942 lines
36 KiB
Python
942 lines
36 KiB
Python
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import warnings
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import io
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import numpy as np
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from numpy.testing import (
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assert_almost_equal, assert_array_equal, assert_array_almost_equal,
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assert_allclose, assert_equal, assert_)
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from pytest import raises as assert_raises
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import pytest
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from scipy.interpolate import (
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KroghInterpolator, krogh_interpolate,
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BarycentricInterpolator, barycentric_interpolate,
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approximate_taylor_polynomial, CubicHermiteSpline, pchip,
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PchipInterpolator, pchip_interpolate, Akima1DInterpolator, CubicSpline,
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make_interp_spline)
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def check_shape(interpolator_cls, x_shape, y_shape, deriv_shape=None, axis=0,
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extra_args={}):
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np.random.seed(1234)
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x = [-1, 0, 1, 2, 3, 4]
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s = list(range(1, len(y_shape)+1))
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s.insert(axis % (len(y_shape)+1), 0)
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y = np.random.rand(*((6,) + y_shape)).transpose(s)
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xi = np.zeros(x_shape)
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if interpolator_cls is CubicHermiteSpline:
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dydx = np.random.rand(*((6,) + y_shape)).transpose(s)
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yi = interpolator_cls(x, y, dydx, axis=axis, **extra_args)(xi)
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else:
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yi = interpolator_cls(x, y, axis=axis, **extra_args)(xi)
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target_shape = ((deriv_shape or ()) + y.shape[:axis]
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+ x_shape + y.shape[axis:][1:])
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assert_equal(yi.shape, target_shape)
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# check it works also with lists
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if x_shape and y.size > 0:
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if interpolator_cls is CubicHermiteSpline:
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interpolator_cls(list(x), list(y), list(dydx), axis=axis,
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**extra_args)(list(xi))
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else:
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interpolator_cls(list(x), list(y), axis=axis,
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**extra_args)(list(xi))
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# check also values
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if xi.size > 0 and deriv_shape is None:
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bs_shape = y.shape[:axis] + (1,)*len(x_shape) + y.shape[axis:][1:]
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yv = y[((slice(None,),)*(axis % y.ndim)) + (1,)]
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yv = yv.reshape(bs_shape)
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yi, y = np.broadcast_arrays(yi, yv)
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assert_allclose(yi, y)
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SHAPES = [(), (0,), (1,), (6, 2, 5)]
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def test_shapes():
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def spl_interp(x, y, axis):
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return make_interp_spline(x, y, axis=axis)
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for ip in [KroghInterpolator, BarycentricInterpolator, CubicHermiteSpline,
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pchip, Akima1DInterpolator, CubicSpline, spl_interp]:
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for s1 in SHAPES:
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for s2 in SHAPES:
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for axis in range(-len(s2), len(s2)):
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if ip != CubicSpline:
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check_shape(ip, s1, s2, None, axis)
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else:
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for bc in ['natural', 'clamped']:
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extra = {'bc_type': bc}
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check_shape(ip, s1, s2, None, axis, extra)
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def test_derivs_shapes():
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for ip in [KroghInterpolator, BarycentricInterpolator]:
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def interpolator_derivs(x, y, axis=0):
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return ip(x, y, axis).derivatives
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for s1 in SHAPES:
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for s2 in SHAPES:
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for axis in range(-len(s2), len(s2)):
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check_shape(interpolator_derivs, s1, s2, (6,), axis)
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def test_deriv_shapes():
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def krogh_deriv(x, y, axis=0):
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return KroghInterpolator(x, y, axis).derivative
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def bary_deriv(x, y, axis=0):
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return BarycentricInterpolator(x, y, axis).derivative
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def pchip_deriv(x, y, axis=0):
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return pchip(x, y, axis).derivative()
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def pchip_deriv2(x, y, axis=0):
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return pchip(x, y, axis).derivative(2)
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def pchip_antideriv(x, y, axis=0):
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return pchip(x, y, axis).antiderivative()
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def pchip_antideriv2(x, y, axis=0):
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return pchip(x, y, axis).antiderivative(2)
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def pchip_deriv_inplace(x, y, axis=0):
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class P(PchipInterpolator):
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def __call__(self, x):
