Saravanan666/pypi-org huggingface dataset:
Package: cobry
This library is currently work in progress and under active developmentCobry is a library for creating powerful and modern CLI applications by providing a simple to follow architecture. It helps you to create clean and easy to use CLI applications like git, kubectl or helm.On further notice, cobry is a python port (WIP) of the popular GOlang CLI creation librarycobra. It aims to be as versatile as the original library and brings the clean architecture and concepts defined for cobra into the python ecosystem.
Package: cobs-index
Compact Bit-Sliced Signature Index (COBS)COBS (COmpact Bit-sliced Signature index) is a cross-over between an inverted index and Bloom filters. Our target application is to index k-mers of DNA samples or q-grams from text documents and processapproximate pattern matchingqueries on the corpus with a user-chosen coverage threshold. Query results may contain a number of false positives which decreases exponentially with the query length and the false positive rate of the index determined at construction time.
COBS' compact but simple data structure outperforms other indexes in construction time and query performance with Mantis by Pandey et al. in second place.
However, unlike Mantis and other previous work, COBS does not need the complete index in RAM and is thus designed to scale to larger document sets.COBS has two interfaces:a command line tool in C++ calledcobs(see below)a Python interface to the C++ library (seehttps://bingmann.github.io/cobs-python-docs/)More information about COBS is presented inour current research paper:
Timo Bingmann, Phelim Bradley, Florian Gauger, and Zamin Iqbal.
"COBS: a Compact Bit-Sliced Signature Index".
In:26th International Symposium on String Processing and Information Retrieval (SPIRE). pages 285-303. Spinger. October 2019.
preprint arXiv:1905.09624.Installation and First StepsInstallationCOBS requires CMake, a C++17 compiler or the Boost.Filesystem library.To download and install COBS run:git clone --recursive https://github.com/bingmann/cobs.git
mkdir cobs/build
cd cobs/build
cmake ..
make -j4and optionally runmake testto check the build.Building an IndexCOBS can read FASTA files (*.fa,*.fasta,*.fa.gz,*.fasta.gz), FASTQ files (*.fq,*.fastq,*.fq.gz.,*.fastq.gz), McCortex files (*.ctx), or text files (*.txt).You can either recursively scan a directory for all files matching any of these files, or pass a*.listfile which lists all paths COBS should index.To check the document list to be indexed, run for examplesrc/cobs doc-list tests/data/fasta/To construct a compact COBS index from these seven example documents runsrc/cobs compact-construct tests/data/fasta/ example.cobs_compactCheck--helpfor many options.Query an IndexCOBS has a simple command line query tool:src/cobs query -i example.cobs_compact AGTCAACGCTAAGGCATTTCCCCCCTGCCTCCTGCCTGCTGCCAAGCCCTor a fasta file of queries withsrc/cobs query -i example.cobs_compact -f query.faPython InterfaceCOBS also has a Python frontend interface which can be used to construct and query an index.
Seehttps://bingmann.github.io/cobs-python-docs/for a tutorial.Experimental ResultsIn our paper we compare COBS against seven other k-mer indexing software packages.
These are the main results, scaling by number of documents in the index, and in the second diagram shown per document.
Package: cobsurv
cobsurv : Cobra Ensemble for Conditional SurvivalCobra Ensemble for Conditional Survival are algorithms, designed for survival prediction
using proximity information. The k-NN survival, Random Survival Forest, Kernel Survival
are some examples of Cobra Ensemble for Conditional Survival. While this package tends
to provide those algorithms later, currently the package provides the following algorithms:COBRA SurvivalFor now other algorithms are taken from scikit-survival and np_survival to provide as
a base learner for the ensemble algorithms.installationpip install cobsurvThe documentation is available athttps://cobsurv.readthedocs.io/en/latest/Citation@misc{goswami2023areanorm,
      title={Area-norm COBRA on Conditional Survival Prediction}, 
      author={Rahul Goswami and Arabin Kr. Dey},
      year={2023},
      eprint={2309.00417},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
Package: cobu
Do you maintain multiple versions of config files for different scenarios and keep
copying them to right location? Do you have this one set of information common to all
your config files and are tired of maintaining it over multiple files? Thencobuis yourconfig builderof choice!It allows you to split your config file in multiple fragments and combine them
as needed, but not only for one type of config files, i.e. not for only for one
tool, but for all of them. I, for example, like to have consistent colorschemes
across applications which i can change quickly, e.g. light schemes for the day
and dark schemes at night. This is achieved with the help ofjinja2templates,
allowing you to reuse most of your existing config files and only changing the parts in
which they differ.
Package: cobutils
cobutilsis a library to access data on files formated with COBOL from
python programs.The package includes ones executable to extract data from a COBOL file and
another to create a file with COBOL formatted data:$ ./cobextract.py
Usage: cobextract.py [OPTIONS] [FILE]
Extract cobol sequential data from FILE or standard input

cobextract.py: error: Record definition [-r|--reg] is required


$ ./cobcreate.py --help
Usage: cobcreate.py [OPTIONS] [CSVFILE]
Create a cobol sequential data from CSV FILE or standard input

