roman_photoz

The roman_photoz package provides tools for processing catalogs produced by the Roman Space Telescope Calibration Pipeline (romancal) to estimate photometric redshift.

Overview

The Roman Space Telescope will produce large catalogs of astronomical objects. This package provides tools for processing these catalogs to estimate photometric redshifts using rail/LePhare. Key features include:

  • Processing Roman catalog data in ASDF format

  • Creating filter definitions based on Roman Space Telescope specifications

  • Generating simulated catalogs for development and testing

  • Estimating photometric redshifts using rail/LePhare

Contents

Modules

API Reference

class roman_photoz.RomanCatalogHandler(catname: str = '', fit_colname: str = 'segment_{}_flux', fit_err_colname: str = 'segment_{}_flux_err')

Bases: object

A class to handle Roman catalog operations including reading and formatting it in a suitable way for roman_photoz.

format_catalog()

Format the catalog by appending necessary fields and columns.

process()

Process the catalog by reading and formatting it.

Returns:

The formatted catalog.

Return type:

np.ndarray

read_catalog()

Read the catalog file and convert it to a numpy structured array.

class roman_photoz.RomanCatalogProcess(config_filename: dict | str = '', model_filename: str = 'roman_model.pkl')

Bases: object

A class to process Roman catalog data using rail and lephare.

data

Dictionary to store the processed data.

Type:

dict

flux_cols

List of flux columns.

Type:

list

flux_err_cols

List of flux error columns.

Type:

list

inform_stage

Informer stage for creating the library of SEDs.

Type:

RailStage

estimated

Estimator stage for finding the best fits from the library.

Type:

RailStage

model_filename

Name of the pickle model file.

Type:

str

create_estimator_stage()

Create the estimator stage to find the best fits from the library.

create_informer_stage()

Create the informer stage to generate the library of SEDs with various parameters.

get_data(input_filename, fit_colname: str = 'segment_{}_flux', fit_err_colname: str = 'segment_{}_flux_err') → Table

Fetch the data from the input file.

Parameters:

input_filename (str, optional) – Name of the input file.

Returns:

The catalog data.

Return type:

Table

property informer_model_exists

Check if the informer model file exists.

Returns:

True if the model file exists, False otherwise.

Return type:

bool

property informer_model_path

Get the path to the informer model file.

The path is determined by checking the INFORMER_MODEL_PATH environment variable first, falling back to LEPHAREWORK if not set.

Returns:

The path to the informer model file.

Return type:

str

process(input_filename, output_filename: str = None, output_format: str = 'parquet', fit_colname: str = 'segment_{}_flux', fit_err_colname: str = 'segment_{}_flux_err')

Process the Roman catalog data.

Parameters:

  • input_filename (str) – Name of the input file.

  • output_filename (str, optional) – Name of the output file.

  • output_format (str, optional) – Format to save the results. Supported formats are “parquet” (default) and “asdf.”

  • flux_type (str, optional) – The type of flux to use for fitting. Options are “psf” (default), “kron”, “segment”, or “aperture.”

save_results(output_filename: str = None, output_format: str = 'parquet')

Save the results to the specified output file.

Parameters:

  • output_filename (str, optional) – Name of the output file.

  • output_format (str, optional) – Format to save the results. Supported formats are “parquet” (default) and “asdf”.

Raises:

ValueError – If there are no results to save.

set_config_file(config_filename: dict | str = '')

Set the configuration file.

Parameters:

config_filename (Union[dict, str], optional) – Path to the configuration file in JSON format or a configuration dictionary.

update_input(input_filename)
class roman_photoz.SimulatedCatalog(nobj: int = 1000, mag_noise: float = 0.1)

Bases: object

A class to create a simulated catalog using the Roman telescope data.

data

A dictionary to store the data.

Type:

OrderedDict

lephare_config

Configuration for LePhare.

Type:

dict

flux_cols

List of flux columns.

Type:

list

flux_err_cols

List of flux error columns.

Type:

list

inform_stage

Placeholder for inform stage.

Type:

None

estimated

Holds all the results from roman_photoz.

Type:

None

filter_lib

Placeholder for filter library.

Type:

None

simulated_data_filename

Filename for the simulated data.

Type:

str

simulated_data

Placeholder for simulated data.

Type:

None

abmag_to_njy(abmag)
add_error(catalog, mag_noise: float = 0.1, seed: int = 42)

Add a Gaussian error to each magnitude column in the catalog.

For each magnitude column, this method adds: + a Gaussian noise with a mean equal to the original value and a standard deviation of mag_noise + an error column (<magnitude_column>_err) with values set to mag_noise.

Parameters:

  • catalog (np.ndarray) – The catalog data.

  • mag_noise (float, optional) – The standard deviation of the Gaussian noise to be added to the observed magnitudes (default: 0.1).

  • seed (int, optional) – The seed for the random number generator.

Returns:

The catalog data with error columns added.

Return type:

np.ndarray

add_ids(catalog)

Add an ID column to the catalog.

Parameters:

catalog (np.ndarray) – The catalog data.

Returns:

The catalog data with an ID column added.

Return type:

np.ndarray

create_column_names()
create_filter_files()

Create filter files for the Roman telescope.

This method checks if the required filter files are present in the specified directory. If not, it generates them using the create_roman_filters module.

Raises:

FileNotFoundError – If the filter files cannot be created or found.

create_header(catalog_name: str)

Create the header for the catalog.

Parameters:

catalog_name (str) – The name of the catalog file.

Returns:

The list of column names for the catalog.

Return type:

list

create_simulated_data()

Generate simulated data using the LePhare configuration.

This method prepares the LePhare environment and generates simulated data for galaxies, stars, and quasars based on the provided configuration.

Raises:

RuntimeError – If the LePhare preparation fails or the configuration is invalid.

create_simulated_input_catalog(output_filename: str = 'roman_simulated_catalog.parquet', output_path: str = '')

Create a simulated input catalog from the simulated data.

  • read the ROMAN_SIMULATED_MAGS.dat produced by LePhare’s

    prepare method in create_simulated_input_catalog. The columns name are defined by LePhare.

  • format the columns name to match Roman catalog’s specifications

create_simulated_roman_catalog(catalog)

Update the Roman catalog template with the simulated data.

Parameters:

catalog (np.ndarray) – The catalog data to update the Roman catalog template with.

is_folder_not_empty(folder_path: str, partial_text: str) → bool

Check if a folder exists and contains files with the specified partial text.

Parameters:

  • folder_path (str) – The path to the folder.

  • partial_text (str) – The partial text to look for in filenames.

Returns:

True if the folder exists and contains files with the partial text, False otherwise.

Return type:

bool

pick_random_lines(num_lines: int)

Pick random lines from the data array.

Parameters:

num_lines (int) – The number of random lines to pick.

Returns:

An array containing the randomly picked lines.

Return type:

np.ndarray

process(output_path: str = '', output_filename: str = 'roman_simulated_catalog.parquet')

Run the process to create the simulated catalog.

read_roman_template_catalog()
roman_photoz.setup_logging(level=20)

Set up logging configuration for roman_photoz module

Indices and tables