def __init__(self, **kwargs):
super(VariableMeshPannerView, self).__init__(**kwargs)
# Create the plot
self.add_trait("field", DelegatesTo("vm_plot"))
plot = self.vm_plot.plot
img_plot = self.vm_plot.img_plot
if self.use_tools:
plot.tools.append(PanTool(img_plot))
zoom = ZoomTool(component=img_plot,
tool_mode="box",
always_on=False)
plot.overlays.append(zoom)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool,
bgcolor="white",
border_visible=True)
img_plot.overlays.append(overlay)
image_value_range = DataRange1D(self.vm_plot.fid)
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=img_plot,
padding_right=40,
resizable='v',
width=30)
self.colorbar.tools.append(
PanTool(self.colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(self.colorbar,
axis="index",
tool_mode="range",
always_on=True,
drag_button="right")
self.colorbar.overlays.append(zoom_overlay)
# create a range selection for the colorbar
range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(img_plot)
self.full_container = HPlotContainer(padding=30)
self.container = OverlayPlotContainer(padding=0)
self.full_container.add(self.colorbar)
self.full_container.add(self.container)
self.container.add(self.vm_plot.plot)
def _create_plot_component():
# Create a scalar field to colormap
xs = linspace(0, 10, 600)
ys = linspace(0, 5, 600)
x, y = meshgrid(xs, ys)
z = exp(-(x**2 + y**2) / 100)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds=(0, 10),
ybounds=(0, 5),
colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "My First Image Plot"
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False)
img_plot.overlays.append(zoom)
return plot
def _create_plot_component(self):
pd = self.pd
# Create the plot
plot = Plot(pd, default_origin="top left",orientation="h")
shape = pd.get_data('imagedata').shape
plot.aspect_ratio = float(shape[1]) / shape[0]
plot.x_axis.orientation = "top"
#plot.y_axis.orientation = "top"
#img_plot = plot.img_plot("imagedata",colormap = jet)[0]
img_plot = plot.img_plot("imagedata",name = 'image', colormap = jet)[0]
# Tweak some of the plot properties
#plot.bgcolor = "white"
plot.bgcolor = bg_color
# Attach some tools to the plot
plot.tools.append(PanTool(plot,constrain_key="shift", drag_button = 'right'))
printer = DataPrinter(component=plot, process = self.process_selection)
plot.tools.append(printer)
plot.overlays.append(ZoomTool(component=plot,
tool_mode="box", always_on=False))
#plot.title = 'Default image'
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
plot.overlays.append(ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool))
return plot
def _add_timePlot_tools(self, imgplot, token):
""" Add LineInspectors, ImageIndexTool, and ZoomTool to the image plots. """
imgplot.overlays.append(
ZoomTool(component=imgplot,
tool_mode="box",
enable_wheel=False,
always_on=False))
imgplot.overlays.append(
LineInspector(imgplot,
axis="index_y",
color="white",
inspect_mode="indexed",
write_metadata=True,
is_listener=True))
imgplot.overlays.append(
LineInspector(imgplot,
axis="index_x",
color="white",
inspect_mode="indexed",
write_metadata=True,
is_listener=True))
imgplot.tools.append(
ImageIndexTool(imgplot,
token=token,
callback=self._indexTime_callback,
wheel_cb=self._wheel_callback))
imgplot.tools.append(PanTool(imgplot))
def render_image(self):
plot = Plot(self.img_plotdata, default_origin="top left")
img = plot.img_plot("imagedata", colormap=gray)[0]
plot.title = "%s of %s: " % (self.img_idx + 1,
self.numfiles) + self.titles[self.img_idx]
plot.aspect_ratio = float(self.sig.data.shape[1]) / float(
self.sig.data.shape[0])
#if not self.ShowCC:
csr = CursorTool(img,
drag_button='left',
color='white',
line_width=2.0)
self.csr = csr
csr.current_position = self.left, self.top
img.overlays.append(csr)
# attach the rectangle tool
plot.tools.append(PanTool(plot, drag_button="right"))
zoom = ZoomTool(plot,
tool_mode="box",
always_on=False,
aspect_ratio=plot.aspect_ratio)
plot.overlays.append(zoom)
self.img_plot = plot
return plot
def _create_plot(self, data, name, type="line"):
p = Plot(self.plot_data)
p.plot(data, name=name, title=name, type=type)
p.tools.append(PanTool(p))
zoom = ZoomTool(component=p, tool_mode="box", always_on=False)
p.overlays.append(zoom)
p.title = name
return p
def _hist_plot_default(self):
plot = Plot(self.hist_plot_data, padding_left=50, padding_top=10, padding_right=10, padding_bottom=30)
plot.plot(('x','y'), style='line', color='blue', name='hist')
plot.index_axis.title = '# counts'
plot.value_axis.title = '# occurences'
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
return plot
def _plot_xy8_default(self):
plot = Plot(self.plot_xy8_data,
padding_left=60,
padding_top=25,
padding_right=10,
padding_bottom=50)
plot.plot(('xy8_plot_x', 'xy8_plot_y'), style='line', color='blue')
plot.index_axis.title = 'tau [micro-s]'
plot.value_axis.title = 'Intensity [a.u.]'
