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tiedarray_movie.py
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# Python code that plots triangulated intensity maps with
# dynamic spectra for 3 frequencies
#
# Functions for background subtraction of data included
#
# Functionality added for saving images fits files
#
#
# Author: Diana Morosan ([email protected])
# Latest version: October 2019
#
# Please acknowledge the use of this code
import h5py
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as pl
import time
import datetime
import numpy as np
from pylab import figure,imshow,xlabel,ylabel,title,close,colorbar
from matplotlib.ticker import MaxNLocator
from matplotlib.dates import date2num
from matplotlib import dates
from matplotlib.colors import LogNorm
import pyfits
import sunpy.coordinates
import astropy.coordinates
from astropy.time import Time
import sunpy.sun
import glob
############################################
# function that writes an image to fits file
############################################
def make_fits(filename, image_data, x, y, x_delta, y_delta, time ):
header = pyfits.Header( [ ('CRVAL1', x),('CRPIX1',0),('CTYPE1','X (arcmin)'),('CDELT1',x_delta),('CRVAL2', y),('CRPIX2',0),('CTYPE2','Y (arcmin)'),('CDELT2',y_delta),('T_OBS',time) ] )
pyfits.writeto(filename, image_data, header)
#####################################
# create quiet time normalising array
#####################################
def quiet_time_array(start_min, end_min):
start_min_quiet = start_min
end_min_quiet = end_min
start_line_quiet = int( (start_min_quiet/(total_time/60.))*t_lines )
end_line_quiet = int( (end_min_quiet/(total_time/60.))*t_lines )
# beam far away from sources to create normalising array
file = obsid + '_SAP000_B070_S0_P000_bf.h5'
f = h5py.File( file, 'r' )
data = f['SUB_ARRAY_POINTING_000/BEAM_070/STOKES_0'][start_line_quiet:end_line_quiet,:]
print 'Quiet Data: ', data.shape
data = np.mean(data, axis = 0)
print data.shape
array_quiet = data
print array_quiet
np.savetxt('Quiet_time_array_1541.txt', array_quiet, fmt = '%10.5f')
return array_quiet
#######################################
# create median array for normalization
#######################################
def median_array(beam_file):
# input: beam far away from sources to create normalising array
file = beam_file
f = h5py.File( file, 'r' )
data = f['SUB_ARRAY_POINTING_000/BEAM_070/STOKES_0'][start_line_norm:end_line_norm,:]
median_arr = []
for sb in xrange(data.shape[1]):
median_arr.append(np.median(data[:,sb]))
#print len(median_arr), median_arr
return median_arr
###########################
# coordinate transformation
###########################
def coordinate_transformation(ra, dec, time):
time_obs = time
# polar north angle
p = sunpy.coordinates.get_sun_P(time_obs).rad # quantities needed in radians
# solar ra and dec on the day
ra0 = astropy.coordinates.get_sun(Time(time_obs)).ra.rad #sunpy.sun.true_rightascension(time_obs).rad
dec0 = astropy.coordinates.get_sun(Time(time_obs)).dec.rad #sunpy.sun.true_declination(time_obs).rad
ra = ra*np.pi/180
dec = dec*np.pi/180
'''
# method 1 - approximation
solar_x = ( -(ra - ra0)*np.cos( dec0 )*np.cos( p ) + (dec - dec0)*np.sin( p ) )*180/np.pi*60
solar_y = ( (ra - ra0)*np.cos( dec0 )*np.sin( p ) + (dec - dec0)*np.cos( p ) )*180/np.pi*60
'''
# method 2 - exact method - based on ALMA transformation reference
rho = np.arccos( np.cos( dec )*np.cos( dec0 )*np.cos(ra - ra0) + np.sin(dec)*np.sin(dec0) )
phi = np.arctan2( ( np.sin(ra-ra0) ), ( np.tan(dec)*np.cos(dec0) - np.sin(dec0)*np.cos(ra-ra0) ) )
solar_x = np.arctan( -np.tan(rho)*np.sin(phi-p))*180/np.pi*60
solar_y = np.arctan(np.tan(rho)*np.cos(phi-p))*180/np.pi*60
return solar_x, solar_y
# extracting file header
files = glob.glob('*.h5')
files.sort()
f = h5py.File( files[0], 'r' )
data = f['SUB_ARRAY_POINTING_000/BEAM_000/STOKES_0'][:,:]
print( 'Data array shape: ', data.shape )
t_lines = data.shape[0]
f_lines = data.shape[1]
total_time = f.attrs.values()[22] #in seconds
lines_per_sec = int(t_lines/total_time)
print( 'Lines per second: ', lines_per_sec )
# extracting time informattion and write it in python format
time = f.attrs['OBSERVATION_START_UTC']
start_time_obs = datetime.datetime.strptime( time, '%Y-%m-%dT%H:%M:%S.%f000Z' )
print( 'Start time observation:', start_time_obs.time() )
'''
Start user input parameters
'''
#########################
# useful input parameters
# can be included as command line arguments instead
###################################################
nfiles = len(files)# number of beams + 1
obsid = 'L608700'
##############################################################################
# parameters to play with depending on which time/freq/chunk of data is needed
##############################################################################
# image cadence
averaging_time = 0.5 #in seconds
# beam to use in dynamic spectrum plot
dynamic_spectrum_beam = 9
#clipping parameters for image
zmin = 0.9
zmax = 20
# start/end minute of movie
start_min = 0.
