# graph_tool - efficient graph analysis and manipulation¶

## Summary¶

 Graph Generic multigraph class. GraphView A view of selected vertices or edges of another graph. Vertex Vertex descriptor. Edge Edge descriptor. PropertyMap This class provides a mapping from vertices, edges or whole graphs to arbitrary properties. PropertyArray This is a ndarray subclass which keeps a reference of its PropertyMap owner. load_graph Load a graph from file_name (which can be either a string or a file-like object). load_graph_from_csv Load a graph from a csv file containing a list of edges and edge properties. group_vector_property Group list of properties props into a vector property map of the same type. ungroup_vector_property Ungroup vector property map vprop into a list of individual property maps. map_property_values Map the values of src_prop to tgt_prop according to the mapping function map_func. infect_vertex_property Propagate the prop values of vertices with value val to all their out-neighbors. edge_endpoint_property Return an edge property map corresponding to the vertex property prop of either the target and source of the edge, according to endpoint. incident_edges_op Return a vertex property map corresponding to a specific operation (sum, product, min or max) on the edge property eprop of incident edges on each vertex, following the direction given by direction. perfect_prop_hash Given a list of property maps props of the same type, a derived list of property maps with integral type htype is returned, where each value is replaced by a perfect (i.e. value_types Return a list of possible properties value types. openmp_enabled Return True if OpenMP was enabled during compilation. openmp_get_num_threads Return the number of OpenMP threads. openmp_set_num_threads Set the number of OpenMP threads. openmp_get_schedule Return the runtime OpenMP schedule and chunk size. openmp_set_schedule Set the runtime OpenMP schedule and chunk size. show_config Show graph_tool build configuration.

This module provides:

1. A Graph class for graph representation and manipulation
2. Property maps for Vertex, Edge or Graph.
3. Fast algorithms implemented in C++.

## How to use the documentation¶

Documentation is available in two forms: docstrings provided with the code, and the full documentation available in the graph-tool homepage.

We recommend exploring the docstrings using IPython, an advanced Python shell with TAB-completion and introspection capabilities.

The docstring examples assume that graph_tool.all has been imported as gt:

>>> import graph_tool.all as gt


Code snippets are indicated by three greater-than signs:

>>> x = x + 1


Use the built-in help function to view a function’s docstring:

>>> help(gt.Graph)


## Contents¶

Basic classes
class graph_tool.Graph(g=None, directed=True, prune=False, vorder=None)[source]

Generic multigraph class.

This class encapsulates either a directed multigraph (default or if directed=True) or an undirected multigraph (if directed=False), with optional internal edge, vertex or graph properties.

If g is specified, the graph (and its internal properties) will be copied.

If prune is set to True, and g is specified, only the filtered graph will be copied, and the new graph object will not be filtered. Optionally, a tuple of three booleans can be passed as value to prune, to specify a different behavior to vertex, edge, and reversal filters, respectively.

If vorder is specified, it should correspond to a vertex PropertyMap specifying the ordering of the vertices in the copied graph.

The graph is implemented as an adjacency list, where both vertex and edge lists are C++ STL vectors.

copy()[source]

Return a deep copy of self. All internal property maps are also copied.

Iterating over vertices and edges

See Iterating over vertices and edges for more documentation and examples.

Iterator-based interface:

vertices()[source]

Return an iterator over the vertices.

Note

The order of the vertices traversed by the iterator always corresponds to the vertex index ordering, as given by the vertex_index property map.

Examples

>>> g = gt.Graph()
>>> vlist2 = []
>>> for v in g.vertices():
...     vlist2.append(v)
...
>>> assert(vlist == vlist2)

edges()[source]

Return an iterator over the edges.

Note

The order of the edges traversed by the iterator does not necessarily correspond to the edge index ordering, as given by the edge_index property map. This will only happen after reindex_edges() is called, or in certain situations such as just after a graph is loaded from a file. However, further manipulation of the graph may destroy the ordering.

Array-based interface:

get_vertices()[source]

Return a numpy.ndarray with the vertex indices.

Note

The order of the vertices is identical to vertices().

Examples

>>> g = gt.Graph()
<...>
>>> g.get_vertices()
array([0, 1, 2, 3, 4], dtype=uint64)

get_edges()[source]

Return a numpy.ndarray containing the edges. The shape of the array will be (E, 3), where E is the number of edges, and each line will contain the source, target and index of an edge.

Note

The order of the edges is identical to edges().

