def __init__(self, tag, timestamp, value, uom=None, *args, **kwargs):
Series.__init__(self,
data=value,
index=timestamp,
name=tag,
*args, **kwargs)
self.tag = tag
self.uom = uom
def __init__(self, data, name=None, dtype=None, index=None, copy=False,
fastpath=False, categorical=False):
"""
A Series with extra information, e.g. categorical.
Parameters
----------
categorical : bool
set categorical label for attribute. If categorical, this attribute
takes on a limited and fixed number of possible values. Examples:
blood type, gender.
"""
Series.__init__(self, data, name=name, dtype=dtype, index=index,
copy=copy, fastpath=fastpath)
# bins can be int (size of histogram bins), str (as algorithm name),
self._bins = ds4ml.params['attribute.bins']
self._min = None
self._max = None
self._step = None
# probability distribution (pr)
self.bins = None
self.prs = None
from pandas.api.types import infer_dtype
# atype: date type for handle different kinds of attributes in data
# synthesis, support: integer, float, string, datetime.
self.atype = infer_dtype(self, skipna=True)
if self.atype == 'integer':
pass
elif self.atype == 'floating' or self.atype == 'mixed-integer-float':
self.atype = 'float'
elif self.atype in ['string', 'mixed-integer', 'mixed']:
self.atype = 'string'
if all(map(utils.is_datetime, self._values)):
self.atype = 'datetime'
# fill the missing values with the most frequent value
self.fillna(self.mode()[0], inplace=True)
# special handling for datetime attribute
if self.atype == 'datetime':
self.update(self.map(self._to_seconds).map(self._date_formatter))
if self.atype == 'float':
self._decimals = self.decimals()
# how to define the attribute is categorical.
self.categorical = categorical or (
self.atype == 'string' and not self.is_unique)
self._set_domain()
self._set_distribution()
def __init__(self, data, dtime, **kwargs):
"""
Time series w/ specific IO methods
"""
self.__dict__.update(kwargs)
TimeSeries.__init__(self, data, index=dtime)
#super(ObsTimeSeries,self).__init__(data,index=dtime)
# Time coordinates
self.nt = self.index.shape
self.tsec = othertime.SecondsSince(self.index,\
basetime = pd.datetime(self.baseyear,1,1))
def __init__(self, input_brain=None, **kwargs):
Series.__init__(self, input_brain, **kwargs)
HasTraits.__init__(self)
self._number_of_neurons = self.get_number_of_neurons()
def __init__(self, label="", input_nodes=None, **kwargs):
Series.__init__(self, input_nodes, name=str(label), **kwargs)
HasTraits.__init__(self)
self._number_of_neurons = self.get_number_of_neurons()
def __init__(self, s, col_name=None, stat=None):
Series.__init__(self, s)
self.s = s
self.stat = stat
self.col_name = col_name
def __init__(self, *args, **kwargs):
Series.__init__(self, *args, **kwargs)
