def test_reversed(self):
ary = array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ds = ArrayDataSource(ary)
r = DataRange1D(ds)
mapper = LinearMapper(range=r, low_pos=100, high_pos=0)
result = mapper.map_screen(ary)
assert_equal(result , array([100, 80, 60, 40, 20, 0]))
return
def test_update_high_pos_dont_stretch_data_with_zero(self):
ary = array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ds = ArrayDataSource(ary)
r = DataRange1D(ds)
mapper = LinearMapper(range=r, stretch_data=False)
# Initialize the bounds, then modify them.
mapper.screen_bounds = (50.0, 100.0)
mapper.high_pos = 100.0
result = mapper.map_screen(ary)
assert_array_almost_equal(result, array([50, 60, 70, 80, 90, 100]))
def test_update_low_pos_stretch_data_with_reverse(self):
ary = array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ds = ArrayDataSource(ary)
r = DataRange1D(ds)
mapper = LinearMapper(range=r, stretch_data=True)
# Initialize the bounds, then modify them.
mapper.screen_bounds = (50.0, 100.0)
mapper.low_pos = 150.0
result = mapper.map_screen(ary)
assert_array_almost_equal(result, array([150, 140, 130, 120, 110, 100]))
def test_reversed_update_high_pos_stretch_data(self):
ary = array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ds = ArrayDataSource(ary)
r = DataRange1D(ds)
mapper = LinearMapper(range=r, stretch_data=True)
# Initialize the bounds, then modify them.
mapper.screen_bounds = (100.0, 50.0)
mapper.high_pos = 40.0
result = mapper.map_screen(ary)
assert_array_almost_equal(result, array([100, 88, 76, 64, 52, 40]))
def test_reversed_update_screen_bounds_stretch_data(self):
ary = array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ds = ArrayDataSource(ary)
r = DataRange1D(ds)
mapper = LinearMapper(range=r, stretch_data=True)
# Initialize the bounds, then modify them.
mapper.screen_bounds = (100.0, 0.0)
mapper.screen_bounds = (120.0, -10.0)
result = mapper.map_screen(ary)
assert_array_almost_equal(
result, array([120.0, 94.0, 68.0, 42.0, 16.0, -10.0]))
def test_reversed_set_screen_bounds(self):
ary = array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ds = ArrayDataSource(ary)
r = DataRange1D(ds)
mapper = LinearMapper(range=r)
self.assertFalse(mapper._low_bound_initialized)
self.assertFalse(mapper._high_bound_initialized)
mapper.screen_bounds = (100.0, 0.0)
self.assertTrue(mapper._low_bound_initialized)
self.assertTrue(mapper._high_bound_initialized)
result = mapper.map_screen(ary)
assert_equal(result , array([100, 80, 60, 40, 20, 0]))
return
def test_basic(self):
ary = array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
ds = ArrayDataSource(ary)
r = DataRange1D(ds)
mapper = LinearMapper(range=r)
self.assertFalse(mapper._low_bound_initialized)
self.assertFalse(mapper._high_bound_initialized)
mapper.low_pos=50
self.assertTrue(mapper._low_bound_initialized)
mapper.high_pos=100
self.assertTrue(mapper._high_bound_initialized)
result = mapper.map_screen(ary)
assert_equal(result , array([50, 60, 70, 80, 90, 100]))
return
def test_reverse_construction(self):
mapper = LinearMapper()
r = DataRange1D()
ds = ArrayDataSource()
ary = array([1,2,3,4,5,6,7])
mapper.range = r
mapper.low_pos = 1.0
mapper.high_pos = 7.0
r.add(ds)
ds.set_data(ary)
self.assertTrue(r.low == 1)
self.assertTrue(r.high == 7)
screen_pts = mapper.map_screen(array([1,3,7]))
self.assertTrue(tuple(screen_pts) == (1.0, 3.0, 7.0))
return
def test_piecewise_construction(self):
ary = array([1,2,3,4,5,6,7])
ds = ArrayDataSource()
ds.set_data(ary)
r = DataRange1D()
r.add(ds)
self.assert_(r.low_setting == "auto")
self.assert_(r.high_setting == "auto")
self.assert_(r.low == 1)
self.assert_(r.high == 7)
mapper = LinearMapper()
mapper.range = r
mapper.low_pos = 1.0
mapper.high_pos = 7.0
screen_pts = mapper.map_screen(array([1,3,7]))
self.assert_(tuple(screen_pts) == (1.0, 3.0, 7.0))
return
def setUp(self):
self.size = (250, 250)
data_source = ArrayDataSource(arange(10))
index_range = DataRange1D()
index_range.add(data_source)
index_mapper = LinearMapper(range=index_range)
self.scatterplot = LineScatterPlot1D(
index=data_source,
index_mapper=index_mapper,
border_visible=False,
)
self.scatterplot.outer_bounds = list(self.size)
def create_colorbar(colormap):
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.tools.append(RangeSelection(component=colorbar))
colorbar.overlays.append(RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
return colorbar
