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labcode
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035e6ba2
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035e6ba2
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Sep 8, 2023
by
JupyterHub User
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hp4155/working_examples/pandas.ipynb
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035e6ba2
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"cells": [
{
"cell_type": "code",
"execution_count":
5
,
"execution_count":
1
,
"id": "f3bb2a53-f571-4da3-b09f-4c8ee8c75a83",
"metadata": {},
"outputs": [],
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},
{
"cell_type": "code",
"execution_count":
6
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"execution_count":
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"id": "a9461575-0bd1-4e25-8403-ed4eb12ccce2",
"metadata": {},
"outputs": [
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},
{
"cell_type": "code",
"execution_count":
7
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"execution_count":
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"id": "b12e1696-12d9-4b94-9028-2610ec7e73c6",
"metadata": {},
"outputs": [
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},
{
"cell_type": "code",
"execution_count":
8
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"execution_count":
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"id": "c7f1a914-cca3-4de7-8cbc-ccdb94197658",
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"2023-09-08 09:
08
:4
3
\n"
"2023-09-08 09:
46
:4
5
\n"
]
}
],
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},
{
"cell_type": "code",
"execution_count":
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"execution_count":
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"id": "09d63039-ef49-4c65-be9d-9c7326428cdc",
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},
{
"cell_type": "code",
"execution_count":
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"execution_count":
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"id": "1f89f490-e901-43cb-b1f4-cda5b4cf6ae8",
"metadata": {},
"outputs": [
...
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%% Cell type:code id:f3bb2a53-f571-4da3-b09f-4c8ee8c75a83 tags:
```
python
import
pandas
as
pd
import
matplotlib.pyplot
as
plt
from
datetime
import
datetime
import
numpy
as
np
from
sklearn.linear_model
import
LinearRegression
```
%% Cell type:code id:a9461575-0bd1-4e25-8403-ed4eb12ccce2 tags:
```
python
voltage_values
=
[
0.0
,
0.05
,
0.1
,
0.15
,
0.2
,
0.25
,
0.3
,
0.35
,
0.4
,
0.45
,
0.5
,
0.55
,
0.6
,
0.65
,
0.7
,
0.75
,
0.8
,
0.85
,
0.9
,
0.95
,
1.0
,
1.05
,
1.1
,
1.15
,
1.2
,
1.25
,
1.3
,
1.35
,
1.4
,
1.45
,
1.5
,
1.55
,
1.6
,
1.65
,
1.7
,
1.75
,
1.8
,
1.85
,
1.9
,
1.95
,
2.0
,
2.05
,
2.1
,
2.15
,
2.2
,
2.25
,
2.3
,
2.35
,
2.4
,
2.45
,
2.5
,
2.55
,
2.6
,
2.65
,
2.7
,
2.75
,
2.8
,
2.85
,
2.9
,
2.95
,
3.0
,
3.05
,
3.1
,
3.15
,
3.2
,
3.25
,
3.3
,
3.35
,
3.4
,
3.45
,
3.5
,
3.55
,
3.6
,
3.65
,
3.7
,
3.75
,
3.8
,
3.85
,
3.9
,
3.95
,
4.0
,
4.05
,
4.1
,
4.15
,
4.2
,
4.25
,
4.3
,
4.35
,
4.4
,
4.45
,
4.5
,
4.55
,
4.6
,
4.65
,
4.7
,
4.75
,
4.8
,
4.85
,
4.9
,
4.95
,
5.0
,
5.05
,
5.1
,
5.15
,
5.2
,
5.25
,
5.3
,
5.35
,
5.4
,
5.45
,
5.5
,
5.55
,
5.6
,
