def test_one_important_number(some_numbers):
errors = np.ones(10)
errors[1:] = np.inf
assert stats.circular_mean(some_numbers, errors=errors) == some_numbers[0]
with pytest.warns(RuntimeWarning):
assert stats.circular_error(some_numbers, errors=errors, t_value_corr=False) ==\
stats.circular_error(errors[0], errors=1., t_value_corr=False)
def test_two_important_number(some_numbers):
errors = np.ones(10)
errors[2:] = np.inf
assert stats.circular_mean(
some_numbers, errors=errors) == stats.circular_mean(some_numbers[:2])
assert stats.circular_error(some_numbers, errors=errors, t_value_corr=False) ==\
stats.circular_error(some_numbers[:2], errors=np.ones(2), t_value_corr=False)
def test_circular_empties():
empty = np.array([])
with pytest.warns(RuntimeWarning):
stats.circular_mean(empty)
with pytest.warns(RuntimeWarning):
stats.circular_error(empty)
def test_circular_nans(a_nan):
assert np.isnan(stats.circular_mean(a_nan))
assert np.isnan(stats.circular_error(a_nan))
def test_circular_zeros(zeros):
assert stats.circular_mean(zeros) == 0
assert stats.circular_error(zeros) == 0
def get_phases(meas_input,
input_files,
model,
plane,
compensate=None,
no_errors=False):
"""
Computes phase advances among all BPMs.
Args:
meas_input: OpticsInput object
input_files: InputFiles object
model: model tfs_panda to be used
plane: "X" or "Y"
compensate: (driven_tune,free_tune,ac2bpm object)
no_errors: if True measured errors shall not be propagated (only their spread)
Returns:
dictionary of DataFrames indexed (BPMi x BPMj) yielding phase advance phi_ij
"MEAS" measured phase advances
"ERRMEAS" errors of measured phase advances
"MODEL" model phase advances
+------++--------+--------+--------+--------+
| || BPM1 | BPM2 | BPM3 | BPM4 |
+======++========+========+========+========+
| BPM1 || 0 | phi_12 | phi_13 | phi_14 |
+------++--------+--------+--------+--------+
| BPM2 || phi_21 | 0 | phi_23 | phi_24 |
+------++--------+--------+--------+--------+
| BPM3 || phi_31 | phi_32 | 0 | phi_34 |
+------++--------+--------+--------+--------+
The phase advance between BPM_i and BPM_j can be obtained via:
phase_advances["MEAS"].loc[BPMi,BPMj]
list of output data frames(for files)
"""
phase_frame = pd.DataFrame(model).loc[:, ['S', 'MU' + plane]]
how = 'outer' if meas_input.union else 'inner'
phase_frame = pd.merge(phase_frame,
input_files.joined_frame(
plane, ['MU' + plane, 'ERR_MU' + plane],
zero_dpp=True,
how=how),
how='inner',
left_index=True,
right_index=True)
phases_mdl = phase_frame.loc[:, 'MU' + plane].values
phase_advances = {
"MODEL":
_get_square_data_frame(
(phases_mdl[np.newaxis, :] - phases_mdl[:, np.newaxis]) % 1.0,
phase_frame.index)
}
phases_meas = input_files.get_data(
phase_frame,
'MU' + plane) * meas_input.accelerator.get_beam_direction()
phases_errors = input_files.get_data(phase_frame, 'ERR_MU' + plane)
if compensate is not None:
(driven_tune, free_tune, ac2bpmac) = compensate
k_bpmac = ac2bpmac[2]
phase_corr = ac2bpmac[1] - phases_meas[k_bpmac] + (0.5 * driven_tune)
phases_meas = phases_meas + phase_corr[np.newaxis, :]
r = get_lambda(driven_tune % 1.0, free_tune % 1.0)
phases_meas[k_bpmac:, :] = phases_meas[k_bpmac:, :] - driven_tune
psi = (np.arctan((1 - r) / (1 + r) * np.tan(2 * np.pi * phases_meas)) /
(2 * np.pi)) % 0.5
phases_meas = np.where(phases_meas % 1.0 > 0.5, psi + .5, psi)
phases_meas[k_bpmac:, :] = phases_meas[k_bpmac:, :] + free_tune
if phases_meas.ndim < 2:
phase_advances["MEAS"] = _get_square_data_frame(
(phases_meas[np.newaxis, :] - phases_meas[:, np.newaxis]) % 1.0,
phase_frame.index)
phase_advances["ERRMEAS"] = _get_square_data_frame(
np.zeros((len(phases_meas), len(phases_meas))), phase_frame.index)
return phase_advances
if meas_input.union:
mask = np.isnan(phases_meas)
phases_meas[mask], phases_errors[mask] = 0.0, np.inf
if no_errors:
phases_errors[~mask] = 1e-10
elif no_errors:
phases_errors = None
phases_3d = phases_meas[np.newaxis, :, :] - phases_meas[:, np.newaxis, :]
if phases_errors is not None:
errors_3d = phases_errors[
np.newaxis, :, :] + phases_errors[:, np.newaxis, :]
else:
errors_3d = None
phase_advances["MEAS"] = _get_square_data_frame(
stats.circular_mean(phases_3d, period=1, errors=errors_3d, axis=2) %
1.0, phase_frame.index)
phase_advances["ERRMEAS"] = _get_square_data_frame(
stats.circular_error(phases_3d, period=1, errors=errors_3d, axis=2),
phase_frame.index)
return phase_advances, [
_create_output_df(phase_advances, phase_frame, plane),
_create_output_df(phase_advances, phase_frame, plane, tot=True)
]
def get_phases(meas_input, input_files, model, plane, compensate=None, no_errors=False):
"""
Computes phase advances among all BPMs.
