In [180]:

    

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sys
from scipy import interpolate
import math
from ipywidgets import *

# use engauge to extract plot data from datasheet
fn = "25L6GT EC2=110V.csv"
df = pd.read_csv(fn)


    

In [181]:

    

df.head(1)


    

    
Out[181]:






  

    

      

      
AnodeVoltage
      
0V
      
-2V
      
-4V
      
-6V
      
-8V
      
-10V
      
-12V
      
-14V
      
-16V
    
  
  

    

      
0
      
0
      
0.00035
      
-0.00034
      
-0.00069
      
0.00035
      
0.00241
      
0.00034
      
-0.00069
      
0.00414
      
0.00034
    
  

In [327]:

    

colnames = df.columns.values
rowcount = len(df[colnames[0]])

Va = 130

x = []
for c in range(1,len(colnames)):
    x.append(float(colnames[c][:-1]))

fig = plt.figure(figsize=(15, 10))

for r in range(0,rowcount):
    if abs(df['AnodeVoltage'][r] - Va) < 5:
        y = []
        for c in range(1,len(colnames)):
            y.append(df[colnames[c]][r])
        plt.plot(x,y,label=df[colnames[0]][r])
        break
        
plt.tick_params(axis='y', which='both', labelleft='off', labelright='on')
plt.grid(True)
plt.legend(loc='upper left')

for i in range(len(x)):
    print "%.6f,%0.6f"%(x[i],y[i])
    
coeff = np.polyfit(x, y, 2)
print coeff


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-327-5bd9988dd13c> in <module>()
     19 
     20 plt.tick_params(axis='y', which='both', labelleft='off', labelright='on')
---> 21 plt.grid(true)
     22 plt.legend(loc='upper left')
     23 

NameError: name 'true' is not defined





    

In [326]:

    

from scipy.fftpack import fft

Rl = 4
n = 33

Vg = -8.0
vg = 4
ra = Rl*n*n

N = 2000
T = 1.0 / 5000.0
f = 100.0

t = np.linspace(0.0, N*T, N)
vi = Vg + vg*np.sin(f*2.0*np.pi*t)
ia = coeff[0]*vi*vi + coeff[1]*vi + coeff[2]
vo = ia*ra

yf = fft(vo)
xf = np.linspace(0.0, 1.0/(2.0*T), N/2)

plt.figure(figsize=(15, 20))
ax = plt.subplot(311)
null = plt.xlim([0,0.05])
ax.plot(t,vo)

ax = plt.subplot(312)
ax.plot(xf[1:], 20*np.log10(2.0/N * np.abs(yf[:N/2][1:]))) # take the dc component off
null = plt.xlim([0,500])

plt.show()

mags = []
for i in range(len(xf)):
    if xf[i]%100 < 1:
        mags.append(2.0/N*np.abs(yf[i]))
        # print xf[i],2.0/N*np.abs(yf[i])

sum = 0
for i in range(2,8):
    sum += mags[i]*mags[i]
    
# print mags
print "%.1f%%"%(100*math.sqrt(sum)/mags[1])

print "%.1fW"%(math.pow((mags[1]/n)/math.sqrt(2),2)/Rl)


    

    













    




9.7%
2.4W

In [324]:

    

# Number of samplepoints
N = 10000
# sample spacing
T = 1.0 / 8000.0
x = np.linspace(0.0, N*T, N)
y = np.sin(50.0 * 2.0*np.pi*x) - 0.1*np.sin(100 * 2.0*np.pi*x)
yf = fft(y)
xf = np.linspace(0.0, 1.0/(2.0*T), N/2)

plt.figure(figsize=(15, 20))
ax = plt.subplot(311)
ax.plot(xf, 2.0/N * np.abs(yf[:N/2]))
null = plt.xlim([0,500])

ax = plt.subplot(312)
ax.plot(x,y)
null = plt.xlim([0,0.1])

plt.show()


    

In [ ]: