Saravanan666/pypi-org huggingface dataset:
Package: graphviz
This package facilitates the creation and rendering of graph descriptions in
theDOTlanguage of theGraphvizgraph drawing software (upstream repo)
from Python.Create a graph object, assemble the graph by adding nodes and edges, and
retrieve its DOT source code string. Save the source code to a file and render
it with the Graphviz installation of your system.Use theviewoption/method to directly inspect the resulting (PDF, PNG,
SVG, etc.) file with its default application. Graphs can also be rendered
and displayed withinJupyter notebooks(formerly known asIPython notebooks,example,nbviewer)
as well as theJupyter QtConsole.LinksGitHub:https://github.com/xflr6/graphvizPyPI:https://pypi.org/project/graphviz/Documentation:https://graphviz.readthedocs.ioChangelog:https://graphviz.readthedocs.io/en/latest/changelog.htmlIssue Tracker:https://github.com/xflr6/graphviz/issuesDownload:https://pypi.org/project/graphviz/#filesInstallationThis package runs under Python 3.7+, usepipto install:$pipinstallgraphvizTo render the generated DOT source code, you also need to installGraphviz(download page,archived versions,installation procedure for Windows).Make sure that the directory containing thedotexecutable is on your
systems’PATH(sometimes done by the installer;
settingPATHonLinux,Mac,
andWindows).Anaconda: see theconda-forgepackageconda-forge/python-graphviz(feedstock),
which should automaticallyconda installconda-forge/graphviz(feedstock) as dependency.QuickstartCreate a graph object:>>>importgraphviz# doctest: +NO_EXE>>>dot=graphviz.Digraph(comment='The Round Table')>>>dot#doctest: +ELLIPSIS<graphviz.graphs.Digraphobjectat0x...>Add nodes and edges:>>>dot.node('A','King Arthur')# doctest: +NO_EXE>>>dot.node('B','Sir Bedevere the Wise')>>>dot.node('L','Sir Lancelot the Brave')>>>dot.edges(['AB','AL'])>>>dot.edge('B','L',constraint='false')Check the generated source code:>>>print(dot.source)# doctest: +NORMALIZE_WHITESPACE +NO_EXE//TheRoundTabledigraph{A[label="King Arthur"]B[label="Sir Bedevere the Wise"]L[label="Sir Lancelot the Brave"]A->BA->LB->L[constraint=false]}Save and render the source code:>>>doctest_mark_exe()>>>dot.render('doctest-output/round-table.gv').replace('\\','/')'doctest-output/round-table.gv.pdf'Save and render and view the result:>>>doctest_mark_exe()>>>dot.render('doctest-output/round-table.gv',view=True)# doctest: +SKIP'doctest-output/round-table.gv.pdf'Caveat:Backslash-escapes and strings of the form<...>have a special meaning in the DOT language.
If you need to render arbitrary strings (e.g. from user input),
check the details in theuser guide.See alsopygraphviz– full-blown interface wrapping the Graphviz C library with SWIGgraphviz-python– official Python bindings
(documentation)pydot– stable pure-Python approach, requires pyparsingLicenseThis package is distributed under theMIT license.DevelopmentDevelopment documentation:https://graphviz.readthedocs.io/en/latest/development.htmlRelease process:https://graphviz.readthedocs.io/en/latest/release_process.html
Package: graphviz2drawio
graphviz2drawioConvert graphviz (dot) files into draw.io (mxGraph) formatGetting StartedPrerequisitesgraphviz2drawio requiresPython 3andGraphvizOn Mac OS these can be installed withHomebrew:brew update; brew install python3 graphviz
