Twopoint correlations

class txpipe.twopoint.Measurement(corr_type, object, i, j)

Bases: tuple

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

static __new__(_cls, corr_type, object, i, j)

Create new instance of Measurement(corr_type, object, i, j)

__repr__()

Return a nicely formatted representation string

corr_type

Alias for field number 0

i

Alias for field number 2

j

Alias for field number 3

object

Alias for field number 1

class txpipe.twopoint.TXTwoPoint(*args: Any, **kwargs: Any)[source]

Bases: ceci.

This is the base stage for real-space two-point correlations.

Input files:

  • shear_tomography_catalog: Shear catalog.

  • shear_photoz_stack: Shear photoz information.

  • lens_tomography_catalog: Lens catalog.

  • lens_photoz_stack: Lens photoz information.

  • random_cats: Random catalog for lenses.

  • patch_centers: Patch centers for Jackknife.

  • tracer_metadata: Metadata for tracers.

Return files:

  • twopoint_data_real_raw: Sacc file with all two-point correlations including jackknife covariance matrices.

  • twopoint_gamma_x: This is the gamma_x component, for null tests, its kept seperate since it is only needed for null tests.

call_treecorr(i, j, k)[source]

This is a wrapper for interaction with treecorr.

get_patch_dir(input_tag, b)[source]

Select a patch directory for the file with the given input tag and with a bin number/label.

To ensure that if you change the catalog the patch dir will also change, the directory path includes the unique ID of the input file.

Parameters

  • input_tag (str) – One of the tags in the class’s inputs attribute

  • b (any) – An additional label used as the last component in the returned directory

Returns

str

Return type

a directory, which has been created if it did not exist already.

prepare_patches(calcs)[source]

For each catalog to be generated, have one process load the catalog and write its patch files out to disc. These are then re-used later by all the different processes.

Parameters

calcs (list) – A list of (bin1, bin2, bin_type) where bin1 and bin2 are indices or bin labels and bin_type is one of the constants SHEAR_SHEAR, SHEAR_POS, or POS_POS.

read_nbin()[source]

Determine the bins to use in this analysis, either from the input file or from the configuration.

run()[source]

Run the analysis for this stage.