def get_cmap_scatter_plot():
boston = datasets.load_boston()
prices = boston['target']
lower_status = boston['data'][:,-1]
nox = boston['data'][:,4]
x, y = get_data_sources(x=lower_status, y=prices)
x_mapper, y_mapper = get_mappers(x, y)
color_source = ArrayDataSource(nox)
color_mapper = dc.reverse(dc.RdYlGn)(
DataRange1D(low=nox.min(), high=nox.max())
)
scatter_plot = ColormappedScatterPlot(
index=x, value=y,
index_mapper=x_mapper, value_mapper=y_mapper,
color_data=color_source,
color_mapper=color_mapper,
marker='circle',
title='Color represents nitric oxides concentration',
render_method='bruteforce',
**PLOT_DEFAULTS
)
add_axes(scatter_plot, x_label='Percent lower status in the population',
y_label='Median house prices')
return scatter_plot
def get_cmap_scatter_plot():
boston = datasets.load_boston()
prices = boston['target']
lower_status = boston['data'][:, -1]
nox = boston['data'][:, 4]
x, y = get_data_sources(x=lower_status, y=prices)
x_mapper, y_mapper = get_mappers(x, y)
color_source = ArrayDataSource(nox)
color_mapper = dc.reverse(dc.RdYlGn)(DataRange1D(low=nox.min(),
high=nox.max()))
scatter_plot = ColormappedScatterPlot(
index=x,
value=y,
index_mapper=x_mapper,
value_mapper=y_mapper,
color_data=color_source,
color_mapper=color_mapper,
marker='circle',
title='Color represents nitric oxides concentration',
render_method='bruteforce',
**PLOT_DEFAULTS)
add_axes(scatter_plot,
x_label='Percent lower status in the population',
y_label='Median house prices')
return scatter_plot
def _create_colormap(self):
if self.colormap_low is None:
self.colormap_low = self.matrix.min()
if self.colormap_high is None:
self.colormap_high = self.matrix.max()
if self.colormap_low >= 0.0:
colormap_factory = Reds
else:
colormap_factory = reverse(RdBu)
colormap = colormap_factory(
DataRange1D(low=self.colormap_low, high=self.colormap_high))
return colormap
def _create_colormap(self):
if self.colormap_low is None:
self.colormap_low = self.matrix.min()
if self.colormap_high is None:
self.colormap_high = self.matrix.max()
if self.colormap_low >= 0.0:
colormap_factory = Reds
else:
colormap_factory = reverse(RdBu)
colormap = colormap_factory(
DataRange1D(low=self.colormap_low, high=self.colormap_high)
)
return colormap
def _update(self):
r = self.series.value.get_bounds()
func = color_map_name_dict[self.cmap]
if self.reverse:
func = reverse(func)
cm = func(DataRange1D(low_setting=0, high_setting=r[1]))
for i, p in enumerate(self.graph.plots[self.plotid].plots.itervalues()):
if i == 0:
continue
p = p[0]
p.color_mapper = cm
p.bgcolor = cm.color_bands[0]
self.series.invalidate_and_redraw()
def _modify_colormap(self):
#myTP.color_mapper.range.low_setting = 0
#myTP.color_mapper.range.high_setting = 1000
# pick out the color map function
myColorMapperFn = default_colormaps.color_map_name_dict[self.colormapNameTE]
# reverse the colormap, if req'd
if self.reversedColormapTB:
myColorMapperFn = default_colormaps.reverse( myColorMapperFn )
## TODO adjust for too low, too high, end cases
myColorMapperFn = default_colormaps.fix(
myColorMapperFn,
(self.colormapLowTR, self.colormapHighTR)
)
myColorMapper = myColorMapperFn( self.myDR1D )
self.myTP.color_mapper = myColorMapper
self.myTP.request_redraw()
def get_4d_scatter_plot():
boston = datasets.load_boston()
prices = boston['target']
lower_status = boston['data'][:, -1]
tax = boston['data'][:, 9]
nox = boston['data'][:, 4]
x, y = get_data_sources(x=lower_status, y=prices)
x_mapper, y_mapper = get_mappers(x, y)
color_source = ArrayDataSource(nox)
color_mapper = dc.reverse(dc.RdYlGn)(DataRange1D(low=nox.min(),
high=nox.max()))
# normalize between 0 and 10
marker_size = tax / tax.max() * 10.
