GauLor_JYmap_633.py

## Uses .txt file from JY Horiba mapping results for 405 nm
## need to add section determining mapping versus individual data text files
## 20191002 expanding bounds based on scatter plots
## 20201207 getting scan details from metadata
## 20230408 altered to allow fitting of any number of Lorentzian peaks, defined lines 29-34
## 20230524 choose baseline fitting order at top 
## 20230628 v.3 now with uncertainties!
## 202308 mixed Gaussian and Lorentzian peak fits
## 20230919 using adjusted R2 instead of R2 for baseline and signal fits



import sys
import numpy as np
from scipy.optimize import curve_fit
from scipy.optimize import minimize
from scipy.optimize import least_squares
import numpy.polynomial.polynomial as poly
import matplotlib.pyplot as plt
#from matplotlib.gridspec import GridSpec
#from scipy import signal
import os
import fnmatch
#import random
#import math
#from time import sleep
import pandas as pd  #needed to read JY data
import linecache
import uncertainties
from uncertainties import ufloat
from uncertainties import unumpy
from uncertainties.umath import sqrt as usqrt
from uncertainties.umath import exp as uexp
from uncertainties.umath import log as ulog

# File Parameters
# =============================================================================
# # Data fitting parameters
# 
FitGOn = 1 # 1 is yes, 0 is no
FitDOn = 1
FitD2On = 0
FitD3On = 1
FitD4On = 0
FitU1On = 0 #unidentified peak but need to include in envelope for 405 etc

fitVersion = 3.01 #changing if there is a change to base fitting subtr or peak fitting or stats calc.  Not for making figures or summarizing data.

base_order = 3 #order of polynomial for bkg fitting, choose 1, 2, or 3
bkd_bounds = [650, 1000, 1750, 2000] #low wavelength limits (low, high) and high wavelength limits (low, high)

G_bounds = [1595, 50, 35, 30] # Center wavelength, wavelength limits, HWHM guess, HWHM limits (currently unused)
D_bounds = [1330, 60, 100, 40]
D2_bounds = [1620, 10, 20, 10]
D3_bounds = [1495, 15, 55, 40]
D4_bounds = [1225, 10, 60, 40]
U1_bounds = [1725, 20, 10, 8]  #no physical basis, trying because weird peak in some 405 data
IIM = 0.8 #Initial intensity multiplier for G and D peaks 
qBWF = 0
# =============================================================================

Ext_Lambda = 000 #nm

TotalNumPeaks = FitGOn + FitDOn + FitD2On + FitD3On + FitD4On + FitU1On
NumPeaks = FitGOn + FitDOn + FitD2On + FitD3On + FitD4On
NumPksApp = str(NumPeaks)+'GnL'
NumParams    = 3*6     #{Number of parameters to fit}
FitParam =np.zeros(NumParams) 
bounds = np.zeros((NumParams,2))
lobounds = np.zeros(18)
hibounds = np.zeros(18)

CollDet = 'NMNH unk det'  #If need to restart, replace with collection details

# only need to set peak location and width bounds once, at the beginning of the program  
# bounds for optimize.curve_fit: an array of lows, an array of highs

lobounds[0] = (G_bounds[0]-G_bounds[1])
lobounds[1] = (D_bounds[0]-D_bounds[1])
lobounds[2] = (D2_bounds[0]-D2_bounds[1])
lobounds[3] = (D3_bounds[0]-D3_bounds[1])
lobounds[4] = (D4_bounds[0]-D4_bounds[1])
lobounds[5] = (U1_bounds[0]-U1_bounds[1])

lobounds[6] = (G_bounds[2]-G_bounds[3])
lobounds[7] = (D_bounds[2]-D_bounds[3])
lobounds[8] = (D2_bounds[2]-D2_bounds[3])
lobounds[9] = (D3_bounds[2]-D3_bounds[3])
lobounds[10] = (D4_bounds[2]-D4_bounds[3])
lobounds[11] = (U1_bounds[2]-U1_bounds[3])

hibounds[0] = (G_bounds[0]+G_bounds[1])
hibounds[1] = (D_bounds[0]+D_bounds[1])
hibounds[2] = (D2_bounds[0]+D2_bounds[1])
hibounds[3] = (D3_bounds[0]+D3_bounds[1])
hibounds[4] = (D4_bounds[0]+D4_bounds[1])
hibounds[5] = (U1_bounds[0]+U1_bounds[1])

