def _create_window(self):
ts = touchstone.read("../tests/data/deemb_mom.s2p")
ts = ts.recombine([1, 2])
circles_source = np.vstack(ts.stability_circle_source()).T
circles_load = np.vstack(ts.stability_circle_load()).T
container = OverlayPlotContainer(padding=50, fill_padding=True, bgcolor="lightgray", use_backbuffer=True)
self.data = ArrayPlotData(
f=ts.freqs,
s11=ts.get_parameter(1, 1),
s12=ts.get_parameter(1, 2),
s21=ts.get_parameter(2, 1),
s22=ts.get_parameter(2, 2),
circles_source=circles_source,
circles_load=circles_load,
)
self.plot = SmithPlot(self.data, title="Smith plot")
self.plot.plot(("f", "s11"), color="auto", line_width=2.0)
self.plot.plot(("f", "s22"), color="auto", line_width=2.0)
# self.plot.plot(("f", "s21"), color="auto", line_width=2.0)
# self.plot.plot(("f", "s12"), color="auto", line_width=2.0)
self.plot.plot_circle(("f", "circles_source"), color="auto", line_width=2.0)
self.plot.plot_circle(("f", "circles_load"), color="auto", line_width=2.0)
container.add(self.plot)
self.plot.tools.append(PanTool(self.plot))
zoom = SimpleZoom(self.plot, tool_mode="box", always_on=False)
self.plot.overlays.append(zoom)
return Window(self, -1, component=container)
def slice_plot( self, anat, coefs, **traits ):
p = Plot( self.plot_data, default_origin = 'bottom left' )
p2 = Plot( self.plot_data, default_origin = 'bottom left' )
p.x_axis.visible = False; p2.x_axis.visible = False
p.y_axis.visible = False; p2.y_axis.visible = False
bounds = self.plot_data.get_data(anat).shape
asp = float( bounds[1] ) / float( bounds[0] )
p.img_plot( anat,
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
colormap = chaco_colormaps.gray )
p2.img_plot( coefs,
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
# bgcolor = 'transparent',
colormap = self.cmap,
interpolation = 'nearest')
# p.aspect_ratio = asp; p2.aspect_ratio = asp
p.aspect_ratio = asp; p2.aspect_ratio = asp
subplot = OverlayPlotContainer( )
subplot.add( p )
subplot.add( p2 )
return subplot
def _container_default(self):
self.plot = None
# Create the data and datasource objects
# In order for the date axis to work, the index data points need to
# be in units of seconds since the epoch. This is because we are using
# the CalendarScaleSystem, whose formatters interpret the numerical values
# as seconds since the epoch.
numpoints = 500
index = create_dates(numpoints)
returns = random.lognormal(0.00, 0.04, size=numpoints)
average = 100.0 * cumprod(returns)
high = average + abs(random.normal(0, 20.0, size=numpoints))
low = average - abs(random.normal(0, 20.0, size=numpoints))
delta = high - low
open = low + delta * random.uniform(0.05, 0.95, size=numpoints)
close = low + delta * random.uniform(0.05, 0.95, size=numpoints)
price = vstack((open, high, low, close, average))
time_ds = ArrayDataSource(index)
price_ds = PriceDataSource(price, sort_order="none")
# Create the price plot
price_plot = self._create_plot(time_ds, price_ds)
self.plot = price_plot
container = OverlayPlotContainer(padding=35)
container.add(price_plot)
return container
def __init__(self, **traits):
super(ContainerExample, self).__init__(**traits)
x = np.linspace(-14, 14, 100)
y = np.sin(x) * x**3 / 1000
data = ArrayPlotData(x=x, y=y)
p1 = Plot(data, padding=30)
p1.plot(("x", "y"), type="scatter", color="blue")
p1.plot(("x", "y"), type="line", color="blue")
p2 = Plot(data, padding=30)
p2.plot(("x", "y"), type="line", color="blue")
p2.set(bounds = [200, 100], position = [70,150],
bgcolor = (0.9,0.9,0.9), unified_draw=True, resizable="")
p3 = Plot(data, padding=30)
p3.plot(("x", "y"), type="line", color="blue", line_width=2.0)
p4 = Plot(data, padding=30)
p4.plot(("x", "y"), type="scatter", color="red", marker="circle")
c1 = OverlayPlotContainer(p1, p2)
c1.fixed_preferred_size = p3.get_preferred_size()
c2 = HPlotContainer(c1, p3)
c3 = VPlotContainer(p4, c2)
self.plot = c3
def test_basics(self):
container = OverlayPlotContainer(resizable='', bounds=[100.0,200.0])
self.assert_tuple(container.get_preferred_size(), (100.0,200.0))
self.assertEquals(container._layout_needed, True)
container.do_layout()
self.assertEquals(container._layout_needed, False)
return
def __init__(self, **traits):
super(ContainerExample, self).__init__(**traits)
x = np.linspace(-14, 14, 100)
y = np.sin(x) * x**3 / 1000
data = ArrayPlotData(x=x, y=y)
p1 = Plot(data, padding=30)
p1.plot(("x", "y"), type="scatter", color="blue")
p1.plot(("x", "y"), type="line", color="blue")
p2 = Plot(data, padding=30)
p2.plot(("x", "y"), type="line", color="blue")
p2.set(bounds=[200, 100],
position=[70, 150],
bgcolor=(0.9, 0.9, 0.9),
unified_draw=True,
resizable="")
p3 = Plot(data, padding=30)
p3.plot(("x", "y"), type="line", color="blue", line_width=2.0)
p4 = Plot(data, padding=30)
p4.plot(("x", "y"), type="scatter", color="red", marker="circle")
c1 = OverlayPlotContainer(p1, p2)
c1.fixed_preferred_size = p3.get_preferred_size()
c2 = HPlotContainer(c1, p3)
c3 = VPlotContainer(p4, c2)
self.plot = c3
def init(self, parent):
factory = self.factory
container = OverlayPlotContainer(bgcolor='transparent',
padding=0, spacing=0)
window = Window(parent, component=container)
interval = self.high - self.low
data = ([self.low, self.high], [0.5]*2)
plot = create_line_plot(data, color='black', bgcolor="sys_window")
plot.x_mapper.range.low = self.low - interval*0.1
plot.x_mapper.range.high = self.high + interval*0.1
plot.y_mapper.range.high = 1.0
plot.y_mapper.range.low = 0.0
range_selection = RangeSelection(plot, left_button_selects=True)
# Do not allow the user to reset the range
range_selection.event_state = "selected"
range_selection.deselect = lambda x: None
range_selection.on_trait_change(self.update_interval, 'selection')
plot.tools.append(range_selection)
plot.overlays.append(RangeKnobsOverlay(plot))
self.plot = plot
container.add(self.plot)
