CSFPA_plots.py

import matplotlib.pyplot as plt
import numpy as np
import os
from CSFPA_dataIO import RetrieveVars, kwavenum, TESPowerCalc, GridPowerCalc, OutputTESPower
import pickle #ignore warning, seems like RetrieveVars uses it
from mpl_toolkits.axes_grid1 import make_axes_locatable

pklrep = '/home/james/files4CSFPA/qbdataioOUTFILES/'


#def TotalIntensityPlot(PixCenX,PixCenY,IntT,xycoords,IT):
def TotalIntensityPlot(plotfname):
    pklrep = '/home/james/files4CSFPA/qbdataioOUTFILES/' + plotfname
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklrep)
    ######################Total Intensity plot - Normalised
    
    TESPower = TESPowerCalc(plotfname)
    GPow = GridPowerCalc(plotfname)
	
    plt.figure()
    plt.subplot(121)
    plt.scatter(PixCenX*1000,PixCenY*1000, c=TESPower, cmap='jet',marker='s')
    plt.axis([-60, 60, -60, 60])
    plt.axis('equal')
    plt.title("Bolometers Total Intensity", fontsize=10)
    plt.subplot(122)
    plt.scatter(xycoords[:,1], xycoords[:,0], c=GPow, cmap='jet', marker='.', s=1)
    plt.axis([-60, 60, -60, 60])
    plt.axis('equal')
    plt.title("Model Power Data", fontsize=10)
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Intensity (1 W Source)")
    plt.show()
    os.system('spd-say "BING! BING! BING!"')
    return

def IntensityXPlot(plotfname):
    pklrep = '/home/james/files4CSFPA/qbdataioOUTFILES/' + plotfname
    ######################IntensityX plot
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklrep)
    
    plt.figure(facecolor='xkcd:pale green')
    plt.subplot(121, facecolor='#d8dcd6')#xkcd reference for this colour
    plt.scatter(PixCenX,PixCenY, c=IntX/max(IntX), s=25, cmap='jet',marker='s') 
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')   
    plt.title("{} as Bolometers Intensity X dir".format(plotfname),fontsize=10)
	
    plt.subplot(122, facecolor='#d8dcd6')#xkcd reference for this colour
    plt.scatter(xycoords[:,0],xycoords[:,1], c=Ix/max(Ix), cmap='jet',marker='.')
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("{}".format(plotfname),fontsize=10)    
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Intensity X")    
    plt.show()
    
    return

def IntensityYPlot(plotfname):
    pklrep = '/home/james/files4CSFPA/qbdataioOUTFILES/' + plotfname
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklrep)
    ######################Intensity Y plot
    plt.figure()
    plt.subplot(121)
    plt.scatter(PixCenX*1000,PixCenY*1000, c=IntY/max(IntY), s=8, cmap='plasma',marker='s')
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')   
    plt.title("CF1 Source as Bolometers Intensity Y dir",fontsize=10)
    plt.subplot(122)
    plt.scatter(xycoords[:,0],xycoords[:,1], c=Iy/max(Iy), cmap='plasma',marker='.')
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("CF1 Source - MODAL",fontsize=10)    
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Intensity Y")    
    plt.show()
    
    return

def MagXPlot(plotfname):
    #load saved variables
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(plotfname)
    #load raw data from file
    dataCF = np.loadtxt(filename, skiprows=1) 
    plt.figure(facecolor='xkcd:pale green')
    plt.subplot(121, facecolor='#d8dcd6')
    plt.scatter(PixCenX,PixCenY, c=MagXarr/max(MagXarr), s=25, cmap='jet',marker='s')   
    plt.axis([-0.055, 0.055, -0.055, 0.055])
    plt.axis('equal')    
    plt.title("{} as Bolometers".format(plotfname),fontsize=10)
    #plt.plot(0, 0, 'o', mfc='none',markersize=57.16*2,color='black')
    plt.subplot(122, facecolor='#d8dcd6')
    plt.scatter(xycoords[:,0],xycoords[:,1], c=dataCF[:,4]/(max(dataCF[:,4])), cmap='jet',marker='.')
    #plt.scatter(xycoords[:,0],xycoords[:,1], c=MagXarr/(max(MagXarr)), cmap='plasma',marker='.')
    #plt.plot(0, 0, 'o', mfc='none',markersize=57.16*2,color='black')
    plt.axis([-0.055, 0.055, -0.055, 0.055])
    plt.axis('equal')    
    plt.title("Source - {}".format(filename),fontsize=10)    
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Mag X")    
    plt.show()
    