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return PchipInterpolator.__call__(self, x, 1)
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pass
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return P(x, y, axis)
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def akima_deriv(x, y, axis=0):
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return Akima1DInterpolator(x, y, axis).derivative()
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def akima_antideriv(x, y, axis=0):
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return Akima1DInterpolator(x, y, axis).antiderivative()
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def cspline_deriv(x, y, axis=0):
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return CubicSpline(x, y, axis).derivative()
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def cspline_antideriv(x, y, axis=0):
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return CubicSpline(x, y, axis).antiderivative()
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def bspl_deriv(x, y, axis=0):
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return make_interp_spline(x, y, axis=axis).derivative()
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def bspl_antideriv(x, y, axis=0):
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return make_interp_spline(x, y, axis=axis).antiderivative()
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for ip in [krogh_deriv, bary_deriv, pchip_deriv, pchip_deriv2, pchip_deriv_inplace,
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pchip_antideriv, pchip_antideriv2, akima_deriv, akima_antideriv,
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cspline_deriv, cspline_antideriv, bspl_deriv, bspl_antideriv]:
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for s1 in SHAPES:
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for s2 in SHAPES:
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for axis in range(-len(s2), len(s2)):
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check_shape(ip, s1, s2, (), axis)
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def test_complex():
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x = [1, 2, 3, 4]
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y = [1, 2, 1j, 3]
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for ip in [KroghInterpolator, BarycentricInterpolator, CubicSpline]:
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p = ip(x, y)
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assert_allclose(y, p(x))
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dydx = [0, -1j, 2, 3j]
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p = CubicHermiteSpline(x, y, dydx)
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assert_allclose(y, p(x))
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assert_allclose(dydx, p(x, 1))
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class TestKrogh:
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def setup_method(self):
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self.true_poly = np.polynomial.Polynomial([-4, 5, 1, 3, -2])
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self.test_xs = np.linspace(-1,1,100)
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self.xs = np.linspace(-1,1,5)
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self.ys = self.true_poly(self.xs)
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def test_lagrange(self):
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P = KroghInterpolator(self.xs,self.ys)
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assert_almost_equal(self.true_poly(self.test_xs),P(self.test_xs))
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def test_scalar(self):
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P = KroghInterpolator(self.xs,self.ys)
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assert_almost_equal(self.true_poly(7),P(7))
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assert_almost_equal(self.true_poly(np.array(7)), P(np.array(7)))
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def test_derivatives(self):
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P = KroghInterpolator(self.xs,self.ys)
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D = P.derivatives(self.test_xs)
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for i in range(D.shape[0]):
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assert_almost_equal(self.true_poly.deriv(i)(self.test_xs),
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D[i])
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def test_low_derivatives(self):
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P = KroghInterpolator(self.xs,self.ys)
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D = P.derivatives(self.test_xs,len(self.xs)+2)
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for i in range(D.shape[0]):
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assert_almost_equal(self.true_poly.deriv(i)(self.test_xs),
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D[i])
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def test_derivative(self):
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P = KroghInterpolator(self.xs,self.ys)
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m = 10
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r = P.derivatives(self.test_xs,m)
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for i in range(m):
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assert_almost_equal(P.derivative(self.test_xs,i),r[i])
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def test_high_derivative(self):
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P = KroghInterpolator(self.xs,self.ys)
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for i in range(len(self.xs), 2*len(self.xs)):
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assert_almost_equal(P.derivative(self.test_xs,i),
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np.zeros(len(self.test_xs)))
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def test_ndim_derivatives(self):
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poly1 = self.true_poly
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poly2 = np.polynomial.Polynomial([-2, 5, 3, -1])
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poly3 = np.polynomial.Polynomial([12, -3, 4, -5, 6])
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ys = np.stack((poly1(self.xs), poly2(self.xs), poly3(self.xs)), axis=-1)
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P = KroghInterpolator(self.xs, ys, axis=0)
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D = P.derivatives(self.test_xs)
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for i in range(D.shape[0]):
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assert_allclose(D[i],
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np.stack((poly1.deriv(i)(self.test_xs),
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poly2.deriv(i)(self.test_xs),
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poly3.deriv(i)(self.test_xs)),
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axis=-1))
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def test_ndim_derivative(self):
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poly1 = self.true_poly
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poly2 = np.polynomial.Polynomial([-2, 5, 3, -1])
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poly3 = np.polynomial.Polynomial([12, -3, 4, -5, 6])
