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pipeline_maps.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 9 14:29:50 2019
@author: jpbreuer
"""
import re
import sys
import os
import subprocess
import numpy as np
import pandas as pd
import scipy.ndimage as ndimage
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from matplotlib import rc
rc('text', usetex=True)
matplotlib.rcParams['mathtext.fontset'] = 'cm'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
from astropy.visualization import astropy_mpl_style
plt.style.use(astropy_mpl_style)
from astropy.io import fits
from astropy import wcs
#%%
temperror = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn70_smooth100/maps/temp_errordepthmap.fits'
densmap = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn33_smooth100/maps/density_map.fits'
denserror = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn33_smooth100/maps/density_errordepthmap.fits'
#presmap = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn33_smooth100/maps/better_pressure_map.fits'
#radtempmap = '/home/jpbreuer/Scripts/chandra_pipeline/radial_temp.fits'
#entropmap = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn33_smooth100/maps/better_entropy_map.fits'
tempmap = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn70_smooth100/maps/temp_map.fits'
#temperrorhighmap = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn142_smooth100/maps/temp_error_high_map.fits'
#temperrorlowmap = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn142_smooth100/maps/temp_error_low_map.fits'
sn_per_region = 100
reg_smoothness = 100
binmap = '/home/jpbreuer/Chandra_data/a2256/merged/contbin_sn' + str(sn_per_region) + '_smooth' + str(reg_smoothness) + '/contbin_binmap.fits'
#binmap = './contbin_binmap_33.fits'
#mapsdir = '/home/jpbreuer/Chandra_data/a2256/specfile_output/results_sn' + str(sn_per_region) + '_smooth' + str(reg_smoothness) + '/maps/'
movex = 725; movey = 759
SPEX=False
XSPEC=True
#%%
def main():
# LoadData('./regions-info-xspec-sn' + str(sn_per_region) + '.data')
# MakeBasicMaps('./regions-info-xspec-sn33.data',binmap)
# MakeBasicMaps('./regions-info-xspec-sn' + str(sn_per_region) + '.data',binmap) #temperature, density, abundance, pressure, entropy
# MixedMaps(tempmap,temperror,densmap,denserror) #better entropy, better pressure
# CleanUp(mapsdir)
MakeRedshiftMap('./regions-info-xspec-redshift-sn100.data',binmap)
# RadialAverage(movex,movey,tempmap)
# ModelDividedMaps(data,model) #resid_div_data.fits
# RadialAverage(movex,movey,presmap,'pressure')
# RadialAverage(movex,movey,entropmap,'entropy')
# RadialCompass(movex,movey,tempmap,temperrorhighmap,temperrorlowmap,0.6215,'temperature')
# RadialCone(movex,movey,tempmap,temperrorhighmap,temperrorlowmap,0.6215,'temperature')
# CheckErrorRatio('/home/jpbreuer/Research/PHD/Data/Chandra/results2256/A2256/Maps/sn33/regions-info-xspec-sn33.data')
# ColdFrontPlot('/home/jpbreuer/Scripts/chandra_pipeline/regions-info-xspec-sn70.data')
# ShockFrontPlot('/home/jpbreuer/Chandra_data/a2256/merged/northprobe/xspec/regions-info-xspec.data')
#%%
def _mkmap(input,output,head):
#hdu = fits.PrimaryHDU(input)
fits.writeto(output,input,head,overwrite=True)
def CleanUp(output):
subprocess.run('mkdir ' + output, shell=True)
subprocess.run('cp *.fits *.data *.png ' + output, shell=True)
#%%
#tempdat = fits.open(tempmap)
#temphead = tempdat[0].header
#tempdata = tempdat[0].data
#
#densdat = fits.open(densmap)
#denshead = densdat[0].header
#densdata = densdat[0].data
#
#presdat = fits.open(presmap)
#preshead = presdat[0].header
#presdata = presdat[0].data
#
#raddat = fits.open(radtempmap)
#radhead = raddat[0].header
#raddata = raddat[0].data
#
#endat = fits.open(entropmap)
#enhead = endat[0].header
#endata = endat[0].data
#RadialAverage(movex,movey,presmap)
#_mkmap()
#_mkmap(densdata*(tempdata*(tempdata/radialtemp)),"temp_div_pressure.fits",temphead)
#_mkmap(presdata-(densdata*(tempdata*(tempdata/radialtemp))),"pressure_residual.fits",preshead)
#%%
#sn33 = 0.2 average
#sn70 = 0.08 average
#sn100 = 0.05 average
#sn142 = 0.04 average
#%%
def CheckErrorRatio(inputdata):
data = pd.read_csv(inputdata, delimiter=' ', index_col=0)
# mapdata = data[0:168]
mapdata = data
region = [];nh = [];temp=[];temp_low=[];temp_high=[];temp_error_low=[];temp_error_high=[];temp_error_diff=[]
abund=[];abund_low=[];abund_high=[];abund_error_low=[];abund_error_high=[];abund_error_diff=[];redshift=[]
norm=[];norm_low=[];norm_high=[];norm_error_low=[];norm_error_high=[];norm_error_diff=[]
chi=[];dof=[];chi2=[]
for index,row in mapdata.iterrows():
region.append(index)
nh.append(round(row['nH'],4))
temp.append(round(row['temperature'],4))
temp_low.append(round(row['temp_low'],4))
temp_high.append(round(row['temp_high'],4))
temp_error_low.append(round(row['temp_error_low'],4))
