269 lines
9.1 KiB
Python
269 lines
9.1 KiB
Python
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import numpy as np
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from numpy.testing import assert_, assert_allclose
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import pytest
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from scipy.special import _ufuncs
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import scipy.special._orthogonal as orth
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from scipy.special._testutils import FuncData
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def test_eval_chebyt():
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n = np.arange(0, 10000, 7)
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x = 2*np.random.rand() - 1
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v1 = np.cos(n*np.arccos(x))
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v2 = _ufuncs.eval_chebyt(n, x)
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assert_(np.allclose(v1, v2, rtol=1e-15))
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def test_eval_genlaguerre_restriction():
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# check it returns nan for alpha <= -1
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assert_(np.isnan(_ufuncs.eval_genlaguerre(0, -1, 0)))
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assert_(np.isnan(_ufuncs.eval_genlaguerre(0.1, -1, 0)))
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def test_warnings():
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# ticket 1334
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with np.errstate(all='raise'):
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# these should raise no fp warnings
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_ufuncs.eval_legendre(1, 0)
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_ufuncs.eval_laguerre(1, 1)
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_ufuncs.eval_gegenbauer(1, 1, 0)
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class TestPolys:
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"""
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Check that the eval_* functions agree with the constructed polynomials
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"""
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def check_poly(self, func, cls, param_ranges=[], x_range=[], nn=10,
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nparam=10, nx=10, rtol=1e-8):
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np.random.seed(1234)
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dataset = []
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for n in np.arange(nn):
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params = [a + (b-a)*np.random.rand(nparam) for a,b in param_ranges]
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params = np.asarray(params).T
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if not param_ranges:
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params = [0]
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for p in params:
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if param_ranges:
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p = (n,) + tuple(p)
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else:
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p = (n,)
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x = x_range[0] + (x_range[1] - x_range[0])*np.random.rand(nx)
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x[0] = x_range[0] # always include domain start point
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x[1] = x_range[1] # always include domain end point
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poly = np.poly1d(cls(*p).coef)
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z = np.c_[np.tile(p, (nx,1)), x, poly(x)]
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dataset.append(z)
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dataset = np.concatenate(dataset, axis=0)
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def polyfunc(*p):
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p = (p[0].astype(int),) + p[1:]
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return func(*p)
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with np.errstate(all='raise'):
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ds = FuncData(polyfunc, dataset, list(range(len(param_ranges)+2)), -1,
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rtol=rtol)
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ds.check()
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def test_jacobi(self):
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self.check_poly(_ufuncs.eval_jacobi, orth.jacobi,
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param_ranges=[(-0.99, 10), (-0.99, 10)],
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x_range=[-1, 1], rtol=1e-5)
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def test_sh_jacobi(self):
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self.check_poly(_ufuncs.eval_sh_jacobi, orth.sh_jacobi,
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param_ranges=[(1, 10), (0, 1)], x_range=[0, 1],
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rtol=1e-5)
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def test_gegenbauer(self):
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self.check_poly(_ufuncs.eval_gegenbauer, orth.gegenbauer,
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param_ranges=[(-0.499, 10)], x_range=[-1, 1],
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rtol=1e-7)
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def test_chebyt(self):
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self.check_poly(_ufuncs.eval_chebyt, orth.chebyt,
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param_ranges=[], x_range=[-1, 1])
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def test_chebyu(self):
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self.check_poly(_ufuncs.eval_chebyu, orth.chebyu,
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param_ranges=[], x_range=[-1, 1])
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def test_chebys(self):
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self.check_poly(_ufuncs.eval_chebys, orth.chebys,
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param_ranges=[], x_range=[-2, 2])
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def test_chebyc(self):
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self.check_poly(_ufuncs.eval_chebyc, orth.chebyc,
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param_ranges=[], x_range=[-2, 2])
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def test_sh_chebyt(self):
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with np.errstate(all='ignore'):
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self.check_poly(_ufuncs.eval_sh_chebyt, orth.sh_chebyt,
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param_ranges=[], x_range=[0, 1])
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def test_sh_chebyu(self):
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self.check_poly(_ufuncs.eval_sh_chebyu, orth.sh_chebyu,
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param_ranges=[], x_range=[0, 1])
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def test_legendre(self):
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self.check_poly(_ufuncs.eval_legendre, orth.legendre,
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param_ranges=[], x_range=[-1, 1])
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def test_sh_legendre(self):
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with np.errstate(all='ignore'):
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self.check_poly(_ufuncs.eval_sh_legendre, orth.sh_legendre,
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param_ranges=[], x_range=[0, 1])
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def test_genlaguerre(self):
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self.check_poly(_ufuncs.eval_genlaguerre, orth.genlaguerre,
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param_ranges=[(-0.99, 10)], x_range=[0, 100])
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def test_laguerre(self):
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self.check_poly(_ufuncs.eval_laguerre, orth.laguerre,
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param_ranges=[], x_range=[0, 100])
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def test_hermite(self):
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self.check_poly(_ufuncs.eval_hermite, orth.hermite,
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param_ranges=[], x_range=[-100, 100])
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def test_hermitenorm(self):
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self.check_poly(_ufuncs.eval_hermitenorm, orth.hermitenorm,
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param_ranges=[], x_range=[-100, 100])
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class TestRecurrence:
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"""
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Check that the eval_* functions sig='ld->d' and 'dd->d' agree.
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"""
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def check_poly(self, func, param_ranges=[], x_range=[], nn=10,
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nparam=10, nx=10, rtol=1e-8):
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np.random.seed(1234)
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dataset = []
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for n in np.arange(nn):
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params = [a + (b-a)*np.random.rand(nparam) for a,b in param_ranges]
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params = np.asarray(params).T
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if not param_ranges:
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params = [0]
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for p in params:
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if param_ranges:
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p = (n,) + tuple(p)
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else:
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p = (n,)
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x = x_range[0] + (x_range[1] - x_range[0])*np.random.rand(nx)
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x[0] = x_range[0] # always include domain start point
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x[1] = x_range[1] # always include domain end point
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kw = dict(sig=(len(p)+1)*'d'+'->d')
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z = np.c_[np.tile(p, (nx,1)), x, func(*(p + (x,)), **kw)]
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dataset.append(z)
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dataset = np.concatenate(dataset, axis=0)
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def polyfunc(*p):
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p = (p[0].astype(int),) + p[1:]
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kw = dict(sig='l'+(len(p)-1)*'d'+'->d')
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return func(*p, **kw)
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with np.errstate(all='raise'):
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ds = FuncData(polyfunc, dataset, list(range(len(param_ranges)+2)), -1,
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rtol=rtol)
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ds.check()
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def test_jacobi(self):
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self.check_poly(_ufuncs.eval_jacobi,
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param_ranges=[(-0.99, 10), (-0.99, 10)],
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x_range=[-1, 1])
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def test_sh_jacobi(self):
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self.check_poly(_ufuncs.eval_sh_jacobi,
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param_ranges=[(1, 10), (0, 1)], x_range=[0, 1])
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def test_gegenbauer(self):
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self.check_poly(_ufuncs.eval_gegenbauer,
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param_ranges=[(-0.499, 10)], x_range=[-1, 1])
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def test_chebyt(self):
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self.check_poly(_ufuncs.eval_chebyt,
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param_ranges=[], x_range=[-1, 1])
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def test_chebyu(self):
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self.check_poly(_ufuncs.eval_chebyu,
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param_ranges=[], x_range=[-1, 1])
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def test_chebys(self):
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self.check_poly(_ufuncs.eval_chebys,
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param_ranges=[], x_range=[-2, 2])
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def test_chebyc(self):
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self.check_poly(_ufuncs.eval_chebyc,
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param_ranges=[], x_range=[-2, 2])
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def test_sh_chebyt(self):
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self.check_poly(_ufuncs.eval_sh_chebyt,
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param_ranges=[], x_range=[0, 1])
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def test_sh_chebyu(self):
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self.check_poly(_ufuncs.eval_sh_chebyu,
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param_ranges=[], x_range=[0, 1])
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def test_legendre(self):
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self.check_poly(_ufuncs.eval_legendre,
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param_ranges=[], x_range=[-1, 1])
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def test_sh_legendre(self):
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self.check_poly(_ufuncs.eval_sh_legendre,
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param_ranges=[], x_range=[0, 1])
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def test_genlaguerre(self):
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self.check_poly(_ufuncs.eval_genlaguerre,
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param_ranges=[(-0.99, 10)], x_range=[0, 100])
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def test_laguerre(self):
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self.check_poly(_ufuncs.eval_laguerre,
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param_ranges=[], x_range=[0, 100])
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def test_hermite(self):
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v = _ufuncs.eval_hermite(70, 1.0)
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a = -1.457076485701412e60
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assert_allclose(v, a)
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def test_hermite_domain():
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# Regression test for gh-11091.
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assert np.isnan(_ufuncs.eval_hermite(-1, 1.0))
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assert np.isnan(_ufuncs.eval_hermitenorm(-1, 1.0))
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@pytest.mark.parametrize("n", [0, 1, 2])
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@pytest.mark.parametrize("x", [0, 1, np.nan])
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def test_hermite_nan(n, x):
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# Regression test for gh-11369.
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assert np.isnan(_ufuncs.eval_hermite(n, x)) == np.any(np.isnan([n, x]))
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assert np.isnan(_ufuncs.eval_hermitenorm(n, x)) == np.any(np.isnan([n, x]))
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@pytest.mark.parametrize('n', [0, 1, 2, 3.2])
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@pytest.mark.parametrize('alpha', [1, np.nan])
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@pytest.mark.parametrize('x', [2, np.nan])
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def test_genlaguerre_nan(n, alpha, x):
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# Regression test for gh-11361.
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nan_laguerre = np.isnan(_ufuncs.eval_genlaguerre(n, alpha, x))
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nan_arg = np.any(np.isnan([n, alpha, x]))
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assert nan_laguerre == nan_arg
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@pytest.mark.parametrize('n', [0, 1, 2, 3.2])
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@pytest.mark.parametrize('alpha', [0.0, 1, np.nan])
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@pytest.mark.parametrize('x', [1e-6, 2, np.nan])
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def test_gegenbauer_nan(n, alpha, x):
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# Regression test for gh-11370.
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nan_gegenbauer = np.isnan(_ufuncs.eval_gegenbauer(n, alpha, x))
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nan_arg = np.any(np.isnan([n, alpha, x]))
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assert nan_gegenbauer == nan_arg
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