def _create_plot_component(obj):
# Spectrogram plot
obj.data = fr.getDataSets(".chi")
frequencies = obj.data[0][0]
index = ArrayDataSource(data=frequencies)
values = [obj.data[i][1] for i in xrange(len(obj.data))]
print len(obj.data[1][1])
print len(obj.data)
p = WaterfallRenderer(
index=index,
values=values,
index_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
value_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
x2_mapper=LinearMapper(low_pos=0, high_pos=100, range=DataRange1D(low=10.0, high=101.0)),
y2_mapper=LinearMapper(low_pos=0, high_pos=100, range=DataRange1D(low=0, high=600000)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
c2 = VPlotContainer()
c2.add(dummy)
return c2
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 _signals_plot_default(self):
print('_signals_plot_default')
"""Create the Plot instance."""
plot = Plot()
plot.add(self.signals_renderer)
x_axis = PlotAxis(component=plot,
mapper=self.signals_renderer.index_mapper,
orientation='bottom')
# y_axis = PlotAxis(component=plot,
# mapper=self.signals_renderer.value_mapper,
# orientation='left')
plot.overlays.extend([x_axis])
plot.origin_axis_visible = False
plot.padding_top = 0
plot.padding_left = 0
plot.padding_right = 0
plot.padding_bottom = 50
plot.border_visible = False
plot.bgcolor = "white"
plot.use_downsampling = True
return plot
class MyPlot(HasTraits):
""" Displays a plot with a few buttons to control which overlay
to display
"""
plot = Instance(Plot)
traits_view = View(Item('plot', editor=ComponentEditor(), show_label=False),
resizable=True)
def __init__(self, x_index, y_index, data, **kw):
super(MyPlot, self).__init__(**kw)
# Create the data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=data)
xs = ArrayDataSource(x_index, sort_order='ascending')
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(y_index, sort_order='ascending')
yrange = DataRange1D()
yrange.add(ys)
mlp = MultiLinePlot(
index = xs,
yindex = ys,
index_mapper = LinearMapper(range=xrange),
value_mapper = LinearMapper(range=yrange),
value=ds,
global_max = np.nanmax(data),
global_min = np.nanmin(data),
**kw)
self.plot = Plot()
self.plot.add(mlp)
class MyPlot(HasTraits):
""" Displays a plot with a few buttons to control which overlay
to display
"""
plot = Instance(Plot)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
resizable=True)
def __init__(self, x_index, y_index, data, **kw):
super(MyPlot, self).__init__(**kw)
# Create the data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=data)
xs = ArrayDataSource(x_index, sort_order='ascending')
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(y_index, sort_order='ascending')
yrange = DataRange1D()
yrange.add(ys)
mlp = MultiLinePlot(index=xs,
yindex=ys,
index_mapper=LinearMapper(range=xrange),
value_mapper=LinearMapper(range=yrange),
value=ds,
global_max=np.nanmax(data),
global_min=np.nanmin(data),
**kw)
self.plot = Plot()
self.plot.add(mlp)
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
color = numpy.random.random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Because this is a non-standard renderer, we can't call plot.plot, which
# sets up the array data sources, mappers and default index/value ranges.
# So, its gotta be done manually for now.
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
color_ds = ArrayDataSource(color)
# Create the plot
plot = Plot(pd)
plot.index_range.add(index_ds)
plot.value_range.add(value_ds)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=plot.index_range)
vmapper = LinearMapper(range=plot.value_range)
# Create the scatter renderer
scatter = ColormappedScatterPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
color_mapper=jet(range=DataRange1D(
low=0.0, high=1.0)),
fill_alpha=0.4,
index_mapper=imapper,
value_mapper=vmapper,
marker='circle',
marker_size=marker_size)
# Append the renderer to the list of the plot's plots
plot.add(scatter)
plot.plots['var_size_scatter'] = [scatter]
# Tweak some of the plot properties
plot.title = "Variable Size and Color Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component(obj):
# Setup the spectrum plot
frequencies = linspace(0.0, float(SAMPLING_RATE)/2, num=NUM_SAMPLES/2)
obj.spectrum_data = ArrayPlotData(frequency=frequencies)
empty_amplitude = zeros(NUM_SAMPLES/2)
obj.spectrum_data.set_data('amplitude', empty_amplitude)
obj.spectrum_plot = Plot(obj.spectrum_data)
spec_renderer = obj.spectrum_plot.plot(("frequency", "amplitude"), name="Spectrum",
color="red")[0]
obj.spectrum_plot.padding = 50
obj.spectrum_plot.title = "Spectrum"
spec_range = list(obj.spectrum_plot.plots.values())[0][0].value_mapper.range
spec_range.low = 0.0
spec_range.high = 5.0
obj.spectrum_plot.index_axis.title = 'Frequency (hz)'
obj.spectrum_plot.value_axis.title = 'Amplitude'
# Time Series plot
times = linspace(0.0, float(NUM_SAMPLES)/SAMPLING_RATE, num=NUM_SAMPLES)
obj.time_data = ArrayPlotData(time=times)
empty_amplitude = zeros(NUM_SAMPLES)
obj.time_data.set_data('amplitude', empty_amplitude)
obj.time_plot = Plot(obj.time_data)
obj.time_plot.plot(("time", "amplitude"), name="Time", color="blue")
obj.time_plot.padding = 50
obj.time_plot.title = "Time"
obj.time_plot.index_axis.title = 'Time (seconds)'
obj.time_plot.value_axis.title = 'Amplitude'
time_range = list(obj.time_plot.plots.values())[0][0].value_mapper.range
time_range.low = -0.2
time_range.high = 0.2
# Spectrogram plot
values = [zeros(NUM_SAMPLES/2) for i in range(SPECTROGRAM_LENGTH)]
p = WaterfallRenderer(index = spec_renderer.index, values = values,
index_mapper = LinearMapper(range = obj.spectrum_plot.index_mapper.range),
value_mapper = LinearMapper(range = DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
y2_mapper = LinearMapper(low_pos=0, high_pos=8,
range=DataRange1D(low=0, high=15)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
container = HPlotContainer()
container.add(obj.spectrum_plot)
container.add(obj.time_plot)
c2 = VPlotContainer()
c2.add(dummy)
c2.add(container)
return c2
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
color = numpy.random.random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Because this is a non-standard renderer, we can't call plot.plot, which
# sets up the array data sources, mappers and default index/value ranges.
# So, its gotta be done manually for now.
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
color_ds = ArrayDataSource(color)
# Create the plot
plot = Plot(pd)
plot.index_range.add(index_ds)
plot.value_range.add(value_ds)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=plot.index_range)
vmapper = LinearMapper(range=plot.value_range)
# Create the scatter renderer
scatter = ColormappedScatterPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
color_mapper=jet(range=DataRange1D(low=0.0, high=1.0)),
fill_alpha=0.4,
index_mapper = imapper,
value_mapper = vmapper,
marker='circle',
marker_size=marker_size)
# Append the renderer to the list of the plot's plots
plot.add(scatter)
plot.plots['var_size_scatter'] = [scatter]
# Tweak some of the plot properties
plot.title = "Variable Size and Color Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _pulse_plot_default(self):
plot = Plot(self.pulse_plot_data, padding=8, padding_left=64, padding_bottom=36)
plot.plot(('x', 'y'), style='line', color='blue', name='data')
edge_marker = LinePlot(index=ArrayDataSource(np.array((0, 0))),
value=ArrayDataSource(np.array((0, 1e9))),
color='red',
index_mapper=LinearMapper(range=plot.index_range),
value_mapper=LinearMapper(range=plot.value_range),
name='marker')
plot.add(edge_marker)
plot.index_axis.title = 'time [ns]'
plot.value_axis.title = 'intensity'
return plot
def _plot_default(self):
"""Create the Plot instance."""
plot = Plot(title="MultiLinePlot Demo")
plot.add(self.multi_line_plot_renderer)
x_axis = PlotAxis(component=plot,
mapper=self.multi_line_plot_renderer.index_mapper,
orientation='bottom',
title='t (seconds)')
y_axis = PlotAxis(component=plot,
mapper=self.multi_line_plot_renderer.value_mapper,
orientation='left',
title='channel')
plot.overlays.extend([x_axis, y_axis])
return plot
def _plot_default(self):
"""Create the Plot instance."""
