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_corr_plot(self):
plot = Plot(self.plotdata, padding=0)
plot.padding_left = 25
plot.padding_bottom = 25
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
self.corr_plot = plot
def _create_corr_plot(self):
plot = Plot(self.plotdata, padding=0)
plot.padding_left = 25
plot.padding_bottom = 25
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
self.corr_plot = plot
def _plot_default(self):
plot = Plot(self.data)
plot.plot( ('t', 'y0'), color=colors[0] )
plot.padding=20
plot.padding_left = 40
plot.tools.append(PanTool(plot))#, constrain=True, constrain_direction="y"))
#TODO: zoomtool works on both axes, should only affect y
plot.tools.append(ZoomTool(plot, tool_mode="range", constrain=True, constrain_direction="y"))
return plot
def _plot1_2d_container_default(self):
if self.cost_data is None or len(self.cost_data) == 0:
return
plot = Plot(self.cost_plot_data)
plot.title = "Cost function"
plot.padding_left = 80
container = HPlotContainer()
container.add(plot)
self.rebuild_renderer(container)
return container
def _plot_default(self):
data = self.data < self.limit
pd = self.pd = ArrayPlotData(imagedata=data,orig=self.data)
plot1 = Plot(pd, default_origin='top left')
plot2 = Plot(pd, default_origin='top left')
img_plot1 = plot1.img_plot("imagedata",colormap=gray,padding=0)[0]
img_plot2 = plot2.img_plot("orig",colormap=gray,padding=0)[0]
container = HPlotContainer(plot1,plot2)
container.spacing=0
plot1.padding_right=0
plot2.padding_left=0
plot2.y_axis.orientation= 'right'
return container
def _plot_default(self):
container = Plot(self.model.plot_data, )
container.renderer_map['bar'] = SelectableBarPlot
container.padding_left = 100
container.padding_bottom = 150
# container.plot(('x', 'y'), type='bar')
self.zoom_tool = ZoomTool(container, )
container.underlays.append(self.zoom_tool)
container.tools.append(self.zoom_tool)
self.pan_tool = PanTool(container, )
container.tools.append(self.pan_tool)
return container
def _time_plot_default(self):
plot = Plot(self.data)
line_plot = plot.plot(('t', 'y'))[0]
line_plot.active_tool = RangeSelection(line_plot, left_button_selects=True)
line_plot.overlays.append(RangeSelectionOverlay(component=line_plot))
self.selection = line_plot.active_tool
plot.padding = 20
plot.padding_left = 50
self.selection.on_trait_change(self.handle_selection_change, 'selection')
return plot
def _plot_default(self):
plot = Plot(self.data)
plot.plot(('t', 'y0'), color=colors[0])
plot.padding = 20
plot.padding_left = 40
plot.tools.append(
PanTool(plot)) #, constrain=True, constrain_direction="y"))
#TODO: zoomtool works on both axes, should only affect y
plot.tools.append(
ZoomTool(plot,
tool_mode="range",
constrain=True,
constrain_direction="y"))
return plot
def _time_plot_default(self):
plot = Plot(self.data)
line_plot = plot.plot(('t', 'y'))[0]
line_plot.active_tool = RangeSelection(line_plot,
left_button_selects=True)
line_plot.overlays.append(RangeSelectionOverlay(component=line_plot))
self.selection = line_plot.active_tool
plot.padding = 20
plot.padding_left = 50
self.selection.on_trait_change(self.handle_selection_change,
'selection')
return plot
def __init__(self):
# Create the data and the PlotData object
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x=x, y=y)
# Create the scatter plot
scatter = Plot(plotdata)
scatter.plot(("x", "y"), type="scatter", color="blue")
# Create the line plot
line = Plot(plotdata)
line.plot(("x", "y"), type="line", color="blue")
# Create a horizontal container and put the two plots inside it
container = HPlotContainer(scatter, line)
