def slice_plot( self, anat, coefs, **traits ):
p = Plot( self.plot_data, default_origin = 'bottom left' )
p2 = Plot( self.plot_data, default_origin = 'bottom left' )
p.x_axis.visible = False; p2.x_axis.visible = False
p.y_axis.visible = False; p2.y_axis.visible = False
bounds = self.plot_data.get_data(anat).shape
asp = float( bounds[1] ) / float( bounds[0] )
p.img_plot( anat,
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
colormap = chaco_colormaps.gray )
p2.img_plot( coefs,
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
# bgcolor = 'transparent',
colormap = self.cmap,
interpolation = 'nearest')
# p.aspect_ratio = asp; p2.aspect_ratio = asp
p.aspect_ratio = asp; p2.aspect_ratio = asp
subplot = OverlayPlotContainer( )
subplot.add( p )
subplot.add( p2 )
return subplot
def _create_plot_component():
# Create a GridContainer to hold all of our plots
container = GridContainer(padding=20, fill_padding=True,
bgcolor="lightgray", use_backbuffer=True,
shape=(3,3), spacing=(12,12))
# Create the initial series of data
x = linspace(-5, 15.0, 100)
pd = ArrayPlotData(index = x)
# Plot some bessel functions and add the plots to our container
for i in range(9):
pd.set_data("y" + str(i), jn(i,x))
plot = Plot(pd)
plot.plot(("index", "y" + str(i)),
color=tuple(COLOR_PALETTE[i]), line_width=2.0,
bgcolor = "white", border_visible=True)
# Tweak some of the plot properties
plot.border_width = 1
plot.padding = 10
# Set each plot's aspect ratio based on its position in the
# 3x3 grid of plots.
n,m = divmod(i, 3)
plot.aspect_ratio = float(n+1) / (m+1)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add to the grid container
container.add(plot)
return container
def __init__(self, signal_instance):
super(TemplatePicker, self).__init__()
try:
import cv
except:
print "OpenCV unavailable. Can't do cross correlation without it. Aborting."
return None
self.OK_custom=OK_custom_handler()
self.sig=signal_instance
if not hasattr(self.sig.mapped_parameters,"original_files"):
self.sig.data=np.atleast_3d(self.sig.data)
self.titles=[self.sig.mapped_parameters.name]
else:
self.numfiles=len(self.sig.mapped_parameters.original_files.keys())
self.titles=self.sig.mapped_parameters.original_files.keys()
tmp_plot_data=ArrayPlotData(imagedata=self.sig.data[self.top:self.top+self.tmp_size,self.left:self.left+self.tmp_size,self.img_idx])
tmp_plot=Plot(tmp_plot_data,default_origin="top left")
tmp_plot.img_plot("imagedata", colormap=jet)
tmp_plot.aspect_ratio=1.0
self.tmp_plot=tmp_plot
self.tmp_plotdata=tmp_plot_data
self.img_plotdata=ArrayPlotData(imagedata=self.sig.data[:,:,self.img_idx])
self.img_container=self._image_plot_container()
self.crop_sig=None
def _create_plot_component(self):
pd = self.pd
# Create the plot
plot = Plot(pd, default_origin="top left",orientation="h")
shape = pd.get_data('imagedata').shape
plot.aspect_ratio = float(shape[1]) / shape[0]
plot.x_axis.orientation = "top"
#plot.y_axis.orientation = "top"
#img_plot = plot.img_plot("imagedata",colormap = jet)[0]
img_plot = plot.img_plot("imagedata",name = 'image', colormap = jet)[0]
# Tweak some of the plot properties
#plot.bgcolor = "white"
plot.bgcolor = bg_color
# Attach some tools to the plot
plot.tools.append(PanTool(plot,constrain_key="shift", drag_button = 'right'))
printer = DataPrinter(component=plot, process = self.process_selection)
plot.tools.append(printer)
plot.overlays.append(ZoomTool(component=plot,
tool_mode="box", always_on=False))
#plot.title = 'Default image'
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
plot.overlays.append(ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool))
