1020 lines
40 KiB
Python
1020 lines
40 KiB
Python
|
import itertools
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from numpy.testing import (assert_allclose, assert_equal, assert_warns,
|
||
|
|
assert_array_almost_equal, assert_array_equal)
|
||
|
|
from pytest import raises as assert_raises
|
||
|
|
|
||
|
|
from scipy.interpolate import (RegularGridInterpolator, interpn,
|
||
|
|
RectBivariateSpline,
|
||
|
|
NearestNDInterpolator, LinearNDInterpolator)
|
||
|
|
|
||
|
|
from scipy.sparse._sputils import matrix
|
||
|
|
|
||
|
|
parametrize_rgi_interp_methods = pytest.mark.parametrize(
|
||
|
|
"method", ['linear', 'nearest', 'slinear', 'cubic', 'quintic', 'pchip']
|
||
|
|
)
|
||
|
|
|
||
|
|
class TestRegularGridInterpolator:
|
||
|
|
def _get_sample_4d(self):
|
||
|
|
# create a 4-D grid of 3 points in each dimension
|
||
|
|
points = [(0., .5, 1.)] * 4
|
||
|
|
values = np.asarray([0., .5, 1.])
|
||
|
|
values0 = values[:, np.newaxis, np.newaxis, np.newaxis]
|
||
|
|
values1 = values[np.newaxis, :, np.newaxis, np.newaxis]
|
||
|
|
values2 = values[np.newaxis, np.newaxis, :, np.newaxis]
|
||
|
|
values3 = values[np.newaxis, np.newaxis, np.newaxis, :]
|
||
|
|
values = (values0 + values1 * 10 + values2 * 100 + values3 * 1000)
|
||
|
|
return points, values
|
||
|
|
|
||
|
|
def _get_sample_4d_2(self):
|
||
|
|
# create another 4-D grid of 3 points in each dimension
|
||
|
|
points = [(0., .5, 1.)] * 2 + [(0., 5., 10.)] * 2
|
||
|
|
values = np.asarray([0., .5, 1.])
|
||
|
|
values0 = values[:, np.newaxis, np.newaxis, np.newaxis]
|
||
|
|
values1 = values[np.newaxis, :, np.newaxis, np.newaxis]
|
||
|
|
values2 = values[np.newaxis, np.newaxis, :, np.newaxis]
|
||
|
|
values3 = values[np.newaxis, np.newaxis, np.newaxis, :]
|
||
|
|
values = (values0 + values1 * 10 + values2 * 100 + values3 * 1000)
|
||
|
|
return points, values
|
||
|
|
|
||
|
|
def _get_sample_4d_3(self):
|
||
|
|
# create another 4-D grid of 7 points in each dimension
|
||
|
|
points = [(0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0)] * 4
|
||
|
|
values = np.asarray([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
|
||
|
|
values0 = values[:, np.newaxis, np.newaxis, np.newaxis]
|
||
|
|
values1 = values[np.newaxis, :, np.newaxis, np.newaxis]
|
||
|
|
values2 = values[np.newaxis, np.newaxis, :, np.newaxis]
|
||
|
|
values3 = values[np.newaxis, np.newaxis, np.newaxis, :]
|
||
|
|
values = (values0 + values1 * 10 + values2 * 100 + values3 * 1000)
|
||
|
|
return points, values
|
||
|
|
|
||
|
|
def _get_sample_4d_4(self):
|
||
|
|
# create another 4-D grid of 2 points in each dimension
|
||
|
|
points = [(0.0, 1.0)] * 4
|
||
|
|
values = np.asarray([0.0, 1.0])
|
||
|
|
values0 = values[:, np.newaxis, np.newaxis, np.newaxis]
|
||
|
|
values1 = values[np.newaxis, :, np.newaxis, np.newaxis]
|
||
|
|
values2 = values[np.newaxis, np.newaxis, :, np.newaxis]
|
||
|
|
values3 = values[np.newaxis, np.newaxis, np.newaxis, :]
|
||
|
|
values = (values0 + values1 * 10 + values2 * 100 + values3 * 1000)
|
||
|
|
return points, values
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_list_input(self, method):
|
||
|
|
points, values = self._get_sample_4d_3()
|
||
|
|
|
||
|
|
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
|
||
|
|
[0.5, 0.5, .5, .5]])
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator(points,
|
||
|
|
values.tolist(),
|
||
|
|
method=method)
|
||
|
|
v1 = interp(sample.tolist())
|
||
|
|
interp = RegularGridInterpolator(points,
|
||
|
|
values,
|
||
|
|
method=method)
|
||
|
|
v2 = interp(sample)
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('method', ['cubic', 'quintic', 'pchip'])
|
||
|
|
def test_spline_dim_error(self, method):
|
||
|
|
points, values = self._get_sample_4d_4()
|
||
|
|
match = "points in dimension"
|
||
|
|
|
||
|
|
# Check error raise when creating interpolator
|
||
|
|
with pytest.raises(ValueError, match=match):
|
||
|
|
RegularGridInterpolator(points, values, method=method)
|
||
|
|
|
||
|
|
# Check error raise when creating interpolator
|
||
|
|
interp = RegularGridInterpolator(points, values)
|
||
|
|
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
|
||
|
|
[0.5, 0.5, .5, .5]])
|
||
|
|
with pytest.raises(ValueError, match=match):
|
||
|
|
interp(sample, method=method)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"points_values, sample",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
_get_sample_4d,
|
||
|
|
np.asarray(
|
||
|
|
[[0.1, 0.1, 1.0, 0.9],
|
||
|
|
[0.2, 0.1, 0.45, 0.8],
|
||
|
|
[0.5, 0.5, 0.5, 0.5]]
|
||
|
|
),
|
||
|
|
),
|
||
|
|
(_get_sample_4d_2, np.asarray([0.1, 0.1, 10.0, 9.0])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_linear_and_slinear_close(self, points_values, sample):
|
||
|
|
points, values = points_values(self)
|
||
|
|
interp = RegularGridInterpolator(points, values, method="linear")
|
||
|
|
v1 = interp(sample)
|
||
|
|
interp = RegularGridInterpolator(points, values, method="slinear")
|
||
|
|
v2 = interp(sample)
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_complex(self, method):
|
||
|
|
points, values = self._get_sample_4d_3()
|
||
|
|
values = values - 2j*values
|
||
|
|
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
|
||
|
|
[0.5, 0.5, .5, .5]])
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator(points, values, method=method)
|
||
|
|
rinterp = RegularGridInterpolator(points, values.real, method=method)
|
||
|
|
iinterp = RegularGridInterpolator(points, values.imag, method=method)
|
||
|
|
|
||
|
|
v1 = interp(sample)
|
||
|
|
v2 = rinterp(sample) + 1j*iinterp(sample)
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
def test_cubic_vs_pchip(self):
|
||
|
|
x, y = [1, 2, 3, 4], [1, 2, 3, 4]
|
||
|
|
xg, yg = np.meshgrid(x, y, indexing='ij')
|
||
|
|
|
||
|
|
values = (lambda x, y: x**4 * y**4)(xg, yg)
|
||
|
|
cubic = RegularGridInterpolator((x, y), values, method='cubic')
|
||
|
|
pchip = RegularGridInterpolator((x, y), values, method='pchip')
|
||
|
|
|
||
|
|
vals_cubic = cubic([1.5, 2])
|
||
|
|
vals_pchip = pchip([1.5, 2])
|
||
|
|
assert not np.allclose(vals_cubic, vals_pchip, atol=1e-14, rtol=0)
|
||
|
|
|
||
|
|
def test_linear_xi1d(self):
|
||
|
|
points, values = self._get_sample_4d_2()
|
||
|
|
interp = RegularGridInterpolator(points, values)
|
||
|
|
sample = np.asarray([0.1, 0.1, 10., 9.])
