import pytest
from scipy.special._testutils import MissingModule, check_version
from scipy.special._mptestutils import mp_assert_allclose
from scipy.special._precompute.utils import lagrange_inversion
try:
import sympy # type: ignore[import]
except ImportError:
sympy = MissingModule('sympy')
try:
import mpmath as mp
except ImportError:
mp = MissingModule('mpmath')
@pytest.mark.slow
@check_version(sympy, '0.7')
@check_version(mp, '0.19')
class TestInversion:
@pytest.mark.xfail_on_32bit("rtol only 2e-9, see gh-6938")
def test_log(self):
with mp.workdps(30):
logcoeffs = mp.taylor(lambda x: mp.log(1 + x), 0, 10)
expcoeffs = mp.taylor(lambda x: mp.exp(x) - 1, 0, 10)
invlogcoeffs = lagrange_inversion(logcoeffs)
mp_assert_allclose(invlogcoeffs, expcoeffs)
@pytest.mark.xfail_on_32bit("rtol only 1e-15, see gh-6938")
def test_sin(self):
the implementation of mp_hyp2f1 below, which modifies mpmath's hyp2f1 to
return the same branch as scipy's on the standard branch cut.
"""
import pytest
import numpy as np
from typing import NamedTuple
from numpy.testing import assert_allclose
from scipy.special import hyp2f1
from scipy.special._testutils import check_version, MissingModule
try:
import mpmath
except ImportError:
mpmath = MissingModule("mpmath")
def mp_hyp2f1(a, b, c, z):
"""Return mpmath hyp2f1 calculated on same branch as scipy hyp2f1.
For most values of a,b,c mpmath returns the x - 0j branch of hyp2f1 on the
branch cut x=(1,inf) whereas scipy's hyp2f1 calculates the x + 0j branch.
Thus, to generate the right comparison values on the branch cut, we
evaluate mpmath.hyp2f1 at x + 1e-15*j.
The exception to this occurs when c-a=-m in which case both mpmath and
scipy calculate the x + 0j branch on the branch cut. When this happens
mpmath.hyp2f1 will be evaluated at the original z point.
"""
on_branch_cut = z.real > 1.0 and abs(z.imag) < 1.0e-15
import itertools
import numpy as np
from numpy.testing import assert_equal
import pytest
import scipy.special as sp
from scipy.special._testutils import (
MissingModule, check_version, FuncData)
from scipy.special._mptestutils import (
Arg, IntArg, get_args, mpf2float, assert_mpmath_equal)
try:
import mpmath
except ImportError:
mpmath = MissingModule('mpmath')
class ProbArg(object):
"""Generate a set of probabilities on [0, 1]."""
def __init__(self):
# Include the endpoints for compatibility with Arg et. al.
self.a = 0
self.b = 1
def values(self, n):
"""Return an array containing approximatively n numbers."""
m = max(1, n//3)
v1 = np.logspace(-30, np.log10(0.3), m)
v2 = np.linspace(0.3, 0.7, m + 1, endpoint=False)[1:]
v3 = 1 - np.logspace(np.log10(0.3), -15, m)
from __future__ import division, print_function, absolute_import
from numpy.testing import assert_equal
from scipy.special._testutils import check_version, MissingModule
from scipy.special._precompute.expn_asy import generate_A
try:
import sympy
from sympy import Poly
except ImportError:
sympy = MissingModule("sympy")
@check_version(sympy, "1.0")
def test_generate_A():
# Data from DLMF 8.20.5
x = sympy.symbols('x')
Astd = [
Poly(1, x),
Poly(1, x),
Poly(1 - 2 * x),
Poly(1 - 8 * x + 6 * x**2)
]
Ares = generate_A(len(Astd))
for p, q in zip(Astd, Ares):
assert_equal(p, q)