def generate_parameters(N, M, nb_local_community, p, carrying_capacity_b,
graph_model):
r = stats.uniform.rvs(
loc=0, scale=1, size=M
) # growth rate, uniform distribution between 0 and 1, vector of size M
while np.any(r == 0.):
for index, value in enumerate(r):
if value == 0.:
r[index] = stats.uniform.rvs(loc=0, scale=1)
# k: carrying capacity
k = stats.beta.rvs(a=1, b=carrying_capacity_b, loc=0, scale=1, size=M)
# Scaling of carrying capacity k between 1 and 100
k = 1. + k * 100
# Interaction matrix A
## Random Erdös-Renyi model
if graph_model == "ER":
A = graph.generate_random_graph(N, p)
elif graph_model == "WS":
A = graph.generate_watts_strogatz_graph(N, p)
else:
raise ValueError("Unknown graph model.")
# Initial abundance of species x_0
x_0 = stats.uniform.rvs(
loc=10, scale=90, size=(nb_local_community,
M)) # Uniform distribution between 10 and 100
return r, k, A, x_0
def main():
"""
Generates new graphs
"""
parser = argparse.ArgumentParser()
parser.add_argument("-p", help="path to data directory", type=str)
parser.add_argument("-nn", help="number of nodes in new graph", type=int)
parser.add_argument("-od",
help="number of OD pairs in new graph",
type=int)
#TODO: manage edge cases when n_OD is too large for instance
args = parser.parse_args()
path = os.path.join('/Users/lucasfuentes/icu/notebooks/Data_comparison',
args.p)
_, _ = generate_random_graph(args.nn, args.od, path)
return
import random
import graph
n = 20
m = 30
G = graph.generate_random_graph(n, m)
(V, E) = G
# print(G)
print("Number of edges is {}, we want {}".format(len(E), m))
start = random.choice(list(V))
stop = random.choice(list(V))
cur = start
print("Starting at {}".format(cur))
if len(graph.neighbours_of(G, cur)) == 0:
raise Exception("Bad luck, {} has no neighbours".format(cur))
num_steps = 0
max_num_steps = 1000
history = [cur]
while cur != stop and num_steps < max_num_steps:
num_steps += 1
# pick a neighbour of cur at random
neighbours = graph.neighbours_of(G, cur)
num_edges = int(argv.pop(0))
elif token == "--start":
start = int(argv.pop(0))
elif token == "--stop":
stop = int(argv.pop(0))
elif token == "--max_steps":
max_num_steps = int(argv.pop(0))
else:
print("Bad argument {}".format(token))
sys.exit(1)
# use supplied seed if given
if seed is not None:
random.seed(seed)
G = graph.generate_random_graph(num_vertices, num_edges)
dot_file_name = "walk-graph.dot"
# display.write_dot_desc(G, dot_file_name)
(V, E) = G
# start and stop vertices
if start is None:
start = random.choice(list(V))
if stop is None:
stop = random.choice(list(V))
# rendering attributes
attr = {"vertex_color":{}, "edge_color":{}}
A.append(vertex_id)
else:
B.append(vertex_id)
return A,B
def random_contraction(g):
A,B = Vertex(),Vertex()
A_ids,B_ids = [],[]
while len(g.get_vertices()) > 2:
print("num verticies",g.num_vertices)
time.sleep(1)
edge = random.choice(g.get_all_edges())
start_edges = g.get_edges(edge[0])
end_edges = g.get_edges(edge[1])
g.remove_vertex(edge[0])
removed_vertex,removed_edges = g.remove_vertex(edge[1])
print("num verticies",g.num_vertices)
A,B = choice(removed_vertex,removed_edges,A,B)
g.add_vertex(edge[0])
start_edges = update_edges(edge,start_edges)
end_edges = update_edges(edge,end_edges)
for edge in start_edges:
g.add_edge(edge[0],edge[1])
for edge in end_edges:
g.add_edge(edge[0],edge[1])
return A,B,g.get_all_edges()
A,B,edges = random_contraction(generate_random_graph())
import code
code.interact(local=locals())
B.append(vertex_id)
return A, B
def random_contraction(g):
A, B = Vertex(), Vertex()
A_ids, B_ids = [], []
while len(g.get_vertices()) > 2:
print("num verticies", g.num_vertices)
time.sleep(1)
edge = random.choice(g.get_all_edges())
start_edges = g.get_edges(edge[0])
end_edges = g.get_edges(edge[1])
g.remove_vertex(edge[0])
removed_vertex, removed_edges = g.remove_vertex(edge[1])
print("num verticies", g.num_vertices)
A, B = choice(removed_vertex, removed_edges, A, B)
g.add_vertex(edge[0])
start_edges = update_edges(edge, start_edges)
end_edges = update_edges(edge, end_edges)
for edge in start_edges:
g.add_edge(edge[0], edge[1])
for edge in end_edges:
g.add_edge(edge[0], edge[1])
return A, B, g.get_all_edges()
A, B, edges = random_contraction(generate_random_graph())
import code
code.interact(local=locals())