def compound_bounds(cpdIds):
"""
This is the zero flux vector.
Parameters:
cpdIds: List of compound IDs from the model
"""
cbounds = [(0.0, 0.0) for i in cpdIds]
if verbose:
print "Number of compound bounds: %d" % len(cbounds)
lp.row_bounds(cbounds)
def compound_bounds(cpids):
"""
This is the zero flux vector.
Parameters:
cpids: List of compound IDs from the model
"""
cbounds = [(0.0, 0.0) for i in cpids]
if verbose:
print(f"Number of compound bounds: {len(cbounds)}")
lp.row_bounds(cbounds)
def test_solve(self):
"""Test the complete linear programing solution, using the
example from the documentation"""
mat = [
[ 1.0, 1.0, 1.0],
[10.0, 4.0, 5.0],
[ 2.0, 2.0, 6.0],
]
lp.load(mat)
lp.objective_coefficients([ 10.0, 6.0, 4.0 ])
lp.row_bounds([(None, 100.0), (None, 600.0), (None, 300.0)])
lp.col_bounds([(0, None), (0, None), (0, None)])
status, result = lp.solve()
r = "%0.3f" % result
self.assertEqual(r, "733.333")
self.assertEqual(status, 'opt')
def test_bound_rows(self):
"""Test adding tuples of boundary conditions for rows"""
mat = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]
]
lp.load(mat)
boundsr = []
# test that we don't have enough values
self.assertRaises(ValueError, lp.row_bounds, boundsr)
# a boundary of None should be infiniity!
boundsr = [(None, 1000), (1000, None), (1000, 1000)]
lp.row_bounds(boundsr)
def test_primals(self):
"""Test getting the primals back as a list"""
mat = [
[ 1.0, 1.0, 1.0],
[10.0, 4.0, 5.0],
[ 2.0, 2.0, 6.0],
]
rh = ['a', 'b', 'c']
ch = ['x', 'y', 'z']
lp.load(mat, rh, ch)
lp.objective_coefficients([ 10.0, 6.0, 4.0 ])
lp.row_bounds([(None, 100.0), (None, 600.0), (None, 300.0)])
lp.col_bounds([(0, None), (0, None), (0, None)])
status, result = lp.solve()
col_pri = [33.333333333333336, 66.66666666666666, 0.0]
col_res = lp.col_primals()
self.assertEqual(col_pri, col_res)
def compound_bounds(cp, lower=0, upper=0):
"""
Impose constraints on the compounds. These constraints limit what
the variation of each compound can be and is essentially 0 for
most compounds except those that are in the media or otherwise
external.
This is the zero flux vector.
Parameters:
cp: the list of compound ids
lower: the default lower value
upper: the default upper value
"""
cbounds = [(lower, upper) for c in cp]
cbvals = {c: (lower, upper) for c in cp}
lp.row_bounds(cbounds)
return cbvals
def compound_bounds(cp, lower=0, upper=0):
"""
Impose constraints on the compounds. These constraints limit what
the variation of each compound can be and is essentially 0 for
most compounds except those that are in the media or otherwise
external.
This is the zero flux vector.
Parameters:
cp: the list of compound ids
lower: the default lower value
upper: the default upper value
"""
cbounds = [(lower, upper) for c in cp]
cbvals = {c: (lower, upper) for c in cp}
lp.row_bounds(cbounds)
return cbvals