Saravanan666/pypi-org huggingface dataset:
Package: pyprojecttemplate
DocumentationExample Project Structure for a Python / PyPI ProjectInstallationNote that this supports Python 3ONLYBasic UsageWarning eggs are deprecated in favor of wheels, and not supported by pip.This project is a sample template used to aggregate the process of:Laying out a python projectUsing setuptools build system toto facilitate packaging Python projectsPython package and module definitionsDistribution package metadataProject installationOutline the process toCreate Python Eggs - a single-file importable distribution formatAutomatically include all packages in your source tree, without listing them individually in setup.pyAutomatically generate wrapper scripts or Windows (console and GUI) .exe files for any number of “main” functions in your project. (Note: this is not a py2exe replacement; the .exe files rely on the local Python installation.)Command Referencehttps://setuptools.readthedocs.io/en/latest/setuptools.html#command-referenceFor example, to produce a source distribution, simply invoke:>>>
python setup.py sdistDetailed instructions to distribute a setuptools project can be found at Packaging project tutorials.https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archivesBefore you begin, make sure you have the latest versions of setuptools and wheel:>>>
python -m pip install --user --upgrade setuptools wheelTo build a setuptools project, run this command from the same directory where setup.py is located:>>>
python setup.py sdist bdist_wheelThis will generate distribution archives in the dist directory.Uploading to test.pypi.orgtest.pypi.orgBefore you upload the generated archives make sure you’re registered onhttps://test.pypi.org/account/register/.You will also need to verify your email to be able to upload any packages.
You should installtwineto be able to upload packages:>>>
python -m pip install --user --upgrade twineNow, to upload these archives, run:>>>
python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*To install your newly uploaded package example_pkg, you can use pip:>>>
python -m pip install --index-url https://test.pypi.org/simple/ example_pkgIf you have issues at any point, please refer to Packaging project tutorials for clarification.CreditsPatrick Shiel:github.com/patrickshielLicenseMIT License
Package: pyproject-validate
pyproject-validateCI/CDPackageMetaValidate and formatpyproject.tomlfiles.Table of ContentsInstallationUsageValidatorsSpecsNamingDependenciesLicenseInstallationpip install pyproject-validateUsageusage: pyproject-validate [-h] [--fix] [--config CONFIG] [--version]optional arguments:-h, --help       show this help message and exit--fix            whether to apply fixes for any encountered errors--config CONFIG  explicit path to the project config file--version        show program's version number and exitValidatorsSpecsAdhere to the data model defined byPEP 517andPEP 621.NamingEnsure normalized project names.Before:[project]name="Foo.bAr"After:name="foo-bar"DependenciesEnsure normalized and sortedPEP 508dependency definitions.Before:[project]dependencies=["python-dateutil","bAr.Baz[TLS]   >=1.2RC5",'Foo;python_version<"3.8"',]After:dependencies=["bar-baz[tls]>=1.2rc5","foo; python_version < '3.8'","python-dateutil",]Licensepyproject-validateis distributed under the terms of theMITlicense.
Package: pyprojectviz
Pyprojectviz (Python Project Visualization Tool)OverviewThis tool generates a Graphviz diagram of the Python project structure, showing classes, methods, and their relationships, including method calls. It's designed to help developers understand the architecture of a Python codebase by visually mapping out its components.Pyprojectviz (Python Project Visualization Tool)OverviewFeaturesRequirementsInstallationSourcePipUsageSourcePipCustomizationLicenseContributionsFeaturesVisualizes classes and methods in a Python project.Shows relationships between methods, including method calls.Allows excluding common built-in method calls (likejoin,split,append) from the visualization.Supports different Graphviz layout engines for various diagram styles.Customizable through configuration file.RequirementsPython 3.8 or higherGraphviz installed on your system (Graphviz Download Page)InstallationSourceEnsure you have Python and Graphviz installed on your system.Clone the repository:gitclonehttps://github.com/codekuu/pyprojectviz.gitInstall the Python packagesgraphvizandtyperusing Poetry:poetryinstallPipEnsure you have Python and Graphviz installed on your system.Install the Python packagepyprojectvizusing Pip:pipinstallpyprojectvizUsageSourceRun the script from the command line with the following arguments:poetryrunpythonpyprojectviz.--configexamples/example_conf_file.yamlPipRun the script from the command line with the following arguments:python-mpyprojectviz.--configexamples/example_conf_file.yamlThis will generate a Graphviz diagram of the project at the "." path, using the specified configuration file.CustomizationPyprojectviz can be customized through a configuration file can be found in theexamplesdirectory.LicenseGNU General Public License (GNU GPL)ContributionsContributions are welcome. Please send pull requests or open issues to discuss proposed changes.
Package: py-proj-init
py-proj-initA simple Python3 project directory initializer.How to use# installpip3installpy-proj-init# runpy-proj-initYOUR-PROJECT-NAME# or juspy-proj-initWhat you will get.
└── [project namespace]
    ├── [project package name]
    │   ├── data
    │   │   └── .gitkeep
    │   │
    │   ├── __main__.py
    │   └── __init__.py
    │
    ├── tests
    │   └── test.py
    │
    ├── .gitignore
    ├── LICENSE[DEFAULT MIT]
    ├── MANIFEST.in
    ├── README.md
    ├── requirements.txt
    └── setup.pyRefshttps://packaging.python.org/tutorials/packaging-projects/https://setuptools.readthedocs.io/en/latest/setuptools.html
Package: pyproj-smthnspcl
pyprojtool to speed up developmentfeatures?creates a project structure for youcompiles your projectpypi integrationimplies you have already created your pypirc and installed twinehow to ..... installpip3installgit+https://github.com/smthnspcl/pyproj... use by cliusage:pyproj[-h]-nNEW[--no-mit]-aAUTHOR