Examples

>>> g = gt.random_graph(6, lambda: 1, directed=False)
>>> g.get_edges()
array([[2, 1, 2],
[3, 4, 0],
[5, 0, 1]], dtype=uint64)

get_out_edges(v)[source]

Return a numpy.ndarray containing the out-edges of vertex v. The shape of the array will be (k, 3), where k is the out-degree of v, and each line will contain the source, target and index of an edge.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_out_edges(66)
array([[   66,    63,  5266],
[   66, 20369,  5267],
[   66, 13980,  5268],
[   66,  8687,  5269],
[   66, 38674,  5270]], dtype=uint64)

get_in_edges(v)[source]

Return a numpy.ndarray containing the out-edges of vertex v. The shape of the array will be (k, 3), where k is the out-degree of v, and each line will contain the source, target and index of an edge.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_in_edges(66)
array([[  8687,     66, 179681],
[ 20369,     66, 255033],
[ 38674,     66, 300230]], dtype=uint64)

get_out_neighbors(v)[source]

Return a numpy.ndarray containing the out-neighbors of vertex v.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_out_neighbors(66)
array([   63, 20369, 13980,  8687, 38674], dtype=uint64)

get_in_neighbors(v)[source]

Return a numpy.ndarray containing the in-neighbors of vertex v.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_in_neighbors(66)
array([ 8687, 20369, 38674], dtype=uint64)

get_out_degrees(vs, eweight=None)[source]

Return a numpy.ndarray containing the out-degrees of vertex list vs. If supplied, the degrees will be weighted according to the edge PropertyMap eweight.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_out_degrees([42, 666])
array([20, 38], dtype=uint64)

get_in_degrees(vs, eweight=None)[source]

Return a numpy.ndarray containing the in-degrees of vertex list vs. If supplied, the degrees will be weighted according to the edge PropertyMap eweight.

Examples

>>> g = gt.collection.data["pgp-strong-2009"]
>>> g.get_in_degrees([42, 666])
array([20, 39], dtype=uint64)

Obtaining vertex and edge descriptors
vertex(i, use_index=True, add_missing=False)[source]

Return the vertex with index i. If use_index=False, the i-th vertex is returned (which can differ from the vertex with index i in case of filtered graphs).

If add_missing == True, and the vertex does not exist in the graph, the necessary number of missing vertices are inserted, and the new vertex is returned.

edge(s, t, all_edges=False, add_missing=False)[source]

Return the edge from vertex s to t, if it exists. If all_edges=True then a list is returned with all the parallel edges from s to t, otherwise only one edge is returned.

If add_missing == True, a new edge is created and returned, if none currently exists.

This operation will take $$O(min(k(s), k(t)))$$ time, where $$k(s)$$ and $$k(t)$$ are the out-degree and in-degree (or out-degree if undirected) of vertices $$s$$ and $$t$$.

Number of vertices and edges
num_vertices(ignore_filter=False)[source]

Get the number of vertices.

If ignore_filter == True, vertex filters are ignored.

Note

If the vertices are being filtered, and ignore_filter == False, this operation is $$O(V)$$. Otherwise it is $$O(1)$$.

num_edges(ignore_filter=False)[source]

Get the number of edges.

If ignore_filter == True, edge filters are ignored.

Note

If the edges are being filtered, and ignore_filter == False, this operation is $$O(E)$$. Otherwise it is $$O(1)$$.

Modifying vertices and edges

The following functions allow for addition and removal of vertices in the graph.

add_vertex(n=1)[source]

Add a vertex to the graph, and return it. If n != 1, n vertices are inserted and an iterator over the new vertices is returned. This operation is $$O(n)$$.

remove_vertex(vertex, fast=False)[source]

Remove a vertex from the graph. If vertex is an iterable, it should correspond to a sequence of vertices to be removed.

Note

If the option fast == False is given, this operation is $$O(V + E)$$ (this is the default). Otherwise it is $$O(k + k_{\text{last}})$$, where $$k$$ is the (total) degree of the vertex being deleted, and $$k_{\text{last}}$$ is the (total) degree of the vertex with the largest index.

Warning

This operation may invalidate vertex descriptors. Vertices are always indexed contiguously in the range $$[0, N-1]$$, hence vertex descriptors with an index higher than vertex will be invalidated after removal (if fast == False, otherwise only descriptors pointing to vertices with the largest index will be invalidated).

Because of this, the only safe way to remove more than one vertex at once is to sort them in decreasing index order:

# 'del_list' is a list of vertex descriptors
for v in reversed(sorted(del_list)):
g.remove_vertex(v)


Alternatively (and preferably), a list (or iterable) may be passed directly as the vertex parameter, and the above is performed internally (in C++).

Warning

If fast == True, the vertex being deleted is ‘swapped’ with the last vertex (i.e. with the largest index), which will in turn inherit the index of the vertex being deleted. All property maps associated with the graph will be properly updated, but the index ordering of the graph will no longer be the same.

