rail.interactive.creation.degraders.photometric_errors module

rail.interactive.creation.degraders.photometric_errors.euclid_deep_error_model(**kwargs)

The Euclid Deep Error model, defined by peEuclidDeepErrorParams and peEuclidDeepErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.EuclidDeepErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.euclid_error_model(**kwargs)

The Euclid Error model, defined by peEuclidErrorParams and peEuclidErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.EuclidErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.euclid_wide_error_model(**kwargs)

The Euclid Wide Error model, defined by peEuclidWideErrorParams and peEuclidWideErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.EuclidWideErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.lsst_error_model(**kwargs)

The LSST Error model, defined by peLsstErrorParams and peLsstErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.LSSTErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.photo_error_model(**kwargs)

The Base Model for photometric errors.

This is a wrapper around the error model from PhotErr. The parameter docstring below is dynamically added by the installed version of PhotErr:

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.PhotoErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.roman_deep_error_model(**kwargs)

The Roman Deep Error model, defined by peRomanDeepErrorParams and peRomanDeepErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.RomanDeepErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.roman_error_model(**kwargs)

The Roman Error model, defined by peRomanErrorParams and peRomanErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.RomanErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.roman_medium_error_model(**kwargs)

The Roman Medium Error model, defined by peRomanMediumErrorParams and peRomanMediumErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.RomanMediumErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.roman_ultra_deep_error_model(**kwargs)

The Roman UltraDeep Error model, defined by peRomanUltraDeepErrorParams and peRomanUltraDeepErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.RomanUltraDeepErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame

rail.interactive.creation.degraders.photometric_errors.roman_wide_error_model(**kwargs)

The Roman WideError model, defined by peRomanWideErrorParams and peRomanWideErrorModel

—

The main interface method for Noisifier.

Adds noise to the input catalog

This will attach the input to this Noisifier

Then it will call the _initNoiseModel() and _addNoise(), which need to be implemented by the sub-classes.

The _initNoiseModel() method will initialize the noise model of the sub-classes, and store the noise model as self.noiseModel

The _addNoise() method will add noise to the flux and magnitude of the column of the catalog.

The finalize() method will check the end results (like preserving number of rows)

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

—

This function was generated from the function rail.creation.degraders.photometric_errors.RomanWideErrorModel.__call__

Parameters:

• sample (TableLike, required) – The sample to be degraded.

• seed (int, optional) – Set to an int to force reproducible results. Default: None An integer to set the numpy random seed, by default None.

Returns:

A handle giving access to a table with degraded sample.

Return type:

pandas.core.frame.DataFrame