plot.title = 'xy8 raw data'
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
return plot
def _plot_density_default(self):
plot = Plot(self.plot_density_data,
padding_left=60,
padding_top=25,
padding_right=10,
padding_bottom=50)
plot.plot(('density_plot_x', 'density_plot_y'),
style='line',
color='blue')
plot.index_axis.title = 'frequency [MHz]'
plot.value_axis.title = 'spectral density [MHz]'
plot.title = 'Spectral density'
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
return plot
def _create_TracePlot_component(self):
plot = DataView(border_visible=True)
line = LinePlot(value=ArrayDataSource(self.Trace),
index=ArrayDataSource(numpy.arange(len(self.Trace))),
color='blue',
index_mapper=LinearMapper(range=plot.index_range),
value_mapper=LinearMapper(range=plot.value_range))
plot.index_range.sources.append(line.index)
plot.value_range.sources.append(line.value)
plot.add(line)
plot.index_axis.title = 'index'
plot.value_axis.title = 'Fluorescence [ counts / s ]'
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
self.TraceLine = line
return plot
def __init__(self, *l, **kw):
# TODO: implement aspect ratio maintaining
HasTraits.__init__(self, *l, **kw)
#self.plotdata = ArrayPlotData(x=self.pointsx, y=self.pointsy)
plot = Plot(self.plotdata)
plot.plot(("x", "y"))
plot.plot(("x", "y"), type='scatter')
plot.tools.append(PanTool(plot, drag_button='left'))
plot.tools.append(ZoomTool(plot, tool_mode='box'))
plot.tools.append(DragZoom(plot, tool_mode='box', drag_button='right'))
plot.tools.append(CustomSaveTool(
plot)) #, filename='/home/pankaj/Desktop/file.png'))
plot.tools.append(TraitsTool(plot))
self.plot = plot
self.set_plotdata()
def _create_window(self, title, xtitle, x, ytitle, y, type="line", color="blue"):
self.plotdata = ArrayPlotData(x=x, y=y)
plot = ToolbarPlot(self.plotdata)
plot.plot(('x', 'y'), type=type, color=color)
plot.title = title
plot.x_axis.title = xtitle
plot.y_axis.title = ytitle
self.plot = plot
self._hid = 0
self._colors = ['blue', 'red', 'black', 'green', 'magenta', 'yellow']
# Add some tools
self.plot.tools.append(PanTool(self.plot, constrain_key="shift"))
self.plot.overlays.append(ZoomTool(component=self.plot, tool_mode="box", always_on=False))
return Window(self, -1, component=plot)
def _plot_xy8norm_default(self):
plot = Plot(self.plot_xy8norm_data,
padding_left=60,
padding_top=25,
padding_right=10,
padding_bottom=50)
plot.plot(('xy8norm_plot_x', 'xy8norm_plot_y'),
style='line',
color='blue')
plot.index_axis.title = 'tau [micro-s]'
plot.value_axis.title = 'normalized Intensity'
plot.title = 'xy8 normalized to rabi contrast'
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
plot.y_axis.mapper.range.set(low=-0.1, high=1.1)
return plot
def __init__(self, name, model, time_src):
'''
Args:
Returns:
Raises:
'''
self.name = name
self.metrics = []
self.model = model
self.plot_data = ArrayPlotData(index=time_src)
self.plot_conactory = Plot(
self.plot_data) #spacing=20, padding = 20, border_visible=False)
self.plot_conactory.padding_bg_color = "white"
self.plot_conactory.bgcolor = "white"
self.plot_conactory.padding_left = 50
self.plot_conactory.padding_bottom = 17
self.plot_conactory.padding_right = 20
self.plot_conactory.padding_top = 2
self.saved_index_range = self.plot_conactory.index_range
self.plot_conactory.legend.visible = True
self.plot_conactory.legend.align = "ll"
self.x_axis = self.plot_conactory.x_axis
self.y_axis = self.plot_conactory.y_axis
self.plot_conactory.legend.tools.append(
MyLegendHighlighter(self.plot_conactory.legend,
drag_button="left",
