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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
|
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import assert_equal, assert_array_equal, assert_allclose, \
run_module_suite, assert_raises
from scipy.interpolate import griddata
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_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)
if __name__ == "__main__":
run_module_suite()
|
