Saravanan666/pypi-org huggingface dataset:
Package: ciclops
CiclopsCross-platform training In CLinical Outcome PredictionS (ciclops) is the winning algorithm in2019 Malaria DREAM Challenge SubChallenge 2.Ciclops performs transfer learning from one transcriptomic platform's samples to another.InstallationInstall this package via pip:pip install ciclopsor clone this program to your local directory:https://github.com/GuanLab/ciclops.gitUsagepython ciclops [-h] [--train_path TRAIN_PATH] [--valid_path VALID_PATH]
               [-m MODEL_TYPE] [--no_quantile] [--shap] [-n TOP_GENES]

Pipeline for building clinical outcome prediction models on training dataset and transfer learning on validation datasets.

optional arguments:
 -h, --help            show this help message and exit
 --train_path TRAIN_PATH
                       Path to your training data, in .csv format; includes sample names as first column and labels as last column
 --valid_path VALID_PATH
                       Path to your transfer validation data, in .csv format; includes sample names as first column and labels as last column
 -m MODEL_TYPE, --model_type MODEL_TYPE
                       Machine learning models to use:
                                   lgb: LightGBM;
                                   xgb: XGBoost;
                                   rf: Random Forest;
                                   gpr: Gaussian Process Regression;
                                   lr: Linear Regression;
                                   default: lgb
 --no_quantile         If specified, do not use quantile normalization.
 --shap                Conduct SHAP analysis on the training and validation set.
                       Only for use with LightGBM, XGBoost, and Random Forest.
 -n TOP_GENES, --top_genes TOP_GENES
                       If --shap is specified, indicate number of top genes from both training and validation sets that will be compared in post-SHAP analysis.
                       Default is 20.It will generate the following folders:./training/: preprocessed training datasets for model training and 10-fold cross validation./validation/: validation dataset for transferring test./params/: trained machine learning model parameters./performance/: model performance in 10-fold cross validation and transferring test./SHAP/: SHAP analysis resultsReferencesFor the original paper, please refer to the Guan Lab's 2022 iScience paper:Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms.STAR Protocol (TBD)External data for testing/example purposes:Shaw, P.J. et al. (2015) ‘Plasmodium parasites mount an arrest response to dihydroartemisinin, as revealed by whole transcriptome shotgun sequencing (RNA-seq) and microarray study’, BMC Genomics. doi:10.1186/s12864-015-2040-0.GSE59098GSE151189
Package: ci-cmg-cruise-schema-orm
ci-cmg-cruise-schema-ormSetting Up a Virtual Environment with pyenvInstall pyenv-virtualenv if you have not alreadybrewinstallpyenv-virtualenv
pyenvinstall3.9.13If setting up pyenv for the first time, you will need to add these lines to your~/.bash_profileor~/.zprofile(if using zsh, the default in macos Catalina and later) file in order for pyenv
to automatically activate / deactivate python environments configured for a given directory (next step).eval"$(pyenvinit-)"eval"$(pyenvvirtualenv-init-)"configure python environment for project directorypyenvvirtualenv3.9.13cruise-schema-orm-3.9.13
pyenvlocalcruise-schema-orm-3.9.13install dependencies into python environmentpipinstall-rrequirements.txt
pipinstall-rtest-requirements.txtBuildingSince this project can use JDBC divers, Apache Maven is used to build and test the library.
Java 8+ and Maven must be installed and on the PATH.  JAVA_HOME and MAVEN_HOME
environment variables should be set.This project uses a docker container running an Oracle database to run tests.  Docker must be installed.To build and test, run the following from the root of the project:mvncleaninstallDeveloping
Package: ci-cmg-mb-cruise-migration
mb-cruise-migrationMigration scripts for migrating multibeam metadata from MB schema to CRUISE schema.Usage: Migration InstructionsCreate a python file and import theci-cmg-mb-cruise-migrationpackage.Create an instance of the "Migrator" class, passing in the path to your config file as the only parameter.Run the "migrate" method.An example run and start_background shell script is provided, which should be used to run the python script in the background.The package and it's dependencies will need to be installed prior to usage. This can be done withpython -m pip install ci-cmg-mb-cruise-migration.NOTE: if enabling pooled connections for cruise db in config, oracle client libraries need to also be installed:https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html#optionally-install-oracle-clientDeveloping ProjectSetup virtual environmentsetup virtualenvspecify python installation for virtualenv to useactivate the virtual environmentverify python version and virtualenvinstall required modulesverify they were installed with pip listdeactivate the virtual environmentvirtualenvmigenv

virtualenv--python=</path/to/python></path/to/new/virtualenv/>source./migenv/bin/activate

pythonwhich
python-V

python-mpipinstall--upgradepip
python-mpipinstall-rrequirements.txt

piplist

deactivaterun testsMany (most) tests are integration tests and require either one or both the MB or CRUISE schema docker
containers be running.To start docker containers:
from test dir:./start-it.shTo run tests:
from project root:python-munittestdiscovertestsTo stop docker containers:
from test dir:./stop-it.shbuild projectUpdate version in pyproject.toml either before or after release.If not already installed:python -m pip install --upgrade buildBuild distribution:python -m buildIf not already installed:python -m pip install --upgrade twineUpload to pypi:twine upload dist/*
Package: cico
cicodeploy CI results to gitcicocommits artefacts generated by a CI enviroinment to a Git results branch.For each tested branch a directory in the results branch gets created.tested branch: The branch you commited an is checked out by the CI toolresults branch: The branch where your results should be deployedcan be in a different repository than the tested branchInstallation$pipinstallcicoUsagedeploy.pyfromcicoimportTravisCIfromcico.resultsimportDirectory,File,BadgeTravisCI(repo=GitHub(USERNAME,# GitHub Username (e.g. 'stefanhoelzl')REPO_NAME,# GitHub Repository (e.g. 'ci-results')TOKEN),# GitHub Personal access tokens# ONLY ENCRYPTED (https://docs.travis-ci.com/user/environment-variables/#Defining-encrypted-variables-in-.travis.yml)branch=RESULT_BRANCH,# Git Branch with the results (e.g. 'cico-testing')results=[# Deploy file 'testresults.tap' into folder 'tap' (destination is optional)File("testresults.tap",destination="tap"),# Deploy file 'wrong_name.tap' as 'correct_name.tap' (rename is optional)File("wrong_name.txt",rename="correct_name.txt"),# Deploy directory 'covhtml' into folder 'coverage' (desitnation is optional)Directory("covhtml",destination="coverage"),# Create a Badge with the label "My Badge" and value "96" as mybadge.svg and mybadge.png# (png is optional) in the directory 'badges'Badge("badges/mybadge",png=True,label="My Badge",value=96,**anybadge_arguments),# https://github.com/jongracecox/anybadge]).commit(# commit message (optional)# {build} gets replaced by build number# {branch} gets replaced by name of tested branchmessage="build{build}on branch{branch}",# perform 'git push' even if not executed in CI environment (default=False)no_ci_push=True).travis.ymlwithafter_scriptsectionafter_script:-python deploy.py.travis.ymlwithdeploysectiondeploy:provider:scriptskip_cleanup:true# prevent TravisCI from cleaning up the files you want to deployscript:python deploy.pydirectory structure afterwards in branchcico-testingof the repositoryci-results+-- master
    o-- correct_name.txt
    +-- tap
    |   o-- testresults.tap
    +-- covhtml
    |   +-- <all contents of covhtml in the tested branch>
    +-- badges
        o-- mybadge.svg
        o-- mybadge.png
Package: cicpy
Custom IC Creator PythonWhyThis is a script package I use transpile from the output of ciccreator to other
formats.ChangelogVersionStatusComment0.0.1:white_check_mark:First version of cicspy0.1.5:white_check_mark:First release to pypiInstall this moduleIf you want to follow the latest and greatestgitclonehttps://github.com/wulffern/cicpycdcicpy
python3-mpipinstall-e.If you want something that does not change that oftenpython3-mpipinstallcicpyCommandsFor the latest help, checkcicpy --help, andcicpy <command> --helpUsage:cicpy[OPTIONS]COMMAND[ARGS]...PythontoolboxforCustomIntegratedCircuitCreator(ciccreator).

Options:--helpShowthismessageandexit.

Commands:jcellExtractacellfrom.cicminecraftMakeamincraftscript*.mcfrom*.cicplacePlaceabunchoftransistorsaccordingtopatternsvgMakeanSVGtranspileTranslate.cicfileintoanotherfileformat...Usage:cicpytranspile[OPTIONS]CICFILETECHFILELIBRARYTranslate.cicfileintoanotherfileformat(SKILL,SPECTRE,SPICE)Options:--layskillWriteSkillLayoutfile--schskillWriteSkillSchematicfile--winfoWriteInfofile[ALPHA]--rinfoTEXTReadInfofile[ALPHA]--verilogWriteverilogfile[EXPERIMENTAL]--spiceWritespicefile--xschemWritexschemschematics--magicWritemagiclayout--smashTEXTListoftransistorstosmashschematichierarchy--excludeTEXTRegexofcellstoignore--helpShowthismessageandexit.
Package: cicriterion
CIC (Cross-Entropy Information Criterion)The CIC is a statistic which can be used to design algorithms for theBoltzmann approximation problem. Inour paperthe CIC is theoretically developed and an algorithm is provided for using the CIC to solve Boltzmann approximation problems.This package contains an implementation for an algorithm described in the paper which solves the Boltzmann approximation problem using the CIC.Installation and UseTo install the latest CIC implementation using pip, run the following command:pip install cicriterionTo see how to use the CIC implementation, you can refer to example scripts in theproject's Github repo. The dependencies for the CIC implementation included with the package will cover any dependencies for experiment scripts found on the Github repo as well.
Package: cicsim
Custom IC Creator Simulation ToolsWhyThis is a script package I use to control ngspice, it canRun corner simulationsCreate IPs (used inwulffern/aicex)Create simulation directoriesView waveformsChangelogVersionStatusComment0.0.1:white_check_mark:First version of cicsim0.0.3:white_check_mark:All in on open source tools0.1.2:white_check_mark:First version on pipy0.1.3:white_check_mark:github action update0.1.4:white_check_mark:Added waveform viewer0.1.5:white_check_mark:Update waveform viewerInstall this moduleIf you want to follow the latest and greatestgitclonehttps://github.com/wulffern/cicsimcdcicsim
python3-mpipinstall--user-e.If you want the latest stablepython3-mpipinstallcicpyGet started with simulationHead over toOpen source analog integrated circuit flow on Skywater
130nmto see cicsim in action.Get started with waveform viewerMake sure you install a python version with tk. On my mac it wasbrewinstallpython-tkOnce installed, I docicsimwaveoutput_tran/tran_SchGtTtKffVh_*CommandsUsage: cicsim [OPTIONS] COMMAND [ARGS]...

  Custom IC Creator Simulator Tools

  This package provides helper scripts for simulating integrated circuits

Options:
  --help  Show this message and exit.

Commands:
  plot         Plot from rawfile
  portreplace  Replace ${PORTS} and ${VPORTS} with the subcircuit ports...
  results      Results of single runfile
  run          Run a ngspice simulation of TESTBENCH
  simcell      Create a ngspice simulation directory for a Cell
  srun         Run a spectre simulation of TESTBENCH
  summary      Generate simulation summary for results
  wave         Open waveform viewer
Package: cid
cidChinese Identity Parser提取身份证号码中的生日、性别、办证区域, 校验身份证号码格式是否正确Installpip install cidUsageIn [1]: from cid import IdParser

In [2]: ip = IdParser()

In [3]: ip.parse('360730198601011111')
Out[3]:
{'birthday': '1986-01-01',
 'gender': '男',
 'region': {'city': '赣州市', 'district': '宁都县', 'province': '江西省'}}

# 校验身份证号码格式是否正确

In [4]: ip.is_valid_cid('360730198601011111')
Out[4]: False


In [5]: ip.extract_gender('360730198601011111')
Out[5]: '男'

In [6]: ip.extract_birthday('360730198601011111')
Out[6]: '1986-01-01'

In [7]: ip.extract_region('360730198601011111')
Out[7]: {'city': '赣州市', 'district': '宁都县', 'province': '江西省'}