end_min = total_time/60.
# start time of movie
start_time = (start_time_obs + datetime.timedelta(minutes = start_min) )
start_min_norm = 0.
end_min_norm = 5#total_time/60.
# 3 input frequencies
start_freq = 30 #in MHz
end_freq = 30.5 #in MHz
start_freq1 =37 #in MHz
end_freq1 = 37.5 #in MHz
start_freq2 = 47 #in MHz
end_freq2 = 47.5 #in MHz
'''
End user input parameters
'''
################################
# calculating imaging parameters
################################
start_line = int( (start_min/(total_time/60.))*t_lines )
end_line = int( (end_min/(total_time/60.))*t_lines )
print( 'Start line/End line: ', start_line, end_line )
start_line_norm = start_line #int( (start_min/(total_time/60.))*t_lines )
end_line_norm = int( (end_min/(total_time/60.))*t_lines )
# no of images based on start/end time
no_images = int( (end_line - start_line)/(lines_per_sec*averaging_time) )
start_freq_ds = f.attrs.values()[30] #in MHz
end_freq_ds = f.attrs.values()[8]
start_freq_ds_plot = start_freq_ds
end_freq_ds_plot = end_freq_ds
t_resolution = (total_time/t_lines)*1000 #in milliseconds
f_resolution = (end_freq_ds - start_freq_ds)/f_lines
start_freq_line_ds = int( (start_freq_ds_plot - start_freq_ds)/f_resolution )
end_freq_line_ds = int( (end_freq_ds_plot - start_freq_ds)/f_resolution )
# constructing a time array from the start time
time_new = np.zeros( end_line - start_line )
for j in range(len(time_new)):
z = (start_time + datetime.timedelta(milliseconds = t_resolution*j))
time_new[j] = matplotlib.dates.date2num(z)
# creating frequency array
freq = np.zeros( int((end_freq_ds_plot - start_freq_ds_plot)/f_resolution) )
for k in range(len(freq)):
freq[k] = start_freq_ds_plot + k*f_resolution
# estimating location of frequency slices
start_freq_line = int( (start_freq - start_freq_ds)/f_resolution ) # 10 MHz corresponds to 800 frequency lines
end_freq_line = int( (end_freq - start_freq_ds)/f_resolution)
start_freq_line1 = int( (start_freq1 - start_freq_ds)/f_resolution ) # 10 MHz corresponds to 800 frequency lines
end_freq_line1 = int( (end_freq1 - start_freq_ds)/f_resolution)
start_freq_line2 = int( (start_freq2 - start_freq_ds)/f_resolution ) # 10 MHz corresponds to 800 frequency lines
end_freq_line2 = int( (end_freq2 - start_freq_ds)/f_resolution)
intensity = np.zeros(( nfiles, no_images ))
intensity1 = np.zeros(( nfiles, no_images ))
intensity2 = np.zeros(( nfiles, no_images ))
ra = np.zeros( nfiles )
dec = np.zeros( nfiles )
INT = np.zeros( nfiles )
INT1 = np.zeros( nfiles )
INT2 = np.zeros( nfiles )
# Background subtraction arrays:
array_quiet = quiet_time_array(0, 0.5)
#array_quiet = np.loadtxt( 'Quiet_time_array_1541.txt' )
median_arr = median_array(obsid + '_SAP000_B070_S0_P000_bf.h5')
###################################################
#extracting intensity information for 3 frequencies
###################################################
count = 0
# looping through beams
for i in xrange(nfiles):
filename = obsid+'_SAP000_B'+str(i).rjust(3,'0')+'_S0_P000_bf.h5'
print filename
f = h5py.File(filename,'r')
# extracting coordinates of individual beam from h5py file
ra[count] = f['SUB_ARRAY_POINTING_000/BEAM_'+str(i).rjust(3,'0')].attrs.values()[17]
dec[count] = f['SUB_ARRAY_POINTING_000/BEAM_'+str(i).rjust(3,'0')].attrs.values()[20] # extracting data at specific frequency in MHz bins, eg 50-55 MHz
# extract entire/big chunk of data set first if normalisation needed later
data = f['SUB_ARRAY_POINTING_000/BEAM_'+str(i).rjust(3,'0')+'/STOKES_0'][start_line_norm:end_line, :]
print data.shape, array_quiet.shape
# normalising to take out the frequency response
for sb in xrange(data.shape[1]):
