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_nanhandling():
vector = np.array([355., 0., 5., np.nan])
assert stats.circular_nanmean(vector) == stats.circular_mean(vector[:-1])
assert stats.weighted_nanmean(vector) == stats.weighted_mean(vector[:-1])
assert stats.weighted_nanrms(vector) == stats.weighted_rms(vector[:-1])
vector = np.array([[355., 0., 5., 0.], [355., 0., 5., 0.],
[355., 0., 5., np.nan]])
assert np.all(
stats.circular_nanerror(vector, axis=1) == stats.circular_error(
vector[:, :-1], axis=1))
def _process_rdt(meas_input, input_files, phase_data, invariants, plane, rdt):
df = pd.DataFrame(phase_data)
second_bpms = df.loc[:, "NAME2"].to_numpy()
df["S2"] = df.loc[second_bpms, "S"].to_numpy()
df["COUNT"] = len(input_files.dpp_frames(plane, 0))
line = _determine_line(rdt, plane)
phase_sign, suffix = get_line_sign_and_suffix(line, input_files, plane)
comp_coeffs1 = to_complex(
input_files.joined_frame(plane, [f"AMP{suffix}"],
dpp_value=0).loc[df.index, :].to_numpy(),
phase_sign *
input_files.joined_frame(plane, [f"PHASE{suffix}"],
dpp_value=0).loc[df.index, :].to_numpy())
# Multiples of tunes needs to be added to phase at second BPM if that is in second turn
phase2 = phase_sign * input_files.joined_frame(
plane, [f"PHASE{suffix}"], dpp_value=0).loc[second_bpms, :].to_numpy()
comp_coeffs2 = to_complex(
input_files.joined_frame(plane, [f"AMP{suffix}"],
dpp_value=0).loc[second_bpms, :].to_numpy(),
_add_tunes_if_in_second_turn(df, input_files, line, phase2))
# Get amplitude and phase of the line from linx/liny file
line_amp, line_phase, line_amp_e, line_phase_e = complex_secondary_lines( # TODO use the errors
df.loc[:, "MEAS"].to_numpy()[:, np.newaxis] *
meas_input.accelerator.beam_direction,
df.loc[:,
"ERRMEAS"].to_numpy()[:,
np.newaxis], comp_coeffs1, comp_coeffs2)
rdt_phases_per_file = _calculate_rdt_phases_from_line_phases(
df, input_files, line, line_phase)
rdt_angles = stats.circular_mean(rdt_phases_per_file, period=1, axis=1) % 1
df[f"PHASE"] = rdt_angles
df[f"{ERR}PHASE"] = stats.circular_error(rdt_phases_per_file,
period=1,
axis=1)
df[AMPLITUDE], df[f"{ERR}{AMPLITUDE}"] = _fit_rdt_amplitudes(
invariants, line_amp, plane, rdt)
df[f"REAL"] = np.cos(
2 * np.pi * rdt_angles) * df.loc[:, AMPLITUDE].to_numpy()
df[f"IMAG"] = np.sin(
2 * np.pi * rdt_angles) * df.loc[:, AMPLITUDE].to_numpy()
# in old files there was "EAMP" and "PHASE_STD"
return df.loc[:, [
"S", "COUNT", AMPLITUDE, f"{ERR}{AMPLITUDE}", "PHASE", f"{ERR}PHASE",
"REAL", "IMAG"
]]
def _calculate_with_compensation(meas_input,
input_files,
tunes,
plane,
model_df,
compensation='none',
no_errors=False):
"""
Calculates phase advances.
Args:
meas_input: the input object including settings and the accelerator class.
input_files: includes measurement tfs.
tunes: `TunesDict` object containing measured and model tunes and ac2bpm object
plane: marking the horizontal or vertical plane, **X** or **Y**.
no_errors: if ``True``, measured errors shall not be propagated (only their spread).
Returns:
A `dictionary` of `TfsDataFrames` 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 |
+------++--------+--------+--------+--------+
| BPM4 || phi_41 | phi_42 | phi_43 | 0 |
+------++--------+--------+--------+--------+
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)
"""
LOGGER.info("Calculating phase advances")
LOGGER.info(f"Measured tune in plane {plane} = {tunes[plane]['Q']}")
df = model_df.loc[:, ["S", f"MU{plane}"]]
how = 'outer' if meas_input.union else 'inner'
dpp_value = meas_input.dpp if "dpp" in meas_input.keys() else 0
df = pd.merge(df,
input_files.joined_frame(plane,
[f"MU{plane}", f"{ERR}MU{plane}"],
dpp_value=dpp_value,
how=how),
how='inner',
left_index=True,
right_index=True)
df[input_files.get_columns(
df, f"MU{plane}")] = (input_files.get_data(df, f"MU{plane}") *
meas_input.accelerator.beam_direction)
phases_mdl = df.loc[:, f"MU{plane}"].to_numpy()
phase_advances = {
"MODEL":
_get_square_data_frame(
(phases_mdl[np.newaxis, :] - phases_mdl[:, np.newaxis]) % 1.0,
df.index)
}
if compensation == "model":
df = _compensate_by_model(input_files, meas_input, df, plane)
phases_meas = input_files.get_data(df, f"MU{plane}")
if meas_input.compensation == "equation":
phases_meas = _compensate_by_equation(phases_meas, plane, tunes)
phases_errors = input_files.get_data(df, f"{ERR}MU{plane}")
if phases_meas.ndim < 2:
phase_advances["MEAS"] = _get_square_data_frame(
(phases_meas[np.newaxis, :] - phases_meas[:, np.newaxis]) % 1.0,
df.index)
phase_advances["ERRMEAS"] = _get_square_data_frame(
np.zeros((len(phases_meas), len(phases_meas))), df.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, df.index)
phase_advances["ERRMEAS"] = _get_square_data_frame(
stats.circular_error(phases_3d, period=1, errors=errors_3d, axis=2),
df.index)
return phase_advances, [
_create_output_df(phase_advances, df, plane),
_create_output_df(phase_advances, df, plane, tot=True)
]
def test_circular_zeros(zeros):
assert stats.circular_mean(zeros) == 0
assert stats.circular_error(zeros) == 0
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]
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_circular_empties():
empty = np.array([])
with pytest.warns(RuntimeWarning):
stats.circular_mean(empty)
with pytest.warns(RuntimeWarning):
stats.circular_error(empty)