# 除了提取发证地，需要实例化 IdParser 外, 其他方法可直接使用

In [8]: from cid import (is_valid_cid, extract_gender, extract_birthday)Changelog0.1.3 - Dec 19, 2017update data.csv0.1.2 - Dec 19, 2017update data.csv0.1.1 - Apr 5, 2016update data.csv0.1 - Apr 1, 2016first version
Package: cidade-ibge-tom
cidade_ibge_tomCidade IBGE TOMDemonstra informações da Cidade pelo código IBGE ou pelo código TOM.Código IBGEé um padrão de código das Cidades brasileiras, que é composto por 7 dígitos, sendo os 2 primeiros o código do Estado.Código TOMé um padrão de código das Cidades brasileiras utilizado por sistemas, por exemplo,SIAFI- Sistema Integrado de Administração Financeira do Governo Federal, que é composto por 4 dígitos. A tabela completa dos códigos pode ser obtidaaqui.Neste pacote python temos o código IBGE, código TOM e o nome da Cidade relacionado.Instalaçãopipinstallcidade_ibge_tomUtilizaçãoInformando o código IBGE 5300108:fromcidade_ibge_tomimportinfo_cidadeprint(info_cidade(codigo='5300108')){'ibge':'5300108','tom':'9701','nome':'Brasília-DF'}Informando o código TOM 7107:fromcidade_ibge_tomimportinfo_cidadeprint(info_cidade(codigo='7107')){'ibge':'3550308','tom':'7107','nome':'São Paulo-SP'}LicençaMIT License
Package: cid-cli
CIDCLI wrapper for py-cid> pip3 install cid-cli

> cid QmaozNR7DZHQK1ZcU9p7QdrshMvXqWK6gpu5rmrkPdT3L4
base58btc - cidv0 - dag-pb - sha2-256-256-b94d27b9934d3e08a52e52d7da7dabfac484efe37a5380ee9088f7ace2efcde9

> cid bafy2bzaceak4ipewhpc3wrrotbfbiquhv2celtjgsyigetmbxlbe5b6rmk4am
base32 - cidv1 - dag-cbor - blake2b-256-256-15c43c963bc5bb462e984a144287ae8445cd269610624d81bac24e87d162b806

