def test_random_unseeded():
num_seeds = get_num_seeds()
nngt.seed(msd=42)
# check that seeds generated by first call indeed allow to reproduce the
# graph in later calls
g1 = ng.gaussian_degree(10, 1, nodes=100)
seeds = nngt.get_config("seeds")
nngt.seed(msd=42, seeds=seeds)
g2 = ng.gaussian_degree(10, 1, nodes=100)
assert np.all(g1.edges_array == g2.edges_array)
def test_group_vs_type():
''' Gaussian degree with groups and types '''
# first with groups
nngt.seed(0)
pop = nngt.NeuralPop.exc_and_inhib(1000)
igroup = pop["inhibitory"]
egroup = pop["excitatory"]
net1 = nngt.Network(population=pop)
all_groups = list(pop.keys()) # necessary to have same order as types
avg_e = 50
std_e = 5
ng.connect_groups(net1, egroup, all_groups, graph_model="gaussian_degree",
avg=avg_e, std=std_e, degree_type="out-degree")
avg_i = 100
std_i = 5
ng.connect_groups(net1, igroup, all_groups, graph_model="gaussian_degree",
avg=avg_i, std=std_i, degree_type="out-degree")
# then with types
nngt.seed(0)
pop = nngt.NeuralPop.exc_and_inhib(1000)
igroup = pop["inhibitory"]
egroup = pop["excitatory"]
net2 = nngt.Network(population=pop)
avg_e = 50
std_e = 5
ng.connect_neural_types(net2, 1, [-1, 1], graph_model="gaussian_degree",
avg=avg_e, std=std_e, degree_type="out-degree")
avg_i = 100
std_i = 5
ng.connect_neural_types(net2, -1, [-1, 1], graph_model="gaussian_degree",
avg=avg_i, std=std_i, degree_type="out-degree")
# call only on root process (for mpi) unless using distributed backend
if nngt.on_master_process() or nngt.get_config("backend") == "nngt":
# check that both networks are equals
assert np.all(net1.get_degrees() == net2.get_degrees())
def test_random_seeded():
num_seeds = get_num_seeds()
# test equality of graph generated with same seeds
nngt.seed(msd=0, seeds=[i for i in range(1, num_seeds + 1)])
g1 = ng.gaussian_degree(10, 1, nodes=100)
nngt.seed(msd=0, seeds=[i for i in range(1, num_seeds + 1)])
g2 = ng.gaussian_degree(10, 1, nodes=100)
assert np.all(g1.edges_array == g2.edges_array)
# check that subsequent graphs are different
g3 = ng.gaussian_degree(10, 1, nodes=100)
# with mpi onnon-distributed backends, test only on master process
if nngt.get_config("backend") == "nngt" or nngt.on_master_process():
if g3.edge_nb() == g2.edge_nb():
assert np.any(g2.edges_array != g3.edges_array)
import matplotlib.pyplot as plt
import nngt
from nngt.geometry import Shape
import pickle
import pdb
print('PDB DEBUGGER ACTIVATED')
# nngt.use_backend("graph-tool")
nngt.use_backend("networkx")
# nngt.set_config({"omp": 8, "palette": 'RdYlBu'})
nngt.set_config("multithreading", False)
nngt.seed(0)
''' Runtime options'''
plot_distribution = True # plot of the connectivity distribution
plot_graphs = False # graphic output of spatial network
simulate_activity = True # whether to run or not a NEST simlation on the model
sim_duration = 2000 # simulation duration in ms
plot_activity = True # whether to plot simulation activity
animation_movie = True # wheter to generate activity on map movie
save_spikes = False # whether to save the spikes of all the neurons
obstacles = True # set to True for simulation with elevated obstacles
print("\n###################\n Runtime options\n------------------")
print("Plot of the spatial graph : {0}".format(plot_graphs))
print("Run Nest simulation on the generated graph : {0}".format(
simulate_activity))
print("Simulation duration (if True) : {0}".format(sim_duration))
import numpy as np
import nngt
import nngt.geospatial as ng
plt.rcParams.update({
'axes.edgecolor': 'grey',
'xtick.color': 'grey',
'ytick.color': 'grey',
"figure.facecolor": (0, 0, 0, 0),
"axes.facecolor": (0, 0, 0, 0),
"axes.labelcolor": "grey",
"text.color": "grey"
})
nngt.seed(2)
# take random countries
num_nodes = 20
world = ng.maps["adaptive"]
units = nngt._rng.choice(50, num_nodes, replace=False)
codes = list(world.iloc[units].SU_A3)
# make random network
g = nngt.generation.erdos_renyi(nodes=num_nodes, avg_deg=3)
# add the A3 code for each country (that's the crucial part that will link
# the graph to the geospatial data)
g.new_node_attribute("code", "string", codes)