5.65
,
5.7
,
5.75
,
5.8
,
5.85
,
5.9
,
5.95
,
6.0
,
6.05
,
6.1
,
6.15
,
6.2
,
6.25
,
6.3
,
6.35
,
6.4
,
6.45
,
6.5
,
6.55
,
6.6
,
6.65
,
6.7
,
6.75
,
6.8
,
6.85
,
6.9
,
6.95
,
7.0
,
7.05
,
7.1
,
7.15
,
7.2
,
7.25
,
7.3
,
7.35
,
7.4
,
7.45
,
7.5
,
7.55
,
7.6
,
7.65
,
7.7
,
7.75
,
7.8
,
7.85
,
7.9
,
7.95
,
8.0
,
8.05
,
8.1
,
8.15
,
8.2
,
8.25
,
8.3
,
8.35
,
8.4
,
8.45
,
8.5
,
8.55
,
8.6
,
8.65
,
8.7
,
8.75
,
8.8
,
8.85
,
8.9
,
8.95
,
9.0
,
9.05
,
9.1
,
9.15
,
9.2
,
9.25
,
9.3
,
9.35
,
9.4
,
9.45
,
9.5
,
9.55
,
9.6
,
9.65
,
9.7
,
9.75
,
9.8
,
9.85
,
9.9
,
9.95
,
10.0
]
print
(
len
(
voltage_values
))
```
%% Output
201
%% Cell type:code id:b12e1696-12d9-4b94-9028-2610ec7e73c6 tags:
```
python
current_values
=
[
-
2.0172e-07
,
1.7837e-05
,
3.7577e-05
,
6.0445e-05
,
8.5833e-05
,
0.000113797
,
0.00014604
,
0.0001823
,
0.00022479
,
0.00027261
,
0.00032461
,
0.00038161
,
0.00044615
,
0.00051651
,
0.00059047
,
0.00066731
,
0.00075451
,
0.0008448
,
0.00093602
,
0.00102376
,
0.0011258
,
0.0012281
,
0.0013295
,
0.0014353
,
0.0015563
,
0.0016701
,
0.0017803
,
0.0018951
,
0.002029
,
0.0021518
,
0.0022691
,
0.0023878
,
0.0025273
,
0.0026616
,
0.0027876
,
0.002916
,
0.0030592
,
0.0032063
,
0.0033439
,
0.003475
,
0.0036308
,
0.0037965
,
0.0039297
,
0.0040698
,
0.0042341
,
0.004404
,
0.0045565
,
0.0046994
,
0.0048693
,
0.0050476
,
0.0052095
,
0.0053543
,
0.0055203
,
0.0057068
,
0.0058664
,
0.0060137
,
0.0061811
,
0.0063717
,
0.0065344
,
0.0066796
,
0.0068391
,
0.0070318
,
0.0072005
,
0.0073433
,
0.0074938
,
0.0076875
,
0.0078528
,
0.0079939
,
0.0081331
,
0.008324
,
0.0084877
,
0.0086322
,
0.0087642
,
0.0089491
,
0.0091169
,
0.0092587
,
0.0094001
,
0.0095756
,
0.0097471
,
0.0098836
,
0.0100007
,
0.0101695
,
0.0103403
,
0.0104739
,
0.0105835
,
0.010747
,
0.0109165
,
0.0110492
,
0.0111549
,
0.0113035
,
0.011437
,
0.011582
,
0.011752
,
0.011885
,
0.01199
,
0.012118
,
0.012294
,
0.012424
,
0.012515
,
0.012631
,
0.012812
,
0.012943
,
0.013039
,
0.013146
,
0.013317
,
0.01345
,
0.013551
,
0.013645
,
0.013811
,
0.01394
,
0.014039
,
0.014122
,
0.014289
,
0.014246
,
0.014127
,
0.014193
,
0.014335
,
0.014895
,
0.014997
,
0.015063
,
0.01521
,
0.01535
,
0.015448
,
0.015513
,
0.015641
,
0.015792
,
0.015895
,
0.015949
,
0.016056
,
0.016203
,
0.016302
,
0.016362
,
0.016492
,
0.016636
,
0.016745
,
0.016774
,
0.016874
,
0.017036
,
0.017124
,
0.017172
,
0.017251
,
0.01741
,
0.017513
,
0.017609
,
0.017646
,
0.017794
,
0.017869
,
0.017955
,
0.01802
,
0.018137
,
0.018244
,
0.018323
,
0.018396
,
0.018502
,
0.018611
,
0.018682
,
0.018713
,
0.018826
,
0.018934
,
0.019005
,
0.019042
,
0.019156
,
0.019268
,
0.019344
,
0.019375
,
0.019478
,
0.019604
,
0.019675
,
0.019701
,
0.019793
,
0.019926
,
0.019996
,
0.020026
,
0.020103
,
0.020242
,
0.020314
,
0.020345
,
0.020408
,
0.020549
,
0.020624
,
0.021077
,
0.020751
,
0.020862
,
0.02093
,
0.020973
,
0.021014
,
0.021131
,
0.021219
,
0.021269
,
0.021204
,
0.02139
,
0.02151
,
0.021572
,
0.02159
,
0.021701
,
0.021798
,
0.021869
,
0.021896
,
0.021991
,
0.022039
,
0.022167
]
print
(
len
(
current_values
))
```
%% Output
201
%% Cell type:code id:c7f1a914-cca3-4de7-8cbc-ccdb94197658 tags:
```
python
#add title to the results
header
=
[
'
Voltage(V)
'
,
'
Current(A)
'
]
data
=
{
header
[
0
]:
voltage_values
,
header
[
1
]:
current_values
}
df
=
pd
.
