def test_out_of_bounds_extrap(self):
points, values = self._get_sample_4d()
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]])
interp = _RegularGridInterpolator(points, sample.T, method="nn")
wanted = np.asarray([0., 1111., 11., 11.])
assert_array_almost_equal(interp(values, fill_value=None), wanted)
interp = _RegularGridInterpolator(points, sample.T, method="lin")
wanted = np.asarray([-111.1, 1222.1, -11068., -1186.9])
assert_array_almost_equal(interp(values, fill_value=None), wanted)
def test_out_of_bounds_extrap(self):
points, values = self._get_sample_4d()
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]])
interp = _RegularGridInterpolator(points, sample.T, method="nn")
wanted = np.asarray([0., 1111., 11., 11.])
assert_array_almost_equal(interp(values, fill_value=None), wanted)
interp = _RegularGridInterpolator(points, sample.T, method="lin")
wanted = np.asarray([-111.1, 1222.1, -11068., -1186.9])
assert_array_almost_equal(interp(values, fill_value=None), wanted)
def test_complex(self):
points, values = self._get_sample_4d()
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]])
for method in ['lin', 'nn']:
interp = _RegularGridInterpolator(points, sample.T, method=method)
rinterp = _RegularGridInterpolator(points, sample.real.T, method=method)
iinterp = _RegularGridInterpolator(points, sample.imag.T, method=method)
v1 = interp(values)
v2 = rinterp(values) + 1j*iinterp(values)
assert_allclose(v1, v2)
def test_list_input(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
[0.5, 0.5, .5, .5]])
for method in ['lin', 'nn']:
interp = _RegularGridInterpolator(points,
sample.T.tolist(), method=method)
v1 = interp(values.tolist())
interp = _RegularGridInterpolator(points,
sample.T, method=method)
v2 = interp(values)
assert_allclose(v1, v2)
def test_list_input(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
[0.5, 0.5, .5, .5]])
for method in ['lin', 'nn']:
interp = _RegularGridInterpolator(points,
sample.T.tolist(),
method=method)
v1 = interp(values.tolist())
interp = _RegularGridInterpolator(points, sample.T, method=method)
v2 = interp(values)
assert_allclose(v1, v2)
def test_linear_xi3d(self):
points, values = self._get_sample_4d()
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])
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values), wanted)
def test_linear_xi3d(self):
points, values = self._get_sample_4d()
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])
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values), wanted)
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 = np.asarray([1, 1, 1])
interpolator = _RegularGridInterpolator(points, xi.T)
interpolator(values, fill_value=0.)
def test_out_of_bounds_fill(self):
points, values = self._get_sample_4d()
sample = np.asarray([[-.1, -.1, -.1, -.1], [1.1, 1.1, 1.1, 1.1],
[2.1, 2.1, -1.1, -1.1]])
interp = _RegularGridInterpolator(points, sample.T, method="nn")
# Assert that all of the elements are masked
assert all(interp(values, fill_value=np.nan).mask)
interp = _RegularGridInterpolator(points, sample.T, method="lin")
assert all(interp(values, fill_value=np.nan).mask)
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])
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values, fill_value=np.nan), wanted)
def test_out_of_bounds_fill(self):
points, values = self._get_sample_4d()
sample = np.asarray([[-.1, -.1, -.1, -.1], [1.1, 1.1, 1.1, 1.1],
[2.1, 2.1, -1.1, -1.1]])
interp = _RegularGridInterpolator(points, sample.T, method="nn")
# Assert that all of the elements are masked
assert all(interp(values, fill_value=np.nan).mask)
interp = _RegularGridInterpolator(points, sample.T, method="lin")
assert all(interp(values, fill_value=np.nan).mask)
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])
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values, fill_value=np.nan), wanted)
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 = np.asarray([1, 1, 1])
interpolator = _RegularGridInterpolator(points, xi.T)
interpolator(values, fill_value=0.)