plot = Plot(title="MultiLinePlot Demo")
plot.add(self.multi_line_plot_renderer)
x_axis = PlotAxis(component=plot,
mapper=self.multi_line_plot_renderer.index_mapper,
orientation='bottom',
title='t (seconds)')
y_axis = PlotAxis(component=plot,
mapper=self.multi_line_plot_renderer.value_mapper,
orientation='left',
title='channel')
plot.overlays.extend([x_axis, y_axis])
return plot
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 _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")
# Tweak some of the plot properties
plot1.title = "My First Line Plot"
plot1.padding = 50
plot1.padding_top = 75
plot1.legend.visible = True
x = linspace(-5, 15.0, 100)
y = jn(5, x)
foreign_plot = create_line_plot((x, y),
color=tuple(COLOR_PALETTE[0]),
width=2.0)
left, bottom = add_default_axes(foreign_plot)
left.orientation = "right"
bottom.orientation = "top"
plot1.add(foreign_plot)
# Attach some tools to the plot
broadcaster = BroadcasterTool()
broadcaster.tools.append(PanTool(plot1))
broadcaster.tools.append(PanTool(foreign_plot))
for c in (plot1, foreign_plot):
zoom = ZoomTool(component=c, tool_mode="box", always_on=False)
broadcaster.tools.append(zoom)
plot1.tools.append(broadcaster)
return plot1
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")
# Tweak some of the plot properties
plot1.title = "My First Line Plot"
plot1.padding = 50
plot1.padding_top = 75
plot1.legend.visible = True
x = linspace(-5, 15.0, 100)
y = jn(5, x)
foreign_plot = create_line_plot((x,y), color=tuple(COLOR_PALETTE[0]), width=2.0)
left, bottom = add_default_axes(foreign_plot)
left.orientation = "right"
bottom.orientation = "top"
plot1.add(foreign_plot)
# Attach some tools to the plot
broadcaster = BroadcasterTool()
broadcaster.tools.append(PanTool(plot1))
broadcaster.tools.append(PanTool(foreign_plot))
for c in (plot1, foreign_plot):
zoom = ZoomTool(component=c, tool_mode="box", always_on=False)
broadcaster.tools.append(zoom)
plot1.tools.append(broadcaster)
return plot1
class PoincarePlotFastMovieMakerWorker(object):
"""
special fast movie generation class which uses chaco library
"""
def __init__(self, pp_specs_manager):
self.manager = pp_specs_manager
self.pp_specs = self.manager.getMiniPoincarePlotSpecs()
if len(self.pp_specs) == 0:
return
self.gc = None
self.p0 = self.pp_specs[0]
#set up specific values for fonts sizes based on products of a movie
#height and arbitrary constants to give looking good picture
if self.manager.movie_axis_font == None:
self.axis_font = 'modern ' + str(nvl_and_positive(
self.manager.movie_axis_font_size, self.manager.movie_height / 38))
else:
self.axis_font = self.manager.movie_axis_font
if self.manager.movie_title_font == None:
self.title_font = 'modern ' + str(nvl_and_positive(
self.manager.movie_title_font_size, self.manager.movie_height / 30))
else:
self.title_font = self.manager.movie_title_font
self.time_label_font = 'modern ' + str(nvl_and_positive(
self.manager.movie_time_label_font_size, self.manager.movie_height / 35))
self.tick_font = nvl(self.manager.movie_tick_font, None)
self.frame_pad = nvl_and_positive(self.manager.movie_frame_pad, 50)
def initiate(self):
if len(self.pp_specs) == 0:
return False
# only positive values are accepted
x = self.p0.x_data[pl.where(self.p0.x_data > 0)]
y = self.p0.y_data[pl.where(self.p0.y_data > 0)]
x_min = pl.amin(x)
x_max = pl.amax(x)
y_min = pl.amin(y)
y_max = pl.amax(y)
value_min = x_min if x_min < y_min else y_min
self.value_max = x_max if x_max > y_max else y_max
self.pd = ArrayPlotData()
self.pd.set_data("index", x)
self.pd.set_data("value", y)
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
# Create the plot
self._plot = Plot(self.pd)
axis_defaults = {
#'axis_line_weight': 2,
#'tick_weight': 2,
#'tick_label_color': 'green',
'title_font': self.axis_font,
}
if self.tick_font:
axis_defaults['tick_label_font'] = self.tick_font
#a very important and weird trick; used to remove default ticks labels
self._plot.x_axis = None
self._plot.y_axis = None
#end trick
#add new x label and x's ticks labels
x_axis = PlotAxis(orientation='bottom',
title=nvl(self.manager.x_label, 'RR(n) [ms]'),
mapper=self._plot.x_mapper,
**axis_defaults)
self._plot.overlays.append(x_axis)
#add new y label and y's ticks labels
y_axis = PlotAxis(orientation='left',
title=nvl(self.manager.y_label, 'RR(n+1) [ms]'),
mapper=self._plot.y_mapper,
**axis_defaults)
self._plot.overlays.append(y_axis)
self._plot.index_range.add(index_ds)
self._plot.value_range.add(value_ds)
self._plot.index_mapper.stretch_data = False
self._plot.value_mapper.stretch_data = False
self._plot.value_range.set_bounds(value_min, self.value_max)
self._plot.index_range.set_bounds(value_min, self.value_max)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=self._plot.index_range)
vmapper = LinearMapper(range=self._plot.value_range)
color = "white"
self.scatter = __PoincarePlotScatterPlot__(
self.p0,
self.manager,
index=index_ds,
value=value_ds,
#color_data=color_ds,
#color_mapper=color_mapper,
#fill_alpha=0.4,
color=color,
index_mapper=imapper,
value_mapper=vmapper,
marker='circle',
marker_size=self.manager.active_point_size,
line_width=0
#outline_color='white'
)
self._plot.add(self.scatter)
#self._plot.plots['var_size_scatter'] = [self.scatter]
# Tweak some of the plot properties
_title = nvl(self.manager.movie_title, "Poincare plot")
if len(_title) > 0:
self._plot.title = _title
self._plot.title_font = self.title_font
self._plot.line_width = 0.5
self._plot.padding = self.frame_pad
self._plot.do_layout(force=True)
self._plot.outer_bounds = [self.manager.movie_width,
self.manager.movie_height]
self.gc = PlotGraphicsContext(self._plot.outer_bounds,
dpi=self.manager.movie_dpi)
self.gc.render_component(self._plot)
self.gc.set_line_width(0)
self.gc.save(self._get_filename(self.p0))
self.x_mean_old = None
self.y_mean_old = None
self._time_label_font = None
return True
def plot(self, idx):
if len(self.pp_specs) == 0:
return
p = self.pp_specs[idx]
p_old = None if idx == 0 else self.pp_specs[idx - 1]
if idx > 0:
self.gc.set_line_width(0)
if not p_old == None and not p_old.mean_plus == None:
r_points = pl.array([[p_old.mean_plus, p_old.mean_minus]])
r_points = self.scatter.map_screen(r_points)
self.gc.set_fill_color((1.0, 1.0, 1.0, 1.0))
self.gc.draw_marker_at_points(r_points,
self.manager.centroid_point_size, CIRCLE)
__update_graphics_context__(self.gc, self.scatter, p, self.manager)
r_points = pl.array([[p.mean_plus, p.mean_minus]])
r_points = self.scatter.map_screen(r_points)
self.gc.set_fill_color(self.manager.centroid_color_as_tuple)
self.gc.draw_marker_at_points(r_points,
self.manager.centroid_point_size, CIRCLE)
#self.gc.save_state()
self._draw_time_text(self.gc, p)
self.gc.save_state()
if self.manager.movie_frame_step > 0 and idx > 0:
# only frames module movie_frame_step are generated
if not self.manager.movie_frame_step % idx == 0:
return
if self.manager.movie_identity_line:
__draw_identity_line__(self.gc, self.value_max, self.scatter)
self.gc.save(self._get_filename(p))
