CompletenessHist#

class lsst.analysis.tools.actions.plot.CompletenessHist(*args, **kw)#

Bases: PlotAction

Makes plots of completeness and purity.

Attributes Summary

`action`	Action to compute completeness/purity (`CalcCompletenessHistogramAction`, default `<class 'lsst.analysis.tools.actions.keyedData.calcCompletenessHistogram.CalcCompletenessHistogramAction'>`)
`color_counts`	Color for the line showing object counts (`str`, default `'#029E73'`)
`color_right`	Color for the line showing the correctly classified fraction (`str`, default `'#949494'`)
`color_wrong`	Color for the line showing the wrongly classified fraction (`str`, default `'#DE8F05'`)
`label_shift`	Fraction of plot width to shift completeness/purity labels by.Ignored if percentiles_style is not 'below_line' (`float`, default `-0.1`)
`legendLocation`	Legend position within main plot (`str`, default `'lower left'`)
`mag_ref_label`	Label for the completeness x axis.
`mag_target_label`	Label for the purity x axis.
`object_label`	Label for measured objects (`str`, default `'Object'`)
`percentiles_style`	Style and locations for completeness threshold percentile labels (`str`, default `'below_line'`)
`publicationStyle`	Make a publication-style of plot (`bool`, default `False`)
`reference_label`	Label for reference objects (`str`, default `'Reference'`)
`show_purity`	Whether to include a purity plot below completness (`bool`, default `True`)

Methods Summary

`__call__`(data, **kwargs)	Call self as a function.
`getInputSchema`()	Return the schema an `AnalysisAction` expects to be present in the arguments supplied to the __call__ method.
`makePlot`(data, plotInfo, **kwargs)	Makes a plot showing the fraction of injected sources recovered by input magnitude.

Attributes Documentation

action#: Action to compute completeness/purity (CalcCompletenessHistogramAction, default <class 'lsst.analysis.tools.actions.keyedData.calcCompletenessHistogram.CalcCompletenessHistogramAction'>)

color_counts#: Color for the line showing object counts (str, default '#029E73')

color_right#: Color for the line showing the correctly classified fraction (str, default '#949494')

color_wrong#: Color for the line showing the wrongly classified fraction (str, default '#DE8F05')

label_shift#: Fraction of plot width to shift completeness/purity labels by.Ignored if percentiles_style is not ‘below_line’ (float, default -0.1)

legendLocation#: Legend position within main plot (str, default 'lower left')

mag_ref_label#: Label for the completeness x axis. (str, default '{band}-band Reference Magnitude')

mag_target_label#: Label for the purity x axis. (str, default '{band}-band Measured Magnitude')

object_label#: Label for measured objects (str, default 'Object')

percentiles_style#

Style and locations for completeness threshold percentile labels (str, default 'below_line')

Allowed values:

'above_plot': Labels in a semicolon-separated list above plot
'below_line': Labels under the horizontal part of each line
'None': Field is optional

publicationStyle#: Make a publication-style of plot (bool, default False)

reference_label#: Label for reference objects (str, default 'Reference')

show_purity#: Whether to include a purity plot below completness (bool, default True)

Methods Documentation

__call__(data: MutableMapping[str, ndarray[tuple[Any, ...], dtype[_ScalarT]] | Scalar | HealSparseMap | Tensor | Mapping], **kwargs) → Mapping[str, Figure] | Figure#: Call self as a function.

getInputSchema() → Iterable[tuple[str, type[ndarray[tuple[Any, ...], dtype[_ScalarT]]] | type[Scalar] | type[HealSparseMap] | type[Tensor] | type[Mapping]]]#

Return the schema an AnalysisAction expects to be present in the arguments supplied to the __call__ method.

Returns#

resultKeyedDataSchema: The schema this action requires to be present when calling this action, keys are unformatted.

makePlot(data, plotInfo, **kwargs)#

Makes a plot showing the fraction of injected sources recovered by input magnitude.

The behavior of this plot is controlled by self.action. This action must be added to a struct (usually self.process.calculateActions) by the tool that calls this plot.

Parameters#

dataKeyedData

All the data

plotInfodict

A dictionary of information about the data being plotted with keys: camera

The camera used to take the data (lsst.afw.cameraGeom.Camera)

"cameraName": The name of camera used to take the data (str).
"filter": The filter used for this data (str).
"ccdKey": The ccd/dectector key associated with this camera (str).
"visit": The visit of the data; only included if the data is from a single epoch dataset (str).
"patch": The patch that the data is from; only included if the data is from a coadd dataset (str).
"tract": The tract that the data comes from (str).
"photoCalibDataset": The dataset used for the calibration, e.g. “jointcal” or “fgcm” (str).
"skyWcsDataset": The sky Wcs dataset used (str).
"rerun": The rerun the data is stored in (str).

Returns#

fig: The figure to be saved (matplotlib.figure.Figure).

Notes#

The behaviour of this plot is largel

Examples#

An example of the plot produced from this code from tract 3828 of the DC2 simulations is here: