def store_special_qbs(self, filln, qbs_ob):
"""
Arguments:
- filln
- qbs_ob
"""
qbs_file = self.get_special_qbs_file(filln)
if not os.path.isdir(os.path.dirname(qbs_file)):
os.mkdir(os.path.dirname(qbs_file))
with h5py.File(qbs_file, 'w') as h5_handle:
h5_handle.create_dataset('timestamps', data=qbs_ob.timestamps)
h5_handle.create_dataset('variables', data=qbs_ob.variables)
qbs_dataset = h5_handle.create_dataset('data', data=qbs_ob.data)
qbs_dataset.attrs[
'time_created'] = tm.UnixTimeStamp2UTCTimberTimeString(
time.time())
def get_fill_dict(filln, h5_storage=None, use_dP=True):
fname = h5_storage.get_qbs_file(filln, use_dP=use_dP)
obhl = ob = mfm.h5_to_obj(fname)
ms0 = 0.*obhl.timestamps
dict_out = {}
for ii, vv in enumerate(obhl.variables):
tv = tm.timber_variable_list()
tv.t_stamps = obhl.timestamps
tv.ms = ms0
tv.values = obhl.data[:, ii]
dict_out[vv] = tv
return dict_out
if '--notrace' in sys.argv:
list_scan_times = []
if '--plotave' in sys.argv:
plotave = True
if '--plotaveonly' in sys.argv:
plotaveonly = True
fill_dict = {}
if os.path.isdir(data_folder_fill+'/fill_basic_data_csvs'):
# 2016+ structure
fill_dict.update(tm.parse_timber_file(
data_folder_fill+'/fill_basic_data_csvs/basic_data_fill_%d.csv'%filln,
verbose=True))
fill_dict.update(tm.parse_timber_file(
(data_folder_fill +
'/fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv'%filln),
verbose=True))
elif os.path.isdir(data_folder_fill+'/fill_basic_data_h5s'):
# 2016+ structure
fill_dict.update(tm.CalsVariables_from_h5(
data_folder_fill+'/fill_basic_data_h5s/basic_data_fill_%d.h5'%filln))
fill_dict.update(tm.CalsVariables_from_h5(
(data_folder_fill +
'/fill_bunchbybunch_data_h5s/bunchbybunch_data_fill_%d.h5'%filln)))
else:
# 2015 structure
fill_dict.update(tm.parse_timber_file(data_folder_fill+'/fill_csvs/fill_%d.csv'%filln, verbose=True))
for filln in sorted(dict_fill_bmodes.keys())[::-1]:
print('Fill %i' % filln)
h5filename = h5folder + '/imp_and_SR_fill_%d.h5' % filln
if dict_fill_bmodes[filln]['flag_complete'] is False:
print("Fill incomplete --> no h5 convesion")
continue
if os.path.isfile(h5filename):
print("Already complete and in h5")
continue
try:
fill_dict = {}
fill_dict.update(
tm.CalsVariables_from_h5(
'fill_basic_data_h5s/basic_data_fill_%d.h5' % filln))
fill_dict.update(
tm.CalsVariables_from_h5(
'fill_bunchbybunch_data_h5s/bunchbybunch_data_fill_%d.h5' %
filln))
fbct_bx = {}
bct_bx = {}
blength_bx = {}
for beam_n in (1, 2):
fbct_bx[beam_n] = FBCT(fill_dict, beam=beam_n)
bct_bx[beam_n] = BCT(fill_dict, beam=beam_n)
blength_bx[beam_n] = blength(fill_dict, beam=beam_n)
hl_imped_fill = fc.HeatLoad_calculated_fill(fill_dict,
hli_calculator,
else:
filln = max(dict_fill_bmodes.keys())
print('--> Processing latest fill: %d' % filln)
# get location of current data
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
t_ref = dict_fill_bmodes[filln]['t_startfill']
tref_string = time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime(t_ref))
fill_dict = {}
if os.path.isdir(data_folder_fill + '/fill_basic_data_csvs'):
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv' %
filln,
verbose=False))
elif os.path.isdir(data_folder_fill + '/fill_basic_data_h5s'):
fill_dict.update(
tm.CalsVariables_from_h5(
data_folder_fill +
'/fill_bunchbybunch_data_h5s/bunchbybunch_data_fill_%d.h5' %
filln, ))
else:
raise ValueError('What?!')
