class PlotUI(HasTraits):
# container for all plots
container = Instance(HPlotContainer)
# Plot components within this container:
polyplot = Instance(ContourPolyPlot)
lineplot = Instance(ContourLinePlot)
cross_plot = Instance(Plot)
cross_plot2 = Instance(Plot)
colorbar = Instance(ColorBar)
# plot data
pd = Instance(ArrayPlotData)
# view options
num_levels = Int(15)
colormap = Enum(colormaps)
#Traits view definitions:
traits_view = View(
Group(UItem('container', editor=ComponentEditor(size=(800,600)))),
resizable=True)
plot_edit_view = View(
Group(Item('num_levels'),
Item('colormap')),
buttons=["OK","Cancel"])
#---------------------------------------------------------------------------
# Private Traits
#---------------------------------------------------------------------------
_image_index = Instance(GridDataSource)
_image_value = Instance(ImageData)
_cmap = Trait(default_colormaps.jet, Callable)
#---------------------------------------------------------------------------
# Public View interface
#---------------------------------------------------------------------------
def __init__(self, *args, **kwargs):
super(PlotUI, self).__init__(*args, **kwargs)
# FIXME: 'with' wrapping is temporary fix for infinite range in initial
# color map, which can cause a distracting warning print. This 'with'
# wrapping should be unnecessary after fix in color_mapper.py.
with errstate(invalid='ignore'):
self.create_plot()
def create_plot(self):
# Create the mapper, etc
self._image_index = GridDataSource(array([]),
array([]),
sort_order=("ascending","ascending"))
image_index_range = DataRange2D(self._image_index)
self._image_index.on_trait_change(self._metadata_changed,
"metadata_changed")
self._image_value = ImageData(data=array([]), value_depth=1)
image_value_range = DataRange1D(self._image_value)
# Create the contour plots
self.polyplot = ContourPolyPlot(index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=
image_index_range),
color_mapper=\
self._cmap(image_value_range),
levels=self.num_levels)
self.lineplot = ContourLinePlot(index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=
self.polyplot.index_mapper.range),
levels=self.num_levels)
# Add a left axis to the plot
left = PlotAxis(orientation='left',
title= "y",
mapper=self.polyplot.index_mapper._ymapper,
component=self.polyplot)
self.polyplot.overlays.append(left)
# Add a bottom axis to the plot
bottom = PlotAxis(orientation='bottom',
title= "x",
mapper=self.polyplot.index_mapper._xmapper,
component=self.polyplot)
self.polyplot.overlays.append(bottom)
# Add some tools to the plot
self.polyplot.tools.append(PanTool(self.polyplot,
constrain_key="shift"))
self.polyplot.overlays.append(ZoomTool(component=self.polyplot,
tool_mode="box", always_on=False))
self.polyplot.overlays.append(LineInspector(component=self.polyplot,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=True,
color="white"))
self.polyplot.overlays.append(LineInspector(component=self.polyplot,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=True))
# Add these two plots to one container
contour_container = OverlayPlotContainer(padding=20,
use_backbuffer=True,
unified_draw=True)
contour_container.add(self.polyplot)
contour_container.add(self.lineplot)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.polyplot,
padding_top=self.polyplot.padding_top,
padding_bottom=self.polyplot.padding_bottom,
padding_right=40,
resizable='v',
width=30)
self.pd = ArrayPlotData(line_index = array([]),
line_value = array([]),
scatter_index = array([]),
scatter_value = array([]),
scatter_color = array([]))
self.cross_plot = Plot(self.pd, resizable="h")
self.cross_plot.height = 100
self.cross_plot.padding = 20
self.cross_plot.plot(("line_index", "line_value"),
line_style="dot")
self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8)
self.cross_plot.index_range = self.polyplot.index_range.x_range
self.pd.set_data("line_index2", array([]))
self.pd.set_data("line_value2", array([]))
self.pd.set_data("scatter_index2", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("scatter_color2", array([]))
self.cross_plot2 = Plot(self.pd, width = 140, orientation="v",
resizable="v", padding=20, padding_bottom=160)
self.cross_plot2.plot(("line_index2", "line_value2"),
line_style="dot")
self.cross_plot2.plot(("scatter_index2",
"scatter_value2",
"scatter_color2"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8)
self.cross_plot2.index_range = self.polyplot.index_range.y_range
# Create a container and add components
self.container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.cross_plot)
inner_cont.add(contour_container)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.cross_plot2)
def update(self, model):
self.minz = model.minz
self.maxz = model.maxz
self.colorbar.index_mapper.range.low = self.minz
self.colorbar.index_mapper.range.high = self.maxz
self._image_index.set_data(model.xs, model.ys)
self._image_value.data = model.zs
self.pd.update_data(line_index=model.xs, line_index2=model.ys)
self.container.invalidate_draw()
self.container.request_redraw()
#---------------------------------------------------------------------------
# Event handlers
#---------------------------------------------------------------------------
def _metadata_changed(self, old, new):
""" This function takes out a cross section from the image data, based
on the line inspector selections, and updates the line and scatter
plots."""
self.cross_plot.value_range.low = self.minz
self.cross_plot.value_range.high = self.maxz
self.cross_plot2.value_range.low = self.minz
self.cross_plot2.value_range.high = self.maxz
if self._image_index.metadata.has_key("selections"):
x_ndx, y_ndx = self._image_index.metadata["selections"]
if y_ndx and x_ndx:
xdata, ydata = self._image_index.get_data()
xdata, ydata = xdata.get_data(), ydata.get_data()
self.pd.update_data(
line_value=self._image_value.data[y_ndx,:],
line_value2=self._image_value.data[:,x_ndx],
scatter_index=array([xdata[x_ndx]]),
scatter_index2=array([ydata[y_ndx]]),
scatter_value=array([self._image_value.data[y_ndx, x_ndx]]),
scatter_value2=array([self._image_value.data[y_ndx, x_ndx]]),
scatter_color=array([self._image_value.data[y_ndx, x_ndx]]),
scatter_color2=array([self._image_value.data[y_ndx, x_ndx]])
)
else:
self.pd.update_data({"scatter_value": array([]),
"scatter_value2": array([]), "line_value": array([]),
"line_value2": array([])})
def _colormap_changed(self):
self._cmap = default_colormaps.color_map_name_dict[self.colormap]
if self.polyplot is not None:
value_range = self.polyplot.color_mapper.range
self.polyplot.color_mapper = self._cmap(value_range)
value_range = self.cross_plot.color_mapper.range
self.cross_plot.color_mapper = self._cmap(value_range)
# FIXME: change when we decide how best to update plots using
# the shared colormap in plot object
self.cross_plot.plots["dot"
][0].color_mapper = self._cmap(value_range)
self.cross_plot2.plots["dot"
][0].color_mapper = self._cmap(value_range)
self.container.request_redraw()
def _num_levels_changed(self):
if self.num_levels > 3:
self.polyplot.levels = self.num_levels
self.lineplot.levels = self.num_levels
class ImageGUI(HasTraits):
# TO FIX : put here the last available shot
#shot = File('L:\\data\\app3\\2011\\1108\\110823\\column_5200.ascii')
#shot = File('/home/pmd/atomcool/lab/data/app3/2012/1203/120307/column_3195.ascii')
#-- Shot traits
shotdir = Directory('/home/pmd/atomcool/lab/data/app3/2012/1203/120320/')
shots = List(Str)
selectedshot = List(Str)
namefilter = Str('column')
#-- Report trait
report = Str
#-- Displayed analysis results
number = Float
#-- Column density plot container
column_density = Instance(HPlotContainer)
#---- Plot components within this container
imgplot = Instance(CMapImagePlot)
cross_plot = Instance(Plot)
cross_plot2 = Instance(Plot)
colorbar = Instance(ColorBar)
#---- Plot data
pd = Instance(ArrayPlotData)
#---- Colorbar
num_levels = Int(15)
colormap = Enum(color_map_name_dict.keys())
#-- Crosshair location
cursor = Instance(BaseCursorTool)
xy = DelegatesTo('cursor', prefix='current_position')
xpos = Float(0.)
ypos = Float(0.)
xpos_read = Float(0.)
ypos_read = Float(0.)
cursor_group = Group( Group(Item('xpos', show_label=True),
Item('xpos_read', show_label=False, style="readonly"),
orientation='horizontal'),
Group(Item('ypos', show_label=True),
Item('ypos_read', show_label=False, style="readonly"),
orientation='horizontal'),
orientation='vertical', layout='normal',springy=True)
#---------------------------------------------------------------------------
# Traits View Definitions
#---------------------------------------------------------------------------
traits_view = View(
Group(
#Directory
Item( 'shotdir',style='simple', editor=DirectoryEditor(), width = 400, \
show_label=False, resizable=False ),
#Bottom
HSplit(
#-- Pane for shot selection
Group(
Item( 'namefilter', show_label=False,springy=False),
Item( 'shots',show_label=False, width=180, height= 360, \
editor = TabularEditor(selected='selectedshot',\
editable=False,multi_select=True,\
adapter=SelectAdapter()) ),
cursor_group,
orientation='vertical',
layout='normal', ),
#-- Pane for column density plots
Group(
Item('column_density',editor=ComponentEditor(), \
show_label=False, width=600, height=500, \
resizable=True ),
Item('report',show_label=False, width=180, \
springy=True, style='custom' ),
layout='tabbed', springy=True),
#-- Pane for analysis results
Group(
Item('number',show_label=False)
)
),
orientation='vertical',
layout='normal',
),
width=1400, height=500, resizable=True)
#-- Pop-up view when Plot->Edit is selcted from the menu
plot_edit_view = View(
Group(Item('num_levels'),
Item('colormap')),
buttons=["OK","Cancel"])
#---------------------------------------------------------------------------
# Private Traits
#---------------------------------------------------------------------------
#-- Represents the region where the data set is defined
_image_index = Instance(GridDataSource)
#-- Represents the data that will be plotted on the grid
_image_value = Instance(ImageData)
#-- Represents the color map that will be used
_cmap = Trait(jet, Callable)
#---------------------------------------------------------------------------
# Public View interface
#---------------------------------------------------------------------------
def __init__(self, *args, **kwargs):
#-- super is used to run the inherited __init__ method
#-- this ensures that all the Traits machinery is properly setup
#-- even though the __init__ method is overridden
super(ImageGUI, self).__init__(*args, **kwargs)
#-- after running the inherited __init__, a plot is created
self.create_plot()
def create_plot(self):
#-- Create the index for the x an y axes and the range over
#-- which they vary
self._image_index = GridDataSource(array([]), array([]),
sort_order=("ascending","ascending"))
image_index_range = DataRange2D(self._image_index)
#-- I believe this is what allows tracking the mouse
self._image_index.on_trait_change(self._metadata_changed,
"metadata_changed")
#-- Create the image values and determine their range
self._image_value = ImageData(data=array([]), value_depth=1)
image_value_range = DataRange1D(self._image_value)
# Create the image plot
self.imgplot = CMapImagePlot( index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=image_index_range),
color_mapper=self._cmap(image_value_range),)
# Add a left axis to the plot
left = PlotAxis(orientation='left',
title= "axial",
mapper=self.imgplot.index_mapper._ymapper,
component=self.imgplot)
self.imgplot.overlays.append(left)
# Add a bottom axis to the plot
bottom = PlotAxis(orientation='bottom',
title= "radial",
mapper=self.imgplot.index_mapper._xmapper,
component=self.imgplot)
self.imgplot.overlays.append(bottom)
# Add some tools to the plot
self.imgplot.tools.append(PanTool(self.imgplot,drag_button="right",
constrain_key="shift"))
self.imgplot.overlays.append(ZoomTool(component=self.imgplot,
tool_mode="box", always_on=False))
# Create a colorbar
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.imgplot,
padding_top=self.imgplot.padding_top,
padding_bottom=self.imgplot.padding_bottom,
padding_right=40,
resizable='v',
width=30)
# Add a cursor
self.cursor = CursorTool( self.imgplot, drag_button="left", color="white")
# the cursor is a rendered component so it goes in the overlays list
self.imgplot.overlays.append(self.cursor)
# Create the two cross plots
self.pd = ArrayPlotData(line_index = array([]),
line_value = array([]),
scatter_index = array([]),
scatter_value = array([]),
scatter_color = array([]))
self.cross_plot = Plot(self.pd, resizable="h")
self.cross_plot.height = 100
self.cross_plot.padding = 20
self.cross_plot.plot(("line_index", "line_value"),
line_style="dot")
self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=6)
self.cross_plot.index_range = self.imgplot.index_range.x_range
self.pd.set_data("line_index2", array([]))
self.pd.set_data("line_value2", array([]))
self.pd.set_data("scatter_index2", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("scatter_color2", array([]))
self.cross_plot2 = Plot(self.pd, width = 140, orientation="v", resizable="v", padding=20, padding_bottom=160)
self.cross_plot2.plot(("line_index2", "line_value2"),
line_style="dot")
self.cross_plot2.plot(("scatter_index2","scatter_value2","scatter_color2"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8)
self.cross_plot2.index_range = self.imgplot.index_range.y_range
# Create a container and add sub-containers and components
self.column_density = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.cross_plot)
self.imgplot.padding =20
inner_cont.add(self.imgplot)
self.column_density.add(self.colorbar)
self.column_density.add(inner_cont)
self.column_density.add(self.cross_plot2)
def update(self):
#print self.cursor.current_index
#self.cursor.current_position = 100.,100.
self.shots = self.populate_shot_list()
print self.selectedshot
imgdata, self.report = self.load_imagedata()
if imgdata is not None:
self.minz = imgdata.min()
self.maxz = imgdata.max()
self.colorbar.index_mapper.range.low = self.minz
self.colorbar.index_mapper.range.high = self.maxz
xs=numpy.linspace(0,imgdata.shape[0],imgdata.shape[0]+1)
ys=numpy.linspace(0,imgdata.shape[1],imgdata.shape[1]+1)
#print xs
#print ys
self._image_index.set_data(xs,ys)
self._image_value.data = imgdata
self.pd.set_data("line_index", xs)
self.pd.set_data("line_index2",ys)
self.column_density.invalidate_draw()
self.column_density.request_redraw()
def populate_shot_list(self):
try:
shot_list = os.listdir(self.shotdir)
fun = lambda x: iscol(x,self.namefilter)
shot_list = filter( fun, shot_list)
shot_list = sorted(shot_list)
except ValueError:
print " *** Not a valid directory path ***"
return shot_list
def load_imagedata(self):
try:
directory = self.shotdir
if self.selectedshot == []:
filename = self.shots[0]
else:
filename = self.selectedshot[0]
#shotnum = filename[filename.rindex('_')+1:filename.rindex('.ascii')]
shotnum = filename[:filename.index('_')]
except ValueError:
print " *** Not a valid path *** "
return None
# Set data path
# Prepare PlotData object
print "Loading file #%s from %s" % (filename,directory)
return import_data.load(directory,filename), import_data.load_report(directory,shotnum)
#---------------------------------------------------------------------------
# Event handlers
#---------------------------------------------------------------------------
def _selectedshot_changed(self):
print self.selectedshot
self.update()
def _shots_changed(self):
self.shots = self.populate_shot_list()
return
def _namefilter_changed(self):
self.shots = self.populate_shot_list()
return
def _xpos_changed(self):
self.cursor.current_position = self.xpos, self.ypos
def _ypos_changed(self):
self.cursor.current_position = self.xpos, self.ypos
def _metadata_changed(self):
self._xy_changed()
def _xy_changed(self):
self.xpos_read = self.cursor.current_index[0]
self.ypos_read = self.cursor.current_index[1]
#print self.cursor.current_index
""" This function takes out a cross section from the image data, based
on the cursor selections, and updates the line and scatter
plots."""
self.cross_plot.value_range.low = self.minz
self.cross_plot.value_range.high = self.maxz
self.cross_plot2.value_range.low = self.minz
self.cross_plot2.value_range.high = self.maxz
if True:
x_ndx, y_ndx = self.cursor.current_index
if y_ndx and x_ndx:
self.pd.set_data("line_value",
self._image_value.data[:,y_ndx])
self.pd.set_data("line_value2",
self._image_value.data[x_ndx,:])
xdata, ydata = self._image_index.get_data()
xdata, ydata = xdata.get_data(), ydata.get_data()
self.pd.set_data("scatter_index", array([ydata[y_ndx]]))
self.pd.set_data("scatter_index2", array([xdata[x_ndx]]))
self.pd.set_data("scatter_value",
array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_value2",
array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_color",
array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_color2",
array([self._image_value.data[y_ndx, x_ndx]]))
else:
self.pd.set_data("scatter_value", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("line_value", array([]))
self.pd.set_data("line_value2", array([]))
def _colormap_changed(self):
self._cmap = color_map_name_dict[self.colormap]
if hasattr(self, "polyplot"):
value_range = self.polyplot.color_mapper.range
self.polyplot.color_mapper = self._cmap(value_range)
value_range = self.cross_plot.color_mapper.range
self.cross_plot.color_mapper = self._cmap(value_range)
# FIXME: change when we decide how best to update plots using
# the shared colormap in plot object
self.cross_plot.plots["dot"][0].color_mapper = self._cmap(value_range)
self.cross_plot2.plots["dot"][0].color_mapper = self._cmap(value_range)
self.column_density.request_redraw()
def _num_levels_changed(self):
if self.num_levels > 3:
self.polyplot.levels = self.num_levels
self.lineplot.levels = self.num_levels
class PlotUI(HasTraits):
# container for all plots
container = Instance(HPlotContainer)
# Plot components within this container:
polyplot = Instance(ContourPolyPlot)
lineplot = Instance(ContourLinePlot)
cross_plot = Instance(Plot)
cross_plot2 = Instance(Plot)
colorbar = Instance(ColorBar)
# plot data
pd = Instance(ArrayPlotData)
# view options
num_levels = Int(15)
colormap = Enum(colormaps)
#Traits view definitions:
traits_view = View(
Group(UItem('container', editor=ComponentEditor(size=(800,600)))),
resizable=True)
plot_edit_view = View(
Group(Item('num_levels'),
Item('colormap')),
buttons=["OK","Cancel"])
#---------------------------------------------------------------------------
# Private Traits
#---------------------------------------------------------------------------
_image_index = Instance(GridDataSource)
_image_value = Instance(ImageData)
_cmap = Trait(default_colormaps.jet, Callable)
#---------------------------------------------------------------------------
# Public View interface
#---------------------------------------------------------------------------
def __init__(self, *args, **kwargs):
super(PlotUI, self).__init__(*args, **kwargs)
