def runTest(self): nx, ny, nz, str_f, pt0, pt1, is_array, mpi_type = 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 fields = Fields(nx, ny, nz, '', 'single', 0, mpi_type=mpi_type) tfunc = lambda tstep: np.sin(0.03 * tstep) incident = DirectIncident(fields, str_f, pt0, pt1, tfunc, value) # host allocations eh = np.zeros(fields.ns_pitch, dtype=fields.dtype) getf = GetFields(fields, str_f, pt0, pt1) # verify eh[slices] = fields.dtype(value) * fields.dtype(tfunc(1)) fields.update_e() fields.update_h() fields.enqueue_barrier() original = eh[slices] getf.get_event().wait() copy = getf.get_fields() 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, mpi_type = 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 fields = Fields(nx, ny, nz, '', 'single', 0, mpi_type=mpi_type) tfunc = lambda tstep: np.sin(0.03*tstep) incident = DirectIncident(fields, str_f, pt0, pt1, tfunc, value) # host allocations eh = np.zeros(fields.ns_pitch, dtype=fields.dtype) getf = GetFields(fields, str_f, pt0, pt1) # verify eh[slices] = fields.dtype(value) * fields.dtype(tfunc(1)) fields.update_e() fields.update_h() fields.enqueue_barrier() original = eh[slices] getf.get_event().wait() copy = getf.get_fields() norm = np.linalg.norm(original - copy) self.assertEqual(norm, 0, '%s, %g' % (self.args, norm))
def runTest(self): ufunc, nx, ny, nz, coeff_use, precision_float, use_cpu_core, split, tmax = self.args fields = Fields(nx, ny, nz, coeff_use, precision_float, use_cpu_core) core = Core(fields) strf_list = ['ex', 'ey', 'ez', 'hx', 'hy', 'hz'] slice_xyz = [slice(None, None), slice(None, None), fields.slice_z] # allocations ns = fields.ns dtype = fields.dtype ehs = common_update.generate_random_ehs(nx, ny, nz, dtype, ufunc) fields.set_ehs(*ehs) ces, chs = common_update.generate_random_cs(coeff_use, nx, ny, nz, dtype) if 'e' in coeff_use: fields.set_ces(*ces) if 'h' in coeff_use: fields.set_chs(*chs) # update if ufunc == 'e': for tstep in xrange(0, tmax): fields.update_e() common_update.update_e(ehs, ces) fields.enqueue_barrier() for strf, eh in zip(strf_list, ehs)[:3]: norm = np.linalg.norm(eh - fields.get(strf)[slice_xyz]) max_diff = np.abs(eh - fields.get(strf)[slice_xyz]).max() self.assertEqual(norm, 0, '%s, %s, %g, %g' % (self.args, strf, norm, max_diff) ) if fields.pad != 0: if strf == 'ez': norm2 = np.linalg.norm(fields.get(strf)[:,:,-fields.pad:]) else: norm2 = np.linalg.norm(fields.get(strf)[:,:,-fields.pad-1:]) self.assertEqual(norm2, 0, '%s, %s, %g, padding' % (self.args, strf, norm2) ) elif ufunc == 'h': for tstep in xrange(0, tmax): fields.update_h() common_update.update_h(ehs, chs) fields.enqueue_barrier() for strf, eh in zip(strf_list, ehs)[3:]: norm = np.linalg.norm(eh - fields.get(strf)[slice_xyz]) max_diff = np.abs(eh - fields.get(strf)[slice_xyz]).max() self.assertEqual(norm, 0, '%s, %s, %g, %g' % \ (self.args, strf, norm, max_diff) ) if fields.pad != 0: if strf == 'hz': norm2 = np.linalg.norm(fields.get(strf)[:,:,-fields.pad:]) else: norm2 = np.linalg.norm(fields.get(strf)[:,:,-fields.pad:]) self.assertEqual(norm2, 0, '%s, %s, %g, padding' % (self.args, strf, norm2) )
