def setUp(self):
N = 10
self.beta = 0.99
self.gamma = 0.0001
dd = degree_distributions.power_law_degree_distrb()
graph, pos = generate_graph(N, dd)
graph = nx.generators.complete_graph(N)
N = len(graph.nodes())
self.Lambda = np.full((N, N), self.beta)
self.Gamma = np.full(N, self.gamma)
self.graph = graph
def power_law_exp(file_root='plaw_T8_10k_q_1_gamma1_g_over_b',num_sims=50, num_nodes=10000, active_gen_sizes_on=False):
## Make power law network
pl_degree_dist = degree_distributions.power_law_degree_distrb(400)
power_law_q2 = degree_distributions.power_law_degree_distrb(400, mu=10)
## set up paramters
beta = 0.004
gamma = 0.001
T = 0.8
## quantities to track:
## dist of cmulative infections by gen, dist of cum infections by time (g/beta*q?), total and active gens, diff of waiting times
# file_root = 'poiss_T8_1k_q_1_gamma1_g_over_b'
print('Running power law experiment')
ensemble.run_ensemble_with_time_distribution(power_law_q2, f'../data/testing/{file_root}', num_sims=num_sims,
num_nodes=num_nodes, init_T=T, recover_rate=gamma,
active_gen_sizes_on=active_gen_sizes_on, kill_by=16)
print('Done')
def random_vax_exp(file_root, num_sims=50, num_nodes=10000):
## Make erdos renyi network
k_mean_degree = 3.04
# k_mean_degree = 5
power_law_q2 = degree_distributions.power_law_degree_distrb(400, mu=10)
## set up paramters
beta = 0.004
gamma = 0.001
T = 0.8
## quantities to track:
## dist of cmulative infections by gen, dist of cum infections by time (g/beta*q?), total and active gens, diff of waiting times
# file_root = 'poiss_T8_1k_q_1_gamma1_g_over_b'
print('Running power law intervention experiment')
s_time = time.time()
ensemble.run_ensemble_intervention_effects(power_law_q2, f'../data/testing/{file_root}', num_sims=num_sims,
num_nodes=num_nodes, init_T=T, gen_intervene=4, T_intervene=0.0,
recover_rate=gamma, prop_reduced=0.5, intervention_type="random",
run_regular=False)
print('Done')
print(f'time all was {time.time() - s_time}')
import matplotlib.pyplot as plt
from analysis import ensemble
from src import degree_distributions
from src import figs_for_paper
from src import pgf_formalism
from src import plotting_util
# ## CREATE A DEGREE DISTRIBUTION
power_law_dd = degree_distributions.power_law_degree_distrb(400)
power_law_q3 = degree_distributions.power_law_degree_distrb(
2000, mu=10) # q is 3, k is 1.7
poisson = degree_distributions.binomial_degree_distb(2000, 2.5)
# ## COMPUTE AND SAVE THE PHASE SPACE MATRICES FOR SPECIFIED GENERATIONS
pgf_formalism.compute_phase_space(
num_gens=17,
num_nodes=2000,
degree_distribution=poisson,
transmissibility=0.8,
save_results=True,
gens_to_save=[2, 3, 4, 5, 6, 7, 8, 10, 12, 15, 16],
file_fmt_to_save='../data/paper/ER_q2.5_T8_ifft_g{0}',
do_non_interv=True,
do_interv=False)
# ## PLOT SIMULATION RESULTS AGAINST THEORETICAL RESULTS, BOTH READ FROM FILES: (LOOK INSIDE FUNCTION FOR FILE FORMAT
# AND LOCATION SPECIFICATION)
plotting_util.plot_sims_vs_analytical_multigens(
[3, 6, 10, 12],
400,
def plots_for_nerccs_talk(list_of_gens, x_lim, fname_sim_results, fname_predict_format,
fname_sim_results_int=None, fname_predict_format_int=None, same_plot=False,
normalize_axis_x=False, plot_distribution_inset=False, grayscale=False, inset_label=None):
## Plain axes:
fig, ax1 = plt.subplots(figsize=(15, 7))
ax1.set_xlim(0, x_lim)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
x_vals = np.arange(1, x_lim)
x_ticks = np.arange(2, x_lim, int((x_lim - 2) / 10))
ax1.set_xlim(0, max(x_vals))
ax1.set_xticks(x_ticks)
ax1.semilogy()
# ax1.set_ylim(.00005, .1)
ax1.set_xlabel('Cumulative Infections', fontsize=20)
ax1.set_ylabel('Probability', fontsize=20)
plt.tight_layout()
plt.show()
## Axes with theory and inset
fig, ax1 = plt.subplots(figsize=(15, 7))
ax1.set_xlim(0, x_lim)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
x_vals = np.arange(1, x_lim)
x_ticks = np.arange(2, x_lim, int((x_lim - 2) / 10))
ax1.set_xlim(0, max(x_vals))
ax1.set_xticks(x_ticks)
ax1.semilogy()
ax1.set_ylim(.00005, .1)
ax1.set_xlabel('Cumulative Infections', fontsize=20)
ax1.set_ylabel('Probability', fontsize=20)
if plot_distribution_inset:
right, bottom, width, height = [0.4, 0.5, 0.25, 0.3]
ax2 = fig.add_axes([right, bottom, width, height])
power_law_dd = degree_distributions.power_law_degree_distrb(400)
ax2.plot(power_law_dd[:15], color='black')
ax2.set_xlim(0, 14)
ax2.set_title('Network Degree Distribution', fontsize=20)
ax2.set_xlabel('Degree $k$', fontsize=20)
ax2.set_ylabel('Fraction of nodes', fontsize=20)
# ax2.set_yticks(np.arange(0, 1, 0.25))
ax2.set_xticks([0, 1, 2, 3, 5, 10])
ax2.semilogy()
plt.tight_layout()
plt.show()
## Axes with just theory
fig, ax1 = plt.subplots(figsize=(15, 7))
ax1.set_xlim(0, x_lim)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
x_vals = np.arange(1, x_lim)
x_ticks = np.arange(2, x_lim, int((x_lim - 2) / 10))
ax1.set_xlim(0, max(x_vals))
ax1.set_xticks(x_ticks)
ax1.semilogy()
ax1.set_ylim(.00005, .1)
ax1.set_xlabel('Cumulative Infections', fontsize=20)
ax1.set_ylabel('Probability', fontsize=20)
color_key = {}
colors = ['blue', 'teal', 'orange', 'crimson', 'purple', 'teal', 'black', 'gold', 'chocolate',
'dodgerblue', 'darkslategray', 'mediumorchid', 'magenta', 'tomato', 'midnightblue',
'cadetblue', 'crimson']
colorcycle = sns.color_palette("mako_r", len(list_of_gens))
## Color test: Uncomment to test plot
# colorcycle = sns.color_palette("mako_r", 4)
# plt.plot(np.arange(100), np.sin(np.arange(100)) + 0, color=colorcycle[0])
# plt.plot(np.arange(100), np.sin(np.arange(100)) + 1, color=colorcycle[1])
# plt.plot(np.arange(100), np.sin(np.arange(100)) + 2, color=colorcycle[2])
# plt.plot(np.arange(100), np.sin(np.arange(100)) + 3, color=colorcycle[3])
# plt.tight_layout()
# plt.show()
style_key = {}
styles = [':', '-.', '--', '-']
for i in range(len(list_of_gens)):
gen = list_of_gens[i]
# color = colors[0]
# colors.remove(color)
color_key[gen] = colorcycle[i]
style = styles[0]
styles.remove(style)
style_key[gen] = style
for gen in list_of_gens:
fname_predict = fname_predict_format.format(gen)
fname_sims = fname_sim_results
if fname_sim_results_int is not None and fname_predict_format_int is not None:
fname_predict_interv = fname_predict_format_int.format(gen)
fname_sims_interv = fname_sim_results_int
else:
fname_sims_interv = None
fname_predict_interv = None
nerccs_plot_sims_vs_analytical_outbreak_sizes(fig, ax1, gen, x_lim, None, fname_predict, None,
None, color_key, style_key, same_plot, normalize_axis_x,
grayscale)
if plot_distribution_inset:
right, bottom, width, height = [0.4, 0.5, 0.25, 0.3]
ax2 = fig.add_axes([right, bottom, width, height])
power_law_dd = degree_distributions.power_law_degree_distrb(400)
ax2.plot(power_law_dd[:15], color='black')
ax2.set_xlim(0, 14)
ax2.set_title('Network Degree Distribution', fontsize=20)
ax2.set_xlabel('Degree $k$', fontsize=20)
ax2.set_ylabel('Fraction of nodes', fontsize=20)
# ax2.set_yticks(np.arange(0, 1, 0.25))
ax2.set_xticks([0, 1, 2, 3, 5, 10])
ax2.semilogy()
plt.tight_layout()
plt.show()
## Axes with theory and sims
fig, ax1 = plt.subplots(figsize=(15, 7))
ax1.set_xlim(0, x_lim)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
x_vals = np.arange(1, x_lim)
x_ticks = np.arange(2, x_lim, int((x_lim - 2) / 10))
ax1.set_xlim(0, max(x_vals))
ax1.set_xticks(x_ticks)
ax1.semilogy()
ax1.set_ylim(.00005, .1)
ax1.set_xlabel('Cumulative Infections', fontsize=20)
ax1.set_ylabel('Probability', fontsize=20)
for gen in list_of_gens:
fname_predict = fname_predict_format.format(gen)
fname_sims = fname_sim_results
if fname_sim_results_int is not None and fname_predict_format_int is not None:
fname_predict_interv = fname_predict_format_int.format(gen)
fname_sims_interv = fname_sim_results_int
else:
fname_sims_interv = None
fname_predict_interv = None
nerccs_plot_sims_vs_analytical_outbreak_sizes(fig, ax1, gen, x_lim, fname_sims, fname_predict, None,
