102 lines
1.8 KiB
Python
102 lines
1.8 KiB
Python
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import numpy as np
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from scipy.optimize import curve_fit
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import matplotlib.pyplot as plt
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# importing module
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from pandas import *
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# reading CSV file
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data = read_csv("20230505_NaCl_optimized.csv")
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# converting column data to list
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#solutionAdded = data['solutionAdded'].tolist() #in ml
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tempReservoir = data['tempReservoir'].tolist() #in C
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adc = data['ECadcAdjusted'].tolist() #adc reading
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solutionConcentration=5924.8 #mg/L NaCl
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startWaterAmount=0.3 #L
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ppmToECfactor=1/0.46
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#concentration = [x*solutionConcentration/(startWaterAmount+x) for x in solutionAdded]
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#ECcalculated = [x*ppmToECfactor for x in concentration] #uS/cm
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ECmeasured = data['ecMeasured'].tolist() #in C
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#print(concentration)
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#print(ECcalculated)
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x = adc
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#y = ECcalculated
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y = ECmeasured
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'''
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def model(x, a, b, c, d, e):
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y = a * x**4 + b *x**3 + c*x**2 + d*x + e
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return y
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popt, pcov = curve_fit(model, x, y)
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#y1 = a * numpy.exp(b * x)
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#print("popt="+str(popt))
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for p in popt:
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print(p)
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'''
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plt.plot(x, y, 'ok')
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xstart = np.min(adc)
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xstop = np.max(adc)
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increment = 10
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xmodel = np.arange(xstart,xstop,increment)
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#ymodel = model(xmodel, *popt)
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#plt.plot(xmodel,ymodel, 'r')
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for model_order in [9,10,11]:
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print("model order="+str(model_order))
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# Finding the Model
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p = np.polyfit(x, y, model_order)
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#print(p)
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polystring=""
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for i in np.arange(model_order+1):
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_c=str(p[i])
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if p[i]>=0:
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_c="+"+_c #add leading +
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polystring+=_c+"*x^"+str(model_order-i)+" "
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print(polystring)
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print("Excel:")
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for i in np.arange(model_order+1):
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print(str(p[i]))
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print("Array:")
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arraystring="{"
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for i in np.arange(model_order+1):
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arraystring+=str(p[model_order-i])
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arraystring+=","
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arraystring=arraystring[:-1]+"}"
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print(arraystring)
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# Plot the Model
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ymodel = np.polyval(p, xmodel)
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plt.plot(xmodel,ymodel,label="order="+str(model_order))
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print()
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plt.legend()
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plt.grid()
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plt.show()
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