How to use the traittypes.traittypes.SciType function in traittypes

foo.bar = [1, 2]  # Should raise a TraitError
        """
        self.validators.extend(validators)
        return self

    def validate(self, obj, value):
        """Validate the value against registered validators."""
        try:
            for validator in self.validators:
                value = validator(self, value)
            return value
        except (ValueError, TypeError) as e:
            raise TraitError(e)


class Array(SciType):

    """A numpy array trait type."""

    info_text = 'a numpy array'
    dtype = None

    def validate(self, obj, value):
        if value is None and not self.allow_none:
            self.error(obj, value)
        if value is None or value is Undefined:
            return super(Array, self).validate(obj, value)
        try:
            r = np.asarray(value, dtype=self.dtype)
            if isinstance(value, np.ndarray) and r is not value:
                warnings.warn(
                    'Given trait value dtype "%s" does not match required type "%s". '

def __init__(self, **kwargs):
        super(SciType, self).__init__(**kwargs)
        self.validators = []

"""A pandas series trait type."""

    info_text = 'a pandas series'
    dtype = None

    def __init__(self, default_value=Empty, allow_none=False, dtype=None, **kwargs):
        if 'klass' not in kwargs and self.klass is None:
            import pandas as pd
            kwargs['klass'] = pd.Series
        super(Series, self).__init__(
            default_value=default_value, allow_none=allow_none, dtype=dtype, **kwargs)
        self.dtype = dtype


class XarrayType(SciType):

    """An xarray dataset or dataarray trait type."""

    info_text = 'an xarray dataset or dataarray'

    klass = None

    def validate(self, obj, value):
        if value is None and not self.allow_none:
            self.error(obj, value)
        if value is None or value is Undefined:
            return super(XarrayType, self).validate(obj, value)
        try:
            value = self.klass(value)
        except (ValueError, TypeError) as e:
            raise TraitError(e)

def __init__(self, default_value=Empty, allow_none=False, dtype=None, **kwargs):
        self.dtype = dtype
        if default_value is Empty:
            default_value = np.array(0, dtype=self.dtype)
        elif default_value is not None and default_value is not Undefined:
            default_value = np.asarray(default_value, dtype=self.dtype)
        super(Array, self).__init__(default_value=default_value, allow_none=allow_none, **kwargs)

    def make_dynamic_default(self):
        if self.default_value is None or self.default_value is Undefined:
            return self.default_value
        else:
            return np.copy(self.default_value)


class PandasType(SciType):

    """A pandas dataframe or series trait type."""

    info_text = 'a pandas dataframe or series'

    klass = None

    def validate(self, obj, value):
        if value is None and not self.allow_none:
            self.error(obj, value)
        if value is None or value is Undefined:
            return super(PandasType, self).validate(obj, value)
        try:
            value = self.klass(value)
        except (ValueError, TypeError) as e:
            raise TraitError(e)