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_window(self):
numpoints = 50
low = -5
high = 15.0
x = arange(low, high, (high - low) / numpoints)
container = OverlayPlotContainer(bgcolor="lightgray")
common_index = None
index_range = None
value_range = None
self.animated_plots = []
for i, color in enumerate(COLOR_PALETTE):
if not common_index:
animated_plot = AnimatedPlot(x, jn(i, x), color)
common_index = animated_plot.plot.index
index_range = animated_plot.plot.index_mapper.range
value_range = animated_plot.plot.value_mapper.range
else:
animated_plot = AnimatedPlot(common_index, jn(i, x), color)
animated_plot.plot.index_mapper.range = index_range
animated_plot.plot.value_mapper.range = value_range
container.add(animated_plot.plot)
self.animated_plots.append(animated_plot)
for i, a_plot in enumerate(self.animated_plots):
a_plot.plot.position = [
50 + (i % 3) * (PLOT_SIZE + 50),
50 + (i // 3) * (PLOT_SIZE + 50)
]
self.timer = Timer(100.0, self.onTimer)
self.container = container
return Window(self, -1, component=container)
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 _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 _ml_plot_default(self):
container = OverlayPlotContainer(padding=CHACO_AXES_PADDING)
index_range = DataRange1D()
value_range = DataRange1D(low=0, high=1)
index_mapper = LinearMapper(range=index_range)
value_mapper = LinearMapper(range=value_range)
# Add the datasources so the psychometric function derived from the
# maximum likelihood estimator is always shown across the full range
# of tested values
for sources in self.ml_track_data:
index_range.add(sources[1])
# Create the datasources for the psychometric function
self.ml_index = FunctionDataSource(data_range=index_range, func=_xfunc)
self.ml_value.data_range = index_range
line = LinePlot(index=self.ml_index, value=self.ml_value,
index_mapper=index_mapper, value_mapper=value_mapper)
ax = PlotAxis(line, orientation='left', title='Yes probability')
line.overlays.append(ax)
ax = PlotAxis(line, orientation='bottom', title='Parameter')
line.overlays.append(ax)
container.add(line)
#line.tools.append(PanTool(line, constrain=True, constrain_key=None,
# constrain_direction='x'))
line.overlays.append(ZoomTool(line, axis='index', tool_mode='range'))
return container
def _plot_default(self):
# Create data
x = linspace(-5, 15.0, 100)
y = jn(3, x)
pd = ArrayPlotData(index=x, value=y)
zoomable_plot = Plot(pd)
zoomable_plot.plot(('index', 'value'),
name='external',
color='red',
line_width=3)
# Attach tools to the plot
zoom = ZoomTool(component=zoomable_plot,
tool_mode="box",
always_on=False)
zoomable_plot.overlays.append(zoom)
zoomable_plot.tools.append(PanTool(zoomable_plot))
# Create a second inset plot, not resizable, not zoom-able
inset_plot = Plot(pd)
inset_plot.plot(('index', 'value'), color='blue')
inset_plot.set(resizable='',
bounds=[250, 150],
position=[450, 350],
border_visible=True)
# Create a container and add our plots
container = OverlayPlotContainer()
container.add(zoomable_plot)
container.add(inset_plot)
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 _plot_default(self):
container = OverlayPlotContainer(bgcolor="white")
plots = self._make_curves()
for plot in plots:
plot.padding = 60
container.add(plot)
bottom_axis = PlotAxis(plot, orientation='bottom')
label_list = [
'var a', 'var b', 'var c', 'var d', 'var e', 'var f', 'var g',
'var h', 'var i'
]
vertical_axis = LabelAxis(plot,
orientation='left',
title='Categories',
positions=list(range(1, 10)),
labels=label_list)
vertical2_axis = LabelAxis(plot,
orientation='right',
positions=list(range(1, 10)),
labels=label_list)
plot.underlays.append(vertical_axis)
plot.underlays.append(vertical2_axis)
plot.underlays.append(bottom_axis)
return container
def _create_window(self):
numpoints = 50
low = -5
high = 15.0
x = arange(low, high, (high-low)/numpoints)
container = OverlayPlotContainer(bgcolor="lightgray")
common_index = None
index_range = None
value_range = None
self.animated_plots = []
for i, color in enumerate(COLOR_PALETTE):
if not common_index:
animated_plot = AnimatedPlot(x, jn(i,x), color)
common_index = animated_plot.plot.index
index_range = animated_plot.plot.index_mapper.range
value_range = animated_plot.plot.value_mapper.range
else:
animated_plot = AnimatedPlot(common_index, jn(i,x), color)
animated_plot.plot.index_mapper.range = index_range
animated_plot.plot.value_mapper.range = value_range
container.add(animated_plot.plot)
self.animated_plots.append(animated_plot)
for i, a_plot in enumerate(self.animated_plots):
a_plot.plot.position = [50 + (i%3)*(PLOT_SIZE+50),
50 + (i//3)*(PLOT_SIZE+50)]
self.timer = Timer(100.0, self.onTimer)
self.container = container
return Window(self, -1, component=container)
class LegendWindow(HasTraits):
plot = Instance(OverlayPlotContainer)
traits_view = View(
UItem('plot', editor=ComponentEditor(bgcolor='white',
width=500, height=500), show_label=False, resizable=True),
title='Legend', scrollable=True,
)
def __init__(self,legend):
super(LegendWindow,self).__init__()
self.legend = self.add_legend(legend)
def _plot_default(self):
self.container = OverlayPlotContainer(bgcolor="white", padding=10)
self.container.add(self.legend)
return self.container
def get_legend(self):
if self.legend:
return self.legend
def add_legend(self, legend):
legend.set(component=None,
padding=10,
error_icon='blank',
visible=True,
resizable='hv',
clip_to_component=True)
return legend
class HistogramPlotHandler(HasTraits):
'''
Class for handling the histograms.
'''
# Index for the histogram plot
index = Array
# The selection handler object for the selected data
selection_handler = Instance(SelectionHandler)
# OVerlayPlotContainer for the histogram plot
container = Instance(OverlayPlotContainer)
# Number of bins of the histogram
nbins = Int(10)
# Whether the data is a pandas dataframe or a numpy array
AS_PANDAS_DATAFRAME = Bool
def __init__(self):
self.index = range(self.nbins)
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def draw_histogram(self):
'''
Default function called when drawing the histogram.
'''
for component in self.container.components:
self.container.remove(component)
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
tuple_list = self.selection_handler.selected_indices[0]
if self.AS_PANDAS_DATAFRAME:
column_name = self.data.columns[tuple_list[1]]
y = self.data[column_name]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type='bar', bar_width=0.5)
self.container.add(plot)
else:
column = tuple_list[1]
y = self.data[:, column]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type='bar', bar_width=0.5)
self.container.add(plot)
self.container.request_redraw()
self.selection_handler.flush()
class HistogramPlotHandler(HasTraits):
"""
Class for handling the histograms.
"""
# Index for the histogram plot
index = Array
# The selection handler object for the selected data
selection_handler = Instance(SelectionHandler)
# OVerlayPlotContainer for the histogram plot
container = Instance(OverlayPlotContainer)
# Number of bins of the histogram
nbins = Int(10)
# Whether the data is a pandas dataframe or a numpy array
AS_PANDAS_DATAFRAME = Bool
def __init__(self):
self.index = range(self.nbins)
self.selection_handler = SelectionHandler()
self.container = OverlayPlotContainer()
def draw_histogram(self):
"""
Default function called when drawing the histogram.
"""
for component in self.container.components:
self.container.remove(component)
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
tuple_list = self.selection_handler.selected_indices[0]
if self.AS_PANDAS_DATAFRAME:
column_name = self.data.columns[tuple_list[1]]
y = self.data[column_name]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="bar", bar_width=0.5)
self.container.add(plot)
else:
column = tuple_list[1]
y = self.data[:, column]
self.index = np.arange(self.nbins)
hist = np.histogram(y, self.nbins)[0]
plotdata = ArrayPlotData(x=self.index, y=hist)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="bar", bar_width=0.5)
self.container.add(plot)
self.container.request_redraw()
self.selection_handler.flush()
def _create_plot_component(title, initial_values=None, on_change_functor=None):
#return OverlayPlotContainer()
container = OverlayPlotContainer(padding = 25, 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 test_min_size(self):
container = OverlayPlotContainer(resizable='', bounds=[50.0,50.0])
component = PlotComponent(resizable='', position=[50.0,60.0],
bounds=[100.0, 110.0])
container.add(component)
container.do_layout()
self.assert_tuple(component.position, (50.0,60.0))
self.assert_tuple(component.bounds, (100.0,110.0))
return
def test_min_size(self):
container = OverlayPlotContainer(resizable='', bounds=[50.0, 50.0])
component = PlotComponent(resizable='',
position=[50.0, 60.0],
bounds=[100.0, 110.0])
container.add(component)
container.do_layout()
self.assert_tuple(component.position, (50.0, 60.0))
self.assert_tuple(component.bounds, (100.0, 110.0))
return
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 _plot_default(self):
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
# Create the initial X-series of data
numpoints = self.numpoints
low = self.low
high = self.high
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 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
class Graph(HasTraits):
plot=None
traits_view=View(Item('plot',editor=ComponentEditor(bgcolor="white"), show_label=False),
width=1200, height=1024, resizable=True, title="BacLog")
def __init__(self):
super(Graph,self).__init__()
self.container = HPlotContainer(padding=20, bgcolor="transparent")
self.plot_area = OverlayPlotContainer(border_visible=True)
def add(self,series,limit=None):
broadcaster = BroadcasterTool()
for name,line in series._plot.line.items():
if limit is not None and name not in limit:
continue
if line.time==[]:
print "Graph.add> empty:", name
continue
plot=create_line_plot((seconds(line.time),line.data),color=line.color)
self.plot_area.add(plot)
axis = PlotAxis(orientation="left", resizable="v",
mapper = plot.y_mapper,
bgcolor="white",
title = name,
title_color = line.color,
title_spacing = -4.0,
border_visible = True,)
## Visual style
axis.bounds = [60,0]
axis.padding_left = 1
axis.padding_right = 1
self.container.add(axis)
## Tools (attach to all for now)
plot.tools.append(broadcaster)
broadcaster.tools.append(PanTool(plot))
broadcaster.tools.append(DragZoom(plot,maintain_aspect_ratio=False,drag_button='right',restrict_domain=True))
def run(self):
## Time axis (first one)
plot=self.plot_area.components[0]
time = PlotAxis(orientation="bottom", component=plot, mapper=plot.x_mapper)
plot.overlays.append(time)
## Plot
self.container.add(self.plot_area)
self.plot=self.container
self.configure_traits()
def test_multiple_min_size(self):
comp1 = StaticPlotComponent([200, 50])
comp2 = StaticPlotComponent([60, 300])
container = OverlayPlotContainer(resizable='hv', bounds=[30,30])
container.fit_components = "hv"
container.add(comp1, comp2)
container.do_layout()
self.assert_tuple(container.get_preferred_size(), (200,300))
self.assert_tuple(comp1.bounds, (200,50))
self.assert_tuple(comp2.bounds, (60,300))
