FocalPlanePlot¶

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

Bases: PlotAction

Plots the focal plane distribution of a parameter.

Given the detector positions in x and y, the focal plane positions are calculated using the camera model. A 2d binned statistic (default is mean) is then calculated and plotted for the parameter z as a function of the focal plane coordinates.

Attributes Summary

`addHistogram`	Add a histogram of all input points (`bool`, default `False`)
`applyContext`	Apply a `Context` to an `AnalysisAction` recursively.
`doUseAdaptiveBinning`	If set to True, the number of bins is adapted to the source density, with lower densities using fewer bins.
`histBins`	Number of bins to use in histogram (`int`, default `30`)
`history`
`identity`	If a configurable action is assigned to a `ConfigurableActionField`, or a `ConfigurableActionStructField` the name of the field will be bound to this variable when it is retrieved.
`nBins`	Number of bins to use within the effective plot ranges along the spatial directions.
`plotMax`	Maximum in z-value to display in the focal plane plot and in the histogram plot, if applicable (`float`, default `None`)
`plotMin`	Minimum in z-value to display in the focal plane plot and in the histogram plot, if applicable (`float`, default `None`)
`showStats`	Show statistics for plotted data (`bool`, default `True`)
`statistic`	Operation to perform in binned_statistic_2d (`str`, default `'mean'`)
`xAxisLabel`	Label to use for the x axis.
`yAxisLabel`	Label to use for the y axis.
`zAxisLabel`	Label to use for the z axis.

Methods Summary

`__call__`(data, **kwargs)	Call self as a function.
`addInputSchema`(inputSchema)	Add the supplied inputSchema argument to the class such that it will be returned along side any other arguments in a call to `getInputSchema`.
`compare`(other[, shortcut, rtol, atol, output])	Compare this configuration to another `Config` for equality.
`formatHistory`(name, **kwargs)	Format a configuration field's history to a human-readable format.
`freeze`()	Make this config, and all subconfigs, read-only.
`getFormattedInputSchema`(**kwargs)	Return input schema, with keys formatted with any arguments supplied by kwargs passed to this method.
`getInputSchema`(**kwargs)	Return the schema an `AnalysisAction` expects to be present in the arguments supplied to the __call__ method.
`getOutputNames`([config])	Returns a list of names that will be used as keys if this action's call method returns a mapping.
`getOutputSchema`()	Return the schema an `AnalysisAction` will produce, if the `__call__` method returns `KeyedData`, otherwise this may return None.
`items`()	Get configurations as `(field name, field value)` pairs.
`keys`()	Get field names.
`load`(filename[, root])	Modify this config in place by executing the Python code in a configuration file.
`loadFromStream`(stream[, root, filename, ...])	Modify this Config in place by executing the Python code in the provided stream.
`loadFromString`(code[, root, filename, ...])	Modify this Config in place by executing the Python code in the provided string.
`makePlot`(data, camera[, plotInfo])	Prep the catalogue and then make a focalPlanePlot of the given column.
`names`()	Get all the field names in the config, recursively.
`save`(filename[, root])	Save a Python script to the named file, which, when loaded, reproduces this config.
`saveToStream`(outfile[, root, skipImports])	Save a configuration file to a stream, which, when loaded, reproduces this config.
`saveToString`([skipImports])	Return the Python script form of this configuration as an executable string.
`setDefaults`()	Subclass hook for computing defaults.
`statsAndText`(arr[, mask])	Calculate some stats from an array and return them and some text.
`toDict`()	Make a dictionary of field names and their values.
`update`(**kw)	Update values of fields specified by the keyword arguments.
`validate`()	Validate the Config, raising an exception if invalid.
`values`()	Get field values.

Attributes Documentation

addHistogram¶: Add a histogram of all input points (bool, default False)

applyContext¶

Apply a Context to an AnalysisAction recursively.

Generally this method is called from within an AnalysisTool to configure all AnalysisActions at one time to make sure that they all are consistently configured. However, it is permitted to call this method if you are aware of the effects, or from within a specific execution environment like a python shell or notebook.

Parameters:

contextContext: The specific execution context, this may be a single context or a joint context, see Context for more info.

doUseAdaptiveBinning¶: If set to True, the number of bins is adapted to the source density, with lower densities using fewer bins. Under these circumstances the nBins parameter sets the minimum number of bins. (bool, default False)

histBins¶: Number of bins to use in histogram (int, default 30)

history¶: Read-only history.

identity: str | None = None¶: If a configurable action is assigned to a ConfigurableActionField, or a ConfigurableActionStructField the name of the field will be bound to this variable when it is retrieved.

nBins¶: Number of bins to use within the effective plot ranges along the spatial directions. (int, default 200)

plotMax¶: Maximum in z-value to display in the focal plane plot and in the histogram plot, if applicable (float, default None)

plotMin¶: Minimum in z-value to display in the focal plane plot and in the histogram plot, if applicable (float, default None)

showStats¶: Show statistics for plotted data (bool, default True)

statistic¶: Operation to perform in binned_statistic_2d (str, default 'mean')

xAxisLabel¶: Label to use for the x axis. (str, default 'x (mm)')

yAxisLabel¶: Label to use for the y axis. (str, default 'y (mm)')

zAxisLabel¶: Label to use for the z axis. (str)

Methods Documentation

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

addInputSchema(inputSchema: Tensor]]]) → None¶

Add the supplied inputSchema argument to the class such that it will be returned along side any other arguments in a call to getInputSchema.

