def __init__(self):
""" """
super().__init__()
self.bpoints = None
self.md = nested_dict()
self.log = get_logger("Benchmark")
self.set_cluster_column()
def __init__(self):
"""Initializes the :class:`clusterking.scan.Scanner` class."""
super().__init__()
# todo: move
self.log = get_logger("Scanner")
#: Points in wilson space
#: Use self.spoints to access this
self._spoints = None # type: Optional[np.ndarray]
#: Instance of SpointCalculator to perform the claculations of
#: the wilson space points.
self._spoint_calculator = SpointCalculator()
# todo: move
self.md = nested_dict()
self.md["git"] = version_info(self.log)
self.md["time"] = time.strftime("%a %d %b %Y %H:%M", time.gmtime())
# todo: shouldn't that be in metadata?
#: Names of the parameters
self._coeffs = [] # type: List[str]
self._no_workers = None # type: Optional[int]
self._progress_bar = True
self._tqdm_kwargs = {}
self.set_imaginary_prefix("im_")
def __init__(self):
"""
Args:
data: :py:class:`~clusterking.data.Data` object
"""
super().__init__()
self.log = get_logger("Scanner")
self.clusters = None
# self.bpoints = None
#: Metadata
self.md = nested_dict()
self.md["git"] = version_info(self.log)
self.md["time"] = time.strftime("%a %d %b %Y %H:%M", time.gmtime())
def __init__(
self,
path: Optional[Union[str, PurePath]] = None,
log: Optional[Union[str, logging.Logger]] = None,
):
"""
Initialize a DFMD object.
Args:
path: Optional: load from this file (specified as string or
:class:`pathlib.PurePath`)
log: Optional: instance of :py:class:`logging.Logger` or name of
logger to be created
"""
# These are the three attributes of this class
#: This will hold all the configuration that we will write out
self.md = None
#: :py:class:`pandas.DataFrame` to hold all of the results
self.df = None # type: Optional[pd.DataFrame]
#: Instance of :py:class:`logging.Logger`
self.log = None
# todo: remember path?
if not path:
# Initialize blank
self.md = nested_dict()
self.df = pd.DataFrame()
self.log = None
else:
self._load(path)
# Overwrite log if user wants that.
if isinstance(log, logging.Logger):
self.log = log
elif isinstance(log, str):
self.log = get_logger(log)
elif log is None:
if not self.log:
self.log = get_logger("DFMD")
else:
raise ValueError(
"Unsupported type '{}' for 'log' argument.".format(type(log)))
def test_nested_dict(self):
nd = metadata.nested_dict()
nd[1][2][3] = None
self.assertEqual(nd, {1: {2: {3: None}}})
def __init__(self):
super().__init__()
self._kmeans_kwargs = {}
self.md = nested_dict()