def _resp_plot_default(self):
# Create plotting components
self.resp_plot_data = ArrayPlotData(
time=self.time,
inhale_times=self.resp_inhale_begin_times,
inhale_values=self.resp_inhale_begin_values,
exhale_times=self.resp_exhale_begin_times,
exhale_values=self.resp_exhale_begin_values,
filtered_resp=self.resp_signal,
lpfilt_resp=self.lpfilt_resp)
plot = Plot(self.resp_plot_data)
plot.plot(("time", "filtered_resp"), color="blue", line_width=1)
plot.plot(("time", "lpfilt_resp"), color="green", line_width=1)
# Plot the inhalation peaks
plot.plot(("inhale_times", "inhale_values"),
type="scatter",
marker="square")
plot.plot(("exhale_times", "exhale_values"),
type="scatter",
marker="circle")
plot.title = "Respiration"
plot.title_position = "right"
plot.title_angle = 270
plot.padding = 20
return plot
def _plot_default(self):
plot = Plot(self.plotdata)
if self.signal in ("tpr", "co", "sv"):
rc_signal = "resp_corrected_" + self.signal
if rc_signal in self.plotdata.arrays and self.plotdata.arrays[
rc_signal].size > 0:
# Plot the resp-uncorrected version underneath
plot.plot(("peak_times", self.signal),
type="line",
color="purple")
signal = rc_signal
signal = self.signal
elif self.signal == "hr":
plot.plot(("peak_times", self.signal), type="line", color="purple")
signal = "mea_hr"
else:
signal = self.signal
# Create the plot
plot.plot(("peak_times", signal), type="line", color="blue")
plot.plot(("peak_times", signal, "beat_type"),
type="cmap_scatter",
color_mapper=jet,
name="my_plot",
marker="circle",
border_visible=False,
outline_color="transparent",
bg_color="white",
index_sort="ascending",
marker_size=3,
fill_alpha=0.8)
# Tweak some of the plot properties
plot.title = self.signal #"Scatter Plot With Lasso Selection"
plot.title_position = "right"
plot.title_angle = 270
plot.line_width = 1
plot.padding = 20
# Right now, some of the tools are a little invasive, and we need the
# actual ScatterPlot object to give to them
my_plot = plot.plots["my_plot"][0]
# Attach some tools to the plot
self.selection = BeatSelection(component=my_plot)
my_plot.active_tool = self.selection
selection_overlay = RangeSelectionOverlay(component=my_plot)
my_plot.overlays.append(selection_overlay)
# Set up the trait handler for the selection
self.selection.on_trait_change(self._selection_changed,
'selection_completed')
return plot
def _z0_plot_default(self):
self.z0_plot_data = ArrayPlotData(time=self.time,
raw_data=self.raw_z0_signal,
cleaned_data=self.resp_corrected_z0)
plot = Plot(self.z0_plot_data)
plot.plot(("time", "raw_data"), color="blue", line_width=1)
plot.plot(("time", "cleaned_data"), color="green", line_width=1)
plot.title = "z0"
plot.title_position = "right"
plot.title_angle = 270
plot.padding = 20
return plot
def _dzdt_plot_default(self):
"""
Creates a plot of the ecg_ts data and the signals derived during
the Pan Tomkins algorithm
"""
# Create plotting components
self.dzdt_plot_data = ArrayPlotData(
time=self.time,
raw_data=self.raw_dzdt_signal,
cleaned_data=self.resp_corrected_dzdt)
plot = Plot(self.dzdt_plot_data)
plot.plot(("time", "raw_data"), color="blue", line_width=1)
plot.plot(("time", "cleaned_data"), color="green", line_width=1)
plot.title = "dZ/dt"
plot.title_position = "right"
plot.title_angle = 270
plot.padding = 20
return plot
def _plot_default(self):
# Create plotting components
plotdata = ArrayPlotData(time=self.time, data=self.data)
plot = Plot(plotdata)
self.renderer = plot.plot(("time", "data"), color=self.line_color)[0]
plot.title = self.name
plot.title_position = "right"
plot.title_angle = 270
plot.line_width = 1
plot.padding = 25
plot.width = 400
# Load the censor regions and add them to the plot
for censor_region in self.censored_regions:
# Make a censor region on the Timeseries object
censor_region.plot = self.renderer
censor_region.viz
censor_region.set_limits(censor_region.start_time,
censor_region.end_time)
return plot