def test_basic():
lmax = 10
nside = 8
rank = MPI.COMM_WORLD.Get_rank()
ms = np.arange(rank, lmax + 1, MPI.COMM_WORLD.Get_size(), dtype=np.int32)
order = libsharp.packed_real_order(lmax, ms=ms)
grid = libsharp.healpix_grid(nside)
alm = np.zeros(order.local_size())
if rank == 0:
alm[0] = 1
elif rank == 1:
alm[0] = 1
map = libsharp.synthesis(grid,
order,
np.repeat(alm[None, None, :], 3, 0),
comm=MPI.COMM_WORLD)
assert np.all(map[2, :] == map[1, :]) and np.all(map[1, :] == map[0, :])
map = map[0, 0, :]
if rank == 0:
healpy.mollzoom(map)
from matplotlib.pyplot import show
show()
def clean_map(file_name, threshold, show_plot=1):
this_map = hp.read_map(setup.out_files_path + file_name + ".fits")
mask = hp.read_map(setup.out_files_path + "mask.fits")
cleaned_map = np.ma.array(this_map, mask=(mask == 0), fill_value=0.)
cleaned_map = (cleaned_map - threshold) * (cleaned_map > threshold
) #*(cleaned_map < 1e19)
if show_plot:
hp.mollzoom(cleaned_map, title="Cleaned Map : " +
file_name) #, min = 0, max = 1e-4)
plt.show()
return cleaned_map
def compare_power(input_file1,
input_file2,
nside=2048,
lmax=2000,
read_inputs=True,
object1=None,
object2=None,
display_plots=True):
''' simple power spectrum comparison '''
map_dens1 = create_healpix_map(input_file1,
nside,
read_inputs=read_inputs,
object1=object1)
map_dens2 = create_healpix_map(input_file2,
nside,
read_inputs=read_inputs,
object1=object2)
# scaling to remove monopole - this helps with power spectrum computation.
mask_octant = compute_mask(nside=nside, octant="upper")
map_dens1 = scale_map(map_dens1, mask_octant, nside=nside)
map_dens2 = scale_map(map_dens2, mask_octant, nside=nside)
print('comparing density maps')
hp.mollview(map_dens1)
hp.mollview(map_dens2)
hp.mollzoom(map_dens1 - map_dens2)
if display_plots:
plt.show()
alm_1 = hp.map2alm(map_dens1, lmax=lmax)
alm_2 = hp.map2alm(map_dens2, lmax=lmax)
cl_1 = hp.alm2cl(alm_1)
cl_2 = hp.alm2cl(alm_2)
print('computed power spectra')
l = np.arange(lmax + 1)
fsky = 1. / 8
plt.figure()
plt.plot(l, cl_1 * l * (l + 1) / (2. * np.pi) / fsky)
plt.plot(l, cl_2 * l * (l + 1) / (2. * np.pi) / fsky)
plt.savefig('cls_' + input_file1 + '_' + input_file2 + '.png', dpi=500)
if display_plots:
plt.show()
plt.figure()
plt.plot(l, np.abs(cl_1 - cl_2) / cl_1)
plt.savefig('cls_fractional_' + input_file1 + '_' + input_file2 + '.png',
dpi=500)
if display_plots:
plt.show()
return map_dens1, map_dens2, cl_1, cl_2
def main():
nb_comp = 2
NSIDE = 2048
F_matrix = []
for i in range(nb_comp):
F_matrix.append(Total_matrix[i])
print 'F_matrix : %s' % F_matrix
truncated_maps = library.get_truncated_maps(NSIDE)
mask = hp.read_map(setup.out_files_path + 'mask.fits')
for i in range(nb_comp):
#if i == 0:
if True:
print 'Computing component %s' % i
e_vector = list()
for j in range(nb_comp):
e_vector.append(0)
e_vector[i] = 1.