def make_colorbar(self,
plot,
width=30,
padding=20,
orientation='v',
resizable='v'):
cm = gray(DataRange1D())
lm = LinearMapper()
colorbar = ColorBar(orientation=orientation,
resizable=resizable,
width=width,
padding=padding,
index_mapper=lm,
color_mapper=cm)
if plot is not None:
colorbar.trait_set(
index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper,
plot=plot)
return colorbar
def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0, scale=1.0):
""" drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1, y1, x2, y2 = self.remove_short_lines(x1c,
y1c,
x2c,
y2c,
min_length=0)
if len(x1) > 0:
xs = ArrayDataSource(x1)
ys = ArrayDataSource(y1)
quiverplot = QuiverPlot(
index=xs,
value=ys,
index_mapper=LinearMapper(range=self._plot.index_mapper.range),
value_mapper=LinearMapper(range=self._plot.value_mapper.range),
origin=self._plot.origin,
arrow_size=0,
line_color=color,
line_width=linewidth,
ep_index=np.array(x2) * scale,
ep_value=np.array(y2) * scale)
self._plot.add(quiverplot)
# we need this to track how many quiverplots are in the current
# plot
self._quiverplots.append(quiverplot)
def test_map_screen_with_null_data_range(self):
r = DataRange1D()
mapper = LinearMapper(range=r)
low_pos = mapper.low_pos
data = array([5.6])
result = mapper.map_screen(data)
self.assertIsInstance(result, ndarray)
self.assertEqual(result.shape, (1, ))
assert_array_almost_equal(result, array([low_pos]))
# Test support for lists and tuples
data = [5.6]
result = mapper.map_screen(data)
self.assertIsInstance(result, ndarray)
self.assertEqual(result.shape, (1, ))
assert_array_almost_equal(result, array([low_pos]))
data = (5.6, )
result = mapper.map_screen(data)
self.assertIsInstance(result, ndarray)
self.assertEqual(result.shape, (1, ))
assert_array_almost_equal(result, array([low_pos]))
def test_errorbarplot(self):
""" Coverage test to check basic case works """
size = (50, 50)
x = np.array([1, 2])
y = np.array([5, 10])
errors = np.array([1, 2])
low = ArrayDataSource(y - errors)
high = ArrayDataSource(y + errors)
errorbar_plot = ErrorBarPlot(
index=ArrayDataSource(x),
values=ArrayDataSource(y),
index_mapper=LinearMapper(range=DataRange1D(low=0, high=3)),
value_mapper=LinearMapper(range=DataRange1D(low=0, high=15)),
value_low=low,
value_high=high,
color='blue',
line_width=3.0,
)
errorbar_plot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(errorbar_plot)
actual = gc.bmp_array[:, :, :]
self.assertFalse(alltrue(actual == 255))
def setUp(self):
self.size = (250, 250)
index_data_source = ArrayDataSource(arange(10))
index_range = DataRange1D()
index_range.add(index_data_source)
index_mapper = LinearMapper(range=index_range)
value_data_source = ArrayDataSource(arange(1, 11))
value_range = DataRange1D()
value_range.add(value_data_source)
value_mapper = LinearMapper(range=value_range)
text_data_source = ArrayDataSource(
array(["one", "two", "three", "four", "five"]))
self.text_plot = TextPlot(
index=index_data_source,
index_mapper=index_mapper,
value=value_data_source,
value_mapper=value_mapper,
border_visible=False,
text=text_data_source,
)
self.text_plot.outer_bounds = list(self.size)
def line_plot(self, x, y, new_plot=True):
if self.plot is None or new_plot:
if isinstance(x, (float, int)):
x = [x]
if isinstance(y, (float, int)):
y = [y]
self.plot = LinePlot(
index=ArrayDataSource(x),
value=ArrayDataSource(y),
index_mapper=LinearMapper(range=self.index_range),
value_mapper=LinearMapper(range=self.value_range))
self.add(self.plot)
else:
datax = self.plot.index.get_data()
datay = self.plot.value.get_data()
nx = hstack((datax, [x]))
ny = hstack((datay, [y]))
self.plot.index.set_data(nx)
self.plot.value.set_data(ny)
def _create_plot_component(model):
# Create a plot data object and give it the model's data array.
pd = ArrayPlotData()
pd.set_data("imagedata", model.data)
# Create the "main" Plot.
plot = Plot(pd, padding=50)
# Use a TransformColorMapper for the color map.
tcm = TransformColorMapper.from_color_map(jet)
# Create the image plot renderer in the main plot.
renderer = plot.img_plot("imagedata",
xbounds=model.x_array,
ybounds=model.y_array,
colormap=tcm)[0]
# Create the colorbar.
lm = LinearMapper(range=renderer.value_range,
domain_limits=(renderer.value_range.low,
renderer.value_range.high))
colorbar = ColorBar(index_mapper=lm,
plot=plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Add pan and zoom tools to the colorbar.
