504 lines
16 KiB
Python
504 lines
16 KiB
Python
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import itertools
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import os
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import numpy as np
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from numpy.testing import (assert_equal, assert_allclose, assert_,
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assert_almost_equal, assert_array_almost_equal)
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from pytest import raises as assert_raises
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import pytest
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from scipy._lib._testutils import check_free_memory
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from scipy.interpolate import RectBivariateSpline
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from scipy.interpolate._fitpack_py import (splrep, splev, bisplrep, bisplev,
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sproot, splprep, splint, spalde, splder, splantider, insert, dblint)
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from scipy.interpolate.dfitpack import regrid_smth
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from scipy.interpolate._fitpack2 import dfitpack_int
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def data_file(basename):
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return os.path.join(os.path.abspath(os.path.dirname(__file__)),
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'data', basename)
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def norm2(x):
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return np.sqrt(np.dot(x.T, x))
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def f1(x, d=0):
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"""Derivatives of sin->cos->-sin->-cos."""
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if d % 4 == 0:
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return np.sin(x)
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if d % 4 == 1:
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return np.cos(x)
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if d % 4 == 2:
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return -np.sin(x)
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if d % 4 == 3:
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return -np.cos(x)
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def makepairs(x, y):
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"""Helper function to create an array of pairs of x and y."""
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xy = np.array(list(itertools.product(np.asarray(x), np.asarray(y))))
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return xy.T
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class TestSmokeTests:
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"""
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Smoke tests (with a few asserts) for fitpack routines -- mostly
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check that they are runnable
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"""
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def check_1(self, per=0, s=0, a=0, b=2*np.pi, at_nodes=False,
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xb=None, xe=None):
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if xb is None:
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xb = a
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if xe is None:
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xe = b
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N = 20
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# nodes and middle points of the nodes
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x = np.linspace(a, b, N + 1)
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x1 = a + (b - a) * np.arange(1, N, dtype=float) / float(N - 1)
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v = f1(x)
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def err_est(k, d):
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# Assume f has all derivatives < 1
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h = 1.0 / N
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tol = 5 * h**(.75*(k-d))
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if s > 0:
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tol += 1e5*s
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return tol
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for k in range(1, 6):
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tck = splrep(x, v, s=s, per=per, k=k, xe=xe)
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tt = tck[0][k:-k] if at_nodes else x1
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for d in range(k+1):
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tol = err_est(k, d)
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err = norm2(f1(tt, d) - splev(tt, tck, d)) / norm2(f1(tt, d))
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assert err < tol
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def check_2(self, per=0, N=20, ia=0, ib=2*np.pi):
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a, b, dx = 0, 2*np.pi, 0.2*np.pi
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x = np.linspace(a, b, N+1) # nodes
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v = np.sin(x)
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def err_est(k, d):
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# Assume f has all derivatives < 1
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h = 1.0 / N
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tol = 5 * h**(.75*(k-d))
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return tol
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nk = []
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for k in range(1, 6):
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tck = splrep(x, v, s=0, per=per, k=k, xe=b)
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nk.append([splint(ia, ib, tck), spalde(dx, tck)])
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k = 1
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for r in nk:
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d = 0
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for dr in r[1]:
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tol = err_est(k, d)
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assert_allclose(dr, f1(dx, d), atol=0, rtol=tol)
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d = d+1
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k = k+1
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def test_smoke_splrep_splev(self):
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self.check_1(s=1e-6)
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self.check_1(b=1.5*np.pi)
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self.check_1(b=1.5*np.pi, xe=2*np.pi, per=1, s=1e-1)
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@pytest.mark.parametrize('per', [0, 1])
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@pytest.mark.parametrize('at_nodes', [True, False])
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def test_smoke_splrep_splev_2(self, per, at_nodes):
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self.check_1(per=per, at_nodes=at_nodes)
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@pytest.mark.parametrize('N', [20, 50])
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@pytest.mark.parametrize('per', [0, 1])
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def test_smoke_splint_spalde(self, N, per):
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self.check_2(per=per, N=N)
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@pytest.mark.parametrize('N', [20, 50])
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@pytest.mark.parametrize('per', [0, 1])
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def test_smoke_splint_spalde_iaib(self, N, per):
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self.check_2(ia=0.2*np.pi, ib=np.pi, N=N, per=per)
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def test_smoke_sproot(self):
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# sproot is only implemented for k=3
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a, b = 0.1, 15
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x = np.linspace(a, b, 20)
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v = np.sin(x)
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for k in [1, 2, 4, 5]:
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tck = splrep(x, v, s=0, per=0, k=k, xe=b)
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with assert_raises(ValueError):
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sproot(tck)
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k = 3
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tck = splrep(x, v, s=0, k=3)
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roots = sproot(tck)
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assert_allclose(splev(roots, tck), 0, atol=1e-10, rtol=1e-10)
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assert_allclose(roots, np.pi * np.array([1, 2, 3, 4]), rtol=1e-3)
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@pytest.mark.parametrize('N', [20, 50])
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@pytest.mark.parametrize('k', [1, 2, 3, 4, 5])
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def test_smoke_splprep_splrep_splev(self, N, k):
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a, b, dx = 0, 2.*np.pi, 0.2*np.pi
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x = np.linspace(a, b, N+1) # nodes
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v = np.sin(x)
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tckp, u = splprep([x, v], s=0, per=0, k=k, nest=-1)
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uv = splev(dx, tckp)
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err1 = abs(uv[1] - np.sin(uv[0]))
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assert err1 < 1e-2
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tck = splrep(x, v, s=0, per=0, k=k)
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err2 = abs(splev(uv[0], tck) - np.sin(uv[0]))
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assert err2 < 1e-2
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# Derivatives of parametric cubic spline at u (first function)
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if k == 3:
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tckp, u = splprep([x, v], s=0, per=0, k=k, nest=-1)
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for d in range(1, k+1):
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uv = splev(dx, tckp, d)
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def test_smoke_bisplrep_bisplev(self):
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xb, xe = 0, 2.*np.pi
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yb, ye = 0, 2.*np.pi
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kx, ky = 3, 3
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Nx, Ny = 20, 20
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def f2(x, y):
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return np.sin(x+y)
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x = np.linspace(xb, xe, Nx + 1)
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y = np.linspace(yb, ye, Ny + 1)
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xy = makepairs(x, y)
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tck = bisplrep(xy[0], xy[1], f2(xy[0], xy[1]), s=0, kx=kx, ky=ky)
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tt = [tck[0][kx:-kx], tck[1][ky:-ky]]
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t2 = makepairs(tt[0], tt[1])
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v1 = bisplev(tt[0], tt[1], tck)
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v2 = f2(t2[0], t2[1])
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v2.shape = len(tt[0]), len(tt[1])
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assert norm2(np.ravel(v1 - v2)) < 1e-2
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class TestSplev:
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def test_1d_shape(self):
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x = [1,2,3,4,5]
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y = [4,5,6,7,8]
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tck = splrep(x, y)
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z = splev([1], tck)
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assert_equal(z.shape, (1,))
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z = splev(1, tck)
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assert_equal(z.shape, ())
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def test_2d_shape(self):
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x = [1, 2, 3, 4, 5]
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y = [4, 5, 6, 7, 8]
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tck = splrep(x, y)
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t = np.array([[1.0, 1.5, 2.0, 2.5],
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[3.0, 3.5, 4.0, 4.5]])
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z = splev(t, tck)
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z0 = splev(t[0], tck)
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z1 = splev(t[1], tck)
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assert_equal(z, np.vstack((z0, z1)))
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def test_extrapolation_modes(self):
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# test extrapolation modes
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# * if ext=0, return the extrapolated value.
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# * if ext=1, return 0
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# * if ext=2, raise a ValueError
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# * if ext=3, return the boundary value.
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x = [1,2,3]
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y = [0,2,4]
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tck = splrep(x, y, k=1)
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rstl = [[-2, 6], [0, 0], None, [0, 4]]
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for ext in (0, 1, 3):
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assert_array_almost_equal(splev([0, 4], tck, ext=ext), rstl[ext])
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assert_raises(ValueError, splev, [0, 4], tck, ext=2)
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class TestSplder:
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def setup_method(self):
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# non-uniform grid, just to make it sure
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x = np.linspace(0, 1, 100)**3
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y = np.sin(20 * x)
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self.spl = splrep(x, y)
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# double check that knots are non-uniform
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assert_(np.ptp(np.diff(self.spl[0])) > 0)
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def test_inverse(self):
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# Check that antiderivative + derivative is identity.
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for n in range(5):
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spl2 = splantider(self.spl, n)
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spl3 = splder(spl2, n)
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assert_allclose(self.spl[0], spl3[0])
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assert_allclose(self.spl[1], spl3[1])
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assert_equal(self.spl[2], spl3[2])
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def test_splder_vs_splev(self):
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# Check derivative vs. FITPACK
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for n in range(3+1):
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# Also extrapolation!
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xx = np.linspace(-1, 2, 2000)
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if n == 3:
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# ... except that FITPACK extrapolates strangely for
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# order 0, so let's not check that.
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xx = xx[(xx >= 0) & (xx <= 1)]
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dy = splev(xx, self.spl, n)
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spl2 = splder(self.spl, n)
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dy2 = splev(xx, spl2)
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if n == 1:
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assert_allclose(dy, dy2, rtol=2e-6)
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else:
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assert_allclose(dy, dy2)
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def test_splantider_vs_splint(self):
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# Check antiderivative vs. FITPACK
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spl2 = splantider(self.spl)
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# no extrapolation, splint assumes function is zero outside
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# range
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xx = np.linspace(0, 1, 20)
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for x1 in xx:
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for x2 in xx:
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y1 = splint(x1, x2, self.spl)
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y2 = splev(x2, spl2) - splev(x1, spl2)
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assert_allclose(y1, y2)
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def test_order0_diff(self):
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assert_raises(ValueError, splder, self.spl, 4)
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def test_kink(self):
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# Should refuse to differentiate splines with kinks
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spl2 = insert(0.5, self.spl, m=2)
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splder(spl2, 2) # Should work
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assert_raises(ValueError, splder, spl2, 3)
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spl2 = insert(0.5, self.spl, m=3)
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splder(spl2, 1) # Should work
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assert_raises(ValueError, splder, spl2, 2)
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spl2 = insert(0.5, self.spl, m=4)
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assert_raises(ValueError, splder, spl2, 1)
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def test_multidim(self):
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# c can have trailing dims
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for n in range(3):
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t, c, k = self.spl
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c2 = np.c_[c, c, c]
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c2 = np.dstack((c2, c2))
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spl2 = splantider((t, c2, k), n)
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spl3 = splder(spl2, n)
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assert_allclose(t, spl3[0])
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assert_allclose(c2, spl3[1])
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assert_equal(k, spl3[2])
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class TestSplint:
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def test_len_c(self):
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n, k = 7, 3
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x = np.arange(n)
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y = x**3
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t, c, k = splrep(x, y, s=0)
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# note that len(c) == len(t) == 11 (== len(x) + 2*(k-1))
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assert len(t) == len(c) == n + 2*(k-1)
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# integrate directly: $\int_0^6 x^3 dx = 6^4 / 4$
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res = splint(0, 6, (t, c, k))
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assert_allclose(res, 6**4 / 4, atol=1e-15)
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# check that the coefficients past len(t) - k - 1 are ignored
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c0 = c.copy()
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c0[len(t)-k-1:] = np.nan
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res0 = splint(0, 6, (t, c0, k))
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assert_allclose(res0, 6**4 / 4, atol=1e-15)
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# however, all other coefficients *are* used
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c0[6] = np.nan
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assert np.isnan(splint(0, 6, (t, c0, k)))
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# check that the coefficient array can have length `len(t) - k - 1`
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c1 = c[:len(t) - k - 1]
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res1 = splint(0, 6, (t, c1, k))
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assert_allclose(res1, 6**4 / 4, atol=1e-15)
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# however shorter c arrays raise. The error from f2py is a
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# `dftipack.error`, which is an Exception but not ValueError etc.
