Saravanan666/pypi-org huggingface dataset:
Package: pyppluss
pyPplusSpyPplusS is a code package accompanying the paper "Fast and precise light-curve model for transiting exoplanets with rings" (doi: 10.1093/mnras/stz2556, ads bibcode: 2019MNRAS.490.1111R).
pyPplusS is a model for the light curve of ringed exoplanets transits’ for uniform and limb darkened stars. The calculation is done using the Polygon+Segments algorithm, described in the above paper.pyPplusS provides calculation of light curves for ringed, oblate or spherical exoplanets in both the uniform and limb darkened cases.InstallationRun the following to install pyPplusS:pip install pypplussPackage StructureThe package is split into five files:polygon_plus_segments is an implementation of the Polygon+Segments algorithm described in the above paper.segment_models contains functions for modelling light curves in the uniform and limb darkened cases.err_order_fin is a script described in appendix C, which aids in setting the order of numerical integration.base_functions contains a variety of "low-level" helpers.fastqs is a python implementation of the Fast Quartic Solver described byStrobach (2010).Detailed descriptions of all inputs and outputs are given by comment lines and documentation text in the code.UsageTo use pyPplusS, import the following function:from pyppluss.segment_models import LC_ringedThis function will return the light curve of ringed exoplanets transits’ for limb darkened stars.DependenciesDependencies: numpy, scipy, python 3.The package was tested with numpy 1.16.3, scipy 0.19.1, python 3.6.8.IssuesIssues can be reported at GitHub, at the EdanRein/pyPplusS repository.LicenseMITAcknowledgmentsThis package was written by Edan Rein and Dr. Aviv Ofir, during work at Prof. Oded Aharonson's lab at the
Center for Planetary Science in the Department of Earth and Planetary Sciences, Weizmann Institute of Science.CitationsIf you use this code, please citeRein and Ofir (2019).
The abstract is availablehere, and
the paper is available on arXivhere.
Package: pyppmd
Introductionpyppmdmodule provides classes and functions for compressing and decompressing text data,
using PPM(Prediction by partial matching) compression algorithm which has several variations of implementations.
PPMd is the implementation by Dmitry Shkarin.
PyPPMD use Igor Pavlov’s range coder introduced in 7-zip.The API is similar to Python’s bz2/lzma/zlib module.Some parts of th codes are derived from7-zip,pyzstdandppmd-cffi.Development statusA project status is considered asStable.Extra input bytePPMdalgorithm and implementation is designed to useExtrainput byte.
The encoder will omit a last null (b”0”) byte when last byte is b”0”.
You may need to provide an extra null byte when you don’t get expected size of
extracted data.You can do like as:dec = pyppmd.Ppmd7Decoder(max_order=6, mem_size=16 << 10)
result = dec.decode(compressed, length)
if len(result) < length:
    if dec.needs_input:
        # ppmd need an extra null byte
        result += dec.decode(b"\0", length - len(result))
    else:
        result += dec.decode(b"", length - len(result))WarningWhen use it on MSYS2/MINGW64 environment, you should set environment variableSETUPTOOLS_USE_DISTUTILS=stdlibCopyright and LicenseSome codes are derived from p7zip/7zip and pyzstd project.
Details are shown in LicenseNotices.rstSPDX-License-Identifier: LGPL-2.1-or-laterSPDX-URL:https://spdx.org/licenses/LGPL-2.1-or-later.htmlPyPPMd is licensed under GNU Lesser General Public License v2.1 or later.Copyright (C) 2020-2023 Hiroshi MiuraCopyright (C) 2020-2021 Ma LinCopyright (C) 2010-2012 Lockless Inc.Copyright (C) 1999-2017 Igor PavlovThis library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
Package: pyppms
PyPPMSPUMAPI - Python InterfaceStratocore'sPPMSbooking system offers an API (the so-calledPUMAPI, short for
PPMS Utility Management API) for fetching information from the booking system as well as
changing its state and properties.This is a Python 3 package for talking to thePUMAPI.Usage ExampleFetch email addresses of all active users:frompyppmsimportppmsfromcredentials_ppmsimportPPMS_URL,PPMS_API_KEYconn=ppms.PpmsConnection(PPMS_URL,PPMS_API_KEY)print("Querying PPMS for emails of active users, can take minutes...")emails=ppms.get_users_emails(active=True)print(f"Got{len(emails)}email addresses from PPMS:")print("\n".join(emails))TestingAutomated testing is described in theTESTINGdocument on github.NoteThe PPMS API sometimes exposes a bit of a surprising behavior. During
development of the package, we came across several issues (this list is
certainly incomplete):HTTP status return code is always200, even on failed authentication.Results of queries are a mixture of CSV (with headers) and and text with
newlines (with no headers and therefore without structural information on
the data). JSON is implemented in some cases only.The CSV headers sometimes do contain spaces between the colons, sometimes
they don't.Some fields are quoted in the CSV output, some are not. Difficult to separate
the values since there are colons in the values too.Semantics of keys is not consistent. Sometimesuseris the user id,
sometimes it refers to the user's full name.Using an invalid permission level (e.g.Z) with thesetrightaction is
silently ignored by PUMAPI, the response is stilldoneeven though this
doesn't make any sense.There is no (obvious) robust way to derive the user id from the user's full
name that is returned e.g. bygetrunningsheet, making it very hard to
cross-reference it with data fromgetuser.The result of thegetrunningsheetquery in general is not suited very well
for automated processing, it seems to be rather tailored for humans and
subject to (mis-) interpretation.UnfortunatelyUsernameandSystemnameare not the unique id, they are
rather the full description. Therefore sometimes looping over all users and
systems is necessary.Some results have a very strange format - for example, the starting time of
the next booking is given asminutes from nowinstead of an absolute time.Official documentation is rather rudimentary, i.e. it contains almost no
information on what is returned in case wrong / invalid parameters are
supplied and similar situations.ReferencesImagopole PPMS Java client
Package: py-ppnet
简介PPNetwork 是无中心节点的网络驱动，可以在任何环境下建立数据连接。PPNetwork采用UDP，自动寻找同类节点，并自动寻找到目标地址的路径，发送数据。至少需要添加一个初始的对等节点。可以通过对等节点获得其邻居信息，逐渐扩大自己的网络。上层应用可以通过寻找好友的方式添加对等节点。安装pip install py-ppnet使用ppnetwork 不能独立运行，需要根据接口，开发上层应用。
需要至少一个可访问到的peer节点，可以通过配置也可以在运行时动态增加。config模式config.yaml:node_port: 54320
nodes: [["172.31.102.109",54320]]程序代码:from ppnet import PPNetNode

ppnetnode = PPNetNode(config=config)
ppnetnode.start()
ppnetnode.send(data,addr1)
data,addr2 = ppnetnode.receive(count)
...
ppnetnode.quit()动态增加模式程序代码:from ppnet import PPNetNode

ppnetnode = PPNetNode(node_port=54320)
ppnetnode.set_peer(addr)
ppnetnode.start()
ppnetnode.send(data,addr1)
data,addr2 = ppnetnode.receive(count)
...
ppnetnode.quit()
Package: pyppr
PyPPRThePyPPRpackage is a collection of financial functions regarding PPRs, or Planos
Poupança Reforma, which are retirement plans specific to Portugal.The documentation for this package is available athttps://goncalovf.github.io/pyppr/and the source code athttps://github.com/goncalovf/pypprThe importable name of the package ispyppr.>>>importpyppr>>>npf.matching_ppr_cost_rate(20,0.07,0.28,0.08,0)0.0048310556726159914
Package: pypprof
pypprofpypprof adds HTTP-based endpoints for collecting profiles from a running Python application a la Go'snet/http/pprof.Under the hood, it useszprofileandmprofileto collect CPU and heap profiles with minimal overhead.UsageAdd profiling endpoints to an applicationRegister the profiling endpoints in your application:frompypprof.net_httpimportstart_pprof_serverstart_pprof_server(port=8081)Fetch profiles from your running applicationFetch a 30s CPU profile, and view as a flamegraph:$gotoolpprof-http=:8088:8081/debug/pprof/profileFetch a heap profile:$gotoolpprof:8081/debug/pprof/heapDump stacks from your application:$curllocalhost:8081/debug/pprof/thread?debug=1Compatibilitypypprof is compatible with Python >= 2.7. Memory profiling is only available by default in Python >= 3.4. To enable memory profiling in earlier Pythons, you must patch Python and manually installmprofile.ContributingPull requests and issues are welcomed!Licensepypprof is released under theGNU Lesser General Public License, Version 3.0
Package: pypptx
Create a pptx from ayamlfile.InstallpipinstallpypptxUsage:To generate the pptx from a yaml filepptxfile.yamlInput yaml exampleslides:-title:first titletext:some textlayout:0-title:second titletext:more textlayout:1images:-path:images/blue.jpgtop:7left:4height:5-path:images/blue.jpgtop:5left:10-title:third slidetext:with the previous layoutThis repository have a yaml file called example.yaml with a example of the input syntax.List the layouts ids to use in yaml filepptx-l0TitleSlide1TitleandContent...With the layout id select which use...-title:titletext:subtitlelayout:0-title:Other slidetext:with other layoutlayout:1...Why?Some people prefer usage a pptx, in this case I prefer write a plain text to generate the pptx.
