def condensation( G, components ): """Return the condensation of G with respect to the given components. Given components C_1 .. C_k which partition the nodes of G, the condensation graph cG has nodes <C_1> .. <C_k> and has edge (<C_i>,<C_j>) iff there was an edge in G from a node in C_i to a node in C_j. Note: the given components can be arbitrary, but if they are the strongly connected components (in the graph theory sense, not the dynamics sense) of G, then cG corresponds to the directed acyclic graph between strongly connected components. Parameters ---------- G : DiGraph components : list of lists of component nodes. Returns ------- cG : DiGraph The condensed graph. """ mapping = dict([(n,c) for c in range(len(components)) for n in components[c]]) cG = DiGraph() for u in mapping: cG.add_node(mapping[u]) for _,v in G.graph.edges_iter(u, data=False): if v not in components[mapping[u]]: cG.add_edge(mapping[u], mapping[v]) return cG
def index_map_to_region_map( hom_mat, reg2gen ):
"""
hom_mat : numpy matrix representing a map on generators (index map).
reg2gen : dictionary mapping region -> generator(s). (I.e, which
regions support which generators)
Returns a DiGraph object of the map on regions in phase space.
"""
H = hom_mat
R = reg2gen
Rinv = invert_dictionary( R )
G = DiGraph()
# find where region k maps to based the index map
for k in R.keys():
# find generator connections
if hasattr( H, 'nnz' ):
if len( R[k] ) == 0:
continue
gen_conns, _J, _V = sparse.find( H[:,R[k]] )
else:
# dense matrix case
gen_conns = np.where( H[:,R[k]] != 0 )[0]
gen_conns = gen_conns.tolist()[0] # fix matrix formatting
for edge in gen_conns:
for glist in Rinv.keys():
if edge in glist:
G.add_edge( k, Rinv[glist][0] )
# return the graph so that we have access to the nodes labels that
# correspond directly to regions with generators.
return G
def condensation_nx( G, components) : """ G : DiGraph object. the nx.DiGraph attribute is extracted from this. components : Given components C_1 .. C_k which partition the nodes of G, the condensation graph cG has nodes <C_1> .. <C_k> and has edge (<C_i>,<C_j>) iff there was an edge in G from a node in C_i to a node in C_j. """ cG = DiGraph() cG.graph = nx.condensation( G.graph, components ) return cG
def construct_mvm_simple(self):
"""
Construct a directed graph on the boxes in self.tree. Boxes
intersecting error_box[i] are mapped to boxes intersecting
error_box[i+1]. No expansion estimates are used.
"""
# store the finite representation
self.mvm = DiGraph()
# generator to save memory
error_boxes = izip(self.data, self.noise)
# pull first time step off the top
pred = self._make_box(error_boxes.next())
pred_ids = self.tree.search(pred)
# iteration starts at second element
for succ in error_boxes:
bx = self._make_box(succ)
self.tree.insert(bx)
succ_ids = self.tree.search(bx)
# loop over boxes in predecessor region and connect to
# those in successor regions
for u in pred_ids:
for v in succ_ids:
self.mvm.add_edge(u, v)
# update predecessor for next time step
pred = succ
pred_ids = succ_ids
def condensation( G, components, loops=False ):
"""Return the condensation of G with respect to the given components.
Given components C_1 .. C_k which partition the nodes of G, the
condensation graph cG has nodes <C_1> .. <C_k> and has edge
(<C_i>,<C_j>) iff there was an edge in G from a node in C_i to a
node in C_j.
Note: the given components can be arbitrary, but if they are the
strongly connected components (in the graph theory sense, not the
dynamics sense) of G, then cG corresponds to the directed acyclic
graph between strongly connected components.
Parameters
----------
G : DiGraph
components : list of lists of component nodes, or dictionary of
component label -> component nodes
loops : whether to allow condensed nodes to have self loops (default: False)
Returns
-------
cG : DiGraph
The condensed graph.
"""
# convert list to dict
if isinstance(components,list):
components = {c:components[c] for c in range(len(components))}
mapping = {n:c for c in components for n in components[c]}
cG = DiGraph()
for u in mapping.keys():
cG.add_node(mapping[u])
for v in G.successors(u):
# if v~u and u,v are in the same component, don't add the loop
# (unless we allow loops)
if loops or v not in components[mapping[u]]:
cG.add_edge(mapping[u], mapping[v])
return cG
def prepare_regions( self ):
"""
Partition the phase space into disjoint, recurrent regions.
