hpmcm.output_tables module

Schema for various output tables produced by hpmcm

class hpmcm.output_tables.ClusterAssocTable(df=None, **kwargs)[source]

Bases: TableInterface

Interface of table with associations between clusters and sources .. rubric:: Notes

ClusterAssocTable schema

Column	Type	Description
cluster_id	int	Unique cluster ID
source_id	int	Source id in input catalog
source_idx	int	Source index in input catalog
catalog_id	int	Associated catalog ID
distance	float	Distance from sources to cluster centroid
cell_idx	int	Index of associated cell

Parameters:

df (pandas.DataFrame | None)
kwargs (Any)

static buildFromCellData(cell_data)[source]

Create object association table

Return type:: ClusterAssocTable
Parameters:: cell_data (CellData) – Cell we are making table for
Return type:: Cluster Association table

class hpmcm.output_tables.ClusterStatsTable(df=None, **kwargs)[source]

Bases: TableInterface

Interface of table of cluster statistics .. rubric:: Notes

ClusterStatsTable schema

Column	Type	Description
cluster_id	int	Parent Cluster Unique ID
n_object	int	Number of objects in cluster
n_unique	int	Number of unique catalogs represented
n_src	int	Number of sources
dist_rms	float	RMS of distance from sources to object centroid
ra	float	RA of object centroid
dec	float	DEC of object centroid
x_cent	float	X-value of cluster centroid in WCS pixels
y_cent	float	Y-value of cluster centroid in WCS pixels
snr	float	Mean signal-to-noise ratio
snr_rms	float	RMS signal-to-noise ratio
cell_idx	int	Index of associated cell
has_ref_cat	bool	Has source from reference catalog

Parameters:

df (pandas.DataFrame | None)
kwargs (Any)

static buildFromCellData(cell_data)[source]

Create object stats table

Return type:: ClusterStatsTable
Parameters:: cell_data (CellData) – Cell we are making table for
Return type:: Object stats table

class hpmcm.output_tables.ObjectAssocTable(df=None, **kwargs)[source]

Bases: TableInterface

Interface of table with associations between objects and sources .. rubric:: Notes

ObjectAssocTable schema

Column	Type	Description
object_id	int	Unique Object ID
cluster_id	int	Parent Cluster Unique ID
source_id	int	Source id in input catalog
source_idx	int	Source index in input catalog
catalog_id	int	Associated catalog ID
distance	float	Distance from sources to object centroid
cell_idx	int	Index of associated cell

Parameters:

df (pandas.DataFrame | None)
kwargs (Any)

static buildFromCellData(cell_data)[source]

Create object association table

Return type:: ObjectAssocTable
Parameters:: cell_data (CellData) – Cell we are making table for
Return type:: Object Association table

class hpmcm.output_tables.ObjectStatsTable(df=None, **kwargs)[source]

Bases: TableInterface

Interface of table of object statistics .. rubric:: Notes

ObjectStatsTable schema

Column	Type	Description
object_id	int	Unique Object ID
cluster_id	int	Parent Cluster Unique ID
n_unique	int	Number of unique catalogs represented
n_src	int	Number of sources
dist_rms	float	RMS of distance from sources to object centroid
ra	float	RA of object centroid
dec	float	DEC of object centroid
x_cent	float	X-value of cluster centroid in WCS pixels
y_cent	float	Y-value of cluster centroid in WCS pixels
snr	float	Mean signal-to-noise ratio
snr_rms	float	RMS signal-to-noise ratio
cell_idx	int	Index of associated cell
has_ref_cat	bool	Has source from the reference catalog

Parameters:

df (pandas.DataFrame | None)
kwargs (Any)

static buildFromCellData(cell_data)[source]

Create object stats table

Return type:: ObjectStatsTable
Parameters:: cell_data (CellData) – Cell we are making table for
Return type:: Object stats table

class hpmcm.output_tables.ShearTable(df=None, **kwargs)[source]

Bases: TableInterface

Interface of table with shear information .. rubric:: Notes

ShearTable schema

Column	Type	Description
good	bool	Has unique match
n_ns	float	number of sourcrs from catalog ns
g_1_ns	float	g 1 for catalog ns
g_2_ns	float	g 2 for catalog ns
n_2p	float	number of sourcrs from catalog 2p
g_1_2p	float	g 1 for catalog 2p
g_2_2p	float	g 2 for catalog 2p
n_2m	float	number of sourcrs from catalog 2m
g_1_2m	float	g 1 for catalog 2m
g_2_2m	float	g 2 for catalog 2m
n_1p	float	number of sourcrs from catalog 1p
g_1_1p	float	g 1 for catalog 1p
g_2_1p	float	g 2 for catalog 1p
n_1m	float	number of sourcrs from catalog 1m
g_1_1m	float	g 1 for catalog 1m
g_2_1m	float	g 2 for catalog 1m
delta_g_1_1	float	delta g 1 for 1p - 1m
delta_g_1_2	float	delta g 1 for 2p - 2m
delta_g_2_1	float	delta g 2 for 1p - 1m
delta_g_2_2	float	delta g 2 for 2p - 2m

Parameters:

df (pandas.DataFrame | None)
kwargs (Any)

classmethod buildClusterShearStats(cell_data)[source]

Create shear stats table for clusters in a cell

Return type:: ShearTable
Parameters:: cell_data (CellData) – Cell we are making table for
Return type:: Shear stats table

classmethod buildObjectShearStats(cell_data)[source]

Create shear stats table for objects in a cell

Return type:: ShearTable
Parameters:: cell_data (CellData) – Cell we are making table for
Return type:: Shear stats table