示例#1
0
 def __init__(self, parent, trace, fig_nrows=1, fig_ncols=1, ax_pos=1):
     super(TracePlot, self).__init__()
     self.parent = parent
     self.fig = parent.fig
     self.ax = self.fig.add_subplot(fig_nrows, fig_ncols, ax_pos, visible=False)
     self.trace = trace
     # Get trace dataseries
     self.signal = trace.signal
     self.time = np.linspace(0, len(self.signal) / trace.fs, num=len(self.signal), endpoint=False)
     self.xmin, self.xmax = 0, self.time[-1]
     # Plot current data
     self._plot_data = self.ax.plot(self.time, self.signal, color='black', rasterized=True)[0]
     self.ax.callbacks.connect('xlim_changed', self.on_xlim_change)
     self.ax.set_xlim(self.xmin, self.xmax)
     # Format axes
     axes_formatter = FuncFormatter(lambda x, pos: clt.float_secs_2_string_date(x, trace.starttime))
     self.ax.xaxis.set_major_formatter(axes_formatter)
     plt.setp(self.ax.get_xticklabels(), visible=False)
     plt.setp(self.ax.get_yticklabels(), visible=False)
     self.ax.grid(True, which='both')
     # Set event markers
     self.marker_select_color = 'r'
     self.marker_color = 'b'
     self.markers = {}
     self.update_markers()
     # Selection parameters
     self.selected = False
     self.selector = self.ax.axvspan(0, self.xmax, fc='LightCoral', ec='r', alpha=0.5, visible=False)#, animated=True)
     # Place legend
     at = AnchoredText(self.trace.short_name, prop=dict(size=12), frameon=True, loc=2)
     at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
     self.ax.add_artist(at)
示例#2
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 def on_position_estimated(self, time, aic, n0_aic):
     self.event_time = time
     time_in_secs = self.event_time / self.record.fs
     self.position_label.setText("Estimated Arrival Time: {}".format(
         clt.float_secs_2_string_date(time_in_secs, starttime=self.record.starttime)))
     # Plot estimated arrival time
     m_event = rc.ApasvoEvent(self.record, time, aic=aic, n0_aic=n0_aic)
     m_event.plot_aic(show_envelope=True, num=self.fig.number)
     self.fig.canvas.draw_idle()
示例#3
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 def on_position_estimated(self, time, aic, n0_aic):
     self.event_time = time
     time_in_secs = self.event_time / self.record.fs
     self.position_label.setText("Estimated Arrival Time: {}".format(
         clt.float_secs_2_string_date(time_in_secs,
                                      starttime=self.record.starttime)))
     # Plot estimated arrival time
     m_event = rc.ApasvoEvent(self.record, time, aic=aic, n0_aic=n0_aic)
     m_event.plot_aic(show_envelope=True, num=self.fig.number)
     self.fig.canvas.draw_idle()
示例#4
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 def __init__(self, parent, trace, fig_nrows=1, fig_ncols=1, ax_pos=1):
     super(TracePlot, self).__init__()
     self.parent = parent
     self.fig = parent.fig
     self.ax = self.fig.add_subplot(fig_nrows,
                                    fig_ncols,
                                    ax_pos,
                                    visible=False)
     self.trace = trace
     # Get trace dataseries
     self.signal = trace.signal
     self.time = np.linspace(0,
                             len(self.signal) / trace.fs,
                             num=len(self.signal),
                             endpoint=False)
     self.xmin, self.xmax = 0, self.time[-1]
     # Plot current data
     self._plot_data = self.ax.plot(self.time,
                                    self.signal,
                                    color='black',
                                    rasterized=True)[0]
     self.ax.callbacks.connect('xlim_changed', self.on_xlim_change)
     self.ax.set_xlim(self.xmin, self.xmax)
     # Format axes
     axes_formatter = FuncFormatter(
         lambda x, pos: clt.float_secs_2_string_date(x, trace.starttime))
     self.ax.xaxis.set_major_formatter(axes_formatter)
     plt.setp(self.ax.get_xticklabels(), visible=False)
     plt.setp(self.ax.get_yticklabels(), visible=False)
     self.ax.grid(True, which='both')
     # Set event markers
     self.marker_select_color = 'r'
     self.marker_color = 'b'
     self.markers = {}
     self.update_markers()
     # Selection parameters
     self.selected = False
     self.selector = self.ax.axvspan(0,
                                     self.xmax,
                                     fc='LightCoral',
                                     ec='r',
                                     alpha=0.5,
                                     visible=False)  #, animated=True)
     # Place legend
     at = AnchoredText(self.trace.short_name,
                       prop=dict(size=12),
                       frameon=True,
                       loc=2)
     at.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
     self.ax.add_artist(at)
示例#5
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 def set_position(self, value):
     time_in_samples = int(value * self.event.record.fs)
     if time_in_samples != self.position:
         if 0 <= time_in_samples <= len(self.event.record.signal):
             self.position = time_in_samples
             time_in_seconds = time_in_samples / float(self.event.record.fs)
             for marker in self.markers:
                 marker.set_xdata(time_in_seconds)
             if 0 <= self.position < len(self.event.record.cf):
                 cf_value = self.event.record.cf[self.position]
             else:
                 cf_value = np.nan
             self.position_label.set_text("Time: %s seconds.\nCF value: %.6g" %
                                          (clt.float_secs_2_string_date(time_in_seconds), cf_value))
             self.minimap.set_marker_position(self.event, time_in_seconds)
示例#6
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 def set_position(self, value):
     time_in_samples = int(value * self.event.record.fs)
     if time_in_samples != self.position:
         if 0 <= time_in_samples <= len(self.event.record.signal):
             self.position = time_in_samples
             time_in_seconds = time_in_samples / float(self.event.record.fs)
             for marker in self.markers:
                 marker.set_xdata(time_in_seconds)
             if 0 <= self.position < len(self.event.record.cf):
                 cf_value = self.event.record.cf[self.position]
             else:
                 cf_value = np.nan
             self.position_label.set_text(
                 "Time: %s seconds.\nCF value: %.6g" %
                 (clt.float_secs_2_string_date(time_in_seconds), cf_value))
             self.minimap.set_marker_position(self.event, time_in_seconds)
示例#7
0
    def plot_signal(self,
                    t_start=0.0,
                    t_end=np.inf,
                    show_events=True,
                    show_x=True,
                    show_cf=True,
                    show_specgram=True,
                    show_envelope=True,
                    threshold=None,
                    num=None,
                    **kwargs):
        """Plots record data.

