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Helper Module for Deep Learning.

Source code for pynet.plotting.signal

# -*- coding: utf-8 -*-
##########################################################################
# NSAp - Copyright (C) CEA, 2019
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################

"""
Common functions to display signals.
"""

# Import
import numpy as np
import matplotlib.pyplot as plt


def _trim_axs(axs, size):
    """ Little helper to massage the axs list to have correct length...
    """
    axs = axs.flat
    for ax in axs[size:]:
        ax.remove()
    return axs[:size]


[docs]def plot_history(history, title=None): """ Plot an history. Parameters ---------- history: pynet History the history to be displayed. title: str, default None the figure title. """ nb_plots = len(history.metrics) cols = 3 rows, rest = divmod(nb_plots, cols) if rest > 0: rows += 1 fig, axs = plt.subplots(rows, cols) fig.title = title axs = _trim_axs(axs, nb_plots) for ax, case in zip(axs, history.metrics): ax.set_title(case) ax.plot(history[case][1])

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