Saravanan666/pypi-org huggingface dataset:
Package: grapeshot-signal-sdk
The grapeshot signal sdk provides a wrapper to the Grapeshot Signal
web service which provides high quality realtime analysis of web
pages. Seehttp://devportal-test.grapeshot.co.ukfor details.Detailed documentation is available in the “docs” directory.Quick StartInstall via pip:pip install grapeshot-signal-sdkFor alternative installation methods see the detailed documentation.Obtain an api key viahttp://devportal-test.grapeshot.co.uk.In your code create an instance of SignalClient, passing in your api key:from grapeshot_signal import SignalClient

signal_client = SignalClient("your api key")Use the client to obtain information about web pages:from grapeshot_signal import rels

url =  "http://news.bbc.co.uk" #for example
page = signal_client.get_page(url, embed=[rels.keywords, rels.segments])

do_my_stuff(results)
Package: graph2img
Prerequisitexelatexandpdf2svg. Installation on Linux:# Ubuntusudoapt-getinstalltexlive-xetex
sudoapt-getinstallpoppler-utils# CentOSyuminstalltexlive-xetex
yuminstallpoppler-utilsFor Windows, you only need to install texlive directly.Installation by pipInstall with pip:pipinstallgraph2imgThen write your edge information intograph.txt. For example:1 2 1
2 3 2
1 3 3This will return a triangular graph.Then run the program:graph2imggraph.txtThis will return a filegraph.png:Or to draw a tree:1 2 1
1 3 2
2 4 2
2 5 3
3 6 3
3 7 2
1 9 1
2 8 2This will return a tree:OptionsYou can run the program by the following command:graph2img<input_file><output_file>Several options are provided:
Three options are provided:--save-temp-files=true/false
--temp-path=[path]
--output-format=png/svg
--show=true/false
--helpIf you want to save the temporary files (like tex codes), set--save-temp-filesastrue;If you want to generate a svg format image, set-output-formatassvg;If your system does not have a user interface, set--showasfalse;To show all options, rungraph2img --help.Installation from sourcegitclonehttps://github.com/ForwardStar/graph_drawer.gitcdgraph_drawer
pythonsetup.pyinstall
Package: graph-alchemy
ContentsThe GoalWhat’s hereUsing this packageWhat’s going to be hereTesting coverageGraph node-edge relationships inSQLAlchemy(and soon more orms) +networkxintegration.The GoalSet up a generalized abstraction/interface for a graph that can be used across
various platforms and within various frameworks that is easy and simple to use,
extensible, and that easily hooks into other graphing tools (likegephinetworkx, etc.)What’s hereA basic set of node/edge abstractions + many-to-one relationships for a graph
represented in SQL withSQLAlchemyUsing this packageDocumentationis available athttp://graphalchemy.readthedocs.orgIt’s very simple to use (and examples are to come). But for now the best thing to do is
to go read the docs onSQLAlchemyA very minimal example (using an SQLite database):from graphalchemy.sqlmodels import create_base_classes, sqlite_connect
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()
Node, Edge = create_base_classes(NodeClass="Node", EdgeClass="Edge", Base=Base)
engine, session = sqlite_connect("database.db", metadata=Base.metadata)

# be sure to use unicode!!
node1 = Node(label=u"First node!")
node2 = Node(label=u"Second node!")
node3 = Node(label=u"Third node!")
edge1 = Edge.connect_nodes(node1, node2)
edge2 = Edge.connect_nodes(node1, node3)

session.add_all([node1, node2, node3, edge1, edge2])
session.commit()


# now we can graph it

import networkx as nx
G = nx.Graph()
G.add_edges_from([edge1, edge2])