# FIXME: 'with' wrapping is temporary fix for infinite range in initial
# color map, which can cause a distracting warning print. This 'with'
# wrapping should be unnecessary after fix in color_mapper.py.
with errstate(invalid='ignore'):
self.create_plot()
def create_plot(self):
# Create the mapper, etc
self._image_index = GridDataSource(array([]),
array([]),
sort_order=("ascending","ascending"))
image_index_range = DataRange2D(self._image_index)
self._image_index.on_trait_change(self._metadata_changed,
"metadata_changed")
self._image_value = ImageData(data=array([]), value_depth=1)
image_value_range = DataRange1D(self._image_value)
# Create the contour plots
self.polyplot = ContourPolyPlot(index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=
image_index_range),
color_mapper=\
self._cmap(image_value_range),
levels=self.num_levels)
self.lineplot = ContourLinePlot(index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=
self.polyplot.index_mapper.range),
levels=self.num_levels)
# Add a left axis to the plot
left = PlotAxis(orientation='left',
title= "y",
mapper=self.polyplot.index_mapper._ymapper,
component=self.polyplot)
self.polyplot.overlays.append(left)
# Add a bottom axis to the plot
bottom = PlotAxis(orientation='bottom',
title= "x",
mapper=self.polyplot.index_mapper._xmapper,
component=self.polyplot)
self.polyplot.overlays.append(bottom)
# Add some tools to the plot
self.polyplot.tools.append(PanTool(self.polyplot,
constrain_key="shift"))
self.polyplot.overlays.append(ZoomTool(component=self.polyplot,
tool_mode="box", always_on=False))
self.polyplot.overlays.append(LineInspector(component=self.polyplot,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=True,
color="white"))
self.polyplot.overlays.append(LineInspector(component=self.polyplot,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=True))
# Add these two plots to one container
contour_container = OverlayPlotContainer(padding=20,
use_backbuffer=True,
unified_draw=True)
contour_container.add(self.polyplot)
contour_container.add(self.lineplot)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.polyplot,
padding_top=self.polyplot.padding_top,
padding_bottom=self.polyplot.padding_bottom,
padding_right=40,
resizable='v',
width=30)
self.pd = ArrayPlotData(line_index = array([]),
line_value = array([]),
scatter_index = array([]),
scatter_value = array([]),
scatter_color = array([]))
self.cross_plot = Plot(self.pd, resizable="h")
self.cross_plot.height = 100
self.cross_plot.padding = 20
self.cross_plot.plot(("line_index", "line_value"),
line_style="dot")
self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8)
self.cross_plot.index_range = self.polyplot.index_range.x_range
self.pd.set_data("line_index2", array([]))
self.pd.set_data("line_value2", array([]))
self.pd.set_data("scatter_index2", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("scatter_color2", array([]))
self.cross_plot2 = Plot(self.pd, width = 140, orientation="v", resizable="v", padding=20, padding_bottom=160)
self.cross_plot2.plot(("line_index2", "line_value2"),
line_style="dot")
self.cross_plot2.plot(("scatter_index2","scatter_value2","scatter_color2"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8)
self.cross_plot2.index_range = self.polyplot.index_range.y_range
# Create a container and add components
self.container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.cross_plot)
inner_cont.add(contour_container)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.cross_plot2)
def update(self, model):
self.minz = model.minz
self.maxz = model.maxz
self.colorbar.index_mapper.range.low = self.minz
self.colorbar.index_mapper.range.high = self.maxz
self._image_index.set_data(model.xs, model.ys)
self._image_value.data = model.zs
self.pd.set_data("line_index", model.xs)
self.pd.set_data("line_index2", model.ys)
self.container.invalidate_draw()
self.container.request_redraw()
#---------------------------------------------------------------------------
# Event handlers
#---------------------------------------------------------------------------
def _metadata_changed(self, old, new):
""" This function takes out a cross section from the image data, based
on the line inspector selections, and updates the line and scatter
plots."""
self.cross_plot.value_range.low = self.minz
self.cross_plot.value_range.high = self.maxz
self.cross_plot2.value_range.low = self.minz
self.cross_plot2.value_range.high = self.maxz
if self._image_index.metadata.has_key("selections"):
x_ndx, y_ndx = self._image_index.metadata["selections"]
if y_ndx and x_ndx:
self.pd.set_data("line_value",
self._image_value.data[y_ndx,:])
self.pd.set_data("line_value2",
self._image_value.data[:,x_ndx])
xdata, ydata = self._image_index.get_data()
xdata, ydata = xdata.get_data(), ydata.get_data()
self.pd.set_data("scatter_index", array([xdata[x_ndx]]))
self.pd.set_data("scatter_index2", array([ydata[y_ndx]]))
self.pd.set_data("scatter_value",
array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_value2",
array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_color",
array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_color2",
array([self._image_value.data[y_ndx, x_ndx]]))
else:
self.pd.set_data("scatter_value", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("line_value", array([]))
self.pd.set_data("line_value2", array([]))
def _colormap_changed(self):
self._cmap = default_colormaps.color_map_name_dict[self.colormap]
if self.polyplot is not None:
value_range = self.polyplot.color_mapper.range
self.polyplot.color_mapper = self._cmap(value_range)
value_range = self.cross_plot.color_mapper.range
self.cross_plot.color_mapper = self._cmap(value_range)
# FIXME: change when we decide how best to update plots using
# the shared colormap in plot object
self.cross_plot.plots["dot"][0].color_mapper = self._cmap(value_range)
self.cross_plot2.plots["dot"][0].color_mapper = self._cmap(value_range)
self.container.request_redraw()
def _num_levels_changed(self):
if self.num_levels > 3:
self.polyplot.levels = self.num_levels
self.lineplot.levels = self.num_levels
class ImagePlot(Atom):
# container for all plots
container = Typed(HPlotContainer)
# Plot components within this container:
color_plot = Typed(CMapImagePlot)
vertical_cross_plot = Typed(Plot)
horizontal_cross_plot = Typed(Plot)
colorbar = Typed(ColorBar)
# plot data
pd_all = Typed(ArrayPlotData)
#pd_horiz=Instance(ArrayPlotData)
#pd_vert=Instance(ArrayPlotData)
#private data storage
_imag_index = Typed(GridDataSource)
_image_value = Typed(ImageData)
def __init__(self, x, y, z):
super(ImagePlot, self).__init__()
self.pd_all = ArrayPlotData(imagedata=z)
#self.pd_horiz = ArrayPlotData(x=x, horiz=z[4, :])
#self.pd_vert = ArrayPlotData(y=y, vert=z[:,5])
self._imag_index = GridDataSource(xdata=x,
ydata=y,
sort_order=("ascending",
"ascending"))
index_mapper = GridMapper(range=DataRange2D(self._imag_index))
self._imag_index.on_trait_change(self._metadata_changed,
"metadata_changed")
self._image_value = ImageData(data=z, value_depth=1)
color_mapper = jet(DataRange1D(self._image_value))
self.color_plot = CMapImagePlot(index=self._imag_index,
index_mapper=index_mapper,
value=self._image_value,
value_mapper=color_mapper,
padding=20,
use_backbuffer=True,
unified_draw=True)
#Add axes to image plot
left = PlotAxis(orientation='left',
title="Frequency (GHz)",
mapper=self.color_plot.index_mapper._ymapper,
component=self.color_plot)
self.color_plot.overlays.append(left)
bottom = PlotAxis(orientation='bottom',
title="Time (us)",
mapper=self.color_plot.index_mapper._xmapper,
component=self.color_plot)
self.color_plot.overlays.append(bottom)
self.color_plot.tools.append(
PanTool(self.color_plot, constrain_key="shift"))
self.color_plot.overlays.append(
ZoomTool(component=self.color_plot,
tool_mode="box",
always_on=False))
#Add line inspector tool for horizontal and vertical
self.color_plot.overlays.append(
LineInspector(component=self.color_plot,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=True,
color="white"))
self.color_plot.overlays.append(
LineInspector(component=self.color_plot,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=True))
myrange = DataRange1D(low=amin(z), high=amax(z))
cmap = jet
self.colormap = cmap(myrange)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=myrange)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.color_plot,
padding_top=self.color_plot.padding_top,
padding_bottom=self.color_plot.padding_bottom,
padding_right=40,
resizable='v',
width=30) #, ytitle="Magvec (mV)")
#create horizontal line plot
self.horiz_cross_plot = Plot(self.pd_horiz, resizable="h")
self.horiz_cross_plot.height = 100
self.horiz_cross_plot.padding = 20
self.horiz_cross_plot.plot(("x", "horiz")) #,
#line_style="dot")
# self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
# type="cmap_scatter",
# name="dot",
# color_mapper=self._cmap(image_value_range),
# marker="circle",
# marker_size=8)
self.horiz_cross_plot.index_range = self.color_plot.index_range.x_range
#create vertical line plot
self.vert_cross_plot = Plot(self.pd_vert,
width=140,
orientation="v",
resizable="v",
padding=20,
padding_bottom=160)
self.vert_cross_plot.plot(("y", "vert")) #,
# line_style="dot")
# self.vert_cross_plot.xtitle="Magvec (mV)"
# self.vertica_cross_plot.plot(("vertical_scatter_index",
# "vertical_scatter_value",
# "vertical_scatter_color"),
# type="cmap_scatter",
# name="dot",
# color_mapper=self._cmap(image_value_range),
# marker="circle",
# marker_size=8)
self.vert_cross_plot.index_range = self.color_plot.index_range.y_range
# Create a container and add components
self.container = HPlotContainer(padding=40,
fill_padding=True,
bgcolor="white",
use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.horiz_cross_plot)
inner_cont.add(self.color_plot)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.vert_cross_plot)
def _metadata_changed(self, old, new):
""" This function takes out a cross section from the image data, based
on the line inspector selections, and updates the line and scatter
plots."""
