Saravanan666/pypi-org huggingface dataset:
Package: graphchem
GraphChem: Graph-based machine learning for chemical property predictionGraphChemis an open source Python package for constructing graph-based machine learning models with a focus on fuel property prediction.Installation:Prerequisites:Have Python 3.11+ installedMethod 1: pip$ pip install graphchemMethod 2: From Source$ git clone https://github.com/ecrl/graphchem
$ cd graphchem
$ python -m pip install .If any errors occur when installing dependencies, namely with RDKit, PyTorch, or torch-geometric, visit their installation pages and follow the installation instructions:RDKit,PyTorch,PyTorch GeometricUsage:For advanced usage, head over to ourAPI documentation page.ExamplesTo view some examples of how GraphChem can be used, head over to ourexamplesfolder on GitHub.Contributing, Reporting Issues and Other Support:To contribute to GraphChem, make a pull request. Contributions should include tests for new features added, as well as extensive documentation.To report problems with the software or feature requests, file an issue. When reporting problems, include information such as error messages, your OS/environment and Python version.For additional support/questions, contact Travis Kessler (Travis_Kessler@student.uml.edu).
Package: graph-cli
graph-cliA CLI utility to create graphs from CSV files.graph-cliis designed to be highly configurable for easy and detailed
graph generation. It has many flags to acquire this detail and uses
reasonable defaults to avoid bothering the user. It also leverages
chaining, so you can create complex graphs from multiple CSV files.Source:mcastorina/graph-cliDonateI develop this software in my spare time. If you find it useful, consider
buying me a coffee! Thank you!
Package: graphcompass
GraphCompassGraphCompass (GraphComparison Tools for DifferentialAnalyses inSpatialSystems) is a Python-based framework that brings together a robust suite of graph analysis and visualization methods, specifically tailored for the differential analysis of cell spatial organization using spatial omics data. It is developed on top onSquidpyandAnnData.Visit ourrepositoryfor documentation and tutorials.GraphCompass key analysis features include:Cell-type-specific subgraphs comparison.Cellular neighborhoods comparison.Entire graphs comparison.InstallationInstall GraphCompass via PyPI by running::pip install graphcompassContributing to GraphCompassWe are happy about any contributions!
Package: graph-compress
graphcompressgraphcompress is a Python library designed to compress graphs from NTRIPLES.FormatsCompressed GraphThe resultant format writes the info of a node in a line. First theNODE ID, next, groups of connections, every connection has theEDGE IDandNODES CONNECTED USING THE EDGE. Considers a negativeEDGE IDas a connection in opposite direction.Example:14 5.3.7 -3.2The line says:Node 14 connects to Node 3 using Edge 5.Node 14 connects to Node 7 using Edge 5.Node 2 connects to Node 14 using Edge 3.Indexed GraphThe resultant format writes the info of a node in a line. First theNODE ID, next, indexes of edges where the node participatesExample:14 3 5 7The line says:Node 14 appears in the edge with the index 3.Node 14 appears in the edge with the index 5.Node 14 appears in the edge with the index 7.Installpipinstallgraph-compressUsageInput file:graph.nt.gz<http://www.wikidata.org/entity/Q1> <http://www.wikidata.org/prop/direct/P5> <http://www.wikidata.org/entity/Q5> .
<http://www.wikidata.org/entity/Q1> <http://www.wikidata.org/prop/direct/P3> <http://www.wikidata.org/entity/Q6> .
<http://www.wikidata.org/entity/Q6> <http://www.wikidata.org/prop/direct/P5> <http://www.wikidata.org/entity/Q5> .
