def _create_corr_plot(self):
plot = Plot(self.plotdata, padding=0)
plot.padding_left = 25
plot.padding_bottom = 25
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
self.corr_plot = plot
def __init__(self):
super(HHCurrentTraits, self).__init__()
# gates
self.vm = linspace(-120,65,1000)
(M, N, H) = self.__gates()
nA = self.__iv()
self.gatedata = ArrayPlotData(x=self.vm, M=M, N=N, H=H)
self.ivdata = ArrayPlotData(x=self.vm, nA=nA)
gateplot = Plot(self.gatedata)
gateplot.plot(("x", "M"), type = "line", color = "blue")
gateplot.plot(("x", "N"), type = "line", color = "green")
gateplot.plot(("x", "H"), type = "line", color = "red")
ivplot = Plot(self.ivdata)
ivplot.plot(("x", "nA"), type = "line", color = "black")
container = VPlotContainer(ivplot, gateplot)
container.spacing = 0
gateplot.x_axis.orientation = "top"
gateplot.padding_bottom = 0
ivplot.padding_top = 0
self.plots = container
def _space_plot_default(self):
self.debug_print('_space_plot_default')
p = Plot(self.plot_data)
# Plot track underlay
x_outer, y_outer = np.transpose(n_gon((0, 0), OUTER_DIAMETER / 2, 48))
x_inner, y_inner = np.transpose(n_gon((0, 0), INNER_DIAMETER / 2, 48))
self.plot_data['x_outer'] = x_outer
self.plot_data['y_outer'] = y_outer
self.plot_data['x_inner'] = x_inner
self.plot_data['y_inner'] = y_inner
self.data_to_keep.extend(['x_outer', 'y_outer', 'x_inner', 'y_inner'])
p.plot(('x_outer', 'y_outer'),
type='polygon',
edge_width=1.0,
edge_color='darkgrey',
face_color='linen')
p.plot(('x_inner', 'y_inner'),
type='polygon',
edge_width=1.0,
edge_color='darkgrey',
face_color='white')
p.plot(('pause_x', 'pause_y'), **PAUSE_FMT)
p.plot(('scan_x', 'scan_y'), **SCAN_FMT)
p.plot(('x', 'y'))
p.plot(('cl_x', 'cl_y'), **SPIKE_FMT)
p.title = 'Space'
p.x_axis.title = 'X (cm)'
p.y_axis.title = 'Y (cm)'
p.padding_bottom = 55
p.range2d.set_bounds((-50, -50), (50, 50))
return p
def _create_corr_plot(self):
plot = Plot(self.plotdata, padding=0)
plot.padding_left = 25
plot.padding_bottom = 25
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
self.corr_plot = 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
def _time_plot_default(self):
self.debug_print('_time_plot_default')
p = Plot(self.plot_data)
p.plot(('time', 'ordinate'))
p.plot(('cl_time', 'cl_ordinate'), **SPIKE_FMT)
p.title = 'Time'
p.x_axis.title = 'Time (s)'
p.padding_bottom = 55
return p
def _behavior_plot_default(self):
self.debug_print('_behavior_plot_default')
p = Plot(self.plot_data)
p.plot(('abscissa', 'ordinate'),
type='scatter',
marker='dot',
marker_size=2.0,
line_width=0,
color='black')
p.plot(('cl_abscissa', 'cl_ordinate'), **SPIKE_FMT)
p.title = 'Behavior'
p.padding_bottom = 55
return p
def _plot_default(self):
container = Plot(self.model.plot_data, )
container.renderer_map['bar'] = SelectableBarPlot
container.padding_left = 100
container.padding_bottom = 150
# container.plot(('x', 'y'), type='bar')
self.zoom_tool = ZoomTool(container, )
container.underlays.append(self.zoom_tool)
container.tools.append(self.zoom_tool)
self.pan_tool = PanTool(container, )
container.tools.append(self.pan_tool)
return container
def _plot_default(self):
container = Plot(
self.model.plot_data,
)
container.renderer_map['bar'] = SelectableBarPlot
container.padding_left = 100
container.padding_bottom = 150
# container.plot(('x', 'y'), type='bar')
self.zoom_tool = ZoomTool(
container,
)
container.underlays.append(self.zoom_tool)
container.tools.append(self.zoom_tool)
self.pan_tool = PanTool(
container,
)
container.tools.append(self.pan_tool)
return container
def __init__(self, ExprmntVm=None, ExprmntnA=None):
super(HHCurrentTraits, self).__init__()
# gates
self.vm = linspace(-120,65,1000)
((MCur1,NCur1,HCur1),(MCur2,NCur2,HCur2)) = self.__gates()
self.Cur1gatedata = ArrayPlotData(x=self.vm, M=MCur1, N=NCur1, H=HCur1)
