Saravanan666/pypi-org huggingface dataset:
Package: gbcrawler
GBcrawlerGBcrawleris a complete GenBank parser in Python 3 meant to be used by other applications.Generates a Python object of GBcrawler class that stores all relevant information within a GenBank file.Table of ContentsFeaturesInstallationUsageDiscrepanciesFuture featuresFeaturesThis script was made using the information from:NCBI-GenBank Flat File Release available atNCBIFeatures table available atINSDC.InstallationPython 3 is requiered.Copy GBcrawler.py to the folder of your project and import it as explained inUsageUsageTo get the GenBank data, create an object with the GenBank filename as a parameter:import GBcrawler from GB crawler
GBobject = GBcrawler("tth1.gb")The following data can be adquired using: attributes or methodsAttribute references:sequenceIDreturns sequence identificationsequenceLengthreturns length of sequencestrandreturns the strand typemoleculeTypereturns molecule typedivisionreturns divison codemodDatereturns datedefinitionreturns definitionaccessionreturns accesionversionreturns versionreferenceListreturns a list, each element is a referencecommentreturns all the comments as a stringfeatureListreturns a list of GBfeatues objectsequenceListreturns the sequence as a list, (see methods to get the sequence as a string)baseCountreturns dictionary with nucleotide countsMethods:getSequence()returns sequence as a stringThe featureList is composed of GBfeature objects and data can be adquired using the following attribute references:beginreturns sequence identificationendreturns length of sequencetypereturns the type of the feauture (gene, CDS, ...)qualifierDictreturns a Dictionary with keys and values for each qualifierDiscrepanciesThe last Flat File release 220.0 has a set of features that is different from the feature table in INSDC. The release indicates "Any discrepancy between the abbreviated feature table description of these release notes and the complete documentation on the Web
should be resolved in favor of the version at the above URL."At the moment, both sets of features will be used to parse the GenBank files, until a large batch of GenBank files can be tested and check how many files uses the "non-standard" featuresFuture featuresexport to FASTAcheck features for mandatory qualifiersfaster performancetag features by its locus_taguse additional info for the features (beyond, between bases, etc)create a Reference class to better reference data managementimprove ACCESION to return a listimprove SOURCE parsing
Package: gb-distributions-rjv
# Upload python package to PyPi### Table of Contents[Installation](#installation)[Project Motivation](#motivation)[File Descriptions](#files)[Results](#results)[Licensing, Authors, and Acknowledgements](#licensing)## Installation <a name=”installation”></a>The code should run with no issues using Python versions 3.*.## Project Motivation<a name=”motivation”></a>This is an example project to upload a python package to PyPi## File Descriptions <a name=”files”></a>Mainly, there is one package named gb_distributions_rjv and 3 .py files under it.## Results<a name=”results”></a>N.A## Licensing, Authors, Acknowledgements<a name=”licensing”></a>You can find the Licensing details in the file licence.txt.
Package: gb-dl
gb-dlA python based utility to download courses from infosec4tc.teachable.com ,ehacking.net ,stackskills.com and designerup.co ...etc for personal offline use.UsageTo use this script:open terminal or cmd and run gb-dl.py
Enter course url , email and password when prompted. OR
run gb-dl -f FILE to load multiple course urls from files
run gb-dl -d (firefox or firefox-snap)
run gb-dl -h for helpInstallationpip install gb-dl

Then get your key here : https://bmc.xyz/l/gbdl 
N.B key will be sent to your email.Version1.7.4xIssuesReport all issues here in githubTested on ubuntu 16 , 18 ,20 LTS , Windows 10Example of course url:https://stackskills.com/courses/enrolled/68582Change Log:Download only a specified section of the course
 Download only specified range of sections
 Show course info without downloading
 Added cookie authentication 
 Downloading multiple courses from a file
 Load cookie from a text file
 Store cookie for future use
 Auto load/detect stored cookie
 Auto load cookie from browser (firefox and firefox-snap)SupportSupport future development
Package: gbd-mapping
Mapping of Global Burden of Disease (GBD) entities to their metadata.There are two packages offered in this distribution.  The first, thegbd_mapping_generatoris a set of scripts that define templates and data gathering code used to produce the second, thegbd_mapping.
Thegbd_mapping_generatorpackage will not function without access to the IHME cluster and some of our
internally used data access libraries. Mapping updates are managed by an automated toolchain, so this shouldn’t
be an issue.Thegbd_mappingis a programmatically accessible (and TAB-complete-able) set of mappings for GBD entities
including:CausesRisksCovariatesEtiologiesSequelaeYou can installgbd_mappingfrom PyPI with pip:> pip install gbd_mappingor build it from source with> git clonehttps://github.com/ihmeuw/gbd_mapping.git> cd gbd_mapping> pip install .Development and Mapping GenerationIn order to generate or regenerate the mappings from data, you must have access to
the Institute for Health Metrics and Evaluation cluster and internal PyPI server.
Contact <collijk@uw.edu> if you need further instructions on that.Given proper permissions, you can set up this library in development mode with> git clonehttps://github.com/ihmeuw/gbd_mapping.git> cd gbd_mapping> pip install-e.['dev']
Package: gbdt
GBDT is a high performance and full featured C++ implementation of [Jerome H. Friedman's Gradient Boosting Decision Trees Algorithm](http://statweb.stanford.edu/~jhf/ftp/stobst.pdf) and its modern offsprings,. It features high efficiency, low memory footprint, collections of loss functions and built-in mechanisms to handle categorical features and missing values.When is GBDT good for you?-----------* **You are looking beyond linear models.*** Gradient Boosting Decision Trees Algorithms is one of the best offshelf ML algorithms with built-in capabilities of non-linear transformation and feature crossing.* **Your data is too big to load into memory with existing ML packages.*** GBDT reduces memory footprint dramatically with feature bucketization. For some tested datasets, it used 1/7 of the memory of its counterpart and took only 1/2 time to train. See [docs/PERFORMANCE_BENCHMARK.md](https://github.com/yarny/gbdt/blob/master/docs/PERFORMANCE_BENCHMARK.md) for more details.* **You want better handling of categorical features and missing values.*** GBDT has built-in mechanisms to figure out how to split categorical features and place missing values in the trees.* **You want to try different loss functions.*** GBDT implements various pointwise, pairwise, listingwis loss functions including mse, logloss, huberized hinge loss, pairwise logloss,[GBRank](http://www.cc.gatech.edu/~zha/papers/fp086-zheng.pdf) and [LambdaMart](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf). It supports easily addition of your own custom loss functions.
Package: gbd-tools
GBD Benchmark DatabaseGBD Benchmark Database (GBD) is about bridging the gap between research on SAT algorithms and data science.GBD has three interfaces:command-line interfacegbdmicro-services and websitegbd-serverpython interfacegbd_core.api.GBDGBD solves several problemsbenchmark instance identification and identification of equivalence classes of benchmark instances (gbdhash, isohash, instance family, ...)distribution of benchmark instances and benchmark meta-dataa simple query language to provide access to filtered sets of benchmark instances and instance featuresinitialization and maintenance of instance feature databases (meta.db, base.db, gate.db, ...)transformation algorithms for benchmark instances such as instance sanitization or transformation of cnf to k-isp instanceskeeping track of contexts (cnf, sanitized-cnf, k-isp, ...) and relations of instances between contextsProgramming LanguagePython 3SQLiteInstallationpip install gbd-toolsConfigurationfetch a database, e.g.,https://benchmark-database.de/getdatabase/meta.dbexport GBD_DB=[path/to/database1]:[path/to/database2:..](and put it in your .bashrc)test command-line interface with commandsgbd infoandgbd --helptest server with commandgbd-serverGBD Python Interfacefrom gbd_core.api import GBD
with GBD(['path/to/database1', 'path/to/database2', ..] as gbd:
    df = gbd.query("family = hardware-bmc", resolve=['verified-result', 'runtime-kissat'])GBD ServerThis runs underhttps://benchmark-database.de/.
The command is available in gbd-tools:gbd-server --helpGBD Command-Line Interfacegbd getWe assumehttps://benchmark-database.de/getdatabase/meta.dbis in your gbd pathGBD_DB.Get list of benchmark instances in database:gbd getGet list of benchmark instances including some meta-datagbd get -r result familyFilter for specific benchmark instances with gbd-querygbd get "family = hardware-bmc" -r filenamegbd initWe assume you installed the python extension modulegdbc.All initialization routines can run in parallel and per-process ressource limits can be set.
Seegbd init --helpfor more info.gbd init localTo initialize a database with local paths to your own benchmarks:gbd -d my.db init local [path/to/benchmarks]After that in my.db, the features local and filename exist and are associated with their corresponding gbd-hash:gbd -d my.db get -r local filenamegbd init isohashTo identify isomorphic instances (approximately by the hash of the sorted degree-sequence of their graph representation):gbd -d my.db init isohashAfter that in my.db, instances can be grouped by their isohash:gbd -d my.db get -r local filename -g isohashgbd init base, gbd init gategbd -d my.db:base.db init --target_db base_db basegbd -d my.db:gate.db init --target_db gate_db gate
Package: gbdxcli
The gbdxcli package provides a Command Line Interface for ease of use.Access to the GBDX API is provided without the need for programming.InstallationThe command is ‘gbdx’ and it is installed when you pip install the gbdxcli package into your virtualenv.UsageNote: gbdx cli uses the gbdxtools package, which requires GBDX credentials to be present in the users home dir.
In order to use gbdx cli, you need GBDX credentials. Emailgeobigdata@digitalglobe.comto get these.Help and usage information are built into the tool, use the –help switch or just throw bad commands to see it.Note how you can walk through the command group and subcommands to get more specific help.]$ gbdx --help
Usage: gbdx [OPTIONS] COMMAND [ARGS]...