|
||
|
|
wanted = 1001.1
|
||
|
|
assert_array_almost_equal(interp(sample), wanted)
|
||
|
|
|
||
|
|
def test_linear_xi3d(self):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values)
|
||
|
|
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
|
||
|
|
[0.5, 0.5, .5, .5]])
|
||
|
|
wanted = np.asarray([1001.1, 846.2, 555.5])
|
||
|
|
assert_array_almost_equal(interp(sample), wanted)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"sample, wanted",
|
||
|
|
[
|
||
|
|
(np.asarray([0.1, 0.1, 0.9, 0.9]), 1100.0),
|
||
|
|
(np.asarray([0.1, 0.1, 0.1, 0.1]), 0.0),
|
||
|
|
(np.asarray([0.0, 0.0, 0.0, 0.0]), 0.0),
|
||
|
|
(np.asarray([1.0, 1.0, 1.0, 1.0]), 1111.0),
|
||
|
|
(np.asarray([0.1, 0.4, 0.6, 0.9]), 1055.0),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_nearest(self, sample, wanted):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values, method="nearest")
|
||
|
|
assert_array_almost_equal(interp(sample), wanted)
|
||
|
|
|
||
|
|
def test_linear_edges(self):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values)
|
||
|
|
sample = np.asarray([[0., 0., 0., 0.], [1., 1., 1., 1.]])
|
||
|
|
wanted = np.asarray([0., 1111.])
|
||
|
|
assert_array_almost_equal(interp(sample), wanted)
|
||
|
|
|
||
|
|
def test_valid_create(self):
|
||
|
|
# create a 2-D grid of 3 points in each dimension
|
||
|
|
points = [(0., .5, 1.), (0., 1., .5)]
|
||
|
|
values = np.asarray([0., .5, 1.])
|
||
|
|
values0 = values[:, np.newaxis]
|
||
|
|
values1 = values[np.newaxis, :]
|
||
|
|
values = (values0 + values1 * 10)
|
||
|
|
assert_raises(ValueError, RegularGridInterpolator, points, values)
|
||
|
|
points = [((0., .5, 1.), ), (0., .5, 1.)]
|
||
|
|
assert_raises(ValueError, RegularGridInterpolator, points, values)
|
||
|
|
points = [(0., .5, .75, 1.), (0., .5, 1.)]
|
||
|
|
assert_raises(ValueError, RegularGridInterpolator, points, values)
|
||
|
|
points = [(0., .5, 1.), (0., .5, 1.), (0., .5, 1.)]
|
||
|
|
assert_raises(ValueError, RegularGridInterpolator, points, values)
|
||
|
|
points = [(0., .5, 1.), (0., .5, 1.)]
|
||
|
|
assert_raises(ValueError, RegularGridInterpolator, points, values,
|
||
|
|
method="undefmethod")
|
||
|
|
|
||
|
|
def test_valid_call(self):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values)
|
||
|
|
sample = np.asarray([[0., 0., 0., 0.], [1., 1., 1., 1.]])
|
||
|
|
assert_raises(ValueError, interp, sample, "undefmethod")
|
||
|
|
sample = np.asarray([[0., 0., 0.], [1., 1., 1.]])
|
||
|
|
assert_raises(ValueError, interp, sample)
|
||
|
|
sample = np.asarray([[0., 0., 0., 0.], [1., 1., 1., 1.1]])
|
||
|
|
assert_raises(ValueError, interp, sample)
|
||
|
|
|
||
|
|
def test_out_of_bounds_extrap(self):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values, bounds_error=False,
|
||
|
|
fill_value=None)
|
||
|
|
sample = np.asarray([[-.1, -.1, -.1, -.1], [1.1, 1.1, 1.1, 1.1],
|
||
|
|
[21, 2.1, -1.1, -11], [2.1, 2.1, -1.1, -1.1]])
|
||
|
|
wanted = np.asarray([0., 1111., 11., 11.])
|
||
|
|
assert_array_almost_equal(interp(sample, method="nearest"), wanted)
|
||
|
|
wanted = np.asarray([-111.1, 1222.1, -11068., -1186.9])
|
||
|
|
assert_array_almost_equal(interp(sample, method="linear"), wanted)
|
||
|
|
|
||
|
|
def test_out_of_bounds_extrap2(self):
|
||
|
|
points, values = self._get_sample_4d_2()
|
||
|
|
interp = RegularGridInterpolator(points, values, bounds_error=False,
|
||
|
|
fill_value=None)
|
||
|
|
sample = np.asarray([[-.1, -.1, -.1, -.1], [1.1, 1.1, 1.1, 1.1],
|
||
|
|
[21, 2.1, -1.1, -11], [2.1, 2.1, -1.1, -1.1]])
|
||
|
|
wanted = np.asarray([0., 11., 11., 11.])
|
||
|
|
assert_array_almost_equal(interp(sample, method="nearest"), wanted)
|
||
|
|
wanted = np.asarray([-12.1, 133.1, -1069., -97.9])
|
||
|
|
assert_array_almost_equal(interp(sample, method="linear"), wanted)
|
||
|
|
|
||
|
|
def test_out_of_bounds_fill(self):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values, bounds_error=False,
|
||
|
|
fill_value=np.nan)
|
||
|
|
sample = np.asarray([[-.1, -.1, -.1, -.1], [1.1, 1.1, 1.1, 1.1],
|
||
|
|
[2.1, 2.1, -1.1, -1.1]])
|
||
|
|
wanted = np.asarray([np.nan, np.nan, np.nan])
|
||
|
|
assert_array_almost_equal(interp(sample, method="nearest"), wanted)
|
||
|
|
assert_array_almost_equal(interp(sample, method="linear"), wanted)
|
||
|
|
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
|
||
|
|
[0.5, 0.5, .5, .5]])
|
||
|
|
wanted = np.asarray([1001.1, 846.2, 555.5])
|
||
|
|
assert_array_almost_equal(interp(sample), wanted)
|
||
|
|
|
||
|
|
def test_nearest_compare_qhull(self):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values, method="nearest")
|
||
|
|
points_qhull = itertools.product(*points)
|
||
|
|
points_qhull = [p for p in points_qhull]
|
||
|
|
points_qhull = np.asarray(points_qhull)
|
||
|
|
values_qhull = values.reshape(-1)
|
||
|
|
interp_qhull = NearestNDInterpolator(points_qhull, values_qhull)
|
||
|
|
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
|
||
|
|
[0.5, 0.5, .5, .5]])
|
||
|
|
assert_array_almost_equal(interp(sample), interp_qhull(sample))
|
||
|
|
|
||
|
|
def test_linear_compare_qhull(self):
|
||
|
|
points, values = self._get_sample_4d()
|
||
|
|
interp = RegularGridInterpolator(points, values)
|
||
|
|
points_qhull = itertools.product(*points)
|
||
|
|
points_qhull = [p for p in points_qhull]
|
||
|
|
points_qhull = np.asarray(points_qhull)
|
||
|
|
values_qhull = values.reshape(-1)
|
||
|
|
interp_qhull = LinearNDInterpolator(points_qhull, values_qhull)
|
||
|
|
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
|
||
|
|
[0.5, 0.5, .5, .5]])
|
||
|
|
assert_array_almost_equal(interp(sample), interp_qhull(sample))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["nearest", "linear"])
|
||
|
|
def test_duck_typed_values(self, method):
|
||
|
|
x = np.linspace(0, 2, 5)
|
||
|
|
y = np.linspace(0, 1, 7)
|
||
|
|
|
||
|
|
values = MyValue((5, 7))
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator((x, y), values, method=method)
|
||
|
|
v1 = interp([0.4, 0.7])
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator((x, y), values._v, method=method)
|
||
|
|
v2 = interp([0.4, 0.7])
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
def test_invalid_fill_value(self):
|
||
|
|
np.random.seed(1234)
|
||
|
|
x = np.linspace(0, 2, 5)
|
||
|
|
y = np.linspace(0, 1, 7)
|
||
|
|
values = np.random.rand(5, 7)
|
||
|
|
|
||
|
|
# integers can be cast to floats
|
||
|
|
RegularGridInterpolator((x, y), values, fill_value=1)
|
||
|
|
|
||
|
|
# complex values cannot
|
||
|
|
assert_raises(ValueError, RegularGridInterpolator,
|
||
|
|
(x, y), values, fill_value=1+2j)
|
||
|
|
|
||
|
|
def test_fillvalue_type(self):
|
||
|
|
# from #3703; test that interpolator object construction succeeds
|
||
|
|
values = np.ones((10, 20, 30), dtype='>f4')
|
||
|
|
points = [np.arange(n) for n in values.shape]
|
||
|
|
# xi = [(1, 1, 1)]
|
||
|
|
RegularGridInterpolator(points, values)
|
||
|
|
RegularGridInterpolator(points, values, fill_value=0.)
|
||
|
|
|
||
|
|
def test_length_one_axis(self):
|
||
|
|
# gh-5890, gh-9524 : length-1 axis is legal for method='linear'.
|
||
|
|
# Along the axis it's linear interpolation; away from the length-1
|
||
|
|
# axis, it's an extrapolation, so fill_value should be used.
|
||
|
|
def f(x, y):
|
||
|
|
return x + y
|
||
|
|
x = np.linspace(1, 1, 1)
|
||
|
|
y = np.linspace(1, 10, 10)
|
||
|
|
data = f(*np.meshgrid(x, y, indexing="ij", sparse=True))
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator((x, y), data, method="linear",
|
||
|
|
bounds_error=False, fill_value=101)
|
||
|
|
|
||
|
|
# check values at the grid
|
||
|
|
assert_allclose(interp(np.array([[1, 1], [1, 5], [1, 10]])),
|
||
|
|
[2, 6, 11],
|
||
|
|
atol=1e-14)
|
||
|
|
|
||
|
|
# check off-grid interpolation is indeed linear
|
||
|
|
assert_allclose(interp(np.array([[1, 1.4], [1, 5.3], [1, 10]])),
|
||
|
|
[2.4, 6.3, 11],
|
||
|
|
atol=1e-14)
|
||
|
|
|
||
|
|
# check exrapolation w/ fill_value
|
||
|
|
assert_allclose(interp(np.array([1.1, 2.4])),
|
||
|
|
interp.fill_value,
|
||
|
|
atol=1e-14)
|
||
|
|
|
||
|
|
# check extrapolation: linear along the `y` axis, const along `x`
|
||
|
|
interp.fill_value = None
|
||
|
|
assert_allclose(interp([[1, 0.3], [1, 11.5]]),
|
||
|
|
[1.3, 12.5], atol=1e-15)
|
||
|
|
|
||
|
|
assert_allclose(interp([[1.5, 0.3], [1.9, 11.5]]),
|
||
|
|
[1.3, 12.5], atol=1e-15)
|
||
|
|
|
||
|
|
# extrapolation with method='nearest'
|
||
|
|
interp = RegularGridInterpolator((x, y), data, method="nearest",
|
||
|
|
bounds_error=False, fill_value=None)
|
||
|
|
assert_allclose(interp([[1.5, 1.8], [-4, 5.1]]),
|
||
|
|
[3, 6],
|
||
|
|
atol=1e-15)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("fill_value", [None, np.nan, np.pi])
|
||
|
|
@pytest.mark.parametrize("method", ['linear', 'nearest'])
|
||
|
|
def test_length_one_axis2(self, fill_value, method):
|
||
|
|
options = {"fill_value": fill_value, "bounds_error": False,
|
||
|
|
"method": method}
|
||
|
|
|
||
|
|
x = np.linspace(0, 2*np.pi, 20)
|
||
|
|
z = np.sin(x)
|
||
|
|
|
||
|
|
fa = RegularGridInterpolator((x,), z[:], **options)
|
||
|
|
fb = RegularGridInterpolator((x, [0]), z[:, None], **options)
|
||
|
|
|
||
|
|
x1a = np.linspace(-1, 2*np.pi+1, 100)
|
||
|
|
za = fa(x1a)
|
||
|
|
|
||
|
|
# evaluated at provided y-value, fb should behave exactly as fa
|
||
|
|
y1b = np.zeros(100)
|
||
|
|
zb = fb(np.vstack([x1a, y1b]).T)
|
||
|
|
assert_allclose(zb, za)
|
||
|
|
|
||
|
|
# evaluated at a different y-value, fb should return fill value
|
||
|
|
y1b = np.ones(100)
|
||
|
|
zb = fb(np.vstack([x1a, y1b]).T)
|
||
|
|
if fill_value is None:
|
||
|
|
assert_allclose(zb, za)
|
||
|
|
else:
|
||
|
|
assert_allclose(zb, fill_value)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ['nearest', 'linear'])
|
||
|
|
def test_nan_x_1d(self, method):
|
||
|
|
# gh-6624 : if x is nan, result should be nan
|
||
|
|
f = RegularGridInterpolator(([1, 2, 3],), [10, 20, 30], fill_value=1,
|
||
|
|
bounds_error=False, method=method)
|
||
|
|
assert np.isnan(f([np.nan]))
|
||
|
|
|
||
|
|
# test arbitrary nan pattern
|
||
|
|
rng = np.random.default_rng(8143215468)
|
||
|
|
x = rng.random(size=100)*4
|
||
|
|
i = rng.random(size=100) > 0.5
|
||
|
|
x[i] = np.nan
|
||
|
|
with np.errstate(invalid='ignore'):
