def draw( self ):
"""Draw data."""
if len(self.fitResults) == 0:
return
#if not hasattr( self, 'subplot1' ):
# self.subplot1 = self.figure.add_subplot( 211 )
# self.subplot2 = self.figure.add_subplot( 212 )
a, ed = numpy.histogram(self.fitResults['tIndex'], self.Size[0]/2)
#self.subplot1.cla()
#self.subplot1.plot(ed[:-1], a, color='b' )
#self.subplot1.set_xticks([0, ed.max()])
#self.subplot1.set_yticks([0, a.max()])
#self.subplot2.cla()
#self.subplot2.plot(ed[:-1], numpy.cumsum(a), color='g' )
#self.subplot2.set_xticks([0, ed.max()])
#self.subplot2.set_yticks([0, a.sum()])
plot1 = create_line_plot(ed[:,-1], a, color = 'blue', bgcolor="white",
add_grid=True, add_axis=True)
plot2 = create_line_plot(ed[:,-1], numpy.cumsum(a), color = 'green', bgcolor="white",
add_grid=True, add_axis=True)
container = HPlotContainer(spacing=20, padding=50, bgcolor="lightgray")
container.add(plot1)
container.add(plot2)
return 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 = 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 _create_plot_component():
numpoints = 100
low = -5
high = 15.0
x = arange(low, high, (high - low) / numpoints)
container = HPlotContainer(resizable="hv", bgcolor="lightgray", fill_padding=True, padding=10)
# container = VPlotContainer(resizable = "hv", bgcolor="lightgray",
# fill_padding=True, padding = 10)
# Plot some bessel functions
value_range = None
for i in range(10):
y = jn(i, x)
plot = create_line_plot((x, y), color=tuple(COLOR_PALETTE[i]), width=2.0, orientation="v")
# orientation="h")
plot.origin_axis_visible = True
plot.origin = "top left"
plot.padding_left = 10
plot.padding_right = 10
plot.border_visible = True
plot.bgcolor = "white"
if value_range is None:
value_range = plot.value_mapper.range
else:
plot.value_range = value_range
value_range.add(plot.value)
if i % 2 == 1:
plot.line_style = "dash"
container.add(plot)
container.padding_top = 50
container.overlays.append(PlotLabel("More Bessels", component=container, font="swiss 16", overlay_position="top"))
return container
def init(self, parent):
factory = self.factory
container = OverlayPlotContainer(bgcolor='transparent',
padding=0, spacing=0)
window = Window(parent, component=container)
interval = self.high - self.low
data = ([self.low, self.high], [0.5]*2)
plot = create_line_plot(data, color='black', bgcolor="sys_window")
plot.x_mapper.range.low = self.low - interval*0.1
plot.x_mapper.range.high = self.high + interval*0.1
plot.y_mapper.range.high = 1.0
plot.y_mapper.range.low = 0.0
range_selection = RangeSelection(plot, left_button_selects=True)
# Do not allow the user to reset the range
range_selection.event_state = "selected"
range_selection.deselect = lambda x: None
range_selection.on_trait_change(self.update_interval, 'selection')
plot.tools.append(range_selection)
plot.overlays.append(RangeKnobsOverlay(plot))
self.plot = plot
container.add(self.plot)