def test_valid_call(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0., 0., 0., 0.], [1., 1., 1., 1.]])
interp = _RegularGridInterpolator(points, sample.T)
assert_raises(ValueError, interp, values, "undefmethod")
# This is actually an error on creation now
sample = np.asarray([[0., 0., 0.], [1., 1., 1.]])
assert_raises(ValueError, _RegularGridInterpolator, points, sample)
def test_valid_call(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0., 0., 0., 0.], [1., 1., 1., 1.]])
interp = _RegularGridInterpolator(points, sample.T)
assert_raises(ValueError, interp, values, "undefmethod")
# This is actually an error on creation now
sample = np.asarray([[0., 0., 0.], [1., 1., 1.]])
assert_raises(ValueError, _RegularGridInterpolator, points, sample)
def test_complex(self):
points, values = self._get_sample_4d()
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]])
for method in ['lin', 'nn']:
interp = _RegularGridInterpolator(points, sample.T, method=method)
rinterp = _RegularGridInterpolator(points,
sample.real.T,
method=method)
iinterp = _RegularGridInterpolator(points,
sample.imag.T,
method=method)
v1 = interp(values)
v2 = rinterp(values) + 1j * iinterp(values)
assert_allclose(v1, v2)
def test_linear_compare_qhull(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
[0.5, 0.5, .5, .5]])
interp = _RegularGridInterpolator(points, sample.T)
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)
assert_array_almost_equal(interp(values), interp_qhull(sample))
def test_linear_compare_qhull(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0.1, 0.1, 1., .9], [0.2, 0.1, .45, .8],
[0.5, 0.5, .5, .5]])
interp = _RegularGridInterpolator(points, sample.T)
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)
assert_array_almost_equal(interp(values), interp_qhull(sample))
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
interp = _RegularGridInterpolator((x, y), [0.4, 0.7])
interp(values, fill_value=1)
# complex values cannot
assert_raises(ValueError, interp, values, fill_value=1+2j)
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
interp = _RegularGridInterpolator((x, y), [0.4, 0.7])
interp(values, fill_value=1)
# complex values cannot
assert_raises(ValueError, interp, values, fill_value=1 + 2j)
def test_nearest(self):
points, values = self._get_sample_4d()
sample = np.asarray([0.1, 0.1, .9, .9])
wanted = 1100.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([0.1, 0.1, 0.1, 0.1])
wanted = 0.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([0., 0., 0., 0.])
wanted = 0.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([1., 1., 1., 1.])
wanted = 1111.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([0.1, 0.4, 0.6, 0.9])
wanted = 1055.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
def test_nearest(self):
points, values = self._get_sample_4d()
sample = np.asarray([0.1, 0.1, .9, .9])
wanted = 1100.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([0.1, 0.1, 0.1, 0.1])
wanted = 0.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([0., 0., 0., 0.])
wanted = 0.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([1., 1., 1., 1.])
wanted = 1111.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
sample = np.asarray([0.1, 0.4, 0.6, 0.9])
wanted = 1055.
interp = _RegularGridInterpolator(points, sample.T, method="nn")
assert_array_almost_equal(interp(values), wanted)
def test_linear_edges(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0., 0., 0., 0.], [1., 1., 1., 1.]])
wanted = np.asarray([0., 1111.])
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values), wanted)
def test_linear_xi1d(self):
points, values = self._get_sample_4d_2()
sample = np.asarray([0.1, 0.1, 10., 9.])
wanted = 1001.1
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values), wanted)
def test_linear_xi1d(self):
points, values = self._get_sample_4d_2()
sample = np.asarray([0.1, 0.1, 10., 9.])
wanted = 1001.1
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values), wanted)
def test_linear_edges(self):
points, values = self._get_sample_4d()
sample = np.asarray([[0., 0., 0., 0.], [1., 1., 1., 1.]])
wanted = np.asarray([0., 1111.])
interp = _RegularGridInterpolator(points, sample.T)
assert_array_almost_equal(interp(values), wanted)