The following functions allow for addition and removal of edges in the graph.

add_edge(source, target, add_missing=True)[source]

Add a new edge from source to target to the graph, and return it. This operation is $$O(1)$$.

If add_missing == True, the source and target vertices are included in the graph if they don’t yet exist.

remove_edge(edge)[source]

Remove an edge from the graph.

Note

This operation is normally $$O(k_s + k_t)$$, where $$k_s$$ and $$k_s$$ are the total degrees of the source and target vertices, respectively. However, if set_fast_edge_removal() is set to True, this operation becomes $$O(1)$$.

Warning

The relative ordering of the remaining edges in the graph is kept unchanged, unless set_fast_edge_removal() is set to True, in which case it can change.

add_edge_list(edge_list, hashed=False, string_vals=False, eprops=None)[source]

Add a list of edges to the graph, given by edge_list, which can be an iterator of (source, target) pairs where both source and target are vertex indexes, or a ndarray of shape (E,2), where E is the number of edges, and each line specifies a (source, target) pair. If the list references vertices which do not exist in the graph, they will be created.

Optionally, if hashed == True, the vertex values in the edge list are not assumed to correspond to vertex indices directly. In this case they will be mapped to vertex indices according to the order in which they are encountered, and a vertex property map with the vertex values is returned. If string_vals == True, the algorithm assumes that the vertex values are strings. Otherwise, they will be assumed to be numeric if edge_list is a ndarray, or arbitrary python objects if it is not.

If given, eprops should specify an iterable containing edge property maps that will be filled with the remaining values at each row, if there are more than two.

set_fast_edge_removal(fast=True)[source]

If fast == True the fast $$O(1)$$ removal of edges will be enabled. This requires an additional data structure of size $$O(E)$$ to be kept at all times. If fast == False, this data structure is destroyed.

get_fast_edge_removal()[source]

Return whether the fast $$O(1)$$ removal of edges is currently enabled.

The following functions allow for easy removal of vertices and edges from the graph.

clear()[source]

Remove all vertices and edges from the graph.

clear_vertex(vertex)[source]

Remove all in and out-edges from the given vertex.

clear_edges()[source]

Remove all edges from the graph.

After the removal of many edges and/or vertices, the underlying containers may have a capacity that significantly exceeds the size of the graph. The function below corrects this.

shrink_to_fit()[source]

Force the physical capacity of the underlying containers to match the graph’s actual size, potentially freeing memory back to the system.

Directedness and reversal of edges

Note

These functions do not actually modify the graph, and are fully reversible. They are also very cheap, with an $$O(1)$$ complexity.

set_directed(is_directed)[source]

Set the directedness of the graph.

Note

This is a $$O(1)$$ operation that does not modify the storage of the graph.

Warning

Changing directedness will invalidate existing vertex and edge descriptors, which will still point to the original graph.

is_directed()[source]

Get the directedness of the graph.

set_reversed(is_reversed)[source]

Reverse the direction of the edges, if is_reversed is True, or maintain the original direction otherwise.

Note

This is a $$O(1)$$ operation that does not modify the storage of the graph.

Warning

Reversing the graph will invalidate existing vertex and edge descriptors, which will still point to the original graph.

is_reversed()[source]

Return True if the edges are reversed, and False otherwise.

Creation of new property maps
new_property(key_type, value_type, vals=None)[source]

Create a new (uninitialized) vertex property map of key type key_type (v, e or g), value type value_type, and return it. If provided, the values will be initialized by vals, which should be a sequence.

new_vertex_property(value_type, vals=None, val=None)[source]

Create a new vertex property map of type value_type, and return it. If provided, the values will be initialized by vals, which should be sequence or by val which should be a single value.

new_vp(value_type, vals=None, val=None)

Alias to new_vertex_property().

new_edge_property(value_type, vals=None, val=None)[source]

Create a new edge property map of type value_type, and return it. If provided, the values will be initialized by vals, which should be sequence or by val which should be a single value.

new_ep(value_type, vals=None, val=None)

Alias to new_edge_property().

new_graph_property(value_type, val=None)[source]

Create a new graph property map of type value_type, and return it. If val is not None, the property is initialized to its value.

new_gp(value_type, val=None)

Alias to new_graph_property().

New property maps can be created by copying already existing ones.

copy_property(src, tgt=None, value_type=None, g=None, full=True)[source]

Copy contents of src property to tgt property. If tgt is None, then a new property map of the same type (or with the type given by the optional value_type parameter) is created, and returned. The optional parameter g specifies the source graph to copy properties from (defaults to self). If full == False, in the case of filtered graphs only the unmasked values are copied (with the remaining ones taking the type-dependent default value).

degree_property_map(deg, weight=None)[source]

Create and return a vertex property map containing the degree type given by deg, which can be any of "in", "out", or "total". If provided, weight should be an edge PropertyMap containing the edge weights which should be summed.

