Saravanan666/pypi-org huggingface dataset:
Package: copius-api
copius_apiThis is a Python-api to the transcription and orthography toolset athttps://copius.eu/ortho.php:“This toolset is a loose conglomeration of applications aiming to help
you handle various character encodings, orthographies, transcriptions
and transliterations you might encounter when working with Uralic
languages and other languages of Europe and Northern Asia that use
variants of the Latin or Cyrillic alphabet.” (Copyright © 2021 COPIUS)How to install:$ pip install copius_apiHow to use:>>> from copius_api import api
>>> api.transcribe("ke̮")
"kɘ">>> from copius_api import api
>>> api.transcribe("lol","kom","lc")
"лол">>> from copius_api import api
>>> api.transcribe("kiki","mns","9c")
"кики">>> from copius_api import api
>>> api.transcribe("буба","mns","c9")
"buba"To see the language abbreviations:>>> from copius_api import api
>>> api.lang_dict
{'Mari (Hill Mari)': 'mhr', 'Udmurt': 'udm', 'Komi': 'kom', 'Erzya': 'myv', 'Moksha': 'mdf', 'Mansi': 'mns', 'Tatar': 'tat', 'Bashkir': 'bak', 'Chuvash': 'chv', 'Russian': 'rus'}To see the script abbrevations:>>> from copius_api import api
>>> api.orth_dict
{'Cyrillic to Cyrillic': 'cc', 'Cyrillic to Latin': 'cl', 'Cyrillic to IPA': 'ci', 'Cyrillic to ISO9': 'c9', 'Latin to Cyrillic': 'lc', 'Latin to Latin': 'll', 'Latin to IPA': 'li', 'Latin to ISO9': 'l9', 'IPA to Cyrillic': 'ic', 'IPA to Latin': 'il', 'IPA to ISO9': 'i9', 'ISO9 to Cyrillic': '9c', 'ISO9 to Latin': '9l', 'ISO9 to IPA': '9i', '<1917 to Cyrillic': '3c', '<1917 to Latin': '3l', '<1917 to IPA': '3i', '<1917 to ISO9': '39'}
Package: coplin-db2
coplin-db2A biblioteca coplin-db2 é um módulo de conveniência para acessar bancos de dados do tipo IBM DB2, desenvolvido pela
Coordenadoria de Planejamento Informacional da UFSM (COPLIN).Com esta biblioteca, é possível definir um arquivo com credenciais de acesso ao banco de dados, no formatojson, que
podem ser utilizadas posteriormente:Arquivocredentials.json:{"user":"nome_de_usuário","password":"sua_senha_aqui","host":"URL_do_host","port":50000,"database":"nome_do_banco"}Arquivodb2_schema.sql:CREATETABLEUSERS_TEST_IBMDB2(IDINTEGERNOTNULLPRIMARYKEY,NAMEVARCHAR(10)NOTNULL,AGEINTEGERNOTNULL);INSERTINTOUSERS_TEST_IBMDB2(ID,NAME,AGE)VALUES(1,'HENRY',32);INSERTINTOUSERS_TEST_IBMDB2(ID,NAME,AGE)VALUES(2,'JOHN',20);Arquivomain.py:importosfromdb2importDB2Connection# arquivo JSON com credenciais de login para o banco de dadoscredentials='credentials.json'withDB2Connection(credentials)asdb2_conn:db2_conn.create_tables('db2_schema.sql')query_str='''SELECT *FROM USERS_TEST_IBMDB2;'''df=db2_conn.query_to_dataframe(query_str)print(df)# deleta a tabela# db2_conn.modify('''DROP TABLE USERS_TEST_IBMDB2;''', suppress=False)A saída esperada deve ser:IDNAMEAGE01HENRY3212JOHN20InstalaçãoPara instalar o pacote pelo pip, digite o seguinte comando:pipinstallcoplin-db2DesenvolvimentoEste passo-a-passo refere-se às instruções paradesenvolvimentodo pacote. Se você deseja apenas usá-lo, siga para
a seçãoInstalação.Instale oPython Anacondana sua máquinaCrie o ambiente virtual do anaconda e instale as bibliotecas necessárias com o comandocondaenvcreate-fenvironment.ymlConstrua o pacote:python-mbuildInstale-o localmente comEste repositório já conta com uma GitHub Action para publicar automaticamente no PyPi e TestPyPi. Consulte o arquivopython-publish.ymlpara detalhes da implementação.Todos os commits serão enviados para o TestPyPi, mas apenas commits com tags serão enviados para o PyPi:# alguma modificação no código fonte# ...gitadd.
gitcommit-m"mensagem do commit"gittag-a<tag_name>-m"título da versão"gitpushoriginmain# envia o commit para o repositório e o pacote para TestPyPigitpushorigin<tag_name># publica a tag e envia o pacote para o PyPiOnde <tag_name> é um número no formato, por exemplo,v1.4.1.Use apenas os comandosgit tag -a <tag_name>egit push origin <tag_name>quando quiser publicar o pacote no
canal PyPi!Um tutorial de publicação com GitHub Actions está disponívelneste linkContatoBiblioteca desenvolvida originalmente por Henry Cagnini:henry.cagnini@ufsm.brCaso encontre algum problema no uso, abra um issue norepositório da biblioteca.Pull requests são bem-vindos!
Package: coppafish
coppafishcoppafishis a data analysis pipeline for decodingcoppaFISH(combinatorial padlock-probe-amplified fluorescence in
situ hybridization) datasets.coppaFISHis a method for in situ transcriptomics which produces a series of images arranged in terms of tiles, rounds