        Draws a figure containing several plots for data stored and computed
        by a Record object: magnitude, envelope and spectrogram plots for
        self.signal, as well as characteristic function if calculated.

        Args:
            t_start: Start time of the plotted data segment, in seconds.
                Default: 0.0, that is the beginning of 'signal'.
            t_end: End time of the plotted data segment, in seconds.
                Default: numpy.inf, that is the end of 'signal'
            show_events: Boolean value to specify whether to plot
                event arrival times or not. Arrival times will be
                indicated by using a vertical line.
                Default: True.
            show_x: Boolean value to specify whether to plot the
                magnitude value of 'signal' or not. This function
                will be drawn preferably on the first axis.
                Default: True.
            show_cf: Boolean value to specify whether to plot the
                characteristic function or not. This function
                will be drawn preferably on the second axis.
                Default: True.
            show_specgram: Boolean value to specify whether to plot the
                spectrogram of 'signal' or not. It will be drawn preferably
                on the third axis.
                Default: True.
            show_envelope: Boolean value to specify whether to plot the
                envelope of 'signal' or not. This function will be drawn
                preferably on the first axis together with amplitude of
                'signal'.
                Default: True.
            threshold: Boolean value to specify whether to plot threshold
                or not. Threshold will be drawn as an horizontal dashed line
                together with characteristic function.
                Default: True.
            num: Identifier of the returned MatplotLib figure, integer type.
                Default None, which means an identifier value will be
                automatically generated.

