

In [1]:

    
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
from scipy import optimize as opt
import matplotlib.image as mpimg



In [2]:

    
from customplotting import *
from dataprocessing import *
from slopes import *

Read in



In [3]:

    
df = pd.read_excel('Histograms.xlsx', sheetname=None,header=None)

Extract Data



In [4]:

    
RedIntensity,GreenIntensity,BlueIntensity,RedSTD,GreenSTD,BlueSTD,ExposureTimes = ExcelExtract(df)

First Plots. HSM and Film Data



In [5]:

    
HSM_Plot(RedIntensity,GreenIntensity,BlueIntensity,RedSTD,GreenSTD,BlueSTD,ExposureTimes)

Curve Fitting



In [6]:

    
RedRef,RedSlopes,RedIntensity,RedSTD,NewExposureTimes = GetSlopes(RedIntensity,RedSTD,ExposureTimes)
GreenRef,GreenSlopes,GreenIntensity,GreenSTD,NewExposureTimes = GetSlopes(GreenIntensity,GreenSTD,ExposureTimes)
BlueRef,BlueSlopes,BlueIntensity,BlueSTD,NewExposureTimes = GetSlopes(BlueIntensity,BlueSTD,ExposureTimes)

Second Plots. Line Fit on Film Data



In [7]:

    
Line_Plot(RedIntensity,GreenIntensity,BlueIntensity,RedSTD,GreenSTD,BlueSTD,RedSlopes,GreenSlopes,BlueSlopes,NewExposureTimes)



In [11]:

    
#Least Difference fitting to guess at layer number values.
#Choosing N such that the difference between calculated reflectivity and measured reflectivity is the least
def NFits(Refl,Color,N_Max):
    i = 2
    difference = abs(Ref(2,Color) - Refl)[0]
    for n in np.arange(3,N_Max+1):
        if abs(Ref(n,Color) - Refl)[0] < difference:
            i = n
            difference = abs(Ref(n,Color) - Refl)[0]
    return i



In [12]:

    
N_Max = 30 #Choose before curves begin to slope downwards.



In [13]:

    
Green_Fits = np.array([NFits(GreenRef[i],'green',N_Max) for i in np.arange(0,len(GreenRef))])
Green_Fits









    Out[13]:





array([14, 15, 16, 21, 22, 29])



In [14]:

    
N_Guesses = Green_Fits

Third Plots. True Reflectivity



In [15]:

    
Ref_Plot(GreenRef,BlueRef,N_Guesses)