def runTest(self): nx, ny, nz, str_f, pt0, pt1, is_array = self.args slice_xyz = common.slice_index_two_points(pt0, pt1) str_fs = common.convert_to_tuple(str_f) # instance fields = Fields(nx, ny, nz, '', 'single') setf = SetFields(fields, str_f, pt0, pt1, is_array) # generate random source if is_array: shape = common.shape_two_points(pt0, pt1, len(str_fs)) value = np.random.rand(*shape).astype(fields.dtype) split_value = np.split(value, len(str_fs)) split_value_dict = dict( zip(str_fs, split_value) ) else: value = np.random.ranf() # host allocations ehs = [np.zeros(fields.ns, dtype=fields.dtype) for i in range(6)] eh_dict = dict( zip(['ex', 'ey', 'ez', 'hx', 'hy', 'hz'], ehs) ) # verify for str_f in str_fs: if is_array: eh_dict[str_f][slice_xyz] = split_value_dict[str_f] else: eh_dict[str_f][slice_xyz] = value setf.set_fields(value) fields.enqueue_barrier() for str_f in str_fs: original = eh_dict[str_f] copy = fields.get(str_f)[:,:,fields.slice_z] 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 slice_xyz = common.slice_index_two_points(pt0, pt1) str_fs = common.convert_to_tuple(str_f) # instance fields = Fields(nx, ny, nz, '', 'single') setf = SetFields(fields, str_f, pt0, pt1, is_array) # generate random source if is_array: shape = common.shape_two_points(pt0, pt1, len(str_fs)) value = np.random.rand(*shape).astype(fields.dtype) split_value = np.split(value, len(str_fs)) split_value_dict = dict(zip(str_fs, split_value)) else: value = np.random.ranf() # host allocations ehs = [np.zeros(fields.ns, dtype=fields.dtype) for i in range(6)] eh_dict = dict(zip(['ex', 'ey', 'ez', 'hx', 'hy', 'hz'], ehs)) # verify for str_f in str_fs: if is_array: eh_dict[str_f][slice_xyz] = split_value_dict[str_f] else: eh_dict[str_f][slice_xyz] = value setf.set_fields(value) fields.enqueue_barrier() for str_f in str_fs: original = eh_dict[str_f] copy = fields.get(str_f)[:, :, fields.slice_z] norm = np.linalg.norm(original - copy) self.assertEqual(norm, 0, '%s, %g' % (self.args, norm))
def runTest(self): ufunc, nx, ny, nz, coeff_use, precision_float, use_cpu_core, split, tmax = self.args fields = Fields(nx, ny, nz, coeff_use, precision_float, use_cpu_core) core = Core(fields) strf_list = ['ex', 'ey', 'ez', 'hx', 'hy', 'hz'] slice_xyz = [slice(None, None), slice(None, None), fields.slice_z] # allocations ns = fields.ns dtype = fields.dtype ehs = common_update.generate_random_ehs(nx, ny, nz, dtype, ufunc) fields.set_ehs(*ehs) ces, chs = common_update.generate_random_cs(coeff_use, nx, ny, nz, dtype) if 'e' in coeff_use: fields.set_ces(*ces) if 'h' in coeff_use: fields.set_chs(*chs) # update if ufunc == 'e': for tstep in