Package: pypqd
pypqdPower quality disturbances dataset generatorInstallation$pipinstallpypqdUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensepypqdwas created by Rafael Angel Montoya Gutierrez. It is licensed under the terms of the GNU General Public License v3.0 license.Creditspypqdwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: pypr0
Python api for pr0gramm.comInstallationpippip3 install pypr0ManuallyFor Debian and Ubuntu users:apt install python python3-pippip3 install requestsThen clone the repositorygit clone https://github.com/itssme/pypr0.gitFor running the tests go into the cloned foldercd pypr0Run the tests with following commandpython3 tests.pyFor running tests with loginUSERNAME="itssme" PASSWORD="1234" LOGIN="true" python3 tests.pyExamplesFor examples see this repository:https://github.com/itssme/pypr0-examplesRelease Notes0.2.8current versionadded newpolflag tocalculate_flagfunction (ref:https://github.com/itssme/pypr0/pull/10); thanks to@iFuzzlefixed sql_manager and made it more resilient to API changes (new values will now be ignored and don't break the sql manager)0.2.7more improvements and refactoring (ref:https://github.com/itssme/pypr0/pull/8); thanks to@chill0r0.2.6improvement of the login functions (ref:https://github.com/itssme/pypr0/pull/7); thanks to@chill0radded more documentation to api classes0.2.5a lot of refactoring and cleaning up in all api functionspassing 'newer' toget_items_by_tagis now deprecated, instead pass 'older' as a boolean and pass an 'item' as id (just like for any other api function)more extensive testspassing multiple tags to items get now works again (ref:https://github.com/itssme/pypr0/pull/6); thanks to@5n0wstorm0.2addedget_collection_itemsandget_collection_items_iteratorwhich gets the content of a collection (replacing favorites)0.1.7updated to python3thanks to@FritzJo
Package: pyPragyanX
pyPragyanXAbout:pyPragyanX have many useful functions, that you can use in your repos/repostory. It'll make you code/bot smooth and fast!Installationpip3installpyPragyanXImport functionsfromPragyanX.functionsimport<functionsname>Functions available:Click HereExample.fromPragyanX.functionsimportban_userfrompyrogramimportClient,filtersfrompyrogram.typesimportMessage@Client.on_message(filters.command("ban"))asyncdefban_(client,message):awaitban_user(client,message)
Package: pyPrairieView
pyPrairieViewA Python libary for interacting with PrarieView software.Highlights (Aspirational)Electrophysiology protocol designElectrophysiology analysis (initially dependent on protocol design)Metadata handling of XML filesImaging file compilationData stream generationOnline motion correctionROI-Mark Points translationMore to come in the future!Installationpip install pyPrairieViewDocumentationHosted atReadtheDocs.
Available locally asHTML,LATEXandPDF.
Package: pypraware
# Description of the module:This module had been created to simplify the code available onhttps://github.com/MISP/misp-privacy-aware-exchangeand to create an export module.
This is also intended to make easier the creation of an integrated export module in MISP.The module pypraware_misp (python privacy aware for MISP) is separated into two different subparts, the first is used for the “crypto systems” called pypraware_crypto while the second one is used for normalisation and is called pypraware_normalize.
Package: pyprec
PyPReC: Python Package Repository CreatorThis package automates the process of repository creation for Python packages.InstallationThe package can be installed with Python's pip package manager.pipinstallpyprecOr manually:gitclonehttps://github.com/marcorossi5/pyprec.gitcdpyprec
pipinstall.The last command installs thepyprecprogram into the environment python path.Running the codePackage help message:$bash--help
usage:pyprec[-h][-rRUNCARD][-l{DEBUG,INFO,WARNING,ERROR,CRITICAL}]optionalarguments:-h,--helpshowthishelpmessageandexit-rRUNCARD,--runcardRUNCARDThesettingsruncardfile.-l{DEBUG,INFO,WARNING,ERROR,CRITICAL}setlogginglevelIf a runcard is specified, settings are loaded from it. Please, find a default
runcardhere.If no runcard is given by the user, the interactive mode is triggered: answer
the prompted questions to proceed.:warning:Note:Both modes require that theprefix_folderdoes not contain anysrcfolder
inside. This is needed to ensure a clean workspace and avoid unintentional file
deletion.
