import importlib.resources import numpy as np from numpy.testing import suppress_warnings import pytest from scipy.special import ( lpn, lpmn, lpmv, lqn, lqmn, sph_harm, eval_legendre, eval_hermite, eval_laguerre, eval_genlaguerre, binom, cbrt, expm1, log1p, zeta, jn, jv, jvp, yn, yv, yvp, iv, ivp, kn, kv, kvp, gamma, gammaln, gammainc, gammaincc, gammaincinv, gammainccinv, digamma, beta, betainc, betaincinv, poch, ellipe, ellipeinc, ellipk, ellipkm1, ellipkinc, elliprc, elliprd, elliprf, elliprg, elliprj, erf, erfc, erfinv, erfcinv, exp1, expi, expn, bdtrik, btdtr, btdtri, btdtria, btdtrib, chndtr, gdtr, gdtrc, gdtrix, gdtrib, nbdtrik, pdtrik, owens_t, mathieu_a, mathieu_b, mathieu_cem, mathieu_sem, mathieu_modcem1, mathieu_modsem1, mathieu_modcem2, mathieu_modsem2, ellip_harm, ellip_harm_2, spherical_jn, spherical_yn, wright_bessel ) from scipy.integrate import IntegrationWarning from scipy.special._testutils import FuncData # The npz files are generated, and hence may live in the build dir. We can only # access them through `importlib.resources`, not an explicit path from `__file__` _datadir = importlib.resources.files('scipy.special.tests.data') _boost_npz = _datadir.joinpath('boost.npz') with importlib.resources.as_file(_boost_npz) as f: DATASETS_BOOST = np.load(f) _gsl_npz = _datadir.joinpath('gsl.npz') with importlib.resources.as_file(_gsl_npz) as f: DATASETS_GSL = np.load(f) _local_npz = _datadir.joinpath('local.npz') with importlib.resources.as_file(_local_npz) as f: DATASETS_LOCAL = np.load(f) def data(func, dataname, *a, **kw): kw.setdefault('dataname', dataname) return FuncData(func, DATASETS_BOOST[dataname], *a, **kw) def data_gsl(func, dataname, *a, **kw): kw.setdefault('dataname', dataname) return FuncData(func, DATASETS_GSL[dataname], *a, **kw) def data_local(func, dataname, *a, **kw): kw.setdefault('dataname', dataname) return FuncData(func, DATASETS_LOCAL[dataname], *a, **kw) def ellipk_(k): return ellipk(k*k) def ellipkinc_(f, k): return ellipkinc(f, k*k) def ellipe_(k): return ellipe(k*k) def ellipeinc_(f, k): return ellipeinc(f, k*k) def zeta_(x): return zeta(x, 1.) def assoc_legendre_p_boost_(nu, mu, x): # the boost test data is for integer orders only return lpmv(mu, nu.astype(int), x) def legendre_p_via_assoc_(nu, x): return lpmv(0, nu, x) def lpn_(n, x): return lpn(n.astype('l'), x)[0][-1] def lqn_(n, x): return lqn(n.astype('l'), x)[0][-1] def legendre_p_via_lpmn(n, x): return lpmn(0, n, x)[0][0,-1] def legendre_q_via_lqmn(n, x): return lqmn(0, n, x)[0][0,-1] def mathieu_ce_rad(m, q, x): return mathieu_cem(m, q, x*180/np.pi)[0] def mathieu_se_rad(m, q, x): return mathieu_sem(m, q, x*180/np.pi)[0] def mathieu_mc1_scaled(m, q, x): # GSL follows a different normalization. # We follow Abramowitz & Stegun, they apparently something else. return mathieu_modcem1(m, q, x)[0] * np.sqrt(np.pi/2) def mathieu_ms1_scaled(m, q, x): return mathieu_modsem1(m, q, x)[0] * np.sqrt(np.pi/2) def mathieu_mc2_scaled(m, q, x): return mathieu_modcem2(m, q, x)[0] * np.sqrt(np.pi/2) def mathieu_ms2_scaled(m, q, x): return mathieu_modsem2(m, q, x)[0] * np.sqrt(np.pi/2) def eval_legendre_ld(n, x): return eval_legendre(n.astype('l'), x) def eval_legendre_dd(n, x): return