|
||
|
|
# out-of-bounds comparisons, `out_of_bounds += x < grid[0]`,
|
||
|
|
# generate numpy warnings if `x` contains nans.
|
||
|
|
# These warnings should propagate to user (since `x` is user
|
||
|
|
# input) and we simply filter them out.
|
||
|
|
res = f(x)
|
||
|
|
|
||
|
|
assert_equal(res[i], np.nan)
|
||
|
|
assert_equal(res[~i], f(x[~i]))
|
||
|
|
|
||
|
|
# also test the length-one axis f(nan)
|
||
|
|
x = [1, 2, 3]
|
||
|
|
y = [1, ]
|
||
|
|
data = np.ones((3, 1))
|
||
|
|
f = RegularGridInterpolator((x, y), data, fill_value=1,
|
||
|
|
bounds_error=False, method=method)
|
||
|
|
assert np.isnan(f([np.nan, 1]))
|
||
|
|
assert np.isnan(f([1, np.nan]))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ['nearest', 'linear'])
|
||
|
|
def test_nan_x_2d(self, method):
|
||
|
|
x, y = np.array([0, 1, 2]), np.array([1, 3, 7])
|
||
|
|
|
||
|
|
def f(x, y):
|
||
|
|
return x**2 + y**2
|
||
|
|
|
||
|
|
xg, yg = np.meshgrid(x, y, indexing='ij', sparse=True)
|
||
|
|
data = f(xg, yg)
|
||
|
|
interp = RegularGridInterpolator((x, y), data,
|
||
|
|
method=method, bounds_error=False)
|
||
|
|
|
||
|
|
with np.errstate(invalid='ignore'):
|
||
|
|
res = interp([[1.5, np.nan], [1, 1]])
|
||
|
|
assert_allclose(res[1], 2, atol=1e-14)
|
||
|
|
assert np.isnan(res[0])
|
||
|
|
|
||
|
|
# test arbitrary nan pattern
|
||
|
|
rng = np.random.default_rng(8143215468)
|
||
|
|
x = rng.random(size=100)*4-1
|
||
|
|
y = rng.random(size=100)*8
|
||
|
|
i1 = rng.random(size=100) > 0.5
|
||
|
|
i2 = rng.random(size=100) > 0.5
|
||
|
|
i = i1 | i2
|
||
|
|
x[i1] = np.nan
|
||
|
|
y[i2] = np.nan
|
||
|
|
z = np.array([x, y]).T
|
||
|
|
with np.errstate(invalid='ignore'):
|
||
|
|
# out-of-bounds comparisons, `out_of_bounds += x < grid[0]`,
|
||
|
|
# generate numpy warnings if `x` contains nans.
|
||
|
|
# These warnings should propagate to user (since `x` is user
|
||
|
|
# input) and we simply filter them out.
|
||
|
|
res = interp(z)
|
||
|
|
|
||
|
|
assert_equal(res[i], np.nan)
|
||
|
|
assert_equal(res[~i], interp(z[~i]))
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_descending_points(self, method):
|
||
|
|
def val_func_3d(x, y, z):
|
||
|
|
return 2 * x ** 3 + 3 * y ** 2 - z
|
||
|
|
|
||
|
|
x = np.linspace(1, 4, 11)
|
||
|
|
y = np.linspace(4, 7, 22)
|
||
|
|
z = np.linspace(7, 9, 33)
|
||
|
|
points = (x, y, z)
|
||
|
|
values = val_func_3d(
|
||
|
|
*np.meshgrid(*points, indexing='ij', sparse=True))
|
||
|
|
my_interpolating_function = RegularGridInterpolator(points,
|
||
|
|
values,
|
||
|
|
method=method)
|
||
|
|
pts = np.array([[2.1, 6.2, 8.3], [3.3, 5.2, 7.1]])
|
||
|
|
correct_result = my_interpolating_function(pts)
|
||
|
|
|
||
|
|
# descending data
|
||
|
|
x_descending = x[::-1]
|
||
|
|
y_descending = y[::-1]
|
||
|
|
z_descending = z[::-1]
|
||
|
|
points_shuffled = (x_descending, y_descending, z_descending)
|
||
|
|
values_shuffled = val_func_3d(
|
||
|
|
*np.meshgrid(*points_shuffled, indexing='ij', sparse=True))
|
||
|
|
my_interpolating_function = RegularGridInterpolator(
|
||
|
|
points_shuffled, values_shuffled, method=method)
|
||
|
|
test_result = my_interpolating_function(pts)
|
||
|
|
|
||
|
|
assert_array_equal(correct_result, test_result)
|
||
|
|
|
||
|
|
def test_invalid_points_order(self):
|
||
|
|
def val_func_2d(x, y):
|
||
|
|
return 2 * x ** 3 + 3 * y ** 2
|
||
|
|
|
||
|
|
x = np.array([.5, 2., 0., 4., 5.5]) # not ascending or descending
|
||
|
|
y = np.array([.5, 2., 3., 4., 5.5])
|
||
|
|
points = (x, y)
|
||
|
|
values = val_func_2d(*np.meshgrid(*points, indexing='ij',
|
||
|
|
sparse=True))
|
||
|
|
match = "must be strictly ascending or descending"
|
||
|
|
with pytest.raises(ValueError, match=match):
|
||
|
|
RegularGridInterpolator(points, values)
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_fill_value(self, method):
|
||
|
|
interp = RegularGridInterpolator([np.arange(6)], np.ones(6),
|
||
|
|
method=method, bounds_error=False)
|
||
|
|
assert np.isnan(interp([10]))
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_nonscalar_values(self, method):
|
||
|
|
# Verify that non-scalar valued values also works
|
||
|
|
points = [(0.0, 0.5, 1.0, 1.5, 2.0, 2.5)] * 2 + [
|
||
|
|
(0.0, 5.0, 10.0, 15.0, 20, 25.0)
|
||
|
|
] * 2
|
||
|
|
|
||
|
|
rng = np.random.default_rng(1234)
|
||
|
|
values = rng.random((6, 6, 6, 6, 8))
|
||
|
|
sample = rng.random((7, 3, 4))
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator(points, values, method=method,
|
||
|
|
bounds_error=False)
|
||
|
|
v = interp(sample)
|
||
|
|
assert_equal(v.shape, (7, 3, 8), err_msg=method)
|
||
|
|
|
||
|
|
vs = []
|
||
|
|
for j in range(8):
|
||
|
|
interp = RegularGridInterpolator(points, values[..., j],
|
||
|
|
method=method,
|
||
|
|
bounds_error=False)
|
||
|
|
vs.append(interp(sample))
|
||
|
|
v2 = np.array(vs).transpose(1, 2, 0)
|
||
|
|
|
||
|
|
assert_allclose(v, v2, atol=1e-14, err_msg=method)
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
@pytest.mark.parametrize("flip_points", [False, True])
|
||
|
|
def test_nonscalar_values_2(self, method, flip_points):
|
||
|
|
# Verify that non-scalar valued values also work : use different
|
||
|
|
# lengths of axes to simplify tracing the internals
|
||
|
|
points = [(0.0, 0.5, 1.0, 1.5, 2.0, 2.5),
|
||
|
|
(0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0),
|
||
|
|
(0.0, 5.0, 10.0, 15.0, 20, 25.0, 35.0, 36.0),
|
||
|
|
(0.0, 5.0, 10.0, 15.0, 20, 25.0, 35.0, 36.0, 47)]
|
||
|
|
|
||
|
|
# verify, that strictly decreasing dimensions work
|
||
|
|
if flip_points:
|
||
|
|
points = [tuple(reversed(p)) for p in points]
|
||
|
|
|
||
|
|
rng = np.random.default_rng(1234)
|
||
|
|
|
||
|
|
trailing_points = (3, 2)
|
||
|
|
# NB: values has a `num_trailing_dims` trailing dimension
|
||
|
|
values = rng.random((6, 7, 8, 9, *trailing_points))
|
||
|
|
sample = rng.random(4) # a single sample point !
