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Allows isometric viewing of a 3D data cube.
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Click or click-drag in any data window to set the slice to view.
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# - need to add line inspectors to side and bottom plots, and synchronize
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# - need to set the various image plots to use the same colormap instance,
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# and that colormap's range needs to be set to min/max of the entire cube
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# - refactor create_window() so there is less code duplication
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# - try to eliminate the use of model.xs, ys, zs in favor of bounds tuples
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from numpy import amin, amax, zeros, fromfile, transpose, uint8
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# Standard library imports
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import os, sys, shutil
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# Major library imports
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from numpy import arange, linspace, nanmin, nanmax, newaxis, pi, sin, cos
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# Enthought library imports
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from enthought.chaco.api import ArrayPlotData, Plot, GridPlotContainer, \
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from enthought.chaco.default_colormaps import *
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from enthought.chaco.tools.api import LineInspector, SimpleZoom
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from enthought.enable.example_support import DemoFrame, demo_main
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from enthought.enable.api import Window
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from enthought.traits.api import Any, Array, Bool, Callable, CFloat, CInt, \
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Event, Float, HasTraits, Int, Trait, on_trait_change
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# Will hold the path that the user chooses to download to. Will be an empty
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# string if the user decides to download to the current directory.
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# Determines if the script should ask the user if they would like to remove the
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# downloaded data. This defaults to False, because data deletion is
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# irreversible, and in the worst case, the user will have to remove it
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# manually themselves.
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class Model(HasTraits):
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def __init__(self, *args, **kwargs):
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super(Model, self).__init__(*args, **kwargs)
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@on_trait_change("npts_+", "min_+", "max_+")
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def compute_model(self):
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return sin(x*z) * cos(y)*sin(z) + sin(0.5*z)
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self.xs = linspace(self.min_x, self.max_x, self.npts_x)
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self.ys = linspace(self.min_y, self.max_y, self.npts_y)
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self.zs = linspace(self.min_z, self.max_z, self.npts_z)
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# Generate a cube of values by using newaxis to span new dimensions
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self.vals = vfunc(self.xs[:, newaxis, newaxis],
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self.ys[newaxis, :, newaxis],
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self.zs[newaxis, newaxis, :])
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self.minval = nanmin(self.vals)
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self.maxval = nanmax(self.vals)
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self.model_changed = True
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class BrainModel(Model):
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def __init__(self, *args, **kwargs):
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super(BrainModel, self).__init__(*args, **kwargs)
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def compute_model(self):
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mrbrain_path = os.path.join(dl_path, 'voldata', 'MRbrain.')
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full_arr = zeros((nx, ny, nz), dtype='f')
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for i in range(1, 110):
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arr = fromfile(mrbrain_path + str(i), dtype='>u2')
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arr.shape = (256,256)
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full_arr[:,:,i-1] = arr
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# Generate a cube of values by using newaxis to span new dimensions
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self.minval = nanmin(self.vals)
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self.maxval = nanmax(self.vals)
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self.model_changed = True
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class ImageIndexTool(BaseTool):
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""" A tool to set the slice of a cube based on the user's mouse movements
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# This callback will be called with the index into self.component's
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# callback(tool, x_index, y_index)
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# where *tool* is a reference to this tool instance. The callback
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# can then use tool.token.
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# This callback (if it exists) will be called with the integer number
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# of mousewheel clicks
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# This token can be used by the callback to decide how to process
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# Whether or not to update the slice info; we enter select mode when
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# the left mouse button is pressed and exit it when the mouse button
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# FIXME: This is not used right now.
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select_mode = Bool(False)
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def normal_left_down(self, event):
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self._update_slices(event)
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def normal_right_down(self, event):
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self._update_slices(event)
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def normal_mouse_move(self, event):
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if event.left_down or event.right_down:
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self._update_slices(event)
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def _update_slices(self, event):
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plot = self.component
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ndx = plot.map_index((event.x, event.y),
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threshold=5.0, index_only=True)
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self.callback(self, *ndx)
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def normal_mouse_wheel(self, event):
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if self.wheel_cb is not None:
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self.wheel_cb(self, event.mouse_wheel)
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class PlotFrame(DemoFrame):
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# These are the indices into the cube that each of the image plot views
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# will show; the default values are non-zero just to make it a little
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#---------------------------------------------------------------------------
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#---------------------------------------------------------------------------
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_cmap = Trait(jet, Callable)
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def _index_callback(self, tool, x_index, y_index):
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self.slice_x = x_index
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self.slice_y = y_index
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# transposed because the plot is oriented vertically
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self.slice_z = x_index
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self.slice_y = y_index
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self.slice_x = x_index
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self.slice_z = y_index
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warnings.warn("Unrecognized plane for _index_callback: %s" % plane)
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self._update_images()
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self.center.invalidate_and_redraw()
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self.right.invalidate_and_redraw()
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self.bottom.invalidate_and_redraw()
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def _wheel_callback(self, tool, wheelamt):
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plane_slice_dict = {"xy": ("slice_z", 2),
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"yz": ("slice_x", 0),
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"xz": ("slice_y", 1)}
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attr, shape_ndx = plane_slice_dict[tool.token]
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val = getattr(self, attr)
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max = self.model.vals.shape[shape_ndx]
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if val + wheelamt > max:
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setattr(self, attr, max-1)
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elif val + wheelamt < 0:
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setattr(self, attr, 0)
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setattr(self, attr, val + wheelamt)
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self._update_images()
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self.center.invalidate_and_redraw()
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self.right.invalidate_and_redraw()
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self.bottom.invalidate_and_redraw()
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def _create_window(self):
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self.model = model = BrainModel()
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print "Unable to load BrainModel, using generated data cube."
