import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('data (1).csv')
data.head()
| pwmint64 | A0int64 | A1int64 | ||
|---|---|---|---|---|
0 | 0 | 188 | 166 | |
1 | 1 | 147 | 127 | |
2 | 2 | 115 | 99 | |
3 | 3 | 92 | 81 | |
4 | 4 | 75 | 65 | |
| 5 rows × 3 columns | ||||
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
Requirement already satisfied: empiricaldist==0.6.0 in /root/venv/lib/python3.7/site-packages (0.6.0)
WARNING: You are using pip version 20.1.1; however, version 21.0.1 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.
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)
PMF {'1': 0.25, '2': 0.25, '3': 0.25, '4': 0.25}
PMF {'1': 0.16666666666666666, '2': 0.16666666666666666, '3': 0.16666666666666666, '4': 0.16666666666666666, '5': 0.16666666666666666, '6': 0.16666666666666666}
PMF {'1': 0.125, '2': 0.125, '3': 0.125, '4': 0.125, '5': 0.125, '6': 0.125, '7': 0.125, '8': 0.125}
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