Saravanan666/pypi-org huggingface dataset:
Package: transip
This library aims to implement theTransIP APIin Python.Quick StartPrerequisiteMake sure you have an account atTransIPEnablethe API (https://www.transip.nl/cp/mijn-account/#api)Whitelist your IP.Generate a new key-pair.Copy-paste the private key into a file.Put the private key in a file calleddecrypted_keybeside thisREADME.rstfile.SetupYou can get the library directly from PyPi:$ pip install transipExampleThe command-line interpreter doesn’t do much yet. By default it does a
getDomainNames() call, but with the ‘-u’ option it’s also possible to add or
update DNS records. When calling it with ‘-h’, it will show all available options.$ transip-api
[example.com, example.org, example.net]

$ transip-api -h
usage: transip-api [-h] [-l LOGINNAME] [-s] [-a] [-u] [-d]
                   [--domain-name DOMAIN_NAME] [--entry-name ENTRY_NAME]
                   [--entry-expire ENTRY_EXPIRE] [--entry-type ENTRY_TYPE]
                   [--entry-content ENTRY_CONTENT] [--api-key PRIVATE_KEY_FILE]

optional arguments:
  -h, --help            show this help message and exit
  -l LOGINNAME, --login-name LOGINNAME
                        TransIP username
  -s, --show-dns-entries
                        show all DNS entries for a domain
  -a, --add-dns-entry   add an entry in the DNS
  -u, --update-dns-entry
                        update an entry in the DNS
  -d, --delete-dns-entry
                        delete an entry in the DNS
  --domain-name DOMAIN_NAME
                        domain name to use
  --entry-name ENTRY_NAME
                        name of the DNS entry
  --entry-expire ENTRY_EXPIRE
                        expire time of the DNS entry
  --entry-type ENTRY_TYPE
                        type of the DNS entry
  --entry-content ENTRY_CONTENT
                        content of the DNS entry
  --api-key PRIVATE_KEY_FILE
                        TransIP private keyExample of adding/updating a record:$ transip-api -l githubuser -u --api-key privatekey --domain-name example.com --entry-name testentry --entry-expire 86400 --entry-type A --entry-content 127.0.0.1
Request finished successfully.DocumentationFurther documentation can be found in thedocsdirectory, or onhttps://transip-api.readthedocs.io/en/latest/.FAQQuestion:When using the library I get SSL errors such as:urllib2.URLError: <urlopen error [Errno 1] _ssl.c:510: error:14077458:SSL routines:SSL23_GET_SERVER_HELLO:tlsv1 unrecognized name>Answer:Thesudslibrary has fairly limited SSL support which is dependent on the Python version, to work around this thesuds_requestslibrary can be used which replacesurllib2with therequestslibrary. Additionally therequestslibrary automatically pools connections which makes the library slightly faster to use.
To install:pip install suds_requests
Package: transipApiV6
Transip-API-v6This repo contains a package for Python3 to connect to the API of transip.For more information about the Transip API go tohttps://api.transip.nl/rest/docs.htmlCurrently this is package mainly supports all actions on DNS. It can be easily extended.Example python code that makes use of this package:https://github.com/jeromba6/transip_ddns/blob/master/transip_ddns.py
Package: transipddnsclient
TransIP dDNS ClientTransIP dDNS Client is a dDNS client for TransIP.
It uses the API to add or update DNS entries with a TTL of 0 seconds.Usageusage: python -m transipddnsclient [-h] [--version] [-v [VERBOSITY]] [-c CFG]

TransIP dDNS Client

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -v [VERBOSITY]        verbosity level
  -c CFG, --config CFG  the configuration file (Default:
                        /etc/transipddnsclient/config.ini)TODOInstallationDocumentationTestingLicenseSee theLICENSEfile for license right and limitations (MIT)
Package: transit2
TRANSITNOTE: TRANSIT2 requires python3.6+.Welcome! This is the distribution for the TRANSIT2 and TPP tools developed by theIoerger Labat Texas A&M University.TRANSIT2 is a tool for processing and statistical analysis of Tn-Seq data.
