def gen_line_plot(series_one, series_two, y_axis_name=''):
"""
Parameters
----------
series_one : nd array
series_two : nd array
"""
size = min(series_one.shape[0],
series_two.shape[0])
idx = ArrayDataSource(arange(size))
series_one_data = ArrayDataSource(series_one[:size])
series_two_data = ArrayDataSource(series_two[:size])
y_range = DataRange1D(series_one_data)
y_range.tight_bounds = False
y_range.margin = 50
x_mapper = LinearMapper(range=DataRange1D(idx))
y_mapper = LinearMapper(range=y_range)
series_one_plot = LinePlot(index=idx,
value=series_one_data, index_mapper=x_mapper,
value_mapper=y_mapper, color='blue')
series_two_plot = LinePlot(index=idx,
value=series_two_data, index_mapper=x_mapper,
value_mapper=y_mapper, color='red')
container = OverlayPlotContainer(bgcolor='white',
padding=25, fill_padding=False, border_visible=True)
y_axis = PlotAxis(mapper=y_mapper, component=container,
orientation='left')
x_axis = PlotAxis(mapper=x_mapper, component=container,
orientation='bottom')
x_axis.title = 'Time'
y_axis.title = y_axis_name
legend = Legend(component=container, padding=10, align='ur')
legend.plots = {
'Predicted': series_one_plot,
'Actual': series_two_plot,
}
container.add(series_one_plot)
container.add(series_two_plot)
container.overlays.append(y_axis)
container.overlays.append(legend)
return container
def _plots_default(self):
plotdata = self.get_data_sets()
plots = Plot(plotdata)
plotsDict = {}
# plot background sonar image and impound lines
xbounds = self.model.survey_rng_m
ybounds = self.model.depth_m
plots.img_plot("sonarimg", colormap=jet,
xbounds=xbounds, ybounds=ybounds)
ip = plots.plot(('impound1_X','impound1_Y'), type='line', marker='square')
plotsDict['Impoundment line'] = ip
plots.x_axis.title = 'Distance along survey line (m)'
plots.y_axis.title = 'Depth (m)'
# add core samples as scatter with separate y-axis
corex = plotdata.get_data('coreX')
corey = plotdata.get_data('coreY')
scatter = create_scatter_plot((corex,corey), marker='diamond',
color='red' )
scatter.index_range = plots.index_range
axis = PlotAxis(scatter, orientation='right')
axis.title = 'Core sample dist from survey line (m)'
scatter.underlays.append(axis)
plots.add(scatter)
# create vertical line for indicating selected core sample position
vals1 = [0 for x in corey]
vline = create_line_plot((corey,vals1), color='blue', orientation='v')
vline.value_range = scatter.index_range
plots.add(vline)
# Add Legend
legend = Legend(component=plots, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="left"))
legend.plots = plotsDict
plots.overlays.append(legend)
# Add tools
scatter.tools.append(PickTool(scatter))
plots.tools.append(TraceTool(plots))
plots.tools.append(PanTool(plots))
plots.tools.append(ZoomTool(plots))
return plots
def _create_plot_component():
container = OverlayPlotContainer(padding=60,
fill_padding=True,
use_backbuffer=True,
border_visible=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = arange(low, high + 0.001, (high - low) / numpoints)
# Plot some bessel functions
plots = {}
broadcaster = BroadcasterTool()
for i in range(4):
y = jn(i, x)
plot = create_line_plot((x, y),
color=tuple(COLOR_PALETTE[i]),
width=2.0)
if i == 0:
add_default_grids(plot)
left_axis, _ = add_default_axes(plot)
left_axis.title = "Bessel j0, j2, j3"
elif i != 1:
# Map correctly j2 and j3 on the first plot's axis:
plot0 = plots["Bessel j_0"]
plot.index_mapper = plot0.index_mapper
plot.value_mapper = plot0.value_mapper
plot0.value_mapper.range.add(plot.value)
# Create a pan/zoom tool and give it a reference to the plot it should
# manipulate, but don't attach it to the plot. Instead, attach it to
# the broadcaster. Do it only for each independent set of axis_mappers:
if i in [0, 1]:
pan = PanTool(component=plot)
broadcaster.tools.append(pan)
zoom = ZoomTool(component=plot)
broadcaster.tools.append(zoom)
container.add(plot)
plots["Bessel j_%d" % i] = plot
# Add an axis on the right-hand side that corresponds to the second plot.
# Note that it uses plot.value_mapper instead of plot0.value_mapper.
plot1 = plots["Bessel j_1"]
axis = PlotAxis(plot1, orientation="right")
plot1.underlays.append(axis)
axis.title = "Bessel j1"
# Add the broadcast tool to one of the renderers: adding it to the
# container instead breaks the box mode of the ZoomTool:
plot0 = plots["Bessel j_0"]
plot0.tools.append(broadcaster)
# Create a legend, with tools to move it around and highlight renderers:
legend = Legend(component=container, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
legend.tools.append(LegendHighlighter(legend))
container.overlays.append(legend)
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(
PlotLabel("Bessel functions",
component=container,
font="swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