modifier="shift"))
self.pan_tool = PanTool(self.plot_conactory,
constrain=True,
constrain_direction="x")
self.plot_conactory.tools.append(self.pan_tool)
self.zoom_tool = ZoomTool(
self.plot_conactory,
drag_button="right",
always_on=True,
always_on_modifier="shift",
tool_mode="range",
axis="index",
max_zoom_out_factor=1.0,
)
self.plot_conactory.tools.append(self.zoom_tool)
self.drag_zoom_tool = DragZoom(self.plot_conactory,
drag_button="right")
self.plot_conactory.tools.append(self.drag_zoom_tool)
def _plot_default(self):
plot = Plot(self.plotdata)
self.plotdata.set_data('t', self.t)
self.plotdata.set_data('x', self.x)
plot.plot(('t', 'x'),
color='red',
type_trait="plot_type",
name='x')
plot.legend.visible = True
plot.title = "Stopped flow"
plot.x_axis.title = 't'
# Add pan and zoom to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(plot)
plot.overlays.append(zoom)
return plot
def __init__(self, **traits):
super(LinePlot, self).__init__(**traits)
x = np.linspace(-14, 14, 100)
y = np.sin(x) * x**3
data = ArrayPlotData(x=x, y=y)
plot = Plot(data)
plot.plot(("x", "y"), type="line", color="blue")
plot.title = "sin(x) * x^3"
plot.tools.append(PanTool(plot))
plot.tools.append(
DragZoom(plot, drag_button="right", maintain_aspect_ratio=False))
plot.overlays.append(ZoomTool(plot))
# plot.overlays.append(ZoomTool(plot, tool_mode="range", axis = "index",
# always_on=True, always_on_modifier="control"))
self.plot = plot
self.data = data
def _create_HistPlot_component(self):
plot = DataView(border_visible=True)
line = LinePlot(
index=ArrayDataSource(self.HistogramBins),
value=ArrayDataSource(self.HistogramN),
color='blue',
#fill_color='blue',
index_mapper=LinearMapper(range=plot.index_range),
value_mapper=LinearMapper(range=plot.value_range))
plot.index_range.sources.append(line.index)
plot.value_range.sources.append(line.value)
plot.add(line)
plot.index_axis.title = 'Fluorescence counts'
plot.value_axis.title = 'number of occurences'
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
self.HistLine = line
return plot
def _create_window(self, title, xtitle, x, ytitle, y, z):
'''
- Left-drag pans the plot.
- Mousewheel up and down zooms the plot in and out.
- Pressing "z" brings up the Zoom Box, and you can click-drag a rectangular
region to zoom. If you use a sequence of zoom boxes, pressing alt-left-arrow
and alt-right-arrow moves you forwards and backwards through the "zoom
history".
'''
self._plotname = title
# Create window
self.data = ArrayPlotData()
self.plot = Plot(self.data)
self.update_plot(x, y, z)
self.plot.title = title
self.plot.x_axis.title = xtitle
self.plot.y_axis.title = ytitle
cmap_renderer = self.plot.plots[self._plotname][0]
# Create colorbar
self._create_colorbar()
self._colorbar.plot = cmap_renderer
self._colorbar.padding_top = self.plot.padding_top
self._colorbar.padding_bottom = self.plot.padding_bottom
# Add some tools
self.plot.tools.append(PanTool(self.plot, constrain_key="shift"))
self.plot.overlays.append(
ZoomTool(component=self.plot, tool_mode="box", always_on=False))
# selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35, selection_type="mask")
# cmap_renderer.overlays.append(selection)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(self.plot)
container.add(self._colorbar)
self.container = container
# Return a window containing our plot container
return Window(self, -1, component=container)
def activate_template(self):
""" Converts all contained 'TDerived' objects to real objects using the
template traits of the object. This method must be overridden in
subclasses.