It provides an easy to use graphical interface and access to three different analysis methods that allow the user to determine essentiality in a single condition as well as between conditions.TRANSIT2 Home page:http://saclab.tamu.edu/essentiality/transit/index_transit2.htmlTRANSIT2 Documentation:https://transit2.readthedocs.io/en/latest/transit_overview.htmlChangelogFeaturesTRANSIT2 offers a variety of features including:More than10 analysis methods, including methods for determiningconditional essentialityas well asgenetic interactions.Ability to analyze datasets from libraries constructed usinghimar1 or tn5 transposons.TrackViewto help visualize read-counts across the genome.Canexport datasetsinto a variety of formats, includingIGV.Includes avariety of normalization methods.Quality Controldiagnostics, to idenfity poor quality datasets.Ability to install as apython package, to import and use in your own personal scripts.SupportFor any questions or comments, please contact Dr. Thomas Ioerger,ioerger@cs.tamu.edu.InstructionsFor full instructions on how to install and run TRANSIT2 (and the optional pre-processor, TPP),
please see the documentation included in this distribution ("src/pytransit/doc" folder) or visit the following web page:https://transit2.readthedocs.io/en/latest/DatasetsThe TRANSIT distribution comes with some example .wig files in the data/ directory, as well as an example annotation file (.prot_table format) in the data/genomes/ directory. Additional genomes may be found on the following website:http://saclab.tamu.edu/essentiality/transit/genomes/Copyright InformationSource code for TRANSIT2 and TPP are available open source under the terms of the GNU General Public License (Version 3.0) as published by the Free Software Foundation. For more information on this license, please see the included LICENSE.md file or visit their website at:http://www.gnu.org/licenses/gpl.html
Package: transitleastsquares
An optimized transit-fitting algorithm to search for periodic transits of small planetsMotivationWe present a new method to detect planetary transits from time-series photometry, theTransit Least Squares(TLS) algorithm. While the commonly used Box Least Squares(BLS, Kovács et al. 2002)algorithm searches for rectangular signals in stellar light curves,TLSsearches for transit-like features with stellar limb-darkening and including the effects of planetary ingress and egress. Moreover,TLSanalyses the entire, unbinned data of the phase-folded light curve. These improvements yield a ~10 % higher detection efficiency (and similar false alarm rates) compared to BLS. The higher detection efficiency of our freely available Python implementation comes at the cost of higher computational load, which we partly compensate by applying an optimized period sampling and transit duration sampling, constrained to the physically plausible range. A typical Kepler K2 light curve, worth of 90 d of observations at a cadence of 30 min, can be searched withTLSin 10 seconds real time on a standard laptop computer, just as with BLS.InstallationTLS can be installed conveniently using:pip install transitleastsquaresIf you have multiple versions of Python and pip on your machine, try:pip3 install transitleastsquaresThe latest version can be pulled from github::git clone https://github.com/hippke/tls.git
cd tls
python setup.py installIf the commandpythondoes not point to Python 3 on your machine, you can try to replace the last line withpython3 setup.py install. If you don't havegiton your machine, you can find installation instructionshere. TLS also runs on Python 2, but without multi-threading.Dependencies:
Python 3,NumPy,numba,batman-package,tqdm,
optional:argparse(for the command line version),astroquery(for LD and stellar density priors from Kepler K1, K2, and TESS).If you have trouble installing, pleaseopen an issue.Getting startedHere is a short animation of a real search for planets in Kepler K2 data. For more examples, have a look at thetutorialsand thedocumentation.AttributionPlease citeHippke & Heller (2019, A&A 623, A39)if you find this code useful in your research. The BibTeX entry for the paper is:@ARTICLE{2019A&A...623A..39H,
       author = {{Hippke}, Michael and {Heller}, Ren{\'e}},
        title = "{Optimized transit detection algorithm to search for periodic transits of small planets}",
      journal = {\aap},
         year = "2019",
        month = "Mar",
       volume = {623},
          eid = {A39},
        pages = {A39},
          doi = {10.1051/0004-6361/201834672},
archivePrefix = {arXiv},
       eprint = {1901.02015},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019A&A...623A..39H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}Contributing Code, Bugfixes, or FeedbackWe welcome and encourage contributions. If you have any trouble,open an issue.Copyright 2019 Michael Hippke & René Heller.
