deepextractor.utils.signal
==========================

.. py:module:: deepextractor.utils.signal




Module Contents
---------------

.. py:function:: whitened_snr_scaling(glitch, snr, srate=4096)

   Scale a glitch signal to the target SNR in the whitened frame.


.. py:function:: quality_factor_conversion(Q, f_0)

   Convert quality factor Q and central frequency f_0 to decay time tau.


.. py:function:: rescale(x)

   Rescale each row of x to the range [-1, 1].


.. py:function:: butter_lowpass(cutoff, fs, order=5)

.. py:function:: butter_highpass(cutoff, fs, order=5)

.. py:function:: butter_filter(data, fs, order=5)

   Apply a bandpass (20–1024 Hz) Butterworth filter to data.


.. py:function:: custom_whiten(self, psd, low_frequency_cutoff=None, return_psd=False, **kwds)

   Return a whitened PyCBC TimeSeries.

   This function is designed to be used with a PyCBC TimeSeries instance
   (as a monkey-patched method). Pass ``self`` as the TimeSeries object.

   :param psd: The power spectral density used for whitening.
   :type psd: FrequencySeries
   :param low_frequency_cutoff: Low frequency cutoff for the inverse spectrum truncation.
   :type low_frequency_cutoff: float, optional
   :param return_psd: If True, return the PSD alongside the whitened data.
   :type return_psd: bool, optional

   :returns: * **white** (*TimeSeries*) -- The whitened time series.
             * **psd** (*FrequencySeries, optional*) -- The PSD used (only returned if ``return_psd=True``).


.. py:function:: generate_gaussian_noise(mean, std_dev, num_samples, sample_shape)

   Generate Gaussian noise samples as a numpy array.