def main():
import pyfits as fits
from myimage_analysis import bin_image
import matplotlib.pyplot as plt
import numpy as np
av_dir = '/d/bip3/ezbc/perseus/data/av/'
av_data, av_header = fits.getdata(av_dir + \
'perseus_av_planck_5arcmin.fits',
header=True)
av_data[av_data > 1] = np.nan
av_data_binned, av_header_binned = bin_image(av_data,
binsize=(11, 11),
statistic=np.nanmean,
header=av_header)
print av_data.shape, av_data_binned.shape
fits.writeto(av_dir + 'test.fits', av_data_binned, av_header_binned,
clobber=True)
if 1:
plt.imshow(av_data, origin='lower left')
plt.show()
plt.imshow(av_data_binned, origin='lower left')
plt.show()
hi_dir = '/d/bip3/ezbc/perseus/data/hi/'
hi_data, hi_header = fits.getdata(hi_dir + \
'perseus_hi_galfa_cube_regrid_planckres.fits',
header=True)
hi_data[hi_data > 10] = np.nan
hi_data_binned, hi_header_binned = bin_image(hi_data,
binsize=(1, 11, 11),
statistic=np.nanmean,
header=hi_header)
fits.writeto(hi_dir + 'test.fits', hi_data_binned, hi_header_binned,
clobber=True)
plt.imshow(hi_data[500], origin='lower left')
plt.colorbar()
plt.show()
plt.imshow(hi_data_binned[500], origin='lower left')
plt.colorbar()
plt.show()
def plot_co_spectra(results,):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_co_spectra'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(cloud.av_filename_bin, header=True)
cloud.load_region(cloud.region_filename, header=cloud.av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
cloudpy.plot_hi_spectrum(cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
def main():
from myimage_analysis import bin_image, calculate_nhi
from mycoords import make_velocity_axis
from astropy.io import fits
import numpy as np
import mystats
import myio
import pickle
# Location of models
#MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_cmode1_3inc/'
MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_highres_2/'
# If true, deletes files to be written
CLOBBER = 0
os.chdir(MODEL_DIR + 'models')
# Ddelete gipsy files, leaving only fits files
filename_delete_list = os.listdir('./')
for filename in filename_delete_list:
if '.image' in filename or '.descr' in filename:
os.system('rm -rf ' + filename)
# get leftover fits files
filename_init_list = os.listdir('./')
filename_list = []
for filename in filename_init_list:
if 'model' in filename and 'regrid' not in filename:
filename_list.append(filename)
#filename_list = filename_list[:5]
stats = {
'logL': np.empty(len(filename_list)),
'model_names': [],
'std': np.empty(len(filename_list)),
'mean_abs_resid': np.empty(len(filename_list)),
'sum_abs_resid': np.empty(len(filename_list)),
}
cube_name = '749237_rebin_cube_regrid.fits'
unbinned_cube_name = '749237_rebin_cube.fits'
cube_error_name = '749237_rebin_cube_error_regrid.fits'
data, header = \
fits.getdata(MODEL_DIR + \
cube_name,
header=True)
error = \
fits.getdata(MODEL_DIR + \
cube_error_name)
data_sum = np.nansum(data)
#binsize = 6
_, unbinned_header = fits.getdata(MODEL_DIR + '../' + \
unbinned_cube_name,
