57 lines
1.1 KiB
Python
57 lines
1.1 KiB
Python
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from scipy.optimize import curve_fit
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from numpy import linspace
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import numpy as np
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from numpy import tanh
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from scipy.constants import e, k
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from numpy import sqrt
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from numpy import array
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import pandas as pd
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# Global variables
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new_U = linspace(-22, 22, 1000)
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# Function for approximation
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def function_IU(U, I_n, T_e, A, B):
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return I_n * tanh(e*U/2/k/T_e) + A*U + B
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# Find the best fit params of In and Te
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def find_FitParams(data_I, data_U):
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popt, pcov = curve_fit(function_IU, data_U, data_I, p0=[0, 50000, 0, 0])
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return popt, pcov
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# Returns disperancy of given value
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def discrepancy(data, popt):
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I = data[0]
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U = data[1]
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tmp = []
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for i in range(len(U)):
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temp = sqrt((function_IU(
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U[i], *popt)-I[i])**2/len(U)/(len(U)-1))
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tmp.append(temp)
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return tmp
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# Returns pandas.DataFrame with approximated Current and Voltage values
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def getApproxValues(data):
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data = data.sort_values(by=['U'])
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popt, pcov = find_FitParams(data['I'], data['U'])
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df = pd.DataFrame()
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df['U'] = new_U
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df['I'] = function_IU(df['U'], *popt)
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error = np.sqrt(np.diag(pcov))
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return [df, popt, error]
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