1.75292
+24-Aug-2019 12:00:00,1,0.4735,1.2318,1.917,1.6445,1.3397,2.4449,1.6628,1.9718,0.49824,4.5271,1.44396,3.03229,2.96273,0.656879,2.48151,0.850349,1.26498,1.73476
+24-Aug-2019 13:00:00,1,0.45545,1.1763,1.837,1.5876,1.2762,2.347,1.6058,1.884,0.47975,4.4192,1.40106,2.96051,2.87552,0.626148,2.38916,0.815075,1.21731,1.68435
+24-Aug-2019 14:00:00,1,0.44155,1.1337,1.7755,1.5437,1.2276,2.2721,1.5615,1.8159,0.46535,4.3328,1.36683,2.9031,2.80764,0.602581,2.31823,0.78789,1.18077,1.64503
+24-Aug-2019 15:00:00,1,0.43674,1.119,1.7542,1.5286,1.2109,2.2463,1.5461,1.7922,0.46034,4.3022,1.35474,2.8828,2.78402,0.59445,2.29374,0.778482,1.16816,1.63132
+24-Aug-2019 16:00:00,1,0.42637,1.0872,1.7082,1.4958,1.175,2.1909,1.5127,1.741,0.44946,4.235,1.32823,2.83823,2.73278,0.576942,2.24097,0.758176,1.14102,1.60157
+24-Aug-2019 17:00:00,1,0.42401,1.08,1.6977,1.4884,1.1668,2.1783,1.5051,1.7293,0.44698,4.2195,1.32213,2.82796,2.72109,0.572975,2.229,0.753566,1.13488,1.59478
+24-Aug-2019 18:00:00,1,0.41993,1.0676,1.6797,1.4756,1.1528,2.1566,1.4919,1.7091,0.44267,4.1925,1.3115,2.81006,2.70083,0.566116,2.20831,0.745585,1.12425,1.58299
+24-Aug-2019 19:00:00,1,0.4133,1.0473,1.6503,1.4546,1.13,2.1214,1.4704,1.6761,0.43564,4.148,1.29398,2.78055,2.66772,0.554963,2.17464,0.732585,1.10696,1.56372
+24-Aug-2019 20:00:00,1,0.40612,1.0254,1.6184,1.432,1.1054,2.0834,1.447,1.6403,0.42798,4.099,1.27474,2.74809,2.6317,0.542915,2.13824,0.71851,1.0883,1.54274
+24-Aug-2019 21:00:00,1,0.38847,0.97171,1.5401,1.3763,1.0453,1.9906,1.389,1.5518,0.40901,3.9751,1.22618,2.66607,2.54246,0.51343,2.04906,0.683914,1.04263,1.49067
+24-Aug-2019 22:00:00,1,0.35954,0.88398,1.4116,1.2849,0.94797,1.8403,1.2926,1.4049,0.37734,3.7606,1.14247,2.52426,2.39366,0.465417,1.90347,0.627107,0.968318,1.40354
+24-Aug-2019 23:00:00,1,0.33033,0.79583,1.2817,1.1927,0.85134,1.6908,1.1934,1.2544,0.34462,3.5291,1.05256,2.37143,2.24015,0.417385,1.75734,0.569648,0.894028,1.31329
+25-Aug-2019 00:00:00,1,0.30342,0.71502,1.1619,1.1076,0.76382,1.5554,1.1003,1.1137,0.31379,3.3017,0.964674,2.22157,2.09567,0.373552,1.62352,0.516617,0.826289,1.22799
+25-Aug-2019 01:00:00,1,0.28568,0.66197,1.0828,1.0515,0.70694,1.4673,1.038,1.0198,0.29308,3.1441,0.903939,2.11777,1.99872,0.34488,1.53574,0.481599,0.782011,1.17057
+25-Aug-2019 02:00:00,1,0.27741,0.63729,1.046,1.0253,0.68066,1.4265,1.0086,0.97568,0.28332,3.0684,0.874843,2.06798,1.95305,0.33158,1.49494,0.46526,0.761479,1.14347
+25-Aug-2019 03:00:00,1,0.27488,0.62976,1.0347,1.0173,0.67266,1.4141,0.99965,0.96216,0.28033,3.045,0.865849,2.05258,1.93903,0.327523,1.48249,0.460265,0.755218,1.13514
+25-Aug-2019 04:00:00,1,0.27366,0.62611,1.0292,1.0134,0.66879,1.4081,0.99528,0.9556,0.27887,3.0337,0.861473,2.04509,1.93223,0.325559,1.47646,0.457844,0.752186,1.1311
+25-Aug-2019 05:00:00,1,0.27909,0.64231,1.0535,1.0306,0.68599,1.4348,1.0146,0.98466,0.28531,3.0839,0.880799,2.07818,1.96236,0.334281,1.50323,0.468583,0.765648,1.14899
+25-Aug-2019 06:00:00,1,0.28933,0.67287,1.0991,1.063,0.71859,1.4853,1.0509,1.0392,0.29737,3.1771,0.916628,2.13947,2.01879,0.350768,1.55378,0.488812,0.791101,1.18247
+25-Aug-2019 07:00:00,1,0.31104,0.73784,1.1958,1.1317,0.78842,1.5935,1.1268,1.1537,0.32258,3.3675,0.990043,2.26487,2.13684,0.385909,1.66129,0.531627,0.84538,1.25233
+25-Aug-2019 08:00:00,1,0.33914,0.82236,1.3209,1.2205,0.88029,1.7356,1.2235,1.3001,0.35456,3.6005,1.08026,2.41856,2.28679,0.431818,1.8013,0.586982,0.916344,1.34075
+25-Aug-2019 09:00:00,1,0.36357,0.89617,1.4295,1.2977,0.96143,1.8611,1.3061,1.4255,0.38179,3.7914,1.15444,2.54457,2.41457,0.472079,1.9237,0.635025,0.978625,1.41581
+25-Aug-2019 10:00:00,1,0.37981,0.9454,1.5017,1.3489,1.016,1.9454,1.3603,1.508,0.3996,3.9124,1.20166,2.62457,2.49823,0.49901,2.00538,0.666915,1.02031,1.46481
+25-Aug-2019 11:00:00,1,0.38592,0.96396,1.5288,1.3682,1.0366,1.9773,1.3806,1.5389,0.40625,3.9568,1.21901,2.65394,2.52948,0.509183,2.0362,0.678913,1.03606,1.48308
+25-Aug-2019 12:00:00,1,0.37596,0.93371,1.4845,1.3368,1.003,1.9253,1.3475,1.4885,0.39539,3.8841,1.19061,2.60586,2.47848,0.49261,1.98598,0.659354,1.0104,1.45325
+25-Aug-2019 13:00:00,1,0.36285,0.89397,1.4263,1.2954,0.959,1.8573,1.3037,1.4218,0.38099,3.7858,1.15229,2.54091,2.4108,0.470875,1.92004,0.633594,0.976762,1.4136
+25-Aug-2019 14:00:00,1,0.35081,0.85758,1.3728,1.2574,0.91891,1.7954,1.2631,1.3602,0.36764,3.693,1.11618,2.47961,2.34812,0.451011,1.8597,0.609941,0.94603,1.37681
+25-Aug-2019 15:00:00,1,0.34199,0.83096,1.3336,1.2295,0.8897,1.7502,1.2332,1.3148,0.35777,3.6234,1.08912,2.43362,2.30182,0.4365,1.81556,0.592593,0.923586,1.34959
+25-Aug-2019 16:00:00,1,0.33498,0.80982,1.3024,1.2073,0.86659,1.7144,1.2093,1.2785,0.34987,3.567,1.06724,2.3964,2.26479,0.424992,1.78052,0.578792,0.90579,1.3278
+25-Aug-2019 17:00:00,1,0.33813,0.8193,1.3164,1.2173,0.87695,1.7305,1.22,1.2948,0.35342,3.5924,1.0771,2.41318,2.28143,0.430154,1.79624,0.584987,0.913771,1.3376
+25-Aug-2019 18:00:00,1,0.3418,0.83039,1.3328,1.2289,0.88908,1.7492,1.2326,1.3138,0.35756,3.6219,1.08854,2.43263,2.30083,0.436192,1.81462,0.592224,0.923109,1.34901
+25-Aug-2019 19:00:00,1,0.33777,0.81821,1.3148,1.2161,0.87576,1.7286,1.2188,1.2929,0.35302,3.5895,1.07597,2.41126,2.27952,0.429561,1.79443,0.584275,0.912854,1.33647
+25-Aug-2019 20:00:00,1,0.33254,0.80247,1.2915,1.1996,0.85857,1.702,1.201,1.2659,0.34712,3.5471,1.05955,2.38331,2.25186,0.420995,1.76834,0.573989,0.899609,1.32018
+25-Aug-2019 21:00:00,1,0.32642,0.78405,1.2643,1.1803,0.83852,1.671,1.18,1.2341,0.34018,3.4969,1.04009,2.35018,2.21932,0.410985,1.73783,0.561941,0.884134,1.30101
+25-Aug-2019 22:00:00,1,0.30919,0.73229,1.1876,1.1258,0.78243,1.5842,1.1204,1.144,0.32045,3.3516,0.983911,2.2544,2.12685,0.382901,1.6521,0.527977,0.840732,1.24643
+25-Aug-2019 23:00:00,1,0.2844,0.65815,1.0771,1.0474,0.70287,1.461,1.0334,1.013,0.29158,3.1325,0.899476,2.11014,1.99168,0.342823,1.52943,0.479075,0.778834,1.16639
+26-Aug-2019 00:00:00,1,0.2637,0.59648,0.98477,0.98191,0.63743,1.3595,0.95964,0.90208,0.26699,2.9399,0.825474,1.98342,1.87665,0.309621,1.42749,0.43815,0.727595,1.09806
+26-Aug-2019 01:00:00,1,0.24862,0.55174,0.91745,0.93418,0.59039,1.2866,0.9052,0.82048,0.2488,2.7939,0.769526,1.88745,1.79164,0.285615,1.35359,0.408311,0.69057,1.04742
+26-Aug-2019 02:00:00,1,0.23847,0.52171,0.87211,0.90205,0.55904,1.2379,0.86821,0.76515,0.23642,2.6928,0.730864,1.82106,1.73382,0.269543,1.30402,0.388212,0.665793,1.01291
+26-Aug-2019 03:00:00,1,0.23298,0.50549,0.84757,0.88466,0.54218,1.2117,0.84808,0.73507,0.22968,2.6372,0.709599,1.78451,1.70233,0.260876,1.27725,0.37733,0.652435,0.994097
+26-Aug-2019 04:00:00,1,0.23283,0.50502,0.84687,0.88417,0.5417,1.211,0.84751,0.73421,0.22948,2.6356,0.708991,1.78347,1.70143,0.26063,1.27649,0.37702,0.652056,0.99356
+26-Aug-2019 05:00:00,1,0.23384,0.50801,0.85139,0.88736,0.54479,1.2158,0.85122,0.73975,0.23073,2.6459,0.712921,1.79022,1.70723,0.262222,1.28141,0.379022,0.654509,0.997028
+26-Aug-2019 06:00:00,1,0.23693,0.51713,0.86519,0.89714,0.55427,1.2305,0.86254,0.75667,0.23452,2.6772,0.724888,1.81079,1.72495,0.267095,1.29646,0.385141,0.66202,1.00761
+26-Aug-2019 07:00:00,1,0.24838,0.55102,0.91636,0.93341,0.58964,1.2854,0.90431,0.81916,0.2485,2.7915,0.76861,1.88588,1.79026,0.285229,1.3524,0.40783,0.689975,1.0466
+26-Aug-2019 08:00:00,1,0.27112,0.61855,1.0179,1.0054,0.66077,1.3957,0.98622,0.94198,0.27585,3.0099,0.852365,2.02949,1.9181,0.321489,1.46396,0.452823,0.745906,1.1227
+26-Aug-2019 09:00:00,1,0.29334,0.68485,1.117,1.0757,0.73141,1.5052,1.065,1.0605,0.30206,3.2129,0.930436,2.16308,2.04074,0.357235,1.57359,0.496721,0.801087,1.19547
+26-Aug-2019 10:00:00,1,0.30814,0.72916,1.1829,1.1225,0.77905,1.579,1.1167,1.1385,0.31924,3.3426,0.980438,2.24848,2.1212,0.381203,1.64691,0.525917,0.838109,1.24309
+26-Aug-2019 11:00:00,1,0.32377,0.77608,1.2525,1.1719,0.82985,1.6576,1.1708,1.2203,0.33716,3.4749,1.03158,2.33569,2.20518,0.406655,1.72462,0.55672,0.877441,1.29267
+26-Aug-2019 12:00:00,1,0.32144,0.76909,1.2422,1.1646,0.82226,1.6458,1.1628,1.2082,0.33451,3.4555,1.02408,2.3229,2.19275,0.402858,1.71304,0.552137,0.871572,1.28534
+26-Aug-2019 13:00:00,1,0.30837,0.72986,1.184,1.1232,0.7798,1.5801,1.1176,1.1398,0.31951,3.3446,0.981217,2.24981,2.12247,0.381584,1.64808,0.526379,0.838697,1.24384
+26-Aug-2019 14:00:00,1,0.29662,0.69467,1.1316,1.0861,0.74194,1.5215,1.0765,1.0778,0.30589,3.2421,0.941661,2.18226,2.05867,0.36254,1.58983,0.503199,0.809279,1.20609
+26-Aug-2019 15:00:00,1,0.2873,0.66679,1.09,1.0566,0.71209,1.4753,1.0437,1.0284,0.29498,3.1587,0.909565,2.12739,2.0076,0.347484,1.54372,0.48479,0.786031,1.17584
+26-Aug-2019 16:00:00,1,0.2831,0.65425,1.0713,1.0433,0.69871,1.4545,1.0288,1.006,0.29004,3.1206,0.894896,2.1023,1.98447,0.340718,1.52298,0.476492,0.775585,1.16212
+26-Aug-2019 17:00:00,1,0.29287,0.68344,1.1149,1.0742,0.72991,1.5029,1.0633,1.058,0.30151,3.2087,0.928822,2.16032,2.03817,0.356475,1.57126,0.495793,0.799914,1.19395
+26-Aug-2019 18:00:00,1,0.30656,0.72443,1.1759,1.1175,0.77395,1.5711,1.1113,1.1302,0.31742,3.329,0.975188,2.23952,2.11268,0.378645,1.6391,0.52281,0.834157,1.23805
+26-Aug-2019 19:00:00,1,0.30847,0.73014,1.1844,1.1235,0.78011,1.5806,1.1179,1.1403,0.31962,3.3454,0.981533,2.25034,2.12298,0.381738,1.64855,0.526566,0.838936,1.24414
+26-Aug-2019 20:00:00,1,0.31253,0.74234,1.2025,1.1364,0.79328,1.601,1.132,1.1616,0.3243,3.3803,0.994987,2.2733,2.14492,0.388344,1.66873,0.53458,0.849143,1.2571
+26-Aug-2019 21:00:00,1,0.30953,0.73333,1.1891,1.1269,0.78355,1.5859,1.1216,1.1458,0.32085,3.3546,0.985067,2.25637,2.12873,0.383467,1.65383,0.528665,0.841607,1.24754
+26-Aug-2019 22:00:00,1,0.29637,0.69389,1.1305,1.0853,0.74111,1.5202,1.0756,1.0765,0.30559,3.2398,0.940783,2.18076,2.05726,0.362123,1.58856,0.502691,0.808636,1.20526
+26-Aug-2019 23:00:00,1,0.27025,0.61596,1.014,1.0026,0.65803,1.3915,0.98311,0.93731,0.27482,3.0018,0.849233,2.02413,1.91325,0.320095,1.45968,0.451103,0.743755,1.11982
+27-Aug-2019 00:00:00,1,0.25231,0.56268,0.93394,0.94588,0.60186,1.3044,0.91859,0.84054,0.25328,2.8301,0.783395,1.91125,1.81256,0.291482,1.37166,0.415623,0.699616,1.05989
+27-Aug-2019 01:00:00,1,0.24547,0.54241,0.90337,0.92421,0.58063,1.2714,0.89374,0.80334,0.24497,2.7628,0.757612,1.867,1.77374,0.280618,1.33818,0.402073,0.682865,1.03674
+27-Aug-2019 02:00:00,1,0.24356,0.53674,0.89482,0.91814,0.57471,1.2622,0.88677,0.79291,0.24264,2.7437,0.750332,1.8545,1.76284,0.277584,1.32883,0.398281,0.678188,1.03024
+27-Aug-2019 03:00:00,1,0.24565,0.54295,0.90419,0.92478,0.58119,1.2723,0.89441,0.80433,0.24519,2.7646,0.758302,1.86818,1.77478,0.280906,1.33907,0.402433,0.68331,1.03736
+27-Aug-2019 04:00:00,1,0.25599,0.5736,0.95037,0.95752,0.61332,1.3221,0.93189,0.86047,0.25772,2.8659,0.797103,1.93477,1.83334,0.297334,1.38968,0.422905,0.708642,1.07227
+27-Aug-2019 05:00:00,1,0.28654,0.66452,1.0866,1.0542,0.70966,1.4715,1.041,1.0243,0.29409,3.1518,0.906916,2.12286,2.00342,0.346257,1.53996,0.483287,0.784137,1.17335
+27-Aug-2019 06:00:00,1,0.34837,0.85022,1.362,1.2497,0.91082,1.7828,1.2549,1.3476,0.36491,3.6739,1.10875,2.46699,2.33535,0.446995,1.84748,0.605145,0.939817,1.36931
+27-Aug-2019 07:00:00,1,0.39817,1.0012,1.5832,1.4069,1.0782,2.0415,1.4209,1.6005,0.41947,4.0439,1.2531,2.71157,2.59166,0.529621,2.09805,0.702938,1.06771,1.51939
+27-Aug-2019 08:00:00,1,0.42431,1.0809,1.6991,1.4894,1.1679,2.1799,1.5061,1.7308,0.44729,4.2215,1.32291,2.82927,2.72258,0.573479,2.23052,0.754152,1.13566,1.59564
+27-Aug-2019 09:00:00,1,0.43878,1.1252,1.7632,1.535,1.218,2.2573,1.5526,1.8023,0.46247,4.3152,1.35989,2.89145,2.79406,0.597902,2.30414,0.782479,1.17351,1.63715
+27-Aug-2019 10:00:00,1,0.45123,1.1633,1.8183,1.5742,1.2614,2.3242,1.5924,1.8633,0.47539,4.3932,1.39076,2.94326,2.85496,0.618977,2.36758,0.806815,1.20619,1.67244
+27-Aug-2019 11:00:00,1,0.46383,1.2021,1.8742,1.614,1.3056,2.3923,1.6323,1.9248,0.48836,4.4699,1.42119,2.99422,2.91614,0.640398,2.432,0.831456,1.23941,1.70784
+27-Aug-2019 12:00:00,1,0.46201,1.1965,1.8661,1.6082,1.2992,2.3825,1.6266,1.916,0.4865,4.459,1.41685,2.98695,2.90733,0.637299,2.42268,0.827897,1.2346,1.70275
+27-Aug-2019 13:00:00,1,0.45914,1.1876,1.8534,1.5992,1.2891,2.3669,1.6175,1.902,0.48355,4.4417,1.40997,2.97544,2.89345,0.632423,2.40803,0.822294,1.22704,1.69472
+27-Aug-2019 14:00:00,1,0.45125,1.1634,1.8185,1.5743,1.2615,2.3243,1.5925,1.8635,0.47542,4.3934,1.39083,2.94337,2.85509,0.619023,2.36773,0.806869,1.20626,1.67252
+27-Aug-2019 15:00:00,1,0.44219,1.1357,1.7783,1.5458,1.2299,2.2756,1.5636,1.819,0.46602,4.3369,1.36844,2.90581,2.81081,0.603674,2.32152,0.789153,1.18246,1.64686
+27-Aug-2019 16:00:00,1,0.42844,1.0935,1.7174,1.5024,1.1821,2.2019,1.5194,1.7512,0.45163,4.2486,1.33358,2.84722,2.74303,0.580433,2.2515,0.762231,1.14643,1.60753
+27-Aug-2019 17:00:00,1,0.42473,1.0822,1.7009,1.4907,1.1693,2.1821,1.5074,1.7329,0.44774,4.2243,1.32401,2.83111,2.72467,0.57419,2.23267,0.754978,1.13676,1.59686
+27-Aug-2019 18:00:00,1,0.42128,1.0717,1.6856,1.4798,1.1574,2.1638,1.4963,1.7158,0.44409,4.2015,1.31502,2.81598,2.70752,0.568377,2.21513,0.748217,1.12775,1.58688
+27-Aug-2019 19:00:00,1,0.41733,1.0596,1.6681,1.4674,1.1438,2.1428,1.4835,1.6962,0.43992,4.1752,1.30466,2.79854,2.68786,0.561738,2.19509,0.740486,1.11746,1.57545
+27-Aug-2019 20:00:00,1,0.41348,1.0479,1.6511,1.4552,1.1306,2.1224,1.471,1.677,0.43583,4.1492,1.29447,2.78136,2.66863,0.555269,2.17556,0.732943,1.10744,1.56425
+27-Aug-2019 21:00:00,1,0.39734,0.99868,1.5795,1.4043,1.0754,2.0372,1.4182,1.5964,0.41858,4.038,1.25082,2.70771,2.58746,0.528233,2.09386,0.701309,1.06556,1.51694
+27-Aug-2019 22:00:00,1,0.35887,0.88194,1.4086,1.2828,0.94572,1.8368,1.2903,1.4015,0.37659,3.7555,1.14046,2.52084,2.39015,0.464304,1.90009,0.625782,0.966595,1.40149
+27-Aug-2019 23:00:00,1,0.32232,0.77172,1.2461,1.1673,0.82511,1.6503,1.1658,1.2127,0.33551,3.4628,1.0269,2.32772,2.19743,0.404286,1.7174,0.553861,0.873779,1.2881
+28-Aug-2019 00:00:00,1,0.30126,0.70855,1.1523,1.1008,0.75685,1.5446,1.0927,1.1023,0.31128,3.2829,0.957389,2.20913,2.08392,0.370046,1.6128,0.512349,0.820873,1.22104
+28-Aug-2019 01:00:00,1,0.28677,0.66523,1.0877,1.0549,0.71042,1.4727,1.0418,1.0256,0.29437,3.154,0.907742,2.12427,2.00472,0.346639,1.54113,0.483755,0.784726,1.17413
+28-Aug-2019 02:00:00,1,0.27693,0.63587,1.0438,1.0238,0.67915,1.4242,1.0069,0.97313,0.28276,3.064,0.873148,2.06508,1.9504,0.330814,1.49259,0.464317,0.760297,1.1419
+28-Aug-2019 03:00:00,1,0.27674,0.63528,1.0429,1.0232,0.67852,1.4232,1.0062,0.97207,0.28252,3.0622,0.872443,2.06387,1.9493,0.330495,1.49161,0.463925,0.759805,1.14124
+28-Aug-2019 04:00:00,1,0.28424,0.65767,1.0764,1.0469,0.70236,1.4602,1.0329,1.0121,0.29139,3.131,0.89891,2.10917,1.99079,0.342562,1.52863,0.478755,0.778432,1.16586
+28-Aug-2019 05:00:00,1,0.31091,0.73746,1.1953,1.1313,0.78801,1.5928,1.1264,1.1531,0.32244,3.3664,0.989624,2.26415,2.13616,0.385703,1.66066,0.531377,0.845061,1.25193
+28-Aug-2019 06:00:00,1,0.36373,0.89665,1.4302,1.2982,0.96196,1.8619,1.3067,1.4263,0.38196,3.7926,1.15491,2.54536,2.41539,0.47234,1.92449,0.635334,0.979028,1.41629
+28-Aug-2019 07:00:00,1,0.40909,1.0345,1.6316,1.4413,1.1155,2.0991,1.4567,1.6551,0.43115,4.1194,1.28273,2.76158,2.64662,0.547895,2.15329,0.724332,1.09601,1.55144
+28-Aug-2019 08:00:00,1,0.43871,1.125,1.7629,1.5348,1.2177,2.2569,1.5524,1.8019,0.46239,4.3147,1.3597,2.89113,2.79369,0.597775,2.30376,0.782331,1.17331,1.63693
+28-Aug-2019 09:00:00,1,0.45393,1.1717,1.8303,1.5828,1.2709,2.3388,1.601,1.8766,0.47819,4.4099,1.39738,2.95434,2.86815,0.623574,2.38142,0.812112,1.21332,1.68008
+28-Aug-2019 10:00:00,1,0.47097,1.224,1.9058,1.6365,1.3307,2.4311,1.6548,1.9595,0.49567,4.5123,1.43805,3.02242,2.95058,0.652566,2.46855,0.845409,1.25828,1.72774
+28-Aug-2019 11:00:00,1,0.48865,1.2784,1.984,1.6922,1.3933,2.5276,1.7102,2.0449,0.51357,4.614,1.47864,3.09016,3.03509,0.682764,2.55918,0.87991,1.30514,1.77648
+28-Aug-2019 12:00:00,1,0.48501,1.2672,1.9679,1.6807,1.3804,2.5077,1.6988,2.0274,0.50991,4.5935,1.47042,3.07647,3.01779,0.676544,2.54052,0.872819,1.29549,1.76652
+28-Aug-2019 13:00:00,1,0.47765,1.2446,1.9353,1.6576,1.3543,2.4675,1.6758,1.9919,0.50246,4.5513,1.45358,3.04837,2.98264,0.663965,2.50278,0.858454,1.27597,1.74624
+28-Aug-2019 14:00:00,1,0.46539,1.2068,1.8811,1.6189,1.3111,2.4008,1.6373,1.9324,0.48996,4.4792,1.42489,3.00041,2.92367,0.643051,2.43997,0.834501,1.24353,1.71219
+28-Aug-2019 15:00:00,1,0.44948,1.158,1.8106,1.5687,1.2553,2.3147,1.5868,1.8548,0.47358,4.3824,1.38648,2.93608,2.84644,0.616015,2.35867,0.803401,1.20159,1.66751
+28-Aug-2019 16:00:00,1,0.43537,1.1148,1.7481,1.5243,1.2062,2.239,1.5417,1.7855,0.4589,4.2934,1.35128,2.87698,2.77727,0.592136,2.28677,0.775802,1.16457,1.62741
+28-Aug-2019 17:00:00,1,0.42983,1.0978,1.7235,1.5068,1.1869,2.2093,1.5239,1.7581,0.4531,4.2576,1.33715,2.85323,2.74992,0.58278,2.25857,0.764955,1.15007,1.61153
+28-Aug-2019 18:00:00,1,0.42704,1.0893,1.7112,1.498,1.1773,2.1945,1.5149,1.7443,0.45017,4.2394,1.32998,2.84116,2.73612,0.578078,2.24439,0.759496,1.14278,1.60351
+28-Aug-2019 19:00:00,1,0.42015,1.0682,1.6806,1.4762,1.1535,2.1577,1.4926,1.7102,0.44289,4.1939,1.31205,2.81099,2.70188,0.566469,2.20937,0.745997,1.12479,1.5836
+28-Aug-2019 20:00:00,1,0.41642,1.0568,1.6641,1.4645,1.1407,2.138,1.4805,1.6917,0.43895,4.1691,1.30226,2.7945,2.68333,0.560213,2.19049,0.738708,1.1151,1.57281
+28-Aug-2019 21:00:00,1,0.39899,1.0037,1.5868,1.4095,1.081,2.0458,1.4236,1.6046,0.42035,4.0496,1.25535,2.71536,2.59579,0.530988,2.10219,0.70454,1.06982,1.5218
+28-Aug-2019 22:00:00,1,0.36357,0.89617,1.4295,1.2977,0.96143,1.8611,1.3061,1.4255,0.38179,3.7914,1.15444,2.54457,2.41457,0.472079,1.9237,0.635025,0.978625,1.41581
+28-Aug-2019 23:00:00,1,0.32861,0.79065,1.2741,1.1872,0.8457,1.6821,1.1875,1.2455,0.34267,3.515,1.04709,2.36211,2.231,0.41457,1.74876,0.56626,0.889677,1.3079
+29-Aug-2019 00:00:00,1,0.30627,0.72356,1.1746,1.1166,0.77301,1.5696,1.1102,1.1287,0.31709,3.3265,0.974217,2.23786,2.11111,0.378173,1.63765,0.522236,0.833428,1.23712
+29-Aug-2019 01:00:00,1,0.29343,0.68511,1.1174,1.076,0.73169,1.5056,1.0653,1.0609,0.30216,3.2137,0.930737,2.16359,2.04122,0.357376,1.57402,0.496894,0.801306,1.19576
+29-Aug-2019 02:00:00,1,0.28772,0.66806,1.0919,1.0579,0.71345,1.4774,1.0452,1.0306,0.29548,3.1626,0.91104,2.12992,2.00994,0.348169,1.54582,0.485629,0.787088,1.17722
+29-Aug-2019 03:00:00,1,0.28947,0.67329,1.0997,1.0635,0.71903,1.486,1.0513,1.0399,0.29753,3.1783,0.917104,2.14029,2.01954,0.35099,1.55446,0.489083,0.791444,1.18291
+29-Aug-2019 04:00:00,1,0.29686,0.69538,1.1327,1.0868,0.7427,1.5227,1.0773,1.0791,0.30617,3.2442,0.942474,2.18365,2.05997,0.362926,1.59101,0.50367,0.809876,1.20686
+29-Aug-2019 05:00:00,1,0.32349,0.77524,1.2513,1.171,0.82893,1.6562,1.1699,1.2188,0.33684,3.4726,1.03068,2.33415,2.20368,0.406196,1.72322,0.556166,0.876731,1.29179
+29-Aug-2019 06:00:00,1,0.37987,0.94558,1.5019,1.3491,1.0162,1.9457,1.3605,1.5083,0.39966,3.9128,1.20183,2.62486,2.49854,0.499109,2.00568,0.667032,1.02046,1.46499
+29-Aug-2019 07:00:00,1,0.42165,1.0728,1.6873,1.481,1.1587,2.1658,1.4975,1.7176,0.44449,4.2039,1.31599,2.81762,2.70938,0.569005,2.21703,0.748948,1.12872,1.58796
+29-Aug-2019 08:00:00,1,0.44731,1.1513,1.801,1.5619,1.2477,2.3031,1.5799,1.8442,0.47134,4.369,1.38115,2.92714,2.83587,0.612344,2.34763,0.799167,1.1959,1.66139
+29-Aug-2019 09:00:00,1,0.45991,1.19,1.8568,1.6016,1.2918,2.3711,1.6199,1.9057,0.48434,4.4463,1.41181,2.97851,2.89715,0.633721,2.41193,0.823785,1.22905,1.69686
+29-Aug-2019 10:00:00,1,0.47513,1.2368,1.9241,1.6496,1.3454,2.4537,1.6679,1.9796,0.49989,4.5366,1.44773,3.0386,2.97052,0.659649,2.48982,0.853518,1.26927,1.73925
+29-Aug-2019 11:00:00,1,0.48557,1.269,1.9703,1.6825,1.3824,2.5107,1.7006,2.0301,0.51047,4.5966,1.47168,3.07857,3.02044,0.677495,2.54337,0.873904,1.29696,1.76804
+29-Aug-2019 12:00:00,1,0.48068,1.2539,1.9487,1.6671,1.365,2.484,1.6853,2.0065,0.50552,4.5687,1.46053,3.05997,2.9971,0.669129,2.51828,0.864355,1.28398,1.75459
+29-Aug-2019 13:00:00,1,0.47599,1.2395,1.928,1.6523,1.3485,2.4584,1.6706,1.9838,0.50077,4.5417,1.44974,3.04196,2.97468,0.66113,2.49427,0.855213,1.27157,1.74165
+29-Aug-2019 14:00:00,1,0.46828,1.2157,1.8938,1.628,1.3213,2.4165,1.6464,1.9464,0.49292,4.4964,1.43173,3.01184,2.93761,0.647975,2.45476,0.840148,1.25116,1.72025
+29-Aug-2019 15:00:00,1,0.45983,1.1898,1.8564,1.6014,1.2915,2.3706,1.6197,1.9053,0.48426,4.4458,1.41162,2.97821,2.89678,0.633591,2.41154,0.823636,1.22885,1.69665
+29-Aug-2019 16:00:00,1,0.44517,1.1448,1.7915,1.5552,1.2403,2.2916,1.5731,1.8337,0.46912,4.3556,1.37585,2.91824,2.8254,0.608715,2.3367,0.794978,1.19028,1.65532
+29-Aug-2019 17:00:00,1,0.44061,1.1308,1.7713,1.5408,1.2244,2.267,1.5585,1.8112,0.46437,4.3268,1.36447,2.89915,2.80303,0.600989,2.31344,0.78605,1.1783,1.64235
+29-Aug-2019 18:00:00,1,0.43608,1.1169,1.7512,1.5265,1.2086,2.2427,1.544,1.7889,0.45964,4.2979,1.35307,2.87998,2.78075,0.593327,2.29036,0.777182,1.16642,1.62942
+29-Aug-2019 19:00:00,1,0.4293,1.0962,1.7212,1.5051,1.1851,2.2065,1.5222,1.7555,0.45254,4.2542,1.33578,2.85093,2.74729,0.581882,2.25586,0.763913,1.14868,1.61
+29-Aug-2019 20:00:00,1,0.42342,1.0782,1.6951,1.4866,1.1648,2.1752,1.5032,1.7264,0.44635,4.2156,1.3206,2.82537,2.71816,0.571979,2.226,0.752408,1.13333,1.59307
+29-Aug-2019 21:00:00,1,0.40386,1.0185,1.6084,1.4248,1.0976,2.0715,1.4396,1.629,0.42557,4.0834,1.26862,2.73777,2.62033,0.53913,2.1268,0.71408,1.08244,1.53612
+29-Aug-2019 22:00:00,1,0.36516,0.90098,1.4366,1.3027,0.96674,1.8693,1.3115,1.4336,0.38354,3.8034,1.15914,2.55252,2.4228,0.474707,1.93168,0.638145,0.982692,1.42063
+29-Aug-2019 23:00:00,1,0.32753,0.78739,1.2692,1.1838,0.84214,1.6766,1.1838,1.2399,0.34144,3.506,1.04364,2.35622,2.22523,0.412797,1.74335,0.564125,0.886936,1.30449
+30-Aug-2019 00:00:00,1,0.30491,0.71949,1.1686,1.1123,0.76862,1.5628,1.1055,1.1216,0.31552,3.3147,0.969672,2.2301,2.10375,0.375968,1.63091,0.519556,0.830022,1.23277
+30-Aug-2019 01:00:00,1,0.29253,0.68243,1.1134,1.0731,0.72882,1.5012,1.0621,1.0562,0.30112,3.2057,0.927659,2.15833,2.03631,0.355929,1.56959,0.495125,0.799071,1.19285
+30-Aug-2019 02:00:00,1,0.28483,0.65943,1.0791,1.0488,0.70424,1.4631,1.035,1.0153,0.29209,3.1364,0.900979,2.11271,1.99405,0.343515,1.53155,0.479924,0.779903,1.1678
+30-Aug-2019 03:00:00,1,0.28478,0.65929,1.0788,1.0486,0.70408,1.4628,1.0348,1.015,0.29203,3.136,0.900805,2.11241,1.99377,0.343435,1.53131,0.479826,0.779779,1.16764
+30-Aug-2019 04:00:00,1,0.29156,0.67951,1.109,1.0701,0.7257,1.4963,1.0587,1.051,0.29997,3.197,0.924297,2.15258,2.03096,0.354352,1.56476,0.493197,0.796635,1.18968
+30-Aug-2019 05:00:00,1,0.31624,0.75346,1.219,1.1481,0.80531,1.6196,1.1449,1.181,0.32856,3.4118,1.00716,2.29405,2.16485,0.394377,1.68715,0.541885,0.858465,1.26888
+30-Aug-2019 06:00:00,1,0.37381,0.92718,1.475,1.33,0.99575,1.9141,1.3403,1.4776,0.39304,3.8682,1.18439,2.59533,2.46741,0.489035,1.97514,0.655125,1.00487,1.44677
+30-Aug-2019 07:00:00,1,0.42135,1.0719,1.6859,1.48,1.1577,2.1641,1.4965,1.7161,0.44416,4.2019,1.31519,2.81628,2.70786,0.568491,2.21547,0.74835,1.12793,1.58708
+30-Aug-2019 08:00:00,1,0.44935,1.1576,1.81,1.5683,1.2549,2.3141,1.5864,1.8542,0.47345,4.3817,1.38618,2.93557,2.84584,0.615804,2.35804,0.803158,1.20127,1.66716
+30-Aug-2019 09:00:00,1,0.46019,1.1909,1.858,1.6025,1.2928,2.3726,1.6208,1.9071,0.48463,4.448,1.41249,2.97966,2.89853,0.634205,2.41338,0.824341,1.2298,1.69766
+30-Aug-2019 10:00:00,1,0.47157,1.2258,1.9084,1.6384,1.3328,2.4343,1.6567,1.9624,0.49627,4.5158,1.43944,3.02474,2.95343,0.653578,2.47159,0.846568,1.25986,1.72939
+30-Aug-2019 11:00:00,1,0.48431,1.2651,1.9648,1.6785,1.3779,2.5038,1.6966,2.024,0.5092,4.5895,1.46883,3.0738,3.01444,0.67534,2.53691,0.871446,1.29362,1.76458
+30-Aug-2019 12:00:00,1,0.47887,1.2483,1.9407,1.6614,1.3586,2.4741,1.6796,1.9977,0.50369,4.5583,1.45638,3.05303,2.98844,0.666036,2.50899,0.860822,1.27918,1.74959
+30-Aug-2019 13:00:00,1,0.46842,1.2162,1.8945,1.6285,1.3217,2.4172,1.6468,1.9471,0.49306,4.4972,1.43205,3.01239,2.93828,0.648213,2.45548,0.840421,1.25153,1.72064
+30-Aug-2019 14:00:00,1,0.45814,1.1846,1.849,1.5961,1.2856,2.3615,1.6143,1.8971,0.48253,4.4356,1.40757,2.97142,2.8886,0.630726,2.40292,0.820342,1.22441,1.69192
+30-Aug-2019 15:00:00,1,0.44664,1.1493,1.798,1.5598,1.2454,2.2995,1.5778,1.8409,0.47064,4.3648,1.37948,2.92434,2.83257,0.6112,2.34418,0.797846,1.19413,1.65948
+30-Aug-2019 16:00:00,1,0.4375,1.1213,1.7575,1.531,1.2136,2.2504,1.5485,1.796,0.46113,4.3071,1.35666,2.88603,2.78776,0.595735,2.29761,0.77997,1.17015,1.63349
+30-Aug-2019 17:00:00,1,0.4362,1.1173,1.7518,1.5269,1.209,2.2434,1.5444,1.7895,0.45977,4.2987,1.35338,2.8805,2.78135,0.593535,2.29099,0.777423,1.16674,1.62978
+30-Aug-2019 18:00:00,1,0.43406,1.1107,1.7423,1.5201,1.2016,2.2319,1.5375,1.779,0.45753,4.285,1.34795,2.87138,2.7708,0.589917,2.28008,0.773232,1.16113,1.62365
+30-Aug-2019 19:00:00,1,0.43323,1.1082,1.7386,1.5175,1.1987,2.2275,1.5348,1.7749,0.45666,4.2797,1.34584,2.86784,2.76672,0.58852,2.27587,0.771612,1.15897,1.62128
+30-Aug-2019 20:00:00,1,0.42769,1.0913,1.714,1.5,1.1795,2.1979,1.517,1.7475,0.45085,4.2437,1.33165,2.84397,2.73932,0.579169,2.24768,0.760763,1.14448,1.60537
+30-Aug-2019 21:00:00,1,0.40387,1.0186,1.6084,1.4249,1.0977,2.0715,1.4396,1.6291,0.42558,4.0835,1.26866,2.73783,2.6204,0.539152,2.12687,0.714107,1.08247,1.53616
+30-Aug-2019 22:00:00,1,0.36892,0.91237,1.4533,1.3146,0.97934,1.8888,1.324,1.4528,0.38768,3.8317,1.17018,2.57125,2.44224,0.480935,1.95058,0.645533,0.99233,1.43203
+30-Aug-2019 23:00:00,1,0.33291,0.80357,1.2932,1.2008,0.85978,1.7039,1.2022,1.2678,0.34753,3.5501,1.06071,2.38528,2.2538,0.421595,1.77017,0.574711,0.900538,1.32133
+31-Aug-2019 00:00:00,1,0.3083,0.72965,1.1837,1.123,0.77958,1.5798,1.1173,1.1394,0.31943,3.344,0.980986,2.24941,2.12209,0.381471,1.64773,0.526241,0.838522,1.24362
+31-Aug-2019 01:00:00,1,0.29456,0.68851,1.1224,1.0796,0.73533,1.5113,1.0693,1.0669,0.30349,3.2238,0.934627,2.17024,2.04742,0.359211,1.57964,0.499135,0.804138,1.19943
+31-Aug-2019 02:00:00,1,0.28717,0.66642,1.0895,1.0562,0.7117,1.4746,1.0432,1.0277,0.29484,3.1576,0.909131,2.12665,2.00692,0.347283,1.5431,0.484544,0.78572,1.17543
+31-Aug-2019 03:00:00,1,0.28871,0.67101,1.0963,1.0611,0.7166,1.4822,1.0487,1.0359,0.29664,3.1715,0.914464,2.13577,2.01536,0.34976,1.55069,0.487578,0.789545,1.18043
+31-Aug-2019 04:00:00,1,0.29657,0.6945,1.1314,1.0859,0.74175,1.5212,1.0763,1.0775,0.30582,3.2416,0.941468,2.18193,2.05836,0.362448,1.58955,0.503087,0.809138,1.20591
+31-Aug-2019 05:00:00,1,0.3227,0.77287,1.2478,1.1685,0.82636,1.6522,1.1672,1.2147,0.33595,3.466,1.02814,2.32983,2.19948,0.404912,1.71931,0.554617,0.874747,1.28931
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+09-Sep-2019 18:00:00,1,0.32857,0.79052,1.2739,1.1871,0.84555,1.6819,1.1873,1.2453,0.34262,3.5146,1.04695,2.36186,2.23076,0.414497,1.74853,0.566171,0.889563,1.30775
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+09-Sep-2019 20:00:00,1,0.34584,0.84257,1.3507,1.2417,0.90243,1.7699,1.2463,1.3346,0.36208,3.6539,1.10099,2.4538,2.32206,0.442828,1.83481,0.600165,0.933372,1.3615
+09-Sep-2019 21:00:00,1,0.33056,0.79651,1.2827,1.1934,0.85207,1.692,1.1942,1.2556,0.34488,3.5309,1.05328,2.37264,2.24134,0.417753,1.75846,0.57009,0.894597,1.31399
+09-Sep-2019 22:00:00,1,0.31517,0.75025,1.2142,1.1447,0.80184,1.6142,1.1412,1.1754,0.32733,3.4027,1.00366,2.28808,2.15911,0.392636,1.68184,0.539778,0.855775,1.26549
+09-Sep-2019 23:00:00,1,0.29175,0.68009,1.1099,1.0707,0.72632,1.4973,1.0594,1.052,0.3002,3.1987,0.924966,2.15373,2.03203,0.354665,1.56572,0.49358,0.797119,1.19031
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+10-Sep-2019 01:00:00,1,0.26778,0.60861,1.003,0.99482,0.65024,1.3794,0.97426,0.92403,0.27187,2.9785,0.840304,2.00883,1.89946,0.316139,1.44752,0.446215,0.737651,1.11162
+10-Sep-2019 02:00:00,1,0.26409,0.59763,0.98649,0.98314,0.63864,1.3614,0.96103,0.90417,0.26745,2.9436,0.826887,1.98584,1.87882,0.310239,1.42939,0.438916,0.728548,1.09935
+10-Sep-2019 03:00:00,1,0.26646,0.60469,0.99709,0.99065,0.6461,1.373,0.96954,0.91695,0.27029,2.9661,0.835527,2.00064,1.8921,0.314032,1.44105,0.44361,0.7344,1.10725
+10-Sep-2019 04:00:00,1,0.27614,0.63351,1.0403,1.0213,0.67664,1.4203,1.0041,0.96888,0.28182,3.0567,0.870328,2.06025,1.94601,0.32954,1.48868,0.46275,0.758331,1.13928
+10-Sep-2019 05:00:00,1,0.30896,0.73162,1.1866,1.1251,0.78171,1.5831,1.1196,1.1429,0.32019,3.3497,0.983175,2.25315,2.12565,0.38254,1.651,0.52754,0.840175,1.24572
+10-Sep-2019 06:00:00,1,0.38036,0.94707,1.5041,1.3507,1.0178,1.9482,1.3621,1.5108,0.4002,3.9164,1.20323,2.62723,2.50105,0.499924,2.00815,0.667994,1.02172,1.46646
+10-Sep-2019 07:00:00,1,0.42628,1.087,1.7078,1.4956,1.1747,2.1904,1.5125,1.7406,0.44937,4.2345,1.32802,2.83787,2.73237,0.576804,2.24055,0.758016,1.14081,1.60133
+10-Sep-2019 08:00:00,1,0.45316,1.1693,1.8269,1.5803,1.2682,2.3346,1.5985,1.8728,0.47739,4.4052,1.39549,2.95118,2.86438,0.62226,2.37746,0.810598,1.21128,1.6779
+10-Sep-2019 09:00:00,1,0.46604,1.2088,1.8839,1.6209,1.3134,2.4043,1.6393,1.9355,0.49062,4.4831,1.42643,3.00298,2.92679,0.644154,2.44328,0.835766,1.24524,1.714
+10-Sep-2019 10:00:00,1,0.48596,1.2702,1.9721,1.6837,1.3838,2.5129,1.7018,2.032,0.51087,4.5989,1.47258,3.08006,3.02232,0.67817,2.5454,0.874673,1.29801,1.76912
+10-Sep-2019 11:00:00,1,0.49903,1.3105,2.0299,1.7249,1.4303,2.5845,1.7424,2.0947,0.52399,4.6718,1.50174,3.12863,3.0843,0.700569,2.61254,0.900169,1.33278,1.80481
+10-Sep-2019 12:00:00,1,0.49695,1.304,2.0207,1.7183,1.4229,2.5731,1.7359,2.0847,0.52191,4.6603,1.49715,3.121,3.07446,0.696995,2.60183,0.896106,1.32723,1.79915
+10-Sep-2019 13:00:00,1,0.49192,1.2885,1.9984,1.7025,1.405,2.5455,1.7203,2.0606,0.51687,4.6324,1.48598,3.1024,3.05064,0.688373,2.57599,0.886298,1.31385,1.78544
+10-Sep-2019 14:00:00,1,0.48068,1.2539,1.9487,1.6671,1.365,2.484,1.6853,2.0065,0.50552,4.5687,1.46053,3.05997,2.9971,0.669129,2.51828,0.864355,1.28398,1.75459
+10-Sep-2019 15:00:00,1,0.47154,1.2258,1.9083,1.6383,1.3327,2.4342,1.6566,1.9623,0.49624,4.5156,1.43938,3.02463,2.9533,0.65353,2.47145,0.846514,1.25978,1.72931
+10-Sep-2019 16:00:00,1,0.45713,1.1815,1.8445,1.5929,1.2821,2.3561,1.6111,1.8922,0.48148,4.4295,1.40513,2.96733,2.88369,0.629006,2.39775,0.818364,1.22174,1.68908
+10-Sep-2019 17:00:00,1,0.45337,1.1699,1.8279,1.581,1.2689,2.3358,1.5992,1.8738,0.47761,4.4065,1.39601,2.95205,2.86543,0.622624,2.37856,0.811017,1.21184,1.6785
+10-Sep-2019 18:00:00,1,0.44934,1.1576,1.81,1.5683,1.2548,2.314,1.5864,1.8541,0.47344,4.3816,1.38614,2.93551,2.84577,0.615781,2.35797,0.803131,1.20123,1.66712
+10-Sep-2019 19:00:00,1,0.4479,1.1531,1.8036,1.5638,1.2498,2.3062,1.5818,1.847,0.47195,4.3726,1.3826,2.92956,2.83873,0.613337,2.35061,0.800312,1.19744,1.66305
+10-Sep-2019 20:00:00,1,0.45085,1.1622,1.8167,1.5731,1.2601,2.3222,1.5912,1.8615,0.475,4.3909,1.38985,2.94173,2.85314,0.618344,2.36568,0.806086,1.20521,1.67139
+10-Sep-2019 21:00:00,1,0.41939,1.0659,1.6773,1.4739,1.1509,2.1537,1.4902,1.7064,0.4421,4.1889,1.31008,2.80766,2.69813,0.565203,2.20555,0.744522,1.12283,1.58142
+10-Sep-2019 22:00:00,1,0.37877,0.94222,1.497,1.3456,1.0124,1.9399,1.3568,1.5027,0.39846,3.9047,1.19866,2.61951,2.49287,0.49727,2.00011,0.664861,1.01761,1.46167
+10-Sep-2019 23:00:00,1,0.34305,0.83414,1.3383,1.2328,0.89319,1.7556,1.2368,1.3202,0.35895,3.6318,1.09238,2.43917,2.30738,0.438234,1.82083,0.594669,0.926267,1.35286
+11-Sep-2019 00:00:00,1,0.31823,0.75943,1.2278,1.1544,0.81178,1.6296,1.1517,1.1914,0.33084,3.4285,1.01364,2.30511,2.17552,0.397614,1.69704,0.545801,0.863468,1.27517
+11-Sep-2019 01:00:00,1,0.30525,0.72049,1.17,1.1134,0.7697,1.5645,1.1067,1.1233,0.3159,3.3176,0.970794,2.23201,2.10556,0.376511,1.63257,0.520216,0.830861,1.23384
+11-Sep-2019 02:00:00,1,0.30054,0.70639,1.1491,1.0985,0.75453,1.541,1.0902,1.0985,0.31045,3.2766,0.954963,2.20498,2.08001,0.368882,1.60924,0.510932,0.819076,1.21873
+11-Sep-2019 03:00:00,1,0.30102,0.70783,1.1512,1.1,0.75607,1.5434,1.0919,1.1011,0.31101,3.2808,0.956581,2.20775,2.08262,0.369658,1.61161,0.511877,0.820274,1.22027
+11-Sep-2019 04:00:00,1,0.30846,0.73012,1.1844,1.1235,0.78009,1.5806,1.1179,1.1402,0.31962,3.3454,0.981512,2.25031,2.12295,0.381728,1.64852,0.526554,0.83892,1.24412
+11-Sep-2019 05:00:00,1,0.33636,0.81398,1.3085,1.2117,0.87113,1.7215,1.214,1.2857,0.35143,3.5781,1.07157,2.40377,2.27209,0.427254,1.78741,0.581507,0.909287,1.3321
+11-Sep-2019 06:00:00,1,0.40494,1.0218,1.6132,1.4283,1.1013,2.0772,1.4431,1.6344,0.42672,4.0909,1.27155,2.74272,2.62578,0.540943,2.13228,0.716202,1.08524,1.53929
+11-Sep-2019 07:00:00,1,0.44395,1.141,1.7861,1.5513,1.236,2.285,1.5692,1.8277,0.46785,4.3479,1.37283,2.91317,2.81944,0.606653,2.33049,0.792596,1.18708,1.65186
+11-Sep-2019 08:00:00,1,0.46796,1.2147,1.8924,1.627,1.3201,2.4147,1.6454,1.9449,0.49259,4.4945,1.43097,3.01058,2.93607,0.647429,2.45312,0.839522,1.25032,1.71936
+11-Sep-2019 09:00:00,1,0.48096,1.2547,1.9499,1.668,1.366,2.4855,1.6862,2.0078,0.50581,4.5703,1.46118,3.06104,2.99844,0.669609,2.51972,0.864903,1.28473,1.75536
+11-Sep-2019 10:00:00,1,0.49765,1.3062,2.0238,1.7206,1.4254,2.577,1.7381,2.0881,0.52262,4.6642,1.49871,3.1236,3.07781,0.69821,2.60547,0.897488,1.32911,1.80107
+11-Sep-2019 11:00:00,1,0.50566,1.331,2.0592,1.7458,1.454,2.6211,1.7629,2.1264,0.53061,4.7079,1.51621,3.1527,3.11556,0.711965,2.64667,0.913102,1.35047,1.82278
+11-Sep-2019 12:00:00,1,0.49958,1.3122,2.0323,1.7266,1.4323,2.5875,1.7441,2.0973,0.52454,4.6748,1.50294,3.13063,3.08688,0.701507,2.61535,0.901233,1.33423,1.80629
+11-Sep-2019 13:00:00,1,0.49141,1.287,1.9962,1.7009,1.4032,2.5427,1.7188,2.0582,0.51635,4.6296,1.48484,3.1005,3.04823,0.687502,2.57338,0.885306,1.31249,1.78405
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+11-Sep-2019 15:00:00,1,0.47642,1.2408,1.9299,1.6537,1.35,2.4608,1.6719,1.9859,0.50121,4.5441,1.45073,3.04361,2.97673,0.661859,2.49646,0.856047,1.2727,1.74284
+11-Sep-2019 16:00:00,1,0.46105,1.1935,1.8618,1.6052,1.2958,2.3773,1.6235,1.9113,0.48551,4.4532,1.41455,2.98311,2.90269,0.635669,2.41778,0.826024,1.23207,1.70007
+11-Sep-2019 17:00:00,1,0.45413,1.1723,1.8312,1.5834,1.2715,2.3398,1.6016,1.8775,0.47839,4.4111,1.39785,2.95513,2.86909,0.623903,2.3824,0.812491,1.21383,1.68063
+11-Sep-2019 18:00:00,1,0.45187,1.1653,1.8212,1.5763,1.2637,2.3277,1.5944,1.8665,0.47606,4.3972,1.39235,2.94592,2.85812,0.620078,2.3709,0.808085,1.2079,1.67428
+11-Sep-2019 19:00:00,1,0.45503,1.175,1.8352,1.5862,1.2747,2.3447,1.6045,1.8819,0.47932,4.4166,1.40003,2.95879,2.87347,0.625431,2.387,0.814249,1.2162,1.68316
+11-Sep-2019 20:00:00,1,0.4421,1.1354,1.7779,1.5455,1.2295,2.275,1.5633,1.8186,0.46592,4.3362,1.3682,2.9054,2.81034,0.603511,2.32103,0.788965,1.18221,1.64659
+11-Sep-2019 21:00:00,1,0.40757,1.0298,1.6248,1.4366,1.1103,2.0911,1.4517,1.6476,0.42954,4.109,1.27866,2.7547,2.63901,0.545353,2.14561,0.72136,1.09208,1.547
+11-Sep-2019 22:00:00,1,0.3722,0.92232,1.4679,1.3249,0.99036,1.9058,1.335,1.4694,0.39128,3.8563,1.17974,2.58745,2.45916,0.486374,1.96708,0.651977,1.00075,1.44194
+11-Sep-2019 23:00:00,1,0.3398,0.82435,1.3238,1.2226,0.88247,1.739,1.2258,1.3035,0.35531,3.6058,1.08231,2.42205,2.29026,0.4329,1.8046,0.588279,0.918017,1.34279
+12-Sep-2019 00:00:00,1,0.31623,0.75344,1.219,1.1481,0.80529,1.6196,1.1448,1.181,0.32855,3.4117,1.00713,2.29402,2.16482,0.394366,1.68712,0.541873,0.858449,1.26886
+12-Sep-2019 01:00:00,1,0.30512,0.72009,1.1695,1.1129,0.76928,1.5638,1.1062,1.1226,0.31575,3.3165,0.970349,2.23126,2.10484,0.376296,1.63191,0.519955,0.830528,1.23342
+12-Sep-2019 02:00:00,1,0.29835,0.69982,1.1393,1.0915,0.74747,1.5301,1.0825,1.0869,0.3079,3.2573,0.94752,2.19227,2.06806,0.365326,1.59836,0.506598,0.813583,1.21165
+12-Sep-2019 03:00:00,1,0.30136,0.70883,1.1527,1.1011,0.75715,1.545,1.0931,1.1028,0.31139,3.2837,0.957708,2.20967,2.08443,0.370199,1.61327,0.512536,0.82111,1.22134
+12-Sep-2019 04:00:00,1,0.31079,0.7371,1.1947,1.1309,0.78762,1.5922,1.1259,1.1524,0.3223,3.3653,0.989225,2.26347,2.13551,0.385507,1.66006,0.531139,0.844758,1.25154
+12-Sep-2019 05:00:00,1,0.33888,0.82156,1.3197,1.2197,0.87942,1.7343,1.2226,1.2987,0.35427,3.5984,1.07943,2.41715,2.28539,0.431383,1.79998,0.586461,0.915672,1.33993
+12-Sep-2019 06:00:00,1,0.40758,1.0299,1.6249,1.4366,1.1104,2.0911,1.4517,1.6476,0.42954,4.1091,1.27868,2.75473,2.63904,0.545364,2.14564,0.721374,1.09209,1.54702
+12-Sep-2019 07:00:00,1,0.45042,1.1609,1.8148,1.5717,1.2586,2.3198,1.5898,1.8594,0.47456,4.3883,1.3888,2.93997,2.85105,0.617618,2.3635,0.805249,1.20408,1.67018
+12-Sep-2019 08:00:00,1,0.46741,1.2131,1.89,1.6253,1.3182,2.4117,1.6436,1.9422,0.49203,4.4912,1.42967,3.00841,2.93341,0.646491,2.4503,0.838446,1.24886,1.71782
+12-Sep-2019 09:00:00,1,0.47104,1.2242,1.9061,1.6367,1.331,2.4315,1.6551,1.9598,0.49574,4.5127,1.43822,3.02269,2.95091,0.652685,2.46891,0.845545,1.25847,1.72793
+12-Sep-2019 10:00:00,1,0.47947,1.2502,1.9434,1.6633,1.3608,2.4774,1.6815,2.0006,0.5043,4.5618,1.45776,3.05535,2.99133,0.667067,2.51209,0.861999,1.28078,1.75126
+12-Sep-2019 11:00:00,1,0.4886,1.2783,1.9838,1.6921,1.3932,2.5273,1.71,2.0447,0.51353,4.6138,1.47854,3.09,3.03489,0.682692,2.55896,0.879828,1.30503,1.77637
+12-Sep-2019 12:00:00,1,0.48674,1.2726,1.9755,1.6862,1.3865,2.5171,1.7042,2.0357,0.51165,4.6033,1.47433,3.08298,3.02601,0.679495,2.54937,0.876184,1.30007,1.77125
+12-Sep-2019 13:00:00,1,0.47882,1.2482,1.9405,1.6612,1.3585,2.4738,1.6795,1.9975,0.50364,4.558,1.45626,3.05284,2.98821,0.665952,2.50874,0.860726,1.27905,1.74946
+12-Sep-2019 14:00:00,1,0.46704,1.2119,1.8884,1.6241,1.3169,2.4097,1.6425,1.9404,0.49165,4.489,1.4288,3.00695,2.93163,0.645862,2.44841,0.837725,1.24789,1.7168
+12-Sep-2019 15:00:00,1,0.45637,1.1791,1.8411,1.5905,1.2794,2.3519,1.6087,1.8885,0.4807,4.4248,1.40329,2.96424,2.87999,0.627712,2.39386,0.816875,1.21973,1.68694
+12-Sep-2019 16:00:00,1,0.44565,1.1463,1.7937,1.5567,1.2419,2.2942,1.5746,1.836,0.46962,4.3586,1.37705,2.92025,2.82775,0.609532,2.33916,0.79592,1.19154,1.65668
+12-Sep-2019 17:00:00,1,0.44713,1.1508,1.8002,1.5613,1.2471,2.3021,1.5793,1.8433,0.47115,4.3678,1.38069,2.92636,2.83496,0.612029,2.34668,0.798803,1.19541,1.66086
+12-Sep-2019 18:00:00,1,0.44707,1.1506,1.7999,1.5611,1.2469,2.3018,1.5791,1.8429,0.47108,4.3674,1.38054,2.92611,2.83466,0.611924,2.34636,0.798681,1.19525,1.66069
+12-Sep-2019 19:00:00,1,0.44929,1.1574,1.8098,1.5682,1.2546,2.3137,1.5862,1.8539,0.47339,4.3813,1.38602,2.93531,2.84553,0.615699,2.35772,0.803036,1.2011,1.66699
+12-Sep-2019 20:00:00,1,0.4488,1.1559,1.8076,1.5666,1.2529,2.3111,1.5846,1.8514,0.47288,4.3782,1.3848,2.93326,2.84311,0.614857,2.35519,0.802065,1.1998,1.66558
+12-Sep-2019 21:00:00,1,0.41461,1.0513,1.6561,1.4588,1.1345,2.1284,1.4746,1.6827,0.43703,4.1569,1.29747,2.78642,2.67427,0.557166,2.18129,0.735155,1.11038,1.56754
+12-Sep-2019 22:00:00,1,0.37654,0.93547,1.4871,1.3386,1.005,1.9283,1.3495,1.4915,0.39603,3.8884,1.19228,2.6087,2.48147,0.493576,1.98891,0.660496,1.0119,1.455
+12-Sep-2019 23:00:00,1,0.33995,0.8248,1.3245,1.223,0.88296,1.7398,1.2263,1.3042,0.35547,3.607,1.08277,2.42283,2.29105,0.433144,1.80534,0.588571,0.918395,1.34326
+13-Sep-2019 00:00:00,1,0.31707,0.75596,1.2227,1.1508,0.80803,1.6238,1.1477,1.1854,0.32952,3.4188,1.00988,2.2987,2.16933,0.395735,1.6913,0.543529,0.860564,1.27152
+13-Sep-2019 01:00:00,1,0.3038,0.71614,1.1636,1.1088,0.76502,1.5572,1.1016,1.1157,0.31422,3.305,0.965924,2.2237,2.09769,0.374155,1.62537,0.517351,0.827221,1.22918
+13-Sep-2019 02:00:00,1,0.2972,0.69638,1.1342,1.0879,0.74377,1.5243,1.0785,1.0808,0.30656,3.2471,0.943608,2.18559,2.06178,0.363464,1.59266,0.504327,0.810708,1.20794
+13-Sep-2019 03:00:00,1,0.29885,0.70133,1.1415,1.0931,0.74909,1.5326,1.0843,1.0896,0.30848,3.2617,0.949229,2.19519,2.0708,0.366141,1.60085,0.507591,0.814841,1.21327
+13-Sep-2019 04:00:00,1,0.3061,0.72305,1.1738,1.1161,0.77246,1.5688,1.1096,1.1278,0.31689,3.325,0.973647,2.23688,2.11018,0.377896,1.63681,0.521899,0.833,1.23657
+13-Sep-2019 05:00:00,1,0.33388,0.80652,1.2975,1.2039,0.86299,1.7089,1.2056,1.2729,0.34864,3.5581,1.06379,2.39054,2.25899,0.423198,1.77505,0.576637,0.903016,1.32438
+13-Sep-2019 06:00:00,1,0.40008,1.007,1.5916,1.4129,1.0847,2.0515,1.4272,1.6101,0.42152,4.0572,1.25832,2.72038,2.60128,0.532804,2.10768,0.70667,1.07264,1.525
+13-Sep-2019 07:00:00,1,0.4409,1.1317,1.7726,1.5417,1.2254,2.2686,1.5594,1.8127,0.46468,4.3287,1.36521,2.90039,2.80448,0.601489,2.31494,0.786627,1.17907,1.64319
+13-Sep-2019 08:00:00,1,0.46184,1.1959,1.8654,1.6077,1.2986,2.3815,1.626,1.9151,0.48633,4.458,1.41645,2.98628,2.90653,0.637015,2.42183,0.827571,1.23416,1.70228
+13-Sep-2019 09:00:00,1,0.47259,1.229,1.9129,1.6416,1.3364,2.4399,1.6599,1.9673,0.49731,4.5218,1.44183,3.02873,2.95834,0.655317,2.47681,0.84856,1.26255,1.73222
+13-Sep-2019 10:00:00,1,0.48368,1.2631,1.962,1.6766,1.3757,2.5004,1.6947,2.021,0.50857,4.5859,1.4674,3.07143,3.01146,0.67427,2.5337,0.870224,1.29196,1.76286
+13-Sep-2019 11:00:00,1,0.49342,1.2932,2.0051,1.7072,1.4103,2.5537,1.725,2.0678,0.51837,4.6408,1.48932,3.10796,3.05775,0.69094,2.58369,0.88922,1.31783,1.78953
+13-Sep-2019 12:00:00,1,0.49006,1.2828,1.9902,1.6967,1.3984,2.5353,1.7146,2.0517,0.515,4.622,1.48182,3.09547,3.04183,0.685192,2.56645,0.882676,1.30891,1.78036
+13-Sep-2019 13:00:00,1,0.48052,1.2534,1.948,1.6666,1.3645,2.4831,1.6848,2.0057,0.50537,4.5678,1.46018,3.05938,2.99636,0.668865,2.51748,0.864054,1.28357,1.75416
+13-Sep-2019 14:00:00,1,0.46969,1.2201,1.9001,1.6325,1.3262,2.4241,1.6508,1.9533,0.49436,4.5047,1.43504,3.01739,2.9444,0.650376,2.46198,0.8429,1.25489,1.72417
+13-Sep-2019 15:00:00,1,0.46242,1.1977,1.8679,1.6095,1.3006,2.3847,1.6279,1.918,0.48692,4.4614,1.41783,2.98859,2.90932,0.637996,2.42478,0.828698,1.23569,1.7039
+13-Sep-2019 16:00:00,1,0.44957,1.1583,1.811,1.569,1.2556,2.3152,1.5871,1.8552,0.47368,4.383,1.3867,2.93645,2.84688,0.616167,2.35913,0.803576,1.20183,1.66777
+13-Sep-2019 17:00:00,1,0.44664,1.1493,1.798,1.5598,1.2454,2.2995,1.5778,1.8409,0.47064,4.3648,1.37948,2.92434,2.83257,0.6112,2.34418,0.797846,1.19413,1.65948
+13-Sep-2019 18:00:00,1,0.44439,1.1424,1.788,1.5527,1.2375,2.2873,1.5706,1.8298,0.4683,4.3507,1.3739,2.91498,2.82156,0.607387,2.3327,0.793443,1.18822,1.65309
+13-Sep-2019 19:00:00,1,0.44794,1.1533,1.8038,1.5639,1.2499,2.3065,1.5819,1.8472,0.47199,4.3729,1.3827,2.92973,2.83893,0.613407,2.35083,0.800393,1.19755,1.66316
+13-Sep-2019 20:00:00,1,0.44611,1.1477,1.7957,1.5581,1.2435,2.2966,1.5761,1.8383,0.47009,4.3614,1.37817,2.92214,2.82998,0.610302,2.34148,0.796809,1.19274,1.65797
+13-Sep-2019 21:00:00,1,0.41625,1.0563,1.6633,1.4639,1.1401,2.137,1.48,1.6908,0.43877,4.1679,1.3018,2.79372,2.68245,0.559917,2.1896,0.738363,1.11464,1.5723
+13-Sep-2019 22:00:00,1,0.3782,0.94049,1.4945,1.3438,1.0105,1.937,1.355,1.4998,0.39784,3.9006,1.19703,2.61674,2.48995,0.496324,1.99724,0.663743,1.01615,1.45997
+13-Sep-2019 23:00:00,1,0.34202,0.83104,1.3337,1.2296,0.88979,1.7503,1.2333,1.3149,0.3578,3.6236,1.0892,2.43376,2.30196,0.436543,1.81568,0.592644,0.923651,1.34967
+14-Sep-2019 00:00:00,1,0.31959,0.76353,1.2339,1.1587,0.81622,1.6365,1.1564,1.1985,0.3324,3.44,1.01808,2.31267,2.18284,0.399838,1.70383,0.54849,0.866906,1.27949
+14-Sep-2019 01:00:00,1,0.30564,0.72167,1.1718,1.1146,0.77097,1.5664,1.108,1.1254,0.31636,3.321,0.972104,2.23425,2.10768,0.377147,1.63452,0.520989,0.831843,1.2351
+14-Sep-2019 02:00:00,1,0.29738,0.69692,1.135,1.0885,0.74436,1.5252,1.0791,1.0818,0.30677,3.2487,0.944229,2.18665,2.06278,0.363759,1.59356,0.504687,0.811163,1.20853
+14-Sep-2019 03:00:00,1,0.29554,0.69143,1.1268,1.0827,0.73847,1.5161,1.0727,1.0721,0.30463,3.2325,0.937975,2.17596,2.05277,0.360793,1.58448,0.501067,0.806582,1.2026
+14-Sep-2019 04:00:00,1,0.30554,0.72136,1.1713,1.1143,0.77064,1.5659,1.1077,1.1249,0.31624,3.3201,0.971766,2.23367,2.10714,0.376983,1.63401,0.52079,0.83159,1.23477
+14-Sep-2019 05:00:00,1,0.33264,0.80278,1.292,1.2,0.85891,1.7026,1.2013,1.2664,0.34723,3.548,1.05987,2.38387,2.2524,0.421163,1.76885,0.574191,0.89987,1.3205
+14-Sep-2019 06:00:00,1,0.39141,0.98062,1.5531,1.3855,1.0552,2.006,1.3987,1.5665,0.41218,3.9961,1.23438,2.67993,2.55738,0.518322,2.06387,0.689668,1.05021,1.49938
+14-Sep-2019 07:00:00,1,0.42767,1.0912,1.714,1.5,1.1795,2.1978,1.5169,1.7475,0.45083,4.2436,1.33161,2.84391,2.73926,0.579146,2.24761,0.760737,1.14444,1.60533
+14-Sep-2019 08:00:00,1,0.44962,1.1584,1.8112,1.5692,1.2558,2.3155,1.5873,1.8554,0.47373,4.3833,1.38682,2.93665,2.84712,0.616249,2.35938,0.80367,1.20196,1.6679
+14-Sep-2019 09:00:00,1,0.45856,1.1859,1.8508,1.5974,1.2871,2.3638,1.6157,1.8991,0.48295,4.4381,1.40857,2.9731,2.89062,0.631433,2.40505,0.821155,1.22551,1.69309
+14-Sep-2019 10:00:00,1,0.47211,1.2275,1.9108,1.6401,1.3347,2.4373,1.6584,1.965,0.49682,4.519,1.4407,3.02684,2.95602,0.654495,2.47435,0.847619,1.26128,1.73088
+14-Sep-2019 11:00:00,1,0.47574,1.2387,1.9269,1.6515,1.3476,2.4571,1.6698,1.9826,0.50052,4.5402,1.44916,3.04098,2.97348,0.6607,2.49298,0.85472,1.2709,1.74096
+14-Sep-2019 12:00:00,1,0.46614,1.2091,1.8843,1.6212,1.3137,2.4048,1.6396,1.936,0.49072,4.4837,1.42666,3.00337,2.92726,0.64432,2.44378,0.835956,1.24549,1.71427
+14-Sep-2019 13:00:00,1,0.45326,1.1696,1.8274,1.5807,1.2685,2.3352,1.5989,1.8733,0.4775,4.4058,1.39574,2.9516,2.86489,0.622436,2.37799,0.810801,1.21155,1.67819
+14-Sep-2019 14:00:00,1,0.43756,1.1215,1.7578,1.5312,1.2138,2.2507,1.5487,1.7963,0.46119,4.3075,1.35682,2.88629,2.78806,0.595839,2.29793,0.780091,1.17031,1.63367
+14-Sep-2019 15:00:00,1,0.42761,1.091,1.7137,1.4998,1.1793,2.1975,1.5167,1.7472,0.45077,4.2432,1.33145,2.84364,2.73895,0.579043,2.2473,0.760617,1.14428,1.60515
+14-Sep-2019 16:00:00,1,0.41562,1.0544,1.6605,1.4619,1.1379,2.1337,1.4779,1.6877,0.4381,4.1637,1.30014,2.79091,2.67931,0.558859,2.1864,0.73713,1.113,1.57047
+14-Sep-2019 17:00:00,1,0.40994,1.0371,1.6353,1.444,1.1184,2.1036,1.4594,1.6594,0.43206,4.1252,1.28501,2.76542,2.65088,0.54932,2.1576,0.725997,1.09822,1.55392
+14-Sep-2019 18:00:00,1,0.40714,1.0285,1.6229,1.4352,1.1089,2.0888,1.4503,1.6454,0.42908,4.1061,1.2775,2.75275,2.63684,0.54463,2.14343,0.720516,1.09096,1.54574
+14-Sep-2019 19:00:00,1,0.41008,1.0375,1.636,1.4445,1.1189,2.1043,1.4599,1.6601,0.43221,4.1261,1.28539,2.76606,2.65159,0.549558,2.15831,0.726275,1.09859,1.55433
+14-Sep-2019 20:00:00,1,0.40506,1.0222,1.6137,1.4286,1.1017,2.0778,1.4435,1.635,0.42685,4.0917,1.27188,2.74327,2.62639,0.541146,2.1329,0.716439,1.08556,1.53965
+14-Sep-2019 21:00:00,1,0.37565,0.93277,1.4832,1.3358,1.0019,1.9237,1.3465,1.4869,0.39506,3.8818,1.18971,2.60435,2.47688,0.492094,1.98442,0.658744,1.0096,1.45232
+14-Sep-2019 22:00:00,1,0.34492,0.83979,1.3466,1.2388,0.89938,1.7652,1.2432,1.3299,0.36105,3.6466,1.09816,2.44899,2.31723,0.441313,1.8302,0.598353,0.931029,1.35865
+14-Sep-2019 23:00:00,1,0.31572,0.75189,1.2167,1.1465,0.80362,1.617,1.143,1.1783,0.32796,3.4073,1.00545,2.29114,2.16205,0.393527,1.68456,0.540857,0.857151,1.26722
+15-Sep-2019 00:00:00,1,0.29466,0.68879,1.1229,1.0799,0.73563,1.5117,1.0696,1.0674,0.3036,3.2246,0.934949,2.17079,2.04794,0.359363,1.58011,0.499321,0.804373,1.19974
+15-Sep-2019 01:00:00,1,0.2823,0.65187,1.0678,1.0408,0.69618,1.4506,1.026,1.0018,0.2891,3.1133,0.892101,2.09752,1.98008,0.339436,1.51905,0.474918,0.773606,1.15951
+15-Sep-2019 02:00:00,1,0.27465,0.62906,1.0336,1.0166,0.67192,1.413,0.99881,0.9609,0.28005,3.0429,0.86501,2.05115,1.93772,0.327146,1.48133,0.4598,0.754636,1.13436
+15-Sep-2019 03:00:00,1,0.27216,0.62166,1.0225,1.0087,0.66406,1.4008,0.98994,0.94758,0.27709,3.0197,0.856114,2.03591,1.92391,0.323161,1.4691,0.454887,0.748486,1.12616
+15-Sep-2019 04:00:00,1,0.27368,0.62619,1.0293,1.0135,0.66887,1.4083,0.99537,0.95573,0.2789,3.0339,0.861561,2.04524,1.93236,0.325598,1.47658,0.457893,0.752248,1.13118
+15-Sep-2019 05:00:00,1,0.27736,0.63715,1.0457,1.0252,0.68051,1.4263,1.0085,0.97541,0.28327,3.068,0.874667,2.06768,1.95277,0.3315,1.4947,0.465162,0.761357,1.1433
+15-Sep-2019 06:00:00,1,0.29077,0.67717,1.1055,1.0676,0.72319,1.4925,1.0559,1.0468,0.29906,3.19,0.9216,2.14797,2.02668,0.353089,1.56089,0.491652,0.794685,1.18714
+15-Sep-2019 07:00:00,1,0.30957,0.73345,1.1893,1.127,0.78368,1.5861,1.1217,1.146,0.32089,3.3549,0.985193,2.25659,2.12894,0.383529,1.65402,0.528739,0.841702,1.24766
+15-Sep-2019 08:00:00,1,0.33606,0.81308,1.3072,1.2108,0.87015,1.7199,1.213,1.2841,0.3511,3.5757,1.07064,2.40218,2.27052,0.426768,1.78592,0.580923,0.908535,1.33117
+15-Sep-2019 09:00:00,1,0.35988,0.88501,1.4131,1.286,0.94911,1.842,1.2937,1.4067,0.37771,3.7632,1.14349,2.52598,2.39543,0.46598,1.90518,0.627775,0.969188,1.40458
+15-Sep-2019 10:00:00,1,0.375,0.93078,1.4803,1.3337,0.99975,1.9203,1.3443,1.4836,0.39434,3.877,1.18782,2.60115,2.47352,0.491008,1.98113,0.657459,1.00792,1.45035
+15-Sep-2019 11:00:00,1,0.38005,0.94612,1.5027,1.3497,1.0168,1.9466,1.3611,1.5092,0.39986,3.9141,1.20234,2.62573,2.49946,0.499406,2.00658,0.667383,1.02092,1.46553
+15-Sep-2019 12:00:00,1,0.37564,0.93273,1.4831,1.3358,1.0019,1.9236,1.3464,1.4869,0.39504,3.8817,1.18967,2.60428,2.47682,0.492072,1.98435,0.658718,1.00957,1.45228
+15-Sep-2019 13:00:00,1,0.35971,0.88447,1.4123,1.2855,0.94852,1.8411,1.2931,1.4058,0.37752,3.7619,1.14296,2.52509,2.39451,0.465688,1.90429,0.627428,0.968736,1.40404
+15-Sep-2019 14:00:00,1,0.34748,0.84753,1.358,1.2468,0.90787,1.7783,1.2519,1.3431,0.36392,3.6669,1.10602,2.46236,2.33068,0.44553,1.84303,0.603395,0.937551,1.36656
+15-Sep-2019 15:00:00,1,0.34104,0.82809,1.3294,1.2265,0.88656,1.7453,1.23,1.3099,0.3567,3.6158,1.08617,2.42861,2.29681,0.434938,1.8108,0.590721,0.92117,1.34665
+15-Sep-2019 16:00:00,1,0.34216,0.83147,1.3343,1.23,0.89026,1.751,1.2338,1.3156,0.35796,3.6247,1.08964,2.43451,2.30271,0.436777,1.8164,0.592924,0.924013,1.35011
+15-Sep-2019 17:00:00,1,0.35213,0.86158,1.3787,1.2615,0.9233,1.8021,1.2676,1.367,0.36911,3.7034,1.12019,2.48643,2.35503,0.453189,1.86632,0.61254,0.949399,1.38087
+15-Sep-2019 18:00:00,1,0.35996,0.88525,1.4135,1.2863,0.94937,1.8424,1.294,1.4071,0.3778,3.7638,1.14372,2.52638,2.39583,0.46611,1.90557,0.62793,0.969388,1.40482
+15-Sep-2019 19:00:00,1,0.36543,0.9018,1.4378,1.3035,0.96765,1.8707,1.3124,1.435,0.38384,3.8055,1.15993,2.55387,2.42419,0.475153,1.93303,0.638674,0.983382,1.42145
+15-Sep-2019 20:00:00,1,0.36229,0.8923,1.4238,1.2936,0.95716,1.8545,1.3018,1.419,0.38038,3.7817,1.15066,2.53814,2.40794,0.469964,1.91728,0.632512,0.975353,1.41192
+15-Sep-2019 21:00:00,1,0.33843,0.82022,1.3177,1.2182,0.87795,1.732,1.2211,1.2964,0.35377,3.5948,1.07804,2.41479,2.28304,0.430652,1.79775,0.585584,0.914541,1.33854
+15-Sep-2019 22:00:00,1,0.31955,0.76341,1.2337,1.1586,0.8161,1.6363,1.1563,1.1983,0.33235,3.4397,1.01795,2.31246,2.18263,0.399776,1.70364,0.548415,0.866809,1.27937
+15-Sep-2019 23:00:00,1,0.29527,0.69061,1.1256,1.0818,0.73758,1.5147,1.0717,1.0706,0.30431,3.23,0.937031,2.17435,2.05126,0.360346,1.58312,0.500522,0.805892,1.20171
+16-Sep-2019 00:00:00,1,0.27508,0.63035,1.0356,1.0179,0.67329,1.4151,1.0004,0.96322,0.28056,3.0469,0.866556,2.05379,1.94013,0.327841,1.48347,0.460657,0.755709,1.13579
+16-Sep-2019 01:00:00,1,0.26047,0.58689,0.97036,0.9717,0.62732,1.3438,0.94804,0.88468,0.26312,2.909,0.813648,1.96314,1.85855,0.304471,1.41165,0.431767,0.71965,1.08728
+16-Sep-2019 02:00:00,1,0.2542,0.56827,0.94235,0.95184,0.60773,1.3135,0.9254,0.85075,0.25556,2.8485,0.790428,1.92332,1.82321,0.294478,1.38089,0.419353,0.704236,1.06624
+16-Sep-2019 03:00:00,1,0.24973,0.55502,0.92239,0.93768,0.59382,1.2919,0.90921,0.82649,0.25014,2.8048,0.773692,1.8946,1.79792,0.287371,1.359,0.410502,0.693278,1.05116
+16-Sep-2019 04:00:00,1,0.25025,0.55657,0.92472,0.93934,0.59545,1.2944,0.91111,0.82934,0.25078,2.8099,0.775658,1.89797,1.80088,0.288202,1.36156,0.411538,0.694559,1.05293
+16-Sep-2019 05:00:00,1,0.25384,0.5672,0.94075,0.9507,0.60661,1.3117,0.9241,0.8488,0.25512,2.845,0.789087,1.92102,1.82118,0.293905,1.37913,0.41864,0.703353,1.06503
+16-Sep-2019 06:00:00,1,0.25841,0.58078,0.96118,0.96519,0.62088,1.3339,0.94063,0.87357,0.26064,2.8893,0.806066,1.95014,1.84698,0.30119,1.40155,0.427696,0.71459,1.08039
+16-Sep-2019 07:00:00,1,0.27,0.61523,1.0129,1.0019,0.65725,1.3903,0.98222,0.93599,0.27452,2.9995,0.848344,2.0226,1.91187,0.3197,1.45847,0.450615,0.743146,1.119
+16-Sep-2019 08:00:00,1,0.29549,0.69126,1.1265,1.0825,0.73829,1.5158,1.0725,1.0718,0.30457,3.232,0.937782,2.17563,2.05246,0.360702,1.5842,0.500956,0.806441,1.20242
+16-Sep-2019 09:00:00,1,0.31954,0.76337,1.2337,1.1586,0.81606,1.6362,1.1563,1.1982,0.33234,3.4396,1.01791,2.31239,2.18256,0.399755,1.70357,0.54839,0.866777,1.27933
+16-Sep-2019 10:00:00,1,0.3378,0.81831,1.3149,1.2162,0.87587,1.7288,1.2189,1.2931,0.35305,3.5897,1.07607,2.41143,2.27969,0.429614,1.79459,0.584339,0.912936,1.33657
+16-Sep-2019 11:00:00,1,0.35228,0.86203,1.3793,1.262,0.9238,1.8029,1.2681,1.3677,0.36928,3.7045,1.12064,2.4872,2.35581,0.453436,1.86707,0.612835,0.949781,1.38133
+16-Sep-2019 12:00:00,1,0.34513,0.84044,1.3476,1.2394,0.90009,1.7663,1.2439,1.331,0.36129,3.6483,1.09882,2.45011,2.31835,0.441665,1.83127,0.598774,0.931573,1.35931
+16-Sep-2019 13:00:00,1,0.32951,0.79336,1.2781,1.1901,0.84864,1.6867,1.1906,1.2502,0.34369,3.5224,1.04995,2.36698,2.23578,0.41604,1.75324,0.568029,0.891949,1.31071
+16-Sep-2019 14:00:00,1,0.31661,0.75457,1.2206,1.1493,0.80651,1.6215,1.1461,1.1829,0.32898,3.4149,1.00836,2.29611,2.16684,0.394978,1.68899,0.542613,0.859394,1.27005
+16-Sep-2019 15:00:00,1,0.30965,0.7337,1.1897,1.1273,0.78394,1.5865,1.122,1.1465,0.32099,3.3556,0.985466,2.25706,2.12938,0.383662,1.65443,0.528902,0.841909,1.24792
+16-Sep-2019 16:00:00,1,0.30767,0.72773,1.1808,1.121,0.77751,1.5766,1.1151,1.136,0.3187,3.3385,0.978858,2.24578,2.11864,0.380432,1.64456,0.524981,0.836918,1.24157
+16-Sep-2019 17:00:00,1,0.31662,0.75461,1.2207,1.1493,0.80655,1.6215,1.1462,1.183,0.329,3.415,1.0084,2.29618,2.16691,0.394999,1.68905,0.542638,0.859426,1.27009
+16-Sep-2019 18:00:00,1,0.3287,0.7909,1.2744,1.1875,0.84597,1.6825,1.1878,1.2459,0.34276,3.5157,1.04736,2.36256,2.23144,0.414707,1.74917,0.566424,0.889888,1.30816
+16-Sep-2019 19:00:00,1,0.34357,0.83571,1.3406,1.2345,0.8949,1.7582,1.2386,1.3229,0.35953,3.6359,1.09398,2.44189,2.3101,0.439086,1.82342,0.595688,0.927584,1.35446
+16-Sep-2019 20:00:00,1,0.35512,0.8706,1.392,1.271,0.93323,1.8175,1.2777,1.3823,0.37243,3.7266,1.12921,2.50175,2.37062,0.458113,1.88128,0.618411,0.957017,1.39002
+16-Sep-2019 21:00:00,1,0.3366,0.81471,1.3096,1.2125,0.87194,1.7227,1.2149,1.2869,0.35171,3.5801,1.07233,2.40507,2.27338,0.427656,1.78863,0.581989,0.909908,1.33286
+16-Sep-2019 22:00:00,1,0.32043,0.76606,1.2377,1.1614,0.81897,1.6407,1.1593,1.2029,0.33336,3.4471,1.02081,2.31733,2.18735,0.401212,1.70802,0.55015,0.869028,1.28215
+16-Sep-2019 23:00:00,1,0.29275,0.68309,1.1144,1.0738,0.72952,1.5023,1.0629,1.0573,0.30137,3.2077,0.928413,2.15962,2.03751,0.356283,1.57068,0.495558,0.799618,1.19356
+17-Sep-2019 00:00:00,1,0.27695,0.63593,1.0439,1.0239,0.67921,1.4243,1.007,0.97323,0.28278,3.0642,0.873214,2.06519,1.95051,0.330843,1.49268,0.464354,0.760343,1.14196
+17-Sep-2019 01:00:00,1,0.26781,0.60872,1.0031,0.99494,0.65036,1.3796,0.9744,0.92423,0.27191,2.9789,0.840438,2.00906,1.89967,0.316198,1.4477,0.446288,0.737742,1.11175
+17-Sep-2019 02:00:00,1,0.26302,0.59445,0.98173,0.97976,0.63529,1.3562,0.95719,0.89841,0.26617,2.9334,0.822983,1.97915,1.87283,0.308533,1.42414,0.436802,0.725916,1.09578
+17-Sep-2019 03:00:00,1,0.26456,0.59906,0.98863,0.98465,0.64015,1.3637,0.96275,0.90675,0.26803,2.9481,0.828635,1.98883,1.8815,0.311005,1.43174,0.439864,0.729729,1.10094
+17-Sep-2019 04:00:00,1,0.27499,0.63008,1.0351,1.0176,0.67299,1.4147,1,0.96272,0.28045,3.046,0.866225,2.05323,1.93961,0.327692,1.48301,0.460473,0.755479,1.13549
+17-Sep-2019 05:00:00,1,0.30734,0.72675,1.1793,1.12,0.77645,1.5749,1.1139,1.1343,0.31832,3.3357,0.977762,2.24391,2.11686,0.379898,1.64293,0.524332,0.836093,1.24052
+17-Sep-2019 06:00:00,1,0.38266,0.95403,1.5143,1.3579,1.0256,1.9602,1.3698,1.5224,0.4027,3.9331,1.20976,2.63829,2.51279,0.503741,2.01972,0.672499,1.02763,1.47332
+17-Sep-2019 07:00:00,1,0.43069,1.1005,1.7274,1.5095,1.19,2.214,1.5267,1.7624,0.45401,4.2633,1.33937,2.85696,2.7542,0.584242,2.26298,0.766652,1.15234,1.61401
+17-Sep-2019 08:00:00,1,0.45653,1.1796,1.8418,1.591,1.28,2.3528,1.6092,1.8892,0.48086,4.4258,1.40367,2.96489,2.88077,0.627982,2.39467,0.817186,1.22015,1.68738
+17-Sep-2019 09:00:00,1,0.46993,1.2208,1.9011,1.6332,1.3271,2.4254,1.6515,1.9544,0.4946,4.5061,1.4356,3.01832,2.94554,0.650781,2.46319,0.843364,1.25552,1.72483
+17-Sep-2019 10:00:00,1,0.48473,1.2664,1.9666,1.6799,1.3794,2.5061,1.6979,2.026,0.50962,4.5919,1.46978,3.0754,3.01645,0.676062,2.53908,0.872269,1.29474,1.76574
+17-Sep-2019 11:00:00,1,0.49556,1.2998,2.0145,1.714,1.4179,2.5655,1.7316,2.0781,0.52052,4.6526,1.49408,3.11589,3.06789,0.694614,2.5947,0.8934,1.32353,1.79537
+17-Sep-2019 12:00:00,1,0.49246,1.2902,2.0008,1.7042,1.4069,2.5484,1.722,2.0632,0.5174,4.6354,1.48718,3.10439,3.05319,0.689293,2.57875,0.887346,1.31527,1.7869
+17-Sep-2019 13:00:00,1,0.48673,1.2725,1.9755,1.6862,1.3865,2.5171,1.7042,2.0356,0.51164,4.6032,1.47431,3.08295,3.02598,0.679483,2.54934,0.876171,1.30005,1.77123
+17-Sep-2019 14:00:00,1,0.47549,1.2379,1.9257,1.6507,1.3467,2.4557,1.669,1.9814,0.50026,4.5387,1.44858,3.04001,2.97227,0.66027,2.49168,0.854228,1.27024,1.74026
+17-Sep-2019 15:00:00,1,0.46604,1.2089,1.8839,1.621,1.3134,2.4043,1.6393,1.9356,0.49063,4.4831,1.42644,3.00301,2.92683,0.644165,2.44332,0.835779,1.24525,1.71402
+17-Sep-2019 16:00:00,1,0.45196,1.1656,1.8216,1.5766,1.264,2.3281,1.5947,1.8669,0.47615,4.3978,1.39256,2.94626,2.85853,0.620219,2.37132,0.808247,1.20811,1.67451
+17-Sep-2019 17:00:00,1,0.44601,1.1473,1.7952,1.5578,1.2432,2.296,1.5757,1.8377,0.46998,4.3608,1.37792,2.92171,2.82947,0.610127,2.34095,0.796607,1.19246,1.65768
+17-Sep-2019 18:00:00,1,0.44667,1.1494,1.7982,1.5599,1.2455,2.2996,1.5779,1.841,0.47068,4.365,1.37957,2.92448,2.83274,0.611258,2.34436,0.797914,1.19422,1.65957
+17-Sep-2019 19:00:00,1,0.45706,1.1813,1.8442,1.5927,1.2818,2.3557,1.6109,1.8918,0.48141,4.429,1.40496,2.96705,2.88336,0.628889,2.3974,0.818229,1.22156,1.68888
+17-Sep-2019 20:00:00,1,0.45677,1.1803,1.8429,1.5917,1.2808,2.3541,1.61,1.8904,0.48111,4.4272,1.40424,2.96584,2.88191,0.628382,2.39588,0.817646,1.22077,1.68805
+17-Sep-2019 21:00:00,1,0.42097,1.0707,1.6843,1.4788,1.1564,2.1621,1.4953,1.7143,0.44377,4.1994,1.31422,2.81464,2.706,0.567863,2.21358,0.747618,1.12695,1.586
+17-Sep-2019 22:00:00,1,0.3836,0.9569,1.5185,1.3609,1.0288,1.9651,1.3729,1.5272,0.40372,3.94,1.21244,2.64282,2.51761,0.50531,2.02447,0.674349,1.03006,1.47614
+17-Sep-2019 23:00:00,1,0.34881,0.85153,1.3639,1.251,0.91226,1.7851,1.2564,1.3499,0.3654,3.6773,1.11008,2.46925,2.33763,0.447712,1.84966,0.606002,0.940926,1.37065
+18-Sep-2019 00:00:00,1,0.32466,0.77876,1.2565,1.1747,0.83276,1.6621,1.1739,1.2249,0.33818,3.4823,1.03444,2.34056,2.20993,0.408107,1.72905,0.558472,0.879686,1.29547
+18-Sep-2019 01:00:00,1,0.31288,0.74336,1.204,1.1375,0.79439,1.6027,1.1332,1.1634,0.3247,3.3832,0.996117,2.27523,2.14676,0.388902,1.67044,0.535256,0.850005,1.25819
+18-Sep-2019 02:00:00,1,0.30283,0.71323,1.1592,1.1057,0.76188,1.5524,1.0982,1.1106,0.3131,3.2965,0.962658,2.21812,2.09241,0.37258,1.62055,0.515434,0.824787,1.22606
+18-Sep-2019 03:00:00,1,0.30231,0.71168,1.1569,1.1041,0.76022,1.5498,1.0964,1.1078,0.3125,3.292,0.960917,2.21515,2.0896,0.371742,1.61799,0.514414,0.823493,1.2244
+18-Sep-2019 04:00:00,1,0.30929,0.73261,1.1881,1.1262,0.78277,1.5847,1.1207,1.1446,0.32057,3.3525,0.984268,2.25501,2.12743,0.383076,1.65264,0.52819,0.841002,1.24677
+18-Sep-2019 05:00:00,1,0.33771,0.81806,1.3146,1.216,0.87559,1.7284,1.2186,1.2927,0.35296,3.5891,1.07581,2.41098,2.27925,0.429476,1.79417,0.584174,0.912723,1.33631
+18-Sep-2019 06:00:00,1,0.40235,1.0139,1.6017,1.4201,1.0925,2.0635,1.4346,1.6215,0.42395,4.073,1.26453,2.73086,2.61275,0.53661,2.11919,0.71113,1.07853,1.5317
+18-Sep-2019 07:00:00,1,0.44193,1.1348,1.7771,1.5449,1.2289,2.2741,1.5627,1.8177,0.46574,4.3352,1.36777,2.90468,2.8095,0.60322,2.32016,0.788629,1.18176,1.6461
+18-Sep-2019 08:00:00,1,0.4595,1.1887,1.855,1.6003,1.2904,2.3688,1.6186,1.9037,0.48392,4.4438,1.41082,2.97687,2.89516,0.633025,2.40983,0.822985,1.22797,1.69571
+18-Sep-2019 09:00:00,1,0.47406,1.2335,1.9194,1.6462,1.3416,2.4479,1.6645,1.9744,0.4988,4.5304,1.44525,3.03444,2.96539,0.657822,2.48434,0.851427,1.26644,1.73629
+18-Sep-2019 10:00:00,1,0.49094,1.2855,1.9941,1.6994,1.4015,2.5401,1.7173,2.0559,0.51588,4.6269,1.48378,3.09874,3.04599,0.686691,2.57095,0.884384,1.31124,1.78276
+18-Sep-2019 11:00:00,1,0.50396,1.3257,2.0517,1.7404,1.4479,2.6117,1.7576,2.1183,0.52892,4.6987,1.51253,3.14657,3.10756,0.709043,2.63792,0.909789,1.34593,1.81818
+18-Sep-2019 12:00:00,1,0.49758,1.306,2.0235,1.7203,1.4252,2.5766,1.7379,2.0878,0.52255,4.6638,1.49856,3.12334,3.07747,0.698088,2.60511,0.897349,1.32893,1.80088
+18-Sep-2019 13:00:00,1,0.49082,1.2851,1.9936,1.699,1.4011,2.5395,1.7169,2.0553,0.51576,4.6263,1.48352,3.09829,3.04542,0.686486,2.57033,0.88415,1.31092,1.78243
+18-Sep-2019 14:00:00,1,0.48043,1.2531,1.9476,1.6663,1.3642,2.4826,1.6845,2.0053,0.50528,4.5673,1.45997,3.05903,2.99593,0.668709,2.51702,0.863876,1.28333,1.75391
+18-Sep-2019 15:00:00,1,0.46866,1.2169,1.8955,1.6292,1.3226,2.4186,1.6476,1.9483,0.49331,4.4987,1.43263,3.01335,2.93946,0.648629,2.45673,0.840898,1.25218,1.72132
+18-Sep-2019 16:00:00,1,0.45127,1.1635,1.8185,1.5744,1.2615,2.3244,1.5925,1.8635,0.47543,4.3935,1.39087,2.94343,2.85516,0.619047,2.3678,0.806896,1.2063,1.67256
+18-Sep-2019 17:00:00,1,0.44813,1.1539,1.8046,1.5645,1.2506,2.3075,1.5825,1.8482,0.47219,4.3741,1.38317,2.93053,2.83988,0.613734,2.35181,0.800771,1.19806,1.66371
+18-Sep-2019 18:00:00,1,0.44625,1.1481,1.7963,1.5586,1.244,2.2974,1.5765,1.839,0.47024,4.3623,1.37853,2.92274,2.83068,0.610547,2.34222,0.797092,1.19312,1.65838
+18-Sep-2019 19:00:00,1,0.4568,1.1805,1.843,1.5918,1.2809,2.3543,1.6101,1.8906,0.48114,4.4274,1.40432,2.96598,2.88208,0.628441,2.39605,0.817714,1.22087,1.68814
+18-Sep-2019 20:00:00,1,0.44412,1.1416,1.7869,1.5518,1.2366,2.2859,1.5697,1.8285,0.46802,4.349,1.37324,2.91386,2.82024,0.606933,2.33134,0.792919,1.18751,1.65233
+18-Sep-2019 21:00:00,1,0.4121,1.0436,1.6449,1.4508,1.1258,2.115,1.4665,1.6701,0.43436,4.1399,1.29078,2.77515,2.6617,0.552944,2.16854,0.730229,1.10384,1.56022
+18-Sep-2019 22:00:00,1,0.37726,0.93764,1.4903,1.3409,1.0074,1.9321,1.3518,1.4951,0.39681,3.8937,1.19434,2.61218,2.48513,0.494762,1.99251,0.661898,1.01373,1.45715
+18-Sep-2019 23:00:00,1,0.34275,0.83324,1.337,1.2319,0.89221,1.7541,1.2358,1.3187,0.35862,3.6294,1.09146,2.43761,2.30581,0.437745,1.81934,0.594083,0.92551,1.35194
+19-Sep-2019 00:00:00,1,0.3187,0.76085,1.2299,1.1559,0.81331,1.632,1.1534,1.1938,0.33138,3.4325,1.01518,2.30773,2.17805,0.398383,1.69938,0.546731,0.864656,1.27667
+19-Sep-2019 01:00:00,1,0.30533,0.72074,1.1704,1.1136,0.76997,1.5649,1.107,1.1238,0.316,3.3183,0.971068,2.23248,2.10601,0.376645,1.63298,0.520378,0.831067,1.2341
+19-Sep-2019 02:00:00,1,0.29908,0.70202,1.1426,1.0939,0.74983,1.5337,1.0851,1.0908,0.30875,3.2637,0.950018,2.19654,2.07206,0.366517,1.602,0.50805,0.815423,1.21402
+19-Sep-2019 03:00:00,1,0.30159,0.70953,1.1537,1.1018,0.7579,1.5462,1.0939,1.104,0.31166,3.2857,0.958495,2.21101,2.0857,0.370577,1.61442,0.512996,0.821693,1.22209
+19-Sep-2019 04:00:00,1,0.30932,0.73269,1.1882,1.1262,0.78286,1.5849,1.1208,1.1447,0.3206,3.3527,0.984352,2.25516,2.12757,0.383117,1.65276,0.52824,0.841066,1.24685
+19-Sep-2019 05:00:00,1,0.33723,0.8166,1.3124,1.2145,0.874,1.7259,1.217,1.2902,0.35241,3.5852,1.07429,2.40841,2.27669,0.428682,1.79175,0.583221,0.911495,1.33481
+19-Sep-2019 06:00:00,1,0.40735,1.0292,1.6239,1.4359,1.1096,2.0899,1.451,1.6465,0.42931,4.1075,1.27808,2.75372,2.63793,0.544991,2.14452,0.720938,1.09152,1.54637
+19-Sep-2019 07:00:00,1,0.44356,1.1398,1.7844,1.5501,1.2346,2.2829,1.5679,1.8257,0.46744,4.3454,1.37183,2.9115,2.81748,0.605978,2.32846,0.791816,1.18603,1.65073
+19-Sep-2019 08:00:00,1,0.45978,1.1896,1.8562,1.6012,1.2914,2.3704,1.6195,1.9051,0.48421,4.4455,1.41151,2.97801,2.89654,0.633508,2.41129,0.823541,1.22872,1.69651
+19-Sep-2019 09:00:00,1,0.46349,1.201,1.8726,1.6129,1.3044,2.3905,1.6313,1.9232,0.48802,4.4679,1.42038,2.99286,2.91449,0.639818,2.43025,0.83079,1.23851,1.70689
+19-Sep-2019 10:00:00,1,0.47292,1.23,1.9144,1.6426,1.3376,2.4417,1.661,1.969,0.49765,4.5237,1.44261,3.03003,2.95995,0.65589,2.47853,0.849215,1.26344,1.73315
+19-Sep-2019 11:00:00,1,0.48087,1.2545,1.9495,1.6677,1.3657,2.485,1.6859,2.0074,0.50572,4.5698,1.46097,3.06069,2.99801,0.669453,2.51925,0.864725,1.28449,1.75511
+19-Sep-2019 12:00:00,1,0.47479,1.2358,1.9227,1.6485,1.3442,2.4519,1.6668,1.978,0.49955,4.5347,1.44695,3.03729,2.96891,0.659075,2.4881,0.852862,1.26838,1.73832
+19-Sep-2019 13:00:00,1,0.46759,1.2136,1.8908,1.6258,1.3188,2.4127,1.6442,1.9431,0.49221,4.4923,1.4301,3.00912,2.93429,0.646799,2.45123,0.8388,1.24934,1.71833
+19-Sep-2019 14:00:00,1,0.45781,1.1836,1.8475,1.595,1.2845,2.3597,1.6133,1.8955,0.48218,4.4336,1.40677,2.97007,2.88699,0.63016,2.40122,0.819692,1.22353,1.69098
+19-Sep-2019 15:00:00,1,0.45143,1.164,1.8193,1.5749,1.2621,2.3253,1.593,1.8643,0.4756,4.3945,1.39127,2.94411,2.85597,0.619328,2.36864,0.80722,1.20673,1.67303
+19-Sep-2019 16:00:00,1,0.43892,1.1256,1.7638,1.5354,1.2185,2.258,1.5531,1.8029,0.46261,4.3161,1.36024,2.89203,2.79474,0.598134,2.30484,0.782747,1.17387,1.63754
+19-Sep-2019 17:00:00,1,0.43632,1.1177,1.7523,1.5273,1.2095,2.2441,1.5448,1.7902,0.4599,4.2995,1.35369,2.88103,2.78196,0.593744,2.29161,0.777665,1.16707,1.63013
+19-Sep-2019 18:00:00,1,0.43968,1.128,1.7672,1.5378,1.2211,2.2621,1.5555,1.8067,0.4634,4.3209,1.36215,2.89524,2.79848,0.599422,2.30872,0.784237,1.17587,1.63971
+19-Sep-2019 19:00:00,1,0.45183,1.1652,1.821,1.5762,1.2635,2.3274,1.5943,1.8663,0.47602,4.397,1.39225,2.94575,2.85792,0.620008,2.37069,0.808004,1.20779,1.67416
+19-Sep-2019 20:00:00,1,0.44831,1.1544,1.8054,1.5651,1.2512,2.3085,1.5831,1.8491,0.47238,4.3752,1.38362,2.93127,2.84075,0.614038,2.35272,0.801121,1.19853,1.66422
+19-Sep-2019 21:00:00,1,0.41458,1.0512,1.656,1.4587,1.1344,2.1282,1.4746,1.6825,0.437,4.1567,1.29739,2.78629,2.67414,0.55712,2.18115,0.735102,1.11031,1.56746
+19-Sep-2019 22:00:00,1,0.37833,0.94091,1.4951,1.3443,1.011,1.9377,1.3554,1.5006,0.39799,3.9016,1.19743,2.61742,2.49067,0.496555,1.99794,0.664016,1.01651,1.46038
+19-Sep-2019 23:00:00,1,0.34201,0.831,1.3336,1.2295,0.88975,1.7503,1.2333,1.3148,0.35778,3.6235,1.08916,2.43369,2.30189,0.436522,1.81562,0.592618,0.923618,1.34963
+20-Sep-2019 00:00:00,1,0.31565,0.75168,1.2164,1.1462,0.80339,1.6166,1.1428,1.1779,0.32788,3.4067,1.00522,2.29075,2.16167,0.393413,1.68421,0.540719,0.856975,1.267
+20-Sep-2019 01:00:00,1,0.30378,0.7161,1.1635,1.1087,0.76498,1.5572,1.1016,1.1156,0.31421,3.3049,0.965882,2.22363,2.09762,0.374135,1.62531,0.517326,0.82719,1.22914
+20-Sep-2019 02:00:00,1,0.29748,0.69722,1.1354,1.0888,0.74468,1.5257,1.0795,1.0823,0.30689,3.2496,0.944571,2.18723,2.06333,0.363922,1.59406,0.504886,0.811415,1.20885
+20-Sep-2019 03:00:00,1,0.30058,0.70651,1.1492,1.0986,0.75465,1.5412,1.0904,1.0987,0.31049,3.2769,0.955091,2.2052,2.08022,0.368944,1.60943,0.511007,0.819171,1.21885
+20-Sep-2019 04:00:00,1,0.30822,0.72938,1.1833,1.1227,0.77929,1.5793,1.117,1.1389,0.31933,3.3432,0.980691,2.24891,2.12162,0.381327,1.64729,0.526067,0.8383,1.24333
+20-Sep-2019 05:00:00,1,0.33583,0.81238,1.3062,1.21,0.86939,1.7188,1.2122,1.2829,0.35083,3.5738,1.06991,2.40095,2.26929,0.426387,1.78477,0.580466,0.907947,1.33045
+20-Sep-2019 06:00:00,1,0.40443,1.0203,1.6109,1.4267,1.0996,2.0745,1.4415,1.6319,0.42618,4.0874,1.27019,2.74041,2.62324,0.540098,2.12973,0.715214,1.08394,1.53781
+20-Sep-2019 07:00:00,1,0.44343,1.1394,1.7838,1.5497,1.2342,2.2822,1.5675,1.8251,0.46731,4.3447,1.37153,2.91099,2.81687,0.605768,2.32783,0.791573,1.18571,1.65038
+20-Sep-2019 08:00:00,1,0.45989,1.19,1.8567,1.6016,1.2918,2.371,1.6199,1.9056,0.48432,4.4462,1.41177,2.97846,2.89708,0.633697,2.41186,0.823758,1.22902,1.69682
+20-Sep-2019 09:00:00,1,0.46936,1.2191,1.8986,1.6314,1.3251,2.4223,1.6498,1.9517,0.49402,4.5028,1.43427,3.0161,2.94282,0.649817,2.4603,0.84226,1.25402,1.72326
+20-Sep-2019 10:00:00,1,0.48409,1.2644,1.9638,1.6778,1.3772,2.5026,1.6959,2.0229,0.50898,4.5883,1.46833,3.07298,3.0134,0.674968,2.53579,0.87102,1.29304,1.76398
+20-Sep-2019 11:00:00,1,0.4939,1.2946,2.0072,1.7087,1.412,2.5563,1.7265,2.0701,0.51885,4.6434,1.49039,3.10974,3.06002,0.691764,2.58616,0.890158,1.31911,1.79084
+20-Sep-2019 12:00:00,1,0.48473,1.2664,1.9666,1.6799,1.3794,2.5061,1.6979,2.026,0.50962,4.5919,1.46978,3.0754,3.01645,0.676062,2.53908,0.872269,1.29474,1.76574
+20-Sep-2019 13:00:00,1,0.48044,1.2532,1.9477,1.6663,1.3642,2.4827,1.6846,2.0053,0.50529,4.5674,1.46,3.05908,2.99599,0.668733,2.51709,0.863903,1.28337,1.75395
+20-Sep-2019 14:00:00,1,0.4723,1.2281,1.9116,1.6407,1.3354,2.4383,1.659,1.9659,0.49702,4.5201,1.44116,3.02761,2.95696,0.654829,2.47535,0.848001,1.2618,1.73142
+20-Sep-2019 15:00:00,1,0.45956,1.1889,1.8552,1.6005,1.2906,2.3692,1.6188,1.904,0.48398,4.4442,1.41097,2.97712,2.89547,0.633131,2.41015,0.823107,1.22814,1.69589
+20-Sep-2019 16:00:00,1,0.44409,1.1415,1.7867,1.5518,1.2365,2.2858,1.5696,1.8284,0.468,4.3488,1.37317,2.91374,2.82011,0.606886,2.3312,0.792865,1.18744,1.65225
+20-Sep-2019 17:00:00,1,0.44084,1.1315,1.7723,1.5415,1.2252,2.2683,1.5592,1.8124,0.46461,4.3283,1.36506,2.90013,2.80417,0.601384,2.31463,0.786506,1.17891,1.64301
+20-Sep-2019 18:00:00,1,0.44294,1.1379,1.7816,1.5481,1.2325,2.2796,1.5659,1.8227,0.46679,4.3415,1.37029,2.90892,2.81445,0.60493,2.32531,0.790605,1.18441,1.64897
+20-Sep-2019 19:00:00,1,0.45729,1.182,1.8452,1.5934,1.2826,2.3569,1.6116,1.893,0.48165,4.4304,1.40551,2.96797,2.88447,0.629277,2.39857,0.818676,1.22216,1.68953
+20-Sep-2019 20:00:00,1,0.45379,1.1712,1.8297,1.5823,1.2704,2.338,1.6005,1.8759,0.47804,4.409,1.39702,2.95375,2.86744,0.623328,2.38067,0.811828,1.21294,1.67967
+20-Sep-2019 21:00:00,1,0.41835,1.0627,1.6726,1.4706,1.1473,2.1482,1.4868,1.7012,0.44099,4.182,1.30733,2.80304,2.69293,0.563447,2.20025,0.742477,1.12011,1.5784
+20-Sep-2019 22:00:00,1,0.38122,0.94968,1.5079,1.3534,1.0207,1.9527,1.365,1.5152,0.40114,3.9227,1.20569,2.63139,2.50546,0.501357,2.0125,0.669686,1.02394,1.46904
+20-Sep-2019 23:00:00,1,0.34398,0.83696,1.3424,1.2358,0.89627,1.7604,1.24,1.325,0.36,3.6392,1.09526,2.44407,2.31228,0.439768,1.8255,0.596504,0.928639,1.35575
+21-Sep-2019 00:00:00,1,0.31887,0.76134,1.2307,1.1564,0.81385,1.6328,1.1539,1.1947,0.33156,3.4339,1.01571,2.30865,2.17894,0.398653,1.70021,0.547057,0.865074,1.27719
+21-Sep-2019 01:00:00,1,0.30648,0.72417,1.1755,1.1172,0.77367,1.5706,1.1109,1.1298,0.31732,3.3282,0.974893,2.23901,2.1122,0.378501,1.63866,0.522635,0.833935,1.23777
+21-Sep-2019 02:00:00,1,0.30155,0.70941,1.1536,1.1017,0.75778,1.546,1.0938,1.1038,0.31162,3.2854,0.958367,2.2108,2.08549,0.370516,1.61424,0.512921,0.821599,1.22197
+21-Sep-2019 03:00:00,1,0.30276,0.71302,1.1589,1.1055,0.76166,1.552,1.098,1.1102,0.31302,3.2959,0.962425,2.21773,2.09204,0.372467,1.62021,0.515297,0.824614,1.22584
+21-Sep-2019 04:00:00,1,0.31093,0.73752,1.1953,1.1313,0.78807,1.5929,1.1264,1.1532,0.32246,3.3665,0.989686,2.26426,2.13626,0.385734,1.66076,0.531414,0.845109,1.25199
+21-Sep-2019 05:00:00,1,0.33708,0.81615,1.3117,1.214,0.87351,1.7251,1.2165,1.2894,0.35225,3.584,1.07383,2.40762,2.27591,0.428439,1.79101,0.582929,0.911119,1.33435
+21-Sep-2019 06:00:00,1,0.40453,1.0206,1.6114,1.427,1.0999,2.0751,1.4418,1.6324,0.42629,4.0881,1.27046,2.74087,2.62375,0.540267,2.13024,0.715411,1.0842,1.53811
+21-Sep-2019 07:00:00,1,0.44626,1.1481,1.7963,1.5586,1.244,2.2974,1.5766,1.839,0.47025,4.3624,1.37855,2.92277,2.83072,0.610558,2.34225,0.797105,1.19313,1.6584
+21-Sep-2019 08:00:00,1,0.46289,1.1991,1.87,1.611,1.3023,2.3872,1.6293,1.9202,0.4874,4.4642,1.41894,2.99045,2.91157,0.638789,2.42716,0.829608,1.23691,1.7052
+21-Sep-2019 09:00:00,1,0.46901,1.218,1.8971,1.6303,1.3238,2.4204,1.6486,1.95,0.49366,4.5007,1.43343,3.0147,2.94111,0.649211,2.45848,0.841565,1.25308,1.72227
+21-Sep-2019 10:00:00,1,0.48686,1.2729,1.976,1.6865,1.387,2.5178,1.7046,2.0363,0.51177,4.6039,1.4746,3.08343,3.02658,0.6797,2.54999,0.876418,1.30039,1.77158
+21-Sep-2019 11:00:00,1,0.49258,1.2906,2.0014,1.7046,1.4073,2.5491,1.7224,2.0638,0.51753,4.6361,1.48746,3.10486,3.05379,0.689511,2.5794,0.887594,1.31561,1.78725
+21-Sep-2019 12:00:00,1,0.48218,1.2585,1.9553,1.6718,1.3704,2.4922,1.69,2.0137,0.50704,4.5773,1.46397,3.06571,3.00428,0.671698,2.52598,0.867289,1.28797,1.75872
+21-Sep-2019 13:00:00,1,0.46802,1.2149,1.8927,1.6272,1.3203,2.415,1.6455,1.9452,0.49265,4.4948,1.4311,3.0108,2.93634,0.647524,2.45341,0.839631,1.25046,1.71951
+21-Sep-2019 14:00:00,1,0.4482,1.1541,1.8049,1.5647,1.2508,2.3079,1.5828,1.8485,0.47226,4.3745,1.38334,2.93081,2.84021,0.613851,2.35216,0.800905,1.19824,1.6639
+21-Sep-2019 15:00:00,1,0.4398,1.1283,1.7677,1.5382,1.2216,2.2627,1.5559,1.8073,0.46353,4.3217,1.36246,2.89576,2.79908,0.599631,2.30935,0.784478,1.17619,1.64006
+21-Sep-2019 16:00:00,1,0.43125,1.1021,1.7298,1.5112,1.1919,2.2169,1.5285,1.7651,0.45459,4.2668,1.34079,2.85934,2.75694,0.585175,2.26579,0.767734,1.15378,1.6156
+21-Sep-2019 17:00:00,1,0.42891,1.095,1.7195,1.5039,1.1838,2.2045,1.5209,1.7536,0.45214,4.2517,1.3348,2.84928,2.7454,0.581238,2.25392,0.763166,1.14768,1.6089
+21-Sep-2019 18:00:00,1,0.43033,1.0993,1.7257,1.5083,1.1887,2.212,1.5255,1.7606,0.45362,4.2609,1.33843,2.85537,2.75238,0.58362,2.2611,0.76593,1.15137,1.61296
+21-Sep-2019 19:00:00,1,0.44099,1.132,1.773,1.542,1.2257,2.2691,1.5597,1.8131,0.46476,4.3292,1.36542,2.90073,2.80488,0.601628,2.31536,0.786788,1.17929,1.64342
+21-Sep-2019 20:00:00,1,0.42353,1.0786,1.6956,1.4869,1.1652,2.1758,1.5036,1.727,0.44647,4.2164,1.3209,2.82588,2.71873,0.572174,2.22658,0.752634,1.13363,1.5934
+21-Sep-2019 21:00:00,1,0.39358,0.98722,1.5628,1.3924,1.0626,2.0174,1.4058,1.5775,0.41452,4.0115,1.24042,2.69013,2.56839,0.521943,2.07483,0.693925,1.05582,1.50581
+21-Sep-2019 22:00:00,1,0.36284,0.89395,1.4263,1.2954,0.95897,1.8573,1.3037,1.4218,0.38098,3.7858,1.15227,2.54088,2.41076,0.470864,1.92001,0.633581,0.976745,1.41358
+21-Sep-2019 23:00:00,1,0.33026,0.79562,1.2814,1.1924,0.8511,1.6905,1.1932,1.2541,0.34454,3.5285,1.05234,2.37104,2.23977,0.417269,1.75699,0.569508,0.89385,1.31307
+22-Sep-2019 00:00:00,1,0.30799,0.7287,1.1822,1.122,0.77855,1.5782,1.1162,1.1377,0.31907,3.3413,0.979933,2.24761,2.12038,0.380956,1.64616,0.525617,0.837728,1.2426
+22-Sep-2019 01:00:00,1,0.29555,0.69145,1.1268,1.0827,0.73849,1.5161,1.0727,1.0721,0.30464,3.2326,0.937996,2.176,2.0528,0.360803,1.58452,0.50108,0.806598,1.20262