#self.cross_plot.value_range.low = self.minz
#self.cross_plot.value_range.high = self.maxz
#self.cross_plot2.value_range.low = self.minz
#self.cross_plot2.value_range.high = self.maxz
if self._imag_index.metadata.has_key("selections"):
x_ndx, y_ndx = self._imag_index.metadata["selections"]
if y_ndx and x_ndx:
# xdata, ydata = self._image_index.get_data()
# xdata, ydata = xdata.get_data(), ydata.get_data()
self.pd_horiz.set_data("horiz",
self._image_value.data[y_ndx, :])
self.pd_vert.set_data("vert", self._image_value.data[:, x_ndx])
# scatter_index=array([xdata[x_ndx]]),
# scatter_index2=array([ydata[y_ndx]]),
# scatter_value=array([self._image_value.data[y_ndx, x_ndx]]),
# scatter_value2=array([self._image_value.data[y_ndx, x_ndx]]),
# scatter_color=array([self._image_value.data[y_ndx, x_ndx]]),
# scatter_color2=array([self._image_value.data[y_ndx, x_ndx]])
# )
# else:
# self.pd.update_data({"scatter_value": array([]),
# "scatter_value2": array([]), "line_value": array([]),
# "line_value2": array([])})
#if __name__ == "__main__":
# filename="/Users/thomasaref/Dropbox/Dad stuff/sample3/digitizer/lt/sample3_digitizer_f_sweep_t_300mk_100nspulse.hdf5"
#
# with h5py.File(filename, 'r') as f:
#
# time=f["Traces"]["d - AvgTrace - t"][:]
# Magvec=f["Traces"]["d - AvgTrace - Magvec"][:]
# frequency=f["Data"]["Data"][:]
# # for name in f["Data"]:
# # print name
#
# time=squeeze(time)
# Magvec=squeeze(Magvec)
# frequency=squeeze(frequency)
#
# x = time[:,0]*1.0e6
# y = frequency[0,:]/1.0e9
# z=transpose(Magvec*1000.0)
#
# ip=ImagePlot(xs,ys,z)
# ip.configure_traits()
#class Image_Plot(Atom):
# plot_control=Instance(Plot_Control)
# xtitle=DelegatesTo('plot_control')
# ytitle=DelegatesTo('plot_control')
# ztitle=DelegatesTo('plot_control')
# request_redraw=DelegatesTo('plot_control')
# #ykeys=DelegatesTo('plot_control')
# container = Typed(HPlotContainer)
# color_plot = Typed(CMapImagePlot)
# plot=Instance(Plot)
# vertical_cross_plot = Typed(Plot)
# horizontal_cross_plot = Typed(Plot)
# colorbar = Typed(ColorBar)
# pd_all = Instance(ArrayPlotData)
# _image_index=Instance(GridDataSource)
# _image_value=Instance(ImageData)
# data=Dict()
#
# pd=Instance(ArrayPlotData)
#
# traits_view = View(Group(Item('container', editor=ComponentEditor(), show_label=False),
# orientation='horizontal'),
# width=1000, height=700, resizable=True, title="Chaco Plot")
#
# def _xtitle_changed(self):
# self.horiz_cross_plot.x_axis.title=self.xtitle
# self.plot.x_axis.title=self.xtitle
#
# def _ytitle_changed(self):
# self.vert_cross_plot.y_axis.title=self.ytitle
# self.plot.y_axis.title=self.ytitle
#
# def _request_redraw_fired(self):
# self.color_plot.request_redraw()
# self.horiz_cross_plot.request_redraw()
# self.vert_cross_plot.request_redraw()
#
# def __init__(self, data, plot_control):
# super(Image_Plot, self).__init__()
# self.plot_control=plot_control
# z=zeros((len(data['y']['0']), len(data['x']['0'])))
# z[:] = nan
# for key, item in data['z'].iteritems():
# z[int(key)]=item
# x=data['x']['0']
# y=data['y']['0']
# self.pd = ArrayPlotData(z=z, x=x, y=y, horiz=z[0, :], vert=z[:, 0])
# self.plot=Plot(self.pd, padding=50, fill_padding=True,
# bgcolor="white", use_backbuffer=True, unified_draw=True)
# xgrid, ygrid = meshgrid(x, y)
#
# color_plot=self.plot.img_plot('z', name="img_plot", xbounds=xgrid, ybounds=ygrid)[0]
# self._image_index = color_plot.index #GridDataSource(xdata=x, ydata=y, sort_order=("ascending","ascending"))
# self._image_index.on_trait_change(self._metadata_changed, "metadata_changed")
# self._image_value=color_plot.value
# self.value_range=DataRange1D(self._image_value)
# color_plot.color_mapper = jet(self.value_range)
# color_plot.tools.append(PanTool(color_plot,
# constrain_key="shift"))
# color_plot.overlays.append(ZoomTool(component=color_plot,
# tool_mode="box", always_on=False))
#
# color_plot.overlays.append(LineInspector(component=color_plot,
# axis='index_x',
# inspect_mode="indexed",
# write_metadata=True,
# is_listener=True,
# color="white"))
#
# color_plot.overlays.append(LineInspector(component=color_plot,
# axis='index_y',
# inspect_mode="indexed",
# write_metadata=True,
# color="white",
# is_listener=True))
#
# cbar_index_mapper = LinearMapper(range=self.value_range)
# self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
# plot=color_plot,
# padding_top=color_plot.padding_top,
# padding_bottom=color_plot.padding_bottom,
# padding_right=40,
# resizable='v',
# width=30)#, ytitle="Magvec (mV)")
#
# #create horizontal line plot
# self.horiz_cross_plot = Plot(self.pd, resizable="h", height=100, padding=50)
# self.horiz_cross_plot.plot(("x", "horiz"))#,
# self.horiz_cross_plot.index_range = color_plot.index_range.x_range
#
# #create vertical line plot
# self.vert_cross_plot = Plot(self.pd, width = 100, orientation="v",
# resizable="v", padding=50, padding_bottom=250)
# self.vert_cross_plot.plot(("y", "vert"))
# self.vert_cross_plot.index_range = color_plot.index_range.y_range
# #self.vert_cross_plot.x_axis.tick_label_formatter = lambda x: '%.2g'%x
# self.color_plot=color_plot
#
# self.container = HPlotContainer(padding=40, fill_padding=True,
# bgcolor = "white", use_backbuffer=False)
# inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
# inner_cont.add(self.horiz_cross_plot)
# inner_cont.add(self.plot)
# self.container.add(self.colorbar)
# self.container.add(inner_cont)
# self.container.add(self.vert_cross_plot)
# #self.vert_cross_plot.y_axis.title="Frequency"
# #self.horiz_cross_plot.x_axis.title="Time (us)"
#
# def _metadata_changed(self, old, new):
# if self._image_index.metadata.has_key("selections"):
# x_ndx, y_ndx = self._image_index.metadata["selections"]
# if y_ndx and x_ndx:
# self.pd.set_data("horiz", self._image_value.data[y_ndx,:])
# self.pd.set_data("vert", self._image_value.data[:,x_ndx])
#
#class Line_Plot(HasTraits):
# plot_control=Instance(Plot_Control)
# request_redraw=DelegatesTo('plot_control')
# new_plot=DelegatesTo('plot_control')
# xtitle=DelegatesTo('plot_control')
# ytitle=DelegatesTo('plot_control')
# title=DelegatesTo('plot_control')
# show_legend=DelegatesTo('plot_control')
# xyformat=DelegatesTo('plot_control')
# plot=Instance(Plot)
# keymap=DelegatesTo('plot_control') #Dict()
# color_index=Int()
# mycolors=List([ 'blue', 'red', 'green', 'purple', 'black', 'darkgray', 'cyan', 'magenta', 'orange'])
# value_scale=DelegatesTo('plot_control')
# index_scale=DelegatesTo('plot_control')
# xcomplex=DelegatesTo('plot_control')
# ycomplex=DelegatesTo('plot_control')
#
# xkeys=DelegatesTo('plot_control')
# zkeys=DelegatesTo('plot_control')
# xindices=DelegatesTo('plot_control')
# zindices=DelegatesTo('plot_control')
# pd = Instance(ArrayPlotData)
#
# def _value_scale_changed(self):
# #if self.color_index!=0:
# self.plot.value_scale = self.value_scale
# self.plot.request_redraw()
#
# def _index_scale_changed(self):
# #if self.color_index!