pip3 install pygraphviz --install-option="--include-path=/usr/local/include/graphviz" --install-option="--library-path=/usr/local/lib/graphviz/"On Ubuntu / Debian based Linux, install graphviz using:sudo apt install python3-pip graphviz graphviz-devIf you encounter installation errors you may need to manually install pygraphviz with links to the graphviz librariesInstallation / Upgradepip3 install graphviz2drawio --upgradeUsageRun the conversion app on your graphviz filegraphviz2drawio example.dotYou can them import the output XML file into draw.ioPython Usagefromgraphviz2drawioimportgraphviz2drawioxml=graphviz2drawio.convert(graph_to_convert)print(xml)wheregraph_to_convertcan be any of a file path, file handle, string of dot language, or PyGraphviz.AGraph objectLimitationsPleaseopen an issuewith your dot file to report crashes or incorrectect conversions.Built WithPyGraphviz- Python interface to GraphvizContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Code FormattingThis project is linted withpyflakesand makes strict use ofBlackfor code formatting.AuthorsHarold Martin- harold.martin at gmailJonah CaplanLicenseGPLv3
Package: graphviz-artist
Graphviz-ArtistYou just become an artist in graph drawing once you start usingGraphviz-Artist.If you don’t have the demand of fine-grained controling upon generated
graphs, the learning curve of graphviz-artist is actually horizontal.This package provides a higher-level encapsulation for the python
packagegraphviz, and you
might want to have a try at the latter.InstallationInstallGraphviz.pip installgraphviz-artistQuickstartUndirected Graphimportgraphviz_artistasga# make a graphg=ga.Graph()# make nodesn1=g.new()n2=g.new()n3=g.new()# (>), (==) and (<) could create edges for graphs.# n2 > n3 / n3 < n2 : there is an edge n2 -> n3# (==) will be introduced later, as it's only meaningful to directed edges._=n1>n2>n3>n1g.view()Directed Graphimportgraphviz_artistasga# use attr module to see which Graphviz Attributes# could be auto-completed.importgraphviz_artist.attrasattr# use HorizontalGraphg=ga.Graph(attr.HorizontalGraph)# `attr.Shape("<name>")` to specify the shape of nodes.n1=g.new(attr.Label('hey'),attr.Shape.diamond)n2=g.new(attr.Label('hey'),attr.Shape.hexagon)n3=g.new(attr.Label('you'),attr.Shape.star)# `attr.Directed()` makes a directed edge.directed=attr.Directed()# `attr.Label` to specify the text that edges displayedge_label=attr.Label("passed_here")# `attr.Penwidth` to decide the width of edge glyph.edge_size=attr.Penwidth(2.)# in `a < b[b_to_c_attrs...] > c`, the edge `b -> c` will have attribute `b_to_c_attrs`._=n3[directed,edge_label,edge_size]>n1[directed]==n2>n3g.view()Expression Treeimportgraphviz_artistasgaimportgraphviz_artist.attrasattr# make a graphg=ga.Graph(directed=True)new=g.new# decl nodesfalse=new(attr.Label("False"))true=new(attr.Label("True"))not_=new(attr.Label("not"))and_=new(attr.Label("and"))unary1=new(attr.Label("unary"))unary2=new(attr.Label("unary"))binary=new(attr.Label("binary"),attr.Width(2),attr.Shape.box)expr=new(attr.Label("expr"))# build graph_=false>unary1<not__=true>unary2_=and_[attr.Label('Op')]>binary# XLabel: For edges, the label will be placed near the center of the edge._=unary1[attr.XLabel("Left operand")]>binary_=unary2[attr.XLabel('Right operand')]>binary_=binary>exprg.view()
Package: graphviz-erd
graphviz-erdDraw Entity Relationship Diagrams (ERD) with python and graphviz.This code is built on top of thegraphvizpython package and provides methods to facilitate the declaration of blocks commonly used in ERD such as entities and attributes.ExampleWe can draw diagrams likeusing the code:gr = graphviz.Graph("ER", filename="erd", engine="dot", format="png")
    obj = ERD(gr)

    # node name and node label
    obj.entity("entity1", "entity 1")
    # the label is optional as it is in graphviz
    obj.attribute("attr")
    # new lines using the html notation
    obj.multivalue("multi", "multivalued<BR/>attribute")
    obj.key("key", "key attribute")
    obj.derived("derived")
    obj.weak_key("weak")

