1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
|
#!/usr/bin/env python
"""
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.
"""
# Author: Prabhu Ramachandran <prabhu_r@users.sf.net>
# Copyright (c) 2007-2020, Enthought, Inc.
# License: BSD Style.
# Standard library imports
import numpy as np
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 = np.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 = (np.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 = np.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 += np.sin(np.random.rand(*sd.shape)*2.0*np.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()
|