i_fig = 0
plt.close('all')
# Loop over beams
beam_col = ['b', 'r']
fill_info = Fills_Info(dict_fill_bmodes)
fill_list = fill_info.fills_in_time_window(t_start_unix, t_end_unix)
# find offset to remove
if zero_at is not None:
print 'Evaluating offsets'
if ':' in zero_at:
t_zero_unix = time.mktime(time.strptime(zero_at, '%d-%m-%Y,%H:%M'))
else:
t_zero_unix = t_ref_unix + float(zero_at)*3600.
filln_offset = fill_info.filln_at_time(t_zero_unix)
data_folder_fill = dict_fill_bmodes[filln_offset]['data_folder']
try:
fill_dict = tm.timber_variables_from_h5(data_folder_fill+'/heatloads_fill_h5s/heatloads_all_fill_%d.h5'%filln_offset)
print 'From h5!'
except IOError:
print "h5 file not found, using csvs"
fill_dict = {}
fill_dict.update(tm.parse_timber_file(data_folder_fill+'/fill_basic_data_csvs/basic_data_fill_%d.csv'%filln_offset, verbose=False))
fill_dict.update(tm.parse_timber_file(data_folder_fill+'/fill_heatload_data_csvs/heatloads_fill_%d.csv'%filln_offset, verbose=False))
if args.use_recalc:
#import GasFlowHLCalculator.qbs_fill as qf
fill_dict.update(qf.get_fill_dict(filln_offset,h5_storage=H5_storage(recalc_h5_folder),use_dP=True))
dict_offsets={}
for kk in hl_varlist:
dict_offsets[kk] = np.interp(t_zero_unix, np.float_(np.array(fill_dict[kk].t_stamps)), fill_dict[kk].float_values())
def load_special_data_file(self, filln):
ob = mfm.h5_to_obj(self.get_special_data_file(filln))
return tm.AlignedTimberData(ob.timestamps, ob.data, ob.variables)
def load_special_qbs(self, filln):
qbs_file = self.get_special_qbs_file(filln)
qbs_ob = mfm.h5_to_obj(qbs_file)
return tm.AlignedTimberData(qbs_ob.timestamps, qbs_ob.data,
qbs_ob.variables)
this_dict_fill_bmodes[kk]['data_folder'] = df
dict_fill_bmodes.update(this_dict_fill_bmodes)
N_snapshots = len(snapshots)
for i_snapshot in xrange(N_snapshots):
filln = snapshots[i_snapshot]['filln']
t_sample_h = snapshots[i_snapshot]['t_h']
t_offset_h = snapshots[i_snapshot]['t_offs_h']
if args.zeroat is not None:
t_offset_h = None
if from_csv:
fill_file = 'fill_heatload_data_csvs/hl_all_cells_fill_%d.csv' % filln
hid = tm.parse_timber_file(fill_file, verbose=args.v)
else:
hid = qf.get_fill_dict(filln, h5_storage=H5_storage(recalc_h5_folder))
# get location of current data
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
t_fill_st = dict_fill_bmodes[filln]['t_startfill']
t_fill_end = dict_fill_bmodes[filln]['t_endfill']
t_ref = t_fill_st
tref_string = time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime(t_ref))
tref_string_short = time.strftime("%d %b %Y %H:%M", time.localtime(t_ref))
# extract standard fill data
fill_dict = {}
if os.path.isdir(data_folder_fill + '/fill_basic_data_csvs'):
# 2016 structure
filln = snapshots[i_snapshot]['filln']
t_sample_h = snapshots[i_snapshot]['t_h']
t_offset_h = snapshots[i_snapshot]['t_offs_h']
# get location of current data
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
t_fill_st = dict_fill_bmodes[filln]['t_startfill']
t_fill_end = dict_fill_bmodes[filln]['t_endfill']
t_ref = t_fill_st
tref_string = time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime(t_ref))
tref_string_short = time.strftime("%d %b %Y %H:%M", time.localtime(t_ref))
if from_published:
fill_file = f"{data_folder_fill}/fill_cell_by_cell_heatload_data_h5s/cell_by_cell_heatloads_fill_{filln}.h5"
hid = tm.CalsVariables_from_h5(fill_file)
else:
hid = qf.get_fill_dict(filln, h5_storage=H5_storage(recalc_h5_folder))
# extract standard fill data
fill_dict = {}
if os.path.isdir(data_folder_fill + '/fill_basic_data_csvs'):
# 2016 structure
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_basic_data_csvs/basic_data_fill_%d.csv' % filln,
verbose=args.v))
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
def recalc_multiple_circuits(raw_data_object, calibration, circuit_selection,
with_P_drop):
if circuit_selection == 'full_lhc':
circuits = calibration.circuits
elif circuit_selection == 'all_instrumented':
circuits = sorted(instrumented_cells_config.keys())
else:
raise ValueError('Not implemented!')