# To set the low and high, we're actually going to set the
# 'selection' metadata on the line plot to the tuple (low,high).
plot.index.metadata["selections"] = (0, 1.0)
# Tell the editor what to display
self.control = window.control
self.control.SetSize((factory.width, factory.height))
def _create_window(self):
self._create_data()
x = self.x_values[:self.current_index]
y = self.y_values[:self.current_index]
value_range = None
index_range = None
plot = create_line_plot((x,y), color="red", width=2.0)
value_range = plot.value_mapper.range
index_range = plot.index_mapper.range
index_range.low = -5
index_range.high = 15
plot.padding = 50
plot.fill_padding = True
plot.bgcolor = "white"
left, bottom = add_default_axes(plot)
hgrid, vgrid = add_default_grids(plot)
bottom.tick_interval = 2.0
vgrid.grid_interval = 2.0
self.plot = plot
plot.tools.append(PanTool(component=plot))
plot.overlays.append(ZoomTool(component=plot, tool_mode="box",
always_on=False))
# Set the timer to generate events to us
timerId = wx.NewId()
self.timer = wx.Timer(self, timerId)
self.Bind(wx.EVT_TIMER, self.onTimer, id=timerId)
self.timer.Start(50.0, wx.TIMER_CONTINUOUS)
return Window(self, -1, component=plot)
def _create_draggable_plot_component(title, initial_values=None, on_change_functor=None):
container = OverlayPlotContainer(padding = 30, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
if initial_values:
x = initial_values[0]
y = initial_values[1]
else:
# Create the initial X-series of data
numpoints = 30
low = -5
high = 15.0
x = linspace(low, high, numpoints)
y = jn(0, x)
lineplot = create_line_plot((x, y), color=tuple(COLOR_PALETTE[0]),
width=2.0)
lineplot.selected_color = 'none'
scatter = ScatterPlot(
index=lineplot.index,
value=lineplot.value,
index_mapper=lineplot.index_mapper,
value_mapper=lineplot.value_mapper,
color=tuple(COLOR_PALETTE[0]),
marker_size=2,
)
scatter.index.sort_order = 'ascending'
scatter.bgcolor = 'white'
scatter.border_visible = True
add_default_grids(scatter)
add_default_axes(scatter)
scatter.tools.append(PanTool(scatter, drag_button='right'))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(scatter, tool_mode='box', always_on=False,
drag_button=None)
scatter.overlays.append(zoom)
point_dragging_tool = PointDraggingTool(scatter)
point_dragging_tool.on_change_functor = on_change_functor
scatter.tools.append(point_dragging_tool)
container.add(lineplot)
container.add(scatter)
# Add the title at the top
container.overlays.append(PlotLabel(title, component=container,
font='swiss 16', overlay_position='top'))
container.mx = lineplot.index.get_data()
container.my = lineplot.value.get_data()
container.lineplot = lineplot
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 _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)
y = jn(0, x)
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[0]), width=2.0)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
add_default_grids(plot)
add_default_axes(plot)
# Add some tools
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add a dynamic label. This can be dragged and moved around using the
# right mouse button. Note the use of padding to offset the label
# from its data point.
label = DataLabel(component=plot, data_point=(x[40], y[40]),
label_position="top left", padding=40,
bgcolor = "lightgray",
border_visible=False)
plot.overlays.append(label)
tool = DataLabelTool(label, drag_button="right", auto_arrow_root=True)
label.tools.append(tool)
# Add some static labels.
label2 = DataLabel(component=plot, data_point=(x[20], y[20]),
label_position="bottom right",
border_visible=False,
bgcolor="transparent",
marker_color="blue",
marker_line_color="transparent",
marker = "diamond",
arrow_visible=False)
plot.overlays.append(label2)
label3 = DataLabel(component=plot, data_point=(x[80], y[80]),
label_position="top", padding_bottom=20,
marker_color="transparent",
marker_size=8,
marker="circle",
arrow_visible=False)
plot.overlays.append(label3)
container.add(plot)
return container
def _create_plot_component():
# Create some x-y data series to plot
plot_area = OverlayPlotContainer(border_visible=True)
container = HPlotContainer(padding=50, bgcolor="transparent")
#container.spacing = 15
x = linspace(-2.0, 10.0, 100)
for i in range(5):
color = tuple(COLOR_PALETTE[i])
y = jn(i, x)
renderer = create_line_plot((x, y), color=color)
plot_area.add(renderer)
#plot_area.padding_left = 20
axis = PlotAxis(orientation="left", resizable="v",
mapper = renderer.y_mapper,
axis_line_color=color,
tick_color=color,
tick_label_color=color,
title_color=color,
bgcolor="transparent",
title = "jn_%d" % i,
border_visible = True,)
axis.bounds = [60,0]
axis.padding_left = 10
axis.padding_right = 10
container.add(axis)
if i == 4:
# Use the last plot's X mapper to create an X axis and a
# vertical grid
x_axis = PlotAxis(orientation="bottom", component=renderer,
mapper=renderer.x_mapper)
renderer.overlays.append(x_axis)
grid = PlotGrid(mapper=renderer.x_mapper, orientation="vertical",
line_color="lightgray", line_style="dot")
renderer.underlays.append(grid)
# Add the plot_area to the horizontal container
container.add(plot_area)
# Attach some tools to the plot
broadcaster = BroadcasterTool()
for plot in plot_area.components:
broadcaster.tools.append(PanTool(plot))
