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)