Args:
meas_input: OpticsInput object
input_files: InputFiles object
model: model tfs_panda to be used
plane: "X" or "Y"
compensate: (driven_tune,free_tune,ac2bpm object)
no_errors: if True measured errors shall not be propagated (only their spread)
Returns:
dictionary of DataFrames indexed (BPMi x BPMj) yielding phase advance phi_ij
"MEAS" measured phase advances
"ERRMEAS" errors of measured phase advances
"MODEL" model phase advances
+------++--------+--------+--------+--------+
| || BPM1 | BPM2 | BPM3 | BPM4 |
+======++========+========+========+========+
| BPM1 || 0 | phi_12 | phi_13 | phi_14 |
+------++--------+--------+--------+--------+
| BPM2 || phi_21 | 0 | phi_23 | phi_24 |
+------++--------+--------+--------+--------+
| BPM3 || phi_31 | phi_32 | 0 | phi_34 |
+------++--------+--------+--------+--------+
The phase advance between BPM_i and BPM_j can be obtained via:
phase_advances["MEAS"].loc[BPMi,BPMj]
list of output data frames(for files)
"""
phase_frame = pd.DataFrame(model).loc[:, ['S', 'MU' + plane]]
how = 'outer' if meas_input.union else 'inner'
phase_frame = pd.merge(phase_frame,
input_files.joined_frame(plane, ['MU' + plane, 'ERR_MU' + plane],
zero_dpp=True, how=how),
how='inner', left_index=True, right_index=True)
phases_mdl = phase_frame.loc[:, 'MU' + plane].values
phase_advances = {"MODEL": _get_square_data_frame(
(phases_mdl[np.newaxis, :] - phases_mdl[:, np.newaxis]) % 1.0, phase_frame.index)}
phases_meas = input_files.get_data(phase_frame, 'MU' + plane) * meas_input.accelerator.get_beam_direction()
phases_errors = input_files.get_data(phase_frame, 'ERR_MU' + plane)
if compensate is not None:
(driven_tune, free_tune, ac2bpmac) = compensate
k_bpmac = ac2bpmac[2]
phase_corr = ac2bpmac[1] - phases_meas[k_bpmac] + (0.5 * driven_tune)
phases_meas = phases_meas + phase_corr[np.newaxis, :]
r = get_lambda(driven_tune % 1.0, free_tune % 1.0)
phases_meas[k_bpmac:, :] = phases_meas[k_bpmac:, :] - driven_tune
psi = (np.arctan((1 - r) / (1 + r) * np.tan(2 * np.pi * phases_meas)) / (2 * np.pi)) % 0.5
phases_meas = np.where(phases_meas % 1.0 > 0.5, psi + .5, psi)
phases_meas[k_bpmac:, :] = phases_meas[k_bpmac:, :] + free_tune
if phases_meas.ndim < 2:
phase_advances["MEAS"] = _get_square_data_frame(
(phases_meas[np.newaxis, :] - phases_meas[:, np.newaxis]) % 1.0, phase_frame.index)
phase_advances["ERRMEAS"] = _get_square_data_frame(
np.zeros((len(phases_meas), len(phases_meas))), phase_frame.index)
return phase_advances
if meas_input.union:
mask = np.isnan(phases_meas)
phases_meas[mask], phases_errors[mask] = 0.0, np.inf
if no_errors:
phases_errors[~mask] = 1e-10
elif no_errors:
phases_errors = None
phases_3d = phases_meas[np.newaxis, :, :] - phases_meas[:, np.newaxis, :]
if phases_errors is not None:
errors_3d = phases_errors[np.newaxis, :, :] + phases_errors[:, np.newaxis, :]
else:
errors_3d = None
phase_advances["MEAS"] = _get_square_data_frame(stats.circular_mean(
phases_3d, period=1, errors=errors_3d, axis=2) % 1.0, phase_frame.index)
phase_advances["ERRMEAS"] = _get_square_data_frame(stats.circular_error(
phases_3d, period=1, errors=errors_3d, axis=2), phase_frame.index)
return phase_advances, [_create_output_df(phase_advances, phase_frame, plane),
_create_output_df(phase_advances, phase_frame, plane, tot=True)]