hibounds[6] = (G_bounds[2]+G_bounds[3])
hibounds[7] = (D_bounds[2]+D_bounds[3])
hibounds[8] = (D2_bounds[2]+D2_bounds[3])
hibounds[9] = (D3_bounds[2]+D3_bounds[3])
hibounds[10] = (D4_bounds[2]+D4_bounds[3])
hibounds[11] = (U1_bounds[2]+U1_bounds[3])

hibounds[12] = 500 #made up intensities just to fill the array out will customize per spectrum
hibounds[13] = 500
hibounds[14] = 500
hibounds[15] = 100
hibounds[16] = 100
hibounds[17] = 500

bounds = lobounds,hibounds
  
def ReadCollDetails(inputFilename):
    global CollDet, Ext_Lambda
    lineList = []
    i=0
    #txtFile = open(inputFilename, "r")
    with open(inputFilename, encoding='latin-1', errors='ignore') as txtFile:
        for line in txtFile:
            lineList.append(line.rstrip("\n"))
            if '#Acq. time' in lineList[i]: 
                acqtime = (lineList[i].split('=')[1][1:])
                #print(str(acqtime)+"acq")
            if '#Accumulations=' in lineList[i]: 
                NoScans = (lineList[i].split('=')[1][1:])
                #print(str(NoScans)+"sc")
            if '#Laser=' in lineList[i]:
                Laser = lineList[i].split('=')[1][1:]
                #print(Laser)
                Ext_Lambda = int(Laser[0:3])
                #print(Ext_Lambda)
            if '#ND' in lineList[i]:
                lp = lineList[i].split('=')[1][1:]
            if '#Date' in lineList[i]:
                DateLine = int(lineList[i].split()[1][6:])
                #print(DateLine)
            i = i+1
    CollDet = 'NMNH '+acqtime+'s '+NoScans+ 'sc ' + Laser + ' lp ' +lp
    txtFile.close()
    return CollDet,Ext_Lambda, DateLine

#physics suggests Raman of solid should be Gaussian--why not here? 
#(gases Lorentzian, liquids Gaussian-Laurentzian or Voigt)

def lorentz(xc, w, I):  
    global x_fit
    s = ((x_fit - xc)/w)
    return (I)*(1/(1+s**2)) #Wikipedia definition.  gives correct value for intensity.  using this definition fit_results you don't need to account for the peak width in the intensity.

def BWF(xc,w,I):
    global qBWF,x_fit
    s = ((x_fit - xc)/w)
    return (I)*((1+s/qBWF)**2/(1+s**2))

def Gaussian(xc,w,I):
    global x_fit
    s = ((x_fit - xc)/w)
    return (I)*np.exp((-1*np.log(2)*s**2))

def GaussianWithUnc(xc,w,I):
    global x_fit
    s = ((x_fit - xc)/w)
    return (I)*unumpy.exp((-1*np.log(2)*s**2))

def EnterData():
    
    global FitParam, G_ints, D_ints, U1_ints, NumParams, G_bounds, D_bounds, D2_bounds, D3_bounds, D4_bounds, U1_bounds
    global bounds, lobounds, hibounds

    FitParam[0] = G_bounds[0] # G peak position
    FitParam[1]  = D_bounds[0] # D peak position
    FitParam[2]  = D2_bounds[0] # D2 peak position
    FitParam[3]  = D3_bounds[0] # D3 peak position
    FitParam[4]  = D4_bounds[0] # D4 peak position
    FitParam[5]  = U1_bounds[0] # U1 peak position

    FitParam[6]  = G_bounds[2]  # G peak width
    FitParam[7]  = D_bounds[2]  # D peak width
    FitParam[8]  = D2_bounds[2]  # D2 peak width
    FitParam[9]  = D3_bounds[2]  # D3 peak width
    FitParam[10]  = D4_bounds[2]  # D4 peak width
    FitParam[11]  = U1_bounds[2] # U1 peak width

    '''need to work G and D starting peak intensities from initial values'''
    FitParam[12]  = IIM*G_ints  # G peak intensity  
    FitParam[13]  = IIM*D_ints  # D peak intensity
    FitParam[14] = (0.4*FitParam[12])
    FitParam[15] = (0.4*FitParam[13])
    FitParam[16] = (0.4*FitParam[13])
    FitParam[17]  = (0.25*FitParam[12]) 

    Gfit = FitGOn*Gaussian(FitParam[0],FitParam[6],FitParam[12])
    Dfit = FitDOn*lorentz(FitParam[1],FitParam[7],FitParam[13])
    D2fit = FitD2On*Gaussian(FitParam[2],FitParam[8],FitParam[14])
    D3fit = FitD3On*Gaussian(FitParam[3],FitParam[9],FitParam[15])
    D4fit = FitD4On*Gaussian(FitParam[4],FitParam[10],FitParam[16])
    U1fit = FitU1On*Gaussian(FitParam[5],FitParam[11],FitParam[17])

