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BQM.py
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BQM.py
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import lhc_log_db_query as lldb
import timber_manag3 as tm
import timestamp_helpers as th
from zip_mat import save_zip
#~ from optics_and_betagamma import beta_opt_lib, retrieve_betagamma
#~
import os
#~ import time
import numpy as np
import scipy.io as sio
#~ import scipy.optimize as op
import shutil
import gzip
#~ import pickle
class BQM(object):
def __init__(self, complete_path):
temp_filename = complete_path.split('.gz')[0] + 'uzipd'
with open(temp_filename, "wb") as tmp:
shutil.copyfileobj(gzip.open(complete_path), tmp)
dict_bqm = sio.loadmat(temp_filename)
os.remove(temp_filename)
self.attenuator_ppm_setting = np.int(np.squeeze(dict_bqm['attenuator_ppm_setting']))
self.filled_buckets = np.squeeze(dict_bqm['filled_buckets'])
self.raw_waveform_inj = dict_bqm['raw_waveform_inj'][0]
self.dict_bqm = dict_bqm
print dict_bqm.keys()
#~
#~ self.bunch_selection = np.squeeze(dict_ws['bunchSelection'])
#~ self.bunch_list = self._bunchSelection2bunchList()
#~ #self.bunch_list_timber = np.squeeze(dict_ws['bunchListTimber'])
#~ self.device_name = str(dict_ws['device_name'][0])
#~ self.gain = np.float_(np.squeeze(dict_ws['gain']))
#~ self.t_stamp_unix = np.float_(np.squeeze(dict_ws['t_stamp_unix']))
#~ self.cycle_time = str(dict_ws['cycleTime'][0])
#~ self.acq_time = str(dict_ws['acqTime'][0])
#~ self.acq_delay = np.float_(np.squeeze(dict_ws['acqDelay']))
#~ self.acq_type = np.int_(np.squeeze(dict_ws['acqType']))
#~ #self.beta_fct = np.squeeze(dict_ws['beta'])
#~ #self.energy_in = np.squeeze(dict_ws['energy1'])
#~ #self.energy_out = np.squeeze(dict_ws['energy2'])
#~
#~ if self.acq_type == 1:
#~ self.n_bunches = 1
#~ #self.bunch_list_timber = np.array([0])
#~ else:
#~ self.n_bunches = np.int_(np.squeeze(dict_ws['nbBunches']))
#~
#~ # Scan 'in'
#~ self.acq_time_in_cycle_set_in = np.float_(np.squeeze(dict_ws['acqTimeInCycleSet1']))
#~ self.proj_position_set_in = np.float_(np.squeeze(dict_ws['projPositionSet1']))
#~ if self.acq_type == 1:
#~ self.proj_profile_set_in = np.float_(np.squeeze(dict_ws['projDataSet1']))
#~ else:
#~ self.proj_profile_set_in = np.reshape(np.float_(np.squeeze(dict_ws['projBunchDataSet1'])), (self.n_bunches, -1))
#~
#~ self._p0_in = [None for i in xrange(self.n_bunches)]
#~ self.p1_in = [None for i in xrange(self.n_bunches)]
#~ self.sigma_in = [None for i in xrange(self.n_bunches)]
#~ self.phys_emit_in = [None for i in xrange(self.n_bunches)]
#~ self.norm_emit_in = [None for i in xrange(self.n_bunches)]
#~ self.area_in = [None for i in xrange(self.n_bunches)]
#~
#~ # Scan 'out'
#~ self.acq_time_in_cycle_set_out = np.float_(np.squeeze(dict_ws['acqTimeInCycleSet2']))
#~ self.proj_position_set_out = np.float_(np.squeeze(dict_ws['projPositionSet2']))
#~ if self.acq_type == 1:
#~ self.proj_profile_set_out = np.float_(np.squeeze(dict_ws['projDataSet2']))
#~ else:
#~ self.proj_profile_set_out = np.reshape(np.float_(np.squeeze(dict_ws['projBunchDataSet2'])), (self.n_bunches, -1))
#~
#~ self._p0_out = [None for i in xrange(self.n_bunches)]
#~ self.p1_out = [None for i in xrange(self.n_bunches)]
#~ self.sigma_out = [None for i in xrange(self.n_bunches)]
#~ self.phys_emit_out = [None for i in xrange(self.n_bunches)]
#~ self.norm_emit_out = [None for i in xrange(self.n_bunches)]
#~ self.area_out = [None for i in xrange(self.n_bunches)]
#~
#~ if init_with_fits:
#~ self.compute_fits(self.scans_in, self.scans_out)
#~
#~ if (self.optics is not None) and (self.cycle_csv_filepath is not None):
#~ self.beta_opt = beta_opt_lib[self.device_name +':'+ self.optics]
#~ self.betagamma_in = retrieve_betagamma(self.cycle_csv_filepath, self.acq_time_in_cycle_set_in)
#~ self.betagamma_out = retrieve_betagamma(self.cycle_csv_filepath, self.acq_time_in_cycle_set_out)
#~ self.compute_emittances()
#~
#~ def get_fitted_profile_in(self, bunch_index=0):
#~ return self._fitfunc(self.p1_in[bunch_index], self.proj_position_set_in)
#~
#~ def get_fitted_profile_out(self, bunch_index=0):
#~ return self._fitfunc(self.p1_out[bunch_index], self.proj_position_set_out)
#~
#~ def _bunchSelection2bunchList(self):
#~ '''
#~ The bunch_selection variable stores the selected bunch pattern
#~ in binary form (two's complement). Each entry in the
#~ bunch_selection list corresponds to 32 bunch slots (32 bit
#~ pattern), i.e. entry 0 describes bunches 0-31, entry 1 bunches
#~ 32-63, etc. The binary patterns are read from the right and they
#~ must be padded to 32 bits from the left. To generate the full
#~ boolean mask, the 32 bit patterns (corresponding to one of the
#~ entries in the bunch_selection list) are reversed and
#~ concatenated. The mask tells us, which of the bunch slots were
#~ selected for measurements assuming the following order [ 1, 2, 3,
#~ ... 32*len(bunch_selection) + 1 ].
#~ '''
#~ bin_list = ''
#~ for selection in self.bunch_selection:
#~ bin_list += ((bin(selection % (1<<32))[2:]).rjust(32, '0'))[::-1]
#~ mask = np.bool_(np.array(list(bin_list)))
#~ return np.array(range(1, 32*len(self.bunch_selection)+1))[mask]
#~
#~ def _compute_single_fit(self, x, y, N_points_ma):
#~ try:
#~ if x[-1]<x[0]:
#~ x=x[::-1]
#~ y=y[::-1]
#~
#~ indx_max = np.argmax(y)
#~ mu0 = x[indx_max]
#~ window = 2*N_points_ma
#~ x_tmp = x[indx_max-window:indx_max+window]
#~ y_tmp = y[indx_max-window:indx_max+window]
#~ offs0 = min(y_tmp)
#~ ampl = max(y_tmp)-offs0
#~ x1 = x_tmp[np.searchsorted(y_tmp[:window], offs0+ampl/2)]
#~ x2 = x_tmp[np.searchsorted(-y_tmp[window:], -offs0+ampl/2)]
#~ FWHM = x2-x1
#~ sigma0 = FWHM/2.355
#~
#~ p0 = [offs0, ampl, mu0, sigma0,0.]
#~ p1, success = op.leastsq(self._errfunc, p0[:], args=(x,y))
#~ if not success:
#~ raise ValueError
#~ except:
#~ p0 = 4 * [0.]
#~ p1 = 4 * [0.]