  GBDX Command Line Interface

  example:     gbdx workflow list_tasks

Options:
  --help  Show this message and exit.

Commands:
  catalog   GBDX Catalog Interface
  idaho     GBDX Idaho Interface
  ordering  GBDX Ordering Interface
  s3        GBDX S3 Interface
  s3temp    GBDX Set temporary S3 credentials
  workflow  GBDX Workflow Interface

]$ gbdx workflow --help
Usage: gbdx workflow [OPTIONS] COMMAND [ARGS]...

  GBDX Workflow Interface

Options:
  --help  Show this message and exit.

Commands:
  describe_task  Show the task description json for the task...
  list_tasks     List workflow tasks available to the user
  status         Display the status information for the...

]$ gbdx workflow describe_task --help
Usage: gbdx workflow describe_task [OPTIONS]

  Show the task description json for the task named

Options:
  -n, --name TEXT  Name of task to describe
  --help           Show this message and exit.Examples]$ gbdx workflow list_tasks
{
"tasks": [
    "Downsample",
    "protogenRAW",
    "protogenUBFP",
    "AComp",
    "StageDataToS3",
    "FastOrtho",
    ... lots more tasks ...
]}

]$ gbdx workflow describe_task --name AComp
{
    "containerDescriptors": [
        {
            "command": "",
            "properties": {
                "image": "tdgp/acomp:latest"
            },
            "type": "DOCKER"
        }
    ],
    "description": "Runs AComp code on an input 1B image.",
    "inputPortDescriptors": [
        {
    ... more task descriptor json ...
}Developmentgbdx cli is a Click application. Learn more about Click athttp://click.pocoo.org/5/Installation is done via the setup.py file in the project’s home dir. Look for the entry_points clause.
Package: gbdx-cloud-harness
This package allows a user to build custom GBDX tasks that can be executed locally and also remotely on the platform. All from a python file and commandline tools.To run remote tasks, this package uses thegbdx-authpackage. Therefore the users credentials must be configured according togbdx-auth.InstallationTo install from pypi:pip install gbdx-cloud-harnessUsageThe cloud-harness package has a template task with example usage inside. To create a new template:cloud-harness create my_app
cd my_app/In the new folder, there will be aapp.pyfile which is the template. Open in your favorite editor to start building a new task. When ready to run the task:cloud-harness run app.pyPlease note, to run the task locally, all port values must be valid filesystem locations. Otherwise errors will be raised.When the task is ready to be ran on the platform, use the–remoteflag:cloud-harness run app.py --remoteThis will run the task on the platform assuming all the port values are S3 locations. Otherwise errors will be raised.If there is local data that needs to be pushed to S3 for the remote run, then the–uploadflag needs to be used:cloud-harness run app.py --remote --uploadThis will push all local data the ports contain to the users account storage, prior to executing the workflow.DevelopmentContributingPlease contribute! Please make pull requests directly to master. Before making a pull request, please:Ensure that all new functionality is covered by unit tests.Verify that all unit tests are passing.Ensure that all functionality is properly documented.tEnsure that all functions/classes have proper docstrings so sphinx can autogenerate documentation.Fix all versions in setup.py (and requirements.txt)Run TestsTests usepytestframeworkpy.test [...]
python -m pytest [...]Create a new versionTo create a new version:bumpversion ( major | minor | patch )
git push --tagsDon’t forget to update the changelog and upload to pypi.
Package: gbdx-task-template