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator(points, values, method=method,
|
||
|
|
bounds_error=False)
|
||
|
|
v = interp(sample)
|
||
|
|
|
||
|
|
# v has a single sample point *per entry in the trailing dimensions*
|
||
|
|
assert v.shape == (1, *trailing_points)
|
||
|
|
|
||
|
|
# check the values, too : manually loop over the trailing dimensions
|
||
|
|
vs = np.empty((values.shape[-2:]))
|
||
|
|
for i in range(values.shape[-2]):
|
||
|
|
for j in range(values.shape[-1]):
|
||
|
|
interp = RegularGridInterpolator(points, values[..., i, j],
|
||
|
|
method=method,
|
||
|
|
bounds_error=False)
|
||
|
|
vs[i, j] = interp(sample)
|
||
|
|
v2 = np.expand_dims(vs, axis=0)
|
||
|
|
assert_allclose(v, v2, atol=1e-14, err_msg=method)
|
||
|
|
|
||
|
|
def test_nonscalar_values_linear_2D(self):
|
||
|
|
# Verify that non-scalar values work in the 2D fast path
|
||
|
|
method = 'linear'
|
||
|
|
points = [(0.0, 0.5, 1.0, 1.5, 2.0, 2.5),
|
||
|
|
(0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0), ]
|
||
|
|
|
||
|
|
rng = np.random.default_rng(1234)
|
||
|
|
|
||
|
|
trailing_points = (3, 4)
|
||
|
|
# NB: values has a `num_trailing_dims` trailing dimension
|
||
|
|
values = rng.random((6, 7, *trailing_points))
|
||
|
|
sample = rng.random(2) # a single sample point !
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator(points, values, method=method,
|
||
|
|
bounds_error=False)
|
||
|
|
v = interp(sample)
|
||
|
|
|
||
|
|
# v has a single sample point *per entry in the trailing dimensions*
|
||
|
|
assert v.shape == (1, *trailing_points)
|
||
|
|
|
||
|
|
# check the values, too : manually loop over the trailing dimensions
|
||
|
|
vs = np.empty((values.shape[-2:]))
|
||
|
|
for i in range(values.shape[-2]):
|
||
|
|
for j in range(values.shape[-1]):
|
||
|
|
interp = RegularGridInterpolator(points, values[..., i, j],
|
||
|
|
method=method,
|
||
|
|
bounds_error=False)
|
||
|
|
vs[i, j] = interp(sample)
|
||
|
|
v2 = np.expand_dims(vs, axis=0)
|
||
|
|
assert_allclose(v, v2, atol=1e-14, err_msg=method)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"dtype",
|
||
|
|
[np.float32, np.float64, np.complex64, np.complex128]
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("xi_dtype", [np.float32, np.float64])
|
||
|
|
def test_float32_values(self, dtype, xi_dtype):
|
||
|
|
# regression test for gh-17718: values.dtype=float32 fails
|
||
|
|
def f(x, y):
|
||
|
|
return 2 * x**3 + 3 * y**2
|
||
|
|
|
||
|
|
x = np.linspace(1, 4, 11)
|
||
|
|
y = np.linspace(4, 7, 22)
|
||
|
|
|
||
|
|
xg, yg = np.meshgrid(x, y, indexing='ij', sparse=True)
|
||
|
|
data = f(xg, yg)
|
||
|
|
|
||
|
|
data = data.astype(dtype)
|
||
|
|
|
||
|
|
interp = RegularGridInterpolator((x, y), data)
|
||
|
|
|
||
|
|
pts = np.array([[2.1, 6.2],
|
||
|
|
[3.3, 5.2]], dtype=xi_dtype)
|
||
|
|
|
||
|
|
# the values here are just what the call returns; the test checks that
|
||
|
|
# that the call succeeds at all, instead of failing with cython not
|
||
|
|
# having a float32 kernel
|
||
|
|
assert_allclose(interp(pts), [134.10469388, 153.40069388], atol=1e-7)
|
||
|
|
|
||
|
|
|
||
|
|
class MyValue:
|
||
|
|
"""
|
||
|
|
Minimal indexable object
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, shape):
|
||
|
|
self.ndim = 2
|
||
|
|
self.shape = shape
|
||
|
|
self._v = np.arange(np.prod(shape)).reshape(shape)
|
||
|
|
|
||
|
|
def __getitem__(self, idx):
|
||
|
|
return self._v[idx]
|
||
|
|
|
||
|
|
def __array_interface__(self):
|
||
|
|
return None
|
||
|
|
|
||
|
|
def __array__(self):
|
||
|
|
raise RuntimeError("No array representation")
|
||
|
|
|
||
|
|
|
||
|
|
class TestInterpN:
|
||
|
|
def _sample_2d_data(self):
|
||
|
|
x = np.array([.5, 2., 3., 4., 5.5, 6.])
|
||
|
|
y = np.array([.5, 2., 3., 4., 5.5, 6.])
