Python Timestamp.max方法代碼示例

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def test_out_of_bounds_value(self):
        one_us = np.timedelta64(1).astype('timedelta64[us]')

        # By definition we can't go out of bounds in [ns], so we
        # convert the datetime64s to [us] so we can go out of bounds
        min_ts_us = np.datetime64(Timestamp.min).astype('M8[us]')
        max_ts_us = np.datetime64(Timestamp.max).astype('M8[us]')

        # No error for the min/max datetimes
        Timestamp(min_ts_us)
        Timestamp(max_ts_us)

        # One us less than the minimum is an error
        with pytest.raises(ValueError):
            Timestamp(min_ts_us - one_us)

        # One us more than the maximum is an error
        with pytest.raises(ValueError):
            Timestamp(max_ts_us + one_us) 

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def __init__(self, stress, rfunc, name, up=True, cutoff=0.999,
                 settings=None, metadata=None, meanstress=None):
        if isinstance(stress, list):
            stress = stress[0]  # TODO Temporary fix Raoul, 2017-10-24

        stress = TimeSeries(stress, settings=settings, metadata=metadata)

        if meanstress is None:
            meanstress = stress.series.std()

        StressModelBase.__init__(self, rfunc, name, stress.series.index.min(),
                                 stress.series.index.max(), up, meanstress,
                                 cutoff)
        self.freq = stress.settings["freq"]
        self.stress = [stress]
        self.set_init_parameters() 

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def test_max_valid(self):
        # Ensure that Timestamp.max is a valid Timestamp
        Timestamp(Timestamp.max) 

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def test_asm8(self):
        np.random.seed(7960929)
        ns = [Timestamp.min.value, Timestamp.max.value, 1000]

        for n in ns:
            assert (Timestamp(n).asm8.view('i8') ==
                    np.datetime64(n, 'ns').view('i8') == n)

        assert (Timestamp('nat').asm8.view('i8') ==
                np.datetime64('nat', 'ns').view('i8')) 

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def test_to_datetime_bijective(self):
        # Ensure that converting to datetime and back only loses precision
        # by going from nanoseconds to microseconds.
        exp_warning = None if Timestamp.max.nanosecond == 0 else UserWarning
        with tm.assert_produces_warning(exp_warning, check_stacklevel=False):
            assert (Timestamp(Timestamp.max.to_pydatetime()).value / 1000 ==
                    Timestamp.max.value / 1000)

        exp_warning = None if Timestamp.min.nanosecond == 0 else UserWarning
        with tm.assert_produces_warning(exp_warning, check_stacklevel=False):
            assert (Timestamp(Timestamp.min.to_pydatetime()).value / 1000 ==
                    Timestamp.min.value / 1000) 

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def set_init_parameters(self):
        self.parameters = self.rfunc.get_init_parameters(self.name)
        tmin = Timestamp.min.toordinal()
        tmax = Timestamp.max.toordinal()
        tinit = self.tstart.toordinal()

        self.parameters.loc[self.name + "_tstart"] = (tinit, tmin, tmax,
                                                      False, self.name) 

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def datetime_column(
        name,
        min_datetime=Timestamp.min,
        max_datetime=Timestamp.max,
        non_nullable=False,
        unique=False,
        ignore_missing_vals=False,
        is_required=None,
    ):
        '''
        Simple constructor for PandasColumns that expresses datetime constraints on 'datetime64[ns]' dtypes.

        Args:
            name (str): Name of the column. This must match up with the column name in the dataframe you
                expect to receive.
            min_datetime (Optional[Union[int,float]]): The lower bound for values you expect in this column.
                Defaults to pandas.Timestamp.min.
            max_datetime (Optional[Union[int,float]]): The upper bound for values you expect in this column.
                Defaults to pandas.Timestamp.max.
            non_nullable (Optional[bool]): If true, this column will enforce a constraint that all values in the column
                ought to be non null values.
            unique (Optional[bool]): If true, this column will enforce a uniqueness constraint on the column values.
            ignore_missing_vals (Optional[bool]): A flag that is passed into most constraints. If true, the constraint will
                only evaluate non-null data. Ignore_missing_vals and non_nullable cannot both be True.
            is_required (Optional[bool]): Flag indicating the optional/required presence of the column.
                If the column exists the validate function will validate the column. Default to True.
        '''
        return PandasColumn(
            name=check.str_param(name, 'name'),
            constraints=[
                ColumnDTypeInSetConstraint({'datetime64[ns]'}),
                InRangeColumnConstraint(
                    min_datetime, max_datetime, ignore_missing_vals=ignore_missing_vals
                ),
            ]
            + _construct_keyword_constraints(
                non_nullable=non_nullable, unique=unique, ignore_missing_vals=ignore_missing_vals
            ),
            is_required=is_required,
        ) 

# 需要導入模塊: from pandas import Timestamp [as 別名]
# 或者: from pandas.Timestamp import max [as 別名]
def add_stressmodel(self, stressmodel, replace=False):
        """Add a stressmodel to the main model.

        Parameters
        ----------
        stressmodel: pastas.stressmodel or list of pastas.stressmodel
            instance of a pastas.stressmodel class. Multiple stress models
            can be provided (e.g., ml.add_stressmodel([sm1, sm2]) in one call.
        replace: bool, optional
            force replace the stressmodel if a stressmodel with the same name
            already exists. Not recommended but useful at times. Default is
            False.

        Notes
        -----
        To obtain a list of the stressmodel names, type:

        >>> ml.get_stressmodel_names()

        Examples
        --------
        >>> sm = ps.StressModel(stress, rfunc=ps.Gamma, name="stress")
        >>> ml.add_stressmodel(sm)

        To add multiple stress models at once you can do the following:

        >>> sm1 = ps.StressModel(stress, rfunc=ps.Gamma, name="stress1")
        >>> sm1 = ps.StressModel(stress, rfunc=ps.Gamma, name="stress2")
        >>> ml.add_stressmodel([sm1, sm2])

        See Also
        --------
        pastas.stressmodels

        """
        # Method can take multiple stressmodels at once through args
        if isinstance(stressmodel, list):
            for sm in stressmodel:
                self.add_stressmodel(sm)
        elif (stressmodel.name in self.stressmodels.keys()) and not replace:
            self.logger.error("The name for the stressmodel you are trying "
                              "to add already exists for this model. Select "
                              "another name.")
        else:
            self.stressmodels[stressmodel.name] = stressmodel
            self.parameters = self.get_init_parameters(initial=False)
            if self.settings["freq"] is None:
                self._set_freq()
            stressmodel.update_stress(freq=self.settings["freq"])

            # Check if stress overlaps with oseries, if not give a warning
            if (stressmodel.tmin > self.oseries.series.index.max()) or \
                    (stressmodel.tmax < self.oseries.series.index.min()):
                self.logger.warning("The stress of the stressmodel has no "
                                    "overlap with ml.oseries.")
        self.check_stressmodel_compatibility()