Metadata-Version: 2.1
Name: abstract
Version: 2020.4.7
Summary: Python library for creating and drawing graphs and taking advantage of graph properties
Home-page: https://github.com/idin/abstract
Author: Idin
Author-email: py@idin.ca
License: MIT
Description: # *Abstract*
        Abstract is a Python library for creating and drawing graphs 
        and taking advantage of graph properties.
        
        ## Installation
        
        ```bash
        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]](https://en.wikipedia.org/wiki/Graph_(abstract_data_type))
        
        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]](https://en.wikipedia.org/wiki/Graph_(abstract_data_type))
        
        
        
        ### Usage
        The `Graph` class allows you to create nodes and edges and 
        visualize the resulting graph. Edges can have direction which
        indicates parent-child relationship.
        
        To construct a new graph, use *Graph()*.
        ```python
        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 recommended
        * `label` (optional): it can be any string, if it is missing the name will be displayed
        * `value` (optional): can be any object
        * `style` (optional): it should be a *NodeStyle* object and is only used for rendering
        * `if_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](https://bigbangtheory.fandom.com/wiki/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.
        
        ```python
        node_list = [
            'scissors', 'paper', 'rock', 'lizard', 'Spock', 'scissors',
            'lizard', 'paper', 'Spock', 'rock', 'scissors'
        ]
        ```
        
        Now let's create `nodes` with the same names:
        ```python
        # create a set to avoid duplicates
        for node in set(node_list):
            node = graph.add_node(name=node)
            
        graph.display(direction='TB') # left-right direction is too tall
        ```
        ![image of the graph](http://idin.ca/storage/python/abstract/images/rock_paper_scissors_lizard_spock_1.png)
        
        **Note**: by default, Graph uses colour theme from the 
        [colouration](http://pypi.org/project/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.
        
        ```python
        for i in range(len(node_list)-1):
            edge = graph.connect(start=node_list[i], end=node_list[i + 1])
        graph.display(direction='LR') # top-bottom direction is too tall
        ```
        ![image of the graph](http://idin.ca/storage/python/abstract/images/rock_paper_scissors_lizard_spock_2.png)
        
        **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.
        ```python
        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. 
        
        ```python
        # 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 library
        * `Pensieve` class from the [pensieve](https://pypi.org/project/pensieve/) library
        * `Page` class from [internet.wikipedia](https://pypi.org/project/internet/) submodule
        
        ```python
        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()
        ```
        ![image of the graph](http://idin.ca/storage/python/abstract/images/pensieve_numbers_graph.png)
        
        
        ### `random(...)`
        The `random` method creates a random Graph.
        
        ```python
        g1 = Graph.random(num_nodes=8, connection_probability=0.4, seed=6)
        g1
        ```
        ![image of the graph](http://idin.ca/storage/python/abstract/images/random_graph_1.png)
        
        ### 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.
        
        ```python
        g2 = Graph.random(num_nodes=7, start_index=3, connection_probability=0.4, seed=41)
        g2
        ```
        ![image of the graph](http://idin.ca/storage/python/abstract/images/random_graph_2.png)
        
        ```python
        g3 = g1 + g2
        g3
        ```
        ![image of the graph](http://idin.ca/storage/python/abstract/images/random_graph_1_plus_2.png)
        
        ### 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.
        
        ```python
        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
        ```
        ![image of the graph](http://idin.ca/storage/python/abstract/images/graph_with_loop.png)
        
        ```python
        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:
        ```text
        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
        
Keywords: graph
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