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ys = np.stack((poly1(self.xs), poly2(self.xs), poly3(self.xs)), axis=-1)
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P = KroghInterpolator(self.xs, ys, axis=0)
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for i in range(P.n):
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assert_allclose(P.derivative(self.test_xs, i),
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np.stack((poly1.deriv(i)(self.test_xs),
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poly2.deriv(i)(self.test_xs),
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poly3.deriv(i)(self.test_xs)),
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axis=-1))
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def test_hermite(self):
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P = KroghInterpolator(self.xs,self.ys)
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assert_almost_equal(self.true_poly(self.test_xs),P(self.test_xs))
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def test_vector(self):
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xs = [0, 1, 2]
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ys = np.array([[0,1],[1,0],[2,1]])
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P = KroghInterpolator(xs,ys)
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Pi = [KroghInterpolator(xs,ys[:,i]) for i in range(ys.shape[1])]
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test_xs = np.linspace(-1,3,100)
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assert_almost_equal(P(test_xs),
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np.asarray([p(test_xs) for p in Pi]).T)
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assert_almost_equal(P.derivatives(test_xs),
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np.transpose(np.asarray([p.derivatives(test_xs) for p in Pi]),
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(1,2,0)))
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def test_empty(self):
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P = KroghInterpolator(self.xs,self.ys)
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assert_array_equal(P([]), [])
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def test_shapes_scalarvalue(self):
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P = KroghInterpolator(self.xs,self.ys)
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assert_array_equal(np.shape(P(0)), ())
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assert_array_equal(np.shape(P(np.array(0))), ())
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assert_array_equal(np.shape(P([0])), (1,))
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assert_array_equal(np.shape(P([0,1])), (2,))
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def test_shapes_scalarvalue_derivative(self):
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P = KroghInterpolator(self.xs,self.ys)
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n = P.n
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assert_array_equal(np.shape(P.derivatives(0)), (n,))
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assert_array_equal(np.shape(P.derivatives(np.array(0))), (n,))
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assert_array_equal(np.shape(P.derivatives([0])), (n,1))
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assert_array_equal(np.shape(P.derivatives([0,1])), (n,2))
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def test_shapes_vectorvalue(self):
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P = KroghInterpolator(self.xs,np.outer(self.ys,np.arange(3)))
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assert_array_equal(np.shape(P(0)), (3,))
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assert_array_equal(np.shape(P([0])), (1,3))
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assert_array_equal(np.shape(P([0,1])), (2,3))
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def test_shapes_1d_vectorvalue(self):
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P = KroghInterpolator(self.xs,np.outer(self.ys,[1]))
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assert_array_equal(np.shape(P(0)), (1,))
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assert_array_equal(np.shape(P([0])), (1,1))
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assert_array_equal(np.shape(P([0,1])), (2,1))
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def test_shapes_vectorvalue_derivative(self):
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P = KroghInterpolator(self.xs,np.outer(self.ys,np.arange(3)))
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n = P.n
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assert_array_equal(np.shape(P.derivatives(0)), (n,3))
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assert_array_equal(np.shape(P.derivatives([0])), (n,1,3))
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assert_array_equal(np.shape(P.derivatives([0,1])), (n,2,3))
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def test_wrapper(self):
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P = KroghInterpolator(self.xs, self.ys)
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ki = krogh_interpolate
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assert_almost_equal(P(self.test_xs), ki(self.xs, self.ys, self.test_xs))
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assert_almost_equal(P.derivative(self.test_xs, 2),
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ki(self.xs, self.ys, self.test_xs, der=2))
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assert_almost_equal(P.derivatives(self.test_xs, 2),
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ki(self.xs, self.ys, self.test_xs, der=[0, 1]))
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def test_int_inputs(self):
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# Check input args are cast correctly to floats, gh-3669
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x = [0, 234, 468, 702, 936, 1170, 1404, 2340, 3744, 6084, 8424,
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13104, 60000]
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offset_cdf = np.array([-0.95, -0.86114777, -0.8147762, -0.64072425,
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-0.48002351, -0.34925329, -0.26503107,
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-0.13148093, -0.12988833, -0.12979296,
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-0.12973574, -0.08582937, 0.05])
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f = KroghInterpolator(x, offset_cdf)
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assert_allclose(abs((f(x) - offset_cdf) / f.derivative(x, 1)),
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0, atol=1e-10)
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def test_derivatives_complex(self):
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# regression test for gh-7381: krogh.derivatives(0) fails complex y
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x, y = np.array([-1, -1, 0, 1, 1]), np.array([1, 1.0j, 0, -1, 1.0j])
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func = KroghInterpolator(x, y)
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cmplx = func.derivatives(0)
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cmplx2 = (KroghInterpolator(x, y.real).derivatives(0) +
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1j*KroghInterpolator(x, y.imag).derivatives(0))
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assert_allclose(cmplx, cmplx2, atol=1e-15)
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def test_high_degree_warning(self):
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|
|
with pytest.warns(UserWarning, match="40 degrees provided,"):
|
||
|
|
KroghInterpolator(np.arange(40), np.ones(40))
|
||
|
|
|
||
|
|
|
||
|
|
class TestTaylor:
|
||
|
|
def test_exponential(self):
|
||
|
|
degree = 5
|
||
|
|
p = approximate_taylor_polynomial(np.exp, 0, degree, 1, 15)
|
||
|
|
for i in range(degree+1):
|
||
|
|
assert_almost_equal(p(0),1)
|
||
|
|
p = p.deriv()
|
||
|
|
assert_almost_equal(p(0),0)
|
||
|
|
|
||
|
|
|
||
|
|
class TestBarycentric:
|
||
|
|
def setup_method(self):
|
||
|
|
self.true_poly = np.polynomial.Polynomial([-4, 5, 1, 3, -2])
|
||
|
|
self.test_xs = np.linspace(-1, 1, 100)
|
||
|
|
self.xs = np.linspace(-1, 1, 5)
|
||
|
|
self.ys = self.true_poly(self.xs)
|
||
|
|
|
||
|
|
def test_lagrange(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
assert_allclose(P(self.test_xs), self.true_poly(self.test_xs))
|
||
|
|
|
||
|
|
def test_scalar(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
assert_allclose(P(7), self.true_poly(7))
|
||
|
|
assert_allclose(P(np.array(7)), self.true_poly(np.array(7)))
|
||
|
|
|
||
|
|
def test_derivatives(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
D = P.derivatives(self.test_xs)
|
||
|
|
for i in range(D.shape[0]):
|
||
|
|
assert_allclose(self.true_poly.deriv(i)(self.test_xs), D[i])
|
||
|
|
|
||
|
|
def test_low_derivatives(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
D = P.derivatives(self.test_xs, len(self.xs)+2)
|
||
|
|
for i in range(D.shape[0]):
|
||
|
|
assert_allclose(self.true_poly.deriv(i)(self.test_xs),
|
||
|
|
D[i],
|
||
|
|
atol=1e-12)
|
||
|
|
|
||
|
|
def test_derivative(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
m = 10
|
||
|
|
r = P.derivatives(self.test_xs, m)
|
||
|
|
for i in range(m):
|
||
|
|
assert_allclose(P.derivative(self.test_xs, i), r[i])
|
||
|
|
|
||
|
|
def test_high_derivative(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
for i in range(len(self.xs), 5*len(self.xs)):
|
||
|
|
assert_allclose(P.derivative(self.test_xs, i),
|
||
|
|
np.zeros(len(self.test_xs)))
|
||
|
|
|
||
|
|
def test_ndim_derivatives(self):
|
||
|
|
poly1 = self.true_poly
|
||
|
|
poly2 = np.polynomial.Polynomial([-2, 5, 3, -1])
|
||
|
|
poly3 = np.polynomial.Polynomial([12, -3, 4, -5, 6])
|
||
|
|
ys = np.stack((poly1(self.xs), poly2(self.xs), poly3(self.xs)), axis=-1)
|
||
|
|
|
||
|
|
P = BarycentricInterpolator(self.xs, ys, axis=0)
|
||
|
|
D = P.derivatives(self.test_xs)
|
||
|
|
for i in range(D.shape[0]):
|
||
|
|
assert_allclose(D[i],
|
||
|
|
np.stack((poly1.deriv(i)(self.test_xs),
|
||
|
|
poly2.deriv(i)(self.test_xs),
|
||
|
|
poly3.deriv(i)(self.test_xs)),
|
||
|
|
axis=-1),
|
||
|
|
atol=1e-12)
|
||
|
|
|
||
|
|
def test_ndim_derivative(self):
|
||
|
|
poly1 = self.true_poly
|
||
|
|
poly2 = np.polynomial.Polynomial([-2, 5, 3, -1])
|
||
|
|
poly3 = np.polynomial.Polynomial([12, -3, 4, -5, 6])
|
||
|
|
ys = np.stack((poly1(self.xs), poly2(self.xs), poly3(self.xs)), axis=-1)
|
||
|
|
|
||
|
|
P = BarycentricInterpolator(self.xs, ys, axis=0)
|
||
|
|
for i in range(P.n):
|
||
|
|
assert_allclose(P.derivative(self.test_xs, i),
|
||
|
|
np.stack((poly1.deriv(i)(self.test_xs),
|
||
|
|
poly2.deriv(i)(self.test_xs),
|
||
|
|
poly3.deriv(i)(self.test_xs)),
|
||
|
|
axis=-1),
|
||
|
|
atol=1e-12)
|
||
|
|
|
||
|
|
def test_delayed(self):
|
||
|
|
P = BarycentricInterpolator(self.xs)
|
||
|
|
P.set_yi(self.ys)
|
||
|
|
assert_almost_equal(self.true_poly(self.test_xs), P(self.test_xs))
|
||
|
|
|
||
|
|
def test_append(self):
|
||
|
|
P = BarycentricInterpolator(self.xs[:3], self.ys[:3])
|
||
|
|
P.add_xi(self.xs[3:], self.ys[3:])
|
||
|
|
assert_almost_equal(self.true_poly(self.test_xs), P(self.test_xs))
|
||
|
|
|
||
|
|
def test_vector(self):
|
||
|
|
xs = [0, 1, 2]
|
||
|
|
ys = np.array([[0, 1], [1, 0], [2, 1]])
|
||
|
|
BI = BarycentricInterpolator
|
||
|
|
P = BI(xs, ys)
|
||
|
|
Pi = [BI(xs, ys[:, i]) for i in range(ys.shape[1])]
|
||
|
|
test_xs = np.linspace(-1, 3, 100)
|
||
|
|
assert_almost_equal(P(test_xs),
|
||
|
|
np.asarray([p(test_xs) for p in Pi]).T)
|
||
|
|
|
||
|
|
def test_shapes_scalarvalue(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
assert_array_equal(np.shape(P(0)), ())
|
||
|
|
assert_array_equal(np.shape(P(np.array(0))), ())
|
||
|
|
assert_array_equal(np.shape(P([0])), (1,))
|
||
|
|
assert_array_equal(np.shape(P([0, 1])), (2,))
|
||
|
|
|
||
|
|
def test_shapes_scalarvalue_derivative(self):
|
||
|
|
P = BarycentricInterpolator(self.xs,self.ys)
|
||
|
|
n = P.n
|
||
|
|
assert_array_equal(np.shape(P.derivatives(0)), (n,))
|
||
|
|
assert_array_equal(np.shape(P.derivatives(np.array(0))), (n,))
|
||
|
|
assert_array_equal(np.shape(P.derivatives([0])), (n,1))
|
||
|
|
assert_array_equal(np.shape(P.derivatives([0,1])), (n,2))
|
||
|
|
|
||
|
|
def test_shapes_vectorvalue(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, np.outer(self.ys, np.arange(3)))
|
||
|
|
assert_array_equal(np.shape(P(0)), (3,))
|
||
|
|
assert_array_equal(np.shape(P([0])), (1, 3))
|
||
|
|
assert_array_equal(np.shape(P([0, 1])), (2, 3))
|
||
|
|
|
||
|
|
def test_shapes_1d_vectorvalue(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, np.outer(self.ys, [1]))
|
||
|
|
assert_array_equal(np.shape(P(0)), (1,))
|
||
|
|
assert_array_equal(np.shape(P([0])), (1, 1))
|
||
|
|
assert_array_equal(np.shape(P([0,1])), (2, 1))
|
||
|
|
|
||
|
|
def test_shapes_vectorvalue_derivative(self):
|
||
|
|
P = BarycentricInterpolator(self.xs,np.outer(self.ys,np.arange(3)))
|
||
|
|
n = P.n
|
||
|
|
assert_array_equal(np.shape(P.derivatives(0)), (n,3))
|
||
|
|
assert_array_equal(np.shape(P.derivatives([0])), (n,1,3))
|
||
|
|
assert_array_equal(np.shape(P.derivatives([0,1])), (n,2,3))
|
||
|
|
|
||
|
|
def test_wrapper(self):
|
||
|
|
P = BarycentricInterpolator(self.xs, self.ys)
|
||
|
|
bi = barycentric_interpolate
|
||
|
|
assert_allclose(P(self.test_xs), bi(self.xs, self.ys, self.test_xs))
|
||
|
|
assert_allclose(P.derivative(self.test_xs, 2),
|
||
|
|
bi(self.xs, self.ys, self.test_xs, der=2))
|
||
|
|
assert_allclose(P.derivatives(self.test_xs, 2),
|
||
|
|
bi(self.xs, self.ys, self.test_xs, der=[0, 1]))
|
||
|
|
|
||
|
|
def test_int_input(self):
|
||
|
|
x = 1000 * np.arange(1, 11) # np.prod(x[-1] - x[:-1]) overflows
|
||
|
|
y = np.arange(1, 11)
|
||
|
|
value = barycentric_interpolate(x, y, 1000 * 9.5)
|
||
|
|
assert_almost_equal(value, 9.5)
|
||
|
|
|
||
|
|
def test_large_chebyshev(self):
|
||
|
|
# The weights for Chebyshev points of the second kind have analytically
|
||
|
|
# solvable weights. Naive calculation of barycentric weights will fail
|
||
|
|
# for large N because of numerical underflow and overflow. We test
|
||
|
|
# correctness for large N against analytical Chebyshev weights.
|
||
|
|
|
||
|
|
# Without capacity scaling or permutation, n=800 fails,
|
||
|
|
# With just capacity scaling, n=1097 fails
|
||
|
|
# With both capacity scaling and random permutation, n=30000 succeeds
|
||
|
|
n = 1100
|
||
|
|
j = np.arange(n + 1).astype(np.float64)
|
||
|
|
x = np.cos(j * np.pi / n)
|
||
|
|
|
||
|
|
# See page 506 of Berrut and Trefethen 2004 for this formula
|
||
|
|
w = (-1) ** j
|
||
|
|
w[0] *= 0.5
|
||
|
|
w[-1] *= 0.5
|
||
|
|
|
||
|
|
P = BarycentricInterpolator(x)