temp_error_high.append(round(row['temp_error_high'],4))
temp_error_diff.append(round(row['temp_error_diff'],4))
abund.append(round(row['abundance'],4))
abund_low.append(round(row['abund_low'],4))
abund_high.append(round(row['abund_high'],4))
abund_error_low.append(round(row['abund_error_low'],4))
abund_error_high.append(round(row['abund_error_high'],4))
abund_error_diff.append(round(row['abund_error_diff'],4))
redshift.append(round(row['redshift'],4))
norm.append(round(row['norm'],4))
norm_low.append(round(row['norm_low'],4))
norm_high.append(round(row['norm_high'],4))
norm_error_low.append(round(row['norm_error_low'],4))
norm_error_high.append(round(row['norm_error_high'],4))
norm_error_diff.append(round(row['norm_error_diff'],4))
chi.append(round(row['chi'],4))
dof.append(round(row['dof'],4))
chi2.append(round(row['chi2'],4))
error_ratio = []
for ii in list(range(len(temp))):
error_ratio.append((temp_error_diff[ii]/2)/temp[ii])
meanerror = np.mean(error_ratio)
plt.title("Mean Error Temperature = " + str(meanerror))
plt.scatter(list(range(len(error_ratio))),error_ratio)
plt.ylim(0,0.3)
plt.savefig('mean_error_temp.png', bbox_inches='tight')
# error_ratio = []
# for ii in list(range(len(temp))):
# error_ratio.append((norm_error_diff[ii]/2)/norm[ii])
#
# meanerror = np.mean(error_ratio)
#
# plt.title("Mean Error Norm = " + str(meanerror))
# plt.scatter(list(range(len(error_ratio))),error_ratio)
# plt.ylim(0,0.01)
# plt.savefig('mean_error_norm.png', bbox_inches='tight')
# entropy = []; pressure = []; entropyerr = []; pressureerr = [];
# for ii in list(range(len(temp))):
# entropy.append(temp[ii]/(norm[ii]**(2/3)))
# pressure.append(norm[ii]*temp[ii])
#
# for ii in list(range(len(temp))):
# entropyerr.append(np.abs(entropy[ii])*np.sqrt((((norm_error_diff[ii]/2)/norm[ii])**2) + (((temp_error_diff[ii]/2)/temp[ii])**2) + ((2*(norm_error_diff[ii]/2)*(temp_error_diff[ii]/2))/(norm[ii]*temp[ii]))))
# pressureerr.append(pressure[ii]*np.sqrt((((temp_error_diff[ii]/2)/temp[ii])**2) + (((2/3)*(norm_error_diff[ii]/2)/norm[ii])**2) - ((2*(norm_error_diff[ii]/2)*(temp_error_diff[ii]/2))/(norm[ii]*temp[ii]))))
#
# error_ratio = []
# for ii in list(range(len(temp))):
# error_ratio.append((entropyerr[ii]/2)/entropy[ii])
#
# meanerror = np.mean(error_ratio)
#
# plt.title("Mean Error Entropy = " + str(meanerror))
# plt.scatter(list(range(len(error_ratio))),error_ratio)
# plt.ylim(0,0.15)
#
# error_ratio = []
# for ii in list(range(len(temp))):
# error_ratio.append((pressureerr[ii]/2)/pressure[ii])
#
# meanerror = np.mean(error_ratio)
#
# plt.title("Mean Error Pressure = " + str(meanerror))
# plt.scatter(list(range(len(error_ratio))),error_ratio)
# plt.ylim(0,0.15)
#%%
def ErrorThreshMasking(inputfile,errordepth,threshold_percent,name):
dat = fits.open(inputfile)
head = dat[0].header
data = dat[0].data
errdat = fits.open(errordepth)
errdata = errdat[0].data
error_ratio = (errdata/2)/data
_mkmap(error_ratio,name + "_error_range_percent_map.fits",head)
error_threshold_mask = np.ones(np.shape(error_ratio))
error_threshold_mask[error_threshold_mask > threshold_percent] = 0
_mkmap(data*error_threshold_mask,name + "_error_thresholded_masked_map.fits",head)
#%%
def MakeBasicMaps(inputdata,binmap):
if SPEX:
#REGION DATA
#da = pd.read_csv(inputdata)
#header = list(da.columns)[0].split()
data = pd.read_csv(inputdata, delimiter=' ', index_col=0)
#datt = pd.read_csv(inputdata, delimiter=' ', index_col=0).transpose()
region=[];
norm=[];norm_low=[];norm_high=[];norm_error_low=[];norm_error_high=[];norm_error_diff=[]
temp=[];temp_low=[];temp_high=[];temp_error_low=[];temp_error_high=[];temp_error_diff=[]
#press=[];press_low=[];press_high=[];press_error_low=[];press_error_high=[];press_error_diff=[]
#entro=[];entro_low=[];entro_high=[];entro_error_low=[];entro_error_high=[];entro_error_diff=[]
for index,row in data.iterrows():
region.append(index)
norm.append(round(row['norm'],4))
norm_low.append(round(row['norm_low'],4))
norm_high.append(round(row['norm_high'],4))
norm_error_low.append(round(row['norm_error_low'],4))
norm_error_high.append(round(row['norm_error_high'],4))
norm_error_diff.append(round(row['norm_error_diff'],4))
temp.append(round(row['temperature'],4))
temp_low.append(round(row['temp_low'],4))
temp_high.append(round(row['temp_high'],4))
temp_error_low.append(round(row['temp_error_low'],4))
temp_error_high.append(round(row['temp_error_high'],4))
temp_error_diff.append(round(row['temp_error_diff'],4))
if XSPEC:
data = pd.read_csv(inputdata, delimiter=' ', index_col=0)
# mapdata = data[0:168]
mapdata = data
region = [];nh = [];temp=[];temp_low=[];temp_high=[];temp_error_low=[];temp_error_high=[];temp_error_diff=[]
abund=[];abund_low=[];abund_high=[];abund_error_low=[];abund_error_high=[];abund_error_diff=[];redshift=[]
norm=[];norm_low=[];norm_high=[];norm_error_low=[];norm_error_high=[];norm_error_diff=[]