        Returns:
            fig: A MatplotLib Figure instance.
        """
        # Lazy matplotlib import
        import matplotlib.pyplot as pl
        from matplotlib import ticker
        # Set limits
        i_from = int(max(0.0, t_start * self.fs))
        if show_cf:
            i_to = int(min(len(self.cf), t_end * self.fs))
        else:
            i_to = int(min(len(self.signal), t_end * self.fs))
        # Create time sequence
        t = np.arange(i_from, i_to) / float(self.fs)
        # Create figure
        nplots = show_x + show_cf + show_specgram
        fig, _ = pl.subplots(nplots, 1, sharex='all', num=num)
        fig.canvas.set_window_title(self.label)
        fig.set_tight_layout(True)
        # Configure axes
        for ax in fig.axes:
            ax.cla()
            ax.grid(True, which='both')
            formatter = ticker.FuncFormatter(
                lambda x, pos: clt.float_secs_2_string_date(x, self.starttime))
            ax.xaxis.set_major_formatter(formatter)
            ax.xaxis.set_major_locator(
                ticker.MaxNLocator(nbins=5, prune='lower'))
            ax.set_xlabel('Time (seconds)')
            pl.setp(ax.get_xticklabels(), visible=True)
        # Draw axes
        ax_idx = 0
        # Draw signal
        if show_x:
            fig.axes[ax_idx].set_title("Signal Amplitude (%gHz)" % self.fs)
            fig.axes[ax_idx].set_ylabel('Amplitude')
            fig.axes[ax_idx].plot(
                t, self.signal[i_from:i_to], color='black', label='Signal')
            # Draw signal envelope
            if show_envelope:
                fig.axes[ax_idx].plot(
                    t,
                    env.envelope(self.signal[i_from:i_to]),
                    color='r',
                    label='Envelope')
                fig.axes[ax_idx].legend(loc=0, fontsize='small')
            ax_idx += 1
        # Draw Characteristic function
        if show_cf:
            fig.axes[ax_idx].set_title('Characteristic Function')
            fig.axes[ax_idx].plot(t, self.cf[i_from:i_to])
            # Draw threshold
            if threshold:
                hline = fig.axes[ax_idx].axhline(threshold, label="Threshold")
                hline.set(color='b', ls='--', lw=2, alpha=0.8)
                fig.axes[ax_idx].legend(loc=0, fontsize='small')
            ax_idx += 1
        # Draw spectrogram
        if show_specgram:
            fig.axes[ax_idx].set_title('Spectrogram')
            fig.axes[ax_idx].set_ylabel('Frequency (Hz)')
            fig.axes[ax_idx].specgram(
                self.signal[i_from:i_to],
                Fs=self.fs,
                xextent=(i_from / self.fs, i_to / self.fs))
            ax_idx += 1
        # Draw event markers
        if show_events:
            for event in self.events:
                arrival_time = event.stime / self.fs
                for ax in fig.axes:
                    xmin, xmax = ax.get_xlim()
                    if arrival_time > xmin and arrival_time < xmax:
                        vline = ax.axvline(arrival_time, label="Event")
                        vline.set(color='r', ls='--', lw=2)
                        ax.legend(loc=0, fontsize='small')
        # Configure limits and draw legend
        for ax in fig.axes:
            ax.set_xlim(t[0], t[-1])
        return fig
示例#8
0
    def plot_aic(self, show_envelope=True, num=None, **kwargs):
        """Plots AIC values for a given event object.

        Draws a figure with two axes: the first one plots magnitude and
        envelope of 'self.signal' and the second one plots AIC values computed
        after applying Takanami AR method to 'event'. Plotted data goes from
        'event.n0_aic' to 'event.n0_aic + len(event.aic)'.

        Args:
            show_envelope: Boolean value to specify whether to plot the
                envelope of 'signal' or not. This function will be drawn
                preferably on the first axis together with amplitude of
                'signal'.
                Default: True.
            num: Identifier of the returned MatplotLib figure, integer type.
                Default None, which means an identifier value will be
                automatically generated.

        Returns:
            fig: A MatplotLib Figure instance.
        """
        if self.aic is None or self.n0_aic is None:
            raise ValueError("Event doesn't have AIC data to plot")