colorbar.tools.append(
PanTool(colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button="right")
colorbar.overlays.append(zoom_overlay)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
return container
def _corr_plot_default(self):
diag = self.covar.diagonal()
corr = self.covar / np.sqrt(np.outer(diag, diag))
N = len(diag)
value_range = DataRange1D(low=-1, high=1)
color_mapper = cmap(range=value_range)
index = GridDataSource()
value = ImageData()
mapper = GridMapper(range=DataRange2D(index),
y_low_pos=1.0,
y_high_pos=0.0)
index.set_data(xdata=np.arange(-0.5, N), ydata=np.arange(-0.5, N))
value.set_data(np.flipud(corr))
self.corr_data = value
cmap_plot = CMapImagePlot(index=index,
index_mapper=mapper,
value=value,
value_mapper=color_mapper,
padding=(40, 40, 100, 40))
yaxis = PlotAxis(
cmap_plot,
orientation='left',
tick_interval=1,
tick_label_formatter=lambda x: self.header[int(N - 1 - x)],
tick_generator=ShowAllTickGenerator(positions=np.arange(N)))
xaxis = PlotAxis(
cmap_plot,
orientation='top',
tick_interval=1,
tick_label_formatter=lambda x: self.header[int(x)],
tick_label_alignment='edge',
tick_generator=ShowAllTickGenerator(positions=np.arange(N)))
cmap_plot.overlays.append(yaxis)
cmap_plot.overlays.append(xaxis)
colorbar = ColorBar(
index_mapper=LinearMapper(range=cmap_plot.value_range),
plot=cmap_plot,
orientation='v',
resizable='v',
width=10,
padding=(40, 5, 100, 40))
container = HPlotContainer(bgcolor='transparent')
container.add(cmap_plot)
container.add(colorbar)
return container
def recompute_colorbar(self, plot):
""" Create a colorbar for the image plot provided, and add to
plot1_2d_container
"""
img_renderer = plot.plots["img_cost"][0]
colormap = img_renderer.color_mapper
# Constant mapper for the color bar so that the colors stay the same
# even when different slices are selected-=
index_mapper = LinearMapper(range=self.colorbar_range)
self.colorbar = ColorBar(index_mapper=index_mapper,
color_mapper=colormap,
padding_top=plot.padding_top,
padding_bottom=plot.padding_bottom,
padding_right=20,
resizable='v',
orientation='v',
width=30)
def setUp(self):
self.size = (250, 250)
data_source = ArrayDataSource(arange(10))
text_data = ArrayDataSource([
'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight',
'nine', 'ten'
])
index_range = DataRange1D()
index_range.add(data_source)
index_mapper = LinearMapper(range=index_range)
self.scatterplot = TextPlot1D(
index=data_source,
index_mapper=index_mapper,
value=text_data,
border_visible=False,
)
self.scatterplot.outer_bounds = list(self.size)
def _figure_image_default(self):
plot = Plot(self.plot_data_image,
width=100,
height=100,
padding=8,
padding_left=48,
padding_bottom=32)
plot.img_plot('image', colormap=RdBu_r, name='image')
plot.aspect_ratio = 1
plot.index_mapper.domain_limits = (self.imager.get_x_range()[0],
self.imager.get_x_range()[1])
plot.value_mapper.domain_limits = (self.imager.get_y_range()[0],
self.imager.get_y_range()[1])
plot_fit = Plot(self.plot_data_image,
width=100,
height=100,
padding=8,
padding_left=48,
padding_bottom=32)
plot_fit.img_plot('fit', colormap=RdBu_r, name='fit')
plot_fit.aspect_ratio = 1
plot_fit.index_mapper.domain_limits = (self.imager.get_x_range()[0],
self.imager.get_x_range()[1])
plot_fit.value_mapper.domain_limits = (self.imager.get_y_range()[0],
self.imager.get_y_range()[1])
container = HPlotContainer()
image = plot.plots['image'][0]
colormap = image.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=16,
height=320,
padding=8,
padding_left=32)
container = HPlotContainer()
container.add(plot)
container.add(plot_fit)
container.add(colorbar)
container.overlays.append(self.image_label)
container.tools.append(SaveTool(container))
return container
def _RegionsPlot_default(self):
plotdata = ArrayPlotData(imagedata=self.RegionsAndLabels[0],
x=self.XPositions,
y=self.YPositions)
plot = Plot(plotdata, width=500, height=500, resizable='hv')
RegionsImage = plot.img_plot(
'imagedata',
colormap=gray,
xbounds=(self.X[0], self.X[-1]),
ybounds=(self.Y[0], self.Y[-1]),
)[0]
RegionsImage.x_mapper.domain_limits = (self.X[0], self.X[-1])
RegionsImage.y_mapper.domain_limits = (self.Y[0], self.Y[-1])
RegionsImage.overlays.append(ZoomTool(RegionsImage))