# now we can draw this! (if you had pylab, matplotlib, etc)And you’d get a picture that looked something like this (clearly, we haven’t added all the traits
and such in, but you get the idea):Now, obviously this is a pretty minimal example, but it shows how you can take advantage
of the power of SQL joins, queries, etc, but also very easilyWhat’s going to be herenetworkxintegrationtesting for multiple sql databases and adaptersabstractions for Google App Engine,mongoalchemy, and possibly Django ORMadapter betweennetworkxand web service requests (maybe?)Testing coverageBasic test suite that gets 100% line coverage for SQLAlchemy models and base
models (still missing a test for Flask-SQLAlchemy). I’ve only run it on SQLite
so far, but presumably it should work with other SQL databases just fine (since
it uses SQLAlchemy’sdeclarative base)
Package: graphalgorithmplayer
GraphAlgorithmPlayerVisualize how a graph is processed by an algorithm with either networkx or bqplot thanks to a player.For each step of your algorithm and using the Networkx view you may change the color and label of each node and edge.
BQPlot only allows the user to change the color of nodes.Binder :Dependencies:valueplayerwidgetbqplotmatplotlibnetworkxipywidgetstraitlets
Package: graph-algorithms
Copyright (c) 2016 David McDougallPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description: Common Graph Algorithms LibraryLibrary of graph algorithms which operate directly on python data structures.This library uses a novel API for representing graphs.  Graph vertexes can be
any hashable python value and the connectivity between vertexes is
represented with a callback function.  This callback is named the ‘adjacent’
function.  The adjacent function has the following form:def adjacent(vertex):‘’’
This function returns all vertexes which the given vertex is connected to.
‘’’
return iterable-of-neighboring-vertexesContents:depth_first_traversal()A lazy depth first traversaldepth_first_search()A depth first searchiterative_deepening_depth_first_search()Searching infinite graphsa_star()Fast optimal pathfindingtopological_sort()Dependency resolution.strongly_connected_components()Determines which areas of the graph can reach which other areas.In the future I would like to implement more algorithms:
- Minimum Spanning Tree
- Min-cut/Max-flow
- Substructure SearchInstallation note:
This package optionally uses numpy.
Numpy is used by some unit tests.
Numpy is used to calculate A-stars effective branching factor (EBF).
If numpy is not available then EBF is not reported.Comments and feedback are welcome
Send to David McDougall  email: dam1784[at]rit.eduKeywords: development,graph
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Provides-Extra: debug
Provides-Extra: test
Package: graphanime
Graphanime - create execution graph in GIF/PDFThis module aim to create GIF, APNG, pdf type standalone or Beamer from a graph
written in LaTex. And showing an excution of one algorithm (for exemple Dijkstra)Install module:pip install graphanimeCommandeload(file.tex)return an object Graph which represented graph write in LaTeXDijkstra(graph,source,sink):excute dijkstra algorithm on graph and return list of graphgen_beamer(anim,file)it's Back-end for slide Beamer with a source is list of graph (anim)gen_pdf(anim,file)it's Back-end for natural pdf with a source is list of graph (anim)gen_gif(anim,file)it's Back-end for GIF with a source is list of graph (anim)gen_apng(anim,file)it's Back-end for APNG with a source is list of graph (anim)WARNING: it's not support everywhereTemplate\documentclass[tikz]{standalone}

\usepackage{tikz}
\usetikzlibrary{quotes}
%\usepackage{}
%\use...library{}

%\tikzset{every figure} = []
\begin{document}
	\begin{tikzpicture} 
		%\node (name node) at(x,y) [options split by comma] {display} ;
		\node () at(,) [] {} ;
		\node () at(,) [] {} ;
		
%(name node): characters prohibited: ) [ ; \node
%at(x,y): optional : x and y must to do a well coordonate : numbers 
%[options split by comma]:  option of node. Few details: 			 
	%fill=color
	%label=text of label ou label={text of label} ou label={:text of label}
	%label={position of label:text of label}
	%label={[color du label]:text of label}
	%label={[color du label]position of label:text of label}
		%text of label : characters prohibited: ; { } ,
	%draw ou draw=
	%draw=color
{Affichage}: charactères interdits: { ] ; 
	
		%\path (name node A) edge[options split by comma] (name node B);
		\path 
		() edge[] ()
		() edge[] ()
		;