and channels.coppafishthen determines the distribution of genes viaimage processing,spot detection,registrationandgene calling.PrerequisitesPython >= 3.8DocumentationFor more information on installation and usage, please see thedocumentation.
Package: coppeliasim-zmqremoteapi-client
CoppeliaSim zmqRemoteApi Python clientPython client for the zmqRemoteApi,protocol verison 2(note: starting from pip package version 2.x, the major version number (e.g. 2) matches with the protocol version used by CoppeliaSim).Installing:python3-mpipinstallcoppeliasim-zmqremoteapi-clientUsagefromcoppeliasim_zmqremoteapi_clientimport*# create a client to connect to zmqRemoteApi server:# (creation arguments can specify different host/port,# defaults are host='localhost', port=23000)client=RemoteAPIClient()# get a remote object:sim=client.require('sim')# call API function:h=sim.getObject('/Floor')print(h)There is also anasyncioversion of the client. Normalasyncioprinciples apply, and all methods are async:fromcoppeliasim_zmqremoteapi_client.asyncioimport*client=RemoteAPIClient()asyncdefmain():asyncwithRemoteAPIClient()asclient:sim=awaitclient.require('sim')h=awaitsim.getObject('/Floor')print(h)asyncio.run(main())on Windows, if it doesn't work properly, before callingasyncio.run(...)call:asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())A note about performance of sequential requests: if performing many commands in one shot, and results will be used later, consider usingasyncio.gatherfor improved throughput.E.g. getting the handles of 100 objects:handles=awaitasyncio.gather(*[sim.getObject(f'/Object{i+1}')foriinrange(100)])Also check out the examples inclients/python.
Package: copper-bem
CopperCopper is a building energy simulation performance curve generator for heating, ventilation, and air-conditioning equipment. It uses a genetic algorithm to modify existing or a set of aggregated existing performance curves to match specific design characteristics including full and part load energy performance metric values.Copper generates performance curves that can be used in most common building energy software such asEnergyPlusorDOE-2.PurposeResults from building energy simulations largely depend on how heating and cooling equipment are modeled. As they rarely operate at their full load and rated performance it is important that reasonable data is used to model an heating or cooling equipment's part load performance. Copper was created to allow building energy modelers, engineers, and researchers to generate simulation-ready performance curves of heating and cooling equipment that not only capture their typical behavior at part load but are also generated to match a set of design characteristics such as full load and part load efficiencies.While chillers, which are rated at both full load and part load through an integrated part load value (IPLV), are currently the main application of Copper, other heating and cooling equipment will be handled in the near future.DocumentationThe documentation for Copper is hostedhere, it includes installation instructions, quick tutorials, examples, API documentation, and more.ContributingContributions (issues and pull requests) are welcomed and greatly appreciated. Guidelines are provided in the documentation.HelpIf you need help using Copper, please open an issue and one of the developers will get back to you at their earliest convenience.
Package: coppertop-bones-demo
coppertop-bones-demoExamples written in Jupyter.Use this -https://nbviewer.jupyter.org- if github doesn't render properly.E.g.https://nbviewer.jupyter.org/github/DangerMouseB/examples/blob/main/think%20bayes/Ch%201%2C2%2C3%20-%20models.ipynbNotebook dependencies and coppertopI usually have matplotlib, plotnine, numpy, pandas, scipy, pymc3, etc, installed.Install coppertop-xxx viapython -m pip install coppertop-xxx. This also installs coppertop-xxx.Clonehttps://github.com/DangerMouseB/coppertop-xxxto get the lastest verson of all three.At the top of each notebook you'll find a cell that includes the following:from dm.std import ensurePath
'/Users/david/repos/github/DangerMouseB/coppertop/src/dm' >> ensurePath       # <= set this to your pathThe notebook should be good to go.
Package: copr-builder
A simple program for building RPM packages from Git repositories inCopr.Usageusage: copr-builder [-h] [-v] [-p [PROJECTS [PROJECTS ...]]] [-c CONFIG]

