def interp(self, attrs=None):
"""Return values of variables in attrs, interpolated by method set
with set_interpolated().
Parameters
----------
attrs : str or list or None
Variable or List of variables to interpolate. If None, the
whole table is returned interpolated.
Returns
-------
X : array (n_inst x n_attrs) or Timeseries
Interpolated variables attrs in columns.
"""
from orangecontrib.timeseries import interpolate_timeseries
# FIXME: This interpolates the whole table, might be an overhead
# if only a single attr is required
interpolated = interpolate_timeseries(self,
self._interp_method,
self._interp_multivariate)
if attrs is None:
return interpolated
if isinstance(attrs, str):
attrs = [attrs]
return Table(Domain([], [], attrs, interpolated.domain), interpolated).metas
def interp(self, attrs=None):
"""Return values of variables in attrs, interpolated by method set
with set_interpolated().
Parameters
----------
attrs : str or list or None
Variable or List of variables to interpolate. If None, the
whole table is returned interpolated.
Returns
-------
X : array (n_inst x n_attrs) or Timeseries
Interpolated variables attrs in columns.
"""
from orangecontrib.timeseries import interpolate_timeseries
# FIXME: This interpolates the whole table, might be an overhead
# if only a single attr is required
interpolated = interpolate_timeseries(self, self._interp_method,
self._interp_multivariate)
if attrs is None:
return interpolated
if isinstance(attrs, str):
attrs = [attrs]
return Table(Domain([], [], attrs, interpolated.domain),
interpolated).metas
def test_methods(self):
for method in ('linear', 'cubic', 'nearest', 'mean'):
interpolated = interpolate_timeseries(self.data, method=method)
self.assertFalse(np.isnan(interpolated.Y).any())
self.assertTrue(np.isnan(self.data.Y).any())