def test_bessel(ctx_factory):
try:
import scipy.special as spec
except ImportError:
from pytest import skip
skip("scipy not present--cannot test Bessel function")
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if not has_double_support(ctx.devices[0]):
from pytest import skip
skip("no double precision support--cannot test bessel function")
nterms = 30
try:
from hellskitchen._native import jfuns2d, hank103_vec
except ImportError:
use_hellskitchen = False
else:
use_hellskitchen = True
if use_hellskitchen:
a = np.logspace(-3, 3, 10**6)
else:
a = np.logspace(-5, 5, 10**6)
for which_func, cl_func, scipy_func, is_rel in [
#("j", clmath.bessel_jn, spec.jn, False),
("y", clmath.bessel_yn, spec.yn, True)
]:
if is_rel:
def get_err(check, ref):
return np.max(np.abs(check - ref)) / np.max(np.abs(ref))
else:
def get_err(check, ref):
return np.max(np.abs(check - ref))
if use_hellskitchen:
hellskitchen_result = np.empty((len(a), nterms),
dtype=np.complex128)
if which_func == "j":
for i, a_i in enumerate(a):
if i % 10000 == 0:
print("%.1f %%" % (100 * i / len(a)))
ier, fjs, _, _ = jfuns2d(nterms, a_i, 1, 0, 10000)
hellskitchen_result[i] = fjs[:nterms]
assert ier == 0
elif which_func == "y":
h0, h1 = hank103_vec(a, ifexpon=1)
hellskitchen_result[:, 0] = h0.imag
hellskitchen_result[:, 1] = h1.imag
a_dev = cl_array.to_device(queue, a)
for n in range(0, nterms):
cl_bessel = cl_func(n, a_dev).get()
scipy_bessel = scipy_func(n, a)
error_scipy = get_err(cl_bessel, scipy_bessel)
assert error_scipy < 1e-10, error_scipy
if use_hellskitchen and (which_func == "j" or
(which_func == "y" and n in [0, 1])):
hk_bessel = hellskitchen_result[:, n]
error_hk = get_err(cl_bessel, hk_bessel)
assert error_hk < 1e-10, error_hk
error_hk_scipy = get_err(scipy_bessel, hk_bessel)
print(n, error_scipy, error_hk, error_hk_scipy)
else:
print(n, error_scipy)
assert not np.isnan(cl_bessel).any()
if 0 and n == 15:
import matplotlib.pyplot as pt
#pt.plot(scipy_bessel)
#pt.plot(cl_bessel)
pt.loglog(a,
np.abs(cl_bessel - scipy_bessel),
label="vs scipy")
if use_hellskitchen:
pt.loglog(a,
np.abs(cl_bessel - hk_bessel),
label="vs hellskitchen")
pt.legend()
pt.show()
def test_bessel_j(ctx_factory):
try:
import scipy.special as spec
except ImportError:
from py.test import skip
skip("scipy not present--cannot test Bessel function")
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if not has_double_support(ctx.devices[0]):
from py.test import skip
skip("no double precision support--cannot test bessel function")
nterms = 30
try:
from hellskitchen._native import jfuns2d
except ImportError:
use_hellskitchen = False
else:
use_hellskitchen = True
if use_hellskitchen:
a = np.logspace(-3, 3, 10 ** 6)
else:
a = np.logspace(-5, 5, 10 ** 6)
if use_hellskitchen:
hellskitchen_result = np.empty((len(a), nterms), dtype=np.complex128)
for i, a_i in enumerate(a):
if i % 10000 == 0:
print "%.1f %%" % (100 * i / len(a))
ier, fjs, _, _ = jfuns2d(nterms, a_i, 1, 0, 10000)
hellskitchen_result[i] = fjs[:nterms]
assert ier == 0
a_dev = cl_array.to_device(queue, a)
for n in range(0, nterms):
cl_bessel = clmath.bessel_jn(n, a_dev).get()
scipy_bessel = spec.jn(n, a)
error_scipy = np.max(np.abs(cl_bessel - scipy_bessel))
assert error_scipy < 1e-10, error_scipy
if use_hellskitchen:
hk_bessel = hellskitchen_result[:, n]
error_hk = np.max(np.abs(cl_bessel - hk_bessel))
assert error_hk < 1e-10, error_hk
error_hk_scipy = np.max(np.abs(scipy_bessel - hk_bessel))
print (n, error_scipy, error_hk, error_hk_scipy)
else:
print (n, error_scipy)
assert not np.isnan(cl_bessel).any()
if 0 and n == 15:
import matplotlib.pyplot as pt
# pt.plot(scipy_bessel)
# pt.plot(cl_bessel)
pt.loglog(a, np.abs(cl_bessel - scipy_bessel), label="vs scipy")
if use_hellskitchen:
pt.loglog(a, np.abs(cl_bessel - hk_bessel), label="vs hellskitchen")
pt.legend()
pt.show()
def test_bessel(ctx_factory):
try:
import scipy.special as spec
except ImportError:
from pytest import skip
skip("scipy not present--cannot test Bessel function")
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
if not has_double_support(ctx.devices[0]):
from pytest import skip
skip("no double precision support--cannot test bessel function")
nterms = 30
try:
from hellskitchen._native import jfuns2d, hank103_vec
except ImportError:
use_hellskitchen = False
else:
use_hellskitchen = True
if use_hellskitchen:
a = np.logspace(-3, 3, 10**6)
else:
a = np.logspace(-5, 5, 10**6)
for which_func, cl_func, scipy_func, is_rel in [
#("j", clmath.bessel_jn, spec.jn, False),
("y", clmath.bessel_yn, spec.yn, True)
]:
if is_rel:
def get_err(check, ref):
return np.max(np.abs(check-ref)) / np.max(np.abs(ref))
else:
def get_err(check, ref):
return np.max(np.abs(check-ref))
if use_hellskitchen:
hellskitchen_result = np.empty((len(a), nterms), dtype=np.complex128)
if which_func == "j":
for i, a_i in enumerate(a):
if i % 10000 == 0:
print("%.1f %%" % (100 * i/len(a)))
ier, fjs, _, _ = jfuns2d(nterms, a_i, 1, 0, 10000)
hellskitchen_result[i] = fjs[:nterms]
assert ier == 0
elif which_func == "y":
h0, h1 = hank103_vec(a, ifexpon=1)
hellskitchen_result[:, 0] = h0.imag
hellskitchen_result[:, 1] = h1.imag
a_dev = cl_array.to_device(queue, a)
for n in range(0, nterms):
cl_bessel = cl_func(n, a_dev).get()
scipy_bessel = scipy_func(n, a)
error_scipy = get_err(cl_bessel, scipy_bessel)
assert error_scipy < 1e-10, error_scipy
if use_hellskitchen and (
which_func == "j"
or
(which_func == "y" and n in [0, 1])):
hk_bessel = hellskitchen_result[:, n]
error_hk = get_err(cl_bessel, hk_bessel)
assert error_hk < 1e-10, error_hk
error_hk_scipy = get_err(scipy_bessel, hk_bessel)
print(n, error_scipy, error_hk, error_hk_scipy)
else:
print(n, error_scipy)
assert not np.isnan(cl_bessel).any()
if 0 and n == 15:
import matplotlib.pyplot as pt
#pt.plot(scipy_bessel)
#pt.plot(cl_bessel)
pt.loglog(a, np.abs(cl_bessel-scipy_bessel), label="vs scipy")
if use_hellskitchen:
pt.loglog(a, np.abs(cl_bessel-hk_bessel), label="vs hellskitchen")
pt.legend()
pt.show()