eval_legendre(n.astype('d'), x) def eval_hermite_ld(n, x): return eval_hermite(n.astype('l'), x) def eval_laguerre_ld(n, x): return eval_laguerre(n.astype('l'), x) def eval_laguerre_dd(n, x): return eval_laguerre(n.astype('d'), x) def eval_genlaguerre_ldd(n, a, x): return eval_genlaguerre(n.astype('l'), a, x) def eval_genlaguerre_ddd(n, a, x): return eval_genlaguerre(n.astype('d'), a, x) def bdtrik_comp(y, n, p): return bdtrik(1-y, n, p) def btdtri_comp(a, b, p): return btdtri(a, b, 1-p) def btdtria_comp(p, b, x): return btdtria(1-p, b, x) def btdtrib_comp(a, p, x): return btdtrib(a, 1-p, x) def gdtr_(p, x): return gdtr(1.0, p, x) def gdtrc_(p, x): return gdtrc(1.0, p, x) def gdtrix_(b, p): return gdtrix(1.0, b, p) def gdtrix_comp(b, p): return gdtrix(1.0, b, 1-p) def gdtrib_(p, x): return gdtrib(1.0, p, x) def gdtrib_comp(p, x): return gdtrib(1.0, 1-p, x) def nbdtrik_comp(y, n, p): return nbdtrik(1-y, n, p) def pdtrik_comp(p, m): return pdtrik(1-p, m) def poch_(z, m): return 1.0 / poch(z, m) def poch_minus(z, m): return 1.0 / poch(z, -m) def spherical_jn_(n, x): return spherical_jn(n.astype('l'), x) def spherical_yn_(n, x): return spherical_yn(n.astype('l'), x) def sph_harm_(m, n, theta, phi): y = sph_harm(m, n, theta, phi) return (y.real, y.imag) def cexpm1(x, y): z = expm1(x + 1j*y) return z.real, z.imag def clog1p(x, y): z = log1p(x + 1j*y) return z.real, z.imag BOOST_TESTS = [ data(assoc_legendre_p_boost_, 'assoc_legendre_p_ipp-assoc_legendre_p', (0,1,2), 3, rtol=1e-11), data(legendre_p_via_assoc_, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=1e-11), data(legendre_p_via_assoc_, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=9.6e-14), data(legendre_p_via_lpmn, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=5e-14, vectorized=False), data(legendre_p_via_lpmn, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=9.6e-14, vectorized=False), data(lpn_, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=5e-14, vectorized=False), data(lpn_, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=3e-13, vectorized=False), data(eval_legendre_ld, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=6e-14), data(eval_legendre_ld, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=2e-13), data(eval_legendre_dd, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=2e-14), data(eval_legendre_dd, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=2e-13), data(lqn_, 'legendre_p_ipp-legendre_p', (0,1), 3, rtol=2e-14, vectorized=False), data(lqn_, 'legendre_p_large_ipp-legendre_p_large', (0,1), 3, rtol=2e-12, vectorized=False), data(legendre_q_via_lqmn, 'legendre_p_ipp-legendre_p', (0,1), 3, rtol=2e-14, vectorized=False), data(legendre_q_via_lqmn, 'legendre_p_large_ipp-legendre_p_large', (0,1), 3, rtol=2e-12, vectorized=False), data(beta, 'beta_exp_data_ipp-beta_exp_data', (0,1), 2, rtol=1e-13), data(beta, 'beta_exp_data_ipp-beta_exp_data', (0,1), 2, rtol=1e-13), data(beta, 'beta_med_data_ipp-beta_med_data', (0,1), 2, rtol=5e-13), data(betainc, 'ibeta_small_data_ipp-ibeta_small_data', (0,1,2), 5, rtol=6e-15), data(betainc, 'ibeta_data_ipp-ibeta_data', (0,1,2), 5, rtol=5e-13), data(betainc, 'ibeta_int_data_ipp-ibeta_int_data', (0,1,2), 5, rtol=2e-14), data(betainc, 