Saravanan666/pypi-org huggingface dataset:
Package: versort
VerSortSort versions according to different versioning schemas.InstallTo use as a CLI app:pipxinstallversortTo use as a library:pipinstallversortUsageSupported version algorithmsPEP440SemVerAs a libraryfromversortimportget_sortersorter=get_sorter("pep440")()print(sorter.sort("v1","2a1","2"))CLIYou can callversortdirectly, or as a Python module withpython -m versort.➤echo22a1v1|versort--stdinpep440
v1
2a12➤versort--reversepep44022a1v122a1
v1

➤versort--firstpep44022a1v1
v1

➤python-mversort--reverse--firstpep44022a1v12
Package: verspec
verspecverspecis a Python library for handling software versions and specifiers,
adapted from thepackagingpackage.An Examplefromverspecimportloose,pythonv1=loose.Version('1.0')s1=loose.SpecifierSet('~=1.0')assertv1ins1v2=python.Version('1.0')s2=python.SpecifierSet('~=1.0')assertv2ins2DocumentationForthcoming! (Sorry about that...)CreditsThe real credit for this package goes to thePython Packaging Authority.LicenseThis project is dual-licensed under the BSD and Apache licenses.
Package: verstack
.. image:: logo.pngverstack 3.9.2is a set of Machine learning tools to make a Data Scientist's work efficient.The package contains multiple tools for: training & tuning machine learning models, creating ensembles, working with datetime objects, data transformation & wrangling, imputing missing values, concurrency work and many more.Please refer to theofficial documentation <https://verstack.readthedocs.io>_ for more information.The project was created by Danil Zherebtsov in 2020.It is currently maintained by a single contributor with occasional contributions by the active members of the community.Installation.. code-block:: console

  $ pip install verstack
  $ pip install --upgrade verstack


Dependencies
------------

- Python (>= 3.6)
- numpy
- pandas<=2.0.3
- xgboost
- scikit-learn>=0.23.2,<=1.1.3
- lightgbm>=3.3.0,<=4.0.0
- optuna>=2.10.0,<=3.2.0
- plotly>=5.3.1,<=5.11.0
- matplotlib
- python-dateutil>=2.8.1,<=2.8.2
- holidays==0.11.3.1
- mlxtend
- tensorflow>=2.6.0,<=2.13.*0*
- keras>=2.6.0,<=2.13.*
- category_encoders>=2.4.0,<=2.5.1
- fastparquet

=======

Development
-----------

I welcome new contributors of all experience levels. ``verstack`` community goals are to be helpful, welcoming, and effective.
`Development Guide <https://scikit-learn.org/stable/developers/index.html>`_
based on scikit-learn best practices has detailed information about contributing code, documentation, tests, and more. 

Important links
---------------

- Official source code repo: https://github.com/DanilZherebtsov/verstack
- Issue tracker: https://github.com/DanilZherebtsov/verstack/issues

Source code
-----------

You can check the latest sources with the command::

    git clone https://github.com/DanilZherebtsov/verstack.git

Submitting a Pull Request
-------------------------

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies
with the following guidelines: https://scikit-learn.org/stable/developers/index.html

Communication
-------------

- Author email: danil.com@me.com
- `Author profile <https://www.linkedin.com/in/danil-zherebtsov/>`_
 