+22-Sep-2019 02:00:00,1,0.28836,0.66996,1.0948,1.0599,0.71548,1.4805,1.0474,1.034,0.29623,3.1683,0.913251,2.1337,2.01344,0.349196,1.54896,0.486887,0.788673,1.17929
+22-Sep-2019 03:00:00,1,0.28487,0.65953,1.0792,1.0489,0.70434,1.4632,1.0351,1.0154,0.29212,3.1367,0.901088,2.11289,1.99422,0.343565,1.53171,0.479986,0.77998,1.1679
+22-Sep-2019 04:00:00,1,0.28558,0.66165,1.0824,1.0511,0.7066,1.4667,1.0376,1.0192,0.29296,3.1431,0.903569,2.11714,1.99813,0.34471,1.53521,0.48139,0.781747,1.17022
+22-Sep-2019 05:00:00,1,0.29024,0.67558,1.1032,1.0659,0.72149,1.4898,1.0541,1.044,0.29844,3.1852,0.919764,2.14483,2.02376,0.352231,1.55826,0.490602,0.79336,1.18542
+22-Sep-2019 06:00:00,1,0.3056,0.72155,1.1716,1.1145,0.77085,1.5663,1.1079,1.1252,0.31631,3.3207,0.971978,2.23403,2.10748,0.377086,1.63433,0.520914,0.831748,1.23497
+22-Sep-2019 07:00:00,1,0.3235,0.77526,1.2513,1.171,0.82895,1.6562,1.1699,1.2189,0.33685,3.4726,1.0307,2.33418,2.20371,0.406206,1.72325,0.556179,0.876747,1.29181
+22-Sep-2019 08:00:00,1,0.34921,0.85275,1.3657,1.2523,0.9136,1.7871,1.2577,1.352,0.36585,3.6805,1.11131,2.47134,2.33974,0.448375,1.85168,0.606794,0.941952,1.37189
+22-Sep-2019 09:00:00,1,0.36688,0.90618,1.4442,1.3081,0.97249,1.8782,1.3172,1.4423,0.38543,3.8164,1.16418,2.56108,2.43167,0.477546,1.94029,0.641514,0.987086,1.42583
+22-Sep-2019 10:00:00,1,0.37515,0.93126,1.481,1.3342,1.0003,1.9211,1.3448,1.4844,0.39451,3.8782,1.18828,2.60192,2.47434,0.491271,1.98193,0.657771,1.00833,1.45083
+22-Sep-2019 11:00:00,1,0.37657,0.93557,1.4873,1.3387,1.0051,1.9285,1.3496,1.4916,0.39607,3.8886,1.19238,2.60886,2.48164,0.493631,1.98908,0.660561,1.01198,1.4551
+22-Sep-2019 12:00:00,1,0.36527,0.90132,1.4371,1.303,0.96712,1.8699,1.3118,1.4342,0.38366,3.8043,1.15946,2.55308,2.42338,0.474892,1.93224,0.638365,0.982978,1.42097
+22-Sep-2019 13:00:00,1,0.3472,0.84669,1.3568,1.246,0.90695,1.7769,1.2509,1.3416,0.36361,3.6647,1.10517,2.46091,2.32922,0.44507,1.84163,0.602846,0.93684,1.3657
+22-Sep-2019 14:00:00,1,0.33053,0.79641,1.2826,1.1933,0.85197,1.6918,1.1941,1.2554,0.34484,3.5307,1.05318,2.37247,2.24117,0.417701,1.7583,0.570027,0.894516,1.31389
+22-Sep-2019 15:00:00,1,0.32324,0.77449,1.2502,1.1702,0.82812,1.6549,1.169,1.2175,0.33656,3.4705,1.02987,2.33278,2.20235,0.405788,1.72198,0.555675,0.876102,1.291
+22-Sep-2019 16:00:00,1,0.32024,0.76548,1.2368,1.1608,0.81834,1.6398,1.1587,1.2019,0.33314,3.4455,1.02019,2.31627,2.18632,0.4009,1.70707,0.549772,0.868546,1.28155
+22-Sep-2019 17:00:00,1,0.33041,0.79604,1.282,1.1929,0.85157,1.6912,1.1936,1.2548,0.3447,3.5297,1.05279,2.37181,2.24052,0.417501,1.75769,0.569787,0.894207,1.31351
+22-Sep-2019 18:00:00,1,0.34252,0.83254,1.3359,1.2312,0.89144,1.7529,1.235,1.3175,0.35836,3.6275,1.09074,2.43638,2.30458,0.437362,1.81818,0.593624,0.924918,1.35122
+22-Sep-2019 19:00:00,1,0.35618,0.87382,1.3967,1.2743,0.93677,1.823,1.2813,1.3877,0.37361,3.7348,1.13241,2.50718,2.37617,0.459869,1.88662,0.620502,0.959733,1.39328
+22-Sep-2019 20:00:00,1,0.35073,0.85733,1.3724,1.2571,0.91863,1.7949,1.2628,1.3597,0.36754,3.6924,1.11592,2.47918,2.34768,0.450872,1.85927,0.609775,0.945814,1.37655
+22-Sep-2019 21:00:00,1,0.32711,0.78614,1.2674,1.1825,0.84078,1.6745,1.1823,1.2377,0.34097,3.5026,1.0423,2.35395,2.22301,0.412116,1.74128,0.563304,0.885883,1.30319
+22-Sep-2019 22:00:00,1,0.31177,0.74003,1.1991,1.134,0.79079,1.5971,1.1293,1.1576,0.32342,3.3737,0.992454,2.26898,2.14078,0.387095,1.66492,0.533066,0.847213,1.25466
+22-Sep-2019 23:00:00,1,0.28988,0.6745,1.1015,1.0648,0.72033,1.488,1.0528,1.0421,0.29801,3.182,0.91851,2.14269,2.02177,0.351645,1.55647,0.489886,0.792456,1.18424
+23-Sep-2019 00:00:00,1,0.26913,0.61264,1.009,0.99911,0.65451,1.386,0.97911,0.93132,0.27348,2.9913,0.845207,2.01723,1.90703,0.318308,1.45419,0.448896,0.740998,1.11612
+23-Sep-2019 01:00:00,1,0.25947,0.58393,0.96591,0.96854,0.62419,1.339,0.94444,0.87929,0.26192,2.8995,0.809973,1.95684,1.85293,0.302878,1.40675,0.429791,0.717193,1.08394
+23-Sep-2019 02:00:00,1,0.25122,0.55943,0.92905,0.9424,0.59845,1.2991,0.91462,0.83459,0.25195,2.8194,0.77929,1.90421,1.80636,0.289739,1.3663,0.413453,0.696929,1.05619
+23-Sep-2019 03:00:00,1,0.2504,0.55702,0.9254,0.93982,0.59592,1.2951,0.91166,0.83016,0.25096,2.8114,0.77623,1.89895,1.80174,0.288444,1.3623,0.411839,0.694931,1.05344
+23-Sep-2019 04:00:00,1,0.25063,0.55768,0.92641,0.94054,0.59662,1.2962,0.91248,0.83138,0.25123,2.8136,0.777075,1.90041,1.80302,0.288801,1.36341,0.412284,0.695483,1.0542
+23-Sep-2019 05:00:00,1,0.25284,0.56424,0.93628,0.94753,0.60349,1.3069,0.92049,0.84338,0.25391,2.8352,0.785355,1.91461,1.81553,0.292315,1.37423,0.416661,0.700901,1.06166
+23-Sep-2019 06:00:00,1,0.25835,0.5806,0.9609,0.96499,0.62069,1.3336,0.94041,0.87324,0.26057,2.8887,0.805841,1.94975,1.84663,0.301093,1.40125,0.427575,0.714439,1.08019
+23-Sep-2019 07:00:00,1,0.26853,0.61084,1.0063,0.9972,0.65261,1.3831,0.97695,0.92807,0.27276,2.9856,0.843024,2.01349,1.90366,0.317341,1.45122,0.447701,0.739506,1.11412
+23-Sep-2019 08:00:00,1,0.29431,0.68776,1.1213,1.0788,0.73453,1.51,1.0684,1.0656,0.3032,3.2216,0.933768,2.16877,2.04605,0.358805,1.5784,0.49864,0.803512,1.19862
+23-Sep-2019 09:00:00,1,0.32019,0.76533,1.2366,1.1606,0.81818,1.6395,1.1585,1.2016,0.33308,3.445,1.02002,2.31599,2.18605,0.400816,1.70681,0.549672,0.868417,1.28139
+23-Sep-2019 10:00:00,1,0.34023,0.82565,1.3258,1.2239,0.8839,1.7412,1.2272,1.3057,0.35579,3.6093,1.08366,2.42434,2.29255,0.433611,1.80676,0.589131,0.919117,1.34414
+23-Sep-2019 11:00:00,1,0.36338,0.8956,1.4287,1.2971,0.96079,1.8601,1.3055,1.4245,0.38158,3.7899,1.15388,2.54361,2.41359,0.471764,1.92274,0.634651,0.978138,1.41523
+23-Sep-2019 12:00:00,1,0.35505,0.8704,1.3917,1.2708,0.93301,1.8172,1.2775,1.3819,0.37236,3.726,1.12901,2.50141,2.37028,0.458006,1.88096,0.618282,0.95685,1.38982
+23-Sep-2019 13:00:00,1,0.34209,0.83125,1.334,1.2298,0.89003,1.7507,1.2335,1.3153,0.35788,3.6241,1.08942,2.43413,2.30233,0.43666,1.81604,0.592784,0.923832,1.34989
+23-Sep-2019 14:00:00,1,0.32743,0.7871,1.2688,1.1835,0.84183,1.6761,1.1834,1.2394,0.34133,3.5052,1.04333,2.3557,2.22472,0.41264,1.74288,0.563935,0.886693,1.30419
+23-Sep-2019 15:00:00,1,0.32251,0.7723,1.2469,1.1679,0.82574,1.6512,1.1665,1.2137,0.33573,3.4644,1.02752,2.32877,2.19845,0.404599,1.71835,0.554239,0.874263,1.2887
+23-Sep-2019 16:00:00,1,0.32077,0.76706,1.2391,1.1624,0.82005,1.6424,1.1605,1.2046,0.33374,3.4498,1.02189,2.31916,2.18912,0.401753,1.70967,0.550804,0.869865,1.2832
+23-Sep-2019 17:00:00,1,0.32959,0.79359,1.2784,1.1903,0.84889,1.6871,1.1908,1.2506,0.34378,3.523,1.0502,2.3674,2.23619,0.416166,1.75362,0.568181,0.892144,1.31095
+23-Sep-2019 18:00:00,1,0.34298,0.83395,1.338,1.2326,0.89298,1.7553,1.2366,1.3199,0.35888,3.6313,1.09218,2.43883,2.30704,0.438128,1.82051,0.594542,0.926103,1.35266
+23-Sep-2019 19:00:00,1,0.36334,0.89548,1.4285,1.2969,0.96066,1.8599,1.3054,1.4243,0.38154,3.7896,1.15377,2.54342,2.41338,0.471699,1.92255,0.634574,0.978038,1.41511
+23-Sep-2019 20:00:00,1,0.35586,0.87283,1.3952,1.2733,0.93568,1.8213,1.2802,1.3861,0.37325,3.7323,1.13143,2.50552,2.37447,0.45933,1.88498,0.619861,0.9589,1.39228
+23-Sep-2019 21:00:00,1,0.33718,0.81646,1.3122,1.2143,0.87385,1.7257,1.2168,1.2899,0.35236,3.5848,1.07415,2.40817,2.27645,0.428608,1.79153,0.583132,0.911381,1.33467
+23-Sep-2019 22:00:00,1,0.32624,0.78352,1.2635,1.1797,0.83793,1.6701,1.1793,1.2332,0.33998,3.4954,1.03952,2.3492,2.21837,0.410692,1.73693,0.561588,0.883681,1.30045
+23-Sep-2019 23:00:00,1,0.3032,0.71436,1.1609,1.1069,0.7631,1.5543,1.0995,1.1126,0.31354,3.2998,0.963931,2.2203,2.09447,0.373194,1.62243,0.516181,0.825735,1.22728
+24-Sep-2019 00:00:00,1,0.28542,0.66118,1.0817,1.0506,0.70611,1.466,1.037,1.0184,0.29278,3.1417,0.903025,2.11621,1.99727,0.344459,1.53445,0.481082,0.78136,1.16971
+24-Sep-2019 01:00:00,1,0.27226,0.62195,1.023,1.009,0.66438,1.4013,0.9903,0.94811,0.27721,3.0206,0.856469,2.03652,1.92446,0.323319,1.46958,0.455082,0.748731,1.12648
+24-Sep-2019 02:00:00,1,0.26718,0.60683,1.0003,0.99293,0.64836,1.3765,0.97212,0.92082,0.27115,2.9729,0.83814,2.00512,1.89612,0.315183,1.44459,0.445034,0.736176,1.10964
+24-Sep-2019 03:00:00,1,0.26856,0.61095,1.0065,0.99732,0.65272,1.3832,0.97709,0.92827,0.27281,2.986,0.843158,2.01372,1.90386,0.317401,1.4514,0.447774,0.739597,1.11424
+24-Sep-2019 04:00:00,1,0.28073,0.6472,1.0608,1.0358,0.6912,1.4429,1.0204,0.99342,0.28725,3.099,0.886588,2.08808,1.97142,0.336916,1.51132,0.471823,0.769716,1.15437
+24-Sep-2019 05:00:00,1,0.31155,0.73938,1.1981,1.1333,0.79009,1.596,1.1286,1.1564,0.32317,3.3719,0.991741,2.26776,2.13961,0.386744,1.66385,0.53264,0.846671,1.25397
+24-Sep-2019 06:00:00,1,0.38608,0.96445,1.5295,1.3687,1.0372,1.9781,1.3811,1.5397,0.40642,3.958,1.21946,2.6547,2.53029,0.509449,2.03701,0.679226,1.03647,1.48356
+24-Sep-2019 07:00:00,1,0.43432,1.1115,1.7434,1.5209,1.2025,2.2333,1.5383,1.7803,0.4578,4.2867,1.34861,2.87249,2.77208,0.590356,2.28141,0.77374,1.16181,1.62439
+24-Sep-2019 08:00:00,1,0.45724,1.1818,1.845,1.5932,1.2825,2.3567,1.6115,1.8927,0.4816,4.4301,1.4054,2.96778,2.88423,0.629195,2.39832,0.818581,1.22203,1.68939
+24-Sep-2019 09:00:00,1,0.46955,1.2196,1.8995,1.632,1.3257,2.4234,1.6504,1.9526,0.49421,4.5039,1.43471,3.01684,2.94373,0.650138,2.46126,0.842628,1.25452,1.72378
+24-Sep-2019 10:00:00,1,0.48466,1.2662,1.9663,1.6796,1.3792,2.5057,1.6977,2.0257,0.50955,4.5915,1.46962,3.07513,3.01612,0.675942,2.53871,0.872132,1.29455,1.76555
+24-Sep-2019 11:00:00,1,0.49031,1.2836,1.9913,1.6974,1.3992,2.5367,1.7153,2.0529,0.51525,4.6234,1.48238,3.09639,3.043,0.685615,2.56772,0.883158,1.30957,1.78104
+24-Sep-2019 12:00:00,1,0.47729,1.2434,1.9337,1.6564,1.3531,2.4655,1.6747,1.9901,0.50209,4.5492,1.45274,3.04696,2.98089,0.663343,2.50091,0.857742,1.275,1.74524
+24-Sep-2019 13:00:00,1,0.47145,1.2255,1.9079,1.638,1.3324,2.4337,1.6563,1.9618,0.49615,4.5151,1.43916,3.02428,2.95286,0.653375,2.47098,0.846336,1.25954,1.72906
+24-Sep-2019 14:00:00,1,0.46552,1.2072,1.8816,1.6193,1.3115,2.4015,1.6376,1.933,0.49009,4.48,1.42519,3.00091,2.92427,0.643264,2.44061,0.834745,1.24386,1.71254
+24-Sep-2019 15:00:00,1,0.46312,1.1999,1.871,1.6117,1.3031,2.3884,1.6301,1.9213,0.48763,4.4656,1.41948,2.99136,2.91268,0.639179,2.42833,0.830056,1.23752,1.70584
+24-Sep-2019 16:00:00,1,0.44737,1.1515,1.8013,1.5621,1.2479,2.3034,1.5801,1.8444,0.4714,4.3693,1.38129,2.92736,2.83614,0.612438,2.34791,0.799274,1.19605,1.66154
+24-Sep-2019 17:00:00,1,0.44237,1.1362,1.7791,1.5463,1.2305,2.2765,1.5641,1.8199,0.4662,4.3379,1.36887,2.90653,2.81165,0.603965,2.3224,0.789489,1.18291,1.64735
+24-Sep-2019 18:00:00,1,0.44422,1.1419,1.7873,1.5521,1.2369,2.2864,1.57,1.829,0.46812,4.3496,1.37348,2.91426,2.82072,0.607096,2.33183,0.793107,1.18777,1.6526
+24-Sep-2019 19:00:00,1,0.45851,1.1857,1.8506,1.5972,1.2869,2.3635,1.6155,1.8989,0.4829,4.4378,1.40844,2.97287,2.89035,0.631339,2.40477,0.821047,1.22536,1.69293
+24-Sep-2019 20:00:00,1,0.44081,1.1314,1.7722,1.5414,1.2251,2.2681,1.5591,1.8122,0.46458,4.3281,1.36497,2.89998,2.804,0.601326,2.31445,0.786439,1.17882,1.64292
+24-Sep-2019 21:00:00,1,0.40471,1.0211,1.6122,1.4275,1.1005,2.076,1.4424,1.6333,0.42648,4.0893,1.27093,2.74167,2.62463,0.54056,2.13113,0.715754,1.08465,1.53862
+24-Sep-2019 22:00:00,1,0.36634,0.90456,1.4418,1.3064,0.9707,1.8754,1.3154,1.4396,0.38484,3.8123,1.16262,2.55843,2.42892,0.476665,1.93762,0.640468,0.985722,1.42422
+24-Sep-2019 23:00:00,1,0.3324,0.80206,1.2909,1.1992,0.85813,1.7013,1.2005,1.2652,0.34696,3.546,1.05912,2.38259,2.25114,0.420774,1.76766,0.573723,0.899267,1.31976
+25-Sep-2019 00:00:00,1,0.31102,0.73778,1.1957,1.1316,0.78836,1.5934,1.1267,1.1536,0.32256,3.3673,0.98998,2.26476,2.13674,0.385878,1.6612,0.53159,0.845332,1.25227
+25-Sep-2019 01:00:00,1,0.29468,0.68884,1.1229,1.0799,0.73569,1.5118,1.0697,1.0675,0.30362,3.2248,0.935014,2.1709,2.04804,0.359393,1.5802,0.499358,0.80442,1.1998
+25-Sep-2019 02:00:00,1,0.28888,0.67151,1.0971,1.0616,0.71714,1.4831,1.0493,1.0368,0.29684,3.173,0.915048,2.13677,2.01628,0.350032,1.55153,0.487911,0.789965,1.18098
+25-Sep-2019 03:00:00,1,0.29166,0.67983,1.1095,1.0704,0.72604,1.4969,1.0591,1.0516,0.3001,3.1979,0.924664,2.15321,2.03155,0.354523,1.56529,0.493407,0.7969,1.19003
+25-Sep-2019 04:00:00,1,0.30263,0.71264,1.1584,1.1051,0.76125,1.5514,1.0975,1.1095,0.31287,3.2948,0.962,2.217,2.09135,0.372263,1.61958,0.515048,0.824298,1.22544
+25-Sep-2019 05:00:00,1,0.33236,0.80193,1.2907,1.1991,0.85798,1.7011,1.2003,1.2649,0.34691,3.5456,1.05898,2.38234,2.2509,0.4207,1.76744,0.573634,0.899153,1.31962
+25-Sep-2019 06:00:00,1,0.39733,0.99864,1.5794,1.4042,1.0754,2.0371,1.4182,1.5963,0.41856,4.038,1.25078,2.70765,2.58739,0.52821,2.09379,0.701283,1.06552,1.5169
+25-Sep-2019 07:00:00,1,0.44035,1.13,1.7701,1.5399,1.2235,2.2656,1.5577,1.81,0.4641,4.3252,1.36382,2.89805,2.80175,0.600548,2.31211,0.785539,1.17761,1.64161
+25-Sep-2019 08:00:00,1,0.46123,1.194,1.8626,1.6058,1.2964,2.3782,1.6241,1.9121,0.48569,4.4543,1.41497,2.98381,2.90353,0.635964,2.41867,0.826363,1.23253,1.70055
+25-Sep-2019 09:00:00,1,0.47208,1.2274,1.9107,1.64,1.3347,2.4371,1.6583,1.9649,0.4968,4.5188,1.44065,3.02676,2.95592,0.654459,2.47424,0.847578,1.26122,1.73082
+25-Sep-2019 10:00:00,1,0.48278,1.2603,1.958,1.6737,1.3725,2.4954,1.6918,2.0166,0.50765,4.5808,1.46534,3.06798,3.00713,0.672719,2.52905,0.868454,1.28955,1.76037
+25-Sep-2019 11:00:00,1,0.49334,1.2929,2.0047,1.707,1.41,2.5533,1.7247,2.0674,0.51829,4.6403,1.48915,3.10767,3.05738,0.690807,2.58329,0.889069,1.31762,1.78931
+25-Sep-2019 12:00:00,1,0.48469,1.2662,1.9664,1.6797,1.3793,2.5059,1.6978,2.0258,0.50958,4.5917,1.46969,3.07524,3.01625,0.67599,2.53886,0.872187,1.29463,1.76563
+25-Sep-2019 13:00:00,1,0.47336,1.2314,1.9163,1.644,1.3392,2.4441,1.6624,1.9711,0.4981,4.5263,1.44363,3.03175,2.96206,0.656641,2.48079,0.850076,1.26461,1.73437
+25-Sep-2019 14:00:00,1,0.46077,1.1926,1.8606,1.6043,1.2948,2.3757,1.6226,1.9099,0.48522,4.4515,1.41387,2.98197,2.90132,0.635184,2.41633,0.825467,1.23132,1.69927
+25-Sep-2019 15:00:00,1,0.45393,1.1716,1.8303,1.5828,1.2708,2.3387,1.601,1.8765,0.47818,4.4099,1.39736,2.95431,2.86812,0.623563,2.38138,0.812099,1.2133,1.68006
+25-Sep-2019 16:00:00,1,0.44279,1.1375,1.781,1.5477,1.232,2.2788,1.5655,1.822,0.46664,4.3406,1.36993,2.90831,2.81374,0.604686,2.32457,0.790323,1.18403,1.64856
+25-Sep-2019 17:00:00,1,0.44479,1.1436,1.7898,1.5539,1.2389,2.2895,1.5719,1.8318,0.46872,4.3532,1.3749,2.91664,2.82351,0.608063,2.33474,0.794224,1.18927,1.65423
+25-Sep-2019 18:00:00,1,0.44986,1.1592,1.8123,1.5699,1.2566,2.3168,1.588,1.8566,0.47398,4.3848,1.38741,2.93764,2.84829,0.616658,2.36061,0.804143,1.20259,1.66858
+25-Sep-2019 19:00:00,1,0.4643,1.2035,1.8762,1.6155,1.3072,2.3948,1.6338,1.9271,0.48884,4.4727,1.4223,2.99607,2.91839,0.641191,2.43438,0.832366,1.24064,1.70914
+25-Sep-2019 20:00:00,1,0.44746,1.1518,1.8017,1.5624,1.2482,2.3039,1.5804,1.8449,0.47149,4.3699,1.38151,2.92773,2.83658,0.61259,2.34836,0.79945,1.19628,1.6618
+25-Sep-2019 21:00:00,1,0.41468,1.0515,1.6564,1.459,1.1347,2.1287,1.4749,1.683,0.4371,4.1573,1.29765,2.78672,2.67461,0.557279,2.18163,0.735288,1.11055,1.56774
+25-Sep-2019 22:00:00,1,0.38221,0.95268,1.5123,1.3565,1.0241,1.9579,1.3683,1.5202,0.40221,3.9299,1.2085,2.63615,2.51052,0.503002,2.01748,0.671626,1.02649,1.472
+25-Sep-2019 23:00:00,1,0.34378,0.83635,1.3415,1.2351,0.89561,1.7593,1.2393,1.324,0.35977,3.6376,1.09464,2.44301,2.31123,0.439437,1.82449,0.596109,0.928128,1.35513
+26-Sep-2019 00:00:00,1,0.31901,0.76176,1.2313,1.1569,0.81431,1.6335,1.1544,1.1954,0.33173,3.4351,1.01617,2.30942,2.17969,0.398882,1.70091,0.547334,0.865427,1.27764
+26-Sep-2019 01:00:00,1,0.30641,0.72398,1.1752,1.117,0.77346,1.5703,1.1107,1.1294,0.31725,3.3277,0.974682,2.23865,2.11186,0.378399,1.63834,0.52251,0.833777,1.23757
+26-Sep-2019 02:00:00,1,0.29972,0.70393,1.1454,1.0959,0.75188,1.5369,1.0873,1.0942,0.30949,3.2693,0.952172,2.20022,2.07552,0.367547,1.60515,0.509305,0.817013,1.21607
+26-Sep-2019 03:00:00,1,0.30346,0.71512,1.1621,1.1077,0.76392,1.5555,1.1004,1.1139,0.31383,3.302,0.96478,2.22175,2.09584,0.373603,1.62368,0.516679,0.826368,1.22809
+26-Sep-2019 04:00:00,1,0.31362,0.74559,1.2073,1.1398,0.7968,1.6064,1.1358,1.1673,0.32555,3.3895,0.998563,2.2794,2.15077,0.390111,1.67413,0.536721,0.851873,1.26056
+26-Sep-2019 05:00:00,1,0.3389,0.82164,1.3198,1.2197,0.87951,1.7344,1.2227,1.2988,0.3543,3.5986,1.07951,2.41729,2.28552,0.431425,1.80011,0.586512,0.915738,1.34001
+26-Sep-2019 06:00:00,1,0.40572,1.0242,1.6166,1.4307,1.104,2.0813,1.4457,1.6383,0.42756,4.0963,1.27366,2.74628,2.6297,0.54225,2.13623,0.717732,1.08727,1.54158
+26-Sep-2019 07:00:00,1,0.44933,1.1575,1.8099,1.5683,1.2548,2.3139,1.5863,1.854,0.47343,4.3815,1.38611,2.93545,2.8457,0.615757,2.3579,0.803104,1.2012,1.66708
+26-Sep-2019 08:00:00,1,0.46968,1.22,1.9,1.6324,1.3262,2.424,1.6508,1.9532,0.49434,4.5047,1.43501,3.01733,2.94433,0.650352,2.4619,0.842873,1.25485,1.72413
+26-Sep-2019 09:00:00,1,0.47534,1.2375,1.9251,1.6503,1.3462,2.4549,1.6686,1.9807,0.50011,4.5379,1.44824,3.03944,2.97156,0.660019,2.49093,0.853941,1.26985,1.73985
+26-Sep-2019 10:00:00,1,0.48908,1.2798,1.9859,1.6935,1.3949,2.5299,1.7115,2.0469,0.51401,4.6165,1.4796,3.09177,3.03714,0.683501,2.56138,0.88075,1.30628,1.77766
+26-Sep-2019 11:00:00,1,0.50167,1.3186,2.0415,1.7332,1.4397,2.5991,1.7506,2.1074,0.52663,4.6862,1.50753,3.13826,3.09676,0.705101,2.62612,0.905315,1.33981,1.81197
+26-Sep-2019 12:00:00,1,0.49271,1.291,2.0019,1.705,1.4078,2.5498,1.7228,2.0644,0.51766,4.6368,1.48775,3.10534,3.0544,0.689729,2.58006,0.887842,1.31595,1.7876
+26-Sep-2019 13:00:00,1,0.48209,1.2582,1.9549,1.6715,1.37,2.4917,1.6897,2.0133,0.50695,4.5768,1.46376,3.06536,3.00384,0.671542,2.52551,0.86711,1.28773,1.75847
+26-Sep-2019 14:00:00,1,0.47299,1.2302,1.9147,1.6429,1.3379,2.4421,1.6612,1.9693,0.49772,4.5242,1.44277,3.03031,2.96028,0.656009,2.47889,0.849352,1.26363,1.73334
+26-Sep-2019 15:00:00,1,0.46239,1.1976,1.8678,1.6095,1.3005,2.3845,1.6278,1.9178,0.48689,4.4613,1.41776,2.98848,2.90918,0.637949,2.42464,0.828644,1.23561,1.70382
+26-Sep-2019 16:00:00,1,0.44974,1.1588,1.8118,1.5696,1.2562,2.3162,1.5877,1.8561,0.47385,4.3841,1.38713,2.93716,2.84772,0.616459,2.36001,0.803913,1.20228,1.66825
+26-Sep-2019 17:00:00,1,0.44838,1.1546,1.8057,1.5653,1.2515,2.3088,1.5833,1.8494,0.47245,4.3756,1.38379,2.93155,2.84109,0.614155,2.35308,0.801256,1.19871,1.66441
+26-Sep-2019 18:00:00,1,0.45446,1.1733,1.8327,1.5844,1.2727,2.3416,1.6027,1.8791,0.47873,4.4131,1.39865,2.95648,2.87071,0.624467,2.3841,0.81314,1.2147,1.68156
+26-Sep-2019 19:00:00,1,0.46954,1.2196,1.8994,1.632,1.3257,2.4233,1.6503,1.9525,0.4942,4.5038,1.43468,3.01678,2.94366,0.650115,2.46119,0.842601,1.25448,1.72374
+26-Sep-2019 20:00:00,1,0.4511,1.163,1.8178,1.5739,1.261,2.3235,1.592,1.8627,0.47526,4.3925,1.39046,2.94275,2.85435,0.618766,2.36695,0.806572,1.20586,1.67209
+26-Sep-2019 21:00:00,1,0.41725,1.0594,1.6678,1.4671,1.1435,2.1424,1.4832,1.6958,0.43983,4.1746,1.30444,2.79817,2.68745,0.561601,2.19468,0.740327,1.11725,1.57521
+26-Sep-2019 22:00:00,1,0.3847,0.96025,1.5234,1.3644,1.0325,1.9709,1.3765,1.5327,0.40492,3.948,1.21556,2.6481,2.52324,0.507146,2.03003,0.676513,1.0329,1.47943
+26-Sep-2019 23:00:00,1,0.34963,0.85403,1.3676,1.2536,0.915,1.7893,1.2592,1.3541,0.36632,3.6838,1.11259,2.47352,2.34196,0.449071,1.8538,0.607626,0.943029,1.37319
+27-Sep-2019 00:00:00,1,0.32279,0.77314,1.2482,1.1688,0.82666,1.6526,1.1675,1.2152,0.33605,3.4668,1.02843,2.33032,2.19996,0.405058,1.71975,0.554793,0.874972,1.28959
+27-Sep-2019 01:00:00,1,0.30887,0.73136,1.1862,1.1248,0.78142,1.5826,1.1193,1.1424,0.32009,3.3489,0.98288,2.25264,2.12517,0.382396,1.65056,0.527365,0.839953,1.24544
+27-Sep-2019 02:00:00,1,0.30486,0.71934,1.1683,1.1121,0.76846,1.5626,1.1053,1.1213,0.31546,3.3143,0.969503,2.22981,2.10347,0.375886,1.63066,0.519456,0.829895,1.23261
+27-Sep-2019 03:00:00,1,0.30603,0.72282,1.1735,1.1158,0.77221,1.5684,1.1094,1.1274,0.3168,3.3243,0.973393,2.23645,2.10977,0.377773,1.63643,0.52175,0.83281,1.23633
+27-Sep-2019 04:00:00,1,0.31757,0.75746,1.2249,1.1523,0.80964,1.6263,1.1495,1.188,0.33008,3.423,1.0115,2.30146,2.172,0.396545,1.69377,0.544508,0.861815,1.2731
+27-Sep-2019 05:00:00,1,0.34261,0.83283,1.3363,1.2315,0.89176,1.7534,1.2353,1.318,0.35847,3.6283,1.09104,2.43689,2.30509,0.437521,1.81866,0.593815,0.925165,1.35152
+27-Sep-2019 06:00:00,1,0.40464,1.0209,1.6118,1.4273,1.1003,2.0756,1.4421,1.6329,0.4264,4.0888,1.27073,2.74133,2.62426,0.540436,2.13075,0.715609,1.08446,1.5384
+27-Sep-2019 07:00:00,1,0.44691,1.1501,1.7992,1.5607,1.2463,2.3009,1.5786,1.8422,0.47093,4.3665,1.38016,2.92548,2.83392,0.611667,2.34559,0.798385,1.19485,1.66026
+27-Sep-2019 08:00:00,1,0.46476,1.2049,1.8783,1.6169,1.3089,2.3974,1.6353,1.9294,0.48932,4.4755,1.42341,2.99793,2.92064,0.641985,2.43676,0.833277,1.24187,1.71045
+27-Sep-2019 09:00:00,1,0.46593,1.2085,1.8834,1.6206,1.313,2.4037,1.6389,1.935,0.49051,4.4824,1.42616,3.00254,2.92626,0.643964,2.44271,0.835548,1.24494,1.71369
+27-Sep-2019 10:00:00,1,0.47872,1.2479,1.94,1.6609,1.3581,2.4733,1.6792,1.997,0.50354,4.5574,1.45604,3.05247,2.98774,0.665785,2.50824,0.860534,1.27879,1.74919
+27-Sep-2019 11:00:00,1,0.48368,1.2631,1.962,1.6765,1.3757,2.5004,1.6947,2.0209,0.50856,4.5859,1.46739,3.0714,3.01142,0.674258,2.53366,0.870211,1.29194,1.76284
+27-Sep-2019 12:00:00,1,0.47588,1.2391,1.9275,1.652,1.3481,2.4578,1.6703,1.9833,0.50066,4.541,1.44949,3.04152,2.97415,0.660939,2.49369,0.854994,1.27128,1.74134
+27-Sep-2019 13:00:00,1,0.46554,1.2073,1.8817,1.6194,1.3116,2.4016,1.6377,1.9331,0.49011,4.4801,1.42524,3.00099,2.92437,0.6433,2.44072,0.834786,1.24391,1.7126
+27-Sep-2019 14:00:00,1,0.45165,1.1646,1.8202,1.5756,1.2629,2.3264,1.5937,1.8654,0.47583,4.3958,1.3918,2.94499,2.85701,0.619691,2.36973,0.807639,1.2073,1.67363
+27-Sep-2019 15:00:00,1,0.44257,1.1368,1.78,1.5469,1.2312,2.2776,1.5648,1.8209,0.46641,4.3392,1.36937,2.90736,2.81263,0.604302,2.32342,0.789879,1.18343,1.64792
+27-Sep-2019 16:00:00,1,0.43496,1.1135,1.7463,1.523,1.2047,2.2368,1.5404,1.7834,0.45847,4.2908,1.35024,2.87523,2.77525,0.591442,2.28468,0.774999,1.1635,1.62623
+27-Sep-2019 17:00:00,1,0.43441,1.1118,1.7438,1.5212,1.2028,2.2338,1.5386,1.7807,0.45789,4.2872,1.34883,2.87287,2.77252,0.590506,2.28186,0.773914,1.16205,1.62465
+27-Sep-2019 18:00:00,1,0.43749,1.1213,1.7575,1.5309,1.2135,2.2503,1.5485,1.7959,0.46112,4.307,1.35665,2.886,2.78772,0.595723,2.29758,0.779957,1.17013,1.63347
+27-Sep-2019 19:00:00,1,0.45453,1.1735,1.833,1.5847,1.273,2.342,1.6029,1.8795,0.4788,4.4136,1.39882,2.95676,2.87105,0.624584,2.38445,0.813275,1.21488,1.68176
+27-Sep-2019 20:00:00,1,0.43763,1.1217,1.7581,1.5314,1.214,2.2511,1.549,1.7966,0.46127,4.3079,1.35699,2.88658,2.7884,0.595955,2.29828,0.780225,1.17049,1.63386
+27-Sep-2019 21:00:00,1,0.40095,1.0097,1.5955,1.4157,1.0877,2.0562,1.4301,1.6145,0.42246,4.0633,1.26072,2.72444,2.60571,0.534274,2.11212,0.708393,1.07491,1.52759
+27-Sep-2019 22:00:00,1,0.36968,0.91467,1.4566,1.317,0.98188,1.8927,1.3266,1.4566,0.38851,3.8374,1.17239,2.575,2.44615,0.48219,1.95438,0.64702,0.994272,1.43432
+27-Sep-2019 23:00:00,1,0.33477,0.80918,1.3014,1.2067,0.86589,1.7134,1.2086,1.2774,0.34963,3.5652,1.06657,2.39526,2.26366,0.424644,1.77946,0.578373,0.905251,1.32714
+28-Sep-2019 00:00:00,1,0.30814,0.72916,1.1829,1.1225,0.77905,1.579,1.1167,1.1385,0.31924,3.3426,0.980438,2.24848,2.1212,0.381203,1.64691,0.525917,0.838109,1.24309
+28-Sep-2019 01:00:00,1,0.29563,0.6917,1.1272,1.0829,0.73875,1.5166,1.073,1.0726,0.30473,3.2333,0.938275,2.17647,2.05325,0.360935,1.58492,0.501241,0.806801,1.20288
+28-Sep-2019 02:00:00,1,0.28965,0.67381,1.1005,1.064,0.71959,1.4869,1.052,1.0409,0.29774,3.1799,0.91771,2.14132,2.0205,0.351272,1.55533,0.489429,0.79188,1.18348
+28-Sep-2019 03:00:00,1,0.29456,0.68851,1.1224,1.0796,0.73533,1.5113,1.0693,1.0669,0.30349,3.2238,0.934627,2.17024,2.04742,0.359211,1.57964,0.499135,0.804138,1.19943
+28-Sep-2019 04:00:00,1,0.30579,0.7221,1.1724,1.1151,0.77144,1.5672,1.1085,1.1262,0.31653,3.3223,0.97259,2.23508,2.10847,0.377383,1.63524,0.521276,0.832207,1.23556
+28-Sep-2019 05:00:00,1,0.33134,0.79887,1.2862,1.1959,0.85465,1.696,1.1969,1.2597,0.34576,3.5374,1.05576,2.37687,2.24551,0.419036,1.76237,0.571634,0.896581,1.31644
+28-Sep-2019 06:00:00,1,0.3968,0.99703,1.5771,1.4026,1.0736,2.0343,1.4164,1.5937,0.41799,4.0342,1.24933,2.70519,2.58472,0.527327,2.09111,0.700246,1.06415,1.51534
+28-Sep-2019 07:00:00,1,0.44126,1.1328,1.7742,1.5428,1.2266,2.2705,1.5606,1.8145,0.46505,4.3309,1.36611,2.90189,2.80623,0.602093,2.31676,0.787326,1.18001,1.64421
+28-Sep-2019 08:00:00,1,0.45896,1.1871,1.8526,1.5986,1.2885,2.3659,1.6169,1.9011,0.48336,4.4405,1.40952,2.97469,2.89254,0.632105,2.40707,0.821928,1.22655,1.69419
+28-Sep-2019 09:00:00,1,0.4632,1.2001,1.8714,1.612,1.3034,2.3889,1.6303,1.9217,0.48772,4.4661,1.41968,2.9917,2.91308,0.639321,2.42876,0.830219,1.23774,1.70607
+28-Sep-2019 10:00:00,1,0.47569,1.2385,1.9266,1.6513,1.3474,2.4568,1.6696,1.9823,0.50046,4.5399,1.44903,3.04076,2.97321,0.660604,2.49269,0.854611,1.27076,1.7408
+28-Sep-2019 11:00:00,1,0.48472,1.2663,1.9666,1.6798,1.3794,2.5061,1.6979,2.026,0.50962,4.5919,1.46977,3.07537,3.01642,0.67605,2.53904,0.872256,1.29472,1.76572
+28-Sep-2019 12:00:00,1,0.47585,1.239,1.9274,1.6519,1.348,2.4577,1.6702,1.9831,0.50063,4.5408,1.44942,3.04142,2.97401,0.660891,2.49355,0.854939,1.2712,1.74127
+28-Sep-2019 13:00:00,1,0.4633,1.2004,1.8718,1.6123,1.3037,2.3894,1.6306,1.9222,0.48782,4.4667,1.41991,2.99208,2.91355,0.639487,2.42926,0.830409,1.238,1.70635
+28-Sep-2019 14:00:00,1,0.44896,1.1564,1.8083,1.5671,1.2535,2.312,1.5852,1.8522,0.47305,4.3792,1.38521,2.93394,2.84392,0.615137,2.35603,0.802389,1.20023,1.66605
+28-Sep-2019 15:00:00,1,0.43777,1.1221,1.7588,1.5318,1.2145,2.2519,1.5494,1.7973,0.46142,4.3088,1.35736,2.88719,2.78911,0.596198,2.29901,0.780507,1.17087,1.63427
+28-Sep-2019 16:00:00,1,0.4267,1.0882,1.7097,1.4969,1.1761,2.1926,1.5138,1.7426,0.44981,4.2372,1.3291,2.83968,2.73443,0.577504,2.24266,0.758829,1.14189,1.60253
+28-Sep-2019 17:00:00,1,0.42692,1.0889,1.7106,1.4976,1.1769,2.1938,1.5145,1.7437,0.45004,4.2386,1.32966,2.84063,2.73551,0.577872,2.24377,0.759256,1.14246,1.60315
+28-Sep-2019 18:00:00,1,0.42968,1.0974,1.7229,1.5063,1.1865,2.2086,1.5234,1.7574,0.45295,4.2567,1.33678,2.85261,2.74921,0.582538,2.25784,0.764674,1.1497,1.61111
+28-Sep-2019 19:00:00,1,0.44731,1.1513,1.801,1.5619,1.2477,2.3031,1.5799,1.8442,0.47134,4.369,1.38115,2.92714,2.83587,0.612344,2.34763,0.799167,1.1959,1.66139
+28-Sep-2019 20:00:00,1,0.42773,1.0914,1.7143,1.5002,1.1797,2.1982,1.5171,1.7478,0.4509,4.244,1.33177,2.84417,2.73956,0.57925,2.24793,0.760857,1.1446,1.60551
+28-Sep-2019 21:00:00,1,0.39272,0.98462,1.559,1.3897,1.0597,2.0129,1.403,1.5732,0.4136,4.0054,1.23804,2.68612,2.56405,0.520516,2.07051,0.692248,1.05361,1.50328
+28-Sep-2019 22:00:00,1,0.36157,0.8901,1.4206,1.2913,0.95473,1.8507,1.2994,1.4153,0.37957,3.7761,1.1485,2.53448,2.40417,0.468761,1.91363,0.631083,0.973492,1.40971
+28-Sep-2019 23:00:00,1,0.33134,0.79885,1.2862,1.1958,0.85462,1.6959,1.1968,1.2596,0.34576,3.5373,1.05574,2.37684,2.24547,0.419026,1.76234,0.571621,0.896565,1.31642
+29-Sep-2019 00:00:00,1,0.3096,0.73354,1.1894,1.1271,0.78378,1.5863,1.1218,1.1462,0.32093,3.3552,0.985298,2.25677,2.12911,0.38358,1.65418,0.528802,0.841782,1.24776
+29-Sep-2019 01:00:00,1,0.29505,0.68997,1.1246,1.0811,0.7369,1.5137,1.071,1.0695,0.30406,3.2282,0.936302,2.1731,2.0501,0.360002,1.58206,0.500101,0.80536,1.20102
+29-Sep-2019 02:00:00,1,0.28487,0.65953,1.0792,1.0489,0.70434,1.4632,1.0351,1.0154,0.29212,3.1367,0.901088,2.11289,1.99422,0.343565,1.53171,0.479986,0.77998,1.1679
+29-Sep-2019 03:00:00,1,0.28121,0.64862,1.0629,1.0373,0.69272,1.4452,1.0221,0.99597,0.28782,3.1034,0.888274,2.09097,1.97407,0.337686,1.51368,0.472768,0.770904,1.15594
+29-Sep-2019 04:00:00,1,0.28135,0.64903,1.0635,1.0378,0.69315,1.4459,1.0226,0.9967,0.28798,3.1046,0.888755,2.09179,1.97482,0.337906,1.51435,0.473038,0.771243,1.15639
+29-Sep-2019 05:00:00,1,0.2871,0.66621,1.0892,1.056,0.71148,1.4743,1.043,1.0274,0.29476,3.157,0.908892,2.12624,2.00654,0.347172,1.54276,0.484408,0.785549,1.17521
+29-Sep-2019 06:00:00,1,0.30144,0.70907,1.1531,1.1013,0.75741,1.5455,1.0934,1.1032,0.31149,3.2844,0.957985,2.21014,2.08488,0.370332,1.61367,0.512697,0.821315,1.22161
+29-Sep-2019 07:00:00,1,0.32088,0.7674,1.2397,1.1628,0.82042,1.643,1.1609,1.2052,0.33387,3.4508,1.02226,2.3198,2.18974,0.401941,1.71024,0.55103,0.870155,1.28356
+29-Sep-2019 08:00:00,1,0.34526,0.84081,1.3481,1.2398,0.9005,1.7669,1.2443,1.3316,0.36143,3.6493,1.09919,2.45075,2.319,0.441867,1.83189,0.599016,0.931886,1.35969
+29-Sep-2019 09:00:00,1,0.36663,0.90542,1.4431,1.3073,0.97165,1.8769,1.3164,1.4411,0.38516,3.8145,1.16345,2.55984,2.43038,0.477133,1.93904,0.641024,0.986446,1.42508
+29-Sep-2019 10:00:00,1,0.38309,0.95534,1.5162,1.3593,1.027,1.9625,1.3712,1.5246,0.40316,3.9363,1.21099,2.64036,2.51499,0.504459,2.02189,0.673345,1.02874,1.47461
+29-Sep-2019 11:00:00,1,0.39121,0.98002,1.5522,1.3849,1.0545,2.0049,1.398,1.5655,0.41196,3.9947,1.23382,2.67899,2.55636,0.517988,2.06286,0.689276,1.04969,1.49879
+29-Sep-2019 12:00:00,1,0.37755,0.93854,1.4916,1.3418,1.0084,1.9336,1.3528,1.4966,0.39714,3.8958,1.19519,2.61363,2.48666,0.495257,1.99401,0.662483,1.0145,1.45804
+29-Sep-2019 13:00:00,1,0.36399,0.89743,1.4314,1.299,0.96281,1.8632,1.3075,1.4276,0.38225,3.7945,1.15567,2.54664,2.41671,0.472763,1.92578,0.635837,0.979684,1.41707
+29-Sep-2019 14:00:00,1,0.35404,0.86735,1.3872,1.2676,0.92965,1.812,1.2741,1.3768,0.37123,3.7182,1.12597,2.49624,2.36501,0.456338,1.87589,0.616295,0.95427,1.38673
+29-Sep-2019 15:00:00,1,0.34687,0.84569,1.3553,1.2449,0.90585,1.7752,1.2498,1.3399,0.36324,3.6621,1.10415,2.45918,2.32748,0.444525,1.83997,0.602195,0.935997,1.36468
+29-Sep-2019 16:00:00,1,0.34652,0.84463,1.3537,1.2438,0.90469,1.7734,1.2486,1.3381,0.36285,3.6593,1.10308,2.45736,2.32564,0.443949,1.83822,0.601505,0.935105,1.3636
+29-Sep-2019 17:00:00,1,0.34975,0.85436,1.3681,1.254,0.91537,1.7899,1.2595,1.3547,0.36645,3.6847,1.11293,2.4741,2.34254,0.449253,1.85435,0.607843,0.943311,1.37353
+29-Sep-2019 18:00:00,1,0.3592,0.88295,1.4101,1.2839,0.94684,1.8385,1.2914,1.4032,0.37696,3.758,1.14146,2.52254,2.39189,0.464855,1.90176,0.626438,0.967448,1.40251
+29-Sep-2019 19:00:00,1,0.36992,0.91541,1.4577,1.3177,0.9827,1.894,1.3274,1.4579,0.38878,3.8392,1.1731,2.5762,2.4474,0.482593,1.95561,0.647498,0.994897,1.43505
+29-Sep-2019 20:00:00,1,0.35669,0.87536,1.3989,1.2759,0.93847,1.8256,1.283,1.3903,0.37418,3.7387,1.13394,2.50978,2.37882,0.46071,1.88917,0.621504,0.961034,1.39484
+29-Sep-2019 21:00:00,1,0.33552,0.81145,1.3048,1.2091,0.86837,1.7172,1.2112,1.2813,0.35049,3.5713,1.06894,2.39929,2.26765,0.42588,1.78322,0.579857,0.907162,1.32949
+29-Sep-2019 22:00:00,1,0.32238,0.77191,1.2463,1.1675,0.82532,1.6506,1.1661,1.2131,0.33558,3.4634,1.02711,2.32807,2.19777,0.40439,1.71771,0.553987,0.87394,1.2883
+29-Sep-2019 23:00:00,1,0.29623,0.6935,1.1299,1.0849,0.74069,1.5195,1.0751,1.0758,0.30544,3.2386,0.940333,2.17999,2.05654,0.36191,1.5879,0.50243,0.808307,1.20483
+30-Sep-2019 00:00:00,1,0.27251,0.62271,1.0241,1.0098,0.66518,1.4025,0.9912,0.94947,0.27751,3.023,0.857377,2.03808,1.92587,0.323725,1.47083,0.455583,0.749357,1.12732
+30-Sep-2019 01:00:00,1,0.25605,0.57376,0.95061,0.9577,0.61349,1.3224,0.93209,0.86077,0.25779,2.8664,0.797307,1.93512,1.83365,0.297422,1.38995,0.423014,0.708777,1.07246
+30-Sep-2019 02:00:00,1,0.24726,0.54771,0.91137,0.92987,0.58617,1.28,0.90025,0.81308,0.24715,2.7805,0.76439,1.87863,1.78392,0.283456,1.34693,0.405617,0.687241,1.04281
+30-Sep-2019 03:00:00,1,0.24417,0.53857,0.89758,0.9201,0.57662,1.2652,0.88902,0.79627,0.24339,2.7499,0.752684,1.85853,1.76636,0.278563,1.33185,0.399504,0.679697,1.03234
+30-Sep-2019 04:00:00,1,0.24714,0.54735,0.91083,0.92949,0.5858,1.2794,0.89981,0.81242,0.247,2.7793,0.763931,1.87785,1.78323,0.283264,1.34634,0.405377,0.686944,1.0424
+30-Sep-2019 05:00:00,1,0.2513,0.55969,0.92943,0.94267,0.59872,1.2995,0.91493,0.83505,0.25205,2.8202,0.77961,1.90475,1.80684,0.289875,1.36671,0.413622,0.697138,1.05648
+30-Sep-2019 06:00:00,1,0.25397,0.5676,0.94135,0.95112,0.60702,1.3124,0.92459,0.84953,0.25528,2.8463,0.789587,1.92187,1.82194,0.294119,1.37978,0.418906,0.703683,1.06548
+30-Sep-2019 07:00:00,1,0.26413,0.59776,0.98669,0.98327,0.63878,1.3616,0.96118,0.9044,0.2675,2.944,0.827044,1.98611,1.87906,0.310308,1.4296,0.439001,0.728654,1.09949
+30-Sep-2019 08:00:00,1,0.28888,0.67153,1.0971,1.0616,0.71716,1.4831,1.0493,1.0368,0.29684,3.173,0.91507,2.13681,2.01632,0.350042,1.55156,0.487923,0.78998,1.181
+30-Sep-2019 09:00:00,1,0.31057,0.73645,1.1938,1.1302,0.78692,1.5911,1.1252,1.1513,0.32205,3.3635,0.988511,2.26225,2.13434,0.385156,1.65899,0.530714,0.844217,1.25086
+30-Sep-2019 10:00:00,1,0.33041,0.79606,1.2821,1.1929,0.85159,1.6912,1.1937,1.2548,0.34471,3.5297,1.05281,2.37184,2.24056,0.417511,1.75772,0.569799,0.894224,1.31353
+30-Sep-2019 11:00:00,1,0.346,0.84306,1.3514,1.2422,0.90297,1.7707,1.2468,1.3355,0.36227,3.6552,1.10149,2.45465,2.32292,0.443095,1.83562,0.600484,0.933784,1.362
+30-Sep-2019 12:00:00,1,0.34192,0.83074,1.3333,1.2293,0.88947,1.7498,1.233,1.3144,0.35769,3.6228,1.0889,2.43325,2.30145,0.436383,1.8152,0.592453,0.923405,1.34937
+30-Sep-2019 13:00:00,1,0.32598,0.78273,1.2623,1.1789,0.83707,1.6688,1.1784,1.2318,0.33968,3.4932,1.03867,2.34777,2.21697,0.410262,1.73563,0.56107,0.883017,1.29962
+30-Sep-2019 14:00:00,1,0.31205,0.74089,1.2003,1.1349,0.79171,1.5986,1.1303,1.1591,0.32375,3.3761,0.993396,2.27059,2.14232,0.38756,1.66634,0.533629,0.84793,1.25557
+30-Sep-2019 15:00:00,1,0.30519,0.72032,1.1698,1.1132,0.76952,1.5642,1.1065,1.123,0.31584,3.3171,0.970603,2.23169,2.10525,0.376419,1.63229,0.520104,0.830718,1.23366
+30-Sep-2019 16:00:00,1,0.30683,0.72523,1.1771,1.1184,0.77481,1.5724,1.1122,1.1316,0.31773,3.3313,0.976075,2.24103,2.11412,0.379076,1.64041,0.523334,0.834823,1.2389
+30-Sep-2019 17:00:00,1,0.32073,0.76694,1.239,1.1623,0.81992,1.6422,1.1604,1.2044,0.33369,3.4495,1.02176,2.31895,2.18892,0.401691,1.70948,0.550728,0.869769,1.28308
+30-Sep-2019 18:00:00,1,0.33948,0.82339,1.3224,1.2216,0.88142,1.7374,1.2247,1.3018,0.35495,3.6033,1.08133,2.42037,2.28859,0.43238,1.80301,0.587656,0.917213,1.34181
+30-Sep-2019 19:00:00,1,0.36401,0.89748,1.4314,1.299,0.96288,1.8633,1.3076,1.4277,0.38227,3.7947,1.15572,2.54674,2.41682,0.472795,1.92587,0.635876,0.979734,1.41713
+30-Sep-2019 20:00:00,1,0.3563,0.87415,1.3972,1.2747,0.93714,1.8235,1.2816,1.3883,0.37373,3.7356,1.13275,2.50775,2.37675,0.460052,1.88717,0.620721,0.960017,1.39362
+30-Sep-2019 21:00:00,1,0.33826,0.81971,1.317,1.2177,0.8774,1.7312,1.2205,1.2955,0.35358,3.5935,1.07752,2.4139,2.28215,0.430376,1.79691,0.585253,0.914115,1.33802
+30-Sep-2019 22:00:00,1,0.32931,0.79276,1.2772,1.1894,0.84799,1.6857,1.1899,1.2491,0.34346,3.5207,1.04932,2.3659,2.23472,0.415714,1.75225,0.567637,0.891446,1.31009
+30-Sep-2019 23:00:00,1,0.30552,0.72131,1.1713,1.1142,0.77058,1.5658,1.1076,1.1248,0.31622,3.32,0.971703,2.23357,2.10703,0.376952,1.63392,0.520752,0.831542,1.23471
+01-Oct-2019 00:00:00,1,0.28865,0.67082,1.0961,1.0609,0.7164,1.4819,1.0484,1.0355,0.29657,3.1709,0.914247,2.1354,2.01501,0.349659,1.55038,0.487454,0.789389,1.18023
+01-Oct-2019 01:00:00,1,0.27712,0.63643,1.0447,1.0244,0.67974,1.4251,1.0076,0.97412,0.28298,3.0658,0.873809,2.06621,1.95143,0.331112,1.49351,0.464685,0.760757,1.14251
+01-Oct-2019 02:00:00,1,0.27344,0.62545,1.0282,1.0127,0.66808,1.4071,0.99449,0.9544,0.27861,3.0316,0.860677,2.04373,1.93099,0.325202,1.47536,0.457404,0.751636,1.13036
+01-Oct-2019 03:00:00,1,0.27708,0.6363,1.0445,1.0243,0.6796,1.4249,1.0075,0.97389,0.28293,3.0654,0.873654,2.06595,1.95119,0.331042,1.49329,0.464599,0.76065,1.14236
+01-Oct-2019 04:00:00,1,0.29072,0.677,1.1053,1.0674,0.72301,1.4922,1.0557,1.0465,0.29899,3.1895,0.921405,2.14764,2.02637,0.352998,1.56061,0.491541,0.794544,1.18696
+01-Oct-2019 05:00:00,1,0.32082,0.76723,1.2394,1.1626,0.82024,1.6427,1.1607,1.2049,0.3338,3.4503,1.02207,2.31948,2.18943,0.401847,1.70996,0.550917,0.87001,1.28338
+01-Oct-2019 06:00:00,1,0.39565,0.99353,1.572,1.3989,1.0696,2.0283,1.4127,1.5879,0.41676,4.0261,1.24615,2.69983,2.57889,0.525404,2.0853,0.697989,1.06118,1.51194
+01-Oct-2019 07:00:00,1,0.44522,1.1449,1.7917,1.5553,1.2404,2.2918,1.5732,1.8339,0.46917,4.3559,1.37597,2.91844,2.82563,0.608797,2.33695,0.795072,1.1904,1.65546
+01-Oct-2019 08:00:00,1,0.46109,1.1936,1.862,1.6053,1.2959,2.3774,1.6237,1.9115,0.48555,4.4534,1.41464,2.98325,2.90286,0.635728,2.41796,0.826092,1.23217,1.70017
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+20-Oct-2019 03:00:00,1,0.29436,0.68789,1.1215,1.0789,0.73467,1.5102,1.0685,1.0658,0.30325,3.222,0.933918,2.16903,2.04629,0.358876,1.57862,0.498726,0.803622,1.19876
+20-Oct-2019 04:00:00,1,0.29747,0.6972,1.1354,1.0888,0.74466,1.5257,1.0795,1.0823,0.30688,3.2496,0.94455,2.18719,2.06329,0.363912,1.59403,0.504873,0.811399,1.20883
+20-Oct-2019 05:00:00,1,0.30306,0.71395,1.1603,1.1065,0.76266,1.5536,1.099,1.1118,0.31337,3.2986,0.963465,2.2195,2.09371,0.372968,1.62174,0.515907,0.825388,1.22683
+20-Oct-2019 06:00:00,1,0.31906,0.76192,1.2315,1.157,0.81448,1.6338,1.1546,1.1957,0.33178,3.4355,1.01634,2.30971,2.17996,0.398965,1.70116,0.547434,0.865556,1.2778
+20-Oct-2019 07:00:00,1,0.34154,0.82959,1.3316,1.2281,0.88821,1.7479,1.2317,1.3124,0.35726,3.6197,1.08771,2.43123,2.29943,0.435756,1.81329,0.591702,0.922435,1.34819
+20-Oct-2019 08:00:00,1,0.36416,0.89796,1.4321,1.2995,0.96341,1.8641,1.3081,1.4285,0.38244,3.7959,1.15619,2.54753,2.41763,0.473056,1.92667,0.636185,0.980137,1.41761
+20-Oct-2019 09:00:00,1,0.38179,0.95141,1.5105,1.3552,1.0227,1.9557,1.3669,1.5181,0.40176,3.9269,1.20731,2.63414,2.50838,0.502306,2.01537,0.670806,1.02541,1.47075
+20-Oct-2019 10:00:00,1,0.39945,1.0051,1.5888,1.4109,1.0826,2.0482,1.4251,1.6069,0.42084,4.0528,1.2566,2.71747,2.59809,0.53175,2.10449,0.705434,1.071,1.52315
+20-Oct-2019 11:00:00,1,0.4101,1.0375,1.6361,1.4445,1.119,2.1044,1.46,1.6602,0.43223,4.1263,1.28544,2.76615,2.65169,0.549592,2.15842,0.726314,1.09864,1.55439
+20-Oct-2019 12:00:00,1,0.40179,1.0122,1.5992,1.4183,1.0906,2.0606,1.4328,1.6187,0.42335,4.0691,1.26299,2.72827,2.60991,0.535667,2.11633,0.710024,1.07707,1.53004
+20-Oct-2019 13:00:00,1,0.38642,0.96548,1.531,1.3698,1.0383,1.9799,1.3822,1.5414,0.40679,3.9604,1.22042,2.65632,2.53202,0.510014,2.03872,0.679892,1.03734,1.48457
+20-Oct-2019 14:00:00,1,0.37363,0.92666,1.4742,1.3294,0.99517,1.9132,1.3398,1.4767,0.39285,3.8669,1.18389,2.59449,2.46653,0.48875,1.97428,0.654788,1.00443,1.44625
+20-Oct-2019 15:00:00,1,0.36705,0.90669,1.445,1.3086,0.97306,1.8791,1.3178,1.4432,0.38562,3.8177,1.16468,2.56193,2.43255,0.477829,1.94115,0.64185,0.987524,1.42635
+20-Oct-2019 16:00:00,1,0.36422,0.89812,1.4324,1.2997,0.96358,1.8644,1.3083,1.4288,0.3825,3.7963,1.15635,2.54779,2.4179,0.473143,1.92693,0.636288,0.980272,1.41776
+20-Oct-2019 17:00:00,1,0.37332,0.92572,1.4728,1.3285,0.99413,1.9116,1.3387,1.4751,0.39251,3.8646,1.18299,2.59296,2.46493,0.488235,1.97272,0.654179,1.00363,1.44532
+20-Oct-2019 18:00:00,1,0.39982,1.0062,1.5905,1.4121,1.0839,2.0502,1.4264,1.6088,0.42124,4.0554,1.25762,2.7192,2.59999,0.532378,2.10639,0.70617,1.07198,1.52425
+20-Oct-2019 19:00:00,1,0.40504,1.0221,1.6136,1.4286,1.1017,2.0777,1.4434,1.6349,0.42683,4.0916,1.27183,2.74318,2.62629,0.541112,2.13279,0.7164,1.08551,1.53959
+20-Oct-2019 20:00:00,1,0.37575,0.93307,1.4836,1.3361,1.0023,1.9242,1.3468,1.4874,0.39516,3.8825,1.19,2.60483,2.47739,0.492258,1.98492,0.658938,1.00986,1.45262
+20-Oct-2019 21:00:00,1,0.3499,0.85481,1.3687,1.2545,0.91587,1.7906,1.26,1.3555,0.36661,3.6859,1.11339,2.47487,2.34332,0.4495,1.8551,0.608137,0.943692,1.37399
+20-Oct-2019 22:00:00,1,0.33516,0.81036,1.3032,1.2079,0.86718,1.7154,1.2099,1.2795,0.35008,3.5684,1.0678,2.39736,2.26574,0.425288,1.78142,0.579147,0.906248,1.32836
+20-Oct-2019 23:00:00,1,0.31163,0.73963,1.1985,1.1335,0.79035,1.5965,1.1289,1.1569,0.32327,3.3726,0.992014,2.26823,2.14006,0.386878,1.66426,0.532803,0.846878,1.25423
+21-Oct-2019 00:00:00,1,0.28905,0.67202,1.0978,1.0621,0.71768,1.4839,1.0498,1.0377,0.29704,3.1745,0.915633,2.13777,2.01721,0.350304,1.55236,0.488244,0.790385,1.18153
+21-Oct-2019 01:00:00,1,0.27504,0.63022,1.0354,1.0178,0.67315,1.4149,1.0002,0.96299,0.28051,3.0465,0.866401,2.05353,1.93989,0.327772,1.48325,0.460571,0.755602,1.13565
+21-Oct-2019 02:00:00,1,0.26773,0.60846,1.0027,0.99466,0.65009,1.3791,0.97409,0.92377,0.27181,2.9781,0.840126,2.00852,1.89918,0.31606,1.44728,0.446118,0.737529,1.11146
+21-Oct-2019 03:00:00,1,0.26452,0.59893,0.98844,0.98452,0.64001,1.3635,0.9626,0.90652,0.26797,2.9477,0.828478,1.98856,1.88126,0.310936,1.43153,0.439779,0.729623,1.1008
+21-Oct-2019 04:00:00,1,0.26803,0.60938,1.0041,0.99564,0.65106,1.3806,0.97519,0.92542,0.27218,2.981,0.841241,2.01044,1.9009,0.316553,1.44879,0.446727,0.738289,1.11248
+21-Oct-2019 05:00:00,1,0.2691,0.61255,1.0089,0.99901,0.65441,1.3859,0.979,0.93115,0.27345,2.991,0.845095,2.01704,1.90685,0.318259,1.45404,0.448835,0.740922,1.11602
+21-Oct-2019 06:00:00,1,0.27153,0.61978,1.0197,1.0067,0.66208,1.3977,0.98769,0.9442,0.27634,3.0138,0.853852,2.03204,1.9204,0.322151,1.466,0.453641,0.746928,1.12407
+21-Oct-2019 07:00:00,1,0.28672,0.66508,1.0875,1.0548,0.71026,1.4724,1.0416,1.0253,0.29431,3.1535,0.907568,2.12398,2.00445,0.346559,1.54088,0.483656,0.784602,1.17396
+21-Oct-2019 08:00:00,1,0.30675,0.72498,1.1767,1.1181,0.77454,1.572,1.1119,1.1312,0.31764,3.3306,0.9758,2.24056,2.11367,0.378943,1.64001,0.523171,0.834617,1.23864
+21-Oct-2019 09:00:00,1,0.32672,0.78496,1.2656,1.1812,0.8395,1.6725,1.181,1.2357,0.34052,3.4994,1.04105,2.35182,2.22093,0.411477,1.73933,0.562534,0.884895,1.30196
+21-Oct-2019 10:00:00,1,0.3436,0.8358,1.3407,1.2346,0.89501,1.7584,1.2387,1.3231,0.35957,3.6362,1.09408,2.44206,2.31027,0.439139,1.82359,0.595752,0.927667,1.35456
+21-Oct-2019 11:00:00,1,0.36254,0.89304,1.4249,1.2944,0.95797,1.8557,1.3026,1.4202,0.38065,3.7835,1.15138,2.53936,2.4092,0.470365,1.9185,0.632989,0.975973,1.41266
+21-Oct-2019 12:00:00,1,0.35709,0.87654,1.4007,1.2772,0.93977,1.8276,1.2843,1.3923,0.37461,3.7417,1.13512,2.51178,2.38087,0.461357,1.89114,0.622274,0.962035,1.39604
+21-Oct-2019 13:00:00,1,0.34047,0.82636,1.3268,1.2247,0.88466,1.7424,1.228,1.3069,0.35605,3.6112,1.08438,2.42557,2.29378,0.433993,1.80792,0.589589,0.919708,1.34486
+21-Oct-2019 14:00:00,1,0.32676,0.78508,1.2658,1.1814,0.83963,1.6727,1.1811,1.2359,0.34057,3.4997,1.04118,2.35203,2.22113,0.41154,1.73952,0.56261,0.884992,1.30208
+21-Oct-2019 15:00:00,1,0.32066,0.76675,1.2387,1.1621,0.81972,1.6419,1.1601,1.2041,0.33362,3.449,1.02155,2.3186,2.18858,0.401587,1.70916,0.550602,0.869608,1.28288
+21-Oct-2019 16:00:00,1,0.32091,0.76748,1.2398,1.1629,0.82051,1.6431,1.161,1.2054,0.3339,3.451,1.02234,2.31994,2.18988,0.401983,1.71037,0.55108,0.870219,1.28365
+21-Oct-2019 17:00:00,1,0.33615,0.81333,1.3076,1.211,0.87043,1.7204,1.2133,1.2846,0.35119,3.5764,1.0709,2.40263,2.27096,0.426905,1.78634,0.581088,0.908748,1.33143
+21-Oct-2019 18:00:00,1,0.36919,0.91319,1.4545,1.3154,0.98025,1.8902,1.3249,1.4541,0.38798,3.8338,1.17097,2.57258,2.44363,0.481382,1.95193,0.646063,0.993022,1.43284
+21-Oct-2019 19:00:00,1,0.38069,0.94807,1.5056,1.3517,1.019,1.95,1.3632,1.5125,0.40056,3.9188,1.20417,2.62883,2.50275,0.500475,2.00982,0.668645,1.02258,1.46745
+21-Oct-2019 20:00:00,1,0.36135,0.88945,1.4196,1.2907,0.954,1.8496,1.2987,1.4142,0.37934,3.7745,1.14785,2.53339,2.40304,0.468404,1.91254,0.630658,0.972939,1.40905
+21-Oct-2019 21:00:00,1,0.34396,0.8369,1.3423,1.2357,0.89621,1.7603,1.2399,1.3249,0.35998,3.639,1.0952,2.44396,2.31218,0.439736,1.8254,0.596466,0.928589,1.35569
+21-Oct-2019 22:00:00,1,0.33599,0.81287,1.3069,1.2105,0.86992,1.7196,1.2128,1.2838,0.35102,3.5752,1.07041,2.40181,2.27014,0.426651,1.78557,0.580784,0.908355,1.33095
+21-Oct-2019 23:00:00,1,0.31015,0.73518,1.1919,1.1289,0.78555,1.589,1.1237,1.1491,0.32156,3.3599,0.987105,2.25985,2.13205,0.384466,1.65688,0.529877,0.84315,1.2495
+22-Oct-2019 00:00:00,1,0.29071,0.67699,1.1053,1.0674,0.72299,1.4921,1.0557,1.0465,0.29898,3.1894,0.921384,2.1476,2.02633,0.352988,1.56058,0.491528,0.794529,1.18694
+22-Oct-2019 01:00:00,1,0.2805,0.64649,1.0597,1.0351,0.69045,1.4417,1.0196,0.99216,0.28697,3.0968,0.885755,2.08666,1.97012,0.336536,1.51015,0.471356,0.76913,1.1536
+22-Oct-2019 02:00:00,1,0.27693,0.63585,1.0438,1.0238,0.67913,1.4242,1.0069,0.97309,0.28275,3.064,0.873126,2.06504,1.95037,0.330804,1.49256,0.464305,0.760281,1.14187
+22-Oct-2019 03:00:00,1,0.2804,0.64622,1.0593,1.0348,0.69015,1.4413,1.0193,0.99166,0.28686,3.096,0.885427,2.0861,1.9696,0.336387,1.50969,0.471172,0.768899,1.15329
+22-Oct-2019 04:00:00,1,0.2956,0.6916,1.127,1.0828,0.73865,1.5164,1.0729,1.0724,0.3047,3.233,0.938168,2.17629,2.05308,0.360884,1.58476,0.501179,0.806723,1.20278
+22-Oct-2019 05:00:00,1,0.32844,0.79013,1.2733,1.1867,0.84513,1.6812,1.1869,1.2446,0.34247,3.5135,1.04654,2.36117,2.23008,0.414287,1.7479,0.565918,0.889239,1.30735
+22-Oct-2019 06:00:00,1,0.40295,1.0158,1.6043,1.422,1.0945,2.0667,1.4366,1.6245,0.42459,4.0772,1.26616,2.73361,2.61576,0.537611,2.12221,0.712302,1.08008,1.53345
+22-Oct-2019 07:00:00,1,0.46348,1.201,1.8726,1.6129,1.3043,2.3904,1.6312,1.9231,0.488,4.4678,1.42034,2.99281,2.91443,0.639794,2.43018,0.830763,1.23847,1.70685
+22-Oct-2019 08:00:00,1,0.48214,1.2584,1.9552,1.6717,1.3702,2.4919,1.6898,2.0135,0.507,4.5771,1.46388,3.06555,3.00408,0.671626,2.52577,0.867206,1.28786,1.75861
+22-Oct-2019 09:00:00,1,0.48475,1.2664,1.9667,1.6799,1.3795,2.5062,1.698,2.0261,0.50965,4.592,1.46983,3.07548,3.01655,0.676099,2.53918,0.872311,1.2948,1.7658
+22-Oct-2019 10:00:00,1,0.49292,1.2916,2.0029,1.7057,1.4085,2.551,1.7235,2.0654,0.51787,4.638,1.48822,3.10612,3.0554,0.690092,2.58114,0.888255,1.31651,1.78818
+22-Oct-2019 11:00:00,1,0.50283,1.3222,2.0467,1.7369,1.4439,2.6054,1.7541,2.1129,0.52778,4.6925,1.51005,3.14246,3.10221,0.70709,2.63207,0.907572,1.3429,1.81511
+22-Oct-2019 12:00:00,1,0.49553,1.2997,2.0144,1.7139,1.4178,2.5653,1.7316,2.078,0.52049,4.6525,1.49402,3.11578,3.06776,0.694566,2.59455,0.893345,1.32346,1.79529
+22-Oct-2019 13:00:00,1,0.49255,1.2905,2.0012,1.7045,1.4072,2.5489,1.7223,2.0636,0.5175,4.6359,1.48738,3.10473,3.05362,0.68945,2.57922,0.887525,1.31552,1.78715
+22-Oct-2019 14:00:00,1,0.4847,1.2663,1.9665,1.6797,1.3793,2.5059,1.6978,2.0258,0.50959,4.5917,1.4697,3.07527,3.01629,0.676002,2.5389,0.872201,1.29465,1.76565
+22-Oct-2019 15:00:00,1,0.47545,1.2378,1.9256,1.6506,1.3465,2.4555,1.6689,1.9812,0.50022,4.5385,1.44848,3.03984,2.97207,0.660198,2.49147,0.854146,1.27013,1.74014
+22-Oct-2019 16:00:00,1,0.46444,1.2039,1.8768,1.6159,1.3077,2.3956,1.6342,1.9278,0.48898,4.4735,1.42263,2.99663,2.91906,0.641428,2.43509,0.832638,1.24101,1.70953
+22-Oct-2019 17:00:00,1,0.46908,1.2182,1.8974,1.6305,1.3241,2.4208,1.6489,1.9503,0.49373,4.5011,1.4336,3.01497,2.94144,0.64933,2.45883,0.841701,1.25327,1.72246
+22-Oct-2019 18:00:00,1,0.48918,1.2801,1.9863,1.6939,1.3952,2.5305,1.7118,2.0475,0.51411,4.6171,1.47984,3.09217,3.03764,0.683682,2.56193,0.880956,1.30657,1.77795
+22-Oct-2019 19:00:00,1,0.49654,1.3028,2.0189,1.7171,1.4214,2.5709,1.7347,2.0828,0.5215,4.6581,1.49626,3.11951,3.07255,0.696302,2.59976,0.895319,1.32615,1.79805
+22-Oct-2019 20:00:00,1,0.46578,1.208,1.8828,1.6201,1.3124,2.4029,1.6385,1.9343,0.49036,4.4815,1.42582,3.00196,2.92555,0.643715,2.44196,0.835262,1.24456,1.71328
+22-Oct-2019 21:00:00,1,0.4313,1.1023,1.7301,1.5114,1.1921,2.2172,1.5286,1.7654,0.45464,4.2672,1.34093,2.85958,2.75721,0.585267,2.26607,0.767841,1.15393,1.61576
+22-Oct-2019 22:00:00,1,0.39783,1.0002,1.5816,1.4058,1.0771,2.0397,1.4198,1.5988,0.4191,4.0415,1.25216,2.70998,2.58993,0.52905,2.09633,0.702268,1.06682,1.51839
+22-Oct-2019 23:00:00,1,0.36118,0.88893,1.4189,1.2901,0.95343,1.8487,1.2981,1.4133,0.37915,3.7732,1.14735,2.53253,2.40216,0.468122,1.91169,0.630323,0.972503,1.40853
+23-Oct-2019 00:00:00,1,0.33547,0.81129,1.3046,1.2089,0.8682,1.7169,1.211,1.2811,0.35043,3.5709,1.06878,2.39902,2.26738,0.425795,1.78296,0.579756,0.907032,1.32933
+23-Oct-2019 01:00:00,1,0.32128,0.76859,1.2414,1.164,0.82171,1.645,1.1623,1.2073,0.33432,3.4541,1.02354,2.32198,2.19186,0.402587,1.71221,0.55181,0.871153,1.28481
+23-Oct-2019 02:00:00,1,0.31448,0.74817,1.2111,1.1425,0.79959,1.6108,1.1387,1.1718,0.32654,3.3968,1.00138,2.28421,2.15538,0.391508,1.67839,0.538412,0.854031,1.26328
+23-Oct-2019 03:00:00,1,0.31778,0.75809,1.2259,1.153,0.81033,1.6274,1.1502,1.1891,0.33033,3.4248,1.01219,2.30263,2.17313,0.396887,1.69482,0.544922,0.862344,1.27376
+23-Oct-2019 04:00:00,1,0.32958,0.79355,1.2784,1.1903,0.84885,1.687,1.1908,1.2505,0.34376,3.5229,1.05016,2.36733,2.23612,0.416145,1.75356,0.568155,0.892111,1.31091
+23-Oct-2019 05:00:00,1,0.35589,0.87293,1.3954,1.2734,0.93579,1.8215,1.2803,1.3862,0.37329,3.7325,1.13153,2.50568,2.37464,0.459384,1.88514,0.619925,0.958983,1.39238
+23-Oct-2019 06:00:00,1,0.42267,1.0759,1.6918,1.4842,1.1622,2.1712,1.5008,1.7227,0.44556,4.2107,1.31865,2.8221,2.71444,0.57072,2.2222,0.750944,1.13138,1.59091
+23-Oct-2019 07:00:00,1,0.48119,1.2555,1.951,1.6687,1.3669,2.4868,1.6869,2.0089,0.50604,4.5717,1.46171,3.06193,2.99955,0.670005,2.5209,0.865356,1.28534,1.756
+23-Oct-2019 08:00:00,1,0.49872,1.3095,2.0285,1.7239,1.4292,2.5828,1.7414,2.0932,0.52368,4.6701,1.50105,3.12749,3.08283,0.700034,2.61094,0.89956,1.33195,1.80396
+23-Oct-2019 09:00:00,1,0.50087,1.3162,2.038,1.7307,1.4369,2.5947,1.7481,2.1036,0.52584,4.6819,1.50579,3.13536,3.09301,0.703736,2.62203,0.903765,1.33769,1.80981
+23-Oct-2019 10:00:00,1,0.50489,1.3286,2.0558,1.7434,1.4513,2.6168,1.7605,2.1228,0.52984,4.7037,1.51455,3.14993,3.11195,0.710644,2.64272,0.911604,1.34842,1.8207
+23-Oct-2019 11:00:00,1,0.50948,1.3428,2.0761,1.7578,1.4677,2.6421,1.7747,2.1447,0.5344,4.7283,1.52444,3.16637,3.13349,0.71853,2.66633,0.920543,1.36066,1.83308
+23-Oct-2019 12:00:00,1,0.50112,1.3169,2.0391,1.7315,1.4378,2.5961,1.7489,2.1047,0.52608,4.6832,1.50633,3.13627,3.09418,0.704162,2.62331,0.904249,1.33835,1.81049
+23-Oct-2019 13:00:00,1,0.49582,1.3006,2.0157,1.7148,1.4189,2.5669,1.7325,2.0793,0.52078,4.6541,1.49466,3.11685,3.06913,0.695064,2.59605,0.893911,1.32423,1.79608
+23-Oct-2019 14:00:00,1,0.48517,1.2677,1.9686,1.6812,1.381,2.5085,1.6993,2.0282,0.51007,4.5944,1.47079,3.07708,3.01857,0.676821,2.54135,0.873135,1.29592,1.76696
+23-Oct-2019 15:00:00,1,0.47736,1.2437,1.934,1.6566,1.3533,2.4659,1.6749,1.9904,0.50216,4.5496,1.4529,3.04723,2.98123,0.663462,2.50127,0.857879,1.27519,1.74543
+23-Oct-2019 16:00:00,1,0.46717,1.2123,1.8889,1.6245,1.3173,2.4104,1.6429,1.941,0.49178,4.4898,1.42909,3.00744,2.93224,0.646075,2.44906,0.83797,1.24822,1.71714
+23-Oct-2019 17:00:00,1,0.4703,1.2219,1.9028,1.6344,1.3284,2.4274,1.6527,1.9562,0.49497,4.5083,1.43647,3.01977,2.94732,0.651411,2.46508,0.844086,1.25649,1.72586
+23-Oct-2019 18:00:00,1,0.49421,1.2956,2.0086,1.7097,1.4131,2.5581,1.7274,2.0716,0.51916,4.6452,1.49108,3.11089,3.0615,0.692298,2.58776,0.890765,1.31994,1.79169
+23-Oct-2019 19:00:00,1,0.50022,1.3142,2.0351,1.7287,1.4346,2.5911,1.7461,2.1004,0.52518,4.6783,1.50436,3.13299,3.08993,0.702615,2.61867,0.902492,1.33595,1.80804
+23-Oct-2019 20:00:00,1,0.46839,1.2161,1.8943,1.6284,1.3216,2.4171,1.6467,1.947,0.49303,4.4971,1.43199,3.01228,2.93815,0.648165,2.45533,0.840366,1.25146,1.72056
+23-Oct-2019 21:00:00,1,0.43523,1.1143,1.7475,1.5238,1.2057,2.2382,1.5412,1.7848,0.45875,4.2925,1.35092,2.87637,2.77657,0.591893,2.28604,0.775521,1.1642,1.627
+23-Oct-2019 22:00:00,1,0.40218,1.0134,1.6009,1.4195,1.0919,2.0626,1.4341,1.6206,0.42377,4.0718,1.26405,2.73006,2.61187,0.536318,2.11831,0.710788,1.07808,1.53118
+23-Oct-2019 23:00:00,1,0.3625,0.89294,1.4248,1.2943,0.95786,1.8556,1.3025,1.4201,0.38061,3.7832,1.15128,2.5392,2.40903,0.470311,1.91833,0.632924,0.975889,1.41256
+24-Oct-2019 00:00:00,1,0.33637,0.81401,1.3086,1.2117,0.87117,1.7215,1.2141,1.2857,0.35145,3.5782,1.07161,2.40384,2.27216,0.427275,1.78747,0.581532,0.90932,1.33214
+24-Oct-2019 01:00:00,1,0.32192,0.77051,1.2443,1.1661,0.8238,1.6482,1.1645,1.2106,0.33505,3.4595,1.0256,2.3255,2.19527,0.403629,1.71539,0.553068,0.872764,1.28683
+24-Oct-2019 02:00:00,1,0.3155,0.75124,1.2157,1.1458,0.80291,1.6159,1.1423,1.1771,0.32771,3.4055,1.00474,2.28993,2.16089,0.393175,1.68348,0.54043,0.856607,1.26654
+24-Oct-2019 03:00:00,1,0.31845,0.7601,1.2288,1.1551,0.8125,1.6307,1.1525,1.1925,0.33109,3.4304,1.01437,2.30635,2.17672,0.397978,1.69815,0.546241,0.86403,1.27588
+24-Oct-2019 04:00:00,1,0.33109,0.79809,1.2851,1.195,0.8538,1.6947,1.196,1.2583,0.34547,3.5353,1.05495,2.37548,2.24414,0.418616,1.76109,0.571128,0.895931,1.31564
+24-Oct-2019 05:00:00,1,0.35705,0.87644,1.4005,1.2771,0.93966,1.8274,1.2842,1.3922,0.37458,3.7415,1.13502,2.51161,2.38069,0.461303,1.89097,0.62221,0.961952,1.39594
+24-Oct-2019 06:00:00,1,0.42222,1.0745,1.6898,1.4828,1.1607,2.1688,1.4993,1.7204,0.44508,4.2077,1.31746,2.8201,2.71218,0.569954,2.21989,0.750052,1.13019,1.58959
+24-Oct-2019 07:00:00,1,0.48207,1.2582,1.9548,1.6715,1.37,2.4916,1.6896,2.0132,0.50693,4.5767,1.46372,3.06528,3.00374,0.671506,2.52541,0.867069,1.28767,1.75842
+24-Oct-2019 08:00:00,1,0.49929,1.3113,2.031,1.7257,1.4313,2.586,1.7432,2.096,0.52425,4.6732,1.50232,3.12959,3.08554,0.70102,2.61389,0.90068,1.33348,1.80552
+24-Oct-2019 09:00:00,1,0.49979,1.3128,2.0332,1.7273,1.433,2.5887,1.7447,2.0984,0.52475,4.6759,1.50341,3.13141,3.08788,0.701872,2.61645,0.901648,1.3348,1.80687
+24-Oct-2019 10:00:00,1,0.5059,1.3317,2.0603,1.7466,1.4549,2.6224,1.7636,2.1276,0.53085,4.7092,1.51674,3.15357,3.1167,0.712381,2.64792,0.913574,1.35111,1.82343
+24-Oct-2019 11:00:00,1,0.51334,1.3547,2.0931,1.77,1.4816,2.6635,1.7866,2.1631,0.53824,4.7489,1.53271,3.1801,3.15161,0.725189,2.68626,0.92808,1.371,1.84348
+24-Oct-2019 12:00:00,1,0.50314,1.3232,2.0481,1.7379,1.445,2.6072,1.7551,2.1144,0.5281,4.6942,1.51075,3.14362,3.10372,0.707639,2.63372,0.908195,1.34375,1.81597
+24-Oct-2019 13:00:00,1,0.49758,1.306,2.0235,1.7203,1.4252,2.5766,1.7379,2.0878,0.52255,4.6638,1.49856,3.12334,3.07747,0.698088,2.60511,0.897349,1.32893,1.80088
+24-Oct-2019 14:00:00,1,0.48838,1.2776,1.9828,1.6913,1.3924,2.5261,1.7093,2.0436,0.5133,4.6125,1.47803,3.08916,3.03382,0.682306,2.5578,0.879388,1.30443,1.77575
+24-Oct-2019 15:00:00,1,0.482,1.2579,1.9545,1.6712,1.3697,2.4912,1.6894,2.0128,0.50686,4.5763,1.46355,3.06501,3.00341,0.671386,2.52505,0.866932,1.28748,1.75822
+24-Oct-2019 16:00:00,1,0.47243,1.2285,1.9122,1.6411,1.3359,2.439,1.6594,1.9666,0.49715,4.5208,1.44145,3.0281,2.95756,0.655043,2.47599,0.848246,1.26213,1.73177
+24-Oct-2019 17:00:00,1,0.47776,1.2449,1.9358,1.6579,1.3547,2.4681,1.6761,1.9924,0.50257,4.5519,1.45383,3.04877,2.98314,0.664144,2.50331,0.858659,1.27625,1.74653
+24-Oct-2019 18:00:00,1,0.497,1.3042,2.0209,1.7185,1.4231,2.5734,1.7361,2.085,0.52197,4.6606,1.49728,3.1212,3.07473,0.697092,2.60212,0.896217,1.32738,1.7993
+24-Oct-2019 19:00:00,1,0.50079,1.3159,2.0377,1.7305,1.4366,2.5943,1.7479,2.1032,0.52576,4.6814,1.50562,3.13508,3.09264,0.703602,2.62163,0.903613,1.33748,1.8096
+24-Oct-2019 20:00:00,1,0.47023,1.2217,1.9025,1.6341,1.3281,2.427,1.6525,1.9559,0.4949,4.5079,1.4363,3.01949,2.94698,0.651292,2.46473,0.84395,1.25631,1.72566
+24-Oct-2019 21:00:00,1,0.43806,1.123,1.76,1.5327,1.2155,2.2534,1.5503,1.7987,0.46171,4.3106,1.35806,2.88838,2.79049,0.596674,2.30044,0.781057,1.17161,1.63508
+24-Oct-2019 22:00:00,1,0.40849,1.0327,1.6289,1.4395,1.1135,2.096,1.4547,1.6522,0.43052,4.1153,1.28114,2.75889,2.64364,0.5469,2.15029,0.723169,1.09447,1.5497
+24-Oct-2019 23:00:00,1,0.36915,0.91305,1.4543,1.3153,0.98009,1.8899,1.3248,1.4539,0.38793,3.8334,1.17083,2.57236,2.44339,0.481306,1.9517,0.645972,0.992904,1.4327
+25-Oct-2019 00:00:00,1,0.33944,0.82326,1.3222,1.2214,0.88127,1.7372,1.2245,1.3016,0.3549,3.6029,1.08118,2.42013,2.28835,0.432306,1.80279,0.587567,0.917099,1.34167
+25-Oct-2019 01:00:00,1,0.32268,0.77282,1.2477,1.1685,0.8263,1.6521,1.1671,1.2146,0.33592,3.4659,1.02808,2.32972,2.19937,0.40488,1.71921,0.554579,0.874698,1.28925
+25-Oct-2019 02:00:00,1,0.31517,0.75023,1.2142,1.1447,0.80182,1.6142,1.1411,1.1754,0.32733,3.4026,1.00363,2.28805,2.15908,0.392626,1.68181,0.539766,0.855759,1.26547
+25-Oct-2019 03:00:00,1,0.31739,0.7569,1.2241,1.1517,0.80904,1.6254,1.1488,1.187,0.32987,3.4214,1.0109,2.30044,2.17101,0.396244,1.69285,0.544144,0.86135,1.27251
+25-Oct-2019 04:00:00,1,0.32789,0.78847,1.2708,1.1849,0.84332,1.6784,1.185,1.2417,0.34185,3.509,1.04478,2.35817,2.22714,0.413385,1.74514,0.564832,0.887844,1.30562
+25-Oct-2019 05:00:00,1,0.35415,0.86766,1.3876,1.2679,0.92999,1.8125,1.2744,1.3773,0.37135,3.719,1.12628,2.49677,2.36555,0.45651,1.87641,0.6165,0.954536,1.38705
+25-Oct-2019 06:00:00,1,0.41543,1.0538,1.6597,1.4613,1.1373,2.1327,1.4773,1.6867,0.4379,4.1624,1.29963,2.79007,2.67836,0.558541,2.18544,0.736759,1.11251,1.56992
+25-Oct-2019 07:00:00,1,0.47037,1.2221,1.9031,1.6346,1.3286,2.4278,1.6529,1.9566,0.49505,4.5087,1.43663,3.02004,2.94766,0.65153,2.46544,0.844223,1.25668,1.72605
+25-Oct-2019 08:00:00,1,0.48877,1.2788,1.9845,1.6926,1.3938,2.5282,1.7105,2.0455,0.5137,4.6148,1.47892,3.09064,3.0357,0.682982,2.55983,0.880158,1.30548,1.77683
+25-Oct-2019 09:00:00,1,0.49047,1.2841,1.992,1.6979,1.3998,2.5376,1.7158,2.0537,0.51541,4.6243,1.48274,3.097,3.04377,0.685893,2.56856,0.883475,1.31,1.78148
+25-Oct-2019 10:00:00,1,0.49791,1.307,2.0249,1.7214,1.4263,2.5784,1.7389,2.0894,0.52287,4.6656,1.49928,3.12453,3.07901,0.698648,2.60678,0.897985,1.32979,1.80177
+25-Oct-2019 11:00:00,1,0.50268,1.3218,2.046,1.7364,1.4434,2.6047,1.7537,2.1122,0.52764,4.6917,1.50974,3.14194,3.10154,0.706846,2.63134,0.907295,1.34252,1.81472
+25-Oct-2019 12:00:00,1,0.49313,1.2923,2.0038,1.7063,1.4093,2.5522,1.7241,2.0665,0.51809,4.6392,1.48869,3.10691,3.05641,0.690455,2.58223,0.888669,1.31708,1.78876
+25-Oct-2019 13:00:00,1,0.48776,1.2757,1.98,1.6894,1.3902,2.5227,1.7074,2.0406,0.51268,4.609,1.47664,3.08683,3.03087,0.681244,2.55462,0.878178,1.30278,1.77405
+25-Oct-2019 14:00:00,1,0.47647,1.2409,1.9301,1.6538,1.3502,2.461,1.6721,1.9861,0.50126,4.5444,1.45085,3.0438,2.97697,0.661943,2.49671,0.856142,1.27283,1.74297
+25-Oct-2019 15:00:00,1,0.46355,1.2012,1.8729,1.6131,1.3046,2.3908,1.6314,1.9234,0.48807,4.4682,1.42051,2.99308,2.91476,0.639913,2.43054,0.830898,1.23866,1.70705
+25-Oct-2019 16:00:00,1,0.4528,1.1682,1.8253,1.5792,1.2669,2.3327,1.5974,1.871,0.47702,4.4029,1.39461,2.94971,2.86263,0.62165,2.37563,0.809895,1.21033,1.67689
+25-Oct-2019 17:00:00,1,0.45439,1.1731,1.8324,1.5842,1.2725,2.3412,1.6024,1.8788,0.47866,4.4127,1.39849,2.9562,2.87037,0.62435,2.38375,0.813005,1.21452,1.68137
+25-Oct-2019 18:00:00,1,0.4729,1.2299,1.9143,1.6426,1.3376,2.4416,1.6609,1.9689,0.49763,4.5236,1.44256,3.02995,2.95985,0.655854,2.47843,0.849175,1.26339,1.73309
+25-Oct-2019 19:00:00,1,0.47498,1.2363,1.9235,1.6491,1.3449,2.4529,1.6674,1.9789,0.49974,4.5358,1.44739,3.03803,2.96982,0.659398,2.48907,0.853231,1.26888,1.73885
+25-Oct-2019 20:00:00,1,0.44182,1.1345,1.7767,1.5446,1.2286,2.2735,1.5624,1.8172,0.46563,4.3345,1.3675,2.90422,2.80896,0.603034,2.3196,0.788414,1.18147,1.64579
+25-Oct-2019 21:00:00,1,0.40966,1.0362,1.6341,1.4431,1.1175,2.1021,1.4585,1.658,0.43176,4.1233,1.28427,2.76417,2.64949,0.548856,2.15619,0.725455,1.0975,1.55311
+25-Oct-2019 22:00:00,1,0.3824,0.95325,1.5131,1.3571,1.0247,1.9589,1.3689,1.5211,0.40241,3.9313,1.20903,2.63705,2.51147,0.503311,2.01841,0.671991,1.02697,1.47255
+25-Oct-2019 23:00:00,1,0.35035,0.85619,1.3708,1.2559,0.91738,1.793,1.2616,1.3578,0.36712,3.6894,1.11477,2.47723,2.3457,0.45025,1.85738,0.609033,0.944852,1.37539
+26-Oct-2019 00:00:00,1,0.32077,0.76707,1.2392,1.1624,0.82007,1.6425,1.1605,1.2047,0.33375,3.4499,1.02191,2.3192,2.18916,0.401764,1.7097,0.550816,0.869881,1.28322
+26-Oct-2019 01:00:00,1,0.3065,0.72422,1.1756,1.1173,0.77373,1.5707,1.111,1.1299,0.31734,3.3284,0.974956,2.23912,2.1123,0.378532,1.63875,0.522672,0.833983,1.23783
+26-Oct-2019 02:00:00,1,0.3,0.70475,1.1466,1.0967,0.75277,1.5383,1.0883,1.0956,0.30981,3.2718,0.95311,2.20182,2.07703,0.367995,1.60653,0.509851,0.817706,1.21696
+26-Oct-2019 03:00:00,1,0.30513,0.72013,1.1695,1.113,0.76932,1.5639,1.1062,1.1227,0.31577,3.3166,0.970392,2.23133,2.10491,0.376317,1.63198,0.51998,0.83056,1.23346
+26-Oct-2019 04:00:00,1,0.31806,0.75893,1.2271,1.1539,0.81124,1.6288,1.1512,1.1905,0.33065,3.4271,1.0131,2.30419,2.17463,0.397344,1.69621,0.545475,0.86305,1.27465
+26-Oct-2019 05:00:00,1,0.34469,0.83909,1.3456,1.238,0.89861,1.764,1.2424,1.3287,0.36079,3.6448,1.09744,2.44777,2.316,0.440929,1.82903,0.597894,0.930435,1.35793
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+26-Oct-2019 07:00:00,1,0.47095,1.2239,1.9057,1.6364,1.3307,2.431,1.6548,1.9594,0.49564,4.5122,1.438,3.02234,2.95048,0.65253,2.46844,0.845368,1.25823,1.72768
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+26-Oct-2019 09:00:00,1,0.48884,1.279,1.9848,1.6928,1.394,2.5286,1.7107,2.0458,0.51376,4.6151,1.47907,3.09088,3.036,0.68309,2.56015,0.880282,1.30565,1.777
+26-Oct-2019 10:00:00,1,0.49485,1.2976,2.0114,1.7117,1.4154,2.5616,1.7294,2.0747,0.51981,4.6487,1.49251,3.11327,3.06455,0.693401,2.59106,0.89202,1.32165,1.79344
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+26-Oct-2019 12:00:00,1,0.49333,1.2929,2.0046,1.7069,1.41,2.5532,1.7247,2.0674,0.51828,4.6402,1.48912,3.10762,3.05731,0.690783,2.58321,0.889041,1.31759,1.78928
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+27-Oct-2019 01:00:00,1,0.31871,0.76086,1.23,1.1559,0.81333,1.632,1.1534,1.1939,0.33138,3.4325,1.0152,2.30776,2.17808,0.398393,1.69942,0.546743,0.864672,1.27669
+27-Oct-2019 02:00:00,1,0.31113,0.73811,1.1962,1.1319,0.78871,1.5939,1.1271,1.1542,0.32268,3.3682,0.990337,2.26537,2.13732,0.386053,1.66173,0.531802,0.845603,1.25262
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+29-Oct-2019 09:00:00,1,0.49193,1.2886,1.9985,1.7025,1.405,2.5455,1.7204,2.0606,0.51687,4.6324,1.486,3.10242,3.05068,0.688385,2.57603,0.886312,1.31386,1.78546
+29-Oct-2019 10:00:00,1,0.49895,1.3102,2.0295,1.7247,1.43,2.5841,1.7422,2.0944,0.52391,4.6713,1.50157,3.12835,3.08393,0.700436,2.61214,0.900017,1.33257,1.8046
+29-Oct-2019 11:00:00,1,0.50454,1.3275,2.0543,1.7423,1.45,2.6149,1.7594,2.1211,0.5295,4.7018,1.51379,3.14868,3.11031,0.710045,2.64093,0.910925,1.34749,1.81976
+29-Oct-2019 12:00:00,1,0.49602,1.3012,2.0166,1.7154,1.4196,2.568,1.7331,2.0803,0.52098,4.6552,1.4951,3.11758,3.07007,0.695403,2.59706,0.894297,1.32476,1.79662
+29-Oct-2019 13:00:00,1,0.4896,1.2814,1.9882,1.6952,1.3967,2.5328,1.7131,2.0495,0.51454,4.6194,1.48079,3.09375,3.03965,0.684407,2.5641,0.881782,1.30769,1.77911
+29-Oct-2019 14:00:00,1,0.48667,1.2723,1.9752,1.686,1.3863,2.5167,1.704,2.0353,0.51158,4.6029,1.47417,3.08272,3.02567,0.679375,2.54901,0.876047,1.29988,1.77106
+29-Oct-2019 15:00:00,1,0.48017,1.2523,1.9465,1.6655,1.3633,2.4812,1.6837,2.004,0.50501,4.5658,1.45938,3.05804,2.99468,0.668266,2.51568,0.863369,1.28264,1.75319
+29-Oct-2019 16:00:00,1,0.47526,1.2372,1.9247,1.65,1.3459,2.4544,1.6683,1.9803,0.50003,4.5374,1.44804,3.03911,2.97116,0.659876,2.4905,0.853777,1.26963,1.73962
+29-Oct-2019 17:00:00,1,0.49772,1.3064,2.0241,1.7208,1.4256,2.5773,1.7383,2.0884,0.52268,4.6646,1.49885,3.12383,3.07811,0.698319,2.6058,0.897612,1.32928,1.80125
+29-Oct-2019 18:00:00,1,0.49971,1.3126,2.0329,1.727,1.4327,2.5883,1.7445,2.098,0.52467,4.6755,1.50324,3.13112,3.08752,0.701738,2.61604,0.901496,1.33459,1.80666
+29-Oct-2019 19:00:00,1,0.48321,1.2617,1.9599,1.6751,1.374,2.4978,1.6932,2.0187,0.50809,4.5832,1.46633,3.06964,3.00921,0.673464,2.53128,0.869305,1.29071,1.76157
+29-Oct-2019 20:00:00,1,0.45263,1.1676,1.8245,1.5787,1.2663,2.3317,1.5968,1.8702,0.47684,4.4019,1.39419,2.949,2.86179,0.621356,2.37474,0.809557,1.20988,1.6764
+29-Oct-2019 21:00:00,1,0.42292,1.0767,1.6929,1.485,1.1631,2.1725,1.5016,1.7239,0.44583,4.2124,1.31931,2.8232,2.71569,0.571144,2.22348,0.751436,1.13204,1.59164
+29-Oct-2019 22:00:00,1,0.39981,1.0062,1.5904,1.4121,1.0838,2.0501,1.4263,1.6087,0.42123,4.0553,1.25759,2.71914,2.59992,0.532355,2.10632,0.706144,1.07194,1.52421
+29-Oct-2019 23:00:00,1,0.36556,0.90218,1.4383,1.3039,0.96806,1.8713,1.3128,1.4356,0.38398,3.8064,1.1603,2.55449,2.42484,0.47536,1.93366,0.63892,0.983702,1.42183
+30-Oct-2019 00:00:00,1,0.34134,0.82899,1.3307,1.2274,0.88755,1.7469,1.231,1.3114,0.35704,3.6181,1.08709,2.43018,2.29838,0.435427,1.81229,0.591307,0.921925,1.34757
+30-Oct-2019 01:00:00,1,0.32798,0.78874,1.2712,1.1852,0.84361,1.6789,1.1853,1.2422,0.34195,3.5097,1.04507,2.35866,2.22762,0.413531,1.74559,0.565009,0.888071,1.3059
+30-Oct-2019 02:00:00,1,0.32023,0.76544,1.2368,1.1607,0.8183,1.6397,1.1586,1.2018,0.33313,3.4454,1.02015,2.3162,2.18625,0.400879,1.707,0.549747,0.868514,1.28151
+30-Oct-2019 03:00:00,1,0.32261,0.7726,1.2474,1.1683,0.82607,1.6517,1.1669,1.2143,0.33584,3.4653,1.02785,2.32933,2.199,0.404766,1.71886,0.554441,0.874521,1.28903
+30-Oct-2019 04:00:00,1,0.32915,0.79226,1.2764,1.1889,0.84744,1.6848,1.1893,1.2483,0.34327,3.5193,1.04879,2.365,2.23383,0.415441,1.75141,0.567308,0.891024,1.30957
+30-Oct-2019 05:00:00,1,0.3513,0.85906,1.375,1.2589,0.92053,1.7979,1.2648,1.3627,0.36818,3.6968,1.11766,2.48213,2.35067,0.451816,1.86214,0.610901,0.947274,1.37831
+30-Oct-2019 06:00:00,1,0.4103,1.0382,1.637,1.4452,1.1197,2.1055,1.4606,1.6612,0.43245,4.1277,1.28599,2.76706,2.65271,0.549931,2.15944,0.726711,1.09917,1.55498
+30-Oct-2019 07:00:00,1,0.46246,1.1978,1.8681,1.6097,1.3008,2.3849,1.628,1.9182,0.48696,4.4617,1.41792,2.98876,2.90952,0.638067,2.42499,0.828779,1.2358,1.70401
+30-Oct-2019 08:00:00,1,0.48173,1.2571,1.9534,1.6704,1.3688,2.4897,1.6886,2.0115,0.50659,4.5748,1.46294,3.06399,3.00213,0.67093,2.52368,0.866411,1.28678,1.75749
+30-Oct-2019 09:00:00,1,0.48667,1.2724,1.9752,1.686,1.3863,2.5168,1.704,2.0354,0.51158,4.6029,1.47419,3.08274,3.02571,0.679387,2.54905,0.876061,1.2999,1.77108
+30-Oct-2019 10:00:00,1,0.49488,1.2977,2.0115,1.7118,1.4155,2.5617,1.7295,2.0748,0.51984,4.6489,1.49257,3.11338,3.06468,0.69345,2.59121,0.892075,1.32173,1.79352
+30-Oct-2019 11:00:00,1,0.49989,1.3131,2.0337,1.7276,1.4334,2.5893,1.7451,2.0988,0.52485,4.6765,1.50363,3.13177,3.08835,0.702042,2.61696,0.901842,1.33506,1.80714
+30-Oct-2019 12:00:00,1,0.4991,1.3107,2.0302,1.7251,1.4306,2.5849,1.7426,2.0951,0.52406,4.6722,1.5019,3.12889,3.08464,0.700691,2.61291,0.900307,1.33297,1.805
+30-Oct-2019 13:00:00,1,0.49669,1.3033,2.0195,1.7175,1.422,2.5717,1.7352,2.0835,0.52165,4.6589,1.49659,3.12006,3.07325,0.696557,2.60052,0.895609,1.32655,1.79845
+30-Oct-2019 14:00:00,1,0.48809,1.2767,1.9815,1.6904,1.3913,2.5245,1.7084,2.0422,0.51301,4.6109,1.47738,3.08807,3.03245,0.681811,2.55632,0.878824,1.30366,1.77496
+30-Oct-2019 15:00:00,1,0.48132,1.2559,1.9516,1.6691,1.3673,2.4875,1.6873,2.0096,0.50618,4.5724,1.46201,3.06244,3.00019,0.670233,2.52159,0.865616,1.2857,1.75637
+30-Oct-2019 16:00:00,1,0.47697,1.2425,1.9323,1.6554,1.3519,2.4637,1.6737,1.9885,0.50176,4.5473,1.452,3.04572,2.97935,0.662792,2.49926,0.857113,1.27415,1.74435
+30-Oct-2019 17:00:00,1,0.50012,1.3138,2.0347,1.7283,1.4342,2.5906,1.7458,2.1,0.52508,4.6778,1.50414,3.13262,3.08946,0.702444,2.61816,0.902298,1.33569,1.80777
+30-Oct-2019 18:00:00,1,0.5016,1.3184,2.0412,1.733,1.4395,2.5987,1.7503,2.107,0.52656,4.6858,1.50737,3.138,3.09642,0.704979,2.62575,0.905177,1.33962,1.81178
+30-Oct-2019 19:00:00,1,0.48479,1.2665,1.9669,1.68,1.3796,2.5064,1.6981,2.0263,0.50968,4.5922,1.46991,3.07561,3.01672,0.676159,2.53936,0.872379,1.29489,1.7659
+30-Oct-2019 20:00:00,1,0.45533,1.1759,1.8365,1.5872,1.2758,2.3463,1.6054,1.8834,0.47963,4.4185,1.40077,2.96003,2.87495,0.625948,2.38855,0.814845,1.217,1.68402
+30-Oct-2019 21:00:00,1,0.4259,1.0858,1.7061,1.4944,1.1734,2.1884,1.5112,1.7387,0.44897,4.232,1.32704,2.83622,2.73048,0.576162,2.23861,0.75727,1.13981,1.60023
+30-Oct-2019 22:00:00,1,0.40146,1.0112,1.5978,1.4173,1.0895,2.0589,1.4317,1.617,0.423,4.0669,1.26211,2.72679,2.60828,0.535127,2.1147,0.709393,1.07624,1.52909
+30-Oct-2019 23:00:00,1,0.36577,0.90281,1.4393,1.3046,0.96877,1.8724,1.3135,1.4367,0.38421,3.808,1.16092,2.55554,2.42593,0.475707,1.93471,0.639333,0.98424,1.42247
+31-Oct-2019 00:00:00,1,0.3399,0.82466,1.3243,1.2229,0.88281,1.7395,1.2261,1.304,0.35542,3.6067,1.08263,2.42259,2.29081,0.433069,1.80511,0.588482,0.91828,1.34312
+31-Oct-2019 01:00:00,1,0.32731,0.78673,1.2683,1.1831,0.84143,1.6755,1.183,1.2387,0.34119,3.5042,1.04294,2.35503,2.22407,0.412441,1.74227,0.563695,0.886385,1.30381
+31-Oct-2019 02:00:00,1,0.32143,0.76905,1.2421,1.1645,0.82221,1.6458,1.1628,1.2081,0.3345,3.4554,1.02403,2.32283,2.19268,0.402837,1.71298,0.552112,0.87154,1.2853
+31-Oct-2019 03:00:00,1,0.32426,0.77755,1.2547,1.1735,0.83144,1.6601,1.1725,1.2228,0.33772,3.479,1.03315,2.33835,2.20778,0.407449,1.72705,0.557678,0.878669,1.29421
+31-Oct-2019 04:00:00,1,0.3312,0.79842,1.2856,1.1954,0.85416,1.6952,1.1964,1.2589,0.3456,3.5361,1.0553,2.37607,2.24472,0.418794,1.76163,0.571343,0.896207,1.31598
+31-Oct-2019 05:00:00,1,0.34938,0.85326,1.3664,1.2528,0.91416,1.788,1.2583,1.3528,0.36604,3.6818,1.11182,2.47221,2.34063,0.448654,1.85253,0.607127,0.942383,1.37241
+31-Oct-2019 06:00:00,1,0.39541,0.9928,1.5709,1.3982,1.0688,2.027,1.4119,1.5867,0.4165,4.0244,1.24549,2.6987,2.57767,0.525001,2.08408,0.697517,1.06055,1.51123
+31-Oct-2019 07:00:00,1,0.43147,1.1028,1.7308,1.512,1.1926,2.2181,1.5292,1.7662,0.45482,4.2683,1.34136,2.86031,2.75805,0.585556,2.26694,0.768175,1.15437,1.61625
+31-Oct-2019 08:00:00,1,0.44495,1.1441,1.7905,1.5545,1.2395,2.2904,1.5724,1.8326,0.46889,4.3542,1.37531,2.91733,2.82432,0.608342,2.33558,0.794547,1.1897,1.65469
+31-Oct-2019 09:00:00,1,0.45319,1.1694,1.8271,1.5805,1.2683,2.3348,1.5986,1.8729,0.47742,4.4054,1.39557,2.95132,2.86455,0.622318,2.37764,0.810666,1.21137,1.678
+31-Oct-2019 10:00:00,1,0.461,1.1933,1.8616,1.6051,1.2956,2.377,1.6234,1.911,0.48546,4.4529,1.41442,2.98289,2.90243,0.635574,2.4175,0.825915,1.23193,1.69991
+31-Oct-2019 11:00:00,1,0.47141,1.2254,1.9077,1.6379,1.3323,2.4335,1.6562,1.9616,0.49611,4.5149,1.43908,3.02414,2.95269,0.653316,2.4708,0.846268,1.25945,1.72896
+31-Oct-2019 12:00:00,1,0.46032,1.1913,1.8586,1.6029,1.2932,2.3733,1.6212,1.9077,0.48476,4.4488,1.41279,2.98016,2.89913,0.634417,2.41402,0.824586,1.23013,1.69801
+31-Oct-2019 13:00:00,1,0.44969,1.1586,1.8115,1.5694,1.256,2.3159,1.5875,1.8558,0.4738,4.3838,1.38701,2.93696,2.84749,0.616377,2.35976,0.803819,1.20216,1.66812
+31-Oct-2019 14:00:00,1,0.44484,1.1438,1.7901,1.5541,1.2391,2.2898,1.572,1.832,0.46877,4.3535,1.37503,2.91687,2.82378,0.608156,2.33502,0.794332,1.18941,1.65438
+31-Oct-2019 15:00:00,1,0.43806,1.123,1.76,1.5327,1.2155,2.2534,1.5503,1.7987,0.46171,4.3106,1.35806,2.88838,2.79049,0.596674,2.30044,0.781057,1.17161,1.63508
+31-Oct-2019 16:00:00,1,0.43638,1.1178,1.7526,1.5274,1.2097,2.2444,1.5449,1.7904,0.45996,4.2999,1.35383,2.88126,2.78223,0.593836,2.29189,0.777772,1.16721,1.63028
+31-Oct-2019 17:00:00,1,0.4664,1.2099,1.8855,1.6221,1.3146,2.4063,1.6404,1.9373,0.49099,4.4852,1.42728,3.00441,2.92854,0.64477,2.44513,0.836473,1.24619,1.71501
+31-Oct-2019 18:00:00,1,0.46691,1.2115,1.8878,1.6237,1.3164,2.409,1.642,1.9398,0.49151,4.4883,1.42849,3.00643,2.93099,0.645636,2.44774,0.837466,1.24754,1.71643
+31-Oct-2019 19:00:00,1,0.45058,1.1614,1.8155,1.5722,1.2591,2.3207,1.5903,1.8602,0.47473,4.3893,1.38919,2.94062,2.85183,0.617887,2.36431,0.80556,1.2045,1.67063
+31-Oct-2019 20:00:00,1,0.41995,1.0676,1.6797,1.4756,1.1528,2.1567,1.492,1.7092,0.44269,4.1926,1.31154,2.81012,2.7009,0.566138,2.20838,0.745611,1.12428,1.58303
+31-Oct-2019 21:00:00,1,0.39475,0.99078,1.5679,1.3961,1.0666,2.0235,1.4097,1.5833,0.41578,4.0198,1.24366,2.69561,2.57432,0.523896,2.08074,0.696218,1.05884,1.50927
+31-Oct-2019 22:00:00,1,0.37741,0.93812,1.491,1.3414,1.0079,1.9329,1.3524,1.4959,0.39698,3.8948,1.19479,2.61295,2.48595,0.495026,1.99331,0.66221,1.01414,1.45762
+31-Oct-2019 23:00:00,1,0.35345,0.86555,1.3845,1.2657,0.92767,1.8089,1.2721,1.3737,0.37057,3.7136,1.12417,2.49319,2.36191,0.455359,1.87291,0.615128,0.952756,1.38491
+01-Nov-2019 00:00:00,1,0.32279,0.77314,1.2482,1.1688,0.82666,1.6526,1.1675,1.2152,0.33605,3.4668,1.02843,2.33032,2.19996,0.405058,1.71975,0.554793,0.874972,1.28959
+01-Nov-2019 01:00:00,1,0.30882,0.73119,1.1859,1.1246,0.78124,1.5823,1.1191,1.1421,0.32003,3.3484,0.982691,2.25232,2.12487,0.382304,1.65028,0.527253,0.83981,1.24525
+01-Nov-2019 02:00:00,1,0.30328,0.71459,1.1613,1.1071,0.76335,1.5546,1.0998,1.113,0.31362,3.3005,0.964186,2.22073,2.09488,0.373316,1.6228,0.51633,0.825925,1.22752
+01-Nov-2019 03:00:00,1,0.30306,0.71395,1.1603,1.1065,0.76266,1.5536,1.099,1.1118,0.31337,3.2986,0.963465,2.2195,2.09371,0.372968,1.62174,0.515907,0.825388,1.22683
+01-Nov-2019 04:00:00,1,0.3043,0.71763,1.1658,1.1103,0.76663,1.5597,1.1033,1.1183,0.3148,3.3093,0.967598,2.22656,2.10039,0.374964,1.62784,0.518335,0.828471,1.23078
+01-Nov-2019 05:00:00,1,0.30939,0.73292,1.1885,1.1265,0.7831,1.5852,1.1211,1.1451,0.32069,3.3534,0.984605,2.25559,2.12798,0.38324,1.65314,0.52839,0.841257,1.2471
+01-Nov-2019 06:00:00,1,0.32402,0.77683,1.2536,1.1727,0.83067,1.6589,1.1717,1.2216,0.33745,3.477,1.03239,2.33706,2.20651,0.407062,1.72587,0.557212,0.878071,1.29346
+01-Nov-2019 07:00:00,1,0.34383,0.83649,1.3417,1.2353,0.89576,1.7596,1.2394,1.3242,0.35983,3.638,1.09478,2.44325,2.31147,0.439512,1.82472,0.596198,0.928243,1.35527
+01-Nov-2019 08:00:00,1,0.35938,0.88348,1.4109,1.2844,0.94743,1.8394,1.292,1.4041,0.37715,3.7594,1.14199,2.52343,2.39281,0.465147,1.90265,0.626785,0.967899,1.40304
+01-Nov-2019 09:00:00,1,0.37284,0.92424,1.4707,1.3269,0.99249,1.9091,1.3371,1.4727,0.39198,3.861,1.18158,2.59057,2.46242,0.487425,1.97026,0.65322,1.00237,1.44385
+01-Nov-2019 10:00:00,1,0.38933,0.9743,1.5439,1.379,1.0482,1.9951,1.3918,1.5561,0.40993,3.9812,1.22857,2.67011,2.5468,0.514852,2.05337,0.685587,1.04484,1.4932
+01-Nov-2019 11:00:00,1,0.40744,1.0294,1.6242,1.4361,1.1099,2.0904,1.4513,1.6469,0.42939,4.1081,1.2783,2.75409,2.63833,0.545127,2.14493,0.721096,1.09173,1.54661
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+11-Nov-2019 01:00:00,1,0.27344,0.62547,1.0282,1.0127,0.6681,1.4071,0.99451,0.95444,0.27862,3.0316,0.860699,2.04377,1.93102,0.325212,1.47539,0.457416,0.751651,1.13038
+11-Nov-2019 02:00:00,1,0.2655,0.60183,0.9928,0.98761,0.64308,1.3683,0.9661,0.91178,0.26914,2.957,0.832039,1.99467,1.88673,0.312498,1.43633,0.441712,0.732033,1.10405
+11-Nov-2019 03:00:00,1,0.26669,0.60536,0.9981,0.99137,0.64681,1.3741,0.97036,0.91817,0.27056,2.9682,0.836353,2.00206,1.89337,0.314396,1.44217,0.44406,0.734961,1.108
