def analyse_with_bbq_corrections(opt):
""" Create amplitude detuning analysis with BBQ correction from timber data.
"""
LOG.info("Starting Amplitude Detuning Analysis")
_save_options(opt)
with DebugMode(active=opt.debug, log_file=opt.logfile):
opt, filter_opt = _check_analyse_opt(opt)
# get data
kick_df = read_two_kick_files_from_folder(opt.kick)
bbq_df = None
if opt.bbq_in is not None:
bbq_df = _get_bbq_data(opt.beam, opt.bbq_in, kick_df)
x_interval = get_approx_bbq_interval(bbq_df, kick_df.index,
opt.window_length)
# add moving average to kick
kick_df, bbq_df = kick_file_modifiers.add_moving_average(
kick_df, bbq_df, filter_opt)
# add corrected values to kick
kick_df = kick_file_modifiers.add_corrected_natural_tunes(kick_df)
kick_df = kick_file_modifiers.add_total_natq_std(kick_df)
kick_plane = opt.plane
# amplitude detuning odr
for tune_plane in PLANES:
for corrected in [False, True]:
if corrected and opt.bbq_in is None:
continue
# get the proper data
data_df = kick_file_modifiers.get_ampdet_data(
kick_df, kick_plane, tune_plane, corrected=corrected)
# make the odr
odr_fit = fitting_tools.do_odr(x=data_df['action'],
y=data_df['tune'],
xerr=data_df['action_err'],
yerr=data_df['tune_err'],
order=opt.detuning_order)
kick_df = kick_file_modifiers.add_odr(kick_df,
odr_fit,
kick_plane,
tune_plane,
corrected=corrected)
# output kick and bbq data
if opt.output:
opt.output.mkdir(parents=True, exist_ok=True)
write_timed_dataframe(opt.output / get_kick_out_name(), kick_df)
if bbq_df is not None:
write_timed_dataframe(opt.output / get_bbq_out_name(),
bbq_df.loc[x_interval[0]:x_interval[1]])
return kick_df, bbq_df
def main(opt):
LOG.info("Plotting Amplitude Detuning Results.")
_save_options(opt)
_check_opt(opt)
kick_plane = opt.plane
figs = {}
_set_plotstyle(opt.manual_style)
limits = opt.get_subdict(['x_lim', 'y_lim'])
for tune_plane in PLANES:
for corrected in [False, True]:
corr_label = "_corrected" if corrected else ""
acd_corr = 1
if opt.correct_acd and (kick_plane == tune_plane):
acd_corr = 0.5
fig = plt.figure()
ax = fig.add_subplot(111)
for idx, (kick, label) in enumerate(zip(opt.kicks, opt.labels)):
kick_df = kick_mod.read_timed_dataframe(kick) if isinstance(kick, str) else kick
try:
data_df = kick_mod.get_ampdet_data(kick_df, kick_plane, tune_plane, corrected=corrected)
except KeyError:
continue # should only happen when there is no 'corrected' columns
odr_fit = kick_mod.get_odr_data(kick_df, kick_plane, tune_plane,
order=opt.detuning_order, corrected=corrected)
data_df, odr_fit = _correct_and_scale(data_df, odr_fit,
opt.action_unit, opt.action_plot_unit,
10**opt.tune_scale, acd_corr)
_plot_detuning(ax, data=data_df, label=label, limits=limits,
odr_fit=odr_fit,
color=pcolors.get_mpl_color(idx),
action_unit=opt.action_plot_unit, tune_scale=10**opt.tune_scale)
ax_labels = const.get_paired_lables(tune_plane, kick_plane, opt.tune_scale)
id_str = f"dQ{tune_plane.upper():s}d2J{kick_plane.upper():s}{corr_label:s}"
pannot.set_name(id_str, fig)
_format_axes(ax, labels=ax_labels, limits=limits)
if opt.show:
plt.show()
if opt.output:
output = Path(opt.output)
fig.savefig(f'{output.with_suffix("")}_{id_str}{output.suffix}')
figs[id_str] = fig
return figs
def analyse_with_bbq_corrections(
opt: DotDict) -> Tuple[TfsDataFrame, TfsDataFrame]:
"""
Create amplitude detuning analysis with BBQ correction from timber data.
Returns:
The amplitude detuning analysis results as a TfsDataFrame and the BBQ data as a TfsDataFrame.
"""
LOG.info("Starting Amplitude Detuning Analysis")
_save_options(opt)
opt, filter_opt = _check_analyse_opt(opt)
LOG.debug("Getting data from kick files")
kick_df = read_two_kick_files_from_folder(opt.kick)
bbq_df = None
if opt.bbq_in is not None:
bbq_df = _get_bbq_data(opt.beam, opt.bbq_in, kick_df)
x_interval = get_approx_bbq_interval(bbq_df, kick_df.index,
opt.window_length)
LOG.debug("Adding moving average data to kick data")
kick_df, bbq_df = kick_file_modifiers.add_moving_average(
kick_df, bbq_df, filter_opt)
LOG.debug("Adding corrected natural tunes and stdev to kick data")
kick_df = kick_file_modifiers.add_corrected_natural_tunes(kick_df)
kick_df = kick_file_modifiers.add_total_natq_std(kick_df)
kick_plane = opt.plane
LOG.info("Performing amplitude detuning odr")
for tune_plane in PLANES:
for corrected in [False, True]:
if corrected and opt.bbq_in is None:
continue
LOG.debug("Getting ampdet data")
data_df = kick_file_modifiers.get_ampdet_data(kick_df,
kick_plane,
tune_plane,
corrected=corrected)
LOG.debug("Fitting ODR to kick data")
odr_fit = fitting_tools.do_odr(
x=data_df["action"],
y=data_df["tune"],
xerr=data_df["action_err"],
yerr=data_df["tune_err"],
order=opt.detuning_order,
)
kick_df = kick_file_modifiers.add_odr(kick_df,
odr_fit,
kick_plane,
tune_plane,
corrected=corrected)
# output kick and bbq data
if opt.output:
LOG.info(
f"Writing kick and BBQ data to files in directory '{opt.output.absolute()}'"
)
opt.output.mkdir(parents=True, exist_ok=True)
write_timed_dataframe(opt.output / get_kick_out_name(), kick_df)
if bbq_df is not None:
write_timed_dataframe(opt.output / get_bbq_out_name(),
bbq_df.loc[x_interval[0]:x_interval[1]])
return kick_df, bbq_df