Classes
class	DiffMatchedTractCatalogConfig

class	DiffMatchedTractCatalogConnections

class	DiffMatchedTractCatalogTask

class	MatchedCatalogFluxesConfig

class	MatchType

class	MeasurementType

class	MeasurementTypeInfo

class	Median

class	Percentile

class	SigmaIQR

class	SigmaMAD

class	SourceType

class	SourceTypeInfo

class	Statistic

Functions
def	is_sequence_set (Sequence x)

def	is_percentile (str x)

def	compute_stats (values_ref, values_target, errors_target, row, stats, suffixes, prefix, skip_diff=False)

Variables
dictionary	DiffMatchedTractCatalogBaseTemplates

Function Documentation

◆ compute_stats()

def lsst.pipe.tasks.diff_matched_tract_catalog.compute_stats	(	values_ref,
		values_target,
		errors_target,
		row,
		stats,
		suffixes,
		prefix,
		skip_diff = `False`
	)

Compute statistics on differences and store results in a row.

Parameters
----------
values_ref : `numpy.ndarray`, (N,)
    Reference values.
values_target : `numpy.ndarray`, (N,)
    Measured values.
errors_target : `numpy.ndarray`, (N,)
    Errors (standard deviations) on `values_target`.
row : `numpy.ndarray`, (1, C)
    A numpy array with pre-assigned column names.
stats : `Dict` [`str`, `Statistic`]
    A dict of `Statistic` values to measure, keyed by their column suffix.
suffixes : `Dict` [`MeasurementType`, `str`]
    A dict of measurement type column suffixes, keyed by the measurement type.
prefix : `str`
    A prefix for all column names (e.g. band).
skip_diff : `bool`
    Whether to skip computing statistics on differences. Note that
    differences will still be computed for chi statistics.

Returns
-------
row_with_stats : `numpy.ndarray`, (1, C)
    The original `row` with statistic values assigned.

Definition at line 409 of file diff_matched_tract_catalog.py.

def compute_stats(values_ref, values_target, errors_target, row, stats, suffixes, prefix, skip_diff=False):
    """Compute statistics on differences and store results in a row.
 
    Parameters
    ----------
    values_ref : `numpy.ndarray`, (N,)
        Reference values.
    values_target : `numpy.ndarray`, (N,)
        Measured values.
    errors_target : `numpy.ndarray`, (N,)
        Errors (standard deviations) on `values_target`.
    row : `numpy.ndarray`, (1, C)
        A numpy array with pre-assigned column names.
    stats : `Dict` [`str`, `Statistic`]
        A dict of `Statistic` values to measure, keyed by their column suffix.
    suffixes : `Dict` [`MeasurementType`, `str`]
        A dict of measurement type column suffixes, keyed by the measurement type.
    prefix : `str`
        A prefix for all column names (e.g. band).
    skip_diff : `bool`
        Whether to skip computing statistics on differences. Note that
        differences will still be computed for chi statistics.
 
    Returns
    -------
    row_with_stats : `numpy.ndarray`, (1, C)
        The original `row` with statistic values assigned.
    """
    n_ref = len(values_ref)
    if n_ref > 0:
        n_target = len(values_target)
        n_target_err = len(errors_target) if errors_target is not None else n_ref
        if (n_target != n_ref) or (n_target_err != n_ref):
            raise ValueError(f'lengths of values_ref={n_ref}, values_target={n_target}'
                             f', error_target={n_target_err} must match')
 
        do_chi = errors_target is not None
        diff = values_target - values_ref
        chi = diff/errors_target if do_chi else diff
        # Could make this configurable, but non-finite values/errors are not really usable
        valid = np.isfinite(chi)
        values_type = {} if skip_diff else {MeasurementType.DIFF: diff[valid]}
        if do_chi:
            values_type[MeasurementType.CHI] = chi[valid]
 
        for suffix_type, suffix in suffixes.items():
            values = values_type.get(suffix_type)
            if values is not None and len(values) > 0:
                for stat_name, stat in stats.items():
                    row[_get_stat_name(prefix, suffix, stat_name)] = stat.value(values)
    return row
 
 
@dataclass(frozen=True)

◆ is_percentile()

def lsst.pipe.tasks.diff_matched_tract_catalog.is_percentile ( str x )

Definition at line 53 of file diff_matched_tract_catalog.py.

def is_percentile(x: str):
    return 0 <= Decimal(x) <= 100

◆ is_sequence_set()

def lsst.pipe.tasks.diff_matched_tract_catalog.is_sequence_set ( Sequence x )

Definition at line 49 of file diff_matched_tract_catalog.py.

def is_sequence_set(x: Sequence):
    return len(x) == len(set(x))

Variable Documentation

◆ DiffMatchedTractCatalogBaseTemplates

dictionary lsst.pipe.tasks.diff_matched_tract_catalog.DiffMatchedTractCatalogBaseTemplates

Initial value:

=  {
    "name_input_cat_ref": "truth_summary",
    "name_input_cat_target": "objectTable_tract",
    "name_skymap": BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
}

Definition at line 57 of file diff_matched_tract_catalog.py.

Classes