# To set the low and high, we're actually going to set the
# 'selection' metadata on the line plot to the tuple (low,high).
plot.index.metadata["selections"] = (0, 1.0)
# Tell the editor what to display
self.control = window.control
self.control.SetSize((factory.width, factory.height))
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 = arange(low, high+0.001, (high-low)/numpoints)
# Plot some bessel functions
plots = {}
broadcaster = BroadcasterTool()
for i in range(4):
y = jn(i, x)
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
if i == 0:
add_default_grids(plot)
add_default_axes(plot)
# Create a pan tool and give it a reference to the plot it should
# manipulate, but don't attach it to the plot. Instead, attach it to
# the broadcaster.
pan = PanTool(plot)
broadcaster.tools.append(pan)
container.add(plot)
plots["Bessel j_%d"%i] = plot
# Add an axis on the right-hand side that corresponds to the second plot.
# Note that it uses plot.value_mapper instead of plot0.value_mapper.
plot1 = plots["Bessel j_1"]
axis = PlotAxis(plot1, orientation="right")
plot1.underlays.append(axis)
# Add the broadcast tool to the container, instead of to an
# individual plot
container.tools.append(broadcaster)
legend = Legend(component=container, padding=10, align="ur")
legend.tools.append(LegendTool(legend, drag_button="right"))
container.overlays.append(legend)
# 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 test_min_size(self):
container = OverlayPlotContainer(resizable='', bounds=[50.0,50.0])
component = PlotComponent(resizable='', position=[50.0,60.0],
bounds=[100.0, 110.0])
container.add(component)
container.do_layout()
self.assert_tuple(component.position, (50.0,60.0))
self.assert_tuple(component.bounds, (100.0,110.0))
return
def __init__(self, **kwargs):
super(VariableMeshPannerView, self).__init__(**kwargs)
# Create the plot
self.add_trait("field", DelegatesTo("vm_plot"))
plot = self.vm_plot.plot
img_plot = self.vm_plot.img_plot
if self.use_tools:
plot.tools.append(PanTool(img_plot))
zoom = ZoomTool(component=img_plot,
tool_mode="box",
always_on=False)
plot.overlays.append(zoom)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool,
bgcolor="white",
border_visible=True)
img_plot.overlays.append(overlay)
image_value_range = DataRange1D(self.vm_plot.fid)
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=img_plot,
padding_right=40,
resizable='v',
width=30)
self.colorbar.tools.append(
PanTool(self.colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(self.colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button="right")
self.colorbar.overlays.append(zoom_overlay)
# create a range selection for the colorbar
range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(img_plot)
self.full_container = HPlotContainer(padding=30)
self.container = OverlayPlotContainer(padding=0)
self.full_container.add(self.colorbar)
self.full_container.add(self.container)
self.container.add(self.vm_plot.plot)
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 = arange(low, high+0.001, (high-low)/numpoints)
y = jn(0, x)
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[0]), width=2.0)
plot.index.sort_order = "ascending"
plot.bgcolor = "white"
plot.border_visible = True
add_default_grids(plot)
add_default_axes(plot)
# Add some tools
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add a dynamic label. This can be dragged and moved around using the
# right mouse button. Note the use of padding to offset the label
# from its data point.
label = DataLabel(component=plot, data_point=(x[40], y[40]),
label_position="top left", padding=40,
bgcolor = "lightgray",
border_visible=False)
plot.overlays.append(label)
tool = DataLabelTool(label, drag_button="right", auto_arrow_root=True)
label.tools.append(tool)
# Add some static labels.
label2 = DataLabel(component=plot, data_point=(x[20], y[20]),
label_position="bottom right",
border_visible=False,
bgcolor="transparent",
marker_color="blue",
marker_line_color="transparent",
marker = "diamond",
arrow_visible=False)
plot.overlays.append(label2)
label3 = DataLabel(component=plot, data_point=(x[80], y[80]),
label_position="top", padding_bottom=20,
marker_color="transparent",
marker_size=8,
marker="circle",
arrow_visible=False)
plot.overlays.append(label3)
container.add(plot)
return container
def _create_plot_component():
# Create some x-y data series to plot
plot_area = OverlayPlotContainer(border_visible=True)
container = HPlotContainer(padding=50, bgcolor="transparent")
#container.spacing = 15
x = linspace(-2.0, 10.0, 100)
for i in range(5):
color = tuple(COLOR_PALETTE[i])
y = jn(i, x)
renderer = create_line_plot((x, y), color=color)
plot_area.add(renderer)
#plot_area.padding_left = 20
axis = PlotAxis(orientation="left", resizable="v",
mapper = renderer.y_mapper,
axis_line_color=color,
tick_color=color,
tick_label_color=color,
title_color=color,
bgcolor="transparent",
title = "jn_%d" % i,
border_visible = True,)
axis.bounds = [60,0]
axis.padding_left = 10
axis.padding_right = 10
container.add(axis)
if i == 4:
# Use the last plot's X mapper to create an X axis and a
# vertical grid
x_axis = PlotAxis(orientation="bottom", component=renderer,
mapper=renderer.x_mapper)
renderer.overlays.append(x_axis)
grid = PlotGrid(mapper=renderer.x_mapper, orientation="vertical",
line_color="lightgray", line_style="dot")
renderer.underlays.append(grid)
# Add the plot_area to the horizontal container
container.add(plot_area)
# Attach some tools to the plot
broadcaster = BroadcasterTool()
for plot in plot_area.components:
broadcaster.tools.append(PanTool(plot))
# Attach the broadcaster to one of the plots. The choice of which
# plot doesn't really matter, as long as one of them has a reference
# to the tool and will hand events to it.
plot.tools.append(broadcaster)
return container
def __init__(self, link):
super(TrackingView, self).__init__()
self.link = link
self.link.add_callback(MSG_TRACKING_SNRS, self.tracking_snrs_callback)
# ======= Line Plot =======
self.plot_data = ArrayPlotData(t=[0.0])
self.plot = Plot(self.plot_data, auto_colors=colours_list)
self.plot.value_range.tight_bounds = False
self.plot.value_range.low_setting = 0.0
for n in range(TRACK_N_CHANNELS):
self.plot_data.set_data('ch'+str(n), [0.0])
self.plot.plot(('t', 'ch'+str(n)), type='line', color='auto')
# ======= Bar Plot =======
idxs = ArrayDataSource(range(1, len(self.snrs)+1))
self.vals = ArrayDataSource(self.snrs, sort_order='none')
# Create the index range
index_range = DataRange1D(idxs, low=0.4, high=TRACK_N_CHANNELS+0.6)
index_mapper = LinearMapper(range=index_range)
# Create the value range
value_range = DataRange1D(low=0.0, high=25.0)
value_mapper = LinearMapper(range=value_range)
plot = BarPlot(index=idxs, value=self.vals,
index_mapper=index_mapper, value_mapper=value_mapper,
line_color='blue', fill_color='blue', bar_width=0.8)
container = OverlayPlotContainer(bgcolor = "white")
plot.padding = 10
plot.padding_left = 30
plot.padding_bottom = 30
container.add(plot)
left_axis = PlotAxis(plot, orientation='left')
bottom_axis = LabelAxis(plot, orientation='bottom',
labels = map(str, range(1, TRACK_N_CHANNELS+1)),
positions = range(1, TRACK_N_CHANNELS+1),
small_haxis_style=True)
plot.underlays.append(left_axis)
plot.underlays.append(bottom_axis)
self.snr_bars = container
self.python_console_cmds = {
'track': self
}
def _create_spectrumplot(self, x, y):
spectrumplot = create_line_plot((x, y), index_bounds=None, value_bounds=None,
orientation='v', color='green', width=1.0, dash='solid',
value_mapper_class=LinearMapper,
bgcolor='transparent', border_visible=True,
add_grid=False, add_axis=False, index_sort='ascending')
add_default_axes(spectrumplot, orientation='flipped', vtitle='Frequency [MHz]', htitle='Amplitude')
spectrumplot.origin = "top left"
spectrumplot.tools.append(PanTool(spectrumplot, drag_button="right"))
zoom = SimpleZoom(component=spectrumplot, tool_mode="box", drag_button="left", always_on=True)
spectrumplot.overlays.append(zoom)
container = OverlayPlotContainer(padding=40, padding_left=60)
container.add(spectrumplot)
self.container = container
def main():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index = x)
for i in range(5):
pd.set_data("y" + str(i), jn(i,x))
# Create some line plots of some of the data
plot = Plot(pd, bgcolor="none", padding=30, border_visible=True,
overlay_border=True, use_backbuffer=False)
plot.legend.visible = True
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="auto")
plot.plot(("index", "y3"), name="j_3", color="auto")
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the mlab test mesh and get references to various parts of the
# VTK pipeline
f = mlab.figure(size=(600,500))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
plot.resizable = ""
plot.bounds = [200,200]
plot.padding = 25
plot.outer_position = [30,30]
plot.tools.append(MoveTool(component=plot,drag_button="right"))
container = OverlayPlotContainer(bgcolor = "transparent",
fit_window = True)
container.add(plot)
# Create the Enable Window
window = EnableVTKWindow(rwi, renderer,
component=container,
#istyle_class = tvtk.InteractorStyleSwitch,
#istyle_class = tvtk.InteractorStyle,
istyle_class = tvtk.InteractorStyleTrackballCamera,
bgcolor = "transparent",
event_passthrough = True,
)
mlab.show()
return window, render_window
def _create_draggable_plot_component(title, initial_values=None, on_change_functor=None):
container = OverlayPlotContainer(padding = 30, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
if initial_values:
x = initial_values[0]
y = initial_values[1]
else:
# Create the initial X-series of data
numpoints = 30
low = -5
high = 15.0
x = linspace(low, high, numpoints)
y = jn(0, x)
lineplot = create_line_plot((x, y), color=tuple(COLOR_PALETTE[0]),
width=2.0)
lineplot.selected_color = 'none'
scatter = ScatterPlot(
index=lineplot.index,
value=lineplot.value,
index_mapper=lineplot.index_mapper,
value_mapper=lineplot.value_mapper,
color=tuple(COLOR_PALETTE[0]),
marker_size=2,
)
scatter.index.sort_order = 'ascending'
scatter.bgcolor = 'white'
scatter.border_visible = True
add_default_grids(scatter)
add_default_axes(scatter)
scatter.tools.append(PanTool(scatter, drag_button='right'))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(scatter, tool_mode='box', always_on=False,
drag_button=None)
scatter.overlays.append(zoom)
point_dragging_tool = PointDraggingTool(scatter)
point_dragging_tool.on_change_functor = on_change_functor
scatter.tools.append(point_dragging_tool)
container.add(lineplot)
container.add(scatter)
# Add the title at the top
container.overlays.append(PlotLabel(title, component=container,
font='swiss 16', overlay_position='top'))
container.mx = lineplot.index.get_data()
container.my = lineplot.value.get_data()
container.lineplot = lineplot
return container
def _image_plot_container(self):
plot = self.render_image()
# Create a container to position the plot and the colorbar side-by-side
self.container = OverlayPlotContainer()
self.container.add(plot)
self.img_container = HPlotContainer(use_backbuffer=False)
self.img_container.add(self.container)
self.img_container.bgcolor = "white"
if self.numpeaks_img > 0:
scatplot = self.render_scatplot()
self.container.add(scatplot)
colorbar = self.draw_colorbar()
self.img_container.add(colorbar)
return self.img_container
def _plot_default(self):
container = OverlayPlotContainer(bgcolor = "white")
self.idx = ArrayDataSource( range(1, self.binsNum+1) )
self.vals = ArrayDataSource(self.value_points, sort_order="none")
index_range = DataRange1D(self.idx, low=0, high=self.binsNum+2 )
index_mapper = LinearMapper(range=index_range)
value_range = DataRange1D(low=0, high=self.value_points.max())
value_mapper = LinearMapper(range=value_range)
# Create the plot
self.histPlot = BarPlot(index=self.idx, value=self.vals,
value_mapper=value_mapper,
index_mapper=index_mapper,
line_color='black',
fill_color=tuple(COLOR_PALETTE[1]),
bar_width=0.8, antialias=False)
plots = [self.histPlot]
for plot in plots:
plot.padding = 50
plot.padding_left = 80
plot.padding_top = 30
container.add(plot)
left_axis = PlotAxis(plot, orientation='left',
title='Number / Probability',
positions = [num for num in self.value_points],
labels = ["%.2f" % num for num in self.value_points],
#small_haxis_style=True
)
bottom_axis = LabelAxis(plot, orientation='bottom',
title='Values',
positions = range(1, self.binsNum+1),
labels = ["%.2f" % num for num in self.index_points],
)
plot.underlays.append(left_axis)
plot.underlays.append(bottom_axis)
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 = 30
low = -5
high = 15.0
x = linspace(low, high, numpoints)
y = jn(0, x)
lineplot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[0]), width=2.0)
lineplot.selected_color = "none"
scatter = ScatterPlot(index = lineplot.index,
value = lineplot.value,
index_mapper = lineplot.index_mapper,
value_mapper = lineplot.value_mapper,
color = tuple(COLOR_PALETTE[0]),
marker_size = 5)
scatter.index.sort_order = "ascending"
scatter.bgcolor = "white"
scatter.border_visible = True
add_default_grids(scatter)
add_default_axes(scatter)
scatter.tools.append(PanTool(scatter, drag_button="right"))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = ZoomTool(scatter, tool_mode="box", always_on=False, drag_button=None)
scatter.overlays.append(zoom)
scatter.tools.append(PointDraggingTool(scatter))
container.add(lineplot)
container.add(scatter)
# Add the title at the top
container.overlays.append(PlotLabel("Line Editor",
component=container,
font = "swiss 16",
overlay_position="top"))
return container
def _create_traceplot(self, y=None):
x = self.x
if y == None:
y = self.y
traceplot = create_line_plot((x, y), index_bounds=None, value_bounds=None,
orientation='v', color='blue', width=1.0, dash='solid',
value_mapper_class=LinearMapper,
bgcolor='transparent', border_visible=True,
add_grid=False, add_axis=False, index_sort='ascending')
add_default_axes(traceplot, orientation='flipped', vtitle='Time [ns]', htitle='Signal')
traceplot.origin = "top left"
traceplot.tools.append(PanTool(traceplot, drag_button="right"))
zoom = SimpleZoom(component=traceplot, tool_mode="box", drag_button="left", always_on=True)
traceplot.overlays.append(zoom)
container = OverlayPlotContainer(padding=40, padding_left=60)
container.add(traceplot)
self.container = container
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index = x)
for i in range(5):
pd.set_data("y" + str(i), jn(i,x))
# Create some line plots of some of the data
plot1 = Plot(pd)
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
plot1.plot(("index", "y3"), name="j_3", color="blue")
# Tweak some of the plot properties
plot1.title = "Inset Plot"
plot1.padding = 50
# Attach some tools to the plot
plot1.tools.append(PanTool(plot1))
zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
plot1.overlays.append(zoom)
# Create a second scatter plot of one of the datasets, linking its
# range to the first plot
plot2 = Plot(pd, range2d=plot1.range2d, padding=50)
plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")
plot2.set(resizable = "",
bounds = [250, 250],
position = [550,150],
bgcolor = "white",
border_visible = True,
unified_draw = True
)
plot2.tools.append(PanTool(plot2))
plot2.tools.append(MoveTool(plot2, drag_button="right"))
zoom = ZoomTool(component=plot2, tool_mode="box", always_on=False)
plot2.overlays.append(zoom)
# Create a container and add our plots
container = OverlayPlotContainer()
container.add(plot1)
container.add(plot2)
return container
def _create_plot_component(use_downsampling=False):
container = OverlayPlotContainer(padding=40, bgcolor="lightgray",
use_backbuffer = True,
border_visible = True,
fill_padding = True)
numpoints = 100000
low = -5
high = 15.0
x = arange(low, high+0.001, (high-low)/numpoints)
# Plot some bessel functionsless ../en
value_mapper = None
index_mapper = None
for i in range(10):
y = jn(i, x)
plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[i]), width=2.0)
plot.use_downsampling = use_downsampling
if value_mapper is None:
index_mapper = plot.index_mapper
value_mapper = plot.value_mapper
add_default_grids(plot)
add_default_axes(plot)
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%2 == 1:
plot.line_style = "dash"
plot.bgcolor = "white"
container.add(plot)
selection_overlay = RangeSelectionOverlay(component = plot)
plot.tools.append(RangeSelection(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(selection_overlay)
plot.overlays.append(zoom)
return container
def main():
plot = create_plot()
plot.bounds = [400,300]
plot.outer_position = [30,30]
plot.resizable = ""
cmap_renderer = plot.plots["my_plot"][0]
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.outer_position = [450,30]
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
container = OverlayPlotContainer(bgcolor = "transparent",
fit_window = True)
container.add(plot)
container.add(colorbar)
start_vtk(container)
def main():
from enthought.tvtk.api import tvtk
from enthought.mayavi import mlab
from enthought.enable.vtk_backend.vtk_window import EnableVTKWindow
f = mlab.figure(size=(900, 850))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
# Create the plot
timer_controller = TimerController()
plots = create_plot_component(timer_controller)
specplot, timeplot, spectrogram = plots
for i, p in enumerate(plots):
p.set(resizable="", bounds=[200, 200], outer_x=0, bgcolor="transparent")
p.outer_y = i * 250
p.tools.append(MoveTool(p, drag_button="right"))
p.tools.append(PanTool(p))
p.tools.append(ZoomTool(p))
spectrogram.tools[-1].set(tool_mode="range", axis="value")
spectrogram.tools[-2].set(constrain=True, constrain_direction="y")
container = OverlayPlotContainer(bgcolor="transparent", fit_window=True)
container.add(*plots)
container.timer_callback = timer_controller.on_timer
window = EnableVTKWindow(
rwi,
renderer,
component=container,
istyle_class=tvtk.InteractorStyleTrackballCamera,
bgcolor="transparent",
event_passthrough=True,
)
mlab.show()
def _create_plot_component():
container = OverlayPlotContainer(bgcolor = "white")
plots = make_curves()
for plot in plots:
plot.padding = 50
container.add(plot)
left_axis = PlotAxis(plot, orientation='left')
bottom_axis = LabelAxis(plot, orientation='bottom',
title='Categories',
positions = range(1, 10),
labels = ['a', 'b', 'c', 'd', 'e',
'f', 'g', 'h', 'i'],
small_haxis_style=True)
plot.underlays.append(left_axis)
plot.underlays.append(bottom_axis)
return container
def _create_window(self):
ts = touchstone.read('tests/data/deemb_mom.s2p')
freqs = ts.freqs
s11 = ts.get_parameter(1 ,1)
container = OverlayPlotContainer(padding=50, fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
plot = create_smith_plot((freqs, s11), color=(0.0, 0.0, 1.0, 1),
width=2.0)
plot.bgcolor = "white"
container.add(plot)
# Add the title at the top
container.overlays.append(PlotLabel("Smith Chart",
component=container,
font = "swiss 16",
overlay_position="top"))
return Window(self, -1, component=container)
def create_plot():
container = OverlayPlotContainer(
padding=50, fill_padding=True, bgcolor="lightgray")
numpoints = 100
low = -5
high = 15.0
x = arange(low, high + 0.001, (high - low) / numpoints)
# Plot some bessel functions
value_mapper = None
index_mapper = None
for i in range(10):
y = jn(i, x)
plot = create_line_plot((x, y),
color=(1.0 - i / 10.0, i / 10.0, 0, 1),
width=2.0,
index_sort="ascending")
if i == 0:
value_mapper = plot.value_mapper
index_mapper = plot.index_mapper
add_default_axes(plot)
add_default_grids(plot)
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 % 2 == 1:
plot.line_style = "dash"
container.add(plot)
return container
def test_multiple_min_size(self):
comp1 = StaticPlotComponent([200, 50])
comp2 = StaticPlotComponent([60, 300])
container = OverlayPlotContainer(resizable='hv', bounds=[30,30])
container.fit_components = "hv"
container.add(comp1, comp2)
container.do_layout()
self.assert_tuple(container.get_preferred_size(), (200,300))
self.assert_tuple(comp1.bounds, (200,50))
self.assert_tuple(comp2.bounds, (60,300))
return
def test_fixed_size_component(self):
container = OverlayPlotContainer(resizable='', bounds=[200.0,300.0])
# non-resizable component
component = PlotComponent(resizable='', position=[50.0,60.0], bounds=[100.0,110.0])
self.assertEquals(container._layout_needed, True)
container.do_layout()
container.add(component)
self.assertEquals(container._layout_needed, True)
container.do_layout()
self.assertEquals(container._layout_needed, False)
# check the results of the layout
self.assert_tuple(container.get_preferred_size(), (200.0,300.0))
self.assert_tuple(component.position, (50.0,60.0))
self.assert_tuple(component.bounds, (100.0,110.0))
return
def __init__(self, **kwargs):
super(VariableMeshPannerView, self).__init__(**kwargs)
# Create the plot
self.add_trait("field", DelegatesTo("vm_plot"))
plot = self.vm_plot.plot
img_plot = self.vm_plot.img_plot
if self.use_tools:
plot.tools.append(PanTool(img_plot))
zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True)
img_plot.overlays.append(overlay)
image_value_range = DataRange1D(self.vm_plot.fid)
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=img_plot,
padding_right=40,
resizable='v',
width=30)
self.colorbar.tools.append(
PanTool(self.colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(self.colorbar, axis="index", tool_mode="range",
always_on=True, drag_button="right")
self.colorbar.overlays.append(zoom_overlay)
# create a range selection for the colorbar
range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.colorbar,
border_color="white",
alpha=0.8, fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(img_plot)
self.full_container = HPlotContainer(padding=30)
self.container = OverlayPlotContainer(padding=0)
self.full_container.add(self.colorbar)
self.full_container.add(self.container)
self.container.add(self.vm_plot.plot)
def _create_window(self):
ts = touchstone.read('tests/data/deemb_mom.s2p')
freqs = ts.freqs
s11 = ts.get_parameter(1, 1)
container = OverlayPlotContainer(padding=50,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
plot = create_smith_plot((freqs, s11),
color=(0.0, 0.0, 1.0, 1),
width=2.0)
plot.bgcolor = "white"
container.add(plot)
# Add the title at the top
container.overlays.append(
PlotLabel("Smith Chart",
component=container,
font="swiss 16",
overlay_position="top"))
return Window(self, -1, component=container)
def _create_plot_component():
container = OverlayPlotContainer(bgcolor = "white")
plots = make_curves()
for plot in plots:
plot.padding = 60
container.add(plot)
bottom_axis = PlotAxis(plot, orientation='bottom')
label_list=['var a', 'var b', 'var c', 'var d', 'var e', 'var f',
'var g', 'var h', 'var i']
vertical_axis = LabelAxis(plot, orientation='left',
title='Categories',
positions = range(1, 10),
labels=label_list)
vertical2_axis = LabelAxis(plot, orientation='right',
positions = range(1, 10),
labels=label_list)
plot.underlays.append(vertical_axis)
plot.underlays.append(vertical2_axis)
plot.underlays.append(bottom_axis)
return container
def rate_plot( self, rates, **traits ):
rp = Plot( self.rate_data, default_origin = 'bottom left' )
rp.x_axis.visible = False
rp.y_axis.visible = False
bounds = self.rate_data.get_data( rates ).shape
# rp.img_plot( rates,
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
# colormap = chaco_colormaps.jet,
# interpolation = 'nearest')
rp.contour_plot( rates,
type = 'line',
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
bgcolor = 'black',
levels = 15,
styles = 'solid',
widths = list( np.linspace( 4.0, 0.1, 15 )),
colors = gmt_drywet )
rp.aspect_ratio = 1
# zoom = ZoomTool( rp, tool_mode = 'box', always_on = False )
# rp.overlays.append(zoom)
# rp.tools.append( PanTool( rp, constrain_key = 'shift' ) )
subplot = OverlayPlotContainer( )
