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import numpy as np
from scipy._lib._array_api import (
xp_assert_equal, xp_assert_close
)
import pytest
from pytest import raises as assert_raises
from scipy.interpolate import (griddata, NearestNDInterpolator,
LinearNDInterpolator,
CloughTocher2DInterpolator)
from scipy._lib._testutils import _run_concurrent_barrier
parametrize_interpolators = pytest.mark.parametrize(
"interpolator", [NearestNDInterpolator, LinearNDInterpolator,
CloughTocher2DInterpolator]
)
parametrize_methods = pytest.mark.parametrize(
'method',
('nearest', 'linear', 'cubic'),
)
parametrize_rescale = pytest.mark.parametrize(
'rescale',
(True, False),
)
class TestGriddata:
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)
xp_assert_equal(yi, [-1., -1, 1])
yi = griddata(x, y, [(1,1), (1,2), (0,0)])
xp_assert_equal(yi, [np.nan, np.nan, 1])
@parametrize_methods
@parametrize_rescale
def test_alternative_call(self, method, rescale):
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
dtype=np.float64)
y = (np.arange(x.shape[0], dtype=np.float64)[:,None]
+ np.array([0,1])[None,:])
msg = repr((method, rescale))
yi = griddata((x[:,0], x[:,1]), y, (x[:,0], x[:,1]), method=method,
rescale=rescale)
xp_assert_close(y, yi, atol=1e-14, err_msg=msg)
@parametrize_methods
@parametrize_rescale
def test_multivalue_2d(self, method, rescale):
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
dtype=np.float64)
y = (np.arange(x.shape[0], dtype=np.float64)[:,None]
+ np.array([0,1])[None,:])
msg = repr((method, rescale))
yi = griddata(x, y, x, method=method, rescale=rescale)
xp_assert_close(y, yi, atol=1e-14, err_msg=msg)
@parametrize_methods
@parametrize_rescale
def test_multipoint_2d(self, method, rescale):
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
dtype=np.float64)
y = np.arange(x.shape[0], dtype=np.float64)
xi = x[:,None,:] + np.array([0,0,0])[None,:,None]
msg = repr((method, rescale))
yi = griddata(x, y, xi, method=method, rescale=rescale)
assert yi.shape == (5, 3), msg
xp_assert_close(yi, np.tile(y[:,None], (1, 3)),
atol=1e-14, err_msg=msg)
@parametrize_methods
@parametrize_rescale
def test_complex_2d(self, method, rescale):
x = np.array([(0,0), (-0.5,-0.5), (-0.5,0.5), (0.5, 0.5), (0.25, 0.3)],
dtype=np.float64)
y = np.arange(x.shape[0], dtype=np.float64)
y = y - 2j*y[::-1]
xi = x[:,None,:] + np.array([0,0,0])[None,:,None]
msg = repr((method, rescale))
yi = griddata(x, y, xi, method=method, rescale=rescale)
assert yi.shape == (5, 3)
xp_assert_close(yi, np.tile(y[:,None], (1, 3)),
atol=1e-14, err_msg=msg)
@parametrize_methods
def test_1d(self, method):
x = np.array([1, 2.5, 3, 4.5, 5, 6])
y = np.array([1, 2, 0, 3.9, 2, 1])
xp_assert_close(griddata(x, y, x, method=method), y,
err_msg=method, atol=1e-14)
xp_assert_close(griddata(x.reshape(6, 1), y, x, method=method), y,
err_msg=method, atol=1e-14)
xp_assert_close(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'
xp_assert_close(griddata(x, y, xi,
method=method), yi_should,
err_msg=method,
atol=1e-14)
xp_assert_close(griddata(x.reshape(6, 1), y, xi,
method=method), yi_should,
err_msg=method,
atol=1e-14)
xp_assert_close(griddata((x, ), y, (xi, ),
method=method), yi_should,
err_msg=method,
atol=1e-14)
@parametrize_methods
def test_1d_unsorted(self, method):
x = np.array([2.5, 1, 4.5, 5, 6, 3])
y = np.array([1, 2, 0, 3.9, 2, 1])
xp_assert_close(griddata(x, y, x, method=method), y,
err_msg=method, atol=1e-10)
xp_assert_close(griddata(x.reshape(6, 1), y, x, method=method), y,
err_msg=method, atol=1e-10)
xp_assert_close(griddata((x,), y, (x,), method=method), y,
err_msg=method, atol=1e-10)
@parametrize_methods
def test_square_rescale_manual(self, method):
points = np.array([(0,0), (0,100), (10,100), (10,0), (1, 5)], dtype=np.float64)
