Example Notebooks

HPMCM comes with several notebooks that demonstrate how to use it to analyze data in a number of different ways.

Here we describe the various notebooks and suggest other ways in which you might study the data.

Checking input data

Comparing signal-to-noise of input catalogs

• 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

Comparing sky-coverage of input catalogs

• 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

Matching Metadetect no-shear catalog to object catalog using 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

Estimating match efficiecy between metadetect no-shear catalog and object catalog

• 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

Matching shear catalogs using cell-based coadd coordinates

• 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

Estimating match efficiecy between shear catalgos

• 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 match data directly

• 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

• High-level meta analysis of shear calibration
  • Standard imports
  • Set up the configuration
  • Plot the match efficiency
  • Make some other plots

Deeper dive in to matching shear catalogs

• 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