obraw = raw_data_object
qbs_recalc = []
issues = []
instrum_cell_recalc_dict = {}
for ii, circuit in enumerate(circuits):
if len(circuits) > 100:
if np.mod(ii, 20) == 0:
print('Circuit %d/%d' % (ii, len(circuits)))
else:
print(ii, circuit)
cell_calib = calibration.get_circuit(circuit)
T1 = obraw.dictionary[cell_calib['T1']]
T3 = obraw.dictionary[cell_calib['T3']]
P1 = obraw.dictionary[cell_calib['P1']]
P4 = obraw.dictionary[cell_calib['P4']]
CV = obraw.dictionary[cell_calib['CV']]
EH = obraw.dictionary[cell_calib['EH']]
# T2 = obraw.dictionary[cell_calib['T2']]
Q_bs, other = hlr.compute_heat_load(
P1,
T1,
T3,
P4,
CV,
EH,
Qs_calib=cell_calib['Qs_calib'],
Kv_calib=cell_calib['Kv_calib'],
R_calib=cell_calib['R_calib'],
cell_length=cell_calib['length'],
n_channels=cell_calib['n_channels_tot'],
channel_radius=cell_calib['channel_radius'],
channel_roughness=cell_calib['roughness'],
with_P_drop=with_P_drop,
N_iter_max=100,
scale_correction=0.3,
iter_toll=1e-3)
qbs_recalc.append(Q_bs)
if len(other['issues']) > 0:
print('Issues found for circuit %s:' % circuit)
print('\n'.join(other['issues']))
issues.append([circuit, other['issues']])
if circuit_selection == 'all_instrumented':
instrum_cell_config = instrumented_cells_config[circuit]
(n_channels_circuits, magnet_lengths_circuits,
in_sensor_names_circuits, out_sensor_names_circuits
) = hlr.extract_info_from_instrum_config_dict(
config_dict=instrum_cell_config)
t_out_magnets_circuits = [[
obraw.dictionary[vv] for vv in out_sensor_names_circuits[ii]
] for ii in [0, 1]]
t_in_magnets_circuits = [[
obraw.dictionary[vv] for vv in in_sensor_names_circuits[ii]
] for ii in [0, 1]]
qbs_magnets_circuits, other_instr = \
hlr.compute_heat_loads_instrumented_cell(
mass_flow = other['mass_flow'], P1=P1,
T_in_magnets_circuits=t_in_magnets_circuits,
T_out_magnets_circuits=t_out_magnets_circuits,
magnet_lengths_circuits=magnet_lengths_circuits,
n_channels_circuits=n_channels_circuits,
channel_radius=cell_calib['channel_radius'],
channel_roughness=cell_calib['roughness'],
dp_toll = 0.001, N_iter_max=200)
magnet_beam_circuits = [
instrum_cell_config['circuit_%s_beam' % cc]
for cc in ['A', 'A']
]
dict_output = hlr.build_instrumented_hl_dict(
config_dict=instrum_cell_config,
circuit=circuit,
Qbs_magnets_circuits=qbs_magnets_circuits)
instrum_cell_recalc_dict.update(dict_output)
avg_loads = []
avg_varnames = []
if circuit_selection == 'full_lhc':
# Build temporary object to compute arc averages
obhl = tm.AlignedTimberData(timestamps=obraw.timestamps,
data=np.array(qbs_recalc).T,
variables=calibration.circuits)
# Compute arc averages
for arc in '12 23 34 45 56 67 78 81'.split():
arc_circuits = HL.arc_cells_by_sector['S' + arc]
arc_loads = np.array([obhl.dictionary[kk] for kk in arc_circuits])
avg_load = np.nanmean(arc_loads, axis=0)
avg_loads.append(avg_load)
avg_varnames.append('S%s_QBS_AVG_ARC.POSST' % arc)
instrum_varnames = sorted(instrum_cell_recalc_dict.keys())
instrum_qbs_recalc = [
instrum_cell_recalc_dict[kk] for kk in instrum_varnames
]
obhl_store = tm.AlignedTimberData(
timestamps=obraw.timestamps,
data=np.array(qbs_recalc + avg_loads + instrum_qbs_recalc).T,
variables=(circuits + avg_varnames + instrum_varnames))
other = {}
other['issues'] = issues
return obhl_store, other
df+'/fills_and_bmodes.json')