    ModelFit = Gfit + Dfit + D2fit + D3fit + D4fit + U1fit    
    
    # # figure with initial fit of various peaks
    
    # fig = plt.figure(5)
    # ax50 = fig.add_subplot(111)
    # ax50.plot(x_fit, signal_fit,'.k', label = 'Experimental')
    # ax50.plot(x_fit, Gfit,'-g', label = 'G Peak Fit')
    # ax50.plot(x_fit, Dfit,'-b', label = 'D Peak Fit')
    # ax50.plot(x_fit, D2fit,'-y', label = 'D2 Peak Fit')
    # ax50.plot(x_fit, D3fit,'-c', label = 'D3 Peak Fit')
    # ax50.plot(x_fit, D4fit,'-m', label = 'D4 Peak Fit')
    # ax50.plot(x_fit, U1fit, '.g', label = 'U1 Peak Fit')
    # ax50.plot(x_fit, ModelFit,'-r', label = 'Summed Peak Fit')
    # ax50.set_xlabel(r'Raman Shift / cm$^-$$^1$')
    # plt.autoscale(enable=True, axis='x', tight=True)
    # plt.autoscale(enable=True, axis='y', tight=True)
    # ax50.set_ylabel('Raman Intensity')
    # ax50.set_ylim(min(signal_fit), max(signal_fit)*1.2)
    # #plt.text(1075, 14100, 'ink', fontsize=20)
    # plt.tick_params(axis='both', which='major')
    # #plt.tight_layout()
    # plt.savefig(SaveName + '_initialfit.jpg')
    # #plt.show()
    # plt.close()
    

    lobounds[12] = 0.5*G_ints  # we might want to let G go to zero depending on D2
    lobounds[13] = 0.5*D_ints
    hibounds[12] = 1.2*G_ints
    hibounds[13] = 1.2*D_ints

    hibounds[14] = 0.5*G_ints
    hibounds[15] = 0.5*D_ints
    hibounds[16] = 0.5*D_ints
    hibounds[17] = 0.5*G_ints
    
    bounds = (lobounds,hibounds)
    
    
def FitFunc(x_fit, *EvalSimp):   
    
    
    Gfit = FitGOn*Gaussian(EvalSimp[0],EvalSimp[6],EvalSimp[12])
    Dfit = FitDOn*lorentz(EvalSimp[1],EvalSimp[7],EvalSimp[13])
    D2fit = FitD2On*Gaussian(EvalSimp[2],EvalSimp[8],EvalSimp[14])
    D3fit = FitD3On*Gaussian(EvalSimp[3],EvalSimp[9],EvalSimp[15])
    D4fit = FitD4On*Gaussian(EvalSimp[4],EvalSimp[10],EvalSimp[16])
    U1fit = FitU1On*Gaussian(EvalSimp[5], EvalSimp[11], EvalSimp[17])

    
    FitY = Gfit + Dfit + D2fit + D3fit + D4fit + U1fit
        
    return(FitY)

def FitFuncWithUnc(x_fit, *EvalSimp):   
    
    
    Gfit = FitGOn*GaussianWithUnc(EvalSimp[0],EvalSimp[6],EvalSimp[12])
    Dfit = FitDOn*lorentz(EvalSimp[1],EvalSimp[7],EvalSimp[13])
    D2fit = FitD2On*GaussianWithUnc(EvalSimp[2],EvalSimp[8],EvalSimp[14])
    D3fit = FitD3On*GaussianWithUnc(EvalSimp[3],EvalSimp[9],EvalSimp[15])
    D4fit = FitD4On*GaussianWithUnc(EvalSimp[4],EvalSimp[10],EvalSimp[16])
    U1fit = FitU1On*GaussianWithUnc(EvalSimp[5], EvalSimp[11], EvalSimp[17])

    
    FitY = Gfit + Dfit + D2fit + D3fit + D4fit + U1fit
        
    return(FitY)


for file in os.listdir('.'):
    if fnmatch.fnmatch(file, '*.txt'):
        Loadfile = file
        print(file)
        filename = file[:-4]
        
