def __init__(self, *args, **kwargs):
super(DemoView, self).__init__(*args, **kwargs)
# Create the plot object, set some options, and add some tools
plot = self.plot = Plot(self.pd, default_origin="top left")
plot.x_axis.orientation = "top"
plot.padding = 50
plot.padding_top = 75
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Load the default image
self._load()
# Plot the image plot with this image
self.plot.img_plot("imagedata")
def add_line(self, str_x, str_y, x1, y1, x2, y2, color1):
""" drawline draws 1 line on the screen by using lineplot x1,y1->x2,y2
parameters:
str_x - label of x coordinate
str_y - label of y coordinate
x1,y1,x2,y2 - start and end coordinates of the line
color1 - color of the line
example usage:
drawline("x_coord","y_coord",100,100,200,200,red)
draws a red line 100,100->200,200
"""
# self._plot_data.set_data(str_x,[x1,x2])
# self._plot_data.set_data(str_y,[y1,y2])
self._plot_data.set_data(str_x, [x1, x2])
self._plot_data.set_data(str_y, [y1, y2])
self._plot.add(Plot((str_x, str_y), type="line", color=color1))
def _create_xy8_plot(self):
plot_data_xy8_line = ArrayPlotData(counts2=np.array((0., 1.)),
time=np.array((0., 0.)),
fit=np.array((0., 0.)))
plot = Plot(plot_data_xy8_line,
width=50,
height=40,
padding=8,
padding_left=64,
padding_bottom=32)
plot.plot(('time', 'counts2'), color='green', line_width=2)
plot.index_axis.title = 'time [ns]'
plot.value_axis.title = 'counts'
#plot.title='counts'
self.plot_data_xy8_line = plot_data_xy8_line
self.xy8_plot = plot
return self.xy8_plot
def _create_returns_plot(self):
plot = Plot(self.plotdata)
plot.legend.visible = False
plot.x_axis = None
x_axis = PlotAxis(
plot,
orientation="bottom",
tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
plot.overlays.append(x_axis)
plot.x_grid.tick_generator = x_axis.tick_generator
for i, name in enumerate(self.plotdata.list_data()):
if name == "times":
continue
renderer = plot.plot(("times", name),
type="line",
name=name,
color="auto",
line_width=2)[0]
self.times_ds = renderer.index
print('chaco: %s') % str(time.time() - tic)
# Tricky: need to set auto_handle_event on the RangeSelection
# so that it passes left-clicks to the PanTool
# FIXME: The range selection is still getting the initial left down
renderer.tools.append(
RangeSelection(renderer,
left_button_selects=False,
auto_handle_event=False))
plot.tools.append(
PanTool(plot,
drag_button="left",
constrain=True,
constrain_direction="x"))
plot.overlays.append(
ZoomTool(plot, tool_mode="range", max_zoom_out=1.0))
# Attach the range selection to the last renderer; any one will do
self._range_selection_overlay = RangeSelectionOverlay(
renderer, metadata_name="selections")
renderer.overlays.append(self._range_selection_overlay)
# Grab a reference to the Time axis datasource and add a listener to its
# selections metadata
self.times_ds = renderer.index
self.times_ds.on_trait_change(self._selections_changed,
'metadata_changed')
self.returns_plot = plot
def _create_digits_plot(self):
data = ArrayPlotData(image=numpy.zeros((2, 2)))
plot = Plot(data,
width=500,
height=500,
resizable='hv',
aspect_ratio=37.0 / 9,
padding=8,
padding_left=48,
padding_bottom=36)
plot.img_plot('image', xbounds=(0, 1), ybounds=(0, 1), colormap=hot)
plot.plots['plot0'][0].value_range.high_setting = 1
plot.plots['plot0'][0].value_range.low_setting = 0
plot.x_axis = None
plot.y_axis = None
self.digits_data = data
self.digits_plot = plot
def create_plot():
numpoints = 100
low = -5
high = 15.0
x = linspace(low, high, numpoints)
pd = ArrayPlotData(index=x)
p = Plot(pd, bgcolor="oldlace", padding=50, border_visible=True)
for i in range(10):
pd.set_data("y" + str(i), jn(i, x))
p.plot(("index", "y" + str(i)), color=tuple(COLOR_PALETTE[i]),
width=2.0 * dpi_scale)
p.x_grid.visible = True
p.x_grid.line_width *= dpi_scale
p.y_grid.visible = True
p.y_grid.line_width *= dpi_scale
p.legend.visible = True
return p
def _create_temperature_plot(self):
plot_data = ArrayPlotData(time=np.array((0., 1.)),
temperature=np.array((0., 40.)),
fit=np.array((0., 0.)))
