# this program corresponds to special.py ### Means test is not done yet # E Means test is giving error (E) # F Means test is failing (F) # EF Means test is giving error and Failing #! Means test is segfaulting # 8 Means test runs forever ### test_besselpoly ### test_mathieu_a ### test_mathieu_even_coef ### test_mathieu_odd_coef ### test_modfresnelp ### test_modfresnelm # test_pbdv_seq ### test_pbvv_seq ### test_sph_harm import functools import itertools import operator import platform import sys import numpy as np from numpy import (array, isnan, r_, arange, finfo, pi, sin, cos, tan, exp, log, zeros, sqrt, asarray, inf, nan_to_num, real, arctan, double, array_equal) import pytest from pytest import raises as assert_raises from numpy.testing import (assert_equal, assert_almost_equal, assert_array_equal, assert_array_almost_equal, assert_approx_equal, assert_, assert_allclose, assert_array_almost_equal_nulp, suppress_warnings) from scipy import special import scipy.special._ufuncs as cephes from scipy.special import ellipe, ellipk, ellipkm1 from scipy.special import elliprc, elliprd, elliprf, elliprg, elliprj from scipy.special import mathieu_odd_coef, mathieu_even_coef, stirling2 from scipy._lib._util import np_long, np_ulong from scipy.special._basic import _FACTORIALK_LIMITS_64BITS, \ _FACTORIALK_LIMITS_32BITS from scipy.special._testutils import with_special_errors, \ assert_func_equal, FuncData import math class TestCephes: def test_airy(self): cephes.airy(0) def test_airye(self): cephes.airye(0) def test_binom(self): n = np.array([0.264, 4, 5.2, 17]) k = np.array([2, 0.4, 7, 3.3]) nk = np.array(np.broadcast_arrays(n[:,None], k[None,:]) ).reshape(2, -1).T rknown = np.array([[-0.097152, 0.9263051596159367, 0.01858423645695389, -0.007581020651518199],[6, 2.0214389119675666, 0, 2.9827344527963846], [10.92, 2.22993515861399, -0.00585728, 10.468891352063146], [136, 3.5252179590758828, 19448, 1024.5526916174495]]) assert_func_equal(cephes.binom, rknown.ravel(), nk, rtol=1e-13) # Test branches in implementation np.random.seed(1234) n = np.r_[np.arange(-7, 30), 1000*np.random.rand(30) - 500] k = np.arange(0, 102) nk = np.array(np.broadcast_arrays(n[:,None], k[None,:]) ).reshape(2, -1).T assert_func_equal(cephes.binom, cephes.binom(nk[:,0], nk[:,1] * (1 + 1e-15)), nk, atol=1e-10, rtol=1e-10) def test_binom_2(self): # Test branches in implementation np.random.seed(1234) n = np.r_[np.logspace(1, 300, 20)] k = np.arange(0, 102) nk = np.array(np.broadcast_arrays(n[:,None], k[None,:]) ).reshape(2, -1).T assert_func_equal(cephes.binom, cephes.binom(nk[:,0], nk[:,1] * (1 + 1e-15)), nk, atol=1e-10, rtol=1e-10) def test_binom_exact(self): @np.vectorize def binom_int(n, k): n = int(n) k = int(k) num = 1 den = 1 for i in range(1, k+1): num *= i + n - k den *= i return float(num/den) np.random.seed(1234) n = np.arange(1, 15) k = np.arange(0, 15) nk = np.array(np.broadcast_arrays(n[:,None], k[None,:]) ).reshape(2, -1).T nk = nk[nk[:,0] >= nk[:,1]] assert_func_equal(cephes.binom, binom_int(nk[:,0], nk[:,1]), nk, atol=0, rtol=0) def test_binom_nooverflow_8346(self): # Test (binom(n, k) doesn't overflow prematurely */ dataset = [ (1000, 500, 2.70288240945436551e+299), (1002, 501, 1.08007396880791225e+300), (1004, 502, 4.31599279169058121e+300), (1006, 503, 1.72468101616263781e+301), (1008, 504, 6.89188009236419153e+301), (1010, 505, 2.75402257948335448e+302), (1012, 506, 1.10052048531923757e+303), (1014, 507, 4.39774063758732849e+303), (1016, 508, 1.75736486108312519e+304), (1018, 509, 7.02255427788423734e+304), (1020, 510, 2.80626776829962255e+305), (1022, 511, 1.12140876377061240e+306), (1024, 512, 4.48125455209897109e+306), (1026, 513, 1.79075474304149900e+307), (1028, 514, 7.15605105487789676e+307) ] dataset = np.asarray(dataset) FuncData(cephes.binom, dataset, (0, 1), 2, rtol=1e-12).check() def test_bdtr(self): assert_equal(cephes.bdtr(1,1,0.5),1.0) def test_bdtri(self): assert_equal(cephes.bdtri(1,3,0.5),0.5) def test_bdtrc(self): assert_equal(cephes.bdtrc(1,3,0.5),0.5) def test_bdtrin(self): assert_equal(cephes.bdtrin(1,0,1),5.0) def test_bdtrik(self): cephes.bdtrik(1,3,0.5) def test_bei(self): assert_equal(cephes.bei(0),0.0) def test_beip(self): assert_equal(cephes.beip(0),0.0) def test_ber(self): assert_equal(cephes.ber(0),1.0) def test_berp(self): assert_equal(cephes.berp(0),0.0) def test_besselpoly(self): assert_equal(cephes.besselpoly(0,0,0),1.0) def test_btdtr(self): with pytest.deprecated_call(match='deprecated in SciPy 1.12.0'): y = special.btdtr(1, 1, 1) assert_equal(y, 1.0) def test_btdtri(self): with pytest.deprecated_call(match='deprecated in SciPy 1.12.0'): y = special.btdtri(1, 1, 1) assert_equal(y, 1.0) def test_btdtria(self): assert_equal(cephes.btdtria(1,1,1),5.0) def test_btdtrib(self): assert_equal(cephes.btdtrib(1,1,1),5.0) def test_cbrt(self): assert_approx_equal(cephes.cbrt(1),1.0) def test_chdtr(self): assert_equal(cephes.chdtr(1,0),0.0) def test_chdtrc(self): assert_equal(cephes.chdtrc(1,0),1.0) def test_chdtri(self): assert_equal(cephes.chdtri(1,1),0.0) def test_chdtriv(self): assert_equal(cephes.chdtriv(0,0),5.0) def test_chndtr(self): assert_equal(cephes.chndtr(0,1,0),0.0) # Each row holds (x, nu, lam, expected_value) # These values were computed using Wolfram Alpha with # CDF[NoncentralChiSquareDistribution[nu, lam], x] values = np.array([ [25.00, 20.0, 400, 4.1210655112396197139e-57], [25.00, 8.00, 250, 2.3988026526832425878e-29], [0.001, 8.00, 40., 5.3761806201366039084e-24], [0.010, 8.00, 40., 5.45396231055999457039e-20], [20.00, 2.00, 107, 1.39390743555819597802e-9], [22.50, 2.00, 107, 7.11803307138105870671e-9], [25.00, 2.00, 107, 3.11041244829864897313e-8], [3.000, 2.00, 1.0, 0.62064365321954362734], [350.0, 300., 10., 0.93880128006276407710], [100.0, 13.5, 10., 0.99999999650104210949], [700.0, 20.0, 400, 0.99999999925680650105], [150.0, 13.5, 10., 0.99999999999999983046], [160.0, 13.5, 10., 0.99999999999999999518], # 1.0 ]) cdf = cephes.chndtr(values[:, 0], values[:, 1], values[:, 2]) assert_allclose(cdf, values[:, 3], rtol=1e-12) assert_almost_equal(cephes.chndtr(np.inf, np.inf, 0), 2.0) assert_almost_equal(cephes.chndtr(2, 1, np.inf), 0.0) assert_(np.isnan(cephes.chndtr(np.nan, 1, 2))) assert_(np.isnan(cephes.chndtr(5, np.nan, 2))) assert_(np.isnan(cephes.chndtr(5, 1, np.nan))) def test_chndtridf(self): assert_equal(cephes.chndtridf(0,0,1),5.0) def test_chndtrinc(self): assert_equal(cephes.chndtrinc(0,1,0),5.0) def test_chndtrix(self): assert_equal(cephes.chndtrix(0,1,0),0.0) def test_cosdg(self): assert_equal(cephes.cosdg(0),1.0) def test_cosm1(self): assert_equal(cephes.cosm1(0),0.0) def test_cotdg(self): assert_almost_equal(cephes.cotdg(45),1.0) def test_dawsn(self): assert_equal(cephes.dawsn(0),0.0) assert_allclose(cephes.dawsn(1.23), 0.50053727749081767) def test_diric(self): # Test behavior near multiples of 2pi. Regression test for issue # described in gh-4001. n_odd = [1, 5, 25] x = np.array(2*np.pi + 5e-5).astype(np.float32) assert_almost_equal(special.diric(x, n_odd), 1.0, decimal=7) x = np.array(2*np.pi + 1e-9).astype(np.float64) assert_almost_equal(special.diric(x, n_odd), 1.0, decimal=15) x = np.array(2*np.pi + 1e-15).astype(np.float64) assert_almost_equal(special.diric(x, n_odd), 1.0, decimal=15) if hasattr(np, 'float128'): # No float128 available in 32-bit numpy x = np.array(2*np.pi + 1e-12).astype(np.float128) assert_almost_equal(special.diric(x, n_odd), 1.0, decimal=19) n_even = [2, 4, 24] x = np.array(2*np.pi + 1e-9).astype(np.float64) assert_almost_equal(special.diric(x, n_even), -1.0, decimal=15) # Test at some values not near a multiple of pi x = np.arange(0.2*np.pi, 1.0*np.pi, 0.2*np.pi) octave_result = [0.872677996249965, 0.539344662916632, 0.127322003750035, -0.206011329583298] assert_almost_equal(special.diric(x, 3), octave_result, decimal=15) def test_diric_broadcasting(self): x = np.arange(5) n = np.array([1, 3, 7]) assert_(special.diric(x[:, np.newaxis], n).shape == (x.size, n.size)) def test_ellipe(self): assert_equal(cephes.ellipe(1),1.0) def test_ellipeinc(self): assert_equal(cephes.ellipeinc(0,1),0.0) def test_ellipj(self): cephes.ellipj(0,1) def test_ellipk(self): assert_allclose(ellipk(0), pi/2) def test_ellipkinc(self): assert_equal(cephes.ellipkinc(0,0),0.0) def test_erf(self): assert_equal(cephes.erf(0), 0.0) def test_erf_symmetry(self): x = 5.905732037710919 assert_equal(cephes.erf(x) + cephes.erf(-x), 0.0) def test_erfc(self): assert_equal(cephes.erfc(0), 1.0) def test_exp10(self): assert_approx_equal(cephes.exp10(2),100.0) def test_exp2(self): assert_equal(cephes.exp2(2),4.0) def test_expm1(self): assert_equal(cephes.expm1(0),0.0) assert_equal(cephes.expm1(np.inf), np.inf) assert_equal(cephes.expm1(-np.inf), -1) assert_equal(cephes.expm1(np.nan), np.nan) def test_expm1_complex(self): expm1 = cephes.expm1 assert_equal(expm1(0 + 0j), 0 + 0j) assert_equal(expm1(complex(np.inf, 0)), complex(np.inf, 0)) assert_equal(expm1(complex(np.inf, 1)), complex(np.inf, np.inf)) assert_equal(expm1(complex(np.inf, 2)), complex(-np.inf, np.inf)) assert_equal(expm1(complex(np.inf, 4)), complex(-np.inf, -np.inf)) assert_equal(expm1(complex(np.inf, 5)), complex(np.inf, -np.inf)) assert_equal(expm1(complex(1, np.inf)), complex(np.nan, np.nan)) assert_equal(expm1(complex(0, np.inf)), complex(np.nan, np.nan)) assert_equal(expm1(complex(np.inf, np.inf)), complex(np.inf, np.nan)) assert_equal(expm1(complex(-np.inf, np.inf)), complex(-1, 0)) assert_equal(expm1(complex(-np.inf, np.nan)), complex(-1, 0)) assert_equal(expm1(complex(np.inf, np.nan)), complex(np.inf, np.nan)) assert_equal(expm1(complex(0, np.nan)), complex(np.nan, np.nan)) assert_equal(expm1(complex(1, np.nan)), complex(np.nan, np.nan)) assert_equal(expm1(complex(np.nan, 1)), complex(np.nan, np.nan)) assert_equal(expm1(complex(np.nan, np.nan)), complex(np.nan, np.nan)) @pytest.mark.xfail(reason='The real part of expm1(z) bad at these points') def test_expm1_complex_hard(self): # The real part of this function is difficult to evaluate when # z.real = -log(cos(z.imag)). y = np.array([0.1, 0.2, 0.3, 5, 11, 20]) x = -np.log(np.cos(y)) z = x + 1j*y # evaluate using mpmath.expm1 with dps=1000 expected = np.array([-5.5507901846769623e-17+0.10033467208545054j, 2.4289354732893695e-18+0.20271003550867248j, 4.5235500262585768e-17+0.30933624960962319j, 7.8234305217489006e-17-3.3805150062465863j, -1.3685191953697676e-16-225.95084645419513j, 8.7175620481291045e-17+2.2371609442247422j]) found = cephes.expm1(z) # this passes. assert_array_almost_equal_nulp(found.imag, expected.imag, 3) # this fails. assert_array_almost_equal_nulp(found.real, expected.real, 20) def test_fdtr(self): assert_equal(cephes.fdtr(1, 1, 0), 0.0) # Computed using Wolfram Alpha: CDF[FRatioDistribution[1e-6, 5], 10] assert_allclose(cephes.fdtr(1e-6, 5, 10), 0.9999940790193488, rtol=1e-12) def test_fdtrc(self): assert_equal(cephes.fdtrc(1, 1, 0), 1.0) # Computed using Wolfram Alpha: # 1 - CDF[FRatioDistribution[2, 1/10], 1e10] assert_allclose(cephes.fdtrc(2, 0.1, 1e10), 0.27223784621293512, rtol=1e-12) def test_fdtri(self): assert_allclose(cephes.fdtri(1, 1, [0.499, 0.501]), array([0.9937365, 1.00630298]), rtol=1e-6) # From Wolfram Alpha: # CDF[FRatioDistribution[1/10, 1], 3] = 0.8756751669632105666874... p = 0.8756751669632105666874 assert_allclose(cephes.fdtri(0.1, 1, p), 3, rtol=1e-12) @pytest.mark.xfail(reason='Returns nan on i686.') def test_fdtri_mysterious_failure(self): assert_allclose(cephes.fdtri(1, 1, 0.5), 1) def test_fdtridfd(self): assert_equal(cephes.fdtridfd(1,0,0),5.0) def test_fresnel(self): assert_equal(cephes.fresnel(0),(0.0,0.0)) def test_gamma(self): assert_equal(cephes.gamma(5),24.0) def test_gammainccinv(self): assert_equal(cephes.gammainccinv(5,1),0.0) def test_gammaln(self): cephes.gammaln(10) def test_gammasgn(self): vals = np.array([-4, -3.5, -2.3, 1, 4.2], np.float64) assert_array_equal(cephes.gammasgn(vals), np.sign(cephes.rgamma(vals))) def test_gdtr(self): assert_equal(cephes.gdtr(1,1,0),0.0) def test_gdtr_inf(self): assert_equal(cephes.gdtr(1,1,np.inf),1.0) def test_gdtrc(self): assert_equal(cephes.gdtrc(1,1,0),1.0) def test_gdtria(self): assert_equal(cephes.gdtria(0,1,1),0.0) def test_gdtrib(self): cephes.gdtrib(1,0,1) # assert_equal(cephes.gdtrib(1,0,1),5.0) def test_gdtrix(self): cephes.gdtrix(1,1,.1) def test_hankel1(self): cephes.hankel1(1,1) def test_hankel1e(self): cephes.hankel1e(1,1) def test_hankel2(self): cephes.hankel2(1,1) def test_hankel2e(self): cephes.hankel2e(1,1) def test_hyp1f1(self): assert_approx_equal(cephes.hyp1f1(1,1,1), exp(1.0)) assert_approx_equal(cephes.hyp1f1(3,4,-6), 0.026056422099537251095) cephes.hyp1f1(1,1,1) def test_hyp2f1(self): assert_equal(cephes.hyp2f1(1,1,1,0),1.0) def test_i0(self): assert_equal(cephes.i0(0),1.0) def test_i0e(self): assert_equal(cephes.i0e(0),1.0) def test_i1(self): assert_equal(cephes.i1(0),0.0) def test_i1e(self): assert_equal(cephes.i1e(0),0.0) def test_it2i0k0(self): cephes.it2i0k0(1) def test_it2j0y0(self): cephes.it2j0y0(1) def test_it2struve0(self): cephes.it2struve0(1) def test_itairy(self): cephes.itairy(1) def test_iti0k0(self): assert_equal(cephes.iti0k0(0),(0.0,0.0)) def test_itj0y0(self): assert_equal(cephes.itj0y0(0),(0.0,0.0)) def test_itmodstruve0(self): assert_equal(cephes.itmodstruve0(0),0.0) def test_itstruve0(self): assert_equal(cephes.itstruve0(0),0.0) def test_iv(self): assert_equal(cephes.iv(1,0),0.0) def test_ive(self): assert_equal(cephes.ive(1,0),0.0) def test_j0(self): assert_equal(cephes.j0(0),1.0) def test_j1(self): assert_equal(cephes.j1(0),0.0) def test_jn(self): assert_equal(cephes.jn(0,0),1.0) def test_jv(self): assert_equal(cephes.jv(0,0),1.0) def test_jve(self): assert_equal(cephes.jve(0,0),1.0) def test_k0(self): cephes.k0(2) def test_k0e(self): cephes.k0e(2) def test_k1(self): cephes.k1(2) def test_k1e(self): cephes.k1e(2) def test_kei(self): cephes.kei(2) def test_keip(self): assert_equal(cephes.keip(0),0.0) def test_ker(self): cephes.ker(2) def test_kerp(self): cephes.kerp(2) def test_kelvin(self): cephes.kelvin(2) def test_kn(self): cephes.kn(1,1) def test_kolmogi(self): assert_equal(cephes.kolmogi(1),0.0) assert_(np.isnan(cephes.kolmogi(np.nan))) def test_kolmogorov(self): assert_equal(cephes.kolmogorov(0), 1.0) def test_kolmogp(self): assert_equal(cephes._kolmogp(0), -0.0) def test_kolmogc(self): assert_equal(cephes._kolmogc(0), 0.0) def test_kolmogci(self): assert_equal(cephes._kolmogci(0), 0.0) assert_(np.isnan(cephes._kolmogci(np.nan))) def test_kv(self): cephes.kv(1,1) def test_kve(self): cephes.kve(1,1) def test_log1p(self): log1p = cephes.log1p assert_equal(log1p(0), 0.0) assert_equal(log1p(-1), -np.inf) assert_equal(log1p(-2), np.nan) assert_equal(log1p(np.inf), np.inf) def test_log1p_complex(self): log1p = cephes.log1p c = complex assert_equal(log1p(0 + 0j), 0 + 0j) assert_equal(log1p(c(-1, 0)), c(-np.inf, 0)) with suppress_warnings() as sup: sup.filter(RuntimeWarning, "invalid value encountered in multiply") assert_allclose(log1p(c(1, np.inf)), c(np.inf, np.pi/2)) assert_equal(log1p(c(1, np.nan)), c(np.nan, np.nan)) assert_allclose(log1p(c(-np.inf, 1)), c(np.inf, np.pi)) assert_equal(log1p(c(np.inf, 1)), c(np.inf, 0)) assert_allclose(log1p(c(-np.inf, np.inf)), c(np.inf, 3*np.pi/4)) assert_allclose(log1p(c(np.inf, np.inf)), c(np.inf, np.pi/4)) assert_equal(log1p(c(np.inf, np.nan)), c(np.inf, np.nan)) assert_equal(log1p(c(-np.inf, np.nan)), c(np.inf, np.nan)) assert_equal(log1p(c(np.nan, np.inf)), c(np.inf, np.nan)) assert_equal(log1p(c(np.nan, 1)), c(np.nan, np.nan)) assert_equal(log1p(c(np.nan, np.nan)), c(np.nan, np.nan)) def test_lpmv(self): assert_equal(cephes.lpmv(0,0,1),1.0) def test_mathieu_a(self): assert_equal(cephes.mathieu_a(1,0),1.0) def test_mathieu_b(self): assert_equal(cephes.mathieu_b(1,0),1.0) def test_mathieu_cem(self): assert_equal(cephes.mathieu_cem(1,0,0),(1.0,0.0)) # Test AMS 20.2.27 @np.vectorize def ce_smallq(m, q, z): z *= np.pi/180 if m == 0: # + O(q^2) return 2**(-0.5) * (1 - .5*q*cos(2*z)) elif m == 1: # + O(q^2) return cos(z) - q/8 * cos(3*z) elif m == 2: # + O(q^2) return cos(2*z) - q*(cos(4*z)/12 - 1/4) else: # + O(q^2) return cos(m*z) - q*(cos((m+2)*z)/(4*(m+1)) - cos((m-2)*z)/(4*(m-1))) m = np.arange(0, 100) q = np.r_[0, np.logspace(-30, -9, 10)] assert_allclose(cephes.mathieu_cem(m[:,None], q[None,:], 0.123)[0], ce_smallq(m[:,None], q[None,:], 0.123), rtol=1e-14, atol=0) def test_mathieu_sem(self): assert_equal(cephes.mathieu_sem(1,0,0),(0.0,1.0)) # Test AMS 20.2.27 @np.vectorize def se_smallq(m, q, z): z *= np.pi/180 if m == 1: # + O(q^2) return