%name node: to reference  to name of node set upper
%[options split by comma]:  options for edge/arc. Few details:
	%-> or <-or -: way of edge
	%"edge_label": indication on edge, for Dijkstra it's weight, for Dijkstra obligatory option 
	% edge_label : characters prohibited: ; , " \node
	%color=color: la color of edge
	\end{tikzpicture}
\end{document}exemplex=load('Exemples/exemple_dijkstra_wiki.tex')A=Dijkstra(x,"node 1","node 5")gen_gif(A,"versusWiki",700)And for more exemple go togithubChange Log0.1.1add Bellman-Ford algorithmupdate list of frobidden charactersfix problem with Ford-Fulkerson0.1.0fix parser (now it's take - )change template FR to ENadd option color for all algotithmadd documentation for Kruskal, Floyd-Warshall and Ford-Fulkerson0.0.9add Kruskal algorithmadd Floyd-Warshall algorithmadd Ford-Fulkerson algorithmfix bug: the parser in funct load() take well the other_option of edgeupdate template0.0.8change name github project0.0.7change documentation FR to EN0.0.5first realese that works well
Package: graphanno
graphannoUse Python 3.6+ type annotations to generate the grapheneObjectTypeclasses.InstallationYou can install the project viapipby running:pipinstallgraphannoInstallation from source:pythonsetup.pyinstallUsageThe@graphanno.graph_annotationsdecorator works only with classes that contains type
annotations. If there are no type annotations within the decorator target,NoAnnotationsErrorexception is raised. Arguments without annotations will
be ignored.To create thegraphene.ObjectTypeobject, you can just decorate your class with@graphanno.graph_annotations.It is recommended to subclassgraphanno.ObjectTypeto provide hints for IDE's.
This is not mandatory, the@graphanno.graph_annotationswill replace your decorated class
with theObjectTypesubclass anyway.importgrapheneimportgraphanno# the class below...@graphanno.graph_annotationsclassGraphanno(graphanno.ObjectType):value:str# ... is equivalent to:classGraphene(graphene.ObjectType):value=graphene.String()If you still need your class with type annotations, set__model__variable to annotated
class within the decorated one:importgrapheneimportgraphannoclassAnnotated:# this class will be still available latervalue:str# the class below...@graphanno.graph_annotationsclassGraphanno(graphanno.ObjectType):"""This class can inherit from graphene.ObjectType already,but it won't change the @graph_annotations behavior."""__model__=Annotated# ... is equivalent to:classGraphene(graphene.ObjectType):value=graphene.String()If another class with the same name will be decorated, theSchemaClashErrorexception
will be raised.Additional parameters__excluded_fields__(tuple): names of the fields that will be excluded from
schema. Private attributes are always excluded.__ignore_unsupported__(bool): do not raise an exception for unsupported annotations.
DefaultFalse.Supported annotationsThe class decorated with@graph_annotationscan use type annotations listed below.strintfloatbooldatetime.datetimedatetime.datedatetime.timetyping.Listcustom classes with supported type annotationsUsing unsupported annotations will raise theUnsupportedAnnotationErrorexception.
To ignore this exception set__ignore_unsupported__toTruein the decorated class.
Package: graph-asp
# GraspGraph with ASP made easy.## Use cases### Write an ASP graph into gexfgrasp convert data.lp target.gexf### Extract the biggest cc, write it anonymized in gmlgrasp split data.lp --biggest-first "data_cc.lp" --slice 0 1grasp convert data_cc_1.lp target.gml --anonymized### Generate a small world graph in gmlgrasp generate data.gml erdos_renyi_graph n=100 p=0.01## Packagingpip install graph-aspWill also populate your env with the executable `grasp`,enabling access to the CLI.Note that the package *grasp* exists, but is unrelated to this work.## CLI commandsCLI provides access to higher level [routines](grasp/routines.py).- `split`: split by connected component.- `info`: give info about given graph.- `convert`: rewrite, anonymize or convert the graph to (clean) ASP or standard format.- `generate`: generate a graph using a given generation method.<!-- - `compress`: produce the powergraph compression of given graph as a bubble file --><!-- - ``:  -->### Other examples# split a graph by ccpython -m grasp split data.lp -o "data_{}.lp"# get infospython -m grasp infos data.lp --graph-properties# there is a shitload of optionspython -m grasp infos --help# conversions between formatspython -m grasp convert data.lp data.gml --anonymize# generation of new graphs to play withpython -m grasp generate graph.gml powerlaw_cluster_graph n=5 m=2 p=0.01
Package: graph-based-clustering
Graph-Based ClusteringGraph-Based Clustering using connected components and minimum spanning trees.Both clustering methods, supported by this library, aretransductive- meaning they are not designed to be applied to new, unseen data.InstallationTo installgraph-based-clusteringrun:pip install graph-based-clusteringUsageThe library has sklearn-likefit/fit_predictinterface.ConnectedComponentsClusteringThis method computes pairwise distances matrix on the input data, and usingthreshold(parameter provided by the user) to binarize pairwise distances matrix makes an undirected graph in order to find connected components to perform the clustering.Required arguments:threshold- paremeter to binarize pairwise distances matrix and make undirected graphOptional arguments:metric- sklearn.metrics.pairwise_distancesparameter (default:"euclidean")n_jobs- sklearn.metrics.pairwise_distancesparameter (default:None)Example:importnumpyasnpfromgraph_based_clusteringimportConnectedComponentsClusteringX=np.array([[0,1],[1,0],[1,1]])clustering=ConnectedComponentsClustering(threshold=0.275,metric="euclidean",n_jobs=-1,)clustering.fit(X)labels_pred=clustering.labels_# alternativelabels_pred=clustering.fit_predict(X)SpanTreeConnectedComponentsClusteringThis method computes pairwise distances matrix on the input data, builds a graph on the obtained matrix, finds minimum spanning tree, and finaly, performs the clustering through dividing the graph inton_clusters(parameter given by the user) by removingn-1edges with the highest weights.Required arguments:n_clusters- the number of clusters to findOptional arguments:metric- sklearn.metrics.pairwise_distancesparameter (default:"euclidean")n_jobs- sklearn.metrics.pairwise_distancesparameter (default:None)Example:importnumpyasnpfromgraph_based_clusteringimportSpanTreeConnectedComponentsClusteringX=np.array([[0,1],[1,0],[1,1]])clustering=SpanTreeConnectedComponentsClustering(n_clusters=3,metric="euclidean",n_jobs=-1,)clustering.fit(X)labels_pred=clustering.labels_# alternativelabels_pred=clustering.fit_predict(X)Comparing on sklearn toy datasetsConnectedComponentsClusteringSpanTreeConnectedComponentsClusteringRequirementsPython >= 3.7CitationIf you usegraph-based-clusteringin a scientific publication, we would appreciate references to the following BibTex entry:@misc{dayyass2021graphbasedclustering,author={El-Ayyass, Dani},title={Graph-Based Clustering using connected components and spanning trees},howpublished={\url{https://github.com/dayyass/graph-based-clustering}},year={2021}}
Package: graphbrain
Graphbrain is an Artificial Intelligence open-source software library and scientific research tool. Its aim is to facilitate automated meaning extraction and text understanding, as well as the exploration and inference of knowledge. It is part of socsemics, a research project funded by the European Research Council and focused on the fragmentation in online public spaces.The ambition of this project is not simply to contribute to advancing the state-of-the-art in AI, but principally to be an effective interdisciplinary research tool. More specifically, we are interested in advancing the possibilities of automated meaning extraction for research in fields such as Computational Social Sciences, Cognitive Science and the Humanities.Graphbrain is built around a unifying concept: the Semantic Hypergraph (SH), which makes it possible to represent a natural language sentence such as “Einstein first published the theory of relativity in 1905” as an ordered, recursive hyperlink of the form:The aim of SH and Graphbrain is to enable hybrid approaches, combining symbolic AI and statistical/connectionist Machine Learning methods. This is for two main reasons: to facilitate understandability and reproducibility for human researchers and to combine the strengths of both approaches.Graphbrain is written in Python, to both take advantage and facilitate integration with the rich environment of scientific libraries available in this language. It is released under the highly permissive MIT open source license.Installation instructions, the manual and more information can be found here:http://graphbrain.net.
Package: graphbutler
A small framework for generating simple, reproducible graphs.Example@recipedefsine_wave():g=Graph()g.x=numpy.arange(0.0,10.0,0.01)g.y=numpy.sin(g.x)returngThis defines a graph of thesin(x)function in the interval0 ≤ x ≤ 10.To save the graph to disk, callsine_wave().save(). To save all
recipes, callgraphbutler.save_all(). To show a preview, callsine_wave().show().For more examples, see the examples directory.StatusThis project is in an experimental stage. Feel free to open an issueon Githuband I’ll get back
to you in a couple of days!
Package: graphby
