CalcBinnedCompletenessAction¶
- class lsst.analysis.tools.actions.keyedData.CalcBinnedCompletenessAction(*args, **kw)¶
Bases:
KeyedDataAction
Calculate completeness and purity in a single magnitude bin.
Completeness is the fraction of matched objects with reference magnitudes within the bin limits, while purity is the fraction of matched objects with measured magnitudes within the bin limits.
Both statistics are also computed separately for objects that are considered “good” and “bad” matches, given a boolean field key.
Attributes Summary
Key for mask to apply for reference objects in completeness (
str
, defaultNone
)Key for mask to apply for target objects in purity (
str
, defaultNone
)Key for column with distance between matched objects (
str
, default'match_distance'
)Key for boolean vector (True if matched objects have the same class as their ref match) (
str
, default'matched_class'
)Field name to append statistic names to (
str
, default''
)Field name to append to statistic names (
str
, default''
)Range selector for reference objects (
RangeSelector
, default<class 'lsst.analysis.tools.actions.vector.selectors.RangeSelector'>
)Range selector for measured objects (
RangeSelector
, default<class 'lsst.analysis.tools.actions.vector.selectors.RangeSelector'>
)Methods Summary
__call__
(data[, band, mask])Compute completeness and purity metrics.
getFormattedOutputKeys
([band])Return the mapping from unformatted output schema keys to formatted.
getInputSchema
(**kwargs)Return the schema an
AnalysisAction
expects to be present in the arguments supplied to the __call__ method.Return the schema an
AnalysisAction
will produce, if the__call__
method returnsKeyedData
, otherwise this may return None.name_mag_completeness
(name_threshold)validate
()Validate the Config, raising an exception if invalid.
Attributes Documentation
- key_match_distance¶
Key for column with distance between matched objects (
str
, default'match_distance'
)
- key_matched_class¶
Key for boolean vector (True if matched objects have the same class as their ref match) (
str
, default'matched_class'
)
- name_completeness¶
- name_completeness_bad_match¶
- name_completeness_good_match¶
- name_count¶
- name_count_ref¶
- name_count_target¶
- name_mask_ref¶
- name_mask_target¶
- name_purity¶
- name_purity_bad_match¶
- name_purity_good_match¶
- name_range_maximum¶
- name_range_minimum¶
- selector_range_ref¶
Range selector for reference objects (
RangeSelector
, default<class 'lsst.analysis.tools.actions.vector.selectors.RangeSelector'>
)
- selector_range_target¶
Range selector for measured objects (
RangeSelector
, default<class 'lsst.analysis.tools.actions.vector.selectors.RangeSelector'>
)
Methods Documentation
- __call__(data: MutableMapping[str, ndarray[Any, dtype[_ScalarType_co]] | Scalar | HealSparseMap | Tensor | Mapping], band: str | None = None, mask=None, **kwargs: Any) MutableMapping[str, ndarray[Any, dtype[_ScalarType_co]] | Scalar | HealSparseMap | Tensor | Mapping] ¶
Compute completeness and purity metrics.
- Parameters:
- data
Input data to read form.
- band
The name of the band, if any.
- mask
An additional mask to select on before computing statistics.
- kwargs
Additional keyword arguments that are unused.
- Returns:
- data
Dictionary with formatted keys:
"name_count"
The number of objects of either type (reference or target) within the bin (and mask).
"name_count_ref"
The number of reference objects within the bin (and mask).
"name_count_target"
The number of target (measured) objects within the bin (and mask).
"name_completeness"
The completeness within the bin.
"name_completeness_bad_match"
The completeness of objects considered bad matches.
"name_completeness_good_match"
The completeness of objects considered good matches.
"name_purity"
The purity within the bin.
"name_purity_bad_match"
The purity of objects considered bad matches.
"name_purity_good_match"
The purity of objects considered good matches.
"name_range_maximum"
The maximum magnitude of the bin selector.
"name_range_minimum"
The minimum magnitude of the bin selector.
- getFormattedOutputKeys(band: str | None = None, **kwargs: Any) dict[str, str] ¶
Return the mapping from unformatted output schema keys to formatted.
- getInputSchema(**kwargs) Mapping]]] ¶
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.
- result
- getOutputSchema() Mapping]]] ¶
Return the schema an
AnalysisAction
will produce, if the__call__
method returnsKeyedData
, 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
.
- result
- 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
andConfigChoiceField
) are defined inlsst.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.