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Format documentation.
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‎docs/source/conf.py‎

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"sphinx.ext.intersphinx",
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"sphinx.ext.autosummary",
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"sphinx.ext.autodoc",
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"sphinx.ext.napoleon",
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]
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autodoc_default_options = {
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"members": True,
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}
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autodoc_typehints = "none" # for cleaner sphinx output, don't show type hints
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intersphinx_mapping = {
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"python": ("https://docs.python.org/3/", None),
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"NumPy [latest]": ("https://docs.scipy.org/doc/numpy/", None),

‎docs/source/fftconvolve.rst‎

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FFT Convolve
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============
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.. automodule:: pydevtips.fftconvolve
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.. GENERATE DOCUMENTATION FOR ALL CLASSES
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.. .. automodule:: pydevtips.fftconvolve
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.. :member-order: bysource
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.. :show-inheritance:
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.. :special-members: __init__, __call__
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.. GENERATE ONE AT A TIME WITH CUSTOM TEXT
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There are two classes in this module for performing convolution in the frequency domain with a fixed filter.
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.. autosummary::
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pydevtips.fftconvolve.RFFTConvolve
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pydevtips.fftconvolve.FFTConvolve
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Both inherit from the base class :class:`pydevtips.fftconvolve.FFTConvolveBase`,
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overwriting the abstract methods: :func:`pydevtips.fftconvolve.FFTConvolveBase._compute_filter_frequency_response` and
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:func:`pydevtips.fftconvolve.FFTConvolveBase.__call__`.
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RFFTConvolve
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------------
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.. autoclass:: pydevtips.fftconvolve.RFFTConvolve
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:member-order: bysource
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:show-inheritance:
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:special-members: __init__, __call__
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FFTConvolve
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-----------
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.. autoclass:: pydevtips.fftconvolve.FFTConvolve
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:member-order: bysource
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:show-inheritance:
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:special-members: __init__, __call__
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FFTConvolveBase
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---------------
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.. autoclass:: pydevtips.fftconvolve.FFTConvolveBase
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:member-order: bysource
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:show-inheritance:
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:members:
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:undoc-members:
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:show-inheritance:
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:special-members: __init__, __call__
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:private-members: _compute_filter_frequency_response

‎pydevtips/fftconvolve.py‎

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class FFTConvolveBase:
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"""Base class for FFT convolve."""
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"""Abstract class for FFT convolve."""
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def __init__(self, filter, length) -> None:
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"""
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Base class for creating a convolver that uses the same filter.
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Parameters
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----------
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filter : np.ndarray
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filter : :py:class:`~numpy.ndarray`
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Filter to convolve with. Must be real.
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length : int
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Length of the signal to convolve with.
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self.filter_frequency_response = self._compute_filter_frequency_response()
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@abstractmethod
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def _compute_filter_frequency_response(self, filter) -> np.ndarray:
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def _compute_filter_frequency_response(self) -> np.ndarray:
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"""
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Compute the filter frequency response.
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Parameters
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----------
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filter : :py:class:`~numpy.ndarray`
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Filter to compute the frequency response for.
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Returns
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-------
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filter_frequency_response : :py:class:`~numpy.ndarray`
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Filter frequency response.
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"""
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raise NotImplementedError
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@abstractmethod
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def __call__(self, signal) -> np.ndarray:
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"""Apply the filter to the signal, in the frequency domain."""
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pass
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class RFFTConvolve(FFTConvolveBase):
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"""Real FFT convolve."""
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def __init__(self, filter, length) -> None:
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"""
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r"""
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Create convolver that uses a real-valued filter.
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Parameters
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----------
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filter : np.ndarray
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filter : :py:class:`~numpy.ndarray`
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Filter to convolve with. Must be real.
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length : int
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Length of the signal to convolve with.
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super(RFFTConvolve, self).__init__(filter, length)
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def _compute_filter_frequency_response(self):
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"""Compute the filter frequency response."""
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r"""Compute the filter frequency response."""
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return np.fft.rfft(self.filter, n=self.pad_length)
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def __call__(self, signal) -> np.ndarray:
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"""
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r"""
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Apply the real-valued filter to the signal, in the frequency domain.
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Parameters
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----------
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signal : np.ndarray
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signal : :py:class:`~numpy.ndarray`
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Signal to convolve with. Must be real.
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Returns
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-------
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result : np.ndarray
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result : :py:class:`~numpy.ndarray`
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Convolved signal.
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"""
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signal_frequency_response = np.fft.rfft(signal, n=self.pad_length)
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"""General FFT convolve."""
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def __init__(self, filter, length) -> None:
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"""
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Create convolver that uses a fixed filter.
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Parameters
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----------
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filter : :py:class:`~numpy.ndarray`
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Filter to convolve with. Must be real.
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length : int
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Length of the signal to convolve with.
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"""
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super(FFTConvolve, self).__init__(filter, length)
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def _compute_filter_frequency_response(self):
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def __call__(self, signal) -> np.ndarray:
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"""
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Apply the filter to the signal, in the frequency domain.
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Parameters
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----------
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signal : np.ndarray
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signal : :py:class:`~numpy.ndarray`
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Signal to convolve with.
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Returns
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-------
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result : np.ndarray
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result : :py:class:`~numpy.ndarray`
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Convolved signal.
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"""
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signal_frequency_response = np.fft.fft(signal, n=self.pad_length)

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