    obj.associative_entity("assoc", "associative<BR/>entity")

    obj.weak_entity("entity2", "weak entity 2")

    # draw relation between entities
    obj.relation("relationship", "entity1", "entity2", "(1,2)", "(2, 1)", "yes")

    # connect the attributes
    obj.gr.edges(
        [
            ("entity1", "attr"),
            ("entity1", "multi"),
            ("entity1", "key"),
            ("entity1", "weak"),
            ("entity1", "derived"),
        ]
    )

    obj.gr.view()Instructions (Development)Assuming a linux environment (Ubuntu), to develop inside a virtual environment using git hooks:python3 -m venv .venv
source .venv/bin/activate
pip install wheel
pip install -r requirements.txt
pre-commit install
Package: graphviz-overlay
Generate templated graphviz source files from structured dataWhyThe Graphviz dot language offers a descriptive and flexible way
to define graphs of various sorts. However, I think it also has
some issues.Maintenance/small changes quickly becomes a hassle, especially
when you want to produce similar looking graphs. I.e. highlighting
certain elements, or selecting specific parts (it is possible
to render just subgraphs, but for me that is not always what
I want).The layout engine is not exactly what you would call intuitive
and getting it to do your bidding takes time and effort to
discover. Also, you need to pull (and remember those) weird
tricks. Basically, it’s just boilerplate work.The defaults does not really produce pretty looking graphs, and
customizing the design is often tedious work and for me often
ends up involving a bunch of copy paste. For SVG output
stylesheets are supported, however that doesn’t really work
with common cooperation tools (such as slack and github)So, to address these issues I decided to put together a tool
to address all that (and more).UsageCLIThegraphviz-overlayexecutable reads from stdin and produces
dot source:cat examples/simple.json | graphviz-overlayVersion History0.1.1:Add initial documentation and project description0.1.0:graph,digraphandercommands.Support for--select,--highlightand--shadevia paths.Support for ranksExternal stylesheet definition.Nodes, edges, and graphs can haveclasses.
Package: graphviz-py
graphviz-pyAllows Python code execution inside ofgraphvizdiagramsExamplegraphpython_graph{{{importmathvalue=0.5sin=math.sin(value)cos=math.cos(value)}}A[label="{{= value }}"];B[label="{{= sin }}"];C[label="{{= cos }}"];A--B[headlabel="sin"];A--C[headlabel="cos"];}OutputInstallationpipinstallgraphviz-pyImportant: Make sure graphviz is installed!Seegraphviz installation instructions.UsageUsing filesgraphviz-py-Tsvgexample/example.py.dot-ooutput.svg
graphviz-py-Tpngexample/example.py.dot-ooutput.pngUsing stdin / pipesecho'digraph { A -> B [label="{{= 38 * 73 }}"] }'|graphviz-py-Tsvg>output.svggraphviz-py passes all unknown arguments to graphviz. So you can use allgraphviz arguments.OutputVariablesgraphviz-py-Tsvg-amyvalue=5example/variable_example.py.dot-ooutput.svgHere we pass a variable called "myvalue" with the value 5OutputSecurityPlease keep in mind that graphviz-py executes all Python code in the diagram. So make sure that your diagrams dies not include harmful code.Coming soonCompartibility with asciidoctor-diagram (Status: Implemented & Approved, waiting for merging)Arguments# graphviz-py --helpusage:graphviz-py[-h][-v][-d][-aARGUMENT][files[files...]]graphviz-pydiagrambuilder

positionalarguments:filesthepathstothegraphviz-pyfiles

optionalarguments:-h,--helpshowthishelpmessageandexit-v,--versionshowprogram'sversionnumberandexit-d,--debugshowdebuginformation-aARGUMENT,--argumentARGUMENT
Package: graphviz-sql
