59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
#!/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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import math
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xcolumn=0 #column with readings
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ycolumn=4 #column with calibration data
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ncoefs=4 #number of coefficients
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#mit log
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#low (0), coeffs=4,, <2.7, ab <=2.3 benutzbar
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#high (1), coeffs=5, >2 ab >=1 knapp benutzbar, ab 2 besser
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xvalues=[]
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yvalues=[]
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with open('20180413_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=math.log(float(xvalue))
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yvalue=float(yvalue)
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if yvalue<2.7:
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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),step=0.001) #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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#for i in range(200,4000,10):
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# yv=np.polyval(coefs, i)
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# print(str(i)+";"+str(yv))
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#exit()
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plt.scatter(xvalues,yvalues,s=0.7,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.xlim(0,4096) #ldr
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plt.ylim(-3,20) #ev
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#limits with lux
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#plt.xlim(0,4096) #ldr
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#plt.xlim(0,500) #ldr
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#plt.ylim(0,1280000) #ev
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#plt.ylim(0,220) #ev
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plt.show()
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