40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
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#!/usr/bin/env python3
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import numpy as np
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import csv
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import matplotlib.pyplot as plt
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xcolumn=1 #column with readings
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ycolumn=4 #column with calibration data
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ncoefs=3 #number of coefficients
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xvalues=[]
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yvalues=[]
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with open('20180123_prototype_calibration.csv', 'r') as csvfile:
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csvreader = csv.reader(csvfile, delimiter=',')
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firstrow=True
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for row in csvreader:
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xvalue=row[xcolumn]
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yvalue=row[ycolumn]
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if len(xvalue)>0 and len(yvalue)>0 and not firstrow:
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xvalue=float(xvalue)
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yvalue=float(yvalue)
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if yvalue>12.5:
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#print(""+str(xvalue)+" - "+str(yvalue))
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xvalues.append(xvalue)
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yvalues.append(yvalue)
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firstrow=False
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coefs=np.polyfit(xvalues,yvalues,ncoefs) #fit polynomial curve
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print(coefs) #coef 0 is the one with highest polynomial
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xtest=np.arange(max(xvalues)) #x values for test visualization
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ytest=np.polyval(coefs, xtest) #calculate y values with polynomial function
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#ytest=[coefs[3]+coefs[2]*pow(x,1)+coefs[1]*pow(x,2)+coefs[0]*pow(x,3) for x in xtest]
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plt.scatter(xvalues,yvalues,s=0.25,c='g') #plot sample data
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plt.plot(xtest,ytest,c='r') #plot approximated curve
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plt.xlabel('LDR Value')
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plt.ylabel('Ev')
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plt.show()
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