    return

def MagYPlot(plotfname, filename):
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(plotfname)
    #load raw data from file
    dataCF = np.loadtxt(filename, skiprows=1) 
    ############################### plot normalised data ################
    plt.figure()
    plt.subplot(121)
    plt.scatter(PixCenX*1000,PixCenY*1000, c=MagYarr/max(MagYarr), s=8, cmap='jet',marker='s')  
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("Mag Y {} as Bolometers".format(filename),fontsize=10)
    plt.subplot(122)
    plt.scatter(xycoords[:,0],xycoords[:,1], c=dataCF[:,6]/(max(dataCF[:,6])), cmap='jet',marker='.')
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("Mag Y {}".format(filename),fontsize=10)    
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Mag Y")    
    plt.show()
    
    return

def PhaXPlot(plotfname):
    #Double check this result. Looks quite odd pattern on TESs
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(plotfname)
    #load raw data from file
    dataCF = np.loadtxt(filename, skiprows=1) 
    ############################### plot normalised data ################
    plt.figure()
    plt.subplot(121)
    plt.scatter(PixCenX*1000,PixCenY*1000, c=PhaXarr/max(PhaXarr), s=8, cmap='plasma',marker='s')  
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("Phase X CF Source as Bolometers",fontsize=10)
    plt.subplot(122)
    plt.scatter(xycoords[:,0],xycoords[:,1], c=dataCF[:,5]/(max(dataCF[:,5])), cmap='plasma',marker='.')
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("Phase X CF Source - MODAL",fontsize=10)    
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Phase X")    
    plt.show()
    
    return

def PhaYPlot():
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars()
    #load raw data from file
    dataCF = np.loadtxt(filename, skiprows=1) 
    ############################### plot normalised data ################
    plt.figure()
    plt.subplot(121)
    plt.scatter(PixCenX*1000,PixCenY*1000, c=PhaYarr/max(PhaYarr), cmap='plasma',marker='s')  
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("Phase Y CF Source as Bolometers",fontsize=10)
    plt.subplot(122)
    plt.scatter(xycoords[:,0],xycoords[:,1], c=dataCF[:,7]/(max(dataCF[:,7])), cmap='plasma',marker='.')
    plt.axis([-0.06, 0.06, -0.06, 0.06])
    plt.axis('equal')    
    plt.title("Phase Y CF Source - MODAL",fontsize=10)    
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Phase Y")    
    plt.show()
    
    return

def IntXCompPlot(pkl1,pkl2):
	#initially going to hardcode for intensity or magnitude
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl1)
	IntX1 = IntX/max(IntX) #NB cross and co polar are mixed up here between MODAL and GRASP

	plt.figure(facecolor='xkcd:pale green')
	plt.subplot(221, facecolor='#d8dcd6')
	plt.scatter(PixCenX*1000,PixCenY*1000, c=IntX1, cmap='jet',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl1),fontsize=10)
	
	plt.subplot(222, facecolor='#d8dcd6')
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl2)
	IntX2 = IntX/max(IntX)
	plt.scatter(PixCenX*1000,PixCenY*1000, c=IntX2, cmap='jet',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl2),fontsize=10)
	
