def _create_plot_component(): # 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 = sin(x) * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds=xbounds[:2],
ybounds=ybounds[:2],
colormap=viridis)[0]
# Tweak some of the plot properties
plot.title = "Image Plot with Lasso"
plot.padding = 50
lasso_selection = LassoSelection(component=img_plot)
lasso_selection.on_trait_change(lasso_updated, "disjoint_selections")
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=img_plot)
img_plot.tools.append(lasso_selection)
img_plot.overlays.append(lasso_overlay)
return plot
def _create_plot_component(): # 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 = sin(x) * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata", xbounds=xbounds[:2], ybounds=ybounds[:2], colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "Image Plot with Lasso"
plot.padding = 50
lasso_selection = LassoSelection(component=img_plot)
lasso_selection.on_trait_change(lasso_updated, "disjoint_selections")
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection, component=img_plot)
img_plot.tools.append(lasso_selection)
img_plot.overlays.append(lasso_overlay)
return plot
def overlay_selection(self, plot):
lasso_selection = LassoSelection(component=plot,
selection_datasource=plot.index,
drag_button="left")
plot.active_tool = lasso_selection
plot.tools.append(ScatterInspector(plot))
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=plot)
plot.overlays.append(lasso_overlay)
# Uncomment this if you would like to see incremental updates:
# lasso_selection.incremental_select = True
self.index_datasource = plot.index
def install(self):
scatter = self.widget.scatter
self._tool = tool = LassoSelection(scatter,
drag_button = 'left',
selection_datasource = scatter.index,
metadata_name = 'lasso_selection',
)
tool.on_trait_change(self._selection_changed, 'selection_changed')
self._overlay = LassoOverlay(scatter,
lasso_selection = self._tool,
)
scatter.tools.append(self._tool)
scatter.overlays.append(self._overlay)
def _create_plot_component():
# Create some data
npts = 2000
x = sort(random(npts))
y = random(npts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="red",
marker_size=4,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot With Lasso Selection"
plot.line_width = 1
plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual ScatterPlot object to give to them
my_plot = plot.plots["my_plot"][0]
# Attach some tools to the plot
lasso_selection = LassoSelection(component=my_plot,
selection_datasource=my_plot.index,
drag_button="left")
#drag_button="right")
my_plot.active_tool = lasso_selection
my_plot.tools.append(ScatterInspector(my_plot))
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=my_plot)
my_plot.overlays.append(lasso_overlay)
# Uncomment this if you would like to see incremental updates:
#lasso_selection.incremental_select = True
return plot
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