utilities package

Submodules

utilities.IOfiles module

utilities.IOfiles.flagging_not_in_allCES(CES_pixs)

Flag all the pixels which are not in common in the considered CES.

utilities.IOfiles.flagging_subscan(unflagged_pix, subscan)

Flag all the samples outside a subscan.

utilities.IOfiles.plot_histogram_eigenvalues(z)

save a plot containing an histogram of the eigenvalues z

utilities.IOfiles.read_from_data(filename, pol, npairs=None)

Read a hdf5 file of one Constant Elevation Scan preprocessed by the AnalysisBackend of the Polarbear Collaboration.

Parameters

• filename:{str}

  path to the hdf5 file

• pol:{int} - 1: read data for intensity data; - 2: read data for polarization data; - 3: read for both intensity and polarization data;

• npairs:{int}

  set how many bolo_pairs to read, default is None.

utilities.IOfiles.read_from_data_with_subscan_resize(filename, pol, npairs=None)

Read a hdf5 file preprocessed by the AnalysisBackend of the Polarbear Collaboration by considering, as chunks of data, only the subscan samples.

Parameters

• filename:{str}

  path to the hdf5 file

• pol:{int} - 1: read data for temperature only data; - 2,3: read for polarization data;

• npairs:{int}

  set how many bolo_pairs to read, default is None.

utilities.IOfiles.read_from_hdf5(filename)

Read from a hdf5 file whose datasets are created by the routine utilities_functions.system_setup()

utilities.IOfiles.read_multiple_ces(filelist, pol, npairs=None, filtersubscan=True)

Read a list of hdf5 files of multiple CES scans preprocessed by the AnalysisBackend of the Polarbear Collaboration.

Parameters

• filelist:{list of str}

  list containing the path to the hdf5 files

• pol:{int} - 1: read data for intensity data; - 2: read data for polarization data; - 3: read for both intensity and polarization data;

• npairs:{int}

  set how many bolo_pairs to read, default is None.

• filtersubscan:{bool}

  activate the subscan selection on to data (default True).

utilities.IOfiles.read_obspix_from_hdf5(filename)

read from hdf5 file the obspix array containing the observed pixels in the Healpix ordering .

utilities.IOfiles.read_ritz_eigenvectors_from_hdf5(filename)

read from hdf5 file the approximated eigenvectors related to the deflation subspace.

utilities.IOfiles.show_matrix_form(A)

Explicit the components of the Linear Operator A as a matrix.

utilities.IOfiles.write_obspix_to_hdf5(filename, obspix)

Save into hdf5 file the obspix array

utilities.IOfiles.write_ritz_eigenvectors_to_hdf5(z, filename)

Save to a file the approximated eigenvectors computed via the deflationlib.arnoldi() routine.

utilities.IOfiles.write_to_hdf5(filename, obs_pixels, noise_values, d, phi=None)

Write onto hdf5 file whose datasets are created by the routine utilities_functions.system_setup().

utilities.healpy_functions module

utilities.healpy_functions.compare_maps(outm, inm, pol, patch, figname=None, remove_offset=True, norm=None)

Output on device or in file the input map, the output one processed from datastream and their difference.

Parameters

• outm :{array,list}

  map in the .fits format;

• inm:{array,list}

  input .fits map to be compared with outm;

• pol : {int}

  see show_map();

• patch: {str}

  Key to a dictionary to get the equatorial coordinates given a name patch, see show_map();

• mask:{array}

  binary map (0=unobserved, 1=observed pixels);

• figname : {str}

  If unset, outputs on screen;

• remove_offset:{bool}

  If True removes the monopole from the input map,`inm`, in the observed region;

• norm : {str}

  key to the normalization of the color scale, ( None, hist, log)

utilities.healpy_functions.obspix2mask(obspix, nside, fname=None)

From the observed pixels to a binary mask, (mask[obspix]=1 , 0 elsewhere)

Parameters

• osbpix:{array}

  pixels observed during the scanning of the telescope and considered as not pathological (ordering in the HEALPIX pixelization).

• nside: {int}

  Healpix parameter to define the pixelization grid of the map

• fname:{str}

  path to the fits file to write the map, if set it writes onto the file

Returns

• mask :{array}

utilities.healpy_functions.reorganize_map(mapin, obspix, npix, nside, pol, fname=None)

From the solution map of the preconditioner to a Healpix map. It specially splits the input array mapin which is a IQU for a polarization analysis in to 3 arrays i,q,u.