Must have initialized self with index_map matrix and
region2gen dictionary, or used one of the load_from_* methods
to initialize these values.
"""
# returns a DiGraph
self.map_on_regions = utils.index_map_to_region_map( self.index_map,
self.region2gen )
# graph_mis from graphs.algorithms
scc_list, scc_components, recurrent_regions = graph_mis( self.map_on_regions,
return_rsccs=True )
nbunch = [ scc_components[i] for i in recurrent_regions ]
self.recurrent_subgraphs = []
# the actual partition of phase space in graph format
for n in nbunch:
# ignore disjoint regions/nodes that have self-loops => no
# entropy
if len( n ) == 1:
continue
G = DiGraph()
G.graph = self.map_on_regions.graph.subgraph( n )
self.recurrent_subgraphs.append( G )
# construct IndexMapProcessor for each disjoint region
# assign entropies of -1 to each recurrent region.
for region in self.recurrent_subgraphs:
self.phase_space.append( [ IndexMapProcessor( self.index_map,
self.region2gen,
region,
debug=self.debug,
verbose=self.verbose ),
-1 # initial entropy for region
] )
def construct_mvm_expansion( self ):
"""
Construct a directed graph on the boxes in self.tree
using. Boxes G_i intersecting error_box[i] are mapped to boxes
G_{i+1} intersecting error_box[i+1] with expansion rate
C. Thus the image boxes are expanded equally in all directions
by a factor C > 1. This image is intersected with boxes in the
tree and the image is updated.
"""
# store the finite representation
self.mvm = DiGraph()
# generator to save memory
error_boxes = izip( self.data, self.noise )
# pull first time step off the top
pred = self._make_box( error_boxes.next() )
pred_ids = self.tree.search( pred )
# loop optimizations
maker = self._make_box
expander = self._expand_equal
tree_insert = self.tree.insert
tree_search = self.tree.search
# iteration starts at second element
for succ in error_boxes:
# error in 'box' form
bx = self._make_box( succ )
# intersect with subdivision
self.tree.insert( bx )
succ_ids = self.tree.search( bx )
# apply expansion to image
ex_box = self._expand_equal( succ_ids )
# DANGER! THIS MIGHT DOUBLE INSERT BOXES
self.tree.insert( ex_box )
# loop over boxes in predecessor region and connect to
# those in successor regions
for u in pred_ids:
for v in succ_ids:
self.mvm.add_edge( u, v )
# update predecessor for next time step
pred = succ
pred_ids = succ_ids
class FiniteRepresentation( object ):
"""
"""
def __init__( self, data, tree, noise, expansion=1. ):
"""
Created a finite representation of the dynamics of a system
from a collection of temporally ordered data points.
data -- list or array of points in R^d. Index order specifies
temporal order.
tree -- BoxTree object initialized with the compact region
containing the data. (See BoxTree class.)
noise -- array of tuples or real numbers specifying the noise
in each dimension of each data point. Hence, if data contains
points in R^2, data[0] <--> noise[0] == ((a1,a2),(b1,b2)) or
(a1, b1) if data[0] is centered within the error box
determined bythe noise. Note: This scheme assume (for the
time being) that noise if uniform about each data point.
"""
self.data = np.asarray( data, dtype=float )
self.noise = noise
self.tree = tree
self.dim = data.ndim
# x_i |--> {grid elements} mapping
self.data_hash = {}
self.expansion = expansion # constant expansion rate (very
# crude approximation of dynamics)
def _add_box( self, box ):
"""
box -- lower left anchor in box[0], widths across dimensions
in box[1].
data point center in noise box, and width in j'th dimension
specified by 2*noise[j].
