#!/usr/bin/env python3
import numpy as np
import csv
import matplotlib.pyplot as plt

xcolumn=1 #column with readings
ycolumn=4 #column with calibration data
ncoefs=3 #number of coefficients

xvalues=[]
yvalues=[]

with open('20180123_prototype_calibration.csv', 'r') as csvfile:
    csvreader = csv.reader(csvfile, delimiter=',')
    firstrow=True
    for row in csvreader:
        xvalue=row[xcolumn]
        yvalue=row[ycolumn]
        if len(xvalue)>0 and len(yvalue)>0 and not firstrow:
            xvalue=float(xvalue)
            yvalue=float(yvalue)
            if yvalue>12.5:
                #print(""+str(xvalue)+" - "+str(yvalue))
                xvalues.append(xvalue)
                yvalues.append(yvalue)
        firstrow=False

    coefs=np.polyfit(xvalues,yvalues,ncoefs) #fit polynomial curve
    print(coefs) #coef 0 is the one with highest polynomial

    xtest=np.arange(max(xvalues)) #x values for test visualization
    ytest=np.polyval(coefs, xtest) #calculate y values with polynomial function
    #ytest=[coefs[3]+coefs[2]*pow(x,1)+coefs[1]*pow(x,2)+coefs[0]*pow(x,3) for x in xtest]

    plt.scatter(xvalues,yvalues,s=0.25,c='g') #plot sample data
    plt.plot(xtest,ytest,c='r') #plot approximated curve
    plt.xlabel('LDR Value')
    plt.ylabel('Ev')
    plt.show()