Returns
-------
None
"""
# If our data sources are still unbound, then just exit; someone must
# have marked them as optional:
if ((self.index.context_data is Undefined)
or (self.value.context_data is Undefined)):
return
# Create a plot data object and give it this data:
pd = ArrayPlotData()
pd.set_data('index', self.index.context_data)
pd.set_data('value', self.value.context_data)
# Create the plot:
self.plot = plot = Plot(pd)
plot.plot(('index', 'value'),
type='scatter',
index_sort='ascending',
marker=self.marker,
color=self.color,
outline_color=self.outline_color,
marker_size=self.marker_size,
line_width=self.line_width,
bgcolor='white')
plot.set(padding_left=50,
padding_right=0,
padding_top=0,
padding_bottom=20)
# Attach some tools to the plot:
plot.tools.append(PanTool(plot, constrain_key='shift'))
zoom = SimpleZoom(component=plot, tool_mode='box', always_on=False)
plot.overlays.append(zoom)
def _plot_default(self):
self._pd = ArrayPlotData()
self._pd.set_data("imagedata", self._image)
plot = Plot(self._pd, default_origin="top left")
plot.x_axis.orientation = "top"
img_plot = plot.img_plot("imagedata")[0]
plot.bgcolor = "white"
# Tweak some of the plot properties
plot.title = "Click to add points, press Enter to clear selection"
plot.padding = 50
plot.line_width = 1
# Attach some tools to the plot
pan = PanTool(plot, drag_button="right", constrain_key="shift")
plot.tools.append(pan)
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create a scalar field to contour
xs = linspace(-2 * pi, 2 * pi, 600)
ys = linspace(-1.5 * pi, 1.5 * pi, 300)
x, y = meshgrid(xs, ys)
z = tanh(x * y / 6) * cosh(exp(-y**2) * x / 3)
z = x * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create a contour polygon plot of the data
plot = Plot(pd, default_origin="top left")
plot.contour_plot("imagedata",
type="poly",
poly_cmap=jet,
xbounds=(xs[0], xs[-1]),
ybounds=(ys[0], ys[-1]))
# Create a contour line plot for the data, too
plot.contour_plot("imagedata",
type="line",
xbounds=(xs[0], xs[-1]),
ybounds=(ys[0], ys[-1]))
# Tweak some of the plot properties
plot.title = "My First Contour Plot"
plot.padding = 50
plot.bg_color = "white"
plot.fill_padding = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def __init__(self, *l, **kw):
# TODO: implement aspect ratio maintaining
HasTraits.__init__(self, *l, **kw)
#self.plotdata = ArrayPlotData(x=self.pointsx, y=self.pointsy)
plot = Plot(self.plotdata)
renderer = plot.plot(("x", "y"))
#lineplot = create_line_plot((self.pointsx,self.pointsy), width=2.0)
#lineplot.tools.append(PanTool(lineplot, drag_button='middle'))
#lineplot.tools.append(ZoomTool(lineplot, tool_mode='box'))
plot.tools.append(PanTool(plot, drag_button='left'))
plot.tools.append(ZoomTool(plot, tool_mode='box'))
plot.tools.append(DragZoom(plot, tool_mode='box', drag_button='right'))
plot.tools.append(CustomSaveTool(
plot)) #, filename='/home/pankaj/Desktop/file.png'))
#plot.overlays.append(PlotLabel('Section : %s' % self.section.type,component=plot))
#plot.tools.append(PlotToolbar(plot))
plot.tools.append(TraitsTool(plot))
#plot.tools.append(ZoomTool(plot, tool_mode='box', axis='index', drag_button='right', always_on=True))
#plot.aspect_ratio = 3
#plot.request_redraw()
#print plot.bounds
#plot.aspect_ratio = 1
#plot.bounds = [500,300]
#print plot.bounds
#plot.range2d = DataRange2D(low=(0,-.5), high=(1,0.5))
#print plot.bounds
for renderer in chain(*plot.plots.values()):
renderer.index_mapper.stretch_data = False
renderer.value_mapper.stretch_data = False
renderer.index_mapper.range.low = 0
renderer.index_mapper.range.high = 1
renderer.value_mapper.range.low = -3 / 8.
renderer.value_mapper.range.high = 3 / 8.
self.plot = plot
def get_plot(self):
pixel_sizes = self.data_source.pixel_sizes
shape = self.data.shape[1:]
m = min(pixel_sizes)
s = [int(d * sz / m) for d, sz in zip(shape, pixel_sizes)]
plot_sizes = dict(xy=(s[1], s[0]))
plot = Plot(
self.plotdata,
padding=30,
fixed_preferred_size=plot_sizes['xy'],
)
image = plot.img_plot('xy', colormap=bone)[0]
image.overlays.append(ZoomTool(image))
image.tools.append(PanTool(image, drag_button='right'))
imgtool = ImageInspectorTool(image)
image.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=image,
bgcolor='white',
image_inspector=imgtool)
image.overlays.append(overlay)
self.image = image
self.plots = dict(xy=image)
return plot
class Demo(HasTraits):
plot = Instance(Component)
fileName = "clusters.cpickle"
case = List(UncertaintyValue)
cases = {}
defaultCase = []
