def runTest(self):
        nx, ny, nz, str_f, pt0, pt1, is_array = self.args

        slices = common.slice_index_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 = [gpu.Fields(context, device, nx, ny, nz) \
                for device in gpu_devices]
        mainf_list.append( cpu.Fields(nx, ny, nz) )
        nodef = NodeFields(mainf_list)
        dtype = nodef.dtype
        anx = nodef.accum_nx_list

        tfunc = lambda tstep: np.sin(0.03*tstep)
        incident = NodeDirectIncident(nodef, str_f, pt0, pt1, tfunc, value) 

        # allocations for verify
        eh = np.zeros(nodef.ns, dtype)
        getf = NodeGetFields(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.slice_index_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 = [gpu.Fields(context, device, nx, ny, nz) \
                for device in gpu_devices]
        mainf_list.append(cpu.Fields(nx, ny, nz))
        nodef = NodeFields(mainf_list)
        dtype = nodef.dtype
        anx = nodef.accum_nx_list

        tfunc = lambda tstep: np.sin(0.03 * tstep)
        incident = NodeDirectIncident(nodef, str_f, pt0, pt1, tfunc, value)

        # allocations for verify
        eh = np.zeros(nodef.ns, dtype)
        getf = NodeGetFields(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))
Exemplo n.º 3
0
# plot
import matplotlib.pyplot as plt
plt.figure(figsize=(12,8))
plt.ion()
for anx in nodef.accum_nx_list[1:]:
	plt.plot((anx, anx), (0, ny), color='w', linewidth=0.2)
imag = plt.imshow(np.zeros((nodef.nx, ny), nodef.dtype).T, cmap=plt.cm.hot, origin='lower', vmin=0, vmax=2.)
plt.colorbar()

# main loop
from datetime import datetime
t0 = datetime.now()

for tstep in xrange(1, tmax+1):
    nodef.update_e()
    pbc_x.update_e()
    exchange.update_e()

    nodef.update_h()
    pbc_x.update_h()
    exchange.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( np.abs(getf.get_fields().T) )
        #plt.savefig('./png/%.6d.png' % tstep)
        plt.draw()