        (CollDet,Ext_Lambda,FileDate) = ReadCollDetails(file)
        Ext_Lambda = int(Ext_Lambda)
        print(Ext_Lambda)
        
        if FileDate < 2023:
            all_data = pd.read_csv(Loadfile, skiprows = (38), sep = None, header = None, engine='python', encoding='latin-1')
        else:
            all_data = pd.read_csv(Loadfile, skiprows = (46), sep = None, header = None, engine='python', encoding='latin-1')
        locations = all_data[0]
        all_data = all_data.transpose()
        
        xy_data = all_data[1:]
        wavenum = np.array(xy_data[0])
        
        for n in range(1,len(xy_data.columns)):
            signal = np.array(xy_data[n])
            SpotNo = n
            print(n)
            SaveName = 'results/' + filename +'_POS'+ str(n).zfill(2) + '_'+ NumPksApp
            #print(SaveName)
            
            #Fit Baseline and cut-down region wavenum and signal to region of interest
            x_fit = wavenum[(bkd_bounds[0] <= wavenum) & (wavenum <= bkd_bounds[3])] # X data cut down to bkg boundaries
            signal_fit = signal[(bkd_bounds[0] <= wavenum) & (wavenum <= bkd_bounds[3])] # y data cut down to bkg boundaries
            
            lower_bkg = (bkd_bounds[0] <= x_fit) & (x_fit <= bkd_bounds[1]) # True/False selection for low part of bkg
            higher_bkg = (bkd_bounds[2] <= x_fit) & (x_fit <= bkd_bounds[3]) # True/False selection for high part of bkg
            
            bkg_x = x_fit[lower_bkg | higher_bkg] # pipe is essentially acting as numpy.or to select only high and low bkg areas
            bkg_signal = signal_fit[lower_bkg | higher_bkg]

            ##  baseline fit
            BasePara, BaseRegrStat = poly.polyfit(bkg_x, bkg_signal, base_order, full = True)
            baseline = poly.polyval(x_fit, BasePara)
            baseline_bkgarea = poly.polyval(bkg_x, BasePara)
            BaseTSS = ((bkg_signal - np.mean(bkg_signal))**2).sum()
            BaseR2= 1-(BaseRegrStat[0]/BaseTSS) #should work regardless of order of fit
            AdjBaseR2 = 1-(1-BaseR2)*(len(baseline_bkgarea)-1)/((len(baseline_bkgarea))-(base_order+1)-1) #wiki definition independent of baseline order fit

            # calculate rise/run for fitted baseline immediately before and after the peak region.
            BaseRise = poly.polyval(bkd_bounds[2], BasePara) - poly.polyval(bkd_bounds[1], BasePara)
            BaseRun = bkd_bounds[2]-bkd_bounds[1]
            PseudoSlope = BaseRise/BaseRun
            
            if base_order ==1:
                BaseSlope = BasePara[1] #only works for first order baseline fits
            else:
                BaseSlope = PseudoSlope
            
            BaseResiduals = bkg_signal-baseline_bkgarea
            
            # figure with baseline fit and residuals
            
            fig = plt.figure(1)
            gs1 = fig.add_gridspec(nrows=2, ncols=1,
                      hspace=0.1, wspace=0, height_ratios=[1, 5])
            ax = fig.add_subplot(gs1[1])
            ax.plot(wavenum , signal,'-k')
            ax.plot(x_fit, baseline, '-r')
            ax.plot(bkg_x , bkg_signal,'.b')
            ax.set_xlabel('Raman Shift / cm$^{-1}$')
            ax.set_ylabel('Raman Intensity')
            ax.set_ylim(0, 1.5*max(signal_fit))
            plt.autoscale(enable=True, axis='x', tight=True)
            plt.autoscale(enable=True, axis='y', tight=True)
            
            ax11 = fig.add_subplot(gs1[0])
            ax11.plot(bkg_x,BaseResiduals, '.b')
            ax11.axhline(0, linestyle='--', color = 'gray', linewidth = 1)
            ax11.set_ylabel('Residuals')
            plt.setp(ax11, xticks=[600,800,1000,1200,1400,1600,1800,2000])
            ax11.tick_params(direction='in',labelbottom=True,labelleft=True)
            plt.autoscale(enable=True, axis='x', tight=True) 
            plt.ylim(min(BaseResiduals)*1.15,max(BaseResiduals)*1.15)
                
            #plt.show()
            gs1.update(left=0.13,right=0.96,top=0.95,bottom=0.12) #as percentages of total figure with 1,1 in upper right
            fig.set_size_inches(6, 5) #width, height
            fname = str(SaveName) + '_base.jpg'
            plt.savefig(fname, dpi=300)
            plt.close()
            
