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Commit ab16ca56 authored by Leah Tacke genannt Unterberg's avatar Leah Tacke genannt Unterberg
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progress on dashboard definition modeling

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with 1995 additions and 5 deletions
from enum import StrEnum
from typing import Literal
from uuid import UUID
import pydantic
from mitm_tooling.representation import ColumnName
from .constants import StrUUID
from ..factories.utils import mk_uuid
DashboardInternalID = str
class DashboardComponentType(StrEnum):
CHART = 'CHART'
HEADER = 'HEADER'
GRID = 'GRID'
ROW = 'ROW'
ROOT = 'ROOT'
class ComponentMeta(pydantic.BaseModel):
pass
class DashboardComponent(pydantic.BaseModel):
id: DashboardInternalID
type: DashboardComponentType
meta: ComponentMeta | None = pydantic.Field(strict=False, default=None)
children: list[DashboardInternalID] = pydantic.Field(default_factory=list)
class HeaderMeta(ComponentMeta):
text: str
class DashboardHeader(DashboardComponent):
type: Literal[DashboardComponentType.HEADER] = DashboardComponentType.HEADER
meta: HeaderMeta
class RowMeta(ComponentMeta):
background: str = 'BACKGROUND_TRANSPARENT'
class DashboardRow(DashboardComponent):
type: Literal[DashboardComponentType.ROW] = DashboardComponentType.ROW
meta: RowMeta
class ChartMeta(ComponentMeta):
uuid: StrUUID
width: int
height: int
chartId: int | None = None
sliceName: str | None = None
class DashboardChart(DashboardComponent):
type: Literal[DashboardComponentType.CHART] = DashboardComponentType.CHART
meta: ChartMeta
DASHBOARD_VERSION_KEY_LITERAL = Literal['DASHBOARD_VERSION_KEY']
DashboardPositionData = dict[DASHBOARD_VERSION_KEY_LITERAL | DashboardInternalID, str | DashboardComponent]
def mk_dashboard_position_data(chart_grid: list[list[DashboardChart]]) -> DashboardPositionData:
res = {
'DASHBOARD_VERSION_KEY': 'v2',
'HEADER_ID': ...
}
return res
class ControlValues(pydantic.BaseModel):
enableEmptyFilter: bool = False
defaultToFirstItem: bool | None = False
multiSelect: bool | None = True
searchAllOptions: bool | None = False
inverseSelection: bool | None = False
class ColName(pydantic.BaseModel):
name: str
class DatasetReference(pydantic.BaseModel):
datasetUuid: StrUUID
class ColumnOfDataset(DatasetReference):
column: ColName
class FilterType(StrEnum):
FILTER_SELECT = 'filter_select'
FILTER_TIME_GRAIN = 'filter_timegrain'
class NativeFilterConfig(pydantic.BaseModel):
id: str
name: str
targets: list[DatasetReference | ColumnOfDataset] = pydantic.Field(default_factory=list)
controlValues: ControlValues = pydantic.Field(default_factory=ControlValues)
filterType: FilterType = FilterType.FILTER_SELECT
type: str = 'NATIVE_FILTER'
def mk_filter_config(name: str, target_cols: list[tuple[ColumnName, UUID]], ft: FilterType = FilterType.FILTER_SELECT,
control_values: ControlValues = ControlValues()) -> NativeFilterConfig:
return NativeFilterConfig(
id=f'NATIVE_FILTER-{mk_uuid().hex[:12]}',
name=name,
filterType=ft,
controlValues=control_values,
targets=[
ColumnOfDataset(column=ColName(name=c), datasetUuid=ds_uuid) for c, ds_uuid in (target_cols or [])
]
)
class DashboardMetadata(pydantic.BaseModel):
color_scheme: str = 'blueToGreen'
cross_filters_enabled: bool = True
native_filter_configuration: list[NativeFilterConfig] = pydantic.Field(default_factory=list)
import itertools import itertools
from collections.abc import Sequence, Mapping from collections.abc import Sequence, Mapping
from typing import TypeVar, Hashable, Iterable, Callable, Any, Union, TypeVarTuple, Unpack, overload from typing import TypeVar, Hashable, Iterable, Callable, Any, Union, TypeVarTuple, Unpack, overload, Literal
def i_th(i: int, result_constr: type | None = tuple): def i_th(i: int, result_constr: type | None = tuple):
...@@ -91,17 +91,17 @@ def inner_list_concat(d1: dict[K, list[Any]], d2: dict[K, list[Any]]) -> dict[K, ...@@ -91,17 +91,17 @@ def inner_list_concat(d1: dict[K, list[Any]], d2: dict[K, list[Any]]) -> dict[K,
@overload @overload
def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K], *, flatten: bool = True) -> list[T]: ... def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K]) -> list[tuple[K, T]]: ...
