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sunpy__synthetic_image.py
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sunpy__synthetic_image.py
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#!/usr/bin/env python3
""" Defines the synthetic_image class to add image realism to the idealized sunrise images.
The synthetic_image class defines a number of routines to take the original image and
convolve it with some psf function, add sky noise, rebin to an appropate pixel scale
(based on telescope), scale to an appropriate image size (based on a petrosian radius
calculation, and add background image.
The majority of the code in this file was developed by Greg Snyder and can be found in
Snyder et al., (2015), http://arxiv.org/abs/1502.07747
"""
import numpy as np
import os
import sys
import math
import gc
try:
import astropy.io.fits as fits
print("Booting up the universe...")
except:
try:
import pyfits as fits
print("loaded pyfits")
except:
print("Error: Unable to access PyFITS or AstroPy modules.")
print("Add PyFITS to your site-packages with:")
print("% pip install pyfits\n")
print(" or ")
print("% easy_install pyfits\n")
print(" or ")
print("download at: www.stsci.edu/institute/software_hardware/pyfits/Download\n")
import cosmocalc
import scipy as sp
import scipy.ndimage
import scipy.signal
import scipy.interpolate
try:
import astropy.convolution.convolve as convolve
CONVOLVE_TYPE = 'astropy'
print("Pumping gas...")
from astropy.convolution import *
except:
try:
from scipy.signal import convolve2d as convolve
CONVOLVE_TYPE = 'scipy'
print("loaded scipy.signal.convolve2d; note that the astropy.convolution.convolve() function is preferred. There may be unexpected sub-pixel or off-by-one behavior with this scipy function.")
except:
print("Error: Unable to access SciPy or AstroPy convolution modules.")
import sunpy__load
import time
import wget
import warnings
__author__ = "Paul Torrey and Greg Snyder"
__copyright__ = "Copyright 2014, The Authors"
__credits__ = ["Paul Torrey", "Greg Snyder"]
__license__ = "GPL"
__version__ = "1.0"
__maintainer__ = "Paul Torrey"
__email__ = "ptorrey@mit.harvard.edu"
__status__ = "Production"
if __name__ == '__main__': # code to execute if called from command-line
pass # do nothing
verbose = False
abs_dist = 0.01
erg_per_joule = 1e7
speedoflight_m = 2.99e8
m2_to_cm2 = 1.0e-4
n_arcsec_per_str = 4.255e10 # (radian per arc second)^2
n_pixels_galaxy_zoo = 424 # changed this to match hst wfc ir
###########################################################
# SDSS background images created by Greg Snyder on 6/18/14#
# SDSS background obtained from: data.sdss3.org/mosaics #
# Ra = 175.0
# Dec = 30.0
# Size (deg) = 0.5
# Pixel Scale = 0.24 "/pixel
#
# HST backgrounds provided by Erica Nelson and Pascal Oesch
# and integrated here by P. Torrey
# #########################################################
dl_base = "http://www.illustris-project.org/files/backgrounds"
bg_base = 'data'
backgrounds = [ [], [], # GALEX 0 1
[bg_base + '/SDSS_backgrounds/J113959.99+300000.0-u.fits'], # 2 SDSS-u
[bg_base + '/SDSS_backgrounds/J113959.99+300000.0-g.fits'], # 3 SDSS-g
[bg_base + '/SDSS_backgrounds/J113959.99+300000.0-r.fits'], # 4 SDSS-r
[bg_base + '/SDSS_backgrounds/J113959.99+300000.0-i.fits'], # 5 SDSS-i
[bg_base + '/SDSS_backgrounds/J113959.99+300000.0-z.fits'], # 6 SDSS-z
[], [], [], [], # 7-8-9-10 IRAC
# 11-12-13-14-15-16-17-18 JOHNSON/COUSINS + 2 mass
[], [], [], [], [], [], [], [], [], [],
[bg_base + '/HST_backgrounds/xdf_noise_775W_30mas.fits'], # 21 ACS-435
[bg_base + '/HST_backgrounds/GOODSN_F606W.fits'], # 22 ACS-606
[bg_base + '/HST_backgrounds/xdf_noise_F775W_30mas.fits'], # 23 ACS-775
[bg_base + '/HST_backgrounds/xdf_noise_F775W_30mas.fits'], # 24 ACS-850
[bg_base + '/HST_backgrounds/XDF_F105W.fits'], # 25 f105w
[bg_base + '/HST_backgrounds/XDF_F125W.fits'], # 26 f125w
[bg_base + '/HST_backgrounds/XDF_F160W.fits'], # 27 f160w
[], [], [], [], [], [], [], [] # NIRCAM
]
bg_zpt = {"u_SDSS.res": [22.5],
"g_SDSS.res": [22.5],
"r_SDSS.res": [22.5],
"i_SDSS.res": [22.5],
"z_SDSS.res": [22.5],
"ACS_F435_NEW.res": [25.69], # 25.16
"ACS_F606_NEW.res": [25.69], # 26.67
"ACS_F775_NEW.res": [25.69], # 26.41
"ACS_F850_NEW.res": [25.69], # 25.95
"f105w.IR.res": [26.27], # 27.69
"f125w.IR.res": [26.23], # 28.02
"f160w.IR.res": [25.94], #
'NIRCAM_prelimfiltersonly_F277W': [29.00],
'NIRCAM_prelimfiltersonly_F356W': [29.00],
'NIRCAM_prelimfiltersonly_F444W': [29.00] } # 28.19
