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NxPy: Nx Python plugin

This project was generated using Nx.

The plugin allows users to create a basic python application using commands. To add the plugin to your project, just follow these steps:

Getting started

Create a Nx Workspace

Before installing the plugin, it is required to have a pre-configured Nx Workspace . If you don't, then proceed to create a new one executing the following command:

Install the plugin

Adding an application to our workspace

To create a new python application based on this plugin, please execute the following command:

Note: Replace with the name of your new application

Usage

After creating the application using the plugin. We can execute the build, lint, test and serve on this new application. Output will be stored on directory.

Building the app

The command is going to compile all the python files inside directory, using the native module.

More information here: py_compile

Linting the application

Unfortunately Python doesn't have a native linting module(yet!). uses the module to lint your application. It is required that you install this module beforehand. More info here: Flake8

After that you can perform the lint process with:

Serving the application

This is going to execute the main file in your python application.

Testing your application

The command is going to execute all the test units inside your python app. You can add new test unit files if you want, but there are two requirements that you must meet:

  • The filename must include the prefix
  • Because we are using the native python module to make our tests, you are going to create the tests based on this approach. More info here: unittest

To test your python app, execute the command:

Contributing

All contributions are welcome. Make sure you follow the code of conduct in this repository.

MIT License

Made with by Code ON | Melvin Vega & Diana Rodriguez

Sours: https://github.com/eulerrapp/nx-python

Tutorial

A Simple Savedata Backup tool¶

We will start with a simple homebrew app that allows the user to backup the savedata of a selection of games.

Firstly, we need to import some libraries to begin work on our homebrew app, primarily the nx package. We also want to show a selection menu to the user, so we should import the AnsiMenu utility class as well:

importnxfromnx.utilsimportAnsiMenu

Next, we create constants that store the title IDs of The Legend of Zelda: Breath of the Wild and Super Mario Odyssey:

# title IDs are hexadecimal numbersBOTW_TITLE_ID=0x01007EF00011E000SMO_TITLE_ID=0x0100000000010000

After that, we create two lists. The names of the titles the user can select from are stored in . is used to store the title IDs of the games in the same order:

title_names=["The Legend of Zelda - Breath of the Wild","Super Mario Odyssey"]title_ids=[BOTW_TITLE_ID,SMO_TITLE_ID]

Once our lists are set up, we can create a menu using the utility class. This menu will allow the user to choose the game of which the savedata backup will be created:

select_title_menu=AnsiMenu(title_names)

The main execution flow of every Python program (homebrew apps are no exception) must be wrapped in a conditional clause as follows:

if__name__=='__main__':

The menu can now be rendered and queried using its method:

selected_index=select_title_menu.query()

The method returns the index of the item selected by the user, which is now stored in the variable. As the order of the two lists we created earlier is equal, we can use the index to get the title ID from the list:

selected_title_id=title_ids[selected_index]

now contains the title ID of the selected title. We can now use this title ID to create a functional object:

selected_title=nx.titles[selected_title_id]

Now we’re interested in accessing and backing up the savedata of the title. To do this, we first need to mount the title’s savedata. This is done by entering a new context with the title’s savedata:

withselected_title.savedataassavedata:

Hint: You can also use , and , however, using a block might save you from a lot of potential headache, and is typically more simple and improves readability. Now that the savedata filesystem of the title is mounted, you can backup its content simply by calling its method:

This creates a backup of the savedata in . You can also provide your own backup path like this:

savedata.backup('/savedata_backups/{}/'.format(title_names[selected_index]))

When the block ends, the savedata filesystem is automatically committed and unmounted.

That’s it! Your code should now look like this:

importnxfromnx.utilsimportAnsiMenu# title IDs are hexadecimal numbersBOTW_TITLE_ID=0x01007EF00011E000SMO_TITLE_ID=0x0100000000010000title_names=["The Legend of Zelda - Breath of the Wild","Super Mario Odyssey"]title_ids=[BOTW_TITLE_ID,SMO_TITLE_ID]select_title_menu=AnsiMenu(title_names)if__name__=='__main__':selected_title=select_title_menu.query()selected_title=title_ids[selected_title]selected_title=nx.titles[selected_title]withselected_title.savedataassavedata:savedata.backup('/savedata_backups/{}/'.format(title_names[selected_index]))

Congratulations, you have created your first Switch homebrew application in Python!