@overload @overload
def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K], *, flatten: bool = False) -> list[tuple[K, T]]: ... def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K], *, flatten: Literal[True]) -> list[T]: ...
@overload @overload
def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K], *, flatten: bool) -> list[T] | list[tuple[K, T]]: ... def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K], *, flatten: Literal[False]) -> list[tuple[K, T]]: ...
# TODO maybe fix type annotations?
def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K], *, flatten: bool = False) -> list[T] | list[tuple[K, T]]: def pick_from_mapping(d: Mapping[K, T], keys: Sequence[K], *, flatten: bool = False) -> list[T] | list[tuple[K, T]]:
if flatten: if flatten:
return [d[k] for k in keys] return [d[k] for k in keys]
......
slice_name: Event Annotations
description: null
certified_by: null
certification_details: null
viz_type: table
params:
datasource: 26__table
viz_type: table
query_mode: raw
groupby: []
time_grain_sqla: P1D
temporal_columns_lookup:
time: true
all_columns:
- object
- type
- time
percent_metrics: []
adhoc_filters:
- expressionType: SIMPLE
subject: time
operator: TEMPORAL_RANGE
comparator: No filter
clause: WHERE
sqlExpression: null
isExtra: false
isNew: false
datasourceWarning: false
filterOptionName: filter_zdzt2v171mi_3t4spqhnfkf
- expressionType: SIMPLE
subject: kind
operator: ==
operatorId: EQUALS
comparator: E
clause: WHERE
sqlExpression: null
isExtra: false
isNew: false
datasourceWarning: false
filterOptionName: filter_02gaxl0iklci_wu8eum560fs
order_by_cols:
- '["time", true]'
row_limit: 1000
server_page_length: 10
order_desc: true
table_timestamp_format: smart_date
allow_render_html: true
show_cell_bars: true
color_pn: true
comparison_color_scheme: Green
comparison_type: values
extra_form_data: {}
dashboards: []
query_context: '{"datasource":{"id":26,"type":"table"},"force":false,"queries":[{"filters":[{"col":"time","op":"TEMPORAL_RANGE","val":"No
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cache_timeout: null
uuid: 323fc686-1f2f-49ce-9fad-5382e8376d8d
version: 1.0.0
dataset_uuid: 9912824b-b393-4acf-9f6b-b0d042ddac29
slice_name: Event Type Counts
description: null
certified_by: null
certification_details: null
viz_type: pie
params:
datasource: 26__table
viz_type: pie
groupby:
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metric:
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column:
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certification_details: null
certified_by: null
column_name: type
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filterable: true
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is_certified: false
is_dttm: false
python_date_format: null
type: VARCHAR
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verbose_name: null
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aggregate: COUNT
sqlExpression: null
datasourceWarning: false
hasCustomLabel: false
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optionName: metric_0hcszvnysjqu_1znn0m0ezlw
adhoc_filters:
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subject: time
operator: TEMPORAL_RANGE
comparator: No filter
clause: WHERE
sqlExpression: null
isExtra: false
isNew: false
datasourceWarning: false
filterOptionName: filter_g4xezxjmej6_ctfpq7bs3ad
- expressionType: SIMPLE
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clause: WHERE
sqlExpression: null
isExtra: false
isNew: false
datasourceWarning: false
filterOptionName: filter_cx1vb3yeeya_ey59yynuao7
row_limit: 100
sort_by_metric: true
color_scheme: supersetColors
show_labels_threshold: 5
show_legend: true
legendType: scroll
legendOrientation: top
label_type: key
number_format: SMART_NUMBER
date_format: smart_date
show_labels: true
labels_outside: true
outerRadius: 70
innerRadius: 30
extra_form_data: {}
dashboards:
- 12
query_context: '{"datasource":{"id":26,"type":"table"},"force":false,"queries":[{"filters":[{"col":"time","op":"TEMPORAL_RANGE","val":"No
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cache_timeout: null
uuid: a23b6fd1-5835-4fd3-9c7b-1c09d189d986
version: 1.0.0
dataset_uuid: 9912824b-b393-4acf-9f6b-b0d042ddac29
slice_name: Event Type Counts
description: null
certified_by: null
certification_details: null
viz_type: echarts_timeseries_line
params:
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x_axis_sort_asc: true
x_axis_sort_series: name
x_axis_sort_series_ascending: true
metrics:
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python_date_format: null
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sqlExpression: null