def _draw_time_text(self, gc, pp_spec):
if self.manager.movie_create_time_label == False:
return
if pp_spec.level == 0:
(H, M, S) = get_time_label_parts_for_miliseconds(0,
hour_label=self.manager.movie_hour_label,
minute_label=self.manager.movie_minute_label,
second_label=self.manager.movie_second_label)
else:
(H, M, S) = get_time_label_parts_for_miliseconds(
pp_spec.cum_inactive,
hour_label=self.manager.movie_hour_label,
minute_label=self.manager.movie_minute_label,
second_label=self.manager.movie_second_label)
if not self._time_label_font:
self._time_label_font = str_to_font(None, None, self.time_label_font)
gc.set_font(self._time_label_font)
shift = 10
if self.manager.movie_time_label_in_line == True:
if self.manager.movie_time_label_prefix:
time_line = '%s %s %s %s' % (self.manager.movie_time_label_prefix, H, M, S)
else:
time_line = '%s %s %s' % (H, M, S)
_, _, tw, th = gc.get_text_extent(time_line)
x = self._plot.outer_bounds[0] / 2 - tw / 2 - self._plot.padding_left
y = self._plot.outer_bounds[1] - self._plot.padding_top - th
gc.set_fill_color((1.0, 1.0, 1.0, 1.0))
gc.rect(x, y, tw, th + shift)
gc.draw_path()
gc.set_fill_color((0.0, 0.0, 0.0, 1.0))
gc.show_text_at_point(time_line, x, y)
else:
for idx, time_e in enumerate([H, M, S]):
_, _, tw, th = gc.get_text_extent(time_e)
x = self._plot.outer_bounds[0] - tw - self._plot.padding_right
y = self._plot.outer_bounds[1] / 2 - idx * (th + shift)
gc.set_fill_color((1.0, 1.0, 1.0, 1.0))
#gc.rect(x, y, tw + shift, th)
gc.rect(x, y, tw, th + shift)
gc.draw_path()
gc.set_fill_color((0.0, 0.0, 0.0, 1.0))
gc.show_text_at_point(time_e, x, y)
return
def _get_filename(self, pp_spec):
if self.manager.movie_frame_filename_with_time:
(H, M, S) = get_time_for_miliseconds(0 if pp_spec.level == 0
else pp_spec.cum_inactive)
if pp_spec.frame_file.endswith(PNG_EXTENSION):
filename = pp_spec.frame_file.rsplit(PNG_EXTENSION)[0]
return '%s_%02d_%02d_%02d%s' % (filename, H, M, S, PNG_EXTENSION)
return pp_spec.frame_file
class PlotWindow(HasTraits):
_plot_data = Instance(ArrayPlotData)
_plot = Instance(Plot)
_click_tool = Instance(ClickerTool)
_img_plot = Instance(ImagePlot)
_right_click_avail = 0
name = Str
view = View(Item(name='_plot', editor=ComponentEditor(),
show_label=False), )
def __init__(self):
super(HasTraits, self).__init__()
# -------------- Initialization of plot system ----------------
padd = 25
self._plot_data = ArrayPlotData()
self._x = []
self._y = []
self.man_ori = [1, 2, 3, 4]
self._plot = Plot(self._plot_data, default_origin="top left")
self._plot.padding_left = padd
self._plot.padding_right = padd
self._plot.padding_top = padd
self._plot.padding_bottom = padd
self._quiverplots = []
# -------------------------------------------------------------
def left_clicked_event(self):
print("left clicked")
if len(self._x) < 4:
self._x.append(self._click_tool.x)
self._y.append(self._click_tool.y)
print(self._x, self._y)
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
def right_clicked_event(self):
print("right clicked")
if len(self._x) > 0:
self._x.pop()
self._y.pop()
print(self._x, self._y)
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
else:
if (self._right_click_avail):
print("deleting point")
self.py_rclick_delete(self._click_tool.x, self._click_tool.y,
self.cameraN)
x = []
y = []
self.py_get_pix_N(x, y, self.cameraN)
self.drawcross("x", "y", x[0], y[0], "blue", 4)
def attach_tools(self):
self._click_tool = ClickerTool(self._img_plot)
self._click_tool.on_trait_change(self.left_clicked_event,
'left_changed')
self._click_tool.on_trait_change(self.right_clicked_event,
'right_changed')
self._img_plot.tools.append(self._click_tool)
self._zoom_tool = SimpleZoom(component=self._plot,
tool_mode="box",
always_on=False)
self._zoom_tool.max_zoom_out_factor = 1.0
self._img_plot.tools.append(self._zoom_tool)
if self._plot.index_mapper is not None:
self._plot.index_mapper.on_trait_change(self.handle_mapper,
'updated',
remove=False)
if self._plot.value_mapper is not None:
self._plot.value_mapper.on_trait_change(self.handle_mapper,
'updated',
remove=False)
def drawcross(self, str_x, str_y, x, y, color1, mrk_size):
"""
Draws crosses on images
"""
self._plot_data.set_data(str_x, x)
self._plot_data.set_data(str_y, y)
self._plot.plot((str_x, str_y),
type="scatter",
color=color1,
marker="plus",
marker_size=mrk_size)
self._plot.request_redraw()
def drawline(self, str_x, str_y, x1, y1, x2, y2, color1):
self._plot_data.set_data(str_x, [x1, x2])
self._plot_data.set_data(str_y, [y1, y2])
self._plot.plot((str_x, str_y), type="line", color=color1)
self._plot.request_redraw()
def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0, scale=1.0):
""" drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1, y1, x2, y2 = self.remove_short_lines(x1c,
y1c,
x2c,
y2c,
min_length=0)
if len(x1) > 0:
xs = ArrayDataSource(x1)
ys = ArrayDataSource(y1)
quiverplot = QuiverPlot(
index=xs,
value=ys,
index_mapper=LinearMapper(range=self._plot.index_mapper.range),
value_mapper=LinearMapper(range=self._plot.value_mapper.range),
origin=self._plot.origin,
arrow_size=0,
line_color=color,
line_width=linewidth,
ep_index=np.array(x2) * scale,
ep_value=np.array(y2) * scale)
self._plot.add(quiverplot)
# we need this to track how many quiverplots are in the current
# plot
self._quiverplots.append(quiverplot)
# import pdb; pdb.set_trace()
def remove_short_lines(self, x1, y1, x2, y2, min_length=2):
""" removes short lines from the array of lines
parameters:
x1,y1,x2,y2 - start and end coordinates of the lines
returns:
x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
example usage:
x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
3 input lines, 1 short line will be removed (100,100->100,102)
returned coordinates:
x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
"""
# dx, dy = 2, 2 # minimum allowable dx,dy
x1f, y1f, x2f, y2f = [], [], [], []
for i in range(len(x1)):
if abs(x1[i] - x2[i]) > min_length or abs(y1[i] -
y2[i]) > min_length:
x1f.append(x1[i])
y1f.append(y1[i])
x2f.append(x2[i])
y2f.append(y2[i])
return x1f, y1f, x2f, y2f
def handle_mapper(self):
for i in range(0, len(self._plot.overlays)):
if hasattr(self._plot.overlays[i], 'real_position'):
coord_x1, coord_y1 = self._plot.map_screen(
[self._plot.overlays[i].real_position])[0]
self._plot.overlays[i].alternate_position = (coord_x1,
coord_y1)
def plot_num_overlay(self, x, y, txt):
for i in range(0, len(x)):
coord_x, coord_y = self._plot.map_screen([(x[i], y[i])])[0]
ovlay = TextBoxOverlay(component=self._plot,
text=str(txt[i]),
alternate_position=(coord_x, coord_y),
real_position=(x[i], y[i]),
text_color="white",
border_color="red")
self._plot.overlays.append(ovlay)
def update_image(self, image, is_float):
if is_float:
self._plot_data.set_data('imagedata', image.astype(np.float))
else:
self._plot_data.set_data('imagedata', image.astype(np.byte))
self._plot.request_redraw()
def clone_plot(clonetool, drop_position):