Options:
  -h, --help           show this help message and exit
  -r FILE, --reg=FILE  read register from FILE
  -o FILE, --out=FILE  output FILE
  -q, --quiet          don't print status messages to stdoutThe project code and bugtracker is hosted onBitbucket.InstallUsing pip:pip install cobutilsOs download and usesetuppython setup.py installLinksProject site.Python package index page.
Package: cobyqa
COBYQA, an acronym forConstrained Optimization BY Quadratic Approximations, is designed to supersedeCOBYLAas a general derivative-free optimization solver.
It can handle unconstrained, bound-constrained, linearly constrained, and nonlinearly constrained problems.
It uses only function values of the objective and constraint functions, if any.
No derivative information is needed.Documentation:https://www.cobyqa.com.InstallationCOBYQA can be installed forPython 3.8 or above.DependenciesThe following Python packages are required by COBYQA:NumPy1.17.0 or higher, andSciPy1.10.0 or higher.If you install COBYQA usingpiporconda(see below), these dependencies will be installed automatically.
More dependencies are required to run the test suite and some examples given in the folderexamples.
See the relevant sections below for more details.User installationThe easiest way to install COBYQA is usingpiporconda.
To install it usingpip, run in a terminal or command windowpipinstallcobyqaIf you are usingconda, you can install COBYQA from theconda-forgechannel by runningcondainstallconda-forge::cobyqaTo check your installation, you can executepython-c"import cobyqa; cobyqa.show_versions()"If your python launcher is notpython, you can replace it with the appropriate command (similarly forpipandconda).
For example, you may need to usepython3instead ofpythonandpip3instead ofpip.TestingTo execute the test suite of COBYQA, you first need to installpytest.
You can then run the test suite by executingpytest--pyargscobyqaThe test suite takes several minutes to run.
It is unnecessary to run the test suite if you installed COBYQA using the recommended method described above.ExamplesThe folderexamplescontains a few examples of how to use COBYQA.
To runpowell2015.py, you first need to installmatplotlib.
These files contains headers explaining what problems they solve.SupportTo report a bug or request a new feature, please open a new issue using theissue tracker.
Package: cochar
CallOfCthulhu Character GeneratorFast way of creating a random character for Call of Cthulhu RPG 7th ed.Summarycocharstands forCall of Cthulhu Character. It's a python package design to create a full characters for Call of Cthulhu RPG 7th ed.www.cochar.pldemonstrates a power ofcocharpackage.Table of ContentsProject TitleSummaryTable of ContentsInstallationUsageDependenciesDocumentationContributionWeb versionAuthorLicenseInstallationpip3 install cocharUsageBasicExample:>>>fromcocharimportcreate_character>>>person=create_character(1925,"US")>>>person>>>Character(year=1925,country='US',first_name='Anthem',last_name='Pharr',age=22,sex='M',occupation='doctor of medicine',strength=33,condition=30,size=78,dexterity=40,appearance=23,education=87,intelligence=65,power=50,move_rate=7,luck=38,skills={'first aid':38,'language [latin]':9,'medicine':73,'science [biology]':48,'ride':64,'anthropology':6,'charm':46,'intimidate':32,'art/craft (sculptor)':9,'credit rating':74,'dodge':20},damage_bonus='0',build=0,dodge=20,sanity_points=50,magic_points=10,hit_points=10)Default settingsDefault settings are defined in./data/settings.json.{"min_age":15,"max_age":90,"max_skill_level":90,"year":1925,"age":null,"sex":null,"first_name":null,"last_name":null,"country":"US","occupation":null,"weights":true,"database":"","show_warnings":false,"occupation_type":null,"era":null,"tags":null}Dependenciescochardepends onrandnamemodule for generating random names.For more details please see:randname githubrandname pypiDocumentationDetailed documentation of module can by found here:cochar documentationContributionIf you want to contribute tococharproject readcontributionfor more information.Web VersionWeb application is not a part ofcocharpackage.Web application was design to present the power ofcocharpackage. You can check it out onwww.cochar.plAuthorAdam WalkiewiczLicenseCochar is licensed under the terms of theGNU AGPL v3
