def plot_mat(syst, ax):
syst, mat, sol = analyze_system(
syst, use_ode_sde_diff=True,
repetition_num=5, tmax=10)
plot_corr_mat(mat, ax)
ax.set_xticks([], [])
ax.set_yticks([], [])
def plot_series(syst, ax, uosd=True):
syst, mat, sol = analyze_system(
syst, use_ode_sde_diff=uosd,
repetition_num=5, tmax=10)
plot_system_evolution(sol[:50], ax, show_legend=False)
ax.set_xticks([], [])
ax.set_yticks([], [])
ax.set_xlabel('')
ax.set_ylabel('')
def handle_systems(raw, enhanced):
""" Simulate given systems
"""
# generate control data
raw_res_diff = analyze_system(raw,
filter_mask=[3],
use_ode_sde_diff=True,
save_stdev='results/corr_stdev')
raw_res = analyze_system(raw, filter_mask=[3], use_ode_sde_diff=False)
if raw_res[1] is None or raw_res_diff[1] is None:
return None
# generate data from altered motifs
row = []
for enh in enhanced:
enh_res_diff = analyze_system(enh,
filter_mask=[3],
use_ode_sde_diff=True)
enh_res = analyze_system(enh, filter_mask=[3], use_ode_sde_diff=False)
row.append((enh_res, enh_res_diff))
return [(raw_res, raw_res_diff), row]
def simulate(syst, reps=1000):
matrices = []
with tqdm(total=reps) as pbar:
while reps >= 0:
_, mat, _ = analyze_system(syst, repetition_num=1)
if mat is None:
continue
pbar.update()
reps -= 1
if mat.shape == (4, 4):
mat = mat[:-1, :-1]
assert mat.shape == (3, 3)
matrices.append(mat)
return np.asarray(matrices)
def simulate(syst, reps=1000):
matrices = []
with tqdm(total=reps) as pbar:
while reps >= 0:
_, mat, sol = analyze_system(syst,
filter_trivial_ss=False,
repetition_num=1)
if mat is None:
continue
pbar.update()
reps -= 1
x, y = mat.shape
if mat.shape != (3, 3):
mat = mat[:-(x - 3), :-(y - 3)]
assert mat.shape == (3, 3), mat.shape
matrices.append(mat)
return np.asarray(matrices), (syst, sol)
def simulate_graph(graph):
""" Generate dynamics on graph
"""
# create system
J = np.copy(nx.to_numpy_matrix(graph))
np.fill_diagonal(J, -1)
D = np.zeros((J.shape[0],))
E = np.zeros((J.shape[0],))
I = np.ones((J.shape[0],))
# add input to nodes of zero in-degree
zero_indgr = []
for i, row in enumerate(J.T):
inp = np.sum(row)
inp += 1 # compensate for self-inhibition
if inp == 0: zero_indgr.append(i)
D[zero_indgr] = 1
E[zero_indgr] = 1
print('>', '{}/{} nodes with zero indegree'.format(len(zero_indgr), len(graph.nodes())))
# simulate system
syst = SDESystem(J, D, E, I)
syst, mat, sol = analyze_system(syst, filter_trivial_ss=False)
# plot results
fig = plt.figure(figsize=(30, 15))
gs = mpl.gridspec.GridSpec(1, 2, width_ratios=[1, 2])
if not mat is None:
# only keep non-zero indegree node correlations
mat = extract_sub_matrix(mat, zero_indgr)
node_inds = list_diff(range(J.shape[0]), zero_indgr)
used_nodes = np.array(graph.nodes())[node_inds]
plot_corr_mat(
mat, plt.subplot(gs[0]),
show_values=False, labels=used_nodes)
plot_system_evolution(sol, plt.subplot(gs[1]), show_legend=False)
save_figure('images/peak_network_simulation.pdf', bbox_inches='tight', dpi=300)
def do(gs, res):
param_range = np.linspace(.1, 5, res)
currow = 0
for k_m in tqdm(param_range):
for k_23 in tqdm(param_range):
syst = generate_basic_system(k_m=k_m, k_23=k_23)
single_run_matrices = []
for r in trange(reps):
_,mat,sol = analyze_system(syst, repetition_num=1)
if mat is None:
continue
sol_extract = sol.T[int(len(sol.T)*3/4):]
if r == 0:
plot_system_evolution(
sol_extract.T,
plt.subplot(gs[currow,2]), show_legend=False)
single_run_mat = compute_correlation_matrix(np.array([sol_extract]))
if single_run_mat.shape == (4, 4):
single_run_mat = single_run_mat[:-1,:-1]
assert single_run_mat.shape == (3, 3)
single_run_matrices.append(single_run_mat)
plot_system(syst, plt.subplot(gs[currow,0]))
single_run_matrices = np.asarray(single_run_matrices)
for i, row in enumerate(single_run_matrices.T):
for j, series in enumerate(row):
if i == j: break
ax = plt.subplot(gs[currow,1])
sns.distplot(series, ax=ax, label=r'$c_{{{},{}}}$'.format(i,j))
ax.set_xlim((-1,1))
currow += 1