xrange(0, tmax): fields.update_e() common_update.update_e(ehs, ces) fields.enqueue_barrier() for strf, eh in zip(strf_list, ehs)[:3]: norm = np.linalg.norm(eh - fields.get(strf)[slice_xyz]) max_diff = np.abs(eh - fields.get(strf)[slice_xyz]).max() self.assertEqual( norm, 0, '%s, %s, %g, %g' % (self.args, strf, norm, max_diff)) if fields.pad != 0: if strf == 'ez': norm2 = np.linalg.norm( fields.get(strf)[:, :, -fields.pad:]) else: norm2 = np.linalg.norm( fields.get(strf)[:, :, -fields.pad - 1:]) self.assertEqual( norm2, 0, '%s, %s, %g, padding' % (self.args, strf, norm2)) elif ufunc == 'h': for tstep in xrange(0, tmax): fields.update_h() common_update.update_h(ehs, chs) fields.enqueue_barrier() for strf, eh in zip(strf_list, ehs)[3:]: norm = np.linalg.norm(eh - fields.get(strf)[slice_xyz]) max_diff = np.abs(eh - fields.get(strf)[slice_xyz]).max() self.assertEqual(norm, 0, '%s, %s, %g, %g' % \ (self.args, strf, norm, max_diff) ) if fields.pad != 0: if strf == 'hz': norm2 = np.linalg.norm( fields.get(strf)[:, :, -fields.pad:]) else: norm2 = np.linalg.norm( fields.get(strf)[:, :, -fields.pad:]) self.assertEqual( norm2, 0, '%s, %s, %g, padding' % (self.args, strf, norm2))
mpl.rc('image', interpolation='nearest', origin='lower') fig = plt.figure(figsize=(14, 8)) # z-axis nx, ny, nz = 180, 160, 2 fields = Fields(QueueTask(), nx, ny, nz) Core(fields) Pbc(fields, 'xyz') IncidentDirect(fields, 'ey', (20, 0, 0), (20, -1, -1), tfunc) IncidentDirect(fields, 'ex', (0, 20, 0), (-1, 20, -1), tfunc) for tstep in xrange(1, tmax + 1): fields.update_e() fields.update_h() fields.enqueue_barrier() ax1 = fig.add_subplot(2, 3, 1) ax1.imshow(fields.get('ey')[:, :, nz / 2].T, vmin=-1.1, vmax=1.1) ax1.set_title('%s, ey[20,:,:]' % repr(fields.ns)) ax1.set_xlabel('x') ax1.set_ylabel('y') ax2 = fig.add_subplot(2, 3, 4) ax2.imshow(fields.get('ex')[:, :, nz / 2].T, vmin=-1.1, vmax=1.1) ax2.set_title('%s, ex[:,20,:]' % repr(fields.ns)) ax2.set_xlabel('x') ax2.set_ylabel('y') # y-axis nx, ny, nz = 180, 2, 160
plt.ion() fig = plt.figure(figsize=(14,8)) # z-axis nx, ny, nz = 180, 160, 2 fields = Fields(nx, ny, nz) Core(fields) Pbc(fields, 'xyz') IncidentDirect(fields, 'ey', (20, 0, 0), (20, ny-1, nz-1), tfunc) IncidentDirect(fields, 'ex', (0, 20, 0), (nx-1, 20, nz-1), tfunc) for tstep in xrange(1, tmax+1): fields.update_e() fields.update_h() fields.enqueue_barrier() ax1 = fig.add_subplot(2, 3, 1) ax1.imshow(fields.get('ey')[:,:,nz/2].T, vmin=-1.1, vmax=1.1) ax1.set_title('%s, ey[20,:,:]' % repr(fields.ns)) ax1.set_xlabel('x') ax1.set_ylabel('y') ax2 = fig.add_subplot(2, 3, 4) ax2.imshow(fields.get('ex')[:,:,nz/2].T, vmin=-1.1, vmax=1.1) ax2.set_title('%s, ex[:,20,:]' % repr(fields.ns)) ax2.set_xlabel('x') ax2.set_ylabel('y') # y-axis nx, ny, nz = 180, 2, 160