container.spacing = 0
scatter.padding_right = 0
line.padding_left = 0
line.y_axis.orientation = "right"
self.plot = container
def _plot_default(self):
# Create the data and the PlotData object
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x = x, y = y)
# Create the scatter plot
scatter = Plot(plotdata)
scatter.plot(("x", "y"), type="scatter", color="blue")
# Create the line plot
line = Plot(plotdata)
line.plot(("x", "y"), type="line", color="blue")
# Create a horizontal container and put the two plots inside it
container = HPlotContainer(scatter, line)
container.spacing = 0
scatter.padding_right = 0
line.padding_left = 0
line.y_axis.orientation = "right"
return container
def _plot_default(self):
container = Plot(
self.model.plot_data,
)
container.renderer_map['bar'] = SelectableBarPlot
container.padding_left = 100
container.padding_bottom = 150
# container.plot(('x', 'y'), type='bar')
self.zoom_tool = ZoomTool(
container,
)
container.underlays.append(self.zoom_tool)
container.tools.append(self.zoom_tool)
self.pan_tool = PanTool(
container,
)
container.tools.append(self.pan_tool)
return container
def _plot_default(self):
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x=x, y=y)
scatter = Plot(plotdata)
scatter.plot(("x", "y"), type="scatter", color="blue")
line = Plot(plotdata)
line.plot(("x", "y"), type="line", color="blue")
container = HPlotContainer(scatter, line)
container.spacing = 0
scatter.padding_right = 0
line.padding_left = 0
line.y_axis_orientation = "right"
return container
def _plot_default(self):
plot = Plot(self.plot_data)
self._update_plot_data()
plot.plot(("counts", "approx"), color="red")
plot.plot(("counts", "upper"), color="gray")
plot.plot(("counts", "lower"), color="gray")
plot.x_axis.title = "Counts (millions of points)"
plot.y_axis.title = "Approximation"
# Add dashed horizontal line at pi.
pi_line = CoordinateLineOverlay(
component=plot,
value_data=[np.pi],
color="green",
line_style="dash",
)
plot.underlays.append(pi_line)
# Allow extra room for the y-axis label.
plot.padding_left = 100
return plot
def __init__(self):
super(ContainerExample, self).__init__()
x = linspace(-14, 14, 100)
y = sin(x) * x ** 3
plotdata = ArrayPlotData(x=x, y=y)
scatter = Plot(plotdata)
scatter.plot(("x", "y"), type="scatter", color="blue")
line = Plot(plotdata)
line.plot(("x", "y"), type="line", color="blue")
container = HPlotContainer(scatter, line)
#Making the plots touch in the middle
container.spacing = 0
scatter.padding_right = 0
line.padding_left = 0
line.y_axis.orientation = "right"
self.plot = 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_plot(self):
# count data
if len(self.df) > 0:
self.indexes = np.arange(len(self.df.date_time))
time_ds = ArrayDataSource(self.indexes)
vol_ds = ArrayDataSource(self.df.volumn.values, sort_order="none")
xmapper = LinearMapper(range=DataRange1D(time_ds))
vol_mapper = LinearMapper(range=DataRange1D(vol_ds))
####################################################################
# create volumn plot
vol_plot = BarPlot(index=time_ds, value=vol_ds,
index_mapper=xmapper,
value_mapper=vol_mapper,
line_color="transparent",
fill_color="blue",
bar_width=0.6,
antialias=False,
height=100,
resizable="h",
origin="bottom left",
bgcolor="white",
border_visible=True
)
vol_plot.padding = 30
vol_plot.padding_left = 40
vol_plot.tools.append(
PanTool(vol_plot, constrain=True, constrain_direction="x"))
add_default_grids(vol_plot)
add_default_axes(vol_plot)
#print vol_plot.index_mapper.range.high
#print vol_plot.value_mapper.range.low, vol_plot.value_mapper.range.high
self.vol_plot = vol_plot
self.container.add(vol_plot)
####################################################################
## Create price plot
sorted_vals = np.vstack(
(self.df.open, self.df.high, self.df.low, self.df.close))
sorted_vals.sort(0)
__bool = self.df.close.values - self.df.open.values
self.up_boolean = __bool >= 0
self.down_boolean = np.invert(self.up_boolean)
pd = ArrayPlotData(
up_index=self.indexes[self.up_boolean],
up_min=sorted_vals[0][self.up_boolean],
up_bar_min=sorted_vals[1][self.up_boolean],
up_bar_max=sorted_vals[2][self.up_boolean],
up_max=sorted_vals[3][self.up_boolean],
down_index=self.indexes[self.down_boolean],
down_min=sorted_vals[0][self.down_boolean],
down_bar_min=sorted_vals[1][self.down_boolean],
down_bar_max=sorted_vals[2][self.down_boolean],
down_max=sorted_vals[3][self.down_boolean],
volumn=self.df.volumn.values,
index=self.indexes
)
price_plot = Plot(pd)
up_plot = price_plot.candle_plot(
("up_index", "up_min", "up_bar_min", "up_bar_max", "up_max"),
color=color_red,
bgcolor="azure",
bar_line_color="black",
stem_color="black",
end_cap=False)[0]
down_plot = price_plot.candle_plot(
("down_index", "down_min", "down_bar_min",
"down_bar_max", "down_max"),
color=color_green,
bar_line_color="black",
stem_color="black",
end_cap=False)[0]
price_plot.fill_padding = True
price_plot.padding = 30
price_plot.padding_left = 40
price_plot.tools.append(ZoomTool(component=price_plot, tool_mode="box", zoom_factor=1.2, always_on=False))
price_plot.tools.append(PanTool(price_plot, drag_button="left"))
price_plot.tools.append(
XYTool(price_plot, callback=self._update_ohlc)) # get data
self._add_line_tool(up_plot)
self._add_line_tool(down_plot)
price_plot.range2d = self._compute_range2d()
price_plot.index_mapper = vol_plot.index_mapper # maper vol_plot and price_plot
self.price_plot = price_plot
self.container.add(price_plot)
def _create_plot_component():
army_lat = np.column_stack([army['start_lat'],
army['end_lat']]).reshape(-1)
army_lon = np.column_stack([army['start_lon'],
army['end_lon']]).reshape(-1)
plot_data = ArrayPlotData(
army_lon=army_lon,
army_lat=army_lat,
army_size=army['size'],
army_color=army['direction'] * army["group"],
towns_lat=towns['lat'],
towns_lon=towns['lon'],
towns=towns['town'],
temp_lon=temperatures['lon'],
temp=temperatures['temp'],
temp_date=temperatures['date'],
)
map_plot = Plot(plot_data)
map_plot.x_grid = None
map_plot.y_grid = None
map_plot.x_axis.orientation = 'top'
map_plot.x_axis.title = 'Longitude'
map_plot.y_axis.title = 'Latitude'
map_plot.title = "Minard's Map of Napoleon's Russian Campaign"
map_plot._title.overlay_position = "inside top"
map_plot._title.hjustify = "left"
map_plot._title.vjustify = "bottom"
map_plot.plot(
("army_lon", "army_lat", "army_color", "army_size"),
type="cmap_segment",
name="my_plot",
color_mapper=viridis,
border_visible=True,
bgcolor="white",
size_min=1.0,
size_max=128.0,
)
map_plot.plot(
("towns_lon", "towns_lat"),
type="scatter",
)
map_plot.plot(
("towns_lon", "towns_lat", "towns"),
type="text",
text_margin=4,
h_position='right',
text_offset=(4, 0),
)
map_plot.plot_1d(
('temp_lon'),
type='line_scatter_1d',
alpha=0.5,
line_style='dot',
)
map_plot.index_range.high_setting = 38
map_plot.index_range.low_setting = 23
map_plot.value_range.high_setting = 56.0
map_plot.value_range.low_setting = 53.5
map_plot.tools.extend([
PanTool(map_plot),
ZoomTool(map_plot),
])
temp_plot = Plot(plot_data, height=100)
temp_plot.index_range = map_plot.index_range
temp_plot.x_grid = None
temp_plot.x_axis = None
temp_plot.y_axis.orientation = 'right'
temp_plot.y_axis.title = u'Temp (°Re)'