points_rescaled = np.array([(0,0), (0,1), (1,1), (1,0), (0.1, 0.05)],
dtype=np.float64)
values = np.array([1., 2., -3., 5., 9.], dtype=np.float64)
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()
msg = method
zi = griddata(points_rescaled, values, xi/np.array([10, 100.]),
method=method)
zi_rescaled = griddata(points, values, xi, method=method,
rescale=True)
xp_assert_close(zi, zi_rescaled, err_msg=msg,
atol=1e-12)
@parametrize_methods
def test_xi_1d(self, method):
# 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.float64)
y = np.arange(x.shape[0], dtype=np.float64)
y = y - 2j*y[::-1]
xi = np.array([0.5, 0.5])
p1 = griddata(x, y, xi, method=method)
p2 = griddata(x, y, xi[None,:], method=method)
xp_assert_close(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)
class TestNearestNDInterpolator:
def test_nearest_options(self):
# 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)
xp_assert_close(nndi(x), nndi_o(x), atol=1e-14)
def test_nearest_list_argument(self):
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])
xp_assert_equal(NI([0.1, 0.9], [0.1, 0.9]), [0.0, 2.0])
# z is list
NI = NearestNDInterpolator((d[0], d[1]), list(d[2]))
xp_assert_equal(NI([0.1, 0.9], [0.1, 0.9]), [0.0, 2.0])
def test_nearest_query_options(self):
nd = np.array([[0, 0.5, 0, 1],
[0, 0, 0.5, 1],
[0, 1, 1, 2]])
delta = 0.1
query_points = [0 + delta, 1 + delta], [0 + delta, 1 + delta]
# case 1 - query max_dist is smaller than
# the query points' nearest distance to nd.
NI = NearestNDInterpolator((nd[0], nd[1]), nd[2])
distance_upper_bound = np.sqrt(delta ** 2 + delta ** 2) - 1e-7
xp_assert_equal(NI(query_points, distance_upper_bound=distance_upper_bound),
[np.nan, np.nan])
# case 2 - query p is inf, will return [0, 2]
distance_upper_bound = np.sqrt(delta ** 2 + delta ** 2) - 1e-7
p = np.inf
xp_assert_equal(
NI(query_points, distance_upper_bound=distance_upper_bound, p=p),
[0.0, 2.0]
)
# case 3 - query max_dist is larger, so should return non np.nan
distance_upper_bound = np.sqrt(delta ** 2 + delta ** 2) + 1e-7
xp_assert_equal(
NI(query_points, distance_upper_bound=distance_upper_bound),
[0.0, 2.0]
)
def test_nearest_query_valid_inputs(self):
nd = np.array([[0, 1, 0, 1],
[0, 0, 1, 1],
[0, 1, 1, 2]])
NI = NearestNDInterpolator((nd[0], nd[1]), nd[2])
with assert_raises(TypeError):
NI([0.5, 0.5], query_options="not a dictionary")
@pytest.mark.thread_unsafe
def test_concurrency(self):
npts, nd = 50, 3
x = np.arange(npts * nd).reshape((npts, nd))
y = np.arange(npts)
nndi = NearestNDInterpolator(x, y)
def worker_fn(_, spl):
spl(x)
_run_concurrent_barrier(10, worker_fn, nndi)
class TestNDInterpolators:
@parametrize_interpolators
def test_broadcastable_input(self, interpolator):
# input data
rng = np.random.RandomState(0)
x = rng.random(10)
y = rng.random(10)
z = np.hypot(x, y)
# x-y grid for interpolation
X = np.linspace(min(x), max(x))
Y = np.linspace(min(y), max(y))
X, Y = np.meshgrid(X, Y)
XY = np.vstack((X.ravel(), Y.ravel())).T
interp = interpolator(list(zip(x, y)), z)
# single array input
interp_points0 = interp(XY)
# tuple input
interp_points1 = interp((X, Y))
interp_points2 = interp((X, 0.0))
# broadcastable input
interp_points3 = interp(X, Y)
interp_points4 = interp(X, 0.0)
assert (interp_points0.size ==
interp_points1.size ==
interp_points2.size ==
interp_points3.size ==
interp_points4.size)
@parametrize_interpolators
def test_read_only(self, interpolator):
# input data
rng = np.random.RandomState(0)
xy = rng.random((10, 2))
x, y = xy[:, 0], xy[:, 1]
z = np.hypot(x, y)
# interpolation points
XY = rng.random((50, 2))
xy.setflags(write=False)
z.setflags(write=False)
XY.setflags(write=False)
interp = interpolator(xy, z)
interp(XY)
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