# Attach the broadcaster to one of the plots. The choice of which
# plot doesn't really matter, as long as one of them has a reference
# to the tool and will hand events to it.
plot.tools.append(broadcaster)
return container
def _create_complexspectrumplot(self, x, y1, y2):
amplitudeplot = create_line_plot((x, y1), index_bounds=None, value_bounds=None,
orientation='h', color='green', width=1.0, dash='solid',
value_mapper_class=LinearMapper,
bgcolor='white', border_visible=True,
add_grid=False, add_axis=False, index_sort='ascending')
add_default_axes(amplitudeplot, orientation='normal', htitle='Frequency [MHz]', vtitle='Amplitude')
amplitudeplot.tools.append(PanTool(amplitudeplot, drag_button="right"))
zoom = SimpleZoom(component=amplitudeplot, tool_mode="box", drag_button="left", always_on=True)
amplitudeplot.overlays.append(zoom)
phaseplot = create_line_plot((x, y2), index_bounds=None,
value_bounds=None,
orientation='h', color='red',
width=1.0, dash='solid',
value_mapper_class=LinearMapper,
bgcolor='white', border_visible=True,
add_grid=False, add_axis=False,
index_sort='ascending')
add_default_axes(phaseplot, orientation='normal',
htitle='Frequency [MHz]', vtitle='Unwrapped phase')
# phaseplot.tools.append(PanTool(phaseplot, drag_button="right"))
zoom = SimpleZoom(component=phaseplot, tool_mode="box",
drag_button="left", always_on=True,
# enter_zoom_key=KeySpec('z')
)
phaseplot.overlays.append(zoom)
self.rangeselect = RangeSelection(phaseplot, left_button_selects=False)
self.rangeselect.on_trait_change(self.phase_center, "selection_completed")
phaseplot.active_tool = self.rangeselect
phaseplot.overlays.append(RangeSelectionOverlay(component=phaseplot))
container = VPlotContainer(padding=40, padding_left=60, spacing=40)
container.add(phaseplot)
container.add(amplitudeplot)
self.container = container
def _create_spectrumplot(self, x, y):
spectrumplot = create_line_plot((x, y), index_bounds=None, value_bounds=None,
orientation='v', color='green', width=1.0, dash='solid',
value_mapper_class=LinearMapper,
bgcolor='transparent', border_visible=True,
add_grid=False, add_axis=False, index_sort='ascending')
add_default_axes(spectrumplot, orientation='flipped', vtitle='Frequency [MHz]', htitle='Amplitude')
spectrumplot.origin = "top left"
spectrumplot.tools.append(PanTool(spectrumplot, drag_button="right"))
zoom = SimpleZoom(component=spectrumplot, tool_mode="box", drag_button="left", always_on=True)
spectrumplot.overlays.append(zoom)
container = OverlayPlotContainer(padding=40, padding_left=60)
container.add(spectrumplot)
self.container = 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 = 30
low = -5
high = 15.0
x = linspace(low, high, numpoints)
y = jn(0, x)
lineplot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[0]), width=2.0)
lineplot.selected_color = "none"
scatter = ScatterPlot(index = lineplot.index,
value = lineplot.value,
index_mapper = lineplot.index_mapper,
value_mapper = lineplot.value_mapper,
color = tuple(COLOR_PALETTE[0]),
