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)
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()
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()
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)
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)
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)
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
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
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)
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
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
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)