# Attributes to use for the plot view.
size = (400, 1600)
traits_view = View(Group(
Group(Item('plot', editor=ComponentEditor(size=size),
show_label=False),
orientation="vertical",
show_border=True,
scrollable=True),
Group(Item('case',
editor=TabularEditor(adapter=CaseAdapter(can_edit=False)),
show_label=False),
orientation="vertical",
show_border=True),
layout='split',
orientation='horizontal'),
title='Interactive Lines',
resizable=True)
def setFileName(self, newName):
self.fileName = newName
def _update_case(self, name):
if name:
self.case = self.cases.get(name)
else:
self.case = self.defaultCase
def _plot_default(self):
#load the data to visualize.
# it is a list of data in the 'results' format, each belonging to a cluster - gonenc
resultsList = cPickle.load(open(self.fileName, 'r'))
#get the names of the outcomes to display
outcome = []
for entry in resultsList:
a = entry[0][1].keys()
outcome.append(a[0])
# outcome = resultsList[0][0][1].keys()
# pop the time axis from the list of outcomes
# outcome.pop(outcome.index('TIME'))
x = resultsList[0][0][1]['TIME']
# the list and number of features (clustering related) stored regarding each run
features = resultsList[0][0][0][0].keys()
noFeatures = len(features)
# Iterate over each cluster to prepare the cases corresponding to indivisdual runs in
# each cluster plot. Each case is labeled as, e.g., y1-2 (3rd run in the 2nd cluster) - gonenc
for c, results in enumerate(resultsList):
for j, aCase in enumerate(results):
aCase = [
UncertaintyValue(name=key, value=value)
for key, value in aCase[0][0].items()
]
self.cases['y' + str(c) + '-' + str(j)] = aCase
# for j, aCase in enumerate(results):
# aCase = [UncertaintyValue(name="blaat", value=aCase[0][0])]
# self.cases['y'+str(j)] = aCase
#make an empty case for default.
#if you have multiple datafields associated with a run, iterate over
#the keys of a dictionary of a case, instead of over lenght(2)
case = []
for i in range(noFeatures):
case.append(UncertaintyValue(name='Default', value='None'))
self.case = case
self.defaultCase = case
# Create some x-y data series to plot
pds = []
# enumerate over the results of all clusters
for c, results in enumerate(resultsList):
pd = ArrayPlotData(index=x)
for j in range(len(results)):
data = np.array(results[j][1].get(outcome[c]))
print "y" + str(c) + '-' + str(j)
pd.set_data("y" + str(c) + '-' + str(j), data)
pds.append(pd)
# Create a container and add our plots
container = GridContainer(bgcolor="lightgray",
use_backbuffer=True,
shape=(len(resultsList), 1))
#plot data
tools = []
for c, results in enumerate(resultsList):
pd1 = pds[c]
# Create some line plots of some of the data
plot = Plot(pd1,
title='Cluster ' + str(c),
border_visible=True,
border_width=1)
plot.legend.visible = False
#plot the results
for i in range(len(results)):
plotvalue = "y" + str(c) + '-' + str(i)
print plotvalue
color = colors[i % len(colors)]
plot.plot(("index", plotvalue), name=plotvalue, color=color)
#make sure that the time axis runs in the right direction
for value in plot.plots.values():
for entry in value:
entry.index.sort_order = 'ascending'
# Attach the selector tools to the plot
selectorTool1 = LineSelectorTool(component=plot)
plot.tools.append(selectorTool1)
tools.append(selectorTool1)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
container.add(plot)
#make sure the selector tools knows the main screen
for tool in tools:
tool._demo = self
return container
def _create_plot_component(self):
# Create a plot data object and give it this data
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.data[self.data_name])
# Create the plot
self.tplot = Plot(self.pd, default_origin="top left")
self.tplot.x_axis.orientation = "top"
self.tplot.img_plot(
"imagedata",
name="my_plot",
#xbounds=(0,10),
#ybounds=(0,10),
colormap=jet)
# Tweak some of the plot properties
self.tplot.title = "Matrix"
self.tplot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
self.my_plot = self.tplot.plots["my_plot"][0]
# Attach some tools to the plot
self.tplot.tools.append(PanTool(self.tplot))
zoom = ZoomTool(component=self.tplot, tool_mode="box", always_on=False)
self.tplot.overlays.append(zoom)
# my custom tool to get the connection information
self.custtool = CustomTool(self.tplot)
self.tplot.tools.append(self.custtool)
# Create the colorbar, handing in the appropriate range and colormap
colormap = self.my_plot.color_mapper
self.colorbar = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
self.colorbar.padding_top = self.tplot.padding_top
self.colorbar.padding_bottom = self.tplot.padding_bottom
# create a range selection for the colorbar
self.range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(self.range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
self.range_selection.listeners.append(self.my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(self.tplot)
container.add(self.colorbar)
container.bgcolor = "white"
return container
class Demo(HasTraits):
plot = Instance(Component)
fileName = "default.txt"
case = List(UncertaintyValue)
cases = {}
defaultCase = []