subplot.add( rp )
return subplot
def __init__(self, parent, id=-1, **kwargs):
wx.Panel.__init__(self, parent, id=id, **kwargs)
self.statusBar = self.GetTopLevelParent().statusBar
self.container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
self.legend = Legend(component=self.container, padding=10, align="ur")
#self.legend.tools.append(LegendTool(self.legend, drag_button="right"))
self.container.overlays.append(self.legend)
self.plot_window = Window(self, component=self.container)
self.container.tools.append(TraitsTool(self.container))
self.firstplot = True
self._palette = ['red', 'blue', 'green', 'purple', 'yellow']
self._current_palette_index = 0
self._traces = []
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(self.plot_window.control, 1, wx.EXPAND)
self.SetSizer(sizer)
self.SetAutoLayout(True)
def _image_plot_container(self):
plot = self.render_image()
# Create a container to position the plot and the colorbar side-by-side
self.container=OverlayPlotContainer()
self.container.add(plot)
self.img_container = HPlotContainer(use_backbuffer = False)
self.img_container.add(self.container)
self.img_container.bgcolor = "white"
if self.numpeaks_img>0:
scatplot = self.render_scatplot()
self.container.add(scatplot)
colorbar = self.draw_colorbar()
self.img_container.add(colorbar)
return self.img_container
class TemplatePicker(HasTraits):
template = Array
CC = Array
peaks = List
zero = Int(0)
tmp_size = Range(low=2, high=512, value=64, cols=4)
max_pos_x = Int(1023)
max_pos_y = Int(1023)
top = Range(low='zero', high='max_pos_x', value=20, cols=4)
left = Range(low='zero', high='max_pos_y', value=20, cols=4)
is_square = Bool
img_plot = Instance(Plot)
tmp_plot = Instance(Plot)
findpeaks = Button
peak_width = Range(low=2, high=200, value=10)
tab_selected = Event
ShowCC = Bool
img_container = Instance(Component)
container = Instance(Component)
colorbar = Instance(Component)
numpeaks_total = Int(0)
numpeaks_img = Int(0)
OK_custom = OK_custom_handler
cbar_selection = Instance(RangeSelection)
cbar_selected = Event
thresh = Trait(None, None, List, Tuple, Array)
thresh_upper = Float(1.0)
thresh_lower = Float(0.0)
numfiles = Int(1)
img_idx = Int(0)
tmp_img_idx = Int(0)
csr = Instance(BaseCursorTool)
traits_view = View(HFlow(
VGroup(Item("img_container",
editor=ComponentEditor(),
show_label=False),
Group(
Spring(),
Item("ShowCC",
editor=BooleanEditor(),
label="Show cross correlation image")),
label="Original image",
show_border=True,
trait_modified="tab_selected"),
VGroup(
Group(HGroup(
Item("left", label="Left coordinate", style="custom"),
Item("top", label="Top coordinate", style="custom"),
),
Item("tmp_size", label="Template size", style="custom"),
Item("tmp_plot",
editor=ComponentEditor(height=256, width=256),
show_label=False,
resizable=True),
label="Template",
show_border=True),
Group(Item("peak_width", label="Peak width", style="custom"),
Group(
Spring(),
Item("findpeaks",
editor=ButtonEditor(label="Find Peaks"),
show_label=False),
Spring(),
),
HGroup(
Item("thresh_lower",
label="Threshold Lower Value",
editor=TextEditor(evaluate=float,
format_str='%1.4f')),
Item("thresh_upper",
label="Threshold Upper Value",
editor=TextEditor(evaluate=float,
format_str='%1.4f')),
),
HGroup(
Item("numpeaks_img",
label="Number of Cells selected (this image)",
style='readonly'),
Spring(),
Item("numpeaks_total", label="Total", style='readonly'),
Spring(),
),
label="Peak parameters",
show_border=True),
)),
buttons=[
Action(name='OK',
enabled_when='numpeaks_total > 0'),
CancelButton
],
title="Template Picker",
handler=OK_custom,
kind='livemodal',
key_bindings=key_bindings,
width=960,
height=600)
def __init__(self, signal_instance):
super(TemplatePicker, self).__init__()
try:
import cv
except:
print "OpenCV unavailable. Can't do cross correlation without it. Aborting."
return None
self.OK_custom = OK_custom_handler()
self.sig = signal_instance
if not hasattr(self.sig.mapped_parameters, "original_files"):
self.sig.data = np.atleast_3d(self.sig.data)
self.titles = [self.sig.mapped_parameters.name]
else:
self.numfiles = len(
self.sig.mapped_parameters.original_files.keys())
self.titles = self.sig.mapped_parameters.original_files.keys()
tmp_plot_data = ArrayPlotData(
imagedata=self.sig.data[self.top:self.top + self.tmp_size,
self.left:self.left + self.tmp_size,
self.img_idx])
tmp_plot = Plot(tmp_plot_data, default_origin="top left")
tmp_plot.img_plot("imagedata", colormap=jet)
tmp_plot.aspect_ratio = 1.0
self.tmp_plot = tmp_plot
self.tmp_plotdata = tmp_plot_data
self.img_plotdata = ArrayPlotData(
imagedata=self.sig.data[:, :, self.img_idx])
self.img_container = self._image_plot_container()
self.crop_sig = None
def render_image(self):
plot = Plot(self.img_plotdata, default_origin="top left")
img = plot.img_plot("imagedata", colormap=gray)[0]
plot.title = "%s of %s: " % (self.img_idx + 1,
self.numfiles) + self.titles[self.img_idx]
plot.aspect_ratio = float(self.sig.data.shape[1]) / float(
self.sig.data.shape[0])
#if not self.ShowCC:
csr = CursorTool(img,
drag_button='left',
color='white',
line_width=2.0)
self.csr = csr
csr.current_position = self.left, self.top
img.overlays.append(csr)
# attach the rectangle tool
plot.tools.append(PanTool(plot, drag_button="right"))
zoom = ZoomTool(plot,
tool_mode="box",
always_on=False,
aspect_ratio=plot.aspect_ratio)
plot.overlays.append(zoom)
self.img_plot = plot
return plot
def render_scatplot(self):
peakdata = ArrayPlotData()
peakdata.set_data("index", self.peaks[self.img_idx][:, 0])
peakdata.set_data("value", self.peaks[self.img_idx][:, 1])
peakdata.set_data("color", self.peaks[self.img_idx][:, 2])
scatplot = Plot(peakdata,
aspect_ratio=self.img_plot.aspect_ratio,
default_origin="top left")
scatplot.plot(
("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=jet(DataRange1D(low=0.0, high=1.0)),
marker="circle",
fill_alpha=0.5,
marker_size=6,
)
scatplot.x_grid.visible = False
scatplot.y_grid.visible = False
scatplot.range2d = self.img_plot.range2d
