Saravanan666/pypi-org huggingface dataset:
Package: cytoolz
Cython implementation of thetoolzpackage,which
provides high performance utility functions for iterables, functions,
and dictionaries.toolzis a pure Python package that borrows heavily from contemporary
functional languanges.  It is designed to interoperate seamlessly with other
libraries includingitertools,functools, and third party libraries.
High performance functional data analysis is possible with builtin types
likelistanddict, and user-defined data structures; and low memory
usage is achieved by using the iterator protocol and returning iterators
whenever possible.cytoolzimplements the same API astoolz.  The main differences are
thatcytoolzis faster (typically 2-5x faster with a few spectacular
exceptions) andcytoolzoffers a C API that is accessible to other
projects developed in Cython.  Sincetoolzis able to process very
large (potentially infinite) data sets, the performance increase gained by
usingcytoolzcan be significant.See the PyToolz documentation athttps://toolz.readthedocs.ioand the fullAPI Documentationfor more details.LICENSENew BSD. SeeLicense File.Installcytoolzis on the Python Package Index (PyPI):pip install cytoolzDependenciescytoolzsupports Python 3.7+ with a common codebase.
It is developed in Cython, but requires no dependecies other than CPython
and a C compiler.  Liketoolz, it is a light weight dependency.Contributions Welcometoolz(andcytoolz) aims to be a repository for utility functions,
particularly those that come from the functional programming and list
processing traditions. We welcome contributions that fall within this scope
and encourage users to scrape theirutil.pyfiles for functions that are
broadly useful.Please take a look at our issue pages fortoolzandcytoolzfor contribution ideas.CommunitySee ourmailing list.
We’re friendly.
Package: cytosim
CytosimCytosim is a cytoskeleton simulation suite designed to handle large systems of flexible filaments with associated proteins such as molecular motors. It is a versatile base that has been used to study actin and microtubule systems in 1D, 2D and 3D. It is built around a cross-platform C++ core engine running on UNIX, Mac OSX, GNU/Linux and within Cygwin on Windows. The code is modular and extensible, making Cytosim a convenient base that can be customized to meet particular tasks. Some of the most common tasks encountered during a simulation project are implemented in Python.Cytosim is a suite of command-line tools with simulation and display capabilities. The simulation is specified in aconfiguration file, defining objects with their parameters and a suite of operations, such as advancing time, saving frames orgenerating reports. Here is a basic example, with parameters specified inunits of seconds, micrometers and pico-Newtons.set simul system
{
    time_step = 0.005
    viscosity = 0.02
}

set space cell
{
    shape = sphere
}

set fiber microtubule
{
    rigidity = 20
    segmentation = 0.5
    confine = inside, 200, cell
}
    
new cell
{
    radius = 5
}

new 5 microtubule
{
    length = 11
}

run 5000 system
{
    nb_frames = 10
}DocumentationLink to documentationThe Brownian dynamics approach was described in:Collective Langevin Dynamics of Flexible Cytoskeletal FibersThe documentation source files useMarkdownand are best viewed withMacDown (Mac OSX only)orTypora (Cross platform)InstallationCytosim is distributed as source code andmust be compiledbefore use. On Mac OS X and Linux this should be uncomplicated even if you are not familiar with program development. Compiling natively on Windows would require changes to the code, but Cytosim shouldcompile within Cygwin.To download the source code, enter these commands in a terminal window:git clone https://gitlab.com/f.nedelec/cytosim
cd cytosimTo compile usingmake, try:makeIf this fails, parameters ofmakefile.incneed to be updated manually.
Altermatively, it is possible to usecmaketo configuremakeautomatically:mkdir build
cd build
cmake ..
makeFor troubleshooting, please checkthe compile instructions.
Oncecytosimis running on your machine, proceed with thetutorials, the page onrunning simulations, and the examples contained in the foldercym.
Inspect in particular the short configuration files (e.g. fiber.cym, self.cym).ContributorsThe project was started in 1995, and received its name in 1999.
We hope cytosim can be useful for your research.