Parameters:

inputSchemaKeyedDataSchema: A schema that is to be merged in with any existing schema when a call to getInputSchema is made.

compare(other, shortcut=True, rtol=1e-08, atol=1e-08, output=None)¶

Compare this configuration to another Config for equality.

Parameters:

otherlsst.pex.config.Config: Other Config object to compare against this config.
shortcutbool, optional: If True, return as soon as an inequality is found. Default is True.
rtolfloat, optional: Relative tolerance for floating point comparisons.
atolfloat, optional: Absolute tolerance for floating point comparisons.
outputcallable, optional: A callable that takes a string, used (possibly repeatedly) to report inequalities.

Returns:

isEqualbool: True when the two lsst.pex.config.Config instances are equal. False if there is an inequality.

See also

lsst.pex.config.compareConfigs

Notes

Unselected targets of RegistryField fields and unselected choices of ConfigChoiceField fields are not considered by this method.

Floating point comparisons are performed by numpy.allclose.

formatHistory(name, **kwargs)¶

Format a configuration field’s history to a human-readable format.

Parameters:

namestr: Name of a Field in this config.
**kwargs: Keyword arguments passed to lsst.pex.config.history.format.

Returns:

historystr: A string containing the formatted history.

See also

lsst.pex.config.history.format

freeze()¶: Make this config, and all subconfigs, read-only.

getFormattedInputSchema(**kwargs) → Tensor]]]¶

Return input schema, with keys formatted with any arguments supplied by kwargs passed to this method.

Returns:

resultKeyedDataSchema: The schema this action requires to be present when calling this action, formatted with any input arguments (e.g. band=’i’)

getInputSchema(**kwargs) → Tensor]]]¶

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.

getOutputNames(config: Config | None = None) → Iterable[str]¶

Returns a list of names that will be used as keys if this action’s call method returns a mapping. Otherwise return an empty Iterable.

Parameters:

configlsst.pex.config.Config, optional: Configuration of the task. This is only used if the output naming needs to be config-aware.

Returns:

resultIterable of str: If a PlotAction produces more than one plot, this should be the keys the action will use in the returned Mapping.

getOutputSchema() → Tensor]]] | None¶

Return the schema an AnalysisAction will produce, if the __call__ method returns KeyedData, otherwise this may return None.

Returns:

resultKeyedDataSchema or None: The schema this action will produce when returning from call. This will be unformatted if any templates are present. Should return None if action does not return KeyedData.

items()¶

Get configurations as (field name, field value) pairs.

Returns:

itemsItemsView

Iterator of tuples for each configuration. Tuple items are:

Field name.
Field value.

keys()¶

Get field names.

Returns:

namesKeysView: List of lsst.pex.config.Field names.

load(filename, root='config')¶

Modify this config in place by executing the Python code in a configuration file.

Parameters:

filenamestr

Name of the configuration file. A configuration file is Python module.

rootstr, optional

Name of the variable in file that refers to the config being overridden.

For example, the value of root is "config" and the file contains:

config.myField = 5

Then this config’s field myField is set to 5.

See also

lsst.pex.config.Config.loadFromStream
lsst.pex.config.Config.loadFromString
lsst.pex.config.Config.save
lsst.pex.config.Config.saveToStream
lsst.pex.config.Config.saveToString

loadFromStream(stream, root='config', filename=None, extraLocals=None)¶

Modify this Config in place by executing the Python code in the provided stream.

Parameters:

streamfile-like object, str, bytes, or CodeType

Stream containing configuration override code. If this is a code object, it should be compiled with mode="exec".

rootstr, optional

Name of the variable in file that refers to the config being overridden.

For example, the value of root is "config" and the file contains:

config.myField = 5

Then this config’s field myField is set to 5.

filenamestr, optional

Name of the configuration file, or None if unknown or contained in the stream. Used for error reporting.

extraLocalsdict of str to object, optional

Any extra variables to include in local scope when loading.

See also

lsst.pex.config.Config.load
lsst.pex.config.Config.loadFromString
lsst.pex.config.Config.save
lsst.pex.config.Config.saveToStream
lsst.pex.config.Config.saveToString

Notes

For backwards compatibility reasons, this method accepts strings, bytes and code objects as well as file-like objects. New code should use loadFromString instead for most of these types.

loadFromString(code, root='config', filename=None, extraLocals=None)¶

Modify this Config in place by executing the Python code in the provided string.

Parameters:

codestr, bytes, or CodeType

Stream containing configuration override code.

rootstr, optional

Name of the variable in file that refers to the config being overridden.

For example, the value of root is "config" and the file contains:

config.myField = 5

Then this config’s field myField is set to 5.

filenamestr, optional

Name of the configuration file, or None if unknown or contained in the stream. Used for error reporting.

extraLocalsdict of str to object, optional

Any extra variables to include in local scope when loading.