marker_size = 5)
scatter.index.sort_order = "ascending"
scatter.bgcolor = "white"
scatter.border_visible = True
add_default_grids(scatter)
add_default_axes(scatter)
scatter.tools.append(PanTool(scatter, drag_button="right"))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(scatter, tool_mode="box", always_on=False, drag_button=None)
scatter.overlays.append(zoom)
scatter.tools.append(PointDraggingTool(scatter))
container.add(lineplot)
container.add(scatter)
# Add the title at the top
container.overlays.append(PlotLabel("Line Editor",
component=container,
font = "swiss 16",
overlay_position="top"))
return container
def _create_traceplot(self, y=None):
x = self.x
if y == None:
y = self.y
traceplot = create_line_plot((x, y), index_bounds=None, value_bounds=None,
orientation='v', color='blue', width=1.0, dash='solid',
value_mapper_class=LinearMapper,
bgcolor='transparent', border_visible=True,
add_grid=False, add_axis=False, index_sort='ascending')
add_default_axes(traceplot, orientation='flipped', vtitle='Time [ns]', htitle='Signal')
traceplot.origin = "top left"
traceplot.tools.append(PanTool(traceplot, drag_button="right"))
zoom = SimpleZoom(component=traceplot, tool_mode="box", drag_button="left", always_on=True)
traceplot.overlays.append(zoom)
container = OverlayPlotContainer(padding=40, padding_left=60)
container.add(traceplot)
self.container = container
def _create_plot_component():
numpoints = 100
low = -5
high = 15.001
x = arange(low, high, (high-low)/numpoints)
# Plot a bessel function
y = jn(0, x)
plot = create_line_plot((x,y), color=(0,0,1,1), width=2.0, index_sort="ascending")
value_range = plot.value_mapper.range
plot.active_tool = RangeSelection(plot, left_button_selects = True)
plot.overlays.append(RangeSelectionOverlay(component=plot))
plot.bgcolor = "white"
plot.padding = 50
add_default_grids(plot)
add_default_axes(plot)
return plot
def _create_plot_component(use_downsampling=False):
container = OverlayPlotContainer(padding=40, bgcolor="lightgray",
use_backbuffer = True,
border_visible = True,
fill_padding = True)
numpoints = 100000
low = -5
high = 15.0
x = arange(low, high+0.001, (high-low)/numpoints)
# Plot some bessel functionsless ../en
value_mapper = None
index_mapper = None
for i in range(10):
y = jn(i, x)
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.use_downsampling = use_downsampling
if value_mapper is None:
index_mapper = plot.index_mapper
value_mapper = plot.value_mapper
add_default_grids(plot)
add_default_axes(plot)
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%2 == 1:
plot.line_style = "dash"
plot.bgcolor = "white"
container.add(plot)
selection_overlay = RangeSelectionOverlay(component = plot)
plot.tools.append(RangeSelection(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(selection_overlay)
plot.overlays.append(zoom)
return container
def do_plotv(session, *args, **kw):
""" Creates a list of plots from the data in ``*args`` and options in
``**kw``, according to the docstring on commands.plot().
