Python library for creating and drawing graphs and taking advantage of graph properties
Project description
Abstract
Abstract is a Python library for creating and drawing graphs and taking advantage of graph properties.
Installation
pip install abstract
Graph
Introduction
In computer science, a graph is an abstract data type that is meant to implement the undirected graph and directed graph concepts from mathematics; specifically, the field of graph theory. [1]
A graph data structure consists of a finite (and possibly mutable) set of vertices or nodes or points, together with a set of unordered pairs of these vertices for an undirected graph or a set of ordered pairs for a directed graph. These pairs are known as edges, arcs, or lines for an undirected graph and as arrows, directed edges, directed arcs, or directed lines for a directed graph. The vertices may be part of the graph structure, or may be external entities represented by integer indices or references. [1]
Usage
The Graph
class allows you to create nodes and edges and
visualize the resulting graph. Edges can have direction which
indicates parentchild relationship.
To construct a new graph, use Graph().
from abstract import Graph graph = Graph(direction='LR') # default direction is 'LR', other options are: 'TB', 'BT', 'RL'
add_node(...)
The add_node
method creates a node in the graph
and returns a Node object.
It takes the following arguments:
name
: name of the new node (should be unique); snake case is recommendedlabel
(optional): it can be any string, if it is missing the name will be displayedvalue
(optional): can be any objectstyle
(optional): it should be a NodeStyle object and is only used for renderingif_node_exists
(optional): what to do if a node with this name exists and can be 'warn', 'error', or 'ignore'; default is 'warn'
Let's use the
Rock, Paper, Scissors, Lizard, Spock
game to show how Graph
works. The following list shows the order in which
an object in the game beats the object to its right of it in the list and
gets beaten by the object left of it. Please note that there are only five
objects and they are repeated to illustrate all possible pairs.
node_list = [ 'scissors', 'paper', 'rock', 'lizard', 'Spock', 'scissors', 'lizard', 'paper', 'Spock', 'rock', 'scissors' ]
Now let's create nodes
with the same names:
# create a set to avoid duplicates for node in set(node_list): node = graph.add_node(name=node) graph.display(direction='TB') # leftright direction is too tall
Note: by default, Graph uses colour theme from the colouration library for roots and uses the directionality of edges to determine the colour of other nodes. In the above example, without any edges, all nodes are roots.
connect(...)
(add an edge)
The connect
method creates an edge
from a start
node to an end
node.
The start
and end
arguments can be either names of nodes or the Node
objects.
for i in range(len(node_list)1): edge = graph.connect(start=node_list[i], end=node_list[i + 1]) graph.display(direction='LR') # topbottom direction is too tall
Note: nodes that form a loop are coloured differently (red circles with yellow colour inside)
get_node
To retrieve a node from the graph you can use the get_node method which returns a Node object.
rock = graph.get_node('rock')
display(...)
The display
method visualizes the graph and if a path
is provided it saves it
to an image file that can be a pdf or png;
you can also provide the resolution with the dpi
argument.
The file format is infered from
the path
argument.
# save as a png file and view the file graph.draw(path='my_graph.png', view=True)
Graph(obj=...)
You can create a graph from any object that has a __graph__()
method.
Examples of such objects are:
Graph
class from this libraryPensieve
class from the pensieve libraryPage
class from internet.wikipedia submodule
from pensieve import Pensieve from abstract import Graph pensieve = Pensieve() pensieve['two'] = 2 pensieve['three'] = 3 pensieve['four'] = lambda two: two * two pensieve['five'] = 5 pensieve['six'] = lambda two, three: two * three pensieve['seven'] = 7 pensieve['eight'] = lambda two, four: two * four pensieve['nine'] = lambda three: three * three pensieve['ten'] = lambda two, five: two * five graph = Graph(obj=pensieve, direction='TB') # or Graph(pensieve) graph.display()
random(...)
The random
method creates a random Graph.
g1 = Graph.random(num_nodes=8, connection_probability=0.4, seed=6) g1
Adding Two Graphs: +
You can easily add two graphs using the +
operator.
The result will have the union of nodes and edges in both graphs.
g2 = Graph.random(num_nodes=7, start_index=3, connection_probability=0.4, seed=41) g2
g3 = g1 + g2 g3
Finding Loops
The node
's is_in_loop
method helps you find nodes that form a loop;
i.e., nodes that have at least one descendant which is also an ancestor.
graph_with_loop = Graph() for letter in 'abcdef': graph_with_loop.add_node(letter) for start, end in [ ('a', 'b'), ('b', 'c'), ('c', 'a'), ('c', 'd'), ('d', 'e'), ('e', 'f'), ('f', 'e') ]: graph_with_loop.connect(start, end) graph_with_loop
for node in graph_with_loop.nodes: if node.is_in_loop_with(other='a') and node.name != 'a': print(node.name, 'is in the same loop as a') elif node.is_in_loop(): print(node.name, 'is in a loop') else: print(node.name, 'is not in a loop')
output:
a is in a loop b is in the same loop as a c is in the same loop as a d is not in a loop e is in a loop f is in a loop
Future Features
 Create a graph from:
 list of dictionaries
 dataframe
 Create a new graph by filtering a graph
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