sin(z) - q/8 * sin(3*z) elif m == 2: # + O(q^2) return sin(2*z) - q*sin(4*z)/12 else: # + O(q^2) return sin(m*z) - q*(sin((m+2)*z)/(4*(m+1)) - sin((m-2)*z)/(4*(m-1))) m = np.arange(1, 100) q = np.r_[0, np.logspace(-30, -9, 10)] assert_allclose(cephes.mathieu_sem(m[:,None], q[None,:], 0.123)[0], se_smallq(m[:,None], q[None,:], 0.123), rtol=1e-14, atol=0) def test_mathieu_modcem1(self): assert_equal(cephes.mathieu_modcem1(1,0,0),(0.0,0.0)) def test_mathieu_modcem2(self): cephes.mathieu_modcem2(1,1,1) # Test reflection relation AMS 20.6.19 m = np.arange(0, 4)[:,None,None] q = np.r_[np.logspace(-2, 2, 10)][None,:,None] z = np.linspace(0, 1, 7)[None,None,:] y1 = cephes.mathieu_modcem2(m, q, -z)[0] fr = -cephes.mathieu_modcem2(m, q, 0)[0] / cephes.mathieu_modcem1(m, q, 0)[0] y2 = (-cephes.mathieu_modcem2(m, q, z)[0] - 2*fr*cephes.mathieu_modcem1(m, q, z)[0]) assert_allclose(y1, y2, rtol=1e-10) def test_mathieu_modsem1(self): assert_equal(cephes.mathieu_modsem1(1,0,0),(0.0,0.0)) def test_mathieu_modsem2(self): cephes.mathieu_modsem2(1,1,1) # Test reflection relation AMS 20.6.20 m = np.arange(1, 4)[:,None,None] q = np.r_[np.logspace(-2, 2, 10)][None,:,None] z = np.linspace(0, 1, 7)[None,None,:] y1 = cephes.mathieu_modsem2(m, q, -z)[0] fr = cephes.mathieu_modsem2(m, q, 0)[1] / cephes.mathieu_modsem1(m, q, 0)[1] y2 = (cephes.mathieu_modsem2(m, q, z)[0] - 2*fr*cephes.mathieu_modsem1(m, q, z)[0]) assert_allclose(y1, y2, rtol=1e-10) def test_mathieu_overflow(self): # Check that these return NaNs instead of causing a SEGV assert_equal(cephes.mathieu_cem(10000, 0, 1.3), (np.nan, np.nan)) assert_equal(cephes.mathieu_sem(10000, 0, 1.3), (np.nan, np.nan)) assert_equal(cephes.mathieu_cem(10000, 1.5, 1.3), (np.nan, np.nan)) assert_equal(cephes.mathieu_sem(10000, 1.5, 1.3), (np.nan, np.nan)) assert_equal(cephes.mathieu_modcem1(10000, 1.5, 1.3), (np.nan, np.nan)) assert_equal(cephes.mathieu_modsem1(10000, 1.5, 1.3), (np.nan, np.nan)) assert_equal(cephes.mathieu_modcem2(10000, 1.5, 1.3), (np.nan, np.nan)) assert_equal(cephes.mathieu_modsem2(10000, 1.5, 1.3), (np.nan, np.nan)) def test_mathieu_ticket_1847(self): # Regression test --- this call had some out-of-bounds access # and could return nan occasionally for k in range(60): v = cephes.mathieu_modsem2(2, 100, -1) # Values from ACM TOMS 804 (derivate by numerical differentiation) assert_allclose(v[0], 0.1431742913063671074347, rtol=1e-10) assert_allclose(v[1], 0.9017807375832909144719, rtol=1e-4) def test_modfresnelm(self): cephes.modfresnelm(0) def test_modfresnelp(self): cephes.modfresnelp(0) def test_modstruve(self): assert_equal(cephes.modstruve(1,0),0.0) def test_nbdtr(self): assert_equal(cephes.nbdtr(1,1,1),1.0) def test_nbdtrc(self): assert_equal(cephes.nbdtrc(1,1,1),0.0) def test_nbdtri(self): assert_equal(cephes.nbdtri(1,1,1),1.0) def test_nbdtrik(self): cephes.nbdtrik(1,.4,.5) def test_nbdtrin(self): assert_equal(cephes.nbdtrin(1,0,0),5.0) def test_ncfdtr(self): assert_equal(cephes.ncfdtr(1,1,1,0),0.0) def test_ncfdtri(self): assert_equal(cephes.ncfdtri(1, 1, 1, 0), 0.0) f = [0.5, 1, 1.5] p = cephes.ncfdtr(2, 3, 1.5, f) assert_allclose(cephes.ncfdtri(2, 3, 1.5, p), f) def test_ncfdtridfd(self): dfd = [1, 2, 3] p = cephes.ncfdtr(2, dfd, 0.25, 15) assert_allclose(cephes.ncfdtridfd(2, p, 0.25, 15), dfd) def test_ncfdtridfn(self): dfn = [0.1, 1, 2, 3, 1e4] p = cephes.ncfdtr(dfn, 2, 0.25, 15) assert_allclose(cephes.ncfdtridfn(p, 2, 0.25, 15), dfn, rtol=1e-5) def test_ncfdtrinc(self): nc = [0.5, 1.5, 2.0] p = cephes.ncfdtr(2, 3, nc, 15) assert_allclose(cephes.ncfdtrinc(2, 3, p, 15), nc) def test_nctdtr(self): assert_equal(cephes.nctdtr(1,0,0),0.5) assert_equal(cephes.nctdtr(9, 65536, 45), 0.0) assert_approx_equal(cephes.nctdtr(np.inf, 1., 1.), 0.5, 5) assert_(np.isnan(cephes.nctdtr(2., np.inf, 10.))) assert_approx_equal(cephes.nctdtr(2., 1., np.inf), 1.) assert_(np.isnan(cephes.nctdtr(np.nan, 1., 1.))) assert_(np.isnan(cephes.nctdtr(2., np.nan, 1.))) assert_(np.isnan(cephes.nctdtr(2., 1., np.nan))) def test_nctdtridf(self): cephes.nctdtridf(1,0.5,0) def test_nctdtrinc(self): cephes.nctdtrinc(1,0,0) def test_nctdtrit(self): cephes.nctdtrit(.1,0.2,.5) def test_nrdtrimn(self): assert_approx_equal(cephes.nrdtrimn(0.5,1,1),1.0) def test_nrdtrisd(self): assert_allclose(cephes.nrdtrisd(0.5,0.5,0.5), 0.0, atol=0, rtol=0) def test_obl_ang1(self): cephes.obl_ang1(1,1,1,0) def test_obl_ang1_cv(self): result = cephes.obl_ang1_cv(1,1,1,1,0) assert_almost_equal(result[0],1.0) assert_almost_equal(result[1],0.0) def test_obl_cv(self): assert_equal(cephes.obl_cv(1,1,0),2.0) def test_obl_rad1(self): cephes.obl_rad1(1,1,1,0) def test_obl_rad1_cv(self): cephes.obl_rad1_cv(1,1,1,1,0) def test_obl_rad2(self): cephes.obl_rad2(1,1,1,0) def test_obl_rad2_cv(self): cephes.obl_rad2_cv(1,1,1,1,0) def test_pbdv(self): assert_equal(cephes.pbdv(1,0),(0.0,1.0)) def test_pbvv(self): cephes.pbvv(1,0) def test_pbwa(self): cephes.pbwa(1,0) def test_pdtr(self): val = cephes.pdtr(0, 1) assert_almost_equal(val, np.exp(-1)) # Edge case: m = 0. val = cephes.pdtr([0, 1, 2], 0) assert_array_equal(val, [1, 1, 1]) def test_pdtrc(self): val = cephes.pdtrc(0, 1) assert_almost_equal(val, 1 - np.exp(-1)) # Edge case: m = 0. val = cephes.pdtrc([0, 1, 2], 0.0) assert_array_equal(val, [0, 0, 0]) def test_pdtri(self): with suppress_warnings() as sup: sup.filter(RuntimeWarning, "floating point number truncated to an integer") cephes.pdtri(0.5,0.5) def test_pdtrik(self): k = cephes.pdtrik(0.5, 1) assert_almost_equal(cephes.gammaincc(k + 1, 1), 0.5) # Edge case: m = 0 or very small. k = cephes.pdtrik([[0], [0.25], [0.95]], [0, 1e-20, 1e-6]) assert_array_equal(k, np.zeros((3, 3))) def test_pro_ang1(self): cephes.pro_ang1(1,1,1,0) def test_pro_ang1_cv(self): assert_array_almost_equal(cephes.pro_ang1_cv(1,1,1,1,0), array((1.0,0.0))) def test_pro_cv(self): assert_equal(cephes.pro_cv(1,1,0),2.0) def test_pro_rad1(self): cephes.pro_rad1(1,1,1,0.1) def test_pro_rad1_cv(self): cephes.pro_rad1_cv(1,1,1,1,0) def test_pro_rad2(self): cephes.pro_rad2(1,1,1,0) def test_pro_rad2_cv(self): cephes.pro_rad2_cv(1,1,1,1,0) def test_psi(self): cephes.psi(1) def test_radian(self): assert_equal(cephes.radian(0,0,0),0) def test_rgamma(self): assert_equal(cephes.rgamma(1),1.0) def test_round(self): assert_equal(cephes.round(3.4),3.0) assert_equal(cephes.round(-3.4),-3.0) assert_equal(cephes.round(3.6),4.0) assert_equal(cephes.round(-3.6),-4.0) assert_equal(cephes.round(3.5),4.0) assert_equal(cephes.round(-3.5),-4.0) def test_shichi(self): cephes.shichi(1) def test_sici(self): cephes.sici(1) s, c = cephes.sici(np.inf) assert_almost_equal(s, np.pi * 0.5) assert_almost_equal(c, 0) s, c = cephes.sici(-np.inf) assert_almost_equal(s, -np.pi * 0.5) assert_(np.isnan(c), "cosine integral(-inf) is not nan") def test_sindg(self): assert_equal(cephes.sindg(90),1.0) def test_smirnov(self): assert_equal(cephes.smirnov(1,.1),0.9) assert_(np.isnan(cephes.smirnov(1,np.nan))) def test_smirnovp(self): assert_equal(cephes._smirnovp(1, .1), -1) assert_equal(cephes._smirnovp(2, 0.75), -2*(0.25)**(2-1)) assert_equal(cephes._smirnovp(3, 0.75), -3*(0.25)**(3-1)) assert_(np.isnan(cephes._smirnovp(1, np.nan))) def test_smirnovc(self): assert_equal(cephes._smirnovc(1,.1),0.1) assert_(np.isnan(cephes._smirnovc(1,np.nan))) x10 = np.linspace(0, 1, 11, endpoint=True) assert_almost_equal(cephes._smirnovc(3, x10), 1-cephes.smirnov(3, x10)) x4 = np.linspace(0, 1, 5, endpoint=True) assert_almost_equal(cephes._smirnovc(4, x4), 1-cephes.smirnov(4, x4)) def test_smirnovi(self): assert_almost_equal(cephes.smirnov(1,cephes.smirnovi(1,0.4)),0.4) assert_almost_equal(cephes.smirnov(1,cephes.smirnovi(1,0.6)),0.6) assert_(np.isnan(cephes.smirnovi(1,np.nan))) def test_smirnovci(self): assert_almost_equal(cephes._smirnovc(1,cephes._smirnovci(1,0.4)),0.4) assert_almost_equal(cephes._smirnovc(1,cephes._smirnovci(1,0.6)),0.6) assert_(np.isnan(cephes._smirnovci(1,np.nan))) def test_spence(self): assert_equal(cephes.spence(1),0.0) def test_stdtr(self): assert_equal(cephes.stdtr(1,0),0.5) assert_almost_equal(cephes.stdtr(1,1), 0.75) assert_almost_equal(cephes.stdtr(1,2), 0.852416382349) def test_stdtridf(self): cephes.stdtridf(0.7,1) def test_stdtrit(self): cephes.stdtrit(1,0.7) def test_struve(self): assert_equal(cephes.struve(0,0),0.0) def test_tandg(self): assert_equal(cephes.tandg(45),1.0) def test_tklmbda(self): assert_almost_equal(cephes.tklmbda(1,1),1.0) def test_y0(self): cephes.y0(1) def test_y1(self): cephes.y1(1) def test_yn(self): cephes.yn(1,1) def test_yv(self): cephes.yv(1,1) def test_yve(self): cephes.yve(1,1) def test_wofz(self): z = [complex(624.2,-0.26123), complex(-0.4,3.), complex(0.6,2.), complex(-1.,1.), complex(-1.,-9.), complex(-1.,9.), complex(-0.0000000234545,1.1234), complex(-3.,5.1), complex(-53,30.1), complex(0.0,0.12345), complex(11,1), complex(-22,-2), complex(9,-28), complex(21,-33), complex(1e5,1e5), complex(1e14,1e14) ] w = [ complex(-3.78270245518980507452677445620103199303131110e-7, 0.000903861276433172057331093754199933411710053155), complex(0.1764906227004816847297495349730234591778719532788, -0.02146550539468457616788719893991501311573031095617), complex(0.2410250715772692146133539023007113781272362309451, 0.06087579663428089745895459735240964093522265589350), complex(0.30474420525691259245713884106959496013413834051768, -0.20821893820283162728743734725471561394145872072738), complex(7.317131068972378096865595229600561710140617977e34, 8.321873499714402777186848353320412813066170427e34), complex(0.0615698507236323685519612934241429530190806818395, -0.00676005783716575013073036218018565206070072304635), complex(0.3960793007699874918961319170187598400134746631, -5.593152259116644920546186222529802777409274656e-9), complex(0.08217199226739447943295069917990417630675021771804, -0.04701291087643609891018366143118110965272615832184), complex(0.00457246000350281640952328010227885008541748668738, -0.00804900791411691821818731763401840373998654987934), complex(0.8746342859608052666092782112565360755791467973338452, 0.), complex(0.00468190164965444174367477874864366058339647648741, 0.0510735563901306197993676329845149741675029197050), complex(-0.0023193175200187620902125853834909543869428763219, -0.025460054739731556004902057663500272721780776336), complex(9.11463368405637174660562096516414499772662584e304, 3.97101807145263333769664875189354358563218932e305), complex(-4.4927207857715598976165541011143706155432296e281, -2.8019591213423077494444700357168707775769028e281), complex(2.820947917809305132678577516325951485807107151e-6, 2.820947917668257736791638444590253942253354058e-6), complex(2.82094791773878143474039725787438662716372268e-15, 2.82094791773878143474039725773333923127678361e-15) ] assert_func_equal(cephes.wofz, w, z, rtol=1e-13) class TestAiry: def test_airy(self): # This tests the airy function to ensure 8 place accuracy in computation x = special.airy(.99) assert_array_almost_equal( x, array([0.13689066,-0.16050153,1.19815925,0.92046818]), 8, ) x = special.airy(.41) assert_array_almost_equal( x, array([0.25238916,-.23480512,0.80686202,0.51053919]), 8, ) x = special.airy(-.36) assert_array_almost_equal( x, array([0.44508477,-0.23186773,0.44939534,0.48105354]), 8, ) def test_airye(self): a = special.airye(0.01) b = special.airy(0.01) b1 = [None]*4 for n in range(2): b1[n] = b[n]*exp(2.0/3.0*0.01*sqrt(0.01)) for n in range(2,4): b1[n] = b[n]*exp(-abs(real(2.0/3.0*0.01*sqrt(0.01)))) assert_array_almost_equal(a,b1,6) def test_bi_zeros(self): bi = special.bi_zeros(2) bia = (array([-1.17371322, -3.2710930]), array([-2.29443968, -4.07315509]), array([-0.45494438, 0.39652284]), array([0.60195789, -0.76031014])) assert_array_almost_equal(bi,bia,4) bi = special.bi_zeros(5) assert_array_almost_equal(bi[0],array([-1.173713222709127, -3.271093302836352, -4.830737841662016, -6.169852128310251, -7.376762079367764]),11) assert_array_almost_equal(bi[1],array([-2.294439682614122, -4.073155089071828, -5.512395729663599, -6.781294445990305, -7.940178689168587]),10) assert_array_almost_equal(bi[2],array([-0.454944383639657, 0.396522836094465, -0.367969161486959, 0.349499116831805, -0.336026240133662]),11) assert_array_almost_equal(bi[3],array([0.601957887976239, -0.760310141492801, 0.836991012619261, -0.88947990142654, 0.929983638568022]),10) def test_ai_zeros(self): ai = special.ai_zeros(1) assert_array_almost_equal(ai,(array([-2.33810741]), array([-1.01879297]), array([0.5357]), array([0.7012])),4) @pytest.mark.fail_slow(2) def test_ai_zeros_big(self): z, zp, ai_zpx, aip_zx = special.ai_zeros(50000) ai_z, aip_z, _, _ = special.airy(z) ai_zp, aip_zp, _, _ = special.airy(zp) ai_envelope = 1/abs(z)**(1./4) aip_envelope = abs(zp)**(1./4) # Check values assert_allclose(ai_zpx, ai_zp, rtol=1e-10) assert_allclose(aip_zx, aip_z, rtol=1e-10) # Check they are zeros assert_allclose(ai_z/ai_envelope, 0, atol=1e-10, rtol=0) assert_allclose(aip_zp/aip_envelope, 0, atol=1e-10, rtol=0) # Check first zeros, DLMF 9.9.1 assert_allclose(z[:6], [-2.3381074105, -4.0879494441, -5.5205598281, -6.7867080901, -7.9441335871, -9.0226508533], rtol=1e-10) assert_allclose(zp[:6], [-1.0187929716, -3.2481975822, -4.8200992112, -6.1633073556, -7.3721772550, -8.4884867340], rtol=1e-10) @pytest.mark.fail_slow(2) def test_bi_zeros_big(self): z, zp, bi_zpx, bip_zx = special.bi_zeros(50000) _, _, bi_z, bip_z = special.airy(z) _, _, bi_zp, bip_zp = special.airy(zp) bi_envelope = 1/abs(z)**(1./4) bip_envelope = abs(zp)**(1./4) # Check values assert_allclose(bi_zpx, bi_zp, rtol=1e-10) assert_allclose(bip_zx, bip_z, rtol=1e-10) # Check they are zeros assert_allclose(bi_z/bi_envelope, 0, atol=1e-10, rtol=0) assert_allclose(bip_zp/bip_envelope, 0, atol=1e-10, rtol=0) # Check first zeros, DLMF 9.9.2 assert_allclose(z[:6], [-1.1737132227, -3.2710933028, -4.8307378417, -6.1698521283, -7.3767620794, -8.4919488465], rtol=1e-10) assert_allclose(zp[:6], [-2.2944396826, -4.0731550891, -5.5123957297, -6.7812944460, -7.9401786892, -9.0195833588], rtol=1e-10) class TestAssocLaguerre: def test_assoc_laguerre(self): a1 = special.genlaguerre(11,1) a2 = special.assoc_laguerre(.2,11,1) assert_array_almost_equal(a2,a1(.2),8) a2 = special.assoc_laguerre(1,11,1) assert_array_almost_equal(a2,a1(1),8) class TestBesselpoly: def test_besselpoly(self): pass class TestKelvin: def test_bei(self): mbei = special.bei(2) assert_almost_equal(mbei, 0.9722916273066613,5) # this may not be exact def test_beip(self): mbeip = special.beip(2) assert_almost_equal(mbeip,0.91701361338403631,5) # this may not be exact def test_ber(self): mber = special.ber(2) assert_almost_equal(mber,0.75173418271380821,5) # this may not be exact def test_berp(self): mberp = special.berp(2) assert_almost_equal(mberp,-0.49306712470943909,5) # this may not be exact def test_bei_zeros(self): # Abramowitz & Stegun, Table 9.12 bi = special.bei_zeros(5) assert_array_almost_equal(bi,array([5.02622, 9.45541, 13.89349, 18.33398, 22.77544]),4) def test_beip_zeros(self): bip = special.beip_zeros(5) assert_array_almost_equal(bip,array([3.772673304934953, 8.280987849760042, 12.742147523633703, 17.193431752512542, 21.641143941167325]),8) def test_ber_zeros(self): ber = special.ber_zeros(5) assert_array_almost_equal(ber,array([2.84892, 7.23883, 11.67396, 16.11356, 20.55463]),4) def test_berp_zeros(self): brp = special.berp_zeros(5) assert_array_almost_equal(brp,array([6.03871, 10.51364, 14.96844, 19.41758, 23.86430]),4) def test_kelvin(self): mkelv = special.kelvin(2) assert_array_almost_equal(mkelv,(special.ber(2) + special.bei(2)*1j, special.ker(2) + special.kei(2)*1j, special.berp(2) + special.beip(2)*1j, special.kerp(2) + special.keip(2)*1j),8) def test_kei(self): mkei = special.kei(2) assert_almost_equal(mkei,-0.20240006776470432,5) def test_keip(self): mkeip = special.keip(2) assert_almost_equal(mkeip,0.21980790991960536,5) def test_ker(self): mker = special.ker(2) assert_almost_equal(mker,-0.041664513991509472,5) def test_kerp(self): mkerp = special.kerp(2) assert_almost_equal(mkerp,-0.10660096588105264,5) def test_kei_zeros(self): kei = special.kei_zeros(5) assert_array_almost_equal(kei,array([3.91467, 8.34422, 12.78256, 17.22314, 21.66464]),4) def test_keip_zeros(self): keip = special.keip_zeros(5) assert_array_almost_equal(keip,array([4.93181, 9.40405, 13.85827, 18.30717, 22.75379]),4) # numbers come from 9.9 of A&S pg. 381 def test_kelvin_zeros(self): tmp = special.kelvin_zeros(5) berz,beiz,kerz,keiz,berpz,beipz,kerpz,keipz = tmp assert_array_almost_equal(berz,array([2.84892, 7.23883, 11.67396, 16.11356, 20.55463]),4) assert_array_almost_equal(beiz,array([5.02622, 