None, color_key, style_key, same_plot, normalize_axis_x,
grayscale)
plt.tight_layout()
plt.show()
## Axes with theory and intervention theory
fig, ax1 = plt.subplots(figsize=(15, 7))
ax1.set_xlim(0, x_lim)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
x_vals = np.arange(1, x_lim)
x_ticks = np.arange(2, x_lim, int((x_lim - 2) / 10))
ax1.set_xlim(0, max(x_vals))
ax1.set_xticks(x_ticks)
ax1.semilogy()
ax1.set_ylim(.00005, .1)
ax1.set_xlabel('Cumulative Infections', fontsize=20)
ax1.set_ylabel('Probability', fontsize=20)
for gen in list_of_gens:
fname_predict = fname_predict_format.format(gen)
fname_sims = fname_sim_results
if fname_sim_results_int is not None and fname_predict_format_int is not None:
fname_predict_interv = fname_predict_format_int.format(gen)
fname_sims_interv = fname_sim_results_int
else:
fname_sims_interv = None
fname_predict_interv = None
nerccs_plot_sims_vs_analytical_outbreak_sizes(fig, ax1, gen, x_lim, None, fname_predict, None,
fname_predict_interv, color_key, style_key, same_plot, normalize_axis_x,
grayscale)
right, bottom, width, height = [0.4, 0.75, 0.25, 0.003]
ax2 = fig.add_axes([right, bottom, width, height], frameon=False)
ax2.set_xlabel(inset_label, fontsize=25)
ax2.set_xticks([])
ax2.set_yticks([])
# power_law_dd = degree_distributions.power_law_degree_distrb(400)
# ax2.plot(power_law_dd[:15], color='black')
# ax2.set_xlim(0, 14)
# ax2.set_xlabel('Degree $k$', fontsize=20)
# ax2.set_ylabel('Fraction of nodes', fontsize=20)
# ax2.set_yticks(np.arange(0, 1, 0.25))
# ax2.set_xticks([0, 1, 2, 3, 5, 10])
# ax2.semilogy()
plt.tight_layout()
plt.show()
## Axes with theory and intervention theory and intervention sims
fig, ax1 = plt.subplots(figsize=(15, 7))
ax1.set_xlim(0, x_lim)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
x_vals = np.arange(1, x_lim)
x_ticks = np.arange(2, x_lim, int((x_lim - 2) / 10))
ax1.set_xlim(0, max(x_vals))
ax1.set_xticks(x_ticks)
ax1.semilogy()
ax1.set_ylim(.00005, .1)
ax1.set_xlabel('Cumulative Infections', fontsize=20)
ax1.set_ylabel('Probability', fontsize=20)
for gen in list_of_gens:
fname_predict = fname_predict_format.format(gen)
fname_sims = fname_sim_results
if fname_sim_results_int is not None and fname_predict_format_int is not None:
fname_predict_interv = fname_predict_format_int.format(gen)
fname_sims_interv = fname_sim_results_int
else:
fname_sims_interv = None
fname_predict_interv = None
nerccs_plot_sims_vs_analytical_outbreak_sizes(fig, ax1, gen, x_lim, None, fname_predict, fname_sims_interv,
fname_predict_interv, color_key, style_key, same_plot, normalize_axis_x,
grayscale)
right, bottom, width, height = [0.4, 0.75, 0.25, 0.003]
ax2 = fig.add_axes([right, bottom, width, height], frameon=False)
ax2.set_xlabel(inset_label, fontsize=25)
ax2.set_xticks([])
ax2.set_yticks([])
# power_law_dd = degree_distributions.power_law_degree_distrb(400)
# ax2.plot(power_law_dd[:15], color='black')
# ax2.set_xlim(0, 14)
# ax2.set_xlabel('Degree $k$', fontsize=20)
# ax2.set_ylabel('Fraction of nodes', fontsize=20)
# ax2.set_yticks(np.arange(0, 1, 0.25))
# ax2.set_xticks([0, 1, 2, 3, 5, 10])
# ax2.semilogy()
plt.tight_layout()
plt.show()
import src.degree_distributions as degree_distributions
import src.pgf_formalism as pgf_formalism
import src.plotting_util as plotting_util
size = 250
def exp_val_dd(dist):
ev = 0
for k in range(len(dist)):
ev += k * dist[k]
return ev
power_law_dd = degree_distributions.power_law_degree_distrb(size, mu=5)
extnct_cake_PL_1, psi_1 = pgf_formalism.compute_extinct_prob_all(power_law_dd,
T=0.8,
n_gens=11,
renorm=True)
power_law_dd = degree_distributions.power_law_degree_distrb(size, mu=7.5)
extnct_cake_PL_2, psi_2 = pgf_formalism.compute_extinct_prob_all(power_law_dd,
T=0.8,
n_gens=11,
renorm=True)
power_law_dd = degree_distributions.power_law_degree_distrb(size, mu=10)
extnct_cake_PL_3, psi_3 = pgf_formalism.compute_extinct_prob_all(power_law_dd,
T=0.8,
n_gens=11,