def test_set_data(self):
ds = GridDataSource(xdata=array([1,2,3]),
ydata=array([1.5, 0.5, -0.5, -1.5]),
sort_order=('ascending', 'descending'))
test_xd = array([0,2,4])
test_yd = array([0,1,2,3,4,5])
test_sort_order = ('none', 'none')
ds.set_data(xdata=test_xd, ydata=test_yd, sort_order=('none', 'none'))
self.assert_(ds.sort_order == test_sort_order)
xd, yd = ds.get_data()
assert_ary_(xd.get_data(), test_xd)
assert_ary_(yd.get_data(), test_yd)
self.assert_(ds.get_bounds() == ((min(test_xd),min(test_yd)),
(max(test_xd),max(test_yd))))
def _corr_plot_default(self):
diag = self.covar.diagonal()
corr = self.covar / np.sqrt(np.outer(diag, diag))
N = len(diag)
value_range = DataRange1D(low=-1, high=1)
color_mapper = cmap(range=value_range)
index = GridDataSource()
value = ImageData()
mapper = GridMapper(range=DataRange2D(index),
y_low_pos=1.0,
y_high_pos=0.0)
index.set_data(xdata=np.arange(-0.5, N), ydata=np.arange(-0.5, N))
value.set_data(np.flipud(corr))
self.corr_data = value
cmap_plot = CMapImagePlot(index=index,
index_mapper=mapper,
value=value,
value_mapper=color_mapper,
padding=(40, 40, 100, 40))
yaxis = PlotAxis(
cmap_plot,
orientation='left',
tick_interval=1,
tick_label_formatter=lambda x: self.header[int(N - 1 - x)],
tick_generator=ShowAllTickGenerator(positions=np.arange(N)))
xaxis = PlotAxis(
cmap_plot,
orientation='top',
tick_interval=1,
tick_label_formatter=lambda x: self.header[int(x)],
tick_label_alignment='edge',
tick_generator=ShowAllTickGenerator(positions=np.arange(N)))
cmap_plot.overlays.append(yaxis)
cmap_plot.overlays.append(xaxis)
colorbar = ColorBar(
index_mapper=LinearMapper(range=cmap_plot.value_range),
plot=cmap_plot,
orientation='v',
resizable='v',
width=10,
padding=(40, 5, 100, 40))
container = HPlotContainer(bgcolor='transparent')
container.add(cmap_plot)
container.add(colorbar)
return container
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.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 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 GridDataSourceTestCase(UnittestTools, unittest.TestCase):
def setUp(self):
self.data_source = GridDataSource(xdata=array([1, 2, 3]),
ydata=array([1.5, 0.5, -0.5, -1.5]),
sort_order=('ascending',
'descending'))
def test_empty(self):
data_source = GridDataSource()
self.assertEqual(data_source.sort_order, ('none', 'none'))
self.assertEqual(data_source.index_dimension, 'image')
self.assertEqual(data_source.value_dimension, 'scalar')
self.assertEqual(data_source.metadata, {
"selections": [],
"annotations": []
})
xdata, ydata = data_source.get_data()
assert_array_equal(xdata.get_data(), array([]))
assert_array_equal(ydata.get_data(), array([]))
self.assertEqual(data_source.get_bounds(), ((0, 0), (0, 0)))
def test_init(self):
test_xd = array([1, 2, 3])
test_yd = array([1.5, 0.5, -0.5, -1.5])
test_sort_order = ('ascending', 'descending')
self.assertEqual(self.data_source.sort_order, test_sort_order)
xd, yd = self.data_source.get_data()
assert_array_equal(xd.get_data(), test_xd)
assert_array_equal(yd.get_data(), test_yd)
self.assertEqual(self.data_source.get_bounds(),
((min(test_xd), min(test_yd)),
(max(test_xd), max(test_yd))))
def test_set_data(self):
test_xd = array([0, 2, 4])
test_yd = array([0, 1, 2, 3, 4, 5])
test_sort_order = ('none', 'none')
self.data_source.set_data(xdata=test_xd,
ydata=test_yd,
sort_order=('none', 'none'))
self.assertEqual(self.data_source.sort_order, test_sort_order)
xd, yd = self.data_source.get_data()
assert_array_equal(xd.get_data(), test_xd)
assert_array_equal(yd.get_data(), test_yd)
self.assertEqual(self.data_source.get_bounds(),
((min(test_xd), min(test_yd)),
(max(test_xd), max(test_yd))))
def test_metadata(self):
self.assertEqual(self.data_source.metadata, {
'annotations': [],
'selections': []
})
def test_metadata_changed(self):
with self.assertTraitChanges(self.data_source,
'metadata_changed',
count=1):
self.data_source.metadata = {'new_metadata': True}
def test_metadata_items_changed(self):
with self.assertTraitChanges(self.data_source,
'metadata_changed',
count=1):
self.data_source.metadata['new_metadata'] = True
class ImagePlotUI(HasTraits):
# Image data source
image_data_source = Instance(ImageDataSource)
# container for all plots
container = Instance(HPlotContainer)
# Plot components within this container
plot = Instance(CMapImagePlot)
colorbar = Instance(ColorBar)
# View options
colormap = Enum(colormaps)
# Traits view definitions:
traits_view = View(Group(
UItem('container', editor=ComponentEditor(size=(500, 450)))),
resizable=True)
plot_edit_view = View(Group(Item('colormap')), buttons=["OK", "Cancel"])
# -------------------------------------------------------------------------
# Private Traits
# -------------------------------------------------------------------------
_image_index = Instance(GridDataSource)
_image_value = Instance(ImageData)
_cmap = Trait(default_colormaps.gray, Callable)
