from data_folders import data_folder_list
import pickle
import numpy as np
csv_folder = 'fill_heatload_data_csvs'
if not os.path.isdir(csv_folder):
os.mkdir(csv_folder)
if len(sys.argv) > 1:
filln = int(sys.argv[1])
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)
t_start_fill = dict_fill_bmodes[filln]['t_startfill']
t_end_fill = dict_fill_bmodes[filln]['t_endfill']
sector_list = HL.sector_list()
variable_list = HL.sector_all_variables(sector_list)
#variable_list=['CMS:LUMI_TOT_INST','S12_QBS_AVG_ARC.POSST']
fill_file = csv_folder + '/hl_all_cells_fill_%d.csv' % filln
lldb.dbquery(variable_list, t_start_fill, t_end_fill, fill_file)
snapshots[i_snapshot]['n_bunches_b2'] = n_bunches_b2
snapshots[i_snapshot]['energy_GeV'] = energy_GeV
snapshots[i_snapshot]['dict_hl_cell_by_cell'] = dict_hl_cell_by_cell
snapshots[i_snapshot]['hl_imped_sample'] = hl_imped_sample
snapshots[i_snapshot]['hl_sr_sample'] = hl_sr_sample
snapshots[i_snapshot]['tref_string_short'] = tref_string_short
if t_offset_h is None:
snapshots[i_snapshot]['t_offs_h_str'] = '-'
else:
snapshots[i_snapshot][
't_offs_h_str'] = '%.2f' % snapshots[i_snapshot]['t_offs_h']
# check consistency in cell naming
for i_snapshot in range(N_snapshots):
for s in hl.sector_list():
if len(snapshots[i_snapshot]['dict_hl_cell_by_cell'][s]['cell_names']
) != len(snapshots[0]['dict_hl_cell_by_cell'][s]['cell_names']):
raise ValueError('Found inconsistency type 1 in dict!')
for c1, c2 in zip(
snapshots[i_snapshot]['dict_hl_cell_by_cell'][s]['cell_names'],
snapshots[0]['dict_hl_cell_by_cell'][s]['cell_names']):
if c1 != c2:
raise ValueError('Found inconsistency type 2 in dict!')
# Plots!
plt.close('all')
myfontsz = 13
ms.mystyle_arial(fontsz=13, dist_tick_lab=3)
if N_snapshots == 1:
width = 0.6
(5386, '25 ns, 48b/tr'),
(5393, '25 ns, 48b/tr'),
(5219, '25 ns, 72b/tr'),
# (4565, '25 ns - 36b/train')
]
list_n_bunches = []
list_bunch_intensity_avg = []
list_bunch_length_avg = []
list_filln = []
list_tags = []
list_imped = []
list_sr = []
dict_hl = {}
for sector in HL.sector_list():
dict_hl[sector] = []
for filln, tag in fills_to_analyze:
i_fill = np.argmin(np.abs(filln - main_dict['filln']))
n_bunches = main_dict[moment]['n_bunches']['b1'][i_fill]
total_intensity_2beams = main_dict[moment]['intensity']['b1'][
i_fill] + main_dict[moment]['intensity']['b2'][i_fill]
bunch_intensity_avg = total_intensity_2beams / 2 / n_bunches
bunch_length_avg = 0.5 * (
main_dict[moment]['blength']['b1']['avg'][i_fill] +
main_dict[moment]['blength']['b2']['avg'][i_fill])
list_n_bunches.append(n_bunches)
list_bunch_intensity_avg.append(bunch_intensity_avg)
dict_fill_bmodes.update(this_dict_fill_bmodes)
# 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))
plt.close('all')
ms.mystyle_arial(fontsz=12, dist_tick_lab=3)
width = 0.6
show_labels = True
sectors = hl.sector_list()
def swap_even_odd(vect):
temp_list = []
for ii in range(len(vect) // 2):
temp_list.append(vect[2 * ii + 1])
temp_list.append(vect[2 * ii])
return np.array(temp_list)
for i, s in enumerate(sectors):
sect_str = str(s)
R_part = 'R' + sect_str[0]
L_part = 'L' + sect_str[1]
snapshots[i_snapshot]['n_bunches_b1'] = n_bunches_b1
snapshots[i_snapshot]['n_bunches_b2'] = n_bunches_b2
snapshots[i_snapshot]['energy_GeV'] = energy_GeV
snapshots[i_snapshot]['dict_hl_cell_by_cell'] = dict_hl_cell_by_cell
snapshots[i_snapshot]['hl_imped_sample'] = hl_imped_sample
snapshots[i_snapshot]['hl_sr_sample'] = hl_sr_sample
snapshots[i_snapshot]['tref_string_short'] = tref_string_short
if t_offset_h is None:
snapshots[i_snapshot]['t_offs_h_str'] = '-'
else:
snapshots[i_snapshot]['t_offs_h_str'] = '%.2f'%snapshots[i_snapshot]['t_offs_h']
# check consistency in cell naming
for i_snapshot in xrange(N_snapshots):
for s in hl.sector_list():
if len(snapshots[i_snapshot]['dict_hl_cell_by_cell'][s]['cell_names'])!=len(snapshots[0]['dict_hl_cell_by_cell'][s]['cell_names']):
raise ValueError('Found inconsistency type 1 in dict!')
for c1, c2 in zip(snapshots[i_snapshot]['dict_hl_cell_by_cell'][s]['cell_names'], snapshots[0]['dict_hl_cell_by_cell'][s]['cell_names']):
if c1!=c2:raise ValueError('Found inconsistency type 2 in dict!')
# Plots!
plt.close('all')
myfontsz = 13
ms.mystyle_arial(fontsz=13,dist_tick_lab=3)
if N_snapshots==1:
width = 0.6
else:
width = 0.8
spshare = None
def save_cell_by_cell(infolder='./'):
str_file = 'fill%dat%.2fh_' % (filln, t_sample_h)
for fig, s in zip(figsect_list, hl.sector_list()):
fig.savefig(infolder + '/correctors_cellbycell_%s_sector%d.png' %
(str_file, s),
dpi=200)
loc = kk.split('.')[2][-2:] + kk.split('.')[1][-2:]
plane = kk.split('RCB')[-1][0]
simplified_name = loc + '_' + beam_name
if '16L2' in kk:
print(kk, simplified_name, plane)
dict_avg[simplified_name] = np.mean(dict_correctors[kk][1])
dict_plane[simplified_name] = plane
# get cell quad association
dict_cell_to_quad = cn.cell_to_quad_dict()
# cell corrector association
dict_correctors_sector = {}
for i_s, s in enumerate(hl.sector_list()):
cells = dict_hl_cell_by_cell[s]['cell_names']
list_corr_B1_B2 = []
list_current_B1 = []
list_current_B2 = []
for cell in cells:
extname = cell.replace('_', '_CV94')
name_quad = dict_cell_to_quad[extname]
place = name_quad.replace('Q', '')
if place.startswith('10') or place.startswith('34'):
print('Skipped %s!' % place)
list_corr_B1_B2.append('')
list_current_B1.append(np.nan)
list_current_B2.append(np.nan)
else:
def sample_and_sort_cell_by_cell(cell_by_cell_db_dictionary,
t_ref,
t_sample_h,
t_offset_h,
first_cell=11):
hid = cell_by_cell_db_dictionary
sectors = hl.sector_list()
dict_hl_cellbycell = {}
for i, s in enumerate(sectors[:]):
sect_str = str(s)
R_part = 'R' + sect_str[0]
L_part = 'L' + sect_str[1]
# Find values at t_sample_h and t2 for each cell.
val1 = []
cells = []
for cell in list(hid.keys()):
if '_D2' in cell or '_D3' in cell or '_D4' in cell or '_Q1' in cell:
continue
if R_part not in cell and L_part not in cell:
continue
try:
ind1 = np.argmin(
np.abs((np.array(hid[cell].t_stamps) - t_ref) / 3600 -
t_sample_h))
except ValueError as e:
print(('Got Error %s, skipping cell %s' % (e, cell)))
continue
cellname = cell.split('_')[1] + '_' + cell.split('.POSST')[0][-1]
if int(cellname[:2]) <= first_cell: continue # skip LSS and DS
#~ if cellname=='11L1_3': print cell, cellname
cells.append(cellname)
# remove offset
if t_offset_h is not None:
ind_offset = np.argmin(
np.abs((np.array(hid[cell].t_stamps) - t_ref) / 3600 -
t_offset_h))
val_offset = float(hid[cell].values[ind_offset])
offset_info = ', no beam at %.2fh' % t_offset_h
else:
val_offset = 0.
offset_info = ''
val1.append(float(hid[cell].float_values()[ind1]) - val_offset)
val1 = np.array(val1)
cells = np.array(cells)
# Sort everything
# it's R(IP) 09, 10, 11, ... L(IP+1) 33, 32, ...
msk_l = (np.char.find(cells, 'L') > -1)
cells_lip = cells[msk_l]
cells_rip = cells[~msk_l]
#~ print val1.shape
#~ print msk_l.shape
val1_lip = val1[msk_l]
val1_rip = val1[~msk_l]
ind_sort = (np.argsort(cells_lip))[::-1]
cells_lip = cells_lip[ind_sort]
val1_lip = val1_lip[ind_sort]
ind_sort = swap_even_odd(np.argsort(cells_rip))
cells_rip = cells_rip[ind_sort]
val1_rip = val1_rip[ind_sort]
cells = np.append(cells_rip, cells_lip)
val1 = np.append(val1_rip, val1_lip)
dict_hl_cellbycell[s] = {'cell_names': cells, 'heat_loads': val1}
return dict_hl_cellbycell