Sincerely yours, The Developers of Cytosim:Francois J. Nedelec     1995-Dietrich Foethke        2003-2007Cleopatra Kozlowski     2003-2007Elizabeth Loughlin      2006-2010Ludovic Brun            2008-2010Beat Rupp               2008-2011Jonathan Ward           2008-2014Antonio Politi          2010-2012Andre-Claude Clapson    2011-2013Jamie-Li Rickman        2014-2019Serge Dmitrieff         2013-Julio Belmonte          2014-Gaelle Letort           2014-Manuel Lera-Ramirez     2017-2022Maud Formanek           2020-2021ContactEmail:cytosim@cytosim.org
Package: cytoskeleton-analyser
cytoskeleton-analyseris a postprocessor for extraction, analysis and human-friendly presentation
of the raw binary results generated by Explicit-Microtubules.Explicit-MicrotubulesExplicit-Microtubules is a C++ simulator of the whole-cell microtubule system.
It reconstructs computationally the microtubule geometry and real-time dynamics in 3d space and
time using stochastic algorithms. In the course of the simulation based on a multitude of bioogical
information, it outputs detailed histories of microtubule dynamics along with three-dimensional
snapshots of microtubules. However, designed to be focuced on versatile generation procedures,
Explicit-Microtubules only includes basic data analysis.Complementing the simulator,cytoskeleton-analyseris designed to extract, analyse
and visualize various characteristics of the stochastically reconstructed microtubule system.##RequirementsCytoskeleton Analyser requirespython 3.9or later.When installing bypip, the following dependencies are installedautomatically:NumPySciPyMatplotlibmeshioSQLAlchemy##InstallationProvided that python is present, in the simplest case the Cytoskeleton Analyser
may be installed globallly usingpip:pipinstallcytoskeleton-analyserHowever, the recommended way is installing the package into apython virtual environment, which adds only a few commans:Create a project directory (here named ‘myproject’) and cd to it.Create a python virtual environment (here named ‘venv3.9’).Activate the virtual environment.Install as above.mkdirmyproject&&cdmyproject
python3-mvenvvenv3.9sourcevenv3.9/bin/activate
pipinstallcytoskeleton-analyserAlternatively, if you want to install the python package straight from the GitHub repository,
replace the last line above withpipinstallgit+git://github.com/vsukhor/cytoskeleton-analyserGetting StartedA more complete introduction, use examples and API are available in thedocumentation.`Licensecytoskeleton-analyseris available under the terms of the BSD 3 Clause license.
See LICENSE.txt for more information.
Package: cytotransform
CytotransfromDescriptionCytotransform is a python package for transforming flow cytometry data. It implements the following transformations
according to the GatingML 2.0 definitions (https://flowcyt.sourceforge.net/gating/latest.pdf):Parametrized logarithmic transformationParametrized inverse hyperbolic sine transformation (asinh)Logicle transformationHyperlog transformationEach transformation is implemented as aTransformclass with atransformmethod that takes a numpy array as input
and returns the transformed array. TheTransformclass also has atransform_inversemethod that takes a numpy array
as input and returns the inverse transformed array. Each implementation includes validation of the input parameters. The
transform classes support multiprocessing out of the box and ifn_jobsis set to more than 0, then the input data will
be split intonbatches depending on the number of cores available and each batch will be transformed in parallel.
Ifn_jobsis set to -1, then all available cores will be used. Ifn_jobsis set to 0, then no multiprocessing will
be used.Cytotransform is thanks to the fantastic community of scientists and developers in the single cell and flow
cytometry data analysis ecosystem. It implements the FastLogicle C++ library for logicle transformations
originally implemented by Wayne A Moore and David R Parks (seehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761345/).
