class test_simcomplex(unittest.TestCase):
def setUp(self):
self.sc1 = SimplicialComplex(simplices=[(1, 2), (2, 3), (1, 2, 3)])
self.sc2 = AlphaComplex(ring, 0.05)
self.ring1d = SimplicialComplex(simplices=ring1d_sc)
self.cylinder = SimplicialComplex(simplices=cylinder_sc)
self.torus = SimplicialComplex(simplices=torus_sc)
self.twosphere = SimplicialComplex(simplices=twosphere_c)
def tearDown(self):
pass
def test_ring1d(self):
self.assertEqual(self.ring1d.betti_number(0), 1)
self.assertEqual(self.ring1d.betti_number(1), 1)
self.assertEqual(self.ring1d.betti_number(2), 0)
def test_cylinder(self):
self.assertEqual(self.cylinder.betti_number(0), 1)
self.assertEqual(self.cylinder.betti_number(1), 0)
self.assertEqual(self.cylinder.betti_number(2), 1)
self.assertEqual(self.cylinder.euler_characteristics(), 2)
def test_torus(self):
self.assertEqual(self.torus.betti_number(0), 1)
self.assertEqual(self.torus.betti_number(1), 2)
self.assertEqual(self.torus.betti_number(2), 1)
self.assertEqual(self.torus.betti_number(3), 0)
self.assertEqual(self.torus.euler_characteristics(), 0)
self.assertEqual(len(self.torus.generate_graph()[0]), 7)
self.assertEqual(len(self.torus.generate_graph()[1]), 21)
def test_twosphere(self):
self.assertEqual(self.twosphere.betti_number(0), 1)
self.assertEqual(self.twosphere.betti_number(1), 0)
self.assertEqual(self.twosphere.betti_number(2), 1)
self.assertEqual(self.twosphere.euler_characteristics(), 2)
def test_sc1(self):
self.assertEqual(self.sc1.betti_number(0), 1)
self.assertEqual(self.sc1.betti_number(1), 0)
self.assertEqual(self.sc1.betti_number(2), 0)
self.assertEqual(self.sc1.euler_characteristics(), 1)
def test_sc2(self):
self.assertEqual(self.sc2.betti_number(0), 1)
self.assertEqual(self.sc2.betti_number(1), 1)
self.assertEqual(self.sc2.betti_number(2), 0)
self.assertEqual(self.sc2.euler_characteristics(), 0)
def betti(complex):
betti = []
# find max dimension
sim_com = SimplicialComplex(simplices=complex)
for i in range(3):
betti.append(sim_com.betti_number(i))
return (betti)
nest.CopyModel("static_synapse", "excitatory", {"weight": g})
num_synapse = 0
for i in range(N):
for j in range(N):
if (M[i][j] == "1"):
# nest.Connect(neurons[i],neurons[j], syn_spec="inhibitory")
nest.Connect(neurons[i], neurons[j], syn_spec="excitatory")
num_synapse += 1
# print("connecting neuron " + str(i) + " to " + str(j))
# measure original homology of network #########################################
simps = mogulist_from_simplist(matrix_name)
SC = SimplicialComplex(simplices=simps)
bettis = [
SC.betti_number(0),
SC.betti_number(1),
SC.betti_number(2),
SC.betti_number(3),
SC.betti_number(4),
SC.betti_number(5),
SC.betti_number(6)
]
print(bettis)
proceed = raw_input("proceed? ")
if (proceed != "y"):
print(asdasd)
# run the simulation ###########################################################
# once to stabilize the network
# -*- coding: utf-8 -*-
# -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.#
#* File Name : topological_data_analysis.py
#
#* Purpose :
#
#* Creation Date : 28-12-2019
#
#* Last Modified : Saturday 28 December 2019 01:00:22 PM IST
#
#* Created By :
#_._._._._._._._._._._._._._._._._._._._._.#
import numpy as np
from mogutda import SimplicialComplex
torus_sc = [(1,2,4), (4,2,5), (2,3,5), (3,5,6), (5,6,1), (1,6,2), (6,7,2), (7,3,2),
(1,3,4), (3,4,6), (4,6,7), (4,5,7), (5,7,1), (7,3,1)]
torus_c = SimplicialComplex(simplices=torus_sc)
print(torus_c.betti_number(0)) # print 1
print(torus_c.betti_number(1)) # print 2
print(torus_c.betti_number(2)) # print 1
import numpy as np from mogutda import SimplicialComplex e1 = [[255, 255], [255, 0]] sc1 = SimplicialComplex(e1) print(sc1.betti_number(0))