"""
# shift center to lower left corner anchor
bx = self._make_box( box )
self.tree.insert( bx )
def _add_boxes_tuple( self ):
""" TODO """
print "NOT IMPLEMENTED!"
def _make_box( self, b ):
"""
b -- tuple or array of
"""
anchor = b[0] - (b[1] / 2.)
if type( b ) == tuple:
width = self.dim * [ b[1] ]
else:
width = b[1]
bx = np.array( [ anchor, width ] )
return bx
def _expand_equal( self, idx ):
"""
Expand box dimension equally by self.expansion in all
directions. Since boxes are anchored in lower left corner,
shift each coordinate by (self.expansion * w - w )/2, where w
is the width of the box in each dimension.
box : array( [[ x1,...,xd ], [ w1,...,wd ]] )
Returns expanded and shifted box
"""
B = self.tree.boxes()
anchors = B.corners[ idx ]
width = B.width
new_width = self.expansion * width
shift = ( new_width - width ) / 2.
new_anchors = anchors - shift
new_box = np.array( [ new_anchors, new_width ] )
print new_box
print ""
return new_box
def add_error_boxes( self ):
"""
Intersect each noise box containing a data point with the grid
contained in Tree. This is used to construct the grid on the
phase space without a MVM. the MVM function perform the same
operation during the construction of the map.
"""
if hasattr( self.noise[0], '__len__' ):
self._add_boxes_tuple() # TODO
else:
errors = izip( self.data, self.noise )
for box in errors:
self._add_box( box )
def boxes( self ):
""" Return a list of all boxes in the tree. """
return self.tree.boxes()
def construct_mvm_simple( self ):
"""
Construct a directed graph on the boxes in self.tree. Boxes
intersecting error_box[i] are mapped to boxes intersecting
error_box[i+1]. [No expansion estimates are used.]
"""
# store the finite representation
self.mvm = DiGraph()
# generator to save memory
error_boxes = izip( self.data, self.noise )
# pull first time step off the top
data_idx = 0
pred = self._make_box( error_boxes.next() )
pred_ids = self.tree.search( pred )
self.data_hash[ data_idx ] = pred_ids
# iteration starts at second element
for succ in error_boxes:
bx = self._make_box( succ )
self.tree.insert( bx )
succ_ids = self.tree.search( bx )
# loop over boxes in predecessor region and create edge to
# those in successor regions
for u in pred_ids:
for v in succ_ids:
self.mvm.add_edge( u, v )
# update predecessor for next time step
pred = succ
pred_ids = succ_ids
data_idx += 1
self.data_hash[ data_idx ] = pred_ids
def construct_mvm_expansion( self ):
"""
Construct a directed graph on the boxes in self.tree
using. Boxes G_i intersecting error_box[i] are mapped to boxes
G_{i+1} intersecting error_box[i+1] with expansion rate
C. Thus the image boxes are expanded equally in all directions
by a factor C > 1. This image is intersected with boxes in the
tree and the image is updated.
"""
# store the finite representation
self.mvm = DiGraph()
# generator to save memory
error_boxes = izip( self.data, self.noise )
# pull first time step off the top
pred = self._make_box( error_boxes.next() )
pred_ids = self.tree.search( pred )
# loop optimizations
maker = self._make_box
expander = self._expand_equal
tree_insert = self.tree.insert
tree_search = self.tree.search
# iteration starts at second element
for succ in error_boxes:
# error in 'box' form
bx = self._make_box( succ )
# intersect with subdivision
self.tree.insert( bx )
succ_ids = self.tree.search( bx )
# apply expansion to image
ex_box = self._expand_equal( succ_ids )
# DANGER! THIS MIGHT DOUBLE INSERT BOXES
self.tree.insert( ex_box )
# loop over boxes in predecessor region and connect to
# those in successor regions
for u in pred_ids:
for v in succ_ids:
self.mvm.add_edge( u, v )
# update predecessor for next time step
pred = succ
pred_ids = succ_ids
def graph_mis( self ):
self.mis = alg.graph_mis( self.mvm )
def trim_graph( self, copy=False ):
"""
Trim the MVM graph to include only nodes from the maximal
invariant set (i.e. the strongly connected component == SCC).
copy : boolean. Default False. If True, copy original MVM to
self.full_mvm. In either case, self.mvm is replaced by the
SCC.
"""
nbunch = set( self.mvm.nodes() )
scc = set( self.mis )
non_scc = nbunch - scc
if copy:
self.full_mvm = DiGraph()
# copy returns NX.DiGraph()
self.full_mvm.graph = self.mvm.copy()
self.mvm.remove_nodes_from( non_scc )
def pickle_tree( self, fname, tree=None ):
"""
Extact necessary information to create a persistent object.