chi=[];dof=[];chi2=[]
for index,row in mapdata.iterrows():
region.append(index)
nh.append(round(row['nH'],4))
temp.append(round(row['temperature'],4))
temp_low.append(round(row['temp_low'],4))
temp_high.append(round(row['temp_high'],4))
temp_error_low.append(round(row['temp_error_low'],4))
temp_error_high.append(round(row['temp_error_high'],4))
temp_error_diff.append(round(row['temp_error_diff'],4))
abund.append(round(row['abundance'],4))
abund_low.append(round(row['abund_low'],4))
abund_high.append(round(row['abund_high'],4))
abund_error_low.append(round(row['abund_error_low'],4))
abund_error_high.append(round(row['abund_error_high'],4))
abund_error_diff.append(round(row['abund_error_diff'],4))
redshift.append(round(row['redshift'],4))
norm.append(round(row['norm'],4))
norm_low.append(round(row['norm_low'],4))
norm_high.append(round(row['norm_high'],4))
norm_error_low.append(round(row['norm_error_low'],4))
norm_error_high.append(round(row['norm_error_high'],4))
norm_error_diff.append(round(row['norm_error_diff'],4))
chi.append(round(row['chi'],4))
dof.append(round(row['dof'],4))
chi2.append(round(row['chi2'],4))
# LoadData(inputdata)
#IMAGE DATA
im = fits.open(binmap)
hdr = im[0].header
imdata = im[0].data
#we = wcs.WCS(hdr)
def _printmap2(input,output):
f = plt.figure()
plt.subplot(111, projection=wcs)
plt.imshow(input, origin='lower', cmap=plt.cm.viridis)
ax = plt.subplot(projection=wcs)
ax.imshow(input, origin='lower', cmap=plt.cm.viridis)
ax.coords.grid(True, color='white', ls='solid')
ax.coords[0].set_axislabel('Galactic Longitude')
ax.coords[1].set_axislabel('Galactic Latitude')
overlay = ax.get_coords_overlay('fk5')
overlay.grid(color='white', ls='dotted')
overlay[0].set_axislabel('Right Ascension (J2000)')
overlay[1].set_axislabel('Declination (J2000)')
f.savefig(output, bbox_inches='tight')
## NORM MAPS
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
boolmask.append(((imreg.astype(np.int))*norm[ii]/region_pixsum[ii]))
# boolmask.append(((621.5*621.5*1000*(3.086*10**(21))*1.18*(1.67*10**(-27))*(imreg.astype(np.int))*norm[ii]/region_pixsum[ii])))
#621.5*621.5*1000: volume kpc to cm, 621.5*621.5*1000*(3.086*10**(21)): particle number density*mean molecular weight in proton mass units
# boolmask.append(n_e*(imreg.astype(np.int)))
normmap = sum(boolmask)
_mkmap(normmap,"norm_map.fits",hdr)
#_printmap(normmap,"norm_map.pdf")
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
boolmask.append(((imreg.astype(np.int))*norm_error_low[ii]/region_pixsum[ii]))
# boolmask.append(((621.5*621.5*1000*(3.086*10**(21))*1.18*(1.67*10**(-27))*(imreg.astype(np.int))*norm[ii]/region_pixsum[ii])))
#621.5*621.5*1000: volume kpc to cm, 621.5*621.5*1000*(3.086*10**(21)): particle number density*mean molecular weight in proton mass units
# boolmask.append(n_e*(imreg.astype(np.int)))
normelowmap = sum(boolmask)
_mkmap(normelowmap,"norm_error_low_map.fits",hdr)
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
boolmask.append(((imreg.astype(np.int))*norm_error_high[ii]/region_pixsum[ii]))
# boolmask.append(((621.5*621.5*1000*(3.086*10**(21))*1.18*(1.67*10**(-27))*(imreg.astype(np.int))*norm[ii]/region_pixsum[ii])))
#621.5*621.5*1000: volume kpc to cm, 621.5*621.5*1000*(3.086*10**(21)): particle number density*mean molecular weight in proton mass units
# boolmask.append(n_e*(imreg.astype(np.int)))
normehighmap = sum(boolmask)
_mkmap(normehighmap,"norm_error_high_map.fits",hdr)
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
boolmask.append(((imreg.astype(np.int))*norm_error_diff[ii]/region_pixsum[ii]))
normerrordepthmap = sum(boolmask)
_mkmap(normerrordepthmap,"norm_error_range_map.fits",hdr)
#_printmap(normerrordepthmap,"norm_errordepthmap.pdf")
## Density MAPS
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
K = norm[ii]; z = 0.058100; d_A = 233.5*(3.08568*10**24)#cm
EI = (K * 4 * np.pi * ((d_A * (z+1))**2))/(10**(-14))
V = 0.6215*0.6215*1000*(2.938*10**(64)) # kpc to cm^3
n_e = np.sqrt(1.18 * EI/(V*region_pixsum[ii]))
# boolmask.append(((imreg.astype(np.int))*norm[ii]/region_pixsum[ii]))
# boolmask.append(((621.5*621.5*1000*(3.086*10**(21))*1.18*(1.67*10**(-27))*(imreg.astype(np.int))*norm[ii]/region_pixsum[ii])))
#621.5*621.5*1000: volume kpc to cm, 621.5*621.5*1000*(3.086*10**(21)): particle number density*mean molecular weight in proton mass units
boolmask.append(n_e*(imreg.astype(np.int)))
densmap = sum(boolmask)
_mkmap(densmap,"density_map.fits",hdr)
#_printmap(normmap,"norm_map.pdf")
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
Kerr = norm_error_diff[ii]; z = 0.058100; d_A = 233.5*(3.08568*10**24)#cm
EIerr = (Kerr * 4 * np.pi * ((d_A * (z+1))**2))/(10**(-14))
V = 0.6215*0.6215*1000*(2.938*10**(64)) # kpc to cm^3
n_e_err = np.sqrt(1.18 * EIerr/(V*region_pixsum[ii]))