plot = Plot(plot_data,
width=50,
height=40,
padding=8,
padding_left=64,
padding_bottom=32)
plot.plot(('time', 'temperature'), color='green')
plot.index_axis.title = 'time [h]'
plot.value_axis.title = 'temperature [C]'
self.plot_data = plot_data
self.temperature_plot = plot
return self.temperature_plot
def _plot_default(self):
# Create the data and the PlotData object
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x=x, y=y)
# Create a Plot and associate it with the PlotData
plot = Plot(plotdata)
# Create a scatter plot in the Plot
plot.plot(("x", "y"), type="scatter", color="blue")
plot.tools.append(PanTool(plot))
# Add our custom overlay to the plot
overlay = CustomOverlay(plot)
# Add the MoveTool to the overlay so it can be dragged around usin gthe
# right mouse button.
overlay.tools.append(MoveTool(overlay, drag_button="right"))
plot.overlays.append(overlay)
return plot
def create_plot():
numpoints = 100
low = -5
high = 15.0
x = linspace(low, high, numpoints)
pd = ArrayPlotData(index=x)
p = Plot(pd, bgcolor="lightgray", padding=50, border_visible=True)
for t,i in sm.zip(cycle(['line','scatter']),sm.range(10)):
pd.set_data("y" + str(i), jn(i,x))
p.plot(("index", "y" + str(i)), color=tuple(COLOR_PALETTE[i]),
width = 2.0 * dpi_scale, type=t)
p.x_grid.visible = True
p.x_grid.line_width *= dpi_scale
p.y_grid.visible = True
p.y_grid.line_width *= dpi_scale
p.legend.visible = True
return p
def __init__(self, **kw):
super(WorldMapPlot, self).__init__(**kw)
self._download_map_image()
image = ImageData.fromfile(self.image_path)
# For now, the locations are hardcoded, though this can be changed
# eassily to take command line args, read from a file, or by other
# means
austin_loc = (30.16, -97.44)
locations_x = numpy.array([austin_loc[1]])
locations_y = numpy.array([austin_loc[0]])
# transform each of the locations to the image data space, including
# moving the origin from bottom left to top left
locations_x = (locations_x + 180) * image.data.shape[1] / 360
locations_y = (locations_y * -1 + 90) * image.data.shape[0] / 180
# Create the plott data, adding the image and the locations
plot_data = ArrayPlotData()
plot_data.set_data("imagedata", image._data)
plot_data.set_data("locations_x", locations_x)
plot_data.set_data("locations_y", locations_y)
# Create the plot with the origin as top left, which matches
# how the image data is aligned
self.plot = Plot(plot_data, default_origin="top left")
self.plot.img_plot('imagedata')
# Plot the locations as a scatter plot to be overlayed on top
# of the map
loc_plot = self.plot.plot(('locations_x', 'locations_y'),
type='scatter',
size=3,
color='yellow',
marker='dot')[0]
loc_plot.x_mapper.range.high = image.data.shape[1]
loc_plot.x_mapper.range.low = 0
loc_plot.y_mapper.range.high = image.data.shape[0]
loc_plot.y_mapper.range.low = -0
# set up any tools, in this case just the zoom tool
zoom = ZoomTool(component=self.plot, tool_mode="box", always_on=False)
self.plot.overlays.append(zoom)
def _create_plot_component(model):
# Create a plot data object and give it the model's data array.
pd = ArrayPlotData()
pd.set_data("imagedata", model.data)
# Create the "main" Plot.
plot = Plot(pd, padding=50)
# Use a TransformColorMapper for the color map.
tcm = TransformColorMapper.from_color_map(jet)
# Create the image plot renderer in the main plot.
renderer = plot.img_plot("imagedata",
xbounds=model.x_array,
ybounds=model.y_array,
colormap=tcm)[0]
# Create the colorbar.
lm = LinearMapper(range=renderer.value_range,
domain_limits=(renderer.value_range.low,
renderer.value_range.high))
colorbar = ColorBar(index_mapper=lm,
plot=plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
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(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
return container
def _bar_default(self):
index = np.arange(0, len(self.current_weight))
bar_data = ArrayPlotData(index=index, value=self.current_weight)
self.bar_data = bar_data
bar = Plot(data=bar_data)
bar.plot(('index', 'value'), type='bar', bar_width=0.8, color='auto')
label_axis = LabelAxis(bar,
orientation='bottom',
title='components',
tick_interval=1,
positions=index,
labels=self.header,
small_haxis_style=True)
bar.underlays.remove(bar.index_axis)
bar.index_axis = label_axis
bar.range2d.y_range.high = 1.0
return bar
def _create_plot_component(self):
plot_data = TableModelPlotData(model = self.model)
self.component = plot = Plot(plot_data,
auto_axis = False,
padding_left = 75, # FIXME: Can Chaco calcuate this automatically?