# A little sketchy...
canvas = clonetool.component.container.component.component
# Create a new Plot object
oldplot = clonetool.component
newplot = Plot(oldplot.data)
basic_traits = [
"orientation", "default_origin", "bgcolor", "border_color",
"border_width", "border_visible", "draw_layer", "unified_draw",
"fit_components", "fill_padding", "visible", "aspect_ratio", "title"
]
for attr in basic_traits:
setattr(newplot, attr, getattr(oldplot, attr))
# copy the ranges
dst = newplot.range2d
src = oldplot.range2d
#for attr in ('_low_setting', '_low_value', '_high_setting', '_high_value'):
# setattr(dst, attr, getattr(src, attr))
dst._xrange.sources = copy(src._xrange.sources)
dst._yrange.sources = copy(src._yrange.sources)
newplot.padding = oldplot.padding
newplot.bounds = oldplot.bounds[:]
newplot.resizable = ""
newplot.position = drop_position
newplot.datasources = copy(oldplot.datasources)
for name, renderers in list(oldplot.plots.items()):
newrenderers = []
for renderer in renderers:
new_r = clone_renderer(renderer)
new_r.index_mapper = LinearMapper(range=newplot.index_range)
new_r.value_mapper = LinearMapper(range=newplot.value_range)
new_r._layout_needed = True
new_r.invalidate_draw()
new_r.resizable = "hv"
newrenderers.append(new_r)
newplot.plots[name] = newrenderers
#newplot.plots = copy(oldplot.plots)
for name, renderers in list(newplot.plots.items()):
newplot.add(*renderers)
newplot.index_axis.title = oldplot.index_axis.title
newplot.index_axis.unified_draw = True
newplot.value_axis.title = oldplot.value_axis.title
newplot.value_axis.unified_draw = True
# Add new tools to the new plot
newplot.tools.append(
AxisTool(component=newplot, range_controller=canvas.range_controller))
# Add tools to the new plot
pan_traits = [
"drag_button", "constrain", "constrain_key", "constrain_direction",
"speed"
]
zoom_traits = [
"tool_mode", "always_on", "axis", "enable_wheel", "drag_button",
"wheel_zoom_step", "enter_zoom_key", "exit_zoom_key", "pointer",
"color", "alpha", "border_color", "border_size", "disable_on_complete",
"minimum_screen_delta", "max_zoom_in_factor", "max_zoom_out_factor"
]
move_traits = [
"drag_button", "end_drag_on_leave", "cancel_keys", "capture_mouse",
"modifier_key"
]
if not MULTITOUCH:
for tool in oldplot.tools:
if isinstance(tool, PanTool):
newtool = tool.clone_traits(pan_traits)
newtool.component = newplot
break
else:
newtool = PanTool(newplot)
# Reconfigure the pan tool to always use the left mouse, because we will
# put plot move on the right mouse button
newtool.drag_button = "left"
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, MoveTool):
newtool = tool.clone_traits(move_traits)
newtool.component = newplot
break
else:
newtool = MoveTool(newplot, drag_button="right")
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, ZoomTool):
newtool = tool.clone_traits(zoom_traits)
newtool.component = newplot
break
else:
newtool = ZoomTool(newplot)
newplot.tools.append(newtool)
else:
pz = MPPanZoom(newplot)
#pz.pan.constrain = True
#pz.pan.constrain_direction = "x"
#pz.zoom.mode = "range"
#pz.zoom.axis = "index"
newplot.tools.append(MPPanZoom(newplot))
#newplot.tools.append(MTMoveTool(
newplot._layout_needed = True
clonetool.dest.add(newplot)
newplot.invalidate_draw()
newplot.request_redraw()
canvas.request_redraw()
return
def create_hplot(self, key=None, mini=False):
if mini:
hpc = HPlotContainer(bgcolor='darkgrey',
height=MINI_HEIGHT,
resizable='h',
padding=0)
else:
hpc = HPlotContainer(bgcolor='lightgrey',
padding=HPLOT_PADDING,
resizable='hv')
# make slice plot for showing intesity profile of main plot
#************************************************************
slice_plot = Plot(self.data,
width=SLICE_PLOT_WIDTH,
orientation="v",
resizable="v",
padding=MAIN_PADDING,
padding_left=MAIN_PADDING_LEFT,
bgcolor='beige',
origin='top left')
slice_plot.x_axis.visible = False
slice_key = key + '_slice'
ydata_key = key + '_y'
slice_plot.plot((ydata_key, slice_key), name=slice_key)
# make main plot for editing depth lines
#************************************************************
main = Plot(
self.data,
border_visible=True,
bgcolor='beige',
origin='top left',
padding=MAIN_PADDING,
padding_left=MAIN_PADDING_LEFT,
)
if mini:
main.padding = MINI_PADDING
# add intensity img to plot and get reference for line inspector
#************************************************************
img_plot = main.img_plot(key,
name=key,
xbounds=self.model.xbounds[key],
ybounds=self.model.ybounds[key],
colormap=self._cmap)[0]
# add line plots: use method since these may change
#************************************************************
self.update_line_plots(key, main, update=True)
# set slice plot index range to follow main plot value range
#************************************************************
slice_plot.index_range = main.value_range
# add vertical core lines to main plots and slices
#************************************************************
# save pos and distance in session dict for view info and control
for core in self.model.core_samples:
loc_index, loc, dist = self.model.core_info_dict[core.core_id]
# add boundarys to slice plot
ref_line = self.model.final_lake_depth
self.plot_core_depths(slice_plot, core, ref_line, loc_index)
# add positions to main plots
self.plot_core(main, core, ref_line, loc_index, loc)
# now add tools depending if it is a mini plot or not
#************************************************************
if mini:
# add range selection tool only
# first add a reference line to attach it to
reference = self.make_reference_plot()
main.add(reference)
# attache range selector to this plot
range_tool = RangeSelection(reference)
reference.tools.append(range_tool)
range_overlay = RangeSelectionOverlay(reference,
metadata_name="selections")
reference.overlays.append(range_overlay)
range_tool.on_trait_change(self._range_selection_handler,
"selection")
# add zoombox to mini plot
main.plot(('zoombox_x', 'zoombox_y'),
type='polygon',
face_color=ZOOMBOX_COLOR,
alpha=ZOOMBOX_ALPHA)
# add to hplot and dict
hpc.add(main)
self.hplot_dict['mini'] = hpc
else:
# add zoom tools
main.tools.append(PanTool(main))
zoom = ZoomTool(main, tool_mode='box', axis='both', alpha=0.5)
main.tools.append(zoom)
main.overlays.append(zoom)
main.value_mapper.on_trait_change(self.zoom_all_value, 'updated')
main.index_mapper.on_trait_change(self.zoom_all_index, 'updated')
# add line inspector and attach to freeze tool
#*********************************************
line_inspector = LineInspector(component=img_plot,
axis='index_x',
inspect_mode="indexed",
is_interactive=True,
write_metadata=True,
metadata_name='x_slice',
is_listener=True,
color="white")
img_plot.overlays.append(line_inspector)
self.inspector_freeze_tool.tool_set.add(line_inspector)
# add listener for changes to metadata made by line inspector
#************************************************************
img_plot.on_trait_change(self.metadata_changed, 'index.metadata')
# set slice plot index range to follow main plot value range
#************************************************************
slice_plot.index_range = main.value_range
# add clickable legend ; must update legend when depth_dict updated
#******************************************************************
legend = Legend(component=main,
padding=0,
align="ur",
font='modern 8')
legend_highlighter = LegendHighlighter(legend, drag_button="right")
legend.tools.append(legend_highlighter)
self.update_legend_plots(legend, main)
legend.visible = False
self.legend_dict[key] = [legend, legend_highlighter]
main.overlays.append(legend)
# add main and slice plot to hplot container and dict
#****************************************************
main.title = 'frequency = {} kHz'.format(key)
main.title_font = TITLE_FONT
hpc.add(main, slice_plot)
self.hplot_dict[key] = hpc
return hpc
class CameraWindow(HasTraits):
""" CameraWindow class contains the relevant information and functions for a single camera window: image, zoom, pan
important members:
_plot_data - contains image data to display (used by update_image)
_plot - instance of Plot class to use with _plot_data
_click_tool - instance of Clicker tool for the single camera window, to handle mouse processing
"""
_plot_data = Instance(ArrayPlotData)
_plot = Instance(Plot)
_click_tool = Instance(Clicker)
rclicked = Int(0)
cam_color = ''
name = Str
view = View(Item(name='_plot', editor=ComponentEditor(), show_label=False))
# view = View( Item(name='_plot',show_label=False) )
def __init__(self, color):
""" Initialization of plot system
"""
padd = 25
self._plot_data = ArrayPlotData()
self._plot = Plot(self._plot_data, default_origin="top left")
self._plot.padding_left = padd
self._plot.padding_right = padd
self._plot.padding_top = padd
self._plot.padding_bottom = padd
self.right_p_x0,self.right_p_y0,self.right_p_x1,self.right_p_y1,self._quiverplots=[],[],[],[],[]
self.cam_color = color
def attach_tools(self):
""" attach_tools(self) contains the relevant tools: clicker, pan, zoom
"""
self._click_tool = Clicker(self._img_plot)
self._click_tool.on_trait_change(
self.left_clicked_event,
'left_changed') #set processing events for Clicker
self._click_tool.on_trait_change(self.right_clicked_event,
'right_changed')
self._img_plot.tools.append(self._click_tool)
pan = PanTool(self._plot, drag_button='middle')
zoom_tool = ZoomTool(self._plot, tool_mode="box", always_on=False)
# zoom_tool = BetterZoom(component=self._plot, tool_mode="box", always_on=False)
zoom_tool.max_zoom_out_factor = 1.0 # Disable "bird view" zoom out
self._img_plot.overlays.append(zoom_tool)
self._img_plot.tools.append(pan)
def left_clicked_event(
self
): #TODO: why do we need the clicker_tool if we can handle mouse clicks here?
