-------
HyperNetX Hypergraph object
+
Example::
+
+ >>> import hypernetx.algorithms.generative_models as gm
+ >>> n = 1000
+ >>> m = n
+ >>> p = 0.01
+ >>> H = gm.erdos_renyi_hypergraph(n, m, p)
- >>> import hypernetx.algorithms.generative_models as gm
- >>> n = 1000
- >>> m = n
- >>> p = 0.01
- >>> H = gm.erdos_renyi_hypergraph(n, m, p)
"""
if node_labels is not None and edge_labels is not None :
@@ -254,12 +257,12 @@ Source code for algorithms.generative_models Example::
- >>> import hypernetx.algorithms.generative_models as gm
- >>> import random
- >>> n = 100
- >>> k1 = {i : random.randint(1, 100) for i in range(n)}
- >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
- >>> H = gm.chung_lu_hypergraph(k1, k2)
+ >>> import hypernetx.algorithms.generative_models as gm
+ >>> import random
+ >>> n = 100
+ >>> k1 = {i : random.randint(1, 100) for i in range(n)}
+ >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
+ >>> H = gm.chung_lu_hypergraph(k1, k2)
"""
# sort dictionary by degree in decreasing order
@@ -337,13 +340,13 @@ Source code for algorithms.generative_models Example::
- >>> n = 100
- >>> k1 = {i : random.randint(1, 100) for i in range(n)}
- >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
- >>> g1 = {i : random.choice([0, 1]) for i in range(n)}
- >>> g2 = {i : random.choice([0, 1]) for i in range(n)}
- >>> omega = np.array([[100, 10], [10, 100]])
- >>> H = gm.dcsbm_hypergraph(k1, k2, g1, g2, omega)
+ >>> n = 100
+ >>> k1 = {i : random.randint(1, 100) for i in range(n)}
+ >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
+ >>> g1 = {i : random.choice([0, 1]) for i in range(n)}
+ >>> g2 = {i : random.choice([0, 1]) for i in range(n)}
+ >>> omega = np.array([[100, 10], [10, 100]])
+ >>> H = gm.dcsbm_hypergraph(k1, k2, g1, g2, omega)
"""
# sort dictionary by degree in decreasing order
diff --git a/docs/build/_modules/algorithms/homology_mod2.html b/docs/build/_modules/algorithms/homology_mod2.html
index 65d495c1..adfc04b4 100644
--- a/docs/build/_modules/algorithms/homology_mod2.html
+++ b/docs/build/_modules/algorithms/homology_mod2.html
@@ -7,7 +7,7 @@
algorithms.homology_mod2 — HyperNetX 1.1.3 documentation
+ algorithms.homology_mod2 — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -930,7 +931,7 @@
Source code for algorithms.homology_mod2
[docs] def homology_basis ( bd , k = None , boundary = False , ** kwargs ):
"""
Compute a basis for the kth-simplicial homology group, $H_k$, defined by a
-
chain complex $C$ with boundary maps given by bd $= \{k:\partial_k$\}$
+
chain complex $C$ with boundary maps given by bd $= \{k:\partial_k \}$
Parameters
----------
diff --git a/docs/build/_modules/algorithms/hypergraph_modularity.html b/docs/build/_modules/algorithms/hypergraph_modularity.html
new file mode 100644
index 00000000..bd0e0df3
--- /dev/null
+++ b/docs/build/_modules/algorithms/hypergraph_modularity.html
@@ -0,0 +1,773 @@
+
+
+
+
+
+
+
+
+
+
algorithms.hypergraph_modularity — HyperNetX 1.2 documentation
+
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+ HyperNetX
+
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+ Module code »algorithms.hypergraph_modularity
+
+
+
+
+
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+
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+
+
+
+
+
+
Source code for algorithms.hypergraph_modularity
+"""
+Hypergraph_Modularity
+---------------------
+Modularity and clustering for hypergraphs using HyperNetX.
+Adapted from F. Théberge's GitHub repository: `Hypergraph Clustering <https://github.com/ftheberge/Hypergraph_Clustering>`_
+See Tutorial 13 in the tutorials folder for library usage.
+
+References
+----------
+.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. "A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering". In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
+.. [2] Kamiński B., Prałat P. and Théberge F. "Community Detection Algorithm Using Hypergraph Modularity". In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
+.. [3] Kamiński B., Poulin V., Prałat P., Szufel P. and Théberge F. "Clustering via hypergraph modularity", Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307
+"""
+
+from collections import Counter
+import numpy as np
+from functools import reduce
+import igraph as ig
+import itertools
+from scipy.special import comb
+
+################################################################################
+
+# we use 2 representations for partitions (0-based part ids):
+# (1) dictionary or (2) list of sets
+
+
+[docs] def dict2part ( D ):
+
"""
+
Given a dictionary mapping the part for each vertex, return a partition as a list of sets; inverse function to part2dict
+
+
Parameters
+
----------
+
D : dict
+
Dictionary keyed by vertices with values equal to integer
+
index of the partition the vertex belongs to
+
+
Returns
+
-------
+
: list
+
List of sets; one set for each part in the partition
+
"""
+
P = []
+
k = list ( D . keys ())
+
v = list ( D . values ())
+
for x in range ( max ( D . values ()) + 1 ):
+
P . append ( set ([ k [ i ] for i in range ( len ( k )) if v [ i ] == x ]))
+
return P [docs] def part2dict ( A ):
+
"""
+
Given a partition (list of sets), returns a dictionary mapping the part for each vertex; inverse function
+
to dict2part
+
+
Parameters
+
----------
+
A : list of sets
+
a partition of the vertices
+
+
Returns
+
-------
+
: dict
+
a dictionary with {vertex: partition index}
+
"""
+
x = []
+
for i in range ( len ( A )):
+
x . extend ([( a , i ) for a in A [ i ]])
+
return { k : v for k , v in x } ################################################################################
+
+
+[docs] def precompute_attributes ( HG ):
+
"""
+
Precompute some values on hypergraph HG for faster computing of hypergraph modularity.
+
This needs to be run before calling either modularity() or last_step().
+
+
Note
+
----
+
+
If HG is unweighted, v.weight is set to 1 for each vertex v in HG.
+
The weighted degree for each vertex v is stored in v.strength.
+
The total edge weigths for each edge cardinality is stored in HG.d_weights.
+
Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef.
+
Isolated vertices found only in edge(s) of size 1 are dropped.
+
+
Parameters
+
----------
+
HG : Hypergraph
+
+
Returns
+
-------
+
H : Hypergraph
+
New hypergraph with added attributes
+
+
"""
+
H = HG . remove_singletons ()
+
# 1. compute node strenghts (weighted degrees)
+
for v in H . nodes :
+
H . nodes [ v ] . strength = 0
+
for e in H . edges :
+
try :
+
w = H . edges [ e ] . weight
+
except :
+
w = 1
+
# add unit weight if none to simplify other functions
+
H . edges [ e ] . weight = 1
+
for v in list ( H . edges [ e ]):
+
H . nodes [ v ] . strength += w
+
# 2. compute d-weights
+
ctr = Counter ([ len ( H . edges [ e ]) for e in H . edges ])
+
for k in ctr . keys ():
+
ctr [ k ] = 0
+
for e in H . edges :
+
ctr [ len ( H . edges [ e ])] += H . edges [ e ] . weight
+
H . d_weights = ctr
+
H . total_weight = sum ( ctr . values ())
+
# 3. compute binomial coeffcients (modularity speed-up)
+
bin_coef = {}
+
for n in H . d_weights . keys ():
+
for k in np . arange ( n // 2 + 1 , n + 1 ):
+
bin_coef [( n , k )] = comb ( n , k , exact = True )
+
H . bin_coef = bin_coef
+
return H ################################################################################
+
+
+[docs] def linear ( d , c ):
+
"""
+
Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
+
This is the default choice for modularity() and last_step() functions.
+
+
Parameters
+
----------
+
d : int
+
Number of vertices in an edge
+
c : int
+
Number of vertices in the majority class
+
+
Returns
+
-------
+
: float
+
c/d if c>d/2 else 0
+
"""
+
return c / d if c > d / 2 else 0 # majority
+
+
+[docs] def majority ( d , c ):
+
"""
+
Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
+
This corresponds to the majority rule [3]_
+
+
Parameters
+
----------
+
d : int
+
Number of vertices in an edge
+
c : int
+
Number of vertices in the majority class
+
+
Returns
+
-------
+
: bool
+
1 if c>d/2 else 0
+
+
"""
+
return 1 if c > d / 2 else 0 # strict
+
+
+[docs] def strict ( d , c ):
+
"""
+
Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
+
This corresponds to the strict rule [3]_
+
+
Parameters
+
----------
+
d : int
+
Number of vertices in an edge
+
c : int
+
Number of vertices in the majority class
+
+
Returns
+
-------
+
: bool
+
1 if c==d else 0
+
"""
+
return 1 if c == d else 0 #########################################
+
+
+def _compute_partition_probas ( HG , A ):
+ """
+ Compute vol(A_i)/vol(V) for each part A_i in A (list of sets)
+
+ Parameters
+ ----------
+ HG : Hypergraph
+ A : list of sets
+
+ Returns
+ -------
+ : list
+ normalized distribution of strengths in partition elements
+ """
+ p = []
+ for part in A :
+ vol = 0
+ for v in part :
+ vol += HG . nodes [ v ] . strength
+ p . append ( vol )
+ s = sum ( p )
+ return [ i / s for i in p ]
+
+
+def _degree_tax ( HG , Pr , wdc ):
+ """
+ Computes the expected fraction of edges falling in
+ the partition as per [2]_
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ Pr : list
+ Probability distribution
+ wdc : func
+ weight function for edge contribution (ex: strict, majority, linear)
+
+ Returns
+ -------
+ float
+
+ """
+ DT = 0
+ for d in HG . d_weights . keys ():
+ tax = 0
+ for c in np . arange ( d // 2 + 1 , d + 1 ):
+ for p in Pr :
+ tax += p ** c * ( 1 - p ) ** ( d - c ) * HG . bin_coef [( d , c )] * wdc ( d , c )
+ tax *= HG . d_weights [ d ]
+ DT += tax
+ DT /= HG . total_weight
+ return DT
+
+
+def _edge_contribution ( HG , A , wdc ):
+ """
+ Edge contribution from hypergraph with respect
+ to partion A.
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ A : list of sets
+
+ wdc : func
+ weight function (ex: strict, majority, linear)
+
+ Returns
+ -------
+ : float
+
+ """
+ EC = 0
+ for e in HG . edges :
+ d = HG . size ( e )
+ for part in A :
+ if HG . size ( e , part ) > d / 2 :
+ EC += wdc ( d , HG . size ( e , part )) * HG . edges [ e ] . weight
+ EC /= HG . total_weight
+ return EC
+
+# HG: HNX hypergraph
+# A: partition (list of sets)
+# wcd: weight function (ex: strict, majority, linear)
+
+
+[docs] def modularity ( HG , A , wdc = linear ):
+
"""
+
Computes modularity of hypergraph HG with respect to partition A.
+
+
Parameters
+
----------
+
HG : Hypergraph
+
The hypergraph with some precomputed attributes via: precompute_attributes(HG)
+
A : list of sets
+
Partition of the vertices in HG
+
wdc : func, optional
+
Hyperparameter for hypergraph modularity [2]_
+
+
Note
+
----
+
For 'wdc', any function of the format w(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used.
+
Default is 'linear'; other supplied choices are 'majority' and 'strict'.
+
+
Returns
+
-------
+
: float
+
The modularity function for partition A on HG
+
"""
+
Pr = _compute_partition_probas ( HG , A )
+
return _edge_contribution ( HG , A , wdc ) - _degree_tax ( HG , Pr , wdc ) ################################################################################
+
+
+[docs] def two_section ( HG ):
+
"""
+
Creates a random walk based [1]_ 2-section igraph Graph with transition weights defined by the
+
weights of the hyperedges.
+
+
Parameters
+
----------
+
HG : Hypergraph
+
+
Returns
+
-------
+
: igraph.Graph
+
The 2-section graph built from HG
+
"""
+
s = []
+
for e in HG . edges :
+
E = HG . edges [ e ]
+
# random-walk 2-section (preserve nodes' weighted degrees)
+
if len ( E ) > 1 :
+
try :
+
w = HG . edges [ e ] . weight / ( len ( E ) - 1 )
+
except :
+
w = 1 / ( len ( E ) - 1 )
+
s . extend ([( k [ 0 ], k [ 1 ], w ) for k in itertools . combinations ( E , 2 )])
+
G = ig . Graph . TupleList ( s , weights = True ) . simplify ( combine_edges = 'sum' )
+
return G ################################################################################
+
+
+[docs] def kumar ( HG , delta = .01 ):
+
"""
+
Compute a partition of the vertices in hypergraph HG as per Kumar's algorithm [1]_
+
+
Parameters
+
----------
+
HG : Hypergraph
+
+
delta : float, optional
+
convergence stopping criterion
+
+
Returns
+
-------
+
: list of sets
+
A partition of the vertices in HG
+
+
"""
+
# weights will be modified -- store initial weights
+
W = { e : HG . edges [ e ] . weight for e in HG . edges } # uses edge id for reference instead of int
+
# build graph
+
G = two_section ( HG )
+
# apply clustering
+
CG = G . community_multilevel ( weights = 'weight' )
+
CH = []
+
for comm in CG . as_cover ():
+
CH . append ( set ([ G . vs [ x ][ 'name' ] for x in comm ]))
+
+
# LOOP
+
diff = 1
+
ctr = 0
+
while diff > delta :
+
# re-weight
+
diff = 0
+
for e in HG . edges :
+
edge = HG . edges [ e ]
+
reweight = sum ([ 1 / ( 1 + HG . size ( e , c )) for c in CH ]) * ( HG . size ( e ) + len ( CH )) / HG . number_of_edges ()
+
diff = max ( diff , 0.5 * abs ( edge . weight - reweight ))
+
edge . weight = 0.5 * edge . weight + 0.5 * reweight
+
# re-run louvain
+
# build graph
+
G = two_section ( HG )
+
# apply clustering
+
CG = G . community_multilevel ( weights = 'weight' )
+
CH = []
+
for comm in CG . as_cover ():
+
CH . append ( set ([ G . vs [ x ][ 'name' ] for x in comm ]))
+
ctr += 1
+
if ctr > 50 : # this process sometimes gets stuck -- set limit
+
break
+
G . vs [ 'part' ] = CG . membership
+
for e in HG . edges :
+
HG . edges [ e ] . weight = W [ e ]
+
return dict2part ({ v [ 'name' ]: v [ 'part' ] for v in G . vs }) ################################################################################
+
+
+def _delta_ec ( HG , P , v , a , b , wdc ):
+ """
+ Computes change in edge contribution --
+ partition P, node v going from P[a] to P[b]
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ P : list of sets
+
+ v : int or str
+ node identifier
+ a : int
+
+ b : int
+
+ wdc : func
+ weight function (ex: strict, majority, linear)
+
+ Returns
+ -------
+ : float
+ """
+ Pm = P [ a ] - { v }
+ Pn = P [ b ] . union ({ v })
+ ec = 0
+ for e in list ( HG . nodes [ v ] . memberships ):
+ d = HG . size ( e )
+ w = HG . edges [ e ] . weight
+ ec += w * ( wdc ( d , HG . size ( e , Pm )) + wdc ( d , HG . size ( e , Pn ))
+ - wdc ( d , HG . size ( e , P [ a ])) - wdc ( d , HG . size ( e , P [ b ])))
+ return ec / HG . total_weight
+
+
+def _bin_ppmf ( d , c , p ):
+ """
+ exponential part of the binomial pmf
+
+ Parameters
+ ----------
+ d : int
+
+ c : int
+
+ p : float
+
+
+ Returns
+ -------
+ : float
+
+ """
+ return p ** c * ( 1 - p ) ** ( d - c )
+
+
+def _delta_dt ( HG , P , v , a , b , wdc ):
+ """
+ Compute change in degree tax --
+ partition P (list), node v going from P[a] to P[b]
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ P : list of sets
+
+ v : int or str
+ node identifier
+ a : int
+
+ b : int
+
+ wdc : func
+ weight function (ex: strict, majority, linear)
+
+ Returns
+ -------
+ : float
+
+ """
+ s = HG . nodes [ v ] . strength
+ vol = sum ([ HG . nodes [ v ] . strength for v in HG . nodes ])
+ vola = sum ([ HG . nodes [ v ] . strength for v in P [ a ]])
+ volb = sum ([ HG . nodes [ v ] . strength for v in P [ b ]])
+ volm = ( vola - s ) / vol
+ voln = ( volb + s ) / vol
+ vola /= vol
+ volb /= vol
+ DT = 0
+
+ for d in HG . d_weights . keys ():
+ x = 0
+ for c in np . arange ( int ( np . floor ( d / 2 )) + 1 , d + 1 ):
+ x += HG . bin_coef [( d , c )] * wdc ( d , c ) * ( _bin_ppmf ( d , c , voln ) + _bin_ppmf ( d , c , volm )
+ - _bin_ppmf ( d , c , vola ) - _bin_ppmf ( d , c , volb ))
+ DT += x * HG . d_weights [ d ]
+ return DT / HG . total_weight
+
+
+[docs] def last_step ( HG , L , wdc = linear , delta = .01 ):
+
"""
+
Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm [2]_
+
+
Note
+
----
+
This is a very simple algorithm that tries moving nodes between communities to improve hypergraph modularity.
+
It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG,
+
or via Kumar's algorithm.
+
+
Parameters
+
----------
+
HG : Hypergraph
+
+
L : list of sets
+
some initial partition of the vertices in HG
+
+
wdc : func, optional
+
Hyperparameter for hypergraph modularity [2]_
+
+
delta : float, optional
+
convergence stopping criterion
+
+
Returns
+
-------
+
: list of sets
+
A new partition for the vertices in HG
+
"""
+
A = L [:] # we will modify this, copy
+
D = part2dict ( A )
+
qH = 0
+
while True :
+
for v in list ( np . random . permutation ( list ( HG . nodes ))):
+
c = D [ v ]
+
s = list ( set ([ c ] + [ D [ i ] for i in HG . neighbors ( v )]))
+
M = []
+
if len ( s ) > 0 :
+
for i in s :
+
if c == i :
+
M . append ( 0 )
+
else :
+
M . append ( _delta_ec ( HG , A , v , c , i , wdc ) - _delta_dt ( HG , A , v , c , i , wdc ))
+
i = s [ np . argmax ( M )]
+
if c != i :
+
A [ c ] = A [ c ] - { v }
+
A [ i ] = A [ i ] . union ({ v })
+
D [ v ] = i
+
Pr = _compute_partition_probas ( HG , A )
+
q2 = _edge_contribution ( HG , A , wdc ) - _degree_tax ( HG , Pr , wdc )
+
if ( q2 - qH ) < delta :
+
break
+
qH = q2
+
return [ a for a in A if len ( a ) > 0 ] ################################################################################
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/_modules/algorithms/laplacians_clustering.html b/docs/build/_modules/algorithms/laplacians_clustering.html
index 10c116ae..5341e06a 100644
--- a/docs/build/_modules/algorithms/laplacians_clustering.html
+++ b/docs/build/_modules/algorithms/laplacians_clustering.html
@@ -7,7 +7,7 @@
-
algorithms.laplacians_clustering — HyperNetX 1.1.3 documentation
+
algorithms.laplacians_clustering — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
diff --git a/docs/build/_modules/algorithms/s_centrality_measures.html b/docs/build/_modules/algorithms/s_centrality_measures.html
index d53fe5a9..ae8081ea 100644
--- a/docs/build/_modules/algorithms/s_centrality_measures.html
+++ b/docs/build/_modules/algorithms/s_centrality_measures.html
@@ -7,7 +7,7 @@
-
algorithms.s_centrality_measures — HyperNetX 1.1.3 documentation
+
algorithms.s_centrality_measures — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
diff --git a/docs/build/_modules/classes/entity.html b/docs/build/_modules/classes/entity.html
index 38a7b4e8..4c494f2e 100644
--- a/docs/build/_modules/classes/entity.html
+++ b/docs/build/_modules/classes/entity.html
@@ -7,7 +7,7 @@
-
classes.entity — HyperNetX 1.1.3 documentation
+
classes.entity — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
diff --git a/docs/build/_modules/classes/hypergraph.html b/docs/build/_modules/classes/hypergraph.html
index a6bc1122..132dd052 100644
--- a/docs/build/_modules/classes/hypergraph.html
+++ b/docs/build/_modules/classes/hypergraph.html
@@ -7,7 +7,7 @@
-
classes.hypergraph — HyperNetX 1.1.3 documentation
+
classes.hypergraph — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -339,6 +340,9 @@
Source code for classes.hypergraph
self . _edges = E
self . _nodes = E . restrict_to_levels ([ 1 ], weights = False , aggregateby = None )
self . _nodes . _memberships = E . memberships
+ for n in self . _nodes :
+ self . _nodes [ n ] . memberships = self . _nodes . _memberships [ n ] ### a bit of a hack to get same functionality from static as dynamic
+ ### we will have to see if it slows things down too much
else :
self . _static = False
if setsystem is None :
@@ -739,17 +743,18 @@ Source code for classes.hypergraph
filepath : None, optional, default : False
Description
+ Returned
+ ------------------
+ hnx.Hypergraph
+ Will have attribute static = True
+
Note
----
Static hypergraphs store the user defined node and edge names in
a dictionary of labeled lists. The order of the lists provides an
index, which the hypergraph uses in place of the node and edge names
- for fast processing.
+ for faster processing.
- No Longer Returned
- ------------------
- hnx.Hypergraph
- Will have attribute static = True
"""
if self . isstatic :
return self
@@ -1313,7 +1318,7 @@ Source code for classes.hypergraph
"""
Helper method to obtain adjacency matrix from
boolean incidence matrix for s-metrics.
- Self loops are note supported.
+ Self loops are not supported.
The adjacency matrix will define an s-linegraph.