|
||
|
|
|
||
|
|
# It's okay to have a constant scaling factor in the weights because it
|
||
|
|
# cancels out in the evaluation of the polynomial.
|
||
|
|
factor = P.wi[0]
|
||
|
|
assert_almost_equal(P.wi / (2 * factor), w)
|
||
|
|
|
||
|
|
def test_warning(self):
|
||
|
|
# Test if the divide-by-zero warning is properly ignored when computing
|
||
|
|
# interpolated values equals to interpolation points
|
||
|
|
P = BarycentricInterpolator([0, 1], [1, 2])
|
||
|
|
with np.errstate(divide='raise'):
|
||
|
|
yi = P(P.xi)
|
||
|
|
|
||
|
|
# Check if the interpolated values match the input values
|
||
|
|
# at the nodes
|
||
|
|
assert_almost_equal(yi, P.yi.ravel())
|
||
|
|
|
||
|
|
def test_repeated_node(self):
|
||
|
|
# check that a repeated node raises a ValueError
|
||
|
|
# (computing the weights requires division by xi[i] - xi[j])
|
||
|
|
xis = np.array([0.1, 0.5, 0.9, 0.5])
|
||
|
|
ys = np.array([1, 2, 3, 4])
|
||
|
|
with pytest.raises(ValueError,
|
||
|
|
match="Interpolation points xi must be distinct."):
|
||
|
|
BarycentricInterpolator(xis, ys)
|
||
|
|
|
||
|
|
|
||
|
|
class TestPCHIP:
|
||
|
|
def _make_random(self, npts=20):
|
||
|
|
np.random.seed(1234)
|
||
|
|
xi = np.sort(np.random.random(npts))
|
||
|
|
yi = np.random.random(npts)
|
||
|
|
return pchip(xi, yi), xi, yi
|
||
|
|
|
||
|
|
def test_overshoot(self):
|
||
|
|
# PCHIP should not overshoot
|
||
|
|
p, xi, yi = self._make_random()
|
||
|
|
for i in range(len(xi)-1):
|
||
|
|
x1, x2 = xi[i], xi[i+1]
|
||
|
|
y1, y2 = yi[i], yi[i+1]
|
||
|
|
if y1 > y2:
|
||
|
|
y1, y2 = y2, y1
|
||
|
|
xp = np.linspace(x1, x2, 10)
|
||
|
|
yp = p(xp)
|
||
|
|
assert_(((y1 <= yp + 1e-15) & (yp <= y2 + 1e-15)).all())
|
||
|
|
|
||
|
|
def test_monotone(self):
|
||
|
|
# PCHIP should preserve monotonicty
|
||
|
|
p, xi, yi = self._make_random()
|
||
|
|
for i in range(len(xi)-1):
|
||
|
|
x1, x2 = xi[i], xi[i+1]
|
||
|
|
y1, y2 = yi[i], yi[i+1]
|
||
|
|
xp = np.linspace(x1, x2, 10)
|
||
|
|
yp = p(xp)
|
||
|
|
assert_(((y2-y1) * (yp[1:] - yp[:1]) > 0).all())
|
||
|
|
|
||
|
|
def test_cast(self):
|
||
|
|
# regression test for integer input data, see gh-3453
|
||
|
|
data = np.array([[0, 4, 12, 27, 47, 60, 79, 87, 99, 100],
|
||
|
|
[-33, -33, -19, -2, 12, 26, 38, 45, 53, 55]])
|
||
|
|
xx = np.arange(100)
|
||
|
|
curve = pchip(data[0], data[1])(xx)
|
||
|
|
|
||
|
|
data1 = data * 1.0
|
||
|
|
curve1 = pchip(data1[0], data1[1])(xx)
|
||
|
|
|
||
|
|
assert_allclose(curve, curve1, atol=1e-14, rtol=1e-14)
|
||
|
|
|
||
|
|
def test_nag(self):
|
||
|
|
# Example from NAG C implementation,
|
||
|
|
# http://nag.com/numeric/cl/nagdoc_cl25/html/e01/e01bec.html
|
||
|
|
# suggested in gh-5326 as a smoke test for the way the derivatives
|
||
|
|
# are computed (see also gh-3453)
|
||
|
|
dataStr = '''
|
||
|
|
7.99 0.00000E+0
|
||
|
|
8.09 0.27643E-4
|
||
|
|
8.19 0.43750E-1
|
||
|
|
8.70 0.16918E+0
|
||
|
|
9.20 0.46943E+0
|
||
|
|
10.00 0.94374E+0
|
||
|
|
12.00 0.99864E+0
|
||
|
|
15.00 0.99992E+0
|
||
|
|
20.00 0.99999E+0
|
||
|
|
'''
|
||
|
|
data = np.loadtxt(io.StringIO(dataStr))
|
||
|
|
pch = pchip(data[:,0], data[:,1])
|
||
|
|
|
||
|
|
resultStr = '''
|
||
|
|
7.9900 0.0000
|
||
|
|
9.1910 0.4640
|
||
|
|
10.3920 0.9645
|
||
|
|
11.5930 0.9965
|
||
|
|
12.7940 0.9992
|
||
|
|
13.9950 0.9998
|
||
|
|
15.1960 0.9999
|
||
|
|
16.3970 1.0000
|
||
|
|
17.5980 1.0000
|
||
|
|
18.7990 1.0000
|
||
|
|
20.0000 1.0000
|
||
|
|
'''
|
||
|
|
result = np.loadtxt(io.StringIO(resultStr))
|
||
|
|
assert_allclose(result[:,1], pch(result[:,0]), rtol=0., atol=5e-5)
|
||
|
|
|
||
|
|
def test_endslopes(self):
|
||
|
|
# this is a smoke test for gh-3453: PCHIP interpolator should not
|
||
|
|
# set edge slopes to zero if the data do not suggest zero edge derivatives
|
||
|
|
x = np.array([0.0, 0.1, 0.25, 0.35])
|
||
|
|
y1 = np.array([279.35, 0.5e3, 1.0e3, 2.5e3])
|
||
|
|
y2 = np.array([279.35, 2.5e3, 1.50e3, 1.0e3])
|
||
|
|
for pp in (pchip(x, y1), pchip(x, y2)):
|
||
|
|
for t in (x[0], x[-1]):
|
||
|
|
assert_(pp(t, 1) != 0)
|
||
|
|
|
||
|
|
def test_all_zeros(self):
|
||
|
|
x = np.arange(10)
|
||
|
|
y = np.zeros_like(x)
|
||
|
|
|
||
|
|
# this should work and not generate any warnings
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.filterwarnings('error')
|
||
|
|
pch = pchip(x, y)
|
||
|
|
|
||
|
|
xx = np.linspace(0, 9, 101)
|
||
|
|
assert_equal(pch(xx), 0.)
|
||
|
|
|
||
|
|
def test_two_points(self):
|
||
|
|
# regression test for gh-6222: pchip([0, 1], [0, 1]) fails because
|
||
|
|
# it tries to use a three-point scheme to estimate edge derivatives,
|
||
|
|
# while there are only two points available.
|
||
|
|
# Instead, it should construct a linear interpolator.
|
||
|
|
x = np.linspace(0, 1, 11)
|
||
|
|
p = pchip([0, 1], [0, 2])
|
||
|
|
assert_allclose(p(x), 2*x, atol=1e-15)
|
||
|
|
|
||
|
|
def test_pchip_interpolate(self):
|
||
|
|
assert_array_almost_equal(
|
||
|
|
pchip_interpolate([1,2,3], [4,5,6], [0.5], der=1),
|
||
|
|
[1.])