""" left_clicked_event - processes left click mouse avents and displays coordinate and grey value information
on the screen
"""
print "x = %d, y= %d, grey= %d " % (self._click_tool.x,
self._click_tool.y,
self._click_tool.data_value)
#need to priny gray value
def right_clicked_event(self):
self.rclicked = 1 #flag that is tracked by main_gui, for right_click_process function of main_gui
#self._click_tool.y,self.name])
#self.drawcross("coord_x","coord_y",self._click_tool.x,self._click_tool.y,"red",5)
#print ("right clicked, "+self.name)
#need to print cross and manage other camera's crosses
def update_image(self, image, is_float):
""" update_image - displays/updates image in the curren camera window
parameters:
image - image data
is_float - if true, displays an image as float array,
else displays as byte array (B&W or gray)
example usage:
update_image(image,is_float=False)
"""
if is_float:
self._plot_data.set_data('imagedata', image.astype(np.float))
else:
self._plot_data.set_data('imagedata', image.astype(np.byte))
if not hasattr(
self,
'_img_plot'): #make a new plot if there is nothing to update
self._img_plot = Instance(ImagePlot)
self._img_plot = self._plot.img_plot('imagedata', colormap=gray)[0]
self.attach_tools()
# self._plot.request_redraw()
def drawcross(self, str_x, str_y, x, y, color1, mrk_size, marker1="plus"):
""" drawcross draws crosses at a given location (x,y) using color and marker in the current camera window
parameters:
str_x - label for x coordinates
str_y - label for y coordinates
x - array of x coordinates
y - array of y coordinates
mrk_size - marker size
makrer1 - type of marker, e.g "plus","circle"
example usage:
drawcross("coord_x","coord_y",[100,200,300],[100,200,300],2)
draws plus markers of size 2 at points (100,100),(200,200),(200,300)
"""
self._plot_data.set_data(str_x, x)
self._plot_data.set_data(str_y, y)
self._plot.plot((str_x, str_y),
type="scatter",
color=color1,
marker=marker1,
marker_size=mrk_size)
#self._plot.request_redraw()
def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0):
""" drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1, y1, x2, y2 = self.remove_short_lines(x1c, y1c, x2c, y2c)
if len(x1) > 0:
xs = ArrayDataSource(x1)
ys = ArrayDataSource(y1)
quiverplot=QuiverPlot(index=xs,value=ys,\
index_mapper=LinearMapper(range=self._plot.index_mapper.range),\
value_mapper=LinearMapper(range=self._plot.value_mapper.range),\
origin = self._plot.origin,arrow_size=0,\
line_color=color,line_width=linewidth,ep_index=np.array(x2),ep_value=np.array(y2)
)
self._plot.add(quiverplot)
self._quiverplots.append(
quiverplot
) #we need this to track how many quiverplots are in the current plot
# import pdb; pdb.set_trace()
def remove_short_lines(self, x1, y1, x2, y2):
""" removes short lines from the array of lines
parameters:
x1,y1,x2,y2 - start and end coordinates of the lines
returns:
x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
example usage:
x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
3 input lines, 1 short line will be removed (100,100->100,102)
returned coordinates:
x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
"""
dx, dy = 2, 2 #minimum allowable dx,dy
x1f, y1f, x2f, y2f = [], [], [], []
for i in range(len(x1)):
if abs(x1[i] - x2[i]) > dx or abs(y1[i] - y2[i]) > dy:
x1f.append(x1[i])
y1f.append(y1[i])
x2f.append(x2[i])
y2f.append(y2[i])
return x1f, y1f, x2f, y2f
def drawline(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.plot((str_x, str_y), type="line", color=color1)
def _get_plot_multiline(self):
if self.data is None or len(self.data.shape) == 1:
return
numpoints = self.data.shape[1]
if self.scale_type == 'Time':
index_x = self._create_dates(numpoints, start=self.first_day)
else:
index_x = np.arange(numpoints)
index_y = np.linspace(1, self.data.shape[0], self.data.shape[0])
xs = ArrayDataSource(index_x)
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(index_y)
yrange = DataRange1D()
yrange.add(ys)
# The data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=self.data)
corr_plot = \
MultiLinePlot(
index=xs,
yindex=ys,
index_mapper=LinearMapper(range=xrange),
value_mapper=LinearMapper(range=yrange),
value=ds,
global_max=self.data.max(),
global_min=self.data.min())
corr_plot.value_mapper.range.low = 0
corr_plot.value_mapper.range.high = self.data.shape[0] + 1
self.multi_line_plot_renderer = corr_plot
plot = Plot(title=self.p_title)
if self.scale_type == 'Time':
# Just the last axis shows tick_labels
bottom_axis = PlotAxis(component=plot,
orientation="bottom",
title=self.x_lbl,
tick_generator=ScalesTickGenerator(
scale=CalendarScaleSystem()))
else:
bottom_axis = PlotAxis(orientation='bottom',
title=self.x_lbl,
title_font="modern 12",
tick_visible=True,
small_axis_style=True,
axis_line_visible=False,
component=plot)
if self.y_lbl_type == 'Custom' and \
len(self.y_labels) == self.data.shape[0]:
# a new value in the list defaults to None so raise an error before
# the operator ends inputing it.
left_axis = LabelAxis(component=plot,
orientation='left',
title=self.x_lbl,
mapper=corr_plot.value_mapper,
tick_interval=1.0,
labels=self.y_labels,
positions=index_y)
else:
left_axis = PlotAxis(
component=plot,
orientation='left',
title=self.y_lbl,
title_font="modern 12",
#title_spacing=0,
tick_label_font="modern 8",
tick_visible=True,
small_axis_style=True,
axis_line_visible=False,
ensure_labels_bounded=True,
#tick_label_color="transparent",
mapper=corr_plot.value_mapper)
plot.overlays.extend([bottom_axis, left_axis])
plot.add(corr_plot)
plot.padding_bottom = 50
return plot
def clone_plot(clonetool, drop_position):
# A little sketchy...
canvas = clonetool.component.container.component.component
# Create a new Plot object
oldplot = clonetool.component
newplot = Plot(oldplot.data)
basic_traits = ["orientation", "default_origin", "bgcolor", "border_color",
"border_width", "border_visible", "draw_layer", "unified_draw",
"fit_components", "fill_padding", "visible", "aspect_ratio",
"title"]
for attr in basic_traits:
setattr(newplot, attr, getattr(oldplot, attr))
# copy the ranges
dst = newplot.range2d
src = oldplot.range2d
#for attr in ('_low_setting', '_low_value', '_high_setting', '_high_value'):
# setattr(dst, attr, getattr(src, attr))
dst._xrange.sources = copy(src._xrange.sources)
dst._yrange.sources = copy(src._yrange.sources)
newplot.padding = oldplot.padding
newplot.bounds = oldplot.bounds[:]
newplot.resizable = ""
newplot.position = drop_position
newplot.datasources = copy(oldplot.datasources)
for name, renderers in oldplot.plots.items():
newrenderers = []
for renderer in renderers:
new_r = clone_renderer(renderer)
new_r.index_mapper = LinearMapper(range=newplot.index_range)
new_r.value_mapper = LinearMapper(range=newplot.value_range)
new_r._layout_needed = True
new_r.invalidate_draw()
new_r.resizable = "hv"
newrenderers.append(new_r)
newplot.plots[name] = newrenderers
#newplot.plots = copy(oldplot.plots)
for name, renderers in newplot.plots.items():
newplot.add(*renderers)
newplot.index_axis.title = oldplot.index_axis.title
newplot.index_axis.unified_draw = True
newplot.value_axis.title = oldplot.value_axis.title
newplot.value_axis.unified_draw = True
# Add new tools to the new plot
newplot.tools.append(AxisTool(component=newplot,
range_controller=canvas.range_controller))
# Add tools to the new plot
pan_traits = ["drag_button", "constrain", "constrain_key", "constrain_direction",
"speed"]
zoom_traits = ["tool_mode", "always_on", "axis", "enable_wheel", "drag_button",
"wheel_zoom_step", "enter_zoom_key", "exit_zoom_key", "pointer",
"color", "alpha", "border_color", "border_size", "disable_on_complete",
"minimum_screen_delta", "max_zoom_in_factor", "max_zoom_out_factor"]
move_traits = ["drag_button", "end_drag_on_leave", "cancel_keys", "capture_mouse",
"modifier_key"]
if not MULTITOUCH:
for tool in oldplot.tools:
if isinstance(tool, PanTool):
newtool = tool.clone_traits(pan_traits)
newtool.component = newplot
break
else:
newtool = PanTool(newplot)
# Reconfigure the pan tool to always use the left mouse, because we will
# put plot move on the right mouse button
newtool.drag_button = "left"
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, MoveTool):
newtool = tool.clone_traits(move_traits)
newtool.component = newplot
break
else:
newtool = MoveTool(newplot, drag_button="right")