scatterplot = plot.plot(('x', 'y'),
type='scatter',
marker='plus',
color='yellow')
colormap = RegionsImage.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=10,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
self.RegionsData = plotdata
self.RegionsImage = RegionsImage
container = HPlotContainer(padding=20,
fill_padding=True,
use_backbuffer=True)
container.add(colorbar)
container.add(plot)
return container
def test_reverse_construction(self):
mapper = LinearMapper()
r = DataRange1D()
ds = ArrayDataSource()
ary = array([1, 2, 3, 4, 5, 6, 7])
mapper.range = r
mapper.low_pos = 1.0
mapper.high_pos = 7.0
r.add(ds)
ds.set_data(ary)
self.assertTrue(r.low == 1)
self.assertTrue(r.high == 7)
screen_pts = mapper.map_screen(array([1, 3, 7]))
self.assertTrue(tuple(screen_pts) == (1.0, 3.0, 7.0))
return
def test_piecewise_construction(self):
ary = array([1, 2, 3, 4, 5, 6, 7])
ds = ArrayDataSource()
ds.set_data(ary)
r = DataRange1D()
r.add(ds)
self.assert_(r.low_setting == "auto")
self.assert_(r.high_setting == "auto")
self.assert_(r.low == 1)
self.assert_(r.high == 7)
mapper = LinearMapper()
mapper.range = r
mapper.low_pos = 1.0
mapper.high_pos = 7.0
screen_pts = mapper.map_screen(array([1, 3, 7]))
self.assert_(tuple(screen_pts) == (1.0, 3.0, 7.0))
return
def _rebuild_plot(self):
container = self.plot
value_range = DataRange1D(low=-1, high=1.)
index_range = DataRange1D(self.index_ds,
high='track',
tracking_amount=24 * 3600 * 365)
color_mapper = cmap(range=value_range)
# Remove old plots
container.remove(*container.components)
for val, row in zip(self.value_ds, self.rows):
horizon = HorizonPlot(
index=self.index_ds,
value=val,
index_mapper=LinearMapper(range=index_range,
stretch_data=False),
value_mapper=BandedMapper(range=DataRange1D(val)),
color_mapper=cmap(range=DataRange1D(val)), #color_mapper,
negative_bands=False,
)
horizon.tools.append(
PanTool(horizon, constrain=True, constrain_direction="x"))
horizon.overlays.append(
PlotLabel(component=horizon,
hjustify='right',
text=row,
overlay_position='outside left'))
container.add(horizon)
bottom_axis = PlotAxis(
horizon,
orientation="bottom",
tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
container.overlays = [bottom_axis]
container.request_redraw()
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)
def create_plot_component(self):# Create a scalar field to colormap
# Create the plot
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.fxydata())
plot = Plot(self.pd)
cmap = default_colormaps.color_map_name_dict[self.colormap]
if self.rev_cmap:
cmap = default_colormaps.reverse(cmap)
img_plot = plot.img_plot("imagedata",
xbounds = self.xbounds,
ybounds = self.ybounds,
colormap=cmap,
)[0]
# Tweak some of the plot properties
plot.title = self.title
plot.padding = 50
# Attach some tools to the plot
#plot.tools.append(PanTool(plot))
#zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
#plot.overlays.append(zoom)
csr = CursorTool(img_plot,
drag_button='left',
color='white',
line_width=2.0
)
self.cursor = csr
csr.current_position = np.mean(self.xbounds), np.mean(self.ybounds)
img_plot.overlays.append(csr)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True,)
#img_plot.overlays.append(overlay)
colorbar = ColorBar(index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.plot = plot
colorbar.padding_top = plot.padding_top + 10
colorbar.padding_bottom = plot.padding_bottom
# Add pan and zoom tools to the colorbar
colorbar.tools.append(PanTool(colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(colorbar, axis="index", tool_mode="range",
always_on=True, drag_button="right")
colorbar.overlays.append(zoom_overlay)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(plot, colorbar, use_backbuffer=True, bgcolor="transparent",)
container.overlays.append(overlay)
#self.plot = plot
self.plot = 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_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=gist_earth,
marker="square",
fill_alpha=0.5,
marker_size=8,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Pan/Zoom Color Bar"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Add pan and zoom to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(plot)