scatter_plot = ColormappedScatterPlot(
index=x,
value=y,
index_mapper=x_mapper,
value_mapper=y_mapper,
color_data=color_source,
color_mapper=color_mapper,
fill_alpha=0.8,
marker='circle',
marker_size=marker_size,
title='Size represents property-tax rate, '
'color nitric oxides concentration',
render_method='bruteforce',
**PLOT_DEFAULTS)
add_axes(scatter_plot,
x_label='Percent lower status in the population',
y_label='Median house prices')
return scatter_plot
def get_4d_scatter_plot():
boston = datasets.load_boston()
prices = boston['target']
lower_status = boston['data'][:,-1]
tax = boston['data'][:,9]
nox = boston['data'][:,4]
x, y = get_data_sources(x=lower_status, y=prices)
x_mapper, y_mapper = get_mappers(x, y)
color_source = ArrayDataSource(nox)
color_mapper = dc.reverse(dc.RdYlGn)(
DataRange1D(low=nox.min(), high=nox.max())
)
# normalize between 0 and 10
marker_size = tax / tax.max() * 10.
scatter_plot = ColormappedScatterPlot(
index=x, value=y,
index_mapper=x_mapper, value_mapper=y_mapper,
color_data=color_source,
color_mapper=color_mapper,
fill_alpha = 0.8,
marker='circle',
marker_size=marker_size,
title='Size represents property-tax rate, '
'color nitric oxides concentration',
render_method='bruteforce',
**PLOT_DEFAULTS
)
add_axes(scatter_plot, x_label='Percent lower status in the population',
y_label='Median house prices')
return scatter_plot
def create_plot_component(self):
if not self.data.size:
return
self.pd = ArrayPlotData()
self.shape = self.data.shape
self.pd.set_data("imagedata", self.data)
self.pd.set_data("left_line", self.data[:, self.shape[0]//2])
self.pd.set_data("top_line", self.data[self.shape[1]//2,:])
plot = Plot(self.pd)
cmap = default_colormaps.color_map_name_dict[self.colormap]
if self.rev_cmap:
cmap = default_colormaps.reverse(cmap)
drange = DataRange1D(ImageData(data=self.data, value_depth=1))
self.ccmap = cmap_constant_range(cmap, drange)(drange)
#from copy import copy
self.img_plot = plot.img_plot("imagedata",
colormap=cmap,
#xbounds=self.xbounds,
#ybounds=self.ybounds,
)[0]
self.cmap = self.img_plot.value_mapper
if not self.autoscale:
self.img_plot.value_mapper = self.ccmap
# Tweak some of the plot properties
plot.title = self.title
plot.padding = 10
# 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(self.img_plot,
drag_button='right',
color='white',
line_width=2.0
)
self.cursor = csr
csr.current_index = self.shape[0]//2, self.shape[1]//2
self.img_plot.overlays.append(csr)
imgtool = ImageInspectorTool(self.img_plot)
self.img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=self.img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True)
self.img_plot.overlays.append(overlay)
self.plot = plot
self.cross_plot = Plot(self.pd,resizable='h')
self.cross_plot.height = 40
self.cross_plot.padding = 15
self.cross_plot.plot("top_line",
line_style="dot")
self.cross_plot.index_range = self.img_plot.index_range.x_range
self.cross_plot2 = Plot(self.pd, width=40, orientation="v",
padding=15, padding_bottom=10, resizable='v')
self.cross_plot2.plot("left_line",
line_style="dot")
self.cross_plot2.index_range = self.img_plot.index_range.y_range
# Create a container and add components
#self.container = HPlotContainer(padding=10, fill_padding=False,
# bgcolor="none", use_backbuffer=False)
#inner_cont = VPlotContainer(padding=0, use_backbuffer=True, bgcolor="none")
#inner_cont.add(self.plot)
#inner_cont.add(self.cross_plot)
self.container = GridPlotContainer(padding=20, fill_padding=False,
bgcolor="none", use_backbuffer=True,
shape=(2, 2), spacing=(12, 20))
#self.container.add(self.colorbar)
self.container.add(self.plot)
self.container.add(self.cross_plot2)
self.container.add(self.cross_plot)
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