)
plot.x_axis = DropAxis(component = plot, mapper = plot.x_mapper,
plot_name = 'x', orientation = 'bottom')
plot.y_axis = DropAxis(component = plot, mapper = plot.y_mapper,
plot_name = 'y', orientation = 'left')
self.scatter = scatter = plot.plot(('x', 'y'),
type = 'scatter',
marker = 'circle',
)[0]
plot.title = self.settings.title
# Create selection overlay.
inspector_overlay = SafeScatterInspectorOverlay(self.model,
scatter,
selection_metadata_name = 'selection',
selection_color = 'blue',
selection_marker_size = 6,
)
scatter.overlays.append(inspector_overlay)
# Create standard tools (always active).
scatter.tools.append(PanTool(scatter, drag_button = 'right'))
self._zoom_tool = ZoomTool(scatter,
drag_button=None,
enter_zoom_key=KeySpec(None),
exit_zoom_key=KeySpec(None)
)
scatter.tools.append(self._zoom_tool)
# Create the active selection and tool.
self._set_selection(self.selected)
self._set_tool(self.tool)
# Register event handlers.
scatter.index.on_trait_change(self._update_selection,
'metadata_changed')
def __init__(self, index, series_a, series_b, series_c, **kw):
super(PlotExample, self).__init__(**kw)
plot_data = ArrayPlotData(index=index)
starting_values = np.ones(10) * 0.5
starting_vals = ArrayDataSource(starting_values, sort_order="none")
series_a = series_a + starting_values
plot_data.set_data('series_a', series_a)
plot_data.set_data('series_b', series_b)
plot_data.set_data('series_c', series_c)
self.plot = Plot(plot_data)
self.plot.plot(('index', 'series_a'),
type='bar',
bar_width=0.8,
color='auto',
starting_value=starting_vals)
# set the plot's value range to 0, otherwise it may pad too much
self.plot.value_range.low = 0
tick_positions_and_labels = {
x: 'label_{}'.format(x)
for x in range(1, 11)
}
tick_generator = ShowAllTickGenerator(
positions=tick_positions_and_labels.keys())
def formatter(value):
return tick_positions_and_labels[int(value)]
plot_axis = PlotAxis(
component=self.plot,
mapper=self.plot.x_mapper,
# labels=labels,
orientation='bottom',
tick_generator=tick_generator,
tick_label_rotate_angle=-45.,
tick_label_alignment='corner',
tick_label_formatter=formatter,
tick_label_offset=3)
self.plot.underlays.remove(self.plot.index_axis)
self.plot.index_axis = plot_axis
self.plot.overlays.append(plot_axis)
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)
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 _add_new_plot(self, new_data):
code = self.data_provider.code
if code in self.dataset.list_data():
create_new_plot = False
else:
create_new_plot = True
self.dataset.set_data(code, new_data)
if create_new_plot:
new_plot = Plot(self.dataset)
new_plot.plot(('dates', code), type='line')
tick_generator = ScalesTickGenerator(scale=CalendarScaleSystem())
new_plot.x_axis.tick_generator = tick_generator
self.container.add(new_plot)
self.container.request_redraw()
def __init__(self):
# Create the data and the PlotData object. For a 2D plot, we need to
# take the row of X points and Y points and create a grid from them
# using meshgrid().
x = linspace(0, 8, 50)
y = linspace(0, 6, 50)
xgrid, ygrid = meshgrid(x, y)
z = exp(-(xgrid * xgrid + ygrid * ygrid) / 100)
plotdata = ArrayPlotData(imagedata=z)
# Create a Plot and associate it with the PlotData
plot = Plot(plotdata)
# Create an image plot in the Plot
self.renderer = plot.img_plot("imagedata",
name="plot1",
xbounds=xgrid,
ybounds=ygrid,
colormap=jet)[0]
self.plot = plot
def _plot_default(self, toolbar=True, **pltkwds):
''' Draw bare plot, including main plotting area, toolbar, etc...