return plot
def _stage_map_default(self):
# RGBA maps
rep_map = array_to_rgba(self.PMap.stage_repr_map, cmap=cm.hot)
cov_map = array_to_rgba(self.PMap.stage_coverage_map, cmap=cm.gray)
# Data sources and plot object
data = ArrayPlotData(fields_x=self.fdata['x'], fields_y=self.fdata['y'],
fields_z=self.fdata['peak'], rep=rep_map, cov=cov_map)
p = Plot(data)
# Plot the field centers
p.plot(('fields_x', 'fields_y', 'fields_z'), name='centers', type='cmap_scatter',
marker='dot', marker_size=5, color_mapper=copper, line_width=1, fill_alpha=0.6)
# Plot the representation and coverage maps
p.img_plot('rep', name='rep', xbounds=(0, self.PMap.W), ybounds=(0, self.PMap.H),
origin='top left')
p.img_plot('cov', name='cov', xbounds=(0, self.PMap.W), ybounds=(0, self.PMap.H),
origin='top left')
# Start with only the representation map visible
p.plots['cov'][0].visible = False
p.plots['centers'][0].visible = False
# Plot tweaks
p.aspect_ratio = 1.0
p.y_axis.title = 'Y (cm)'
p.x_axis.title = 'X (cm)'
p.x_axis.orientation = 'bottom'
p.title = 'Stage Maps'
return p
def render_image(self):
plot = Plot(self.img_plotdata, default_origin="top left")
img = plot.img_plot("imagedata", colormap=gray)[0]
plot.title = "%s of %s: " % (self.img_idx + 1,
self.numfiles) + self.titles[self.img_idx]
plot.aspect_ratio = float(self.sig.data.shape[1]) / float(
self.sig.data.shape[0])
#if not self.ShowCC:
csr = CursorTool(img,
drag_button='left',
color='white',
line_width=2.0)
self.csr = csr
csr.current_position = self.left, self.top
img.overlays.append(csr)
# attach the rectangle tool
plot.tools.append(PanTool(plot, drag_button="right"))
zoom = ZoomTool(plot,
tool_mode="box",
always_on=False,
aspect_ratio=plot.aspect_ratio)
plot.overlays.append(zoom)
self.img_plot = plot
return plot
def __init__(self, signal_instance):
super(TemplatePicker, self).__init__()
try:
import cv
except:
print "OpenCV unavailable. Can't do cross correlation without it. Aborting."
return None
self.OK_custom = OK_custom_handler()
self.sig = signal_instance
if not hasattr(self.sig.mapped_parameters, "original_files"):
self.sig.data = np.atleast_3d(self.sig.data)
self.titles = [self.sig.mapped_parameters.name]
else:
self.numfiles = len(
self.sig.mapped_parameters.original_files.keys())
self.titles = self.sig.mapped_parameters.original_files.keys()
tmp_plot_data = ArrayPlotData(
imagedata=self.sig.data[self.top:self.top + self.tmp_size,
self.left:self.left + self.tmp_size,
self.img_idx])
tmp_plot = Plot(tmp_plot_data, default_origin="top left")
tmp_plot.img_plot("imagedata", colormap=jet)
tmp_plot.aspect_ratio = 1.0
self.tmp_plot = tmp_plot
self.tmp_plotdata = tmp_plot_data
self.img_plotdata = ArrayPlotData(
imagedata=self.sig.data[:, :, self.img_idx])
self.img_container = self._image_plot_container()
self.crop_sig = None
def _vr_image_plot_default(self):
plot = Plot(self.vr_image_data, default_origin="top left",
size=(512,512))
plot.aspect_ratio = 1.0
#plot.x_axis.orientation = "top"
img_plot = plot.img_plot("vr_image_data")[0]
plot.bgcolor = "black"
return plot
def _update_img_plot(self, plot_name, image, title):
"""Update an image plot."""
plotdata = ArrayPlotData(imagedata=image)
xbounds = (0, image.shape[1] - 1)
ybounds = (0, image.shape[0] - 1)
plot = Plot(plotdata)
plot.aspect_ratio = float(xbounds[1]) / float(ybounds[1])
plot.img_plot("imagedata", colormap=bone, xbounds=xbounds,
ybounds=ybounds)
plot.title = title
setattr(self, plot_name, plot)
getattr(self, plot_name).request_redraw()
def _update_img_plot(self, plot_name, image, title):
"""Update an image plot."""