boolmask.append(n_e_err*(imreg.astype(np.int)))
denserrordepthmap = sum(boolmask)
_mkmap(denserrordepthmap/2,"density_error_range_map.fits",hdr)
#_printmap(normerrordepthmap,"norm_errordepthmap.pdf")
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
K = norm_error_low[ii]; z = 0.058100; d_A = 233.5*(3.08568*10**24)#cm
EI = (K * 4 * np.pi * ((d_A * (z+1))**2))/(10**(-14))
V = 0.6215*0.6215*1000*(2.938*10**(64)) # kpc to cm^3
n_e = np.sqrt(1.18 * EI/(V*region_pixsum[ii]))
# boolmask.append(((imreg.astype(np.int))*norm[ii]/region_pixsum[ii]))
# boolmask.append(((621.5*621.5*1000*(3.086*10**(21))*1.18*(1.67*10**(-27))*(imreg.astype(np.int))*norm[ii]/region_pixsum[ii])))
#621.5*621.5*1000: volume kpc to cm, 621.5*621.5*1000*(3.086*10**(21)): particle number density*mean molecular weight in proton mass units
boolmask.append(n_e*(imreg.astype(np.int)))
denselowmap = sum(boolmask)
_mkmap(denselowmap,"density_error_low_map.fits",hdr)
boolmask = [];region_pixsum = []
for ii in region:
imreg = imdata == ii
region_pixsum.append(np.count_nonzero(imreg.astype(np.int)))
K = norm_error_high[ii]; z = 0.058100; d_A = 233.5*(3.08568*10**24)#cm
EI = (K * 4 * np.pi * ((d_A * (z+1))**2))/(10**(-14))
V = 0.6215*0.6215*1000*(2.938*10**(64)) # kpc to cm^3
n_e = np.sqrt(1.18 * EI/(V*region_pixsum[ii]))
# boolmask.append(((imreg.astype(np.int))*norm[ii]/region_pixsum[ii]))
# boolmask.append(((621.5*621.5*1000*(3.086*10**(21))*1.18*(1.67*10**(-27))*(imreg.astype(np.int))*norm[ii]/region_pixsum[ii])))
#621.5*621.5*1000: volume kpc to cm, 621.5*621.5*1000*(3.086*10**(21)): particle number density*mean molecular weight in proton mass units
boolmask.append(n_e*(imreg.astype(np.int)))
densehighmap = sum(boolmask)
_mkmap(densehighmap,"density_error_high_map.fits",hdr)
## TEMP MAPS
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*temp[ii])
tempmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(tempmap,"temp_map.fits",hdr)
#_printmap(tempmap,"temp_map.pdf")
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*temp_error_diff[ii])
temperrordepthmap = sum(boolmask)
_mkmap((temperrordepthmap/2),"temp_error_range_map.fits",hdr)
#_printmap(temperrordepthmap,"temp_errordepthmap.pdf")
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*temp_error_low[ii])
tempelowmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(tempelowmap,"temp_error_low_map.fits",hdr)
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*temp_error_high[ii])
tempehighmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(tempehighmap,"temp_error_high_map.fits",hdr)
## ABUND MAPS
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*abund[ii])
abundmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(abundmap,"abund_map.fits",hdr)
#_printmap(tempmap,"temp_map.pdf")
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*abund_error_diff[ii])
abunderrordepthmap = sum(boolmask)
_mkmap((abunderrordepthmap/2),"abund_error_range_map.fits",hdr)
#_printmap(temperrordepthmap,"temp_errordepthmap.pdf")
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*abund_error_low[ii])
abundelowmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(abundelowmap,"abund_error_low_map.fits",hdr)
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*abund_error_high[ii])
abundehighmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(abundehighmap,"abund_error_high_map.fits",hdr)
##ENTROPY/PRESSURE MAPS
entropymap = tempmap/(densmap**(2/3))
pressuremap = densmap*tempmap
entroerrordepthmap = np.abs(entropymap)*np.sqrt((((denserrordepthmap/2)/densmap)**2) + (((temperrordepthmap/2)/tempmap)**2) + ((2*(denserrordepthmap/2)*(temperrordepthmap/2))/(densmap*tempmap)))
presserrordepthmap = pressuremap*np.sqrt((((temperrordepthmap/2)/tempmap)**2) + (((2/3)*(denserrordepthmap/2)/densmap)**2) - ((2*(denserrordepthmap/2)*(temperrordepthmap/2))/(densmap*tempmap)))
sigmat = 6.65245*10**-25
pseudocompy = (sigmat*pressuremap*(3.086*10**24))/511.0
_mkmap(pseudocompy, "pseudocomptony_map.fits",hdr)
_mkmap(entropymap,"entropy_map.fits",hdr)
_mkmap(pressuremap,"pressure_map.fits",hdr)
_mkmap(entroerrordepthmap,"entropy_error_range_map.fits",hdr)
_mkmap(presserrordepthmap,"pressure_error_range_map.fits",hdr)
_mkmap((presserrordepthmap/2)/pressuremap,"pressure_error_range_percent_map.fits",hdr)
entropyelow = tempelowmap/(denselowmap**(2/3))
entropyehigh = tempehighmap/(densehighmap**(2/3))
pressureelow = denselowmap*tempelowmap
pressureehigh = densehighmap*tempehighmap
_mkmap(entropyelow,"entropy_error_low_map.fits",hdr)
_mkmap(entropyehigh,"entropy_error_high_map.fits",hdr)
_mkmap(pressureelow,"pressure_error_low_map.fits",hdr)
_mkmap(pressureehigh,"pressure_error_high_map.fits",hdr)
#%%
def MixedMaps(tempmap,temperror,densmap,denserrordepthmap):
###--- BETTER MAPS --->>>
temp = fits.open(tempmap)
temp_imdata = temp[0].data