9.45541, 13.89349, 18.33398, 22.77544]),4) assert_array_almost_equal(kerz,array([1.71854, 6.12728, 10.56294, 15.00269, 19.44382]),4) assert_array_almost_equal(keiz,array([3.91467, 8.34422, 12.78256, 17.22314, 21.66464]),4) assert_array_almost_equal(berpz,array([6.03871, 10.51364, 14.96844, 19.41758, 23.86430]),4) assert_array_almost_equal(beipz,array([3.77267, # table from 1927 had 3.77320 # but this is more accurate 8.28099, 12.74215, 17.19343, 21.64114]),4) assert_array_almost_equal(kerpz,array([2.66584, 7.17212, 11.63218, 16.08312, 20.53068]),4) assert_array_almost_equal(keipz,array([4.93181, 9.40405, 13.85827, 18.30717, 22.75379]),4) def test_ker_zeros(self): ker = special.ker_zeros(5) assert_array_almost_equal(ker,array([1.71854, 6.12728, 10.56294, 15.00269, 19.44381]),4) def test_kerp_zeros(self): kerp = special.kerp_zeros(5) assert_array_almost_equal(kerp,array([2.66584, 7.17212, 11.63218, 16.08312, 20.53068]),4) class TestBernoulli: def test_bernoulli(self): brn = special.bernoulli(5) assert_array_almost_equal(brn,array([1.0000, -0.5000, 0.1667, 0.0000, -0.0333, 0.0000]),4) class TestBeta: """ Test beta and betaln. """ def test_beta(self): assert_equal(special.beta(1, 1), 1.0) assert_allclose(special.beta(-100.3, 1e-200), special.gamma(1e-200)) assert_allclose(special.beta(0.0342, 171), 24.070498359873497, rtol=1e-13, atol=0) bet = special.beta(2, 4) betg = (special.gamma(2)*special.gamma(4))/special.gamma(6) assert_allclose(bet, betg, rtol=1e-13) def test_beta_inf(self): assert_(np.isinf(special.beta(-1, 2))) def test_betaln(self): assert_equal(special.betaln(1, 1), 0.0) assert_allclose(special.betaln(-100.3, 1e-200), special.gammaln(1e-200)) assert_allclose(special.betaln(0.0342, 170), 3.1811881124242447, rtol=1e-14, atol=0) betln = special.betaln(2, 4) bet = log(abs(special.beta(2, 4))) assert_allclose(betln, bet, rtol=1e-13) class TestBetaInc: """ Tests for betainc, betaincinv, betaincc, betainccinv. """ def test_a1_b1(self): # betainc(1, 1, x) is x. x = np.array([0, 0.25, 1]) assert_equal(special.betainc(1, 1, x), x) assert_equal(special.betaincinv(1, 1, x), x) assert_equal(special.betaincc(1, 1, x), 1 - x) assert_equal(special.betainccinv(1, 1, x), 1 - x) # Nontrivial expected values computed with mpmath: # from mpmath import mp # mp.dps = 100 # p = mp.betainc(a, b, 0, x, regularized=True) # # or, e.g., # # p = 0.25 # a, b = 0.0342, 171 # x = mp.findroot( # lambda t: mp.betainc(a, b, 0, t, regularized=True) - p, # (8e-21, 9e-21), # solver='anderson', # ) # @pytest.mark.parametrize( 'a, b, x, p', [(2, 4, 0.3138101704556974, 0.5), (0.0342, 171.0, 1e-10, 0.552699169018070910641), # gh-3761: (0.0342, 171, 8.42313169354797e-21, 0.25), # gh-4244: (0.0002742794749792665, 289206.03125, 1.639984034231756e-56, 0.9688708782196045), # gh-12796: (4, 99997, 0.0001947841578892121, 0.999995)]) def test_betainc_betaincinv(self, a, b, x, p): p1 = special.betainc(a, b, x) assert_allclose(p1, p, rtol=1e-15) x1 = special.betaincinv(a, b, p) assert_allclose(x1, x, rtol=5e-13) # Expected values computed with mpmath: # from mpmath import mp # mp.dps = 100 # p = mp.betainc(a, b, x, 1, regularized=True) @pytest.mark.parametrize('a, b, x, p', [(2.5, 3.0, 0.25, 0.833251953125), (7.5, 13.25, 0.375, 0.43298734645560368593), (0.125, 7.5, 0.425, 0.0006688257851314237), (0.125, 18.0, 1e-6, 0.72982359145096327654), (0.125, 18.0, 0.996, 7.2745875538380150586e-46), (0.125, 24.0, 0.75, 3.70853404816862016966e-17), (16.0, 0.75, 0.99999999975, 5.4408759277418629909e-07), # gh-4677 (numbers from stackoverflow question): (0.4211959643503401, 16939.046996018118, 0.000815296167195521, 1e-7)]) def test_betaincc_betainccinv(self, a, b, x, p): p1 = special.betaincc(a, b, x) assert_allclose(p1, p, rtol=5e-15) x1 = special.betainccinv(a, b, p) assert_allclose(x1, x, rtol=8e-15) @pytest.mark.parametrize( 'a, b, y, ref', [(14.208308325339239, 14.208308325339239, 7.703145458496392e-307, 8.566004561846704e-23), (14.0, 14.5, 1e-280, 2.9343915006642424e-21), (3.5, 15.0, 4e-95, 1.3290751429289227e-28), (10.0, 1.25, 2e-234, 3.982659092143654e-24), (4.0, 99997.0, 5e-88, 3.309800566862242e-27)] ) def test_betaincinv_tiny_y(self, a, b, y, ref): # Test with extremely small y values. This test includes # a regression test for an issue in the boost code; # see https://github.com/boostorg/math/issues/961 # # The reference values were computed with mpmath. For example, # # from mpmath import mp # mp.dps = 1000 # a = 14.208308325339239 # p = 7.703145458496392e-307 # x = mp.findroot(lambda t: mp.betainc(a, a, 0, t, # regularized=True) - p, # x0=8.566e-23) # print(float(x)) # x = special.betaincinv(a, b, y) assert_allclose(x, ref, rtol=1e-14) @pytest.mark.parametrize('func', [special.betainc, special.betaincinv, special.betaincc, special.betainccinv]) @pytest.mark.parametrize('args', [(-1.0, 2, 0.5), (0, 2, 0.5), (1.5, -2.0, 0.5), (1.5, 0, 0.5), (1.5, 2.0, -0.3), (1.5, 2.0, 1.1)]) def test_betainc_domain_errors(self, func, args): with special.errstate(domain='raise'): with pytest.raises(special.SpecialFunctionError, match='domain'): special.betainc(*args) class TestCombinatorics: def test_comb(self): assert_allclose(special.comb([10, 10], [3, 4]), [120., 210.]) assert_allclose(special.comb(10, 3), 120.) assert_equal(special.comb(10, 3, exact=True), 120) assert_equal(special.comb(10, 3, exact=True, repetition=True), 220) assert_allclose([special.comb(20, k, exact=True) for k in range(21)], special.comb(20, list(range(21))), atol=1e-15) ii = np.iinfo(int).max + 1 assert_equal(special.comb(ii, ii-1, exact=True), ii) expected = 100891344545564193334812497256 assert special.comb(100, 50, exact=True) == expected def test_comb_with_np_int64(self): n = 70 k = 30 np_n = np.int64(n) np_k = np.int64(k) res_np = special.comb(np_n, np_k, exact=True) res_py = special.comb(n, k, exact=True) assert res_np == res_py def test_comb_zeros(self): assert_equal(special.comb(2, 3, exact=True), 0) assert_equal(special.comb(-1, 3, exact=True), 0) assert_equal(special.comb(2, -1, exact=True), 0) assert_equal(special.comb(2, -1, exact=False), 0) assert_allclose(special.comb([2, -1, 2, 10], [3, 3, -1, 3]), [0., 0., 0., 120.]) def test_comb_exact_non_int_dep(self): msg = "`exact=True`" with pytest.deprecated_call(match=msg): special.comb(3.4, 4, exact=True) def test_perm(self): assert_allclose(special.perm([10, 10], [3, 4]), [720., 5040.]) assert_almost_equal(special.perm(10, 3), 720.) assert_equal(special.perm(10, 3, exact=True), 720) def test_perm_zeros(self): assert_equal(special.perm(2, 3, exact=True), 0) assert_equal(special.perm(-1, 3, exact=True), 0) assert_equal(special.perm(2, -1, exact=True), 0) assert_equal(special.perm(2, -1, exact=False), 0) assert_allclose(special.perm([2, -1, 2, 10], [3, 3, -1, 3]), [0., 0., 0., 720.]) def test_perm_iv(self): # currently `exact=True` only support scalars with pytest.raises(ValueError, match="scalar integers"): special.perm([1, 2], [4, 5], exact=True) # Non-integral scalars with N < k, or N,k < 0 used to return 0, this is now # deprecated and will raise an error in SciPy 1.16.0 with pytest.deprecated_call(match="Non-integer"): special.perm(4.6, 6, exact=True) with pytest.deprecated_call(match="Non-integer"): special.perm(-4.6, 3, exact=True) with pytest.deprecated_call(match="Non-integer"): special.perm(4, -3.9, exact=True) # Non-integral scalars which aren't included in the cases above an raise an # error directly without deprecation as this code never worked with pytest.raises(ValueError, match="Non-integer"): special.perm(6.0, 4.6, exact=True) class TestTrigonometric: def test_cbrt(self): cb = special.cbrt(27) cbrl = 27**(1.0/3.0) assert_approx_equal(cb,cbrl) def test_cbrtmore(self): cb1 = special.cbrt(27.9) cbrl1 = 27.9**(1.0/3.0) assert_almost_equal(cb1,cbrl1,8) def test_cosdg(self): cdg = special.cosdg(90) cdgrl = cos(pi/2.0) assert_almost_equal(cdg,cdgrl,8) def test_cosdgmore(self): cdgm = special.cosdg(30) cdgmrl = cos(pi/6.0) assert_almost_equal(cdgm,cdgmrl,8) def test_cosm1(self): cs = (special.cosm1(0),special.cosm1(.3),special.cosm1(pi/10)) csrl = (cos(0)-1,cos(.3)-1,cos(pi/10)-1) assert_array_almost_equal(cs,csrl,8) def test_cotdg(self): ct = special.cotdg(30) ctrl = tan(pi/6.0)**(-1) assert_almost_equal(ct,ctrl,8) def test_cotdgmore(self): ct1 = special.cotdg(45) ctrl1 = tan(pi/4.0)**(-1) assert_almost_equal(ct1,ctrl1,8) def test_specialpoints(self): assert_almost_equal(special.cotdg(45), 1.0, 14) assert_almost_equal(special.cotdg(-45), -1.0, 14) assert_almost_equal(special.cotdg(90), 0.0, 14) assert_almost_equal(special.cotdg(-90), 0.0, 14) assert_almost_equal(special.cotdg(135), -1.0, 14) assert_almost_equal(special.cotdg(-135), 1.0, 14) assert_almost_equal(special.cotdg(225), 1.0, 14) assert_almost_equal(special.cotdg(-225), -1.0, 14) assert_almost_equal(special.cotdg(270), 0.0, 14) assert_almost_equal(special.cotdg(-270), 0.0, 14) assert_almost_equal(special.cotdg(315), -1.0, 14) assert_almost_equal(special.cotdg(-315), 1.0, 14) assert_almost_equal(special.cotdg(765), 1.0, 14) def test_sinc(self): # the sinc implementation and more extensive sinc tests are in numpy assert_array_equal(special.sinc([0]), 1) assert_equal(special.sinc(0.0), 1.0) def test_sindg(self): sn = special.sindg(90) assert_equal(sn,1.0) def test_sindgmore(self): snm = special.sindg(30) snmrl = sin(pi/6.0) assert_almost_equal(snm,snmrl,8) snm1 = special.sindg(45) snmrl1 = sin(pi/4.0) assert_almost_equal(snm1,snmrl1,8) class TestTandg: def test_tandg(self): tn = special.tandg(30) tnrl = tan(pi/6.0) assert_almost_equal(tn,tnrl,8) def test_tandgmore(self): tnm = special.tandg(45) tnmrl = tan(pi/4.0) assert_almost_equal(tnm,tnmrl,8) tnm1 = special.tandg(60) tnmrl1 = tan(pi/3.0) assert_almost_equal(tnm1,tnmrl1,8) def test_specialpoints(self): assert_almost_equal(special.tandg(0), 0.0, 14) assert_almost_equal(special.tandg(45), 1.0, 14) assert_almost_equal(special.tandg(-45), -1.0, 14) assert_almost_equal(special.tandg(135), -1.0, 14) assert_almost_equal(special.tandg(-135), 1.0, 14) assert_almost_equal(special.tandg(180), 0.0, 14) assert_almost_equal(special.tandg(-180), 0.0, 14) assert_almost_equal(special.tandg(225), 1.0, 14) assert_almost_equal(special.tandg(-225), -1.0, 14) assert_almost_equal(special.tandg(315), -1.0, 14) assert_almost_equal(special.tandg(-315), 1.0, 14) class TestEllip: def test_ellipj_nan(self): """Regression test for #912.""" special.ellipj(0.5, np.nan) def test_ellipj(self): el = special.ellipj(0.2,0) rel = [sin(0.2),cos(0.2),1.0,0.20] assert_array_almost_equal(el,rel,13) def test_ellipk(self): elk = special.ellipk(.2) assert_almost_equal(elk,1.659623598610528,11) assert_equal(special.ellipkm1(0.0), np.inf) assert_equal(special.ellipkm1(1.0), pi/2) assert_equal(special.ellipkm1(np.inf), 0.0) assert_equal(special.ellipkm1(np.nan), np.nan) assert_equal(special.ellipkm1(-1), np.nan) assert_allclose(special.ellipk(-10), 0.7908718902387385) def test_ellipkinc(self): elkinc = special.ellipkinc(pi/2,.2) elk = special.ellipk(0.2) assert_almost_equal(elkinc,elk,15) alpha = 20*pi/180 phi = 45*pi/180 m = sin(alpha)**2 elkinc = special.ellipkinc(phi,m) assert_almost_equal(elkinc,0.79398143,8) # From pg. 614 of A & S assert_equal(special.ellipkinc(pi/2, 0.0), pi/2) assert_equal(special.ellipkinc(pi/2, 1.0), np.inf) assert_equal(special.ellipkinc(pi/2, -np.inf), 0.0) assert_equal(special.ellipkinc(pi/2, np.nan), np.nan) assert_equal(special.ellipkinc(pi/2, 2), np.nan) assert_equal(special.ellipkinc(0, 0.5), 0.0) assert_equal(special.ellipkinc(np.inf, 0.5), np.inf) assert_equal(special.ellipkinc(-np.inf, 0.5), -np.inf) assert_equal(special.ellipkinc(np.inf, np.inf), np.nan) assert_equal(special.ellipkinc(np.inf, -np.inf), np.nan) assert_equal(special.ellipkinc(-np.inf, -np.inf), np.nan) assert_equal(special.ellipkinc(-np.inf, np.inf), np.nan) assert_equal(special.ellipkinc(np.nan, 0.5), np.nan) assert_equal(special.ellipkinc(np.nan, np.nan), np.nan) assert_allclose(special.ellipkinc(0.38974112035318718, 1), 0.4, rtol=1e-14) assert_allclose(special.ellipkinc(1.5707, -10), 0.79084284661724946) def test_ellipkinc_2(self): # Regression test for gh-3550 # ellipkinc(phi, mbad) was NaN and mvals[2:6] were twice the correct value mbad = 0.68359375000000011 phi = 0.9272952180016123 m = np.nextafter(mbad, 0) mvals = [] for j in range(10): mvals.append(m) m = np.nextafter(m, 1) f = special.ellipkinc(phi, mvals) assert_array_almost_equal_nulp(f, np.full_like(f, 1.0259330100195334), 1) # this bug also appears at phi + n * pi for at least small n f1 = special.ellipkinc(phi + pi, mvals) assert_array_almost_equal_nulp(f1, np.full_like(f1, 5.1296650500976675), 2) def test_ellipkinc_singular(self): # ellipkinc(phi, 1) has closed form and is finite only for phi in (-pi/2, pi/2) xlog = np.logspace(-300, -17, 25) xlin = np.linspace(1e-17, 0.1, 25) xlin2 = np.linspace(0.1, pi/2, 25, endpoint=False) assert_allclose(special.ellipkinc(xlog, 1), np.arcsinh(np.tan(xlog)), rtol=1e14) assert_allclose(special.ellipkinc(xlin, 1), np.arcsinh(np.tan(xlin)), rtol=1e14) assert_allclose(special.ellipkinc(xlin2, 1), np.arcsinh(np.tan(xlin2)), rtol=1e14) assert_equal(special.ellipkinc(np.pi/2, 1), np.inf) assert_allclose(special.ellipkinc(-xlog, 1), np.arcsinh(np.tan(-xlog)), rtol=1e14) assert_allclose(special.ellipkinc(-xlin, 1), np.arcsinh(np.tan(-xlin)), rtol=1e14) assert_allclose(special.ellipkinc(-xlin2, 1), np.arcsinh(np.tan(-xlin2)), rtol=1e14) assert_equal(special.ellipkinc(-np.pi/2, 1), np.inf) def test_ellipe(self): ele = special.ellipe(.2) assert_almost_equal(ele,1.4890350580958529,8) assert_equal(special.ellipe(0.0), pi/2) assert_equal(special.ellipe(1.0), 1.0) assert_equal(special.ellipe(-np.inf), np.inf) assert_equal(special.ellipe(np.nan), np.nan) assert_equal(special.ellipe(2), np.nan) assert_allclose(special.ellipe(-10), 3.6391380384177689) def test_ellipeinc(self): eleinc = special.ellipeinc(pi/2,.2) ele = special.ellipe(0.2) assert_almost_equal(eleinc,ele,14) # pg 617 of A & S alpha, phi = 52*pi/180,35*pi/180 m = sin(alpha)**2 eleinc = special.ellipeinc(phi,m) assert_almost_equal(eleinc, 0.58823065, 8) assert_equal(special.ellipeinc(pi/2, 0.0), pi/2) assert_equal(special.ellipeinc(pi/2, 1.0), 1.0) assert_equal(special.ellipeinc(pi/2, -np.inf), np.inf) assert_equal(special.ellipeinc(pi/2, np.nan), np.nan) assert_equal(special.ellipeinc(pi/2, 2), np.nan) assert_equal(special.ellipeinc(0, 0.5), 0.0) assert_equal(special.ellipeinc(np.inf, 0.5), np.inf) assert_equal(special.ellipeinc(-np.inf, 0.5), -np.inf) assert_equal(special.ellipeinc(np.inf, -np.inf), np.inf) assert_equal(special.ellipeinc(-np.inf, -np.inf), -np.inf) assert_equal(special.ellipeinc(np.inf, np.inf), np.nan) assert_equal(special.ellipeinc(-np.inf, np.inf), np.nan) assert_equal(special.ellipeinc(np.nan, 0.5), np.nan) assert_equal(special.ellipeinc(np.nan, np.nan), np.nan) assert_allclose(special.ellipeinc(1.5707, -10), 3.6388185585822876) def test_ellipeinc_2(self): # Regression test for gh-3550 # ellipeinc(phi, mbad) was NaN and mvals[2:6] were twice the correct value mbad = 0.68359375000000011 phi = 0.9272952180016123 m = np.nextafter(mbad, 0) mvals = [] for j in range(10): mvals.append(m) m = np.nextafter(m, 1) f = special.ellipeinc(phi, mvals) assert_array_almost_equal_nulp(f, np.full_like(f, 0.84442884574781019), 2) # this bug also appears at phi + n * pi for at least small n f1 = special.ellipeinc(phi + pi, mvals) assert_array_almost_equal_nulp(f1, np.full_like(f1, 3.3471442287390509), 4) class TestEllipCarlson: """Test for Carlson elliptic integrals ellipr[cdfgj]. The special values used in these tests can be found in Sec. 3 of Carlson (1994), https://arxiv.org/abs/math/9409227 """ def test_elliprc(self): assert_allclose(elliprc(1, 1), 1) assert elliprc(1, inf) == 0.0 assert isnan(elliprc(1, 0)) assert elliprc(1, complex(1, inf)) == 0.0 args = array([[0.0, 0.25], [2.25, 2.0], [0.0, 1.0j], [-1.0j, 1.0j], [0.25, -2.0], [1.0j, -1.0]]) expected_results = array([np.pi, np.log(2.0), 1.1107207345396 * (1.0-1.0j), 1.2260849569072-0.34471136988768j, np.log(2.0) / 3.0, 0.77778596920447+0.19832484993429j]) for i, arr in enumerate(args): assert_allclose(elliprc(*arr), expected_results[i]) def test_elliprd(self): assert_allclose(elliprd(1, 1, 1), 1) assert_allclose(elliprd(0, 2, 1) / 3.0, 0.59907011736779610371) assert elliprd(1, 1, inf) == 0.0 assert np.isinf(elliprd(1, 1, 0)) assert np.isinf(elliprd(1, 1, complex(0, 0))) assert np.isinf(elliprd(0, 1, complex(0, 0))) assert isnan(elliprd(1, 1, -np.finfo(np.float64).tiny / 2.0)) assert isnan(elliprd(1, 1, complex(-1, 0))) args = array([[0.0, 2.0, 1.0], [2.0, 3.0, 4.0], [1.0j, -1.0j, 2.0], [0.0, 1.0j, -1.0j], [0.0, -1.0+1.0j, 1.0j], [-2.0-1.0j, -1.0j, -1.0+1.0j]]) expected_results = array([1.7972103521034, 0.16510527294261, 0.65933854154220, 1.2708196271910+2.7811120159521j, -1.8577235439239-0.96193450888839j, 1.8249027393704-1.2218475784827j]) for i, arr in enumerate(args): assert_allclose(elliprd(*arr), expected_results[i]) def test_elliprf(self): assert_allclose(elliprf(1, 1, 1), 1) assert_allclose(elliprf(0, 1, 2), 1.31102877714605990523) assert elliprf(1, inf, 1) == 0.0 assert np.isinf(elliprf(0, 1, 0)) assert isnan(elliprf(1, 1, -1)) assert elliprf(complex(inf), 0, 1) == 0.0 assert isnan(elliprf(1, 1, complex(-inf, 1))) args = array([[1.0, 2.0, 0.0], [1.0j, -1.0j, 0.0], [0.5, 1.0, 0.0], [-1.0+1.0j, 1.0j, 0.0], [2.0, 3.0, 4.0], [1.0j, -1.0j, 2.0], [-1.0+1.0j, 1.0j, 1.0-1.0j]]) expected_results = array([1.3110287771461, 1.8540746773014, 1.8540746773014, 0.79612586584234-1.2138566698365j, 0.58408284167715, 1.0441445654064, 0.93912050218619-0.53296252018635j]) for i, arr in enumerate(args): assert_allclose(elliprf(*arr), expected_results[i]) def test_elliprg(self): assert_allclose(elliprg(1, 1, 1), 1) assert_allclose(elliprg(0, 0, 1), 0.5) assert_allclose(elliprg(0, 0, 0), 0) assert np.isinf(elliprg(1, inf, 1)) assert np.isinf(elliprg(complex(inf), 1, 1)) args = array([[0.0, 16.0, 16.0], [2.0, 3.0, 4.0], [0.0, 1.0j, -1.0j], [-1.0+1.0j, 1.0j, 0.0], [-1.0j, -1.0+1.0j, 1.0j], [0.0, 0.0796, 4.0]]) expected_results = array([np.pi, 1.7255030280692, 0.42360654239699, 0.44660591677018+0.70768352357515j, 0.36023392184473+0.40348623401722j, 1.0284758090288]) for i, arr in enumerate(args): assert_allclose(elliprg(*arr), expected_results[i]) def test_elliprj(self): assert_allclose(elliprj(1, 1, 1, 1), 1) assert elliprj(1, 1, inf, 1) == 0.0 assert isnan(elliprj(1, 0, 0, 0)) assert isnan(elliprj(-1, 1, 1, 1)) assert elliprj(1, 1, 1, inf) == 0.0 args = array([[0.0, 1.0, 2.0, 3.0], [2.0, 3.0, 4.0, 5.0], [2.0, 3.0, 4.0, -1.0+1.0j], [1.0j, -1.0j, 0.0, 2.0], [-1.0+1.0j, -1.0-1.0j, 1.0, 2.0], [1.0j, -1.0j, 0.0, 1.0-1.0j], [-1.0+1.0j, -1.0-1.0j, 1.0, -3.0+1.0j], [2.0, 3.0, 4.0, -0.5], # Cauchy principal value [2.0, 3.0, 4.0, -5.0]]) # Cauchy principal value expected_results = array([0.77688623778582, 0.14297579667157, 0.13613945827771-0.38207561624427j, 1.6490011662711, 0.94148358841220, 1.8260115229009+1.2290661908643j, -0.61127970812028-1.0684038390007j, 0.24723819703052, # Cauchy principal value -0.12711230042964]) # Caucny principal value for i, arr in enumerate(args): assert_allclose(elliprj(*arr), expected_results[i]) @pytest.mark.xfail(reason="Insufficient accuracy on 32-bit") def test_elliprj_hard(self): assert_allclose(elliprj(6.483625725195452e-08, 1.1649136528196886e-27, 3.6767340167168e+13, 0.493704617023468), 8.63426920644241857617477551054e-6, rtol=5e-15, atol=1e-20) assert_allclose(elliprj(14.375105857849121, 9.993988969725365e-11, 1.72844262269944e-26, 5.898871222598245e-06), 829774.1424801627252574054378691828, rtol=5e-15, atol=1e-20) class TestEllipLegendreCarlsonIdentities: """Test identities expressing the Legendre elliptic integrals in terms of Carlson's symmetric integrals. These identities can be found in the DLMF https://dlmf.nist.gov/19.25#i . """ def setup_class(self): self.m_n1_1 = np.arange(-1., 1., 0.01) # For double, this is -(2**1024) self.max_neg = finfo(double).min # Lots of very negative numbers self.very_neg_m = -1. * 2.**arange(-1 + np.log2(-self.max_neg), 0., -1.) self.ms_up_to_1 = np.concatenate(([self.max_neg], self.very_neg_m, self.m_n1_1)) def test_k(self): """Test identity: K(m) = R_F(0, 1-m, 1) """ m = self.ms_up_to_1 assert_allclose(ellipk(m), elliprf(0., 1.-m, 1.)) def test_km1(self): """Test identity: K(m) = R_F(0, 1-m, 1) But with the ellipkm1 function """ # For double, this is 2**-1022 tiny = finfo(double).tiny # All these small powers of 2, up to 2**-1 m1 = tiny * 2.**arange(0., -np.log2(tiny)) assert_allclose(ellipkm1(m1), elliprf(0., m1, 1.)) def test_e(self): """Test identity: E(m) = 2*R_G(0, 1-k^2, 1) """ m = self.ms_up_to_1 assert_allclose(ellipe(m), 2.*elliprg(0., 1.-m, 1.)) class TestErf: def test_erf(self): er = special.erf(.25) assert_almost_equal(er,0.2763263902,8) def test_erf_zeros(self): erz = special.erf_zeros(5) erzr = array([1.45061616+1.88094300j, 2.24465928+2.61657514j, 2.83974105+3.17562810j, 3.33546074+3.64617438j, 3.76900557+4.06069723j]) assert_array_almost_equal(erz,erzr,4) def _check_variant_func(self, func, other_func, rtol, atol=0): np.random.seed(1234) n = 10000 x = np.random.pareto(0.02, n) * (2*np.random.randint(0, 2, n) - 1) y = np.random.pareto(0.02, n) * (2*np.random.randint(0, 2, n) - 1) z = x + 1j*y with np.errstate(all='ignore'): w = other_func(z) w_real = other_func(x).real mask = np.isfinite(w) w = w[mask] z = z[mask] mask = np.isfinite(w_real) w_real = w_real[mask] x = x[mask] # test both real and complex variants assert_func_equal(func, w, z, rtol=rtol, atol=atol) assert_func_equal(func, w_real, x, rtol=rtol, atol=atol) def test_erfc_consistent(self): self._check_variant_func( cephes.erfc, lambda z: 1 - cephes.erf(z), rtol=1e-12, atol=1e-14 # <- the test function loses precision ) def test_erfcx_consistent(self): self._check_variant_func( cephes.erfcx, lambda z: np.exp(z*z) * cephes.erfc(z), rtol=1e-12 ) def test_erfi_consistent(self): self._check_variant_func( cephes.erfi, lambda z: -1j * cephes.erf(1j*z), rtol=1e-12 ) def test_dawsn_consistent(self): self._check_variant_func( cephes.dawsn, lambda z: sqrt(pi)/2 * np.exp(-z*z) * cephes.erfi(z), rtol=1e-12 ) def test_erf_nan_inf(self): vals = [np.nan, -np.inf, np.inf] expected = [np.nan, -1, 1] assert_allclose(special.erf(vals), expected, rtol=1e-15) def test_erfc_nan_inf(self): vals = [np.nan, -np.inf, np.inf] expected = [np.nan, 2, 0] assert_allclose(special.erfc(vals), expected, rtol=1e-15) def test_erfcx_nan_inf(self): vals = [np.nan, -np.inf, np.inf] expected = [np.nan, np.inf, 0] assert_allclose(special.erfcx(vals), expected, rtol=1e-15) def test_erfi_nan_inf(self): vals = [np.nan, -np.inf, np.inf] expected = [np.nan, -np.inf, np.inf] assert_allclose(special.erfi(vals), expected, rtol=1e-15) def test_dawsn_nan_inf(self): vals = [np.nan, -np.inf, np.inf] expected = [np.nan, -0.0, 0.0] assert_allclose(special.dawsn(vals), expected, rtol=1e-15) def test_wofz_nan_inf(self): vals = [np.nan, -np.inf, np.inf] expected = [np.nan + np.nan * 1.j, 0.-0.j, 0.+0.j] assert_allclose(special.wofz(vals), expected, rtol=1e-15) class TestEuler: def test_euler(self): eu0 = special.euler(0) eu1 = special.euler(1) eu2 = special.euler(2) # just checking segfaults assert_allclose(eu0, [1], rtol=1e-15) assert_allclose(eu1, [1, 0], rtol=1e-15) assert_allclose(eu2, [1, 0, -1], rtol=1e-15) eu24 = special.euler(24) mathworld = [1,1,5,61,1385,50521,2702765,199360981, 19391512145,2404879675441, 370371188237525,69348874393137901, 15514534163557086905] correct = zeros((25,),'d') for k in range(0,13): if (k % 2): correct[2*k] = -float(mathworld[k]) else: correct[2*k] = float(mathworld[k]) with np.errstate(all='ignore'): err = nan_to_num((eu24-correct)/correct) errmax = max(err) assert_almost_equal(errmax, 0.0, 14) class TestExp: def test_exp2(self): ex = special.exp2(2) exrl = 2**2 assert_equal(ex,exrl) def test_exp2more(self): exm = special.exp2(2.5) exmrl = 2**(2.5) assert_almost_equal(exm,exmrl,8) def test_exp10(self): ex = special.exp10(2) exrl = 10**2 assert_approx_equal(ex,exrl) def test_exp10more(self): exm = special.exp10(2.5) exmrl = 10**(2.5) assert_almost_equal(exm,exmrl,8) def test_expm1(self): ex = (special.expm1(2),special.expm1(3),special.expm1(4)) exrl = (exp(2)-1,exp(3)-1,exp(4)-1) assert_array_almost_equal(ex,exrl,8) def test_expm1more(self): ex1 = (special.expm1(2),special.expm1(2.1),special.expm1(2.2)) exrl1 = (exp(2)-1,exp(2.1)-1,exp(2.2)-1) assert_array_almost_equal(ex1,exrl1,8) class TestFactorialFunctions: @pytest.mark.parametrize("exact", [True, False]) def test_factorialx_scalar_return_type(self, exact): assert np.isscalar(special.factorial(1, exact=exact)) assert np.isscalar(special.factorial2(1, exact=exact)) assert np.isscalar(special.factorialk(1, 3, exact=exact)) @pytest.mark.parametrize("n", [-1, -2, -3]) @pytest.mark.parametrize("exact", [True, False]) def test_factorialx_negative(self, exact, n): assert_equal(special.factorial(n, exact=exact), 0) assert_equal(special.factorial2(n, exact=exact), 0) assert_equal(special.factorialk(n, 3, exact=exact), 0) @pytest.mark.parametrize("exact", [True, False]) def test_factorialx_negative_array(self, exact): assert_func = assert_array_equal if exact else assert_allclose # Consistent output for n < 0 assert_func(special.factorial([-5, -4, 0, 1], exact=exact), [0, 0, 1, 1]) assert_func(special.factorial2([-5, -4, 0, 1], exact=exact), [0, 0, 1, 1]) assert_func(special.factorialk([-5, -4, 0, 1], 3, exact=exact), [0, 0, 1, 1]) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("content", [np.nan, None, np.datetime64('nat')], ids=["NaN", "None", "NaT"]) def test_factorialx_nan(self, content, exact): # scalar assert special.factorial(content, exact=exact) is np.nan assert special.factorial2(content, exact=exact) is np.nan assert special.factorialk(content, 3, exact=exact) is np.nan # array-like (initializes np.array with default dtype) if content is not np.nan: # None causes object dtype, which is not supported; as is datetime with pytest.raises(ValueError, match="Unsupported datatype.*"): special.factorial([content], exact=exact) elif exact: with pytest.raises(ValueError, match="factorial with `exact=Tr.*"): special.factorial([content], exact=exact) else: assert np.isnan(special.factorial([content], exact=exact)[0]) # factorial{2,k} don't support array case due to dtype constraints with pytest.raises(ValueError, match="factorial2 does not support.*"): special.factorial2([content], exact=exact) with pytest.raises(ValueError, match="factorialk does not support.*"): special.factorialk([content], 3, exact=exact) # array-case also tested in test_factorial{,2,k}_corner_cases @pytest.mark.parametrize("levels", range(1, 5)) @pytest.mark.parametrize("exact", [True, False]) def test_factorialx_array_shape(self, levels, exact): def _nest_me(x, k=1): """ Double x and nest it k times For example: >>> _nest_me([3, 4], 2) [[[3, 4], [3, 4]], [[3, 4], [3, 4]]] """ if k == 0: return x else: return _nest_me([x, x], k-1) def _check(res, nucleus): exp = np.array(_nest_me(nucleus, k=levels), dtype=object) # test that ndarray shape is maintained # need to cast to float due to numpy/numpy#21220 assert_allclose(res.astype(np.float64), exp.astype(np.float64)) n = np.array(_nest_me([5, 25], k=levels)) exp_nucleus = {1: [120, math.factorial(25)], # correctness of factorial{2,k}() is tested elsewhere 2: [15, special.factorial2(25, exact=True)], 3: [10, special.factorialk(25, 3, exact=True)]} _check(special.factorial(n, exact=exact), exp_nucleus[1]) _check(special.factorial2(n, exact=exact), exp_nucleus[2]) _check(special.factorialk(n, 3, exact=exact), exp_nucleus[3]) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("dtype", [ None, int, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64 ]) @pytest.mark.parametrize("dim", range(0, 5)) def test_factorialx_array_dimension(self, dim, dtype, exact): n = np.array(5, dtype=dtype, ndmin=dim) exp = {1: 120, 2: 15, 3: 10} assert_allclose(special.factorial(n, exact=exact), np.array(exp[1], ndmin=dim)) assert_allclose(special.factorial2(n, exact=exact), np.array(exp[2], ndmin=dim)) assert_allclose(special.factorialk(n, 3, exact=exact), np.array(exp[3], ndmin=dim)) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("level", range(1, 5)) def test_factorialx_array_like(self, level, exact): def _nest_me(x, k=1): if k == 0: return x else: return _nest_me([x], k-1) n = _nest_me([5], k=level-1) # nested list exp_nucleus = {1: 120, 2: 15, 3: 10} assert_func = assert_array_equal if exact else assert_allclose assert_func(special.factorial(n, exact=exact), np.array(exp_nucleus[1], ndmin=level)) assert_func(special.factorial2(n, exact=exact), np.array(exp_nucleus[2], ndmin=level)) assert_func(special.factorialk(n, 3, exact=exact), np.array(exp_nucleus[3], ndmin=level)) # note that n=170 is the last integer such that factorial(n) fits float64 @pytest.mark.parametrize('n', range(30, 180, 10)) def test_factorial_accuracy(self, n): # Compare exact=True vs False, i.e. that the accuracy of the # approximation is better than the specified tolerance. rtol = 6e-14 if sys.platform == 'win32' else 1e-15 # need to cast exact result to float due to numpy/numpy#21220 assert_allclose(float(special.factorial(n, exact=True)), special.factorial(n, exact=False), rtol=rtol) assert_allclose(special.factorial([n], exact=True).astype(float), special.factorial([n], exact=False), rtol=rtol) @pytest.mark.parametrize('n', list(range(0, 22)) + list(range(30, 180, 10))) def test_factorial_int_reference(self, n): # Compare all with math.factorial correct = math.factorial(n) assert_array_equal(correct, special.factorial(n, True)) assert_array_equal(correct, special.factorial([n], True)[0]) rtol = 6e-14 if sys.platform == 'win32' else 1e-15 assert_allclose(float(correct), special.factorial(n, False), rtol=rtol) assert_allclose(float(correct), special.factorial([n], False)[0], rtol=rtol) def test_factorial_float_reference(self): def _check(n, expected): assert_allclose(special.factorial(n), expected) assert_allclose(special.factorial([n])[0], expected) # using floats with exact=True is deprecated for scalars... with pytest.deprecated_call(match="Non-integer values.*"): assert_allclose(special.factorial(n, exact=True), expected) # ... and already an error for arrays with pytest.raises(ValueError, match="factorial with `exact=Tr.*"): special.factorial([n], exact=True) # Reference values from mpmath for gamma(n+1) _check(0.01, 0.994325851191506032181932988) _check(1.11, 1.051609009483625091514147465) _check(5.55, 314.9503192327208241614959052) _check(11.1, 50983227.84411615655137170553) _check(33.3, 2.493363339642036352229215273e+37) _check(55.5, 9.479934358436729043289162027e+73) _check(77.7, 3.060540559059579022358692625e+114) _check(99.9, 5.885840419492871504575693337e+157) # close to maximum for float64 _check(170.6243, 1.79698185749571048960082e+308) @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.complex128, object]) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("dim", range(0, 5)) # test empty & non-empty arrays, with nans and mixed @pytest.mark.parametrize("content", [[], [1], [1.1], [np.nan], [np.nan, 1]], ids=["[]", "[1]", "[1.1]", "[NaN]", "[NaN, 1]"]) def test_factorial_array_corner_cases(self, content, dim, exact, dtype): if dtype == np.int64 and any(np.isnan(x) for x in content): pytest.skip("impossible combination") # np.array(x, ndim=0) will not be 0-dim. unless x is too content = content if (dim > 0 or len(content) != 1) else content[0] n = np.array(content, ndmin=dim, dtype=dtype) result = None if not content: result = special.factorial(n, exact=exact) elif not (np.issubdtype(n.dtype, np.integer) or np.issubdtype(n.dtype, np.floating)): with pytest.raises(ValueError, match="Unsupported datatype*"): special.factorial(n, exact=exact) elif exact and not np.issubdtype(n.dtype, np.integer): with pytest.raises(ValueError, match="factorial with `exact=.*"): special.factorial(n, exact=exact) else: # no error result = special.factorial(n, exact=exact) # assert_equal does not distinguish scalars and 0-dim arrays of the same value, # see https://github.com/numpy/numpy/issues/24050 def assert_really_equal(x, y): assert type(x) == type(y), f"types not equal: {type(x)}, {type(y)}" assert_equal(x, y) if result is not None: # keep 0-dim.; otherwise n.ravel().ndim==1, even if n.ndim==0 n_flat = n.ravel() if n.ndim else n ref = special.factorial(n_flat, exact=exact) if n.size else [] # expected result is empty if and only if n is empty, # and has the same dtype & dimension as n expected = np.array(ref, ndmin=dim, dtype=dtype) assert_really_equal(result, expected) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("n", [1, 1.1, 2 + 2j, np.nan, None], ids=["1", "1.1", "2+2j", "NaN", "None"]) def test_factorial_scalar_corner_cases(self, n, exact): if (n is None or n is np.nan or np.issubdtype(type(n), np.integer) or np.issubdtype(type(n), np.floating)): # no error if (np.issubdtype(type(n), np.floating) and exact and n is not np.nan): with pytest.deprecated_call(match="Non-integer values.