=0:
# self.plot.index_scale = self.index_scale
# self.plot.request_redraw()
#
# def _show_legend_changed(self):
# self.plot.legend.visible = self.show_legend
# self.plot.request_redraw()
#
# def _title_changed(self):
# self.plot.title = self.title
# self.plot.request_redraw()
#
# def _xtitle_changed(self):
# self.plot.x_axis.title=self.xtitle
# self.plot.request_redraw()
#
# def _ytitle_changed(self):
# self.plot.y_axis.title=self.ytitle
# self.plot.request_redraw()
#
# def _request_redraw_fired(self):
# self.plot.request_redraw()
#
# def _new_plot_fired(self):
# for key in self.plot.plots.keys():
# self.remove_plot(key)
# self.color_index=0
# for n, name in enumerate(self.zkeys):
# key='z'+str(name)
# self.add_plot(key)
#
# def _zkeys_changed(self, name, old, new):
# #print self.pd.list_data()
# n=0
# for key in self.pd.list_data():
# if int(key[1:]) in new:
# self.add_plot(key)
# n=n+1
# else:
# self.remove_plot(key)
#
## for key, plot in self.plot.plots.iteritems():
## if int(key[1:]) in new:
## if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
## color=self.mycolors[mod(n, len(self.mycolors))]
## else:
## color=self.xyformat.t_color
## plot[0].color=color
## #plot[0].outline_color=self.xyformat.outline_color,
## n=n+1
##
## else:
## plot[0].color="none"
# #plot[0].outline_color="none"
#
# def add_plot(self, key, z, xkey='x0', x=None):
# if key not in self.plot.plots.keys() and key[0]!='x':
# if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
# color=self.mycolors[mod(self.color_index, len(self.mycolors))]
# else:
# color=self.xyformat.t_color
#
# if x!=None:
# self.pd.set_data(xkey, x)
# self.pd.set_data(key, z)
#
# #if self.color_index<len(self.xkeys):
# # xkey='x'+str(self.xkeys[self.color_index])
# #else:
# # xkey='x'+str(self.xkeys[0])
# self.plot.plot((xkey, key),
# name=key,
# type=self.xyformat.plot_type,
# line_width=self.xyformat.line_width,
# color=color,
# outline_color=self.xyformat.outline_color,
# marker = self.xyformat.marker,
# marker_size = self.xyformat.marker_size)
# self.color_index=self.color_index+1
#
# def remove_plot(self, key):
# if key in self.plot.plots.keys():
# self.plot.delplot(key)
#
# def __init__(self, data, plot_control, *args, **kws):
# super(Line_Plot, self).__init__(*args, **kws)
# self.plot_control=plot_control
# self.pd = ArrayPlotData()
#
# for name, arr in sorted(data['z'].iteritems()):
# self.pd.set_data('z'+str(name), arr)
#
# for name, arr in sorted(data['x'].iteritems()):
# self.pd.set_data('x'+str(name), arr)
#
# plot = Plot(self.pd, padding=50, fill_padding=True,
# bgcolor="white", use_backbuffer=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)
# plot.legend.tools.append(LegendTool(plot.legend, drag_button="right"))
# self.plot=plot
#
# for n, item in enumerate(self.zkeys):
# key='z'+str(item)
# if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
# color=self.mycolors[mod(n, len(self.mycolors))]
# else:
# color=self.xyformat.t_color
# if n<len(self.xkeys):
# xkey='x'+str(self.xkeys[n])
# else:
# xkey='x'+str(self.xkeys[0])
# #n=n+1
# #self.pd.set_data(key, magphase(self._image_value.data[int(item)], self.ycomplex))
# self.plot.plot((xkey, key),
# name=key,
# type=self.xyformat.plot_type,
# line_width=self.xyformat.line_width,
# color=color,
# outline_color=self.xyformat.outline_color,
# marker = self.xyformat.marker,
# marker_size = self.xyformat.marker_size)
# self.plot.value_scale = self.value_scale
# self.plot.index_scale= self.index_scale
#
#
# traits_view = View(Item('plot', style='custom',editor=ComponentEditor(),
# show_label=False),
# resizable=True, title="Chaco Plot",
# width=800, height=700, #kind='modal',
# buttons=[OKButton, CancelButton]
# )
class ImagePlot(Atom):
# container for all plots
container = Typed(HPlotContainer)
# Plot components within this container:
color_plot = Typed(CMapImagePlot)
vertical_cross_plot = Typed(Plot)
horizontal_cross_plot = Typed(Plot)
colorbar = Typed(ColorBar)
# plot data
pd_all = Typed(ArrayPlotData)
#pd_horiz=Instance(ArrayPlotData)
#pd_vert=Instance(ArrayPlotData)
#private data storage
_imag_index=Typed(GridDataSource)
_image_value=Typed(ImageData)
def __init__(self, x,y,z):
super(ImagePlot, self).__init__()
self.pd_all = ArrayPlotData(imagedata = z)
#self.pd_horiz = ArrayPlotData(x=x, horiz=z[4, :])
#self.pd_vert = ArrayPlotData(y=y, vert=z[:,5])
self._imag_index = GridDataSource(xdata=x, ydata=y, sort_order=("ascending","ascending"))
index_mapper = GridMapper(range=DataRange2D(self._imag_index))
self._imag_index.on_trait_change(self._metadata_changed,
"metadata_changed")
self._image_value = ImageData(data=z, value_depth=1)
color_mapper = jet(DataRange1D(self._image_value))
self.color_plot= CMapImagePlot(
index=self._imag_index,
index_mapper=index_mapper,
value=self._image_value,
value_mapper=color_mapper,
padding=20,
use_backbuffer=True,
unified_draw=True)
#Add axes to image plot
left = PlotAxis(orientation='left',
title= "Frequency (GHz)",
mapper=self.color_plot.index_mapper._ymapper,
component=self.color_plot)
self.color_plot.overlays.append(left)
bottom = PlotAxis(orientation='bottom',
title= "Time (us)",
mapper=self.color_plot.index_mapper._xmapper,
component=self.color_plot)
self.color_plot.overlays.append(bottom)
self.color_plot.tools.append(PanTool(self.color_plot,
constrain_key="shift"))
self.color_plot.overlays.append(ZoomTool(component=self.color_plot,
tool_mode="box", always_on=False))
#Add line inspector tool for horizontal and vertical
self.color_plot.overlays.append(LineInspector(component=self.color_plot,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=True,
color="white"))
self.color_plot.overlays.append(LineInspector(component=self.color_plot,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=True))
myrange = DataRange1D(low=amin(z),
high=amax(z))
cmap=jet
self.colormap = cmap(myrange)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=myrange)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.color_plot,
padding_top=self.color_plot.padding_top,
padding_bottom=self.color_plot.padding_bottom,
padding_right=40,
resizable='v',
width=30)#, ytitle="Magvec (mV)")
#create horizontal line plot
self.horiz_cross_plot = Plot(self.pd_horiz, resizable="h")
self.horiz_cross_plot.height = 100
self.horiz_cross_plot.padding = 20
self.horiz_cross_plot.plot(("x", "horiz"))#,
#line_style="dot")
# self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
# type="cmap_scatter",
# name="dot",
# color_mapper=self._cmap(image_value_range),
# marker="circle",
# marker_size=8)
self.horiz_cross_plot.index_range = self.color_plot.index_range.x_range
#create vertical line plot
self.vert_cross_plot = Plot(self.pd_vert, width = 140, orientation="v",
resizable="v", padding=20, padding_bottom=160)
self.vert_cross_plot.plot(("y", "vert"))#,
# line_style="dot")
# self.vert_cross_plot.xtitle="Magvec (mV)"
# self.vertica_cross_plot.plot(("vertical_scatter_index",
# "vertical_scatter_value",
# "vertical_scatter_color"),
# type="cmap_scatter",
# name="dot",
# color_mapper=self._cmap(image_value_range),
# marker="circle",
# marker_size=8)
self.vert_cross_plot.index_range = self.color_plot.index_range.y_range
# Create a container and add components
self.container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.horiz_cross_plot)
inner_cont.add(self.color_plot)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.vert_cross_plot)
def _metadata_changed(self, old, new):
""" This function takes out a cross section from the image data, based
on the line inspector selections, and updates the line and scatter
plots."""