self.scatplot = scatplot
self.peakdata = peakdata
return scatplot
def _image_plot_container(self):
plot = self.render_image()
# Create a container to position the plot and the colorbar side-by-side
self.container = OverlayPlotContainer()
self.container.add(plot)
self.img_container = HPlotContainer(use_backbuffer=False)
self.img_container.add(self.container)
self.img_container.bgcolor = "white"
if self.numpeaks_img > 0:
scatplot = self.render_scatplot()
self.container.add(scatplot)
colorbar = self.draw_colorbar()
self.img_container.add(colorbar)
return self.img_container
def draw_colorbar(self):
scatplot = self.scatplot
cmap_renderer = scatplot.plots["my_plot"][0]
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="range")
cmap_renderer.overlays.append(selection)
if self.thresh is not None:
cmap_renderer.color_data.metadata['selections'] = self.thresh
cmap_renderer.color_data.metadata_changed = {
'selections': self.thresh
}
# Create the colorbar, handing in the appropriate range and colormap
colormap = scatplot.color_mapper
colorbar = ColorBar(
index_mapper=LinearMapper(range=DataRange1D(low=0.0, high=1.0)),
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar_selection = RangeSelection(component=colorbar)
colorbar.tools.append(colorbar_selection)
ovr = colorbar.overlays.append(
RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray",
metadata_name='selections'))
#ipshell('colorbar, colorbar_selection and ovr available:')
self.cbar_selection = colorbar_selection
self.cmap_renderer = cmap_renderer
colorbar.plot = cmap_renderer
colorbar.padding_top = scatplot.padding_top
colorbar.padding_bottom = scatplot.padding_bottom
self.colorbar = colorbar
return colorbar
@on_trait_change('ShowCC')
def toggle_cc_view(self):
if self.ShowCC:
self.CC = cv_funcs.xcorr(
self.sig.data[self.top:self.top + self.tmp_size,
self.left:self.left + self.tmp_size,
self.img_idx], self.sig.data[:, :, self.img_idx])
self.img_plotdata.set_data("imagedata", self.CC)
else:
self.img_plotdata.set_data("imagedata",
self.sig.data[:, :, self.img_idx])
self.redraw_plots()
@on_trait_change("img_idx")
def update_img_depth(self):
if self.ShowCC:
self.CC = cv_funcs.xcorr(
self.sig.data[self.top:self.top + self.tmp_size,
self.left:self.left + self.tmp_size,
self.img_idx], self.sig.data[:, :, self.img_idx])
self.img_plotdata.set_data("imagedata", self.CC)
else:
self.img_plotdata.set_data("imagedata",
self.sig.data[:, :, self.img_idx])
self.img_plot.title = "%s of %s: " % (
self.img_idx + 1, self.numfiles) + self.titles[self.img_idx]
self.redraw_plots()
@on_trait_change('tmp_size')
def update_max_pos(self):
max_pos_x = self.sig.data.shape[0] - self.tmp_size - 1
if self.left > max_pos_x: self.left = max_pos_x
self.max_pos_x = max_pos_x
max_pos_y = self.sig.data.shape[1] - self.tmp_size - 1
if self.top > max_pos_y: self.top = max_pos_y
self.max_pos_y = max_pos_y
return
def increase_img_idx(self, info):
if self.img_idx == (self.numfiles - 1):
self.img_idx = 0
else:
self.img_idx += 1
def decrease_img_idx(self, info):
if self.img_idx == 0:
self.img_idx = self.numfiles - 1
else:
self.img_idx -= 1
@on_trait_change('left, top')
def update_csr_position(self):
self.csr.current_position = self.left, self.top
@on_trait_change('csr:current_position')
def update_top_left(self):
self.left, self.top = self.csr.current_position
@on_trait_change('left, top, tmp_size')
def update_tmp_plot(self):
self.tmp_plotdata.set_data(
"imagedata",
self.sig.data[self.top:self.top + self.tmp_size,
self.left:self.left + self.tmp_size, self.img_idx])
grid_data_source = self.tmp_plot.range2d.sources[0]
grid_data_source.set_data(np.arange(self.tmp_size),
np.arange(self.tmp_size))
self.tmp_img_idx = self.img_idx
return
@on_trait_change('left, top, tmp_size')
def update_CC(self):
if self.ShowCC:
self.CC = cv_funcs.xcorr(
self.sig.data[self.top:self.top + self.tmp_size,
self.left:self.left + self.tmp_size,
self.tmp_img_idx], self.sig.data[:, :,
self.img_idx])
self.img_plotdata.set_data("imagedata", self.CC)
grid_data_source = self.img_plot.range2d.sources[0]
grid_data_source.set_data(np.arange(self.CC.shape[1]),
np.arange(self.CC.shape[0]))
if self.numpeaks_total > 0:
self.peaks = [np.array([[0, 0, -1]])]
@on_trait_change('cbar_selection:selection')
def update_thresh(self):
try:
thresh = self.cbar_selection.selection
self.thresh = thresh
self.cmap_renderer.color_data.metadata['selections'] = thresh
self.thresh_lower = thresh[0]
self.thresh_upper = thresh[1]
#cmap_renderer.color_data.metadata['selection_masks']=self.thresh
self.cmap_renderer.color_data.metadata_changed = {
'selections': thresh
}
self.container.request_redraw()
self.img_container.request_redraw()
except:
pass
@on_trait_change('thresh_upper,thresh_lower')
def manual_thresh_update(self):
self.thresh = [self.thresh_lower, self.thresh_upper]
self.cmap_renderer.color_data.metadata['selections'] = self.thresh
self.cmap_renderer.color_data.metadata_changed = {
'selections': self.thresh
}
self.container.request_redraw()
self.img_container.request_redraw()
@on_trait_change('peaks,cbar_selection:selection,img_idx')
def calc_numpeaks(self):
try:
thresh = self.cbar_selection.selection
self.thresh = thresh
except:
thresh = []
if thresh == [] or thresh == () or thresh == None:
thresh = (0, 1)
self.numpeaks_total = int(
np.sum([
np.sum(
np.ma.masked_inside(self.peaks[i][:, 2], thresh[0],
thresh[1]).mask)
for i in xrange(len(self.peaks))
]))
try:
self.numpeaks_img = int(
np.sum(
np.ma.masked_inside(self.peaks[self.img_idx][:, 2],
thresh[0], thresh[1]).mask))
except:
self.numpeaks_img = 0
@on_trait_change('findpeaks')
def locate_peaks(self):
from hyperspy import peak_char as pc
peaks = []
for idx in xrange(self.numfiles):
self.CC = cv_funcs.xcorr(
self.sig.data[self.top:self.top + self.tmp_size,
self.left:self.left + self.tmp_size,
self.tmp_img_idx], self.sig.data[:, :, idx])
# peak finder needs peaks greater than 1. Multiply by 255 to scale them.
pks = pc.two_dim_findpeaks(self.CC * 255,
peak_width=self.peak_width,
medfilt_radius=None)
pks[:, 2] = pks[:, 2] / 255.