Code was inspired by the great work by Scott White (https://github.com/whitews/FlowKit) and Brian Teague
(https://github.com/cytoflow).InstallationCytotransform can be installed from PyPi using pip:pipinstallcytotransformUsageThetransformandinverse_transformmethods take a numpy array or Pandas DataFrame as input and return a numpy the
transformed array/dataframe.Parametrized logarithmic transformationfromcytotransformimportParametrizedLogTransformtransformer=ParametrizedLogTransform(m=4.5,t=262144,n_jobs=-1)transformed_data=transformer.transform(data)data=transformer.inverse_transform(transformed_data)Parametrized inverse hyperbolic sine transformation (asinh)fromcytotransformimportAsinhTransformtransformer=AsinhTransform(m=4.5,t=262144,a=0.0,n_jobs=-1)transformed_data=transformer.transform(data)data=transformer.inverse_transform(transformed_data)Logicle transformationfromcytotransformimportLogicleTransformtransformer=LogicleTransform(t=262144,w=0.5,m=4.5,a=0.0,n_jobs=-1)transformed_data=transformer.transform(data)data=transformer.inverse_transform(transformed_data)Hyperlog transformationfromcytotransformimportHyperlogTransformtransformer=HyperlogTransform(t=262144,w=0.5,m=4.5,a=0.0,n_jobs=-1)transformed_data=transformer.transform(data)data=transformer.inverse_transform(transformed_data)LicenseCytotransform is licensed under the MIT license, is free to use, and comes with no warranty whatsoever.
Package: cytube-bot
OverviewAsyncIOCyTubebotRequirementsPython >=3.4Installationpipinstallcytube-botpipinstallcytube-bot[proxy]gitclonehttps://github.com/dead-beef/cytube-botcdcytube-botpipinstall-e.[dev]Building./build.shTesting./testUsageModule documentationExamplesLicensescytube-bot
Package: cyuuid
There is0speedup for this module.Clone of built in uuid module.run tests:python -m unittest discoverBuild:python setup.py build_extInstall:pip install cyuuidUpload to pypi:python setup.py sdist
twine upload -u<USERNAME> -p<PASSWORD> dist/cyuuid-<VERSION>.tar.gzUsage:Construct from Hexadecimal:from cyuuid import UUID
uuid = UUID('a8098c1a-f86e-11da-bd1a-00112444be1e')Construct from python built in uuid:from uuid import UUID as PY_UUID
py_uuid = PY_UUID('a8098c1a-f86e-11da-bd1a-00112444be1e')
cy_uuid = UUID(uuid=PY_UUID)
Package: cyvcf
CyVCFA Cython port of the PyVCF library maintained by @jamescasbon.The goal of this project is to provide a very fast Python library for parsing and manipulating large VCF files.
Cython has been used to optimize speed.  This version is approximately 4 times faster than PyVCF,
and the parsing speed is essentially identical to that of C/C++ libraries provided by PLINKSEQ and VCFLIB.The functionality and interface are currently the same as documented here:http://pyvcf.rtfd.org/Installationpython setup.py build
python setup.py installTestingpython setup.py testBasic usage>>> import cyvcf
>>> vcf_reader = cyvcf.Reader(open('test/example-4.0.vcf', 'rb'))
>>> for record in vcf_reader:
...     print record
20  14370   G       A       29.0    .       H2=True;NS=3;DB=True;DP=14;AF=0.5       GT:GQ:DP:HQ     0|0:48:1:51,51  1|0:48:8:51,51  1/1:43:5:.,.
20  17330   T       A       3.0     q10     NS=3;DP=11;AF=0.017     GT:GQ:DP:HQ     0|0:49:3:58,50  0|1:3:5:65,3    0/0:41:3:.
20  1110696 A       G,T     67.0    .       AA=T;NS=2;DB=True;DP=10;AF=0.333,0.667  GT:GQ:DP:HQ     1|2:21:6:23,27  2|1:2:0:18,2    2/2:35:4:.
20  1230237 T       .       47.0    .       AA=T;NS=3;DP=13 GT:GQ:DP:HQ     0|0:54:7:56,60  0|0:48:4:51,51  0/0:61:2:.
