Python pylab.subplot2grid方法代码示例

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplot2grid [as 别名]
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() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplot2grid [as 别名]
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() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplot2grid [as 别名]
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) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplot2grid [as 别名]
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) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplot2grid [as 别名]
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