def create_smatrix(cpids, rctnids, stm, obfunc):
"""Create the stiochiomatrix with the fba module"""
# Use stoichiometric Dictionary to create S matrix
# Preallocate size of array
st_mat = [[0.0] * len(rctnids) for i in cpids]
if verbose:
print(f"Number of total compounds: {len(cpids)}")
print(f"Number of total reactions: {len(rctnids)}")
print(
f"Stiochiomatrix dimensions before fill: {len(st_mat)} x {len(st_mat[0])}"
)
# Fill in S matrix
for cidx, c in enumerate(cpids):
for ridx, r in enumerate(rctnids):
try:
val = stm[c][r]
except KeyError:
val = 0.0
st_mat[cidx][ridx] = val
if verbose:
print(
f"Stiochiomatrix dimensions after fill: fill: {len(st_mat)} x {len(st_mat[0])}"
)
# Load the data
lp.load(st_mat, cpids, rctnids)
# Objective function
lp.objective_coefficients(obfunc)
return cpids, rctnids
def createSMatrix(cpdIds, rxnIds, sm, objFunc):
"""Create the SMatrix with the fba module"""
# Use stoichiometric Dictionary to create S matrix
# Preallocate size of array
SMat = [[0.0] * len(rxnIds) for i in cpdIds]
if verbose:
print "Number of total compounds: %d" % len(cpdIds)
print "Number of total reactions: %d" % len(rxnIds)
print "SMat dimensions before fill: %d x %d" % (len(SMat), len(
SMat[0]))
# Fill in S matrix
for cIdx, c in enumerate(cpdIds):
for rIdx, r in enumerate(rxnIds):
try:
val = sm[c][r]
except KeyError:
val = 0.0
SMat[cIdx][rIdx] = val
if verbose:
print "SMat dimensions after fill: %d x %d" % (len(SMat), len(SMat[0]))
# Load the data
lp.load(SMat, cpdIds, rxnIds)
# Objective function
lp.objective_coefficients(objFunc)
return cpdIds, rxnIds
def createSMatrix(cpdIds, rxnIds, sm, objFunc):
"""Create the SMatrix with the fba module"""
# Use stoichiometric Dictionary to create S matrix
# Preallocate size of array
SMat = [[0.0] * len(rxnIds) for i in cpdIds]
if verbose:
print "Number of total compounds: %d" % len(cpdIds)
print "Number of total reactions: %d" % len(rxnIds)
print "SMat dimensions before fill: %d x %d" % (len(SMat),
len(SMat[0]))
# Fill in S matrix
for cIdx, c in enumerate(cpdIds):
for rIdx, r in enumerate(rxnIds):
try:
val = sm[c][r]
except KeyError:
val = 0.0
SMat[cIdx][rIdx] = val
if verbose:
print "SMat dimensions after fill: %d x %d" % (len(SMat), len(SMat[0]))
# Load the data
lp.load(SMat, cpdIds, rxnIds)
# Objective function
lp.objective_coefficients(objFunc)
return cpdIds, rxnIds
def test_objective_coeff(self):
"""Test adding the objective coefficients. Note that there
should be as many coefficients as columns in the matrix."""
mat = [
[ 1.0, 1.0, 1.0],
[10.0, 4.0, 5.0],
[ 2.0, 2.0, 6.0],
[ 2.0, 2.0, 6.0],
]
lp.load(mat)
lp.objective_coefficients([ 10.0, 6.0, 4.0 ])
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_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)