将tree.export_graphviz构建决策树代码转换为标准sql1. 工具简介通常我们会采用sklearn框架tree模块进行决策树相关的挖掘分析，并使用tree.export_graphviz将决策树过程导出为graphviz.dot文件
再配合外部程序graphviz进行画图。但在有些时候，我们会根据决策树图形来抽取关键路径，将其翻译成标准sql，部署在数据库当中，持续化产生价值。目前sklearn暂未提供此功能，故我们开发此插件来进行完善。2. 安装环境、依赖安装方法:支持在线安装或离线安装downloadpip install graphviz_sql内部依赖sklearn外部依赖Graphviz:找到适合自己版本下载即可3. 一个简单的示例将tree.export_graphviz输出结果转换为标准sqltree.export_graphviz参数说明为了能够准确的输出决策树规则，方法tree.export_graphviz当中一下参数必须设置成以下形式。其余参数使用默认的即可。feature_names：特征名称，顺序必须和训练样本的数据一致class_names：类别名称，输入的时候，必须要排序。如将原来的['1', '0']设置为['0', '1']，注意：数据类型必须为str型的。filled：填充，必须为Truenode_ids：节点id，必须为Truerounded：画的图形边缘是否美化，必须为Truespecial_characters：必须为True=========================eg:importpandasaspdimportnumpyasnpfromsklearnimporttreefromsklearnimportmetricsfromsklearn.model_selectionimporttrain_test_splitfromsklearn.datasetsimportload_irisiris=load_iris()clf=tree.DecisionTreeClassifier()feature_names=['sepal_length','sepal_width','petal_length','petal_width']data=pd.DataFrame(iris.data,columns=feature_names)data['target']=iris.targetX_train,X_test,y_train,y_test=train_test_split(data[feature_names],data['target'],test_size=0.33,random_state=42)clf=clf.fit(data[feature_names],data['target'])# tree_rule_2.txt为输出文件的路径tree.export_graphviz(model,out_file='tree_rule.dot',feature_names=feture_selected_res,class_names=['0','1'],filled=True,node_ids=True,rounded=True,special_characters=True)# 调用插件fromgraphviz_sql.extractimportto_sql# tree_rule.txt: 通过决策树生成的用于graphviz画图的文件路径# tree_rule_2.sql: 转换后的sql文件路径to_sql('tree_rule.dot','tree_rule_2.sql')4. 代码原理通过一种反向搜索的方法遍历决策树结果，将结果转换成标准sql5. 常见问题说明乱码此问题可百度一下，网上有具体的处理办法，其它问题请及时反馈6. 加入进来强大的工具需要你我共同完善，期待技术大佬的加入。email:wang_xup@163.com857956556@qq.com
Package: graph-weather
Graph WeatherImplementation of the Graph Weather paper (https://arxiv.org/pdf/2202.07575.pdf) in PyTorch. Additionally, an implementation
of a modified model that assimilates raw or processed observations into analysis files.InstallationThis library can be installed throughpipinstallgraph-weatherExample UsageThe models generate the graphs internally, so the only thing that needs to be passed to the model is the node features
in the same order as thelat_lons.importtorchfromgraph_weatherimportGraphWeatherForecasterfromgraph_weather.models.lossesimportNormalizedMSELosslat_lons=[]forlatinrange(-90,90,1):forloninrange(0,360,1):lat_lons.append((lat,lon))model=GraphWeatherForecaster(lat_lons)features=torch.randn((2,len(lat_lons),78))out=model(features)criterion=NormalizedMSELoss(lat_lons=lat_lons,feature_variance=torch.randn((78,)))loss=criterion(out,features)loss.backward()And for the assimilation model, which assumes each lat/lon point also has a height above ground, and each observation
is a single value + the relative time. The assimlation model also assumes the desired output grid is given to it as
well.importtorchfromgraph_weatherimportGraphWeatherAssimilatorfromgraph_weather.models.lossesimportNormalizedMSELossobs_lat_lons=[]forlatinrange(-90,90,7):forloninrange(0,180,6):obs_lat_lons.append((lat,lon,np.random.random(1)))forlonin360*np.random.random(100):obs_lat_lons.append((lat,lon,np.random.random(1)))output_lat_lons=[]forlatinrange(-90,90,5):forloninrange(0,360,5):output_lat_lons.append((lat,lon))model=GraphWeatherAssimilator(output_lat_lons=output_lat_lons,analysis_dim=24)features=torch.randn((1,len(obs_lat_lons),2))lat_lon_heights=torch.tensor(obs_lat_lons)out=model(features,lat_lon_heights)assertnottorch.isnan(out).all()assertout.size()==(1,len(output_lat_lons),24)criterion=torch.nn.MSELoss()loss=criterion(out,torch.randn((1,len(output_lat_lons),24)))loss.backward()Pretrained WeightsComing soon! We plan to train a model on GFS 0.25 degree operational forecasts, as well as MetOffice NWP forecasts.