Package: cochlear-nucleus
A Python package implementing computational models of theglobular
bushy cells(GBCs) in the mammalianventral cochlear nucleus.
Phenomenological models of endbulbs of Held with with short term
synaptic depressions are also included.UsageLook for examples in theexamplesdirectory (e.g. nrn_gbc_demo.py and
nrn_gbc_demo_with_default_weights.py).  Note that some examples
requirecochleato be installed.InstallationInstall the dependencies:Python 2.7NumpyNEURON (with Python bindings):http://neuron.yale.edu/neuron/Brian:http://briansimulator.org/Clone the git repo with:git clone https://github.com/mrkrd/cochlear_nucleus.git
cd cochlear_nucleusCompile NEURON’s models:makeor manually:cd cochlear_nucleus/nrn && nrnivmodl
cd -Install the package itself:python setup.py develop --userCitingRudnicki M. and Hemmert W. (2017).High entrainment constrains
synaptic depression levels of an in vivo globular bushy cell model.
Frontiers in Computational Neuroscience, Frontiers Media SA, 2017, 11,
pp. 1-11.
doi:10.3389/fncom.2017.00016
https://www.frontiersin.org/articles/10.3389/fncom.2017.00016/fullBibTeX entry:@Article{Rudnicki2017,
  author    = {Marek Rudnicki and Werner Hemmert},
  title     = {High Entrainment Constrains Synaptic Depression Levels of an In vivo Globular Bushy Cell Model},
  journal   = {Frontiers in Computational Neuroscience},
  year      = {2017},
  volume    = {11},
  pages     = {1--11},
  month     = {mar},
  doi       = {10.3389/fncom.2017.00016},
  publisher = {Frontiers Media {SA}},
  url       = {https://www.frontiersin.org/articles/10.3389/fncom.2017.00016/full},
}AcknowledgmentsThis work was supported by the German Research Foundation (DFG) within
the Priority Program “Ultrafast and temporally precise information
processing: normal and dysfunctional hearing” SPP 1608 (HE6713), the
Technische Universität München within the funding programme Open
Access Publishing, and the German Federal Ministry of Education and
Research within the Munich Bernstein Center for Computational
Neuroscience (reference number 01GQ1004B).LicenseThe project is licensed under the GNU General Public License v3 or
later (GPLv3+).
Package: cochl-sense-api
Cochl.Sense API allows to detect what is contained inside sound data. Send audio data over the internet to discover what it contains.  &#x60;&#x60;&#x60; npx @openapitools/openapi-generator-cli generate -i openapi.json -g python -o python-client &#x60;&#x60;&#x60;   # noqa: E501
Package: coci
COCICollectiveObservation onCausalInferencesCoci makes it easy to observe the changes in predictions from machine learning models based on the alterations of
feature values.Why Coci?Machine learning has always been understood as a black box algorithm, which makes the decision makers hesitant to trust
the predictions from this approach.ShapandLimehas
unveiled a lot of mysteries around the effects of the presence of each feature on outcomes. However, these methods
cannot show the change in outcomes when features are tweaked.Coci takes it a step further, and reveals the effects on outcomes when changing feature values.Installationpip install coci==0.1.7Summary PlotSample codeimport coci