+11-Nov-2019 04:00:00,1,0.27116,0.61866,1.018,1.0055,0.66089,1.3959,0.98635,0.94218,0.2759,3.0103,0.852498,2.02972,1.9183,0.321548,1.46414,0.452897,0.745997,1.12283
+11-Nov-2019 05:00:00,1,0.26863,0.61115,1.0068,0.99753,0.65294,1.3836,0.97733,0.92863,0.27289,2.9866,0.843403,2.01414,1.90424,0.317509,1.45173,0.447908,0.739765,1.11447
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+11-Nov-2019 08:00:00,1,0.30077,0.70707,1.1501,1.0992,0.75526,1.5421,1.091,1.0997,0.31071,3.2786,0.95573,2.20629,2.08125,0.36925,1.61036,0.51138,0.819644,1.21946
+11-Nov-2019 09:00:00,1,0.32485,0.77934,1.2573,1.1753,0.83338,1.6631,1.1746,1.2259,0.33839,3.4839,1.03506,2.34161,2.21096,0.408421,1.73001,0.558851,0.880171,1.29608
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+12-Nov-2019 01:00:00,1,0.29479,0.68918,1.1234,1.0803,0.73605,1.5124,1.0701,1.0681,0.30375,3.2258,0.9354,2.17156,2.04866,0.359576,1.58076,0.499581,0.804702,1.20016
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+14-Nov-2019 01:00:00,1,0.33209,0.80111,1.2895,1.1982,0.85709,1.6997,1.1994,1.2635,0.34661,3.5434,1.05812,2.38089,2.24947,0.420258,1.76609,0.573102,0.898469,1.31878
+14-Nov-2019 02:00:00,1,0.32653,0.78438,1.2648,1.1806,0.83887,1.6716,1.1803,1.2347,0.3403,3.4978,1.04044,2.35078,2.2199,0.411163,1.73837,0.562155,0.884409,1.30135
+14-Nov-2019 03:00:00,1,0.33107,0.79804,1.285,1.195,0.85374,1.6946,1.1959,1.2582,0.34545,3.5351,1.05489,2.37538,2.24404,0.418584,1.76099,0.57109,0.895882,1.31558
+14-Nov-2019 04:00:00,1,0.34161,0.82981,1.3319,1.2283,0.88844,1.7482,1.2319,1.3128,0.35734,3.6203,1.08793,2.43161,2.29981,0.435873,1.81365,0.591842,0.922616,1.34841
+14-Nov-2019 05:00:00,1,0.36657,0.90526,1.4429,1.3071,0.97147,1.8766,1.3162,1.4408,0.3851,3.8141,1.16329,2.55958,2.43011,0.477046,1.93877,0.64092,0.986311,1.42492
+14-Nov-2019 06:00:00,1,0.42887,1.0949,1.7193,1.5037,1.1836,2.2042,1.5208,1.7534,0.45209,4.2514,1.33468,2.84907,2.74516,0.581158,2.25368,0.763072,1.14756,1.60876
+14-Nov-2019 07:00:00,1,0.4791,1.249,1.9417,1.6621,1.3595,2.4754,1.6803,1.9988,0.50392,4.5596,1.45691,3.05392,2.98955,0.666432,2.51018,0.861273,1.2798,1.75023
+14-Nov-2019 08:00:00,1,0.49582,1.3006,2.0157,1.7148,1.4189,2.5669,1.7325,2.0793,0.52078,4.6541,1.49466,3.11685,3.06913,0.695064,2.59605,0.893911,1.32423,1.79608
+14-Nov-2019 09:00:00,1,0.49199,1.2888,1.9987,1.7027,1.4052,2.5459,1.7206,2.061,0.51694,4.6328,1.48614,3.10266,3.05098,0.688494,2.57635,0.886436,1.31403,1.78563
+14-Nov-2019 10:00:00,1,0.50075,1.3158,2.0375,1.7303,1.4365,2.594,1.7477,2.103,0.52571,4.6812,1.50552,3.13493,3.09244,0.703529,2.62141,0.90353,1.33737,1.80949
+14-Nov-2019 11:00:00,1,0.50752,1.3367,2.0674,1.7516,1.4607,2.6313,1.7686,2.1353,0.53246,4.7178,1.52023,3.15937,3.12429,0.715159,2.65624,0.916723,1.35542,1.8278
+14-Nov-2019 12:00:00,1,0.50137,1.3177,2.0402,1.7323,1.4387,2.5974,1.7496,2.1059,0.52633,4.6846,1.50687,3.13717,3.09535,0.704589,2.62458,0.904734,1.33902,1.81116
+14-Nov-2019 13:00:00,1,0.49891,1.3101,2.0293,1.7245,1.4299,2.5839,1.742,2.0941,0.52387,4.6711,1.50148,3.12819,3.08373,0.700363,2.61192,0.899934,1.33246,1.80448
+14-Nov-2019 14:00:00,1,0.49576,1.3004,2.0154,1.7146,1.4186,2.5666,1.7323,2.079,0.52072,4.6537,1.49452,3.11662,3.06883,0.694954,2.59572,0.893787,1.32406,1.79591
+14-Nov-2019 15:00:00,1,0.4927,1.291,2.0019,1.705,1.4078,2.5498,1.7228,2.0644,0.51765,4.6368,1.48773,3.10531,3.05436,0.689717,2.58002,0.887828,1.31593,1.78758
+14-Nov-2019 16:00:00,1,0.49807,1.3075,2.0256,1.7219,1.4269,2.5792,1.7394,2.0901,0.52303,4.6665,1.49962,3.1251,3.07975,0.698915,2.60759,0.898289,1.33021,1.80219
+14-Nov-2019 17:00:00,1,0.52595,1.3937,2.1489,1.8097,1.527,2.7334,1.8252,2.223,0.55067,4.8145,1.55916,3.22394,3.2104,0.746945,2.75133,0.952652,1.40478,1.8772
+14-Nov-2019 18:00:00,1,0.52366,1.3866,2.1387,1.8025,1.5187,2.7207,1.8181,2.2121,0.54841,4.8027,1.5544,3.21605,3.19972,0.742974,2.73945,0.948174,1.39861,1.87108
+14-Nov-2019 19:00:00,1,0.50783,1.3377,2.0688,1.7526,1.4618,2.633,1.7696,2.1368,0.53277,4.7195,1.5209,3.16049,3.12576,0.715698,2.65785,0.917334,1.35626,1.82864
+14-Nov-2019 20:00:00,1,0.47616,1.24,1.9287,1.6529,1.3491,2.4593,1.6711,1.9846,0.50095,4.5426,1.45013,3.04261,2.97549,0.661417,2.49513,0.855541,1.27202,1.74212
+14-Nov-2019 21:00:00,1,0.44577,1.1466,1.7942,1.557,1.2423,2.2948,1.575,1.8366,0.46974,4.3593,1.37734,2.92073,2.82833,0.60973,2.33976,0.796149,1.19185,1.65702
+14-Nov-2019 22:00:00,1,0.41806,1.0618,1.6714,1.4697,1.1463,2.1467,1.4859,1.6998,0.44069,4.18,1.30659,2.80178,2.69151,0.562968,2.19881,0.741919,1.11937,1.57757
+14-Nov-2019 23:00:00,1,0.38117,0.94952,1.5077,1.3532,1.0206,1.9525,1.3648,1.5149,0.40108,3.9223,1.20553,2.63114,2.50519,0.501269,2.01223,0.669582,1.02381,1.46888
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+15-Nov-2019 01:00:00,1,0.3388,0.82135,1.3194,1.2194,0.87919,1.7339,1.2224,1.2983,0.35419,3.5978,1.07921,2.41678,2.28501,0.431266,1.79962,0.586321,0.915492,1.3397
+15-Nov-2019 02:00:00,1,0.33466,0.80885,1.301,1.2063,0.86553,1.7128,1.2082,1.2769,0.34951,3.5643,1.06622,2.39468,2.26308,0.424465,1.77891,0.578158,0.904974,1.32679
+15-Nov-2019 03:00:00,1,0.33773,0.81812,1.3146,1.216,0.87566,1.7285,1.2187,1.2928,0.35298,3.5892,1.07587,2.41108,2.27935,0.429508,1.79427,0.584212,0.912772,1.33637
+15-Nov-2019 04:00:00,1,0.34799,0.84906,1.3603,1.2484,0.90955,1.7809,1.2536,1.3457,0.36449,3.6709,1.10757,2.465,2.33334,0.446364,1.84556,0.604391,0.938841,1.36812
+15-Nov-2019 05:00:00,1,0.37089,0.91834,1.462,1.3208,0.98595,1.899,1.3306,1.4628,0.38984,3.8465,1.17592,2.58099,2.4524,0.484199,1.96048,0.6494,0.997382,1.43798
+15-Nov-2019 06:00:00,1,0.43318,1.1081,1.7384,1.5173,1.1986,2.2273,1.5347,1.7747,0.45661,4.2793,1.34572,2.86764,2.76648,0.588439,2.27563,0.771519,1.15884,1.62114
+15-Nov-2019 07:00:00,1,0.48775,1.2757,1.98,1.6894,1.3901,2.5227,1.7074,2.0406,0.51267,4.609,1.47662,3.0868,3.03084,0.681232,2.55458,0.878164,1.30276,1.77403
+15-Nov-2019 08:00:00,1,0.50528,1.3298,2.0575,1.7446,1.4527,2.619,1.7617,2.1246,0.53023,4.7058,1.51538,3.15132,3.11375,0.711304,2.6447,0.912353,1.34944,1.82174
+15-Nov-2019 09:00:00,1,0.50801,1.3382,2.0696,1.7532,1.4624,2.634,1.7701,2.1377,0.53295,4.7205,1.52129,3.16113,3.1266,0.716005,2.65877,0.917681,1.35674,1.82912
+15-Nov-2019 10:00:00,1,0.51311,1.354,2.0921,1.7692,1.4807,2.6622,1.7858,2.162,0.538,4.7477,1.53221,3.17926,3.15051,0.724783,2.68504,0.927621,1.37037,1.84285
+15-Nov-2019 11:00:00,1,0.5189,1.3719,2.1177,1.7875,1.5016,2.6943,1.8036,2.1895,0.54373,4.7781,1.54446,3.19959,3.17757,0.734768,2.71492,0.93891,1.38587,1.85838
+15-Nov-2019 12:00:00,1,0.51841,1.3704,2.1155,1.7859,1.4998,2.6915,1.8021,2.1872,0.54324,4.7755,1.54342,3.19787,3.17527,0.733916,2.71237,0.937947,1.38455,1.85706
+15-Nov-2019 13:00:00,1,0.51578,1.3623,2.1039,1.7777,1.4903,2.677,1.794,2.1747,0.54065,4.7617,1.53788,3.18868,3.16301,0.729388,2.69882,0.93283,1.37752,1.85003
+15-Nov-2019 14:00:00,1,0.50999,1.3443,2.0783,1.7594,1.4695,2.645,1.7762,2.1471,0.53491,4.7311,1.52555,3.1682,3.1359,0.719414,2.66897,0.921543,1.36203,1.83446
+15-Nov-2019 15:00:00,1,0.50781,1.3376,2.0687,1.7526,1.4617,2.6329,1.7695,2.1367,0.53275,4.7194,1.52086,3.16042,3.12566,0.715662,2.65774,0.917293,1.3562,1.82858
+15-Nov-2019 16:00:00,1,0.51196,1.3504,2.087,1.7656,1.4766,2.6558,1.7823,2.1565,0.53687,4.7416,1.52976,3.1752,3.14513,0.722803,2.67912,0.925381,1.36729,1.83976
+15-Nov-2019 17:00:00,1,0.53215,1.4129,2.1762,1.8292,1.5494,2.7679,1.844,2.2523,0.55674,4.846,1.57187,3.24497,3.2391,0.75765,2.78332,0.964712,1.42141,1.89364
+15-Nov-2019 18:00:00,1,0.52593,1.3937,2.1487,1.8096,1.5269,2.7333,1.8251,2.2228,0.55065,4.8144,1.5591,3.22385,3.21027,0.746895,2.75118,0.952596,1.4047,1.87713
+15-Nov-2019 19:00:00,1,0.51152,1.3491,2.0851,1.7642,1.475,2.6534,1.7809,2.1544,0.53643,4.7392,1.52881,3.17363,3.14305,0.722041,2.67684,0.924518,1.36611,1.83857
+15-Nov-2019 20:00:00,1,0.47915,1.2492,1.9419,1.6623,1.3596,2.4756,1.6805,1.9991,0.50397,4.5599,1.45702,3.05411,2.98979,0.666516,2.51043,0.861369,1.27993,1.75037
+15-Nov-2019 21:00:00,1,0.45056,1.1613,1.8154,1.5721,1.259,2.3206,1.5902,1.86,0.4747,4.3891,1.38912,2.94051,2.85169,0.61784,2.36417,0.805506,1.20443,1.67055
+15-Nov-2019 22:00:00,1,0.4252,1.0837,1.703,1.4922,1.171,2.1847,1.509,1.7352,0.44823,4.2274,1.32522,2.83316,2.727,0.57498,2.23505,0.755897,1.13798,1.59821
+15-Nov-2019 23:00:00,1,0.38624,0.96491,1.5302,1.3692,1.0377,1.9789,1.3816,1.5405,0.40658,3.9591,1.21989,2.65543,2.53107,0.509704,2.03778,0.679526,1.03686,1.48401
+16-Nov-2019 00:00:00,1,0.35012,0.85548,1.3697,1.2552,0.9166,1.7918,1.2608,1.3566,0.36686,3.6876,1.11406,2.47601,2.34448,0.449864,1.85621,0.608572,0.944255,1.37467
+16-Nov-2019 01:00:00,1,0.33596,0.81277,1.3067,1.2104,0.86981,1.7194,1.2126,1.2836,0.35098,3.5749,1.07031,2.40163,2.26997,0.426598,1.78541,0.58072,0.908273,1.33085
+16-Nov-2019 02:00:00,1,0.33103,0.79794,1.2848,1.1949,0.85363,1.6944,1.1958,1.2581,0.34541,3.5348,1.05479,2.37521,2.24387,0.418531,1.76083,0.571027,0.895801,1.31548
+16-Nov-2019 03:00:00,1,0.33482,0.80933,1.3017,1.2068,0.86606,1.7136,1.2088,1.2777,0.34969,3.5657,1.06673,2.39554,2.26393,0.424728,1.77971,0.578475,0.905382,1.3273
+16-Nov-2019 04:00:00,1,0.34364,0.83594,1.3409,1.2347,0.89516,1.7586,1.2388,1.3233,0.35962,3.6365,1.09422,2.4423,2.31051,0.439214,1.82381,0.595841,0.927782,1.3547
+16-Nov-2019 05:00:00,1,0.36575,0.90275,1.4392,1.3045,0.9687,1.8723,1.3134,1.4366,0.38419,3.8078,1.16086,2.55545,2.42582,0.475675,1.93461,0.639294,0.98419,1.42241
+16-Nov-2019 06:00:00,1,0.42481,1.0825,1.7013,1.491,1.1696,2.1826,1.5077,1.7333,0.44782,4.2248,1.32422,2.83147,2.72508,0.574327,2.23308,0.755138,1.13697,1.59709
+16-Nov-2019 07:00:00,1,0.48271,1.2602,1.9577,1.6735,1.3723,2.4951,1.6916,2.0163,0.50759,4.5804,1.46519,3.06774,3.00683,0.672611,2.52872,0.868331,1.28939,1.76019
+16-Nov-2019 08:00:00,1,0.50526,1.3297,2.0574,1.7445,1.4526,2.6189,1.7617,2.1245,0.53021,4.7057,1.51535,3.15127,3.11369,0.71128,2.64462,0.912325,1.3494,1.8217
+16-Nov-2019 09:00:00,1,0.50941,1.3425,2.0757,1.7576,1.4674,2.6417,1.7744,2.1443,0.53433,4.728,1.52429,3.16612,3.13315,0.718408,2.66596,0.920404,1.36047,1.83289
+16-Nov-2019 10:00:00,1,0.52113,1.3788,2.1276,1.7945,1.5096,2.7066,1.8104,2.2001,0.54593,4.7897,1.54913,3.20733,3.18796,0.738614,2.72642,0.943253,1.39184,1.86434
+16-Nov-2019 11:00:00,1,0.52348,1.3861,2.1379,1.8019,1.5181,2.7197,1.8176,2.2113,0.54824,4.8018,1.55404,3.21546,3.19892,0.742676,2.73857,0.947838,1.39815,1.87062
+16-Nov-2019 12:00:00,1,0.51428,1.3576,2.0973,1.7729,1.4849,2.6687,1.7894,2.1675,0.53916,4.7538,1.5347,3.1834,3.15599,0.726801,2.69108,0.929904,1.3735,1.846
+16-Nov-2019 13:00:00,1,0.50429,1.3267,2.0531,1.7415,1.4491,2.6135,1.7587,2.1199,0.52924,4.7004,1.51324,3.14775,3.1091,0.709605,2.63961,0.910426,1.3468,1.81907
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+16-Nov-2019 21:00:00,1,0.43291,1.1072,1.7372,1.5165,1.1976,2.2258,1.5338,1.7734,0.45633,4.2776,1.34504,2.86649,2.76517,0.587989,2.27427,0.770997,1.15815,1.62038
+16-Nov-2019 22:00:00,1,0.41363,1.0483,1.6517,1.4557,1.1311,2.1232,1.4715,1.6778,0.43599,4.1502,1.29486,2.78203,2.66937,0.555519,2.17632,0.733234,1.10783,1.56469
+16-Nov-2019 23:00:00,1,0.37779,0.93927,1.4927,1.3426,1.0092,1.9348,1.3536,1.4978,0.3974,3.8976,1.19587,2.61478,2.48788,0.495653,1.99521,0.66295,1.01511,1.45875
+17-Nov-2019 00:00:00,1,0.34703,0.84616,1.356,1.2454,0.90636,1.776,1.2503,1.3407,0.36341,3.6633,1.10463,2.46,2.3283,0.444782,1.84075,0.602501,0.936394,1.36516
+17-Nov-2019 01:00:00,1,0.33136,0.79893,1.2863,1.1959,0.85471,1.6961,1.1969,1.2598,0.34579,3.5375,1.05582,2.37698,2.24561,0.419068,1.76247,0.571672,0.89663,1.3165
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+17-Nov-2019 07:00:00,1,0.34471,0.83915,1.3457,1.2381,0.89868,1.7641,1.2424,1.3288,0.36081,3.645,1.0975,2.44787,2.3161,0.440961,1.82913,0.597932,0.930485,1.35799
+17-Nov-2019 08:00:00,1,0.37106,0.91884,1.4628,1.3213,0.9865,1.8998,1.3312,1.4636,0.39002,3.8477,1.1764,2.5818,2.45325,0.484472,1.96131,0.649724,0.997804,1.43848
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+19-Nov-2019 01:00:00,1,0.29919,0.70234,1.143,1.0942,0.75018,1.5343,1.0855,1.0914,0.30888,3.2647,0.950381,2.19716,2.07265,0.366691,1.60253,0.508261,0.815691,1.21437
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+19-Nov-2019 05:00:00,1,0.34579,0.84242,1.3505,1.2415,0.90226,1.7696,1.2461,1.3343,0.36203,3.6535,1.10083,2.45353,2.32179,0.442742,1.83455,0.600063,0.93324,1.36134
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+20-Nov-2019 06:00:00,1,0.42608,1.0863,1.7069,1.4949,1.174,2.1893,1.5118,1.7396,0.44916,4.2331,1.3275,2.83699,2.73136,0.57646,2.23951,0.757616,1.14028,1.60074
+20-Nov-2019 07:00:00,1,0.48486,1.2668,1.9672,1.6803,1.3799,2.5068,1.6983,2.0266,0.50975,4.5926,1.47007,3.07588,3.01706,0.676279,2.53973,0.872517,1.29508,1.76609
+20-Nov-2019 08:00:00,1,0.49979,1.3128,2.0333,1.7273,1.4331,2.5888,1.7448,2.0984,0.52476,4.676,1.50342,3.13143,3.08792,0.701884,2.61648,0.901662,1.33482,1.80689
+20-Nov-2019 09:00:00,1,0.50277,1.322,2.0464,1.7367,1.4437,2.6051,1.754,2.1126,0.52773,4.6922,1.50993,3.14225,3.10194,0.706992,2.63178,0.907461,1.34275,1.81495
+20-Nov-2019 10:00:00,1,0.51142,1.3488,2.0847,1.7639,1.4747,2.6529,1.7806,2.1539,0.53634,4.7387,1.52862,3.1733,3.14262,0.721882,2.67636,0.924338,1.36586,1.83832
+20-Nov-2019 11:00:00,1,0.52009,1.3756,2.123,1.7912,1.5058,2.7009,1.8072,2.1952,0.5449,4.7843,1.54696,3.20373,3.18312,0.73682,2.72105,0.941228,1.38906,1.86156
+20-Nov-2019 12:00:00,1,0.51317,1.3542,2.0924,1.7695,1.481,2.6626,1.786,2.1623,0.53807,4.748,1.53235,3.17949,3.15081,0.724893,2.68537,0.927746,1.37054,1.84302
+20-Nov-2019 13:00:00,1,0.50982,1.3438,2.0776,1.7589,1.4689,2.644,1.7757,2.1463,0.53474,4.7302,1.52518,3.16759,3.13509,0.719119,2.66809,0.92121,1.36157,1.834
+20-Nov-2019 14:00:00,1,0.50536,1.33,2.0579,1.7449,1.453,2.6194,1.762,2.125,0.53031,4.7062,1.51557,3.15163,3.11416,0.711451,2.64513,0.91252,1.34967,1.82197
+20-Nov-2019 15:00:00,1,0.50335,1.3238,2.049,1.7385,1.4458,2.6083,1.7558,2.1154,0.52831,4.6954,1.5112,3.14436,3.10469,0.707993,2.63478,0.908597,1.3443,1.81653
+20-Nov-2019 16:00:00,1,0.51216,1.351,2.0879,1.7663,1.4773,2.657,1.7829,2.1574,0.53706,4.7426,1.53018,3.1759,3.14606,0.723147,2.68015,0.92577,1.36783,1.8403
+20-Nov-2019 17:00:00,1,0.53333,1.4166,2.1814,1.8329,1.5536,2.7745,1.8476,2.2578,0.5579,4.8519,1.57428,3.24895,3.24457,0.759697,2.78944,0.967016,1.42459,1.89677
+20-Nov-2019 18:00:00,1,0.52684,1.3965,2.1528,1.8125,1.5302,2.7383,1.8279,2.2272,0.55154,4.8191,1.56099,3.22697,3.21451,0.748473,2.75589,0.954375,1.40715,1.87956
+20-Nov-2019 19:00:00,1,0.50988,1.344,2.0779,1.7591,1.4692,2.6444,1.7759,2.1466,0.53481,4.7305,1.52532,3.16782,3.1354,0.71923,2.66842,0.921335,1.36174,1.83417
+20-Nov-2019 20:00:00,1,0.47856,1.2474,1.9393,1.6604,1.3575,2.4724,1.6786,1.9962,0.50338,4.5565,1.45567,3.05185,2.98697,0.665509,2.50741,0.860219,1.27837,1.74874
+20-Nov-2019 21:00:00,1,0.44832,1.1544,1.8055,1.5651,1.2512,2.3085,1.5831,1.8491,0.47238,4.3752,1.38363,2.9313,2.84079,0.61405,2.35276,0.801135,1.19855,1.66424
+20-Nov-2019 22:00:00,1,0.41809,1.0619,1.6715,1.4697,1.1464,2.1468,1.4859,1.7,0.44072,4.1802,1.30666,2.8019,2.69164,0.563014,2.19894,0.741972,1.11944,1.57765
+20-Nov-2019 23:00:00,1,0.37918,0.94347,1.4988,1.3469,1.0138,1.9421,1.3582,1.5048,0.39891,3.9078,1.19984,2.6215,2.49498,0.497952,2.00218,0.665667,1.01867,1.4629
+21-Nov-2019 00:00:00,1,0.35144,0.85947,1.3756,1.2593,0.92099,1.7986,1.2652,1.3634,0.36833,3.6979,1.11808,2.48284,2.35139,0.452041,1.86283,0.61117,0.947623,1.37873
+21-Nov-2019 01:00:00,1,0.33731,0.81685,1.3128,1.2147,0.87427,1.7263,1.2173,1.2906,0.35251,3.5858,1.07456,2.40885,2.27714,0.42882,1.79217,0.583386,0.911708,1.33507
+21-Nov-2019 02:00:00,1,0.33191,0.80057,1.2887,1.1976,0.8565,1.6988,1.1988,1.2626,0.3464,3.542,1.05755,2.37992,2.24851,0.419963,1.76519,0.572748,0.898013,1.31821
+21-Nov-2019 03:00:00,1,0.33347,0.80526,1.2957,1.2026,0.86162,1.7067,1.2041,1.2707,0.34817,3.5547,1.06247,2.38829,2.25677,0.422513,1.77296,0.575813,0.901956,1.32308
+21-Nov-2019 04:00:00,1,0.34147,0.8294,1.3313,1.2279,0.888,1.7475,1.2315,1.3121,0.35719,3.6192,1.08751,2.43089,2.29909,0.43565,1.81297,0.591574,0.92227,1.34799
+21-Nov-2019 05:00:00,1,0.36232,0.89238,1.424,1.2937,0.95724,1.8546,1.3019,1.4191,0.38041,3.7819,1.15073,2.53828,2.40808,0.470008,1.91741,0.632564,0.97542,1.412
+21-Nov-2019 06:00:00,1,0.42172,1.073,1.6876,1.4812,1.1589,2.1661,1.4977,1.718,0.44456,4.2044,1.31617,2.81792,2.70972,0.569119,2.21737,0.749081,1.1289,1.58816
+21-Nov-2019 07:00:00,1,0.47774,1.2448,1.9357,1.6578,1.3546,2.4679,1.6761,1.9923,0.50255,4.5518,1.45378,3.04869,2.98304,0.664108,2.50321,0.858618,1.27619,1.74648
+21-Nov-2019 08:00:00,1,0.49573,1.3003,2.0153,1.7145,1.4185,2.5664,1.7322,2.0789,0.52069,4.6536,1.49446,3.11651,3.0687,0.694906,2.59557,0.893731,1.32399,1.79583
+21-Nov-2019 09:00:00,1,0.49627,1.3019,2.0177,1.7162,1.4205,2.5694,1.7338,2.0815,0.52123,4.6566,1.49565,3.11849,3.07124,0.695828,2.59834,0.894781,1.32542,1.7973
+21-Nov-2019 10:00:00,1,0.50462,1.3278,2.0546,1.7425,1.4503,2.6153,1.7597,2.1215,0.52958,4.7022,1.51396,3.14896,3.11068,0.71018,2.64133,0.911078,1.34769,1.81997
+21-Nov-2019 11:00:00,1,0.51494,1.3597,2.1002,1.775,1.4873,2.6724,1.7915,2.1707,0.53982,4.7573,1.53611,3.18574,3.1591,0.727946,2.69451,0.9312,1.37528,1.84778
+21-Nov-2019 12:00:00,1,0.5076,1.3369,2.0677,1.7519,1.461,2.6318,1.7689,2.1357,0.53253,4.7183,1.5204,3.15965,3.12466,0.715294,2.65664,0.916876,1.35563,1.82801
+21-Nov-2019 13:00:00,1,0.50334,1.3238,2.0489,1.7385,1.4457,2.6083,1.7557,2.1154,0.5283,4.6953,1.51118,3.14434,3.10465,0.707981,2.63474,0.908583,1.34428,1.81651
+21-Nov-2019 14:00:00,1,0.4931,1.2922,2.0036,1.7062,1.4092,2.552,1.724,2.0663,0.51805,4.639,1.48861,3.10678,3.05624,0.690395,2.58205,0.8886,1.31698,1.78866
+21-Nov-2019 15:00:00,1,0.4865,1.2718,1.9744,1.6854,1.3857,2.5158,1.7035,2.0345,0.51141,4.6019,1.47379,3.08208,3.02487,0.679085,2.54814,0.875717,1.29943,1.77059
+21-Nov-2019 16:00:00,1,0.49462,1.2969,2.0104,1.711,1.4146,2.5603,1.7287,2.0736,0.51958,4.6474,1.49199,3.11241,3.06344,0.693001,2.58986,0.891565,1.32103,1.7928
+21-Nov-2019 17:00:00,1,0.5194,1.3734,2.1199,1.789,1.5033,2.697,1.8051,2.1919,0.54422,4.7807,1.5455,3.20131,3.17988,0.735621,2.71747,0.939873,1.38719,1.85971
+21-Nov-2019 18:00:00,1,0.51679,1.3654,2.1084,1.7808,1.494,2.6826,1.7971,2.1795,0.54165,4.767,1.54001,3.19221,3.16772,0.731126,2.70402,0.934795,1.38022,1.85273
+21-Nov-2019 19:00:00,1,0.49928,1.3112,2.031,1.7257,1.4312,2.5859,1.7432,2.0959,0.52424,4.6731,1.50229,3.12954,3.08547,0.700995,2.61382,0.900653,1.33344,1.80548
+21-Nov-2019 20:00:00,1,0.46563,1.2076,1.8821,1.6196,1.3119,2.4021,1.638,1.9335,0.4902,4.4806,1.42545,3.00135,2.92481,0.643454,2.44118,0.834963,1.24415,1.71285
+21-Nov-2019 21:00:00,1,0.43358,1.1093,1.7402,1.5186,1.1999,2.2294,1.5359,1.7766,0.45703,4.2819,1.34673,2.86933,2.76844,0.589109,2.27765,0.772295,1.15988,1.62228
+21-Nov-2019 22:00:00,1,0.40994,1.0371,1.6354,1.444,1.1185,2.1036,1.4595,1.6594,0.43207,4.1252,1.28503,2.76545,2.65091,0.549331,2.15763,0.72601,1.09824,1.55394
+21-Nov-2019 23:00:00,1,0.37564,0.93273,1.4831,1.3358,1.0019,1.9236,1.3464,1.4869,0.39504,3.8817,1.18967,2.60428,2.47682,0.492072,1.98435,0.658718,1.00957,1.45228
+22-Nov-2019 00:00:00,1,0.35131,0.8591,1.375,1.2589,0.92057,1.7979,1.2648,1.3628,0.36819,3.697,1.1177,2.4822,2.35074,0.451837,1.86221,0.610927,0.947307,1.37835
+22-Nov-2019 01:00:00,1,0.33923,0.82263,1.3213,1.2208,0.88059,1.7361,1.2238,1.3005,0.35467,3.6013,1.08054,2.41904,2.28726,0.431966,1.80175,0.58716,0.916574,1.34103
+22-Nov-2019 02:00:00,1,0.32958,0.79357,1.2784,1.1903,0.84887,1.687,1.1908,1.2505,0.34377,3.5229,1.05018,2.36736,2.23616,0.416156,1.75359,0.568168,0.892128,1.31093
+22-Nov-2019 03:00:00,1,0.33148,0.79928,1.2868,1.1963,0.85509,1.6966,1.1973,1.2604,0.34592,3.5385,1.05619,2.3776,2.24622,0.419257,1.76304,0.5719,0.896923,1.31687
+22-Nov-2019 04:00:00,1,0.34029,0.82583,1.326,1.2241,0.88409,1.7415,1.2274,1.306,0.35586,3.6098,1.08384,2.42464,2.29286,0.433706,1.80705,0.589246,0.919265,1.34432
+22-Nov-2019 05:00:00,1,0.36096,0.88826,1.4179,1.2894,0.95269,1.8476,1.2973,1.4122,0.3789,3.7715,1.14669,2.53141,2.401,0.467754,1.91057,0.629886,0.971933,1.40785
+22-Nov-2019 06:00:00,1,0.42088,1.0704,1.6839,1.4785,1.156,2.1616,1.495,1.7138,0.44367,4.1988,1.31397,2.81422,2.70553,0.567703,2.2131,0.747432,1.1267,1.58572
+22-Nov-2019 07:00:00,1,0.47953,1.2504,1.9436,1.6635,1.361,2.4777,1.6817,2.0009,0.50437,4.5621,1.45791,3.05559,2.99163,0.667175,2.51241,0.862122,1.28095,1.75143
+22-Nov-2019 08:00:00,1,0.4887,1.2786,1.9842,1.6924,1.3935,2.5279,1.7103,2.0451,0.51363,4.6144,1.47876,3.09037,3.03536,0.682861,2.55946,0.880021,1.30529,1.77664
+22-Nov-2019 09:00:00,1,0.48424,1.2649,1.9645,1.6783,1.3777,2.5034,1.6964,2.0236,0.50913,4.5891,1.46867,3.07354,3.01411,0.67522,2.53655,0.871309,1.29343,1.76439
+22-Nov-2019 10:00:00,1,0.4912,1.2863,1.9952,1.7002,1.4024,2.5415,1.7181,2.0572,0.51614,4.6284,1.48437,3.09971,3.04722,0.687139,2.57229,0.884893,1.31193,1.78347
+22-Nov-2019 11:00:00,1,0.49788,1.3069,2.0248,1.7213,1.4262,2.5782,1.7388,2.0892,0.52284,4.6655,1.49921,3.12443,3.07888,0.698599,2.60664,0.89793,1.32972,1.80169
+22-Nov-2019 12:00:00,1,0.49477,1.2973,2.011,1.7115,1.4151,2.5611,1.7292,2.0743,0.51972,4.6483,1.49232,3.11296,3.06414,0.693256,2.59063,0.891855,1.32142,1.79321
+22-Nov-2019 13:00:00,1,0.49405,1.2951,2.0078,1.7092,1.4125,2.5572,1.7269,2.0708,0.519,4.6443,1.49072,3.11029,3.06073,0.692019,2.58692,0.890448,1.3195,1.79124
+22-Nov-2019 14:00:00,1,0.48666,1.2723,1.9752,1.6859,1.3863,2.5167,1.704,2.0353,0.51157,4.6028,1.47415,3.08269,3.02564,0.679363,2.54897,0.876033,1.29986,1.77104
+22-Nov-2019 15:00:00,1,0.48459,1.2659,1.966,1.6794,1.3789,2.5054,1.6975,2.0253,0.50948,4.5911,1.46946,3.07487,3.01578,0.675822,2.53835,0.871995,1.29437,1.76536
+22-Nov-2019 16:00:00,1,0.49352,1.2935,2.0055,1.7076,1.4107,2.5543,1.7253,2.0683,0.51848,4.6413,1.48956,3.10835,3.05825,0.691122,2.58423,0.889427,1.31811,1.78982
+22-Nov-2019 17:00:00,1,0.51914,1.3726,2.1187,1.7882,1.5024,2.6956,1.8043,2.1906,0.54396,4.7793,1.54496,3.20041,3.17868,0.735176,2.71614,0.939371,1.3865,1.85902
+22-Nov-2019 18:00:00,1,0.51302,1.3537,2.0917,1.769,1.4804,2.6617,1.7855,2.1615,0.53791,4.7472,1.53201,3.17894,3.15007,0.724623,2.68456,0.92744,1.37012,1.8426
+22-Nov-2019 19:00:00,1,0.49608,1.3014,2.0168,1.7156,1.4198,2.5684,1.7333,2.0806,0.52104,4.6555,1.49524,3.11782,3.07037,0.695513,2.59739,0.894422,1.32493,1.79679
+22-Nov-2019 20:00:00,1,0.46311,1.1998,1.871,1.6117,1.3031,2.3884,1.63,1.9213,0.48762,4.4656,1.41947,2.99134,2.91265,0.639167,2.4283,0.830043,1.2375,1.70582
+22-Nov-2019 21:00:00,1,0.43621,1.1173,1.7518,1.5269,1.2091,2.2434,1.5444,1.7896,0.45978,4.2988,1.35339,2.88053,2.78139,0.593547,2.29102,0.777437,1.16676,1.62979
+22-Nov-2019 22:00:00,1,0.4128,1.0458,1.648,1.4531,1.1283,2.1187,1.4688,1.6736,0.43511,4.1446,1.29265,2.7783,2.66522,0.554123,2.1721,0.731606,1.10566,1.56227
+22-Nov-2019 23:00:00,1,0.37882,0.9424,1.4973,1.3458,1.0126,1.9402,1.357,1.503,0.39852,3.9052,1.19883,2.6198,2.49318,0.497369,2.00041,0.664978,1.01777,1.46185
+23-Nov-2019 00:00:00,1,0.35106,0.85833,1.3739,1.2581,0.91973,1.7966,1.264,1.3614,0.36791,3.695,1.11693,2.48089,2.34941,0.451419,1.86093,0.610428,0.94666,1.37757
+23-Nov-2019 01:00:00,1,0.33806,0.81911,1.3161,1.2171,0.87674,1.7301,1.2198,1.2945,0.35335,3.5919,1.0769,2.41283,2.28109,0.430048,1.79591,0.58486,0.913607,1.3374
+23-Nov-2019 02:00:00,1,0.33215,0.80129,1.2898,1.1984,0.85728,1.7,1.1996,1.2638,0.34667,3.5439,1.05831,2.3812,2.24977,0.420352,1.76638,0.573216,0.898616,1.31896
+23-Nov-2019 03:00:00,1,0.33519,0.81044,1.3033,1.208,0.86727,1.7155,1.21,1.2796,0.35011,3.5686,1.06788,2.3975,2.26588,0.42533,1.78155,0.579198,0.906313,1.32844
+23-Nov-2019 04:00:00,1,0.33973,0.82413,1.3235,1.2223,0.88223,1.7386,1.2255,1.3031,0.35523,3.6053,1.08209,2.42167,2.28989,0.432783,1.80424,0.588139,0.917837,1.34257
+23-Nov-2019 05:00:00,1,0.35871,0.88147,1.4079,1.2823,0.9452,1.836,1.2898,1.4007,0.37642,3.7543,1.13999,2.52005,2.38934,0.464045,1.8993,0.625474,0.966194,1.40101
+23-Nov-2019 06:00:00,1,0.41537,1.0536,1.6594,1.4612,1.1371,2.1324,1.4771,1.6864,0.43783,4.162,1.29947,2.7898,2.67806,0.558438,2.18513,0.73664,1.11235,1.56974
+23-Nov-2019 07:00:00,1,0.47123,1.2248,1.9069,1.6373,1.3317,2.4325,1.6557,1.9608,0.49593,4.5138,1.43866,3.02343,2.95182,0.653006,2.46987,0.845913,1.25897,1.72846
+23-Nov-2019 08:00:00,1,0.48572,1.2694,1.971,1.683,1.3829,2.5115,1.701,2.0308,0.51062,4.5975,1.47202,3.07913,3.02115,0.677748,2.54413,0.874192,1.29736,1.76845
+23-Nov-2019 09:00:00,1,0.49227,1.2896,2,1.7036,1.4062,2.5474,1.7214,2.0623,0.51721,4.6343,1.48675,3.10368,3.05228,0.688966,2.57777,0.886973,1.31477,1.78638
+23-Nov-2019 10:00:00,1,0.49831,1.3082,2.0267,1.7226,1.4277,2.5806,1.7402,2.0913,0.52327,4.6678,1.50015,3.12599,3.08089,0.699329,2.60882,0.898759,1.33085,1.80284
+23-Nov-2019 11:00:00,1,0.50643,1.3333,2.0626,1.7482,1.4568,2.6253,1.7653,2.1301,0.53137,4.712,1.51788,3.15546,3.11917,0.713286,2.65063,0.9146,1.35252,1.82486
+23-Nov-2019 12:00:00,1,0.50242,1.3209,2.0449,1.7356,1.4424,2.6032,1.7529,2.111,0.52738,4.6903,1.50917,3.14099,3.1003,0.706394,2.62999,0.906783,1.34182,1.81401
+23-Nov-2019 13:00:00,1,0.49034,1.2837,1.9914,1.6975,1.3993,2.5368,1.7154,2.053,0.51528,4.6236,1.48244,3.0965,3.04314,0.685663,2.56787,0.883213,1.30964,1.78112
+23-Nov-2019 14:00:00,1,0.48026,1.2526,1.9468,1.6658,1.3636,2.4817,1.684,2.0044,0.5051,4.5663,1.45957,3.05836,2.99509,0.66841,2.51612,0.863533,1.28287,1.75342
+23-Nov-2019 15:00:00,1,0.47466,1.2354,1.9221,1.6481,1.3438,2.4512,1.6664,1.9774,0.49942,4.5339,1.44666,3.0368,2.96831,0.65886,2.48745,0.852616,1.26805,1.73797
+23-Nov-2019 16:00:00,1,0.48361,1.2629,1.9617,1.6763,1.3754,2.5,1.6944,2.0206,0.50849,4.5855,1.46723,3.07113,3.01109,0.674138,2.5333,0.870073,1.29175,1.76265
+23-Nov-2019 17:00:00,1,0.49899,1.3104,2.0297,1.7248,1.4302,2.5844,1.7423,2.0946,0.52396,4.6716,1.50166,3.1285,3.08414,0.700509,2.61236,0.9001,1.33268,1.80471
+23-Nov-2019 18:00:00,1,0.4898,1.282,1.989,1.6958,1.3974,2.5338,1.7137,2.0504,0.51473,4.6205,1.48122,3.09447,3.04056,0.684733,2.56508,0.882153,1.3082,1.77963
+23-Nov-2019 19:00:00,1,0.47145,1.2255,1.9079,1.638,1.3324,2.4337,1.6563,1.9618,0.49615,4.5151,1.43916,3.02428,2.95286,0.653375,2.47098,0.846336,1.25954,1.72906
+23-Nov-2019 20:00:00,1,0.44237,1.1362,1.7791,1.5463,1.2305,2.2765,1.5641,1.8199,0.46621,4.338,1.36889,2.90656,2.81169,0.603976,2.32243,0.789503,1.18293,1.64737
+23-Nov-2019 21:00:00,1,0.41425,1.0502,1.6545,1.4576,1.1332,2.1264,1.4735,1.6808,0.43664,4.1544,1.2965,2.78478,2.67245,0.556552,2.17944,0.73444,1.10943,1.56648
+23-Nov-2019 22:00:00,1,0.39715,0.99811,1.5786,1.4037,1.0748,2.0362,1.4176,1.5954,0.41838,4.0367,1.2503,2.70684,2.58651,0.527919,2.09291,0.700941,1.06507,1.51639
+23-Nov-2019 23:00:00,1,0.36441,0.8987,1.4332,1.3003,0.96422,1.8654,1.3089,1.4298,0.38271,3.7977,1.15691,2.54875,2.41889,0.473458,1.92789,0.636662,0.980759,1.41834
+24-Nov-2019 00:00:00,1,0.33426,0.80767,1.2992,1.2051,0.86424,1.7108,1.2069,1.2748,0.34907,3.5612,1.06499,2.39257,2.261,0.423821,1.77695,0.577385,0.903978,1.32557
+24-Nov-2019 01:00:00,1,0.31753,0.75734,1.2247,1.1522,0.80952,1.6261,1.1493,1.1878,0.33004,3.4227,1.01138,2.30125,2.17179,0.396482,1.69358,0.544433,0.861719,1.27297
+24-Nov-2019 02:00:00,1,0.31104,0.73786,1.1958,1.1317,0.78844,1.5935,1.1268,1.1538,0.32259,3.3675,0.990064,2.2649,2.13688,0.385919,1.66132,0.53164,0.845396,1.25235
+24-Nov-2019 03:00:00,1,0.31074,0.73697,1.1945,1.1307,0.78748,1.592,1.1258,1.1522,0.32225,3.365,0.989078,2.26322,2.13527,0.385435,1.65984,0.531052,0.844647,1.2514
+24-Nov-2019 04:00:00,1,0.31382,0.74619,1.2082,1.1405,0.79744,1.6074,1.1365,1.1683,0.32578,3.3912,0.999211,2.2805,2.15183,0.390432,1.67511,0.537109,0.852369,1.26118
+24-Nov-2019 05:00:00,1,0.31415,0.7472,1.2097,1.1415,0.79853,1.6091,1.1376,1.1701,0.32617,3.3941,1.00032,2.28239,2.15364,0.39098,1.67678,0.537773,0.853216,1.26225
+24-Nov-2019 06:00:00,1,0.3208,0.76715,1.2393,1.1625,0.82015,1.6426,1.1606,1.2048,0.33377,3.4501,1.02199,2.31934,2.18929,0.401805,1.70983,0.550866,0.869946,1.2833
+24-Nov-2019 07:00:00,1,0.34921,0.85273,1.3657,1.2523,0.91358,1.7871,1.2577,1.3519,0.36584,3.6804,1.11129,2.4713,2.33971,0.448365,1.85165,0.606782,0.941936,1.37187
+24-Nov-2019 08:00:00,1,0.37826,0.94069,1.4948,1.3441,1.0108,1.9373,1.3552,1.5002,0.39791,3.9011,1.19722,2.61707,2.49029,0.496434,1.99758,0.663873,1.01632,1.46016
+24-Nov-2019 09:00:00,1,0.3982,1.0013,1.5833,1.407,1.0783,2.0417,1.421,1.6007,0.4195,4.0441,1.25318,2.71169,2.59179,0.529666,2.09819,0.70299,1.06778,1.51947
+24-Nov-2019 10:00:00,1,0.41237,1.0445,1.6461,1.4517,1.1268,2.1165,1.4673,1.6715,0.43465,4.1417,1.2915,2.77636,2.66305,0.553398,2.16991,0.730759,1.10454,1.56101
+24-Nov-2019 11:00:00,1,0.42131,1.0718,1.6858,1.4799,1.1575,2.1639,1.4964,1.7159,0.44412,4.2016,1.31509,2.8161,2.70766,0.568422,2.21527,0.74827,1.12782,1.58696
+24-Nov-2019 12:00:00,1,0.41732,1.0596,1.6681,1.4673,1.1438,2.1427,1.4834,1.6961,0.4399,4.1751,1.30462,2.79848,2.68779,0.561715,2.19503,0.740459,1.11743,1.57541
+24-Nov-2019 13:00:00,1,0.40136,1.0109,1.5973,1.4169,1.0891,2.0583,1.4314,1.6165,0.42289,4.0661,1.26182,2.72629,2.60774,0.534948,2.11416,0.709182,1.07596,1.52877
+24-Nov-2019 14:00:00,1,0.39274,0.98468,1.5591,1.3898,1.0598,2.013,1.4031,1.5733,0.41362,4.0056,1.2381,2.68621,2.56415,0.520549,2.07061,0.692287,1.05366,1.50334
+24-Nov-2019 15:00:00,1,0.39351,0.98702,1.5625,1.3922,1.0624,2.017,1.4056,1.5771,0.41445,4.011,1.24023,2.68982,2.56805,0.521832,2.07449,0.693794,1.05564,1.50561
+24-Nov-2019 16:00:00,1,0.41087,1.0399,1.6395,1.447,1.1216,2.1086,1.4625,1.6641,0.43306,4.1316,1.28752,2.76965,2.65558,0.550893,2.16235,0.727835,1.10066,1.55666
+24-Nov-2019 17:00:00,1,0.43526,1.1144,1.7476,1.5239,1.2058,2.2384,1.5414,1.7849,0.45879,4.2927,1.351,2.87651,2.77673,0.591951,2.28621,0.775588,1.16429,1.62709
+24-Nov-2019 18:00:00,1,0.43264,1.1064,1.736,1.5156,1.1967,2.2244,1.5329,1.772,0.45605,4.2759,1.34434,2.86532,2.76382,0.587527,2.27288,0.770462,1.15743,1.6196
+24-Nov-2019 19:00:00,1,0.41168,1.0424,1.6431,1.4495,1.1244,2.1128,1.4651,1.6681,0.43392,4.1371,1.28968,2.7733,2.65964,0.552253,2.16645,0.729422,1.10277,1.55902
+24-Nov-2019 20:00:00,1,0.38042,0.94725,1.5044,1.3509,1.018,1.9485,1.3623,1.5111,0.40026,3.9169,1.2034,2.62752,2.50136,0.500023,2.00845,0.668112,1.02188,1.46664
+24-Nov-2019 21:00:00,1,0.36022,0.88602,1.4146,1.2871,0.95022,1.8438,1.2949,1.4084,0.37808,3.7658,1.14448,2.52767,2.39716,0.466531,1.90686,0.628431,0.970041,1.4056
+24-Nov-2019 22:00:00,1,0.35333,0.86518,1.384,1.2653,0.92726,1.8083,1.2716,1.3731,0.37044,3.7126,1.1238,2.49256,2.36126,0.455155,1.87229,0.614885,0.95244,1.38453
+24-Nov-2019 23:00:00,1,0.32894,0.79164,1.2755,1.1883,0.84677,1.6838,1.1886,1.2472,0.34304,3.5177,1.04814,2.36388,2.23274,0.415105,1.75039,0.566904,0.890504,1.30892
+25-Nov-2019 00:00:00,1,0.30569,0.72182,1.172,1.1148,0.77113,1.5667,1.1082,1.1257,0.31642,3.3214,0.972273,2.23454,2.10796,0.377229,1.63477,0.521089,0.83197,1.23526
+25-Nov-2019 01:00:00,1,0.288,0.6689,1.0932,1.0588,0.71434,1.4787,1.0462,1.0321,0.29581,3.1651,0.912016,2.13158,2.01148,0.348622,1.5472,0.486184,0.787787,1.17814
+25-Nov-2019 02:00:00,1,0.28025,0.64577,1.0586,1.0343,0.68968,1.4405,1.0187,0.99086,0.28669,3.0946,0.884901,2.0852,1.96878,0.336147,1.50896,0.470877,0.768529,1.15281
+25-Nov-2019 03:00:00,1,0.27699,0.63604,1.0441,1.024,0.67933,1.4245,1.0071,0.97343,0.28282,3.0646,0.873346,2.06542,1.95071,0.330903,1.49287,0.464427,0.760435,1.14208
+25-Nov-2019 04:00:00,1,0.27542,0.63137,1.0371,1.019,0.67436,1.4168,1.0016,0.96504,0.28097,3.05,0.86777,2.05587,1.94202,0.328387,1.48514,0.46133,0.756552,1.13692
+25-Nov-2019 05:00:00,1,0.26945,0.61357,1.0104,1.0001,0.6555,1.3875,0.98024,0.933,0.27386,2.9942,0.846342,2.01917,1.90878,0.318811,1.45574,0.449517,0.741774,1.11716
+25-Nov-2019 06:00:00,1,0.26308,0.59466,0.98203,0.97997,0.6355,1.3565,0.95743,0.89878,0.26625,2.934,0.82323,1.97957,1.87321,0.30864,1.42447,0.436936,0.726082,1.09601
+25-Nov-2019 07:00:00,1,0.27446,0.62851,1.0328,1.016,0.67133,1.4121,0.99815,0.9599,0.27983,3.0411,0.864347,2.05001,1.93669,0.326848,1.48042,0.459433,0.754177,1.13375
+25-Nov-2019 08:00:00,1,0.29784,0.69831,1.137,1.0899,0.74585,1.5276,1.0808,1.0843,0.30731,3.2528,0.945811,2.18935,2.06531,0.364512,1.59587,0.505605,0.812326,1.21003
+25-Nov-2019 09:00:00,1,0.32146,0.76913,1.2422,1.1646,0.8223,1.6459,1.1629,1.2082,0.33453,3.4556,1.02412,2.32297,2.19281,0.402878,1.7131,0.552162,0.871604,1.28538
+25-Nov-2019 10:00:00,1,0.34114,0.82838,1.3298,1.2268,0.88688,1.7458,1.2303,1.3104,0.35681,3.6165,1.08647,2.42912,2.29732,0.435097,1.81129,0.590912,0.921416,1.34695
+25-Nov-2019 11:00:00,1,0.36614,0.90395,1.4409,1.3058,0.97002,1.8744,1.3147,1.4386,0.38462,3.8108,1.16202,2.55741,2.42786,0.476327,1.9366,0.640068,0.9852,1.4236
+25-Nov-2019 12:00:00,1,0.3631,0.89474,1.4274,1.2962,0.95985,1.8586,1.3045,1.4231,0.38127,3.7878,1.15305,2.5422,2.41212,0.471298,1.92133,0.634097,0.977417,1.41438
+25-Nov-2019 13:00:00,1,0.34591,0.84279,1.351,1.2419,0.90267,1.7702,1.2465,1.335,0.36216,3.6545,1.10121,2.45418,2.32244,0.442945,1.83517,0.600305,0.933553,1.36172
+25-Nov-2019 14:00:00,1,0.3301,0.79514,1.2807,1.1919,0.85058,1.6897,1.1926,1.2532,0.34436,3.5272,1.05183,2.37018,2.23892,0.417007,1.75619,0.569192,0.893443,1.31256
+25-Nov-2019 15:00:00,1,0.32907,0.79202,1.2761,1.1887,0.84719,1.6844,1.1891,1.2479,0.34319,3.5187,1.04854,2.36458,2.23342,0.415315,1.75103,0.567157,0.890829,1.30932
+25-Nov-2019 16:00:00,1,0.34657,0.84479,1.354,1.244,0.90486,1.7736,1.2488,1.3384,0.3629,3.6597,1.10324,2.45763,2.32591,0.444034,1.83848,0.601607,0.935237,1.36376
+25-Nov-2019 17:00:00,1,0.37884,0.94246,1.4974,1.3459,1.0127,1.9403,1.3571,1.5031,0.39854,3.9053,1.19889,2.61989,2.49328,0.497402,2.00051,0.665017,1.01782,1.46191
+25-Nov-2019 18:00:00,1,0.38367,0.95712,1.5188,1.3611,1.029,1.9655,1.3731,1.5275,0.4038,3.9405,1.21264,2.64317,2.51798,0.505432,2.02484,0.674492,1.03025,1.47636
+25-Nov-2019 19:00:00,1,0.37757,0.93859,1.4917,1.3419,1.0084,1.9337,1.3529,1.4967,0.39715,3.8959,1.19523,2.61369,2.48673,0.495279,1.99408,0.662509,1.01453,1.45808
+25-Nov-2019 20:00:00,1,0.36024,0.88608,1.4147,1.2871,0.95029,1.8439,1.2949,1.4085,0.3781,3.7659,1.14454,2.52777,2.39726,0.466564,1.90695,0.62847,0.970091,1.40566
+25-Nov-2019 21:00:00,1,0.34651,0.84459,1.3537,1.2438,0.90464,1.7733,1.2486,1.3381,0.36283,3.6592,1.10304,2.45729,2.32557,0.443927,1.83815,0.60148,0.935072,1.36356
+25-Nov-2019 22:00:00,1,0.34291,0.83373,1.3377,1.2324,0.89274,1.7549,1.2363,1.3195,0.3588,3.6307,1.09196,2.43846,2.30666,0.438011,1.82015,0.594401,0.925922,1.35244
+25-Nov-2019 23:00:00,1,0.31867,0.76077,1.2298,1.1558,0.81323,1.6319,1.1533,1.1937,0.33135,3.4323,1.01509,2.30759,2.17791,0.398341,1.69926,0.546681,0.864592,1.27659
+26-Nov-2019 00:00:00,1,0.2942,0.68742,1.1208,1.0784,0.73416,1.5094,1.068,1.065,0.30306,3.2206,0.933381,2.16811,2.04543,0.358623,1.57784,0.498417,0.80323,1.19826
+26-Nov-2019 01:00:00,1,0.28186,0.65055,1.0658,1.0394,0.69477,1.4484,1.0244,0.99942,0.28858,3.1093,0.890549,2.09486,1.97764,0.338726,1.51687,0.474046,0.772509,1.15806
+26-Nov-2019 02:00:00,1,0.2788,0.64144,1.0522,1.0297,0.68507,1.4334,1.0136,0.9831,0.28497,3.0813,0.879767,2.07641,1.96074,0.333812,1.50179,0.468006,0.764924,1.14803
+26-Nov-2019 03:00:00,1,0.28679,0.66528,1.0878,1.055,0.71048,1.4728,1.0419,1.0257,0.29439,3.1542,0.907807,2.12439,2.00482,0.346669,1.54122,0.483792,0.784773,1.17419
+26-Nov-2019 04:00:00,1,0.30539,0.72091,1.1707,1.1138,0.77015,1.5652,1.1072,1.1241,0.31607,3.3188,0.971259,2.23281,2.10631,0.376737,1.63326,0.520491,0.831209,1.23429
+26-Nov-2019 05:00:00,1,0.3349,0.80957,1.302,1.2071,0.86632,1.714,1.209,1.2781,0.34978,3.5663,1.06697,2.39595,2.26434,0.424855,1.7801,0.578627,0.905578,1.32754
+26-Nov-2019 06:00:00,1,0.40487,1.0216,1.6129,1.428,1.1011,2.0768,1.4429,1.6341,0.42665,4.0904,1.27137,2.74241,2.62544,0.54083,2.13194,0.71607,1.08507,1.5391
+26-Nov-2019 07:00:00,1,0.46895,1.2178,1.8968,1.6301,1.3236,2.4201,1.6485,1.9497,0.4936,4.5004,1.4333,3.01448,2.94084,0.649116,2.45819,0.841456,1.25293,1.72211
+26-Nov-2019 08:00:00,1,0.48385,1.2637,1.9627,1.6771,1.3763,2.5013,1.6952,2.0218,0.50874,4.5869,1.46779,3.07207,3.01226,0.674559,2.53456,0.870554,1.29241,1.76333
+26-Nov-2019 09:00:00,1,0.4873,1.2743,1.978,1.6879,1.3885,2.5202,1.706,2.0384,0.51222,4.6065,1.4756,3.0851,3.02869,0.68046,2.55226,0.877284,1.30157,1.77279
+26-Nov-2019 10:00:00,1,0.4977,1.3064,2.024,1.7207,1.4256,2.5773,1.7383,2.0884,0.52267,4.6645,1.49882,3.12378,3.07804,0.698295,2.60573,0.897584,1.32925,1.80121
+26-Nov-2019 11:00:00,1,0.51147,1.3489,2.0849,1.7641,1.4748,2.6531,1.7808,2.1541,0.53638,4.7389,1.52871,3.17345,3.14282,0.721955,2.67658,0.924421,1.36598,1.83844
+26-Nov-2019 12:00:00,1,0.50794,1.338,2.0693,1.753,1.4622,2.6336,1.7699,2.1373,0.53287,4.7201,1.52113,3.16088,3.12627,0.715882,2.6584,0.917543,1.35655,1.82893
+26-Nov-2019 13:00:00,1,0.50576,1.3313,2.0596,1.7461,1.4544,2.6216,1.7632,2.1269,0.53071,4.7084,1.51643,3.15306,3.11603,0.712136,2.64719,0.913296,1.35073,1.82305
+26-Nov-2019 14:00:00,1,0.49855,1.309,2.0278,1.7234,1.4286,2.5819,1.7409,2.0925,0.52352,4.6692,1.5007,3.1269,3.08206,0.699754,2.6101,0.899243,1.33151,1.80352
+26-Nov-2019 15:00:00,1,0.4951,1.2983,2.0125,1.7125,1.4163,2.5629,1.7302,2.0759,0.52006,4.6501,1.49306,3.11419,3.06572,0.693826,2.59234,0.892503,1.32231,1.79412
+26-Nov-2019 16:00:00,1,0.50314,1.3232,2.0481,1.7379,1.445,2.6072,1.7551,2.1144,0.5281,4.6942,1.51075,3.14362,3.10372,0.707639,2.63372,0.908195,1.34375,1.81597
+26-Nov-2019 17:00:00,1,0.51868,1.3712,2.1167,1.7868,1.5008,2.693,1.8029,2.1885,0.54351,4.7769,1.544,3.19882,3.17654,0.734385,2.71377,0.938478,1.38528,1.85779
+26-Nov-2019 18:00:00,1,0.50936,1.3424,2.0755,1.7574,1.4673,2.6415,1.7743,2.1441,0.53428,4.7277,1.52418,3.16594,3.13292,0.718322,2.66571,0.920306,1.36033,1.83275
+26-Nov-2019 19:00:00,1,0.49246,1.2902,2.0008,1.7042,1.4069,2.5485,1.722,2.0632,0.51741,4.6354,1.48719,3.10442,3.05322,0.689305,2.57878,0.887359,1.31529,1.78692
+26-Nov-2019 20:00:00,1,0.4616,1.1952,1.8643,1.607,1.2978,2.3802,1.6253,1.914,0.48608,4.4565,1.41587,2.98531,2.90535,0.636602,2.42059,0.827096,1.23352,1.7016
+26-Nov-2019 21:00:00,1,0.43118,1.1019,1.7295,1.511,1.1916,2.2166,1.5282,1.7648,0.45451,4.2664,1.34061,2.85905,2.7566,0.58506,2.26544,0.7676,1.15361,1.6154
+26-Nov-2019 22:00:00,1,0.40446,1.0204,1.6111,1.4267,1.0997,2.0747,1.4416,1.632,0.42621,4.0876,1.27026,2.74053,2.62338,0.540143,2.12987,0.715266,1.08401,1.53789
+26-Nov-2019 23:00:00,1,0.36602,0.90357,1.4404,1.3054,0.9696,1.8737,1.3143,1.438,0.38448,3.8099,1.16165,2.55679,2.42722,0.476121,1.93597,0.639823,0.98488,1.42322
+27-Nov-2019 00:00:00,1,0.33642,0.81417,1.3088,1.2119,0.87134,1.7218,1.2142,1.286,0.3515,3.5786,1.07177,2.40411,2.27243,0.42736,1.78773,0.581634,0.909451,1.3323
+27-Nov-2019 01:00:00,1,0.32327,0.77456,1.2503,1.1703,0.8282,1.655,1.1691,1.2177,0.33659,3.4707,1.02996,2.33292,2.20248,0.40583,1.72211,0.555725,0.876166,1.29108
+27-Nov-2019 02:00:00,1,0.32006,0.76493,1.236,1.1602,0.81774,1.6389,1.158,1.2009,0.33293,3.4439,1.01959,2.31525,2.18533,0.400598,1.70614,0.549408,0.868079,1.28096
+27-Nov-2019 03:00:00,1,0.32414,0.7772,1.2542,1.1731,0.83106,1.6595,1.1721,1.2222,0.33759,3.478,1.03278,2.33772,2.20716,0.407261,1.72647,0.557451,0.878378,1.29384
+27-Nov-2019 04:00:00,1,0.33796,0.8188,1.3156,1.2168,0.8764,1.7296,1.2195,1.2939,0.35323,3.591,1.07657,2.41228,2.28055,0.429878,1.7954,0.584656,0.913345,1.33708
+27-Nov-2019 05:00:00,1,0.36304,0.89457,1.4272,1.296,0.95965,1.8583,1.3043,1.4228,0.3812,3.7873,1.15287,2.5419,2.41182,0.4712,1.92103,0.633981,0.977265,1.4142
+27-Nov-2019 06:00:00,1,0.42846,1.0936,1.7175,1.5024,1.1822,2.202,1.5195,1.7513,0.45166,4.2487,1.33363,2.8473,2.74314,0.580468,2.2516,0.762271,1.14649,1.60759
+27-Nov-2019 07:00:00,1,0.4881,1.2767,1.9815,1.6905,1.3914,2.5245,1.7084,2.0422,0.51302,4.611,1.4774,3.0881,3.03248,0.681823,2.55635,0.878838,1.30368,1.77498
+27-Nov-2019 08:00:00,1,0.50595,1.3318,2.0604,1.7467,1.455,2.6226,1.7638,2.1278,0.53089,4.7094,1.51683,3.15372,3.1169,0.712454,2.64814,0.913657,1.35123,1.82355
+27-Nov-2019 09:00:00,1,0.50287,1.3224,2.0469,1.737,1.4441,2.6057,1.7543,2.1131,0.52783,4.6928,1.51016,3.14264,3.10244,0.707175,2.63233,0.907669,1.34303,1.81524
+27-Nov-2019 10:00:00,1,0.50863,1.3401,2.0723,1.7551,1.4647,2.6375,1.772,2.1406,0.53356,4.7238,1.52262,3.16335,3.12951,0.717071,2.66196,0.91889,1.35839,1.83079
+27-Nov-2019 11:00:00,1,0.51647,1.3644,2.107,1.7798,1.4928,2.6808,1.7961,2.1779,0.54133,4.7654,1.53933,3.19109,3.16622,0.730571,2.70236,0.934168,1.37935,1.85187
+27-Nov-2019 12:00:00,1,0.51334,1.3547,2.0931,1.77,1.4816,2.6635,1.7865,2.1631,0.53823,4.7489,1.5327,3.18007,3.15158,0.725176,2.68622,0.928066,1.37098,1.84347
+27-Nov-2019 13:00:00,1,0.51319,1.3542,2.0925,1.7695,1.481,2.6626,1.7861,2.1623,0.53808,4.7481,1.53238,3.17954,3.15087,0.724918,2.68545,0.927774,1.37058,1.84306
+27-Nov-2019 14:00:00,1,0.50926,1.3421,2.0751,1.7571,1.4669,2.641,1.774,2.1436,0.53419,4.7272,1.52399,3.16561,3.13249,0.718162,2.66523,0.920126,1.36009,1.8325
+27-Nov-2019 15:00:00,1,0.50682,1.3345,2.0643,1.7494,1.4582,2.6275,1.7665,2.132,0.53176,4.7141,1.51872,3.15687,3.12101,0.713959,2.65265,0.915363,1.35356,1.82591
+27-Nov-2019 16:00:00,1,0.5126,1.3524,2.0899,1.7677,1.4789,2.6594,1.7843,2.1596,0.5375,4.745,1.53113,3.17747,3.14813,0.72391,2.68243,0.926633,1.36901,1.84149
+27-Nov-2019 17:00:00,1,0.52715,1.3974,2.1541,1.8135,1.5313,2.7401,1.8288,2.2286,0.55185,4.8206,1.56163,3.22802,3.21594,0.749007,2.75749,0.954978,1.40798,1.88038
+27-Nov-2019 18:00:00,1,0.52261,1.3834,2.1341,1.7992,1.5149,2.7149,1.815,2.2071,0.54739,4.7974,1.55223,3.21247,3.19488,0.741177,2.73408,0.946146,1.39582,1.8683
+27-Nov-2019 19:00:00,1,0.5054,1.3302,2.0581,1.745,1.4531,2.6197,1.7621,2.1252,0.53035,4.7065,1.51566,3.15178,3.11436,0.711524,2.64535,0.912603,1.34978,1.82209
+27-Nov-2019 20:00:00,1,0.47779,1.245,1.9359,1.658,1.3548,2.4682,1.6763,1.9925,0.5026,4.5521,1.45391,3.04891,2.98331,0.664204,2.50349,0.858728,1.27634,1.74663
+27-Nov-2019 21:00:00,1,0.44545,1.1456,1.7927,1.556,1.2412,2.293,1.574,1.835,0.4694,4.3573,1.37653,2.91939,2.82674,0.609182,2.33811,0.795516,1.191,1.6561
+27-Nov-2019 22:00:00,1,0.41762,1.0605,1.6694,1.4683,1.1448,2.1443,1.4844,1.6976,0.44022,4.1771,1.30543,2.79983,2.68931,0.562228,2.19657,0.741056,1.11822,1.57629
+27-Nov-2019 23:00:00,1,0.37787,0.93951,1.493,1.3428,1.0094,1.9353,1.3539,1.4982,0.39748,3.8982,1.1961,2.61517,2.48829,0.495785,1.99561,0.663106,1.01532,1.45899
+28-Nov-2019 00:00:00,1,0.34809,0.84937,1.3607,1.2488,0.90989,1.7814,1.2539,1.3462,0.3646,3.6717,1.10789,2.46554,2.33388,0.446535,1.84608,0.604596,0.939106,1.36845
+28-Nov-2019 01:00:00,1,0.33439,0.80805,1.2998,1.2055,0.86466,1.7115,1.2073,1.2755,0.34921,3.5622,1.06539,2.39326,2.26168,0.424032,1.77759,0.577638,0.904305,1.32597
+28-Nov-2019 02:00:00,1,0.32994,0.79463,1.28,1.1914,0.85003,1.6888,1.192,1.2524,0.34417,3.5258,1.0513,2.36927,2.23803,0.416733,1.75535,0.568864,0.893021,1.31204
+28-Nov-2019 03:00:00,1,0.33658,0.81466,1.3095,1.2124,0.87187,1.7226,1.2148,1.2868,0.35169,3.5799,1.07227,2.40497,2.27328,0.427624,1.78853,0.581951,0.909859,1.3328
+28-Nov-2019 04:00:00,1,0.34659,0.84485,1.354,1.244,0.90492,1.7737,1.2488,1.3385,0.36293,3.6599,1.1033,2.45773,2.32602,0.444066,1.83857,0.601645,0.935287,1.36382
+28-Nov-2019 05:00:00,1,0.37043,0.91694,1.46,1.3193,0.9844,1.8966,1.3291,1.4604,0.38934,3.843,1.17458,2.57871,2.45002,0.483434,1.95816,0.648494,0.996198,1.43659
+28-Nov-2019 06:00:00,1,0.43514,1.114,1.7471,1.5235,1.2054,2.2377,1.541,1.7843,0.45866,4.2919,1.35069,2.87599,2.77613,0.591743,2.28558,0.775347,1.16396,1.62674
+28-Nov-2019 07:00:00,1,0.49457,1.2967,2.0101,1.7109,1.4144,2.56,1.7286,2.0733,0.51953,4.6472,1.49188,3.11223,3.06321,0.692916,2.58961,0.891468,1.3209,1.79267
+28-Nov-2019 08:00:00,1,0.50988,1.344,2.0779,1.7591,1.4692,2.6444,1.7759,2.1466,0.53481,4.7305,1.52532,3.16782,3.1354,0.71923,2.66842,0.921335,1.36174,1.83417
+28-Nov-2019 09:00:00,1,0.50813,1.3386,2.0701,1.7536,1.4629,2.6347,1.7705,2.1382,0.53307,4.7211,1.52155,3.16156,3.12717,0.716213,2.65939,0.917917,1.35706,1.82945
+28-Nov-2019 10:00:00,1,0.51565,1.3619,2.1033,1.7773,1.4899,2.6763,1.7936,2.1741,0.54052,4.7611,1.53761,3.18823,3.16241,0.729166,2.69816,0.932579,1.37717,1.84968
+28-Nov-2019 11:00:00,1,0.52445,1.3891,2.1422,1.805,1.5216,2.7251,1.8206,2.2159,0.5492,4.8068,1.55605,3.21879,3.20342,0.74435,2.74357,0.949727,1.40075,1.8732
+28-Nov-2019 12:00:00,1,0.51847,1.3706,2.1158,1.7861,1.5,2.6919,1.8023,2.1875,0.54331,4.7758,1.54356,3.19809,3.17557,0.734027,2.7127,0.938073,1.38472,1.85723
+28-Nov-2019 13:00:00,1,0.5153,1.3608,2.1018,1.7762,1.4886,2.6743,1.7926,2.1724,0.54018,4.7592,1.53687,3.18699,3.16077,0.728562,2.69635,0.931896,1.37623,1.84874
+28-Nov-2019 14:00:00,1,0.51055,1.3461,2.0808,1.7612,1.4715,2.648,1.7779,2.1498,0.53546,4.734,1.52674,3.17018,3.13851,0.720371,2.67184,0.922627,1.36352,1.83596
+28-Nov-2019 15:00:00,1,0.50999,1.3443,2.0783,1.7594,1.4695,2.645,1.7762,2.1471,0.53491,4.7311,1.52555,3.1682,3.1359,0.719414,2.66897,0.921543,1.36203,1.83446
+28-Nov-2019 16:00:00,1,0.51776,1.3684,2.1126,1.7839,1.4974,2.6879,1.8001,2.1841,0.5426,4.7721,1.54205,3.19559,3.17223,0.732792,2.70901,0.936678,1.3828,1.85532
+28-Nov-2019 17:00:00,1,0.52824,1.4008,2.159,1.8169,1.5352,2.7461,1.8321,2.2338,0.55292,4.8262,1.56388,3.23175,3.22101,0.750897,2.76314,0.957108,1.41092,1.88328
+28-Nov-2019 18:00:00,1,0.52254,1.3832,2.1338,1.7989,1.5146,2.7144,1.8147,2.2068,0.54731,4.797,1.55207,3.21219,3.19451,0.74104,2.73367,0.945992,1.39561,1.86809
+28-Nov-2019 19:00:00,1,0.50481,1.3283,2.0554,1.7431,1.451,2.6164,1.7603,2.1224,0.52976,4.7032,1.51436,3.14963,3.11155,0.710497,2.64228,0.911438,1.34819,1.82047
+28-Nov-2019 20:00:00,1,0.47105,1.2243,1.9061,1.6367,1.331,2.4315,1.6551,1.9599,0.49574,4.5128,1.43823,3.02272,2.95095,0.652696,2.46894,0.845559,1.25849,1.72795
+28-Nov-2019 21:00:00,1,0.43887,1.1255,1.7636,1.5353,1.2183,2.2577,1.5529,1.8027,0.46256,4.3158,1.36012,2.89183,2.7945,0.598053,2.3046,0.782653,1.17375,1.6374
+28-Nov-2019 22:00:00,1,0.41537,1.0536,1.6594,1.4612,1.1371,2.1324,1.4771,1.6864,0.43783,4.162,1.29947,2.7898,2.67806,0.558438,2.18513,0.73664,1.11235,1.56974
+28-Nov-2019 23:00:00,1,0.37796,0.93977,1.4934,1.3431,1.0097,1.9357,1.3542,1.4986,0.39758,3.8988,1.19635,2.61559,2.48873,0.495928,1.99604,0.663275,1.01554,1.45925
+29-Nov-2019 00:00:00,1,0.34595,0.84289,1.3512,1.242,0.90278,1.7704,1.2466,1.3351,0.3622,3.6548,1.10131,2.45434,2.32261,0.442998,1.83533,0.600369,0.933636,1.36182
+29-Nov-2019 01:00:00,1,0.33211,0.80117,1.2896,1.1983,0.85716,1.6998,1.1995,1.2636,0.34663,3.5436,1.05819,2.38099,2.24957,0.420289,1.76619,0.57314,0.898518,1.31884
+29-Nov-2019 02:00:00,1,0.32617,0.7833,1.2632,1.1795,0.8377,1.6697,1.1791,1.2328,0.3399,3.4948,1.03929,2.34882,2.21799,0.410576,1.73658,0.561449,0.883503,1.30023
+29-Nov-2019 03:00:00,1,0.32698,0.78575,1.2668,1.1821,0.84036,1.6739,1.1819,1.237,0.34082,3.5015,1.04189,2.35325,2.22233,0.411907,1.74064,0.563052,0.885559,1.30278
+29-Nov-2019 04:00:00,1,0.3366,0.81471,1.3096,1.2125,0.87194,1.7227,1.2149,1.2869,0.35171,3.5801,1.07233,2.40507,2.27338,0.427656,1.78863,0.581989,0.909908,1.33286
+29-Nov-2019 05:00:00,1,0.35668,0.87532,1.3989,1.2759,0.93842,1.8255,1.2829,1.3903,0.37416,3.7386,1.1339,2.50971,2.37876,0.460688,1.88911,0.621478,0.961001,1.3948
+29-Nov-2019 06:00:00,1,0.4167,1.0577,1.6653,1.4654,1.1417,2.1394,1.4814,1.6931,0.43925,4.1709,1.303,2.79574,2.68472,0.560679,2.1919,0.739252,1.11582,1.57362
+29-Nov-2019 07:00:00,1,0.47571,1.2386,1.9267,1.6514,1.3475,2.4569,1.6697,1.9824,0.50048,4.54,1.44908,3.04085,2.97331,0.66064,2.4928,0.854652,1.27081,1.74086
+29-Nov-2019 08:00:00,1,0.49151,1.2873,1.9966,1.7012,1.4035,2.5432,1.7191,2.0586,0.51645,4.6301,1.48506,3.10087,3.0487,0.687671,2.57389,0.885499,1.31276,1.78432
+29-Nov-2019 09:00:00,1,0.49029,1.2835,1.9912,1.6974,1.3992,2.5366,1.7153,2.0528,0.51523,4.6233,1.48233,3.09631,3.0429,0.685579,2.56761,0.883117,1.30951,1.78098
+29-Nov-2019 10:00:00,1,0.49676,1.3035,2.0198,1.7178,1.4222,2.5721,1.7354,2.0839,0.52172,4.6593,1.49674,3.12032,3.07359,0.696679,2.60088,0.895747,1.32674,1.79864
+29-Nov-2019 11:00:00,1,0.50356,1.3245,2.0499,1.7392,1.4465,2.6095,1.7564,2.1164,0.52852,4.6965,1.51166,3.14513,3.10569,0.708359,2.63588,0.909013,1.34487,1.81711
+29-Nov-2019 12:00:00,1,0.4998,1.3129,2.0333,1.7273,1.4331,2.5888,1.7448,2.0984,0.52477,4.676,1.50344,3.13146,3.08795,0.701896,2.61652,0.901676,1.33484,1.80691
+29-Nov-2019 13:00:00,1,0.49851,1.3089,2.0276,1.7233,1.4285,2.5817,1.7408,2.0922,0.52348,4.6689,1.5006,3.12674,3.08186,0.699681,2.60988,0.89916,1.3314,1.8034
+29-Nov-2019 14:00:00,1,0.49405,1.2951,2.0078,1.7092,1.4125,2.5572,1.7269,2.0708,0.519,4.6443,1.49072,3.11029,3.06073,0.692019,2.58692,0.890448,1.3195,1.79124
+29-Nov-2019 15:00:00,1,0.49525,1.2988,2.0132,1.713,1.4168,2.5638,1.7307,2.0766,0.52021,4.6509,1.49339,3.11474,3.06642,0.69408,2.5931,0.892793,1.3227,1.79452
+29-Nov-2019 16:00:00,1,0.50136,1.3177,2.0402,1.7322,1.4386,2.5973,1.7496,2.1059,0.52632,4.6845,1.50684,3.13712,3.09528,0.704565,2.62451,0.904706,1.33898,1.81112
+29-Nov-2019 17:00:00,1,0.51267,1.3526,2.0901,1.7679,1.4791,2.6598,1.7845,2.1599,0.53757,4.7453,1.53127,3.1777,3.14843,0.72402,2.68276,0.926758,1.36918,1.84166
+29-Nov-2019 18:00:00,1,0.50243,1.321,2.0449,1.7356,1.4425,2.6033,1.7529,2.111,0.52739,4.6903,1.50918,3.14102,3.10034,0.706406,2.63003,0.906796,1.34184,1.81403
+29-Nov-2019 19:00:00,1,0.48298,1.261,1.9589,1.6743,1.3732,2.4966,1.6925,2.0176,0.50786,4.5819,1.4658,3.06876,3.0081,0.673068,2.53009,0.868852,1.29009,1.76093
+29-Nov-2019 20:00:00,1,0.45214,1.1661,1.8224,1.5771,1.2646,2.3291,1.5953,1.8678,0.47633,4.3989,1.39299,2.947,2.8594,0.620524,2.37224,0.808598,1.20859,1.67502
+29-Nov-2019 21:00:00,1,0.42452,1.0816,1.7,1.49,1.1686,2.181,1.5068,1.7319,0.44751,4.2229,1.32346,2.83019,2.72363,0.573834,2.23159,0.754565,1.13621,1.59625
+29-Nov-2019 22:00:00,1,0.40318,1.0164,1.6054,1.4227,1.0953,2.0679,1.4374,1.6256,0.42484,4.0787,1.26678,2.73466,2.61691,0.537993,2.12337,0.71275,1.08068,1.53412
+29-Nov-2019 23:00:00,1,0.36876,0.91187,1.4526,1.314,0.97879,1.8879,1.3235,1.4519,0.3875,3.8305,1.1697,2.57043,2.44139,0.480662,1.94975,0.645209,0.991908,1.43153
+30-Nov-2019 00:00:00,1,0.34171,0.8301,1.3323,1.2286,0.88876,1.7487,1.2322,1.3133,0.35745,3.6211,1.08823,2.43212,2.30032,0.436032,1.81413,0.592033,0.922862,1.34871
+30-Nov-2019 01:00:00,1,0.32886,0.79141,1.2752,1.188,0.84652,1.6834,1.1884,1.2468,0.34295,3.517,1.04789,2.36347,2.23233,0.414979,1.75001,0.566752,0.890309,1.30868
+30-Nov-2019 02:00:00,1,0.32335,0.77483,1.2507,1.1706,0.82849,1.6555,1.1694,1.2181,0.33669,3.4714,1.03024,2.33341,2.20296,0.405976,1.72255,0.555902,0.876392,1.29136
+30-Nov-2019 03:00:00,1,0.32764,0.78772,1.2697,1.1841,0.8425,1.6772,1.1841,1.2404,0.34156,3.5069,1.04398,2.35681,2.22581,0.412976,1.7439,0.564339,0.887212,1.30484
+30-Nov-2019 04:00:00,1,0.33668,0.81495,1.31,1.2127,0.87219,1.7231,1.2151,1.2873,0.35179,3.5807,1.07258,2.40549,2.27379,0.427783,1.78902,0.582142,0.910105,1.3331
+30-Nov-2019 05:00:00,1,0.35841,0.88054,1.4066,1.2813,0.94417,1.8344,1.2888,1.3991,0.37608,3.7519,1.13907,2.51849,2.38774,0.463537,1.89776,0.62487,0.965408,1.40007
+30-Nov-2019 06:00:00,1,0.41873,1.0639,1.6744,1.4718,1.1487,2.1502,1.488,1.7032,0.4414,4.1845,1.30835,2.80475,2.69485,0.564097,2.20221,0.743234,1.12112,1.57952
+30-Nov-2019 07:00:00,1,0.47639,1.2407,1.9297,1.6536,1.3499,2.4606,1.6718,1.9857,0.50117,4.5439,1.45065,3.04347,2.97656,0.6618,2.49628,0.855978,1.27261,1.74274
+30-Nov-2019 08:00:00,1,0.49307,1.2921,2.0035,1.7061,1.4091,2.5518,1.7239,2.0661,0.51802,4.6388,1.48855,3.10668,3.0561,0.690346,2.58191,0.888545,1.31691,1.78858
+30-Nov-2019 09:00:00,1,0.49193,1.2886,1.9985,1.7025,1.405,2.5455,1.7204,2.0606,0.51687,4.6324,1.486,3.10242,3.05068,0.688385,2.57603,0.886312,1.31386,1.78546
+30-Nov-2019 10:00:00,1,0.4947,1.2971,2.0107,1.7113,1.4149,2.5608,1.729,2.074,0.51966,4.6479,1.49218,3.11272,3.06384,0.693146,2.5903,0.89173,1.32125,1.79304
+30-Nov-2019 11:00:00,1,0.50468,1.3279,2.0549,1.7427,1.4505,2.6157,1.7599,2.1218,0.52963,4.7026,1.51409,3.14916,3.11094,0.710277,2.64162,0.911188,1.34785,1.82013
+30-Nov-2019 12:00:00,1,0.49734,1.3053,2.0224,1.7196,1.4243,2.5753,1.7372,2.0866,0.52231,4.6625,1.49803,3.12245,3.07633,0.697675,2.60387,0.89688,1.32828,1.80022
+30-Nov-2019 13:00:00,1,0.48853,1.2781,1.9835,1.6918,1.3929,2.5269,1.7098,2.0443,0.51346,4.6134,1.47838,3.08974,3.03456,0.682571,2.5586,0.87969,1.30484,1.77617
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+30-Nov-2019 17:00:00,1,0.51251,1.3521,2.0895,1.7674,1.4786,2.6589,1.784,2.1591,0.53741,4.7445,1.53093,3.17714,3.1477,0.72375,2.68195,0.926452,1.36876,1.84124
+30-Nov-2019 18:00:00,1,0.50378,1.3251,2.0509,1.7399,1.4473,2.6107,1.7571,2.1174,0.52873,4.6977,1.51212,3.1459,3.10669,0.708726,2.63697,0.909428,1.34544,1.81768
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+30-Nov-2019 21:00:00,1,0.43194,1.1043,1.7329,1.5134,1.1943,2.2206,1.5307,1.7686,0.45532,4.2714,1.34257,2.86233,2.76038,0.586351,2.26934,0.769098,1.15561,1.6176
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+01-Dec-2019 00:00:00,1,0.35604,0.87338,1.396,1.2739,0.93629,1.8222,1.2808,1.387,0.37345,3.7337,1.13198,2.50645,2.37542,0.459632,1.8859,0.62022,0.959366,1.39284
+01-Dec-2019 01:00:00,1,0.3381,0.81923,1.3163,1.2172,0.87687,1.7303,1.22,1.2947,0.35339,3.5922,1.07702,2.41304,2.2813,0.430112,1.79611,0.584936,0.913706,1.33752
+01-Dec-2019 02:00:00,1,0.32933,0.79282,1.2773,1.1895,0.84805,1.6858,1.19,1.2492,0.34349,3.5209,1.04938,2.36601,2.23482,0.415746,1.75234,0.567675,0.891494,1.31015
+01-Dec-2019 03:00:00,1,0.32461,0.77862,1.2563,1.1746,0.83261,1.6619,1.1738,1.2247,0.33813,3.4819,1.0343,2.34032,2.20969,0.408034,1.72883,0.558384,0.879573,1.29533
+01-Dec-2019 04:00:00,1,0.32387,0.77637,1.2529,1.1722,0.83016,1.6581,1.1712,1.2208,0.33727,3.4757,1.03189,2.33622,2.20569,0.406812,1.7251,0.556909,0.877683,1.29298
+01-Dec-2019 05:00:00,1,0.31929,0.76263,1.2326,1.1578,0.81525,1.635,1.1554,1.1969,0.33205,3.4375,1.0171,2.31101,2.18123,0.39935,1.70233,0.547899,0.86615,1.27854
+01-Dec-2019 06:00:00,1,0.32048,0.76619,1.2379,1.1615,0.81911,1.641,1.1595,1.2031,0.33341,3.4474,1.02095,2.31758,2.18759,0.401285,1.70824,0.550238,0.869141,1.28229
+01-Dec-2019 07:00:00,1,0.34468,0.83907,1.3455,1.238,0.89859,1.7639,1.2424,1.3286,0.36078,3.6447,1.09742,2.44773,2.31596,0.440918,1.829,0.597881,0.930419,1.35791
+01-Dec-2019 08:00:00,1,0.3717,0.92078,1.4656,1.3233,0.98865,1.9032,1.3333,1.4669,0.39073,3.8525,1.17826,2.58495,2.45654,0.485532,1.96452,0.650979,0.999446,1.44041
+01-Dec-2019 09:00:00,1,0.39497,0.99145,1.5689,1.3968,1.0673,2.0247,1.4104,1.5844,0.41602,4.0213,1.24427,2.69664,2.57544,0.524264,2.08185,0.696651,1.05941,1.50993
+01-Dec-2019 10:00:00,1,0.40881,1.0336,1.6303,1.4405,1.1146,2.0976,1.4558,1.6538,0.43086,4.1175,1.28199,2.76033,2.64523,0.547431,2.15189,0.72379,1.0953,1.55063
+01-Dec-2019 11:00:00,1,0.41301,1.0464,1.649,1.4537,1.129,2.1199,1.4695,1.6747,0.43533,4.1461,1.29323,2.77927,2.6663,0.554486,2.1732,0.732029,1.10623,1.5629
+01-Dec-2019 12:00:00,1,0.41088,1.0399,1.6395,1.447,1.1217,2.1086,1.4625,1.6641,0.43306,4.1316,1.28754,2.76968,2.65561,0.550905,2.16238,0.727848,1.10068,1.55668
+01-Dec-2019 13:00:00,1,0.39805,1.0008,1.5826,1.4065,1.0778,2.0409,1.4206,1.5999,0.41934,4.043,1.25277,2.71101,2.59105,0.529419,2.09744,0.702701,1.0674,1.51904
+01-Dec-2019 14:00:00,1,0.39091,0.9791,1.5509,1.384,1.0535,2.0034,1.397,1.564,0.41164,3.9925,1.23299,2.67758,2.55483,0.517487,2.06134,0.688687,1.04892,1.4979
+01-Dec-2019 15:00:00,1,0.39025,0.9771,1.548,1.3819,1.0513,1.9999,1.3949,1.5607,0.41093,3.9878,1.23114,2.67446,2.55148,0.516386,2.05801,0.687392,1.04721,1.49594
+01-Dec-2019 16:00:00,1,0.4096,1.036,1.6338,1.443,1.1173,2.1018,1.4583,1.6577,0.4317,4.1229,1.28411,2.76389,2.64919,0.548754,2.15589,0.725336,1.09735,1.55293
+01-Dec-2019 17:00:00,1,0.43375,1.1098,1.7409,1.5191,1.2005,2.2303,1.5365,1.7775,0.45721,4.283,1.34716,2.87006,2.76928,0.589397,2.27852,0.772629,1.16033,1.62277
+01-Dec-2019 18:00:00,1,0.43281,1.1069,1.7368,1.5162,1.1973,2.2253,1.5335,1.7729,0.45623,4.277,1.34478,2.86605,2.76466,0.587816,2.27375,0.770796,1.15788,1.62009
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+01-Dec-2019 20:00:00,1,0.38675,0.96647,1.5325,1.3708,1.0394,1.9816,1.3833,1.5431,0.40714,3.9627,1.22134,2.65787,2.53368,0.510557,2.04036,0.680532,1.03819,1.48554
+01-Dec-2019 21:00:00,1,0.36942,0.91387,1.4555,1.3161,0.981,1.8913,1.3257,1.4553,0.38822,3.8354,1.17162,2.57369,2.44479,0.481753,1.95306,0.646502,0.993596,1.43352
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+01-Dec-2019 23:00:00,1,0.3445,0.83852,1.3447,1.2374,0.89799,1.763,1.2417,1.3277,0.36058,3.6433,1.09686,2.44678,2.31501,0.44062,1.82809,0.597524,0.929957,1.35735
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+02-Dec-2019 01:00:00,1,0.30345,0.7151,1.162,1.1077,0.7639,1.5555,1.1004,1.1138,0.31382,3.302,0.964758,2.22171,2.0958,0.373593,1.62365,0.516666,0.826352,1.22807
+02-Dec-2019 02:00:00,1,0.29276,0.68311,1.1144,1.0739,0.72954,1.5023,1.0629,1.0574,0.30138,3.2077,0.928434,2.15966,2.03755,0.356293,1.57071,0.495571,0.799633,1.19358
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+12-Dec-2019 04:00:00,1,0.36978,0.91497,1.4571,1.3173,0.98221,1.8932,1.3269,1.4571,0.38862,3.8382,1.17268,2.57549,2.44666,0.482353,1.95488,0.647214,0.994525,1.43462
+12-Dec-2019 05:00:00,1,0.38955,0.97497,1.5449,1.3797,1.0489,1.9962,1.3926,1.5572,0.41017,3.9828,1.22919,2.67115,2.54792,0.515219,2.05448,0.686019,1.0454,1.49386
+12-Dec-2019 06:00:00,1,0.44787,1.153,1.8035,1.5637,1.2497,2.3061,1.5817,1.8469,0.47191,4.3724,1.38251,2.92942,2.83856,0.613279,2.35044,0.800245,1.19735,1.66295
+12-Dec-2019 07:00:00,1,0.50447,1.3273,2.0539,1.742,1.4498,2.6145,1.7592,2.1207,0.52942,4.7014,1.51362,3.14839,3.10994,0.709911,2.64052,0.910773,1.34728,1.81955
+12-Dec-2019 08:00:00,1,0.51977,1.3746,2.1216,1.7902,1.5047,2.6991,1.8063,2.1937,0.54459,4.7826,1.5463,3.20263,3.18165,0.736276,2.71943,0.940613,1.38821,1.86072
+12-Dec-2019 09:00:00,1,0.52081,1.3778,2.1262,1.7935,1.5084,2.7049,1.8095,2.1986,0.54562,4.788,1.54847,3.20624,3.18649,0.738069,2.72479,0.942638,1.391,1.8635
+12-Dec-2019 10:00:00,1,0.5274,1.3982,2.1553,1.8143,1.5322,2.7415,1.8296,2.2298,0.55209,4.8219,1.56215,3.22888,3.21711,0.749442,2.75879,0.955468,1.40866,1.88105
+12-Dec-2019 11:00:00,1,0.5342,1.4193,2.1853,1.8357,1.5568,2.7793,1.8503,2.262,0.55875,4.8563,1.57605,3.25189,3.2486,0.761209,2.79395,0.968717,1.42693,1.89908
+12-Dec-2019 12:00:00,1,0.52854,1.4017,2.1603,1.8178,1.5363,2.7478,1.833,2.2352,0.55321,4.8277,1.56449,3.23275,3.22238,0.751408,2.76467,0.957683,1.41171,1.88407
+12-Dec-2019 13:00:00,1,0.52353,1.3862,2.1382,1.8021,1.5182,2.72,1.8178,2.2115,0.54829,4.8021,1.55414,3.21563,3.19915,0.742763,2.73882,0.947936,1.39829,1.87075
+12-Dec-2019 14:00:00,1,0.51622,1.3636,2.1058,1.779,1.4919,2.6794,1.7954,2.1768,0.54108,4.764,1.53881,3.19021,3.16505,0.73014,2.70107,0.93368,1.37868,1.8512
+12-Dec-2019 15:00:00,1,0.51441,1.358,2.0979,1.7733,1.4854,2.6694,1.7898,2.1682,0.53929,4.7545,1.53497,3.18385,3.15659,0.727023,2.69174,0.930155,1.37384,1.84634
+12-Dec-2019 16:00:00,1,0.52812,1.4005,2.1584,1.8165,1.5348,2.7455,1.8318,2.2332,0.5528,4.8256,1.56363,3.23133,3.22045,0.750686,2.76251,0.956869,1.41059,1.88296
+12-Dec-2019 17:00:00,1,0.54426,1.4505,2.2297,1.8673,1.5933,2.8355,1.8808,2.3094,0.56854,4.9061,1.59622,3.28519,3.29494,0.778642,2.84601,0.988301,1.45401,1.92558
+12-Dec-2019 18:00:00,1,0.53802,1.4311,2.2021,1.8477,1.5706,2.8006,1.8619,2.28,0.56247,4.8754,1.58376,3.26462,3.26621,0.767816,2.81369,0.976145,1.4372,1.90916
+12-Dec-2019 19:00:00,1,0.52836,1.4012,2.1595,1.8173,1.5357,2.7468,1.8325,2.2344,0.55304,4.8268,1.56413,3.23217,3.22158,0.751109,2.76377,0.957346,1.41125,1.88361
+12-Dec-2019 20:00:00,1,0.50226,1.3204,2.0441,1.7351,1.4419,2.6023,1.7524,2.1102,0.52722,4.6894,1.50881,3.1404,3.09953,0.706113,2.62915,0.906464,1.34138,1.81357
+12-Dec-2019 21:00:00,1,0.47585,1.239,1.9273,1.6519,1.348,2.4576,1.6701,1.9831,0.50063,4.5408,1.4494,3.04139,2.97398,0.660879,2.49351,0.854925,1.27118,1.74125
+12-Dec-2019 22:00:00,1,0.45971,1.1894,1.8559,1.601,1.2911,2.37,1.6193,1.9047,0.48413,4.4451,1.41132,2.9777,2.89617,0.633379,2.4109,0.823392,1.22852,1.6963
+12-Dec-2019 23:00:00,1,0.43056,1.1,1.7268,1.5091,1.1895,2.2132,1.5262,1.7617,0.45386,4.2624,1.33902,2.85637,2.75353,0.584012,2.26228,0.766384,1.15198,1.61362
+13-Dec-2019 00:00:00,1,0.40463,1.0209,1.6118,1.4273,1.1003,2.0755,1.4421,1.6329,0.42639,4.0888,1.27072,2.7413,2.62422,0.540425,2.13072,0.715596,1.08444,1.53839
+13-Dec-2019 01:00:00,1,0.38832,0.97124,1.5394,1.3758,1.0448,1.9898,1.3885,1.551,0.40884,3.974,1.22575,2.66534,2.54168,0.513175,2.04829,0.683614,1.04224,1.49021
+13-Dec-2019 02:00:00,1,0.37935,0.94401,1.4996,1.3475,1.0144,1.943,1.3588,1.5057,0.3991,3.9091,1.20035,2.62236,2.49589,0.49825,2.00308,0.666018,1.01913,1.46344
+13-Dec-2019 03:00:00,1,0.37433,0.92876,1.4773,1.3316,0.9975,1.9168,1.3421,1.4802,0.39361,3.872,1.1859,2.59788,2.47009,0.4899,1.97777,0.656149,1.00621,1.44834
+13-Dec-2019 04:00:00,1,0.38018,0.9465,1.5033,1.3501,1.0172,1.9473,1.3615,1.5099,0.4,3.9151,1.2027,2.62634,2.5001,0.499616,2.00722,0.66763,1.02125,1.4659
+13-Dec-2019 05:00:00,1,0.39931,1.0047,1.5882,1.4105,1.0821,2.0475,1.4247,1.6063,0.42069,4.0518,1.25623,2.71685,2.59742,0.531526,2.10381,0.705171,1.07066,1.52275
+13-Dec-2019 06:00:00,1,0.45357,1.1705,1.8287,1.5816,1.2696,2.3368,1.5998,1.8748,0.47781,4.4077,1.39648,2.95284,2.86637,0.622952,2.37954,0.811396,1.21235,1.67905
+13-Dec-2019 07:00:00,1,0.50738,1.3363,2.0668,1.7512,1.4602,2.6305,1.7682,2.1346,0.53232,4.7171,1.51992,3.15886,3.12362,0.714914,2.6555,0.916446,1.35504,1.82741
+13-Dec-2019 08:00:00,1,0.52018,1.3759,2.1234,1.7915,1.5062,2.7014,1.8075,2.1956,0.545,4.7848,1.54715,3.20405,3.18355,0.736981,2.72153,0.941409,1.38931,1.86181
+13-Dec-2019 09:00:00,1,0.51868,1.3712,2.1167,1.7868,1.5008,2.693,1.8029,2.1885,0.54351,4.7769,1.544,3.19882,3.17654,0.734385,2.71377,0.938478,1.38528,1.85779
+13-Dec-2019 10:00:00,1,0.52115,1.3789,2.1276,1.7946,1.5097,2.7068,1.8105,2.2002,0.54595,4.7898,1.54918,3.20741,3.18806,0.738651,2.72653,0.943295,1.3919,1.8644
+13-Dec-2019 11:00:00,1,0.52672,1.3961,2.1523,1.8121,1.5298,2.7377,1.8275,2.2266,0.55143,4.8185,1.56075,3.22658,3.21397,0.748274,2.7553,0.954151,1.40684,1.87925
+13-Dec-2019 12:00:00,1,0.52025,1.3761,2.1237,1.7917,1.5064,2.7017,1.8077,2.1959,0.54506,4.7851,1.54729,3.20427,3.18385,0.737092,2.72187,0.941535,1.38948,1.86199
+13-Dec-2019 13:00:00,1,0.51628,1.3638,2.1061,1.7792,1.4921,2.6798,1.7956,2.1771,0.54114,4.7644,1.53894,3.19043,3.16535,0.730251,2.7014,0.933805,1.37886,1.85137
+13-Dec-2019 14:00:00,1,0.51071,1.3466,2.0815,1.7617,1.4721,2.6489,1.7785,2.1505,0.53563,4.7349,1.52709,3.17076,3.13927,0.720653,2.67268,0.922947,1.36395,1.8364
+13-Dec-2019 15:00:00,1,0.5076,1.337,2.0678,1.7519,1.461,2.6318,1.7689,2.1357,0.53254,4.7183,1.52041,3.15968,3.12469,0.715306,2.65668,0.91689,1.35565,1.82803
+13-Dec-2019 16:00:00,1,0.52064,1.3773,2.1254,1.793,1.5078,2.7039,1.8089,2.1978,0.54545,4.7871,1.54811,3.20564,3.18569,0.737772,2.7239,0.942303,1.39054,1.86304
+13-Dec-2019 17:00:00,1,0.53507,1.422,2.1891,1.8384,1.5599,2.7842,1.8529,2.2661,0.5596,4.8607,1.57781,3.25479,3.25261,0.762709,2.79843,0.970404,1.42926,1.90137
+13-Dec-2019 18:00:00,1,0.52534,1.3918,2.1461,1.8078,1.5247,2.73,1.8233,2.22,0.55007,4.8114,1.55788,3.22183,3.20753,0.745877,2.74813,0.951448,1.40312,1.87556
+13-Dec-2019 19:00:00,1,0.51142,1.3488,2.0847,1.7639,1.4747,2.6529,1.7806,2.1539,0.53634,4.7387,1.52862,3.1733,3.14262,0.721882,2.67636,0.924338,1.36586,1.83832
+13-Dec-2019 20:00:00,1,0.48405,1.2643,1.9636,1.6777,1.377,2.5024,1.6958,2.0227,0.50893,4.588,1.46823,3.07282,3.0132,0.674895,2.53557,0.870938,1.29293,1.76387
+13-Dec-2019 21:00:00,1,0.46293,1.1993,1.8702,1.6112,1.3024,2.3875,1.6295,1.9205,0.48745,4.4645,1.41905,2.99064,2.9118,0.638872,2.42741,0.829703,1.23704,1.70534
+13-Dec-2019 22:00:00,1,0.44511,1.1446,1.7912,1.555,1.24,2.2912,1.5729,1.8334,0.46905,4.3552,1.3757,2.91799,2.82509,0.608611,2.33639,0.794857,1.19011,1.65514
+13-Dec-2019 23:00:00,1,0.41485,1.052,1.6571,1.4595,1.1353,2.1296,1.4754,1.6838,0.43728,4.1585,1.29809,2.78747,2.67546,0.557563,2.18249,0.735619,1.11099,1.56823
+14-Dec-2019 00:00:00,1,0.38716,0.96772,1.5343,1.3721,1.0408,1.9838,1.3847,1.5452,0.40759,3.9657,1.2225,2.65984,2.53578,0.511245,2.04244,0.681341,1.03925,1.48677
+14-Dec-2019 01:00:00,1,0.36627,0.90434,1.4415,1.3062,0.97046,1.875,1.3152,1.4393,0.38476,3.8118,1.1624,2.55807,2.42854,0.476545,1.93726,0.640326,0.985536,1.424
+14-Dec-2019 02:00:00,1,0.35384,0.86672,1.3862,1.2669,0.92895,1.8109,1.2734,1.3757,0.371,3.7166,1.12534,2.49517,2.36392,0.455994,1.87484,0.615884,0.953737,1.38609
+14-Dec-2019 03:00:00,1,0.35253,0.86278,1.3804,1.2628,0.92462,1.8042,1.2689,1.369,0.36955,3.7064,1.12139,2.48847,2.35711,0.453844,1.86831,0.613321,0.950412,1.38209
+14-Dec-2019 04:00:00,1,0.35887,0.88194,1.4086,1.2828,0.94572,1.8368,1.2903,1.4015,0.37659,3.7555,1.14046,2.52084,2.39015,0.464304,1.90009,0.625782,0.966595,1.40149
+14-Dec-2019 05:00:00,1,0.37866,0.9419,1.4965,1.3453,1.0121,1.9394,1.3565,1.5022,0.39834,3.904,1.19836,2.61899,2.49233,0.497094,1.99958,0.664653,1.01734,1.46136
+14-Dec-2019 06:00:00,1,0.43125,1.1021,1.7298,1.5112,1.1919,2.2169,1.5285,1.7651,0.45459,4.2668,1.34079,2.85934,2.75694,0.585175,2.26579,0.767734,1.15378,1.6156
+14-Dec-2019 07:00:00,1,0.49116,1.2862,1.9951,1.7001,1.4023,2.5413,1.718,2.057,0.5161,4.6281,1.48427,3.09955,3.04702,0.687066,2.57207,0.884811,1.31182,1.78335
+14-Dec-2019 08:00:00,1,0.50784,1.3377,2.0688,1.7527,1.4618,2.6331,1.7696,2.1368,0.53278,4.7196,1.52092,3.16052,3.1258,0.715711,2.65789,0.917348,1.35628,1.82866
+14-Dec-2019 09:00:00,1,0.50899,1.3413,2.0739,1.7563,1.466,2.6395,1.7732,2.1424,0.53392,4.7257,1.5234,3.16464,3.13122,0.717696,2.66383,0.919598,1.35936,1.83177
+14-Dec-2019 10:00:00,1,0.51229,1.3514,2.0885,1.7667,1.4778,2.6577,1.7833,2.1581,0.53719,4.7433,1.53046,3.17636,3.14666,0.723369,2.68081,0.926021,1.36817,1.84064
+14-Dec-2019 11:00:00,1,0.51152,1.3491,2.0851,1.7642,1.475,2.6534,1.7809,2.1544,0.53643,4.7392,1.52881,3.17363,3.14305,0.722041,2.67684,0.924518,1.36611,1.83857
+14-Dec-2019 12:00:00,1,0.50105,1.3167,2.0388,1.7313,1.4375,2.5957,1.7487,2.1044,0.52601,4.6828,1.50617,3.13601,3.09384,0.70404,2.62294,0.904111,1.33816,1.8103
+14-Dec-2019 13:00:00,1,0.49251,1.2904,2.0011,1.7044,1.4071,2.5487,1.7222,2.0635,0.51746,4.6357,1.4873,3.1046,3.05346,0.68939,2.57904,0.887456,1.31542,1.78706
+14-Dec-2019 14:00:00,1,0.48437,1.2653,1.965,1.6787,1.3781,2.5042,1.6968,2.0243,0.50926,4.5899,1.46897,3.07404,3.01474,0.675449,2.53723,0.871569,1.29379,1.76476
+14-Dec-2019 15:00:00,1,0.47861,1.2475,1.9395,1.6606,1.3577,2.4727,1.6788,1.9965,0.50343,4.5568,1.45578,3.05203,2.9872,0.665593,2.50766,0.860315,1.2785,1.74888
+14-Dec-2019 16:00:00,1,0.49279,1.2912,2.0023,1.7052,1.4081,2.5503,1.723,2.0648,0.51774,4.6373,1.48792,3.10563,3.05476,0.689862,2.58045,0.887993,1.31616,1.78781
+14-Dec-2019 17:00:00,1,0.50752,1.3367,2.0674,1.7516,1.4607,2.6313,1.7686,2.1353,0.53246,4.7178,1.52023,3.15937,3.12429,0.715159,2.65624,0.916723,1.35542,1.8278
+14-Dec-2019 18:00:00,1,0.49528,1.2989,2.0133,1.7131,1.417,2.564,1.7308,2.0768,0.52024,4.6511,1.49347,3.11487,3.06659,0.694141,2.59328,0.892862,1.3228,1.79462
+14-Dec-2019 19:00:00,1,0.47218,1.2277,1.9111,1.6403,1.335,2.4377,1.6586,1.9654,0.4969,4.5194,1.44088,3.02714,2.95639,0.654626,2.47474,0.847769,1.26148,1.73109
+14-Dec-2019 20:00:00,1,0.43761,1.1216,1.758,1.5313,1.2139,2.251,1.5489,1.7965,0.46124,4.3078,1.35694,2.88649,2.7883,0.59592,2.29817,0.780185,1.17044,1.63381
+14-Dec-2019 21:00:00,1,0.41244,1.0447,1.6464,1.4519,1.127,2.1169,1.4676,1.6719,0.43472,4.1422,1.2917,2.7767,2.66342,0.553522,2.17029,0.730904,1.10473,1.56122
+14-Dec-2019 22:00:00,1,0.39798,1.0006,1.5823,1.4063,1.0776,2.0405,1.4203,1.5996,0.41927,4.0426,1.25259,2.7107,2.59071,0.529307,2.09711,0.70257,1.06722,1.51884
+14-Dec-2019 23:00:00,1,0.36781,0.909,1.4483,1.311,0.97562,1.883,1.3203,1.4471,0.38646,3.8234,1.16692,2.56573,2.4365,0.479093,1.94499,0.643348,0.989479,1.42866
+15-Dec-2019 00:00:00,1,0.33832,0.81989,1.3173,1.2179,0.87759,1.7315,1.2207,1.2958,0.35364,3.5939,1.0777,2.41421,2.28246,0.430472,1.7972,0.585368,0.914263,1.3382
+15-Dec-2019 01:00:00,1,0.32022,0.76543,1.2367,1.1607,0.81828,1.6397,1.1586,1.2018,0.33312,3.4453,1.02013,2.31617,2.18622,0.400869,1.70697,0.549735,0.868497,1.28149
+15-Dec-2019 02:00:00,1,0.30957,0.73345,1.1893,1.127,0.78368,1.5861,1.1217,1.146,0.32089,3.3549,0.985193,2.25659,2.12894,0.383529,1.65402,0.528739,0.841702,1.24766
+15-Dec-2019 03:00:00,1,0.30766,0.72771,1.1808,1.121,0.77749,1.5765,1.1151,1.136,0.31869,3.3384,0.978837,2.24574,2.1186,0.380422,1.64453,0.524968,0.836902,1.24155
+15-Dec-2019 04:00:00,1,0.30876,0.73102,1.1857,1.1245,0.78105,1.5821,1.1189,1.1418,0.31996,3.3479,0.982501,2.252,2.12456,0.382211,1.64999,0.527141,0.839667,1.24507
+15-Dec-2019 05:00:00,1,0.30758,0.72747,1.1804,1.1207,0.77722,1.5761,1.1148,1.1356,0.31859,3.3377,0.978563,2.24528,2.11816,0.380288,1.64412,0.524806,0.836696,1.24129
+15-Dec-2019 06:00:00,1,0.30939,0.7329,1.1885,1.1265,0.78308,1.5852,1.1211,1.1451,0.32068,3.3533,0.984584,2.25555,2.12795,0.38323,1.65311,0.528377,0.841241,1.24708
+15-Dec-2019 07:00:00,1,0.33872,0.82109,1.319,1.2192,0.87891,1.7335,1.2221,1.2979,0.35409,3.5972,1.07895,2.41633,2.28457,0.431129,1.7992,0.586156,0.915279,1.33944
+15-Dec-2019 08:00:00,1,0.36687,0.90616,1.4442,1.3081,0.97246,1.8781,1.3172,1.4423,0.38542,3.8163,1.16416,2.56105,2.43164,0.477535,1.94026,0.641501,0.987069,1.42581
+15-Dec-2019 09:00:00,1,0.38706,0.96742,1.5338,1.3718,1.0405,1.9832,1.3843,1.5447,0.40748,3.965,1.22222,2.65936,2.53528,0.511078,2.04194,0.681145,1.03899,1.48647
+15-Dec-2019 10:00:00,1,0.40444,1.0203,1.611,1.4267,1.0996,2.0746,1.4415,1.6319,0.42619,4.0875,1.27021,2.74044,2.62328,0.540109,2.12976,0.715227,1.08395,1.53783
+15-Dec-2019 11:00:00,1,0.41221,1.044,1.6454,1.4512,1.1262,2.1156,1.4668,1.6707,0.43448,4.1406,1.29109,2.77566,2.66227,0.553137,2.16912,0.730454,1.10414,1.56055
+15-Dec-2019 12:00:00,1,0.40814,1.0316,1.6274,1.4384,1.1123,2.0941,1.4536,1.6504,0.43015,4.1129,1.2802,2.7573,2.64188,0.546313,2.14851,0.722483,1.09356,1.54868
+15-Dec-2019 13:00:00,1,0.39348,0.98694,1.5623,1.3921,1.0623,2.0169,1.4055,1.577,0.41442,4.0108,1.24016,2.68969,2.56792,0.521787,2.07435,0.693741,1.05557,1.50554
+15-Dec-2019 14:00:00,1,0.38626,0.96497,1.5303,1.3693,1.0378,1.979,1.3817,1.5406,0.40661,3.9592,1.21995,2.65553,2.53117,0.509737,2.03788,0.679566,1.03692,1.48407
+15-Dec-2019 15:00:00,1,0.38672,0.96637,1.5323,1.3707,1.0393,1.9814,1.3832,1.5429,0.4071,3.9625,1.22124,2.65772,2.53351,0.510502,2.04019,0.680466,1.0381,1.48544
+15-Dec-2019 16:00:00,1,0.40526,1.0228,1.6146,1.4293,1.1024,2.0789,1.4442,1.636,0.42707,4.0931,1.27243,2.74419,2.6274,0.541484,2.13392,0.716835,1.08608,1.54024
+15-Dec-2019 17:00:00,1,0.42919,1.0958,1.7207,1.5047,1.1847,2.2059,1.5218,1.7549,0.45242,4.2535,1.3355,2.85046,2.74675,0.581698,2.25531,0.7637,1.14839,1.60968
+15-Dec-2019 18:00:00,1,0.42742,1.0904,1.7129,1.4992,1.1786,2.1965,1.5161,1.7462,0.45057,4.2419,1.33096,2.84281,2.73801,0.578721,2.24633,0.760243,1.14378,1.60461
+15-Dec-2019 19:00:00,1,0.41163,1.0422,1.6429,1.4494,1.1242,2.1126,1.4649,1.6678,0.43386,4.1367,1.28954,2.77305,2.65937,0.552162,2.16618,0.729316,1.10262,1.55886
+15-Dec-2019 20:00:00,1,0.38487,0.96077,1.5241,1.3649,1.0331,1.9718,1.3771,1.5336,0.40511,3.9492,1.21605,2.64893,2.52412,0.507434,2.0309,0.676852,1.03335,1.47995
+15-Dec-2019 21:00:00,1,0.37033,0.91664,1.4595,1.319,0.98407,1.8961,1.3287,1.4599,0.38923,3.8423,1.17429,2.57822,2.44951,0.48327,1.95766,0.6483,0.995945,1.43629
+15-Dec-2019 22:00:00,1,0.36248,0.89286,1.4247,1.2942,0.95777,1.8554,1.3024,1.4199,0.38058,3.783,1.1512,2.53907,2.40889,0.470268,1.9182,0.632873,0.975822,1.41248
+15-Dec-2019 23:00:00,1,0.33758,0.81765,1.314,1.2156,0.87515,1.7277,1.2182,1.292,0.35281,3.588,1.07538,2.41026,2.27853,0.429254,1.7935,0.583907,0.912379,1.33589
+16-Dec-2019 00:00:00,1,0.31286,0.74331,1.2039,1.1374,0.79433,1.6026,1.1331,1.1633,0.32468,3.383,0.996054,2.27512,2.14666,0.388871,1.67034,0.535218,0.849957,1.25813
+16-Dec-2019 01:00:00,1,0.29537,0.69091,1.126,1.0821,0.73791,1.5152,1.0721,1.0712,0.30443,3.2309,0.937375,2.17493,2.05181,0.360509,1.58361,0.50072,0.806143,1.20203
+16-Dec-2019 02:00:00,1,0.28621,0.66355,1.0852,1.0532,0.70863,1.4699,1.0398,1.0226,0.29371,3.1489,0.905786,2.12093,2.00163,0.345734,1.53836,0.482646,0.78333,1.1723
+16-Dec-2019 03:00:00,1,0.28362,0.65581,1.0736,1.045,0.70037,1.4571,1.0307,1.0088,0.29066,3.1254,0.896729,2.10544,1.98736,0.341559,1.52556,0.477525,0.776884,1.16383
+16-Dec-2019 04:00:00,1,0.28521,0.66055,1.0807,1.05,0.70543,1.4649,1.0363,1.0173,0.29253,3.1398,0.902285,2.11494,1.99611,0.344117,1.5334,0.480663,0.780833,1.16902
+16-Dec-2019 05:00:00,1,0.28106,0.64816,1.0622,1.0368,0.69222,1.4445,1.0216,0.99514,0.28763,3.102,0.887726,2.09003,1.97321,0.337436,1.51291,0.472461,0.770518,1.15543
+16-Dec-2019 06:00:00,1,0.27266,0.62313,1.0247,1.0103,0.66562,1.4032,0.99171,0.95023,0.27768,3.0243,0.857887,2.03895,1.92666,0.323953,1.47153,0.455864,0.749709,1.12779
+16-Dec-2019 07:00:00,1,0.28676,0.66519,1.0876,1.0549,0.71038,1.4726,1.0418,1.0255,0.29435,3.1539,0.907698,2.1242,2.00465,0.346619,1.54107,0.48373,0.784695,1.17409
+16-Dec-2019 08:00:00,1,0.31014,0.73516,1.1918,1.1288,0.78552,1.589,1.1237,1.149,0.32155,3.3598,0.987084,2.25982,2.13202,0.384456,1.65685,0.529864,0.843134,1.24948
+16-Dec-2019 09:00:00,1,0.33366,0.80584,1.2965,1.2032,0.86225,1.7077,1.2048,1.2717,0.34838,3.5562,1.06308,2.38933,2.25779,0.422829,1.77393,0.576193,0.902445,1.32368
+16-Dec-2019 10:00:00,1,0.35718,0.87682,1.4011,1.2775,0.94008,1.8281,1.2846,1.3928,0.37471,3.7424,1.13539,2.51224,2.38134,0.461508,1.8916,0.622454,0.962269,1.39632
+16-Dec-2019 11:00:00,1,0.38032,0.94693,1.5039,1.3505,1.0177,1.948,1.362,1.5106,0.40015,3.9161,1.2031,2.62701,2.50081,0.499847,2.00792,0.667903,1.0216,1.46632
