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)
fig1.patch.set_facecolor('w')
ax0 = plt.subplot(211)
ax1 = plt.subplot(212)
ms.mystyle()
ax0.plot((bct.t_stamps-t_ref)/3600., bct.values, color=beam_col[beam-1], lw=2)
for i in xrange(0, n_traces):
t_cut_h = traces_times[i]
t_curr = t_ref+t_cut_h*3600.
fbct_curr, t_fbct_curr = fbct.nearest_older_sample(t_curr, flag_return_time=True)
ax1.plot(fbct_curr, color=ms.colorprog(i, n_traces),
label='%.2f h'%((t_fbct_curr-t_ref)/3600.))
ax0.axvline((t_fbct_curr-t_ref)/3600., lw=1.5, color=ms.colorprog(i, n_traces))
ax1.set_xlabel('25 ns slot')
ax1.set_xlim(0, 3500)
ax1.set_ylabel('Bunch intensity [p$^+$]')
ax1.grid('on')
ax0.set_xlabel('Time [h]')
ax0.grid('on')
ax0.set_ylim(bottom=0)
plt.subplots_adjust(top=0.9, bottom=0.1, right=0.95,
left=0.1, hspace=0.3, wspace=0.4)
fig1.suptitle('Fill %d: B%d, started on %s'%(filln, beam, tref_string), fontsize=20)
filename_out = 'outputs/Fill_%d_FBCTraw_B%d.png'%(filln, beam)
i_fig += 1
fig = plt.figure()
fig.canvas.set_window_title(title_str)
plt.suptitle(title_str,fontsize=25)
for dev_ctr, device in enumerate(devices):
sp = plt.subplot(2,2,dev_ctr+1)
sp.set_xlabel('Chamber bin position [cm]')
sp.set_ylabel('Number of $e^-$ per bin')
sp.set_title(device_labels_dict[device])
for coast_ctr, coast_str in enumerate(coast_strs):
for sey_ctr, sey_str in enumerate(this_sey_list):
if dev_ctr == 1 and sey_ctr == 0:
label = coast_str + ' e9 coasting'
elif dev_ctr == 2 and coast_ctr == 0:
label = 'SEY %s' % sey_str
else:
label = None
ls = coast_linestyle_dict[coast_str]
color = colorprog(sey_ctr, len(this_sey_list))
try:
nel_hist = nel_hist_dict[main_key][device][coast_str][sey_str][beam]
except KeyError:
#print(main_key,device,coast_str,sey_str,beam)
nel_hist = yy_zero
sp.plot(xg_hist*100,nel_hist,label=label,ls=ls,color=color)
sp.legend(bbox_to_anchor=(1.1, 1))
plt.show()
# 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):
# continue
colorcurr = ms.colorprog(i_prog=ii, Nplots=len(heatloads.variable_list))
kwplt = {}
# # Enhance S12
# if 'S12' in kk:
# kwplt = {'zorder':10}
# colorcurr = tk'
# Labels
if first_fill:
label = ''
for st in kk.split('.POSST')[0].split('_'):
if 'QRL' in st or 'QBS' in st or 'AVG' in st or 'ARC' in st:
pass
else:
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))
if t_zero is not None:
offset = np.interp(t_ref+t_zero*3600, heatloads.timber_variables[kk].t_stamps, heatloads.timber_variables[kk].values)
else:
offset=0.
sphlcell.plot((heatloads.timber_variables[kk].t_stamps-t_ref)/3600, heatloads.timber_variables[kk].values-offset,
'-', color=colorcurr, lw=2., label=kk.split('.POSST')[0])#.split('_QBS')[0])
if plot_model and group_name == 'Arcs':
kk = 'LHC.QBS_CALCULATED_ARC.TOTAL'
label='Imp.+SR'
sphlcell.plot((hl_model.timber_variables[kk].t_stamps-t_ref)/3600., hl_model.timber_variables[kk].values,
'--', color='grey', lw=2., label=label)
if flag_average:
if t_zero is not None:
sp2.set_ylim(0, 7)
sp2.set_ylabel('Energy [TeV]')
sp2.plot(energy.t_stamps,
energy.energy / 1e3,
c='black',
lw=2.,
label='Energy')
summed_log, summed_re = 0., 0.