self.Cur2gatedata = ArrayPlotData(x=self.vm, M=MCur2, N=NCur2, H=HCur2)
Cur1gatesplot = Plot(self.Cur1gatedata)
Cur1gatesplot.plot(("x", "M"), type = "line", color = "blue")
Cur1gatesplot.plot(("x", "N"), type = "line", color = "green")
Cur1gatesplot.plot(("x", "H"), type = "line", color = "red")
Cur2gatesplot = Plot(self.Cur2gatedata)
Cur2gatesplot.plot(("x", "M"), type = "line", color = "blue")
Cur2gatesplot.plot(("x", "N"), type = "line", color = "green")
Cur2gatesplot.plot(("x", "H"), type = "line", color = "red")
(Cur1,Cur2) = self.__iv()
self.ivdata = ArrayPlotData(x=self.vm, nA1=Cur1, nA2=Cur2, combin=Cur1+Cur2)
ivplot = Plot(self.ivdata)
ivplot.plot(("x", "nA1"), type = "line", color = "blue")
ivplot.plot(("x", "nA2"), type = "line", color = "green")
ivplot.plot(("x", "combin"), type = "line", color = "black")
if ExprmntVm is not None:
self.ivdata.set_data('ExptVm',ExprmntVm)
self.ivdata.set_data('ExptnA',ExprmntnA)
ivplot.plot(("ExptVm", "ExptnA"),
type = "scatter", color = "red", marker_size = 5)
self.plots = VPlotContainer(ivplot, Cur2gatesplot, Cur1gatesplot)
self.plots.spacing = 0
ivplot.padding_top = 0
Cur1gatesplot.padding_bottom = 0
Cur2gatesplot.padding_top = 0
self.write_button = Button(label="Print_Pars")
def _create_1D1_plot(self):
index = 0
plot0 = Plot(self.plotdata, padding=0)
plot0.padding_left = 5
plot0.padding_bottom = 5
Container = OverlayPlotContainer(padding = 50, fill_padding = True, bgcolor = "lightgray", use_backbuffer=True)
y1 = range(len(self.PCAData.batchs[0].prescores))
points = []
for batch in self.PCAData.batchs:
if (self.active_scores_combobox == "Post Scores"):
x1 = self.PCAData.batchs[index].postscores
else:
x1 = self.PCAData.batchs[index].prescores
if (self.Shape == self.phenotypes[0]):
a = 1
elif (self.Shape == self.phenotypes[1]):
a = batch.number
elif (self.Shape == self.phenotypes[2]):
a = batch.type
else:
a = 0
if (self.Color == self.phenotypes[0]):
b = 0
elif(self.Color == self.phenotypes[1]):
b = batch.number
elif(self.Color == self.phenotypes[2]):
b = batch.type
else:
b = 0
tmarker = shapes[a]
bcolor = self.colors[b]
for i in range(len(x1)):
points.append((x1[i],y1[i]))
plot0 = create_scatter_plot((x1,y1), marker = tmarker, color=getColor(bcolor))
if batch.isSelected:
plot0.alpha = 1
else:
plot0.alpha = 0.2
plot0.bgcolor = "white"
plot0.border_visible = True
if index == 0:
value_mapper = plot0.value_mapper
index_mapper = plot0.index_mapper
add_default_grids(plot0)
add_default_axes(plot0, vtitle='PCA Indices', htitle='PCA Scores')
plot0.index_range.tight_bounds = False
plot0.index_range.refresh()
plot0.value_range.tight_bounds = False
plot0.value_range.refresh()
plot0.tools.append(PanTool(plot0))
zoom = ZoomTool(plot0, tool_mode="box", always_on=False, maintain_aspect_ratio=False)
plot0.overlays.append(zoom)
dragzoom = DragZoom(plot0, drag_button="right", maintain_aspect_ratio=False)
plot0.tools.append(dragzoom)
else:
plot0.value_mapper = value_mapper
value_mapper.range.add(plot0.value)
plot0.index_mapper = index_mapper
index_mapper.range.add(plot0.index)
Container.add(plot0)
index = index +1
self.RightPlot = Container
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 _create_plot_component():
army_lat = np.column_stack([army['start_lat'],
army['end_lat']]).reshape(-1)
army_lon = np.column_stack([army['start_lon'],
army['end_lon']]).reshape(-1)
plot_data = ArrayPlotData(
army_lon=army_lon,
army_lat=army_lat,
army_size=army['size'],
army_color=army['direction'] * army["group"],
towns_lat=towns['lat'],
towns_lon=towns['lon'],
towns=towns['town'],
temp_lon=temperatures['lon'],
temp=temperatures['temp'],
temp_date=temperatures['date'],
)
map_plot = Plot(plot_data)
map_plot.x_grid = None
map_plot.y_grid = None
map_plot.x_axis.orientation = 'top'
map_plot.x_axis.title = 'Longitude'
map_plot.y_axis.title = 'Latitude'