'ibeta_large_data_ipp-ibeta_large_data', (0,1,2), 5, rtol=4e-10), data(betaincinv, 'ibeta_inv_data_ipp-ibeta_inv_data', (0,1,2), 3, rtol=1e-5), data(btdtr, 'ibeta_small_data_ipp-ibeta_small_data', (0,1,2), 5, rtol=6e-15), data(btdtr, 'ibeta_data_ipp-ibeta_data', (0,1,2), 5, rtol=4e-13), data(btdtr, 'ibeta_int_data_ipp-ibeta_int_data', (0,1,2), 5, rtol=2e-14), data(btdtr, 'ibeta_large_data_ipp-ibeta_large_data', (0,1,2), 5, rtol=4e-10), data(btdtri, 'ibeta_inv_data_ipp-ibeta_inv_data', (0,1,2), 3, rtol=1e-5), data(btdtri_comp, 'ibeta_inv_data_ipp-ibeta_inv_data', (0,1,2), 4, rtol=8e-7), data(btdtria, 'ibeta_inva_data_ipp-ibeta_inva_data', (2,0,1), 3, rtol=5e-9), data(btdtria_comp, 'ibeta_inva_data_ipp-ibeta_inva_data', (2,0,1), 4, rtol=5e-9), data(btdtrib, 'ibeta_inva_data_ipp-ibeta_inva_data', (0,2,1), 5, rtol=5e-9), data(btdtrib_comp, 'ibeta_inva_data_ipp-ibeta_inva_data', (0,2,1), 6, rtol=5e-9), data(binom, 'binomial_data_ipp-binomial_data', (0,1), 2, rtol=1e-13), data(binom, 'binomial_large_data_ipp-binomial_large_data', (0,1), 2, rtol=5e-13), data(bdtrik, 'binomial_quantile_ipp-binomial_quantile_data', (2,0,1), 3, rtol=5e-9), data(bdtrik_comp, 'binomial_quantile_ipp-binomial_quantile_data', (2,0,1), 4, rtol=5e-9), data(nbdtrik, 'negative_binomial_quantile_ipp-negative_binomial_quantile_data', (2,0,1), 3, rtol=4e-9), data(nbdtrik_comp, 'negative_binomial_quantile_ipp-negative_binomial_quantile_data', (2,0,1), 4, rtol=4e-9), data(pdtrik, 'poisson_quantile_ipp-poisson_quantile_data', (1,0), 2, rtol=3e-9), data(pdtrik_comp, 'poisson_quantile_ipp-poisson_quantile_data', (1,0), 3, rtol=4e-9), data(cbrt, 'cbrt_data_ipp-cbrt_data', 1, 0), data(digamma, 'digamma_data_ipp-digamma_data', 0, 1), data(digamma, 'digamma_data_ipp-digamma_data', 0j, 1), data(digamma, 'digamma_neg_data_ipp-digamma_neg_data', 0, 1, rtol=2e-13), data(digamma, 'digamma_neg_data_ipp-digamma_neg_data', 0j, 1, rtol=1e-13), data(digamma, 'digamma_root_data_ipp-digamma_root_data', 0, 1, rtol=1e-15), data(digamma, 'digamma_root_data_ipp-digamma_root_data', 0j, 1, rtol=1e-15), data(digamma, 'digamma_small_data_ipp-digamma_small_data', 0, 1, rtol=1e-15), data(digamma, 'digamma_small_data_ipp-digamma_small_data', 0j, 1, rtol=1e-14), data(ellipk_, 'ellint_k_data_ipp-ellint_k_data', 0, 1), data(ellipkinc_, 'ellint_f_data_ipp-ellint_f_data', (0,1), 2, rtol=1e-14), data(ellipe_, 'ellint_e_data_ipp-ellint_e_data', 0, 1), data(ellipeinc_, 'ellint_e2_data_ipp-ellint_e2_data', (0,1), 2, rtol=1e-14), data(erf, 'erf_data_ipp-erf_data', 0, 1), data(erf, 'erf_data_ipp-erf_data', 0j, 1, rtol=1e-13), data(erfc, 'erf_data_ipp-erf_data', 0, 2, rtol=6e-15), data(erf, 'erf_large_data_ipp-erf_large_data', 0, 1), data(erf, 'erf_large_data_ipp-erf_large_data', 0j, 1), data(erfc, 'erf_large_data_ipp-erf_large_data', 0, 2, rtol=4e-14), data(erf, 'erf_small_data_ipp-erf_small_data', 0, 1), data(erf, 'erf_small_data_ipp-erf_small_data', 0j, 1, rtol=1e-13), data(erfc, 'erf_small_data_ipp-erf_small_data', 0, 2), data(erfinv, 'erf_inv_data_ipp-erf_inv_data', 0, 1), data(erfcinv, 'erfc_inv_data_ipp-erfc_inv_data', 0, 1), data(erfcinv, 'erfc_inv_big_data_ipp-erfc_inv_big_data', 0, 1, param_filter=(lambda s: s > 0)), data(exp1, 'expint_1_data_ipp-expint_1_data', 