S = library.compute_S_matrix(truncated_maps, F_matrix, e_vector)
# hp.mollview(S[i], title="Component %s" % i, norm='hist')
if i == 0:
hp.gnomview(S, rot=[0, 90])
hp.mollzoom(S, norm='hist')
hp.write_map(
setup.out_files_path + "ILC_SZ_comp_" + str(NSIDE) +
".fits", S)
S = np.ma.array(S, mask=(mask == 0), fill_value=hp.UNSEEN)
hp.mollview(S, title="Component_%s" % i, norm='hist')
library.save_figure('Component_%s' % i, sub_folder='figures/ILC')
del S
plt.show()
return 0
def test_basic():
lmax = 10
nside = 8
rank = MPI.COMM_WORLD.Get_rank()
ms = np.arange(rank, lmax + 1, MPI.COMM_WORLD.Get_size(), dtype=np.int32)
order = libsharp.packed_real_order(lmax, ms=ms)
grid = libsharp.healpix_grid(nside)
alm = np.zeros(order.local_size())
if rank == 0:
alm[0] = 1
elif rank == 1:
alm[0] = 1
map = libsharp.synthesis(grid, order, np.repeat(alm[None, None, :], 3, 0), comm=MPI.COMM_WORLD)
assert np.all(map[2, :] == map[1, :]) and np.all(map[1, :] == map[0, :])
map = map[0, 0, :]
if rank == 0:
healpy.mollzoom(map)
from matplotlib.pyplot import show
show()
mp.clf()
display(convolved_sky, 'Original map', iplot=1)
display(solution_fusion['x'], 'Reconstructed map', iplot=4)
res_fusion = display(solution_fusion['x'] - convolved_sky,
'Difference map', iplot=7)
mp.figure(3)
for res, color in zip((res_qubic, res_fusion), ('blue', 'green')):
for i, kind in enumerate('IQU'):
axis = mp.subplot(3, 1, i+1)
x, y = profile(res[i]**2)
x *= 5 / 60
y = np.sqrt(y)
y *= np.degrees(np.sqrt(4 * np.pi / acq_qubic.scene.npixel))
mp.plot(x, y, color=color)
mp.title(kind)
mp.ylabel('Sensitivity [$\mu$K deg]')
mp.xlabel('Angular distance [degrees]')
# BICEP-2 / Planck
# sigmas = 1.2 * np.array([1 / np.sqrt(2), 1, 1])
sigmas = np.std(acq_planck.sigma, 0)
for i, sigma in enumerate(sigmas):
axis = mp.subplot(3, 1, i+1)
mp.axhline(sigma, color='red')
hp.mollzoom(solution_fusion['x'][:, 0] - convolved_sky[:, 0])
mp.show()
vmin = -cc
vmax = cc
ax.pcolormesh(x_grid, y_grid, arr.T, rasterized=True, vmin=vmin,
vmax=vmax) #, cmap=cmap)
ax.axis('off')
#ax.set_title(title, y=0.975)
# Plot equiangular map
'''fig, ax = plt.subplots(1) #,1)
myplot_moll(f_mw.real, L, ax, title=r'Input CMB map') #map converted to MW')
fig.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.95)
plt.savefig('/Users/keir/Documents/s2let_ilc_latex/s2let_ilc_papers/s2let_ilc_temp/cmb_orig_mollweide.pdf',dpi=1200)'''
#plt.show()
'''LIM = 150
hp.mollzoom(f_ini[0]*1e6,unit=r'$\mu\mathrm{K}$',title=r'Input CMB Stokes $Q$ map',min=-1.*LIM,max=LIM)
plt.savefig('/Users/keir/Documents/s2let_ilc_latex/s2let_ilc_papers/s2let_ilc_pol/fg_353_q.pdf')
hp.mollzoom(f_ini[1]*1e6,unit=r'$\mu\mathrm{K}$',title=r'Input CMB Stokes $U$ map',min=-1.*LIM,max=LIM)
plt.savefig('/Users/keir/Documents/s2let_ilc_latex/s2let_ilc_papers/s2let_ilc_pol/fg_353_u.pdf')'''
# Create giant array figure
plt.rc('font', family='serif', size=14.0)
fig, axs = plt.subplots(nrows,
3,
sharex=True,
sharey=True,
figsize=(8., 6.4 * (nrows / 4.)))
