AnalysisPlot

class lsst.analysis.tools.AnalysisPlot

Bases: lsst.analysis.tools.AnalysisTool

Specialized AnalysisTool for producing plots.

The produce stage of AnalysisPlot has been specialized such that it expects to be assigned to a PlotAction.

Attributes Summary

applyContext
history
identity
parameterizedBand
prep Action to run to prepare inputs (KeyedDataAction, default <class 'lsst.analysis.tools.interfaces.KeyedDataAction'>)
process Action to process data into intended form (AnalysisAction, default <class 'lsst.analysis.tools.interfaces.AnalysisAction'>)
produce Action which returns a plot (PlotAction, default <class 'lsst.analysis.tools.interfaces.PlotAction'>)

Methods Summary

__call__(data, numpy.ndarray[typing.Any, …) 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() Return the schema an AnalysisAction expects to be present in the arguments supplied to the __call__ method.
getOutputNames() Return the names of the plots produced by this action.
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.
names() Get all the field names in the config, recursively.
populatePrepFromProcess() Add additional inputs to the prep stage if supported.
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.
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

applyContext
history
identity = None
parameterizedBand = True
prep

Action to run to prepare inputs (KeyedDataAction, default <class 'lsst.analysis.tools.interfaces.KeyedDataAction'>)

process

Action to process data into intended form (AnalysisAction, default <class 'lsst.analysis.tools.interfaces.AnalysisAction'>)

produce

Action which returns a plot (PlotAction, default <class 'lsst.analysis.tools.interfaces.PlotAction'>)

Methods Documentation

__call__(data: MutableMapping[str, numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]] | lsst.analysis.tools.interfaces.Scalar[numpy.ndarray[Any, numpy.dtype[ScalarType]], lsst.analysis.tools.interfaces.Scalar]], **kwargs) → Union[Mapping[str, matplotlib.figure.Figure], matplotlib.figure.Figure, Mapping[str, lsst.verify.measurement.Measurement], lsst.verify.measurement.Measurement]

Call self as a function.

addInputSchema(inputSchema: Iterable[Tuple[str, Union[Type[numpy.ndarray[Any, numpy.dtype[ScalarType]]], Type[numbers.Number], Type[numpy.number]]]]) → 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:
inputSchema : KeyedDataSchema

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:
other : lsst.pex.config.Config

Other Config object to compare against this config.

shortcut : bool, optional

If True, return as soon as an inequality is found. Default is True.

rtol : float, optional

Relative tolerance for floating point comparisons.

atol : float, optional

Absolute tolerance for floating point comparisons.

output : callable, optional

A callable that takes a string, used (possibly repeatedly) to report inequalities.

Returns:
isEqual : bool

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

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:
name : str

Name of a Field in this config.

kwargs

Keyword arguments passed to lsst.pex.config.history.format.

Returns:
history : str

A string containing the formatted history.

freeze()

Make this config, and all subconfigs, read-only.

getFormattedInputSchema(**kwargs) → Iterable[Tuple[str, Union[Type[numpy.ndarray[Any, numpy.dtype[ScalarType]]], Type[numbers.Number], Type[numpy.number]]]]

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

Returns:
result : KeyedDataSchema

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

getInputSchema() → Iterable[Tuple[str, Union[Type[numpy.ndarray[Any, numpy.dtype[ScalarType]]], Type[numbers.Number], Type[numpy.number]]]]

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

Returns:
result : KeyedDataSchema

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

getOutputNames() → Iterable[str]

Return the names of the plots produced by this action.

This will either come from the PlotAction if it defines a getOutputNames method (likely if it returns a mapping of figures), or a default value is used and a single figure is assumed.

Returns:
result : tuple of str

Names for each plot produced by this action.

getOutputSchema() → Optional[Iterable[Tuple[str, Union[Type[numpy.ndarray[Any, numpy.dtype[ScalarType]]], Type[numbers.Number], Type[numpy.number]]]], None]

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

Returns:
result : KeyedDataSchema 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:
items : dict_items

Iterator of tuples for each configuration. Tuple items are:

  1. Field name.
  2. Field value.
keys()

Get field names.

Returns:
names : dict_keys

List of lsst.pex.config.Field names.

See also

lsst.pex.config.Config.iterkeys
load(filename, root='config')

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

Parameters:
filename : str

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

root : str, 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.

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

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

Parameters:
stream : file-like object, str, bytes, or compiled string

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

root : str, 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.

filename : str, optional

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

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)

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

Parameters:
code : str, bytes, or compiled string

Stream containing configuration override code.

root : str, 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.

filename : str, optional

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

names()

Get all the field names in the config, recursively.

Returns:
names : list of str

Field names.

populatePrepFromProcess()

Add additional inputs to the prep stage if supported.

If the configured prep action supports adding to it’s input schema, attempt to add the required inputs schema from the process stage to the prep stage.

This method will be a no-op if the prep action does not support this feature.

save(filename, root='config')

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

Parameters:
filename : str

Desination filename of this configuration.

root : str, optional

Name to use for the root config variable. The same value must be used when loading (see lsst.pex.config.Config.load).

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

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

Parameters:
outfile : file-like object

Destination file object write the config into. Accepts strings not bytes.

root

Name to use for the root config variable. The same value must be used when loading (see lsst.pex.config.Config.load).

skipImports : bool, 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.

saveToString(skipImports=False)

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

Parameters:
skipImports : bool, 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:
code : str

A code string readable by 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.

toDict()

Make a dictionary of field names and their values.

Returns:
dict_ : dict

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

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:
values : dict_values

Iterator of field values.