rail.estimation.summarizer module

Abstract base classes defining Summarizers of the redshift distribution of an ensemble of galaxies

class rail.estimation.summarizer.CatSummarizer(args, comm=None)

Bases: RailStage

The base class for classes that go from catalog-like tables to ensemble NZ estimates.

CatSummarizer take as “input” a catalog-like table. I.e., a table with fluxes in photometric bands among the set of columns.

provide as “output” a QPEnsemble, with per-ensemble n(z).

config_options = {'chunk_size': 10000, 'output_mode': <ceci.config.StageParameter object>}

inputs = [('input', <class 'rail.core.data.TableHandle'>)]

name = 'CatSummarizer'

outputs = [('output', <class 'rail.core.data.QPHandle'>)]

summarize(input_data)

The main run method for the summarization, should be implemented in the specific subclass.

This will attach the input_data to this CatSummarizer (for introspection and provenance tracking).

Then it will call the run() and finalize() methods, which need to be implemented by the sub-classes.

The run() method will need to register the data that it creates to this CatSummarizer by using self.add_data(‘output’, output_data).

Finally, this will return a QPHandle providing access to that output data.

Parameters:

input_data (dict or ModelHandle) – Either a dictionary of all input data or a ModelHandle providing access to the same

Returns:

output – Ensemble with n(z), and any ancilary data

Return type:

qp.Ensemble

class rail.estimation.summarizer.PZSummarizer(args, comm=None)

Bases: RailStage

The base class for classes that go from per-galaxy PZ estimates to ensemble NZ estimates

PZSummarizer take as “input” a qp.Ensemble with per-galaxy PDFs, and provide as “output” a QPEnsemble, with per-ensemble n(z).

config_options = {'chunk_size': 10000, 'output_mode': <ceci.config.StageParameter object>}

inputs = [('model', <class 'rail.core.data.ModelHandle'>), ('input', <class 'rail.core.data.QPHandle'>)]

name = 'PZtoNZSummarizer'

outputs = [('output', <class 'rail.core.data.QPHandle'>)]

summarize(input_data)

The main run method for the summarization, should be implemented in the specific subclass.

This will attach the input_data to this PZtoNZSummarizer (for introspection and provenance tracking).

Then it will call the run() and finalize() methods, which need to be implemented by the sub-classes.

The run() method will need to register the data that it creates to this Estimator by using self.add_data(‘output’, output_data).

Finally, this will return a QPHandle providing access to that output data.

Parameters:

input_data (qp.Ensemble) – Per-galaxy p(z), and any ancilary data associated with it

Returns:

output – Ensemble with n(z), and any ancilary data

Return type:

qp.Ensemble

class rail.estimation.summarizer.SZPZSummarizer(args, comm=None)

Bases: RailStage

The base class for classes that use two sets of data: a photometry sample with spec-z values, and a photometry sample with unknown redshifts, e.g. minisom_som and outputs a QP Ensemble with bootstrap realization of the N(z) distribution

config_options = {'chunk_size': 10000, 'output_mode': <ceci.config.StageParameter object>}

inputs = [('input', <class 'rail.core.data.TableHandle'>), ('spec_input', <class 'rail.core.data.TableHandle'>), ('model', <class 'rail.core.data.ModelHandle'>)]

name = 'SZPZtoNZSummarizer'

open_model(**kwargs)

Load the mode and/or attach it to this Summarizer

Notes

Keywords are potentially used to build the model. By default we just use ‘model’ which can be an object, str or ModelHandle

I.e., either an object with a trained model, a path pointing to a file that can be read to obtain the trained model, or a ModelHandle providing access to the trained model.

Returns:

self.model – The object encapsulating the trained model.

Return type:

object

outputs = [('output', <class 'rail.core.data.QPHandle'>)]

summarize(input_data, spec_data)

The main run method for the summarization, should be implemented in the specific subclass.

This will attach the input_data to this SZandPhottoNZSummarizer (for introspection and provenance tracking).

Then it will call the run() and finalize() methods, which need to be implemented by the sub-classes.

The run() method will need to register the data that it creates to this Estimator by using self.add_data(‘output’, output_data).

Finally, this will return a QPHandle providing access to that output data.

Parameters:

input_data (qp.Ensemble) – Per-galaxy p(z), and any ancilary data associated with it

Returns:

output – Ensemble with n(z), and any ancilary data

Return type:

qp.Ensemble