def _create_plot_component():
# Create a random scattering of XY pairs
x = random.uniform(0.0, 10.0, 50)
y = random.uniform(0.0, 5.0, 50)
pd = ArrayPlotData(x = x, y = y)
plot = Plot(pd, border_visible=True, overlay_border=True)
scatter = plot.plot(("x", "y"), type="scatter", color="lightblue")[0]
# Tweak some of the plot properties
plot.set(title="Scatter Inspector Demo", padding=50)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
# Attach the inspector and its overlay
inspector = ScatterInspector(scatter)
scatter.tools.append(inspector)
overlay = ScatterInspectorOverlay(scatter,
hover_color="red",
hover_marker_size=6,
selection_marker_size=6,
selection_color="yellow",
selection_outline_color="purple",
selection_line_width=3)
scatter.overlays.append(overlay)
# Optional: add a listener on inspector events:
def echo(new):
print("{} event on element {}".format(new.event_type, new.event_index))
inspector.on_trait_change(echo, "inspector_event")
return plot
def _create_plot_component():
# Create a random scattering of XY pairs
x = random.uniform(0.0, 10.0, 50)
y = random.uniform(0.0, 5.0, 50)
pd = ArrayPlotData(x=x, y=y)
plot = Plot(pd, border_visible=True, overlay_border=True)
scatter = plot.plot(("x", "y"), type="scatter", color="lightblue")[0]
# Tweak some of the plot properties
plot.set(title="Scatter Inspector Demo", padding=50)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
# Attach the inspector and its overlay
scatter.tools.append(ScatterInspector(scatter))
overlay = ScatterInspectorOverlay(scatter,
hover_color="red",
hover_marker_size=6,
selection_marker_size=6,
selection_color="yellow",
selection_outline_color="purple",
selection_line_width=3)
scatter.overlays.append(overlay)
return plot
def install(self):
scatter = self.widget.scatter
self._tool = ScatterInspector(scatter,
selection_metadata_name = 'selection',
selection_mode = 'toggle',
)
scatter.tools.append(self._tool)
def _create_plot_component():
# Create some data
npts = 100
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)
pd.set_data("value2", y * 2)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="slategray",
marker_size=6,
bgcolor="white")
plot.plot(("index", "value2"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="red",
marker_size=6,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot With 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
my_plot.tools.append(
ScatterInspector(my_plot,
selection_mode="toggle",
persistent_hover=False))
my_plot.overlays.append(
ScatterInspectorOverlay(my_plot,
hover_color="transparent",
hover_marker_size=10,
hover_outline_color="purple",
hover_line_width=2,
selection_marker_size=8,
selection_color="lawngreen"))
my_plot.tools.append(PanTool(my_plot))
my_plot.overlays.append(ZoomTool(my_plot, drag_button="right"))
return plot
def _create_plot_component(self):
""" Creates the plot component of the to be used in the FeatureScatter
instance.
"""
x = np.zeros(len(self.data))
y = np.zeros(len(self.data))
c = np.zeros(len(self.data))
for i, (coord, count) in enumerate(self.data.items()):
x[i], y[i] = coord
c[i] = count
c = np.log2(c)
pd = ArrayPlotData()
pd.set_data("x", x)
pd.set_data("y", y)
pd.set_data("color", c)
cm = Map(DataRange1D(low=-c.max() / 2, high=c.max()))
plot = Plot(pd)
plot.plot(("x", "y", "color"),
type="cmap_scatter",
name="my_plot",
marker="dot",
index_sort="ascending",
color_mapper=cm,
marker_size=2,
bgcolor=0xF7F7F7,
)
plot.title = "Scatter Plot With Lasso Selection"
plot.line_width = 1
plot.padding = 50
my_plot = plot.plots["my_plot"][0]
my_plot.data = self.data
my_plot.out_file = self.out_file
