def convert(map_file, history_file=None):
"""Main function."""
map = algebra.load(map_file)
map = algebra.make_vect(map)
if map.axes != ('freq', 'ra', 'dec'):
raise NotImplementedError("Exepected input map to be organized "
"('freq', 'ra', 'dec').")
new_shape = map.shape[1:] + (map.shape[0], )
# Make the out file name assuming the input file end in .npy. This is a
# hack and someone should fix it sometime.
out_fname = map_file.split('/')[-1][:-4] + '.fits'
Map_fits = data_map.DataMap(sp.rollaxis(map, 0, 3))
# Set axis names
Map_fits.set_field('CTYPE3', 'FREQ--HZ', (), '32A')
Map_fits.set_field('CTYPE1', 'RA---DEG', (), '32A')
Map_fits.set_field('CTYPE2', 'DEC--DEG', (), '32A')
# Copy frequency axis (now the third axis not the first).
Map_fits.set_field('CRVAL3', map.info['freq_centre'], (), 'D')
Map_fits.set_field('CRPIX3', new_shape[2] // 2 + 1, (), 'D')
Map_fits.set_field('CDELT3', map.info['freq_delta'], (), 'D')
# Set the other two axes.
Map_fits.set_field('CRVAL1', map.info['ra_centre'], (), 'D')
Map_fits.set_field('CRPIX1', new_shape[0] // 2 + 1, (), 'D')
Map_fits.set_field('CDELT1', map.info['ra_delta'], (), 'D')
Map_fits.set_field('CRVAL2', map.info['dec_centre'], (), 'D')
Map_fits.set_field('CRPIX2', new_shape[1] // 2 + 1, (), 'D')
Map_fits.set_field('CDELT2', map.info['dec_delta'], (), 'D')
# Copy the file history if provided.
if not history_file is None:
history = hist.read(history_file)
history.add("Converted map to fits.", ("File name: " + out_fname, ))
Map_fits.history = history
# Verify contents and write out.
Map_fits.verify()
fits_map.write(Map_fits, out_fname)
def convert(map_file, history_file=None):
"""Main function."""
map = algebra.load(map_file)
map = algebra.make_vect(map)
if map.axes != ("freq", "ra", "dec"):
raise NotImplementedError("Exepected input map to be organized " "('freq', 'ra', 'dec').")
new_shape = map.shape[1:] + (map.shape[0],)
# Make the out file name assuming the input file end in .npy. This is a
# hack and someone should fix it sometime.
out_fname = map_file.split("/")[-1][:-4] + ".fits"
Map_fits = data_map.DataMap(sp.rollaxis(map, 0, 3))
# Set axis names
Map_fits.set_field("CTYPE3", "FREQ--HZ", (), "32A")
Map_fits.set_field("CTYPE1", "RA---DEG", (), "32A")
Map_fits.set_field("CTYPE2", "DEC--DEG", (), "32A")
# Copy frequency axis (now the third axis not the first).
Map_fits.set_field("CRVAL3", map.info["freq_centre"], (), "D")
Map_fits.set_field("CRPIX3", new_shape[2] // 2 + 1, (), "D")
Map_fits.set_field("CDELT3", map.info["freq_delta"], (), "D")
# Set the other two axes.
Map_fits.set_field("CRVAL1", map.info["ra_centre"], (), "D")
Map_fits.set_field("CRPIX1", new_shape[0] // 2 + 1, (), "D")
Map_fits.set_field("CDELT1", map.info["ra_delta"], (), "D")
Map_fits.set_field("CRVAL2", map.info["dec_centre"], (), "D")
Map_fits.set_field("CRPIX2", new_shape[1] // 2 + 1, (), "D")
Map_fits.set_field("CDELT2", map.info["dec_delta"], (), "D")
# Copy the file history if provided.
if not history_file is None:
history = hist.read(history_file)
history.add("Converted map to fits.", ("File name: " + out_fname,))
Map_fits.history = history
# Verify contents and write out.
Map_fits.verify()
fits_map.write(Map_fits, out_fname)
def execute(self, nprocesses=1) :
"""Worker funciton."""
params = self.params
# Make parent directory and write parameter file.
kiyopy.utils.mkparents(params['output_root'])
parse_ini.write_params(params, params['output_root'] + 'params.ini',
prefix='mm_')
save_noise_diag = params['save_noise_diag']
in_root = params['input_root']
all_out_fname_list = []
all_in_fname_list = []
# Loop over files to process.
for pol_str in params['polarizations']:
dmap_fname = in_root + 'dirty_map_' + pol_str + '.npy'
noise_fname = in_root + 'noise_inv_' + pol_str + '.npy'
all_in_fname_list.append(
kiyopy.utils.abbreviate_file_path(dmap_fname))
all_in_fname_list.append(
kiyopy.utils.abbreviate_file_path(noise_fname))
# Load the dirty map and the noise matrix.
dirty_map = algebra.load(dmap_fname)
dirty_map = algebra.make_vect(dirty_map)
if dirty_map.axes != ('freq', 'ra', 'dec') :
raise ce.DataError("Expeced dirty map to have axes "
"('freq', 'ra', 'dec'), but it has axes: "
+ str(dirty_map.axes))
shape = dirty_map.shape
noise_inv = algebra.open_memmap(noise_fname, 'r')
noise_inv = algebra.make_mat(noise_inv)
