def main():
sns.set_style('white')
plt.style.use('seaborn-poster')
detailed_system()
configuration_overview(
lambda s,a: plot_system(s, a, netx_plot=True),
'sub', draw_all=False)
configuration_overview(
lambda s,a: plot_system(s, a, netx_plot=True), 'all')
configuration_overview(
lambda s,a: plot_series(s,a,uosd=False), 'series_orig')
configuration_overview(plot_series, 'series')
configuration_overview(plot_mat, 'mat')
configuration_overview(plot_hist, 'hist')
distribution_filter_threshold()
def single_corr_coeff_hist(reps=5000):
""" Plot distribution of single correlation coefficients
"""
def do(syst, ax):
# data
single_run_matrices = []
for _ in trange(reps):
sol = solve_system(syst)
sol_extract = sol.T[int(len(sol.T)*3/4):]
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)
single_run_matrices = np.asarray(single_run_matrices)
# plotting
cols = cycle(['b', 'r', 'g', 'c', 'm', 'y', 'k'])
for i, row in enumerate(single_run_matrices.T):
for j, series in enumerate(row):
if i == j: break
plot_histogram(
series[series!=1], ax,
label=r'$c_{{{},{}}}$'.format(i,j),
facecolor=next(cols), alpha=0.5,
bins=100)
# data
syst = generate_basic_system()
more = add_node_to_system(syst)[::10]
print('#more', len(more))
# plot
f, axes = plt.subplots(len(more), 2, figsize=(9,20))
do(syst, axes[0,0]); print()
for i, m in tqdm(enumerate(more), total=len(more)):
if i > 0:
plot_system(m, axes[i,0])
do(m, axes[i,1])
plt.tight_layout()
save_figure('images/correlation_distribution.pdf', 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 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 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
def detailed_system():
syst = generate_basic_system()
plt.figure()
plot_system(syst, plt.gca())
plt.savefig('presentation/images/FFL.pdf', dpi=300)
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()