for kk in this_dict_fill_bmodes:
this_dict_fill_bmodes[kk]['data_folder'] = df
dict_fill_bmodes.update(this_dict_fill_bmodes)
# get location of current data
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
#load data
if os.path.isdir(data_folder_fill+'/fill_basic_data_csvs'):
# 2016+ structure
fill_dict = {}
fill_dict.update(tm.parse_timber_file(
data_folder_fill+'/fill_basic_data_csvs/basic_data_fill_%d.csv'%filln,
verbose=True))
fill_dict.update(tm.parse_timber_file(
data_folder_fill + ('/fill_bunchbybunch_data_csvs/'
'bunchbybunch_data_fill_%d.csv'%filln), verbose=True))
if not use_recalculated:
fill_dict.update(tm.parse_timber_file(
data_folder_fill + ('/fill_heatload_data_csvs/'
'heatloads_fill_%d.csv'%filln), verbose=False))
elif os.path.isdir(data_folder_fill+'/fill_basic_data_h5s'):
# 2016+ structure
fill_dict = {}
fill_dict.update(tm.CalsVariables_from_h5(
data_folder_fill+'/fill_basic_data_h5s/basic_data_fill_%d.h5'%filln))
fill_dict.update(tm.CalsVariables_from_h5(
data_folder_fill + ('/fill_bunchbybunch_data_h5s/'
channel_roughness=cell_calib['roughness'],
with_P_drop=with_P_drop,
N_iter_max=100,
scale_correction=0.3,
iter_toll=1e-3)
qbs_recalc.append(Q_bs)
if len(other['issues']) > 0:
print('Issues found for circuit %s:' % circuit)
print('\n'.join(other['issues']))
issues.append([circuit, other['issues']])
# Build temporary object to compute arc averages
obhl = tm.AlignedTimberData(timestamps=obraw.timestamps,
data=np.array(qbs_recalc).T,
variables=calibration.circuits)
avg_loads = []
avg_varnames = []
# Compute arc averages
for arc in '12 23 34 45 56 67 78 81'.split():
arc_circuits = HL.arc_cells_by_sector['S' + arc]
arc_loads = np.array([obhl.dictionary[kk] for kk in arc_circuits])
avg_load = np.nanmean(arc_loads, axis=0)
avg_loads.append(avg_load)
avg_varnames.append('S%s_QBS_AVG_ARC.POSST' % arc)
# Build temporary object to compute arc averages
obhl_store = tm.AlignedTimberData(timestamps=obraw.timestamps,
import sys, os
BIN = os.path.expanduser("../")
sys.path.append(BIN)
# Load fills
import LHCMeasurementTools.TimberManager as tm
filln = 6740
fill_dict = {}
fill_dict.update(
tm.CalsVariables_from_h5(
('../../LHC_followup_download_scripts/fill_basic_data_h5s'
'/basic_data_fill_%d.h5' % filln)))
fill_dict.update(
tm.CalsVariables_from_h5(
('../../LHC_followup_download_scripts/fill_bunchbybunch_data_h5s/'
'bunchbybunch_data_fill_%d.h5' % filln)))
# Build heat load calculators
import impedance_heatload as ihl
import synchrotron_radiation_heatload as srhl
hli_calculator = ihl.HeatLoadCalculatorImpedanceLHCArc()
hlsr_calculator = srhl.HeatLoadCalculatorSynchrotronRadiationLHCArc()
# Use fill calculator
import FillCalculator as fc
hl_imped_fill = fc.HeatLoad_calculated_fill(fill_dict, hli_calculator)
hl_sr_fill = fc.HeatLoad_calculated_fill(fill_dict, hlsr_calculator)
import LHCMeasurementTools.TimberManager as tm
data_folder_fill = './'
filln = 6741
varlist = []
data = tm.CalsVariables_from_h5(data_folder_fill +
'/fill_basic_data_h5s/basic_data_fill_%d.h5' %
filln)
varlist += data.keys()
data = tm.CalsVariables_from_h5(
data_folder_fill +
'/fill_bunchbybunch_data_h5s/bunchbybunch_data_fill_%d.h5' % filln)
varlist += data.keys()
data = tm.CalsVariables_from_h5(
data_folder_fill + '/fill_heatload_data_h5s/heatloads_fill_%d.h5' % filln)