We also plan trying out adaptive meshes, and predicting future satellite imagery as well.Training DataTraining data will be available through HuggingFace Datasets for the GFS forecasts. The initial set of data is available forGFSv16 forecasts, raw observations, and FNL Analysis files from 2016 to 2022, and forERA5 Reanlaysis. MetOffice NWP forecasts we cannot
redistribute, but can be accessed throughCEDA.Contributors ✨Thanks goes to these wonderful people (emoji key):Jacob Bieker💻Jack Kelly🤔byphilipp🤔Markus Kaukonen💬MoHawastaken🐛Mihai💬Vitus Benson🐛dongZheX💬sabbir2331💬This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: graphworks
GraphworksA Python module for efficient graph theoretic programmingUsageSee thewikiTLDRFirst,pip install graphworksimportjsonfromsrc.graphworks.graphimportGraphjson_graph={"label":"my graph","edges":{"A":["B"],"B":[]}}graph=Graph("my graph",input_graph=json.dumps(json_graph))print(graph)DevelopmentRequirementsPython 3.8+virtualenvnumpygraphvizInstall the required packagespipinstallvirtualenv
virtualenvenvStart the virtualenvsource./env/bin/activateYou can deactivate the virtualenv withdeactivateLastly, install the required librariespipinstall-rrequirements.txtBuilding the packageUpdate the version number ingraphworks.__init__.pyRunpython -m buildRuntwine check dist/*Upload to test PyPi:twine upload --repository-url https://test.pypi.org/legacy/ dist/*Upload to PyPi main:twine upload --skip-existing dist/*To autopublish, tag commit withgit tag -a vX.Y.Z -m 'release messageThengit push --tagsDiagnosticsRun the unit tests:python -m unittest discover tests '*_tests.py'Run unit test coverage:coverage run --source=graphworks/ -m unittest discover tests '*_tests.py'Generate test coverage reports (either works):coverage report --omit="*/test*,*/venv/*"coverage html --omit="*/test*,*/venv/*"TODOCreate Vertex classhttps://www.python-course.eu/graphs_python.phpBuild out directed graphs algorithmshttps://algs4.cs.princeton.edu/42digraph/Allow for weighted graph algorithmsJarnik's algorithmDijkstra's algorithmC++ binaries for speeding up graph computations
Package: graphy2
graphy2 is designed to try and insure that a given table or graph standard can be constructed
from any given statistical or python platform. It is built on top of many external libraries listed below and acts as
an API for these libraries. The core libraries graphy2 is currently using are:Seaborn:       <https://github.com/mwaskom/seaborn>
Pandas:        <https://github.com/pandas-dev/pandas>
matplotlib:    <https://github.com/pandas-dev/pandas>It is designed to be as simple as possible, with most commands being pushed to a single line. graphy2 also contains a
list of styles for well used Custom_Graphs/tables that should reflect the standards expected from certain journals at
some point in the future (hopefully).
Package: graphysio
GraPhysioGraPhysio is a graphical time series visualizer created for biometric data
signals from ICU patient monitors. It is however not limited to this. It can
handle low frequency and high frequency data as well as aggregating and
synchronizing signals from different sources. GraPhysio supports basic
mathematical operations and filters and can help selecting and exporting time
periods. GraPhysio can read data from CSV, Parquet and EDF files and can write
CSV, Parquet, EDF and Matlab files.Install instructionsFor the best experience, conda is recommended:conda install -c conda-forge graphysioAlternatively you can then install the latest version of GraPhysio from PyPi by
tying the following command:python -m pip install graphysioYou can launch GraPhysio by typing:python -m graphysio
Package: graphyte
graphyte is a small Python library that sends data to a Graphite metrics
server (Carbon). We wrote it because the existinggraphitesendlibrary
didn’t support Python 3, and it also required gevent for asyncronous use.