# bg_zpt = [ [], [], # GALEX
# [22.5],
# [22.5],
# [22.5],
# [22.5],
# [22.5],
# [], [], [], [], # 7-8-9-10 IRAC
# [], [], [], [], [], [], [], [], [], [], # 11-12-13-14-15-16-17-18 JOHNSON/COUSINS + 2 mass
# [25.69],
# [25.69],
# [25.69],
# [25.69],
# [25.69],
# [25.69],
# [25.69],
# [], [], [], [], [], [], [], [] # NIRCAM
# ]
def build_synthetic_image(filename, band, r_petro_kpc=None, openlist=None, **kwargs):
""" build a synthetic image from a SUNRISE fits file and return the image to the user """
obj = synthetic_image(filename, band=band, #this function uses the synthetic image class (defined below)
r_petro_kpc=r_petro_kpc, openlist=openlist, **kwargs)
image = obj.bg_image.return_image()
rp = obj.r_petro_kpc
seed = obj.seed
failed = obj.bg_failed
fitsfn = obj.fitsfn
openlist = obj.openlist
del obj
gc.collect()
return image, rp, seed, failed, fitsfn, openlist
def load_resolved_broadband_apparent_magnitudes(filename, redshift, camera=0, seed=12345, n_bands=36, **kwargs):
""" loads n_band x n_pix x n_pix image array with apparent mags for synthetic images """
mags = sunpy.sunpy__load.load_all_broadband_photometry(filename, camera=0)
for band in np.arange(n_bands):
obj = synthetic_image(filename, band=int(
band), seed=seed, redshift=redshift, **kwargs)
img = obj.bg_image.return_image() # muJy / str
if band == 0:
n_pixels = img.shape[0]
all_images = np.zeros((n_bands, n_pixels, n_pixels))
all_images[band, :, :] = img # muJy / str
pixel_in_sr = (1e3 * obj.bg_image.pixel_in_kpc / 10.0)**2
all_images *= pixel_in_sr / 1e6 # in Jy
for band in np.arange(n_bands):
# total image flux in Jy
tot_img_in_Jy = np.sum(all_images[band, :, :])
abmag = -2.5 * np.log10(tot_img_in_Jy / 3631)
if verbose:
print("the ab magnitude of band " + str(band) +
" is :" + str(abmag) + " " + str(mags[band]))
print(abmag / mags[band], abmag - mags[band])
print(" ")
# abmag in each pixel
all_images = -2.5 * np.log10(all_images / 3631)
dist = (cosmocalc.cosmocalc(redshift, H0=70.4,
WM=0.2726, WV=0.7274))['DL_Mpc'] * 1e6
dist_modulus = 5.0 * (np.log10(dist) - 1.0)
apparent_magnitudes = dist_modulus + all_images
del mags, obj, img, n_pixels, all_images, pixel_in_sr, tot_img_in_Jy, abmag, dist, dist_modulus
gc.collect()
return apparent_magnitudes
class synthetic_image:
""" main class for loading and manipulating SUNRISE data into real data format """
def __init__(self,
filename, band=0, camera=0,
redshift=0.01,
psf_fwhm_arcsec=0.13, pixelsize_arcsec=0.12,
r_petro_kpc=None, save_fits=False,
seed=None,
add_background=True,
add_psf=True,
add_noise=True,
rebin_phys=True,
rebin_gz=True,
n_target_pixels=n_pixels_galaxy_zoo,
resize_rp=True,
sn_limit=29.0,
sky_sig=None,
verbose=False,
fix_seed=True,
bg_tag=None,
bb_label='broadband_',
output_label='',
psf_fits=None,
psf_pixsize_arcsec=None,
psf_truncate_pixels=None,
psf_hdu_num=0,
custom_fitsfile=None,
bb_header=None,
openlist=None,
jwst=False,
**kwargs):
if (not os.path.exists(filename)):
print("file not found:", filename)
sys.exit()
start_time = time.time()
self.filename = filename
self.cosmology = cosmology(redshift)
self.telescope = telescope(psf_fwhm_arcsec, pixelsize_arcsec, psf_fits,
psf_pixsize_arcsec, rebin_phys, add_psf, psf_truncate_pixels, psf_hdu_num)
band_names = sunpy__load.load_broadband_names(filename)
hdulist = fits.open(filename)
if type(band) is not int:
band = int(
np.where([this_band == band for this_band in band_names])[0][0])
self.camera = camera
self.band = band
self.band_name = band_names[band]
self.image_header = hdulist['CAMERA' +
str(camera) + '-BROADBAND-NONSCATTER'].header
bb_header = self.image_header
self.broadband_header = hdulist['BROADBAND'].header
self.param_header = hdulist['CAMERA' +
str(camera) + '-PARAMETERS'].header
self.int_quant_data = hdulist['INTEGRATED_QUANTITIES'].data
self.filter_data = hdulist['FILTERS'].data
self.lambda_eff = (self.filter_data['lambda_eff'])[band]
self.ewidth_lambda = (self.filter_data['ewidth_lambda'])[band]
self.ewidth_nu = (self.filter_data['ewidth_nu'])[band]
self.sunrise_absolute_mag = (
self.filter_data['AB_mag_nonscatter' + str(self.camera)])[band]
hdulist.close()
#============= DECLARE ALL IMAGES HERE =================#
self.sunrise_image = single_image() # orig sunrise image
self.psf_image = single_image() # supersampled image + psf convolution
self.rebinned_image = single_image() # rebinned by appropriate pixel scale
self.noisy_image = single_image() # noise added via gaussian draw