|
||
|
|
with assert_raises(Exception, match=r">=n-k-1"):
|
||
|
|
splint(0, 1, (np.ones(10), np.ones(5), 3))
|
||
|
|
|
||
|
|
|
||
|
|
class TestBisplrep:
|
||
|
|
def test_overflow(self):
|
||
|
|
from numpy.lib.stride_tricks import as_strided
|
||
|
|
if dfitpack_int.itemsize == 8:
|
||
|
|
size = 1500000**2
|
||
|
|
else:
|
||
|
|
size = 400**2
|
||
|
|
# Don't allocate a real array, as it's very big, but rely
|
||
|
|
# on that it's not referenced
|
||
|
|
x = as_strided(np.zeros(()), shape=(size,))
|
||
|
|
assert_raises(OverflowError, bisplrep, x, x, x, w=x,
|
||
|
|
xb=0, xe=1, yb=0, ye=1, s=0)
|
||
|
|
|
||
|
|
def test_regression_1310(self):
|
||
|
|
# Regression test for gh-1310
|
||
|
|
with np.load(data_file('bug-1310.npz')) as loaded_data:
|
||
|
|
data = loaded_data['data']
|
||
|
|
|
||
|
|
# Shouldn't crash -- the input data triggers work array sizes
|
||
|
|
# that caused previously some data to not be aligned on
|
||
|
|
# sizeof(double) boundaries in memory, which made the Fortran
|
||
|
|
# code to crash when compiled with -O3
|
||
|
|
bisplrep(data[:,0], data[:,1], data[:,2], kx=3, ky=3, s=0,
|
||
|
|
full_output=True)
|
||
|
|
|
||
|
|
@pytest.mark.skipif(dfitpack_int != np.int64, reason="needs ilp64 fitpack")
|
||
|
|
def test_ilp64_bisplrep(self):
|
||
|
|
check_free_memory(28000) # VM size, doesn't actually use the pages
|
||
|
|
x = np.linspace(0, 1, 400)
|
||
|
|
y = np.linspace(0, 1, 400)
|
||
|
|
x, y = np.meshgrid(x, y)
|
||
|
|
z = np.zeros_like(x)
|
||
|
|
tck = bisplrep(x, y, z, kx=3, ky=3, s=0)
|
||
|
|
assert_allclose(bisplev(0.5, 0.5, tck), 0.0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_dblint():
|
||
|
|
# Basic test to see it runs and gives the correct result on a trivial
|
||
|
|
# problem. Note that `dblint` is not exposed in the interpolate namespace.
|
||
|
|
x = np.linspace(0, 1)
|
||
|
|
y = np.linspace(0, 1)
|
||
|
|
xx, yy = np.meshgrid(x, y)
|
||
|
|
rect = RectBivariateSpline(x, y, 4 * xx * yy)
|
||
|
|
tck = list(rect.tck)
|
||
|
|
tck.extend(rect.degrees)
|
||
|
|
|
||
|
|
assert_almost_equal(dblint(0, 1, 0, 1, tck), 1)
|
||
|
|
assert_almost_equal(dblint(0, 0.5, 0, 1, tck), 0.25)
|
||
|
|
assert_almost_equal(dblint(0.5, 1, 0, 1, tck), 0.75)
|
||
|
|
assert_almost_equal(dblint(-100, 100, -100, 100, tck), 1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_splev_der_k():
|
||
|
|
# regression test for gh-2188: splev(x, tck, der=k) gives garbage or crashes
|
||
|
|
# for x outside of knot range
|
||
|
|
|
||
|
|
# test case from gh-2188
|
||
|
|
tck = (np.array([0., 0., 2.5, 2.5]),
|
||
|
|
np.array([-1.56679978, 2.43995873, 0., 0.]),
|
||
|
|
1)
|
||
|
|
t, c, k = tck
|
||
|
|
x = np.array([-3, 0, 2.5, 3])
|
||
|
|
|
||
|
|
# an explicit form of the linear spline
|
||
|
|
assert_allclose(splev(x, tck), c[0] + (c[1] - c[0]) * x/t[2])
|
||
|
|
assert_allclose(splev(x, tck, 1), (c[1]-c[0]) / t[2])
|
||
|
|
|
||
|
|
# now check a random spline vs splder
|
||
|
|
np.random.seed(1234)
|
||
|
|
x = np.sort(np.random.random(30))
|
||
|
|
y = np.random.random(30)
|
||
|
|
t, c, k = splrep(x, y)
|
||
|
|
|
||
|
|
x = [t[0] - 1., t[-1] + 1.]
