def capitulo_7():
file = open("resultados_capitulo_7.txt", "w")
model = create_test_model("textbook")
s = sample(model, 100)
s.head()
print("One process:")
s = sample(model, 1000)
print("Two processes:")
s = sample(model, 1000, processes=2)
s = sample(model, 100, method="achr")
from cobra.flux_analysis.sampling import OptGPSampler, ACHRSampler
achr = ACHRSampler(model, thinning=10)
optgp = OptGPSampler(model, processes=4)
s1 = achr.sample(100)
s2 = optgp.sample(100)
import numpy as np
bad = np.random.uniform(-1000, 1000, size=len(model.reactions))
achr.validate(np.atleast_2d(bad))
achr.validate(s1)
counts = [
np.mean(s.Biomass_Ecoli_core > 0.1) for s in optgp.batch(100, 10)
]
file.write("Usually {:.2f}% +- {:.2f}% grow...".format(
np.mean(counts) * 100.0,
np.std(counts) * 100.0))
file.write("\n")
co = model.problem.Constraint(
model.reactions.Biomass_Ecoli_core.flux_expression, lb=0.1)
model.add_cons_vars([co])
s = sample(model, 10)
file.write(s.Biomass_Ecoli_core)
file.write("\n")
file.close()
def fluxSampling(model):
from cobra.test import create_test_model
from cobra.flux_analysis import sample
model = create_test_model("textbook")
s = sample(model, 100) #number of samples to generate
s.head()
s = sample(model, 1000)
s
#The sampling process can be controlled on a lower level by using the sampler classes directly.
from cobra.flux_analysis.sampling import OptGPSampler, ACHRSampler
achr = ACHRSampler(
model, thinning=10
) #“Thinning” means only recording samples every n iterations. A higher thinning factor means less correlated samples but also larger computation times.
optgp = OptGPSampler(
model, processes=4
) #an additional processes argument specifying how many processes are used to create parallel sampling chains.
#For OptGPSampler the number of samples should be a multiple of the number of
# processes, otherwise it will be increased to the nearest multiple automatically.
s1 = achr.sample(100)
s2 = optgp.sample(100)
# Sampling and validation
import numpy as np
bad = np.random.uniform(-1000, 1000, size=len(model.reactions))
achr.validate(np.atleast_2d(bad)) #should not be feasible
achr.validate(s1)
# Batch sampling
counts = [
np.mean(s.Biomass_Ecoli_core > 0.1) for s in optgp.batch(100, 10)
]
print("Usually {:.2f}% +- {:.2f}% grow...".format(
np.mean(counts) * 100.0,
np.std(counts) * 100.0))
# Adding constraints
co = model.problem.Constraint(
model.reactions.Biomass_Ecoli_core.flux_expression, lb=0.1)
model.add_cons_vars([co])
# Note that this is only for demonstration purposes. usually you could set
# the lower bound of the reaction directly instead of creating a new constraint.
s = sample(model, 10)
print(s.Biomass_Ecoli_core)