temperr = fits.open(temperror)
temperr_hdr = temperr[0].header
temperr_imdata = temperr[0].data
dens = fits.open(densmap)
dens_imdata = dens[0].data
denserr = fits.open(denserrordepthmap)
denserr_imdata = denserr[0].data
betterentropy = temp_imdata/(dens_imdata**(2/3))
betterpressure = dens_imdata*temp_imdata
betterpresserrordepthmap = np.abs(betterpressure)*np.sqrt(((denserr_imdata/dens_imdata)**2) + ((temperr_imdata/temp_imdata)**2) + ((2*denserr_imdata*temperr_imdata)/(dens_imdata*temp_imdata)))
betterentroerrordepthmap = betterentropy*np.sqrt(((temperr_imdata/temp_imdata)**2) + (((2/3)*denserr_imdata/dens_imdata)**2) - ((2*denserr_imdata*temperr_imdata)/(dens_imdata*temp_imdata)))
_mkmap(betterentropy,"better_entropy_map.fits",temperr_hdr)
_mkmap(1/betterentropy,"better_entropy_map_inverse.fits",temperr_hdr)
_mkmap(betterpressure,"better_pressure_map.fits",temperr_hdr)
_mkmap(betterentroerrordepthmap,"better_entropy_errordepthmap.fits",temperr_hdr)
_mkmap(betterpresserrordepthmap,"better_pressure_errordepthmap.fits",temperr_hdr)
sigmat = 6.65245*10**-25
pseudocompy = (sigmat*betterpressure*(3.086*10**24))/511.0
_mkmap(pseudocompy, "pseudocomptony_map.fits",hdr)
percentmask = (presserrordepthmap/2)/pressuremap
#%%
def ModelDividedMaps(data,model):
dat = fits.open(data)
head = dat[0].header
data = dat[0].data
modat = fits.open(model)
modata = modat[0].data
_mkmap(data*(data/modata),"resid_div_data.fits",head)
#%%
def MakeRedshiftMap(inputdata,binmap):#regions-info-xspec-redshift.data
if XSPEC:
data = pd.read_csv(inputdata, delimiter=' ', index_col=0)
# mapdata = data[0:168]
mapdata = data
region = [];nh = [];temp=[];temp_low=[];temp_high=[];temp_error_low=[];temp_error_high=[];temp_error_diff=[]
abund=[];abund_low=[];abund_high=[];abund_error_low=[];abund_error_high=[];abund_error_diff=[];
redshift=[];redshift_low=[];redshift_high=[];redshift_error_low=[];redshift_error_high=[];redshift_error_diff=[];
norm=[];norm_low=[];norm_high=[];norm_error_low=[];norm_error_high=[];norm_error_diff=[]
chi=[];dof=[];chi2=[]
for index,row in mapdata.iterrows():
region.append(index)
nh.append(round(row['nH'],4))
temp.append(round(row['temperature'],4))
temp_low.append(round(row['temp_low'],4))
temp_high.append(round(row['temp_high'],4))
temp_error_low.append(round(row['temp_error_low'],4))
temp_error_high.append(round(row['temp_error_high'],4))
temp_error_diff.append(round(row['temp_error_diff'],4))
abund.append(round(row['abundance'],4))
abund_low.append(round(row['abund_low'],4))
abund_high.append(round(row['abund_high'],4))
abund_error_low.append(round(row['abund_error_low'],4))
abund_error_high.append(round(row['abund_error_high'],4))
abund_error_diff.append(round(row['abund_error_diff'],4))
redshift.append(round(row['redshift'],4))
redshift_low.append(round(row['redshift_low'],4))
redshift_high.append(round(row['redshift_high'],4))
redshift_error_low.append(round(row['redshift_error_low'],4))
redshift_error_high.append(round(row['redshift_error_high'],4))
redshift_error_diff.append(round(row['redshift_error_diff'],4))
norm.append(round(row['norm'],4))
norm_low.append(round(row['norm_low'],4))
norm_high.append(round(row['norm_high'],4))
norm_error_low.append(round(row['norm_error_low'],4))
norm_error_high.append(round(row['norm_error_high'],4))
norm_error_diff.append(round(row['norm_error_diff'],4))
chi.append(round(row['chi'],4))
dof.append(round(row['dof'],4))
chi2.append(round(row['chi2'],4))
# LoadData(inputdata)
#IMAGE DATA
im = fits.open(binmap)
hdr = im[0].header
imdata = im[0].data
#we = wcs.WCS(hdr)
def _printmap2(input,output):
f = plt.figure()
plt.subplot(111, projection=wcs)
plt.imshow(input, origin='lower', cmap=plt.cm.viridis)
ax = plt.subplot(projection=wcs)
ax.imshow(input, origin='lower', cmap=plt.cm.viridis)
ax.coords.grid(True, color='white', ls='solid')
ax.coords[0].set_axislabel('Galactic Longitude')
ax.coords[1].set_axislabel('Galactic Latitude')
overlay = ax.get_coords_overlay('fk5')
overlay.grid(color='white', ls='dotted')
overlay[0].set_axislabel('Right Ascension (J2000)')
overlay[1].set_axislabel('Declination (J2000)')
f.savefig(output, bbox_inches='tight')
## Redshift MAPS
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*redshift[ii])
redmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(redmap,"redshift_map.fits",hdr)
#_printmap(tempmap,"temp_map.pdf")
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*redshift_error_diff[ii])
rederrordepthmap = sum(boolmask)
_mkmap((rederrordepthmap/2),"redshift_error_range_map.fits",hdr)
#_printmap(temperrordepthmap,"temp_errordepthmap.pdf")
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*redshift_error_low[ii])
redelowmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(redelowmap,"redshift_error_low_map.fits",hdr)
boolmask = []
for ii in region:
imreg = imdata == ii