# very important data normalising step: either by median in each beam
# or by median_array/quiet_time calculated from far-away quiet beam
# can also be calculated at a later stage
data[:,sb] = data[:,sb]/np.median(data[:end_line_norm,sb])#median_arr[sb]#array_quiet[sb]#
data0 = data[start_line-start_line_norm:, start_freq_line:end_freq_line]
data1 = data[start_line-start_line_norm:, start_freq_line1:end_freq_line1]
data2 = data[start_line-start_line_norm:, start_freq_line2:end_freq_line2]
# average over frequency range
data0 = np.mean(data0, axis = 1)
data1 = np.mean(data1, axis = 1)
data2 = np.mean(data2, axis = 1)
for j in xrange(no_images):
# average intensity over N sec
intensity[ count ][ j ] = np.mean(data0[int(j*lines_per_sec*averaging_time):int((j+1)*lines_per_sec*averaging_time)]) # 1 second = lines_per_sec lines of time
intensity1[ count ][ j ] = np.mean(data1[int(j*lines_per_sec*averaging_time):int((j+1)*lines_per_sec*averaging_time)])
intensity2[ count ][ j ] = np.mean(data2[int(j*lines_per_sec*averaging_time):int((j+1)*lines_per_sec*averaging_time)])
count = count + 1
#######################################
#extracting dynamic spectra information
#######################################
for i in xrange(dynamic_spectrum_beam,dynamic_spectrum_beam+1):
filename = obsid+'_SAP000_B'+str(i).rjust(3,'0')+'_S0_P000_bf.h5'
print filename
f = h5py.File(filename,'r')
data = f['SUB_ARRAY_POINTING_000/BEAM_'+str(i).rjust(3,'0')+'/STOKES_0'][start_line:end_line,start_freq_line_ds:end_freq_line_ds]
for sb in xrange(data.shape[1]):
data[:,sb] = data[:,sb]/np.median(data[:,sb])
data = np.transpose(data)
f.close()
##################################################
#plotting the data to check background subtraction
##################################################
print intensity.shape
# time array
time_int = []#np.zeros(intensity.shape[1])
for i in range(intensity.shape[1]):
time_int.append( start_time + datetime.timedelta(seconds = i*averaging_time))
pl.figure(0,figsize=(22,14))
pl.plot( time_int, intensity[dynamic_spectrum_beam, :], color = 'r', label = '40 MHz' )
pl.plot( time_int, intensity1[dynamic_spectrum_beam, :], color = 'b', label = '50 MHz' )
pl.plot( time_int, intensity2[dynamic_spectrum_beam, :], color = 'g', label = '60 MHz' )
pl.legend()
ax = pl.gca()
#ax.set_yscale('log')
ax.xaxis_date()
ax.xaxis.set_major_locator(dates.MinuteLocator())
ax.xaxis.set_major_formatter(dates.DateFormatter('%H:%M:%S'))
ax.xaxis.set_major_locator( MaxNLocator(nbins = 7) )
pl.savefig('Intensity_before.png')
pl.show()
# dynamic spectrum extent
xmin1 = np.min(time_new)
xmax1 = np.max(time_new)
ymin1 = np.min(freq)
ymax1 = np.max(freq)
# looping over time
for i in range(no_images):
pl.figure(1,figsize=(22,14))
'''
plotting dynamic spectrum and time bar
'''
ax1 = pl.subplot2grid((2,3), (0,0), colspan=3)
imshow(data, vmin = zmin , vmax = zmax, aspect='auto', norm = LogNorm(), extent=(xmin1,xmax1,end_freq_ds_plot,start_freq_ds_plot))
xlabel('Start Time: ' + str( start_time.time() ))
ylabel('Frequency (MHz)')
title(str(dynamic_spectrum_beam))
time_im = start_time + datetime.timedelta(seconds = i*averaging_time)
x = np.zeros( len(freq) ) + matplotlib.dates.date2num( time_im )
pl.plot( x, freq, '-w' )
pl.xlim(xmin1, xmax1)
pl.ylim(ymin1, ymax1)
pl.colorbar()
# reversing y axis
ax1.set_ylim(ax1.get_ylim()[::-1])
#setting x axis as a time axis
ax1.xaxis_date()
ax1.xaxis.set_major_locator(dates.MinuteLocator())
ax1.xaxis.set_major_formatter(dates.DateFormatter('%H:%M:%S'))
ax1.xaxis.set_major_locator( MaxNLocator(nbins = 7) )
# coordinate transformation
solar_x, solar_y = coordinate_transformation(ra, dec, time_im)
# finding min/max for plotting
xmin = np.min(solar_x)
xmax = np.max(solar_x)
ymin = np.min(solar_y)
ymax = np.max(solar_y)
'''
plotting images for each frequency slice
'''
################################ 1
INT = intensity2[:,i:i+1].flatten()
