# Script to convert a series of FITS data files into a sequences of png images. # Specially designed for the massive HI4PI data set, which is difficult to process # as a single FITS file. # Adapted from a FRELLED script and stripped to the bare minimum of functionality. import os import matplotlib import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np try: import pyfits except: from astropy.io import fits as pyfits import glob import math import os import sys import multiprocessing import time import traceback from sys import platform def clearscreen(): try: if platform == 'win32': os.system('cls') else: os.system('clear') finally: pass # Change to directory where script is located abspath = os.path.abspath(__file__) dname = os.path.dirname(abspath) os.chdir(dname) clearscreen() Smin = 0.1 Smax = 150.0 colours = 1 logs = True minchan = 200 maxchan = 750 basename = 'Channel_' # Available colours from matplotlib # 1 = grey # 2 = hot # 3 = cold # 4 = fire # 5 = red # 6 = yel # 7 = green # 8 = cyan # 9 = purple # 10 = high contrast # Define custom colourbars # COLD cdict = {'red': ((0.0, 0.0, 0.0), (0.75, 0.0, 0.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25, 0.0,0.0), (0.75, 1.0, 1.0), (1.00, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.25, 1.0, 1.0), (1.0, 1.0, 1.0))} cmap1 = matplotlib.colors.LinearSegmentedColormap('cold',cdict,N=256) plt.register_cmap(cmap=cmap1) # FIRE cdict2 = {'red': ((0.0, 0.0, 0.0), (0.25, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.75, 1.0, 1.0), (1.00, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.75, 0.0, 0.0), (1.0, 1.0, 1.0))} cmap2 = matplotlib.colors.LinearSegmentedColormap('fire',cdict2,N=256) plt.register_cmap(cmap=cmap2) # RED cdict3 = {'red': ((0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.00, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0))} cmap3 = matplotlib.colors.LinearSegmentedColormap('red',cdict3,N=256) plt.register_cmap(cmap=cmap3) # YELLOW cdict4 = {'red': ((0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.00, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0))} cmap4 = matplotlib.colors.LinearSegmentedColormap('yellow',cdict4,N=256) plt.register_cmap(cmap=cmap4) # GREEN cdict5 = {'red': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.00, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0))} cmap5 = matplotlib.colors.LinearSegmentedColormap('green',cdict5,N=256) plt.register_cmap(cmap=cmap5) # CYAN cdict6 = {'red': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.00, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0))} cmap6 = matplotlib.colors.LinearSegmentedColormap('cyan',cdict6,N=256) plt.register_cmap(cmap=cmap6) # PURPLE cdict7 = {'red': ((0.0, 0.0, 0.0), (0.5, 0.5, 0.5), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.00, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0))} cmap7 = matplotlib.colors.LinearSegmentedColormap('purple',cdict7,N=256) plt.register_cmap(cmap=cmap7) # HIGH CONTRAST x = 5.0 cdict8 = {'red': ((0.0, pow(0.0,x), pow(0.0,x)), (0.10, pow(0.1,x), pow(0.1,x)), (0.20, pow(0.2,x), pow(0.2,x)), (0.30, pow(0.3,x), pow(0.3,x)), (0.40, pow(0.4,x), pow(0.4,x)), (0.50, pow(0.5,x), pow(0.5,x)), (0.60, pow(0.6,x), pow(0.6,x)), (0.70, pow(0.7,x), pow(0.7,x)), (0.80, pow(0.8,x), pow(0.8,x)), (0.90, pow(0.9,x), pow(0.9,x)), (1.00, pow(1.0,x), pow(1.0,x))), 