|
||
|
|
z = np.array(
|
||
|
|
[
|
||
|
|
[1, 2, 1, 2, 1, 1],
|
||
|
|
[1, 2, 1, 2, 1, 1],
|
||
|
|
[1, 2, 3, 2, 1, 1],
|
||
|
|
[1, 2, 2, 2, 1, 1],
|
||
|
|
[1, 2, 1, 2, 1, 1],
|
||
|
|
[1, 2, 2, 2, 1, 1],
|
||
|
|
]
|
||
|
|
)
|
||
|
|
return x, y, z
|
||
|
|
|
||
|
|
def test_spline_2d(self):
|
||
|
|
x, y, z = self._sample_2d_data()
|
||
|
|
lut = RectBivariateSpline(x, y, z)
|
||
|
|
|
||
|
|
xi = np.array([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
|
||
|
|
[1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T
|
||
|
|
assert_array_almost_equal(interpn((x, y), z, xi, method="splinef2d"),
|
||
|
|
lut.ev(xi[:, 0], xi[:, 1]))
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_list_input(self, method):
|
||
|
|
x, y, z = self._sample_2d_data()
|
||
|
|
xi = np.array([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
|
||
|
|
[1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T
|
||
|
|
|
||
|
|
v1 = interpn((x, y), z, xi, method=method)
|
||
|
|
v2 = interpn(
|
||
|
|
(x.tolist(), y.tolist()), z.tolist(), xi.tolist(), method=method
|
||
|
|
)
|
||
|
|
assert_allclose(v1, v2, err_msg=method)
|
||
|
|
|
||
|
|
def test_spline_2d_outofbounds(self):
|
||
|
|
x = np.array([.5, 2., 3., 4., 5.5])
|
||
|
|
y = np.array([.5, 2., 3., 4., 5.5])
|
||
|
|
z = np.array([[1, 2, 1, 2, 1], [1, 2, 1, 2, 1], [1, 2, 3, 2, 1],
|
||
|
|
[1, 2, 2, 2, 1], [1, 2, 1, 2, 1]])
|
||
|
|
lut = RectBivariateSpline(x, y, z)
|
||
|
|
|
||
|
|
xi = np.array([[1, 2.3, 6.3, 0.5, 3.3, 1.2, 3],
|
||
|
|
[1, 3.3, 1.2, -4.0, 5.0, 1.0, 3]]).T
|
||
|
|
actual = interpn((x, y), z, xi, method="splinef2d",
|
||
|
|
bounds_error=False, fill_value=999.99)
|
||
|
|
expected = lut.ev(xi[:, 0], xi[:, 1])
|
||
|
|
expected[2:4] = 999.99
|
||
|
|
assert_array_almost_equal(actual, expected)
|
||
|
|
|
||
|
|
# no extrapolation for splinef2d
|
||
|
|
assert_raises(ValueError, interpn, (x, y), z, xi, method="splinef2d",
|
||
|
|
bounds_error=False, fill_value=None)
|
||
|
|
|
||
|
|
def _sample_4d_data(self):
|
||
|
|
points = [(0., .5, 1.)] * 2 + [(0., 5., 10.)] * 2
|
||
|
|
values = np.asarray([0., .5, 1.])
|
||
|
|
values0 = values[:, np.newaxis, np.newaxis, np.newaxis]
|
||
|
|
values1 = values[np.newaxis, :, np.newaxis, np.newaxis]
|
||
|
|
values2 = values[np.newaxis, np.newaxis, :, np.newaxis]
|
||
|
|
values3 = values[np.newaxis, np.newaxis, np.newaxis, :]
|
||
|
|
values = (values0 + values1 * 10 + values2 * 100 + values3 * 1000)
|
||
|
|
return points, values
|
||
|
|
|
||
|
|
def test_linear_4d(self):
|
||
|
|
# create a 4-D grid of 3 points in each dimension
|
||
|
|
points, values = self._sample_4d_data()
|
||
|
|
interp_rg = RegularGridInterpolator(points, values)
|
||
|
|
sample = np.asarray([[0.1, 0.1, 10., 9.]])
|
||
|
|
wanted = interpn(points, values, sample, method="linear")
|
||
|
|
assert_array_almost_equal(interp_rg(sample), wanted)
|
||
|
|
|
||
|
|
def test_4d_linear_outofbounds(self):
|
||
|
|
# create a 4-D grid of 3 points in each dimension
|
||
|
|
points, values = self._sample_4d_data()
|
||
|
|
sample = np.asarray([[0.1, -0.1, 10.1, 9.]])
|
||
|
|
wanted = 999.99
|
||
|
|
actual = interpn(points, values, sample, method="linear",
|
||
|
|
bounds_error=False, fill_value=999.99)
|
||
|
|
assert_array_almost_equal(actual, wanted)
|
||
|
|
|
||
|
|
def test_nearest_4d(self):
|
||
|
|
# create a 4-D grid of 3 points in each dimension
|
||
|
|
points, values = self._sample_4d_data()
|
||
|
|
interp_rg = RegularGridInterpolator(points, values, method="nearest")
|
||
|
|
sample = np.asarray([[0.1, 0.1, 10., 9.]])
|
||
|
|
wanted = interpn(points, values, sample, method="nearest")
|
||
|
|
assert_array_almost_equal(interp_rg(sample), wanted)
|
||
|
|
|
||
|
|
def test_4d_nearest_outofbounds(self):
|
||
|
|
# create a 4-D grid of 3 points in each dimension
|
||
|
|
points, values = self._sample_4d_data()
|
||
|
|
sample = np.asarray([[0.1, -0.1, 10.1, 9.]])
|
||
|
|
wanted = 999.99
|
||
|
|
actual = interpn(points, values, sample, method="nearest",
|
||
|
|
bounds_error=False, fill_value=999.99)
|
||
|
|
assert_array_almost_equal(actual, wanted)
|
||
|
|
|
||
|
|
def test_xi_1d(self):
|
||
|
|
# verify that 1-D xi works as expected
|
||
|
|
points, values = self._sample_4d_data()
|
||
|
|
sample = np.asarray([0.1, 0.1, 10., 9.])