temp_plot.plot(
('temp_lon', 'temp'),
type='line',
)
temp_plot.plot_1d(
('temp_lon'),
type='line_scatter_1d',
alpha=0.5,
line_style='dot',
)
temp_plot.plot_1d(
('temp_lon', 'temp_date'),
type='textplot_1d',
alpha=0.5,
line_style='dot',
alignment='bottom',
)
temp_plot.value_range.high_setting = 5
temp_plot.value_range.low_setting = -35
container = VPlotContainer(temp_plot, map_plot)
container.spacing = 0
map_plot.padding_bottom = 0
map_plot.padding_left = 70
map_plot.padding_right = 70
map_plot.padding_top = 50
temp_plot.padding_top = 0
temp_plot.padding_bottom = 15
temp_plot.padding_right = 70
temp_plot.padding_left = 70
temp_plot.height = 100
temp_plot.resizable = 'h'
return container
def _create_plot(self):
# count data
if len(self.df) > 0:
self.indexes = np.arange(len(self.df.date_time))
time_ds = ArrayDataSource(self.indexes)
vol_ds = ArrayDataSource(self.df.volumn.values, sort_order="none")
xmapper = LinearMapper(range=DataRange1D(time_ds))
vol_mapper = LinearMapper(range=DataRange1D(vol_ds))
####################################################################
# create volumn plot
vol_plot = BarPlot(
index=time_ds,
value=vol_ds,
index_mapper=xmapper,
value_mapper=vol_mapper,
line_color="transparent",
fill_color="blue",
bar_width=0.6,
antialias=False,
height=100,
resizable="h",
origin="bottom left",
bgcolor="white",
border_visible=True,
)
vol_plot.padding = 30
vol_plot.padding_left = 40
vol_plot.tools.append(PanTool(vol_plot, constrain=True, constrain_direction="x"))
add_default_grids(vol_plot)
add_default_axes(vol_plot)
# print vol_plot.index_mapper.range.high
# print vol_plot.value_mapper.range.low, vol_plot.value_mapper.range.high
self.vol_plot = vol_plot
self.container.add(vol_plot)
####################################################################
## Create price plot
sorted_vals = np.vstack((self.df.open, self.df.high, self.df.low, self.df.close))
sorted_vals.sort(0)
__bool = self.df.close.values - self.df.open.values
self.up_boolean = __bool >= 0
self.down_boolean = np.invert(self.up_boolean)
pd = ArrayPlotData(
up_index=self.indexes[self.up_boolean],
up_min=sorted_vals[0][self.up_boolean],
up_bar_min=sorted_vals[1][self.up_boolean],
up_bar_max=sorted_vals[2][self.up_boolean],
up_max=sorted_vals[3][self.up_boolean],
down_index=self.indexes[self.down_boolean],
down_min=sorted_vals[0][self.down_boolean],
down_bar_min=sorted_vals[1][self.down_boolean],
down_bar_max=sorted_vals[2][self.down_boolean],
down_max=sorted_vals[3][self.down_boolean],
volumn=self.df.volumn.values,
index=self.indexes,
)
price_plot = Plot(pd)
up_plot = price_plot.candle_plot(
("up_index", "up_min", "up_bar_min", "up_bar_max", "up_max"),
color=color_red,
bgcolor="azure",
bar_line_color="black",
stem_color="black",
end_cap=False,
)[0]
down_plot = price_plot.candle_plot(
("down_index", "down_min", "down_bar_min", "down_bar_max", "down_max"),
color=color_green,
bar_line_color="black",
stem_color="black",
end_cap=False,
)[0]
price_plot.fill_padding = True
price_plot.padding = 30
price_plot.padding_left = 40
price_plot.tools.append(ZoomTool(component=price_plot, tool_mode="box", zoom_factor=1.2, always_on=False))
price_plot.tools.append(PanTool(price_plot, drag_button="left"))
price_plot.tools.append(XYTool(price_plot, callback=self._update_ohlc)) # get data
self._add_line_tool(up_plot)
self._add_line_tool(down_plot)
price_plot.range2d = self._compute_range2d()
price_plot.index_mapper = vol_plot.index_mapper # maper vol_plot and price_plot
self.price_plot = price_plot
self.container.add(price_plot)
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
