Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
190 lines
7.4 KiB
190 lines
7.4 KiB
4 years ago
|
import numpy as np
|
||
|
|
from numpy.testing import assert_equal, assert_array_equal, assert_allclose
|
||
|
|
from pytest import raises as assert_raises
|
||
|
|
|
||
|
|
from scipy.interpolate import griddata, NearestNDInterpolator
|
||
|
|
|
||
|
|
|
||
|
|
class TestGriddata(object):
|
||
|
|
def test_fill_value(self):
|
||
|
|
x = [(0,0), (0,1), (1,0)]
|
||
|
|
y = [1, 2, 3]
|
||
|
|
|
||
|
|
yi = griddata(x, y, [(1,1), (1,2), (0,0)], fill_value=-1)
|
||
|
|
assert_array_equal(yi, [-1., -1, 1])
|
||
|
|
|
||
|
|
yi = griddata(x, y, [(1,1), (1,2), (0,0)])
|
||
|
|
assert_array_equal(yi, [np.nan, np.nan, 1])
|
||
|
|
|
||
|
|
def test_alternative_call(self):
|
||
|
|
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
|
||
|
|
dtype=np.double)
|
||
|
|
y = (np.arange(x.shape[0], dtype=np.double)[:,None]
|
||
|
|
+ np.array([0,1])[None,:])
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
for rescale in (True, False):
|
||
|
|
msg = repr((method, rescale))
|
||
|
|
yi = griddata((x[:,0], x[:,1]), y, (x[:,0], x[:,1]), method=method,
|
||
|
|
rescale=rescale)
|
||
|
|
assert_allclose(y, yi, atol=1e-14, err_msg=msg)
|
||
|
|
|
||
|
|
def test_multivalue_2d(self):
|
||
|
|
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
|
||
|
|
dtype=np.double)
|
||
|
|
y = (np.arange(x.shape[0], dtype=np.double)[:,None]
|
||
|
|
+ np.array([0,1])[None,:])
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
for rescale in (True, False):
|
||
|
|
msg = repr((method, rescale))
|
||
|
|
yi = griddata(x, y, x, method=method, rescale=rescale)
|
||
|
|
assert_allclose(y, yi, atol=1e-14, err_msg=msg)
|
||
|
|
|
||
|
|
def test_multipoint_2d(self):
|
||
|
|
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
|
||
|
|
dtype=np.double)
|
||
|
|
y = np.arange(x.shape[0], dtype=np.double)
|
||
|
|
|
||
|
|
xi = x[:,None,:] + np.array([0,0,0])[None,:,None]
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
for rescale in (True, False):
|
||
|
|
msg = repr((method, rescale))
|
||
|
|
yi = griddata(x, y, xi, method=method, rescale=rescale)
|
||
|
|
|
||
|
|
assert_equal(yi.shape, (5, 3), err_msg=msg)
|
||
|
|
assert_allclose(yi, np.tile(y[:,None], (1, 3)),
|
||
|
|
atol=1e-14, err_msg=msg)
|
||
|
|
|
||
|
|
def test_complex_2d(self):
|
||
|
|
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
|
||
|
|
dtype=np.double)
|
||
|
|
y = np.arange(x.shape[0], dtype=np.double)
|
||
|
|
y = y - 2j*y[::-1]
|
||
|
|
|
||
|
|
xi = x[:,None,:] + np.array([0,0,0])[None,:,None]
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
for rescale in (True, False):
|
||
|
|
msg = repr((method, rescale))
|
||
|
|
yi = griddata(x, y, xi, method=method, rescale=rescale)
|
||
|
|
|
||
|
|
assert_equal(yi.shape, (5, 3), err_msg=msg)
|
||
|
|
assert_allclose(yi, np.tile(y[:,None], (1, 3)),
|
||
|
|
atol=1e-14, err_msg=msg)
|
||
|
|
|
||
|
|
def test_1d(self):
|
||
|
|
x = np.array([1, 2.5, 3, 4.5, 5, 6])
|
||
|
|
y = np.array([1, 2, 0, 3.9, 2, 1])
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
assert_allclose(griddata(x, y, x, method=method), y,
|
||
|
|
err_msg=method, atol=1e-14)
|
||
|
|
assert_allclose(griddata(x.reshape(6, 1), y, x, method=method), y,
|
||
|
|
err_msg=method, atol=1e-14)
|
||
|
|
assert_allclose(griddata((x,), y, (x,), method=method), y,
|
||
|
|
err_msg=method, atol=1e-14)
|
||
|
|
|
||
|
|
def test_1d_borders(self):