<http://www.wikidata.org/entity/Q6> <http://www.wikidata.org/prop/direct/P5> <http://www.wikidata.org/entity/Q2> .(The lines above are the uncompressed file)Compressed Graphfromgraphcompress.readersimportReader_NT,Reader_NTgz,Reader_CGgzfromgraphcompress.parsersimportWikidataParser,CGParserfromgraphcompress.builders.compress_graphimportCGBuilderr=Reader_NTgz('graph.nt.gz')p=WikidataParser()b=CGBuilder(r,p,'tmp','output.gz')b.make_partitions()b.merge_partitions()Output file:output.gz1 5.5 3.6
2 -5.6
5 -5.1.6
6 5.5.2 -3.1(The lines above are the uncompressed file)Indexed Graphfromgraphcompress.readersimportReader_NT,Reader_NTgz,Reader_CGgzfromgraphcompress.parsersimportWikidataParser,CGParserfromgraphcompress.builders.index_graphimportIGBuilderr=Reader_NTgz('graph.nt.gz')p=WikidataParser()b=IGBuilder(r,p,'tmp','output.gz')b.make_partitions()b.merge_partitions()Output file:output.gz1 0 1
2 3
5 0 2
6 1 2 3(The lines above are the uncompressed file)
Package: graphcompute
The GraphCompute SDK for Python allows user to accessGraphCompute Serviceon Alibaba Cloud. You can access Graph Compute service without the need to generate accesskey-related signature manually. This README document introduces how to obtain and call GraphCompute SDK for Python. If you have any problem while using GraphCompute SDK for Python, pleasesubmit an issue.RequirementsTo use GraphCompute SDK for Python, you must have an Alibaba Cloud account as well as anAccessKey IDand anAccessKey Secret. Create and view your AccessKey on theRAM consoleor contact your system administrator.To use  GraphCompute SDK for Python to access the APIs of a product, you must first activate the product on theAlibaba Cloud consoleif required.Python 2.7 is strongly recommendedInstallationInstall the official release version through PIP:$pipinstallgraphcomputeYou can also install the unzipped installer package directly:$ python setup.py installGetting Started# -*- coding: utf-8 -*-fromgraphcomputeimportGraphCompute_Clientfromgraphcompute.utils.authimportCredentialsaccess_key_id="your access_key_id"access_key_secret="your access_key_secret"cred=Credentials(access_key_id,access_key_secret)instance_domain="your instance domain with port"remote_endpoint="ws://%s/gremlin"%str(instance_domain)client=GraphCompute_Client(remote_endpoint,'g',cred)query="g.V()"result=client.submit(query).one()client.close()More examples can be referred inbasic_func_examples.pyunder “examples” directory.LicenseApache-2.0
Package: graph-cordinates
See the logic and an example implementation on my githubhttps://github.com/kumarUjjawal3621/mypython_libHow to use:import graph_cordinates.graph_cordinates as gcgc.get_graphcordinates(image_path,x_range,y_range,background_value)Will return an array of 2D cordinates: [[x1,y1], [x2,y2], .......n-points] ; n=pixel width of ImageHow to pass arguments-image_path == path to a graph imagex_range == [Value of left most point on graph's x-axis, Value of right most point on graph's x-axis]y_range == [Value of bottom most point on graph's y-axis, Value of top most point on graph's y-axis]background_value == 0 or 10: When the graph's background color is relatively darker than the curve1: Otherwise)Caution- Try to pass the Image by erasing the axes
Package: graphdat
Graphdat lets you visualise the performance of your servers and applications in real time and shows you what to fix.It takes 5 minutes to setup after which you can watch your server / app performance in real time on interfaces customised for TV, PC and large / small touch devices.See an issue, drill into it. Find out the process or line of code causing the bottleneck. Fix it. Deploy code. Watch in real time the performance improvement.To find out more about graphdat visithttp://www.graphdat.com/Graphdat reference <http://www.graphdat.com/python>Bug tracker <https://github.com/alphashack/graphdat-sdk-python/issues>Browse source code <https://github.com/alphashack/graphdat-sdk-python>Detailed changelog <https://github.com/alphashack/graphdat-sdk-python/commits/master>
Package: graphdatamodel
graphdatamodelTool for modeling real world entities and relationshipsFree software: MIT licenseDocumentation:https://graphdatamodel.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-10-20)First release on PyPI.