boolmask.append((imreg.astype(np.int))*redshift_error_high[ii])
redehighmap = sum(boolmask)
#tempmap[tempmap >= 18] = np.nan
_mkmap(redehighmap,"redshift_error_high_map.fits",hdr)
_mkmap((rederrordepthmap/2)/redmap,"redshift_error_range_percent_map.fits",hdr)
#%%
def ColdFrontPlot(inputdata):
if XSPEC:
data = pd.read_csv(inputdata, delimiter=' ', index_col=0)
# mapdata = data[0:168]
mapdata = data
region = [];nh = [];temp=[];temp_low=[];temp_high=[];temp_error_low=[];temp_error_high=[];temp_error_diff=[]
abund=[];abund_low=[];abund_high=[];abund_error_low=[];abund_error_high=[];abund_error_diff=[];redshift=[]
norm=[];norm_low=[];norm_high=[];norm_error_low=[];norm_error_high=[];norm_error_diff=[]
chi=[];dof=[];chi2=[]
for index,row in mapdata.iterrows():
region.append(index)
nh.append(round(row['nH'],4))
temp.append(round(row['temperature'],4))
temp_low.append(round(row['temp_low'],4))
temp_high.append(round(row['temp_high'],4))
temp_error_low.append(round(row['temp_error_low'],4))
temp_error_high.append(round(row['temp_error_high'],4))
temp_error_diff.append(round(row['temp_error_diff'],4))
abund.append(round(row['abundance'],4))
abund_low.append(round(row['abund_low'],4))
abund_high.append(round(row['abund_high'],4))
abund_error_low.append(round(row['abund_error_low'],4))
abund_error_high.append(round(row['abund_error_high'],4))
abund_error_diff.append(round(row['abund_error_diff'],4))
redshift.append(round(row['redshift'],4))
norm.append(round(row['norm'],4))
norm_low.append(round(row['norm_low'],4))
norm_high.append(round(row['norm_high'],4))
norm_error_low.append(round(row['norm_error_low'],4))
norm_error_high.append(round(row['norm_error_high'],4))
norm_error_diff.append(round(row['norm_error_diff'],4))
chi.append(round(row['chi'],4))
dof.append(round(row['dof'],4))
chi2.append(round(row['chi2'],4))
cfdata = data[168:len(data)]
cftemp = [];cfabund = [];cfnorm = [];cfchi2 = []
cftlow = [];cfthigh = [];cfalow = [];cfahigh = [];cfnlow = [];cfnhigh = []
for index,row in cfdata.iterrows():
cftemp.append(round(row['temperature'],4))
cfabund.append(round(row['abundance'],4))
cfnorm.append(round(row['norm'],4))
cfchi2.append(round(row['chi2'],4))
cftlow.append(round(row['temp_error_low'],4))
cfthigh.append(round(row['temp_error_high'],4))
cfalow.append(round(row['abund_error_low'],4))
cfahigh.append(round(row['abund_error_high'],4))
cfnlow.append(round(row['norm_error_low'],4))
cfnhigh.append(round(row['norm_error_high'],4))
#regions are all mixed up, had to fix manually
longtemp = [cftemp[4],cftemp[3],cftemp[2],cftemp[1],cftemp[0],cftemp[5],cftemp[6],cftemp[7]]
longabund = [cfabund[4],cfabund[3],cfabund[2],cfabund[1],cfabund[0],cfabund[5],cfabund[6],cfabund[7]]
longnorm = [cfnorm[4],cfnorm[3],cfnorm[2],cfnorm[1],cfnorm[0],cfnorm[5],cfnorm[6],cfnorm[7]]
longchi2 = [cfchi2[4],cfchi2[3],cfchi2[2],cfchi2[1],cfchi2[0],cfchi2[5],cfchi2[6],cfchi2[7]]
shlefttemp = [cftemp[9],cftemp[11],cftemp[12],cftemp[13],cftemp[14],cftemp[15],cftemp[16],cftemp[17],cftemp[8],cftemp[25],cftemp[26]]
shleftabund = [cfabund[9],cfabund[11],cfabund[12],cfabund[13],cfabund[14],cfabund[15],cfabund[16],cfabund[17],cfabund[8],cfabund[25],cfabund[26]]
shleftnorm = [cfnorm[9],cfnorm[11],cfnorm[12],cfnorm[13],cfnorm[14],cfnorm[15],cfnorm[16],cfnorm[17],cfnorm[8],cfnorm[25],cfnorm[26]]
shleftchi2 = [cfchi2[9],cfchi2[11],cfchi2[12],cfchi2[13],cfchi2[14],cfchi2[15],cfchi2[16],cfchi2[17],cfchi2[8],cfchi2[25],cfchi2[26]]
shrighttemp = [cftemp[10],cftemp[18],cftemp[19],cftemp[20],cftemp[21],cftemp[22],cftemp[23],cftemp[24],cftemp[27],cftemp[28],cftemp[29]]
shrightabund = [cfabund[10],cfabund[18],cfabund[19],cfabund[20],cfabund[21],cfabund[22],cfabund[23],cfabund[24],cfabund[27],cfabund[28],cfabund[29]]
shrightnorm = [cfnorm[10],cfnorm[18],cfnorm[19],cfnorm[20],cfnorm[21],cfnorm[22],cfnorm[23],cfnorm[24],cfnorm[27],cfnorm[28],cfnorm[29]]
shrightchi2 = [cfchi2[10],cfchi2[18],cfchi2[19],cfchi2[20],cfchi2[21],cfchi2[22],cfchi2[23],cfchi2[24],cfchi2[27],cfchi2[28],cfchi2[29]]
longtlow = [cftlow[4],cftlow[3],cftlow[2],cftlow[1],cftlow[0],cftlow[5],cftlow[6],cftlow[7]]
longalow = [cfalow[4],cfalow[3],cfalow[2],cfalow[1],cfalow[0],cfalow[5],cfalow[6],cfalow[7]]
longnlow = [cfnlow[4],cfnlow[3],cfnlow[2],cfnlow[1],cfnlow[0],cfnlow[5],cfnlow[6],cfnlow[7]]
shlefttlow = [cftlow[9],cftlow[11],cftlow[12],cftlow[13],cftlow[14],cftlow[15],cftlow[16],cftlow[17],cftlow[8],cftlow[25],cftlow[26]]
shleftalow = [cfalow[9],cfalow[11],cfalow[12],cfalow[13],cfalow[14],cfalow[15],cfalow[16],cfalow[17],cfalow[8],cfalow[25],cfalow[26]]
shleftnlow = [cfnlow[9],cfnlow[11],cfnlow[12],cfnlow[13],cfnlow[14],cfnlow[15],cfnlow[16],cfnlow[17],cfnlow[8],cfnlow[25],cfnlow[26]]
shrighttlow = [cftlow[10],cftlow[18],cftlow[19],cftlow[20],cftlow[21],cftlow[22],cftlow[23],cftlow[24],cftlow[27],cftlow[28],cftlow[29]]
shrightalow = [cfalow[10],cfalow[18],cfalow[19],cfalow[20],cfalow[21],cfalow[22],cfalow[23],cfalow[24],cfalow[27],cfalow[28],cfalow[29]]
shrightnlow = [cfnlow[10],cfnlow[18],cfnlow[19],cfnlow[20],cfnlow[21],cfnlow[22],cfnlow[23],cfnlow[24],cfnlow[27],cfnlow[28],cfnlow[29]]
longthigh = [cfthigh[4],cfthigh[3],cfthigh[2],cfthigh[1],cfthigh[0],cfthigh[5],cfthigh[6],cfthigh[7]]
longahigh = [cfahigh[4],cfahigh[3],cfahigh[2],cfahigh[1],cfahigh[0],cfahigh[5],cfahigh[6],cfahigh[7]]
longnhigh = [cfnhigh[4],cfnhigh[3],cfnhigh[2],cfnhigh[1],cfnhigh[0],cfnhigh[5],cfnhigh[6],cfnhigh[7]]
shleftthigh = [cfthigh[9],cfthigh[11],cfthigh[12],cfthigh[13],cfthigh[14],cfthigh[15],cfthigh[16],cfthigh[17],cfthigh[8],cfthigh[25],cfthigh[26]]
shleftahigh = [cfahigh[9],cfahigh[11],cfahigh[12],cfahigh[13],cfahigh[14],cfahigh[15],cfahigh[16],cfahigh[17],cfahigh[8],cfahigh[25],cfahigh[26]]
shleftnhigh = [cfnhigh[9],cfnhigh[11],cfnhigh[12],cfnhigh[13],cfnhigh[14],cfnhigh[15],cfnhigh[16],cfnhigh[17],cfnhigh[8],cfnhigh[25],cfnhigh[26]]
shrightthigh = [cfthigh[10],cfthigh[18],cfthigh[19],cfthigh[20],cfthigh[21],cfthigh[22],cfthigh[23],cfthigh[24],cfthigh[27],cfthigh[28],cfthigh[29]]
shrightahigh = [cfahigh[10],cfahigh[18],cfahigh[19],cfahigh[20],cfahigh[21],cfahigh[22],cfahigh[23],cfahigh[24],cfahigh[27],cfahigh[28],cfahigh[29]]
shrightnhigh = [cfnhigh[10],cfnhigh[18],cfnhigh[19],cfnhigh[20],cfnhigh[21],cfnhigh[22],cfnhigh[23],cfnhigh[24],cfnhigh[27],cfnhigh[28],cfnhigh[29]]
longterr = [longtlow, longthigh];longaerr = [longalow, longahigh];longnerr = [longnlow, longnhigh]
shleftterr = [shlefttlow, shleftthigh];shleftaerr = [shleftalow, shleftahigh];shleftnerr = [shleftnlow, shleftnhigh]
shrightterr = [shrighttlow, shrightthigh];shrightaerr = [shrightalow, shrightahigh];shrightnerr = [shrightnlow, shrightnhigh]
f, axarr = plt.subplots(2,2)
f.suptitle('Cold Front Long Regions')
axarr[0, 0].errorbar(list(range(len(longtemp))), longtemp, yerr=longterr, fmt='-o')
axarr[0, 0].set_title('temperature')
axarr[0, 1].errorbar(list(range(len(longtemp))), longabund, yerr=longaerr, fmt='-o')
axarr[0, 1].set_title('abundance')
axarr[1, 0].errorbar(list(range(len(longtemp))), longnorm, yerr=longnerr, fmt='-o')
axarr[1, 0].set_title('norm')
axarr[1, 1].scatter(list(range(len(longtemp))), longchi2)
axarr[1, 1].set_title('$\chi^2$')
f.savefig('longregions.png', bbox_inches='tight')
f, axarr = plt.subplots(2,2)
f.suptitle('Cold Front Short Left Regions')
axarr[0, 0].errorbar(list(range(len(shlefttemp))), shlefttemp, yerr=shleftterr, fmt='-o')
axarr[0, 0].set_title('temperature')
axarr[0, 1].errorbar(list(range(len(shlefttemp))), shleftabund, yerr=shleftaerr, fmt='-o')
axarr[0, 1].set_title('abundance')
axarr[1, 0].errorbar(list(range(len(shlefttemp))), shleftnorm, yerr=shleftnerr, fmt='-o')
axarr[1, 0].set_title('norm')
axarr[1, 1].scatter(list(range(len(shlefttemp))), shleftchi2)
axarr[1, 1].set_title('$\chi^2$')
f.savefig('shortleftregions.png', bbox_inches='tight')
f, axarr = plt.subplots(2,2)
f.suptitle('Cold Front Short Right Regions')
axarr[0, 0].errorbar(list(range(len(shrighttemp))), shrighttemp, yerr=shrightterr, fmt='-o')
axarr[0, 0].set_title('temperature')
axarr[0, 1].errorbar(list(range(len(shrighttemp))), shrightabund, yerr=shrightaerr, fmt='-o')
axarr[0, 1].set_title('abundance')
axarr[1, 0].errorbar(list(range(len(shrighttemp))), shrightnorm, yerr=shrightnerr, fmt='-o')
axarr[1, 0].set_title('norm')
axarr[1, 1].scatter(list(range(len(shrighttemp))), shrightchi2)
axarr[1, 1].set_title('$\chi^2$')
f.savefig('shortrightregions.png', bbox_inches='tight')
subprocess.run('mkdir plots', shell=True)
subprocess.run('mv longregions.png shortleftregions.png shortrightregions.png plots', shell=True)
#%%
def ShockFrontPlot(inputdata):
if XSPEC:
data = pd.read_csv(inputdata, delimiter=' ', index_col=0)
# mapdata = data[0:168]
mapdata = data
region = [];nh = [];temp=[];temp_low=[];temp_high=[];temp_error_low=[];temp_error_high=[];temp_error_diff=[]
abund=[];abund_low=[];abund_high=[];abund_error_low=[];abund_error_high=[];abund_error_diff=[];redshift=[]
norm=[];norm_low=[];norm_high=[];norm_error_low=[];norm_error_high=[];norm_error_diff=[]
chi=[];dof=[];chi2=[]
for index,row in mapdata.iterrows():
region.append(index)
nh.append(round(row['nH'],4))
temp.append(round(row['temperature'],4))
temp_low.append(round(row['temp_low'],4))
temp_high.append(round(row['temp_high'],4))
temp_error_low.append(round(row['temp_error_low'],4))
temp_error_high.append(round(row['temp_error_high'],4))
temp_error_diff.append(round(row['temp_error_diff'],4))
abund.append(round(row['abundance'],4))
abund_low.append(round(row['abund_low'],4))
abund_high.append(round(row['abund_high'],4))
abund_error_low.append(round(row['abund_error_low'],4))
abund_error_high.append(round(row['abund_error_high'],4))
