nn.modules.ButterMelFilter

class nn.modules.ButterMelFilter(*args, **kwargs)[source]

Bases: FilterBankBase

Define a Butterworth filter bank (mel spacing) filtering layer with continuous sampled output

Attributes overview

`class_name`	Class name of `self`
`full_name`	The full name of this module (class plus module name)
`name`	The name of this module, or an empty string if `None`
`shape`	The shape of this module
`size`	(DEPRECATED) The output size of this module
`size_in`	The input size of this module
`size_out`	The output size of this module
`spiking_input`	If `True`, this module receives spiking input.
`spiking_output`	If `True`, this module sends spiking output.
`fs`	(float) Input sampling frequency in Hz
`cutoff_fs`	(float) Post-filtering output low-pass cutoff frequency in Hz
`order`	(int) Filter order
`num_workers`	(int) Number of workers to use in filtering
`mean_subtraction`	(bool) Iff `True`, subtract the mean filter output value from each output
`normalize`	(bool) Iff `True`, collectively normalise the filter outputs [-1, 1]
`use_lowpass`	(bool) Iff `True`, perform a low-pass filter after filtering

Methods overview

`__init__`([shape, fs, cutoff_fs, ...])	Layer which applies the butterworth filter in MEL scale to a one-dimensional input signal.
`as_graph`()	Convert this module to a computational graph
`attributes_named`(name)	Search for attributes of this or submodules by time
`evolve`(input, args, *kwargs)	Evolve the state of the filterbanks, given an input
`modules`()	Return a dictionary of all sub-modules of this module
`parameters`([family])	Return a nested dictionary of module and submodule Parameters
`reset_parameters`()	Reset all parameters in this module
`reset_state`()	Reset the state of this module
`set_attributes`(new_attributes)	Set the attributes and sub-module attributes from a dictionary
`simulation_parameters`([family])	Return a nested dictionary of module and submodule SimulationParameters
`state`([family])	Return a nested dictionary of module and submodule States
`timed`([output_num, dt, add_events])	Convert this module to a `TimedModule`

__init__(shape: tuple | int = (1, 64), fs: float = 44100.0, cutoff_fs: float = 100.0, filter_width: float = 2.0, mean_subtraction: bool = False, normalize: bool = False, order: int = 2, num_workers: int = 1, plot: bool = False, use_lowpass: bool = True, *args, **kwargs)[source]

Layer which applies the butterworth filter in MEL scale to a one-dimensional input signal. Further dimensions can be passed through the layer without being filtered.

Parameters:

shape (tuple) – Module shape (1, N)
fs (float) – input signal sampling frequency
name (str) – name of the layer. Default "unnamed"
cutoff_fs (float) – lowpass frequency to get only the enveloppe of filters output. Also the lowest frequency of the filter bank. Default: 100 Hz Don’t set it yourself unless you know what you’re doing.
filter_width (float) – The width of the filters which is scaled with the number of filters. This determines the overlap between channels. Default: 2.
order (int) – filter order. Default: 2
mean_subtraction (bool) – subtract the mean of output signals (per channel). Default False
normalize (bool) – divide output signals by their maximum value (i.e. filter responses in the range [-1, 1]). Default: False
num_workers (int) – Number of CPU cores to use in simulation. Default: 1
use_lowpass (bool) – Iff True, return the filtered rectified smoothed signal. Default: True. If False, simply perform the band-pass filtering.
plot (bool) – Plots the filter response. Default: False

_abc_impl = <_abc._abc_data object>

_auto_batch(data: ndarray, states: Tuple = (), target_shapes: Tuple | None = None) → Tuple[ndarray, Tuple[ndarray]]

Automatically replicate states over batches and verify input dimensions

Examples

>>> data, (state0, state1, state2) = self._auto_batch(data, (self.state0, self.state1, self.state2))

This will verify that data has the correct final dimension (i.e. self.size_in).

If data has only two dimensions (T, Nin), then it will be augmented to (1, T, Nin). The individual states will be replicated out from shape (a, b, c, ...) to (n_batches, a, b, c, ...) and returned.

If data has only a single dimension (T,), it will be expanded to (1, T, self.size_in).

state0, state1, state2 will be replicated out along the batch dimension.

>>> data, (state0,) = self._auto_batch(data, (self.state0,), ((10, -1, self.size_in),))

Attempt to replicate state0 to a specified size (10, -1, self.size_in).