1, 2, rtol=1e-13), data(exp1, 'expint_1_data_ipp-expint_1_data', 1j, 2, rtol=5e-9), data(expi, 'expinti_data_ipp-expinti_data', 0, 1, rtol=1e-13), data(expi, 'expinti_data_double_ipp-expinti_data_double', 0, 1, rtol=1e-13), data(expi, 'expinti_data_long_ipp-expinti_data_long', 0, 1), data(expn, 'expint_small_data_ipp-expint_small_data', (0,1), 2), data(expn, 'expint_data_ipp-expint_data', (0,1), 2, rtol=1e-14), data(gamma, 'test_gamma_data_ipp-near_0', 0, 1), data(gamma, 'test_gamma_data_ipp-near_1', 0, 1), data(gamma, 'test_gamma_data_ipp-near_2', 0, 1), data(gamma, 'test_gamma_data_ipp-near_m10', 0, 1), data(gamma, 'test_gamma_data_ipp-near_m55', 0, 1, rtol=7e-12), data(gamma, 'test_gamma_data_ipp-factorials', 0, 1, rtol=4e-14), data(gamma, 'test_gamma_data_ipp-near_0', 0j, 1, rtol=2e-9), data(gamma, 'test_gamma_data_ipp-near_1', 0j, 1, rtol=2e-9), data(gamma, 'test_gamma_data_ipp-near_2', 0j, 1, rtol=2e-9), data(gamma, 'test_gamma_data_ipp-near_m10', 0j, 1, rtol=2e-9), data(gamma, 'test_gamma_data_ipp-near_m55', 0j, 1, rtol=2e-9), data(gamma, 'test_gamma_data_ipp-factorials', 0j, 1, rtol=2e-13), data(gammaln, 'test_gamma_data_ipp-near_0', 0, 2, rtol=5e-11), data(gammaln, 'test_gamma_data_ipp-near_1', 0, 2, rtol=5e-11), data(gammaln, 'test_gamma_data_ipp-near_2', 0, 2, rtol=2e-10), data(gammaln, 'test_gamma_data_ipp-near_m10', 0, 2, rtol=5e-11), data(gammaln, 'test_gamma_data_ipp-near_m55', 0, 2, rtol=5e-11), data(gammaln, 'test_gamma_data_ipp-factorials', 0, 2), data(gammainc, 'igamma_small_data_ipp-igamma_small_data', (0,1), 5, rtol=5e-15), data(gammainc, 'igamma_med_data_ipp-igamma_med_data', (0,1), 5, rtol=2e-13), data(gammainc, 'igamma_int_data_ipp-igamma_int_data', (0,1), 5, rtol=2e-13), data(gammainc, 'igamma_big_data_ipp-igamma_big_data', (0,1), 5, rtol=1e-12), data(gdtr_, 'igamma_small_data_ipp-igamma_small_data', (0,1), 5, rtol=1e-13), data(gdtr_, 'igamma_med_data_ipp-igamma_med_data', (0,1), 5, rtol=2e-13), data(gdtr_, 'igamma_int_data_ipp-igamma_int_data', (0,1), 5, rtol=2e-13), data(gdtr_, 'igamma_big_data_ipp-igamma_big_data', (0,1), 5, rtol=2e-9), data(gammaincc, 'igamma_small_data_ipp-igamma_small_data', (0,1), 3, rtol=1e-13), data(gammaincc, 'igamma_med_data_ipp-igamma_med_data', (0,1), 3, rtol=2e-13), data(gammaincc, 'igamma_int_data_ipp-igamma_int_data', (0,1), 3, rtol=4e-14), data(gammaincc, 'igamma_big_data_ipp-igamma_big_data', (0,1), 3, rtol=1e-11), data(gdtrc_, 'igamma_small_data_ipp-igamma_small_data', (0,1), 3, rtol=1e-13), data(gdtrc_, 'igamma_med_data_ipp-igamma_med_data', (0,1), 3, rtol=2e-13), data(gdtrc_, 'igamma_int_data_ipp-igamma_int_data', (0,1), 3, rtol=4e-14), data(gdtrc_, 'igamma_big_data_ipp-igamma_big_data', (0,1), 3, rtol=1e-11), data(gdtrib_, 'igamma_inva_data_ipp-igamma_inva_data', (1,0), 2, rtol=5e-9), data(gdtrib_comp, 'igamma_inva_data_ipp-igamma_inva_data', (1,0), 3, rtol=5e-9), data(poch_, 'tgamma_delta_ratio_data_ipp-tgamma_delta_ratio_data', (0,1), 2, rtol=2e-13), data(poch_, 'tgamma_delta_ratio_int_ipp-tgamma_delta_ratio_int', (0,1), 2,), data(poch_, 'tgamma_delta_ratio_int2_ipp-tgamma_delta_ratio_int2', (0,1), 2,), data(poch_minus, 'tgamma_delta_ratio_data_ipp-tgamma_delta_ratio_data', (0,1), 3, rtol=2e-13), data(poch_minus, 