Parameters
diff --git a/docs/build/_modules/classes/staticentity.html b/docs/build/_modules/classes/staticentity.html
index 7eb3626f..0fe3a4c8 100644
--- a/docs/build/_modules/classes/staticentity.html
+++ b/docs/build/_modules/classes/staticentity.html
@@ -7,7 +7,7 @@
classes.staticentity — HyperNetX 1.1.3 documentation
+ classes.staticentity — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -233,7 +234,7 @@
Source code for classes.staticentity
arr = None ,
labels = None ,
uid = None ,
- weights = None ,
+ weights = None , ### in this context weights is just a column of values corresponding to the rows in data.
keep_weights = True ,
aggregateby = "sum" ,
** props ,
diff --git a/docs/build/_modules/drawing/rubber_band.html b/docs/build/_modules/drawing/rubber_band.html
index 3d55ac06..09543511 100644
--- a/docs/build/_modules/drawing/rubber_band.html
+++ b/docs/build/_modules/drawing/rubber_band.html
@@ -7,7 +7,7 @@
drawing.rubber_band — HyperNetX 1.1.3 documentation
+ drawing.rubber_band — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -175,6 +176,7 @@
Source code for drawing.rubber_band
from hypernetx import Hypergraph
from .util import (
get_frozenset_label ,
+ get_collapsed_size ,
get_set_layering ,
inflate_kwargs ,
transpose_inflated_kwargs ,
@@ -496,7 +498,6 @@ Source code for drawing.rubber_band
-
[docs] def draw (
H ,
pos = None ,
@@ -568,7 +569,7 @@
Source code for drawing.rubber_band
with_color: bool
set to False to disable color cycling of edges
with_node_counts: bool
- set to True to label collapsed nodes with number of elements
+ set to True to replace the label for collapsed nodes with the number of elements
with_edge_counts: bool
set to True to label collapsed edges with number of elements
layout: function
@@ -603,9 +604,11 @@ Source code for drawing.rubber_band
r0 = get_default_radius ( H , pos )
a0 = np . pi * r0 ** 2
+
+
def get_node_radius ( v ):
if node_radius is None :
- return np . sqrt ( a0 * ( len ( v ) if type ( v ) == frozenset else 1 ) / np . pi )
+ return np . sqrt ( a0 * get_collapsed_size ( v ) / np . pi )
elif hasattr ( node_radius , "get" ):
return node_radius . get ( v , 1 ) * r0
return node_radius * r0
diff --git a/docs/build/_modules/drawing/two_column.html b/docs/build/_modules/drawing/two_column.html
index 519f0802..fb163bdc 100644
--- a/docs/build/_modules/drawing/two_column.html
+++ b/docs/build/_modules/drawing/two_column.html
@@ -7,7 +7,7 @@
drawing.two_column — HyperNetX 1.1.3 documentation
+ drawing.two_column — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
diff --git a/docs/build/_modules/drawing/util.html b/docs/build/_modules/drawing/util.html
index 7c9bcce2..2e179d57 100644
--- a/docs/build/_modules/drawing/util.html
+++ b/docs/build/_modules/drawing/util.html
@@ -7,7 +7,7 @@
-
drawing.util — HyperNetX 1.1.3 documentation
+
drawing.util — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -213,6 +214,15 @@
Source code for drawing.util
return [ dict ( zip ( inflated , v )) for v in zip ( * inflated . values ())]
+
[docs] def get_collapsed_size ( v ):
+
try :
+
if type ( v ) == str and ':' in v :
+
return int ( v . split ( ':' )[ - 1 ])
+
except :
+
pass
+
+
return 1
+
[docs] def get_frozenset_label ( S , count = False , override = {}):
"""
Helper function for rendering the labels of possibly collapsed nodes and edges
@@ -231,13 +241,12 @@
Source code for drawing.util
"""
def helper ( v ):
- if type ( v ) == frozenset :
- if count and len ( v ) > 1 :
- return f "x { len ( v ) } "
+ if type ( v ) == str :
+ n = get_collapsed_size ( v )
+ if count and n > 1 :
+ return f "x { n } "
elif count :
return ""
- else :
- return ", " . join ([ str ( override . get ( s , s )) for s in v ])
return str ( v )
return { v : override . get ( v , helper ( v )) for v in S }
diff --git a/docs/build/_modules/index.html b/docs/build/_modules/index.html
index 0196ccad..5054e8f9 100644
--- a/docs/build/_modules/index.html
+++ b/docs/build/_modules/index.html
@@ -7,7 +7,7 @@
-
Overview: module code — HyperNetX 1.1.3 documentation
+
Overview: module code — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -171,6 +172,7 @@
All modules for which code is available
algorithms.contagion.epidemics algorithms.generative_models algorithms.homology_mod2
+
algorithms.hypergraph_modularity algorithms.laplacians_clustering algorithms.s_centrality_measures classes.entity
diff --git a/docs/build/_modules/reports/descriptive_stats.html b/docs/build/_modules/reports/descriptive_stats.html
index c61d1897..f7d4d96b 100644
--- a/docs/build/_modules/reports/descriptive_stats.html
+++ b/docs/build/_modules/reports/descriptive_stats.html
@@ -7,7 +7,7 @@
-
reports.descriptive_stats — HyperNetX 1.1.3 documentation
+
reports.descriptive_stats — HyperNetX 1.2 documentation
@@ -68,7 +68,7 @@
- 1.1
+ 1.2
@@ -101,6 +101,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
diff --git a/docs/build/_sources/algorithms/algorithms.rst.txt b/docs/build/_sources/algorithms/algorithms.rst.txt
index 2070dbee..5a819963 100644
--- a/docs/build/_sources/algorithms/algorithms.rst.txt
+++ b/docs/build/_sources/algorithms/algorithms.rst.txt
@@ -28,6 +28,14 @@ algorithms.homology\_mod2 module
:undoc-members:
:show-inheritance:
+algorithms.hypergraph\_modularity module
+----------------------------------------
+
+.. automodule:: algorithms.hypergraph_modularity
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
algorithms.laplacians\_clustering module
----------------------------------------
diff --git a/docs/build/_sources/index.rst.txt b/docs/build/_sources/index.rst.txt
index 72ba936a..d14f4f16 100644
--- a/docs/build/_sources/index.rst.txt
+++ b/docs/build/_sources/index.rst.txt
@@ -40,6 +40,7 @@ Contents
core
NWHypergraph C++ Optimization
HyperNetX Visualization Widget
+ Algorithms: Modularity and Clustering
Publications
license
diff --git a/docs/build/_sources/modularity.rst.txt b/docs/build/_sources/modularity.rst.txt
new file mode 100644
index 00000000..8738ced1
--- /dev/null
+++ b/docs/build/_sources/modularity.rst.txt
@@ -0,0 +1,114 @@
+.. _modularity:
+
+
+=========================
+Modularity and Clustering
+=========================
+
+.. image:: images/ModularityScreenShot.png
+ :width: 300px
+ :align: right
+
+Overview
+--------
+The hypergraph_modularity submodule in HNX provides functions to compute **hypergraph modularity** for a
+given partition of the vertices in a hypergraph. In general, higher modularity indicates a better
+partitioning of the vertices into dense communities.
+
+Two functions to generate such hypergraph
+partitions are provided: **Kumar's** algorithm, and the simple **Last-Step** refinement algorithm.
+
+The submodule also provides a function to generate the **two-section graph** for a given hypergraph which can then be used to find
+vertex partitions via graph-based algorithms.
+
+
+Installation
+------------
+Since it is part of HNX, no extra installation is required.
+The submodule can be imported as follows::
+
+ import hypernetx.algorithms.hypergraph_modularity as hmod
+
+Using the Tool
+--------------
+
+
+Precomputation
+^^^^^^^^^^^^^^
+
+In order to make the computation of hypergraph modularity more efficient, some quantities need to be pre-computed.
+Given hypergraph H, calling::
+
+ HG = hmod.precompute_attributes(H)
+
+will pre-compute quantities such as node strength (weighted degree), d-weights (total weight for each edge cardinality) and binomial coefficients.
+
+Modularity
+^^^^^^^^^^
+
+Given hypergraph HG and a partition A of its vertices, hypergraph modularity is a measure of the quality of this partition.
+Random partitions typically yield modularity near zero (it can be negative) while positive modularity is indicative of the presence
+of dense communities, or modules. There are several variations for the definition of hypergraph modularity, and the main difference lies in the
+weight given to different edges. Modularity is computed via::
+
+ q = hmod.modularity(HG, A, wdc=linear)
+
+In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increases the modularity)
+or between communities.
+
+With hypergraphs, we consider edges of size *d=2* or more. Given some vertex partition A and some *d*-edge *e*, let *c* be the number of nodes
+that belong to the most represented part in *e*; if *c > d/2*, we consider this edge to be within the part.
+Hyper-parameters *0 <= w(d,c) <= 1* control the weight
+given to such edges. Three functions are supplied in this submodule, namely:
+
+**linear**
+ $w(d,c) = c/d$ if $c > d/2$, else $0$.
+**majority**
+ $w(d,c) = 1$ if $c > d/2$, else $0$.
+**strict**
+ $w(d,c) = 1$ if $c == d$, else $0$.
+
+The 'linear' function is used by default. More details in [2].
+
+Two-section graph
+^^^^^^^^^^^^^^^^^
+
+There are several good partitioning algorithms for graphs such as the Louvain algorithm and ECG, a consensus clustering algorithm.
+One way to obtain a partition for hypergraph HG is to build its corresponding two-section graph G and run a graph clustering algorithm.
+Code is provided to build such graph via::
+
+ G = hmod.two_section(HG)
+
+which returns an igraph.Graph object.
+
+
+Clustering Algorithms
+^^^^^^^^^^^^^^^^^^^^^
+
+Two clustering (vertex partitioning) algorithms are supplied. The first one is a hybrid method proposed by Kumar et al. (see [1])
+that uses the Louvain algorithm on the two-section graph, but re-weights the edges according to the distibution of vertices
+from each part inside each edge. Given hypergraph HG, this is called as::
+
+ K = hmod.kumar(HG)
+
+The other supplied algorithm is a simple method to improve hypergraph modularity directely. Given some
+initial partition of the vertices (for example via Louvain on the two-section graph), move vertices between parts in order
+to improve hypergraph modularity. Given hypergraph HG and initial partition A, this is called as::
+
+ L = hmod.last_step(HG, A, wdc=linear)
+
+where the 'wdc' parameter is the same as in the modularity function.
+
+
+Other Features
+^^^^^^^^^^^^^^
+
+We represent a vertex partition A as a list of sets, but another conveninent representation is via a dictionary.
+We provide two utility functions to switch representation, namely `A = dict2part(D)` and `D = part2dict(A)`.
+
+References
+^^^^^^^^^^
+[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
+
+[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
+
diff --git a/docs/build/_sources/overview/index.rst.txt b/docs/build/_sources/overview/index.rst.txt
index f7e2a271..30cb329a 100644
--- a/docs/build/_sources/overview/index.rst.txt
+++ b/docs/build/_sources/overview/index.rst.txt
@@ -20,8 +20,8 @@ PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830
* Visualization: Dustin Arendt, Ji Young Yun
* High Performance Computing: Tony Liu, Andrew Lumsdaine
* Principal Investigator: Cliff Joslyn
-* Program Manager: Mark Raugas, Brian Kritzstein
-* Mathematics, methods, and algorithms: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Tony Liu, Andrew Lumsdaine, Brenda Praggastis, and Emilie Purvine
+* Program Manager: Brian Kritzstein
+* Mathematics, methods, and algorithms: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Tony Liu, Andrew Lumsdaine, Brenda Praggastis, and Emilie Purvine, François Théberge
@@ -44,6 +44,10 @@ New Features in Version 1.1
#. Clustering module for clustering vertices based on hyperedge incidence and weighting.
#. Generator module for synthetic generation of ChungLu and DCSBM hypergraphs.
+New Features in Version 1.2
+---------------------------
+#. Added algorithm module and tutorial for Modularity and Clustering
+
.. _colab:
diff --git a/docs/build/_static/documentation_options.js b/docs/build/_static/documentation_options.js
index 4c685415..12dbdc97 100644
--- a/docs/build/_static/documentation_options.js
+++ b/docs/build/_static/documentation_options.js
@@ -1,6 +1,6 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '1.1.3',
+ VERSION: '1.2',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
BUILDER: 'html',
diff --git a/docs/build/algorithms/algorithms.contagion.html b/docs/build/algorithms/algorithms.contagion.html
index 44f41d3c..7f702b65 100644
--- a/docs/build/algorithms/algorithms.contagion.html
+++ b/docs/build/algorithms/algorithms.contagion.html
@@ -7,7 +7,7 @@
algorithms.contagion package — HyperNetX 1.1.3 documentation
+ algorithms.contagion package — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -108,6 +108,7 @@
Submodules algorithms.generative_models module algorithms.homology_mod2 module algorithms.hypergraph_modularity module algorithms.laplacians_clustering module algorithms.s_centrality_measures module Module contents NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License algorithms package — HyperNetX 1.1.3 documentation
+ algorithms package — HyperNetX 1.2 documentation
@@ -71,7 +71,7 @@
- 1.1
+ 1.2
@@ -109,6 +109,7 @@
Submodules algorithms.generative_models module algorithms.homology_mod2 module algorithms.hypergraph_modularity module algorithms.laplacians_clustering module algorithms.s_centrality_measures module Module contents NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License Submodules
@@ -505,7 +510,7 @@
Homology Mod2[source] Compute a basis for the kth-simplicial homology group, \(H_k\) , defined by a
-chain complex \(C\) with boundary maps given by bd \(= \{k:\partial_k\) }$
+chain complex \(C\) with boundary maps given by bd \(= \{k:\partial_k \}\)
Parameters
@@ -804,6 +809,246 @@ Homology Mod2
diff --git a/docs/build/algorithms/modules.html b/docs/build/algorithms/modules.html
index 03ed0491..fe64cff6 100644
--- a/docs/build/algorithms/modules.html
+++ b/docs/build/algorithms/modules.html
@@ -7,7 +7,7 @@
-
algorithms — HyperNetX 1.1.3 documentation
+
algorithms — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -112,6 +112,7 @@
NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -207,6 +208,10 @@
algorithms.laplacians_clustering module
diff --git a/docs/build/classes/classes.html b/docs/build/classes/classes.html
index 8bf9fcc3..43d031d2 100644
--- a/docs/build/classes/classes.html
+++ b/docs/build/classes/classes.html
@@ -7,7 +7,7 @@
classes package — HyperNetX 1.1.3 documentation
+ classes package — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -119,6 +119,7 @@
NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -1248,6 +1249,9 @@
Submodules classes — HyperNetX 1.1.3 documentation
+ classes — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -112,6 +112,7 @@
NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
diff --git a/docs/build/core.html b/docs/build/core.html
index 71612045..7def6ab2 100644
--- a/docs/build/core.html
+++ b/docs/build/core.html
@@ -7,7 +7,7 @@
-
HyperNetX Packages — HyperNetX 1.1.3 documentation
+
HyperNetX Packages — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -109,6 +109,7 @@
NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -214,6 +215,10 @@
+
algorithms.hypergraph_modularity module
+algorithms.laplacians_clustering module
diff --git a/docs/build/drawing/drawing.html b/docs/build/drawing/drawing.html
index 588f3c19..a632acf4 100644
--- a/docs/build/drawing/drawing.html
+++ b/docs/build/drawing/drawing.html
@@ -7,7 +7,7 @@
drawing package — HyperNetX 1.1.3 documentation
+ drawing package — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -119,6 +119,7 @@
NWHypergraph C++ Optimization HyperNetX Visualization Widget
+
Algorithms: Modularity and Clustering Publications License
@@ -242,7 +243,7 @@
Submodules) – the entity to be drawn
pos (dict ) – mapping of node and edge positions to R^2
with_color (bool ) – set to False to disable color cycling of edges
-
with_node_counts (bool ) – set to True to label collapsed nodes with number of elements
+
with_node_counts (bool ) – set to True to replace the label for collapsed nodes with the number of elements
with_edge_counts (bool ) – set to True to label collapsed edges with number of elements
layout (function ) – layout algorithm to compute
layout_kwargs (dict ) – keyword arguments passed to layout function
@@ -529,6 +530,11 @@
Submodules
levelset() (classes.entity.Entity method)
+linear() (in module algorithms.hypergraph_modularity)
logical_dot() (in module algorithms.homology_mod2)
L
M
merge_entities() (classes.entity.Entity static method)
+modularity() (in module algorithms.hypergraph_modularity)
module
@@ -737,6 +759,8 @@ M
algorithms.generative_models
algorithms.homology_mod2
+algorithms.hypergraph_modularity
algorithms.laplacians_clustering
O
P
diff --git a/docs/build/glossary.html b/docs/build/glossary.html
index 6e839de2..b6de0188 100644
--- a/docs/build/glossary.html
+++ b/docs/build/glossary.html
@@ -7,7 +7,7 @@
Glossary of HNX terms — HyperNetX 1.1.3 documentation
+ Glossary of HNX terms — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -103,6 +103,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License HyperNetX (HNX) — HyperNetX 1.1.3 documentation
+ HyperNetX (HNX) — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -106,6 +106,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License HyperNetX (HNX) — HyperNetX 1.1.3 documentation
+ HyperNetX (HNX) — HyperNetX 1.2 documentation
@@ -69,7 +69,7 @@
- 1.1
+ 1.2
@@ -102,6 +102,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License License — HyperNetX 1.1.3 documentation
+ License — HyperNetX 1.2 documentation
@@ -69,7 +69,7 @@
- 1.1
+ 1.2
@@ -102,6 +102,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License Modularity and Clustering — HyperNetX 1.2 documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ HyperNetX
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Modularity and Clustering
+
+
+
Overview
+
The hypergraph_modularity submodule in HNX provides functions to compute hypergraph modularity for a
+given partition of the vertices in a hypergraph. In general, higher modularity indicates a better
+partitioning of the vertices into dense communities.
+
Two functions to generate such hypergraph
+partitions are provided: Kumar’s algorithm, and the simple Last-Step refinement algorithm.
+
The submodule also provides a function to generate the two-section graph for a given hypergraph which can then be used to find
+vertex partitions via graph-based algorithms.
+
+
+
Installation
+
Since it is part of HNX, no extra installation is required.
+The submodule can be imported as follows:
+
import hypernetx.algorithms.hypergraph_modularity as hmod
+
+
+
+
+
Using the Tool
+
+
Precomputation
+
In order to make the computation of hypergraph modularity more efficient, some quantities need to be pre-computed.
+Given hypergraph H, calling:
+
HG = hmod . precompute_attributes ( H )
+
+
+
will pre-compute quantities such as node strength (weighted degree), d-weights (total weight for each edge cardinality) and binomial coefficients.
+
+
+
Modularity
+
Given hypergraph HG and a partition A of its vertices, hypergraph modularity is a measure of the quality of this partition.
+Random partitions typically yield modularity near zero (it can be negative) while positive modularity is indicative of the presence
+of dense communities, or modules. There are several variations for the definition of hypergraph modularity, and the main difference lies in the
+weight given to different edges. Modularity is computed via:
+
q = hmod . modularity ( HG , A , wdc = linear )
+
+
+
In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increases the modularity)
+or between communities.
+
With hypergraphs, we consider edges of size d=2 or more. Given some vertex partition A and some d -edge e , let c be the number of nodes
+that belong to the most represented part in e ; if c > d/2 , we consider this edge to be within the part.
+Hyper-parameters 0 <= w(d,c) <= 1 control the weight
+given to such edges. Three functions are supplied in this submodule, namely:
+
+linear \(w(d,c) = c/d\) if \(c > d/2\) , else \(0\) .
+majority \(w(d,c) = 1\) if \(c > d/2\) , else \(0\) .
+strict \(w(d,c) = 1\) if \(c == d\) , else \(0\) .
+
+
The ‘linear’ function is used by default. More details in [2].
+
+
+
Two-section graph
+
There are several good partitioning algorithms for graphs such as the Louvain algorithm and ECG, a consensus clustering algorithm.
+One way to obtain a partition for hypergraph HG is to build its corresponding two-section graph G and run a graph clustering algorithm.
+Code is provided to build such graph via:
+
G = hmod . two_section ( HG )
+
+
+
which returns an igraph.Graph object.
+
+
+
Clustering Algorithms
+
Two clustering (vertex partitioning) algorithms are supplied. The first one is a hybrid method proposed by Kumar et al. (see [1])
+that uses the Louvain algorithm on the two-section graph, but re-weights the edges according to the distibution of vertices
+from each part inside each edge. Given hypergraph HG, this is called as:
+
+
The other supplied algorithm is a simple method to improve hypergraph modularity directely. Given some
+initial partition of the vertices (for example via Louvain on the two-section graph), move vertices between parts in order
+to improve hypergraph modularity. Given hypergraph HG and initial partition A, this is called as:
+
L = hmod . last_step ( HG , A , wdc = linear )
+
+
+
where the ‘wdc’ parameter is the same as in the modularity function.
+
+
+
Other Features
+
We represent a vertex partition A as a list of sets, but another conveninent representation is via a dictionary.
+We provide two utility functions to switch representation, namely A = dict2part(D) and D = part2dict(A) .
+
+
+
References
+
[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
+
[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
+
+
+
+
+
+
+
+
+
+
+
+
+
NWHy — HyperNetX 1.1.3 documentation
+ NWHy — HyperNetX 1.2 documentation
@@ -71,7 +71,7 @@
- 1.1
+ 1.2
@@ -125,6 +125,7 @@
HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License Overview — HyperNetX 1.1.3 documentation
+ Overview — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -100,6 +100,7 @@
Overview
+
+
+ algorithms.hypergraph_modularity
+
diff --git a/docs/build/reports/modules.html b/docs/build/reports/modules.html
index e2b83705..0ae521ba 100644
--- a/docs/build/reports/modules.html
+++ b/docs/build/reports/modules.html
@@ -7,7 +7,7 @@
reports — HyperNetX 1.1.3 documentation
+ reports — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -112,6 +112,7 @@
NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License reports package — HyperNetX 1.1.3 documentation
+ reports package — HyperNetX 1.2 documentation
@@ -70,7 +70,7 @@
- 1.1
+ 1.2
@@ -117,6 +117,7 @@
NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License Search — HyperNetX 1.1.3 documentation
+ Search — HyperNetX 1.2 documentation
@@ -71,7 +71,7 @@
- 1.1
+ 1.2
@@ -104,6 +104,7 @@
HyperNetX Packages NWHypergraph C++ Optimization HyperNetX Visualization Widget Algorithms: Modularity and Clustering Publications License Hypernetx-Widget — HyperNetX 1.1.3 documentation
+ Hypernetx-Widget — HyperNetX 1.2 documentation
@@ -42,7 +42,7 @@
- 1.1
+ 1.2
@@ -114,6 +114,7 @@
+Algorithms: Modularity and Clustering Publications License Other FeaturesNext
+ Next
HyperNetX Visualization Widget
+ Algorithms: Modularity and Clustering
Publications
license
diff --git a/docs/source/modularity.rst b/docs/source/modularity.rst
new file mode 100644
index 00000000..8738ced1
--- /dev/null
+++ b/docs/source/modularity.rst
@@ -0,0 +1,114 @@
+.. _modularity:
+
+
+=========================
+Modularity and Clustering
+=========================
+
+.. image:: images/ModularityScreenShot.png
+ :width: 300px
+ :align: right
+
+Overview
+--------
+The hypergraph_modularity submodule in HNX provides functions to compute **hypergraph modularity** for a
+given partition of the vertices in a hypergraph. In general, higher modularity indicates a better
+partitioning of the vertices into dense communities.
+
+Two functions to generate such hypergraph
+partitions are provided: **Kumar's** algorithm, and the simple **Last-Step** refinement algorithm.
+
+The submodule also provides a function to generate the **two-section graph** for a given hypergraph which can then be used to find
+vertex partitions via graph-based algorithms.
+
+
+Installation
+------------
+Since it is part of HNX, no extra installation is required.
+The submodule can be imported as follows::
+
+ import hypernetx.algorithms.hypergraph_modularity as hmod
+
+Using the Tool
+--------------
+
+
+Precomputation
+^^^^^^^^^^^^^^
+
+In order to make the computation of hypergraph modularity more efficient, some quantities need to be pre-computed.
+Given hypergraph H, calling::
+
+ HG = hmod.precompute_attributes(H)
+
+will pre-compute quantities such as node strength (weighted degree), d-weights (total weight for each edge cardinality) and binomial coefficients.
+
+Modularity
+^^^^^^^^^^
+
+Given hypergraph HG and a partition A of its vertices, hypergraph modularity is a measure of the quality of this partition.
