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_result(motifs, data, fname_app='', sub_num=10):
""" Create result plot
"""
mpl.style.use('default')
sub_num = min(sub_num, len(motifs))
print(' > Plotting results ({})'.format(fname_app[1:]))
# overview plots
plt.figure(figsize=(30, 4 * sub_num))
gs = mpl.gridspec.GridSpec(sub_num, 3, width_ratios=[1, 2, 1])
idx = map(int,
np.linspace(0, len(motifs), num=sub_num, endpoint=False))
corrs = []
for ai, i in tqdm(enumerate(idx), total=sub_num):
c1, c2, c3 = motifs[i]
# plot all possible correlations and select optimal one
sel, all_corrs = plot_all_correlations(
(c1, c2, c3), data,
plt.subplot(gs[ai, 2]) if ai < sub_num else None)
# get intensities
sols = []
for foo in [c1, c2, c3]:
sols.append(data[foo]['intensities'][sel[foo]])
# compute correlation matrix
corr_mat = get_correlation_matrix(sols)
corrs.extend(all_corrs)
# plot rest
plotter.plot_corr_mat(corr_mat, plt.subplot(gs[ai, 0]))
series_ax = plt.subplot(gs[ai, 1])
plotter.plot_system_evolution(
sols, series_ax,
xlabel='sample')
series_ax.set_title('{}\n{}\n{}'.format(c1, c2, c3))
plt.tight_layout()
plotter.save_figure('images/rl_motifs{}.pdf'.format(fname_app), bbox_inches='tight')
# correlation histogram
plt.figure()
plotter.plot_histogram(corrs, plt.gca())
plt.tight_layout()
plotter.save_figure('images/rl_corr_hist{}.pdf'.format(fname_app), bbox_inches='tight')
def plot_system_overview(data, sample_size=20):
""" Plot systems vs correlations
"""
# extract sample
dsample = [data[i]
for i in sorted(random.sample(range(len(data)), min(len(data), sample_size)))]
# plot sample
fig = plt.figure(figsize=(13, 4*len(dsample)))
gs = gridspec.GridSpec(len(dsample), 3, width_ratios=[1, 1, 2])
for i, (system, corr_mat, solution) in enumerate(dsample):
plot_system(system, plt.subplot(gs[i, 0]))
plot_corr_mat(corr_mat, plt.subplot(gs[i, 1]))
plot_system_evolution(solution, plt.subplot(gs[i, 2]))
plt.tight_layout()
save_figure('images/overview.pdf', bbox_inches='tight', dpi=300)
plt.close()
def main(fname, skip_filtered=True):
""" Main interface
"""
if os.path.isfile(fname):
systems = load_systems(fname)
if systems.ndim == 0:
systems = [np.asscalar(systems)]
print('Integrating %d systems' % len(systems))
core_num = int(multiprocessing.cpu_count() * 4 / 5)
print('Using %d cores' % core_num)
data = []
with tqdm(total=len(systems)) as pbar:
with multiprocessing.Pool(core_num) as p:
for res in p.imap(analyze_system, systems, chunksize=10):
if not skip_filtered or not res[1] is None:
data.append(res)
pbar.update()
print('Found result for %d systems' % len(data))
if not skip_filtered:
data = cluster_data(data)
cache_data(data)
else:
syst = system_from_string(fname)
syst, mat, sol = analyze_system(syst, use_ode_sde_diff=False)
if mat is None:
print('No sensible steady-state found')
else:
print(mat)
fig = plt.figure(figsize=(20, 6))
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 2])
plt.style.use('seaborn-poster')
plot_system(syst, plt.subplot(gs[0, 0]))
plot_corr_mat(mat, plt.subplot(gs[0, 1]))
plot_system_evolution(sol, plt.subplot(gs[0, 2]))
plt.tight_layout()
save_figure('images/single_case.pdf', bbox_inches='tight', dpi=300)
plt.close()
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 plot_system_overview(data, sample_size=20):
""" Plot systems vs correlations
"""
# extract sample
dsample = [
data[i] for i in sorted(
random.sample(range(len(data)), min(len(data), sample_size)))
]
# plot sample
fig = plt.figure(figsize=(13, 4 * len(dsample)))
gs = gridspec.GridSpec(len(dsample), 3, width_ratios=[1, 1, 2])
for i, (system, corr_mat, solution) in enumerate(dsample):
plot_system(system, plt.subplot(gs[i, 0]))
plot_corr_mat(corr_mat, plt.subplot(gs[i, 1]))
plot_system_evolution(solution, plt.subplot(gs[i, 2]))
plt.tight_layout()
save_figure('images/overview.pdf', bbox_inches='tight', dpi=300)
plt.close()
def plot_individuals(examples, fname, val_func=None):
""" Plot a selection of individual results
"""
if val_func is None:
mod = -1
else:
mod = 0
# plot selected networks
if len(examples[0]) == 2: # is pair of networks
fig = plt.figure(figsize=(50, 4 * len(examples)))
gs = mpl.gridspec.GridSpec(
len(examples),
6 + mod,
width_ratios=[1, 2, 1, 2, 1 + (-3 * mod), 4])
else: # each entry is single network
fig = plt.figure(figsize=(25, 4 * len(examples)))
gs = mpl.gridspec.GridSpec(len(examples), 3, width_ratios=[1, 1, 2])
counter = 0
for i, net in enumerate(examples):
if len(net) == 2: # pair of networks
raw_p, enh_p = net
# -.- ...
if len(raw_p) == 2:
_, raw = raw_p
_, enh = enh_p
else:
raw = raw_p
enh = enh_p
plot_system(raw[0], plt.subplot(gs[i, 0]))
plot_corr_mat(raw[1], plt.subplot(gs[i, 1]))
plot_system(enh[0], plt.subplot(gs[i, 2]))
plot_corr_mat(enh[1], plt.subplot(gs[i, 3]))
plot_system_evolution(enh[2], plt.subplot(gs[i, 5 + mod]))
# plot marker
mark_ax = plt.subplot(gs[i, 4])
if not val_func is None:
mark_ax.imshow([[handle_enh_entry(raw_p, enh_p, val_func)]],
cmap=get_matrix_cmap(),
vmin=0,
vmax=3)
mark_ax.axis('off')
else:
print('Tried to use `val_func`, but it\'s None')
else: # single network
if net[1] is None:
counter += 1
plot_system(net[0], plt.subplot(gs[i, 0]))
plot_system_evolution(net[2], plt.subplot(gs[i, 2]))
continue
plot_system(net[0], plt.subplot(gs[i, 0]))
plot_corr_mat(net[1], plt.subplot(gs[i, 1]))
plot_system_evolution(net[2], plt.subplot(gs[i, 2]))
if counter > 0:
#print('{} broken results'.format(counter))
pass
plt.tight_layout()
save_figure('%s_zoom.pdf' % fname.replace('.pdf', ''),
bbox_inches='tight',
dpi=300)
plt.close()
def plot_individuals(examples, fname, val_func=None):
""" Plot a selection of individual results
"""
if val_func is None:
mod = -1
else:
mod = 0
# plot selected networks
if len(examples[0]) == 2: # is pair of networks
fig = plt.figure(figsize=(50, 4*len(examples)))
gs = mpl.gridspec.GridSpec(
len(examples), 6+mod,
width_ratios=[1, 2, 1, 2, 1+(-3*mod), 4])
else: # each entry is single network
fig = plt.figure(figsize=(25, 4*len(examples)))
gs = mpl.gridspec.GridSpec(len(examples), 3, width_ratios=[1, 1, 2])
counter = 0
for i, net in enumerate(examples):
if len(net) == 2: # pair of networks
raw_p, enh_p = net
# -.- ...
if len(raw_p) == 2:
_, raw = raw_p
_, enh = enh_p
else:
raw = raw_p
enh = enh_p
plot_system(raw[0], plt.subplot(gs[i, 0]))
plot_corr_mat(raw[1], plt.subplot(gs[i, 1]))
plot_system(enh[0], plt.subplot(gs[i, 2]))
plot_corr_mat(enh[1], plt.subplot(gs[i, 3]))
plot_system_evolution(enh[2], plt.subplot(gs[i, 5+mod]))
# plot marker
mark_ax = plt.subplot(gs[i, 4])
if not val_func is None:
mark_ax.imshow(
[[handle_enh_entry(raw_p, enh_p, val_func)]],
cmap=get_matrix_cmap(), vmin=0, vmax=3)
mark_ax.axis('off')
else:
print('Tried to use `val_func`, but it\'s None')
else: # single network
if net[1] is None:
counter += 1
plot_system(net[0], plt.subplot(gs[i, 0]))
plot_system_evolution(net[2], plt.subplot(gs[i, 2]))
continue
plot_system(net[0], plt.subplot(gs[i, 0]))
plot_corr_mat(net[1], plt.subplot(gs[i, 1]))
plot_system_evolution(net[2], plt.subplot(gs[i, 2]))
if counter > 0:
#print('{} broken results'.format(counter))
pass
plt.tight_layout()
save_figure('%s_zoom.pdf' % fname.replace('.pdf', ''), bbox_inches='tight', dpi=300)
plt.close()
