def test_WrongTrackingFunction(self):
with self.assertRaises(RuntimeError):
SparseSlices(self.rf_station,
self.beam,
self.n_slices_rf,
self.filling_pattern,
tracker='something horribly wrong')
nonuniform_profile = SparseSlices(self.rf_station, self.beam,
self.n_slices_rf,
self.filling_pattern)
self.assertEqual(nonuniform_profile.bin_centers_array.shape,
(2, self.n_slices_rf),
msg='Wrong shape of bin_centers_array!')
def test_Ctracker(self):
rtol = 1e-6 # relative tolerance
atol = 0 # absolute tolerance
nonuniform_profile = SparseSlices(self.rf_station,
self.beam,
self.n_slices_rf,
self.filling_pattern,
tracker='C',
direct_slicing=True)
for bunch in range(2):
indexes = (self.uniform_profile.bin_centers>nonuniform_profile.cut_left_array[bunch])\
* (self.uniform_profile.bin_centers<nonuniform_profile.cut_right_array[bunch])
np.testing.assert_allclose(
self.uniform_profile.bin_centers[indexes],
nonuniform_profile.bin_centers_array[bunch],
rtol=rtol,
atol=atol,
err_msg=f'Bins for bunch {bunch} do not agree ' +
'for tracker="C"')
np.testing.assert_allclose(
self.uniform_profile.n_macroparticles[indexes],
nonuniform_profile.n_macroparticles_array[bunch],
rtol=rtol,
atol=atol,
err_msg=f'Profiles for bunch {bunch} do not agree ' +
'for tracker="C"')
distribution_type=distribution_type,
distribution_variable=distribution_variable,
seed=134253)
indexes = np.arange(n_macroparticles)
for i in range(int(np.sum(filling_pattern))):
beam.dt[indexes[int(i * len(beam.dt) //
np.sum(filling_pattern))]:indexes[int(
(i + 1) * len(beam.dt) // np.sum(filling_pattern) -
1)]] += (bucket_length *
np.where(filling_pattern)[0][i])
import time
slice_beam = SparseSlices(RF_sct_par, beam, n_slices, filling_pattern)
t0 = time.time()
slice_beam.track()
print('Time for optimized C++ track ', time.time() - t0)
plt.figure()
for i in range(int(np.sum(filling_pattern))):
plt.plot(slice_beam.profiles_list[i].bin_centers,
slice_beam.profiles_list[i].n_macroparticles,
'b',
lw=2)
for i in range(int(np.sum(filling_pattern))):
slice_beam.profiles_list[i].n_macroparticles *= 0
slice_beam = SparseSlices(RF_sct_par,
matched_from_distribution_function(beam, full_tracker, emittance=emittance,
distribution_type=distribution_type,
distribution_variable=distribution_variable
, seed=134253)
indexes = np.arange(n_macroparticles)
for i in range(int(np.sum(filling_pattern))):
beam.dt[indexes[int(i*len(beam.dt)//np.sum(filling_pattern))]:
indexes[int((i+1)*len(beam.dt)//np.sum(filling_pattern)-1)]] += (
bucket_length * np.where(filling_pattern)[0][i])
import time
slice_beam = SparseSlices(RF_sct_par, beam, n_slices, filling_pattern)
t0 = time.time()
slice_beam.track()
print( 'Time for optimized C++ track ', time.time() - t0 )
plt.figure()
for i in range(int(np.sum(filling_pattern))):
plt.plot(slice_beam.slices_array[i].bin_centers,
slice_beam.slices_array[i].n_macroparticles,'b',lw=2)
for i in range(int(np.sum(filling_pattern))):
slice_beam.slices_array[i].n_macroparticles *= 0