'tgamma_delta_ratio_int_ipp-tgamma_delta_ratio_int', (0,1), 3), data(poch_minus, 'tgamma_delta_ratio_int2_ipp-tgamma_delta_ratio_int2', (0,1), 3), data(eval_hermite_ld, 'hermite_ipp-hermite', (0,1), 2, rtol=2e-14), data(eval_laguerre_ld, 'laguerre2_ipp-laguerre2', (0,1), 2, rtol=7e-12), data(eval_laguerre_dd, 'laguerre2_ipp-laguerre2', (0,1), 2, knownfailure='hyp2f1 insufficiently accurate.'), data(eval_genlaguerre_ldd, 'laguerre3_ipp-laguerre3', (0,1,2), 3, rtol=2e-13), data(eval_genlaguerre_ddd, 'laguerre3_ipp-laguerre3', (0,1,2), 3, knownfailure='hyp2f1 insufficiently accurate.'), data(log1p, 'log1p_expm1_data_ipp-log1p_expm1_data', 0, 1), data(expm1, 'log1p_expm1_data_ipp-log1p_expm1_data', 0, 2), data(iv, 'bessel_i_data_ipp-bessel_i_data', (0,1), 2, rtol=1e-12), data(iv, 'bessel_i_data_ipp-bessel_i_data', (0,1j), 2, rtol=2e-10, atol=1e-306), data(iv, 'bessel_i_int_data_ipp-bessel_i_int_data', (0,1), 2, rtol=1e-9), data(iv, 'bessel_i_int_data_ipp-bessel_i_int_data', (0,1j), 2, rtol=2e-10), data(ivp, 'bessel_i_prime_int_data_ipp-bessel_i_prime_int_data', (0,1), 2, rtol=1.2e-13), data(ivp, 'bessel_i_prime_int_data_ipp-bessel_i_prime_int_data', (0,1j), 2, rtol=1.2e-13, atol=1e-300), data(jn, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1), 2, rtol=1e-12), data(jn, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1j), 2, rtol=1e-12), data(jn, 'bessel_j_large_data_ipp-bessel_j_large_data', (0,1), 2, rtol=6e-11), data(jn, 'bessel_j_large_data_ipp-bessel_j_large_data', (0,1j), 2, rtol=6e-11), data(jv, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1), 2, rtol=1e-12), data(jv, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1j), 2, rtol=1e-12), data(jv, 'bessel_j_data_ipp-bessel_j_data', (0,1), 2, rtol=1e-12), data(jv, 'bessel_j_data_ipp-bessel_j_data', (0,1j), 2, rtol=1e-12), data(jvp, 'bessel_j_prime_int_data_ipp-bessel_j_prime_int_data', (0,1), 2, rtol=1e-13), data(jvp, 'bessel_j_prime_int_data_ipp-bessel_j_prime_int_data', (0,1j), 2, rtol=1e-13), data(jvp, 'bessel_j_prime_large_data_ipp-bessel_j_prime_large_data', (0,1), 2, rtol=1e-11), data(jvp, 'bessel_j_prime_large_data_ipp-bessel_j_prime_large_data', (0,1j), 2, rtol=2e-11), data(kn, 'bessel_k_int_data_ipp-bessel_k_int_data', (0,1), 2, rtol=1e-12), data(kv, 'bessel_k_int_data_ipp-bessel_k_int_data', (0,1), 2, rtol=1e-12), data(kv, 'bessel_k_int_data_ipp-bessel_k_int_data', (0,1j), 2, rtol=1e-12), data(kv, 'bessel_k_data_ipp-bessel_k_data', (0,1), 2, rtol=1e-12), data(kv, 'bessel_k_data_ipp-bessel_k_data', (0,1j), 2, rtol=1e-12), data(kvp, 'bessel_k_prime_int_data_ipp-bessel_k_prime_int_data', (0,1), 2, rtol=3e-14), data(kvp, 'bessel_k_prime_int_data_ipp-bessel_k_prime_int_data', (0,1j), 2, rtol=3e-14), data(kvp, 'bessel_k_prime_data_ipp-bessel_k_prime_data', (0,1), 2, rtol=7e-14), data(kvp, 'bessel_k_prime_data_ipp-bessel_k_prime_data', (0,1j), 2, rtol=7e-14), data(yn, 'bessel_y01_data_ipp-bessel_y01_data', (0,1), 2, rtol=1e-12), data(yn, 'bessel_yn_data_ipp-bessel_yn_data', (0,1), 2, rtol=1e-12), data(yv, 'bessel_yn_data_ipp-bessel_yn_data', (0,1), 2, rtol=1e-12), data(yv, 'bessel_yn_data_ipp-bessel_yn_data', (0,1j), 2, rtol=1e-12), data(yv, 'bessel_yv_data_ipp-bessel_yv_data', (0,1), 