20  1234567 GTCT    G,GTACT 50.0    .       AA=G;NS=3;DP=9  GT:GQ:DP        ./.     0/2:17:2        1/1:40:3
Package: cyvidia-ai-utils
ai-utilsInstallationpipinstallcyvidia-ai-utilsCross validationHuggingface Trainer APIfromcyvidia_ai_utilsimportTransformerCrossValidationModel,cross_validate,EvaluationResultfromtransformersimportAutoTokenizer,AutoModelForSequenceClassification,TrainingArgumentsfromdatasetsimportload_dataset,DatasetfromtransformersimportDataCollatorWithPadding,Trainerfolds=load_dataset("dipesh/Intent-Classification-small",split=[f"train[{k}%:{k+10}%]"forkinrange(0,100,10)])assert(isinstance(folds,list))defcreate_model():model=AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-tiny",num_labels=21)tokenizer=AutoTokenizer.from_pretrained("prajjwal1/bert-tiny")returnTransformerCrossValidationModel(model=model,tokenizer=tokenizer,training_args=TrainingArguments(output_dir=f'tests/test_models/{uuid.uuid4()}',learning_rate=2e-5,per_device_train_batch_size=16,per_device_eval_batch_size=16,num_train_epochs=0.001,weight_decay=0.01,evaluation_strategy="epoch",save_strategy="epoch",load_best_model_at_end=True,))# folds can either be a list of Dataset's or a dictionary of label to Datasetfolds_dict={f"train[{k}%:{k+10}%]":foldfork,foldinenumerate(folds)}results=cross_validate(create_model,folds_dict,target_id_column="label",input_text_column="text")aggragated_result=EvaluationResult.aggregate(list(results.values()))Custom Trainerfromcyvidia_ai_utilsimportCrossValidationModelclassMyCustomCrossValidationModel(CrossValidationModel):defget_label_for_id(self,id:int)->str:# Implementdeftrain(self,train_ds,val_ds)->CrossValidationModel:# Implementdefpredict_values(self,values)->Dict[str,Any]:# Implementdefcreate_model():returnMyCustomCrossValidationModel()results=cross_validate(create_model,folds,target_id_column="label",input_text_column="text")
Package: cyvincenty
CyvincentyA fast Cython implementation of the Vincenty algorithm for calculating the distance in kilometers between 2 co-ordinates.This module is heavily inspired byuvincentyandvincenty. It is just as fast if not slightly faster than uvincenty which is a pure C Python extension despite being written in Python! (technically Cython).InstallationpipinstallcyvincentyUsage>>fromcyvincentyimportvincenty>>boston=(42.3541165,-71.0693514)>>newyork=(40.7791472,-73.9680804)>>vincenty(*boston,*newyork)BenchmarksUsing ipython>>importcyvincenty>>importuvincenty>>importvincenty>>importgeopy.distance>>boston=(42.3541165,-71.0693514)>>newyork=(40.7791472,-73.9680804)>>%timeituvincenty.vincenty(*boston,*newyork)744ns±2.58nsperloop(mean±std.dev.of7runs,1,000,000loopseach)>>%timeitcyvincenty.vincenty(*boston,*newyork)736ns±2.82nsperloop(mean±std.dev.of7runs,1,000,000loopseach)>>%timeitvincenty.vincenty(boston,newyork)10.2µs±60.1nsperloop(mean±std.dev.of7runs,100,000loopseach)>>%timeitgeopy.distance.geodesic(boston,newyork)191µs±1.52µsperloop(mean±std.dev.of7runs,10,000loopseach)
Package: cyw-bogus
[Homepage](http://www.otree.org/)These are the core oTree libraries.