#self.cross_plot.value_range.low = self.minz
#self.cross_plot.value_range.high = self.maxz
#self.cross_plot2.value_range.low = self.minz
#self.cross_plot2.value_range.high = self.maxz
if self._imag_index.metadata.has_key("selections"):
x_ndx, y_ndx = self._imag_index.metadata["selections"]
if y_ndx and x_ndx:
# xdata, ydata = self._image_index.get_data()
# xdata, ydata = xdata.get_data(), ydata.get_data()
self.pd_horiz.set_data("horiz", self._image_value.data[y_ndx,:])
self.pd_vert.set_data("vert", self._image_value.data[:,x_ndx])
# scatter_index=array([xdata[x_ndx]]),
# scatter_index2=array([ydata[y_ndx]]),
# scatter_value=array([self._image_value.data[y_ndx, x_ndx]]),
# scatter_value2=array([self._image_value.data[y_ndx, x_ndx]]),
# scatter_color=array([self._image_value.data[y_ndx, x_ndx]]),
# scatter_color2=array([self._image_value.data[y_ndx, x_ndx]])
# )
# else:
# self.pd.update_data({"scatter_value": array([]),
# "scatter_value2": array([]), "line_value": array([]),
# "line_value2": array([])})
#if __name__ == "__main__":
# filename="/Users/thomasaref/Dropbox/Dad stuff/sample3/digitizer/lt/sample3_digitizer_f_sweep_t_300mk_100nspulse.hdf5"
#
# with h5py.File(filename, 'r') as f:
#
# time=f["Traces"]["d - AvgTrace - t"][:]
# Magvec=f["Traces"]["d - AvgTrace - Magvec"][:]
# frequency=f["Data"]["Data"][:]
# # for name in f["Data"]:
# # print name
#
# time=squeeze(time)
# Magvec=squeeze(Magvec)
# frequency=squeeze(frequency)
#
# x = time[:,0]*1.0e6
# y = frequency[0,:]/1.0e9
# z=transpose(Magvec*1000.0)
#
# ip=ImagePlot(xs,ys,z)
# ip.configure_traits()
#class Image_Plot(Atom):
# plot_control=Instance(Plot_Control)
# xtitle=DelegatesTo('plot_control')
# ytitle=DelegatesTo('plot_control')
# ztitle=DelegatesTo('plot_control')
# request_redraw=DelegatesTo('plot_control')
# #ykeys=DelegatesTo('plot_control')
# container = Typed(HPlotContainer)
# color_plot = Typed(CMapImagePlot)
# plot=Instance(Plot)
# vertical_cross_plot = Typed(Plot)
# horizontal_cross_plot = Typed(Plot)
# colorbar = Typed(ColorBar)
# pd_all = Instance(ArrayPlotData)
# _image_index=Instance(GridDataSource)
# _image_value=Instance(ImageData)
# data=Dict()
#
# pd=Instance(ArrayPlotData)
#
# traits_view = View(Group(Item('container', editor=ComponentEditor(), show_label=False),
# orientation='horizontal'),
# width=1000, height=700, resizable=True, title="Chaco Plot")
#
# def _xtitle_changed(self):
# self.horiz_cross_plot.x_axis.title=self.xtitle
# self.plot.x_axis.title=self.xtitle
#
# def _ytitle_changed(self):
# self.vert_cross_plot.y_axis.title=self.ytitle
# self.plot.y_axis.title=self.ytitle
#
# def _request_redraw_fired(self):
# self.color_plot.request_redraw()
# self.horiz_cross_plot.request_redraw()
# self.vert_cross_plot.request_redraw()
#
# def __init__(self, data, plot_control):
# super(Image_Plot, self).__init__()
# self.plot_control=plot_control
# z=zeros((len(data['y']['0']), len(data['x']['0'])))
# z[:] = nan
# for key, item in data['z'].iteritems():
# z[int(key)]=item
# x=data['x']['0']
# y=data['y']['0']
# self.pd = ArrayPlotData(z=z, x=x, y=y, horiz=z[0, :], vert=z[:, 0])
# self.plot=Plot(self.pd, padding=50, fill_padding=True,
# bgcolor="white", use_backbuffer=True, unified_draw=True)
# xgrid, ygrid = meshgrid(x, y)
#
# color_plot=self.plot.img_plot('z', name="img_plot", xbounds=xgrid, ybounds=ygrid)[0]
# self._image_index = color_plot.index #GridDataSource(xdata=x, ydata=y, sort_order=("ascending","ascending"))
# self._image_index.on_trait_change(self._metadata_changed, "metadata_changed")
# self._image_value=color_plot.value
# self.value_range=DataRange1D(self._image_value)
# color_plot.color_mapper = jet(self.value_range)
# color_plot.tools.append(PanTool(color_plot,
# constrain_key="shift"))
# color_plot.overlays.append(ZoomTool(component=color_plot,
# tool_mode="box", always_on=False))
#
# color_plot.overlays.append(LineInspector(component=color_plot,
# axis='index_x',
# inspect_mode="indexed",
# write_metadata=True,
# is_listener=True,
# color="white"))
#
# color_plot.overlays.append(LineInspector(component=color_plot,
# axis='index_y',
# inspect_mode="indexed",
# write_metadata=True,
# color="white",
# is_listener=True))
#
# cbar_index_mapper = LinearMapper(range=self.value_range)
# self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
# plot=color_plot,
# padding_top=color_plot.padding_top,
# padding_bottom=color_plot.padding_bottom,
# padding_right=40,
# resizable='v',
# width=30)#, ytitle="Magvec (mV)")
#
# #create horizontal line plot
# self.horiz_cross_plot = Plot(self.pd, resizable="h", height=100, padding=50)
# self.horiz_cross_plot.plot(("x", "horiz"))#,
# self.horiz_cross_plot.index_range = color_plot.index_range.x_range
#
# #create vertical line plot
# self.vert_cross_plot = Plot(self.pd, width = 100, orientation="v",
# resizable="v", padding=50, padding_bottom=250)
# self.vert_cross_plot.plot(("y", "vert"))
# self.vert_cross_plot.index_range = color_plot.index_range.y_range
# #self.vert_cross_plot.x_axis.tick_label_formatter = lambda x: '%.2g'%x
# self.color_plot=color_plot
#
# self.container = HPlotContainer(padding=40, fill_padding=True,
# bgcolor = "white", use_backbuffer=False)
# inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
# inner_cont.add(self.horiz_cross_plot)
# inner_cont.add(self.plot)
# self.container.add(self.colorbar)
# self.container.add(inner_cont)
# self.container.add(self.vert_cross_plot)
# #self.vert_cross_plot.y_axis.title="Frequency"
# #self.horiz_cross_plot.x_axis.title="Time (us)"
#
# def _metadata_changed(self, old, new):
# if self._image_index.metadata.has_key("selections"):
# x_ndx, y_ndx = self._image_index.metadata["selections"]
# if y_ndx and x_ndx:
# self.pd.set_data("horiz", self._image_value.data[y_ndx,:])
# self.pd.set_data("vert", self._image_value.data[:,x_ndx])
#
#class Line_Plot(HasTraits):
# plot_control=Instance(Plot_Control)
# request_redraw=DelegatesTo('plot_control')
# new_plot=DelegatesTo('plot_control')
# xtitle=DelegatesTo('plot_control')
# ytitle=DelegatesTo('plot_control')
# title=DelegatesTo('plot_control')
# show_legend=DelegatesTo('plot_control')
# xyformat=DelegatesTo('plot_control')
# plot=Instance(Plot)
# keymap=DelegatesTo('plot_control') #Dict()
# color_index=Int()
# mycolors=List([ 'blue', 'red', 'green', 'purple', 'black', 'darkgray', 'cyan', 'magenta', 'orange'])
# value_scale=DelegatesTo('plot_control')
# index_scale=DelegatesTo('plot_control')
# xcomplex=DelegatesTo('plot_control')
# ycomplex=DelegatesTo('plot_control')
#
# xkeys=DelegatesTo('plot_control')
# zkeys=DelegatesTo('plot_control')
# xindices=DelegatesTo('plot_control')
# zindices=DelegatesTo('plot_control')
# pd = Instance(ArrayPlotData)
#
# def _value_scale_changed(self):
# #if self.color_index!=0:
# self.plot.value_scale = self.value_scale
# self.plot.request_redraw()
#
# def _index_scale_changed(self):
# #if self.color_index!