> cid QmaozNR7DZHQK1ZcU9p7QdrshMvXqWK6gpu5rmrkPdT3L
Unsupported hash code 80

> cid bafy2bzaceak4ipewhpc3wrrotbfbiquhv2celtjgsyigetmbxlbe5b6rmk4a
Inconsistent multihash length 31 != 32
Package: cidc-ngs-pipeline-api
CIDC NGS Pipeline APIOverviewThis repository serves as an interface between the CIDC and Bioinformatics teams to determine specifications and documentation related to NGS pipelines.Repository structure:.
├── README.md
├── cidc_ngs_pipeline_api
│   ├── output_API.schema.json
│   ├── rna
│   │   ├── rna.md
│   │   ├── rna_config.schema.json
│   │   ├── rna_output_API.json
│   │   └── imgs
│   |       └── RIMA.png
│   ├── atacseq
│   │   ├── atacseq.md
│   │   ├── atacseq_output_API.json
│   │   └── imgs
│   |       └── atacseq.png
│   ├── tcr
│   │   ├── tcr.md
│   │   └── imgs
│   |       └── TCRseq.png
│   └── wes
│       ├── wes.md
│       ├── wes_config.schema.json
│       ├── wes_output_API.json
│       ├── wes_tumor_only_output_API.json
│       ├── wes_output_API.py
│       └── imgs
│          └── wes.png
├── tests
│   └── test_apis.py
├── requirements.dev.txt
├── requirements.txt
├── MANIFEST.in
├── CHANGELOG.md
├── setup.py
└── .github
    └── workflows
       └── ci.ymlcidc_ngs_pipeline_api moduleTheoutput_API.schema.jsonfile defines the schema structure:filter_group: Filter under which the file would appear during faceted search. It is the GCS-URI top-level hierarchyfile_path_template: Local file path used for CLI uploadshort_description: Description to appear on hovering over file name in file browserlong_description: Longer description to appear on file documentation pagefile_purpose: Assigns a tag to show up in a particular file-browser view configuration. Permissible values:Source view,Analysis view,Clinical view,MiscellaneousWithin the directory for each assay:The defined schema is used to structure information about pipeline-related files in the respective< assay >_output_API.json.Information related to YAML configurations (which are generated by the CIDC and configured with CIMAC IDs to run the pipelines), are described in the respective< assay > config.schema.json.Documentation related to each pipeline is in the respective< assay > .md.Developer SetupInstall necessary dependencies.pipinstall-rrequirements.dev.txtInstall and configure pre-commit hooks.pre-commitinstall
Package: cider
Start with a clean slateCider is a simple wrapper forHomebrewandHomebrew Caskthat allows you to save your setup across different machines. This lets you to restore a backup without having to deal with the mess that was the state of your previous installation, or painstakingly babysit the process step-by-step.Simply run the following on a new machine:git clone [YOUR_REPO] ~/.cider
cider restore... and you'll be back up and running, with all of your applications and command line utilities re-installed (and configurations restored).In addition to Homebrew, Cider also supports managing your user defaults, restoring symlinks, and running scripts to conveniently manage other settings such as your dotfiles.Installationpip install -U ciderConfigurationAll configuration files are stored in the~/.ciderdirectory as JSON. For instance, here's an example bootstrap file:{
    "after-scripts": [
        "brew linkapps"
    ],
    "casks": [
        "adobe-creative-cloud",
        "dropbox",
        "firefox",
        "flash",
        "flux",
        "github",
        "google-chrome",
        "google-hangouts",
        "heroku-toolbelt",
        "iterm2",
        "mplayerx",
        "sublime-text",
        "transmission",
    ],
    "formulas": [
        "brew-cask",
        "emacs",
        "fish",
        "git",
        "go",
        "macvim --overwrite-system-vi",
        "python",
        "python3",
        "xctool"
    ],
	"icons": {
		"iTerm": "https://dribbble.com/shots/1702947-iTerm-Replacement-Icon/attachments/271548"
	},
	"symlinks": {
		"bash/.*": "~",
		"bin/*": "~/bin/",
		"git/.*": "~",
		"sh/.*": "~",
		"vim/.*": "~"
	},
    "taps": [
        "caskroom/cask"
    ]
}User defaults are stored similarly:{
    "NSGlobalDomain": {
        "ApplePressAndHoldEnabled": false
    },
    "com.apple.dock": {
        "tilesize": 48
    },
    "com.iconfactor.mac.xScope": {
        "generalShowDockIcon": false
    }
}Cider also supports YAML if you'd like to add comments to either of these. To see how this works out in practice, feel free to take a look atmy dotfiles.Backup your existing setupTo save the state of your existing setup:cider missing
cider tap missing
cider cask missingManage symlinksCider supports the following commands to manage symlinks (inspired in part byGNU Stow).cider addlink NAME ITEM...
cider relink # (invoked automatically by restore)For example,cider addlink git ~/.gitconfigwill move~/.gitconfigto~/.cider/symlinks/git/, create a link back to its original location, and add an entry to your bootstrap denoting this:"symlinks": {
    "git/.*": "~"
