firecrown.data_functions

This module deals with two-point data functions.

It contains functions to manipulate two-point data objects.

Attributes

BinSpec

Classes

TwoPointTracerSpec	Class defining a tracer bin specification.
TwoPointBinFilter	Class defining a filter for a bin.
TwoPointBinFilterCollection	Class defining a collection of bin filters.

Functions

cov_hash(sacc_data)	Return a hash of the covariance matrix.
extract_all_harmonic_data(sacc_data[, ...])	Extract the two-point function metadata and data from a sacc file.
extract_all_real_data(sacc_data[, allowed_data_type, ...])	Extract the two-point function metadata and data from a sacc file.
ensure_no_overlaps(measurement, index_set, index_sets, ...)	Check if the indices of the measurement-space two-point functions overlap.
check_consistence(measurements, is_type_func, type_name)	Check the indices of the two-point functions.
check_two_point_consistence_harmonic(two_point_harmonics)	Check the indices of the harmonic-space two-point functions.
check_two_point_consistence_real(two_point_reals)	Check the indices of the real-space two-point functions.
make_interval_from_list(values)	Create an interval from a list of values.
bin_spec_from_metadata(metadata)	Return the bin spec from the metadata.

Module Contents

firecrown.data_functions.cov_hash(sacc_data)

Return a hash of the covariance matrix.

Parameters:

sacc_data (sacc.Sacc) – The SACC data object containing the covariance matrix.

Returns:

The hash of the covariance matrix.

Return type:

str

firecrown.data_functions.extract_all_harmonic_data(sacc_data, allowed_data_type=None, include_maybe_types=False)

Extract the two-point function metadata and data from a sacc file.

Parameters:

• sacc_data (sacc.Sacc)

• allowed_data_type (None | list[str])

Return type:

list[firecrown.data_types.TwoPointMeasurement]

firecrown.data_functions.extract_all_real_data(sacc_data, allowed_data_type=None, include_maybe_types=False)

Extract the two-point function metadata and data from a sacc file.

Parameters:

• sacc_data (sacc.Sacc)

• allowed_data_type (None | list[str])

Return type:

list[firecrown.data_types.TwoPointMeasurement]

firecrown.data_functions.ensure_no_overlaps(measurement, index_set, index_sets, other_measurements)

Check if the indices of the measurement-space two-point functions overlap.

Raises a ValueError if they do.

Parameters:

• measurement (str) – The TwoPointHarmonic to check.

• index_set (set[int]) – The indices of the current TwoPointHarmonic.

• index_sets (list[set[int]]) – The indices of the other TwoPointHarmonics.

• other_measurements (list[str]) – The other TwoPointHarmonics.

Return type:

None

firecrown.data_functions.check_consistence(measurements, is_type_func, type_name)

Check the indices of the two-point functions.

Make sure the indices of the two-point functions are consistent.

Parameters:

• measurements (collections.abc.Sequence[firecrown.data_types.TwoPointMeasurement]) – The measurements to check.

• is_type_func (collections.abc.Callable[[firecrown.data_types.TwoPointMeasurement], bool]) – A function to verify the type of the measurements.

• type_name (str) – The type of the measurements.

Return type:

None

firecrown.data_functions.check_two_point_consistence_harmonic(two_point_harmonics)

Check the indices of the harmonic-space two-point functions.

Parameters:

two_point_harmonics (collections.abc.Sequence[firecrown.data_types.TwoPointMeasurement])

Return type:

None

firecrown.data_functions.check_two_point_consistence_real(two_point_reals)

Check the indices of the real-space two-point functions.

Parameters:

two_point_reals (collections.abc.Sequence[firecrown.data_types.TwoPointMeasurement])

Return type:

None

class firecrown.data_functions.TwoPointTracerSpec(/, **data)

Bases: pydantic.BaseModel

Class defining a tracer bin specification.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: Annotated[str, Field(description='The name of the tracer bin.')]

measurement: Annotated[firecrown.metadata_types.Measurement, Field(description='The measurement of the tracer bin.'), BeforeValidator(make_measurement)]

classmethod serialize_measurement(value)

Serialize the Measurement.