either at initialization or global redo'''
if toolbar:
self.plot = ToolbarPlot(self.plotdata, **pltkwds)
else:
self.plot = Plot(self.plotdata, **pltkwds)
self.plot.title = self.title
self.plot.padding = 50
self.plot.legend.visible = False
self.plot.tools.append(PanTool(self.plot))
zoom = BetterSelectingZoom(component=self.plot,
tool_mode="box",
always_on=False)
self.plot.overlays.append(zoom)
def _create_filter_function_plot(self):
plot_data_filter_function = ArrayPlotData(freq=np.array((0., 1.)),
value=np.array((0., 0.)),
fit=np.array((0., 0.)))
plot = Plot(plot_data_filter_function,
width=50,
height=40,
padding=8,
padding_left=64,
padding_bottom=32)
plot.plot(('freq', 'value'), color='red', type='line', line_width=3)
plot.index_axis.title = 'frequency [MHz]'
plot.value_axis.title = 'Filter Function Fourier Transform'
#plot.title='Fourier transform of filter function'
self.plot_data_filter_function = plot_data_filter_function
self.filter_function_plot = plot
return self.filter_function_plot
def _creat_line_plot(self):
line_data = ArrayPlotData(frequency=self.freq,
counts=np.mean(self.matrix, 0))
line_plot = Plot(line_data,
padding=8,
padding_left=64,
padding_bottom=32)
line_plot.plot(('frequency', 'counts'), style='line', color='blue')
line_plot.index_axis.title = 'Frequency [MHz]'
line_plot.value_axis.title = 'Fluorescence counts'
line_label = PlotLabel(text='',
hjustify='left',
vjustify='bottom',
position=[64, 128])
line_plot.overlays.append(line_label)
self.line_label = line_label
self.line_data = line_data
self.line_plot = line_plot
def test_draw_border_simple(self):
""" Borders should have the correct height and width.
"""
size = (5,5)
container = Plot(padding=1, border_visible=True)
container.outer_bounds = list(size)
gc = PlotGraphicsContext(size)
gc.render_component(container)
desired = array(((255, 255, 255, 255, 255, 255),
(255, 0, 0, 0, 0, 255),
(255, 0, 255, 255, 0, 255),
(255, 0, 255, 255, 0, 255),
(255, 0, 0, 0, 0, 255),
(255, 255, 255, 255, 255, 255)))
actual = gc.bmp_array[:,:,0]
self.assertRavelEqual(actual, desired)
def _create_plot_component():
red = util.Color(255, 0, 0, 255)
green = util.Color(0, 255, 0, 255)
blue = util.Color(0, 0, 255, 255)
purple = util.Color(125, 0, 255, 255)
white = util.Color(255, 255, 255, 255)
black = util.Color(0, 0, 0, 255)
shape = glyphset.ShapeCodes.RECT
glyphs = blzg.load_csv(
"../data/circlepoints.csv",
"x",
"y",
"series",
schema="{r:float32, theta:float32, x:float32, y:float32, series:int32}"
)
screen = (800, 600)
ivt = util.zoom_fit(screen, glyphs.bounds())
with Timer("Abstract-Render") as arTimer:
image = core.render(
glyphs, infos.val(), blzg.CountCategories("int32"),
categories.HDAlpha([red, blue, green, purple, black]), screen, ivt)
# image = core.render(glyphs,
# infos.valAt(4,0),
# blzg.Count(),
# numeric.BinarySegment(white, black, 1),
# screen,
# ivt)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.title = "Abstract Rendering"
plot.padding = 50
return plot
def plot_robot(self):
plot = Plot(self.plot_data)
plot.range2d.x_range = self.x_range
plot.range2d.y_range = self.y_range
plot.x_mapper = LinearMapper(range=self.x_range)
plot.y_mapper = LinearMapper(range=self.y_range)
plot.title = "robot"
plot.plot(('robot_x', 'robot_y'),
type='polygon',
marker='dot',
marker_size=1.0,
color=(0, 0, 0, 0.8))
wheel_outline_color = (0, 0, 0, 1.0)
wheel_color = (0, 0, 0, 0.8)
plot.plot(('wheel_bl_x', 'wheel_bl_y'),
type='polygon',
marker='dot',
marker_size=1.0,
face_color=wheel_color,
color=wheel_outline_color)
plot.plot(('wheel_br_x', 'wheel_br_y'),
type='polygon',
marker='dot',
marker_size=1.0,
face_color=wheel_color,
color=wheel_outline_color)
plot.plot(('wheel_tr_x', 'wheel_tr_y'),
type='polygon',
marker='dot',
marker_size=1.0,
face_color=wheel_color,
color=wheel_outline_color)
plot.plot(('wheel_tl_x', 'wheel_tl_y'),
type='polygon',
marker='dot',
marker_size=1.0,
face_color=wheel_color,
color=wheel_outline_color)
return plot
def _load_image(self, path):
self.container = self._container_factory()
im = Image.open(path)
# oim = array(im)
im = im.convert('L')
odim = ndim = array(im)