}To undo this change, simply runcider unlink git.Directories in targets are automatically expanded, so the entry"bin/*": "~/bin/"will first create the directory~/bin/if it doesn't exist already, and then link all items insymlinks/bin/*to children of that directory.CaveatsThere doesn't seem to be a way to re-install purchases made from Mac App Store via the command line just yet, so those have to be done by hand.Note: Cider is a work-in-progress, but it's well-tested and should be kind to your machine.For more information, see theGitHub Repository.
Package: cider-chem
ChemIcal DatasEt comparatoR (CIDER)ChemIcal DatasEt comparatoR (CIDER) is a Python package which primarily utilizes RDKit to compare and visualize different chemical compounds from two different datasets.UsageTo use CIDER, clone the repository to your local disk and make sure you install all the necessary requirements.We recommend to use CIDER inside a Conda environment to facilitate the installation of the dependencies.Conda can be downloaded as part of theAnacondaor theMinicondaplatforms (Python 3.8). We recommend to install miniconda3. Using Linux you can get it with:$wgethttps://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
$bashMiniconda3-latest-Linux-x86_64.shInstallation$gitclonehttps://github.com/hannbus/ChemIcal_DatasEt_compaRator.git
$cdChemIcal_DatasEt_compaRator
$condacreate--nameChemIcal_DatasEt_compaRatorpython=3.8
$condaactivateChemIcal_DatasEt_compaRator
$condainstallpip
$python-mpipinstall-Upip#Upgrade pip$pipinstallnotebookrdkit-pypimatplotlib==3.5.1seaborn==0.11.2chemplot==1.2.0matplotlib_venn==0.11.6FPDF==1.7.2Alternative$python-mpipinstall-Upip#Upgrade pip$pipinstallgit+https://github.com/hannbus/ChemIcal_DatasEt_compaRator.gitInstall from PyPI$todoBasic usage:TodoCite usBusch, H., Schaub, J., Brinkhaus, H. O., Rajan, K., & Steinbeck, C. (2022). ChemIcal DatasEt comparatoR CIDER (Version 0.0.1-dev) [Computer software].https://doi.org/10.5281/zenodo.6630494More information about our research group
Package: cidict
Everyone needs it, but there is no package implementing it.
Every project rolls its own.When I was thinking that I should write unit tests for mine, I extracted it
into a separate package.This is a work in progress.TestsRun withnose.LicenseReleased under the 2 clause BSD license.Referenceshttp://docs.python.org/2/library/stdtypes.html#mapping-types-dict
Package: ci-diff-helper
Diff Helper for Continuous Integration (CI) ServicesFor an open source project, running unit tests, system tests, torture tests,
fuzz tests, integration tests, code quality checks, etc. can quickly become
a large task.In order to limit the amount of time and resources that these jobs require,
this tool provides a way to determine which files have changed and provides
a Python API for these changes. In addition, this library provides the
corresponding commit SHA (or other artifact) that is used as the diffbase.The library supports (planned)Continuous Integration ServicesTravis CIAppVeyorCircleCIVerson Control SystemsgitProject Hosting SitesGitHubInstall$pipinstall--upgradeci-diff-helperSee It In ActionThetest-appbranchis set up to run every time a change is made.
Check out thelatest Travis buildsintest-app, thelatest CircleCI buildsand thelatest AppVeyor buildsto see the computed values at work.For example, in aTravis PR build:Config object: <Travis (active=True)>
----------------------------------------
    active: True
      base: 'test-app'
    branch: 'test-app'
event_type: <TravisEventType.pull_request: 'pull_request'>
     in_pr: True
  is_merge: True
 merged_pr: None
        pr: 10
      slug: 'dhermes/ci-diff-helper'
       tag: None
       ...and in aTravis push build:Config object: <Travis (active=True)>
----------------------------------------
    active: True
      base: u'4a02445642735ba0d3ce5b944f4b2647b4c55813'
    branch: 'test-app'
event_type: <TravisEventType.push: 'push'>
     in_pr: False
  is_merge: True
 merged_pr: 10
        pr: None
      slug: 'dhermes/ci-diff-helper'
       tag: None
       ...ACircleCI push build:Config object: <CircleCI (active=True)>
----------------------------------------
    active: True
    branch: 'test-app'
  is_merge: True
       tag: None
       ...AnAppVeyor push build:Config object: <AppVeyor (active=True)>
----------------------------------------
    active: True
    branch: 'test-app'
  is_merge: True
  provider: <AppVeyorRepoProvider.github: 'github'>
       tag: None
       ...LicenseApache 2.0 - SeeLICENSEfor more information.
Package: cidr-list
# cidr-list[![Build Status](https://travis-ci.org/mpicard/cidr_list.svg?branch=master)](https://travis-ci.org/mpicard/cidr_list)Convert CIDR to list of IPs.# InstallationIf you don’t usepipsi, you’re missing out.
Here are [installation instructions](https://github.com/mitsuhiko/pipsi#readme).Simply run:$ pipsi install cidr-listOr with pip$ pip install cidr-list# UsageTo use it:$ cidr-list –help
$ cidr-list 10.10.10.0/30
> 10.10.10.0,10.10.10.1,10.10.10.2,10.10.10.3
$ cidr-list 10.10.10.0/30 –range
> 10.10.10.0-10.10.10.3
$ cidr-list 10.10.10.0/30 –file output.txt
$ cidr-list 10.10.10.0/30 –seperator :
> 10.10.10.0:10.10.10.1:10.10.10.2:10.10.10.3