Package: graph-datasets
Graph DatasetsInstallationpython>=3.8torch>=1.12torch_geometric>=2.0dgl>=1.1$python-mpipinstallgraph_datasetsUsageSeeGraph Datasetsfor docs.fromgraph_datasetsimportload_datagraph,label,n_clusters=load_data(dataset_name='cora',directory='./data',source='pyg',verbosity=1,)Statisticsidxsourcedatasetn_nodesn_featsn_edgesn_clusters1pygcora2,7081,43310,55672pygciteseer3,3273,7039,10463pygpubmed19,71750088,64834pygcorafull19,7938,710126,842705pygreddit232,965602114,615,892416pygchameleon2,2772,32562,74257pygsquirrel5,2012,089396,70658pygactor7,60093253,31859pygcornell1831,703554510pygtexas1831,703558511pygwisconsin2511,703900512pygcomputers13,752767491,7221013pygphoto7,650745238,162814pygcs18,3336,805163,7881515pygphysics34,4938,415495,924516pygwikics11,701300431,2061017dglcora2,7081,43310,556718dglciteseer3,3273,7039,104619dglpubmed19,71750088,648320dglcorafull19,7938,710126,8427021dglreddit232,965602114,615,8924122dglchameleon2,2772,32562,742523dglsquirrel5,2012,089396,706524dglactor7,60093253,318525dglcornell1831,703554526dgltexas1831,703558527dglwisconsin2511,703900528ogbproducts2,449,029100123,718,0244729ogbarxiv169,3431282,315,5984030sdcndblp4,0573347,056431sdcnacm3,0251,87026,256332colablogcatalog5,1968,189343,486633colaflickr7,57512,047479,476934linkxsnap-patents2,923,92226927,945,090535linkxpokec1,632,8036544,603,928336linkxgenius421,961121,845,736237linkxarxiv-year169,3431282,315,598538linkxPenn9441,5544,8142,724,458339linkxtwitch-gamers168,114713,595,114240linkxwiki1,925,342600485,014,138641linkxcornell1831,703554542linkxchameleon2,2772,32562,742543linkxfilm7,60093253,318544linkxsquirrel5,2012,089396,706545linkxtexas1831,703558546linkxwisconsin2511,703900547linkxyelp-chi45,954327,693,958248linkxdeezer-europe28,28131,241185,504249linkxAmherst412,2351,193181,908350linkxCornell518,6604,7351,581,554351linkxJohns Hopkins555,1802,406373,172352linkxReed9896274537,624353criticalroman-empire22,66230065,8541854criticalamazon-ratings24,492300186,100555criticalminesweeper10,000778,804256criticaltolokers11,758101,038,000257criticalquestions48,921301307,080258criticalsquirrel2,2232,08993,996559criticalchameleon8902,32517,7085RequirementsSeerequirements-dev.txtandrequirements.txt.ContributingSeeCONTRIBUTING.md.
Package: graph-dfs
Graph-DFSThis package allows for the creation of graphs. It also provides the depth first search algorithm.Installationpipinstallgraph-dfsHow to use it?To create a graph, do:g=Graph(n)wherenis the number of vertices. To create an arc from
vertexato vertexb, do:g.link(a,b)whereaandbare indices in the range[0, n - 1]. You can perform a depth first search on the graph
with:dfs=DFS(g)dfs.start()If you desire to obtain a topological sorting of the graph, then do so after a DFS with:dfs.topological_sorting()
Package: graphdiff
graph-diffUtilities to view diff between graphsGetting startedInstall prerequisitesDebian / Ubuntu:sudo apt-get install libgraph-easy-perlInstallFrom sourcegit clone https://github.com/yeger00/graph-diff
pip install -e .From PyPIpip install graphdiffGenerate and view diffcat samples/before.dot | graph-easy --as boxart
cat samples/after.dot | graph-easy --as boxart
graph-diff samples/before.dot samples/after.dot > ./diff.dot
cat ./diff.dot | graph-diff-colorgit-graph-diff-toolIt is possible to use graph-diff with git, withgit-graph-diff-toolprovided in this library. An usage example:InstallFor every repository you would like to install you need to add to .gitattributes file a rules to know how to handle .dot files. For example:echo "*.dot diff=graph_diff" >> .gitattributesThen, configure the difftool to be thegit-graph-diff-tool. For example:git config diff.graph_diff.command git-graph-diff-toolThen, you can use git as usual, while adding--ext-diffflag to enable external difftools.git log -p --ext-diff
Package: graphdna
GraphDNAGraphDNA is a tool that uses multiple heuristics to fingerprint GraphQL endpoints.Getting StartedIt takes only two simple steps to fingerprint an endpoint using GraphDNA.pipinstallgraphdna
