data = pd.read_csv('data (1).csv') data.head()

start = 61 end = 75 pwm = data.pwm[start:end] vtop = (data.A0*2/1023)[start:end] vbot = (data.A1*2/1023)[start:end] vp = 3.3 plt.plot(pwm,vtop) plt.plot(pwm,vbot) plt.grid plt.xlabel('Time (s)') plt.ylabel('Voltage (V)') plt.title('Voltage as Time Passes')

!pip install empiricaldist==0.6.0

import empiricaldist as emp die4={'1':1,'2':1,'3':1,'4':1} die6={'1':1,'2':1,'3':1,'4':1,'5':1,'6':1} die8={'1':1,'2':1,'3':1,'4':1,'5':1,'6':1,'7':1,'8':1} def buildPMF(dictOfThings): pmfDict = {} things = dictOfThings.items() numThings = np.array(list(dictOfThings.values())).sum() for k,v in things: pmfDict[k]=v/numThings return pmfDict pmf4 = buildPMF(die4) pmf6 = buildPMF(die6) pmf8 = buildPMF(die8) print("\nPMF", pmf4) print("\nPMF", pmf6) print("\nPMF", pmf8)

pmfHypos = emp.Pmf(1/3,['4','6','8']) pmfHypos

liklihood = [pmf4['1'],pmf6['1'],pmf8['1']] post1 = pmfHypos*liklihood post1.normalize() post1

liklihood = [pmf4['4'],pmf6['4'],pmf8['4']] post2 = post1*liklihood post2.normalize() post2

liklihood = [pmf4['2'],pmf6['2'],pmf8['2']] post3 = post2*liklihood post3.normalize() post3

liklihood = [0,pmf6['5'],pmf8['5']] post4 = post3*liklihood post4.normalize() post4