plot.overlays.append(zoom)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = ColorBar(
index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.plot = plot
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Add pan and zoom tools to the colorbar
colorbar.tools.append(
PanTool(colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button="right")
colorbar.overlays.append(zoom_overlay)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(plot,
colorbar,
use_backbuffer=True,
bgcolor="lightgray")
return 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():
# Create the data and datasource objects
numpoints = 500
index = arange(numpoints)
returns = random.lognormal(0.01, 0.1, size=numpoints)
price = 100.0 * cumprod(returns)
volume = abs(random.normal(1000.0, 1500.0, size=numpoints) + 2000.0)
time_ds = ArrayDataSource(index)
vol_ds = ArrayDataSource(volume, sort_order="none")
price_ds = ArrayDataSource(price, sort_order="none")
xmapper = LinearMapper(range=DataRange1D(time_ds))
vol_mapper = LinearMapper(range=DataRange1D(vol_ds))
price_mapper = LinearMapper(range=DataRange1D(price_ds))
price_plot = FilledLinePlot(index=time_ds,
value=price_ds,
index_mapper=xmapper,
value_mapper=price_mapper,
edge_color="blue",
face_color="paleturquoise",
alpha=0.5,
bgcolor="white",
border_visible=True)
add_default_grids(price_plot)
price_plot.overlays.append(PlotAxis(price_plot, orientation='left'))
price_plot.overlays.append(PlotAxis(price_plot, orientation='bottom'))
price_plot.tools.append(
PanTool(price_plot, constrain=True, constrain_direction="x"))
price_plot.overlays.append(
ZoomTool(price_plot,
drag_button="right",
always_on=True,
tool_mode="range",
axis="index"))
vol_plot = BarPlot(index=time_ds,
value=vol_ds,
index_mapper=xmapper,
value_mapper=vol_mapper,
line_color="transparent",
fill_color="black",
bar_width=1.0,
bar_width_type="screen",
antialias=False,
height=100,
resizable="h",
bgcolor="white",
border_visible=True)
add_default_grids(vol_plot)
vol_plot.underlays.append(PlotAxis(vol_plot, orientation='left'))
vol_plot.tools.append(
PanTool(vol_plot, constrain=True, constrain_direction="x"))
container = VPlotContainer(bgcolor="lightblue",
spacing=20,
padding=50,
fill_padding=False)
container.add(vol_plot)
container.add(price_plot)
container.overlays.append(
PlotLabel(
"Financial Plot",
component=container,
#font="Times New Roman 24"))
font="Arial 24"))
return container
def _create_plot(self):
plot_data = ArrayPlotData(image=self.image)
plot = Plot(plot_data,
width=500,
height=500,
resizable='hv',
aspect_ratio=1.0,
padding=8,
padding_left=32,
padding_bottom=32)
plot.img_plot('image',
colormap=jet,
xbounds=(self.X[0], self.X[-1]),
ybounds=(self.Y[0], self.Y[-1]),
name='image')
image = plot.plots['image'][0]
bwplot_data = ArrayPlotData(bwimage=self.bwimage)
bwplot = Plot(bwplot_data,
width=500,
height=500,
resizable='hv',
aspect_ratio=1.0,
padding=8,
padding_left=32,
padding_bottom=32)
bwplot.img_plot('bwimage',
colormap=jet,
xbounds=(self.X[0], self.X[-1]),
ybounds=(self.Y[0], self.Y[-1]),
name='bwimage')
bwimage = bwplot.plots['bwimage'][0]
image.x_mapper.domain_limits = (self.confocal.scanner.getXRange()[0],
self.confocal.scanner.getXRange()[1])
image.y_mapper.domain_limits = (self.confocal.scanner.getYRange()[0],
self.confocal.scanner.getYRange()[1])
bwimage.x_mapper.domain_limits = (self.confocal.scanner.getXRange()[0],
self.confocal.scanner.getXRange()[1])
bwimage.y_mapper.domain_limits = (self.confocal.scanner.getYRange()[0],
self.confocal.scanner.getYRange()[1])
colormap = image.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=16,
height=320,
padding=8,
padding_left=32)
container = HPlotContainer()
container.add(plot)
container.add(colorbar)
container.add(bwplot)
container.tools.append(SaveTool(container))
self.plot_data = plot_data
self.scan_plot = image
self.figure = plot
self.bwplot_data = bwplot_data
self.bwscan_plot = bwimage
self.bwfigure = bwplot
self.figure_container = container
def _create_plot_component(obj):
# Setup the spectrum plot
frequencies = linspace(0.0, float(SAMPLING_RATE) / 2, num=NUM_SAMPLES / 2)
obj.spectrum_data = ArrayPlotData(frequency=frequencies)
empty_amplitude = zeros(NUM_SAMPLES / 2)
obj.spectrum_data.set_data('amplitude', empty_amplitude)