Index property maps
vertex_index

Vertex index map.

It maps for each vertex in the graph an unique integer in the range [0, num_vertices() - 1].

Note

Like edge_index, this is a special instance of a PropertyMap class, which is immutable, and cannot be accessed as an array.

edge_index

Edge index map.

It maps for each edge in the graph an unique integer.

Note

Like vertex_index, this is a special instance of a PropertyMap class, which is immutable, and cannot be accessed as an array.

Additionally, the indexes may not necessarily lie in the range [0, num_edges() - 1]. However this will always happen whenever no edges are deleted from the graph.

edge_index_range

The size of the range of edge indexes.

reindex_edges()[source]

Reset the edge indexes so that they lie in the [0, num_edges() - 1] range. The index ordering will be compatible with the sequence returned by the edges() function.

Warning

Calling this function will invalidate all existing edge property maps, if the index ordering is modified! The property maps will still be usable, but their contents will still be tied to the old indexes, and thus may become scrambled.

Internal property maps

Internal property maps are just like regular property maps, with the only exception that they are saved and loaded to/from files together with the graph itself. See internal property maps for more details.

Note

All dictionaries below are mutable. However, any dictionary returned below is only an one-way proxy to the internally-kept properties. If you modify this object, the change will be propagated to the internal dictionary, but not vice-versa. Keep this in mind if you intend to keep a copy of the returned object.

properties

Dictionary of internal properties. Keys must always be a tuple, where the first element if a string from the set {‘v’, ‘e’, ‘g’}, representing a vertex, edge or graph property, respectively, and the second element is the name of the property map.

Examples

>>> g = gt.Graph()
>>> g.properties[("e", "foo")] = g.new_edge_property("vector<double>")
>>> del g.properties[("e", "foo")]

vertex_properties

Dictionary of internal vertex properties. The keys are the property names.

vp

Alias to vertex_properties.

edge_properties

Dictionary of internal edge properties. The keys are the property names.

ep

Alias to edge_properties.

graph_properties

Dictionary of internal graph properties. The keys are the property names.

gp

Alias to graph_properties.

list_properties()[source]

Print a list of all internal properties.

Examples

>>> g = gt.Graph()
>>> g.properties[("e", "foo")] = g.new_edge_property("vector<double>")
>>> g.vertex_properties["foo"] = g.new_vertex_property("double")
>>> g.vertex_properties["bar"] = g.new_vertex_property("python::object")
>>> g.graph_properties["gnat"] = g.new_graph_property("string", "hi there!")
>>> g.list_properties()
gnat           (graph)   (type: string, val: hi there!)
bar            (vertex)  (type: python::object)
foo            (vertex)  (type: double)
foo            (edge)    (type: vector<double>)

Filtering of vertices and edges.

See Graph filtering for more details.

Note

These functions do not actually modify the graph, and are fully reversible. They are also very cheap, and have an $$O(1)$$ complexity.

set_filters(eprop, vprop, inverted_edges=False, inverted_vertices=False)[source]

Set the boolean properties for edge and vertex filters, respectively. Only the vertices and edges with value different than False are kept in the filtered graph. If either the inverted_edges or inverted_vertex options are supplied with the value True, only the edges or vertices with value False are kept. If any of the supplied property is None, an empty filter is constructed which allows all edges or vertices.

Note

This is a $$O(1)$$ operation that does not modify the storage of the graph.

Warning

Setting vertex or edge filters will invalidate existing vertex and edge descriptors, which will still point to the unfiltered graph.

set_vertex_filter(prop, inverted=False)[source]

Set the vertex boolean filter property. Only the vertices with value different than False are kept in the filtered graph. If the inverted option is supplied with value True, only the vertices with value False are kept. If the supplied property is None, the filter is replaced by an uniform filter allowing all vertices.

Note

This is a $$O(1)$$ operation that does not modify the storage of the graph.

Warning

Setting vertex filters will invalidate existing vertex and edge descriptors, which will still point to the unfiltered graph.

get_vertex_filter()[source]

Return a tuple with the vertex filter property and bool value indicating whether or not it is inverted.

set_edge_filter(prop, inverted=False)[source]

Set the edge boolean filter property. Only the edges with value different than False are kept in the filtered graph. If the inverted option is supplied with value True, only the edges with value False are kept. If the supplied property is None, the filter is replaced by an uniform filter allowing all edges.

Note

This is a $$O(1)$$ operation that does not modify the storage of the graph.