return
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 test_multiple_min_size(self):
comp1 = StaticPlotComponent([200, 50])
comp2 = StaticPlotComponent([60, 300])
container = OverlayPlotContainer(resizable='hv', bounds=[30, 30])
container.fit_components = "hv"
container.add(comp1, comp2)
container.do_layout()
self.assert_tuple(container.get_preferred_size(), (200, 300))
self.assert_tuple(comp1.bounds, (200, 50))
self.assert_tuple(comp2.bounds, (60, 300))
return
def main():
# 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
plot = Plot(
pd,
bgcolor="none",
padding=30,
border_visible=True,
overlay_border=True,
use_backbuffer=False)
plot.legend.visible = True
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="auto")
plot.plot(("index", "y3"), name="j_3", color="auto")
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the mlab test mesh and get references to various parts of the
# VTK pipeline
f = mlab.figure(size=(600, 500))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
plot.resizable = ""
plot.bounds = [200, 200]
plot.padding = 25
plot.bgcolor = "lightgray"
plot.outer_position = [30, 30]
plot.tools.append(MoveTool(component=plot, drag_button="right"))
container = OverlayPlotContainer(bgcolor="transparent", fit_window=True)
container.add(plot)
# Create the Enable Window
window = EnableVTKWindow(
rwi,
renderer,
component=container,
#istyle_class = tvtk.InteractorStyleSwitch,
#istyle_class = tvtk.InteractorStyle,
istyle_class=tvtk.InteractorStyleTrackballCamera,
bgcolor="transparent",
event_passthrough=True, )
mlab.show()
return window, render_window
def _create_plot_component(max_pop, index_ds, value_ds, color_ds, paths):
tile_cache = HTTPTileManager(min_level=2, max_level=4,
server='tile.cloudmade.com',
url='/1a1b06b230af4efdbb989ea99e9841af/20760/256/%(zoom)d/%(col)d/%(row)d.png') # noqa
color_range = DataRange1D(color_ds, low_setting=0)
choro = ChoroplethPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
index_mapper=LinearMapper(range=DataRange1D(index_ds)),
value_mapper=LinearMapper(range=DataRange1D(value_ds)),
color_mapper=colormap(range=color_range),
outline_color='white',
line_width=1.5,
fill_alpha=1.,
compiled_paths=paths,
tile_cache=tile_cache,
zoom_level=3,
)
container = OverlayPlotContainer(
bgcolor='sys_window', padding=50, fill_padding=False,
border_visible=True,
)
container.add(choro)
for dir in ['left']:
axis = PlotAxis(tick_label_formatter=convert_lat,
mapper=choro.value_mapper, component=container,
orientation=dir)
container.overlays.append(axis)
for dir in ['top', 'bottom']:
axis = PlotAxis(tick_label_formatter=convert_lon,
mapper=choro.index_mapper, component=container,
orientation=dir)
container.overlays.append(axis)
choro.tools.append(PanTool(choro))
choro.tools.append(ZoomTool(choro))
colorbar = create_colorbar(choro)
colorbar.padding_top = container.padding_top
colorbar.padding_bottom = container.padding_bottom
plt = HPlotContainer(use_backbuffer=True)
plt.add(container)
plt.add(colorbar)
plt.bgcolor = "sys_window"
return plt
def create_container(self, plugin):
index_range = ChannelDataRange(trig_delay=self.trig_delay, span=self.span)
index_mapper = LinearMapper(range=index_range)
container = OverlayPlotContainer(padding=[20, 20, 50, 50])
for child in self.children:
plot = child.create_plot(plugin, index_mapper)
container.add(plot)
# Add the time axis to the final plot
add_time_axis(plot)
add_default_grids(plot, major_index=5, minor_index=1)
return container
def __init__(self, link):
super(TrackingView, self).__init__()
self.link = link
self.link.add_callback(MSG_TRACKING_SNRS, self.tracking_snrs_callback)
# ======= Line Plot =======
self.plot_data = ArrayPlotData(t=[0.0])
self.plot = Plot(self.plot_data, auto_colors=colours_list)
self.plot.value_range.tight_bounds = False
self.plot.value_range.low_setting = 0.0
for n in range(TRACK_N_CHANNELS):
self.plot_data.set_data('ch'+str(n), [0.0])
self.plot.plot(('t', 'ch'+str(n)), type='line', color='auto')
# ======= Bar Plot =======
idxs = ArrayDataSource(range(1, len(self.snrs)+1))
self.vals = ArrayDataSource(self.snrs, sort_order='none')
# Create the index range
index_range = DataRange1D(idxs, low=0.4, high=TRACK_N_CHANNELS+0.6)
index_mapper = LinearMapper(range=index_range)
# Create the value range
value_range = DataRange1D(low=0.0, high=25.0)
value_mapper = LinearMapper(range=value_range)
plot = BarPlot(index=idxs, value=self.vals,
index_mapper=index_mapper, value_mapper=value_mapper,
line_color='blue', fill_color='blue', bar_width=0.8)
container = OverlayPlotContainer(bgcolor = "white")
plot.padding = 10
plot.padding_left = 30
plot.padding_bottom = 30
container.add(plot)
left_axis = PlotAxis(plot, orientation='left')
bottom_axis = LabelAxis(plot, orientation='bottom',
labels = map(str, range(1, TRACK_N_CHANNELS+1)),
positions = range(1, TRACK_N_CHANNELS+1),
small_haxis_style=True)
plot.underlays.append(left_axis)
plot.underlays.append(bottom_axis)
self.snr_bars = container
self.python_console_cmds = {
'track': self
}
def __init__(self, sensors = std_sensors.offset_str):
m = MapPlot(map = themap, logger = logger)
self.map_scale = themap.scale
self.map_info = "%s" % themap
self.noise_sensor = std_sensors.ultra_noise
self.noise_move = noise_params['move']
self.noise_turn = noise_params['turn']
#start_pose = Pose(themap.x_inches/2, themap.y_inches/2, 0.0)
start_pose = Pose(4, 7.75, 0.0)
robot = SimRobot(start_pose, sensors, noise_params = noise_params)
rplotter = RobotPlotter(robot = robot, xsize = themap.x_inches, ysize = themap.y_inches)
localizer = ParticleLocalizer(sensors, noise_params, themap, particle_count, start_pose, logger = logger)