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, ZoomTool):
newtool = tool.clone_traits(zoom_traits)
newtool.component = newplot
break
else:
newtool = ZoomTool(newplot)
newplot.tools.append(newtool)
else:
pz = MPPanZoom(newplot)
#pz.pan.constrain = True
#pz.pan.constrain_direction = "x"
#pz.zoom.mode = "range"
#pz.zoom.axis = "index"
newplot.tools.append(MPPanZoom(newplot))
#newplot.tools.append(MTMoveTool(
newplot._layout_needed = True
clonetool.dest.add(newplot)
newplot.invalidate_draw()
newplot.request_redraw()
canvas.request_redraw()
return
def _create_plot_component(obj):
# Setup the spectrum plot
frequencies = linspace(0.0, 16, num=16) ##
obj.spectrum_data = ArrayPlotData(frequency=frequencies)
empty_amplitude = zeros(16) ##
obj.spectrum_data.set_data('amplitude', empty_amplitude)
background = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) * 20
obj.spectrum_data.set_data('background', background)
obj.spectrum_plot = Plot(obj.spectrum_data)
obj.spectrum_plot.plot(("frequency", "background"), value_scale="linear",\
type='bar', bar_width=0.8, color='auto')
obj.spectrum_plot.plot(("frequency", "amplitude"), value_scale="linear",\
name="Spectrum", type='bar', bar_width=0.8, color='auto')#[0]
obj.spectrum_plot.padding = 50
obj.spectrum_plot.title = "Spectrum"
spec_range = obj.spectrum_plot.plots.values()[0][0].value_mapper.range
spec_range.low = 0.0
spec_range.high = 75.0
obj.spectrum_plot.index_axis.title = 'Frequency (hz)'
obj.spectrum_plot.value_axis.title = 'Amplitude'
# Word Plots
# Setup the unseen spectrum plot
dum_frequencies = linspace(0.0,
float(SAMPLING_RATE) / 4,
num=NUM_SAMPLES / 4)
obj.dum_spectrum_data = ArrayPlotData(frequency=dum_frequencies)
dum_empty_amplitude = zeros(NUM_SAMPLES / 4)
obj.dum_spectrum_data.set_data('amplitude', dum_empty_amplitude)
obj.dum_spectrum_plot = Plot(obj.dum_spectrum_data)
spec_renderer = obj.dum_spectrum_plot.plot(("frequency", "amplitude"),
name="Spectrum",
color="red")[0]
values = [zeros(NUM_SAMPLES / 4) for i in xrange(SPECTROGRAM_LENGTH)]
p = WaterfallRenderer(index = spec_renderer.index, values = values,
index_mapper = LinearMapper(range = obj.dum_spectrum_plot.\
index_mapper.range),
value_mapper = LinearMapper(range = DataRange1D(low=0,\
high=SPECTROGRAM_LENGTH)),
y2_mapper = LinearMapper(low_pos=0, high_pos=8,
range=DataRange1D(low=0, high=15)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.title = "Current Spectral Envelope"
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
values2 = [zeros(NUM_SAMPLES / 4) for i in xrange(SPECTROGRAM_LENGTH)]
p2 = WaterfallRenderer(index = spec_renderer.index, values = values2,
index_mapper = LinearMapper(range = obj.dum_spectrum_plot.\
index_mapper.range),
value_mapper = LinearMapper(range = DataRange1D(low=0,\
high=SPECTROGRAM_LENGTH)),
y2_mapper = LinearMapper(low_pos=0, high_pos=8,
range=DataRange1D(low=0, high=15)),
)
spectrogram_plot2 = p2
obj.spectrogram_plot2 = p2
dummy2 = Plot()
dummy2.title = "Target Spectral Envelope"
dummy2.padding = 50
dummy2.index_axis.mapper.range = p.index_mapper.range
dummy2.index_axis.title = "Frequency (hz)"
dummy2.add(p2)
container = HPlotContainer()
container.add(dummy)
container.add(dummy2)
c2 = VPlotContainer()
c2.add(container)
c2.add(obj.spectrum_plot)
return c2
def _create_plot_component(obj):
# Setup the spectrum plot
frequencies = linspace(0.0, float(SAMPLING_RATE) / 2, num=NUM_SAMPLES / 2)
obj.spectrum_data = ArrayPlotData(frequency=frequencies)
empty_amplitude = zeros(NUM_SAMPLES / 2)
obj.spectrum_data.set_data('amplitude', empty_amplitude)
obj.spectrum_plot = Plot(obj.spectrum_data)
spec_renderer = obj.spectrum_plot.plot(("frequency", "amplitude"),
name="Spectrum",
color="red")[0]
obj.spectrum_plot.padding = 50
obj.spectrum_plot.title = "Spectrum"
spec_range = obj.spectrum_plot.plots.values()[0][0].value_mapper.range
spec_range.low = 0.0
spec_range.high = 5.0
obj.spectrum_plot.index_axis.title = 'Frequency (hz)'
obj.spectrum_plot.value_axis.title = 'Amplitude'
# Time Series plot
times = linspace(0.0, float(NUM_SAMPLES) / SAMPLING_RATE, num=NUM_SAMPLES)
obj.time_data = ArrayPlotData(time=times)
empty_amplitude = zeros(NUM_SAMPLES)
obj.time_data.set_data('amplitude', empty_amplitude)
obj.time_plot = Plot(obj.time_data)
obj.time_plot.plot(("time", "amplitude"), name="Time", color="blue")
obj.time_plot.padding = 50
obj.time_plot.title = "Time"
obj.time_plot.index_axis.title = 'Time (seconds)'
obj.time_plot.value_axis.title = 'Amplitude'
time_range = obj.time_plot.plots.values()[0][0].value_mapper.range
time_range.low = -0.2
time_range.high = 0.2
# Spectrogram plot
values = [zeros(NUM_SAMPLES / 2) for i in xrange(SPECTROGRAM_LENGTH)]
p = WaterfallRenderer(
index=spec_renderer.index,
values=values,
index_mapper=LinearMapper(range=obj.spectrum_plot.index_mapper.range),
value_mapper=LinearMapper(
range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
y2_mapper=LinearMapper(low_pos=0,
high_pos=8,
range=DataRange1D(low=0, high=15)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
container = HPlotContainer()
container.add(obj.spectrum_plot)
container.add(obj.time_plot)
c2 = VPlotContainer()
c2.add(dummy)
c2.add(container)
return c2
class plot_window (HasTraits):
_plot_data = Instance(ArrayPlotData)
_plot = Instance(Plot)
_click_tool = Instance(clicker_tool)
_img_plot = Instance(ImagePlot)
_right_click_avail = 0
name = Str
view = View(
Item(name='_plot', editor=ComponentEditor(), show_label=False),
)
def __init__(self):
# -------------- Initialization of plot system ----------------
padd = 25
self._plot_data = ArrayPlotData()
self._x = []
self._y = []
self.man_ori = [1, 2, 3, 4]
self._plot = Plot(self._plot_data, default_origin="top left")
self._plot.padding_left = padd
self._plot.padding_right = padd
self._plot.padding_top = padd
self._plot.padding_bottom = padd
self._quiverplots = []
# -------------------------------------------------------------
def left_clicked_event(self):
print ("left clicked")
if len(self._x) < 4:
self._x.append(self._click_tool.x)
self._y.append(self._click_tool.y)
print self._x
print self._y
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
def right_clicked_event(self):
print ("right clicked")
if len(self._x) > 0:
self._x.pop()
self._y.pop()
print self._x
print self._y
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
else:
if (self._right_click_avail):
print "deleting point"
self.py_rclick_delete(self._click_tool.x,
self._click_tool.y, self.cameraN)
x = []
y = []
self.py_get_pix_N(x, y, self.cameraN)
self.drawcross("x", "y", x[0], y[0], "blue", 4)
def attach_tools(self):
self._click_tool = clicker_tool(self._img_plot)
self._click_tool.on_trait_change(
self.left_clicked_event, 'left_changed')
self._click_tool.on_trait_change(
self.right_clicked_event, 'right_changed')
self._img_plot.tools.append(self._click_tool)
self._zoom_tool = SimpleZoom(
component=self._plot, tool_mode="box", always_on=False)
self._zoom_tool.max_zoom_out_factor = 1.0
self._img_plot.tools.append(self._zoom_tool)
if self._plot.index_mapper is not None:
self._plot.index_mapper.on_trait_change(
self.handle_mapper, 'updated', remove=False)
if self._plot.value_mapper is not None:
self._plot.value_mapper.on_trait_change(
self.handle_mapper, 'updated', remove=False)
def drawcross(self, str_x, str_y, x, y, color1, mrk_size):
self._plot_data.set_data(str_x, x)
self._plot_data.set_data(str_y, y)
self._plot.plot((str_x, str_y), type="scatter",
color=color1, marker="plus", marker_size=mrk_size)
self._plot.request_redraw()
def drawline(self, str_x, str_y, x1, y1, x2, y2, color1):
self._plot_data.set_data(str_x, [x1, x2])
self._plot_data.set_data(str_y, [y1, y2])
self._plot.plot((str_x, str_y), type="line", color=color1)
self._plot.request_redraw()
def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0):
""" drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1, y1, x2, y2 = self.remove_short_lines(x1c, y1c, x2c, y2c)
if len(x1) > 0:
xs = ArrayDataSource(x1)
ys = ArrayDataSource(y1)
quiverplot = QuiverPlot(index=xs, value=ys,
index_mapper=LinearMapper(
range=self._plot.index_mapper.range),
value_mapper=LinearMapper(
range=self._plot.value_mapper.range),
origin=self._plot.origin, arrow_size=0,
line_color=color, line_width=linewidth, ep_index=np.array(x2), ep_value=np.array(y2)
)
