time_self_eval))
print('Direct target evaluation took: {:0.1f}'.format(
time_target_eval))
else:
# by FMMLIB2D, if available
try:
import pyfmmlib2d
source = np.row_stack([px, py])
target = np.row_stack([rx, ry])
dumb_targ = np.row_stack(
[np.array([0.6, 0.6]),
np.array([0.5, 0.5])])
st = time.time()
out = pyfmmlib2d.RFMM(source,
dumb_targ,
charge=tau,
compute_target_potential=True,
precision=reference_precision)
tform = time.time() - st
print('FMMLIB generation took: {:0.1f}'.format(
tform * 1000))
print('...Points/Second/Core (thousands) \033[1m',
int(N_source / tform / cpu_num / 1000), '\033[0m ')
st = time.time()
out = pyfmmlib2d.RFMM(source,
charge=tau,
compute_source_potential=True,
precision=reference_precision)
self_reference_eval = -0.5 * out['source']['u'] / np.pi
tt = time.time() - st - tform
print('FMMLIB self only eval took: {:0.1f}'.format(tt *
# get reference solution
reference = True
if N_total <= 50000:
# by Direct Sum
st = time.time()
reference_eval = np.zeros(N_total, dtype=float)
Kernel_Self_Apply(px, py, tau, reference_eval)
time_direct_eval = (time.time() - st) * 1000
print('\nDirect evaluation took: {:0.1f}'.format(time_direct_eval))
# by FMMLIB2D, if available
try:
source = np.row_stack([px, py])
st = time.time()
if problem == 'Laplace':
out = pyfmmlib2d.RFMM(source,
charge=tau,
compute_source_potential=True)
reference_eval = -out['source']['u'] / (2 * np.pi)
elif problem == 'Modified Helmholtz':
out = pyfmmlib2d.HFMM(source,
charge=tau,
compute_source_potential=True,
helmholtz_parameter=1j * helmholtz_k)
reference_eval = out['source']['u'] * 2 * np.pi
elif problem == 'Biharmonic':
out = pyfmmlib2d.BFMM(source,
charge=tau.astype(complex),
compute_source_velocity=True)
else:
raise Exception('No FMMLIB function provided.')
et = time.time()
print('Direct target evaluation took: {:0.1f}'.format(
time_target_eval))
else:
# by FMMLIB2D, if available
try:
import pyfmmlib2d
source = np.row_stack([px, py])
target = np.row_stack([rx, ry])
dumb_targ = np.row_stack(
[np.array([0.6, 0.6]),
np.array([0.5, 0.5])])
st = time.time()
out = pyfmmlib2d.RFMM(source,
dumb_targ,
charge=slp,
dipstr=dlp,
dipvec=dipvec,
compute_target_potential=True,
precision=reference_precision)
tform = time.time() - st
print('FMMLIB generation took: {:0.1f}'.format(
tform * 1000))
print('...Points/Second/Core (thousands) \033[1m',
int(N_source / tform / cpu_num / 1000), '\033[0m ')
st = time.time()
out = pyfmmlib2d.RFMM(source,
charge=slp,
dipstr=dlp,
dipvec=dipvec,
compute_source_potential=True,
precision=reference_precision)
time_self_eval = (time.time() - st)*1000
st = time.time()
target_reference_eval = np.zeros(N_target, dtype=complex)
KA(px, py, rx, ry, tau, out=target_reference_eval)
time_target_eval = (time.time() - st)*1000
print('\nDirect self evaluation took: {:0.1f}'.format(time_self_eval))
print('Direct target evaluation took: {:0.1f}'.format(time_target_eval))
else:
# by FMMLIB2D, if available
try:
import pyfmmlib2d
source = np.row_stack([px, py])
target = np.row_stack([rx, ry])
dumb_targ = np.row_stack([np.array([0.6, 0.6]), np.array([0.5, 0.5])])
st = time.time()
out = pyfmmlib2d.RFMM(source, dumb_targ, charge=tau, compute_target_potential=True)
tform = time.time() - st
print('FMMLIB generation took: {:0.1f}'.format(tform*1000))
print('...Points/Second/Core (thousands) \033[1m', int(N_source/tform/cpu_num/1000), '\033[0m ')
st = time.time()
out = pyfmmlib2d.RFMM(source, charge=tau, compute_source_potential=True)
self_reference_eval = -0.5*out['source']['u']/np.pi
tt = time.time() - st - tform
print('FMMLIB self only eval took: {:0.1f}'.format(tt*1000))
print('...Points/Second/Core (thousands) \033[1m', int(N_source/tt/cpu_num/1000), '\033[0m ')
st = time.time()
out = pyfmmlib2d.RFMM(source, target, charge=tau, compute_target_potential=True)
target_reference_eval = -0.5*out['target']['u']/np.pi
tt = time.time() - st - tform
print('FMMLIB target only eval took: {:0.1f}'.format(tt*1000))
print('...Points/Second/Core (thousands) \033[1m', int(N_target/tt/cpu_num/1000), '\033[0m ')