obj.spectrum_plot = Plot(obj.spectrum_data)
spec_renderer = obj.spectrum_plot.plot(("frequency", "amplitude"),
name="Spectrum",
color="red")[0]
obj.spectrum_plot.padding = 50
obj.spectrum_plot.title = "Spectrum"
spec_range = obj.spectrum_plot.plots.values()[0][0].value_mapper.range
spec_range.low = 0.0
spec_range.high = 5.0
obj.spectrum_plot.index_axis.title = 'Frequency (hz)'
obj.spectrum_plot.value_axis.title = 'Amplitude'
# Time Series plot
times = linspace(0.0, float(NUM_SAMPLES) / SAMPLING_RATE, num=NUM_SAMPLES)
obj.time_data = ArrayPlotData(time=times)
empty_amplitude = zeros(NUM_SAMPLES)
obj.time_data.set_data('amplitude', empty_amplitude)
obj.time_plot = Plot(obj.time_data)
obj.time_plot.plot(("time", "amplitude"), name="Time", color="blue")
obj.time_plot.padding = 50
obj.time_plot.title = "Time"
obj.time_plot.index_axis.title = 'Time (seconds)'
obj.time_plot.value_axis.title = 'Amplitude'
time_range = obj.time_plot.plots.values()[0][0].value_mapper.range
time_range.low = -0.2
time_range.high = 0.2
# Spectrogram plot
values = [zeros(NUM_SAMPLES / 2) for i in xrange(SPECTROGRAM_LENGTH)]
p = WaterfallRenderer(
index=spec_renderer.index,
values=values,
index_mapper=LinearMapper(range=obj.spectrum_plot.index_mapper.range),
value_mapper=LinearMapper(
range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
y2_mapper=LinearMapper(low_pos=0,
high_pos=8,
range=DataRange1D(low=0, high=15)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
container = HPlotContainer()
container.add(obj.spectrum_plot)
container.add(obj.time_plot)
c2 = VPlotContainer()
c2.add(dummy)
c2.add(container)
return c2
def clone_plot(clonetool, drop_position):
# A little sketchy...
canvas = clonetool.component.container.component.component
# Create a new Plot object
oldplot = clonetool.component
newplot = Plot(oldplot.data)
basic_traits = [
"orientation", "default_origin", "bgcolor", "border_color",
"border_width", "border_visible", "draw_layer", "unified_draw",
"fit_components", "fill_padding", "visible", "aspect_ratio", "title"
]
for attr in basic_traits:
setattr(newplot, attr, getattr(oldplot, attr))
# copy the ranges
dst = newplot.range2d
src = oldplot.range2d
#for attr in ('_low_setting', '_low_value', '_high_setting', '_high_value'):
# setattr(dst, attr, getattr(src, attr))
dst._xrange.sources = copy(src._xrange.sources)
dst._yrange.sources = copy(src._yrange.sources)
newplot.padding = oldplot.padding
newplot.bounds = oldplot.bounds[:]
newplot.resizable = ""
newplot.position = drop_position
newplot.datasources = copy(oldplot.datasources)
for name, renderers in list(oldplot.plots.items()):
newrenderers = []
for renderer in renderers:
new_r = clone_renderer(renderer)
new_r.index_mapper = LinearMapper(range=newplot.index_range)
new_r.value_mapper = LinearMapper(range=newplot.value_range)
new_r._layout_needed = True
new_r.invalidate_draw()
new_r.resizable = "hv"
newrenderers.append(new_r)
newplot.plots[name] = newrenderers
#newplot.plots = copy(oldplot.plots)
for name, renderers in list(newplot.plots.items()):
newplot.add(*renderers)
newplot.index_axis.title = oldplot.index_axis.title
newplot.index_axis.unified_draw = True
newplot.value_axis.title = oldplot.value_axis.title
newplot.value_axis.unified_draw = True
# Add new tools to the new plot
newplot.tools.append(
AxisTool(component=newplot, range_controller=canvas.range_controller))
# Add tools to the new plot
pan_traits = [
"drag_button", "constrain", "constrain_key", "constrain_direction",
"speed"
]
zoom_traits = [
"tool_mode", "always_on", "axis", "enable_wheel", "drag_button",
"wheel_zoom_step", "enter_zoom_key", "exit_zoom_key", "pointer",
"color", "alpha", "border_color", "border_size", "disable_on_complete",
"minimum_screen_delta", "max_zoom_in_factor", "max_zoom_out_factor"
]
move_traits = [
"drag_button", "end_drag_on_leave", "cancel_keys", "capture_mouse",
"modifier_key"
]
if not MULTITOUCH:
for tool in oldplot.tools:
if isinstance(tool, PanTool):
newtool = tool.clone_traits(pan_traits)
newtool.component = newplot
break
else:
newtool = PanTool(newplot)
# Reconfigure the pan tool to always use the left mouse, because we will
# put plot move on the right mouse button
newtool.drag_button = "left"