# -------------------------------------------------------------------------
# Public View interface
# -------------------------------------------------------------------------
def __init__(self, image_data_source=None):
super(ImagePlotUI, self).__init__()
with errstate(invalid='ignore'):
self.create_plot()
self.image_data_source = image_data_source
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 colormapped scalar plot
self.plot = CMapImagePlot(
index=self._image_index,
index_mapper=GridMapper(range=image_index_range),
value=self._image_value,
value_mapper=self._cmap(image_value_range))
# Add a left axis to the plot
left = PlotAxis(orientation='left',
title="y",
mapper=self.plot.index_mapper._ymapper,
component=self.plot)
self.plot.overlays.append(left)
# Add a bottom axis to the plot
bottom = PlotAxis(orientation='bottom',
title="x",
mapper=self.plot.index_mapper._xmapper,
component=self.plot)
self.plot.overlays.append(bottom)
# Add some tools to the plot
self.plot.tools.append(PanTool(self.plot, constrain_key="shift"))
self.plot.overlays.append(
ZoomTool(component=self.plot, 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.plot,
padding_top=self.plot.padding_top,
padding_bottom=self.plot.padding_bottom,
padding_right=40,
resizable='v',
width=10)
# Create a container and add components
self.container = HPlotContainer(padding=40,
fill_padding=True,
bgcolor="white",
use_backbuffer=False)
self.container.add(self.colorbar)
self.container.add(self.plot)
@on_trait_change('image_data_source.data_source_changed')
def update_plot(self):
xs = self.image_data_source.xs
ys = self.image_data_source.ys
zs = self.image_data_source.zs
self.colorbar.index_mapper.range.low = zs.min()
self.colorbar.index_mapper.range.high = zs.max()
self._image_index.set_data(xs, ys)
self._image_value.data = zs
self.container.invalidate_draw()
self.container.request_redraw()
# -------------------------------------------------------------------------
# Event handlers
# -------------------------------------------------------------------------
def _colormap_changed(self):
self._cmap = default_colormaps.color_map_name_dict[self.colormap]
if self.plot is not None:
value_range = self.plot.color_mapper.range
self.plot.color_mapper = self._cmap(value_range)
self.container.request_redraw()
class GridDataSourceTestCase(UnittestTools, unittest.TestCase):
def setUp(self):
self.data_source = GridDataSource(
xdata=array([1, 2, 3]),
ydata=array([1.5, 0.5, -0.5, -1.5]),
sort_order=('ascending', 'descending'))
def test_empty(self):
data_source = GridDataSource()
self.assertEqual(data_source.sort_order, ('none', 'none'))
self.assertEqual(data_source.index_dimension, 'image')
self.assertEqual(data_source.value_dimension, 'scalar')
self.assertEqual(data_source.metadata,
{"selections":[], "annotations":[]})
xdata, ydata = data_source.get_data()
assert_array_equal(xdata.get_data(), array([]))
assert_array_equal(ydata.get_data(), array([]))
self.assertEqual(data_source.get_bounds(), ((0,0),(0,0)))
def test_init(self):
test_xd = array([1, 2, 3])
test_yd = array([1.5, 0.5, -0.5, -1.5])
test_sort_order = ('ascending', 'descending')
self.assertEqual(self.data_source.sort_order, test_sort_order)
xd, yd = self.data_source.get_data()
assert_array_equal(xd.get_data(), test_xd)
assert_array_equal(yd.get_data(), test_yd)
self.assertEqual(self.data_source.get_bounds(),
((min(test_xd),min(test_yd)),
(max(test_xd),max(test_yd))))
def test_set_data(self):
test_xd = array([0,2,4])
test_yd = array([0,1,2,3,4,5])
test_sort_order = ('none', 'none')
self.data_source.set_data(xdata=test_xd, ydata=test_yd,
sort_order=('none', 'none'))
self.assertEqual(self.data_source.sort_order, test_sort_order)
xd, yd = self.data_source.get_data()
assert_array_equal(xd.get_data(), test_xd)
assert_array_equal(yd.get_data(), test_yd)
self.assertEqual(self.data_source.get_bounds(),
((min(test_xd),min(test_yd)),
(max(test_xd),max(test_yd))))
def test_metadata(self):
self.assertEqual(self.data_source.metadata,
{'annotations': [], 'selections': []})
def test_metadata_changed(self):
with self.assertTraitChanges(self.data_source, 'metadata_changed', count=1):
self.data_source.metadata = {'new_metadata': True}
def test_metadata_items_changed(self):
with self.assertTraitChanges(self.data_source, 'metadata_changed', count=1):
self.data_source.metadata['new_metadata'] = True
class ImagePlot(HasTraits):
#create UI interface
plot = Instance(GridPlotContainer)
LineScans = Instance(GridPlotContainer)
value = Str
saved = Bool(True)
filedir = Str
filename = Str
path = Str
xLo = Str
xHi = Str
yLo = Str
yHi = Str
Cmin = Str
Cmax = Str
Cmin2 = Str
Cmax2 = Str
loaded = False
Rtrue = Bool(True)
R2true = Bool(False)
Fluortrue = Bool(False)
#Catch variables
bounds = []
xLow = []
yLow = []
xHigh = []
yHigh = []
vscale = None
notes = Str
#UI buttons
reload_button = Button("Reload")
save_plot_button = Button("Save Plots..")
save_LineScans_button = Button("Save LineScans..")
