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)
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)
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
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)
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,