	plt.subplot(223, facecolor='#d8dcd6')
	comp = (IntX1 - IntX2)*100
	analysisarray = ([])
	#okay so here i am finding all of the outer pixels and setting to zero
	#this allows me to analyse valid pixels between grasp and modal
	#maybe i should delete these elements of the array to make data analysis easier
	for i in range(len(PixCenX)):
		if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:	
			comp[i] = 0
			PixCenX[i] = 0.05
			PixCenY[i] = 0.05
		else:
			analysisarray = np.append(comp[i], analysisarray)
			#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
			#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')

	plt.scatter(PixCenX*1000,PixCenY*1000, c=comp, cmap='jet',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("Data Comparison",fontsize=10)	
	
	plt.subplot(224, facecolor='#d8dcd6')
    #do histogram here	
	#binarr = [-0.35, -0.25, -0.15, -0.05, 0.05, 0.015]
	#binarr = [-0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025, 0.025, 0.075, 0.125]
	#binarr = [-32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5]
	#binarr = [0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25]
	comp = np.abs(comp)
	#analysisarray = np.abs(analysisarray)
	print "analysis info, max, length, mean", np.max(analysisarray), len(analysisarray), np.mean(analysisarray)
	n, bins, patches = plt.hist(analysisarray)
	print "hist data", n, bins, patches
			 
	plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
	cax = plt.axes([0.85, 0.1, 0.05, 0.8])
	plt.colorbar(cax=cax,label="% Difference Comparison")    
	plt.show()	
	
	return

def IntYCompPlot(pkl1,pkl2):
	#initially going to hardcode for intensity or magnitude
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl1)
	IntY1 = IntY/max(IntY) #NB cross and co polar are mixed up here between MODAL and GRASP

	plt.figure(facecolor='xkcd:pale green')
	plt.subplot(221, facecolor='#d8dcd6')
	plt.scatter(PixCenX*1000,PixCenY*1000, c=IntY1, cmap='jet',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl1),fontsize=10)
	
	plt.subplot(222, facecolor='#d8dcd6')
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl2)
	IntY2 = IntY/max(IntY)
	plt.scatter(PixCenX*1000,PixCenY*1000, c=IntY2, cmap='jet',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl2),fontsize=10)
	
	plt.subplot(223, facecolor='#d8dcd6')
	comp = (IntY1 - IntY2)*100
	analysisarray = ([])
	#okay so here i am finding all of the outer pixels and setting to zero
	#this allows me to analyse valid pixels between grasp and modal
	#maybe i should delete these elements of the array to make data analysis easier
	for i in range(len(PixCenX)):
		if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:	
			comp[i] = 0
			PixCenX[i] = 0.05
			PixCenY[i] = 0.05
		else:
			analysisarray = np.append(comp[i], analysisarray)
			#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
			#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')

	plt.scatter(PixCenX*1000,PixCenY*1000, c=comp, cmap='PiYG',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("Data Comparison",fontsize=10)	
	
	plt.subplot(224, facecolor='#d8dcd6')
    #do histogram here	
	#binarr = [-0.35, -0.25, -0.15, -0.05, 0.05, 0.015]
	#binarr = [-0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025, 0.025, 0.075, 0.125]
	#binarr = [-32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5]
	#binarr = [0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25]
	comp = np.abs(comp)
	#analysisarray = np.abs(analysisarray)
	print "analysis info, max, length, mean", np.max(analysisarray), len(analysisarray), np.mean(analysisarray)
	n, bins, patches = plt.hist(analysisarray)
	print "hist data", n, bins, patches
			 
	plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
	cax = plt.axes([0.85, 0.1, 0.05, 0.8])
	plt.colorbar(cax=cax,label="% Difference Comparison")    
	plt.show()	
	
	return

def TotIntCompPlot(pkl1,pkl2):
	#initially going to hardcode for intensity or magnitude
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl1)
	IntT1 = IntT/max(IntT)

	plt.figure(facecolor='xkcd:pale green')
	plt.subplot(221, facecolor='#d8dcd6')
	plt.scatter(PixCenX*1000,PixCenY*1000, c=IntT1, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl1),fontsize=10)
	
	plt.subplot(222, facecolor='#d8dcd6')
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl2)
	IntT2 = IntT/max(IntT) #Normalise to first files peak
	plt.scatter(PixCenX*1000,PixCenY*1000, c=IntT2, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl2),fontsize=10)
	
	plt.subplot(223, facecolor='#d8dcd6')
    
