def create_plot(value_ds, index_ds, horizontal_val_ds, horizontal_index_ds, int_val_ds, int_index_ds):
xmapper = LinearMapper(range=DataRange1D(index_ds))
value_mapper = LinearMapper(range=DataRange1D(value_ds))
xmapper_val = LinearMapper(range=DataRange1D(horizontal_index_ds))
horizontal_val_mapper = LinearMapper(range=DataRange1D(horizontal_val_ds))
int_xmapper = LinearMapper(range=DataRange1D(int_index_ds))
int_val_mapper = LinearMapper(range=DataRange1D(int_val_ds))
value_plot = FilledLinePlot(index = index_ds, value = value_ds,
index_mapper = xmapper,
value_mapper = value_mapper,
line_color = "black",
render_style='connectedhold',
fill_color = (0,0,1,0.3),
antialias=False)
add_default_grids(value_plot)
value_plot.overlays.append(PlotAxis(value_plot, orientation='left'))
value_plot.overlays.append(PlotAxis(value_plot, orientation='bottom', title='steps backwards'))
value_plot.overlays.append(PlotLabel('position of max power: vertical axes', component=value_plot, font = 'swiss 16', overlay_position='top'))
value_plot.padding = 50
horizontal_pos_plot = FilledLinePlot(index = horizontal_index_ds, value = horizontal_val_ds,
index_mapper = xmapper_val,
value_mapper = horizontal_val_mapper,
line_color = "black",
render_style='connectedhold',
fill_color = (0,1,0,0.3),
antialias=False)
horizontal_pos_plot.padding = 50
add_default_grids(horizontal_pos_plot)
horizontal_pos_plot.overlays.append(PlotAxis(horizontal_pos_plot, orientation='left'))
horizontal_pos_plot.overlays.append(PlotAxis(horizontal_pos_plot, orientation='bottom', title='steps backwards'))
horizontal_pos_plot.overlays.append(PlotLabel('position of max power: horizontal axes', component=horizontal_pos_plot, font = 'swiss 16', overlay_position='top'))
intensity_plot = FilledLinePlot(index = int_index_ds, value = int_val_ds,
index_mapper = int_xmapper,
value_mapper = int_val_mapper,
line_color = "black",
fill_color = (0,0,0,0.4),
antialias=False)
intensity_plot.padding = 50
add_default_grids(intensity_plot)
intensity_plot.overlays.append(PlotAxis(intensity_plot, orientation='left', title= 'Power [uW]'))
intensity_plot.overlays.append(PlotAxis(intensity_plot, orientation='bottom', title='steps backwards'))
intensity_plot.y_axis.tick_label_formatter = lambda x: ('%.1e'%(x*1e6))
intensity_plot.overlays.append(PlotLabel('Transmission power', component=intensity_plot, font = 'swiss 16', overlay_position='top'))
container = VPlotContainer(use_backbuffer = True)
container.add(horizontal_pos_plot)
container.add(value_plot)
#container.add(intensity_plot)
container_h = HPlotContainer(use_backbuffer = True)
container_h.add(container)
container_h.add(intensity_plot)
return container_h
def _plot_container_default(self):
container = OverlayPlotContainer( padding = 60, fill_padding = False,
bgcolor = "white", use_backbuffer=True)
# Plot some distribution functions
plots = {}
broadcaster = BroadcasterTool()
#""" Plot
# view = DataView(border_visible = True)
#
index = ArrayDataSource(self.x_array)
value = ArrayDataSource(self.pdf_array, sort_order="none")
index_range = DataRange1D()
index_range.add(index)
index_mapper = LinearMapper(range=index_range)
value_range = DataRange1D()
value_range.add(value)
value_mapper = LinearMapper(range=value_range)
pdf_plot = FilledLinePlot(index = index, value = value,
index_mapper = index_mapper,
value_mapper = value_mapper,
edge_color = tuple(COLOR_PALETTE[0]),
face_color = "paleturquoise",
bgcolor = "white",
border_visible = True)
add_default_grids(pdf_plot)
add_default_axes(pdf_plot)
#***************************Label*************************************
pdf_label = DataLabel(component=pdf_plot, data_point=(2.4,0.15),
label_position=(15,15), padding=5,
label_format = 'PDF',
bgcolor = "transparent",
marker_color = "transparent",
marker_line_color = "transparent",
border_visible=False)
pdf_plot.overlays.append(pdf_label)
# tool = DataLabelTool(pdf_label, drag_button="right", auto_arrow_root=True)
# pdf_label.tools.append(tool)
container.add(pdf_plot)
pan = PanTool(pdf_plot)
zoom = SimpleZoom(pdf_plot, tool_mode="box", always_on=False)
broadcaster.tools.append(pan)
broadcaster.tools.append(zoom)
#*********************************CDF****************************
plot = create_line_plot((self.x_array,self.pdf_array),
color=tuple(COLOR_PALETTE[0]), width=2.0)
plot.bgcolor = "white"
plot.border_visible = True
add_default_grids(plot)
add_default_axes(plot)
container.add(plot)
# # Create a pan 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.
pan = PanTool(plot)
zoom = SimpleZoom(plot, tool_mode="box", always_on=False)
broadcaster.tools.append(pan)
broadcaster.tools.append(zoom)
#""" PDF 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.
pdf_plot = create_line_plot((self.x_array,self.cdf_array),
color=tuple(COLOR_PALETTE[1]), width=2.0)
pdf_plot.bgcolor = "white"
pdf_plot.border_visible = True
# Label
cdf_text = TextBoxOverlay(text = 'CDF', alternate_position = (200,390) )
pdf_plot.overlays.append(cdf_text)
tool = DataLabelTool(cdf_text, drag_button="right", auto_arrow_root=True)
cdf_text.tools.append(tool)
container.add(pdf_plot)
# vertical_axis = LabelAxis(pdf_plot, orientation='top',
# title='Categories')
# pdf_plot.underlays.append(vertical_axis)
pdf_pan = PanTool(pdf_plot)
pdf_zoom = SimpleZoom(pdf_plot, tool_mode="box", always_on=False)
broadcaster.tools.append(pdf_pan)
broadcaster.tools.append(pdf_zoom)
axis = PlotAxis(pdf_plot, orientation="right")
pdf_plot.underlays.append(axis)
#"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# Add the broadcast tool to the container, instead of to an
# individual plot
container.tools.append(broadcaster)
container.underlays.append(PlotLabel("CDF",
component=container,
font = "swiss 16",
overlay_position="right"))
legend = Legend(component=container, padding=10, align="ul")
legend.tools.append(LegendTool(legend, drag_button="right"))
container.overlays.append(legend)
# Set the list of plots on the legend
plots["pdf"] = plot
plots["cdf"] = pdf_plot
legend.plots = plots
#*******************************************************************************
x = ArrayDataSource( array( [ 0.0, 0.0 ], dtype = float ) )
y = ArrayDataSource( array( [ 0.0, self.mean ], dtype = float ) )
mean_plot = create_line_plot((x,y),
color=tuple(COLOR_PALETTE[2]), width=2.0)
# vertical_plot = LinePlot(index = ArrayDataSource( array( [ 0.0, 1.0 ], dtype = float ) ),
# value = ArrayDataSource( array( [ 0.0, 1.0 ], dtype = float ) ),
# color = "green"),
# index_mapper = LinearMapper(range=index_mapper),
# value_mapper = LinearMapper(range=value_mapper))
container.add(mean_plot)
# Create a pan 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.
mean_pan = PanTool(mean_plot)
mean_zoom = SimpleZoom(mean_plot, tool_mode="box", always_on=False)
broadcaster.tools.append(mean_pan)
broadcaster.tools.append(mean_zoom)
#**************************************************************************
x = ArrayDataSource( array( [ 0.0, 0.0 ], dtype = float ) )
y = ArrayDataSource( array( [ 0.0, 2.0 ], dtype = float ) )
print "self.standard_deviation", self.standard_deviation
st_plot = create_line_plot((x,y),
color=tuple(COLOR_PALETTE[4]), width=2.0)
container.add(st_plot)
# Create a pan 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.
st_pan = PanTool(st_plot)
st_zoom = SimpleZoom(st_plot, tool_mode="box", always_on=False)
broadcaster.tools.append(st_pan)
broadcaster.tools.append(st_zoom)
#*****************************************************************************
# Add the title at the top
container.overlays.append(PlotLabel("Distribution plots",
component=container,
font = "swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