A super class template for custom tasks to be run using gbdx-cloud-harness, seehttps://github.com/TDG-Platform/cloud-harness.DevelopmentContributingPlease contribute! Please make pull requests directly to master. Before making a pull request, please:Ensure that all new functionality is covered by unit tests.Verify that all unit tests are passing.Ensure that all functionality is properly documented.tEnsure that all functions/classes have proper docstrings so sphinx can autogenerate documentation.Fix all versions in setup.py (and requirements.txt)Run TestsTests usepytestframeworkpy.test [...]
python -m pytest [...]Create a new versionTo create a new version:bumpversion ( major | minor | patch )
git push --tagsDon’t forget to update the changelog
Package: gbe0-log
gbe0 LogMy standard logging module.InstallationTo install from PyPI:python3-mpipinstallgbe0-logUsageTo use in scriptscheck example.py.Example:## Import modulefromgbe0_logimportLoggerimportloggingaslog## Initialize logger## This only needs to be done onceLogger(verbosity=5)## Log some messageslog.critical('A message to log at critical level')# Always logged (verbosity level 0)log.error('A message to log at error level')# Always logged (verbosity level 0)log.warning('A message to log at warning level')# Verbosity level 1log.info('A message to log at info level')# Verbosity level 2log.verbose('A message to log at verbose level')# Verbosity level 3log.debug('A message to log at verbose level')# Verbosity level 4log.trace('A message to log at trace level')# Verbosity level 5Example OutputOutput is to stderr and looks like this:
Package: gbfr-auto-restart
gbfr-auto-restart그랑블루 판타지 리링크 전투 자동 재시작 스크립트다운로드https://github.com/Bing-su/gbfr_auto_restart/releases/latest/download/gbfr_auto_restart.exe작동원리3초마다image폴더에 있는 이미지와 일치하는 부분을 모니터에서 찾으면W, enter를 순서대로 누릅니다.찾는 이미지는 '아니요'에 커서가 올려져 있는 이미지로, '계속 도전하시겠습니까?' 이외에 다른 상황에도 동작하니 유의해주세요.주의사항백그라운드에서 동작하지 않음주 모니터만 인식함바이러스로 인식될 가능성이 높음windows에서만 동작함게임을 시작한 뒤, 한번이라도 키보드나 마우스를 게임 내에서 사용해야 하는 것 같음 (게임패드로만 플레이 했을 경우 동작하지 않았음)해상도 1080에서만 동작함파이썬 사용법python-mgbfr_auto_restart또는 그냥gbfr_auto_restart기타치트엔진을 사용한 nexusmod의https://www.nexusmods.com/granbluefantasyrelink/mods/84가 더 유용할 수 있습니다.
Package: gbif
A simple and pythonic client for Gbif Taxon Webservice. Pretty much work in progress.
See [Gbif Taxon Webservice API](http://data.gbif.org/ws/rest/taxon) for details.OVERVIEW:This is the way to use it:>>> from gbif import Client
>>> c = Client()
>>> taxon = client.get_by(key=5661746)
<Genus Orchid key=5661746 status='unconfirmed'>
>>> taxon.key
5661746
>>> taxon.url
'http://data.gbif.org/ws/rest/taxon/get/5661746/'
>>> taxon.fullname
'Orchid'
>>> taxon.status
'unconfirmed'
>>> taxon.primary
True
>>> # searching by scientific name and rank
>>> c.search("Cymbidium")
[<Genus Cymbidium key=16286243 status=unconfirmed>, <Genus Cymbidium key=16308408 status=unconfirmed>,
<Genus Cymbidium key=14751259 status=accepted>, <SubGenus Cymbidium key=16069947 status=unconfirmed>]
>>> c.search("Cymbidium", rank="subgenus")
[<SubGenus Cymbidium key=6313072 status=unconfirmed>, <SubGenus Cymbidium key=7843799 status=unconfirmed>,
<SubGenus Cymbidium key=7926640 status=unconfirmed>, <SubGenus Cymbidium key=16069947 status=unconfirmed>,
<SubGenus Cymbidium key=7709563 status=unconfirmed>, <SubGenus Cymbidium key=17001172 status=unconfirmed>,
<SubGenus Cymbidium key=7033101 status=unconfirmed>]
>>> c.search("Cymbidium", rank="genus")
[<Genus Cymbidium key=16286243 status=unconfirmed>, <Genus Cymbidium key=16308408 status=unconfirmed>,
<Genus Cymbidium key=14751259 status=accepted>]
>>> c.search("Cymbidium", rank="species")
[]
>>> c.search("aerides", rank="genus")
[<Genus Aerides key=14750373 status=accepted>]
>>> c.search("orchid", rank="genus")
[<Genus Orchid key=5661746 status=unconfirmed>, <Genus Orchid key=16409837 status=unconfirmed>,
<Genus Orchid key=23768848 status=unconfirmed>]
>>> c.search("cattleya", rank="genus")
[<Genus Cattleya key=13232033 status=accepted>]
>>> c.search("Dendrobium", rank="genus")
[<Genus Dendrobium key=13232902 status=accepted>]
>>> c.search("Dendrobium", rank="species")
[]
>>> c.search("Dendrobium", rank="subgenus")
[<SubGenus Dendrobium key=7927123 status=unconfirmed>, <SubGenus Dendrobium key=16133082 status=unconfirmed>,
<SubGenus Dendrobium key=6417149 status=unconfirmed>, <SubGenus Dendrobium key=6525783 status=unconfirmed>,
<SubGenus Dendrobium key=17000374 status=unconfirmed>, <SubGenus Dendrobium key=7035708 status=unconfirmed>]REQUIREMENTS:Please before using, install [restkit](http://benoitc.github.com/restkit/)
Package: gbinaryclf
gbinaryclfGeneric Python Package for Binary Classification that seeks to include the latest over time and making it easy to use. 
Thus, including what is current and stable for non-profit or social use.UsageSee -> ./tests/test_example/test_hello.pyFeaturesFeature selection using curated machine learningModel selection using curated machine learningWhyhttps://www.clinfo.eu/mean-median/https://www.health.harvard.edu/blog/the-11-most-expensive-medications-201202094228ReleasesYou can see the list of available releases on theGitHub Releasespage.  
We useRelease Drafter. As pull requests are merged, a draft release is kept up-to-date listing the changes.LicenseThis project is licensed under the terms of theGNU GPL v3.0license.📃 Citation@misc{gbinaryclf,author={altcp},title={Generic Python Package for Binary Classifications},year={2021},publisher={GitHub},journal={GitHub repository},howpublished={https://github.com/altcp/gbinaryclf}}CreditsThis project was generated withpython-package-template
Package: gbkviz
GBKviz: Genbank Data Visualization WebAppOverviewGBKviz is a web-based Genbank data visualization and comparison tool developed with streamlit web framework.
GBKviz allows user to easily and flexibly draw CDSs in user-specified genomic region (PNG or SVG format is available).
It also supports drawing genome comparison results by MUMmer.
GenomeDiagram, a part of BioPython module, is used to draw the diagram.
This software is developed under the strong inspiration ofEasyFig.If you are interested, clickhereto try GBKviz on Streamlit Cloud.:warning: Due to the limited resources in Streamlit Cloud, it may be unstable.InstallationGBKviz is implemented in Python3 (Tested on Ubuntu20.04)Install PyPI stable version with pip:pip install gbkvizIf you want to enable genome comparison in GBKviz, MUMmer is required.Install MUMmer with apt command (Ubuntu):sudo apt install mummerGBKviz can also be available with Docker:docker pull moshi4/gbkviz:latest
docker run -d -p 8501:8501 moshi4/gbkviz:latestDependenciesStreamlitSimple web framework for data analysisBioPythonUtility tools for computational molecular biologyMUMmerGenome alignment tool for comparative genomicsCommand UsageLaunch GBKviz in web browser (http://localhost:8501):gbkviz_webappIf you are using Docker to start, above command is already executed.ExampleExample of GBKviz genome comparison and visualization results.Fig.1: 4 phage whole genomes comparison resultFig.2: 4 E.coli partial genomes comparison resultFig.3: 4 E.coli whole genomes comparison resultFig.4: Simple CDS visualization with gene labelGenome ComparisonIn GBKviz,MUMmeris used as genome comparison tool.Following four genome comparison methods are available.Nucleotide One-to-One MappingNucleotide Many-to-Many MappingProtein One-to-One MappingProtein Many-to-Many MappingUser can download and check genome comparison results file.Genome comparison results file is in the following tsv format.ColumnsContentsREF_STARTReference genome alignment start positionREF_ENDReference genome alignment end positionQUERY_STARTQuery genome alignment start positionQUERY_ENDQuery genome alignment end positionREF_LENGTHReference genome alignment lengthQUERY_LENGTHQuery genome alignment lengthIDENTITYReference and query genome alignment identity (%)REF_NAMEReference genome name tagQUERY_NAMEQuery genome name tag