Warning

Setting edge filters will invalidate existing vertex and edge descriptors, which will still point to the unfiltered graph.

get_edge_filter()[source]

Return a tuple with the edge filter property and bool value indicating whether or not it is inverted.

clear_filters()[source]

Remove vertex and edge filters, and set the graph to the unfiltered state.

Note

This is a $$O(1)$$ operation that does not modify the storage of the graph.

Warning

Clearing vertex and edge filters will invalidate existing vertex and edge descriptors.

Warning

The purge functions below irreversibly remove the filtered vertices or edges from the graph. Note that, contrary to the functions above, these are $$O(V)$$ and $$O(E)$$ operations, respectively.

purge_vertices(in_place=False)[source]

Remove all vertices of the graph which are currently being filtered out. This operation is not reversible.

If the option in_place == True is given, the algorithm will remove the filtered vertices and re-index all property maps which are tied with the graph. This is a slow operation which has an $$O(V^2)$$ complexity.

If in_place == False, the graph and its vertex and edge property maps are temporarily copied to a new unfiltered graph, which will replace the contents of the original graph. This is a fast operation with an $$O(V + E)$$ complexity. This is the default behaviour if no option is given.

purge_edges()[source]

Remove all edges of the graph which are currently being filtered out. This operation is not reversible.

I/O operations

See Graph I/O for more details.

load(file_name, fmt='auto', ignore_vp=None, ignore_ep=None, ignore_gp=None)[source]

Load graph from file_name (which can be either a string or a file-like object). The format is guessed from file_name, or can be specified by fmt, which can be either “gt”, “graphml”, “xml”, “dot” or “gml”. (Note that “graphml” and “xml” are synonyms).

If provided, the parameters ignore_vp, ignore_ep and ignore_gp, should contain a list of property names (vertex, edge or graph, respectively) which should be ignored when reading the file.

Warning

The only file formats which are capable of perfectly preserving the internal property maps are “gt” and “graphml”. Because of this, they should be preferred over the other formats whenever possible.

save(file_name, fmt='auto')[source]

Save graph to file_name (which can be either a string or a file-like object). The format is guessed from the file_name, or can be specified by fmt, which can be either “gt”, “graphml”, “xml”, “dot” or “gml”. (Note that “graphml” and “xml” are synonyms).

Warning

The only file formats which are capable of perfectly preserving the internal property maps are “gt” and “graphml”. Because of this, they should be preferred over the other formats whenever possible.

class graph_tool.GraphView(g, vfilt=None, efilt=None, directed=None, reversed=False, skip_properties=False, skip_vfilt=False, skip_efilt=False)[source]

A view of selected vertices or edges of another graph.

This class uses shared data from another Graph instance, but allows for local filtering of vertices and/or edges, edge directionality or reversal. See Graph views for more details and examples.

The existence of a GraphView object does not affect the original graph, except if the graph view is modified (addition or removal of vertices or edges), in which case the modification is directly reflected in the original graph (and vice-versa), since they both point to the same underlying data. Because of this, instances of PropertyMap can be used interchangeably with a graph and its views.

The argument g must be an instance of a Graph class. If specified, vfilt and efilt select which vertices and edges are filtered, respectively. These parameters can either be a boolean-valued PropertyMap or a ndarray, which specify which vertices/edges are selected, or an unary function that returns True if a given vertex/edge is to be selected, or False otherwise.

The boolean parameter directed can be used to set the directionality of the graph view. If directed is None, the directionality is inherited from g.

If reversed == True, the direction of the edges is reversed.

If vfilt or efilt is anything other than a PropertyMap instance, the instantiation running time is $$O(V)$$ and $$O(E)$$, respectively. Otherwise, the running time is $$O(1)$$.

If either skip_properties, skip_vfilt or skip_efilt is True, then the internal properties, vertex filter or edge filter of the original graph are ignored, respectively.

base

Base graph.

class graph_tool.Vertex

Vertex descriptor.

This class represents a vertex in a Graph instance.

Vertex instances are hashable, and are convertible to integers, corresponding to its index (see vertex_index).

Raises an exception This class cannot be instantiated from Python

all_edges()

Return an iterator over all edges (both in or out).

all_neighbors()

Return an iterator over all neighbors (both in or out).

in_degree(weight=None)

Return the in-degree of the vertex. If provided, weight should be a scalar edge PropertyMap, and the in-degree will correspond to the sum of the weights of the in-edges.

in_edges()

Return an iterator over the in-edges.

in_neighbors()

Return an iterator over the in-neighbors.

is_valid()

Returns True if the descriptor corresponds to an existing vertex in the graph, False otherwise.

out_degree(weight=None)

Return the out-degree of the vertex. If provided, weight should be a scalar edge PropertyMap, and the out-degree will correspond to the sum of the weights of the out-edges.

out_edges()

Return an iterator over the out-edges.

out_neighbors()

Return an iterator over the out-neighbors.

class graph_tool.Edge

Edge descriptor.