# Size of regular grid
ny, nx = 200, 200
# Generate a regular grid to interpolate the data.
xi = np.linspace(xmin, xmax, nx)
deltax = xi[1] - xi[0]
yi = np.linspace(ymin, ymax, ny)
deltay = yi[1] - yi[0]
xi, yi = np.meshgrid(xi, yi)
# Interpolate using delaunay triangularization
zi = matplotlib.mlab.griddata(solar_x,solar_y,INT,xi,yi,interp='linear')#interp='nn'
ax2 = pl.subplot2grid((2,3), (1,0))
pl.pcolormesh(xi,yi,zi, vmin = zmin, vmax = zmax, norm = LogNorm() )
pl.xlabel('X (arcmin)')
pl.ylabel('Y (arcmin)')
pl.title( str(start_freq2) + '-' + str(end_freq2) + ('MHz, Time: ') + str( time_im.time() )[:10] )
# plots the solar limb
SUN = pl.Circle((0,0), radius = 16., color = 'y', fc ='none')
ax2.add_patch( SUN )
ax2.add_patch( SUN )
#print zi.shape, xmin, ymin,deltax, deltay, np.min(zi), np.max(zi)
zi = np.asarray( zi )
# save image as fits file
filename = format( start_min, '.0f' ) + 'min' + '_' + str(start_freq2) + '-' + str(end_freq2) + 'MHz_' + str(i).zfill(4) + '.fits'
make_fits( filename, zi, xmin, ymin, deltax, deltay, str( time_im.date() ) + ' ' + str( time_im.time() ) )
################################ 2
INT1 = intensity1[:,i:i+1].flatten()
# Interpolate using delaunay triangularization
zi = matplotlib.mlab.griddata(solar_x,solar_y,INT1,xi,yi,interp='linear')
ax3 = pl.subplot2grid((2,3), (1,1))
pl.pcolormesh(xi,yi,zi, vmin = zmin, vmax = zmax, norm = LogNorm() )
pl.xlabel('X (arcmin)')
pl.ylabel('Y (arcmin)')
pl.title( str(start_freq1) + '-' + str(end_freq1) + ('MHz, Time: ') + str( time_im.time() )[:10] )
# plots the solar limb
SUN = pl.Circle((0,0), radius = 16., color = 'y', fc ='none')
ax3.add_patch( SUN )
#print zi.shape, xmin, ymin,deltax, deltay, np.min(zi), np.max(zi)
zi = np.asarray( zi )
# save image as fits file
filename = format( start_min, '.0f' ) + 'min' + '_' + str(start_freq1) + '-' + str(end_freq1) + 'MHz_' + str(i).zfill(4) + '.fits'
make_fits( filename, zi, xmin, ymin, deltax, deltay, str( time_im.date() ) + ' ' + str( time_im.time() ) )
################################ 3
INT2 = intensity[:,i:i+1].flatten()
# Interpolate using delaunay triangularization
zi = matplotlib.mlab.griddata(solar_x,solar_y,INT2,xi,yi,interp='linear')
ax4 = pl.subplot2grid((2,3), (1,2))
pl.pcolormesh(xi,yi,zi, vmin = zmin, vmax = zmax, norm = LogNorm() )
pl.xlabel('X (arcmin)')
pl.ylabel('Y (arcmin)')
pl.title( str(start_freq) + '-' + str(end_freq) + ('MHz, Time: ') + str( time_im.time() )[:10] )
# plots the solar limb
SUN = pl.Circle((0,0), radius = 16., color = 'y', fc ='none')
ax4.add_patch( SUN )
#print zi.shape, xmin, ymin,deltax, deltay, np.min(zi), np.max(zi)
zi = np.asarray( zi )
# save image as fits file
filename = format( start_min, '.0f' ) + 'min' + '_' + str(start_freq) + '-' + str(end_freq) + 'MHz_' + str(i).zfill(4) + '.fits'
make_fits( filename, zi, xmin, ymin, deltax, deltay, str( time_im.date() ) + ' ' + str( time_im.time() ) )
pl.savefig(str(i)+'.png')
print i