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""" A module that integrates through a tensor field to generate a
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hyperstreamline. The integration is along the maximum
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eigenvector and the cross section of the hyperstreamline is
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defined by the two other eigenvectors. Thus the shape of the
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hyperstreamline is "tube-like", with the cross section being
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elliptical. Hyperstreamlines are used to visualize tensor fields.
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# Authors: KK Rai (kk.rai [at] iitb.ac.in)
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# R. Ambareesha (ambareesha [at] iitb.ac.in)
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# Prabhu Ramachandran <prabhu_r@users.sf.net>
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# Enthought library imports.
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from enthought.traits.api import Instance, Delegate
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from enthought.traits.ui.api import View, Group, Item
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from enthought.tvtk.api import tvtk
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from enthought.mayavi.core.module import Module
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from enthought.mayavi.components.actor import Actor
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######################################################################
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# `HyperStreamline` class.
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######################################################################
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class HyperStreamline(Module):
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# The version of this class. Used for persistence.
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# The hyper streamline object.
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streamline = Instance(tvtk.HyperStreamline, allow_none=False)
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# The actor for the streamlines.
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actor = Instance(Actor, allow_none=False)
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widget = Instance(tvtk.PointWidget, args=(),
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kw={'outline': False, 'x_shadows': False,
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'y_shadows': False, 'z_shadows': False},
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# Create the UI for the traits.
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view = View(Group(Item(name='actor', style='custom'),
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Group(Item(name='widget', style='custom', resizable=True),
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Group(Item(name='streamline', style='custom',
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######################################################################
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######################################################################
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def setup_pipeline(self):
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"""Override this method so that it *creates* the tvtk
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This method is invoked when the object is initialized via
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`__init__`. Note that at the time this method is called, the
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tvtk data pipeline will *not* yet be setup. So upstream data
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will not be available. The idea is that you simply create the
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basic objects and setup those parts of the pipeline not
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dependent on upstream sources and filters. You should also
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set the `actors` attribute up at this point.
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self.widget.on_trait_change(self._start_position_changed)
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self.streamline = tvtk.HyperStreamline()
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self.streamline.start_position = self.widget.position
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self.streamline.integrate_minor_eigenvector()
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self.streamline.maximum_propagation_distance = 10.0
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self.streamline.integration_step_length =0.1
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self.streamline.step_length = 0.01
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self.streamline.radius = 0.25
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self.streamline.number_of_sides = 18
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self.streamline.integration_direction = 2 #integrate both direction
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self.streamline.on_trait_change(self.render)
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self.widgets.append(self.widget)
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def update_pipeline(self):
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"""Override this method so that it *updates* the tvtk pipeline
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when data upstream is known to have changed.
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This method is invoked (automatically) when any of the inputs
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sends a `pipeline_changed` event.
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mm = self.module_manager
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src = mm.source.outputs[0]
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self.streamline.input = src
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if old_inp is None or src != old_inp:
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self.streamline.update()
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self.outputs = [self.streamline.output]
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self.pipeline_changed = True
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def update_data(self):
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"""Override this method so that it flushes the vtk pipeline if
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This method is invoked (automatically) when any of the inputs
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sends a `data_changed` event.
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# Just set data_changed, the component should do the rest.
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self.data_changed = True
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######################################################################
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# Non-public methods.
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######################################################################
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def _streamline_changed(self, old, new):
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old.on_trait_change(self.render, remove=True)
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new.on_trait_change(self.render)
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mm = self.module_manager
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new.input = mm.source.outputs[0]
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# A default output so there are no pipeline errors. The
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# update_pipeline call corrects this if needed.
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self.outputs = [new.output]
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self.update_pipeline()
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def _start_position_changed(self, value):
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self.streamline.start_position = self.widget.position
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def _actor_changed(self, old, new):
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new.scene = self.scene
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self._change_components(old, new)
added
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enthought.mayavi/enthought/mayavi/modules/hyper_streamline.py