*"): result = special.factorial(n, exact=exact) else: result = special.factorial(n, exact=exact) exp = np.nan if n is np.nan or n is None else special.factorial(n) assert_equal(result, exp) else: with pytest.raises(ValueError, match="Unsupported datatype*"): special.factorial(n, exact=exact) # use odd increment to make sure both odd & even numbers are tested! @pytest.mark.parametrize('n', range(30, 180, 11)) def test_factorial2_accuracy(self, n): # Compare exact=True vs False, i.e. that the accuracy of the # approximation is better than the specified tolerance. rtol = 2e-14 if sys.platform == 'win32' else 1e-15 # need to cast exact result to float due to numpy/numpy#21220 assert_allclose(float(special.factorial2(n, exact=True)), special.factorial2(n, exact=False), rtol=rtol) assert_allclose(special.factorial2([n], exact=True).astype(float), special.factorial2([n], exact=False), rtol=rtol) @pytest.mark.parametrize('n', list(range(0, 22)) + list(range(30, 180, 11))) def test_factorial2_int_reference(self, n): # Compare all with correct value # Cannot use np.product due to overflow correct = functools.reduce(operator.mul, list(range(n, 0, -2)), 1) assert_array_equal(correct, special.factorial2(n, True)) assert_array_equal(correct, special.factorial2([n], True)[0]) assert_allclose(float(correct), special.factorial2(n, False)) assert_allclose(float(correct), special.factorial2([n], False)[0]) @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.complex128, object]) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("dim", range(0, 5)) # test empty & non-empty arrays, with nans and mixed @pytest.mark.parametrize("content", [[], [1], [np.nan], [np.nan, 1]], ids=["[]", "[1]", "[NaN]", "[NaN, 1]"]) def test_factorial2_array_corner_cases(self, content, dim, exact, dtype): if dtype == np.int64 and any(np.isnan(x) for x in content): pytest.skip("impossible combination") # np.array(x, ndim=0) will not be 0-dim. unless x is too content = content if (dim > 0 or len(content) != 1) else content[0] n = np.array(content, ndmin=dim, dtype=dtype) if np.issubdtype(n.dtype, np.integer) or (not content): # no error result = special.factorial2(n, exact=exact) # expected result is identical to n for exact=True resp. empty # arrays (assert_allclose chokes on object), otherwise up to tol func = assert_equal if exact or (not content) else assert_allclose func(result, n) else: with pytest.raises(ValueError, match="factorial2 does not*"): special.factorial2(n, 3) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("n", [1, 1.1, 2 + 2j, np.nan, None], ids=["1", "1.1", "2+2j", "NaN", "None"]) def test_factorial2_scalar_corner_cases(self, n, exact): if n is None or n is np.nan or np.issubdtype(type(n), np.integer): # no error result = special.factorial2(n, exact=exact) exp = np.nan if n is np.nan or n is None else special.factorial(n) assert_equal(result, exp) else: with pytest.raises(ValueError, match="factorial2 does not*"): special.factorial2(n, exact=exact) @pytest.mark.parametrize("k", range(1, 5)) # note that n=170 is the last integer such that factorial(n) fits float64; # use odd increment to make sure both odd & even numbers are tested @pytest.mark.parametrize('n', range(170, 20, -29)) def test_factorialk_accuracy(self, n, k): # Compare exact=True vs False, i.e. that the accuracy of the # approximation is better than the specified tolerance. # need to cast exact result to float due to numpy/numpy#21220 assert_allclose(float(special.factorialk(n, k=k, exact=True)), special.factorialk(n, k=k, exact=False)) assert_allclose(special.factorialk([n], k=k, exact=True).astype(float), special.factorialk([n], k=k, exact=False)) @pytest.mark.parametrize('k', list(range(1, 5)) + [10, 20]) @pytest.mark.parametrize('n', list(range(0, 22)) + list(range(22, 100, 11))) def test_factorialk_int_reference(self, n, k): # Compare all with correct value # Would be nice to use np.product here, but that's # broken on windows, see numpy/numpy#21219 correct = functools.reduce(operator.mul, list(range(n, 0, -k)), 1) assert_array_equal(correct, special.factorialk(n, k, True)) assert_array_equal(correct, special.factorialk([n], k, True)[0]) assert_allclose(float(correct), special.factorialk(n, k, False)) assert_allclose(float(correct), special.factorialk([n], k, False)[0]) @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.complex128, object]) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("dim", range(0, 5)) # test empty & non-empty arrays, with nans and mixed @pytest.mark.parametrize("content", [[], [1], [np.nan], [np.nan, 1]], ids=["[]", "[1]", "[NaN]", "[NaN, 1]"]) def test_factorialk_array_corner_cases(self, content, dim, exact, dtype): if dtype == np.int64 and any(np.isnan(x) for x in content): pytest.skip("impossible combination") # np.array(x, ndim=0) will not be 0-dim. unless x is too content = content if (dim > 0 or len(content) != 1) else content[0] n = np.array(content, ndmin=dim, dtype=dtype if exact else np.float64) if np.issubdtype(n.dtype, np.integer) or (not content): # no error; expected result is identical to n assert_equal(special.factorialk(n, 3, exact=exact), n) else: with pytest.raises(ValueError, match="factorialk does not*"): special.factorialk(n, 3, exact=exact) @pytest.mark.parametrize("exact", [True, False, None]) @pytest.mark.parametrize("k", range(1, 5)) @pytest.mark.parametrize("n", [1, 1.1, 2 + 2j, np.nan, None], ids=["1", "1.1", "2+2j", "NaN", "None"]) def test_factorialk_scalar_corner_cases(self, n, k, exact): if n is None or n is np.nan or np.issubdtype(type(n), np.integer): if exact is None: with pytest.deprecated_call(match="factorialk will default.*"): result = special.factorialk(n, k=k, exact=exact) else: # no error result = special.factorialk(n, k=k, exact=exact) nan_cond = n is np.nan or n is None # factorialk(1, k) == 1 for all k expected = np.nan if nan_cond else 1 assert_equal(result, expected) else: with pytest.raises(ValueError, match="factorialk does not*"): with suppress_warnings() as sup: sup.filter(DeprecationWarning, "factorialk will default") special.factorialk(n, k=k, exact=exact) @pytest.mark.parametrize("k", [0, 1.1, np.nan, "1"]) def test_factorialk_raises_k(self, k): with pytest.raises(ValueError, match="k must be a positive integer*"): special.factorialk(1, k) @pytest.mark.parametrize("exact", [True, False]) @pytest.mark.parametrize("k", range(1, 12)) def test_factorialk_dtype(self, k, exact): kw = {"k": k, "exact": exact} if exact and k in _FACTORIALK_LIMITS_64BITS.keys(): n = np.array([_FACTORIALK_LIMITS_32BITS[k]]) assert_equal(special.factorialk(n, **kw).dtype, np_long) assert_equal(special.factorialk(n + 1, **kw).dtype, np.int64) # assert maximality of limits for given dtype assert special.factorialk(n + 1, **kw) > np.iinfo(np.int32).max n = np.array([_FACTORIALK_LIMITS_64BITS[k]]) assert_equal(special.factorialk(n, **kw).dtype, np.int64) assert_equal(special.factorialk(n + 1, **kw).dtype, object) assert special.factorialk(n + 1, **kw) > np.iinfo(np.int64).max else: n = np.array([_FACTORIALK_LIMITS_64BITS.get(k, 1)]) # for exact=True and k >= 10, we always return object; # for exact=False it's always float dtype = object if exact else np.float64 assert_equal(special.factorialk(n, **kw).dtype, dtype) def test_factorial_mixed_nan_inputs(self): x = np.array([np.nan, 1, 2, 3, np.nan]) expected = np.array([np.nan, 1, 2, 6, np.nan]) assert_equal(special.factorial(x, exact=False), expected) with pytest.raises(ValueError, match="factorial with `exact=True.*"): special.factorial(x, exact=True) class TestFresnel: @pytest.mark.parametrize("z, s, c", [ # some positive value (.5, 0.064732432859999287, 0.49234422587144644), (.5 + .0j, 0.064732432859999287, 0.49234422587144644), # negative half annulus # https://github.com/scipy/scipy/issues/12309 # Reference values can be reproduced with # https://www.wolframalpha.com/input/?i=FresnelS%5B-2.0+%2B+0.1i%5D # https://www.wolframalpha.com/input/?i=FresnelC%5B-2.0+%2B+0.1i%5D ( -2.0 + 0.1j, -0.3109538687728942-0.0005870728836383176j, -0.4879956866358554+0.10670801832903172j ), ( -0.1 - 1.5j, -0.03918309471866977+0.7197508454568574j, 0.09605692502968956-0.43625191013617465j ), # a different algorithm kicks in for "large" values, i.e., |z| >= 4.5, # make sure to test both float and complex values; a different # algorithm is used (6.0, 0.44696076, 0.49953147), (6.0 + 0.0j, 0.44696076, 0.49953147), (6.0j, -0.44696076j, 0.49953147j), (-6.0 + 0.0j, -0.44696076, -0.49953147), (-6.0j, 0.44696076j, -0.49953147j), # inf (np.inf, 0.5, 0.5), (-np.inf, -0.5, -0.5), ]) def test_fresnel_values(self, z, s, c): frs = array(special.fresnel(z)) assert_array_almost_equal(frs, array([s, c]), 8) # values from pg 329 Table 7.11 of A & S # slightly corrected in 4th decimal place def test_fresnel_zeros(self): szo, czo = special.fresnel_zeros(5) assert_array_almost_equal(szo, array([2.0093+0.2885j, 2.8335+0.2443j, 3.4675+0.2185j, 4.0026+0.2009j, 4.4742+0.1877j]),3) assert_array_almost_equal(czo, array([1.7437+0.3057j, 2.6515+0.2529j, 3.3204+0.2240j, 3.8757+0.2047j, 4.3611+0.1907j]),3) vals1 = special.fresnel(szo)[0] vals2 = special.fresnel(czo)[1] assert_array_almost_equal(vals1,0,14) assert_array_almost_equal(vals2,0,14) def test_fresnelc_zeros(self): szo, czo = special.fresnel_zeros(6) frc = special.fresnelc_zeros(6) assert_array_almost_equal(frc,czo,12) def test_fresnels_zeros(self): szo, czo = special.fresnel_zeros(5) frs = special.fresnels_zeros(5) assert_array_almost_equal(frs,szo,12) class TestGamma: def test_gamma(self): gam = special.gamma(5) assert_equal(gam,24.0) def test_gammaln(self): gamln = special.gammaln(3) lngam = log(special.gamma(3)) assert_almost_equal(gamln,lngam,8) def test_gammainccinv(self): gccinv = special.gammainccinv(.5,.5) gcinv = special.gammaincinv(.5,.5) assert_almost_equal(gccinv,gcinv,8) @with_special_errors def test_gammaincinv(self): y = special.gammaincinv(.4,.4) x = special.gammainc(.4,y) assert_almost_equal(x,0.4,1) y = special.gammainc(10, 0.05) x = special.gammaincinv(10, 2.5715803516000736e-20) assert_almost_equal(0.05, x, decimal=10) assert_almost_equal(y, 2.5715803516000736e-20, decimal=10) x = special.gammaincinv(50, 8.20754777388471303050299243573393e-18) assert_almost_equal(11.0, x, decimal=10) @with_special_errors def test_975(self): # Regression test for ticket #975 -- switch point in algorithm # check that things work OK at the point, immediately next floats # around it, and a bit further away pts = [0.25, np.nextafter(0.25, 0), 0.25 - 1e-12, np.nextafter(0.25, 1), 0.25 + 1e-12] for xp in pts: y = special.gammaincinv(.4, xp) x = special.gammainc(0.4, y) assert_allclose(x, xp, rtol=1e-12) def test_rgamma(self): rgam = special.rgamma(8) rlgam = 1/special.gamma(8) assert_almost_equal(rgam,rlgam,8) def test_infinity(self): assert_(np.isinf(special.gamma(-1))) assert_equal(special.rgamma(-1), 0) class TestHankel: def test_negv1(self): assert_almost_equal(special.hankel1(-3,2), -special.hankel1(3,2), 14) def test_hankel1(self): hank1 = special.hankel1(1,.1) hankrl = (special.jv(1,.1) + special.yv(1,.1)*1j) assert_almost_equal(hank1,hankrl,8) def test_negv1e(self): assert_almost_equal(special.hankel1e(-3,2), -special.hankel1e(3,2), 14) def test_hankel1e(self): hank1e = special.hankel1e(1,.1) hankrle = special.hankel1(1,.1)*exp(-.1j) assert_almost_equal(hank1e,hankrle,8) def test_negv2(self): assert_almost_equal(special.hankel2(-3,2), -special.hankel2(3,2), 14) def test_hankel2(self): hank2 = special.hankel2(1,.1) hankrl2 = (special.jv(1,.1) - special.yv(1,.1)*1j) assert_almost_equal(hank2,hankrl2,8) def test_neg2e(self): assert_almost_equal(special.hankel2e(-3,2), -special.hankel2e(3,2), 14) def test_hankl2e(self): hank2e = special.hankel2e(1,.1) hankrl2e = special.hankel2e(1,.1) assert_almost_equal(hank2e,hankrl2e,8) class TestHyper: def test_h1vp(self): h1 = special.h1vp(1,.1) h1real = (special.jvp(1,.1) + special.yvp(1,.1)*1j) assert_almost_equal(h1,h1real,8) def test_h2vp(self): h2 = special.h2vp(1,.1) h2real = (special.jvp(1,.1) - special.yvp(1,.1)*1j) assert_almost_equal(h2,h2real,8) def test_hyp0f1(self): # scalar input assert_allclose(special.hyp0f1(2.5, 0.5), 1.21482702689997, rtol=1e-12) assert_allclose(special.hyp0f1(2.5, 0), 1.0, rtol=1e-15) # float input, expected values match mpmath x = special.hyp0f1(3.0, [-1.5, -1, 0, 1, 1.5]) expected = np.array([0.58493659229143, 0.70566805723127, 1.0, 1.37789689539747, 1.60373685288480]) assert_allclose(x, expected, rtol=1e-12) # complex input x = special.hyp0f1(3.0, np.array([-1.5, -1, 0, 1, 1.5]) + 0.j) assert_allclose(x, expected.astype(complex), rtol=1e-12) # test broadcasting x1 = [0.5, 1.5, 2.5] x2 = [0, 1, 0.5] x = special.hyp0f1(x1, x2) expected = [1.0, 1.8134302039235093, 1.21482702689997] assert_allclose(x, expected, rtol=1e-12) x = special.hyp0f1(np.vstack([x1] * 2), x2) assert_allclose(x, np.vstack([expected] * 2), rtol=1e-12) assert_raises(ValueError, special.hyp0f1, np.vstack([x1] * 3), [0, 1]) def test_hyp0f1_gh5764(self): # Just checks the point that failed; there's a more systematic # test in test_mpmath res = special.hyp0f1(0.8, 0.5 + 0.5*1J) # The expected value was generated using mpmath assert_almost_equal(res, 1.6139719776441115 + 1J*0.80893054061790665) def test_hyp1f1(self): hyp1 = special.hyp1f1(.1,.1,.3) assert_almost_equal(hyp1, 1.3498588075760032,7) # test contributed by Moritz Deger (2008-05-29) # https://github.com/scipy/scipy/issues/1186 (Trac #659) # reference data obtained from mathematica [ a, b, x, m(a,b,x)]: # produced with test_hyp1f1.nb ref_data = array([ [-8.38132975e+00, -1.28436461e+01, -2.91081397e+01, 1.04178330e+04], [2.91076882e+00, -6.35234333e+00, -1.27083993e+01, 6.68132725e+00], [-1.42938258e+01, 1.80869131e-01, 1.90038728e+01, 1.01385897e+05], [5.84069088e+00, 1.33187908e+01, 2.91290106e+01, 1.59469411e+08], [-2.70433202e+01, -1.16274873e+01, -2.89582384e+01, 1.39900152e+24], [4.26344966e+00, -2.32701773e+01, 1.91635759e+01, 6.13816915e+21], [1.20514340e+01, -3.40260240e+00, 7.26832235e+00, 1.17696112e+13], [2.77372955e+01, -1.99424687e+00, 3.61332246e+00, 3.07419615e+13], [1.50310939e+01, -2.91198675e+01, -1.53581080e+01, -3.79166033e+02], [1.43995827e+01, 9.84311196e+00, 1.93204553e+01, 2.55836264e+10], [-4.08759686e+00, 1.34437025e+01, -1.42072843e+01, 1.70778449e+01], [8.05595738e+00, -1.31019838e+01, 1.52180721e+01, 3.06233294e+21], [1.81815804e+01, -1.42908793e+01, 9.57868793e+00, -2.84771348e+20], [-2.49671396e+01, 1.25082843e+01, -1.71562286e+01, 2.36290426e+07], [2.67277673e+01, 1.70315414e+01, 6.12701450e+00, 7.77917232e+03], [2.49565476e+01, 2.91694684e+01, 6.29622660e+00, 2.35300027e+02], [6.11924542e+00, -1.59943768e+00, 9.57009289e+00, 1.32906326e+11], [-1.47863653e+01, 2.41691301e+01, -1.89981821e+01, 2.73064953e+03], [2.24070483e+01, -2.93647433e+00, 8.19281432e+00, -6.42000372e+17], [8.04042600e-01, 1.82710085e+01, -1.97814534e+01, 5.48372441e-01], [1.39590390e+01, 1.97318686e+01, 2.37606635e+00, 5.51923681e+00], [-4.66640483e+00, -2.00237930e+01, 7.40365095e+00, 4.50310752e+00], [2.76821999e+01, -6.36563968e+00, 1.11533984e+01, -9.28725179e+23], [-2.56764457e+01, 1.24544906e+00, 1.06407572e+01, 1.25922076e+01], [3.20447808e+00, 1.30874383e+01, 2.26098014e+01, 2.03202059e+04], [-1.24809647e+01, 4.15137113e+00, -2.92265700e+01, 2.39621411e+08], [2.14778108e+01, -2.35162960e+00, -1.13758664e+01, 4.46882152e-01], [-9.85469168e+00, -3.28157680e+00, 1.67447548e+01, -1.07342390e+07], [1.08122310e+01, -2.47353236e+01, -1.15622349e+01, -2.91733796e+03], [-2.67933347e+01, -3.39100709e+00, 2.56006986e+01, -5.29275382e+09], [-8.60066776e+00, -8.02200924e+00, 1.07231926e+01, 1.33548320e+06], [-1.01724238e-01, -1.18479709e+01, -2.55407104e+01, 1.55436570e+00], [-3.93356771e+00, 2.11106818e+01, -2.57598485e+01, 2.13467840e+01], [3.74750503e+00, 1.55687633e+01, -2.92841720e+01, 1.43873509e-02], [6.99726781e+00, 2.69855571e+01, -1.63707771e+01, 3.08098673e-02], [-2.31996011e+01, 3.47631054e+00, 9.75119815e-01, 1.79971073e-02], [2.38951044e+01, -2.91460190e+01, -2.50774708e+00, 9.56934814e+00], [1.52730825e+01, 5.77062507e+00, 1.21922003e+01, 1.32345307e+09], [1.74673917e+01, 1.89723426e+01, 4.94903250e+00, 9.90859484e+01], [1.88971241e+01, 2.86255413e+01, 5.52360109e-01, 1.44165360e+00], [1.02002319e+01, -1.66855152e+01, -2.55426235e+01, 6.56481554e+02], [-1.79474153e+01, 1.22210200e+01, -1.84058212e+01, 8.24041812e+05], [-1.36147103e+01, 1.32365492e+00, -7.22375200e+00, 9.92446491e+05], [7.57407832e+00, 2.59738234e+01, -1.34139168e+01, 3.64037761e-02], [2.21110169e+00, 1.28012666e+01, 1.62529102e+01, 