	IntT1[IntT1 == 0] = 0.000001
	IntT2[IntT2 == 0] = 0.000001	 	 
	comp = ((IntT1 - IntT2) / IntT1) * 100 #can delete this % conversion
	analysisarray = ([])
	#okay so here i am finding all of the outer pixels and setting to zero
	#this allows me to analyse valid pixels between grasp and modal
	#maybe i should delete these elements of the array to make data analysis easier
	for i in range(len(PixCenX)):
		if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:	
			comp[i] = np.mean(comp)
			PixCenX[i] = 0.05
			PixCenY[i] = 0.05
		else:
			analysisarray = np.append(comp[i], analysisarray)
			#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
			#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')

	plt.scatter(PixCenX*1000,PixCenY*1000, c=comp, cmap='RdPu',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("Data Comparison",fontsize=10)	
	
	plt.subplot(224, facecolor='#d8dcd6')
    #do histogram here	
	#binarr = [-0.35, -0.25, -0.15, -0.05, 0.05, 0.015]
	#binarr = [-0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025, 0.025, 0.075, 0.125]
	#binarr = [-32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5]
	#binarr = [0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25]
	#binarr = [0, 1, 2, 3, 4, 5, 6, 7, 8]
	#comp = np.abs(comp)
	#analysisarray = np.abs(analysisarray)
	print "analysis info, max, length, mean", np.max(analysisarray), len(analysisarray), np.mean(analysisarray)
	n, bins, patches = plt.hist(analysisarray)
	print "hist data", n, bins, patches
			 
	plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
	cax = plt.axes([0.85, 0.1, 0.05, 0.8])
	plt.colorbar(cax=cax,label="% Difference Comparison - Total Intensity")    
	plt.show()	
	
	return

def PhaXCompPlot(pkl1,pkl2):
	pklfrep1 = pklrep + pkl1
	pklfrep2 = pklrep + pkl2
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklfrep1)
	PhaX1 = PhaXarr#/max(PhaXarr) # cross and co polar mixed up

	plt.figure(facecolor='xkcd:pale green')
	plt.subplot(221, facecolor='#d8dcd6')
	plt.scatter(PixCenX*1000,PixCenY*1000, c=PhaX1, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl1),fontsize=10)
	
	plt.subplot(222, facecolor='#d8dcd6')
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklfrep2)
	PhaX2 = PhaYarr#/max(PhaXarr)
	plt.scatter(PixCenX*1000,PixCenY*1000, c=PhaX2, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl2),fontsize=10)
	
	plt.subplot(223, facecolor='#d8dcd6')
	comp = (PhaX1 - PhaX2) / PhaX1 * 100
	analysisarray = ([])
	#okay so here i am finding all of the outer pixels and setting to zero
	#this allows me to analyse valid pixels between grasp and modal
	#maybe i should delete these elements of the array to make data analysis easier
	for i in range(len(PixCenX)):
		if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:	
			comp[i] = np.mean(comp)
			PixCenX[i] = 0.05
			PixCenY[i] = 0.05
		else:
			analysisarray = np.append(comp[i], analysisarray)
			#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
			#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')

	plt.scatter(PixCenX*1000,PixCenY*1000, c=comp, cmap='PiYG',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("Data Comparison",fontsize=10)	
	
	plt.subplot(224, facecolor='#d8dcd6')
    #do histogram here	
	#binarr = [-0.35, -0.25, -0.15, -0.05, 0.05, 0.015]
	#binarr = [-0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025, 0.025, 0.075, 0.125]
	#binarr = [-32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5]
	#binarr = [0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25]