pplotter = ParticlePlotter(particles = localizer.p, xsize = themap.x_inches, ysize = themap.y_inches, color = 'red')
guessbot = Robot(start_pose)
guessplotter = RobotPlotter(robot = guessbot, xsize = themap.x_inches, ysize = themap.y_inches, color = 'green' )
c = OverlayPlotContainer()
c.add(m.plot)
c.add(pplotter.qplot)
c.add(guessplotter.plot)
c.add(rplotter.plot)
self.container = c
self.mapplot = m
self.pplotter = pplotter
self.rplotter = rplotter
self.guessplotter = guessplotter
self.localizer = localizer
def _create_plot_component_overlay(signals, use_downsampling=False):
container = OverlayPlotContainer(padding=40,
bgcolor="lightgray",
use_backbuffer=True,
border_visible=True,
fill_padding=True)
nSignal, nSample = np.shape(signals)
time = arange(nSample)
value_mapper = None
index_mapper = None
plots = {}
for i in range(nSignal):
plot = create_line_plot(
(time, signals[i]),
color=tuple(COLOR_PALETTE[i % len(COLOR_PALETTE)]),
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"
plots["Corr fun %d" % i] = plot
container.add(plot)
# 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)
# legend.plots = plots
# 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 main():
# 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
plot = Plot(pd, bgcolor="none", padding=30, border_visible=True,
overlay_border=True, use_backbuffer=False)
plot.legend.visible = True
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="auto")
plot.plot(("index", "y3"), name="j_3", color="auto")
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the mlab test mesh and get references to various parts of the
# VTK pipeline
f = mlab.figure(size=(600,500))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
plot.resizable = ""
plot.bounds = [200,200]
plot.padding = 25
plot.bgcolor = "lightgray"
plot.outer_position = [30,30]
plot.tools.append(MoveTool(component=plot,drag_button="right"))
container = OverlayPlotContainer(bgcolor = "transparent",
fit_window = True)
container.add(plot)
# Create the Enable Window
window = EnableVTKWindow(rwi, renderer,
component=container,
#istyle_class = tvtk.InteractorStyleSwitch,
#istyle_class = tvtk.InteractorStyle,
istyle_class = tvtk.InteractorStyleTrackballCamera,
bgcolor = "transparent",
event_passthrough = True,
)
mlab.show()
return window, render_window
def _create_plot_component_overlay(signals, use_downsampling=False):
container = OverlayPlotContainer(padding=40, bgcolor="lightgray",
use_backbuffer=True,
border_visible=True,
fill_padding=True)
nSignal, nSample = np.shape(signals)
time = arange(nSample)
value_mapper = None
index_mapper = None
plots = {}
for i in range(nSignal):
plot = create_line_plot((time, signals[i]),
color=tuple(COLOR_PALETTE[i % len(COLOR_PALETTE)]),
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"
plots["Corr fun %d" % i] = plot
container.add(plot)
# 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)
# legend.plots = plots
# 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 test_fixed_size_component(self):
container = OverlayPlotContainer(resizable='', bounds=[200.0,300.0])
# non-resizable component
component = PlotComponent(resizable='', position=[50.0,60.0], bounds=[100.0,110.0])
self.assertEqual(container._layout_needed, True)
container.do_layout()
container.add(component)
self.assertEqual(container._layout_needed, True)
container.do_layout()
self.assertEqual(container._layout_needed, False)
# check the results of the layout
self.assert_tuple(container.get_preferred_size(), (200.0,300.0))
self.assert_tuple(component.position, (50.0,60.0))
self.assert_tuple(component.bounds, (100.0,110.0))
return
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)
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 main():
from tvtk.api import tvtk
from mayavi import mlab
from enable.vtk_backend.vtk_window import EnableVTKWindow
f = mlab.figure(size=(900, 850))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
# Create the plot
timer_controller = TimerController()
plots = create_plot_component(timer_controller)
specplot, timeplot, spectrogram = plots
for i, p in enumerate(plots):
p.resizable = ""
p.bgcolor = "transparent"
p.bounds = [200, 200]
p.outer_x = 0
p.outer_y = i * 250
p.tools.append(MoveTool(p, drag_button="right"))
p.tools.append(PanTool(p))
p.tools.append(ZoomTool(p))
spectrogram.tools[-1].tool_mode = "range"
spectrogram.tools[-1].axis = "value"
spectrogram.tools[-2].constrain = True
spectrogram.tools[-2].constrain_direction = "y"
container = OverlayPlotContainer(bgcolor="transparent", fit_window=True)
container.add(*plots)
container.timer_callback = timer_controller.on_timer
window = EnableVTKWindow(
rwi,
renderer,
component=container,
istyle_class=tvtk.InteractorStyleTrackballCamera,
bgcolor="transparent",
event_passthrough=True,
)
mlab.show()
def _create_plot_component(use_downsampling=True):
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 test_fixed_size_component(self):
container = OverlayPlotContainer(resizable='', bounds=[200.0, 300.0])
# non-resizable component
component = PlotComponent(resizable='',
position=[50.0, 60.0],
bounds=[100.0, 110.0])
self.assertEqual(container._layout_needed, True)
container.do_layout()
container.add(component)
self.assertEqual(container._layout_needed, True)
container.do_layout()
self.assertEqual(container._layout_needed, False)
# check the results of the layout
self.assert_tuple(container.get_preferred_size(), (200.0, 300.0))
self.assert_tuple(component.position, (50.0, 60.0))
self.assert_tuple(component.bounds, (100.0, 110.0))
return
class PCPlotHandler(HasTraits):
'''
Class for handling principal component plots.
