def plot_core(self, main, core, ref_depth_line=None):
''' plot core info on main plot'''
logger.debug('replotting main cores')
loc_index, loc, dist = self.model.core_info_dict[core.core_id]
# first plot vertical line
y_range = main.value_range
ys = np.array([y_range.low, y_range.high])
xs = ys * 0 + loc
line = create_line_plot((xs, ys), color='lightgreen',
width=CORE_LINE_WIDTH)
line.origin = 'top left'
line.index_range = main.index_range
main.add(line)
# then plot boundary layers as dots on line
if ref_depth_line is None:
ref_depth_line = self.model.survey_line.core_depth_reference
if ref_depth_line:
ref_depth = ref_depth_line.depth_array[loc_index]
else:
ref_depth = 0
layer_depths = core.layer_boundaries
ys = ref_depth + layer_depths
xs = ys * 0 + loc
scatter = create_scatter_plot((xs, ys), color='darkgreen',
marker='circle',
marker_size=CORE_LINE_WIDTH + 1)
scatter.origin = 'top left'
scatter.value_range = main.value_range
scatter.index_range = main.index_range
old_scatter = main.plots.get('core_plot', [])
if old_scatter:
main.components.remove(old_scatter[0])
main.add(scatter)
main.plots['core_plot'] = [scatter]
def _plot_default(self):
plot_data = ArrayPlotData(index=self.sigma, value=self.mu)
self.plot_data = plot_data
plot = Plot(data=plot_data)
line = create_line_plot([self.sigma, self.mu],
add_grid=True,
value_bounds=(min(self.mean), max(self.mean)),
add_axis=True,
index_sort='ascending',
orientation='h')
scatter = create_scatter_plot(
[np.sqrt(np.diag(self.covar)),
np.squeeze(self.mean)],
index_bounds=(line.index_range.low, line.index_range.high),
value_bounds=(line.value_range.low, line.value_range.high),
marker='circle',
color='blue')
plot.add(line)
left, bottom = line.underlays[-2:]
left.title = 'Return'
bottom.title = 'Risk'
plot.add(scatter)
cursor = CursorTool(line, drag_button='left', color='blue')
self.cursor = cursor
#cursor.current_position = self.sigma[0], self.mu[0]
line.overlays.append(cursor)
line.tools.append(PanTool(line, drag_button='right'))
#line.overlays.append(ZoomTool(line))
return plot
def plot_core(self, main, core, ref_line, loc_index, loc):
''' plot core info on main plot'''
# first plot vertical line
y_range = main.value_range
ys = np.array([y_range.low, y_range.high])
xs = ys * 0 + loc
line = create_line_plot((xs, ys),
color='lightgreen',
width=CORE_LINE_WIDTH)
line.origin = 'top left'
line.index_range = main.index_range
main.add(line)
# then plot boundary layers as dots on line
layer_depths = core.layer_boundaries
ref_depth_line = self.model.get_ref_depth_line()
if ref_depth_line:
ref_depth = ref_depth_line.depth_array[loc_index]
else:
ref_depth = 0
ys = ref_depth + layer_depths
xs = ys * 0 + loc
scatter = create_scatter_plot((xs, ys),
color='darkgreen',
marker='circle',
marker_size=CORE_LINE_WIDTH + 1)
scatter.origin = 'top left'
scatter.value_range = main.value_range
scatter.index_range = main.index_range
main.add(scatter)
def _setup_plots(self):
"""Creates series of Bessel function plots"""
plots = {}
x = arange(self.low, self.high + 0.001, (self.high - self.low) / self.numpoints)
for i in range(self.num_funs):
y = jn(i, x)
if i % 2 == 1:
plot = create_line_plot((x, y), color=tuple(COLOR_PALETTE[i]), width=2.0)
else:
plot = create_scatter_plot((x, y), color=tuple(COLOR_PALETTE[i]))
if i == 0:
value_mapper, index_mapper, legend = self._setup_plot_tools(plot)
else:
self._setup_mapper(plot, value_mapper, index_mapper)
self.add(plot)
plots["Bessel j_%d" % i] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
self.overlays.append(PlotLabel("Bessel functions", component=self, font="swiss 16", overlay_position="top"))
# Add the traits inspector tool to the container
self.tools.append(TraitsTool(self))
def make_reference_plot(self):
''' make reference plot for mini plot range selector'''
x_pts = np.array(
[self.model.distance_array.min(),
self.model.distance_array.max()])
y_pts = 0 * x_pts
ref_plot = create_scatter_plot((x_pts, y_pts), color='black')
return ref_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 make_reference_plot(self):
''' make reference plot for mini plot range selector'''
x_pts = np.array([self.model.distance_array.min(),
self.model.distance_array.max()
]
)
y_pts = 0 * x_pts
ref_plot = create_scatter_plot((x_pts, y_pts), color='black')
return ref_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 test_scatter_fast(self):
""" Coverage test to check basic case works """
size = (50, 50)
scatterplot = create_scatter_plot(
data=[range(10), range(10)],
border_visible=False,
)
scatterplot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(scatterplot)
actual = gc.bmp_array[:, :, :]
self.assertFalse(alltrue(actual == 255))