If you are developing oTree games, install this instead:https://github.com/oTree-org/otreeYou should only install this if you want to modify the behavior of the oTree core engine.Install this withpip install -e ..[![Build Status](https://travis-ci.org/oTree-org/otree-core.svg?branch=master)](https://travis-ci.org/oTree-org/otree-core)
Package: cy-weather-api
Caiyun Weather API Python SDKNOTE: This project is not officially supported by ColorfulClouds
Technology.It's open sourced under MIT License and designed as a debug tool for our
internal systems instead of online services, or third party technology
support.InstallPython 3.8.1+ is required.pipinstallcy-weather-apiUsageRequest Caiyun APINOTE:TAkhjf8d1nlSlspNis test token, no one can ensure its availability.fromcy_weather_apiimportCyWeatherAPIClientclient=CyWeatherAPIClient(token="TAkhjf8d1nlSlspN")api_result=client.fetch(lng=101.8551,lat=26.6832,lang="zh_CN",alert=True)print(api_result.result.hourly.description)api_result=client.fetch(lng=-0.2008,lat=51.5024,lang="en_GB")print(api_result.result.hourly.description)api_result=client.fetch(lng=73.9808,lat=40.7648,lang="en_US")print(api_result.result.hourly.description)Output sample:晴，今天晚间20点钟后转小雨，其后多云
clear weather over the next 24 hours
clear weather, overcast after 20 o'clock this afternoon, followed by cloudyUse our dataclass modelsThe default HTTP client is httpx, you can use other HTTP client to request API,
and pass the response dict to our models (based ondataclass):fromcy_weather_apiimportCyWeatherResponsedata={"status":"ok","api_version":"v2.6","api_status":"active","lang":"en_US","unit":"metric","tzshift":28800,"timezone":"Asia/Shanghai","server_time":1589125757,"location":[39.888888,116.674501],"result":{"forecast_keypoint":"test forecast_keypoint","primary":0},}result=CyWeatherResponse.from_dict(data)
Package: cywheel
Cython packaging for python wheelsRequirementspipinstall-rrequirements.txt
pipinstallcywheelGetting StartedfromcywheelimportCyMakeif__name__=="__main__":maker=CyMake(root="/home/user/python/project-python",setup="setup.py",version="1.0a1")maker.make(submodule="submodule1")ContributionsEmail me atj.cadic@9dw-lab.comfor any questions.
Package: cyxmltv
Project CyXmlTvThis project is for generating anxmltvcompatible
electronic programming guide for cypriot channels. It's primarily used for
operating the Plex DVR.How to installOption 1: Install through pip withpip install cyxmltvOption 2: Download the source code and create a distribution viapython setup.py sdist. Then install viapip install dist/cyxmltv-<version>.tar.gz.How to runSimply runcyxmltv <filename>, where<filename>is the target file name
you want to use. Then, you can use the aforementioned file in Plex'sDVR setup
Package: cyzil
cyzilDescriptionCyzil provides tools that enable quick and in-depth analysis of sequence generation models such as machine translation models. It contains a Cython module that provides fast computation of standard metrics. It coversedit distance (Levenstein Distance)and BLEU score proposed byPapineni et al. (2002)so far.Command-line toolPython APIUser guideRequirementsPython 3.7+InstallationCyzil requires Python 3.7+. Please install it by running the following code:pipinstallcyzilCommand-line toolUser GuideWith cyzil, you can compute BLEU score and Edit distance on your terminal. All you have to do is
to specify the path to a reference file (correct translations) and a candidate file
(translation generated by a machine translation model).
The reference and candidate sentences should be stored in separate lines, e.g.
sentence 1\n sentence 2\n ... sentence k\n.
Please see exampleshere.
For computing score, you can tokenize sentences by white space ornltk tokenizer.
By default, it tokenizes sentences
by white space.UsageThe following code shows an example for corpus-leve BLEU score. It prints out the precision, the brevity penalty and BLEU score.>cyzil-bleu-corpus\--referencedata/ref.en\--candidatedata/can.en\--ngram4\--tokenizernltk[0.9041149616241455,1.0,0.9041149616241455]The below is an example for corpus-level edit distance.>cyzil-edit-distance-corpus\--referencedata/ref.en\--candidatedata/can.en\--tokenizernltk[0.5,0.04545454680919647]Computing Score for Each PairCyzil also computes the metric of each reference-candidate pair to for in-depth analysis of sequence generation models. The output can be stored in a csv file. Each row of output corresponds to each reference-candidate pair.Here is an example for BLEU score. The first column of the output is the precision, the second is the brevity penalty and the last column is the BLEU score.>cyzil-bleu-points\--referencedata/ref.en\--candidatedata/can.en\--ngram4\--tokenizernltk\--outputoutput.csvEdit distance can be computed as follows. The first column of the output is edit distance and the second column is normalized edit distance.>cyzil-edit-distance-points\--referencedata/ref.en\--candidatedata/can.en\--tokenizernltk\--outputoutput.csvFor more details, please refer to help of each command, e.g.cyzil-bleu-corpus -h.Python APICyzil can be imported as a python module into your program.
