def test_run(self):
sens_vode = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable)
sens_vode.run()
npt.assert_almost_equal(self.sens.p_matrix,
self.p_simulated,
decimal=3)
def test_unique_simulations_only(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(
os.path.join(self.output_dir, 'test4_P_H_P_prime.png'))
def test_all_params(self):
vals = [.9, 1.1]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sens_type='all')
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4', out_dir=None)
assert os.path.exists('test4_P_H_P_prime.png')
def test_run(self):
sens_vode = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable
)
sens_vode.run()
npt.assert_almost_equal(self.sens.p_matrix, self.p_simulated,
decimal=3)
def test_param_pair(self):
vals = [.9, 1.0, 1.1]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sample_list=['k1', 'cdc0'])
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(
os.path.join(self.output_dir, 'test4_P_H_P_prime.png'))
def test_unique_simulations_only(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver
)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir,
'test4_P_H_P_prime.png'))
def test_all_params(self):
vals = [.9, 1.1]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sens_type='all'
)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=None)
assert os.path.exists('test4_P_H_P_prime.png')
def test_param_pair(self):
vals = [.9, 1.0, 1.1]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sample_list=['k1', 'cdc0']
)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir,
'test4_P_H_P_prime.png'))
def setUp(self):
self.tspan = np.linspace(0, 200, 5001)
self.observable = 'Y3'
self.savename = 'sens_out_test'
self.output_dir = tempfile.mkdtemp()
self.vals = np.linspace(.8, 1.2, 5)
self.vals = [.8, .9, 1., 1.1, 1.2]
self.model = model
self.solver = ScipyOdeSimulator(self.model,
tspan=self.tspan,
integrator='lsoda',
integrator_options={'rtol': 1e-8,
'atol': 1e-8,
'mxstep': 20000})
self.sens = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable
)
self.p_simulated = np.array(
[[0., 0., 0., 0., 0., 5.0301, 2.6027, 0., -2.5118, -4.5758],
[0., 0., 0., 0., 0., 5.0301, 2.5832, 0., -2.5313, -4.5823],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.5953],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.6082],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.60829],
[5.0301, 5.0301, 5.0301, 5.0301, 5.0301, 0., 0., 0., 0., 0.],
[2.6027, 2.5832, 2.5767, 2.5767, 2.5767, 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[-2.5118, -2.5313, -2.5313, -2.5313, -2.5313, 0., 0., 0., 0., 0.],
[-4.5758, -4.5823, -4.5953, -4.6082, -4.6082, 0., 0., 0., 0.,
0.]])
self.sens.run()
def test_bad_solver(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=None,
sample_list=None,
sens_type=None)
def test_param_not_in_model(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
solver = ScipyOdeSimulator(self.model,
tspan=self.tspan,
integrator='lsoda',
integrator_options={
'rtol': 1e-8,
'atol': 1e-8,
'mxstep': 20000
})
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=solver,
sample_list=['a0'])
def setUp(self):
self.tspan = np.linspace(0, 200, 5001)
self.observable = 'Y3'
self.savename = 'sens_out_test'
self.output_dir = tempfile.mkdtemp()
self.vals = np.linspace(.8, 1.2, 5)
self.vals = [.8, .9, 1., 1.1, 1.2]
self.model = model
self.solver = ScipyOdeSimulator(self.model,
tspan=self.tspan,
integrator='lsoda',
integrator_options={
'rtol': 1e-8,
'atol': 1e-8,
'mxstep': 20000
})
self.sens = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable)
self.p_simulated = np.array(
[[0., 0., 0., 0., 0., 5.0301, 2.6027, 0., -2.5118, -4.5758],
[0., 0., 0., 0., 0., 5.0301, 2.5832, 0., -2.5313, -4.5823],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.5953],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.6082],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.60829],
[5.0301, 5.0301, 5.0301, 5.0301, 5.0301, 0., 0., 0., 0., 0.],
[2.6027, 2.5832, 2.5767, 2.5767, 2.5767, 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[-2.5118, -2.5313, -2.5313, -2.5313, -2.5313, 0., 0., 0., 0., 0.],
[-4.5758, -4.5823, -4.5953, -4.6082, -4.6082, 0., 0., 0., 0.,
0.]])
self.sens.run()
class TestSensitivityAnalysis(object):
def setUp(self):
self.tspan = np.linspace(0, 200, 5001)
self.observable = 'Y3'
self.savename = 'sens_out_test'
self.output_dir = tempfile.mkdtemp()
self.vals = np.linspace(.8, 1.2, 5)
self.vals = [.8, .9, 1., 1.1, 1.2]
self.model = model
self.solver = ScipyOdeSimulator(self.model,
tspan=self.tspan,
integrator='lsoda',
integrator_options={
'rtol': 1e-8,
'atol': 1e-8,
'mxstep': 20000
})
self.sens = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable)
self.p_simulated = np.array(
[[0., 0., 0., 0., 0., 5.0301, 2.6027, 0., -2.5118, -4.5758],
[0., 0., 0., 0., 0., 5.0301, 2.5832, 0., -2.5313, -4.5823],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.5953],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.6082],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.60829],
[5.0301, 5.0301, 5.0301, 5.0301, 5.0301, 0., 0., 0., 0., 0.],
[2.6027, 2.5832, 2.5767, 2.5767, 2.5767, 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[-2.5118, -2.5313, -2.5313, -2.5313, -2.5313, 0., 0., 0., 0., 0.],
[-4.5758, -4.5823, -4.5953, -4.6082, -4.6082, 0., 0., 0., 0.,
0.]])
self.sens.run()
def tearDown(self):
shutil.rmtree(self.output_dir)
def obj_func_cell_cycle(self, out):
timestep = self.tspan[:-1]
y = out[:-1] - out[1:]
freq = 0
local_times = []
prev = y[0]
for n in range(1, len(y)):
if y[n] > 0 > prev:
local_times.append(timestep[n])
freq += 1
prev = y[n]
local_times = np.array(local_times)
local_freq = np.average(local_times) / len(local_times) * 2
return local_freq
def test_run(self):
sens_vode = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable)
sens_vode.run()
npt.assert_almost_equal(self.sens.p_matrix,
self.p_simulated,
decimal=3)
def test_old_class_naming(self):
sens_vode = InitialsSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable)
sens_vode.run()
npt.assert_almost_equal(self.sens.p_matrix,
self.p_simulated,
decimal=3)
def test_p_matrix_shape(self):
assert self.sens.p_matrix.shape == (10, 10)
def test_p_matrix(self):
npt.assert_almost_equal(self.sens.p_matrix,
self.p_simulated,
decimal=3)
def test_pmatrix_outfile_exists(self):
self.sens.run(save_name=self.savename, out_dir=self.output_dir)
assert os.path.exists(
os.path.join(self.output_dir,
'{}_p_matrix.csv'.format(self.savename)))
assert os.path.exists(
os.path.join(self.output_dir,
'{}_p_prime_matrix.csv'.format(self.savename)))
def test_create_png(self):
self.sens.create_boxplot_and_heatplot(save_name='test',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir, 'test.png'))
assert os.path.exists(os.path.join(self.output_dir, 'test.eps'))
assert os.path.exists(os.path.join(self.output_dir, 'test.svg'))
self.sens.create_individual_pairwise_plots(save_name='test2',
out_dir=self.output_dir)
assert os.path.exists(
os.path.join(self.output_dir, 'test2_subplots.png'))
self.sens.create_plot_p_h_pprime(save_name='test3',
out_dir=self.output_dir)
assert os.path.exists(
os.path.join(self.output_dir, 'test3_P_H_P_prime.png'))
def test_unique_simulations_only(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(
os.path.join(self.output_dir, 'test4_P_H_P_prime.png'))
def test_param_pair(self):
vals = [.9, 1.0, 1.1]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sample_list=['k1', 'cdc0'])
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(
os.path.join(self.output_dir, 'test4_P_H_P_prime.png'))
def test_all_params(self):
vals = [.9, 1.1]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sens_type='all')
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4', out_dir=None)
assert os.path.exists('test4_P_H_P_prime.png')
@raises(ValueError)
def test_param_not_in_model(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
solver = ScipyOdeSimulator(self.model,
tspan=self.tspan,
integrator='lsoda',
integrator_options={
'rtol': 1e-8,
'atol': 1e-8,
'mxstep': 20000
})
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=solver,
sample_list=['a0'])
@raises(ValueError)
def test_sens_type_and_list_none(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sample_list=None,
sens_type=None)
@raises(TypeError)
def test_bad_solver(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=None,
sample_list=None,
sens_type=None)
class TestSensitivityAnalysis(object):
def setUp(self):
self.tspan = np.linspace(0, 200, 5001)
self.observable = 'Y3'
self.savename = 'sens_out_test'
self.output_dir = tempfile.mkdtemp()
self.vals = np.linspace(.8, 1.2, 5)
self.vals = [.8, .9, 1., 1.1, 1.2]
self.model = model
self.solver = ScipyOdeSimulator(self.model,
tspan=self.tspan,
integrator='lsoda',
integrator_options={'rtol': 1e-8,
'atol': 1e-8,
'mxstep': 20000})
self.sens = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable
)
self.p_simulated = np.array(
[[0., 0., 0., 0., 0., 5.0301, 2.6027, 0., -2.5118, -4.5758],
[0., 0., 0., 0., 0., 5.0301, 2.5832, 0., -2.5313, -4.5823],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.5953],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.6082],
[0., 0., 0., 0., 0., 5.0301, 2.5767, 0., -2.5313, -4.60829],
[5.0301, 5.0301, 5.0301, 5.0301, 5.0301, 0., 0., 0., 0., 0.],
[2.6027, 2.5832, 2.5767, 2.5767, 2.5767, 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[-2.5118, -2.5313, -2.5313, -2.5313, -2.5313, 0., 0., 0., 0., 0.],
[-4.5758, -4.5823, -4.5953, -4.6082, -4.6082, 0., 0., 0., 0.,
0.]])
self.sens.run()
def tearDown(self):
shutil.rmtree(self.output_dir)
def obj_func_cell_cycle(self, out):
timestep = self.tspan[:-1]
y = out[:-1] - out[1:]
freq = 0
local_times = []
prev = y[0]
for n in range(1, len(y)):
if y[n] > 0 > prev:
local_times.append(timestep[n])
freq += 1
prev = y[n]
local_times = np.array(local_times)
local_freq = np.average(local_times) / len(local_times) * 2
return local_freq
def test_run(self):
sens_vode = PairwiseSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable
)
sens_vode.run()
npt.assert_almost_equal(self.sens.p_matrix, self.p_simulated,
decimal=3)
def test_old_class_naming(self):
sens_vode = InitialsSensitivity(
solver=self.solver,
values_to_sample=self.vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable
)
sens_vode.run()
npt.assert_almost_equal(self.sens.p_matrix, self.p_simulated,
decimal=3)
def test_p_matrix_shape(self):
assert self.sens.p_matrix.shape == (10, 10)
def test_p_matrix(self):
npt.assert_almost_equal(self.sens.p_matrix, self.p_simulated,
decimal=3)
def test_pmatrix_outfile_exists(self):
self.sens.run(save_name=self.savename,
out_dir=self.output_dir)
assert os.path.exists(os.path.join(
self.output_dir, '{}_p_matrix.csv'.format(self.savename)
))
assert os.path.exists(os.path.join(
self.output_dir, '{}_p_prime_matrix.csv'.format(self.savename)
))
def test_create_png(self):
self.sens.create_boxplot_and_heatplot(save_name='test',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir, 'test.png'))
assert os.path.exists(os.path.join(self.output_dir, 'test.eps'))
assert os.path.exists(os.path.join(self.output_dir, 'test.svg'))
self.sens.create_individual_pairwise_plots(save_name='test2',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir,
'test2_subplots.png'))
self.sens.create_plot_p_h_pprime(save_name='test3',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir,
'test3_P_H_P_prime.png'))
def test_unique_simulations_only(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver
)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir,
'test4_P_H_P_prime.png'))
def test_param_pair(self):
vals = [.9, 1.0, 1.1]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sample_list=['k1', 'cdc0']
)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=self.output_dir)
assert os.path.exists(os.path.join(self.output_dir,
'test4_P_H_P_prime.png'))
def test_all_params(self):
vals = [.9, 1.1]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver,
sens_type='all'
)
sens.run()
self.sens.create_plot_p_h_pprime(save_name='test4',
out_dir=None)
assert os.path.exists('test4_P_H_P_prime.png')
@raises(ValueError)
def test_param_not_in_model(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
solver = ScipyOdeSimulator(self.model,
tspan=self.tspan,
integrator='lsoda',
integrator_options={'rtol': 1e-8,
'atol': 1e-8,
'mxstep': 20000})
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=solver, sample_list=['a0']
)
@raises(ValueError)
def test_sens_type_and_list_none(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=self.solver, sample_list=None, sens_type=None
)
@raises(TypeError)
def test_bad_solver(self):
vals = [.8, .9, 1.1, 1.2, 1.3]
sens = PairwiseSensitivity(
values_to_sample=vals,
objective_function=self.obj_func_cell_cycle,
observable=self.observable,
solver=None, sample_list=None, sens_type=None
)