def plot_ModuleLoads(MappingFile, CMSSW_Silicon, CMSSW_Scintillator):
#Load external data
data = loadDataFile(MappingFile) #dataframe
data_tcs_passing, data_tcs_passing_scin = getTCsPassing(
CMSSW_Silicon, CMSSW_Scintillator) #from CMSSW
lpgbt_loads_tcs, lpgbt_loads_words, lpgbt_layers = getHexModuleLoadInfo(
data, data_tcs_passing, data_tcs_passing_scin)
plot(lpgbt_loads_tcs,
"loads_tcs.png",
binwidth=0.1,
xtitle='Number of TCs on a single lpGBT')
plot(lpgbt_loads_words,
"loads_words.png",
binwidth=0.1,
xtitle='Number of words on a single lpGBT')
plot2D(lpgbt_loads_tcs,
lpgbt_layers,
"tcs_vs_layer.png",
xtitle='Number of TCs on a single lpGBT')
plot2D(lpgbt_loads_words,
lpgbt_layers,
"words_vs_layer.png",
xtitle='Number of words on a single lpGBT')
def check_for_missing_modules_inCMSSW(MappingFile, CMSSW_Silicon,
CMSSW_Scintillator):
#Load external data
data = loadDataFile(MappingFile) #dataframe
data_tcs_passing, data_tcs_passing_scin = getTCsPassing(
CMSSW_Silicon, CMSSW_Scintillator) #from CMSSW
getHexModuleLoadInfo(data, data_tcs_passing, data_tcs_passing_scin, True)
def main():
MappingFile = "data/FeMappingV7.txt"
FileLocation = "../outputs/"
data = loadDataFile(MappingFile) #dataframe
minigroups, minigroups_swap = getMinilpGBTGroups(data)
correlation = np.zeros((len(minigroups_swap), len(minigroups_swap)))
listofarrs = []
# listofarrs.append(arr1)
# listofarrs.append(arr2)
# for job in range (60):
# listofarrs.append(np.load(FileLocation + "bundles_job_"+str(job)+".npy",allow_pickle=True))
for filename in glob(FileLocation + "bundles_job_*[0-9].npy"):
listofarrs.append(np.load(filename, allow_pickle=True))
for b, bundles in enumerate(listofarrs):
print("Bundle " + str(b) + " of " + str(len(listofarrs)))
for bundle in bundles:
combination = combinations(bundle, 2)
for comb in combination:
#print(correlation(comb))
correlation[comb[0], comb[1]] += 1
correlation[comb[1], comb[0]] += 1
fig, (ax1, ax2) = plt.subplots(1, 2)
fig1 = ax1.imshow(correlation,
vmin=1,
vmax=np.amax(correlation),
cmap='viridis_r',
norm=LogNorm(vmin=1, vmax=np.amax(correlation)))
fig2 = ax2.imshow(correlation,
vmin=0,
vmax=np.amax(correlation),
cmap='viridis_r')
#plt.pcolormesh(correlation,vmin=0, vmax=np.amax(correlation), cmap='gray_r')
#plt.pcolormesh(correlation,vmin=0.1, vmax=np.amax(correlation), cmap='gray_r',norm=LogNorm(vmin=0.01, vmax=np.amax(correlation)))
divider1 = make_axes_locatable(ax1)
divider2 = make_axes_locatable(ax2)
cax1 = divider1.append_axes("right", size="5%", pad=0.05)
cax2 = divider2.append_axes("right", size="5%", pad=0.05)
fig.colorbar(fig1, cax=cax1)
fig.colorbar(fig2, cax=cax2)
fig.tight_layout()
# plt.savefig("lin.png")
# plt.savefig("lin.pdf")
#plt.savefig("log.png",)
plt.savefig("both_v2.png", dpi=2000)
def produce_AllocationFile(MappingFile, allocation, minigroup_type="minimal"):
#Load mapping file
data = loadDataFile(MappingFile)
#List of which minigroups are assigned to each bundle
configuration = np.hstack(np.load(allocation, allow_pickle=True))
#Get minigroups
minigroups, minigroups_swap = getMinilpGBTGroups(data, minigroup_type)
#Bundle together minigroup configuration
bundles = getBundles(minigroups_swap, configuration)
#Open output file
fileout = open('allocation_20200729_1.txt', 'w')
fileout.write(
'(lpGBT_number) (number_modules) (sil=0scin=1) (layer) (u/eta) (v/phi) (number_elinks)\n'
)
for b, bundle in enumerate(bundles):
fileout.write(str(b) + "\n")
for minigroup in bundle:
#list lpgbts in minigroup:
for lpgbt in minigroups_swap[minigroup]:
fileout.write(str(lpgbt) + " ")
#Get modules associated to each lpgbt:
data_list = data[((data['TPGId1'] == lpgbt) |
(data['TPGId2'] == lpgbt))]
fileout.write(str(len(data_list)) + " ")
for index, row in data_list.iterrows():
if (row['density'] == 2):
fileout.write("1 " + str(row['layer']) + " " +
str(row['u']) + " " + str(row['v']) +
" " + str(row['TPGeLinkSum']) + " ")
else:
fileout.write("0 " + str(row['layer']) + " " +
str(row['u']) + " " + str(row['v']) +