my_plot.label = self.label
lasso_selection = FeatureLasso(component=my_plot,
selection_datasource=my_plot.index,
drag_button="left")
my_plot.tools.append(lasso_selection)
my_plot.tools.append(BetterZoom(my_plot, zoom_factor=1.2))
my_plot.tools.append(PanTool(my_plot, drag_button="right"))
my_plot.tools.append(ScatterInspector(my_plot))
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=my_plot,
selection_fill_color=0xEF8A62)
my_plot.overlays.append(lasso_overlay)
my_plot.overlays.append(ScatterInspectorOverlay(my_plot,
hover_marker_size=4))
return plot
def _plot_default(self):
import numpy as np
data = np.random.normal(size=(10, 2))
apl = ArrayPlotData(x=data[:, 0], y=data[:, 1])
plot = Plot(apl)
scatter = plot.plot(('x', 'y'), type='scatter')[0]
self.inspector = ScatterInspector(scatter)
scatter.tools.append(self.inspector)
return plot
def setUp(self):
values = numpy.arange(10)
self.plot = create_scatter_plot((values, values))
self.plot.bounds = [100, 100]
self.plot._window = self.create_mock_window()
self.tool = ScatterInspector(component=self.plot)
self.plot.active_tool = self.tool
self.plot.do_layout()
self.insp_event = None
def _create_plot_component():
# Create some data
npts = 200
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 Rectangular 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
rect_selection = RectangularSelection(
component=my_plot,
selection_datasource=my_plot.index,
drag_button="left",
metadata_name='selections',
)
my_plot.tools.append(rect_selection)
my_plot.tools.append(ScatterInspector(my_plot, selection_mode='toggle'))
my_plot.active_tool = rect_selection
lasso_overlay = LassoOverlay(lasso_selection=rect_selection,
component=my_plot)
my_plot.overlays.append(lasso_overlay)
scatter_overlay = ScatterInspectorOverlay(
component=my_plot,
selection_color='cornflowerblue',
selection_marker_size=int(my_plot.marker_size) + 3,
selection_marker='circle')
my_plot.overlays.append(scatter_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 _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):
container = DisableTrackingPlot(
self.model.plot_data,
live_plot=self,
)
container.padding_left = 100
container.plot(('x', 'y'), type='line')
scatter = container.plot(
('x', 'y'),
type='scatter',
marker_size=1,
show_selection=False,
color='lightskyblue',
)
self.scatter = scatter[0]
inspector = ScatterInspector(
self.scatter,
selection_mode='single',
)
self.scatter.tools.append(inspector)
overlay = ScatterInspectorOverlay(self.scatter,
hover_color="lightskyblue",
hover_marker_size=2,
selection_marker_size=3,
selection_color="lightskyblue",
selection_outline_color="black",
selection_line_width=1)
self.scatter.overlays.append(overlay)
self.scatter.index.on_trait_change(
self._metadata_changed, "metadata_changed")
self.zoom_tool = ZoomTool(
container,
)
container.underlays.append(self.zoom_tool)
container.tools.append(self.zoom_tool)
self.pan_tool = PanTool(
container,
)
container.tools.append(self.pan_tool)
return container
def _add_scatter_inspector_overlay(self, scatter_plot):
inspector = ScatterInspector(
scatter_plot,
threshold=10,
multiselect_modifier=KeySpec(None, "shift"),
selection_mode="multi",
)
overlay = ScatterInspectorOverlay(
scatter_plot,
hover_color=(0, 0, 1, 1),
hover_marker_size=6,
selection_marker_size=20,
selection_color=(0, 0, 1, 0.5),
selection_outline_color=(0, 0, 0, 0.8),
selection_line_width=3,
)
scatter_plot.tools.append(inspector)
scatter_plot.overlays.append(overlay)
def _create_plot_component(self):
""" Creates the plot component of the to be used in the FeatureScatter
instance.