Sours: https://nx-python.readthedocs.io/en/latest/getting_started/tutorial.html
  1. Red arrows wallpaper
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https://github.com/networkx/networkx/workflows/test/badge.svg?tag=networkx-2.6.3https://codecov.io/gh/networkx/networkx/branch/main/graph/badge.svghttps://img.shields.io/github/labels/networkx/networkx/Good%20First%20Issue?color=green&label=Contribute%20&style=flat-square

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Simple example

Find the shortest path between two nodes in an undirected graph:

>>>importnetworkxasnx>>>G=nx.Graph()>>>G.add_edge('A','B',weight=4)>>>G.add_edge('B','D',weight=2)>>>G.add_edge('A','C',weight=3)>>>G.add_edge('C','D',weight=4)>>>nx.shortest_path(G,'A','D',weight='weight')['A','B','D']

Install

Install the latest version of NetworkX:

$ pip install networkx

Install with all optional dependencies:

$ pip install networkx[all]

For additional details, please see INSTALL.rst.

Bugs

Please report any bugs that you find here. Or, even better, fork the repository on GitHub and create a pull request (PR). We welcome all changes, big or small, and we will help you make the PR if you are new to git (just ask on the issue and/or see CONTRIBUTING.rst).

License

Released under the 3-Clause BSD license (see LICENSE.txt):

Copyright (C) 2004-2021 NetworkX Developers Aric Hagberg <[email protected]> Dan Schult <[email protected]> Pieter Swart <[email protected]>
Sours: https://pypi.org/project/networkx/
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Tutorial¶

This guide can help you start working with NetworkX.

Creating a graph¶

Create an empty graph with no nodes and no edges.

>>> importnetworkxasnx>>> G=nx.Graph()

By definition, a is a collection of nodes (vertices) along with identified pairs of nodes (called edges, links, etc). In NetworkX, nodes can be any hashable object e.g., a text string, an image, an XML object, another Graph, a customized node object, etc.

Note

Python’s object is not allowed to be used as a node. It determines whether optional function arguments have been assigned in many functions. And it can be used as a sentinel object meaning “not a node”.

Nodes¶

The graph can be grown in several ways. NetworkX includes many graph generator functions and facilities to read and write graphs in many formats. To get started though we’ll look at simple manipulations. You can add one node at a time,

>>> G.add_node(1)

or add nodes from any iterable container, such as a list

>>> G.add_nodes_from([2,3])

You can also add nodes along with node attributes if your container yields 2-tuples of the form :

>>> G.add_nodes_from([... (4,{"color":"red"}),... (5,{"color":"green"}),... ])

Node attributes are discussed further below.

Nodes from one graph can be incorporated into another:

>>> H=nx.path_graph(10)>>> G.add_nodes_from(H)

now contains the nodes of as nodes of . In contrast, you could use the graph as a node in .

>>> G.add_node(H)

The graph now contains as a node. This flexibility is very powerful as it allows graphs of graphs, graphs of files, graphs of functions and much more. It is worth thinking about how to structure your application so that the nodes are useful entities. Of course you can always use a unique identifier in and have a separate dictionary keyed by identifier to the node information if you prefer.

Note

You should not change the node object if the hash depends on its contents.

Edges¶

can also be grown by adding one edge at a time,

>>> G.add_edge(1,2)>>> e=(2,3)>>> G.add_edge(*e)# unpack edge tuple*

by adding a list of edges,

>>> G.add_edges_from([(1,2),(1,3)])

or by adding any ebunch of edges. An ebunch is any iterable container of edge-tuples. An edge-tuple can be a 2-tuple of nodes or a 3-tuple with 2 nodes followed by an edge attribute dictionary, e.g., . Edge attributes are discussed further below.

>>> G.add_edges_from(H.edges)

There are no complaints when adding existing nodes or edges. For example, after removing all nodes and edges,

we add new nodes/edges and NetworkX quietly ignores any that are already present.