" " + str(row['TPGeLinkSum']) + " ")
fileout.write("\n")
fileout.close()
def check_for_missing_modules_inMappingFile(MappingFile, CMSSW_Silicon,
CMSSW_Scintillator):
#Check for modules missing in the mapping file
#Load external data
data = loadDataFile(MappingFile) #dataframe
data_tcs_passing, data_tcs_passing_scin = getTCsPassing(
CMSSW_Silicon, CMSSW_Scintillator) #from CMSSW
mappingfile_sil = data[data['density'] < 2][['layer', 'u', 'v']]
mappingfile_scin = data[data['density'] == 2][['layer', 'u', 'v']]
cmssw_sil = data_tcs_passing[['u', 'v', 'layer', 'nTCs']]
cmssw_scin = data_tcs_passing_scin[['u', 'v', 'layer', 'nTCs']]
#onlymapping_sil = mappingfile.merge(cmssw.drop_duplicates(), on=['u','v','layer'],how='left', indicator=True)
onlycmssw_sil = cmssw_sil.merge(mappingfile_sil.drop_duplicates(),
on=['u', 'v', 'layer'],
how='left',
indicator=True)
onlycmssw_scin = cmssw_scin.merge(mappingfile_scin.drop_duplicates(),
on=['u', 'v', 'layer'],
how='left',
indicator=True)
onlycmssw_sil = onlycmssw_sil[onlycmssw_sil['_merge'] == 'left_only']
onlycmssw_scin = onlycmssw_scin[onlycmssw_scin['_merge'] == 'left_only']
print("Silicon")
print(onlycmssw_sil[onlycmssw_sil['nTCs'] > 0][['layer', 'u', 'v'
]].to_string(index=False))
print("Scintillator")
print(onlycmssw_scin[onlycmssw_scin['nTCs'] > 0][[
'layer', 'u', 'v'
]].to_string(index=False))
def produce_nTCsPerModuleHists(MappingFile,
allocation,
CMSSW_ModuleHists,
minigroup_type="minimal",
correctionConfig=None):
#Load mapping file
data = loadDataFile(MappingFile)
#List of which minigroups are assigned to each bundle
configuration = np.hstack(np.load(allocation, allow_pickle=True))
#Get minigroups
minigroups, minigroups_swap = getMinilpGBTGroups(data, minigroup_type)
#Get list of which modules are in each minigroup
minigroups_modules = getMiniModuleGroups(data, minigroups_swap)
#Bundle together minigroup configuration
bundles = getBundles(minigroups_swap, configuration)
#Get nTC hists per module
module_hists = getModuleTCHists(CMSSW_ModuleHists)
#Open output file
outfile = ROOT.TFile.Open("hists_per_bundle.root", "RECREATE")
for b, bundle in enumerate(bundles):
outfile.mkdir("bundle_" + str(b))
outfile.cd("bundle_" + str(b))
for minigroup in bundle:
for module in minigroups_modules[minigroup]:
module_hists[tuple(module)].Write()
outfile.cd()
def study_mapping(MappingFile,
CMSSW_ModuleHists,
algorithm="random_hill_climb",
initial_state="best_so_far",
random_seed=None,
max_iterations=100000,
output_dir=".",
print_level=0,
minigroup_type="minimal",
correctionConfig=None,
phisplitConfig=None,
chi2Config=None,
TowerMappingFile=None):
#Load external data
data = loadDataFile(MappingFile) #dataframe
try:
#Configuration for how to divide TCs into phidivisionX and phidivisionY (traditionally phi > 60 and phi < 60)
split = "per_roverz_bin"
phidivisionX_fixvalue_min = 55
phidivisionY_fixvalue_max = None
if phisplitConfig != None:
split = phisplitConfig['type']
if 'phidivisionX_fixvalue_min' in phisplitConfig.keys():
phidivisionX_fixvalue_min = phisplitConfig[
'phidivisionX_fixvalue_min']
if 'phidivisionY_fixvalue_max' in phisplitConfig.keys():
phidivisionY_fixvalue_max = phisplitConfig[
'phidivisionY_fixvalue_max']
inclusive_hists, module_hists = getModuleHists(
CMSSW_ModuleHists,
split=split,
phidivisionX_fixvalue_min=phidivisionX_fixvalue_min,
phidivisionY_fixvalue_max=phidivisionY_fixvalue_max)
except EnvironmentError:
print("File " + CMSSW_ModuleHists + " does not exist")
exit()
# Apply various corrections to r/z distributions from CMSSW
if correctionConfig != None:
print("Applying geometry corrections")
applyGeometryCorrections(inclusive_hists, module_hists,
correctionConfig)
include_errors_in_chi2 = False
include_max_modules_in_chi2 = False
include_max_towers_in_chi2 = False
max_modules_weighting_factor = 1000
if chi2Config != None:
if 'include_errors_in_chi2' in chi2Config.keys():
include_errors_in_chi2 = chi2Config['include_errors_in_chi2']
