def fastMAPmap(datapath,ironmap, oxygen1map, oxygen2map, basepath='/Users/jhamer/Research/Data'):
"""NAME: BMAPmap
PURPOSE: Returns the MAP solution for B for each pixel in the sun.
INPUTS: datapath - The final directory in which the FITS files are located
ironmap - The array of indices of the iron line core.
oxygen1map - The array of indices of the first oxygen line core
oxygen2map - The array of indices of the second oxygen line core
OUTPUTS: marginalmap - An array of the MAP solution for B for each pixel
EXAMPLE:
"""
dispersion=Dispersion(oxygen1map=oxygen1map, oxygen2map=oxygen2map) #angstroms/wavelength pixel
lambdanaughts=LambdaNaught(ironmap=ironmap, oxygen1map=oxygen1map,dispersion=dispersion) #wavelength of iron line core
alphas=Alpha(lambdanaught=lambdanaughts) #alpha values for each pixel
fMAPmap=[]
BMAPmap=[]
for i in np.arange(1135,1165,1): #currently only calculating for a region
print(i, str(datetime.now()))
frow=[]
Brow=[]
ssI=v9s.SingleStokes(datapath=datapath, stokes='I', slicenumber=i, basepath=basepath) #row stokes I data
ssV=v9s.SingleStokes(datapath=datapath, stokes='V', slicenumber=i, basepath=basepath) #row stokes V data
for j in np.arange(440, 470,1): #currently only calculating for a region
if lambdanaughts[i][j]==0:
frow.append(0) #if the pixel is not on the Sun, the most likely value is zero
Brow.append(0) #if the pixel is not on the Sun, the most likely value is zero
else:
fMAP, BMAP=FastMAP(Islicedata=ssI, Vslicedata=ssV, col=j, dispersion=dispersion[i][j], alpha=alphas[i][j], ironloc=ironmap[i][j], sigmamax=3000., sigmamin=1.)
frow.append(fMAP)
Brow.append(BMAP)
fMAPmap.append(frow)
BMAPmap.append(Brow)
return fMAPmap, BMAPmap
def Bfullmarginal(datapath, ironmap, oxygen1map, oxygen2map):
"""NAME: Bfullmarginal
PURPOSE: Returns the pdf for B for each pixel in the disk of the sun
INPUTS: datapath - The final directory in which the FITS files are located
ironmap - The array of indices of the iron line core.
oxygen1map - The array of indices of the first oxygen line core
oxygen2map - The array of indices of the second oxygen line core
OUTPUTS: marginalmap - An array for pdfs for B for each pixel
EXAMPLE:
"""
dispersion=Dispersion(oxygen1map=oxygen1map, oxygen2map=oxygen2map) #angstroms/wavelength pixel
lambdanaughts=LambdaNaught(ironmap=ironmap, oxygen1map=oxygen1map,dispersion=dispersion) #wavelength of iron line core
alphas=Alpha(lambdanaught=lambdanaughts) #alpha values for each pixel
marginalmap=[]
for i in range(2048):
row=[]
ssI=v9s.SingleStokes(datapath=datapath, stokes='I', row=i) #row stokes I data
ssV=v9s.SingleStokes(datapath=datapath, stokes='V', row=i) #row stokes V data
for j in range(2048):
if lambdanaughts[i][j]==0:
pdf=np.zeros(400)
pdf[199]=1 #if the pixel is not on the Sun, you want the most likely value to be B=0
row.append(pdf)
else:
row.append(Bpixelmarginal(Islicedata=ssI, Vslicedata=ssV, col=j, dispersion=dispersion[i][j], alpha=alphas[i][j], ironloc=ironmap[i][j], sigmamax=3000., sigmamin=1., integration='quad'))
marginalmap.append(row)
return marginalmap
def BMAPmap(datapath, ironmap, oxygen1map, oxygen2map):
"""NAME: BMAPmap
PURPOSE: Returns the MAP solution for B for each pixel in the sun.