+Random partitions typically yield modularity near zero (it can be negative) while positive modularity is indicative of the presence
+of dense communities, or modules. There are several variations for the definition of hypergraph modularity, and the main difference lies in the
+weight given to different edges. Modularity is computed via::
+
+ q = hmod.modularity(HG, A, wdc=linear)
+
+In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increases the modularity)
+or between communities.
+
+With hypergraphs, we consider edges of size *d=2* or more. Given some vertex partition A and some *d*-edge *e*, let *c* be the number of nodes
+that belong to the most represented part in *e*; if *c > d/2*, we consider this edge to be within the part.
+Hyper-parameters *0 <= w(d,c) <= 1* control the weight
+given to such edges. Three functions are supplied in this submodule, namely:
+
+**linear**
+ $w(d,c) = c/d$ if $c > d/2$, else $0$.
+**majority**
+ $w(d,c) = 1$ if $c > d/2$, else $0$.
+**strict**
+ $w(d,c) = 1$ if $c == d$, else $0$.
+
+The 'linear' function is used by default. More details in [2].
+
+Two-section graph
+^^^^^^^^^^^^^^^^^
+
+There are several good partitioning algorithms for graphs such as the Louvain algorithm and ECG, a consensus clustering algorithm.
+One way to obtain a partition for hypergraph HG is to build its corresponding two-section graph G and run a graph clustering algorithm.
+Code is provided to build such graph via::
+
+ G = hmod.two_section(HG)
+
+which returns an igraph.Graph object.
+
+
+Clustering Algorithms
+^^^^^^^^^^^^^^^^^^^^^
+
+Two clustering (vertex partitioning) algorithms are supplied. The first one is a hybrid method proposed by Kumar et al. (see [1])
+that uses the Louvain algorithm on the two-section graph, but re-weights the edges according to the distibution of vertices
+from each part inside each edge. Given hypergraph HG, this is called as::
+
+ K = hmod.kumar(HG)
+
+The other supplied algorithm is a simple method to improve hypergraph modularity directely. Given some
+initial partition of the vertices (for example via Louvain on the two-section graph), move vertices between parts in order
+to improve hypergraph modularity. Given hypergraph HG and initial partition A, this is called as::
+
+ L = hmod.last_step(HG, A, wdc=linear)
+
+where the 'wdc' parameter is the same as in the modularity function.
+
+
+Other Features
+^^^^^^^^^^^^^^
+
+We represent a vertex partition A as a list of sets, but another conveninent representation is via a dictionary.
+We provide two utility functions to switch representation, namely `A = dict2part(D)` and `D = part2dict(A)`.
+
+References
+^^^^^^^^^^
+[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
+
+[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
+
diff --git a/docs/source/overview/index.rst b/docs/source/overview/index.rst
index f7e2a271..30cb329a 100644
--- a/docs/source/overview/index.rst
+++ b/docs/source/overview/index.rst
@@ -20,8 +20,8 @@ PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830
* Visualization: Dustin Arendt, Ji Young Yun
* High Performance Computing: Tony Liu, Andrew Lumsdaine
* Principal Investigator: Cliff Joslyn
-* Program Manager: Mark Raugas, Brian Kritzstein
-* Mathematics, methods, and algorithms: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Tony Liu, Andrew Lumsdaine, Brenda Praggastis, and Emilie Purvine
+* Program Manager: Brian Kritzstein
+* Mathematics, methods, and algorithms: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Tony Liu, Andrew Lumsdaine, Brenda Praggastis, and Emilie Purvine, François Théberge
@@ -44,6 +44,10 @@ New Features in Version 1.1
#. Clustering module for clustering vertices based on hyperedge incidence and weighting.
#. Generator module for synthetic generation of ChungLu and DCSBM hypergraphs.
+New Features in Version 1.2
+---------------------------
+#. Added algorithm module and tutorial for Modularity and Clustering
+
.. _colab:
diff --git a/hypernetx/algorithms/__init__.py b/hypernetx/algorithms/__init__.py
index 6e9889fc..a3b6fd6a 100644
--- a/hypernetx/algorithms/__init__.py
+++ b/hypernetx/algorithms/__init__.py
@@ -3,3 +3,4 @@
from .contagion import *
from .laplacians_clustering import *
from .generative_models import *
+from .hypergraph_modularity import *
diff --git a/hypernetx/algorithms/generative_models.py b/hypernetx/algorithms/generative_models.py
index 5687e35e..fe74c81a 100644
--- a/hypernetx/algorithms/generative_models.py
+++ b/hypernetx/algorithms/generative_models.py
@@ -29,13 +29,15 @@ def erdos_renyi_hypergraph(n, m, p, node_labels=None, edge_labels=None):
-------
HyperNetX Hypergraph object
+
Example::
+
+ >>> import hypernetx.algorithms.generative_models as gm
+ >>> n = 1000
+ >>> m = n
+ >>> p = 0.01
+ >>> H = gm.erdos_renyi_hypergraph(n, m, p)
- >>> import hypernetx.algorithms.generative_models as gm
- >>> n = 1000
- >>> m = n
- >>> p = 0.01
- >>> H = gm.erdos_renyi_hypergraph(n, m, p)
"""
if node_labels is not None and edge_labels is not None:
@@ -83,12 +85,12 @@ def chung_lu_hypergraph(k1, k2):
Example::
- >>> import hypernetx.algorithms.generative_models as gm
- >>> import random
- >>> n = 100
- >>> k1 = {i : random.randint(1, 100) for i in range(n)}
- >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
- >>> H = gm.chung_lu_hypergraph(k1, k2)
+ >>> import hypernetx.algorithms.generative_models as gm
+ >>> import random
+ >>> n = 100
+ >>> k1 = {i : random.randint(1, 100) for i in range(n)}
+ >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
+ >>> H = gm.chung_lu_hypergraph(k1, k2)
"""
# sort dictionary by degree in decreasing order
@@ -166,13 +168,13 @@ def dcsbm_hypergraph(k1, k2, g1, g2, omega):
Example::
- >>> n = 100
- >>> k1 = {i : random.randint(1, 100) for i in range(n)}
- >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
- >>> g1 = {i : random.choice([0, 1]) for i in range(n)}
- >>> g2 = {i : random.choice([0, 1]) for i in range(n)}
- >>> omega = np.array([[100, 10], [10, 100]])
- >>> H = gm.dcsbm_hypergraph(k1, k2, g1, g2, omega)
+ >>> n = 100
+ >>> k1 = {i : random.randint(1, 100) for i in range(n)}
+ >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
+ >>> g1 = {i : random.choice([0, 1]) for i in range(n)}
+ >>> g2 = {i : random.choice([0, 1]) for i in range(n)}
+ >>> omega = np.array([[100, 10], [10, 100]])
+ >>> H = gm.dcsbm_hypergraph(k1, k2, g1, g2, omega)
"""
# sort dictionary by degree in decreasing order
diff --git a/hypernetx/algorithms/homology_mod2.py b/hypernetx/algorithms/homology_mod2.py
index 199b2c5f..363b3677 100644
--- a/hypernetx/algorithms/homology_mod2.py
+++ b/hypernetx/algorithms/homology_mod2.py
@@ -759,7 +759,7 @@ def betti_numbers(h, k=None):
def homology_basis(bd, k=None, boundary=False, **kwargs):
"""
Compute a basis for the kth-simplicial homology group, $H_k$, defined by a
- chain complex $C$ with boundary maps given by bd $= \{k:\partial_k$\}$
+ chain complex $C$ with boundary maps given by bd $= \{k:\partial_k \}$
Parameters
----------
diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py
new file mode 100644
index 00000000..bfe63f97
--- /dev/null
+++ b/hypernetx/algorithms/hypergraph_modularity.py
@@ -0,0 +1,555 @@
+"""
+Hypergraph_Modularity
+---------------------
+Modularity and clustering for hypergraphs using HyperNetX.
+Adapted from F. Théberge's GitHub repository: `Hypergraph Clustering `_
+See Tutorial 13 in the tutorials folder for library usage.
+
+References
+----------
+.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. "A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering". In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
+.. [2] Kamiński B., Prałat P. and Théberge F. "Community Detection Algorithm Using Hypergraph Modularity". In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
+.. [3] Kamiński B., Poulin V., Prałat P., Szufel P. and Théberge F. "Clustering via hypergraph modularity", Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307
+"""
+
+from collections import Counter
+import numpy as np
+from functools import reduce
+import igraph as ig
+import itertools
+from scipy.special import comb
+
+################################################################################
+
+# we use 2 representations for partitions (0-based part ids):
+# (1) dictionary or (2) list of sets
+
+
+def dict2part(D):
+ """
+ Given a dictionary mapping the part for each vertex, return a partition as a list of sets; inverse function to part2dict
+
+ Parameters
+ ----------
+ D : dict
+ Dictionary keyed by vertices with values equal to integer
+ index of the partition the vertex belongs to
+
+ Returns
+ -------
+ : list
+ List of sets; one set for each part in the partition
+ """
+ P = []
+ k = list(D.keys())
+ v = list(D.values())
+ for x in range(max(D.values()) + 1):
+ P.append(set([k[i] for i in range(len(k)) if v[i] == x]))
+ return P
+
+
+def part2dict(A):
+ """
+ Given a partition (list of sets), returns a dictionary mapping the part for each vertex; inverse function
+ to dict2part
+
+ Parameters
+ ----------
+ A : list of sets
+ a partition of the vertices
+
+ Returns
+ -------
+ : dict
+ a dictionary with {vertex: partition index}
+ """
+ x = []
+ for i in range(len(A)):
+ x.extend([(a, i) for a in A[i]])
+ return {k: v for k, v in x}
+
+################################################################################
+
+
+def precompute_attributes(HG):
+ """
+ Precompute some values on hypergraph HG for faster computing of hypergraph modularity.
+ This needs to be run before calling either modularity() or last_step().
+
+ Note
+ ----
+
+ If HG is unweighted, v.weight is set to 1 for each vertex v in HG.
+ The weighted degree for each vertex v is stored in v.strength.
+ The total edge weigths for each edge cardinality is stored in HG.d_weights.
+ Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef.
+ Isolated vertices found only in edge(s) of size 1 are dropped.
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ Returns
+ -------
+ H : Hypergraph
+ New hypergraph with added attributes
+
+ """
+ H = HG.remove_singletons()
+ # 1. compute node strenghts (weighted degrees)
+ for v in H.nodes:
+ H.nodes[v].strength = 0
+ for e in H.edges:
+ try:
+ w = H.edges[e].weight
+ except:
+ w = 1
+ # add unit weight if none to simplify other functions
+ H.edges[e].weight = 1
+ for v in list(H.edges[e]):
+ H.nodes[v].strength += w
+ # 2. compute d-weights
+ ctr = Counter([len(H.edges[e]) for e in H.edges])
+ for k in ctr.keys():
+ ctr[k] = 0
+ for e in H.edges:
+ ctr[len(H.edges[e])] += H.edges[e].weight
+ H.d_weights = ctr
+ H.total_weight = sum(ctr.values())
+ # 3. compute binomial coeffcients (modularity speed-up)
+ bin_coef = {}
+ for n in H.d_weights.keys():
+ for k in np.arange(n // 2 + 1, n + 1):
+ bin_coef[(n, k)] = comb(n, k, exact=True)
+ H.bin_coef = bin_coef
+ return H
+
+################################################################################
+
+
+def linear(d, c):
+ """
+ Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
+ This is the default choice for modularity() and last_step() functions.
+
+ Parameters
+ ----------
+ d : int
+ Number of vertices in an edge
+ c : int
+ Number of vertices in the majority class
+
+ Returns
+ -------
+ : float
+ c/d if c>d/2 else 0
+ """
+ return c / d if c > d / 2 else 0
+
+# majority
+
+
+def majority(d, c):
+ """
+ Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
+ This corresponds to the majority rule [3]_
+
+ Parameters
+ ----------
+ d : int
+ Number of vertices in an edge
+ c : int
+ Number of vertices in the majority class
+
+ Returns
+ -------
+ : bool
+ 1 if c>d/2 else 0
+
+ """
+ return 1 if c > d / 2 else 0
+
+# strict
+
+
+def strict(d, c):
+ """
+ Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
+ This corresponds to the strict rule [3]_
+
+ Parameters
+ ----------
+ d : int
+ Number of vertices in an edge
+ c : int
+ Number of vertices in the majority class
+
+ Returns
+ -------
+ : bool
+ 1 if c==d else 0
+ """
+ return 1 if c == d else 0
+
+#########################################
+
+
+def _compute_partition_probas(HG, A):
+ """
+ Compute vol(A_i)/vol(V) for each part A_i in A (list of sets)
+
+ Parameters
+ ----------
+ HG : Hypergraph
+ A : list of sets
+
+ Returns
+ -------
+ : list
+ normalized distribution of strengths in partition elements
+ """
+ p = []
+ for part in A:
+ vol = 0
+ for v in part:
+ vol += HG.nodes[v].strength
+ p.append(vol)
+ s = sum(p)
+ return [i / s for i in p]
+
+
+def _degree_tax(HG, Pr, wdc):
+ """
+ Computes the expected fraction of edges falling in
+ the partition as per [2]_
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ Pr : list
+ Probability distribution
+ wdc : func
+ weight function for edge contribution (ex: strict, majority, linear)
+
+ Returns
+ -------
+ float
+
+ """
+ DT = 0
+ for d in HG.d_weights.keys():
+ tax = 0
+ for c in np.arange(d // 2 + 1, d + 1):
+ for p in Pr:
+ tax += p**c * (1 - p)**(d - c) * HG.bin_coef[(d, c)] * wdc(d, c)
+ tax *= HG.d_weights[d]
+ DT += tax
+ DT /= HG.total_weight
+ return DT
+
+
+def _edge_contribution(HG, A, wdc):
+ """
+ Edge contribution from hypergraph with respect
+ to partion A.
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ A : list of sets
+
+ wdc : func
+ weight function (ex: strict, majority, linear)
+
+ Returns
+ -------
+ : float
+
+ """
+ EC = 0
+ for e in HG.edges:
+ d = HG.size(e)
+ for part in A:
+ if HG.size(e, part) > d / 2:
+ EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight
+ EC /= HG.total_weight
+ return EC
+
+# HG: HNX hypergraph
+# A: partition (list of sets)
+# wcd: weight function (ex: strict, majority, linear)
+
+
+def modularity(HG, A, wdc=linear):
+ """
+ Computes modularity of hypergraph HG with respect to partition A.
+
+ Parameters
+ ----------
+ HG : Hypergraph
+ The hypergraph with some precomputed attributes via: precompute_attributes(HG)
+ A : list of sets
+ Partition of the vertices in HG
+ wdc : func, optional
+ Hyperparameter for hypergraph modularity [2]_
+
+ Note
+ ----
+ For 'wdc', any function of the format w(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used.
+ Default is 'linear'; other supplied choices are 'majority' and 'strict'.
+
+ Returns
+ -------
+ : float
+ The modularity function for partition A on HG
+ """
+ Pr = _compute_partition_probas(HG, A)
+ return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc)
+
+################################################################################
+
+
+def two_section(HG):
+ """
+ Creates a random walk based [1]_ 2-section igraph Graph with transition weights defined by the
+ weights of the hyperedges.
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ Returns
+ -------
+ : igraph.Graph
+ The 2-section graph built from HG
+ """
+ s = []
+ for e in HG.edges:
+ E = HG.edges[e]
+ # random-walk 2-section (preserve nodes' weighted degrees)
+ if len(E) > 1:
+ try:
+ w = HG.edges[e].weight / (len(E) - 1)
+ except:
+ w = 1 / (len(E) - 1)
+ s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)])
+ G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum')
+ return G
+
+################################################################################
+
+
+def kumar(HG, delta=.01):
+ """
+ Compute a partition of the vertices in hypergraph HG as per Kumar's algorithm [1]_
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ delta : float, optional
+ convergence stopping criterion
+
+ Returns
+ -------
+ : list of sets
+ A partition of the vertices in HG
+
+ """
+ # weights will be modified -- store initial weights
+ W = {e: HG.edges[e].weight for e in HG.edges} # uses edge id for reference instead of int
+ # build graph
+ G = two_section(HG)
+ # apply clustering
+ CG = G.community_multilevel(weights='weight')
+ CH = []
+ for comm in CG.as_cover():
+ CH.append(set([G.vs[x]['name'] for x in comm]))
+
+ # LOOP
+ diff = 1
+ ctr = 0
+ while diff > delta:
+ # re-weight
+ diff = 0
+ for e in HG.edges:
+ edge = HG.edges[e]
+ reweight = sum([1 / (1 + HG.size(e, c)) for c in CH]) * (HG.size(e) + len(CH)) / HG.number_of_edges()
+ diff = max(diff, 0.5 * abs(edge.weight - reweight))
+ edge.weight = 0.5 * edge.weight + 0.5 * reweight
+ # re-run louvain
+ # build graph
+ G = two_section(HG)
+ # apply clustering
+ CG = G.community_multilevel(weights='weight')
+ CH = []
+ for comm in CG.as_cover():
+ CH.append(set([G.vs[x]['name'] for x in comm]))
+ ctr += 1
+ if ctr > 50: # this process sometimes gets stuck -- set limit
+ break
+ G.vs['part'] = CG.membership
+ for e in HG.edges:
+ HG.edges[e].weight = W[e]
+ return dict2part({v['name']: v['part'] for v in G.vs})
+
+################################################################################
+
+
+def _delta_ec(HG, P, v, a, b, wdc):
+ """
+ Computes change in edge contribution --
+ partition P, node v going from P[a] to P[b]
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ P : list of sets
+
+ v : int or str
+ node identifier
+ a : int
+
+ b : int
+
+ wdc : func
+ weight function (ex: strict, majority, linear)
+
+ Returns
+ -------
+ : float
+ """
+ Pm = P[a] - {v}
+ Pn = P[b].union({v})
+ ec = 0
+ for e in list(HG.nodes[v].memberships):
+ d = HG.size(e)
+ w = HG.edges[e].weight
+ ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn))
+ - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b])))
+ return ec / HG.total_weight
+
+
+def _bin_ppmf(d, c, p):
+ """
+ exponential part of the binomial pmf
+
+ Parameters
+ ----------
+ d : int
+
+ c : int
+
+ p : float
+
+
+ Returns
+ -------
+ : float
+
+ """
+ return p**c * (1 - p)**(d - c)
+
+
+def _delta_dt(HG, P, v, a, b, wdc):
+ """
+ Compute change in degree tax --
+ partition P (list), node v going from P[a] to P[b]
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ P : list of sets
+
+ v : int or str
+ node identifier
+ a : int
+
+ b : int
+
+ wdc : func
+ weight function (ex: strict, majority, linear)
+
+ Returns
+ -------
+ : float
+
+ """
+ s = HG.nodes[v].strength
+ vol = sum([HG.nodes[v].strength for v in HG.nodes])
+ vola = sum([HG.nodes[v].strength for v in P[a]])
+ volb = sum([HG.nodes[v].strength for v in P[b]])
+ volm = (vola - s) / vol
+ voln = (volb + s) / vol
+ vola /= vol
+ volb /= vol
+ DT = 0
+
+ for d in HG.d_weights.keys():
+ x = 0
+ for c in np.arange(int(np.floor(d / 2)) + 1, d + 1):
+ x += HG.bin_coef[(d, c)] * wdc(d, c) * (_bin_ppmf(d, c, voln) + _bin_ppmf(d, c, volm)
+ - _bin_ppmf(d, c, vola) - _bin_ppmf(d, c, volb))
+ DT += x * HG.d_weights[d]
+ return DT / HG.total_weight
+
+
+def last_step(HG, L, wdc=linear, delta=.01):
+ """
+ Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm [2]_
+
+ Note
+ ----
+ This is a very simple algorithm that tries moving nodes between communities to improve hypergraph modularity.
+ It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG,
+ or via Kumar's algorithm.
+
+ Parameters
+ ----------
+ HG : Hypergraph
+
+ L : list of sets
+ some initial partition of the vertices in HG
+
+ wdc : func, optional
+ Hyperparameter for hypergraph modularity [2]_
+
+ delta : float, optional
+ convergence stopping criterion
+
+ Returns
+ -------
+ : list of sets
+ A new partition for the vertices in HG
+ """
+ A = L[:] # we will modify this, copy
+ D = part2dict(A)
+ qH = 0
+ while True:
+ for v in list(np.random.permutation(list(HG.nodes))):
+ c = D[v]
+ s = list(set([c] + [D[i] for i in HG.neighbors(v)]))
+ M = []
+ if len(s) > 0:
+ for i in s:
+ if c == i:
+ M.append(0)
+ else:
+ M.append(_delta_ec(HG, A, v, c, i, wdc) - _delta_dt(HG, A, v, c, i, wdc))
+ i = s[np.argmax(M)]
+ if c != i:
+ A[c] = A[c] - {v}
+ A[i] = A[i].union({v})
+ D[v] = i
+ Pr = _compute_partition_probas(HG, A)
+ q2 = _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc)
+ if (q2 - qH) < delta:
+ break
+ qH = q2
+ return [a for a in A if len(a) > 0]
+
+################################################################################
diff --git a/hypernetx/algorithms/tests/conftest.py b/hypernetx/algorithms/tests/conftest.py
index f3da6c41..895bbb6a 100644
--- a/hypernetx/algorithms/tests/conftest.py
+++ b/hypernetx/algorithms/tests/conftest.py
@@ -127,6 +127,22 @@ def __init__(self):
self.hypergraph = hnx.Hypergraph.from_numpy_array(mat)
+class ModularityExample:
+ """
+ ## build a hypergraph from a list of sets (the hyperedges)
+ """
+
+ def __init__(self):
+ E = [{'A', 'B'}, {'A', 'C'}, {'A', 'B', 'C'}, {'A', 'D', 'E', 'F'}, {'D', 'F'}, {'E', 'F'}]
+ self.E = E
+ self.HG = hnx.Hypergraph(E, static=True)
+ A1 = [{'A', 'B', 'C'}, {'D', 'E', 'F'}]
+ A2 = [{'B', 'C'}, {'A', 'D', 'E', 'F'}]
+ A3 = [{'A', 'B', 'C', 'D', 'E', 'F'}]
+ A4 = [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]
+ self.partitions = [A1, A2, A3, A4]
+
+
@pytest.fixture
def triloop():
return TriLoop()
@@ -145,3 +161,8 @@ def bigfish():
@pytest.fixture
def sixbyfive():
return SixByFive()
+
+
+@pytest.fixture
+def modularityexample():
+ return ModularityExample()
diff --git a/hypernetx/algorithms/tests/test_modularity.py b/hypernetx/algorithms/tests/test_modularity.py
new file mode 100644
index 00000000..c4f9540a
--- /dev/null
+++ b/hypernetx/algorithms/tests/test_modularity.py
@@ -0,0 +1,33 @@
+import numpy as np
+import pytest
+import warnings
+from hypernetx.algorithms.hypergraph_modularity import *
+import random
+import hypernetx as hnx
+
+warnings.simplefilter("ignore")
+
+
+def test_precompute(modularityexample):
+ HG = modularityexample.HG
+ HG = precompute_attributes(HG)
+ assert HG.nodes['F'].strength == 3
+ assert HG.total_weight == 6
+ assert HG.edges['e2'].weight == 1
+
+
+def test_modularity(modularityexample):
+ HG = modularityexample.HG
+ A1, A2, A3, A4 = modularityexample.partitions
+ HG = precompute_attributes(HG)
+ assert np.abs(modularity(HG, A1) - 0.41444526) < 10e-5
+ assert np.abs(modularity(HG, A1, strict) - 0.434906995) < 10e-5
+ assert np.abs(modularity(HG, A1, majority) - 0.39379753) < 10e-5
+
+
+def test_clustering(modularityexample):
+ HG = modularityexample.HG
+ A1, A2, A3, A4 = modularityexample.partitions
+ HG = precompute_attributes(HG)
+ assert {'A', 'B', 'C'} in kumar(HG)
+ assert {'C', 'A', 'B'} in last_step(HG, A4)
diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py
index 0af2c2ce..cf6c04d6 100644
--- a/hypernetx/classes/hypergraph.py
+++ b/hypernetx/classes/hypergraph.py
@@ -168,6 +168,9 @@ def __init__(
self._edges = E
self._nodes = E.restrict_to_levels([1], weights=False, aggregateby=None)
self._nodes._memberships = E.memberships
+ for n in self._nodes:
+ self._nodes[n].memberships = self._nodes._memberships[n] ### a bit of a hack to get same functionality from static as dynamic
+ ### we will have to see if it slows things down too much
else:
self._static = False
if setsystem is None:
@@ -568,17 +571,18 @@ def convert_to_static(
filepath : None, optional, default : False
Description
+ Returned
+ ------------------
+ hnx.Hypergraph
+ Will have attribute static = True
+
Note
----
Static hypergraphs store the user defined node and edge names in
a dictionary of labeled lists. The order of the lists provides an
index, which the hypergraph uses in place of the node and edge names
- for fast processing.