# Attributes to use for the plot view.
size = (400, 250)
traits_view = View(Group(
Group(Item('plot', editor=ComponentEditor(size=size),
show_label=False),
orientation="vertical",
show_border=True),
Group(Item('case',
editor=TabularEditor(adapter=CaseAdapter(can_edit=False)),
show_label=False),
orientation="vertical",
show_border=True),
layout='split',
orientation='horizontal'),
title='Interactive Lines',
resizable=True)
def setFileName(self, newName):
self.fileName = newName
def _update_case(self, name):
if name:
self.case = self.cases.get(name)
else:
self.case = self.defaultCase
def _plot_default(self):
results = cPickle.load(open(self.fileName, 'r'))
outcomes = results[0][1].keys()
outcomes.pop(outcomes.index('TIME'))
x = results[0][1]['TIME']
for j, aCase in enumerate(results):
aCase = [
UncertaintyValue(name=key, value=value)
for key, value in aCase[0][0].items()
]
self.cases['y' + str(j)] = aCase
uncertainties = results[0][0][0]
uncertaintynames = uncertainties.keys()
uncertaintyvalues = []
for key in uncertainties.keys():
uncertaintyvalues.append(uncertainties[key])
case = []
for i in range(len(uncertainties)):
case.append(
UncertaintyValue(name=str(uncertaintynames[i]),
value="")) #haydaa
self.case = case
self.defaultCase = case
# Create some x-y data series to plot
pds = []
for i, outcome in enumerate(outcomes):
pd = ArrayPlotData(index=x)
for j in range(len(results)):
pd.set_data("y" + str(j), results[j][1].get(outcome))
pds.append(pd)
# Create a container and add our plots
container = GridContainer(bgcolor="lightgray",
use_backbuffer=True,
shape=(1, 1))
#plot data
tools = []
for j in range(len(outcomes)):
pd1 = pds[j]
# Create some line plots of some of the data
plot = Plot(pd1,
title=outcomes[j],
border_visible=True,
border_width=1)
plot.legend.visible = False
for i in range(len(results)):
plotvalue = "y" + str(i)
color = colors[i % len(colors)]
plot.plot(("index", plotvalue), name=plotvalue, color=color)
for value in plot.plots.values():
for entry in value:
entry.index.sort_order = 'ascending'
# Attach the selector tools to the plot
selectorTool1 = LineSelectorTool(component=plot)
plot.tools.append(selectorTool1)
tools.append(selectorTool1)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
container.add(plot)
#make sure the selector tools know each other
for tool in tools:
tool._demo = self
return container
def _end_pan(self, event):
if hasattr(event, "bid"):
event.window.release_blob(event.bid)
PanTool._end_pan(self, event)
def panning_mouse_move(self,event):
PanTool.panning_mouse_move(self,event)
self.component.immediate_invalidate()
def __init__(self,xdata=None,ydata=None,weights=None,model=None,
include_models=None,exclude_models=None,fittype=None,**traits):
"""
:param xdata: the first dimension of the data to be fit
:type xdata: array-like
:param ydata: the second dimension of the data to be fit
:type ydata: array-like
:param weights:
The weights to apply to the data. Statistically interpreted as inverse
errors (*not* inverse variance). May be any of the following forms:
* None for equal weights
* an array of points that must match `ydata`
* a 2-sequence of arrays (xierr,yierr) such that xierr matches the
`xdata` and yierr matches `ydata`
* a function called as f(params) that returns an array of weights
that match one of the above two conditions
:param model: the initial model to use to fit this data
:type model:
None, string, or :class:`pymodelfit.core.FunctionModel1D`
instance.
:param include_models:
With `exclude_models`, specifies which models should be available in
the "new model" dialog (see `models.list_models` for syntax).
:param exclude_models:
With `include_models`, specifies which models should be available in
the "new model" dialog (see `models.list_models` for syntax).
:param fittype:
The fitting technique for the initial fit (see
:class:`pymodelfit.core.FunctionModel`).
:type fittype: string
kwargs are passed in as any additional traits to apply to the
application.
