Covariance

These stages compute covariances of measurements

class txpipe.covariance.TXFourierGaussianCovariance(args, comm=None, aliases=None)[source]

Compute a Gaussian Fourier-space covariance with TJPCov using f_sky only

It imports TJPCov to do so, and runs at a fiducial cosmology.

This version does not account for mask geometry, only the total sky area measured.

Inputs:

  • fiducial_cosmology: FiducialCosmology

  • twopoint_data_fourier: SACCFile

  • tracer_metadata: HDFFile

Outputs:

  • summary_statistics_fourier: SACCFile

Parallel: No - Serial

Configuration
  • pickled_wigner_transform: (str) Default=.
  • use_true_shear: (bool) Default=False.
  • galaxy_bias: (list) Default=[0.0].
  • gaussian_sims_factor: (list) Default=[1.0].
class txpipe.covariance.TXRealGaussianCovariance(args, comm=None, aliases=None)[source]

Compute a Gaussian real-space covariance with TJPCov using f_sky only

This version does not account for mask geometry, only the total sky area measured.

It is implemented as a subclass of the Fourier-space version, so also uses TJPCov and a fiducial cosmology.

Inputs:

  • fiducial_cosmology: FiducialCosmology

  • twopoint_data_real: SACCFile

  • tracer_metadata: HDFFile

Outputs:

  • summary_statistics_real: SACCFile

Parallel: No - Serial

Configuration
  • min_sep: (float) Default=2.5.
  • max_sep: (int) Default=250.
  • nbins: (int) Default=20.
  • pickled_wigner_transform: (str) Default=.
  • use_true_shear: (bool) Default=False.
  • galaxy_bias: (list) Default=[0.0].
  • gaussian_sims_factor: (list) Default=[1.0].
class txpipe.covariance.TXFourierTJPCovariance(args, comm=None, aliases=None)[source]

Compute a Gaussian Fourier-space covariance with TJPCov using mask geometry

This also calls out to TJPCov, using more recent additions to that package.

This version, for speed, re-uses the workspace objects cached in the twopoint fourier measurement stage.

Inputs:

  • fiducial_cosmology: FiducialCosmology

  • twopoint_data_fourier: SACCFile

  • tracer_metadata_yml: YamlFile

  • mask: MapsFile

  • density_maps: MapsFile

  • source_maps: MapsFile

Outputs:

  • summary_statistics_fourier: SACCFile

Parallel: Yes - MPI

Configuration
  • galaxy_bias: (list) Default=[0.0].
  • IA: (float) Default=0.5.
  • cache_dir: (str) Default=.
  • cov_type: (list) Default=['FourierGaussianNmt', 'FourierSSCHaloModel'].
class txpipe.covariance_nmt.TXFourierNamasterCovariance(args, comm=None, aliases=None)[source]

Compute a Gaussian Fourier-space covariance with NaMaster

This functionality duplicates that of TXFourierTJPCovariance, and we should rationalize.

Inputs:

  • fiducial_cosmology: FiducialCosmology

  • twopoint_data_fourier: SACCFile

  • tracer_metadata: HDFFile

  • mask: MapsFile

Outputs:

  • summary_statistics_fourier: SACCFile

Parallel: Yes - MPI

Configuration
  • pickled_wigner_transform: (str) Default=.
  • use_true_shear: (bool) Default=False.
  • scratch_dir: (str) Default=temp.
  • nside: (int) Default=1024.
class txpipe.covariance_nmt.TXRealNamasterCovariance(args, comm=None, aliases=None)[source]

Compute a Gaussian real-space covariance with NaMaster

We don’t yet have another stage for this, but should rationalize when comparing to TJPCov.

Inputs:

  • fiducial_cosmology: FiducialCosmology

  • twopoint_data_real: SACCFile

  • tracer_metadata: HDFFile

  • mask: MapsFile

Outputs:

  • summary_statistics_real: SACCFile

Parallel: Yes - MPI

Configuration
  • min_sep: (float) Default=2.5.
  • max_sep: (int) Default=250.
  • nbins: (int) Default=20.
  • pickled_wigner_transform: (str) Default=.
  • use_true_shear: (bool) Default=False.
  • galaxy_bias: (list) Default=[0.0].