optionalarguments:-h,--helpshowthishelpmessageandexit-nNEW,--newNEWprojectname--no-mit-aAUTHOR,--authorAUTHORauthorname

eg.:pyproj-n"test"-a"Pascal Eberlein"... use by codefrompyprojimportProjectProject.new("testProject",True,"Pascal Eberlein")
Package: pyprojstencil
GistSource Code RepositoryRepositoryDocumentationBadgesDescriptionCreate a python project from template.What does it doCreate a pythonprojecttree with the following featuresgit initvirtualenv.venvlocated inproject’srootdocs (sphinx-readthedocs)unit testscoverage reportcode-aid for planningplan.organd common actions
- init_venv (initialize venv with linter, importmagic, etc)
- install (install in local environment)
- coverage (test code and create a coverage badge)
- pypi (upload to testpypi repository)
Package: pypromice
pypromicepypromice is designed for processing and handlingPROMICEautomated weather station (AWS) data.It is envisioned for pypromice to be the go-to toolbox for handling and processingPROMICEandGC-Netdatasets. New releases of pypromice are uploaded alongside PROMICE AWS data releases to ourDataversefor transparency purposes and to encourage collaboration on improving our data. Please visit the pypromicereadthedocsfor more information.If you intend to use PROMICE AWS data and/or pypromice in your work, please cite these publications below, along with any other applicable PROMICE publications where possible:Fausto, R.S., van As, D., Mankoff, K.D., Vandecrux, B., Citterio, M., Ahlstrøm, A.P., Andersen, S.B., Colgan, W., Karlsson, N.B., Kjeldsen, K.K., Korsgaard, N.J., Larsen, S.H., Nielsen, S., Pedersen, A.Ø., Shields, C.L., Solgaard, A.M., and Box, J.E. (2021) Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth Syst. Sci. Data, 13, 3819–3845,https://doi.org/10.5194/essd-13-3819-2021How, P., Wright, P.J., Mankoff, K., Vandecrux, B., Fausto, R.S. and Ahlstrøm, A.P. (2023) pypromice: A Python package for processing automated weather station data, Journal of Open Source Software, 8(86), 5298,https://doi.org/10.21105/joss.05298How, P., Lund, M.C., Nielsen, R.B., Ahlstrøm, A.P., Fausto, R.S., Larsen, S.H., Mankoff, K.D., Vandecrux, B., Wright, P.J. (2023) pypromice, GEUS Dataverse,https://doi.org/10.22008/FK2/3TSBF0InstallationQuick installThe latest release of pypromice can installed using conda or pip:$ conda install pypromice -c conda-forge$ pip install pypromiceFor the most up-to-date version, pypromice can be installed directly from the repo:$ pip install --upgrade git+http://github.com/GEUS-Glaciology-and-Climate/pypromice.gitDeveloper installpypromice can be ran in an environment with the pypromice repo:$ conda create --name pypromice python=3.8
$ conda activate pypromice
$ git clone git@github.com:GEUS-Glaciology-and-Climate/pypromice.git
$ cd pypromice/
$ pip install .Additional dependenciesAdditional packages are required if you wish to use pypromice's post-processing functionality.eccodesis the official package for BUFR encoding and decoding. Try firstly to install with conda-forge like so:$ conda install -c conda-forge eccodesIf the environment cannot resolve the eccodes installation then follow the steps documentedhereto download eccodes and then install eccodes' python bindings using pip.$ pip3 install eccodes-python
Package: pyprompt
PyPromptLLM Prompt Validation and ManipulationThis project provides functionality for creating prompts for LLMs, including their validation and manipulation. This includes functionality for limiting the number of tokens used for LLM prompts and distributing these tokens among the parts of the LLM message.Note that the tokenizer used here may not be exactly the same as the one used by the LLM, so the actual number of tokens used may be greater than or less than this module estimates. If using more tokens the the context window is problematic, you can add a margin to this module's estimate by simply passing in a smaller number of tokens than the context window.DependenciesThe required packages can be installed by runningpoetry install. This depends onpoetrybeing installed locally; instructions can be foundhere.UsageFor direction on how to use this module, please see functionexampleinpyprompt/prompt_validation.pyand read the appropriate docstrings as necessary.TestsThe test suite can be run usingpoetry run pytest.
Package: pypromptpay
pypromptpayQR Code PromptPay in Python 3.Installpip install pypromptpayUsingfrompypromptpayimportqr_codeqr_code(account,one_time=True,path_qr_code="",country="TH",money="",currency="THB")account is phone number or  identification number.one_time : if you use once than it's True.path_qr_code : path save file qr code image.country : THmoney : money (if have)currency : THBreturn True (if have path_qr_code) or text (if haven't path_qr_code)LicenseApache Software License 2.0DevelopWannaphong Phatthiyaphaibun (wannaphong@kkumail.com)Referenceแนวนโยบายการใช้มาตรฐาน Thai QR Code ในธุรกรรมการชำระเงิน
Package: pyproms
# PyPROMSPyPROMS is a library of Python classes that helps Python coders generate provenance reports using the PROV and PROMS OWL Ontologies. It also has a function to send a Report to an instances of the [PROMS Server](http://promsns.org/wiki/proms) for storage.The Python classes match the PROMS-O OWL classes, for example the class ‘Report’ matches the PROMS-O ‘Report’ class.PyProms is expected to be extended so if you need so add to it to get your reporting job done, please do so!## License
PyPROMS is licensed using the MIT License, an a copy of the deed is contained within this repository:[License deed](LICENSE.txt)## ContactThe development of PyProms is lead by Nicholas Car at the [CSIRO](http://csiro.au) and includes members of the CSIRO informatics community. Please contact him for any help.Nicholas CarSenior Experimental ScientistCSIRO Land & Water
Brisbane, Queensland
<nicholas.car@csiro.au>
<https://orcid.org/0000-0002-8742-7730>We are interested in any extensions people have made to PyPROMS and are keen to take on suggestions so please get in touch!
Package: pyproofpoint
PyProofpointPython Wrapper for Proofpoint's Threat Insight APIInstallpip3 install pyproofpointDocshttps://help.proofpoint.com/Threat_Insight_Dashboard/API_Documentation/Campaign_APIUsagefrom pyproofpoint import proofpoint
from datetime import date

servicePrincipal = "Service Account"
APISecret = "Service AccountSecret"

pp = proofpoint.ProofPoint(servicePrincipal, APISecret)