|
||
|
|
tck2 = splder((t, c, k), k)
|
||
|
|
assert_allclose(splev(x, (t, c, k), k), splev(x, tck2))
|
||
|
|
|
||
|
|
|
||
|
|
def test_splprep_segfault():
|
||
|
|
# regression test for gh-3847: splprep segfaults if knots are specified
|
||
|
|
# for task=-1
|
||
|
|
t = np.arange(0, 1.1, 0.1)
|
||
|
|
x = np.sin(2*np.pi*t)
|
||
|
|
y = np.cos(2*np.pi*t)
|
||
|
|
tck, u = splprep([x, y], s=0)
|
||
|
|
np.arange(0, 1.01, 0.01)
|
||
|
|
|
||
|
|
uknots = tck[0] # using the knots from the previous fitting
|
||
|
|
tck, u = splprep([x, y], task=-1, t=uknots) # here is the crash
|
||
|
|
|
||
|
|
|
||
|
|
def test_bisplev_integer_overflow():
|
||
|
|
np.random.seed(1)
|
||
|
|
|
||
|
|
x = np.linspace(0, 1, 11)
|
||
|
|
y = x
|
||
|
|
z = np.random.randn(11, 11).ravel()
|
||
|
|
kx = 1
|
||
|
|
ky = 1
|
||
|
|
|
||
|
|
nx, tx, ny, ty, c, fp, ier = regrid_smth(
|
||
|
|
x, y, z, None, None, None, None, kx=kx, ky=ky, s=0.0)
|
||
|
|
tck = (tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)], kx, ky)
|
||
|
|
|
||
|
|
xp = np.zeros([2621440])
|
||
|
|
yp = np.zeros([2621440])
|
||
|
|
|
||
|
|
assert_raises((RuntimeError, MemoryError), bisplev, xp, yp, tck)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.xslow
|
||
|
|
def test_gh_1766():
|
||
|
|
# this should fail gracefully instead of segfaulting (int overflow)
|
||
|
|
size = 22
|
||
|
|
kx, ky = 3, 3
|
||
|
|
def f2(x, y):
|
||
|
|
return np.sin(x+y)
|
||
|
|
|
||
|
|
x = np.linspace(0, 10, size)
|
||
|
|
y = np.linspace(50, 700, size)
|
||
|
|
xy = makepairs(x, y)
|
||
|
|
tck = bisplrep(xy[0], xy[1], f2(xy[0], xy[1]), s=0, kx=kx, ky=ky)
|
||
|
|
# the size value here can either segfault
|
||
|
|
# or produce a MemoryError on main
|
||
|
|
tx_ty_size = 500000
|
||
|
|
tck[0] = np.arange(tx_ty_size)
|
||
|
|
tck[1] = np.arange(tx_ty_size) * 4
|
||
|
|
tt_0 = np.arange(50)
|
||
|
|
tt_1 = np.arange(50) * 3
|
||
|
|
with pytest.raises(MemoryError):
|
||
|
|
bisplev(tt_0, tt_1, tck, 1, 1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_spalde_scalar_input():
|
||
|
|
# Ticket #629
|
||
|
|
x = np.linspace(0, 10)
|
||
|
|
y = x**3
|
||
|
|
tck = splrep(x, y, k=3, t=[5])
|
||
|
|
res = spalde(np.float64(1), tck)
|
||
|
|
des = np.array([1., 3., 6., 6.])
|
||
|
|
assert_almost_equal(res, des)
|
||
|
|
|
||
|
|
|
||
|
|
def test_spalde_nc():
|
||
|
|
# regression test for https://github.com/scipy/scipy/issues/19002
|
||
|
|
# here len(t) = 29 and len(c) = 25 (== len(t) - k - 1)
|
||
|
|
x = np.asarray([-10., -9., -8., -7., -6., -5., -4., -3., -2.5, -2., -1.5,
|
||
|
|
-1., -0.5, 0., 0.5, 1., 1.5, 2., 2.5, 3., 4., 5., 6.],
|
||
|
|
dtype="float")
|
||
|
|
t = [-10.0, -10.0, -10.0, -10.0, -9.0, -8.0, -7.0, -6.0, -5.0, -4.0, -3.0,
|
||
|
|
-2.5, -2.0, -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0,
|
||
|
|
5.0, 6.0, 6.0, 6.0, 6.0]
|
||
|
|
c = np.asarray([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
|
||
|
|
0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
|
||
|
|
k = 3
|
||
|
|
|
||
|
|
res = spalde(x, (t, c, k))
|
||
|
|
res_splev = np.asarray([splev(x, (t, c, k), nu) for nu in range(4)])
|
||
|
|
assert_allclose(res, res_splev.T, atol=1e-15)
|