"""
feature_types = self.features.keys()
print(feature_types)
x = np.array(self.features["BORDER"]["x"] + self.features["ADDITIONAL_BORDER"]["x"])
y = np.array(self.features["BORDER"]["y"] + self.features["ADDITIONAL_BORDER"]["y"])
pd = ArrayPlotData()
pd.set_data("x", x)
pd.set_data("y", y)
plot = Plot(pd)
plot.plot(("x", "y"),
type="scatter",
name="my_plot",
color="red",
index_sort="ascending",
marker_size=2.0,
bgcolor="black",
line_width=0,
)
my_plot = plot.plots["my_plot"][0]
my_plot.tools.append(BetterZoom(my_plot, zoom_factor=1.2))
my_plot.tools.append(PanTool(my_plot, drag_button="right"))
my_plot.tools.append(ScatterInspector(my_plot))
my_plot.overlays.append(ScatterInspectorOverlay(my_plot,
hover_marker_size=4))
return plot
def _create_plot_component(self, recalc=False):
container = VPlotContainer()
### Assemble the scatter plot of the Efficient Frontier
x, y = self.get_stock_data()
if not hasattr(self, "efx") or recalc:
efx, efy, allocations = self.get_ef_data()
else:
efx = self.efx
efy = self.efy
p = self.portfolio
symbs = p.symbols
pd = ArrayPlotData(x=x, y=y, efx=efx, efy=efy, mp_x=[self.model_portfolio_x], mp_y=[self.model_portfolio_y])
# Create some plots of the data
plot = Plot(pd, title="Efficient Frontier")
# Create a scatter plot (and keep a handle on it)
stockplt = plot.plot(("x", "y"), color="transparent",
type="scatter",
marker="dot",
marker_line_color="transparent",
marker_color="transparent",
marker_size=1)[0]
efplt = plot.plot(("efx", "efy"), color=(0.0,0.5,0.0,0.25),
type="scatter",
marker="circle",
marker_size=6)[0]
efpltline = plot.plot(("efx", "efy"), color=(0.1,0.4,0.1,0.7),
type="line")[0]
# Create another one-point scatter for a model portfolio
mp_plot = plot.plot(("mp_x", "mp_y"), color=(1.0, 0.5, 0.5, 0.25),
type="scatter",
market="triangle",
market_size=7)[0]
for i in range(len(p.stocks)):
label = DataPointLabel(component=plot, data_point=(x[i], y[i]),
label_position="bottom right",
padding=4,
bgcolor="transparent",
border_visible=False,
text=self.symbols[i],
marker="circle",
marker_color=(0.0,0.0,0.5,0.25),
marker_line_color="lightgray",
marker_size=6,
arrow_size=8.0,
arrow_min_length=7.0,
font_size=14)
plot.overlays.append(label)
tool = DataLabelTool(label, drag_button="left", auto_arrow_root=True)
label.tools.append(tool)
stockplt.tools.append(ScatterInspector(stockplt, selection_mode="toggle",
persistent_hover=False))
scatinsp = ScatterInspectorOverlay(stockplt,
hover_color = "red",
hover_marker_size = 8,
hover_outline_color = (0.7, 0.7, 0.7, 0.5),
hover_line_width = 1)
stockplt.overlays.append(scatinsp)
# Tweak some of the plot properties
plot.padding = 50
stockplt.value_range.low=0.0
stockplt.value_range.high=0.1
stockplt.index_range.low=0.0
stockplt.index_range.high=0.1
# Attach some tools to the plot
plot.tools.append(PanTool(plot, drag_button="right"))
plot.overlays.append(ZoomTool(plot))
#### Assemble the "stacked area" plot
if not hasattr(self, "efx") or recalc:
a = self.get_ef_data()[2]
else:
a = self.allocations
rts = a.keys()
rts.sort()
rts = np.array(rts)
symbs = a[rts[0]].keys()
symbs.sort()
# "Transpose" symbols' weights to get vectors of weights for each symbol
symb_data = np.array([[a[rt][symb] for rt in rts] for symb in symbs])
self.symbols2 = [Symbol(symbol=symbs[i], color=COLORS[i]) for i in range(len(symbs))]
# Create a plot data object and give it this data
bpd = ArrayPlotData()
bpd.set_data("index", rts)
bpd.set_data("allocations", symb_data)
# Create a contour polygon plot of the data
bplot = Plot(bpd, title="Allocations")
bplot.stacked_bar_plot(("index", "allocations"),
color = COLORS,
outline_color = "gray")
bplot.padding = 50
#bplot.legend.visible = True
# Add a plot of the stocks
stock_obj_list = [p.stocks[symb] for symb in symbs]
#for itm in stock_obj_list:
#itm.print_traits()
#print "Has Cache?:", itm.stock_data_cache is not None
splot = StockPlot(stocks=[p.stocks[symb] for symb in symbs], colors=COLORS).plot
container.add(bplot)
container.add(plot)
container.add(splot)
return container