map_plot.title = "Minard's Map of Napoleon's Russian Campaign"
map_plot._title.overlay_position = "inside top"
map_plot._title.hjustify = "left"
map_plot._title.vjustify = "bottom"
map_plot.plot(
("army_lon", "army_lat", "army_color", "army_size"),
type="cmap_segment",
name="my_plot",
color_mapper=viridis,
border_visible=True,
bgcolor="white",
size_min=1.0,
size_max=128.0,
)
map_plot.plot(
("towns_lon", "towns_lat"),
type="scatter",
)
map_plot.plot(
("towns_lon", "towns_lat", "towns"),
type="text",
text_margin=4,
h_position='right',
text_offset=(4, 0),
)
map_plot.plot_1d(
('temp_lon'),
type='line_scatter_1d',
alpha=0.5,
line_style='dot',
)
map_plot.index_range.high_setting = 38
map_plot.index_range.low_setting = 23
map_plot.value_range.high_setting = 56.0
map_plot.value_range.low_setting = 53.5
map_plot.tools.extend([
PanTool(map_plot),
ZoomTool(map_plot),
])
temp_plot = Plot(plot_data, height=100)
temp_plot.index_range = map_plot.index_range
temp_plot.x_grid = None
temp_plot.x_axis = None
temp_plot.y_axis.orientation = 'right'
temp_plot.y_axis.title = u'Temp (°Re)'
temp_plot.plot(
('temp_lon', 'temp'),
type='line',
)
temp_plot.plot_1d(
('temp_lon'),
type='line_scatter_1d',
alpha=0.5,
line_style='dot',
)
temp_plot.plot_1d(
('temp_lon', 'temp_date'),
type='textplot_1d',
alpha=0.5,
line_style='dot',
alignment='bottom',
)
temp_plot.value_range.high_setting = 5
temp_plot.value_range.low_setting = -35
container = VPlotContainer(temp_plot, map_plot)
container.spacing = 0
map_plot.padding_bottom = 0
map_plot.padding_left = 70
map_plot.padding_right = 70
map_plot.padding_top = 50
temp_plot.padding_top = 0
temp_plot.padding_bottom = 15
temp_plot.padding_right = 70
temp_plot.padding_left = 70
temp_plot.height = 100
temp_plot.resizable = 'h'
return container
def _create_1D1_plot(self):
index = 0
plot0 = Plot(self.plotdata, padding=0)
plot0.padding_left = 5
plot0.padding_bottom = 5
Container = OverlayPlotContainer(padding=50,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True)
y1 = range(len(self.PCAData.batchs[0].prescores))
points = []
for batch in self.PCAData.batchs:
if (self.active_scores_combobox == "Post Scores"):
x1 = self.PCAData.batchs[index].postscores
else:
x1 = self.PCAData.batchs[index].prescores
'''if (self.Shape == self.phenotypes[0]):
a = 1
elif (self.Shape == self.phenotypes[1]):
a = batch.number
elif (self.Shape == self.phenotypes[2]):
a = batch.type
else:
a = 0
if (self.Color == self.phenotypes[0]):
b = 0
elif(self.Color == self.phenotypes[1]):
b = batch.number
elif(self.Color == self.phenotypes[2]):
b = batch.type
else:
b = 0 '''
a = batch.type
b = batch.number
tmarker = shapes[a]
bcolor = self.colors[b]
for i in range(len(x1)):
points.append((x1[i], y1[i]))
plot0 = create_scatter_plot((x1, y1),
marker=tmarker,
color=getColor(bcolor))
if batch.isSelected:
plot0.alpha = 1
plot0.alpha = 1
else:
plot0.fill_alpha = 0.2
plot0.edge_alpha = 0.2
plot0.bgcolor = "white"
plot0.border_visible = True
if index == 0:
value_mapper = plot0.value_mapper
index_mapper = plot0.index_mapper
add_default_grids(plot0)
add_default_axes(plot0,
vtitle='PCA Indices',
htitle='PCA Scores')
plot0.index_range.tight_bounds = False
plot0.index_range.refresh()
plot0.value_range.tight_bounds = False
plot0.value_range.refresh()
plot0.tools.append(PanTool(plot0))
zoom = ZoomTool(plot0,
tool_mode="box",
always_on=False,
maintain_aspect_ratio=False)
plot0.overlays.append(zoom)
dragzoom = DragZoom(plot0,
drag_button="right",
maintain_aspect_ratio=False)
plot0.tools.append(dragzoom)
else:
plot0.value_mapper = value_mapper
value_mapper.range.add(plot0.value)
plot0.index_mapper = index_mapper
index_mapper.range.add(plot0.index)
if batch.isSelected:
Container.add(plot0)
index = index + 1
self.RightPlot = Container
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 _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