# if self.contrast_equalize:
# ndim = self._contrast_equalize(ndim)
# self._ndim = ndim
# low = self.low
# high = self.high
# if self.use_threshold:
# tim = zeros_like(ndim)
# tim[where((ndim > low) & (ndim < high))] = 255
# self.area = (sum(tim) / (ndim.shape[0] * ndim.shape[1])) / 255.
# else:
# tim = ndim
pd = ArrayPlotData()
pd.set_data('img', odim)
plot = Plot(data=pd, padding=[30, 5, 5, 30], default_origin='top left')
img_plot = plot.img_plot('img',
colormap=color_map_name_dict[self.colormap_name_1]
)[0]
self.add_inspector(img_plot)
self.add_tools(img_plot)
self.oplot = plot
# pd = ArrayPlotData()
# pd.set_data('img', tim)
# plot = Plot(data=pd,
# padding=[30, 5, 5, 30], default_origin='top left')
# img_plot = plot.img_plot('img', colormap=color_map_name_dict[self.colormap_name_2])[0]
# self.add_inspector(img_plot)
# self.plot = plot
#
# self.plot.range2d = self.oplot.range2d
self.container.add(self.oplot)
# self.container.add(self.plot)
self.container.request_redraw()
def __init__(self, link):
super(BaselineView, self).__init__()
self.log_file = None
self.num_hyps = 0
self.plot_data = ArrayPlotData(n=[0.0], e=[0.0], d=[0.0], t=[0.0], ref_n=[0.0], ref_e=[0.0], ref_d=[0.0])
self.plot = Plot(self.plot_data)
self.plot.plot(('e', 'n'), type='line', name='line', color=(0, 0, 0, 0.1))
self.plot.plot(('e', 'n'), type='scatter', name='points', color='blue', marker='dot', line_width=0.0, marker_size=1.0)
self.plot.plot(('ref_e', 'ref_n'),
type='scatter',
color='red',
marker='plus',
marker_size=5,
line_width=1.5
)
self.plot.index_axis.tick_label_position = 'inside'
self.plot.index_axis.tick_label_color = 'gray'
self.plot.index_axis.tick_color = 'gray'
self.plot.value_axis.tick_label_position = 'inside'
self.plot.value_axis.tick_label_color = 'gray'
self.plot.value_axis.tick_color = 'gray'
self.plot.padding = (0, 1, 0, 1)
self.plot.tools.append(PanTool(self.plot))
zt = ZoomTool(self.plot, zoom_factor=1.1, tool_mode="box", always_on=False)
self.plot.overlays.append(zt)
self.week = None
self.nsec = 0
self.link = link
self.link.add_callback(sbp_messages.SBP_BASELINE_NED, self._baseline_callback_ned)
self.link.add_callback(sbp_messages.SBP_BASELINE_ECEF, self._baseline_callback_ecef)
self.link.add_callback(sbp_messages.IAR_STATE, self.iar_state_callback)
self.link.add_callback(sbp_messages.SBP_GPS_TIME, self.gps_time_callback)
self.python_console_cmds = {
'baseline': self
}
def __init__(self, link):
super(MagView, self).__init__()
self.mag = np.zeros((NUM_POINTS, 3))
self.plot_data = ArrayPlotData(t=np.arange(NUM_POINTS),
mag_x=[0.0],
mag_y=[0.0],
mag_z=[0.0])
self.plot = Plot(self.plot_data,
auto_colors=colours_list,
emphasized=True)
self.plot.title = 'Raw Magnetometer Data'
self.plot.title_color = [0, 0, 0.43]
self.plot.value_axis.orientation = 'right'
self.plot.value_axis.axis_line_visible = False
call_repeatedly(0.2, self.mag_set_data)
self.legend_visible = True
self.plot.legend.visible = True
self.plot.legend.align = 'll'
self.plot.legend.line_spacing = 1
self.plot.legend.font = 'modern 8'
self.plot.legend.draw_layer = 'overlay'
self.plot.legend.tools.append(
LegendTool(self.plot.legend, drag_button="right"))
mag_x = self.plot.plot(('t', 'mag_x'),