header=True)
beamsize = unbinned_header['BMAJ']
cdelt = np.abs(unbinned_header['CDELT1'])
binsize = int(beamsize / cdelt)
mask = (np.isnan(data) | np.isnan(error))
# Load the images into miriad
for i, model_name in enumerate(filename_list):
model_bin_name = model_name.replace('.FITS', '_regrid.FITS')
exists = myio.check_file(model_bin_name, clobber=CLOBBER)
if exists:
print('Loading cube:\n' + model_bin_name)
model_bin = fits.getdata(model_bin_name)
else:
print('Binning cube:\n' + model_name)
model = fits.getdata(model_name)
print('\tBinsize = ' + str(binsize))
# bin the model
model_bin = bin_image(model,
binsize=(1, binsize, binsize),
statistic=np.nanmean,
quick_bin=True
)
# normalize the model to have same sum as data
model_bin = model_bin / np.nansum(model_bin) * data_sum
#assert np.nansum(model_bin) == data_sum
# write the model to a file
fits.writeto(model_bin_name,
model_bin,
header,
clobber=CLOBBER)
residuals = model_bin[~mask] - data[~mask]
stats['model_names'].append(model_bin_name)
stats['logL'][i] = mystats.calc_logL(model_bin[~mask],
data[~mask],
data_error=error[~mask])
stats['std'][i] = np.nanstd(residuals)
stats['mean_abs_resid'][i] = np.nanmean(np.abs(residuals))
stats['sum_abs_resid'][i] = np.nansum(np.abs(residuals))
with open(MODEL_DIR + 'statistics.pickle', 'wb') as f:
pickle.dump(stats, f)
with open(MODEL_DIR + 'statistics.pickle', 'rb') as f:
stats = pickle.load(f)
def test_bin_image():
import numpy as np
from numpy.testing import assert_array_almost_equal
from myimage_analysis import bin_image
unbinned = np.arange(0, 12, 1).reshape((3, 4))
#array([[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]])
binned = bin_image(unbinned, binsize=(2, 2), statistic=np.nansum)
answer = np.array([[10, 18],
])
assert_array_almost_equal(binned, answer)
# bin whole axis
binned = bin_image(unbinned, binsize=(3, 1), statistic=np.nansum)
answer = np.array([[12, 15, 18, 21],
])
assert_array_almost_equal(binned, answer)
# Bin
binned = bin_image(unbinned, binsize=(1, 2), statistic=np.nansum)
answer = np.array([[1, 5],
[9, 13],
[17, 21]])
assert_array_almost_equal(binned, answer)
# Test binsize being a float
binned = bin_image(unbinned, binsize=(1.1, 2.0), statistic=np.nansum)
answer = np.array([[1, 5],
[9, 13],
[17, 21]])
assert_array_almost_equal(binned, answer)
# Bin
if 0:
def statistic(image, axis=None):
return np.nansum(image, axis=axis)
binned = bin_image(unbinned, binsize=(1, 2), statistic=statistic)
answer = np.array([[1, 13],
[41, 85],
[145, 221]])
assert_array_almost_equal(binned, answer)
m = np.arange(0,100,1).reshape((10,10))
n = bin_image(m, binsize=(2,2))
answer = np.array([[ 22, 30, 38, 46, 54],
[102, 110, 118, 126, 134],
[182, 190, 198, 206, 214],
[262, 270, 278, 286, 294],
[342, 350, 358, 366, 374]])
assert_array_almost_equal(n, answer)
def plot_hi_spectrum(cloud_results, plot_co=1):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_hi_spectrum'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
if plot_co:
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
av_filename = cloud.av_filename_bin
hi_filename = cloud.hi_filename_bin
else:
av_filename = cloud.av_filename
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(av_filename, header=True)
cloud.hi_data, cloud.hi_header = \
fits.getdata(hi_filename, header=True)
cloud.load_region(cloud.region_filename, header=av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
import matplotlib.pyplot as plt
plt.close(); plt.clf();
co = np.copy(cloud.co_data[30,:,:])
co[co_mask] = np.nan
plt.imshow(co, origin='lower')
plt.savefig('/usr/users/ezbc/Desktop/comap_' + cloud.region + '.png')
assert all((cloud.hi_data.shape, cloud.co_data.shape,
cloud.region_mask.shape))
cloudpy.plot_hi_spectrum(cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
def main():
from myimage_analysis import bin_image, calculate_nhi
from mycoords import make_velocity_axis
from astropy.io import fits
import numpy as np
os.chdir('/d/bip3/ezbc/shield/749237_lowres/')
# If true, deletes files to be written
clobber = 1
# First, change zeros in lee image to nans
in_images = ('749237_rebin_cube.fits',)
# Load the images into miriad
out_images = []
for in_image in in_images:
print('Binning cube:\n' + in_image)
cube, header = fits.getdata(in_image, header=True)
# set freq0 setting
header['FREQ0'] = 1.4204058E+09
header['RESTFREQ'] = 1.4204058E+09
header['CTYPE3'] = 'VELO'
beamsize = header['BMAJ']
cdelt = np.abs(header['CDELT1'])
binsize = int(beamsize / cdelt)
print('\tBinsize = ' + str(binsize))
if 1:
# cube measurement error = 700 uJy/Beam = 0.7 mJy/Beam
# cube flux calibration error = 10%
# add errors quadratically
cube_std = np.nanstd(cube[0, :, :])
cube_error = ((0.1 * cube)**2 + cube_std**2)**0.5
cube_bin, header_bin = bin_image(cube,
binsize=(1, binsize, binsize),
header=header,
statistic=np.nanmean,
)
# cube measurement error = 700 uJy/Beam = 0.7 mJy/Beam
# cube flux calibration error = 10%
# add errors quadratically
cube_bin_std = np.nanstd(cube_bin[0, :, :])
cube_error_bin = ((0.1 * cube_bin)**2 + cube_bin_std**2)**0.5
if 0:
noise_func = lambda x: (1 / np.nansum(x**-2))**0.5
cube_error_bin = bin_image(cube_error,
binsize=(1, binsize, binsize),
statistic=noise_func,
)
fits.writeto(in_image,
cube,
header,
clobber=clobber)
fits.writeto(in_image.replace('cube', 'cube_error'),
cube_error,
header,
clobber=clobber)
fits.writeto(in_image.replace('cube.fits', 'cube_regrid.fits'),
cube_bin,
header_bin,
clobber=clobber)
fits.writeto(in_image.replace('cube', 'cube_error_regrid'),
cube_error_bin,
header_bin,
clobber=clobber)
#else:
# cube_bin, header_bin = \
# fits.getdata(in_image.replace('cube.fits', 'cube_regrid.fits'),
# clobber=clobber, header=True)
# make nhi_image
velocity_axis = make_velocity_axis(header_bin)
# convert to T_B
cube_bin_tb = 1.36 * 21**2 * cube_bin * 1000.0 / \
(header_bin['BMAJ'] * 3600.) / \
(3600. * header_bin['BMIN'])
cube_tb = 1.36 * 21**2 * cube * 1000.0 / \
(header['BMAJ'] * 3600.) / \
(3600. * header['BMIN'])
