def generate_ASN_grid(numOfNetworks, wires_per_network):
import wires
adj_list = []
graph_list = []
for i in range(numOfNetworks):
wires_dict = wires.generate_wires_distribution(
number_of_wires=wires_per_network,
wire_av_length=150,
wire_dispersion=50,
gennorm_shape=3,
centroid_dispersion=60,
Lx=3e2,
Ly=3e2,
this_seed=i * 5)
wires_dict = wires.detect_junctions(wires_dict)
wires_dict = wires.generate_graph(wires_dict)
while not wires.check_connectedness(wires_dict):
wires_dict = wires.generate_wires_distribution(
number_of_wires=wires_per_network,
wire_av_length=150,
wire_dispersion=50,
gennorm_shape=3,
centroid_dispersion=60,
Lx=3e2,
Ly=3e2,
this_seed=np.random.randint(1000))
wires_dict = wires.detect_junctions(wires_dict)
wires_dict = wires.generate_graph(wires_dict)
adj_list.append(wires_dict['adj_matrix'])
graph_list.append(wires_dict['G'])
num_wires_list = [len(graph_list[i]) for i in range(len(graph_list))]
total_wires = sum(num_wires_list)
bigMat = np.zeros((total_wires + 2, total_wires + 2))
inList = np.zeros(len(adj_list))
outList = np.zeros(len(adj_list))
node_count = 0
for i in range(len(adj_list)):
bigMat[node_count:node_count + num_wires_list[i],
node_count:node_count + num_wires_list[i]] = adj_list[i]
inList[i], outList[i] = get_farthest_pairing(adj_list[i]) + node_count
node_count += num_wires_list[i]
inList = inList.astype(int)
outList = outList.astype(int)
bigMat[total_wires, inList] = 1
bigMat[inList, total_wires] = 1
bigMat[total_wires + 1, outList] = 1
bigMat[outList, total_wires + 1] = 1
return bigMat
def generateNetwork(numOfWires = 100, dispersion=100, mean_length = 100, this_seed = 42, iterations=0, max_iters = 10):
import wires
wires_dict = wires.generate_wires_distribution(number_of_wires = numOfWires,
wire_av_length = mean_length,
wire_dispersion = 20,
gennorm_shape = 3,
centroid_dispersion = dispersion,
Lx = 5e2,
Ly = 5e2,
this_seed = this_seed)
wires_dict = wires.detect_junctions(wires_dict)
wires_dict = wires.generate_graph(wires_dict)
if wires.check_connectedness(wires_dict):
logging.info(f'The returned network has {wires_dict["number_of_junctions"]} junctions.')
return wires_dict
elif iterations < max_iters:
generateNetwork(numOfWires, dispersion, mean_length, this_seed+1, iterations+1, max_iters)
else:
logging.warning('No network is generated.')
return None
def wires_tester(number_of_wires,
avg_length,
centroid_dispersion,
out_mode='number_of_junctions',
attempt_limit=5):
counter = 0
out = -1
while (out == -1) & (counter < attempt_limit):
temp_seed = np.random.randint(2000)
temp_distribution = wires.generate_wires_distribution(
number_of_wires=number_of_wires,
wire_av_length=avg_length,
wire_dispersion=20,
gennorm_shape=3,
centroid_dispersion=centroid_dispersion,
Lx=2e2,
Ly=2e2,
this_seed=temp_seed)
counter += 1
temp_distribution = wires.detect_junctions(temp_distribution)
temp_distribution = wires.generate_graph(temp_distribution)
if wires.check_connectedness(temp_distribution):
logging.info(
f"{temp_distribution['number_of_wires']} nanowire network is connected with seed {temp_seed}, average length of {temp_distribution['avg_length']}, dispersion of {temp_distribution['centroid_dispersion']}."
)
temp_distribution['clustering'] = nx.average_clustering(
temp_distribution['G'])
temp_distribution[
'avg_path_length'] = nx.average_shortest_path_length(
temp_distribution['G'])
out = temp_distribution[out_mode]
else:
logging.warning(
f"{temp_distribution['number_of_wires']} nanowire network is NOT connected. Current seed is {temp_seed}, average length is {temp_distribution['avg_length']}, dispersion is {temp_distribution['centroid_dispersion']}."
)
return out
from dataStruct import *
import wires
import logging
import pickle
sparse_ranger = range(20, 100, 10)
realization_list = []
for this_sparse in sparse_ranger:
logging.info(
f"Simulation #{sparse_ranger.index(this_sparse)+1}/{len(sparse_ranger)}"
)
wires_dict = wires.generate_wires_distribution(
number_of_wires=100,
wire_av_length=100,
wire_dispersion=10,
gennorm_shape=3,
centroid_dispersion=this_sparse,
Lx=3e2,
Ly=3e2)
# Get junctions list and their positions
wires_dict = wires.detect_junctions(wires_dict)
wires_dict = wires.generate_graph(wires_dict)
if not wires.check_connectedness(wires_dict):
logging.warning(
f"This network is not connected, will move on to next one! Current dispersion is {wires_dict['centroid_dispersion']}."
)
continue
Connectivity = connectivity__(wires_dict=wires_dict)
parser.add_argument('--view_only',
dest='view_only',
action='store_true',
default=False,
help='Flag to not save the file.')
parser.set_defaults(plot_network=False)
args = parser.parse_args()
# Generate the network
wires_dict = wires.generate_wires_distribution(
number_of_wires=args.nwires,
wire_av_length=args.mean_length,
wire_dispersion=args.std_length,
gennorm_shape=args.shape,
centroid_dispersion=args.cent_dispersion,
this_seed=args.seed,
Lx=args.Lx,
Ly=args.Ly)
# Get junctions list and their positions
wires_dict = wires.detect_junctions(wires_dict)
# Genreate graph object and adjacency matrix
wires_dict = wires.generate_graph(wires_dict)
if not wires.check_connectedness(wires_dict):
logging.warning("Will select the largest connected component.")
wires_dict = wires.select_largest_component(wires_dict)
for nwires in wireList:
for seed in seedList:
for Lx in LxList:
if square:
Ly = Lx
if fixedDensity:
nwires = int(density * Lx * Ly)
print('Now generating: nwires = ', nwires, ', seed = ', seed,
' grid = ', Lx, 'x', Ly, 'um^2')
# Generate the network
wires_dict = wires.generate_wires_distribution(
number_of_wires=nwires,
wire_av_length=mean_length,
wire_dispersion=std_length,
gennorm_shape=shape,
centroid_dispersion=cent_dispersion,
this_seed=seed,
Lx=Lx,
Ly=Ly,
oldNameConvention=oldNameConvention)
# Get junctions list and their positions
wires_dict = wires.detect_junctions(wires_dict)
# Genreate graph object and adjacency matrix
wires_dict = wires.generate_graph(wires_dict)
if not wires.check_connectedness(wires_dict):
wires_dict = wires.select_largest_component(wires_dict)
#Calculate network statistics