def testWitnessStreamPH():
from WitnessFiltration import WitnessStream
from src.WeakWitnessFiltration import WeakWitnessStream
from src.ComputeInterval import IntervalComputation
# mean = [0, 0]
# cov = [[1, 0], [0, 1]]
# matrixofpoints_inR2 = np.random.multivariate_normal(mean, cov, 100)
# p = pc.PointCloud(matrixofpoints_inR2)
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename, 'point_cloud')
p.compute_distancematrix()
# Create Selector
# pointcloud_sel = sel.PointCloudSelector(p, 10, "RandomSelector")
pointcloud_sel = sel.PointCloudSelector(p, 50, "MaxminSelector")
# Measuring system time elapsed since the epoch (UTC time-zone)
start_time = time()
pointcloud_sel.select()
elapsed_time = time() - start_time
R = float(pointcloud_sel.get_maxdistance_landmarktoPointcloud())/3
# R = 0
print 'R = ', R
numdivision = 10
maxdim = 2
# ws = WitnessStream(landmarkselector=pointcloud_sel, maxdistance=R, numdivision=numdivision, maxdimension=maxdim)
ws = WeakWitnessStream(mu=2, landmarkselector=pointcloud_sel, maxdistance=R, numdivision=numdivision,
maxdimension=maxdim)
ws.construct()
print "Total number of Simplices in the Filtration: ", len(ws)
ci = IntervalComputation(ws)
ci.compute_intervals(
maxdim) # I should check everything is ok in this function since the deg for simplices can have real value now.
ci.print_BettiNumbers()
def testWitnessStreamPerCom():
from WitnessFiltration import WitnessStream
from src.WeakWitnessFiltration import WeakWitnessStream
from src.AbsoluteCohomologyOptimized import *
from src.ComputeInterval import IntervalComputation
# mean = [0, 0]
# cov = [[1, 0], [0, 1]]
# matrixofpoints_inR2 = np.random.multivariate_normal(mean, cov, 100)
# p = pc.PointCloud(matrixofpoints_inR2)
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
h = hpy() # guppy heap object
p = pc.MatlabPointCloud(filename, 'point_cloud')
p.compute_distancematrix()
# Create Selector
# pointcloud_sel = sel.PointCloudSelector(p, 10, "RandomSelector")
pointcloud_sel = sel.PointCloudSelector(p, 500, "MaxminSelector")
# Measuring system time elapsed since the epoch (UTC time-zone)
start_time = time()
pointcloud_sel.select()
elapsed_time = time() - start_time
R = float(pointcloud_sel.get_maxdistance_landmarktoPointcloud())
# R = 0
print 'R = ', R
numdivision = 10
maxdim = 2
# ws = WitnessStream(landmarkselector=pointcloud_sel, maxdistance=R, numdivision=numdivision, maxdimension=maxdim)
ws = WeakWitnessStream(mu=2, landmarkselector=pointcloud_sel, maxdistance=R, numdivision=numdivision,
maxdimension=maxdim)
ws.construct()
print "Total number of Simplices in the Filtration: ", len(ws)
print "After ws.construct():\n",h.heap().size
start_time = time()
cohom = FiltrationArrayCohomologyComputer(filtr=ws,
maxdim=maxdim,maxfilter=R)
total_mem_consumed = h.heap().size
cohom.compute()
total_mem_consumed = h.heap().size - total_mem_consumed
elapsed_time = time() - start_time
print "time: ", elapsed_time
print "After cohomology:\n",total_mem_consumed
start_time = time()
ci = IntervalComputation(ws)
total_mem_consumed = h.heap().size
ci.compute_intervals(
maxdim) # I should check everything is ok in this function since the deg for simplices can have real value now.
