Python matplotlib.pylab.subplot2grid() Examples

The following are 5 code examples of matplotlib.pylab.subplot2grid(). 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. You may also want to check out all available functions/classes of the module matplotlib.pylab , or try the search function .
Example #1
Source File: training_dataset_memory.py    From deepecg with MIT License 6 votes vote down vote up 
def plot(self, index):

        # Get time array
        time = np.arange(self.length) * 1 / self.fs

        # Setup plot
        fig = plt.figure(figsize=(15, 6))
        fig.subplots_adjust(hspace=0.25)
        ax1 = plt.subplot2grid((1, 1), (0, 0))
        ax1.set_title(
            'File Name: ' + self.labels.loc[index, 'file_name'] + '\n'
            'Label: ' + self.labels.loc[index, 'label_str'], fontsize=20
        )

        # Plot waveform
        ax1.plot(time, self.data.loc[index, :], '-k', label='Filtered')
        ax1.set_xlabel('Time, seconds', fontsize=25)
        ax1.set_ylabel('Normalized Amplitude', fontsize=25)
        ax1.set_xlim([0, self.duration])
        ax1.set_ylim([-0.75, 1.5])
        ax1.tick_params(labelsize=18)

        plt.show()
Example #2
Source File: training_data_validation.py    From deepecg with MIT License 5 votes vote down vote up 
def interval_plot(label_id, labels, path, dataset):
    """Plot measure vs time."""
    # Label lookup
    label_lookup = {0: 'N', 1: 'A', 2: 'O', 3: '~'}

    # File name
    file_name = list(labels.keys())[label_id]

    # Get label
    label = labels[file_name]

    # Load data
    data = np.load(os.path.join(path, dataset, 'waveforms', file_name + '.npy'))

    # Time array
    time = np.arange(data.shape[0]) * 1 / 300

    # Setup figure
    fig = plt.figure(figsize=(15, 5), facecolor='w')
    fig.subplots_adjust(wspace=0, hspace=0.05)
    ax1 = plt.subplot2grid((1, 1), (0, 0))

    # ECG
    ax1.set_title('Dataset: {}\nFile Name: {}\nLabel: {}'.format(dataset, file_name, label_lookup[label]), fontsize=20)
    ax1.plot(time, data, '-k', lw=2)

    # Axes labels
    ax1.set_xlabel('Time, seconds', fontsize=20)
    ax1.set_ylabel('ECG', fontsize=20)
    ax1.set_xlim([time.min(), time.max()])
    plt.yticks(fontsize=12)

    plt.show()
Example #3
Source File: waveforms.py    From deepecg with MIT License 5 votes vote down vote up 
def plot_waveforms(index, waveforms):
    """Plots one univariate time series."""
    # Get file name
    file_name = list(waveforms.keys())[index]

    # Setup plot
    fig = plt.figure(figsize=(15, 6))
    fig.subplots_adjust(hspace=0.25)
    ax1 = plt.subplot2grid((1, 1), (0, 0))
    ax1.set_title(
        'File Name: ' + file_name + '\n'  
        'Label: ' + waveforms[file_name]['label_str'], fontsize=20
    )

    # Plot waveform
    ax1.plot(waveforms[file_name]['time'], waveforms[file_name]['filtered'], '-k', label='Filtered')
    ax1.vlines(
        waveforms[file_name]['rpeaks_ts'],
        waveforms[file_name]['filtered'].min() - 0.01,
        waveforms[file_name]['filtered'].max() + 0.01,
        color=[0.7, 0.7, 0.7],
        linewidth=4,
        label='R-Peaks'
    )

    ax1.set_xlabel('Time, seconds', fontsize=25)
    ax1.set_ylabel('Normalized Amplitude', fontsize=25)
    ax1.set_xlim([0, waveforms[file_name]['duration']])
    ax1.set_ylim([waveforms[file_name]['filtered'].min() - 0.01, waveforms[file_name]['filtered'].max() + 0.01])
    ax1.tick_params(labelsize=18)
Example #4
Source File: elecsus_gui.py    From ElecSus with Apache License 2.0 4 votes vote down vote up 
def OnCreateResidualPlot(self,fig,canvas):
		"""
		Create a plot with main data, residuals and (optionally) histogram of residuals, using
		subplot2grid in matplotlib 
		"""
		
		fig.clf()
		
		print('Debugging...')
		print(self.fit_datatype)
		print(self.y_optimised)
		