graphdna-uhttps://example.com/graphqlOptionsGraphDNA supports the following options:graphdna\# Url to fingerprint--url/-uhttps://example.com/graphql\# Header (chainable)--header/-H"Authorization: Bearer token"Supported enginesNameSupportedNameSupportedNameSupportedAgoo✅Apollo✅Ariadne✅AWS AppSync✅Caliban✅DGraph✅Dianajl✅Directus✅Flutter✅GQLGen✅Graphene✅GraphQLApiForWp✅GraphQL-Go✅gopher/GraphQL-Go✅GraphQL-Java✅GraphQL-PHP✅GraphQL Yoga✅Hasura✅HyperGraphQL✅Jaal✅Juniper✅Lacinia✅Lighthouse✅Mercurius✅MorpheusGraphQL✅GraphQL Ruby✅Sangria✅Shopify✅Stepzen✅Strawberry✅Tartiflette✅WPGraphQL✅Environment VariablesLogger-No effect if you pass your own loggerNameValuesDefaultBehaviorLOG_FORMATconsole,jsonconsoleChange the log format accordinglyDEBUGTrue,FalseFalseEnable debug loggingIntegrationimportloggingfromtypingimportDict,Optionalfromgraphdnaimportdetect_engine,detect_engine_asyncfromgraphdna.entitiesimportGraphQLEnginedefdetect_engine(url:str,headers:Optional[Dict[str,str]]=None,logger:Optional[logging.Logger]=None,)->Optional[GraphQLEngine]:...asyncdefdetect_engine_async(url:str,headers:Optional[Dict[str,str]]=None,logger:Optional[logging.Logger]=None,)->Optional[GraphQLEngine]:...Local installationgitclonegit@github.com:Escape-Technologies/graphdna.gitcdgraphdna
chmod+x./install-dev.sh
./install-dev.shCreditsGraphw00fDolev FarhiContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: graphdoc
Graphdoc - Generate docs for your GraphQL APIGraphdoc uses your GraphQL schema to generate an HTML page documenting your API.
It works with Graphene and Ariadne and any other framework generatingGraphQLSchemainstances from thegraphql-corepackage.You can see an example of autogenerated docs using theSWAPIschemain this pageInstallationInstall using pip:pip install graphdocUsageUsegraphdoc.to_docto create HTML documentation
from aGraphQLSchemainstance or a string with your schema in SDL.
Usegraphdoc.to_mdto create Markdown documentation.ExamplesDjango and Graphene# view.pyfromdjango.httpimportHttpResponsefromgraphene_django.viewsimportGraphQLViewimportgraphdocdefgraphql_docs(request):# For graphene>=3 use schema.graphql_schema# html = graphdoc.to_doc(GraphQLView().schema.graphql_schema)html=graphdoc.to_doc(GraphQLView().schema)returnHttpResponse(html,content_type='text/html')# urls.pyfromgraphene_django.viewsimportGraphQLViewfrom.viewsimportgraphql_docsurlpatterns=[path('graphql',GraphQLView.as_view(),name='graphql_endpoint'),path('docs',graphql_docs,name='graphql_docs'),]FastAPI and Ariadne# schema.pyfromariadneimportload_schema_from_pathschema=load_schema_from_path("schema.graphql")# main.pyfromfastapiimportFastAPI,Responsefromschemaimportschemaimportgraphdocapp=FastAPI()@app.get("/docs")asyncdefgraphql_docs():html=graphdoc.to_doc(schema)returnResponse(content=html,media_type="text/html")APIgraphdoc.to_doc(schema, templates_folder=None, context=None)schema:GraphQLSchemainstance or a str with a schema in SDL.templates_folder: Optional path to a templates folder. If received,
Jinja will look for templates in this folder before falling back to the defaultshere.context: An optional custom dictionary to be passed to the templates variables.Custom templatesYou can find more example apps using custom templates and documenting multiple schemas
in theexamples folder
Package: graphdot
The GraphDot LibraryGraphDot is a GPU-accelerated Python library that carries out graph dot product operations to compute graph similarity. Currently, the library implements the Marginalized Graph Kernel algorithm, which uses a random walk process to compare subtree patterns and thus defining a generalized graph convolution process. The library can operate on undirected graphs, either weighted or unweighted, that contain arbitrary nodal and edge labels and attributes. It implements state-of-the-art GPU acceleration algorithms and supports versatile customization through just-in-time code generation and compilation.For more details, please checkout the latest documentation onreadthedocs.CopyrightGraphDot Copyright (c) 2019-2020, The Regents of the University of California,
through Lawrence Berkeley National Laboratory (subject to receipt of any