type='line',
color='auto',
name='Mag. X (uT)')
mag_y = self.plot.plot(('t', 'mag_y'),
type='line',
color='auto',
name='Mag. Y (uT)')
mag_z = self.plot.plot(('t', 'mag_z'),
type='line',
color='auto',
name='Mag. Z (uT)')
self.link = link
self.link.add_callback(self.mag_raw_callback, SBP_MSG_MAG_RAW)
self.python_console_cmds = {'track': self}
def plot_factory(legend_kw=None, **kw):
"""
"""
p = Plot(data=ArrayPlotData(), **kw)
vis = kw['show_legend'] if 'show_legend' in kw else False
if not isinstance(vis, bool):
align = vis
vis = True
else:
align = 'lr'
p.legend.visible = vis
p.legend.align = align
if legend_kw:
p.legend.trait_set(**legend_kw)
return p
def test_bounds_2d_case():
# test for bug: contour and image plots should support the case where
# xbounds and ybounds are 2d arrays resulting from meshgrids
xs = np.linspace(-10, 10, 200)
ys = np.linspace(-10, 10, 400)
x, y = np.meshgrid(xs, ys)
z = x + y
plotdata = ArrayPlotData()
plotdata.set_data("z", z)
plot = Plot(plotdata)
plot.contour_plot("z", xbounds=x, ybounds=y)
# try to display it, that's when the exception is raised
with store_exceptions_on_all_threads():
pv = PlotViewer(plot=plot)
pv.edit_traits()
def __init__(self, link, legend_visible=True):
super(TrackingView, self).__init__()
self._at_least_one_track_received = False
self.t_init = monotonic()
self.time = deque([x * 1 / TRK_RATE for x in range(-NUM_POINTS, 0, 1)],
maxlen=NUM_POINTS)
self.CN0_lock = threading.Lock()
self.CN0_dict = defaultdict(
lambda: deque([0] * NUM_POINTS, maxlen=NUM_POINTS))
self.CN0_age = defaultdict(lambda: -1)
self.sv_labels = []
self.glo_fcn_dict = {}
self.glo_slot_dict = {}
self.n_channels = None
self.plot_data = ArrayPlotData(t=[0.0])
self.plot = Plot(self.plot_data, emphasized=True)
self.plot.title = 'Tracking C/N0'
self.plot.title_color = [0, 0, 0.43]
self.ylim = self.plot.value_mapper.range
self.ylim.low = SNR_THRESHOLD
self.ylim.high = 60
self.plot.value_range.bounds_func = lambda l, h, m, tb: (0, h *
(1 + m))
self.plot.value_axis.orientation = 'right'
self.plot.value_axis.axis_line_visible = False
self.plot.value_axis.title = 'dB-Hz'
self.plot_data.set_data('t', self.time)
self.plot.index_axis.title = 'seconds'
self.legend_visible = True
self.plot.legend.visible = legend_visible
self.plot.legend.align = 'll'
self.plot.legend.line_spacing = 1
self.plot.legend.font = 'monospace 8'
self.plot.legend.draw_layer = 'overlay'
self.plot.legend.tools.append(
LegendTool(self.plot.legend, drag_button="right"))
self.link = link
self.link.add_callback(self.measurement_state_callback,
SBP_MSG_MEASUREMENT_STATE)
self.link.add_callback(self.tracking_state_callback,
SBP_MSG_TRACKING_STATE)
self.python_console_cmds = {'track': self}
self.update_scheduler = UpdateScheduler()
def _create_plot_component():
# make 10 random points
x = arange(10)
x = ArrayDataSource(x, sort_order="ascending")
y = random_sample(10)
# Plot the data
pd = ArrayPlotData(x=x, y=y)
plot = Plot(pd)
plot.orientation = 'v'
line_plot = plot.plot(("x", "y"))[0]
# Add the tool to the plot both as a tool and as an overlay
tool = HittestTool(component=plot, line_plot=line_plot)
plot.tools.append(tool)
plot.overlays.append(tool)
return plot