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optionName: metric_mdfvifcu2zf_eayy1rictqi
groupby:
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- type
adhoc_filters:
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operator: TEMPORAL_RANGE
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clause: WHERE
sqlExpression: null
isExtra: false
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filterOptionName: filter_m1tuiynrv8_5m5j3jrqjp9
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order_desc: true
row_limit: 10000
truncate_metric: true
show_empty_columns: true
comparison_type: values
annotation_layers: []
forecastPeriods: 10
forecastInterval: 0.8
x_axis_title_margin: 15
y_axis_title_margin: 15
y_axis_title_position: Left
sort_series_type: sum
color_scheme: supersetColors
time_shift_color: true
seriesType: line
only_total: true
opacity: 0.2
markerSize: 6
show_legend: true
legendType: scroll
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x_axis_time_format: smart_date
rich_tooltip: true
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showTooltipPercentage: true
tooltipTimeFormat: smart_date
y_axis_format: SMART_NUMBER
truncateXAxis: true
y_axis_bounds:
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extra_form_data: {}
dashboards:
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cache_timeout: null
uuid: 55f30587-c90f-4c35-8ada-1892b2305a12
version: 1.0.0
dataset_uuid: 9912824b-b393-4acf-9f6b-b0d042ddac29
slice_name: Events per Object
description: null
certified_by: null
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time_shift_color: true
only_total: true
show_legend: true
legendType: scroll
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x_axis_time_format: smart_date
y_axis_format: SMART_NUMBER
y_axis_bounds:
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truncateXAxis: true
rich_tooltip: true
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tooltipTimeFormat: smart_date
extra_form_data: {}
dashboards:
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datasourceWarning: false
expressionType: SIMPLE
hasCustomLabel: false
label: COUNT(type)
optionName: metric_ixu59vrx32j_imygsjnt0rj
sqlExpression: null
only_total: true
order_desc: true
orientation: vertical
result_format: json
result_type: full
rich_tooltip: true
row_limit: 10000
showTooltipPercentage: true
showTooltipTotal: true
show_empty_columns: true
show_legend: true
sort_series_type: sum
time_grain_sqla: P1D
time_shift_color: true
tooltipTimeFormat: smart_date
truncateXAxis: true
truncate_metric: true
viz_type: echarts_timeseries_bar
x_axis: object
x_axis_sort_asc: true
x_axis_sort_series: name
x_axis_sort_series_ascending: true
x_axis_time_format: smart_date
x_axis_title_margin: 15
y_axis_bounds:
- null
- null
y_axis_format: SMART_NUMBER
y_axis_title_margin: 15
y_axis_title_position: Left
queries:
- annotation_layers: []
applied_time_extras: {}
columns:
- columnType: BASE_AXIS
expressionType: SQL
label: object
sqlExpression: object
timeGrain: P1D
- type
custom_form_data: {}
custom_params: {}
extras:
having: ''
time_grain_sqla: P1D
where: ''
filters:
- col: time
op: TEMPORAL_RANGE
val: No filter
- col: kind
op: ==
val: E
metrics:
- aggregate: COUNT
column:
advanced_data_type: null
certification_details: null
certified_by: null
column_name: type
description: null
expression: null
filterable: true
groupby: true
id: 772
is_certified: false
is_dttm: false
python_date_format: null
type: VARCHAR
type_generic: 1
verbose_name: null
warning_markdown: null
datasourceWarning: false
expressionType: SIMPLE
hasCustomLabel: false
label: COUNT(type)
optionName: metric_ixu59vrx32j_imygsjnt0rj
sqlExpression: null
order_desc: true
orderby:
- - aggregate: COUNT
column:
advanced_data_type: null
certification_details: null
certified_by: null
column_name: type
description: null
expression: null
filterable: true
groupby: true
id: 772
is_certified: false
is_dttm: false
python_date_format: null
type: VARCHAR
type_generic: 1
verbose_name: null
warning_markdown: null
datasourceWarning: false
expressionType: SIMPLE
hasCustomLabel: false
label: COUNT(type)
optionName: metric_ixu59vrx32j_imygsjnt0rj
sqlExpression: null
- false
post_processing:
- operation: pivot
options:
aggregates:
COUNT(type):
operator: mean
columns:
- type
drop_missing_columns: false
index:
- object
- operation: rename
options:
columns:
COUNT(type): null
inplace: true
level: 0
- operation: flatten
row_limit: 10000
series_columns:
- type
series_limit: 0
time_offsets: []
url_params: {}
result_format: json
result_type: full
\ No newline at end of file
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