Package: pyprecag
A suite of tools for Precision Agriculture data analysisHomepage:https://github.com/CSIRO-Precision-Agriculture/pyprecagDocumentation:https://CSIRO-Precision-Agriculture.github.io/pyprecag_docsInstallationVersion: 0.4.0License:CSIRO Open Source Software License Agreementpyprecag is currently supported for Python version 2.7 and 3.7DependenciesPython version 2.7, 3.7GDALFionaRasterioGeopandasVESPER(for Kriging)DevelopmentThere is a makefile in the project root with targets for the most common
development operations such as lint checks, running unit tests, building the
documentation, and building installer packages.toxdoes not have a target,
asmake toxis more typing thantox.Run make with no target to see the list of targets:$ makeBumpversionis used to manage the
package version numbers. This ensures that the version number is correctly
incremented in all required files. Please see the bumpversion documentation for
usage instructions, and do not edit the version strings directly.Version numbers follow theSemantic versioning guidelines.
Package: pypreclin
Python processing for PreClinical data.Home-page:https://github.com/neurospin/pypreclinAuthor:
Antoine Grigis <antoine.grigis@cea.fr>
Jordy Tasserie <jordy.tasserie@cea.fr>
Bechir Jarraya <bechir.jarraya@cea.fr>Author-email:antoine.grigis@cea.frLicense: CeCILL-B
Description: pypreclin is a Python project that provides a collection of Python scripts for processing MRI preclinical datasets. This work is made available by a community of people, amoung which the CEA Neurospin UNATI and CEA NeuroSpin UNICOG laboratories, in particular A. Grigis, J. Tasserie, and B. Jarraya.Platform: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: X11 Applications :: Qt
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Provides-Extra: gui
Package: pypredictor
pypredictorThe Python library that makes AI predictions simple.View theGitHub PageWhat can it do?pypredictor uses an RNN (Recurrent Neural Network) to predict the next n numbers in a sequence. As an example, using this code:pred=NumPredictor(500)print(pred.predict([1,2,3,4,5,6,7,8,9,10],5,3))pypredictor generated the following output, which is quite accurate:[10.969043, 11.950292, 12.920968, 13.859894, 14.789316]pypredictor also has the ability to generate a pandas DataFrame and a seaborn line graph, from an initial sequence/DataFrame which you provide.Plus, there are examples in theexamples/directory, so you can take a look for yourself :)How to installInstall via pip:$ pip install pypredictorTo get examples, clone this repository and enterpypredictor/examples:$ git clone https://github.com/hamdivazim/pypredictor.git
$ cd pypredictor/examplesanaconda support hopefully coming soon!Licensepypredictor is licensed by Hamd Waseem (codingboy_CW) underthe Apache License 2.0.
Package: pyPrediktorMapClient
pyPrediktorMapClientHelper functions for Prediktor Map ServicesHelper functions for communicating with Prediktors OPC UA ModelIndex REST-API and OPC UA
Values REST-API. Typically used for data anlytics purposes, you’ll find Jypiter Notebooks
with examples in the notebooks folder.NoteThis project has been set up using PyScaffold 4.3. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: pyPrediktorUtilities
pyPrediktorUtilitiesHelper functions for Prediktor projectsThe included classes and functions allows for easier and more maintainable code when
working with Prediktor projects. The main features are:Sending emailsWorking with FTP and SFTP serverLoggingGenerating files through templatesNoteThis project has been set up using PyScaffold 4.3. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: pypref
PYthon PREFrences or pypref is a python implementation to creating application’s configuration, preferences or settings file and dynamically interacting, updating and pulling them. Most applications have default preferences and in general those are created and stored in a text file (.ini, xml, json, etc). At some point preferences are pulled and updated by the application and/or users. pypref is especially designed to take care of this task automatically and make programming application’s preferences more desirable. In addition, pypref allows creating dynamic preferences that will be evaluated real time. This becomes handy in plethora of applications for authentication, automatic key generation, etc.
Package: pypremiumsim
pyPremiumSIMA Python API for accessing yourPremiumSIM.deaccount balance & usage stats. This is an unoffical API and the author/contributors are in no way connected to PremiumSIM or Drillisch. The API provides a method to:Get your current month's data balances (consumed and remaining GB)Installationpip install pypremiumsimUsageIt's very easy to use. Try the following to get your balance data:frompypremiumsimimport*frompprintimportpprintsession=PremiumSimSession()session.try_login("<username>","<password>")data_used=session.current_month_data_usage()pprint(vars(data_used))returns:{'consumed_data_gb':0.73585937,'tariff_total_data_gb':20.0,'used_percentage':3.7}Testspython ./tests/tests.pyContributingFork this repo, make some changes and create a new pull request!
Package: pyprep
pyprepFor documentation, see the:stable documentationlatest (development) documentationpyprepis a Python implementation of thePreprocessing Pipeline (PREP)for EEG data, working withMNE-Python.ALPHA SOFTWARE.This package is currently in its early stages of iteration.It may change both its internals or its user-facing API in the near future.Any feedback and ideas on how to improve either of these is welcome!Use this software at your own risk.Installationpypreprequires Python version3.8or higher to run properly.
We recommend to runpyprepin a dedicated virtual environment
(for example usingconda).For installing thestableversion ofpyprep, call:pip install pyprepor, as an alternative topip, call:conda install -c conda-forge pyprepFor installing thelatest (development)version ofpyprep, call:pip install git+https://github.com/sappelhoff/pyprep.git@mainBoth thestableand thelatestinstallation will additionally install
all required dependencies automatically.
The dependencies are defined in thesetup.cfgfile under theoptions.install_requiressection.ContributionsWe are actively looking for contributors!Please chime in with your ideas on how to improve this software by opening
a GitHub issue, or submitting a pull request.See also ourCONTRIBUTING.mdfile for help with submitting a pull request.Potential contributors should installpyprepin the following way:First they should forkpyprepto their own GitHub account.Then they should run the following commands,
adequately replacing<gh-username>with their GitHub username.git clone https://github.com/<gh-username>/pyprep
cd pyprep
pip install -r requirements-dev.txt
pre-commit install
pip install -e .CitingIf you use this software in academic work, please cite it using theZenodo entry.
Please also consider citing the original publication on PREP (see “References” below).
Metadata is encoded in theCITATION.cfffile.ReferencesBigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., & Robbins, K. A.
(2015). The PREP pipeline: standardized preprocessing for large-scale EEG
analysis. Frontiers in Neuroinformatics, 9, 16. doi:10.3389/fninf.2015.00016
Package: pyPreprocessing
pyPreprocessingGeneral informationFor preprocessing of datasets like Raman spectra, infrared spectra, UV/Vis
spectra, but also HPLC data and many other types of data, currently via
baseline correction, smoothing, filtering, transformation, normalization and
derivative. It relies on numpy, pandas, scipy, tqdm and scikit-learn, but also
onhttps://github.com/AlexanderSouthan/pyDataFittingfor the introduction of
equality constraints into the polynomial baseline estimation methods, andhttps://github.com/AlexanderSouthan/little_helpers.DocumentationPlease visit:https://alexandersouthan.github.io/pyPreprocessing/
Package: pypreql-nlp
PreQL NLPNatural language interface for generating PreQL query objects.PreQL is easier for a large language model to interact with as it requires only extract relevant concepts from a text,
classifying them as metrics or dimensions, and mapping them to what is available in a model.This makes it more testable and less prone to hallucination than generating SQL directly.Requires setting the following environment variablesOPENAI_API_KEYOPENAI_MODELRecommended to use "gpt-3.5-turbo" or higher as the model.ExamplesBasic BQ examplefromtrilogy_public_modelsimportmodelsfrompreqlimportExecutor,Dialectsfrompreql_nlpimportbuild_query# define the model we want to parseenvironment=models["bigquery.stack_overflow"]# set up preql executor# default bigquery executor requires local default credentials configuredexecutor=Dialects.BIGQUERY.default_executor(environment=environment)# build a query off text and the selected modelprocessed_query=build_query("How many questions are asked per year?",environment,)# make sure we got reasonable outputsforconceptinprocessed_query.output_columns:print(concept.name)# and run that to get our answerresults=executor.execute_query(processed_query)forrowinresults:print(row)Don't Expecct PerfectionResults are non-determistic and may not always be accurate.#generatedfromprompt:WhatisTaylorSwift'sbirthday?Howmanyquestionswereaskedonthatdayin2020?SELECTquestion.count,answer.creation_date.year,question.creation_date.year,question.creation_date,WHEREquestion.creation_date.year=1989ORDERBYquestion.countdesc,question.countdescLIMIT100;Setting Up Your EnvironmentRecommend that you work in a virtual environment with requirements from both requirements.txt and requirements-test.txt installed. The latter is necessary to run
tests (surprise).Pypreql-nlp is python 3.10+