1.33433085e+02], [-2.64297569e+01, -1.63176658e+01, -1.11642006e+01, -2.44797251e+13], [-2.46622944e+01, -3.02147372e+00, 8.29159315e+00, -3.21799070e+05], [-1.37215095e+01, -1.96680183e+01, 2.91940118e+01, 3.21457520e+12], [-5.45566105e+00, 2.81292086e+01, 1.72548215e-01, 9.66973000e-01], [-1.55751298e+00, -8.65703373e+00, 2.68622026e+01, -3.17190834e+16], [2.45393609e+01, -2.70571903e+01, 1.96815505e+01, 1.80708004e+37], [5.77482829e+00, 1.53203143e+01, 2.50534322e+01, 1.14304242e+06], [-1.02626819e+01, 2.36887658e+01, -2.32152102e+01, 7.28965646e+02], [-1.30833446e+00, -1.28310210e+01, 1.87275544e+01, -9.33487904e+12], [5.83024676e+00, -1.49279672e+01, 2.44957538e+01, -7.61083070e+27], [-2.03130747e+01, 2.59641715e+01, -2.06174328e+01, 4.54744859e+04], [1.97684551e+01, -2.21410519e+01, -2.26728740e+01, 3.53113026e+06], [2.73673444e+01, 2.64491725e+01, 1.57599882e+01, 1.07385118e+07], [5.73287971e+00, 1.21111904e+01, 1.33080171e+01, 2.63220467e+03], [-2.82751072e+01, 2.08605881e+01, 9.09838900e+00, -6.60957033e-07], [1.87270691e+01, -1.74437016e+01, 1.52413599e+01, 6.59572851e+27], [6.60681457e+00, -2.69449855e+00, 9.78972047e+00, -2.38587870e+12], [1.20895561e+01, -2.51355765e+01, 2.30096101e+01, 7.58739886e+32], [-2.44682278e+01, 2.10673441e+01, -1.36705538e+01, 4.54213550e+04], [-4.50665152e+00, 3.72292059e+00, -4.83403707e+00, 2.68938214e+01], [-7.46540049e+00, -1.08422222e+01, -1.72203805e+01, -2.09402162e+02], [-2.00307551e+01, -7.50604431e+00, -2.78640020e+01, 4.15985444e+19], [1.99890876e+01, 2.20677419e+01, -2.51301778e+01, 1.23840297e-09], [2.03183823e+01, -7.66942559e+00, 2.10340070e+01, 1.46285095e+31], [-2.90315825e+00, -2.55785967e+01, -9.58779316e+00, 2.65714264e-01], [2.73960829e+01, -1.80097203e+01, -2.03070131e+00, 2.52908999e+02], [-2.11708058e+01, -2.70304032e+01, 2.48257944e+01, 3.09027527e+08], [2.21959758e+01, 4.00258675e+00, -1.62853977e+01, -9.16280090e-09], [1.61661840e+01, -2.26845150e+01, 2.17226940e+01, -8.24774394e+33], [-3.35030306e+00, 1.32670581e+00, 9.39711214e+00, -1.47303163e+01], [7.23720726e+00, -2.29763909e+01, 2.34709682e+01, -9.20711735e+29], [2.71013568e+01, 1.61951087e+01, -7.11388906e-01, 2.98750911e-01], [8.40057933e+00, -7.49665220e+00, 2.95587388e+01, 6.59465635e+29], [-1.51603423e+01, 1.94032322e+01, -7.60044357e+00, 1.05186941e+02], [-8.83788031e+00, -2.72018313e+01, 1.88269907e+00, 1.81687019e+00], [-1.87283712e+01, 5.87479570e+00, -1.91210203e+01, 2.52235612e+08], [-5.61338513e-01, 2.69490237e+01, 1.16660111e-01, 9.97567783e-01], [-5.44354025e+00, -1.26721408e+01, -4.66831036e+00, 1.06660735e-01], [-2.18846497e+00, 2.33299566e+01, 9.62564397e+00, 3.03842061e-01], [6.65661299e+00, -2.39048713e+01, 1.04191807e+01, 4.73700451e+13], [-2.57298921e+01, -2.60811296e+01, 2.74398110e+01, -5.32566307e+11], [-1.11431826e+01, -1.59420160e+01, -1.84880553e+01, -1.01514747e+02], [6.50301931e+00, 2.59859051e+01, -2.33270137e+01, 1.22760500e-02], [-1.94987891e+01, -2.62123262e+01, 3.90323225e+00, 1.71658894e+01], [7.26164601e+00, -1.41469402e+01, 2.81499763e+01, -2.50068329e+31], [-1.52424040e+01, 2.99719005e+01, -2.85753678e+01, 1.31906693e+04], [5.24149291e+00, -1.72807223e+01, 2.22129493e+01, 2.50748475e+25], [3.63207230e-01, -9.54120862e-02, -2.83874044e+01, 9.43854939e-01], [-2.11326457e+00, -1.25707023e+01, 1.17172130e+00, 1.20812698e+00], [2.48513582e+00, 1.03652647e+01, -1.84625148e+01, 6.47910997e-02], [2.65395942e+01, 2.74794672e+01, 1.29413428e+01, 2.89306132e+05], [-9.49445460e+00, 1.59930921e+01, -1.49596331e+01, 3.27574841e+02], [-5.89173945e+00, 9.96742426e+00, 2.60318889e+01, -3.15842908e-01], [-1.15387239e+01, -2.21433107e+01, -2.17686413e+01, 1.56724718e-01], [-5.30592244e+00, -2.42752190e+01, 1.29734035e+00, 1.31985534e+00] ]) for a,b,c,expected in ref_data: result = special.hyp1f1(a,b,c) assert_(abs(expected - result)/expected < 1e-4) def test_hyp1f1_gh2957(self): hyp1 = special.hyp1f1(0.5, 1.5, -709.7827128933) hyp2 = special.hyp1f1(0.5, 1.5, -709.7827128934) assert_almost_equal(hyp1, hyp2, 12) def test_hyp1f1_gh2282(self): hyp = special.hyp1f1(0.5, 1.5, -1000) assert_almost_equal(hyp, 0.028024956081989643, 12) def test_hyp2f1(self): # a collection of special cases taken from AMS 55 values = [ [0.5, 1, 1.5, 0.2**2, 0.5/0.2*log((1+0.2)/(1-0.2))], [0.5, 1, 1.5, -0.2**2, 1./0.2*arctan(0.2)], [1, 1, 2, 0.2, -1/0.2*log(1-0.2)], [3, 3.5, 1.5, 0.2**2, 0.5/0.2/(-5)*((1+0.2)**(-5)-(1-0.2)**(-5))], [-3, 3, 0.5, sin(0.2)**2, cos(2*3*0.2)], [3, 4, 8, 1, special.gamma(8) * special.gamma(8-4-3) / special.gamma(8-3) / special.gamma(8-4)], [3, 2, 3-2+1, -1, 1./2**3*sqrt(pi) * special.gamma(1+3-2) / special.gamma(1+0.5*3-2) / special.gamma(0.5+0.5*3)], [5, 2, 5-2+1, -1, 1./2**5*sqrt(pi) * special.gamma(1+5-2) / special.gamma(1+0.5*5-2) / special.gamma(0.5+0.5*5)], [4, 0.5+4, 1.5-2*4, -1./3, (8./9)**(-2*4)*special.gamma(4./3) * special.gamma(1.5-2*4) / special.gamma(3./2) / special.gamma(4./3-2*4)], # and some others # ticket #424 [1.5, -0.5, 1.0, -10.0, 4.1300097765277476484], # negative integer a or b, with c-a-b integer and x > 0.9 [-2,3,1,0.95,0.715], [2,-3,1,0.95,-0.007], [-6,3,1,0.95,0.0000810625], [2,-5,1,0.95,-0.000029375], # huge negative integers (10, -900, 10.5, 0.99, 1.91853705796607664803709475658e-24), (10, -900, -10.5, 0.99, 3.54279200040355710199058559155e-18), ] for i, (a, b, c, x, v) in enumerate(values): cv = special.hyp2f1(a, b, c, x) assert_almost_equal(cv, v, 8, err_msg='test #%d' % i) def test_hyperu(self): val1 = special.hyperu(1,0.1,100) assert_almost_equal(val1,0.0098153,7) a,b = [0.3,0.6,1.2,-2.7],[1.5,3.2,-0.4,-3.2] a,b = asarray(a), asarray(b) z = 0.5 hypu = special.hyperu(a,b,z) hprl = (pi/sin(pi*b))*(special.hyp1f1(a,b,z) / (special.gamma(1+a-b)*special.gamma(b)) - z**(1-b)*special.hyp1f1(1+a-b,2-b,z) / (special.gamma(a)*special.gamma(2-b))) assert_array_almost_equal(hypu,hprl,12) def test_hyperu_gh2287(self): assert_almost_equal(special.hyperu(1, 1.5, 20.2), 0.048360918656699191, 12) class TestBessel: def test_itj0y0(self): it0 = array(special.itj0y0(.2)) assert_array_almost_equal( it0, array([0.19933433254006822, -0.34570883800412566]), 8, ) def test_it2j0y0(self): it2 = array(special.it2j0y0(.2)) assert_array_almost_equal( it2, array([0.0049937546274601858, -0.43423067011231614]), 8, ) def test_negv_iv(self): assert_equal(special.iv(3,2), special.iv(-3,2)) def test_j0(self): oz = special.j0(.1) ozr = special.jn(0,.1) assert_almost_equal(oz,ozr,8) def test_j1(self): o1 = special.j1(.1) o1r = special.jn(1,.1) assert_almost_equal(o1,o1r,8) def test_jn(self): jnnr = special.jn(1,.2) assert_almost_equal(jnnr,0.099500832639235995,8) def test_negv_jv(self): assert_almost_equal(special.jv(-3,2), -special.jv(3,2), 14) def test_jv(self): values = [[0, 0.1, 0.99750156206604002], [2./3, 1e-8, 0.3239028506761532e-5], [2./3, 1e-10, 0.1503423854873779e-6], [3.1, 1e-10, 0.1711956265409013e-32], [2./3, 4.0, -0.2325440850267039], ] for i, (v, x, y) in enumerate(values): yc = special.jv(v, x) assert_almost_equal(yc, y, 8, err_msg='test #%d' % i) def test_negv_jve(self): assert_almost_equal(special.jve(-3,2), -special.jve(3,2), 14) def test_jve(self): jvexp = special.jve(1,.2) assert_almost_equal(jvexp,0.099500832639235995,8) jvexp1 = special.jve(1,.2+1j) z = .2+1j jvexpr = special.jv(1,z)*exp(-abs(z.imag)) assert_almost_equal(jvexp1,jvexpr,8) def test_jn_zeros(self): jn0 = special.jn_zeros(0,5) jn1 = special.jn_zeros(1,5) assert_array_almost_equal(jn0,array([2.4048255577, 5.5200781103, 8.6537279129, 11.7915344391, 14.9309177086]),4) assert_array_almost_equal(jn1,array([3.83171, 7.01559, 10.17347, 13.32369, 16.47063]),4) jn102 = special.jn_zeros(102,5) assert_allclose(jn102, array([110.89174935992040343, 117.83464175788308398, 123.70194191713507279, 129.02417238949092824, 134.00114761868422559]), rtol=1e-13) jn301 = special.jn_zeros(301,5) assert_allclose(jn301, array([313.59097866698830153, 323.21549776096288280, 331.22338738656748796, 338.39676338872084500, 345.03284233056064157]), rtol=1e-13) def test_jn_zeros_slow(self): jn0 = special.jn_zeros(0, 300) assert_allclose(jn0[260-1], 816.02884495068867280, rtol=1e-13) assert_allclose(jn0[280-1], 878.86068707124422606, rtol=1e-13) assert_allclose(jn0[300-1], 941.69253065317954064, rtol=1e-13) jn10 = special.jn_zeros(10, 300) assert_allclose(jn10[260-1], 831.67668514305631151, rtol=1e-13) assert_allclose(jn10[280-1], 894.51275095371316931, rtol=1e-13) assert_allclose(jn10[300-1], 957.34826370866539775, rtol=1e-13) jn3010 = special.jn_zeros(3010,5) assert_allclose(jn3010, array([3036.86590780927, 3057.06598526482, 3073.66360690272, 3088.37736494778, 3101.86438139042]), rtol=1e-8) def test_jnjnp_zeros(self): jn = special.jn def jnp(n, x): return (jn(n-1,x) - jn(n+1,x))/2 for nt in range(1, 30): z, n, m, t = special.jnjnp_zeros(nt) for zz, nn, tt in zip(z, n, t): if tt == 0: assert_allclose(jn(nn, zz), 0, atol=1e-6) elif tt == 1: assert_allclose(jnp(nn, zz), 0, atol=1e-6) else: raise AssertionError("Invalid t return for nt=%d" % nt) def test_jnp_zeros(self): jnp = special.jnp_zeros(1,5) assert_array_almost_equal(jnp, array([1.84118, 5.33144, 8.53632, 11.70600, 14.86359]),4) jnp = special.jnp_zeros(443,5) assert_allclose(special.jvp(443, jnp), 0, atol=1e-15) def test_jnyn_zeros(self): jnz = special.jnyn_zeros(1,5) assert_array_almost_equal(jnz,(array([3.83171, 7.01559, 10.17347, 13.32369, 16.47063]), array([1.84118, 5.33144, 8.53632, 11.70600, 14.86359]), array([2.19714, 5.42968, 8.59601, 11.74915, 14.89744]), array([3.68302, 6.94150, 10.12340, 13.28576, 16.44006])),5) def test_jvp(self): jvprim = special.jvp(2,2) jv0 = (special.jv(1,2)-special.jv(3,2))/2 assert_almost_equal(jvprim,jv0,10) def test_k0(self): ozk = special.k0(.1) ozkr = special.kv(0,.1) assert_almost_equal(ozk,ozkr,8) def test_k0e(self): ozke = special.k0e(.1) ozker = special.kve(0,.1) assert_almost_equal(ozke,ozker,8) def test_k1(self): o1k = special.k1(.1) o1kr = special.kv(1,.1) assert_almost_equal(o1k,o1kr,8) def test_k1e(self): o1ke = special.k1e(.1) o1ker = special.kve(1,.1) assert_almost_equal(o1ke,o1ker,8) def test_jacobi(self): a = 5*np.random.random() - 1 b = 5*np.random.random() - 1 P0 = special.jacobi(0,a,b) P1 = special.jacobi(1,a,b) P2 = special.jacobi(2,a,b) P3 = special.jacobi(3,a,b) assert_array_almost_equal(P0.c,[1],13) assert_array_almost_equal(P1.c,array([a+b+2,a-b])/2.0,13) cp = [(a+b+3)*(a+b+4), 4*(a+b+3)*(a+2), 4*(a+1)*(a+2)] p2c = [cp[0],cp[1]-2*cp[0],cp[2]-cp[1]+cp[0]] assert_array_almost_equal(P2.c,array(p2c)/8.0,13) cp = [(a+b+4)*(a+b+5)*(a+b+6),6*(a+b+4)*(a+b+5)*(a+3), 12*(a+b+4)*(a+2)*(a+3),8*(a+1)*(a+2)*(a+3)] p3c = [cp[0],cp[1]-3*cp[0],cp[2]-2*cp[1]+3*cp[0],cp[3]-cp[2]+cp[1]-cp[0]] assert_array_almost_equal(P3.c,array(p3c)/48.0,13) def test_kn(self): kn1 = special.kn(0,.2) assert_almost_equal(kn1,1.7527038555281462,8) def test_negv_kv(self): assert_equal(special.kv(3.0, 2.2), special.kv(-3.0, 2.2)) def test_kv0(self): kv0 = special.kv(0,.2) assert_almost_equal(kv0, 1.7527038555281462, 10) def test_kv1(self): kv1 = special.kv(1,0.2) assert_almost_equal(kv1, 4.775972543220472, 10) def test_kv2(self): kv2 = special.kv(2,0.2) assert_almost_equal(kv2, 49.51242928773287, 10) def test_kn_largeorder(self): assert_allclose(special.kn(32, 1), 1.7516596664574289e+43) def test_kv_largearg(self): assert_equal(special.kv(0, 1e19), 0) def test_negv_kve(self): assert_equal(special.kve(3.0, 2.2), special.kve(-3.0, 2.2)) def test_kve(self): kve1 = special.kve(0,.2) kv1 = special.kv(0,.2)*exp(.2) assert_almost_equal(kve1,kv1,8) z = .2+1j kve2 = special.kve(0,z) kv2 = special.kv(0,z)*exp(z) assert_almost_equal(kve2,kv2,8) def test_kvp_v0n1(self): z = 2.2 assert_almost_equal(-special.kv(1,z), special.kvp(0,z, n=1), 10) def test_kvp_n1(self): v = 3. z = 2.2 xc = -special.kv(v+1,z) + v/z*special.kv(v,z) x = special.kvp(v,z, n=1) assert_almost_equal(xc, x, 10) # this function (kvp) is broken def test_kvp_n2(self): v = 3. z = 2.2 xc = (z**2+v**2-v)/z**2 * special.kv(v,z) + special.kv(v+1,z)/z x = special.kvp(v, z, n=2) assert_almost_equal(xc, x, 10) def test_y0(self): oz = special.y0(.1) ozr = special.yn(0,.1) assert_almost_equal(oz,ozr,8) def test_y1(self): o1 = special.y1(.1) o1r = special.yn(1,.1) assert_almost_equal(o1,o1r,8) def test_y0_zeros(self): yo,ypo = special.y0_zeros(2) zo,zpo = special.y0_zeros(2,complex=1) all = r_[yo,zo] allval = r_[ypo,zpo] assert_array_almost_equal(abs(special.yv(0.0,all)),0.0,11) assert_array_almost_equal(abs(special.yv(1,all)-allval),0.0,11) def test_y1_zeros(self): y1 = special.y1_zeros(1) assert_array_almost_equal(y1,(array([2.19714]),array([0.52079])),5) def test_y1p_zeros(self): y1p = special.y1p_zeros(1,complex=1) assert_array_almost_equal( y1p, (array([0.5768+0.904j]), array([-0.7635+0.5892j])), 3, ) def test_yn_zeros(self): an = special.yn_zeros(4,2) assert_array_almost_equal(an,array([5.64515, 9.36162]),5) an = special.yn_zeros(443,5) assert_allclose(an, [450.13573091578090314, 463.05692376675001542, 472.80651546418663566, 481.27353184725625838, 488.98055964441374646], rtol=1e-15,) def test_ynp_zeros(self): ao = special.ynp_zeros(0,2) assert_array_almost_equal(ao,array([2.19714133, 5.42968104]),6) ao = special.ynp_zeros(43,5) assert_allclose(special.yvp(43, ao), 0, atol=1e-15) ao = special.ynp_zeros(443,5) assert_allclose(special.yvp(443, ao), 0, atol=1e-9) def test_ynp_zeros_large_order(self): ao = special.ynp_zeros(443,5) assert_allclose(special.yvp(443, ao), 0, atol=1e-14) def test_yn(self): yn2n = special.yn(1,.2) assert_almost_equal(yn2n,-3.3238249881118471,8) def test_yn_gh_20405(self): # Enforce correct asymptotic behavior for large n. observed = cephes.yn(500, 1) assert observed == -np.inf def test_negv_yv(self): assert_almost_equal(special.yv(-3,2), -special.yv(3,2), 14) def test_yv(self): yv2 = special.yv(1,.2) assert_almost_equal(yv2,-3.3238249881118471,8) def test_negv_yve(self): assert_almost_equal(special.yve(-3,2), -special.yve(3,2), 14) def test_yve(self): yve2 = special.yve(1,.2) assert_almost_equal(yve2,-3.3238249881118471,8) yve2r = special.yv(1,.2+1j)*exp(-1) yve22 = special.yve(1,.2+1j) assert_almost_equal(yve22,yve2r,8) def test_yvp(self): yvpr = (special.yv(1,.2) - special.yv(3,.2))/2.0 yvp1 = special.yvp(2,.2) assert_array_almost_equal(yvp1,yvpr,10) def _cephes_vs_amos_points(self): """Yield points at which to compare Cephes implementation to AMOS""" # check several points, including large-amplitude ones v = [-120, -100.3, -20., -10., -1., -.5, 0., 1., 12.49, 120., 301] z = [-1300, -11, -10, -1, 1., 10., 200.5, 401., 600.5, 700.6, 1300, 10003] yield from itertools.product(v, z) # check half-integers; these are problematic points at least # for cephes/iv yield from itertools.product(0.5 + arange(-60, 60), [3.5]) def check_cephes_vs_amos(self, f1, f2, rtol=1e-11, atol=0, skip=None): for v, z in self._cephes_vs_amos_points(): if skip is not None and skip(v, z): continue c1, c2, c3 = f1(v, z), f1(v,z+0j), f2(int(v), z) if np.isinf(c1): assert_(np.abs(c2) >= 1e300, (v, z)) elif np.isnan(c1): assert_(c2.imag != 0, (v, z)) else: assert_allclose(c1, c2, err_msg=(v, z), rtol=rtol, atol=atol) if v == int(v): assert_allclose(c3, c2, err_msg=(v, z), rtol=rtol, atol=atol) @pytest.mark.xfail(platform.machine() == 'ppc64le', reason="fails on ppc64le") def test_jv_cephes_vs_amos(self): self.check_cephes_vs_amos(special.jv, special.jn, rtol=1e-10, atol=1e-305) @pytest.mark.xfail(platform.machine() == 'ppc64le', reason="fails on ppc64le") def test_yv_cephes_vs_amos(self): self.check_cephes_vs_amos(special.yv, special.yn, rtol=1e-11, atol=1e-305) def test_yv_cephes_vs_amos_only_small_orders(self): def skipper(v, z): return abs(v) > 50 self.check_cephes_vs_amos(special.yv, special.yn, rtol=1e-11, atol=1e-305, skip=skipper) def test_iv_cephes_vs_amos(self): with np.errstate(all='ignore'): self.check_cephes_vs_amos(special.iv, special.iv, rtol=5e-9, atol=1e-305) @pytest.mark.slow def test_iv_cephes_vs_amos_mass_test(self): N = 1000000 np.random.seed(1) v = np.random.pareto(0.5, N) * (-1)**np.random.randint(2, size=N) x = np.random.pareto(0.2, N) * (-1)**np.random.randint(2, size=N) imsk = (np.random.randint(8, size=N) == 0) v[imsk] = v[imsk].astype(np.int64) with np.errstate(all='ignore'): c1 = special.iv(v, x) c2 = special.iv(v, x+0j) # deal with differences in the inf and zero cutoffs c1[abs(c1) > 1e300] = np.inf c2[abs(c2) > 1e300] = np.inf c1[abs(c1) < 1e-300] = 0 c2[abs(c2) < 1e-300] = 0 dc = abs(c1/c2 - 1) dc[np.isnan(dc)] = 0 k = np.argmax(dc) # Most error apparently comes from AMOS and not our implementation; # there are some problems near integer orders there assert_( dc[k] < 