load_button = Button("Load")
load1_button =Button("Load 1")
load2_button =Button("Load 2")
load3_button =Button("Load 3")
load4_button =Button("Load 4")
catch_bounds = Button("Catch Bounds")
to_csv = Button("Generate .CSV")
scale_set = Button("Set Scale")
reset = Button("Reset")
nextfile = Button("Next")
prevfile = Button("Prev")
flatten = Button("Flatten")
readline = Button("LineScan")
colormap = Button("ApplyColorMap")
genfilelist = Button("Generate File List")
out_to_mat = Button("Generate .mat")
special = Button("Special")
presmooth = Bool
plot1button = Bool
plot2button = Bool
plot3button = Bool
plot4button = Bool
fastline = Str
slowline = Str
fastscanline = 0
slowscanline = 0
fastline = str(fastscanline)
slowline = str(slowscanline)
fastlinevar = Str
fastlinemean = Str
fastlinemax = Str
fastlinemin = Str
slowlinevar = Str
slowlinemean = Str
slowlinemax = Str
slowlinemin = Str
x_f = Bool(label = "Fast Axis x:")
y_f = Bool(label = "y:")
z_f = Bool(label = "z:")
x_s = Bool(label = "Slow Axis x:")
y_s = Bool(label = "y:")
z_s = Bool(label = "z:")
xbounds=None
ybounds=None
#wildcard patterns
file_wildcard = Str("zis File (*.zis)|*.zis|All files|*")
file_wildcard2 = Str("png File (*.png)|*.png|All files|*")
file_wildcard3 = Str("mat File (*.mat)|*.mat|All files|*")
fastlinevars = Group( Item('fastlinevar'),
Item('fastlinemean'),
Item('fastlinemax'),
Item('fastlinemin'),show_border=True)
slowlinevars = Group( Item('slowlinevar'),
Item('slowlinemean'),
Item('slowlinemax'),
Item('slowlinemin'),show_border=True)
linescangroup = HGroup(Item('LineScans', editor=ComponentEditor(),show_label=False),
VGroup(
Item(name="plte", style = 'simple'),
HGroup(
Item('slowline'),
Item('fastline'),),
UItem('readline'),
fastlinevars,
slowlinevars
),label = "LineScan")
colorgroup = VGroup(HGroup(
Item('Cmin',width = -50),
Item('Cmax',width = -50),
Item('Cmin2',width = -50),
Item('Cmax2',width = -50),
),
HGroup(
UItem('colormap'),
UItem('genfilelist'),
UItem('out_to_mat'),
UItem('special'),
),
show_border =True)
tabs = Group(Item('plot', editor=ComponentEditor(),show_label=False),
linescangroup,
Item('notes', style = 'custom', width=.1), layout = "tabbed")
plta = Enum("R", "Phase", "R0","R/R0","Fluor", "X", "Y")
pltb = Enum("R", "Phase", "R0","R/R0","Fluor", "X", "Y")
pltc = Enum("R", "Phase", "R0","R/R0","Fluor", "X", "Y")
pltd = Enum("R", "Phase", "R0","R/R0","Fluor", "X", "Y")
plte = Enum("R", "Phase", "R0","R/R0","Fluor", "X", "Y")
lefttop = Group(
VGroup(
HGroup(
Item('value', label = "File", width = 300),
Item('presmooth'),
),
HGroup(
UItem('save_plot_button'),
UItem('save_LineScans_button'),
UItem('load_button'),
UItem('nextfile'),
UItem('prevfile'),
),
HGroup(
Item(name="plta", style = 'simple'),
Item(name="pltb", style = 'simple'),
Item(name="pltc", style = 'simple'),
Item(name="pltd", style = 'simple'),
UItem('reload_button'),
),
),
)
righttop = Group(
VGroup(
HGroup(
Item('xLo',width = -50, height = 25),
Item('xHi',width = -50),
Item('yLo',width = -50, height = 25),
Item('yHi',width = -50),
),
HGroup(
UItem('flatten'),
UItem('catch_bounds'),
UItem('scale_set'),
),
HGroup(
UItem("load1_button"),
UItem("load2_button"),
UItem("load3_button"),
UItem("load4_button"),
),
),
show_border = True)
traits_view = View(
VGroup(
HGroup(
lefttop,
righttop,
colorgroup,
),
tabs,
),
width=1200, height=700, resizable=True, title="Scan Image Reconstruction")
#USED DICTIONARY
plotA = {"plot": plta, "data" : "", "shape" : "", "range" : np.zeros((2,2))}
plotB = {"plot": pltb, "data" : "", "shape" : "", "range" : np.zeros((2,2))}
plotC = {"plot": pltc, "data" : "", "shape" : "", "range" : np.zeros((2,2))}
plotD = {"plot": pltd, "data" : "", "shape" : "", "range" : np.zeros((2,2))}
plotE = {"plot": plte, "data" : "", "shape" : "", "range" : np.zeros((2,2))}
def _Rtrue_changed(self):
if self.Rtrue:
self.R2true = False
self.Fluortrue = False
def _R2true_changed(self):
if self.R2true:
self.Rtrue = False
self.Fluortrue = False
def _Fluortrue_changed(self):
if self.Fluortrue:
self.Rtrue = False
self.R2true = False
def _plot1button_changed(self):
if self.plot1button:
self.plot2button = False
self.plot3button = False
self.plot4button = False
self._plot2()
def _plot2button_changed(self):
if self.plot2button:
self.plot1button=False
self.plot3button = False
self.plot4button = False
self._plot2()
def _plot3button_changed(self):
if self.plot3button:
self.plot1button=False
self.plot2button = False
self.plot4button = False
self._plot2()
def _plot4button_changed(self):
if self.plot4button:
self.plot1button=False
self.plot2button = False
self.plot3button = False
self._plot2()
def _colormap_fired(self):
self.plot1.color_mapper.range.low = float(self.Cmin)
self.plot1.color_mapper.range.high = float(self.Cmax)
self.plot2.color_mapper.range.low = float(self.Cmin2)
self.plot2.color_mapper.range.high = float(self.Cmax2)
self.plot4.color_mapper.range.low = float(self.Cmin)
self.plot4.color_mapper.range.high = float(self.Cmax)
if self.plot1button or self.plot3button or self.plot4button:
self.plot1lines.color_mapper.range.low = float(self.Cmin)
self.plot1lines.color_mapper.range.high = float(self.Cmax)
if self.plot2button:
self.plot1lines.color_mapper.range.low = float(self.Cmin2)
self.plot1lines.color_mapper.range.high = float(self.Cmax2)
#self._plot()
print "Color Mapped"
def _scale_set_fired(self):
self.plot1.range2d.x_range.low = float(self.xLo)
self.plot1.range2d.x_range.high = float(self.xHi)
self.plot1.range2d.y_range.low = float(self.yLo)
self.plot1.range2d.y_range.high = float(self.yHi)
self.plot2.range2d.x_range.low = float(self.xLo)
self.plot2.range2d.x_range.high = float(self.xHi)
self.plot2.range2d.y_range.low = float(self.yLo)
self.plot2.range2d.y_range.high = float(self.yHi)
self.plot3.range2d.x_range.low = float(self.xLo)
self.plot3.range2d.x_range.high = float(self.xHi)
self.plot3.range2d.y_range.low = float(self.yLo)
self.plot3.range2d.y_range.high = float(self.yHi)
self.plot4.range2d.x_range.low = float(self.xLo)
self.plot4.range2d.x_range.high = float(self.xHi)
self.plot4.range2d.y_range.low = float(self.yLo)
self.plot4.range2d.y_range.high = float(self.yHi)
self.plot1lines.range2d.x_range.low = float(self.xLo)/self.plotE["range"][0][1]*self.plotE["shape"][0]
self.plot1lines.range2d.x_range.high = float(self.xHi)/self.plotE["range"][0][1]*self.plotE["shape"][0]
self.plot1lines.range2d.y_range.low = float(self.yLo)/self.plotE["range"][1][1]*self.plotE["shape"][1]
self.plot1lines.range2d.y_range.high = float(self.yHi)/self.plotE["range"][1][1]*self.plotE["shape"][1]
self.slow_plot.range2d.x_range.low = float(self.xLo)
self.slow_plot.range2d.x_range.high = float(self.xHi)
self.fast_plot.range2d.x_range.low = float(self.yLo)
self.fast_plot.range2d.x_range.high = float(self.yHi)
def _reset_fired(self):
self.vscale = None
self.Cmin = ""
self.Cmax = ""
self._refresh()
def _value_changed(self):
#self.saved = False
self.value = self.value
def _refresh(self):
try: self._plot()
except: print "Option will be applied when plotting"
def _readline_fired(self):
#select which line to scan (this should be input as int value of line number until changed)
self.fastscanline = int(self.fastline)#float(self.slowline)*float(self.dataset["range"][0][1])/self.dataset["shape"][0])
self.slowscanline = int(self.slowline)#float(self.fastline)*float(self.dataset["range"][1][1])/self.dataset["shape"][1])
slowstart = int(float(self.xLo)/float(self.plotE["range"][0][1])*self.plotE["shape"][0])
slowend = int(float(self.xHi)/float(self.plotE["range"][0][1])*self.plotE["shape"][0]-1)
faststart = int(float(self.yLo)/float(self.plotE["range"][1][1])*self.plotE["shape"][1])
fastend = int(float(self.yHi)/float(self.plotE["range"][1][1])*(self.plotE["shape"][1])-1)
fastarray = np.array(self._image_value.data[:,int(self.slowline)])
slowarray = np.array(self._image_value.data[int(self.fastline),:])
self.pd.set_data("line_value2",
self._image_value.data[:,self.fastscanline])
self.pd.set_data("line_value",
self._image_value.data[self.slowscanline,:])
self.fastlinevar = str(np.std(fastarray))
self.fastlinemean = str(np.mean(fastarray))
self.fastlinemax = str(np.amax(fastarray))
self.fastlinemin = str(np.amin(fastarray))
self.slowlinevar = str(np.std(slowarray))
self.slowlinemean = str(np.mean(slowarray))
self.slowlinemax = str(np.amax(slowarray))
self.slowlinemin = str(np.amin(slowarray))
self.slow_plot.title = "Slowline : " + str(self.fastscanline)
self.fast_plot.title = "Fastline : " + str(self.fastscanline)
def _flatten_fired(self):
self._flatten()
def _out_to_mat_fired(self):
dialog = FileDialog(default_filename = self.filename+"_MATLAB_Phase"+str(self.plotA["notes"]["start"][2]), action="save as", wildcard=self.file_wildcard3)
dialog.open()
if dialog.return_code == OK:
savefiledir = dialog.directory
savefilename = dialog.filename
path = os.path.join(savefiledir, savefilename)
dataset = {self.plotA, self.plotB, self.plotC, self.plotD}
scio.savemat(path, dataset, True)
def _flatten(self):
y=0
x=0
for line in self.plotA['data']:
x_axis = np.arange(0,int(len(line)))
y_axis = np.array(line)
slope,intercept,r_value,p_value,std_err = stats.linregress(x_axis,y_axis)
x=0
for point in line:
self.plotA['data'][y,x] = point - (slope*x + intercept)
x+=1
y+=1
y=0
x=0
for line in self.plotB['data']:
x_axis = np.arange(0,int(len(line)))
y_axis = np.array(line)
slope,intercept,r_value,p_value,std_err = stats.linregress(x_axis,y_axis)
x=0
for point in line:
self.plotB['data'][y,x] = point - (slope*x + intercept)
x+=1
y+=1
y=0
x=0
for line in self.plotC['data']:
x_axis = np.arange(0,int(len(line)))
y_axis = np.array(line)
slope,intercept,r_value,p_value,std_err = stats.linregress(x_axis,y_axis)
x=0
for point in line:
self.plotC['data'][y,x] = point - (slope*x + intercept)
x+=1
y+=1
y=0
x=0
for line in self.plotD['data']:
x_axis = np.arange(0,int(len(line)))
y_axis = np.array(line)
slope,intercept,r_value,p_value,std_err = stats.linregress(x_axis,y_axis)
x=0
for point in line:
self.plotD['data'][y,x] = point - (slope*x + intercept)