varlist += data.keys()
data = tm.CalsVariables_from_h5(data_folder_fill +
'/fill_extra_data_h5s/extra_data_fill_%d.h5' %
filln)
varlist += data.keys()
import LHCMeasurementTools.LHC_Fills as Fills
varlist += Fills.get_varlist()
# Variables for heat load recalculation
varfiles = [
'../tools/GasFlowHLCalculator/variable_list_complete.txt',
dict_fill_bmodes = pickle.load(fid)
if len(sys.argv) > 1:
print '--> Processing fill {:s}'.format(sys.argv[1])
filln = int(sys.argv[1])
else:
filln = max(dict_fill_bmodes.keys())
print '--> Processing latest fill: %d' % filln
t_ref = dict_fill_bmodes[filln]['t_startfill']
tref_string = time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime(t_ref))
fill_dict = {}
fill_dict.update(
tm.parse_timber_file(
'fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv' % filln,
verbose=False))
bint_thresh = 8e9
totint_thresh = 2e11
t_inter = 60. #seconds
i_fig = 0
plt.close('all')
# Loop over beams
beam_col = ['b', 'r']
for beam in [1, 2]:
print '\nPreparing plot beam %d...' % beam
fbct = FBCT.FBCT(fill_dict, beam=beam)
help='Plot a legend for Imp/SR',
action='store_true')
args = parser.parse_args()
filln = args.filln
t1 = args.t
min_scale = args.min_scale
max_scale = args.max_scale
tagfname = args.tag
#if args.t_offset:
# t_offset = args.t_offset
t_offset = None
fill_file = 'fill_heatload_data_csvs/t3_all_cells_fill_%d.csv' % filln
hid = tm.parse_timber_file(fill_file, verbose=args.v)
varlist = cq.config_qbs.TT94x_list
hid_set = shv.SetOfHomogeneousNumericVariables(varlist, hid)
# merge pickles and add info on location
dict_fill_bmodes = {}
for df in data_folder_list:
with open(df + '/fills_and_bmodes.pkl', 'rb') as fid:
this_dict_fill_bmodes = pickle.load(fid)
for kk in this_dict_fill_bmodes:
this_dict_fill_bmodes[kk]['data_folder'] = df
dict_fill_bmodes.update(this_dict_fill_bmodes)
# get location of current data
#~ filln=4252
#~ filln=4260
#~ filln=4261
if len(sys.argv) > 1:
print('--> Processing fill {:s}'.format(sys.argv[1]))
filln = int(sys.argv[1])
myfontsz = 16
with open('fills_and_bmodes.pkl', 'rb') as fid:
dict_fill_bmodes = pickle.load(fid)
dict_fill_data = {}
dict_fill_data.update(
tm.parse_timber_file('fill_basic_data_csvs/basic_data_fill_%d.csv' % filln,
verbose=True))
dict_fill_data.update(
tm.parse_timber_file('fill_extra_data_csvs/extra_data_fill_%d.csv' % filln,
verbose=True))
dict_beam = dict_fill_data
dict_fbct = dict_fill_data
colstr = {}
colstr[1] = 'b'
colstr[2] = 'r'
energy = Energy.energy(dict_fill_data, beam=1)
t_fill_st = dict_fill_bmodes[filln]['t_startfill']
t_fill_end = dict_fill_bmodes[filln]['t_endfill']
#pkl_name = 'fills_and_bmodes.pkl'
json_name = 'fills_and_bmodes.json'
ldb = pytimber.LoggingDB(source='nxcals')
#ldb = pytimber.LoggingDB()
dict_fill_info = {}
for period in periods:
t_start_string = period.split('!')[0]
t_stop_string = period.split('!')[1]
t_start = th.localtime2unixstamp(t_start_string)
t_stop = th.localtime2unixstamp(t_stop_string)
# Get data from database
data_fnum = tm.CalsVariables_from_pytimber(
ldb.get(['HX:FILLN'], t_start, t_stop))
list_bmodes = ldb.getLHCFillsByTime(t_start, t_stop)
# Generate dictionary
dict_fill_info.update(
Fills.make_fill_info_dict(data_fnum, list_bmodes, t_stop))