        # Lazy matplotlib import
        import matplotlib.pyplot as pl
        from matplotlib import ticker

        # Set limits
        i_from = int(max(0, self.n0_aic))
        i_to = int(min(len(self.trace.signal), self.n0_aic + len(self.aic)))
        # Create time sequence
        t = np.arange(i_from, i_to) / float(self.trace.fs)
        # Create figure
        fig, _ = pl.subplots(2, 1, sharex='all', num=num)
        fig.canvas.set_window_title(self.trace.label)
        fig.set_tight_layout(True)
        # Configure axes
        for ax in fig.axes:
            ax.cla()
            ax.grid(True, which='both')
            formatter = ticker.FuncFormatter(lambda x, pos: clt.float_secs_2_string_date(x, self.trace.starttime))
            ax.xaxis.set_major_formatter(formatter)
            ax.xaxis.set_major_locator(
                ticker.MaxNLocator(nbins=5, prune='lower'))
            ax.set_xlabel('Time (seconds)')
            pl.setp(ax.get_xticklabels(), visible=True)
        # Draw signal
        fig.axes[0].set_title('Signal Amplitude')
        fig.axes[0].set_ylabel('Amplitude')
        fig.axes[0].plot(
            t, self.trace.signal[i_from:i_to], color='black', label='Signal')
        # Draw envelope
        if show_envelope:
            fig.axes[0].plot(
                t,
                env.envelope(self.trace.signal[i_from:i_to]),
                color='r',
                label='Envelope')
            fig.axes[0].legend(loc=0, fontsize='small')
        # Draw AIC
        fig.axes[1].set_title('AIC')
        fig.axes[1].plot(t, self.aic)
        # Draw event
        for ax in fig.axes:
            vline = ax.axvline(self.stime / self.trace.fs, label="Event")
            vline.set(color='r', ls='--', lw=2)
        # Configure limits and draw legend
        for ax in fig.axes:
            ax.set_xlim(t[0], t[-1])
            ax.legend(loc=0, fontsize='small')
        return fig
示例#9
0
    def __init__(self, parent, document=None):
        super(SignalViewerWidget, self).__init__(parent)

        self.document = document
        self.xmin = 0.0
        self.xmax = 0.0
        self.xleft = 0.0
        self.xright = 0.0
        self.time = np.array([])

        self.fs = 0.0
        self.signal = None
        self.envelope = None
        self.cf = None
        self.time = None
        self._signal_data = None
        self._envelope_data = None
        self._cf_data = None

        self.fig, _ = plt.subplots(3, 1)

        self.signal_ax = self.fig.axes[0]
        self.cf_ax = self.fig.axes[1]
        self.specgram_ax = self.fig.axes[2]

        self.canvas = FigureCanvas(self.fig)
        self.canvas.setSizePolicy(
            QtGui.QSizePolicy(QtGui.QSizePolicy.Policy.Expanding,
                              QtGui.QSizePolicy.Policy.Expanding))
        self.canvas.setMinimumHeight(320)
        self.graphArea = QtGui.QScrollArea(self)
        self.graphArea.setWidget(self.canvas)
        self.graphArea.setWidgetResizable(True)

        self.eventMarkers = {}
        self.last_right_clicked_event = None
        self.thresholdMarker = None
        self.playback_marker = None
        self.selector = SpanSelector(self.fig)
        self.minimap = MiniMap(self, self.signal_ax, None)

        # Load Spectrogram settings
        self.update_specgram_settings()

        # Animation related attributes
        self.background = None
        self.animated = False

        # Create context menus
        self.event_context_menu = QtGui.QMenu(self)
        self.takanami_on_event_action = QtGui.QAction(
            "Apply Takanami to Event", self)
        self.takanami_on_event_action.setStatusTip(
            "Refine event position by using Takanami algorithm")
        self.event_context_menu.addAction(self.takanami_on_event_action)
        self.takanami_on_event_action.triggered.connect(
            self.apply_takanami_to_selected_event)

        self.selection_context_menu = QtGui.QMenu(self)
        self.create_event_action = QtGui.QAction(
            "Create New Event on Selection", self)
        self.create_event_action.setStatusTip(
            "Create a new event on selection")
        self.takanami_on_selection_action = QtGui.QAction(
            "Apply Takanami to Selection", self)
        self.takanami_on_selection_action.setStatusTip(
            "Apply Takanami algorithm to selection")
        self.selection_context_menu.addAction(self.create_event_action)
        self.selection_context_menu.addAction(
            self.takanami_on_selection_action)
        self.create_event_action.triggered.connect(
            self.create_event_on_selection)
        self.takanami_on_selection_action.triggered.connect(
            self.apply_takanami_to_selection)

        # format axes
        formatter = FuncFormatter(lambda x, pos: clt.float_secs_2_string_date(
            x, self.document.record.starttime))
        for ax in self.fig.axes:
            ax.callbacks.connect('xlim_changed', self.on_xlim_change)
            ax.xaxis.set_major_formatter(formatter)
            plt.setp(ax.get_xticklabels(), visible=True)
            ax.grid(True, which='both')
        self.specgram_ax.callbacks.connect('ylim_changed', self.on_ylim_change)
        self.specgram_ax.set_xlabel('Time (seconds)')
        plt.setp(self.signal_ax.get_yticklabels(), visible=False)
        #self.signal_ax.set_ylabel('Signal Amp.')
        self.cf_ax.set_ylabel('CF Amp.')
        self.specgram_ax.set_ylabel('Frequency (Hz)')

        # Set the layout
        self.layout = QtGui.QVBoxLayout(self)
        self.layout.addWidget(self.graphArea)
        self.layout.addWidget(self.minimap)

        self.selector.toggled.connect(self.minimap.set_selection_visible)
        self.selector.valueChanged.connect(self.minimap.set_selection_limits)
        self.selector.right_clicked.connect(self.on_selector_right_clicked)

        if self.document is not None:
            self.set_record(document)
示例#10
0
    def plot_signal(self, t_start=0.0, t_end=np.inf, show_events=True,
                    show_x=True, show_cf=True, show_specgram=True,
                    show_envelope=True, threshold=None, num=None, **kwargs):
        """Plots record data.

        Draws a figure containing several plots for data stored and computed
        by a Record object: magnitude, envelope and spectrogram plots for
        self.signal, as well as characteristic function if calculated.

        Args:
            t_start: Start time of the plotted data segment, in seconds.
                Default: 0.0, that is the beginning of 'signal'.
            t_end: End time of the plotted data segment, in seconds.
                Default: numpy.inf, that is the end of 'signal'
            show_events: Boolean value to specify whether to plot
                event arrival times or not. Arrival times will be
                indicated by using a vertical line.
                Default: True.
            show_x: Boolean value to specify whether to plot the
                magnitude value of 'signal' or not. This function
                will be drawn preferably on the first axis.
                Default: True.
            show_cf: Boolean value to specify whether to plot the
                characteristic function or not. This function
                will be drawn preferably on the second axis.
                Default: True.
            show_specgram: Boolean value to specify whether to plot the
                spectrogram of 'signal' or not. It will be drawn preferably
                on the third axis.
                Default: True.
            show_envelope: Boolean value to specify whether to plot the
                envelope of 'signal' or not. This function will be drawn
                preferably on the first axis together with amplitude of
                'signal'.
                Default: True.
            threshold: Boolean value to specify whether to plot threshold
                or not. Threshold will be drawn as an horizontal dashed line
                together with characteristic function.
                Default: True.
            num: Identifier of the returned MatplotLib figure, integer type.
                Default None, which means an identifier value will be
                automatically generated.

        Returns:
            fig: A MatplotLib Figure instance.
        """
        # Lazy matplotlib import
        import matplotlib.pyplot as pl
        from matplotlib import ticker
        # Set limits
        i_from = int(max(0.0, t_start * self.fs))
        if show_cf:
            i_to = int(min(len(self.cf), t_end * self.fs))
        else:
            i_to = int(min(len(self.signal), t_end * self.fs))
        # Create time sequence
        t = np.arange(i_from, i_to) / float(self.fs)
        # Create figure
        nplots = show_x + show_cf + show_specgram
        fig, _ = pl.subplots(nplots, 1, sharex='all', num=num)
        fig.canvas.set_window_title(self.label)
        fig.set_tight_layout(True)
        # Configure axes
        for ax in fig.axes:
            ax.cla()
            ax.grid(True, which='both')
            formatter = ticker.FuncFormatter(lambda x, pos: clt.float_secs_2_string_date(x, self.starttime))
            ax.xaxis.set_major_formatter(formatter)
            ax.xaxis.set_major_locator(ticker.MaxNLocator(nbins=5, prune='lower'))
            ax.set_xlabel('Time (seconds)')
            pl.setp(ax.get_xticklabels(), visible=True)
        # Draw axes
        ax_idx = 0
        # Draw signal
        if show_x:
            fig.axes[ax_idx].set_title("Signal Amplitude (%gHz)" % self.fs)
            fig.axes[ax_idx].set_ylabel('Amplitude')