|
||
|
|
# Test for nearest neighbor case with xi outside
|
||
|
|
# the range of the values.
|
||
|
|
x = np.array([1, 2.5, 3, 4.5, 5, 6])
|
||
|
|
y = np.array([1, 2, 0, 3.9, 2, 1])
|
||
|
|
xi = np.array([0.9, 6.5])
|
||
|
|
yi_should = np.array([1.0, 1.0])
|
||
|
|
|
||
|
|
method = 'nearest'
|
||
|
|
assert_allclose(griddata(x, y, xi,
|
||
|
|
method=method), yi_should,
|
||
|
|
err_msg=method,
|
||
|
|
atol=1e-14)
|
||
|
|
assert_allclose(griddata(x.reshape(6, 1), y, xi,
|
||
|
|
method=method), yi_should,
|
||
|
|
err_msg=method,
|
||
|
|
atol=1e-14)
|
||
|
|
assert_allclose(griddata((x, ), y, (xi, ),
|
||
|
|
method=method), yi_should,
|
||
|
|
err_msg=method,
|
||
|
|
atol=1e-14)
|
||
|
|
|
||
|
|
def test_1d_unsorted(self):
|
||
|
|
x = np.array([2.5, 1, 4.5, 5, 6, 3])
|
||
|
|
y = np.array([1, 2, 0, 3.9, 2, 1])
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
assert_allclose(griddata(x, y, x, method=method), y,
|
||
|
|
err_msg=method, atol=1e-10)
|
||
|
|
assert_allclose(griddata(x.reshape(6, 1), y, x, method=method), y,
|
||
|
|
err_msg=method, atol=1e-10)
|
||
|
|
assert_allclose(griddata((x,), y, (x,), method=method), y,
|
||
|
|
err_msg=method, atol=1e-10)
|
||
|
|
|
||
|
|
def test_square_rescale_manual(self):
|
||
|
|
points = np.array([(0,0), (0,100), (10,100), (10,0), (1, 5)], dtype=np.double)
|
||
|
|
points_rescaled = np.array([(0,0), (0,1), (1,1), (1,0), (0.1, 0.05)], dtype=np.double)
|
||
|
|
values = np.array([1., 2., -3., 5., 9.], dtype=np.double)
|
||
|
|
|
||
|
|
xx, yy = np.broadcast_arrays(np.linspace(0, 10, 14)[:,None],
|
||
|
|
np.linspace(0, 100, 14)[None,:])
|
||
|
|
xx = xx.ravel()
|
||
|
|
yy = yy.ravel()
|
||
|
|
xi = np.array([xx, yy]).T.copy()
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
msg = method
|
||
|
|
zi = griddata(points_rescaled, values, xi/np.array([10, 100.]),
|
||
|
|
method=method)
|
||
|
|
zi_rescaled = griddata(points, values, xi, method=method,
|
||
|
|
rescale=True)
|
||
|
|
assert_allclose(zi, zi_rescaled, err_msg=msg,
|
||
|
|
atol=1e-12)
|
||
|
|
|
||
|
|
def test_xi_1d(self):
|
||
|
|
# Check that 1-D xi is interpreted as a coordinate
|
||
|
|
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
|
||
|
|
dtype=np.double)
|
||
|
|
y = np.arange(x.shape[0], dtype=np.double)
|
||
|
|
y = y - 2j*y[::-1]
|
||
|
|
|
||
|
|
xi = np.array([0.5, 0.5])
|
||
|
|
|
||
|
|
for method in ('nearest', 'linear', 'cubic'):
|
||
|
|
p1 = griddata(x, y, xi, method=method)
|
||
|
|
p2 = griddata(x, y, xi[None,:], method=method)
|
||
|
|
assert_allclose(p1, p2, err_msg=method)
|
||
|
|
|
||
|
|
xi1 = np.array([0.5])
|
||
|
|
xi3 = np.array([0.5, 0.5, 0.5])
|
||
|
|
assert_raises(ValueError, griddata, x, y, xi1,
|
||
|
|
method=method)
|
||
|
|
assert_raises(ValueError, griddata, x, y, xi3,
|
||
|
|
method=method)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nearest_options():
|
||
|
|
# smoke test that NearestNDInterpolator accept cKDTree options
|
||
|
|
npts, nd = 4, 3
|
||
|
|
x = np.arange(npts*nd).reshape((npts, nd))
|
||
|
|
y = np.arange(npts)
|
||
|
|
nndi = NearestNDInterpolator(x, y)
|
||
|
|
|
||
|
|
opts = {'balanced_tree': False, 'compact_nodes': False}
|
||
|
|
nndi_o = NearestNDInterpolator(x, y, tree_options=opts)
|
||
|
|
assert_allclose(nndi(x), nndi_o(x), atol=1e-14)
|
||
|
|
|
||
|
|
|
||
|
|
def test_nearest_list_argument():
|
||
|
|
nd = np.array([[0, 0, 0, 0, 1, 0, 1],
|
||
|
|
[0, 0, 0, 0, 0, 1, 1],
|
||
|
|
[0, 0, 0, 0, 1, 1, 2]])
|
||
|
|
d = nd[:, 3:]
|
||
|
|
|
||
|
|
# z is np.array
|
||
|
|
NI = NearestNDInterpolator((d[0], d[1]), d[2])
|
||
|
|
assert_array_equal(NI([0.1, 0.9], [0.1, 0.9]), [0, 2])
|
||
|
|
|
||
|
|
# z is list
|
||
|
|
NI = NearestNDInterpolator((d[0], d[1]), list(d[2]))
|
||
|
|
assert_array_equal(NI([0.1, 0.9], [0.1, 0.9]), [0, 2])
|