hpmcm

Getting Started

  • Overview
  • Installation
    • Production Installation
    • Developer Installation
    • Adding your kernel to jupyter

Contributing

  • Contribution Overview
    • Contributing to HPMCM
    • Where to contribute:
    • Contribution workflow
      • Issue
      • Branch
        • Merge
        • Reviewing a PR
        • Naming conventions

Demonstrations

  • Example Notebooks
    • Checking input data
      • Simple notebook to compare the Signal-to-noise between different input catalogs
        • Standard imports
        • Configuration data
        • Read the input files
        • Plot the SNR for the various catalgos
      • Compare sky coverage between metadected catalog an object catalog
        • Standard imports
        • Read the input data
        • Plot the footprints of the object table and metedetect table
    • Matching catalogs using a specified WCS
      • Run wcs-based matching to compare object tables to metadetect no-shear catalogs
        • Standard imports
        • Set up the configuration
        • Make the matcher, reduce the data
        • Make a plot comparing the signal-to-noise in the two catalogs
        • This should have made 3 x 3 cells
        • Run the loop over cells
        • Show a single cluster
        • Classify the objects by match type
        • Classify objcts using the object table as the reference
        • Display an object
      • Extract the efficiency of the matching between the shear catalog and the object catalog
        • Standard import
        • Set up the configuration
        • Make maskes of different types of matches
        • Make a histogram of the different match types
        • Make a scatter plot of positions of missing matches, to make sure we haven’t messed up the sky overlap
        • Estimate the good match efficiency as a function of SNR
        • Plot the good match efficiency as a function of SNR
        • Plot the good match efficiency w.r.t. the metadataect catalog as a function of SNR
        • Estimate the good match efficiency w.r.t. the metadataect catalog
    • Matching shear catalogs
      • Compare metadetect catalog types
        • Standard imports
        • Set up the configuration
        • Make the matcher, reduce the data
        • Run the data
        • Classify the objects by match type
      • Extract the efficiency of the matching between the shear catalogs
        • Standard imports
        • Set up the configuration
        • Merge the two tables we read
        • Make maskes of different types of matches
        • Make a histogram of the different match types
        • Estimate the good match efficiency as a function of SNR
        • Plot the good match efficiency as a function of SNR
    • Shear calibration
      • Reading shear data from an existing file
        • Standard imports
        • Set up the configuration
        • Load the requested data
        • Make some plots
      • High-level meta analysis of shear calibration
        • Standard imports
        • Set up the configuration
        • Plot the match efficiency
        • Make some other plots
      • Run cell-based matching
        • Standard imports
        • Set up the configuration
        • Make the matcher, reduce the data
        • This should have made 200 x 200 cells
        • Run the data
        • Show the source counts map for a single cell
        • Show a single cluster
        • Extract the output of the matching
        • Get the offsets between the cluster centroid and the sources
        • Plots the residuals, they should be flat
        • Look at how the sources lie within the cells
        • Classify the objects by match type
        • Measure the matching efficiency for objects above the SNRCut
        • Classify the clusters by match type
        • Display a few objects

Command line interface

  • hpmcm CLI
    • Matching using global WCS
    • Matching using cell-based coadd frame
    • Splitting input catalogs for shear matching
    • Making shear calibration reports
    • Merging shear calibration reports

API

  • hpmcm package
    • Matchers
      • hpmcm.match module
        • Match
      • hpmcm.shear_match module
        • ShearMatch
      • hpmcm.wcs_match module
        • WcsMatch
        • createGlobalWcs()
    • Input/Output Table Formats
      • hpmcm.input_tables module
        • CoaddSourceTable
        • ShearCoaddSourceTable
        • SourceTable
      • hpmcm.output_tables module
        • ClusterAssocTable
        • ClusterStatsTable
        • ObjectAssocTable
        • ObjectStatsTable
        • ShearTable
      • hpmcm.table module
        • TableColumnInfo
        • TableInterface
    • Internal Data Structures
      • hpmcm.cell module
        • CellData
        • ShearCellData
      • hpmcm.cluster module
        • ClusterData
        • ShearClusterData
      • hpmcm.object module
        • ObjectData
        • ShearObjectData
      • hpmcm.shear_data module
        • ShearData
        • ShearHistogramStats
        • ShearHistograms
        • ShearProfileHistogramStats
        • ShearStats
      • hpmcm.footprint module
        • Footprint
        • FootprintSet
    • Utility Functions
      • hpmcm.classify module
        • classifyClusters()
        • classifyObjects()
        • clusterStats()
        • matchObjectsAgainstRef()
        • printClusterTypes()
        • printObjectMatchTypes()
        • printObjectTypes()
        • printSummaryStats()
      • hpmcm.match_utils module
        • heirarchicalProcessCluster()
        • heirarchicalProcessObject()
        • heirarchicalSplitObject()
      • hpmcm.package_utils module
        • setupTestDataArea()
        • teardownTestDataArea()
      • hpmcm.shear_utils module
        • makeMatchedShearSourceCatalogs()
        • mergeShearReports()
        • reduceShearDataForCell()
        • shearReport()
        • shearStats()
        • splitByTypeAndClean()
      • hpmcm.utils module
        • associateSourcesToFootprints()
        • fillCountsMapFromArrays()
        • fillCountsMapFromDf()
        • findClusterIds()
        • findClusterIdsFromArrays()
        • getFootprints()
        • reduceObjectTable()
      • hpmcm.viz_utils module
        • showCluster()
        • showObjects()
        • showObjectsV2()
        • showShearObj()
        • showShearObjs()
hpmcm
  • HPMCM: Heirarchical Pixel Multi-Catalog Matcher
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HPMCM: Heirarchical Pixel Multi-Catalog Matcher

Description

HPMCM does multi-catalog cross matching by clustering sources from individual catalogs using a heirarchical pixelization scheme.

Getting Started

  • Overview
  • Installation
    • Production Installation
    • Developer Installation
    • Adding your kernel to jupyter

Contributing

  • Contribution Overview
    • Contributing to HPMCM
    • Where to contribute:
    • Contribution workflow

Demonstrations

  • Example Notebooks
    • Checking input data
    • Matching catalogs using a specified WCS
    • Matching shear catalogs
    • Shear calibration

Command line interface

  • hpmcm CLI
    • Matching using global WCS
    • Matching using cell-based coadd frame
    • Splitting input catalogs for shear matching
    • Making shear calibration reports
    • Merging shear calibration reports

API

  • hpmcm package
    • Matchers
      • hpmcm.match module
      • hpmcm.shear_match module
      • hpmcm.wcs_match module
    • Input/Output Table Formats
      • hpmcm.input_tables module
      • hpmcm.output_tables module
      • hpmcm.table module
    • Internal Data Structures
      • hpmcm.cell module
      • hpmcm.cluster module
      • hpmcm.object module
      • hpmcm.shear_data module
      • hpmcm.footprint module
    • Utility Functions
      • hpmcm.classify module
      • hpmcm.match_utils module
      • hpmcm.package_utils module
      • hpmcm.shear_utils module
      • hpmcm.utils module
      • hpmcm.viz_utils module

© Copyright 2025, Eric Charles.

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