for ctr, affix in enumerate(affix_list):
var = cell_vars[affix]
row_ctr = heatloads.variables.index(var)
values = heatloads.data[:, row_ctr]
summed_log += values
summed_re += special_hl[cell][affix]['Sum']
color = ms.colorprog(ctr, cell_vars)
if logged:
sp.plot(timestamps,
values,
label=affix + ' logged',
ls='--',
lw=2.,
color=color)
sp.plot(special_tt,
special_hl[cell][affix]['Sum'],
ls='-',
lw=2.,
color=color,
label=affix)
#sp.axvline(avg_time_hrs, color='black')
# summed
sp2.set_ylim(10, 105)
sp2.set_xlim(2, 4)
for sp_ in (sp, sphlcell):
sp_.set_title('Average arc half cells')
sp_.grid(True)
sp_.set_ylabel('Heat load [W]')
def plot_both(*args, **kwargs):
sp.plot(*args, **kwargs)
sphlcell.plot(*args, **kwargs)
tt = (qbs_ob.timestamps - t_ref) / 3600.
for arc_ctr, key in enumerate(arc_keys_list):
color = ms.colorprog(arc_ctr, len(arc_keys_list) + 1)
label = key[:3]
if args.selected and label not in ('S12', 'S56'):
continue
# Logged
# Recalculated
if arc_ctr == 0:
label1 = label + ' with dP'
label2 = label + ' without dP'
else:
label1 = label
label2 = None
if args.subtract_offset:
subtract = find_offset(tt, qbs_arc_avg[:, arc_ctr])
else:
subtract = 0
# CORRECT ARC AVERAGES
if not use_recalculated and 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()
# Loop for arcs
fill_arc_hl_dict[filln] = {}
for ii, kk in enumerate(heatloads.variable_list):
colorcurr = ms.colorprog(ii, heatloads.variable_list)
# offset
if t_zero is not None:
offset = np.interp(t_ref + t_zero * 3600,
heatloads.timber_variables[kk].t_stamps,
heatloads.timber_variables[kk].values)
else:
offset = 0.
label = ''
for st in kk.split('.POSST')[0].split('_'):
if 'QRL' in st or 'QBS' in st or 'AVG' in st or 'ARC' in st:
pass
else:
label += st + ' '
if args.vs_bunch_inten:
fig_bint = pl.figure(100+ii, figsize=(8*1.4, 6*1.4))
fig_bint.set_facecolor('w')
spbint = pl.subplot(1,1,1)
if args.beam_events:
keys_labels = [('t_start_INJPHYS', 'Injection'),
('t_start_RAMP', 'Ramp'),
('t_start_FLATTOP', 'Flat top'),
('t_start_SQUEEZE', 'Squeeze'),
('t_start_ADJUST', 'Adjust'),
('t_start_STABLE', 'Stable beams')
]
for ctr, (key, label) in enumerate(keys_labels):
color = ms.colorprog(ctr, len(keys_labels)+1)
tt = (dict_fill_bmodes[filln][key] - t_ref)/3600.
spenergy.axvline(tt, ls='-', color=color, label=label)
n_bunches_bx = {}
for beam_n in beams_list:
if flag_fbct: sptotint.plot((fbct_bx[beam_n].t_stamps-t_ref)/3600., fbct_bx[beam_n].totint, '.--', color=colstr[beam_n])
sptotint.plot((bct_bx[beam_n].t_stamps-t_ref)/3600., bct_bx[beam_n].values/1e14, '-', color=colstr[beam_n], lw=2., label='Intensity B%i' % beam_n)
sptotint.set_ylabel('Total intensity\n[10$^{14}$ p$^+$]',fontsize = 24)
sptotint.grid('on')
sptotint.set_ylim(0, None)
if flag_bunch_length and not normtointen:
spavbl.plot((blength_bx[beam_n].t_stamps-t_ref)/3600., blength_bx[beam_n].avblen/1e-9, '.-', color=colstr[beam_n])
spavbl.set_ylabel('Bunch length\n[ns]',fontsize = 24)
spavbl.set_ylim(0.8,1.8)
ax0 = plt.subplot(211)
ax1 = plt.subplot(212)
ms.mystyle()
ax0.plot((bct.t_stamps - t_ref) / 3600.,
bct.values,
color=beam_col[beam - 1],
lw=2)
for i in xrange(0, n_traces):
t_cut_h = traces_times[i]
t_curr = t_ref + t_cut_h * 3600.
fbct_curr, t_fbct_curr = fbct.nearest_older_sample(
t_curr, flag_return_time=True)
ax1.plot(fbct_curr,
color=ms.colorprog(i, n_traces),
label='%.2f h' % ((t_fbct_curr - t_ref) / 3600.))
ax0.axvline((t_fbct_curr - t_ref) / 3600.,
lw=1.5,
color=ms.colorprog(i, n_traces))
ax1.set_xlabel('25 ns slot')
ax1.set_xlim(0, 3500)
ax1.set_ylabel('Bunch intensity [p$^+$]')
ax1.grid('on')
ax0.set_xlabel('Time [h]')
ax0.grid('on')
ax0.set_ylim(bottom=0)
plt.subplots_adjust(top=0.9,
bottom=0.1,
right=0.95,
axhist.plot(x_hist, tempy, linewidth=3, color='blue', alpha=0.8)
axhist.grid('on')
axhist.set_xlim(minhist, maxhist)
y_list.append(tempy)
figlist.append(fig_sect)
plt.figure(2000)
bad_sectors = [12, 23, 81, 78]
good_sectors = [34, 45, 56, 67]
bad_distr = 0 * x_hist
good_distr = 0 * x_hist
for ii, s in enumerate(sectors[:]):
colorcurr = ms.colorprog(ii, 8)
plt.plot(x_hist, y_list[ii], color=colorcurr, linewidth=3)
if s in bad_sectors:
bad_distr += y_list[ii]
else:
good_distr += y_list[ii]
plt.figure(3000)
plt.plot(x_hist, bad_distr / np.sum(bad_distr))
plt.plot(x_hist, good_distr / np.sum(good_distr))
if args.o:
for fig, s in zip(figlist, sectors[:]):
fig.savefig(
'cell_by_cell_plots/cellbycell_fill%d_t%.2fh_%s_sector%d.png' %
(filln, t1, tagfname, s),
ax2_sect.bar(ind,
np.abs(dict_correctors_sector[s]['list_current_B2']),
width / 2.,
color='r',
alpha=.5)
ax2_sect.set_xticks(ind)
ax2_sect.set_xticklabels(dict_correctors_sector[s]['list_corr_B1_B2'],
rotation=90)
ax2_sect.yaxis.grid(True)
ax2_sect.set_ylabel('Corrector current (abs.) [A]')
for ax in [ax1_sect, ax2_sect]:
for igrey in ind[1::2]:
ax.axvspan(igrey - 0.5, igrey + 0.5, color='k', alpha=.1)
colorcurr = ms.colorprog(i_s, 8, v1=0.9, v2=1.0, cm='hsv')
axcorr.plot(np.abs(dict_correctors_sector[s]['list_current_B1']),
dict_hl_cell_by_cell[s]['heat_loads'],
'.',
markersize=10,
color=colorcurr)
axcorrlog.semilogx(np.abs(dict_correctors_sector[s]['list_current_B1']),
dict_hl_cell_by_cell[s]['heat_loads'],
'.',
markersize=10,
color=colorcurr)
fig_sect.subplots_adjust(left=.06,
right=.96,
top=0.9,
hspace=0.12,
hl_keys = main_dict[moment]['heat_load'].keys()
# Heat load
fig2 = plt.figure(2, figsize=(8 * 1.5, 6 * 1.5))
fig2.set_facecolor('w')
sphl1 = plt.subplot(2, 1, 1, sharex=sp3)
sphl2 = plt.subplot(2, 1, 2, sharex=sp3)
hl_keys.sort()
arc_ctr = 0
for key in hl_keys:
if key[0] != 'S':
continue
color = colorprog(arc_ctr, 8)
sphl1.plot(main_dict['filln'],
main_dict[moment]['heat_load']['arc_averages'][key],
'.',
label=key,
color=color,
markersize=12)
sphl1.grid('on')
sphl2.plot(main_dict['filln'],
main_dict[moment]['heat_load']['arc_averages'][key] /
main_dict[moment]['intensity']['total'],
'.',
label=key,
color=color,
markersize=12)