graphbyGenerate simple visualizations on terminal.Importgraphbycan now be used on your Python projects through PyPi by running pip command on a Python-ready environment.pip import --upgrade graphbyUsage1. Import packagefromgraphbyimportBar2. Basic usagefromgraphbyimportBarlabels=["a","b","c","d"]values=[1,2,3,4]Bar(labels,values).plot()---a:**1b:*****2c:********3d:**********43. Negative numbersfromgraphbyimportBarlabels=["a","b","c","d"]values=[200,-350,20,999]Bar(labels,values).plot()---a:****200b:-350c:***20d:**********9994. Custom symbolfromgraphbyimportBarlabels=["a","b","c","d"]values=[200,-350,20,999]Bar(labels,values,bar="+").plot()---a:++++200b:-350c:+++20d:++++++++++9995. Expand graphfromgraphbyimportBarlabels=["a","b","c","d"]values=[200,-350,20,999]Bar(labels,values,limit=15).plot()---a:******200b:-350c:****20d:***************9996. CustomizationsfromgraphbyimportBarlabels=["a","b","c","d"]values=[200,-350,20,999]Bar(labels,values,bar="#",limit=5).plot()---a:##     200b:-350c:#       20d:#####  999
Package: graphcache
graphcachePython Library to store connected nodes and their properties on cache storage (redis)InstallationTo installgraphcache, simply:pipinstallgraphcacheDevelopmentInstallpyenvandpyenv-virtualenvRunpyenv install 3.7.0 --skip-existing
pyenv virtualenv 3.7.0 graphcacheUpdate requirementspip install -r requirements.txtInstallRedisBasic UseEnsure you haveredisservice runningOn macosredis-server/usr/local/etc/redis.confTo use graphcache, you must first create an instance of GraphCache,
and construct your nodes and edges:# Import GraphCachefromgraphcacheimportGraphCache# default# host = localhost# port = 6379# db = 0g=GraphCache(host='localhost',port=6379,db=0)# Add optimisation keysg.optimise_for('bananas')g.optimise_for('apples')# Create Nodes (need 'apples', 'bananas' keys in all nodes, as they have been added to optimisation_keys)n1=g.add_vertex({'name':'Tom','age':24,'bananas':5,'apples':4})n2=g.add_vertex({'name':'Bob','bananas':0,'apples':8})n3=g.add_vertex({'name':'Harry','gender':'Male','bananas':3,'apples':1})n4=g.add_vertex({'name':'Jill','bananas':8,'apples':1})# Connect themg.add_edge(g.entry,n2)# g.entry specifies the entry point for graphg.add_edge(g.entry,n3)g.add_edge(n2,n3)g.add_edge(n3,n4)g.add_edge(n2,n1)Then you can perform filter/sort operations on any of the node to get the required adjacent nodes from that node:# Filter By# Get all outgoing nodes (only adjacent) from n2 which have only 1 applesnodes1=n2.get_outgoing().filter_by('apples',[1]).get_all_nodes()# Sort By# Get all incoming nodes (only adjacent) to n3 sorted by number of bananas they havenodes2=n3.get_incoming().sort_by('bananas').get_all_nodes()# Get first incoming node to n1 sorted by bananasnode1=n1.get_incoming().sort_by('bananas').get_node_indexed_at(0)Also you can perform chained complex operations:# Filter By, Sort By# Get all outgoing nodes (only adjacent) from n2 with apples less than 5 and sorted by bananasnodes1=n2.get_outgoing().filter_by('apples',5,"lt").sort_by('bananas').get_all_nodes()# Sort By, Filter By, Filter Bynodes2=n3.get_incoming().sort_by('bananas').filter_by('bananas',[1,5],"in").filter_by('apples',[1]).get_all_nodes()Get the key for the graphcache objectg.cache_key# graphcache-MZ5SQRRetrieve previously stored graphcache object from redis cache (cache_key =graphcache-MZ5SQR)# using default redis connection# host = localhost# port = 6379# db = 0g1=GraphCache(graphcache_ref='graphcache-MZ5SQR')
Package: graphcall
GraphCallLicenseThis project is licensed under the GNU GPLv3 LicenseIntroductionBack when I first discovered GraphQL, I started using it for y data driven projects.But I was in shock when I saw that no good java framework was able to make a decent request.I decided to make one then...Use caseimportcom.nwmqpa.graphcall.requests.Query;publicclassMain{publicintmain(Stringargv[]){Queryquery=newQuery();query.addNode("hero").addNode("friends").addEdge("friendsName").buildNode().addEdge("name");System.out.println(query.compile);Requestrequest=newRequest(query.compile());Resultresult=request.send(newURL("http://api.example.com/graphql/"));System.out.println(result.data);return(0);}}Resolves to ...query{hero{friends{friendsName}name}}{"data":{"hero":[{"friends":[{"friendsName":"Leia"},{"firendsName":"Han"}],"name":"Luke"}]}}TODOMake mutation resolves correctyImplement queries arguments.Implements fragments and name queries.CreditsThomas Nicollet (@nwmqpa)
Package: graphcanvas
graphcanvas is an library for interacting with visualizations of complex
graphs. The aim is to allow the developer to declare the graph by the
simplest means and be able to visualize the graph immediately.For example:from graphcanvas.api import GraphView, graph_from_dict
g = {'a':['b'], 'b':['c', 'd'], 'c':[], 'd':[]}
GraphView(graph=graph_from_dict(g)).configure_traits()PrerequisitesNetworkXdistributeenable