x+=1
y+=1
self._plot()
print "Flattened"
def _catch_bounds_fired(self):
try:
self.xLow = float(self.plot1.range2d.x_range.low)
self.xHigh = float(self.plot1.range2d.x_range.high)
self.yLow = float(self.plot1.range2d.y_range.low)
self.yHigh = float(self.plot1.range2d.y_range.high)
self.xLo = str(self.xLow)
self.xHi = str(self.xHigh)
self.yLo = str(self.yLow)
self.yHi = str(self.yHigh)
except: print "Please plot first"
def _save_plot_button_fired(self):
dialog = FileDialog(default_filename = self.filename+"_Plots_", action="save as", wildcard=self.file_wildcard2)
dialog.open()
if dialog.return_code == OK:
savefiledir = dialog.directory
savefilename = dialog.filename
path = os.path.join(savefiledir, savefilename)
#self.plot.do_layout(force=True)
plot_gc = PlotGraphicsContext(self.plot.outer_bounds)
plot_gc.render_component(self.plot)
plot_gc.save(path)
def _save_LineScans_button_fired(self):
dialog = FileDialog(default_filename = self.filename+"_LineScan_", action="save as", wildcard=self.file_wildcard2)
dialog.open()
if dialog.return_code == OK:
savefiledir = dialog.directory
savefilename = dialog.filename
path = os.path.join(savefiledir, savefilename)
self.LineScans.do_layout(force=True)
plot_gc = PlotGraphicsContext(self.LineScans.outer_bounds)
plot_gc.render_component(self.LineScans)
plot_gc.save(path)
def _load_button_fired(self):
dialog = FileDialog(action="open", wildcard=self.file_wildcard)
dialog.open()
if dialog.return_code == OK:
self.value = dialog.filename
title = self.value
self.path = dialog.path
self.filedir = dialog.directory
self.allfiles =[]
for filenames in os.walk(self.filedir):
for files in filenames:
for afile in files:
if ".zis" in str(afile):
if ".png" not in str(afile)and ".mat" not in str(afile):
self.allfiles.append(self.filedir+"\\"+afile)
self.filename = dialog.filename
self.saved = True
self.loaded = True
self._loader(self.path)
self._plot()
def _nextfile_fired(self):
if self.loaded:
nextone = False
path2=self.path
## self.allfiles =[]
## for filenames in os.walk(self.filedir):
## for files in filenames:
## for afile in files:
## if ".zis" in str(afile):
## if ".png" not in str(afile):
## self.allfiles.append(self.filedir+"\\"+afile)
for afile in self.allfiles:
if nextone == True:
self.path = afile
junk,self.value = afile.split(self.filedir+"\\")
self.filename =self.value
self._loader(self.path)
self._plot()
nextone=False
break
if afile == path2:
nextone = True
def _prevfile_fired(self):
if self.loaded:
nextone = False
path2=self.path
for afile in self.allfiles:
if afile == path2:
self.path = prevfile
junk,self.value = prevfile.split(self.filedir+"\\")
self.filename = self.value
self._loader(self.path)
self._plot()
break
prevfile = afile
def _genfilelist_fired(self):
if self.loaded:
event = {'trial': 0 , "settings" : "", "notes": "", "time": ""}
eventlist = {"Description": "", "0" : event}
i=1
for afile in self.allfiles:
#grab file name
junk,currentfilename = afile.split(self.filedir+"\\")
print "Working on file : " + currentfilename
#unpickle file and grab data
try:
currentfile = open(afile,'rb')
data = pickle.load(currentfile)
currentfile.close()
foldername,time = currentfilename.split("_")
time,junk = time.split(".")
settings = data['settings']['scan']
strsettings = ""
for key, value in settings.iteritems() :
strsettings += str(key) + " " + str(value)+ "\n"
newtrial = {'trial': i, "settings" : strsettings, "notes": "", "time": time}
eventlist[str(i)] = newtrial
i +=1
except:
print "\tcorrupt file, skipping"
settings = ""
strsettings =""
newtrial = ""
#save data to data logger compatible file
a = os.getcwd() + "/eventlist_"+foldername
if not os.path.isdir(a):
print "made"
os.makedirs(a)
b = a+ "/filelist.log"
f1 = open(b, "w")
pickle.dump(eventlist, f1)
f1.close()
print "File Write Complete"
else:
print "Please load a folder first"
def _reload_button_fired(self):
self._loader(self.path)
self._plot()
#-----------------------------------------------
# Private API
#-----------------------------------------------
def _plot(self):
print "...plotting"
self.notes = ""
for key, value in self.plotA["notes"].iteritems() :
self.notes += str(key) + " " + str(value)+ "\n"
self.container1 = GridPlotContainer(shape = (2,4), spacing = (0,0), use_backbuffer=True,
valign = 'top', bgcolor = 'white')
print"\t assigning data"
self.plotdata1 = ArrayPlotData(imagedata = self.plotA["data"])
self.plotdata2 = ArrayPlotData(imagedata = self.plotB["data"])
self.plotdata3 = ArrayPlotData(imagedata = self.plotC["data"])
self.plotdata4 = ArrayPlotData(imagedata = self.plotD["data"])
print"\t calling names"
self.plot1 = Plot(self.plotdata1, title = self.plotA["plot"]+ str(self.plotA["notes"]["start"]))
self.plot2 = Plot(self.plotdata2, title = self.plotB["plot"])
self.plot3 = Plot(self.plotdata3, title = self.plotC["plot"])
self.plot4 = Plot(self.plotdata4, title = self.plotD["plot"])
self.plot1.img_plot("imagedata", xbounds = (self.plotA["range"][0][0],self.plotA["range"][0][1]), ybounds = (self.plotA["range"][1][0],self.plotA["range"][1][1]))