'''
# The container for the plot.
container = OverlayPlotContainer()
# the sklearn.decmoposition.PCA object
pca = PCA
# Whether or not to normalize the data, one of the parameters of the PCA
# object
whiten = Bool
# The input data.
table = Array
# The selection_handler instance for the tableview
selection_handler = Instance(SelectionHandler)
def __init__(self):
self.pca = PCA(n_components=2)
self.pca.whiten = True
self.container = OverlayPlotContainer()
self.selection_handler = SelectionHandler()
def draw_pc_plot(self):
'''
Called to draw the PCA plot.
'''
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
top_left = self.selection_handler.selected_indices[0][0:2]
bot_right = self.selection_handler.selected_indices[0][2:4]
data = self.table[top_left[0]:bot_right[0],
top_left[1]:bot_right[1]]
pc_red = self.pca.fit_transform(data)
plotdata = ArrayPlotData(x=pc_red[:, 0], y=pc_red[:, 1])
plot = Plot(plotdata)
plot.plot(("x", "y"), type='scatter')
self.container.add(plot)
self.container.request_redraw()
def main():
plot = create_plot()
plot.bounds = [400, 300]
plot.outer_position = [30, 30]
plot.resizable = ""
cmap_renderer = plot.plots["my_plot"][0]
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.outer_position = [450, 30]
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
container = OverlayPlotContainer(bgcolor="transparent", fit_window=True)
container.add(plot)
container.add(colorbar)
start_vtk(container)
class PCPlotHandler(HasTraits):
"""
Class for handling principal component plots.
"""
# The container for the plot.
container = OverlayPlotContainer()
# the sklearn.decmoposition.PCA object
pca = PCA
# Whether or not to normalize the data, one of the parameters of the PCA
# object
whiten = Bool
# The input data.
table = Array
# The selection_handler instance for the tableview
selection_handler = Instance(SelectionHandler)
def __init__(self):
self.pca = PCA(n_components=2)
self.pca.whiten = True
self.container = OverlayPlotContainer()
self.selection_handler = SelectionHandler()
def draw_pc_plot(self):
"""
Called to draw the PCA plot.
"""
self.selection_handler.create_selection()
if len(self.selection_handler.selected_indices) == 1:
top_left = self.selection_handler.selected_indices[0][0:2]
bot_right = self.selection_handler.selected_indices[0][2:4]
data = self.table[top_left[0] : bot_right[0], top_left[1] : bot_right[1]]
pc_red = self.pca.fit_transform(data)
plotdata = ArrayPlotData(x=pc_red[:, 0], y=pc_red[:, 1])
plot = Plot(plotdata)
plot.plot(("x", "y"), type="scatter")
self.container.add(plot)
self.container.request_redraw()
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")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Tweak some of the plot properties
plot1.title = "Inset Plot"
plot1.padding = 50
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Create a second scatter plot of one of the datasets, linking its
# range to the first plot
plot2 = Plot(pd, range2d=plot1.range2d, padding=50)
plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")
plot2.set(resizable = "",
bounds = [250, 250],
position = [550,150],
bgcolor = "white",
border_visible = True,
unified_draw = True
)
plot2.tools.append(PanTool(plot2))
plot2.tools.append(MoveTool(plot2, drag_button="right"))
zoom = ZoomTool(component=plot2, tool_mode="box", always_on=False)
plot2.overlays.append(zoom)
# Create a container and add our plots
container = OverlayPlotContainer()
container.add(plot1)
container.add(plot2)
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")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Tweak some of the plot properties
plot1.title = "Inset Plot"
plot1.padding = 50
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Create a second scatter plot of one of the datasets, linking its
# range to the first plot
plot2 = Plot(pd, range2d=plot1.range2d, padding=50)
plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")
plot2.resizable = ""
plot2.bounds = [250, 250]
plot2.position = [550, 150]
plot2.bgcolor = "white"
plot2.border_visible = True
plot2.unified_draw = True
plot2.tools.append(PanTool(plot2))
plot2.tools.append(MoveTool(plot2, drag_button="right"))
zoom = ZoomTool(component=plot2, tool_mode="box", always_on=False)
plot2.overlays.append(zoom)
# Create a container and add our plots
container = OverlayPlotContainer()
container.add(plot1)
container.add(plot2)
return container
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(1):
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 main():
plot = create_plot()
plot.bounds = [400,300]
plot.outer_position = [30,30]
plot.resizable = ""
cmap_renderer = plot.plots["my_plot"][0]
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.outer_position = [450,30]
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
container = OverlayPlotContainer(bgcolor = "transparent",
fit_window = True)
container.add(plot)
container.add(colorbar)
start_vtk(container)
def main():
from tvtk.api import tvtk
from mayavi import mlab
from enable.vtk_backend.vtk_window import EnableVTKWindow
f = mlab.figure(size=(900,850))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
# Create the plot
timer_controller = TimerController()