self._plot.add(quiverplot)
# we need this to track how many quiverplots are in the current
# plot
self._quiverplots.append(quiverplot)
# import pdb; pdb.set_trace()
def remove_short_lines(self, x1, y1, x2, y2):
""" removes short lines from the array of lines
parameters:
x1,y1,x2,y2 - start and end coordinates of the lines
returns:
x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
example usage:
x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
3 input lines, 1 short line will be removed (100,100->100,102)
returned coordinates:
x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
"""
dx, dy = 2, 2 # minimum allowable dx,dy
x1f, y1f, x2f, y2f = [], [], [], []
for i in range(len(x1)):
if abs(x1[i] - x2[i]) > dx or abs(y1[i] - y2[i]) > dy:
x1f.append(x1[i])
y1f.append(y1[i])
x2f.append(x2[i])
y2f.append(y2[i])
return x1f, y1f, x2f, y2f
def handle_mapper(self):
for i in range(0, len(self._plot.overlays)):
if hasattr(self._plot.overlays[i], 'real_position'):
coord_x1, coord_y1 = self._plot.map_screen(
[self._plot.overlays[i].real_position])[0]
self._plot.overlays[i].alternate_position = (
coord_x1, coord_y1)
def plot_num_overlay(self, x, y, txt):
for i in range(0, len(x)):
coord_x, coord_y = self._plot.map_screen([(x[i], y[i])])[0]
ovlay = TextBoxOverlay(component=self._plot,
text=str(txt[i]), alternate_position=(coord_x, coord_y),
real_position=(x[i], y[i]),
text_color="white",
border_color="red"
)
self._plot.overlays.append(ovlay)
def update_image(self, image, is_float):
if is_float:
self._plot_data.set_data('imagedata', image.astype(np.float))
else:
self._plot_data.set_data('imagedata', image.astype(np.byte))
self._plot.request_redraw()
def create_hplot(self, key=None, mini=False):
if mini:
hpc = HPlotContainer(bgcolor='darkgrey',
height=MINI_HEIGHT,
resizable='h',
padding=HPLOT_PADDING
)
else:
hpc = HPlotContainer(bgcolor='lightgrey',
padding=HPLOT_PADDING,
resizable='hv'
)
# make slice plot for showing intesity profile of main plot
#************************************************************
slice_plot = Plot(self.data,
width=SLICE_PLOT_WIDTH,
orientation="v",
resizable="v",
padding=MAIN_PADDING,
padding_left=MAIN_PADDING_LEFT,
padding_bottom=MAIN_PADDING_BOTTOM,
bgcolor='beige',
origin='top left'
)
mini_slice = Plot(self.data,
width=SLICE_PLOT_WIDTH,
orientation="v",
resizable="v",
padding=MAIN_PADDING,
padding_left=MAIN_PADDING_LEFT,
padding_bottom=MAIN_PADDING_BOTTOM,
bgcolor='beige',
origin='top left'
)
slice_plot.x_axis.visible = True
slice_key = key + '_slice'
ydata_key = key + '_y'
slice_plot.plot((ydata_key, slice_key), name=slice_key)
# make plot to show line at depth of cursor. y values constant
slice_depth_key = key + '_depth'
slice_plot.plot(('slice_depth_depth', 'slice_depth_y'),
name=slice_depth_key, color='red')
self.update_slice_depth_line_plot(slice_plot, depth=0)
# make main plot for editing depth lines
#************************************************************
main = Plot(self.data,
border_visible=True,
bgcolor='beige',
origin='top left',
padding=MAIN_PADDING,
padding_left=MAIN_PADDING_LEFT,
padding_bottom=MAIN_PADDING_BOTTOM
)
if mini:
#main.padding = MINI_PADDING
main.padding_bottom = MINI_PADDING_BOTTOM
# add intensity img to plot and get reference for line inspector
#************************************************************
img_plot = main.img_plot(key, name=key,
xbounds=self.model.xbounds[key],
ybounds=self.model.ybounds[key],
colormap=self._cmap
)[0]
# add line plots: use method since these may change
#************************************************************
self.update_line_plots(key, main, update=True)
self.plot_mask_array(key, main)
# set slice plot index range to follow main plot value range
#************************************************************
slice_plot.index_range = main.value_range
# add vertical core lines to main plots and slices
#************************************************************
# save pos and distance in session dict for view info and control
for core in self.model.core_samples:
# add boundarys to slice plot
self.plot_core_depths(slice_plot, core, ref_depth_line=None)
# add positions to main plots
self.plot_core(main, core, ref_depth_line=None)
# now add tools depending if it is a mini plot or not
#************************************************************
if mini:
# add range selection tool only
# first add a reference line to attach it to
reference = self.make_reference_plot()
main.add(reference)
main.plots['reference'] = [reference]
# attache range selector to this plot
range_tool = RangeSelection(reference)
reference.tools.append(range_tool)
range_overlay = RangeSelectionOverlay(reference,
metadata_name="selections")
reference.overlays.append(range_overlay)
range_tool.on_trait_change(self._range_selection_handler,
"selection")
# add zoombox to mini plot
main.plot(('zoombox_x', 'zoombox_y'), type='polygon',
face_color=ZOOMBOX_COLOR, alpha=ZOOMBOX_ALPHA)
# add to hplot and dict
hpc.add(main, mini_slice)
self.hplot_dict['mini'] = hpc
else:
# add zoom tools
zoom = ZoomTool(main, tool_mode='box', axis='both', alpha=0.5,
drag_button="left")
main.tools.append(zoom)
main.overlays.append(zoom)
self.zoom_tools[key] = zoom
main.value_mapper.on_trait_change(self.zoom_all_value, 'updated')
main.index_mapper.on_trait_change(self.zoom_all_index, 'updated')
# add line inspector and attach to freeze tool
#*********************************************
line_inspector = LineInspector(component=img_plot,
axis='index_x',
inspect_mode="indexed",
is_interactive=True,
write_metadata=True,
metadata_name='x_slice',
is_listener=True,
color="white")
img_plot.overlays.append(line_inspector)
self.inspector_freeze_tool.tool_set.add(line_inspector)
# add listener for changes to metadata made by line inspector
#************************************************************
img_plot.on_trait_change(self.metadata_changed, 'index.metadata')
# set slice plot index range to follow main plot value range
#************************************************************
slice_plot.index_range = main.value_range
# add clickable legend ; must update legend when depth_dict updated
#******************************************************************
legend = Legend(component=main, padding=0,
align="ur", font='modern 8')
legend_highlighter = LegendHighlighter(legend,
drag_button="right")
legend.tools.append(legend_highlighter)
self.legend_dict[key] = [legend, legend_highlighter]
self.update_legend_plots(legend, main)
legend.visible = False
main.overlays.append(legend)
legend_highlighter.on_trait_change(self.legend_moved, '_drag_state')
# add pan tool
pan_tool = PanTool(main, drag_button="right")
main.tools.append(pan_tool)
self.pan_tool_dict[key] = pan_tool
# add main and slice plot to hplot container and dict
#****************************************************
main.title = 'frequency = {} kHz'.format(key)
main.title_font = TITLE_FONT
hpc.add(main, slice_plot)
self.hplot_dict[key] = hpc
return hpc
def _get_plot_multiline(self):
if self.data is None or len(self.data.shape) == 1:
return
numpoints = self.data.shape[1]
if self.scale_type == 'Time':
index_x = self._create_dates(numpoints, start=self.first_day)
else:
index_x = np.arange(numpoints)
index_y = np.linspace(1, self.data.shape[0], self.data.shape[0])
xs = ArrayDataSource(index_x)
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(index_y)
yrange = DataRange1D()
yrange.add(ys)
# The data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=self.data)
corr_plot = \
MultiLinePlot(
index=xs,
yindex=ys,
index_mapper=LinearMapper(range=xrange),
value_mapper=LinearMapper(range=yrange),
value=ds,
global_max=self.data.max(),
global_min=self.data.min())
corr_plot.value_mapper.range.low = 0
corr_plot.value_mapper.range.high = self.data.shape[0] + 1
self.multi_line_plot_renderer = corr_plot
plot = Plot(title=self.p_title)
if self.scale_type == 'Time':
# Just the last axis shows tick_labels
bottom_axis = PlotAxis(component=plot, orientation="bottom", title=self.x_lbl,
tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
else:
bottom_axis = PlotAxis(orientation='bottom',
title=self.x_lbl,
title_font="modern 12",
tick_visible=True,
small_axis_style=True,
axis_line_visible=False,
component=plot)
if self.y_lbl_type == 'Custom' and \
len(self.y_labels) == self.data.shape[0]:
# a new value in the list defaults to None so raise an error before
# the operator ends inputing it.
left_axis = LabelAxis(component=plot,
orientation='left',
title=self.x_lbl,
mapper=corr_plot.value_mapper,
tick_interval=1.0,
labels=self.y_labels,
positions=index_y)
else:
left_axis = PlotAxis(component=plot,
orientation='left',
title=self.y_lbl,
title_font="modern 12",
#title_spacing=0,
tick_label_font="modern 8",
tick_visible=True,
small_axis_style=True,
axis_line_visible=False,
ensure_labels_bounded=True,
#tick_label_color="transparent",
mapper=corr_plot.value_mapper)
plot.overlays.extend([bottom_axis, left_axis])
plot.add(corr_plot)
plot.padding_bottom = 50
return plot
class CameraWindow (HasTraits):
""" CameraWindow class contains the relevant information and functions for a single camera window: image, zoom, pan
important members:
_plot_data - contains image data to display (used by update_image)
_plot - instance of Plot class to use with _plot_data
_click_tool - instance of Clicker tool for the single camera window, to handle mouse processing
"""
_plot_data=Instance(ArrayPlotData)
_plot=Instance(Plot)
_click_tool=Instance(Clicker)
rclicked=Int(0)
cam_color = ''
name=Str
view = View( Item(name='_plot',editor=ComponentEditor(), show_label=False) )
# view = View( Item(name='_plot',show_label=False) )
def __init__(self, color):
""" Initialization of plot system
"""
padd=25
self._plot_data=ArrayPlotData()
self._plot=Plot(self._plot_data, default_origin="top left")
self._plot.padding_left=padd
self._plot.padding_right=padd
self._plot.padding_top=padd
self._plot.padding_bottom=padd
self.right_p_x0,self.right_p_y0,self.right_p_x1,self.right_p_y1,self._quiverplots=[],[],[],[],[]
self.cam_color = color
def attach_tools(self):
""" attach_tools(self) contains the relevant tools: clicker, pan, zoom
"""
self._click_tool=Clicker(self._img_plot)
self._click_tool.on_trait_change(self.left_clicked_event, 'left_changed') #set processing events for Clicker
self._click_tool.on_trait_change(self.right_clicked_event, 'right_changed')
self._img_plot.tools.append(self._click_tool)
pan = PanTool(self._plot, drag_button = 'middle')
zoom_tool= ZoomTool(self._plot, tool_mode="box", always_on=False)
# zoom_tool = BetterZoom(component=self._plot, tool_mode="box", always_on=False)
zoom_tool.max_zoom_out_factor=1.0 # Disable "bird view" zoom out
self._img_plot.overlays.append(zoom_tool)
self._img_plot.tools.append(pan)
def left_clicked_event(self): #TODO: why do we need the clicker_tool if we can handle mouse clicks here?
""" left_clicked_event - processes left click mouse avents and displays coordinate and grey value information
on the screen
"""
print "x = %d, y= %d, grey= %d " % (self._click_tool.x,self._click_tool.y,self._click_tool.data_value)
#need to priny gray value
def right_clicked_event(self):
self.rclicked=1 #flag that is tracked by main_gui, for right_click_process function of main_gui
#self._click_tool.y,self.name])
#self.drawcross("coord_x","coord_y",self._click_tool.x,self._click_tool.y,"red",5)
#print ("right clicked, "+self.name)
#need to print cross and manage other camera's crosses
def update_image(self,image,is_float):
""" update_image - displays/updates image in the curren camera window
parameters:
image - image data
is_float - if true, displays an image as float array,
else displays as byte array (B&W or gray)
example usage:
update_image(image,is_float=False)
"""
if is_float:
self._plot_data.set_data('imagedata',image.astype(np.float))
else:
self._plot_data.set_data('imagedata',image.astype(np.byte))
if not hasattr(self,'_img_plot'): #make a new plot if there is nothing to update
self._img_plot=Instance(ImagePlot)
self._img_plot=self._plot.img_plot('imagedata',colormap=gray)[0]
self.attach_tools()
# self._plot.request_redraw()
def drawcross(self, str_x,str_y,x,y,color1,mrk_size,marker1="plus"):
""" drawcross draws crosses at a given location (x,y) using color and marker in the current camera window
parameters:
str_x - label for x coordinates
str_y - label for y coordinates
x - array of x coordinates
y - array of y coordinates
mrk_size - marker size
makrer1 - type of marker, e.g "plus","circle"
example usage:
drawcross("coord_x","coord_y",[100,200,300],[100,200,300],2)
draws plus markers of size 2 at points (100,100),(200,200),(200,300)
"""
self._plot_data.set_data(str_x,x)
self._plot_data.set_data(str_y,y)
self._plot.plot((str_x,str_y),type="scatter",color=color1,marker=marker1,marker_size=mrk_size)
#self._plot.request_redraw()
def drawquiver(self,x1c,y1c,x2c,y2c,color,linewidth=1.0):
""" drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1,y1,x2,y2=self.remove_short_lines(x1c,y1c,x2c,y2c)
if len(x1)>0:
xs=ArrayDataSource(x1)
ys=ArrayDataSource(y1)
quiverplot=QuiverPlot(index=xs,value=ys,\
index_mapper=LinearMapper(range=self._plot.index_mapper.range),\
value_mapper=LinearMapper(range=self._plot.value_mapper.range),\
origin = self._plot.origin,arrow_size=0,\
line_color=color,line_width=linewidth,ep_index=np.array(x2),ep_value=np.array(y2)
)
self._plot.add(quiverplot)
self._quiverplots.append(quiverplot) #we need this to track how many quiverplots are in the current plot
# import pdb; pdb.set_trace()
def remove_short_lines(self,x1,y1,x2,y2):
""" removes short lines from the array of lines
parameters:
x1,y1,x2,y2 - start and end coordinates of the lines
returns:
x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
example usage:
x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
3 input lines, 1 short line will be removed (100,100->100,102)
returned coordinates:
x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
"""
dx,dy=2,2 #minimum allowable dx,dy
x1f,y1f,x2f,y2f=[],[],[],[]
for i in range(len(x1)):
if abs(x1[i]-x2[i])>dx or abs(y1[i]-y2[i])>dy:
x1f.append(x1[i])
y1f.append(y1[i])
x2f.append(x2[i])
y2f.append(y2[i])
return x1f,y1f,x2f,y2f
def drawline(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.plot((str_x,str_y),type="line",color=color1)
def _create_plot_component(obj):
# Setup the spectrum plot
frequencies = linspace(0.0, 16, num=16) ##
obj.spectrum_data = ArrayPlotData(frequency=frequencies)
empty_amplitude = zeros(16) ##
obj.spectrum_data.set_data("amplitude", empty_amplitude)
background = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) * 20
obj.spectrum_data.set_data("background", background)
obj.spectrum_plot = Plot(obj.spectrum_data)
obj.spectrum_plot.plot(("frequency", "background"), value_scale="linear", type="bar", bar_width=0.8, color="auto")
obj.spectrum_plot.plot(
("frequency", "amplitude"), value_scale="linear", name="Spectrum", type="bar", bar_width=0.8, color="auto"
) # [0]
obj.spectrum_plot.padding = 50
obj.spectrum_plot.title = "Spectrum"
spec_range = obj.spectrum_plot.plots.values()[0][0].value_mapper.range
spec_range.low = 0.0
spec_range.high = 75.0
obj.spectrum_plot.index_axis.title = "Frequency (hz)"
obj.spectrum_plot.value_axis.title = "Amplitude"
# Word Plots
# Setup the unseen spectrum plot
dum_frequencies = linspace(0.0, float(SAMPLING_RATE) / 4, num=NUM_SAMPLES / 4)
obj.dum_spectrum_data = ArrayPlotData(frequency=dum_frequencies)
dum_empty_amplitude = zeros(NUM_SAMPLES / 4)
obj.dum_spectrum_data.set_data("amplitude", dum_empty_amplitude)
obj.dum_spectrum_plot = Plot(obj.dum_spectrum_data)
spec_renderer = obj.dum_spectrum_plot.plot(("frequency", "amplitude"), name="Spectrum", color="red")[0]
values = [zeros(NUM_SAMPLES / 4) for i in xrange(SPECTROGRAM_LENGTH)]
p = WaterfallRenderer(
index=spec_renderer.index,
values=values,
index_mapper=LinearMapper(range=obj.dum_spectrum_plot.index_mapper.range),
value_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
y2_mapper=LinearMapper(low_pos=0, high_pos=8, range=DataRange1D(low=0, high=15)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.title = "Current Spectral Envelope"
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
values2 = [zeros(NUM_SAMPLES / 4) for i in xrange(SPECTROGRAM_LENGTH)]
p2 = WaterfallRenderer(
index=spec_renderer.index,
values=values2,
index_mapper=LinearMapper(range=obj.dum_spectrum_plot.index_mapper.range),
value_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
y2_mapper=LinearMapper(low_pos=0, high_pos=8, range=DataRange1D(low=0, high=15)),
)
spectrogram_plot2 = p2
obj.spectrogram_plot2 = p2
dummy2 = Plot()
dummy2.title = "Target Spectral Envelope"
dummy2.padding = 50
dummy2.index_axis.mapper.range = p.index_mapper.range
dummy2.index_axis.title = "Frequency (hz)"
dummy2.add(p2)
container = HPlotContainer()
container.add(dummy)
container.add(dummy2)
c2 = VPlotContainer()
c2.add(container)
c2.add(obj.spectrum_plot)
return c2