"""
sort = kw.get("sort", "none")
sources_list = make_data_sources(session, index_sort=sort, *args)
plot_type = kw.get("type", "line")
if plot_type == "scatter":
plots = [create_scatter_plot(sources) for sources in sources_list]
elif plot_type == "line":
plots = [create_line_plot(sources) for sources in sources_list]
else:
raise ChacoShellError, "Unknown plot type '%s'." % plot_type
for plot in plots:
plot.orientation = kw.get("orientation", "h")
return plots
def create_plot():
container = OverlayPlotContainer(
padding=50, fill_padding=True, bgcolor="lightgray")
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
for i in range(10):
y = jn(i, x)
plot = create_line_plot((x, y),
color=(1.0 - i / 10.0, i / 10.0, 0, 1),
width=2.0,
index_sort="ascending")
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
add_default_axes(plot)
add_default_grids(plot)
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 % 2 == 1:
plot.line_style = "dash"
container.add(plot)
return container
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")
# Tweak some of the plot properties
plot1.title = "My First Line Plot"
plot1.padding = 50
plot1.padding_top = 75
plot1.legend.visible = True
x = linspace(-5, 15.0, 100)
y = jn(5, x)
foreign_plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[0]), width=2.0)
left, bottom = add_default_axes(foreign_plot)
left.orientation = "right"
bottom.orientation = "top"
plot1.add(foreign_plot)
# Attach some tools to the plot
broadcaster = BroadcasterTool()
broadcaster.tools.append(PanTool(plot1))
broadcaster.tools.append(PanTool(foreign_plot))
for c in (plot1, foreign_plot):
zoom = ZoomTool(component=c, tool_mode="box", always_on=False)
broadcaster.tools.append(zoom)
plot1.tools.append(broadcaster)
return plot1
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))
def make_plots(self):
main_plot = chaco.Plot(self.plotdata, padding=0)
colors = {
'pos': 'black',
'vel': 'blue',
'accel': 'red',
'jerk': 'green',
'power': 'purple'
}
left_y_axis_title_list = []
legend_dict = {}
if self.plot_vel:
vel_plot = main_plot.plot(("sample_times", "velocities"),
type="line",
color=colors['vel'],
line_width=2)
max_vel_plot = main_plot.plot(("endpoint_times", "max_vel"),
color=colors['vel'],
line_style='dash',
line_width=0.60)
min_vel_plot = main_plot.plot(("endpoint_times", "min_vel"),
color=colors['vel'],
line_style='dash',
line_width=0.60)
left_y_axis_title_list.append("Velocity (m/s)")
legend_dict['vel'] = vel_plot
if self.plot_accel:
accel_plot = main_plot.plot(("knot_times", "accelerations"),
type="line",
color=colors['accel'],
line_width=2)
max_accel_plot = main_plot.plot(("endpoint_times", "max_accel"),
color=colors['accel'],
line_style='dash',
line_width=0.55)
min_accel_plot = main_plot.plot(("endpoint_times", "min_accel"),
color=colors['accel'],
line_style='dash',
line_width=0.55)
left_y_axis_title_list.append("Accel (m/s2)")
legend_dict['accel'] = accel_plot
if self.plot_jerk:
jerk_plot = main_plot.plot(("knot_times", "jerks"),
type="line",
color=colors['jerk'],
line_width=2,
render_style="connectedhold")
max_jerk_plot = main_plot.plot(("endpoint_times", "max_jerk"),
color=colors['jerk'],
line_style='dash',
line_width=0.45)
min_jerk_plot = main_plot.plot(("endpoint_times", "min_jerk"),
color=colors['jerk'],
line_style='dash',
line_width=0.45)
left_y_axis_title_list.append("Jerk (m/s3)")
legend_dict['jerk'] = jerk_plot
if self.plot_power:
power_plot = main_plot.plot(("sample_times", "powers"),
type="line",
color=colors['power'],
line_width=2)
left_y_axis_title_list.append("Power (KW)")
legend_dict['power'] = power_plot
main_plot.y_axis.title = ", ".join(left_y_axis_title_list)
self.container.add(main_plot)
# plot positions (on a separate scale from the others)
if self.plot_pos:
pos_plot = chaco.create_line_plot([
self.plotdata.arrays["sample_times"],
self.plotdata.arrays["positions"]
],
color=colors['pos'],
width=2)
legend_dict['pos'] = pos_plot
self.container.add(pos_plot)
# add a second y-axis for the positions
pos_y_axis = chaco.PlotAxis(pos_plot,
orientation="right",
title="Position (meters)")
self.container.overlays.append(pos_y_axis)
# make Legend
legend = chaco.Legend(component=self.container, padding=20, align="ul")
legend.plots = legend_dict
legend.tools.append(tools.LegendTool(legend, drag_button="left"))
self.container.overlays.append(legend)
# Add title, if any
if self.title:
main_plot.title = self.title
main_plot.title_position = "inside top"