self.plot2.img_plot("imagedata", xbounds = (self.plotB["range"][0][0],self.plotB["range"][0][1]), ybounds = (self.plotB["range"][1][0],self.plotB["range"][1][1]))
self.plot3.img_plot("imagedata", xbounds = (self.plotC["range"][0][0],self.plotC["range"][0][1]), ybounds = (self.plotC["range"][1][0],self.plotC["range"][1][1]))
self.plot4.img_plot("imagedata", xbounds = (self.plotD["range"][0][0],self.plotD["range"][0][1]), ybounds = (self.plotD["range"][1][0],self.plotD["range"][1][1]))
# self.scale = Str(self.plot3.color_mapper.high)
# plot1.index_axis.title = str(f) + ' (um)'
##ADD TOOLS
self.plot1.tools.append(PanTool(self.plot1))
zoom1 = ZoomTool(component=self.plot1, tool_mode="box", always_on=False)
self.plot1.overlays.append(zoom1)
self.plot2.tools.append(PanTool(self.plot2))
zoom2 = ZoomTool(component=self.plot2, tool_mode="box", always_on=False)
self.plot2.overlays.append(zoom2)
self.plot3.tools.append(PanTool(self.plot3))
zoom3 = ZoomTool(component=self.plot3, tool_mode="box", always_on=False)
self.plot3.overlays.append(zoom3)
self.plot4.tools.append(PanTool(self.plot4))
zoom4 = ZoomTool(component=self.plot4, tool_mode="box", always_on=False)
self.plot4.overlays.append(zoom4)
##ADD COLORBARS
self.colorbar1 = ColorBar(index_mapper=LinearMapper(range=self.plot1.color_mapper.range),
color_mapper=self.plot1.color_mapper,
orientation='v',
resizable='v',
width=20,
padding=5)
self.colorbar1.plot = self.plot1
self.colorbar1.padding_left = 45
self.colorbar1.padding_right= 5
self.colorbar2 = ColorBar(index_mapper=LinearMapper(range=self.plot2.color_mapper.range),
color_mapper=self.plot2.color_mapper,
orientation='v',
resizable='v',
width=20,
padding=5)
self.colorbar2.plot = self.plot2
self.colorbar2.padding_left = 10
self.colorbar3 = ColorBar(index_mapper=LinearMapper(range=self.plot3.color_mapper.range),
color_mapper=self.plot3.color_mapper,
orientation='v',
resizable='v',
width=20,
padding=5)
self.colorbar3.plot = self.plot3
self.colorbar3.padding_left = 45
self.colorbar3.padding_right= 5
self.colorbar4 = ColorBar(index_mapper=LinearMapper(range=self.plot4.color_mapper.range),
color_mapper=self.plot4.color_mapper,
orientation='v',
resizable='v',
width=20,
padding=5)
self.colorbar4.plot = self.plot4
self.colorbar4.padding_left = 15
self.colorbar1.tools.append(PanTool(self.colorbar1, constrain_direction="y", constrain=True))
self.zoom_overlay1 = ZoomTool(self.colorbar1, axis="index", tool_mode="range",
always_on=True, drag_button="right")
self.colorbar1.overlays.append(self.zoom_overlay1)
self.colorbar2.tools.append(PanTool(self.colorbar2, constrain_direction="y", constrain=True))
self.zoom_overlay2 = ZoomTool(self.colorbar2, axis="index", tool_mode="range",
always_on=True, drag_button="right")
self.colorbar2.overlays.append(self.zoom_overlay2)
self.colorbar3.tools.append(PanTool(self.colorbar3, constrain_direction="y", constrain=True))
self.zoom_overlay3 = ZoomTool(self.colorbar3, axis="index", tool_mode="range",
always_on=True, drag_button="right")
self.colorbar3.overlays.append(self.zoom_overlay3)
self.colorbar4.tools.append(PanTool(self.colorbar4, constrain_direction="y", constrain=True))
self.zoom_overlay4 = ZoomTool(self.colorbar4, axis="index", tool_mode="range",
always_on=True, drag_button="right")
self.colorbar4.overlays.append(self.zoom_overlay4)
self.container1.add(self.colorbar1)
self.container1.add(self.plot1)
self.container1.add(self.plot2)
self.container1.add(self.colorbar2)
self.container1.add(self.colorbar3)
self.container1.add(self.plot3)
self.container1.add(self.plot4)
self.container1.add(self.colorbar4)
self.plot1.padding_right = 5
self.plot2.padding_left = 5
self.plot1.padding_bottom = 15
self.plot2.padding_bottom = 15
self.plot3.padding_top = 15
self.plot4.padding_top = 15
self.plot1.x_axis.orientation = "top"
self.plot2.x_axis.orientation = "top"
self.plot2.y_axis.orientation = "right"
self.plot3.padding_right = 5
self.plot4.padding_left = 5
self.plot4.y_axis.orientation = "right"
self.colorbar1.padding_top = self.plot1.padding_top
self.colorbar1.padding_bottom = self.plot1.padding_bottom
self.colorbar2.padding_top = self.plot2.padding_top
self.colorbar2.padding_bottom = self.plot2.padding_bottom
self.colorbar3.padding_top = self.plot3.padding_top
self.colorbar3.padding_bottom = self.plot3.padding_bottom
self.colorbar4.padding_top = self.plot4.padding_top
self.colorbar4.padding_bottom = self.plot4.padding_bottom
imgtool = ImageInspectorTool(self.plot1)
self.plot1.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=self.plot1, image_inspector=imgtool,
bgcolor="white", border_visible=True)
self.plot1.overlays.append(overlay)
self.plot = self.container1
self._plot2()
#######line plots##############################################################################################
def _plot2(self):
print"...plotting line scans"
title = str(self.plotE['plot']) + str(self.plotE["notes"]["start"])
self.plotdata5 = ArrayPlotData(imagedata = self.plotE['data'])