# with open(pkl_name, 'wb') as fid:
# pickle.dump(dict_fill_info, fid)
import json
with open(json_name, 'w') as fid:
json.dump(dict_fill_info, fid)
sp_blen_vs_int.grid('on')
sp_blen_vs_int.set_xlabel('Bunch intensity [p+]')
sp_blen_vs_int.set_ylabel('Bunch length [ns]')
hli_calculator = ihl.HeatLoadCalculatorImpedanceLHCArc()
hlsr_calculator = srhl.HeatLoadCalculatorSynchrotronRadiationLHCArc()
fills_string = ''
for i_fill, filln in enumerate(filln_list):
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
fills_string += '_%d' % filln
fill_dict = {}
if os.path.isdir(data_folder_fill + '/fill_basic_data_csvs'):
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_basic_data_csvs/basic_data_fill_%d.csv' % filln,
verbose=False))
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv' %
filln,
verbose=False))
if use_recalculated:
fill_dict.update(qf.get_fill_dict(filln))
else:
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_heatload_data_csvs/heatloads_fill_%d.csv' % filln,
verbose=False))
if avg_time_hrs == -1.:
if dict_fill_bmodes[filln]['t_start_STABLE'] != -1:
avg_time_hrs = (dict_fill_bmodes[filln]['t_start_STABLE'] -
dict_fill_bmodes[filln]['t_startfill']) / 3600.
else:
print('Warning: Avg time hrs = 0.5')
avg_time_hrs = 0.5
# get location of current data
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
fill_dict = {}
if filln < 4857:
fill_dict.update(
tm.parse_timber_file(
'/afs/cern.ch/project/spsecloud/LHC_2015_PhysicsAfterTS2/fill_csvs/fill_%d.csv'
% filln,
verbose=False))
fill_dict.update(
tm.parse_timber_file(
'/afs/cern.ch/project/spsecloud/LHC_2015_PhysicsAfterTS2/heatloads_fill_h5s/heatloads_all_fill_%i.h5'
% filln,
verbose=False))
else:
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_basic_data_csvs/basic_data_fill_%d.csv' % filln,
verbose=False))
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
def load_qbs(self, filln, use_dP):
qbs_file = self.get_qbs_file(filln, use_dP=use_dP)
qbs_ob = mfm.h5_to_obj(qbs_file)
#print('Loaded file %s' % qbs_file)
return tm.AlignedTimberData(qbs_ob.timestamps, qbs_ob.data,
qbs_ob.variables)
for filln in sorted(dict_fill_bmodes.keys()):
print('Fill n.',filln)
h5filename = h5folder+'/heatloads_all_fill_%d.h5'%filln
if dict_fill_bmodes[filln]['flag_complete'] is False:
print("Fill incomplete --> no h5 convesion")
continue
if os.path.isfile(h5filename) and dict_fill_bmodes[filln]['flag_complete'] is True:
print("Already complete and in h5")
continue
try:
dict_fill_data = {}
dict_fill_data.update(tm.parse_timber_file('fill_basic_data_csvs/basic_data_fill_%d.csv'%filln, verbose=False))
dict_fill_data.update(tm.parse_timber_file('fill_heatload_data_csvs/heatloads_fill_%d.csv'%filln, verbose=False))
varlist = []
varlist += LHC_BCT.variable_list()
varlist += LHC_Energy.variable_list()
for kk in list(LHC_Heatloads.variable_lists_heatloads.keys()):
varlist+=LHC_Heatloads.variable_lists_heatloads[kk]
dict_to_h5 = {}
for varname in varlist:
#~ print varname
fill_info = Fills_Info(dict_fill_bmodes)
fill_list = fill_info.fills_in_time_window(t_start_unix, t_end_unix)
# find offset to remove
if zero_at is not None:
print('Evaluating offsets')
if ':' in zero_at:
t_zero_unix = time.mktime(time.strptime(zero_at, '%d-%m-%Y,%H:%M'))
else:
t_zero_unix = t_ref_unix + float(zero_at)*3600.