	#analysisarray = np.abs(analysisarray)
	analysisarray = analysisarray[~np.isnan(analysisarray)]
	#print "analysis info, max, length, mean", np.max(analysisarray), len(analysisarray), np.mean(analysisarray)
	n, bins, patches = plt.hist(analysisarray)
	print "hist data", n, bins, patches
			 
	plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
	cax = plt.axes([0.85, 0.1, 0.05, 0.8])
	plt.colorbar(cax=cax,label="% Difference Comparison")    
	plt.show()	
	
	return

def PhaYCompPlot(pkl1,pkl2):
	#initially going to hardcode for intensity or magnitude
	#Is this a Pha Y plot?
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl1)
	PhaY1 = PhaYarr/max(PhaYarr) # cross and co polar mixed up

	plt.figure(facecolor='xkcd:pale green')
	plt.subplot(221, facecolor='#d8dcd6')
	plt.scatter(PixCenX*1000,PixCenY*1000, c=PhaY1, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl1),fontsize=10)
	
	plt.subplot(222, facecolor='#d8dcd6')
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl2)
	PhaY2 = PhaYarr/max(PhaYarr)
	plt.scatter(PixCenX*1000,PixCenY*1000, c=PhaY2, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl2),fontsize=10)
	
	plt.subplot(223, facecolor='#d8dcd6')
	PhaY1[PhaY1 == 0] = 0.000001
	PhaY2[PhaY2 == 0] = 0.000001	
	comp = PhaY1 / PhaY2

	analysisarray = ([])
	#okay so here i am finding all of the outer pixels and setting to zero
	#this allows me to analyse valid pixels between grasp and modal
	#maybe i should delete these elements of the array to make data analysis easier
	for i in range(len(PixCenX)):
		if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:	
			comp[i] = 0
			PixCenX[i] = 0.05
			PixCenY[i] = 0.05
		else:
			analysisarray = np.append(comp[i], analysisarray)
			#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
			#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')

	plt.scatter(PixCenX*1000,PixCenY*1000, c=comp, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("Data Comparison",fontsize=10)	
	
	plt.subplot(224, facecolor='#d8dcd6')
	#analysisarray = np.abs(analysisarray)
	analysisarray = analysisarray[~np.isnan(analysisarray)]
	#print "analysis info, max, length, mean", np.max(analysisarray), len(analysisarray), np.mean(analysisarray)
	binarr = [-3, -2, -1, 0, 1, 2, 3]
	n, bins, patches = plt.hist(analysisarray, bins=binarr)
	print "hist data", n, bins, patches
			 
	plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
	cax = plt.axes([0.85, 0.1, 0.05, 0.8])
	plt.colorbar(cax=cax,label="% Difference Comparison")    
	plt.show()	
	
	return

def MagXCompPlot(pkl1,pkl2):
	pklfrep1 = pklrep + pkl1
	pklfrep2 = pklrep + pkl2
	#initially going to hardcode for intensity or magnitude
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklfrep1)
	MagX1 = MagXarr#/max(MagXarr) #since cross and co polar are mixed up

	plt.figure(facecolor='xkcd:pale green')
	plt.subplot(221, facecolor='#d8dcd6')
	plt.scatter(PixCenX*1000,PixCenY*1000, c=MagX1, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl1),fontsize=10)
	
	plt.subplot(222, facecolor='#d8dcd6')
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklfrep2)
	MagX2 = MagYarr#/max(MagYarr)
	plt.scatter(PixCenX*1000,PixCenY*1000, c=MagX2, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("FP - {}".format(pkl2),fontsize=10)
	
	plt.subplot(223, facecolor='#d8dcd6')
	comp = (MagX1 - MagX2) / MagX1 * 100
	analysisarray = ([])
	#okay so here i am finding all of the outer pixels and setting to zero
	#this allows me to analyse valid pixels between grasp and modal
	#maybe i should delete these elements of the array to make data analysis easier
	for i in range(len(PixCenX)):
		if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:	
			comp[i] = np.mean(comp)
			PixCenX[i] = 0.05
			PixCenY[i] = 0.05
		else:
			analysisarray = np.append(comp[i], analysisarray)
			#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
			#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')

	plt.scatter(PixCenX*1000,PixCenY*1000, c=comp, cmap='jet',marker='s',s=5)
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("Data Comparison",fontsize=10)	
	
	plt.subplot(224, facecolor='#d8dcd6')
    #do histogram here	
	#binarr = [-0.35, -0.25, -0.15, -0.05, 0.05, 0.015]
	#binarr = [-0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025, 0.025, 0.075, 0.125]
	#binarr = [-32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5]
	#binarr = [0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25]

	analysisarray = np.abs(analysisarray)
	#analysisarray = analysisarray[~np.isnan(analysisarray)]
	print "analysis info, max, length, mean", np.max(analysisarray), len(analysisarray), np.mean(analysisarray)
	n, bins, patches = plt.hist(analysisarray)
	print "hist data", n, bins, patches
			 
	plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
	cax = plt.axes([0.85, 0.1, 0.05, 0.8])
	plt.colorbar(cax=cax,label="% Difference Comparison")    
	plt.show()	
	
	return

def FPComparisonPlotRAW(pkl1,pkl2):
	#initially going to hardcode for intensity or magnitude
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl1)
	IntX1 = IT/max(IT) # cx and co mixed
	print "pkl1 max intensity", max(IT)
	
	plt.figure(facecolor='xkcd:pale green')
	plt.subplot(221, facecolor='#d8dcd6')
	plt.scatter(xycoords[:,1]*1000, xycoords[:,0]*1000, c=IntX1, cmap='jet',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("pkl1",fontsize=10)
	
	#delete vars
	del MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename
	MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pkl2)
	IntX2 = IT/max(IT) # norm to first plot
	print "pkl2 max intensity", max(IT)
	plt.subplot(222, facecolor='#d8dcd6')
	plt.scatter(xycoords[:,0]*1000, xycoords[:,1]*1000, c=IntX2, cmap='jet',marker='s')
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("pkl2",fontsize=10)
	#initialise comparison array and plot
	#IntX1[IntX1 == 0] = min(IntX1)
	#IntX2[IntX2 == 0] = min(IntX1)
	comp = (( IntX1 - IntX2 ) / IntX1) *100
	
	plt.subplot(223, facecolor='#d8dcd6')
	plt.scatter(xycoords[:,0]*1000, xycoords[:,1]*1000, c=comp, cmap='RdYlGn',marker='s')		#RdPu, 	YlGnBu
	plt.axis([-60, 60, -60, 60])
	plt.axis('equal')
	plt.title("Data Comparison",fontsize=10)
	#Now do histogram
	plt.subplot(224, facecolor='#d8dcd6')
	print "analysis info, max, length, mean", np.max(comp), len(comp), np.mean(comp)
	n, bins, patches = plt.hist(comp)
	print "hist data", n, bins, patches
	#Set colorbar	
	plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
	cax = plt.axes([0.85, 0.1, 0.05, 0.8])
	plt.colorbar(cax=cax,label="Model % Diff Comparison")    
	plt.show()	
	
	return

def TESPowAnalysis(plotfname):
    pklrep = '/home/james/files4CSFPA/qbdataioOUTFILES/' + plotfname
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(pklrep)
    ######################Total Intensity plot - Normalised
    
