/usr/lib/python2.7/dist-packages/PySPH-1.0a4.dev0-py2.7-linux-x86_64.egg/pysph/tools/tests/test_interpolator.py is in python-pysph 0~20160514.git91867dc-4build1.
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# Copyright (c) 2014 Prabhu Ramachandran
# License: BSD Style.
# Standard library imports
try:
# This is for Python-2.6.x
import unittest2 as unittest
except ImportError:
import unittest
# Library imports.
import numpy as np
# Local imports
from pysph.tools.interpolator import get_nx_ny_nz, Interpolator
from pysph.base.utils import get_particle_array
class TestGetNxNyNz(unittest.TestCase):
def test_should_work_for_1d_data(self):
# When
num_points = 100
bounds = (0.0, 1.5, 0.0, 0.0, 0.0, 0.0)
dims = get_nx_ny_nz(num_points, bounds)
# Then
self.assertListEqual(list(dims), [100, 1, 1])
# When
num_points = 100
bounds = (0.0, 0.0, 0.0, 1.123, 0.0, 0.0)
dims = get_nx_ny_nz(num_points, bounds)
# Then
self.assertListEqual(list(dims), [1, 100, 1])
# When
num_points = 100
bounds = (0.0, 0.0, 0.0, 0.0, 0.0, 3.0)
dims = get_nx_ny_nz(num_points, bounds)
# Then
self.assertListEqual(list(dims), [1, 1, 100])
def test_should_work_for_2d_data(self):
# When
num_points = 100
bounds = (0.0, 1.0, 0.5, 1.5, 0.0, 0.0)
dims = get_nx_ny_nz(num_points, bounds)
# Then
self.assertListEqual(list(dims), [10, 10, 1])
# When
num_points = 100
bounds = (0.0, 0.0, 0, 1, 0.5, 1.5)
dims = get_nx_ny_nz(num_points, bounds)
# Then
self.assertListEqual(list(dims), [1, 10, 10])
def test_should_work_for_3d_data(self):
# When
num_points = 1000
bounds = (0.0, 1.0, 0.5, 1.5, -1.0, 0.0)
dims = get_nx_ny_nz(num_points, bounds)
# Then
self.assertListEqual(list(dims), [10, 10, 10])
# When
num_points = 1000
bounds = (0.0, 1.0, 0.5, 1.5, -1.0, 2.0)
dims = get_nx_ny_nz(num_points, bounds)
# Then
self.assertListEqual(list(dims), [7, 7, 21])
class TestInterpolator(unittest.TestCase):
def _make_2d_grid(self, name='fluid'):
n = 11
x, y = np.mgrid[-1:1:n*1j,-1:1:n*1j]
dx = 2.0/(n-1)
z = np.zeros_like(x)
x, y, z = x.ravel(), y.ravel(), z.ravel()
m = np.ones_like(x)
p = np.ones_like(x)*2.0
h = np.ones_like(x)*2*dx
u = np.ones_like(x)*0.1
pa = get_particle_array(name=name, x=x, y=y, z=z, h=h, m=m, p=p, u=u)
return pa
def test_should_work_on_2d_data(self):
# Given
pa = self._make_2d_grid()
# When.
ip = Interpolator([pa], num_points=1000)
p = ip.interpolate('p')
u = ip.interpolate('u')
# Then.
expect = np.ones_like(p)*2.0
self.assertTrue(np.allclose(p, expect))
expect = np.ones_like(u)*0.1
self.assertTrue(np.allclose(u, expect))
def test_should_work_with_multiple_arrays(self):
# Given
pa1 = self._make_2d_grid()
pa2 = self._make_2d_grid('solid')
pa2.p[:] = 4.0
pa2.u[:] = 0.2
# When.
ip = Interpolator([pa1, pa2], num_points=1000)
p = ip.interpolate('p')
u = ip.interpolate('u')
# Then.
expect = np.ones_like(p)*3.0
self.assertTrue(np.allclose(p, expect))
expect = np.ones_like(u)*0.15
self.assertTrue(np.allclose(u, expect))
def test_should_work_with_ghost_particles(self):
# Given
pa = self._make_2d_grid()
# Make half the particles ghosts.
n = pa.get_number_of_particles()
pa.tag[n/2:] = 1
pa.align_particles()
# When.
ip = Interpolator([pa], num_points=1000)
p = ip.interpolate('p')
u = ip.interpolate('u')
# Then.
expect = np.ones_like(p)*2.0
self.assertTrue(np.allclose(p, expect))
expect = np.ones_like(u)*0.1
self.assertTrue(np.allclose(u, expect))
def test_should_work_with_changed_data(self):
# Given
pa = self._make_2d_grid()
ip = Interpolator([pa], num_points=1000)
p = ip.interpolate('p')
# When.
pa.p *= 2.0
p = ip.interpolate('p')
# Then.
expect = np.ones_like(p)*4.0
self.assertTrue(np.allclose(p, expect))
def test_should_be_able_to_update_particle_arrays(self):
# Given
pa = self._make_2d_grid()
pa_new = self._make_2d_grid()
pa_new.p[:] = 10.0
ip = Interpolator([pa], num_points=1000)
p = ip.interpolate('p')
# When.
ip.update_particle_arrays([pa_new])
p = ip.interpolate('p')
# Then.
expect = np.ones_like(p)*10.0
self.assertTrue(np.allclose(p, expect))
def test_should_correctly_update_domain(self):
# Given
pa = self._make_2d_grid()
ip = Interpolator([pa], num_points=1000)
p = ip.interpolate('p')
# When.
ip.set_domain((0.0, 1.0, 0.0, 1.0, 0.0, 0.0), (11, 11, 1))
p = ip.interpolate('p')
# Then.
expect = np.ones_like(p)*2.0
self.assertTrue(np.allclose(p, expect))
def test_should_work_when_arrays_have_different_props(self):
# Given
pa1 = self._make_2d_grid()
pa1.add_property('junk', default=2.0)
pa2 = self._make_2d_grid('solid')
# When.
ip = Interpolator([pa1, pa2], num_points=1000)
junk = ip.interpolate('junk')
# Then.
expect = np.ones_like(junk)*1.0
self.assertTrue(np.allclose(junk, expect))
def test_should_work_with_explicit_points_in_constructor(self):
# Given
pa = self._make_2d_grid()
x, y = np.random.random((2, 5, 5))
z = np.zeros_like(x)
# When.
ip = Interpolator([pa], x=x, y=y, z=z)
p = ip.interpolate('p')
# Then.
self.assertEqual(p.shape, x.shape)
expect = np.ones_like(x)*2.0
self.assertTrue(np.allclose(p, expect))
def test_should_work_with_explicit_points_without_z(self):
# Given
pa = self._make_2d_grid()
x, y = np.random.random((2, 5, 5))
# When.
ip = Interpolator([pa], x=x, y=y)
p = ip.interpolate('p')
# Then.
self.assertEqual(p.shape, x.shape)
expect = np.ones_like(x)*2.0
self.assertTrue(np.allclose(p, expect))
def test_that_set_interpolation_points_works(self):
# Given
pa = self._make_2d_grid()
ip = Interpolator([pa], num_points=1000)
# When.
x, y = np.random.random((2, 5, 5))
ip.set_interpolation_points(x=x, y=y)
p = ip.interpolate('p')
# Then.
self.assertEqual(p.shape, x.shape)
expect = np.ones_like(x)*2.0
self.assertTrue(np.allclose(p, expect))
if __name__ == '__main__':
unittest.main()
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