def _render_scatter_overlay(base_plot, array_plot_data,
marker="circle", fill_alpha=0.5,
marker_size=6, tools=[]):
#if 'index' not in array_plot_data.arrays:
#return base_plot, None
scatter_plot = Plot(array_plot_data, aspect_ratio = base_plot.aspect_ratio,
default_origin="top left")
if "color" in array_plot_data.arrays:
scatter_plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="scatter_plot",
color_mapper=jet,
marker = marker,
fill_alpha = fill_alpha,
marker_size = marker_size,
)
scatter_renderer = scatter_plot.plots['scatter_plot'][0]
colorbar = _create_colorbar(scatter_plot.color_mapper, tools=tools)
colorbar.plot = scatter_renderer
#colorbar.padding_top = scatter_renderer.padding_top
#colorbar.padding_bottom = scatter_renderer.padding_bottom
if 'colorbar' in tools:
# this part is for making the colormapped points fade when they
# are not selected by the threshold.
# The renderer is the actual class that does the rendering -
# the Plot class calls this other class. When we say
# plots['scatter_plot']
# we are getting the named plot that we created above.
# The extra [0] on the end is because we get a list - in case
# there is more than one plot named "scatter_plot"
selection = ColormappedSelectionOverlay(scatter_renderer,
fade_alpha=0.35,
selection_type="range")
scatter_renderer.overlays.append(selection)
else:
colorbar = None
if 'inspector' in tools:
# Attach the inspector and its overlay
scatter_renderer = scatter_plot.plots['scatter_plot'][0]
#scatter_plot.tools.append(PeakSelectionTool(scatter_renderer))
selection = ColormappedSelectionOverlay(scatter_renderer,
fade_alpha=0.35,
selection_type="mask")
scatter_plot.overlays.append(selection)
scatter_plot.x_grid.visible = False
scatter_plot.y_grid.visible = False
scatter_plot.range2d = base_plot.range2d
return scatter_plot, colorbar
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = randint(0, 7, numpts)
# 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=accent,
marker="square",
fill_alpha=0.5,
marker_size=6,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Range-selectable Data Points"
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"
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# 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"
return container
def _create_plot_component():
# Load state data
states = pandas.read_csv('states.csv')
lon = (states['longitude'] + 180.) / 360.
lat = numpy.radians(states['latitude'])
lat = (1 - (1. - numpy.log(numpy.tan(lat) +
(1. / numpy.cos(lat))) / numpy.pi) / 2.0)
populations = pandas.read_csv('state_populations.csv')
data = populations['2010']
lon = lon.view(numpy.ndarray)
lat = lat.view(numpy.ndarray)
data = data.view(numpy.ndarray)
plot = Plot(ArrayPlotData(index=lon, value=lat, color=data))
renderers = plot.plot(
("index", "value", "color"),
type="cmap_scatter",
name="unfunded",
color_mapper=OrRd,
marker="circle",
outline_color='lightgray',
line_width=1.,
marker_size=10,
)
tile_cache = MBTileManager(filename='./map.mbtiles',
min_level=2,
max_level=4)
# Need a better way add the overlays
cmap = renderers[0]
map = Map(cmap, tile_cache=tile_cache, zoom_level=3)
cmap.underlays.append(map)
plot.title = "2010 Population"
plot.tools.append(PanTool(plot))
plot.tools.append(ZoomTool(plot))
plot.index_axis.title = "Longitude"
plot.index_axis.tick_label_formatter = convert_lon
plot.value_axis.title = "Latitude"
plot.value_axis.tick_label_formatter = convert_lat
cmap.overlays.append(
ColormappedSelectionOverlay(cmap,
fade_alpha=0.35,
selection_type="mask"))
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
def create_plot():
# Create some data
numpts = 200
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=jet,
marker="square",
fill_alpha=0.5,
marker_size=6,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot"
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"
# Set colors
#plot.title_color = "white"
#for axis in plot.x_axis, plot.y_axis:
# axis.trait_set(title_color="white", tick_label_color="white")
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
plot.tools.append(MoveTool(plot, drag_button="right"))
return plot
def generate_colorbar(self, desc):
""" Generate the colorbar to display along side the plot.
"""
super(CmapScatterPlotFactory, self).generate_colorbar(desc)
colorbar = self.colorbar
cmap_renderer = self._get_cmap_renderer()
select_tool = "colorbar_selector" in self.plot_tools
if select_tool:
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
colorbar.tools.append(RangeSelection(component=colorbar))
overlay = RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray")
colorbar.overlays.append(overlay)