|
||
|
|
|
||
|
|
assert_array_almost_equal(
|
||
|
|
pchip_interpolate([1,2,3], [4,5,6], [0.5], der=0),
|
||
|
|
[3.5])
|
||
|
|
|
||
|
|
assert_array_almost_equal(
|
||
|
|
pchip_interpolate([1,2,3], [4,5,6], [0.5], der=[0, 1]),
|
||
|
|
[[3.5], [1]])
|
||
|
|
|
||
|
|
def test_roots(self):
|
||
|
|
# regression test for gh-6357: .roots method should work
|
||
|
|
p = pchip([0, 1], [-1, 1])
|
||
|
|
r = p.roots()
|
||
|
|
assert_allclose(r, 0.5)
|
||
|
|
|
||
|
|
|
||
|
|
class TestCubicSpline:
|
||
|
|
@staticmethod
|
||
|
|
def check_correctness(S, bc_start='not-a-knot', bc_end='not-a-knot',
|
||
|
|
tol=1e-14):
|
||
|
|
"""Check that spline coefficients satisfy the continuity and boundary
|
||
|
|
conditions."""
|
||
|
|
x = S.x
|
||
|
|
c = S.c
|
||
|
|
dx = np.diff(x)
|
||
|
|
dx = dx.reshape([dx.shape[0]] + [1] * (c.ndim - 2))
|
||
|
|
dxi = dx[:-1]
|
||
|
|
|
||
|
|
# Check C2 continuity.
|
||
|
|
assert_allclose(c[3, 1:], c[0, :-1] * dxi**3 + c[1, :-1] * dxi**2 +
|
||
|
|
c[2, :-1] * dxi + c[3, :-1], rtol=tol, atol=tol)
|
||
|
|
assert_allclose(c[2, 1:], 3 * c[0, :-1] * dxi**2 +
|
||
|
|
2 * c[1, :-1] * dxi + c[2, :-1], rtol=tol, atol=tol)
|
||
|
|
assert_allclose(c[1, 1:], 3 * c[0, :-1] * dxi + c[1, :-1],
|
||
|
|
rtol=tol, atol=tol)
|
||
|
|
|
||
|
|
# Check that we found a parabola, the third derivative is 0.
|
||
|
|
if x.size == 3 and bc_start == 'not-a-knot' and bc_end == 'not-a-knot':
|
||
|
|
assert_allclose(c[0], 0, rtol=tol, atol=tol)
|
||
|
|
return
|
||
|
|
|
||
|
|
# Check periodic boundary conditions.
|
||
|
|
if bc_start == 'periodic':
|
||
|
|
assert_allclose(S(x[0], 0), S(x[-1], 0), rtol=tol, atol=tol)
|
||
|
|
assert_allclose(S(x[0], 1), S(x[-1], 1), rtol=tol, atol=tol)
|
||
|
|
assert_allclose(S(x[0], 2), S(x[-1], 2), rtol=tol, atol=tol)
|
||
|
|
return
|
||
|
|
|
||
|
|
# Check other boundary conditions.
|
||
|
|
if bc_start == 'not-a-knot':
|
||
|
|
if x.size == 2:
|
||
|
|
slope = (S(x[1]) - S(x[0])) / dx[0]
|
||
|
|
assert_allclose(S(x[0], 1), slope, rtol=tol, atol=tol)
|
||
|
|
else:
|
||
|
|
assert_allclose(c[0, 0], c[0, 1], rtol=tol, atol=tol)
|
||
|
|
elif bc_start == 'clamped':
|
||
|
|
assert_allclose(S(x[0], 1), 0, rtol=tol, atol=tol)
|
||
|
|
elif bc_start == 'natural':
|
||
|
|
assert_allclose(S(x[0], 2), 0, rtol=tol, atol=tol)
|
||
|
|
else:
|
||
|
|
order, value = bc_start
|
||
|
|
assert_allclose(S(x[0], order), value, rtol=tol, atol=tol)
|
||
|
|
|
||
|
|
if bc_end == 'not-a-knot':
|
||
|
|
if x.size == 2:
|
||
|
|
slope = (S(x[1]) - S(x[0])) / dx[0]
|
||
|
|
assert_allclose(S(x[1], 1), slope, rtol=tol, atol=tol)
|
||
|
|
else:
|
||
|
|
assert_allclose(c[0, -1], c[0, -2], rtol=tol, atol=tol)
|
||
|
|
elif bc_end == 'clamped':
|
||
|
|
assert_allclose(S(x[-1], 1), 0, rtol=tol, atol=tol)
|
||
|
|
elif bc_end == 'natural':
|
||
|
|
assert_allclose(S(x[-1], 2), 0, rtol=2*tol, atol=2*tol)
|
||
|
|
else:
|
||
|
|
order, value = bc_end
|
||
|
|
assert_allclose(S(x[-1], order), value, rtol=tol, atol=tol)
|
||
|
|
|
||
|
|
def check_all_bc(self, x, y, axis):
|
||
|
|
deriv_shape = list(y.shape)
|
||
|
|
del deriv_shape[axis]
|
||
|
|
first_deriv = np.empty(deriv_shape)
|
||
|
|
first_deriv.fill(2)
|
||
|
|
second_deriv = np.empty(deriv_shape)
|
||
|
|
second_deriv.fill(-1)
|
||
|
|
bc_all = [
|
||
|
|
'not-a-knot',
|
||
|
|
'natural',
|
||
|
|
'clamped',
|
||
|
|
(1, first_deriv),
|
||
|
|
(2, second_deriv)
|
||
|
|
]
|
||
|
|
for bc in bc_all[:3]:
|
||
|
|
S = CubicSpline(x, y, axis=axis, bc_type=bc)
|
||
|
|
self.check_correctness(S, bc, bc)
|
||
|
|
|
||
|
|
for bc_start in bc_all:
|
||
|
|
for bc_end in bc_all:
|
||
|
|
S = CubicSpline(x, y, axis=axis, bc_type=(bc_start, bc_end))
|
||
|
|
self.check_correctness(S, bc_start, bc_end, tol=2e-14)
|
||
|
|
|
||
|
|
def test_general(self):
|
||
|
|
x = np.array([-1, 0, 0.5, 2, 4, 4.5, 5.5, 9])
|
||
|
|
y = np.array([0, -0.5, 2, 3, 2.5, 1, 1, 0.5])
|
||
|
|
for n in [2, 3, x.size]:
|
||
|
|
self.check_all_bc(x[:n], y[:n], 0)
|
||
|
|
|
||
|
|
Y = np.empty((2, n, 2))
|
||
|
|
Y[0, :, 0] = y[:n]
|
||
|
|
Y[0, :, 1] = y[:n] - 1
|
||
|
|
Y[1, :, 0] = y[:n] + 2
|
||
|
|
Y[1, :, 1] = y[:n] + 3
|
||
|
|
self.check_all_bc(x[:n], Y, 1)
|
||
|
|
|
||
|
|
def test_periodic(self):
|
||
|
|
for n in [2, 3, 5]:
|
||
|
|
x = np.linspace(0, 2 * np.pi, n)