graphyte is tested on Python 3.5+ as well as Python 2.7, and uses the
standard library’sthreadingmodule for asynchronous use.The library ison the Python Package Index (PyPI), so to install it, fire up
a command prompt, activate your virtualenv if you’re using one, and type:pip install graphyteUsing graphyte is simple – just callinit()to initialize the default
sender and thensend()to send a message. For example, to sendsystem.sync.foo.bar 42{timestamp}\nto graphite.example.com:2003
synchronously:importgraphytegraphyte.init('graphite.example.com',prefix='system.sync')graphyte.send('foo.bar',42)If you want to send asynchronously on a background thread (for example, in a
web server context), just specify a send interval. For example, this will
setup a background thread to send every 10 seconds:graphyte.init('graphite.example.com',prefix='system.async',interval=10)graphyte.send('foo.bar',42)If you want to send tagged metrics, the usage is as follows:graphite.send('foo.bar',42,tags={'ding':'dong'})For more advanced usage, for example if you want to send to multiple servers
or if you want to subclassSender, you can instantiate instances ofSenderdirectly. For example, to instantiate two senders sending to
different   servers (one synchronous, one using a background thread with send
interval 10   seconds), use something like the following:sender1=graphyte.Sender('graphite1.example.com',prefix='system.one')sender2=graphyte.Sender('graphite2.example.com',prefix='system.two',interval=10)sender1.send('foo.bar1',42)sender2.send('foo.bar2',43)If you want to send via UDP instead of TCP, just addprotocol='udp'to
theinit()orSender()call.Or, to customize how messages are logged or sent to the socket, subclassSenderand overridesend_message(or evensend_socketif you
want to override logging and exception handling):classCustomSender(graphyte.Sender):defsend_message(self,message):print('Sending bytes in some custom way:{!r}'.format(message))By default, exceptions that occur when sending a message are logged. If you
want to raise and propagate exceptions instead, instantiateSenderwithraise_send_errors=True. It’s an error to setraise_send_errorswhenintervalis specified.Socket sending errors are logged using the Python logging system (using
logger name “graphyte”). If the sender is initialized withlog_sends=True, all sends are logged at the INFO level.You can also use graphyte to send metrics directly from the command line:python -m graphyte foo.bar 42There are command line arguments to specify the server and port and other
configuration. Typepython-mgraphyte--helpfor help.Read the code ingraphyte.pyfor more details – it’s pretty small!graphyte was written byBen Hoytand is licensed with a
permissive MIT license (seeLICENSE.txt).Related work:delphidhas a fork of graphyte which supports the statsd
protocol. See the changes ondelphid’s branch.
Package: graphy-test-zhaohany
Your Package NameShort description or tagline for your package.Table of ContentsInstallationUsageFeaturesContributingLicenseInstallationhttps://pypi.org/project/graphy-test-zhaohany/You can install graphy-test-zhaohany using pip:pipinstallgraphy-test-zhaohanyUsageProvide examples and explanations of how to use your package here.fromgraphy_test_zhaohanyimportmainmain.test()main.test_ref('code_gen_test','test')main.test_ref('gas_api_test','test')main.test_ref('new_script_test','test')# Example usage code hereexpected result:helloworld
hellofromcode_gen_test
hellofromgas_api_test
hellofromnew_script_testFeaturesConcisely list key features and functionalities of your package.ContributingContributions are welcome! Here's how you can get involved:Fork the repository and clone it to your local machine.Create a new branch for your feature/bug fix:git checkout -b feature/your-feature-name.Make your changes, add tests if applicable, and ensure the code lints.Commit your changes:git commit -am 'Add some feature'.Push to the branch:git push origin feature/your-feature-name.Create a pull request, explaining your changes and why they should be merged.Please ensure your code follows the project's coding style and includes tests.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.