franc.evaluation.filter_interface

Shared functionality and interface for all filtering techniques

Attributes

FilterTypeT

Classes

FilterInterface

common interface definition for Filter implementations

Functions

make_2d_array(A)

add a dimension to 1D arrays and leave 2D arrays as they are

handle_from_dict(init_func)

A decorator for the init functions of classes derived from FitlerInterface

Module Contents

franc.evaluation.filter_interface.FilterTypeT
franc.evaluation.filter_interface.make_2d_array(A)

add a dimension to 1D arrays and leave 2D arrays as they are This is intended to allow 1D array input for single channel application

Parameters:

A (collections.abc.Sequence | collections.abc.Sequence[collections.abc.Sequence] | numpy.typing.NDArray) – input array

Returns:

extended array

Raises:

ValueError if the input shape is not compatible

Return type:

numpy.typing.NDArray

>>> import franc as fnc
>>> fnc.evaluation.make_2d_array([1, 2])
array([[1, 2]])

>>> import franc as fnc
>>> fnc.evaluation.make_2d_array([[1, 2], [3, 4]])
array([[1, 2],
       [3, 4]])
franc.evaluation.filter_interface.handle_from_dict(init_func)

A decorator for the init functions of classes derived from FitlerInterface

If the _from_dict keyword argument is passed, the __init__() function is ignored and the class is initialized based on the passed dictionary. Otherwise, the constructor is called the usual way.

Parameters:

init_func (collections.abc.Callable) –

class franc.evaluation.filter_interface.FilterInterface(n_channel, _from_dict=None)

Bases: abc.ABC

common interface definition for Filter implementations

Parameters:

n_channel (int) – Number of witness sensor channels

requires_apply_target: bool
n_channel: int
method_hash_value: bytes
supports_multi_sequence = True
filter_name = 'FilterInterface'
default_args = [None]
static supports_saving_loading()

Indicates whether saving and loading is supported Due to the way dataclasses work with inheritance, class values with default values don’t work in the parent dataclass. Thus, this is a function

Return type:

bool

condition(witness, target)

Use an input dataset to condition the filter

Parameters:

  • witness (collections.abc.Sequence | collections.abc.Sequence[collections.abc.Sequence] | numpy.typing.NDArray) – Witness sensor data

  • target (collections.abc.Sequence | numpy.typing.NDArray) – Target sensor data

abstract condition_multi_sequence(witness, target)

Similar to condition(), but expects multiple sequences

First index to the given data objects indicates the sequence. The last index indicates the time within a single sequence. Sequences must not have the same length.

Parameters:

  • witness (collections.abc.Sequence | collections.abc.Sequence[collections.abc.Sequence] | numpy.typing.NDArray) –

  • target (collections.abc.Sequence | numpy.typing.NDArray) –

Return type:

Any

apply(witness, target=None, pad=True, update_state=False)

Apply the filter to a single sequence of input data

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) – Witness sensor data (1D or 2D array)

  • target (collections.abc.Sequence | numpy.typing.NDArray | None) – Target sensor data (1D array)

  • pad (bool) – if True, apply padding zeros so that the length matches the target signal

  • update_state (bool) – if True, the filter state will be changed. If false, the filter state will remain

Returns:

prediction

Return type:

numpy.typing.NDArray

abstract apply_multi_sequence(witness, target, pad=True, update_state=False)

Apply the filter to multiple sequences of input data.

Similar to apply() but expects multiple sequences. First index to the given data objects indicates the sequence. The last index indicates the time within a single sequence. Sequences must not have the same length.

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) –

  • target (collections.abc.Sequence | numpy.typing.NDArray | None) –

  • pad (bool) –

  • update_state (bool) –

Return type:

collections.abc.Sequence[numpy.typing.NDArray]

check_data_dimensions(witness, target=None)

Check the dimensions of the provided input data and apply make_2d_array()

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) – Witness sensor data

  • target (collections.abc.Sequence | numpy.typing.NDArray | None) – Target sensor data

Returns:

data as (target, witness)

Raises:

AssertionError

Return type:

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

check_data_dimensions_multi_sequence(witness: collections.abc.Sequence | numpy.typing.NDArray, target: None) → tuple[list[numpy.typing.NDArray], None]
check_data_dimensions_multi_sequence(witness: collections.abc.Sequence | numpy.typing.NDArray, target: collections.abc.Sequence | numpy.typing.NDArray) → tuple[list[numpy.typing.NDArray], list[numpy.typing.NDArray]]

Check the dimensions of the provided input data and apply make_2d_array()

Parameters:

  • witness – Witness sensor data

  • target – Target sensor data

Returns:

data as (target, witness)

Raises:

AssertionError

as_dict()

Returns a dictionary that represents the state of this filter.

Return type:

dict[str, Any]

classmethod from_dict(input_dict)

Create a filter instance from a dictionary that was created from as_dict()

Parameters:

input_dict (dict[str, Any]) –

Return type:

FilterTypeT

classmethod make_filename(filename)

Append the file type of save files for this class to the given filename, if it is not already present

Parameters:

filename (str | pathlib.Path) –

save(filename, warn_incompatible=False)

Save the filter state as a numpy file

The given filename will be autocompleted with a “.<filter_name>.npz” filename extension, unless a matching extension is detected.

warn_incompatible: set to True to warn for object types might not

compatible with np.save(allow_pickle=False) during development

Parameters:

  • filename (str | pathlib.Path) –

  • warn_incompatible (bool) –

classmethod load(filename)

Load a filter state from the supplied filename.

The given filename will be autocompleted with a “.<filter_name>.npz” filename extension, unless a matching extension is detected.

Return type:

FilterTypeT

classmethod file_hash()

Calculates a hash value based on the file in which this method was defined.

Return type:

bytes

property method_hash: bytes

A hash of the method and parameters NOTE: This is not a hash of the conditioned filter! Thus, the same filter configuration applied to a different dataset will result in the same hash!

Return type:

bytes

property method_filename_part: str

string that can be used in a file name

Return type:

str