self._image_index = GridDataSource(array([]),
array([]),
sort_order=("ascending","ascending"))
self.xs = linspace(self.plotE["range"][0][0], self.plotE["range"][0][1], self.plotE["shape"][0])
self.ys = linspace(self.plotE["range"][1][0], self.plotE["range"][1][1], self.plotE["shape"][1])
self._image_index.set_data(self.xs, self.ys)
image_index_range = DataRange2D(self._image_index)
self._image_value = ImageData(data=array([]), value_depth=1)
self._image_value.data = self.plotE['data']
image_value_range = DataRange1D(self._image_value)
s = ""
f = ""
if self.x_s: s = "X"
if self.y_s: s = "Y"
if self.z_s: s = "Z"
if self.x_f: f = "X"
if self.y_f: f = "Y"
if self.z_f: f = "Z"
self.plot1lines = Plot(self.plotdata5, title = title)
self.plot1lines.img_plot("imagedata", xbounds = (self.plotE["range"][0][0],self.plotE["range"][0][1]), ybounds = (self.plotE["range"][1][0],self.plotE["range"][1][1]), colormap=jet)
img_plot = self.plot1lines.img_plot("imagedata")[0]
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True)
self.plot1lines.overlays.append(overlay)
##ADD TOOLS
self.plot1lines.tools.append(PanTool(self.plot1lines))
zoom1 = ZoomTool(component=self.plot1lines, tool_mode="box", always_on=False)
self.plot1lines.overlays.append(zoom1)
self.plot1lines.overlays.append(LineInspector(component=self.plot1lines,
axis='index_x',
inspect_mode="indexed",
write_metadata=True,
is_listener=False,
#constrain_key="right",
color="white"))
self.plot1lines.overlays.append(LineInspector(component=self.plot1lines,
axis='index_y',
inspect_mode="indexed",
write_metadata=True,
color="white",
is_listener=False))
##ADD COLORBAR
self.colorbar5 = ColorBar(index_mapper=LinearMapper(range=self.plot1lines.color_mapper.range),
color_mapper=self.plot1lines.color_mapper,
orientation='v',
resizable='v',
width=20,
padding=5)
self.colorbar5.plot = self.plot1lines
self.colorbar5.tools.append(PanTool(self.colorbar5, constrain_direction="y", constrain=True))
self.zoom_overlay5 = ZoomTool(self.colorbar5, axis="index", tool_mode="range",
always_on=True, drag_button="right")
self.colorbar5.overlays.append(self.zoom_overlay5)
self.pd = ArrayPlotData(line_index = array([]),
line_value = array([]))
self.slow_plot = Plot(self.pd, title = "Slowline : " + self.slowline)
self.slow_plot.plot(("line_index", "line_value"),
line_style='solid')
self.pd.set_data("line_index2", array([]))
self.pd.set_data("line_value2", array([]))
self.fast_plot = Plot(self.pd, title = "Fastline : " + self.fastline)
self.fast_plot.plot(("line_index2", "line_value2"),
line_style='solid')
self.pd.set_data("line_index", self.xs)
self.pd.set_data("line_index2", self.ys)
self.pd.set_data("line_value",
self._image_value.data[self.fastscanline,:])
self.pd.set_data("line_value2",
self._image_value.data[:,self.slowscanline])
self.colorbar5.padding= 0
self.colorbar5.padding_left = 15
#self.colorbar5.height = 400
self.colorbar5.padding_top =50
self.colorbar5.padding_bottom = 0
self.colorbar5.padding_right = 25
self.colorbar5.padding_left = 50
self.plot1lines.width = 300
self.plot1lines.padding_top = 50
self.plot1lines.index_axis.title = 'fast axis (um)'
self.plot1lines.value_axis.title = 'slow axis (um)'
self.slow_plot.width = 100
self.slow_plot.padding_right = 20
self.fast_plot.width = 100
self.fast_plot.padding_right = 20
self.container2 = GridPlotContainer(shape = (1,2), spacing = ((0,0)), use_backbuffer=True,
valign = 'top', halign = 'center', bgcolor = 'white')
self.container3 = GridPlotContainer(shape = (2,1), spacing = (0,0), use_backbuffer=True,
valign = 'top', halign = 'center', bgcolor = 'grey')
self.container4 = GridPlotContainer(shape = (1,2), spacing = (0,0), use_backbuffer=True,
valign = 'top', halign = 'center', bgcolor = 'grey')
self.container2.add(self.colorbar5)
self.container3.add(self.fast_plot)
self.container3.add(self.slow_plot)
self.container4.add(self.container3)
self.container4.add(self.plot1lines)
self.container2.add(self.container4)
self.LineScans = self.container2
self._readline_fired()
self._scale_set_fired()
def _load_file(self,i):
file = open(str(i),'rb')
print "\n\n" + str(i) + " is open"
self.data = pickle.load(file)
file.close()
print "\nfile.closed"
return
#########################################################
def _special_fired(self):
#load stitch
print "All resolutions must be the same"
a,b = 2,3 #raw_input("\tset rows and cols: ")
size = 512 #raw_input("\tsize:")
self.stitchdataA = np.zeros((size*a, size*b))
self.stitchdataB = np.zeros((size*a, size*b))
i = 1
col = 0
while i < a*b:
j = 1
row = 0
while j < b:
self._load_file(self._single_load())
self.plotA['plot'] = self.plta
self.plotA = self._pick_plots(self.plotA)
print col*size, row*size
self.stitchdataA[col*size : col*self.plotA["shape"][0], row*size : row*self.plotA["shape"][1]] = self.plotA
row = row+1
j = j+1
i = i+1
col = col+1
i = 1
col = 0
while i < a*b:
j = 1
row = 0
while j < b:
self._load_file(self._single_load())
self.plotB['plot'] = self.pltb
self.plotB = self._pick_plots(self.plotB)
