In [1]:

import cobra
from cobra.io import read_sbml_model, load_model
from cobra import Model

In [2]:

cobra.__path__

Out[2]:

['/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra']

In [3]:

cobra.__version__

Out[3]:

'0.24.0'

In [4]:

benchmark_results = {}

In [5]:

m = load_model('iJO1366')

Scaling...
 A: min|aij| =  1.000e+00  max|aij| =  1.000e+00  ratio =  1.000e+00
Problem data seem to be well scaled

In [6]:

%%timeit -o
m2 = Model()
for r in m.reactions:
    m2.add_reaction(r)

12 s ± 1.45 s per loop (mean ± std. dev. of 7 runs, 1 loop each)

Out[6]:

<TimeitResult : 12 s ± 1.45 s per loop (mean ± std. dev. of 7 runs, 1 loop each)>

In [8]:

benchmark_results['add_reaction'] = _

In [9]:

%%timeit -o
m2 = Model()
m2.add_reactions(m.reactions)

565 ms ± 83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Out[9]:

<TimeitResult : 565 ms ± 83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)>

In [10]:

benchmark_results['add_reactions'] = _

In [11]:

%%timeit -o
model = load_model('iJO1366')
for reaction in model.reactions:
    model.remove_reactions([reaction])

/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/core/group.py:148: UserWarning: need to pass in a list
  warn("need to pass in a list")

7.06 s ± 368 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Out[11]:

<TimeitResult : 7.06 s ± 368 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)>

In [12]:

benchmark_results['remove_reaction'] = _

In [13]:

%%timeit -o
model = load_model('e_coli_core')

291 ms ± 18.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Out[13]:

<TimeitResult : 291 ms ± 18.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)>

In [14]:

benchmark_results['load_model'] = _

In [15]:

model = load_model('e_coli_core')

In [16]:

%%timeit -o
for reaction in model.reactions:
    model.objective = reaction
    solution = model.optimize()
    solution.objective_value

133 ms ± 5.07 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Out[16]:

<TimeitResult : 133 ms ± 5.07 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)>

In [17]:

benchmark_results['iteratete_all_reactions_set_objective_solve'] = _

In [18]:

%%timeit -o
model.copy()

15.9 ms ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

Out[18]:

<TimeitResult : 15.9 ms ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)>

In [19]:

benchmark_results['model_copy'] = _

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