hpmcm.shear_utils module

hpmcm.shear_utils.makeMatchedShearSourceCatalogs(source_base_name, match_base_name)

Use the associations to join the source tables to their match obects

Return type:

dict[str, DataFrame]

Parameters:

• source_base_name (str) – _base file name for souces catalogs

• match_base_name (str) – _base file naem for match tables

Returns:

• Dict of tables, keyed by shear type, which have the

• souces catalogs joined to the associated objects

hpmcm.shear_utils.mergeShearReports(inputs, output_file)

Merge reports on the shear calibration

Return type:

None

Parameters:

• inputs (list[str]) – List of input ShearData pickle files

• output_file (str) – Where to write the merged file

hpmcm.shear_utils.reduceShearDataForCell(cell, i_cat, dataframe)

Filters dataframe to keep only sources in the cell

Return type:

DataFrame

Parameters:

• cell (CellData) – The cell being analyzed

• i_cat (int) – Catalog index

• dataframe (DataFrame) – Input dataframe

Return type:

Filtered datasets

Notes

This will optionally deshear the source positions if matcher.deshear is not None.

This will add these columns to the output dataframes

Column	Description
x_cell	X-coordinate in cell frame
y_cell	Y-coordinate in cell frame
x_pix	X-coordinate in global WCS frame
y_pix	Y-coordinate in global WCS frame
dx_shear	Change in X position when desheared
dy_shear	Change in X position when desheared

hpmcm.shear_utils.shearReport(basefile, output_file_base, shear, cat_type, tract, snr_cut=7.5)

Report on the shear calibration

Return type:

None

Parameters:

• basefile (str) – Input base file name (see notes)

• output_file_base (str | None) – Output file name (see notes)

• shear (float) – Applied shear

• cat_type (str) – Catalog type (one of [“pgauss”, “gauss”, “wmom”]

• tract (int) – Tract, written to outout data

• snr_cut (float) – Signal-to-noise cut.

Notes

This will read the object shear data from “{basefile}_cluster_shear.pq” This will read the object statisticis from “{basefile}_cluster_stats.pq”

This will write the shear stats to “{output_file_base}.pkl” This will write the figures to “{output_file_base}_{figure}.png”

hpmcm.shear_utils.shearStats(df)

Return the shear statistics

{st} is the shear type, one of “gauss”, “pgauss”, “wmom”

{i}, {j} index the shear parameters 1, 2

Return type:

dict

Parameters:

df (DataFrame) – Input DataFrame, must have hpmcm.ShearTable schema

Return type:

Shear stats in a dict.

Notes

Shear stats include:

Key	Description
n_{st}	Number of sources from that catalog
g_{i}_{st}	g_{i} shear parameter for that catalog
delta_g_{i}_{j}	g_{i,j} shear measurment: g_{i}_{j}p - g_{i}_{j}m
good	True if every catalog has one source in this object

If the matching is not good, then delta_g_1 = delta_g_2 = np.nan

hpmcm.shear_utils.splitByTypeAndClean(basefile, tract, shear, cat_type, *, clean=False)

Split a parquet file by shear catalog type and tract

Return type:

None

Parameters:

• basefile (str) – Original file name

• tract (int) – Tract to select

• shear (float) – Applied shear, saved to output

• cat_type (str) – Catalog type to select

• clean (bool) – Remove duplicates

Notes

This will create 5 files with the pattern: “{basefile}_uncleaned_{tract}_{type}.pq”

Column	Description
id	Index of object inside catalog
orig_id	Original object id
cell_idx_x	X-index of Cell
cell_idx_y	Y-index of Cell
x_cell_coadd	X-coordinate in cell frame
y_cell_coadd	Y-coordinate in cell frame
x_pix	X-coordinate in global WCS frame
y_pix	Y-coordinate in global WCS frame
g_1	Shear g_1 component estimate
g_2	Shear g_2 component estimate
snr	Signal-to-noise ratio