|
||
|
|
v1 = interpn(points, values, sample, bounds_error=False)
|
||
|
|
v2 = interpn(points, values, sample[None,:], bounds_error=False)
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
def test_xi_nd(self):
|
||
|
|
# verify that higher-d xi works as expected
|
||
|
|
points, values = self._sample_4d_data()
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
sample = np.random.rand(2, 3, 4)
|
||
|
|
|
||
|
|
v1 = interpn(points, values, sample, method='nearest',
|
||
|
|
bounds_error=False)
|
||
|
|
assert_equal(v1.shape, (2, 3))
|
||
|
|
|
||
|
|
v2 = interpn(points, values, sample.reshape(-1, 4),
|
||
|
|
method='nearest', bounds_error=False)
|
||
|
|
assert_allclose(v1, v2.reshape(v1.shape))
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_xi_broadcast(self, method):
|
||
|
|
# verify that the interpolators broadcast xi
|
||
|
|
x, y, values = self._sample_2d_data()
|
||
|
|
points = (x, y)
|
||
|
|
|
||
|
|
xi = np.linspace(0, 1, 2)
|
||
|
|
yi = np.linspace(0, 3, 3)
|
||
|
|
|
||
|
|
sample = (xi[:, None], yi[None, :])
|
||
|
|
v1 = interpn(points, values, sample, method=method, bounds_error=False)
|
||
|
|
assert_equal(v1.shape, (2, 3))
|
||
|
|
|
||
|
|
xx, yy = np.meshgrid(xi, yi)
|
||
|
|
sample = np.c_[xx.T.ravel(), yy.T.ravel()]
|
||
|
|
|
||
|
|
v2 = interpn(points, values, sample,
|
||
|
|
method=method, bounds_error=False)
|
||
|
|
assert_allclose(v1, v2.reshape(v1.shape))
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_nonscalar_values(self, method):
|
||
|
|
# Verify that non-scalar valued values also works
|
||
|
|
points = [(0.0, 0.5, 1.0, 1.5, 2.0, 2.5)] * 2 + [
|
||
|
|
(0.0, 5.0, 10.0, 15.0, 20, 25.0)
|
||
|
|
] * 2
|
||
|
|
|
||
|
|
rng = np.random.default_rng(1234)
|
||
|
|
values = rng.random((6, 6, 6, 6, 8))
|
||
|
|
sample = rng.random((7, 3, 4))
|
||
|
|
|
||
|
|
v = interpn(points, values, sample, method=method,
|
||
|
|
bounds_error=False)
|
||
|
|
assert_equal(v.shape, (7, 3, 8), err_msg=method)
|
||
|
|
|
||
|
|
vs = [interpn(points, values[..., j], sample, method=method,
|
||
|
|
bounds_error=False) for j in range(8)]
|
||
|
|
v2 = np.array(vs).transpose(1, 2, 0)
|
||
|
|
|
||
|
|
assert_allclose(v, v2, atol=1e-14, err_msg=method)
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_nonscalar_values_2(self, method):
|
||
|
|
# Verify that non-scalar valued values also work : use different
|
||
|
|
# lengths of axes to simplify tracing the internals
|
||
|
|
points = [(0.0, 0.5, 1.0, 1.5, 2.0, 2.5),
|
||
|
|
(0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0),
|
||
|
|
(0.0, 5.0, 10.0, 15.0, 20, 25.0, 35.0, 36.0),
|
||
|
|
(0.0, 5.0, 10.0, 15.0, 20, 25.0, 35.0, 36.0, 47)]
|
||
|
|
|
||
|
|
rng = np.random.default_rng(1234)
|
||
|
|
|
||
|
|
trailing_points = (3, 2)
|
||
|
|
# NB: values has a `num_trailing_dims` trailing dimension
|
||
|
|
values = rng.random((6, 7, 8, 9, *trailing_points))
|
||
|
|
sample = rng.random(4) # a single sample point !
|
||
|
|
|
||
|
|
v = interpn(points, values, sample, method=method, bounds_error=False)
|
||
|
|
|
||
|
|
# v has a single sample point *per entry in the trailing dimensions*
|
||
|
|
assert v.shape == (1, *trailing_points)
|
||
|
|
|
||
|
|
# check the values, too : manually loop over the trailing dimensions
|
||
|
|
vs = [[
|
||
|
|
interpn(points, values[..., i, j], sample, method=method,
|
||
|
|
bounds_error=False) for i in range(values.shape[-2])
|
||
|
|
] for j in range(values.shape[-1])]
|
||
|
|
|
||
|
|
assert_allclose(v, np.asarray(vs).T, atol=1e-14, err_msg=method)
|
||
|
|
|
||
|
|
def test_non_scalar_values_splinef2d(self):
|
||
|
|
# Vector-valued splines supported with fitpack
|
||
|
|
points, values = self._sample_4d_data()
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
values = np.random.rand(3, 3, 3, 3, 6)
|
||
|
|
sample = np.random.rand(7, 11, 4)
|
||
|
|
assert_raises(ValueError, interpn, points, values, sample,
|
||
|
|
method='splinef2d')
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_complex(self, method):
|
||
|
|
x, y, values = self._sample_2d_data()
|
||
|
|
points = (x, y)
|
||
|
|
values = values - 2j*values
|
||
|
|
|
||
|
|
sample = np.array([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
|
||
|
|
[1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T
|
||
|
|
|
||
|
|
v1 = interpn(points, values, sample, method=method)
|
||
|
|
v2r = interpn(points, values.real, sample, method=method)
|
||
|
|
v2i = interpn(points, values.imag, sample, method=method)
|
||
|
|
v2 = v2r + 1j*v2i
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
def test_complex_spline2fd(self):
|
||
|
|
# Complex-valued data not supported by spline2fd
|
||
|
|
x, y, values = self._sample_2d_data()
|
||
|
|
points = (x, y)
|
||
|
|
values = values - 2j*values
|
||
|
|
|
||
|
|
sample = np.array([[1, 2.3, 5.3, 0.5, 3.3, 1.2, 3],
|
||
|
|
[1, 3.3, 1.2, 4.0, 5.0, 1.0, 3]]).T
|
||
|
|
with assert_warns(np.ComplexWarning):
|
||
|
|
interpn(points, values, sample, method='splinef2d')
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"method",
|
||
|
|
["linear", "nearest"]
|
||
|
|
)
|
||
|
|
def test_duck_typed_values(self, method):
|
||
|
|
x = np.linspace(0, 2, 5)
|
||
|
|
y = np.linspace(0, 1, 7)
|
||
|
|
|
||
|
|
values = MyValue((5, 7))
|
||
|
|
|
||
|
|
v1 = interpn((x, y), values, [0.4, 0.7], method=method)
|
||
|
|
v2 = interpn((x, y), values._v, [0.4, 0.7], method=method)
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
@parametrize_rgi_interp_methods
|
||
|
|
def test_matrix_input(self, method):
|
||
|
|
x = np.linspace(0, 2, 6)
|
||
|
|
y = np.linspace(0, 1, 7)
|
||
|
|
|
||
|
|
values = matrix(np.random.rand(6, 7))
|
||
|
|
|
||
|
|
sample = np.random.rand(3, 7, 2)
|
||
|
|
|
||
|
|
v1 = interpn((x, y), values, sample, method=method)
|
||
|
|
v2 = interpn((x, y), np.asarray(values), sample, method=method)
|
||
|
|
assert_allclose(v1, v2)
|
||
|
|
|
||
|
|
def test_length_one_axis(self):