+ for faster processing.
- No Longer Returned
- ------------------
- hnx.Hypergraph
- Will have attribute static = True
"""
if self.isstatic:
return self
@@ -1142,7 +1146,7 @@ def _incidence_to_adjacency(M, s=1, weights=False):
"""
Helper method to obtain adjacency matrix from
boolean incidence matrix for s-metrics.
- Self loops are note supported.
+ Self loops are not supported.
The adjacency matrix will define an s-linegraph.
Parameters
diff --git a/hypernetx/classes/staticentity.py b/hypernetx/classes/staticentity.py
index a84fd71b..8e8f59ea 100644
--- a/hypernetx/classes/staticentity.py
+++ b/hypernetx/classes/staticentity.py
@@ -62,7 +62,7 @@ def __init__(
arr=None,
labels=None,
uid=None,
- weights=None,
+ weights=None, ### in this context weights is just a column of values corresponding to the rows in data.
keep_weights=True,
aggregateby="sum",
**props,
diff --git a/hypernetx/drawing/rubber_band.py b/hypernetx/drawing/rubber_band.py
index 3473e953..55b32749 100644
--- a/hypernetx/drawing/rubber_band.py
+++ b/hypernetx/drawing/rubber_band.py
@@ -4,6 +4,7 @@
from hypernetx import Hypergraph
from .util import (
get_frozenset_label,
+ get_collapsed_size,
get_set_layering,
inflate_kwargs,
transpose_inflated_kwargs,
@@ -325,7 +326,6 @@ def draw_hyper_labels(H, pos, node_radius={}, ax=None, labels={}, **kwargs):
}
)
-
def draw(
H,
pos=None,
@@ -397,7 +397,7 @@ def draw(
with_color: bool
set to False to disable color cycling of edges
with_node_counts: bool
- set to True to label collapsed nodes with number of elements
+ set to True to replace the label for collapsed nodes with the number of elements
with_edge_counts: bool
set to True to label collapsed edges with number of elements
layout: function
@@ -432,9 +432,11 @@ def draw(
r0 = get_default_radius(H, pos)
a0 = np.pi * r0 ** 2
+
+
def get_node_radius(v):
if node_radius is None:
- return np.sqrt(a0 * (len(v) if type(v) == frozenset else 1) / np.pi)
+ return np.sqrt(a0 * get_collapsed_size(v) / np.pi)
elif hasattr(node_radius, "get"):
return node_radius.get(v, 1) * r0
return node_radius * r0
diff --git a/hypernetx/drawing/util.py b/hypernetx/drawing/util.py
index 7846ace8..67d16968 100644
--- a/hypernetx/drawing/util.py
+++ b/hypernetx/drawing/util.py
@@ -42,6 +42,15 @@ def transpose_inflated_kwargs(inflated):
return [dict(zip(inflated, v)) for v in zip(*inflated.values())]
+def get_collapsed_size(v):
+ try:
+ if type(v) == str and ':' in v:
+ return int(v.split(':')[-1])
+ except:
+ pass
+
+ return 1
+
def get_frozenset_label(S, count=False, override={}):
"""
Helper function for rendering the labels of possibly collapsed nodes and edges
@@ -60,13 +69,12 @@ def get_frozenset_label(S, count=False, override={}):
"""
def helper(v):
- if type(v) == frozenset:
- if count and len(v) > 1:
- return f"x {len(v)}"
+ if type(v) == str:
+ n = get_collapsed_size(v)
+ if count and n > 1:
+ return f"x {n}"
elif count:
return ""
- else:
- return ", ".join([str(override.get(s, s)) for s in v])
return str(v)
return {v: override.get(v, helper(v)) for v in S}
diff --git a/hypernetx/utils/toys/GoT.pkl b/hypernetx/utils/toys/GoT.pkl
new file mode 100644
index 00000000..1690cec1
Binary files /dev/null and b/hypernetx/utils/toys/GoT.pkl differ
diff --git a/setup.py b/setup.py
index ace3121d..708c3f5d 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,7 @@
from setuptools import setup
import sys
-__version__ = "1.1.3"
+__version__ = "1.2"
if sys.version_info < (3, 7):
sys.exit("HyperNetX requires Python 3.7 or later.")
@@ -19,7 +19,7 @@
"hypernetx.utils.toys",
],
version=__version__,
- author="Brenda Praggastis, Dustin Arendt, Sinan Aksoy, Emilie Purvine, Cliff Joslyn, Nicholas Landry",
+ author="Brenda Praggastis, Dustin Arendt, Sinan Aksoy, Emilie Purvine, Cliff Joslyn",
author_email="hypernetx@pnnl.gov",
url="https://github.com/pnnl/HyperNetX",
description="HyperNetX is a Python library for the creation and study of hypergraphs.",
@@ -30,7 +30,6 @@
"matplotlib>3.0",
"scikit-learn>=0.20.0",
"pandas>=0.23",
- "celluloid>=0.2.0",
],
license="3-Clause BSD license",
long_description="""
@@ -46,8 +45,8 @@
* Visualization: Dustin Arendt, Ji Young Yun
* High Performance Computing: Tony Liu, Andrew Lumsdaine
* Principal Investigator: Cliff Joslyn
- * Program Manager: Mark Raugas, Brian Kritzstein
- * Contributors: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Andrew Lumsdaine, Tony Liu, Brenda Praggastis, Emilie Purvine, Mirah Shi, Francois Theberge
+ * Program Manager: Brian Kritzstein
+ * Contributors: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Andrew Lumsdaine, Tony Liu, Brenda Praggastis, Emilie Purvine, Mirah Shi, François Théberge
The code in this repository is intended to support researchers modeling data
as hypergraphs. We have a growing community of users and contributors.
@@ -67,10 +66,14 @@
1. Static Hypergraph refactored to improve performance across all methods.
2. Added modules and tutorials for Contagion Modeling, Community Detection, Clustering, and Hypergraph Generation.
3. Cell weights for incidence matrices may be added to static hypergraphs on construction.
+
+ **New Features of Version 1.2**
+
+ 1. Added module and tutorial for Modularity and Clustering
""",
extras_require={
"testing": ["pytest>=4.0"],
- "tutorials": ["jupyter>=1.0"],
+ "tutorials": ["jupyter>=1.0", "python-igraph>=0.9.6", "celluloid>=0.2.0", ],
"documentation": ["sphinx>=1.8.2", "nb2plots>=0.6", "sphinx-rtd-theme>=0.4.2"],
"all": [
"sphinx>=1.8.2",
@@ -78,6 +81,8 @@
"sphinx-rtd-theme>=0.4.2",
"pytest>=4.0",
"jupyter>=1.0",
+ "python-igraph>=0.9.6",
+ "celluloid>=0.2.0",
],
},
)
diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb
new file mode 100644
index 00000000..200eb688
--- /dev/null
+++ b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb
@@ -0,0 +1,836 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import random\n",
+ "import igraph as ig ## pip install python-igraph\n",
+ "import partition_igraph ## pip install partition-igraph\n",
+ "import hypernetx as hnx\n",
+ "import hypernetx.algorithms.hypergraph_modularity as hmod\n",
+ "import hypernetx.algorithms.generative_models as gm\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Main functions for Hypergraph Modularity using HyperNetX\n",
+ "\n",
+ "### Pre-computing key hypergraph quantities\n",
+ "\n",
+ "Given some hnx hypergraph HG, the following function needs to be called first\n",
+ "to pre-compute node strengths (weighted degrees), d-degrees and binomial coefficients\n",
+ "and add these as attributes to HG:\n",
+ "\n",
+ "```python\n",
+ "hmod.precompute_attributes(HG)\n",
+ "```\n",
+ "\n",
+ "### H-modularity (qH)\n",
+ "\n",
+ "The function to compute H-modularity for HG w.r.t. partition A (list of sets covering the vertices):\n",
+ "\n",
+ "```python\n",
+ "hmod.hypergraph_modularity(HG, A, wcd=linear)\n",
+ "```\n",
+ "\n",
+ "where 'wcd' is the weight function (default = 'linear'). Other choices are 'strict'\n",
+ "and 'majority', or any user-supplied function with the following format:\n",
+ "\n",
+ "```python\n",
+ "def linear(d,c):\n",
+ " return c/d if c>d/2 else 0\n",
+ "```\n",
+ "\n",
+ "where $d$ is the edge size, and $c$ is the number of nodes in the majority class, $d \\geq c > \\frac{d}{2}$\n",
+ "\n",
+ "### Two-section graph\n",
+ "\n",
+ "Build the random-walk based $2$-section graph given some hypergraph HG:\n",
+ "\n",
+ "```python\n",
+ "G = hmod.two_section(HG)\n",
+ "```\n",
+ "\n",
+ "where G is an igraph Graph.\n",
+ "\n",
+ "### Clustering: Kumar algorithm\n",
+ "\n",
+ "Given hypergraph HG, compute a partition of the vertices as per Kumar's algorithm described in [1].\n",
+ "\n",
+ "```python\n",
+ "K = hmod.kumar(HG, delta=.01)\n",
+ "```\n",
+ "\n",
+ "where delta is the convergence stopping criterion. Partition is returned as a list of sets.\n",
+ "\n",
+ "[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) *A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering*. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24\n",
+ "\n",
+ "\n",
+ "### Clustering: Simple qH-based algorithm\n",
+ "\n",
+ "Given hypergraph HG and initial partition L, \n",
+ "compute a partition of the vertices as per Last-Step algorithm described in [2].\n",
+ "\n",
+ "```python\n",
+ "A = hmod.last_step(HG, L, wdc=linear, delta = .01)\n",
+ "```\n",
+ "\n",
+ "where 'wcd' is the the weight function (default = 'linear') and delta is the convergence stopping criterion.\n",
+ "Returned partition is a list of sets.\n",
+ "\n",
+ "[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13\n",
+ "\n",
+ "### Utility functions\n",
+ "\n",
+ "We use two representations for partitions: list of sets (the parts) or dictionary.\n",
+ "Those functions are used to map from one to the other:\n",
+ "\n",
+ "```python\n",
+ "dict2part(D)\n",
+ "part2dict(A)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Toy example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "## build a hypergraph from a list of sets (the hyperedges)\n",
+ "E = [{'A','B'},{'A','C'},{'A','B','C'},{'A','D','E','F'},{'D','F'},{'E','F'}]\n",
+ "HG = hnx.Hypergraph(E,static=True)\n",
+ "hnx.draw(HG)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## compute node strength (add unit weight if unweighted), d-degrees, binomial coefficients\n",
+ "HG = hmod.precompute_attributes(HG)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "e0 has weight 1\n",
+ "e1 has weight 1\n",
+ "e2 has weight 1\n",
+ "e3 has weight 1\n",
+ "e4 has weight 1\n",
+ "e5 has weight 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "## list the edges (unit weights added by default)\n",
+ "for e in HG.edges:\n",
+ " print(e,'has weight',HG.edges[e].weight)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "B has strength 2\n",
+ "A has strength 4\n",
+ "C has strength 2\n",
+ "E has strength 2\n",
+ "D has strength 2\n",
+ "F has strength 3\n"
+ ]
+ }
+ ],
+ "source": [
+ "## list the nodes (here strength = degree since all weights are 1)\n",
+ "for v in HG.nodes:\n",
+ " print(v,'has strength',HG.nodes[v].strength) \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({2: 4, 3: 1, 4: 1})"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "## total edge weight for each edge cardinality\n",
+ "HG.d_weights\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "linear edge contribution:\n",
+ "qH(A1): 0.414445267489712 qH(A2): -0.03746831275720153 qH(A3): 0.0 qH(A4): -0.19173004115226341\n",
+ "strict edge contribution:\n",
+ "qH(A1): 0.43490699588477366 qH(A2): -0.02385843621399164 qH(A3): 0.0 qH(A4): -0.12887572016460908\n",
+ "majority edge contribution:\n",
+ "qH(A1): 0.39379753086419755 qH(A2): -0.0343506172839505 qH(A3): 0.0 qH(A4): -0.22078024691358022\n"
+ ]
+ }
+ ],
+ "source": [
+ "## compute hypergraph modularity (qH) for the following partitions:\n",
+ "A1 = [{'A','B','C'},{'D','E','F'}]\n",
+ "A2 = [{'B','C'},{'A','D','E','F'}]\n",
+ "A3 = [{'A','B','C','D','E','F'}]\n",
+ "A4 = [{'A'},{'B'},{'C'},{'D'},{'E'},{'F'}]\n",
+ "\n",
+ "## we compute with 3 different choices of functions for the edge contribution: linear (default), strict and majority\n",
+ "strict = hmod.strict\n",
+ "majority = hmod.majority\n",
+ "\n",
+ "print('linear edge contribution:')\n",
+ "print('qH(A1):',hmod.modularity(HG,A1),\n",
+ " 'qH(A2):',hmod.modularity(HG,A2),\n",
+ " 'qH(A3):',hmod.modularity(HG,A3),\n",
+ " 'qH(A4):',hmod.modularity(HG,A4))\n",
+ "print('strict edge contribution:')\n",
+ "print('qH(A1):',hmod.modularity(HG,A1,strict),\n",
+ " 'qH(A2):',hmod.modularity(HG,A2,strict),\n",
+ " 'qH(A3):',hmod.modularity(HG,A3,strict),\n",
+ " 'qH(A4):',hmod.modularity(HG,A4,strict))\n",
+ "print('majority edge contribution:')\n",
+ "print('qH(A1):',hmod.modularity(HG,A1,majority),\n",
+ " 'qH(A2):',hmod.modularity(HG,A2,majority),\n",
+ " 'qH(A3):',hmod.modularity(HG,A3,majority),\n",
+ " 'qH(A4):',hmod.modularity(HG,A4,majority))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
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+ "\n",
+ " \n",
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+ "\n",
+ " \n",
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+ "\n",
+ " \n",
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+ " \n",
+ " \n",
+ "\n",
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+ " \n",
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+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {
+ "image/svg+xml": {
+ "isolated": true
+ }
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "## 2-section graph\n",
+ "G = hmod.two_section(HG)\n",
+ "G.vs['label'] = G.vs['name']\n",
+ "ig.plot(G,bbox=(0,0,250,250))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "## 2-section clustering with ECG\n",
+ "G.vs['community'] = G.community_ecg().membership\n",
+ "hmod.dict2part({v['name']:v['community'] for v in G.vs})\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "## Clustering with Kumar's algorithm\n",
+ "hmod.kumar(HG)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n",
+ "final partition: [{'B', 'A', 'C'}, {'E', 'D', 'F'}]\n"
+ ]
+ }
+ ],
+ "source": [
+ "## hypergraph clustering -- start from trivial partition A4 defined above\n",
+ "print('start from:',A4)\n",
+ "A = hmod.last_step(HG,A4)\n",
+ "print('final partition:',A)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Chung-Lu hypergraph example\n",
+ "\n",
+ "We build a Chung-Lu hypergraph and compute modularity for partitions from 3 algorithms:\n",
+ "* Louvain, on the 2-section graph\n",
+ "* Kumar algorithm\n",
+ "* LastStep algorithm\n",
+ "\n",
+ "We use the **strict** modularity, so only edges where all vertices are in the same part will add to the modularity.\n",
+ "For each algorithm, we compute the modularity qH and compare with the number of edges where all vertices are in the same part.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Chung-Lu hypergraph\n",
+ "n = 200\n",
+ "k1 = {i : random.randint(2, 10) for i in range(n)} ## node degrees\n",
+ "k2 = {i : sorted(k1.values())[i] for i in range(n)} ## edge sizes\n",
+ "H = gm.chung_lu_hypergraph(k1, k2)\n",
+ "\n",
+ "## pre-compute required quantities\n",
+ "HG = hmod.precompute_attributes(H)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "qH = 0.0569711443530809\n",
+ "edges with all vertices in same part: 28\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Louvain algorithm on the 2-section graph\n",
+ "G = hmod.two_section(HG)\n",
+ "G.vs['louvain'] = G.community_multilevel().membership\n",
+ "D = {v['name']:v['louvain'] for v in G.vs}\n",
+ "ML = hmod.dict2part(D)\n",
+ "\n",
+ "## Compute qH\n",
+ "print('qH =',hmod.modularity(HG, ML, strict))\n",
+ "\n",
+ "## number of edges where all vertices belong to the same community\n",
+ "print('edges with all vertices in same part:',\n",
+ " sum([len(set([D[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "qH = 0.19310099840187803\n",
+ "edges with all vertices in same part: 54\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Kumar algorithm\n",
+ "KU = hmod.kumar(HG)\n",
+ "\n",
+ "## Compute qH\n",
+ "print('qH =',hmod.modularity(HG, KU, strict))\n",
+ "\n",
+ "## number of edges where all vertices belong to the same community\n",
+ "print('edges with all vertices in same part:',\n",
+ " sum([len(set([hmod.part2dict(KU)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "qH = 0.21300020106295142\n",
+ "edges with all vertices in same part: 57\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Last-step algorithm using previous result as initial partition\n",
+ "LS = hmod.last_step(HG, KU, strict)\n",
+ "\n",
+ "## Compute qH\n",
+ "print('qH =',hmod.modularity(HG, LS, strict))\n",
+ "\n",
+ "## number of edges where all vertices belong to the same community\n",
+ "print('edges with all vertices in same part:',\n",
+ " sum([len(set([hmod.part2dict(LS)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Game of Thrones scenes hypergraph\n",
+ "\n",
+ "REF: https://github.com/jeffreylancaster/game-of-thrones\n",
+ "\n",
+ "We built an hypergraph from the game of thrones scenes with he following elements:\n",
+ "\n",
+ "* **Nodes** are characters in the series\n",
+ "* **Hyperedges** are groups of character appearing in the same scene(s)\n",
+ "* **Hyperedge weights** are total scene(s) duration in seconds involving those characters\n",
+ "\n",
+ "We kept hyperedges with at least 2 characters.\n",
+ "Moreover, we discarded characters with degree below 5.\n",
+ "\n",
+ "We saved the following:\n",
+ "\n",
+ "* *Edges*: list of sets where the nodes are 0-based integers represents as strings\n",
+ "* *Names*: dictionary; mapping of nodes to character names\n",
+ "* *Weights*: list; hyperedge weights (in same order as Edges)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "198 nodes and 1492 edges\n"
+ ]
+ }
+ ],
+ "source": [
+ "## load the GoT dataset\n",
+ "Edges, Names, Weights = pickle.load(open( \"../hypernetx/utils/toys/GoT.pkl\", \"rb\" ))\n",
+ "print(len(Names),'nodes and',len(Edges),'edges')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Build weighted GoT hypergraph "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Nodes are represented as strings from '0' to 'n-1'\n",
+ "H = hnx.Hypergraph(dict(enumerate(Edges)))\n",
+ "## add edge weights\n",
+ "for e in H.edges:\n",
+ " H.edges[e].weight = Weights[e]\n",
+ "## add full names\n",
+ "for v in H.nodes:\n",
+ " H.nodes[v].name = Names[v]\n",
+ "## pre-compute required quantities for modularity and clustering\n",
+ "GoT = hmod.precompute_attributes(H)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Modularity (qH) on a random partition\n",
+ "\n",
+ "We use the default choice for the modularity (**linear** weights).\n",
+ "Result for the random partition should be close to 0 and can be negative."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-0.0054328760823038336"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "## generate a random partition into K parts to compare results\n",
+ "K = 5\n",
+ "V = list(GoT.nodes)\n",
+ "p = np.random.choice(K, size=len(V))\n",
+ "RandPart = hmod.dict2part({V[i]:p[i] for i in range(len(V))})\n",
+ "## compute qH\n",
+ "hmod.modularity(GoT, RandPart)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate the 2-section igraph Graph and cluster with Louvain Algorithm\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "qH = 0.5372359319251633\n"
+ ]
+ }
+ ],
+ "source": [
+ "## build 2-section\n",
+ "G = hmod.two_section(GoT)\n",
+ "## Louvain algorithm\n",
+ "G.vs['louvain'] = G.community_multilevel(weights='weight').membership\n",
+ "ML = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n",
+ "\n",
+ "## Compute qH\n",
+ "print('qH =',hmod.modularity(GoT, ML))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Cluster hypergraph with Kumar's algorithm\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "qH = 0.5382594158646983\n"
+ ]
+ }
+ ],
+ "source": [
+ "## run Kumar's algorithm, get partition\n",
+ "KU = hmod.kumar(GoT)\n",
+ "## Compute qH\n",
+ "print('qH =',hmod.modularity(GoT, KU))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Cluster with simple H-based (Last Step) Algorithm\n",
+ "\n",
+ "We use Louvain on the 2-section or Kumar algorithm for the initial partition"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "qH = 0.5460841945299417\n"
+ ]
+ }
+ ],
+ "source": [
+ "## H-based last step with Louvain parition already computed\n",
+ "LS = hmod.last_step(GoT, ML)\n",
+ "## Compute qH\n",
+ "print('qH =',hmod.modularity(GoT, LS))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example: show top nodes in same cluster as Daenerys Targaryen\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " character \n",
+ " strength \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " Daenerys Targaryen \n",
+ " 31103 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Jorah Mormont \n",
+ " 19344 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " Missandei \n",
+ " 13683 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Grey Worm \n",
+ " 10497 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " Barristan Selmy \n",
+ " 6514 \n",
+ " \n",
+ " \n",
+ "
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+ "
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]
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\n",
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\n",
"text/plain": [
""
]
@@ -140,19 +149,25 @@
"metadata": {},
"source": [
"## Collapsing Vertices\n",
- "By passing in a hypergraph with its nodes collapsed (using `H.collapse_nodes()`), we show nodes with identical hyper edge membership to be collapsed into a single dot. The drawing tool automatically detects if nodes and edges have been collapsed, and the dot is labeled with the list of nodes it represents. In this case, `{CN, CC, BR}` and `{CH, JU}` were collapsed. The size of the dot increases to reflect the number of members.\n",
- "\n",
- "We will use a consistent random state across the next few diagrams to make the layout consistent."