Citation
--------

If you use verstack in a media/research publication, we would appreciate citations to this repository.
Package: verstr
verstrMake comparing version strings super simple.Quick startIf you want codes using your package to be able to verify its version as easily as:# user_code.pyimportmy_packageassertmy_package.__version__>="1.1"Just customize your package as follows:# my_package/__init__.pyimportverstr__version__=verstr.verstr("1.2.4")or if you are using a tool such assetuptools_scmto generate a_version.pysubmodule:# my_package/__init__.pyimportverstrtry:from.import_version__version__=verstr.verstr(_version.version)exceptImportError:__version__=NoneInstallationInstallverstrwith pippipinstallverstrContributingYou are welcome to help the project, seehow.LicenseMIT
Package: versuchung
versuchung - a toolbox for experimentsIn some fields of science it is common practice to not publish any
experiment descriptions or the raw measurement results of the
experiment. This is not only opposed to the idea of science, where
results should be reproducible to verify them by a larger audience,
but also harmful for other researchers, who want to extend the results
in the field.This lack of experiment descriptions and raw data is not necessary a
result of mean intention by the original researcher, more a lack of
tools for documenting and specifying experiements and the raw data
analysis. This is especially true for some fields of computer
science. But as these people are experts in using formal descriptions
(also known as programming languages) it is surprising that there is
no easy-to-use tool for specifying experiments and analyses.Versuchung tries to fill this gap, by providing such a tool, where
experiments can be specified with proper input and output parameter
declarations, versioned result sets and the possibility to process the
result sets further.DocumentationFor the documentation, please refer tohttps://versuchung.readthedocs.ioGetting the latest versionversuchung is hosted at github:https://github.com/stettberger/versuchung
Package: versy
Usageversy[-h][--changelogCHANGELOG][--dry-run][--edit][--fileFILE][--messageMESSAGE][--push][--rootROOT][--verbose][action]DescriptionAutomatically update the version number and change log for a Python repositoryPositional argumentsactionActions are: patch, minor, major, new, show, <new version number>Optional arguments-h,--helpShow this help message and exit--changelogCHANGELOG,-cCHANGELOGSpecify the name of a new change log--dry-run,-dDon’t actually make the changes, just print the diffs--edit,-eBring up the change log in an editor--fileFILE,-fFILESpecify the file containing __version__--messageMESSAGE,-mMESSAGESet change log message--push,-pGit push after committing changes--rootROOT,-rROOTRoot directory to search for a version number--verbose,-vPrint more stuff(automatically generated bydokson 2020-11-07T12:35:56.188835)
Package: vertexarray
VertexArray===========A simple library for efficiently storing & manipulating vertex arrays in python.Requirements------------1. Python 3.+This library is pure python 3 and has no other requirements.Installation------------`python3 setup.py install`Usage-----```python>>> from vertexarray import VertexArray>>> va = VertexArray()>>> face = va.extend([(1, 0, 0), (0, 1, 0), (0, 0, 1)])```0.1.0 (2016-04-06)------------------- Initial development version, copied & renamed from maze-builder.
Package: vertex-protocol
Vertex Protocol Python SDKThis is the Python SDK for theVertex Protocol API.SeeSDK docsto get started.RequirementsPython 3.9 or aboveInstallationYou can install the SDK via pip:pipinstallvertex-protocolBasic usageImport the necessary utilities:fromvertex_protocol.clientimportcreate_vertex_client,ClientModefromvertex_protocol.contracts.typesimportDepositCollateralParamsfromvertex_protocol.engine_client.types.executeimport(OrderParams,PlaceOrderParams)fromvertex_protocol.utils.expirationimportOrderType,get_expiration_timestampfromvertex_protocol.utils.mathimportto_pow_10,to_x18fromvertex_protocol.utils.nonceimportgen_order_nonceCreate the VertexClient providing your private key:print("setting up vertex client...")private_key="xxx"client=create_vertex_client(ClientMode.MAINNET,private_key)Perform basic operations:# Depositing collateralsprint("approving allowance...")approve_allowance_tx_hash=client.spot.approve_allowance(0,to_pow_10(100000,6))print("approve allowance tx hash:",approve_allowance_tx_hash)print("querying my allowance...")token_allowance=client.spot.get_token_allowance(0,client.context.signer.address)print("token allowance:",token_allowance)print("depositing collateral...")deposit_tx_hash=client.spot.deposit(DepositCollateralParams(subaccount_name="default",product_id=0,amount=to_pow_10(100000,6)))print("deposit collateral tx hash:",deposit_tx_hash)# Placing ordersprint("placing order...")owner=client.context.engine_client.signer.addressproduct_id=1order=OrderParams(sender=SubaccountParams(subaccount_owner=owner,subaccount_name="default",),priceX18=to_x18(20000),amount=to_pow_10(1,17),expiration=get_expiration_timestamp(OrderType.POST_ONLY,int(time.time())+40),nonce=gen_order_nonce(),)res=client.market.place_order({"product_id":product_id,"order":order})print("order result:",res.json(indent=2))SeeGetting Startedfor more.Running locallyClonegithub repoInstall poetry$ curl -sSL https://install.python-poetry.org | python3 -Setup a virtual environment and activate it$ python3 -m venv venv
$ source ./venv/bin/activateInstall dependencies viapoetry installSetup an.envfile and set the following envvarsCLIENT_MODE='mainnet|sepolia-testnet|devnet'SIGNER_PRIVATE_KEY="0x..."LINKED_SIGNER_PRIVATE_KEY="0x..."# not requiredRun tests$ poetry run testRun sanity checkspoetry run client-sanity: runs sanity checks for the top-level client.poetry run engine-sanity: runs sanity checks for theengine-client.poetry run indexer-sanity: runs sanity checks for theindexer-client.poetry run contracts-sanity: runs sanity checks for the contracts module.Build DocsTo build the docs locally run:$ poetry run sphinx-build docs/source docs/build
Package: vertezml
Vertez is a cutting-edge open-source framework company dedicated to developing powerful and versatile libraries for statistical analysis, machine learning, and deep learning. We empower data scientists, researchers, and developers with a comprehensive suite of tools to drive insights and innovations in diverse domains. At Vertez, we believe in democratizing access to advanced statistical techniques and the latest advancements in artificial intelligence. Our libraries provide an intuitive and flexible platform for statistical modeling, predictive analytics, and complex data-driven decision making. With a focus on simplicity, performance, and scalability, we enable practitioners to efficiently explore, analyze, and infer from their data.
Package: vertibench
VertiBench: Vertical Federated Learning BenchmarkIntroductionVertiBench is a benchmark forfederated learning,split learning, andassisted learningon vertical partitioned data. It provides tools to synthetic vertical partitioned data from a given global dataset. VertiBench supports partition under variousimbalanceandcorrelationlevel, effectively simulating a wide-range of real-world vertical federated learning scenarios.InstallationVertiBench has already been published on PyPI. The installation requires the installation ofpython>=3.9. To further install VertiBench, run the following command:pipinstallvertibenchGetting StartedThis examples includes the pipeline of split and evaluate. First,
load your datasets or generate synthetic datasets.fromsklearn.datasetsimportmake_classification# Generate a large datasetX,y=make_classification(n_samples=10000,n_features=10)To split the dataset by importance,fromvertibench.SplitterimportImportanceSplitterimp_splitter=ImportanceSplitter(num_parties=4,weights=[1,1,1,3])Xs=imp_splitter.split(X)To split the dataset by correlation,fromvertibench.SplitterimportCorrelationSplittercorr_splitter=CorrelationSplitter(num_parties=4)Xs=corr_splitter.fit_split(X)To evaluate a feature splitXsin terms of party importance,fromvertibench.EvaluatorimportImportanceEvaluatorfromsklearn.linear_modelimportLogisticRegressionimportnumpyasnpmodel=LogisticRegression()X=np.concatenate(Xs,axis=1)model.fit(X,y)imp_evaluator=ImportanceEvaluator()imp_scores=imp_evaluator.evaluate(Xs,model.predict)alpha=imp_evaluator.evaluate_alpha(scores=imp_scores)print(f"Importance scores:{imp_scores}, alpha:{alpha}")To evaluate a feature split in terms of correlation,fromvertibench.EvaluatorimportCorrelationEvaluatorcorr_evaluator=CorrelationEvaluator()corr_scores=corr_evaluator.fit_evaluate(Xs)beta=corr_evaluator.evaluate_beta()print(f"Correlation scores:{corr_scores}, beta:{beta}")
Package: vertibird
Vertibird is a dead-simple virtualization library based around direct access to
QEMU. Because I couldn't be bothered to figure out how the hell the crappy
libvirt C bindings work. I also couldn't be bothered to write a million lines
of XML. Screw that.Currently only tested on x86_64, and probably only works on x86_64. Usage is
also only possible on Linux, this library is NOT cross-platform. It would've
taken an infinitely longer amount of time to write this for Windows, since it
makes use of features such as named pipes.PyPi:https://pypi.org/project/vertibird/GitHub & Further Information:https://github.com/naphthasl/vertibirdLicense: MIT (see LICENSE for details)
Package: vertica-connector-talenttech
Customized vertica connectorThe Python Wrapper to vertica_python lib for reconnectiong across server nodesUsagepip3installvertica-connector-talenttechimportosimportjsonfromvconnector.vertica_connectorimportVerticaConnectoruser="test_user",password="test_password"database="test_database"vertica_configs=json.loads(os.getenv("VERTICA_CONFIGS"))withVerticaConnector(user=user,password=password,database=database,vertica_configs=vertica_configs)asv_connector:cur=v_connector.cnx.cursor()sql="SELECT 1"cur.execute(sql)VERTICA_CONFIGS variable structure{"host":<VERTICA_HOST>,"port":<VERTICA_PORT>,"backup_server_node":[<SERVER_NODE_1>,<SERVER_NODE_2>,<SERVER_NODE_3>}INSERT TABLE  EXAMPLEwith VerticaConnector(
            user=os.getenv("VERTICA_USER"),
            password=os.getenv("VERTICA_PASSWORD"),
            database=os.getenv("DATABASE"),
            vertica_configs=json.loads(os.getenv("VERTICA_CONFIGS")),
    ) as v_connector:
        cursor = v_connector.cnx.cursor("dict")
        cursor.execute("drop table if exists test_staging.test cascade")
        cursor.execute(
            """create table netology_staging.test
                             (
                              a int,
                              b varchar(10),
                              c long varbinary(1000),
                              d long varchar default MAPTOSTRING(c)
                             )"""
        )
        v_connector.insert(
            table_name="test",
            schema="test_staging",
            data=list(
                [
                    {
                        "a": 1,
                        "b": "test2",
                        "c": '[{"x": 1, "y": 2}, {"x": 1, "y": 2}]',
                        "d": "fuck",
                    }
                ]
            ),
        )
Package: vertica-locust-client
The package includes an object that you can use as a client for the locust.io
load testing tool.  The client can be used to write load testing workloads that
automatically connect to a Vertica database.The client also integrates with a graphite backend to be able to flow metrics to it.InstallationTo install this package:pip install vertica_locust_clientLocust Command Line ParametersThis will automatically add parameters to locust that will control how to
connect to Vertica and the graphite backend.  These parameters are also exposed
in the webUI.--user              ID of user to connect to Vertica with (defaults to dbadmin)
--password          Password of the Vertica user ID (defaults to empty string)
--carbon-endpoint   The carbon endpoint to connect to.
                    The endpoint is defined as: host[:port][,host[:port]...]
--carbon-namespace  The namespace for the carbon metric.  This is treated as
                    the prefix when constructing the full metric name.Sample locustfileHere is a sample locustfile.py to show the client in action.  This will issue a
simple query to count the number of sessions in the database.from locust import task
from vertica_locust_client import VerticaUser