|
||
|
|
# gh-5890, gh-9524 : length-1 axis is legal for method='linear'.
|
||
|
|
# Along the axis it's linear interpolation; away from the length-1
|
||
|
|
# axis, it's an extrapolation, so fill_value should be used.
|
||
|
|
|
||
|
|
values = np.array([[0.1, 1, 10]])
|
||
|
|
xi = np.array([[1, 2.2], [1, 3.2], [1, 3.8]])
|
||
|
|
|
||
|
|
res = interpn(([1], [2, 3, 4]), values, xi)
|
||
|
|
wanted = [0.9*0.2 + 0.1, # on [2, 3) it's 0.9*(x-2) + 0.1
|
||
|
|
9*0.2 + 1, # on [3, 4] it's 9*(x-3) + 1
|
||
|
|
9*0.8 + 1]
|
||
|
|
|
||
|
|
assert_allclose(res, wanted, atol=1e-15)
|
||
|
|
|
||
|
|
# check extrapolation
|
||
|
|
xi = np.array([[1.1, 2.2], [1.5, 3.2], [-2.3, 3.8]])
|
||
|
|
res = interpn(([1], [2, 3, 4]), values, xi,
|
||
|
|
bounds_error=False, fill_value=None)
|
||
|
|
|
||
|
|
assert_allclose(res, wanted, atol=1e-15)
|
||
|
|
|
||
|
|
def test_descending_points(self):
|
||
|
|
def value_func_4d(x, y, z, a):
|
||
|
|
return 2 * x ** 3 + 3 * y ** 2 - z - a
|
||
|
|
|
||
|
|
x1 = np.array([0, 1, 2, 3])
|
||
|
|
x2 = np.array([0, 10, 20, 30])
|
||
|
|
x3 = np.array([0, 10, 20, 30])
|
||
|
|
x4 = np.array([0, .1, .2, .30])
|
||
|
|
points = (x1, x2, x3, x4)
|
||
|
|
values = value_func_4d(
|
||
|
|
*np.meshgrid(*points, indexing='ij', sparse=True))
|
||
|
|
pts = (0.1, 0.3, np.transpose(np.linspace(0, 30, 4)),
|
||
|
|
np.linspace(0, 0.3, 4))
|
||
|
|
correct_result = interpn(points, values, pts)
|
||
|
|
|
||
|
|
x1_descend = x1[::-1]
|
||
|
|
x2_descend = x2[::-1]
|
||
|
|
x3_descend = x3[::-1]
|
||
|
|
x4_descend = x4[::-1]
|
||
|
|
points_shuffled = (x1_descend, x2_descend, x3_descend, x4_descend)
|
||
|
|
values_shuffled = value_func_4d(
|
||
|
|
*np.meshgrid(*points_shuffled, indexing='ij', sparse=True))
|
||
|
|
test_result = interpn(points_shuffled, values_shuffled, pts)
|
||
|
|
|
||
|
|
assert_array_equal(correct_result, test_result)
|
||
|
|
|
||
|
|
def test_invalid_points_order(self):
|
||
|
|
x = np.array([.5, 2., 0., 4., 5.5]) # not ascending or descending
|
||
|
|
y = np.array([.5, 2., 3., 4., 5.5])
|
||
|
|
z = np.array([[1, 2, 1, 2, 1], [1, 2, 1, 2, 1], [1, 2, 3, 2, 1],
|
||
|
|
[1, 2, 2, 2, 1], [1, 2, 1, 2, 1]])
|
||
|
|
xi = np.array([[1, 2.3, 6.3, 0.5, 3.3, 1.2, 3],
|
||
|
|
[1, 3.3, 1.2, -4.0, 5.0, 1.0, 3]]).T
|
||
|
|
|
||
|
|
match = "must be strictly ascending or descending"
|
||
|
|
with pytest.raises(ValueError, match=match):
|
||
|
|
interpn((x, y), z, xi)
|
||
|
|
|
||
|
|
def test_invalid_xi_dimensions(self):
|
||
|
|
# https://github.com/scipy/scipy/issues/16519
|
||
|
|
points = [(0, 1)]
|
||
|
|
values = [0, 1]
|
||
|
|
xi = np.ones((1, 1, 3))
|
||
|
|
msg = ("The requested sample points xi have dimension 3, but this "
|
||
|
|
"RegularGridInterpolator has dimension 1")
|
||
|
|
with assert_raises(ValueError, match=msg):
|
||
|
|
interpn(points, values, xi)
|
||
|
|
|
||
|
|
def test_readonly_grid(self):
|
||
|
|
# https://github.com/scipy/scipy/issues/17716
|
||
|
|
x = np.linspace(0, 4, 5)
|
||
|
|
y = np.linspace(0, 5, 6)
|
||
|
|
z = np.linspace(0, 6, 7)
|
||
|
|
points = (x, y, z)
|
||
|
|
values = np.ones((5, 6, 7))
|
||
|
|
point = np.array([2.21, 3.12, 1.15])
|
||
|
|
for d in points:
|
||
|
|
d.flags.writeable = False
|
||
|
|
values.flags.writeable = False
|
||
|
|
point.flags.writeable = False
|
||
|
|
interpn(points, values, point)
|
||
|
|
RegularGridInterpolator(points, values)(point)
|
||
|
|
|
||
|
|
def test_2d_readonly_grid(self):
|
||
|
|
# https://github.com/scipy/scipy/issues/17716
|
||
|
|
# test special 2d case
|
||
|
|
x = np.linspace(0, 4, 5)
|
||
|
|
y = np.linspace(0, 5, 6)
|
||
|
|
points = (x, y)
|
||
|
|
values = np.ones((5, 6))
|
||
|
|
point = np.array([2.21, 3.12])
|
||
|
|
for d in points:
|
||
|
|
d.flags.writeable = False
|
||
|
|
values.flags.writeable = False
|
||
|
|
point.flags.writeable = False
|
||
|
|
interpn(points, values, point)
|
||
|
|
RegularGridInterpolator(points, values)(point)
|
||
|
|
|
||
|
|
def test_non_c_contiguous_grid(self):
|
||
|
|
# https://github.com/scipy/scipy/issues/17716
|
||
|
|
x = np.linspace(0, 4, 5)
|
||
|
|
x = np.vstack((x, np.empty_like(x))).T.copy()[:, 0]
|
||
|
|
assert not x.flags.c_contiguous
|
||
|
|
y = np.linspace(0, 5, 6)
|
||
|
|
z = np.linspace(0, 6, 7)
|
||
|
|
points = (x, y, z)
|
||
|
|
values = np.ones((5, 6, 7))
|
||
|
|
point = np.array([2.21, 3.12, 1.15])
|
||
|
|
interpn(points, values, point)
|
||
|
|
RegularGridInterpolator(points, values)(point)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", ['>f8', '<f8'])
|
||
|
|
def test_endianness(self, dtype):
|
||
|
|
# https://github.com/scipy/scipy/issues/17716
|
||
|
|
# test special 2d case
|
||
|
|
x = np.linspace(0, 4, 5, dtype=dtype)
|
||
|
|
y = np.linspace(0, 5, 6, dtype=dtype)
|
||
|
|
points = (x, y)
|
||
|
|
values = np.ones((5, 6), dtype=dtype)
|
||
|
|
point = np.array([2.21, 3.12], dtype=dtype)
|
||
|
|
interpn(points, values, point)
|
||
|
|
RegularGridInterpolator(points, values)(point)
|