+16-Dec-2019 12:00:00,1,0.37685,0.9364,1.4885,1.3396,1.006,1.9299,1.3505,1.493,0.39636,3.8906,1.19316,2.61018,2.48303,0.494081,1.99044,0.661093,1.01268,1.45591
+16-Dec-2019 13:00:00,1,0.36427,0.89828,1.4326,1.2999,0.96376,1.8647,1.3085,1.4291,0.38256,3.7967,1.1565,2.54806,2.41818,0.47323,1.92719,0.636391,0.980406,1.41792
+16-Dec-2019 14:00:00,1,0.35618,0.8738,1.3967,1.2743,0.93675,1.8229,1.2813,1.3877,0.3736,3.7347,1.13239,2.50715,2.37614,0.459858,1.88658,0.62049,0.959716,1.39326
+16-Dec-2019 15:00:00,1,0.35598,0.87321,1.3958,1.2737,0.9361,1.8219,1.2806,1.3867,0.37339,3.7332,1.1318,2.50615,2.37511,0.459535,1.8856,0.620105,0.959216,1.39266
+16-Dec-2019 16:00:00,1,0.37184,0.92122,1.4662,1.3238,0.98914,1.9039,1.3338,1.4676,0.39088,3.8536,1.17869,2.58567,2.45729,0.485773,1.96525,0.651264,0.999818,1.44084
+16-Dec-2019 17:00:00,1,0.39592,0.99436,1.5732,1.3998,1.0706,2.0297,1.4136,1.5892,0.41705,4.0281,1.24691,2.7011,2.58028,0.525862,2.08668,0.698527,1.06189,1.51275
+16-Dec-2019 18:00:00,1,0.40226,1.0136,1.6013,1.4198,1.0922,2.063,1.4343,1.621,0.42385,4.0724,1.26427,2.73043,2.61228,0.536453,2.11871,0.710946,1.07829,1.53142
+16-Dec-2019 19:00:00,1,0.3931,0.98576,1.5606,1.3909,1.061,2.0149,1.4043,1.575,0.414,4.0081,1.23908,2.68788,2.56595,0.52114,2.0724,0.692981,1.05457,1.50439
+16-Dec-2019 20:00:00,1,0.37564,0.93275,1.4831,1.3358,1.0019,1.9237,1.3465,1.4869,0.39505,3.8818,1.18969,2.60431,2.47685,0.492083,1.98439,0.658731,1.00958,1.4523
+16-Dec-2019 21:00:00,1,0.36613,0.90393,1.4409,1.3058,0.97,1.8743,1.3147,1.4386,0.38461,3.8108,1.162,2.55738,2.42783,0.476317,1.93656,0.640055,0.985183,1.42358
+16-Dec-2019 22:00:00,1,0.36622,0.90419,1.4413,1.306,0.97028,1.8748,1.315,1.439,0.38471,3.8114,1.16225,2.55781,2.42827,0.476458,1.93699,0.640223,0.985402,1.42384
+16-Dec-2019 23:00:00,1,0.34097,0.82788,1.329,1.2263,0.88633,1.745,1.2297,1.3095,0.35662,3.6152,1.08595,2.42823,2.29643,0.434821,1.81044,0.590581,0.920989,1.34642
+17-Dec-2019 00:00:00,1,0.31603,0.75283,1.2181,1.1475,0.80463,1.6186,1.1441,1.1799,0.32832,3.41,1.00647,2.29288,2.16373,0.394035,1.68611,0.541471,0.857936,1.26821
+17-Dec-2019 01:00:00,1,0.30189,0.71043,1.1551,1.1027,0.75888,1.5477,1.0949,1.1056,0.31201,3.2884,0.959515,2.21276,2.08734,0.371067,1.61592,0.513593,0.822451,1.22307
+17-Dec-2019 02:00:00,1,0.29817,0.69929,1.1385,1.091,0.7469,1.5292,1.0819,1.086,0.30769,3.2557,0.946922,2.19125,2.0671,0.365041,1.59749,0.50625,0.813143,1.21108
+17-Dec-2019 03:00:00,1,0.30328,0.71459,1.1613,1.1071,0.76335,1.5546,1.0998,1.113,0.31362,3.3005,0.964186,2.22073,2.09488,0.373316,1.6228,0.51633,0.825925,1.22752
+17-Dec-2019 04:00:00,1,0.31523,0.75042,1.2145,1.1449,0.80202,1.6145,1.1413,1.1757,0.3274,3.4032,1.00384,2.2884,2.15942,0.392729,1.68212,0.539891,0.855919,1.26567
+17-Dec-2019 05:00:00,1,0.34262,0.83285,1.3364,1.2315,0.89178,1.7534,1.2354,1.318,0.35847,3.6284,1.09106,2.43692,2.30513,0.437532,1.81869,0.593828,0.925181,1.35154
+17-Dec-2019 06:00:00,1,0.40885,1.0337,1.6305,1.4406,1.1147,2.0978,1.4559,1.654,0.4309,4.1178,1.2821,2.76051,2.64543,0.547499,2.1521,0.72387,1.0954,1.55075
+17-Dec-2019 07:00:00,1,0.47216,1.2277,1.911,1.6402,1.3349,2.4376,1.6586,1.9653,0.49688,4.5193,1.44083,3.02706,2.95629,0.65459,2.47463,0.847728,1.26143,1.73104
+17-Dec-2019 08:00:00,1,0.49348,1.2934,2.0053,1.7074,1.4105,2.5541,1.7252,2.0681,0.51843,4.6411,1.48946,3.1082,3.05805,0.691049,2.58401,0.889344,1.318,1.7897
+17-Dec-2019 09:00:00,1,0.49281,1.2913,2.0024,1.7053,1.4081,2.5504,1.7231,2.0649,0.51776,4.6374,1.48797,3.1057,3.05486,0.689898,2.58056,0.888035,1.31621,1.78787
+17-Dec-2019 10:00:00,1,0.49627,1.3019,2.0177,1.7162,1.4205,2.5694,1.7338,2.0815,0.52123,4.6566,1.49565,3.11849,3.07124,0.695828,2.59834,0.894781,1.32542,1.7973
+17-Dec-2019 11:00:00,1,0.50215,1.3201,2.0437,1.7347,1.4415,2.6017,1.7521,2.1097,0.52711,4.6888,1.50858,3.14001,3.09903,0.70593,2.6286,0.906256,1.3411,1.81328
+17-Dec-2019 12:00:00,1,0.5005,1.315,2.0364,1.7295,1.4356,2.5926,1.7469,2.1018,0.52546,4.6798,1.50496,3.13399,3.09123,0.70309,2.62009,0.903032,1.33669,1.80879
+17-Dec-2019 13:00:00,1,0.50004,1.3136,2.0344,1.7281,1.4339,2.5901,1.7455,2.0996,0.52501,4.6773,1.50397,3.13234,3.08909,0.70231,2.61776,0.902146,1.33548,1.80756
+17-Dec-2019 14:00:00,1,0.49271,1.291,2.0019,1.705,1.4078,2.5498,1.7228,2.0644,0.51766,4.6368,1.48775,3.10534,3.0544,0.689729,2.58006,0.887842,1.31595,1.7876
+17-Dec-2019 15:00:00,1,0.49063,1.2846,1.9927,1.6985,1.4004,2.5385,1.7163,2.0544,0.51557,4.6252,1.4831,3.09761,3.04454,0.686171,2.56939,0.883792,1.31043,1.78193
+17-Dec-2019 16:00:00,1,0.49968,1.3125,2.0328,1.727,1.4326,2.5881,1.7444,2.0978,0.52464,4.6753,1.50317,3.13102,3.08738,0.701689,2.6159,0.901441,1.33451,1.80658
+17-Dec-2019 17:00:00,1,0.51354,1.3553,2.094,1.7706,1.4823,2.6646,1.7872,2.164,0.53844,4.75,1.53314,3.1808,3.15255,0.725533,2.68729,0.92847,1.37153,1.84402
+17-Dec-2019 18:00:00,1,0.50635,1.3331,2.0622,1.748,1.4565,2.6249,1.765,2.1297,0.5313,4.7116,1.51771,3.15518,3.11881,0.713151,2.65023,0.914448,1.35231,1.82464
+17-Dec-2019 19:00:00,1,0.49167,1.2878,1.9973,1.7017,1.4041,2.5441,1.7195,2.0594,0.51661,4.631,1.48541,3.10145,3.04944,0.687937,2.57468,0.885802,1.31317,1.78474
+17-Dec-2019 20:00:00,1,0.46137,1.1945,1.8633,1.6062,1.2969,2.379,1.6246,1.9128,0.48584,4.4551,1.41532,2.98439,2.90424,0.636212,2.41942,0.826648,1.23292,1.70096
+17-Dec-2019 21:00:00,1,0.43794,1.1226,1.7595,1.5324,1.2151,2.2527,1.5499,1.7981,0.46159,4.3099,1.35777,2.88789,2.78992,0.596477,2.29985,0.780829,1.1713,1.63474
+17-Dec-2019 22:00:00,1,0.41838,1.0628,1.6728,1.4706,1.1474,2.1483,1.4869,1.7014,0.44102,4.1821,1.30741,2.80316,2.69306,0.563492,2.20039,0.74253,1.12018,1.57847
+17-Dec-2019 23:00:00,1,0.37796,0.93977,1.4934,1.3431,1.0097,1.9357,1.3542,1.4986,0.39758,3.8988,1.19635,2.61559,2.48873,0.495928,1.99604,0.663275,1.01554,1.45925
+18-Dec-2019 00:00:00,1,0.34561,0.84189,1.3497,1.2409,0.90168,1.7687,1.2455,1.3334,0.36183,3.6521,1.10029,2.45262,2.32087,0.442454,1.83367,0.599718,0.932794,1.3608
+18-Dec-2019 01:00:00,1,0.33012,0.79517,1.2808,1.192,0.85062,1.6897,1.1927,1.2533,0.34437,3.5273,1.05187,2.37024,2.23899,0.417028,1.75625,0.569218,0.893476,1.3126
+18-Dec-2019 02:00:00,1,0.32349,0.77524,1.2513,1.171,0.82893,1.6562,1.1699,1.2188,0.33684,3.4726,1.03068,2.33415,2.20368,0.406196,1.72322,0.556166,0.876731,1.29179
+18-Dec-2019 03:00:00,1,0.32583,0.78226,1.2617,1.1784,0.83657,1.668,1.1779,1.231,0.3395,3.492,1.03818,2.34693,2.21615,0.410011,1.73486,0.560767,0.882629,1.29914
+18-Dec-2019 04:00:00,1,0.33695,0.81574,1.3111,1.2136,0.87306,1.7244,1.216,1.2887,0.35209,3.5829,1.0734,2.4069,2.27519,0.428216,1.79034,0.582662,0.910775,1.33392
+18-Dec-2019 05:00:00,1,0.35923,0.88303,1.4102,1.284,0.94692,1.8387,1.2915,1.4033,0.37699,3.7582,1.14154,2.52267,2.39202,0.464899,1.9019,0.626489,0.967515,1.40259
+18-Dec-2019 06:00:00,1,0.42006,1.068,1.6803,1.476,1.1532,2.1573,1.4923,1.7098,0.44281,4.1934,1.31184,2.81063,2.70147,0.566332,2.20896,0.745837,1.12458,1.58337
+18-Dec-2019 07:00:00,1,0.47882,1.2482,1.9405,1.6612,1.3585,2.4738,1.6795,1.9975,0.50364,4.558,1.45626,3.05284,2.98821,0.665952,2.50874,0.860726,1.27905,1.74946
+18-Dec-2019 08:00:00,1,0.49664,1.3031,2.0193,1.7174,1.4218,2.5714,1.735,2.0833,0.5216,4.6586,1.49646,3.11985,3.07299,0.69646,2.60023,0.895499,1.3264,1.7983
+18-Dec-2019 09:00:00,1,0.49268,1.2909,2.0018,1.7049,1.4077,2.5497,1.7227,2.0643,0.51763,4.6367,1.48768,3.10523,3.05426,0.68968,2.57991,0.887787,1.31588,1.78752
+18-Dec-2019 10:00:00,1,0.49756,1.3059,2.0234,1.7203,1.4251,2.5765,1.7379,2.0877,0.52253,4.6637,1.49851,3.12326,3.07737,0.698052,2.605,0.897308,1.32887,1.80082
+18-Dec-2019 11:00:00,1,0.50371,1.3249,2.0505,1.7396,1.447,2.6103,1.7569,2.1171,0.52866,4.6973,1.51197,3.14565,3.10636,0.708604,2.63661,0.90929,1.34525,1.81749
+18-Dec-2019 12:00:00,1,0.49936,1.3115,2.0313,1.7259,1.4315,2.5863,1.7434,2.0963,0.52432,4.6736,1.50246,3.12983,3.08584,0.701129,2.61422,0.900805,1.33365,1.80569
+18-Dec-2019 13:00:00,1,0.49713,1.3046,2.0215,1.7189,1.4235,2.5741,1.7365,2.0856,0.52209,4.6613,1.49756,3.12167,3.07533,0.69731,2.60278,0.896465,1.32772,1.79965
+18-Dec-2019 14:00:00,1,0.49086,1.2853,1.9937,1.6992,1.4012,2.5397,1.717,2.0555,0.5158,4.6265,1.48361,3.09845,3.04562,0.686558,2.57055,0.884232,1.31103,1.78254
+18-Dec-2019 15:00:00,1,0.4886,1.2783,1.9837,1.692,1.3932,2.5273,1.71,2.0446,0.51352,4.6138,1.47853,3.08998,3.03486,0.68268,2.55892,0.879814,1.30501,1.77635
+18-Dec-2019 16:00:00,1,0.49875,1.3096,2.0286,1.724,1.4293,2.583,1.7415,2.0934,0.52372,4.6703,1.50113,3.12762,3.083,0.700095,2.61112,0.89963,1.33204,1.80406
+18-Dec-2019 17:00:00,1,0.51113,1.3479,2.0834,1.763,1.4736,2.6513,1.7797,2.1525,0.53604,4.7372,1.52799,3.17226,3.14125,0.721378,2.67485,0.923767,1.36508,1.83753
+18-Dec-2019 18:00:00,1,0.50627,1.3328,2.0619,1.7477,1.4562,2.6244,1.7648,2.1293,0.53121,4.7111,1.51752,3.15488,3.1184,0.713005,2.64979,0.914281,1.35208,1.82441
+18-Dec-2019 19:00:00,1,0.4935,1.2934,2.0054,1.7075,1.4106,2.5542,1.7253,2.0682,0.51845,4.6412,1.48951,3.10827,3.05815,0.691085,2.58412,0.889386,1.31806,1.78976
+18-Dec-2019 20:00:00,1,0.46429,1.2035,1.8762,1.6154,1.3072,2.3948,1.6338,1.9271,0.48884,4.4727,1.42228,2.99605,2.91836,0.641179,2.43434,0.832352,1.24062,1.70912
+18-Dec-2019 21:00:00,1,0.43986,1.1285,1.768,1.5384,1.2218,2.263,1.5561,1.8076,0.46359,4.3221,1.3626,2.896,2.79935,0.599724,2.30963,0.784586,1.17634,1.64022
+18-Dec-2019 22:00:00,1,0.41937,1.0658,1.6772,1.4738,1.1508,2.1536,1.4901,1.7063,0.44208,4.1888,1.31002,2.80757,2.69803,0.565169,2.20545,0.744482,1.12278,1.58136
+18-Dec-2019 23:00:00,1,0.37989,0.94564,1.502,1.3492,1.0162,1.9458,1.3606,1.5084,0.39969,3.913,1.20188,2.62496,2.49864,0.499142,2.00578,0.667071,1.02051,1.46505
+19-Dec-2019 00:00:00,1,0.34535,0.84109,1.3485,1.2401,0.9008,1.7673,1.2446,1.3321,0.36153,3.65,1.09947,2.45123,2.31947,0.442017,1.83234,0.599195,0.932117,1.35997
+19-Dec-2019 01:00:00,1,0.32857,0.79052,1.2739,1.1871,0.84555,1.6819,1.1873,1.2453,0.34262,3.5146,1.04695,2.36186,2.23076,0.414497,1.74853,0.566171,0.889563,1.30775
+19-Dec-2019 02:00:00,1,0.32208,0.77101,1.245,1.1666,0.82434,1.6491,1.165,1.2115,0.33524,3.4608,1.02614,2.32642,2.19616,0.4039,1.71622,0.553396,0.873183,1.28735
+19-Dec-2019 03:00:00,1,0.32479,0.77914,1.257,1.1751,0.83317,1.6627,1.1743,1.2256,0.33832,3.4834,1.03485,2.34126,2.21061,0.408317,1.72969,0.558724,0.88001,1.29588
+19-Dec-2019 04:00:00,1,0.33473,0.80908,1.3013,1.2066,0.86579,1.7132,1.2085,1.2773,0.3496,3.565,1.06647,2.39509,2.26349,0.424591,1.7793,0.57831,0.90517,1.32704
+19-Dec-2019 05:00:00,1,0.3571,0.87658,1.4007,1.2772,0.93982,1.8277,1.2844,1.3924,0.37463,3.7418,1.13516,2.51184,2.38093,0.461379,1.8912,0.6223,0.962069,1.39608
+19-Dec-2019 06:00:00,1,0.4169,1.0583,1.6662,1.466,1.1423,2.1405,1.4821,1.694,0.43946,4.1722,1.30352,2.79661,2.6857,0.561009,2.19289,0.739637,1.11633,1.57419
+19-Dec-2019 07:00:00,1,0.47578,1.2388,1.9271,1.6517,1.3477,2.4573,1.67,1.9828,0.50056,4.5404,1.44926,3.04114,2.97368,0.660772,2.49319,0.854802,1.27102,1.74107
+19-Dec-2019 08:00:00,1,0.494,1.295,2.0076,1.7091,1.4124,2.5569,1.7268,2.0706,0.51896,4.644,1.49063,3.11013,3.06053,0.691946,2.5867,0.890365,1.31939,1.79113
+19-Dec-2019 09:00:00,1,0.49081,1.2851,1.9935,1.699,1.401,2.5394,1.7169,2.0553,0.51575,4.6262,1.4835,3.09826,3.04538,0.686473,2.5703,0.884136,1.3109,1.78241
+19-Dec-2019 10:00:00,1,0.48776,1.2757,1.98,1.6894,1.3902,2.5227,1.7074,2.0406,0.51268,4.609,1.47664,3.08683,3.03087,0.681244,2.55462,0.878178,1.30278,1.77405
+19-Dec-2019 11:00:00,1,0.4931,1.2922,2.0036,1.7062,1.4092,2.552,1.724,2.0663,0.51805,4.639,1.48861,3.10678,3.05624,0.690395,2.58205,0.8886,1.31698,1.78866
+19-Dec-2019 12:00:00,1,0.49402,1.295,2.0077,1.7091,1.4124,2.557,1.7269,2.0707,0.51897,4.6441,1.49066,3.11019,3.06059,0.69197,2.58677,0.890392,1.31943,1.79117
+19-Dec-2019 13:00:00,1,0.4904,1.2839,1.9917,1.6977,1.3996,2.5372,1.7156,2.0533,0.51534,4.6239,1.48258,3.09674,3.04344,0.685772,2.56819,0.883337,1.30981,1.78129
+19-Dec-2019 14:00:00,1,0.48154,1.2565,1.9525,1.6698,1.3681,2.4887,1.688,2.0106,0.5064,4.5737,1.46251,3.06327,3.00123,0.670605,2.5227,0.866041,1.28627,1.75696
+19-Dec-2019 15:00:00,1,0.47826,1.2464,1.938,1.6595,1.3565,2.4708,1.6777,1.9948,0.50308,4.5548,1.45499,3.05071,2.98556,0.665006,2.5059,0.859644,1.27759,1.74793
+19-Dec-2019 16:00:00,1,0.48827,1.2773,1.9823,1.691,1.392,2.5255,1.709,2.043,0.51319,4.6119,1.47778,3.08873,3.03328,0.682112,2.55722,0.879168,1.30413,1.77544
+19-Dec-2019 17:00:00,1,0.50334,1.3238,2.0489,1.7385,1.4457,2.6083,1.7557,2.1154,0.5283,4.6953,1.51118,3.14434,3.10465,0.707981,2.63474,0.908583,1.34428,1.81651
+19-Dec-2019 18:00:00,1,0.49913,1.3108,2.0303,1.7252,1.4307,2.5851,1.7427,2.0952,0.52409,4.6723,1.50196,3.129,3.08477,0.70074,2.61305,0.900362,1.33304,1.80508
+19-Dec-2019 19:00:00,1,0.48347,1.2625,1.9611,1.6759,1.375,2.4992,1.694,2.02,0.50835,4.5847,1.46692,3.07063,3.01045,0.673909,2.53262,0.869813,1.2914,1.76228
+19-Dec-2019 20:00:00,1,0.45562,1.1768,1.8378,1.5881,1.2768,2.3479,1.6063,1.8848,0.47992,4.4202,1.40146,2.96118,2.87633,0.62643,2.39,0.815399,1.21775,1.68481
+19-Dec-2019 21:00:00,1,0.42869,1.0943,1.7185,1.5032,1.183,2.2033,1.5202,1.7525,0.4519,4.2502,1.33422,2.84831,2.74428,0.580859,2.25278,0.762725,1.14709,1.60825
+19-Dec-2019 22:00:00,1,0.40731,1.029,1.6237,1.4357,1.1094,2.0897,1.4509,1.6463,0.42926,4.1072,1.27795,2.75351,2.63769,0.544912,2.14428,0.720846,1.09139,1.54623
+19-Dec-2019 23:00:00,1,0.37083,0.91814,1.4617,1.3206,0.98573,1.8986,1.3304,1.4624,0.38977,3.846,1.17573,2.58066,2.45206,0.48409,1.96015,0.649271,0.997213,1.43778
+20-Dec-2019 00:00:00,1,0.34173,0.83016,1.3324,1.2287,0.88883,1.7488,1.2323,1.3134,0.35747,3.6212,1.08829,2.43222,2.30042,0.436064,1.81423,0.592071,0.922911,1.34877
+20-Dec-2019 01:00:00,1,0.32718,0.78635,1.2677,1.1827,0.84101,1.6749,1.1826,1.2381,0.34105,3.5032,1.04253,2.35434,2.22339,0.412231,1.74163,0.563443,0.886061,1.30341
+20-Dec-2019 02:00:00,1,0.32255,0.77241,1.2471,1.1681,0.82586,1.6514,1.1666,1.2139,0.33577,3.4647,1.02765,2.32898,2.19866,0.404661,1.71854,0.554315,0.87436,1.28883
+20-Dec-2019 03:00:00,1,0.32282,0.77324,1.2483,1.1689,0.82676,1.6528,1.1676,1.2154,0.33609,3.467,1.02853,2.33049,2.20013,0.40511,1.71991,0.554856,0.875053,1.28969
+20-Dec-2019 04:00:00,1,0.33101,0.79786,1.2847,1.1948,0.85355,1.6943,1.1957,1.2579,0.34539,3.5346,1.0547,2.37507,2.24373,0.418489,1.7607,0.570976,0.895736,1.3154
+20-Dec-2019 05:00:00,1,0.34619,0.84363,1.3523,1.2428,0.90359,1.7717,1.2475,1.3364,0.36248,3.6567,1.10206,2.45563,2.3239,0.443404,1.83656,0.600854,0.934263,1.36258
+20-Dec-2019 06:00:00,1,0.39385,0.98806,1.564,1.3933,1.0635,2.0188,1.4067,1.5788,0.41482,4.0134,1.24118,2.69142,2.56978,0.5224,2.07621,0.694462,1.05652,1.50662
+20-Dec-2019 07:00:00,1,0.44953,1.1582,1.8108,1.5689,1.2555,2.315,1.587,1.855,0.47364,4.3828,1.38662,2.93631,2.84671,0.616108,2.35895,0.803508,1.20174,1.66767
+20-Dec-2019 08:00:00,1,0.46937,1.2191,1.8987,1.6314,1.3251,2.4224,1.6498,1.9517,0.49403,4.5028,1.43429,3.01612,2.94285,0.649829,2.46033,0.842274,1.25404,1.72328
+20-Dec-2019 09:00:00,1,0.46523,1.2064,1.8803,1.6184,1.3105,2.3999,1.6367,1.9316,0.4898,4.4783,1.42451,2.99978,2.9229,0.642778,2.43915,0.834188,1.2431,1.71175
+20-Dec-2019 10:00:00,1,0.46689,1.2115,1.8877,1.6236,1.3164,2.4089,1.642,1.9397,0.4915,4.4882,1.42845,3.00637,2.93093,0.645613,2.44767,0.837439,1.2475,1.71639
+20-Dec-2019 11:00:00,1,0.47259,1.229,1.9129,1.6416,1.3364,2.4399,1.6599,1.9673,0.49731,4.5218,1.44183,3.02873,2.95834,0.655317,2.47681,0.84856,1.26255,1.73222
+20-Dec-2019 12:00:00,1,0.46694,1.2116,1.8879,1.6238,1.3165,2.4092,1.6421,1.9399,0.49155,4.4885,1.42857,3.00656,2.93116,0.645696,2.44792,0.837534,1.24763,1.71652
+20-Dec-2019 13:00:00,1,0.46136,1.1945,1.8632,1.6062,1.2969,2.379,1.6245,1.9128,0.48583,4.4551,1.4153,2.98436,2.90421,0.6362,2.41938,0.826634,1.2329,1.70094
+20-Dec-2019 14:00:00,1,0.45492,1.1747,1.8347,1.5859,1.2743,2.3441,1.6041,1.8814,0.47921,4.416,1.39978,2.95837,2.87296,0.625254,2.38647,0.814046,1.21592,1.68287
+20-Dec-2019 15:00:00,1,0.45438,1.173,1.8323,1.5842,1.2724,2.3412,1.6024,1.8787,0.47864,4.4126,1.39845,2.95614,2.8703,0.624326,2.38368,0.812978,1.21448,1.68133
+20-Dec-2019 16:00:00,1,0.46482,1.2051,1.8785,1.6171,1.3091,2.3977,1.6355,1.9296,0.48938,4.4758,1.42354,2.99815,2.92091,0.642079,2.43705,0.833386,1.24202,1.7106
+20-Dec-2019 17:00:00,1,0.47653,1.2411,1.9304,1.654,1.3504,2.4614,1.6723,1.9864,0.50132,4.5448,1.45099,3.04404,2.97727,0.662051,2.49703,0.856265,1.273,1.74315
+20-Dec-2019 18:00:00,1,0.46703,1.2119,1.8883,1.6241,1.3168,2.4097,1.6424,1.9404,0.49164,4.489,1.42877,3.00689,2.93157,0.645838,2.44834,0.837698,1.24785,1.71676
+20-Dec-2019 19:00:00,1,0.45108,1.1629,1.8177,1.5738,1.2609,2.3234,1.5919,1.8626,0.47524,4.3923,1.39041,2.94266,2.85425,0.618731,2.36684,0.806532,1.20581,1.67204
+20-Dec-2019 20:00:00,1,0.42415,1.0804,1.6984,1.4889,1.1673,2.179,1.5056,1.73,0.44712,4.2204,1.32249,2.82856,2.72177,0.573204,2.22969,0.753832,1.13523,1.59517
+20-Dec-2019 21:00:00,1,0.40511,1.0223,1.6139,1.4288,1.1019,2.0781,1.4437,1.6353,0.4269,4.0921,1.27201,2.74349,2.62663,0.541224,2.13314,0.716532,1.08568,1.53979
+20-Dec-2019 22:00:00,1,0.39309,0.98574,1.5606,1.3909,1.0609,2.0148,1.4042,1.575,0.414,4.008,1.23906,2.68785,2.56592,0.521129,2.07236,0.692968,1.05456,1.50437
+20-Dec-2019 23:00:00,1,0.36123,0.88909,1.4191,1.2903,0.95361,1.849,1.2983,1.4136,0.3792,3.7736,1.1475,2.5328,2.40243,0.468209,1.91195,0.630426,0.972637,1.40869
+21-Dec-2019 00:00:00,1,0.33394,0.80668,1.2977,1.204,0.86316,1.7091,1.2057,1.2731,0.3487,3.5585,1.06395,2.39081,2.25926,0.423283,1.77531,0.576738,0.903146,1.32454
+21-Dec-2019 01:00:00,1,0.31723,0.75644,1.2234,1.1513,0.80854,1.6246,1.1483,1.1862,0.3297,3.4201,1.0104,2.29958,2.17019,0.395995,1.69209,0.543843,0.860965,1.27203
+21-Dec-2019 02:00:00,1,0.30879,0.73109,1.1858,1.1245,0.78113,1.5822,1.119,1.1419,0.31999,3.3482,0.982586,2.25214,2.1247,0.382252,1.65012,0.52719,0.83973,1.24515
+21-Dec-2019 03:00:00,1,0.30757,0.72745,1.1804,1.1207,0.7772,1.5761,1.1148,1.1355,0.31859,3.3377,0.978542,2.24524,2.11812,0.380278,1.64409,0.524793,0.83668,1.24127
+21-Dec-2019 04:00:00,1,0.31466,0.74872,1.212,1.1431,0.80019,1.6117,1.1394,1.1727,0.32675,3.3984,1.00199,2.28524,2.15637,0.391808,1.67931,0.538776,0.854495,1.26387
+21-Dec-2019 05:00:00,1,0.32927,0.79264,1.277,1.1893,0.84786,1.6855,1.1898,1.2489,0.34342,3.5204,1.0492,2.36569,2.23452,0.415651,1.75205,0.567561,0.891348,1.30997
+21-Dec-2019 06:00:00,1,0.36656,0.90522,1.4428,1.3071,0.97143,1.8765,1.3161,1.4407,0.38508,3.814,1.16325,2.55951,2.43004,0.477024,1.93871,0.640894,0.986277,1.42488
+21-Dec-2019 07:00:00,1,0.42063,1.0697,1.6827,1.4777,1.1552,2.1603,1.4942,1.7126,0.4434,4.1971,1.31331,2.81311,2.70428,0.56728,2.21182,0.74694,1.12605,1.585
+21-Dec-2019 08:00:00,1,0.44705,1.1505,1.7999,1.5611,1.2468,2.3017,1.5791,1.8429,0.47107,4.3673,1.3805,2.92605,2.83459,0.611901,2.34629,0.798655,1.19522,1.66065
+21-Dec-2019 09:00:00,1,0.45166,1.1647,1.8203,1.5756,1.2629,2.3265,1.5938,1.8654,0.47584,4.3959,1.39183,2.94504,2.85708,0.619715,2.36981,0.807666,1.20733,1.67367
+21-Dec-2019 10:00:00,1,0.45589,1.1777,1.839,1.589,1.2777,2.3494,1.6072,1.8861,0.48021,4.4219,1.40213,2.9623,2.87767,0.6269,2.39142,0.815941,1.21848,1.68559
+21-Dec-2019 11:00:00,1,0.45713,1.1815,1.8445,1.5928,1.282,2.356,1.6111,1.8921,0.48148,4.4294,1.40511,2.9673,2.88366,0.628995,2.39772,0.818351,1.22172,1.68906
+21-Dec-2019 12:00:00,1,0.44841,1.1547,1.8059,1.5654,1.2516,2.309,1.5834,1.8495,0.47248,4.3758,1.38385,2.93167,2.84122,0.614202,2.35322,0.80131,1.19878,1.66449
+21-Dec-2019 13:00:00,1,0.43503,1.1137,1.7466,1.5232,1.205,2.2371,1.5406,1.7838,0.45855,4.2912,1.35041,2.87552,2.77559,0.591558,2.28503,0.775133,1.16368,1.62643
+21-Dec-2019 14:00:00,1,0.42805,1.0924,1.7156,1.5012,1.1808,2.1998,1.5181,1.7493,0.45123,4.246,1.33258,2.84553,2.74111,0.579778,2.24952,0.761471,1.14542,1.60641
+21-Dec-2019 15:00:00,1,0.42372,1.0791,1.6965,1.4875,1.1659,2.1768,1.5042,1.7279,0.44667,4.2177,1.32139,2.82671,2.71968,0.572494,2.22755,0.753007,1.13413,1.59395
+21-Dec-2019 16:00:00,1,0.43831,1.1238,1.7611,1.5335,1.2164,2.2547,1.5511,1.7999,0.46197,4.3122,1.3587,2.88945,2.79174,0.597102,2.30173,0.781553,1.17227,1.6358
+21-Dec-2019 17:00:00,1,0.45441,1.1731,1.8324,1.5843,1.2725,2.3414,1.6025,1.8789,0.47868,4.4128,1.39854,2.95628,2.87047,0.624385,2.38385,0.813045,1.21457,1.68143
+21-Dec-2019 18:00:00,1,0.44919,1.1571,1.8093,1.5678,1.2543,2.3132,1.5859,1.8534,0.47328,4.3806,1.38577,2.93488,2.84503,0.615523,2.35719,0.802834,1.20083,1.66669
+21-Dec-2019 19:00:00,1,0.43281,1.1069,1.7367,1.5162,1.1973,2.2252,1.5335,1.7728,0.45622,4.2769,1.34476,2.86603,2.76463,0.587804,2.27372,0.770783,1.15786,1.62007
+21-Dec-2019 20:00:00,1,0.40622,1.0257,1.6189,1.4323,1.1057,2.084,1.4473,1.6408,0.4281,4.0998,1.27503,2.74858,2.63224,0.543096,2.13879,0.718721,1.08858,1.54306
+21-Dec-2019 21:00:00,1,0.38592,0.96394,1.5288,1.3682,1.0366,1.9773,1.3806,1.5389,0.40624,3.9568,1.21899,2.65391,2.52944,0.509172,2.03617,0.6789,1.03604,1.48306
+21-Dec-2019 22:00:00,1,0.37331,0.92568,1.4728,1.3284,0.99408,1.9115,1.3387,1.4751,0.3925,3.8645,1.18295,2.5929,2.46486,0.488213,1.97265,0.654153,1.00359,1.44528
+21-Dec-2019 23:00:00,1,0.34238,0.83213,1.3353,1.2307,0.89099,1.7522,1.2345,1.3168,0.35821,3.6265,1.09032,2.43566,2.30387,0.437138,1.8175,0.593357,0.924572,1.3508
+22-Dec-2019 00:00:00,1,0.31741,0.75698,1.2242,1.1518,0.80912,1.6255,1.1489,1.1871,0.3299,3.4216,1.01098,2.30058,2.17115,0.396285,1.69298,0.544194,0.861414,1.27259
+22-Dec-2019 01:00:00,1,0.30014,0.70519,1.1473,1.0972,0.75324,1.539,1.0888,1.0964,0.30998,3.273,0.9536,2.20265,2.07782,0.36823,1.60724,0.510137,0.818068,1.21743
+22-Dec-2019 02:00:00,1,0.29019,0.67543,1.1029,1.0657,0.72133,1.4896,1.0539,1.0437,0.29838,3.1848,0.919591,2.14454,2.02349,0.35215,1.55802,0.490503,0.793235,1.18525
+22-Dec-2019 03:00:00,1,0.28564,0.66184,1.0826,1.0513,0.7068,1.4671,1.0378,1.0196,0.29303,3.1437,0.903786,2.11751,1.99848,0.34481,1.53552,0.481513,0.781902,1.17042
+22-Dec-2019 04:00:00,1,0.28546,0.6613,1.0818,1.0508,0.70623,1.4662,1.0372,1.0186,0.29282,3.1421,0.903155,2.11643,1.99748,0.344519,1.53463,0.481156,0.781453,1.16983
+22-Dec-2019 05:00:00,1,0.28479,0.6593,1.0789,1.0487,0.7041,1.4629,1.0348,1.015,0.29204,3.136,0.900827,2.11245,1.99381,0.343445,1.53134,0.479838,0.779795,1.16766
+22-Dec-2019 06:00:00,1,0.2846,0.65875,1.078,1.0481,0.70351,1.4619,1.0342,1.014,0.29182,3.1343,0.900173,2.11133,1.99278,0.343144,1.53041,0.479469,0.77933,1.16704
+22-Dec-2019 07:00:00,1,0.30676,0.72502,1.1768,1.1181,0.77458,1.572,1.1119,1.1313,0.31765,3.3307,0.975843,2.24063,2.11374,0.378963,1.64007,0.523196,0.834649,1.23868
+22-Dec-2019 08:00:00,1,0.33448,0.80831,1.3002,1.2058,0.86494,1.7119,1.2076,1.2759,0.34931,3.5629,1.06566,2.39371,2.26213,0.424169,1.77801,0.577803,0.904517,1.32623
+22-Dec-2019 09:00:00,1,0.35805,0.87945,1.405,1.2802,0.94298,1.8326,1.2875,1.3973,0.37568,3.7491,1.138,2.51666,2.38587,0.462943,1.89596,0.624163,0.96449,1.39897
+22-Dec-2019 10:00:00,1,0.36997,0.91555,1.4579,1.3179,0.98286,1.8942,1.3275,1.4581,0.38883,3.8396,1.17324,2.57643,2.44764,0.48267,1.95584,0.647589,0.995015,1.43519
+22-Dec-2019 11:00:00,1,0.37867,0.94192,1.4966,1.3453,1.0121,1.9394,1.3565,1.5022,0.39835,3.904,1.19838,2.61902,2.49236,0.497105,1.99961,0.664666,1.01736,1.46138
+22-Dec-2019 12:00:00,1,0.3781,0.94019,1.494,1.3435,1.0102,1.9364,1.3546,1.4993,0.39773,3.8998,1.19675,2.61626,2.48944,0.496159,1.99674,0.663548,1.01589,1.45967
+22-Dec-2019 13:00:00,1,0.37145,0.92004,1.4645,1.3225,0.98783,1.9019,1.3325,1.4656,0.39046,3.8507,1.17755,2.58375,2.45528,0.485128,1.9633,0.6505,0.998819,1.43967
+22-Dec-2019 14:00:00,1,0.36803,0.90968,1.4493,1.3118,0.97636,1.8842,1.3211,1.4482,0.3867,3.8251,1.16758,2.56684,2.43765,0.479463,1.94611,0.643788,0.990052,1.42934
+22-Dec-2019 15:00:00,1,0.36909,0.91289,1.454,1.3151,0.97992,1.8897,1.3246,1.4536,0.38787,3.833,1.17068,2.57209,2.44312,0.481218,1.95144,0.645869,0.992769,1.43255
+22-Dec-2019 16:00:00,1,0.3875,0.96875,1.5358,1.3732,1.042,1.9855,1.3858,1.5469,0.40796,3.9682,1.22345,2.66145,2.53751,0.51181,2.04416,0.682007,1.04013,1.48778
+22-Dec-2019 17:00:00,1,0.40525,1.0228,1.6145,1.4292,1.1024,2.0788,1.4441,1.636,0.42706,4.093,1.27239,2.74413,2.62734,0.541461,2.13385,0.716809,1.08605,1.5402
+22-Dec-2019 18:00:00,1,0.40033,1.0078,1.5927,1.4137,1.0856,2.0529,1.428,1.6114,0.42179,4.059,1.25902,2.72156,2.60256,0.53323,2.10897,0.70717,1.0733,1.52575
+22-Dec-2019 19:00:00,1,0.38773,0.96944,1.5368,1.3739,1.0427,1.9867,1.3865,1.548,0.4082,3.9698,1.22409,2.66253,2.53867,0.512187,2.0453,0.682451,1.04071,1.48845
+22-Dec-2019 20:00:00,1,0.35902,0.8824,1.4093,1.2833,0.94622,1.8376,1.2908,1.4022,0.37676,3.7566,1.14091,2.52161,2.39094,0.464553,1.90085,0.626078,0.96698,1.40195
+22-Dec-2019 21:00:00,1,0.3419,0.83067,1.3332,1.2292,0.88938,1.7497,1.2329,1.3143,0.35766,3.6226,1.08882,2.43311,2.30131,0.436341,1.81507,0.592402,0.923339,1.34929
+22-Dec-2019 22:00:00,1,0.33808,0.81917,1.3162,1.2171,0.8768,1.7302,1.2199,1.2946,0.35337,3.592,1.07696,2.41294,2.28119,0.43008,1.79601,0.584898,0.913657,1.33746
+22-Dec-2019 23:00:00,1,0.31239,0.7419,1.2018,1.1359,0.7928,1.6003,1.1315,1.1608,0.32414,3.379,0.994506,2.27248,2.14413,0.388107,1.66801,0.534292,0.848776,1.25664
+23-Dec-2019 00:00:00,1,0.28806,0.66907,1.0934,1.059,0.71452,1.479,1.0464,1.0324,0.29588,3.1656,0.912211,2.13192,2.01179,0.348712,1.54748,0.486295,0.787927,1.17832
+23-Dec-2019 01:00:00,1,0.27031,0.61615,1.0143,1.0028,0.65822,1.3918,0.98333,0.93764,0.27489,3.0024,0.849455,2.02451,1.91359,0.320194,1.45998,0.451225,0.743908,1.12003
+23-Dec-2019 02:00:00,1,0.26202,0.59148,0.97726,0.97659,0.63216,1.3513,0.9536,0.89302,0.26497,2.9238,0.819321,1.97287,1.86722,0.306936,1.41923,0.434823,0.723452,1.09244
+23-Dec-2019 03:00:00,1,0.25665,0.57554,0.95329,0.95959,0.61536,1.3253,0.93425,0.86401,0.25851,2.8722,0.799528,1.93893,1.83702,0.298375,1.39289,0.424199,0.710247,1.07447
+23-Dec-2019 04:00:00,1,0.25394,0.56751,0.94121,0.95103,0.60693,1.3122,0.92448,0.84936,0.25525,2.846,0.789473,1.92168,1.82176,0.29407,1.37963,0.418845,0.703608,1.06537
+23-Dec-2019 05:00:00,1,0.24922,0.55352,0.92013,0.93609,0.59226,1.2894,0.90738,0.82375,0.24953,2.7998,0.771793,1.89134,1.79505,0.28657,1.35653,0.409503,0.692042,1.04945
+23-Dec-2019 06:00:00,1,0.24272,0.53425,0.89106,0.91548,0.57211,1.2582,0.8837,0.78832,0.24161,2.7353,0.747124,1.84899,1.75805,0.276252,1.32472,0.396614,0.676135,1.02738
+23-Dec-2019 07:00:00,1,0.25772,0.57873,0.95809,0.963,0.61872,1.3305,0.93813,0.86983,0.25981,2.8826,0.803512,1.94576,1.84309,0.300089,1.39816,0.426328,0.712891,1.07808
+23-Dec-2019 08:00:00,1,0.28239,0.65213,1.0681,1.0411,0.69645,1.451,1.0263,1.0022,0.2892,3.1141,0.892407,2.09804,1.98056,0.339576,1.51947,0.47509,0.773822,1.15979
+23-Dec-2019 09:00:00,1,0.3082,0.72935,1.1832,1.1227,0.77925,1.5793,1.117,1.1389,0.31932,3.3431,0.980649,2.24884,2.12155,0.381306,1.64723,0.526042,0.838268,1.24329
+23-Dec-2019 10:00:00,1,0.33357,0.80557,1.2961,1.2029,0.86195,1.7073,1.2045,1.2712,0.34828,3.5555,1.0628,2.38884,2.25732,0.422681,1.77348,0.576016,0.902217,1.3234
+23-Dec-2019 11:00:00,1,0.35922,0.88301,1.4102,1.2839,0.9469,1.8386,1.2915,1.4033,0.37698,3.7582,1.14152,2.52264,2.39199,0.464888,1.90186,0.626476,0.967498,1.40257
+23-Dec-2019 12:00:00,1,0.35777,0.8786,1.4037,1.2793,0.94204,1.8311,1.2866,1.3958,0.37537,3.747,1.13716,2.51523,2.3844,0.462479,1.89455,0.62361,0.963771,1.39811
+23-Dec-2019 13:00:00,1,0.34502,0.84011,1.3471,1.2391,0.89973,1.7657,1.2435,1.3304,0.36117,3.6475,1.09848,2.44953,2.31777,0.441484,1.83072,0.598557,0.931293,1.35897
+23-Dec-2019 14:00:00,1,0.33646,0.81429,1.309,1.212,0.87147,1.722,1.2144,1.2862,0.35155,3.579,1.07189,2.40432,2.27263,0.427423,1.78792,0.58171,0.909549,1.33242
+23-Dec-2019 15:00:00,1,0.33446,0.80825,1.3001,1.2057,0.86488,1.7118,1.2075,1.2758,0.34929,3.5627,1.0656,2.39361,2.26202,0.424137,1.77791,0.577765,0.904468,1.32617
+23-Dec-2019 16:00:00,1,0.34747,0.84749,1.3579,1.2468,0.90783,1.7782,1.2518,1.343,0.36391,3.6668,1.10598,2.46229,2.33061,0.445508,1.84296,0.603369,0.937518,1.36652
+23-Dec-2019 17:00:00,1,0.36814,0.91,1.4498,1.3121,0.97672,1.8847,1.3214,1.4488,0.38682,3.8259,1.16789,2.56736,2.4382,0.479637,1.94664,0.643994,0.990322,1.42966
+23-Dec-2019 18:00:00,1,0.36849,0.91108,1.4514,1.3132,0.97791,1.8866,1.3226,1.4506,0.38721,3.8285,1.16893,2.56912,2.44003,0.480226,1.94843,0.644692,0.991233,1.43073
+23-Dec-2019 19:00:00,1,0.35845,0.88066,1.4067,1.2815,0.94431,1.8346,1.2889,1.3993,0.37612,3.7522,1.13919,2.51869,2.38794,0.463602,1.89796,0.624947,0.965509,1.40019
+23-Dec-2019 20:00:00,1,0.33824,0.81965,1.3169,1.2177,0.87734,1.7311,1.2204,1.2954,0.35355,3.5933,1.07746,2.41379,2.28205,0.430345,1.79682,0.585215,0.914066,1.33796
+23-Dec-2019 21:00:00,1,0.327,0.78581,1.2669,1.1821,0.84042,1.674,1.182,1.2371,0.34084,3.5017,1.04196,2.35336,2.22243,0.411938,1.74073,0.563089,0.885608,1.30284
+23-Dec-2019 22:00:00,1,0.32118,0.76828,1.241,1.1637,0.82138,1.6445,1.1619,1.2068,0.3342,3.4533,1.02321,2.32142,2.19131,0.40242,1.7117,0.551609,0.870895,1.28449
+23-Dec-2019 23:00:00,1,0.29311,0.68416,1.1159,1.075,0.73067,1.504,1.0642,1.0592,0.30179,3.2109,0.92964,2.16172,2.03947,0.35686,1.57244,0.496263,0.800508,1.19472
+24-Dec-2019 00:00:00,1,0.26706,0.60648,0.99978,0.99256,0.64799,1.3759,0.9717,0.92019,0.27101,2.9718,0.837716,2.0044,1.89547,0.314996,1.44401,0.444802,0.735888,1.10925
+24-Dec-2019 01:00:00,1,0.25065,0.55774,0.92649,0.94059,0.59668,1.2963,0.91254,0.83148,0.25126,2.8138,0.777144,1.90052,1.80312,0.28883,1.36349,0.412321,0.695528,1.05426
+24-Dec-2019 02:00:00,1,0.24287,0.53472,0.89177,0.91598,0.5726,1.2589,0.88428,0.78918,0.2418,2.7369,0.747724,1.85002,1.75895,0.276501,1.32549,0.396926,0.676519,1.02791
+24-Dec-2019 03:00:00,1,0.24417,0.53857,0.89758,0.9201,0.57662,1.2652,0.88902,0.79627,0.24339,2.7499,0.752684,1.85853,1.76636,0.278563,1.33185,0.399504,0.679697,1.03234
+24-Dec-2019 04:00:00,1,0.24944,0.55415,0.92108,0.93676,0.59292,1.2905,0.90815,0.82491,0.24979,2.8019,0.772594,1.89271,1.79626,0.286908,1.35757,0.409924,0.692563,1.05017
+24-Dec-2019 05:00:00,1,0.25678,0.57594,0.95389,0.96002,0.61578,1.3259,0.93474,0.86474,0.25867,2.8735,0.800026,1.93978,1.83778,0.298589,1.39355,0.424465,0.710577,1.07492
+24-Dec-2019 06:00:00,1,0.26128,0.5893,0.97398,0.97426,0.62985,1.3478,0.95095,0.88905,0.26409,2.9168,0.816621,1.96824,1.86309,0.305761,1.41562,0.433367,0.721641,1.08999
+24-Dec-2019 07:00:00,1,0.28618,0.66346,1.0851,1.0531,0.70853,1.4697,1.0397,1.0224,0.29367,3.1486,0.905678,2.12075,2.00146,0.345684,1.5382,0.482584,0.783252,1.17219
+24-Dec-2019 08:00:00,1,0.31163,0.73963,1.1985,1.1335,0.79035,1.5965,1.1289,1.1569,0.32327,3.3726,0.992014,2.26823,2.14006,0.386878,1.66426,0.532803,0.846878,1.25423
+24-Dec-2019 09:00:00,1,0.32661,0.78463,1.2652,1.1809,0.83914,1.672,1.1806,1.2351,0.3404,3.4985,1.04071,2.35123,2.22035,0.411299,1.73879,0.56232,0.88462,1.30162
+24-Dec-2019 10:00:00,1,0.33581,0.81232,1.3061,1.21,0.86933,1.7187,1.2121,1.2828,0.35081,3.5737,1.06985,2.40084,2.26919,0.426355,1.78467,0.580428,0.907898,1.33039
+24-Dec-2019 11:00:00,1,0.34906,0.85228,1.365,1.2518,0.91308,1.7863,1.2572,1.3512,0.36568,3.6793,1.11083,2.47053,2.33893,0.448118,1.8509,0.606488,0.941555,1.37141
+24-Dec-2019 12:00:00,1,0.35116,0.85863,1.3743,1.2585,0.92006,1.7971,1.2643,1.362,0.36802,3.6957,1.11723,2.48139,2.34992,0.45158,1.86142,0.61062,0.946909,1.37787
+24-Dec-2019 13:00:00,1,0.33901,0.82195,1.3203,1.2201,0.87985,1.7349,1.223,1.2993,0.35441,3.5994,1.07984,2.41784,2.28607,0.431595,1.80062,0.586715,0.916,1.34033
+24-Dec-2019 14:00:00,1,0.32338,0.77491,1.2508,1.1707,0.82858,1.6556,1.1695,1.2183,0.33672,3.4717,1.03033,2.33355,2.2031,0.406018,1.72268,0.555952,0.876457,1.29145
+24-Dec-2019 15:00:00,1,0.31545,0.75107,1.2155,1.1456,0.80273,1.6156,1.1421,1.1768,0.32765,3.405,1.00455,2.28961,2.16058,0.393081,1.6832,0.540317,0.856463,1.26635
+24-Dec-2019 16:00:00,1,0.32785,0.78836,1.2707,1.1848,0.84319,1.6782,1.1849,1.2415,0.3418,3.5087,1.04466,2.35796,2.22694,0.413322,1.74495,0.564756,0.887747,1.3055
+24-Dec-2019 17:00:00,1,0.34704,0.84618,1.356,1.2454,0.90639,1.776,1.2503,1.3408,0.36342,3.6634,1.10465,2.46003,2.32833,0.444792,1.84078,0.602514,0.93641,1.36518
+24-Dec-2019 18:00:00,1,0.33313,0.80423,1.2941,1.2015,0.8605,1.705,1.203,1.2689,0.34778,3.5519,1.0614,2.38646,2.25496,0.421954,1.77126,0.575141,0.901092,1.32201
+24-Dec-2019 19:00:00,1,0.30702,0.7258,1.1779,1.119,0.77542,1.5733,1.1128,1.1326,0.31795,3.3329,0.976708,2.24211,2.11515,0.379384,1.64136,0.523708,0.835299,1.23951
+24-Dec-2019 20:00:00,1,0.29005,0.67502,1.1023,1.0653,0.72089,1.4889,1.0534,1.043,0.29822,3.1835,0.919115,2.14372,2.02273,0.351928,1.55734,0.490232,0.792892,1.18481
+24-Dec-2019 21:00:00,1,0.28646,0.66429,1.0863,1.0539,0.70943,1.4711,1.0407,1.0239,0.294,3.1512,0.906656,2.12242,2.00301,0.346137,1.53959,0.483139,0.78395,1.17311
+24-Dec-2019 22:00:00,1,0.29085,0.6774,1.1059,1.0678,0.72343,1.4928,1.0562,1.0472,0.29915,3.1907,0.921859,2.14841,2.02709,0.35321,1.56126,0.4918,0.794872,1.18739
+24-Dec-2019 23:00:00,1,0.27133,0.61918,1.0188,1.0061,0.66143,1.3967,0.98697,0.94311,0.2761,3.0119,0.85312,2.03078,1.91927,0.321825,1.46499,0.453238,0.746425,1.1234
+25-Dec-2019 00:00:00,1,0.2528,0.56413,0.93612,0.94742,0.60338,1.3067,0.92035,0.84318,0.25387,2.8349,0.785218,1.91438,1.81532,0.292257,1.37405,0.416588,0.700811,1.06154
+25-Dec-2019 01:00:00,1,0.23366,0.50749,0.85061,0.88681,0.54426,1.2149,0.85058,0.7388,0.23051,2.6441,0.712243,1.78906,1.70623,0.261947,1.28056,0.378676,0.654085,0.99643
+25-Dec-2019 02:00:00,1,0.2248,0.48135,0.81099,0.85874,0.51719,1.1729,0.81793,0.69005,0.21956,2.553,0.677449,1.72923,1.65512,0.247997,1.23744,0.361105,0.632592,0.96586
+25-Dec-2019 03:00:00,1,0.22376,0.47829,0.80635,0.85545,0.51402,1.168,0.81409,0.68432,0.21827,2.5422,0.673328,1.72214,1.6491,0.246366,1.23239,0.359044,0.630079,0.962258
+25-Dec-2019 04:00:00,1,0.22615,0.48531,0.817,0.863,0.52128,1.1793,0.8229,0.69746,0.22123,2.5669,0.682769,1.73838,1.6629,0.25011,1.24397,0.363771,0.635847,0.970515
+25-Dec-2019 05:00:00,1,0.22446,0.48035,0.80947,0.85766,0.51615,1.1713,0.81667,0.68818,0.21914,2.5495,0.676099,1.72691,1.65314,0.247462,1.23578,0.360429,0.631769,0.96468
+25-Dec-2019 06:00:00,1,0.21785,0.46086,0.77987,0.8367,0.49607,1.1401,0.79214,0.6516,0.2109,2.4802,0.649674,1.68144,1.61471,0.237083,1.20366,0.347301,0.615782,0.941665
+25-Dec-2019 07:00:00,1,0.22867,0.49276,0.82829,0.871,0.52898,1.1912,0.83221,0.71137,0.22435,2.593,0.692721,1.7555,1.67748,0.254082,1.25625,0.368779,0.641966,0.979242
+25-Dec-2019 08:00:00,1,0.24964,0.55475,0.92198,0.9374,0.59354,1.2914,0.90888,0.826,0.25003,2.8039,0.773349,1.89401,1.7974,0.287227,1.35855,0.410321,0.693055,1.05085
+25-Dec-2019 09:00:00,1,0.27633,0.63408,1.0411,1.0219,0.67725,1.4213,1.0048,0.96991,0.28205,3.0585,0.871011,2.06142,1.94707,0.329849,1.48963,0.463129,0.758807,1.13992
+25-Dec-2019 10:00:00,1,0.29966,0.70376,1.1451,1.0957,0.7517,1.5366,1.0871,1.0939,0.30943,3.2688,0.95198,2.19989,2.07522,0.367455,1.60487,0.509193,0.816871,1.21589
+25-Dec-2019 11:00:00,1,0.32225,0.77151,1.2457,1.1671,0.82488,1.6499,1.1656,1.2124,0.33543,3.4622,1.02668,2.32733,2.19705,0.404171,1.71705,0.553723,0.873602,1.28788
+25-Dec-2019 12:00:00,1,0.312,0.74074,1.2001,1.1347,0.79155,1.5983,1.1302,1.1588,0.32369,3.3757,0.993229,2.2703,2.14204,0.387477,1.66608,0.533529,0.847803,1.2554
+25-Dec-2019 13:00:00,1,0.28834,0.66991,1.0947,1.0599,0.71542,1.4804,1.0474,1.0339,0.29621,3.1681,0.913186,2.13359,2.01333,0.349165,1.54887,0.48685,0.788627,1.17923
+25-Dec-2019 14:00:00,1,0.28119,0.64857,1.0628,1.0373,0.69266,1.4451,1.0221,0.99587,0.28779,3.1032,0.888208,2.09086,1.97397,0.337656,1.51358,0.472731,0.770858,1.15588
+25-Dec-2019 15:00:00,1,0.28053,0.64659,1.0599,1.0352,0.69055,1.4419,1.0197,0.99232,0.28701,3.0971,0.885865,2.08685,1.97029,0.336586,1.5103,0.471417,0.769207,1.1537
+25-Dec-2019 16:00:00,1,0.29453,0.68841,1.1223,1.0795,0.73523,1.5111,1.0692,1.0668,0.30345,3.2235,0.93452,2.17006,2.04725,0.35916,1.57948,0.499073,0.80406,1.19933
+25-Dec-2019 17:00:00,1,0.3155,0.75124,1.2157,1.1458,0.80291,1.6159,1.1423,1.1771,0.32771,3.4055,1.00474,2.28993,2.16089,0.393175,1.68348,0.54043,0.856607,1.26654
+25-Dec-2019 18:00:00,1,0.32234,0.77178,1.2461,1.1674,0.82517,1.6504,1.1659,1.2128,0.33553,3.463,1.02697,2.32782,2.19753,0.404317,1.71749,0.553899,0.873828,1.28816
+25-Dec-2019 19:00:00,1,0.31299,0.74369,1.2045,1.1378,0.79474,1.6033,1.1336,1.1639,0.32482,3.3841,0.996473,2.27583,2.14734,0.389078,1.67097,0.535469,0.850276,1.25854
+25-Dec-2019 20:00:00,1,0.29777,0.69811,1.1367,1.0897,0.74563,1.5272,1.0805,1.0839,0.30723,3.2522,0.945576,2.18895,2.06494,0.3644,1.59552,0.505469,0.812153,1.2098
+25-Dec-2019 21:00:00,1,0.29045,0.6762,1.1041,1.0666,0.72215,1.4908,1.0548,1.0451,0.29868,3.1871,0.920477,2.14605,2.02489,0.352564,1.55928,0.49101,0.793874,1.18609
+25-Dec-2019 22:00:00,1,0.29102,0.67792,1.1067,1.0684,0.72399,1.4937,1.0568,1.0482,0.29935,3.1922,0.922463,2.14945,2.02805,0.353493,1.56213,0.492146,0.795309,1.18796
+25-Dec-2019 23:00:00,1,0.26862,0.61112,1.0067,0.99749,0.6529,1.3835,0.97728,0.92857,0.27287,2.9865,0.843358,2.01406,1.90417,0.317489,1.45167,0.447884,0.739734,1.11442
+26-Dec-2019 00:00:00,1,0.23924,0.52397,0.87554,0.90448,0.5614,1.2416,0.87101,0.76934,0.23736,2.7005,0.733813,1.82612,1.73821,0.270754,1.30775,0.38973,0.66766,1.01553
+26-Dec-2019 01:00:00,1,0.22269,0.47512,0.80154,0.85205,0.51076,1.1629,0.81012,0.67839,0.21694,2.531,0.669058,1.7148,1.64287,0.24468,1.22717,0.356914,0.627482,0.958532
+26-Dec-2019 02:00:00,1,0.21614,0.45585,0.77225,0.8313,0.49091,1.1321,0.78581,0.64215,0.20877,2.4622,0.642809,1.66963,1.60477,0.234416,1.19539,0.343919,0.611673,0.935713
+26-Dec-2019 03:00:00,1,0.21563,0.45435,0.76997,0.82968,0.48937,1.1297,0.78391,0.63932,0.20814,2.4568,0.640748,1.66608,1.6018,0.233617,1.19292,0.342906,0.610444,0.933928
+26-Dec-2019 04:00:00,1,0.21988,0.46685,0.78897,0.84314,0.50223,1.1497,0.79969,0.66285,0.21344,2.5016,0.657834,1.69549,1.62654,0.24027,1.21352,0.351336,0.620689,0.948754
+26-Dec-2019 05:00:00,1,0.22111,0.47046,0.79445,0.84703,0.50595,1.1554,0.80424,0.66964,0.21497,2.5144,0.662738,1.70393,1.63367,0.242193,1.21948,0.353769,0.623651,0.953022
+26-Dec-2019 06:00:00,1,0.21851,0.4628,0.78282,0.83879,0.49806,1.1432,0.79459,0.65525,0.21173,2.4871,0.652324,1.68601,1.61855,0.238117,1.20686,0.34861,0.617373,0.943966
+26-Dec-2019 07:00:00,1,0.2298,0.4961,0.83336,0.87459,0.53244,1.1966,0.83639,0.7176,0.22575,2.6046,0.697168,1.76314,1.68402,0.255864,1.26176,0.371024,0.644713,0.983148
+26-Dec-2019 08:00:00,1,0.251,0.5588,0.92809,0.94173,0.59779,1.298,0.91384,0.83343,0.25169,2.8173,0.778491,1.90283,1.80515,0.289401,1.36525,0.413032,0.696407,1.05548
+26-Dec-2019 09:00:00,1,0.27526,0.63089,1.0364,1.0185,0.67386,1.416,1.001,0.96418,0.28078,3.0485,0.867196,2.05489,1.94113,0.328129,1.48435,0.461011,0.756153,1.13639
+26-Dec-2019 10:00:00,1,0.296,0.6928,1.1288,1.0841,0.73994,1.5184,1.0743,1.0745,0.30517,3.2366,0.93954,2.17863,2.05527,0.361534,1.58675,0.501972,0.807726,1.20408
+26-Dec-2019 11:00:00,1,0.3162,0.75333,1.2188,1.148,0.80517,1.6194,1.1447,1.1808,0.32851,3.4114,1.00701,2.2938,2.16462,0.394304,1.68693,0.541797,0.858352,1.26874
+26-Dec-2019 12:00:00,1,0.31753,0.75734,1.2247,1.1522,0.80952,1.6261,1.1493,1.1878,0.33004,3.4227,1.01138,2.30125,2.17179,0.396482,1.69358,0.544433,0.861719,1.27297
+26-Dec-2019 13:00:00,1,0.30036,0.70585,1.1483,1.0979,0.75395,1.5401,1.0896,1.0976,0.31024,3.275,0.954346,2.20393,2.07902,0.368587,1.60834,0.510572,0.818619,1.21814
+26-Dec-2019 14:00:00,1,0.29273,0.68301,1.1142,1.0738,0.72944,1.5021,1.0628,1.0572,0.30134,3.2075,0.928327,2.15947,2.03738,0.356243,1.57055,0.495509,0.799555,1.19348
+26-Dec-2019 15:00:00,1,0.29037,0.67596,1.1037,1.0663,0.72189,1.4904,1.0545,1.0447,0.29858,3.1863,0.920196,2.14557,2.02445,0.352432,1.55888,0.490849,0.793671,1.18582
+26-Dec-2019 16:00:00,1,0.2993,0.70266,1.1435,1.0945,0.75052,1.5348,1.0859,1.0919,0.309,3.2656,0.950744,2.19778,2.07323,0.366864,1.60306,0.508473,0.815958,1.21471
+26-Dec-2019 17:00:00,1,0.32069,0.76683,1.2388,1.1622,0.8198,1.642,1.1602,1.2042,0.33365,3.4492,1.02164,2.31874,2.18871,0.401628,1.70929,0.550653,0.869672,1.28296
+26-Dec-2019 18:00:00,1,0.32565,0.78174,1.2609,1.1779,0.836,1.6671,1.1773,1.2301,0.33931,3.4905,1.03763,2.34599,2.21522,0.409729,1.734,0.560427,0.882192,1.2986
+26-Dec-2019 19:00:00,1,0.32011,0.76508,1.2362,1.1603,0.81791,1.6391,1.1582,1.2012,0.33299,3.4443,1.01975,2.31553,2.1856,0.400681,1.7064,0.549508,0.868208,1.28112
+26-Dec-2019 20:00:00,1,0.30965,0.7337,1.1897,1.1273,0.78394,1.5865,1.122,1.1465,0.32099,3.3556,0.985466,2.25706,2.12938,0.383662,1.65443,0.528902,0.841909,1.24792
+26-Dec-2019 21:00:00,1,0.30265,0.71272,1.1585,1.1052,0.76133,1.5515,1.0976,1.1097,0.3129,3.295,0.962085,2.21715,2.09149,0.372304,1.61971,0.515098,0.824361,1.22552
+26-Dec-2019 22:00:00,1,0.30052,0.70634,1.149,1.0984,0.75447,1.5409,1.0902,1.0984,0.31043,3.2764,0.954899,2.20487,2.07991,0.368852,1.60915,0.510895,0.819029,1.21867
+26-Dec-2019 23:00:00,1,0.27793,0.63883,1.0482,1.0269,0.68229,1.4291,1.0105,0.97843,0.28393,3.0732,0.876669,2.07111,1.9559,0.332407,1.49748,0.466278,0.762755,1.14516
+27-Dec-2019 00:00:00,1,0.25163,0.56064,0.93087,0.9437,0.59972,1.301,0.91609,0.8368,0.25244,2.8234,0.780819,1.90683,1.80867,0.290388,1.36829,0.414261,0.697929,1.05757
+27-Dec-2019 01:00:00,1,0.23789,0.51998,0.8695,0.9002,0.55724,1.2351,0.86607,0.76195,0.2357,2.6869,0.72861,1.81718,1.73047,0.268619,1.30116,0.387052,0.664368,1.01091
+27-Dec-2019 02:00:00,1,0.22943,0.49498,0.83167,0.87339,0.53129,1.1948,0.83499,0.71552,0.22529,2.6008,0.695686,1.7606,1.68184,0.25527,1.25993,0.370275,0.643797,0.981846
+27-Dec-2019 03:00:00,1,0.22676,0.48713,0.81976,0.86495,0.52316,1.1822,0.82517,0.70086,0.22199,2.5733,0.685201,1.74257,1.66646,0.251078,1.24697,0.364992,0.637339,0.972646
+27-Dec-2019 04:00:00,1,0.23139,0.50077,0.84043,0.8796,0.53728,1.2041,0.84221,0.7263,0.22771,2.6209,0.703367,1.7738,1.69314,0.258358,1.26947,0.374163,0.648556,0.988603
+27-Dec-2019 05:00:00,1,0.23748,0.51877,0.86767,0.8989,0.55598,1.2331,0.86457,0.75971,0.2352,2.6828,0.727029,1.81447,1.72813,0.267971,1.29916,0.38624,0.66337,1.00951
+27-Dec-2019 06:00:00,1,0.24793,0.54969,0.91435,0.93199,0.58824,1.2832,0.90268,0.81672,0.24796,2.7871,0.766914,1.88296,1.78771,0.284516,1.3502,0.40694,0.688875,1.04508
+27-Dec-2019 07:00:00,1,0.27641,0.63432,1.0415,1.0222,0.6775,1.4216,1.0051,0.97034,0.28214,3.0592,0.871298,2.06191,1.94752,0.329978,1.49003,0.463288,0.759007,1.14018
+27-Dec-2019 08:00:00,1,0.31469,0.74882,1.2121,1.1432,0.80029,1.6118,1.1395,1.1729,0.32679,3.3986,1.00209,2.28542,2.15655,0.39186,1.67947,0.538838,0.854575,1.26397
+27-Dec-2019 09:00:00,1,0.33568,0.81194,1.3055,1.2096,0.8689,1.718,1.2117,1.2822,0.35067,3.5726,1.06944,2.40015,2.26851,0.426144,1.78402,0.580175,0.907571,1.32999
+27-Dec-2019 10:00:00,1,0.34943,0.8534,1.3666,1.253,0.91431,1.7882,1.2584,1.3531,0.36609,3.6822,1.11196,2.47245,2.34087,0.448729,1.85276,0.607216,0.942499,1.37255
+27-Dec-2019 11:00:00,1,0.36052,0.88693,1.416,1.288,0.95123,1.8453,1.2959,1.4099,0.37842,3.7681,1.14538,2.52919,2.39872,0.467029,1.90837,0.629023,0.970811,1.40652
+27-Dec-2019 12:00:00,1,0.35782,0.87878,1.404,1.2795,0.94223,1.8314,1.2868,1.3961,0.37543,3.7474,1.13733,2.51553,2.38471,0.462576,1.89484,0.623726,0.963922,1.39829
+27-Dec-2019 13:00:00,1,0.35584,0.87279,1.3952,1.2733,0.93564,1.8212,1.2801,1.386,0.37323,3.7322,1.13139,2.50545,2.3744,0.459309,1.88491,0.619835,0.958866,1.39224
+27-Dec-2019 14:00:00,1,0.35517,0.87074,1.3922,1.2711,0.93338,1.8177,1.2778,1.3825,0.37248,3.7269,1.12935,2.50198,2.37086,0.458189,1.88151,0.618501,0.957133,1.39016
+27-Dec-2019 15:00:00,1,0.35773,0.87848,1.4035,1.2792,0.94191,1.8309,1.2865,1.3956,0.37532,3.7467,1.13704,2.51503,2.3842,0.462414,1.89435,0.623533,0.963671,1.39799
+27-Dec-2019 16:00:00,1,0.38179,0.95141,1.5105,1.3552,1.0227,1.9557,1.3669,1.5181,0.40176,3.9269,1.20731,2.63414,2.50838,0.502306,2.01537,0.670806,1.02541,1.47075
+27-Dec-2019 17:00:00,1,0.40704,1.0282,1.6225,1.4349,1.1085,2.0883,1.45,1.6449,0.42897,4.1054,1.27723,2.75229,2.63634,0.544461,2.14292,0.720318,1.09069,1.54544
+27-Dec-2019 18:00:00,1,0.41109,1.0406,1.6405,1.4477,1.1224,2.1097,1.4632,1.6651,0.43329,4.133,1.2881,2.77062,2.65666,0.551256,2.16344,0.728258,1.10122,1.55729
+27-Dec-2019 19:00:00,1,0.39632,0.99556,1.5749,1.401,1.0719,2.0318,1.4149,1.5912,0.41748,4.0308,1.248,2.70294,2.58228,0.526521,2.08868,0.699301,1.06291,1.51392
+27-Dec-2019 20:00:00,1,0.37011,0.91596,1.4586,1.3183,0.98332,1.8949,1.328,1.4588,0.38898,3.8406,1.17364,2.57711,2.44835,0.482899,1.95654,0.64786,0.99537,1.43561
+27-Dec-2019 21:00:00,1,0.35232,0.86213,1.3795,1.2621,0.92391,1.8031,1.2682,1.3679,0.36931,3.7048,1.12074,2.48737,2.35599,0.45349,1.86723,0.612899,0.949864,1.38143
+27-Dec-2019 22:00:00,1,0.33785,0.81847,1.3152,1.2164,0.87604,1.7291,1.2191,1.2934,0.35311,3.5901,1.07623,2.4117,2.27997,0.429698,1.79485,0.58444,0.913067,1.33673
+27-Dec-2019 23:00:00,1,0.30803,0.72883,1.1824,1.1222,0.7787,1.5784,1.1164,1.138,0.31912,3.3417,0.98008,2.24787,2.12062,0.381028,1.64638,0.525705,0.837839,1.24275
+28-Dec-2019 00:00:00,1,0.27608,0.63332,1.04,1.0211,0.67644,1.42,1.0039,0.96855,0.28175,3.0561,0.870108,2.05988,1.94566,0.329441,1.48838,0.462627,0.758178,1.13908
+28-Dec-2019 01:00:00,1,0.26077,0.58777,0.97168,0.97263,0.62824,1.3453,0.9491,0.88627,0.26347,2.9119,0.81473,1.965,1.8602,0.30494,1.41309,0.432349,0.720373,1.08826
+28-Dec-2019 02:00:00,1,0.25682,0.57606,0.95408,0.96015,0.61592,1.3262,0.93489,0.86497,0.25873,2.8739,0.800185,1.94005,1.83802,0.298657,1.39376,0.424549,0.710683,1.07506
+28-Dec-2019 03:00:00,1,0.25963,0.58438,0.96659,0.96902,0.62467,1.3397,0.94499,0.88012,0.2621,2.9009,0.810537,1.95781,1.85379,0.303123,1.4075,0.430094,0.71757,1.08445
+28-Dec-2019 04:00:00,1,0.26455,0.59902,0.98858,0.98462,0.64011,1.3637,0.96271,0.90668,0.26801,2.948,0.82859,1.98876,1.88143,0.310985,1.43168,0.439839,0.729699,1.1009
+28-Dec-2019 05:00:00,1,0.27438,0.62825,1.0324,1.0157,0.67106,1.4117,0.99784,0.95944,0.27973,3.0403,0.864038,2.04948,1.93621,0.326709,1.47999,0.459262,0.753962,1.13347
+28-Dec-2019 06:00:00,1,0.28971,0.674,1.1008,1.0642,0.71979,1.4872,1.0522,1.0412,0.29781,3.1804,0.917926,2.14169,2.02085,0.351373,1.55564,0.489552,0.792035,1.18369
+28-Dec-2019 07:00:00,1,0.32127,0.76857,1.2414,1.164,0.82169,1.645,1.1622,1.2073,0.33431,3.4541,1.02352,2.32195,2.19182,0.402576,1.71218,0.551797,0.871137,1.28479
+28-Dec-2019 08:00:00,1,0.35006,0.8553,1.3694,1.255,0.91641,1.7915,1.2606,1.3563,0.36679,3.6871,1.11388,2.47571,2.34417,0.449768,1.85591,0.608457,0.944106,1.37449
+28-Dec-2019 09:00:00,1,0.36455,0.89913,1.4339,1.3008,0.9647,1.8661,1.3094,1.4305,0.38287,3.7988,1.15733,2.54947,2.41964,0.473697,1.92861,0.636946,0.981129,1.41878
+28-Dec-2019 10:00:00,1,0.37162,0.92054,1.4653,1.3231,0.98839,1.9027,1.333,1.4665,0.39064,3.8519,1.17803,2.58456,2.45613,0.485401,1.96412,0.650824,0.999242,1.44017
+28-Dec-2019 11:00:00,1,0.37904,0.94306,1.4982,1.3465,1.0134,1.9414,1.3578,1.5041,0.39876,3.9068,1.19946,2.62085,2.4943,0.497732,2.00151,0.665407,1.01833,1.46251
+28-Dec-2019 12:00:00,1,0.37919,0.94351,1.4989,1.347,1.0139,1.9421,1.3583,1.5049,0.39892,3.9078,1.19988,2.62156,2.49504,0.497974,2.00224,0.665693,1.0187,1.46294
+28-Dec-2019 13:00:00,1,0.36979,0.91501,1.4571,1.3173,0.98226,1.8933,1.3269,1.4572,0.38864,3.8383,1.17272,2.57555,2.44672,0.482375,1.95495,0.647239,0.994559,1.43466
+28-Dec-2019 14:00:00,1,0.36667,0.90554,1.4433,1.3074,0.97178,1.8771,1.3165,1.4413,0.3852,3.8148,1.16356,2.56003,2.43058,0.477198,1.93924,0.641101,0.986547,1.4252
+28-Dec-2019 15:00:00,1,0.36375,0.89671,1.4303,1.2982,0.96202,1.862,1.3067,1.4264,0.38199,3.7927,1.15497,2.54546,2.41549,0.472372,1.92459,0.635373,0.979079,1.41635
+28-Dec-2019 16:00:00,1,0.37572,0.93299,1.4835,1.336,1.0022,1.9241,1.3467,1.4873,0.39513,3.8824,1.18992,2.6047,2.47726,0.492214,1.98479,0.658886,1.00979,1.45254
+28-Dec-2019 17:00:00,1,0.3937,0.98759,1.5633,1.3928,1.063,2.018,1.4062,1.5781,0.41465,4.0123,1.24075,2.6907,2.569,0.522144,2.07543,0.69416,1.05613,1.50617
+28-Dec-2019 18:00:00,1,0.38789,0.96993,1.5375,1.3744,1.0433,1.9876,1.3871,1.5488,0.40837,3.9709,1.22454,2.66329,2.53948,0.512454,2.0461,0.682765,1.04112,1.48893
+28-Dec-2019 19:00:00,1,0.37344,0.92608,1.4734,1.3288,0.99453,1.9122,1.3391,1.4757,0.39264,3.8655,1.18334,2.59355,2.46554,0.488432,1.97332,0.654412,1.00393,1.44568
+28-Dec-2019 20:00:00,1,0.34954,0.85375,1.3672,1.2534,0.9147,1.7888,1.2588,1.3537,0.36622,3.6831,1.11232,2.47305,2.34148,0.448921,1.85334,0.607447,0.942797,1.37291
+28-Dec-2019 21:00:00,1,0.33574,0.81211,1.3058,1.2097,0.86909,1.7183,1.2119,1.2825,0.35073,3.5731,1.06962,2.40046,2.26881,0.426239,1.78431,0.580289,0.907718,1.33017
+28-Dec-2019 22:00:00,1,0.32854,0.79044,1.2738,1.187,0.84546,1.6818,1.1873,1.2451,0.34259,3.5144,1.04687,2.36173,2.23062,0.414455,1.74841,0.566121,0.889498,1.30767
+28-Dec-2019 23:00:00,1,0.3016,0.70956,1.1538,1.1018,0.75794,1.5463,1.0939,1.1041,0.31168,3.2858,0.958538,2.21109,2.08577,0.370597,1.61449,0.513021,0.821725,1.22213
+29-Dec-2019 00:00:00,1,0.27558,0.63183,1.0378,1.0195,0.67485,1.4175,1.0021,0.96587,0.28115,3.0515,0.868321,2.05682,1.94288,0.328636,1.48591,0.461636,0.756935,1.13743
+29-Dec-2019 01:00:00,1,0.25972,0.58467,0.96703,0.96933,0.62498,1.3402,0.94535,0.88064,0.26222,2.9019,0.810898,1.95843,1.85435,0.303279,1.40798,0.430288,0.717811,1.08478
+29-Dec-2019 02:00:00,1,0.25019,0.55639,0.92445,0.93915,0.59526,1.2941,0.91089,0.82901,0.2507,2.8093,0.77543,1.89758,1.80054,0.288106,1.36126,0.411417,0.69441,1.05272
+29-Dec-2019 03:00:00,1,0.24659,0.54573,0.90839,0.92776,0.5841,1.2768,0.89783,0.80945,0.24633,2.7739,0.761865,1.8743,1.78013,0.282397,1.34367,0.404295,0.685608,1.04055
+29-Dec-2019 04:00:00,1,0.2488,0.55228,0.91826,0.93476,0.59096,1.2874,0.90586,0.82147,0.24902,2.7957,0.770213,1.88863,1.79268,0.285904,1.35448,0.408672,0.691016,1.04804
+29-Dec-2019 05:00:00,1,0.2505,0.55731,0.92584,0.94013,0.59622,1.2956,0.91201,0.83069,0.25108,2.8123,0.776595,1.89958,1.80229,0.288598,1.36278,0.412031,0.69517,1.05377
+29-Dec-2019 06:00:00,1,0.25287,0.56433,0.93642,0.94763,0.60359,1.307,0.9206,0.84355,0.25395,2.8355,0.785468,1.91481,1.8157,0.292363,1.37438,0.416721,0.700976,1.06176
+29-Dec-2019 07:00:00,1,0.2718,0.62057,1.0209,1.0075,0.66291,1.399,0.98864,0.94562,0.27666,3.0163,0.854806,2.03367,1.92188,0.322577,1.4673,0.454166,0.747585,1.12495
+29-Dec-2019 08:00:00,1,0.2931,0.68412,1.1159,1.0749,0.73063,1.504,1.0641,1.0592,0.30178,3.2108,0.929597,2.16164,2.0394,0.35684,1.57238,0.496239,0.800477,1.19468
+29-Dec-2019 09:00:00,1,0.31346,0.74512,1.2066,1.1393,0.79629,1.6056,1.1352,1.1664,0.32537,3.3882,0.998041,2.27851,2.14991,0.389853,1.67334,0.536408,0.851474,1.26005
+29-Dec-2019 10:00:00,1,0.3277,0.78791,1.27,1.1843,0.84271,1.6775,1.1844,1.2408,0.34164,3.5075,1.04419,2.35716,2.22615,0.413081,1.74422,0.564466,0.887374,1.30504
+29-Dec-2019 11:00:00,1,0.33815,0.81936,1.3165,1.2173,0.87702,1.7306,1.2201,1.2949,0.35345,3.5925,1.07716,2.41328,2.28153,0.430186,1.79633,0.585025,0.91382,1.33766
+29-Dec-2019 12:00:00,1,0.34077,0.82727,1.3281,1.2256,0.88567,1.7439,1.2291,1.3085,0.3564,3.6136,1.08533,2.42717,2.29538,0.434492,1.80944,0.590187,0.92048,1.3458
+29-Dec-2019 13:00:00,1,0.33289,0.80352,1.2931,1.2007,0.85971,1.7038,1.2021,1.2677,0.34751,3.5499,1.06064,2.38518,2.2537,0.421564,1.77007,0.574673,0.900489,1.32127
+29-Dec-2019 14:00:00,1,0.32934,0.79284,1.2773,1.1895,0.84807,1.6858,1.19,1.2493,0.34349,3.5209,1.0494,2.36604,2.23486,0.415756,1.75238,0.567688,0.891511,1.31017
+29-Dec-2019 15:00:00,1,0.32525,0.78053,1.2591,1.1766,0.83468,1.6651,1.1759,1.228,0.33885,3.4872,1.03633,2.34378,2.21307,0.40907,1.73199,0.559632,0.881173,1.29733
+29-Dec-2019 16:00:00,1,0.33463,0.80875,1.3008,1.2062,0.86543,1.7126,1.2081,1.2767,0.34947,3.5641,1.06612,2.3945,2.26291,0.424412,1.77875,0.578095,0.904892,1.32669
+29-Dec-2019 17:00:00,1,0.36507,0.9007,1.4362,1.3024,0.96644,1.8688,1.3111,1.4331,0.38344,3.8027,1.15886,2.55206,2.42232,0.474555,1.93122,0.637965,0.982457,1.42036
+29-Dec-2019 18:00:00,1,0.36776,0.90884,1.4481,1.3109,0.97544,1.8827,1.3201,1.4468,0.3864,3.823,1.16677,2.56547,2.43622,0.479005,1.94472,0.643245,0.989344,1.4285
+29-Dec-2019 19:00:00,1,0.35882,0.88178,1.4084,1.2827,0.94555,1.8365,1.2901,1.4012,0.37653,3.7551,1.14031,2.52058,2.38988,0.464218,1.89983,0.625679,0.966461,1.40133
+29-Dec-2019 20:00:00,1,0.33201,0.80088,1.2892,1.198,0.85684,1.6994,1.1991,1.2631,0.34652,3.5428,1.05788,2.38047,2.24906,0.420131,1.76571,0.57295,0.898274,1.31853
+29-Dec-2019 21:00:00,1,0.31944,0.76307,1.2332,1.1582,0.81572,1.6357,1.1559,1.1977,0.33222,3.4387,1.01758,2.31183,2.18202,0.399589,1.70306,0.548188,0.86652,1.27901
+29-Dec-2019 22:00:00,1,0.31253,0.74234,1.2025,1.1364,0.79328,1.601,1.132,1.1616,0.3243,3.3803,0.994987,2.2733,2.14492,0.388344,1.66873,0.53458,0.849143,1.2571
+29-Dec-2019 23:00:00,1,0.28732,0.66685,1.0901,1.0566,0.71215,1.4753,1.0437,1.0285,0.29501,3.1589,0.90963,2.1275,2.00771,0.347514,1.54381,0.484827,0.786078,1.1759
+30-Dec-2019 00:00:00,1,0.25994,0.58533,0.96801,0.97003,0.62567,1.3413,0.94614,0.88184,0.26248,2.904,0.81171,1.95982,1.85559,0.30363,1.40906,0.430724,0.718353,1.08552
+30-Dec-2019 01:00:00,1,0.2418,0.53153,0.88695,0.91257,0.56927,1.2538,0.88035,0.7833,0.24049,2.7262,0.743611,1.84295,1.7528,0.274797,1.32023,0.394793,0.673891,1.02425
+30-Dec-2019 02:00:00,1,0.23221,0.5032,0.84411,0.88221,0.5398,1.208,0.84524,0.73082,0.22872,2.6293,0.706578,1.77932,1.69787,0.259654,1.27348,0.375794,0.650553,0.991433
+30-Dec-2019 03:00:00,1,0.23094,0.49946,0.83845,0.8782,0.53593,1.202,0.84058,0.72386,0.22716,2.6163,0.70163,1.77082,1.69058,0.257659,1.26731,0.373283,0.647478,0.987074
+30-Dec-2019 04:00:00,1,0.23362,0.50735,0.85039,0.88666,0.54411,1.2147,0.8504,0.73853,0.23045,2.6436,0.712056,1.78874,1.70596,0.261871,1.28033,0.378581,0.653969,0.996265
+30-Dec-2019 05:00:00,1,0.23224,0.50329,0.84424,0.8823,0.53989,1.2082,0.84535,0.73098,0.22876,2.6296,0.706695,1.77952,1.69804,0.259702,1.27362,0.375853,0.650626,0.991536
+30-Dec-2019 06:00:00,1,0.22483,0.48142,0.8111,0.85882,0.51726,1.173,0.81802,0.69018,0.21959,2.5532,0.677543,1.7294,1.65525,0.248035,1.23755,0.361152,0.63265,0.965942
+30-Dec-2019 07:00:00,1,0.23539,0.51259,0.85832,0.89228,0.54955,1.2232,0.85691,0.74826,0.23264,2.6616,0.718946,1.80058,1.71614,0.26467,1.28897,0.382098,0.658283,1.00235
+30-Dec-2019 08:00:00,1,0.25272,0.56389,0.93576,0.94717,0.60313,1.3063,0.92007,0.84275,0.25377,2.8341,0.784922,1.91387,1.81487,0.292131,1.37366,0.416432,0.700617,1.06127
+30-Dec-2019 09:00:00,1,0.27464,0.62904,1.0336,1.0165,0.6719,1.413,0.99879,0.96086,0.28004,3.0428,0.864988,2.05111,1.93769,0.327136,1.4813,0.459788,0.754621,1.13434
+30-Dec-2019 10:00:00,1,0.29136,0.67893,1.1082,1.0694,0.72507,1.4954,1.058,1.05,0.29975,3.1953,0.923628,2.15144,2.0299,0.354038,1.5638,0.492813,0.796151,1.18905
+30-Dec-2019 11:00:00,1,0.31716,0.75621,1.2231,1.151,0.80829,1.6242,1.148,1.1858,0.32961,3.4195,1.01015,2.29916,2.16978,0.39587,1.69171,0.543692,0.860772,1.27178
+30-Dec-2019 12:00:00,1,0.32215,0.7712,1.2453,1.1668,0.82455,1.6494,1.1653,1.2118,0.33531,3.4614,1.02635,2.32677,2.1965,0.404004,1.71654,0.553522,0.873344,1.28756
+30-Dec-2019 13:00:00,1,0.30818,0.72927,1.1831,1.1226,0.77917,1.5791,1.1169,1.1387,0.31929,3.3429,0.980565,2.24869,2.12141,0.381265,1.6471,0.525992,0.838205,1.24321
+30-Dec-2019 14:00:00,1,0.30114,0.70819,1.1517,1.1004,0.75646,1.544,1.0923,1.1017,0.31114,3.2818,0.956985,2.20844,2.08327,0.369852,1.61221,0.512113,0.820574,1.22065
+30-Dec-2019 15:00:00,1,0.29726,0.69658,1.1345,1.0881,0.74399,1.5247,1.0787,1.0812,0.30664,3.2477,0.943844,2.18599,2.06216,0.363576,1.593,0.504464,0.810881,1.20816
+30-Dec-2019 16:00:00,1,0.31068,0.73678,1.1942,1.1305,0.78727,1.5917,1.1256,1.1519,0.32217,3.3644,0.988868,2.26286,2.13493,0.385331,1.65953,0.530927,0.844488,1.2512
+30-Dec-2019 17:00:00,1,0.33656,0.81458,1.3094,1.2123,0.87179,1.7225,1.2147,1.2867,0.35166,3.5797,1.07219,2.40483,2.27314,0.427582,1.7884,0.581901,0.909794,1.33272
+30-Dec-2019 18:00:00,1,0.34207,0.83119,1.3339,1.2297,0.88996,1.7506,1.2335,1.3152,0.35786,3.624,1.08936,2.43403,2.30223,0.436628,1.81594,0.592746,0.923783,1.34983
+30-Dec-2019 19:00:00,1,0.33374,0.80607,1.2969,1.2034,0.8625,1.7081,1.2051,1.2721,0.34847,3.5569,1.06332,2.38974,2.2582,0.422956,1.77431,0.576345,0.902641,1.32392
+30-Dec-2019 20:00:00,1,0.31791,0.75847,1.2264,1.1534,0.81074,1.628,1.1506,1.1897,0.33047,3.4258,1.0126,2.30334,2.17381,0.397095,1.69545,0.545173,0.862665,1.27417
+30-Dec-2019 21:00:00,1,0.30676,0.72502,1.1768,1.1181,0.77458,1.572,1.1119,1.1313,0.31765,3.3307,0.975843,2.24063,2.11374,0.378963,1.64007,0.523196,0.834649,1.23868
+30-Dec-2019 22:00:00,1,0.30136,0.70885,1.1527,1.1011,0.75717,1.5451,1.0931,1.1028,0.3114,3.2837,0.957729,2.20971,2.08447,0.370209,1.6133,0.512548,0.821126,1.22136
+30-Dec-2019 23:00:00,1,0.2781,0.63935,1.049,1.0275,0.68284,1.4299,1.0111,0.97936,0.28414,3.0748,0.877284,2.07216,1.95686,0.332686,1.49834,0.466621,0.763186,1.14573
+31-Dec-2019 00:00:00,1,0.25459,0.56945,0.94413,0.95309,0.60896,1.3154,0.92684,0.8529,0.25604,2.8523,0.791905,1.92585,1.82545,0.295109,1.38283,0.420138,0.70521,1.06757
+31-Dec-2019 01:00:00,1,0.23945,0.52458,0.87645,0.90513,0.56203,1.2425,0.87176,0.77046,0.23761,2.7026,0.734602,1.82748,1.73938,0.271079,1.30876,0.390137,0.66816,1.01623
+31-Dec-2019 02:00:00,1,0.23318,0.50607,0.84846,0.88529,0.54278,1.2127,0.84881,0.73616,0.22992,2.6392,0.710371,1.78584,1.70347,0.261188,1.27822,0.377723,0.652917,0.994778
+31-Dec-2019 03:00:00,1,0.23151,0.50114,0.841,0.88,0.53767,1.2047,0.84268,0.72699,0.22786,2.6222,0.703859,1.77465,1.69386,0.258557,1.27009,0.374413,0.648862,0.989037
+31-Dec-2019 04:00:00,1,0.23925,0.52401,0.87559,0.90451,0.56143,1.2416,0.87106,0.76941,0.23738,2.7007,0.733859,1.8262,1.73827,0.270773,1.30781,0.389754,0.667689,1.01557
+31-Dec-2019 05:00:00,1,0.24404,0.53817,0.89699,0.91968,0.57621,1.2645,0.88854,0.79555,0.24323,2.7485,0.752177,1.85766,1.7656,0.278352,1.33119,0.39924,0.679371,1.03189
+31-Dec-2019 06:00:00,1,0.24169,0.53121,0.88647,0.91222,0.56894,1.2533,0.87995,0.78271,0.24035,2.7251,0.743195,1.84224,1.75218,0.274624,1.3197,0.394577,0.673625,1.02388
+31-Dec-2019 07:00:00,1,0.25908,0.58276,0.96416,0.9673,0.62297,1.3371,0.94303,0.87717,0.26144,2.8957,0.808529,1.95436,1.85073,0.302254,1.40483,0.429016,0.71623,1.08263
+31-Dec-2019 08:00:00,1,0.28216,0.65146,1.0671,1.0403,0.69574,1.4499,1.0255,1.001,0.28894,3.1121,0.89162,2.0967,1.97932,0.339216,1.51837,0.474648,0.773266,1.15906
+31-Dec-2019 09:00:00,1,0.3013,0.70866,1.1524,1.1009,0.75697,1.5448,1.0929,1.1025,0.31133,3.2832,0.957517,2.20934,2.08412,0.370107,1.61299,0.512424,0.820968,1.22116
+31-Dec-2019 10:00:00,1,0.31036,0.73583,1.1928,1.1295,0.78624,1.5901,1.1245,1.1502,0.32181,3.3617,0.987819,2.26107,2.13322,0.384816,1.65795,0.530302,0.843691,1.25019
+31-Dec-2019 11:00:00,1,0.32044,0.76608,1.2377,1.1614,0.81899,1.6408,1.1594,1.2029,0.33337,3.4471,1.02083,2.31737,2.18738,0.401222,1.70805,0.550162,0.869044,1.28217
+31-Dec-2019 12:00:00,1,0.32815,0.78926,1.272,1.1858,0.84418,1.6798,1.1859,1.2431,0.34215,3.5112,1.04562,2.3596,2.22854,0.413815,1.74646,0.56535,0.888509,1.30645
+31-Dec-2019 13:00:00,1,0.32722,0.78645,1.2678,1.1828,0.84112,1.675,1.1827,1.2382,0.34108,3.5035,1.04263,2.35451,2.22356,0.412284,1.74179,0.563506,0.886142,1.30351
+31-Dec-2019 14:00:00,1,0.31856,0.76042,1.2293,1.1554,0.81286,1.6313,1.1529,1.1931,0.33121,3.4313,1.01472,2.30695,2.1773,0.398154,1.69869,0.546454,0.864303,1.27622
+31-Dec-2019 15:00:00,1,0.31733,0.75673,1.2238,1.1516,0.80885,1.6251,1.1486,1.1867,0.32981,3.421,1.01071,2.30012,2.1707,0.39615,1.69257,0.544031,0.861205,1.27233
+31-Dec-2019 16:00:00,1,0.33352,0.80544,1.2959,1.2027,0.86181,1.707,1.2043,1.271,0.34823,3.5551,1.06265,2.3886,2.25708,0.422608,1.77325,0.575927,0.902103,1.32326
+31-Dec-2019 17:00:00,1,0.35824,0.88002,1.4058,1.2808,0.94361,1.8335,1.2882,1.3982,0.37589,3.7506,1.13856,2.51763,2.38685,0.463257,1.89691,0.624535,0.964974,1.39955
+31-Dec-2019 18:00:00,1,0.35514,0.87066,1.392,1.271,0.93329,1.8176,1.2778,1.3824,0.37245,3.7267,1.12927,2.50185,2.37072,0.458146,1.88138,0.618449,0.957067,1.39008
+31-Dec-2019 19:00:00,1,0.33307,0.80408,1.2939,1.2013,0.86033,1.7047,1.2028,1.2686,0.34772,3.5515,1.06123,2.38618,2.25469,0.421869,1.771,0.57504,0.900962,1.32185
+31-Dec-2019 20:00:00,1,0.30299,0.71372,1.16,1.1062,0.76241,1.5532,1.0988,1.1114,0.31329,3.2979,0.96321,2.21907,2.0933,0.372846,1.62136,0.515758,0.825198,1.22659
+31-Dec-2019 21:00:00,1,0.29268,0.68288,1.114,1.0736,0.7293,1.5019,1.0627,1.057,0.30129,3.2071,0.928176,2.15921,2.03714,0.356172,1.57033,0.495422,0.799446,1.19334
+31-Dec-2019 22:00:00,1,0.2931,0.68414,1.1159,1.075,0.73065,1.504,1.0641,1.0592,0.30178,3.2108,0.929618,2.16168,2.03943,0.35685,1.57241,0.496251,0.800493,1.1947
+31-Dec-2019 23:00:00,1,0.2793,0.64292,1.0544,1.0313,0.68664,1.4358,1.0153,0.98576,0.28556,3.0858,0.881523,2.07942,1.96349,0.33461,1.50424,0.468988,0.766156,1.14967
diff --git a/sampledata/03-PRODUCTS/electricity/DE/required_average_nodal_secured_capacity.csv b/sampledata/03-PRODUCTS/electricity/DE/required_average_nodal_secured_capacity.csv
new file mode 100644
index 0000000..3879fa6
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/DE/required_average_nodal_secured_capacity.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+MW,-inf,-inf,-inf,-inf,-inf,-inf,-inf,-inf,
diff --git a/sampledata/03-PRODUCTS/electricity/DE/required_relative_nodal_secured_capacity.csv b/sampledata/03-PRODUCTS/electricity/DE/required_relative_nodal_secured_capacity.csv
new file mode 100644
index 0000000..fef3124
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/DE/required_relative_nodal_secured_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,value,comments
+1,,,
+2,1,,
+3,1,,
+4,1,,
+5,1,,
+6,1,,
+7,1,,
+8,1,,
+9,1,,
+10,1,,
+11,1,,
+12,1,,
+13,1,,
+14,1,,
+15,1,,
+16,1,,
+17,1,,
+18,1,,
diff --git a/sampledata/03-PRODUCTS/electricity/DE/required_total_secured_capacity.csv b/sampledata/03-PRODUCTS/electricity/DE/required_total_secured_capacity.csv
new file mode 100644
index 0000000..0cf6182
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/DE/required_total_secured_capacity.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+MW,65743,65743,65743,65743,65743,65743,65743,65743,
diff --git a/sampledata/03-PRODUCTS/electricity/README.md b/sampledata/03-PRODUCTS/electricity/README.md
new file mode 100644
index 0000000..672a3d7
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/README.md
@@ -0,0 +1,4 @@
+# README - ELECTRICITY
+
+This data package contains all necessary information
+about the product: **electricity**. Grid-dependent information can be modified in the folder named similar to the employed grid. Further, the cost of a product can be used either as real cost for product import, or as a slack variable for debugging, when unrealistically high cost are chosen instead of entirely forbidding imports of the product.
diff --git a/sampledata/03-PRODUCTS/electricity/costs_yearly_average.csv b/sampledata/03-PRODUCTS/electricity/costs_yearly_average.csv
new file mode 100644
index 0000000..74c703c
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/costs_yearly_average.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+kiloEuro / MWh,3000,3000,3000,3000,3000,3000,3000,3000,
diff --git a/sampledata/03-PRODUCTS/electricity/costs_yearly_timeseries.csv b/sampledata/03-PRODUCTS/electricity/costs_yearly_timeseries.csv
new file mode 100644
index 0000000..af4164c
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/costs_yearly_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,value
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diff --git a/sampledata/03-PRODUCTS/electricity/ecoinvent_operation.csv b/sampledata/03-PRODUCTS/electricity/ecoinvent_operation.csv
new file mode 100644
index 0000000..8c34c24
--- /dev/null
+++ b/sampledata/03-PRODUCTS/electricity/ecoinvent_operation.csv
@@ -0,0 +1,2 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor
+%,(MWh ** (-1)),1,1,1,1,1,1,1,1,,,,,,,,,
diff --git a/sampledata/03-PRODUCTS/natural_gas/DE/README.md b/sampledata/03-PRODUCTS/natural_gas/DE/README.md
new file mode 100644
index 0000000..9a78e82
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/DE/README.md
@@ -0,0 +1,4 @@
+# README - NATURAL GAS - DE
+
+This data package contains all necessary information
+about the product **natural gas** in the grid: **DE**
diff --git a/sampledata/03-PRODUCTS/natural_gas/DE/demand_nodal_average.csv b/sampledata/03-PRODUCTS/natural_gas/DE/demand_nodal_average.csv
new file mode 100644
index 0000000..b73e2a1
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/DE/demand_nodal_average.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+MW,,,,,,,,,
diff --git a/sampledata/03-PRODUCTS/natural_gas/DE/demand_nodal_timeseries.csv b/sampledata/03-PRODUCTS/natural_gas/DE/demand_nodal_timeseries.csv
new file mode 100644
index 0000000..089ad8b
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/DE/demand_nodal_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18
+01-Jan-2019 00:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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+01-Jan-2019 02:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 03:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 04:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 05:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 06:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 07:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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+01-Jan-2019 09:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 10:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+01-Jan-2019 11:00:00,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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diff --git a/sampledata/03-PRODUCTS/natural_gas/DE/required_average_nodal_secured_capacity.csv b/sampledata/03-PRODUCTS/natural_gas/DE/required_average_nodal_secured_capacity.csv
new file mode 100644
index 0000000..3879fa6
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/DE/required_average_nodal_secured_capacity.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+MW,-inf,-inf,-inf,-inf,-inf,-inf,-inf,-inf,
diff --git a/sampledata/03-PRODUCTS/natural_gas/DE/required_relative_nodal_secured_capacity.csv b/sampledata/03-PRODUCTS/natural_gas/DE/required_relative_nodal_secured_capacity.csv
new file mode 100644
index 0000000..fef3124
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/DE/required_relative_nodal_secured_capacity.csv
@@ -0,0 +1,19 @@
+node,unit,value,comments
+1,,,
+2,1,,
+3,1,,
+4,1,,
+5,1,,
+6,1,,
+7,1,,
+8,1,,
+9,1,,
+10,1,,
+11,1,,
+12,1,,
+13,1,,
+14,1,,
+15,1,,
+16,1,,
+17,1,,
+18,1,,
diff --git a/sampledata/03-PRODUCTS/natural_gas/DE/required_total_secured_capacity.csv b/sampledata/03-PRODUCTS/natural_gas/DE/required_total_secured_capacity.csv
new file mode 100644
index 0000000..3879fa6
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/DE/required_total_secured_capacity.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+MW,-inf,-inf,-inf,-inf,-inf,-inf,-inf,-inf,
diff --git a/sampledata/03-PRODUCTS/natural_gas/README.md b/sampledata/03-PRODUCTS/natural_gas/README.md
new file mode 100644
index 0000000..b02e972
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/README.md
@@ -0,0 +1,4 @@
+# README - NATURAL GAS
+
+This data package contains all necessary information
+about the product: **natural gas**. Grid-dependent information can be modified in the folder named similar to the employed grid. Further, the cost of a product can be used either as real cost for product import, or as a slack variable for debugging, when unrealistically high cost are chosen instead of entirely forbidding imports of the product.
diff --git a/sampledata/03-PRODUCTS/natural_gas/costs_yearly_average.csv b/sampledata/03-PRODUCTS/natural_gas/costs_yearly_average.csv
new file mode 100644
index 0000000..79f38bd
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/costs_yearly_average.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+kiloEuro / MWh,0.032372,0.032372,0.032372,0.032372,0.032372,0.032372,0.032372,0.032372,
diff --git a/sampledata/03-PRODUCTS/natural_gas/costs_yearly_timeseries.csv b/sampledata/03-PRODUCTS/natural_gas/costs_yearly_timeseries.csv
new file mode 100644
index 0000000..f08c33a
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/costs_yearly_timeseries.csv
@@ -0,0 +1,8761 @@
+time slice,unit,value
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+30-Dec-2019 04:00:00,1,1.0093
+30-Dec-2019 05:00:00,1,1.0093
+30-Dec-2019 06:00:00,1,1.0093
+30-Dec-2019 07:00:00,1,1.0093
+30-Dec-2019 08:00:00,1,1.0093
+30-Dec-2019 09:00:00,1,1.0093
+30-Dec-2019 10:00:00,1,1.0093
+30-Dec-2019 11:00:00,1,1.0093
+30-Dec-2019 12:00:00,1,1.0093
+30-Dec-2019 13:00:00,1,1.0093
+30-Dec-2019 14:00:00,1,1.0093
+30-Dec-2019 15:00:00,1,1.0093
+30-Dec-2019 16:00:00,1,1.0093
+30-Dec-2019 17:00:00,1,1.0093
+30-Dec-2019 18:00:00,1,1.0093
+30-Dec-2019 19:00:00,1,1.0093
+30-Dec-2019 20:00:00,1,1.0093
+30-Dec-2019 21:00:00,1,1.0093
+30-Dec-2019 22:00:00,1,1.0093
+30-Dec-2019 23:00:00,1,1.0093
+31-Dec-2019 00:00:00,1,1.0093
+31-Dec-2019 01:00:00,1,1.0093
+31-Dec-2019 02:00:00,1,1.0093
+31-Dec-2019 03:00:00,1,1.0093
+31-Dec-2019 04:00:00,1,1.0093
+31-Dec-2019 05:00:00,1,1.0093
+31-Dec-2019 06:00:00,1,1.0093
+31-Dec-2019 07:00:00,1,1.0093
+31-Dec-2019 08:00:00,1,1.0093
+31-Dec-2019 09:00:00,1,1.0093
+31-Dec-2019 10:00:00,1,1.0093
+31-Dec-2019 11:00:00,1,1.0093
+31-Dec-2019 12:00:00,1,1.0093
+31-Dec-2019 13:00:00,1,1.0093
+31-Dec-2019 14:00:00,1,1.0093
+31-Dec-2019 15:00:00,1,1.0093
+31-Dec-2019 16:00:00,1,1.0093
+31-Dec-2019 17:00:00,1,1.0093
+31-Dec-2019 18:00:00,1,1.0093
+31-Dec-2019 19:00:00,1,1.0093
+31-Dec-2019 20:00:00,1,1.0093
+31-Dec-2019 21:00:00,1,1.0093
+31-Dec-2019 22:00:00,1,1.0093
+31-Dec-2019 23:00:00,1,1.0093
diff --git a/sampledata/03-PRODUCTS/natural_gas/ecoinvent_operation.csv b/sampledata/03-PRODUCTS/natural_gas/ecoinvent_operation.csv
new file mode 100644
index 0000000..8c34c24
--- /dev/null
+++ b/sampledata/03-PRODUCTS/natural_gas/ecoinvent_operation.csv
@@ -0,0 +1,2 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor
+%,(MWh ** (-1)),1,1,1,1,1,1,1,1,,,,,,,,,
diff --git a/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/README.md b/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/README.md
new file mode 100644
index 0000000..b217c07
--- /dev/null
+++ b/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/README.md
@@ -0,0 +1,8 @@
+# README - ECOINVENT SUBASSEMBLIES
+
+This directory contains all subassemblies used for the ecoinvent
+based processes. A subassembly has a unique name and contains
+one or multiple ecoinvent processes, which then can be used in
+the impact definition of all processes.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
\ No newline at end of file
diff --git a/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/insulators_150kV.csv b/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/insulators_150kV.csv
new file mode 100644
index 0000000..5bf5492
--- /dev/null
+++ b/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/insulators_150kV.csv
@@ -0,0 +1,3 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+flat_glass_uncoated_RER_flat_glass_production_uncoated,1,320,320,320,320,320,320,320,320,Insulators 150kV,nach "Life cycle assessment of electricity transmission and distribution—part 1: power lines and cables Raquel Santos Jorge & Troy R. Hawkins & Edgar G. Hertwich" (DOI: 10.1007/s11367-011-0335-1),flat glass. uncoated,RER,flat glass production. uncoated,200,0,1,1,,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,1,110,110,110,110,110,110,110,110,Insulators 150kV,,steel. low-alloyed,RER,steel production. converter. low-alloyed,300,0.85,1,1,,1
diff --git a/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/insulators_and_conductors_400kV.csv b/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/insulators_and_conductors_400kV.csv
new file mode 100644
index 0000000..f9006bd
--- /dev/null
+++ b/sampledata/04-ECOINVENT/00-ECOINVENT-SUBASSEMBLIES/insulators_and_conductors_400kV.csv
@@ -0,0 +1,6 @@
+process_name,unit,2016,2020,2025,2030,2035,2040,2045,2050,build unit,group,comments,product,location,activity,amountBeforeRecyclingAndScaling,recyclingRate,EOLfactor,scalingFactor,scalingFactor2
+flat_glass_uncoated_RER_flat_glass_production_uncoated,1,1840,1840,1840,1840,1840,1840,1840,1840,kg,Insulators 400kV,nach "Life cycle assessment of electricity transmission and distribution—part 1: power lines and cables Raquel Santos Jorge & Troy R. Hawkins & Edgar G. Hertwich" (DOI: 10.1007/s11367-011-0335-1),flat glass. uncoated,RER,flat glass production. uncoated,1840,0,1,1,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,1,80,80,80,80,80,80,80,80,kg,Insulators 400kV,,steel. low-alloyed,RER,steel production. converter. low-alloyed,700,0.85,1,1,1
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,1,750,750,750,750,750,750,750,750,kg,Conductors 400kV,,steel. low-alloyed,RER,steel production. converter. low-alloyed,3800,0.85,1,1,1
+lubricating_oil_RER_lubricating_oil_production,1,500,500,500,500,500,500,500,500,,Conductors 400kV,mineral fat,lubricating oil,RER,lubricating oil production,290,0,1,1,1
+waste_mineral_oil_Europe_without_Switzerland_treatment_of_waste_mineral_oil_hazardous_waste_incineration,1,280,280,280,280,280,280,280,280,kg,waste treatment,,waste mineral oil,Europe without Switzerland,treatment of waste mineral oil. hazardous waste incineration,300,0,1,1,1
diff --git a/sampledata/04-ECOINVENT/README.md b/sampledata/04-ECOINVENT/README.md
new file mode 100644
index 0000000..6a1cb11
--- /dev/null
+++ b/sampledata/04-ECOINVENT/README.md
@@ -0,0 +1,5 @@
+# README - ECOINVENT
+
+This data package contains the life cycle impacts for all ecoinvent processes. These processes are used to define the environmental impact of the used processes. It includes life cycle impacts following the ILCD2 midpoint method, but can be easily extended by other assessment methods given e.g., in the ecoinvent table.