		#normalised_residuals = False
		if self.normalised_residuals:
			### not done yet! -- requires error bars in imported data
			residuals = 100*(self.y_fit_array-self.y_optimised)
		else:
			residuals = 100*(self.y_fit_array-self.y_optimised)
		
		fig = plt.figure(2)
		yy = 4
		xx = 6
		if self.residual_histogram:
			ax_main = plt.subplot2grid((yy,xx),(0,0),colspan=xx-1,rowspan=yy-1)
			ax_residual = plt.subplot2grid((yy,xx),(yy-1,0),colspan=xx-1,sharex=ax_main)
			ax_hist = plt.subplot2grid((yy,xx), (yy-1,xx-1), sharey=ax_residual)

			plt.setp(ax_hist.get_yticklabels(),visible=False)
			ax_hist.set_xticklabels([])

		else:
			ax_main = plt.subplot2grid((yy,xx),(0,0),colspan=xx,rowspan=yy-1)
			ax_residual = plt.subplot2grid((yy,xx),(yy-1,0),colspan=xx,sharex=ax_main)
			
		plt.setp(ax_main.get_xticklabels(),visible=False)
		
		ax_residual.set_xlabel('Detuning (GHz)')
		ax_residual.set_ylabel('Residuals (%)')

		ax_main.set_ylabel(self.expt_type)
		
		ax_main.plot(self.x_fit_array,self.y_fit_array,color=d_olive)
		print(len(self.x_fit_array), len(self.y_optimised))
		ax_main.plot(self.x_fit_array,self.y_optimised)
		ax_residual.plot(self.x_fit_array,residuals,lw=1.25)
		ax_residual.axhline(0,color='k',linestyle='dashed')
		
		if self.residual_histogram:
			bins = 25
			ax_hist.hist(residuals, bins=bins, orientation='horizontal')
			ax_hist.axhline(0,color='k', linestyle='dashed')

		ax_main.autoscale_view(tight=True)
		
		self._draw_fig(fig,canvas)
Example #5
Source File: state.py    From deepecg with MIT License 4 votes vote down vote up 
def _plot_image(self, index):

        # Setup figure
        fig = plt.figure(figsize=(20., 8.), dpi=80)
        fig.subplots_adjust(wspace=0, hspace=0)
        ax1 = plt.subplot2grid((2, 1), (0, 0))
        ax2 = plt.subplot2grid((2, 1), (1, 0))

        # Label lookup
        label_lookup = ['Normal Sinus Rhythm', 'Atrial Fibrillation', 'Other Rhythm', 'Noisy']

        # Get time array
        time_array = np.arange(self.waveforms.shape[1]) * 1 / 300

        # Get labels
        label = self.labels[index]

        # Get logits
        logits = self.logits[index, :]

        # Get softmax
        softmax = self._softmax(scores=logits)

        # Get prediction
        prediction = int(np.squeeze(np.argmax(softmax)))

        # Get non-zero-pad indices
        non_zero_index = np.where(self.waveforms[index, :, 0] != 0)[0]

        # Title
        title_string = 'True Label: {}\nPredicted Label: {}\nN: {} %  A: {} %  O: {} %  ~: {} %'
        ax1.set_title(title_string.format(label_lookup[int(label)], label_lookup[prediction],
                                          np.round(softmax[0] * 100., 2), np.round(softmax[1] * 100., 2),
                                          np.round(softmax[2] * 100., 2), np.round(softmax[3] * 100., 2)),
                      fontsize=20, y=1.03)

        # Plot ECG waveform
        ax1.plot(time_array[non_zero_index], self.waveforms[index, non_zero_index, 0], '-k')
        ax1.set_xlim([time_array[non_zero_index].min(), time_array[non_zero_index].max()])
        ax1.axes.get_xaxis().set_visible(False)
        ax1.axes.get_yaxis().set_visible(False)
        # ax1.set_ylabel('Normalized Amplitude', fontsize=22)
        # ax1.yaxis.set_tick_params(labelsize=16)

        # Plot Class Activation Map
        ax2.plot(time_array[non_zero_index], self.cams[index, non_zero_index, prediction], '-k')
        ax2.set_xlim([time_array[non_zero_index].min(), time_array[non_zero_index].max()])
        ax2.axes.get_xaxis().set_visible(False)
        ax2.axes.get_yaxis().set_visible(False)
        # ax2.set_xlabel('Time, seconds', fontsize=22)
        # ax2.set_ylabel('Class Activation Map', fontsize=22)
        # ax2.xaxis.set_tick_params(labelsize=16)
        # ax2.yaxis.set_tick_params(labelsize=16)

        # Get image buffer
        buf = io.BytesIO()
        plt.savefig(buf, format='png', bbox_inches='tight')
        buf.seek(0)
        plt.close(fig)

        return buf