HistPlot

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

Bases: PlotAction

Make an N-panel plot with a configurable number of histograms displayed in each panel. Reference lines showing values of interest may also be added to each histogram. Panels are configured using the HistPanel class.

Attributes Summary

cmap	Color map used for histogram lines.
panels	A configurable dict describing the panels to be plotted, and the histograms for each panel.
panelsPerRow	Maximum number of histogram panels to place in each row.

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])	Make an N-panel plot with a user-configurable number of histograms displayed in each panel.
validate()	Validate the Config, raising an exception if invalid.

Attributes Documentation

cmap

Color map used for histogram lines. All types available via plt.cm may be used. A number of custom color maps are also defined: newtab10, bright, vibrant. (str, default 'rubin')

panels

A configurable dict describing the panels to be plotted, and the histograms for each panel. (ConfigDict, default {})

panelsPerRow

Maximum number of histogram panels to place in each row. Set to 1 to stack panels vertically. (int, default 2)

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: MutableMapping[str, ndarray[tuple[Any, ...], dtype[_ScalarT]] | Scalar | HealSparseMap | Tensor | Mapping], plotInfo: Mapping[str, str] = None, **kwargs) → Figure

Make an N-panel plot with a user-configurable number of histograms displayed in each panel.

Parameters

datapandas.core.frame.DataFrame

The catalog to plot the points from.

plotInfodict

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

"run"

Output run for the plots (str).

"tractTableType"

Table from which results are taken (str).

"plotName"

Output plot name (str)

"SN"

The global signal-to-noise data threshold (float)

"skymap"

The type of skymap used for the data (str).

"tract"

The tract that the data comes from (int).

"bands"

The bands used for this data (str or list).

"visit"

The visit that the data comes from (int)

Returns

figmatplotlib.figure.Figure

The resulting figure.

Examples

An example histogram plot may be seen below:

For further details on how to generate a plot, please refer to the getting started guide.

validate()

Validate the Config, raising an exception if invalid.

Raises

lsst.pex.config.FieldValidationError

Raised if verification fails.

Notes

The base class implementation performs type checks on all fields by calling their validate methods.

Complex single-field validation can be defined by deriving new Field types. For convenience, some derived lsst.pex.config.Field-types (ConfigField and ConfigChoiceField) are defined in lsst.pex.config that handle recursing into subconfigs.

Inter-field relationships should only be checked in derived Config classes after calling this method, and base validation is complete.