axs = axs.ravel()
# Loop through scales j and directions n
for j in xrange(0, 3):
mp.figure(2)
mp.clf()
display(conv_sky, 'Original map', iplot=1)
display(x_rec_fusion, 'Reconstructed map', iplot=4)
res_fusion = display(x_rec_fusion - conv_sky, 'Difference map', iplot=7)
mp.figure(3)
for res, color in zip((res_qubic, res_fusion), ('blue', 'green')):
for i, kind in enumerate('IQU'):
axis = mp.subplot(3, 1, i + 1)
x, y = profile(res[i]**2)
x *= 5 / 60
y = np.sqrt(y)
y *= np.degrees(np.sqrt(4 * np.pi / acq.scene.npixel))
mp.plot(x, y, color=color)
mp.title(kind)
mp.ylim(0, 1.8)
mp.ylabel('Sensitivity [$\mu$K deg]')
mp.xlabel('Angular distance [degrees]')
# BICEP-2 / Planck
# sigmas = 1.2 * np.array([1 / np.sqrt(2), 1, 1])
sigmas = np.std(acq_planck.sigma, 0)
for i, sigma in enumerate(sigmas):
axis = mp.subplot(3, 1, i + 1)
mp.axhline(sigma, color='red')
hp.mollzoom(x_rec_fusion[:, 0] - conv_sky[:, 0])
mp.show()
display(convolved_sky, 'Original map', iplot=1)
display(solution_fusion['x'], 'Reconstructed map', iplot=4)
res_fusion = display(solution_fusion['x'] - convolved_sky,
'Difference map',
iplot=7)
mp.figure(3)
for res, color in zip((res_qubic, res_fusion), ('blue', 'green')):
for i, kind in enumerate('IQU'):
axis = mp.subplot(3, 1, i + 1)
x, y = profile(res[i]**2)
x *= 5 / 60
y = np.sqrt(y)
y *= np.degrees(np.sqrt(4 * np.pi / acq_qubic.scene.npixel))
mp.plot(x, y, color=color)
mp.title(kind)
mp.ylabel('Sensitivity [$\mu$K deg]')
mp.xlabel('Angular distance [degrees]')
# BICEP-2 / Planck
# sigmas = 1.2 * np.array([1 / np.sqrt(2), 1, 1])
sigmas = np.std(acq_planck.sigma, 0)
for i, sigma in enumerate(sigmas):
axis = mp.subplot(3, 1, i + 1)
mp.axhline(sigma, color='red')
hp.mollzoom(solution_fusion['x'][:, 0] - convolved_sky[:, 0])
mp.show()
# create_healpix_map.py
Create a healpix map showing the Galactic DM contribution.
"""
import healpy as hp
import numpy as np
import pyymw16
import pylab as plt
import os
filename = 'ymw16_allsky.fits'
if os.path.exists(filename):
print("File %s exists, skipping write" % filename)
d_2016 = hp.read_map(filename)
else:
print("Generating healpix map... (this may take a while)")
NSIDE = 128
pix_id = np.arange(hp.nside2npix(NSIDE))
gl, gb = hp.pix2ang(NSIDE, pix_id, lonlat=True)
d_2016 = np.zeros_like(pix_id, dtype='float32')
for ii in pix_id:
dm, tau = pyymw16.dist_to_dm(gl[ii], gb[ii], 30000)
d_2016[ii] = dm.value
print("Writing to %s" % filename)
hp.write_map(filename, d_2016, coord='G')
hp.mollzoom(np.log(d_2016), title='YMW16', cmap='magma')
plt.show()
mp.figure(2)
mp.clf()
display(conv_sky, 'Original map', iplot=1)
display(x_rec_fusion, 'Reconstructed map', iplot=4)
res_fusion = display(x_rec_fusion - conv_sky, 'Difference map', iplot=7)
mp.figure(3)
for res, color in zip((res_qubic, res_fusion), ('blue', 'green')):
for i, kind in enumerate('IQU'):
axis = mp.subplot(3, 1, i+1)
x, y = profile(res[i]**2)
x *= 5 / 60
y = np.sqrt(y)
y *= np.degrees(np.sqrt(4 * np.pi / acq.scene.npixel))
mp.plot(x, y, color=color)
mp.title(kind)
mp.ylim(0, 1.8)
mp.ylabel('Sensitivity [$\mu$K deg]')
mp.xlabel('Angular distance [degrees]')
# BICEP-2 / Planck
# sigmas = 1.2 * np.array([1 / np.sqrt(2), 1, 1])
sigmas = np.std(acq_planck.sigma, 0)
for i, sigma in enumerate(sigmas):
axis = mp.subplot(3, 1, i+1)
mp.axhline(sigma, color='red')
hp.mollzoom(x_rec_fusion[:, 0] - conv_sky[:, 0])
mp.show()
def plot_map(hpxmap, unit='unit'):
mpl.rcParams['image.cmap'] = 'jet'
hp.mollzoom(hpxmap, unit=unit, title='title')
hp.graticule()
# diffAbsMax = diffMax
# if os.path.exists(phiMapFilename):
# print "read phi map"
# phiMap = hp.read_map(phiMapFilename)
# else:
# print "read phi alm"
# phiAlm = hp.read_alm(phiAlmFilename)
# phiMap = hp.alm2map(phiAlm, 2048)
# hp.write_map(phiMapFilename, phiMap)
# diffScheme = colors.SymLogNorm(linthresh=9.e-5, linscale=0.03, vmin=-diffMax, vmax=diffMax)
# normScheme = colors.SymLogNorm(linthresh=1.5e-8, linscale=1., vmin=minTemp, vmax=maxTemp)
print 'plot unlensed'
hp.mollzoom(unlensedMap, xsize=2048, cmap=cibCmap, title="Unlensed @ %.2f<zmax<%.2f smoothed fwhm=0.0035" % (zMinCib,zMaxCib), min=unlensedMin, max=unlensedMax, rot=rotAngle)#, norm=normScheme)
# hp.set_g_clim(minTemp, maxTemp) #sets min/max of zoomed portion
hp.set_g_clim(unlensedMin, unlensedMax)
# plt.savefig('/scratch2/r/rbond/phamloui/pics/jul17_unlensed_fwhm_0.0035/unlensed_zmin%.1f_zmax%.1f.png' % (zMinCib, zMaxCib), dpi=220)
print 'plot lensed'
hp.mollzoom(lensedUnseen, xsize=2048, cmap=cibCmap, title="Unlensed @ %.2f<zmax<%.2f" % (zMinCib,zMaxCib), min=lensedMin, max=lensedMax, rot=rotAngle)#, norm=normScheme)
# hp.set_g_clim(minTemp, maxTemp)
hp.set_g_clim(lensedMin, lensedMax)