Package: transitspectroscopy
TransitSpectroscopy 🪐 --- a package for all your transit spectroscopy needstransitspectroscopyis a package containing algorithms and wrappers useful for performing transit (transmission and emission) spectroscopy of exoplanetary systems.Author: Nestor Espinoza (nespinoza@stsci.edu)Statement of needI might fill this in later, but basically: I needed a package to make available all the tools useful for transit spectroscopy: from tracing, spectral extraction to actual lightcurve fitting of wavelength-dependant transits.InstallationInstallation is as simple as:python setup.py installOr via PyPi:pip install transitspectroscopyNote you need to have theGNU Scientific Library (GSL)installed for these scripts to successfully installtransitspectroscopyon your machine. In MacOS, installing the GSL library is as easy as doingbrew install gsl.Licence and attributionRead theLICENCEfile for licencing details on how to use the code.CitationYou can cite this software via its DOI: 10.5281/zenodo.6960923 (seehttps://zenodo.org/record/6960924#.YutMaezMLUI). Here's the citation snippet:@software{espinoza_nestor_2022_6960924,
  author       = {Espinoza, Nestor},
  title        = {TransitSpectroscopy},
  month        = aug,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {0.3.11},
  doi          = {10.5281/zenodo.6960924},
  url          = {https://doi.org/10.5281/zenodo.6960924}
}
Package: transkribus-client
Transkribus API Clienttranskribus-clientprovides a Python 3.6+ API client to interact with Transkribus.AuthenticationMost of the API requires an authentication with a Transkribus account.
To authenticate, you can give your email and password to the client:fromgetpassimportgetpassfromtranskribusimportTranskribusAPIapi=TranskribusAPI()TranskribusAPI.login('user@example.com',getpass())Alternatively, you can use theoptions_from_envhelper and some environment variables:fromtranskribusimportTranskribusAPI,options_from_envapi=TranskribusAPI(**options_from_env())You can define the following environment variables:TRANSKRIBUS_API_URL: Base URL of the Transkribus API. Defaults tohttps://transkribus.eu/TrpServer/rest.TRANSKRIBUS_EMAIL: Email address of the user to authenticate with.TRANSKRIBUS_PASSWORD: Password of the user to authenticate with.UsageBrowsing from collections to transcriptsfromtranskribus.apiimportTranskribusAPI,options_from_envfromtranskribus.modelsimportCollectionapi=TranskribusAPI(**options_from_env())forcollection_datainapi.list_collections():fordocumentinCollection(collection_data).get_documents(api):forpageindocument.get_pages(api):print(str(page.get_transcript()))Exporting a collectioncollection=Collection(COLLECTION_ID)export_job=collection.export(api)export_job.wait_for_result(api)export_job.download_result('path/to/export.zip')Parsing a PageXML filefromtranskribus.pagexmlimportPageXmlPageforregioninPageXmlPage('/path/to/transcript.xml').page.text_regions:forlineinregion.lines:print(line.text)ContributingIssues and patches are welcome! Here are some tips to help you get started coding.Unit testsWe usetoxfor unit tests. You can install and run it like so:pip install tox
toxLintingWe usepre-commitwithblackto automatically format the Python source code of this project.To be efficient, you should run pre-commit before committing (hence the name…).To do that, run once:pip install pre-commit
pre-commit installThe linting workflow will now run on modified files before committing, and will fix issues for you.If you want to run the full workflow on all the files:pre-commit run -a.
Package: transkribus-metagrapho-api
Transkribus Metagrapho API ClientUsagewith ContextManagerfromtimeimportsleepfromtranskribus_metagrapho_apiimporttranskribus_metagrapho_apiwithtranskribus_metagrapho_api(USERNAME,PASSWORD)asapi:process_id=api.process(IMAGE_PATH,line_detection=49272,htr_id=51170)whileTrue:matchapi.status(process_id).upper():case"FINISHED":print(api.apge(process_id))breakcase"FAILED":print("FAILED")breaksleep(10)orwithtranskribus_metagrapho_api(USERNAME,PASSWORD)asapi:forimage_path,page_xmlinzip(IMAGES,api(IMAGES*,line_detection=49272,htr_id=51170)):withopen(Path(image_path.parent,image_path.name.replace(image_path.suffix,".xml")),"w",encoding="utf8")asf:f.write(page_xml)from command line$python3-mtranskribus_metagrapho_api--usernameUSERNAME--passwordPASSWORD--imagesimages/*.tiff
Package: translatability
Assessing-TranslatabilityAlgorithmic procedure for scoring German compounds according to English-Translatability.OverviewIf you have done any translation or are familiar with the vocabulary of a second language, then it is apparent that some words or concepts are easier to translate than others. Many are familiar with the German-English loanwords ‘Schadenfreude’ or ‘Wanderlust,’ which mean, respectively, a perverse pleasure gained at the expense of another’s pain and a desire to leave an area of comfort and see the outside world.This project aims to develop an algorithmic procedure for quantifying a 'translatability' score using the statistical properties of words and their translations. The procedure only works for German compound nouns.Installationpip install translatabilityCLIpython -m translatability --helpCorporaYou can download parallel corpora necessary for the calculations from the links below.http://opus.nlpl.eu/Europarl.phphttp://opus.nlpl.eu/Wikipedia.phphttp://opus.nlpl.eu/OpenSubtitles-v2018.phpEnsure you download the above corpora as aligned MOSES format. The image below demonstrates the appropriate download link for the OpenSubtitles corpus, which is found in the second of the two download matrices, within the lower-left triangle where the indices for "EN" and "DE" align. Please do not use the other "DE-EN" link in the top-right triangle because these data are in the wrong format.You may pass the absolute path to each of the files when running the main script, as in the example below.python -m translatability -w <path to Wikipedia corpus> -e <path to Europarl corpus> -s <path to OpenSubtitles corpus> -f <path to file>Usage Examplepython -m translatability -w "./wikipedia" -f res/test_short.txt

>>> ...