+ "By passing in a hypergraph with its nodes collapsed (using `H.collapse_nodes()`), we show nodes with identical hyper edge membership to be collapsed into a single dot. The drawing tool automatically detects if nodes and edges have been collapsed, and the dot is labeled with the list of nodes it represents. In this case, `{CN, CC, BR}` and `{CH, JU}` were collapsed. The size of the dot increases to reflect the number of members. We pass `with_node_counts=True` to show the number of nodes in the collapsed node instead of its label."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A note on random seeds\n",
+ "We will use a consistent random seed across the next few diagrams to make the layout consistent. This is done by passing an arbitrary integer `{'seed': 39}` to the layout algorithm. The default layout algorithm is `nx.spring_layout` which takes a `seed` parameter which determines the inital random positioning of the vertices. Thus, `39` is passed into this function for that parameter to make the initial position (and final position) consistent each time the cell is executed."
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -164,7 +179,7 @@
}
],
"source": [
- "kwargs = {'layout_kwargs': {'seed': 39}}\n",
+ "kwargs = {'layout_kwargs': {'seed': 39}, 'with_node_counts': True}\n",
"\n",
"hnx.drawing.draw(H.collapse_nodes(), **kwargs)"
]
@@ -179,28 +194,6 @@
"The collapsed nodes from above have been replaced with `x3` and `x2`, and the rest of the labels have disappeared."
]
},
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "hnx.drawing.draw(H.collapse_nodes(), with_node_counts=True, **kwargs)"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -215,7 +208,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -245,7 +238,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -289,7 +282,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -328,7 +321,7 @@
"outputs": [
{
"data": {
- "image/png": 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/rrDB1/hMIGrTps3sMWPG9D9x4sSXiZQsTDU2wzTGmPg1AJYEeDwC10B6DS5rM7E9ztTkO2CKSEUReVhE5uPGehnwAVBaVTup6th0D5YigshzuNn6c0BP4CFgBvAbIuUTefn48847775WrVptzp079/4vvvjivNy5c79fuXLlx2JfV7Vq1UdKlSo1OL77fPbZZ2VKlSo1ODg4eMy1117bzs/QLWAaY0z8LiT9yt35lWDAFJGaIjLQO7e4CNenczhQUlVvUtXJyS0kkEoGAE8TOP5cBHyDSN74XhwSEnJy586d5bdv3x4aHh6+afr06Q3z5ct31n7uX3/9lXfHjh2VT5w4kW/GjBnFA92nSpUqR5544olPLr744q/8DtwCpjHGBOAl11xI4BlmRjorYIpTX0SeE5FVuL3IskB/3Eyyt6rOVNWTGTTeM0SKAs8kctV5wF0JXVCnTp2lzz///IWFCxfetXjx4jqNGzf+NfbzQ4cObVyrVq0/GzZs+Os777zTLNA9mjRpcqhPnz4bg4ODfS+lW8A0xpjAKuGSa3Zl9EDiKArsFZFmIvIKsAFXQCAPcAdQXlUfUNUfU7vqTiq4BvDTgSTBEni9evVaMHv27Ob58uXbt3fv3iLNmzdfH/v5n376qXmXLl1+vfvuu39duHBh85jHu3bt2rpr166tkzd0C5jGGBOfTLUcKyIhItIKuAW4FRgFHMMFl6qq+piqLkivqjvJVNbndeUSerJ3796b9+/fX/z7778vW65cubNmzgsWLCh04MCBUv3791/btWvXHSIS/fHHH5cDmDhx4g8TJ078IZljt4BpjDHxyPDlWBHJJSLtROQjXGGEF3HViF5X1fNVdZCqLs2IqjvJ5Le4Q6LX1atX789vvvnm6jp16pxV1vCll15qevLkyXyFCxd+s0CBAm8ePny4+OjRo5vHd5+ksIBpjDGBxZchm6ZEJL+IdBGRz3CZuI8Dy4GGqnoR8C+udVZW9C3g5/zo9MQuGDhw4NymTZuuql69+r+xH1+wYEHzYcOGDT98+HCfw4cP95k8efITS5YsCbiPmVR2DtMYYwJLtyVZEQkHrsWdkWyFO6YyBeirqnFnWxnWqSTFVHcg8iouUzY+G3E9NBPUunXrfT/88MP+IkWK7Dpy5AgAM2fOLHb48OFiffv2XRdzXfv27XeHhYUdf+2116ouWLCgEril2V9++aXQlVdeOfTUqVN5AM2XL1/bpUuXPla9evVAVZwA61ZijDHn8A75/wUUTavlThEpyZlCAs1w2a1TgK9UNd6ydyKyBLhDVf9Mi3GlOZFg4E3gvgDPrgE64vpggsv0Pac0Xox77733mUsvvXRq9+7dl6f+QE8rD7wENsM0xphALgSWpHawFJEKQCdckKwLfAN8CHRW1SM+b5PmnUrSlGoUcD8iHwM3A1VwlX5+BCagesLPbX799dcyx44dK3XttdemW5cYC5jGGHOuVFuOFZEauEzWzkBl3P7cS8D36jM4xJEmnUrSnepCzhS9T7JvvvmmddWqVecUKFAg3Y7OWNKPMcacK9kZsl4hgXoiMlhEVgJzcct6A3CFBG5X1a+SEyy99lshuI4pOdaePXty/fvvv5d06NDhx/R8X5thGmPMuRoAg/xeLCJBQGPcTPJ6IBjXIusu4LdUPBtZFNibhY6RpIm33nqrc7FixVbWr18/XZemLWAaY0wsIlIIKAkk2DBZREKAS3ABshNwCBckbwDS6mxk9liOTYExY8Y03Lx5c9NBgwY9md7vbQHTGGPOVh9Yri455RwiUg640/u1HRck26hqepyNzGkBczuxGjhv2LChyN9//333Aw888HHlypULA4XTaQyAHSsxxpiziEhfoJqq3h/rMcGdj7wPaAl8BryrqivTeWxdgO6q2jk93zczEJHawDTgLVV9OyPGYDNMY4w524XAPABxZwbvAR7EVagZiWu4fDiDxpbTZpgAiMiNwFvAAFX9X0aNwwKmMcac7ULgdREpDnyK6wJyJ/BzJki2yVEBU1xfzJeAq4ErVTVDa/vasRJjjPGISB6gGpAf+BN3FrOVqs7PBMESclDAFJGrgJVAONAoo4Ml2AzTGGNiuwAXkCbhys/NyODxxFUUF0SyLREpAbyGKxd4r6rOyuAhnWYzTGOMwXUJwe1R5gaaZcJgCdl4hukVfOiN+0DwH3B+ZgqWYDNMY4yJyYL9BCgEDFXVjRk8pPhky4ApIjWB94G8uL3KpRk7osBshmmMMfAwUAE4QArqm6aDYmTlwutxeA2yBwG/4Dq1NMuswRJshmmMyeFEpAWuzmsLYJn3K6n3EFx1oMq47htlcc2f/8H1d9yWSuXxss0MU0QuAT7AVVS6UFXjbeOVWVjANMbkWF6CyefA7bjjI/8l5YyliJwH3ItrU3WKWAESl0BUBRdEC4nIF7g90t+Tk3HrnQkthGuFlWV5zbJfBK4B+gBTM0kGcqIsYBpjciQvAI0HPlHVmSLSEx8dSrzZ5HXAQ0BNYBRwgar+l8BrigK3AeOAAyLyOvBpEgNFYeBQfCX7Mjvv+9YVlwE7FThPVQ9m7KiSxgKmMSanehCXx/GM9/tEe2CKyEW4H/j5gaHANFU9ldgbqepe4BURGQFcCbwAtBeRO1T1kM/xZtnlWBGphJtdl8c1y16QsSNKHkv6McbkON7ssg/weKwZWwPimWGKSDkRGQt8CYwGGqrqJD/BMjZVjVbVb3FnDA8AC0XkAp8vz3IBU0RCROQRYBEwH2iQVYMlWMA0xuRMV+H6Si6E0/0s6xMnYIpIfhF5DpcI9C9QU1X/l9JlUVU9oap34WapP4pIQx8vS8+AWQBoDZRK7g28r+kPoC3QVFVfSOoHjMzGlmSNMTnRfbglwhiVgYOqugdOB9BewPPAXFwW5+bUHoSqjhGRY8AkEWmkqvsSuDw9AmYQUAdoA+TCfV8+wSU0+eIVgBgCdAceI+l7tZmWBUxjTI4iIpWBJrgElBinl2NF5HJgBHACuF5Vf0/L8ajqZO9oyycicl0Cx0/SOmAWw+2vVgB2ACeBcrjvja/vgYhcC7wDzMFV6sk2Z0bBAqYxJue5G5cZeyzWYxcC/4rIVO//HwcmptHMqAdQOvYDJ0+e3Pnqq692qlKlyjTg50AvGj169FXBwcGngP4peO/twNg4j4UCjYCLgeO4pefY118KrCeBYC0ipYE3cN+721X1hxSMMdOygGmMyTFEJBR3vKNFrMfCccuHRYFhuAbNJ9JwGKWBsw7ph4WF0axZs5Eff/zx4+3bt5+YN2/ec/ZI165dq6VKldoa97VJVD7A79vijqxsA+K+bxRupt0GmAicNfv1lq7vwi3Bvg/0UtXjKRhfpmZJP8aYnKQKcFhV14tIqIg8AKzBVem5TFWHp3GwjNfll1++JU+ePLsnTpzYINDzJ0+ezJ8/f/4jqfR2eXGJTzd7v9/CucEyxh6gElA79oNe0Yb5QE+gpao+lZ2DJVjANMbkLJWBjSLSDliOK0DQBbdftzTFdxepiMjDiLyCyGOI1EnKyxs0aDB70aJFbQI9d/LkyfyFChXyXYUoATWBO4Dzgc2An3vuwM0y84tIbhEZgkuG+hS4WFWzdcuxGLYka4zJSS7GzZReBR4BvgYuAjamaL/Snet8Hre/GHsi8hIinwD3cfae6VkWLFhQ6Pbbb+/x33//VQ8NDS06adKkod26dZtavHjxo0OGDHm0YMGCu06dOlV2+fLlTbp06bIq7uvr1q1797p16y7MkyfPoX379sW3x5kXV64vCNiF2688x4gRI6pdccUV2+vWrXs01sMngaDRo0c/gCsjuByop6rb4v2eZEM2wzTGZHsiUlJE3gf6An/iStnN9IJkFVz915QYjivgHuhnai9gTHwvjIqKolOnTv0uuOCC1YcPH36oe/fuqx966KGv//vvv6IAZcuWXbNnz56B11577aFly5bVe+eddyrHvUe3bt3mjRgxYngiY6wCFMcl9ZwTLOfPn1+4Zs2afR599NFnvvvuuzKxn9u0aVP+xx9/vMOWLVse79Sp00uq2iWnBUuwgGmMyca85cMBwCrgCPADMC7OAfrKpCRgilTHzVYT0hmRgEutzz333HlBQUGREydO/AEgf/78u/Lnz59rypQpp5snR0dHAxQoXbr0xhUrVpSMe48nn3xyTYUKFRLb3/wHiCTAyuJ1113XtmPHjgMaNmy4olatWgumTp16Ucz7vv/++xc/88wzL4eFhR27//77n54yZUoI7oxmjmMB0xiT7YjTDZfQ0xjXZ/ERXFZo3OCY0hlmV0B8XNct0IPLly8vV758+U0xvy9cuPCuffv2lYh9zb59+3KdPHlSt23bVrV58+b/zZs3r3ClSpWSerzkKC5onnWkZf/+/cEAr7/++lvjx4+f07lz55/27t1bdMqUKRX79Okz8M8//2zXvXv3l4cMGTK2aNGie3FVgBon8b2zBdvDNMZkKyLSBFcgPTdwq6rOjfV0eSBuV5HywLQUvGVFn9dV8nPR+PHjz/vvv/8qjxw58vwHH3xw3NatW2vVrFlziKoGt23b9suePXv+B7Bp06aXkjHWrbjzlIWAgwDh4eFRX3755TcxF5w8eTI0IiKi3JQpU56oW7fu9D59+nyTO3fu2MdJtgPNcX0sdyZjDFmWzTCNMdmCiFQQkXHAFFxj4oviBEuA3biKNok9lhS7U3Jd3bp1/9uyZUulmN/37Nlzac+ePRccP368ILg9zP/9738fdevWbX3Msm0KRAPf4s5dnjUrjoiIkJkzZ1bbv39/t507d5YqUaLEJ/37958ZGhoat/JQFC6z9kogOIXjyVIsYBpjsjQRKSAiz+NK263HFUj/OJ4C6RtxS7CJPZYUsxK/BHCB6hyDBg1aFRUVFXbDDTdcAXDgwIESoaGhZ/WJ3LZtW4n8+fPvSsEYY9uK6x5yeml2x44deQYPHtzr888/79e4cePpjRs3nrFq1ariAMHBAWPifqAMLus2x7CAaYzJkkQkWER6A2txy6L1VfUZVU0o+eUfXJJPbCkLmKo/EU8wjGU58HmgJ4KDg5k8efKrK1asqF2gQIE33n///YvHjx/f+Kabbhofc83evXtLFCpU6HTAjLuHWatWrQduuOGG5w4cOFA6X758b998882XJzKeX4AIIM8nn3zSaMCAAS9HRkaGDh069LE777zz11y5ckUcPXo0D7gs3nhsx3U0KZTIe2Ubkk2KyBtjchARaYUrkH4E6BvTpsvH6/oBFVT14ViPXQoMU9WLUzCgIril4MsCPLsS6IDqP97v+xNPebvo6Gh69+795u233/7iJZdcsjXm8ccee+y+KlWqrLj33nvnJ3uMTnngJYCPPvro4sOHD7+3fPny8Pbt23/YsWPHNVFRUQQHB/PEE0/U/eijjzpv3br1mXhmmDHKAX8BM1M4rizBkn6MMVmGiNQAXsYtBfYHvkhiwYGNwOUBHquaooGp7kOkNXADrnJQTMePr4Cx+CwZN23atNrBwcEnW7RosTX240eOHClZpkyZVClo7jXPvk9EBg0cOPCHt99+e2GePHm2gZvtRkVFMWzYsOXDhg1bntBtcEu6J3ABM0ewgGmMyfTEzeAGAbcALwLdklnzNdCS7DbghIhc5HemGpDbM/2ceJZe/fj555/bnHfeebODgs7slq1evbrwkSNHyjRp0iTF/ThXrlxZCvgVOKmqlw4dOnQbrkxeKF7Py0RmlAAFgXBgMa6zSrwVjLIb28M0xqSF3LhZYL6U3MQrkP4Q7jxlLqCOqr6cggLp64EKIlI05gGv/+R7uKbSGWbNmjWFd+/efUG3bt3OWnadMmVKq/Llyy8oVapUsgub79u3L+zZZ5+98fPPP78TGAVcrqqrcUdLfiTO2cx4BOOWdKNwNWS/IwcFS7CAaYxJXcHAecCdQGdc7dYk8woPdMDt/7XFdcO4V1VTlCmqqkdxZy5vjfPUR0Cn2IE0vY0ePfqGChUq/FKmTJnTgfHYsWPB69ata9WmTZvvk3vfCRMmXPDoo4++dPjw4WL333//a6r6YZwm1ctxCTzhCdymGC4rdh7wCeeeZc0RMu2SrLfOXhW3fFLF+5ULVwdxk/fff4E9adTk1RiTNBVxWZPFcGcO9wL1cF1AfB9wF5F6uISeUsBDqppYBmpSjQTGichrMYFDVXeLyAxcIH01ld8v8QGNHHnpnj17ag4bNuyp2I9PnDixQZ48eXa3bNkyycux69evL/jee+/dsnfv3ppXXnnlR927d1/Guf0wwc0YZ+G+9oOc3fMyF+7PYRMwmwSaSOcEmS5gikhJ3Jr63bg/yPW4fYeNuIr5FXGfWiviKmfkEpGY4LmJcwPqjjifpowxqasYcClQA3c+L3YG6FGgJTABSPCDrYiUwnX8aA8MBj5Q1cg0GO8fuMDQhrPPUI4ExorIG2n0vjG2EytwrVy5stS2bdt6PPLII++XLFmyeMzj0dHR7Ny5s0P79u1/J3CgCyg6OpoZM2Y0XLhw4TW1atX6s1u3bm8WKFAgwrvH9nhethNYgCt5txWX1FMKFzxnAKuJ0zw6J8o0x0pEpCKu4v/VwCTgXVVd4uN1BXHBM+ZXpTj/Xwj3Dzi+gLo1jf9xGJNd5QWa4H7IHif+ijcVcf+mNwR6UkTy4LqI9ANGA0NV9UBqDzbOe/YGrlPVDrEeE1y7r+Wq+nhavn+s96yNO5LxtKqOi/PcPbh91UaqGuHzfjWA93H1Xu/08zM0lly41l1huD/b5bgl2NRqWp3lZYqAKSJtcf9Q3gHeSs1/LCKSF5fiHV9ALY771BVfQN2iqidTazzGZAOhuObDl+PyIHaQ8Owjn3fdaLxMTDgdoLoDLwALgcdVNWBQTW3ez4UtQENV3RTr8WK49l99VPXLNB5DR1wJv/6q+nGc5xoB3wAtVPVvH/cKAx4HHgKGAG/HU+koMTHL6j/ifg6aWDI0YHr7lM8CtwE3qurPGTCGMNzh20oEDqhlgD2cG0hP/78m0BjWmGxEcLkEV+BWbnbiqsX4UQG3/LkUQESa4QqkhwD91FXLSVci8hqQR1XvifN4U2A6rsNJqgdwEQnCLTn3BDqr6qI4zxfBBe3HVHWyj/tdjAu8G4D7VTXFx09MYBkdMN8HauLOVGXKqvdeUC9D/AG1PK4QcUIB9ay6kMZkQaG4rNdKuA+QRwNdtHLlynznn39+oOfCgCItW7b8eu7cuYNxeQhPAJ9mVI6BiBTGBaYnVHVCnOcexC1PNlefRQeS8J7jcLPurnGzfr1gOh34W1X7+bjXi8C1uJllUos4mCTKsKQfEemFKyN1kaoezqhxJMZb1tji/TqnLJX3F7wEZwfU2ri92EpARRE5RQIBFdhrf9FNJie47YtduP3Kc7Rt27bDzz//3HrEiBGv3nnnnWfNcnbs2BE8c+bMdiEhIc/iMmB7e0c8MoyqHhCRG4BZIrJMVdfEevptoBHwg4h0VdUUH6MQkfOBqbg9y8fiNLFGRMKBj3H7h/HuoXpL2TfgZuhf4s6m2ofydJAhM0wRqYvrfH65qq5KhVv2wN/B29SwHRjr92LvL3cR4p+hVsR9ek8ooO60TF+TCdQGOgBnBcN33nmn8sCBAx+qXr36smefffar9u3bn07+iYiIkPfff//yRYsW3VC6dOnlDzzwwPxy5cq9RiY6niAid+JmaE1iB3Hvw/BjwMNAT1WdnYL36ILLwu2nqp8GeL4BMBk3u+wfX5KPlxz5Du5nx12q+mtyx2SSLt1nmN4S5wTcX5zUCJbggmXAYsZpwHd6N4A3c9zr/foz0DUiUohzM30bcSagFhCRLcQfULcmc4PfmKRYi9u3PN18GKBWrVoHIyIi8nzxxRfjK1WqdHLWrFlFS5cufWLt2rWVv/vuu1tCQ0OP3nzzzS9feeWV/+BWYy7DFSrPLD4EWgDviUjPmNUe70PqiyLyO+7c5vvA80n58Or9vHseuBG4WlUXx3lecEUehuL2HyfGc58QoA9uGfs14Hq/mbMm9aT7DFNE2gHPqGrjVLxtvNX/08Dpav/pRUTykXCmbzFcPcxNBA6oW+wfl0kl5YGbcX+vTrv44otv3Lp1a4VixYrt3LJlS43g4OCiVatW1R49eozu3bv3H7Fro+L+3o4nzkw1I3lZs78BH6nq6wGeL42rEZsLeB2Ykti/KS955zPcxKSbqu6J9VwwbtvmQVzSYWdVXRvPfRrgytkdBO5W1XVJ/fpM6siIPcz7cEsTxidvmWi19+scIpIL94MsdhC9PNb/lxaR3cR/dObf1ExsMNnaFtzfw4rEOnf57bffTqxYseLwwoULF7zyyiuL7dmzZ8Xu3buP7Nu370CcYAmuuMEVuBJrmWJlRFWPiUgn4GsRqQU8HLterapuF9eN5Drcz7DXReRDXHGFcwK/t+001fs1IOast4gUxyUT3YNLnhoJfB7o35+I5Mdl096CmxSMsVyHjJWuAVNEquAOOXdJwosK4f7CNMZl2q0EPiEVNuGzC++c6Hrv1zm85ZyynB1QG+MSByoB5UXkIGcqKsX+7+K0PkRuspz5uKo+wUDU0aNHg997770rW7VqVbhGjRq/3n777S9WrVr1cNWqVftt3ry5cIDXH8KtmNQCUmtbJsVUdYOIXAT8D/hZRG7QMz0s8YLeF8AXXsGBe4AlIrIK+Jsz/2aq4goxvIz7N9nP+9lXFbfVMg2XIRtvZxRvJe4d4CfgfFWNryiESUfpPcPsRjyfpgISuQy331kyzjNPI/IAqh8m9PLQ0NDRp06dug2gcePGPVetWtVk//79D4SFhZ3zKW3p0qX527Vr9/CuXbuq1qtXb96iRYs+9jXGLMD7hx4zmzyHl9xQCle3N6Z27yVAL+ACEfkCGBl3/8XkWPuAP6Kjoy8aO3Zs6Xnz5t2UP3/+HX369Bkc0/R4zpw54ZGRkWFlypQ5FM89dgGtcGcHk9t5JNWp6iER6YrbL/xNRHqr6lcBrlsNPCQiTwLNcP9uqgLPeP9/GLdvGfPhcwUuo/V3Vd0X3/t75QHfwAXWO1OSaGRSX3oHzBrAL76uFKmJa76aP8CzuYAPENmF6vTEbhURESF//fXXRfnz59/74osv1nr66afPWdosUqTIqQcffHDS4sWLy23cuDFJiT1ZnZfEsM37ddafj4iUAHoDU0VkO24JaWIK2iuZbKB27donb7rpprt37NiR56qrrhrdrVu3FVFRUSxfvjzfLbfccsv27dsrNG/e/Ocnn3xyTTy3OIHbe2+A68+YaXjLnm+IyELgc68wwDBVPSf4q+oRYLZXIehz3L5sC1VNUhawiOTGFXB5DpeEdJsVRMl80ru9VxXcpy0/niNwsIwhwMu4LLMEDR06tE6xYsW2XH311bOnTJnSPNA1FSpUODlgwIC1uXPnPhXo+ZxKVXep6gu4P7uhuISPxd6SlMlhRKS0iHy0Zs2aaWvWrPns9ddff7tbt24rwDUeLly4cGTt2rXXL1y48Lkvv/zym4RuhSvGfn66DDwZvCMbDXHbGJtEZKSIXBD3OhG5EFfabxHQNinBUkSqiMiLuEB7HdBaVQdasMyc0jtgVsZPwHRBsL2P+9UA6iR20bRp05q3bNny16eeemrR33//3eDQoUPBAH379m3QsmVL//upOZiqRqnqDFW9CngF+ElEumf0uEz6EJG8IvI0LodgN1Bz3Lhxz4WGhu7DFfoG3AfPCRMm/FCpUqWE6i8Xwe1hrsTNyjItVd2tqt1xgX0n8K2IzBeR7iISJiI340r+9VfVAX6Od4lIsIi0E5GZuM4pwbhZ6dWqujwtvx6TMum2JOudNyqNK9ScmHAgj89bl0noyf379wevW7fuwilTpoytWrXqidKlS68fMmTIBS+//PLS1157bTGQI/flvAPQtTjTa7Q8LmtvY6xfqwL9AFDVj0RkMTDZW67qZwXqsydvf/smYBiu/VOj2IkwuB6JN+L27BKTD7cMuxmXPOPnZ0GmoKrbgMEiMgxXvOF+3NJpNK4TS1ERuRL372azqkZ4yXblOPNvLCY/oBnuQ8c7QBfLUM860i1gqqp6e2AxzUgTsh9XfstP0NwKXBjfk88991y9iIiIPPXr138JIDIyMmzmzJknX3755aW+Bp6NiEgobtnnfuA8YBlnMvuW4n6YVcYdSakBhIvIdFxW3w9x0uyXikhDXAeKH0TkCtvXzF5EpAXukDxAd1UNlH+wCZe4UxL3gSuQUNy/+0O4ajYbSKQ3ZmalqqdEZD7wAG4Zdgzua2+IyzqvDJQVkX24mfQuzv4QOhMYrqrLMmD4JoXSO+lnI+4T1qYEr1JV3A/qboncby3uTNi18V3wzTffNL/ttttGjRo16leAzZs356pZs+YbW7duDStbtmyOOMzvzRIeAh7FpbmPBKb6OHhdBRdg++MqnXyHC55fq+oBVT0oIp2BibgfrPem3Vdh0ouIVMYV9W4KDAQ+S6C6jQJzcGcLY/YlT98Kt6oUDXyPyxTN0jkCXtutL3DlMZ8JtALjfTAtCey2lZfsJV0r/YjIaOAXTeQ4iHdxDdwmeoF4rlCgAy7l+5xKP0eOHAkqWrTo+wArV67sU7169dPLHtWqVevbunXrBXnz5o1YunRplTlz5kwGKFCgwJsRERF5oqOjQ8LCwo6+9957L/To0WNrnPdN90o/KeFVGxmD+7R7t6quSOZ9iuP2lTviZqC/4Q5lT8ctxy0CngtUJ9NkDV6JxieAO3DVbF5NQvJJG6AuLtMa3GpFXtws7Hfi6W6SlYjIrbh/+3er6tQMHo7JAOk9w1yHWwpMnOrfuMO7E3HLObGdAO4jwPmoGJ999lm5QoUK7di1a9fTcZ9bv379a7F+e3oP8/Dhw318jS2L8JZMJ+MC2+NxuyMkhXdw+iPgI68CyZVAJ1zm7Dpcp/o3RWSpqq5M8eBNuvH22u7A9ab9GrjA27NLigXABbgPZgVwfyfmEf8ybZbhzRhH4P7OX66qf2XwkEwGSe+AOR34RkQe8VXAWHW+N9O8mbMr/YwhgX/QXbt2bT179uyr77vvvjGpNO4sR0TOA74F7lUfTWiTwjt7NgWY4v0wuRQ381TgTxF5G5cI8Yd1WcncvESVEbgklLaquiSZtzqCW5q9EHd+ejNZdJ8yNhEpifu7fBBobG20craMKL6+DHhIVeem4m2zdfH1pBKRArilsOGq+nE6vq8Ac3E/PCviZhtf4vY956RjAfi8uGMAW3GZmJmiXmlm4p2jfRWojmth9aXVKT2biDTBrdD8D7fdYB/+criMCJj9geqqemcq3tYCpscLWuOBI6n4Pfbdb3T+/PnV5s6de+2TTz75+oYNG4ouWrTovE2bNp1/5MiREiVLllxbo0aNVU2bNl1buHBhv8kQSeo/6qmPm/EeAyJwiWFrcftrOSLRKz5eRZpncQl1w4B3rJPNuUSkN/ACcIf6qCZmcoaMCJjlcUcYyqRiBpkFTI+I3IT7fjRLxfNdvr+/kZGRcvfdd7/SsWPH99u3b/93zONr164tNGvWrIbr169vtG/fvppFihRZW7169YVXX3314urVqye0zJWc73cP3JGkw7hth3AgN26muQH4C/gPF1BzBK+jzYPA47iWU4OTWr4tJxCRMFwt18uBjvG13DI5U7q391LVLSKyEmiLW6pLDdtJYmPnFL5XZvYw8GRGHYYOCQnROnXqfD9v3rw2sQNmzZo1D9asWfNH4McdO3bk+eqrr+qtXr260XPPPXdT/vz5t1auXHnR5ZdfvrBx48Y7UziEApzdUDySM22oBNe1pQZuf20Lbk98C7EaImcn3opDJ9yHjtXAJaoaX33XHM3reTkZd3aySaDasSZnS/cZJoCI3AW0UdUb0v3NszGvNdFEoJqfEl1JkKQZ/JYtW/I98cQTrz/zzDOPVKtWLcEfOocPHw6ZOXNmnWXLll20bdu2hqGhoUcqVKiwqGnTpguvuOKKf4KCgpI6w6yFq8TiZ7wFgUK4QLoLd05wOdlk2dbLkh6Bm2H3U9XvM3hImZaINMf923kXeMH2K00gGRUwi+Cqy1SwrLPUIyIfAWtV9cUkvOhKXGmzSrgjAN8B4zh7hprkJe8+ffr0v+iii77v0aOH79KDkZGRMmvWrKoLFy5stGXLlouioqJyNWvWbM0999wzCPjJ57GYLkBx4EBSxosr21YGV7kovZb304SIlMUd97kKGAR8lMofoLINbwZ+F67Zw22q+nUGD8lkYuldfB0Arx/cHNxSkUkF3j/8G4CPfb4gDyKTcIWjbwNaeq8fBfyJSLWEXh4UFDSuaNGiL8T8mjlzZrEhQ4bUFpHx/fr1a1CgQIGdu3btKlm5cuXHhgwZck5nk6FDh9YqVqzYsKCgoE/vv//+xuCWc9u1a7f+2Wef/XzUqFGP3HnnncPy589/EJecskNExojI9SKSL55h5caVJgv4ISwqKsGYcQKX3Ztl6pvGJSL5ROQZ3Cx5G1BTVUdZsAzM29f9ANf78mILliYxGRIwPeNx5ytN6igJHFdVv3uAb+NmY4HUBmYQf2AiODg4Yu/evQNjfrVr124PQN68efeNGzeuY3h4+K79+/eXiO/19evX3/Piiy++V7t27YD9UYOCgmjRosW2m2++ea6qNgHq4aoL3QNsF5HpInKbV4EoRlnOLc8GwODBg+tceOGFd5UsWXJI//796wcInoVwh+2zXOk2EQkSkZ7AGtySdANVfcL24OLnzcLnAYVx+5XrMnZEJivIyIA5A2jkbbSblKuCW+ZOnGvOfVsiV9Xycc05SpQo8W/u3LmPrVq1Ks/hw4eLx3ddu3bt9vTu3XtzUFCQrz0BVf1PVUeq6pW4M54TgGuA9SIyT0Qenj9//qW4ov1nWbx4cYFXXnnlrrZt2/7eo0ePKRMnTrzy559/Lhznsny4gJOliMgluBZR9wFdVbW7qv6bwcPK1GJ9z6bivmdHMnhIJovIsIDpZXF+ids/MymXlObc1+BmYolpF98TUVFRYTHLsdWqVesX+7l77rln6qxZsxoePXr0rBlmy5Ytu/Tt27eBzzHGS1X3q+o4L2msJPBy7ty5L/jqq6/evPfeex95/vnnO//4448VoqOjOXLkSNDQoUObVa1adeWLL7647JVXXlkSERGRe9u2bXlj3TJmVvpfSseWXrzGw5OBT3EFCJqp6oIMHlamJs79uEzY21X1RSvWYJIi3Y+VxDEOtz/1WmIXmkSVwGV6+lEypdfFLMkGem7gwIFr33rrrcjNmzeX5MyRDmKK3Kcmr6XYV8DyyMjIv7/++us8ixYtavTpp5/2Gzt2rOTPn3/5xo0bCzRv3nwBwLRp00pUq1Zt7YIFC8p07949prxiQeBfAsxOMxsRKQw8iZv9jwB6WD/FxIlIblwGbEOguapuyOAhmSwoowPmHKCciNRQ1b8TvdokZCvQ3Oe1m1P5unN06NDh5+nTp3fPlStXcm+RVNVCQkJOdujQYXOHDh3WRkdHj/vpp5/Kjx079ooDBw6ct2PHjjqPP/54/cOHD289dOhQwUqVKh2I9dqCwE/pNdDk8Aqk34XLep0BnK+qWTZBKT15xVKm4FrbNVPVLN85xWSMjNzDRFUjcXtRlvyTcjG9Rv2YhjvQn5hkzwgbN258KCIiImrXrl0Vk3uPJAjCdcHZd/qBoCAuv/zyLTVq1Pjl+PHj2wYNGvREeHj45pUrV15+/PjxSzZu3Nj+vffeu3jz5s0xiU2ZdjlWRNriMl87A1ep6p0WLP0Rkctx+5WfAzdZsDQpkaEB0zMOuNk7FmGSbyPuSEXiXKeXYYlcNR/3QyZZdu/eXbx58+arjx07ViTmsdh7mG+++WaVfPnyvb169eomH3744R3h4eEpKTdYEnekJGCGa/78+Q/Ur19/T3Bw8PqdO3duvuyyyz4877zzFq1Zs6bxsGHD3uzbt28vEenpzUQyDRE5T0S+xfWmfBy4QlWXZeyosgZvv/Ih3N/hHqr6qu1XmpTKkMIFZw3ABcq1wC2q+keGDiYL876PMSW9Ek/+EQkChgADOPeD07fALZypNZrkwgUDBw7sXaJEic19+/adnZTXBeCn0k9zoCkByhYeOnQouFWrVj1WrVrVtESJEpsuu+yyX8eMGXN6+fXIkSOVn3zyyeNvvvlmPeBaYBNuBj4V+CsjfsiKSAlgMG5G+TzwnhVI909E8gLv4zrWdFLVTRk7IpNdZHjABBCRZ4FwVX0oo8eSlYnICOCkqgZMxonnRZWBrpyp9DML1Z/jXJXkgHn//fc/2aJFi5k33XTT0qS8LoDEAmZMpZYIXPGBgDZt2pRr6dKlhTp27Bg3Mao8rn3TXm+f8BJcp5OO3j2neb9+S+sCAN5B+odw3++xwBCvyIfxSUQq4fYr/wLuUtUcU2DfpL2MTvqJMQ6Y7zWW9rO3ZgJ7D/d9fNZ3JxjVfwD/pfR8WLVqVfihQ4cqtW7dOj3ONRbBFR1IMKBXqlTpZKVKleIGyzy4qkD74PSe+hxgjog8jGuG3BGXXVlCRKbjgucPqdhpJ2Z1oDPug8EKXBanJcElkYhcgTtm8wLwpi3BmtSWGfYw8apsbAZaZ/RYsjLvh+wyXIm7DDN16tRW5cuX/7VkyZLxzvhSUXkgF8n7uxyOS6Y55werOotVdZCq1gVa4LYOBgI7RWSCiHQXkUIpGHtMwfyfgKdwvRevs2CZNN5+5SO4WfmNqvqGBUuTFjJFwPSMA27K6EFkAy8DQ71Gwenu2LFjwevXr2911VVXpXTv0q//cB8SSuKCZwn8r5wE4bM6kqpu8BJHLsG1B/sOl929RURmicg9IlLG76BFpJyIjMUV7xgNNFTVH/2+3jheXeHxQHfc/v3cjB2Ryc4yy5IsuOMlz4pIXtt3SD5VnS0iE4BPReSaVGpT5Lvf6Pz58y+oVavWgcsuu0z8vsbHeydkDzAdCMN1G6mOq4WbC3d0Zj8QaPk0FLfn6bfYw2mqugu37/k/ESmA6wrSERgmIn/jJQ0Faj7s/YB/HLgft9RbU1UPJ3UMxlU7wiVnLcH1+bQCDiZNZYqknxgi8h3wP1WdkNFjycq85JUfge9V9bl0fN8g3HGUNzP4zzAYN+OsAlyAayodjWv5FXMOryRuOTbVZnUiEgZcxpmkoUOcSRpaiKvPO9l73/6qmuzCEDmdiFwFjMG15RppS7AmPWS2gNkLuF5Vr8vosWR1XlH7P3CJQOnSEFdEnsLNtlpmouQtAYrhsoDPx/XKVNwM81NSUM0owTd1Hx4acSZ4lsIlGb0DPJHYMRHvQ095XNCvglsN2ohbQv43NZOOshIvQao/Lpu4m6rOz+AhmRwkswXMgrhsx8qWTp9yXgujCbhM0B5p+T31MhTHAI3UFUbIrArjAlFJXHunNG3n5R0VGYErZD8NaALUBL7xfv+tqh72AmQ74HZcYC+H680ZEyQjcYUpqnjP7cR1VxkLTPJq6mZrIpIft99bAeisqpm2OpPJnjJVwAQQkYm4tP33M3os2YGIhALDgeuBR4HpqpqqQUJEyuGWHG9S1Tmpee+sTEQqApNwiUm3qepB7/EyQAfczLMFrg5wcVxgfAv4Bdgc3yzUC67lcIXE78IdfxmNK3Dgr8VbFiOuofk0XE/U+3PqDNtkrMyUJRtjHFZbNtWo6ilVfQR4AHgY2CQizyQlozMh3r7dBOAtC5ZneEviv+C+N51jgiWANwP/HJfQFIULmIuAasCdwHUkkDClqpGquklVv1DVq3BBNwRY6DXWLptGX1aGEJFrgF9xHybutGBpMkpmnGGGAdtwXeMtKSKViUhd4F5cH9IleEkpyflee/t0Y3DNlzunxz5pVuDNAL8H5qrqswGer49L/pkFPKmqB7zHcwGtcDPP63Ct0abhZYImltjilYR7BNdM+hZV/SE1vp6M4v39Goj7+9pVVX/N4CGZHC7TBUwAEXkf2KiqqVqBxpzh/XBtg/vh3B7XD3Ka92uln6xDEXkZV8e1jR0FOkNEhuOWSa+JW05PRG7HVVZ6SFXHJ3CPYNx+Z0egE+6YzDTv108JJVWJSGtcQtPbpFPCV2rz8hk+we01d8nk++Imh8isAfMy3BJf3YweS07gzYgu5kxGZyRnfjgvCFRDVUT64pYPL7YErTNEpAMuUDVQ1T2xHs8NjMQVie+iqn8l4Z4C1OHMn09lXL3Ud1V1STyviUn4OgR0j70knNmJSE3c3725uA8WVnjeZAqZNWAG4bpGtFPVFRk8nBzF++Fcn7OPQ8SuoXpCRG7EVRRqYcvmZ3gH6RcA16nqb7EeL46r6PMfcLuqHknh+5QHegD34PY/RwIT4gYWL+HrPdyxmk5ZYabpfeD4EHf05sOMHo8xsWXKgAkgIi8C0UnqvGFSnRcEOnq/6gJLccuNbW1P6QzvQ94fwCeq+lasx2sDXwGfAYNSM2h5KwPXAH1x+8g3qOq/ca4Jwx2fmaqqKek5mqa879/TwB24GfjvGTwkY86RmQNmXWAG7kxmpv9knBOISCvcTHMFroLOb7iElOmqujUDh5bhvMozw3FLseo91hpX57S/qn6Shu8tuKD5OO74ytdxni+PO/bTTVXnpdU4kssrYD8WVwz/BlXdkcFDMiagzHisJMYK4DAuZT4hwUDBtB9Oziaub+ZY3A/kFri6re/jkn5WiMjvIjJQRGpl5Dgz0H3AO7GCZW9csOyWlsESTndWGYFrEfaB17kj9vNbgF7AeO+4S6YhInVwM/N/gdYWLE1mlmlnmAAiMhCoqKr3xH0Klz1XE6iHO4M2ChdgTSrz9uB+AV5X1ZEBng/l7BqqRziTNPRHdl8h8AoULMZVoDkODMMFr3aazq26vGSfP3AfbL6L89zzQFVV7Z6eY4qPiHQCPgAeU9WPM3g4xiQqswfMisCfQBkvoaEI7nB3A9ys8hSwFxc8v8XNSk0q8sqR/QjMVtUnfVwfhKtA09H7FY5LeJkGzMmOGY8iMhS3h/gE7lxqCVxN5D0JvjDtxnM5rjDCRd7sMubxwrhqQrUzcibnHZl5FuiJO7+7KKPGYkxSZKb2XudQ1X/Dw8P/vuWWW+7GzR5L4Cqj7MPVnI1xABdELWCmIm/mOBFYhWtwnChvNrnQ+/WkiNTABc5ngM9FJHYN1UNpMOx05RUb6I2bUc7FNZluk47VaHoAZy2zqirjxo1b/O+///586tSpd0NDQ6NjHh85cuTaAgUKfEzqdGnZjlum901EwnHVvPLiAnqS26sZk1Ey6x5mbtxya7cPP/xwe5UqVW7HdZfYgqsCFLfQ9BHcLLNIuo4yG/MSSUbhOnvc5aeQQSCq+reqvqSqzXFnCecBtwH/icjXInKniJRMtYGnv+txs7ZxuCS1nulcuq007t/FWb+6dev26ebNmyPGjx9fPPbjNWvWnPbDDz9cdOLEia2BXpfEX0naDxWR83EfpGI+VFiwNFlKZgqYobgD2e1xdU+vA8IbNGgwa/HixTV37tyZWLuoKNxyrUkdz+MaMXdNrWLtqrpdVd9X1ba44uGfAK2BtSLyi4g85hXZzkpuA84DBqrqkOR+sEhtISEheuGFF85auHBhm9iPt27d+t88efLsmTBhQoP0HI+I3ADMAZ5V1b6p3QDAmPSQGQJmCeAKXAf6G4CKuFnkFuBg5cqVDxcpUmT11KlTGyVyn/24ZVlJy8HmBCLyANAFl7RyNLHrk0NVD6nqBFW9Ebc6MASoCswXkZUi8ryINPRmupmSiNyHS3Z6UlU/S6M3KYnIVYi0w+3p+3bTTTf9fvjw4Qq//fZbqdiPn3/++XOWLVuWWPZ5qhCRYK9U4EvAVar6aXq8rzFpITMEzCtxSSJ7cEFyL24Z8LQLLrjgl1WrVl0c94V79uwJ+euvv/J6vz2OSwTKyst7GU5EuuAKXl+dXkkrqnpSVb/1sqHL4kruheIO+28WkbdEpLW3p5rhvCDwGvAg7u/rV2nwJgURGYOrDvSt9x7/IPIVCXSaWbp0af6iRYu+ULRo0RdKlCjx1qRJk3Jdc801zxYtWvSFkJCQjwGqV6+++ciRI6V69ux5aaNGjW4NdI+yZcs+FRoaOjrQ8/6/BCkCfI1rpH2Rqi5O7r2MyQwyQ8BcDUTjllQD6tix4+IDBw5UW7duXaGYx6Kiohg3blzFdu3a3b9nz56Y5KVTQI20HW725WVXjsTNLDOkr6KqRqvqAlV9HLePfRUuueQFYIeIjBGR60UkX0aMz8sanoqrenQ5UJSzE9BS403yAj/hEnpiJ+YJrsn0QkRKBXpp/fr1j+zdu3fg3r17BzZr1uz7pk2b/tq9e/cte/fuHSgiClC7du1dx48fLxHf6nGRIkVOPfjgg5M6deqU7NmgiNTDtSxbTjp++DImLWWGgLmBRJZRw8LCIkuVKrV4xowZTcEFy+DgYB566KENwcHBkf369WvuXboXVwc1OE1HnA15lZUmAjeq6tIMHg5w+kD+X6o6TFUb4/5sf8e1e9ru9X68zTsnmua8RtnzgV3A1UAh4L+EOock09O488XxKQO86edGISEhR44dO1Yi9mPly5c/CmhkZGTuQK+pUKHCyQEDBqzNnTt3svYZvVrD3+PqwT6WBt8fYzJEZjhWcgA3gyhAPIUHOnTocP3atWsr1qtXr0qLFi0WNGnS5FCs5+Z9/fXXzXGfyE8BeXDLelYU3CfvvOvXQB9VTY3jBmnCO1P4DvCOdzzhGlzrq9dFZClu5vdlWsyOReRCXBH6t4GXVFW96kcbU/u9cPVUE9MJkSIkkmMUEhJyNCIiIvzYsWPBUVFRYUWLFn0BIDo6OiQiIqJT7dq1fwfo27dvg6VLl1aZM2fO5OQO2qttOxz3Z3KFqi5L7r2MyYwyQ8AE18i4LfEEzBIlSuz7448/CmzatCnvDTfc8HDx4sW3PP300zPnzZtXbuLEidc0b978t1iXn8Bld1rA9EFEiuL2yF5R1c8zejx+qep+3FGOcSKSB5dt2wl4QkS2cabx8vKUZq6KSHvgI+BeVY0dUCrjuuqkHpESuO4iiQnBx/ZDUFBQdFhY2IHVq1cXDQ4Ojti7d+9AgL59+z68bt26gzt2uPoFr7322mJctaJkDluK4dqJReL2K63lm8l2MkvAjJkRCHESfgBeeeWVn3/66adLr7/++o27d++WrVu37rnzzjsfKlq06I569eotfffdd+fGunwvLs1/DpDtqsqkJq+J9Axghqq+nsHDSTZVPY5LivnKqyLTHFcsYSruSOk0XAD9JSnLg16G7kPAY7h93T/iXHIct6KRmo6n9rVRUVFh+fPnPxn3saCgoEhSYftCRBrg+nN+BjylAfqnGpMdZJaAeRS3tFUSdzzkLBUqVDiZJ0+eo+vWrTtcsGDBFl999dUjkZGRM8HtZ+bJkyd2kI3CfV3lcfujJgBv+exz3PdoQAYPJ9V4P6zn446nPIrrqtIReA0oLyIzcMFzthdoA/K+P2/gjo00j9s2y7MRN8tMzS/gMCKrcB/6ErIfVwDgqoQuioqKComOjs5VvXr1sxpIHz16tERYWNjfQOGUDFdEegAjgPtUdVJK7mVMZpcZkn5iLAfyx/fkI488MqlatWrrVTXo+++/rxwWFqZhYWEaJ1iCqxKUC6iSloPNyryZ00jc96l3di2O7iUNLVfV51S1AXARsAzXCmuHiHwhIj284w+niUhB3My7Kq5JdqBgCW5lJC3+nr3o45o3UY1b8eockZGR+XPnzr07KCgo9mNy4sSJ4rly5TqdC9C3b98GLVu27BLz+wIFCrw5YcKEHsuWLbssX758b48dO7Zs7PseO3YseNSoUe2BQUBLC5YmJ8hMxddz4Sr87MAdMwloyJAhXSIiInIPGTIkdsp7KC69PxQ4hNuLWeP9v4lDRJ4FrsX9oMuRHV68PbdrcbPPVriSbVNxRyFG4WapfRJawvUKzR8DiqjqsVQe4Fu4fw+BTAeux82m+5PAsZZPPvmk0Z9//tnyzTfffDnmsVWrVoW/9NJLQz/55JP7kjO09evXFxwxYsRDVapUCXrssceaefvJxmR7mWmGeRJ3JjPBerCtW7f+dfPmzc0jIiJCcFWCyuOWlRbjOkW8j2tvZMEyABG5G7gFtyeXI4MlgKruUdWPVbUjribqW7ijIr/gjotsA2omVGnIm5lvIrWXZd3NHwS6Agtw/zYicasw9wId8blPuGLFiqYVK1ZcGfux9evXl8ibN+/u5Axr1qxZlYcOHfp86dKl1zz88MOjLVianCSz7GHGWAWcn8Dz0rx58xNz5sw5umDBguaXXXbZl8BfwFYSKHxgHBHpiOsacomq7szg4WQaqnrUSxZqgus6chA38/waiIiVNPRbgISWmH3MVWkwsEnAJFzQDvIbJGOsW7eu0M6dO+v17dv3o9iPr1q1qnZ4ePimpA5n5MiRl/722283tW7d+sNevXotwn1YNSbHyEwzTHCB7yRuaTW2grh/nGWBbXPnzv2kbdu2eXDHITZjwTJRInIxbqmxg6paMpRHnP7A67hap9NUdY6qPgRUArrhslHfBbaKyAcico3X1gtgHS6xKO2oalKDJcDkyZNbli1b9o+yZcueXi4+ceJE0N9//926devWvs/bHj16NPjJJ5+89c8//7zu7rvvfs4LlsbkOJlthhmJS8q4EHcmMxx31OQ/3J7SJuDY999//zuwUkRyq4/Eh5xORM4DvgBusWa9Z3i1ad/F1TJupqr/xX7eO78Zcz5xkNdJ5Tpcrd3xIjILN7PsLSIvZqbkqS1btuRbu3Ztm1tuueWl2I9PnDjxwjx58uxr3bp1fIlMZ1m7dm2h119//eGwsLCjQ4YMeapMmTJJOfZiTLaS2QImuFT5xt7/f49b8jorJV5Vt4nIElzSRrIrk+QEXjm3b4BHVXVWRo8ns/AqBU3GHWm6RFWPJPYaVV0PvAq86vXwbI9buq0M/C4i/wOmq+q2NBu4D5GRkfLKK6/cW65cuT+uuOKKswLjwoUL29SvX3+2n/t8/fXXVSdOnNi3evXqPz7++ONTQ0JCMk2GoDEZITMGzO24Iw9HCVDEIJZxwM1YwIyXFxS+Bd5S1bEZPZ7MQkSqADNx35tHk3PQ3tsD/hD4UEQexCVSXQK8ICJrcRm301R1beqNPKDtxNlLnDhx4mUlS5Ys9sgjj0yK/dy6deuKFipUqErPnj0nxH1NXNOmTbto0aJFV99yyy1fXHHFFX/h+pcGem9jcozMdKwkSUSkEG7/spJl6p1LRHID3wF/Av0yS2PjjCYizXHL00NUdWQq3TM/8C+uOPxOXLGDTrjl20O4hKFpwMK0XrYVkQ7AB0BjVd0c6/Fg3IeEX1X1uQRen4szBRs6pkPANybLyLIBE0BEJgPfquqHafgeAhQHIlT1QFq9T2ryfjhOxBWjvykz7a1lJBHpjuvy0VNVv0nle78JHFTVp2M9FoTrBdkRF0ALAl/igudcVU210o3en/lg4Fagi6r+Fuf5Qbh6u63jO1sqrs/mZFzQ76WqdjTLmFiyesC8HnhQVVumwr0K4ZbUqni/Ksf670kgDBeANnq//vH+ux6Yn5o//FLCC/BvA7WAa1T1ZCIvyWoq4T7ALCLhJfvTvO/JU7guIO1VdXlqD0pEauPqF1eP73yriNTCzTo74np9foMLnt+m5Eyst586Dpcgd1PcI0MiciUwGmikqgGXUUWkBa54+rvAC/Yhy5hzZfWAmRt3wLxu3AzHJNyjPu4weFfcD+G/OTso/qOqB70fusU4E0hjfp3nPfYh8EHsZbCMICJPAjcAl2azGYLgljyvwh2HmoVbbk74RW6JcRSug02H+AJGqgxQ5ANc0YMbE1sC92ZzHXDBM6Y93TRcIXxfZ2S9lmP3AV1wbc+eibsfKyLlcVWMblTVuQHuIcDduNnpbar6tZ/3NiYnytIBE0BEPgTWqOorSXhNLtwPmfuACrjqQB+q6o5kjqE2cA8u8WM+Lmnp+/T+lC4it+OaDzdPy8CQAYJxe2pNcEeMwCWhTCSBAvte67IpwB6gR6qXrzv3/XIDvwKjVfWtJLyuEK63Z0fcB4KVwDzc1xazkhHJ2R/WrsKdS34P+F+gIOsdg5kKjFPV4fGM922gKW6/cr3fMRuTE2WHgNkSGKGqF/q4Ngjoh2vXtAwX2L5KrY7wXvJHd+B+IB/wEu6HZ5p3nBeRa3Gz3MuyWaJGblyv1Bq4mqkxf2Hz4MoojsXtuZ1FRGrgklymAAPT68OLl4H7G242+1ti1wd4fS7cXuNFnB0gQzgTPDfiyj9+k8B+ZCfcB8FngXfjzni940Zf4BLnbvNzrMaYnC47BMxg3D/6Nqr6VwLXxfxwLYTr0JFmQcVb5moBDMUVhe+P++GWJt9sEWmK665xrar+nhbvkUEK4WZdxXBL73EVxFWFGkOs2sEichluP+5pVR2V9sM8m5ep+hbQUFX3pPN7hwIv4FZQuuq5PTwRkUtxrd3eAF6yDGpj/MnyARNARF4BTqrqk/E8fxEwCfeJeoCqnornOsFVfanJuck/EZz5dB+zv7k8ocDr3a89bqb5H+7M39JkfInx8hJJ5uI+BMxMzXtnsFK4vVhwS6rxKY4rbPE5cFJEegKv4JJfvk/bIcZPRF7E7blek5xznsl8z/LAeFyVrB6qujfO84Jb/XgalylshSyMSYLsEjAvxC29VYn9adn7AXEvLqHhblWdEs/rCwM9cXuaAEs4OxP2H1yWbNyEnybe8yOBL+LLlPU+9d+J6x04C9eVPt6WTH55iSO/AM+p6uiU3i8TqQpcj5s1+klcKnPq1Kl/8uXL1+DUqVM34TqxrE7TESbCa0A9Hde2rruq7krj97sBl/jzGnBOmT5vu+B9XHODTqq6MS3HY0x2lF0CpuBqet6hqr96j4XgUunr4s6lrQvwunq4T9w34Kq+jAR+9rtE5QXC9rhAez7wP+D9+DJlxTUmfhyXIPQe7gdbsjJZvUSRecBEVR2WnHtkQoI7t3gFri+qrzrBBw8eDB0zZswjs2fPDp4xY0artA5OfnnbBc8Ct+GyVH9Og/fIjztbegluVr0wwDW1cecrfwfuV1WrB2tMMmS2biXJ4gW48bhSeTEGA2VwRbXPCpYiEiwiz+OC5L9AbVXtrqrzk7Kfo6qnVHWKql4BXI5L9FkiIiO8PdO41x/ylo3r47I8/xaRe73A65uXGDIV+Bm3X5UdhOAC5RW45euAwfL48eNn9adcv359wQEDBjy1efPmIxMmTJigqmXSfqj+qGqUV8jgLuALEennfbhLFd5WwxLvtxfGEyxvxB1ZeVVVb7dgaUzyZYsZJpyVnVgWl3L/Li7pYlec60rggmsQbqksVftCeofIn8UlXbwAvBNf8QBvKfkVb8z9cWfwEju/FwR8hjtq0S299sfSWDCuEk5Vzs6EBeD3338v+MorrzSZNGnSbICIiAgJCwvT+fPnlx09evRjlStX/nngwIFfhISEhOC+l5/jltEzDRGphNtH34LLSj2Y8CsSvFcw7u9LX9yMcVKAa3LhCsVfjVthWZrc9zPGONlihgng7cmsx52F/B9uCSxusGyBO+z+Oy6rNtWbKKvqTlW9F7gUN+tcLSLdAs0sVHUJbkbVDxdc54hIo/ju7d3jNVxCzC3ZJFiC+3sYDhwhTrCcMGFC6TZt2gyeMWNGl1atWnUGCAsL0wkTJpw/atSopy+66KIvnn766cleJ41TuCMm1wMl0vlrSJCqbgIuxmX7bhSR172ErSTxEnt+wAXCRnGDpYiUE5HncK3wSuA+NC5N2eiNMZCNAqZnAm7GNlxVf4n9hIh0xCUG3aOqT6Z1sFHV1araAeiNmw0sENfEOe516lVXqYcrbzZdRMaJSMUAt30MaAVcp9mrD+gpXAZzCG5Z+7SIiIigFi1azJk6deqTq1atqn/zzTdf/tZbb7WcOXPm/ddff/3r99577/w49zqO63TTBSiQLqP3SVVPquoDQAPcGOeKyA8i0tnbc0+QiHTBfeD7DmgVs1cuIkEicoWITAWW4z58tFbVrimZyRpjzpZtlmQBRORj4CagSOyD2CJSHVeB5ZpA+zzpMK4gb1xDceX3BgRKQvKuzY8LjA/gChG8oKoHvOMSQ4AWmswygFlAOdw+9A7cMR4A1q1bl6d69erHn3rqqQveeeedBy666KJTQ4YMeb5JkyYJVWYqAezDVQPKlPV0vWXT63FJY1WAT4HVnDm6tE1Vo72/EzEdRG7CnTuOydiuAdyI2/MdiavqY0UIjEkD2WaGKSLNgJa4M4nXxXo8L272MigjgiWAqkar6qe4guh/4Gabb4pIsQDXHlHVZ4ALcEUP1orIu8DLQNtsHCzBJft8iUvWCo55sHr16sf37dsXdvTo0dZ169Y9/Oeffx7LlSvXEYBRo0ZViOdeu4CSnKk9m+l4M87PVPUS3BLrcVyVnxdw9V+Picgm3DJzG9yHiDnACuB1XIZ2KHA7UF9V37dgaUzayTYzTBEZi8sY3IlLr2/n7fmNxi319cgsFU1EpDju8Hh33BLyG/EtsYprSfUJ7ut6CJiaWb6ONNQMt/+7CWDNmjWFX3vttUcLFiy4ddCgQaPuuOOOy2bPnn1VVFRUSMeOHad+8skncZdlY+TBBc33cbPNLMNL7HkCeBi30rCAM80AslNRfWOyjEz5yTupvAB0LfAxbobSwnusF+5c392ZKcio6m5V7YMrn9cEN4u82Vu6Pc0rnv0q7pxob+AZYL5XCi87+w1X67f83Llzy7/44ovPVaxYcdGLL774boECBSKrV6++69ChQyVbt249K4FgWQi3lzeBrBcsywHf42aVDVT1cVWdpqrLLFgak3GyxQxTRAYANVT1du/343F7lo8STz3NzERELsHNNEOAR1R1rnc85VdccYMPvOuCcRWJhuAq/AzMxhVbQj///PMhixcvvr969eof33nnnb8C7N+/P7hRo0YPXXfddXNHjBixOJ7XFsd195iMW5rNMkSkM24v8i3c/nV2yYQ2JsvL8gHTCyIbcGfNFnmPtcMFoEOq2iSNh9ADKJ3Sm0RHRzNr1qy6v/32W9sCBQrsOnbsWHilSpWW9+zZ85x6qIcPHw6bNm3axWvWrLmkUqVKf7Zt23Z8uXLl3kvpGDITEbk/f/78T0+fPv2Tli1b/gvsj3nu0KFDwQULFowvkJQBduN6S2aZ2ZiI5MPtS7YCbtZkdDoxxqSt7BAwr8Ul9DSO9Vgo7kzfk5qEPpnJ1B93GD1V7N69O9eAAQOGnThxoljFihXn3XbbbV9Ur1494NGAtWvXFvr444+7BAcHNx06dOggEiiSkFV4H4BexSXrtPOORfTEFRQ/mtBLcb1NV+MqOGWZ74OINMQV0/gNeNCWXY3JnLJDwPwCmKmqH8V6rCquCe9wVR2cxkNItYAZGRkpAwYMuPfUqVN5+vTp88HHH3983b///ntJ7dq1v7n77ru/LlKkSMDi7qtXr25Yp06dmsB5wABgUmbas/XLOz7xGS5Zp4uqHvCeKo87TrEdd2YzrlDckZRfcA2807Vxd3J5e9aPer/6qOrnGTwkY0wCskPAXIPrvrA61mMv4UqkXQicl8bBI9UC5jPPPHPTrl27agwdOnRYTHBcuHBhic8+++zGffv21WjSpMmE3r17/xwWFhb36ykPvCQirXBL0RG4VmKpXuw7rXiJLjNwB/Pv1XNbsJ2PO0axmbMDYl5cv8xvcIf2swQRKYvr4xmGq9r0bwYPyRiTiCwdML1P6EdxhQqOx3p8AS6QjQGu90rQJecNGuLacp2PC0J/AO/hypzFSJWA+fLLL1+zdu3aloMGDXq2QoUK5yw9fvXVV9Vnzpx5c1RUVK42bdqMu+GGG1bGero8rudmzPfkZlyRhIUkUCQhsxCRBrjs5jeBVxL4gHOx9ysmuBTGzUa/iPVYpicinXDdat7GJfZEZvCQjDE+ZPVjJWWA/QE6MFTBJQKNxy3lJZ3IM7iAczfu+EdLXGuuVYh0TeilQUFB44oWLfpC0aJFhxcrVmzYyy+/XB1g5syZxURk/OWXXx7TGJnFixcXCAoK+vTDDz/s0qdPn+Gxg+XSpUvzly1b9qnQ0NDRzz77bIt33nnn2WbNmk395ptvevfp06f/vHnzysV9b69IwlhcE+yFJFAkITMQketwPUIfUtWXE1kN+BW31F4Wd75SgbFkkWApIvlE5APcHm1HVR1iwdKYrCOrB8wquBJip3nZhgVx5dXGAd29RBL/RG7DdRwJ1IopLzCWBIqkBwcHR+zdu3fg3r17B9x1112fv/baazfGPFegQIFdK1eubBDz+yeffLJjoUKFyJs37+K6devujX2fIkWKnHrwwQcnderU6VOAoKAgbrvttj/eeuutRytVqrTsf//731MDBgy4c+vWrefUTFXV46o6HKjtfR2rRaS/iORO0vcijYjTF3eE4hqNp7l3HNG4OqrbcZmwn3r/zfS8WfSfQG5cVZ4FGTwkY0wSZYeAGbeNU2VgkzfT+gvYg+sc4o8rgj0kkavCcAE1Ufv378+TJ0+e07PGkJCQiOLFi299++23K3///fcVFy9efGXdunXnBQcHn1PSrEKFCicHDBiwNnfu3Gft5+XLly+qX79+s1544YV+uXLlOjpy5Mh+IvKMlzRzFq9IwoO4pcxmxFMkIT15hcZH4horN0tiycIIXPuuz3GZ0JmaVxj9MdwserCq9rQsWGOypkQ7JGRylYkzw+TcWec43J7eHJ/3PA+35JeYNogEEWAFMSoqKqxo0aIvREVFhR47diz8tddeez7281dfffWvY8aMaV2hQoUGISEh2ypWrLj2r7/+qgLQt2/fBkuXLq0yZ86cyYkNoGzZsscGDx48/p9//lk5bNiwGrhg+AwwOu6Bd1VdC3TyiiS8CvQVkUdVda6PrzXViEhBXEF0BS5OZvDIEof5vcSeT3Czyov07L1vY0wWk9VnmBU5d/+qQpzHPgOu9zpD+FHc53VhuPJr54hZkj1w4MCjw4YNG/7UU0/dGxV15md8r169Nq5bt+7SnTt3rrvkkktmxX7ta6+9tthPsIytcuXK+1X1ZqAjrpDCUhFpG08PzvlAU1w27WgRmS4itZPyfsnltSz7BfeBpn12nml57eQW4465XG7B0pisL6sHzOKcW/rsrMe87h7LgGt83tNvAskhVPcndtGjjz667sSJEwUWLVpU0BuPvPvuu4+UKFFi66JFi2oMGDAg1cr2eUublwNP4RpNfyci9QNcF+2d+asFzAN+EpGRIpJmTZdFpDEuaed/wP3ZNdnFS+x5HxiBO+40OLt+rcbkNFk9YBbD7VEm9th43LJs4twRDD/HUCb6ud1nn31WRlWD6tate/jUqVPBqlq0UKFC/z3xxBPvdO3adXz9+vVTdR/O9aPWL3HtwaYC34rIaO+cY9xrT6rqq7jAeRL4S0Se9FqipRqv8fFMXPPu17NiUQU/RORCXGJPPuBCVf01g4dkjElFWX0Psxiw18djk4FXRKSQ+utA/wBu5hXf92cnMCi+F8fsYXq/lQcffPDdXLly6YwZM24EePbZZz/MmzdvVK9evc7pbRl3D7NAgQJvRkRE5ImOjg7Jly9fo/fee++FHj16bE3sC/AO/o8UkXG44zDLvL6aL6rq4TjX7sXtab6N68W4VkSeAj5NSfFvb0m4P+77eWWyz8Nmcl4CVV9claWHVXVcBg/JGJMGsnrhggNAZY21NCoi3+OCwuw4104Bvo3p/OHj5lfhzvjF3dNcDXTBZeCCz8IFgwcP7rZ9+/bzn3/++eeLFSuW2nVOTxcuiI+IlAeeB64EBgMfxrdUKCLNcXuceYDHVPWcAvCJEZEwXCZsA9x+ZaJBPisSkTK4tnL5cUXT42ZtG2OyiSy7JOsVWM8HxJ0xBlqSBVdZ5d5AiTABqc4CKgG3AMOB53Cl2c6PFSx9ee21167cvHlz48cff/ylNAiWvqjqFlXtBbTD9ddcISLXxpMY9CuuWMPzwHsi8rWInO/3vUQkHFeqrgRwaTYOlh1wiT2/4r5OC5bGZGNZNmACRYF9qhq30HZ8AfN73CzAf/Nl1WOojkN1IKrPoPoV575fgj766KPGy5Ytu+7+++8fXrly5cOJvyJtqepi4ArgEeBF4EevW0bc61RVvwDq4M4Q/igio0QkwVZmXuH7BbhEq06qmunPSiaViOT1lrffADqr6rOW2GNM9peVA+Y5gdGbLQXaw8QLrO8C96fL6IApU6bUnjt37u09evR4qUGDBpmmIo0XDL8G6uGO3cwQkbEiUiHAtRGq+gau1N4BYKVXJCFf3GtFpAXwM/CGqvbLjs2PvazjRbhqUvVV9ZeMHZExJr1kq4CJW6KNUtVj8bxmNNBKRK5I05EBc+bMqTBt2rSHOnTo8Fbr1q0zZa1TVY309nRr4s5GLhGR4SJyzvlSVd2vqo8BDb3r/xaRO2LKDorITbis3NtU9d30+yrSh1expx8wGximqjf7TCAzxmQTWTlL1u+RktNUdb+I3AyMF5FGqbS3th2XdHPapk2bCs+ZM+e+7t27f9W2bdtDcZ9PA9tT8mIva/YZrzD4c7hg+DzwXtw2W94B/JtE5CJcYtBDIrIEV36wtaquSMlYMiNvGfpj3KyyiarGrS5ljMkBclTABFDVOSLyJjBRRC4P0HcxqcbG/o2IFMUtSz6rqq+n8N7pyvsA0VtE6uKybh8UkceBaXHPTqrqQhG5EvgWl0T0B5C0IvdZgIi0B0YB7wPWXcSYHCy7LckmGjA9L+K6XMxIzbZXXom5n4Avs1qwjE1Vl6vq1cCDuCMoP4lIk9jXeB8MZuP2i0viCjnEWyQhq/ESe0YCbwFdVPUZC5bG5Gw5MmB6CUBdgOXAn3GDQXKIyI24YPmqqg5I6f0yA3VHay7E7f1+ISKfi0hlEakB/IY7TtFVVQ+p6ju4vc3tuCIJQ0TknLZjWYGI1MMl9oTjEnt+zuAhGWMygaweMP1U+QnIS3jpDzyEm2k+k5xaqiJSQ0Q+xJ1ZbKOqHyX1HpmZqkZ5X1NNYBWwFFc68C1VHRD7WI+qHlTVJ4D6uCL4f4tI5/QfdfJ4iT19cUeQhgM3qeqBjB2VMSazyMoBsyjnziYDPZYgVZ2GO5tZHlcSbryIXJxQgQMRCRGRjiIyG9eNYjfQSFWXJuW9sxJVPQpsxvWj/BF4SkT6BuoCE6tIQkdcScIRXqGJTEtESgFfA92Apqo6JrvWvDXGJE9WDpgp2cM8i6puVNU7cL00/8AleRwRkVUiMkNE3hSR90TkOxFZBxwFHsNlTlZQ1YHZeSbizbyex9XPvVRV2wMtgda4gu03xFMx6HfcMZQawFxx/SEzHRG5FjdrXghcoqobMnhIxphMKMvWkhWRf3DHGDbGemwSMElVfXUSSeT+BXANqqt4/43AnVXcCPyrqidS+h5ZgYjkwe1hlgc6quruOM+3wh0vOQk8Guggv1ec/HGgD9AjObVp04L3tb2MK3l4i7peocYYE1COO1bil3c2cbn3K0fy9nS/BDbhPpyc8yFBVX8UkUa49mmficgfwABVXR/rmmjgBRH5HfhURF5W1dfS5YuIh3d0ZjywEqiXnVcIjDGpI0suyYpIbiAXELc2a6oFzJxOROrgMmG/wyW/xDujVteQeiwuMehPYIFXICLudT8CjYF+ItIubUaeMHEeAn7AzS67W7A0xviRJZdkvb2whapaJs7j24GGqrotY0aWPYhIG2Ac8IgXCJP6+nq4HqSzgb6qejLO8y2AKbiqOZtSPmLf4yqJ23cugmvFtT7hVxhjzBlZcoZJ/IXXi+LzWIkJTETuxFUv6pKcYAmgqsuARriCBj+LSKU4z/+CO7YxKVCWbVrwZrRLcTPgiy1YGmOSKtsETFydzxNxZzPGHy8T9mVc9u8lqvpTSu7nFSbvguuG8ptX7CC214F/gTTdyxSRPCLyFq6ZdTdVfSoVyiEaY3KgrJr0k6YJPzmN16rrU1xlm6aqui817uudYxwhIsdxs8lmMZ1kVFVF5HZgkYjcrKrjUuM9YxORC3CJPX/hKvbsT+33MMbkHFl1hpkqRQsMiEgZYB5wELgytYJlHO/hslHfiX1eU1UP4Qq3vyEieVPrzbzEnj7AHGAEcKMFS2NMSmXVgJmisnjG8Y5WLOBMH8uItHgfb6Z5Ny5D9vY4zy3zxnBjaryXdxTmK+AWoJmqjraKPcaY1JCVA6YtyaaAN7P8FndmcmhaBxVVPYLb0xwuIufHeXokcH9C5Qj9EJG2uMSeZUALVV2XkvsZY0xsFjBzIK+u6wRgpKp+ll7vq6qrcWcfH43z1Czc/ulFybmviOQWkTdwPStvUtUnLLHHGJPaLOknZxqGK/owLJXu1wMo7efCnTt35n3jjTe67t69e1Px4sVjEoD47LPP/tq7d+87wKREbrGdWE27vdnqeGAtLrEnLfZgjTHGZpg5jYg0xe0X9ojdmiuFSgNb/PwqUaLE2lOnTi0cPXp09diPN23adPrvv/9ee9OmTfsTuUdp7+sQEXkAl9jzOq4vpwVLY0yasYCZ8zwAjFDVDEuQatmy5ew1a9ZcERkZeXrPsnLlyodLliy5ZNq0ac0Se72X2DMd6AU0V9WPLLHHGJPWLGDmIF6gaYcrD5dhrrrqqg2hoaFHJ02adEHsx0uXLr1x9+7dZeJ7HcC8efNq4FpxrcQSe4wx6SjL7WF65/UEOBbnKQuYibsdmJKkM4ku8/R2oDbue/4L8Bax2qolVVBQEOXLl1+yYcOGGsTqBlOiRIld//77b91Arzl48GDoK6+8cqOINAOuV9U5yX1/Y4xJjqw4wywG7AmwBGcBM3GdiJUwkyCRIEQ+Ar7GHQc5D5fF+jCwHJHrE7tFaGjo6AIFCrwxYcKEsxKCmjRp0mPWrFllDh48WCLmscsuu6zrPffcc+e7775bL+595s+fX/bRRx8dcuTIkaJ9+vR53YKlMSYjZMWAeU5FH69BcThgSR8Jqwqs9nnt08Bt8TyXDxjHuecpz1G/fv0FH3zwwel9yYiICFm5cmWT9u3b/37kyJHiMY9fd911iydPnvwMIDF7m9HR0bz++uttRo0aNahevXrfvvrqq68XK1bsuM/xG2NMqsqKATNQRZ9CwFE7exc/ESkI5AF2+bi4AOeelYwrN/BEYre69dZbf/3zzz+bx/z+xRdfrFWwYMHd11xzzbpjx46VjHm8X79+69u2bbsLYN26dYU2bNhQoG/fvo+uXr368vvuu++ZBx54YG5QUFb862qMyS6y4k8gS/hJnsrAPz6zSZsB+X1cd0ViF/Tu3XuziESPGjWqAsDUqVObt2jR4tdatWrtnz59eqF9+/aFxXmJzpw5s9mQIUOGh4eHbx0xYsSgpk2b7vAxFmOMSVMWMHOOIvhfsg73fZ2PcnaNGjX6dcyYMc2PHDkStGbNmoaPPfbY7yEhIdqhQ4dD27dvzxdz3eHDh0MAli9f3vHaa68d+eyzz36WL1++KJ9jMcaYNGUBM+fYDJT3ee0/Pq/bhI8Z6/333//rsmXLmj7//PPnFy9efHOTJk0OHTx4MDQyMjJv9erVD4BL7HnkkUeGANKpU6fXu3TpssrnGIwxJl1YwMw5NgNlvDqyiVkI+DnfON7PG3fs2HFX7ty5D48aNar7ZZdd9ivAqlWriufOnXtvSEiIvvHGG1eMGjVq0AUXXPAdQJMmTTb5ua8xxqQnC5g5hJcQtR2o4Odi4B4goeXQNbhC6gEdOXIkKCgo6HQSVtOmTX89cOBAmSeffHIhwKZNm0pMnTq1cN++fR9ZtWpVy7Vr1y4aMGDADZGRkVSvXv3lVq1adfb5pRljTLrIcoULsICZEquAJsCGRK9U/RGR9sBooGScZ+cAN+NadgX02WeflStUqNDOmN9Pnz79G+Cb0wNZtareNddcI4ULF94+bNiw1/Plyxc1c+bMatOnT7/1/ffffyppX5YxxqS97BQwrURa4kYDD3JmKTXhLiOqcOLEG/z0U2127SpBaGgE1atvokGD/7zXxrgMOBDzm1mzZlXbvXt3zW+++WYxcG3sW546dSrojz/+qFe+fPlq0dHRPw0ePPj0su62bdtK5MuXL/FjL8YYkwGyYsA8p3BBPI+Zc30JvCEi56vqSs50GYlf7txw5ZWbErnvAWJl4F511VV/XHXVVX/EvWjr1q3hs2fPviJPnjyHixQpElW/fv2ZsZ/fs2dPiYIFC1rANMZkSllqD1PcEYZAhQtsSdYHbx9zFHB/Or8vv/zyy3kzZsy4vnr16isqV668rkCBAgfinq/cvHnzhVWqVLGVAmNMppTVZpj5gChVjVseLVAQNe4DRingKKqHvEffA5aKyLj06Ih18ODB3N9++22r48ePF2jfvv2UsmXL7h87dmznJk2anBUYv//++4rHjx8v0rVr1yVpPihjjEmGrBYw45tJ2gwzNpH8wLO4LiPh3mOrgBdVdayI3AZ8vn379o9Klz53C/OXX34p1Lt3757btm2rGhwcfKpQoUK7X3rppTEA/fv377lv377SQUFBUSVLltz88ccff9KsWbMDuMIIHD58OKhhw4ZtTp06FRwZGRnapEmTsL59+65v0qTJHyEhIbJjx46qefPmLVS1atUjxDoX+vfff3e84oorFuXOnbtsIl/d9pR+e4wxJjmyfMAUkWCgMOC/ZVV2JhIO/AzUifPMecAYRBqq6sMi8tGYMWNu6tu376CwsLDTU82oqCg6d+7c79JLL/1pzZo1bwF88MEHFTds2FBo+PDhd/fu3fvTESNGLAYYPHhwnbVr1xZo1qzZzzGvz5s3L19++eX3n3/+eceNGzc2nzVr1sHmzZt/1qJFi/UAr7zyygNFihT5Kjg4+E/gJTdkKQwMBGrfcsstVgbPGJMpZak9TALPJMOBg6oamQHjyYze5dxgGdtDiHQEBgcHB0c++uijj23atOl03dghQ4acFxQUFDVx4sQfYh676667/l21alWpcuXKrYsJlgDPPPPMX7feeut/sW/++++/l3nttdeeOnDgQOkHH3zwmejo6AgRUYCRI0detn///kq9e/f+Ns6YegLfqqoFS2NMppUdAqYtx8YQKQvc4OPKvqoa1adPn4/Dw8P/Gzx48LBvvvmmKsCyZcvKlS9f/pzSeOvXry9fvXr1gCXz5s2bV7hSpUr933zzzVbvvffeM7Vq1frx008/LdaiRYtXatWqtaJv374bfvzxxwq//fbbTb169Xq9ZMmSJ84MWQS4DxiZvC/aGGPSR5Zfko3nsZyqAf4+BDUECAsLix48ePD4Tz755O/PPvvssUWLFv1w6tSpJLVIi46O5tChQyU6dOigK1eubHP33XcPbtGixbZ+/fp9/9dff+Vt06ZNv3feeafan3/+eV+LFi3GXHrppf/FucXdwBHcMrIxxmRaFjCzl2Df14kIXpZsr169Fl1wwQX/Tp48+doTJ05csmnTplOTJ09e3LZt2zUx3UKqVq3639KlS2vH3GDr1q15v/jii2YrVqxoEx0dHVanTp3Zd9111+wCBQqcXhqvU6fOsZo1a64ZM2bMXa1bt15x9913/xJ7ECLSCBgCtPDZdswYYzJMVgyYywI8ZgHTWeH7ujgBqkGDBrsbNGgweuvWrZ/Xr19/+IgRI+6ZOnVqobCwsAMHDhw4FBQUdGjz5s212rRpU6106dL5o6Ojw/bv3/9v3bp1Zw0ZMmRuSEiIAixcuLBAvnz5ourUqXNs/vz5JVeuXNm2YcOG2wYOHDg29vvt2bMnDzAJuFdV/06Vr94YY9JQVtvDtCo/CVHdAHzn48p343uibNmyxydPnvzM3r17182YMWPftGnTIhcvXhxdsWLFtXffffcXa9asOfrll1+emDlz5p4NGzbs7dChw+JffvmlUKVKlfoDLF++PPzSSy99ukiRIm+0b9/+1cqVK6+fMWPGoNgzz8jISPnkk0+6AVNVdXKKv25jjEkHkpVWwkRkDvCcqs6J9djLwC5VjbdzRo4iUg74g/hrxE5GNSYxqD+JlcZLooiICHn55Zc7/f3331d06NDh7c6dO/8V95rhw4d3UNVmTzzxRHWv+pAxxmR6WW2GGV9ZPKvyE0P1P1xSzyQg9lGbPcBTQLe0eNt169YVeuGFF667++6739i+fXutAQMGPBEoWL733nsXr169+uoePXp8asHSGJOVZMU9TEv6SYzqdqArIkWAasAx4G9UI1LzbaKjo5k5c2aNefPmXblz5856ZcuW/eOmm24a0aZNm01xrz148GDo8OHDe+7ateu822677YVy5cql5lCMMSbNZZmA6Z3XK4oVXvdPdR9ueTZVbdq0Kf8XX3xxyerVq1uqanCdOnW+f+ihh0aXL1/+aKDrlyxZUuy9997rmzdv3t1Dhw59slSpUseJVRbPGGOygiwTMIGCwAlVPRnncQuY6SAyMlKmTZtW57fffmu5c+fO+iVLllzcrl270dddd93qoKD4V/Y//fTTC2fPnn1X3bp1p/ft2/ebhK41xpjMLCsFTCu8ngHWrl1baOrUqZf9/fffLYODg0/Wrl17TkKzyRh//PFHyWnTpl2zbdu2hl26dBnRvn17a9tljMnSskyWrIg0Ad5S1caxHgsFjgNhqhqdYYPLunoQIJs2MjJSfv755xqLFy9usnfv3iply5Zd0aRJkz8uvPDCLQnNECMjI2Xu3Lm1lyxZ0vTgwYNlq1Spsqhdu3Y/lSxZMlBw3Q6MDfC4McZkSll9hlkE2JdDgmUQUA6oB/wHpEbfyLMClohUxLUEux3YBrwBfK6qhxO6iYiUBHrjytxtA4YCk1T1REKvM8aYrCSrB8ycsBxbCKgJXATkB04AlYGVQIqPZXiz9A7AHUBjYDzQTlWXJ/K6IKCN97o2uGMsnVR1cUKvM8aYrCorBcycVOUnBKiAK6Ze2XtsN7DP+//y3vMbkvsGIlIDNyvsBawFRgHXq+rxRF5XDrjNe+0+73V3qOrB5I7FGGOygqwUMNNzhhlwby8d5AFKAAVwM8ijuOXXuA7jgmmSAqaI5AGuB+4EagOfAJep6tpEXhcCtPNe1xyYgAuuNps0xuQYWS1g/hvgsbSo8lOaVC4Zl4Bg3NdRxftvNBCayPsfwM08C3v/nyARuQAX7G4CFgFvA9M1kUIGIlIVN5O8FfgHN5vspqoJZsgaY0x2lNUCZnbbwywLXID7czjOmeBfJIHX5MUF9EpALeC3QBeJSH7gRlygLAt8BDRS1U0JDUhEcgGdcHuT9XCJQW1UdZWfL8gYY7Kr7BAwtyb5TiKX4ZYW8wCrgWkksneXRnLj/gz2JXJdMG6/tjJuyTYayIVLtlkIRMHpakgX4YJkF2Ae8BzwrapGJfQGIlIHFyR7AMtxs8mpAQpFGGNMjpQdAmbc/pjxc8cmJgBN4jyzF5HbUJ2R0Mt/+eWXQr179+65bdu2qsHBwacKFSq0+6WXXhpzzz339Nu3b1//mOtatWrVOW/evCe++uqrmbFf37t37xbTpk1rDxAaGnryxRdfHNerV69432/58uWlp0yZck14eHjVCy64YMcll1yyOjQ0NGYWWgC3jFteRA4CN+MCZX7gQ6COupqyCXw7JB9wg/e6ysDHQFN1bcKMMcbEkpXqlKVsSVakMDCHc4MluNnbVERaxffyqKgoOnfu3K9u3bp/HTp06OH9+/c/9sQTT0zYsGFDIV/vD9SqVWvXvHnzhuzdu3dAr169pvTv378HsAvIF+uyIKDgihUr2s+YMePZihUrlmvYsOHyqVOnVv3hhx+Kx1ykqke2bdtW9bHHHhuD219sAfQDqqvqCwkFSxFpICLv4vZJuwAvARVV9QkLlsYYE1iWmGF6Z/6KcO7SZVL2MAdy5ohGIMHAu4jUIkD1oyFDhpwXFBQUNXHixB9iHrvrrrv+nTlzZjGf789jjz12ujxcjx491r/99ttFgE2484/RQDhQHMh/wQUXLC5fvvz4woULnwKYN29etePHj4ceOHAgz5IlS2pt3LjxvCJFikjbtm2Xnjhxov5bb721KaH3FpFCuKSfO3AfEP4H1FXXDswYY0wiskTAxGWDHg7QPzEpAfNGH9fUAC4M9MSyZcvKlS9f/p9Azx06dKhk0aJFX4j5/bFjxwq3bt36K4CuXbu2BogdaAEGDBhwebVq1Zbijo5UwP1ZnMIVJggGjhcuXJjNmzfne/vtt6/Yt29f0VOnTpXatm3b5WXKlNl46aWXfl+tWrV9IlK0VatWRXGB9yzenmYz3JJrJ2A28ATwfWJ7msYYY86WlQLmgSQ8fjaRYPy3k0poFhpQwYIFd+7du3dgzO9btWrVOeb/4wZKgMGDB9f5+eefW86dO/dZ3BLsCVwC0LHY1+3ZsyffX3/9dV6ePHmKN27c+MiGDRtOiciGq6++elGePHliAl40Lvlniff/iEgxXPLOHbg/4w+Bx1V1V1K/NmOMMU5W2cPcDZTwZkyx7QJKJvpqN5s64PO9As5Y69at+9+WLVuSHEzj+t///lfh1VdfveuDDz54pX79+kdwRQg2AWEAUVFRQbt27Qr//PPPr500adJNEREReWrVqjVn2LBhumnTpj07d+4svHv37lyxbnkcqH7w4MFyItJaRD4H1uMKG9wL1FLVly1YGmNMymSJgOkV/z7GucFxIy5T1I/ZPq45QDwNlwcNGrQqKioqtHv37i1jHnvzzTerLFmypHig6wOZNWtW0b59+/Z9+umn3+nateuOWE+tPnr0aPDatWtrzZ0799Jt27aVqVSp0vqePXuO7tChwzzgWFhYWOSaNWuKnjx5MqhkyZKni5rv27cvavny5U3vu+++34ERwM9AZVXtoao/aVZpR2OMMZlcVlmSBRccKwOxA80/+A+Yz+OKjOdO4Jph8Z3HDA4OZuLEia/eddddPQsWLHhdcHBwROHChfe8/PLLYxJ609h7mP3797/+5MmT+YcPH3778OHDCQoKin7hhRe+WrlyZas2bdpUq1mz5t6GDRsuLFy4cBCwBmDPnj25Pv744wvLlCmTa8+ePXn37du3Njg4WBcvXlxp3bp1dfbt21e2Tp06WwYNGvT966+/fk/x4sWtCo8xxqSBrNQP8zPgK1UdF+uxfkANVb3H502uAz7FnVWM622gD+4b0p80LI03f/78st99913Lf//995KCBQv+W79+/R979ux5PCwsrDWwH3fMZHFkZCRRUVFB11133TU1atTYKyKnVq5cWaVbt255c+fOfaRatWp/1a9ff13evHmDcPu5Q0nKuVRjjDG+ZaUZ5j9A1TiPTQN+F5G+iXXZAED1S0TOB+7DZY/mxVX6+RDVeak73LPt2bMn18SJE5usWLGi1bFjx0pUrVp17kMPPfR0w4YNY/YWCwEHcdWHAAgJCWHfvn0he/bsydOrV6/iq1evLrFhwwb99NNPdwwfPvzn5s2bx+4QIrjkn+VA1vgUZIwxWUhWCpg/A8/iSr0BoKobReQPoBuuSk3iVP8FHk/94QU2e/bsSnPmzGm1ZcuWZkWKFFnbvHnz6TfccMPS3Llzx216fRDXmaQ2ELlt27bCS5curbNq1apaoaGhjBw5Mmrjxo3Hjh49mqts2bKRzZs3P3jy5EnJlSuX4qr+5MLt8QYDken19RljTE6RlZZkg3HZn11VdWGsx9sBg1Q1UAWf5ErRkuy2bdvyTJo0qcWqVatanTp1Kn/16tV/7NChw0/nn39+gjVjjx07VungwYO37du3r+TPP/8cUqFChTWlS5f++/77729YtGjR43ffffdfO3bsyP3GG280XL58+Te4pWUFtnnjDcMtLVv9V2OMSWVZJmACiMjjQE1VvT3WY8G4BJn+qjo1ld4qyQEzOjqamTNn1pg/f36r7du3NypevPiKJk2a/Ni5c+eVISEhCX6T58yZU+H7779vuWPHjha33nrrsTp16hwrWLDg16GhoXFnoUFLliwp9d1335W666671oSHh/8N7ORMgCwPTMdLGDLGGJN6slrALA78DVRV1X2xHm8MfAU0S6VaqL4D5j///FNg8uTJl6xdu7alqgbVrFnzx+uvv35+tWrVDiX0up07d+aeOHFis5UrV7Y6efJkeLVq1eZee+21c+vXr18O16x5Hl4hAiDXqVOn8gcHB0cHBQVt9sZ2IMBtC+CKICSYuWuMMSbpslTABBCRscAyVX0lzuP340rANfOVAJSwHriekwFFRUXJggULqv7555+Nd+7cWaN06dKrGzVq9EeTJk3+CQqK/2hrdHQ0S5YsKf/777833rp16wVFixbd2KBBg98vvvjiv2PNQgviWo9tx7XtCsYVN9iAm03GLQ8YV3lgNK7YgzHGmFSSFQPm+biuI5eq6upYjwswDpf52sMrdpDa710auA3oDRzB9Ywcp6r7E3ldEc6038qLK1X3STwdRQR4GGiIqwD0Hy4hKDH5cAXqo3EtzNLsWIwxxuREWS5gAohIb1wrqyaqeiTW47mBN4FLgc6quioV3isEuBoX7C4FJuEC5aKEquh4AfxS73XXAt94r5urqnH3JuOqC1yFC5YJCcZ1NwnDzSgX4maixxJ6kTHGmKTLkgETQEQ+wh2luCVu4BKRXsAruOMjY1Q1yccsRKQSbiZ5Gy5wjQImxA7Q8byuJNALV/j8lPe6saq6N6HXxZEPd1Z0K4HPVBbAFSqIBFZ4v3bGc60xxphUkJUDZl5gAfC+qo4M8Hxd4B1cOb0PgFEJNVX2XhMGXIcLdg1xS7wfquqKRF4XjCsacAfQGpiKC5S/paCWaztc2b+YYvAhuNlkCC44/o5bsj0R6MXGGGNSV5YNmAAiUg1X0GAE8HKg4OQFzntx/TCX4M5y/oOrTXsMF1CreP9tCvyFC3ZTVDXBYCQi5Tmzp7nbe91nqppghqxP5XENnw/iqgBFAItxlYms84gxxqSzLB0wAUSkAm5fcQfQS1UPxHNdQVw5vJgAWQWXgBMTPDfism83JvJ+objZ353e/T7HzV6XpMbXE0sQLlEoCliEm01GpPJ7GGOM8SnLB0w4vZT6Ci6QdUmD4IWIVMUtud6KS6wZBUxS1bRMsBFsX9IYYzKFbBEwY4jIjcBbwHjgXVVNUcUbL+u2E242eT4wFrenuTrBFxpjjMl2slXAhNP7ivfgZoMrcYk/05OSKSsi53mvvwVYiptNfqmqVqPVGGNyqGwXMGOISC7getzxjMrAr5zZq4yb9BN7X/MCXJWf0cBHie1pGmOMyRmybcCMTURqA/U4ExTjS/rZiMui/SU5ZzeNMcZkXzkiYBpjjDEpFX+lcGOMMcacZgHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA8WMI0xxhgfLGAaY4wxPljANMYYY3ywgGmMMcb4YAHTGGOM8cECpjHGGOODBUxjjDHGBwuYxhhjjA//B0En4woi9yHpAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -367,7 +360,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -410,7 +403,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -444,7 +437,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -481,7 +474,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.10"
+ "version": "3.8.5"
}
},
"nbformat": 4,
diff --git a/tutorials/Tutorial 9 - HNXWidget.ipynb b/tutorials/Tutorial 9 - HNXWidget.ipynb
index 3b11bbe0..23b201ca 100644
--- a/tutorials/Tutorial 9 - HNXWidget.ipynb
+++ b/tutorials/Tutorial 9 - HNXWidget.ipynb
@@ -16,9 +16,123 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " FullName \n",
+ " Description \n",
+ " \n",
+ " \n",
+ " Symbol \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " AZ \n",
+ " Anzelma \n",
+ " daughter of TH and TM \n",
+ " \n",
+ " \n",
+ " BA \n",
+ " Bahorel \n",
+ " `Friends of the ABC' cutup \n",
+ " \n",
+ " \n",
+ " BB \n",
+ " Babet \n",
+ " tooth-pulling bandit of Paris \n",
+ " \n",
+ " \n",
+ " BJ \n",
+ " Brujon \n",
+ " notorious criminal \n",
+ " \n",
+ " \n",
+ " BL \n",
+ " Blacheville \n",
+ " Parisian student from Montauban \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " TS \n",
+ " Toussaint \n",
+ " servant of JV at Rue Plumet \n",
+ " \n",
+ " \n",
+ " VI \n",
+ " Madame Victurnien \n",
+ " snoop in M-- sur M-- \n",
+ " \n",
+ " \n",
+ " XA \n",
+ " Child 1 \n",
+ " son of TH sold to MN \n",
+ " \n",
+ " \n",
+ " XB \n",
+ " Child 2 \n",
+ " son of TH sold to MN \n",
+ " \n",
+ " \n",
+ " ZE \n",
+ " Zephine \n",
+ " lover of FA \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
80 rows × 2 columns
\n",
+ "