Parameters

• mapin:{array}

  solution array map (size=npix*pol);

• obspix:{array}

  array containing the observed pixels in the Healpix ordering;

• npix:{int}

• nside: {int}

  the same as in obspix2mask;

• pol:{int}

• fname:{str}

Returns

• healpix_map:{list of arrays}

  pixelized map with Healpix.

utilities.healpy_functions.show_map(outm, pol, patch, figname=None, norm=None, title=None)

Output the map outm to screen or to a file.

Parameters

• outm :

  map in the fullsky format;

• pol : {int}

• patch: {str}

  Key to a dictionary to get the equatorial coordinates given a name patch (Polarbear collaboration is now observing in 3 patches: ra23, ra12, lst4p5);

• figname : {str}

  If unset, outputs on screen;

• norm : {str}

  key to the normalization of the color scale, ( None, hist, log)

utilities.healpy_functions.subtract_offset(mapp, obspix, pol)

remove the average from the observed pixels of mapp.

utilities.linear_algebra_funcs module

utilities.linear_algebra_funcs.dgemm(A, B)

Compute Matrix-Matrix multiplication from the BLAS routine DGEMM If A ,B are ordered as lists it convert them as matrices via the `` numpy.asarray`` function.

utilities.linear_algebra_funcs.get_legendre_polynomials(polyorder, size)

Returns a size x polyorder matrix whose columns contain the respective Legendre polynomial in :math:`left[ -1,1

ight` normalized.

utilities.linear_algebra_funcs.norm2(q)

Compute the euclidean norm of an array q by calling the BLAS routine

utilities.linear_algebra_funcs.scalprod(a, b)

Scalar product of two vectors a and b.

utilities.utilities_functions module

utilities.utilities_functions.angles_gen(theta0, n, sample_freq=200.0, whwp_freq=2.5)

Generate polarization angle given the sample frequency of the instrument, the frequency of HWP and the size n of the timestream.

class utilities.utilities_functions.bash_colors

This class contains the necessary definitions to print to bash screen with colors. Sometimes it can be useful...

BOLD = '\x1b[1m'

ENDC = '\x1b[0m'

FAIL = '\x1b[91m'

HEADER = '\x1b[95m'

OKBLUE = '\x1b[94m'

OKGREEN = '\x1b[92m'

UNDERLINE = '\x1b[4m'

WARNING = '\x1b[93m'

blue(string)

bold(string)

fail(string)

green(string)

header(string)

underline(string)

warning(string)

utilities.utilities_functions.checking_output(info)

utilities.utilities_functions.filter_warnings(wfilter)

wfilter: {string} - “ignore”: never print matching warnings; - “always”: always print matching warnings

utilities.utilities_functions.is_sorted(seq)

Check if sequence is sorted bool

utilities.utilities_functions.noise_val(nb, bandwidth=1)

Generate elements to fill the noise covariance matrix with a random ditribution .

Parameters

• nb : {int}

  number of noise stationary intervals, i.e. number of blocks in N_tt’.

• bandwidth : {int}

  the width of the diagonal band,e.g. :

  • bandwidth=1 define the first up and low diagonal terms
  • bandwidth=2 2 off diagonal terms.

Returns

• t: {list of arrays }

  shape=(nb,bandwidth)

• diag : {list }, size = nb

  diagonal values of each block .

utilities.utilities_functions.output_profile(pr)

Output of the profiling with profile_run().

Parameter

• pr:

  instance returned by profile_run()

utilities.utilities_functions.pairs_gen(nrows, ncols)

Generate random int numbers to fill the pointing matrix for observed pixels. Implemented even for polarization runs.

utilities.utilities_functions.profile_run()

Profile the execution with cProfile

utilities.utilities_functions.rescalepixels(pixs)

utilities.utilities_functions.subscan_resize(data, subscan)

Resize a tod-size array by considering only the subscan intervals.

utilities.utilities_functions.system_setup(nt, npix, nb)

Setup the linear system

Returns

• d :{array}

  a nt array of random numbers;

• pairs: {array }

  the non-null indices of the pointing matrix;

• phi :{array}

  angles if pol=3

• t,diag : {outputs of noise_val()}

  noise values to construct the noise covariance matrix

Module contents

This module contains the utilities functions strictly related to the computation, the input/output.