deepextractor.utils.visualization
=================================

.. py:module:: deepextractor.utils.visualization




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

.. py:function:: save_predictions_as_plots(loader, model, folder='saved_predictions/', device='cuda')

   Save model prediction vs target plots for each sample in the loader.


.. py:function:: plot_examples(Difference_ts, clean_glitch_subtract, snrs, signal_type, PLOTS_PATH, indices_to_plot, noisy=False)

   Plot up to 3 example time series and save to disk.


.. py:function:: plot_q_transform(data, srate=4096.0, crop=None, whiten=True, ax=None, colourbar=True, qrange=[4, 64], frange=[10, 1200], clim=(0, 25.5))

   Plot the Q-transform of a time series using gwpy.

   :param data: Input time-domain data.
   :type data: array-like
   :param srate: Sample rate in Hz.
   :type srate: float
   :param crop: ``(center_time, duration)`` window in seconds for the Q-transform.
   :type crop: tuple or list, optional
   :param whiten: If True, apply whitening before the Q-transform.
   :type whiten: bool
   :param ax: Axes on which to plot. A new figure is created if not provided.
   :type ax: matplotlib.axes.Axes, optional
   :param colourbar: If True, add a colorbar to the plot.
   :type colourbar: bool
   :param qrange: [q_min, q_max] range for the Q-transform.
   :type qrange: list
   :param frange: [f_min, f_max] frequency range in Hz.
   :type frange: list
   :param clim: (vmin, vmax) colour axis limits for normalised energy.
   :type clim: tuple