total_mem_consumed = h.heap().size - total_mem_consumed
ci.print_BettiNumbers()
elapsed_time = time() - start_time
print "time: ", elapsed_time
print "After Homology:\n", total_mem_consumed
def testRipsstream():
# maxdim = 2
# filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/pointcloud.txt'
# p = pc.TextPointCloud(filename, ' ')
maxdim = 2
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename, 'point_cloud')
print p.dimension, p.size
p.compute_distancematrix()
print p.getdistance(0, 1)
from src.RipsFiltration import BruteForceRips
rf = BruteForceRips(p, 100, maxdim, 0.02)
rf.construct()
print rf.write_boundarylists_to(
'/Users/naheed/PycharmProjects/PersistenceHomology/data/eight_mat.fil')
print len(rf)
from src.ComputeInterval import IntervalComputation
ci = IntervalComputation(rf)
ci.compute_intervals(maxdim)
npar = ci.get_intervals_asnumpyarray()
print npar[:10]
print npar.shape
import numpy as np
with open("test.csv", 'wb') as f:
f.write("Dimension,Birth,Death\n")
np.savetxt(f, npar, delimiter=",", fmt='%d,%.4f,%.4f')
ci.print_BettiNumbers()
from src.Intervalviz import PersistenceViz
pv = PersistenceViz(ci.betti_intervals, replace_Inf=rf.maxfiltration_val)
pv.draw_barcodes()
def test_pers_cohomology():
from src.AbsoluteCohom import FiltrationArrayCohomologyComputer
fil = Filtration()
# name = "/Users/naheed/NetBeansProjects/Toy-1"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/3437-7"
# name = "/Users/naheed/NetBeansProjects/Toy-2 262143"
# name = "/Users/naheed/NetBeansProjects/Toy-3 2047 (11 Vertex Big Cylce)"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/netscience_10101023"
# fil.add_simplices_from_cliquefiles(name)
# fil.add_simplices_from_file('../../data/zomorodian_fil.txt')
fil.add_simplices_from_file('../../data/test_simplexfromfile.txt')
print 'total number of Simplices: ', len(fil)
cohomc = FiltrationArrayCohomologyComputer(filtr=fil, maxdim=2, maxfilter=fil.maxdeg + 1)
cohomc.compute()
cohomc.print_BettiNumbers()
ci = IntervalComputation(fil)
ci.compute_intervals(2)
ci.print_BettiNumbers()
def testRipsstream():
# maxdim = 2
# filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/pointcloud.txt'
# p = pc.TextPointCloud(filename, ' ')
maxdim = 2
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename,'point_cloud')
print p.dimension, p.size
p.compute_distancematrix()
print p.getdistance(0,1)
from src.RipsFiltration import BruteForceRips
rf = BruteForceRips(p, 100, maxdim, 0.02)
rf.construct()
print rf.write_boundarylists_to('/Users/naheed/PycharmProjects/PersistenceHomology/data/eight_mat.fil')
print len(rf)
from src.ComputeInterval import IntervalComputation
ci = IntervalComputation(rf)
ci.compute_intervals(maxdim)
npar= ci.get_intervals_asnumpyarray()
print npar[:10]
print npar.shape
import numpy as np
with open("test.csv",'wb') as f:
f.write("Dimension,Birth,Death\n")
np.savetxt(f, npar, delimiter=",", fmt='%d,%.4f,%.4f')
ci.print_BettiNumbers()
from src.Intervalviz import PersistenceViz
pv = PersistenceViz(ci.betti_intervals, replace_Inf=rf.maxfiltration_val)
pv.draw_barcodes()
def test_persistencehomology():
fil = Filtration()
# name = "/Users/naheed/NetBeansProjects/Toy-1"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/3437-7"
# name = "/Users/naheed/NetBeansProjects/Toy-2 262143"
# name = "/Users/naheed/NetBeansProjects/Toy-3 2047 (11 Vertex Big Cylce)"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/netscience_10101023"
# fil.add_simplices_from_cliquefiles(name)
# fil.add_simplices_from_file('../../data/zomorodian_fil.txt')
fil.add_simplices_from_file('../../data/test_simplexfromfile.txt')
print 'total number of Simplices: ', len(fil)
ci = IntervalComputation(fil)
ci.compute_intervals(3)
ci.print_BettiNumbers()
ci.get_representativs()
def test_persistencehomology():
fil = Filtration()
# name = "/Users/naheed/NetBeansProjects/Toy-1"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/3437-7"
# name = "/Users/naheed/NetBeansProjects/Toy-2 262143"
# name = "/Users/naheed/NetBeansProjects/Toy-3 2047 (11 Vertex Big Cylce)"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/netscience_10101023"
# fil.add_simplices_from_cliquefiles(name)
# fil.add_simplices_from_file('../../data/zomorodian_fil.txt')
fil.add_simplices_from_file('../../data/test_simplexfromfile.txt')
print 'total number of Simplices: ', len(fil)
ci = IntervalComputation(fil)
ci.compute_intervals(3)
ci.print_BettiNumbers()
ci.get_representativs()
def comparebars():
maxdim = 2
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename, 'point_cloud')
print p.dimension, p.size
p.compute_distancematrix()
print p.getdistance(0, 1)
from src.RipsFiltration import BruteForceRips
rf = BruteForceRips(p, 100, maxdim, 0.01)
rf.construct()
from src.ComputeInterval import IntervalComputation
ci = IntervalComputation(rf)
ci.compute_intervals(maxdim)
print ci.betti_intervals
from src.WeakWitnessFiltration import WeakWitnessStream
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p2 = pc.MatlabPointCloud(filename, 'point_cloud')
print p2.dimension, p2.size
p2.compute_distancematrix()
pointcloud_sel = sel.PointCloudSelector(p2, 200, "MaxminSelector")
pointcloud_sel.select()
R = 0.01
print "R= ", R
ws = WeakWitnessStream(mu=1,
landmarkselector=pointcloud_sel,
maxdistance=R,
numdivision=100,
maxdimension=maxdim)
ws.construct()
ci2 = IntervalComputation(ws)
ci2.compute_intervals(maxdim)
# print len(ci2.betti_intervals), len(ci.betti_intervals)
from src.Intervalviz import PersistenceViz
pv = PersistenceViz(ci.betti_intervals, replace_Inf=rf.maxfiltration_val)
pv.qual_compare_barcodes(ci2.betti_intervals, R)
def testWitnessStreamPH():
from WitnessFiltration import WitnessStream
from src.WeakWitnessFiltration import WeakWitnessStream
from src.ComputeInterval import IntervalComputation
# mean = [0, 0]
# cov = [[1, 0], [0, 1]]
# matrixofpoints_inR2 = np.random.multivariate_normal(mean, cov, 100)
# p = pc.PointCloud(matrixofpoints_inR2)
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename, 'point_cloud')
p.compute_distancematrix()
# Create Selector
# pointcloud_sel = sel.PointCloudSelector(p, 10, "RandomSelector")
pointcloud_sel = sel.PointCloudSelector(p, 50, "MaxminSelector")
# Measuring system time elapsed since the epoch (UTC time-zone)
start_time = time()
pointcloud_sel.select()
elapsed_time = time() - start_time
R = float(pointcloud_sel.get_maxdistance_landmarktoPointcloud()) / 3
# R = 0
print 'R = ', R
numdivision = 10
maxdim = 2
# ws = WitnessStream(landmarkselector=pointcloud_sel, maxdistance=R, numdivision=numdivision, maxdimension=maxdim)
ws = WeakWitnessStream(mu=2,
landmarkselector=pointcloud_sel,
maxdistance=R,
numdivision=numdivision,
maxdimension=maxdim)
ws.construct()
print "Total number of Simplices in the Filtration: ", len(ws)
ci = IntervalComputation(ws)
ci.compute_intervals(
maxdim
) # I should check everything is ok in this function since the deg for simplices can have real value now.
ci.print_BettiNumbers()
def test_pers_cohomology():
from src.AbsoluteCohom import FiltrationArrayCohomologyComputer
fil = Filtration()
# name = "/Users/naheed/NetBeansProjects/Toy-1"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/3437-7"
# name = "/Users/naheed/NetBeansProjects/Toy-2 262143"
# name = "/Users/naheed/NetBeansProjects/Toy-3 2047 (11 Vertex Big Cylce)"
# name = "/Users/naheed/NetBeansProjects/jplex_explore/netscience_10101023"
# fil.add_simplices_from_cliquefiles(name)
# fil.add_simplices_from_file('../../data/zomorodian_fil.txt')
fil.add_simplices_from_file('../../data/test_simplexfromfile.txt')
print 'total number of Simplices: ', len(fil)
cohomc = FiltrationArrayCohomologyComputer(filtr=fil,
maxdim=2,
maxfilter=fil.maxdeg + 1)
cohomc.compute()
cohomc.print_BettiNumbers()
ci = IntervalComputation(fil)
ci.compute_intervals(2)
ci.print_BettiNumbers()
def comparebars():
maxdim = 2
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename, 'point_cloud')
print p.dimension, p.size
p.compute_distancematrix()
print p.getdistance(0, 1)
from src.RipsFiltration import BruteForceRips
rf = BruteForceRips(p, 100, maxdim, 0.01)
rf.construct()
from src.ComputeInterval import IntervalComputation
ci = IntervalComputation(rf)
ci.compute_intervals(maxdim)
print ci.betti_intervals
from src.WeakWitnessFiltration import WeakWitnessStream
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p2 = pc.MatlabPointCloud(filename, 'point_cloud')
print p2.dimension, p2.size
p2.compute_distancematrix()
pointcloud_sel = sel.PointCloudSelector(p2, 200, "MaxminSelector")
pointcloud_sel.select()
R = 0.01
print "R= ",R
ws = WeakWitnessStream(mu=1, landmarkselector=pointcloud_sel, maxdistance=R, numdivision=100,
maxdimension=maxdim)
ws.construct()
ci2 = IntervalComputation(ws)
ci2.compute_intervals(maxdim)
# print len(ci2.betti_intervals), len(ci.betti_intervals)
from src.Intervalviz import PersistenceViz
pv = PersistenceViz(ci.betti_intervals, replace_Inf=rf.maxfiltration_val)
pv.qual_compare_barcodes(ci2.betti_intervals, R)
def test_persistence_algorithm(N=10, D=50):
from time import time
from src.WeakWitnessFiltration import WeakWitnessStream
import src.Selector as sel
import src.PointCloud as pc
abs_cohom_exectime = [0.0] * N
abs_hom_exectime = [0.0] * N
filtration_sizes = [0.0] * N
for n in xrange(N):
# print "Iteration: ", n
# mean = [0, 0]
# cov = [[1, 0], [0, 1]]
# matrixofpoints_inR2 = np.random.multivariate_normal(mean, cov, 100)
# p = pc.PointCloud(matrixofpoints_inR2)
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename, 'point_cloud')
p.compute_distancematrix()
# Typically for 2D, according to paper <= N/20. so N/20, N/25, N/30, N/35, N/40 is fine
NumLandmarks = int(p.size / D)
# print "#Landmark: ", NumLandmarks
# Create Selector
# pointcloud_sel = sel.PointCloudSelector(p, 10, "RandomSelector")
pointcloud_sel = sel.PointCloudSelector(p, NumLandmarks,
"MaxminSelector")
# Select point
pointcloud_sel.select()
R = float(pointcloud_sel.get_maxdistance_landmarktoPointcloud())
# R = 0
# print 'R = ', R
numdivision = 10
maxdim = 2
# Run Lazy Witness stream
ws = WeakWitnessStream(mu=2,
landmarkselector=pointcloud_sel,
maxdistance=R,
numdivision=numdivision,
maxdimension=maxdim)
ws.construct()
filtration_sizes[n] = len(ws)
start_time = time()
cohom = FiltrationArrayCohomologyComputer(ws, maxdim, R)
cohom.compute()
abs_cohom_exectime[n] = time() - start_time
start_time = time()
ci = IntervalComputation(ws)
ci.compute_intervals(maxdim) # I should
abs_hom_exectime[n] = time() - start_time
abs_hom_exectime = np.array(abs_hom_exectime)
abs_cohom_exectime = np.array(abs_cohom_exectime)
print "Average number of simplices in the filtration: \n", np.mean(
filtration_sizes)
print "Mean (,", N, " run) runtime for Absolute Homology: \n", np.mean(
abs_hom_exectime)
print "Standard deviation (", N, " run) runtime for Absolute Homology: \n", np.std(
abs_hom_exectime)
print "Mean (,", N, " run) runtime for Absolute Cohomology: \n", np.mean(
abs_cohom_exectime)
print "Standard deviation (", N, " run) runtime for Absolute Cohomology: \n", np.std(
abs_cohom_exectime)
def testWitnessStreamPerCom():
from WitnessFiltration import WitnessStream
from src.WeakWitnessFiltration import WeakWitnessStream
from src.AbsoluteCohomologyOptimized import *
from src.ComputeInterval import IntervalComputation
# mean = [0, 0]
# cov = [[1, 0], [0, 1]]
# matrixofpoints_inR2 = np.random.multivariate_normal(mean, cov, 100)
# p = pc.PointCloud(matrixofpoints_inR2)
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
h = hpy() # guppy heap object
p = pc.MatlabPointCloud(filename, 'point_cloud')
p.compute_distancematrix()
# Create Selector
# pointcloud_sel = sel.PointCloudSelector(p, 10, "RandomSelector")
pointcloud_sel = sel.PointCloudSelector(p, 500, "MaxminSelector")
# Measuring system time elapsed since the epoch (UTC time-zone)
start_time = time()
pointcloud_sel.select()
elapsed_time = time() - start_time
R = float(pointcloud_sel.get_maxdistance_landmarktoPointcloud())
# R = 0
print 'R = ', R
numdivision = 10
maxdim = 2
# ws = WitnessStream(landmarkselector=pointcloud_sel, maxdistance=R, numdivision=numdivision, maxdimension=maxdim)
ws = WeakWitnessStream(mu=2,
landmarkselector=pointcloud_sel,
maxdistance=R,
numdivision=numdivision,
maxdimension=maxdim)
ws.construct()
print "Total number of Simplices in the Filtration: ", len(ws)
print "After ws.construct():\n", h.heap().size
start_time = time()
cohom = FiltrationArrayCohomologyComputer(filtr=ws,
maxdim=maxdim,
maxfilter=R)
total_mem_consumed = h.heap().size
cohom.compute()
total_mem_consumed = h.heap().size - total_mem_consumed
elapsed_time = time() - start_time
print "time: ", elapsed_time
print "After cohomology:\n", total_mem_consumed
start_time = time()
ci = IntervalComputation(ws)
total_mem_consumed = h.heap().size
ci.compute_intervals(
maxdim
) # I should check everything is ok in this function since the deg for simplices can have real value now.
total_mem_consumed = h.heap().size - total_mem_consumed
ci.print_BettiNumbers()
elapsed_time = time() - start_time
print "time: ", elapsed_time
print "After Homology:\n", total_mem_consumed
def test_persistence_algorithm(N=10, D=50):
from time import time
from src.WeakWitnessFiltration import WeakWitnessStream
import src.Selector as sel
import src.PointCloud as pc
abs_cohom_exectime = [0.0] * N
abs_hom_exectime = [0.0] * N
filtration_sizes = [0.0] * N
for n in xrange(N):
# print "Iteration: ", n
# mean = [0, 0]
# cov = [[1, 0], [0, 1]]
# matrixofpoints_inR2 = np.random.multivariate_normal(mean, cov, 100)
# p = pc.PointCloud(matrixofpoints_inR2)
filename = '/Users/naheed/PycharmProjects/PersistenceHomology/data/eight.mat'
p = pc.MatlabPointCloud(filename, 'point_cloud')
p.compute_distancematrix()
# Typically for 2D, according to paper <= N/20. so N/20, N/25, N/30, N/35, N/40 is fine
NumLandmarks = int(p.size / D)
# print "#Landmark: ", NumLandmarks
# Create Selector
# pointcloud_sel = sel.PointCloudSelector(p, 10, "RandomSelector")
pointcloud_sel = sel.PointCloudSelector(p, NumLandmarks, "MaxminSelector")
# Select point
pointcloud_sel.select()
R = float(pointcloud_sel.get_maxdistance_landmarktoPointcloud())
# R = 0
# print 'R = ', R
numdivision = 10
maxdim = 2
# Run Lazy Witness stream
ws = WeakWitnessStream(mu=2, landmarkselector=pointcloud_sel, maxdistance=R, numdivision=numdivision,
maxdimension=maxdim)
ws.construct()
filtration_sizes[n] = len(ws)
start_time = time()
cohom = FiltrationArrayCohomologyComputer(ws, maxdim, R)
cohom.compute()
abs_cohom_exectime[n] = time() - start_time
start_time = time()
ci = IntervalComputation(ws)
ci.compute_intervals(maxdim) # I should
abs_hom_exectime[n] = time() - start_time
abs_hom_exectime = np.array(abs_hom_exectime)
abs_cohom_exectime = np.array(abs_cohom_exectime)
print "Average number of simplices in the filtration: \n", np.mean(filtration_sizes)
print "Mean (,", N, " run) runtime for Absolute Homology: \n", np.mean(abs_hom_exectime)
print "Standard deviation (", N, " run) runtime for Absolute Homology: \n", np.std(
abs_hom_exectime)
print "Mean (,", N, " run) runtime for Absolute Cohomology: \n", np.mean(abs_cohom_exectime)
print "Standard deviation (", N, " run) runtime for Absolute Cohomology: \n", np.std(
abs_cohom_exectime)
def test_manual_Filtration():
# This example is a challenging one for cohomology
fil = Filtration()
# fil.add_simplex_toith_filtration(0, [0])
# fil.add_simplex_toith_filtration(0, [1])
# fil.add_simplex_toith_filtration(1, [2])
# fil.add_simplex_toith_filtration(1, [3])
# fil.add_simplex_toith_filtration(1, [0, 1])
# fil.add_simplex_toith_filtration(1, [1, 2]) # The simplices should be sorted
# fil.add_simplex_toith_filtration(2, [0, 2])
# Another example. a square shape
# print fil
# fil.add_simplex_toith_filtration(0, [0])
# fil.add_simplex_toith_filtration(1, [1])
# # fil.add_simplex_toith_filtration(3, [3])
# fil.add_simplex_toith_filtration(2, [2])
# fil.add_simplex_toith_filtration(3, [3])
# fil.add_simplex_toith_filtration(4, [0, 1])
# fil.add_simplex_toith_filtration(5, [1, 2])
# fil.add_simplex_toith_filtration(6, [2, 3])
# fil.add_simplex_toith_filtration(7, [0, 3])
# fil.add_simplex_toith_filtration(8, [0, 2])
# fil.add_simplex_toith_filtration(9, [1, 3])
# fil.add_simplex_toith_filtration(10, [0, 2, 3])
# fil.add_simplex_toith_filtration(11, [0, 1, 2])
# fil.add_simplex_toith_filtration(12, [1, 2, 3])
# fil.add_simplex_toith_filtration(13, [0, 1, 3])
fil.add_simplex_toith_filtration(0, [0])
fil.add_simplex_toith_filtration(0, [1])
fil.add_simplex_toith_filtration(0, [2])
fil.add_simplex_toith_filtration(0, [3])
fil.add_simplex_toith_filtration(0, [0, 1])
fil.add_simplex_toith_filtration(0, [1, 2])
fil.add_simplex_toith_filtration(0, [2, 3])
fil.add_simplex_toith_filtration(0, [0, 3])
fil.add_simplex_toith_filtration(0, [0, 2])
fil.add_simplex_toith_filtration(0, [1, 3])
fil.add_simplex_toith_filtration(0, [0, 1, 2])
fil.add_simplex_toith_filtration(0, [0, 2, 3])
fil.add_simplex_toith_filtration(0, [0, 1, 3])
fil.add_simplex_toith_filtration(13, [1, 2, 3])
# print [str(sigma)+" id: "+ str(sigma.id) for sigma in fil.get_ksimplices_from_ithFiltration(0,0)]
from src.AbsoluteCohom import *
# simplices = [
# [0], [1],[0,1],[3],[1,3],[0,3],[0,1,3]
# ]
# filtration_vals = [0, 1, 2, 3, 4, 5, 6]
# cohomc = NaiveCohomologyComputer(simplices,filtration_vals)
# print cohomc.compute()
cohomc = FiltrationArrayCohomologyComputer(filtr=fil, maxdim=2, maxfilter=16)
cohomc.compute()
cohomc.print_BettiNumbers()
print "\n\n"
ci = IntervalComputation(fil)
ci.compute_intervals(2)
ci.print_BettiNumbers()
def test_manual_Filtration():
# This example is a challenging one for cohomology
fil = Filtration()
# fil.add_simplex_toith_filtration(0, [0])
# fil.add_simplex_toith_filtration(0, [1])
# fil.add_simplex_toith_filtration(1, [2])
# fil.add_simplex_toith_filtration(1, [3])
# fil.add_simplex_toith_filtration(1, [0, 1])
# fil.add_simplex_toith_filtration(1, [1, 2]) # The simplices should be sorted
# fil.add_simplex_toith_filtration(2, [0, 2])
# Another example. a square shape
# print fil
# fil.add_simplex_toith_filtration(0, [0])
# fil.add_simplex_toith_filtration(1, [1])
# # fil.add_simplex_toith_filtration(3, [3])
# fil.add_simplex_toith_filtration(2, [2])
# fil.add_simplex_toith_filtration(3, [3])
# fil.add_simplex_toith_filtration(4, [0, 1])
# fil.add_simplex_toith_filtration(5, [1, 2])
# fil.add_simplex_toith_filtration(6, [2, 3])
# fil.add_simplex_toith_filtration(7, [0, 3])
# fil.add_simplex_toith_filtration(8, [0, 2])
# fil.add_simplex_toith_filtration(9, [1, 3])
# fil.add_simplex_toith_filtration(10, [0, 2, 3])
# fil.add_simplex_toith_filtration(11, [0, 1, 2])
# fil.add_simplex_toith_filtration(12, [1, 2, 3])
# fil.add_simplex_toith_filtration(13, [0, 1, 3])
fil.add_simplex_toith_filtration(0, [0])
fil.add_simplex_toith_filtration(0, [1])
fil.add_simplex_toith_filtration(0, [2])
fil.add_simplex_toith_filtration(0, [3])
fil.add_simplex_toith_filtration(0, [0, 1])
fil.add_simplex_toith_filtration(0, [1, 2])
fil.add_simplex_toith_filtration(0, [2, 3])
fil.add_simplex_toith_filtration(0, [0, 3])
fil.add_simplex_toith_filtration(0, [0, 2])
fil.add_simplex_toith_filtration(0, [1, 3])
fil.add_simplex_toith_filtration(0, [0, 1, 2])
fil.add_simplex_toith_filtration(0, [0, 2, 3])
fil.add_simplex_toith_filtration(0, [0, 1, 3])
fil.add_simplex_toith_filtration(13, [1, 2, 3])
# print [str(sigma)+" id: "+ str(sigma.id) for sigma in fil.get_ksimplices_from_ithFiltration(0,0)]
from src.AbsoluteCohom import *
# simplices = [
# [0], [1],[0,1],[3],[1,3],[0,3],[0,1,3]
# ]
# filtration_vals = [0, 1, 2, 3, 4, 5, 6]
# cohomc = NaiveCohomologyComputer(simplices,filtration_vals)
# print cohomc.compute()
cohomc = FiltrationArrayCohomologyComputer(filtr=fil,
maxdim=2,
maxfilter=16)
cohomc.compute()
cohomc.print_BettiNumbers()
print "\n\n"
ci = IntervalComputation(fil)
ci.compute_intervals(2)
ci.print_BettiNumbers()