2, rtol=1e-10), data(yv, 'bessel_yv_data_ipp-bessel_yv_data', (0,1j), 2, rtol=1e-10), data(yvp, 'bessel_yv_prime_data_ipp-bessel_yv_prime_data', (0, 1), 2, rtol=4e-9), data(yvp, 'bessel_yv_prime_data_ipp-bessel_yv_prime_data', (0, 1j), 2, rtol=4e-9), data(zeta_, 'zeta_data_ipp-zeta_data', 0, 1, param_filter=(lambda s: s > 1)), data(zeta_, 'zeta_neg_data_ipp-zeta_neg_data', 0, 1, param_filter=(lambda s: s > 1)), data(zeta_, 'zeta_1_up_data_ipp-zeta_1_up_data', 0, 1, param_filter=(lambda s: s > 1)), data(zeta_, 'zeta_1_below_data_ipp-zeta_1_below_data', 0, 1, param_filter=(lambda s: s > 1)), data(gammaincinv, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 2, rtol=1e-11), data(gammaincinv, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 2, rtol=1e-14), data(gammaincinv, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 2, rtol=1e-11), data(gammainccinv, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 3, rtol=1e-12), data(gammainccinv, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 3, rtol=1e-14), data(gammainccinv, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 3, rtol=1e-14), data(gdtrix_, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 2, rtol=3e-13, knownfailure='gdtrix unflow some points'), data(gdtrix_, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 2, rtol=3e-15), data(gdtrix_, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 2), data(gdtrix_comp, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 2, knownfailure='gdtrix bad some points'), data(gdtrix_comp, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 3, rtol=6e-15), data(gdtrix_comp, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 3), data(chndtr, 'nccs_ipp-nccs', (2,0,1), 3, rtol=3e-5), data(chndtr, 'nccs_big_ipp-nccs_big', (2,0,1), 3, rtol=5e-4, knownfailure='chndtr inaccurate some points'), data(sph_harm_, 'spherical_harmonic_ipp-spherical_harmonic', (1,0,3,2), (4,5), rtol=5e-11, param_filter=(lambda p: np.ones(p.shape, '?'), lambda p: np.ones(p.shape, '?'), lambda p: np.logical_and(p < 2*np.pi, p >= 0), lambda p: np.logical_and(p < np.pi, p >= 0))), data(spherical_jn_, 'sph_bessel_data_ipp-sph_bessel_data', (0,1), 2, rtol=1e-13), data(spherical_yn_, 'sph_neumann_data_ipp-sph_neumann_data', (0,1), 2, rtol=8e-15), data(owens_t, 'owens_t_ipp-owens_t', (0, 1), 2, rtol=5e-14), data(owens_t, 'owens_t_large_data_ipp-owens_t_large_data', (0, 1), 2, rtol=8e-12), # -- test data exists in boost but is not used in scipy -- # ibeta_derivative_data_ipp/ibeta_derivative_data.txt # ibeta_derivative_int_data_ipp/ibeta_derivative_int_data.txt # ibeta_derivative_large_data_ipp/ibeta_derivative_large_data.txt # ibeta_derivative_small_data_ipp/ibeta_derivative_small_data.txt # bessel_y01_prime_data_ipp/bessel_y01_prime_data.txt # bessel_yn_prime_data_ipp/bessel_yn_prime_data.txt # sph_bessel_prime_data_ipp/sph_bessel_prime_data.txt # sph_neumann_prime_data_ipp/sph_neumann_prime_data.txt # ellint_d2_data_ipp/ellint_d2_data.txt # ellint_d_data_ipp/ellint_d_data.txt # ellint_pi2_data_ipp/ellint_pi2_data.txt # ellint_pi3_data_ipp/ellint_pi3_data.txt # ellint_pi3_large_data_ipp/ellint_pi3_large_data.txt data(elliprc, 'ellint_rc_data_ipp-ellint_rc_data', (0, 1), 2, rtol=5e-16), data(elliprd, 