+
+All ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database.
diff --git a/sampledata/04-ECOINVENT/ecoinvent.csv b/sampledata/04-ECOINVENT/ecoinvent.csv
new file mode 100644
index 0000000..229ba6d
--- /dev/null
+++ b/sampledata/04-ECOINVENT/ecoinvent.csv
@@ -0,0 +1,21 @@
+process,impact category,unit,2016,2020,2025,2030,2035,2040,2045,2050
+gas_power_plant_combined_cycle_400MW_electrical_RER_gas_power_plant_construction_combined_cycle_400MW_electrical,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,65000000,65000000,65000000,65000000,65000000,65000000,65000000,65000000
+electricity_high_voltage_DE_electricity_production_natural_gas_combined_cycle_power_plant,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,0.86,0.86,0.86,0.86,0.86,0.86,0.86,0.86
+wind_power_plant_2MW_offshore_fixed_parts_GLO_market_for_wind_power_plant_2MW_offshore_fixed_parts,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,500000,500000,500000,500000,500000,500000,500000,500000
+wind_power_plant_2MW_offshore_moving_parts_GLO_market_for_wind_power_plant_2MW_offshore_moving_parts,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,930000,930000,930000,930000,930000,930000,930000,930000
+electricity_high_voltage_DE_electricity_production_wind_1_3MW_turbine_offshore,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,0.005,0.005,0.005,0.005,0.005,0.005,0.005,0.005
+wind_turbine_2MW_onshore_GLO_wind_turbine_construction_2MW_onshore,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,1300000,1300000,1300000,1300000,1300000,1300000,1300000,1300000
+wind_turbine_network_connection_2MW_onshore_RoW_wind_turbine_network_connection_construction_2MW_onshore,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,15000,15000,15000,15000,15000,15000,15000,15000
+transport_freight_lorry_7_5_16_metric_ton_EURO6_RER_market_for_transport_freight_lorry_7_5_16_metric_ton_EURO6,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,0.12,0.12,0.12,0.12,0.12,0.12,0.12,0.12
+lubricating_oil_RER_lubricating_oil_production,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,2.5,2.5,2.5,2.5,2.5,2.5,2.5,2.5
+waste_mineral_oil_Europe_without_Switzerland_treatment_of_waste_mineral_oil_hazardous_waste_incineration,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,5.1,5.1,5.1,5.1,5.1,5.1,5.1,5.1
+hydropower_plant_reservoir_non_alpine_regions_RER_hydropower_plant_construction_reservoir_non_alpine_regions,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,19000000000,19000000000,19000000000,19000000000,19000000000,19000000000,19000000000,19000000000
+concrete_normal_CH_unreinforced_concrete_production_with_cement_CEM_II/A,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,114,114,114,114,114,114,114,114
+cast_iron_RER_cast_iron_production,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,6.1,6.1,6.1,6.1,6.1,6.1,6.1,6.1
+transport_freight_lorry_7_5_16_metric_ton_EURO3_RER_transport_freight_lorry_7_5_16_metric_ton_EURO3,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08
+steel_low_alloyed_RER_steel_production_converter_low_alloyed,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9
+zinc_RoW_primary_zinc_production_from_concentrate,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,5.9,5.9,5.9,5.9,5.9,5.9,5.9,5.9
+aluminium_primary_cast_alloy_slab_from_continuous_casting_RoW_aluminium_production_primary_cast_alloy_slab_from_continuous_casting,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,7,7,7,7,7,7,7,7
+waste_concrete_CH_treatment_of_waste_concrete_inert_material_landfill,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,0.0064,0.0064,0.0064,0.0064,0.0064,0.0064,0.0064,0.0064
+inert_waste_for_final_disposal_CH_treatment_of_inert_waste_inert_material_landfill,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,0.0058,0.0058,0.0058,0.0058,0.0058,0.0058,0.0058,0.0058
+flat_glass_uncoated_RER_flat_glass_production_uncoated,ILCD 2.0 2018 midpoint:climate change:climate change total,kg CO2_eq,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3
diff --git a/sampledata/04-ECOINVENT/ecoinvent_units_to_change.json b/sampledata/04-ECOINVENT/ecoinvent_units_to_change.json
new file mode 100644
index 0000000..4212b57
--- /dev/null
+++ b/sampledata/04-ECOINVENT/ecoinvent_units_to_change.json
@@ -0,0 +1,23 @@
+{
+ "MJ-Eq": "MJ_eq",
+ "CTUh.m3.yr": "CTUh.m3.year",
+ "cubic meter": "cubic_meter",
+ "kg 1,4-DC.": "kg DCB_eq",
+ "kg CFC-11.": "kg CFC_11_eq",
+ "kg CO2-Eq": "kg CO2_eq",
+ "kg Fe-Eq": "kg Fe_eq",
+ "kg N-Eq": "kg N_eq",
+ "kg P-Eq": "kg P_eq",
+ "kg PM10-Eq": "kg PM10_eq",
+ "kg PM2.5-.": "kg PM2_5_eq",
+ "kg SO2-Eq": "kg SO2_eq",
+ "kg Sb-Eq": "kg Sb_eq",
+ "kg Soil O.": "kg SOC",
+ "kg U235-Eq": "kg U235_eq",
+ "kg ethyle.": "kg ethylen_eq",
+ "kg oil-Eq": "kg oil_eq",
+ "mol H+-Eq": "mol H_plus_eq",
+ "mol N-Eq": "mol N_eq",
+ "square meter": "square_meter",
+ "square meter-year": "square_meter.year"
+}
\ No newline at end of file
diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/README.md b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/README.md
new file mode 100644
index 0000000..e2ff6c6
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/README.md
@@ -0,0 +1,5 @@
+# README - IMPACT CATEGORY - ILCD 2.0 2018 midpoint:climate change:GWP 100a - DE
+
+This directory contains all necessary information about this
+impact category, including the nodal and total impact limits in the grid DE.
+Impact limits can be defined on a nodal or a total level for either the total impacts, or operational or invest impacts only.
diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_impact_limits.csv
new file mode 100644
index 0000000..2e62341
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_impact_limits.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+kg CO2_eq,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_nodal_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_nodal_impact_limits.csv
new file mode 100644
index 0000000..79c0824
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/invest_nodal_impact_limits.csv
@@ -0,0 +1,417 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,kg CO2_eq,,,,,,,,,
+2,kg CO2_eq,,,,,,,,,
+3,kg CO2_eq,,,,,,,,,
+4,kg CO2_eq,,,,,,,,,
+5,kg CO2_eq,,,,,,,,,
+6,kg CO2_eq,,,,,,,,,
+7,kg CO2_eq,,,,,,,,,
+8,kg CO2_eq,,,,,,,,,
+9,kg CO2_eq,,,,,,,,,
+10,kg CO2_eq,,,,,,,,,
+11,kg CO2_eq,,,,,,,,,
+12,kg CO2_eq,,,,,,,,,
+13,kg CO2_eq,,,,,,,,,
+14,kg CO2_eq,,,,,,,,,
+15,kg CO2_eq,,,,,,,,,
+16,kg CO2_eq,,,,,,,,,
+17,kg CO2_eq,,,,,,,,,
+18,kg CO2_eq,,,,,,,,,
+19,kg CO2_eq,,,,,,,,,
+20,kg CO2_eq,,,,,,,,,
+21,kg CO2_eq,,,,,,,,,
+22,kg CO2_eq,,,,,,,,,
+23,kg CO2_eq,,,,,,,,,
+24,kg CO2_eq,,,,,,,,,
+25,kg CO2_eq,,,,,,,,,
+26,kg CO2_eq,,,,,,,,,
+27,kg CO2_eq,,,,,,,,,
+28,kg CO2_eq,,,,,,,,,
+29,kg CO2_eq,,,,,,,,,
+30,kg CO2_eq,,,,,,,,,
+31,kg CO2_eq,,,,,,,,,
+32,kg CO2_eq,,,,,,,,,
+33,kg CO2_eq,,,,,,,,,
+34,kg CO2_eq,,,,,,,,,
+35,kg CO2_eq,,,,,,,,,
+36,kg CO2_eq,,,,,,,,,
+37,kg CO2_eq,,,,,,,,,
+38,kg CO2_eq,,,,,,,,,
+39,kg CO2_eq,,,,,,,,,
+40,kg CO2_eq,,,,,,,,,
+41,kg CO2_eq,,,,,,,,,
+42,kg CO2_eq,,,,,,,,,
+43,kg CO2_eq,,,,,,,,,
+44,kg CO2_eq,,,,,,,,,
+45,kg CO2_eq,,,,,,,,,
+46,kg CO2_eq,,,,,,,,,
+47,kg CO2_eq,,,,,,,,,
+48,kg CO2_eq,,,,,,,,,
+49,kg CO2_eq,,,,,,,,,
+50,kg CO2_eq,,,,,,,,,
+51,kg CO2_eq,,,,,,,,,
+52,kg CO2_eq,,,,,,,,,
+53,kg CO2_eq,,,,,,,,,
+54,kg CO2_eq,,,,,,,,,
+55,kg CO2_eq,,,,,,,,,
+56,kg CO2_eq,,,,,,,,,
+57,kg CO2_eq,,,,,,,,,
+58,kg CO2_eq,,,,,,,,,
+59,kg CO2_eq,,,,,,,,,
+60,kg CO2_eq,,,,,,,,,
+61,kg CO2_eq,,,,,,,,,
+62,kg CO2_eq,,,,,,,,,
+63,kg CO2_eq,,,,,,,,,
+64,kg CO2_eq,,,,,,,,,
+65,kg CO2_eq,,,,,,,,,
+66,kg CO2_eq,,,,,,,,,
+67,kg CO2_eq,,,,,,,,,
+68,kg CO2_eq,,,,,,,,,
+69,kg CO2_eq,,,,,,,,,
+70,kg CO2_eq,,,,,,,,,
+71,kg CO2_eq,,,,,,,,,
+72,kg CO2_eq,,,,,,,,,
+73,kg CO2_eq,,,,,,,,,
+74,kg CO2_eq,,,,,,,,,
+75,kg CO2_eq,,,,,,,,,
+76,kg CO2_eq,,,,,,,,,
+77,kg CO2_eq,,,,,,,,,
+78,kg CO2_eq,,,,,,,,,
+79,kg CO2_eq,,,,,,,,,
+80,kg CO2_eq,,,,,,,,,
+81,kg CO2_eq,,,,,,,,,
+82,kg CO2_eq,,,,,,,,,
+83,kg CO2_eq,,,,,,,,,
+84,kg CO2_eq,,,,,,,,,
+85,kg CO2_eq,,,,,,,,,
+86,kg CO2_eq,,,,,,,,,
+87,kg CO2_eq,,,,,,,,,
+88,kg CO2_eq,,,,,,,,,
+89,kg CO2_eq,,,,,,,,,
+90,kg CO2_eq,,,,,,,,,
+91,kg CO2_eq,,,,,,,,,
+92,kg CO2_eq,,,,,,,,,
+93,kg CO2_eq,,,,,,,,,
+94,kg CO2_eq,,,,,,,,,
+95,kg CO2_eq,,,,,,,,,
+96,kg CO2_eq,,,,,,,,,
+97,kg CO2_eq,,,,,,,,,
+98,kg CO2_eq,,,,,,,,,
+99,kg CO2_eq,,,,,,,,,
+100,kg CO2_eq,,,,,,,,,
+101,kg CO2_eq,,,,,,,,,
+102,kg CO2_eq,,,,,,,,,
+103,kg CO2_eq,,,,,,,,,
+104,kg CO2_eq,,,,,,,,,
+105,kg CO2_eq,,,,,,,,,
+106,kg CO2_eq,,,,,,,,,
+107,kg CO2_eq,,,,,,,,,
+108,kg CO2_eq,,,,,,,,,
+109,kg CO2_eq,,,,,,,,,
+110,kg CO2_eq,,,,,,,,,
+111,kg CO2_eq,,,,,,,,,
+112,kg CO2_eq,,,,,,,,,
+113,kg CO2_eq,,,,,,,,,
+114,kg CO2_eq,,,,,,,,,
+115,kg CO2_eq,,,,,,,,,
+116,kg CO2_eq,,,,,,,,,
+117,kg CO2_eq,,,,,,,,,
+118,kg CO2_eq,,,,,,,,,
+119,kg CO2_eq,,,,,,,,,
+120,kg CO2_eq,,,,,,,,,
+121,kg CO2_eq,,,,,,,,,
+122,kg CO2_eq,,,,,,,,,
+123,kg CO2_eq,,,,,,,,,
+124,kg CO2_eq,,,,,,,,,
+125,kg CO2_eq,,,,,,,,,
+126,kg CO2_eq,,,,,,,,,
+127,kg CO2_eq,,,,,,,,,
+128,kg CO2_eq,,,,,,,,,
+129,kg CO2_eq,,,,,,,,,
+130,kg CO2_eq,,,,,,,,,
+131,kg CO2_eq,,,,,,,,,
+132,kg CO2_eq,,,,,,,,,
+133,kg CO2_eq,,,,,,,,,
+134,kg CO2_eq,,,,,,,,,
+135,kg CO2_eq,,,,,,,,,
+136,kg CO2_eq,,,,,,,,,
+137,kg CO2_eq,,,,,,,,,
+138,kg CO2_eq,,,,,,,,,
+139,kg CO2_eq,,,,,,,,,
+140,kg CO2_eq,,,,,,,,,
+141,kg CO2_eq,,,,,,,,,
+142,kg CO2_eq,,,,,,,,,
+143,kg CO2_eq,,,,,,,,,
+144,kg CO2_eq,,,,,,,,,
+145,kg CO2_eq,,,,,,,,,
+146,kg CO2_eq,,,,,,,,,
+147,kg CO2_eq,,,,,,,,,
+148,kg CO2_eq,,,,,,,,,
+149,kg CO2_eq,,,,,,,,,
+150,kg CO2_eq,,,,,,,,,
+151,kg CO2_eq,,,,,,,,,
+152,kg CO2_eq,,,,,,,,,
+153,kg CO2_eq,,,,,,,,,
+154,kg CO2_eq,,,,,,,,,
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diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_impact_limits.csv
new file mode 100644
index 0000000..36eee81
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_impact_limits.csv
@@ -0,0 +1,2 @@
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diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_nodal_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_nodal_impact_limits.csv
new file mode 100644
index 0000000..79c0824
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/operational_nodal_impact_limits.csv
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diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/total_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/total_impact_limits.csv
new file mode 100644
index 0000000..2e62341
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/total_impact_limits.csv
@@ -0,0 +1,2 @@
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diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/total_nodal_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/total_nodal_impact_limits.csv
new file mode 100644
index 0000000..79c0824
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/DE/total_nodal_impact_limits.csv
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+345,kg CO2_eq,,,,,,,,,
+346,kg CO2_eq,,,,,,,,,
+347,kg CO2_eq,,,,,,,,,
+348,kg CO2_eq,,,,,,,,,
+349,kg CO2_eq,,,,,,,,,
+350,kg CO2_eq,,,,,,,,,
+351,kg CO2_eq,,,,,,,,,
+352,kg CO2_eq,,,,,,,,,
+353,kg CO2_eq,,,,,,,,,
+354,kg CO2_eq,,,,,,,,,
+355,kg CO2_eq,,,,,,,,,
+356,kg CO2_eq,,,,,,,,,
+357,kg CO2_eq,,,,,,,,,
+358,kg CO2_eq,,,,,,,,,
+359,kg CO2_eq,,,,,,,,,
+360,kg CO2_eq,,,,,,,,,
+361,kg CO2_eq,,,,,,,,,
+362,kg CO2_eq,,,,,,,,,
+363,kg CO2_eq,,,,,,,,,
+364,kg CO2_eq,,,,,,,,,
+365,kg CO2_eq,,,,,,,,,
+366,kg CO2_eq,,,,,,,,,
+367,kg CO2_eq,,,,,,,,,
+368,kg CO2_eq,,,,,,,,,
+369,kg CO2_eq,,,,,,,,,
+370,kg CO2_eq,,,,,,,,,
+371,kg CO2_eq,,,,,,,,,
+372,kg CO2_eq,,,,,,,,,
+373,kg CO2_eq,,,,,,,,,
+374,kg CO2_eq,,,,,,,,,
+375,kg CO2_eq,,,,,,,,,
+376,kg CO2_eq,,,,,,,,,
+377,kg CO2_eq,,,,,,,,,
+378,kg CO2_eq,,,,,,,,,
+379,kg CO2_eq,,,,,,,,,
+380,kg CO2_eq,,,,,,,,,
+381,kg CO2_eq,,,,,,,,,
+382,kg CO2_eq,,,,,,,,,
+383,kg CO2_eq,,,,,,,,,
+384,kg CO2_eq,,,,,,,,,
+385,kg CO2_eq,,,,,,,,,
+386,kg CO2_eq,,,,,,,,,
+387,kg CO2_eq,,,,,,,,,
+388,kg CO2_eq,,,,,,,,,
+389,kg CO2_eq,,,,,,,,,
+390,kg CO2_eq,,,,,,,,,
+391,kg CO2_eq,,,,,,,,,
+392,kg CO2_eq,,,,,,,,,
+393,kg CO2_eq,,,,,,,,,
+394,kg CO2_eq,,,,,,,,,
+395,kg CO2_eq,,,,,,,,,
+396,kg CO2_eq,,,,,,,,,
+397,kg CO2_eq,,,,,,,,,
+398,kg CO2_eq,,,,,,,,,
+399,kg CO2_eq,,,,,,,,,
+400,kg CO2_eq,,,,,,,,,
+401,kg CO2_eq,,,,,,,,,
+402,kg CO2_eq,,,,,,,,,
+403,kg CO2_eq,,,,,,,,,
+404,kg CO2_eq,,,,,,,,,
+405,kg CO2_eq,,,,,,,,,
+406,kg CO2_eq,,,,,,,,,
+407,kg CO2_eq,,,,,,,,,
+408,kg CO2_eq,,,,,,,,,
+409,kg CO2_eq,,,,,,,,,
+410,kg CO2_eq,,,,,,,,,
+411,kg CO2_eq,,,,,,,,,
+412,kg CO2_eq,,,,,,,,,
+413,kg CO2_eq,,,,,,,,,
+414,kg CO2_eq,,,,,,,,,
+415,kg CO2_eq,,,,,,,,,
+416,kg CO2_eq,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/README.md b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/README.md
new file mode 100644
index 0000000..7f0d35b
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/README.md
@@ -0,0 +1,5 @@
+# README - IMPACT CATEGORY - ILCD 2.0 2018 midpoint:climate change:GWP 100a
+
+This directory contains all necessary information about this
+impact category, including the nodal and total impact limits. The objective factor indicates whether this category is part of the objective function. The overshoot allows to exceed given impact limits at a penalty.
+Grid-dependent information can be modified in the folder with the same name as the grid.
diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/objective_factor_impact.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/objective_factor_impact.csv
new file mode 100644
index 0000000..fa1a1c5
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/objective_factor_impact.csv
@@ -0,0 +1,3 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,(kg CO2_eq) ** (-1),,,,,,,,,
+operation,(kg CO2_eq) ** (-1),,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/objective_factor_impact_overshot.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/objective_factor_impact_overshot.csv
new file mode 100644
index 0000000..92aac4d
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/objective_factor_impact_overshot.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+(kg CO2_eq) ** (-1),10000,10000,10000,10000,10000,10000,10000,10000,
diff --git a/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/properties.csv b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/properties.csv
new file mode 100644
index 0000000..00f178e
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/ILCD_2.0_2018_midpoint_climate_change_climate_change_total/properties.csv
@@ -0,0 +1,2 @@
+property,value,comments
+ecoinvent name,ILCD 2.0 2018 midpoint:climate change:climate change total,
diff --git a/sampledata/05-IMPACT-CATEGORIES/README.md b/sampledata/05-IMPACT-CATEGORIES/README.md
new file mode 100644
index 0000000..2a9d57a
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/README.md
@@ -0,0 +1,6 @@
+# README - IMPACT CATEGORIES
+
+This directory contains all impact categories used in the optimization.
+A impact category has a unique name and a corresponding name used
+in the ecoinvent database. Every impact category has data about
+the nodal and total impact limits for operation, invest and total impact.
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/DE/README.md b/sampledata/05-IMPACT-CATEGORIES/cost/DE/README.md
new file mode 100644
index 0000000..1723c30
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/DE/README.md
@@ -0,0 +1,4 @@
+# README - IMPACT CATEGORY - cost - DE
+
+This directory contains all necessary information about this
+impact category, including the nodal and total impact limits in the grid DE. Impact limits can be defined on a nodal or a total level for either the total impacts, or operational or invest impacts only.
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/DE/invest_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/cost/DE/invest_impact_limits.csv
new file mode 100644
index 0000000..846a326
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/DE/invest_impact_limits.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+kiloEuro,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/DE/invest_nodal_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/cost/DE/invest_nodal_impact_limits.csv
new file mode 100644
index 0000000..9a1ab9a
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/DE/invest_nodal_impact_limits.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,kiloEuro,,,,,,,,,
+2,kiloEuro,,,,,,,,,
+3,kiloEuro,,,,,,,,,
+4,kiloEuro,,,,,,,,,
+5,kiloEuro,,,,,,,,,
+6,kiloEuro,,,,,,,,,
+7,kiloEuro,,,,,,,,,
+8,kiloEuro,,,,,,,,,
+9,kiloEuro,,,,,,,,,
+10,kiloEuro,,,,,,,,,
+11,kiloEuro,,,,,,,,,
+12,kiloEuro,,,,,,,,,
+13,kiloEuro,,,,,,,,,
+14,kiloEuro,,,,,,,,,
+15,kiloEuro,,,,,,,,,
+16,kiloEuro,,,,,,,,,
+17,kiloEuro,,,,,,,,,
+18,kiloEuro,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/DE/operational_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/cost/DE/operational_impact_limits.csv
new file mode 100644
index 0000000..846a326
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/DE/operational_impact_limits.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+kiloEuro,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/DE/operational_nodal_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/cost/DE/operational_nodal_impact_limits.csv
new file mode 100644
index 0000000..9a1ab9a
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/DE/operational_nodal_impact_limits.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,kiloEuro,,,,,,,,,
+2,kiloEuro,,,,,,,,,
+3,kiloEuro,,,,,,,,,
+4,kiloEuro,,,,,,,,,
+5,kiloEuro,,,,,,,,,
+6,kiloEuro,,,,,,,,,
+7,kiloEuro,,,,,,,,,
+8,kiloEuro,,,,,,,,,
+9,kiloEuro,,,,,,,,,
+10,kiloEuro,,,,,,,,,
+11,kiloEuro,,,,,,,,,
+12,kiloEuro,,,,,,,,,
+13,kiloEuro,,,,,,,,,
+14,kiloEuro,,,,,,,,,
+15,kiloEuro,,,,,,,,,
+16,kiloEuro,,,,,,,,,
+17,kiloEuro,,,,,,,,,
+18,kiloEuro,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/DE/total_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/cost/DE/total_impact_limits.csv
new file mode 100644
index 0000000..846a326
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/DE/total_impact_limits.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+kiloEuro,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/DE/total_nodal_impact_limits.csv b/sampledata/05-IMPACT-CATEGORIES/cost/DE/total_nodal_impact_limits.csv
new file mode 100644
index 0000000..9a1ab9a
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/DE/total_nodal_impact_limits.csv
@@ -0,0 +1,19 @@
+node,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+1,kiloEuro,,,,,,,,,
+2,kiloEuro,,,,,,,,,
+3,kiloEuro,,,,,,,,,
+4,kiloEuro,,,,,,,,,
+5,kiloEuro,,,,,,,,,
+6,kiloEuro,,,,,,,,,
+7,kiloEuro,,,,,,,,,
+8,kiloEuro,,,,,,,,,
+9,kiloEuro,,,,,,,,,
+10,kiloEuro,,,,,,,,,
+11,kiloEuro,,,,,,,,,
+12,kiloEuro,,,,,,,,,
+13,kiloEuro,,,,,,,,,
+14,kiloEuro,,,,,,,,,
+15,kiloEuro,,,,,,,,,
+16,kiloEuro,,,,,,,,,
+17,kiloEuro,,,,,,,,,
+18,kiloEuro,,,,,,,,,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/README.md b/sampledata/05-IMPACT-CATEGORIES/cost/README.md
new file mode 100644
index 0000000..ac8a803
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/README.md
@@ -0,0 +1,5 @@
+# README - IMPACT CATEGORY - cost
+
+This directory contains all necessary information about this
+impact category, including the nodal and total impact limits. The objective factor indicates whether this category is part of the objective function. The overshoot allows to exceed given impact limits at a penalty.
+Grid-dependent information can be modified in the folder with the same name as the grid.
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/objective_factor_impact.csv b/sampledata/05-IMPACT-CATEGORIES/cost/objective_factor_impact.csv
new file mode 100644
index 0000000..22d6de2
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/objective_factor_impact.csv
@@ -0,0 +1,3 @@
+phase,unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+invest,(kiloEuro) ** (-1),1,1,1,1,1,1,1,1,
+operation,(kiloEuro) ** (-1),1,1,1,1,1,1,1,1,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/objective_factor_impact_overshot.csv b/sampledata/05-IMPACT-CATEGORIES/cost/objective_factor_impact_overshot.csv
new file mode 100644
index 0000000..eb9f1ba
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/objective_factor_impact_overshot.csv
@@ -0,0 +1,2 @@
+unit,2016,2020,2025,2030,2035,2040,2045,2050,comments
+(kiloEuro) ** (-1),5,5,5,5,5,5,5,5,
diff --git a/sampledata/05-IMPACT-CATEGORIES/cost/properties.csv b/sampledata/05-IMPACT-CATEGORIES/cost/properties.csv
new file mode 100644
index 0000000..64335f6
--- /dev/null
+++ b/sampledata/05-IMPACT-CATEGORIES/cost/properties.csv
@@ -0,0 +1,2 @@
+property,value,comments
+ecoinvent name,cost,
diff --git a/sampledata/README.md b/sampledata/README.md
new file mode 100644
index 0000000..999f57b
--- /dev/null
+++ b/sampledata/README.md
@@ -0,0 +1,7 @@
+# SecMOD Data
+
+This project includes a small data set documented in our publication, representing case study 1.
+
+Due to license restrictions, all ecoinvent files contain randomly assigned exemplary values which were not taken from the ecoinvent database. If used for other projects, the ecoinvent file `04-ECOINVENT/ecoinvent.csv` and the LCIs should be updated with the given format and original data.
+
+
diff --git a/sampledata/config.py b/sampledata/config.py
new file mode 100644
index 0000000..ec18654
--- /dev/null
+++ b/sampledata/config.py
@@ -0,0 +1,72 @@
+grid_name = "DE" #name of the used grid topology
+invest_years_per_optimization = 1 #foresight (number of considered investment periods in each optimization)
+invest_years = [2016,2020,2025,2030,2035,2040] #considered investment periods for the transition pathway
+interest_rate = 0.05
+economic_period = "30 years" #determines the maximal annualization horizon of a process
+# Choose LCA framework - e.g., "ReCiPe Midpoint (H)", "ILCD 2.0 2018 midpoint" or "cumulative energy demand"
+LCA_framework = "ReCiPe Midpoint (H)"
+# Override used impact categories to reduce preparation time - "None" if FRAMEWORK should be used
+LCA_manual_impact_categories = ["cost", "ILCD 2.0 2018 midpoint:climate change:climate change total"]
+
+
+# Set parameters for the timeseries aggregation, done by TSAM
+# Documentation of all parameters is here https://github.com/FZJ-IEK3-VSA/tsam/blob/master/tsam/timeseriesaggregation.py
+# See "class TimeSeriesAggregation"
+time_series_aggregation_config = {
+ "resolution" : None,
+ "noTypicalPeriods" : 10,
+ "hoursPerPeriod" : 2,
+ "clusterMethod" : "hierarchical",
+ "evalSumPeriods" : False,
+ "sortValues" : False,
+ "sameMean" : False,
+ "rescaleClusterPeriods" : True,
+ "extremePeriodMethod" : 'None',
+ "predefClusterOrder" : None,
+ "predefClusterCenterIndices": None,
+ "solver_tsa" : "gurobi",
+ "roundOutput" : None,
+ "addPeakMin" : None,
+ "addPeakMax" : None,
+ "addMeanMin" : None,
+ "addMeanMax" : None,
+ "use_time_series_aggregation": True,
+ "correct_dateformat" : '%d.%m.%Y %H:%M', # desired format of timeseries indizes
+ "partially_correct_dateformat" : '%d.%m.%Y %H:%M:%S', # partially correct format of timeseries indizes that is meant to be changed
+ "wrong_dateformat" : '%d-%b-%Y %H:%M:%S' # current format of timeseries indizes that is meant to be changed
+}
+
+# scaling parameters
+scaling_options = {
+ 'algorithm': 'geom_mean', # 'equi': Equilibration, 'geom_mean': Geometric Mean, 'approx_geom_mean': Approximated Geometric Mean, 'arith_mean': Arithmetic Mean
+ 'Round_to_power_of_2': True, # rounds scaling factors to power of two (often numerically better)
+ 'scale_constraints': True, # scaled while model instantiated
+ 'scale_variables': True, # scaled after model instatiated
+ 'analyze_numerics': True, # retrieves information about numerics
+ 'eps_bound': 1e-8 # threshold for printing constraint bound
+}
+
+# choose between gurobi_persistent, cplex, and other solvers
+solver = "gurobi_persistent"
+solver_options = {
+ "ObjScale": 0,
+ "LogToConsole": 1,
+ "NumericFocus": 3,
+ "ScaleFlag": 0,
+ "Method": 0,
+ "BarHomogeneous": 1,
+ "InfUnbdInfo": 0,
+ "ResultFile": "model.ilp", # writes largest feasible model, in the case that the full model is infeasible
+ }
+
+debug_optimization = True
+
+load_raw_input = True #loads input from working directory, if fals, existing input must be loaded
+load_existing_input_dict = False #loads input from existing input dictionary
+load_existing_results = False #loads results from previous calculation
+new_pint_units = "./SecMOD/00-INPUT/00-RAW-INPUT/00-UNITS-DEFINITIONS.txt" #Units employed in the model
+
+fix_slack_variables = True #, if the non_served_demand of the in config.fix_products specified products should be fixed to 0.
+# fix_slack_variables = False allows to import products at the cost given in the products folder any may be used as slack variable for debugging.
+fix_products = ["electricity"]
+
diff --git a/secmod/__main__.py b/secmod/__main__.py
new file mode 100644
index 0000000..daca493
--- /dev/null
+++ b/secmod/__main__.py
@@ -0,0 +1,107 @@
+from pathlib import Path
+import logging
+import time
+import os
+from datetime import datetime
+from pyomo.opt import TerminationCondition
+import pickle
+
+log_format = '%(asctime)s %(filename)s: %(levelname)s: %(message)s'
+if not os.path.exists("logs"):
+ os.mkdir("logs")
+logging.basicConfig(filename='logs/secmod.log', level=logging.INFO, format=log_format,
+ datefmt='%Y-%m-%d %H:%M:%S')
+logging.getLogger().addHandler(logging.StreamHandler())
+logging.propagate = False
+
+import secmod.data_preprocessing as dataprep
+import secmod.setup as setup
+import secmod.helpers as helpers
+import secmod.data_processing as data_processing
+from secmod.classes import ImpactCategory, ProcessImpacts, ProductionProcess, StorageProcess, TransshipmentProcess, TransmissionProcess, units, config
+from secmod.optimization import Optimization
+import secmod.evaluation as evaluation
+import secmod.scaling as scaling
+
+# Get current working directory
+working_directory = Path.cwd()
+setup.setup(working_directory, reset=False)
+
+def main():
+ """This is the main method of SecMOD.
+
+ This method is called when SecMOD is called as a script instead of being imported,
+ e.g. using the following command line in the command line::
+
+ python -m secmod
+
+ It is the main entrance point for starting a run of SecMOD.
+ """
+
+ helpers.log_heading("SecMOD - Startup")
+
+ computed_input_path = working_directory / "SecMOD" / "00-INPUT" / "01-COMPUTED-INPUT"
+
+ # Setup static variables
+ ProcessImpacts.interest_rate = config.interest_rate
+ ProcessImpacts.economic_period = units(config.economic_period)
+
+ # Choose LCA framework - "ReCiPe Midpoint (H)", "ILCD 1.0.8 2016 midpoint" or "cumulative energy demand"
+ ImpactCategory.FRAMEWORK = config.LCA_framework
+
+ # Override used impact categories to reduce preparation time - "None" if FRAMEWORK should be used
+ if hasattr(config, "LCA_manual_impact_categories"):
+ ImpactCategory.MANUAL_IMPACT_CATEGORY_SELECTION = config.LCA_manual_impact_categories
+
+ # Load raw data
+ if config.load_raw_input:
+ data_processing.load_raw_input(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT")
+
+ # Sort invest years in case someone put them in the wrong order
+ config.invest_years.sort()
+
+ # Go over all years using a rolling horizon for the optimization
+ for reference_year in config.invest_years:
+ # Select invest years for the current optimization horizon
+ ProcessImpacts.invest_years = [year for year in config.invest_years if (year >= reference_year) and ((config.invest_years.index(year) - config.invest_years.index(reference_year)) < (config.invest_years_per_optimization))]
+ helpers.log_heading("Start optimization horizon of {0}, including {1}".format(reference_year, ProcessImpacts.invest_years))
+
+ # Generate input dictionary or start with an existing one (only senseful for debugging)
+ input_dictionary = data_processing.generate_input_dictionary(computed_input_path, config.load_existing_input_dict)
+ input_dictionary[None]['type_of_optimization'] = {None: ['InvestmentOptimization']}
+ # Make sure that in the next optimization horizon a new input dictionary is created
+ config.load_existing_input_dict = False
+
+ # Initialize Optimization class
+ thisInvestmentOptimization = Optimization()
+ if not os.path.exists(working_directory / "SecMOD" / "01-MODEL-RESULTS"):
+ os.mkdir(working_directory / "SecMOD" / "01-MODEL-RESULTS")
+
+ if config.load_existing_results:
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "InvestmentModel_{0}.pickle".format(min(ProcessImpacts.invest_years)), "rb") as input_file:
+ thisInvestmentOptimization = pickle.load(input_file)
+ config.load_existing_results = False
+ else:
+ thisInvestmentOptimization.run(input_dict=input_dictionary, solver=config.solver, solver_options=config.solver_options, debug=config.debug_optimization)
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "InvestmentModel_{0}.pickle".format(min(ProcessImpacts.invest_years)), "wb") as input_file:
+ pickle.dump(thisInvestmentOptimization.model_instance, input_file)
+
+ # if infeasible, compute and write Irreducible Inconsistent Subsystem (subset model with infeasible constraints)
+ if config.solver == 'gurobi_persistent' and config.debug_optimization and (thisInvestmentOptimization.results.solver.termination_condition == getattr(TerminationCondition,'infeasible') or thisInvestmentOptimization.results.solver.termination_condition == getattr(TerminationCondition,'infeasibleOrUnbounded')):
+ thisInvestmentOptimization.mysolver._solver_model.computeIIS()
+ thisInvestmentOptimization.mysolver._solver_model.write("{0}/SecMOD/01-MODEL-RESULTS/gurobi_model_{1}.ilp".format(working_directory, min(ProcessImpacts.invest_years)))
+ # add new capacity
+ data_processing.add_new_capacity_from_results(thisInvestmentOptimization)
+
+ logging.info("\n\n")
+ logging.info("========================================")
+ logging.info("= THE END! =")
+ logging.info("========================================")
+ # start evaluation
+ evaluation.start_evaluation(working_directory)
+
+ logging.getLogger().handlers.clear()
+ logging.shutdown()
+
+if __name__ == "__main__":
+ main()
diff --git a/secmod/classes.py b/secmod/classes.py
new file mode 100644
index 0000000..e5a7ff7
--- /dev/null
+++ b/secmod/classes.py
@@ -0,0 +1,3347 @@
+import abc
+import copy
+import logging
+import os
+import json
+from pathlib import Path
+from clint.textui import progress
+
+import numpy as np
+import sympy as sp
+import pandas as pd
+import pint
+import importlib.util
+import secmod.helpers
+from geopy import distance
+
+# Get current working directory
+working_directory = Path.cwd()
+
+# Load config.py
+spec = importlib.util.spec_from_file_location("config", "SecMOD/00-INPUT/00-RAW-INPUT/config.py")
+config = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(config)
+
+units = pint.UnitRegistry()
+# Load new unit definitions for the pint package, if path to TXT-file is provided
+if config.new_pint_units != None:
+ logging.info("Load new unit definitions for the pint package")
+ units.load_definitions(str(working_directory / config.new_pint_units))
+ logging.info("Done")
+# load target_units
+pint.set_application_registry(units)
+target_units = json.load(open("SecMOD/00-INPUT/00-RAW-INPUT/01-UNITS-TARGET-OPTIMIZATION.json", 'r'))
+target_units_dictionary = {}
+for target_unit in target_units:
+ target_pint_unit = units(target_unit)
+ target_units_dictionary[target_pint_unit.dimensionality] = target_unit
+
+class Grid(object):
+ """This class is used to manage grids of nodes and connections.
+
+ This class represents a grid, which consists of a name, nodes and connections between these nodes.
+ It can be created from scratch by providing at least a unique name and optionally pandas dataframes for
+ data about the nodes and connections. It can alternatively be created from a path to a directory of an
+ existing grid. The name will then be taken from the name of the grid directory and the node data
+ and connection data will be loaded from .csv-files.
+
+ Furthermore the class has a static list of all created instances and a single static variable
+ which represents the selected grid for the optimization.
+
+ Args:
+ grid_path (Path): A path of the directory of an existing grid definition, which includes a
+ CSV-file with data about nodes and connections in the grid.
+
+ name (str): Name for a new grid, if no grid_path is provided. The name has to be unique,
+ since no two grids can have the same name.
+
+ nodes (pd.Dataframe): A pandas dataframe which has all the information about the nodes of
+ a new grid, if no grid_path is provided. This dataframe must have the columns
+ ["node","latitude","longitude"].
+
+ connections(pd.Dataframe): A pandas dataframe which has all the information about the
+ connections of a new grid, if no grid_path is provided. This dataframe must have
+ the columns ["connection","node1","node2"].
+
+ Attributes:
+ grids (list): Static list of all instanciated grids.
+
+ _selected (Grid): Static reference to a single Grid instance which is selected to be
+ used in the optimization. Can be reached via the selected()-method.
+
+ Raises:
+ ValueError: If no name is provided, if no path is provided as well.
+
+ FileNotFoundError: If "nodes.csv" or "connections.csv" does not exist in the provided directory.
+
+ NotADirectoryError: If the provided path is no directory.
+ """
+
+ CONNECTION_NODES = {None: ["node1", "node2"]}
+
+ grids = []
+ _selected = None
+
+ def __init__(self, grid_path: Path = None, name: str = None, nodes: pd.DataFrame = None, connections: pd.DataFrame = None):
+ """See class documentation above for more information on the initialization."""
+
+ # If no path to an existing grid is provided, a new grid is created
+ if grid_path == None:
+ # If a name for the new grid is provided it is set as name
+ if name != None:
+ self.name = name
+ logging.debug(
+ "Initialize a new instance of Grid from scratch - name: {0}...".format(self.name))
+ # If no name is provided, an exception is raised. Every grid needs a name.
+ else:
+ raise ValueError(
+ 'A grid without a name is no grid. Please provide a unique name for your grid.')
+ # If existing node data is provided it is set as nodes
+ if nodes != None:
+ self.nodes = nodes
+ # If no node data is provided, a new empty dataframe with the needed columns is created
+ else:
+ self.nodes = pd.DataFrame(
+ columns=["latitude", "longitude", "comments"])
+ self.nodes.index.name = "node"
+ logging.debug(
+ "=== Empty dataframe for information about nodes created.")
+ # If existing connection data is provided it is set as connections
+ if connections != None:
+ self.connections = connections
+ # If no connection data is provided, a new empty dataframe with the needed columns is created
+ else:
+ self.connections = pd.DataFrame(
+ columns=["node1", "node2", "comments"])
+ self.connections.index.name = "connection"
+ logging.debug(
+ "=== Empty dataframe for information about connections created.")
+ # If a path to an existing grid is provided, the name, nodes and connections are loaded from that directory
+ else:
+ # checks, if the provided path is a directory
+ if grid_path.is_dir():
+ self.name = str(grid_path.name).replace("_", " ")
+ logging.debug(
+ "Initialize a new instance of Grid from directory - name: {0}...".format(self.name))
+ # checks, if the file "nodes.csv" exists, otherwise raises an exception
+ if not (grid_path / "nodes.csv").is_file():
+ raise FileNotFoundError(
+ "A grid exists out of nodes and connections. Without nodes a grid can't exist. Please provide a file 'nodes.csv' in your grid directory.")
+ # checks, if the file "connections.csv" exists, otherwise raises an exception
+ if not (grid_path / "connections.csv").is_file():
+ raise FileNotFoundError(
+ "A grid exists out of nodes and connections. Without connections a grid can't exist. Please provide a file 'connection.csv' in your grid directory.")
+ self.nodes = pd.read_csv(
+ grid_path / "nodes.csv", index_col="node", usecols=lambda x: "Unnamed" not in x, float_precision="high")
+ logging.debug("=== Nodal information read.")
+ self.connections = pd.read_csv(
+ grid_path / "connections.csv", index_col="connection", usecols=lambda x: "Unnamed" not in x, float_precision="high")
+ logging.debug("=== Connection information read.")
+ # if the provided path is no directory, an exception is raised
+ else:
+ raise NotADirectoryError(
+ "Path {0} is not a directory. Please provide a path to the directory of the grid.".format(str(grid_path)))
+ Grid.grids.append(self)
+ logging.debug("=== Grid instance appended to the static Grid list.")
+ logging.debug("=== Grid instance initialized.")
+
+ @classmethod
+ def load_grids_from_directory(cls, grids_category_path: Path):
+ """Loads all grids of a specific class into their class."""
+
+ logging.info("Load grids of class '{0}".format(str(cls)))
+
+ # Get a list of all files and folders in the directory
+ grid_paths = os.listdir(grids_category_path)
+
+ # Iterate through the members of the grid directory
+ for grid in progress.bar(grid_paths):
+ # Check if the file ending is "csv"
+ if (grids_category_path / grid).is_dir():
+ # If the file is csv, read it in as a dataframe
+ cls(grids_category_path / grid)
+
+ @property
+ def name(self):
+ """This property contains a unique name for the grid.
+
+ Getter:
+ Gets the value of this property.
+
+ Returns:
+ Name of the grid as string.
+
+ Setter:
+ Sets the property to the provided name, if it isn't already used by another grid.
+
+ Raises:
+ ValueError: If the name is already used by another grid.
+
+ Type:
+ str
+ """
+
+ return self._name
+
+ @name.setter
+ def name(self, name):
+ """See property docstring above for setter information."""
+
+ # If the provided name is equal to the current name, do nothing
+ if hasattr(self, "_name"):
+ if self.name == name:
+ return
+ # Check if any other defined grid has the provided name
+ for grid in Grid.grids:
+ if grid.name == name:
+ raise ValueError(
+ "There is already a grid with the name '{0}'. Please choose another name.".format(name))
+ self._name = name
+
+ @property
+ def nodes(self):
+ """This property contains all information about the nodes of a grid.
+
+ Getter:
+ Gets the value of this property.
+
+ Returns:
+ Dataframe with the information about the nodes of the grid.
+
+ Setter:
+ Sets the property to the provided dataframe, if the dataframe
+ has the columns ["node","latitude","longitude"].
+
+ Type:
+ pd.dataframe
+ """
+
+ return self._nodes
+
+ @nodes.setter
+ def nodes(self, nodes):
+ """See property docstring above for setter information."""
+
+ # Checks if the necessary columns exist in the provided dataframe
+ if set(["latitude", "longitude"]).issubset(nodes.columns):
+ # Sets the provided dataframe as new node information
+ self._nodes = nodes
+ else:
+ # Raises ValueError, if the necessary columns do not exist
+ raise ValueError(
+ 'The file "nodes.csv" does not provide the necessary columns: ["node","latitude","longitude"]')
+
+ @staticmethod
+ def get_list_of_node_ids():
+ """Return a list of all node ID numbers of the selected grid."""
+
+ return(list(Grid.selected()._nodes.index.values))
+
+ @property
+ def connections(self):
+ """This property contains all information about the connections between nodes of a grid.
+
+ Getter:
+ Gets the value of this property.
+
+ Returns:
+ Dataframe with the information about the connections of the grid.
+
+ Setter:
+ Sets the property to the provided dataframe, if the dataframe
+ has the columns ["connection","node1","node2"].
+
+ Type:
+ pd.dataframe
+ """
+
+ return self._connections
+
+ @connections.setter
+ def connections(self, connections):
+ """See property docstring above for setter information."""
+
+ # Checks if the necessary columns exist in the provided dataframe
+ if set(["node1", "node2"]).issubset(connections.columns):
+ # Sets the provided dataframe as new connection information
+ self._connections = connections
+ else:
+ # Raises ValueError, if the necessary columns do not exist
+ raise ValueError(
+ 'The provided data of connections does not have the necessary columns: ["node1","node2"]')
+
+ @staticmethod
+ def get_list_of_connection_ids():
+ """Return a list of all node ID numbers of the selected grid."""
+
+ return(list(Grid.selected()._connections.index.values))
+
+ def setup_distances(self):
+ """Sets up the distances of all connections, using pint units.
+
+ Uses geodesic distance from the package GeoPy (https://geopy.readthedocs.io/en/stable/#module-geopy.distance)
+ to calculate the distance between the two nodes of a connection.
+ """
+
+ # Get coordinates of both nodes of all connections of the grid
+ coordinates = self.connections.join(self.nodes[["longitude", "latitude"]], on="node1").join(
+ self.nodes[["longitude", "latitude"]], on="node2", lsuffix="_1", rsuffix="_2")
+
+ # Determine distance between both nodes of all connections of the grid
+ self.connections["distance"] = coordinates.apply(
+ lambda connection: Grid.calculate_distances(connection), axis=1)
+ # Convert dataframe to correct unit
+ self.connections["distance"] = convert_quantity_to_correct_unit(self.connections["distance"])
+
+ @staticmethod
+ def calculate_distances(connection):
+ """Calculates the distance or takes it from existing data.
+
+ Uses geodesic distance from the package GeoPy (https://geopy.readthedocs.io/en/stable/#module-geopy.distance)
+ to calculate the distance between the two nodes of a connection, if no distance is provided in the data.
+
+ Args:
+ connection: An itertuple from the connections DataFrame.
+ """
+
+ # Check if the column for manual distance exists in the loaded connection data
+ if hasattr(connection, "manual_distance"):
+ # If a distance value does not exist, calculate it with the geodetic distance
+ if pd.isnull(connection.manual_distance):
+ return units(str(distance.distance((connection.latitude_1, connection.longitude_1), (connection.latitude_2, connection.longitude_2))))
+ else:
+ # Check if the column for the unit of manual distances exists in the loaded connection data
+ if hasattr(connection, "manual_distance_unit"):
+ # If a unit value does not exist, assume the distance to be in kilometers
+ if pd.isnull(connection.manual_distance_unit):
+ # Check if the manual distance is smaller than the minimal possible geodetic distance and issue a warning, if it is
+ if (units(str(connection.manual_distance) + " km") < units(str(distance.distance((connection.latitude_1, connection.longitude_1), (connection.latitude_2, connection.longitude_2))))):
+ logging.warning(
+ "Manual distance of connection {0} is smaller than minimal geodetic distance!".format(connection.name))
+ logging.warning("=== Manual distance: {0}".format(
+ units(str(connection.manual_distance) + " km")))
+ logging.warning("=== Geodetic distance: {0}".format(units(str(distance.distance(
+ (connection.latitude_1, connection.longitude_1), (connection.latitude_2, connection.longitude_2))))))
+ # Return distance in the assumed unit of kilometers
+ return units(str(connection.manual_distance) + " km")
+ else:
+ # Check if the manual distance is smaller than the minimal possible geodetic distance and issue a warning, if it is
+ if (units(str(connection.manual_distance) + str(connection.manual_distance_unit)) < units(str(distance.distance((connection.latitude_1, connection.longitude_1), (connection.latitude_2, connection.longitude_2))))):
+ logging.warning(
+ "Manual distance of connection {0} is smaller than minimal geodetic distance!".format(connection.name))
+ logging.warning("=== Manual distance: {0}".format(
+ units(str(connection.manual_distance) + " km")))
+ logging.warning("=== Geodetic distance: {0}".format(units(str(distance.distance(
+ (connection.latitude_1, connection.longitude_1), (connection.latitude_2, connection.longitude_2))))))
+ # Return distance in the provided manual distance unit
+ return units(str(connection.manual_distance) + str(connection.manual_distance_unit))
+ else:
+ # Assume the unit to be kilometers, if the column of the manual distance unit does not exist
+ logging.warning(
+ "No unit provided for manual distance. Kilometers are assumed.")
+ return units(str(connection.manual_distance) + " km")
+ else:
+ # Return the geodetic calculated distance
+ return units(str(distance.distance((connection.latitude_1, connection.longitude_1), (connection.latitude_2, connection.longitude_2))))
+
+ @staticmethod
+ def selected():
+ """This methods gets the value of the static variable "selected".
+
+ This method returns the Grid object which is selected to be used
+ as grid for the optimization. The static variable "selected" can
+ only contain one Grid object and is set using the method "select()"
+ of an instance of the Grid class.
+
+ Returns:
+ Value of _selected, which is either None or a Grid instance.
+ """
+
+ return Grid._selected
+
+ def select(self):
+ """This methods sets the value of the static member "selected".
+
+ The static member "selected" is set to reference the Grid instance
+ which called this method.
+ """
+
+ # Setup the distances of all connections in the grid, based on node coordinates or manual distances
+ self.setup_distances()
+ # Set the static variable Grid._selected to the current instance
+ Grid._selected = self
+ # Return True, if successfull
+ return True
+
+
+class Product(object):
+ """This class is used for all product related data, e.g. demand, impacts, etc."""
+
+ # Default value for average yearly costs, if value is missing
+ DEFAULT_COST = 3000
+ # List of all products
+ products = []
+
+ def __init__(self, product_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of Product.")
+
+ if product_path == None:
+ # Do things if None has been given as path.
+ raise NotImplementedError
+ else:
+ # Check if product directory exists
+ if product_path.is_dir():
+ # Get name by directory name
+ self.name = str(product_path.name).replace("_", " ")
+ # Load average yearly product price
+ self._costs_yearly_average = pd.read_csv(
+ product_path / "costs_yearly_average.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ # Fill empty values with class-wide default costs
+ self._costs_yearly_average.fillna(Product.DEFAULT_COST, inplace=True)
+ # Convert dataframe to correct unit
+ self._costs_yearly_average = convert_quantity_to_correct_unit(self._costs_yearly_average)
+ # Load relative yearly product price timeseries
+ self._costs_yearly_timeseries = pd.read_csv(
+ product_path / "costs_yearly_timeseries.csv", index_col="time slice", float_precision="high", usecols=(lambda column: column != "comments"))
+ # correct time stamp format
+ self._costs_yearly_timeseries = secmod.helpers.correct_time_stamp_of_timeseries(self._costs_yearly_timeseries)
+ # Fill empty values with 1 as the yearly average should be used then.
+ self._costs_yearly_timeseries.fillna(1, inplace=True)
+ # Convert dataframe to correct unit
+ self._costs_yearly_timeseries = convert_quantity_to_correct_unit(self._costs_yearly_timeseries)
+ # Load impact definition for non-served demand
+ self._processes_non_served_demand = pd.read_csv(
+ product_path / "ecoinvent_operation.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+
+ # Check if subdirectory for grid-dependent data exists
+ if (product_path / Grid.selected().name).is_dir():
+ # Load average nodal demand
+ self._demand_nodal_average = pd.read_csv(
+ product_path / Grid.selected().name / "demand_nodal_average.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ self._target_unit = get_target_unit_from_unit(self._demand_nodal_average['unit'].values[0])
+ # Fill empty values with 0 as there should be no demand then.
+ self._demand_nodal_average.fillna(0, inplace=True)
+ # Convert dataframe to correct unit
+ self._demand_nodal_average = convert_quantity_to_correct_unit(self._demand_nodal_average)
+ # Load relative yearly nodal demand timeseries
+ self._demand_nodal_timeseries = pd.read_csv(
+ product_path / Grid.selected().name / "demand_nodal_timeseries.csv", index_col="time slice", float_precision="high", usecols=(lambda column: column != "comments"))
+ # correct time stamp format
+ self._demand_nodal_timeseries = secmod.helpers.correct_time_stamp_of_timeseries(self._demand_nodal_timeseries)
+ # Fill empty values with 1 as the yearly average should be used then.
+ self._demand_nodal_timeseries.fillna(1, inplace=True)
+ # Convert dataframe to correct unit
+ self._demand_nodal_timeseries = convert_quantity_to_correct_unit(self._demand_nodal_timeseries)
+
+ # Required secured capacity
+ self._required_total_secured_capacity = pd.read_csv(
+ product_path / Grid.selected().name / "required_total_secured_capacity.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ # Fill empty values with minus infinity as there should be no required secured capacity then.
+ self._required_total_secured_capacity.fillna(-np.inf, inplace=True)
+ # Convert dataframe to correct unit
+ self._required_total_secured_capacity = convert_quantity_to_correct_unit(self._required_total_secured_capacity)
+ # Required secured capacity
+ self._required_average_nodal_secured_capacity = pd.read_csv(
+ product_path / Grid.selected().name / "required_average_nodal_secured_capacity.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ # Fill empty values with minus infinity as there should be no required secured capacity then.
+ self._required_average_nodal_secured_capacity.fillna(-np.inf, inplace=True)
+ # Convert dataframe to correct unit
+ self._required_average_nodal_secured_capacity = convert_quantity_to_correct_unit(self._required_average_nodal_secured_capacity)
+ # Load relative yearly nodal required secured capacity
+ self._required_relative_nodal_secured_capacity = pd.read_csv(
+ product_path / Grid.selected().name / "required_relative_nodal_secured_capacity.csv", index_col="node", float_precision="high", usecols=["node", "unit", "value"])
+ # Fill empty values with 1 as the yearly average should be used then.
+ self._required_relative_nodal_secured_capacity.fillna(1, inplace=True)
+ # Convert dataframe to correct unit
+ self._required_relative_nodal_secured_capacity = convert_quantity_to_correct_unit(self._required_relative_nodal_secured_capacity)
+ else:
+ logging.warning("No demand data found for process '{0}' in grid '{1}'! Existing and potential capacities are assumed.".format(
+ self._name, Grid.selected().name))
+ # Create empty DataFrames, if no grid-dependent data is provided
+ self._demand_nodal_average = pd.DataFrame(index=Grid.selected().nodes.index.tolist(
+ ), data=0, columns=ProcessImpacts.invest_years)
+ self._demand_nodal_timeseries = pd.DataFrame(index=Grid.selected().nodes.index.tolist(
+ ), data=1, columns=Grid.selected().get_list_of_node_ids())
+ else:
+ raise NotADirectoryError(
+ "Path {0} is not a directory. Please provide a path to the directory of the product.".format(str(product_path)))
+ # Append product instance to static list of products
+ Product.products.append(self)
+
+ @classmethod
+ def load_products_from_directory(cls, products_path: Path):
+ """Loads all products of a specific class into their class."""
+
+ logging.info("Load products of class '{0}".format(str(cls)))
+
+ # Get a list of all files and folders in the directory
+ product_paths = os.listdir(products_path)
+
+ # Iterate through the members of the process directory
+ for process in progress.bar(product_paths):
+ # Check if the file ending is "csv"
+ if (products_path / process).is_dir():
+ # If the file is csv, read it in as a dataframe
+ cls(products_path / process)
+
+ @property
+ def name(self):
+ """This property contains a unique name for the grid.
+
+ Getter:
+ Gets the value of this property.
+
+ Returns:
+ Name of the grid as string.
+
+ Setter:
+ Sets the property to the provided name, if it isn't already used by another grid.
+
+ Raises:
+ ValueError: If the name is already used by another grid.
+
+ Type:
+ str
+ """
+
+ return self._name
+
+ @name.setter
+ def name(self, name):
+ """See property docstring above for setter information."""
+
+ # If the provided name is equal to the current name, do nothing
+ if hasattr(self, "_name"):
+ if self.name == name:
+ return
+ # Check if any other defined grid has the provided name
+ for product in Product.products:
+ if product.name == name:
+ raise ValueError(
+ "There is already a product with the name '{0}'. Please choose another name.".format(name))
+ self._name = name
+
+ def _get_price_time_series(self, year: int):
+ """Returns the absolute price time series"""
+
+ df = self._costs_yearly_timeseries* self._costs_yearly_average[str(year)].values[0]
+ return df
+
+ def _get_cost_non_served_demand(self, reference_year: int, invest_year: int):
+ """This method gets the costs of non-served demand of a product.
+
+ Calculates the present value of the selected impact year and its investment period
+ in the reference year for a product.
+
+ Args:
+ reference_year (int): Reference year of the optimized time horizon, e.g. 2020
+
+ invest_year (int): An investment year of the optimized time horizon, e.g. 2025
+ """
+
+ # Determine the length of the current investment period
+ investment_period_duration = ProcessImpacts._get_investment_period_duration(
+ invest_year)
+ # Calculate annuity present value factor for the current investment period
+ investment_period_annuity_present_value_factor = ((1+ProcessImpacts.interest_rate) ** (investment_period_duration / units.year) - 1) / (
+ (1+ProcessImpacts.interest_rate) ** (investment_period_duration / units.year) * ProcessImpacts.interest_rate)
+
+ return (
+ # Get costs per non-served demand
+ self._get_price_time_series(invest_year)
+ # multiplied with the annuity present value factor of the current investment period
+ * investment_period_annuity_present_value_factor
+ # discounted to reference year
+ / (1 + ProcessImpacts.interest_rate)**(invest_year - reference_year)
+ )
+
+ def get_impact_non_served_demand(self, impact_categories: list, reference_year: int, invest_years: list):
+ """This methods gets the impact of non-served demand of a product in one or multiple impact categories for a specific year.
+
+ If the impact category is "cost", the corresponding method Product._get_cost_non_served_demand is used.
+ In all other cases the impacts of all ecoinvent processes defined by the process itself as the
+ impacts of non-served demand, are multiplied with their corresponding weight factors and summed up together.
+ """
+
+ impact_non_served_demand_list = []
+ impact_non_served_demand_combined = pd.DataFrame()
+
+ for invest_year in invest_years:
+ # Create empty dictionary for the operational impacts
+ impact_non_served_demand = pd.DataFrame(data=units("0"), index=self._costs_yearly_timeseries.index, columns=["product", "invest year"] + impact_categories)
+ impact_non_served_demand["product"] = self.name
+ impact_non_served_demand["invest year"] = invest_year
+
+ # Loop over all requested impact categories
+ for impact_category in impact_categories:
+ # Check if impact category is "cost", if it is, use special methods from ProcessImpacts
+ if impact_category == "cost":
+ impact_non_served_demand[impact_category] = self._get_cost_non_served_demand(
+ reference_year, invest_year)
+
+ # If impact category is not "cost" calculate the sum of the impacts of all invest processes
+ else:
+ impact_non_served_demand[impact_category] = EcoinventImpacts._recursively_get_impact(self._processes_non_served_demand, impact_category, invest_year, invest_year)
+
+ impact_non_served_demand.reset_index(inplace=True)
+ impact_non_served_demand.set_index(["product", "invest year", "time slice"], inplace=True)
+ impact_non_served_demand_list.append(impact_non_served_demand)
+
+ impact_non_served_demand_combined = impact_non_served_demand_combined.append(other=impact_non_served_demand_list, verify_integrity=True, sort=True)
+
+ return impact_non_served_demand_combined
+
+ @staticmethod
+ def get_combined_impact_non_served_demand():
+ """Returns the combined impact time series for all products and years."""
+
+ combined_impact_time_series = pd.DataFrame()
+ impact_time_series_list = []
+ for product in progress.bar(Product.products):
+ impact_time_series_list.append(product.get_impact_non_served_demand(ImpactCategory.get_list_of_names_of_active_impact_categories(), min(ProcessImpacts.invest_years), ProcessImpacts.invest_years))
+
+ combined_impact_time_series = combined_impact_time_series.append(other=impact_time_series_list, verify_integrity=True, sort=True)
+ index_names = combined_impact_time_series.index.names
+ combined_impact_time_series = combined_impact_time_series.stack()
+ combined_impact_time_series.index.names = index_names + ["impact category"]
+ combined_impact_time_series = combined_impact_time_series.reset_index()
+ combined_impact_time_series.set_index(["impact category"] + index_names, inplace=True)
+ return combined_impact_time_series.sort_index()
+
+ def get_nodal_demand_time_series(self, year:int):
+ """Returns the absolute nodal demand time series."""
+
+ df = self._demand_nodal_timeseries*self._demand_nodal_average[str(year)].values[0]
+ df["product"] = self.name
+ df["invest year"] = year
+ df.reset_index(inplace=True)
+ df.set_index(["product", "invest year", "time slice"], inplace=True)
+ df.columns = [int(column) for column in df.columns]
+
+ return df
+
+ @staticmethod
+ def get_combined_demand_time_series():
+ """Returns the combined demand time series for all products and years."""
+
+ logging.info("Get combined demand time series")
+
+ combined_demand_time_series = pd.DataFrame()
+ demand_time_series_list = []
+ for product in progress.bar(Product.products):
+ for year in ProcessImpacts.invest_years:
+ demand_time_series_list.append(product.get_nodal_demand_time_series(year))
+
+ combined_demand_time_series = combined_demand_time_series.append(other=demand_time_series_list, verify_integrity=True, sort=True)
+ combined_demand_time_series.fillna(0, inplace=True)
+ index_names = combined_demand_time_series.index.names
+ combined_demand_time_series = combined_demand_time_series.stack()
+ combined_demand_time_series.index.names = index_names + ["node"]
+
+ return combined_demand_time_series.sort_index()
+
+ def get_required_total_secured_capacity(self, invest_years: list):
+ """Return the required secured capacity in the whole grid for product in a specific year."""
+
+ required_total_secured_capacity = self._required_total_secured_capacity.reindex(index=[0], columns=[str(year) for year in invest_years])
+ required_total_secured_capacity["product"] = self.name
+ required_total_secured_capacity.set_index(["product"], inplace=True)
+ required_total_secured_capacity.columns = [int(column) for column in required_total_secured_capacity.columns]
+
+ return required_total_secured_capacity
+
+ @staticmethod
+ def get_combined_required_total_secured_capacity():
+ """Returns the combined required total secured capacity for all products and years."""
+
+ logging.info("Get combined required total secured capacity")
+
+ combined_required_total_secured_capacity = pd.DataFrame()
+ required_total_secured_capacity_list = []
+ for product in progress.bar(Product.products):
+ required_total_secured_capacity_list.append(product.get_required_total_secured_capacity(ProcessImpacts.invest_years))
+
+ combined_required_total_secured_capacity = combined_required_total_secured_capacity.append(other=required_total_secured_capacity_list, verify_integrity=True, sort=True)
+ combined_required_total_secured_capacity.fillna(0, inplace=True)
+ index_names = combined_required_total_secured_capacity.index.names
+ combined_required_total_secured_capacity = combined_required_total_secured_capacity.stack()
+ combined_required_total_secured_capacity.index.names = index_names + ["invest year"]
+
+ return combined_required_total_secured_capacity.sort_index()
+
+ def get_required_nodal_secured_capacity(self, nodes: list, invest_years: list):
+ """Return the required secured capacity in the whole grid for product in a specific year."""
+
+ required_average_nodal_secured_capacity = self._required_average_nodal_secured_capacity.reindex(index=[0], columns=[str(year) for year in invest_years])
+ required_relative_nodal_secured_capacity = self._required_relative_nodal_secured_capacity.reindex(index=nodes, columns=["value"])
+
+ required_nodal_secured_capacity = pd.DataFrame(required_relative_nodal_secured_capacity.values * required_average_nodal_secured_capacity.values)
+ required_nodal_secured_capacity.index = required_relative_nodal_secured_capacity.index
+ required_nodal_secured_capacity.columns = invest_years
+ required_nodal_secured_capacity["product"] = self.name
+ required_nodal_secured_capacity.reset_index(inplace=True)
+ required_nodal_secured_capacity.set_index(["product", "node"], inplace=True)
+ required_nodal_secured_capacity.columns = [int(column) for column in required_nodal_secured_capacity.columns]
+
+ return required_nodal_secured_capacity
+
+ @staticmethod
+ def get_combined_required_nodal_secured_capacity():
+ """Returns the combined required nodal secured capacity for all products and years."""
+
+ logging.info("Get combined required nodal secured capacity")
+
+ combined_required_nodal_secured_capacity = pd.DataFrame()
+ required_nodal_secured_capacity_list = []
+ for product in progress.bar(Product.products):
+ required_nodal_secured_capacity_list.append(product.get_required_nodal_secured_capacity(Grid.selected().get_list_of_node_ids(), ProcessImpacts.invest_years))
+
+ combined_required_nodal_secured_capacity = combined_required_nodal_secured_capacity.append(other=required_nodal_secured_capacity_list, verify_integrity=True, sort=True)
+ combined_required_nodal_secured_capacity.fillna(0, inplace=True)
+ index_names = combined_required_nodal_secured_capacity.index.names
+ combined_required_nodal_secured_capacity = combined_required_nodal_secured_capacity.stack()
+ combined_required_nodal_secured_capacity.index.names = index_names + ["invest year"]
+
+ return combined_required_nodal_secured_capacity.sort_index()
+
+ @classmethod
+ def get_target_units(cls):
+ """ returns dict with base units of products """
+ base_unit_dict = {}
+ for product in progress.bar(cls.products):
+ if hasattr(product,'_target_unit'):
+ base_unit_dict[product.name] = product._target_unit
+ else:
+ logging.warning("Could not find base unit for {}. Base unit is set to no_target_unit".format(product.name))
+ base_unit_dict[product.name] = "no_target_unit"
+ return base_unit_dict
+
+class ImpactCategory(object):
+ """This class is used to manage impact categories, their corresponding limits and costs of overshoot."""
+
+ impact_categories = []
+ FRAMEWORK = "ReCiPe Midpoint (H)"
+ MANUAL_IMPACT_CATEGORY_SELECTION = []
+
+ def __init__(self, impact_category_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of ImpactCategory.")
+
+ if impact_category_path == None:
+ # Do things if None has been given as path.
+ raise NotImplementedError
+ else:
+ # Check if product directory exists
+ if impact_category_path.is_dir():
+ # Get name by directory name
+ self.name = str(impact_category_path.name).replace("_", " ")
+ # Get properties
+ self._properties = pd.read_csv(impact_category_path / "properties.csv", float_precision="high", usecols=["property", "value"], index_col=["property"])
+ # Get name in ecoinvent
+ self.ecoinvent_name = self._properties.at["ecoinvent name", "value"]
+ # Load the objective factor of the impact category
+ self._objective_factor_impact = pd.read_csv(
+ impact_category_path / "objective_factor_impact.csv", float_precision="high", index_col="phase", usecols=(lambda column: column != "comments"))
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._objective_factor_impact.fillna(0, inplace=True)
+ # Convert dataframe to correct unit
+ ### IMPORTANT: CHANGE AGAIN ###
+ # self._objective_factor_impact = secmod.helpers.unitize_dataframe(self._objective_factor_impact)
+ self._objective_factor_impact = convert_quantity_to_correct_unit(self._objective_factor_impact)
+ # Load the objective factor of limit overshoots of the impact category
+ self._objective_factor_impact_overshoot = pd.read_csv(
+ impact_category_path / "objective_factor_impact_overshot.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._objective_factor_impact_overshoot.fillna(0, inplace=True)
+ # Convert dataframe to correct unit
+ ### IMPORTANT: CHANGE AGAIN ###
+ # self._objective_factor_impact_overshoot = secmod.helpers.unitize_dataframe(self._objective_factor_impact_overshoot)
+ self._objective_factor_impact_overshoot = convert_quantity_to_correct_unit(self._objective_factor_impact_overshoot)
+
+ # Initialize dictionaries for results to be loaded in
+ result_total_nodal_impact = {}
+ result_total_impact = {}
+ result_operational_nodal_impact = {}
+ result_operational_impact = {}
+ result_invest_nodal_impact = {}
+ result_invest_impact = {}
+
+ # Check if subdirectory for grid-dependent data exists
+ if (impact_category_path / Grid.selected().name).is_dir():
+ # Load the objective factor of the impact category
+ self._operational_nodal_impact_limits = pd.read_csv(
+ impact_category_path / Grid.selected().name / "operational_nodal_impact_limits.csv", float_precision="high", usecols=(lambda column: column != "comments"), index_col="node")
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._operational_nodal_impact_limits.fillna(float("inf"), inplace=True)
+ # Convert dataframe to correct unit
+ self._operational_nodal_impact_limits = convert_quantity_to_correct_unit(self._operational_nodal_impact_limits)
+
+ # Load the objective factor of the impact category
+ self._operational_impact_limits = pd.read_csv(
+ impact_category_path / Grid.selected().name / "operational_impact_limits.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._operational_impact_limits.fillna(float("inf"), inplace=True)
+ # Convert dataframe to correct unit
+ self._operational_impact_limits = convert_quantity_to_correct_unit(self._operational_impact_limits)
+
+ # Load the objective factor of the impact category
+ self._invest_nodal_impact_limits = pd.read_csv(
+ impact_category_path / Grid.selected().name / "invest_nodal_impact_limits.csv", float_precision="high", usecols=(lambda column: column != "comments"), index_col="node")
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._invest_nodal_impact_limits.fillna(float("inf"), inplace=True)
+ # Convert dataframe to correct unit
+ self._invest_nodal_impact_limits = convert_quantity_to_correct_unit(self._invest_nodal_impact_limits)
+
+ # Load the objective factor of the impact category
+ self._invest_impact_limits = pd.read_csv(
+ impact_category_path / Grid.selected().name / "invest_impact_limits.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._invest_impact_limits.fillna(float("inf"), inplace=True)
+ # Convert dataframe to correct unit
+ self._invest_impact_limits = convert_quantity_to_correct_unit(self._invest_impact_limits)
+
+ # Load the objective factor of the impact category
+ self._total_nodal_impact_limits = pd.read_csv(
+ impact_category_path / Grid.selected().name / "total_nodal_impact_limits.csv", float_precision="high", usecols=(lambda column: column != "comments"), index_col="node")
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._total_nodal_impact_limits.fillna(float("inf"), inplace=True)
+ # Convert dataframe to correct unit
+ self._total_nodal_impact_limits = convert_quantity_to_correct_unit(self._total_nodal_impact_limits)
+
+ # Load the objective factor of the impact category
+ self._total_impact_limits = pd.read_csv(
+ impact_category_path / Grid.selected().name / "total_impact_limits.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ self._target_unit = get_target_unit_from_unit(self._total_impact_limits['unit'].values[0])
+ # Fill empty value with 0 as there should be no impact on the objective then
+ self._total_impact_limits.fillna(float("inf"), inplace=True)
+ # Convert dataframe to correct unit
+ self._total_impact_limits = convert_quantity_to_correct_unit(self._total_impact_limits)
+
+ else:
+ logging.warning("No grid dependent data found for impact category '{0}' in grid '{1}'! .".format(
+ self._name, Grid.selected().name))
+ # Create empty DataFrames, if no grid-dependent data is provided
+ self._operational_nodal_impact_limits = pd.DataFrame(index=Grid.selected().nodes.index.tolist(), data=float("inf"), columns=ProcessImpacts.invest_years)
+ self._operational_impact_limits = pd.DataFrame(index=[0], data=float("inf"), columns=ProcessImpacts.invest_years)
+ self._invest_nodal_impact_limits = pd.DataFrame(index=Grid.selected().nodes.index.tolist(), data=float("inf"), columns=ProcessImpacts.invest_years)
+ self._invest_impact_limits = pd.DataFrame(index=[0], data=float("inf"), columns=ProcessImpacts.invest_years)
+ self._total_nodal_impact_limits = pd.DataFrame(index=Grid.selected().nodes.index.tolist(), data=float("inf"), columns=ProcessImpacts.invest_years)
+ self._total_impact_limits = pd.DataFrame(index=[0], data=float("inf"), columns=ProcessImpacts.invest_years)
+
+ else:
+ raise NotADirectoryError(
+ "Path {0} is not a directory. Please provide a path to the directory of the product.".format(str(impact_category_path)))
+ # Append product instance to static list of products
+ ImpactCategory.impact_categories.append(self)
+
+ @property
+ def name(self):
+ """This property contains a unique name for the impact categoy.
+
+ Getter:
+ Gets the value of this property.
+
+ Returns:
+ Name of the impact categoy as string.
+
+ Setter:
+ Sets the property to the provided name, if it isn't already used by another impact categoy.
+
+ Raises:
+ ValueError: If the name is already used by another impact categoy.
+
+ Type:
+ str
+ """
+
+ return self._name
+
+ @name.setter
+ def name(self, name):
+ """See property docstring above for setter information."""
+
+ # If the provided name is equal to the current name, do nothing
+ if hasattr(self, "_name"):
+ if self.name == name:
+ return
+ # Check if any other defined grid has the provided name
+ for impact_category in ImpactCategory.impact_categories:
+ if impact_category.name == name:
+ raise ValueError(
+ "There is already an impact category with the name '{0}'. Please choose another name.".format(name))
+ self._name = name
+
+ @property
+ def ecoinvent_name(self):
+ """This property contains a unique name for the impact category, which is used by ecoinvent.
+
+ Getter:
+ Gets the value of this property.
+
+ Returns:
+ ecoinvent name of the impact category as string.
+
+ Setter:
+ Sets the property to the provided ecoinvent name, if it isn't already used by another impact category.
+
+ Raises:
+ ValueError: If the ecoinvent name is already used by another impact category.
+
+ Type:
+ str
+ """
+
+ return self._ecoinvent_name
+
+ @ecoinvent_name.setter
+ def ecoinvent_name(self, ecoinvent_name):
+ """See property docstring above for setter information."""
+
+ # If the provided ecoinvent_name is equal to the current ecoinvent_name, do nothing
+ if hasattr(self, "_ecoinvent_name"):
+ if self.ecoinvent_name == ecoinvent_name:
+ return
+ # Check if any other defined grid has the provided ecoinvent_name
+ for impact_category in ImpactCategory.impact_categories:
+ if impact_category.ecoinvent_name == ecoinvent_name:
+ raise ValueError(
+ "There is already an impact category with the ecoinvent_name '{0}'. Please choose another ecoinvent_name.".format(ecoinvent_name))
+ self._ecoinvent_name = ecoinvent_name
+
+ @classmethod
+ def load_impact_categories_from_directory(cls, impact_categories_path: Path):
+ """Loads all impact categories of a specific class into their class."""
+
+ logging.info("Load impact categories of class '{0}".format(str(cls)))
+ if ImpactCategory.MANUAL_IMPACT_CATEGORY_SELECTION: # if Manual Impact Categories selected, only import these Impact Categories
+ impact_category_paths = [secmod.helpers.clear_filename_special_character(impact_category) for impact_category in ImpactCategory.MANUAL_IMPACT_CATEGORY_SELECTION]
+ else: # else get a list of all files and folders in the directory
+ impact_category_paths_total = os.listdir(impact_categories_path)
+ impact_category_paths = []
+ if ImpactCategory.FRAMEWORK: # if Framework selected
+ impact_category_paths = [path for path in impact_category_paths_total if secmod.helpers.clear_filename_special_character(ImpactCategory.FRAMEWORK) in path or path == 'cost']
+ else: # if neither Framework nor Manual Impact Categories selected
+ impact_category_paths = impact_category_paths_total
+
+
+ # Iterate through the members of the impact_category directory
+ for impact_category in progress.bar(impact_category_paths):
+ # Check if the file ending is "csv"
+ if (impact_categories_path / impact_category).is_dir():
+ # If the file is csv, read it in as a dataframe
+ cls(impact_categories_path / impact_category)
+
+ @staticmethod
+ def get_list_of_active_impact_categories():
+ """Returns a list of the all active impact categories in the currently selected framework."""
+
+ # Create empty list
+ impact_categories = []
+
+ if ImpactCategory.MANUAL_IMPACT_CATEGORY_SELECTION:
+ for impact_category in ImpactCategory.impact_categories:
+ # If it includes the name of the framework append it to the list
+ if (impact_category._ecoinvent_name in ImpactCategory.MANUAL_IMPACT_CATEGORY_SELECTION) or (impact_category._ecoinvent_name == "cost"):
+ impact_categories.append(impact_category)
+ else:
+ # Check for all instances of ImpactCategory whether its ecoinvent name includes the name of the framework
+ for impact_category in ImpactCategory.impact_categories:
+ # If it includes the name of the framework append it to the list
+ if (ImpactCategory.FRAMEWORK in impact_category._ecoinvent_name) or (impact_category._ecoinvent_name == "cost"):
+ impact_categories.append(impact_category)
+
+ # Return the list of active impact categories
+ return impact_categories
+
+ @staticmethod
+ def get_list_of_names_of_active_impact_categories():
+ """Returns a list of the ecoinvent names of all impact categories active in the currently selected framework."""
+
+ # Create list of names
+ ecoinvent_names = [impact_category._ecoinvent_name for impact_category in ImpactCategory.get_list_of_active_impact_categories()]
+
+ return ecoinvent_names
+
+ def _get_operational_nodal_impact_limits(self, nodes: list, invest_years: list):
+ """Return the operational nodal impact limit for an impact category in a specific year."""
+
+ operational_nodal_impact_limits = self._operational_nodal_impact_limits.reindex(index=nodes, columns=[str(year) for year in invest_years])
+
+ operational_nodal_impact_limits.columns = invest_years
+ operational_nodal_impact_limits["impact category"] = self._ecoinvent_name
+ operational_nodal_impact_limits.reset_index(inplace=True)
+ operational_nodal_impact_limits.set_index(["node", "impact category"], inplace=True)
+ operational_nodal_impact_limits.columns = [int(column) for column in operational_nodal_impact_limits.columns]
+
+ return operational_nodal_impact_limits
+
+ @staticmethod
+ def get_combined_operational_nodal_impact_limits():
+ """Returns the combined operational nodal impact limit for all impact categories and years."""
+
+ logging.info("Get combined operational nodal impact limit")
+
+ combined_operational_nodal_impact_limits = pd.DataFrame()
+ operational_nodal_impact_limits_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ operational_nodal_impact_limits_list.append(impact_category._get_operational_nodal_impact_limits(Grid.selected().get_list_of_node_ids(), ProcessImpacts.invest_years))
+
+ combined_operational_nodal_impact_limits = combined_operational_nodal_impact_limits.append(other=operational_nodal_impact_limits_list, verify_integrity=True, sort=True)
+ combined_operational_nodal_impact_limits.fillna(float("inf"), inplace=True)
+ index_names = combined_operational_nodal_impact_limits.index.names
+ combined_operational_nodal_impact_limits = combined_operational_nodal_impact_limits.stack()
+ combined_operational_nodal_impact_limits.index.names = index_names + ["invest year"]
+
+ return combined_operational_nodal_impact_limits.sort_index()
+
+ def _get_invest_nodal_impact_limits(self, nodes: list, invest_years: list):
+ """Return the invest nodal impact limit for an impact category in a specific year."""
+
+ invest_nodal_impact_limits = self._invest_nodal_impact_limits.reindex(index=nodes, columns=[str(year) for year in invest_years])
+
+ invest_nodal_impact_limits.columns = invest_years
+ invest_nodal_impact_limits["impact category"] = self._ecoinvent_name
+ invest_nodal_impact_limits.reset_index(inplace=True)
+ invest_nodal_impact_limits.set_index(["node", "impact category"], inplace=True)
+ invest_nodal_impact_limits.columns = [int(column) for column in invest_nodal_impact_limits.columns]
+
+ return invest_nodal_impact_limits
+
+ @staticmethod
+ def get_combined_invest_nodal_impact_limits():
+ """Returns the combined invest nodal impact limit for all impact categories and years."""
+
+ logging.info("Get combined invest nodal impact limit")
+
+ combined_invest_nodal_impact_limits = pd.DataFrame()
+ invest_nodal_impact_limits_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ invest_nodal_impact_limits_list.append(impact_category._get_invest_nodal_impact_limits(Grid.selected().get_list_of_node_ids(), ProcessImpacts.invest_years))
+
+ combined_invest_nodal_impact_limits = combined_invest_nodal_impact_limits.append(other=invest_nodal_impact_limits_list, verify_integrity=True, sort=True)
+ combined_invest_nodal_impact_limits.fillna(float("inf"), inplace=True)
+ index_names = combined_invest_nodal_impact_limits.index.names
+ combined_invest_nodal_impact_limits = combined_invest_nodal_impact_limits.stack()
+ combined_invest_nodal_impact_limits.index.names = index_names + ["invest year"]
+
+ return combined_invest_nodal_impact_limits.sort_index()
+
+ def _get_total_nodal_impact_limits(self, nodes: list, invest_years: list):
+ """Return the total nodal impact limit for an impact category in a specific year."""
+
+ total_nodal_impact_limits = self._total_nodal_impact_limits.reindex(index=nodes, columns=[str(year) for year in invest_years])
+
+ total_nodal_impact_limits.columns = invest_years
+ total_nodal_impact_limits["impact category"] = self._ecoinvent_name
+ total_nodal_impact_limits.reset_index(inplace=True)
+ total_nodal_impact_limits.set_index(["node", "impact category"], inplace=True)
+ total_nodal_impact_limits.columns = [int(column) for column in total_nodal_impact_limits.columns]
+
+ return total_nodal_impact_limits
+
+ @staticmethod
+ def get_combined_total_nodal_impact_limits():
+ """Returns the combined total nodal impact limit for all impact categories and years."""
+
+ logging.info("Get combined total nodal impact limit")
+
+ combined_total_nodal_impact_limits = pd.DataFrame()
+ total_nodal_impact_limits_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ total_nodal_impact_limits_list.append(impact_category._get_total_nodal_impact_limits(Grid.selected().get_list_of_node_ids(), ProcessImpacts.invest_years))
+
+ combined_total_nodal_impact_limits = combined_total_nodal_impact_limits.append(other=total_nodal_impact_limits_list, verify_integrity=True, sort=True)
+ combined_total_nodal_impact_limits.fillna(float("inf"), inplace=True)
+ index_names = combined_total_nodal_impact_limits.index.names
+ combined_total_nodal_impact_limits = combined_total_nodal_impact_limits.stack()
+ combined_total_nodal_impact_limits.index.names = index_names + ["invest year"]
+
+ return combined_total_nodal_impact_limits.sort_index()
+
+ def _get_operational_impact_limits(self, invest_years: list):
+ """Return the operational impact limit for an impact category in a specific year."""
+
+ operational_impact_limits = self._operational_impact_limits.reindex(index=[0], columns=[str(year) for year in invest_years])
+
+ operational_impact_limits.columns = invest_years
+ operational_impact_limits["impact category"] = self._ecoinvent_name
+ operational_impact_limits.set_index(["impact category"], inplace=True)
+ operational_impact_limits.columns = [int(column) for column in operational_impact_limits.columns]
+
+ return operational_impact_limits
+
+ @staticmethod
+ def get_combined_operational_impact_limits():
+ """Returns the combined operational impact limit for all impact categories and years."""
+
+ logging.info("Get combined operational impact limit")
+
+ combined_operational_impact_limits = pd.DataFrame()
+ operational_impact_limits_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ operational_impact_limits_list.append(impact_category._get_operational_impact_limits(ProcessImpacts.invest_years))
+
+ combined_operational_impact_limits = combined_operational_impact_limits.append(other=operational_impact_limits_list, verify_integrity=True, sort=True)
+ combined_operational_impact_limits.fillna(float("inf"), inplace=True)
+ index_names = combined_operational_impact_limits.index.names
+ combined_operational_impact_limits = combined_operational_impact_limits.stack()
+ combined_operational_impact_limits.index.names = index_names + ["invest year"]
+
+ return combined_operational_impact_limits.sort_index()
+
+ def _get_invest_impact_limits(self, invest_years: list):
+ """Return the invest impact limit for an impact category in a specific year."""
+
+ invest_impact_limits = self._invest_impact_limits.reindex(index=[0], columns=[str(year) for year in invest_years])
+
+ invest_impact_limits.columns = invest_years
+ invest_impact_limits["impact category"] = self._ecoinvent_name
+ invest_impact_limits.set_index(["impact category"], inplace=True)
+ invest_impact_limits.columns = [int(column) for column in invest_impact_limits.columns]
+
+ return invest_impact_limits
+
+ @staticmethod
+ def get_combined_invest_impact_limits():
+ """Returns the combined invest impact limit for all impact categories and years."""
+
+ logging.info("Get combined invest impact limit")
+
+ combined_invest_impact_limits = pd.DataFrame()
+ invest_impact_limits_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ invest_impact_limits_list.append(impact_category._get_invest_impact_limits(ProcessImpacts.invest_years))
+
+ combined_invest_impact_limits = combined_invest_impact_limits.append(other=invest_impact_limits_list, verify_integrity=True, sort=True)
+ combined_invest_impact_limits.fillna(float("inf"), inplace=True)
+ index_names = combined_invest_impact_limits.index.names
+ combined_invest_impact_limits = combined_invest_impact_limits.stack()
+ combined_invest_impact_limits.index.names = index_names + ["invest year"]
+
+ return combined_invest_impact_limits.sort_index()
+
+ def _get_total_impact_limits(self, invest_years: list):
+ """Return the total impact limit for an impact category in a specific year."""
+
+ total_impact_limits = self._total_impact_limits.reindex(index=[0], columns=[str(year) for year in invest_years])
+
+ total_impact_limits.columns = invest_years
+ total_impact_limits["impact category"] = self._ecoinvent_name
+ total_impact_limits.set_index(["impact category"], inplace=True)
+ total_impact_limits.columns = [int(column) for column in total_impact_limits.columns]
+
+ return total_impact_limits
+
+ @staticmethod
+ def get_combined_total_impact_limits():
+ """Returns the combined total impact limit for all impact categories and years."""
+
+ logging.info("Get combined total impact limit")
+
+ combined_total_impact_limits = pd.DataFrame()
+ total_impact_limits_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ total_impact_limits_list.append(impact_category._get_total_impact_limits(ProcessImpacts.invest_years))
+
+ combined_total_impact_limits = combined_total_impact_limits.append(other=total_impact_limits_list, verify_integrity=True, sort=True)
+ combined_total_impact_limits.fillna(float("inf"), inplace=True)
+ index_names = combined_total_impact_limits.index.names
+ combined_total_impact_limits = combined_total_impact_limits.stack()
+ combined_total_impact_limits.index.names = index_names + ["invest year"]
+
+ return combined_total_impact_limits.sort_index()
+
+ def _get_objective_factor_impact(self, invest_years: list):
+ """Return the total impact limit for an impact category in a specific year."""
+
+ objective_factor_impact = self._objective_factor_impact.reindex(index=["invest", "operation"], columns=[str(year) for year in invest_years])
+
+ objective_factor_impact.columns = invest_years
+ objective_factor_impact["impact category"] = self._ecoinvent_name
+ objective_factor_impact.reset_index(inplace=True)
+ objective_factor_impact.set_index(["phase", "impact category"], inplace=True)
+ objective_factor_impact.columns = [int(column) for column in objective_factor_impact.columns]
+
+ return objective_factor_impact
+
+ @staticmethod
+ def get_combined_objective_factor_impact():
+ """Returns the combined total impact limit for all impact categories and years."""
+
+ logging.info("Get combined objective factors for impacts")
+
+ combined_objective_factor_impact = pd.DataFrame()
+ objective_factor_impact_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ objective_factor_impact_list.append(impact_category._get_objective_factor_impact(ProcessImpacts.invest_years))
+
+ combined_objective_factor_impact = combined_objective_factor_impact.append(other=objective_factor_impact_list, verify_integrity=True, sort=True)
+ combined_objective_factor_impact.fillna(float("inf"), inplace=True)
+ index_names = combined_objective_factor_impact.index.names
+ combined_objective_factor_impact = combined_objective_factor_impact.stack()
+ combined_objective_factor_impact.index.names = index_names + ["invest year"]
+
+ return combined_objective_factor_impact.sort_index()
+
+ def _get_objective_factor_impact_overshoot(self, invest_years: list):
+ """Return the objective factors for impact overshoots for an impact category in a specific year."""
+
+ objective_factor_impact_overshoot = self._objective_factor_impact_overshoot.reindex(index=[0], columns=[str(year) for year in invest_years])
+
+ objective_factor_impact_overshoot.columns = invest_years
+ objective_factor_impact_overshoot["impact category"] = self._ecoinvent_name
+ objective_factor_impact_overshoot.set_index(["impact category"], inplace=True)
+ objective_factor_impact_overshoot.columns = [int(column) for column in objective_factor_impact_overshoot.columns]
+
+ return objective_factor_impact_overshoot
+
+ @staticmethod
+ def get_combined_objective_factor_impact_overshoot():
+ """Returns the combined objective factors for impact overshoots for all impact categories and years."""
+
+ logging.info("Get combined objective factors for impact overshoots")
+
+ combined_objective_factor_impact_overshoot = pd.DataFrame()
+ objective_factor_impact_overshoot_list = []
+ for impact_category in progress.bar(ImpactCategory.get_list_of_active_impact_categories()):
+ objective_factor_impact_overshoot_list.append(impact_category._get_objective_factor_impact_overshoot(ProcessImpacts.invest_years))
+
+ combined_objective_factor_impact_overshoot = combined_objective_factor_impact_overshoot.append(other=objective_factor_impact_overshoot_list, verify_integrity=True, sort=True)
+ combined_objective_factor_impact_overshoot.fillna(float("inf"), inplace=True)
+ index_names = combined_objective_factor_impact_overshoot.index.names
+ combined_objective_factor_impact_overshoot = combined_objective_factor_impact_overshoot.stack()
+ combined_objective_factor_impact_overshoot.index.names = index_names + ["invest year"]
+
+ return combined_objective_factor_impact_overshoot.sort_index()
+
+ @classmethod
+ def get_target_units(cls):
+ """ returns dict with base units of impacts """
+ base_unit_dict = {}
+ for impact_category in progress.bar(cls.impact_categories):
+ if hasattr(impact_category,'_target_unit'):
+ base_unit_dict[impact_category.ecoinvent_name] = impact_category._target_unit
+ else:
+ logging.warning("Could not find base unit for {}. Base unit is set to no_target_unit".format(impact_category.ecoinvent_name))
+ base_unit_dict[impact_category.ecoinvent_name] = "no_target_unit"
+ return base_unit_dict
+
+class ProcessImpacts(abc.ABC):
+ """This class is used as an interface for process impacts.
+
+ In this class two abstract methods are defined which need to be implemented
+ by subclasses. Therefore this class acts as an interface which is implemented by
+ the inheriting classes. This way it is assured that the methods used to get the
+ impacts of investment and operation can be called in all inheriting classes.
+
+ Furthermore it includes static variables for the economic time period and assumed
+ interest rate. And since costs are special impacts, which all processes must have,
+ the instance variables for costs are defined in this class. Furthermore the
+ initialization of this class imports the costs from a processes directory, if provided.
+ Methods to get costs of investment and operation are implemented as well.
+
+ Args:
+ process_path (Path): A path of the directory of an existing process definition, which
+ includes a CSV-file with data about the costs of the process.
+
+ Attributes:
+ interest_rate (float): The interest rate used for economic impacts as share of 1.
+
+ economic_period (pint quantity): The time period used for the calculation of all
+ annualized impacts.
+
+ Raises:
+ FileNotFoundError: If no file "costs.csv" can be found in the process directory.
+ """
+
+ IMPACT_SOURCES = {None: ["operation", "invest"]}
+
+ # Set static variables used for all impacts
+ # These values are not used, but instead replaced by values from the config.py
+ # interest rate in share of 1
+ interest_rate = 0.05
+ # time period used to calculate the annualized impacts
+ economic_period = 30 * units.year
+ # invest years
+ invest_years = [2016, 2020, 2025, 2030, 2035, 2040, 2045, 2050]
+ # construction year (will be determined automatically, if not set)
+ construction_years = []
+ # list of all processes
+ processes = []
+
+ def __init__(self, process_path: Path = None):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of ProcessImpact.")
+
+ # Check if a process path is provided and create an empty costs table, if not
+ if process_path == None:
+ self.name = ""
+ self._costs = pd.DataFrame(data=np.nan, columns=["unit"] + ProcessImpacts.invest_years + [
+ "comments"], index=["invest", "operation", "maintenance_absolute", "maintenace_relative_invest"])
+ self._costs.index.name = "phase"
+ self._lifetime_duration = pd.DataFrame(index=[1], data=np.nan, columns=[
+ "unit"] + ProcessImpacts.invest_years + ["comments"])
+ else:
+ if process_path.is_dir():
+ self.name = str(process_path.name).replace("_", " ")
+ self._costs = pd.read_csv(
+ process_path / "costs.csv", index_col="phase", float_precision="high", usecols=(lambda column: column != "comments"))
+ self._lifetime_duration = pd.read_csv(
+ process_path / "lifetime_duration.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ else:
+ raise NotADirectoryError(
+ "Path {0} is not a directory. Please provide a path to the directory of the process.".format(str(process_path)))
+
+ @property
+ def name(self):
+ """This property contains a unique name for the grid.
+
+ Getter:
+ Gets the value of this property.
+
+ Returns:
+ Name of the grid as string.
+
+ Setter:
+ Sets the property to the provided name, if it isn't already used by another grid.
+
+ Raises:
+ ValueError: If the name is already used by another grid.
+
+ Type:
+ str
+ """
+
+ return self._name
+
+ @name.setter
+ def name(self, name):
+ """See property docstring above for setter information."""
+
+ # If the provided name is equal to the current name, do nothing
+ if hasattr(self, "_name"):
+ if self.name == name:
+ return
+ # Check if any other defined grid has the provided name
+ for process in Process.processes:
+ if process.name == name:
+ raise ValueError(
+ "There is already a process with the name '{0}'. Please choose another name.".format(name))
+ self._name = name
+
+ @abc.abstractmethod
+ def get_impact_invest(self, impact_categories: list, construction_year: int, invest_year: int):
+ """This method gets the annual investment impacts of a process
+
+ Args:
+ impacts_categories (list): List of strings, which define for which impact categories
+ the yearly investment impacts shall be returned
+
+ construction_year (int): Year of construction of an instance of this process
+
+ invest_year (int): Year for which the impact is calculated
+ """
+
+ raise NotImplementedError
+
+ @abc.abstractmethod
+ def get_impact_operation(self, impact_categories: list, construction_year: int, reference_year: int, invest_year: int):
+ """This method gets the operational impacts of a process
+
+ Args:
+ impacts_categories (list): List of strings, which define for which impact categories
+ the yearly investment impacts shall be returned
+
+ construction_year (int): Year of construction of an instance of this process
+
+ invest_year (int): Year for which the impact is calculated
+ """
+
+ raise NotImplementedError
+
+ @staticmethod
+ def _get_investment_period_duration(invest_year: int):
+ """Gets the duration of the period between this and the next invest year.
+
+ If it is the last year in the list of invest years, a period duration of
+ one year is returned.
+
+ Args:
+ invest_year (int): A year, e.g. 2020.
+ """
+
+ # Get the index of the selected invest year in the list of invest years
+ index = config.invest_years.index(invest_year)
+ # Get the number of invest years
+ number_of_invest_years = len(config.invest_years)
+ # Check if invest year is not the last invest year
+ if index < (number_of_invest_years - 1):
+ # if not the last year, calculate the duration to the next invest year
+ return (config.invest_years[index + 1] - invest_year) * units.year
+ # Check if there are more than one invest year
+ elif number_of_invest_years > 1:
+ # if it is the last year, but not the only, take the previous duration
+ return (invest_year - config.invest_years[index - 1]) * units.year
+ else:
+ # if it is the only year, calculate only for that year
+ return 1 * units.year
+
+ def _get_cost_invest(self, construction_year: int, reference_year: int, invest_year: int):
+ """This method gets the annual investment costs of a process
+
+ Calculates the present value of the selected impact year and its investment period
+ in the reference year for a process instance build in the construction year.
+
+ Args:
+ construction_year (int): Year of construction, e.g. 2016. Earliest year is used,
+ if construction year is earlier than the earliest year in the data.
+
+ reference_year (int): Reference year of the optimized time horizon, e.g. 2020
+
+ invest_year (int): An investment year of the optimized time horizon, e.g. 2025
+ """
+
+ # Determine whether lifetime duration or economic period should be used, based on which is smaller
+ full_annuity_period = min(ProcessImpacts.economic_period,
+ units(str(self._lifetime_duration.at[0, str(max(construction_year, int(min(self._lifetime_duration.columns))))]) + str(self._lifetime_duration.at[0, "unit"])))
+ # Calculate the annuity present value factor for the full annuity period
+ full_annuity_present_value_factor = ((1+ProcessImpacts.interest_rate) ** (full_annuity_period / units.year) - 1) / (
+ (1+ProcessImpacts.interest_rate) ** (full_annuity_period / units.year) * ProcessImpacts.interest_rate)
+ # Determine the length of the current investment period
+ investment_period_duration = ProcessImpacts._get_investment_period_duration(
+ invest_year)
+ # Calculate annuity present value factor for the current investment period
+ investment_period_annuity_present_value_factor = ((1+ProcessImpacts.interest_rate) ** (investment_period_duration / units.year) - 1) / (
+ (1+ProcessImpacts.interest_rate) ** (investment_period_duration / units.year) * ProcessImpacts.interest_rate)
+
+ # Check if invest year is within the full annuity period after the construction
+ # and therefore has investment and maintenance costs
+ if (invest_year >= construction_year) and ((invest_year - construction_year) * units.year < full_annuity_period):
+ return (
+ (
+ # Get total invest costs
+ convert_quantity_to_correct_unit(
+ self._costs.loc["invest", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+ # divide it by the full annuity present value factor to get the invest cost annuity
+ / full_annuity_present_value_factor
+
+ # Add maintenance cost in relative share of invest costs
+ + convert_quantity_to_correct_unit(
+ self._costs.loc["maintenance_relative_invest", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+ # Multiplied with the total invest costs
+ * convert_quantity_to_correct_unit(
+ self._costs.loc["invest", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+
+ # Add maintenance costs in absolute value
+ + convert_quantity_to_correct_unit(
+ self._costs.loc["maintenance_absolute", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+
+ # all together multiplied with the annuity present value factor of the current investment period
+ ) * investment_period_annuity_present_value_factor
+
+ # Discount from invest year to reference year
+ / (1 + ProcessImpacts.interest_rate)**(invest_year - reference_year)
+ )
+
+ # Else check if invest year is still within the lifetime duration of the process instance
+ # and therefore still has maintenance costs
+ elif (invest_year >= construction_year) and (invest_year - construction_year) * units.year < units(str(self._lifetime_duration.at[0, str(max(construction_year, int(min(self._lifetime_duration.columns))))]) + str(self._lifetime_duration.at[0, "unit"])):
+ return (
+ ( # Get maintenance cost in relative share of invest costs
+ convert_quantity_to_correct_unit(
+ self._costs.loc["maintenance_relative_invest", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+ # Multiplied with the total invest costs
+ * convert_quantity_to_correct_unit(
+ self._costs.loc["invest", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+
+ # Add maintenance costs in absolute value
+ + convert_quantity_to_correct_unit(
+ self._costs.loc["maintenance_absolute", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+
+ # all together multiplied with the annuity present value factor of the current investment period
+ ) * investment_period_annuity_present_value_factor
+
+ # Discount from invest year to reference year
+ / (1 + ProcessImpacts.interest_rate)**(invest_year - reference_year)
+ )
+
+ else:
+ # If invest year is outside the lifetime duration of the process instance return zero costs
+ return 0
+
+ def _get_cost_operation(self, construction_year: int, reference_year: int, invest_year: int):
+ """This method gets the annual investment costs of a process
+
+ Calculates the present value of the selected impact year and its investment period
+ in the reference year for a process instance build in the construction year.
+
+ Args:
+ construction_year (int): Year of construction, e.g. 2016. Earliest year is used,
+ if construction year is earlier than the earliest year in the data.
+
+ reference_year (int): Reference year of the optimized time horizon, e.g. 2020
+
+ invest_year (int): An investment year of the optimized time horizon, e.g. 2025
+ """
+
+ # Determine the length of the current investment period
+ investment_period_duration = ProcessImpacts._get_investment_period_duration(
+ invest_year)
+ # Calculate annuity present value factor for the current investment period
+ investment_period_annuity_present_value_factor = ((1+ProcessImpacts.interest_rate) ** (investment_period_duration / units.year) - 1) / (
+ (1+ProcessImpacts.interest_rate) ** (investment_period_duration / units.year) * ProcessImpacts.interest_rate)
+
+ return (
+ # Get operational costs per used capacity
+ convert_quantity_to_correct_unit(
+ self._costs.loc["operation", [str(max(construction_year, int(min(self._costs.columns)))),'unit']])
+
+ # multiplied with the annuity present value factor of the current investment period
+ * investment_period_annuity_present_value_factor
+ # discounted to reference year
+ / (1 + ProcessImpacts.interest_rate)**(invest_year - reference_year)
+ )
+
+ @classmethod
+ def get_combined_impact_matrix(cls):
+ """Method that is used to return the combined impact matrix of all process of a process category.
+
+ """
+
+ reference_year = min(cls.invest_years)
+
+ if len(cls.construction_years) == 0:
+ cls.setup_construction_years()
+
+ combined_impact_matrix = pd.DataFrame()
+ combined_impact_matrix_list = []
+ for process in progress.bar(cls.processes):
+ EcoinventImpacts._get_single_combined_impact_matrix(combined_impact_matrix_list, process, ImpactCategory.get_list_of_names_of_active_impact_categories(), cls.construction_years, reference_year, cls.invest_years)
+
+ if len(combined_impact_matrix_list) != 0:
+ combined_impact_matrix = combined_impact_matrix.append(other=combined_impact_matrix_list, verify_integrity=True, sort=True)
+
+ index_names = combined_impact_matrix.index.names
+ combined_impact_matrix = combined_impact_matrix.stack()
+ combined_impact_matrix.index.names = index_names + ["impact category"]
+ combined_impact_matrix = combined_impact_matrix.reset_index()
+ combined_impact_matrix.set_index(["impact category"] + index_names, inplace=True)
+ return combined_impact_matrix.sort_index()
+
+ @staticmethod
+ def _get_single_combined_impact_matrix(combined_impact_matrix_list, process, impact_categories, construction_years, reference_year, invest_years):
+ """Returns impacts for component investment and operation"""
+ combined_impact_matrix_list.append(copy.deepcopy(process.get_impact_invest(impact_categories, construction_years, reference_year, invest_years)))
+ combined_impact_matrix_list.append(copy.deepcopy(process.get_impact_operation(impact_categories, construction_years, reference_year, invest_years)))
+
+ def get_lifetime_duration(self, construction_years: list):
+ """Return the lifetime duration of the process for a list of construction years"""
+ # Unitize Dataframe
+ unitized_lifetime = secmod.helpers.unitize_dataframe(self._lifetime_duration, units)
+
+ lifetime_duration = pd.DataFrame()
+ for construction_year in construction_years:
+ if str(construction_year) in unitized_lifetime.columns:
+ lifetime_duration[str(construction_year)] = unitized_lifetime[str(construction_year)]
+ elif construction_year < int(min(unitized_lifetime.columns)):
+ lifetime_duration[str(construction_year)] = unitized_lifetime[min(unitized_lifetime.columns)]
+ elif construction_year > int(max(unitized_lifetime.columns)):
+ lifetime_duration[str(construction_year)] = unitized_lifetime[max(unitized_lifetime.columns)]
+ lifetime_duration[str(construction_year)] = lifetime_duration[str(construction_year)].apply(lambda row: row.magnitude)
+ lifetime_duration.columns = [int(column) for column in lifetime_duration.columns]
+ lifetime_duration["process"] = self.name
+ lifetime_duration.set_index(["process"], inplace=True)
+
+ return lifetime_duration
+
+ @classmethod
+ def get_combined_lifetime_duration(cls):
+ """Returns the combined lifetime durations of all processes in the class using the construction years of the class."""