plots = create_plot_component(timer_controller)
specplot, timeplot, spectrogram = plots
for i, p in enumerate(plots):
p.set(resizable = "", bounds = [200,200], outer_x = 0,
bgcolor = "transparent",
)
p.outer_y = i*250
p.tools.append(MoveTool(p, drag_button="right"))
p.tools.append(PanTool(p))
p.tools.append(ZoomTool(p))
spectrogram.tools[-1].set(tool_mode="range", axis="value")
spectrogram.tools[-2].set(constrain=True, constrain_direction="y")
container = OverlayPlotContainer(bgcolor = "transparent",
fit_window = True)
container.add(*plots)
container.timer_callback = timer_controller.on_timer
window = EnableVTKWindow(rwi, renderer,
component = container,
istyle_class = tvtk.InteractorStyleTrackballCamera,
bgcolor = "transparent",
event_passthrough = True,
)
mlab.show()
def test_resizable_component(self):
container = OverlayPlotContainer(resizable='', bounds=[200.0, 300.0])
component = PlotComponent(resizable='hv',
position=[50.0, 56.0],
bounds=[100.0, 110.0])
container.add(component)
container.do_layout()
self.assert_tuple(component.position, (0.0, 0.0))
self.assert_tuple(component.bounds, (200.0, 300.0))
comp2 = PlotComponent(resizable="h",
position=[10, 20],
bounds=[100, 150])
container.add(comp2)
container.do_layout()
self.assert_tuple(comp2.position, (0.0, 20.0))
self.assert_tuple(comp2.bounds, (200.0, 150.0))
comp3 = PlotComponent(resizable="v",
position=[30, 40],
bounds=[100, 150])
container.add(comp3)
container.do_layout()
self.assert_tuple(comp3.position, (30.0, 0.0))
self.assert_tuple(comp3.bounds, (100, 300))
return
def _ml_track_plot_default(self):
container = OverlayPlotContainer(padding=CHACO_AXES_PADDING)
index_range = DataRange1D()
value_range = DataRange1D()
index_mapper = LinearMapper(range=index_range)
value_mapper = LinearMapper(range=value_range)
for i, v, i_hit, v_hit, i_miss, v_miss in self.ml_track_data:
index_range.add(i)
value_range.add(v)
kwargs = dict(index_mapper=index_mapper,
value_mapper=value_mapper)
# The connecting line
line = LinePlot(index=i, value=v, **kwargs)
container.add(line)
# Black for hit
scatter = ScatterPlot(index=i_hit, value=v_hit, marker='circle',
color='black', **kwargs)
container.add(scatter)
# Red for miss
scatter = ScatterPlot(index=i_miss, value=v_miss, marker='circle',
color='red', **kwargs)
container.add(scatter)
# Add the overlays to the last plot
line.overlays.append(PlotAxis(line, orientation='left', title='Parameter'))
line.overlays.append(PlotAxis(line, orientation='bottom', title='Trial number'))
return container
def _container_default(self):
container = OverlayPlotContainer(padding=[50, 50, 50, 50])
index_range = DataRange1D(low=0, high=0.0012)
value_range = DataRange1D(low=-0.00025, high=0.0005)
index_mapper = LinearMapper(range=index_range)
value_mapper = LinearMapper(range=value_range)
plot = SnippetChannelPlot(history=100, channel=self.snippet_store,
value_mapper=value_mapper, index_mapper=index_mapper)
self.plot = plot
# Add the axes
axis = PlotAxis(orientation='left', component=plot)
plot.overlays.append(axis)
axis = PlotAxis(orientation='bottom', component=plot)
plot.overlays.append(axis)
plot.overlays.append(ZoomTool(plot, axis='value'))
self.tool = WindowTool(component=plot)
plot.overlays.append(self.tool)
container.add(plot)
# plot = SnippetChannelPlot(history=100, channel=self.snippet_store,
# value_mapper=value_mapper, index_mapper=index_mapper,
# classifier=1, line_color='red')
# container.add(plot)
#
#plot = SnippetChannelPlot(history=100, channel=self.snippet_store,
# value_mapper=value_mapper, index_mapper=index_mapper,
# classifier=2, line_color='green')
#container.add(plot)
#plot = SnippetChannelPlot(history=5, channel=self.snippet_store,
# value_mapper=value_mapper, index_mapper=index_mapper,
# classifier=2, line_color='blue')
#container.add(plot)
print 'adding container'
return container
def _plot_default(self):
container = OverlayPlotContainer(bgcolor = "white")
plots = self._make_curves()
for plot in plots:
plot.padding = 60
container.add(plot)
bottom_axis = PlotAxis(plot, orientation='bottom')
label_list=['var a', 'var b', 'var c', 'var d', 'var e', 'var f',
'var g', 'var h', 'var i']
vertical_axis = LabelAxis(plot, orientation='left',
title='Categories',
positions = list(range(1, 10)),
labels=label_list)
vertical2_axis = LabelAxis(plot, orientation='right',
positions = list(range(1, 10)),
labels=label_list)
plot.underlays.append(vertical_axis)
plot.underlays.append(vertical2_axis)
plot.underlays.append(bottom_axis)
return container
def _create_plot_component(self):
container = OverlayPlotContainer(padding=50,
fill_padding=True,
bgcolor="white",
use_backbuffer=True)
types = {"line": LinePlot, "scatter": ScatterPlot}
# Create the initial X-series of data
if len(self) > 0: # Only create a plot if we ahve datat
if self.p_type == "scatter and line":
lineplot = self._create_plot(p_type=LinePlot)
#lineplot.selected_color = "none"
scatter = self._create_plot(p_type=ScatterPlot)
scatter.bgcolor = "white"
scatter.index_mapper = lineplot.index_mapper
scatter.value_mapper = lineplot.value_mapper
add_default_grids(scatter)
add_default_axes(scatter)
container.add(lineplot)