This class represents an edge in a Graph.

Edge instances are hashable, iterable and thus are convertible to a tuple, which contains the source and target vertices.

Raises an exception This class cannot be instantiated from Python

is_valid()

Returns True if the descriptor corresponds to an existing edge in the graph, False otherwise.

source()

Returns the source of the edge (a Vertex instance).

target()

Returns the target of the edge (a Vertex instance).

class graph_tool.PropertyMap(pmap, g, key_type)[source]

This class provides a mapping from vertices, edges or whole graphs to arbitrary properties.

See Property maps for more details.

The possible property value types are listed below.

Type name Alias
bool uint8_t
int16_t short
int32_t int
int64_t long, long long
double float
long double
string
vector<bool> vector<uint8_t>
vector<int16_t> short
vector<int32_t> vector<int>
vector<int64_t> vector<long>, vector<long long>
vector<double> vector<float>
vector<long double>
vector<string>
python::object object
copy(value_type=None, full=True)[source]

Return a copy of the property map. If value_type is specified, the value type is converted to the chosen type. If full == False, in the case of filtered graphs only the unmasked values are copied (with the remaining ones taking the type-dependent default value).

get_graph()[source]

Get the graph class to which the map refers.

key_type()[source]

Return the key type of the map. Either ‘g’, ‘v’ or ‘e’.

value_type()[source]

Return the value type of the map.

python_value_type()[source]

Return the python-compatible value type of the map.

get_array()[source]

Get a numpy.ndarray subclass (PropertyArray) with the property values.

Note

An array is returned only if the value type of the property map is a scalar. For vector, string or object types, None is returned instead. For vector and string objects, indirect array access is provided via the get_2d_array() and set_2d_array() member functions.

Warning

The returned array does not own the data, which belongs to the property map. Therefore, if the graph changes, the array may become invalid. Do not store the array if the graph is to be modified; store a copy instead.

a

Shortcut to the get_array() method as an attribute. This makes assignments more convenient, e.g.:

>>> g = gt.Graph()
<...>
>>> prop = g.new_vertex_property("double")
>>> prop.a = np.random.random(10)           # Assignment from array

fa

The same as the a attribute, but instead an indexed array is returned, which contains only entries for vertices/edges which are not filtered out. If there are no filters in place, the array is not indexed, and is identical to the a attribute.

Note that because advanced indexing is triggered, a copy of the array is returned, not a view, as for the a attribute. Nevertheless, the assignment of values to the whole array at once works as expected.

ma

The same as the a attribute, but instead a MaskedArray object is returned, which contains only entries for vertices/edges which are not filtered out. If there are no filters in place, a regular PropertyArray is returned, which is identical to the a attribute.

get_2d_array(pos)[source]

Return a two-dimensional array with a copy of the entries of the vector-valued property map. The parameter pos must be a sequence of integers which specifies the indexes of the property values which will be used.

set_2d_array(a, pos=None)[source]

Set the entries of the vector-valued property map from a two-dimensional array a. If given, the parameter pos must be a sequence of integers which specifies the indexes of the property values which will be set.

is_writable()[source]

Return True if the property is writable.

set_value(val)[source]

Sets all values in the property map to val.

reserve(size)[source]

Reserve enough space for size elements in underlying container. If the original size is already equal or larger, nothing will happen.

resize(size)[source]

Resize the underlying container to contain exactly size elements.

shrink_to_fit()[source]

Shrink size of underlying container to accommodate only the necessary amount, and thus potentially freeing memory.

data_ptr()[source]

Return the pointer to memory where the data resides.

class graph_tool.PropertyArray[source]

Bases: numpy.ndarray

This is a ndarray subclass which keeps a reference of its PropertyMap owner.

prop_map

PropertyMap owner instance.

I/O functions
graph_tool.load_graph(file_name, fmt='auto', ignore_vp=None, ignore_ep=None, ignore_gp=None)[source]

Load a graph from file_name (which can be either a string or a file-like object).

The format is guessed from file_name, or can be specified by fmt, which can be either “gt”, “graphml”, “xml”, “dot” or “gml”. (Note that “graphml” and “xml” are synonyms).

If provided, the parameters ignore_vp, ignore_ep and ignore_gp, should contain a list of property names (vertex, edge or graph, respectively) which should be ignored when reading the file.

