phot_class.utils

A collection of general utilities used across the parent package.

phot_class.utils.calc_model_chisq(data, result, model)

Calculate the chi-squared for a given data table and model

Chi-squareds are calculated using parameter values from model. Degrees of freedom are calculated using the number of varied parameters specified is the result object.

Parameters:

• data (Table) – An sncosmo input table
• model (Model) – An sncosmo Model
• result (Result) – sncosmo fitting result

Returns:

The un-normalized chi-squared The number of data points used in the calculation

phot_class.utils.classification_filter_factory(classifications, ftype='exclude')

Returns function to determine whether data should be skipped/kept in an

iterator based on its classification

The function returned by this factory has signature returned_function(table: astropy.Table) -> boolean. The boolean indicates whether the data should kept (i.e. not skipped). The class of each object is determined from table.meta['classification']. If there is no ‘classification’ key in the meta data, the return is True.

Parameters:

• classifications (list[str]) – A list of classifications to allow
• ftype (str) – ‘exclude’ or ‘include’ the given classes

Returns:

A filter function for sndata

phot_class.utils.parse_config_dict(obj_id, config_dict)

Return the priors and kwargs for a given object from a config file

Parameters:

• obj_id (str) – The object id in the dictionary
• config_dict (dict) – A dictionary with data from a config file

Returns:

• A dictionary with object priors for the hsiao_x1 model
• A dictionary of fitting kwargs for the hsiao_x1 model
• A dictionary with object priors for the sn91bg model
• A dictionary of fitting kwargs for the sn91bg model

phot_class.utils.split_bands(bands, lambda_eff, redshift=0)

Split band-passes into collections of blue and red bands

Blue bands have an rest frame effective wavelength < 5500 Ang. Red bands have a rest frame effective wavelength >= 5500 Ang.

Parameters:

• bands (array[str]) – Name of band-passes
• lambda_eff (array[float]) – Effective wavelength of band-passes
• redshift (float) – The redshift of the rest frame

Returns:

An array of blue filter names An array of red filter names

phot_class.utils.split_data(data_table, band_names, lambda_eff, z, cutoff=700)

Split a data table into blue and red data (by rest frame)

Wavelengths are expected to be in angstroms. Split data by keeping filters that are red-ward or blue-ward of 5500 Ang. If the closest rest frame filter for an observation is more than cutoff angstroms away, drop the observation.

Parameters:

• data_table (Table) – An SNCosmo input table with column ‘band’
• band_names (iter) – List of all bands available in the survey
• lambda_eff (iter) – The effective wavelength of each band in band_names
• z (float) – The redshift of the observed target
• cutoff (float) – The cutoff distance for dropping an observation

Returns:

A SNCosmo input table with only blue bands A SNCosmo input table with only red bands

class phot_class.utils.timeout(seconds=1, error_message='Timeout')

A timeout context manager