'ellint_rd_data_ipp-ellint_rd_data', (0, 1, 2), 3, rtol=5e-16), data(elliprd, 'ellint_rd_0xy_ipp-ellint_rd_0xy', (0, 1, 2), 3, rtol=5e-16), data(elliprd, 'ellint_rd_0yy_ipp-ellint_rd_0yy', (0, 1, 2), 3, rtol=5e-16), data(elliprd, 'ellint_rd_xxx_ipp-ellint_rd_xxx', (0, 1, 2), 3, rtol=5e-16), # Some of the following rtol for elliprd may be larger than 5e-16 to # work around some hard cases in the Boost test where we get slightly # larger error than the ideal bound when the x (==y) input is close to # zero. # Also the accuracy on 32-bit builds with g++ may suffer from excess # loss of precision; see GCC bugzilla 323 # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=323 data(elliprd, 'ellint_rd_xxz_ipp-ellint_rd_xxz', (0, 1, 2), 3, rtol=6.5e-16), data(elliprd, 'ellint_rd_xyy_ipp-ellint_rd_xyy', (0, 1, 2), 3, rtol=6e-16), data(elliprf, 'ellint_rf_data_ipp-ellint_rf_data', (0, 1, 2), 3, rtol=5e-16), data(elliprf, 'ellint_rf_xxx_ipp-ellint_rf_xxx', (0, 1, 2), 3, rtol=5e-16), data(elliprf, 'ellint_rf_xyy_ipp-ellint_rf_xyy', (0, 1, 2), 3, rtol=5e-16), data(elliprf, 'ellint_rf_xy0_ipp-ellint_rf_xy0', (0, 1, 2), 3, rtol=5e-16), data(elliprf, 'ellint_rf_0yy_ipp-ellint_rf_0yy', (0, 1, 2), 3, rtol=5e-16), # The accuracy of R_G is primarily limited by R_D that is used # internally. It is generally worse than R_D. Notice that we increased # the rtol for R_G here. The cases with duplicate arguments are # slightly less likely to be unbalanced (at least two arguments are # already balanced) so the error bound is slightly better. Again, # precision with g++ 32-bit is even worse. data(elliprg, 'ellint_rg_ipp-ellint_rg', (0, 1, 2), 3, rtol=8.0e-16), data(elliprg, 'ellint_rg_xxx_ipp-ellint_rg_xxx', (0, 1, 2), 3, rtol=6e-16), data(elliprg, 'ellint_rg_xyy_ipp-ellint_rg_xyy', (0, 1, 2), 3, rtol=7.5e-16), data(elliprg, 'ellint_rg_xy0_ipp-ellint_rg_xy0', (0, 1, 2), 3, rtol=5e-16), data(elliprg, 'ellint_rg_00x_ipp-ellint_rg_00x', (0, 1, 2), 3, rtol=5e-16), data(elliprj, 'ellint_rj_data_ipp-ellint_rj_data', (0, 1, 2, 3), 4, rtol=5e-16, atol=1e-25, param_filter=(lambda s: s <= 5e-26,)), # ellint_rc_data_ipp/ellint_rc_data.txt # ellint_rd_0xy_ipp/ellint_rd_0xy.txt # ellint_rd_0yy_ipp/ellint_rd_0yy.txt # ellint_rd_data_ipp/ellint_rd_data.txt # ellint_rd_xxx_ipp/ellint_rd_xxx.txt # ellint_rd_xxz_ipp/ellint_rd_xxz.txt # ellint_rd_xyy_ipp/ellint_rd_xyy.txt # ellint_rf_0yy_ipp/ellint_rf_0yy.txt # ellint_rf_data_ipp/ellint_rf_data.txt # ellint_rf_xxx_ipp/ellint_rf_xxx.txt # ellint_rf_xy0_ipp/ellint_rf_xy0.txt # ellint_rf_xyy_ipp/ellint_rf_xyy.txt # ellint_rg_00x_ipp/ellint_rg_00x.txt # ellint_rg_ipp/ellint_rg.txt # ellint_rg_xxx_ipp/ellint_rg_xxx.txt # ellint_rg_xy0_ipp/ellint_rg_xy0.txt # ellint_rg_xyy_ipp/ellint_rg_xyy.txt # ellint_rj_data_ipp/ellint_rj_data.txt # ellint_rj_e2_ipp/ellint_rj_e2.txt # ellint_rj_e3_ipp/ellint_rj_e3.txt # ellint_rj_e4_ipp/ellint_rj_e4.txt # ellint_rj_zp_ipp/ellint_rj_zp.txt # jacobi_elliptic_ipp/jacobi_elliptic.txt # jacobi_elliptic_small_ipp/jacobi_elliptic_small.txt # jacobi_large_phi_ipp/jacobi_large_phi.txt # jacobi_near_1_ipp/jacobi_near_1.txt # jacobi_zeta_big_phi_ipp/jacobi_zeta_big_phi.txt # jacobi_zeta_data_ipp/jacobi_zeta_data.txt # heuman_lambda_data_ipp/heuman_lambda_data.txt # hypergeometric_0F2_ipp/hypergeometric_0F2.txt # hypergeometric_1F1_big_ipp/hypergeometric_1F1_big.txt # hypergeometric_1F1_ipp/hypergeometric_1F1.txt # hypergeometric_1F1_small_random_ipp/hypergeometric_1F1_small_random.txt # hypergeometric_1F2_ipp/hypergeometric_1F2.txt # hypergeometric_1f1_large_regularized_ipp/hypergeometric_1f1_large_regularized.txt # noqa: E501 # hypergeometric_1f1_log_large_unsolved_ipp/hypergeometric_1f1_log_large_unsolved.txt # noqa: E501 # hypergeometric_2F0_half_ipp/hypergeometric_2F0_half.txt # hypergeometric_2F0_integer_a2_ipp/hypergeometric_2F0_integer_a2.txt # hypergeometric_2F0_ipp/hypergeometric_2F0.txt # hypergeometric_2F0_large_z_ipp/hypergeometric_2F0_large_z.txt # hypergeometric_2F1_ipp/hypergeometric_2F1.txt # hypergeometric_2F2_ipp/hypergeometric_2F2.txt # ncbeta_big_ipp/ncbeta_big.txt # nct_small_delta_ipp/nct_small_delta.txt # nct_asym_ipp/nct_asym.txt # ncbeta_ipp/ncbeta.txt # powm1_data_ipp/powm1_big_data.txt # powm1_sqrtp1m1_test_hpp/sqrtp1m1_data.txt # sinc_data_ipp/sinc_data.txt # test_gamma_data_ipp/gammap1m1_data.txt # tgamma_ratio_data_ipp/tgamma_ratio_data.txt # trig_data_ipp/trig_data.txt # trig_data2_ipp/trig_data2.txt ] @pytest.mark.parametrize('test', BOOST_TESTS, ids=repr) def test_boost(test): # Filter deprecation warnings of any deprecated functions. if test.func in [btdtr, btdtri, btdtri_comp]: with pytest.deprecated_call(): _test_factory(test) else: _test_factory(test) GSL_TESTS = [ data_gsl(mathieu_a, 'mathieu_ab', (0, 1), 2, rtol=1e-13, atol=1e-13), data_gsl(mathieu_b, 'mathieu_ab', (0, 1), 3, rtol=1e-13, atol=1e-13), # Also the GSL output has limited accuracy... data_gsl(mathieu_ce_rad, 'mathieu_ce_se', (0, 1, 2), 3, rtol=1e-7, atol=1e-13), data_gsl(mathieu_se_rad, 'mathieu_ce_se', (0, 1, 2), 4, rtol=1e-7, atol=1e-13), data_gsl(mathieu_mc1_scaled, 'mathieu_mc_ms', (0, 1, 2), 3, rtol=1e-7, atol=1e-13), data_gsl(mathieu_ms1_scaled, 'mathieu_mc_ms', (0, 1, 2), 4, rtol=1e-7, atol=1e-13), data_gsl(mathieu_mc2_scaled, 'mathieu_mc_ms', (0, 1, 2), 5, rtol=1e-7, atol=1e-13), data_gsl(mathieu_ms2_scaled, 'mathieu_mc_ms', (0, 1, 2), 6, rtol=1e-7, atol=1e-13), ] @pytest.mark.parametrize('test', GSL_TESTS, ids=repr) def test_gsl(test): _test_factory(test) LOCAL_TESTS = [ data_local(ellipkinc, 'ellipkinc_neg_m', (0, 1), 2), data_local(ellipkm1, 'ellipkm1', 0, 1), data_local(ellipeinc, 'ellipeinc_neg_m', (0, 1), 2), data_local(clog1p, 'log1p_expm1_complex', (0,1), (2,3), rtol=1e-14), data_local(cexpm1, 'log1p_expm1_complex', (0,1), (4,5), rtol=1e-14), data_local(gammainc, 'gammainc', (0, 1), 2, rtol=1e-12), data_local(gammaincc, 'gammaincc', (0, 1), 2, rtol=1e-11), data_local(ellip_harm_2, 'ellip',(0, 1, 2, 3, 4), 6, rtol=1e-10, atol=1e-13), data_local(ellip_harm, 'ellip',(0, 1, 2, 3, 4), 5, rtol=1e-10, atol=1e-13), data_local(wright_bessel, 'wright_bessel', (0, 1, 2), 3, rtol=1e-11), ] @pytest.mark.parametrize('test', LOCAL_TESTS, ids=repr) def test_local(test): _test_factory(test) def _test_factory(test, dtype=np.float64): """Boost test""" with suppress_warnings() as sup: sup.filter(IntegrationWarning, "The occurrence of roundoff error is detected") with np.errstate(all='ignore'): test.check(dtype=dtype)