filln_offset = fill_info.filln_at_time(t_zero_unix)
data_folder_fill = dict_fill_bmodes[filln_offset]['data_folder']
try:
fill_dict = tm.timber_variables_from_h5(data_folder_fill+'/heatloads_fill_h5s/heatloads_all_fill_%d.h5'%filln_offset)
print('From h5!')
except IOError:
print("h5 file not found, using h5s :-P")
fill_dict = {}
fill_dict.update(tm.CalsVariables_from_h5(
data_folder_fill + ('/fill_basic_data_h5s/'
'basic_data_fill_%d.h5'%filln_offset)))
fill_dict.update(tm.CalsVariables_from_h5(
data_folder_fill + ('/fill_heatload_data_h5s/'
'heatloads_fill_%d.h5'%filln_offset)))
if args.use_recalc:
fill_dict.update(qf.get_fill_dict(filln_offset,
h5_storage=H5_storage(recalc_h5_folder),
use_dP=True))
print('Interval manually set: %.2fh to %.2fh' %
(t_start_man, t_end_man))
if N_traces_set == None: N_traces_set = 30
list_scan_times = np.linspace(t_start_man, t_end_man, N_traces_set)
xlim = t_start_man, t_end_man
else:
xlim = None, None
if '--notrace' in sys.argv:
list_scan_times = []
fill_dict = {}
if os.path.isdir(data_folder_fill + '/fill_basic_data_csvs'):
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_basic_data_csvs/basic_data_fill_%d.csv' % filln,
verbose=True))
elif os.path.isdir(data_folder_fill + '/fill_basic_data_h5s'):
fill_dict.update(
tm.CalsVariables_from_h5(
data_folder_fill +
'/fill_basic_data_h5s/basic_data_fill_%d.h5' % filln, ))
pl.close('all')
sp_ploss = None
# START PLOT
fig_h = pl.figure(1, figsize=(8 * 1.6, 6 * 1.3))
fig_h.patch.set_facecolor('w')
ms.mystyle_arial(fontsz=16, dist_tick_lab=5)
fig_h.suptitle('Fill %d: started on %s' % (filln, tref_string), fontsize=18)
dict_hl_groups['Q6s_IR28'] = HL.variable_lists_heatloads[
'Q6s_IR2'] + HL.variable_lists_heatloads['Q6s_IR8']
dict_hl_groups['special_HC_Q1'] = HL.variable_lists_heatloads['special_HC_Q1']
dict_hl_groups['special_HC_dipoles'] = HL.variable_lists_heatloads['special_HC_D2']+\
HL.variable_lists_heatloads['special_HC_D3']+HL.variable_lists_heatloads['special_HC_D4']
dict_hl_groups['Q4D2s_IR15'] = HL.variable_lists_heatloads[
'Q4D2s_IR1'] + HL.variable_lists_heatloads['Q4D2s_IR5']
dict_hl_groups['Q4D2s_IR28'] = HL.variable_lists_heatloads[
'Q4D2s_IR2'] + HL.variable_lists_heatloads['Q4D2s_IR8']
with open('fills_and_bmodes.pkl', 'rb') as fid:
dict_fill_bmodes = pickle.load(fid)
fill_dict = {}
fill_dict.update(
tm.parse_timber_file('fill_basic_data_csvs/basic_data_fill_%d.csv' % filln,
verbose=False))
fill_dict.update(
tm.parse_timber_file('fill_heatload_data_csvs/heatloads_fill_%d.csv' %
filln,
verbose=False))
fill_dict.update(
tm.parse_timber_file(
'fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv' % filln,
verbose=False))
dict_beam = fill_dict
dict_fbct = fill_dict
colstr = {}
colstr[1] = 'b'
colstr[2] = 'r'
with open(df+'/fills_and_bmodes.pkl', 'rb') as fid:
this_dict_fill_bmodes = pickle.load(fid)
for kk in this_dict_fill_bmodes:
this_dict_fill_bmodes[kk]['data_folder'] = df
dict_fill_bmodes.update(this_dict_fill_bmodes)
# get location of current data
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
#load data
if os.path.isdir(data_folder_fill+'/fill_basic_data_csvs'):
# 2016+ structure
fill_dict = {}
fill_dict.update(tm.parse_timber_file(data_folder_fill+'/fill_basic_data_csvs/basic_data_fill_%d.csv'%filln, verbose=True))
fill_dict.update(tm.parse_timber_file(data_folder_fill+'/fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv'%filln, verbose=True))
if not use_recalculated:
fill_dict.update(tm.parse_timber_file(data_folder_fill+'/fill_heatload_data_csvs/heatloads_fill_%d.csv'%filln, verbose=False))
else:
# 2015 structure
fill_dict = {}
fill_dict.update(tm.parse_timber_file(data_folder_fill+'/fill_csvs/fill_%d.csv'%filln, verbose=True))
if use_recalculated:
print 'Using recalc data'
# remove db values from dictionary (for 2015 cases)
for kk in fill_dict.keys():
if 'QBS' in kk and '.POSST'in kk:
# get location of current data
data_folder_fill = dict_fill_bmodes[filln]['data_folder']
t_start_fill = dict_fill_bmodes[filln]['t_startfill']
t_end_fill = dict_fill_bmodes[filln]['t_endfill']
t_fill_len = t_end_fill - t_start_fill
t_ref = t_start_fill
n_traces = 50.