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self.model = model = Model()
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self._update_model(cmap)
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datacube = self.colorcube
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self.plotdata = ArrayPlotData()
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self._update_images()
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centerplot = Plot(self.plotdata, padding=0)
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imgplot = centerplot.img_plot("xy", xbounds=model.xs, ybounds=model.ys,
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self._add_plot_tools(imgplot, "xy")
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self.center = imgplot
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rightplot = Plot(self.plotdata, width=150, resizable="v", padding=0)
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rightplot.value_range = centerplot.value_range
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imgplot = rightplot.img_plot("yz", xbounds=model.zs, ybounds=model.ys,
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self._add_plot_tools(imgplot, "yz")
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bottomplot = Plot(self.plotdata, height=150, resizable="h", padding=0)
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bottomplot.index_range = centerplot.index_range
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imgplot = bottomplot.img_plot("xz", xbounds=model.xs, ybounds=model.zs,
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self._add_plot_tools(imgplot, "xz")
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self.bottom = imgplot
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# Create Container and add all Plots
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container = GridPlotContainer(padding=20, fill_padding=True,
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bgcolor="white", use_backbuffer=True,
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shape=(2,2), spacing=(12,12))
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container.add(centerplot)
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container.add(rightplot)
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container.add(bottomplot)
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self.container = container
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return Window(self, -1, component=container)
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def _add_plot_tools(self, imgplot, token):
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""" Add LineInspectors, ImageIndexTool, and SimpleZoom to the image plots. """
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imgplot.overlays.append(SimpleZoom(component=imgplot, tool_mode="box",
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enable_wheel=False, always_on=False))
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imgplot.overlays.append(LineInspector(imgplot, axis="index_y", color="white",
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inspect_mode="indexed", write_metadata=True, is_listener=True))
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imgplot.overlays.append(LineInspector(imgplot, axis="index_x", color="white",
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inspect_mode="indexed", write_metadata=True, is_listener=True))
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imgplot.tools.append(ImageIndexTool(imgplot, token=token,
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callback=self._index_callback, wheel_cb=self._wheel_callback))
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def _update_model(self, cmap):
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range = DataRange1D(low=amin(self.model.vals),
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high=amax(self.model.vals))
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self.colormap = cmap(range)
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self.colorcube = (self.colormap.map_screen(self.model.vals) * 255).astype(uint8)
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def _update_images(self):
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""" Updates the image data in self.plotdata to correspond to the
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cube = self.colorcube
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# These are transposed because img_plot() expects its data to be in
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pd.set_data("xy", transpose(cube[:, :, self.slice_z], (1,0,2)))
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pd.set_data("xz", transpose(cube[:, self.slice_y, :], (1,0,2)))
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pd.set_data("yz", cube[self.slice_x, :, :])
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global dl_path, run_cleanup
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print 'Please enter the location of the "voldata" subdirectory containing'
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print 'the data files for this demo, or enter a path to download to (7.8MB).'
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print 'Press <ENTER> to download to the current directory.'
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dl_path = raw_input('Path: ').strip().rstrip("/").rstrip("\\")
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if not dl_path.endswith("voldata"):
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voldata_path = os.path.join(dl_path, 'voldata')
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voldata_path = dl_path
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tar_path = os.path.join(dl_path, 'MRbrain.tar.gz')
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for i in range(1,110):
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if not os.path.isfile(os.path.join(voldata_path, "MRbrain.%d" % i)):
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if len(dl_path) > 0 and not os.path.exists(dl_path):
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print 'The given path does not exist.'
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if not os.path.isabs(dl_path):
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print 'Downloading to: ' + os.path.join(os.getcwd(), dl_path)
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print 'Downloading to: ' + dl_path
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# download and extract the file
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print "Downloading data, Please Wait (7.8MB)"
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opener = urllib.urlopen('http://www-graphics.stanford.edu/data/voldata/MRbrain.tar.gz')
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print 'Download error. Opening backup data.'
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open(tar_path, 'wb').write(opener.read())
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print 'Cannot write to the destination directory specified. ' \
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'Opening backup data.'
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tar_file = tarfile.open(tar_path)
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os.mkdir(voldata_path)
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tar_file.extractall(voldata_path)
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print 'Previously downloaded data detected.'
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answer = raw_input('Remove downloaded files? [Y/N]: ')
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if answer.lower() == 'y':
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shutil.rmtree(os.path.join(dl_path, 'voldata'))
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if __name__ == "__main__":
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demo_main(PlotFrame, size=(800,700), title="Cube analyzer")
added
removed
examples/advanced/data_cube.py