explainer = coci.TreeExplainer(model)

explainer.sensitivity(X_test, 
                    feature_names=feature_names,
                    split_num=2,
                    sample_size=300)

explainer.summary_plot(max_display=10)Reading the summary plotTrend PlotSample codeimport coci 

explainer = coci.TreeExplainer(model)

explainer.sensitivity(X_test, 
                    feature_names=feature_names,
                    split_num=2,
                    sample_size=300)

explainer.trend_plot(feature_name=['要介護認定等基準時間（食事）'])

## or show by index
explainer.trend_plot(feature_index=[1276])

## or show the top ranked features
explainer.trend_plot(max_display=10)Reading the trend plot
Package: cocinero
🍳 cocinerococinero é um utilitário escrito em Python para facilitar a geração de projetos novos a partir de boilerplates. Este projeto permite:Criar novos projetos a partir de um repositório baseVerificar no ambiente do usuário se ele possui todos os requisitos para criar/executar um projetoExecutar tarefas pré-definidas em cima dos novos projetosInstalaçãoPara instalar o cocinero, você pode utilizar tanto o pip, quanto o pipx, executando:pipinstallcocineroou ...pipxinstallcocineroPara utilizar boilerplates oriundos do GitHub, você também precisa instalar o git.Como usarApós instalado, para criar um novo projeto com o cocinero, execute:cocinerocookgithub.com/des467/webservicepythonmeu_novo_projeto_incrivelO comando acima irá:Clonar o repositório template passadoLer o arquivococinero-recipe.ymlna raiz do repositório e a partir daí.... gerar o seu incrível projeto.Para ver mais informações sobre como criarrecipes,clique aqui.Como contribuirVeja como contribuirclicando aqui.LicençaEste projeto utiliza a licença GPL v3. Para ver mais sobre a licença,clique aqui
Package: co-citation
Co-citation graph generatorGenerate a co-citation graph from an article list in two steps:Get the references of each article and their corresponding journalsGenerate the co-citation pairs and add them the graph. The weights are the
number of times the journals are co-cited.Examplefromco_citationimportCoCitationcites=CoCitation(["arxiv:1602.05112","pubmed:8113053","sciencedirect:S0167923610001703","scopus:10.1016/j.cmet.2020.11.014",],data_type="journal",# or "article", "institution"wait=None,# None or the time to wait between requests (in seconds)retries=None,# None or the number of retries for HTTPS requestsfirst_last_author=False,# Set to True to only get the institution of the first and last authorsnode_weights="eigenvector",# Or "betweenness")cites.write_graph_edges("graph")cites.plot_graph(display=False,k=10,# The spacing between the nodesseed=42,# Use the seed argument for reproducibilitymargin=dict(b=0,l=110,r=150,t=40))DocumentationSeethe documentation.
Package: cockatiel
[![Build Status](https://travis-ci.org/raphaelm/cockatiel.svg?branch=master)](https://travis-ci.org/raphaelm/cockatiel)
[![Documentation Status](https://readthedocs.org/projects/cockatiel/badge/?version=latest)](http://cockatiel.readthedocs.org/en/latest/?badge=latest)THIS IS HIGHLY EXPERIMENTAL, DON’T USE IT IN PRODUCTION (YET)Cockatiel is a replicating file server for small-scale setups. It is intended
to be used e.g. for handling user-uploaded files to a web application
in a redundant way.Cockatiel doesn’t try to be a CDN, but to implement the simplest
solution that fulfills our needs. Currently, cockatiel makes the following
assumptions. If those won’t work for you, you should probably be looking
for a CDN-like solution.Cockatiel makes some very strong assumption about your use case, be sure to
read them in our [Documentation](https://cockatiel.readthedocs.org/en/latest/design.html#assumptions)
before you decide to use cockatiel.Read morePlease head to our [Documentation](https://cockatiel.readthedocs.org/en/latest/) for
more information on how to install, use and contribute!There is a [Django storage backend](https://github.com/raphaelm/django-cockatiel)
available that uses cockatiel.AuthorRaphael Michel <mail@raphaelmichel.de>
Package: cockatoo
A similarity metric for macromolecular crystallization conditionsWhat is cockatoo?cockatoo is an implementation of a similarity metric used in the comparison of
macromolecular crystallization conditions (or cocktails).Documention is online at:http://ubccr.github.io/cockatoo/Install with CondaFirst download and installed eitherAnacondaorMiniconda.Create a new conda environment, checkout the cockatoo source code, and install
the dependencies:$ conda create -c keiserlab -c rdkit -c sdaxen --name cockatoo-dev e3fp
    $ conda activate cockatoo-dev

