interp_maps module

Script to generate additional 3x2pt field maps by linear interpolation if there are redshift sample points that fall between where Flask generates maps for (i.e. the no. z bins used to generate maps with Flask is less than the total number of redshift sample points). Repeated over a given number of realisations/iterations. This was extremely painful to code so…you’re welcome lol.

catalogue_sim.interp_maps.execute_interpolation(config, ras, decs, field)

Execute the map interpolation. First determine what field to load, then iterate through pairs of Flask maps in the redshift sample to generate interpolated field maps and then save to disk.

Parameters

config(dict): Dictionary of pipeline config parameters
ras(arr): Array of RA values corresponding to each Healpix pixel (following Healpix indexing from 0 -> Npix)
decs(arr): Array of Dec values corresponding to each Healpix pixel (following Healpix indexing from 0 -> Npix)
field(str): Given field type - used to find and load Flask-generated maps

Returns

Executes interpolation of field maps covering given redshift range

catalogue_sim.interp_maps.interp_config(pipeline_variables_path)

Set up a config dictionary to execute the map interpolation based on the catalogue simulation inputs

Parameters

pipeline_variables_path(str): Path to location of set_variables_cat.ini file

Returns

Dictionary of pipeline, 3x2pt and redshift parameters

catalogue_sim.interp_maps.load_map_slices(config, slice_i, slice_j, field_type)

Load a given pair of field maps of a given type from the Flask output to prepare for interpolation between the two maps’ pixel values

Parameters

config(dict): Dictionary of the pipeline parameters used for the catalogue simulation
slice_i(int): Redshift-space ID of the first map to load in for pair-wise pixel interpolation
slice_j(int): Redshift-space ID of the second map to load in for pair-wise pixel interpolation
field_type(str): The given 3x2pt field to interpolate field values for. Must be one of ‘Clustering’, ‘Convergence’, ‘Shear_y1’, or ‘Shear_y2’

Returns

Array containing the map data for the two fields between which to interpolate pixel values

catalogue_sim.interp_maps.main(): Main function to run the interpolation routine. Load in the pipeline variables to prep into a config dictionary, then execute the interpolation for each field type.

catalogue_sim.interp_maps.save_interp_map(config, ras, decs, map_name, map_arr, z_at_slice, field_type)

Save the interpolated field map (Healpix array) of a given type at a given redshift to disk

Parameters

config(dict): Config dictionary of pipeline parameters
ras(arr): Array of RA values corresponding to each Healpix pixel (index ordered from 0 -> Npix)
decs(arr): Array of Dec values corresponding to each Healpix pixel (index ordered from 0 -> Npix)
map_name(str): Name with which to save interpolated map to disk
map_arr(str): The Healpix map array containing the interpolated field values
z_at_slice(float): Redshift at which the interpolated field map is evaluated
field_type(str): Field type of given interpolated map - will dictate which folder on disk to save map into. Must be one of ‘Clustering’, ‘Convergence’, or ‘Shear’. If type is ‘Shear’, assumes that the map_arr array is of the form [map_arr_1, map_arr_2], corresponding to the two shear components y1, y2

Returns

Saves interpolated field maps

catalogue_sim.interp_maps.setup_interpolation(config, field, pair_type, pair_id=0)

Set up the interpolation functions depending on where the maps are located in redshift-space. If the pair of maps to interpolate between are at the wings of the redshift distribution we perform a linear extrapolation to fill in additional redshift sample points. If the pair of maps are anywhere in the ‘middle’ of the redshift distribution (i.e.) not the pairs on either end, then set up a linear interpolation.

Parameters

config(dict): Config dict on pipeline parameters
field(str): Given field type - used to find directory to load Flask compiled maps and set up interpolation functions
pair_type(str): ‘First’, ‘Middle’, or ‘Last’ - pair type of Flask maps to set up either interpolation or extrapolation
pair_id(float): The ‘ID’ corresponding to where the maps are in redshift-space

Returns

Loads given pairs of field maps at given id (redshift)