spavbl.set_ylim(0.8, 1.8)
spavbl.grid('on')
spavbl.set_xlabel('Time [h]')
group_name = group_names[ii]
pl.suptitle(' Fill. %d started on %s\n%s' %
(filln, tref_string, group_name))
fig_h.canvas.set_window_title(group_name)
hl_var_names = dict_hl_groups[group_name][:]
hl_var_names_copy = dict_hl_groups[group_name][:]
for varname in hl_var_names_copy:
if varname in blacklist:
hl_var_names.remove(varname)
heatloads = SetOfHomogeneousNumericVariables(variable_list=hl_var_names,
timber_variables=dict_hl_data)
hl_model = SetOfHomogeneousNumericVariables(
variable_list=HL.variable_lists_heatloads['MODEL'],
timber_variables=fill_dict)
# CORRECT ARC AVERAGES
if group_name == 'Arcs' and filln < first_correct_filln:
hl_corr_factors = []
for ii, varname in enumerate(dict_hl_groups[group_name]):
if varname not in blacklist:
hl_corr_factors.append(arc_correction_factor_list[ii])
heatloads.correct_values(hl_corr_factors)
if flag_average: hl_ts_curr, hl_aver_curr = heatloads.mean()
for ii, kk in enumerate(heatloads.variable_list):
colorcurr = ms.colorprog(i_prog=ii,
if plot_model:
try:
fill_dict.update(tm.timber_variables_from_h5(data_folder_fill+'/heatloads_fill_h5s/imp_and_SR_fill_%i.h5' % filln))
except IOError:
print "model datafile not found"
bct_b1 = BCT.BCT(fill_dict, beam=1)
bct_b2 = BCT.BCT(fill_dict, beam=2)
energy = Energy.energy(fill_dict, beam=1, t_start_fill=t_startfill, t_end_fill=t_endfill)
ax1.plot(tc(bct_b1.t_stamps), bct_b1.values*1e-14, lw=2, c='b', label = 'Intensity B1' if i_fill==0 else "")
ax1.plot(tc(bct_b2.t_stamps), bct_b2.values*1e-14, lw=2, c='r', label = 'Intensity B2' if i_fill==0 else "")
ax11.plot(tc(energy.t_stamps), energy.energy/1e3, c='black', linestyle = '--',lw=2,label='Energy' if i_fill==0 else "") #was alpha=.5
heatloads = SetOfHomogeneousNumericVariables(variable_list=hl_varlist, timber_variables=fill_dict)
# remove offset
if zero_at is not None:
for device in hl_varlist:
heatloads.timber_variables[device].values = heatloads.timber_variables[device].values - dict_offsets[device]
# normalize to the length
if normalization_to_length_of is not None:
for device in hl_varlist:
heatloads.timber_variables[device].values = heatloads.timber_variables[device].values/norm_length_dict[device]
if plot_all:
for ii, kk in enumerate(heatloads.variable_list):
#if '13L5' in kk and ('D2' in kk or 'D3' in kk):
spavbl.set_ylabel('Bunch length')
spavbl.set_ylim(0.8,1.8)
spavbl.grid('on')
spavbl.set_xlabel('Time [h]')
group_name = group_names[ii]
pl.suptitle(' Fill. %d started on %s\n%s'%(filln, tref_string, group_name))
fig_h.canvas.set_window_title(group_name)
hl_var_names = dict_hl_groups[group_name][:]
hl_var_names_copy = dict_hl_groups[group_name][:]
for varname in hl_var_names_copy:
if varname in blacklist:
hl_var_names.remove(varname)
heatloads = SetOfHomogeneousNumericVariables(variable_list=hl_var_names, timber_variables=dict_hl_data)
hl_model = SetOfHomogeneousNumericVariables(variable_list=HL.variable_lists_heatloads['MODEL'], timber_variables=fill_dict)
# CORRECT ARC AVERAGES
if group_name == 'Arcs' and filln < first_correct_filln:
hl_corr_factors = []
for ii, varname in enumerate(dict_hl_groups[group_name]):
if varname not in blacklist:
hl_corr_factors.append(arc_correction_factor_list[ii])
heatloads.correct_values(hl_corr_factors)
if flag_average: hl_ts_curr, hl_aver_curr = heatloads.mean()
for ii, kk in enumerate(heatloads.variable_list):
colorcurr = ms.colorprog(i_prog=ii, Nplots=len(heatloads.variable_list))
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_heatload_data_csvs/heatloads_fill_%d.csv' % filln,
verbose=False))
energy = Energy.energy(fill_dict, beam=1)
energy.t_stamps = (energy.t_stamps - energy.t_stamps[0]) / 3600.
t_ref = dict_fill_bmodes[filln]['t_startfill']
tref_string = time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime(t_ref))
heatloads = SetOfHomogeneousNumericVariables(
variable_list=variable_list,
timber_variables=fill_dict).aligned_object(dt_seconds=60)
## Swap 13L5 D2 and D4, as the correct gas flow is in reverse direction
#for ctr,var in enumerate(heatloads.variables):
# if '13R4' in var:
# if 'D2' in var:
# ctr_2 = ctr
# var_2 = var
# elif 'D4' in var:
# ctr_4 = ctr
# var_4 = var
#heatloads.variables[ctr_2] = var_4
#heatloads.variables[ctr_4] = var_2
y_min, y_max = -10, np.max(heatloads.data) + 5
timestamps = (heatloads.timestamps - heatloads.timestamps[0]) / 3600.
table_bint = total_bint.nearest_older_sample(tt_blen)
ii = 0
group_names = list(dict_hl_groups.keys())
group_name = group_names[ii]
pl.suptitle(' Fill. %d started on %s\n%s (%s)' %
(filln, tref_string, group_name, title_string))
fig_h.canvas.set_window_title(group_name + ' %i' % filln)
hl_var_names = dict_hl_groups[group_name][:]
hl_var_names_copy = dict_hl_groups[group_name][:]
for varname in hl_var_names_copy:
if varname in blacklist:
hl_var_names.remove(varname)
heatloads = SetOfHomogeneousNumericVariables(variable_list=hl_var_names,
timber_variables=fill_dict)
# Model heat load
hl_imped_fill = fc.HeatLoad_calculated_fill(fill_dict,
hli_calculator,
bct_dict=bct_bx,
fbct_dict=fbct_bx,
blength_dict=blength_bx)
hl_sr_fill = fc.HeatLoad_calculated_fill(fill_dict,
hlsr_calculator,
bct_dict=bct_bx,
fbct_dict=fbct_bx,
blength_dict=blength_bx)
hl_total_model = (hl_imped_fill.heat_load_calculated_total +
hl_sr_fill.heat_load_calculated_total) * 53.45
hl_model_t_stamps = (hl_imped_fill.t_stamps -
data_folder +
'/fill_bunchbybunch_data_csvs/bunchbybunch_data_fill_%d.csv' %
filln,
verbose=False))
except Exception as e:
print e
pass
if not args.nolog:
fill_dict.update(
tm.parse_timber_file(
data_folder +
'./fill_heatload_data_csvs/heatloads_fill_%d.csv' % filln,
verbose=False))
heatloads = SetOfHomogeneousNumericVariables(variable_list=arc_keys_list +
quad_keys_list,
timber_variables=fill_dict)
energy = Energy.energy(fill_dict, beam=1)
bct_bx = {}
for beam_n in colstr:
bct_bx[beam_n] = BCT(fill_dict, beam=beam_n)
hli_calculator = ihl.HeatLoadCalculatorImpedanceLHCArc()
hlsr_calculator = srhl.HeatLoadCalculatorSynchrotronRadiationLHCArc()
hl_imped_fill = fc.HeatLoad_calculated_fill(fill_dict, hli_calculator)
hl_sr_fill = fc.HeatLoad_calculated_fill(fill_dict, hlsr_calculator)
figs = []
ms.mystyle(12)
title = 'Recalculated arc heat loads %i' % filln
t_startfill = fill_info.dict_fill_bmodes[filln]['t_startfill']
t_endfill = fill_info.dict_fill_bmodes[filln]['t_endfill']
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))
bct_b1 = BCT.BCT(fill_dict, beam=1)
bct_b2 = BCT.BCT(fill_dict, beam=2)
energy = Energy.energy(fill_dict, beam=1, t_start_fill=t_startfill, t_end_fill=t_endfill)
ax1.plot(tc(bct_b1.t_stamps), bct_b1.values*1e-14, lw=2, c='b')
ax1.plot(tc(bct_b2.t_stamps), bct_b2.values*1e-14, lw=2, c='r')
ax11.plot(tc(energy.t_stamps), energy.energy/1e3, c='black', lw=1.5, alpha=0.5)
heatloads = SetOfHomogeneousNumericVariables(variable_list=hl_varlist, timber_variables=fill_dict)
hl_model = SetOfHomogeneousNumericVariables(variable_list=HL.variable_lists_heatloads['MODEL'], timber_variables=fill_dict)
# remove offset
if zero_at is not None:
for device in hl_varlist:
heatloads.timber_variables[device].values = heatloads.timber_variables[device].values - dict_offsets[device]
# normalize to the length
if normalization_to_length_of is not None:
for device in hl_varlist:
heatloads.timber_variables[device].values = heatloads.timber_variables[device].values/norm_length_dict[device]
for device in HL.variable_lists_heatloads['MODEL']:
hl_model.timber_variables[device].values = hl_model.timber_variables[device].values/53.45
bct_b1 = BCT.BCT(fill_dict, beam=1)
bct_b2 = BCT.BCT(fill_dict, beam=2)
energy = Energy.energy(fill_dict,
beam=1,
t_start_fill=t_startfill,
t_end_fill=t_endfill)
ax1.plot(tc(bct_b1.t_stamps), bct_b1.values * 1e-14, lw=2, c='b')
ax1.plot(tc(bct_b2.t_stamps), bct_b2.values * 1e-14, lw=2, c='r')
ax11.plot(tc(energy.t_stamps),
energy.energy / 1e3,
c='black',
lw=1.5,
alpha=0.5)
heatloads = SetOfHomogeneousNumericVariables(variable_list=hl_varlist,
timber_variables=fill_dict)
hl_model = SetOfHomogeneousNumericVariables(
variable_list=HL.variable_lists_heatloads['MODEL'],
timber_variables=fill_dict)
# remove offset
if zero_at is not None:
for device in hl_varlist:
heatloads.timber_variables[
device].values = heatloads.timber_variables[
device].values - dict_offsets[device]
# normalize to the length
if normalization_to_length_of is not None:
for device in hl_varlist:
heatloads.timber_variables[
raise ValueError('%s does not have a .POSST' % var)
if not os.path.isdir(h5_dir):
os.mkdir(h5_dir)
for filln in fill_sublist_2:
h5_file = data_file_func(filln)
if h5_file in os.listdir(os.path.dirname(h5_file)):
continue
this_temp_file = temp_file % filln
print('Downloading csv for fill %i' % filln)
t_start_fill = dict_fill_bmodes[filln]['t_startfill']
t_end_fill = dict_fill_bmodes[filln]['t_endfill']
lldb.dbquery(varlist, t_start_fill, t_end_fill, this_temp_file)
print('Aligning data for fill %i' % filln)
htd_ob = SetOfHomogeneousNumericVariables(
varlist, this_temp_file).aligned_object(dt_seconds)
print('Creating h5 file for fill %i' % filln)
n_tries_max = 5
for n_try in xrange(n_tries_max):
try:
mfm.aligned_obj_to_h5(htd_ob, h5_file)
break
except Exception as e:
print('Saving of h5 failed')
time.sleep(10)
else:
print('Raise error after trying to save the h5 file %i times' %
n_tries_max)
raise
if os.path.isfile(h5_file) and os.path.getsize(h5_file) > 500: