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, default- None)- Key for mask to apply for target objects in purity ( - str, default- None)- 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 - AnalysisActionexpects to be present in the arguments supplied to the __call__ method.- Return the schema an - AnalysisActionwill produce, if the- __call__method returns- KeyedData, 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[tuple[int, ...], dtype[_ScalarType_co]] | Scalar | HealSparseMap | Tensor | Mapping], band: str | None = None, mask=None, **kwargs: Any) MutableMapping[str, ndarray[tuple[int, ...], 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 - AnalysisActionexpects 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. 
 
- result
 
 - getOutputSchema() Mapping]]]¶
- Return the schema an - AnalysisActionwill produce, if the- __call__method returns- KeyedData, otherwise this may return None.- Returns:
- resultKeyedDataSchemaor 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 - validatemethods.- Complex single-field validation can be defined by deriving new Field types. For convenience, some derived - lsst.pex.config.Field-types (- ConfigFieldand- ConfigChoiceField) are defined in- lsst.pex.configthat handle recursing into subconfigs.- Inter-field relationships should only be checked in derived - Configclasses after calling this method, and base validation is complete.