>>> G.add_edges_from([(1,2),(1,3)])>>> G.add_node(1)>>> G.add_edge(1,2)>>> G.add_node("spam")# adds node "spam">>> G.add_nodes_from("spam")# adds 4 nodes: 's', 'p', 'a', 'm'>>> G.add_edge(3,'m')

At this stage the graph consists of 8 nodes and 3 edges, as can be seen by:

>>> G.number_of_nodes()8>>> G.number_of_edges()3

Note

The order of adjacency reporting (e.g., G.adj, G.successors, G.predecessors) is the order of edge addition. However, the order of G.edges is the order of the adjacencies which includes both the order of the nodes and each node’s adjacencies. See example below:

>>> DG=nx.DiGraph()>>> DG.add_edge(2,1)# adds the nodes in order 2, 1>>> DG.add_edge(1,3)>>> DG.add_edge(2,4)>>> DG.add_edge(1,2)>>> assertlist(DG.successors(2))==[1,4]>>> assertlist(DG.edges)==[(2,1),(2,4),(1,3),(1,2)]

Examining elements of a graph¶

We can examine the nodes and edges. Four basic graph properties facilitate reporting: , , and . These are set-like views of the nodes, edges, neighbors (adjacencies), and degrees of nodes in a graph. They offer a continually updated read-only view into the graph structure. They are also dict-like in that you can look up node and edge data attributes via the views and iterate with data attributes using methods , . If you want a specific container type instead of a view, you can specify one. Here we use lists, though sets, dicts, tuples and other containers may be better in other contexts.

>>> list(G.nodes)[1, 2, 3, 'spam', 's', 'p', 'a', 'm']>>> list(G.edges)[(1, 2), (1, 3), (3, 'm')]>>> list(G.adj[1])# or list(G.neighbors(1))[2, 3]>>> G.degree[1]# the number of edges incident to 12

One can specify to report the edges and degree from a subset of all nodes using an nbunch. An nbunch is any of: (meaning all nodes), a node, or an iterable container of nodes that is not itself a node in the graph.

>>> G.edges([2,'m'])EdgeDataView([(2, 1), ('m', 3)])>>> G.degree([2,3])DegreeView({2: 1, 3: 2})

Using the graph constructors¶

Graph objects do not have to be built up incrementally - data specifying graph structure can be passed directly to the constructors of the various graph classes. When creating a graph structure by instantiating one of the graph classes you can specify data in several formats.

>>> G.add_edge(1,2)>>> H=nx.DiGraph(G)# create a DiGraph using the connections from G>>> list(H.edges())[(1, 2), (2, 1)]>>> edgelist=[(0,1),(1,2),(2,3)]>>> H=nx.Graph(edgelist)

What to use as nodes and edges¶

You might notice that nodes and edges are not specified as NetworkX objects. This leaves you free to use meaningful items as nodes and edges. The most common choices are numbers or strings, but a node can be any hashable object (except ), and an edge can be associated with any object using .

As an example, and could be protein objects from the RCSB Protein Data Bank, and could refer to an XML record of publications detailing experimental observations of their interaction.

We have found this power quite useful, but its abuse can lead to surprising behavior unless one is familiar with Python. If in doubt, consider using to obtain a more traditional graph with integer labels.

Accessing edges and neighbors¶

In addition to the views , and , access to edges and neighbors is possible using subscript notation.

>>> G=nx.Graph([(1,2,{"color":"yellow"})])>>> G[1]# same as G.adj[1]AtlasView({2: {'color': 'yellow'}})>>> G[1][2]{'color': 'yellow'}>>> G.edges[1,2]{'color': 'yellow'}

You can get/set the attributes of an edge using subscript notation if the edge already exists.

>>> G.add_edge(1,3)>>> G[1][3]['color']="blue">>> G.edges[1,2]['color']="red">>> G.edges[1,2]{'color': 'red'}

Fast examination of all (node, adjacency) pairs is achieved using , or . Note that for undirected graphs, adjacency iteration sees each edge twice.

>>> FG=nx.Graph()>>> FG.add_weighted_edges_from([(1,2,0.125),(1,3,0.75),(2,4,1.2),(3,4,0.375)])>>> forn,nbrsinFG.adj.items():... fornbr,eattrinnbrs.items():... wt=eattr['weight']... ifwt<0.5:print(f"({n}, {nbr}, {wt:.3})")(1, 2, 0.125)(2, 1, 0.125)(3, 4, 0.375)(4, 3, 0.375)

Convenient access to all edges is achieved with the edges property.

>>> for(u,v,wt)inFG.edges.data('weight'):... ifwt<0.5:... print(f"({u}, {v}, {wt:.3})")(1, 2, 0.125)(3, 4, 0.375)

Adding attributes to graphs, nodes, and edges¶

Attributes such as weights, labels, colors, or whatever Python object you like, can be attached to graphs, nodes, or edges.

Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). By default these are empty, but attributes can be added or changed using , or direct manipulation of the attribute dictionaries named , , and for a graph .

Graph attributes¶

Assign graph attributes when creating a new graph

>>> G=nx.Graph(day="Friday")>>> G.graph{'day': 'Friday'}

Or you can modify attributes later

>>> G.graph['day']="Monday">>> G.graph{'day': 'Monday'}

Node attributes¶

Add node attributes using , , or

>>> G.add_node(1,time='5pm')>>> G.add_nodes_from([3],time='2pm')>>> G.nodes[1]{'time': '5pm'}>>> G.nodes[1]['room']=714>>> G.nodes.data()NodeDataView({1: {'time': '5pm', 'room': 714}, 3: {'time': '2pm'}})

Note that adding a node to does not add it to the graph, use to add new nodes. Similarly for edges.

Edge Attributes¶

Add/change edge attributes using , , or subscript notation.

>>> G.add_edge(1,2,weight=4.7)>>> G.add_edges_from([(3,4),(4,5)],color='red')>>> G.add_edges_from([(1,2,{'color':'blue'}),(2,3,{'weight':8})])>>> G[1][2]['weight']=4.7>>> G.edges[3,4]['weight']=4.2

The special attribute should be numeric as it is used by algorithms requiring weighted edges.

Directed graphs¶

The class provides additional methods and properties specific to directed edges, e.g., , , , etc. To allow algorithms to work with both classes easily, the directed versions of is equivalent to while reports the sum of and even though that may feel inconsistent at times.

>>> DG=nx.DiGraph()>>> DG.add_weighted_edges_from([(1,2,0.5),(3,1,0.75)])>>> DG.out_degree(1,weight='weight')0.5>>> DG.degree(1,weight='weight')1.25>>> list(DG.successors(1))[2]>>> list(DG.neighbors(1))[2]

Some algorithms work only for directed graphs and others are not well defined for directed graphs. Indeed the tendency to lump directed and undirected graphs together is dangerous. If you want to treat a directed graph as undirected for some measurement you should probably convert it using or with

>>> H=nx.Graph(G)# create an undirected graph H from a directed graph G

Multigraphs¶

NetworkX provides classes for graphs which allow multiple edges between any pair of nodes. The and classes allow you to add the same edge twice, possibly with different edge data. This can be powerful for some applications, but many algorithms are not well defined on such graphs. Where results are well defined, e.g., we provide the function. Otherwise you should convert to a standard graph in a way that makes the measurement well defined.

>>> MG=nx.MultiGraph()>>> MG.add_weighted_edges_from([(1,2,0.5),(1,2,0.75),(2,3,0.5)])>>> dict(MG.degree(weight='weight')){1: 1.25, 2: 1.75, 3: 0.5}>>> GG=nx.Graph()>>> forn,nbrsinMG.adjacency():... fornbr,edictinnbrs.items():... minvalue=min([d['weight']fordinedict.values()])... GG.add_edge(n,nbr,weight=minvalue)...>>> nx.shortest_path(GG,1,3)[1, 2, 3]

Graph generators and graph operations¶

In addition to constructing graphs node-by-node or edge-by-edge, they can also be generated by

  1. Applying classic graph operations, such as:

(G, nbunch)

Returns the subgraph induced on nodes in nbunch.

(G, H[, rename, name])

Return the union of graphs G and H.

(G, H)

Return the disjoint union of graphs G and H.

(G, H)

Returns the Cartesian product of G and H.

(G, H)

Returns a new graph of G composed with H.

(G)

Returns the graph complement of G.

(G[, with_data])

Returns a copy of the graph G with all of the edges removed.

(graph)

Returns an undirected view of the graph .

(graph)

Returns a directed view of the graph .

  1. Using a call to one of the classic small graphs, e.g.,

([create_using])

Returns the Petersen graph.

([create_using])

Returns the Tutte graph.

([create_using])

Return a small maze with a cycle.

([create_using])

Return the 3-regular Platonic Tetrahedral graph.

  1. Using a (constructive) generator for a classic graph, e.g.,

(n[, create_using])

Return the complete graph with n nodes.

(n1, n2[, create_using])

Returns the complete bipartite graph .

(m1, m2[, create_using])

Returns the Barbell Graph: two complete graphs connected by a path.

(m, n[, create_using])

Returns the Lollipop Graph; connected to .

like so:

>>> K_5=nx.complete_graph(5)>>> K_3_5=nx.complete_bipartite_graph(3,5)>>> barbell=nx.barbell_graph(10,10)>>> lollipop=nx.lollipop_graph(10,20)
  1. Using a stochastic graph generator, e.g,

(n, p[, seed, directed])

Returns a \(G_{n,p}\) random graph, also known as an Erdős-Rényi graph or a binomial graph.

(n, k, p[, seed])

Returns a Watts–Strogatz small-world graph.

(n, m[, seed, …])

Returns a random graph using Barabási–Albert preferential attachment

(n, p1, p2[, seed])

Returns a random lobster graph.

like so:

>>> er=nx.erdos_renyi_graph(100,0.15)>>> ws=nx.watts_strogatz_graph(30,3,0.1)>>> ba=nx.barabasi_albert_graph(100,5)>>> red=nx.random_lobster(100,0.9,0.9)
  1. Reading a graph stored in a file using common graph formats, such as edge lists, adjacency lists, GML, GraphML, pickle, LEDA and others.

>>> nx.write_gml(red,"path.to.file")>>> mygraph=nx.read_gml("path.to.file")

For details on graph formats see Reading and writing graphs and for graph generator functions see Graph generators

Analyzing graphs¶

The structure of can be analyzed using various graph-theoretic functions such as:

>>> G=nx.Graph()>>> G.add_edges_from([(1,2),(1,3)])>>> G.add_node("spam")# adds node "spam">>> list(nx.connected_components(G))[{1, 2, 3}, {'spam'}]>>> sorted(dforn,dinG.degree())[0, 1, 1, 2]>>> nx.clustering(G){1: 0, 2: 0, 3: 0, 'spam': 0}

Some functions with large output iterate over (node, value) 2-tuples. These are easily stored in a structure if you desire.

>>> sp=dict(nx.all_pairs_shortest_path(G))>>> sp[3]{3: [3], 1: [3, 1], 2: [3, 1, 2]}

See Algorithms for details on graph algorithms supported.

Drawing graphs¶

NetworkX is not primarily a graph drawing package but basic drawing with Matplotlib as well as an interface to use the open source Graphviz software package are included. These are part of the module and will be imported if possible.

First import Matplotlib’s plot interface (pylab works too)

>>> importmatplotlib.pyplotasplt

To test if the import of was successful draw using one of

>>> G=nx.petersen_graph()>>> subax1=plt.subplot(121)>>> nx.draw(G,with_labels=True,font_weight='bold')>>> subax2=plt.subplot(122)>>> nx.draw_shell(G,nlist=[range(5,10),range(5)],with_labels=True,font_weight='bold')

(png, hires.png, pdf)

_images/tutorial-34.png

when drawing to an interactive display. Note that you may need to issue a Matplotlib

command if you are not using matplotlib in interactive mode (see this Matplotlib FAQ).

>>> options={... 'node_color':'black',... 'node_size':100,... 'width':3,... }>>> subax1=plt.subplot(221)>>> nx.draw_random(G,**options)>>> subax2=plt.subplot(222)>>> nx.draw_circular(G,**options)>>> subax3=plt.subplot(223)>>> nx.draw_spectral(G,**options)>>> subax4=plt.subplot(224)>>> nx.draw_shell(G,nlist=[range(5,10),range(5)],**options)

(png, hires.png, pdf)

_images/tutorial-35.png

You can find additional options via and layouts via . You can use multiple shells with .

>>> G=nx.dodecahedral_graph()>>> shells=[[2,3,4,5,6],[8,1,0,19,18,17,16,15,14,7],[9,10,11,12,13]]>>> nx.draw_shell(G,nlist=shells,**options)

(png, hires.png, pdf)

_images/tutorial-36.png

To save drawings to a file, use, for example

>>> nx.draw(G)>>> plt.savefig("path.png")

writes to the file in the local directory. If Graphviz and PyGraphviz or pydot, are available on your system, you can also use or to get the node positions, or write the graph in dot format for further processing.

>>> fromnetworkx.drawing.nx_pydotimportwrite_dot>>> pos=nx.nx_agraph.graphviz_layout(G)>>> nx.draw(G,pos=pos)>>> write_dot(G,'file.dot')

See Drawing for additional details.

Sours: https://networkx.org/documentation/stable/tutorial.html

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