def __call__(self, data):
if data.domain != self.domain:
data = data.from_table(self.domain, data)
xs, xsind, mon, X = _transform_to_sorted_features(data)
x = xs[xsind]
newd = np.zeros_like(data.X)
for rowi, row in enumerate(X):
# remove NaNs which ConvexHull can not handle
source = np.column_stack((x, row))
source = source[~np.isnan(source).any(axis=1)]
try:
v = ConvexHull(source).vertices
except QhullError:
# FIXME notify user
baseline = np.zeros_like(row)
else:
if self.peak_dir == RubberbandBaseline.PeakPositive:
v = np.roll(v, -v.argmin())
v = v[:v.argmax() + 1]
elif self.peak_dir == RubberbandBaseline.PeakNegative:
v = np.roll(v, -v.argmax())
v = v[:v.argmin() + 1]
# If there are NaN values at the edges of data then convex hull
# does not include the endpoints. Because the same values are also
# NaN in the current row, we can fill them with NaN (bounds_error
# achieves this).
baseline = interp1d(source[v, 0],
source[v, 1],
bounds_error=False)(x)
finally:
if self.sub == 0:
newd[rowi] = row - baseline
else:
newd[rowi] = baseline
return _transform_back_to_features(xsind, mon, newd)
def transformed(self, X, x):
newd = np.zeros_like(X)
for rowi, row in enumerate(X):
# remove NaNs which ConvexHull can not handle
source = np.column_stack((x, row))
source = source[~np.isnan(source).any(axis=1)]
try:
v = ConvexHull(source).vertices
except (QhullError, ValueError):
# FIXME notify user
baseline = np.zeros_like(row)
else:
if self.peak_dir == RubberbandBaseline.PeakPositive:
v = np.roll(v, -v.argmin())
v = v[:v.argmax() + 1]
elif self.peak_dir == RubberbandBaseline.PeakNegative:
v = np.roll(v, -v.argmax())
v = v[:v.argmin() + 1]
# If there are NaN values at the edges of data then convex hull
# does not include the endpoints. Because the same values are also
# NaN in the current row, we can fill them with NaN (bounds_error
# achieves this).
baseline = interp1d(source[v, 0],
source[v, 1],
bounds_error=False)(x)
finally:
if self.sub == 0:
newd[rowi] = row - baseline
else:
newd[rowi] = baseline
return newd
def transformed(self, X, x):
newd = np.zeros_like(X)
for rowi, row in enumerate(X):
# remove NaNs which ConvexHull can not handle
source = np.column_stack((x, row))
source = source[~np.isnan(source).any(axis=1)]
try:
v = ConvexHull(source).vertices
except QhullError:
# FIXME notify user
baseline = np.zeros_like(row)
else:
if self.peak_dir == RubberbandBaseline.PeakPositive:
v = np.roll(v, -v.argmin())
v = v[:v.argmax() + 1]
elif self.peak_dir == RubberbandBaseline.PeakNegative:
v = np.roll(v, -v.argmax())
v = v[:v.argmin() + 1]
# If there are NaN values at the edges of data then convex hull
# does not include the endpoints. Because the same values are also
# NaN in the current row, we can fill them with NaN (bounds_error
# achieves this).
baseline = interp1d(source[v, 0], source[v, 1], bounds_error=False)(x)
finally:
if self.sub == 0:
newd[rowi] = row - baseline
else:
newd[rowi] = baseline
return newd