INPUTS: datapath - The final directory in which the FITS files are located
ironmap - The array of indices of the iron line core.
oxygen1map - The array of indices of the first oxygen line core
oxygen2map - The array of indices of the second oxygen line core
OUTPUTS: marginalmap - An array of the MAP solution for B for each pixel
EXAMPLE:
"""
dispersion=Dispersion(oxygen1map=oxygen1map, oxygen2map=oxygen2map) #angstroms/wavelength pixel
lambdanaughts=LambdaNaught(ironmap=ironmap, oxygen1map=oxygen1map,dispersion=dispersion) #wavelength of iron line core
alphas=Alpha(lambdanaught=lambdanaughts) #alpha values for each pixel
MAPmap=[]
for i in np.arange(1470,1490,1): #currently only calculating for a region
print(i, str(datetime.now()))
row=[]
ssI=v9s.SingleStokes(datapath=datapath, stokes='I', row=i) #row stokes I data
ssV=v9s.SingleStokes(datapath=datapath, stokes='V', row=i) #row stokes V data
for j in np.arange(1445, 1465,1): #currently only calculating for a region
if lambdanaughts[i][j]==0:
row.append(0) #if the pixel is not on the Sun, the most likely value is zero
else:
MAP=BpixelMAP(Islicedata=ssI, Vslicedata=ssV, col=j, dispersion=dispersion[i][j], alpha=alphas[i][j], ironloc=ironmap[i][j], sigmamax=3000., sigmamin=1., integration='quad')
row.append(MAP)
MAPmap.append(row)
return MAPmap
def IndexMaps(datapath, array):
"""NAME: IndexMap
PURPOSE: Given a folder of FITS files and an array of full disk intensity information, return an array containing the index of the core of the absorption line.
INPUTS: datapath - The folder name containing all of the FITS files for each slice.
array - The array of full-disk intensity information.
line - The absorption line to look at: Calcium or Oxygen
OUTPUTS: indexmap - The array containing the indices of the core of the absorption line for each pixel.
EXAMPLE:
In [6]: datapath='k4v9s160517t213238_oid114635206372132_cleaned'
disk=fits.getdata('/Users/jhamer/Research/Output/k4v9s160517t213238/FullDiskPlots /k4v9s160517t213238_oid114635206372132_cleanedI80fulldisk.fits')
indexmap=CL.IndexMap(datapath, disk[1020:1028])
In [7]: indexmap[4][29:35]
Out[7]: array([ 0. , 0. , 63.016, 60.264, 61.748, 61.472])
"""
ironmap=np.zeros(len(array))
oxygen1map=np.zeros(len(array))
oxygen2map=np.zeros(len(array))
solarboolean=LF.ContainsSun(datapath=datapath, disk=array)
gapcols=LF.GapCols(datapath)
limbs=LF.LimbIndices(datapath, array, solarboolean)
slicedata=v9s.SingleStokes(datapath=datapath, stokes='I', slicenumber=1024)
j=0
while(j<2048):
PixelIndices(slicedata, ironmap, j, line='Iron')
PixelIndices(slicedata, oxygen1map, j, line='Oxygen1')
PixelIndices(slicedata, oxygen2map, j, line='Oxygen2')
j=j+1
return ironmap, oxygen1map, oxygen2map
import os
os.chdir('/Users/jhamer/Research/Scripts/Synthetic/')
import v9sDataHandler as v9s
import WeakFieldApprox as WFA
#import FourParamWFA as FPW
import CoreLocation as CL
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from astropy.io import fits
os.chdir('/Users/jhamer/Research/Data/')
datapath = 'k4v9s160617t000000_1024_wfa_noscat'
ls = ['I', 'Q', 'U', 'V']
stokes = []
for l in ls:
stokes.append(v9s.SingleStokes(datapath, l))
fe, ox1, ox2 = CL.IndexMaps(datapath, stokes[0][70])
dispersion = WFA.Dispersion(ox1, ox2)
lambdanaught = WFA.LambdaNaught(fe, ox1, dispersion)
alpha = WFA.Alpha(lambdanaught)
#beta=FPW.Beta(lambdanaught)
Islicedata = stokes[0]
#Qslicedata=stokes[1]
#Uslicedata=stokes[2]
Vslicedata = stokes[3]
col = 256
dispersion = dispersion[col]
alpha = alpha[col]
#beta=beta[col]
ironloc = fe[col]
params = fits.getdata('/Users/jhamer/Downloads/synth_spec_params.fits')