plotdata = ArrayPlotData(imagedata=image)
xbounds = (0, image.shape[1] - 1)
ybounds = (0, image.shape[0] - 1)
plot = Plot(plotdata)
plot.aspect_ratio = float(xbounds[1]) / float(ybounds[1])
plot.img_plot("imagedata",
colormap=bone,
xbounds=xbounds,
ybounds=ybounds)
plot.title = title
setattr(self, plot_name, plot)
getattr(self, plot_name).request_redraw()
def _unit_map_default(self):
# Set the initial unit map
data = ArrayPlotData(unit_map=self._get_unit_map_data())
p = Plot(data)
# Plot the map
p.img_plot('unit_map', name='unit', xbounds=(0, self.PMap.W), ybounds=(0, self.PMap.H),
origin='top left')
# Plot tweaks
p.aspect_ratio = 1.0
p.y_axis.title = 'Y (cm)'
p.x_axis.title = 'X (cm)'
p.x_axis.orientation = 'bottom'
p.title = 'Single Unit Maps'
return p
def render_image(self):
plot = Plot(self.img_plotdata,default_origin="top left")
img=plot.img_plot("imagedata", colormap=gray)[0]
plot.title="%s of %s: "%(self.img_idx+1,self.numfiles)+self.titles[self.img_idx]
plot.aspect_ratio=float(self.sig.data.shape[1])/float(self.sig.data.shape[0])
#if not self.ShowCC:
csr = CursorTool(img, drag_button='left', color='white',
line_width=2.0)
self.csr=csr
csr.current_position=self.left, self.top
img.overlays.append(csr)
# attach the rectangle tool
plot.tools.append(PanTool(plot,drag_button="right"))
zoom = ZoomTool(plot, tool_mode="box", always_on=False, aspect_ratio=plot.aspect_ratio)
plot.overlays.append(zoom)
self.img_plot=plot
return plot
def rate_plot( self, rates, **traits ):
rp = Plot( self.rate_data, default_origin = 'bottom left' )
rp.x_axis.visible = False
rp.y_axis.visible = False
bounds = self.rate_data.get_data( rates ).shape
# rp.img_plot( rates,
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
# colormap = chaco_colormaps.jet,
# interpolation = 'nearest')
rp.contour_plot( rates,
type = 'line',
# xbounds = np.linspace( 0, 1, bounds[1] + 1 ),
# ybounds = np.linspace( 0, 1, bounds[0] + 1 ),
bgcolor = 'black',
levels = 15,
styles = 'solid',
widths = list( np.linspace( 4.0, 0.1, 15 )),
colors = gmt_drywet )
rp.aspect_ratio = 1
# zoom = ZoomTool( rp, tool_mode = 'box', always_on = False )
# rp.overlays.append(zoom)
# rp.tools.append( PanTool( rp, constrain_key = 'shift' ) )
subplot = OverlayPlotContainer( )
subplot.add( rp )
return subplot
def _plot_default(self):
outcomes, results, time = self._prepare_data()
self.outcomes = outcomes
self.time = time
self.low = int(np.min(time))
self.high = int(np.max(time))
self.data = {}
for outcome in outcomes:
self.data[outcome] = results[outcome]
# Create some data
pd = ArrayPlotData()
for entry in outcomes:
if self.startValue == 'low':
pd.set_data(entry, self.data[entry][:,0])
elif self.startValue == 'high':
pd.set_data(entry, self.data[entry][:,-1])
self.plotdata = pd
# outcomes = outcomes[0:3]
container = GridContainer(shape=(len(outcomes),len(outcomes)))
combis = [(field1, field2) for field1 in outcomes for field2 in outcomes]
selectorTools = []
for entry1, entry2 in combis:
# Create the plot
if entry1 == entry2:
plot = Plot(pd)
else:
plot = Plot(pd)
#set limits for x and y to global limits
plot.range2d._xrange._high_setting = np.max(self.data[entry1])
plot.range2d._xrange._high_value = np.max(self.data[entry1])
plot.range2d._xrange._low_setting = np.min(self.data[entry1])
plot.range2d._xrange._low_value = np.min(self.data[entry1])
plot.range2d._yrange._high_setting =np.max(self.data[entry2])
plot.range2d._yrange._high_value = np.max(self.data[entry2])
plot.range2d._yrange._low_setting = np.min(self.data[entry2])
plot.range2d._yrange._low_value = np.min(self.data[entry2])
#make the scatter plot
plot.plot((entry1, entry2),
type="scatter",
marker="circle",
color="blue",
marker_size=3,
bgcolor="white")[0]
tool = ScatterSelectorTool(component=plot)
tool._index = entry1
plot.tools.append(tool)
selectorTools.append(tool)
plot.height = 500
plot.width = 500
plot.border_width = 1
plot.aspect_ratio = 1.
container.add(plot)
for tool in selectorTools:
tool._other_selectors = list(set(selectorTools) - set([tool]))
tool._demo = self
return container