Parameters:

value (firecrown.metadata_types.Measurement)

Return type:

dict[str, str]

firecrown.data_functions.make_interval_from_list(values)

Create an interval from a list of values.

Parameters:

values (list[float] | tuple[float, float])

Return type:

tuple[float, float]

class firecrown.data_functions.TwoPointBinFilter(/, **data)

Bases: pydantic.BaseModel

Class defining a filter for a bin.

Parameters:

• spec – The two-point bin specification.

• interval – The range of the bin to filter.

• method – The filter method.

• data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

spec: Annotated[list[TwoPointTracerSpec], Field(description='The two-point bin specification.')]

interval: Annotated[tuple[float, float], BeforeValidator(make_interval_from_list), Field(description='The range of the bin to filter.')]

method: Annotated[firecrown.metadata_types.TwoPointFilterMethod, Field(description='The filter method.')]

check_bin_filter()

Check the bin filter.

Return type:

TwoPointBinFilter

classmethod serialize_interval(value)

Serialize the Measurement.

Parameters:

value (tuple[float, float])

Return type:

list[float]

classmethod from_args(name1, measurement1, name2, measurement2, lower, upper, method=TwoPointFilterMethod.SUPPORT)

Create a TwoPointBinFilter from the arguments.

Parameters:

• name1 (str)

• measurement1 (firecrown.metadata_types.Measurement)

• name2 (str)

• measurement2 (firecrown.metadata_types.Measurement)

• lower (float)

• upper (float)

• method (firecrown.metadata_types.TwoPointFilterMethod)

Return type:

TwoPointBinFilter

classmethod from_args_auto(name, measurement, lower, upper, method=TwoPointFilterMethod.SUPPORT)

Create a TwoPointBinFilter from the arguments.

Parameters:

• name (str)

• measurement (firecrown.metadata_types.Measurement)

• lower (float)

• upper (float)

• method (firecrown.metadata_types.TwoPointFilterMethod)

Return type:

TwoPointBinFilter

firecrown.data_functions.BinSpec

firecrown.data_functions.bin_spec_from_metadata(metadata)

Return the bin spec from the metadata.

Parameters:

metadata (firecrown.metadata_types.TwoPointReal | firecrown.metadata_types.TwoPointHarmonic)

Return type:

BinSpec

class firecrown.data_functions.TwoPointBinFilterCollection(/, **data)

Bases: pydantic.BaseModel

Class defining a collection of bin filters.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

require_filter_for_all: bool = None

allow_empty: bool = None

filters: list[TwoPointBinFilter] = None

check_bin_filters()

Check the bin filters.

Return type:

TwoPointBinFilterCollection

property bin_filter_dict: dict[BinSpec, TwoPointBinFilter]

Return the bin filter dictionary.

Return type:

dict[BinSpec, TwoPointBinFilter]

filter_match(tpm)

Check if the TwoPointMeasurement matches the filter.

Parameters:

tpm (firecrown.data_types.TwoPointMeasurement)

Return type:

bool

run_bin_filter(bin_filter, vals)

Run the filter merge.

Parameters:

• bin_filter (TwoPointBinFilter)

• vals (numpy.typing.NDArray[numpy.float64] | numpy.typing.NDArray[numpy.int64])

Return type:

numpy.typing.NDArray[numpy.bool_]

apply_filter_single(tpm)

Apply the filter to a single TwoPointMeasurement.

Parameters:

tpm (firecrown.data_types.TwoPointMeasurement)

Return type:

tuple[numpy.typing.NDArray[numpy.bool_], numpy.typing.NDArray[numpy.bool_]]

__call__(tpms)

Filter the two-point measurements.

Parameters:

tpms (collections.abc.Sequence[firecrown.data_types.TwoPointMeasurement])

Return type:

list[firecrown.data_types.TwoPointMeasurement]