import pandas as pd import numpy as np import math import matplotlib.pyplot as plt df = pd.read_csv('Data1.txt') # read in the data taken previously T = df.Time VD = df.Vo VO = df.Vd plt.plot(T, VO * 2.0 / 16383, label='Vd') plt.plot(T, VD * 2.0 / 16383, label='Vo') plt.legend() plt.xlabel('time (s)') plt.ylabel('voltage (V)')

R = 130 Vd = df.Vd[140:195] * 2.0 / 16383 Vo = df.Vo[140:195] * 2.0 / 16383 V = Vd - Vo I = V / R A, B = np.polyfit(np.log(I), Vd, 1) Vth = A*np.log(I) + B Bconst = 1.380649 * 10**-23 Echarge = 1.602176634 * 10**-19 Tabs = 294.27 # or 70 degrees farenheit N = (A*Echarge) / (Tabs * Bconst) Io = 2.71828**(-B/A) print("N: "+ str(N) + " Io: " + str(Io)) plt.plot( np.log(I), Vd, 'b.') plt.plot(np.log(I), Vth, 'r-') plt.xlabel("Natural Log of Current") plt.ylabel("Voltage Drop")

mu = 25 std = 6 N = 10000 xs = np.random.normal(loc=mu, scale=std, size=N) res = plt.hist(xs,bins = 20)

xs = np.array(sorted(xs)) zs = np.array(sorted(np.random.normal(loc=0, scale=1.0, size=N))) plt.plot(zs,xs,'b.') plt.plot(zs, zs*std + mu,'r-') print("x > 35 =", len(xs[xs>35])/N)