The following shows example of API calls.
For more details, please refer toUser Guide.importcyzilreference=['this','is','a','test']candidate=['this','is','a','test']cyzil.bleu_sentence(reference,candidate,max_ngram=4)cyzil.bleu_corpus([reference],[candidate],max_ngram=4)cyzil.bleu_points([reference],[candidate],max_ngram=4)cyzil.edit_distance_sentence(reference,candidate)cyzil.edit_distance_corpus([reference],[candidate])cyzil.edit_distance_points([reference],[candidate])
Package: cz-bitbucket-jira-conventional
cz-bitbucket-jira-conventionalcz-bitbucket-jira-conventionalis a plugin for thecommitizen tools, a toolset that helps you to createconventional commit messages. Since the structure of conventional commits messages is standardized they are machine readable and allow commitizen to automaticially calculate and tagsemantic version numbersas well as createCHANGELOG.mdfiles for your releases.This plugin extends the commitizen tools by:require a Jira issue idin the commit messagecreate links to GitHubcommits in the CHANGELOG.mdcreate links to Jiraissues in the CHANGELOG.mdWhen you call commitizencommityou will be required you to enter the scope of your commit as a Jira issue id (or multiple issue ids, prefixed or without prefix, see config below).> cz commit
? Select the type of change you are committing fix: A bug fix. Correlates with PATCH in SemVer
? JIRA issue number (multiple "42, 123"). XX-
...The changelog created by cz (cz bump --changelog)will contain links to the commits in Github and the Jira issues.## v1.0.0 (2021-08-06)### Features-**[XX-123](https://myproject.atlassian.net/browse/XX-123)**: create changelogs with links to issues and commits [a374b](https://github.com/apheris/cz-bitbucket-jira-conventional/commit/a374b93f39327964f5ab5290252b795647906008)-**[XX-42](https://myproject.atlassian.net/browse/XX-42),[XX-13](https://myproject.atlassian.net/browse/XX-13)**: allow multiple issue to be referenced in the commit [07ab0](https://github.com/apheris/cz-bitbucket-jira-conventional/commit/07ab0e09de36712ab1db93fff0c821ecd80b5849)InstallationInstall with pippython -m pip install cz-bitbucket-jira-conventionalYou need to use a cz config file that has therequiredadditional valuesjira_base_urlandbitbucket_repoand may contain theoptionalvaluejira_prefix.Example.cz.yamlconfig for this repositorycommitizen:name:cz_bitbucket_jira_conventionaltag_format:v$versionversion:1.0.0jira_prefix:XX-jira_base_url:https://myproject.atlassian.netbitbucket_repo:apheris/cz-bitbucket-jira-conventionalThejira_prefixcan be eitherempty (the user must write the prefix for each issue)a string (the prefix will be added automatically)a list (for multiple projects, the user will be asked to choose a prefix)jira_prefix:-XX--XY--YY-pre-commitAdd this plugin to the dependencies of your commit message linting withpre-commit.Example.pre-commit-config.yamlfile.repos:-repo:https://github.com/commitizen-tools/commitizenrev:v2.17.13hooks:-id:commitizenstages:[commit-msg]additional_dependencies:[cz-bitbucket-jira-conventional]Install the hook withpre-commitinstall--hook-typecommit-msgLicenseDistributed under the MIT License. SeeLICENSEfor more information.AcknowledgementsThis plugin would not have been possible without the fantastic work from:commitizen toolsconventional_JIRAOriginalcz-github-jira-conventional