            # Baseline Correction
            signal_fit = signal_fit - baseline #corrects for baseline 
            
            #Finds an initial G intensity and D intensity to give a decent start to the peak fitting
            G_ints = max(signal_fit[(G_bounds[0]-G_bounds[1] <= x_fit) & (x_fit <= G_bounds[0]+G_bounds[1])])
            D_ints = max(signal_fit[(D_bounds[0]-D_bounds[1] <= x_fit) & (x_fit <= D_bounds[0]+D_bounds[1])]) 
            U1_ints = max(signal_fit[(U1_bounds[0]-U1_bounds[1] <= x_fit) & (x_fit <= U1_bounds[0]+U1_bounds[1])])
            #print 'initial D/G intensity ratio: ',D_ints/G_ints 
            
            
            EnterData()
            #print 'FitParam ', FitParam
            try:
                minres = curve_fit(FitFunc,x_fit,signal_fit,p0=FitParam,method = 'trf', bounds=bounds, full_output=True) 
            except RuntimeError as error:  #if fit is unsuccessful
                    print("No fit found")
                    print(error)
                    fit_results = np.zeros(NumParams)  #shouldn't need this, but just in case, not passing bad fit data
                    continue
            else:  #if fit is successful
                fit_results=minres[0]
                covMatrix = minres[1] 
                dFit = np.sqrt(np.diag(covMatrix))
            
            #setting fit results to zero if peak is off
            
            if FitGOn == 0:
                fit_results[0],fit_results[6],fit_results[12] = 0,1,0
            if FitDOn == 0:
                fit_results[1],fit_results[7],fit_results[13] = 0,1,0
            if FitD2On == 0:
                fit_results[2],fit_results[8],fit_results[14] = 0,1,0
            if FitD3On == 0:
                fit_results[3],fit_results[9],fit_results[15] = 0,1,0
            if FitD4On == 0:
                fit_results[4],fit_results[10],fit_results[16] = 0,1,0
            if FitU1On == 0:
                fit_results[5],fit_results[11],fit_results[17] = 0,1,0
            
            # setting intensities using uncertainties library
            (Gloc, Dloc, D2loc, D3loc, D4loc, U1loc, Gwid, Dwid, D2wid, D3wid, D4wid, U1wid, 
                 G_ints, D_ints, D2_ints, D3_ints, D4_ints, U1_ints) = uncertainties.correlated_values(fit_results, covMatrix)
            
            Gfit_nom = FitGOn*Gaussian(fit_results[0],fit_results[6],fit_results[12])
            Dfit_nom = FitDOn*lorentz(fit_results[1],fit_results[7],fit_results[13])
            D2fit_nom = FitD2On*Gaussian(fit_results[2],fit_results[8],fit_results[14])
            D3fit_nom = FitD3On*Gaussian(fit_results[3],fit_results[9],fit_results[15])
            D4fit_nom = FitD4On*Gaussian(fit_results[4],fit_results[10],fit_results[16])
            U1fit_nom = FitU1On*Gaussian(fit_results[5],fit_results[11],fit_results[17])
            
            Gfit = FitGOn*GaussianWithUnc(Gloc,Gwid,G_ints)
            Dfit = FitDOn*lorentz(Dloc,Dwid,D_ints)
            D2fit = FitD2On*GaussianWithUnc(D2loc,D2wid,D2_ints)
            D3fit = FitD3On*GaussianWithUnc(D3loc,D3wid,D3_ints)
            D4fit = FitD4On*GaussianWithUnc(D4loc,D4wid,D4_ints)
            U1fit = FitU1On*GaussianWithUnc(U1loc,U1wid,U1_ints)
    
            ModelFit = Gfit + Dfit +D2fit + D3fit + D4fit + U1fit
            
            
            D2_ints = FitD2On*D2_ints
            D3_ints = FitD3On*D3_ints
            D4_ints = FitD4On*D4_ints
            
            TotalIntensity = G_ints + D_ints + D2_ints + D3_ints + D4_ints
                      
            Residuals = signal_fit - ModelFit
            ss_res = np.sum((Residuals) ** 2)
            ss_tot = np.sum((signal_fit - np.mean(signal_fit)) ** 2)
            R2_fit = 1-ss_res/ss_tot # not meaningful because can't use R2 on Gaussian or Lorentzian fits, so need to use standard error regression
            AdjR2_fit = 1-(1-R2_fit)*(len(ModelFit)-1)/((len(ModelFit))-(NumParams)-1) # wiki definition, adjusted to account for no. variables but still non-linear so ish?
            SEE_fit = usqrt(ss_res/(len(signal_fit)-NumPeaks*3))
            
            # Figure 4 Plot of individual peak fits and total peak fit with experimental
            
            ModelFit_nom = np.fromiter((ModelFit[i].n for i in range(len(ModelFit))),float, count = len(ModelFit))
            ModelFit_unc = np.fromiter((ModelFit[i].s for i in range(len(ModelFit))),float, count = len(ModelFit))
            Residuals_nom = np.fromiter((Residuals[i].n for i in range(len(ModelFit))),float, count = len(ModelFit))
    
            
            fig = plt.figure(4)
            gs4 = fig.add_gridspec(nrows=2, ncols=1,
                      hspace=0, wspace=0, height_ratios=[1, 5])
            ax40 = fig.add_subplot(gs4[1])
            ax40.plot(x_fit, signal_fit,'.k', label = 'Experimental')
            ax40.plot(x_fit, Gfit_nom,'-g', label = 'G Peak Fit')
            ax40.plot(x_fit, Dfit_nom,'-b', label = 'D Peak Fit')
            if FitD2On == 1:
                ax40.plot(x_fit , D2fit_nom,'-y', label = 'D2 Peak Fit')
            if FitD3On == 1:
                ax40.plot(x_fit, D3fit_nom,'-c', label = 'D3 Peak Fit')
            if FitD4On == 1:
                ax40.plot(x_fit , D4fit_nom,'-m', label = 'D4 Peak Fit')
            if FitU1On == 1:
                ax40.plot(x_fit, U1fit_nom, '-y', label = 'U1 peak fit')
            ax40.plot(x_fit, ModelFit_nom,'-r', label = 'Summed Peak Fit')
            ax40.fill_between(x_fit, ModelFit_nom - ModelFit_unc, ModelFit_nom + ModelFit_unc,  facecolor='red', alpha = 0.25)
            ax40.set_xlabel(r'Raman Shift / cm$^{-1}$', fontsize=16)
            plt.autoscale(enable=True, axis='x', tight=True)
            plt.autoscale(enable=True, axis='y')
            plt.setp(ax40, xticks=[1000,1200,1400,1600,1800,2000])
            ax40.set_ylabel('Raman Intensity', fontsize=16)
            plt.tick_params(axis='both', which='major', labelsize=14)
            
            ax41 = fig.add_subplot(gs4[0])
            ax41.plot(x_fit,Residuals_nom, '.b')
            ax41.axhline(0, linestyle='--', color = 'gray', linewidth = 1)
            ax41.set_ylabel('Residuals')
            plt.setp(ax41, xticks=[800,1000,1200,1400,1600,1800])
            ax41.tick_params(direction='in',labelbottom=False,labelleft=True)
            plt.autoscale(enable=True, axis='x', tight=True) 
            
            plt.ylim(min(Residuals_nom)*1.15,max(Residuals_nom)*1.15)
            
            gs4.update(left=0.16,right=0.94,top=0.95,bottom=0.15) #as percentages of total figure with 1,1 in upper right
            fig.set_size_inches(6, 5) #width, height
            plt.savefig(SaveName + '_fit.jpg', dpi=300)
            plt.close()
            
            # ID/IG Ratio
            Exp_ratio = D_ints/G_ints #with uncertainties
            Exp_ratio = fit_results[13]/fit_results[12] # w/o uncertainties calc
            
            # Calculation of uncertainties with covariances
            
            Exp_ratio_stdev = Exp_ratio*((dFit[12]/fit_results[12])**2 + (dFit[13]/fit_results[13])**2 - 
                                        (2*(covMatrix[13,12])/fit_results[12]/fit_results[13]))**0.5
     # =============================================================================
     #         TotIntstdev = (dFit[12]**2 + dFit[13]**2 + dFit[14]**2 + dFit[15]**2 + dFit[16]**2 + 
     #                       2*((covMatrix[13,12]) + (covMatrix[13,14]) + 
     #                           (covMatrix[13,15]) + (covMatrix[13,16]) + 
     #                           (covMatrix[14,12]) + (covMatrix[14,15]) +
     #                           (covMatrix[14,16]) + (covMatrix[15,12]) + 
     #                           (covMatrix[15,16]) + (covMatrix[16,12])))**0.5
     # =============================================================================
            IDIG = D_ints/G_ints  #with uncertainties calc
            ID2IG = D2_ints/G_ints
            ID3ID = D3_ints/D_ints
            ID4ID = D4_ints/D_ints
            IDIT = D_ints/TotalIntensity
            IGIT = G_ints/TotalIntensity
            ID2IT = D2_ints/TotalIntensity
            ID3IT = D3_ints/TotalIntensity
            ID4IT = D4_ints/TotalIntensity
             