            fig.axes[ax_idx].plot(t, self.signal[i_from:i_to], color='black',
                                  label='Signal')
            #fig.axes[ax_idx].plot(t, signal_norm, color='black',
                                  #label='Signal')
            # Draw signal envelope
            if show_envelope:
                fig.axes[ax_idx].plot(t, env.envelope(self.signal[i_from:i_to]),
                                  color='r', label='Envelope')
                fig.axes[ax_idx].legend(loc=0, fontsize='small')
            ax_idx += 1
        # Draw Characteristic function
        if show_cf:
            fig.axes[ax_idx].set_title('Characteristic Function')
            fig.axes[ax_idx].plot(t, self.cf[i_from:i_to])
            # Draw threshold
            if threshold:
                hline = fig.axes[ax_idx].axhline(threshold, label="Threshold")
                hline.set(color='b', ls='--', lw=2, alpha=0.8)
                fig.axes[ax_idx].legend(loc=0, fontsize='small')
            ax_idx += 1
        # Draw spectrogram
        if show_specgram:
            fig.axes[ax_idx].set_title('Spectrogram')
            fig.axes[ax_idx].set_ylabel('Frequency (Hz)')
            fig.axes[ax_idx].specgram(self.signal[i_from:i_to], Fs=self.fs,
                                  xextent=(i_from / self.fs, i_to / self.fs))
            ax_idx += 1
        # Draw event markers
        if show_events:
            for event in self.events:
                arrival_time = event.stime / self.fs
                for ax in fig.axes:
                    xmin, xmax = ax.get_xlim()
                    if arrival_time > xmin and arrival_time < xmax:
                        vline = ax.axvline(arrival_time, label="Event")
                        vline.set(color='r', ls='--', lw=2)
                        ax.legend(loc=0, fontsize='small')
        # Configure limits and draw legend
        for ax in fig.axes:
            ax.set_xlim(t[0], t[-1])
        return fig
示例#11
0
    def plot_aic(self, show_envelope=True, num=None, **kwargs):
        """Plots AIC values for a given event object.

        Draws a figure with two axes: the first one plots magnitude and
        envelope of 'self.signal' and the second one plots AIC values computed
        after applying Takanami AR method to 'event'. Plotted data goes from
        'event.n0_aic' to 'event.n0_aic + len(event.aic)'.

        Args:
            show_envelope: Boolean value to specify whether to plot the
                envelope of 'signal' or not. This function will be drawn
                preferably on the first axis together with amplitude of
                'signal'.
                Default: True.
            num: Identifier of the returned MatplotLib figure, integer type.
                Default None, which means an identifier value will be
                automatically generated.

        Returns:
            fig: A MatplotLib Figure instance.
        """
        if self.aic is None or self.n0_aic is None:
            raise ValueError("Event doesn't have AIC data to plot")

        # Lazy matplotlib import
        import matplotlib.pyplot as pl
        from matplotlib import ticker

        # Set limits
        i_from = int(max(0, self.n0_aic))
        i_to = int(min(len(self.trace.signal), self.n0_aic + len(self.aic)))
        # Create time sequence
        t = np.arange(i_from, i_to) / float(self.trace.fs)
        # Create figure
        fig, _ = pl.subplots(2, 1, sharex='all', num=num)
        fig.canvas.set_window_title(self.trace.label)
        fig.set_tight_layout(True)
        # Configure axes
        for ax in fig.axes:
            ax.cla()
            ax.grid(True, which='both')
            formatter = ticker.FuncFormatter(lambda x, pos: clt.float_secs_2_string_date(x, self.trace.starttime))
            ax.xaxis.set_major_formatter(formatter)
            ax.xaxis.set_major_locator(ticker.MaxNLocator(nbins=5, prune='lower'))
            ax.set_xlabel('Time (seconds)')
            pl.setp(ax.get_xticklabels(), visible=True)
        # Draw signal
        fig.axes[0].set_title('Signal Amplitude')
        fig.axes[0].set_ylabel('Amplitude')
        fig.axes[0].plot(t, self.trace.signal[i_from:i_to], color='black',
                         label='Signal')
        # Draw envelope
        if show_envelope:
            fig.axes[0].plot(t, env.envelope(self.trace.signal[i_from:i_to]),
                         color='r', label='Envelope')
            fig.axes[0].legend(loc=0, fontsize='small')
        # Draw AIC
        fig.axes[1].set_title('AIC')
        fig.axes[1].plot(t, self.aic)
        # Draw event
        for ax in fig.axes:
            vline = ax.axvline(self.stime / self.trace.fs, label="Event")
            vline.set(color='r', ls='--', lw=2)
        # Configure limits and draw legend
        for ax in fig.axes:
            ax.set_xlim(t[0], t[-1])
            ax.legend(loc=0, fontsize='small')
        return fig
示例#12
0
    def __init__(self, parent, document=None):
        super(SignalViewerWidget, self).__init__(parent)

