def __init__(self, x, y, z, ex=None, ey=None):
assert ex is None
x = Coord(x, ex)
y = Coord(y, ey)
z = np.asarray(z, dtype=self.dtype)
assert x.v.shape + y.v.shape == z.shape
pin(locals())
def __init__(self, pdfset, values):
assert isinstance(pdfset, PDFSet)
if isinstance(values, PDFInput):
inp = values
values = AutoDict(self._from_input)
else:
assert isinstance(values, (Sequence, np.ndarray))
assert len(pdfset) == len(values)
pin(locals())
def __init__(self, v, e=None):
v = np.asarray(v)
assert v.ndim == 1
if e is None:
e = np.zeros_like(v, dtype=bool)
else:
e = np.asarray(e, dtype=bool)
assert e.shape == v.shape
e[0] = e[-1] = True
pin(locals())
def __init__(
self,
label=None,
xtitle=None,
ytitle="Entries",
binning=[],
unit=None,
metadata={},
register=True,
):
pin(locals())
def __init__(self, path, config=None):
path = Path(path)
if path.suffix == ".info" and path.is_file():
config = parse_config(path, config)
path = path.parent
elif path.is_dir():
config = parse_config(
path.joinpath(path.name).with_suffix(".info"), config)
else:
raise ValueError("path not understood: %r" % path)
_pdfs = CacheDict(self._make_pdf)
pin(locals())
def __init__(self, analysis, category, variable, hists, config_inst, year):
if not self._processes:
raise Exception("Processes have to be defined!")
if not issubclass(self._processes, IntEnum):
raise Exception(
f"{self.processes} has to be subclassed from <IntEnum>")
# e.g.: category = "ee_2b"
channel = category.split("_")[0]
_nuisances_names = []
_valid_processes = []
_nuisances_renaming = {}
_newhists = {}
console = Console()
pin(locals())
def auto(cls, model_path, **kwargs):
if not isinstance(model_path, dict):
model_path = dict(default=model_path)
tmp = TemporaryDirectory(prefix="tf_server_ahoc")
tmp_path = Path(tmp.name)
dirs = {}
for model, path in model_path.items():
path = Path(path)
if not path.is_dir():
raise RuntimeError("%s: %s is not a dir" % (model, path))
b = tmp_path.joinpath(model)
dirs[model] = b.absolute().as_posix()
if path.joinpath("saved_model.pb").is_file():
b.mkdir()
b = b.joinpath("1")
b.symlink_to(path.absolute(), True)
del model, path, b
if len(dirs) > 1:
mc = tmp_path.joinpath("model_config.txt.pb")
kwargs["model_config_file"] = mc.absolute()
with mc.open("w") as f:
f.write(
str(
ModelServerConfig(model_config_list=ModelConfigList(
config=[
ModelConfig(name=key,
base_path=value,
model_platform="tensorflow")
for key, value in dirs.items()
]))))
else:
((kwargs["model_name"],
kwargs["model_base_path"]), ) = dirs.items()
kwargs.setdefault("grpc_socket_path",
tmp_path.joinpath("sock").absolute())
self = cls.relieable(**kwargs)
pin(locals(), cls)
return self
def __init__(self, x, y, e=None):
x = Coord(x, e)
del e
pin(locals())
def __init__(self, block, xfx):
pin(locals())
def __init__(self, config):
pin(locals())
# assert None not in set(map(self.threshold, flavors))
assert None not in set(map(self.mass, flavors))
def __init__(self, func):
assert callable(func)
pin(locals())
def __init__(self, pdf, x, q, pdgId, xfx, eq):
assert xfx.ndim == 3
assert xfx.shape == (len(pdgId), ) + x.shape + q.shape
xfx = np.ascontiguousarray(xfx)
pin(locals())
def __init__(self, path, config=None):
self.name = path.name
config, skip = self._parse_config(path, config)
assert config["Format"] == "lhagrid1"
blocks = list(self._merge_blocks(self._parse_blocks(path, skip)))
pin(locals())
def __init__(self, path, config=None):
path = Path(path)
assert path.is_dir()
config = parse_config(path.joinpath("lhapdf.conf"), config)
pin(locals())
def __init__(self, address, **kwargs):
if isinstance(address, BaseServer):
self.server = address
address = address.address
pin(locals())