# First, we create two arrays of data, x and y. 'x' will be a sequence of
# 100 points spanning the range -2pi to 2pi, and 'y' will be sin(x).
from scipy import arange, pi, sin
numpoints = 100
step = 4*pi / numpoints
x = arange(-2*pi, 2*pi+step/2, step)
y = sin(x)
# Now that we have our data, we can use a factory function to create the
# line plot for us. Chaco provides a few factories to simplify creating common
# plot types (line, scatter, etc.). In later tutorials we'll see what the
# factories are actually doing, and how to manually assemble plotting
# primitives in more powerful ways. For now, factories suit our needs.
from enthought.chaco import api as chaco
myplot = chaco.create_line_plot((x,y), bgcolor="white", add_grid=True, add_axis=True)
# We now need to set the plot's size, and add a little padding for the axes.
# (Normally, when Chaco plots are placed inside WX windows, the bounds are
# set automatically by the window.)
myplot.padding = 50
myplot.bounds = [400,400]
def main():
# Now we create a canvas of the appropriate size and ask it to render
# our component. (If we wanted to display this plot in a window, we
# would not need to create the graphics context ourselves; it would be
# created for us by the window.)
plot_gc = chaco.PlotGraphicsContext(myplot.outer_bounds)
plot_gc.render_component(myplot)
def __init__(self):
#The delegates views don't work unless we caller the superclass __init__
super(CursorTest, self).__init__()
container = HPlotContainer(padding=0, spacing=20)
self.plot = container
#a subcontainer for the first plot.
#I'm not sure why this is required. Without it, the layout doesn't work right.
subcontainer = OverlayPlotContainer(padding=40)
container.add(subcontainer)
#make some data
index = numpy.linspace(-10,10,512)
value = numpy.sin(index)
#create a LinePlot instance and add it to the subcontainer
line = create_line_plot([index, value], add_grid=True,
add_axis=True, index_sort='ascending',
orientation = 'h')
subcontainer.add(line)
#here's our first cursor.
csr = CursorTool(line,
drag_button="left",
color='blue')
self.cursor1 = csr
#and set it's initial position (in data-space units)
csr.current_position = 0.0, 0.0
#this is a rendered component so it goes in the overlays list
line.overlays.append(csr)
#some other standard tools
line.tools.append(PanTool(line, drag_button="right"))
line.overlays.append(ZoomTool(line))
#make some 2D data for a colourmap plot
xy_range = (-5, 5)
x = numpy.linspace(xy_range[0], xy_range[1] ,100)
y = numpy.linspace(xy_range[0], xy_range[1] ,100)
X,Y = numpy.meshgrid(x, y)
Z = numpy.sin(X)*numpy.arctan2(Y,X)
#easiest way to get a CMapImagePlot is to use the Plot class
ds = ArrayPlotData()
ds.set_data('img', Z)
img = Plot(ds, padding=40)
cmapImgPlot = img.img_plot("img",
xbounds = xy_range,
ybounds = xy_range,
colormap = jet)[0]
container.add(img)
#now make another cursor
csr2 = CursorTool(cmapImgPlot,
drag_button='left',
color='white',
line_width=2.0
)
self.cursor2 = csr2
csr2.current_position = 1.0, 1.5
cmapImgPlot.overlays.append(csr2)
#add some standard tools. Note, I'm assigning the PanTool to the
#right mouse-button to avoid conflicting with the cursors
cmapImgPlot.tools.append(PanTool(cmapImgPlot, drag_button="right"))
cmapImgPlot.overlays.append(ZoomTool(cmapImgPlot))
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 _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 _create_plot_component(self):
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = 0xc9e1eb,
use_backbuffer=True)
self.container = container
self.value_mapper = None
self.index_mapper = None
self.plots = {}
if sys.platform == 'linux2':
plot_font = 'sans-serif'
elif sys.platform == 'darwin':
plot_font = 'Helvetica'
else:
plot_font = 'Verdana'
# Store Python object ids to distinguish between different plots
# with the same label
self.plot_ids = []
self.beams = {}
x = numpy.arange(0)
y = numpy.arange(0)
plot = create_line_plot((x,y), color=tuple(self.get_plot_color()), width=2.0)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
self.value_mapper = plot.value_mapper
self.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, drag_button="middle"))