Warning

The only file formats which are capable of perfectly preserving the internal property maps are “gt” and “graphml”. Because of this, they should be preferred over the other formats whenever possible.

graph_tool.load_graph_from_csv(file_name, directed=True, eprop_types=None, eprop_names=None, string_vals=True, hashed=False, skip_first=False, ecols=(0, 1), csv_options={'delimiter': ', ', 'quotechar': '"'})[source]

Load a graph from a csv file containing a list of edges and edge properties.

Parameters: file_name : str or file-like object File in :mod:csv format, with edges given in each row. directed : bool (optional, default: False) Whether or not the graph is directed. eprop_types : list of str (optional, default: None) List of edge property types to be read from remaining columns (if this is None, all properties will be of type string. eprop_names : list of str (optional, default: None) List of edge property names to be used for the remaining columns (if this is None, the properties will be called “c1, c2, …”). string_vals : bool (optional, default: True) If True, the vertex values are assumed to be arbitrary strings, otherwise they will be assumed to correspond to integers. hashed : bool (optional, default: False) If True and string_vals == False, the vertex values in the edge list are not assumed to correspond to vertex indices directly. In this case they will be mapped to vertex indices according to the order in which they are encountered, and a vertex property map with the vertex values is returned. If string_vals == True, this automatically means hashed = True. skip_first : bool (optional, default: False) If True the first line of the file will be skipped. ecols : pair of int (optional, default: (0,1)) Line columns used as source and target for the edges. csv_options : dict (optional, default: {"delimiter": ",", "quotechar": '"'}) Options to be passed to the csv.reader() parser. The loaded graph. It will contain additional columns in the file as internal edge property maps. If hashed == True, it will also contain an internal vertex property map with the vertex names.
Property map operations
graph_tool.group_vector_property(props, value_type=None, vprop=None, pos=None)[source]

Group list of properties props into a vector property map of the same type.

Parameters: props : list of PropertyMap Properties to be grouped. value_type : string (optional, default: None) If supplied, defines the value type of the grouped property. vprop : PropertyMap (optional, default: None) If supplied, the properties are grouped into this property map. pos : list of ints (optional, default: None) If supplied, should contain a list of indexes where each corresponding element of props should be inserted. vprop : PropertyMap A vector property map with the grouped values of each property map in props.

Examples

>>> from numpy.random import seed, randint
>>> from numpy import array
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> props = [g.new_vertex_property("int") for i in range(3)]
>>> for i in range(3):
...    props[i].a = randint(0, 100, g.num_vertices())
>>> gprop = gt.group_vector_property(props)
>>> print(gprop[g.vertex(0)].a)
[51 25  8]
>>> print(array([p[g.vertex(0)] for p in props]))
[51 25  8]

graph_tool.ungroup_vector_property(vprop, pos, props=None)[source]

Ungroup vector property map vprop into a list of individual property maps.

Parameters: vprop : PropertyMap Vector property map to be ungrouped. pos : list of ints A list of indexes corresponding to where each element of vprop should be inserted into the ungrouped list. props : list of PropertyMap (optional, default: None) If supplied, should contain a list of property maps to which vprop should be ungroupped. props : list of PropertyMap A list of property maps with the ungrouped values of vprop.

Examples

>>> from numpy.random import seed, randint
>>> from numpy import array
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> prop = g.new_vertex_property("vector<int>")
>>> for v in g.vertices():
...    prop[v] = randint(0, 100, 3)
>>> uprops = gt.ungroup_vector_property(prop, [0, 1, 2])
>>> print(prop[g.vertex(0)].a)
[51 92 14]
>>> print(array([p[g.vertex(0)] for p in uprops]))
[51 92 14]

graph_tool.map_property_values(src_prop, tgt_prop, map_func)[source]

Map the values of src_prop to tgt_prop according to the mapping function map_func.

Parameters: src_prop : PropertyMap Source property map. tgt_prop : PropertyMap Target property map. map_func : function or callable object Function mapping values of src_prop to values of tgt_prop. None

Examples

>>> g = gt.collection.data["lesmis"]
>>> label_len = g.new_vertex_property("int64_t")
>>> gt.map_property_values(g.vp.label, label_len,
...                        lambda x: len(x))
>>> print(label_len.a)
[ 6  8 14 11 12  8 12  8  5  6  7  7 10  6  7  7  9  9  7 11  9  6  7  7
13 10  7  6 12 10  8  8 11  6  5 12  6 10 11  9 12  7  7  6 14  7  9  9
8 12  6 16 12 11 14  6  9  6  8 10  9  7 10  7  7  4  9 14  9  5 10 12
9  6  6  6 12]

graph_tool.infect_vertex_property(g, prop, vals=None)[source]

Propagate the prop values of vertices with value val to all their out-neighbors.