def test_scatter_circle(self):
""" Coverage test to check circles work """
size = (50, 50)
scatterplot = create_scatter_plot(
data=[list(sm.xrange(10)), list(sm.xrange(10))],
marker="circle",
border_visible=False,
)
scatterplot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(scatterplot)
actual = gc.bmp_array[:, :, :]
self.assertFalse(alltrue(actual == 255))
def test_scatter_circle(self):
""" Coverage test to check circles work """
size = (50, 50)
scatterplot = create_scatter_plot(
data=[list(range(10)), list(range(10))],
marker="circle",
border_visible=False,
)
scatterplot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(scatterplot)
actual = gc.bmp_array[:, :, :]
self.assertFalse(alltrue(actual == 255))
def test_scatter_slow(self):
""" Coverage test to check multiple marker size works """
size = (50, 50)
scatterplot = create_scatter_plot(
data=[range(10), range(10)],
marker="circle",
border_visible=False,
marker_size=range(1, 11),
)
scatterplot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(scatterplot)
actual = gc.bmp_array[:, :, :]
self.assertFalse(alltrue(actual == 255))
def _plots_default(self):
plotdata = self.get_data_sets()
plots = Plot(plotdata)
plotsDict = {}
# plot background sonar image and impound lines
xbounds = self.model.survey_rng_m
ybounds = self.model.depth_m
plots.img_plot("sonarimg", colormap=jet,
xbounds=xbounds, ybounds=ybounds)
ip = plots.plot(('impound1_X','impound1_Y'), type='line', marker='square')
plotsDict['Impoundment line'] = ip
plots.x_axis.title = 'Distance along survey line (m)'
plots.y_axis.title = 'Depth (m)'
# add core samples as scatter with separate y-axis
corex = plotdata.get_data('coreX')
corey = plotdata.get_data('coreY')
scatter = create_scatter_plot((corex,corey), marker='diamond',
color='red' )
scatter.index_range = plots.index_range
axis = PlotAxis(scatter, orientation='right')
axis.title = 'Core sample dist from survey line (m)'
scatter.underlays.append(axis)
plots.add(scatter)
# create vertical line for indicating selected core sample position
vals1 = [0 for x in corey]
vline = create_line_plot((corey,vals1), color='blue', orientation='v')
vline.value_range = scatter.index_range
plots.add(vline)
# Add Legend
legend = Legend(component=plots, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="left"))
legend.plots = plotsDict
plots.overlays.append(legend)
# Add tools
scatter.tools.append(PickTool(scatter))
plots.tools.append(TraceTool(plots))
plots.tools.append(PanTool(plots))
plots.tools.append(ZoomTool(plots))
return plots
def test_data_label_arrow_not_visible(self):
# Regression test for https://github.com/enthought/chaco/issues/281
# Before the problem was fixed, this test (specifically, using
# arrow_visible=False in the DataLabel constructor) would raise an
# exception because of an undefined reference.
size = (50, 50)
plot = create_scatter_plot(data=[range(10), range(10)])
label = DataLabel(component=plot,
data_point=(4, 4),
marker_color="red",
marker_size=3,
label_position=(20, 50),
label_style='bubble',
label_text="Something interesting",
label_format="at x=%(x).2f, y=%(y).2f",
arrow_visible=False)
plot.overlays.append(label)
plot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(plot)
def do_plotv(session, *args, **kw):
""" Creates a list of plots from the data in ``*args`` and options in
``**kw``, according to the docstring on commands.plot().
"""
sort = kw.get("sort", "none")
sources_list = make_data_sources(session, sort, *args)
plot_type = kw.get("type", "line")
if plot_type == "scatter":
plots = [create_scatter_plot(sources) for sources in sources_list]
elif plot_type == "line":
plots = [create_line_plot(sources) for sources in sources_list]
else:
raise ChacoShellError("Unknown plot type '%s'." % plot_type)
for plot in plots:
plot.orientation = kw.get("orientation", "h")
return plots
def test_data_label_arrow_not_visible(self):
# Regression test for https://github.com/enthought/chaco/issues/281
# Before the problem was fixed, this test (specifically, using
# arrow_visible=False in the DataLabel constructor) would raise an
# exception because of an undefined reference.
size = (50, 50)
plot = create_scatter_plot(data=[range(10), range(10)])
label = DataLabel(component=plot,
data_point=(4, 4),
marker_color="red",
marker_size=3,
label_position=(20, 50),
label_style='bubble',
label_text="Something interesting",
label_format="at x=%(x).2f, y=%(y).2f",
arrow_visible=False)
plot.overlays.append(label)
plot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(plot)
def do_plotv(session, *args, **kw):
""" Creates a list of plots from the data in ``*args`` and options in
``**kw``, according to the docstring on commands.plot().
"""
sort = kw.get("sort", "none")
sources_list = make_data_sources(session, sort, *args)
plot_type = kw.get("type", "line")
if plot_type == "scatter":
plots = [create_scatter_plot(sources) for sources in sources_list]
elif plot_type == "line":
plots = [create_line_plot(sources) for sources in sources_list]
else:
raise ChacoShellError, "Unknown plot type '%s'." % plot_type
for plot in plots:
plot.orientation = kw.get("orientation", "h")
return plots
def _make_container(self):
container = chaco.HPlotContainer(padding=40, spacing=25)
broadcaster=chaco_tools.BroadcasterTool()
self.data.append((chaco.ArrayDataSource([]), chaco.ArrayDataSource([])))
plot = chaco.create_scatter_plot((self.data[-1][0], self.data[-1][1]),
marker='circle',
marker_size=2.0,
add_grid=True,
add_axis=True,
border_visible=True)
plot.line_width=0.0
zoom = chaco_tools.ZoomTool(plot)
pan = chaco_tools.PanTool(plot)
broadcaster.tools.append(zoom)
broadcaster.tools.append(pan)
plot.bgcolor = 'white'
container.add(plot)
container.tools.append(broadcaster)
return container
def test_scatter_custom(self):
""" Coverage test to check custom markers work """
# build path
path = CompiledPath()
path.move_to(-5, -5)
path.line_to(5, 5)
path.line_to(5, -5)
path.line_to(-5, 5)
path.line_to(-5, -5)
size = (50, 50)
scatterplot = create_scatter_plot(
data=[range(10), range(10)],
marker='custom',
border_visible=False,
)
scatterplot.custom_symbol = path
scatterplot.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(scatterplot)
actual = gc.bmp_array[:, :, :]
self.assertFalse(alltrue(actual == 255))
def _setup_plots(self):
"""Creates series of Bessel function plots"""
plots = {}
x = arange(self.low, self.high + 0.001,
(self.high - self.low) / self.numpoints)
for i in range(self.num_funs):
y = jn(i, x)
if i % 2 == 1:
plot = create_line_plot((x, y),
color=tuple(COLOR_PALETTE[i]),
width=2.0)
else:
plot = create_scatter_plot((x, y),
color=tuple(COLOR_PALETTE[i]))
if i == 0:
value_mapper, index_mapper, legend = \
self._setup_plot_tools(plot)
else:
self._setup_mapper(plot, value_mapper, index_mapper)
self.add(plot)
plots["Bessel j_%d" % i] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
self.overlays.append(
PlotLabel("Bessel functions",
component=self,
font="swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
self.tools.append(TraitsTool(self))
def _create_plot_component():
container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = linspace(low, high, numpoints)
now = time()
timex = linspace(now, now+7*24*3600, numpoints)
# Plot some bessel functions
value_mapper = None
index_mapper = None
plots = {}
for i in range(10):
y = jn(i, x)
if i%2 == 1:
plot = create_line_plot((timex,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
else:
plot = create_scatter_plot((timex,y), color=tuple(COLOR_PALETTE[i]))
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
left, bottom = add_default_axes(plot)
left.tick_generator = ScalesTickGenerator()
bottom.tick_generator = ScalesTickGenerator(scale=CalendarScaleSystem())
add_default_grids(plot, tick_gen=bottom.tick_generator)
else:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
if i==0:
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
container.add(plot)
plots["Bessel j_%d"%i] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(PlotLabel("Bessel functions",
component=container,
font = "swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
def _create_plot_component():
container = OverlayPlotContainer(
padding=50,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
# Create the initial X-series of data
numpoints = 100
low = -5
high = 15.0
x = linspace(low, high, numpoints)
now = time()
timex = linspace(now, now + 7 * 24 * 3600, numpoints)
# Plot some bessel functions
value_mapper = None
index_mapper = None
plots = {}
for i in range(10):
y = jn(i, x)
if i % 2 == 1:
plot = create_line_plot(
(timex, y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
else:
plot = create_scatter_plot(
(timex, y), color=tuple(COLOR_PALETTE[i]))
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
left, bottom = add_default_axes(plot)
left.tick_generator = ScalesTickGenerator()
bottom.tick_generator = ScalesTickGenerator(
scale=CalendarScaleSystem())
add_default_grids(plot, tick_gen=bottom.tick_generator)
else:
plot.value_mapper = value_mapper
value_mapper.range.add(plot.value)
plot.index_mapper = index_mapper
index_mapper.range.add(plot.index)
if i == 0:
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add a legend in the upper right corner, and make it relocatable
legend = Legend(component=plot, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
plot.overlays.append(legend)
container.add(plot)
plots["Bessel j_%d" % i] = plot
# Set the list of plots on the legend
legend.plots = plots
# Add the title at the top
container.overlays.append(
PlotLabel(
"Bessel functions",
component=container,
font="swiss 16",
overlay_position="top"))
# Add the traits inspector tool to the container
container.tools.append(TraitsTool(container))
return container