    TESPower = TESPowerCalc(plotfname)
    GPow = GridPowerCalc(plotfname)
	
    plt.figure(facecolor='xkcd:pale green')
    plt.subplot(221, facecolor='#d8dcd6')
    plt.scatter(PixCenX*1000,PixCenY*1000, c=TESPower, cmap='jet',marker='s',s=4)
    plt.axis([-60, 60, -60, 60])
    plt.axis('equal')
    plt.title("{} Bolometers Total Instensity".format(plotfname),fontsize=10)
	
    plt.subplot(222, facecolor='#d8dcd6')
    plt.scatter(xycoords[:,0],xycoords[:,1], c=GPow, cmap='jet',marker='.')
    plt.axis([-60, 60, -60, 60])
    plt.axis('equal')
    plt.title("RAW - {}".format(filename),fontsize=10)
	
    plt.subplot(212, facecolor='#d8dcd6')
    plt.plot(TESPower, marker='_', linestyle="", markersize=0.75)
	
	#output TES Power to file
    OutputTESPower(TESPower, filename)
	
    plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
    cax = plt.axes([0.85, 0.1, 0.05, 0.8])
    plt.colorbar(cax=cax,label="Intensity")
    plt.show()
    os.system('spd-say "BING! BING! BING!"')	

    return

def TESPowPlot(plotfname):
    rep = '/home/james/files4CSFPA/qbdataioOUTFILES/'
    repfile = rep + plotfname
    #load pickle data
    MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(repfile)
    #load qbdata for TES and Save function 
    qbrep = '/home/james/files4CSFPA/Fromqbdataio/'
    qbrepfile = qbrep + filename
    #load TES power array    
    TESPower = TESPowerCalc(plotfname)
    GPow = GridPowerCalc(plotfname)
    #plot tes power

    fig = plt.figure(facecolor='xkcd:pale green')
    fig.suptitle("File Analysis: '{}'".format(plotfname),fontsize=12)

    ax1 = fig.add_subplot(221, facecolor='#d8dcd6', aspect='equal')
    ax1.set_title("Bolometers Total Instensity",fontsize=10)				   
    sc = ax1.scatter(PixCenX,PixCenY, c=TESPower, cmap='jet',marker='s',s=4)
    cbar = fig.colorbar(sc, label="Intensity per Bolometer (W)")

    ax2 = fig.add_subplot(222, facecolor='#d8dcd6', aspect='equal')
    sc = ax2.scatter(xycoords[:,0],xycoords[:,1], c=GPow, cmap='jet',marker='.')
    ax2.set_title("RAW Grasp Total Instensity", fontsize=10)
    cbar = fig.colorbar(sc, label="Intensity per GRASP data point (W)")

    ax3 = fig.add_subplot(212, facecolor='#d8dcd6')
    tp = ax3.plot(TESPower, marker='_', linestyle="", markersize=0.75)
    ax3.set_title("TES Detector Power Plot")
    ax3.set_ylabel("Intensity per Bolometer (W)")
    ax3.set_xlabel("TES Bolometer Number (qubicsoft order)")
    ax3.set_xlim(1,992)

    plt.show()

    #output TES Power to file + use filename for file naming system
    OutputTESPower(TESPower, filename)

    return

def PowDiffCalc(file1,file2):
	#compare power difference between models
	rep = '/home/james/files4CSFPA/qbdataioOUTFILES/'
	repfile1 = rep + file1
	repfile2 = rep + file2
	
	data1 = np.loadtxt(repfile1, skiprows=1)
	data2 = np.loadtxt(repfile2, skiprows=1)
	
	diff = ( (data1[:,1] - data2[:,1]) / data1[:,1] ) * 100
	
	plt.plot(diff, marker='_', linestyle="")
	plt.xlabel("Bolometer Number")
	plt.ylabel("% Difference")
	plt.title("% difference between old/new CAL_SOU coordinates")
	plt.show()
	
	return

def FPCompV2(gpkl, modaldat):
	
	return