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Commit e3386689 authored by felixk1990's avatar felixk1990
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update package structure

parent 94c20324
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LICENSE 0 → 100644
View file @ e3386689
Copyright (c) 2018 The Python Packaging Authority
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
analyze_graph.py → cycle_analysis/cycle_coalescence.py
View file @ e3386689
# @Author: Felix Kramer <kramer>
# @Date: 18-02-2019
# @Email: felix.kramer@hotmail.de
# @Project: cycle-coalescecne-algorithm
# @Last modified by: kramer
# @Last modified time: 09-01-2021
import networkx as nx
import numpy as np
import random as rd
import sys
BASIS_MODE='minimum_tile'
def calc_cycle_asymmetry(input_graph):
minimum_basis=construct_minimum_basis(input_graph)
......@@ -37,10 +41,7 @@ def calc_cycle_coalescence(input_graph,cycle_basis):
for i,cycle in enumerate(cycle_basis):
if cycle.has_edge(*e):
if 'minimum_weight' == BASIS_MODE:
cycles_with_edge.update({i:cycle.graph['cycle_weight']})
else:
cycles_with_edge.update({i:nx.number_of_edges(cycle)})
cycles_with_edge.update({i:nx.number_of_edges(cycle)})
if len(cycles_with_edge.values()) >= 2:
......@@ -116,12 +117,7 @@ def merge_cycles(input_graph,cycle_1,cycle_2):
merged_cycle.remove_edge(*e)
else:
merged_cycle.add_edge(*e)
L=len(merged_cycle.edges())
# print(L)
# if L==4:
# print(cycle_1.edges())
# print(cycle_2.edges())
# print(merged_cycle.edges())
for e in merged_cycle.edges():
merged_cycle.graph['cycle_weight']+=input_graph.edges[e]['weight']
......@@ -136,103 +132,57 @@ def generate_cycle_lists(input_graph):
total_cycle_dict={}
total_cycle_list=[]
super_list=[]
# check for graph_type, then check for paralles in the Graph, if existent insert dummy nodes to resolve conflict, cast the network onto simple graph afterwards
# choose method to perform construction of minimal basis
if 'minimum_weight' == BASIS_MODE:
counter=0
spanning_tree=nx.minimum_spanning_tree(input_graph,weight='weight')
diff_graph=nx.difference(input_graph,spanning_tree)
for n in input_graph.nodes():
for e in diff_graph.edges():
p_in=nx.shortest_path(input_graph,source=n,target=e[0],weight='weight')
p_out=nx.all_shortest_paths(input_graph,source=n,target=e[1],weight='weight')
for p in p_out:
simple_cycle=nx.Graph(cycle_weight=0.)
nx.add_path(simple_cycle,p_in)
nx.add_path(simple_cycle,p)
simple_cycle.add_edge(*e)
if nx.is_eulerian(simple_cycle):
# relabeling and weighting graph
simple_cycle=nx.MultiGraph(simple_cycle)
for m in simple_cycle.edges():
simple_cycle.graph['cycle_weight']+=input_graph.edges[m]['weight']
total_cycle_list.append(simple_cycle)
total_cycle_dict.update({counter:simple_cycle.graph['cycle_weight']})
counter+=1
break
if 'minimum_tile' == BASIS_MODE:
counter=0
for n in input_graph.nodes():
# building new tree using breadth first
# spanning_tree=nx.minimum_spanning_tree(graph,weight='weight')
spanning_tree=breadth_first_tree(input_graph,n)
diff_graph=nx.difference(input_graph,spanning_tree)
labels_e={}
for e in diff_graph.edges():
p_in=nx.shortest_path(spanning_tree,source=n,target=e[0])
p_out=nx.shortest_path(spanning_tree,source=n,target=e[1])
# label pathways
simple_cycle=nx.MultiGraph(cycle_weight=0.)