container.add(scatter)
else:
plot = self._create_plot(p_type=types[self.p_type])
add_default_grids(plot)
add_default_axes(plot)
container.add(plot)
scatter = plot
scatter.tools.append(PanTool(scatter, drag_button="left"))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(scatter,
tool_mode="box",
always_on=True,
drag_button="right")
scatter.overlays.append(zoom)
csr = CursorTool(scatter, color="black", drag_button="left")
scatter.overlays.append(csr)
self.plot = container
return container
def _create_component(self):
rect1 = Region("orchid", position=[50, 50])
rect2 = Region("cornflowerblue", position=[200, 50])
rect1.overlays.append(Overlay("One", component=rect1))
rect2.overlays.append(Overlay("Two", component=rect2))
container1 = OverlayPlotContainer(bounds=[400, 400], resizable="")
container1.add(rect1, rect2)
container1.bgcolor = (0.60, 0.98, 0.60, 0.5) # "palegreen"
rect3 = Region("purple", position=[50, 50])
rect4 = Region("teal", position=[200, 50])
rect3.overlays.append(Overlay("Three", component=rect3))
rect4.overlays.append(Overlay("Four", component=rect4))
container2 = OverlayPlotContainer(bounds=[400, 400], resizable="")
container2.add(rect3, rect4)
container2.bgcolor = "navajowhite"
container2.position = [200, 200]
top_container = OverlayPlotContainer()
top_container.add(container1, container2)
return top_container
def _create_plot_component(max_pop, index_ds, value_ds, color_ds, paths):
tile_cache = HTTPTileManager(
min_level=2,
max_level=4,
server='tile.cloudmade.com',
url=
'/1a1b06b230af4efdbb989ea99e9841af/20760/256/%(zoom)d/%(row)d/%(col)d.png'
)
color_range = DataRange1D(color_ds,
low_setting=0) #, high_setting=max_pop)
choro = ChoroplethPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
index_mapper=LinearMapper(range=DataRange1D(index_ds)),
value_mapper=LinearMapper(range=DataRange1D(value_ds)),
color_mapper=colormap(range=color_range),
outline_color='white',
line_width=1.5,
fill_alpha=1.,
compiled_paths=paths,
tile_cache=tile_cache,
zoom_level=3,
)
container = OverlayPlotContainer(
bgcolor='sys_window',
padding=50,
fill_padding=False,
border_visible=True,
)
container.add(choro)
for dir in ['left']:
axis = PlotAxis(tick_label_formatter=convert_lat,
mapper=choro.value_mapper,
component=container,
orientation=dir)
container.overlays.append(axis)
for dir in ['top', 'bottom']:
axis = PlotAxis(tick_label_formatter=convert_lon,
mapper=choro.index_mapper,
component=container,
orientation=dir)
container.overlays.append(axis)
choro.tools.append(PanTool(choro))
choro.tools.append(ZoomTool(choro))
colorbar = create_colorbar(choro)
colorbar.padding_top = container.padding_top
colorbar.padding_bottom = container.padding_bottom
plt = HPlotContainer(use_backbuffer=True)
plt.add(container)
plt.add(colorbar)
plt.bgcolor = "sys_window"
return plt
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():
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_default(self):
stations = self.stations
lon, lat = self._proj(stations['LON'].view(numpy.ndarray),
stations['LAT'].view(numpy.ndarray))
shift = self._shift
lon = (lon + shift / 2.) / (shift)
lat = (lat + shift / 2.) / (shift)
plot = Plot(ArrayPlotData(index=lon, value=lat))
plot.plot(
("index", "value"),
type="scatter",
name="stations",
marker="dot",
outline_color='black',
color=(1., 0., 0., 0.2),
line_width=1.,
marker_size=1,
)
mbtiles_fname = os.path.join('Data', 'tiles.mbtiles')
if os.path.exists(mbtiles_fname):
tile_cache = MBTileManager(filename=mbtiles_fname,
min_level=0,
max_level=7)
else:
tile_cache = HTTPTileManager(
min_level=0,
max_level=6,
server='oatile1.mqcdn.com',
url='/tiles/1.0.0/sat/%(zoom)d/%(row)d/%(col)d.jpg',
)
# Right now, some of the tools are a little invasive, and we need the
# actual ScatterPlot object to give to them
scatter = plot.plots['stations'][0]
map = Map(scatter, tile_cache=tile_cache, alpha=0.8, zoom_level=2)
scatter.underlays.append(map)
map.on_trait_change(lambda new: self._update_scatter(scatter, new),
'zoom_level')
self._update_scatter(scatter, map.zoom_level)
lasso_selection = LassoSelection(component=scatter,
selection_datasource=scatter.index)
scatter.tools.append(lasso_selection)
scatter.overlays.append(
LassoOverlay(component=scatter,
selection_fill_color='lawngreen',
selection_border_color='lightgreen',
selection_alpha=0.5,
selection_border_width=2.0,
lasso_selection=lasso_selection))
scatter.overlays.append(
ScatterInspectorOverlay(scatter,
selection_metadata_name='selection',
selection_marker_size=4,
selection_color="lawngreen"))
scatter.index.on_trait_change(self._metadata_handler,
"metadata_changed")
scatter.tools.append(PanTool(scatter, drag_button='right'))
scatter.tools.append(ZoomTool(scatter))
plot.index_axis.title = "Longitude"
plot.index_axis.tick_label_formatter = self._convert_lon
plot.value_axis.title = "Latitude"
plot.value_axis.tick_label_formatter = self._convert_lat
plot.padding_right = plot.padding_top = 2
container = OverlayPlotContainer(use_backbuffer=True,
bgcolor="sys_window")
container.add(plot)
container.bgcolor = "sys_window"
return container
def __init__(self, smaps, *args, **kwargs):
super(MyPlot, self).__init__(*args, **kwargs)
indexdata = []
