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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 09:08:43
2023-09-08 09:46:45
%% 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 = '\results '+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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