In [427]:

    

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from __future__ import division
import pandas as pd


    

In [428]:

    

V = np.linspace(0,1000,1000)
plt.plot(V, 6.43 - 5e-14*(np.exp(V/2.6) - 1)) #in V and A
plt.ylim(0,10)
plt.xlim(0,100)


    

    
Out[428]:





(0, 100)






    

In [429]:

    

V = np.linspace(0,100,1000)
I_o = 5e-14 #A
I_L = 6.43 #A
R_s = 0 #ohm
R_sh = 1e6 #ohm
plt.plot(V, I_L - I_o*(np.exp(V/2.6) - 1)) #in V and A
plt.ylim(0,10)
plt.xlim(0,100)


    

    
Out[429]:





(0, 100)






    

In [430]:

    

I = 100
plt.plot(I - I_L - I_o*(np.exp((V+I*R_s)/2.6) - 1)) #this


    

    
Out[430]:





[<matplotlib.lines.Line2D at 0x12b237290>]






    

In [431]:

    

from sympy import solve, Symbol, exp
x = Symbol('x')
y = Symbol('y')
solve([x + 5*y - 2, -3*x + 6*y - 15], [x, y])


    

    
Out[431]:





{x: -3, y: 1}

In [433]:

    

V = Symbol('V')
I = Symbol('I')
I_o = 5e-14 #A
I_L = 6.43 #A
R_s = 10 #mohm
R_sh = 1e5 #mohm
n = 1

#solve(I - I_L - I_o*(exp( (V + I * R_s) /(26*n)) - 1) , I)


    

In [434]:

    

from scipy.optimize import fsolve
import math

def equations(p):
    x, y = p
    return (x+y**2-4, math.exp(x) + x*y - 3)

x, y =  fsolve(equations, (1, 1))

print equations((x, y))


    

    




(4.4508396968012676e-11, -1.0512035686360832e-11)

In [435]:

    

import scipy.optimize as optimize
from math import sqrt

def f(c):
    return sqrt(c[0]**2 + c[1]**2 + (c[2]-2)**2)

result = optimize.minimize(f, [1,1,1])
print result.values()[6]


    

    




[ -7.29867252e-09  -7.29867252e-09   1.99999999e+00]

In [436]:

    

import scipy.optimize as optimize
from math import sqrt

# I, c[0]
I_L = 6.43 #A
# I_o = 5e-14 #A,  c[2]
# n = 1, c[2]
V = 1 #mV
# R_s = 1 #mohm, c[3]
# R_sh = 1e5 #mohm c[4]


def f(c):
    
    
    I - I_L - I_o*(exp( (V + I * R_s) /(26*n)) - 1)
    return I 

result = optimize.minimize(f, [1,1,1])
print result.values()[6]


    

    




[ 1.  1.  1.]

In [437]:

    

irrad_df = pd.read_csv('data/ASTMG173.csv')


    

In [438]:

    

irrad_df.head()


    

    
Out[438]:






  

    

      

      
wavelength
      
etr
      
globaltilt
      
directcircum
    
  
  

    

      
0
      
280.0
      
0.082
      
4.730000e-23
      
2.540000e-26
    
    

      
1
      
280.5
      
0.099
      
1.230000e-21
      
1.090000e-24
    
    

      
2
      
281.0
      
0.150
      
5.690000e-21
      
6.130000e-24
    
    

      
3
      
281.5
      
0.212
      
1.570000e-19
      
2.750000e-22
    
    

      
4
      
282.0
      
0.267
      
1.190000e-18
      
2.830000e-21
    
  

In [439]:

    

irrad_df['globaltilt'].plot()


    

    
Out[439]:





<matplotlib.axes._subplots.AxesSubplot at 0x351f7dc10>






    

In [440]:

    

eqe_df = pd.read_csv('data/eqe_sunpower_25.csv')


    

In [441]:

    

eqe_df.head()


    

    
Out[441]:






  

    

      

      
wavelength
      
percent
    
  
  

    

      
0
      
299.768250
      
64.683427
    
    

      
1
      
303.939745
      
67.215777
    
    

      
2
      
308.806489
      
70.355838
    
    

      
3
      
322.016223
      
73.598574
    
    

      
4
      
331.054461
      
76.840606
    
  

In [442]:

    

eqe_df['percent'].values


    

    
Out[442]:





array([ 64.68342696,  67.21577658,  70.35583848,  73.5985743 ,
        76.84060607,  79.57642607,  86.66984467,  93.45970049,
        95.99603972,  97.11454009,  98.13494287,  98.5429397 ,
        98.44707159,  98.75345058,  98.95762501,  99.26916702,
        98.7698784 ,  98.77257726,  98.97862916,  98.58530003,
        98.79158661,  98.79510685,  98.79804039,  98.80038723,
        99.00585241,  99.00972469,  98.81118266,  98.51125746,
        97.70465113,  96.39218505,  93.15578568,  89.20935213,
        86.47705238,  75.84824794,  69.976473  ,  66.02839667,
        39.80782375,  33.63236869,  24.21722868,   8.42457134,   1.44074981])

In [443]:

    

from scipy import interpolate
x = eqe_df['wavelength'].values
y = eqe_df['percent'].values
f = interpolate.interp1d(x, y)

wav_new = np.arange(300,1180, 0.5)
eqe_new = f(xnew)   # use interpolation function returned by `interp1d`
plt.plot(x, y, 'o', wav_new, eqe_new, '-')
plt.show()


    

In [444]:

    

irrad_df[irrad_df['wavelength']==300]


    

    
Out[444]:






  

    

      

      
wavelength
      
etr
      
globaltilt
      
directcircum
    
  
  

    

      
40
      
300
      
0.458
      
0.00102
      
0.000456
    
  

In [445]:

    

irrad_df[irrad_df['wavelength']==1180]


    

    
Out[445]:






  

    

      

      
wavelength
      
etr
      
globaltilt
      
directcircum
    
  
  

    

      
1020
      
1180
      
0.522
      
0.441
      
0.421
    
  

In [ ]:

    




    

In [448]:

    

from scipy import interpolate

x = irrad_df['wavelength'][40:1021].values
irrad_global = irrad_df['globaltilt'][40:1021].values #AM1.5 spectrum

f = interpolate.interp1d(x, irrad_global)
wav_new = np.arange(300,1180, 0.5) #300 nm to 1180 nm with 0.5 nm spacing
irrad_new = f(xnew)   #recreate AM1.5 with 0.5 nm spacing

plt.plot(x, irrad_global, 'o', wav_new, irrad_new, '-')
plt.show()


    

In [449]:

    

plt.plot(wav_new,eqe_new*irrad_new*wav_new)


    

    
Out[449]:





[<matplotlib.lines.Line2D at 0x11f1d2490>]






    

In [450]:

    

(1/1240)*sum(eqe_new*irrad_new*wav_new)*.5/1e3 #mA/cm^2


    

    
Out[450]:





41.796068304420977

In [451]:

    

iv_df = pd.read_csv('data/i_v_sunpower_25.csv')


    

In [465]:

    

plt.plot(iv_df.voltage,iv_df.current, 'r--')
I_o = 3.6e-10 #mA/cm^2
I_L = 41.74 #mA/cm^2
plt.plot(iv_df.voltage, I_L - I_o*(np.exp(iv_df.voltage/.0283) - 1)) #in V and A
plt.ylim(0,50)


    

    
Out[465]:





(0, 50)






    

In [ ]:

    




    

In [ ]:

    




    

In [ ]: