Commit ea74dcb6 authored by Felix's avatar Felix
Browse files

last update before rebuild

parent 791b3796
......@@ -26,7 +26,7 @@ class coalescence(cycle_tools_simple.simple,object):
def calc_cycle_coalescence(self,input_graph,cycle_basis):
#create cycle_map_tree with cycles' edges as tree nodes
# print([len(cb.edges()) for cb in cycle_basis])
cycle_tree=nx.Graph()
for cycle in cycle_basis:
cycle_tree.add_node(tuple(cycle.edges(keys=True)),label='base',weight=1.,branch_type='none',pos=(-1,-1))
......@@ -206,45 +206,8 @@ class coalescence(cycle_tools_simple.simple,object):
EC.add_edge(*e,label=counter)
counter+=1
cycle_edges_in_basis=False
#if cycle edges where not part of the supergraph yet then it becomes automatically part of the basis
# if not cycle_edges_in_basis:
# minimum_basis.append(total_cycle_list[c])
#
# #if cycle edges are already included we check for linear dependece
# else:
# linear_independent=False
# rows=len(list(EC.edges()))
# columns=len(minimum_basis)+1
# E=np.zeros((rows,columns))
# # translate the existent basis vectors into z2 representation
# for idx_c,cycle in enumerate(minimum_basis+[total_cycle_list[c]]):
# for m in cycle.edges(keys=True):
# if EC.has_edge(*m):
# E[EC.edges[m]['label'],idx_c]=1
#
# # calc echelon form
# a_columns=np.arange(columns-1)
# zwo=np.ones(columns)*2
# for column in a_columns:
# idx_nz=np.nonzero(E[column:,column])[0]
# if idx_nz.size:
# if len(idx_nz)==1:
# E[column,:],E[idx_nz[0]+column,:]=E[idx_nz[0]+column,:].copy(),E[column,:].copy()
# else:
# for r in idx_nz[1:]:
# aux_E=np.add(E[r+column],E[idx_nz[0]+column])
# E[r+column]=np.mod(aux_E,zwo)
# E[column,:],E[idx_nz[0]+column,:]=E[idx_nz[0]+column,:].copy(),E[column,:].copy()
# else:
# sys.exit('Error: minimum_weight_basis containing inconsistencies ...')
# # test echelon form for inconsistencies
# for r in range(rows):
# line_check=np.nonzero(E[r])[0]
# if len(line_check)==1 and line_check[0]==(columns-1):
# linear_independent=True
# break
# if linear_independent:
# minimum_basis.append(total_cycle_list[c])
if not cycle_edges_in_basis:
minimum_basis.append(new_cycle)
......@@ -253,7 +216,7 @@ class coalescence(cycle_tools_simple.simple,object):
#if cycle edges are already included we check for linear dependece
else:
E=self.edge_matrix(EC, minimum_basis, minimum_label ,new_cycle)
E=self.edge_matrix(EC, len(minimum_basis), minimum_label ,new_cycle)
linear_independent=self.compute_linear_independence(E)
# print(linear_independent)
......@@ -272,13 +235,13 @@ class coalescence(cycle_tools_simple.simple,object):
def edge_matrix(self,*args):
EC, minimum_basis, minimum_label,new_cycle=args
EC,length_basis, minimum_label,new_cycle=args
rows=len(EC.edges())
columns=len(minimum_basis)+1
columns=length_basis+1
E=np.zeros((rows,columns))
for idx_c,cycle in enumerate(minimum_basis):
E[minimum_label[idx_c],idx_c]=1
for i in range(length_basis):
E[minimum_label[i],i]=1
for m in new_cycle.edges(keys=True):
E[EC.edges[m]['label'],-1]=1
......
......@@ -7,7 +7,7 @@
"outputs": [
{
"data": {
"image/png": 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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -20,17 +20,15 @@
],
"source": [
"import sys\n",
"sys.path.insert(0, \"../cycle_analysis\")\n",
"import cycle_tools_coalescence as ctc\n",
"import test as cat\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"# import cycle_analysis.cycle_tools_coalescence as ctc\n",
"# import cycle_analysis.test as cat\n",
"import cycle_analysis.cycle_tools_coalescence as ctc\n",
"import cycle_analysis.test as cat\n",
"\n",
"# generate a dummy graph for testing\n",
"# put an edge weight distribution on the system, available are random/gradient/bigradient/nested_square\n",
"G=nx.grid_graph((7,7,1))\n",
"n=5\n",
"G=nx.grid_graph(( n,n,1))\n",
"G=cat.generate_pattern(G,'random')\n",
"\n",
"weights=[G.edges[e]['weight'] for e in G.edges()]\n",
......@@ -56,9 +54,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"79.6 ms ± 2.85 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": []
},
{
......@@ -85,7 +91,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.9"
}
},
"nbformat": 4,
......
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