             
     # =============================================================================
     #         Calculating the conjugation length, La
     # =============================================================================
             
             # from Herdman and Miller 2011, based on Cancado et al. 2011 and Luchhese et al. 2010
    
            ra = 3.1 #Cancado has 3.1 nm,  Luchhese has 3.00 +/- 0.03 nm
            rs = 1.0 # Luchhese has 1.00 +/- 0.04 nm, Cancado also uses 1.0
            CA_mult = ufloat(160,48)# 160 +/- 48 from Cancado et al.
            CA = CA_mult*((1240*Ext_Lambda**-1)**-4)  # unitless
            La_model = np.arange(0.1, 100, step = 0.001)
            Ratio_model = [0]*len(La_model)
            Ratio_model =CA*(ra**2 - rs**2)/(ra**2 - 2*(rs**2))*(np.exp((-np.pi*rs**2)/(La_model**2))-np.exp((-np.pi*(ra**2 - rs**2))/(La_model**2))) 
             
             # find the La from ID/IG values for the lower part of the curve and the higher part of the curve
             # no equations from ID/IG for high defect frequency so have to do it the hard way
                     
            Ratio_model_low = np.where(La_model <=3, Ratio_model, np.nan)
            La_low = np.where(La_model <=3, La_model, np.nan)
            low_La = La_low[np.where(abs(Ratio_model_low - Exp_ratio) == min(abs(Ratio_model_low - Exp_ratio)))[0][0]]
    
            Ratio_model_high = np.where(La_model >3, Ratio_model, 100)
            La_high = np.where(La_model >3, La_model, np.nan)
            high_La = La_high[np.where(abs(Ratio_model_high - Exp_ratio) == min(abs(Ratio_model_high - Exp_ratio)))[0][0]]
            high_La = La_high[np.where(abs(Ratio_model_high - Exp_ratio) == min(abs(Ratio_model_high - Exp_ratio)))[0][0]]
             
             # when resolving eq from Herdman and Miller for LsubA, if LsubA approaches 1, 
             # we can neglect second exponential term as it gets very small compared to first term (by e-10)
             # however for LsubA close to 20, the two terms are close in value, so uncertainty only for low_La
             
            low_La_calc = usqrt((-1*np.pi)/(ulog(((ra**2-2)/(ra**2-1))*(IDIG/CA))))
            low_label = u'{:.2fP}'.format(low_La_calc)

            
            Ratio = [Exp_ratio, Exp_ratio]
            La_calc = [low_La, high_La]
            
            if high_La > 8:  #10 via Cancado et al. but with uncertainty...actually okay.
                high_La_calc = usqrt(1/((IDIG)/CA/(np.pi*(3.1**2-1))))
                La_calc2 = [low_La_calc.n, high_La_calc.n]
                high_label = u'{:.2fP}'.format(high_La_calc)
            else:
                La_calc2 = [low_La_calc.n, high_La]
                high_label = '{:.2f}'.format(high_La)
                high_La_calc = ufloat(high_La, np.nan)
         
     # =============================================================================
            

            
            Ratio_model_plot = np.fromiter((Ratio_model[i].n for i in range(len(Ratio_model))),float, count = len(Ratio_model))
            dRatio_model = np.fromiter((Ratio_model[i].s for i in range(len(Ratio_model))),float, count = len(Ratio_model))
            
            
            # fig = plt.figure(3)
            # ax = fig.add_subplot(111)
            # ax.plot(La_model , Ratio_model_plot ,'-k')
            # ax.fill_between(La_model, Ratio_model_plot - dRatio_model, Ratio_model_plot + dRatio_model,  facecolor='gray', alpha = 0.25)
            # # will need to double the dRatio_model in fill line for 95% confidence only one stdev now
            # ax.loglog(La_calc2 , Ratio,'or')
            # ax.text(high_La, Exp_ratio, high_label, va="top", ha = "center", size = 10) #offset for legibility
            # ax.text(low_La, Exp_ratio, low_label, va="top", ha = "center", size = 10) #offset for legibility
            # ax.set_xlabel('$L_a$', fontsize=16)
            # ax.set_ylabel('$I_D$ / $I_G$', fontsize=16)
            # ax.set_xlim(0.1, 100)
            # ax.set_ylim(0.01, 100)
            # fig.set_size_inches(6, 5) #width, height
            # plt.savefig(SaveName + '_Ratio.jpg',dpi=300)
            
            # plt.close()
            
            
            f = open(SaveName+'_fitfile.txt',"w")
            f.write("{}\t{}\t{}\n".format('Collection Details',CollDet,''))
            f.write("{}\t{}\t{}\n".format('Original File',Loadfile,''))
            f.write("{}\t{}\t{}\n".format('position',str(n).zfill(2),'0'))
            f.write("{}\t{}\t{}\n".format('Location', locations[n],''))            
            f.write("{}\t{}\t{}\n".format('Laser Wavelength', Ext_Lambda,'0'))
            f.write("{}\t{}\t{}\n".format('Num Peaks and fit version', NumPeaks,fitVersion))

            f.write("{}\t{}\t{}\n".format('Baseline Order', base_order, 0) )            
            f.write("{}\t{}\t{}\n".format('Baseline R2', AdjBaseR2[0], 0) )
            f.write("{}\t{}\t{}\n".format('Baseline Slope', BaseSlope, 0) )
            
            f.write("{}\t{}\t{}\n".format('Peak Fit R2ish', float(AdjR2_fit.n), float(AdjR2_fit.s)) )
            f.write("{}\t{}\t{}\n".format('Peak Fit SEE', float(SEE_fit.n), float(SEE_fit.s)) )
            
            f.write("{}\t{}\t{}\n".format('qBWF', qBWF, 0) )
            
            f.write("{}\t{}\t{}\n".format('G Band Peak Position', fit_results[0], dFit[0]) )
            f.write("{}\t{}\t{}\n".format('G Band Peak Width', fit_results[6], dFit[6] ))
            f.write("{}\t{}\t{}\n".format('G Band Peak Intensity', fit_results[12],dFit[12]))
            
            f.write("{}\t{}\t{}\n".format('D Band Peak Position',fit_results[1],dFit[1]))
            f.write("{}\t{}\t{}\n".format('D Band Peak Width',fit_results[7],dFit[7]))
            f.write("{}\t{}\t{}\n".format('D Band Peak Intensity', fit_results[13],dFit[13]))
            
            f.write("{}\t{}\t{}\n".format('D2 Band Peak Position',fit_results[2],dFit[2]))
            f.write("{}\t{}\t{}\n".format('D2 Band Peak Width',fit_results[8],dFit[8]))
            f.write("{}\t{}\t{}\n".format('D2 Band Peak Intensity', fit_results[14],dFit[14]))
            
            f.write("{}\t{}\t{}\n".format('D3 Band Peak Position',fit_results[3],dFit[3]))
            f.write("{}\t{}\t{}\n".format('D3 Band Peak Width',fit_results[9],dFit[9]))
            f.write("{}\t{}\t{}\n".format('D3 Band Peak Intensity', fit_results[15],dFit[15]))
            
            f.write("{}\t{}\t{}\n".format('D4 Band Peak Position',fit_results[4],dFit[4]))
            f.write("{}\t{}\t{}\n".format('D4 Band Peak Width',fit_results[10],dFit[10]))
            f.write("{}\t{}\t{}\n".format('D4 Band Peak Intensity', fit_results[16],dFit[16]))
            
            f.write("{}\t{}\t{}\n".format('Conjugation Length (low)',low_La_calc.n,low_La_calc.s))
            f.write("{}\t{}\t{}\n".format('Conjugation Length (high)',high_La,high_La_calc.s))
            f.write("{}\t{}\t{}\n".format('ID/IG',Exp_ratio,Exp_ratio_stdev))
            
            f.write("{}\t{}\t{}\n".format('ID/total ratio', IDIT.n,IDIT.s))
            f.write("{}\t{}\t{}\n".format('ID2/total ratio', ID2IT.n,ID2IT.s))
            f.write("{}\t{}\t{}\n".format('ID3/total ratio', ID3IT.n,ID3IT.s))
            f.write("{}\t{}\t{}\n".format('ID4/total ratio', ID4IT.n,ID4IT.s))
            f.write("{}\t{}\t{}\n".format('IG/total ratio',IGIT.n,IGIT.s))
            f.write("{}\t{}\t{}\n".format('ID2/IG ratio', ID2IG.n,ID2IG.s))
            f.write("{}\t{}\t{}\n".format('ID3/ID ratio', ID3ID.n,ID3ID.s))
            f.write("{}\t{}\t{}\n".format('ID4/ID ratio', ID4ID.n,ID4ID.s))
            f.write("{}\t{}\t{}\n".format('bkd_low',bkd_bounds[0],bkd_bounds[1]))
            f.write("{}\t{}\t{}\n".format('bkd_hi',bkd_bounds[2],bkd_bounds[3])) 
            f.close()