def evaluate(self, ast_node):
qv = ast_visitor()
#print(ast.dump(ast_node))
qv.visit(ast_node)
if isinstance(self.dataset_source, str):
data_pathname = self.dataset_source
else:
data_pathname = 'temp.awkd'
awkward.save(data_pathname, self.dataset_source)
f = open('temp.py', 'w')
f.write('import awkward\n')
source = ast_node.source
while hasattr(source, 'source'):
source = source.source
if data_pathname[-5:] == '.awkd':
f.write(source.rep + " = awkward.load('" + data_pathname + "')\n")
elif data_pathname[-5:] == '.root':
f.write('import uproot\n')
f.write("input_file = uproot.open('" + data_pathname + "')\n")
f.write(
source.rep +
" = input_file[input_file.keys()[0]].lazyarrays(namedecode='utf-8')\n"
)
else:
raise BaseException('unimplemented file type: ' + data_pathname)
f.write('output_array = awkward.fromiter(' + ast_node.rep + ')\n')
f.write("awkward.save('output.awkd', output_array)\n")
f.close()
os.system('python temp.py')
if not isinstance(self.dataset_source, str):
os.remove(data_pathname)
os.remove('temp.py')
output = awkward.load('output.awkd')
os.remove('output.awkd')
return output
def save_awk(scores, labels, observers):
"""
Saves as .awkd
:param scores:
:param labels:
:param observers:
:return:
"""
import awkward
output = {'scores': scores}
output.update(labels)
output.update(observers)
name_remap = {}
arraynames = list(output)
for i in range(len(arraynames)):
for j in range(i + 1, len(arraynames)):
if arraynames[i].startswith(arraynames[j]):
name_remap[arraynames[j]] = '%s_%d' % (arraynames[j],
len(name_remap))
if arraynames[j].startswith(arraynames[i]):
name_remap[arraynames[i]] = '%s_%d' % (arraynames[i],
len(name_remap))
_logger.info('Renamed the following variables in the output file: %s',
str(name_remap))
output = {
name_remap[k] if k in name_remap else k: v
for k, v in output.items()
}
awkward.save(args.predict_output, output, mode='w')
def merge_npzs_to_ak(rawdir, outfile=None, nmax=None):
"""
Loops over all .npz files in rawdir, stacks all events into an ak array,
and dumps it to a file.
"""
if outfile is None: outfile = osp.dirname(rawdir) + '/merged.awkd'
bbefp.logger.info(f'Merging {rawdir} --> {outfile}')
merged = ak.fromiter(_iter_npzs(rawdir, nmax))
ak.save(outfile, ak_transpose(merged))
def main():
""" Loop over all combinations of mass and width. """
args = parse_input()
masses = np.linspace(
args.mass_min, args.mass_max,
np.ceil((args.mass_max - args.mass_min) / args.mass_step) + 1)
widths = np.linspace(
args.width_min, args.width_max,
np.ceil((args.width_max - args.width_min) / args.width_step) + 1)
def generator():
with tqdm.tqdm(unit='event',
total=masses.size * widths.size * args.nevents,
desc='Generating') as pbar:
for mass in masses:
for width in widths:
yield from run(args.nevents, mass, width)
pbar.update(args.nevents)
events = ak.fromiter(generator())
ak.save('events.awkd', events, mode='w')
def convert(source, destdir, basename, step=None, limit=None):
df = pd.read_hdf(source, key='table')
logging.info('Total events: %s' % str(df.shape[0]))
if limit is not None:
df = df.iloc[0:limit]
logging.info('Restricting to the first %s events:' % str(df.shape[0]))
if step is None:
step = df.shape[0]
idx=-1
while True:
idx+=1
start=idx*step
if start>=df.shape[0]: break
if not os.path.exists(destdir):
os.makedirs(destdir)
output = os.path.join(destdir, '%s_%d.awkd'%(basename, idx))
logging.info(output)
if os.path.exists(output):
logging.warning('... file already exist: continue ...')
continue
v=_transform(df, start=start, stop=start+step)
awkward.save(output, v, mode='x')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-c',
'--data-config',
type=str,
default='data/ak15_points_pf_sv_v0.yaml',
help='data config YAML file')
parser.add_argument('-i',
'--data-train',
nargs='*',
default=[],
help='training files')
parser.add_argument('-t',
'--data-test',
nargs='*',
default=[],
help='testing files')
parser.add_argument(
'--data-fraction',
type=float,
default=1,
help=
'fraction of events to load from each file; for training, the events are randomly selected for each epoch'
)
parser.add_argument(
'--data-dilation',
type=int,
default=1,
help=
'reduce number of file by a factor of `d` for training. NOT recommended in general - use `--data-fraction` instead.'
)
parser.add_argument(
'--files-per-fetch',
type=int,
default=20,
help=
'number of files to load each time; shuffling is done within these events, so choose a number large enough to get events from all classes'
)
parser.add_argument('--train-val-split',
type=float,
default=0.8,
help='training/validation split fraction')
parser.add_argument(
'--demo',
action='store_true',
default=False,
help=
'quickly test the setup by running over only a small number of events')
parser.add_argument(
'--lr-finder',
type=str,
default=None,
help=
'run learning rate finder instead of the actual training; format: ``start_lr, end_lr, num_iters``'
)
parser.add_argument(
'-n',
'--network-config',
type=str,
default='networks/particle_net_pfcand_sv.py',
help=
'network architecture configuration file; the path must be relative to the current dir'
)
parser.add_argument(
'--network-option',
nargs=2,
action='append',
default=[],
help=
'options to pass to the model class constructor, e.g., `--network-option use_counts False`'
)
parser.add_argument(
'-m',
'--model-prefix',
type=str,
default='test_output/model_name',
help=
'path to save or load the model; for training, this will be used as a prefix; for testing, this should be the full path including extension'
)
parser.add_argument('--num-epochs',
type=int,
default=20,
help='number of epochs')
parser.add_argument(
'--optimizer',
type=str,
default='ranger',
choices=['adam', 'ranger'], # TODO: add more
help='optimizer for the training')
parser.add_argument(
'--load-epoch',
type=int,
default=None,
help=
'used to resume interrupted training, load model and optimizer state saved in the `epoch-%d_state.pt` and `epoch-%d_optimizer.pt` files'
)
parser.add_argument('--start-lr',
type=float,
default=5e-3,
help='start learning rate')
parser.add_argument(
'--lr-steps',
type=str,
default='10,20',
help=
'steps to reduce the lr; currently only used when setting `--optimizer` to adam'
)
parser.add_argument('--batch-size',
type=int,
default=128,
help='batch size')
parser.add_argument(
'--use-amp',
action='store_true',
default=False,
help='use mixed precision training (fp16); NOT WORKING YET')
parser.add_argument(
'--gpus',
type=str,
default='0',
help='device for the training/testing; to use CPU, set to empty string ('
'); to use multiple gpu, set it as a comma separated list, e.g., `1,2,3,4`'
)
parser.add_argument(
'--num-workers',
type=int,
default=2,
help=
'number of threads to load the dataset; memory consuption and disk access load increases (~linearly) with this numbers'
)
parser.add_argument('--predict',
action='store_true',
default=False,
help='run prediction instead of training')
parser.add_argument(
'--predict-output',
type=str,
help=
'path to save the prediction output, support `.root` and `.awkd` format'
)
parser.add_argument(
'--export-onnx',
type=str,
default=None,
help=
'export the PyTorch model to ONNX model and save it at the given path (path must ends w/ .onnx); '
'needs to set `--data-config`, `--network-config`, and `--model-prefix` (requires the full model path)'
)
args = parser.parse_args()
_logger.info(args)
if args.use_amp:
raise NotImplementedError
# from apex import amp
if args.data_dilation > 1:
_logger.warning(
'Use of `data-dilation` is not recomended in general -- consider using `data-fraction` instead.'
)
# training/testing mode
training_mode = not args.predict
# device
if args.gpus:
gpus = [int(i) for i in args.gpus.split(',')]
dev = torch.device(gpus[0])
else:
gpus = None
dev = torch.device('cpu')
# load data
if training_mode:
filelist = sorted(sum([glob.glob(f) for f in args.data_train], []))
# np.random.seed(1)
np.random.shuffle(filelist)
if args.demo:
filelist = filelist[:20]
_logger.info(filelist)
args.data_fraction = 0.1
args.files_per_fetch = 5
train_data = SimpleIterDataset(filelist,
args.data_config,
for_training=True,
partial_load=((0, args.train_val_split),
args.data_fraction),
dilation=args.data_dilation,
files_per_fetch=args.files_per_fetch)
val_data = SimpleIterDataset(filelist,
args.data_config,
for_training=True,
partial_load=((args.train_val_split, 1),
args.data_fraction),
dilation=args.data_dilation,
files_per_fetch=args.files_per_fetch)
train_loader = DataLoader(train_data,
num_workers=args.num_workers,
batch_size=args.batch_size,
drop_last=True,
pin_memory=True)
val_loader = DataLoader(val_data,
num_workers=args.num_workers,
batch_size=args.batch_size,
drop_last=True,
pin_memory=True)
data_config = train_data.config
else:
filelist = sorted(sum([glob.glob(f) for f in args.data_test], []))
test_data = SimpleIterDataset(filelist,
args.data_config,
for_training=False,
files_per_fetch=1)
test_loader = DataLoader(test_data,
num_workers=args.num_workers,
batch_size=args.batch_size,
drop_last=False,
pin_memory=True)
data_config = test_data.config
# model
network_module = import_module(
args.network_config.replace('.py', '').replace('/', '.'))