2e-7, (v[k], x[k], special.iv(v[k], x[k]), special.iv(v[k], x[k]+0j)) ) def test_kv_cephes_vs_amos(self): self.check_cephes_vs_amos(special.kv, special.kn, rtol=1e-9, atol=1e-305) self.check_cephes_vs_amos(special.kv, special.kv, rtol=1e-9, atol=1e-305) def test_ticket_623(self): assert_allclose(special.jv(3, 4), 0.43017147387562193) assert_allclose(special.jv(301, 1300), 0.0183487151115275) assert_allclose(special.jv(301, 1296.0682), -0.0224174325312048) def test_ticket_853(self): """Negative-order Bessels""" # cephes assert_allclose(special.jv(-1, 1), -0.4400505857449335) assert_allclose(special.jv(-2, 1), 0.1149034849319005) assert_allclose(special.yv(-1, 1), 0.7812128213002887) assert_allclose(special.yv(-2, 1), -1.650682606816255) assert_allclose(special.iv(-1, 1), 0.5651591039924851) assert_allclose(special.iv(-2, 1), 0.1357476697670383) assert_allclose(special.kv(-1, 1), 0.6019072301972347) assert_allclose(special.kv(-2, 1), 1.624838898635178) assert_allclose(special.jv(-0.5, 1), 0.43109886801837607952) assert_allclose(special.yv(-0.5, 1), 0.6713967071418031) assert_allclose(special.iv(-0.5, 1), 1.231200214592967) assert_allclose(special.kv(-0.5, 1), 0.4610685044478945) # amos assert_allclose(special.jv(-1, 1+0j), -0.4400505857449335) assert_allclose(special.jv(-2, 1+0j), 0.1149034849319005) assert_allclose(special.yv(-1, 1+0j), 0.7812128213002887) assert_allclose(special.yv(-2, 1+0j), -1.650682606816255) assert_allclose(special.iv(-1, 1+0j), 0.5651591039924851) assert_allclose(special.iv(-2, 1+0j), 0.1357476697670383) assert_allclose(special.kv(-1, 1+0j), 0.6019072301972347) assert_allclose(special.kv(-2, 1+0j), 1.624838898635178) assert_allclose(special.jv(-0.5, 1+0j), 0.43109886801837607952) assert_allclose(special.jv(-0.5, 1+1j), 0.2628946385649065-0.827050182040562j) assert_allclose(special.yv(-0.5, 1+0j), 0.6713967071418031) assert_allclose(special.yv(-0.5, 1+1j), 0.967901282890131+0.0602046062142816j) assert_allclose(special.iv(-0.5, 1+0j), 1.231200214592967) assert_allclose(special.iv(-0.5, 1+1j), 0.77070737376928+0.39891821043561j) assert_allclose(special.kv(-0.5, 1+0j), 0.4610685044478945) assert_allclose(special.kv(-0.5, 1+1j), 0.06868578341999-0.38157825981268j) assert_allclose(special.jve(-0.5,1+0.3j), special.jv(-0.5, 1+0.3j)*exp(-0.3)) assert_allclose(special.yve(-0.5,1+0.3j), special.yv(-0.5, 1+0.3j)*exp(-0.3)) assert_allclose(special.ive(-0.5,0.3+1j), special.iv(-0.5, 0.3+1j)*exp(-0.3)) assert_allclose(special.kve(-0.5,0.3+1j), special.kv(-0.5, 0.3+1j)*exp(0.3+1j)) assert_allclose( special.hankel1(-0.5, 1+1j), special.jv(-0.5, 1+1j) + 1j*special.yv(-0.5,1+1j) ) assert_allclose( special.hankel2(-0.5, 1+1j), special.jv(-0.5, 1+1j) - 1j*special.yv(-0.5,1+1j) ) def test_ticket_854(self): """Real-valued Bessel domains""" assert_(isnan(special.jv(0.5, -1))) assert_(isnan(special.iv(0.5, -1))) assert_(isnan(special.yv(0.5, -1))) assert_(isnan(special.yv(1, -1))) assert_(isnan(special.kv(0.5, -1))) assert_(isnan(special.kv(1, -1))) assert_(isnan(special.jve(0.5, -1))) assert_(isnan(special.ive(0.5, -1))) assert_(isnan(special.yve(0.5, -1))) assert_(isnan(special.yve(1, -1))) assert_(isnan(special.kve(0.5, -1))) assert_(isnan(special.kve(1, -1))) assert_(isnan(special.airye(-1)[0:2]).all(), special.airye(-1)) assert_(not isnan(special.airye(-1)[2:4]).any(), special.airye(-1)) def test_gh_7909(self): assert_(special.kv(1.5, 0) == np.inf) assert_(special.kve(1.5, 0) == np.inf) def test_ticket_503(self): """Real-valued Bessel I overflow""" assert_allclose(special.iv(1, 700), 1.528500390233901e302) assert_allclose(special.iv(1000, 1120), 1.301564549405821e301) def test_iv_hyperg_poles(self): assert_allclose(special.iv(-0.5, 1), 1.231200214592967) def iv_series(self, v, z, n=200): k = arange(0, n).astype(double) r = (v+2*k)*log(.5*z) - special.gammaln(k+1) - special.gammaln(v+k+1) r[isnan(r)] = inf r = exp(r) err = abs(r).max() * finfo(double).eps * n + abs(r[-1])*10 return r.sum(), err def test_i0_series(self): for z in [1., 10., 200.5]: value, err = self.iv_series(0, z) assert_allclose(special.i0(z), value, atol=err, err_msg=z) def test_i1_series(self): for z in [1., 10., 200.5]: value, err = self.iv_series(1, z) assert_allclose(special.i1(z), value, atol=err, err_msg=z) def test_iv_series(self): for v in [-20., -10., -1., 0., 1., 12.49, 120.]: for z in [1., 10., 200.5, -1+2j]: value, err = self.iv_series(v, z) assert_allclose(special.iv(v, z), value, atol=err, err_msg=(v, z)) def test_i0(self): values = [[0.0, 1.0], [1e-10, 1.0], [0.1, 0.9071009258], [0.5, 0.6450352706], [1.0, 0.4657596077], [2.5, 0.2700464416], [5.0, 0.1835408126], [20.0, 0.0897803119], ] for i, (x, v) in enumerate(values): cv = special.i0(x) * exp(-x) assert_almost_equal(cv, v, 8, err_msg='test #%d' % i) def test_i0e(self): oize = special.i0e(.1) oizer = special.ive(0,.1) assert_almost_equal(oize,oizer,8) def test_i1(self): values = [[0.0, 0.0], [1e-10, 0.4999999999500000e-10], [0.1, 0.0452984468], [0.5, 0.1564208032], [1.0, 0.2079104154], [5.0, 0.1639722669], [20.0, 0.0875062222], ] for i, (x, v) in enumerate(values): cv = special.i1(x) * exp(-x) assert_almost_equal(cv, v, 8, err_msg='test #%d' % i) def test_i1e(self): oi1e = special.i1e(.1) oi1er = special.ive(1,.1) assert_almost_equal(oi1e,oi1er,8) def test_iti0k0(self): iti0 = array(special.iti0k0(5)) assert_array_almost_equal( iti0, array([31.848667776169801, 1.5673873907283657]), 5, ) def test_it2i0k0(self): it2k = special.it2i0k0(.1) assert_array_almost_equal( it2k, array([0.0012503906973464409, 3.3309450354686687]), 6, ) def test_iv(self): iv1 = special.iv(0,.1)*exp(-.1) assert_almost_equal(iv1,0.90710092578230106,10) def test_negv_ive(self): assert_equal(special.ive(3,2), special.ive(-3,2)) def test_ive(self): ive1 = special.ive(0,.1) iv1 = special.iv(0,.1)*exp(-.1) assert_almost_equal(ive1,iv1,10) def test_ivp0(self): assert_almost_equal(special.iv(1,2), special.ivp(0,2), 10) def test_ivp(self): y = (special.iv(0,2) + special.iv(2,2))/2 x = special.ivp(1,2) assert_almost_equal(x,y,10) class TestLaguerre: def test_laguerre(self): lag0 = special.laguerre(0) lag1 = special.laguerre(1) lag2 = special.laguerre(2) lag3 = special.laguerre(3) lag4 = special.laguerre(4) lag5 = special.laguerre(5) assert_array_almost_equal(lag0.c,[1],13) assert_array_almost_equal(lag1.c,[-1,1],13) assert_array_almost_equal(lag2.c,array([1,-4,2])/2.0,13) assert_array_almost_equal(lag3.c,array([-1,9,-18,6])/6.0,13) assert_array_almost_equal(lag4.c,array([1,-16,72,-96,24])/24.0,13) assert_array_almost_equal(lag5.c,array([-1,25,-200,600,-600,120])/120.0,13) def test_genlaguerre(self): k = 5*np.random.random() - 0.9 lag0 = special.genlaguerre(0,k) lag1 = special.genlaguerre(1,k) lag2 = special.genlaguerre(2,k) lag3 = special.genlaguerre(3,k) assert_equal(lag0.c, [1]) assert_equal(lag1.c, [-1, k + 1]) assert_almost_equal( lag2.c, array([1,-2*(k+2),(k+1.)*(k+2.)])/2.0 ) assert_almost_equal( lag3.c, array([-1,3*(k+3),-3*(k+2)*(k+3),(k+1)*(k+2)*(k+3)])/6.0 ) # Base polynomials come from Abrahmowitz and Stegan class TestLegendre: def test_legendre(self): leg0 = special.legendre(0) leg1 = special.legendre(1) leg2 = special.legendre(2) leg3 = special.legendre(3) leg4 = special.legendre(4) leg5 = special.legendre(5) assert_equal(leg0.c, [1]) assert_equal(leg1.c, [1,0]) assert_almost_equal(leg2.c, array([3,0,-1])/2.0, decimal=13) assert_almost_equal(leg3.c, array([5,0,-3,0])/2.0) assert_almost_equal(leg4.c, array([35,0,-30,0,3])/8.0) assert_almost_equal(leg5.c, array([63,0,-70,0,15,0])/8.0) @pytest.mark.parametrize('n', [1, 2, 3, 4, 5]) @pytest.mark.parametrize('zr', [0.5241717, 12.80232, -9.699001, 0.5122437, 0.1714377]) @pytest.mark.parametrize('zi', [9.766818, 0.2999083, 8.24726, -22.84843, -0.8792666]) def test_lpn_against_clpmn(self, n, zr, zi): reslpn = special.lpn(n, zr + zi*1j) resclpmn = special.clpmn(0, n, zr+zi*1j) assert_allclose(reslpn[0], resclpmn[0][0]) assert_allclose(reslpn[1], resclpmn[1][0]) class TestLambda: def test_lmbda(self): lam = special.lmbda(1,.1) lamr = ( array([special.jn(0,.1), 2*special.jn(1,.1)/.1]), array([special.jvp(0,.1), -2*special.jv(1,.1)/.01 + 2*special.jvp(1,.1)/.1]) ) assert_array_almost_equal(lam,lamr,8) class TestLog1p: def test_log1p(self): l1p = (special.log1p(10), special.log1p(11), special.log1p(12)) l1prl = (log(11), log(12), log(13)) assert_array_almost_equal(l1p,l1prl,8) def test_log1pmore(self): l1pm = (special.log1p(1), special.log1p(1.1), special.log1p(1.2)) l1pmrl = (log(2),log(2.1),log(2.2)) assert_array_almost_equal(l1pm,l1pmrl,8) class TestLegendreFunctions: def test_clpmn(self): z = 0.5+0.3j clp = special.clpmn(2, 2, z, 3) assert_array_almost_equal(clp, (array([[1.0000, z, 0.5*(3*z*z-1)], [0.0000, sqrt(z*z-1), 3*z*sqrt(z*z-1)], [0.0000, 0.0000, 3*(z*z-1)]]), array([[0.0000, 1.0000, 3*z], [0.0000, z/sqrt(z*z-1), 3*(2*z*z-1)/sqrt(z*z-1)], [0.0000, 0.0000, 6*z]])), 7) def test_clpmn_close_to_real_2(self): eps = 1e-10 m = 1 n = 3 x = 0.5 clp_plus = special.clpmn(m, n, x+1j*eps, 2)[0][m, n] clp_minus = special.clpmn(m, n, x-1j*eps, 2)[0][m, n] assert_array_almost_equal(array([clp_plus, clp_minus]), array([special.lpmv(m, n, x), special.lpmv(m, n, x)]), 7) def test_clpmn_close_to_real_3(self): eps = 1e-10 m = 1 n = 3 x = 0.5 clp_plus = special.clpmn(m, n, x+1j*eps, 3)[0][m, n] clp_minus = special.clpmn(m, n, x-1j*eps, 3)[0][m, n] assert_array_almost_equal(array([clp_plus, clp_minus]), array([special.lpmv(m, n, x)*np.exp(-0.5j*m*np.pi), special.lpmv(m, n, x)*np.exp(0.5j*m*np.pi)]), 7) def test_clpmn_across_unit_circle(self): eps = 1e-7 m = 1 n = 1 x = 1j for type in [2, 3]: assert_almost_equal(special.clpmn(m, n, x+1j*eps, type)[0][m, n], special.clpmn(m, n, x-1j*eps, type)[0][m, n], 6) def test_inf(self): for z in (1, -1): for n in range(4): for m in range(1, n): lp = special.clpmn(m, n, z) assert_(np.isinf(lp[1][1,1:]).all()) lp = special.lpmn(m, n, z) assert_(np.isinf(lp[1][1,1:]).all()) def test_deriv_clpmn(self): # data inside and outside of the unit circle zvals = [0.5+0.5j, -0.5+0.5j, -0.5-0.5j, 0.5-0.5j, 1+1j, -1+1j, -1-1j, 1-1j] m = 2 n = 3 for type in [2, 3]: for z in zvals: for h in [1e-3, 1e-3j]: approx_derivative = (special.clpmn(m, n, z+0.5*h, type)[0] - special.clpmn(m, n, z-0.5*h, type)[0])/h assert_allclose(special.clpmn(m, n, z, type)[1], approx_derivative, rtol=1e-4) def test_lpmn(self): lp = special.lpmn(0,2,.5) assert_array_almost_equal(lp,(array([[1.00000, 0.50000, -0.12500]]), array([[0.00000, 1.00000, 1.50000]])),4) def test_lpn(self): lpnf = special.lpn(2,.5) assert_array_almost_equal(lpnf,(array([1.00000, 0.50000, -0.12500]), array([0.00000, 1.00000, 1.50000])),4) def test_lpmv(self): lp = special.lpmv(0,2,.5) assert_almost_equal(lp,-0.125,7) lp = special.lpmv(0,40,.001) assert_almost_equal(lp,0.1252678976534484,7) # XXX: this is outside the domain of the current implementation, # so ensure it returns a NaN rather than a wrong answer. with np.errstate(all='ignore'): lp = special.lpmv(-1,-1,.001) assert_(lp != 0 or np.isnan(lp)) def test_lqmn(self): lqmnf = special.lqmn(0,2,.5) lqf = special.lqn(2,.5) assert_array_almost_equal(lqmnf[0][0],lqf[0],4) assert_array_almost_equal(lqmnf[1][0],lqf[1],4) def test_lqmn_gt1(self): """algorithm for real arguments changes at 1.0001 test against analytical result for m=2, n=1 """ x0 = 1.0001 delta = 0.00002 for x in (x0-delta, x0+delta): lq = special.lqmn(2, 1, x)[0][-1, -1] expected = 2/(x*x-1) assert_almost_equal(lq, expected) def test_lqmn_shape(self): a, b = special.lqmn(4, 4, 1.1) assert_equal(a.shape, (5, 5)) assert_equal(b.shape, (5, 5)) a, b = special.lqmn(4, 0, 1.1) assert_equal(a.shape, (5, 1)) assert_equal(b.shape, (5, 1)) def test_lqn(self): lqf = special.lqn(2,.5) assert_array_almost_equal(lqf,(array([0.5493, -0.7253, -0.8187]), array([1.3333, 1.216, -0.8427])),4) @pytest.mark.parametrize("function", [special.lpn, special.lqn]) @pytest.mark.parametrize("n", [1, 2, 4, 8, 16, 32]) @pytest.mark.parametrize("z_complex", [False, True]) @pytest.mark.parametrize("z_inexact", [False, True]) @pytest.mark.parametrize( "input_shape", [ (), (1, ), (2, ), (2, 1), (1, 2), (2, 2), (2, 2, 1), (2, 2, 2) ] ) def test_array_inputs_lxn(self, function, n, z_complex, z_inexact, input_shape): """Tests for correct output shapes.""" rng = np.random.default_rng(1234) if z_inexact: z = rng.integers(-3, 3, size=input_shape) else: z = rng.uniform(-1, 1, size=input_shape) if z_complex: z = 1j * z + 0.5j * z P_z, P_d_z = function(n, z) assert P_z.shape == (n + 1, ) + input_shape assert P_d_z.shape == (n + 1, ) + input_shape @pytest.mark.parametrize("function", [special.lqmn]) @pytest.mark.parametrize( "m,n", [(0, 1), (1, 2), (1, 4), (3, 8), (11, 16), (19, 32)] ) @pytest.mark.parametrize("z_inexact", [False, True]) @pytest.mark.parametrize( "input_shape", [ (), (1, ), (2, ), (2, 1), (1, 2), (2, 2), (2, 2, 1) ] ) def test_array_inputs_lxmn(self, function, m, n, z_inexact, input_shape): """Tests for correct output shapes and dtypes.""" rng = np.random.default_rng(1234) if z_inexact: z = rng.integers(-3, 3, size=input_shape) else: z = rng.uniform(-1, 1, size=input_shape) P_z, P_d_z = function(m, n, z) assert P_z.shape == (m + 1, n + 1) + input_shape assert P_d_z.shape == (m + 1, n + 1) + input_shape @pytest.mark.parametrize("function", [special.clpmn, special.lqmn]) @pytest.mark.parametrize( "m,n", [(0, 1), (1, 2), (1, 4), (3, 8), (11, 16), (19, 32)] ) @pytest.mark.parametrize( "input_shape", [ (), (1, ), (2, ), (2, 1), (1, 2), (2, 2), (2, 2, 1) ] ) def test_array_inputs_clxmn(self, function, m, n, input_shape): """Tests for correct output shapes and dtypes.""" rng = np.random.default_rng(1234) z = rng.uniform(-1, 1, size=input_shape) z = 1j * z + 0.5j * z P_z, P_d_z = function(m, n, z) assert P_z.shape == (m + 1, n + 1) + input_shape assert P_d_z.shape == (m + 1, n + 1) + input_shape class TestMathieu: def test_mathieu_a(self): pass def test_mathieu_even_coef(self): special.mathieu_even_coef(2,5) # Q not defined broken and cannot figure out proper reporting order def test_mathieu_odd_coef(self): # same problem as above pass class TestFresnelIntegral: def test_modfresnelp(self): pass def test_modfresnelm(self): pass class TestOblCvSeq: def test_obl_cv_seq(self): obl = special.obl_cv_seq(0,3,1) assert_array_almost_equal(obl,array([-0.348602, 1.393206, 5.486800, 11.492120]),5) class TestParabolicCylinder: def test_pbdn_seq(self): pb = special.pbdn_seq(1,.1) assert_array_almost_equal(pb,(array([0.9975, 0.0998]), array([-0.0499, 0.9925])),4) def test_pbdv(self): special.pbdv(1,.2) 1/2*(.2)*special.pbdv(1,.2)[0] - special.pbdv(0,.2)[0] def test_pbdv_seq(self): pbn = special.pbdn_seq(1,.1) pbv = special.pbdv_seq(1,.1) assert_array_almost_equal(pbv,(real(pbn[0]),real(pbn[1])),4) def test_pbdv_points(self): # simple case eta = np.linspace(-10, 10, 5) z = 2**(eta/2)*np.sqrt(np.pi)/special.gamma(.5-.5*eta) assert_allclose(special.pbdv(eta, 0.)