=0:
# self.plot.index_scale = self.index_scale
# self.plot.request_redraw()
#
# def _show_legend_changed(self):
# self.plot.legend.visible = self.show_legend
# self.plot.request_redraw()
#
# def _title_changed(self):
# self.plot.title = self.title
# self.plot.request_redraw()
#
# def _xtitle_changed(self):
# self.plot.x_axis.title=self.xtitle
# self.plot.request_redraw()
#
# def _ytitle_changed(self):
# self.plot.y_axis.title=self.ytitle
# self.plot.request_redraw()
#
# def _request_redraw_fired(self):
# self.plot.request_redraw()
#
# def _new_plot_fired(self):
# for key in self.plot.plots.keys():
# self.remove_plot(key)
# self.color_index=0
# for n, name in enumerate(self.zkeys):
# key='z'+str(name)
# self.add_plot(key)
#
# def _zkeys_changed(self, name, old, new):
# #print self.pd.list_data()
# n=0
# for key in self.pd.list_data():
# if int(key[1:]) in new:
# self.add_plot(key)
# n=n+1
# else:
# self.remove_plot(key)
#
## for key, plot in self.plot.plots.iteritems():
## if int(key[1:]) in new:
## if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
## color=self.mycolors[mod(n, len(self.mycolors))]
## else:
## color=self.xyformat.t_color
## plot[0].color=color
## #plot[0].outline_color=self.xyformat.outline_color,
## n=n+1
##
## else:
## plot[0].color="none"
# #plot[0].outline_color="none"
#
# def add_plot(self, key, z, xkey='x0', x=None):
# if key not in self.plot.plots.keys() and key[0]!='x':
# if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
# color=self.mycolors[mod(self.color_index, len(self.mycolors))]
# else:
# color=self.xyformat.t_color
#
# if x!=None:
# self.pd.set_data(xkey, x)
# self.pd.set_data(key, z)
#
# #if self.color_index<len(self.xkeys):
# # xkey='x'+str(self.xkeys[self.color_index])
# #else:
# # xkey='x'+str(self.xkeys[0])
# self.plot.plot((xkey, key),
# name=key,
# type=self.xyformat.plot_type,
# line_width=self.xyformat.line_width,
# color=color,
# outline_color=self.xyformat.outline_color,
# marker = self.xyformat.marker,
# marker_size = self.xyformat.marker_size)
# self.color_index=self.color_index+1
#
# def remove_plot(self, key):
# if key in self.plot.plots.keys():
# self.plot.delplot(key)
#
# def __init__(self, data, plot_control, *args, **kws):
# super(Line_Plot, self).__init__(*args, **kws)
# self.plot_control=plot_control
# self.pd = ArrayPlotData()
#
# for name, arr in sorted(data['z'].iteritems()):
# self.pd.set_data('z'+str(name), arr)
#
# for name, arr in sorted(data['x'].iteritems()):
# self.pd.set_data('x'+str(name), arr)
#
# plot = Plot(self.pd, padding=50, fill_padding=True,
# bgcolor="white", use_backbuffer=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)
# plot.legend.tools.append(LegendTool(plot.legend, drag_button="right"))
# self.plot=plot
#
# for n, item in enumerate(self.zkeys):
# key='z'+str(item)
# if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
# color=self.mycolors[mod(n, len(self.mycolors))]
# else:
# color=self.xyformat.t_color
# if n<len(self.xkeys):
# xkey='x'+str(self.xkeys[n])
# else:
# xkey='x'+str(self.xkeys[0])
# #n=n+1
# #self.pd.set_data(key, magphase(self._image_value.data[int(item)], self.ycomplex))
# self.plot.plot((xkey, key),
# name=key,
# type=self.xyformat.plot_type,
# line_width=self.xyformat.line_width,
# color=color,
# outline_color=self.xyformat.outline_color,
# marker = self.xyformat.marker,
# marker_size = self.xyformat.marker_size)
# self.plot.value_scale = self.value_scale
# self.plot.index_scale= self.index_scale
#
#
# traits_view = View(Item('plot', style='custom',editor=ComponentEditor(),
# show_label=False),
# resizable=True, title="Chaco Plot",
# width=800, height=700, #kind='modal',
# buttons=[OKButton, CancelButton]
# )
class myImagePlot(HasTraits):
# container for all plots
container = Instance(HPlotContainer)
# Plot components within this container:
color_plot = Instance(CMapImagePlot)
vertical_cross_plot = Instance(Plot)
horizontal_cross_plot = Instance(Plot)
colorbar = Instance(ColorBar)
# plot data
pd_all = Instance(ArrayPlotData)
pd_horiz=Instance(ArrayPlotData)
pd_vert=Instance(ArrayPlotData)
#private data storage
_imag_index=Instance(GridDataSource)
_image_value=Instance(ImageData)
traits_view = View(
Item('container', editor=ComponentEditor(), show_label=False),
width=1000, height=700, resizable=True, title="Chaco Plot")
def __init__(self, x,y,z):
super(myImagePlot, self).__init__()
self.pd_all = ArrayPlotData(imagedata = z)
self.pd_horiz = ArrayPlotData(x=x, horiz=z[4, :])
self.pd_vert = ArrayPlotData(y=y, vert=z[:,5])
self._imag_index = GridDataSource(xdata=x, ydata=y, sort_order=("ascending","ascending"))
index_mapper = GridMapper(range=DataRange2D(self._imag_index))
self._imag_index.on_trait_change(self._metadata_changed,
"metadata_changed")
self._image_value = ImageData(data=z, value_depth=1)
color_mapper = jet(DataRange1D(self._image_value))
self.color_plot= CMapImagePlot(
index=self._imag_index,
index_mapper=index_mapper,
value=self._image_value,
value_mapper=color_mapper,
padding=20,
use_backbuffer=True,
unified_draw=True)
#Add axes to image plot
left = PlotAxis(orientation='left',
title= "Frequency (GHz)",
mapper=self.color_plot.index_mapper._ymapper,
component=self.color_plot)
self.color_plot.overlays.append(left)
bottom = PlotAxis(orientation='bottom',
title= "Time (us)",
mapper=self.color_plot.index_mapper._xmapper,
component=self.color_plot)
self.color_plot.overlays.append(bottom)
self.color_plot.tools.append(PanTool(self.color_plot,
constrain_key="shift"))
self.color_plot.overlays.append(ZoomTool(component=self.color_plot,
tool_mode="box", always_on=False))
#Add line inspector tool for horizontal and vertical
self.color_plot.overlays.append(LineInspector(component=self.color_plot,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=True,
color="white"))
self.color_plot.overlays.append(LineInspector(component=self.color_plot,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=True))
myrange = DataRange1D(low=amin(z),
high=amax(z))
cmap=jet
self.colormap = cmap(myrange)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=myrange)
self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
plot=self.color_plot,
padding_top=self.color_plot.padding_top,
padding_bottom=self.color_plot.padding_bottom,
padding_right=40,
resizable='v',
width=30)#, ytitle="Magvec (mV)")
#create horizontal line plot
self.horiz_cross_plot = Plot(self.pd_horiz, resizable="h")
self.horiz_cross_plot.height = 100
self.horiz_cross_plot.padding = 20
self.horiz_cross_plot.plot(("x", "horiz"))#,
#line_style="dot")
# self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
# type="cmap_scatter",
# name="dot",
# color_mapper=self._cmap(image_value_range),
# marker="circle",
# marker_size=8)
self.horiz_cross_plot.index_range = self.color_plot.index_range.x_range
#create vertical line plot
self.vert_cross_plot = Plot(self.pd_vert, width = 140, orientation="v",
resizable="v", padding=20, padding_bottom=160)
self.vert_cross_plot.plot(("y", "vert"))#,
# line_style="dot")
# self.vert_cross_plot.xtitle="Magvec (mV)"
# self.vertica_cross_plot.plot(("vertical_scatter_index",
# "vertical_scatter_value",
# "vertical_scatter_color"),
# type="cmap_scatter",
# name="dot",
# color_mapper=self._cmap(image_value_range),
# marker="circle",
# marker_size=8)
self.vert_cross_plot.index_range = self.color_plot.index_range.y_range
# Create a container and add components
self.container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.horiz_cross_plot)
inner_cont.add(self.color_plot)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.vert_cross_plot)
def _metadata_changed(self, old, new):
""" This function takes out a cross section from the image data, based
on the line inspector selections, and updates the line and scatter
plots."""