required approvals from the U.S. Dept. of Energy).  All rights reserved.If you have questions about your rights to use or distribute this software,
please contact Berkeley Lab's Intellectual Property Office atIPO@lbl.gov.NOTICE.  This Software was developed under funding from the U.S. Department
of Energy and the U.S. Government consequently retains certain rights.  As
such, the U.S. Government has been granted for itself and others acting on
its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
Software to reproduce, distribute copies to the public, prepare derivative
works, and perform publicly and display publicly, and to permit other to do
so.Like the package?Please cite:Tang, Yu-Hang, and Wibe A. de Jong. "Prediction of atomization energy using graph kernel and active learning." The Journal of chemical physics 150, no. 4 (2019): 044107.Tang, Yu-Hang, Oguz Selvitopi, Doru Thom Popovici, and Aydın Buluç. "A High-Throughput Solver for Marginalized Graph Kernels on GPU." In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 728-738. IEEE, 2020.
Package: graph-dynamo
Graph-DynamoGraph-Dynamo is a computational framework developed to model cellular processes by leveraging the dynamical systems theory, addressing a significant bottleneck in genome-wide modeling due to the absence of sufficient quantitative data. This innovative framework is capable of extracting dynamical information from high-throughput snapshot single cell data, courtesy of advances in single-cell techniques.FeaturesTangent Space Projection (TSP):
Graph-Dynamo introduces a graph-based machine learning procedure that constrains RNA velocity vectors to lie in the tangent space of the low-dimensional manifold represented by the single cell expression data. Unlike the traditional cosine correlation kernel, TSP maintains the vector's magnitude information while transitioning between different data manifold representations.Data-Driven Graph Fokker-Planck (FPE) Equation Formalism:
The framework incorporates a data-driven graph FPE equation formalism to model the cellular state transition dynamics as a convection-diffusion process on a data-formed graph network. This formalism ensures invariance under representation transformation while preserving the topological and dynamical properties of system dynamics.Dynamo Framework Integration:
Building upon our previously developed dynamo framework, Graph-Dynamo reconstructs genome-wide gene regulation relations from single-cell expression states and RNA velocity data derived from either splicing or metabolic labeling.Installationpipinstallgraph-dynamoQuick StartAfter installation, you can easily start working with thegraph-dynamopackage:importgraph_dynamoasgd# Assuming data is loaded as datatsp_result=gd.tangent_space_projection(data)fpe_result=gd.graph_fokker_planck_equation(data)DocumentationThe detailed documentation can be found atDocumentation LinkUsage Examples# More elaborate examples of how to use graph-dynamoPerformanceNumerical tests on both synthetic data and experimental scRNA-seq data underline the capability of the Graph-Dynamo framework. By utilizing the graph TSP/FPE formalism constructed from snapshot single cell data, it successfully recapitulates system dynamics, marking a significant stride in single-cell studies and systems biology.CitationIf you utilize Graph-Dynamo in your research, please consider citing our paper:@article{author2023graph,title={Graph-Dynamo: Learning stochastic cellular state transition dynamics from single cell data},author={Yan Zhang, Xiaojie Qiu, Ke Ni, Jonathan Weissman, Ivet Bahar, Jianhua Xing},year={2023},publisher={https://www.biorxiv.org/content/10.1101/2023.09.24.559170v1}}SupportFor support and further inquiries, feel free to reach out atxing1@pitt.edu.LicenseGraph-Dynamo is released under the MIT License. See the LICENSE file for further details.This README follows the standard structure, and is intended to provide a basic understanding of the Graph-Dynamo framework, its capabilities, installation instructions, and how to get started quickly. For a more comprehensive insight, please refer to the documentation and the referenced paper.
Package: graph-editor
Interactive Graph Editor for JupyterAn interactive graph editor function designed for Jupyter notebooks.edit(graph: nx.Graph)function allows usersto manipulate a graph by creating vertices, edges, and adding labels directly within a Jupyter environment.Parametersgraph(networkx.Graph): The graph object to be edited.
It should be an instance of the NetworkX Graph class or a subclass.Functions of buttons (from left to right)Select whether you want to edit graph structure.Select whether you want to edit labels.Toggle nodes clickable option.Toggle edges clickable option.Close editing window.Mouse FunctionsClick and drag vertices to move them around the canvas.To create an edge, click on one vertex and then click on another vertex.An edge will be created between the two selected vertices.To create a vertex, click on empty space of a canvas.To delete an object, double-click on it.DependenciesJupyter notebook web environment.NetworkX library for graph manipulation.NotesThis function relies on Jupyter ipywidgets, so it should work only in web versions of Jupyter.Examplesimportnetworkxasnx# Create a sample graphG=nx.Graph()G.add_nodes_from([1,2,3])G.add_edges_from([(1,2),(2,3)])# Call the interactive graph editoredit(G)
Package: grapheekdb
Getting started :Documentation indexQuick install guideGrapheekDB featuresIntro : What is a graph database ?Tutorial :Part 1 : Adding and removing nodes and edgesPart 2 : Lookup nodes and edgesPart 3 : Path traversal, collecting and aggregating dataPart 4 : Scaling our app : indexes, performance tipsPart 5 : Production use : client/server configurationLinks:Home Page :https://bitbucket.org/nidusfr/grapheekdbTests and documentation :code base test coverage is currently : 99 %documentation is a work in progressBackends :It can use various KVS (Key/Value Store) backends :Local memory (default backend - not transactionnal)Kyoto Cabinet :http://fallabs.com/kyotocabinet/Symas LMDB :http://symas.com/mdb/Sqlite3 - not transactionnal - graph can become inconsistentShelve - not transactionnal - graph can become inconsistentKnown issues :There’s an issue when running all tests as it opens a lot of filesYou may need to raise limits before running tests.If you want to raise those limits permanently, on Linux, you’ll need to edit /etc/security/limits.conf and add those lines :username soft nofile 8192
username hard nofile 8192Change username to fit your needs8192 appeared to be enough for tests to runThen, depending on your linux distribution, you may need to edit /etc/pam.d/login to add :session required /lib/security/pam_limits.soAbout me :I’m a freelance working with Python and Django since 2000.
You can find more information on my Website :Nidus(In French)If you have interesting projects (involving GrapheekDB or not), don’t hesitate to contact me :)See youContributors :Edwin Cox (sqlite3 and shelve backends)Pasdavoine (bug tracking and fixing)
Package: graphembedding
GraphEmbeddingPython Graph Embedding Libary for Knowledge graphThis project provides Tensorflow2.0 implementatinons of several different popular graph embeddings for knowledge graph.transEcomplExInstallation:graphembeddingwill be released on pypi soon.pythonsetup.pyinstallBasic Usages:It's simple. example code is below.
The embedding object is returned aspd.Dataframe, so it can be used easily.fromgraphembedding.playgroundimportload_githubfromgraphembeddingimportcomplEx,transE# Load Sample datasetgithub_dataset=load_github()triplets=github_dataset[['subject','relation','object']].values# That's all. One line code.node_embedding,edge_embedding=complEx(triplets)# if you wanna use transE,# node_embedding, edge_embedding = transE(triplets)