def write(fname, data, splits=None, type="auto", maxmaps=1000, **kwargs):
import numpy as np
from pixell import enmap, utils
if type == "auto": type = infer_type(fname)
if type == "zip":
work, flexopen = zipfile.ZipFile(fname, "w"), zip_flexopen
elif type == "dir":
utils.mkdir(fname)
work, flexopen = fname, dir_flexopen
else:
raise ValueError("Unrecognized type '%s'" % str(type))
with flexopen(work, "info.txt", "w") as f:
write_info(f, data)
with flexopen(work, "beam.txt", "w") as f:
np.savetxt(f,
np.array([np.arange(len(data.beam)), data.beam]).T,
fmt="%5.0f %15.7e")
for i, m in enumerate(data.maps):
with flexopen(work, "map%d.fits" % (i + 1), "w") as f:
enmap.write_fits(f, m)
for i, m in enumerate(data.ivars):
with flexopen(work, "ivar%d.fits" % (i + 1), "w") as f:
enmap.write_fits(f, m)
version = 'v6.0.0_uncalibrated_mask_version_padded_v1'
#version = 'v4.0_mask_version_mr3c_20190215_pickupsub_190303' # for s16
# our test data set
#season, array, patch, freq = ('s16', 'pa2', 'cmb', 'f150')
#season, array, patch, freq = ('s13', 'pa1', 'deep1', 'f150')
season, array, patch, freq = ('s15', 'pa3', 'deep56', 'f150')
# We initialize the sim generator with the mask version
simgen = simgen.SimGen(version=version)
# We can then get just signal = cmb + fg (and loop over season,patch,array,sim_num after the above initialization)
# with bench.show("signal"):
# simgen.get_signal(season, patch, array, freq, sim_num=0,mask_patch="patch006")
# Or get just cmb
#imap = simgen.get_cmb(season, patch, array, freq, sim_num= 0,mask_patch="patch006")
# Or get just foregrounds
#simgen.get_fg(season, patch, array, freq, sim_num=0)#,mask_patch="patch006")
# Or get just noise. NOTE: You can't get this per frequency, but you get a stack of maps for the entire dichroic array
with bench.show("noise"):
omap = simgen.get_noise(season, patch, array, sim_num=0) #, freq, 0)
print(omap.shape)
enmap.write_fits("simpa3.fits", omap[:, 0, 0, ...])
# Or signal + noise. Same note as above for stack of dichroic
# @Steve: hopefully this is the only function you will need
#imap = simgen.get_sim(season, patch, array,sim_num=0,mask_patch="patch006")
# simgen = simgen.SimGen(version=version)
# imap = simgen.get_signal(season, patch, array, freq, sim_num=0)
# print(imap.shape)
def project_mask(mask, shape, wcs, fname=None):
sim_mask = enmap.project(mask, shape, wcs, order=1)
if fname != None:
enmap.write_fits(fname, sim_mask, extra={})
np.deg2rad(pfwhm / 60.)),
div=divstamp,
ps=ps,
beam=pfwhm,
n2d=None)
# model = pointsrcs.sim_srcs(stamp.shape, stamp.wcs,
# np.array((dec,ra,famp.reshape(-1)[0]))[None],
# maps.sigma_from_fwhm(np.deg2rad(pfwhm/60.)))
# io.plot_img(np.log10(stamp),"stamp_%d.png" % k)
# io.plot_img(divstamp,"divstamp_%d.png" % k)
# io.plot_img(model,"model_%d.png" % k)
# io.plot_img(stamp-model,"residual_%d.png" % k)
# if k==1: sys.exit()
sdecs.append(dec)
sras.append(ra)
amps.append(famp.reshape(-1)[0])
print(famp, sns[k])
print("Done with source ", k + 1, " / ", len(ras))
srcs = np.stack((sdecs, sras, amps)).T
shape, wcs = imap.shape, imap.wcs
model = pointsrcs.sim_srcs(shape[-2:], wcs, srcs,
maps.sigma_from_fwhm(np.deg2rad(pfwhm / 60.)))
omap = imap - model
mname = fname.replace('.fits', '_pccs_sub_model.fits')
cname = fname.replace('.fits', '_pccs_sub_catalog.txt')
io.save_cols(cname, (sras, sdecs, amps))
enmap.write_fits(mname, model[None])
enmap.write_fits(oname, omap[None])
if numerical_parameters['verbose']:
print('I am in index = ' + str(ii))
# generate a single tSZ map
# Note: the runtime of this function is dominated by the
# mfunc.throw_clusters function, which is just array
# manipulation.
# The usage of numba.jit is very helpful in keeping
# runtime reasonable.
stub = '_{:d}_{:d}'.format(ii, jj)
if numerical_parameters['do_maps']:
sz_map = mfunc.map_generate_final_map(numerical_parameters, \
cosmology_parameters, \
dndOmega, thetas, \
yprofiles, wcss[jj])
enmap.write_fits(path + 'tsz_map' + stub + '.fits', sz_map)
else:
sz_map = enmap.read_map(path + 'tsz_map' + stub + '.fits')
# generates a noise map of the same shape as the final map
if numerical_parameters['do_maps']:
noise_map = \
mfunc.map_generate_random_noise(numerical_parameters, \
cosmology_parameters, \
wcss[jj])
enmap.write_fits(path + 'noise_map' + stub + '.fits',
noise_map)
else:
noise_map = enmap.read_map(path + 'noise_map' + stub +
'.fits')