|
||
|
|
y = np.cos(x)
|
||
|
|
S = CubicSpline(x, y, bc_type='periodic')
|
||
|
|
self.check_correctness(S, 'periodic', 'periodic')
|
||
|
|
|
||
|
|
Y = np.empty((2, n, 2))
|
||
|
|
Y[0, :, 0] = y
|
||
|
|
Y[0, :, 1] = y + 2
|
||
|
|
Y[1, :, 0] = y - 1
|
||
|
|
Y[1, :, 1] = y + 5
|
||
|
|
S = CubicSpline(x, Y, axis=1, bc_type='periodic')
|
||
|
|
self.check_correctness(S, 'periodic', 'periodic')
|
||
|
|
|
||
|
|
def test_periodic_eval(self):
|
||
|
|
x = np.linspace(0, 2 * np.pi, 10)
|
||
|
|
y = np.cos(x)
|
||
|
|
S = CubicSpline(x, y, bc_type='periodic')
|
||
|
|
assert_almost_equal(S(1), S(1 + 2 * np.pi), decimal=15)
|
||
|
|
|
||
|
|
def test_second_derivative_continuity_gh_11758(self):
|
||
|
|
# gh-11758: C2 continuity fail
|
||
|
|
x = np.array([0.9, 1.3, 1.9, 2.1, 2.6, 3.0, 3.9, 4.4, 4.7, 5.0, 6.0,
|
||
|
|
7.0, 8.0, 9.2, 10.5, 11.3, 11.6, 12.0, 12.6, 13.0, 13.3])
|
||
|
|
y = np.array([1.3, 1.5, 1.85, 2.1, 2.6, 2.7, 2.4, 2.15, 2.05, 2.1,
|
||
|
|
2.25, 2.3, 2.25, 1.95, 1.4, 0.9, 0.7, 0.6, 0.5, 0.4, 1.3])
|
||
|
|
S = CubicSpline(x, y, bc_type='periodic', extrapolate='periodic')
|
||
|
|
self.check_correctness(S, 'periodic', 'periodic')
|
||
|
|
|
||
|
|
def test_three_points(self):
|
||
|
|
# gh-11758: Fails computing a_m2_m1
|
||
|
|
# In this case, s (first derivatives) could be found manually by solving
|
||
|
|
# system of 2 linear equations. Due to solution of this system,
|
||
|
|
# s[i] = (h1m2 + h2m1) / (h1 + h2), where h1 = x[1] - x[0], h2 = x[2] - x[1],
|
||
|
|
# m1 = (y[1] - y[0]) / h1, m2 = (y[2] - y[1]) / h2
|
||
|
|
x = np.array([1.0, 2.75, 3.0])
|
||
|
|
y = np.array([1.0, 15.0, 1.0])
|
||
|
|
S = CubicSpline(x, y, bc_type='periodic')
|
||
|
|
self.check_correctness(S, 'periodic', 'periodic')
|
||
|
|
assert_allclose(S.derivative(1)(x), np.array([-48.0, -48.0, -48.0]))
|
||
|
|
|
||
|
|
def test_periodic_three_points_multidim(self):
|
||
|
|
# make sure one multidimensional interpolator does the same as multiple
|
||
|
|
# one-dimensional interpolators
|
||
|
|
x = np.array([0.0, 1.0, 3.0])
|
||
|
|
y = np.array([[0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])
|
||
|
|
S = CubicSpline(x, y, bc_type="periodic")
|
||
|
|
self.check_correctness(S, 'periodic', 'periodic')
|
||
|
|
S0 = CubicSpline(x, y[:, 0], bc_type="periodic")
|
||
|
|
S1 = CubicSpline(x, y[:, 1], bc_type="periodic")
|
||
|
|
q = np.linspace(0, 2, 5)
|
||
|
|
assert_allclose(S(q)[:, 0], S0(q))
|
||
|
|
assert_allclose(S(q)[:, 1], S1(q))
|
||
|
|
|
||
|
|
def test_dtypes(self):
|
||
|
|
x = np.array([0, 1, 2, 3], dtype=int)
|
||
|
|
y = np.array([-5, 2, 3, 1], dtype=int)
|
||
|
|
S = CubicSpline(x, y)
|
||
|
|
self.check_correctness(S)
|
||
|
|
|
||
|
|
y = np.array([-1+1j, 0.0, 1-1j, 0.5-1.5j])
|
||
|
|
S = CubicSpline(x, y)
|
||
|
|
self.check_correctness(S)
|
||
|
|
|
||
|
|
S = CubicSpline(x, x ** 3, bc_type=("natural", (1, 2j)))
|
||
|
|
self.check_correctness(S, "natural", (1, 2j))
|
||
|
|
|
||
|
|
y = np.array([-5, 2, 3, 1])
|
||
|
|
S = CubicSpline(x, y, bc_type=[(1, 2 + 0.5j), (2, 0.5 - 1j)])
|
||
|
|
self.check_correctness(S, (1, 2 + 0.5j), (2, 0.5 - 1j))
|
||
|
|
|
||
|
|
def test_small_dx(self):
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
x = np.sort(rng.uniform(size=100))
|
||
|
|
y = 1e4 + rng.uniform(size=100)
|
||
|
|
S = CubicSpline(x, y)
|
||
|
|
self.check_correctness(S, tol=1e-13)
|
||
|
|
|
||
|
|
def test_incorrect_inputs(self):
|
||
|
|
x = np.array([1, 2, 3, 4])
|
||
|
|
y = np.array([1, 2, 3, 4])
|
||
|
|
xc = np.array([1 + 1j, 2, 3, 4])
|
||
|
|
xn = np.array([np.nan, 2, 3, 4])
|
||
|
|
xo = np.array([2, 1, 3, 4])
|
||
|
|
yn = np.array([np.nan, 2, 3, 4])
|
||
|
|
y3 = [1, 2, 3]
|
||
|
|
x1 = [1]
|
||
|
|
y1 = [1]
|
||
|
|
|
||
|
|
assert_raises(ValueError, CubicSpline, xc, y)
|
||
|
|
assert_raises(ValueError, CubicSpline, xn, y)
|
||
|
|
assert_raises(ValueError, CubicSpline, x, yn)
|
||
|
|
assert_raises(ValueError, CubicSpline, xo, y)
|
||
|
|
assert_raises(ValueError, CubicSpline, x, y3)
|
||
|
|
assert_raises(ValueError, CubicSpline, x[:, np.newaxis], y)
|
||
|
|
assert_raises(ValueError, CubicSpline, x1, y1)
|
||
|
|
|
||
|
|
wrong_bc = [('periodic', 'clamped'),
|
||
|
|
((2, 0), (3, 10)),
|
||
|
|
((1, 0), ),
|
||
|
|
(0., 0.),
|
||
|
|
'not-a-typo']
