import numpy as np from scipy.optimize import curve_fit import matplotlib.pyplot as plt # importing module from pandas import * # reading CSV file data = read_csv("20230620_NaCl_raw.csv") # converting column data to list #solutionAdded = data['solutionAdded'].tolist() #in ml tempReservoir = data['tempReservoir'].tolist() #in C adc = data['ECadcAdjusted'].tolist() #adc reading solutionConcentration=5924.8 #mg/L NaCl startWaterAmount=0.3 #L ppmToECfactor=1/0.46 #concentration = [x*solutionConcentration/(startWaterAmount+x) for x in solutionAdded] #ECcalculated = [x*ppmToECfactor for x in concentration] #uS/cm ECmeasured = data['ecMeasured'].tolist() #in C #print(concentration) #print(ECcalculated) x = adc #y = ECcalculated y = ECmeasured ''' def model(x, a, b, c, d, e): y = a * x**4 + b *x**3 + c*x**2 + d*x + e return y popt, pcov = curve_fit(model, x, y) #y1 = a * numpy.exp(b * x) #print("popt="+str(popt)) for p in popt: print(p) ''' plt.plot(x, y, 'ok') xstart = np.min(adc) xstop = np.max(adc) increment = 10 xmodel = np.arange(xstart,xstop,increment) #ymodel = model(xmodel, *popt) #plt.plot(xmodel,ymodel, 'r') for model_order in [3,4,5,6]: print("model order="+str(model_order)) # Finding the Model p = np.polyfit(x, y, model_order) #print(p) polystring="" for i in np.arange(model_order+1): _c=str(p[i]) if p[i]>=0: _c="+"+_c #add leading + polystring+=_c+"*x^"+str(model_order-i)+" " print(polystring) print("Excel:") for i in np.arange(model_order+1): print(str(p[i])) print("Array:") arraystring="{" for i in np.arange(model_order+1): arraystring+=str(p[model_order-i]) arraystring+="," arraystring=arraystring[:-1]+"}" print(arraystring) # Plot the Model ymodel = np.polyval(p, xmodel) plt.plot(xmodel,ymodel,label="order="+str(model_order)) print() plt.legend() plt.grid() plt.show()