self.nmag_image = single_image() # converted to nanomaggies units
self.rp_image = single_image() # scale image based on rp radius criteria (for GZ)
# add backgrounds (only possible for 5 SDSS bands at the moment)
self.bg_image = single_image()
#============ SET ORIGINAL IMAGE ======================#
all_images, self.openlist = sunpy__load.load_all_broadband_images(
filename, camera=camera, openlist=openlist)
# to_nu = ((self.lambda_eff**2 ) / (speedoflight_m)) #* pixel_area_in_str
to_nu = (self.ewidth_lambda / self.ewidth_nu)
# 1 muJy/str (1Jy = 1e-26 W/m^2/Hz)
to_microjanskies = (1.0e6) * to_nu * (1.0e26)
this_image = all_images[band, :, :]
this_image = this_image * to_microjanskies # to microjanskies / str
if True: # verbose:
print("SUNRISE calculated the abmag for this system to be: {:.2f}".format(
self.filter_data.AB_mag_nonscatter0[band]))
self.sunrise_image.init_image(
this_image, self, comoving_to_phys_fov=False)
# assume now that all images are in micro-Janskies per str
self.convolve_with_psf(add_psf=add_psf)
# self.add_gaussian_psf(add_psf=add_psf) add_gaussian_psf now called in convolve_with_psf, if appropriate
self.rebin_to_physical_scale(rebin_phys=rebin_phys)
self.add_noise(add_noise=add_noise, sn_limit=sn_limit, sky_sig=sky_sig)
self.calc_r_petro(r_petro_kpc=r_petro_kpc, resize_rp=resize_rp)
self.resize_image_from_rp(resize_rp=resize_rp)
self.seed = seed
self.bg_failed = False
self.seed = self.add_background(seed=self.seed, add_background=add_background,
rebin_gz=rebin_gz, n_target_pixels=n_target_pixels, fix_seed=fix_seed)
end_time = time.time()
# print "init images + adding realism took "+str(end_time - start_time)+" seconds"
num_label = len(bb_label)
if verbose:
print("preparing to save " + filename[:filename.index(bb_label)] + 'synthetic_image_' + filename[filename.index(
bb_label) + num_label:filename.index('.fits')] + '_band_' + str(self.band) + '_camera_' + str(camera) + '_' + str(int(self.seed)) + '.fits')
if save_fits:
if custom_fitsfile != None:
self.save_bgimage_fits_mujyas(
custom_fitsfile, add_noise=add_noise, add_background=add_background)
self.fitsfn = custom_fitsfile
else:
orig_dir = filename[:filename.index('broadband')]
if bg_tag != None:
outputfitsfile = orig_dir + output_label + 'synthetic_image_' + filename[filename.index(bb_label) + num_label:filename.index(
'.fits')] + '_band_' + str(self.band) + '_camera_' + str(camera) + '_bg_' + str(int(bg_tag)) + '.fits'
else:
outputfitsfile = orig_dir + output_label + 'synthetic_image_' + filename[filename.index(bb_label) + num_label:filename.index(
'.fits')] + '_band_' + str(self.band) + '_camera_' + str(camera) + '_bg_' + '.fits'
self.save_bgimage_fits(outputfitsfile)
self.fitsfn = outputfitsfile
del self.sunrise_image, self.psf_image, self.rebinned_image, self.noisy_image, self.nmag_image, self.rp_image
gc.collect()
def convolve_with_psf(self, add_psf=True):
if add_psf:
if self.telescope.psf_fits_file != None:
# first, rebin to psf pixel scale
n_pixel_orig = self.sunrise_image.n_pixels
n_pixel_new = self.sunrise_image.n_pixels * \
self.sunrise_image.pixel_in_arcsec / self.telescope.psf_pixsize_arcsec
# print np.sum(self.sunrise_image.image)
new_image = congrid(self.sunrise_image.image,
(n_pixel_new, n_pixel_new))
# print np.sum(new_image)
# second, convolve with PSF
if CONVOLVE_TYPE == 'astropy':
# astropy.convolution.convolve()
print("convolving with astropy")
conv_im = convolve_fft(new_image, self.telescope.psf_kernel, boundary='fill',
fill_value=0.0, normalize_kernel=True) # boundary option?
# print np.sum(conv_im)
else:
# scipy.signal.convolve2d()
conv_im = convolve(new_image, self.telescope.psf_kernel / np.sum(
self.telescope.psf_kernel), boundary='fill', fillvalue=0.0, mode='same') # boundary option?
self.psf_image.init_image(conv_im, self)
del new_image, conv_im
else:
self.add_gaussian_psf(add_psf=add_psf)
else:
self.psf_image.init_image(self.sunrise_image.image, self)
gc.collect()
# operates on sunrise_image -> creates psf_image
def add_gaussian_psf(self, add_psf=True, sample_factor=1.0):
if add_psf:
current_psf_sigma_pixels = self.telescope.psf_fwhm_arcsec * \
(1.0 / 2.355) / self.sunrise_image.pixel_in_arcsec
# want the psf sigma to be resolved with (at least) 8 pixels...
if current_psf_sigma_pixels < 8:
target_psf_sigma_pixels = 8.0
n_pixel_new = np.floor(
self.sunrise_image.n_pixels * target_psf_sigma_pixels / current_psf_sigma_pixels)
if n_pixel_new > 1500: # for speed, beyond this, the PSF is already very small...