Parameters:

data (np.ndarray) – Input data tensor. Either (batches, T, Nin) or (T, Nin)
states (Tuple) – Tuple of state variables. Each will be replicated out over batches by prepending a batch dimension
target_shapes (Tuple) – A tuple of target size tuples, each corresponding to each state argument. The individual states will be replicated out to match the corresponding target sizes. If not provided (the default), then states will be only replicated along batches.

Returns:

(np.ndarray, Tuple[np.ndarray]) data, states

_force_set_attributes: (bool) If True, do not sanity-check attributes when setting.

static _generate_chunks(l, n) → list: Generates chunks of data

_get_attribute_family(type_name: str, family: Tuple | List | str | None = None) → dict

Search for attributes of this module and submodules that match a given family

This method can be used to conveniently get all weights for a network; or all time constants; or any other family of parameters. Parameter families are defined simply by a string: "weights" for weights; "taus" for time constants, etc. These strings are arbitrary, but if you follow the conventions then future developers will thank you (that includes you in six month’s time).

Parameters:

type_name (str) – The class of parameters to search for. Must be one of ["Parameter", "SimulationParameter", "State"] or another future subclass of ParameterBase
family (Union[str, Tuple[str]]) – A string or list or tuple of strings, that define one or more attribute families to search for

Returns:

A nested dictionary of attributes that match the provided type_name and family

Return type:

dict

_get_attribute_registry() → Tuple[Dict, Dict]

Return or initialise the attribute registry for this module

Returns:: registered_attributes, registered_modules
Return type:: (tuple)

_has_registered_attribute(name: str) → bool

Check if the module has a registered attribute

Parameters:: name (str) – The name of the attribute to check
Returns:: True if the attribute name is in the attribute registry, False otherwise.
Return type:: bool

_in_Module_init: (bool) If exists and True, indicates that the module is in the __init__ chain.

_name: str | None: Name of this module, if assigned

static _process_filters(args) → list: Method for processing the filters each worker executes

_register_attribute(name: str, val: ParameterBase)

Record an attribute in the attribute registry

Parameters:

name (str) – The name of the attribute to register
val (ParameterBase) – The ParameterBase subclass object to register. e.g. Parameter, SimulationParameter or State.

_register_module(name: str, mod: ModuleBase)

Register a sub-module in the module registry

Parameters:

name (str) – The name of the module to register
mod (ModuleBase) – The ModuleBase object to register

_reset_attribute(name: str) → ModuleBase

Reset an attribute to its initialisation value

Parameters:: name (str) – The name of the attribute to reset
Returns:: For compatibility with the functional API
Return type:: self (Module)

_shape: The shape of this module

_spiking_input: bool: Whether this module receives spiking input

_spiking_output: bool: Whether this module produces spiking output

_submodulenames: List[str]: Registry of sub-module names

_terminate(): Terminates all processes in the worker _pool

_wrap_recorded_state(recorded_dict: dict, t_start: float) → Dict[str, TimeSeries]

Convert a recorded dictionary to a TimeSeries representation

This method is optional, and is provided to make the timed() conversion to a TimedModule work better. You should override this method in your custom Module, to wrap each element of your recorded state dictionary as a TimeSeries

Parameters:

state_dict (dict) – A recorded state dictionary as returned by evolve()
t_start (float) – The initial time of the recorded state, to use as the starting point of the time series

Returns:

The mapped recorded state dictionary, wrapped as TimeSeries objects

Return type:

Dict[str, TimeSeries]

as_graph() → GraphModuleBase

Convert this module to a computational graph

Returns:: The computational graph corresponding to this module
Return type:: GraphModuleBase
Raises:: NotImplementedError – If as_graph() is not implemented for this subclass

attributes_named(name: Tuple[str] | List[str] | str) → dict

Search for attributes of this or submodules by time

Parameters:: name (Union[str, Tuple[str]) – The name of the attribute to search for
Returns:: A nested dictionary of attributes that match name
Return type:: dict

property class_name: str

Class name of self

Type:: str

cutoff_fs: P_float: (float) Post-filtering output low-pass cutoff frequency in Hz

evolve(input: ndarray, *args, **kwargs) → Tuple[ndarray, dict, dict]

Evolve the state of the filterbanks, given an input

Parameters:: input (np.ndarray) – Raw input signal

fs: P_float: (float) Input sampling frequency in Hz

property full_name: str

The full name of this module (class plus module name)

