# lumping = do_lumping(
# system["equations"],
# [SparsePolynomial.from_string("C3GActive", system["variables"])],
# print_reduction=False,
# discard_useless_matrices=True,
# )
# time += timeit.default_timer() - start
# print("Average Time: ", time/N)
# check_lumping("BIOMD0000000033", system["equations"], lumping)
# MODEL1502270000
system = read_system("../examples/RationalFunctions/MODEL1502270000.ode")
lumping = do_lumping(
system["equations"],
[SparsePolynomial.from_string("gmax*Kp+a", system["variables"])],
print_reduction=False,
discard_useless_matrices=True,
)
print("Lumping Size: ", len(lumping['rhs']))
check_lumping("MODEL1502270000", system["equations"], lumping)
lumping = do_lumping(
system["equations"],
[SparsePolynomial.from_string("si", system["variables"])],
print_reduction=False,
discard_useless_matrices=False,
)
print("Lumping Size: ", len(lumping['rhs']))
check_lumping("MODEL1502270000", system["equations"], lumping)
# print(lumping)
############################################
# Model generated from:
# Borisov, N. M., Chistopolsky, A. S., Faeder, J. R., & Kholodenko, B. N.
# Domain-oriented reduction of rule-based network models. IET systems biology, 2(5), 342-351, 2008.
# Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2628550/bin/NIHMS80246-supplement-Supplement_4.doc
##
import sys
import time
from sympy import QQ
sys.path.insert(0, "../")
sys.path.insert(0, "./../../")
import parser
import clue
from sparse_polynomial import SparsePolynomial
system = parser.read_system("OrderedPhosphorylation.ode")
obs = SparsePolynomial.from_string("s0", system['variables'])
start = time.time()
lumped = clue.do_lumping(system['equations'], [obs])
end = time.time()
print(f"The size of the original model is {len(system['equations'])}")
print(f"The size of the reduced model is {len(lumped['polynomials'])}")
print(f"Computation took {end - start} seconds")
from sympy import QQ
sys.path.insert(0, "../")
sys.path.insert(0, "./../../")
import parser
import clue
from sparse_polynomial import SparsePolynomial
system = parser.read_system("BIOMD0000000504.ode")
obs_sets = [["cFos_P", "cJun_P"], ["MMP1_mRNA", "MMP13_mRNA", "TIMP1_mRNA"],
["MMP1", "MMP13", "ColFrag"],
[
"JAK1_P", "JNK_P", "cJun_P", "cJun_dimer", "STAT3_P_nuc",
"STAT3_P_cyt"
]]
for obs_set in obs_sets:
print("===============================================")
obs_polys = [
SparsePolynomial.from_string(s, system['variables']) for s in obs_set
]
start = time.time()
lumped = clue.do_lumping(system['equations'], obs_polys)
end = time.time()
print(f"The size of the original model is {len(system['equations'])}")
print(f"The size of the reduced model is {len(lumped['polynomials'])}")
print(f"Computation took {end - start} seconds")
###############################################
obss = {
"BIOMD0000000504.ode":
[["cFos_P", "cJun_P"], ["MMP1_mRNA", "MMP13_mRNA", "TIMP1_mRNA"],
["MMP1", "MMP13", "ColFrag"],
["JAK1_P", "JNK_P", "cJun_P", "cJun_dimer", "STAT3_P_nuc",
"STAT3_P_cyt"]],
"fceri_ji.ode": [["S0"], ["S2", "S178", "S267", "S77"],
["S2 + S178 + S267 + S77"], ["S7"], ["S1"]],
"e2.ode": [["S0"], ["S1"]],
"Barua.ode": [["aS000"], ["aS027"]]
}
if __name__ == "__main__":
path = sys.argv[1]
name = path[path.rindex('/') + 1:]
system = read_system("{0}".format(path))
obs_sets = obss[name]
for obs_set in obs_sets:
obs_polys = [
SparsePolynomial.from_string(s, system['variables'])
for s in obs_set
]
do_lumping(system["equations"], obs_polys)
polys_values = [evalp(p, specialization) for p in polys]
polys_values_lumped = [dot_product(polys_values, var) for var in new_vars]
specialization_lumped = [dot_product(specialization, var) for var in new_vars]
lumped_polys_values = [evalp(p, specialization_lumped) for p in lumped_system]
assert(polys_values_lumped == lumped_polys_values)
print(test_name + ": lumping is correct")
###############################################
if __name__ == "__main__":
# Example 1
R = sympy.polys.rings.vring(["x0", "x1", "x2"], QQ)
polys = [x0**2 + x1 + x2, x2, x1]
lumping = do_lumping(polys, [x0], print_reduction=False, initial_conditions={"x0" : 1, "x1" : 2, "x2" : 5})
check_lumping("Example 1", polys, lumping, 2)
assert lumping["new_ic"] == [QQ(1), QQ(7)]
# Example 2
polys = [x1**2 + 4 * x1 * x2 + 4 * x2**2, x1 + 2 * x0**2, x2 - x0**2]
lumping = do_lumping(polys, [x0], print_reduction=False)
check_lumping("Example 2", polys, lumping, 2)
# PP for n = 2
system = read_system("e2.ode")
lumping = do_lumping(
system["equations"],
[SparsePolynomial.from_string("S0", system["variables"])],
print_reduction=False
)
import sys
import time
from sympy import QQ
sys.path.insert(0, "../")
sys.path.insert(0, "./../../")
import parser
import clue
from sparse_polynomial import SparsePolynomial
system = parser.read_system("BIOMD0000000033.ode")
obs = SparsePolynomial.from_string("AktInactive", system['variables'])
start = time.time()
lumped = clue.do_lumping(system['equations'], [obs], print_system=True)
end = time.time()
print(f"The size of the original model is {len(system['equations'])}")
print(f"The size of the reduced model is {len(lumped['polynomials'])}")
print(f"Computation took {end - start} seconds")