n_pixel_new = 1500
target_psf_sigma_pixels = n_pixel_new * \
current_psf_sigma_pixels / self.sunrise_image.n_pixels
new_image = congrid(self.sunrise_image.image,
(n_pixel_new, n_pixel_new))
current_psf_sigma_pixels = target_psf_sigma_pixels * (
(self.sunrise_image.n_pixels * target_psf_sigma_pixels
/ current_psf_sigma_pixels) / n_pixel_new)
else:
new_image = self.sunrise_image.image
psf_image = np.zeros_like(new_image) * 1.0
dummy = sp.ndimage.filters.gaussian_filter(new_image,
current_psf_sigma_pixels, output=psf_image, mode='constant')
self.psf_image.init_image(psf_image, self)
del new_image, psf_image, dummy
else:
self.psf_image.init_image(self.sunrise_image.image, self)
gc.collect()
def rebin_to_physical_scale(self, rebin_phys=True):
if rebin_phys:
n_pixel_new = np.floor((self.psf_image.pixel_in_arcsec /
self.telescope.pixelsize_arcsec) * self.psf_image.n_pixels)
rebinned_image = congrid(
self.psf_image.image, (n_pixel_new, n_pixel_new))
self.rebinned_image.init_image(rebinned_image, self)
del n_pixel_new, rebinned_image
gc.collect()
else:
self.rebinned_image.init_image(self.psf_image.image, self)
def add_noise(self, add_noise=True, sky_sig=None, sn_limit=29.0):
if add_noise:
if sky_sig == None:
total_flux = np.sum(self.rebinned_image.image)
area = 1.0 * self.rebinned_image.n_pixels * self.rebinned_image.n_pixels
sky_sig = np.sqrt((total_flux / sn_limit)**2 / (area**2))
noise_image = sky_sig * \
np.random.randn(self.rebinned_image.n_pixels,
self.rebinned_image.n_pixels)
new_image = self.rebinned_image.image + noise_image
self.noisy_image.init_image(new_image, self)
del noise_image, new_image
gc.collect()
else:
self.noisy_image.init_image(self.rebinned_image.image, self)
def calc_r_petro(self, r_petro_kpc=None, resize_rp=True): # rename to "set_r_petro"
" this routine is not working well. Must manually set r_p until this is fixed..."
if (resize_rp == False):
r_petro_kpc = 1.0
elif(r_petro_kpc == None):
RadiusObject = RadialInfo(
self.noisy_image.n_pixels, self.noisy_image.image)
# do this outside of the RadialInfo class'
r_petro_kpc = RadiusObject.PetroRadius * self.noisy_image.pixel_in_kpc
if verbose:
print(" we've calculated a r_p of " + str(r_petro_kpc))
del RadiusObject
gc.collect()
else:
r_petro_kpc = r_petro_kpc
if r_petro_kpc < 3.0:
r_petro_kpc = 3.0
if r_petro_kpc > 100.0:
r_petro_kpc = 100.0
r_petro_pixels = r_petro_kpc / self.noisy_image.pixel_in_kpc
self.r_petro_pixels = r_petro_pixels
self.r_petro_kpc = r_petro_kpc
def resize_image_from_rp(self, resize_rp=True, resize_factor=1, max_rp=1000.0):
if resize_rp:
if self.r_petro_kpc < max_rp:
# The target scale; was 0.008, upping to 0.016 for GZ based on feedback
rp_pixel_in_kpc = resize_factor * self.r_petro_kpc
else:
self.r_petro_kpc = max_rp
rp_pixel_in_kpc = resize_factor * self.r_petro_kpc
Ntotal_new = int((self.noisy_image.pixel_in_kpc /
rp_pixel_in_kpc) * self.noisy_image.n_pixels)
rebinned_image = congrid(
self.noisy_image.image, (Ntotal_new, Ntotal_new))
diff = n_pixels_galaxy_zoo - Ntotal_new #
if diff >= 0:
shift = int(np.floor(1.0 * diff / 2.0))
lp = shift
up = shift + Ntotal_new
tmp_image = np.zeros(
(n_pixels_galaxy_zoo, n_pixels_galaxy_zoo))
tmp_image[lp:up,
lp:up] = rebinned_image[0:Ntotal_new, 0:Ntotal_new]
rp_image = tmp_image
else:
shift = int(np.floor(-1.0 * diff / 2.0))
lp = int(shift)
up = int(shift + n_pixels_galaxy_zoo)
rp_image = rebinned_image[lp:up, lp:up]
self.rp_image.init_image(rp_image, self, fov=(
1.0 * n_pixels_galaxy_zoo) * (resize_factor * self.r_petro_kpc))
del rebinned_image, rp_image
gc.collect()
else:
self.rp_image.init_image(
self.noisy_image.image, self, fov=self.noisy_image.pixel_in_kpc * self.noisy_image.n_pixels)
def add_background(self, seed=1, add_background=True, rebin_gz=True, n_target_pixels=n_pixels_galaxy_zoo, fix_seed=True):
if add_background and (len(backgrounds[self.band]) > 0):
bg_image = 10.0 * self.rp_image.image # dummy values for while loop condition
tot_bg = np.sum(bg_image)
tot_img = np.sum(self.rp_image.image)
tol_fac = 1.0
while(tot_bg > tol_fac * tot_img):
#=== load *full* bg image, and its properties ===#
bg_filename = (backgrounds[self.band])[0]
if not (os.path.isfile(bg_filename)):
# Note from RM: If I try to wget the files from the URLs given below, I get
# "Not Found" errors. So I think these are wrong and should be updated.
print(" Background files were not found... ")
print(
" The standard files used in Torrey al. (2015), Snyder et al., (2015) and Genel et al., (2014) ...")