#~
#~ return p0, p1
#~
#~ def compute_fits(self, scans_in=True, scans_out=False, N_points_ma=50):
#~ if scans_in:
#~ for i in xrange(self.n_bunches):
#~ if self.acq_type == 1:
#~ proj_prof = self.proj_profile_set_in
#~ else:
#~ proj_prof = self.proj_profile_set_in[i]
#~ self._p0_in[i], self.p1_in[i] = self._compute_single_fit(self.proj_position_set_in, proj_prof, N_points_ma)
#~ self.sigma_in[i] = abs(self.p1_in[i][3]*1e-6)
#~ self.area_in[i] = abs(self.p1_in[i][1]*self.p1_in[i][3])
#~
#~ if self.scans_out:
#~ for i in xrange(self.n_bunches):
#~ if self.acq_type == 1:
#~ proj_prof = self.proj_profile_set_out
#~ else:
#~ proj_prof = self.proj_profile_set_out[i]
#~ self._p0_out[i], self.p1_out[i] = self._compute_single_fit(self.proj_position_set_out, self.proj_profile_set_out[i], N_points_ma)
#~ self.sigma_out[i] = abs(self.p1_out[i][3]*1e-6)
#~ self.area_out[i] = abs(self.p1_out[i][1]*self.p1_out[i][3])
#~
#~ def compute_emittances(self):
#~
#~ if self.scans_in:
#~ if self.p1_in == None:
#~ self.compute_fits(self.scans_in, False)
#~ self.phys_emit_in = np.array(self.sigma_in)**2/self.beta_opt
#~ self.norm_emit_in = self.betagamma_in*self.phys_emit_in
#~ self.phys_emit_in[np.array(self.sigma_in)==0] = 0.
#~ self.norm_emit_in[np.array(self.sigma_in)==0] = 0.
#~
#~ if self.scans_out:
#~ if self.p1_out == None:
#~ self.compute_fits(False, self.scans_out)
#~ self.phys_emit_out = np.array(self.sigma_out)**2/self.beta_opt
#~ self.norm_emit_out = self.betagamma_out*self.phys_emit_out
#~ self.phys_emit_out[np.array(self.sigma_out)==0] = 0.
#~ self.norm_emit_out[np.array(self.sigma_out)==0] = 0.
def timber_to_csv(start_time, end_time='Now', filename_prefix='SPSmeas_', outp_folder=None,
t_query_interval=1800., t_query_interval_fine=60.,
filename_retrieved='bqm_retrieved.txt'):
try:
with open(filename_retrieved, 'r') as fid:
list_retrieved = fid.read().split('\n')
except IOError:
list_retrieved = []
if type(start_time) is str:
start_tstamp_unix = th.localtime2unixstamp(start_time)
else:
start_tstamp_unix = start_time
if end_time == 'Now':
end_tstamp_unix = time.mktime(time.localtime())
elif type(end_time) is str:
end_tstamp_unix = th.localtime2unixstamp(end_time)
else:
end_tstamp_unix = end_time
if outp_folder == None:
outp_folder = 'BQM_CSVfromTimberDB'
if not os.path.exists(outp_folder):
os.makedirs(outp_folder)
varlist = get_timber_varlist()
print 'VARLIST', varlist
t_queries = np.arange(start_tstamp_unix, end_tstamp_unix, t_query_interval)
for t_query in t_queries:
outp_compl_path = outp_folder + '/' + filename_prefix + '%d_%d.csv'%(t_query, t_query_interval)
if outp_compl_path in list_retrieved:
continue
lldb.dbquery(varlist, t_query, t_query+t_query_interval, outp_compl_path)
with open(filename_retrieved, 'a+') as fid:
fid.write(outp_compl_path + '\n')
# In case something goes wrong with the request -> use a shorter interval.
if not os.path.exists(outp_compl_path):
t_queries_fine = np.arange(t_query, t_query + t_query_interval, t_query_interval_fine)
for t_query_fine in t_queries_fine:
time_str = (th.unixstamp2localtime(t_query_fine)).replace(' ', '_')
outp_compl_path = outp_folder + '/' + filename_prefix + '%d_%d.csv'%(t_query_fine, t_query_interval_fine)
if outp_compl_path in list_retrieved:
continue
lldb.dbquery(varlist, t_query_fine, t_query_fine+t_query_interval_fine, outp_compl_path)
with open(filename_retrieved, 'a+') as fid:
fid.write(outp_compl_path + '\n')
if not os.path.exists(outp_compl_path):
print '!!! WARNING: Could not retrieve data in %s, %d s'%(time_str, t_query_interval_fine)
def csv_to_file(in_complete_path, mat_filename_prefix='SPSmeas_', outp_folder='bqm',
filename_converted='bqm_converted.txt'):
try:
with open(filename_converted, 'r') as fid:
list_converted = fid.read().split('\n')
except IOError:
list_converted = []
if in_complete_path in list_converted:
return
variables = tm.parse_timber_file(in_complete_path, verbose=False)
N_meas = len(variables['SPS.BQM:NO_BUNCHES'].t_stamps)
for i in [0]: #xrange(N_meas):
dict_meas = {\
'attenuator_ppm_setting': np.int_(variables['SPS.APWL.ATTENUATOR:PPM_SETTING'].values[i][0]),
#~ 'beam_ok': variables['SPS.BQM:BEAM_OK'].values[i][0],
#'no_bunches': variables['SPS.BQM:NO_BUNCHES'].values[i][0],
#'bunch_intensities': variables['SPS.BQM:BUNCH_INTENSITIES'].values[i],
#~ 'bunch_intensity_max_thresh': variables['SPS.BQM:BUNCH_INTENSITY_MAX_THRESH'].values[i][0],
#~ 'bunch_intensity_min_thresh': variables['SPS.BQM:BUNCH_INTENSITY_MIN_THRESH'].values[i][0],
#'bunch_intensity_ok': variables['SPS.BQM:BUNCH_INTENSITY_OK'].values[i][0],
#'bunch_lengths': variables['SPS.BQM:BUNCH_LENGTHS'].values[i],
#'bunch_lengths_inj': variables['SPS.BQM:BUNCH_LENGTHS_INJ'].values[i],
#~ 'bunch_lengths_inj_max_thresh': variables['SPS.BQM:BUNCH_LENGTH_INJ_MAX_THRESH'].values[i][0],
#~ 'bunch_length_max_thresh': variables['SPS.BQM:BUNCH_LENGTH_MAX_THRESH'].values[i][0],
#~ 'bunch_length_min_thresh': variables['SPS.BQM:BUNCH_LENGTH_MIN_THRESH'].values[i][0],
#~ 'bunch_length_ok': variables['SPS.BQM:BUNCH_LENGTH_OK'].values[i][0],
#~ 'bunch_length_stddev_ok': variables['SPS.BQM:BUNCH_LENGTH_STDDEV_OK'].values[i][0],
#~ 'bunch_length_stddev_thresh': variables['SPS.BQM:BUNCH_LENGTH_STDDEV_THRESH'].values[i][0],
#~ 'bunch_pattern_ok': variables['SPS.BQM:BUNCH_PATTERN_OK'].values[i][0],
#'bunch_peaks': variables['SPS.BQM:BUNCH_PEAKS'].values[i],
#'bunch_peaks_inj': variables['SPS.BQM:BUNCH_PEAKS_INJ'].values[i],
#~ 'bunch_peak_max_thresh': variables['SPS.BQM:BUNCH_PEAK_MAX_THRESH'].values[i][0],
#~ 'bunch_peak_min_thresh': variables['SPS.BQM:BUNCH_PEAK_MIN_THRESH'].values[i][0],
#'bunch_peak_ok': variables['SPS.BQM:BUNCH_PEAK_OK'].values[i][0],
#'bunch_peak_stddev_ok': variables['SPS.BQM:BUNCH_PEAK_STDDEV_OK'].values[i][0],
#~ 'bunch_peak_stddev_thresh': variables['SPS.BQM:BUNCH_PEAK_STDDEV_THRESH'].values[i][0],
#'bunch_positions': variables['SPS.BQM:BUNCH_POSITIONS'].values[i],
#'bunch_positions_inj': variables['SPS.BQM:BUNCH_POSITIONS_INJ'].values[i],
#'diagnostics': variables['SPS.BQM:DIAGNOSTIC'].values[i][0],
#'doublets': variables['SPS.BQM:DOUBLETS'].values[i][0],
#'doublets_int_splits': variables['SPS.BQM:DOUBLET_INT_SPLITS'].values[i],
#'doublets_int_splits_max_thres': variables['SPS.BQM:DOUBLET_INT_SPLIT_MAX_THRESH'].values[i][0],
#'doublets_int_splits_min_thres': variables['SPS.BQM:DOUBLET_INT_SPLIT_MIN_THRESH'].values[i][0],
#~ 'dump_enabled': variables['SPS.BQM:DUMP_ENABLED'].values[i][0],
#'doublets_int_splits_ok': variables['SPS.BQM:DOUBLET_INT_SPLIT_OK'].values[i][0],
'filled_buckets': np.int_(np.float_(variables['SPS.BQM:FILLED_BUCKETS'].values[i])),
#'first_bunch_pos_inj_ok': variables['SPS.BQM:FIRST_BUNCH_POS_INJ_OK'].values[i][0],
'raw_waveform_inj': np.float_(variables['SPS.BQM:RAW_WAVEFORM_INJ'].values[i]),
}
t_stamp_unix = variables['SPS.BQM:NO_BUNCHES'].t_stamps[i]
out_filename = mat_filename_prefix + 'SPS.BQM' + ('_%d'%t_stamp_unix)
out_complete_path = outp_folder +'/'+ out_filename
print out_complete_path
if not os.path.isdir(outp_folder):
print 'I create folder: '+ outp_folder
os.makedirs(outp_folder)
sio.savemat(out_complete_path, dict_meas, oned_as='row')
save_zip(out_complete_path)
'''
with open(filename_converted, 'a+') as fid:
fid.write(in_complete_path + '\n')
'''
def make_mat_files(start_time, end_time='Now', csv_data_folder='BQM_CSVfromTimberDB',
filename_converted='bqm_converted.txt', filename_retrieved='bqm_retrieved.txt'):
if type(start_time) is str:
start_tstamp_unix = th.localtime2unixstamp(start_time)
else:
start_tstamp_unix = start_time
if end_time == 'Now':
end_tstamp_unix = time.mktime(time.localtime())
elif type(end_time) is str:
end_tstamp_unix = th.localtime2unixstamp(end_time)
else:
end_tstamp_unix = end_time
# 1. Retrieve from timber and save as .csv.
timber_to_csv(start_tstamp_unix, end_tstamp_unix, outp_folder=csv_data_folder,
filename_retrieved=filename_retrieved)
# 2. Convert from csv to mat in given range.
file_list = os.listdir(csv_data_folder)
for filename in file_list:
tstart_filename = int(filename.split('.csv')[0].split('_')[-2])
tend_filename = tstart_filename + int(filename.split('.csv')[0].split('_')[-1])
if (tend_filename < start_tstamp_unix) or (tstart_filename > end_tstamp_unix):
continue
in_complete_path = csv_data_folder + '/' + filename
csv_to_file(in_complete_path, filename_converted=filename_converted)
'''
def make_pickle(start_from_last=True, pickle_name_ws='ws_overview.pkl', pickle_name_bct='bct_overview.pkl',
mat_folder='wirescanner', mat_file_prefix='SPSmeas_', inj_delay_to_cycle_start=1015e-3,
cycle_csv_filepath='SPSMeasurementTools/cycle_momenta/MD_SCRUB_26_L26400_Q20_2014_V1.csv'):
if not os.path.isfile(pickle_name_bct):
raise RuntimeError('Must have BCT pickle.')
with open(pickle_name_bct, 'rb') as fid:
beams = pickle.load(fid)
if os.path.isfile(pickle_name_ws) or start_from_last:
with open(pickle_name_ws) as fid:
ws_dict = pickle.load(fid)
else:
ws_dict = {}
for SPSuser in beams.keys():
if not(SPSuser in ws_dict.keys()):
ws_dict[SPSuser] = {}
ws_dict[SPSuser]['timestamp_bct'] = []
ws_dict[SPSuser]['timestamp_ws'] = []
ws_dict[SPSuser]['device_name'] = []
ws_dict[SPSuser]['bunch_list'] = []
ws_dict[SPSuser]['norm_emit_in'] = []
ws_dict[SPSuser]['norm_emit_out'] = []
ws_dict[SPSuser]['area_in'] = []
ws_dict[SPSuser]['area_out'] = []
ws_dict[SPSuser]['acq_time_in_cycle_set_in'] = []
ws_dict[SPSuser]['acq_time_in_cycle_set_out'] = []
ws_dict[SPSuser]['acq_type'] = []
list_files = os.listdir(mat_folder)
n_files = len(list_files)
n_files_no_bct = 0
if n_files == 0:
print('No ws mat files found.')
for ctr, filename in enumerate(list_files):
print('Processing file %d/%d'%(ctr, n_files))
wsobj = WireScan(mat_folder +'/'+ filename, optics='Q20', cycle_csv_filepath=cycle_csv_filepath,
scans_in=True, scans_out=True)
t_stamp = th.localtime2unixstamp(wsobj.acq_time.split('.')[0], strformat='%Y/%m/%d %H:%M:%S')
t_stamp_bct = None
SPSuser = None
guess_t_stamp_bct = int(t_stamp - inj_delay_to_cycle_start - wsobj.acq_time_in_cycle_set_in/1000.)
for SPSuser_loop in beams.keys():
t_stamps_bct = np.int_(beams[SPSuser_loop]['timestamp_float'])
for jj in xrange(4, -1, -1):
idx_tstamp = np.where(t_stamps_bct == (guess_t_stamp_bct+jj))[0]
if len(idx_tstamp) == 1:
t_stamp_bct = t_stamps_bct[idx_tstamp]
SPSuser = SPSuser_loop
break
if not(t_stamp_bct == None):
break
# mask_past = np.array(t_stamps_bct) <= t_stamp
# if any(mask_past):
# t_stamp_bct_curr = np.max(np.array(t_stamps_bct)[mask_past])
# if t_stamp_bct is None:
# t_stamp_bct = t_stamp_bct_curr
# SPSuser = SPSuser_loop
# elif t_stamp_bct_curr > t_stamp_bct:
# t_stamp_bct = t_stamp_bct_curr
# SPSuser = SPSuser_loop
if t_stamp_bct == None:
print('No corresponding BCT time stamp found')
n_files_no_bct += 1
continue
if start_from_last and len(ws_dict[SPSuser]['timestamp_ws']) > 0:
if t_stamp <= ws_dict[SPSuser]['timestamp_ws'][-1]:
continue
elif t_stamp in ws_dict[SPSuser]['timestamp_ws']:
continue
# if np.abs(t_stamp - t_stamp_bct) > 30:
# print('WARNING: WS time stamp differs from BCT time stamp by more ' +
# 'than 30s! Difference is %ds. Saving anyway.'%np.abs(t_stamp-t_stamp_bct))
ws_dict[SPSuser]['timestamp_bct'].append(t_stamp_bct)
ws_dict[SPSuser]['timestamp_ws'].append(t_stamp)
ws_dict[SPSuser]['device_name'].append(wsobj.device_name)
ws_dict[SPSuser]['bunch_list'].append(wsobj.bunch_list)
# ws_dict[SPSuser]['bunch_list'].append(wsobj.bunch_list_timber)
ws_dict[SPSuser]['norm_emit_in'].append(wsobj.norm_emit_in)
ws_dict[SPSuser]['norm_emit_out'].append(wsobj.norm_emit_out)
ws_dict[SPSuser]['area_in'].append(wsobj.area_in)
ws_dict[SPSuser]['area_out'].append(wsobj.area_out)
ws_dict[SPSuser]['acq_time_in_cycle_set_in'].append(wsobj.acq_time_in_cycle_set_in/1000.-inj_delay_to_cycle_start)
ws_dict[SPSuser]['acq_time_in_cycle_set_out'].append(wsobj.acq_time_in_cycle_set_out/1000.-inj_delay_to_cycle_start)
ws_dict[SPSuser]['acq_type'].append(wsobj.acq_type)