>>> The final list of scored words:
	0.7139908155580019 Salzwasser
	0.6117190378041573 Landzunge
	0.5171641715308692 SchnapsideeIn the/resdirectory you will find several sample texts which may be used for similar evaluations.DocumentationFor further details on the algorithm, its evaluation, and application in a small study, please consult the/docsdirectory.NotesThesrc/split.pymodule is a modified version of theCharSplitalgorithm by dtuggener. All credit goes to this individual.At the end of each run of the scripts, there is created in the current working directory aresultsandsegmentsdirectory containing temporary data. You should clean these files between runs or else results will be contaminated.
Package: translatable-enums
Translatable-EnumsTranslatable-Enums is an i18n tool which uses built-in Enums as a convenient way to store translation keys.InstallationYou can use PIP:pipinstalltranslatable-enumsOr Poetry:poetryaddtranslatable-enumsGetting-Startedfromi18nimport(TranslatableEnum,set_domain,set_language)classMessages(TranslatableEnum):HELLO='Hello,'WORLD='World!'set_domain('app','./resources/languages')set_language('en_US')print(Messages.HELLO,Messages.WORLD)# Hello, World!set_language('uk_UA')print(Messages.HELLO,Messages.WORLD)# Привіт, Світ!set_language('fr_FR')print(Messages.HELLO,Messages.WORLD)# Bonjour le monde!print(Messages.HELLO.language('uk'),Messages.WORLD.language('en'))# Привіт, World!Extraction-ToolsTo extract the translation-keys from application:python-mi18nmain.pyapplication.potYou will obtain a.potfile like this:msgid ""
msgstr ""
"Project-Id-Version: \n"
"POT-Creation-Date: \n"
"Last-Translator: \n"
"Language-Team: \n"
"Language: \n"
"MIME-Version: \n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"

msgid "Hello,"
msgstr ""

msgid "World!"
msgstr ""Status0.0.3- RELEASEDLicenceTranslatable-Enums is released under the MIT License. See the bundledLICENSEfile for details.
Package: translate4colors
automates text translation using Google Translatepip install translate4colorsExample Usagetextx=r"""Python. was created in the early 1990s by Guido van Rossum at Stichting MathematischCentrum (CWI, see https://www.cwi.nl/) in the Netherlands as a successor of alanguage called ABC. Guido remains Python’s principal author, although it includesmany contributions from others.In 1995, Guido. continued his work on Python at the Corporation forNational Research Initiatives(CNRI, see https://www.cnri.reston.va.us/) in Reston,Virginia where he released several versions of the software."""fromtranslate4colorsimportGtranslatewithGtranslate("en","de")asgo:data=go.translate(textx)print(data)google=Gtranslate("en","de")translated=google.translate(textx)print(translated)google.quit_translator(soft_kill_first=True)# [[('Python. was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl/) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others. In 1995, Guido. continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us/) in Reston, Virginia where he released several versions of the software.', 'Python. wurde Anfang der 1990er Jahre von Guido van Rossum am Stichting Mathematisch Centrum (CWI, siehe https://www.cwi.nl/) in den Niederlanden als Nachfolger einer Sprache namens ABC entwickelt. Guido bleibt der Hauptautor von Python, obwohl es viele Beiträge anderer enthält. Im Jahr 1995, Guido. setzte seine Arbeit an Python bei der Corporation for National Research Initiatives (CNRI, siehe https://www.cnri.reston.va.us/) in Reston, Virginia fort, wo er mehrere Versionen der Software veröffentlichte.')]]# [[('Python. was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl/) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others. In 1995, Guido. continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us/) in Reston, Virginia where he released several versions of the software.', 'Python. wurde Anfang der 1990er Jahre von Guido van Rossum am Stichting Mathematisch Centrum (CWI, siehe https://www.cwi.nl/) in den Niederlanden als Nachfolger einer Sprache namens ABC entwickelt. Guido bleibt der Hauptautor von Python, obwohl es viele Beiträge anderer enthält. Im Jahr 1995, Guido. setzte seine Arbeit an Python bei der Corporation for National Research Initiatives (CNRI, siehe https://www.cnri.reston.va.us/) in Reston, Virginia fort, wo er mehrere Versionen der Software veröffentlichte.')]]
Package: translate6frames
Useful tool to translate gene sequences to protein sequencesDescriptionThis tool consists one module:translate6frames: tool to translate gene sequences to protein sequences (all 6 frames)InstallationNormal installationpipinstalltranslate6framesDevelopment installationgitclonehttps://github.com/khaledianehdieh/translate6frames.gitUsagepython3
>>>fromtranslate6framesimporttranslate6framesasTF
>>>p=TF.translate6frames(FASTA_FILE,TYPE)# FASTA_FILE is the input gene file, the TYPE is "DNA", or "RNA">>>p.Output()# returns 6 fasta files, one for each frame (direct/reverse), and a csv file that contains all 6 frames in 6 columnsChange log[0.1] - 2021-08-01Created
Package: translate-by-country
With this library, you can translate the text without choosing the language and with the characteristics of the countriesinstallation:pip install translate-by-countryUsage:Translates text based on country alpha2fromtranslate_by_countryimporttranslate_text_by_alpha2print(translate_text_by_alpha2("translate by country","ax"))Translates text based on country alpha3fromtranslate_by_countryimporttranslate_text_by_alpha3print(translate_text_by_alpha3("translate by country","aut"))Translates text based on country codefromtranslate_by_countryimporttranslate_text_by_codeprint(translate_text_by_code("translate by country",98))Translates text based on country namefromtranslate_by_countryimporttranslate_text_by_nameprint(translate_text_by_name("translate by country","albania"))Translates text based on country emojifromtranslate_by_countryimporttranslate_text_by_emojiprint(translate_text_by_emoji("translate by country",flag_emoji))If there was any problem, pleaseopen an issuefor them on github.
Package: translatedoc
translatedocPDFなどのドキュメントを翻訳するツール。Unstructuredで読み込み、OpenAI APIに渡しているだけ。インストール1. Unstructuredインストール例:sudoaptinstallpoppler-utilspoppler-datatesseract-ocrtesseract-ocr-jpn
pipinstallunstructured[all-docs]--extra-index-urlhttps://download.pytorch.org/whl/cu118cu118のところは環境に合わせて変更する。詳細はUnstructuredのドキュメントを参照。2. translatedocpipinstalltranslatedoc使い方基本exportOPENAI_API_KEY=<your_api_key>
translatedoc--language=Japanese<input_files_and_or_urls>カレントディレクトリにファイル名.Source.txtとファイル名.Japanese.txtが生成される。詳細はtranslatedoc --helpを参照。応用その1テキストの抽出と翻訳を別々に行うこともできる。translatedoc-step1<input_files_and_or_urls>
translatedoc-step2--language=Japanese<input_text_files>応用その2Pythonライブラリとしても使用可能。importosimporttranslatedocresult_text:str=translatedoc.extract_text("path_or_url.pdf")model="gpt-3.5-turbo-1106"openai_client=openai.OpenAI(base_url=os.environ.get("OPENAI_API_BASE"))chunks:list[str]=translatedoc.partition(result_text,model)translated="\n\n".join(translatedoc.translate(chunk,model,"Japanese",openai_client)forchunkinchunks)環境変数環境変数名概要デフォルト値OPENAI_API_BASEOpenAI APIのベースURLhttps://api.openai.com/v1OPENAI_API_KEYOpenAI APIのキー-TRANSLATEDOC_MODEL翻訳に使用するモデルgpt-3.5-turbo-1106TRANSLATEDOC_STRATEGYドキュメントのパーティション化に使用する戦略hi_res