firecrown.data_functions.TwoPointBinFilterCollection

class firecrown.data_functions.TwoPointBinFilterCollection(**data)

Bases: BaseModel

Class defining a collection of bin filters.

Parameters:

data (Any) –

Public Methods:

check_bin_filters()	Check the bin filters.
filter_match(tpm)	Check if the TwoPointMeasurement matches the filter.
run_bin_filter(bin_filter, vals)	Run the filter merge.
apply_filter_single(tpm)	Apply the filter to a single TwoPointMeasurement.
__call__(tpms)	Filter the two-point measurements.
model_post_init(context, /)	This function is meant to behave like a BaseModel method to initialise private attributes.
__hash__()	Return hash(self).
__signature__	The synthesized __init__ [Signature][inspect.Signature] of the model.

Inherited from BaseModel

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
model_dump(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
model_dump_json(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(_BaseModel__context)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
model_validate_strings(obj, *[, strict, context])	Validate the given object with string data against the Pydantic model.
__get_pydantic_core_schema__(source, handler, /)	Hook into generating the model's CoreSchema.
__get_pydantic_json_schema__(core_schema, ...)	Hook into generating the model's JSON schema.
__pydantic_init_subclass__(**kwargs)	This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized.
__class_getitem__(typevar_values)	param typevar_values:
__copy__()	Returns a shallow copy of the model.
__deepcopy__([memo])	Returns a deep copy of the model.
__getattr__(item)	param item:
__setattr__(name, value)	Implement setattr(self, name, value).
__delattr__(item)	Implement delattr(self, name).
__replace__(**changes)	param changes:
__getstate__()	Helper for pickle.
__setstate__(state)	param state:
__eq__(other)	Return self==value.
__iter__()	So dict(model) works.
__repr__()	Return repr(self).
__repr_args__()
__repr_name__()	Name of the instance's class, used in __repr__.
__repr_recursion__(object)	Returns the string representation of a recursive object.
__repr_str__(join_str)	param join_str:
__pretty__(fmt, **kwargs)	Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
__rich_repr__()	Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
__str__()	Return str(self).
dict(*[, include, exclude, by_alias, ...])	param include:
json(*[, include, exclude, by_alias, ...])	param include:
parse_obj(obj)	param obj:
parse_raw(b, *[, content_type, encoding, ...])	param b:
parse_file(path, *[, content_type, ...])	param path:
from_orm(obj)	param obj:
construct([_fields_set])	param _fields_set:
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
schema([by_alias, ref_template])	param by_alias:
schema_json(*[, by_alias, ref_template])	param by_alias:
validate(value)	param value:
update_forward_refs(**localns)	param localns:

Private Methods:

Inherited from BaseModel

_check_frozen(name, value)	param name:
_iter(*args, **kwargs)	param args:
_copy_and_set_values(*args, **kwargs)	param args:
_get_value(*args, **kwargs)	param args:
_calculate_keys(*args, **kwargs)	param args:

---

__call__(tpms)

Filter the two-point measurements.

Parameters:

tpms (Sequence[TwoPointMeasurement]) –

Return type:

list[TwoPointMeasurement]

apply_filter_single(tpm)

Apply the filter to a single TwoPointMeasurement.

Parameters:

tpm (TwoPointMeasurement) –

Return type:

tuple[ndarray[tuple[int, ...], dtype[bool]], ndarray[tuple[int, ...], dtype[bool]]]

property bin_filter_dict: dict[frozenset[firecrown.data_functions.TwoPointTracerSpec], tuple[float, float]]

Return the bin filter dictionary.

check_bin_filters()

Check the bin filters.

Return type:

TwoPointBinFilterCollection

filter_match(tpm)

Check if the TwoPointMeasurement matches the filter.

Parameters:

tpm (TwoPointMeasurement) –

Return type:

bool

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True}

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

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• context (Any) – The context.

Return type:

None

run_bin_filter(bin_filter, vals)

Run the filter merge.

Parameters:

• bin_filter (tuple[float, float]) –

• vals (ndarray[tuple[int, ...], dtype[float64]] | ndarray[tuple[int, ...], dtype[int64]]) –

Return type:

ndarray[tuple[int, ...], dtype[bool]]