def _plot_default(self):
# Create data
x = linspace(-5, 15.0, 100)
y = jn(3, x)
pd = ArrayPlotData(index=x, value=y)
zoomable_plot = Plot(pd)
zoomable_plot.plot(('index', 'value'),
name='external',
color='red',
line_width=3)
# Attach tools to the plot
zoom = ZoomTool(component=zoomable_plot,
tool_mode="box",
always_on=False)
zoomable_plot.overlays.append(zoom)
zoomable_plot.tools.append(PanTool(zoomable_plot))
# Create a second inset plot, not resizable, not zoom-able
inset_plot = Plot(pd)
inset_plot.plot(('index', 'value'), color='blue')
inset_plot.resizable = ''
inset_plot.bounds = [250, 150]
inset_plot.position = [450, 350]
inset_plot.border_visible = True
# Create a container and add our plots
container = OverlayPlotContainer()
container.add(zoomable_plot)
container.add(inset_plot)
return container
def _signals_plot_default(self):
print('_signals_plot_default')
"""Create the Plot instance."""
plot = Plot()
plot.add(self.signals_renderer)
x_axis = PlotAxis(component=plot,
mapper=self.signals_renderer.index_mapper,
orientation='bottom')
# y_axis = PlotAxis(component=plot,
# mapper=self.signals_renderer.value_mapper,
# orientation='left')
plot.overlays.extend([x_axis])
plot.origin_axis_visible = False
plot.padding_top = 0
plot.padding_left = 0
plot.padding_right = 0
plot.padding_bottom = 50
plot.border_visible = False
plot.bgcolor = "white"
plot.use_downsampling = True
return plot
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index=x)
for i in range(5):
pd.set_data("y" + str(i), jn(i, x))
# Create some line plots of some of the data
plot1 = Plot(pd)
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Tweak some of the plot properties
plot1.title = "Inset Plot"
plot1.padding = 50
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Create a second scatter plot of one of the datasets, linking its
# range to the first plot
plot2 = Plot(pd, range2d=plot1.range2d, padding=50)
plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")
plot2.resizable = ""
plot2.bounds = [250, 250]
plot2.position = [550, 150]
plot2.bgcolor = "white"
plot2.border_visible = True
plot2.unified_draw = True
plot2.tools.append(PanTool(plot2))
plot2.tools.append(MoveTool(plot2, drag_button="right"))
zoom = ZoomTool(component=plot2, tool_mode="box", always_on=False)
plot2.overlays.append(zoom)
# Create a container and add our plots
container = OverlayPlotContainer()
container.add(plot1)
container.add(plot2)
return container
def _plot_default(self):
distr_len = len(self.data)
# PolygonPlot holding the circles of the Hinton diagram
polyplot = Plot(self.plot_data)
for idx in range(distr_len):
p = polyplot.plot(('x%d' % idx, 'y%d' % idx),
type="polygon",
face_color=get_class_color(idx),
edge_color='black')
self._set_title(polyplot)
self._remove_grid_and_axes(polyplot)
# create x axis for labels
axis = self._create_increment_one_axis(polyplot, 1., distr_len,
'bottom')
self._add_index_axis(polyplot, axis)
# create y axis for probability density
#prob_axis = self._create_probability_axis(polyplot)
#polyplot.value_axis = prob_axis
#polyplot.underlays.append(prob_axis)
# tweak some of the plot properties
range2d = DataRange2D(low=(0.5, 0.), high=(distr_len + 0.5, 1.))
polyplot.range2d = range2d
polyplot.aspect_ratio = ((range2d.x_range.high - range2d.x_range.low) /
(range2d.y_range.high - range2d.y_range.low))
polyplot.border_visible = False
polyplot.padding = [0, 0, 25, 25]
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True, valign='center')
container.add(polyplot)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
self.decorate_plot(container, self.data)
return container
def _plot_default(self):
distr_len = len(self.data)
# PolygonPlot holding the circles of the Hinton diagram
polyplot = Plot(self.plot_data)
for idx in range(distr_len):
p = polyplot.plot(('x%d' % idx, 'y%d' % idx),
type="polygon",
face_color=get_class_color(idx),
edge_color='black')
self._set_title(polyplot)
self._remove_grid_and_axes(polyplot)
# create x axis for labels
axis = self._create_increment_one_axis(polyplot, 1., distr_len, 'bottom')
self._add_index_axis(polyplot, axis)
# create y axis for probability density
#prob_axis = self._create_probability_axis(polyplot)
#polyplot.value_axis = prob_axis
#polyplot.underlays.append(prob_axis)
# tweak some of the plot properties
range2d = DataRange2D(low=(0.5, 0.), high=(distr_len+0.5, 1.))
polyplot.range2d = range2d
polyplot.aspect_ratio = ((range2d.x_range.high - range2d.x_range.low)
/ (range2d.y_range.high - range2d.y_range.low))
polyplot.border_visible = False
polyplot.padding = [0, 0, 25, 25]
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True, valign='center')
container.add(polyplot)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
self.decorate_plot(container, self.data)
return container
def _create_1D1_plot(self):
index = 0
plot0 = Plot(self.plotdata, padding=0)
plot0.padding_left = 5
plot0.padding_bottom = 5
Container = OverlayPlotContainer(padding = 50, fill_padding = True, bgcolor = "lightgray", use_backbuffer=True)
y1 = range(len(self.PCAData.batchs[0].prescores))
points = []
for batch in self.PCAData.batchs:
if (self.active_scores_combobox == "Post Scores"):
x1 = self.PCAData.batchs[index].postscores
else:
x1 = self.PCAData.batchs[index].prescores
if (self.Shape == self.phenotypes[0]):
a = 1
elif (self.Shape == self.phenotypes[1]):
a = batch.number
elif (self.Shape == self.phenotypes[2]):
a = batch.type
else:
a = 0
if (self.Color == self.phenotypes[0]):
b = 0
elif(self.Color == self.phenotypes[1]):
b = batch.number
elif(self.Color == self.phenotypes[2]):
b = batch.type
else:
b = 0
tmarker = shapes[a]
bcolor = self.colors[b]
for i in range(len(x1)):
points.append((x1[i],y1[i]))
plot0 = create_scatter_plot((x1,y1), marker = tmarker, color=getColor(bcolor))
if batch.isSelected:
plot0.alpha = 1
else:
plot0.alpha = 0.2
plot0.bgcolor = "white"
plot0.border_visible = True
if index == 0:
value_mapper = plot0.value_mapper
index_mapper = plot0.index_mapper
add_default_grids(plot0)
add_default_axes(plot0, vtitle='PCA Indices', htitle='PCA Scores')
plot0.index_range.tight_bounds = False
plot0.index_range.refresh()
plot0.value_range.tight_bounds = False
plot0.value_range.refresh()
plot0.tools.append(PanTool(plot0))
zoom = ZoomTool(plot0, tool_mode="box", always_on=False, maintain_aspect_ratio=False)
plot0.overlays.append(zoom)
dragzoom = DragZoom(plot0, drag_button="right", maintain_aspect_ratio=False)
plot0.tools.append(dragzoom)
else:
plot0.value_mapper = value_mapper
value_mapper.range.add(plot0.value)
plot0.index_mapper = index_mapper
index_mapper.range.add(plot0.index)
Container.add(plot0)
index = index +1
self.RightPlot = Container
def _create_1D1_plot(self):
index = 0
plot0 = Plot(self.plotdata, padding=0)
plot0.padding_left = 5
plot0.padding_bottom = 5
Container = OverlayPlotContainer(padding=50,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
y1 = range(len(self.PCAData.batchs[0].prescores))
points = []
for batch in self.PCAData.batchs:
if (self.active_scores_combobox == "Post Scores"):
x1 = self.PCAData.batchs[index].postscores
else:
x1 = self.PCAData.batchs[index].prescores
'''if (self.Shape == self.phenotypes[0]):
a = 1
elif (self.Shape == self.phenotypes[1]):
a = batch.number
elif (self.Shape == self.phenotypes[2]):
a = batch.type
else:
a = 0
if (self.Color == self.phenotypes[0]):
b = 0
elif(self.Color == self.phenotypes[1]):
b = batch.number
elif(self.Color == self.phenotypes[2]):
b = batch.type
else:
b = 0 '''
a = batch.type
b = batch.number
tmarker = shapes[a]
bcolor = self.colors[b]
for i in range(len(x1)):
points.append((x1[i], y1[i]))
plot0 = create_scatter_plot((x1, y1),
marker=tmarker,
color=getColor(bcolor))
if batch.isSelected:
plot0.alpha = 1
plot0.alpha = 1
else:
plot0.fill_alpha = 0.2
plot0.edge_alpha = 0.2
plot0.bgcolor = "white"
plot0.border_visible = True
if index == 0:
value_mapper = plot0.value_mapper
index_mapper = plot0.index_mapper
add_default_grids(plot0)
add_default_axes(plot0,
vtitle='PCA Indices',
htitle='PCA Scores')
plot0.index_range.tight_bounds = False
plot0.index_range.refresh()
plot0.value_range.tight_bounds = False
plot0.value_range.refresh()
plot0.tools.append(PanTool(plot0))
zoom = ZoomTool(plot0,
tool_mode="box",
always_on=False,
maintain_aspect_ratio=False)
plot0.overlays.append(zoom)
dragzoom = DragZoom(plot0,
drag_button="right",
maintain_aspect_ratio=False)
plot0.tools.append(dragzoom)
else:
plot0.value_mapper = value_mapper
value_mapper.range.add(plot0.value)
plot0.index_mapper = index_mapper
index_mapper.range.add(plot0.index)
if batch.isSelected:
Container.add(plot0)
index = index + 1
self.RightPlot = Container
def update_plots(self, waveforms, results):
mic_psd = db(psd(waveforms, self.model.fs, 'hanning')).mean(axis=0)
results['ref_mic_psd'] = mic_psd[1]
results['exp_mic_psd'] = mic_psd[0]
results['freq_psd'] = psd_freq(waveforms, self.model.fs)
result = MicToneCalibrationResult(**results)
frequency = results['frequency']
ds = ArrayPlotData(freq_psd=results['freq_psd'],
exp_mic_psd=results['exp_mic_psd'],
ref_mic_psd=results['ref_mic_psd'],
time=results['time'],
exp_mic_waveform=results['exp_mic_waveform'],
ref_mic_waveform=results['ref_mic_waveform'])
# Set up the waveform plot
container = HPlotContainer(bgcolor='white', padding=10)
plot = Plot(ds)
plot.plot(('time', 'ref_mic_waveform'), color='black')
plot.index_range.low_setting = self.model.trim
plot.index_range.high_setting = 5.0/frequency+self.model.trim
container.add(plot)
plot = Plot(ds)
plot.plot(('time', 'exp_mic_waveform'), color='red')
plot.index_range.low_setting = self.model.trim
plot.index_range.high_setting = 5.0/frequency+self.model.trim
container.add(plot)
result.waveform_plots = container
# Set up the spectrum plot
plot = Plot(ds)
plot.plot(('freq_psd', 'ref_mic_psd'), color='black')
plot.plot(('freq_psd', 'exp_mic_psd'), color='red')
plot.index_scale = 'log'
plot.title = 'Microphone response'
plot.padding = 50
plot.index_range.low_setting = 100
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode='box', always_on=False)
plot.overlays.append(zoom)
result.spectrum_plots = plot
# Plot the fundamental (i.e. the tone) and first even/odd harmonics
harmonic_container = HPlotContainer(resizable='hv', bgcolor='white',
fill_padding=True, padding=10)
for i in range(3):
f_harmonic = results['exp_harmonics'][i]['frequency']
plot = Plot(ds)
plot.plot(('freq_psd', 'ref_mic_psd'), color='black')
plot.plot(('freq_psd', 'exp_mic_psd'), color='red')
plot.index_range.low_setting = f_harmonic-500
plot.index_range.high_setting = f_harmonic+500
plot.origin_axis_visible = True
plot.padding_left = 10
plot.padding_right = 10
plot.border_visible = True
plot.title = 'F{}'.format(i+1)
harmonic_container.add(plot)
result.harmonic_plots = harmonic_container
self.model.tone_data.append(result)
# Update the master overview
self.model.measured_freq.append(results['frequency'])
self.model.measured_spl.append(results['output_spl'])
self.model.exp_mic_sens.append(results['exp_mic_sens'])
for mic in ('ref', 'exp'):
for h in range(3):
v = results['{}_harmonics'.format(mic)][h]['mic_rms']
name = 'measured_{}_f{}'.format(mic, h+1)
getattr(self.model, name).append(v)
v = results['{}_thd'.format(mic)]
getattr(self.model, 'measured_{}_thd'.format(mic)).append(v)
ds = ArrayPlotData(
frequency=self.model.measured_freq,
spl=self.model.measured_spl,
measured_exp_thd=self.model.measured_exp_thd,
measured_ref_thd=self.model.measured_ref_thd,
exp_mic_sens=self.model.exp_mic_sens,
)
container = VPlotContainer(padding=10, bgcolor='white',
fill_padding=True, resizable='hv')
plot = Plot(ds)
plot.plot(('frequency', 'spl'), color='black')
plot.plot(('frequency', 'spl'), color='black', type='scatter')
plot.index_scale = 'log'
plot.title = 'Speaker output (dB SPL)'
container.add(plot)
plot = Plot(ds)
plot.plot(('frequency', 'measured_ref_thd'), color='black')
plot.plot(('frequency', 'measured_ref_thd'), color='black', type='scatter')
plot.plot(('frequency', 'measured_exp_thd'), color='red')
plot.plot(('frequency', 'measured_exp_thd'), color='red', type='scatter')
plot.index_scale = 'log'
plot.title = 'Total harmonic distortion (frac)'
container.add(plot)
plot = Plot(ds)
plot.plot(('frequency', 'exp_mic_sens'), color='red')
plot.plot(('frequency', 'exp_mic_sens'), color='red', type='scatter')
plot.index_scale = 'log'
plot.title = 'Experiment mic. sensitivity V (dB re Pa)'
container.add(plot)
self.model.spl_plots = container