my_date = date(2020, 12, 12).isoformat()
interval = "PT12H/" + my_date

campaigns = pp.get_campaign_ids(interval=interval)
print(campaigns)
Package: pyprop8
pyprop8This package provides a lightweight Python implementation of the seismogram calculation algorithm set out inO'Toole & Woodhouse (2011), together with the source derivatives set out inO'Toole, Valentine & Woodhouse (2012). It is intended to provide a lightweight, easy-to-install seismological forward model suitable for use in teaching and research (in particular, to provide a computationally-cheap yet physically-realistic forward problem for use in the development and testing of inversion algorithms).Full documentation isavailable here, and you can try the package outusing Binder.A list of projects that have made use ofpyprop8can be foundhere; please feel free toadd your own.InstallationThe easiest way to get this package is throughpip, e.g.pip install pyprop8Alternatively, you can clone this repository and ensure that the contents of thesrc/directory are placed somewhere where they can be found by your Python installation, e.g. by setting the $PYTHONPATH environment variable.DependenciesThe core functions of this package depend only on:numpyscipypyprop8will also usetqdmif it is available, to display progress bars.Some of the examples found inexamples/may have additional dependencies and requirements, includingJupyterandmatplotlib. Look for arequirements.txtfile in the relevant example directory; you can install the necessary pacakges using the commandpip install -r requirements.txt.Citing this packageIf you make use of this code, please acknowledge the work that went into developing it! In particular, if you are preparing a publication, we would appreciate it if you cite both the paper describing the general method, and this specific implementation:O'Toole, T.B. & J.H. Woodhouse (2011), "Numerically stable computation of complete synthetic seismograms including the static displacement in plane layered media", Geophysical Journal International, 187, pp.1516-1536, doi:10.1111/j.1365-246X.2011.05210.xValentine, A.P. & M. Sambridge (2022), "pyprop8: A lightweight code to simulate seismic observables in a layered half-space", Journal of Open Source SoftwareIf your work relies on being able to calculate the source parameter derivatives, you should also cite the paper describing how these are obtained:O'Toole, Valentine & Woodhouse (2012), "Centroid–moment tensor inversions using high-rate GPS waveforms", Geophysical Journal International, 191, pp.257-270, doi:10.1111/j.1365-246X.2012.05608.xAcknowledgementsThis package was developed at the Australian National University and Durham University by Andrew Valentine. It builds on earlier work by Tom O'Toole and John Woodhouse, and has benefited from sight of code written by those authors. In particular, the exponential rescaling of the propagator matrices is inspired by their implementation. However, the present code has been developed 'from scratch' based on the published algorithms.This work has received support from the Australian Research Council under grants DE180100040 and DP200100053.
Package: pyprophet
PyProphetPyProphet: Semi-supervised learning and scoring of OpenSWATH results.PyProphet is a Python re-implementation of the mProphet algorithm [1] optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in [2] and has since been extended to support new data types and analysis modes [3,4].Please consult theOpenSWATH websitefor usage instructions and help.Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R.mProphet: automated data processing and statistical validation for large-scale
SRM experiments.Nat Methods.2011 May;8(5):430-5.doi:
10.1038/nmeth.1584.Epub 2011 Mar 20.Teleman J, Röst HL, Rosenberger G, Schmitt U, Malmström L, Malmström J, Levander F.DIANA--algorithmic improvements for analysis of data-independent acquisition MS data.Bioinformatics.2015 Feb 15;31(4):555-62.doi: 10.1093/bioinformatics/btu686.Epub 2014 Oct 27.Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R.Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS.Nat Biotechnol2017 Aug;35(8):781-788.doi: 10.1038/nbt.3908.Epub 2017 Jun 12.Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R.Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.Nat Methods.2017 Sep;14(9):921-927.doi: 10.1038/nmeth.4398.Epub 2017 Aug 21.InstallationWe strongly advice to install PyProphet in a Pythonvirtualenv. PyProphet is compatible with Python 3.Install the development version ofpyprophetfrom GitHub:$ pip install git+https://github.com/PyProphet/pyprophet.git@masterInstall the stable version ofpyprophetfrom the Python Package Index (PyPI):$ pip install pyprophetRunning pyprophetpyprophetis not only a Python package, but also a command line tool:$ pyprophet --helpor:$ pyprophet score --in=tests/test_data.txtDockerPyProphet is also available from Docker (automated builds):Pull the stable version (e.g. 2.1.2) ofpyprophetfrom DockerHub (synced with releases):$ docker pull pyprophet/pyprophet:2.1.2Running testsThepyprophettests are best executed usingpy.testand thepytest-regtestplugin:$ pip install pytest
    $ pip install pytest-regtest
    $ py.test ./tests
Package: pypros
PyPROS, precipitation type calculation: rain or snowThe PyPROS package applies several algorithms to estimate the precipitation type and discriminate it among rain, sleet and snow.Given an air temperature and dew point temperature fields a precipitation type field can be obtained. In addition, if a reflectivity field is supplied, the precipitation area is masked.InstallationTo install pyPROS you can have a look atpyPROS installationdocumentation.UseDifferent precipitation type discrimination schemes are implemented in this package. More information inpyPROS documentationA set of examples were designed to explore the different possibilities of PROS.Another repository was created to store sample data and the examples in jupyter notebookspypros-examples
Package: pyprosail
PyProSAILPyProSAIL is a Python interface to the ProSAIL combined leaf and canopy optical model.PyProSAIL was developed byRobin Wilsonand is now maintained by theEarth Observation of Agroecosystems Teamat theSwiss Center For Excellence in Agricultural Research, Agroscope.This Pyhon interface (note: not the ProSAIL model itself) is released under the GNU Lesser General Public License.
For more information seehttp://pyprosail.readthedocs.org.
Package: pyprosegur
PyProsegurPython library to retrieve information fromProsegur Residential AlarmsRequirementsbackoffaiohttpaiofileClickChangelogSee CHANGES.mdNOTES:This project has no relationship with Prosegur (unofficial library).The component uses the API provided by the Web Application.Has been developed/tested in Portugal, but should also work in other countries (please open an Issue and report your success!)LICENCE:Code is under MIT licence.
Package: pyprost
PROST python package v0.2.15PRotein Ortholog Search Tool is a new homolog detection tool that utilizes ESM-1b language model and iDCT quantization method.
PROST is fast and accurate compared to traditional tools.InstallationThe package can be installed with:pip install pyprostOn the initial run, PROST will download required files to~/.config/prostor an user defined directory viaPROSTDIRenvironment variable.How to useFollowing commands can be used to create databases and perform homology search.prost.py makedb db/sp.fa db/sp.prdb
prost.py makedb db/covid.fa db/covid.prdb
prost.py search --thr 0.05 --jobs 8 db/covid.prdb db/sp.prdb results
prost.py searchsp --thr 0.05 --jobs 8 db/covid.prdb results
prost.py tosjonwp -a -i 'info' results.tsv websitemakedb: creates a PROST database from given fasta file. The fasta file usually contains more than one entry.search: searches a query database agains a target database. Query database can contain one or more sequences embedded using makedb command.--thrcan be used to specify an e-value threshold. The default threshold is 0.05. You can paralelize the search by using--jobsoption.searchsp: searches a query database agains a SwissProt February 2023 database. Performs GO enrichment analysis on found homologs. Query database can contain one or more sequences embedded using makedb command. Again--thrcan be used to specify an e-value threshold.--gothrcan be used to specifiy different e-value threshold for GO enrichment analysis.searchspproduces a tab seprataed file.tsvThis file can be converted into a.jsonfile that can be used with the toolJSONWPusing the commandprost.py tojsonwp -i 'Here is an info string to shown on website' results.tsv websiteHere is an example result:Sequence alignment done automatically using thePROTSUBmatrix which is better for aligning remote proteins then BLOSUM62 matrix.
Protein structures are fetched from the Alphafold 2 database. No alignment produced this time.Scriptingimport pyprost