    # Note on Linux may want to install ncurses if using Miniconda
    $ conda install -c default ncurses

    $ git clone git://github.com/ubccr/cockatoo.git cockatoo
    $ cd cockatoo
    $ pip install -e .Licensecockatoo is released under the GNU General Public License ("GPL") Version 3.0.
See the LICENSE file.
Package: cockatoo-genome
CockatooInstallation optionsInstall from pipInstall latest release via pip.pipinstallcockatoo-genomeInstall from sourceCreate conda env fromcockatoo.ymland install from source.gitclonehttps://github.com/AroneyS/cockatoo.gitcdcockatoo
condaenvcreate-fcockatoo.yml
condaactivatecockatoo
pipinstall-e.Cockatoo coassembleSnakemake pipeline to discover coassembly sample clusters based on co-occurrence of single-copy marker genes, excluding those genes present in reference genomes (e.g. previously recovered genomes).
Creates graph with samples as nodes and the number of overlapping sequences provided by SingleM.
The taxa of the considered sequences can be filtered to target a specific taxon (e.g. the phylum Planctomycetota).
The graph is clustered using the Girvan-Newman algorithm to provide sample groupings.
Aviary assemble/recover commands are generated based on proposed coassemblies.
Optionally, reads can be mapped to the matched bins with only unmapped reads being assembled.# Example: cluster reads into proposed coassemblies based on unbinned sequencescockatoocoassemble--forwardreads_1.1.fq...--reversereads_1.2.fq...--genomesgenome_1.fna...# Example: cluster reads into proposed coassemblies based on unbinned sequences and coassemble only unbinned readscockatoocoassemble--forwardreads_1.1.fq...--reversereads_1.2.fq...--genomesgenome_1.fna...--assemble-unmapped# Example: cluster reads into proposed coassemblies based on unbinned sequences from a specific taxacockatoocoassemble--forwardreads_1.1.fq...--reversereads_1.2.fq...--genomesgenome_1.fna...--taxa-of-interest"p__Planctomycetota"# Example: find relevant samples for differential coverage binning (no coassembly)cockatoocoassemble--forwardreads_1.1.fq...--reversereads_1.2.fq...--single-assemblyCockatoo evaluateEvaluates the recovery of target genes by coassemblies suggested by above, finding the number of target genes present in the newly recovered genomes.
Compares the recovery by phyla and by single-copy marker gene.# Example: evaluate a completed coassemblycockatooevaluate--coassemble-outputcoassemble_dir--aviary-outputscoassembly_0_dir...Cockatoo unmapApplies unmapping to a previous Cockatoo coassemble run, generating unmapped reads files and Aviary commands.# Example: generate unmapped reads and commands for completed coassemblycockatoounmap--coassemble-outputcoassemble_dir--forwardreads_1.1.fq...--reversereads_1.2.fq...--genomesgenome_1.fna...
Package: cockatrice
# What’s Cockatrice?Cockatrice is the open source search and indexing server written in [Python](https://www.python.org) that provides scalable indexing and search, faceting, hit highlighting and advanced analysis/tokenization capabilities.
Indexing and search are implemented by [Whoosh](https://whoosh.readthedocs.io/en/latest/). Cockatrice provides it via the [RESTful](https://en.wikipedia.org/wiki/Representational_state_transfer) API using [Flask](http://flask.pocoo.org/docs/).
In cluster mode, Cockatrice uses [Raft Consensus Algorithm](https://raft.github.io) by [PySyncObj](https://pysyncobj.readthedocs.io/en/latest/) to achieve consensus across all the instances of the nodes, ensuring that every change made to the system is made to a quorum of nodes.# Documenthttps://cockatrice.readthedocs.io/en/latest/# RequirementsPython 3.x interpreter# FeaturesFull-text search and indexingFacetingResult highlightingEasy deploymentBringing up clusterIndex replicationAn easy-to-use RESTful API
Package: cockli-gen
cockli-genA tool to randomly createcock.liemail addresses on the commandline. This tool is not meant for spam-usage (you still have to fill in the captcha) but rather for "privacy-conscious" people who want to use different email addresses for every service they sign up.Installationpip3 install cockli-genUsagefe@mememachine:~$ cockligen --help
usage: cockli-gen [-h] [-d DOMAIN] [-p PASSWORD_LENGTH]
                  [-mil MINIMUM_USERNAME_LENGTH]
                  [-mal MAXIMUM_USERNAME_LENGTH] [-s SIMPLE]

Create a random cock.li mail address

optional arguments:
  -h, --help            show this help message and exit
  -d DOMAIN, --domain DOMAIN
                        Select one of cock.li's many domains; default =
                        cock.li
  -p PASSWORD_LENGTH, --password-length PASSWORD_LENGTH
                        Override the default password length; default = 32,
                        min = 8, max = 255
  -mil MINIMUM_USERNAME_LENGTH, --minimum-username-length MINIMUM_USERNAME_LENGTH
                        Override the default minimum username length; default
                        = 10, min = 1
  -mal MAXIMUM_USERNAME_LENGTH, --maximum-username-length MAXIMUM_USERNAME_LENGTH
                        Override the default maximum username length; default
                        = 16, max = 32
  -s SIMPLE, --simple SIMPLE
                        Simple output, username:password
Package: cockpit-api
Cockpit CMS APIVersion: 0.3.0Cockpit CMS API Python WrapperIncludes standard Cockpit API CallsAPI Calls:Get ContentAdd/Update ContentGet AssetGet Image AssetThis repository is actively maintained and will continue adding more API callsContribution Opportunities:Support new API callsUsage:#!/usr/bin/python# coding: utf-8importcockpit_apitoken="<TOKEN>"api_url="<COCKPIT API URL>"client=cockpit_api.Api(url=api_url,token=token)content=client.get_content(model="<COLLECTION/SINGLETON>")print(content)Installation Instructions:python-mpipinstallcockpit-apiRepository Owners: