def test():
filenames = get_filenames()
print("current reactions file: " + filenames[-1])
rxn_list_d2 = get_rxn_list_d2_example(filenames[-1])
parsed_rxn_list_d4 = list_of_reaction_strings_to_parsed_reaction_list(
rxn_list_d2)
bounds_value = 5
bounds = give_upper_lower_bounds_list_d2(parsed_rxn_list_d4, bounds_value)
mtrices = create_stoichiometric_matrix(parsed_rxn_list_d4)
S_w_cmpnds = mtrices[0]
S = mtrices[1]
print("Stoichiometric Matrix with compound and reaction names")
print(S_w_cmpnds)
print("Stoichiometric Matrix by itself: ")
print(S)
#We assume the objective value is the index of the last added reaction.
#objective_index = len(parsed_rxn_list_d4)-1
#New objective indeces (subtract one from reaction number):
objective_index = len(parsed_rxn_list_d4) - 1
#objective_index = 10
#The objective direction is 'max', or 'min'
objective_direction = "max"
#We solve the system of solutions using optlang
status, model = stoichiomatrix_solution(S, bounds, objective_index,
objective_direction)
model_print(model)
fluxes = np.asarray(make_fluxes(model))
print("FLUXES: ")
print(fluxes)
Product_Vector = np.matmul(S, fluxes)
print("Product Vector: (all values should be zero or close to zero)")
print(Product_Vector)
return 0
def quick_process(filepath, objective_index, objective_direction,
bounds_value):
rxn_list_d2 = get_rxn_list_d2_example(filepath)
parsed_rxn_list_d4 = list_of_reaction_strings_to_parsed_reaction_list(
rxn_list_d2)
bounds = give_upper_lower_bounds_list_d2(parsed_rxn_list_d4, bounds_value)
mtrices = create_stoichiometric_matrix(parsed_rxn_list_d4)
S_w_cmpnds = mtrices[0]
S = mtrices[1]
print("Stoichiometric Matrix with Compound Names and Reactions.")
print(S_w_cmpnds)
print("\n")
print("Stoichiometric Matrix")
print(S)
print("\n")
#We solve the system of solutions using optlang
status, model = stoichiomatrix_solution(S, bounds, objective_index,
objective_direction)
model_print(model)
fluxes = np.asarray(make_fluxes(model))
print("Fluxes: \n")
print(fluxes)
#This is a mini-test, product_vector should be zero
#For now product_vector should be zero
Product_Vector = np.matmul(S, fluxes)
print("\n")
print("Product Vector: (All values should be zero or close to zero)")
print(Product_Vector)
return 0
def user_sub(total_file_path):
bounds_string = input(
"Please enter an integer for the highest bound, common values are 100 or 1000: "
)
bounds_value = int(bounds_string)
if type(bounds_value) == int and bounds_value >= 0:
mtrices = get_Stoichiometric_Matrix_from_File(total_file_path,
bounds_value)
S = mtrices[1]
parsed_rxn_list_d4 = mtrices[2]
bounds = give_upper_lower_bounds_list_d2(parsed_rxn_list_d4,
bounds_value)
n_rows = len(S[0])
objective_index_string = input(
"What is the index of the reaction you want to optimize? Enter an int between 1 and "
+ str(n_rows) + ": ")
objective_index = int(objective_index_string)
objective_direction = input(
"Enter 'max' if you want to maximize obj, 'min' if minimize ")
if type(
objective_index
) == int and objective_index >= 1 and objective_index <= n_rows and objective_direction in [
"max", "min"
]:
status, model = stoichiomatrix_solution(S, bounds,
objective_index - 1,
objective_direction)
model_print(model)
fluxes = np.asarray(make_fluxes(model))
print("FLUXES: ")
print(fluxes)
#This is a mini-test, product_vector should be zero
#For now product_vector should be zero
Product_Vector = np.matmul(S, fluxes)
print(
"TEST: Product Vector. If there is a non-zero (or not close to zero) value in the Product_Vector then there is an issue with the solution."
)
print(Product_Vector)
else:
print("One of the inputs is incorrect. Stopping program")