Copr builder

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         print debug messages
  -p [PROJECTS [PROJECTS ...]], --projects [PROJECTS [PROJECTS ...]]
                        projects to build; if not given, all projects from
                        config will be built
  -c CONFIG, --config CONFIG
                        config file locationConfig file structure[project_name]
copr_user = username
copr_repo = reponame
package = mypackage
git_url = https://github.com/<user>/mypackage
pre_archive_cmd = ./autogen.sh && ./configure
archive_cmd = make local
git_branch = 1.1-devel
git_merge_branch = 1.1-release

[another_project_name]
copr_user =
...project_name– some unique name for project/packagecopr_user– FAS username or groupnamecopr_repo– name of the Copr repository (without the username!)package– name of the package that will be builtaspecfile must be in the repopre_archive_cmd–(optional)command which will be run before the spec file is read. This can be used to generate or download spec file.archive_cmd– command for creating archive from the source (e.g. “make local” or “git archive HEAD –prefix=package/ -o package.tar.gz”)this command must create a single archive (.tar.[gz|bz|bz2|xz]) in the current directorygit_url– URL of the Git repo (will be used for “git clone”)git_branch– branch to use from the Git repo (e.g. “master”)git_merge_branch– optional; if you need to merge another branch intogit_branchbefore running thearchive_cmd
Package: copt
Note: This package is no longer actively maintained. I won’t be actively responding to issues. If you’d like to volunteer to maintain it, please drop me a line atf@bianp.netcopt: composite optimization in Pythoncopt is an optimization library for Python. Its goal is to provide a high quality implementation of classical optimization algorithms under a consistent API.Docs|ExamplesInstallationIf you already have a working installation of numpy and scipy,
the easiest way to install copt is usingpippip install -U coptAlternatively, you can install the latest development from github with the command:pip install git+https://github.com/openopt/copt.gitCitingIf this software is useful for your research, please consider citing it as@article{copt,
  author       = {Fabian Pedregosa, Geoffrey Negiar, Gideon Dresdner},
  title        = {copt: composite optimization in Python},
  year         = 2020,
  DOI          = {10.5281/zenodo.1283339},
  url={http://openopt.github.io/copt/}
}DevelopmentThe recommended way to work on the development versionis the following:Clone locally the github repo. This can be done with the command:git clone https://github.com/openopt/copt.gitThis will create a copt directory.2. Link this directory to your Python interpreter. This can be done by
running the following command from the copt directory created with the
previous step:python setup.py developNow you can run the tests withpy.test tests/
Package: coptictranslit
The vision of this project is to provide an easy, bulk transliteration service between Coptic and Latin script. The primary use case (Coptic –> Latin script) is to allow English speakers who do not read Coptic to follow along with church services by offering transliterated text with accurate and transparent pronunciations. For more information visit out Github page athttps://github.com/shehatamichael/coptic-transliteration
Package: copul
copulas_in_systemic_riskCopula propertiesFor any of the copula families below, e.g.copula = copul.Galambos(), get the following properties:Cumulative distribution function via copula.cdfDensity function via copula.pdfSupported copula families:Archimedean CopulasThe 22 copula families from Nelsen, accessible viacopul.archimedean.Nelsen1,copul.archimedean.Nelsen2, etc.
Letcopulabe any instance of those classes, e.g.copula = copul.archimedean.Nelsen1().For these families, the following properties are available:generator function is available via e.g.copula.generatorinverse generator function is available via e.g.copula.inverse_generatorCI char function is available via e.g.copula.ci_charthe MTP2 char function is available via e.g.copula.mtp2_charExtreme Value CopulasBB5GalambosGumbelHusler-ReissJoeMarshall-OlkintEVtawnLetcopulabe any instance of those classes, e.g.copula = copul.extreme_value.Galambos().
Then, the Pickand function is available via e.g.copula.pickand.OtherFarlie-Gumbell-MorgensternFrechetPlackettSample Usageimport copul

galambos = copul.extreme_value.Galambos()
params = galambos.sample_parameters(3)
galambos.plot_pickand(params)

clayton = copul.archimedean.Clayton()
clayton(theta=1.5).plot_cdf()
clayton(theta=2.5).plot_pdf()
Package: copula
CopulaA python library for sampling and generating new Data points by multivariate Gaussian copulas.OverviewA python libray to build multivariate gaussian copula for given data points and sample arbitary number of new data points from input data distribution. In shortgiven some input data points, it can generate more  such data points which follow the input data distributionInstallationInstall using pippip install copulaUsageIf you have some data points and distribution of this multivariate data points is not explicitly given. Then this library can be used to sample new points from this dritribution.gendata()funtion number of new samples to be generated as input>>>fromcopulaimportpyCopula>>>data=[[2,1,2,4],[3,1,7,4],[2,9,1,0],[3,6,1,6]]>>>cop=pyCopula.Copula(data)>>>samples=cop.gendata(3)>>>print(samples)[2.697128268374179,6.29726013955287,2.983951810593502,2.1149729235834496],[3.0,1.0,6.831369733333171,4.631091408593663],[2.147377031275032,6.75098812552581,1.9789800708813163,1.1200891337867478]]Assumptionsdata provided to copula must be a 2D numeric arrayData must be clean (no null values)Every row of input data must contain same number of columns
Package: copulabayesnet
copulabayesnetLoad dataTest CopulasPredict from multivariate normal copulasGenerate several useful plotsDocumentationOnline documentationInstallationInstall basicpip install copulabayesnetIf you are looking for latest updates, consider installation directly from sources.git clone https://github.com/SjoerdGn/copulabayesnet.git
cd copulabayesnet
python setup.py installFunctionalities-- make table hereGaussian copula
Package: copulae
CopulaeProbably the second most popular copula package in Python. 😣Copulae is a package used to model complex dependency structures. Copulae implements common and popular copula
structures to bind multiple univariate streams of data together. All copula implemented are multivariate by default.VersionsContinuous IntegrationDocumentationCoverageInstallingInstall and update usingpipand on conda.# condacondainstall-cconda-forgecopulae# PyPIpipinstall-UcopulaeDocumentationThe documentation is located athttps://copulae.readthedocs.io/en/latest/. Please check it out. :)Simple UsagefromcopulaeimportNormalCopulaimportnumpyasnpnp.random.seed(8)data=np.random.normal(size=(300,8))cop=NormalCopula(8)cop.fit(data)cop.random(10)# simulate random number# getting parametersp=cop.params# cop.params = ...  # you can override parameters too, even after it's fitted!# get a summary of the copula. If it's fitted, fit details will be present toocop.summary()# overriding parameters, for Elliptical Copulae, you can override the correlation matrixcop[:]=np.eye(8)# in this case, this will be equivalent to an Independent CopulaMost of the copulae work roughly the same way. They share pretty much the same API. The difference lies in the way they are parameterized. Read the docs to learn more about them. 😊AcknowledgementsMost of the code has been implemented by learning from others. Copulas are not the easiest beasts to understand but here are some items that helped me along the way. I would recommend all the works listed below.Elements of Copula Modeling with RI referred quite a lot to the textbook when first learning. The authors give a pretty thorough explanation of copula from ground up. They go from describing when you can use copulas for modeling to the different classes of copulas to how to fit them and more.Blogpost from Thomas WieckiThis blogpost gives a very gentle introduction to copulas. Before diving into all the complex math you'd find in textbooks, this is probably the best place to start.MotivationsI started working on the copulae package because I couldn't find a good existing package that does multivariate copula modeling. Presently, I'm building up the package according to my needs at work. If you feel that you'll need some features, you can drop me a message. I'll see how I can schedule it. 😊TODOSSet up package for pip and conda installationMore documentation on usage and post docs on rtd (Permanently in the works 😊)Elliptical CopulasGaussian (Normal)Student (T)Implement in Archimedean copulasClaytonGumbelFrankEmpiricalJoeAMHRho finding via CubaturesMixture copulasGaussian Mixture CopulaGeneric Mixture CopulaMarginal CopulaVine CopulasCopula TestsRadial SymmetryExchangeabilityGoodness of FitPairwise RosenblattMulti-IndependenceGeneral GOFModel SelectionCross-Validated AIC/BIC
Package: copulalib
CopulalibThis is a package/library in python to model the copulas.
This was developed as part of my PhD thesis.This contains module for the following copula:FrankClaytonGumbelInstalling copulalibInstalling copulalib is done bypython setup.py installwith the usual Distutils options availableUsageImport required modulesimport numpy as np
import matplotlib.pyplot as plt
from copulalib.copulalib import CopulaGenerate random (normal distributed) numbersx = np.random.normal(size=100)
y = 2.5*x+ np.random.normal(size=100)Make the instance of Copula class with x, y and clayton familyfoo = Copula(x, y, family='clayton')Print the Kendall's rank correlationprint(foo.tau)Print spearmen's correlationprint(foo.sr)Print pearson's correlationprint(foo.pr)Print the parameter (theta) of copulaprint(foo.theta)Generate the 1000 samples (U,V) of copulaX1, Y1 = foo.generate_xy(1000)For more details see the test.py file inside module.ChangesVersion 1.0.0 -- May, 2011 --- Initial releaseVersion 1.1.0 -- June, 2011 --- changed from function orieted to object oriented, documentation improvedversion 2.0.0 -- Oct, 2022 --- Updated for Python 3, removed the dependancy on statistics libraryAny questions/commentsIf you have any comment/suggestion/question,
please feel free to write me atsatkumartomer@gmail.comYou may go throughhttps://github.com/tomersk/learn-pythonto see the examples.
Package: copulogram
CopulogramThis package is provides a new data visualisation tool to explore multivariate datasets developped with V.Chabridon.Acopulogramis an innovative plot as it decomposes a mutivariate dataset between the effects of the marginals and those of the dependence between features. To do so, it represents the marginals with univariate kernel density estimation plots or histograms (diagonal), and the dependence structure with scatter plots in the ranked space (upper triangle). On the bottom triangle the scatter plots are set in the physical space, gathering the effects of the marginals and the dependencies. Since the dependence structure is theoretically modeled by an underlying copula, this plot is calledcopulogram, generalizing the well-known ``correlogram'' to nonlinear dependencies. It gives a synthetic and empirical decomposition of the dataset.Copulogram of a wind-wave datasetInstallationThe following commands install the current version of the copulogram package.~$ pip install copulogramExample on iris datasetUsing the famousiris dataset, let us plot copulograms with different settings:>>> import seaborn as sns
>>> import copulogram as cp