Parameters: Property map to be modified. vals : list (optional, default: None) List of values to be propagated. If not provided, all values will be propagated. None : None

Examples

>>> from numpy.random import seed
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> prop = g.vertex_index.copy("int32_t")
>>> gt.infect_vertex_property(g, prop, [10])
>>> print(sum(prop.a == 10))
4

graph_tool.edge_endpoint_property(g, prop, endpoint, eprop=None)[source]

Return an edge property map corresponding to the vertex property prop of either the target and source of the edge, according to endpoint.

Parameters: Vertex property map to be used to propagated to the edge. endpoint : “source” or “target” Edge endpoint considered. If the graph is undirected, the source is always the vertex with the lowest index. eprop : PropertyMap (optional, default: None) If provided, the resulting edge properties will be stored here. eprop : PropertyMap Propagated edge property.

Examples

>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> esource = gt.edge_endpoint_property(g, g.vertex_index, "source")
>>> print(esource.a)
[ 0  0  0 96 96 96 92 92 92 88 88 88 84 84 84 80 80 80 76 76 76 72 72 72
68 68 68 64 64 64 60 60 60 56 56 56 52 52 52 48 48 48 44 44 44 40 40 40
36 36 36 32 32 32 28 28 28 24 24 24 20 20 20 16 16 16 12 12 12  8  8  8
4  4  4 99 99 99  1  1  1  2  2  2  3  3  3  5  5  5  6  6  6  7  7  7
9  9  9 10 10 10 14 14 14 19 19 19 25 25 25 30 30 30 35 35 35 41 41 41
46 46 46 51 51 51 57 57 57 62 62 62 67 67 67 73 73 73 78 78 78 83 83 83
89 89 89 94 94 94 11 11 11 98 98 98 97 97 97 95 95 95 93 93 93 91 91 91
90 90 90 87 87 87 86 86 86 85 85 85 82 82 82 81 81 81 79 79 79 77 77 77
75 75 75 74 74 74 71 71 71 69 69 69 61 61 61 54 54 54 47 47 47 39 39 39
33 33 33 26 26 26 18 18 18 70 70 70 13 13 13 15 15 15 17 17 17 21 21 21
22 22 22 23 23 23 27 27 27 29 29 29 31 31 31 34 34 34 37 37 37 38 38 38
42 42 42 43 43 43 45 45 45 49 49 49 50 50 50 53 53 53 55 55 55 58 58 58
59 59 59 63 63 63 65 65 65 66 66 66]

graph_tool.incident_edges_op(g, direction, op, eprop, vprop=None)[source]

Return a vertex property map corresponding to a specific operation (sum, product, min or max) on the edge property eprop of incident edges on each vertex, following the direction given by direction.

Parameters: direction : “in” or “out” Direction of the incident edges. op : “sum”, “prod”, “min” or “max” Operation performed on incident edges. eprop : PropertyMap Edge property map to be summed. vprop : PropertyMap (optional, default: None) If provided, the resulting vertex properties will be stored here. vprop : PropertyMap Resulting vertex property.

Examples

>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> vsum = gt.incident_edges_op(g, "out", "sum", g.edge_index)
>>> print(vsum.a)
[  3 237 246 255 219 264 273 282 210 291 300 453 201 687 309 696 192 705
669 318 183 714 723 732 174 327 660 741 165 750 336 759 156 651 768 345
147 777 786 642 138 354 795 804 129 813 363 633 120 822 831 372 111 840
624 849 102 381 858 867  93 615 390 876  84 885 894 399  75 606 678 597
66 408 588 579  57 570 417 561  48 552 543 426  39 534 525 516  30 435
507 498  21 489 444 480  12 471 462 228]

graph_tool.perfect_prop_hash(props, htype='int32_t')[source]

Given a list of property maps props of the same type, a derived list of property maps with integral type htype is returned, where each value is replaced by a perfect (i.e. unique) hash value.

Note

The hash value is deterministic, but it will not be necessarily the same for different values of props.

graph_tool.value_types()[source]

Return a list of possible properties value types.

OpenMP control
graph_tool.openmp_enabled()[source]

Return True if OpenMP was enabled during compilation.

graph_tool.openmp_get_num_threads()[source]

Return the number of OpenMP threads.

graph_tool.openmp_set_num_threads(n)[source]

Set the number of OpenMP threads.

graph_tool.openmp_get_schedule()[source]

Return the runtime OpenMP schedule and chunk size. The schedule can by any of: "static", "dynamic", "guided", "auto".

graph_tool.openmp_set_schedule(schedule, chunk=0)[source]

Set the runtime OpenMP schedule and chunk size. The schedule can by any of: "static", "dynamic", "guided", "auto".

Misc
graph_tool.show_config()[source]

Show graph_tool build configuration.