def add_legend(self, padding=10, align="ur"):
legend = Legend(component=self.plot_container, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
multibarplot = self.multibarplot
legend.plots = dict(zip(multibarplot.legend_labels,
multibarplot.barplots))
self.plot_container.overlays.append(legend)
def _create_plot_component():
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=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)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
add_default_grids(plot)
add_default_axes(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)
broadcaster.tools.append(pan)
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)
# Add the broadcast tool to the container, instead of to an
# individual plot
container.tools.append(broadcaster)
legend = Legend(component=container, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
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
def _plot_targets_fired(self):
print 'bla'
numpoints = 100
low = -5
high = 15.0
x = np.arange(low, high+0.001, (high-low)/numpoints)
# Plot some bessel functions
value_mapper = None
index_mapper = None
plots = {}
for i in range(10):
y = jn(i, x)
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
#plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
if i != 0:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
else:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
add_default_grids(plot)
add_default_axes(plot)
plot.index_range.tight_bounds = False
plot.index_range.refresh()
plot.value_range.tight_bounds = False
plot.value_range.refresh()
plot.tools.append(PanTool(plot))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# The DragZoom tool just zooms in and out as the user drags
# the mouse vertically.
dragzoom = DragZoom(plot, drag_button="right")
plot.tools.append(dragzoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
self.LinePlotContainer.add(plot)
plots["Bessel j_%d"%i] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
self.LinePlotContainer.overlays.append(PlotLabel("Bessel functions",
component=self.LinePlotContainer,
font = "swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
self.LinePlotContainer.tools.append(TraitsTool(self.LinePlotContainer))
self.show_lines=True
print 'hallo'
def __init__(self):
super(LinePlot, self).__init__()
x = linspace(-14, 14, 100)
y1 = sin(x) * x**3
y2 = cos(x) * x**3
plotdata = ArrayPlotData(x=x, y1=y1, y2=y2)
plot = Plot(plotdata)
plot.plot(("x", "y1"), type="line", color="blue", name="sin(x) * x**3")
plot.plot(("x", "y2"), type="line", color="red", name="cos(x) * x**3")
plot.plot(("x", "y2"), type="scatter", color="red", marker = "circle",
marker_size = 2, name="cos(x) * x**3 points")
plot.title = "Multiple Curves"
self.plot = plot
legend = Legend(padding=10, align="ur")
legend.plots = plot.plots
plot.overlays.append(legend)
def __init__(self):
super(LinePlot, self).__init__()
x = linspace(-14, 14, 100)
y1 = sin(x) * x**3
y2 = cos(x) * x**3
plotdata = ArrayPlotData(x=x, y1=y1, y2=y2)
plot = Plot(plotdata)
plot.plot(("x", "y1"), type="line", color="blue", name="sin(x) * x**3")
plot.plot(("x", "y2"), type="line", color="red", name="cos(x) * x**3")
plot.plot(("x", "y2"),
type="scatter",
color="red",
marker="circle",
marker_size=2,
name="cos(x) * x**3 points")
plot.title = "Multiple Curves"
self.plot = plot
legend = Legend(padding=10, align="ur")
legend.plots = plot.plots
plot.overlays.append(legend)
def __init__(self, parent, id=-1, **kwargs):
wx.Panel.__init__(self, parent, id=id, **kwargs)
self.statusBar = self.GetTopLevelParent().statusBar
self.container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
self.legend = Legend(component=self.container, padding=10, align="ur")
#self.legend.tools.append(LegendTool(self.legend, drag_button="right"))
self.container.overlays.append(self.legend)
self.plot_window = Window(self, component=self.container)
self.container.tools.append(TraitsTool(self.container))
self.firstplot = True
self._palette = ['red', 'blue', 'green', 'purple', 'yellow']
self._current_palette_index = 0
self._traces = []
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(self.plot_window.control, 1, wx.EXPAND)
self.SetSizer(sizer)
self.SetAutoLayout(True)
def _refresh_container( self ):
''' rebuild the container for the current data
'''
broadcaster = BroadcasterTool()
mfn_line = self.value
ydata = transpose(mfn_line.ydata)
adapter = self.adapter
if adapter.var_x != '':
# Get the x-label text from the object's trait var_x
label_x = getattr( self.object, adapter.var_x )
else:
# Get the x-label from the adapter
label_x = adapter.label_x
if adapter.var_y != '':
label_y = getattr( self.object, adapter.var_y )
else:
label_y = adapter.label_y
index = ArrayDataSource(mfn_line.xdata)
index_range = DataRange1D()
index_range.add(index)
index_mapper = LinearMapper(range=index_range)
value_range = DataRange1D( low_setting = 0.0 )
colors = []
colors = adapter.line_color #self.line_color_chaco.values()
styles = []
styles = adapter.line_style #self.line_style_chaco.values()
s_item = styles.items()
color_chaco = []
style_chaco = []
c_index = 0 # loop for colors
s_index = 0 # loop for styles
i=0 # for colors and styles
plots = {} # for legend
pd = ArrayPlotData(index = mfn_line.xdata)
self.splot = Plot(pd)
for vector in ydata[:]:
if len(colors) == c_index:
c_index = 0
if len(styles) == s_index:
s_index = 0
style_name = s_item[s_index][0]
color_chaco.append(colors[c_index])
style_chaco.append(style_name)
c_index = c_index + 1
s_index = s_index + 1
y = ArrayDataSource(vector, sort_order="none")
value_range.add(y)
value_mapper = LinearMapper(range=value_range)
self.line_plot = LinePlot(index = index, value = y,
index_mapper = index_mapper,
value_mapper = value_mapper,
color = color_chaco[i],
line_width = adapter.linewidth,
edge_color = 'blue',
border_visible = False,
line_style = style_chaco[i])
add_default_grids(self.line_plot)
add_default_axes(self.line_plot, vtitle= label_y, htitle= label_x)
self.plot_container.add(self.line_plot)
# pan = PanTool(line_plot)
# zoom = SimpleZoom(line_plot, tool_mode="box", always_on=False)
# broadcaster.tools.append(pan)
# broadcaster.tools.append(zoom)
# Add the traits inspector tool to the container
#
# self.plot_container.tools.append(TraitsTool( self.plot_container ))
self.line_plot.tools.append(PanTool(self.line_plot))
self.line_plot.overlays.append(ZoomTool(self.line_plot))
# Legend
lgnd = adapter.legend_labels[i]
plots[lgnd] = self.line_plot
# change the color of the curves
i = i +1
legend = Legend(component=self.plot_container, padding=10, align="ul")
legend.tools.append(LegendTool(legend, drag_button="right"))
self.plot_container.overlays.append(legend)
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
self.plot_container.overlays.append(PlotLabel(adapter.title,
component=self.plot_container,
font = "swiss 16",
overlay_position="top"))
def _create_window(self):
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
self.container = container
# 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
value_mapper = None
index_mapper = None
plots = {}
for i in range(10):
y = jn(i, x)
if i%2 == 1:
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
else:
plot = create_scatter_plot((x,y), color=tuple(COLOR_PALETTE[i]))
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
add_default_grids(plot)
add_default_axes(plot)
plot.index_range.tight_bounds = False
plot.index_range.refresh()
plot.value_range.tight_bounds = False
plot.value_range.refresh()
else:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
if i==0:
plot.tools.append(PanTool(plot))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# The DragZoom tool just zooms in and out as the user drags
# the mouse vertically.
dragzoom = DragZoom(plot, drag_button="right")
plot.tools.append(dragzoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
container.add(plot)
plots["Bessel j_%d"%i] = plot
# 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"))
container.overlays.append(PlotLabel("height",component=container,overlay_position="bottom"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return Window(self, -1, component=container)
def _create_plot_component():
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = linspace(low, high, numpoints)
now = time()
timex = linspace(now, now+7*24*3600, numpoints)
# Plot some bessel functions
value_mapper = None
index_mapper = None
plots = {}
for i in range(10):
y = jn(i, x)
if i%2 == 1:
plot = create_line_plot((timex,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
else:
plot = create_scatter_plot((timex,y), color=tuple(COLOR_PALETTE[i]))
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
left, bottom = add_default_axes(plot)
left.tick_generator = ScalesTickGenerator()
bottom.tick_generator = ScalesTickGenerator(scale=CalendarScaleSystem())
add_default_grids(plot, tick_gen=bottom.tick_generator)
else:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
if i==0:
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
container.add(plot)
plots["Bessel j_%d"%i] = plot
# 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
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
def _refresh_container(self,container):
""" Plot some distribution functions """
plots = {}
broadcaster = BroadcasterTool()
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( low_setting = 0.0 )
value_range.add(value)
value_mapper = LinearMapper(range=value_range)
"""Plot probability distribution function(pdf) with marking the area under the function """
plot_pdf = FilledLinePlot(index = index, value = value,
index_mapper = index_mapper,
value_mapper = value_mapper,
edge_color = tuple(COLOR_PALETTE[0]),
face_color = "paleturquoise",
border_visible = True)
"""define the grid, axes and title of the vertical grid """
add_default_grids(plot_pdf)
add_default_axes(plot_pdf, vtitle="PDF")
"""create a label for the pdf and append it to the plot_pdf """
label_pdf = DataLabel(component=plot_pdf, data_point=(2.4,0.13),
label_position=(15,15), padding=5,
label_format = 'PDF',
bgcolor = "transparent",
marker_color = "transparent",
marker_line_color = "transparent",
arrow_color = tuple(COLOR_PALETTE[0]),
border_visible=False)
plot_pdf.overlays.append(label_pdf)
container.add(plot_pdf)
"""create a label for the x coordinate """
container.overlays.append(PlotLabel("X",
component=container,
font = "swiss 16",
overlay_position="bottom"))
pan = PanTool(plot_pdf)
# zoom = SimpleZoom(plot_pdf, tool_mode="box", always_on=False)
broadcaster.tools.append(pan)
# broadcaster.tools.append(zoom)
#"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
#""" Mean Plot
x = ArrayDataSource( array( [ self.mean, self.mean ], dtype = float ) )
y = ArrayDataSource( array( [ 0.0, max( self.pdf_array ) ], dtype = float ) )
""" Plot the mean value"""
plot_mean = LinePlot(index = x, value = y,
color = "pink",
index_mapper = index_mapper,
value_mapper = value_mapper )
container.add(plot_mean)
# 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(plot_mean)
# mean_zoom = SimpleZoom(plot_mean, tool_mode="box", always_on=False)
broadcaster.tools.append(mean_pan)
# broadcaster.tools.append(mean_zoom)
#"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# 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.
""" Plot cdf and its label """
plot_cdf = create_line_plot((self.x_array,self.cdf_array),
color=tuple(COLOR_PALETTE[1]), width=2.0)
plot_cdf.bgcolor = "white"
plot_cdf.border_visible = True
label_cdf = DataLabel(component=plot_cdf, data_point=(2.4,0.9),
label_position=(-35,20), padding=5,
label_format = 'CDF',
bgcolor = "transparent",
marker_color = "transparent",
marker_line_color = "transparent",
arrow_color = tuple(COLOR_PALETTE[1]),
border_visible=False)
plot_cdf.overlays.append(label_cdf)
container.add(plot_cdf)
pan1 = PanTool(plot_cdf)
# zoom1 = SimpleZoom(plot_cdf, tool_mode="box", always_on=False)
broadcaster.tools.append(pan1)
# broadcaster.tools.append(zoom1)
axis = PlotAxis(plot_cdf, title="CDF", orientation="right")
plot_cdf.underlays.append(axis)
#"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# Add the broadcast tool to the container, instead of to an
# individual plot
container.tools.append(broadcaster)
##**************************************************************************
"""Plot standard deviation """
stdev_low = self.mean - self.standard_deviation
stdev_high = self.mean + self.standard_deviation
stdev_low_height = self.distr.pdf( stdev_low )
stdev_high_height = self.distr.pdf( stdev_high )
x = ArrayDataSource( array( [ stdev_low, stdev_low, stdev_high, stdev_high ], dtype = float ) )
y = ArrayDataSource( array( [ 0, stdev_low_height, stdev_high_height, 0. ], dtype = float ) )
plot_st = PolygonPlot(index = x, value = y,
edge_color = "purple",
index_mapper = index_mapper,
value_mapper = value_mapper )
container.add(plot_st)
##*****************************************************************************
"""create the legend for the mean """
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["Mean"] = plot_mean
legend.plots = plots
# Add the title at the top
container.overlays.append(PlotLabel("probability distribution plots",
component=container,
font = "swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