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, MoveTool):
newtool = tool.clone_traits(move_traits)
newtool.component = newplot
break
else:
newtool = MoveTool(newplot, drag_button="right")
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, ZoomTool):
newtool = tool.clone_traits(zoom_traits)
newtool.component = newplot
break
else:
newtool = ZoomTool(newplot)
newplot.tools.append(newtool)
else:
pz = MPPanZoom(newplot)
#pz.pan.constrain = True
#pz.pan.constrain_direction = "x"
#pz.zoom.mode = "range"
#pz.zoom.axis = "index"
newplot.tools.append(MPPanZoom(newplot))
#newplot.tools.append(MTMoveTool(
newplot._layout_needed = True
clonetool.dest.add(newplot)
newplot.invalidate_draw()
newplot.request_redraw()
canvas.request_redraw()
return
def new_series(self, x=None, y=None, plotid=0, **kw):
'''
'''
plot, scatter, _line = super(ResidualsGraph,
self).new_series(x=x,
y=y,
plotid=plotid,
**kw)
for underlay in plot.underlays:
if underlay.orientation == 'bottom':
underlay.visible = False
underlay.padding_bottom = 0
plot.padding_bottom = 0
x, y, res = self.calc_residuals(plotid=plotid)
ressplit = self._split_residual(x, res)
resneg = ArrayDataSource(ressplit[1])
xneg = ArrayDataSource(ressplit[0])
respos = ArrayDataSource(ressplit[3])
xpos = ArrayDataSource(ressplit[2])
yrange = DataRange1D(ArrayDataSource(res))
ymapper = LinearMapper(range=yrange)
container = self._container_factory(type='o',
padding=[50, 15, 0, 30],
height=75,
resizable='h')
bar = BarPlot(
index=xneg,
value=resneg,
index_mapper=scatter.index_mapper,
value_mapper=ymapper,
bar_width=0.2,
line_color='blue',
fill_color='blue',
border_visible=True,
)
# left_axis = PlotAxis(bar, orientation = 'left')
# bottom_axis=PlotAxis(bar,orientaiton='bottom')
kw = dict(vtitle='residuals')
if self.xtitle:
kw['htitle'] = self.xtitle
add_default_axes(bar, **kw)
hgrid = PlotGrid(mapper=ymapper,
component=bar,
orientation='horizontal',
line_color='lightgray',
line_style='dot')
bar.underlays.append(hgrid)
# bar.underlays.append(left_axis)
# bar.underlays.append(bottom_axis)
bar2 = BarPlot(
index=xpos,
value=respos,
index_mapper=scatter.index_mapper,
value_mapper=ymapper,
bar_width=0.2,
line_color='green',
fill_color='green',
# bgcolor = 'green',
resizable='hv',
border_visible=True,
# padding = [30, 5, 0, 30]
)
bar2.overlays.append(GuideOverlay(bar2, value=0, color=(0, 0, 0)))
bar2.underlays.append(hgrid)
container.add(bar)
container.add(bar2)
# container.add(PlotLabel('foo'))
self.residual_plots = [bar, bar2]
self.plotcontainer.add(container)
def __init__(self, x, y, z):
super(ImagePlot, self).__init__()
self.pd_all = ArrayPlotData(imagedata=z)
#self.pd_horiz = ArrayPlotData(x=x, horiz=z[4, :])
#self.pd_vert = ArrayPlotData(y=y, vert=z[:,5])
self._imag_index = GridDataSource(xdata=x,
ydata=y,
sort_order=("ascending",
"ascending"))
index_mapper = GridMapper(range=DataRange2D(self._imag_index))
self._imag_index.on_trait_change(self._metadata_changed,
"metadata_changed")
self._image_value = ImageData(data=z, value_depth=1)
color_mapper = jet(DataRange1D(self._image_value))
self.color_plot = CMapImagePlot(index=self._imag_index,
index_mapper=index_mapper,
value=self._image_value,
value_mapper=color_mapper,
padding=20,
use_backbuffer=True,
unified_draw=True)
#Add axes to image plot
left = PlotAxis(orientation='left',
title="Frequency (GHz)",
mapper=self.color_plot.index_mapper._ymapper,
component=self.color_plot)
self.color_plot.overlays.append(left)
bottom = PlotAxis(orientation='bottom',
title="Time (us)",
mapper=self.color_plot.index_mapper._xmapper,
component=self.color_plot)
self.color_plot.overlays.append(bottom)
self.color_plot.tools.append(
PanTool(self.color_plot, constrain_key="shift"))
self.color_plot.overlays.append(
ZoomTool(component=self.color_plot,
tool_mode="box",
always_on=False))
#Add line inspector tool for horizontal and vertical
self.color_plot.overlays.append(
LineInspector(component=self.color_plot,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=True,
color="white"))
self.color_plot.overlays.append(
LineInspector(component=self.color_plot,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=True))
myrange = DataRange1D(low=amin(z), high=amax(z))
cmap = jet
