fig.tight_layout()
fig.savefig("plots/size_of_dynamical_volume_" + save_name + ".pdf")
#plt.show()
plt.close()
critical_alpha0 = np.transpose(critical_alpha0)
connectance = np.real(largest_eigenvalue_region[0, 0][:, 3])
nestedness = np.real(largest_eigenvalue_region[0, 0][:, 2])
for k in range(len(largest_eigenvalue_region)):
# plot critical alpha0
fig = plt.figure()
ax = fig.add_subplot(111)
for nest in all_nestedness:
indices = [
int(i)
for i in range(len(nestedness)) if cf.closest_element_in_list(
nestedness[i], all_nestedness) == nest
]
sorted_indices = np.array(
[indices[a] for a in np.argsort(connectance[indices])])
connectance_to_plot = [connectance[i] for i in sorted_indices]
critical_alpha0_to_plot = [
critical_alpha0[k][i] for i in sorted_indices
]
ax.plot(connectance_to_plot,
critical_alpha0_to_plot,
label=r'$\eta_G\approx' + str(nest) + '$',
markersize=10,
linewidth=2.5,
markeredgewidth=3)
ax.set_xlabel(r'Connectance $\kappa_G$')
ax.set_ylabel(r'$\alpha_0^D(G,A)$')
local_dynamical_stability_region=cf.load_data_region(alpha_mode,alpha0, filename_lds,optimal_LRI_folder)
feasibility_region=cf.load_data_region(alpha_mode, alpha0, filename_feasib, optimal_LRI_folder)
alpha0=np.array(alpha0)
Nmatrices=len(local_dynamical_stability_region[0,0])
print(Nmatrices)
for i in range(Nmatrices):
fig = plt.figure()
ax = fig.add_subplot(111)
NR = local_dynamical_stability_region[0,0,0,0]
NS = local_dynamical_stability_region[0,0,0,1]
nest = local_dynamical_stability_region[0,0,i, 2]
conn = local_dynamical_stability_region[0,0,i, 3]
title = r'$\eta_G='+str(cf.closest_element_in_list(nest, all_nestedness))+', \kappa_G='+str(cf.closest_element_in_list(conn, all_connectance))+'$'
for k in range(len(alpha_mode)):
to_plot=[cf.vol_dyna_if_feas(feasibility_region, local_dynamical_stability_region, k, j, i) for j in range(len(alpha0))]
ax.plot(alpha0, to_plot, label=label[k])
ax.set_xlabel(r'$\alpha_0$')
ax.set_ylabel(r'Prob$\left(\mathcal{D}_L | \mathcal{F}\right)\left(G, A, \alpha_0\right)$')
ax.set_title(title)
ax.legend()
fig.tight_layout()
fig.savefig('plots/prob_dyn_stab_if_feas_NR'+str(NR)+'_NS'+str(NS)+'_Nest'+str(nest)+'_Conn'+str(conn)+'.pdf')
plt.close()
##### OLD VERSION BELOW #######
# import numpy as np
data = np.loadtxt(file,
usecols=tuple([
i for i in range(
1, 2 * (len(alpha_mode) *
(len(syntrophy_mode) - 1) + 1) + 1)
]))
#### DATA LOADING ######
# str_stab dictionary is organized this way
# str_stab[Nest][Conn][syntrophy mode][alpha mode]
nest = []
conn = []
for i in range(len(mat_name)):
a = mat_name[i].split("_", 7)
actual_nest = float(a[5][4:])
round_nest = cf.closest_element_in_list(actual_nest, all_nestedness)
actual_conn = float(a[6][4:-4])
round_conn = cf.closest_element_in_list(actual_conn, all_connectance)
nest.append(round_nest)
conn.append(round_conn)
str_stab = {}
# build structural stability dictionary
for n in all_nestedness:
dnest = {}
for c in all_connectance:
dconn = {}
index = 0
for syn in syntrophy_mode:
dsyn = {}
for al in alpha_mode:
dalpha = {}
suptitle = r'FC'
save_name = 'plots/energy_FC'
file = 'energy_optimal_RS_matrices'
suptitle = r'RS'
save_name = 'plots/energy_RS'
data = (np.loadtxt(folder + '/' + file + '.out'))
connG = data[:, 0]
nestG = data[:, 1]
connA = data[:, 2]
nestA = data[:, 3]
energy = data[:, 4]
for i in range(len(connG)):
connG[i] = cr.closest_element_in_list(connG[i], cr.all_connectance)
for i in range(len(nestG)):
nestG[i] = cr.closest_element_in_list(nestG[i], cr.all_nestedness)
fig = plt.figure()
ax = fig.add_subplot(111)
for j in range(len(cr.all_nestedness)):
nest = cr.all_nestedness[j]
points = [i for i in range(len(connG)) if nestG[i] == nest]
points = [points[i] for i in np.argsort(connG[points])]
ax.plot(connG[points],
energy[points],
color=cr.nest_colours[j],
label=r'$\eta_G=' + str(nest) + '$')
ax.legend(bbox_to_anchor=(1., 1.))
feasability[mat_index],
cmap=cmap,
levels=[0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.00001])
#ax1.plot(fit_g0, fit_S0, color='black', marker='None',linestyle='-.', linewidth=2)
ax1.plot(fit_g0,
expected_S0,
color='black',
marker='None',
linestyle='--',
linewidth=2)
ax1.set_xlim(0., 1.)
ax1.set_ylim(0., 1.)
ax1.set_xlabel(r'$\gamma_0$')
ax1.set_ylabel(r'$S_0$')
title_axis = r'$\eta_G=' + str(
closest_element_in_list(nestedness[mat_index], all_nestedness))
title_axis += r', \kappa_G=' + str(
closest_element_in_list(connectance[mat_index], all_connectance)) + '$'
ax1.set_title(title_axis)
ax12 = fig1.add_subplot(122, aspect='equal')
im = ax12.tricontourf(
gamma0[mat_index],
S0[mat_index_2],
feasability[mat_index_2],
cmap=cmap,
levels=[0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.00001])
#ax12.plot(fit_g0_2, fit_S0_2, color='black', marker='None',linestyle='-.', linewidth=2)
ax12.plot(fit_g0_2,
expected_S0_2,
color='black',
fig.savefig('plots/local_dynamical_stability_region_' + save_name + '.pdf')
plt.close()
decline = []
critical_alpha0 = []
err_decline = []
for j in range(len(local_dynamical_stability_region[0][0])):
fig, ax = plt.subplots(1, 1)
local_critical_alpha0 = []
local_decline = []
for i in range(len(local_dynamical_stability_region)):
data = local_dynamical_stability_region[i, :, j]
NR = data[0, 0]
NS = data[0, 1]
nestedness = cf.closest_element_in_list(data[0, 2], all_nestedness)
connectance = cf.closest_element_in_list(data[0, 3], all_connectance)
lds_volume = cf.shrink_volume_for_one_matrix(data)
fit_function = cf.exponential_function
take_zero_points = False
fitted_alpha0, fitted_dyn_volume, estimated_alpha_crit, estimated_vol_zero_syntrophy, (
est_decay_rate,
err_decay_rate) = cf.fit_shrinkage_curve(alpha0, lds_volume,
fit_function,
take_zero_points)
print(err_decay_rate)
ax.plot(fitted_alpha0,
fitted_dyn_volume,
color=alpha_mode_colours[i],
marker='None',
linestyle='solid')
# data is organized this way: decay_rate[NR][alpha_mode][nest][conn]
decay_rate = {}
dNR25 = {}
dNR50 = {}
for i in range(len(alpha_mode)):
dNR25_almode = {}
dNR50_almode = {}
data_50 = df_50[i]
data_25 = df_25[i]
# we first take all NR=50 points and match them to a NR=25 point
NR = data_25[:, 0]
NS = data_25[:, 1]
nestedness_25 = [
cf.closest_element_in_list(n, all_nestedness) for n in data_25[:, 2]
]
connectance_25 = [
cf.closest_element_in_list(k, all_connectance) for k in data_25[:, 3]
]
decay_rate_25 = data_25[:, 4]
nestedness_50 = [
cf.closest_element_in_list(n, all_nestedness) for n in data_50[:, 2]
]
connectance_50 = [
cf.closest_element_in_list(k, all_connectance) for k in data_50[:, 3]
]
decay_rate_50 = data_50[:, 4]
# first for NR=25