abund_error_diff.append(round(row['abund_error_diff'],4))
redshift.append(round(row['redshift'],4))
norm.append(round(row['norm'],4))
norm_low.append(round(row['norm_low'],4))
norm_high.append(round(row['norm_high'],4))
norm_error_low.append(round(row['norm_error_low'],4))
norm_error_high.append(round(row['norm_error_high'],4))
norm_error_diff.append(round(row['norm_error_diff'],4))
chi.append(round(row['chi'],4))
dof.append(round(row['dof'],4))
chi2.append(round(row['chi2'],4))
terr = [temp_error_low, temp_error_high];aerr = [abund_error_low, abund_error_high];nerr = [norm_error_low, norm_error_high]
f, axarr = plt.subplots(2,2)
f.suptitle('Shock Front Regions')
axarr[0, 0].errorbar(list(range(len(temp))), temp, yerr=terr, fmt='o-')
axarr[0, 0].set_title('temperature')
axarr[0, 1].errorbar(list(range(len(abund))), abund, yerr=aerr, fmt='o-')
axarr[0, 1].set_title('abundance')
axarr[1, 0].errorbar(list(range(len(norm))), norm, yerr=nerr, fmt='o-')
axarr[1, 0].set_title('norm')
axarr[1, 1].scatter(list(range(len(chi2))), chi2)
axarr[1, 1].set_title('$\chi^2$')
f.savefig('shockregions.png', bbox_inches='tight')
subprocess.run('mkdir plots', shell=True)
subprocess.run('mv shockregions.png plots', shell=True)
#%%
# entropmap,'entropy'
def RadialAverage(centerx,centery,data,outputlabel):
# outputlabel = 'entropy'
tempdat = fits.open(data)
temphead = tempdat[0].header
#tempdata = np.flipud(tempdat[0].data)
tempdata = tempdat[0].data
y,x = np.shape(tempdata)
#movex = centerx
#movey = centery
zeroarray = np.zeros([y, x]).astype(int)
xpositions = np.zeros([y, x]).astype(int)
ypositions = np.zeros([y, x]).astype(int)
xarray = np.array(range(-movex,x-movex)).astype(int)
yarray = np.array(range(-movey,y-movey)).astype(int)
for ii in np.arange(y):
xpositions[ii,:] = np.c_[xarray[None,:]]
for ii in np.arange(x):
ypositions[:,ii] = np.r_[yarray[None,:]]
#ypositions = np.flipud(ypositions)
grid = zeroarray
for ii in np.arange(y):
for jj in np.arange(x):
grid[ii][jj] = int(np.sqrt(((np.abs(xpositions[ii][jj])**2) + ((np.abs(ypositions[ii][jj])**2)))))
# grid[ii][jj] = int(np.sqrt((int(newx[ii][jj])**2) + ((int(newy[ii][jj])**2))))
_mkmap(grid,"radial_long.fits",temphead)
radialtemp = np.zeros([y, x]).astype(int)
for ii in list(range(np.amax(grid)+1)):
#ii = 1048
imreg = grid == ii
imregfloat = imreg.astype(np.float)
imreg = grid == ii
imreg = imreg.astype(np.float)
imreg[imreg == 0] = np.nan
radialtemp = radialtemp + (imregfloat*np.nanmean(np.ma.masked_invalid(tempdata*imreg)))
_mkmap(radialtemp,"radial_" + outputlabel + ".fits",temphead)
_mkmap(tempdata/radialtemp,outputlabel + "_div_avg.fits",temphead)
#%%
def RadialCompass(centerx,centery,data,erbarup,erbardown,pixelsizekpc,outputlabel):
#centerx = 725;centery = 759
tempdat = fits.open(data) #tempmap
temphead = tempdat[0].header
#tempdata = np.flipud(tempdat[0].data)
tempdata = tempdat[0].data
tempelowdat = fits.open(erbardown)
tempelowdata = tempelowdat[0].data
tempehighdat = fits.open(erbarup)
tempehighdata = tempehighdat[0].data
y,x = np.shape(tempdata)
testgrid = np.reshape(range(x*y),(x,y))
magicnum = testgrid[centerx,centery]
lrtest = np.fliplr(testgrid)
flx,fly = np.where(lrtest==magicnum)
#movex = int(flx)
#movey = int(fly)
movex = centerx
movey = centery
xpositions = np.zeros([y, x]).astype(int)
ypositions = np.zeros([y, x]).astype(int)
xarray = np.array(range(-movex,x-movex)).astype(int)
yarray = np.array(range(-movey,y-movey)).astype(int)
for ii in np.arange(y):
xpositions[ii,:] = np.c_[xarray[None,:]]
for ii in np.arange(x):
ypositions[:,ii] = np.r_[yarray[None,:]]
#ypositions = np.flip(ypositions)
#NORTH
northgrid = np.zeros([y, x]).astype(int)
for ii in list(range(0,np.abs(np.amin(ypositions))+1)):
northgrid[ii,movex] = 1
#SOUTH
southgrid = np.zeros([y, x]).astype(int)
for ii in list(range(np.abs(np.amin(ypositions)),y)):
southgrid[ii,movex] = 1
#EAST (IMG LEFT)
eastgrid = np.zeros([y, x]).astype(int)
for ii in list(range(0,np.abs(np.amin(xpositions))+1)):
eastgrid[movey,ii] = 1
#WEST (IMG RIGHT)
westgrid = np.zeros([y, x]).astype(int)
for ii in list(range(np.abs(np.amin(xpositions)),x)):
westgrid[movey,ii] = 1
#NORTHEAST (IMG TOP LEFT)
negrid = np.zeros([y, x]).astype(int)
nequad = np.zeros([y, x]).astype(int)
nediag = np.eye(y,M=x,k=movex-movey)
for ii in list(range(np.amin(ypositions),1)):
for jj in list(range(np.amin(xpositions),1)):
nequad[movey+ii,movex+jj] = 1
negrid = np.multiply(nequad,nediag)
#SOUTHWEST (IMG BOTTOM RIGHT)
swgrid = np.zeros([y, x]).astype(int)
swquad = np.zeros([y, x]).astype(int)
#swdiag = np.eye(y,M=x,k=movex-movey)
for ii in list(range(0,np.amax(ypositions)+1)):
for jj in list(range(0,np.amax(xpositions)+1)):
swquad[movey+ii,movex+jj] = 1
swgrid = np.multiply(swquad,nediag)
#NORTHWEST (IMG TOP RIGHT)
#testgrid = np.reshape(range(x*y),(x,y))
#magicnum = testgrid[movex,movey]