Python matplotlib.show() Examples

The following are 4 code examples for showing how to use matplotlib.show(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: retentioneering-tools   Author: retentioneering   File: utils.py    License: Mozilla Public License 2.0 6 votes vote down vote up
def pairwise_time_distribution(self, event_order, time_col=None, index_col=None,
                                   event_col=None, bins=100, limit=180, topk=3):
        self._init_cols(locals())
        if 'next_event' not in self._obj.columns:
            self._get_shift(index_col, event_col)
        self._obj['time_diff'] = (self._obj['next_timestamp'] - self._obj[
            time_col or self.retention_config['event_time_col']]).dt.total_seconds()
        f_cur = self._obj[self._event_col()] == event_order[0]
        f_next = self._obj['next_event'] == event_order[1]
        s_next = self._obj[f_cur & f_next].copy()
        s_cur = self._obj[f_cur & (~f_next)].copy()

        s_cur.time_diff[s_cur.time_diff < limit].hist(alpha=0.5, log=True,
                                                      bins=bins, label='Others {:.2f}'.format(
                                                          (s_cur.time_diff < limit).sum() / f_cur.sum()
                                                      ))
        s_next.time_diff[s_next.time_diff < limit].hist(alpha=0.7, log=True,
                                                        bins=bins,
                                                        label='Selected event order {:.2f}'.format(
                                                            (s_next.time_diff < limit).sum() / f_cur.sum()
                                                        ))
        plot.sns.mpl.pyplot.legend()
        plot.sns.mpl.pyplot.show()
        (s_cur.next_event.value_counts() / f_cur.sum()).iloc[:topk].plot.bar() 
Example 2
Project: indras_net   Author: gcallah   File: emp_model.py    License: GNU General Public License v3.0 5 votes vote down vote up
def draw(self):
        """
        Draw a network graph of the employee relationship.
        """
        if self.graph is not None:
            nx.draw_networkx(self.graph)
            plt.show() 
Example 3
Project: retentioneering-tools   Author: retentioneering   File: utils.py    License: Mozilla Public License 2.0 5 votes vote down vote up
def calculate_delays(self, plotting=True, time_col=None, index_col=None, event_col=None, bins=15, **kwargs):
        """
        Displays the logarithm of delay between ``time_col`` with the next value in nanoseconds as a histogram.

        Parameters
        --------
        plotting: bool, optional
            If ``True``, then histogram is plotted as a graph. Default: ``True``
        time_col: str, optional
            Name of custom time column for more information refer to ``init_config``. For instance, if in config you have defined ``event_time_col`` as ``server_timestamp``, but want to use function over ``user_timestamp``. By default the column defined in ``init_config`` will be used as ``time_col``.
        index_col: str, optional
            Name of custom index column, for more information refer to ``init_config``. For instance, if in config you have defined ``index_col`` as ``user_id``, but want to use function over sessions. By default the column defined in ``init_config`` will be used as ``index_col``.
        event_col: str, optional
            Name of custom event column, for more information refer to ``init_config``. For instance, you may want to aggregate some events or rename and use it as new event column. By default the column defined in ``init_config`` will be used as ``event_col``.
        bins: int, optional
            Number of bins for visualisation. Default: ``50``

        Returns
        -------
        Delays in seconds for each ``time_col``. Index is preserved as in original dataset.

        Return type
        -------
        List
        """
        self._init_cols(locals())
        self._get_shift(self._index_col(), self._event_col())

        delays = np.log((self._obj['next_timestamp'] - self._obj[self._event_time_col()]) // pd.Timedelta('1s'))

        if plotting:
            fig, ax = plot.sns.mpl.pyplot.subplots(figsize=kwargs.get('figsize', (15, 7)))  # control figsize for proper display on large bin numbers
            _, bins, _ = plt.hist(delays[~np.isnan(delays) & ~np.isinf(delays)], bins=bins, log=True)
            if not kwargs.get('logvals', False):  # test & compare with logarithmic and normal
                plt.xticks(bins, np.around(np.exp(bins), 1))
            plt.show()

        return np.exp(delays) 
Example 4
Project: NoisePy   Author: mdenolle   File: noise_module.py    License: MIT License 5 votes vote down vote up
def spect(tr,fmin = 0.1,fmax = None,wlen=10,title=None):
    import matplotlib as plt
    if fmax is None:
        fmax = tr.stats.sampling_rate/2
    fig = plt.figure()
    ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2]) #[left bottom width height]
    ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], sharex=ax1)
    ax3 = fig.add_axes([0.83, 0.1, 0.03, 0.6])

    #make time vector
    t = np.arange(tr.stats.npts) / tr.stats.sampling_rate

    #plot waveform (top subfigure)    
    ax1.plot(t, tr.data, 'k')

    #plot spectrogram (bottom subfigure)
    tr2 = tr.copy()
    fig = tr2.spectrogram(per_lap=0.9,wlen=wlen,show=False, axes=ax2)
    mappable = ax2.images[0]
    plt.colorbar(mappable=mappable, cax=ax3)
    ax2.set_ylim(fmin, fmax)
    ax2.set_xlabel('Time [s]')
    ax2.set_ylabel('Frequency [Hz]')
    if title:
        plt.suptitle(title)
    else:
        plt.suptitle('{}.{}.{} {}'.format(tr.stats.network,tr.stats.station,
                  tr.stats.channel,tr.stats.starttime))
    plt.show()