[0], z, rtol=1e-14, atol=1e-14) # some points assert_allclose(special.pbdv(10.34, 20.44)[0], 1.3731383034455e-32, rtol=1e-12) assert_allclose(special.pbdv(-9.53, 3.44)[0], 3.166735001119246e-8, rtol=1e-12) def test_pbdv_gradient(self): x = np.linspace(-4, 4, 8)[:,None] eta = np.linspace(-10, 10, 5)[None,:] p = special.pbdv(eta, x) eps = 1e-7 + 1e-7*abs(x) dp = (special.pbdv(eta, x + eps)[0] - special.pbdv(eta, x - eps)[0]) / eps / 2. assert_allclose(p[1], dp, rtol=1e-6, atol=1e-6) def test_pbvv_gradient(self): x = np.linspace(-4, 4, 8)[:,None] eta = np.linspace(-10, 10, 5)[None,:] p = special.pbvv(eta, x) eps = 1e-7 + 1e-7*abs(x) dp = (special.pbvv(eta, x + eps)[0] - special.pbvv(eta, x - eps)[0]) / eps / 2. assert_allclose(p[1], dp, rtol=1e-6, atol=1e-6) def test_pbvv_seq(self): res1, res2 = special.pbvv_seq(2, 3) assert_allclose(res1, np.array([2.976319645712036, 1.358840996329579, 0.5501016716383508])) assert_allclose(res2, np.array([3.105638472238475, 0.9380581512176672, 0.533688488872053])) class TestPolygamma: # from Table 6.2 (pg. 271) of A&S def test_polygamma(self): poly2 = special.polygamma(2,1) poly3 = special.polygamma(3,1) assert_almost_equal(poly2,-2.4041138063,10) assert_almost_equal(poly3,6.4939394023,10) # Test polygamma(0, x) == psi(x) x = [2, 3, 1.1e14] assert_almost_equal(special.polygamma(0, x), special.psi(x)) # Test broadcasting n = [0, 1, 2] x = [0.5, 1.5, 2.5] expected = [-1.9635100260214238, 0.93480220054467933, -0.23620405164172739] assert_almost_equal(special.polygamma(n, x), expected) expected = np.vstack([expected]*2) assert_almost_equal(special.polygamma(n, np.vstack([x]*2)), expected) assert_almost_equal(special.polygamma(np.vstack([n]*2), x), expected) class TestProCvSeq: def test_pro_cv_seq(self): prol = special.pro_cv_seq(0,3,1) assert_array_almost_equal(prol,array([0.319000, 2.593084, 6.533471, 12.514462]),5) class TestPsi: def test_psi(self): ps = special.psi(1) assert_almost_equal(ps,-0.57721566490153287,8) class TestRadian: def test_radian(self): rad = special.radian(90,0,0) assert_almost_equal(rad,pi/2.0,5) def test_radianmore(self): rad1 = special.radian(90,1,60) assert_almost_equal(rad1,pi/2+0.0005816135199345904,5) class TestRiccati: def test_riccati_jn(self): N, x = 2, 0.2 S = np.empty((N, N)) for n in range(N): j = special.spherical_jn(n, x) jp = special.spherical_jn(n, x, derivative=True) S[0,n] = x*j S[1,n] = x*jp + j assert_array_almost_equal(S, special.riccati_jn(n, x), 8) def test_riccati_yn(self): N, x = 2, 0.2 C = np.empty((N, N)) for n in range(N): y = special.spherical_yn(n, x) yp = special.spherical_yn(n, x, derivative=True) C[0,n] = x*y C[1,n] = x*yp + y assert_array_almost_equal(C, special.riccati_yn(n, x), 8) class TestRound: def test_round(self): rnd = list(map(int, (special.round(10.1), special.round(10.4), special.round(10.5), special.round(10.6)))) # Note: According to the documentation, scipy.special.round is # supposed to round to the nearest even number if the fractional # part is exactly 0.5. On some platforms, this does not appear # to work and thus this test may fail. However, this unit test is # correctly written. rndrl = (10,10,10,11) assert_array_equal(rnd,rndrl) def test_sph_harm(): # Tests derived from tables in # https://en.wikipedia.org/wiki/Table_of_spherical_harmonics sh = special.sph_harm pi = np.pi exp = np.exp sqrt = np.sqrt sin = np.sin cos = np.cos assert_array_almost_equal(sh(0,0,0,0), 0.5/sqrt(pi)) assert_array_almost_equal(sh(-2,2,0.,pi/4), 0.25*sqrt(15./(2.*pi)) * (sin(pi/4))**2.) assert_array_almost_equal(sh(-2,2,0.,pi/2), 0.25*sqrt(15./(2.*pi))) assert_array_almost_equal(sh(2,2,pi,pi/2), 0.25*sqrt(15/(2.*pi)) * exp(0+2.*pi*1j)*sin(pi/2.)**2.) assert_array_almost_equal(sh(2,4,pi/4.,pi/3.), (3./8.)*sqrt(5./(2.*pi)) * exp(0+2.*pi/4.*1j) * sin(pi/3.)**2. * (7.*cos(pi/3.)**2.-1)) assert_array_almost_equal(sh(4,4,pi/8.,pi/6.), (3./16.)*sqrt(35./(2.*pi)) * exp(0+4.*pi/8.*1j)*sin(pi/6.)**4.) def test_sph_harm_ufunc_loop_selection(): # see https://github.com/scipy/scipy/issues/4895 dt = np.dtype(np.complex128) assert_equal(special.sph_harm(0, 0, 0, 0).dtype, dt) assert_equal(special.sph_harm([0], 0, 0, 0).dtype, dt) assert_equal(special.sph_harm(0, [0], 0, 0).dtype, dt) assert_equal(special.sph_harm(0, 0, [0], 0).dtype, dt) assert_equal(special.sph_harm(0, 0, 0, [0]).dtype, dt) assert_equal(special.sph_harm([0], [0], [0], [0]).dtype, dt) class TestStruve: def _series(self, v, z, n=100): """Compute Struve function & error estimate from its power series.""" k = arange(0, n) r = (-1)**k * (.5*z)**(2*k+v+1)/special.gamma(k+1.5)/special.gamma(k+v+1.5) err = abs(r).max() * finfo(double).eps * n return r.sum(), err def test_vs_series(self): """Check Struve function versus its power series""" for v in [-20, -10, -7.99, -3.4, -1, 0, 1, 3.4, 12.49, 16]: for z in [1, 10, 19, 21, 30]: value, err = self._series(v, z) assert_allclose(special.struve(v, z), value, rtol=0, atol=err), (v, z) def test_some_values(self): assert_allclose(special.struve(-7.99, 21), 0.0467547614113, rtol=1e-7) assert_allclose(special.struve(-8.01, 21), 0.0398716951023, rtol=1e-8) assert_allclose(special.struve(-3.0, 200), 0.0142134427432, rtol=1e-12) assert_allclose(special.struve(-8.0, -41), 0.0192469727846, rtol=1e-11) assert_equal(special.struve(-12, -41), -special.struve(-12, 41)) assert_equal(special.struve(+12, -41), -special.struve(+12, 41)) assert_equal(special.struve(-11, -41), +special.struve(-11, 41)) assert_equal(special.struve(+11, -41), +special.struve(+11, 41)) assert_(isnan(special.struve(-7.1, -1))) assert_(isnan(special.struve(-10.1, -1))) def test_regression_679(self): """Regression test for #679""" assert_allclose(special.struve(-1.0, 20 - 1e-8), special.struve(-1.0, 20 + 1e-8)) assert_allclose(special.struve(-2.0, 20 - 1e-8), special.struve(-2.0, 20 + 1e-8)) assert_allclose(special.struve(-4.3, 20 - 1e-8), special.struve(-4.3, 20 + 1e-8)) def test_chi2_smalldf(): assert_almost_equal(special.chdtr(0.6,3), 0.957890536704110) def test_ch2_inf(): assert_equal(special.chdtr(0.7,np.inf), 1.0) def test_chi2c_smalldf(): assert_almost_equal(special.chdtrc(0.6,3), 1-0.957890536704110) def test_chi2_inv_smalldf(): assert_almost_equal(special.chdtri(0.6,1-0.957890536704110), 3) def test_agm_simple(): rtol = 1e-13 # Gauss's constant assert_allclose(1/special.agm(1, np.sqrt(2)), 0.834626841674073186, rtol=rtol) # These values were computed using Wolfram Alpha, with the # function ArithmeticGeometricMean[a, b]. agm13 = 1.863616783244897 agm15 = 2.604008190530940 agm35 = 3.936235503649555 assert_allclose(special.agm([[1], [3]], [1, 3, 5]), [[1, agm13, agm15], [agm13, 3, agm35]], rtol=rtol) # Computed by the iteration formula using mpmath, # with mpmath.mp.prec = 1000: agm12 = 1.4567910310469068 assert_allclose(special.agm(1, 2), agm12, rtol=rtol) assert_allclose(special.agm(2, 1), agm12, rtol=rtol) assert_allclose(special.agm(-1, -2), -agm12, rtol=rtol) assert_allclose(special.agm(24, 6), 13.458171481725614, rtol=rtol) assert_allclose(special.agm(13, 123456789.5), 11111458.498599306, rtol=rtol) assert_allclose(special.agm(1e30, 1), 2.229223055945383e+28, rtol=rtol) assert_allclose(special.agm(1e-22, 1), 0.030182566420169886, rtol=rtol) assert_allclose(special.agm(1e150, 1e180), 2.229223055945383e+178, rtol=rtol) assert_allclose(special.agm(1e180, 1e-150), 2.0634722510162677e+177, rtol=rtol) assert_allclose(special.agm(1e-150, 1e-170), 3.3112619670463756e-152, rtol=rtol) fi = np.finfo(1.0) assert_allclose(special.agm(fi.tiny, fi.max), 1.9892072050015473e+305, rtol=rtol) assert_allclose(special.agm(0.75*fi.max, fi.max), 1.564904312298045e+308, rtol=rtol) assert_allclose(special.agm(fi.tiny, 3*fi.tiny), 4.1466849866735005e-308, rtol=rtol) # zero, nan and inf cases. assert_equal(special.agm(0, 0), 0) assert_equal(special.agm(99, 0), 0) assert_equal(special.agm(-1, 10), np.nan) assert_equal(special.agm(0, np.inf), np.nan) assert_equal(special.agm(np.inf, 0), np.nan) assert_equal(special.agm(0, -np.inf), np.nan) assert_equal(special.agm(-np.inf, 0), np.nan) assert_equal(special.agm(np.inf, -np.inf), np.nan) assert_equal(special.agm(-np.inf, np.inf), np.nan) assert_equal(special.agm(1, np.nan), np.nan) assert_equal(special.agm(np.nan, -1), np.nan) assert_equal(special.agm(1, np.inf), np.inf) assert_equal(special.agm(np.inf, 1), np.inf) assert_equal(special.agm(-1, -np.inf), -np.inf) assert_equal(special.agm(-np.inf, -1), -np.inf) def test_legacy(): # Legacy behavior: truncating arguments to integers with suppress_warnings() as sup: sup.filter(RuntimeWarning, "floating point number truncated to an integer") assert_equal(special.expn(1, 0.3), special.expn(1.8, 0.3)) assert_equal(special.nbdtrc(1, 2, 0.3), special.nbdtrc(1.8, 2.8, 0.3)) assert_equal(special.nbdtr(1, 2, 0.3), special.nbdtr(1.8, 2.8, 0.3)) assert_equal(special.nbdtri(1, 2, 0.3), special.nbdtri(1.8, 2.8, 0.3)) assert_equal(special.pdtri(1, 0.3), special.pdtri(1.8, 0.3)) assert_equal(special.kn(1, 0.3), special.kn(1.8, 0.3)) assert_equal(special.yn(1, 0.3), special.yn(1.8, 0.3)) assert_equal(special.smirnov(1, 0.3), special.smirnov(1.8, 0.3)) assert_equal(special.smirnovi(1, 0.3), special.smirnovi(1.8, 0.3)) @with_special_errors def test_error_raising(): assert_raises(special.SpecialFunctionError, special.iv, 1, 1e99j) def test_xlogy(): def xfunc(x, y): with np.errstate(invalid='ignore'): if x == 0 and not np.isnan(y): return x else: return x*np.log(y) z1 = np.asarray([(0,0), (0, np.nan), (0, np.inf), (1.0, 2.0)], dtype=float) z2 = np.r_[z1, [(0, 1j), (1, 1j)]] w1 = np.vectorize(xfunc)(z1[:,0], z1[:,1]) assert_func_equal(special.xlogy, w1, z1, rtol=1e-13, atol=1e-13) w2 = np.vectorize(xfunc)(z2[:,0], z2[:,1]) assert_func_equal(special.xlogy, w2, z2, rtol=1e-13, atol=1e-13) def test_xlog1py(): def xfunc(x, y): with np.errstate(invalid='ignore'): if x == 0 and not np.isnan(y): return x else: return x * np.log1p(y) z1 = np.asarray([(0,0), (0, np.nan), (0, np.inf), (1.0, 2.0), (1, 1e-30)], dtype=float) w1 = np.vectorize(xfunc)(z1[:,0], z1[:,1]) assert_func_equal(special.xlog1py, w1, z1, rtol=1e-13, atol=1e-13) def test_entr(): def xfunc(x): if x < 0: return -np.inf else: return -special.xlogy(x, x) values = (0, 0.5, 1.0, np.inf) signs = [-1, 1] arr = [] for sgn, v in itertools.product(signs, values): arr.append(sgn * v) z = np.array(arr, dtype=float) w = np.vectorize(xfunc, otypes=[np.float64])(z) assert_func_equal(special.entr, w, z, rtol=1e-13, atol=1e-13) def test_kl_div(): def xfunc(x, y): if x < 0 or y < 0 or (y == 0 and x != 0): # extension of natural domain to preserve convexity return np.inf elif np.isposinf(x) or np.isposinf(y): # limits within the natural domain return np.inf elif x == 0: return y else: return special.xlogy(x, x/y) - x + y values = (0, 0.5, 1.0) signs = [-1, 1] arr = [] for sgna, va, sgnb, vb in itertools.product(signs, values, signs, values): arr.append((sgna*va, sgnb*vb)) z = np.array(arr, dtype=float) w = np.vectorize(xfunc, otypes=[np.float64])(z[:,0], z[:,1]) assert_func_equal(special.kl_div, w, z, rtol=1e-13, atol=1e-13) def test_rel_entr(): def xfunc(x, y): if x > 0 and y > 0: return special.xlogy(x, x/y) elif x == 0 and y >= 0: return 0 else: return np.inf values = (0, 0.5, 1.0) signs = [-1, 1] arr = [] for sgna, va, sgnb, vb in itertools.product(signs, values, signs, values): arr.append((sgna*va, sgnb*vb)) z = np.array(arr, dtype=float) w = np.vectorize(xfunc, otypes=[np.float64])(z[:,0], z[:,1]) assert_func_equal(special.rel_entr, w, z, rtol=1e-13, atol=1e-13) def test_huber(): assert_equal(special.huber(-1, 1.5), np.inf) assert_allclose(special.huber(2, 1.5), 0.5 * np.square(1.5)) assert_allclose(special.huber(2, 2.5), 2 * (2.5 - 0.5 * 2)) def xfunc(delta, r): if delta < 0: return np.inf elif np.abs(r) < delta: return 0.5 * np.square(r) else: return delta * (np.abs(r) - 0.5 * delta) z = np.random.randn(10, 2) w = np.vectorize(xfunc, otypes=[np.float64])(z[:,0], z[:,1]) assert_func_equal(special.huber, w, z, rtol=1e-13, atol=1e-13) def test_pseudo_huber(): def xfunc(delta, r): if delta < 0: return np.inf elif (not delta) or (not r): return 0 else: return delta**2 * (np.sqrt(1 + (r/delta)**2) - 1) z = np.array(np.random.randn(10, 2).tolist() + [[0, 0.5], [0.5, 0]]) w = np.vectorize(xfunc, otypes=[np.float64])(z[:,0], z[:,1]) assert_func_equal(special.pseudo_huber, w, z, rtol=1e-13, atol=1e-13) def test_pseudo_huber_small_r(): delta = 1.0 r = 1e-18 y = special.pseudo_huber(delta, r) # expected computed with mpmath: # import mpmath # mpmath.mp.dps = 200 # r = mpmath.mpf(1e-18) # expected = float(mpmath.sqrt(1 + r**2) - 1) expected = 5.0000000000000005e-37 assert_allclose(y, expected, rtol=1e-13) def test_runtime_warning(): with pytest.warns(RuntimeWarning, match=r'Too many predicted coefficients'): mathieu_odd_coef(1000, 1000) with pytest.warns(RuntimeWarning, match=r'Too many predicted coefficients'): mathieu_even_coef(1000, 1000) class TestStirling2: table = [ [1], [0, 1], [0, 1, 1], [0, 1, 3, 1], [0, 1, 7, 6, 1], [0, 1, 15, 25, 10, 1], [0, 1, 31, 90, 65, 15, 1], [0, 1, 63, 301, 350, 140, 21, 1], [0, 1, 127, 966, 1701, 1050, 266, 28, 1], [0, 1, 255, 3025, 7770, 6951, 2646, 462, 36, 1], [0, 1, 511, 9330, 34105, 42525, 22827, 5880, 750, 45, 1], ] @pytest.mark.parametrize("is_exact, comp, kwargs", [ (True, assert_equal, {}), (False, assert_allclose, {'rtol': 1e-12}) ]) def test_table_cases(self, is_exact, comp, kwargs): for n in range(1, len(self.table)): k_values = list(range(n+1)) row = self.table[n] comp(row, stirling2([n], k_values, exact=is_exact), **kwargs) @pytest.mark.parametrize("is_exact, comp, kwargs", [ (True, assert_equal, {}), (False, assert_allclose, {'rtol': 1e-12}) ]) def test_valid_single_integer(self, is_exact, comp, kwargs): comp(stirling2(0, 0, exact=is_exact), self.table[0][0], **kwargs) comp(stirling2(4, 2, exact=is_exact), self.table[4][2], **kwargs) # a single 2-tuple of integers as arguments must return an int and not # an array whereas arrays of single values should return array comp(stirling2(5, 3, exact=is_exact), 25, **kwargs) comp(stirling2([5], [3], exact=is_exact), [25], **kwargs) @pytest.mark.parametrize("is_exact, comp, kwargs", [ (True, assert_equal, {}), (False, assert_allclose, {'rtol': 1e-12}) ]) def test_negative_integer(self, is_exact, comp, kwargs): # negative integers for n or k arguments return 0 comp(stirling2(-1, -1, exact=is_exact), 0, **kwargs) comp(stirling2(-1, 2, exact=is_exact), 0, **kwargs) comp(stirling2(2, -1, exact=is_exact), 0, **kwargs) @pytest.mark.parametrize("is_exact, comp, kwargs", [ (True, assert_equal, {}), (False, assert_allclose, {'rtol': 1e-12}) ]) def test_array_inputs(self, is_exact, comp, kwargs): ans = [self.table[10][3], self.table[10][4]] comp(stirling2(asarray([10, 10]), asarray([3, 4]), exact=is_exact), ans) comp(stirling2([10, 10], asarray([3, 4]), exact=is_exact), ans) comp(stirling2(asarray([10, 10]), [3, 4], exact=is_exact), ans) @pytest.mark.parametrize("is_exact, comp, kwargs", [ (True, assert_equal, {}), (False, assert_allclose, {'rtol': 1e-13}) ]) def test_mixed_values(self, is_exact, comp, kwargs): # negative values-of either n or k-should return 0 for the entry ans = [0, 1, 3, 25, 1050, 5880, 9330] n = [-1, 0, 3, 5, 8, 10, 10] k = [-2, 0, 2, 3, 5, 7, 3] comp(stirling2(n, k, exact=is_exact), ans, **kwargs) def test_correct_parity(self): """Test parity follows well known identity. en.wikipedia.org/wiki/Stirling_numbers_of_the_second_kind#Parity """ n, K = 100, np.arange(101) assert_equal( stirling2(n, K, exact=True) % 2, [math.comb(n - (k // 2) - 1, n - k) % 2 for k in K], ) def test_big_numbers(self): # via mpmath (bigger than 32bit) ans = asarray([48063331393110, 48004081105038305]) n = [25, 30] k = [17, 4] assert array_equal(stirling2(n, k, exact=True), ans) # bigger than 64 bit ans = asarray([2801934359500572414253157841233849412, 14245032222277144547280648984426251]) n = [42, 43] k = [17, 23] assert array_equal(stirling2(n, k, exact=True), ans) @pytest.mark.parametrize("N", [4.5, 3., 4+1j, "12", np.nan]) @pytest.mark.parametrize("K", [3.5, 3, "2", None]) @pytest.mark.parametrize("is_exact", [True, False]) def test_unsupported_input_types(self, N, K, is_exact): # object, float, string, complex are not supported and raise TypeError with pytest.raises(TypeError): stirling2(N, K, exact=is_exact) @pytest.mark.parametrize("is_exact", [True, False]) def test_numpy_array_int_object_dtype(self, is_exact): # python integers with arbitrary precision are *not* allowed as # object type in numpy arrays are inconsistent from api perspective ans = asarray(self.table[4][1:]) n = asarray([4, 4, 4, 4], dtype=object) k = asarray([1, 2, 3, 4], dtype=object) with pytest.raises(TypeError): array_equal(stirling2(n, k, exact=is_exact), ans) @pytest.mark.parametrize("is_exact, comp, kwargs", [ (True, assert_equal, {}), (False, assert_allclose, {'rtol': 1e-13}) ]) def test_numpy_array_unsigned_int_dtype(self, is_exact, comp, kwargs): # numpy unsigned integers are allowed as dtype in numpy arrays ans = asarray(self.table[4][1:]) n = asarray([4, 4, 4, 4], dtype=np_ulong) k = asarray([1, 2, 3, 4], dtype=np_ulong) comp(stirling2(n, k, exact=False), ans, **kwargs) @pytest.mark.parametrize("is_exact, comp, kwargs", [ (True, assert_equal, {}), (False, assert_allclose, {'rtol': 1e-13}) ]) def test_broadcasting_arrays_correctly(self, is_exact, comp, kwargs): # broadcasting is handled by stirling2 # test leading 1s are replicated ans = asarray([[1, 15, 25, 10], [1, 7, 6, 1]]) # shape (2,4) n = asarray([[5, 5, 5, 5], [4, 4, 4, 4]]) # shape (2,4) k = asarray([1, 2, 3, 4]) # shape (4,) comp(stirling2(n, k, exact=is_exact), ans, **kwargs) # test that dims both mismatch broadcast correctly (5,1) & (6,) n = asarray([[4], [4], [4], [4], [4]]) k = asarray([0, 1, 2, 3, 4, 5]) ans = asarray([[0, 1, 7, 6, 1, 0] for _ in range(5)]) comp(stirling2(n, k, exact=False), ans, **kwargs) def test_temme_rel_max_error(self): # python integers with arbitrary precision are *not* allowed as # object type in numpy arrays are inconsistent from api perspective x = list(range(51, 101, 5)) for n in x: k_entries = list(range(1, n+1)) denom = stirling2([n], k_entries, exact=True) num = denom - stirling2([n], k_entries, exact=False) assert np.max(np.abs(num / denom)) < 2e-5