network_options = {k: ast.literal_eval(v) for k, v in args.network_option}
if args.export_onnx:
network_options['for_inference'] = True
model, model_info = network_module.get_model(data_config,
**network_options)
_logger.info(model)
# export to ONNX
if args.export_onnx:
assert (args.export_onnx.endswith('.onnx'))
model_path = args.model_prefix
_logger.info('Exporting model %s to ONNX' % model_path)
model.load_state_dict(torch.load(model_path, map_location='cpu'))
model = model.cpu()
model.eval()
os.makedirs(os.path.dirname(args.export_onnx), exist_ok=True)
inputs = tuple(
torch.ones(model_info['input_shapes'][k], dtype=torch.float32)
for k in model_info['input_names'])
torch.onnx.export(model,
inputs,
args.export_onnx,
input_names=model_info['input_names'],
output_names=model_info['output_names'],
dynamic_axes=model_info.get('dynamic_axes', None),
opset_version=11)
_logger.info('ONNX model saved to %s', args.export_onnx)
return
# note: we should always save/load the state_dict of the original model, not the one wrapped by nn.DataParallel
# so we do not convert it to nn.DataParallel now
model = model.to(dev)
# loss function
try:
loss_func = network_module.get_loss(data_config, **network_options)
_logger.info(loss_func)
except AttributeError:
loss_func = torch.nn.CrossEntropyLoss()
_logger.warning(
'Loss function not defined in %s. Will use `torch.nn.CrossEntropyLoss()` by default.',
args.network_config)
if training_mode:
# optimizer & learning rate
if args.optimizer == 'adam':
opt = torch.optim.Adam(model.parameters(), lr=args.start_lr)
if args.lr_finder is None:
lr_steps = [int(x) for x in args.lr_steps.split(',')]
scheduler = torch.optim.lr_scheduler.MultiStepLR(
opt, milestones=lr_steps, gamma=0.1)
else:
from utils.nn.optimizer.ranger import Ranger
opt = Ranger(model.parameters(), lr=args.start_lr)
if args.lr_finder is None:
lr_decay_epochs = max(1, int(args.num_epochs * 0.3))
lr_decay_rate = 0.01**(1. / lr_decay_epochs)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
opt,
milestones=list(
range(args.num_epochs - lr_decay_epochs,
args.num_epochs)),
gamma=lr_decay_rate)
# TODO: mixed precision training
if args.use_amp:
# model, opt = amp.initialize(
# model, opt, opt_level="O2",
# keep_batchnorm_fp32=True, loss_scale="dynamic"
# )
model, opt = amp.initialize(model,
opt,
opt_level="O1",
keep_batchnorm_fp32=None,
loss_scale="dynamic")
# load previous training and resume if `--load-epoch` is set
if args.load_epoch is not None:
_logger.info('Resume training from epoch %d' % args.load_epoch)
model_state = torch.load(args.model_prefix +
'_epoch-%d_state.pt' % args.load_epoch,
map_location=dev)
model.load_state_dict(model_state)
opt_state = torch.load(args.model_prefix +
'_epoch-%d_optimizer.pt' % args.load_epoch,
map_location=dev)
opt.load_state_dict(opt_state)
# mutli-gpu
if gpus is not None and len(gpus) > 1:
model = torch.nn.DataParallel(
model, device_ids=gpus
) # model becomes `torch.nn.DataParallel` w/ model.module being the orignal `torch.nn.Module`
model = model.to(dev)
# lr finder: keep it after all other setups
if args.lr_finder is not None:
start_lr, end_lr, num_iter = args.lr_finder.replace(' ',
'').split(',')
from utils.lr_finder import LRFinder
lr_finder = LRFinder(model,
opt,
loss_func,
device=dev,
input_names=train_data.config.input_names,
label_names=train_data.config.label_names)
lr_finder.range_test(train_loader,
start_lr=float(start_lr),
end_lr=float(end_lr),
num_iter=int(num_iter))
lr_finder.plot(output='lr_finder.png'
) # to inspect the loss-learning rate graph
return
# training loop
best_valid_acc = 0
for epoch in range(args.num_epochs):
if args.load_epoch is not None:
if epoch <= args.load_epoch:
continue
print('-' * 50)
_logger.info('Epoch #%d training' % epoch)
train(model, loss_func, opt, scheduler, train_loader, dev)
if args.model_prefix:
dirname = os.path.dirname(args.model_prefix)
if dirname and not os.path.exists(dirname):
os.makedirs(dirname)
state_dict = model.module.state_dict() if isinstance(
model, torch.nn.DataParallel) else model.state_dict()
torch.save(state_dict,
args.model_prefix + '_epoch-%d_state.pt' % epoch)
torch.save(
opt.state_dict(),
args.model_prefix + '_epoch-%d_optimizer.pt' % epoch)
_logger.info('Epoch #%d validating' % epoch)
valid_acc = evaluate(model, val_loader, dev, loss_func=loss_func)
if valid_acc > best_valid_acc:
best_valid_acc = valid_acc
if args.model_prefix:
shutil.copy2(
args.model_prefix + '_epoch-%d_state.pt' % epoch,
args.model_prefix + '_best_acc_state.pt')
torch.save(model, args.model_prefix + '_best_acc_full.pt')
_logger.info(
'Epoch #%d: Current validation acc: %.5f (best: %.5f)' %
(epoch, valid_acc, best_valid_acc))
else:
# run prediction
if args.model_prefix.endswith('.onnx'):
_logger.info('Loading model %s for eval' % args.model_prefix)
from utils.nn.tools import evaluate_onnx
test_acc, scores, labels, observers = evaluate_onnx(
args.model_prefix, test_loader)
else:
model_path = args.model_prefix if args.model_prefix.endswith(
'.pt') else args.model_prefix + '_best_acc_state.pt'
_logger.info('Loading model %s for eval' % model_path)
model.load_state_dict(torch.load(model_path, map_location=dev))
if gpus is not None and len(gpus) > 1:
model = torch.nn.DataParallel(model, device_ids=gpus)
model = model.to(dev)
test_acc, scores, labels, observers = evaluate(model,
test_loader,
dev,
for_training=False)
_logger.info('Test acc %.5f' % test_acc)
if args.predict_output:
os.makedirs(os.path.dirname(args.predict_output), exist_ok=True)
if args.predict_output.endswith('.root'):
from utils.data.fileio import _write_root
output = {}
for idx, label_name in enumerate(data_config.label_value):
output[label_name] = (
labels[data_config.label_names[0]] == idx)
output['score_' + label_name] = scores[:, idx]
for k, v in labels.items():
if k == data_config.label_names[0]:
continue
if v.ndim > 1:
_logger.warning('Ignoring %s, not a 1d array.', k)
continue
output[k] = v
for k, v in observers.items():
if v.ndim > 1:
_logger.warning('Ignoring %s, not a 1d array.', k)
continue
output[k] = v
_write_root(args.predict_output, output)
else:
import awkward
output = {'scores': scores}
output.update(labels)
output.update(observers)
awkward.save(args.predict_output, output, mode='w')
_logger.info('Written output to %s' % args.predict_output)
def flatten_ntuple_write(input_filename, tree_name, output_filename, class_json_filename=None, verbose=False):
"""Convert ntuple to flattened file with awkward array table.
All data for a given method are output as one long list, ignoring event splitting.
Tried to output as ROOT TTree, but very difficult in PyROOT with
variable size branches.
Parameters
----------
input_filename : std
Input Ntuple filename
tree_name : str
Name of TTree inside input file
output_filename : str
Output filename
class_json_filename : None, optional
If a str, output class info dicts to this file in JSON format
verbose : bool, optional
If True, print class info
"""
f_in = ROOT.TFile(input_filename)
if f_in.IsZombie():
raise RuntimeError("Cannot open ROOT file %s" % input_filename)
tree = f_in.Get(tree_name)
check_tobj(tree)
tree_info, class_infos, method_list = parse_tree(tree)
if verbose:
print_tree_summary(tree_info, class_infos, 'tree')
print(tree.GetEntries(), "entries in tree")
print(len(method_list), "hists in tree")
# store list of values for each method call, where each event is a dict of {method:value}
tree_data = defaultdict(list)
# Use tqdm for nice progressbar, disable on non-TTY
for ind in trange(tree.GetEntries(), disable=None):
this_data = get_data(tree, ind, method_list)
# flatten all events into one long list per method, makes for a much
# more compact output, we don't care about individual events
# guess we could compare those events with the same number of entries
# in both files? i.e. 1 or 0, but hard to do for
# eg jets, in which jet #1 may not be the same object in both files
# don't use items() as not iterator in python2
for key in this_data:
# may be a single scalar, or iterable - use extend where possible
try:
_ = iter(this_data[key])
tree_data[key].extend(this_data[key])
except TypeError:
tree_data[key].append(this_data[key])
print("tree_data size:", get_size(tree_data))
# Save JSON data
if class_infos and class_json_filename:
with open(class_json_filename, 'w') as jf:
json.dump(class_infos, jf, indent=2, sort_keys=True)
is_hdf5 = "hdf5" in os.path.splitext(output_filename)[1]
if is_hdf5:
# Save to HDF5
import h5py
import numpy as np
with h5py.File(output_filename, "w") as f:
for k in tree_data:
f.create_dataset(k, data=np.array(tree_data[k]),
compression="gzip", compression_opts=9)
else:
# Save to awkward array
# make awkward table, save with compression
import awkward
# Use awkeard 0.12/13/14 as 0.15 has a bug that means it can't load()
# the file
# And awkward 1 doesn't even allow this format
# And the awkward 0.9 in CMSSW_10_6 is too old for this
major, minor, _ = awkward.version.version_info
major = int(major)
minor = int(minor)
if major == 1:
raise ImportError("Need awkward 0.12.X, you have %s" % awkward.__version__)
elif minor > 14:
raise ImportError("Need awkward 0.12 / 0.13 / 0.14, you have %s" % awkward.__version__)
elif minor < 12:
raise ImportError("Need awkward 0.12 / 0.13 / 0.14, you have %s" % awkward.__version__)
awkd_table = awkward.fromiter([tree_data])
awkward.save(output_filename, awkd_table, mode='w', compression=True)
# if any(n.startswith(name) for n in namelist):
# raise KeyError("cannot add {0} to zipfile because the following already exist: {1}".format(repr(name), ", ".join(repr(n) for n in namelist if n.startswith(name))))
# sed -i 's/raise KeyError/print/' ~/Library/Python/3.7/lib/python/site-packages/awkward/persist.py
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("filename", help="filename")
args = parser.parse_args()
f = uproot.open(str(args.filename))
t = f["Events"]
ac = [dict(
types=[np.bool_,bool,np.integer,np.float32],
pair=(lambda x: blosc.compress(x,cname="lz4hc"), ("blosc", "decompress")),
minsize=8192,
contexts="*",
)]
bnames = [bn.decode("ascii") for bn in t.keys()]
t0 = time.time()
fname = "table.awkd"
for iname,name in enumerate(tqdm(bnames)):
arr = t.array(name)
awkward.save(fname,arr,name=name,compression=ac,mode="w" if iname==0 else "a",)
t1 = time.time()
print(t1-t0)
ew.selected_event = event_n
pts = getattr(ew, j_name + "_Total_PT")
top_4 = np.argsort(pts)[-4:]
num_found = len(top_4)
for var_num, var_name in enumerate(kinematics):
wanted = j_name + "_Total_" + var_name
vals = getattr(ew, wanted)[top_4]
all_kinematics[j_class][order, var_num, j, event_n, :num_found] = vals
energy, px, py, pz = all_kinematics[j_class][order, 1:, j, event_n, :num_found]
if num_found > 1:
shape_vals = ShapeVariables.shape(energy, px, py, pz)[1]
for var_num, var_name in enumerate(shapes):
all_shapes[j_class][order, var_num, j, event_n] = shape_vals[var_name]
content = {"shape_names": shapes, "kinematic_names": kinematics, "orders": ["nlo", "lo"], "jet_names": [spectral_names, traditional_names, iterative_names], "kinematics" : awkward.fromiter(all_kinematics), "shapes": awkward.fromiter(all_shapes)}
awkward.save("../megaIgnore/IRC_shapes.awkd", content)
else:
data = awkward.load("../megaIgnore/IRC_shapes.awkd")
shapes = data["shape_names"]
kinematics = data["kinematic_names"]
spectral_names, traditional_names, iterative_names = data["jet_names"]
all_kinematics = data["kinematics"]
all_shapes = data["shapes"]
def plot_jet_name(name, variable, bounds=None, ax=None):
colours = ['blue', 'purple', 'orange']
line_styles = ['--', '-', '-.']
if variable in kinematics:
table = all_kinematics
v_index = kinematics.index(variable)
def run_query(input_filenames=None, tree_name=None, branches=None):
import awkward, uproot
a = (lambda event: (awkward.Table if hasattr(awkward, 'Table') else awkward['Table'])((event.MVA3lCERN_weight_ttH if hasattr(event, 'MVA3lCERN_weight_ttH') else event['MVA3lCERN_weight_ttH'])))
b = (lambda event: event[(((((((((((((((((((((((((((event.trilep_type if hasattr(event, 'trilep_type') else event['trilep_type']) > 0) & ((event.nTaus_OR_Pt25 if hasattr(event, 'nTaus_OR_Pt25') else event['nTaus_OR_Pt25']) == 0)) & (abs((event.total_charge if hasattr(event, 'total_charge') else event['total_charge'])) == 1)) & ((event.nJets_OR_T if hasattr(event, 'nJets_OR_T') else event['nJets_OR_T']) >= 2)) & ((event.nJets_OR_T_MV2c10_70 if hasattr(event, 'nJets_OR_T_MV2c10_70') else event['nJets_OR_T_MV2c10_70']) > 0)) & ((event.lep_Pt_1 if hasattr(event, 'lep_Pt_1') else event['lep_Pt_1']) > 15000.0)) & ((event.lep_Pt_2 if hasattr(event, 'lep_Pt_2') else event['lep_Pt_2']) > 15000.0)) & ((event.lep_isolationFixedCutLoose_0 if hasattr(event, 'lep_isolationFixedCutLoose_0') else event['lep_isolationFixedCutLoose_0']) > 0)) & (abs(((event.Mlll012 if hasattr(event, 'Mlll012') else event['Mlll012']) - 91200.0)) > 10000.0)) & (((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0']) != (-(event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1']))) | ((event.Mll01 if hasattr(event, 'Mll01') else event['Mll01']) > 12000.0))) & (((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0']) != (-(event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2']))) | ((event.Mll02 if hasattr(event, 'Mll02') else event['Mll02']) > 12000.0))) & (((((abs((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0'])) == 13) & ((event.lep_isMedium_0 if hasattr(event, 'lep_isMedium_0') else event['lep_isMedium_0']) > 0)) | (abs((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0'])) == 11)) & (((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) == 11) & (abs((event.lep_Eta_1 if hasattr(event, 'lep_Eta_1') else event['lep_Eta_1'])) < 2.0)) | ((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) == 13) & ((event.lep_isMedium_1 if hasattr(event, 'lep_isMedium_1') else event['lep_isMedium_1']) > 0)))) & (((abs((event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2'])) == 11) & (abs((event.lep_Eta_2 if hasattr(event, 'lep_Eta_2') else event['lep_Eta_2'])) < 2.0)) | ((abs((event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2'])) == 13) & ((event.lep_isMedium_2 if hasattr(event, 'lep_isMedium_2') else event['lep_isMedium_2']) > 0))))) & ((((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) * abs((event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2']))) != 169) & ((event.DRll12 if hasattr(event, 'DRll12') else event['DRll12']) > 0.5)) | ((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) * abs((event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2']))) == 169))) & ((event.Mll12 if hasattr(event, 'Mll12') else event['Mll12']) > 12000.0)) & (((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0']) != (-(event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1']))) | (abs(((event.Mll01 if hasattr(event, 'Mll01') else event['Mll01']) - 91200.0)) > 10000.0))) & (((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0']) != (-(event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2']))) | (abs(((event.Mll02 if hasattr(event, 'Mll02') else event['Mll02']) - 91200.0)) > 10000.0))) & ((event.MVA3lCERN_weight_ttH if hasattr(event, 'MVA3lCERN_weight_ttH') else event['MVA3lCERN_weight_ttH']) > (-1))) & (((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0']) != (-(event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1']))) | (abs(((event.Mll01 if hasattr(event, 'Mll01') else event['Mll01']) - 91200.0)) > 10000.0))) & (((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0']) != (-(event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2']))) | (abs(((event.Mll02 if hasattr(event, 'Mll02') else event['Mll02']) - 91200.0)) > 10000.0))) & ((event.MVA3lCERN_weight_ttH if hasattr(event, 'MVA3lCERN_weight_ttH') else event['MVA3lCERN_weight_ttH']) > 0.3)) & ((event.MVA3lCERN_weight_ttW if hasattr(event, 'MVA3lCERN_weight_ttW') else event['MVA3lCERN_weight_ttW']) < 0.75)) & ((event.MVA3lCERN_weight_ttZ if hasattr(event, 'MVA3lCERN_weight_ttZ') else event['MVA3lCERN_weight_ttZ']) < 0.75)) & ((event.MVA3lCERN_weight_VV if hasattr(event, 'MVA3lCERN_weight_VV') else event['MVA3lCERN_weight_VV']) < 0.75)) & ((event.MVA3lCERN_weight_ttbar if hasattr(event, 'MVA3lCERN_weight_ttbar') else event['MVA3lCERN_weight_ttbar']) < 0.3)) & ((((((((event.dilep_type if hasattr(event, 'dilep_type') else event['dilep_type']) > 0) & (((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0']) * (event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) > 0)) & ((event.lep_isQMisID_1 if hasattr(event, 'lep_isQMisID_1') else event['lep_isQMisID_1']) == 0)) & ((event.lep_isQMisID_0 if hasattr(event, 'lep_isQMisID_0') else event['lep_isQMisID_0']) == 0)) | ((((event.trilep_type if hasattr(event, 'trilep_type') else event['trilep_type']) > 0) & ((event.lep_isQMisID_2 if hasattr(event, 'lep_isQMisID_2') else event['lep_isQMisID_2']) == 0)) & ((event.lep_isQMisID_1 if hasattr(event, 'lep_isQMisID_1') else event['lep_isQMisID_1']) == 0))) | (((event.quadlep_type if hasattr(event, 'quadlep_type') else event['quadlep_type']) > 0) & ((event.FSF_4L_tot if hasattr(event, 'FSF_4L_tot') else event['FSF_4L_tot']) == 1))) & (((((((((event.dilep_type if hasattr(event, 'dilep_type') else event['dilep_type']) > 0) & (((((abs((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0'])) == 13) & ((event.lep_isMedium_0 if hasattr(event, 'lep_isMedium_0') else event['lep_isMedium_0']) > 0)) & ((event.lep_isolationFixedCutLoose_0 if hasattr(event, 'lep_isolationFixedCutLoose_0') else event['lep_isolationFixedCutLoose_0']) > 0)) & ((event.lep_promptLeptonVeto_TagWeight_0 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_0') else event['lep_promptLeptonVeto_TagWeight_0']) < (-0.5))) | ((((((abs((event.lep_ID_0 if hasattr(event, 'lep_ID_0') else event['lep_ID_0'])) == 11) & ((event.lep_isolationFixedCutLoose_0 if hasattr(event, 'lep_isolationFixedCutLoose_0') else event['lep_isolationFixedCutLoose_0']) > 0)) & ((event.lep_isTightLH_0 if hasattr(event, 'lep_isTightLH_0') else event['lep_isTightLH_0']) > 0)) & ((event.lep_chargeIDBDTTight_0 if hasattr(event, 'lep_chargeIDBDTTight_0') else event['lep_chargeIDBDTTight_0']) > 0.7)) & ((event.lep_ambiguityType_0 if hasattr(event, 'lep_ambiguityType_0') else event['lep_ambiguityType_0']) == 0)) & ((event.lep_promptLeptonVeto_TagWeight_0 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_0') else event['lep_promptLeptonVeto_TagWeight_0']) < (-0.7))))) & (((((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) == 13) & ((event.lep_isMedium_1 if hasattr(event, 'lep_isMedium_1') else event['lep_isMedium_1']) > 0)) & ((event.lep_isolationFixedCutLoose_1 if hasattr(event, 'lep_isolationFixedCutLoose_1') else event['lep_isolationFixedCutLoose_1']) > 0)) & ((event.lep_promptLeptonVeto_TagWeight_1 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_1') else event['lep_promptLeptonVeto_TagWeight_1']) < (-0.5))) | ((((((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) == 11) & ((event.lep_isolationFixedCutLoose_1 if hasattr(event, 'lep_isolationFixedCutLoose_1') else event['lep_isolationFixedCutLoose_1']) > 0)) & ((event.lep_isTightLH_1 if hasattr(event, 'lep_isTightLH_1') else event['lep_isTightLH_1']) > 0)) & ((event.lep_chargeIDBDTTight_1 if hasattr(event, 'lep_chargeIDBDTTight_1') else event['lep_chargeIDBDTTight_1']) > 0.7)) & ((event.lep_ambiguityType_1 if hasattr(event, 'lep_ambiguityType_1') else event['lep_ambiguityType_1']) == 0)) & ((event.lep_promptLeptonVeto_TagWeight_1 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_1') else event['lep_promptLeptonVeto_TagWeight_1']) < (-0.7))))) | ((((event.trilep_type if hasattr(event, 'trilep_type') else event['trilep_type']) > 0) & ((event.nTaus_OR_Pt25 if hasattr(event, 'nTaus_OR_Pt25') else event['nTaus_OR_Pt25']) == 0)) & (((((abs((event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2'])) == 13) & ((event.lep_isolationFixedCutLoose_2 if hasattr(event, 'lep_isolationFixedCutLoose_2') else event['lep_isolationFixedCutLoose_2']) > 0)) & ((event.lep_promptLeptonVeto_TagWeight_2 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_2') else event['lep_promptLeptonVeto_TagWeight_2']) < (-0.5))) | ((((((abs((event.lep_ID_2 if hasattr(event, 'lep_ID_2') else event['lep_ID_2'])) == 11) & ((event.lep_isolationFixedCutLoose_2 if hasattr(event, 'lep_isolationFixedCutLoose_2') else event['lep_isolationFixedCutLoose_2']) > 0)) & ((event.lep_isTightLH_2 if hasattr(event, 'lep_isTightLH_2') else event['lep_isTightLH_2']) > 0)) & ((event.lep_chargeIDBDTTight_2 if hasattr(event, 'lep_chargeIDBDTTight_2') else event['lep_chargeIDBDTTight_2']) > 0.7)) & ((event.lep_promptLeptonVeto_TagWeight_2 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_2') else event['lep_promptLeptonVeto_TagWeight_2']) < (-0.7))) & ((event.lep_ambiguityType_2 if hasattr(event, 'lep_ambiguityType_2') else event['lep_ambiguityType_2']) == 0))) & ((((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) == 13) & ((event.lep_isolationFixedCutLoose_1 if hasattr(event, 'lep_isolationFixedCutLoose_1') else event['lep_isolationFixedCutLoose_1']) > 0)) & ((event.lep_promptLeptonVeto_TagWeight_1 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_1') else event['lep_promptLeptonVeto_TagWeight_1']) < (-0.5))) | ((((((abs((event.lep_ID_1 if hasattr(event, 'lep_ID_1') else event['lep_ID_1'])) == 11) & ((event.lep_isolationFixedCutLoose_1 if hasattr(event, 'lep_isolationFixedCutLoose_1') else event['lep_isolationFixedCutLoose_1']) > 0)) & ((event.lep_isTightLH_1 if hasattr(event, 'lep_isTightLH_1') else event['lep_isTightLH_1']) > 0)) & ((event.lep_chargeIDBDTTight_1 if hasattr(event, 'lep_chargeIDBDTTight_1') else event['lep_chargeIDBDTTight_1']) > 0.7)) & ((event.lep_promptLeptonVeto_TagWeight_1 if hasattr(event, 'lep_promptLeptonVeto_TagWeight_1') else event['lep_promptLeptonVeto_TagWeight_1']) < (-0.7))) & ((event.lep_ambiguityType_1 if hasattr(event, 'lep_ambiguityType_1') else event['lep_ambiguityType_1']) == 0)))))) | (((event.dilep_type if hasattr(event, 'dilep_type') else event['dilep_type']) > 0) & ((event.nTaus_OR_Pt25 if hasattr(event, 'nTaus_OR_Pt25') else event['nTaus_OR_Pt25']) > 1))) | ((((event.dilep_type if hasattr(event, 'dilep_type') else event['dilep_type']) > 0) | ((event.trilep_type if hasattr(event, 'trilep_type') else event['trilep_type']) > 0)) == 0)) | ((event.quadlep_type if hasattr(event, 'quadlep_type') else event['quadlep_type']) > 0)) | (((event.trilep_type if hasattr(event, 'trilep_type') else event['trilep_type']) > 0) & ((event.nTaus_OR_Pt25 if hasattr(event, 'nTaus_OR_Pt25') else event['nTaus_OR_Pt25']) > 0))))) | ((event.is1L2Tau if hasattr(event, 'is1L2Tau') else event['is1L2Tau']) > 0))])
out = awkward.Table()
out['0'] =[]
for i in uproot.iterate(input_filenames,tree_name,branches=branches,namedecode="utf-8",entrysteps=50000, reportentries=False):
out = awkward.concatenate([out, (a)((b)(awkward.Table(i)))])
# for i in uproot.iterate(input_filenames,tree_name,branches=branches,namedecode="utf-8",entrysteps=10000, reportentries=True):
# print("Entry range: ", i[0], i[1])
# out = awkward.concatenate([out, (a)((b)(awkward.Table(i[2])))])
return out
if __name__ == '__main__':
branch_list = ['trilep_type', 'nTaus_OR_Pt25', 'total_charge', 'nJets_OR_T', 'nJets_OR_T_MV2c10_70', 'lep_Pt_1', 'lep_Pt_2', 'lep_isolationFixedCutLoose_0', 'Mlll012', 'lep_ID_0', 'lep_ID_1', 'Mll01', 'lep_ID_2', 'Mll02', 'lep_isMedium_0', 'lep_Eta_1', 'lep_isMedium_1', 'lep_Eta_2', 'lep_isMedium_2', 'DRll12', 'Mll12', 'MVA3lCERN_weight_ttH', 'MVA3lCERN_weight_ttW', 'MVA3lCERN_weight_ttZ', 'MVA3lCERN_weight_VV', 'MVA3lCERN_weight_ttbar', 'dilep_type', 'lep_promptLeptonVeto_TagWeight_0', 'lep_isTightLH_0', 'lep_chargeIDBDTTight_0', 'lep_ambiguityType_0', 'lep_isolationFixedCutLoose_1', 'lep_promptLeptonVeto_TagWeight_1', 'lep_isTightLH_1', 'lep_chargeIDBDTTight_1', 'lep_ambiguityType_1', 'lep_isolationFixedCutLoose_2', 'lep_promptLeptonVeto_TagWeight_2', 'lep_isTightLH_2', 'lep_chargeIDBDTTight_2', 'lep_ambiguityType_2', 'quadlep_type', 'lep_isQMisID_1', 'lep_isQMisID_0', 'lep_isQMisID_2', 'FSF_4L_tot', 'is1L2Tau']
a = run_query('/data/kyungeon/ttHML_v09_01/user.kchoi.ttHML_80fb_VV/ttHML_80fb_364286_mc16d.root', 'nominal', branch_list)
# a = run_query('/scratch/data/ttHML_v09_01/user.kchoi.ttHML_80fb_VV/ttHML_80fb_364253_mc16d.root', 'nominal', branch_list)
import awkward
awkward.save('h.awkd', a)
info["label"] = label
info["iter"] = i
ac = copy.deepcopy(awkward.persist.compression)
ac[0]["types"] += [np.float32]
if label.startswith("lz4"):
ac[0]["pair"] = (fcomp, ("lz4.frame", "decompress"))
if label.startswith("blosc"):
ac[0]["pair"] = (fcomp, ("blosc", "decompress"))
if label.startswith("lzma"):
ac[0]["pair"] = (fcomp, ("backports.lzma", "decompress"))
fname = "tables/table_{}.awkd".format(label)
t0 = time.time()
awkward.save(fname, table, compression=ac, mode="w")
t1 = time.time()
info["t_compress_ms"] = 1e3 * (t1 - t0)
t0 = time.time()
tmp = awkward.load(fname,
whitelist=awkward.persist.whitelist + [
['lz4.frame', 'decompress'],
['lz4.block', 'decompress'],
['blosc', 'decompress'],
['backports.lzma', 'decompress'],
])
t1 = time.time()
info["t_decompress_ms"] = 1e3 * (t1 - t0)
info["uncompressed_bytes"] = table.nbytes
start = time.time()
nn = ParticleNetJetTagsProducer(args.model, args.preprocess)
diff = time.time() - start
print('--- Setup model: %f s total' % (diff,))
start = time.time()
outputs = nn.predict(taginfo, eval_flags)
diff = time.time() - start
print('--- Run prediction: %f s total, %f s per jet ---' % (diff, diff / outputs['probQCDbb'].counts.sum()))
# print(outputs)
# for k in outputs:
# print(k, outputs[k].content.mean())
if 'FatJet_ParticleNetMD_probXbb' in table:
print('Compare w/ stored values')
print('Stored values:\n ...', table['FatJet_ParticleNetMD_probXbb'][:5])
print('Computed values:\n ...', outputs['probXbb'][:5])
print('Diff (50%, 95%, 99%, 100%) = ', np.percentile(
np.abs(outputs['probXbb'] - table['FatJet_ParticleNetMD_probXbb']).content, [50, 95, 99, 100]))
# assert(np.array_equal(jetmass.counts, outputs['probQCDbb'].counts))
alloutputs = awkward.JaggedArray.zip(outputs)
if args.make_baseline:
with open('baseline.awkd', 'wb') as fout:
awkward.save(fout, alloutputs)
else:
if os.path.exists('baseline.awkd'):
with open('baseline.awkd', 'rb') as fin:
baseline = awkward.load(fin)
print("Comparison to baseline:", (alloutputs == baseline).all().all())
