def set_record(self, document, step=120.0):
self.document = document
self.fs = self.document.record.fs
self.signal = self.document.record.signal
self.envelope = env.envelope(self.signal)
self.cf = self.document.record.cf
self.time = np.linspace(0,
len(self.signal) / self.fs,
num=len(self.signal),
endpoint=False)
self.xmax = self.time[-1]
# Draw minimap
self.minimap.set_record(self.document.record, step)
# Plot signal
self._signal_data = self.signal_ax.plot(self.time,
self.signal,
color='black',
rasterized=True)[0]
# Plot envelope
self._envelope_data = self.signal_ax.plot(self.time,
self.envelope,
color='red',
rasterized=True)[0]
plotting.adjust_axes_height(self.signal_ax)
# Plot CF
cf_loaded = (self.cf.size != 0)
self.set_cf_visible(cf_loaded)
self.CF_loaded.emit(cf_loaded)
self._cf_data = self.cf_ax.plot(self.time[:len(self.cf)],
self.cf,
color='black',
rasterized=True)[0]
plotting.adjust_axes_height(self.signal_ax)
self.thresholdMarker = ThresholdMarker(self.cf_ax)
# Plot espectrogram
plotting.plot_specgram(self.specgram_ax,
self.signal,
self.fs,
nfft=self.specgram_windowlen,
noverlap=self.specgram_noverlap,
window=self.specgram_window)
# Set the span selector
self.selector.fs = self.fs
self.selector.set_active(False)
self.selector.set_selection_limits(self.xmin, self.xmax)
# Set the playback marker
self.playback_marker = PlayBackMarker(self.fig, self)
# Set the initial xlimits
self.set_xlim(0, step)
self.subplots_adjust()
def set_record(self, document, step=120.0):
self.document = document
self.fs = self.document.record.fs
self.signal = self.document.record.signal
self.envelope = env.envelope(self.signal)
self.cf = self.document.record.cf
self.time = np.linspace(0, len(self.signal) / self.fs, num=len(self.signal), endpoint=False)
self.xmax = self.time[-1]
# Draw minimap
self.minimap.set_record(self.document.record, step)
# Plot signal
self._signal_data = self.signal_ax.plot(self.time,
self.signal,
color='black',
rasterized=True)[0]
# Plot envelope
self._envelope_data = self.signal_ax.plot(self.time,
self.envelope,
color='red',
rasterized=True)[0]
plotting.adjust_axes_height(self.signal_ax)
# Plot CF
cf_loaded = (self.cf.size != 0)
self.set_cf_visible(cf_loaded)
self.CF_loaded.emit(cf_loaded)
self._cf_data = self.cf_ax.plot(self.time[:len(self.cf)],
self.cf,
color='black', rasterized=True)[0]
plotting.adjust_axes_height(self.signal_ax)
self.thresholdMarker = ThresholdMarker(self.cf_ax)
# Plot espectrogram
plotting.plot_specgram(self.specgram_ax, self.signal, self.fs,
nfft=self.specgram_windowlen,
noverlap=self.specgram_noverlap,
window=self.specgram_window)
# Set the span selector
self.selector.fs = self.fs
self.selector.set_active(False)
self.selector.set_selection_limits(self.xmin, self.xmax)
# Set the playback marker
self.playback_marker = PlayBackMarker(self.fig, self)
# Set the initial xlimits
self.set_xlim(0, step)
self.subplots_adjust()
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 set_record(self, document, step=120.0):
self.document = document
self.fs = self.document.record.fs
self.signal = self.document.record.signal
self.envelope = env.envelope(self.signal)
self.cf = self.document.record.cf
self.time = np.linspace(0,
len(self.signal) / self.fs,
num=len(self.signal),
endpoint=False)
self.xmax = self.time[-1]
# Draw minimap
self.minimap.minimapSelector.set(
visible=False) # Hide minimap selector while loading
self.minimap.set_record(self.document.record, step)
# Plot signal
step_samples = step * self.fs
self._signal_data = self.signal_ax.plot(self.time[:step_samples],
self.signal[:step_samples],
color='black',
rasterized=True)[0]
# Plot envelope
self._envelope_data = self.signal_ax.plot(self.time[:step_samples],
self.envelope[:step_samples],
color='red',
rasterized=True)[0]
# Adjust y axis for signal plot
signal_yaxis_max_value = max(np.max(self.signal),
np.max(self.envelope))
signal_yaxis_min_value = np.min(self.signal)
plotting.adjust_axes_height(self.signal_ax,
max_value=signal_yaxis_max_value,
min_value=signal_yaxis_min_value)
# Plot CF
cf_loaded = (self.cf.size != 0)
self.set_cf_visible(cf_loaded)
self.CF_loaded.emit(cf_loaded)
cf_step_samples = min(step_samples, len(self.cf))
self._cf_data = self.cf_ax.plot(self.time[:cf_step_samples],
self.cf[:cf_step_samples],
color='black',
rasterized=True)[0]
# Adjust y axis for CF plot
if cf_loaded:
plotting.adjust_axes_height(self.cf_ax,
max_value=np.max(self.cf),
min_value=np.min(self.cf))
self.thresholdMarker = ThresholdMarker(self.cf_ax)
# Plot espectrogram
plotting.plot_specgram(self.specgram_ax,
self.signal,
self.fs,
nfft=self.specgram_windowlen,
noverlap=self.specgram_noverlap,
window=self.specgram_window)
# Set the span selector
self.selector.fs = self.fs
self.selector.set_active(False)
self.selector.set_selection_limits(self.xmin, self.xmax)
# Set the playback marker
self.playback_marker = PlayBackMarker(self.fig, self)
# Set the initial xlimits
self.set_xlim(0, step)
self.subplots_adjust()
# Set event markers
self.eventMarkers = {}
for event in self.document.record.events:
self.create_event(event)
# Now activate selector again on minimap
self.minimap.minimapSelector.set(visible=True)
self.minimap.draw()
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 set_record(self, document, step=120.0):
self.document = document
self.fs = self.document.record.fs
self.signal = self.document.record.signal
self.envelope = env.envelope(self.signal)
self.cf = self.document.record.cf
self.time = np.linspace(0, len(self.signal) / self.fs, num=len(self.signal), endpoint=False)
self.xmax = self.time[-1]
# Draw minimap
self.minimap.minimapSelector.set(visible=False) # Hide minimap selector while loading
self.minimap.set_record(self.document.record, step)
# Plot signal
step_samples = step * self.fs
self._signal_data = self.signal_ax.plot(self.time[:step_samples],
self.signal[:step_samples],
color='black',
rasterized=True)[0]
# Plot envelope
self._envelope_data = self.signal_ax.plot(self.time[:step_samples],
self.envelope[:step_samples],
color='red',
rasterized=True)[0]
# Adjust y axis for signal plot
signal_yaxis_max_value = max(np.max(self.signal), np.max(self.envelope))
signal_yaxis_min_value = np.min(self.signal)
plotting.adjust_axes_height(self.signal_ax,
max_value=signal_yaxis_max_value,
min_value=signal_yaxis_min_value)
# Plot CF
cf_loaded = (self.cf.size != 0)
self.set_cf_visible(cf_loaded)
self.CF_loaded.emit(cf_loaded)
cf_step_samples = min(step_samples,len(self.cf))
self._cf_data = self.cf_ax.plot(self.time[:cf_step_samples],
self.cf[:cf_step_samples],
color='black',
rasterized=True)[0]
# Adjust y axis for CF plot
if cf_loaded:
plotting.adjust_axes_height(self.cf_ax,
max_value=np.max(self.cf),
min_value=np.min(self.cf))
self.thresholdMarker = ThresholdMarker(self.cf_ax)
# Plot espectrogram
plotting.plot_specgram(self.specgram_ax, self.signal, self.fs,
nfft=self.specgram_windowlen,
noverlap=self.specgram_noverlap,
window=self.specgram_window)
# Set the span selector
self.selector.fs = self.fs
self.selector.set_active(False)
self.selector.set_selection_limits(self.xmin, self.xmax)
# Set the playback marker
self.playback_marker = PlayBackMarker(self.fig, self)
# Set the initial xlimits
self.set_xlim(0, step)
self.subplots_adjust()
# Set event markers
self.eventMarkers = {}
for event in self.document.record.events:
self.create_event(event)
# Now activate selector again on minimap
self.minimap.minimapSelector.set(visible=True)
self.minimap.draw()