>>> data = sns.load_dataset('iris')
>>> copulogram = cp.Copulogram(data)
>>> copulogram.draw()>>> copulogram.draw(alpha=0.8, hue='species', kde_on_marginals=False)>>> copulogram.draw(hue='species', quantile_contour_levels=[0.2, 0.4, 0.6, 0.8])ReferencesEmpirical Bernstein copula: Sancetta, A., & Satchell, S. (2004). The Bernstein Copula and Its Applications to Modeling and Approximations of Multivariate Distributions. Econometric Theory, 20(3), 535–562.Nonparametric copula estimation: Nagler, T., Schellhase, C. & Czado, C. (2017). Nonparametric estimation of simplified vine copula models: comparison of methods. Dependence Modeling, 5(1), 99-120.OpenTURNS: Baudin, M.,  Lebrun, R., Iooss, B., Popelin, A.L. (2017). OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation.Wind-waves environmental dataset: The data was generated by a
numerical model from ANEMOC (Digital Atlas of Ocean
and Coastal Sea States, seehttp://anemoc.cetmef.developpement-durable.gouv.fr/)
Package: copybook
CopybookCopybook is a python module for parsing VSAM copybooks.FeaturesSupports all numeric PIC variations including:integersfloats described with dot notationfloats described with V notationleading SIGNEDtrailing SIGNEDCharacter PIC notationsGroupsOCCURS clausesREDEFINES clausesIn addition, it provides indexes of column locations to help with parsing fixed width files based on positions within each lineInstallationpip install copybookUsageCopybook provides two methods for parsing copybooks:parse_fileandparse_string. The result is aFieldGroupobject that represents the root of the copybook tree.Examples:import copybook
text = """
       01  WORK-BOOK.
        10  TAX-RATE        PIC S9(13)V9(2)
                    SIGN LEADING SEPARATE.
"""
root = copybook.parse_string(text)Using Copybook module to parse a fixed width lineTheFieldGroupobject provides aflattenmethod that return a flat list ofFieldobjects.Example:import copybook
text = """
       01  WORK-BOOK.
        10  TAX-RATE        PIC S9(13)V9(2)
                    SIGN LEADING SEPARATE.
        10  AMOUNT        PIC S9(4)V9(2).
"""
# copybook also provides a parse_file method that receives a text filename
root = copybook.parse_string(text)

# flatten returns a list of Fields and FieldGroups instead of traversing the tree
list_of_fields = root.flatten()

# dummy sample input
line = "          -13452987654"

# loop over the fields and parse the relevant position in the line
for field in list_of_fields:

  # FieldGroups are Copybook groups and contain Field objects as children
  if type(field)==copybook.Field:

    # each Field has a start_pos and a get_total_length method
    # to identify the position within the raw line input
    str_field = line[field.start_pos:field.start_pos+field.get_total_length()]

    # Field provides a parse method that returns a str, int, or float based on the PIC
    print(f"{field.name}: {field.parse(str_field)}")DevelopmentPRs are always welcome!SupportIf you encounter an unsupported copybook feature, please paste the copybook example along with whatever logs or error message you have received and open an issue.GratitudeCopybook uses the awesome PyParsing library for tokenizationLicenseMIT
Package: copycurse
CopyCurse is a simple tool for recursively copying files from one directory to another.InstallationWindows:pip install copycurseLinux:pip3 install copycurseUsagecopycurse source destination [--version] [-h]ArgumentsDescriptionsourceSource directory [Default=.]destinationDestination directory--versionShow version and exit-h, --helpShow help and exitExamplesCopy from your current directory:copycurse . C:\directoryCopy from directory to another directory:copycurse C:\directory D:\directoryCopy from your current directory to network share:copycurse . \\192.168.1.1\directory