def runTest(self):
nx, ny, nz, str_f, pt0, pt1, is_array = self.args
slices = common.slices_two_points(pt0, pt1)
# generate random source
if is_array:
shape = common.shape_two_points(pt0, pt1)
value = np.random.rand(*shape).astype(np.float32)
else:
value = np.random.ranf()
# instance
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
mainf_list = [cpu.Fields(nx, ny, nz)]
mainf_list += [gpu.Fields(context, device, nx, ny, nz) \
for device in gpu_devices]
nodef = Fields(mainf_list)
dtype = nodef.dtype
anx = nodef.accum_nx_list
tfunc = lambda tstep: np.sin(0.03 * tstep)
incident = IncidentDirect(nodef, str_f, pt0, pt1, tfunc, value)
# allocations for verify
eh = np.zeros(nodef.ns, dtype)
getf = GetFields(nodef, str_f, pt0, pt1)
# verify
eh[slices] = dtype(value) * dtype(tfunc(1))
e_or_h = str_f[0]
nodef.update_e()
nodef.update_h()
getf.wait()
original = eh[slices]
copy = getf.get_fields(str_f)
norm = np.linalg.norm(original - copy)
self.assertEqual(norm, 0, '%s, %g' % (self.args, norm))
def runTest(self):
nx, ny, nz, str_f, pt0, pt1, is_array = self.args
slices = common.slices_two_points(pt0, pt1)
# generate random source
if is_array:
shape = common.shape_two_points(pt0, pt1)
value = np.random.rand(*shape).astype(np.float32)
else:
value = np.random.ranf()
# instance
gpu_devices = common_gpu.gpu_device_list(print_info=False)
context = cl.Context(gpu_devices)
mainf_list = [ cpu.Fields(nx, ny, nz) ]
mainf_list += [gpu.Fields(context, device, nx, ny, nz) \
for device in gpu_devices]
nodef = Fields(mainf_list)
dtype = nodef.dtype
anx = nodef.accum_nx_list
tfunc = lambda tstep: np.sin(0.03*tstep)
incident = IncidentDirect(nodef, str_f, pt0, pt1, tfunc, value)
# allocations for verify
eh = np.zeros(nodef.ns, dtype)
getf = GetFields(nodef, str_f, pt0, pt1)
# verify
eh[slices] = dtype(value) * dtype(tfunc(1))
e_or_h = str_f[0]
nodef.update_e()
nodef.update_h()
getf.wait()
original = eh[slices]
copy = getf.get_fields(str_f)
norm = np.linalg.norm(original - copy)
self.assertEqual(norm, 0, '%s, %g' % (self.args, norm))
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(12,8))
imag = plt.imshow(np.zeros((nx, ny), fields.dtype).T, interpolation='nearest', origin='lower', vmin=-1.1, vmax=1.1)
plt.colorbar()
from datetime import datetime
from time import time
t0 = datetime.now()
t00 = time()
# main loop
gtmp = gpu.GetFields(gpuf, 'ez', (0, 0, 0), (0, 0, 0))
for tstep in xrange(1, tmax+1):
fields.update_e()
fields.update_h()
if tstep % tgap == 0 and rank == 0:
print('[%s] %d/%d (%d %%)\r' % (datetime.now() - t0, tstep, tmax, float(tstep)/tmax*100)),
sys.stdout.flush()
getf.get_event().wait()
imag.set_array( getf.get_fields().T )
#plt.savefig('./png/%.6d.png' % tstep)
plt.draw()
gtmp.get_event().wait()
if rank == 0:
dt = time() - t00
print dt
print('[%s] %d/%d (%d %%) %f Mpoint/s' % (datetime.now() - t0, tstep, tmax, float(tstep)/tmax*100, nx*ny*nz*tmax/dt/1e6) )
# plot
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(12,8))
for i in fields.accum_nx_list[1:]:
plt.plot((i,i), (0,ny), color='k', linewidth=2)
imag = plt.imshow(np.zeros((nx, ny), fields.dtype).T, interpolation='nearest', origin='lower', vmin=-1.1, vmax=1.1)
plt.colorbar()
# main loop
from datetime import datetime
t0 = datetime.now()
for tstep in xrange(1, tmax+1):
fields.update_e()
fields.update_h()
if tstep % tgap == 0:
print('[%s] %d/%d (%d %%)\r' % (datetime.now() - t0, tstep, tmax, float(tstep)/tmax*100)),
sys.stdout.flush()
getf.wait()
imag.set_array( getf.get_fields().T )
#plt.savefig('./png/%.6d.png' % tstep)
plt.draw()
plt.show()
print('\n[%s] %d/%d (%d %%)' % (datetime.now() - t0, tstep, tmax, float(tstep)/tmax*100))
print('')