startdata = []
stopdata = []
lastval = 0
for mem in smaps:
if mem.size != 0:
print mem.canread, mem.canwrite
indexdata.append(mem.canread << 0 | mem.canwrite << 1
| mem.isprivate << 2)
startdata.append(lastval)
stopdata.append(lastval + mem.size)
lastval += mem.size
print mem.size
indexsrc = ArrayDataSource(data=indexdata)
startsrc = ArrayDataSource(data=startdata)
stopsrc = ArrayDataSource(data=stopdata)
idxrange = [min(indexdata), max(indexdata)]
valrange = [min(startdata), max(stopdata)]
indexmapper = LinearMapper(
range=DataRange1D(ArrayDataSource(idxrange)))
valuemapper = LinearMapper(
range=DataRange1D(ArrayDataSource(valrange)))
barlist = []
barlist.append(
self.drawBar(indexsrc, indexmapper, startsrc, valuemapper,
0xc09090, stopsrc))
#barlist.append(self.drawBar(indexsrc, indexmapper, rsssrc, valuemapper, 0xffa0a0))
#barlist.append(self.drawBar(idxs, indexmapper, start, valuemapper, 0x0000ff, stop))
bottom_axis = PlotAxis(barlist[0],
orientation='bottom',
tick_label_formatter=lambda x: str(x))
barlist[0].underlays.append(bottom_axis)
modelist = []
for i in range(8):
mstr = ""
mstr += ['r', '-'][i & 1 == 0]
mstr += ['w', '-'][i & 2 == 0]
mstr += ['p', 's'][i & 4 == 0]
modelist.append(mstr)
vaxis1 = LabelAxis(barlist[0],
orientation='left',
title="Mode",
positions=range(len(modelist)),
labels=modelist,
tick_interval=1)
#vaxis2 = LabelAxis(barlist[0], orientation='right',
# title="Map Name",
# positions = range(idx),
# labels=["%s" % os.path.basename(x) for x in namedata])
barlist[0].underlays.append(vaxis1)
#barlist[0].underlays.append(vaxis2)
barlist[0].tools.append(ZoomTool(barlist[0]))
barlist[0].tools.append(PanTool(barlist[0]))
#add_default_axes(plot, orientation = 'v')
add_default_grids(barlist[0], orientation='v')
container = OverlayPlotContainer(bgcolor="white")
for p in barlist:
p.padding = [200, 200, 20, 30]
p.bgcolor = "white"
container.add(p)
self.plot = container
def create_plot(self):
# Create the mapper, etc
self._image_index = GridDataSource(array([]),
array([]),
sort_order=("ascending","ascending"))
image_index_range = DataRange2D(self._image_index)
self._image_index.on_trait_change(self._metadata_changed,
"metadata_changed")
self._image_value = ImageData(data=array([]), value_depth=1)
image_value_range = DataRange1D(self._image_value)
# Create the contour plots
self.polyplot = ContourPolyPlot(index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=
image_index_range),
color_mapper=\
self._cmap(image_value_range),
levels=self.num_levels)
self.lineplot = ContourLinePlot(index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=
self.polyplot.index_mapper.range),
levels=self.num_levels)
# Add a left axis to the plot
left = PlotAxis(orientation='left',
title= "y",
mapper=self.polyplot.index_mapper._ymapper,
component=self.polyplot)
self.polyplot.overlays.append(left)
# Add a bottom axis to the plot
bottom = PlotAxis(orientation='bottom',
title= "x",
mapper=self.polyplot.index_mapper._xmapper,
component=self.polyplot)
self.polyplot.overlays.append(bottom)
# Add some tools to the plot
self.polyplot.tools.append(PanTool(self.polyplot,
constrain_key="shift"))
self.polyplot.overlays.append(ZoomTool(component=self.polyplot,
tool_mode="box", always_on=False))
self.polyplot.overlays.append(LineInspector(component=self.polyplot,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=True,
color="white"))
self.polyplot.overlays.append(LineInspector(component=self.polyplot,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=True))
# Add these two plots to one container
contour_container = OverlayPlotContainer(padding=20,
use_backbuffer=True,
unified_draw=True)
contour_container.add(self.polyplot)
contour_container.add(self.lineplot)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.polyplot,
padding_top=self.polyplot.padding_top,
padding_bottom=self.polyplot.padding_bottom,
padding_right=40,
resizable='v',
width=30)
self.pd = ArrayPlotData(line_index = array([]),
line_value = array([]),
scatter_index = array([]),
scatter_value = array([]),
scatter_color = array([]))
self.cross_plot = Plot(self.pd, resizable="h")
self.cross_plot.height = 100
self.cross_plot.padding = 20
self.cross_plot.plot(("line_index", "line_value"),
line_style="dot")
self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8)
self.cross_plot.index_range = self.polyplot.index_range.x_range
self.pd.set_data("line_index2", array([]))
self.pd.set_data("line_value2", array([]))
self.pd.set_data("scatter_index2", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("scatter_color2", array([]))
self.cross_plot2 = Plot(self.pd, width = 140, orientation="v", resizable="v", padding=20, padding_bottom=160)
self.cross_plot2.plot(("line_index2", "line_value2"),
line_style="dot")
self.cross_plot2.plot(("scatter_index2","scatter_value2","scatter_color2"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8)
self.cross_plot2.index_range = self.polyplot.index_range.y_range
# Create a container and add components
self.container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.cross_plot)
inner_cont.add(contour_container)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.cross_plot2)