#self.cross_plot.value_range.low = self.minz
#self.cross_plot.value_range.high = self.maxz
#self.cross_plot2.value_range.low = self.minz
#self.cross_plot2.value_range.high = self.maxz
if self._imag_index.metadata.has_key("selections"):
x_ndx, y_ndx = self._imag_index.metadata["selections"]
if y_ndx and x_ndx:
# xdata, ydata = self._image_index.get_data()
# xdata, ydata = xdata.get_data(), ydata.get_data()
self.pd_horiz.set_data("horiz", self._image_value.data[y_ndx,:])
self.pd_vert.set_data("vert", self._image_value.data[:,x_ndx])
class ImageGUI(HasTraits):
# TO FIX : put here the last available shot
shot = File("L:\\data\\app3\\2011\\1108\\110823\\column_5200.ascii")
# ---------------------------------------------------------------------------
# Traits View Definitions
# ---------------------------------------------------------------------------
traits_view = View(
HSplit(
Item(
"shot",
style="custom",
editor=FileEditor(filter=["column_*.ascii"]),
show_label=False,
resizable=True,
width=400,
),
Item("container", editor=ComponentEditor(), show_label=False, width=800, height=800),
),
width=1200,
height=800,
resizable=True,
title="APPARATUS 3 :: Analyze Images",
)
plot_edit_view = View(Group(Item("num_levels"), Item("colormap")), buttons=["OK", "Cancel"])
num_levels = Int(15)
colormap = Enum(color_map_name_dict.keys())
# ---------------------------------------------------------------------------
# Private Traits
# ---------------------------------------------------------------------------
_image_index = Instance(GridDataSource)
_image_value = Instance(ImageData)
_cmap = Trait(jet, Callable)
# ---------------------------------------------------------------------------
# Public View interface
# ---------------------------------------------------------------------------
def __init__(self, *args, **kwargs):
super(ImageGUI, self).__init__(*args, **kwargs)
self.create_plot()
def create_plot(self):
# Create the mapper, etc
self._image_index = GridDataSource(array([]), array([]), sort_order=("ascending", "ascending"))
image_index_range = DataRange2D(self._image_index)
self._image_index.on_trait_change(self._metadata_changed, "metadata_changed")
self._image_value = ImageData(data=array([]), value_depth=1)
image_value_range = DataRange1D(self._image_value)
# Create the image plot
self.imgplot = CMapImagePlot(
index=self._image_index,
value=self._image_value,
index_mapper=GridMapper(range=image_index_range),
color_mapper=self._cmap(image_value_range),
)
# Create the contour plots
# ~ self.polyplot = ContourPolyPlot(index=self._image_index,
# ~ value=self._image_value,
# ~ index_mapper=GridMapper(range=
# ~ image_index_range),
# ~ color_mapper=\
# ~ self._cmap(image_value_range),
# ~ levels=self.num_levels)
# ~ self.lineplot = ContourLinePlot(index=self._image_index,
# ~ value=self._image_value,
# ~ index_mapper=GridMapper(range=
# ~ self.polyplot.index_mapper.range),
# ~ levels=self.num_levels)
# Add a left axis to the plot
left = PlotAxis(
orientation="left", title="axial", mapper=self.imgplot.index_mapper._ymapper, component=self.imgplot
)
self.imgplot.overlays.append(left)
# Add a bottom axis to the plot
bottom = PlotAxis(
orientation="bottom", title="radial", mapper=self.imgplot.index_mapper._xmapper, component=self.imgplot
)
self.imgplot.overlays.append(bottom)
# Add some tools to the plot
# ~ self.polyplot.tools.append(PanTool(self.polyplot,
# ~ constrain_key="shift"))
self.imgplot.overlays.append(ZoomTool(component=self.imgplot, tool_mode="box", always_on=False))
self.imgplot.overlays.append(
LineInspector(
component=self.imgplot,
axis="index_x",
inspect_mode="indexed",
write_metadata=True,
is_listener=False,
color="white",
)
)
self.imgplot.overlays.append(
LineInspector(
component=self.imgplot,
axis="index_y",
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=False,
)
)
# Add these two plots to one container
contour_container = OverlayPlotContainer(padding=20, use_backbuffer=True, unified_draw=True)
contour_container.add(self.imgplot)
# ~ contour_container.add(self.polyplot)
# ~ contour_container.add(self.lineplot)
# Create a colorbar
cbar_index_mapper = LinearMapper(range=image_value_range)
self.colorbar = ColorBar(
index_mapper=cbar_index_mapper,
plot=self.imgplot,
padding_top=self.imgplot.padding_top,
padding_bottom=self.imgplot.padding_bottom,
padding_right=40,
resizable="v",
width=30,
)
# Create the two cross plots
self.pd = ArrayPlotData(
line_index=array([]),
line_value=array([]),
scatter_index=array([]),
scatter_value=array([]),
scatter_color=array([]),
)
self.cross_plot = Plot(self.pd, resizable="h")
self.cross_plot.height = 100
self.cross_plot.padding = 20
self.cross_plot.plot(("line_index", "line_value"), line_style="dot")
self.cross_plot.plot(
("scatter_index", "scatter_value", "scatter_color"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8,
)
self.cross_plot.index_range = self.imgplot.index_range.x_range
self.pd.set_data("line_index2", array([]))
self.pd.set_data("line_value2", array([]))
self.pd.set_data("scatter_index2", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("scatter_color2", array([]))
self.cross_plot2 = Plot(self.pd, width=140, orientation="v", resizable="v", padding=20, padding_bottom=160)
self.cross_plot2.plot(("line_index2", "line_value2"), line_style="dot")
self.cross_plot2.plot(
("scatter_index2", "scatter_value2", "scatter_color2"),
type="cmap_scatter",
name="dot",
color_mapper=self._cmap(image_value_range),
marker="circle",
marker_size=8,
)
self.cross_plot2.index_range = self.imgplot.index_range.y_range
# Create a container and add components
self.container = HPlotContainer(padding=40, fill_padding=True, bgcolor="white", use_backbuffer=False)
inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
inner_cont.add(self.cross_plot)
inner_cont.add(contour_container)
self.container.add(self.colorbar)
self.container.add(inner_cont)
self.container.add(self.cross_plot2)
def update(self):
imgdata = self.load_imagedata()
if imgdata is not None:
self.minz = imgdata.min()
self.maxz = imgdata.max()
self.colorbar.index_mapper.range.low = self.minz
self.colorbar.index_mapper.range.high = self.maxz
xs = numpy.linspace(0, imgdata.shape[0], imgdata.shape[0] + 1)
ys = numpy.linspace(0, imgdata.shape[1], imgdata.shape[1] + 1)
print xs
print ys
self._image_index.set_data(xs, ys)
self._image_value.data = imgdata
self.pd.set_data("line_index", xs)
self.pd.set_data("line_index2", ys)
self.container.invalidate_draw()
self.container.request_redraw()
def load_imagedata(self):
try:
dir = self.shot[self.shot.index(":\\") + 2 : self.shot.rindex("\\") + 1]
shotnum = self.shot[self.shot.rindex("_") + 1 : self.shot.rindex(".ascii")]
except ValueError:
print " *** Not a valid column density path *** "
return None
# Set data path
# Prepare PlotData object
print dir
print shotnum
return load(dir, shotnum)
# ---------------------------------------------------------------------------
# Event handlers
# ---------------------------------------------------------------------------
def _shot_changed(self):
self.update()
def _metadata_changed(self, old, new):
""" This function takes out a cross section from the image data, based
on the line inspector selections, and updates the line and scatter
plots."""
self.cross_plot.value_range.low = self.minz
self.cross_plot.value_range.high = self.maxz
self.cross_plot2.value_range.low = self.minz
self.cross_plot2.value_range.high = self.maxz
if self._image_index.metadata.has_key("selections"):
x_ndx, y_ndx = self._image_index.metadata["selections"]
if y_ndx and x_ndx:
self.pd.set_data("line_value", self._image_value.data[y_ndx, :])
self.pd.set_data("line_value2", self._image_value.data[:, x_ndx])
xdata, ydata = self._image_index.get_data()
xdata, ydata = xdata.get_data(), ydata.get_data()
self.pd.set_data("scatter_index", array([xdata[x_ndx]]))
self.pd.set_data("scatter_index2", array([ydata[y_ndx]]))
self.pd.set_data("scatter_value", array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_value2", array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_color", array([self._image_value.data[y_ndx, x_ndx]]))
self.pd.set_data("scatter_color2", array([self._image_value.data[y_ndx, x_ndx]]))
else:
self.pd.set_data("scatter_value", array([]))
self.pd.set_data("scatter_value2", array([]))
self.pd.set_data("line_value", array([]))
self.pd.set_data("line_value2", array([]))
def _colormap_changed(self):
self._cmap = color_map_name_dict[self.colormap]
if hasattr(self, "polyplot"):
value_range = self.polyplot.color_mapper.range
self.polyplot.color_mapper = self._cmap(value_range)
value_range = self.cross_plot.color_mapper.range
self.cross_plot.color_mapper = self._cmap(value_range)
# FIXME: change when we decide how best to update plots using
# the shared colormap in plot object
self.cross_plot.plots["dot"][0].color_mapper = self._cmap(value_range)
self.cross_plot2.plots["dot"][0].color_mapper = self._cmap(value_range)
self.container.request_redraw()
def _num_levels_changed(self):
if self.num_levels > 3:
self.polyplot.levels = self.num_levels
self.lineplot.levels = self.num_levels