fill_dict = {}
if os.path.isdir(data_folder_fill + '/fill_basic_data_csvs'):
# 2016 structure
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_basic_data_csvs/basic_data_fill_%d.csv' % filln,
verbose=True))
fill_dict.update(
tm.parse_timber_file(
data_folder_fill +
'/fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv' %
filln,
verbose=True))
else:
# 2015 structure
fill_dict.update(
tm.parse_timber_file(data_folder_fill +
'/fill_csvs/fill_%d.csv' % filln,
verbose=True))
beam_col = ['k', 'b', 'r']
time_conv = TH.TimeConverter(time_in, t_ref_unix, t_plot_tick_h=t_plot_tick_h)
tc = time_conv.from_unix
fill_info = Fills_Info('fills_and_bmodes.pkl')
fill_list = fill_info.fills_in_time_window(t_start_unix, t_end_unix)
# find offset to remove
if zero_at is not None:
if ':' in zero_at:
t_zero_unix = time.mktime(time.strptime(zero_at, '%d-%m-%Y,%H:%M'))
else:
t_zero_unix = t_ref_unix + float(zero_at)*3600.
filln_offset = fill_info.filln_at_time(t_zero_unix)
fill_dict = {}
fill_dict.update(tm.parse_timber_file('fill_basic_data_csvs/basic_data_fill_%d.csv'%filln_offset, verbose=False))
fill_dict.update(tm.parse_timber_file('fill_heatload_data_csvs/heatloads_fill_%d.csv'%filln_offset, verbose=False))
dict_offsets={}
for kk in hl_varlist:
dict_offsets[kk] = np.interp(t_zero_unix, np.float_(np.array(fill_dict[kk].t_stamps)), fill_dict[kk].float_values())
pl.close('all')
ms.mystyle_arial(fontsz=fontsz, dist_tick_lab=9)
#ms.mystyle(fontsz=fontsz)
fig = pl.figure(1, figsize=figsz)
fig.patch.set_facecolor('w')
ax1 = fig.add_subplot(311)
ax11 = ax1.twinx()
ax2 = fig.add_subplot(312, sharex=ax1)
ax3 = fig.add_subplot(313, sharex=ax1)
traces_times = []
plt.rcParams.update({'axes.labelsize': 18,
'axes.linewidth': 2,
'xtick.labelsize': 'large',
'ytick.labelsize': 'large',
'xtick.major.pad': 14,
'ytick.major.pad': 14})
format_datetime = mdt.DateFormatter('%m-%d %H:%M')
fill_dict = {}
fill_dict.update(tm.parse_timber_file('fill_basic_data_csvs/basic_data_fill_%d.csv'%filln, verbose=False))
fill_dict.update(tm.parse_timber_file('fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv'%filln, verbose=False))
n_traces = len(traces_times)
bint_thresh = 8e9
i_fig = 0
plt.close('all')
beam_col = ['b','r']
for beam in [1,2]:
fbct = FBCT.FBCT(fill_dict, beam=beam)
bct = BCT.BCT(fill_dict, beam=beam)
fig1 = plt.figure(i_fig, figsize=(14, 8), tight_layout=False)
def __getitem__(self, kk):
var = tm.timber_variable_list()
var.t_stamps, var.values = self.db.get(kk, self.t_start, self.t_end)[kk]
return var