nx.add_path(simple_cycle,p_in)
nx.add_path(simple_cycle,p_out)
simple_cycle.add_edge(*e)
list_n=list(simple_cycle.nodes())
seen={}
for m in list(simple_cycle.edges()):
num_conncetions=simple_cycle.number_of_edges(*m)
if num_conncetions > 1 and m not in seen.keys():
seen[m]=1
elif num_conncetions > 1:
seen[m]+=1
for m in seen:
for i in range(seen[m]):
simple_cycle.remove_edge(m[0],m[1],i)
for q in list_n:
if simple_cycle.degree(q)==0:
simple_cycle.remove_node(q)
if nx.is_eulerian(simple_cycle):
# relabeling and weighting graph
for m in simple_cycle.edges():
simple_cycle.graph['cycle_weight']+=1.
total_cycle_list.append(simple_cycle)
total_cycle_dict.update({counter:nx.number_of_edges(simple_cycle)})
counter+=1
# sorted_cycle_list=sorted(total_cycle_dict ,key=total_cycle_dict.__getitem__)
# total_cycle_list=[total_cycle_list[i] for i in sorted_cycle_list]
# print([len(tcl.edges()) for tcl in total_cycle_list ])
else:
# create cycle subgraphs from super_list
for n in input_graph.nodes():
if input_graph.degree(n) > 1:
c_list=nx.cycle_basis(input_graph,n)
if not super_list:
super_list=list(c_list)
else:
super_list+=c_list
for idx_c,c in enumerate(super_list):
J=nx.MultiGraph()
nx.add_cycle(J,c)
total_cycle_list.append(J)
total_cycle_dict.update({idx_c:nx.number_of_edges(J)})
counter=0
for n in input_graph.nodes():
# building new tree using breadth first
# spanning_tree=nx.minimum_spanning_tree(graph,weight='weight')
spanning_tree=breadth_first_tree(input_graph,n)
diff_graph=nx.difference(input_graph,spanning_tree)
labels_e={}
for e in diff_graph.edges():
p_in=nx.shortest_path(spanning_tree,source=n,target=e[0])
p_out=nx.shortest_path(spanning_tree,source=n,target=e[1])
# label pathways
simple_cycle=nx.MultiGraph(cycle_weight=0.)
nx.add_path(simple_cycle,p_in)
nx.add_path(simple_cycle,p_out)
simple_cycle.add_edge(*e)
list_n=list(simple_cycle.nodes())
seen={}
for m in list(simple_cycle.edges()):
num_conncetions=simple_cycle.number_of_edges(*m)
if num_conncetions > 1 and m not in seen.keys():
seen[m]=1
elif num_conncetions > 1:
seen[m]+=1
for m in seen:
for i in range(seen[m]):
simple_cycle.remove_edge(m[0],m[1],i)
for q in list_n:
if simple_cycle.degree(q)==0:
simple_cycle.remove_node(q)
if nx.is_eulerian(simple_cycle):
# relabeling and weighting graph
for m in simple_cycle.edges():
simple_cycle.graph['cycle_weight']+=1.
total_cycle_list.append(simple_cycle)
total_cycle_dict.update({counter:nx.number_of_edges(simple_cycle)})
counter+=1
return total_cycle_dict,total_cycle_list,super_list
return total_cycle_dict,total_cycle_list
def construct_minimum_basis(input_graph):
# calc minimum weight basis and construct dictionary for weights of edges, takes a leave-less, connected, N > 1 SimpleGraph as input, no self-loops optimally, deviations are not raising any warnings
#sort basis vectors according to weight, creating a new minimum weight basis from the total_cycle_list
nullity=nx.number_of_edges(input_graph)-nx.number_of_nodes(input_graph)+nx.number_connected_components(input_graph)
total_cycle_dict,total_cycle_list,super_list=generate_cycle_lists(input_graph)
total_cycle_dict,total_cycle_list=generate_cycle_lists(input_graph)
sorted_cycle_list=sorted(total_cycle_dict,key=total_cycle_dict.__getitem__)
minimum_basis=[]
EC=nx.MultiGraph()
......@@ -365,180 +315,3 @@ def path_list(input_graph,root):
paths[tuple(p)]=len(p)
return paths
# generate example pattern random/nested/gradient
def generate_pattern(input_graph,mode):
iteration=0
list_n=list(input_graph.nodes())
pos=nx.spectral_layout(input_graph)
for n in pos.keys():
input_graph.nodes[n]['pos']=pos[n]
if 'random' == mode:
for e in input_graph.edges():
input_graph.edges[e]['weight']=rd.uniform(0.,1.)*10.
elif 'gradient' == mode:
idx_min=np.argmin([ input_graph.nodes[n]['pos'][0] for n in list_n])
ref_p=input_graph.nodes[list_n[idx_min]]['pos']
for e in input_graph.edges():
p=(np.array(input_graph.nodes[e[0]]['pos'])+np.array(input_graph.nodes[e[1]]['pos']))/2.
r=np.linalg.norm(p-ref_p)
input_graph.edges[e]['weight']=1./r
elif 'nested_square' == mode:
corners=get_corners(input_graph)
my_tiles=get_first_tile(input_graph, corners)
E=nx.number_of_edges(input_graph)
N=nx.number_of_nodes(input_graph)
counter=0
go_on=True
while go_on:
new_my_tiles=[]
graph_seen=nx.Graph()
dict_seen={}
for tile in my_tiles:
list_e=list(tile.edges())
sub_tile=nx.Graph()
sub_tile.add_edge(*list_e[0],weight=tile.edges[list_e[0]]['weight'])
push_1=[0]
push_2=[]
for i,e in enumerate(list_e[1:]):
if ( sub_tile.has_node(e[0]) or sub_tile.has_node(e[1]) ):
push_2.append(i+1)
else:
push_1.append(i+1)
pos=[]
for i,n in enumerate(tile):
p=tile.nodes[n]['pos']
sub_tile.add_node(n,pos=p)
pos.append(p)
my_center=counter
sub_tile.add_node(my_center,pos=np.mean(pos,axis=0))
counter+=1
for i,e in enumerate(list_e[1:]):
sub_tile.add_edge(*e,weight=tile.edges[e]['weight'])
sub_w=np.amin(list(nx.get_edge_attributes(sub_tile,'weight').values()))/2.
push_nodes_1=[]
push_nodes_2=[]
for i,e in enumerate(list_e):
if i in push_1:
if graph_seen.has_edge(*e):
sub_tile,node_id=use_a_brick( sub_tile, e, dict_seen)
push_nodes_1.append(node_id)
else:
push_nodes_1.append(counter)
sub_tile=form_a_brick(tile,sub_tile,counter,e, dict_seen)
graph_seen.add_edge(*e)
counter+=1
elif i in push_2:
if graph_seen.has_edge(*e):
sub_tile,node_id=use_a_brick( sub_tile, e, dict_seen)
push_nodes_2.append(node_id)
else:
push_nodes_2.append(counter)
sub_tile=form_a_brick(tile,sub_tile,counter,e, dict_seen)
graph_seen.add_edge(*e)
counter+=1
for i in push_nodes_1:
sub_tile.add_edge(my_center,i,weight=sub_w)
for i in push_nodes_2:
sub_tile.add_edge(my_center,i,weight=sub_w*0.9)
new_my_tiles.append(sub_tile)
new_input_graph=nx.Graph()
for tile in new_my_tiles:
new_input_graph=nx.compose(new_input_graph,tile)
input_graph=nx.Graph(new_input_graph)
basis=construct_minimum_basis(new_input_graph)
simple_basis=[nx.Graph(b) for b in basis]
for b in simple_basis:
for n in b.nodes():
b.nodes[n]['pos']=input_graph.nodes[n]['pos']
for e in b.edges():
b.edges[e]['weight']=input_graph.edges[e]['weight']
my_tiles=simple_basis
iteration+=1
if (nx.number_of_edges(new_input_graph)==E or nx.number_of_nodes(new_input_graph)==N) or iteration==3 :
input_graph=new_input_graph
go_on=False
break
return input_graph
def get_corners(input_graph):
dim=len(list(nx.get_node_attributes(input_graph,'pos').values())[0])
corners=[]
if dim==2:
corners=[n for n in input_graph.nodes() if input_graph.degree(n)==2]
elif dim==3:
corners=[n for n in input_graph.nodes() if input_graph.degree(n)==3]
return corners
def get_first_tile(input_graph, corners):
side_length=np.sqrt(nx.number_of_nodes( input_graph) )
w=5.
tile=nx.Graph()
for i,n in enumerate(corners):
tile.add_node(n,pos=input_graph.nodes[n]['pos'])
for i,n in enumerate(corners[:-1]):
for j,m in enumerate(corners[i+1:]):
path=nx.shortest_path(input_graph,n,m)
if len(path)==side_length:
tile.add_edge(n,m,weight=w)
return [tile]
def use_a_brick( sub_tile, edge, dict_seen):
if edge in dict_seen:
brick=dict_seen[edge]
else:
brick=dict_seen[(edge[1],edge[0])]
for n in brick.nodes():
if brick.degree(n)==2:
node_id=n
sub_tile=nx.compose(sub_tile,brick)
sub_tile.remove_edge(*edge)
return sub_tile,node_id
def form_a_brick(tile, sub_tile, node_id, edge,dict_seen):
pos=(tile.nodes[ edge[0]]['pos'] + tile.nodes[ edge[1]]['pos'])/2.
brick=nx.Graph()
brick.add_node(node_id,pos=pos)
brick.add_edge(node_id, edge[0],weight=sub_tile.edges[ edge]['weight'])
brick.add_edge(node_id, edge[1],weight=sub_tile.edges[ edge]['weight'])
sub_tile=nx.compose(sub_tile,brick)
sub_tile.remove_edge(*edge)
dict_seen[edge]=brick
return sub_tile
cycle_analysis/test.py 0 → 100644
View file @ e3386689
# @Author: Felix Kramer <kramer>
# @Date: 18-02-2019
# @Email: felix.kramer@hotmail.de
# @Project: cycle-coalescecne-algorithm
# @Last modified by: kramer
# @Last modified time: 09-01-2021
import networkx as nx
import numpy as np
import random as rd
import cycle_analysis.cycle_coalescence as cc
# generate example pattern random/nested/gradient for testing baseline values of the order parameter
def generate_pattern(input_graph,mode):
iteration=0
list_n=list(input_graph.nodes())
pos=nx.spectral_layout(input_graph)
for n in pos.keys():
input_graph.nodes[n]['pos']=pos[n]
if 'random' == mode:
for e in input_graph.edges():
input_graph.edges[e]['weight']=rd.uniform(0.,1.)*10.
elif 'gradient' == mode:
idx_min=np.argmin([ input_graph.nodes[n]['pos'][0] for n in list_n])
ref_p=input_graph.nodes[list_n[idx_min]]['pos']
for e in input_graph.edges():
p=(np.array(input_graph.nodes[e[0]]['pos'])+np.array(input_graph.nodes[e[1]]['pos']))/2.
r=np.linalg.norm(p-ref_p)
input_graph.edges[e]['weight']=1./r
elif 'nested_square' == mode:
corners=get_corners(input_graph)
my_tiles=get_first_tile(input_graph, corners)
E=nx.number_of_edges(input_graph)
N=nx.number_of_nodes(input_graph)
counter=0
go_on=True
while go_on:
new_my_tiles=[]
graph_seen=nx.Graph()
dict_seen={}
for tile in my_tiles:
list_e=list(tile.edges())
sub_tile=nx.Graph()
sub_tile.add_edge(*list_e[0],weight=tile.edges[list_e[0]]['weight'])
push_1=[0]
push_2=[]
for i,e in enumerate(list_e[1:]):
if ( sub_tile.has_node(e[0]) or sub_tile.has_node(e[1]) ):
push_2.append(i+1)
else:
push_1.append(i+1)
pos=[]
for i,n in enumerate(tile):
p=tile.nodes[n]['pos']
sub_tile.add_node(n,pos=p)
pos.append(p)
my_center=counter
sub_tile.add_node(my_center,pos=np.mean(pos,axis=0))
counter+=1
for i,e in enumerate(list_e[1:]):
sub_tile.add_edge(*e,weight=tile.edges[e]['weight'])
sub_w=np.amin(list(nx.get_edge_attributes(sub_tile,'weight').values()))/2.
push_nodes_1=[]
push_nodes_2=[]
for i,e in enumerate(list_e):
if i in push_1:
if graph_seen.has_edge(*e):
sub_tile,node_id=use_a_brick( sub_tile, e, dict_seen)
push_nodes_1.append(node_id)
else:
push_nodes_1.append(counter)
sub_tile=form_a_brick(tile,sub_tile,counter,e, dict_seen)
graph_seen.add_edge(*e)
counter+=1
elif i in push_2:
if graph_seen.has_edge(*e):
sub_tile,node_id=use_a_brick( sub_tile, e, dict_seen)
push_nodes_2.append(node_id)
else:
push_nodes_2.append(counter)
sub_tile=form_a_brick(tile,sub_tile,counter,e, dict_seen)
graph_seen.add_edge(*e)
counter+=1
for i in push_nodes_1:
sub_tile.add_edge(my_center,i,weight=sub_w)
for i in push_nodes_2:
sub_tile.add_edge(my_center,i,weight=sub_w*0.9)
new_my_tiles.append(sub_tile)
new_input_graph=nx.Graph()
for tile in new_my_tiles:
new_input_graph=nx.compose(new_input_graph,tile)
input_graph=nx.Graph(new_input_graph)
basis=cc.construct_minimum_basis(new_input_graph)
simple_basis=[nx.Graph(b) for b in basis]
for b in simple_basis:
for n in b.nodes():
b.nodes[n]['pos']=input_graph.nodes[n]['pos']
for e in b.edges():
b.edges[e]['weight']=input_graph.edges[e]['weight']
my_tiles=simple_basis
iteration+=1
if (nx.number_of_edges(new_input_graph)==E or nx.number_of_nodes(new_input_graph)==N) or iteration==3 :
input_graph=new_input_graph
go_on=False
break
return input_graph
def get_corners(input_graph):
dim=len(list(nx.get_node_attributes(input_graph,'pos').values())[0])
corners=[]
if dim==2:
corners=[n for n in input_graph.nodes() if input_graph.degree(n)==2]
elif dim==3:
corners=[n for n in input_graph.nodes() if input_graph.degree(n)==3]
return corners
def get_first_tile(input_graph, corners):
side_length=np.sqrt(nx.number_of_nodes( input_graph) )
w=5.
tile=nx.Graph()
for i,n in enumerate(corners):
tile.add_node(n,pos=input_graph.nodes[n]['pos'])
for i,n in enumerate(corners[:-1]):
for j,m in enumerate(corners[i+1:]):
path=nx.shortest_path(input_graph,n,m)
if len(path)==side_length:
tile.add_edge(n,m,weight=w)
return [tile]
def use_a_brick( sub_tile, edge, dict_seen):
if edge in dict_seen:
brick=dict_seen[edge]
else:
brick=dict_seen[(edge[1],edge[0])]
for n in brick.nodes():
if brick.degree(n)==2:
node_id=n
sub_tile=nx.compose(sub_tile,brick)
sub_tile.remove_edge(*edge)
return sub_tile,node_id
def form_a_brick(tile, sub_tile, node_id, edge,dict_seen):
pos=(tile.nodes[ edge[0]]['pos'] + tile.nodes[ edge[1]]['pos'])/2.
brick=nx.Graph()
brick.add_node(node_id,pos=pos)
brick.add_edge(node_id, edge[0],weight=sub_tile.edges[ edge]['weight'])
brick.add_edge(node_id, edge[1],weight=sub_tile.edges[ edge]['weight'])
sub_tile=nx.compose(sub_tile,brick)
sub_tile.remove_edge(*edge)
dict_seen[edge]=brick
return sub_tile
notebook/cycle_coalescence_asymmetry.ipynb
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notebook/cycle_coalescence_plot.ipynb
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setup.py 0 → 100644
View file @ e3386689
import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="cycle_analysis", # Replace with your own username
version="0.0.4",
author="felixk1990",
author_email="felix@hotmail.de",
description="My cycle_analysis module, performing and testing the cycle coalescence algorithm.",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/felixk1990/cycle-coalescence-algorithm",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",