def __call__(self, data):
data = self.transform_domain(data)
if "edge_jump" in data.domain:
edges = data.transform(Orange.data.Domain([data.domain["edge_jump"]]))
I_jumps = edges.X[:, 0]
else:
raise NoEdgejumpProvidedException(
'Invalid meta data: Intensity jump at edge is missing')
# order X by wavenumbers:
# xs non ordered energies
# xsind - indecies corresponding to the ordered energies
# mon = True
# X spectra as corresponding to the ordered energies
xs, xsind, mon, X = transform_to_sorted_features(data)
# for the missing data
X, nans = nan_extend_edges_and_interpolate(xs[xsind], X)
# TODO notify the user if some unknown values were interpolated
# Replace remaining NaNs (where whole rows were NaN) with
# with some values so that the function does not crash.
# Results are going to be discarded later.
nan_rows = np.isnan(X).all(axis=1)
X[nan_rows] = 1.
# do the transformation
X = self.transformed(X, xs[xsind], I_jumps)
# discard nan rows
X[nan_rows] = np.nan
# k scores are always ordered, so do not restore order
return X
def __call__(self, data):
data = self.transform_domain(data)
if "edge_jump" in data.domain:
edges = data.transform(Orange.data.Domain([data.domain["edge_jump"]]))
I_jumps = edges.X[:, 0]
else:
raise NoEdgejumpProvidedException(
'Invalid meta data: Intensity jump at edge is missing')
# order X by wavenumbers:
# xs non ordered energies
# xsind - indecies corresponding to the ordered energies
# mon = True
# X spectra as corresponding to the ordered energies
xs, xsind, mon, X = transform_to_sorted_features(data)
# for the missing data
X, nans = nan_extend_edges_and_interpolate(xs[xsind], X)
# TODO notify the user if some unknown values were interpolated
# do the transformation
X = self.transformed(X, xs[xsind], I_jumps)
# k scores are always ordered, so do not restore order
return X