+
+ if len(cls.construction_years) == 0:
+ cls.setup_construction_years()
+
+ combined_lifetime_duration = pd.DataFrame()
+ combined_lifetime_duration_list = []
+ for process in progress.bar(cls.processes):
+ combined_lifetime_duration_list.append(process.get_lifetime_duration(cls.construction_years))
+
+ if len(combined_lifetime_duration_list) != 0:
+ combined_lifetime_duration = combined_lifetime_duration.append(other=combined_lifetime_duration_list, verify_integrity=True, sort=True)
+
+ index_names = combined_lifetime_duration.index.names
+ combined_lifetime_duration = combined_lifetime_duration.stack()
+ combined_lifetime_duration.index.names = index_names + ["construction year"]
+ combined_lifetime_duration = combined_lifetime_duration.reset_index()
+ combined_lifetime_duration.set_index(index_names + ["construction year"], inplace=True)
+ return combined_lifetime_duration.sort_index()
+
+ @abc.abstractclassmethod
+ def setup_construction_years(cls):
+ """Abstract method to setup the construction years from existing capacity data."""
+
+ raise NotImplementedError
+
+ def setup_years(self):
+ """"""
+
+ raise NotImplementedError
+
+
+class EcoinventImpacts(ProcessImpacts):
+ """This class provides impacts based on a list of ecoinvent processes.
+
+ An instance based on this class gets its impacts from the ecoinvent
+ database. The impacts are calculated by adding a list of ecoinvent
+ processes multiplied with a scaling factor, which are used to define
+ the environmental impact of a process in SecMOD.
+
+ Args:
+ process_path (Path) = None: A path to the directory of an ecoinvent process.
+ If no path is provided, an empty instance will be created.
+
+ Raises:
+ NotADirectoryError: If provided path is not a directory.
+ """
+
+ # Static variable for the ecoinvent database to be loaded
+ database = None
+ # Static dictionary with the definitions of all subassemblies
+ subassemblies = {}
+
+ def __init__(self, process_path: Path = None):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of EcoinventImpacts.")
+
+ # Call inherited constructors
+ super(EcoinventImpacts, self).__init__(process_path)
+
+ # Check if a path has been provided as parameter
+ if process_path == None:
+ # Create empty dataframes for invest ecoinvent processes, if no directory is provided
+ self._processes_invest = pd.DataFrame(columns=["process_name", "unit"] + ProcessImpacts.invest_years + [
+ "group", "comments", "product", "location", "activity", "amountBeforeRecyclingAndScaling", "recyclingRate", "EOLfactor", "scalingFactor"])
+ # Create empty dataframes for operational ecoinvent processes, if no directory is provided
+ self._processes_operation = pd.DataFrame(columns=["process_name", "unit"] + ProcessImpacts.invest_years + [
+ "group", "comments", "product", "location", "activity", "amountBeforeRecyclingAndScaling", "recyclingRate", "EOLfactor", "scalingFactor"])
+ else:
+ # Read invest ecoinvent processes
+ self._processes_invest = pd.read_csv(
+ process_path / "ecoinvent_invest.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ # Read operational ecoinvent processes
+ self._processes_operation = pd.read_csv(
+ process_path / "ecoinvent_operation.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+
+ @staticmethod
+ def load_ecoinvent_database(database_path: Path, units_to_change_path: Path = None):
+ """Loads the impacts of all ecoinvent processes from a multiindexed CSV-file.
+
+ Additionally this methods loads new unit defintions to be used in pint and
+ translates ecoinvent units to the necessary format to be used with pint.
+
+ Furthermore the process names are edited to match previous naming logic from SecMOD 1.0.
+
+ Args:
+ database_path (Path): A path which includes the filename of the CSV-file.
+
+ units_to_change_path (Path) = None: A path to a JSON-file which defines a dictionary of
+ existing units and the units they shall be replaced with.
+
+ new_pint_units (Path) = None: A path to a TXT-file which includes new unit definitions
+ to be used as pint units. E.g. "car = []" or "mass_CO2_equivalent = [GWP100] = CO2_eq"
+ """
+
+ logging.info("Load ecoinvent database")
+ # Load database from CSV file
+ EcoinventImpacts.database = pd.read_csv(database_path, index_col=[
+ "process", "impact category"], usecols=lambda x: "Unnamed" not in x, float_precision="high")
+
+ # Change units from ecoinvent to fit the defined pint units
+ if units_to_change_path != None:
+ logging.info("Translate ecoinvent units to pint format")
+ EcoinventImpacts._translate_units_to_pint(units_to_change_path)
+
+ # Get list of all process names
+ process_names = list(EcoinventImpacts.database.index.levels[0])
+ # Translate every single process name and save the new list to process_names
+ process_names = [EcoinventImpacts._translate_process_name_to_ecoinvent_identifier(
+ process_name) for process_name in process_names]
+ # Set the process name index in the database to the translated process names
+ EcoinventImpacts.database.index.set_levels(
+ process_names, level=0, inplace=True)
+
+ @staticmethod
+ def _translate_process_name_to_ecoinvent_identifier(process_name: str):
+ """Modifies a process name to match the ecoinvent identifiers.
+
+ Deletes special charaters, spaces, commas, dots, etc. and replaces
+ them by underscores. Furthermore secures that there is only one underscore
+ in a row.
+ e.g.:
+ "passenger car, electric, without battery//[GLO] passenger car production, electric, without battery"
+ becomes
+ "passenger_car_electric_without_battery_GLO_passenger_car_production_electric_without_battery"
+
+ .. WARNING::
+ Translating the process names from the original string to the (sadly) used style of SecMOD 1.0
+ This behavior should be avoided and abandoned in the future.
+ See https://git.rwth-aachen.de/ltt/secmod/secmod/issues/48 for more information.
+ """
+
+ # Replace special characters
+ process_name = process_name.replace(",", "_")
+ process_name = process_name.replace("/", "_")
+ process_name = process_name.replace("-", "_")
+ process_name = process_name.replace(".", "_")
+ process_name = process_name.replace(" ", "_")
+ process_name = process_name.replace("//", "_")
+ process_name = process_name.replace("[", "")
+ process_name = process_name.replace("]", "")
+
+ if process_name != "%":
+ process_name = process_name.replace("%", "")
+
+ # Making sure that there are never multiple underscore at the same time
+ # it's inefficient, but does the job safely
+ for _ in range(0, process_name.count("_")):
+ process_name = process_name.replace("__", "_")
+
+ return process_name
+
+ @staticmethod
+ def _translate_units_to_pint(json_path: Path):
+ """Modifies units of impacts to match pint unit definitions.
+
+ This method simply exchanges some units from the ecoinvent database to fit
+ the corresponding unit definitions in pint. It uses a dictionary to replace
+ the units. This dictionary is provided as a JSON-file.
+ The path to the file has to be given to this method or preferably to the
+ method load_ecoinvent_database.
+
+ .. NOTE::
+ If new units are needed they have to be added to this JSON-file.
+ e.g. like this
+
+ "kg PM2.5-.": "kg PM2_5_eq"
+ """
+
+ # Open JSON-file with dictionary of units to translate
+ with open(json_path) as f:
+ # Load JSON-file into a dictionary
+ units_to_change = json.load(f)
+
+ # Iterate over all units to translate
+ for unit in progress.bar(units_to_change):
+ # Change the unit of each row which unit equals the unit to translate
+ EcoinventImpacts.database.loc[EcoinventImpacts.database.unit ==
+ unit, 'unit'] = units_to_change[unit]
+
+ @staticmethod
+ def load_ecoinvent_subassemblies(subassemblies_path: Path):
+ """Loads ecoinvent subassemblies from a directory.
+
+ This methods automatically detects all CSV-files in a directory and
+ assumes that all of them are ecoinvent subassemblies.
+ """
+
+ logging.info("Load ecoinvent subassemblies")
+
+ # Get a list of all files and folders in the directory
+ subassembly_paths = os.listdir(subassemblies_path)
+
+ # Iterate through the members of the subassembly directory
+ for subassembly in progress.bar(subassembly_paths):
+ # Split the names at each "."
+ filename = subassembly.split(".")
+ # Check if the file ending is "csv"
+ if filename[1] == "csv":
+ # If the file is csv, read it in as a subassembly dataframe
+ EcoinventImpacts.subassemblies[filename[0]] = pd.read_csv(
+ subassemblies_path / subassembly, float_precision="high", usecols=(lambda column: column != "comments"))
+
+ def get_impact_invest(self, impact_categories: list, construction_years: list, reference_year: int, invest_years: list):
+ """This method gets the invest impact of a process in one or multiple impact categories for a specific year.
+
+ This method determines the value of the requested impact categories for a process in a specific year.
+ It takes into account when the process capacity was constructed, which year is the reference year
+ of the current optimization horizon and which year is actually the current invest year.
+
+ If the impact category is "cost", the corresponding method ProcessImpacts._get_cost_invest is used.
+ In all other cases the impacts of all ecoinvent processes defined by the process itself as the
+ invest phase, are multiplied with their corresponding weight factors and summed up together.
+
+ It returns the results as a dictionary of impact categories and their corresponding value,
+ including the correct units.
+
+ Args:
+ impact_categories (list): A list of one or more impact categories to be determined.
+
+ construction_year (int): The year in which the process capacity was build. It is used to
+ determine the actual impact a capacity build in that year has. Earliest year is used,
+ if construction year is earlier than the earliest year in the data.
+
+ reference_year (int): The reference year of the current optimization horizon. It is only
+ used to discount the cost of the process during the whole optimization horizon to
+ the reference year value.
+
+ invest_year(int): The invest year investigated right now. It is used to determine whether
+ a process still got invest annuities to pay, only maintenance costs remaining or
+ is already beyond its lifetime duration and therefore has no invest impacts anymore
+ at all.
+ """
+
+ impact_invest_combined = []
+
+ for invest_year in invest_years:
+ for construction_year in construction_years:
+ # Calculate annuity periods to determine whether invest impacts have to considered or not
+ # Determine whether lifetime duration or economic period should be used, based on which is smaller
+ full_annuity_period = min(ProcessImpacts.economic_period,
+ units(str(self._lifetime_duration.at[0, str(max(construction_year, int(min(self._lifetime_duration.columns))))]) + str(self._lifetime_duration.at[0, "unit"])))
+
+ # Create empty dictionary for the invest impacts
+ impact_invest = {}
+ impact_invest["process"] = self.name
+ impact_invest["invest year"] = invest_year
+ impact_invest["impact source"] = "invest"
+ impact_invest["construction year"] = construction_year
+
+ # Check if invest year is within the full annuity period after the construction
+ # and therefore has investment impacts
+ if (invest_year >= construction_year) and ((invest_year - construction_year) * units.year < full_annuity_period):
+ # Loop over all requested impact categories
+ for impact_category in impact_categories:
+ # Set the impact of the current impact category to 0 by default, before adding impacts from ecoinvent
+ impact_invest[impact_category] = units("0")
+
+ # Check if impact category is "cost", if it is, use special methods from ProcessImpacts
+ if impact_category == "cost":
+ impact_invest[impact_category] = self._get_cost_invest(
+ construction_year, reference_year, invest_year)
+
+ # If impact category is not "cost" calculate the sum of the impacts of all invest processes
+ else:
+ impact_invest[impact_category] = (self._recursively_get_impact(self._processes_invest, impact_category, construction_year, construction_year)
+ / full_annuity_period.to("years").magnitude)
+
+ # If invest year is outside the full annuity period of the process instance return zero costs
+ else:
+ for impact_category in impact_categories:
+ # Check if impact category is "cost", if it is, use special methods from ProcessImpacts
+ if impact_category == "cost":
+ # Get invest and maintenance costs
+ impact_invest[impact_category] = self._get_cost_invest(
+ construction_year, reference_year, invest_year)
+ else:
+ # Return 0 as impact since the invest impact has been fully accounted for already
+ impact_invest[impact_category] = units("0")
+
+ impact_invest_combined.append(impact_invest)
+
+ impact_invest_combined = pd.DataFrame(impact_invest_combined)
+ impact_invest_combined.set_index(["impact source", "process", "invest year", "construction year"], inplace=True)
+
+ return impact_invest_combined
+
+ def get_impact_operation(self, impact_categories: list, construction_years: int, reference_year: int, invest_years: int):
+ """This method gets the operational impact of a process in one or multiple impact categories for a specific year.
+
+ This method determines the value of the requested impact categories for a process in a specific year.
+ It takes into account when the process capacity was constructed, which year is the reference year
+ of the current optimization horizon and which year is actually the current invest year.
+
+ If the impact category is "cost", the corresponding method ProcessImpacts._get_cost_operation is used.
+ In all other cases the impacts of all ecoinvent processes defined by the process itself as the
+ operational phase, are multiplied with their corresponding weight factors and summed up together.
+
+ It returns the results as a DataFrame of impact categories and their corresponding value,
+ including the correct units.
+
+ Args:
+ impact_categories (list): A list of one or more impact categories to be determined.
+
+ construction_year (int): The year in which the process capacity was build. It is used to
+ determine the actual impact a capacity build in that year has. Earliest year is used,
+ if construction year is earlier than the earliest year in the data.
+
+ reference_year (int): The reference year of the current optimization horizon. It is only
+ used to discount the cost of the process during the whole optimization horizon to
+ the reference year value.
+
+ invest_year(int): The invest year investigated right now. It is only used to
+ discount the cost of the process during the whole optimization horizon to
+ the reference year value.
+ """
+
+ impact_operation_combined = []
+
+ for invest_year in invest_years:
+ for construction_year in construction_years:
+ # Create empty dictionary for the operational impacts
+ impact_operation = {}
+ impact_operation["process"] = self.name
+ impact_operation["invest year"] = invest_year
+ impact_operation["impact source"] = "operation"
+ impact_operation["construction year"] = construction_year
+
+ # Loop over all requested impact categories
+ for impact_category in impact_categories:
+ # Set the impact of the current impact category to 0 by default, before adding impacts from ecoinvent
+ impact_operation[impact_category] = units("0")
+
+ # Check if impact category is "cost", if it is, use special methods from ProcessImpacts
+ if impact_category == "cost":
+ impact_operation[impact_category] = self._get_cost_operation(
+ construction_year, reference_year, invest_year)
+
+ # If impact category is not "cost" calculate the sum of the impacts of all invest processes
+ else:
+ impact_operation[impact_category] = self._recursively_get_impact(self._processes_operation, impact_category, construction_year, invest_year)
+
+ impact_operation_combined.append(impact_operation)
+
+ impact_operation_combined = pd.DataFrame(impact_operation_combined)
+ impact_operation_combined.set_index(["impact source", "process", "invest year", "construction year"], inplace=True)
+
+ return impact_operation_combined
+
+ @staticmethod
+ def _get_ecoinvent_process_impact(construction_year_attribute_name, process, impact_category: str, construction_year: int, impact_year: int):
+ """Get the impact of a process in a specific impact category and construction year.
+
+ Takes in an interable tuple from a process list (e.g. self._processes_invest) and returns
+ the corresponding impact value.
+
+ Args:
+ process_list (pd.Dataframe): A DataFrame, which represents a list of processes, e.g. self._processes_invest
+
+ process: An element of process_list.itertuple() which is currently evaluated.
+
+ impact_category (str): The name of the evaluated impact category.
+
+ construction_year (int): The year of construction for which the impact is determined.
+ If the year of construction is earlier than the earliest year in the data, the
+ earliest year is used.
+
+ impact_year (int): The year in which the impact actually occurs. For invest impacts
+ the impact_year is equal to the construction_year. If the impact_year is
+ earlier than the earliest year in the data, the earliest year is used.
+
+ Raises:
+ KeyError: If the searched process does not exist in the ecoinvent database and seems to be used,
+ since its weight factor is not zero.
+ """
+
+ # Check if process does not exist in ecoinvent database
+ if not getattr(process, "process_name") in EcoinventImpacts.database.index.get_level_values("process"):
+ # If the process does not exist, but would be multiplied by 0 anyway, output a warning, but continue
+ if getattr(process, construction_year_attribute_name) == 0:
+ logging.info("The process '{0}' does not exist in the ecoinvent database. Though its impact factor is 0, therefore it is skipped.".format(
+ getattr(process, "process_name")))
+ return 0
+ # If the process does not exist, but seems to be used, raise a KeyError
+ else:
+ logging.error("The process '{0}' does not exist in the ecoinvent database, but its impact factor is not 0. Make sure to only use existing processes.".format(
+ getattr(process, "process_name")))
+ raise KeyError("The process '{0}' does not exist in the ecoinvent database, but its impact factor is not 0. Make sure to only use existing processes.".format(
+ getattr(process, "process_name")))
+
+ # If process exists, return impact
+ else:
+ return (
+ # Get impact of process from ecoinvent database
+ (convert_quantity_to_correct_unit(
+ EcoinventImpacts.database.loc[(getattr(process, "process_name"), impact_category), [str(max(impact_year, int(min(EcoinventImpacts.database.columns)))),'unit']])
+ # Multiplied with its weight factor
+ * getattr(process, construction_year_attribute_name))
+ )
+
+ @staticmethod
+ def _isSubassembly(process_name: str):
+ """Checks whether a process is actually a subassembly"""
+
+ # Check if any subassemblies exist
+ if EcoinventImpacts.subassemblies:
+ # Check if the process name is in the dictionary of subassemblies ...
+ if process_name in EcoinventImpacts.subassemblies:
+ # ... return True, if it is
+ return True
+ else:
+ # ... return False, if it isn't
+ return False
+ else:
+ # If there are no subassemblies at all, issue a warning, in case the user forgot to load the subassembly definitions
+ logging.warning(
+ "There are no subassemblies. Assure that you have read in your ecoinvent subassemblies using load_ecoinvent_subassemblies.")
+ return False
+
+ @staticmethod
+ def _recursively_get_impact(process_list: pd.DataFrame, impact_category: str, construction_year: int, impact_year: int):
+ """Recursively calculates the impact of a process list"""
+
+ # Iterate of the defined invest processes
+ impact = 0
+ for process in process_list.loc[process_list['process_name']!='%'].itertuples():
+ # Check if process is not a comment line
+ if not getattr(process, "process_name") == "%":
+ logging.debug("Get {0} \tof {1}".format(impact_category, getattr(process, "process_name")))
+ # Determine the attribute name of the construction year, since Integers column labels are transformed to
+ # numbers following an underscore (e.g. "_03") to follow attribute naming conventions, when calling DataFrame.itertuples()
+ construction_year_attribute_name = "_" + \
+ str(process_list.columns.get_loc(
+ str(max(construction_year, int(min(EcoinventImpacts.database.columns))))) + 1)
+
+ # Check if process is not a subassembly
+ if not EcoinventImpacts._isSubassembly(getattr(process, "process_name")):
+ # Add impact of ecoinvent process to the total impact
+ impact += (EcoinventImpacts._get_ecoinvent_process_impact(
+ construction_year_attribute_name, process, impact_category, construction_year, impact_year)
+ * convert_quantity_to_correct_unit(str(process_list.at[getattr(process, "Index"), "unit"])))
+ # If the process is actually a subassembly, iterate over all processes in that subassembly
+ # and add their impacts to the total impact
+ else:
+ impact += (EcoinventImpacts._recursively_get_impact(EcoinventImpacts.subassemblies[getattr(process, "process_name")], impact_category, construction_year, impact_year)
+ * getattr(process, construction_year_attribute_name)
+ * convert_quantity_to_correct_unit(str(process_list.at[getattr(process, "Index"), "unit"])))
+ return impact
+
+
+class ManualImpact(ProcessImpacts):
+ """ This is the class for manual impact definitions, when own databases are used. Currently, this is not fully implemented yet.
+
+ """
+
+ def __init__(self, process_path: Path = None):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of ManualImpact.")
+
+ if process_path == None:
+ raise NotImplementedError
+ else:
+ self._impact_invest = pd.read_csv(
+ process_path / "impact_invest.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ self._impact_operation = pd.read_csv(
+ process_path / "impact_operation.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+
+ def get_impact_invest(self, impact_categories: list, construction_year: int, reference_year: int, invest_year: int):
+ """ Get manual invest impacts.
+
+ """
+
+ raise NotImplementedError
+
+ def get_impact_operation(self, impact_categories: list, construction_year: int, reference_year: int, invest_year: int):
+ """ Get operational invest impacts.
+
+ """
+
+ raise NotImplementedError
+
+
+class Process(ProcessImpacts):
+ """This is the base class for processes of all kind.
+
+ It inherits from ProcessImpacts, since all processes have impacts and costs.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ construction_years = []
+ # List of either nodes or connections, to be filled by subclasses
+ locations = []
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of Process.")
+
+ super(Process, self).__init__(process_path)
+
+ if process_path == None:
+ self._properties = pd.DataFrame(columns=["value"])
+ self._properties.index.name = "property"
+ else:
+ self._properties = pd.read_csv(
+ process_path / "properties.csv", index_col="property", float_precision="high", usecols=(lambda column: column != "comments"))
+
+ if (process_path / Grid.selected().name).is_dir():
+ self._existing_capacity = pd.read_csv(
+ process_path / Grid.selected().name / "existing_capacity.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ self._target_unit = get_target_unit_from_unit(self._existing_capacity['unit'].values[0])
+ self._potential_capacity = pd.read_csv(
+ process_path / Grid.selected().name / "potential_capacity.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ else:
+ logging.warning("No capacity data found for process '{0}' in grid '{1}'! Existing and potential capacities are assumed.".format(
+ self.name, Grid.selected().name))
+ self._existing_capacity = pd.DataFrame(index=locations, data=np.nan, columns=["unit"] + ProcessImpacts.invest_years + ["comments"])
+ self._potential_capacity = pd.DataFrame(index=locations, data=np.nan, columns=["unit"] + ProcessImpacts.invest_years + ["comments"])
+ Process.processes.append(self)
+
+ @classmethod
+ def load_processes_from_directory(cls, process_category_path: Path):
+ """Loads all processes of a specific class into their class."""
+
+ logging.info("Load processes of class '{0}".format(str(cls)))
+
+ # Get a list of all files and folders in the directory
+ process_paths = os.listdir(process_category_path)
+
+ # Iterate through the members of the process directory
+ for process in progress.bar(process_paths):
+ # Check if the file ending is "csv"
+ if (process_category_path / process).is_dir():
+ # If the file is csv, read it in as a dataframe
+ cls(process_category_path / process)
+
+ def get_numerical_property(self, property_to_return: str):
+ """
+
+ """
+ return_property = convert_quantity_to_correct_unit(self._properties.loc[property_to_return])
+ return return_property
+ # return (units(self._properties.at[str(property_to_return), "value"]) * units(str(self._properties.at[str(property_to_return), "unit"])))
+
+ @abc.abstractmethod
+ def get_existing_capacity(self, locations: list, invest_years: list):
+ """
+
+ """
+
+ raise NotImplementedError
+
+ @abc.abstractmethod
+ def get_potential_capacity(self, locations: list, invest_years: list):
+ """
+
+ """
+
+ raise NotImplementedError
+
+ def add_capacity(self, construction_year: int, new_capacity: float):
+ """Abstract method that adds capacity to a specific node or connection in a specific construction year."""
+ self._existing_capacity = self._existing_capacity.fillna(0)
+ if str(construction_year) in self._existing_capacity:
+ self._existing_capacity[str(construction_year)] += new_capacity
+ else:
+ self._existing_capacity[str(construction_year)] = new_capacity
+
+ @classmethod
+ def get_combined_existing_capacity(cls, numtol=-10e-6):
+ """
+ Numerical tolerance: float indicating the tolerance for negative values in existing capacities: small negative values are assumed to be 0
+ """
+
+ combined_existing_capacity = pd.DataFrame()
+ combined_existing_capacity_list = []
+ for process in progress.bar(cls.processes):
+ if combined_existing_capacity.empty:
+ combined_existing_capacity = process.get_existing_capacity(cls.locations, cls.invest_years)
+ else:
+ combined_existing_capacity_list.append(copy.deepcopy(process.get_existing_capacity(cls.locations, cls.invest_years)))
+
+ if len(combined_existing_capacity_list) != 0:
+ combined_existing_capacity = combined_existing_capacity.append(other=combined_existing_capacity_list, verify_integrity=True, sort=True)
+
+ combined_existing_capacity.fillna(0, inplace=True)
+
+ # replace entries in combined_existing_capacity that are small negative values due to instability by 0
+ combined_existing_capacity[(combined_existing_capacity <= 0) & (combined_existing_capacity > numtol)] = 0
+
+ combined_existing_capacity = combined_existing_capacity.loc[:, (combined_existing_capacity != 0).any(axis=0)]
+
+ if len(combined_existing_capacity.columns) == 0:
+ combined_existing_capacity[min(ProcessImpacts.invest_years)] = 0
+
+ index_names = combined_existing_capacity.index.names
+ combined_existing_capacity = combined_existing_capacity.stack()
+ combined_existing_capacity.index.names = index_names + ["construction year"]
+
+ return combined_existing_capacity.sort_index()
+
+ @classmethod
+ def get_combined_potential_capacity(cls):
+ """ Returns the potential capacity
+
+ """
+
+ combined_potential_capacity = pd.DataFrame()
+ combined_potential_capacity_list = []
+ for process in progress.bar(cls.processes):
+ if combined_potential_capacity.empty:
+ combined_potential_capacity = process.get_potential_capacity(cls.locations, cls.invest_years)
+ else:
+ combined_potential_capacity_list.append(copy.deepcopy(process.get_potential_capacity(cls.locations, cls.invest_years)))
+
+ if len(combined_potential_capacity_list) != 0:
+ combined_potential_capacity = combined_potential_capacity.append(other=combined_potential_capacity_list, verify_integrity=True, sort=True)
+
+ combined_potential_capacity.fillna(0, inplace=True)
+
+
+ index_names = combined_potential_capacity.index.names
+ combined_potential_capacity = combined_potential_capacity.stack()
+ combined_potential_capacity.index.names = index_names + ["construction year"]
+
+ return combined_potential_capacity.sort_index()
+
+ @classmethod
+ def setup_construction_years(cls, combined_existing_capacity=pd.Series()):
+ """Looks up the required construction years for this process class.
+
+ A matrix of combined existing capacity can be provided to speed up the process.
+ Otherwise the method will create a new combined_existing_capacity matrix.
+ """
+
+ if combined_existing_capacity.empty:
+ combined_existing_capacity = cls.get_combined_existing_capacity()
+
+ existing_construction_years = combined_existing_capacity.index.levels[combined_existing_capacity.index.names.index("construction year")]
+
+ cls.construction_years = sorted(set([int(construction_year) for construction_year in existing_construction_years] + ProcessImpacts.invest_years))
+
+ # return
+ return
+
+ @classmethod
+ def get_target_units(cls):
+ """ returns dict with base units of processes """
+ base_unit_dict = {}
+ for process in progress.bar(cls.processes):
+ if hasattr(process,'_target_unit'):
+ base_unit_dict[process.name] = process._target_unit
+ else:
+ logging.warning("Could not find base unit for {}. Base unit is set to no_target_unit".format(process.name))
+ base_unit_dict[process.name] = "no_target_unit"
+ return base_unit_dict
+
+class NodalProcess(Process):
+ """ This is the class for nodal processes.
+ It inherits from Process.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ # list of nodes used
+ locations = []
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization.
+
+ """
+
+ logging.debug("Initialize a new instance of NodalProcess.")
+
+ super(NodalProcess, self).__init__(process_path)
+
+ # Setup nodes, if it hasn't happened yet
+ if len(NodalProcess.locations) == 0:
+ NodalProcess.locations = Grid.get_list_of_node_ids()
+
+ if process_path == None:
+ self._existing_capacity.columns = [
+ "node", "unit"] + ProcessImpacts.invest_years + ["comments"]
+ self._existing_capacity.index.name = "node"
+ self._secured_capacity_factor = pd.DataFrame(
+ columns=["unit"] + ProcessImpacts.invest_years + ["comments"])
+ self._potential_capacity.index.name = "node"
+ else:
+ self._existing_capacity.set_index("node", inplace=True)
+ self._potential_capacity.set_index("node", inplace=True)
+ self._secured_capacity_factor = pd.read_csv(
+ process_path / "secured_capacity_factor.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ self._technologymatrix = pd.read_csv(
+ process_path / "technologymatrix.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+ self._maximum_production_share = pd.read_csv(
+ process_path / Grid.selected().name / "maximum_production_share.csv", index_col="node", float_precision="high", usecols=(lambda column: column != "comments"))
+
+ def get_existing_capacity(self, nodes: list, invest_years: list):
+ """Returns existing capacity from previous investment years.
+
+ """
+
+ existing_capacity_combined = pd.DataFrame
+ existing_capacity_combined_list = []
+
+ for invest_year in invest_years:
+ construction_years = [column for column in self._existing_capacity.columns if ((np.char.isnumeric(str(column)) and (int(column) <= invest_year) and (invest_year - int(column) < self._lifetime_duration.at[0, str(max(int(column), int(min(self._lifetime_duration.columns))))])))]
+ if len(construction_years) == 0:
+ construction_years = [str(min(invest_years))]
+ existing_capacity = self._existing_capacity.reindex(
+ index=nodes, columns=construction_years + ["unit"])
+ existing_capacity["process"] = self.name
+ existing_capacity["invest year"] = invest_year
+ existing_capacity.reset_index(inplace=True)
+ existing_capacity.set_index(["node", "process", "invest year"], inplace=True)
+ existing_capacity.fillna(0, inplace=True)
+ existing_capacity = convert_quantity_to_correct_unit(existing_capacity)
+
+ if existing_capacity_combined.empty:
+ existing_capacity_combined = existing_capacity
+ else:
+ existing_capacity_combined_list.append(copy.deepcopy(existing_capacity))
+
+ if len(existing_capacity_combined_list) != 0:
+ existing_capacity_combined = existing_capacity_combined.append(other=existing_capacity_combined_list, verify_integrity=True, sort=True)
+
+ existing_capacity_combined.columns = [int(column) for column in existing_capacity_combined.columns]
+ existing_capacity_combined.fillna(0, inplace=True)
+ existing_capacity_combined.sort_index(inplace=True)
+
+ return existing_capacity_combined
+
+ def get_potential_capacity(self, locations: list, invest_years: list):
+ """Returns potential capacity.
+
+ """
+
+ column_year = {}
+ for invest_year in invest_years:
+ column_year[invest_year] = str(max(invest_year, int(min(self._potential_capacity.columns))))
+
+ potential_capacity = self._potential_capacity.reindex(index=locations, columns= [str(column_year[invest_year]) for invest_year in invest_years] + ["unit"])
+ potential_capacity["process"] = self.name
+ potential_capacity.fillna(np.inf, inplace=True)
+ potential_capacity.reset_index(inplace=True)
+ potential_capacity.set_index(["node", "process"], inplace=True)
+ potential_capacity.columns = invest_years + ["unit"]
+ potential_capacity.fillna(np.inf, inplace=True)
+ potential_capacity = convert_quantity_to_correct_unit(potential_capacity)
+ potential_capacity.sort_index(inplace=True)
+ potential_capacity.columns = [int(column) for column in potential_capacity.columns]
+ return potential_capacity
+
+ def get_secured_capacity_factor(self, construction_years: list):
+ """Returns secured capacity factor.
+
+ """
+
+ column_year = {}
+ for construction_year in construction_years:
+ column_year[construction_year] = str(max(construction_year, int(min(self._secured_capacity_factor.columns))))
+
+ secured_capacity_factor = self._secured_capacity_factor.reindex(index=[0], columns=[str(column_year[construction_year]) for construction_year in construction_years] + ["unit"])
+ secured_capacity_factor.columns = construction_years + ["unit"]
+ secured_capacity_factor["process"] = self.name
+ secured_capacity_factor.fillna(1, inplace=True)
+ secured_capacity_factor.set_index(["process"], inplace=True)
+ secured_capacity_factor = convert_quantity_to_correct_unit(secured_capacity_factor)
+
+ secured_capacity_factor.sort_index(inplace=True)
+
+ return secured_capacity_factor
+
+ @classmethod
+ def get_combined_secured_capacity_factor(cls):
+ """Gets the secured capacity factors for all processes of a process category."""
+
+ if len(cls.construction_years) == 0:
+ cls.setup_construction_years()
+
+ combined_secured_capacity_factor = pd.DataFrame()
+ combined_secured_capacity_factor_list = []
+ for process in progress.bar(cls.processes):
+ combined_secured_capacity_factor_list.append(copy.deepcopy(process.get_secured_capacity_factor(cls.construction_years)))
+
+ if len(combined_secured_capacity_factor_list) != 0:
+ combined_secured_capacity_factor = combined_secured_capacity_factor.append(other=combined_secured_capacity_factor_list, verify_integrity=True, sort=True)
+
+ index_names = combined_secured_capacity_factor.index.names
+ combined_secured_capacity_factor = combined_secured_capacity_factor.stack()
+ combined_secured_capacity_factor.index.names = index_names + ["construction year"]
+ return combined_secured_capacity_factor.sort_index()
+
+ @abc.abstractmethod
+ def get_technologymatrix(self, construction_years: list, products: list):
+ """
+
+ """
+
+ raise NotImplementedError
+
+ @classmethod
+ def get_combined_technology_matrix(cls, products: list):
+ """Returns technology matrix.
+
+ """
+
+ if len(cls.construction_years) == 0:
+ cls.setup_construction_years()
+
+ combined_technology_matrix = pd.DataFrame()
+ combined_technology_matrix_list = []
+ for process in progress.bar(cls.processes):
+ combined_technology_matrix_list.append(copy.deepcopy(process.get_technologymatrix(cls.construction_years, products)))
+
+ if len(combined_technology_matrix_list) != 0:
+ combined_technology_matrix = combined_technology_matrix.append(other=combined_technology_matrix_list, verify_integrity=True, sort=True)
+
+ index_names = combined_technology_matrix.index.names
+ combined_technology_matrix = combined_technology_matrix.stack()
+ combined_technology_matrix.index.names = index_names + ["construction year"]
+ return combined_technology_matrix.sort_index()
+
+ def get_maximum_production_share(self, nodes: list, invest_years: list):
+ """Returns maximum production share.
+
+ """
+
+ column_year = {}
+ for invest_year in invest_years:
+ column_year[invest_year] = str(max(invest_year, int(min(self._secured_capacity_factor.columns))))
+
+ maximum_production_share = self._maximum_production_share.reindex(index=nodes, columns= [str(column_year[invest_year]) for invest_year in invest_years] +["unit"])
+ maximum_production_share["process"] = self.name
+ maximum_production_share.fillna(1, inplace=True)
+ maximum_production_share.reset_index(inplace=True)
+ maximum_production_share.set_index(["node", "process"], inplace=True)
+ maximum_production_share.sort_index(inplace=True)
+
+ return maximum_production_share
+
+
+class ConnectionProcess(Process):
+ """This is the class for connection processes.
+ It inherits from Process.
+
+ Args:
+ process_path (Path): The path to a process directory.
+
+ """
+
+ # List of connections used
+ locations = []
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of ConnectionProcess.")
+
+ super(ConnectionProcess, self).__init__(process_path)
+
+ # Setup connections, if it hasn't happened yet
+ if len(ConnectionProcess.locations) == 0:
+ ConnectionProcess.locations = Grid.get_list_of_connection_ids()
+
+ if process_path == None:
+ self._existing_capacity.index.name = "connection"
+ self._potential_capacity.index.name = "connection"
+ else:
+ self._existing_capacity.set_index("connection", inplace=True)
+ self._potential_capacity.set_index("connection", inplace=True)
+
+ def get_existing_capacity(self, connections: list, invest_years: list):
+ """Returns existing capacity.
+
+ """
+
+ existing_capacity_combined = pd.DataFrame()
+ existing_capacity_combined_list = []
+
+ for invest_year in invest_years:
+ existing_capacity = pd.DataFrame()
+ construction_years = [column for column in self._existing_capacity.columns if ((np.char.isnumeric(str(column)) and (float(column) <= invest_year) and (invest_year - int(column) < self._lifetime_duration.at[0, str(max(int(column), int(min(self._lifetime_duration.columns))))])))]
+ if len(construction_years) == 0:
+ construction_years = [min(invest_years)]
+ existing_capacity = self._existing_capacity.reindex(
+ index=connections, columns=construction_years + ["unit"])
+ existing_capacity["process"] = self.name
+ existing_capacity["invest year"] = invest_year
+ existing_capacity.reset_index(inplace=True)
+ existing_capacity.set_index(["connection", "process", "invest year"], inplace=True)
+ existing_capacity.fillna(0, inplace=True)
+ existing_capacity = convert_quantity_to_correct_unit(existing_capacity)
+
+ existing_capacity_combined_list.append(copy.deepcopy(existing_capacity))
+
+ if len(existing_capacity_combined_list) != 0:
+ existing_capacity_combined = existing_capacity_combined.append(other=existing_capacity_combined_list, verify_integrity=True, sort=True)
+
+ existing_capacity_combined.columns = [int(column) for column in existing_capacity_combined.columns]
+ existing_capacity_combined.fillna(0, inplace=True)
+ existing_capacity_combined.sort_index(inplace=True)
+
+ return existing_capacity_combined
+
+
+ @abc.abstractmethod
+ def get_potential_capacity(self, locations: list, invest_years: list):
+ """Returns potential capacity.
+
+ """
+
+ raise NotImplementedError
+
+
+class ProductionProcess(NodalProcess):
+ """ This is the class for production processes.
+ It inherits from nodal process.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ processes = []
+ construction_years = []
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of ProductionProcess.")
+
+ super(ProductionProcess, self).__init__(process_path)
+
+ if process_path == None:
+ self._existing_capacity.columns = [
+ "unit"] + ProcessImpacts.invest_years + ["comments"]
+ self._technologymatrix = pd.DataFrame(
+ columns=["product", "unit"] + ProcessImpacts.invest_years + ["comments"])
+ else:
+ self._technologymatrix.set_index("product", inplace=True)
+ self._technologymatrix_timeseries = pd.read_csv(
+ process_path / "technologymatrix_timeseries.csv", index_col="time slice", float_precision="high", usecols=(lambda column: column != "comments"))
+ # correct time stamp format
+ self._technologymatrix_timeseries = secmod.helpers.correct_time_stamp_of_timeseries(self._technologymatrix_timeseries)
+ self._availability_timeseries = pd.read_csv(
+ process_path / Grid.selected().name / "availability_timeseries.csv", index_col="time slice", float_precision="high", usecols=(lambda column: column != "comments"))
+ # correct time stamp format
+ self._availability_timeseries = secmod.helpers.correct_time_stamp_of_timeseries(self._availability_timeseries)
+
+ ProductionProcess.processes.append(self)
+
+ def get_technologymatrix(self, construction_years: list, products: list):
+ """Returns technologymatrix
+
+ """
+
+ column_years = [str(max(construction_year, int(min(
+ self._technologymatrix.columns)))) for construction_year in construction_years]
+
+ technologymatrix = self._technologymatrix.reindex(
+ index=products, columns=column_years + ["unit"])
+ technologymatrix.columns = construction_years + ["unit"]
+ technologymatrix["process"] = self.name
+ technologymatrix.fillna(0, inplace=True)
+ technologymatrix.reset_index(inplace=True)
+ technologymatrix.set_index(["product", "process"], inplace=True)
+ technologymatrix = convert_quantity_to_correct_unit(technologymatrix)
+
+ technologymatrix.sort_index(inplace=True)
+
+ return technologymatrix
+
+ def get_availability_timeseries(self):
+ """Returns availability timeseries
+
+ """
+
+ time_slices = list(self._availability_timeseries.index)
+ availability_timeseries = self._availability_timeseries.reindex(index=time_slices, columns=[str(location) for location in self.locations])
+ availability_timeseries.columns = self.locations
+ availability_timeseries["process"] = self.name
+ availability_timeseries.reset_index(inplace=True)
+ availability_timeseries.set_index(["time slice", "process"], inplace=True)
+
+ return availability_timeseries
+
+ @staticmethod
+ def get_combined_availability_timeseries():
+ """Returns combined availability timeseries
+
+ """
+
+ combined_availability_timeseries = pd.DataFrame()
+ combined_availability_timeseries_list = []
+
+ for process in ProductionProcess.processes:
+ combined_availability_timeseries_list.append(copy.deepcopy(process.get_availability_timeseries()))
+
+ if len(combined_availability_timeseries_list) != 0:
+ combined_availability_timeseries = combined_availability_timeseries.append(other=combined_availability_timeseries_list, verify_integrity=True, sort=True)
+
+ combined_availability_timeseries.fillna(1, inplace=True)
+ combined_availability_timeseries = combined_availability_timeseries.stack()
+ combined_availability_timeseries = combined_availability_timeseries.reset_index()
+ combined_availability_timeseries.columns = ["time slice", "process", "node", "value"]
+ combined_availability_timeseries.set_index(["node", "process", "time slice"], inplace=True)
+ combined_availability_timeseries.sort_index(inplace=True)
+
+ return combined_availability_timeseries.sort_index()
+
+ def get_technology_matrix_timeseries(self):
+ """Returns time-dependent technologymatrix
+
+ """
+
+ technologymatrix_timeseries = self._technologymatrix_timeseries.drop(["unit"], axis=1)
+ technologymatrix_timeseries["process"] = self.name
+ technologymatrix_timeseries.reset_index(inplace=True)
+ technologymatrix_timeseries.set_index(["time slice", "process"], inplace=True)
+
+ return technologymatrix_timeseries
+
+ @staticmethod
+ def get_combined_technology_matrix_timeseries():
+ """Returns combined time-dependent technologymatrix
+
+ """
+
+ combined_technologymatrix_timeseries = pd.DataFrame()
+ combined_technologymatrix_timeseries_list = []
+
+ for process in ProductionProcess.processes:
+ combined_technologymatrix_timeseries_list.append(copy.deepcopy(process.get_technology_matrix_timeseries()))
+
+ if len(combined_technologymatrix_timeseries_list) != 0:
+ combined_technologymatrix_timeseries = combined_technologymatrix_timeseries.append(other=combined_technologymatrix_timeseries_list, verify_integrity=True, sort=True)
+
+ combined_technologymatrix_timeseries.fillna(1, inplace=True)
+ combined_technologymatrix_timeseries = combined_technologymatrix_timeseries.stack()
+ combined_technologymatrix_timeseries = combined_technologymatrix_timeseries.reset_index()
+ combined_technologymatrix_timeseries.columns = ["time slice", "process", "product", "value"]
+ combined_technologymatrix_timeseries.set_index(["product", "process", "time slice"], inplace=True)
+ combined_technologymatrix_timeseries.sort_index(inplace=True)
+
+ return combined_technologymatrix_timeseries.sort_index()
+
+ @staticmethod
+ def get_combined_production_products(products):
+ """Return a dictionary of all production processes and their corresponding products."""
+
+ combined_production_products_dict = {}
+
+ for process in progress.bar(ProductionProcess.processes):
+ for product in products:
+ if product in process._technologymatrix.index:
+ combined_production_products_dict[(product,process.name)] = True
+ return combined_production_products_dict
+
+
+class StorageProcess(NodalProcess):
+ """This is the class for storage processes.
+ It inherits from nodal process.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ DIRECTIONS_STORAGE = {None: ["deposit", "withdraw"]}
+ STORAGE_LEVEL_FACTOR = {"deposit": 1, "withdraw": -1}
+
+ processes = []
+ construction_years = []
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of StorageProcess.")
+
+ super(StorageProcess, self).__init__(process_path)
+
+ if process_path == None:
+ self._existing_capacity.columns = [
+ "unit"] + ProcessImpacts.invest_years + ["comments"]
+ self._technologymatrix = pd.DataFrame(
+ columns=["unit"] + ProcessImpacts.invest_years + ["comments"], index=["deposit", "withdraw"])
+ self._technologymatrix.index.name = "direction"
+ else:
+ self._technologymatrix.set_index("direction", inplace=True)
+ self._flow_to_storage_capacity_factor = pd.read_csv(
+ process_path / "flow_to_storage_capacity_factor.csv", float_precision="high", usecols=(lambda column: column != "comments"))
+
+ StorageProcess.processes.append(self)
+
+ def get_technologymatrix(self, construction_years: list, products: list):
+ """Return the technologymatrix
+
+ """
+
+ column_years = {}
+ for construction_year in construction_years:
+ column_years[construction_year] = str(
+ max(construction_year, int(min(self._technologymatrix.columns))))
+
+ technologymatrix = []
+
+ for product in products:
+ for direction in ["deposit", "withdraw"]:
+ # Set index values
+ row = {"product": product, "process": self.name,
+ "direction": direction}
+
+ # Set value
+ for construction_year in construction_years:
+
+ if product != self._properties.at["reference product", "value"]:
+ row[construction_year] = np.nan
+ else:
+ row[construction_year] = self._technologymatrix.at[direction,
+ column_years[construction_year]]
+ # Add row to list
+ technologymatrix.append(copy.deepcopy(row))
+
+ technologymatrix = pd.DataFrame(technologymatrix)
+ technologymatrix.fillna(0, inplace=True)
+ technologymatrix.set_index(
+ ["product", "process", "direction"], inplace=True)
+ return (technologymatrix)
+
+ def get_flow_to_storage_capacity_factor(self, construction_years: list):
+ """Returns the flow-to-storagecapacity-factor
+
+ """
+
+ column_year = {}
+ for construction_year in construction_years:
+ column_year[construction_year] = str(max(construction_year, int(min(self._flow_to_storage_capacity_factor.columns))))
+
+ flow_to_storage_capacity_factor = self._flow_to_storage_capacity_factor.reindex(index=[0], columns=[str(column_year[construction_year]) for construction_year in construction_years] + ["unit"])
+ flow_to_storage_capacity_factor.columns = construction_years + ["unit"]
+ flow_to_storage_capacity_factor["process"] = self.name
+ flow_to_storage_capacity_factor.fillna(1, inplace=True)
+ flow_to_storage_capacity_factor.set_index(["process"], inplace=True)
+ flow_to_storage_capacity_factor = convert_quantity_to_correct_unit(flow_to_storage_capacity_factor)
+
+ return flow_to_storage_capacity_factor
+
+ @staticmethod
+ def get_combined_flow_to_storage_capacity_factor():
+ """Returns the combined flow to storagecapacity factor
+
+ """
+
+ combined_flow_to_storage_capacity_factor = pd.DataFrame()
+ combined_flow_to_storage_capacity_factor_list = []
+
+ for process in progress.bar(StorageProcess.processes):
+ combined_flow_to_storage_capacity_factor_list.append(copy.deepcopy(process.get_flow_to_storage_capacity_factor(StorageProcess.construction_years)))
+
+ if len(combined_flow_to_storage_capacity_factor_list) != 0:
+ combined_flow_to_storage_capacity_factor = combined_flow_to_storage_capacity_factor.append(other=combined_flow_to_storage_capacity_factor_list, verify_integrity=True, sort=True)
+
+ combined_flow_to_storage_capacity_factor.fillna(1, inplace=True)
+ combined_flow_to_storage_capacity_factor = combined_flow_to_storage_capacity_factor.stack()
+ combined_flow_to_storage_capacity_factor.sort_index(inplace=True)
+
+ return combined_flow_to_storage_capacity_factor.sort_index()
+
+ @staticmethod
+ def get_combined_storage_products(products):
+ """Return a dictionary of all storage processes and their storeable product."""
+
+ combined_storage_products_dict = {}
+
+ for process in progress.bar(StorageProcess.processes):
+ combined_storage_products_dict[process.name] = process._properties.at["reference product", "value"]
+
+ return combined_storage_products_dict
+
+
+class TransshipmentProcess(ConnectionProcess):
+ """ This is the class for transshipment processes.
+ It inherits from connection process.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ DIRECTIONS_TRANSSHIPMENT = {None: ["forward", "backward"]}
+ DIRECTION_FACTOR_TRANSSHIPMENT = {"forward": 1, "backward": -1}
+
+ processes = []
+ construction_years = []
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of TransshipmentProcess.")
+
+ super(TransshipmentProcess, self).__init__(process_path)
+
+ if process_path == None:
+ self._existing_capacity.columns = ["connection", "2016", "unit", "comments"]
+ else:
+ pass
+
+ TransshipmentProcess.processes.append(self)
+
+ def get_transshipment_efficiency(self):
+ """Return a dataframe with the efficiency of every connection for a transshipment process."""
+
+ specific_efficiency = self.get_numerical_property("transshipment efficiency")
+ efficiency_exponent = self.get_numerical_property("efficiency exponent")
+
+ transshipment_efficiency = pd.DataFrame(Grid.selected().connections["distance"])
+ transshipment_efficiency["efficiency"] = specific_efficiency ** (efficiency_exponent * transshipment_efficiency.distance).astype(float)
+
+ transshipment_efficiency["process"] = self.name
+ transshipment_efficiency.drop(labels=["distance"], axis=1, inplace=True)
+
+ transshipment_efficiency.reset_index(inplace=True)
+ transshipment_efficiency.set_index(["process", "connection"], inplace=True)
+
+ return transshipment_efficiency
+
+ def get_potential_capacity(self, locations: list, invest_years: list):
+ """Return potential capacity.
+
+ """
+
+ column_year = {}
+ for invest_year in invest_years:
+ column_year[invest_year] = str(max(invest_year, int(min(self._potential_capacity.columns))))
+
+ potential_capacity = self._potential_capacity.reindex(index=locations, columns= [str(column_year[invest_year]) for invest_year in invest_years] + ["unit"])
+ potential_capacity["process"] = self.name
+ potential_capacity.fillna(np.inf, inplace=True)
+ potential_capacity.reset_index(inplace=True)
+ potential_capacity.set_index(["connection", "process"], inplace=True)
+ potential_capacity.sort_index(inplace=True)
+ potential_capacity.fillna(np.inf, inplace=True)
+ potential_capacity.columns = invest_years + ["unit"]
+ potential_capacity = convert_quantity_to_correct_unit(potential_capacity)
+
+ return potential_capacity
+
+ @classmethod
+ def get_combined_transshipment_efficiency(cls):
+ """Return a dataframe with the efficiencies of every connection for every transshipment process."""
+
+ combined_transshipment_efficiency = pd.DataFrame()
+ combined_transshipment_efficiency_list = []
+ for process in progress.bar(cls.processes):
+ combined_transshipment_efficiency_list.append(copy.deepcopy(process.get_transshipment_efficiency()))
+
+ if len(combined_transshipment_efficiency_list) != 0:
+ combined_transshipment_efficiency = combined_transshipment_efficiency.append(other=combined_transshipment_efficiency_list, verify_integrity=True, sort=True)
+
+ return combined_transshipment_efficiency.sort_index()
+
+ @classmethod
+ def get_combined_transhipment_products(cls):
+ """Returns a dictionary with the names and products of all transshipment processes"""
+
+ combined_transshipment_products = {}
+
+ for process in progress.bar(cls.processes):
+ combined_transshipment_products[process.name] = process._properties.at["product", "value"]
+
+ return combined_transshipment_products
+
+
+class TransmissionProcess(ConnectionProcess):
+ """ This is the class for transmission processes.
+ It inherits from connection process.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ PRODUCTS = {None: ["electricity"]}
+
+ processes = []
+ construction_years = []
+
+ per_unit_base = units("500 MVA")
+ per_unit_base = (per_unit_base/units(target_units_dictionary[per_unit_base.dimensionality])).to_base_units().magnitude
+
+ reference_node = 1
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of TransmissionProcess.")
+
+ super(TransmissionProcess, self).__init__(process_path)
+
+ if process_path == None:
+ self._existing_capacity.columns = ["circuits", "comments"]
+ else:
+ pass
+
+ TransmissionProcess.processes.append(self)
+
+ def get_potential_capacity(self, locations: list, invest_years: list):
+ """Returns potential capacity
+
+ """
+
+ column_year = {}
+ for invest_year in invest_years:
+ column_year[invest_year] = str(max(invest_year, int(min(self._potential_capacity.columns))))
+
+ potential_capacity = self._potential_capacity.reindex(index=locations, columns= [str(column_year[invest_year]) for invest_year in invest_years] + ["unit"])
+
+ if self._properties.at["process type", "value"] == "power line":
+ potential_capacity["process"] = self.name
+ potential_capacity.fillna(np.inf, inplace=True)
+ elif self._properties.at["process type", "value"] == "voltage switch":
+ if potential_capacity.isnull().values.any():
+ base_voltage = convert_quantity_to_correct_unit(str(self._properties.at["base voltage", "value"]) + " " + str(self._properties.at["base voltage", "unit"]))
+ base_voltage_process = TransmissionProcess._get_process_by_voltage(base_voltage)
+ base_voltage_existing_capacity = base_voltage_process.get_existing_capacity(locations, invest_years)
+ construction_years = [column for column in base_voltage_existing_capacity.columns if (np.char.isnumeric(str(column)))]
+ base_voltage_existing_capacity["total existing circuits"] = 0
+ for construction_year in construction_years:
+ base_voltage_existing_capacity["total existing circuits"] += base_voltage_existing_capacity[construction_year]
+
+ nan_matrix = potential_capacity[[str(column_year[invest_year]) for invest_year in invest_years]].isnull().values
+
+ potential_capacity = potential_capacity[[str(column_year[invest_year]) for invest_year in invest_years]].fillna(0) + nan_matrix.astype(float) * base_voltage_existing_capacity["total existing circuits"].unstack("invest year").values
+
+ potential_capacity["process"] = self.name
+ potential_capacity["unit"] = 1
+
+ potential_capacity.reset_index(inplace=True)
+ potential_capacity.set_index(["connection", "process"], inplace=True)
+ potential_capacity.sort_index(inplace=True)
+ potential_capacity.fillna(np.inf, inplace=True)
+ potential_capacity.columns = invest_years + ["unit"]
+ potential_capacity = convert_quantity_to_correct_unit(potential_capacity)
+
+ return potential_capacity
+
+ def _get_process_connection_properties(self, connections: list, invest_years: list):
+ """Returns connection properties, such as circuits, power limit, safety margin, and more.
+
+ """
+
+ properties = []
+
+ existing_capacity = self.get_existing_capacity(connections, invest_years)
+ construction_years = [column for column in existing_capacity.columns if (np.char.isnumeric(str(column)))]
+ existing_capacity["total existing circuits"] = 0
+ for construction_year in construction_years:
+ existing_capacity["total existing circuits"] += existing_capacity[construction_year]
+
+ for connection in connections:
+ connection_properties = {}
+ connection_properties["connection"] = connection
+
+ for invest_year in invest_years:
+ connection_properties["invest year"] = invest_year
+
+ if not (existing_capacity.at[(connection, self.name, invest_year), "total existing circuits"] == 0):
+ if self._properties.at["process type", "value"] == "power line":
+ connection_properties["power limit"] = calculate_power_line_power_limit(self.get_numerical_property("power limit"),
+ self.get_numerical_property("safety margin"), existing_capacity.at[(connection, self.name, invest_year), "total existing circuits"])
+
+ connection_properties["resistance per unit"] = calculate_power_line_resistance_per_unit(self.get_numerical_property("specific resistance"),
+ Grid.selected().connections.at[connection, "distance"], existing_capacity.at[(connection, self.name, invest_year), "total existing circuits"],
+ self.get_numerical_property("voltage"), TransmissionProcess.per_unit_base)
+
+ connection_properties["reactance per unit"] = calculate_power_line_resistance_per_unit(self.get_numerical_property("specific reactance"),
+ Grid.selected().connections.at[connection, "distance"], existing_capacity.at[(connection, self.name, invest_year), "total existing circuits"],
+ self.get_numerical_property("voltage"), TransmissionProcess.per_unit_base)
+
+ connection_properties["susceptance per unit"] = calculate_power_line_susceptance_per_unit(connection_properties["reactance per unit"],
+ connection_properties["resistance per unit"])
+
+ elif self._properties.at["process type", "value"] == "voltage switch":
+ base_voltage_process = TransmissionProcess._get_process_by_voltage(self.get_numerical_property("base voltage"))
+ base_voltage = base_voltage_process.get_numerical_property("voltage")
+ target_voltage_process = TransmissionProcess._get_process_by_voltage(self.get_numerical_property("target voltage"))
+ target_voltage = target_voltage_process.get_numerical_property("voltage")
+
+ connection_properties["power limit"] = calculate_voltage_switch_power_limit(
+ target_voltage_process.get_numerical_property("power limit"), base_voltage_process.get_numerical_property("power limit"),
+ self.get_numerical_property("safety margin"), existing_capacity.at[(connection, self.name, invest_year), "total existing circuits"])
+
+ connection_properties["resistance per unit"] = calculate_voltage_switch_resistance_per_unit(target_voltage, base_voltage,
+ target_voltage_process.get_numerical_property("specific resistance"), base_voltage_process.get_numerical_property("specific resistance"),
+ TransmissionProcess.per_unit_base, existing_capacity.at[(connection, self.name, invest_year), "total existing circuits"],
+ Grid.selected().connections.at[connection, "distance"])
+
+ connection_properties["reactance per unit"] = calculate_voltage_switch_resistance_per_unit(target_voltage, base_voltage,
+ target_voltage_process.get_numerical_property("specific reactance"), base_voltage_process.get_numerical_property("specific reactance"),
+ TransmissionProcess.per_unit_base, existing_capacity.at[(connection, self.name, invest_year), "total existing circuits"],
+ Grid.selected().connections.at[connection, "distance"])
+
+ connection_properties["susceptance per unit"] = calculate_power_line_susceptance_per_unit(connection_properties["reactance per unit"],
+ connection_properties["resistance per unit"])
+ else:
+ connection_properties["power limit"] = 0
+ connection_properties["resistance per unit"] = np.inf
+ connection_properties["reactance per unit"] = np.inf
+ connection_properties["susceptance per unit"] = 0
+
+ properties.append(copy.deepcopy(connection_properties))
+
+ properties = pd.DataFrame(properties)
+
+ properties.set_index(["connection", "invest year"], inplace=True)
+ properties.sort_index(inplace=True)
+
+ return properties
+
+ @staticmethod
+ def _get_process_by_voltage(voltage: pint.quantity):
+ """Returns processes sorted by voltage.
+
+ """
+
+ for process in TransmissionProcess.processes:
+ if process._properties.at["process type", "value"] == "power line":
+ if convert_quantity_to_correct_unit(process._properties.loc["voltage", ["value","unit"]]) == voltage:
+ return process
+
+ return None
+
+ @staticmethod
+ def get_connection_properties():
+ """Returns connection properties.
+
+ """
+ connection_properties = pd.DataFrame
+ for process in progress.bar(TransmissionProcess.processes):
+ process_properties = process._get_process_connection_properties(TransmissionProcess.locations, ProcessImpacts.invest_years)
+ if connection_properties.empty:
+ connection_properties = process_properties
+ else:
+ connection_properties["power limit"] += process_properties["power limit"]
+ connection_properties["resistance per unit"] = (
+ (((connection_properties["resistance per unit"])**(-1) + (process_properties["resistance per unit"]) ** (-1)).replace(0, np.nan) **(-1)).fillna(np.inf))
+ connection_properties["reactance per unit"] = (
+ (((connection_properties["reactance per unit"])**(-1) + (process_properties["reactance per unit"]) ** (-1)).replace(0, np.nan) **(-1)).fillna(np.inf))
+ connection_properties["susceptance per unit"] = (
+ connection_properties["reactance per unit"] / (
+ connection_properties["resistance per unit"] ** 2 + connection_properties["reactance per unit"] ** 2))
+
+ return connection_properties
+
+ @staticmethod
+ def get_combined_power_limit_per_circuit():
+ """Returns a dictionary of the power limits per circuit of all transmission processes."""
+
+ combined_power_limit = {}
+ for process in TransmissionProcess.processes:
+ if process._properties.at["process type", "value"] == "power line":
+ combined_power_limit[process.name] = process.get_numerical_property("power limit")
+ elif process._properties.at["process type", "value"] == "voltage switch":
+ base_voltage_process = TransmissionProcess._get_process_by_voltage(process.get_numerical_property("base voltage"))
+ target_voltage_process = TransmissionProcess._get_process_by_voltage(process.get_numerical_property("target voltage"))
+ combined_power_limit[process.name] = target_voltage_process.get_numerical_property("power limit") - base_voltage_process.get_numerical_property("power limit")
+
+ return combined_power_limit
+
+ @staticmethod
+ def get_combined_safety_margin():
+ """Returns a dictionary of the safety margin of all transmission processes."""
+
+ combined_safety_margin = {}
+ for process in progress.bar(TransmissionProcess.processes):
+ combined_safety_margin[process.name] = process.get_numerical_property("safety margin")
+
+ return combined_safety_margin
+
+class ProductionProcessEcoinvent(ProductionProcess, EcoinventImpacts):
+ """ This is the class for ecoinvent production processes.
+ It inherits from ProductionProcess and EcoinventImpacts.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug(
+ "Initialize a new instance of ProductionProcessEcoinvent.")
+
+ super(ProductionProcessEcoinvent, self).__init__(process_path)
+
+
+class ProductionProcessManual(ProductionProcess, ManualImpact):
+ """ This is the class for manually defined production processes.
+ It inherits from ProductionProcess and ManualImpact.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of ProductionProcessManual.")
+
+ super(ProductionProcessManual, self).__init__(process_path)
+
+
+class StorageProcessEcoinvent(StorageProcess, EcoinventImpacts):
+ """ This is the class for ecoinvent storage processes.
+ It inherits from StorageProcess and Econinvent Impacts.
+
+ Args:
+ process_path (Path): The path to a process directory.
+
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of StorageProcessEcoinvent.")
+
+ super(StorageProcessEcoinvent, self).__init__(process_path)
+
+
+class StorageProcessManual(StorageProcess, ManualImpact):
+ """ This is the class for manually defined storage processes.
+ It inherits from StorageProcess and ManualImpact.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug("Initialize a new instance of StorageProcessManual.")
+
+ super(StorageProcessManual, self).__init__(process_path)
+
+
+class TransshipmentProcessEcoinvent(TransshipmentProcess, EcoinventImpacts):
+ """ This is the class for ecoinvent transshipment processes.
+ It inherits from TransshipmentProcess and EconinventImpacts.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug(
+ "Initialize a new instance of TransshipmentProcessEcoinvent.")
+
+ super(TransshipmentProcessEcoinvent, self).__init__(process_path)
+
+
+class TransshipmentProcessManual(TransshipmentProcess, ManualImpact):
+ """ This is the class for manually defined transshipment processes.
+ It inherits from TransshipmentProcess and Econinvent Impacts.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug(
+ "Initialize a new instance of TransshipmentProcessManual.")
+
+ super(TransshipmentProcessManual, self).__init__(process_path)
+
+
+class TransmissionProcessEcoinvent(TransmissionProcess, EcoinventImpacts):
+ """ This is the class for ecoinvent transmission processes.
+ It inherits from TransmissionProcess and EconinventImpacts.
+
+ Args:
+ process_path (Path): The path to a process directory.
+
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug(
+ "Initialize a new instance of TransmissionProcessEcoinvent.")
+
+ super(TransmissionProcessEcoinvent, self).__init__(process_path)
+
+
+class TransmissionProcessManual(TransmissionProcess, ManualImpact):
+ """ This is the class for manually-defined tranmission processes.
+ It inherits from TransmissionProcess and EconinventImpacts.
+
+ Args:
+ process_path (Path): The path to a process directory.
+ """
+
+ def __init__(self, process_path: Path):
+ """See class documentation above for more information on the initialization."""
+
+ logging.debug(
+ "Initialize a new instance of TransmissionProcessManual.")
+
+ super(TransmissionProcessManual, self).__init__(process_path)
+
+# Helper functions for pint
+
+# @units.wraps(units.MW, [convert_to_target_unit('MW'), convert_to_target_unit('dimensionless'), convert_to_target_unit('circuits')], False)
+def calculate_power_line_power_limit(power_limit, safety_margin, circuits):
+ return (power_limit * (1 - safety_margin) * circuits)
+
+# @units.wraps(units.MW * units.ohm / units.kV ** 2, [convert_to_target_unit('ohm/km'), convert_to_target_unit('km'), convert_to_target_unit('circuits'), convert_to_target_unit('kV'), convert_to_target_unit('MVA')],False)
+def calculate_power_line_resistance_per_unit(specific_resistance, distance, circuits, voltage, per_unit_base):
+ return(specific_resistance * distance / circuits / ((voltage ** 2) / per_unit_base))
+
+# @units.wraps(1 / (units.MW * units.ohm / units.kV ** 2), [units.MW * units.ohm / units.kV ** 2, units.MW * units.ohm / units.kV ** 2])
+def calculate_power_line_susceptance_per_unit(reactance_per_unit, resistance_per_unit):
+ return (reactance_per_unit / (resistance_per_unit ** 2 + reactance_per_unit ** 2))
+
+# @units.wraps(units.MW, [units.MW, units.MW, units.dimensionless, units.circuits], False)
+def calculate_voltage_switch_power_limit(target_power_limit, base_power_limit, safety_margin, circuits):
+ return ((target_power_limit - base_power_limit) * (1 - safety_margin) * circuits)
+
+# @units.wraps(units.MW * units.ohm / units.kV ** 2, [units.kV, units.kV, units.ohm / units.km, units.ohm / units.km, units.MVA, units.circuits, units.km])
+def calculate_voltage_switch_resistance_per_unit(target_voltage, base_voltage, target_specific_resistance, base_specific_resistance, per_unit_base, circuits, distance):
+ return((target_voltage ** 2 / target_specific_resistance - base_voltage ** 2 / base_specific_resistance) ** (-1)
+ * per_unit_base / circuits * distance)
+
+def get_target_unit_from_unit(unit: str):
+ """ convert Unit String to Target Unit and returns target unit string
+
+ Args:
+ unit(str): string of unit
+ """
+ try:
+ # get unit from input
+ unit = units(str(unit))
+ # if dimension explicitly stated in Target Unit Dictionary, return target_unit
+ if unit.dimensionality in target_units_dictionary:
+ target_unit = target_units_dictionary[unit.dimensionality]
+ else:
+ unit_dict = unit._units._d # get dict of subunits
+ # create empty target_unit
+ target_unit = ''
+ # iterate through subunits
+ for subunit in unit_dict:
+ # raise subunit to corresponding power (e.g. meter^3)
+ subunit_raised_to_power = subunit+'**'+str(abs(unit_dict[subunit]))
+ # if subunit_raised_to_power in target_units_dictionary (e.g. m^3 == cubic_meter)
+ if units(subunit_raised_to_power).dimensionality in target_units_dictionary:
+ sub_target_unit = target_units_dictionary[units(subunit_raised_to_power).dimensionality]
+ power = np.sign(unit_dict[subunit])
+ # if subunit_raised_to_power not in target_units_dictionary (e.g. MW^-1 is not in target_units_dictionary)
+ else:
+ sub_target_unit = target_units_dictionary[units(subunit).dimensionality]
+ power = unit_dict[subunit]
+ if power>0: # multiply subunit
+ if target_unit != '':
+ target_unit += '*'
+ else: # divide subunit
+ if target_unit != '':
+ target_unit += '/'
+ else:
+ target_unit += '1/'
+ target_unit += sub_target_unit
+ if abs(power) != 1:
+ target_unit += '^{}'.format(str(abs(power)))
+ except KeyError: # target unit not defined for unit
+ logging.error("No target unit was defined for '{0}'. Please define a target unit for the dimensionality: {1}".format(unit, unit.dimensionality))
+ raise KeyError("No target unit was defined for '{0}'. Please define a target unit for the dimensionality: {1}".format(unit, unit.dimensionality))
+
+ else:
+ return target_unit
+
+def convert_quantity_to_correct_unit(unit_input):
+ """ compare unit from csv with unit in target unit dictionary
+
+ m'*d' = m_target * d_target <-> m_target = m'*d'/d_target
+ with m: magnitude, d: dimensionality
+
+ Args:
+ unit_input: unit_input which oughts to be converted into correct unit.
+ currently supported for pd.Dataframe, pd.Series and str
+
+ Return:
+ converted_unit_input: unit_input converted into correct unit.
+ """
+ if hasattr(unit_input,'unit'): # if unit_input has unit column
+ # calculate multiplication factor for each unit in unit_input
+ multiplication_factors = []
+ if isinstance(unit_input, pd.DataFrame): # if unit_input is pd.DataFrame
+ for unit in unit_input["unit"]:
+ # calculate multiplication factor for each unit
+ multiplication_factors = calculate_multiplication_factors(unit,multiplication_factors)
+ converted_unit_input = unit_input.drop("unit", axis=1)
+ # multiply column with multiplication factor
+ converted_unit_input = converted_unit_input.apply(lambda column: column * multiplication_factors)
+ elif isinstance(unit_input, pd.Series): # if unit_input is pd.Series
+ unit = unit_input["unit"]
+ # calculate multiplication factor for unit
+ multiplication_factors = calculate_multiplication_factors(unit,multiplication_factors)[0]
+ converted_unit_input = unit_input.drop("unit")
+ # multiply with multiplication factor
+ converted_unit_input = (converted_unit_input.astype(float) * multiplication_factors).values[0]
+
+ elif isinstance(unit_input,pd.Series) and hasattr(unit_input.iloc[0],'dimensionality'): # if unit_input is array of quantities
+ for idx_quantity,quantity in enumerate(unit_input):
+ try:
+ unit_input.iloc[idx_quantity] = calculate_multiplication_factors(quantity)[0]
+ except KeyError:
+ logging.error("No target unit was defined for '{0}'. Please define a target unit for the dimensionality: {1}".format(quantity, quantity.dimensionality))
+ raise KeyError("No target unit was defined for '{0}'. Please define a target unit for the dimensionality: {1}".format(quantity, quantity.dimensionality))
+
+ converted_unit_input = unit_input.values
+
+ elif isinstance(unit_input,str): # if input is string
+ if unit_input == '1':
+ converted_unit_input = 1
+ else:
+ try:
+ converted_unit_input = calculate_multiplication_factors(unit_input)[0]
+ except KeyError:
+ logging.error("Check if {0} is valid unit".format(unit_input))
+ raise KeyError("Check if {0} is valid unit".format(unit_input))
+ return converted_unit_input
+
+def calculate_multiplication_factors(unit,multiplication_factors=None):
+ """ returns multiplication factor of conversion from current unit to target unit
+
+ Args:
+ unit: current unit or quantity
+ multiplication_factors (list): list with multiplication factors, to which the current factor is added
+ """
+ # set default multiplication_factors
+ if multiplication_factors is None:
+ multiplication_factors = []
+ # if unit is string -> convert string to quantity
+ if isinstance(unit,str):
+ unit = units(unit)
+ # if unit is integer (also if just converted from string) -> no unit
+ if not isinstance(unit,pint.quantity._Quantity):
+ multiplication_factors.append(unit)
+ return multiplication_factors
+ try:
+ # if dimension of unit explicitly stated in Target Unit Dictionary, convert dimension to base unit
+ if unit.dimensionality in target_units_dictionary:
+ factor = (unit/units(target_units_dictionary[unit.dimensionality])).to_base_units().magnitude
+ # round to five significant digits after leading power --> 1.0000001e-6 -> 1e-6
+ factor = round(factor,int(abs(np.log10(factor))+5))
+ multiplication_factors.append(factor)
+ # else split unit in subunits
+ else:
+ # get dict of subunits
+ unit_dict = unit._units._d
+ # factor = d'/d_target
+ factor = unit.magnitude
+ for subunit in unit_dict:
+ subunit_raised_to_power = subunit+'**'+str(abs(unit_dict[subunit]))
+ if units(subunit_raised_to_power).dimensionality in target_units_dictionary:
+ subfactor = (units(subunit_raised_to_power)/units(target_units_dictionary[units(subunit_raised_to_power).dimensionality])).to_base_units().magnitude
+ # round to five significant digits after leading power --> 1.0000001e-6 -> 1e-6
+ subfactor = round(subfactor,int(abs(np.log10(subfactor))+5))
+ power = np.sign(unit_dict[subunit])
+ else:
+ subfactor = (units(subunit)/units(target_units_dictionary[units(subunit).dimensionality])).to_base_units().magnitude
+ # round to five significant digits after leading power --> 1.0000001e-6 -> 1e-6
+ subfactor = round(subfactor,int(abs(np.log10(subfactor))+5))
+ power = unit_dict[subunit]
+ factor *= subfactor**power
+ multiplication_factors.append(factor)
+ except KeyError:
+ logging.error("No target unit was defined for '{0}'. Please define a target unit for the dimensionality: {1}".format(unit, unit.dimensionality))
+ raise KeyError("No target unit was defined for '{0}'. Please define a target unit for the dimensionality: {1}".format(unit, unit.dimensionality))
+ # return multiplication factors
+ return multiplication_factors
\ No newline at end of file
diff --git a/secmod/data_preprocessing.py b/secmod/data_preprocessing.py
new file mode 100644
index 0000000..7c98be7
--- /dev/null
+++ b/secmod/data_preprocessing.py
@@ -0,0 +1,164 @@
+import os
+import logging
+import shutil
+import requests
+from clint.textui import progress
+from pathlib import Path
+
+import datapackage as dp
+
+
+def download_datapackage(url: str, download_path: Path) -> bool:
+ """This method is used to download an Open Source Data Package.
+
+ Using the URL to the meta data JSON-file of the data package,
+ the complete data package is downloaded into a download folder.
+ If the download has been successfull a boolean True is returned.
+
+ Args:
+ url (str): URL to the directory of the meta data JSON-file of the data package
+ (e.g. https://data.open-power-system-data.org/renewable_power_plants/latest/datapackage.json)
+ download_path (Path): Path of the directory where the data package is to be saved
+ """
+
+ # try to open the remote datapackage
+ try:
+ datapackage = dp.Package(url + "datapackage.json")
+ # handle exception, if data package is invalid
+ except dp.exceptions.ValidationError as e:
+ # show error message
+ logging.error("Datapackage is invalid!\n\nPlease check the source:\n{0}\n\nDetails:\n{1}".format(url, str(e)))
+ # return False since the download wasn´t successfull
+ return False
+ # handle exception, if some other data package exception has been raised
+ except dp.exceptions.DataPackageException as e:
+ # show error message
+ logging.error("Datapackage could not be opened!\n\nPlease check the provided URL and your network connection:\n{0}\n\nDetails:\n{1}".format(url, str(e)))
+ # return False since the download wasn´t successfull
+ return False
+
+ # create download directory, if not existent
+ create_directory(download_path)
+ # download meta data JSON-file
+ download_file(
+ url + "datapackage.json", download_path / "datapackage.json")
+ # download readme.md, if existing
+ try:
+ download_file(url + "README.md", download_path / "README.md")
+ except Exception:
+ pass
+ # download all ressources, stated in the meta data JSON-file
+ for resource in datapackage.resources:
+ try:
+ download_file(resource.source, download_path /
+ resource.descriptor['path'], resource.descriptor['bytes'])
+ except Exception:
+ download_file(resource.source, download_path /
+ resource.descriptor['path'])
+
+ return True
+
+
+def datapackage_update_available(url: str, download_path: Path) -> bool:
+ """This method checks if there is an update available for a remote data package.
+
+ First the methods tries to open the remote data package to check its availability.
+ If it is not available, False is returned.
+
+ After that the local data package is opened, if it exists.
+ If it does not exist, True is returned.
+
+ If both sources are accessible the version information from the meta data is compared.
+ Because the remote source is expected to have the newest version, any difference in the
+ version information returns True.
+
+ Args:
+ url (str): URL to the directory of the meta data JSON-file of the data package
+ (e.g. https://data.open-power-system-data.org/renewable_power_plants/latest/datapackage.json)
+ download_path (Path): Path of the directory where the data package is to be saved
+ """
+
+ # try to open the remote data package meta data
+ try:
+ remote_dp = dp.Package(url + "datapackage.json")
+ full_path = download_path / remote_dp.descriptor['name']
+ # handle exception, if data package is invalid
+ except dp.exceptions.ValidationError as e:
+ # show error message
+ logging.error("Remote datapackage is invalid!\n\nDetails:\n" + str(e))
+ # return False since the download wasn´t successfull
+ return False
+ # handle exception, if some other data package exception has been raised
+ except dp.exceptions.DataPackageException as e:
+ # show error message
+ logging.error("Remote datapackage could not be opened!\nPlease check the provided URL and your network connection.\n\nDetails:\n" + str(e))
+ # return False since the download wasn´t successfull
+ return False
+
+ # check if data package exists locally
+ if ((full_path / "datapackage.json").exists()):
+ local_dp = dp.Package(str(full_path / "datapackage.json"))
+ if (local_dp.descriptor['version'] == remote_dp.descriptor['version']):
+ return False
+ else:
+ return True
+ else:
+ return True
+
+
+def download_file(url: str, save_to: str, expected_size: int = None):
+ """This method downloads files using streaming.
+
+ A get request is used to download the file from the URL.
+ The response is streamed into a local file to minimize RAM usage.
+
+ Args:
+ url (str): URL to the file
+ save_to (str): local path and filename to be saved to
+ """
+ print("Downloading " + url.split('/')[-1])
+ response = requests.get(url, stream=True)
+
+ chunk_size = 8192
+ file_handle = open(save_to, "wb")
+ if expected_size == None:
+ try:
+ total_length = int(response.headers.get('content-length'))
+ except Exception:
+ total_length = 100000000
+ else:
+ total_length = expected_size
+ for chunk in progress.bar(response.iter_content(chunk_size=chunk_size), expected_size=(total_length/chunk_size) + 1):
+ if chunk:
+ file_handle.write(chunk)
+
+
+def is_setup(working_directory: Path) -> bool:
+ """This method checks if SecMOD has been set up before.
+
+ This is achieved by checking if the folder "SecMOD" exists in
+ the working directory and returning a boolean. If it does not exist,
+ the method returns False.
+
+ Args:
+ working_directory (Path): Path of the working directory
+ """
+
+ if (working_directory / "SecMOD").exists():
+ return True
+ else:
+ return False
+
+
+def create_directory(path: Path):
+ """This method creates a new directory, if it does not exist, yet.
+
+ The method uses pathlib to check for the existence of a path.
+ If the path does not exist yet, it get created.
+
+ Args:
+ path (Path): Path of the directory to be created
+ """
+
+ if not path.exists():
+ path.mkdir(parents=True)
diff --git a/secmod/data_processing.py b/secmod/data_processing.py
new file mode 100644
index 0000000..09ff3b0
--- /dev/null
+++ b/secmod/data_processing.py
@@ -0,0 +1,327 @@
+from pathlib import Path
+from secmod.classes import units, Grid, ImpactCategory, Product, ProcessImpacts, EcoinventImpacts, Process, ProductionProcess, ProductionProcessEcoinvent, StorageProcess, StorageProcessEcoinvent, TransshipmentProcess, TransshipmentProcessEcoinvent, TransmissionProcess, TransmissionProcessEcoinvent, convert_quantity_to_correct_unit
+from secmod.optimization import Optimization
+import secmod.time_series_aggregation as tsa
+import os
+import importlib.util
+import logging
+import pandas as pd
+import pickle
+import json
+import copy
+from clint.textui import progress
+
+# Load config.py
+spec = importlib.util.spec_from_file_location("config", "SecMOD/00-INPUT/00-RAW-INPUT/config.py")
+config = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(config)
+
+time_slices = pd.DataFrame()
+target_units = json.load(open("SecMOD/00-INPUT/00-RAW-INPUT/01-UNITS-TARGET-OPTIMIZATION.json", 'r'))
+
+def load_raw_input(raw_input_directory: Path):
+ """Loads all data from raw input.
+
+ Args:
+ raw_input_directory (Path): Path to the raw input directory
+ """
+ global time_slices
+ global target_units
+
+ # Load ecoinvent database
+ EcoinventImpacts.load_ecoinvent_database(raw_input_directory / "04-ECOINVENT" / "ecoinvent.csv", raw_input_directory / "04-ECOINVENT" / "ecoinvent_units_to_change.json")
+ # Load ecoinvent subassemblies
+ EcoinventImpacts.load_ecoinvent_subassemblies(raw_input_directory / "04-ECOINVENT" / "00-ECOINVENT-SUBASSEMBLIES")
+ # Load all grids and select the one from config
+ Grid.load_grids_from_directory(raw_input_directory / "01-GRID")
+ for grid in Grid.grids:
+ if grid.name == config.grid_name:
+ grid.select()
+ # Load all impact categories
+ ImpactCategory.load_impact_categories_from_directory(raw_input_directory / "05-IMPACT-CATEGORIES")
+ # Load all products
+ Product.load_products_from_directory(raw_input_directory / "03-PRODUCTS")
+ # Load all processes
+ load_all_processes(raw_input_directory / "02-PROCESSES")
+ # Load time slices
+ if not (hasattr(config,'time_series_aggregation_config') and config.time_series_aggregation_config["use_time_series_aggregation"]):
+ time_slices = pd.read_csv(raw_input_directory / "06-AGGREGATED-TIMESERIES" / "time_slices.csv", index_col="time_slice")
+
+def load_all_processes(processes_directory: Path):
+ """Loads all processes in their corresponding class.
+
+ Args:
+ raw_input_directory (Path): Path to the raw input directory
+ """
+ logging.info("Load processes")
+ directories = os.listdir(processes_directory)
+ directories = [processes_directory / directory for directory in directories if (processes_directory / directory).is_dir()]
+
+ for directory in directories:
+ subdirectories = os.listdir(directory)
+ subdirectories = [directory / subdirectory for subdirectory in subdirectories if (directory / subdirectory).is_dir()]
+
+ for subdirectory in subdirectories:
+ if subdirectory.name.upper() == "01-ECOINVENT-BASED":
+ if directory.name.upper() == "01-PRODUCTION":
+ ProductionProcessEcoinvent.load_processes_from_directory(subdirectory)
+ elif directory.name.upper() == "02-STORAGE":
+ StorageProcessEcoinvent.load_processes_from_directory(subdirectory)
+ elif directory.name.upper() == "03-TRANSSHIPMENT":
+ TransshipmentProcessEcoinvent.load_processes_from_directory(subdirectory)
+ elif directory.name.upper() == "04-TRANSMISSION":
+ TransmissionProcessEcoinvent.load_processes_from_directory(subdirectory)
+
+ logging.info("All processes loaded")
+
+def generate_input_dictionary(computed_input_path: Path, load: bool = False):
+ """Takes all loaded data and generates an input dictionary for the optimization model.
+
+ Args:
+ computed_input_path (Path): Path to an already computed input directory for reading
+ load (bool), default False: specifies whether an existing input dictionary will be loaded (True) or an input dictionary will be computed (False)
+
+ Returns:
+ input_dictionary (Dict): returns a Dictionary with all the necessary data for optimization
+ """
+
+ if load and computed_input_path.exists():
+ logging.info("Load existing input dictionary...")
+ with open(computed_input_path / "input-{0}.pickle".format(str(min(ProcessImpacts.invest_years))), 'rb') as input_file:
+ input_dictionary = pickle.load(input_file)
+ else:
+
+ logging.info("Start generation of input dictionary...")
+ input_dictionary = {}
+
+ logging.info("Setup nodes")
+ input_dictionary["nodes"] = {None: Grid.selected().get_list_of_node_ids()}
+ logging.info("Setup connections")
+ input_dictionary["connections"] = {None: Grid.selected().get_list_of_connection_ids()}
+ logging.info("Setup connection nodes")
+ input_dictionary["connection_nodes"] = Grid.CONNECTION_NODES
+ input_dictionary["connected_nodes"] = Grid.selected().connections[Grid.CONNECTION_NODES[None]].stack().to_dict()
+ input_dictionary["connection_length"] = Grid.selected()._connections["distance"].to_dict()
+ logging.info("Setup products")
+ input_dictionary["products"] = {None: [product.name for product in Product.products]}
+ logging.info("Setup process list production")
+ input_dictionary["processes_production"] = {None: [process.name for process in ProductionProcess.processes]}
+ logging.info("Setup process list storage")
+ input_dictionary["processes_storage"] = {None: [process.name for process in StorageProcess.processes]}
+ logging.info("Setup process list transshipment")
+ input_dictionary["processes_transshipment"] = {None: [process.name for process in TransshipmentProcess.processes]}
+ logging.info("Setup process list transmission")
+ input_dictionary["processes_transmission"] = {None: [process.name for process in TransmissionProcess.processes]}
+
+ logging.info("Setup constant factors")
+ input_dictionary["directions_transshipment"] = TransshipmentProcess.DIRECTIONS_TRANSSHIPMENT
+ input_dictionary["factor_transshipment"] = TransshipmentProcess.DIRECTION_FACTOR_TRANSSHIPMENT
+ input_dictionary["directions_storage"] = StorageProcess.DIRECTIONS_STORAGE
+ input_dictionary["storage_level_factor"] = StorageProcess.STORAGE_LEVEL_FACTOR
+ input_dictionary["products_transmission"] = TransmissionProcess.PRODUCTS
+
+ logging.info("Setup years")
+ input_dictionary["years"] = {None: ProcessImpacts.invest_years}
+
+ logging.info("Setup existing capacity")
+ existing_capacity = ProductionProcess.get_combined_existing_capacity()
+ input_dictionary["existing_capacity_production"] = existing_capacity.to_dict()
+ ProductionProcess.setup_construction_years(existing_capacity)
+ existing_capacity = StorageProcess.get_combined_existing_capacity()
+ input_dictionary["existing_capacity_storage"] = existing_capacity.to_dict()
+ StorageProcess.setup_construction_years(existing_capacity)
+ existing_capacity = TransshipmentProcess.get_combined_existing_capacity()
+ input_dictionary["existing_capacity_transshipment"] = existing_capacity.to_dict()
+ TransshipmentProcess.setup_construction_years(existing_capacity)
+ existing_capacity = TransmissionProcess.get_combined_existing_capacity()
+ input_dictionary["existing_capacity_transmission"] = existing_capacity.to_dict()
+ TransmissionProcess.setup_construction_years(existing_capacity)
+
+ logging.info("Setup construction years production")
+ input_dictionary["construction_years_production"] = {None: ProductionProcess.construction_years}
+ logging.info("Setup construction years storage")
+ input_dictionary["construction_years_storage"] = {None: StorageProcess.construction_years}
+ logging.info("Setup construction years transshipment")
+ input_dictionary["construction_years_transshipment"] = {None: TransshipmentProcess.construction_years}
+ logging.info("Setup construction years transmission")
+ input_dictionary["construction_years_transmission"] = {None: TransmissionProcess.construction_years}
+
+ logging.info("Setup lifetime duration production")
+ input_dictionary["lifetime_duration_production"] = ProductionProcess.get_combined_lifetime_duration()[0].to_dict()
+ logging.info("Setup lifetime duration storage")
+ input_dictionary["lifetime_duration_storage"] = StorageProcess.get_combined_lifetime_duration()[0].to_dict()
+ logging.info("Setup lifetime duration transshipment")
+ input_dictionary["lifetime_duration_transshipment"] = TransshipmentProcess.get_combined_lifetime_duration()[0].to_dict()
+ logging.info("Setup lifetime duration transmission")
+ input_dictionary["lifetime_duration_transmission"] = TransmissionProcess.get_combined_lifetime_duration()[0].to_dict()
+
+ logging.info("Setup impact categories and sources")
+ input_dictionary["impact_categories"] = {None: ImpactCategory.get_list_of_names_of_active_impact_categories()}
+ input_dictionary["impact_sources"] = ProcessImpacts.IMPACT_SOURCES
+
+ logging.info("Setup technology matrices")
+ input_dictionary["technology_matrix_production"] = ProductionProcess.get_combined_technology_matrix(input_dictionary["products"][None]).to_dict()
+ input_dictionary["efficiency_matrix_storage"] = StorageProcess.get_combined_technology_matrix(input_dictionary["products"][None]).to_dict()
+
+ # logging.info("Setup production products")
+ # input_dictionary["production_products"] = ProductionProcess.get_combined_production_products(input_dictionary["products"][None])
+
+ logging.info("Setup stored products")
+ input_dictionary["storage_products"] = StorageProcess.get_combined_storage_products(input_dictionary["products"][None])
+
+ logging.info("Setup transshipment parameters")
+ input_dictionary["transshipment_efficiency"] = TransshipmentProcess.get_combined_transshipment_efficiency()["efficiency"].to_dict()
+ input_dictionary["transshipment_products"] = TransshipmentProcess.get_combined_transhipment_products()
+
+ logging.info("Setup power transmission properties")
+ input_dictionary["power_limit_per_circuit"] = TransmissionProcess.get_combined_power_limit_per_circuit()
+ input_dictionary["power_line_property_categories"] = {None: ["power limit", "susceptance per unit"]}
+ input_dictionary["power_line_properties"] = TransmissionProcess.get_connection_properties()[["power limit", "susceptance per unit"]].stack().to_dict()
+ input_dictionary["transmission_reference_node"] = {None: int(TransmissionProcess.reference_node)}
+ input_dictionary["per_unit_base"] = {None: TransmissionProcess.per_unit_base}
+
+ logging.info("Setup impact matrices")
+ input_dictionary["impact_matrix_production"] = ProductionProcess.get_combined_impact_matrix().to_dict()[0]
+ input_dictionary["impact_matrix_storage"] = StorageProcess.get_combined_impact_matrix().to_dict()[0]
+ input_dictionary["impact_matrix_transshipment"] = TransshipmentProcess.get_combined_impact_matrix().to_dict()[0]
+ input_dictionary["impact_matrix_transmission"] = TransmissionProcess.get_combined_impact_matrix().to_dict()[0]
+
+ logging.info("Setup potential capacity")
+ input_dictionary["potential_capacity_production"] = ProductionProcess.get_combined_potential_capacity().to_dict()
+ input_dictionary["potential_capacity_storage"] = StorageProcess.get_combined_potential_capacity().to_dict()
+ input_dictionary["potential_capacity_transshipment"] = TransshipmentProcess.get_combined_potential_capacity().to_dict()
+ input_dictionary["potential_capacity_transmission"] = TransmissionProcess.get_combined_potential_capacity().to_dict()
+
+ logging.info("Setup transmission reliability margins")
+ input_dictionary["reliability_margin_transmission"] = TransmissionProcess.get_combined_safety_margin()
+
+ logging.info("Setup the flow to storage capacity factor for storages")
+ input_dictionary["flow_to_storage_capacity_factor"] = StorageProcess.get_combined_flow_to_storage_capacity_factor().to_dict()
+
+ input_dictionary["operational_nodal_impact_limits"] = ImpactCategory.get_combined_operational_nodal_impact_limits().to_dict()
+ input_dictionary["operational_impact_limits"] = ImpactCategory.get_combined_operational_impact_limits().to_dict()
+ input_dictionary["invest_nodal_impact_limits"] = ImpactCategory.get_combined_invest_nodal_impact_limits().to_dict()
+ input_dictionary["invest_impact_limits"] = ImpactCategory.get_combined_invest_impact_limits().to_dict()
+ input_dictionary["total_nodal_impact_limits"] = ImpactCategory.get_combined_total_nodal_impact_limits().to_dict()
+ input_dictionary["total_impact_limits"] = ImpactCategory.get_combined_total_impact_limits().to_dict()
+
+ input_dictionary["objective_factor_impacts"] = ImpactCategory.get_combined_objective_factor_impact().to_dict()
+ input_dictionary["objective_factor_impact_overshoot"] = ImpactCategory.get_combined_objective_factor_impact_overshoot().to_dict()
+
+ input_dictionary["secured_capacity_factors_production"] = ProductionProcess.get_combined_secured_capacity_factor().to_dict()
+ input_dictionary["secured_capacity_factors_storage"] = StorageProcess.get_combined_secured_capacity_factor().to_dict()
+ input_dictionary["required_total_secured_capacity"] = Product.get_combined_required_total_secured_capacity().to_dict()
+ input_dictionary["required_nodal_secured_capacity"] = Product.get_combined_required_nodal_secured_capacity().to_dict()
+
+ logging.info("Get target Units of Processes, Products and Impacts")
+ input_dictionary["target_units"]={}
+ input_dictionary["target_units"]["processes"] = Process.get_target_units()
+ input_dictionary["target_units"]["products"] = Product.get_target_units()
+ input_dictionary["target_units"]["impacts"] = ImpactCategory.get_target_units()
+
+ # select if timeseries aggregation is used
+ logging.info("Setup time series aggregation dependent variables")
+ if hasattr(config,'time_series_aggregation_config') and config.time_series_aggregation_config["use_time_series_aggregation"]:
+ time_series_data = tsa.get_time_series_data()
+ input_dictionary["time_slices"] = {None: time_series_data["aggregated_time_slices"]["time_slice"]}
+ input_dictionary["time_slice_yearly_weight"] = time_series_data["aggregated_time_slices"]["time_slice_yearly_weights"]
+ input_dictionary["time_slice_duration"] = time_series_data["aggregated_time_slices"]["time_slice_duration"]
+ input_dictionary["next_time_slice"] = time_series_data["aggregated_time_slices"]["next_time_slice"]
+ input_dictionary["time_series_aggregation_info"] = {"cluster_order": None, "hours_per_period": None, "no_typical_periods": None}
+ input_dictionary["time_series_aggregation_info"]["cluster_order"] = time_series_data['aggregation'].clusterOrder
+ input_dictionary["time_series_aggregation_info"]["hours_per_period"] = time_series_data['aggregation'].hoursPerPeriod
+ input_dictionary["time_series_aggregation_info"]["no_typical_periods"] = time_series_data['aggregation'].noTypicalPeriods
+
+ input_dictionary["technology_matrix_production_timeseries"] = time_series_data["aggregated_time_series"]["technologymatrix"].to_dict()
+ input_dictionary["usable_capacity_factor_timeseries_production"] = time_series_data["aggregated_time_series"]["availability"].to_dict()
+ input_dictionary["impact_matrix_non_served_demand"] = time_series_data["aggregated_time_series"]["impact_non_served_demand"].to_dict()
+ input_dictionary["demand"] = time_series_data["aggregated_time_series"]["demand"].to_dict()
+ else:
+ input_dictionary["time_slices"] = {None: list(time_slices.index)}
+ input_dictionary["time_slice_yearly_weight"] = time_slices["yearly_hours"].to_dict()
+ input_dictionary["time_slice_duration"] = time_slices["duration"].to_dict()
+ input_dictionary["next_time_slice"] = time_slices["next_time_slice"].to_dict()
+
+ input_dictionary["technology_matrix_production_timeseries"] = ProductionProcess.get_combined_technology_matrix_timeseries().to_dict()["value"]
+ input_dictionary["usable_capacity_factor_timeseries_production"] = ProductionProcess.get_combined_availability_timeseries().to_dict()["value"]
+ input_dictionary["impact_matrix_non_served_demand"] = Product.get_combined_impact_non_served_demand().to_dict()[0]
+ input_dictionary["demand"] = Product.get_combined_demand_time_series().to_dict()
+ logging.info("Generation of input dictionary finished.")
+
+ logging.info("Store input dictionary in {0}".format(computed_input_path / "input-{0}.pickle".format(str(min(ProcessImpacts.invest_years)))))
+ if not os.path.exists(computed_input_path):
+ os.mkdir(computed_input_path)
+
+ with open(computed_input_path / "input-{0}.pickle".format(str(min(ProcessImpacts.invest_years))), 'wb') as input_file:
+ pickle.dump(input_dictionary, input_file, protocol=pickle.HIGHEST_PROTOCOL)
+
+ with open(computed_input_path / "Dimensions-{0}.json".format(str(min(ProcessImpacts.invest_years))), "w") as dimensions:
+ json.dump(get_dimensionalities_from_input_dictionary(input_dictionary), dimensions)
+
+ return {None: input_dictionary}
+ # return deunitize_input_dictionary({None: input_dictionary})
+
+def get_dimensionalities_from_input_dictionary(input_dictionary: dict):
+ """Returns a dictionary with all unique dimensionalities and an example unit.
+
+ Args:
+ input_dictionary (Dict): a Dictionary with all mandatory inputs
+
+ Returns:
+ dimensions (Dict): returns a Dictionary with dimensions used in the Input Dictionary
+ """
+ dimensions = {}
+ example_unit = units("50 MW")
+ for dictionary in input_dictionary:
+ for element in input_dictionary[dictionary]:
+ if type(input_dictionary[dictionary][element]) == type(example_unit):
+ dimensions[str(input_dictionary[dictionary][element].dimensionality)] = str(input_dictionary[dictionary][element].units)
+
+ return dimensions
+
+def add_new_capacity_from_results(optimization_model: Optimization, years: list = None):
+ """Add the returned new capacity from the optimization to the existing capacity in the data.
+
+ Args:
+ optimization_model (Optimization): Instance of optimization class with all the computed results
+ years (list), default None: a list with all years considered in the investment period
+ """
+
+ logging.info("Add new capacity to processes from optimization")
+ if years == None:
+ years = [min(optimization_model.model_instance.years.ordered_data())]
+ new_capacity = optimization_model.get_new_capacity_from_result()
+
+ target_units_dictionary = {}
+
+ for target_unit in target_units:
+ target_pint_unit = units(target_unit)
+ target_units_dictionary[target_pint_unit.dimensionality] = target_unit
+ idx = pd.IndexSlice
+ for year in progress.bar(years):
+ logging.info("Add new capacity from {0} to production processes".format(year))
+ for process in progress.bar(ProductionProcess.processes):
+ multiplication_factor = convert_quantity_to_correct_unit(process._existing_capacity.at[1, "unit"])
+ # for node in Grid.selected().get_list_of_node_ids():
+ process.add_capacity(year, new_capacity["production"].loc[idx[:,process.name],year].sum(level='node') / multiplication_factor)
+ logging.info("Add new capacity from {0} to storage processes".format(year))
+ for process in progress.bar(StorageProcess.processes):
+ multiplication_factor = convert_quantity_to_correct_unit(process._existing_capacity.at[1, "unit"])
+ # for node in Grid.selected().get_list_of_node_ids():
+ process.add_capacity(year, new_capacity["storage"].loc[idx[:,process.name],year].sum(level='node') / multiplication_factor)
+ logging.info("Add new capacity from {0} to transshipment processes".format(year))
+ for process in progress.bar(TransshipmentProcess.processes):
+ multiplication_factor = convert_quantity_to_correct_unit(process._existing_capacity.at[1, "unit"])
+ # for node in Grid.selected().get_list_of_connection_ids():
+ process.add_capacity(year, new_capacity["transshipment"].loc[idx[:,process.name],year].sum(level='connection') / multiplication_factor)
+ logging.info("Add new capacity from {0} to transmission processes".format(year))
+ for process in progress.bar(TransmissionProcess.processes):
+ multiplication_factor = convert_quantity_to_correct_unit(process._existing_capacity.at[1, "unit"])
+ # for node in Grid.selected().get_list_of_connection_ids():
+ process.add_capacity(year, new_capacity["transmission"].loc[idx[:,process.name],year].sum(level='connection') / multiplication_factor)
+
+ # Reset construction years of all process classes
+ ProductionProcess.construction_years = []
+ StorageProcess.construction_years = []
+ TransshipmentProcess.construction_years = []
+ TransmissionProcess.construction_years = []
diff --git a/secmod/evaluation.py b/secmod/evaluation.py
new file mode 100644
index 0000000..be70cf9
--- /dev/null
+++ b/secmod/evaluation.py
@@ -0,0 +1,1082 @@
+import abc
+import logging
+import pickle
+import os
+import platform
+import pint
+import json
+import copy
+import tkinter as tk
+from tkinter.messagebox import showinfo
+from tkinter import font
+import pandas as pd
+import numpy as np
+import tikzplotlib as plt2tikz
+import pyomo.environ as pyo
+
+from cycler import cycler
+import matplotlib
+import matplotlib.pyplot as plt
+matplotlib.use("TkAgg")
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+from matplotlib.figure import Figure
+from matplotlib.lines import Line2D
+from itertools import compress
+from pathlib import Path
+from clint.textui import progress
+
+from pandastable import Table, TableModel, IndexHeader
+import pandastable.config as pt_config
+import pandastable.core as pt_core
+import pandastable.util as pt_util
+
+import secmod.helpers as helpers
+import secmod.result_processing as result_processing
+from secmod.classes import (
+ Process, Product, config, units,get_target_unit_from_unit)
+
+target_units = json.load(open("SecMOD/00-INPUT/00-RAW-INPUT/01-UNITS-TARGET-OPTIMIZATION.json", 'r'))
+target_units_dictionary = {}
+for target_unit in target_units:
+ target_pint_unit = units(target_unit)
+ target_units_dictionary[target_pint_unit.dimensionality] = target_unit
+
+class AbstractFrame_evaluation(tk.Frame):
+ """ This is the abstract GUI Frame class
+
+ It is used to define the common parts of the MainFrame and the DetailedFrame."""
+ def __init__(self,parent,optimization_results,working_directory,frac_height = 0.8, frac_width = 0.8):
+ self.parent = parent
+ self.optimization_results = optimization_results
+ self.working_directory = working_directory
+ self.parent.resizable(True,True)
+ self.utilities={}
+ # get width and height of screen
+ self.utilities['screen_height'] = parent.winfo_screenheight()
+ self.utilities['screen_width'] = parent.winfo_screenwidth()
+ # define fraction of used screen
+ self.utilities['fraction_height'] = frac_height
+ self.utilities['fraction_width'] = frac_width
+ # set width and height of window
+ self.utilities['window_height'] = int(self.utilities['fraction_height']*self.utilities['screen_height'])
+ self.utilities['window_width'] = int(self.utilities['fraction_width']*self.utilities['screen_width'])
+ # define properties for plot items
+ self.utilities['default_linewidth_areabar'] = 0
+ self.utilities['default_linewidth_lineplot'] = 2
+ self.utilities['changed_linewidth'] = 3.5
+ self.utilities['changed_alpha'] = 1
+ self.utilities['default_alpha'] = 0.8
+ self.utilities['default_alpha_legend'] = 0.6
+ self.utilities['min_shade']= 0.35
+ self.utilities['colormap'] = helpers.get_rwth_colors()
+ # set font
+ if 'Arial' in font.families(): # if Arial available on operation system, select Arial (Corporate Design of the RWTH)
+ self.utilities['standard_font'] = 'Arial'
+ self.parent.option_add('*Font', self.utilities['standard_font'] + ' 9')
+ # set geometry
+ self.parent.geometry('{}x{}+{}+{}'.format(int(self.utilities['window_width']),int(self.utilities['window_height']),int((1-self.utilities['fraction_width'])/2*self.utilities['screen_width']),int((1-self.utilities['fraction_height'])/2*self.utilities['screen_height'])))
+ # frames
+ self.utilities['rows'] = [7,3] # height fraction of frames, important: only integer values
+ self.utilities['columns'] = [6,4] # width fraction of frames, important: only integer values
+ self.utilities['rowints'] = sum(self.utilities['rows'])
+ self.utilities['columnints'] = sum(self.utilities['columns'])
+ # set sizes of Frames
+ for [idxrow, row] in enumerate(self.utilities['rows']):
+ self.parent.grid_rowconfigure(idxrow, weight=row, uniform="x")
+ for [idxcolumn, column] in enumerate(self.utilities['columns']):
+ self.parent.grid_columnconfigure(idxcolumn, weight=column, uniform="x")
+ self.parent.grid_propagate(False)
+ # secmod icon
+ if os.path.exists(str(self.working_directory)+'\\icon\\icon_secmod.ico'):
+ self.parent.iconbitmap(r'icon\\icon_secmod.ico')
+ ## setup Frame Layout
+ # create frames
+ self.frame_south = tk.Frame(self.parent)
+ self.frame_north = tk.Frame(self.parent)
+ self.frame_east = tk.Frame(self.parent)
+ self.frame_southwest = tk.Frame(self.frame_south)
+ self.frame_southeast = tk.Frame(self.frame_south)
+ self.frame_south.grid(row = 1, column = 0,sticky = 'nsew')
+ self.frame_north.grid(row = 0, column = 0,sticky = 'nsew')
+ self.frame_east.grid(row = 0, column = 1,rowspan=2,sticky = 'nsew')
+ self.frame_southwest.grid(row=0, column=0,sticky = 'nsew')
+ self.frame_southeast.grid(row=0, column=1,sticky = 'nse')
+ # adjust south south frame to south frame size
+ self.frame_south.grid_columnconfigure(index = 1, weight = 1)
+ self.frame_south.grid_rowconfigure(index = 1, weight = 1)
+ # southeast frame
+ self.button_save = tk.Button(self.frame_southeast,text = 'Save Plot/Data',command = self.savePlotData)
+ self.button_save.grid(sticky='ne')
+ # initialize attributes which are overridden by Child Class
+ self.plot_type = None
+ self.plot_types = None
+ self.plotted_balance = None
+ self.grouped_balance = None
+ self.extended_balance = None
+ self.unit_time_summation = None
+ self.plotted_balance_name = None
+ self.title_string = None
+ self.selection_balance = None
+ self.list_products = None
+ self.selection_product = None
+ self.codes_of_singleindex_balance = None
+
+ def startPlot(self):
+ """ start setup or plot of figure"""
+ # if figure already created, change plot; otherwise first create figure
+ if not hasattr(self,'fig'):
+ self.setupFigure()
+ self.plotFigure()
+
+ def setupFigure(self):
+ """ set up figure in north frame"""
+ # setup figure
+ matplotlib.style.use('fast')
+ self.utilities['dpi']=100
+ self.fig = Figure(figsize = (self.calculateFrameGeometry(0,0)['width']/self.utilities['dpi'],self.calculateFrameGeometry(0,0)['height']/self.utilities['dpi']))
+ if 'standard_font' in self.utilities: # if standard_font manually set
+ try: # if possible to set standard_font in matplotlib
+ plt.rcParams.update({'font.family': self.utilities['standard_font']})
+ except:
+ logging.info('Unable to set Font in Plot. Used default Font instead.')
+ # plot in north frame
+ self.plot_canvas = FigureCanvasTkAgg(self.fig, master = self.frame_north)
+ self.plot_canvas.get_tk_widget().grid(sticky = 'nsew')
+
+ def calculateFrameGeometry(self,idxrow=0,idxcolumn=0):
+ """ Calculate width and height of frame in GUI """
+ geometry={'width': 0,'height': 0}
+ geometry['width']= int(self.utilities['window_width']*self.utilities['columns'][idxcolumn]/self.utilities['columnints'])
+ geometry['height']= int(self.utilities['window_height']*self.utilities['rows'][idxrow]/self.utilities['rowints'])
+ return geometry
+
+ def plotFigure(self):
+ """ plot balance """
+ if hasattr(self, 'annotation'): # remove annotation
+ self.annotation.set_visible(False)
+ self.fig.clf()
+ self.axes = self.fig.add_axes([0.1,0.1,0.8,0.8])
+ # group balance by Checkbutton Selection (only in Detailed Frame)
+ self.grouped_balance = self.groupBalanceByIndex(self.plotted_balance)
+ # select plot type
+ self.selectPlotType()
+ # adjust axes
+ self.axes.set_xlabel(self.grouped_balance.columns.name.replace('_',' ').title())
+ unit_string = self.getbalanceUnits(self.grouped_balance,self.unit_time_summation)[0]
+ self.axes.set_ylabel(self.selection_balance.get() + ' [{}]'.format(unit_string))
+ # set legend
+ self.createLegend()
+ # title
+ self.title_string = self.getTitleString()
+ self.axes.set_title(self.title_string)
+ # draw
+ self.plot_canvas.draw()
+ # show table
+ self.showTable()
+ # highlight selection over which mouse hovers
+ self.fig.canvas.mpl_connect('motion_notify_event', self.highlightMouseSelection)
+
+ def getbalanceUnits(self,grouped_balance,unit_time_summation, b_extendedDataframe = False):
+ """ Get units of balance in optimization"""
+ unit_string = ''
+ # get name of parameter
+ if 'process' in grouped_balance.index.names:
+ index_name_parameter = 'process'
+ elif 'products' in grouped_balance.index.names:
+ index_name_parameter = 'products'
+ elif 'impact_category' in grouped_balance.index.names:
+ index_name_parameter = 'impact_category'
+
+ # create list of parameters in index level
+ parameter_list = grouped_balance.index.get_level_values(index_name_parameter)
+ unit_list = []
+ # get unit and append to list
+ for parameter in parameter_list:
+ concat_unit_str = self.optimization_results['Utilities']['Unit_dict'][parameter]+unit_time_summation
+ try: # if subunits in target_units_dictionary
+ unit_list.append(get_target_unit_from_unit(concat_unit_str))
+ except: # if not in dictionary
+ unit_list.append(concat_unit_str)
+ logging.warning('Cannot create unit string of {} from input. Used unit string instead'.format(concat_unit_str))
+ if self.plot_type.get() == 'line': # if plot type == line plot, Unit is percent
+ unit_string = '%'
+ else:
+ if not b_extendedDataframe:
+ unit_set = list(np.unique(np.array(unit_list)[self.getNonzeroIndizes()])) # get unique list of units whose corresponding values are nonzero
+ else:
+ unit_set = list(np.unique(np.array(unit_list))) # get unique list of units
+ # create unit_string
+ for unit in unit_set:
+ if unit == unit_set[0]:
+ unit_string+=unit
+ else:
+ unit_string+='/'+unit
+ return unit_string, unit_list
+
+ def selectPlotType(self):
+ """ plots the data according to specified plot type """
+ colors = self.setColors()
+ if self.plot_type.get() == self.plot_types[0][1]: # Area Plot
+ try:
+ self.plot_balance = self.grouped_balance[self.getNonzeroIndizes()].transpose().plot.area(ax=self.axes, linewidth=0,color = colors, alpha = self.utilities['default_alpha'], picker=True,stacked = True)
+ except ValueError as valerr:
+ self.plot_balance = self.grouped_balance[self.getNonzeroIndizes()].transpose().plot.area(ax=self.axes, linewidth=0,color = colors, alpha = self.utilities['default_alpha'], picker=True, stacked = False)
+ logging.warning(str(valerr)+'\nUnstacked Area Plot used')
+ except TypeError as typeerr:
+ logging.warning(str(typeerr))
+ self.plot_balance.set_xticks(self.grouped_balance.transpose().index.values)
+
+ elif self.plot_type.get() == self.plot_types[1][1]: # Bar Plot
+ try:
+ self.plot_balance = self.grouped_balance[self.getNonzeroIndizes()].transpose().plot.bar(ax=self.axes, linewidth=0,color = colors,alpha = self.utilities['default_alpha'],stacked = True, picker=True)
+ self.plot_balance.set_xticklabels(self.plot_balance.get_xticklabels(), rotation='horizontal')
+ except TypeError as typeerr:
+ logging.warning(str(typeerr))
+ elif self.plot_type.get() == self.plot_types[2][1]: # Line Plot
+ # divide by columns of first year to show development in percentage
+ line_balance = self.grouped_balance.div(self.grouped_balance[self.grouped_balance.columns[0]],axis = 0).fillna(0)*100
+ try:
+ self.plot_balance = line_balance[self.getNonzeroIndizes()].transpose().plot(ax=self.axes,linewidth = self.utilities['default_linewidth_lineplot'],color = colors, alpha = self.utilities['default_alpha'], picker=5)
+ self.plot_balance.set_xticks(self.grouped_balance.transpose().index.values)
+ except TypeError as typeerr:
+ logging.warning(str(typeerr))
+ # set ylabel to scientific notation 10^n for n < -4 and n > 2
+ self.axes.ticklabel_format(axis='y',style='sci',scilimits=(-4,3))
+ def createLegend(self):
+ """ creates seperate Legends """
+ if isinstance(self, MainFrame_evaluation): # if in Main Frame
+ if isinstance(self.grouped_balance.index, pd.MultiIndex): # if multiindex
+ # first index
+ level_index = 0
+ custom_lines = [Line2D([0], [0], color=color, lw=4) for color in self.legend_maincolors] # create dummy lines
+ custom_text = list(map(lambda x: str(x).title(),np.unique(self.grouped_balance.index.get_level_values(level_index)[self.getNonzeroIndizes()]))) # get list of level names that have non-zero values
+ legend_title = self.grouped_balance.index.names[level_index].replace('_',' ').title() # legend title
+ self.axes.add_artist(self.axes.legend(custom_lines,custom_text,loc='lower left',title = legend_title,fontsize = 'small',framealpha = self.utilities['default_alpha_legend'])) # add legend
+ # second index
+ level_index = 1
+ custom_lines = [Line2D([0], [0], color=color, lw=4) for color in self.legend_shades] # create dummy lines
+ custom_text = list(map(lambda x: str(x).title(),np.unique(self.grouped_balance.index.get_level_values(level_index)[self.getNonzeroIndizes()]))) # get list of level names that have non-zero values
+ legend_title = self.grouped_balance.index.names[1].replace('_',' ').title() # legend title
+ self.axes.add_artist(self.axes.legend(custom_lines,custom_text,loc='lower right',title = legend_title,fontsize = 'small',framealpha = self.utilities['default_alpha_legend'])) # add legend
+ else: # no multiindex
+ custom_lines = [Line2D([0], [0], color=color, lw=4) for color in self.legend_maincolors] # create dummy lines
+ custom_text = list(map(lambda x: str(x).title(),np.unique(self.grouped_balance.index.values[self.getNonzeroIndizes()]))) # get list of level names that have non-zero values
+ legend_title = self.grouped_balance.index.names[0].replace('_',' ').title() # legend title
+ self.axes.add_artist(self.axes.legend(custom_lines,custom_text,loc='lower left',title = legend_title,fontsize = 'small',framealpha = self.utilities['default_alpha_legend'])) # add legend
+ elif isinstance(self, DetailedFrame_evaluation): # if in Detailed Frame
+ self.axes.get_legend().remove() # if none of the indizes below --> remove legend
+ if 'nodes' in self.grouped_balance.index.names: # select level of nodes
+ level_index = self.grouped_balance.index.names.index('nodes')
+ custom_lines = [Line2D([0], [0], color=color, lw=4) for color in self.legend_maincolors] # create dummy lines
+ custom_text = np.unique(self.grouped_balance.index.get_level_values(level_index)[self.getNonzeroIndizes()]) # get list of level names that have non-zero values
+ legend_title = self.grouped_balance.index.names[level_index].replace('_',' ').title() # legend title
+ self.axes.add_artist(self.axes.legend(custom_lines,custom_text,loc='lower left',title = legend_title,fontsize = 'small',framealpha = self.utilities['default_alpha_legend'])) # add legend
+ elif 'connections' in self.grouped_balance.index.names: # select level of connections
+ level_index = self.grouped_balance.index.names.index('connections')
+ custom_lines = [Line2D([0], [0], color=color, lw=4) for color in self.legend_maincolors] # create dummy lines
+ custom_text = np.unique(self.grouped_balance.index.get_level_values(level_index)[self.getNonzeroIndizes()]) # get list of level names that have non-zero values
+ legend_title = self.grouped_balance.index.names[level_index].replace('_',' ').title() # legend title
+ self.axes.add_artist(self.axes.legend(custom_lines,custom_text,loc='lower left',title = legend_title,fontsize = 'small',framealpha = self.utilities['default_alpha_legend'])) # add legend
+ if 'construction_years' in self.grouped_balance.index.names: # select level of construction years
+ level_index = self.grouped_balance.index.names.index('construction_years')
+ custom_lines = [Line2D([0], [0], color=color, lw=4) for color in self.legend_shades] # create dummy lines
+ custom_text = np.unique(self.grouped_balance.index.get_level_values(level_index)[self.getNonzeroIndizes()]) # get list of level names that have non-zero values
+ legend_title = self.grouped_balance.index.names[level_index].replace('_',' ').title() # legend title
+ self.axes.add_artist(self.axes.legend(custom_lines,custom_text,loc='lower right',title = legend_title,fontsize = 'small',framealpha = self.utilities['default_alpha_legend'])) # add legend
+
+ def setColors(self):
+ """ return list of colors for plot """
+ if isinstance(self.grouped_balance.index, pd.MultiIndex): # if multiindex
+ if isinstance(self, MainFrame_evaluation): # if in Main Frame
+ # main colors
+ level_index = 0
+ nonzero_main = self.grouped_balance.index.codes[level_index][self.getNonzeroIndizes()] # get main indizes which are nonzero
+ nonzero_main_unique = np.unique(nonzero_main) # get unique values of nonzero main indizes
+ self.legend_maincolors = self.utilities['colormap'][nonzero_main_unique%len(self.utilities['colormap'])] # get main colors correlating with main indices
+ maincolors = np.array([self.legend_maincolors[nonzero_main_unique == main_index] for main_index in nonzero_main]).reshape(len(nonzero_main),3) # create color array correlating with main index
+ # shades
+ level_index = 1
+ nonzero_shades = self.grouped_balance.index.codes[level_index][self.getNonzeroIndizes()] # get lesser indizes which are nonzero
+ nonzero_shades_unique = np.unique(nonzero_shades) # get unique values of nonzero lesser indizes
+ self.legend_shades = np.linspace(1.0,self.utilities['min_shade'],len(nonzero_shades_unique)).reshape(len(nonzero_shades_unique),1) # create shades by percentage
+ shades = np.array([self.legend_shades[nonzero_shades_unique == shade] for shade in nonzero_shades]).reshape(len(nonzero_shades),1) # create color array correlating with lesser index
+ elif isinstance(self, DetailedFrame_evaluation): # if in Detailed Frame
+ maincolors = np.tile(self.utilities['colormap'][0],(len(self.grouped_balance[self.getNonzeroIndizes()]),1)) # default main color. Just one main color, so repeat for length of index
+ shades = np.array(1)
+ self.legend_shades = shades
+ if 'nodes' in self.grouped_balance.index.names: # select level of nodes
+ level_index = self.grouped_balance.index.names.index('nodes')
+ nonzero_main = self.grouped_balance.index.codes[level_index][self.getNonzeroIndizes()] # get main indizes which are nonzero
+ nonzero_main_unique = np.unique(nonzero_main) # get unique values of nonzero main indizes
+ self.legend_maincolors = self.utilities['colormap'][nonzero_main_unique%len(self.utilities['colormap'])] # get main colors correlating with index of nodes
+ maincolors = np.array([self.legend_maincolors[nonzero_main_unique == main_index] for main_index in nonzero_main]).reshape(len(nonzero_main),3) # create color array correlating with index of nodes
+ elif 'connections' in self.grouped_balance.index.names: # select level of connections
+ level_index = self.grouped_balance.index.names.index('connections')
+ nonzero_main = self.grouped_balance.index.codes[level_index][self.getNonzeroIndizes()] # get main indizes which are nonzero
+ nonzero_main_unique = np.unique(nonzero_main) # get unique values of nonzero main indizes
+ self.legend_maincolors = self.utilities['colormap'][nonzero_main_unique%len(self.utilities['colormap'])] # get main colors correlating with main indices
+ maincolors = np.array([self.legend_maincolors[nonzero_main_unique == main_index] for main_index in nonzero_main]).reshape(len(nonzero_main),3) # create color array correlating with main index
+ if 'construction_years' in self.grouped_balance.index.names: # select level of construction years
+ level_index = self.grouped_balance.index.names.index('construction_years')
+ nonzero_shades = self.grouped_balance.index.codes[level_index][self.getNonzeroIndizes()] # get lesser indizes which are nonzero
+ nonzero_shades_unique = np.unique(nonzero_shades) # get unique values of nonzero lesser indizes
+ self.legend_shades = np.linspace(1.0,self.utilities['min_shade'],len(nonzero_shades_unique)).reshape(len(nonzero_shades_unique),1) # create shades by percentage
+ shades = np.array([self.legend_shades[nonzero_shades_unique == shade] for shade in nonzero_shades]).reshape(len(nonzero_shades),1) # create color array correlating with index of construction years
+ endcolors = np.multiply(maincolors, shades) # multiply maincolors with shades
+ main_black = np.tile(0.6,3) # grayscale
+ self.legend_shades = np.multiply(main_black,self.legend_shades.reshape(self.legend_shades.size,1)) # create color of first maincolor with used shades
+ else: # no multiindex
+ # endcolors = np.array([self.utilities['colormap'][code%len(self.utilities['colormap'])] for code in range(self.grouped_balance.index.size)])
+ if self.selection_product.get() != self.list_products[0] and isinstance(self, MainFrame_evaluation): # single product and Mainframe
+ maincolors = self.utilities['colormap'][self.codes_of_singleindex_balance[self.getNonzeroIndizes()]%len(self.utilities['colormap'])]
+ else:
+ maincolors = self.utilities['colormap'][np.arange(self.grouped_balance.index.size)[self.getNonzeroIndizes()]%len(self.utilities['colormap'])]
+ self.legend_maincolors = maincolors
+ # endcolors = np.ones((self.grouped_balance.index.size,3)) # create array of ones
+ # endcolors[self.getNonzeroIndizes()] = maincolors # create array correlating with main index, if values zero -> set to 1
+ endcolors = maincolors
+ return endcolors
+
+ def highlightMouseSelection(self,event):
+ """ highlight selection over which mouse currently hovers """
+ def getAnnotation(selection_label,event):
+ """ annonate cursor with label of current selection """
+ if not hasattr(self, 'annotation'): # if no already existing annotation
+ self.annotation = self.axes.text(event.xdata, event.ydata, selection_label.title(),zorder = 6,bbox = dict(boxstyle='round',fc='0.5',alpha=self.utilities['default_alpha_legend'])) # (zorder puts annotation on top of legend)
+ elif not self.annotation.get_visible(): # if already existing but invisible
+ self.annotation = self.axes.text(event.xdata, event.ydata, selection_label.title(),zorder = 6,bbox = dict(boxstyle='round',fc='0.5',alpha=self.utilities['default_alpha_legend']))
+ else:
+ self.annotation.set_position((event.xdata,event.ydata))
+ self.annotation.set_text(selection_label.title())
+ event_in_any_plot_member = True # set bool if mouse over any plot member to False
+ return event_in_any_plot_member
+
+ if event.inaxes == self.axes:
+ event_in_any_plot_member = False # set bool if mouse over any plot member to False
+ if self.plot_type.get() == self.plot_types[0][1]: # area plot
+ for collection in self.axes.collections:
+ # Searching which data member corresponds to current mouse position
+ if collection.contains(event)[0]:
+ collection.set_linewidth(self.utilities['changed_linewidth'])
+ collection.set_alpha(self.utilities['changed_alpha'])
+ event_in_any_plot_member = getAnnotation(collection.get_label(),event) # get Annotation
+ else:
+ collection.set_linewidth(self.utilities['default_linewidth_areabar'])
+ collection.set_alpha(self.utilities['default_alpha'])
+ elif self.plot_type.get() == self.plot_types[1][1]: # bar plot
+ for container in self.axes.containers:
+ for bar in container:
+ if bar.contains(event)[0]:
+ bar.set_linewidth(self.utilities['changed_linewidth'])
+ bar.set_alpha(self.utilities['changed_alpha'])
+ event_in_any_plot_member = getAnnotation(container.get_label(),event) # get Annotation
+ else:
+ bar.set_linewidth(self.utilities['default_linewidth_areabar'])
+ bar.set_alpha(self.utilities['default_alpha'])
+ else: # line plot
+ for line in self.axes.get_lines():
+ # Searching which data member corresponds to current mouse position
+ if line.contains(event)[0]:
+ line.set_linewidth(self.utilities['changed_linewidth'])
+ line.set_alpha(self.utilities['changed_alpha'])
+ event_in_any_plot_member = getAnnotation(line.get_label(),event) # get Annotation
+ else:
+ line.set_linewidth(self.utilities['default_linewidth_lineplot'])
+ line.set_alpha(self.utilities['default_alpha'])
+ if hasattr(self, 'annotation') and not event_in_any_plot_member: # if already existing annotation and mouse not over plot member
+ self.annotation.set_visible(False) # set annotation to invisible
+ # draw
+ self.plot_canvas.draw_idle()
+
+ def showTable(self,extendedDataframe = False):
+ """ Show table with values of dataframe """
+ if extendedDataframe: # if extended Dataframe selected in Detailed Frame --> create Table for extended balance
+ grouped_extended_balance = self.groupBalanceByIndex(self.extended_balance)
+ self.balance_table = copy.deepcopy(grouped_extended_balance)
+ unit_list = self.getbalanceUnits(grouped_extended_balance,self.unit_time_summation, b_extendedDataframe = True)[1]
+ else: # otherwise for plotted balance
+ self.balance_table = copy.deepcopy(self.grouped_balance)
+ unit_list = self.getbalanceUnits(self.grouped_balance,self.unit_time_summation)[1]
+ # insert unit column
+ self.balance_table.insert(0,'unit',unit_list)
+ if not hasattr(self,'table'): # if table not yet created
+ self.table = Table(self.frame_east, dataframe=self.balance_table)
+ self.table.grid()
+ pt_config.apply_options({'fontsize':8,'cellwidth':40},self.table)
+ if 'standard_font' in self.utilities: # if standard_font manually set
+ if self.utilities['standard_font'] in pt_util.getFonts(): # if possible to set standard_font in table
+ pt_config.apply_options({'font': self.utilities['standard_font']},self.table)
+ else:
+ logging.info('Unable to set Font in Table. Used default Font instead.')
+ self.table.show()
+ self.table.showIndex()
+ else:
+ self.table.updateModel(TableModel(self.balance_table))
+ self.table.rowindexheader = IndexHeader(self.table.parentframe, self.table)
+ self.table.rowindexheader.grid(row=0,column=0,rowspan=1,sticky='news')
+ self.adjustManualColumnWidths()
+ self.table.redraw()
+
+ def adjustManualColumnWidths(self):
+ """ Adjust column width of table.
+ Copied from pandastable documentation and cleared line "if w > 200 """
+ self.table.cols = self.table.model.getColumnCount()
+ for col in range(self.table.cols):
+ colname = self.table.model.getColumnName(col)
+ l = self.table.model.getlongestEntry(col)
+ txt = ''.join(['X' for i in range(l+1)])
+ tw = pt_util.getTextLength(txt, self.table.maxcellwidth,
+ font=self.table.thefont)[0]
+ if tw >= self.table.maxcellwidth:
+ tw = self.table.maxcellwidth
+ elif tw < self.table.cellwidth:
+ tw = self.table.cellwidth
+ self.table.columnwidths[colname] = tw
+
+ def savePlotData(self):
+ """ opens new Save Frame """
+ self.newWindow = tk.Toplevel(self.parent)
+ SaveFrame(self.newWindow, self)
+
+ def groupBalanceByIndex(self,input_balance):
+ """ in mainframe no grouping necessary. Method overwritten in Detailed Frame """
+ return input_balance
+
+ def getNonzeroIndizes(self,summed_level = None):
+ """ get indizes of balance whose values are nonzero """
+ if summed_level is not None: # level specified
+ balance_by_level = self.grouped_balance.sum(level = summed_level)
+ else: # no level specified
+ balance_by_level = self.grouped_balance
+ # drop rows that are zero
+ b_nonzero = list((balance_by_level != 0).any(axis=1)) # get entries that are nonzero
+ # if b_nonzero empty, select at least first row
+ if True not in b_nonzero and b_nonzero:
+ b_nonzero[0] = True
+ return b_nonzero
+
+ @abc.abstractmethod
+ def getTitleString(self):
+ """ abstract method. Needs to be implemented in Child Class"""
+
+class MainFrame_evaluation(AbstractFrame_evaluation):
+ """ This is the Main Frame of the evaluation GUI
+
+ It is a child class of AbstractFrame_evaluation"""
+ def __init__(self,parent,optimization_results,working_directory):
+ """ sets up Main Frame of Evaluation GUI """
+ # setup Abstract Frame
+ super().__init__(parent,optimization_results,working_directory,frac_height=0.8, frac_width=0.9)
+ self.parent.title('SecMOD Data Viewer')
+ ## Southwest Frame:
+ # Level 0: Select Product
+ tk.Label(self.frame_southwest,text = 'Select Product',bg = 'grey').grid(row = 0,column=0,sticky='ew')
+ self.list_products = [product.title() for product in self.optimization_results['Utilities']['Parameters']['products']]
+ self.list_products.insert(0,'All Products')
+ self.selection_product = tk.StringVar()
+ self.selection_product.set(self.list_products[0])
+ self.option_menu_product = tk.OptionMenu(self.frame_southwest, self.selection_product, *self.list_products,command=self.getDataframeFromSelection)
+ self.option_menu_product.grid(row=1,column=0, sticky = 'w')
+
+ # Level 1: Select Balance
+ tk.Label(self.frame_southwest,text = 'Select Balance',bg = 'grey').grid(row = 0,column=1,sticky='ew')
+ self.list_balances = ['Product Flow','Demand','Capacities', 'Impacts']
+ self.selection_balance = tk.StringVar()
+ self.selection_balance.set(self.list_balances[0])
+ self.option_menu_balance = tk.OptionMenu(self.frame_southwest, self.selection_balance, *self.list_balances,command=self.getDataframeFromSelection)
+ self.option_menu_balance.grid(row=1,column=1, sticky = 'w')
+
+ # Level 2 + 3: Select Impact/Process specification
+ self.setUpRadiobuttons()
+ self.selection_balance.trace('w',self.setUpRadiobuttons) # if selection_balance changes, set up radiobuttons
+ self.selection_product.trace('w',self.setUpRadiobuttons) # if selection_product changes, set up radiobuttons
+
+ # Level 4: Select Plot Type
+ tk.Label(self.frame_southwest,text = 'Select Plot Type',bg = 'grey').grid(row = 0,column = 4,sticky='ew')
+ self.plot_types = [
+ ('Area Plot','area',1),
+ ('Bar Plot','bar',2),
+ ('Line Plot','line',3)
+ ]
+ self.plot_type = tk.StringVar()
+ self.plot_type.set(self.plot_types[0][1])
+ for txt_plot_type,value_plot_type, row_plot_type in self.plot_types:
+ tk.Radiobutton(self.frame_southwest,
+ text = txt_plot_type,
+ variable = self.plot_type,
+ command = self.plotFigure,
+ value = value_plot_type).grid(row = row_plot_type,column = 4,sticky ='w')
+
+ ## get Dataframe and start Plot
+ self.getDataframeFromSelection()
+
+ def setUpRadiobuttons(self,*args):
+ """ Set up and change radiobuttons for product flows/impacts/processes """
+ self.dict_radiobuttons = {}
+ # delete current widgets
+ list_widgets = []
+ for widget in self.frame_southwest.children:
+ if self.frame_southwest.children[widget].grid_info()['column'] in [2,3]:
+ list_widgets.append(widget)
+ # delete every widget with column = 2 or 3
+ for widget in list_widgets:
+ self.frame_southwest.children[widget].destroy()
+ # Product flows
+ self.list_product_flows = [
+ ('Total Product Flows','total',1),
+ ('Production Processes','production',2),
+ ('Storage Processes','storage',3),
+ ('Transshipment Processes','transshipment',4),
+ ('Transmission Processes','transmission',5)
+ ]
+ self.selection_product_flows = tk.StringVar()
+ self.selection_product_flows.set(self.list_product_flows[0][1])
+ # Demand
+ self.list_demand = [
+ ('Demand','demand',1),
+ ('Non Served Demand','non_served_demand',2),
+ ]
+ self.selection_demand = tk.StringVar()
+ self.selection_demand.set(self.list_demand[0][1])
+ # Processes
+ self.list_processes = [
+ ('Production Processes','production',1),
+ ('Storage Processes','storage',2),
+ ('Transshipment Processes','transshipment',3),
+ ('Transmission Processes','transmission',4)
+ ]
+ self.selection_process = tk.StringVar()
+ self.selection_process.set(self.list_processes[0][1])
+ # Impacts
+ # impact type
+ self.list_impacts = [
+ ('Total Impact','total',1),
+ ('Invest Impact','invest',2),
+ ('Operational Impact','operational',3)
+ ]
+ self.selection_impact = tk.StringVar()
+ self.selection_impact.set(self.list_impacts[0][1])
+ # impact of processes/non served demand
+ self.list_impact_process = [
+ ('All Categories','total',1),
+ ('Production Processes','production',2),
+ ('Storage Processes','storage',3),
+ ('Transshipment Processes','transshipment',4),
+ ('Transmission Processes','transmission',5),
+ ('Non Served Demand','non_served_demand',6)
+ ]
+ self.selection_impact_process = tk.StringVar()
+ if self.selection_product.get() == self.list_products[0]: # if all products selected
+ self.selection_impact_process.set(self.list_impact_process[0][1])
+ else: # if single product selected, not impacts of all categories selectable
+ self.selection_impact_process.set(self.list_impact_process[1][1])
+
+ if self.selection_balance.get() == self.list_balances[0]: # Product Flows
+ tk.Label(self.frame_southwest,text = 'Select Product Flow by Process Category',bg = 'grey').grid(row = 0,column=2,sticky='ew')
+ # radiobuttons of PROCESS Category
+ for txt_selection_product_flows,value_selection_product_flows, row_selection_product_flows in self.list_product_flows:
+ self.dict_radiobuttons[value_selection_product_flows] = tk.Radiobutton(
+ self.frame_southwest,
+ text = txt_selection_product_flows,
+ variable = self.selection_product_flows,
+ command = self.getDataframeFromSelection,
+ value = value_selection_product_flows)
+ self.dict_radiobuttons[value_selection_product_flows].grid(row = row_selection_product_flows,column = 2,sticky ='w')
+ elif self.selection_balance.get() == self.list_balances[1]: # Demand
+ tk.Label(self.frame_southwest,text = 'Select Demand',bg = 'grey').grid(row = 0,column=2,sticky='ew')
+ # radiobuttons of DEMAND Type
+ for txt_selection_demand,value_selection_demand, row_selection_demand in self.list_demand:
+ tk.Radiobutton(
+ self.frame_southwest,
+ text = txt_selection_demand,
+ variable = self.selection_demand,
+ command = self.getDataframeFromSelection,
+ value = value_selection_demand).grid(row = row_selection_demand,column = 2,sticky ='w')
+ elif self.selection_balance.get() == self.list_balances[2]: # Capacities
+ tk.Label(self.frame_southwest,text = 'Select Processes',bg = 'grey').grid(row = 0,column=2,sticky='ew')
+ # radiobuttons of PROCESS Category
+ for txt_selection_process,value_selection_process, row_selection_process in self.list_processes:
+ self.dict_radiobuttons[value_selection_process] = tk.Radiobutton(
+ self.frame_southwest,
+ text = txt_selection_process,
+ variable = self.selection_process,
+ command = self.getDataframeFromSelection,
+ value = value_selection_process)
+ self.dict_radiobuttons[value_selection_process].grid(row = row_selection_process,column = 2,sticky ='w')
+ elif self.selection_balance.get() == self.list_balances[3]: # Impacts
+ tk.Label(self.frame_southwest,text = 'Select Impact Category',bg = 'grey').grid(row = 0,column=2,sticky='ew')
+ # radiobuttons of IMPACT Type
+ for txt_selection_impact,value_selection_impact, row_selection_impact in self.list_impacts:
+ tk.Radiobutton(self.frame_southwest,
+ text = txt_selection_impact,
+ variable = self.selection_impact,
+ command =self.getDataframeFromSelection,
+ value = value_selection_impact).grid(row = row_selection_impact,column = 2,sticky ='w')
+ tk.Label(self.frame_southwest,text = 'Select Impacts by Producer',bg = 'grey').grid(row = 0,column=3,sticky='ew')
+ # radiobuttons of IMPACT Producer
+ for txt_selection_impact_process,value_selection_impact_process, row_selection_impact_process in self.list_impact_process:
+ self.dict_radiobuttons[value_selection_impact_process] = tk.Radiobutton(self.frame_southwest,
+ text = txt_selection_impact_process,
+ variable = self.selection_impact_process,
+ command =self.getDataframeFromSelection,
+ value = value_selection_impact_process)
+ self.dict_radiobuttons[value_selection_impact_process].grid(row = row_selection_impact_process,column = 3,sticky ='w')
+ if self.selection_product.get() != self.list_products[0] and row_selection_impact_process == 1: # if single product and first radiobutton, disable radiobutton
+ self.dict_radiobuttons[value_selection_impact_process].config(state=tk.DISABLED)
+
+ def getDataframeFromSelection(self,*args):
+ """ Create Dataframe which contains the information representing the selection,
+ i.e. selection of Product, Balance and Impact/Capacities
+ """
+ # get Balance by selection balances
+ if self.selection_balance.get() == self.list_balances[0]: # Product Flows
+ if self.selection_product_flows.get() == self.list_product_flows[0][1]: # Total Product Flows
+ self.plotted_balance_name = 'product_flow'
+ elif self.selection_product_flows.get() == self.list_product_flows[1][1]: # Production processes
+ self.plotted_balance_name = 'product_flow_production'
+ elif self.selection_product_flows.get() == self.list_product_flows[2][1]: # Storage processes
+ self.plotted_balance_name = 'product_flow_storage'
+ elif self.selection_product_flows.get() == self.list_product_flows[3][1]: # Transshipment processes
+ self.plotted_balance_name = 'product_flow_transshipment'
+ elif self.selection_product_flows.get() == self.list_product_flows[4][1]: # Transmission processes
+ self.plotted_balance_name = 'product_flow_transmission'
+ elif self.selection_balance.get() == self.list_balances[1]: # Demand
+ if self.selection_demand.get() == self.list_demand[0][1]: # Demand
+ self.plotted_balance_name = 'demand'
+ elif self.selection_demand.get() == self.list_demand[1][1]: # Non Served Demand
+ self.plotted_balance_name = 'non_served_demand'
+ elif self.selection_balance.get() == self.list_balances[2]: # Capacities
+ if self.selection_process.get() == self.list_processes[0][1]: # Production Processes
+ self.plotted_balance_name = 'existing_capacity_production'
+ elif self.selection_process.get() == self.list_processes[1][1]: # Storage Processes
+ self.plotted_balance_name = 'existing_capacity_storage'
+ elif self.selection_process.get() == self.list_processes[2][1]: # Transshipment Processes
+ self.plotted_balance_name = 'existing_capacity_transshipment'
+ elif self.selection_process.get() == self.list_processes[3][1]: # Transmission Processes
+ self.plotted_balance_name = 'existing_capacity_transmission'
+ elif self.selection_balance.get() == self.list_balances[3]: # Impacts
+ if self.selection_impact.get() == self.list_impacts[0][1]: # Total Impact
+ self.plotted_balance_name = 'total_impact'
+ elif self.selection_impact.get() == self.list_impacts[1][1]: # Invest Impact
+ self.plotted_balance_name = 'invest_impact'
+ elif self.selection_impact.get() == self.list_impacts[2][1]: # Operational Impact
+ self.plotted_balance_name = 'operational_impact'
+ # select by Category
+ if self.selection_impact_process.get() == self.list_impact_process[1][1]: # Production Processes
+ self.plotted_balance_name += '_production'
+ elif self.selection_impact_process.get() == self.list_impact_process[2][1]: # Storage Processes
+ self.plotted_balance_name += '_storage'
+ elif self.selection_impact_process.get() == self.list_impact_process[3][1]: # Transshipment Processes
+ self.plotted_balance_name += '_transshipment'
+ elif self.selection_impact_process.get() == self.list_impact_process[4][1]: # Transmission Processes
+ self.plotted_balance_name += '_transmission'
+ elif self.selection_impact_process.get() == self.list_impact_process[5][1]: # Non Served Demand
+ self.plotted_balance_name += '_non_served_demand'
+ # get balance from optimization results
+ # plotted balance is already grouped --> grouping in mainframe not necessary
+ # for detailed frame plotted extended balance
+ self.plotted_balance = self.optimization_results['Results'][self.plotted_balance_name+'_grouped']
+ self.plotted_extended_balance = self.optimization_results['Results'][self.plotted_balance_name]
+ self.unit_time_summation = self.optimization_results['Utilities']['Unit_time_summation'][self.plotted_balance_name]
+
+ # SINGLE PRODUCT
+ if self.selection_product.get() != self.list_products[0]:
+ if self.selection_balance.get() == self.list_balances[0]: # Product Flows
+ # get list of products in balance
+ product_list_balance = self.plotted_balance.index.get_level_values('products')
+ # get indices of corresponding products
+ idx_list = [product_index for product_index, product_name in enumerate(product_list_balance) if product_name == self.selection_product.get().lower()]
+ elif self.selection_balance.get() == self.list_balances[1]: # Demand
+ # get list of products in balance
+ product_list_balance = self.plotted_balance.index.get_level_values('products')
+ # get indices of corresponding products
+ idx_list = [product_index for product_index, product_name in enumerate(product_list_balance) if product_name == self.selection_product.get().lower()]
+ elif self.selection_balance.get() == self.list_balances[2]: # Capacities
+ # get list of processes in balance
+ process_list_balance = self.plotted_balance.index.get_level_values('process')
+ # get indices of processes of corresponding products
+ idx_list = [process_index for process_index, process_name in enumerate(process_list_balance) if process_name in self.optimization_results['Utilities']['ProductsOfProcess'][self.selection_process.get()+'_products'][self.selection_product.get().lower()]]
+ elif self.selection_balance.get() == self.list_balances[3]: # Impacts
+ if self.selection_impact_process.get() == self.list_impact_process[0][1]: # All
+ idx_list = range(self.plotted_balance.shape[0])
+ elif self.selection_impact_process.get() == self.list_impact_process[5][1]: # Non Served Demand
+ # get list of products in balance
+ product_list_balance = self.plotted_balance.index.get_level_values('impact_products')
+ # get indices of corresponding products
+ idx_list = [product_index for product_index, product_name in enumerate(product_list_balance) if product_name == self.selection_product.get().lower()]
+ else: # Processes
+ # get list of processes in balance
+ process_list_balance = self.plotted_balance.index.get_level_values('impact_process')
+ # get indices of processes of corresponding products
+ idx_list = [process_index for process_index, process_name in enumerate(process_list_balance) if process_name in self.optimization_results['Utilities']['ProductsOfProcess'][self.selection_impact_process.get()+'_products'][self.selection_product.get().lower()]]
+ self.plotted_balance = self.plotted_balance.iloc[idx_list]
+ if not isinstance(self.plotted_balance.index, pd.MultiIndex): # if not multiindex
+ self.codes_of_singleindex_balance = np.array(idx_list)
+
+ if self.plotted_balance.empty: # empty Dataframe selected
+ logging.warning('Empty Dataframe selected, since no {} matching {}. Set Balance to Power Flow'.format(self.selection_balance.get(),self.selection_product.get()))
+ self.selection_balance.set(self.list_balances[0]) # set balance to power flow
+ self.getDataframeFromSelection() # get new Dataframe
+ # enable/disable radiobuttons and checkbuttons
+ self.disableEnableRadiobuttons()
+ # plot
+ self.startPlot()
+
+ def disableEnableRadiobuttons(self):
+ """ Disable radiobuttons of balance specification, which is not available for product
+
+ Only necessary for single product"""
+ # SINGLE PRODUCT
+ if self.selection_product.get() != self.list_products[0]:
+ # disable radiobutton of process category which no process corresponding to selected product
+ for process_category in self.list_processes:
+ # process category has no process corresponding to selected product
+ if not self.optimization_results['Utilities']['ProductsOfProcess'][process_category[1]+'_products'][self.selection_product.get().lower()]:
+ if self.dict_radiobuttons:
+ self.dict_radiobuttons[process_category[1]].config(state=tk.DISABLED)
+
+ def plotFigure(self):
+ """ plot balance
+
+ inherited from AbstractFrame_evaluation but click functionality added"""
+ super().plotFigure()
+ # click on plot to get detailed view
+ self.fig.canvas.mpl_connect('pick_event', self.openDetailedWindow)
+
+ def openDetailedWindow(self, event):
+ """ open Detailed Frame to further inspect line/bar/area clicked on"""
+ # get set of indices of selected line/bar/area
+ if hasattr(event, 'artist'): # mouse on plot
+ if self.plot_type.get() != self.plot_types[1][1]: # if not bar plot
+ self.selected_label = event.artist.get_label().strip('()')
+ label_set = set(self.selected_label.split(', '))
+ else: # bar plot
+ label_list = [container.get_label() for container in event.artist.axes.containers if event.artist in container.get_children()]
+ self.selected_label = label_list[0].strip('()')
+ label_set = set(self.selected_label.split(', '))
+ # get dataframe of selected line/bar/area
+ index_list = [index for index in self.plotted_extended_balance.index if label_set.issubset(set(index))]
+ reduced_balance = self.plotted_extended_balance.loc[index_list]
+ # create new DetailedFrame
+ self.newWindow = tk.Toplevel(self.parent)
+ DetailedFrame_evaluation(self.newWindow, self.optimization_results, self.working_directory,reduced_balance,self)
+
+ def getTitleString(self):
+ """ Get Title String depended on selection of balance """
+ if self.selection_balance.get() == self.list_balances[0]: # Product Flows
+ title_string = 'Product Flow'
+ if self.selection_product_flows.get() != self.list_product_flows[0][1]:
+ title_string += ' of ' + self.selection_product_flows.get().title() + ' Processes'
+ elif self.selection_balance.get() == self.list_balances[1]: # Demand
+ title_string = self.selection_demand.get().replace('_',' ').title()
+ elif self.selection_balance.get() == self.list_balances[2]: # Capacities
+ title_string = 'Existing Capacity of ' + self.selection_process.get().title() + ' Processes'
+ elif self.selection_balance.get() == self.list_balances[3]: # Impacts
+ title_string = self.selection_impact.get().title() + ' Impact'
+ if self.selection_impact_process.get() != self.list_impact_process[0][1]: # not total
+ if self.selection_impact_process.get() != self.list_impact_process[5][1]: # process category
+ title_string += ' of ' + self.selection_impact_process.get().title() + ' Processes'
+ else: # not non served demand
+ title_string += ' of ' + self.selection_impact_process.get().replace('_',' ').title()
+ if self.selection_product.get() != self.list_products[0]: # single product
+ title_string += ' for ' + self.selection_product.get()
+ return title_string
+
+class DetailedFrame_evaluation(AbstractFrame_evaluation):
+ """ This is the Detailed Frame of the evaluation GUI
+
+ It is a child class of AbstractFrame_evaluation"""
+ def __init__(self,parent,optimization_results,working_directory,reduced_balance,mainframe):
+ """ sets up Detailed Frame of Evaluation GUI """
+ super().__init__(parent,optimization_results,working_directory,frac_height=0.8, frac_width=0.9)
+ self.parent.title('Detailed SecMOD Data Viewer')
+ self.parent.grab_set()
+ # copy values from mainframe
+ self.mainframe = mainframe
+ self.plotted_balance = reduced_balance
+ self.plotted_balance_name = self.mainframe.plotted_balance_name
+ self.extended_balance = self.mainframe.plotted_extended_balance
+ self.list_balances = self.mainframe.list_balances
+ self.list_products = self.mainframe.list_products
+ self.list_impact_process = self.mainframe.list_impact_process
+ self.selection_balance = self.mainframe.selection_balance
+ self.selection_product_flows = self.mainframe.selection_product_flows
+ self.selection_demand = self.mainframe.selection_demand
+ self.selection_process = self.mainframe.selection_process
+ self.selection_impact = self.mainframe.selection_impact
+ self.selection_impact_process = self.mainframe.selection_impact_process
+ self.selection_product = self.mainframe.selection_product
+ self.unit_time_summation = self.mainframe.unit_time_summation
+ self.codes_of_singleindex_balance = self.mainframe.codes_of_singleindex_balance
+ # setup Frame Layout
+ # Level 0: Select Level of Aggregation
+ tk.Label(self.frame_southwest,text = 'Sum Results over',bg = 'grey').grid(row = 0,column=0,sticky='ew')
+ self.b_nodes = tk.IntVar()
+ self.b_nodes.set(1)
+ self.b_time_slices = tk.IntVar()
+ self.b_time_slices.set(1)
+ self.b_construction_years = tk.IntVar()
+ self.b_construction_years.set(1)
+ self.checkbutton_nodes = tk.Checkbutton(self.frame_southwest,text='nodes/connections',variable = self.b_nodes, command=self.startPlot)
+ self.checkbutton_construction_years = tk.Checkbutton(self.frame_southwest,text='construction years',variable = self.b_construction_years, command=self.startPlot)
+ self.checkbutton_nodes.grid(row = 1,column=0, sticky = 'w')
+ self.checkbutton_construction_years.grid(row = 2,column=0, sticky = 'w')
+ # comment toggled since of no suitable use yet
+ # self.checkbutton_time_slices = tk.Checkbutton(self.frame_southwest,text='time slices',variable = self.b_time_slices, command=lambda:self.plotFigure())
+ # self.checkbutton_time_slices.grid(row = 3,column=0, sticky = 'w')
+
+ # Level 1: Select Plot Type
+ tk.Label(self.frame_southwest,text = 'Select Plot Type',bg = 'grey').grid(row = 0,column = 1,sticky='ew')
+ self.plot_types = [
+ ('Area Plot','area',1),
+ ('Bar Plot','bar',2),
+ ('Line Plot','line',3)
+ ]
+ self.plot_type = tk.StringVar()
+ self.plot_type.set(self.mainframe.plot_type.get())
+ for txt_plot_type,value_plot_type, row_plot_type in self.plot_types:
+ tk.Radiobutton(self.frame_southwest,
+ text = txt_plot_type,
+ variable = self.plot_type,
+ command = self.plotFigure,
+ value = value_plot_type).grid(row = row_plot_type,column = 1,sticky ='w')
+
+ # Level 2: Select DataFrame of Table
+ tk.Label(self.frame_southwest,text = 'Show Table for',bg = 'grey').grid(row = 0,column = 2,sticky='ew')
+ self.table_options = [
+ ('Detailed Dataset','detailed',1),
+ ('Entire Dataset','total',2)
+ ]
+ self.table_option = tk.StringVar()
+ self.table_option.set(self.table_options[0][1])
+ for txt_table_option,value_table_option, row_table_option in self.table_options:
+ tk.Radiobutton(self.frame_southwest,
+ text = txt_table_option,
+ variable = self.table_option,
+ command = self.showTable,
+ value = value_table_option).grid(row = row_table_option,column = 2,sticky ='w')
+ # start Plot
+ self.disableEnableCheckbuttons()
+ self.startPlot()
+
+ def disableEnableCheckbuttons(self):
+ """ Disable checkbuttons of aggregation level, which is not available in balance """
+ # reset Checkbuttons to default value
+ self.b_nodes.set(1)
+ self.b_time_slices.set(1)
+ self.b_construction_years.set(1)
+ # get index names of balance
+ index_names = self.optimization_results['Utilities']['Index_names'][self.plotted_balance_name]
+ # enable/disable nodes or connections
+ if ('nodes' or 'connections') in index_names:
+ # enable Checkbutton
+ self.checkbutton_nodes.config(state=tk.NORMAL)
+ else:
+ # disable Checkbutton
+ self.checkbutton_nodes.config(state=tk.DISABLED)
+
+ # enable/disable construction years
+ if 'construction_years' in index_names:
+ # enable Checkbutton
+ self.checkbutton_construction_years.config(state=tk.NORMAL)
+ else:
+ # disable Checkbutton
+ self.checkbutton_construction_years.config(state=tk.DISABLED)
+
+ # comment toggled since of no suitable use yet
+ # enable/disable time slices
+ # if 'time_slices' in index_names:
+ # # enable Checkbutton
+ # self.checkbutton_time_slices.config(state=tk.NORMAL)
+ # else:
+ # # disable Checkbutton
+ # self.checkbutton_time_slices.config(state=tk.DISABLED)
+
+ def groupBalanceByIndex(self,input_balance):
+ """ Group balance by level of aggregation. Index selected by checkbuttons"""
+ # simplify Plot Results
+ grouped_balance = copy.deepcopy(input_balance)
+ index_names = self.optimization_results['Utilities']['Index_names'][self.plotted_balance_name]
+ # sum over nodes, if 'nodes' in index
+ if 'nodes' in index_names and len(index_names)>1 and self.b_nodes.get()==1:
+ index_names = [index_of_var for index_of_var in index_names if index_of_var != 'nodes']
+ grouped_balance = grouped_balance.groupby(index_names).sum()
+ # sum over connections, if 'connections' in index
+ if 'connections' in index_names and len(index_names)>1 and self.b_nodes.get()==1:
+ index_names = [index_of_var for index_of_var in index_names if index_of_var != 'connections']
+ grouped_balance = grouped_balance.groupby(index_names).sum()
+ # sum over construction years, if 'construction_years' in index
+ if 'construction_years' in index_names and len(index_names)>1 and self.b_construction_years.get()==1:
+ index_names = [index_of_var for index_of_var in index_names if index_of_var != 'construction_years']
+ grouped_balance = grouped_balance.groupby(index_names).sum()
+ grouped_balance = grouped_balance.astype(float)
+ return grouped_balance
+
+ def showTable(self):
+ """ show table
+
+ select whether detailed or entire dataset shown in table """
+ if self.table_option.get() == self.table_options[0][1]: # detailed dataset
+ super().showTable(extendedDataframe= False)
+ else: # entire dataset
+ super().showTable(extendedDataframe= True)
+
+ def getTitleString(self):
+ """ Get Title String depended on selection of balance
+
+ Same Title as MainFrame, only 'Detailed' added """
+ title_string = '{} ({})'.format(self.mainframe.title_string,self.mainframe.selected_label.title())
+
+ return title_string
+
+class SaveFrame:
+ """ New Frame in order to save plot and Data """
+ def __init__(self,parent,mainframe):
+ """ sets up save frame """
+ self.parent = parent
+ self.parent.title('Save Plot and Data')
+ self.parent.grab_set()
+ self.mainframe = mainframe
+ # set font
+ if 'Arial' in font.families(): # if Arial available on operation system, select Arial (Corporate Design of the RWTH)
+ standard_font = 'Arial'
+ self.parent.option_add('*Font', standard_font + ' 9')
+ # set up Message how to use save Frame
+ message_save = 'Save plots and table of current data in the SecMOD DataViewer.\nSelect export format, insert filename for exports and select folder path.\nCreates export folder if not yet created.'
+ tk.Label(self.parent, text=message_save, anchor = 'w',justify = 'left').grid(row=0, column = 0, columnspan = 3, sticky = 'nsew')
+ # set up checkbuttons - export format
+ tk.Label(self.parent,text = 'Export Format',bg = 'grey').grid(row = 1,column=0,sticky='ew')
+ # xlsx
+ self.b_xlsx= tk.IntVar()
+ self.b_xlsx.set(0)
+ # PNG
+ self.b_png = tk.IntVar()
+ self.b_png.set(0)
+ # PDF
+ self.b_pdf = tk.IntVar()
+ self.b_pdf.set(0)
+ # Tikz
+ self.b_tikz = tk.IntVar()
+ self.b_tikz.set(0)
+ # checkbuttons
+ self.checkbutton_xlsx = tk.Checkbutton(self.parent,text='XLSX',variable = self.b_xlsx,command = self.toggleSaveButton)
+ self.checkbutton_png = tk.Checkbutton(self.parent,text='PNG',variable = self.b_png,command = self.toggleSaveButton)
+ self.checkbutton_pdf = tk.Checkbutton(self.parent,text='PDF',variable = self.b_pdf,command = self.toggleSaveButton)
+ self.checkbutton_tikz = tk.Checkbutton(self.parent,text='Tikz',variable = self.b_tikz,command = self.toggleSaveButton)
+ self.checkbutton_xlsx.grid(row=2,column=0,sticky='ew')
+ self.checkbutton_png.grid(row=3,column=0,sticky='ew')
+ self.checkbutton_pdf.grid(row=4,column=0,sticky='ew')
+ self.checkbutton_tikz.grid(row=5,column=0,sticky='ew')
+ # set up title selection with default filename
+ # default filename defined by balance
+ default_filename = 'Results'
+ if self.mainframe.selection_balance.get() == self.mainframe.list_balances[0]: # Product Flows
+ default_filename += '_product_flow_' + self.mainframe.selection_product_flows.get()
+ elif self.mainframe.selection_balance.get() == self.mainframe.list_balances[1]: # Demand
+ default_filename += '_' + self.mainframe.selection_demand.get()
+ elif self.mainframe.selection_balance.get() == self.mainframe.list_balances[2]: # Capacities
+ default_filename += '_existing_capacity_' + self.mainframe.selection_process.get()
+ elif self.mainframe.selection_balance.get() == self.mainframe.list_balances[3]: # Impacts
+ default_filename += '_' + self.mainframe.selection_impact.get() + '_impact'
+ if self.mainframe.selection_impact_process.get() != self.mainframe.list_impact_process[0][1]: # by process category
+ default_filename += '_' + self.mainframe.selection_impact_process.get()
+ if self.mainframe.selection_product.get() != self.mainframe.list_products[0]: # single product
+ default_filename += '_' + self.mainframe.selection_product.get().lower()
+ tk.Label(self.parent,text = 'Select Filename',bg = 'grey').grid(row = 1,column=1,sticky='ew')
+ # create entry to alter filename
+ self.entry_filename = tk.Entry(self.parent)
+ self.entry_filename.insert(tk.END,default_filename)
+ self.entry_filename.grid(row = 2,column=1,columnspan = 2,sticky='ew')
+ # set up path to save plot/data
+ tk.Label(self.parent,text = 'Select Folderpath',bg = 'grey').grid(row = 3,column=1,sticky='ew')
+ self.string_folderpath = tk.StringVar()
+ self.string_folderpath.set(str(self.mainframe.working_directory)+'/SecMOD/01-MODEL-RESULTS')
+ tk.Label(self.parent,textvariable = self.string_folderpath).grid(row = 4,column=1,columnspan = 2,sticky='ew')
+ self.button_folderpath = tk.Button(self.parent,text = 'Browse Folder',command = self.browseFolder)
+ self.button_folderpath.grid(row = 3,column=2,sticky='ew')
+ # set up save button and message
+ self.button_save = tk.Button(self.parent,text = 'Save Selection',command = self.saveSelection)
+ self.button_save.grid(row = 6,column=0,sticky='ew')
+ self.string_save_message = tk.StringVar()
+ tk.Label(self.parent,textvariable = self.string_save_message).grid(row = 6,column=1,sticky='ew')
+ # set up destroy button
+ self.button_destroy = tk.Button(self.parent,text = 'Close',command = self.parent.destroy)
+ self.button_destroy.grid(row = 6,column=2,sticky='ew')
+ # toggle save button
+ self.toggleSaveButton()
+
+ def toggleSaveButton(self):
+ """ disables the SaveButton if no checkbutton selected """
+ # clear save message label
+ self.string_save_message.set('')
+ # toggle save button
+ if self.b_xlsx.get() == 0 and self.b_png.get() == 0 and self.b_pdf.get() == 0 and self.b_tikz.get() == 0: # no checkbutton selected
+ self.button_save.config(state=tk.DISABLED)
+ else:
+ self.button_save.config(state=tk.NORMAL)
+
+ def browseFolder(self):
+ """ Browses Folder and selects new Folder """
+ # clear save message label
+ self.string_save_message.set('')
+ # get folderpath
+ folderpath = tk.filedialog.askdirectory(title = 'Please select a directory')
+ if folderpath:
+ # if folderpath specified. If no new path selected, use default folderpath
+ self.string_folderpath.set(folderpath)
+
+ def saveSelection(self):
+ """ saves the selection as <filename> in <folderpath> """
+ if self.entry_filename.get() == '': # if no name specified
+ showinfo('No Filename','Please specify Filename')
+ else:
+ if os.path.split(self.string_folderpath.get())[1] != 'Export': # if not already in Export Folder
+ exportfolder = self.string_folderpath.get()+"/Export" # add Export to path
+ if not os.path.exists(exportfolder): # if "Export" Folder doesn't exist
+ os.mkdir(exportfolder) # create Export Folder
+ if self.b_xlsx.get() == 1: # save xlsx
+ self.mainframe.balance_table.to_excel("{0}/{1}.{2}".format(exportfolder,self.entry_filename.get(), "xlsx"))
+ if self.b_png.get() == 1: # save png
+ self.mainframe.fig.savefig("{0}/{1}.{2}".format(exportfolder,self.entry_filename.get(), "png"))
+ if self.b_pdf.get() == 1: # save pdf
+ self.mainframe.fig.savefig("{0}/{1}.{2}".format(exportfolder,self.entry_filename.get(), "pdf"))
+ if self.b_tikz.get() == 1: # save tikz
+ plt2tikz.save("{0}/{1}.{2}".format(exportfolder,self.entry_filename.get(), "tex"),figure = self.mainframe.fig)
+ logging.info('Results exported')
+ # set save message
+ self.string_save_message.set('Selection saved!')
+
+def evaluate(working_directory):
+ """Evaluates the results of the optimization."""
+ # open optimization results
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "Extracted Results" / "Optimization_Results.pickle", 'rb') as input_file:
+ optimization_results = pickle.load(input_file)
+ # set up GUI
+ root = tk.Tk()
+ MainFrame_evaluation(root,optimization_results,working_directory)
+ root.mainloop()
+
+def start_evaluation(working_directory, debug_evaluation=False):
+ helpers.log_heading("Processing results")
+ # check whether Results already extracted
+ if os.path.exists(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "Extracted Results" / "Date_Log_Raw_Results.pickle"):
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "Extracted Results" / "Date_Log_Raw_Results.pickle", "rb") as input_file:
+ date_creation_log = pickle.load(input_file)
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "InvestmentModel_{0}.pickle".format(config.invest_years[0]), "rb") as input_file:
+ # get creation date of Raw Results:"InvestmentModel_"+year[0]+".pickle"
+ if platform.system() == 'Windows':
+ date_creation_result_file = os.path.getmtime(input_file.name)
+ # different system
+ else:
+ date_creation_result_file = os.stat(input_file.name).st_mtime
+ # if modification date of current result file is not the modification date of logged file
+ if date_creation_log != date_creation_result_file or debug_evaluation:
+ result_processing.extract_results(working_directory)
+ else:
+ result_processing.extract_results(working_directory)
+ evaluate(working_directory)
+
+if __name__ == "__main__":
+ working_directory = Path().cwd()
+ # start evaluation
+ start_evaluation(working_directory,debug_evaluation=False)
\ No newline at end of file
diff --git a/secmod/helpers.py b/secmod/helpers.py
new file mode 100644
index 0000000..bc2091a
--- /dev/null
+++ b/secmod/helpers.py
@@ -0,0 +1,126 @@
+import pandas as pd
+import pint
+import logging
+import copy
+import numpy as np
+import importlib.util
+from datetime import datetime
+
+def isInteger(text: str):
+ """Checks if string could be an integer."""
+
+ try:
+ int(text)
+ except ValueError:
+ return False
+ else:
+ return True
+
+def unitize_dataframe(dataframe: pd.DataFrame, units: pint.UnitRegistry):
+ """Multiplied every row of a dataframe with the corresponding unit and drops the unit column."""
+
+ df_units = dataframe["unit"].apply(lambda row: units(str(row)))
+ unitized_dataframe = dataframe.drop("unit", axis=1)
+ unitized_dataframe = unitized_dataframe.apply(lambda column: column * df_units)
+
+ return unitized_dataframe
+
+def log_heading(message: str):
+ """Takes a message and creates a nicely formatted heading log message."""
+
+ message = "= {0} =".format(message)
+ length = len(message)
+ logging.info("\n\n\n" + "=" * length + "\n" + message + "\n" + "=" * length + "\n")
+
+def clear_filename_special_character(filename: str):
+
+ filename = filename.replace(", ","_")
+ filename = filename.replace(":","_")
+ filename = filename.replace(" ","_")
+ filename = filename.replace("\\","_")
+ filename = filename.replace("/","_")
+ filename = filename.replace("*","_")
+ filename = filename.replace("?","_")
+ filename = filename.replace("<","_")
+ filename = filename.replace(">","_")
+ filename = filename.replace("|","_")
+ filename = filename.replace('"',"_")
+ filename = filename.replace('(',"")
+ filename = filename.replace(')',"")
+
+ return filename
+
+def convert_date(date_old, correct_dateformat, partially_correct_dateformat, wrong_dateformat):
+ """ converts date to correct dateformat.
+ The old date must be of a known dateformat
+
+ Args:
+ date_old(str): String of date in old format
+ correct_dateformat(str): String of known dateformat, to which the date is converted
+ partially_correct_dateformat(str): String of known, wrong dateformat, which is similar to correct_dateformat
+ (though it actually does not matter how similar it is)
+ wrong_dateformat(str): String of known, wrong dateformat
+ """
+ if type(date_old)!= str:
+ raise TypeError("Date format of timeseries indizes needs to be of type 'str'. The type of '{}' is {}".format(date_old,type(date_old)))
+ try: # check if date already in right format
+ date = datetime.strptime(date_old, correct_dateformat)
+ return date_old
+ except: # if known wrong format, change format
+ try:
+ date = datetime.strptime(date_old, partially_correct_dateformat) # if known partially correct format, change format
+ date = date.strftime(correct_dateformat)
+ return date
+ except:
+ try:
+ date = datetime.strptime(date_old, wrong_dateformat) # if known wrong format, change format
+ date = date.strftime(correct_dateformat)
+ return date
+ except: # unknown wrong format
+ raise TypeError("Date format of timeseries index unknown. {} is not of any known dateformat type".format(date_old))
+
+def correct_time_stamp_of_timeseries(timeseries):
+ """ corrects time stamp of timeseries
+
+ format desired, which is given in config (e.g. dd.mm.yyyy HH:MM)
+
+ Args:
+ timeseries(pd.MultiIndex): timeseries with dates
+ """
+
+ # Load config.py
+ spec = importlib.util.spec_from_file_location("config", "SecMOD/00-INPUT/00-RAW-INPUT/config.py")
+ config = importlib.util.module_from_spec(spec)
+ spec.loader.exec_module(config)
+
+ if hasattr(config,'time_series_aggregation_config') and config.time_series_aggregation_config["use_time_series_aggregation"]: # if time series aggregation is used
+ if not isinstance(timeseries, pd.MultiIndex): # if not a multiindex
+ if timeseries.index.name == 'time slice':
+ corrected_timeseries = copy.deepcopy(timeseries)
+ correct_dateformat = config.time_series_aggregation_config["correct_dateformat"]
+ partially_correct_dateformat = config.time_series_aggregation_config["partially_correct_dateformat"]
+ wrong_dateformat = config.time_series_aggregation_config["wrong_dateformat"]
+ corrected_timeseries['time slice'] = list(map(lambda date: convert_date(date, correct_dateformat, partially_correct_dateformat, wrong_dateformat),timeseries.index.values)) # get corrected index
+ corrected_timeseries.set_index('time slice',drop=True,inplace = True)
+ else:
+ raise NotImplementedError
+ return corrected_timeseries
+ else:
+ return timeseries
+
+def get_rwth_colors():
+ """ return RWTH colors as array """
+ rwth_colors = [
+ (0,104,180),
+ (0,97,101),
+ (0,152,161),
+ (87,171,39),
+ (189,205,0),
+ (246,168,0),
+ (204,7,30),
+ (161,16,53),
+ (97,33,88),
+ (122,111,172),
+ ]
+ rwth_colors = np.divide(rwth_colors,256) # percentage of 256 spectrum
+ return rwth_colors
\ No newline at end of file
diff --git a/secmod/optimization.py b/secmod/optimization.py
new file mode 100644
index 0000000..1559fea
--- /dev/null
+++ b/secmod/optimization.py
@@ -0,0 +1,2706 @@
+# import namespace for abstract classes
+import abc
+import logging
+import secmod.scaling as scaling
+import secmod.helpers as helpers
+from datetime import datetime
+import pandas as pd
+import importlib.util
+import numpy as np
+import scipy.stats.mstats as sp
+import cProfile
+
+# import necessary elements of pyomo - alternatively use "from pyomo.environ import *", if you are missing something
+import pyomo.environ as pe
+from pyomo.opt import TerminationCondition as TerminationCondition
+from pyomo.core.expr import current as EXPR
+
+# Load config.py
+spec = importlib.util.spec_from_file_location("config", "SecMOD/00-INPUT/00-RAW-INPUT/config.py")
+config = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(config)
+
+class Optimization(abc.ABC):
+ """This is the class for optimization models.
+
+ It includes all declarations of Sets, Parameters, Variables and Constraints.
+ Furthermore it contains all optimization methods.
+ """
+ # PROPERTIES #
+ # empty properties later set
+ variable_dict = {}
+ num_constraints = {}
+ numerics = {}
+
+ def __init__(self):
+ """This method initializes the model as an abstract pyomo model.
+
+ Hence, all necessary pe.Sets, pe.Params (including scaling parameters), pe.Vars, pe.Constraints as well as the Objective Function are setup here"""
+ # set necessary properties
+ setattr(Optimization,'variable_dict',{})
+ setattr(Optimization,'num_constraints',{'evaluated':0,'skipped':0})
+ setattr(Optimization,'numerics',{'unscaled':{'max_val':{'var':None,'val': None},'min_val':{'var':None,'val': None},'sum_coeff_sq':0},'scaled':{'max_val':{'var':None,'val': None},'min_val':{'var':None,'val': None},'sum_coeff_sq':0}})
+
+ # create Pyomo AbstractModel
+ self.model = pe.AbstractModel()
+
+ # SETS # Declaration of sets used in the optimization
+ self.setupSets()
+
+ # PARAMETERS # Declaration of parameters used in the optimization
+ self.setupParameters()
+
+ # PARAMETERS # Declaration of scaling parameters used in the optimization
+ self.setupScalingParameters()
+
+ # VARIABLES # Declaration of variables used in the optimization
+ self.setupVariables()
+
+ # CONSTRAINTS # Definition of constraints used in the optimization
+ self.setupConstraints()
+
+ # pe.Objective
+ self.setupObjective()
+
+ # METHODS #
+ @staticmethod
+ def calculate_scaling_factor(scaling_vector):
+ """ returns scaling factor r or c for scaling_vector as Arg and scaling algorithm selected in config
+ Args:
+ scaling_vector(list): list with coefficients
+
+ Returns:
+ scaling_factor(float): scaling factor corresponding to scaling_vector
+ """
+ scaling_vector = np.abs(scaling_vector)
+ # if scaling_vector not empty vector
+ if not scaling_vector.size == 0:
+ # if all coefficients == 1
+ if np.all(scaling_vector == 1):
+ scaling_factor = 1
+ # else, apply one of the following algorithms
+ else:
+ if config.scaling_options['algorithm'] == 'approx_geom_mean': # geometric mean scaling
+ scaling_factor_til = np.sqrt(1/(scaling_vector.max()*scaling_vector.min()))
+ elif config.scaling_options['algorithm'] == 'geom_mean': # exact geometric mean scaling
+ scaling_factor_til = 1/sp.gmean(scaling_vector)
+ elif config.scaling_options['algorithm'] == 'arith_mean': # arithmetic mean scaling
+ scaling_factor_til = 1/np.mean(scaling_vector)
+ elif config.scaling_options['algorithm'] == 'equi': # equilibrium scaling
+ scaling_factor_til = 1/np.max(scaling_vector)
+ else:
+ raise KeyError('Scaling Algorithm {} not found'.format(config.scaling_options['algorithm']))
+
+ if config.scaling_options['Round_to_power_of_2'] and not config.scaling_options['algorithm'] == 'equi':
+ # round to the closest power of two
+ scaling_factor = 2**(np.round(np.log2(scaling_factor_til)))
+ else:
+ scaling_factor = scaling_factor_til
+ else:
+ scaling_factor = None
+ # return scaling factor
+ return scaling_factor
+
+ @classmethod
+ def set_coefficients_for_numerics(cls,item,scaled_model:bool,constraint_name='<constraint unknown>'):
+ """ this classmethod receives an item containing a tuple of a variable and its corresponding coefficient.
+ It subsequently compares the coefficient to the saved max and min value and saves the coefficient if it is larger/smaller.
+ Furthermore it sums up the coefficients in order to calculate the frobenius norm.
+
+ Args:
+ cls: class Optimization
+ item(tuple): tuple which contains coefficient [0] and variable [1]
+ scaled_model(bool): boolean whether scaled or unscaled model
+ constraint_name(str): str which contains name of constraint.
+ During the construction of the constraint, it is impossible and impractical to pass the constraint name.
+ If constraint of min/max coefficient can not be derived from context, disable scaling
+ """
+ # get if coefficients from unscaled or scaled model
+ if scaled_model:
+ _model_type = 'scaled'
+ else:
+ _model_type = 'unscaled'
+ item_val = abs(pe.value(item[0]))
+ # ||A||_F = \sqrt(\sum(\abs(a_ij)^2))
+ cls.numerics[_model_type]['sum_coeff_sq'] += item_val**2
+ if cls.numerics[_model_type]['max_val']['var'] is None: # set first item as min max
+ cls.numerics[_model_type]['max_val']['var'] = item[1].name + ' in ' + constraint_name
+ cls.numerics[_model_type]['max_val']['val'] = item_val
+ cls.numerics[_model_type]['min_val']['var'] = item[1].name + ' in ' + constraint_name
+ cls.numerics[_model_type]['min_val']['val'] = item_val
+ elif item_val > cls.numerics[_model_type]['max_val']['val']:
+ cls.numerics[_model_type]['max_val']['var'] = item[1].name + ' in ' + constraint_name
+ cls.numerics[_model_type]['max_val']['val'] = item_val
+ elif item_val < cls.numerics[_model_type]['min_val']['val']:
+ cls.numerics[_model_type]['min_val']['var'] = item[1].name + ' in ' + constraint_name
+ cls.numerics[_model_type]['min_val']['val'] = item_val
+
+ @classmethod
+ def calculate_matrix_properties(cls):
+ """ this classmethod calculates the highest and lowest matrix coefficient, and the frobenius norm for the scaled and unscaled model
+ Performed after initialization of model.
+ Args:
+ cls: class Optimization
+ """
+ # unscaled model
+ logging.info('Numeric properties of unscaled model:')
+ # if properties exist for unscaled model --> scaling enabled
+ if cls.numerics['unscaled']['max_val']['val'] is not None:
+ _model_type = 'unscaled'
+ logging.info('Largest matrix coefficient: {:.3e} in {}'.format(cls.numerics[_model_type]['max_val']['val'],cls.numerics[_model_type]['max_val']['var']))
+ logging.info('Smallest (nonzero) matrix coefficient: {:.3e} in {}'.format(cls.numerics[_model_type]['min_val']['val'],cls.numerics[_model_type]['min_val']['var']))
+ _matrix_range = np.round(np.log10(cls.numerics[_model_type]['max_val']['val']))-np.round(np.log10(cls.numerics[_model_type]['min_val']['val']))
+ logging.info('Range of order of magnitude: {}'.format(_matrix_range))
+ logging.info('Frobenius norm: {:.3e}'.format(np.sqrt(cls.numerics[_model_type]['sum_coeff_sq'])))
+ # scaled model
+ logging.info('Numeric properties of scaled model:')
+ _model_type = 'scaled'
+ logging.info('Largest matrix coefficient: {:.3e} in {}'.format(cls.numerics[_model_type]['max_val']['val'],cls.numerics[_model_type]['max_val']['var']))
+ logging.info('Smallest (nonzero) matrix coefficient: {:.3e} in {}'.format(cls.numerics[_model_type]['min_val']['val'],cls.numerics[_model_type]['min_val']['var']))
+ _matrix_range = np.round(np.log10(cls.numerics[_model_type]['max_val']['val']))-np.round(np.log10(cls.numerics[_model_type]['min_val']['val']))
+ logging.info('Range of order of magnitude: {}'.format(_matrix_range))
+ logging.info('Frobenius norm: {:.3e}'.format(np.sqrt(cls.numerics[_model_type]['sum_coeff_sq'])))
+
+ @classmethod
+ def set_variable_dict(cls,variable,variable_coefficient,constraint_scaling_factor):
+ """ appends variable_coefficient to variable_dict
+ Args:
+ cls: class Optimization
+ variable(pe.Var): indexed variable
+ variable_coefficient(float): value of coefficient corresponding to variable
+ constraint_scaling_factor(float): scaling factor of constraint
+ """
+ # if id(variable) already in variable_dict, append to key
+ try:
+ cls.variable_dict[id(variable)].append(variable_coefficient*constraint_scaling_factor)
+ # if not yet in variable_dict, create new key
+ except KeyError:
+ cls.variable_dict[id(variable)] = [variable_coefficient*constraint_scaling_factor]
+
+ @classmethod
+ def add_evaluated_constraint(cls):
+ """ this method increases the counter of evaluated constraints by one """
+ cls.num_constraints['evaluated'] += 1
+
+ @classmethod
+ def add_skipped_constraint(cls):
+ """ this method increases the counter of skipped constraints by one """
+ cls.num_constraints['skipped'] += 1
+
+ @classmethod
+ def scale_expr(cls,unscaled_expr):
+ """ this method scales an constraint expression, if config.scaling_options['scale_constraints'] or config.scaling_options['scale_variables'] selected.
+ If config.scaling_options['scale_constraints'] the constraint is scaled.
+ If config.scaling_options['scale_variables'] selected, this methods saves the variable coefficients in cls.variable_dict
+
+ Args:
+ cls: class Optimization
+ unscaled_expr(EXPR.InequalityExpression/EXPR.EqualityExpression): unscaled inequality or equality expression,
+ which is generated by the calling constraint
+
+ Returns:
+ scaled_expr(EXPR.InequalityExpression/EXPR.EqualityExpression): unscaled inequality or equality expression,
+ used in the calling constraint
+ """
+ # if either constraints or variables scaled
+ if config.scaling_options['scale_constraints'] or config.scaling_options['scale_variables']:
+ merged_expr = unscaled_expr.args[0] + unscaled_expr.args[1]
+ variable_coefficient_list = EXPR.decompose_term(merged_expr)[1]
+ # if constraints not scaled
+ if not config.scaling_options['scale_constraints']:
+ if config.scaling_options['scale_variables']:
+ for item in variable_coefficient_list:
+ # if item[1] is variable, which is not fixed
+ if item[1] is not None and item[1].value != 0:
+ # multiply variable coefficient with 1, since constraint not scaled
+ cls.set_variable_dict(item[1],item[0].value,1)
+ # collect coefficient information for numerical analysis
+ if config.scaling_options['analyze_numerics']:
+ cls.set_coefficients_for_numerics(item,scaled_model=False)
+ return unscaled_expr
+ # if constraints scaled
+ # calculate constraint_scaling_factor
+ constraint_scaling_factor = cls.calculate_scaling_factor([pe.value(item[0]) for item in variable_coefficient_list if item[1] is not None and item[1].value != 0])
+ if constraint_scaling_factor:
+ if constraint_scaling_factor == 1:
+ a=1
+
+ for item in variable_coefficient_list:
+ # if item[1] is variable, which is not fixed
+ if item[1] is not None and item[1].value != 0:
+ # if variables scaled
+ if config.scaling_options['scale_variables']:
+ # multiply variable coefficient with scaling factor
+ cls.set_variable_dict(item[1],pe.value(item[0]),constraint_scaling_factor)
+ # collect coefficient information for numerical analysis
+ if config.scaling_options['analyze_numerics']:
+ cls.set_coefficients_for_numerics(item,scaled_model=False)
+ # if constraints_scaling_factor == 1, no scaling necessary
+ if constraint_scaling_factor != 1:
+ # scale lhs and rhs of unscaled_expr with constraint_scaling_factor
+ if unscaled_expr.args[0].__class__ not in EXPR.native_numeric_types and not isinstance(unscaled_expr.args[0], EXPR.NumericConstant):
+ # if lhs is not a numeric value (hence it's an expression)
+ scaled_lhs = constraint_scaling_factor*unscaled_expr.args[0]
+ else:
+ scaled_lhs = EXPR.NumericConstant(constraint_scaling_factor*unscaled_expr.args[0])
+ if unscaled_expr.args[1].__class__ not in EXPR.native_numeric_types and not isinstance(unscaled_expr.args[1], EXPR.NumericConstant):
+ # if lhs is not a numeric value (hence it's an expression)
+ scaled_rhs = constraint_scaling_factor*unscaled_expr.args[1]
+ else:
+ scaled_rhs = EXPR.NumericConstant(constraint_scaling_factor*unscaled_expr.args[1])
+ # reconstruct expression
+ if isinstance(unscaled_expr, EXPR.InequalityExpression): # if inequality
+ return(EXPR.inequality(scaled_lhs,scaled_rhs))
+ else: # if equality
+ return(scaled_lhs==scaled_rhs)
+ else:
+ return unscaled_expr
+ else:
+ # if constraint_scaling_factor == 1, return unscaled_expr
+ return unscaled_expr
+ else:
+ # if scaling disabled, return unscaled_expr
+ return unscaled_expr
+
+ @staticmethod
+ def fix_variables(*args):
+ """ This method sets the variables that need to be fixed to 0 at initialization
+
+ By setting the default value to 0, the fixed variables can be identified before explicitly fixing them.
+ If condition is met, function returns 0, otherwise None. This is subsequently set as the variable's default value
+ Args:
+ *args:
+ args[0](str): string of variable name to identify variable
+ args[1](pe.ConcreteModel): ConcreteModel with necessary sets and parameters
+ args[2:]: Set indizes which define the current variable index (e.g. year, product, production_process, node etc.)
+ """
+ # select items from *args, see variable definition for order of data
+ variable_name = args[0]
+ model = args[1]
+ data = args[2:]
+ # get type_of_optimization
+ type_of_optimization = model.type_of_optimization.data()[0]
+ # used_production
+ if variable_name == 'used_production':
+ if ((data[2] - data[3]) >= model.lifetime_duration_production[data[1], data[3]]):
+ # year - construction_year >= lifetime
+ return 0
+ # existing_capacity == 0
+ elif model.existing_capacity_production[data[0], data[1], data[2], data[3]] == 0:
+ # additional fixed variables for InvestmentOptimization
+ if type_of_optimization == 'InvestmentOptimization':
+ if (sum([model.existing_capacity_production[data[0], data[1], data[2], construction_year] for construction_year in model.construction_years_production]) >= model.potential_capacity_production[data[0], data[1], data[2]]):
+ # sum(existing capacity) >= potential capacity
+ return 0
+ elif data[3] not in model.years:
+ # construction_year not in invest_year
+ return 0
+ # additional fixed variables for OperationalOptimization
+ elif type_of_optimization == 'OperationalOptimization':
+ return 0
+ # optimization type unknown
+ else:
+ raise NotImplementedError('Not implemented for {}'.format(type_of_optimization))
+ # used_storage
+ elif variable_name == 'used_storage':
+ # year - construction_year >= lifetime
+ if ((data[3] - data[4]) >= model.lifetime_duration_storage[data[1], data[4]]):
+ return 0
+ elif model.existing_capacity_storage[data[0], data[1], data[3], data[4]] == 0:
+ # additional fixed variables for 'InvestmentOptimization'
+ if type_of_optimization == 'InvestmentOptimization':
+ if (sum([model.existing_capacity_storage[data[0], data[1], data[3], construction_year] for construction_year in model.construction_years_storage]) >= model.potential_capacity_storage[data[0], data[1], data[3]]):
+ # sum(existing capacity) >= potential capacity
+ return 0
+ elif data[3] not in model.years:
+ # construction_year not in invest_year
+ return 0
+ # additional fixed variables for OperationalOptimization
+ elif type_of_optimization == 'OperationalOptimization':
+ return 0
+ else:
+ raise NotImplementedError('Not implemented for {}'.format(type_of_optimization))
+ # used_transshipment
+ elif variable_name == 'used_transshipment':
+ # year - construction_year >= lifetime
+ if ((data[3] - data[4]) >= model.lifetime_duration_transshipment[data[1], data[4]]):
+ return 0
+ # existing_capacity == 0
+ elif model.existing_capacity_transshipment[data[0], data[1], data[3], data[4]] == 0:
+ # additional fixed variables for 'InvestmentOptimization'
+ if type_of_optimization == 'InvestmentOptimization':
+ if (sum([model.existing_capacity_transshipment[data[0], data[1], data[3], construction_year] for construction_year in model.construction_years_transshipment]) >= model.potential_capacity_transshipment[data[0], data[1], data[3]]):
+ # sum(existing capacity) >= potential capacity
+ return 0
+ elif data[3] not in model.years:
+ # construction_year not in invest_year
+ return 0
+ elif type_of_optimization == 'OperationalOptimization':
+ return 0
+ else:
+ raise NotImplementedError('Not implemented for {}'.format(type_of_optimization))
+ # fixing the transmission phase difference of one node to zero.
+ elif variable_name == 'phase_difference':
+ # if node == transmission_reference_node
+ if data[0] == pe.value(model.transmission_reference_node):
+ return 0
+ # different Optimization subclasses
+ if type_of_optimization == 'InvestmentOptimization':
+ # Fixes the new capacity variables for all processes which have no potential left and the used capacity variables which therefore have no existing capacity either.
+ if variable_name == 'new_capacity_production':
+ # if sum(existing_capacity) >= potential capacity
+ if (sum(model.existing_capacity_production[data[0], data[1], data[2], construction_year] for construction_year in model.construction_years_production) >= model.potential_capacity_production[data[0], data[1], data[2]]):
+ return 0
+ elif variable_name == 'new_capacity_storage':
+ # if sum(existing_capacity) >= potential capacity
+ if (sum(model.existing_capacity_storage[data[0], data[1], data[2], construction_year] for construction_year in model.construction_years_storage) >= model.potential_capacity_storage[data[0], data[1], data[2]]):
+ return 0
+ elif variable_name == 'new_capacity_transshipment':
+ # if sum(existing_capacity) >= potential capacity
+ if (sum(model.existing_capacity_transshipment[data[0], data[1], data[2], construction_year] for construction_year in model.construction_years_transshipment) >= model.potential_capacity_transshipment[data[0], data[1], data[2]]):
+ return 0
+ elif variable_name == 'new_capacity_transmission':
+ # if sum(existing_capacity) >= potential capacity
+ if (sum(model.existing_capacity_transmission[data[0], data[1], data[2], construction_year] for construction_year in model.construction_years_transmission) >= model.potential_capacity_transmission[data[0], data[1], data[2]]):
+ return 0
+ # if OperationalOptimization, fix new capacities
+ elif type_of_optimization == 'OperationalOptimization':
+ if variable_name == 'new_capacity_production':
+ return 0
+ elif variable_name == 'new_capacity_storage':
+ return 0
+ elif variable_name == 'new_capacity_transshipment':
+ return 0
+ elif variable_name == 'new_capacity_transmission':
+ return 0
+ # unknown type of optimization
+ else:
+ raise NotImplementedError('Not implemented for {}'.format(type_of_optimization))
+ # if not fixed, return None
+ return None
+
+ def setupSets(self):
+ """This method sets up all Sets required the optimization model.
+
+ Some Sets are initialized with default values, if no other values are specified in the input file.
+ """
+ # pe.Set of the type of optimization
+ self.model.type_of_optimization = pe.Set()
+
+ # pe.Set of the nodes of the grid
+ self.model.nodes = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of the connections between the nodes of the grid
+ self.model.connections = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of two nodes to identify both nodes of a connection
+ self.model.connection_nodes = pe.Set(
+ initialize={"node1", "node2"})
+ # pe.Set of the used products for which there is a demand
+ self.model.products = pe.Set()
+ # pe.Set of the used processes for production
+ self.model.processes_production = pe.Set()
+ # pe.Set of the used processes for storage of products
+ self.model.processes_storage = pe.Set()
+ # pe.Set of the used processes for transshipment of products between nodes
+ self.model.processes_transshipment = pe.Set()
+ # pe.Set of the used processes for transshipment of products between nodes
+ self.model.processes_transmission = pe.Set()
+ # pe.Set of storage directions (deposit and withdraw)
+ self.model.directions_storage = pe.Set(
+ initialize={"deposit", "withdraw"})
+ # pe.Set of transshipment direction (forward and backward)
+ self.model.directions_transshipment = pe.Set(
+ initialize={"forward", "backward"})
+ # pe.Set of the properties of a transmission process/power line
+ self.model.power_line_property_categories = pe.Set(
+ initialize={
+ "power limit",
+ "susceptance per unit"})
+ # pe.Set of products using transmission via DC load flow
+ self.model.products_transmission = pe.Set(
+ initialize={"electricity"})
+ # pe.Set of the years of construction of existing capacity
+ self.model.construction_years_production = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of the years of construction of existing capacity
+ self.model.construction_years_storage = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of the years of construction of existing capacity
+ self.model.construction_years_transshipment = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of the years of construction of existing capacity
+ self.model.construction_years_transmission = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of the years in the current optimization period
+ self.model.years = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of the time slices of a year
+ self.model.time_slices = pe.Set(
+ within=pe.NonNegativeIntegers)
+ # pe.Set of the impact categories, including environmental and monetary costs
+ self.model.impact_categories = pe.Set()
+ # pe.Set of the impact sources, e.g. whether the impacts result from invest or operation
+ self.model.impact_sources = pe.Set(
+ initialize={"operation", "invest"})
+
+ def setupParameters(self):
+ """This method sets up all Parameters required by the optimization model.
+
+ """
+
+ # length of the connections of the grid
+ self.model.connection_length = pe.Param(
+ self.model.connections,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the length of all connections of the grid.")
+ # technology matrices, which define how much of any product is produces by a process
+ # technology matrix of production processes like power plant processes
+ self.model.technology_matrix_production = pe.Param(
+ self.model.products,
+ self.model.processes_production,
+ self.model.construction_years_production,
+ default=0,
+ doc="Parameter which contains the data about which products take part in a production process.")
+ # technology matrix of storage processes like pumped hydro storage
+ self.model.efficiency_matrix_storage = pe.Param(
+ self.model.products,
+ self.model.processes_storage,
+ self.model.directions_storage,
+ self.model.construction_years_storage,
+ default=0,
+ doc="Parameter which contains the data about which products take part in a storage process.")
+
+ # products stored by storage processes
+ self.model.storage_products = pe.Param(
+ self.model.processes_storage,
+ within=self.model.products,
+ doc="Parameter which contains the product stored by a storage process.")
+ # efficiency of transshipment processes along all connections
+ self.model.transshipment_efficiency = pe.Param(
+ self.model.processes_transshipment,
+ self.model.connections,
+ default=0,
+ doc="Parameter which contains the efficiency of a transshipment process along a connection.")
+ # products which are transshipped by the corresponding transshipment processes
+ self.model.transshipment_products = pe.Param(
+ self.model.processes_transshipment,
+ within = pe.Any,
+ default=None,
+ doc="Parameter which contains the transshipped product of each transshipment process.")
+ # properties of each existing power line, regarding power limit and susceptance per unit
+ self.model.power_line_properties = pe.Param(
+ self.model.connections,
+ self.model.years,
+ self.model.power_line_property_categories,
+ within=pe.NonNegativeReals,
+ default=0,
+ doc="Parameter which contains the power limit and susceptance per unit of the existing capacity.")
+
+ # timeseries multiplier of the technology matrix of production processes
+ self.model.technology_matrix_production_timeseries = pe.Param(
+ self.model.products,
+ self.model.processes_production,
+ self.model.time_slices,
+ default=1,
+ doc="Parameter which contains the time series factor for the technology matrix of a production process.")
+
+ # impact matrices, which define how big the life cycle impact of any process is - includes monetary costs
+ # impact matrix of production processes
+ self.model.impact_matrix_production = pe.Param(
+ self.model.impact_categories,
+ self.model.impact_sources,
+ self.model.processes_production,
+ self.model.years,
+ self.model.construction_years_production,
+ doc="Parameter which contains the data about the impacts of a production process. \
+ First 'year' marks the year for which the value is valid. Second 'year' marks the \
+ year of construction.")
+ # impact matrix of storage processes
+ self.model.impact_matrix_storage = pe.Param(
+ self.model.impact_categories,
+ self.model.impact_sources,
+ self.model.processes_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ doc="Parameter which contains the data about the impacts of a storage process. \
+ Operational impact data is based on the **withdrawn** product. \
+ First 'year' marks the year for which the value is valid. Second 'year' marks the \
+ year of construction.")
+ # impact matrix of transshipment processes
+ self.model.impact_matrix_transshipment = pe.Param(
+ self.model.impact_categories,
+ self.model.impact_sources,
+ self.model.processes_transshipment,
+ self.model.years,
+ self.model.construction_years_transshipment,
+ doc="Parameter which contains the data about the impacts of a transshipment process. \
+ First 'year' marks the year for which the value is valid. Second 'year' marks the \
+ year of construction.")
+ # impact matrix of transmssion processes
+ self.model.impact_matrix_transmission = pe.Param(
+ self.model.impact_categories,
+ self.model.impact_sources,
+ self.model.processes_transmission,
+ self.model.years,
+ self.model.construction_years_transmission,
+ doc="Parameter which contains the data about the impacts of transmission processes \
+ in the transmission grid. First 'year' marks the year for which the value is valid. \
+ Second 'year' marks the year of construction.")
+ # impact matrix of non-served demand of a product
+ self.model.impact_matrix_non_served_demand = pe.Param(
+ self.model.impact_categories,
+ self.model.products,
+ self.model.years,
+ self.model.time_slices,
+ doc="Parameter which contains the data about the impacts of non-served demand.")
+
+ # lifetime duration of production process
+ self.model.lifetime_duration_production = pe.Param(
+ self.model.processes_production,
+ self.model.construction_years_production,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the lifetime duration of each production process\
+ in the corresponding construction year.")
+ # lifetime duration of storage process
+ self.model.lifetime_duration_storage = pe.Param(
+ self.model.processes_storage,
+ self.model.construction_years_storage,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the lifetime duration of each storage process\
+ in the corresponding construction year.")
+ # lifetime duration of transshipment process
+ self.model.lifetime_duration_transshipment = pe.Param(
+ self.model.processes_transshipment,
+ self.model.construction_years_transshipment,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the lifetime duration of each production process\
+ in the corresponding construction year.")
+ # lifetime duration of transmission process
+ self.model.lifetime_duration_transmission = pe.Param(
+ self.model.processes_transmission,
+ self.model.construction_years_transmission,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the lifetime duration of each production process\
+ in the corresponding construction year.")
+
+ # existing capacity of all technologies
+ # installed capacity of production technologies based on nodes and investment years
+ self.model.existing_capacity_production = pe.Param(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ self.model.construction_years_production,
+ within=pe.NonNegativeReals,
+ default=0,
+ doc="Parameter which contains the data about the existing capacity of a production process. \
+ First 'year' marks the year for which the value is valid. Second 'year' marks the \
+ year of construction.")
+ # installed capacity of storage technologies based on nodes and investment years
+ self.model.existing_capacity_storage = pe.Param(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ default=0,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the existing capacity of a storage process. \
+ First 'year' marks the year for which the value is valid. Second 'year' marks the \
+ year of construction.")
+ # installed capacity of transshipment technologies based on nodes and investment years
+ self.model.existing_capacity_transshipment = pe.Param(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.years,
+ self.model.construction_years_transshipment,
+ default=0,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the existing capacity of a transshipment process. \
+ First 'year' marks the year for which the value is valid. Second 'year' marks the \
+ year of construction.")
+ # installed capacity of transshipment technologies based on nodes and investment years
+ self.model.existing_capacity_transmission = pe.Param(
+ self.model.connections,
+ self.model.processes_transmission,
+ self.model.years,
+ self.model.construction_years_transmission,
+ default=0,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the existing capacity of a transshipment process. \
+ First 'year' marks the year for which the value is valid. Second 'year' marks the \
+ year of construction.")
+ # product flow to storage capacity factor based on processes and investment years
+ self.model.flow_to_storage_capacity_factor = pe.Param(
+ self.model.processes_storage,
+ self.model.construction_years_storage,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the relation between the maximum input \
+ and output of a storage process and its storage capacity.")
+ # lists of nodes connected by connections
+ self.model.connected_nodes = pe.Param(
+ self.model.connections,
+ self.model.connection_nodes,
+ within=pe.NonNegativeIntegers,
+ doc="Parameter which contains the two nodes connected by a connection process.")
+ # reference node of which the phase difference is pe.Set to zero
+ self.model.transmission_reference_node = pe.Param(
+ within=self.model.nodes,
+ doc="Parameter which contains one node which is used as reference node for the \
+ calculation of the phase difference between the nodes. Therefore, its \
+ phase difference is set to zero.")
+ # apparent power base value for the per-unit calculations
+ self.model.per_unit_base = pe.Param(
+ within=pe.NonNegativeReals,
+ default=500,
+ doc="Parameter which contains the base value in MVA for the per-unit calculations.")
+ # time dependent parameters
+ # weight of every time slice regarding the full year - sum of all shares equals 8760 hours
+ self.model.time_slice_yearly_weight = pe.Param(
+ self.model.time_slices,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the share of a year represented by each time slice.")
+ # actual duration of one time slice - necessary for storage level calculations
+ self.model.time_slice_duration = pe.Param(
+ self.model.time_slices,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the duration of each time slice.")
+ # next connected time slice of the current time slice, used for storage level boundaries
+ self.model.next_time_slice = pe.Param(
+ self.model.time_slices,
+ doc="Parameter which contains the data about the connections between the time slices.")
+ # timeseries for the available share of any capacity e.g. production from renewable energy
+ self.model.usable_capacity_factor_timeseries_production = pe.Param(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.time_slices,
+ within=pe.NonNegativeReals,
+ default=1,
+ doc="Parameter which contains the data about the available share of capacity of a production \
+ process in a specific time slice.")
+ # demand for every product
+ self.model.demand = pe.Param(
+ self.model.products,
+ self.model.years,
+ self.model.time_slices,
+ self.model.nodes,
+ default=0,
+ doc="Parameter which contains the data about the demand for each product at each node.")
+ # parameters regarding the impact of the system
+ # limits for operational impact
+ # nodal limits for the operational impacts
+ self.model.operational_nodal_impact_limits = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default=float("inf"),
+ doc="Parameter which contains all limits for the operational impacts on nodal base.")
+ # overall limits for the operational impacts
+ self.model.operational_impact_limits = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default=float("inf"),
+ doc="Parameter which contains all limits for the operational impacts overall.")
+ # limits for invest impact
+ # nodal limits for the invest impacts
+ self.model.invest_nodal_impact_limits = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default=float("inf"),
+ doc="Parameter which contains all limits for the invest impacts on nodal base.")
+ # overall limits for the invest impacts
+ self.model.invest_impact_limits = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default=float("inf"),
+ doc="Parameter which contains all limits for the invest impacts overall.")
+ # limits for total impact
+ # nodal limits for the total impacts
+ self.model.total_nodal_impact_limits = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default=float("inf"),
+ doc="Parameter which contains all limits for the total impacts on nodal base.")
+ # overall limits for the total impacts
+ self.model.total_impact_limits = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default=float("inf"),
+ doc="Parameter which contains all limits for the total impacts overall.")
+ # parameters which specify with which weight impacts is taken into account in the pe.Objective rule
+ self.model.objective_factor_impacts = pe.Param(
+ self.model.impact_sources,
+ self.model.impact_categories,
+ self.model.years,
+ default=0,
+ doc="Parameter which defines the factor used to multiply an impact category with in the objective rule.")
+ # parameters for the weight of overshoot and non-served demand variables
+ # parameters which specify with which weight impact overshoots are taken into account in the pe.Objective rule
+ self.model.objective_factor_impact_overshoot = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ doc="Parameters which specify with which weight impact overshoots are taken into account in the objective rule.")
+ # parameter used for the transshipment in different directions.
+ # Therefore its values need to be
+ self.model.factor_transshipment = pe.Param(
+ self.model.directions_transshipment,
+ initialize={"forward": 1, "backward": -1},
+ doc="Parameter which is used for transshipment equations and does not need further initialization.")
+ # additional parameter to be used for the calculation of the impact of storage usage on the storage level
+ # therefore its values need to be {"withdraw": -1, "deposit": 1}
+ self.model.storage_level_factor = pe.Param(
+ self.model.directions_storage,
+ initialize={"withdraw": -1, "deposit": 1},
+ doc="Parameter which is used for storage equations and does not need further initialization.")
+ # capacity limits of all processes on nodal base
+ # capacity limits of production processes
+ self.model.potential_capacity_production = pe.Param(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ within=pe.NonNegativeReals,
+ default=float("inf"),
+ doc="Parameter which contains the data about the capacity limit of a production process at each node.")
+ # capacity limits of storage processes
+ self.model.potential_capacity_storage = pe.Param(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ within=pe.NonNegativeReals,
+ default=float("inf"),
+ doc="Parameter which contains the data about the capacity limit of a storage process at each node.")
+ # capacity limits of transshipment processes
+ self.model.potential_capacity_transshipment = pe.Param(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.years,
+ within=pe.NonNegativeReals,
+ default=float("inf"),
+ doc="Parameter which contains the data about the capacity limit of a transshipment process at each node.")
+ # capacity limits of transmission processes
+ self.model.potential_capacity_transmission = pe.Param(
+ self.model.connections,
+ self.model.processes_transmission,
+ self.model.years,
+ within=pe.NonNegativeReals,
+ default=float("inf"),
+ doc="Parameter which contains the data about the capacity limit of a transmission process at each connection.")
+ # power limit per circuit of each transmission process
+ self.model.power_limit_per_circuit = pe.Param(
+ self.model.processes_transmission,
+ default=0,
+ doc="Parameter which contains the power limit per circuit of each transmission process.")
+ # reliability margin for a transmission process
+ self.model.reliability_margin_transmission = pe.Param(
+ self.model.processes_transmission,
+ within=pe.NonNegativeReals,
+ default=0,
+ doc="Parameter which contains the data about the share of the power limit of a process that can be used.")
+
+ # parameters necessary for reaching the secured capacity
+ # secured capacity factor for production processes
+ self.model.secured_capacity_factors_production = pe.Param(
+ self.model.processes_production,
+ self.model.construction_years_production,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the secured capacity factor of a production process.")
+ # secured capacity factor for storage processes
+ self.model.secured_capacity_factors_storage = pe.Param(
+ self.model.processes_storage,
+ self.model.construction_years_storage,
+ within=pe.NonNegativeReals,
+ doc="Parameter which contains the data about the secured capacity factor of a storage process.")
+ # required total secured capacity e.g. peak load
+ self.model.required_total_secured_capacity = pe.Param(
+ self.model.products,
+ self.model.years,
+ default=0,
+ doc="Parameter which contains the data about the required total secured capacity for a product")
+ # required nodal secured capacity
+ self.model.required_nodal_secured_capacity = pe.Param(
+ self.model.products,
+ self.model.nodes,
+ self.model.years,
+ default=0,
+ doc="Parameter which contains the data about the required nodal secured capacity for a product")
+
+ def setupScalingParameters(self):
+ """ This functions sets up the scaling parameters of the variables required by the optimization model.
+
+ The notation is the following: The scaling parameter for the component <component> is self.model.scaling_<component>
+ It is crucial to select the same pe.Sets as the corresponding component, to select default = 1, and mutable=True.
+ Check setupVariables() for details on the variable declaration """
+
+ ## set up Parameters for scaling variables
+ # slack variables
+ self.model.scaling_nodal_slack_variable = pe.Param(
+ self.model.products,
+ self.model.nodes,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ self.model.scaling_total_slack_variable = pe.Param(
+ self.model.products,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # usage of production technologies based on nodes and time
+ self.model.scaling_used_production = pe.Param(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ self.model.construction_years_production,
+ self.model.time_slices,
+ default = 1,
+ mutable = True)
+ # usage of storage technologies based on nodes and time
+ self.model.scaling_used_storage = pe.Param(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.directions_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ self.model.time_slices,
+ default = 1,
+ mutable = True)
+ # usage of transshipment technologies based on nodes and time
+ self.model.scaling_used_transshipment = pe.Param(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.directions_transshipment,
+ self.model.years,
+ self.model.construction_years_transshipment,
+ self.model.time_slices,
+ default = 1,
+ mutable = True)
+ # non-served demand of a product based on nodes and time
+ self.model.scaling_non_served_demand = pe.Param(
+ self.model.nodes,
+ self.model.products,
+ self.model.years,
+ self.model.time_slices,
+ default = 1,
+ mutable = True)
+ # storage level of storage technologies based on nodes, years and time slices
+ self.model.scaling_storage_level = pe.Param(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ self.model.time_slices,
+ default = 1,
+ mutable = True)
+ # DC load flow - transmission related variables
+ self.model.scaling_phase_difference = pe.Param(
+ self.model.nodes,
+ self.model.years,
+ self.model.time_slices,
+ default = 1,
+ mutable = True)
+ # impact related variables
+ # operational impact
+ # nodal operational environmental and monetary impact based on impact categories and years
+ self.model.scaling_operational_nodal_impact = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overshoot of nodal operational environmental and monetary impact based on impact categories and years
+ self.model.scaling_operational_nodal_impact_overshoot = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overall operational environmental and monetary impact based on impact categories and years
+ self.model.scaling_operational_impact = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overshoot of overall operational environmental and monetary impact based on impact categories and years
+ self.model.scaling_operational_impact_overshoot = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # invest impact
+ # nodal invest environmental and monetary impact based on impact categories and years
+ self.model.scaling_invest_nodal_impact = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overshoot of nodal invest environmental and monetary impact based on impact categories and years
+ self.model.scaling_invest_nodal_impact_overshoot = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overall invest environmental and monetary impact based on impact categories and years
+ self.model.scaling_invest_impact = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overshoot of overall invest environmental and monetary impact based on impact categories and years
+ self.model.scaling_invest_impact_overshoot = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # total impact
+ # nodal total environmental and monetary impact based on impact categories and years
+ self.model.scaling_total_nodal_impact = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overshoot of nodal total environmental and monetary impact based on impact categories and years
+ self.model.scaling_total_nodal_impact_overshoot = pe.Param(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overall total environmental and monetary impact based on impact categories and years
+ self.model.scaling_total_impact = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # overshoot of overall total environmental and monetary impact based on impact categories and years
+ self.model.scaling_total_impact_overshoot = pe.Param(
+ self.model.impact_categories,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # newly installed capacity of production technologies based on nodes and investment years
+ self.model.scaling_new_capacity_production = pe.Param(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # newly installed capacity of storage technologies based on nodes and investment years
+ self.model.scaling_new_capacity_storage = pe.Param(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # newly installed capacity of transshipment technologies based on nodes and investment years
+ self.model.scaling_new_capacity_transshipment = pe.Param(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.years,
+ default = 1,
+ mutable = True)
+ # newly installed transmission capacity from switching
+ self.model.scaling_new_capacity_transmission = pe.Param(
+ self.model.connections,
+ self.model.processes_transmission,
+ self.model.years,
+ default = 1,
+ mutable = True)
+
+ def setupVariables(self):
+ """This method sets up all Variables required by the optimization model.
+
+ """
+ # slack variable required for constraint_nodal_secured_capacity
+ self.model.nodal_slack_variable = pe.Var(
+ self.model.products,
+ self.model.nodes,
+ self.model.years,
+ within=pe.NonNegativeReals)
+
+ # slack variable required for constraint_total_secured_capacity
+ self.model.total_slack_variable = pe.Var(
+ self.model.products,
+ self.model.years,
+ within = pe.NonNegativeReals)
+
+ # usage of production technologies based on nodes and time
+ self.model.used_production = pe.Var(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ self.model.construction_years_production,
+ self.model.time_slices,
+ rule = lambda *args: self.fix_variables('used_production',*args),
+ within=pe.NonNegativeReals)
+ # usage of storage technologies based on nodes and time
+ self.model.used_storage = pe.Var(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.directions_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ self.model.time_slices,
+ rule = lambda *args: self.fix_variables('used_storage',*args),
+ within=pe.NonNegativeReals)
+ # usage of transshipment technologies based on nodes and time
+ self.model.used_transshipment = pe.Var(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.directions_transshipment,
+ self.model.years,
+ self.model.construction_years_transshipment,
+ self.model.time_slices,
+ rule = lambda *args: self.fix_variables('used_transshipment',*args),
+ within=pe.NonNegativeReals)
+ # non-served demand of a product based on nodes and time
+ self.model.non_served_demand = pe.Var(
+ self.model.nodes,
+ self.model.products,
+ self.model.years,
+ self.model.time_slices,
+ within=pe.NonNegativeReals)
+ # storage level of storage technologies based on nodes, years and time slices
+ self.model.storage_level = pe.Var(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ self.model.time_slices,
+ within=pe.NonNegativeReals)
+ # DC load flow - transmission related variables
+ self.model.phase_difference = pe.Var(
+ self.model.nodes,
+ self.model.years,
+ self.model.time_slices,
+ rule = lambda *args: self.fix_variables('phase_difference',*args),
+ within=pe.Reals)
+ # impact related variables
+ # operational impact
+ # nodal operational environmental and monetary impact based on impact categories and years
+ self.model.operational_nodal_impact = pe.Var(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.Reals)
+ # overshoot of nodal operational environmental and monetary impact based on impact categories and years
+ self.model.operational_nodal_impact_overshoot = pe.Var(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.NonNegativeReals)
+ # overall operational environmental and monetary impact based on impact categories and years
+ self.model.operational_impact = pe.Var(
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.Reals)
+ # overshoot of overall operational environmental and monetary impact based on impact categories and years
+ self.model.operational_impact_overshoot = pe.Var(
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.NonNegativeReals)
+ # invest impact
+ # nodal invest environmental and monetary impact based on impact categories and years
+ self.model.invest_nodal_impact = pe.Var(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.Reals)
+ # overshoot of nodal invest environmental and monetary impact based on impact categories and years
+ self.model.invest_nodal_impact_overshoot = pe.Var(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.NonNegativeReals)
+ # overall invest environmental and monetary impact based on impact categories and years
+ self.model.invest_impact = pe.Var(
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.Reals)
+ # overshoot of overall invest environmental and monetary impact based on impact categories and years
+ self.model.invest_impact_overshoot = pe.Var(
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.NonNegativeReals)
+ # total impact
+ # nodal total environmental and monetary impact based on impact categories and years
+ self.model.total_nodal_impact = pe.Var(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.Reals)
+ # overshoot of nodal total environmental and monetary impact based on impact categories and years
+ self.model.total_nodal_impact_overshoot = pe.Var(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.NonNegativeReals)
+ # overall total environmental and monetary impact based on impact categories and years
+ self.model.total_impact = pe.Var(
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.Reals)
+ # overshoot of overall total environmental and monetary impact based on impact categories and years
+ self.model.total_impact_overshoot = pe.Var(
+ self.model.impact_categories,
+ self.model.years,
+ within=pe.NonNegativeReals)
+ # NEW capacities
+ # newly installed capacity of production technologies based on nodes and investment years
+ self.model.new_capacity_production = pe.Var(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ rule = lambda *args: self.fix_variables('new_capacity_production',*args),
+ within=pe.NonNegativeReals)
+ # newly installed capacity of storage technologies based on nodes and investment years
+ self.model.new_capacity_storage = pe.Var(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ rule = lambda *args: self.fix_variables('new_capacity_storage',*args),
+ within=pe.NonNegativeReals)
+ # newly installed capacity of transshipment technologies based on nodes and investment years
+ self.model.new_capacity_transshipment = pe.Var(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.years,
+ rule = lambda *args: self.fix_variables('new_capacity_transshipment',*args),
+ within=pe.NonNegativeReals)
+ # newly installed transmission capacity from switching
+ self.model.new_capacity_transmission = pe.Var(
+ self.model.connections,
+ self.model.processes_transmission,
+ self.model.years,
+ rule = lambda *args: self.fix_variables('new_capacity_transmission',*args),
+ within=pe.NonNegativeReals)
+
+ def setupConstraints(self):
+ """This method sets up all Constraints required by the optimization model.
+
+ """
+ # declaring the balance equation for all products used in the optimization
+ self.model.constraint_product_balance = pe.Constraint(
+ self.model.nodes,
+ self.model.products,
+ self.model.years,
+ self.model.time_slices,
+ rule=self.constraint_product_balance_rule)
+ # declaring the limit of production, limited by the capacity
+ self.model.constraint_production_limit_by_capacity = pe.Constraint(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ self.model.construction_years_production,
+ self.model.time_slices,
+ rule=self.constraint_production_limit_by_capacity_rule)
+ # declaring the limit of the used storage, limited by the storage capacity and flow-to-capacity factor
+ self.model.constraint_used_storage_limit_by_capacity = pe.Constraint(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.directions_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ self.model.time_slices,
+ rule=self.constraint_used_storage_limit_by_capacity_rule)
+ # declaring the limit of the storage level, limited by the storage capacity
+ self.model.constraint_storage_level_limit_by_capacity = pe.Constraint(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ self.model.time_slices,
+ rule=self.constraint_storage_level_limit_by_capacity_rule)
+ # declaring the calculation of the storage level at the end of each time slice
+ self.model.constraint_storage_level = pe.Constraint(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ self.model.construction_years_storage,
+ self.model.time_slices,
+ rule=self.constraint_storage_level_rule)
+ # declaring the limit of transshipment, limited by the capacity
+ self.model.constraint_transshipment_limit_by_capacity = pe.Constraint(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.directions_transshipment,
+ self.model.years,
+ self.model.construction_years_transshipment,
+ self.model.time_slices,
+ rule=self.constraint_transshipment_limit_by_capacity_rule)
+ # constraints for DC load flow modelling of transmission
+ # declaring the limit of positive transmission, limited by the capacity
+ self.model.constraint_transmission_limit_by_capacity_positive = pe.Constraint(
+ self.model.connections,
+ self.model.years,
+ self.model.time_slices,
+ rule=self.constraint_transmission_limit_by_capacity_positive_rule)
+ # declaring the limit of negative transmission, limited by the capacity
+ self.model.constraint_transmission_limit_by_capacity_negative = pe.Constraint(
+ self.model.connections,
+ self.model.years,
+ self.model.time_slices,
+ rule=self.constraint_transmission_limit_by_capacity_negative_rule)
+ # impact calculation and limitation
+ # operational impacts nodal based
+ # declaring the calculation of all operational impact categories,
+ # summed over all processes on nodal base
+ self.model.constraint_operational_nodal_impact = pe.Constraint(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_operational_nodal_impact_rule)
+ # declaring the calculation of all operational impact categories,
+ # summed over all processes on nodal base
+ self.model.constraint_operational_nodal_impact_limits = pe.Constraint(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_operational_nodal_impact_limits_rule)
+ # operational impacts over all nodes
+ # declaring the calculation of all operational impact categories,
+ # summed over all processes
+ self.model.constraint_operational_impact = pe.Constraint(
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_operational_impact_rule)
+ # declaring the calculation of all operational impact categories,
+ # summed over all processes
+ self.model.constraint_operational_impact_limits = pe.Constraint(
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_operational_impact_limits_rule)
+ # invest impacts nodal based
+ # declaring the calculation of all invest impact categories,
+ # summed over all processes on nodal base
+ self.model.constraint_invest_nodal_impact = pe.Constraint(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_invest_nodal_impact_rule)
+ # declaring the calculation of all invest impact categories,
+ # summed over all processes on nodal base
+ self.model.constraint_invest_nodal_impact_limits = pe.Constraint(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_invest_nodal_impact_limits_rule)
+ # invest impacts over all nodes
+ # declaring the calculation of all invest impact categories,
+ # summed over all processes
+ self.model.constraint_invest_impact = pe.Constraint(
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_invest_impact_rule)
+ # declaring the calculation of all invest impact categories,
+ # summed over all processes
+ self.model.constraint_invest_impact_limits = pe.Constraint(
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_invest_impact_limits_rule)
+ # total impacts nodal based
+ # declaring the calculation of all impact categories,
+ # summed over all processes on nodal base
+ self.model.constraint_total_nodal_impact = pe.Constraint(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_total_nodal_impact_rule)
+ # declaring the limitation of all impact categories on nodal scale
+ self.model.constraint_total_nodal_impact_limits = pe.Constraint(
+ self.model.nodes,
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_total_nodal_impact_limits_rule)
+ # total impacts over all nodes
+ # declaring the calculation of all impact categories,
+ # summed over all processes and nodes
+ self.model.constraint_total_impact = pe.Constraint(
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_total_impact_rule)
+ # declaring the limitation of all impact categories on global scale
+ self.model.constraint_total_impact_limits = pe.Constraint(
+ self.model.impact_categories,
+ self.model.years,
+ rule=self.constraint_total_impact_limits_rule)
+ # declaring the limit of production capacity, limited by the provided input
+ self.model.constraint_production_potential_capacity_by_input = pe.Constraint(
+ self.model.nodes,
+ self.model.processes_production,
+ self.model.years,
+ rule=self.constraint_production_potential_capacity_by_input_rule)
+ # declaring the limit of storage capacity, limited by the provided input
+ self.model.constraint_storage_potential_capacity_by_input = pe.Constraint(
+ self.model.nodes,
+ self.model.processes_storage,
+ self.model.years,
+ rule=self.constraint_storage_potential_capacity_by_input_rule)
+ # declaring the limit of transshipment capacity, limited by the provided input
+ self.model.constraint_transshipment_potential_capacity_by_input = pe.Constraint(
+ self.model.connections,
+ self.model.processes_transshipment,
+ self.model.years,
+ rule=self.constraint_transshipment_potential_capacity_by_input_rule)
+ # declaring the limit of transmission capacity, limited by the provided input
+ self.model.constraint_transmission_potential_capacity_by_input_rule = pe.Constraint(
+ self.model.connections,
+ self.model.processes_transmission,
+ self.model.years,
+ rule=self.constraint_transmission_potential_capacity_by_input_rule_rule)
+ # declaring the necessity of providing the secured capacity
+ self.model.constraint_total_secured_capacity = pe.Constraint(
+ self.model.products,
+ self.model.years,
+ rule=self.constraint_total_secured_capacity_rule)
+ # declaring the necessity of providing the secured capacity
+ self.model.constraint_nodal_secured_capacity = pe.Constraint(
+ self.model.nodes,
+ self.model.products,
+ self.model.years,
+ rule=self.constraint_nodal_secured_capacity_rule)
+
+ def setupObjective(self):
+ """This method declares the pe.Objective required by the optimization model."""
+
+ self.model.Objective = pe.Objective(rule=self.objectiveRule)
+
+ def scale_model_variables(self):
+ """ scales model variables, which are in Optimization.variable_dict """
+ logging.info('Scale Variables')
+ if config.debug_optimization:
+ _time_start = datetime.now()
+ variable_dict = getattr(Optimization,'variable_dict')
+ # iterate through variables in model_instance
+ for variable in self.model_instance.component_objects(pe.Var, active =True):
+ for data in variable:
+ try:
+ # try to retrieve coefficient of variable from variable_dict
+ variable_scaling_factor = self.calculate_scaling_factor(variable_dict[id(variable[data])])
+ # if scaling factor exists
+ if variable_scaling_factor:
+ # overwrite scaling_<variable> with scaling factor
+ self.model_instance.find_component('scaling_'+str(variable.name))[data] = variable_scaling_factor
+ # Tries to retrieve coefficient from variable_dict. Passes to next iterate, if retrievement fails
+ except KeyError:
+ pass
+ # log time to scale variables
+ if config.debug_optimization:
+ time_delta_1 = datetime.now() -_time_start
+ logging.info('Scale Variables: {:,.2f} s for {:,d} variables - {:,.2f} ms/variable'.format(time_delta_1.total_seconds(),len(variable_dict),time_delta_1.total_seconds()*1000/len(variable_dict)))
+
+ def retrieve_small_bounds_and_calculate_numerics(self):
+ """ retrieves and prints bound that are smaller than eps_bound.
+ Furthermore set coefficients for numerics of scaled model and calculate matrix properties
+ """
+ # set threshold to print coefficients
+ if 'eps_bound' in config.scaling_options:
+ eps_bound = config.scaling_options['eps_bound']
+ else:
+ eps_bound = 1e-8
+
+ small_bound_list = []
+ for cons in self.model_instance.component_objects(pe.Constraint,active=True):
+ cons_name = cons.name
+ for data in cons:
+ # calculate bound
+ const_lhs = 0
+ for item in EXPR.decompose_term(cons[data].body)[1]:
+ if item[1] is None:
+ const_lhs += item[0]
+ elif item[1].value != 0:
+ # retrieves scaled coefficients
+ Optimization.set_coefficients_for_numerics(item, scaled_model=True,constraint_name = cons_name)
+ # check if 0 < bound < eps_bound
+ if cons[data].has_ub():
+ const = cons[data].upper - const_lhs
+ elif cons[data].has_lb():
+ const = cons[data].lower - const_lhs
+ if abs(const) < eps_bound and abs(const) >0:
+ small_bound_list.append((cons[data].name, const))
+ # if bound < eps_bound exists, print bounds
+ if len(small_bound_list) > 0:
+ print("Constraints with non-zero bounds lower than {:.0e}:".format(eps_bound))
+ for small_bound in small_bound_list:
+ print('{}: {:.3e}'.format(small_bound[0],small_bound[1]))
+ # calculate norm and log
+ logging.info('Numerical Properties of Optimization Problem')
+ Optimization.calculate_matrix_properties()
+
+ def instantiate_model(self, input_dict: dict = None, filepath: str = None, skip_instantiation: bool = False):
+ """ This method creates a model instance, fixes variables, and, if selected, analyzes numerics
+
+ Args:
+ input_dict(dict): input dictionary
+ filepath(str): filepath to input dictionary
+ skip_instantiation(bool): bool if instantiation is skipped. Default is False
+ """
+ if config.debug_optimization:
+ _time_start = datetime.now()
+ if not skip_instantiation:
+ # create instance
+ if input_dict:
+ self.model_instance = self.model.create_instance(data=input_dict)
+ elif filepath:
+ self.model_instance = self.model.create_instance(filename=filepath)
+ else:
+ raise AttributeError("Please provide input data either as input dictionary for pyomo or as filepath to an input file.")
+ # log skipped constraints
+ logging.info("{} out of {} constraints skipped (trivially feasible) - {:,.2f} %".format(Optimization.num_constraints['skipped'],Optimization.num_constraints['skipped']+Optimization.num_constraints['evaluated'],Optimization.num_constraints['skipped']/(Optimization.num_constraints['skipped']+Optimization.num_constraints['evaluated'])*100))
+ # fix unneeded_variables
+ self.fix_unneeded_variables()
+ if config.fix_slack_variables:
+ # if true, fix the non served demand of the products specified in config.fix_products to 0 to prevent using them as slack variable
+ self.fix_slack_variables(fix_products=config.fix_products)
+ # scale variables
+ if config.scaling_options['scale_variables']:
+ self.scale_model_variables()
+ if config.debug_optimization:
+ logging.info('Instantiation of Model Instance took {:,.2f}s.'.format((datetime.now()-_time_start).total_seconds()))
+ if config.scaling_options['analyze_numerics']:
+ self.retrieve_small_bounds_and_calculate_numerics()
+
+ def run(self, input_dict: dict = None, filepath: str = None, solver: str = 'glpk', solver_options: dict = None, debug: bool = False, skip_instantiation: bool = False):
+ """This method is used to start an optimization.
+
+ To start an optimization it is necessary to provide a file with the necessary input data.
+ This file includes all information required to instantiate the optimization problem.
+
+ Args:
+ input_dict (dict): Input dictionary that contains all the necessary data for the optimization
+ filename (str): Filename of the previously generated input file
+ solve (str): Name of the solve to be used. Standard is 'gurobi'
+ solver_options (dict): dict with options directly forwarded to the solver
+ debug (bool): bool if solver is debugged. Default is False
+ skip_instantiation (bool): bool if instantiation is skipped. Default is False
+ """
+
+ logging.info("Instantiate optimization")
+ # log scaling type
+ if not config.scaling_options['scale_constraints'] and not config.scaling_options['scale_variables']:
+ logging.info('Scaling disabled')
+ else:
+ if not config.scaling_options['scale_variables']:
+ logging.info('Row (Constraint) scaling enabled')
+ elif not config.scaling_options['scale_constraints']:
+ logging.info('Column (Variable) scaling enabled')
+ else:
+ logging.info('Row (Constraint) and Column (Variable) scaling enabled')
+ self.mysolver = pe.SolverFactory(solver, options=solver_options)
+ self.instantiate_model(input_dict,filepath,skip_instantiation)
+
+ # delete value init rule of variables, because it can not be pickled into the result file
+ for variable in self.model_instance.component_objects(pe.Var, active =True):
+ variable._value_init_rule = None
+ if (solver == "gurobi_persistent"):
+ self.mysolver.set_instance(self.model_instance, symbolic_solver_labels=True)
+ self.results = self.mysolver.solve(tee=debug)
+ # write kappa
+ logging.info('Condition Number Kappa = {:.2e}'.format(self.mysolver._solver_model.Kappa_exact))
+ else:
+ self.results = self.mysolver.solve(self.model_instance, tee=debug)
+ logging.info("Termination condition is {}".format(self.results.solver.termination_condition))
+ if self.results.solver.termination_condition == getattr(TerminationCondition,'optimal'):
+ self.model_instance.solutions.load_from(self.results)
+
+ def fix_unneeded_variables(self):
+ """Fixes variables to 0. The variables have to been labeled as to be fixed in self.fix_variables
+ Here, we iterate through each variable and check if the default value is 0. If yes, the variable is fixed
+ """
+
+ logging.info("Fix unneeded variables")
+ number_of_fixed_variables = 0
+ number_of_variables = 0
+ # iterate through variables in model
+ for variable in self.model_instance.component_objects(pe.Var,active=True):
+ for data in variable:
+ number_of_variables += 1
+ # if variable was set to be fixed in construction
+ if variable[data].value == 0:
+ variable[data].fix(0)
+ number_of_fixed_variables += 1
+ logging.info("{} of {} variables fixed in Optimization - {:,.2f} %".format(number_of_fixed_variables, number_of_variables, number_of_fixed_variables / number_of_variables * 100))
+
+ def fix_slack_variables(self,fix_products):
+ """
+ Fixes the non_served_demand of specified products( e.g. heat, electricity, and mobility) to 0.
+ Although the import of these products may be undesired in some models,
+ their import is usually allowed at arbitrarily high environmental or financial impacts to keep the optimization feasible.
+ By fixing the non-served-demand to 0, these variables are no longer used as slack variables and their import is strictly prevented
+ """
+ logging.info("Fix slack variables")
+ number_of_variables = 0
+ # iterate through variables in model
+ for variable in self.model_instance.component_objects(pe.Var,active=True):
+ if variable.name=='non_served_demand':
+ for data in variable:
+ if data[1] in fix_products:
+ variable[data].fix(0)
+ number_of_variables += 1
+ logging.info("{} slack variables in non_served_demand were fixed.".format(number_of_variables))
+ # METHODS #
+ # Evaluation methods
+ def get_new_capacity_from_result(self):
+ """Return a dictionary of dataframes with all new capacities from the optimization."""
+
+ # Create empty dictionary
+ new_capacity_categories = ["production", "storage", "transshipment", "transmission"]
+ unstack = {"production": [2], "storage": [2], "transshipment": [2], "transmission": [2]}
+ index = {"production": ["node", "process"], "storage": ["node", "process"], "transshipment": ["connection", "process"], "transmission": ["connection", "process"]}
+ new_capacity = {}
+ for category in new_capacity_categories:
+ new_capacity[category] = self.get_dataframe_from_result(getattr(self.model_instance, "new_capacity_" + category),getattr(self.model_instance, "scaling_new_capacity_" + category), unstack=unstack[category])
+ new_capacity[category].index.names = index[category]
+
+ return new_capacity
+
+ @staticmethod
+ def get_dataframe_from_result(model_instance_variable, variable_scaling_factors, unstack: list = None):
+ """Returns a dataframe with the results from the optimization model."""