self.stitchdataB[col*size : col*self.plotB["shape"][0], row*size : row*self.plotB["shape"][1]] = self.plotB
row = row+1
j = j+1
i = i+1
col = col+1
self.plotA["data"] = self.stitchdataA
self.plotB["data"] = self.stitchdataB
self.plotC["data"] = self.stitchdataA
self.plotD["data"] = self.stitchdataB
self.plotA["range"][0][0] = 0
self.plotA["range"][1][0] = 0
self.plotA["range"][0][1] = size*a
self.plotA["range"][1][1] = size*b
self.plotA["shape"] = (size*b, size*a)
self._plot()
gc.collect()
return
########################################################
def _pick_plots(self, plotdata):
#plotA = Enum("R", "Phase", "R0","R/R0","Fluor", "X", "Y")
print "...loading plot"
#gather shape info
plotdata["notes"] = self.data['settings']['scan']
print "\t filling shapes"
for x in xrange(3):
if plotdata["notes"]['axes'][x] == 0:
fast = plotdata["notes"]['npoints'][x]
plotdata["range"][0][1] = plotdata["notes"]["fast_axis_range"]
if plotdata["notes"]['axes'][x] == 1:
slow = plotdata["notes"]['npoints'][x]
plotdata["range"][1][1] = plotdata["notes"]['range'][x]
plotdata["shape"] =(fast,slow)
print "\t filling data"
data =np.zeros((fast,slow))
try:
if plotdata['plot']== "R":
print "\t\tplotting R"
data = np.sqrt(np.multiply(np.array(self.data['data']['lia_x']["0"]),np.array(self.data['data']['lia_x']["0"])) + np.multiply(np.array(self.data['data']['lia_y']["0"]),np.array(self.data['data']['lia_y']["0"])))
if plotdata['plot'] == "Phase":
print "\t\tplotting Phase"
data = np.arctan2(np.array(self.data['data']['lia_y']["0"]),np.array(self.data['data']['lia_x']["0"]))
if plotdata['plot']== "R0":
print "\t\tplotting R0"
data = np.sqrt(np.multiply(np.array(self.data['data']['lia_x']["3"]),np.array(self.data['data']['lia_x']["3"])) + np.multiply(np.array(self.data['data']['lia_y']["3"]),np.array(self.data['data']['lia_y']["3"])))
if plotdata['plot']== "R/R0":
print "\t\tplotting R/R0"
data = np.sqrt(np.multiply(np.array(self.data['data']['lia_x']["0"]),np.array(self.data['data']['lia_x']["0"])) + np.multiply(np.array(self.data['data']['lia_y']["0"]),np.array(self.data['data']['lia_y']["0"])))/np.sqrt(np.multiply(np.array(self.data['data']['lia_x']["3"]),np.array(self.data['data']['lia_x']["3"])) + np.multiply(np.array(self.data['data']['lia_y']["3"]),np.array(self.data['data']['lia_y']["3"])))
if plotdata['plot']== "Fluor":
print "\t\tplotting Fluor"
data = np.array(self.data['data']['dio2'])
if plotdata['plot']=="X":
print "\t\tplotting X"
data = np.array(self.data['data']['lia_x']["0"])
if plotdata['plot']=="Y":
print "\t\tplotting Y"
data = np.array(self.data['data']['lia_y']["0"])
except:
print "Process failed-- check dropdown assignments"
data.shape = (plotdata["shape"])
plotdata['data'] = data
return plotdata
def _single_load(self):
dialog = FileDialog(action="open", wildcard=self.file_wildcard)
dialog.open()
if dialog.return_code == OK:
return dialog.path
def _loader(self, i):
#open file with specified path unpickle and prepare to pass to dictionary
self._load_file(i)
#--APPLY LOGIC CONDITIONS TO DATA AND ASSIGN TO PLOTING 2D ARRAYS
self.plotA['plot'] = self.plta
self.plotB['plot'] = self.pltb
self.plotC['plot'] = self.pltc
self.plotD['plot'] = self.pltd
self.plotE['plot'] = self.plte
self.plotA = self._pick_plots(self.plotA)
self.plotB = self._pick_plots(self.plotB)
self.plotC = self._pick_plots(self.plotC)
self.plotD = self._pick_plots(self.plotD)
self.plotE = self._pick_plots(self.plotE)
if self.xLo == "":
print "...populating ranges"
self.xLo = str(self.plotA["range"][0][0])
self.xHi = str(self.plotA["range"][0][1])
self.yLo = str(self.plotA["range"][1][0])
self.yHi = str(self.plotA["range"][1][1])
gc.collect()
return
def _load1_button_fired(self):
#open file with specified path unpickle and prepare to pass to dictionary
self._load_file(self._single_load())
#--APPLY LOGIC CONDITIONS TO DATA AND ASSIGN TO PLOTING 2D ARRAYS
self.plotA['plot'] = self.plta
self.plotA = self._pick_plots(self.plotA)
gc.collect()
self._plot()
return
def _load2_button_fired(self):
#open file with specified path unpickle and prepare to pass to dictionary
self._load_file(self._single_load())
#--APPLY LOGIC CONDITIONS TO DATA AND ASSIGN TO PLOTING 2D ARRAYS
self.plotB['plot'] = self.pltb
self.plotB = self._pick_plots(self.plotB)
gc.collect()
self._plot()
return
def _load3_button_fired(self):
#open file with specified path unpickle and prepare to pass to dictionary
self._load_file(self._single_load())
#--APPLY LOGIC CONDITIONS TO DATA AND ASSIGN TO PLOTING 2D ARRAYS
self.plotC['plot'] = self.pltc
self.plotC = self._pick_plots(self.plotC)
gc.collect()
self._plot()
return
def _load4_button_fired(self):
#open file with specified path unpickle and prepare to pass to dictionary
self._load_file(self._single_load())
#--APPLY LOGIC CONDITIONS TO DATA AND ASSIGN TO PLOTING 2D ARRAYS
self.plotD['plot'] = self.pltd
self.plotD = self._pick_plots(self.plotD)
gc.collect()
self._plot()
return
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