class SessionCounterUser(VerticaUser):
    def __init__(self, environment):
        super().__init__(environment)

    @task
    def query_users(self):
        with self.client.make_cursor(carbon_metric="query") as cur:
            cur.execute('''
                select count(session_id) sessions
                from v_monitor.sessions
                where session_id not in (
                    select session_id
                    from current_session
                )
                ''')
Package: verticaltable
This is an structured text parser.formatexample 1:source:[Q] This is a question.
[A] This is an answer.
[Q] This is a question2.
# This is comment
[A] This is an answer2.parsed:[
  {
    'Q': 'This is a question.',
    'A': 'This is an answer.',
  },
  {
    'Q': 'This is a question2.',
    'A': 'This is an answer2.',
  },
]If the same key appeared, Next record.Actuary, output is OrderedDict list. Not Dict list.source:[Key1]
Value1
[Key2]
Value2
----
[Key3]
Value3
next line
next line
[Key4]

Value4    4!

[Key5]
Value5parsed:[
  {
    'Key1': 'Value1',
    'Key2': 'Value2',
  },
  {
    'Key3': 'Value3\nnext line\nnext line',
    'Key4': 'Value4    4!',
    'Key5': 'Value5',
  }Whitespace trimmed.“—-” (Hyphens >= 4) is list separator.usageimport verticaltable

fp = open('sample.txt')

parsed_list = verticaltable.loads(fp.read())