+
+ # Create empty dictionary
+ variable = {}
+ # for all indices in the model_instance_variable put its key-value pair in the dictionary
+ for index in model_instance_variable:
+ variable[index] = pe.value(model_instance_variable[index])*pe.value(variable_scaling_factors[index])
+
+ # Create pandas Series from
+ variable = pd.Series(variable)
+ if unstack:
+ variable = variable.unstack(unstack)
+
+ return variable
+
+ @staticmethod
+ def get_dataframe_from_parameter(model_instance_variable, unstack: list = None):
+ """Returns a dataframe with the results from the optimization model."""
+
+ # Create empty dictionary
+ variable = {}
+ # for all indices in the model_instance_variable put its key-value pair in the dictionary
+ for index in model_instance_variable:
+ variable[index] = model_instance_variable[index]
+
+ # Create pandas Series from
+ variable = pd.Series(variable)
+ if unstack:
+ variable = variable.unstack(unstack)
+
+ return variable
+
+ # RULES FOR pe.Objective AND CONSTRAINTS #
+ # CONSTRAINTS # methods used as rules for constraints
+ @staticmethod
+ def constraint_product_balance_rule(model, node: int, product: str, year: int, time_slice: str) -> "pyomo rule":
+ """This method is used as rule for the product balance equation.
+
+ This expression represents the product balance equation which is unique for each node, product, year and time slice.
+ It can be summarized as::
+
+ Production + Transshipment + Storage + Transmission >= Demand
+
+ Therefore it takes the named above as parameters.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ product (str): A product at which is constrained
+ year (int): A year at which the pe.Constraint is active
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ # returns the expression which is the product balance equation
+ return Optimization.scale_expr(
+ # Sets the demand for a product at a node in a specific year and time slice equal to
+ # the sum of all process contributions to that product balance
+ model.demand[product, year, time_slice, node] <=
+ # sum of the production of a product by all used production processes
+ sum(
+ # sum over all construction years to differentiate between the used capacities
+ sum(model.technology_matrix_production[product, process_production, year_construction]
+ * model.used_production[node, process_production, year, year_construction, time_slice]*model.scaling_used_production[node, process_production, year, year_construction, time_slice]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ for process_production in model.processes_production)
+
+ # sum over all storage processes
+ + sum(
+ # sum over all construction years to differentiate between the used capacities
+ + sum(
+ sum(model.used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]*model.scaling_used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]
+ * (- model.storage_level_factor[direction_storage])
+ # sum over both storage direction (deposit and withdraw)
+ for direction_storage in model.directions_storage)
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ for process_storage in filter(lambda process_storage: product == model.storage_products[process_storage], model.processes_storage))
+
+ # sum of the transshipment of a product by all used transshipment processes
+ + sum(
+ # sum over all construction years to differentiate between the used capacities
+ sum(
+ # Sum of all streams of connections which have this node as first node
+ sum(model.transshipment_efficiency[process_transshipment, connection]
+ * model.used_transshipment[connection, process_transshipment, "backward", year, year_construction, time_slice] * model.scaling_used_transshipment[connection, process_transshipment, "backward", year, year_construction, time_slice]
+ - (model.used_transshipment[connection, process_transshipment, "forward", year, year_construction, time_slice] * model.scaling_used_transshipment[connection, process_transshipment, "forward", year, year_construction, time_slice])
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # Sum of all streams of connections, which have this node as second node, therefore multiplied with the efficiency of the connection
+ + sum(model.transshipment_efficiency[process_transshipment, connection]
+ * model.used_transshipment[connection, process_transshipment, "forward", year, year_construction, time_slice] * model.scaling_used_transshipment[connection, process_transshipment, "forward", year, year_construction, time_slice]
+ - (model.used_transshipment[connection, process_transshipment, "backward", year, year_construction, time_slice] * model.scaling_used_transshipment[connection, process_transshipment, "backward", year, year_construction, time_slice])
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transshipment[process_transshipment, year_construction]), model.construction_years_transshipment))
+ # sum over all transshipment processes that use this product
+ for process_transshipment in filter(lambda process_transshipment: product == model.transshipment_products[process_transshipment], model.processes_transshipment))
+
+ # adding the power from DC load flow transmission, if the product is electricity
+ + ( # Sum of the power from all connections which have this node as first node
+ sum(
+ model.power_line_properties[connection, year, "susceptance per unit"]
+ * (model.phase_difference[model.connected_nodes[connection, "node2"], year, time_slice]*model.scaling_phase_difference[model.connected_nodes[connection, "node2"], year, time_slice]
+ - model.phase_difference[node, year, time_slice]*model.scaling_phase_difference[node, year, time_slice])
+ for connection in filter(lambda connection: (model.connected_nodes[connection, "node1"] == node) and (product in model.products_transmission), model.connections))
+ # Sum of the power from all connections which have this node as second node
+ + sum(
+ model.power_line_properties[connection, year, "susceptance per unit"]
+ * (model.phase_difference[model.connected_nodes[connection, "node1"], year, time_slice]*model.scaling_phase_difference[model.connected_nodes[connection, "node1"], year, time_slice]
+ - model.phase_difference[node, year, time_slice]*model.scaling_phase_difference[node, year, time_slice])
+ for connection in filter(lambda connection: (model.connected_nodes[connection, "node2"] == node) and (product in model.products_transmission), model.connections))
+ ) * model.per_unit_base
+ # variable for non-served demand to avoid infeasibilities
+ + model.non_served_demand[node, product, year, time_slice]*model.scaling_non_served_demand[node, product, year, time_slice]
+
+ )
+
+ @staticmethod
+ def constraint_production_limit_by_capacity_rule(model, node: int, process_production: str, year: int, year_construction: int, time_slice: str) -> "pyomo rule":
+ """This method is used as rule for limitting the production to the sum of existing and new capacity.
+
+ The rule is applied to every year of operation AND every year of construction.
+ This means that for every year the capacity of existing infrastructure is used as limit,
+ creating a constraint for every year of construction as well. This way the operation can
+ differentiate between older and newer capacities which might have different impacts.
+ Additionally for the years of construction which also are investment years,
+ the newly build capacities are added to the limit.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ process_production (str): A production process which is constrained
+ year (int): A year at which the pe.Constraint is active
+ year_construction (int): The year of construction of a specific capacity
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+ if not model.used_production[node, process_production, year, year_construction, time_slice].value == 0:
+ Optimization.add_evaluated_constraint()
+ if year_construction not in model.years:
+ return Optimization.scale_expr(
+ model.used_production[node, process_production, year, year_construction, time_slice]*model.scaling_used_production[node, process_production, year, year_construction, time_slice]
+ <= model.existing_capacity_production[node, process_production, year, year_construction]
+ * model.usable_capacity_factor_timeseries_production[node, process_production, time_slice]
+ )
+ else:
+ return Optimization.scale_expr(
+ model.used_production[node, process_production, year, year_construction, time_slice]*model.scaling_used_production[node, process_production, year, year_construction, time_slice]
+ <= (model.existing_capacity_production[node, process_production, year, year_construction]
+ + model.new_capacity_production[node, process_production, year_construction]*model.scaling_new_capacity_production[node, process_production, year_construction])
+ * model.usable_capacity_factor_timeseries_production[node, process_production, time_slice]
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_transshipment_limit_by_capacity_rule(model, connection: int, process_transshipment: str, direction_transshipment: str, year: int, year_construction: int, time_slice: str) -> "pyomo rule":
+ """This method is used rule for limiting the used transmission up to the sum of existing and new capacity.
+
+ The rule is applied to every year of operation AND every year of construction.
+ This means that for every year the capacity of existing infrastructure is used as limit,
+ creating a constraint for every year of construction as well. This way the operation can
+ differentiate between older and newer capacities which might have different impacts.
+ Additionally for the years of construction which also are investment years,
+ the newly build capacities are added to the limit.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ process_transshipment (str): A transshipment process which is constrained
+ direction_transshipment (str): A transshipment direction which is constrained
+ year (int): A year at which the pe.Constraint is active
+ year_construction (int): The year of construction of a specific capacity
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+ if not model.used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice].value == 0:
+ Optimization.add_evaluated_constraint()
+ if year_construction not in model.years:
+ return Optimization.scale_expr(
+ model.used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]*model.scaling_used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]
+ <= model.existing_capacity_transshipment[connection, process_transshipment, year, year_construction]
+ )
+ else:
+ return Optimization.scale_expr(
+ model.used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]*model.scaling_used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]
+ <= (model.existing_capacity_transshipment[connection, process_transshipment, year, year_construction]
+ + model.new_capacity_transshipment[connection, process_transshipment, year_construction]*model.scaling_new_capacity_transshipment[connection, process_transshipment, year_construction])
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_storage_level_limit_by_capacity_rule(model, node: int, process_storage: str, year: int, year_construction: int, time_slice: str) -> "pyomo rule":
+ """This method is used as rule for limiting the used storage up to the sum of existing and new capacity.
+
+ The rule is applied to every year of operation AND every year of construction.
+ This means that for every year the capacity of existing infrastructure is used as limit,
+ creating a constraint for every year of construction as well. This way the operation can
+ differentiate between older and newer capacities which might have different impacts.
+ Additionally for the years of construction which also are investment years,
+ the newly build capacities are added to the limit.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ process_storage (str): A storage process which is constrained
+ year (int): A year at which the pe.Constraint is active
+ year_construction (int): The year of construction of a specific capacity
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ if year_construction not in model.years:
+ return Optimization.scale_expr(
+ model.storage_level[node, process_storage, year, year_construction, time_slice]*model.scaling_storage_level[node, process_storage, year, year_construction, time_slice]
+ <= model.existing_capacity_storage[node, process_storage, year, year_construction]
+ )
+ else:
+ return Optimization.scale_expr(
+ model.storage_level[node, process_storage, year, year_construction, time_slice]*model.scaling_storage_level[node, process_storage, year, year_construction, time_slice]
+ <= (model.existing_capacity_storage[node, process_storage, year, year_construction]
+ + model.new_capacity_storage[node,process_storage, year_construction]*model.scaling_new_capacity_storage[node,process_storage, year_construction])
+ )
+
+ @staticmethod
+ def constraint_used_storage_limit_by_capacity_rule(model, node: int, process_storage: str, direction_storage: str, year: int, year_construction: int, time_slice: str) -> "pyomo rule":
+ """This abstract method is used as rule for limiting the output and input of storage processes.
+
+ The maximum flow out of or into a storage instance is limited by the available storage capacity
+ multiplied with a flow-to-storage-capacity factor.
+
+ Although the available capacity can be defined differently, e.g.::
+
+ storage usage <= flow-to-storage-capacity factor * (installed capacity)
+
+ or::
+
+ storage usage <= flow-to-storage-capacity factor * (installed capacity + new capacity)
+
+ Because of this, the abstract pe.Constraint rule needs to be defined in a subclass.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ process_storage (str): A storage process which is constrained
+ direction_storage (str): A direction of "withdraw" or "deposit" a product
+ year (int): A year at which the pe.Constraint is active
+ year_construction (int): The year of construction of a specific capacity
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+ if not model.storage_level[node, process_storage, year, year_construction, time_slice].value == 0:
+ Optimization.add_evaluated_constraint()
+ if year_construction not in model.years:
+ return Optimization.scale_expr(
+ model.used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]*model.scaling_used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]
+ <= model.flow_to_storage_capacity_factor[process_storage, year_construction] * model.existing_capacity_storage[node, process_storage, year, year_construction]
+ )
+ else:
+ return Optimization.scale_expr(
+ model.used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]*model.scaling_used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]
+ <= model.flow_to_storage_capacity_factor[process_storage, year_construction] * (
+ model.existing_capacity_storage[node, process_storage, year, year_construction]
+ + model.new_capacity_storage[node, process_storage, year_construction]*model.scaling_new_capacity_storage[node, process_storage, year_construction])
+
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_storage_level_rule(model, node: int, process_storage: str, year: int, year_construction: int, time_slice: str) -> "pyomo rule":
+ """This method is used as rule for the calculation of the storage level.
+
+ Since the storage should not act as a source or sink, this method calculates the storage level after
+ each time_slice and couples it with the storage level at the beginning of the next time slice.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ process_storage (str): A storage process which is constrained
+ year (int): A year at which the pe.Constraint is active
+ year_construction (int): The year of construction of a specific capacity
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # sum of the current storage level, the deposits and withdrawals
+
+ (model.storage_level[node, process_storage, year, year_construction, time_slice]*model.scaling_storage_level[node, process_storage, year, year_construction, time_slice]
+ # multiplication of the duration of the time slice with...
+ + (model.time_slice_duration[time_slice]
+ # ... the sum of all deposits and withdrawals from the storage
+ * sum(model.storage_level_factor[direction_storage]
+ * model.used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]*model.scaling_used_storage[node, process_storage, direction_storage, year, year_construction, time_slice]
+ * (model.efficiency_matrix_storage[model.storage_products[process_storage], process_storage, direction_storage, year_construction]
+ ** model.storage_level_factor[direction_storage])
+ for direction_storage in model.directions_storage))
+ # the sum above equal the storage level of the next time slice
+ ) == model.storage_level[node, process_storage, year, year_construction, model.next_time_slice[time_slice]]*model.scaling_storage_level[node, process_storage, year, year_construction, model.next_time_slice[time_slice]]
+ )
+
+ @staticmethod
+ def constraint_transmission_limit_by_capacity_positive_rule(model, connection: int, year: int, time_slice: str) -> "pyomo rule":
+ """This method is used as rule for limiting the load flow through a transmission power line.
+
+ The load flow needs to be limited by the available capacity, which is why this abstract rule
+ exists and is used for a constraint in the base class for all optimizations.
+ Although the available capacity can be defined differently, e.g.::
+
+ load flow <= installed capacity
+
+ or::
+
+ load flow <= installed capacity + capacity from switching + capacity from new power lines
+
+ Because of this, the abstract pe.Constraint rule needs to be defined in a subclass.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ connection (int): A power line which is constrained
+ year (int): A year at which the pe.Constraint is active
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # Positive power flow from node1 to node2
+ + model.power_line_properties[connection, year, "susceptance per unit"]
+ * (model.phase_difference[model.connected_nodes[connection, "node1"], year, time_slice]*model.scaling_phase_difference[model.connected_nodes[connection, "node1"], year, time_slice]
+ - model.phase_difference[model.connected_nodes[connection, "node2"], year, time_slice]*model.scaling_phase_difference[model.connected_nodes[connection, "node2"], year, time_slice])
+ * model.per_unit_base
+ # has to be smaller or equal than the power limit of the existing capacity of that connection
+ <= (model.power_line_properties[connection, year, "power limit"]
+ # adding the sum of the new power limit capacity from all transmission processes
+ + sum(
+ # sum over all invest years which are earlier or equal to the current invest years
+ sum(
+ # new installed circuits of each transmission technology
+ model.new_capacity_transmission[connection, process_transmission, earlier_year]*model.scaling_new_capacity_transmission[connection, process_transmission, earlier_year]
+ # multiplied by their power limit per circuit
+ * model.power_limit_per_circuit[process_transmission]
+ # multiplied by 1 minus the safety margin
+ * (1 - model.reliability_margin_transmission[process_transmission])
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transmission[process_transmission, earlier_year]), model.years))
+ for process_transmission in model.processes_transmission))
+ )
+
+ @staticmethod
+ def constraint_transmission_limit_by_capacity_negative_rule(model, connection: int, year: int, time_slice: str) -> "pyomo rule":
+ """This method is used as rule for limiting the load flow through a transmission power line.
+
+ The load flow needs to be limited by the available capacity, which is why this abstract rule
+ exists and is used for a constraint in the base class for all optimizations.
+ Although the available capacity can be defined differently, e.g.::
+
+ load flow <= installed capacity
+
+ or::
+
+ load flow <= installed capacity + capacity from switching + capacity from new power lines
+
+ Because of this, the abstract pe.Constraint rule needs to be defined in a subclass.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ power_line (int): A power line which is constrained
+ year (int): A year at which the pe.Constraint is active
+ time_slice (str): A time_slice at which the pe.Constraint is active
+ """
+
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # Negative power flow from node1 to node2
+ - model.power_line_properties[connection, year, "susceptance per unit"]
+ * (model.phase_difference[model.connected_nodes[connection, "node1"], year, time_slice]*model.scaling_phase_difference[model.connected_nodes[connection, "node1"], year, time_slice]
+ - model.phase_difference[model.connected_nodes[connection, "node2"], year, time_slice]*model.scaling_phase_difference[model.connected_nodes[connection, "node2"], year, time_slice])
+ * model.per_unit_base
+ # has to be smaller or equal than the power limit of the existing capacity of that connection
+ <= (model.power_line_properties[connection, year, "power limit"]
+ # adding the sum of the new power limit capacity from all transmission processes
+ + sum(
+ # sum over all invest years which are earlier or equal to the current invest years
+ sum(
+ # new installed circuits of each transmission technology
+ model.new_capacity_transmission[connection, process_transmission, earlier_year]*model.scaling_new_capacity_transmission[connection, process_transmission, earlier_year]
+ # multiplied by their power limit per circuit
+ * model.power_limit_per_circuit[process_transmission]
+ # multiplied by 1 minus the safety margin
+ * (1 - model.reliability_margin_transmission[process_transmission])
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transmission[process_transmission, earlier_year]), model.years))
+ for process_transmission in model.processes_transmission))
+ )
+
+ # constraints for impacts
+ # nodal pe.Constraint rules for the operational impact
+ @staticmethod
+ def constraint_operational_nodal_impact_rule(model, node: int, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the calculation of the operational impact vector summed over all processes.
+
+ The operational impact of one node over all time slices is calculated in this method.
+ By summing over the construction years of all capacities, the corresponding impact matrices from
+ the actual construction year can be used. Furthermore the impact calculated for each time slice
+ is multiplied with the weight in hours of a year the time slice has.
+
+ The operational impact of production processes equals the sum of the products of process usage and
+ the corresponding impact matrix. The same method is used for calculating the impact of transshipment
+ processes, but the resulting impact is shared equally between the corresponding nodes.
+
+ The operational impact of storage processes is calculated based on the **withdrawn** product flow.
+
+ For the electric transmission grid it is assumed that the operational impact is negligible.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # calculate the operational nodal impact
+ model.operational_nodal_impact[node, impact_category, year]*model.scaling_operational_nodal_impact[node, impact_category, year]
+ ==
+ # sum over all time slices of the year
+ sum(
+ (
+ # Sum over the impacts of all production processes
+ sum(
+ # sum over the construction years of the production process
+ sum(model.used_production[node, process_production, year, year_construction, time_slice]*model.scaling_used_production[node, process_production, year, year_construction, time_slice]
+ * model.impact_matrix_production[impact_category, 'operation', process_production, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ for process_production in model.processes_production)
+
+ # sum over all storage processes
+ + sum(
+ # sum over the construction years of the storage process
+ sum(
+ # impact is assumed to be the impact per withdrawn unit of stored product
+ model.impact_matrix_storage[impact_category, 'operation', process_storage, year, year_construction]
+ * model.used_storage[node, process_storage, "withdraw", year, year_construction, time_slice]* model.scaling_used_storage[node, process_storage, "withdraw", year, year_construction, time_slice]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ for process_storage in model.processes_storage)
+
+ # sum over all transshipment processes
+ + sum(
+ # sum over the construction years of the transshipment process
+ sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transshipment[impact_category, 'operation', process_transshipment, year, year_construction]
+ # sum over both transshipment directions
+ * sum(
+ # sum over all connections, which have this node as first node
+ sum(model.used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]*model.scaling_used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]*model.scaling_used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ for direction_transshipment in model.directions_transshipment)
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transshipment[process_transshipment, year_construction]), model.construction_years_transshipment))
+ for process_transshipment in model.processes_transshipment)
+
+ # adding the sum of all non-served demands multiplied by the corresponding impacts
+ # sum over all products
+ + sum(model.non_served_demand[node, product, year, time_slice]*model.scaling_non_served_demand[node, product, year, time_slice]
+ * model.impact_matrix_non_served_demand[impact_category, product, year, time_slice]
+ for product in model.products)
+ )
+ # multiply the sum of all impacts with the weight of a time slice regarding the full year
+ * model.time_slice_yearly_weight[time_slice]
+ for time_slice in model.time_slices)
+ )
+
+ @staticmethod
+ def constraint_operational_nodal_impact_limits_rule(model, node: int, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the limitation of the operational impact vector summed over all processes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.operational_nodal_impact_limits[node, impact_category, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # limit nodal operational impact
+ model.operational_nodal_impact[node, impact_category, year]*model.scaling_operational_nodal_impact[node, impact_category, year] <=
+ (model.operational_nodal_impact_limits[node, impact_category, year]
+ # adding an overshoot variable mainly for debugging infeasibility issues
+ + model.operational_nodal_impact_overshoot[node, impact_category, year]*model.scaling_operational_nodal_impact_overshoot[node, impact_category, year])
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ # overall pe.Constraint rules for the operational impact
+ @staticmethod
+ def constraint_operational_impact_rule(model, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the calculation of the operational impact vector summed over all processes and nodes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ model.operational_impact[impact_category, year]*model.scaling_operational_impact[impact_category, year] ==
+ sum(model.operational_nodal_impact[node, impact_category, year]*model.scaling_operational_nodal_impact[node, impact_category, year]
+ for node in model.nodes)
+ )
+
+ @staticmethod
+ def constraint_operational_impact_limits_rule(model, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the limitation of the operational impact vector summed over all processes and nodes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.operational_impact_limits[impact_category, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # limit overall operational impact
+ model.operational_impact[impact_category, year]*model.scaling_operational_impact[impact_category, year] <=
+ (model.operational_impact_limits[impact_category, year]
+ # adding an overshoot variable mainly for debugging feasibility issues
+ + model.operational_impact_overshoot[impact_category, year]*model.scaling_operational_impact_overshoot[impact_category, year])
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ # pe.Constraint rules for the invest impact
+ # nodal pe.Constraint rules for the invest impact
+ @staticmethod
+ def constraint_invest_nodal_impact_rule(model, node: int, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the calculation of the invest impact vector
+ summed over all processes.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # calculate the invest nodal impact
+ model.invest_nodal_impact[node, impact_category, year]*model.scaling_invest_nodal_impact[node, impact_category, year] ==
+
+ # sum over all time slice, while yearly impact is weighted with the share of a year the time slice represents
+ sum(# Multiply everything with the yearly weight of the time slice
+ model.time_slice_yearly_weight[time_slice] / sum(model.time_slice_yearly_weight[time_slice] for time_slice in model.time_slices)
+
+ # existing infrastructure
+ # Sum over the impacts of all production processes
+ * (sum(
+ # sum over the construction years of the production process
+ sum(model.existing_capacity_production[node, process_production, year, year_construction]
+ * model.impact_matrix_production[impact_category, 'invest', process_production, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ for process_production in model.processes_production)
+
+ # Adding the sum over all storage processes
+ + sum(
+ # sum over the construction years of the storage process
+ sum(model.existing_capacity_storage[node, process_storage, year, year_construction]
+ * model.impact_matrix_storage[impact_category, 'invest', process_storage, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ for process_storage in model.processes_storage)
+
+ # Adding the sum over all transshipment processes
+ + sum(
+ # sum over the construction years of the transshipment process
+ sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transshipment[impact_category, 'invest', process_transshipment, year, year_construction]
+ # multiplied with the length and capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.existing_capacity_transshipment[connection, process_transshipment, year, year_construction]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.existing_capacity_transshipment[connection, process_transshipment, year, year_construction]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transshipment[process_transshipment, year_construction]), model.construction_years_transshipment))
+ for process_transshipment in model.processes_transshipment)
+
+ # adding the sum of the impacts of the transmission grid
+ + sum(
+ # sum over the construction years of the transmission process
+ sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transmission[impact_category, 'invest', process_transmission, year, year_construction]
+ # multiplied with the length and power capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.existing_capacity_transmission[connection, process_transmission, year, year_construction]
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.existing_capacity_transmission[connection, process_transmission, year, year_construction]
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transmission[process_transmission, year_construction]), model.construction_years_transmission))
+ for process_transmission in model.processes_transmission)
+
+
+ # adding NEW CAPACITIES multiplied with the corresponding impact
+ # sum over all invest years until the current year
+ # Sum over the impacts of all production processes
+ + sum(
+ # sum over the construction years of the production process
+ sum(model.new_capacity_production[node, process_production, earlier_year]*model.scaling_new_capacity_production[node, process_production, earlier_year]
+ * model.impact_matrix_production[impact_category, 'invest', process_production, year, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_production[process_production, earlier_year]), model.years))
+ for process_production in model.processes_production)
+
+ # Adding the sum over all storage processes
+ + sum(
+ # sum over the construction years of the storage process
+ sum(model.new_capacity_storage[node, process_storage, earlier_year]*model.scaling_new_capacity_storage[node, process_storage, earlier_year]
+ * model.impact_matrix_storage[impact_category, 'invest', process_storage, year, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_storage[process_storage, earlier_year]), model.years))
+ for process_storage in model.processes_storage)
+
+ # Adding the sum over all transshipment processes
+ + sum(
+ # sum over the construction years of the transshipment process
+ sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transshipment[impact_category, 'invest', process_transshipment, year, earlier_year]
+ # multiplied with the length and capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.new_capacity_transshipment[connection, process_transshipment, earlier_year]*model.scaling_new_capacity_transshipment[connection, process_transshipment, earlier_year]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.new_capacity_transshipment[connection, process_transshipment, earlier_year]*model.scaling_new_capacity_transshipment[connection, process_transshipment, earlier_year]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transshipment[process_transshipment, earlier_year]), model.years))
+ for process_transshipment in model.processes_transshipment)
+
+ # adding the sum of the impacts of the transmission grid
+ + sum(
+ # sum over the construction years of the transmission process
+ sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transmission[impact_category, 'invest', process_transmission, year, earlier_year]
+ # multiplied with the length and power capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.new_capacity_transmission[connection, process_transmission, earlier_year]*model.scaling_new_capacity_transmission[connection, process_transmission, earlier_year]
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.new_capacity_transmission[connection, process_transmission, earlier_year]*model.scaling_new_capacity_transmission[connection, process_transmission, earlier_year]
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transmission[process_transmission, earlier_year]), model.years))
+ for process_transmission in model.processes_transmission)
+ )
+ for time_slice in model.time_slices)
+ )
+
+ @staticmethod
+ def constraint_invest_nodal_impact_limits_rule(model, node: int, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the limitation of the invest impact vector summed over all processes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.invest_nodal_impact_limits[node, impact_category, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # limit nodal invest impact
+ model.invest_nodal_impact[node, impact_category, year]*model.scaling_invest_nodal_impact[node, impact_category, year] <=
+ (model.invest_nodal_impact_limits[node, impact_category, year]
+ # adding an overshoot variable mainly for debugging feasibility issues
+ + model.invest_nodal_impact_overshoot[node, impact_category, year]*model.scaling_invest_nodal_impact_overshoot[node, impact_category, year])
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ # overall pe.Constraint rules for the invest impact
+ @staticmethod
+ def constraint_invest_impact_rule(model, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the calculation of the invest impact vector summed over all processes and nodes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ model.invest_impact[impact_category, year]*model.scaling_invest_impact[impact_category, year] ==
+ sum(model.invest_nodal_impact[node, impact_category, year]*model.scaling_invest_nodal_impact[node, impact_category, year]
+ for node in model.nodes)
+ )
+
+ @staticmethod
+ def constraint_invest_impact_limits_rule(model, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the limitation of the invest impact vector summed over all processes and nodes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.invest_impact_limits[impact_category, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # limit overall operational impact
+ model.invest_impact[impact_category, year]*model.scaling_invest_impact[impact_category, year] <=
+ model.invest_impact_limits[impact_category, year]
+ # adding an overshoot variable mainly for debugging feasibility issues
+ + model.invest_impact_overshoot[impact_category, year]*model.scaling_invest_impact_overshoot[impact_category, year]
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ # pe.Constraint rules for the total impact
+ # nodal pe.Constraint rules for the total impact
+ @staticmethod
+ def constraint_total_nodal_impact_rule(model, node: int, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the calculation of the total impact vector summed over all processes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ model.total_nodal_impact[node, impact_category, year]*model.scaling_total_nodal_impact[node, impact_category, year] ==
+ model.operational_nodal_impact[node, impact_category, year]*model.scaling_operational_nodal_impact[node, impact_category, year]
+ + model.invest_nodal_impact[node, impact_category, year]*model.scaling_invest_nodal_impact[node, impact_category, year]
+ )
+
+ @staticmethod
+ def constraint_total_nodal_impact_limits_rule(model, node: int, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the limitation of the total impact vector summed over all processes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.total_nodal_impact_limits[node, impact_category, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # limit nodal total impact
+ model.total_nodal_impact[node, impact_category, year]*model.scaling_total_nodal_impact[node, impact_category, year] <=
+ model.total_nodal_impact_limits[node, impact_category, year]
+ # adding an overshoot variable mainly for debugging feasibility issues
+ + model.total_nodal_impact_overshoot[node, impact_category, year]*model.scaling_total_nodal_impact_overshoot[node, impact_category, year]
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ # overall pe.Constraint rules for the total impact
+ @staticmethod
+ def constraint_total_impact_rule(model, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the calculation of the total impact vector summed over all processes and nodes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ model.total_impact[impact_category, year]*model.scaling_total_impact[impact_category, year] ==
+ sum(model.total_nodal_impact[node, impact_category, year]*model.scaling_total_nodal_impact[node, impact_category, year]
+ for node in model.nodes)
+ )
+
+ @staticmethod
+ def constraint_total_impact_limits_rule(model, impact_category: str, year: int) -> "pyomo rule":
+ """This method is used as rule for the limitation of the total impact vector summed over all processes and nodes
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ impact_category (str): An impact category which is assessed
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.total_impact_limits[impact_category, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # limit overall total impact
+ model.total_impact[impact_category, year]*model.scaling_total_impact[impact_category, year] <=
+ model.total_impact_limits[impact_category, year]
+ # adding an overshoot variable mainly for debugging feasibility issues
+ + model.total_impact_overshoot[impact_category, year]*model.scaling_total_impact_overshoot[impact_category, year]
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_production_potential_capacity_by_input_rule(model, node: int, process_production: str, year: int) -> "pyomo rule":
+ """This method is used as rule for limitting the capacity of production processes by limits
+ from the input e.g. maximum potential.
+
+ Some production technologies like renewable energies have local capacity limits based on
+ the availability of ressources or regulatory restrictions. These limits are given to the
+ optimization as parameter and can change by time.
+
+ Args;
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ process_production (str): A production process which is constrained
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.potential_capacity_production[node, process_production, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ combined_existing_capacity_production = sum(model.existing_capacity_production[node,process_production, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ # if combined existing capacity >= potential capacity --> new capacity == 0
+ if combined_existing_capacity_production >= model.potential_capacity_production[node, process_production, year]:
+ return Optimization.scale_expr(0 >=
+ sum(model.new_capacity_production[node,process_production, earlier_year]*model.scaling_new_capacity_production[node,process_production, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_production[process_production, earlier_year]), model.years))
+ )
+ # else new capacity <= potential capacity - existing capacity
+ else:
+ return Optimization.scale_expr(
+ model.potential_capacity_production[node, process_production, year] - combined_existing_capacity_production
+ >= sum(model.new_capacity_production[node,process_production, earlier_year]*model.scaling_new_capacity_production[node,process_production, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_production[process_production, earlier_year]), model.years))
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+
+ @staticmethod
+ def constraint_storage_potential_capacity_by_input_rule(model, node: int, process_storage: str, year: int) -> "pyomo rule":
+ """This method is used as rule for limitting the capacity of storage processes by limits
+ from the input e.g. maximum potential
+
+ Some storage technologies like gas cavern storages have local capacity limits based on
+ the availability of ressources or regulatory restrictions. These limits are given to the
+ optimization as parameter and can change by time.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ node (int): A node at which the pe.Constraint is active
+ process_storage (str): A storage process which is constrained
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.potential_capacity_storage[node, process_storage, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ combined_existing_capacity_storage = sum(model.existing_capacity_storage[node,process_storage, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ # if combined existing capacity >= potential capacity --> new capacity == 0
+ if combined_existing_capacity_storage >= model.potential_capacity_storage[node, process_storage, year]:
+ return Optimization.scale_expr(0 >=
+ sum(model.new_capacity_storage[node,process_storage, earlier_year]*model.scaling_new_capacity_storage[node,process_storage, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_storage[process_storage, earlier_year]), model.years))
+ )
+ # else new capacity <= potential capacity - existing capacity
+ else:
+ return Optimization.scale_expr(
+ model.potential_capacity_storage[node, process_storage, year] - combined_existing_capacity_storage
+ >= sum(model.new_capacity_storage[node,process_storage, earlier_year]*model.scaling_new_capacity_storage[node,process_storage, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_storage[process_storage, earlier_year]), model.years))
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_transshipment_potential_capacity_by_input_rule(model, connection: int, process_transshipment: str, year: int) -> "pyomo rule":
+ """This method is used as rule for limitting the capacity of transshipment processes by limits
+ from the input e.g. maximum potential.
+
+ Some transshipment technologies like pipelines have local capacity limits based on
+ the availability of ressources or regulatory restrictions. These limits are given to the
+ optimization as parameter and can change by time.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ connection (int): A connection of the grid
+ process_transshipment (str): A transshipment process which is constrained
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.potential_capacity_transshipment[connection, process_transshipment, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ combined_existing_capacity_transshipment = sum(model.existing_capacity_transshipment[connection,process_transshipment, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transshipment[process_transshipment, year_construction]), model.construction_years_transshipment))
+ # if combined existing capacity >= potential capacity --> new capacity == 0
+ if combined_existing_capacity_transshipment >= model.potential_capacity_transshipment[connection, process_transshipment, year]:
+ return Optimization.scale_expr(0 ==
+ sum(model.new_capacity_transshipment[connection,process_transshipment, earlier_year]*model.scaling_new_capacity_transshipment[connection,process_transshipment, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transshipment[process_transshipment, earlier_year]), model.years))
+ )
+ # else new capacity <= potential capacity - existing capacity
+ else:
+ return Optimization.scale_expr(
+ model.potential_capacity_transshipment[connection, process_transshipment, year] - combined_existing_capacity_transshipment
+ >= sum(model.new_capacity_transshipment[connection,process_transshipment, earlier_year]*model.scaling_new_capacity_transshipment[connection,process_transshipment, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transshipment[process_transshipment, earlier_year]), model.years))
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_transmission_potential_capacity_by_input_rule_rule(model, connection: int, process_transmission: str, year: int) -> "pyomo rule":
+ """This method is used as rule for limitting the capacity of transmission processes by limits
+ from the input e.g. maximum potential.
+
+ Some transmission technologies have local capacity limits based on the availability of
+ ressources or regulatory restrictions. These limits are given to the optimization as
+ parameter and can change by time.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ connection (int): A connection of the grid
+ process_transmission (str): A transsmission process which is constrained
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.potential_capacity_transmission[connection, process_transmission, year] != np.inf:
+ Optimization.add_evaluated_constraint()
+ combined_existing_capacity_transmission = sum(model.existing_capacity_transmission[connection,process_transmission, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transmission[process_transmission, year_construction]), model.construction_years_transmission))
+ # if combined existing capacity >= potential capacity --> new capacity == 0
+ if combined_existing_capacity_transmission >= model.potential_capacity_transmission[connection, process_transmission, year]:
+ return Optimization.scale_expr(0 ==
+ sum(model.new_capacity_transmission[connection,process_transmission, earlier_year]*model.scaling_new_capacity_transmission[connection,process_transmission, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transmission[process_transmission, earlier_year]), model.years))
+ )
+ # else new capacity <= potential capacity - existing capacity
+ else:
+ return Optimization.scale_expr(
+ model.potential_capacity_transmission[connection, process_transmission, year] - combined_existing_capacity_transmission
+ >= sum(model.new_capacity_transmission[connection,process_transmission, earlier_year]*model.scaling_new_capacity_transmission[connection,process_transmission, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transmission[process_transmission, earlier_year]), model.years))
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_total_secured_capacity_rule(model, product: str, year: int) -> "pyomo rule":
+ """This method is used as rule for providing the necessary secured capacity for a product.
+
+ A baseline of required secured capacity can be given as parameter. This required secured
+ capacity needs to be lower or equal to the sum of capacity secured by existing and new
+ capacities.
+
+ Since some technologies might increase the need for secured capacity for a product,
+ their impact is taken into account as well.
+
+ Only production processes are taken into account for the secured capacity calculation.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ product (str): A product for which the pe.Constraint is active
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.required_total_secured_capacity[product, year] != -np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # The required secured capacity has to be smaller or equal than the existing secured capacity
+ (model.required_total_secured_capacity[product, year]
+ + model.total_slack_variable[product, year]*model.scaling_total_slack_variable[product, year])
+ <=
+ # Sum over all nodes to aggregate the total secured capacity
+ sum(
+ # Sum over all production processes
+ sum(
+ # Sum over all existing capacity
+ sum(# Existing capacity of a production process
+ model.existing_capacity_production[node, process_production, year, year_construction]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_production[process_production, year_construction]
+ # Multiplied by the value for the product in the technology matrix
+ # Note, that negative values can exist and therefore increase the need for secured capacity
+ * model.technology_matrix_production[product, process_production, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ # Adding the sum over all new capacity built before or in this year
+ + sum(# Existing capacity of a production process
+ model.new_capacity_production[node, process_production, earlier_year]*model.scaling_new_capacity_production[node, process_production, earlier_year]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_production[process_production, earlier_year]
+ # Multiplied by the value for the product in the technology matrix
+ # Note, that negative values can exist and therefore increase the need for secured capacity
+ * model.technology_matrix_production[product, process_production, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_production[process_production, earlier_year]), model.years))
+ for process_production in model.processes_production)
+
+ # Adding the sum over all storage processes
+ + sum(
+ # Sum over all existing capacity
+ sum(# Existing capacity of a storage process
+ model.existing_capacity_storage[node, process_storage, year, year_construction]
+ # Multiplied by the flow to storage capacity factor of its construction year
+ * model.flow_to_storage_capacity_factor[process_storage, year_construction]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_storage[process_storage, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ # Adding the sum over all new capacity built before or in this year
+ + sum(# Existing capacity of a storage process
+ model.new_capacity_storage[node, process_storage, earlier_year]*model.scaling_new_capacity_storage[node, process_storage, earlier_year]
+ # Multiplied by the flow to storage capacity factor of its construction year
+ * model.flow_to_storage_capacity_factor[process_storage, earlier_year]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_storage[process_storage, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_storage[process_storage, earlier_year]), model.years))
+ for process_storage in filter(lambda process_storage: product == model.storage_products[process_storage], model.processes_storage))
+ for node in model.nodes)
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ @staticmethod
+ def constraint_nodal_secured_capacity_rule(model, node: int, product: str, year: int) -> "pyomo rule":
+ """This method is used as rule for providing the necessary secured capacity for a product.
+
+ A baseline of required secured capacity can be given as parameter. This required secured
+ capacity needs to be lower or equal to the sum of capacity secured by existing and new
+ capacities.
+
+ Since some technologies might increase the need for secured capacity for a product,
+ their impact is taken into account as well.
+
+ Only production processes are taken into account for the secured capacity calculation.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ product (str): A product for which the pe.Constraint is active
+ year (int): A year at which the pe.Constraint is active
+ """
+ if model.required_nodal_secured_capacity[product, node, year] != -np.inf:
+ Optimization.add_evaluated_constraint()
+ return Optimization.scale_expr(
+ # The required secured capacity has to be smaller or equal than the existing secured capacity
+ (model.required_nodal_secured_capacity[product, node, year]
+ + model.nodal_slack_variable[product, node, year]*model.scaling_nodal_slack_variable[product, node, year])
+ <=
+ # Sum over all production processes
+ sum(
+ # Sum over all existing capacity
+ sum(# Existing capacity of a production process
+ model.existing_capacity_production[node, process_production, year, year_construction]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_production[process_production, year_construction]
+ # Multiplied by the value for the product in the technology matrix
+ # Note, that negative values can exist and therefore increase the need for secured capacity
+ * model.technology_matrix_production[product, process_production, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ # Adding the sum over all new capacity built before or in this year
+ + sum(# Existing capacity of a production process
+ model.new_capacity_production[node, process_production, earlier_year]*model.scaling_new_capacity_production[node, process_production, earlier_year]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_production[process_production, earlier_year]
+ # Multiplied by the value for the product in the technology matrix
+ # Note, that negative values can exist and therefore increase the need for secured capacity
+ * model.technology_matrix_production[product, process_production, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_production[process_production, earlier_year]), model.years))
+ for process_production in model.processes_production)
+
+ # Adding the sum over all storage processes
+ + sum(
+ # Sum over all existing capacity
+ sum(# Existing capacity of a storage process
+ model.existing_capacity_storage[node, process_storage, year, year_construction]
+ # Multiplied by the flow to storage capacity factor of its construction year
+ * model.flow_to_storage_capacity_factor[process_storage, year_construction]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_storage[process_storage, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ # Adding the sum over all new capacity built before or in this year
+ + sum(# Existing capacity of a storage process
+ model.new_capacity_storage[node, process_storage, earlier_year]*model.scaling_new_capacity_storage[node, process_storage, earlier_year]
+ # Multiplied by the flow to storage capacity factor of its construction year
+ * model.flow_to_storage_capacity_factor[process_storage, earlier_year]
+ # Multiplied by the secured capacity factor of its construction year
+ * model.secured_capacity_factors_storage[process_storage, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_storage[process_storage, earlier_year]), model.years))
+ for process_storage in filter(lambda process_storage: product == model.storage_products[process_storage], model.processes_storage))
+ )
+ else:
+ Optimization.add_skipped_constraint()
+ return pe.Constraint.Skip
+
+ # pe.Objective #
+ @staticmethod
+ def objectiveRule(model):
+ """This method is used as an pe.Objective for the optimization, using weight factors for all impact categories.
+
+ Args:
+ model: The equivalent of "self" in pyomo optimization models
+ """
+
+ return (
+ # sum over all years
+ sum(
+ # sum over all impact categories
+ sum(
+ # sum of the operational impact over all impact categories multiplied by the corresponding weight factor
+ model.operational_impact[impact_category, year]*model.scaling_operational_impact[impact_category, year]
+ * model.objective_factor_impacts['operation', impact_category, year]
+ # sum of the invest impact over all impact categories multiplied by the corresponding weight factor
+ + model.invest_impact[impact_category, year]*model.scaling_invest_impact[impact_category, year]
+ * model.objective_factor_impacts['invest', impact_category, year]
+
+ # adding the sum of all impact overshoots multiplied by the corresponding weight factor
+ # sum nodal and overall overshoots
+ + (
+ # nodal limit overshoots
+ sum(model.operational_nodal_impact_overshoot[node, impact_category, year]*model.scaling_operational_nodal_impact_overshoot[node, impact_category, year]
+ + model.invest_nodal_impact_overshoot[node, impact_category, year]*model.scaling_invest_nodal_impact_overshoot[node, impact_category, year]
+ + model.total_nodal_impact_overshoot[node, impact_category, year]*model.scaling_total_nodal_impact_overshoot[node, impact_category, year]
+ for node in model.nodes)
+ # overall limit overshoots
+ + model.operational_impact_overshoot[impact_category, year]*model.scaling_operational_impact_overshoot[impact_category, year]
+ + model.invest_impact_overshoot[impact_category, year]*model.scaling_invest_impact_overshoot[impact_category, year]
+ + model.total_impact_overshoot[impact_category, year]*model.scaling_total_impact_overshoot[impact_category, year]
+ ) * model.objective_factor_impact_overshoot[impact_category, year]
+ for impact_category in model.impact_categories)
+ for year in model.years)
+ )
diff --git a/secmod/result_processing.py b/secmod/result_processing.py
new file mode 100644
index 0000000..0a4bf53
--- /dev/null
+++ b/secmod/result_processing.py
@@ -0,0 +1,929 @@
+import logging
+import pickle
+import os
+import platform
+from pathlib import Path
+from clint.textui import progress
+import pandas as pd
+import numpy as np
+import pyomo.environ as pe
+import pyomo.core.base as pyo_base
+import csv
+import copy
+
+import secmod.data_processing as dataproc
+from secmod.classes import (
+Process, Product, config, units, convert_quantity_to_correct_unit)
+from secmod.optimization import Optimization
+
+def extract_results(working_directory, debug=False):
+ """This method extracts the results of the optimization and dumps them into a pickle file.
+
+ Args:
+ working_directory: Path to the working directory
+ debug (bool), default False : specifies whether to debug the file e.g. to extract result for a certain year
+
+ Returns:
+ None
+ """
+ if debug == True:
+ config.invest_years = [2035]
+
+ logging.info("Load processes")
+ optimization_results = {'Results': {}, 'Utilities': {'Unit_dict':{},'Index_names':{},'Unit_time_summation': {}, 'Parameters':{},'ProductsOfProcess':{},'Misc':{}}}
+ optimization_results['Utilities']['Parameters'] = {'processes':{},'products':{},'impact_categories':[]}
+ # deunitized_input_dictionary = {'Units':{}}
+ index_names = {}
+ # define output balances selected from model instance
+ list_output_balances =[
+ 'non_served_demand',
+ 'new_capacity_production',
+ 'new_capacity_storage',
+ 'new_capacity_transshipment',
+ 'new_capacity_transmission',
+ 'existing_capacity_production',
+ 'existing_capacity_storage',
+ 'existing_capacity_transshipment',
+ 'existing_capacity_transmission',
+ 'demand'
+ ]
+ # Load units and Process/Product/Impact names
+ with open(working_directory / "SecMOD" / "00-INPUT" / "01-COMPUTED-INPUT" / "input-{0}.pickle".format(config.invest_years[0]), "rb") as input_file:
+ input_dictionary = pickle.load(input_file)
+
+ # Load optimization results
+ logging.info("Loading optimization results")
+
+ for year in progress.bar(config.invest_years):
+ # create abstract optimization model
+ # optimization = Optimization()
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "InvestmentModel_{0}.pickle".format(year), "rb") as input_file:
+ # get creation date of Raw Results:"InvestmentModel_"+year[0]+".pickle"
+ if year == config.invest_years[0]:
+ if platform.system() == 'Windows':
+ date_creation_result_file = os.path.getmtime(input_file.name)
+ # different system
+ else:
+ date_creation_result_file = os.stat(input_file.name).st_mtime
+ # load optimization result instance
+ model_instance = pickle.load(input_file)
+
+ # iterate through variables in result
+ optimization_results, index_names = get_dataframes_from_results(model_instance, optimization_results, index_names, year, list_output_balances)
+
+ # apply variable scaling factors to variables
+ logging.info("Apply variable scaling factors to variable for year {}".format(str(year)))
+ model_instance = overwrite_variable_values(model_instance)
+
+ # calculate product flows for each year
+ optimization_results, index_names = calculate_product_flows(model_instance, optimization_results, index_names, input_dictionary, year)
+
+ # calculate impacts for each year
+ optimization_results, index_names = calculate_impacts(model_instance, optimization_results, index_names, input_dictionary, year)
+
+ ### End of for year in progress.bar(config.invest_years):
+ # sum operational impact and invest impact for total impact
+ # OVERALL
+ index_names['total_impact'] = index_names['operational_impact']
+ optimization_results['Results']['total_impact'] = optimization_results['Results']['operational_impact'].add(optimization_results['Results']['invest_impact'], fill_value = 0)
+ # PROCESSES
+ process_types = ['production','storage','transshipment','transmission']
+ for process_type in process_types:
+ index_names['total_impact_'+process_type] = index_names['operational_impact_'+process_type]
+ optimization_results['Results']['total_impact_'+process_type] = optimization_results['Results']['operational_impact_'+process_type].add(optimization_results['Results']['invest_impact_'+process_type], fill_value = 0)
+ # NON SERVED DEMAND
+ index_names['total_impact_non_served_demand'] = index_names['operational_impact_non_served_demand']
+ optimization_results['Results']['total_impact_non_served_demand'] = optimization_results['Results']['operational_impact_non_served_demand'].add(optimization_results['Results']['invest_impact_non_served_demand'], fill_value = 0)
+
+ # iterate through sets in result for last year to get names of parameters
+ for balance_in_optimization in model_instance.component_objects(pe.Set,active=True):
+ if 'processes' in balance_in_optimization.name:
+ optimization_results['Utilities']['Parameters']['processes'][balance_in_optimization.name] = balance_in_optimization.ordered_data()
+ if 'products' == balance_in_optimization.name:
+ optimization_results['Utilities']['Parameters'][balance_in_optimization.name] = balance_in_optimization.ordered_data()
+ if 'impact_categories' == balance_in_optimization.name:
+ optimization_results['Utilities']['Parameters']['impact_categories'] = balance_in_optimization.ordered_data()
+
+ # get processes corresponding to products
+ optimization_results = get_processes_of_products(optimization_results,input_dictionary)
+
+ # set index names
+ for balance_in_optimization in optimization_results['Results']:
+ optimization_results['Results'][balance_in_optimization].index.names = index_names[balance_in_optimization]
+
+ # add new capacity to existing capacity: existing_capacity_x(node, construction_year, year) += new_capacity_x(node,year)
+ for process_category in progress.bar(optimization_results['Utilities']['Parameters']['processes'].keys()):
+ category = process_category.replace('processes','')
+ for year in config.invest_years:
+ # ATTENTION index of construction years explicitly used
+ optimization_results['Results']['existing_capacity'+category].loc[(slice(None),slice(None),year),year]+=optimization_results['Results']['new_capacity'+category][year]
+ logging.info('New capacities added to existing capacities')
+
+ #remove values that are smaller than a threshold value (numerical error)
+ for key, result in optimization_results['Results'].items():
+ result[(result<10**(-10)) & (result>-10**(-10))] = 0
+
+ ## get units
+ # get unit of capacities
+ for process_type in optimization_results['Utilities']['Parameters']['processes']:
+ for process in optimization_results['Utilities']['Parameters']['processes'][process_type]:
+ unit = input_dictionary["target_units"]["processes"][process]
+ optimization_results['Utilities']['Unit_dict'][process] = unit
+ # get unit of products
+ for product in optimization_results['Utilities']['Parameters']['products']:
+ unit = input_dictionary["target_units"]["products"][product]
+ optimization_results['Utilities']['Unit_dict'][product] = unit
+ # get unit of impacts
+ for impact in optimization_results['Utilities']['Parameters']['impact_categories']:
+ unit = input_dictionary["target_units"]["impacts"][impact]
+ optimization_results['Utilities']['Unit_dict'][impact] = unit
+
+ # group balance by time slice and nodes/connections/construction years
+ # and copy as new balance
+ results_temp = {}
+ for balance_in_optimization in progress.bar(optimization_results['Results'].keys()):
+ # group by time slice and return balance, unit_time_summation appendix and index name
+ optimization_results['Results'][balance_in_optimization], optimization_results['Utilities']['Unit_time_summation'][balance_in_optimization], index_names[balance_in_optimization] = \
+ group_by_time_slice(optimization_results['Results'][balance_in_optimization], balance_in_optimization, index_names[balance_in_optimization], model_instance.time_slice_yearly_weight,optimization_results['Utilities']['Unit_dict'])
+ # copy balance and group by nodes/connections and construction year
+ # copy index_names and unit_time_summation
+ results_temp[balance_in_optimization+'_grouped'],index_names[balance_in_optimization+'_grouped'] = \
+ group_by_lower_index(optimization_results['Results'][balance_in_optimization],index_names[balance_in_optimization])
+ optimization_results['Utilities']['Unit_time_summation'][balance_in_optimization +'_grouped'] = optimization_results['Utilities']['Unit_time_summation'][balance_in_optimization]
+ # update optimization_results['Results']
+ optimization_results['Results'].update(results_temp)
+
+ # copy index_names and time slice yearly weights for export
+ optimization_results['Utilities']['Index_names']= index_names
+ optimization_results['Utilities']['Misc']['time_slice_yearly_weights'] = model_instance.time_slice_yearly_weight
+
+ logging.info('Results simplified and unitized')
+ # create Result folder
+ if not os.path.exists(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "Extracted Results"):
+ os.mkdir(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "Extracted Results")
+
+ # save optimization results
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "Extracted Results" / "Optimization_Results.pickle", 'wb') as output_file:
+ pickle.dump(optimization_results, output_file)
+ logging.info('Results saved')
+
+ # save date log file
+ with open(working_directory / "SecMOD" / "01-MODEL-RESULTS" / "Extracted Results" / "Date_Log_Raw_Results.pickle", 'wb') as output_file:
+ pickle.dump(date_creation_result_file, output_file)
+
+def get_dataframes_from_results(model_instance, optimization_results, index_names, year, list_output_balances):
+ """This method retrieves dataframes with parameters and variables from. results
+ Args:
+ model_instance: optimization result instance
+ optimization_results(dict): dictionary to write the results to
+ index_names(dict): dictionary to write the index names to
+ year: the year in which the results are evaluated
+ list_output_balances(list): list that defines output balances selected from model instance
+
+ Returns:
+ optimization_results(dict): dictionary filled with results
+ index_names(dict): dictionary of the index names of the optimization_results dictionary
+ """
+
+ logging.info("Loading optimization variable for year {}".format(str(year)))
+ for balance_in_optimization_name in progress.bar(list_output_balances):
+ balance_in_optimization = getattr(model_instance,balance_in_optimization_name)
+ # get index of 'year' index and list of index_names
+ index_names[balance_in_optimization_name], idxyear = get_list_of_index_names(balance_in_optimization)
+ if isinstance(balance_in_optimization, pyo_base.var.Var): # if variable
+ variable_scaling_factors = getattr(model_instance,'scaling_'+balance_in_optimization.name)
+ if balance_in_optimization_name not in optimization_results['Results']:
+ # get results for first year
+ optimization_results['Results'][balance_in_optimization_name] = pd.DataFrame(Optimization().get_dataframe_from_result(balance_in_optimization,variable_scaling_factors, idxyear)[year])
+ else:
+ # append results for subsequent years
+ optimization_results['Results'][balance_in_optimization_name] = optimization_results['Results'][balance_in_optimization_name].join(pd.DataFrame(Optimization().get_dataframe_from_result(balance_in_optimization,variable_scaling_factors, idxyear)[year]),how='outer')
+ elif isinstance(balance_in_optimization, pyo_base.param.Param): # if parameter
+ if balance_in_optimization_name not in optimization_results['Results']:
+ # get results for first year
+ optimization_results['Results'][balance_in_optimization_name] = pd.DataFrame(Optimization().get_dataframe_from_parameter(balance_in_optimization, idxyear)[year])
+ else:
+ # append results for subsequent years
+ optimization_results['Results'][balance_in_optimization_name] = optimization_results['Results'][balance_in_optimization_name].join(pd.DataFrame(Optimization().get_dataframe_from_parameter(balance_in_optimization, idxyear)[year]),how='outer')
+ optimization_results['Results'][balance_in_optimization_name].fillna(0,inplace=True)
+ optimization_results['Results'][balance_in_optimization_name].columns.names = ['invest_year']
+ return optimization_results, index_names
+
+def calculate_product_flows(model_instance, optimization_results, index_names, input_dictionary, year):
+ """This method calculates product flows.
+ Args:
+ model_instance: optimization result instance
+ optimization_results(dict): dictionary to write the results to
+ index_names(dict): dictionary to write the index names to
+ year: the year in which the results are evaluated
+
+ Returns:
+ optimization_results(dict): dictionary filled with results
+ index_names(dict): dictionary of the index names
+ """
+ # calculate PRODUCT FLOWS for production, storage, transmission and transshipment and total
+ logging.info("Calculate product flows for year {}".format(str(year)))
+ # set up Dataframe [products, nodes, time_slices]/[products,product_process, nodes, time_slices]
+ if year == config.invest_years[0]:
+ # set index of total product flow
+ index_names['product_flow'] = ['products','nodes','time_slices']
+ # get product, nodes and time slices of product flow
+ index_product_flow = [model_instance.products.ordered_data(),model_instance.nodes.ordered_data(),model_instance.time_slices.ordered_data()]
+ # create multiindex of total product flow
+ index_product_flow = pd.MultiIndex.from_product(index_product_flow, names = index_names['product_flow'])
+ # create dataframe of total product flow
+ optimization_results['Results']['product_flow'] = pd.DataFrame(index = index_product_flow,columns=config.invest_years)
+ optimization_results['Results']['product_flow'].columns.names = ['invest_year']
+ # analogously create empty dataframe for process types without transmission
+ process_types = ['production','storage','transshipment']
+ # set index of product flow
+ index_names_product_flow_processes = ['products','product_process','nodes','time_slices']
+ for process_type in process_types:
+ # get technologymatrix/efficiency matrix for process type
+ if process_type == 'production':
+ technology_matrix = input_dictionary['technology_matrix_production']
+ elif process_type == 'storage':
+ technology_matrix = input_dictionary['efficiency_matrix_storage']
+ # elif process_type == 'transshipment':
+ # technology_matrix = input_dictionary['transshipment_products']
+ # get product, process, nodes and time slices of product flow
+ index_product_flow = pd.MultiIndex.from_product([model_instance.products.ordered_data(),eval('model_instance.processes_'+process_type+'.ordered_data()')])
+ index_nodes_time_slices = pd.MultiIndex.from_product([model_instance.nodes.ordered_data(),model_instance.time_slices.ordered_data()])
+ # get indizes where technologymatrix != 0 (exclude transshipment for now)
+ if process_type != 'transshipment':
+ if process_type == 'production':
+ _boolean_technologymatrix = [technology_matrix[row+(year,)]!=0 for row in index_product_flow]
+ elif process_type == 'storage':
+ _boolean_technologymatrix = [technology_matrix[row+('deposit',year)]!=0 for row in index_product_flow]
+ index_product_flow = index_product_flow[_boolean_technologymatrix]
+ index_names['product_flow_'+process_type] = index_names_product_flow_processes
+ # index_product_flow = pd.MultiIndex.from_product(index_product_flow, names = index_names['product_flow_'+process_type])
+ # create dataframe of total product flow
+ optimization_results['Results']['product_flow_'+ process_type] = pd.DataFrame(data = 0, index = index_product_flow,columns=index_nodes_time_slices).stack([0,1]).to_frame().drop(0,axis=1)
+ optimization_results['Results']['product_flow_'+ process_type].index.names = index_names['product_flow_'+process_type]
+ optimization_results['Results']['product_flow_'+ process_type].columns.names = ['invest_year']
+ # copy of dataframe(product_flow) for transmission
+ optimization_results['Results']['product_flow_transmission'] = copy.deepcopy(optimization_results['Results']['product_flow'])
+ index_names['product_flow_transmission'] = copy.deepcopy(index_names['product_flow'])
+
+ # calculate Product flows
+ # production processes
+ optimization_results['Results']['product_flow_production'][year] = list(map(lambda x: get_product_flow_production(model_instance,year,x),
+ optimization_results['Results']['product_flow_production'].index.values))
+ # storage processes
+ optimization_results['Results']['product_flow_storage'][year] = list(map(lambda x: get_product_flow_storage(model_instance,year,x),
+ optimization_results['Results']['product_flow_storage'].index.values))
+ # transshipment processes
+ optimization_results['Results']['product_flow_transshipment'][year] = list(map(lambda x: get_product_flow_transshipment(model_instance,year,x),
+ optimization_results['Results']['product_flow_transshipment'].index.values))
+ # transmission processes
+ optimization_results['Results']['product_flow_transmission'][year] = list(map(lambda x: get_product_flow_transmission(model_instance,year,x),
+ optimization_results['Results']['product_flow_transmission'].index.values))
+ # total
+ optimization_results['Results']['product_flow'][year] = optimization_results['Results']['product_flow_production'][year].sum(level=[0,2,3]).add(
+ optimization_results['Results']['product_flow_storage'][year].sum(level=[0,2,3]), fill_value = 0).add(
+ optimization_results['Results']['product_flow_transshipment'][year].sum(level=[0,2,3]),fill_value = 0).add(
+ optimization_results['Results']['product_flow_transmission'][year], fill_value = 0)
+
+ # return optimization_results and index_names
+ return optimization_results, index_names
+
+def calculate_impacts(model_instance, optimization_results, index_names, input_dictionary, year):
+ """ calculates impacts
+ Args:
+ model_instance: optimization result instance
+ optimization_results (dict): dictionary to write the results to
+ index_names (dict): dictionary to write the index names to
+ year: the year in which the results are evaluated
+
+ Returns:
+ optimization_results (dict): dictionary filled with results
+ index_names (dict): dictionary of the index names
+ """
+ # calculate IMPACTS for production, storage, transmission and transshipment and total
+ logging.info("Calculate impacts for year {}".format(str(year)))
+ # set up Dataframe [impact_category, nodes, time_slices]/[impact_category, impact_process/impact_product, nodes, time_slices]
+ if year == config.invest_years[0]:
+ # for OVERALL
+ # set index of impacts
+ index_names['operational_impact'] = ['impact_category','nodes','time_slices']
+ index_names['invest_impact'] = ['impact_category','nodes','time_slices']
+ # get product, nodes and time slices of impacts
+ index_impact = [model_instance.impact_categories.ordered_data(),
+ model_instance.nodes.ordered_data(),
+ model_instance.time_slices.ordered_data()]
+ # create multiindex of impacts
+ index_impact = pd.MultiIndex.from_product(index_impact, names = index_names['operational_impact'])
+ # create dataframe of impacts
+ optimization_results['Results']['operational_impact'] = pd.DataFrame(index = index_impact,columns=config.invest_years)
+ optimization_results['Results']['operational_impact'].columns.names = ['invest_year']
+ optimization_results['Results']['invest_impact'] = copy.deepcopy(optimization_results['Results']['operational_impact'])
+
+ # for PROCESSES
+ # analogously create empty dataframe for process types
+ process_types = ['production','storage','transshipment','transmission']
+ # set index of impact
+ index_names_impacts_processes = ['impact_category','impact_process','nodes','time_slices']
+ for process_type in process_types:
+ # get impact category, process, nodes and time slices of product flow
+ index_impact = [model_instance.impact_categories.ordered_data(),eval('model_instance.processes_'+process_type+'.ordered_data()'), model_instance.nodes.ordered_data(),model_instance.time_slices.ordered_data()]
+ # create multiindex of product flow
+ index_names['operational_impact_'+process_type] = index_names_impacts_processes
+ index_names['invest_impact_'+process_type] = index_names_impacts_processes
+ index_impact = pd.MultiIndex.from_product(index_impact, names = index_names['operational_impact_'+process_type])
+ # create dataframe of total product flow
+ optimization_results['Results']['operational_impact_'+ process_type] = pd.DataFrame(index = index_impact,columns=config.invest_years)
+ optimization_results['Results']['operational_impact_'+ process_type].columns.names = ['invest_year']
+ optimization_results['Results']['invest_impact_'+ process_type] = copy.deepcopy(optimization_results['Results']['operational_impact_'+ process_type])
+
+ # for NON_SERVED_DEMAND
+ # analogously create empty dataframe for non served demand
+ demand_type = 'non_served_demand'
+ # set index of impact
+ index_names_impacts_demand = ['impact_category','impact_products','nodes','time_slices']
+ # get impact category, process, nodes and time slices of product flow
+ index_impact = [model_instance.impact_categories.ordered_data(),model_instance.products.ordered_data(), model_instance.nodes.ordered_data(),model_instance.time_slices.ordered_data()]
+ # create multiindex of product flow
+ index_names['operational_impact_'+demand_type] = index_names_impacts_demand
+ index_names['invest_impact_'+demand_type] = index_names_impacts_demand
+ index_impact = pd.MultiIndex.from_product(index_impact, names = index_names['operational_impact_'+demand_type])
+ # create dataframe of total product flow
+ optimization_results['Results']['operational_impact_'+ demand_type] = pd.DataFrame(index = index_impact,columns=config.invest_years)
+ optimization_results['Results']['operational_impact_'+ demand_type].columns.names = ['invest_year']
+ optimization_results['Results']['invest_impact_'+ demand_type] = copy.deepcopy(optimization_results['Results']['operational_impact_'+ demand_type])
+
+ # calculate Impacts
+ # production processes
+ optimization_results['Results']['operational_impact_production'][year] = list(map(lambda x: get_operational_impact_production(model_instance,year,x),
+ optimization_results['Results']['operational_impact_production'].index.values))
+ optimization_results['Results']['invest_impact_production'][year] = list(map(lambda x: get_invest_impact_production(model_instance,year,x),
+ optimization_results['Results']['invest_impact_production'].index.values))
+ # storage processes
+ optimization_results['Results']['operational_impact_storage'][year] = list(map(lambda x: get_operational_impact_storage(model_instance,year,x),
+ optimization_results['Results']['operational_impact_storage'].index.values))
+ optimization_results['Results']['invest_impact_storage'][year] = list(map(lambda x: get_invest_impact_storage(model_instance,year,x),
+ optimization_results['Results']['invest_impact_storage'].index.values))
+ # transshipment processes
+ optimization_results['Results']['operational_impact_transshipment'][year] = list(map(lambda x: get_operational_impact_transshipment(model_instance,year,x),
+ optimization_results['Results']['operational_impact_transshipment'].index.values))
+ optimization_results['Results']['invest_impact_transshipment'][year] = list(map(lambda x: get_invest_impact_transshipment(model_instance,year,x),
+ optimization_results['Results']['invest_impact_transshipment'].index.values))
+ # transmission processes
+ optimization_results['Results']['operational_impact_transmission'][year] = list(map(lambda x: get_operational_impact_transmission(model_instance,year,x),
+ optimization_results['Results']['operational_impact_transmission'].index.values))
+ optimization_results['Results']['invest_impact_transmission'][year] = list(map(lambda x: get_invest_impact_transmission(model_instance,year,x),
+ optimization_results['Results']['invest_impact_transmission'].index.values))
+ # non served demand
+ optimization_results['Results']['operational_impact_non_served_demand'][year] = list(map(lambda x: get_operational_impact_non_served_demand(model_instance,year,x),
+ optimization_results['Results']['operational_impact_non_served_demand'].index.values))
+ optimization_results['Results']['invest_impact_non_served_demand'][year] = list(map(lambda x: get_invest_impact_non_served_demand(model_instance,year,x),
+ optimization_results['Results']['invest_impact_non_served_demand'].index.values))
+ # overall
+ optimization_results['Results']['operational_impact'][year] = (
+ optimization_results['Results']['operational_impact_production'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['operational_impact_storage'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['operational_impact_transshipment'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['operational_impact_transmission'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['operational_impact_non_served_demand'][year].sum(level=[0,2,3])
+ )
+ optimization_results['Results']['invest_impact'][year] = (
+ optimization_results['Results']['invest_impact_production'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['invest_impact_storage'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['invest_impact_transshipment'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['invest_impact_transmission'][year].sum(level=[0,2,3])
+ + optimization_results['Results']['invest_impact_non_served_demand'][year].sum(level=[0,2,3])
+ )
+ return optimization_results, index_names
+
+def overwrite_variable_values(model_instance):
+ """This method overwrites variable values of unscaled model instance with rescaled optimized variable values
+ Args:
+ model_instance: optimization result instance
+
+ Returns:
+ model_instance: optimization result instance with rescaled values
+ """
+ for variable in model_instance.component_objects(pe.Var, active=True):
+ scaling_factor = getattr(model_instance,'scaling_'+variable.name)
+ for data in variable:
+ if variable[data].value:
+ variable[data].value *= scaling_factor[data].value
+ return model_instance
+
+def get_list_of_index_names(balance_in_optimization):
+ """ get index of 'years' in matrix """
+ index_names= []
+ for [idxcount,index_of_var] in enumerate(balance_in_optimization._index.subsets()):
+ if index_of_var.name == 'years':
+ idxyear = idxcount
+ else:
+ # create list of index names with processes substituted by 'process', and 'construction_years_x' substituted by 'construction_years', in order to identify level in multiindex
+ if index_of_var.name in ['processes_production','processes_storage','processes_transmission','processes_transshipment']:
+ index_names.append('process')
+ elif 'construction_years' in index_of_var.name:
+ index_names.append('construction_years')
+ else:
+ index_names.append(index_of_var.name)
+ return index_names, idxyear
+
+def get_processes_of_products(optimization_results,input_dictionary):
+ """ get process corresponding to products """
+ optimization_results['Utilities']['ProductsOfProcess']['production_products'] = {}
+ optimization_results['Utilities']['ProductsOfProcess']['storage_products'] = {}
+ optimization_results['Utilities']['ProductsOfProcess']['transmission_products'] = {}
+ optimization_results['Utilities']['ProductsOfProcess']['transshipment_products'] = {}
+ for product in optimization_results['Utilities']['Parameters']['products']:
+ optimization_results['Utilities']['ProductsOfProcess']['production_products'][product] = []
+ optimization_results['Utilities']['ProductsOfProcess']['storage_products'][product] = []
+ optimization_results['Utilities']['ProductsOfProcess']['transmission_products'][product] = []
+ optimization_results['Utilities']['ProductsOfProcess']['transshipment_products'][product] = []
+ # get production processes
+ for production_process in optimization_results['Utilities']['Parameters']['processes']['processes_production']:
+ if input_dictionary['technology_matrix_production'][(product, production_process, input_dictionary['years'][None][0])] != 0:
+ optimization_results['Utilities']['ProductsOfProcess']['production_products'][product].append(production_process)
+ # get storage processes
+ for storage_process in optimization_results['Utilities']['Parameters']['processes']['processes_storage']:
+ if input_dictionary['storage_products'][storage_process] == product:
+ optimization_results['Utilities']['ProductsOfProcess']['storage_products'][product].append(storage_process)
+ # get storage processes
+ for transmission_process in optimization_results['Utilities']['Parameters']['processes']['processes_transmission']:
+ if product in input_dictionary['products_transmission'][None]:
+ optimization_results['Utilities']['ProductsOfProcess']['transmission_products'][product].append(transmission_process)
+ # get storage processes
+ for transshipment_process in optimization_results['Utilities']['Parameters']['processes']['processes_transshipment']:
+ if input_dictionary['transshipment_products'][transshipment_process] == product:
+ optimization_results['Utilities']['ProductsOfProcess']['transshipment_products'][product].append(transshipment_process)
+ return optimization_results
+
+def group_by_time_slice(balance_in_optimization,balance_name, index_name, time_slice_weight, unit_dict):
+ """ group balance by time slices. Eliminates time slice index from dataframe """
+ unit_time_summation = ''
+ if 'time_slices' in index_name and len(index_name)>1:
+ # unit of impacts already integrated and multiplied with time slice weights
+ if 'impact' not in balance_name:
+ # add time slice weight list
+ list_time_slice_weight = []
+ # get weights of time slices for row in effect
+ for row in balance_in_optimization.index.get_level_values('time_slices'):
+ list_time_slice_weight.append(time_slice_weight[row])
+ # multiply each column with time slice weight
+ balance_in_optimization = balance_in_optimization.apply(lambda column: column * list_time_slice_weight)
+ # add unit_time_summation
+ unit_time_summation = '*hour'
+ # get units
+ if 'products' in balance_in_optimization.index.names:
+ unit_list = [unit_dict[row]+unit_time_summation for row in balance_in_optimization.index.get_level_values('products')]
+ elif 'process' in balance_in_optimization.index.names:
+ unit_list = [unit_dict[row]+unit_time_summation for row in balance_in_optimization.index.get_level_values('process')]
+ # append units to dataframe
+ balance_in_optimization['unit']=unit_list
+ # convert dataframe to correct unit
+ balance_in_optimization = convert_quantity_to_correct_unit(balance_in_optimization)
+ index_name = [index_of_var for index_of_var in index_name if index_of_var != 'time_slices']
+ balance_in_optimization = balance_in_optimization.groupby(index_name).sum() # sum over time slices
+ return balance_in_optimization, unit_time_summation, index_name
+
+def group_by_lower_index(balance_in_optimization,index_name):
+ """ group balance by nodes/connections and construction years. Eliminates indices from dataframe """
+ # sum over nodes, if 'nodes' in index
+ if 'nodes' in index_name and len(index_name)>1:
+ index_name = [index_of_var for index_of_var in index_name if index_of_var != 'nodes']
+ balance_in_optimization = balance_in_optimization.groupby(index_name).sum()
+ # sum over connections, if 'connections' in index
+ if 'connections' in index_name and len(index_name)>1:
+ index_name = [index_of_var for index_of_var in index_name if index_of_var != 'connections']
+ balance_in_optimization = balance_in_optimization.groupby(index_name).sum()
+ # sum over construction years, if 'construction_years' in index
+ if 'construction_years' in index_name and len(index_name)>1:
+ index_name = [index_of_var for index_of_var in index_name if index_of_var != 'construction_years']
+ balance_in_optimization = balance_in_optimization.groupby(index_name).sum()
+ return balance_in_optimization, index_name
+
+def get_product_flow_production(model, year: int, multiindex_entry):
+ """ returns the product flow for PRODUCTION at a node in a specific year and time slice equal to the sum of all process contributions to that product balance
+ identical structure as constraint_product_balance_rule in secmod.optimization"""
+ product = multiindex_entry[0]
+ process_production = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ return (
+ # sum over all construction years to differentiate between the used capacities
+ sum(model.technology_matrix_production[product, process_production, year_construction]
+ * model.used_production[node, process_production, year, year_construction, time_slice].value
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ )
+
+def get_product_flow_storage(model, year: int, multiindex_entry):
+ """ returns the product flow for STORAGE at a node in a specific year and time slice equal to the sum of all process contributions to that product balance
+ identical structure as constraint_product_balance_rule in secmod.optimization"""
+ product = multiindex_entry[0]
+ process_storage = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ if product == model.storage_products[process_storage]:
+ return (
+ # sum over all construction years to differentiate between the used capacities
+ + sum(
+ sum(model.used_storage[node, process_storage, direction_storage, year, year_construction, time_slice].value
+ * (- model.storage_level_factor[direction_storage])
+ # sum over both storage direction (deposit and withdraw)
+ for direction_storage in model.directions_storage)
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ )
+ else:
+ return 0
+
+def get_product_flow_transshipment(model, year: int, multiindex_entry):
+ """ returns the product flow for TRANSSHIPMENT at a node in a specific year and time slice equal to the sum of all process contributions to that product balance
+ identical structure as constraint_product_balance_rule in secmod.optimization"""
+ product = multiindex_entry[0]
+ process_transshipment = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ if product == model.transshipment_products[process_transshipment]:
+ return (
+ # sum over all construction years to differentiate between the used capacities
+ sum(
+ # Sum of all streams of connections which have this node as first node
+ sum(model.transshipment_efficiency[process_transshipment, connection]
+ * model.used_transshipment[connection, process_transshipment, "backward", year, year_construction, time_slice].value
+ - model.used_transshipment[connection, process_transshipment, "forward", year, year_construction, time_slice].value
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # Sum of all streams of connections, which have this node as second node, therefore multiplied with the efficiency of the connection
+ + sum(model.transshipment_efficiency[process_transshipment, connection]
+ * model.used_transshipment[connection, process_transshipment, "forward", year, year_construction, time_slice].value
+ - model.used_transshipment[connection, process_transshipment, "backward", year, year_construction, time_slice].value
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transshipment[process_transshipment, year_construction]), model.construction_years_transshipment))
+ )
+ else:
+ return 0
+
+def get_product_flow_transmission(model, year: int, multiindex_entry):
+ """ returns the product flow for TRANSMISSION at a node in a specific year and time slice equal to the sum of all process contributions to that product balance
+ identical structure as constraint_product_balance_rule in secmod.optimization"""
+ product = multiindex_entry[0]
+ node = multiindex_entry[1]
+ time_slice = multiindex_entry[2]
+ return (
+ # adding the power from DC load flow transmission, if the product is electricity
+ ( # Sum of the power from all connections which have this node as first node
+ sum(
+ model.power_line_properties[connection, year, "susceptance per unit"]
+ * (model.phase_difference[model.connected_nodes[connection, "node2"], year, time_slice].value
+ - model.phase_difference[node, year, time_slice].value)
+ for connection in filter(lambda connection: (model.connected_nodes[connection, "node1"] == node) and (product in model.products_transmission), model.connections))
+ # Sum of the power from all connections which have this node as second node
+ + sum(
+ model.power_line_properties[connection, year, "susceptance per unit"]
+ * (model.phase_difference[model.connected_nodes[connection, "node1"], year, time_slice].value
+ - model.phase_difference[node, year, time_slice].value)
+ for connection in filter(lambda connection: (model.connected_nodes[connection, "node2"] == node) and (product in model.products_transmission), model.connections))
+ ) * model.per_unit_base.value
+ )
+
+def get_operational_impact_production(model, year: int, multiindex_entry):
+ """ returns the oprerational impact for PRODUCTION at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization"""
+ impact_category = multiindex_entry[0]
+ process_production = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+ # sum over the construction years of the production process
+ return(
+ sum(model.used_production[node, process_production, year, year_construction, time_slice].value
+ * model.impact_matrix_production[impact_category, 'operation', process_production, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ # multiply the impact with the weight of the current time slice
+ * model.time_slice_yearly_weight[time_slice] / annuity_correction)
+
+def get_operational_impact_storage(model, year: int, multiindex_entry):
+ """ returns the oprerational impact for STORAGE at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization"""
+ impact_category = multiindex_entry[0]
+ process_storage = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+ # sum over the construction years of the storage process
+ return(
+ sum(
+ # impact is assumed to be the impact per withdrawn unit of stored product
+ model.impact_matrix_storage[impact_category, 'operation', process_storage, year, year_construction]
+ * model.used_storage[node, process_storage, "withdraw", year, year_construction, time_slice].value
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ # multiply the impact with the weight of the current time slice
+ * model.time_slice_yearly_weight[time_slice] / annuity_correction)
+
+def get_operational_impact_transshipment(model, year: int, multiindex_entry):
+ """ returns the oprerational impact for TRANSSHIPMENT at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization"""
+ impact_category = multiindex_entry[0]
+ process_transshipment = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+ return(
+ # sum over the construction years of the transshipment process
+ sum(
+ # impact of transshipment is distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transshipment[impact_category, 'operation', process_transshipment, year, year_construction]
+ # sum over both transshipment directions
+ * sum(
+ # sum over all connections, which have this node as first node
+ sum(model.used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice].value
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.used_transshipment[connection, process_transshipment, direction_transshipment, year, year_construction, time_slice].value
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ for direction_transshipment in model.directions_transshipment)
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transshipment[process_transshipment, year_construction]), model.construction_years_transshipment))
+ * model.time_slice_yearly_weight[time_slice] / annuity_correction)
+
+def get_operational_impact_transmission(model, year: int, multiindex_entry):
+ """ returns the oprerational impact for TRANSMISSION at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization
+ Assumed to be negligible
+ """
+ return 0
+
+def get_operational_impact_non_served_demand(model, year: int, multiindex_entry):
+ """ returns the oprerational impact for NON SERVED DEMAND at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization
+ """
+ impact_category = multiindex_entry[0]
+ product = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+ return(
+ model.non_served_demand[node, product, year, time_slice].value
+ * model.impact_matrix_non_served_demand[impact_category, product, year, time_slice]
+ * model.time_slice_yearly_weight[time_slice] / annuity_correction)
+
+def get_invest_impact_production(model, year: int, multiindex_entry):
+ """ returns the invest impact for PRODUCTION at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization"""
+ impact_category = multiindex_entry[0]
+ process_production = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+ return(
+ # EXISTING CAPACITY
+ # sum over the construction years of the production process
+ (sum(model.existing_capacity_production[node, process_production, year, year_construction]
+ * model.impact_matrix_production[impact_category, 'invest', process_production, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_production[process_production, year_construction]), model.construction_years_production))
+ +
+ # NEW CAPACITY
+ # sum over the construction years of the production process
+ sum(model.new_capacity_production[node, process_production, earlier_year].value
+ * model.impact_matrix_production[impact_category, 'invest', process_production, year, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_production[process_production, earlier_year]), model.years)))
+ * model.time_slice_yearly_weight[time_slice] / sum(model.time_slice_yearly_weight[time_slice] for time_slice in model.time_slices) / annuity_correction)
+
+def get_invest_impact_storage(model, year: int, multiindex_entry):
+ """ returns the invest impact for STORAGE at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization"""
+ impact_category = multiindex_entry[0]
+ process_storage = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+
+ return(
+ # EXISTING CAPACITY
+ # sum over the construction years of the storage process
+ (sum(model.existing_capacity_storage[node, process_storage, year, year_construction]
+ * model.impact_matrix_storage[impact_category, 'invest', process_storage, year, year_construction]
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_storage[process_storage, year_construction]), model.construction_years_storage))
+ +
+ # NEW CAPACITY
+ # sum over the construction years of the storage process
+ sum(model.new_capacity_storage[node, process_storage, earlier_year].value
+ * model.impact_matrix_storage[impact_category, 'invest', process_storage, year, earlier_year]
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_storage[process_storage, earlier_year]), model.years)))
+ * model.time_slice_yearly_weight[time_slice] / sum(model.time_slice_yearly_weight[time_slice] for time_slice in model.time_slices) / annuity_correction)
+
+def get_invest_impact_transshipment(model, year: int, multiindex_entry):
+ """ returns the invest impact for TRANSSHIPMENT at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization"""
+ impact_category = multiindex_entry[0]
+ process_transshipment = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+ return(
+ # EXISTING CAPACITY
+ # sum over the construction years of the transshipment process
+ (sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transshipment[impact_category, 'invest', process_transshipment, year, year_construction]
+ # multiplied with the length and capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.existing_capacity_transshipment[connection, process_transshipment, year, year_construction]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.existing_capacity_transshipment[connection, process_transshipment, year, year_construction]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transshipment[process_transshipment, year_construction]), model.construction_years_transshipment))
+ +
+ # NEW CAPACITY
+ # sum over the construction years of the transshipment process
+ sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transshipment[impact_category, 'invest', process_transshipment, year, earlier_year]
+ # multiplied with the length and capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.new_capacity_transshipment[connection, process_transshipment, earlier_year].value
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.new_capacity_transshipment[connection, process_transshipment, earlier_year].value
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transshipment[process_transshipment, earlier_year]), model.years)))
+ * model.time_slice_yearly_weight[time_slice] / sum(model.time_slice_yearly_weight[time_slice] for time_slice in model.time_slices) / annuity_correction)
+
+def get_invest_impact_transmission(model, year: int, multiindex_entry):
+ """ returns the invest impact for TRANSMISSION at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_operational_nodal_impact_rule in secmod.optimization"""
+ impact_category = multiindex_entry[0]
+ process_transmission = multiindex_entry[1]
+ node = multiindex_entry[2]
+ time_slice = multiindex_entry[3]
+ annuity_correction = 1
+ if impact_category == 'cost':
+ # if the impact category is cost, we divide by the annuity factor of the investment period:
+ # In the optimization we annualize the total cost by determining cost factors in the impact matrix that
+ # represent the full investment period (i.e., the present value of the cost in the full
+ # investment period, e.g., the present value of the cost for the investment period 2020-2025). This is necessary
+ # for the rolling horizon optimization if we consider foresight with multiple investment periods of different
+ # lengths.
+ # see `classes.py`: `_get_cost_invest` and `_get_cost_operation` in `ProcessImpacts` class
+ # However, in the results we want to know the cost per year (e.g., the total annualized cost in 2020).
+ # Therefore, we need to divide the cost by the `investment_period_annuity_present_value_factor`.
+ investment_period_duration = Process._get_investment_period_duration(year)
+ annuity_correction = ((1 + Process.interest_rate) ** (investment_period_duration / units.year) - 1) / \
+ ((1 + Process.interest_rate) ** (investment_period_duration / units.year) * Process.interest_rate)
+ return(
+ # EXISTING CAPACITY
+ # sum over the construction years of the transmission process
+ (sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transmission[impact_category, 'invest', process_transmission, year, year_construction]
+ # multiplied with the length and power capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.existing_capacity_transmission[connection, process_transmission, year, year_construction]
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.existing_capacity_transmission[connection, process_transmission, year, year_construction]
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for year_construction in filter(lambda year_construction: (year_construction <= year) and (year - year_construction < model.lifetime_duration_transmission[process_transmission, year_construction]), model.construction_years_transmission))
+ +
+ # NEW CAPACITY
+ # sum over the construction years of the transmission process
+ sum(
+ # impact distributed equally (0.5:0.5) between the two connected nodes
+ 0.5 * model.impact_matrix_transmission[impact_category, 'invest', process_transmission, year, earlier_year]
+ # multiplied with the length and power capacity of all connections of the node
+ * (
+ # sum over all connections, which have this node as first node
+ sum(model.new_capacity_transmission[connection, process_transmission, earlier_year].value
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node1"] == node, model.connections))
+ # sum over all connections, which have this node as second node
+ + sum(model.new_capacity_transmission[connection, process_transmission, earlier_year].value
+ * model.power_limit_per_circuit[process_transmission]
+ * model.connection_length[connection]
+ for connection in filter(lambda connection: model.connected_nodes[connection, "node2"] == node, model.connections))
+ )
+ for earlier_year in filter(lambda earlier_year: (earlier_year <= year) and (year - earlier_year <= model.lifetime_duration_transmission[process_transmission, earlier_year]), model.years)))
+ * model.time_slice_yearly_weight[time_slice] / sum(model.time_slice_yearly_weight[time_slice] for time_slice in model.time_slices) / annuity_correction)
+
+def get_invest_impact_non_served_demand(model, year: int, multiindex_entry):
+ """ returns the oprerational impact for NON SERVED DEMAND at a node in a specific year and time slice equal to the sum of all process contributions to that impact
+ identical structure as constraint_invest_nodal_impact_rule in secmod.optimization
+ Obviously 0, since no invest necessary for non served demand
+ """
+ return 0
+
+if __name__ == "__main__":
+ log_format = '%(asctime)s %(filename)s: %(levelname)s: %(message)s'
+ if not os.path.exists("logs"):
+ os.mkdir("logs")
+ logging.basicConfig(filename='logs/secmod.log', level=logging.INFO, format=log_format,
+ datefmt='%Y-%m-%d %H:%M:%S')
+ logging.getLogger().addHandler(logging.StreamHandler())
+ working_directory = Path().cwd()
+ extract_results(working_directory, debug=False)
+
+ logging.getLogger().handlers.clear()
+ logging.shutdown()
diff --git a/secmod/scaling.py b/secmod/scaling.py
new file mode 100644
index 0000000..87d7e1d
--- /dev/null
+++ b/secmod/scaling.py
@@ -0,0 +1,35 @@
+import numpy as np
+import scipy.stats.mstats as sp
+import importlib.util
+# Load config.py
+spec = importlib.util.spec_from_file_location("config", "SecMOD/00-INPUT/00-RAW-INPUT/config.py")
+config = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(config)
+
+def calculate_scaling_factor(scaling_vector):
+ """ returns scaling factor r or c for vector
+
+ select scaling algorithm"""
+ scaling_vector = np.abs(scaling_vector)
+ if not scaling_vector.size == 0 and not np.all(scaling_vector==1):
+ if config.scaling_options['Algorithm'] == 'approx_geom_mean': # geometric mean scaling
+ scaling_factor_til = np.sqrt(1/(scaling_vector.max()*scaling_vector.min()))
+ elif config.scaling_options['Algorithm'] == 'geom_mean': # exact geometric mean scaling
+ scaling_factor_til = 1/sp.gmean(scaling_vector)
+ elif config.scaling_options['Algorithm'] == 'arith_mean': # arithmetic mean scaling
+ scaling_factor_til = 1/np.mean(scaling_vector)
+ elif config.scaling_options['Algorithm'] == 'equi': # equilibrium scaling
+ scaling_factor_til = 1/np.max(scaling_vector)
+ else:
+ raise KeyError('Scaling Algorithm {} not found'.format(config.scaling_options['Algorithm']))
+
+ if config.scaling_options['Round_to_power_of_2'] and not config.scaling_options['Algorithm'] == 'equi':
+ # round to the closest power of two
+ scaling_factor = 2**(np.round(np.log2(scaling_factor_til)))
+ else:
+ scaling_factor = scaling_factor_til
+ else:
+ scaling_factor = None
+ return scaling_factor
+
+
diff --git a/secmod/setup.py b/secmod/setup.py
new file mode 100644
index 0000000..2108d45
--- /dev/null
+++ b/secmod/setup.py
@@ -0,0 +1,103 @@
+import logging
+import os
+import shutil
+import sys
+from pathlib import Path
+
+import datapackage as dp
+import secmod.data_preprocessing as dataprep
+
+
+def setup(working_directory: Path, reset: bool = False, download_ext_datapackages: bool = False):
+ """This methods sets up SecMOD for the first use in the working directory.
+
+ This method copies the embedded sample data to the working directory.
+ Furthermore it downloads the external data packages for the first time.
+ Last but not least it creates a python file and a batch file to easily start SecMOD.
+
+ Please note that you need to acitvate the download of external data packages here.
+
+ Args:
+ working_directory (Path): The folder which is used as working directory
+ by SecMOD
+ reset (bool): Boolean value which decides whether an existing working directory
+ is overwritten
+ download_external_datapackages (bool): Boolean value which decides whether
+ external datapackages are downloaded
+ """
+ if (dataprep.is_setup(working_directory) and not reset):
+ logging.info("SecMOD folder found! Setup has already been run.")
+ else:
+ logging.info("Setting up SecMOD in {0}...".format(str(working_directory)))
+ copy_sample_data(working_directory)
+ create_startup_helper(working_directory)
+
+ if download_ext_datapackages:
+ # Manual download of data packages might be required due to defect meta data
+ download_external_datapackages(working_directory)
+ logging.info("Setup completed!")
+
+
+def copy_sample_data(working_directory: Path):
+ """This method copies sample data from the package to the working directory.
+
+ Args:
+ working_directory (Path): The folder which is used as working directory
+ by SecMOD
+ """
+
+ # get path of the installed package
+ current_path = os.path.dirname(os.path.abspath(__file__))
+ # join path to get sample data
+ sampledata_path = os.path.join(os.path.dirname(current_path), 'sampledata')
+ # remove SecMOD folder from working directory, if existing
+ shutil.rmtree(working_directory / "SecMOD", ignore_errors=True)
+ # copy sample data to new SecMOD folder in working directory
+ logging.info("Copy sample data from package to working directory...")
+ shutil.copytree(sampledata_path, working_directory / "SecMOD" / "00-INPUT" /
+ "00-RAW-INPUT", ignore=shutil.ignore_patterns('__init__.py'))
+ logging.info("Done!")
+
+
+def create_startup_helper(working_directory: Path):
+ """This method creates startup helpers in the working directory.
+
+ The startup helpers created by this method allow to run SecMOD with one click.
+
+ Args:
+ working_directory (Path): The folder which is used as working directory
+ by SecMOD
+ """
+
+ # creating the startup helper files for SecMOD
+ logging.info("Creating startup helper files...")
+ content = "@Echo off\nEcho Edit this file to configure your startup!\n:start\nEcho To run SecMOD on default Anaconda environment choose 1\nEcho To run SecMOD on your a specific virtual environment choose 2\nset /p choice=Type number to choose environment:\nif %choice%==1 goto default_environment\nif %choice%==2 goto virtual_environment\nEcho Unvalid choice.\ngoto start\n:default_environment\nC:\\Anaconda3\\python.exe -m secmod\ngoto end\n:virtual_environment\ncall C:\\Anaconda3\\Scripts\\conda env list\nset /p environment_name=Enter the name of the virtual environment you want to choose:\ncall C:\\Anaconda3\\Scripts\\activate %environment_name%\npython -m secmod\ngoto end\n:end\npause"
+ file = open(working_directory / "start.bat", "w")
+ file.write(content)
+ logging.info("Done!")
+
+
+def download_external_datapackages(working_directory: Path):
+ # access data package "external_data_packages" to retrieve source URLs of external data packages
+ if (working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / "external_data_packages" / "datapackage.json").exists():
+ logging.info("Reading external datapackages...")
+ package = dp.Package(str(working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" /
+ "00-EXTERNAL" / "external_data_packages" / "datapackage.json"))
+ # read data about external data packages (internal name, source URL)
+ external_data_packages = package.get_resource(
+ 'external_data_packages').read()
+ logging.info("Done!")
+ # download all external data packages
+ for package in external_data_packages:
+ logging.info("Downloading external data package: {0}...".format(package[0]))
+ if dataprep.download_datapackage(package[1], working_directory / "SecMOD" / "00-INPUT" / "00-RAW-INPUT" / "00-EXTERNAL" / package[0]):
+ logging.info("Download of data package {0} finished.".format(package[0]))
+ else:
+ logging.info(
+ "Download of data package {0} was unsuccessful.".format(package[0]))
+ logging.info("You need to retry downloading all necessary data packages later.")
+ else:
+ logging.warning("No external datapackages set up!")
+
+if __name__ == "__main__":
+ setup(Path.cwd(), True, False)
diff --git a/secmod/time_series_aggregation.py b/secmod/time_series_aggregation.py
new file mode 100644
index 0000000..45d0a52
--- /dev/null
+++ b/secmod/time_series_aggregation.py
@@ -0,0 +1,228 @@
+import pandas as pd
+import logging
+import numpy as np
+import copy as copy
+import tsam.timeseriesaggregation as tsam
+from secmod.classes import config, Product, ProductionProcess
+import secmod.helpers as helpers
+
+def get_all_timeseries():
+ """ returns a dictionary of timeseries dataframes with the timeseries data provided for each product and process
+
+ Returns:
+ full_time_series (dict)
+ """
+
+ logging.info("Get all time series")
+ full_time_series = {}
+ full_time_series["demand"] = Product.get_combined_demand_time_series()
+ full_time_series["impact_non_served_demand"] = Product.get_combined_impact_non_served_demand()
+ full_time_series["technologymatrix"] = ProductionProcess.get_combined_technology_matrix_timeseries()
+ full_time_series["availability"] = ProductionProcess.get_combined_availability_timeseries()
+
+ for series in full_time_series:
+ # convert date string in index
+ full_time_series[series] = format_as_timeseries(full_time_series[series], series)
+ return full_time_series
+
+def format_as_timeseries(timeseries: pd.Series, series: str):
+ """ Formats a parametrized time series obtained from classes as columns of individual time series.
+
+ """
+
+ # make sure that column has no identifier, transforming DataFrames to Series
+ if isinstance(timeseries, pd.DataFrame):
+ timeseries = timeseries[list(timeseries.columns)[0]]
+ # Get the names of all indices of the provided series
+ index_names = list(timeseries.index.names)
+
+ # Remove the level "time slice" from the list of indices
+ try:
+ index_names.remove("time slice")
+ except ValueError as e:
+ print(e)
+ raise ValueError("Index of the provided Series '{0}' does not include the level 'time slice'!".format(series))
+
+ # Use the list of indices different from "time slice" to unstack all of them
+ unstacked_timeseries = timeseries.unstack(index_names)
+
+ # return the unstacked time series
+ return unstacked_timeseries
+
+def get_columns_for_all_timeseries(full_time_series: list):
+ """ Return simple indices for all indices of all time series which are about to be joined. """
+
+ simple_columns = {}
+ num_of_columns = 0
+ for series in full_time_series:
+ columns = list(full_time_series[series].columns)
+ simple_columns[series] = {}
+ for column in columns:
+ simple_columns[series][column] = num_of_columns
+ num_of_columns += 1
+
+ return simple_columns
+
+def get_combined_timeseries_table(full_time_series: dict):
+ """ Returns a dataframe with all timeseries as columns. """
+
+ logging.info("Combine timeseries tables")
+ # Get unique identifiers for every column of the combined time series
+ column_identifiers = get_columns_for_all_timeseries(full_time_series)
+ # Initialize a new dictionary to take in all used time series
+ combined_timeseries = {}
+ # initialize dictionary to save column multiindex
+ column_index_timeseries = {}
+ # For all time series in the input data...
+ for timeseries in full_time_series:
+ # Copy the dataframe in order to not manipulate the input data
+ combined_timeseries[timeseries] = full_time_series[timeseries].copy(deep=True)
+ # Overwrite the columns index with the unique integer column identifiers in order to have a simple joinable column index
+ combined_timeseries[timeseries].columns = [column_identifiers[timeseries][index] for index in column_identifiers[timeseries]]
+ # save column multiindex for later reconstruction of index
+ column_index_timeseries[timeseries] = full_time_series[timeseries].columns
+ list_of_timeseries = [combined_timeseries[index] for index in combined_timeseries]
+ combined_timeseries_data = list_of_timeseries.pop(0)
+ combined_timeseries_data = combined_timeseries_data.join(list_of_timeseries)
+
+ combined_timeseries = {}
+ combined_timeseries["timeseries"] = combined_timeseries_data
+ combined_timeseries["column_identifiers"] = column_identifiers
+ combined_timeseries["column_index_timeseries"] = column_index_timeseries
+
+ return combined_timeseries
+
+def separate_timeseries(combined_timeseries: pd.DataFrame, column_identifiers: dict, column_index: dict, time_slices: dict):
+ """ Method to separate a combined time series DataFrame into multiple DataFrames.
+
+ The parameter column_identifiers contains a dictionary of dictionaries for every
+ targeted DataFrame. These dictionaries contain the information about all columns
+ of the combined time series which belong to the targeted DataFrame.
+ """
+
+ logging.info("Separate combined time series")
+ # Create an empty dictionary for the separated time series
+ separated_timeseries = {}
+ combined_timeseries.index = time_slices["time_slice"]
+ combined_timeseries.index.set_names("time_slice",inplace = True)
+ for element in column_identifiers:
+ separated_timeseries[element] = pd.DataFrame()
+ for index in column_identifiers[element]:
+ separated_timeseries[element][index] = combined_timeseries[column_identifiers[element][index]]
+ separated_timeseries[element].columns = column_index[element]
+
+ return separated_timeseries
+
+def reorder_timeseries(aggregated_time_series: dict):
+ """ Reorder indizes of timeseries in order to match format in generate_input_dictionary (data_processing) """
+ for timeseries in aggregated_time_series:
+ # get names of columns
+ column_names = list(aggregated_time_series[timeseries].columns.names)
+ desired_order = copy.deepcopy(column_names)
+ if timeseries != 'demand': # if not demand, append 'time_slice'
+ desired_order.append('time_slice')
+ else: # if demand
+ desired_order.insert(2, 'time_slice')
+ # stack and reorder
+ aggregated_time_series[timeseries] = aggregated_time_series[timeseries].stack(column_names).reorder_levels(desired_order)
+
+ return aggregated_time_series
+def get_all_weights():
+ """ TO-DO: Get all weights for the time series aggregation
+
+ """
+
+ return None
+
+def aggregate_and_get_time_slices(aggregation):
+ """ return time slices of aggregation. Corresponding to format of time slices without time series aggregation """
+ aggregated_time_series = aggregation.createTypicalPeriods()
+ aggregated_time_slices = {"time_slice": [], "next_time_slice": [], "time_slice_duration":[],"time_slice_yearly_weights": []}
+ # create array of time slices
+ # ! durch Aufrufen von aggregation.clusterPeriodIdx wird aggregation.createTypicalPeriods() ausgeführt, wenn aggregation.createTypicalPeriods() vorher noch nicht ausgeführt wurde !
+ for cluster in aggregation.clusterPeriodIdx:
+ # list of time slices for each cluster
+ time_slices = list(range(1+cluster*aggregation.hoursPerPeriod,1+aggregation.hoursPerPeriod*(1+cluster)))
+ aggregated_time_slices["time_slice"].extend(time_slices)
+ # shift index of time slices to get next time slice
+ time_slices.append(time_slices.pop(0))
+ aggregated_time_slices["next_time_slice"].extend(time_slices)
+ # get duration of time slice
+ aggregated_time_slices["time_slice_duration"].extend(list(np.ones(aggregation.hoursPerPeriod)*aggregation.resolution))
+ # get weight of time slice
+ aggregated_time_slices["time_slice_yearly_weights"].extend(list(np.ones(aggregation.hoursPerPeriod)*aggregation.clusterPeriodNoOccur[cluster]))
+
+ # create dicts
+ aggregated_time_slices["next_time_slice"] = dict(zip(aggregated_time_slices["time_slice"],aggregated_time_slices["next_time_slice"]))
+ aggregated_time_slices["time_slice_duration"] = dict(zip(aggregated_time_slices["time_slice"],aggregated_time_slices["time_slice_duration"]))
+ aggregated_time_slices["time_slice_yearly_weights"] = dict(zip(aggregated_time_slices["time_slice"],aggregated_time_slices["time_slice_yearly_weights"]))
+ return aggregated_time_slices, aggregated_time_series
+
+def separate_and_reorder_timeseries(combined_timeseries, aggregated_time_series, aggregated_time_slices):
+ """ Returns all aggregated time_series. """
+
+ # Create the individual time series DataFrames
+ aggregated_time_series = separate_timeseries(aggregated_time_series, combined_timeseries["column_identifiers"],combined_timeseries["column_index_timeseries"],aggregated_time_slices)
+ # Reorder time series
+ aggregated_time_series = reorder_timeseries(aggregated_time_series)
+
+ return aggregated_time_series
+
+def get_time_series_data():
+ """ Return the aggregated time series data """
+
+ helpers.log_heading("Time Series Aggregation")
+ aggregated_time_series = {}
+
+ # Get all time series
+ aggregated_time_series["full_time_series"] = get_all_timeseries()
+ # Combine time series to a single dataframe
+ aggregated_time_series["combined_timeseries"] = get_combined_timeseries_table(aggregated_time_series["full_time_series"])
+ # Get weight of every single time series
+ weights = get_all_weights()
+
+ logging.info("Aggregate time series")
+ # Constant time series do not need to be used as input for TSAM as they do not affect the aggregation outcome but lead to longer runtimes and higher memory requirements
+ aggregated_time_series['combined_timeseries']['fluctuating_timeseries']=pd.DataFrame(index = aggregated_time_series['combined_timeseries']['timeseries'].index)
+ for column_identifier_type in aggregated_time_series['combined_timeseries']['column_identifiers']:
+ for column_identifier in aggregated_time_series['combined_timeseries']['column_identifiers'][column_identifier_type].values():
+ time_series_values = aggregated_time_series['combined_timeseries']['timeseries'][column_identifier].to_numpy()
+ if not (time_series_values[0] == time_series_values).all(0):
+ # values in time series column are not constant, thus the column needs to be used as input for TSAM
+ aggregated_time_series['combined_timeseries']['fluctuating_timeseries'][column_identifier] = aggregated_time_series['combined_timeseries']['timeseries'][column_identifier]
+
+ # Create aggregation object
+ aggregated_time_series["aggregation"] = tsam.TimeSeriesAggregation(
+ timeSeries = aggregated_time_series["combined_timeseries"]['fluctuating_timeseries'],
+ resolution = config.time_series_aggregation_config["resolution"],
+ noTypicalPeriods = config.time_series_aggregation_config["noTypicalPeriods"],
+ hoursPerPeriod = config.time_series_aggregation_config["hoursPerPeriod"],
+ clusterMethod = config.time_series_aggregation_config["clusterMethod"],
+ evalSumPeriods = config.time_series_aggregation_config["evalSumPeriods"],
+ sortValues = config.time_series_aggregation_config["sortValues"],
+ sameMean = config.time_series_aggregation_config["sameMean"],
+ rescaleClusterPeriods = config.time_series_aggregation_config["rescaleClusterPeriods"],
+ # weightDict = weights,
+ extremePeriodMethod = config.time_series_aggregation_config["extremePeriodMethod"],
+ predefClusterOrder = config.time_series_aggregation_config["predefClusterOrder"],
+ predefClusterCenterIndices = config.time_series_aggregation_config["predefClusterCenterIndices"],
+ solver = config.time_series_aggregation_config["solver_tsa"],
+ roundOutput = config.time_series_aggregation_config["roundOutput"],
+ addPeakMin = config.time_series_aggregation_config["addPeakMin"],
+ addPeakMax = config.time_series_aggregation_config["addPeakMax"],
+ addMeanMin = config.time_series_aggregation_config["addMeanMin"],
+ addMeanMax = config.time_series_aggregation_config["addMeanMax"]
+ )
+
+ aggregated_time_series["aggregated_time_slices"], aggregated_time_series["aggregated_time_series"] = aggregate_and_get_time_slices(aggregated_time_series["aggregation"])
+ # Merge the constant timeseries with the aggregated timeseries
+ for column in aggregated_time_series["combined_timeseries"]['timeseries'].columns:
+ if column not in aggregated_time_series["aggregated_time_series"].columns:
+ # the first time step in a constant time series represents all time steps
+ aggregated_time_series["aggregated_time_series"][column] = aggregated_time_series["combined_timeseries"]['timeseries'][column][0]
+ # sort columns according to their number
+ aggregated_time_series["aggregated_time_series"] = aggregated_time_series["aggregated_time_series"][np.sort(aggregated_time_series["aggregated_time_series"].columns.tolist())]
+ aggregated_time_series["aggregated_time_series"] = separate_and_reorder_timeseries(aggregated_time_series["combined_timeseries"], aggregated_time_series["aggregated_time_series"], aggregated_time_series["aggregated_time_slices"])
+
+
+ return aggregated_time_series
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..ea565b8
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,13 @@
+[metadata]
+# This includes the license file(s) in the wheel.
+# https://wheel.readthedocs.io/en/stable/user_guide.html#including-license-files-in-the-generated-wheel-file
+license_files = LICENSE.txt
+
+[bdist_wheel]
+# This flag says to generate wheels that support both Python 2 and Python
+# 3. If your code will not run unchanged on both Python 2 and 3, you will
+# need to generate separate wheels for each Python version that you
+# support. Removing this line (or setting universal to 0) will prevent
+# bdist_wheel from trying to make a universal wheel. For more see:
+# https://packaging.python.org/guides/distributing-packages-using-setuptools/#wheels
+universal=1
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..000075b
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,193 @@
+"""A setuptools based setup module.
+
+See:
+https://packaging.python.org/guides/distributing-packages-using-setuptools/
+https://github.com/pypa/sampleproject
+"""
+
+# Always prefer setuptools over distutils
+from setuptools import setup, find_packages
+from os import path
+
+here = path.abspath(path.dirname(__file__))
+
+# Get the long description from the README file
+with open(path.join(here, 'README.md'), encoding='utf-8') as f:
+ long_description = f.read()
+
+# Arguments marked as "Required" below must be included for upload to PyPI.
+# Fields marked as "Optional" may be commented out.
+
+setup(
+ # This is the name of your project. The first time you publish this
+ # package, this name will be registered for you. It will determine how
+ # users can install this project, e.g.:
+ #
+ # $ pip install sampleproject
+ #
+ # And where it will live on PyPI: https://pypi.org/project/sampleproject/
+ #
+ # There are some restrictions on what makes a valid project name
+ # specification here:
+ # https://packaging.python.org/specifications/core-metadata/#name
+ name='SecMOD', # Required
+
+ # Versions should comply with PEP 440:
+ # https://www.python.org/dev/peps/pep-0440/
+ #
+ # For a discussion on single-sourcing the version across setup.py and the
+ # project code, see
+ # https://packaging.python.org/en/latest/single_source_version.html
+ version='2.0.0', # Required
+
+ # This is a one-line description or tagline of what your project does. This
+ # corresponds to the "Summary" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#summary
+ description='A framework for the optimization and life-cycle assessment of multi-sector energy systems.', # Optional
+
+ # This is an optional longer description of your project that represents
+ # the body of text which users will see when they visit PyPI.
+ #
+ # Often, this is the same as your README, so you can just read it in from
+ # that file directly (as we have already done above)
+ #
+ # This field corresponds to the "Description" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#description-optional
+ long_description=long_description, # Optional
+
+ # Denotes that our long_description is in Markdown; valid values are
+ # text/plain, text/x-rst, and text/markdown
+ #
+ # Optional if long_description is written in reStructuredText (rst) but
+ # required for plain-text or Markdown; if unspecified, "applications should
+ # attempt to render [the long_description] as text/x-rst; charset=UTF-8 and
+ # fall back to text/plain if it is not valid rst" (see link below)
+ #
+ # This field corresponds to the "Description-Content-Type" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
+ long_description_content_type='text/markdown', # Optional (see note above)
+
+ # This should be a valid link to your project's main homepage.
+ #
+ # This field corresponds to the "Home-Page" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#home-page-optional
+ url='https://git-ce.rwth-aachen.de/ltt/secmod', # Optional
+
+ # This should be your name or the name of the organization which owns the
+ # project.
+ author='Institute of Technical Thermodynamics - RWTH Aachen University', # Optional
+
+ # Classifiers help users find your project by categorizing it.
+ #
+ # For a list of valid classifiers, see https://pypi.org/classifiers/
+ classifiers=[ # Optional
+ # How mature is this project? Common values are
+ # 3 - Alpha
+ # 4 - Beta
+ # 5 - Production/Stable
+ 'Development Status :: 5 - Stable',
+
+ # Indicate who your project is intended for
+ 'Intended Audience :: Science/Research',
+ 'Topic :: Scientific/Engineering',
+
+ # License
+ #'License :: OSI Approved :: MIT License',
+
+ # Specify the Python versions you support here. In particular, ensure
+ # that you indicate whether you support Python 2, Python 3 or both.
+ # These classifiers are *not* checked by 'pip install'. See instead
+ # 'python_requires' below.
+ 'Programming Language :: Python :: 3.7',
+ ],
+
+ # This field adds keywords for your project which will appear on the
+ # project page. What does your project relate to?
+ #
+ # Note that this is a string of words separated by whitespace, not a list.
+ keywords='sector coupling optimization life cycle assessment',
+
+ # You can just specify package directories manually here if your project is
+ # simple. Or you can use find_packages().
+ #
+ # Alternatively, if you just want to distribute a single Python file, use
+ # the `py_modules` argument instead as follows, which will expect a file
+ # called `my_module.py` to exist:
+ #
+ # py_modules=["my_module"],
+ #
+ packages=find_packages(exclude=['data', 'contrib', 'docs', 'tests']), # Required
+
+ # Specify which Python versions you support. In contrast to the
+ # 'Programming Language' classifiers above, 'pip install' will check this
+ # and refuse to install the project if the version does not match. If you
+ # do not support Python 2, you can simplify this to '>=3.5' or similar, see
+ # https://packaging.python.org/guides/distributing-packages-using-setuptools/#python-requires
+ python_requires='>=3.7.1',
+
+ # This field lists other packages that your project depends on to run.
+ # Any package you put here will be installed by pip when your project is
+ # installed, so they must be valid existing projects.
+ #
+ # For an analysis of "install_requires" vs pip's requirements files see:
+ # https://packaging.python.org/en/latest/requirements.html
+ install_requires=['pyomo==5.7.1', 'datapackage==1.15.1', 'geopy==2.1.0', 'pint==0.16.1', 'pathlib==1.0.1', 'numpy==1.19.5', 'pandas==1.2.1', 'requests==2.25.1', 'clint==0.5.1', 'tikzplotlib==0.9.6','sympy==1.7.1', 'scipy==1.6.0', 'tsam==1.1.0', 'pandastable==0.12.2.post1'], # Optional
+
+ # List additional groups of dependencies here (e.g. development
+ # dependencies). Users will be able to install these using the "extras"
+ # syntax, for example:
+ #
+ # $ pip install sampleproject[dev]
+ #
+ # Similar to `install_requires` above, these must be valid existing
+ # projects.
+ # extras_require={ # Optional
+ # 'dev': ['check-manifest'],
+ # 'test': ['coverage'],
+ # },
+
+ # If there are data files included in your packages that need to be
+ # installed, specify them here.
+ #
+ # If using Python 2.6 or earlier, then these have to be included in
+ # MANIFEST.in as well.
+ # TO-DO
+ # package_data={ # Optional
+ # 'sample': ['package_data.dat'],
+ # },
+
+ # Although 'package_data' is the preferred approach, in some case you may
+ # need to place data files outside of your packages. See:
+ # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files
+ #
+ # In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
+ # TO-DO
+ # data_files=[('my_data', ['data/data_file'])], # Optional
+
+ # To provide executable scripts, use entry points in preference to the
+ # "scripts" keyword. Entry points provide cross-platform support and allow
+ # `pip` to create the appropriate form of executable for the target
+ # platform.
+ #
+ # For example, the following would provide a command called `sample` which
+ # executes the function `main` from this package when invoked:
+ entry_points={ # Optional
+ 'console_scripts': [
+ 'secmod=secmod:main',
+ ],
+ },
+
+ # List additional URLs that are relevant to your project as a dict.
+ #
+ # This field corresponds to the "Project-URL" metadata fields:
+ # https://packaging.python.org/specifications/core-metadata/#project-url-multiple-use
+ #
+ # Examples listed include a pattern for specifying where the package tracks
+ # issues, where the source is hosted, where to say thanks to the package
+ # maintainers, and where to support the project financially. The key is
+ # what's used to render the link text on PyPI.
+ project_urls={ # Optional
+ 'Bug Reports': 'https://git-ce.rwth-aachen.de/ltt/secmod/issues',
+ 'Source': 'https://git-ce.rwth-aachen.de/ltt/secmod',
+ },
+)
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