Python库 | abstract-2021.8.20.1.tar.gz

# *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 ```  **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 ```  **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() ```  ### `random(...)` The `random` method creates a random Graph. ```python 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. ```python g2 = Graph.random(num_nodes=7, start_index=3, connection_probability=0.4, seed=41) g2 ```  ```python 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. ```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 ```  ```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