# %% Configuration
# ================
GRAPH = 'er'
TAUS = np.concatenate([[0.001],
np.arange(0.01, 0.3, step=0.05),
np.arange(0.3, 3.01, step=0.1)])
DISTANCE = 25
# %% Run the required simulations
# ===============================
graph = load_graph(GRAPH)
out = data_path(f'MFPT/{GRAPH}_UndirectedNetwork_ExponentialWalker')
if not out.exists():
network = UndirectedNetwork(graph)
walker = ExponentialWalker(timescale=0.1)
sim = MFPTSimulation(network, walker, num_sims=5000)
results = sim.run()
results.to_csv(str(out))
for tau in TAUS:
out = data_path(
f'MFPT/{GRAPH}_SwitchingNetwork_ExponentialWalker_tau{tau:e}')
if out.exists():
continue
print(f'Running simulation: {out}')
network = SwitchingNetwork(graph, timescale=tau, memory=False)
# %% Configuration
# ================
GRAPH = 'hex'
TAU = 3.
NUM_TRAJS = 10000
MAX_TIME = 200
# %% Run the required simulations
# ===============================
graph = load_graph(GRAPH)
# Undirected network
out = data_path(
f'trajs/{GRAPH}_UndirectedNetwork_ExponentialWalker_N{NUM_TRAJS}_max_time{MAX_TIME}',
suffix='.parquet')
if not out.exists():
print(f'Generating trajectories: {out}')
network = UndirectedNetwork(graph)
walker = ExponentialWalker(timescale=0.1)
generator = TrajectoryGenerator(network, walker)
trajs = generator.trajectories(NUM_TRAJS, MAX_TIME)
trajs.to_parquet(str(out))
# Active network
out = data_path(
f'trajs/{GRAPH}_SwitchingNetwork_ExponentialWalker_tau{TAU:e}_N{NUM_TRAJS}_max_time{MAX_TIME}',
suffix='.parquet')
if not out.exists():
print(f'Generating trajectories: {out}')
# %% Configuration
# ================
GRAPH = 'hex'
TAUS = np.concatenate([[0.001],
np.arange(0.01, 0.075, step=0.005),
np.arange(0.75, 0.3, step=0.01),
np.arange(0.3, 2.3, step=0.1)])
DISTANCE = 25
# %% Run the required simulations
# ===============================
graph = load_graph(GRAPH)
out = data_path(f'MFPT/{GRAPH}_UndirectedNetwork_ExponentialWalker')
if not out.exists():
network = UndirectedNetwork(graph)
walker = ExponentialWalker(timescale=0.1)
sim = MFPTSimulation(network, walker, num_sims=5000)
results = sim.run()
results.to_csv(str(out))
for tau in TAUS:
out = data_path(
f'MFPT/{GRAPH}_SwitchingNetworkConstantRate_ExponentialWalker_tau{tau:e}_sim1000'
)
if out.exists():
continue
print(f'Running simulation: {out}')
# %% Configuration
# ================
TAU = 0.03
VMAX = 3000
GRAPH = "er"
# %% Run required simulations
# ===========================
graph = load_graph(GRAPH)
out = data_path(f'MFPT/{GRAPH}_SwitchingNetwork_ExponentialWalker_tau{TAU:e}')
if not out.exists():
network = SwitchingNetwork(graph, timescale=TAU, memory=False)
walker = ExponentialWalker(timescale=0.1)
sim = MFPTSimulation(network, walker, num_sims=5000)
print(f'Running simulation: {out}')
res = sim.run()
res.to_csv(str(out))
# %% MFPT heatmap
# ===============
data = load_data(f'MFPT/{GRAPH}_SwitchingNetwork_ExponentialWalker_tau{TAU:e}')
# data = load_data(f'MFPT/{GRAPH}_UndirectedNetwork_ExponentialWalker')
# %% Configuration
GRAPH = 'hex'
TAU = 1e9 # “frozen” active network
MAX_TIME = 60
NUM_RUNS = 10 # number of independent simulations to run
# %% Run the required simulations
# ===============================
graph = load_graph(GRAPH)
for n_run in range(1, NUM_RUNS + 1):
out = data_path(
f'trajs/{GRAPH}_SwitchingNetwork_ExponentialWalker_tau{TAU:e}_uniform_10_max_time{MAX_TIME}_run{n_run}',
suffix='.parquet')
if out.exists():
continue
print(f'Generating trajectories: {out}')
# Start from uniform distribution, 10 particles per node.
network = SwitchingNetwork(graph, timescale=TAU)
walker = ExponentialWalker(timescale=0.1)
num_walkers = 10 * network.graph.number_of_nodes()
start_nodes = np.repeat(list(network.graph.nodes), 10)
generator = TrajectoryGenerator(network, walker)
trajs = generator.trajectories(num_walkers,
MAX_TIME,
start_nodes=start_nodes)
trajs.to_parquet(str(out))
TAUS = [0.03, 0.3, 3]
N = 10000
MAX_TIME = 120
GRAPH = 'hex'
TARGET = 146
# %% Run the required simulations
# ===============================
graph = load_graph(GRAPH)
for tau in TAUS:
out = data_path(
f'trajs/{GRAPH}_SwitchingNetwork_ExponentialWalker_tau{tau:e}_N{N}_max_time{MAX_TIME}',
suffix='.parquet')
if out.exists():
continue
print(f'Generating trajectories: {out}')
network = SwitchingNetwork(graph, timescale=tau)
walker = ExponentialWalker(timescale=0.1)
generator = TrajectoryGenerator(network, walker)
trajs = generator.trajectories(N, MAX_TIME)
trajs.to_parquet(str(out))
# %% Generate the figures
# =======================
for tau in TAUS:
GRAPH = 'hex'
NUM_TRAJS = 1000
TARGET = 146
TAUS = [0.03, 0.3, 3.]
NUM_SIMS = 1000
# %% Run required simulations
# ===========================
graph = load_graph(GRAPH)
distance = nx.shortest_path_length(graph, 0, TARGET)
for tau in TAUS:
out = data_path(
f'trajs/{GRAPH}_SwitchingNetwork_ExponentialWalker_tau{tau:e}_N{NUM_TRAJS}_1st_trajectories_S0_T{TARGET}',
suffix='.parquet')
if out.exists():
continue
print(f'Generating trajectories: {out}')
network = SwitchingNetwork(graph, timescale=tau)
walker = ExponentialWalker(timescale=0.1)
# We generate simulations with NUM_TRAJS particles and keep only the first
# to arrive. We repeat this NUM_SIMS times to obtain the statistics of the
# first particle to arrive.
trajs = []
for sim_id in trange(NUM_SIMS):
network.reset()