# convert moment zero images to column density units.
# Recall: 1 K = (7.354E-8)*[Bmaj(")*Bmin(")/lamda^2(m)] Jy/Bm
# Here, units of images are Jy/Bm m/s; cellsize = 2";
# lambda = 0.211061140507 m
# Thus, for the 21 cm line of Hydrogen, we have:
# 1 K = Bmaj(")*Bmin(")/(6.057493205E5) Jy/Bm
# ---- OR ----
# 1 Jy/Bm = (6.057493205E5)/[Bmaj(")*Bmin(")]
# Now, recall that: N_HI = (1.8224E18 cm^-2)*[T_b (K)]*int(dv)
# -- For moment maps in K km/sec, just input the values
# & multiply by coefficient.
# -- Assure that units are Jy/Bm km/sec (i.e., divide by 1000)
# Leave in units of 1E20 cm^-2 by dividing by 1E20:
# For a x beam:
# N_HI (cm^-2) = (image) *
# [(6.057493205E5)/(*)] * (1/1000) * (1.8224E18 cm^-2) *
# (1/1E20)
# N_HI (cm^-2) = (image)*
nhi_image = calculate_nhi(cube_bin_tb,
velocity_axis=velocity_axis,
header=header_bin,
fits_filename=\
in_image.replace('cube.fits', 'nhi_regrid.fits') )
nhi_image = calculate_nhi(cube_tb,
velocity_axis=velocity_axis,
header=header,
fits_filename=\
in_image.replace('cube.fits', 'nhi.fits') )
def plot_co_spectra(results, ):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_co_spectra'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(
co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(
cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(cloud.av_filename_bin, header=True)
cloud.load_region(cloud.region_filename, header=cloud.av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
cloudpy.plot_hi_spectrum(
cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
def plot_hi_spectrum(cloud_results, plot_co=1):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_hi_spectrum'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
if plot_co:
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(
co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(
cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
av_filename = cloud.av_filename_bin
hi_filename = cloud.hi_filename_bin
else:
av_filename = cloud.av_filename
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(av_filename, header=True)
cloud.hi_data, cloud.hi_header = \
fits.getdata(hi_filename, header=True)
cloud.load_region(cloud.region_filename, header=av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
import matplotlib.pyplot as plt
plt.close()
plt.clf()
co = np.copy(cloud.co_data[30, :, :])
co[co_mask] = np.nan
plt.imshow(co, origin='lower')
plt.savefig('/usr/users/ezbc/Desktop/comap_' + cloud.region + '.png')
assert all(
(cloud.hi_data.shape, cloud.co_data.shape, cloud.region_mask.shape))
cloudpy.plot_hi_spectrum(
cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
def main():
from myimage_analysis import bin_image, calculate_nhi
from mycoords import make_velocity_axis
from astropy.io import fits
import numpy as np
import mystats
import myio
import pickle
# Location of models
#MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_cmode1_3inc/'
MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_highres_2/'
# If true, deletes files to be written
CLOBBER = 0
os.chdir(MODEL_DIR + 'models')
# Ddelete gipsy files, leaving only fits files
filename_delete_list = os.listdir('./')
for filename in filename_delete_list:
if '.image' in filename or '.descr' in filename:
os.system('rm -rf ' + filename)
# get leftover fits files
filename_init_list = os.listdir('./')
filename_list = []
for filename in filename_init_list:
if 'model' in filename and 'regrid' not in filename:
filename_list.append(filename)
#filename_list = filename_list[:5]
stats = {
'logL': np.empty(len(filename_list)),
'model_names': [],
'std': np.empty(len(filename_list)),
'mean_abs_resid': np.empty(len(filename_list)),
'sum_abs_resid': np.empty(len(filename_list)),
}
cube_name = '749237_rebin_cube_regrid.fits'
unbinned_cube_name = '749237_rebin_cube.fits'
cube_error_name = '749237_rebin_cube_error_regrid.fits'
data, header = \
fits.getdata(MODEL_DIR + \
cube_name,
header=True)
error = \
fits.getdata(MODEL_DIR + \
cube_error_name)
data_sum = np.nansum(data)
#binsize = 6
_, unbinned_header = fits.getdata(MODEL_DIR + '../' + \
unbinned_cube_name,
header=True)
beamsize = unbinned_header['BMAJ']
cdelt = np.abs(unbinned_header['CDELT1'])
binsize = int(beamsize / cdelt)
mask = (np.isnan(data) | np.isnan(error))
# Load the images into miriad
for i, model_name in enumerate(filename_list):
model_bin_name = model_name.replace('.FITS', '_regrid.FITS')
exists = myio.check_file(model_bin_name, clobber=CLOBBER)
if exists:
print('Loading cube:\n' + model_bin_name)
model_bin = fits.getdata(model_bin_name)
else:
print('Binning cube:\n' + model_name)
model = fits.getdata(model_name)
print('\tBinsize = ' + str(binsize))
# bin the model
model_bin = bin_image(model,
binsize=(1, binsize, binsize),
statistic=np.nanmean,
quick_bin=True)
# normalize the model to have same sum as data
model_bin = model_bin / np.nansum(model_bin) * data_sum
#assert np.nansum(model_bin) == data_sum
# write the model to a file
fits.writeto(model_bin_name, model_bin, header, clobber=CLOBBER)
residuals = model_bin[~mask] - data[~mask]
stats['model_names'].append(model_bin_name)
stats['logL'][i] = mystats.calc_logL(model_bin[~mask],
data[~mask],
data_error=error[~mask])
stats['std'][i] = np.nanstd(residuals)
stats['mean_abs_resid'][i] = np.nanmean(np.abs(residuals))
stats['sum_abs_resid'][i] = np.nansum(np.abs(residuals))
with open(MODEL_DIR + 'statistics.pickle', 'wb') as f:
pickle.dump(stats, f)
with open(MODEL_DIR + 'statistics.pickle', 'rb') as f:
stats = pickle.load(f)