# plt.savefig('/scratch2/r/rbond/phamloui/pics/jul17_lensed_fwhm_0.0035/lensed_zmin%.1f_zmax%.1f.png' % (zMinCib, zMaxCib), dpi=220)
# print 'plot diff'
# hp.mollzoom(diffUnseen, xsize=2048, cmap=diffCmap, title="Diff @ %.2f<zmax<%.2f" % (zMinCib,zMaxCib), min=-diffMax, max=diffMax)#, norm=diffScheme)
# hp.set_g_clim(-diffMax, diffMax)
# plt.savefig('/scratch2/r/rbond/phamloui/pics/jul17_diff_fwhm_0.0035/diff_zmin%.1f_zmax%.1f.png' % (zMinCib, zMaxCib), dpi=220)
# print 'plot kappa'
# hp.mollzoom(kappaMap, xsize=2048, cmap=lensingCmap, title="Kappa @ 0.20<zmax<%.2f" % zMax)
# print 'plot phi'
assert (lmax_in >
lmax_out), "New lmax must be less than lmax of input alms"
alm_out = np.zeros(hp.Alm.getsize(lmax_out), dtype=np.complex)
for il in xrange(0, lmax_out + 1):
alm_out[hp.Alm.getidx(lmax_out, il,
np.arange(0, il + 1))] = alm_in[hp.Alm.getidx(
lmax_in, il, np.arange(0, il + 1))]
return alm_out
# Usage:
new_lmax = 8000
alm = lower_lmax(alm, new_lmax)
# Make map from alms
tmap = hp.alm2map(alm, nside)
# Look at map interactively
hp.mollzoom(tmap)
# SPT-only products:
# Read filter transfer functions
tlm = hp.read_alm('tlm_150GHz_lmax10000.fits')
# Read beam file
bl = np.fromfile('bl_gauss_fwhm1p75am_lmax10000.bin')
# Create new beam:
fwhm_arcmin_new = 1.85 # FWHM of new beam
bl_new = hp.gauss_beam(fwhm_arcmin_new / 60. * np.pi / 180., lmax=10000)
fig = plt.figure(1)
hp.mollview(gravPotential, fig=1, xsize=2048, hold=True)
plt.quiver(x[nanIndices],
y[nanIndices],
gradphiI[nanIndices],
gradphiR[nanIndices],
units='x',
width=0.0006,
scale=0.045,
pivot='mid') #lower scale -> larger arrows
plt.figure(2)
hp.mollzoom(unlensed,
fig=2,
xsize=2048,
title="Unlensed",
min=cibMin,
max=cibMax)
hp.set_g_clim(cibMin, cibMax)
plt.figure(3)
hp.mollzoom(lensedUnseen,
fig=3,
xsize=2048,
title="Lensed",
min=cibMin,
max=cibMax)
hp.set_g_clim(cibMin, cibMax)
# plt.figure(4)
# diffMap = lensed - unlensed
# diffRange = np.absolute(diffMap).max()
zMinCib, zMaxCib)
primary_smoothed_file = appendToFilename(primary_file, "fwhm_%s" % _fwhm)
# if not os.path.exists(primary_smoothed_file):
# primary_map = hp.read_map(primary_file)
# hp.mollzoom(primary_map, xsize=2048, title="Unlensed %.1f<z<%.1f" % (zMinCib, zMaxCib))
# print "smoothing primary..."
# primary_map_smoothed = hp.smoothing(primary_map, fwhm=_fwhm)
# hp.mollzoom(primary_map_smoothed, xsize=2048, title="Unlensed smoothed %.1f<z<%.1f" % (zMinCib, zMaxCib))
# plt.show()
# print "writing smoothed primary to file..."
# hp.write_map(primary_smoothed_file, primary_map_smoothed)
# else:
# print "smoothed primary already exists - skipping"
if not os.path.exists(kappa_smoothed_file):
kappa_map = hp.read_map(kappa_file)
hp.mollzoom(kappa_map, xsize=2048, title="Kappa 0.2<z<%.1f" % zMax)
print "smoothing kappa..."
kappa_map_smoothed = hp.smoothing(kappa_map, fwhm=_fwhm)
hp.mollzoom(kappa_map_smoothed,
xsize=2048,
title="kappa smoothed 0.2<z<%.1f" % zMax)
plt.show()
print "writing smoothed kappa to file..."
hp.write_map(kappa_smoothed_file, kappa_map_smoothed)
else:
print "smoothed kappa already exists - skipping"
i += 1