"""
if not tree:
tree = self.tree
b = tree.boxes()
tree_dict = { 'corners' : b.corners,
'width' : b.width,
'dim' : b.dim,
'size' : b.size
}
with open( fname, 'wb' ) as fh:
pkl.dump( tree_dict, fh )
def write_mvm( self, fname, stype='dot' ):
"""
A wrapper around NX's graph writers.
fname : full path to save graph to
type : 'pkl' or 'dot' (default). 'pkl' => pickle the
graph. 'dot' => save graph in dot format (more portable)
"""
if stype == 'pkl':
write_gpickle( self.mvm.graph, fname )
else:
write_dot( self.mvm.graph, fname )
def show_boxes( self, color='b', alpha=0.6 ):
"""
"""
fig = gfx.show_uboxes( self.boxes(), col=color )
return fig
def show_error_boxes( self, box=None, color='r', alpha=0.6, fig=None ):
"""
"""
error_boxes = izip( self.data, self.noise )
# boxes = [ self._make_box( b ) for b in error_boxes ]
# gfx.show_boxes( error_boxes, S=range( len(boxes) ), col=color,
# alpha=alpha, fig=fig )
for b in error_boxes:
# shift center to lower left corner anchor
bx = self._make_box( b )
fig = gfx.show_box( bx, col=color, alpha=alpha, fig=fig )
return fig
[0,1,0,0,0],
[0,0,1,1,-1],
[0,0,-1,-1,1]]
).T
# A <--> 0, B <--> 1, etc.
region2gen = { 0 : [0,1],
1 : [2],
2 : [3,4]
}
symbols = numpy.matrix( [[0,1,1],
[1,0,0],
[0,1,1]]
)
map_on_regions = DiGraph()
map_on_regions.from_numpy_matrix( symbols )
if 1:
hom_matrix = utils.load_matlab_matrix( 'henon_index.mat', 'hom_matrix' )
region2gen = utils.convert_matlab_gens( 'henon_gens.mat' )
map_on_regions = utils.index_map_to_region_map( hom_matrix, region2gen, shift=-1)
if 0:
hom_matrix = utils.load_matlab_matrix( 'leslie_index.mat', 'hom_matrix' )
region2gen = utils.convert_matlab_gens( 'leslie_gens.mat' )
map_on_regions = utils.index_map_to_region_map( hom_matrix, region2gen, shift=-1)
########################
import networkx as nx from rads.graphs import DiGraph from rads.graphs.algorithms import graph_mis if __name__ == "__main__": E = [(0, 8), (0, 5), (1, 19), (1, 10), (1, 3), (1, 5), (2, 11), (2, 4), (2, 7), (4, 19), (6, 19), (6, 3), (6, 5), (7, 19), (7, 3), (8, 0), (9, 16), (9, 14), (9, 6), (10, 3), (10, 12), (11, 19), (11, 6), (12, 3), (12, 12), (13, 8), (13, 19), (13, 11), (13, 5), (14, 1), (14, 9), (16, 3), (17, 1), (18, 16), (18, 13), (19, 9)] G = DiGraph() G.add_edges_from(E) correct_answer = [0, 1, 6, 8, 9, 10, 12, 14, 19] my_answer = sorted(graph_mis(G)) print 'graph_mis(G) = ', my_answer print 'correct answer =', correct_answer E = [(0,1), (1,2), (2,0), (2,3), (3,4), (1,5), (5,6), (6,6), (7,0), (8,7)] G = DiGraph() G.add_edges_from(E) my_answer = sorted(graph_mis(G)) print 'graph_mis(G) = ', my_answer print 'correct answer =', [0,1,2,5,6]
1 : [1,2],
2 : [3],
3 : [4],
4 : [5],
5 : [6,7]
}
adjmatrix = numpy.matrix( [[0,0,1,0,0,0],
[0,0,0,1,0,0],
[0,0,0,0,1,0],
[1,1,0,0,0,0],
[0,0,0,0,0,1],
[1,1,0,0,0,0]]
)
P = DiGraph()
P.from_numpy_matrix( adjmatrix )
G = DiGraph( )
G.from_numpy_matrix( generators )
edgeset = defaultdict( list )
# length-1 walks
k = 1
for s,t in P.edges():
r2g = ( regions[ s ], regions[ t ] )
edge_matrix = generators[ r2g[0],r2g[1] ]
edgeset[k].append( Walk( s, t, set( [(s,t)] ), edge_matrix, k, sparse=False ) )