|
||
|
|
|
||
|
|
for bc_type in wrong_bc:
|
||
|
|
assert_raises(ValueError, CubicSpline, x, y, 0, bc_type, True)
|
||
|
|
|
||
|
|
# Shapes mismatch when giving arbitrary derivative values:
|
||
|
|
Y = np.c_[y, y]
|
||
|
|
bc1 = ('clamped', (1, 0))
|
||
|
|
bc2 = ('clamped', (1, [0, 0, 0]))
|
||
|
|
bc3 = ('clamped', (1, [[0, 0]]))
|
||
|
|
assert_raises(ValueError, CubicSpline, x, Y, 0, bc1, True)
|
||
|
|
assert_raises(ValueError, CubicSpline, x, Y, 0, bc2, True)
|
||
|
|
assert_raises(ValueError, CubicSpline, x, Y, 0, bc3, True)
|
||
|
|
|
||
|
|
# periodic condition, y[-1] must be equal to y[0]:
|
||
|
|
assert_raises(ValueError, CubicSpline, x, y, 0, 'periodic', True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_CubicHermiteSpline_correctness():
|
||
|
|
x = [0, 2, 7]
|
||
|
|
y = [-1, 2, 3]
|
||
|
|
dydx = [0, 3, 7]
|
||
|
|
s = CubicHermiteSpline(x, y, dydx)
|
||
|
|
assert_allclose(s(x), y, rtol=1e-15)
|
||
|
|
assert_allclose(s(x, 1), dydx, rtol=1e-15)
|
||
|
|
|
||
|
|
|
||
|
|
def test_CubicHermiteSpline_error_handling():
|
||
|
|
x = [1, 2, 3]
|
||
|
|
y = [0, 3, 5]
|
||
|
|
dydx = [1, -1, 2, 3]
|
||
|
|
assert_raises(ValueError, CubicHermiteSpline, x, y, dydx)
|
||
|
|
|
||
|
|
dydx_with_nan = [1, 0, np.nan]
|
||
|
|
assert_raises(ValueError, CubicHermiteSpline, x, y, dydx_with_nan)
|
||
|
|
|
||
|
|
|
||
|
|
def test_roots_extrapolate_gh_11185():
|
||
|
|
x = np.array([0.001, 0.002])
|
||
|
|
y = np.array([1.66066935e-06, 1.10410807e-06])
|
||
|
|
dy = np.array([-1.60061854, -1.600619])
|
||
|
|
p = CubicHermiteSpline(x, y, dy)
|
||
|
|
|
||
|
|
# roots(extrapolate=True) for a polynomial with a single interval
|
||
|
|
# should return all three real roots
|
||
|
|
r = p.roots(extrapolate=True)
|
||
|
|
assert_equal(p.c.shape[1], 1)
|
||
|
|
assert_equal(r.size, 3)
|
||
|
|
|
||
|
|
|
||
|
|
class TestZeroSizeArrays:
|
||
|
|
# regression tests for gh-17241 : CubicSpline et al must not segfault
|
||
|
|
# when y.size == 0
|
||
|
|
# The two methods below are _almost_ the same, but not quite:
|
||
|
|
# one is for objects which have the `bc_type` argument (CubicSpline)
|
||
|
|
# and the other one is for those which do not (Pchip, Akima1D)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('y', [np.zeros((10, 0, 5)),
|
||
|
|
np.zeros((10, 5, 0))])
|
||
|
|
@pytest.mark.parametrize('bc_type',
|
||
|
|
['not-a-knot', 'periodic', 'natural', 'clamped'])
|
||
|
|
@pytest.mark.parametrize('axis', [0, 1, 2])
|
||
|
|
@pytest.mark.parametrize('cls', [make_interp_spline, CubicSpline])
|
||
|
|
def test_zero_size(self, cls, y, bc_type, axis):
|
||
|
|
x = np.arange(10)
|
||
|
|
xval = np.arange(3)
|
||
|
|
|
||
|
|
obj = cls(x, y, bc_type=bc_type)
|
||
|
|
assert obj(xval).size == 0
|
||
|
|
assert obj(xval).shape == xval.shape + y.shape[1:]
|
||
|
|
|
||
|
|
# Also check with an explicit non-default axis
|
||
|
|
yt = np.moveaxis(y, 0, axis) # (10, 0, 5) --> (0, 10, 5) if axis=1 etc
|
||
|
|
|
||
|
|
obj = cls(x, yt, bc_type=bc_type, axis=axis)
|
||
|
|
sh = yt.shape[:axis] + (xval.size, ) + yt.shape[axis+1:]
|
||
|
|
assert obj(xval).size == 0
|
||
|
|
assert obj(xval).shape == sh
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('y', [np.zeros((10, 0, 5)),
|
||
|
|
np.zeros((10, 5, 0))])
|
||
|
|
@pytest.mark.parametrize('axis', [0, 1, 2])
|
||
|
|
@pytest.mark.parametrize('cls', [PchipInterpolator, Akima1DInterpolator])
|
||
|
|
def test_zero_size_2(self, cls, y, axis):
|
||
|
|
x = np.arange(10)
|
||
|
|
xval = np.arange(3)
|
||
|
|
|
||
|
|
obj = cls(x, y)
|
||
|
|
assert obj(xval).size == 0
|
||
|
|
assert obj(xval).shape == xval.shape + y.shape[1:]
|
||
|
|
|
||
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# Also check with an explicit non-default axis
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yt = np.moveaxis(y, 0, axis) # (10, 0, 5) --> (0, 10, 5) if axis=1 etc
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obj = cls(x, yt, axis=axis)
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sh = yt.shape[:axis] + (xval.size, ) + yt.shape[axis+1:]
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assert obj(xval).size == 0
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assert obj(xval).shape == sh
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