DataFrame
(
data
)
date
=
str
(
datetime
.
today
().
replace
(
microsecond
=
0
))
print
(
date
)
```
%% Output
2023-09-08 09:
08
:4
3
2023-09-08 09:
46
:4
5
%% Cell type:code id:30ac24c1-d6ed-4e41-8d7c-fa0fe44e072f tags:
```
python
#export table in pdf file
fig
=
plt
.
figure
(
figsize
=
(
8
,
2
))
fig
.
suptitle
(
'
I-V measurement results at:
'
+
date
,
y
=
9
)
ax
=
fig
.
add_subplot
(
111
)
ax
.
table
(
cellText
=
df
.
values
,
rowLabels
=
df
.
index
,
colLabels
=
df
.
columns
,
loc
=
"
center
"
)
plt
.
axis
(
'
off
'
)
plt
.
savefig
(
'
results.pdf
'
,
format
=
"
pdf
"
,
bbox_inches
=
"
tight
"
)
```
%% Output
%% Cell type:code id:09d63039-ef49-4c65-be9d-9c7326428cdc tags:
```
python
#exporting the data frame in an excel file
#file_name = 'results '+date+'.xlsx'
#df.to_excel(file_name)
file_name
=
'
\r
esults
'
+
date
+
'
.txt
'
file_name
=
'
\
results
'
+
date
+
'
.txt
'
path
=
"
\\
FILESERVER\public\Datentransfer\Asonitis, Alexandros
"
+
file_name
#export DataFrame to text file (keep header row and index column)
with
open
(
path
,
'
a
'
)
as
f
:
f
.
write
(
'
title.
\n\n
'
)
df_string
=
df
.
to_string
()
f
.
write
(
df_string
)
```
%% Cell type:code id:1f89f490-e901-43cb-b1f4-cda5b4cf6ae8 tags:
```
python
x
=
np
.
array
(
voltage_values
).
reshape
((
-
1
,
1
))
#column matrix
y
=
np
.
array
(
current_values
)
#create a model
model
=
LinearRegression
()
model
.
fit
(
x
,
y
)
r_sq
=
model
.
score
(
x
,
y
)
print
(
f
"
coefficient of determination:
{
r_sq
}
"
)
print
(
f
"
intercept:
{
model
.
intercept_
}
"
)
print
(
f
"
slope:
{
model
.
coef_
}
"
)
plt
.
figure
()
plt
.
plot
(
voltage_values
,
current_values
,
label
=
'
real curve
'
)
plt
.
plot
(
x
,
model
.
coef_
*
x
+
model
.
intercept_
,
label
=
'
linear
'
)
plt
.
xlabel
(
'
Voltage(V)
'
)
plt
.
ylabel
(
'
Current(A)
'
)
plt
.
title
(
"
I-V plot
"
)
plt
.
legend
()
plt
.
show
()
```
%% Output
coefficient of determination: 0.9873880135298887
intercept: -0.00043234755330870747
slope: [0.00243783]
%% Cell type:code id:49c0c86e-bc82-42c6-b6fa-a3b8fa926f6d tags:
```
python
```
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