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CST
labcode
Commits
0fdf6ec4
Commit
0fdf6ec4
authored
1 year ago
by
JupyterHub User
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added labels, titles to all plots
parent
27c49cab
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2 changed files
hp4155/measurements.py
+6
-1
6 additions, 1 deletion
hp4155/measurements.py
hp4155/working_examples/pandas.ipynb
+49
-6
49 additions, 6 deletions
hp4155/working_examples/pandas.ipynb
with
55 additions
and
7 deletions
hp4155/measurements.py
+
6
−
1
View file @
0fdf6ec4
...
...
@@ -43,6 +43,9 @@ def I_V_Measurement(start,stop,step):
# show plot
plt
.
plot
(
voltage_values
,
current_values
)
plt
.
xlabel
(
'
Voltage(V)
'
)
plt
.
ylabel
(
'
Current(A)
'
)
plt
.
title
(
"
I-V plot
"
)
plt
.
show
()
#export data to csv file
...
...
@@ -125,6 +128,9 @@ def stress_sampling(V2_stress=10,V3_stress=3,stress_time=30,V2_sampling=10,V3_sa
fig
=
plt
.
figure
()
plt
.
plot
(
time_values
,
I2_values
,
label
=
'
I2
'
)
plt
.
plot
(
time_values
,
I3_values
,
label
=
'
I3
'
)
plt
.
xlabel
(
'
Time(s)
'
)
plt
.
ylabel
(
'
Current(A)
'
)
plt
.
title
(
"
stress + sampilng plot
"
)
plt
.
legend
()
plt
.
show
()
...
...
@@ -226,7 +232,6 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
break
#close the connection and plot all the diagramms
plt
.
legend
()
plt
.
show
()
del
device
This diff is collapsed.
Click to expand it.
hp4155/working_examples/pandas.ipynb
+
49
−
6
View file @
0fdf6ec4
...
...
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count":
1
,
"execution_count":
2
,
"id": "f3bb2a53-f571-4da3-b09f-4c8ee8c75a83",
"metadata": {},
"outputs": [],
...
...
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
"execution_count":
2
,
"execution_count":
3
,
"id": "a9461575-0bd1-4e25-8403-ed4eb12ccce2",
"metadata": {},
"outputs": [
...
...
@@ -33,7 +33,7 @@
},
{
"cell_type": "code",
"execution_count":
3
,
"execution_count":
4
,
"id": "b12e1696-12d9-4b94-9028-2610ec7e73c6",
"metadata": {},
"outputs": [
...
...
@@ -52,7 +52,7 @@
},
{
"cell_type": "code",
"execution_count":
4
,
"execution_count":
5
,
"id": "c7f1a914-cca3-4de7-8cbc-ccdb94197658",
"metadata": {},
"outputs": [
...
...
@@ -60,7 +60,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"2023-09-0
5 10:27
:1
6
\n"
"2023-09-0
7 09:38
:1
0
\n"
]
}
],
...
...
@@ -142,9 +142,52 @@
},
{
"cell_type": "code",
"execution_count":
null
,
"execution_count":
13
,
"id": "48dc546e-c0a7-4052-a63a-a816f4678d8d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.plot(voltage_values,current_values,label='1') \n",
"plt.figure()\n",
"plt.plot(voltage_values,current_values,label='2') \n",
"plt.legend()\n",
"plt.xlabel('Voltage(V)')\n",
"plt.ylabel('Current(A)')\n",
"plt.title(\"I-V plot\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f89f490-e901-43cb-b1f4-cda5b4cf6ae8",
"metadata": {},
"outputs": [],
"source": []
}
...
...
%% Cell type:code id:f3bb2a53-f571-4da3-b09f-4c8ee8c75a83 tags:
```
python
import
pandas
as
pd
import
matplotlib.pyplot
as
plt
from
datetime
import
datetime
```
%% 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
2023-09-0
5 10:27
:1
6
2023-09-0
7 09:38
:1
0
%% 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
'
path
=
f
"
\FILESERVER\public\Datentransfer\Asonitis, Alexandros\New folder
{
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
)
```
%% Output
File "/tmp/ipykernel_7647/3126428292.py", line 7
path = f"\FILESERVER\public\Datentransfer\Asonitis, Alexandros\New folder{file_name}"
^
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 53-54: malformed \N character escape
%% Cell type:code id:48dc546e-c0a7-4052-a63a-a816f4678d8d tags:
```
python
plt
.
figure
()
plt
.
plot
(
voltage_values
,
current_values
,
label
=
'
1
'
)
plt
.
figure
()
plt
.
plot
(
voltage_values
,
current_values
,
label
=
'
2
'
)
plt
.
legend
()
plt
.
xlabel
(
'
Voltage(V)
'
)
plt
.
ylabel
(
'
Current(A)
'
)
plt
.
title
(
"
I-V plot
"
)
plt
.
show
()
```
%% Output
%% Cell type:code id:1f89f490-e901-43cb-b1f4-cda5b4cf6ae8 tags:
```
python
```
...
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