# Compute in thread example

This script demonstrates how one can do a computation in another thread and update the mayavi pipeline. It also shows how to create a numpy array data and visualize it as image data using a few modules.

Python source code: `compute_in_thread.py`

    # Author: Prabhu Ramachandran <prabhu_r@users.sf.net>
    # Copyright (c) 2007-2015, Enthought, Inc.
    # License: BSD Style.

    from __future__ import print_function

    # Standard library imports
    import numpy
    from threading import Thread
    from time import sleep

    # Enthought library imports
    from mayavi.scripts import mayavi2
    from traits.api import HasTraits, Button, Instance
    from traitsui.api import View, Item
    from mayavi.sources.array_source import ArraySource
    from mayavi.modules.outline import Outline
    from mayavi.modules.image_plane_widget import ImagePlaneWidget
    from pyface.api import GUI

    def make_data(dims=(128, 128, 128)):
        """Creates some simple array data of the given dimensions to test
        with."""
        np = dims[0]*dims[1]*dims[2]

        # Create some scalars to render.
        x, y, z = numpy.ogrid[-5:5:dims[0]*1j,-5:5:dims[1]*1j,-5:5:dims[2]*1j]
        x = x.astype('f')
        y = y.astype('f')
        z = z.astype('f')

        scalars = (numpy.sin(x*y*z)/(x*y*z))
        # The copy makes the data contiguous and the transpose makes it
        # suitable for display via tvtk.  Please note that we assume here
        # that the ArraySource is configured to not transpose the data.
        s = numpy.transpose(scalars).copy()
        # Reshaping the array is needed since the transpose messes up the
        # dimensions of the data.  The scalars themselves are ravel'd and
        # used internally by VTK so the dimension does not matter for the
        # scalars.
        s.shape = s.shape[::-1]
        return s

    class ThreadedAction(Thread):
        def __init__(self, data, **kwargs):
            Thread.__init__(self, **kwargs)
            self.data = data

        def run(self):
            print("Performing expensive calculation in %s..."%self.getName(), end=' ')
            sleep(3)
            sd = self.data.scalar_data
            sd += numpy.sin(numpy.random.rand(*sd.shape)*2.0*numpy.pi)
            GUI.invoke_later(self.data.update)
            print('done.')

    class Controller(HasTraits):
        run_calculation = Button('Run calculation')
        data = Instance(ArraySource)

        view = View(Item(name='run_calculation'))

        def _run_calculation_changed(self, value):
            action = ThreadedAction(self.data)
            action.start()

    @mayavi2.standalone
    def view_numpy():
        """Example showing how to view a 3D numpy array in mayavi2.
        """
        # 'mayavi' is always defined on the interpreter.
        mayavi.new_scene()
        # Make the data and add it to the pipeline.
        data = make_data()
        src = ArraySource(transpose_input_array=False)
        src.scalar_data = data
        mayavi.add_source(src)
        # Visualize the data.
        o = Outline()
        mayavi.add_module(o)
        ipw = ImagePlaneWidget()
        mayavi.add_module(ipw)
        ipw.module_manager.scalar_lut_manager.show_scalar_bar = True

        ipw_y = ImagePlaneWidget()
        mayavi.add_module(ipw_y)
        ipw_y.ipw.plane_orientation = 'y_axes'

        computation = Controller(data=src)
        computation.edit_traits()

    if __name__ == '__main__':
        view_numpy()