parsed_list2 = verticaltable.load(open('sample2.txt'))
Package: vertica-sqlalchemy
Vertica dialect for sqlalchemyThis module implements a Vertica dialect for SQLAlchemy.  You can use it:>>> import sqlalchemy as sa
>>> sa.create_engine(sa.engine.url.URL(
    drivername='vertica+pyodbc',
    username='myusername',
    password='mypassword',
    host='hostname',
    database='dbname',
))This work is mainly a package of code [posted](https://groups.google.com/d/msg/sqlalchemy/ttJzN-t9R74/9W1d9KfHK_0J)
to the SQLAlchemy mailing list by ‘Bo’ (Google groups won’t let me see the
original author).I have tested this for table introspection, selects and joins.  However, I do
not have access to a Vertica database to test against.  If you do, let me know.The primary, foreign and index constraints return nothing.  This is because I’m
told these are ‘enforced on select’.  Maybe a Vertica expert can tell me the
correct way to handle this.InstallationWith pippip install vertica-sqlalchemyFrom git:git clonehttps://github.com/jamescasbon/vertica-sqlalchemycd vertica-sqlalchemy
python setup.py install
Package: vertica-sqlalchemy-0.2
Vertica dialect for sqlalchemyThis module implements a Vertica dialect for SQLAlchemy.  You can use it:>>> import sqlalchemy as sa
>>> sa.create_engine(sa.engine.url.URL(
    drivername='vertica+pyodbc',
    username='myusername',
    password='mypassword',
    host='hostname',
    database='dbname',
))This work is mainly a package of code [posted](https://groups.google.com/d/msg/sqlalchemy/ttJzN-t9R74/9W1d9KfHK_0J)
to the SQLAlchemy mailing list by ‘Bo’ (Google groups won’t let me see the
original author).I have tested this for table introspection, selects and joins.  However, I do
not have access to a Vertica database to test against.  If you do, let me know.The primary, foreign and index constraints return nothing.  This is because I’m
told these are ‘enforced on select’.  Maybe a Vertica expert can tell me the
correct way to handle this.InstallationWith pippip install vertica-sqlalchemy-0.2From git:git clonehttps://bitbucket.com/shivamshukla/vertica-sqlalchemy-0.2cd vertica-sqlalchemy-0.2
python setup.py install
Package: vertices-to-h5m
This is a minimal Python package that provides a Python API interfaces for converting mesh vertices into a DAGMC h5m file ready for use in simulation.Convert a set of vertices with their connectivity in to a DAGMC h5m file complete with material tags and ready for use neutronics simulations.warningthis approach does not imprint and merge the geometry and therefore
requires that the mesh is well formed and does not overlap. Overlaps could lead
to particles being lost during transport. If imprinting and merging is required
consider usingParamak export_dagmc_h5m()method orcad-to-h5mto make the
DAGMC geometry.It is strongly advised to used the DAGMC overlap checker to check the
resulting h5m file (see checking for overlaps section below).Installation - CondaThis single line command should install the package and dependencies (including moab)condainstall-cfusion-energy-cfusion-energyvertices_to_h5mInstallation - Pip + CondaThese two commands should install the package and dependencies. Moab requires a separate install as it is not available onpipcondainstall-cconda-forgemoab
pipinstallvertices_to_h5mExamplesThese examples with volumes made from just four triangles to keep the examples minimal.
The package can also convert larger meshes as shown in the picture below.Usage - single volumeTo convert a single volume mesh into a h5m file. This also tags the volume with the
material tag mat1.fromvertices_to_h5mimportvertices_to_h5mimportnumpyasnp# these are the x,y,z coordinates of each vertex. Entries should be floatsvertices=np.array([[0.0,0.0,0.0],[1.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,1.0],])# These are the triangle that connect individual vertices together to form a continious surface and also a closed volume. Entries should be intstriangles=[np.array([[0,1,2],[3,1,2],[0,2,3],[0,1,3]]),]# This will produce a h5m file called one_volume.h5m ready for use with DAGMC enabled codes.vertices_to_h5m(vertices=vertices,triangles=triangles,material_tags=["mat1"],h5m_filename="one_volume.h5m",)Usage - multiple volumesTo convert multiple mesh volumes files into a h5m file. This also tags the relevant volumes with material tags called mat1 and mat2. This example also uses numpy arrays instead of lists, both are acceptable.fromvertices_to_h5mimportvertices_to_h5mimportnumpyasnp# These are the x,y,z coordinates of each vertex. Numpy array is set to type float to enforce floatsvertices=np.array([[0,0,0],[1,0,0],[0,1,0],[0,0,1],[1,1,1],[1,1,0]],dtype="float64")# These are the two sets triangle that connect individual vertices together to form a continious surfaces and also two closed volume.triangles=[np.array([[0,1,2],[3,1,2],[0,2,3],[0,1,3]]),np.array([[4,5,1],[4,5,2],[4,1,2],[5,1,2]]),]# This will produce a h5m file called two_volume_touching_edge.h5m ready for use with DAGMC enabled codesvertices_to_h5m(vertices=vertices,triangles=triangles,material_tags=["mat1","mat2"],h5m_filename="two_volume_touching_edge.h5m",)Checking for overlapsTo check for overlaps in the resulting h5m file one can use the DAGMC
overlap checker. -p is the number of points to check on each linecondainstall-cconda-forge
overlap_checkdagmc.h5m-p1000
Package: verto
Verto is an extension of the Python Markdown package, which allows
authors to include complex HTML elements with simple text tags in their
Markdown files.Basic UsageVerto allows for an author to quickly include images and content and display
them in a panel (similar to a Bootstrap Collapsible Panel) with the following
markdown:# Example Header

Example Paragraph

{panel type="example"}

# Example Panel

{image file-path="http://placehold.it/350x150" caption="Example Image"}

{panel end}While Verto has many configuration options it can be used immediately
with little code. For example, if the previous markdown is saved in the file
calledexample.mdthen the following would convert that file and print the
output to stdout:fromvertoimportVertotext=open('example.md','r').read()converter=Verto()result=converter.convert(text)print(result.html_string)DocumentationInstallation and usage documentation for Verto can be found onReadTheDocs, and can also be built from the documentation source
within thedocs/directory of the development distribution.LicenseVerto is licensed under the MIT License. Read thelicense filefor
more details.Bugs and feature requestsHave a bug or a feature request? Please first search forexisting and
closed issuesin our issue tracker. If your problem or idea is not
addressed yet, pleaseopen a new issue.FAQWhere is the changelog?The changelog is available within thedocumentation.How do I install the development version as local package?$ git clonehttps://github.com/uccser/verto.git$ cd verto$ pip3 install .