def test_complex_bessel(ctx_factory, ref_src):
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 complex bessel function")
v = 40
n = 10**5
np.random.seed(13)
z = (np.logspace(-5, 2, n) * np.exp(1j * 2 * np.pi * np.random.rand(n)))
if ref_src == "pyfmmlib":
pyfmmlib = pytest.importorskip("pyfmmlib")
jv_ref = np.zeros(len(z), 'complex')
vin = v + 1
for i in range(len(z)):
ier, fjs, _, _ = pyfmmlib.jfuns2d(vin, z[i], 1, 0, 10000)
assert ier == 0
jv_ref[i] = fjs[v]
elif ref_src == "scipy":
spec = pytest.importorskip("scipy.special")
jv_ref = spec.jv(v, z)
else:
raise ValueError("ref_src")
z_dev = cl_array.to_device(queue, z)
jv_dev = clmath.bessel_jn(v, z_dev)
abs_err_jv = np.abs(jv_dev.get() - jv_ref)
abs_jv_ref = np.abs(jv_ref)
rel_err_jv = abs_err_jv / abs_jv_ref
# use absolute error instead if the function value itself is too small
tiny = 1e-300
ind = abs_jv_ref < tiny
rel_err_jv[ind] = abs_err_jv[ind]
# if the reference value is inf or nan, set the error to zero
ind1 = np.isinf(abs_jv_ref)
ind2 = np.isnan(abs_jv_ref)
rel_err_jv[ind1] = 0
rel_err_jv[ind2] = 0
if 0:
print(abs(z))
print(np.abs(jv_ref))
print(np.abs(jv_dev.get()))
print(rel_err_jv)
max_err = np.max(rel_err_jv)
assert max_err <= 2e-13, max_err
print("Jv", np.max(rel_err_jv))
if 0:
import matplotlib.pyplot as pt
pt.loglog(np.abs(z), rel_err_jv)
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_complex_bessel(ctx_factory, ref_src):
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 complex bessel function")
v = 40
n = 10**5
np.random.seed(13)
z = (
np.logspace(-5, 2, n)
* np.exp(1j * 2 * np.pi * np.random.rand(n)))
if ref_src == "pyfmmlib":
pyfmmlib = pytest.importorskip("pyfmmlib")
jv_ref = np.zeros(len(z), 'complex')
vin = v+1
for i in range(len(z)):
ier, fjs, _, _ = pyfmmlib.jfuns2d(vin, z[i], 1, 0, 10000)
assert ier == 0
jv_ref[i] = fjs[v]
elif ref_src == "scipy":
spec = pytest.importorskip("scipy.special")
jv_ref = spec.jv(v, z)
else:
raise ValueError("ref_src")
z_dev = cl_array.to_device(queue, z)
jv_dev = clmath.bessel_jn(v, z_dev)
abs_err_jv = np.abs(jv_dev.get() - jv_ref)
abs_jv_ref = np.abs(jv_ref)
rel_err_jv = abs_err_jv/abs_jv_ref
# use absolute error instead if the function value itself is too small
tiny = 1e-300
ind = abs_jv_ref < tiny
rel_err_jv[ind] = abs_err_jv[ind]
# if the reference value is inf or nan, set the error to zero
ind1 = np.isinf(abs_jv_ref)
ind2 = np.isnan(abs_jv_ref)
rel_err_jv[ind1] = 0
rel_err_jv[ind2] = 0
if 0:
print(abs(z))
print(np.abs(jv_ref))
print(np.abs(jv_dev.get()))
print(rel_err_jv)
max_err = np.max(rel_err_jv)
assert max_err <= 2e-13, max_err
print("Jv", np.max(rel_err_jv))
if 0:
import matplotlib.pyplot as pt
pt.loglog(np.abs(z), rel_err_jv)
pt.show()