        self.document = document
        self.xmin = 0.0
        self.xmax = 0.0
        self.xleft = 0.0
        self.xright = 0.0
        self.time = np.array([])

        self.fs = 0.0
        self.signal = None
        self.envelope = None
        self.cf = None
        self.time = None
        self._signal_data = None
        self._envelope_data = None
        self._cf_data = None

        self.fig, _ = plt.subplots(3, 1)

        self.signal_ax = self.fig.axes[0]
        self.cf_ax = self.fig.axes[1]
        self.specgram_ax = self.fig.axes[2]

        self.canvas = FigureCanvas(self.fig)
        self.canvas.setSizePolicy(QtGui.QSizePolicy(QtGui.QSizePolicy.Policy.Expanding,
                                                    QtGui.QSizePolicy.Policy.Expanding))
        self.canvas.setMinimumHeight(320)
        self.graphArea = QtGui.QScrollArea(self)
        self.graphArea.setWidget(self.canvas)
        self.graphArea.setWidgetResizable(True)

        self.eventMarkers = {}
        self.last_right_clicked_event = None
        self.thresholdMarker = None
        self.playback_marker = None
        self.selector = SpanSelector(self.fig)
        self.minimap = MiniMap(self, self.signal_ax, None)

        # Load Spectrogram settings
        self.update_specgram_settings()

        # Animation related attributes
        self.background = None
        self.animated = False

        # Create context menus
        self.event_context_menu = QtGui.QMenu(self)
        self.takanami_on_event_action = QtGui.QAction("Apply Takanami to Event", self)
        self.takanami_on_event_action.setStatusTip("Refine event position by using Takanami algorithm")
        self.event_context_menu.addAction(self.takanami_on_event_action)
        self.takanami_on_event_action.triggered.connect(self.apply_takanami_to_selected_event)

        self.selection_context_menu = QtGui.QMenu(self)
        self.create_event_action = QtGui.QAction("Create New Event on Selection", self)
        self.create_event_action.setStatusTip("Create a new event on selection")
        self.takanami_on_selection_action = QtGui.QAction("Apply Takanami to Selection", self)
        self.takanami_on_selection_action.setStatusTip("Apply Takanami algorithm to selection")
        self.selection_context_menu.addAction(self.create_event_action)
        self.selection_context_menu.addAction(self.takanami_on_selection_action)
        self.create_event_action.triggered.connect(self.create_event_on_selection)
        self.takanami_on_selection_action.triggered.connect(self.apply_takanami_to_selection)

        # format axes
        formatter = FuncFormatter(lambda x, pos: clt.float_secs_2_string_date(x, self.document.record.starttime))
        for ax in self.fig.axes:
            ax.callbacks.connect('xlim_changed', self.on_xlim_change)
            ax.xaxis.set_major_formatter(formatter)
            plt.setp(ax.get_xticklabels(), visible=True)
            ax.grid(True, which='both')
        self.specgram_ax.callbacks.connect('ylim_changed', self.on_ylim_change)
        self.specgram_ax.set_xlabel('Time (seconds)')
        plt.setp(self.signal_ax.get_yticklabels(), visible=False)
        #self.signal_ax.set_ylabel('Signal Amp.')
        self.cf_ax.set_ylabel('CF Amp.')
        self.specgram_ax.set_ylabel('Frequency (Hz)')

        # Set the layout
        self.layout = QtGui.QVBoxLayout(self)
        self.layout.addWidget(self.graphArea)
        self.layout.addWidget(self.minimap)

        self.selector.toggled.connect(self.minimap.set_selection_visible)
        self.selector.valueChanged.connect(self.minimap.set_selection_limits)
        self.selector.right_clicked.connect(self.on_selector_right_clicked)

        if self.document is not None:
            self.set_record(document)