"""
self.modelpanel = View(Label('empty'),kind='subpanel',title='model editor')
self.tmodel = TraitedModel(model)
if model is not None and fittype is not None:
self.tmodel.model.fittype = fittype
if xdata is None or ydata is None:
if not hasattr(self.tmodel.model,'data') or self.tmodel.model.data is None:
raise ValueError('data not provided and no data in model')
if xdata is None:
xdata = self.tmodel.model.data[0]
if ydata is None:
ydata = self.tmodel.model.data[1]
if weights is None:
weights = self.tmodel.model.data[2]
self.on_trait_change(self._paramsChanged,'tmodel.paramchange')
self.modelselector = NewModelSelector(include_models,exclude_models)
self.data = [xdata,ydata]
if weights is None:
self.weights = np.ones_like(xdata)
self.weighttype = 'equal'
else:
self.weights = np.array(weights,copy=True)
self.savews = True
weights1d = self.weights
while len(weights1d.shape)>1:
weights1d = np.sum(weights1d**2,axis=0)
pd = ArrayPlotData(xdata=self.data[0],ydata=self.data[1],weights=weights1d)
self.plot = plot = Plot(pd,resizable='hv')
self.scatter = plot.plot(('xdata','ydata','weights'),name='data',
color_mapper=_cmapblack if self.weights0rem else _cmap,
type='cmap_scatter', marker='circle')[0]
self.errorplots = None
if not isinstance(model,FunctionModel1D):
self.fitmodel = True
self.updatemodelplot = False #force plot update - generates xmod and ymod
plot.plot(('xmod','ymod'),name='model',type='line',line_style='dash',color='black',line_width=2)
del plot.x_mapper.range.sources[-1] #remove the line plot from the x_mapper source so only the data is tied to the scaling
self.on_trait_change(self._rangeChanged,'plot.index_mapper.range.updated')
self.pantool = PanTool(plot,drag_button='left')
plot.tools.append(self.pantool)
self.zoomtool = ZoomTool(plot)
self.zoomtool.prev_state_key = KeySpec('a')
self.zoomtool.next_state_key = KeySpec('s')
plot.overlays.append(self.zoomtool)
self.scattertool = None
self.scatter.overlays.append(ScatterInspectorOverlay(self.scatter,
hover_color = "black",
selection_color="black",
selection_outline_color="red",
selection_line_width=2))
self.colorbar = colorbar = ColorBar(index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper.range,
plot=plot,
orientation='v',
resizable='v',
width = 30,
padding = 5)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
colorbar._axis.title = 'Weights'
self.plotcontainer = container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
super(FitGui,self).__init__(**traits)
self.on_trait_change(self._scale_change,'plot.value_scale,plot.index_scale')
if weights is not None and len(weights)==2:
self.weightsChanged() #update error bars
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 __init__(self):
super(CyclesPlot, self).__init__()
# Normally you'd pass in the data, but I'll hardwire things for this
# one-off plot.
srecs = read_time_series_from_csv("./biz_cycles2.csv",
date_col=0,
date_format="%Y-%m-%d")
dt = srecs["Date"]
# Industrial production compared with trend (plotted on value axis)
iprod_vs_trend = srecs["Metric 1"]
# Industrial production change in last 6 Months (plotted on index axis)
iprod_delta = srecs["Metric 2"]
self._dates = dt
self._series1 = self._selected_s1 = iprod_delta
self._series2 = self._selected_s2 = iprod_vs_trend
end_x = np.array([self._selected_s1[-1]])
end_y = np.array([self._selected_s2[-1]])
plotdata = ArrayPlotData(x=self._series1,
y=self._series2,
dates=self._dates,
selected_x=self._selected_s1,
selected_y=self._selected_s2,
endpoint_x=end_x,
endpoint_y=end_y)
cycles = Plot(plotdata, padding=20)
cycles.plot(("x", "y"), type="line", color=(.2, .4, .5, .4))
cycles.plot(("selected_x", "selected_y"),
type="line",
marker="circle",
line_width=3,
color=(.2, .4, .5, .9))
cycles.plot(("endpoint_x", "endpoint_y"),
type="scatter",
marker_size=4,
marker="circle",
color=(.2, .4, .5, .2),
outline_color=(.2, .4, .5, .6))
cycles.index_range = DataRange1D(low_setting=80., high_setting=120.)
cycles.value_range = DataRange1D(low_setting=80., high_setting=120.)