'green': ((0.0, pow(0.0,x), pow(0.0,x)), (0.10, pow(0.1,x), pow(0.1,x)), (0.20, pow(0.2,x), pow(0.2,x)), (0.30, pow(0.3,x), pow(0.3,x)), (0.40, pow(0.4,x), pow(0.4,x)), (0.50, pow(0.5,x), pow(0.5,x)), (0.60, pow(0.6,x), pow(0.6,x)), (0.70, pow(0.7,x), pow(0.7,x)), (0.80, pow(0.8,x), pow(0.8,x)), (0.90, pow(0.9,x), pow(0.9,x)), (1.00, pow(1.0,x), pow(1.0,x))), 'blue': ((0.0, pow(0.0,x), pow(0.0,x)), (0.10, pow(0.1,x), pow(0.1,x)), (0.20, pow(0.2,x), pow(0.2,x)), (0.30, pow(0.3,x), pow(0.3,x)), (0.40, pow(0.4,x), pow(0.4,x)), (0.50, pow(0.5,x), pow(0.5,x)), (0.60, pow(0.6,x), pow(0.6,x)), (0.70, pow(0.7,x), pow(0.7,x)), (0.80, pow(0.8,x), pow(0.8,x)), (0.90, pow(0.9,x), pow(0.9,x)), (1.00, pow(1.0,x), pow(1.0,x)))} cmap8 = matplotlib.colors.LinearSegmentedColormap('highcont',cdict8,N=256) plt.register_cmap(cmap=cmap8) if colours == 1: colours=str('gray') elif colours == 2: colours=str('hot') elif colours == 3: colours=cmap1 elif colours == 4: colours=cmap2 elif colours == 5: colours=cmap3 elif colours == 6: colours=cmap4 elif colours == 7: colours=cmap5 elif colours == 8: colours=cmap6 elif colours == 9: colours=cmap7 elif colours == 10: colours=cmap8 elif colours == 11: colours=str('jet') elif colours == 12: colours=str('RdBu') elif colours == 13: colours=str('cool') elif colours == 14: colours=str('RdYlBu') elif colours == 15: colours=str('RdYlGn') elif colours == 16: colours=str('spectral') # Disable interactive plotting in matplotlib plt.ioff() # Get base size basefile = pyfits.open('Channel_000.fits', mode='update') baseimage = basefile[0].data nz = baseimage.shape[0] ny = baseimage.shape[1] nx = baseimage.shape[2] basefile.close() minv = Smin maxv = Smax def plotxy(i): # Render the channels clearscreen() print 'Rendering channels...'+str(i).zfill(3)+'/'+str(maxchan).zfill(3) fitsfile = pyfits.open(basename+str(i).zfill(3)+'.fits', mode='update') image = fitsfile[0].data # Converted original cube with miriad, which always outputs two channels even though we only # want one. imslice=image[0,:,:] fig=plt.figure() # This works since dpi=100, so images will have correct size and shape fig.set_size_inches(float(nx)/100.0,float(ny)/100.0) # Trick to remove any axes and whitespace from the images ax=plt.Axes(fig, [0.,0.,1.,1.,]) ax.set_axis_off() fig.add_axes(ax) plt.set_cmap(colours) if logs==0: try: ax.imshow(imslice,aspect='auto',interpolation='none',vmin=minv,vmax=maxv) except: ax.imshow(imslice,aspect='auto',interpolation='nearest',vmin=minv,vmax=maxv) if logs==1: try: ax.imshow(imslice,aspect='auto',interpolation='none',vmin=minv,vmax=maxv,norm=matplotlib.colors.LogNorm()) except: ax.imshow(imslice,aspect='auto',interpolation='nearest',vmin=minv,vmax=maxv,norm=matplotlib.colors.LogNorm()) plt.gca().invert_yaxis() # Format numbers so have trailing zeros (ensures compatibility with kvis) n = str(i+1).zfill(3) filename = str(dname)+'/Images/'+'Channel_'+str(n)+str('.png') plt.savefig(filename,dpi=100,facecolor='black') plt.close(fig) fitsfile.close() if __name__ == '__main__': pool = multiprocessing.Pool() # Create a subdirectory to hold the files, if it doesn't already exist if os.path.isdir('./Images') == False: os.system('mkdir Images') pool.map(plotxy, range(minchan,maxchan)) print 'Done !'