self.colormap = cmap(myrange)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=myrange)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.color_plot,
padding_top=self.color_plot.padding_top,
padding_bottom=self.color_plot.padding_bottom,
padding_right=40,
resizable='v',
width=30) #, ytitle="Magvec (mV)")
#create horizontal line plot
self.horiz_cross_plot = Plot(self.pd_horiz, resizable="h")
self.horiz_cross_plot.height = 100
self.horiz_cross_plot.padding = 20
self.horiz_cross_plot.plot(("x", "horiz")) #,
#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.horiz_cross_plot.index_range = self.color_plot.index_range.x_range
#create vertical line plot
self.vert_cross_plot = Plot(self.pd_vert,
width=140,
orientation="v",
resizable="v",
padding=20,
padding_bottom=160)
self.vert_cross_plot.plot(("y", "vert")) #,
# line_style="dot")
# self.vert_cross_plot.xtitle="Magvec (mV)"
# self.vertica_cross_plot.plot(("vertical_scatter_index",
# "vertical_scatter_value",
# "vertical_scatter_color"),
# type="cmap_scatter",
# name="dot",
# color_mapper=self._cmap(image_value_range),
# marker="circle",
# marker_size=8)
self.vert_cross_plot.index_range = self.color_plot.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.horiz_cross_plot)
inner_cont.add(self.color_plot)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.vert_cross_plot)
def create_gridded_scatter_plot(x,
y,
orientation="h",
color="red",
width=1.0,
fill_color="red",
marker="square",
marker_size=2,
value_mapper_class=LinearMapper,
padding=30):
assert len(x) == len(y)
# If you know it is monotonically increasing, sort_order can
# be set to 'ascending'
index = ArrayDataSource(x, sort_order='none')
value = ArrayDataSource(y, sort_order="none")
index_range = DataRange1D(tight_bounds=False)
index_range.add(index)
index_mapper = LinearMapper(range=index_range)
value_range = DataRange1D(tight_bounds=False)
value_range.add(value)
value_mapper = value_mapper_class(range=value_range)
plot = ScatterPlot(
index=index,
value=value,
index_mapper=index_mapper,
value_mapper=value_mapper,
orientation=orientation,
color=color,
fill_color=fill_color,
marker=marker,
marker_size=marker_size,
padding=[40, 15, 15, 20], # left, right, top, bottom
border_visible=True,
border_width=1,
bgcolor="white",
use_backbuffer=True,
backbuffer_padding=False,
unified_draw=True,
draw_layer="plot",
overlay_border=True)
vertical_grid = PlotGrid(component=plot,
mapper=index_mapper,
orientation='vertical',
line_color="gray",
line_style='dot',
use_draw_order=True)
horizontal_grid = PlotGrid(component=plot,
mapper=value_mapper,
orientation='horizontal',
line_color="gray",
line_style='dot',
use_draw_order=True)
vertical_axis = PlotAxis(orientation='left',
mapper=plot.value_mapper,
use_draw_order=True)
horizontal_axis = PlotAxis(orientation='bottom',
title='Time (s)',
mapper=plot.index_mapper,
use_draw_order=True)
plot.underlays.append(vertical_grid)
plot.underlays.append(horizontal_grid)
# Have to add axes to overlays because we are backbuffering the main plot,
# and only overlays get to render in addition to the backbuffer.
plot.overlays.append(vertical_axis)
plot.overlays.append(horizontal_axis)
return plot
def _create_plot_component():
# Create a scalar field to colormap# Create a scalar field to colormap
xbounds = (-2 * pi, 2 * pi, 600)
ybounds = (-1.5 * pi, 1.5 * pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs, ys)
z = jn(2, x) * y * x
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
plot.img_plot(
"imagedata",
name="my_plot",
xbounds=xbounds[:2],
ybounds=ybounds[:2],
colormap=jet)
# Tweak some of the plot properties
plot.title = "Selectable Image Plot"
plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
my_plot = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the colorbar, handing in the appropriate range and colormap
colormap = my_plot.color_mapper
colorbar = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a range selection for the colorbar
range_selection = RangeSelection(component=colorbar)
colorbar.tools.append(range_selection)
colorbar.overlays.append(
RangeSelectionOverlay(
component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
#my_plot.set_value_selection((-1.3, 6.9))
return container