# dig down to use actual Plot object
cyc_plot = cycles.components[0]
# Add the labels in the quadrants
cyc_plot.overlays.append(
PlotLabel("\nSlowdown" + 40 * " " + "Expansion",
component=cyc_plot,
font="swiss 24",
color=(.2, .4, .5, .6),
overlay_position="inside top"))
cyc_plot.overlays.append(
PlotLabel("Downturn" + 40 * " " + "Recovery\n ",
component=cyc_plot,
font="swiss 24",
color=(.2, .4, .5, .6),
overlay_position="inside bottom"))
timeline = Plot(plotdata, resizable='h', height=50, padding=20)
timeline.plot(("dates", "x"),
type="line",
color=(.2, .4, .5, .8),
name='x')
timeline.plot(("dates", "y"),
type="line",
color=(.5, .4, .2, .8),
name='y')
# Snap on the tools
zoomer = ZoomTool(timeline,
drag_button="right",
always_on=True,
tool_mode="range",
axis="index",
max_zoom_out_factor=1.1)
panner = PanTool(timeline, constrain=True, constrain_direction="x")
# dig down to get Plot component I want
x_plt = timeline.plots['x'][0]
range_selection = RangeSelection(x_plt, left_button_selects=True)
range_selection.on_trait_change(self.update_interval, 'selection')
x_plt.tools.append(range_selection)
x_plt.overlays.append(RangeSelectionOverlay(x_plt))
# Set the plot's bottom axis to use the Scales ticking system
scale_sys = CalendarScaleSystem(
fill_ratio=0.4,
default_numlabels=5,
default_numticks=10,
)
tick_gen = ScalesTickGenerator(scale=scale_sys)
bottom_axis = ScalesPlotAxis(timeline,
orientation="bottom",
tick_generator=tick_gen)
# Hack to remove default axis - FIXME: how do I *replace* an axis?
del (timeline.underlays[-2])
timeline.overlays.append(bottom_axis)
container = GridContainer(padding=20,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True,
shape=(2, 1),
spacing=(30, 30))
# add a central "x" and "y" axis
x_line = LineInspector(cyc_plot,
is_listener=True,
color="gray",
width=2)
y_line = LineInspector(cyc_plot,
is_listener=True,
color="gray",
width=2,
axis="value")
cyc_plot.overlays.append(x_line)
cyc_plot.overlays.append(y_line)
cyc_plot.index.metadata["selections"] = 100.0
cyc_plot.value.metadata["selections"] = 100.0
container.add(cycles)
container.add(timeline)
container.title = "Business Cycles"
self.plot = container
def add_plot(self, signal, sweep_point=None):
## waveform = signal.get_waveform()
## x = waveform.get_x()[-1][0].tolist()
## y = np.real(waveform.get_y()[0].tolist())
if sweep_point is None:
sweep_point = signal.get_circuit()._sweep_set._points[0]
trace = Trace(signal, self, self._next_color(), sweep_point)
x_name = trace.index_label
y_name = trace.label
x = trace.get_indices()
y = trace.get_values()
if type(y[0]) == complex:
y = [value.real for value in y]
#print x_name, len(x)
#print y_name, len(y)
#print x
#print y
if self.firstplot:
self.plotdata = ArrayPlotData()
self.plotdata.set_data(x_name, x)
self.plotdata.set_data(y_name, y)
plot = Plot(self.plotdata)
plot.padding = 1
plot.bgcolor = "white"
plot.border_visible = True
add_default_grids(plot)
add_default_axes(plot)
plot.tools.append(PanTool(plot))
# The ZoomTool tool is stateful and allows drawing a zoom
# box to select a zoom region.
zoom = CustomZoomTool(plot)
plot.overlays.append(zoom)
# The DragZoom tool just zooms in and out as the user drags
# the mouse vertically.
dragzoom = DragZoom(plot, drag_button="right")
plot.tools.append(dragzoom)
#~ # Add a legend in the upper right corner, and make it relocatable
#~ self.legend = Legend(component=plot, padding=10, align="ur")
#~ self.legend.tools.append(LegendTool(self.legend, drag_button="right"))
#~ plot.overlays.append(self.legend)
#~ self.legend.plots = {}
self.firstplot = False
self.container.add(plot)
self.plot = plot
else:
self.plotdata.set_data(x_name, x)
self.plotdata.set_data(y_name, y)
#self.plot.plot(self.plotdata.list_data())
pl = self.plot.plot( (x_name, y_name), name=trace.label, type="line",
color=trace.color, line_style=trace.line_style,
line_width=trace.line_width, marker=trace.marker,
marker_size=trace.marker_size, marker_color=trace.marker_color)
self.legend.plots[trace.label] = pl
self.Refresh()