Type:: str

mean_subtraction: P_bool: (bool) Iff True, subtract the mean filter output value from each output

modules() → Dict

Return a dictionary of all sub-modules of this module

Returns:: A dictionary containing all sub-modules. Each item will be named with the sub-module name.
Return type:: dict

property name: str

The name of this module, or an empty string if None

Type:: str

normalize: P_bool: (bool) Iff True, collectively normalise the filter outputs [-1, 1]

num_workers: P_int: (int) Number of workers to use in filtering

order: P_int: (int) Filter order

parameters(family: Tuple | List | str | None = None) → Dict

Return a nested dictionary of module and submodule Parameters

Use this method to inspect the Parameters from this and all submodules. The optional argument family allows you to search for Parameters in a particular family — for example "weights" for all weights of this module and nested submodules.

Although the family argument is an arbitrary string, reasonable choises are "weights", "taus" for time constants, "biases" for biases…

Examples

Obtain a dictionary of all Parameters for this module (including submodules):

>>> mod.parameters()
dict{ ... }

Obtain a dictionary of Parameters from a particular family:

>>> mod.parameters("weights")
dict{ ... }

Parameters:: family (str) – The family of Parameters to search for. Default: None; return all parameters.
Returns:: A nested dictionary of Parameters of this module and all submodules
Return type:: dict

reset_parameters()

Reset all parameters in this module

Returns:: The updated module is returned for compatibility with the functional API
Return type:: Module

reset_state() → ModuleBase

Reset the state of this module

Returns:: The updated module is returned for compatibility with the functional API
Return type:: Module

set_attributes(new_attributes: dict) → ModuleBase

Set the attributes and sub-module attributes from a dictionary

This method can be used with the dictionary returned from module evolution to set the new state of the module. It can also be used to set multiple parameters of a module and submodules.

Examples

Use the functional API to evolve, obtain new states, and set those states:

>>> _, new_state, _ = mod(input)
>>> mod = mod.set_attributes(new_state)

Obtain a parameter dictionary, modify it, then set the parameters back:

>>> params = mod.parameters()
>>> params['w_input'] *= 0.
>>> mod.set_attributes(params)

Parameters:: new_attributes (dict) – A nested dictionary containing parameters of this module and sub-modules.

property shape: tuple

The shape of this module

Type:: tuple

simulation_parameters(family: Tuple | List | str | None = None) → Dict

Return a nested dictionary of module and submodule SimulationParameters

Use this method to inspect the SimulationParameters from this and all submodules. The optional argument family allows you to search for SimulationParameters in a particular family.

Examples

Obtain a dictionary of all SimulationParameters for this module (including submodules):

>>> mod.simulation_parameters()
dict{ ... }

Parameters:: family (str) – The family of SimulationParameters to search for. Default: None; return all SimulationParameter attributes.
Returns:: A nested dictionary of SimulationParameters of this module and all submodules
Return type:: dict

property size: int

(DEPRECATED) The output size of this module

Type:: int

property size_in: int

The input size of this module

Type:: int

property size_out: int

The output size of this module

Type:: int

property spiking_input: bool

If True, this module receives spiking input. If False, this module expects continuous input.

Type:: bool

property spiking_output

If True, this module sends spiking output. If False, this module sends continuous output.

Type:: bool

state(family: Tuple | List | str | None = None) → Dict

Return a nested dictionary of module and submodule States

Use this method to inspect the States from this and all submodules. The optional argument family allows you to search for States in a particular family.

Examples

Obtain a dictionary of all States for this module (including submodules):

>>> mod.state()
dict{ ... }

Parameters:: family (str) – The family of States to search for. Default: None; return all State attributes.
Returns:: A nested dictionary of States of this module and all submodules
Return type:: dict

timed(output_num: int = 0, dt: float | None = None, add_events: bool = False)

Convert this module to a TimedModule

Parameters:

output_num (int) – Specify which output of the module to take, if the module returns multiple output series. Default: 0, take the first (or only) output.
dt (float) – Used to provide a time-step for this module, if the module does not already have one. If self already defines a time-step, then self.dt will be used. Default: None
add_events (bool) – Iff True, the TimedModule will add events occurring on a single timestep on input and output. Default: False, don’t add time steps.

Returns: TimedModule: A timed module that wraps this module

use_lowpass: P_bool: (bool) Iff True, perform a low-pass filter after filtering