def update_param(paramset, x, y0):
search_idx = search_parameter_index()
if os.path.isfile('./out/%d/generation.npy' % (paramset)):
best_generation = np.load('./out/%d/generation.npy' % (paramset))
best_indiv = np.load('./out/%d/fit_param%d.npy' %
(paramset, int(best_generation)))
for i, j in enumerate(search_idx[0]):
x[j] = best_indiv[i]
for i, j in enumerate(search_idx[1]):
y0[j] = best_indiv[i + len(search_idx[0])]
return x, y0
def optimize(nth_paramset):
np.random.seed(time.time_ns() * nth_paramset % 2**32)
search_idx = search_parameter_index()
search_region = get_search_region()
max_generation = 10000
n_population = int(5 * search_region.shape[1])
n_children = 50
n_gene = search_region.shape[1]
allowable_error = 0.35
(best_indiv, best_fitness) = ga_v2(nth_paramset, max_generation,
n_population, n_children, n_gene,
allowable_error, search_idx,
search_region)
def optimize_continue(nth_paramset):
np.random.seed(time.time_ns() * nth_paramset % 2**32)
search_idx = search_parameter_index()
search_region = get_search_region()
max_generation = 10000
n_population = int(5 * search_region.shape[1])
n_children = 50
n_gene = search_region.shape[1]
allowable_error = 0.35
p0_bounds = [0.1, 10.0] # [lower_bounds, upper bounds]
(best_indiv,
best_fitness) = ga_v2_continue(nth_paramset, max_generation, n_population,
n_children, n_gene, allowable_error,
search_idx, search_region, p0_bounds)
def save_param_range(n_file, x, y0, portrait):
search_idx = search_parameter_index()
search_param_matrix = np.empty(
(len(n_file), len(search_idx[0]) + len(search_idx[1])))
for k, nth_paramset in enumerate(n_file):
if os.path.isfile('./out/%d/generation.npy' % (nth_paramset)):
best_generation = np.load('./out/%d/generation.npy' %
(nth_paramset))
best_indiv = np.load('./out/%d/fit_param%d.npy' %
(nth_paramset, int(best_generation)))
else:
best_indiv = np.empty(len(search_idx[0]) + len(search_idx[1]))
for i, j in enumerate(search_idx[0]):
best_indiv[i] = x[j]
for i, j in enumerate(search_idx[1]):
best_indiv[i + len(search_idx[0])] = y0[j]
search_param_matrix[k, :] = best_indiv
plot_func.param_range(search_idx, search_param_matrix, portrait)
def analyze_sensitivity(metric, nonzero_idx):
"""Compute sensitivity coefficients
Parameters
----------
metric: str
- 'amplitude': The maximum value.
- 'duration': The time it takes to decline below 10% of its maximum.
- 'integral': The integral of concentration over the observation time.
nonzero_idx: list
for i in nonzero_idx:
y0[i] != 0.0
Returns
-------
sensitivity_coefficients: numpy array
"""
sim = NumericalSimulation()
rate = 1.01 # 1% change
epsilon = 1e-9 # If |M - M*| < epsilon, sensitivity_coefficient = 0
x = f_params()
y0 = initial_values()
nonzero_idx = []
for i, val in enumerate(y0):
if val != 0.0:
nonzero_idx.append(i)
n_file = []
fitparam_files = os.listdir('./out')
for file in fitparam_files:
if re.match(r'\d', file):
n_file.append(int(file))
signaling_metric = np.full((len(n_file), len(nonzero_idx) + 1,
len(observables), len(sim.conditions)), np.nan)
search_idx = search_parameter_index()
for i, nth_paramset in enumerate(n_file):
if os.path.isfile('./out/%d/generation.npy' % (nth_paramset)):
best_generation = np.load('./out/%d/generation.npy' %
(nth_paramset))
best_indiv = np.load('./out/%d/fit_param%d.npy' %
(nth_paramset, int(best_generation)))
for m, n in enumerate(search_idx[0]):
x[n] = best_indiv[m]
for m, n in enumerate(search_idx[1]):
y0[n] = best_indiv[m + len(search_idx[0])]
copy_y0 = y0[:]
for j, idx in enumerate(nonzero_idx):
y0 = copy_y0[:]
y0[idx] = copy_y0[idx] * rate
if sim.simulate(x, y0) is None:
for k, _ in enumerate(observables):
for l, _ in enumerate(sim.conditions):
if metric == 'amplitude':
signaling_metric[i, j, k, l] = np.max(
sim.simulations[k, :, l])
elif metric == 'duration':
signaling_metric[i, j, k, l] = get_duration(
sim.simulations[k, :, l])
elif metric == 'integral':
signaling_metric[i, j, k, l] = simps(
sim.simulations[k, :, l])
else:
raise ValueError(
"metric ∈ {'amplitude', 'duration', 'integral'}"
)
sys.stdout.write('\r%d / %d' %
(i * len(nonzero_idx) + j + 1,
len(n_file) * len(nonzero_idx)))
y0 = copy_y0[:]
if sim.simulate(x, y0) is None:
for k, _ in enumerate(observables):
for l, _ in enumerate(sim.conditions):
if metric == 'amplitude':
signaling_metric[i, -1, k,
l] = np.max(sim.simulations[k, :,
l])
elif metric == 'duration':
signaling_metric[i, -1, k, l] = get_duration(
sim.simulations[k, :, l])
elif metric == 'integral':
signaling_metric[i, -1, k,
l] = simps(sim.simulations[k, :,
l])
else:
sys.exit()
sensitivity_coefficients = np.empty(
(len(n_file), len(nonzero_idx), len(observables), len(sim.conditions)))
for i, _ in enumerate(n_file):
for j, _ in enumerate(nonzero_idx):
for k, _ in enumerate(observables):
for l, _ in enumerate(sim.conditions):
if np.isnan(signaling_metric[i, j, k, l]):
sensitivity_coefficients[i, j, k, l] = np.nan
elif fabs(signaling_metric[i, j, k, l] -
signaling_metric[i, -1, k, l]) < epsilon or (
signaling_metric[i, j, k, l] /
signaling_metric[i, -1, k, l]) < 0:
sensitivity_coefficients[i, j, k, l] = 0.0
else:
sensitivity_coefficients[i, j, k, l] = (
log(signaling_metric[i, j, k, l] /
signaling_metric[i, -1, k, l]) / log(rate))
return sensitivity_coefficients