print(
" can be downloaded using the download_backgrounds routine or manually from: ")
print(
" http://illustris.rc.fas.harvard.edu/data/illustris_images_aux/backgrounds/SDSS_backgrounds/J113959.99+300000.0-u.fits ")
print(
" http://illustris.rc.fas.harvard.edu/data/illustris_images_aux/backgrounds/SDSS_backgrounds/J113959.99+300000.0-g.fits ")
print(" ")
# was pyfits.open(bg_filename)
file = fits.open(bg_filename)
header = file[0].header
pixsize = get_pixelsize_arcsec(header)
print("Pixel Size:",pixsize)
Nx = header.get('NAXIS1')
Ny = header.get('NAXIS2')
#=== figure out how much of the image to extract ===#
Npix_get = np.floor(self.rp_image.n_pixels *
self.rp_image.pixel_in_arcsec / pixsize) # number of pixels *
# this is in some native units (nmaggies, for SDSS )
im = file[0].data
halfval_i = np.floor(np.float(Nx) / 1.3)
halfval_j = np.floor(np.float(Ny) / 1.3)
np.random.seed(seed=int(seed))
starti = np.random.randint(5, halfval_i) #changed to randint, random_integer deprecated
startj = np.random.randint(5, halfval_j)
# the extracted patch...
bg_image_raw = im[int(starti):int(starti) +
int(Npix_get), int(starti):int(starti) + int(Npix_get)]
#=== need to convert to microJy / str ===#
# if you got your zero points right, this is now in muJy
bg_image_muJy = bg_image_raw * \
10.0**(-0.4 * (bg_zpt[self.band_name][0] - 23.9))
pixel_area_in_str = pixsize**2 / n_arcsec_per_str
bg_image = bg_image_muJy / pixel_area_in_str
#=== need to rebin bg_image ===#
bg_image = congrid(
bg_image, (self.rp_image.n_pixels, self.rp_image.n_pixels))
#=== compare sum(bg_image) to sum(self.rp_image.image) ===#
if (fix_seed):
# if seed is fixed, no need for brightness check...
tot_bg = 0
else:
tot_bg = np.sum(bg_image)
tot_img = np.sum(self.rp_image.image)
if(tot_bg > tol_fac * tot_img):
seed += 1
new_image = bg_image + self.rp_image.image
new_image[new_image < self.rp_image.image.min()
] = self.rp_image.image.min() # does this match the minimum pixel values?
if (new_image.mean() > (5 * self.rp_image.image.mean())):
self.bg_failed = True
del im, bg_image_raw, bg_image_muJy
else:
new_image = self.rp_image.image
if rebin_gz:
new_image = congrid(new_image, (n_target_pixels, n_target_pixels))
self.bg_image.init_image(
new_image, self, fov=self.rp_image.pixel_in_kpc * self.rp_image.n_pixels) # background FOV field of view
del new_image
gc.collect()
return seed
def save_bgimage_fits(self, outputfitsfile, save_img_in_muJy=False):
""" Written by G. Snyder 8/4/2014 to output FITS files from Sunpy module """
theobj = self.bg_image
image = np.copy(theobj.return_image()) # in muJy / str
pixel_area_in_str = theobj.pixel_in_arcsec**2 / n_arcsec_per_str
image *= pixel_area_in_str # in muJy
print(np.sum(image))
if save_img_in_muJy == False:
print(bg_zpt[self.band_name])
if len(bg_zpt[self.band_name]) > 0:
image = image / \
(10.0**(-0.4 * (bg_zpt[self.band_name][0] - 23.9)))
print((10.0**(-0.4 * (bg_zpt[self.band_name][0] - 23.9))))
else:
print('saving image in muJy!!!!!')