if 'include_max_modules_in_chi2' in chi2Config.keys():
include_max_modules_in_chi2 = chi2Config[
'include_max_modules_in_chi2']
if 'max_modules_weighting_factor' in chi2Config.keys():
max_modules_weighting_factor = chi2Config[
'max_modules_weighting_factor']
if 'include_max_towers_in_chi2' in chi2Config.keys():
include_max_towers_in_chi2 = chi2Config[
'include_max_towers_in_chi2']
if 'max_modules_weighting_factor' in chi2Config.keys():
max_towers_weighting_factor = chi2Config[
'max_towers_weighting_factor']
#Load tower data if required
if include_max_towers_in_chi2:
try:
towerdata = loadModuleTowerMappingFile(TowerMappingFile)
except EnvironmentError:
print("File " + TowerMappingFile + " does not exist")
exit()
#Form hists corresponding to each lpGBT from module hists
lpgbt_hists = getlpGBTHists(data, module_hists)
minigroups, minigroups_swap = getMinilpGBTGroups(data, minigroup_type)
minigroup_hists = getMiniGroupHists(
lpgbt_hists,
minigroups_swap,
return_error_squares=include_errors_in_chi2)
minigroup_hists_root = getMiniGroupHists(lpgbt_hists,
minigroups_swap,
root=True)
#Get list of which modules are in each minigroup
minigroups_modules = getMiniModuleGroups(data, minigroups_swap)
#Get list of which towers are in each minigroup
if include_max_towers_in_chi2:
minigroups_towers = getMiniTowerGroups(towerdata, minigroups_modules)
def mapping_max(state):
global chi2_min
global combbest
max_modules = None
max_towers = None
chi2 = 0
bundles = getBundles(minigroups_swap, state)
bundled_lpgbthists = getBundledlpgbtHists(minigroup_hists, bundles)
if include_max_modules_in_chi2:
max_modules = getMaximumNumberOfModulesInABundle(
minigroups_modules, bundles)
if include_max_towers_in_chi2:
bundled_towers = getTowerBundles(minigroups_towers, bundles)
max_towers = len(
max(bundled_towers, key=len)
) #Get the length of bundle with the greatest number of towers
chi2 = calculateChiSquared(inclusive_hists, bundled_lpgbthists,
max_modules, max_modules_weighting_factor,
max_towers, max_towers_weighting_factor)
typicalchi2 = 600000000000
if include_errors_in_chi2:
typicalchi2 = 10000000
if (chi2 < chi2_min):
chi2_min = chi2
combbest = np.copy(state)
if (print_level > 0):
print(algorithm, " ", chi2_min, " ", chi2_min / typicalchi2)
if include_max_towers_in_chi2:
print("max_towers = ", max_towers)
if include_max_modules_in_chi2:
print("max_modules = ", max_modules)
if (print_level > 1):
print(repr(combbest))
return chi2
init_state = []
if (initial_state == "example"):
init_state = example_minigroup_configuration
if (initial_state[-4:] == ".npy"):
print(initial_state)
init_state = np.hstack(np.load(initial_state, allow_pickle=True))
if (len(init_state) != len(minigroups_swap)):
print(
"Initial state should be the same length as the number of mini groups"
)
exit()
elif (initial_state == "random"):
np.random.seed(random_seed)
init_state = np.arange(len(minigroups_swap))
np.random.shuffle(init_state)
fitness_cust = mlrose.CustomFitness(mapping_max)
# Define optimization problem object
problem_cust = mlrose.DiscreteOpt(length=len(init_state),
fitness_fn=fitness_cust,
maximize=False,
max_val=len(minigroups_swap),
minigroups=minigroups_swap)
# Define decay schedule
schedule = mlrose.ExpDecay()
#schedule = mlrose.ArithDecay()
filename = "bundles_job_"
filenumber = ""
if (len(sys.argv) > 2):
filenumber = str(sys.argv[2])
else:
filenumber = "default"
filename += filenumber
if (algorithm == "save_root"):
#Save best combination so far into a root file
bundles = getBundles(minigroups_swap, init_state)
bundled_hists_root = getBundledlpgbtHistsRoot(minigroup_hists_root,
bundles)
bundled_hists = getBundledlpgbtHists(minigroup_hists, bundles)
chi2 = calculateChiSquared(inclusive_hists, bundled_hists)
newfile = ROOT.TFile("lpgbt_10.root", "RECREATE")
np.save(output_dir + "/" + filename + "_saveroot.npy",
np.array(bundles, dtype=object))
for sector in bundled_hists_root:
for key, value in sector.items():
value.Write()
for sector in inclusive_hists:
sector.Scale(1. / 24.)
sector.Write()
newfile.Close()
print("Chi2:", chi2)
print("List of Bundles:")
for b, bundle in enumerate(bundles):
print("")
print("bundle" + str(b))
for minigroup in bundle:
#print (minigroup)
lpgbts = minigroups_swap[minigroup]
for lpgbt in lpgbts:
print(str(lpgbt) + ", ", end='')
elif algorithm == "random_hill_climb" or algorithm == "simulated_annealing":
try:
if (algorithm == "random_hill_climb"):
best_state, best_fitness = mlrose.random_hill_climb(
problem_cust,
max_attempts=10000,
max_iters=max_iterations,
restarts=0,
init_state=init_state,
random_state=random_seed)
elif (algorithm == "simulated_annealing"):
best_state, best_fitness = mlrose.simulated_annealing(
problem_cust,
schedule=schedule,
max_attempts=100000,
max_iters=10000000,
init_state=init_state,
random_state=random_seed)
except ValueError:
print("interrupt received, stopping and saving")
finally:
bundles = getBundles(minigroups_swap, combbest)
np.save(output_dir + "/" + filename + ".npy",
np.array(bundles, dtype=object))
file1 = open(output_dir + "/chi2_" + filenumber + ".txt", "a")
file1.write("bundles[" + filenumber + "] = " + str(chi2_min) +
"\n")
file1.close()
else:
print("Algorithm " + algorithm + " currently not implemented")
def checkFluctuations(initial_state,
cmsswNtuple,
mappingFile,
outputName="alldata",
tcPtConfig=None,
correctionConfig=None,
phisplitConfig=None,
truncationConfig=None,
save_ntc_hists=False,
beginEvent=-1,
endEvent=-1):
nROverZBins = 42
#To get binning for r/z histograms
inclusive_hists = np.histogram(np.empty(0),
bins=nROverZBins,
range=(0.076, 0.58))
roverzBinning = inclusive_hists[1]
#List of which minigroups are assigned to each bundle
init_state = np.hstack(np.load(initial_state, allow_pickle=True))
#Load the truncation options, if need to truncate based on E_T when running over ntuple (save_sum_tcPt == True)
truncation_options = []
ABratios = []
nLinks = []
save_sum_tcPt = False
if (tcPtConfig != None):
save_sum_tcPt = tcPtConfig['save_sum_tcPt']
options_to_study = tcPtConfig['options_to_study']
if (truncationConfig != None):
for option in options_to_study:
truncation_options.append(
truncationConfig['option' +
str(option)]['predetermined_values'])
ABratios.append(
truncationConfig['option' + str(option)]['maxTCsA'] /
truncationConfig['option' + str(option)]['maxTCsB'])
nLinks.append(truncationConfig['option' +
str(option)]['nLinks'])
#Load the CMSSW ntuple to get per event and per trigger cell information
rootfile = ROOT.TFile.Open(cmsswNtuple, "READ")
tree = rootfile.Get("HGCalTriggerNtuple")
#Load mapping file
data = loadDataFile(mappingFile)
#Load geometry corrections
if correctionConfig['nTCCorrectionFile'] != None:
modulesToCorrect = loadSiliconNTCCorrectionFile(
correctionConfig['nTCCorrectionFile'])
else:
modulesToCorrect = pd.DataFrame()
#Get list of which lpgbts are in each minigroup
minigroups, minigroups_swap = getMinilpGBTGroups(data)
#Get list of which modules are in each minigroup
minigroups_modules = getMiniModuleGroups(data, minigroups_swap)
bundles = getBundles(minigroups_swap, init_state)
bundled_lpgbthists_allevents = []
bundled_pt_hists_allevents = []
ROverZ_per_module_phidivisionX = {} #traditionally phi > 60 degrees
ROverZ_per_module_phidivisionY = {} #traditionally phi < 60 degrees
ROverZ_per_module_phidivisionX_tcPt = {}
ROverZ_per_module_phidivisionY_tcPt = {}
nTCs_per_module = {}
#Value of split in phi (traditionally 60 degrees)
if phisplitConfig == None:
phi_split_phidivisionX = np.full(nROverZBins, np.pi / 3)
phi_split_phidivisionY = np.full(nROverZBins, np.pi / 3)
else:
if phisplitConfig['type'] == "fixed":
phi_split_phidivisionX = np.full(
nROverZBins,
np.radians(phisplitConfig['phidivisionX_fixvalue_min']))
phi_split_phidivisionY = np.full(
nROverZBins,
np.radians(phisplitConfig['phidivisionY_fixvalue_max']))
else:
file_roverz_inclusive = ROOT.TFile(
str(phisplitConfig['splitfile']), "READ")
PhiVsROverZ_Total = file_roverz_inclusive.Get("ROverZ_Inclusive")
split_indices_phidivisionX = getPhiSplitIndices(
PhiVsROverZ_Total, split="per_roverz_bin")
split_indices_phidivisionY = getPhiSplitIndices(
PhiVsROverZ_Total, split="per_roverz_bin")
phi_split_phidivisionX = np.zeros(nROverZBins)
phi_split_phidivisionY = np.zeros(nROverZBins)
for i, (idxX, idxY) in enumerate(
zip(split_indices_phidivisionX,
split_indices_phidivisionY)):
phi_split_phidivisionX[i] = PhiVsROverZ_Total.GetYaxis(
).GetBinLowEdge(int(idxY))
phi_split_phidivisionY[i] = PhiVsROverZ_Total.GetYaxis(
).GetBinLowEdge(int(idxY))
if save_ntc_hists:
for i in range(15):
for j in range(15):
for k in range(1, 53):
if k < 28 and k % 2 == 0:
continue
nTCs_per_module[0, i, j, k] = ROOT.TH1D(
"nTCs_silicon_" + str(i) + "_" + str(j) + "_" + str(k),
"", 49, -0.5, 48.5)
for i in range(5):
for j in range(12):
for k in range(37, 53):
nTCs_per_module[1, i, j, k] = ROOT.TH1D(
"nTCs_scintillator_" + str(i) + "_" + str(j) + "_" +
str(k), "", 49, -0.5, 48.5)
for z in (-1, 1):
for sector in (0, 1, 2):
key1 = (z, sector)
ROverZ_per_module_phidivisionX[key1] = {}
ROverZ_per_module_phidivisionY[key1] = {}
if save_sum_tcPt:
ROverZ_per_module_phidivisionX_tcPt[key1] = {}
ROverZ_per_module_phidivisionY_tcPt[key1] = {}
for i in range(15):
for j in range(15):
for k in range(1, 53):
if k < 28 and k % 2 == 0:
continue
ROverZ_per_module_phidivisionX[key1][0, i, j,
k] = np.empty(0)
ROverZ_per_module_phidivisionY[key1][0, i, j,
k] = np.empty(0)
if save_sum_tcPt:
ROverZ_per_module_phidivisionX_tcPt[key1][
0, i, j, k] = [] #np.empty(0)
ROverZ_per_module_phidivisionY_tcPt[key1][
0, i, j, k] = [] #np.empty(0)
for i in range(5):
for j in range(12):
for k in range(37, 53):
ROverZ_per_module_phidivisionX[key1][1, i, j,
k] = np.empty(0)
ROverZ_per_module_phidivisionY[key1][1, i, j,
k] = np.empty(0)
if save_sum_tcPt:
ROverZ_per_module_phidivisionX_tcPt[key1][
0, i, j, k] = [] #np.empty(0)
ROverZ_per_module_phidivisionY_tcPt[key1][
0, i, j, k] = [] #np.empty(0)
try:
for entry, event in enumerate(tree):
if (beginEvent != -1 and entry < beginEvent):
if (entry == 0):
print("Event number less than " + str(beginEvent) +
", continue")
continue
if (endEvent != -1 and entry > endEvent):
print("Event number greater than " + str(endEvent) + ", break")
break
# if entry > 10:
# break
print("Event number " + str(entry))
for key1 in ROverZ_per_module_phidivisionX.keys():
for key2 in ROverZ_per_module_phidivisionX[key1].keys():
ROverZ_per_module_phidivisionX[key1][key2] = np.empty(0)
ROverZ_per_module_phidivisionY[key1][key2] = np.empty(0)
if save_sum_tcPt:
ROverZ_per_module_phidivisionX_tcPt[key1][key2] = [
] #np.empty(0)
ROverZ_per_module_phidivisionY_tcPt[key1][key2] = [
] #np.empty(0)
#Loop over list of trigger cells in a particular
#event and fill R/Z histograms for each module
#for phidivisionX and phidivisionY (traditionally phi > 60 degrees and phi < 60 degrees respectively)
#Check if tc_pt exists (needed if we want to save the sum of (truncated) TC's pT)
eventzip = zip(event.tc_waferu, event.tc_waferv, event.tc_layer,
event.tc_x, event.tc_y, event.tc_z, event.tc_cellu,
event.tc_cellv)
if (save_sum_tcPt):
if hasattr(event, 'tc_pt'):
eventzip = zip(event.tc_waferu, event.tc_waferv,
event.tc_layer, event.tc_x, event.tc_y,
event.tc_z, event.tc_cellu, event.tc_cellv,
event.tc_pt)
else:
print(
'tc_pt not found in TTree - switching to non-save_sum_pt mode'
)
save_sum_tcPt = False
for variables in eventzip:
u, v, layer, x, y, z, cellu, cellv = variables[:8]
if save_sum_tcPt: pt = variables[8]
if (u > -990): #Silicon
uv, sector = rotate_to_sector_0(u, v, layer)
roverz_phi = getROverZPhi(x, y, z, sector)
roverz_bin = np.argmax(roverzBinning > abs(roverz_phi[0]))
if (roverz_phi[1] >=
phi_split_phidivisionX[roverz_bin - 1]):
#There should be no r/z values lower than 0.076
ROverZ_per_module_phidivisionX[np.sign(z), sector][
0, uv[0], uv[1], layer] = np.append(
ROverZ_per_module_phidivisionX[np.sign(z),
sector][0,
uv[0],
uv[1],
layer],
abs(roverz_phi[0]))
if save_sum_tcPt:
ROverZ_per_module_phidivisionX_tcPt[
np.sign(z),
sector][0, uv[0], uv[1],
layer].append([abs(roverz_phi[0]), pt])
if (roverz_phi[1] <
phi_split_phidivisionY[roverz_bin - 1]):
ROverZ_per_module_phidivisionY[np.sign(z), sector][
0, uv[0], uv[1], layer] = np.append(
ROverZ_per_module_phidivisionY[np.sign(z),
sector][0,
uv[0],
uv[1],
layer],
abs(roverz_phi[0]))
if save_sum_tcPt:
ROverZ_per_module_phidivisionY_tcPt[
np.sign(z),
sector][0, uv[0], uv[1],
layer].append([abs(roverz_phi[0]), pt])
else: #Scintillator
eta = cellu
phi = cellv
etaphi, sector = etaphiMapping(layer, [eta, phi])
roverz_phi = getROverZPhi(x, y, z, sector)
roverz_bin = np.argmax(roverzBinning > abs(roverz_phi[0]))
if (roverz_phi[1] >=
phi_split_phidivisionX[roverz_bin - 1]):
ROverZ_per_module_phidivisionX[np.sign(z), sector][
1, etaphi[0], etaphi[1], layer] = np.append(
ROverZ_per_module_phidivisionX[
np.sign(z), sector][1, etaphi[0],
etaphi[1], layer],
abs(roverz_phi[0]))
if save_sum_tcPt:
ROverZ_per_module_phidivisionX_tcPt[
np.sign(z),
sector][1, etaphi[0], etaphi[1],
layer].append([abs(roverz_phi[0]), pt])
if (roverz_phi[1] <
phi_split_phidivisionY[roverz_bin - 1]):
ROverZ_per_module_phidivisionY[np.sign(z), sector][
1, etaphi[0], etaphi[1], layer] = np.append(
ROverZ_per_module_phidivisionY[
np.sign(z), sector][1, etaphi[0],
etaphi[1], layer],
abs(roverz_phi[0]))
if save_sum_tcPt:
ROverZ_per_module_phidivisionY_tcPt[
np.sign(z),
sector][1, etaphi[0], etaphi[1],
layer].append([abs(roverz_phi[0]), pt])
#Bin the TC module data
module_hists_phidivisionX = {}
module_hists_phidivisionY = {}
for key1, value1 in ROverZ_per_module_phidivisionX.items():
module_hists_phidivisionX[key1] = {}
for key2, value2 in value1.items():
module_hists_phidivisionX[key1][key2] = np.histogram(
value2, bins=nROverZBins, range=(0.076, 0.58))[0]
for key1, value1 in ROverZ_per_module_phidivisionY.items():
module_hists_phidivisionY[key1] = {}
for key2, value2 in value1.items():
module_hists_phidivisionY[key1][key2] = np.histogram(
value2, bins=nROverZBins, range=(0.076, 0.58))[0]
for z in (-1, 1):
for sector in (0, 1, 2):
#the module hists are a numpy array of size 42
module_hists = [
module_hists_phidivisionX[z, sector],
module_hists_phidivisionY[z, sector]
]
#Apply geometry corrections
applyGeometryCorrectionsNumpy(module_hists,
modulesToCorrect)
#Save the integral of module_hists, per event
if save_ntc_hists:
for module, hist in nTCs_per_module.items():
hist.Fill(
np.round(
np.sum(module_hists[0][module]) +
np.sum(module_hists[1][module])))
#Sum the individual module histograms to get the minigroup histograms
minigroup_hists = getMiniGroupHistsNumpy(
module_hists, minigroups_modules)
#Sum the minigroup histograms to get the bundle histograms
bundled_lpgbthists = getBundledlpgbtHists(
minigroup_hists, bundles)
bundled_lpgbthists_allevents.append(bundled_lpgbthists)
#Collect the individual TC Pt values for a given minigroup, with the view to truncate and sum
if (save_sum_tcPt):
tc_Pt_rawdata = [
ROverZ_per_module_phidivisionX_tcPt[z, sector],
ROverZ_per_module_phidivisionY_tcPt[z, sector]
]
#Apply geometry corrections
applyGeometryCorrectionsTCPtRawData(
tc_Pt_rawdata, modulesToCorrect)
#Get lists of (r/z, pt) pairs, first for minigroups and then for bundles
minigroup_tc_Pt_rawdata = getMiniGroupTCPtRawData(
tc_Pt_rawdata, minigroups_modules)
bundled_tc_Pt_rawdata = getBundledTCPtRawData(
minigroup_tc_Pt_rawdata, bundles)
#Get histograms of (truncated) sum pT per r/z bin
bundled_pt_hists = applyTruncationAndGetPtSums(
bundled_tc_Pt_rawdata, truncation_options,
ABratios, roverzBinning, nLinks)
bundled_pt_hists_allevents.append(bundled_pt_hists)
except KeyboardInterrupt:
print("interrupt received, stopping and saving")
finally:
#Write all data to file for later analysis (Pickling)
if (beginEvent != -1):
outputName = outputName + "_from" + str(beginEvent)
if (endEvent != -1):
outputName = outputName + "_to" + str(endEvent)
with open(outputName + ".txt", "wb") as filep:
pickle.dump(bundled_lpgbthists_allevents, filep)
if save_sum_tcPt:
with open(outputName + "_sumpt.txt", "wb") as filep:
pickle.dump(bundled_pt_hists_allevents, filep)
if save_ntc_hists:
outfile = ROOT.TFile(outputName + "_nTCs.root", "RECREATE")
for hist in nTCs_per_module.values():
hist.Write()
def study_mapping(MappingFile,
CMSSW_ModuleHists,
algorithm="random_hill_climb",
initial_state="best_so_far",
random_seed=1,
max_iterations=100000,
output_dir=".",
print_level=0,
minigroup_type="minimal"):
#Load external data
data = loadDataFile(MappingFile) #dataframe
inclusive_hists, module_hists = getModuleHists(CMSSW_ModuleHists)
#Form hists corresponding to each lpGBT from module hists
lpgbt_hists = getlpGBTHists(data, module_hists)
minigroups, minigroups_swap = getMinilpGBTGroups(data, minigroup_type)
# minigroup_hists = getMiniGroupHists(lpgbt_hists,minigroups_swap)
minigroup_hists = getMiniGroupHists(lpgbt_hists, minigroups_swap)
minigroup_hists_root = getMiniGroupHists(lpgbt_hists,
minigroups_swap,
root=True)
def mapping_max(state):
global chi2_min
global combbest
chi2 = 0
bundles = getBundles(minigroups_swap, state)
#bundled_lpgbthists = getBundledlpgbtHists(minigroup_hists,bundles)
bundled_lpgbthists = getBundledlpgbtHists(minigroup_hists, bundles)
chi2 = calculateChiSquared(inclusive_hists, bundled_lpgbthists)
typicalchi2 = 600000000000
if (chi2 < chi2_min):
chi2_min = chi2
combbest = np.copy(state)
if (print_level > 0):
print(algorithm, " ", chi2_min, " ", chi2_min / typicalchi2)
if (print_level > 1):
print(repr(combbest))
return chi2
init_state = []
if (initial_state == "best_so_far"):
init_state = bestsofar
if (initial_state[-4:] == ".npy"):
print(initial_state)
init_state = np.hstack(np.load(initial_state, allow_pickle=True))
elif (initial_state == "random"):
init_state = np.arange(len(minigroups_swap))
np.random.shuffle(init_state)
fitness_cust = mlrose.CustomFitness(mapping_max)
# Define optimization problem object
problem_cust = mlrose.DiscreteOpt(length=len(init_state),
fitness_fn=fitness_cust,
maximize=False,
max_val=len(minigroups_swap),
minigroups=minigroups_swap)
# Define decay schedule
schedule = mlrose.ExpDecay()
#schedule = mlrose.ArithDecay()
filename = "bundles_job_"
filenumber = ""
if (len(sys.argv) > 2):
filenumber = str(sys.argv[2])
else:
filenumber = "default"
filename += filenumber
if (algorithm == "save_root"):
#Save best combination so far into a root file
bundles = getBundles(minigroups_swap, init_state)
bundled_hists = getBundledlpgbtHistsRoot(minigroup_hists_root, bundles)
chi2 = calculateChiSquared(inclusive_hists, bundled_hists)
newfile = ROOT.TFile("lpgbt_10.root", "RECREATE")
np.save(output_dir + "/" + filename + ".npy", bundles)
for sector in bundled_hists:
for key, value in sector.items():
value.Write()
for sector in inclusive_hists:
sector.Scale(1. / 24.)
sector.Write()
newfile.Close()
print("Chi2:", chi2)
print("List of Bundles:")
for b, bundle in enumerate(bundles):
print("")
print("bundle" + str(b))
for minigroup in bundle:
#print (minigroup)
lpgbts = minigroups_swap[minigroup]
for lpgbt in lpgbts:
print(str(lpgbt) + ", ", end='')
elif (algorithm == "random_hill_climb"):
try:
best_state, best_fitness = mlrose.random_hill_climb(
problem_cust,
max_attempts=10000,
max_iters=max_iterations,
restarts=0,
init_state=init_state,
random_state=random_seed)
print(repr(best_state))
except ValueError:
print("interrupt received, stopping and saving")
finally:
bundles = getBundles(minigroups_swap, combbest)
np.save(output_dir + "/" + filename + ".npy", bundles)
file1 = open(output_dir + "/chi2_" + filenumber + ".txt", "a")
file1.write("bundles[" + filenumber + "] = " + str(chi2_min) +
"\n")
file1.close()
elif (algorithm == "genetic_alg"):
best_state, best_fitness = mlrose.genetic_alg(problem_cust,
pop_size=200,
mutation_prob=0.1,
max_attempts=1000,
max_iters=10000000,
curve=False,
random_state=random_seed)
elif (algorithm == "mimic"):
best_state, best_fitness = mlrose.mimic(problem_cust,
pop_size=200,
keep_pct=0.2,
max_attempts=10,
max_iters=np.inf,
curve=False,
random_state=random_seed)
elif (algorithm == "simulated_annealing"):
best_state, best_fitness = mlrose.simulated_annealing(
problem_cust,
schedule=schedule,
max_attempts=100000,
max_iters=10000000,
init_state=init_state,
random_state=1)
else:
print("Algorithm " + algorithm + " not known")
def checkFluctuations(initial_state, cmsswNtuple, mappingFile):
#List of which minigroups are assigned to each bundle
init_state = np.hstack(np.load(initial_state, allow_pickle=True))
#Load the CMSSW ntuple to get per event and per trigger cell information
rootfile = ROOT.TFile.Open(cmsswNtuple, "READ")
tree = rootfile.Get("HGCalTriggerNtuple")
#Load mapping file
data = loadDataFile(mappingFile)
#Get list of which lpgbts are in each minigroup
minigroups, minigroups_swap = getMinilpGBTGroups(data)
#Get list of which modules are in each minigroup
minigroups_modules = getMiniModuleGroups(data, minigroups_swap)
bundles = getBundles(minigroups_swap, init_state)
bundled_lpgbthists_allevents = []
ROverZ_per_module = {}
ROverZ_per_module_Phi60 = {}
for i in range(15):
for j in range(15):
for k in range(1, 53):
if k < 28 and k % 2 == 0:
continue
ROverZ_per_module[0, i, j, k] = np.empty(0)
ROverZ_per_module_Phi60[0, i, j, k] = np.empty(0)
for i in range(5):
for j in range(12):
for k in range(37, 53):
ROverZ_per_module[1, i, j, k] = np.empty(0)
ROverZ_per_module_Phi60[1, i, j, k] = np.empty(0)
try:
for entry, event in enumerate(tree):
# if entry > 10:
# break
print("Event number " + str(entry))
for key in ROverZ_per_module.keys():
ROverZ_per_module[key] = np.empty(0)
ROverZ_per_module_Phi60[key] = np.empty(0)
#Loop over list of trigger cells in a particular
#event and fill R/Z histograms for each module
#(inclusively and for phi < 60)
for u, v, layer, x, y, z, cellu, cellv in zip(
event.tc_waferu, event.tc_waferv, event.tc_layer,
event.tc_x, event.tc_y, event.tc_z, event.tc_cellu,
event.tc_cellv):
eta_phi = getROverZPhi(x, y, z)
if (u > -990): #Silicon
uv = rotate_to_sector_0(u, v, layer)
ROverZ_per_module[0, uv[0], uv[1], layer] = np.append(
ROverZ_per_module[0, uv[0], uv[1], layer],
abs(eta_phi[0]))
if (eta_phi[1] < np.pi / 3):
ROverZ_per_module_Phi60[
0, uv[0], uv[1], layer] = np.append(
ROverZ_per_module_Phi60[0, uv[0],
uv[1], layer],
abs(eta_phi[0]))
else: #Scintillator
eta = cellu
phi = cellv
etaphi = etaphiMapping(layer, [eta, phi])
ROverZ_per_module[1, etaphi[0], etaphi[1],
layer] = np.append(
ROverZ_per_module[1, etaphi[0],
etaphi[1], layer],
abs(eta_phi[0]))
if (eta_phi[1] < np.pi / 3):
ROverZ_per_module_Phi60[
1, etaphi[0], etaphi[1], layer] = np.append(
ROverZ_per_module_Phi60[1, etaphi[0],
etaphi[1], layer],
abs(eta_phi[0]))
ROverZ_Inclusive = np.empty(0)
ROverZ_Inclusive_Phi60 = np.empty(0)
for key, value in ROverZ_per_module.items():
ROverZ_Inclusive = np.append(ROverZ_Inclusive, value)
for key, value in ROverZ_per_module_Phi60.items():
ROverZ_Inclusive_Phi60 = np.append(ROverZ_Inclusive_Phi60,
value)
#Bin the TC module data
module_hists_inc = {}
module_hists_phi60 = {}
inclusive_hists = np.histogram(ROverZ_Inclusive,
bins=42,
range=(0.076, 0.58))
inclusive_hists_phi60 = np.histogram(ROverZ_Inclusive_Phi60,
bins=42,
range=(0.076, 0.58))
for key, value in ROverZ_per_module.items():
module_hists_inc[key] = np.histogram(value,
bins=42,
range=(0.076, 0.58))[0]
for key, value in ROverZ_per_module_Phi60.items():
module_hists_phi60[key] = np.histogram(value,
bins=42,
range=(0.076, 0.58))[0]
module_hists = [module_hists_inc, module_hists_phi60]
#Sum the individual module histograms to get the minigroup histograms
minigroup_hists = getMiniGroupHistsNumpy(module_hists,
minigroups_modules)
#Sum the minigroup histograms to get the bundle histograms
bundled_lpgbthists = getBundledlpgbtHists(minigroup_hists, bundles)
bundled_lpgbthists_allevents.append(bundled_lpgbthists)
except KeyboardInterrupt:
print("interrupt received, stopping and saving")
finally:
#Write all data to file for later analysis (Pickling)
with open("alldata.txt", "wb") as filep:
pickle.dump(bundled_lpgbthists_allevents, filep)