# Initialize the clean map.
clean_map = algebra.info_array(sp.zeros(dirty_map.shape))
clean_map.info = dict(dirty_map.info)
clean_map = algebra.make_vect(clean_map)
# If needed, initialize a map for the noise diagonal.
if save_noise_diag :
noise_diag = algebra.zeros_like(clean_map)
# Two cases for the noise. If its the same shape as the map then
# the noise is diagonal. Otherwise, it should be block diagonal in
# frequency.
if noise_inv.ndim == 3 :
if noise_inv.axes != ('freq', 'ra', 'dec') :
raise ce.DataError("Expeced noise matrix to have axes "
"('freq', 'ra', 'dec'), but it has: "
+ str(noise_inv.axes))
# Noise inverse can fit in memory, so copy it.
noise_inv_memory = sp.array(noise_inv, copy=True)
# Find the non-singular (covered) pixels.
max_information = noise_inv_memory.max()
good_data = noise_inv_memory < 1.0e-10*max_information
# Make the clean map.
clean_map[good_data] = (dirty_map[good_data]
/ noise_inv_memory[good_data])
if save_noise_diag :
noise_diag[good_data] = 1/noise_inv_memory[good_data]
elif noise_inv.ndim == 5 :
if noise_inv.axes != ('freq', 'ra', 'dec', 'ra', 'dec') :
raise ce.DataError("Expeced noise matrix to have axes "
"('freq', 'ra', 'dec', 'ra', 'dec'), "
"but it has: "
+ str(noise_inv.axes))
# Arrange the dirty map as a vector.
dirty_map_vect = sp.array(dirty_map) # A view.
dirty_map_vect.shape = (shape[0], shape[1]*shape[2])
frequencies = dirty_map.get_axis('freq')/1.0e6
# Allowcate memory only once.
noise_inv_freq = sp.empty((shape[1], shape[2], shape[1],
shape[2]), dtype=float)
if self.feedback > 1 :
print "Inverting noise matrix."
# Block diagonal in frequency so loop over frequencies.
for ii in xrange(dirty_map.shape[0]) :
if self.feedback > 1:
print "Frequency: ", "%5.1f"%(frequencies[ii]),
if self.feedback > 2:
print ", start mmap read:",
sys.stdout.flush()
noise_inv_freq[...] = noise_inv[ii, ...]
if self.feedback > 2:
print "done, start eig:",
sys.stdout.flush()
noise_inv_freq.shape = (shape[1]*shape[2],
shape[1]*shape[2])
# Solve the map making equation by diagonalization.
noise_inv_diag, Rot = sp.linalg.eigh(noise_inv_freq,
overwrite_a=True)
if self.feedback > 2:
print "done",
map_rotated = sp.dot(Rot.T, dirty_map_vect[ii])
# Zero out infinite noise modes.
bad_modes = noise_inv_diag < 1.0e-5*noise_inv_diag.max()
if self.feedback > 1:
print ", discarded: ",
print "%4.1f"%(100.0*sp.sum(bad_modes)/bad_modes.size),
print "% of modes",
if self.feedback > 2:
print ", start rotations:",
sys.stdout.flush()
map_rotated[bad_modes] = 0.
noise_inv_diag[bad_modes] = 1.0
# Solve for the clean map and rotate back.
map_rotated /= noise_inv_diag
map = sp.dot(Rot, map_rotated)
if self.feedback > 2:
print "done",
sys.stdout.flush()
# Fill the clean array.
map.shape = (shape[1], shape[2])
clean_map[ii, ...] = map
if save_noise_diag :
# Using C = R Lambda R^T
# where Lambda = diag(1/noise_inv_diag).
temp_noise_diag = 1/noise_inv_diag
temp_noise_diag[bad_modes] = 0
# Multiply R by the diagonal eigenvalue matrix.
# Broadcasting does equivalent of mult by diag matrix.
temp_mat = Rot*temp_noise_diag
# Multiply by R^T, but only calculate the diagonal
# elements.
for jj in range(shape[1]*shape[2]) :
temp_noise_diag[jj] = sp.dot(temp_mat[jj,:],
Rot[jj,:])
temp_noise_diag.shape = (shape[1], shape[2])
noise_diag[ii, ...] = temp_noise_diag
# Return workspace memory to origional shape.
noise_inv_freq.shape = (shape[1], shape[2],
shape[1], shape[2])
if self.feedback > 1:
print ""
sys.stdout.flush()
elif noise_inv.ndim == 6 :
raise NotImplementedError("Full noise matrix not yet "
"implemented. Best we can do is "
"block diagonal in frequency.")
else :
raise ce.DataError("Noise matrix has bad shape.")
# Write the clean map to file.
out_fname = params['output_root'] + 'clean_map_' + pol_str + '.npy'
algebra.save(out_fname, clean_map)
all_out_fname_list.append(
kiyopy.utils.abbreviate_file_path(out_fname))
if save_noise_diag :
noise_diag_fname = (params['output_root'] + 'noise_diag_'
+ pol_str + '.npy')
algebra.save(noise_diag_fname, noise_diag)
all_out_fname_list.append(
kiyopy.utils.abbreviate_file_path(noise_diag_fname))
# Finally update the history object.
history = hist.read(in_root + 'history.hist')
history.add('Read map and noise files:', all_in_fname_list)
history.add('Converted dirty map to clean map.', all_out_fname_list)
h_fname = params['output_root'] + "history.hist"
history.write(h_fname)