Raises:

ValueError: Raised if a key in extraLocals is the same value as the value of the root argument.

See also

lsst.pex.config.Config.load
lsst.pex.config.Config.loadFromStream
lsst.pex.config.Config.save
lsst.pex.config.Config.saveToStream
lsst.pex.config.Config.saveToString

makePlot(data: MutableMapping[str, ndarray[Any, dtype[ScalarType]] | Scalar | HealSparseMap | Tensor], camera: Camera, plotInfo: Mapping[str, str] | None = None, **kwargs) → Figure¶

Prep the catalogue and then make a focalPlanePlot of the given column.

Uses the axisLabels config options x and y to make an image, where the color corresponds to the 2d binned statistic (the mean is the default) applied to the z column. A summary panel is shown in the upper right corner of the resultant plot. The code uses the selectorActions to decide which points to plot and the statisticSelector actions to determine which points to use for the printed statistics.

Parameters:

datapandas.core.frame.DataFrame

The catalog to plot the points from.

cameralsst.afw.cameraGeom.Camera

The camera used to map from pixel to focal plane positions.

plotInfodict

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

"run": The output run for the plots (str).
"skymap": The type of skymap used for the data (str).
"filter": The filter used for this data (str).
"tract": The tract that the data comes from (str).
"bands": The band(s) that the data comes from (list of str).

Returns:

figmatplotlib.figure.Figure: The resulting figure.

names()¶

Get all the field names in the config, recursively.

Returns:

nameslist of str: Field names.

save(filename, root='config')¶

Save a Python script to the named file, which, when loaded, reproduces this config.

Parameters:

filenamestr: Desination filename of this configuration.
rootstr, optional: Name to use for the root config variable. The same value must be used when loading (see lsst.pex.config.Config.load).

See also

lsst.pex.config.Config.saveToStream
lsst.pex.config.Config.saveToString
lsst.pex.config.Config.load
lsst.pex.config.Config.loadFromStream
lsst.pex.config.Config.loadFromString

saveToStream(outfile, root='config', skipImports=False)¶

Save a configuration file to a stream, which, when loaded, reproduces this config.

Parameters:

outfilefile-like object: Destination file object write the config into. Accepts strings not bytes.
rootstr, optional: Name to use for the root config variable. The same value must be used when loading (see lsst.pex.config.Config.load).
skipImportsbool, optional: If True then do not include import statements in output, this is to support human-oriented output from pipetask where additional clutter is not useful.

See also

lsst.pex.config.Config.save
lsst.pex.config.Config.saveToString
lsst.pex.config.Config.load
lsst.pex.config.Config.loadFromStream
lsst.pex.config.Config.loadFromString

saveToString(skipImports=False)¶

Return the Python script form of this configuration as an executable string.

Parameters:

skipImportsbool, optional: If True then do not include import statements in output, this is to support human-oriented output from pipetask where additional clutter is not useful.

Returns:

codestr: A code string readable by loadFromString.

See also

lsst.pex.config.Config.save
lsst.pex.config.Config.saveToStream
lsst.pex.config.Config.load
lsst.pex.config.Config.loadFromStream
lsst.pex.config.Config.loadFromString

setDefaults()¶

Subclass hook for computing defaults.

Notes

Derived Config classes that must compute defaults rather than using the Field instances’s defaults should do so here. To correctly use inherited defaults, implementations of setDefaults must call their base class’s setDefaults.

statsAndText(arr, mask=None)¶: Calculate some stats from an array and return them and some text.

toDict()¶

Make a dictionary of field names and their values.

Returns:

dict_dict: Dictionary with keys that are Field names. Values are Field values.

See also

lsst.pex.config.Field.toDict

Notes

This method uses the toDict method of individual fields. Subclasses of Field may need to implement a toDict method for this method to work.

update(**kw)¶

Update values of fields specified by the keyword arguments.

Parameters:

**kw: Keywords are configuration field names. Values are configuration field values.

Notes

The __at and __label keyword arguments are special internal keywords. They are used to strip out any internal steps from the history tracebacks of the config. Do not modify these keywords to subvert a Config instance’s history.

Examples

This is a config with three fields:

>>> from lsst.pex.config import Config, Field
>>> class DemoConfig(Config):
...     fieldA = Field(doc='Field A', dtype=int, default=42)
...     fieldB = Field(doc='Field B', dtype=bool, default=True)
...     fieldC = Field(doc='Field C', dtype=str, default='Hello world')
...
>>> config = DemoConfig()

These are the default values of each field:

>>> for name, value in config.iteritems():
...     print(f"{name}: {value}")
...
fieldA: 42
fieldB: True
fieldC: 'Hello world'

Using this method to update fieldA and fieldC:

>>> config.update(fieldA=13, fieldC='Updated!')

Now the values of each field are:

>>> for name, value in config.iteritems():
...     print(f"{name}: {value}")
...
fieldA: 13
fieldB: True
fieldC: 'Updated!'

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.

values()¶

Get field values.

Returns:

valuesValuesView: Iterator of field values.

Navigation

FocalPlanePlot¶