# 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,
drag_button="right", always_on_modifier='control')#,
# x_min_zoom_factor=0.5,
# y_min_zoom_factor=0.5,
# x_max_zoom_factor=40.,
# y_max_zoom_factor=40.)
zoom.zoom_factor = 1.2
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")
# dragzoom.speed = 0.2
# plot.tools.append(dragzoom)
# Add a legend in the upper right corner, and make it relocatable
self.legend = Legend(component=plot, padding=10, align="ur")
self.legend.tools.append(LegendTool(self.legend, drag_button="right"))
self.legend.font = plot_font+' 12'
plot.overlays.append(self.legend)
self.legend.visible = False
# The Legend tool allows plots to be selected by left clicking
# on the label in the plot legend. This tool sets the ChacoPlot
# selected plot trait.
highlight_legend = HighlightLegend(self.legend)
highlight_legend.parent = self
self.legend.tools.append(highlight_legend)
# The PlotSelectTool allows plots to be selected by left clicking
# on the actual plot trace. This tools sets the ChacoPlot selected
# plot trait.
plot_select_tool = PlotSelectTool(self.container)
plot_select_tool.parent = self
plot.tools.append(plot_select_tool)
plot.x_axis.title = "Distance (cm)"
plot.x_axis.title_font = plot_font+" 12"
plot.x_axis.tick_label_font = plot_font+" 10"
plot.y_axis.title = "% Dose"
plot.y_axis.title_font = plot_font+" 12"
plot.y_axis.tick_label_font = plot_font+" 10"
container.add(plot)
# Set the list of plots on the legend
self.legend.plots = self.plots
# Add the title at the top
self.title = PlotLabel("Scans",
component=container,
font = plot_font+' 16',
overlay_position="top")
container.overlays.append(self.title)
return container
def add_plot(self, label, beam):
# Check to see if beam has already been plotted.
if id(beam) in self.plot_ids:
return
# Remove extraneous whitespace
label = '|'.join([i.strip() for i in label.split('|')])
# Remove spaces in fields within the label so that when the '|' are
# replaced with spaces, the label will be able to be split on spaces.
label = label.replace(' ','_')
# The plot_type trait is defined by the geometry of the scanned plot
# (inline, crossline, depth dose, etc.).
if self.plot_type is not None:
self.plot_type = beam.get_scan_type()
# # Reformat labels
# fields = [i.strip() for i in label.split('|')]
# if self.plot_type == 'Depth Dose':
# label = ' '.join(fields[:-1])
if self.plot_type in ['Inplane Profile','Crossplane Profile']:
label = label+'_cm_depth'
x, y = (beam.Data_Abscissa, beam.Data_Ordinate)
if label in self.plots.keys():
#label = increment(label)
label = label + '_' + str(len(self.plots))
plot = create_line_plot((x,y), color=tuple(
self.get_plot_color()), width=2.0)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
plot.value_mapper = self.value_mapper
self.value_mapper.range.add(plot.value)
plot.index_mapper = self.index_mapper
self.index_mapper.range.add(plot.index)
self.container.add(plot)
# beams and plots are dictionaries that map plot objects to beam
# objects and plot labels to plot objects. They are used to
# determine plot titles and legend labels as well as mapping
# the selected_plot trait to the selected_beam trait.
self.beams[plot] = beam
self.plots[label] = plot
self.plot_ids.append(id(beam))
self.legend.visible = True
self.legend.plots = self.plots
self.legend.labels = self.get_legend_labels()
# legend.plots dictionary must have keys that match the labels in
# legend.labels in order for the labels to be visible.
legend_plot_dict = {}
for i in self.legend.plots.keys():
for j in self.legend.labels:
label_set = set(j.split(' '))
plot_set = set(i.split('|'))
if label_set.issubset(plot_set):
legend_plot_dict[j] = self.legend.plots[i]
self.legend.plots = legend_plot_dict
self.title.text = self.get_title()
self.container.request_redraw()
return self.title.text
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)