hpo30 = '''MPLIMYKFLLVTSIFLIVSGLILTAFSLFSPLWEVVDFPRSHLSHHHGLWWDCIVHHET
LIPLHEDQAELRGDRCDSKMDSSVQASLRVALEKGDEEARELLLHRFLPHHKGVIFFAVF
TFVFGLISILIGSCSPCFPPNALLYVVGVFMTGACSLLADIIYIFAFNQKPIFTKEQSEP
HQEVLSRRERGSIGPIYKRLGIATYMHMFGSMLLIAAFIFSIFCAYFLITSKHAHDVCCT
SRKEYREQTKWKNNGLILKTGRVNHQSHRPFVVIDDDSSM'''

clc2 = '''MSQAVSYAILVLTIIAFLLTAAALCTPAWQVVYAREIRQWVQSGLWLSCQTRPNGMYSCT
YTFSHDDFNTYFSDEVSGFRTPSFYPWQRTLFHIYLISQAFAMLSLISFCVSVSHKESKM
PNILRSVFLVLAAVIAFGCLIAFAVYSYMVEYRFFHVSVSGIYEKHRGYSWYIALTGAFV
YLVAIILSVVHVLLQARNSNTTMSRQNINSSLQSDFFEYQYHPNRSMESFEDRFAMRTLP
PVPRQEKKTTVF'''

hpo30embedding = pyprost.quantSeq(hpo30)
clc2embedding = pyprost.quantSeq(clc2)

dist = pyprost.prostDistance(hpo30embedding,clc2embedding)
print('HPO30-CLC2 prost distance:',dist)
#Should print: HPO30-CLC2 prost distance: 3479.0
#Distance smaller than 6875 may indicate homologyResourcesYeast analysis:https://mesihk.github.io/prostyeastUnannotated Human Proteins Analysis:https://mesihk.github.io/prosthumanWebserver:https://mesihk.github.io/prostPROST Python package:https://github.com/MesihK/prostPROST Research Data:https://github.com/MesihK/prost-dataJSONWP:https://jsonwp.onrender.com/