print(" ")
print(" ")
print(image.shape)
print(np.sum(image))
# print 22.5 - 2.5*np.log10( np.sum(image) )
# print -2.5*np.log10( np.sum(image) )
print(" ")
primhdu = fits.PrimaryHDU(image)
primhdu.header.set('IMUNIT', 'NMAGGIE', comment='approx 3.63e-6 Jy')
# THIS SHOULD BE CORRECT FOR NANOMAGGIE IMAGES ONLY
primhdu.header.set('ABABSZP', 22.5, 'For Final Image')
# primhdu.header.update(
# 'ORIGZP', theobj.ab_abs_zeropoint, 'For Original Image')
primhdu.header.set(
'PIXSCALE', theobj.pixel_in_arcsec, 'For Final Image, arcsec')
primhdu.header.set(
'PIXORIG', theobj.camera_pixel_in_arcsec, 'For Original Image, arcsec')
primhdu.header.set('PIXKPC', theobj.pixel_in_kpc, 'KPC')
primhdu.header.set(
'ORIGKPC', self.sunrise_image.pixel_in_kpc, 'For Original Image, KPC')
primhdu.header.set('NPIX', theobj.n_pixels)
primhdu.header.set('NPIXORIG', self.sunrise_image.n_pixels)
primhdu.header.set('REDSHIFT', self.cosmology.redshift)
primhdu.header.set('LUMDIST', self.cosmology.lum_dist, 'MPC')
primhdu.header.set('ANGDIST', self.cosmology.ang_diam_dist, 'MPC')
primhdu.header.set('PSCALE', self.cosmology.kpc_per_arcsec, 'KPC')
primhdu.header.set('H0', self.cosmology.H0)
primhdu.header.set('WM', self.cosmology.WM)
primhdu.header.set('WV', self.cosmology.WV)
primhdu.header.set(
'PSFFWHM', self.telescope.psf_fwhm_arcsec, 'arcsec')
primhdu.header.set(
'TPIX', self.telescope.pixelsize_arcsec, 'arcsec')
primhdu.header.set('FILTER', self.band_name)
primhdu.header.set('FILE', self.filename)
# primhdu.update_ext_name('SYNTHETIC_IMAGE')
primhdu.header.name = "SYNTHETIC_IMAGE"
# primhdu.header[keyword] = value
# Optionally, we can save additional images alongside these final ones
# e.g., the raw sunrise image below
# simhdu = pyfits.ImageHDU(self.sunriseimage, header=self.image_header) ; zhdu.update_ext_name('SIMULATED_IMAGE')
# newlist = pyfits.HDUList([primhdu, simhdu])
# create HDU List container
newlist = fits.HDUList([primhdu])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# save container to file, overwriting as needed
newlist.writeto(outputfitsfile, clobber=True)
def save_bgimage_fits_mujyas(self, outputfitsfile, save_img_in_muJy=False, add_noise=False, add_background=False):
""" Written by G. Snyder 8/4/2014 to output FITS files from Sunpy module """
""" Updated 9/24/2015 """
theobj = self.bg_image
image = np.copy(theobj.return_image()) # in muJy / str
image *= 1.0 / n_arcsec_per_str # in muJy/Arcsec**2
sunobj = self.sunrise_image
sunimage = np.copy(sunobj.return_image())
sunimage *= 1.0 / n_arcsec_per_str
# print theobj.pixel_in_arcsec
AB_zeropoint = -2.5 * np.log10(theobj.pixel_in_arcsec**2) - 2.5 * \
(-6.0) + 2.5 * np.log10(3631.0) # for image in muJy/Arcsec**2
total_apparent_mag = -2.5 * np.log10(np.sum(image)) + AB_zeropoint
total_absolute_mag = -2.5 * \
np.log10(np.sum(image)) + AB_zeropoint - \
self.cosmology.distance_modulus
sunrise_absolute_mag = self.sunrise_absolute_mag
sun_AB_app_zp = -2.5 * \
np.log10(sunobj.pixel_in_arcsec**2) - 2.5 * \
(-6.0) + 2.5 * np.log10(3631.0)
sun_AB_cam_zp = -2.5 * \
np.log10(sunobj.camera_pixel_in_arcsec**2) - \
2.5 * (-6.0) + 2.5 * np.log10(3631.0)
sun_AB_abs_zp = sun_AB_app_zp - self.cosmology.distance_modulus
sunrise_image_camera_mag = -2.5 * \
np.log10(np.sum(sunimage)) + sun_AB_cam_zp
sunrise_image_apparent_mag = -2.5 * \
np.log10(np.sum(sunimage)) + sun_AB_app_zp
sunrise_image_absolute_mag = -2.5 * \
np.log10(np.sum(sunimage)) + sun_AB_abs_zp
primhdu = fits.PrimaryHDU(np.float32(image))
primhdu.header.update('IMUNIT', 'muJy/SqArcsec',
comment='microjanskies per square arcsecond')
primhdu.header.update('ABZP', round(
AB_zeropoint, 6), 'For Final Image')
primhdu.header.update('PIXSCALE', round(
theobj.pixel_in_arcsec, 6), 'For Final Image, arcsec')
primhdu.header.update('PIXORIG', round(
theobj.camera_pixel_in_arcsec, 6), 'For Original Image, arcsec')
primhdu.header.update('PIXKPC', round(theobj.pixel_in_kpc, 6), 'KPC')
primhdu.header.update('ORIGKPC', round(
self.sunrise_image.pixel_in_kpc, 6), 'For Original Image, KPC')
primhdu.header.update('NPIX', theobj.n_pixels)
primhdu.header.update('NPIXORIG', self.sunrise_image.n_pixels)
primhdu.header.update('REDSHIFT', self.cosmology.redshift)
primhdu.header.update('LUMDIST', round(
self.cosmology.lum_dist, 6), 'MPC')
primhdu.header.update('ANGDIST', round(
self.cosmology.ang_diam_dist, 6), 'MPC')
primhdu.header.update('PSCALE', round(
self.cosmology.kpc_per_arcsec, 6), 'KPC')
primhdu.header.update('DISTMOD', round(
self.cosmology.distance_modulus, 6), 'Mag')
primhdu.header.update('H0', round(self.cosmology.H0, 6))
primhdu.header.update('WM', round(self.cosmology.WM, 6))
primhdu.header.update('WV', round(self.cosmology.WV, 6))
if self.telescope.psf_fits_file == None:
primhdu.header.update('PSFFWHM', round(
self.telescope.psf_fwhm_arcsec, 6), 'arcsec')
else:
primhdu.header.update('PSFFILE', os.path.join(os.path.basename(os.path.dirname(
self.telescope.psf_fits_file)), os.path.basename(self.telescope.psf_fits_file)))
primhdu.header.update('TPIX', round(
self.telescope.pixelsize_arcsec, 6), 'arcsec')
primhdu.header.update('FILTER', self.band_name)
primhdu.header.update('FILE', self.filename)
primhdu.header.update('EFLAMBDA', round(
self.lambda_eff * 1.0e6, 6), 'filter effective wavelength [microns]')
primhdu.header.update('MAG', round(total_apparent_mag, 6), 'AB system')
primhdu.header.update('ABSMAG', round(
total_absolute_mag, 6), 'AB system')
primhdu.header.update('SUNMAG', round(
sunrise_absolute_mag, 6), 'from spectrum, Note: excludes Lyman absorption')
primhdu.header.update('SUNCMAG', round(
sunrise_image_camera_mag, 6), 'from image, camera mag')
primhdu.header.update('SUNAPMAG', round(
sunrise_image_apparent_mag, 6), 'from image, apparent mag')
primhdu.header.update('SUABSMAG', round(
sunrise_image_absolute_mag, 6), 'from image, absolute mag')
if add_noise == False and add_background == False:
primhdu.header.update('SKYSIG', 0.0, 'image units')
elif sky_sig != None:
primhdu.header.update('SKYSIG', round(
self.sky_sig, 6), 'image units')
if add_background == True:
primhdu.header.update(
'BGFILE', os.path.basename(backgrounds[self.band]))
camera_param_cards = self.param_header.cards[13:]
for card in camera_param_cards:
# print card
primhdu.header.append(card)
primhdu.update_ext_name('SYNTHETIC_IMAGE')
# Optionally, we can save additional images alongside these final ones
# e.g., the raw sunrise image below
# simhdu = pyfits.ImageHDU(self.sunriseimage, header=self.image_header) ; zhdu.update_ext_name('SIMULATED_IMAGE')
# newlist = pyfits.HDUList([primhdu, simhdu])
# create HDU List container
newlist = fits.HDUList([primhdu])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# save container to file, overwriting as needed
newlist.writeto(outputfitsfile, clobber=True)
def get_pixelsize_arcsec(header):
cd1_1 = header.get('CD1_1') # come in degrees
cd1_2 = header.get('CD1_2')
if cd1_2 == None:
cd1_2 = header.get('CD2_2')
try:
pix_arcsec = 3600.0 * (cd1_1**2 + cd1_2**2)**0.5
except:
pix_arcsec = 0.06
return pix_arcsec
from scipy.optimize import curve_fit
def my_fit(r, a, b, c):
return a * np.exp(-r / b) + c
class RadialInfo:
""" Class for giving radial profile info for rp calculations """
def __init__(self, N, image, num_pts=100, max_pixels_for_fit=10000):
self.Npix = N
self.RadiusGrid = np.linspace(0.01, 1.0 * N, num=num_pts)
self.PetroRatio = np.ones_like(self.RadiusGrid)
xgrid = np.linspace(float(-self.Npix) / 2.0 + 0.5,
float(self.Npix) / 2.0 - 0.5, num=self.Npix)
xsquare = np.zeros((self.Npix, self.Npix))
ysquare = np.zeros_like(xsquare)
ones = np.ones((self.Npix, self.Npix))
for j in range(self.Npix):
xsquare[j, :] = xgrid
ysquare[:, j] = xgrid
self.rsquare = (xsquare**2 + ysquare**2)**0.5
x0 = np.array(self.rsquare).flatten()
y0 = np.array(image).flatten()
# print x0.shape
x0 = x0[y0 > 0]
y0 = y0[y0 > 0]
if x0.shape[0] > max_pixels_for_fit:
index_list = np.arange(x0.shape[0])
index_list = np.random.choice(index_list, max_pixels_for_fit)
x0 = x0[index_list]
y0 = y0[index_list]
popt, pcov = curve_fit(my_fit, x0, np.log10(y0))
y1 = 10.0**(my_fit(self.RadiusGrid, *popt))
fake_image1 = 10.0**(my_fit(self.rsquare, *popt))
y2 = 10.0**(my_fit(self.RadiusGrid, *popt)) - 10.0**popt[2]
fake_image2 = 10.0**(my_fit(self.rsquare, *popt)) - 10.0**popt[2]
y1sum = np.zeros_like(y1)
y2sum = np.zeros_like(y2)
for index, val in enumerate(y1[:-1]):
if index == 0:
this_r = 0.5 * \
(self.RadiusGrid[index] + self.RadiusGrid[index + 1])
y1sum[index] = 3.14159 * this_r**2 * y1[index]
y2sum[index] = 3.14159 * this_r**2 * y2[index]
else:
y1sum[index] = y1sum[index - 1] + 3.14159 * \
(self.RadiusGrid[index + 1]**2 - self.RadiusGrid[index]
** 2) * (y1[index] + y1[index + 1]) / 2.0
y2sum[index] = y2sum[index - 1] + 3.14159 * \
(self.RadiusGrid[index + 1]**2 - self.RadiusGrid[index]
** 2) * (y2[index] + y2[index + 1]) / 2.0
for index, val in enumerate(y1sum[:-1]):
this_r = (
0.5 * (self.RadiusGrid[index] + self.RadiusGrid[index + 1]))
y1sum[index] = val / (3.14159 * this_r ** 2)
y2sum[index] = y2sum[index] / (3.14159 * this_r ** 2)
self.PetroRatio = np.array(y2 / y2sum)
self.PetroRatio[np.isnan(self.PetroRatio)] = 0.0
self.PetroRatio[np.isinf(self.PetroRatio)] = 0.0
self.Pind = np.argmin(np.absolute(np.flipud(self.PetroRatio) - 0.2))
self.PetroRadius = np.flipud(self.RadiusGrid)[self.Pind]
if verbose:
print(y2)
print(" Saving Figure ...")
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(22, 5))
ax = fig.add_subplot(1, 5, 1)
print(x0.shape)
ax.plot(x0, y0, 'ro', ms=5)
ax.plot(self.RadiusGrid, y1, 'g', lw=3, ls='-')
ax.plot(self.RadiusGrid, y2, 'b', lw=3, ls='-')
ax.plot(self.RadiusGrid, y1sum, 'g', lw=3, ls='-.')
ax.plot(self.RadiusGrid, y2sum, 'b', lw=3, ls='-.')
ax.plot([self.PetroRadius, self.PetroRadius],
[1, 1e20], 'k', lw=1, ls='-')
ax.set_yscale('log')
ax.set_ylim([1e7, 1e13])
ax = fig.add_subplot(1, 5, 2)
ax.plot(self.RadiusGrid, y1 / y1sum, 'g', lw=3)
ax.plot(self.RadiusGrid, y2 / y2sum, 'b', lw=3)
ax.plot([self.PetroRadius, self.PetroRadius],
[-10, 10], 'k', lw=1, ls='-')
ax.plot(self.RadiusGrid, np.ones_like(self.RadiusGrid) * 0.2)
ax.set_ylim([0, 1])
ax = fig.add_subplot(1, 5, 3)
ax.imshow(np.log10(image), vmin=7, vmax=13)
ax = fig.add_subplot(1, 5, 4)
ax.imshow(np.log10(fake_image1), vmin=7, vmax=13)
ax = fig.add_subplot(1, 5, 5)
ax.imshow(np.log10(fake_image2), vmin=7, vmax=13)
fig.savefig('temp1.png')
fig.clf()
plt.close()
# print " Figure has been saved ... "
# for i,rad in enumerate(self.RadiusGrid):
# tf_annulus = np.logical_and( self.rsquare < 1.25*rad, self.rsquare > 0.80*rad )
# tf_annulus = np.logical_and( tf_annulus, image > min_img_thresh )
# self.annulus_indices.append( np.where(tf_annulus) ) #np.logical_and( self.rsquare < 1.25*rad, self.rsquare > 0.80*rad )) )#
# tf_int = np.logical_and( self.rsquare < rad, image > min_img_thresh )
# self.interior_indices.append( np.where(tf_int) )
# self.annulus_sums.append( np.sum(ones[self.annulus_indices[i]] ) )
# self.interior_sums.append( np.sum(ones[self.interior_indices[i]]) )
# this_sum = np.sum( image[self.interior_indices[i]])
# for radius in RadiusObject.RadiusGrid:
# pflux_annulus = image[ self.annulus_indices[i] ]
# pflux_interior = image[ self.interior_indices[i] ]
# self.sumI_r[i] = np.sum(pflux_interior)
# if(self.annulus_sums[i]*self.interior_sums[i] != 0.0):
# self.AnnulusSB[i] = (np.sum(pflux_annulus)/self.annulus_sums[i])
# self.IntSB[i] = (np.sum(pflux_interior)/self.interior_sums[i])
# self.PetroRatio[i] = (np.sum(pflux_annulus)/self.annulus_sums[i])/(np.sum(pflux_interior)/self.interior_sums[i])
class fits_header:
def __init__(self, filename):
if (not os.path.exists(filename)):
print("file not found:", filename)
sys.exit()
hdulist = fits.open(filename)
self.info = hdulist.info()
def my_fits_open(filename):
if (not os.path.exists(filename)):
print("file not found:", filename)
sys.exit()
return fits.open(filename)
#============ COSMOLOGY PARAMETERS =====================#
# cosmology class:
#
# used to track (i) the cosmological parameters and
# (ii) image properties set by our adopted cosmology
#
# This class is used to distinguish features of the telescope
# (e.g., pixel size in arcseconds) from features of our
# adopted cosmology (e.g.,image kpc per arcsec)
#
#=======================================================#
class cosmology:
def __init__(self, redshift, H0=70.4, WM=0.2726, WV=0.7274):
self.H0 = H0
self.WM = WM
self.WV = WV
self.redshift = redshift
self.lum_dist = (cosmocalc.cosmocalc(self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))[
'DL_Mpc'] # luminosity dist in mpc
print(self.lum_dist, "Megaparsecs away")
self.ang_diam_dist = (cosmocalc.cosmocalc(
self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['DA_Mpc']
self.kpc_per_arcsec = (cosmocalc.cosmocalc(
self.redshift, H0=self.H0, WM=self.WM, WV=self.WV))['PS_kpc']
self.distance_modulus = 5.0 * (np.log10(self.lum_dist * 1.0e6) - 1.0)
#============ TELESCOPE PARAMETERS =====================#
# telescope class:
#
# used to track the psf size in arcsec and pixelsize in arcsec
#=======================================================#
class telescope:
def __init__(self, psf_fwhm_arcsec, pixelsize_arcsec, psf_fits, psf_pixsize_arcsec, rebin_phys, add_psf, psf_truncate_pixels, psf_hdu_num):
self.psf_fwhm_arcsec = psf_fwhm_arcsec
self.pixelsize_arcsec = pixelsize_arcsec
self.psf_truncate_pixels = psf_truncate_pixels
self.psf_hdu_num = psf_hdu_num