# Sort for ws timestamps.
for SPSuser in beams.keys():
ind_sorted = np.argsort(ws_dict[SPSuser]['timestamp_ws'])
for kk in ws_dict[SPSuser].keys():
ws_dict[SPSuser][kk] = list(np.take(ws_dict[SPSuser][kk], ind_sorted))
with open(pickle_name_ws, 'wb') as fid:
pickle.dump(ws_dict, fid)
print('Done! %d files could not be matched to a BCT timestamp.'%n_files_no_bct)
'''
def get_timber_varlist():
varlist = [\
'SPS.APWL.ATTENUATOR:PPM_SETTING',
#~ 'SPS.APWL.ATTENUATOR:SETTING',
#~ 'SPS.BQM:1ST_BUNCH_INJ',
#~ 'SPS.BQM:1ST_BUNCH_POS_FRACT',
#~ 'SPS.BQM:1ST_BUNCH_POS_OK',
'SPS.BQM:BEAM_OK',
'SPS.BQM:BUNCH_INTENSITIES',
'SPS.BQM:BUNCH_INTENSITY_MAX_THRESH',
'SPS.BQM:BUNCH_INTENSITY_MIN_THRESH',
'SPS.BQM:BUNCH_INTENSITY_OK',
'SPS.BQM:BUNCH_LENGTHS',
'SPS.BQM:BUNCH_LENGTHS_INJ',
'SPS.BQM:BUNCH_LENGTH_INJ_MAX_THRESH',
'SPS.BQM:BUNCH_LENGTH_MAX_THRESH',
'SPS.BQM:BUNCH_LENGTH_MIN_THRESH',
'SPS.BQM:BUNCH_LENGTH_OK',
'SPS.BQM:BUNCH_LENGTH_STDDEV_OK',
'SPS.BQM:BUNCH_LENGTH_STDDEV_THRESH',
'SPS.BQM:BUNCH_PATTERN_OK',
'SPS.BQM:BUNCH_PEAKS',
'SPS.BQM:BUNCH_PEAKS_INJ',
'SPS.BQM:BUNCH_PEAK_MAX_THRESH',
'SPS.BQM:BUNCH_PEAK_MIN_THRESH',
#~ 'SPS.BQM:BUNCH_PEAK_MOD_INDEX',
#~ 'SPS.BQM:BUNCH_PEAK_MOD_INDEX_OK',
#~ 'SPS.BQM:BUNCH_PEAK_MOD_INDEX_THRESH',
'SPS.BQM:BUNCH_PEAK_OK',
'SPS.BQM:BUNCH_PEAK_STDDEV_OK',
'SPS.BQM:BUNCH_PEAK_STDDEV_THRESH',
'SPS.BQM:BUNCH_POSITIONS',
'SPS.BQM:BUNCH_POSITIONS_INJ',
'SPS.BQM:DIAGNOSTIC',
'SPS.BQM:DOUBLETS',
'SPS.BQM:DOUBLET_INT_SPLITS',
'SPS.BQM:DOUBLET_INT_SPLIT_MAX_THRESH',
'SPS.BQM:DOUBLET_INT_SPLIT_MIN_THRESH',
'SPS.BQM:DOUBLET_INT_SPLIT_OK',
'SPS.BQM:DUMP_ENABLED',
'SPS.BQM:FILLED_BUCKETS',
'SPS.BQM:FIRST_BUNCH_POS_INJ_OK',
'SPS.BQM:NO_BUNCHES',
#~ 'SPS.BQM:NO_SATELLITES_INT',
#~ 'SPS.BQM:NO_SATELLITES_MID_BUCKET',
#~ 'SPS.BQM:N_ALLOWED_LONG_BUNCHES',
#~ 'SPS.BQM:OSCILLATION_DELTA_PCT',
#~ 'SPS.BQM:OSCILLATION_DELTA_PCT_THRESH',
#~ 'SPS.BQM:OSCILLATION_PEAK_PCT',
#~ 'SPS.BQM:OSCILLATION_PEAK_THRESH_PCT',
#~ 'SPS.BQM:RAW_WAVEFORM_FLATTOP',
'SPS.BQM:RAW_WAVEFORM_INJ',
#~ 'SPS.BQM:RAW_WAVEFORM_RAMP',
#~ 'SPS.BQM:SATELLITES_INT_THRESH',
#~ 'SPS.BQM:SATELLITES_MID_BUCKET_THRESH',
#~ 'SPS.BQM:SATELLITES_OK',
#~ 'SPS.BQM:STABILITY_OK',
#~ 'SPS.BQM:WHICH_WAVEFORM_INJ'\
]
return varlist