def transform_streamels(self, streamels):
check_streamels_1D(streamels)
check_streamels_continuous(streamels)
if self.A0 is not None:
check_streamels_1D_size(streamels, self.A0.shape[1])
self.A = self._get_A(streamels)
if self.A.shape[1] != streamels.size:
msg = ('I expect a matrix with rows %d, got %s' %
(streamels.size, self.A.shape))
raise ValueError(msg)
# Save original constraints that we can pass to inverse()
self.old_streamels = streamels.copy()
streamels2 = np.zeros(self.A.shape[0], streamel_dtype)
streamels2['kind'][:] = ValueFormats.Continuous
find_polytope_bounds_after_linear(streamels, self.A, streamels2)
self.lower = streamels2['lower'].copy()
self.upper = streamels2['upper'].copy()
streamels2['default'] = self.transform_value(streamels['default'])
return streamels2
def find_polytope_bounds_after_linear(streamels, A, streamels2):
'''
Fills the 'lower' and 'upper' part of streamels2
given the A transform.
'''
M, N = A.shape
check_streamels_1D_size(streamels, N)
check_streamels_1D_size(streamels2, M)
is_diagonal = (M == N) and allclose(np.diag(np.diagonal(A)), A)
if is_diagonal:
streamels2['lower'] = np.dot(A, streamels['lower'])
streamels2['upper'] = np.dot(A, streamels['upper'])
else: # TODO: do something smarter here
norm = np.abs(A).sum() # XXX
lb = np.max(np.abs(streamels['lower']))
ub = np.max(np.abs(streamels['upper']))
B = max(lb, ub)
streamels2['lower'] = -B * norm
streamels2['upper'] = +B * norm