peaks.append(pks)
self.peaks = peaks
def mask_peaks(self, idx):
thresh = self.cbar_selection.selection
if thresh == []:
thresh = (0, 1)
mpeaks = np.ma.asarray(self.peaks[idx])
mpeaks[:, 2] = np.ma.masked_outside(mpeaks[:, 2], thresh[0], thresh[1])
return mpeaks
@on_trait_change("peaks")
def redraw_plots(self):
oldplot = self.img_plot
self.container.remove(oldplot)
newplot = self.render_image()
self.container.add(newplot)
self.img_plot = newplot
try:
# if these haven't been created before, this will fail. wrap in try to prevent that.
oldscat = self.scatplot
self.container.remove(oldscat)
oldcolorbar = self.colorbar
self.img_container.remove(oldcolorbar)
except:
pass
if self.numpeaks_img > 0:
newscat = self.render_scatplot()
self.container.add(newscat)
self.scatplot = newscat
colorbar = self.draw_colorbar()
self.img_container.add(colorbar)
self.colorbar = colorbar
self.container.request_redraw()
self.img_container.request_redraw()
def crop_cells_stack(self):
from eelslab.signals.aggregate import AggregateCells
if self.numfiles == 1:
self.crop_sig = self.crop_cells()
return
else:
crop_agg = []
for idx in xrange(self.numfiles):
crop_agg.append(self.crop_cells(idx))
self.crop_sig = AggregateCells(*crop_agg)
return
def crop_cells(self, idx=0):
print "cropping cells..."
from hyperspy.signals.image import Image
# filter the peaks that are outside the selected threshold
peaks = np.ma.compress_rows(self.mask_peaks(idx))
tmp_sz = self.tmp_size
data = np.zeros((tmp_sz, tmp_sz, peaks.shape[0]))
if not hasattr(self.sig.mapped_parameters, "original_files"):
parent = self.sig
else:
parent = self.sig.mapped_parameters.original_files[
self.titles[idx]]
for i in xrange(peaks.shape[0]):
# crop the cells from the given locations
data[:, :, i] = self.sig.data[peaks[i, 1]:peaks[i, 1] + tmp_sz,
peaks[i,
0]:peaks[i, 0] + tmp_sz, idx]
crop_sig = Image({
'data': data,
'mapped_parameters': {
'name': 'Cropped cells from %s' % self.titles[idx],
'record_by': 'image',
'locations': peaks,
'parent': parent,
}
})
return crop_sig
# attach a class member that has the locations from which the images were cropped
print "Complete. "
class VariableMeshPannerView(HasTraits):
plot = Instance(Plot)
spawn_zoom = Button
vm_plot = Instance(VMImagePlot)
use_tools = Bool(True)
full_container = Instance(HPlotContainer)
container = Instance(OverlayPlotContainer)
traits_view = View(
Group(Item('full_container',
editor=ComponentEditor(size=(512, 512)),
show_label=False),
Item('field', show_label=False),
orientation="vertical"),
width=800,
height=800,
resizable=True,
title="Pan and Scan",
)
def _vm_plot_default(self):
return VMImagePlot(panner=self.panner)
def __init__(self, **kwargs):
super(VariableMeshPannerView, self).__init__(**kwargs)
# Create the plot
self.add_trait("field", DelegatesTo("vm_plot"))
plot = self.vm_plot.plot
img_plot = self.vm_plot.img_plot
if self.use_tools:
plot.tools.append(PanTool(img_plot))
zoom = ZoomTool(component=img_plot,
tool_mode="box",
always_on=False)
plot.overlays.append(zoom)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool,
bgcolor="white",
border_visible=True)
img_plot.overlays.append(overlay)
image_value_range = DataRange1D(self.vm_plot.fid)
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=img_plot,
padding_right=40,
resizable='v',
width=30)
self.colorbar.tools.append(
PanTool(self.colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(self.colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button="right")
self.colorbar.overlays.append(zoom_overlay)
# create a range selection for the colorbar
range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(img_plot)
self.full_container = HPlotContainer(padding=30)
self.container = OverlayPlotContainer(padding=0)
self.full_container.add(self.colorbar)
self.full_container.add(self.container)
self.container.add(self.vm_plot.plot)
class PlotView(wx.Panel):
def __init__(self, parent, id=-1, **kwargs):
wx.Panel.__init__(self, parent, id=id, **kwargs)
self.statusBar = self.GetTopLevelParent().statusBar
self.container = OverlayPlotContainer(padding = 50, fill_padding = True,
bgcolor = "lightgray", use_backbuffer=True)
self.legend = Legend(component=self.container, padding=10, align="ur")
#self.legend.tools.append(LegendTool(self.legend, drag_button="right"))
self.container.overlays.append(self.legend)
self.plot_window = Window(self, component=self.container)
self.container.tools.append(TraitsTool(self.container))
self.firstplot = True
self._palette = ['red', 'blue', 'green', 'purple', 'yellow']
self._current_palette_index = 0
self._traces = []
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(self.plot_window.control, 1, wx.EXPAND)
self.SetSizer(sizer)
self.SetAutoLayout(True)
def _next_color(self):
if self._current_palette_index == len(self._palette):
self._current_palette_index = 0
self._current_palette_index += 1
return self._palette[self._current_palette_index - 1]
def add_plot(self, signal, sweep_point=None):
## waveform = signal.get_waveform()
## x = waveform.get_x()[-1][0].tolist()
## y = np.real(waveform.get_y()[0].tolist())
if sweep_point is None:
sweep_point = signal.get_circuit()._sweep_set._points[0]
trace = Trace(signal, self, self._next_color(), sweep_point)
x_name = trace.index_label
y_name = trace.label
x = trace.get_indices()
y = trace.get_values()
if type(y[0]) == complex:
y = [value.real for value in y]
#print x_name, len(x)
#print y_name, len(y)
#print x
#print y
if self.firstplot:
self.plotdata = ArrayPlotData()
self.plotdata.set_data(x_name, x)
self.plotdata.set_data(y_name, y)
plot = Plot(self.plotdata)
plot.padding = 1
plot.bgcolor = "white"
plot.border_visible = True
add_default_grids(plot)
add_default_axes(plot)
plot.tools.append(PanTool(plot))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = CustomZoomTool(plot)
plot.overlays.append(zoom)
# The DragZoom tool just zooms in and out as the user drags
# the mouse vertically.
dragzoom = DragZoom(plot, drag_button="right")
plot.tools.append(dragzoom)
#~ # Add a legend in the upper right corner, and make it relocatable
#~ self.legend = Legend(component=plot, padding=10, align="ur")
#~ self.legend.tools.append(LegendTool(self.legend, drag_button="right"))
#~ plot.overlays.append(self.legend)
#~ self.legend.plots = {}
self.firstplot = False
self.container.add(plot)
self.plot = plot
else:
self.plotdata.set_data(x_name, x)
self.plotdata.set_data(y_name, y)
#self.plot.plot(self.plotdata.list_data())
pl = self.plot.plot( (x_name, y_name), name=trace.label, type="line",
color=trace.color, line_style=trace.line_style,
line_width=trace.line_width, marker=trace.marker,
marker_size=trace.marker_size, marker_color=trace.marker_color)
self.legend.plots[trace.label] = pl
self.Refresh()