def runTest(self): axis, nx, ny, nz, mpi_type = self.args fields = Fields(nx, ny, nz, mpi_type=mpi_type) core = Core(fields) pbc = Pbc(fields, axis) # allocations ehs = common_update.generate_random_ehs(nx, ny, nz, fields.dtype) fields.set_ehs(*ehs) # update fields.update_e() fields.update_h() fields.enqueue_barrier() # verify getf0, getf1 = {}, {} strfs_e = {'x':['ey', 'ez'], 'y':['ex', 'ez'], 'z':['ex', 'ey']}[axis] strfs_h = {'x':['hy', 'hz'], 'y':['hx', 'hz'], 'z':['hx', 'hy']}[axis] pt0 = (0, 0, 0) pt1 = { 'x': (0, ny-2, nz-2), \ 'y': (nx-2, 0, nz-2), \ 'z': (nx-2, ny-2, 0) }[axis] getf0['e'] = GetFields(fields, strfs_e, pt0, pt1) pt0 = { 'x': (nx-1, 0, 0), \ 'y': (0, ny-1, 0), \ 'z': (0, 0, nz-1) }[axis] pt1 = { 'x': (nx-1, ny-2, nz-2), \ 'y': (nx-2, ny-1, nz-2), \ 'z': (nx-2, ny-2, nz-1) }[axis] getf1['e'] = GetFields(fields, strfs_e, pt0, pt1) pt0 = { 'x': (0, 1, 1), \ 'y': (1, 0, 1), \ 'z': (1, 1, 0) }[axis] pt1 = { 'x': (0, ny-1, nz-1), \ 'y': (nx-1, 0, nz-1), \ 'z': (nx-1, ny-1, 0) }[axis] getf0['h'] = GetFields(fields, strfs_h, pt0, pt1) pt0 = { 'x': (nx-1, 1, 1), \ 'y': (1, ny-1, 1), \ 'z': (1, 1, nz-1) }[axis] pt1 = (nx-1, ny-1, nz-1) getf1['h'] = GetFields(fields, strfs_h, pt0, pt1) for getf in getf0.values() + getf1.values(): getf.get_event().wait() for eh in ['e', 'h']: g0 = getf0[eh].get_fields() g1 = getf1[eh].get_fields() norm = np.linalg.norm(g0 - g1) ''' print eh print g0 print g1 ''' self.assertEqual(norm, 0, '%g, %s, %s' % (norm, self.args, eh))
def runTest(self): axis, nx, ny, nz, mpi_type = self.args fields = Fields(nx, ny, nz, mpi_type=mpi_type) core = Core(fields) pbc = Pbc(fields, axis) # allocations ehs = common_update.generate_random_ehs(nx, ny, nz, fields.dtype) fields.set_ehs(*ehs) # update fields.update_e() fields.update_h() fields.enqueue_barrier() # verify getf0, getf1 = {}, {} strfs_e = { 'x': ['ey', 'ez'], 'y': ['ex', 'ez'], 'z': ['ex', 'ey'] }[axis] strfs_h = { 'x': ['hy', 'hz'], 'y': ['hx', 'hz'], 'z': ['hx', 'hy'] }[axis] pt0 = (0, 0, 0) pt1 = { 'x': (0, ny-2, nz-2), \ 'y': (nx-2, 0, nz-2), \ 'z': (nx-2, ny-2, 0) }[axis] getf0['e'] = GetFields(fields, strfs_e, pt0, pt1) pt0 = { 'x': (nx-1, 0, 0), \ 'y': (0, ny-1, 0), \ 'z': (0, 0, nz-1) }[axis] pt1 = { 'x': (nx-1, ny-2, nz-2), \ 'y': (nx-2, ny-1, nz-2), \ 'z': (nx-2, ny-2, nz-1) }[axis] getf1['e'] = GetFields(fields, strfs_e, pt0, pt1) pt0 = { 'x': (0, 1, 1), \ 'y': (1, 0, 1), \ 'z': (1, 1, 0) }[axis] pt1 = { 'x': (0, ny-1, nz-1), \ 'y': (nx-1, 0, nz-1), \ 'z': (nx-1, ny-1, 0) }[axis] getf0['h'] = GetFields(fields, strfs_h, pt0, pt1) pt0 = { 'x': (nx-1, 1, 1), \ 'y': (1, ny-1, 1), \ 'z': (1, 1, nz-1) }[axis] pt1 = (nx - 1, ny - 1, nz - 1) getf1['h'] = GetFields(fields, strfs_h, pt0, pt1) for getf in getf0.values() + getf1.values(): getf.get_event().wait() for eh in ['e', 'h']: g0 = getf0[eh].get_fields() g1 = getf1[eh].get_fields() norm = np.linalg.norm(g0 - g1) ''' print eh print g0 print g1 ''' self.assertEqual(norm, 0, '%g, %s, %s' % (norm, self.args, eh))