Saravanan666/pypi-org huggingface dataset:
Package: coalescing
This module is intended to ease access to nested data
structures—in any combination of key or attribue access to each
level of a hierarchy—where many paths in the tree may be missing.
Typically, such data is encountered in semi-structured formats
like JSON, YAML, or some XML, that is deserialized into Python
data structures.PEP 505 attempts to address this need with the addition of a
number of new syntactic operators for attribute or item access
where a special type of failure mode “coalesces” None values.
Other libraries such as PyMaybe (https://pymaybe.readthedocs.io)
perform a similar operation, and are more mature and robust.On the other hand, by utilizingwrapt.ObjectProxy, this simple
module manages to do some pretty neat stuff that even PyMaybe does
not.In this library, I simply want to provide simple wrapper classes that
provide both what I think is more useful behavior and behavior that
is semantically equivalent to PEP 505.Classes:GreedyAccess:Wrap an object so that nested attribute and item access
simply does not fail, but propagates a wrapped object
across such nested access.NullCoalesce:Wrap an object so that encountering a None (or other
indicated sentinel) at any level stops accessing further
attributes and unboxes as a None (or other sentinel).Aliased asNoneCoalesce
Package: coalition
coalition |pypi| |travis| |landscape|=============================.. |pypi| image:: https://badge.fury.io/py/coalition.svg?style=flat:target: https://pypi.python.org/pypi/coalition:alt: PyPi Package.. |travis| image:: https://travis-ci.com/thee-engineer/coalition.svg?token=ySNrvJx6Lqs7r3y3HqKN&branch=master&style=flat:target: https://travis-ci.com/thee-engineer/coalition:alt: Travis-CI Status.. |landscape| image:: https://landscape.io/github/thee-engineer/coalition/master/landscape.svg?style=flat:target: https://landscape.io/github/thee-engineer/coalition/master:alt: Code Health
Package: coalringbuf
coalringbufis Python port of CoalescingRingBuffer fromLMAXCollections.coalringbufworks with:CPython 2.x >= 2.5, 3.x >= 3.2PyPy 1.9+Supported platforms: platform independent.StatusIt’s usable and it passes port of original test suite. It’s currently based on LMAXCollections 1.1.0.UsageThis port mimics original CoalescingRingBuffer API as closely as possible, however it was modified to make it more Pythonic.Example intepreter session:>>>fromcoalringbufimportCoalescingRingBuffer>>>buffer=CoalescingRingBuffer(3)>>>buffer.capacity4>>>buffer.emptyTrue>>>buffer.offer('something')True>>>buffer.emptyFalse>>>buffer.offer('something else')True>>>buffer.offer('quack')True>>>buffer.offer('id','value')True>>>buffer.size4>>>buffer.fullTrue>>>buffer.offer('id','this will overwrite "value"')True>>>buffer.size4>>>buffer.offer('this will be rejected')False>>>buffer.size4>>>bucket=[]>>>buffer.poll(bucket)4>>>bucket['something','something else','quack','this will overwrite "value"']>>>buffer.emptyTrueTODOimplement performance testsprovide more efficient bucket class if neededCopyrightOriginal implementation (C)LMAX/Nick Zeeb.Python implementation (C) 2013Jakub Stasiak.This project is licensed under MIT license, see LICENSE file for details.
Package: coar
coarcoaris implementation of clustering of association rules based on user defined thresholds.InstallationUse the package managerpipto installcoar.pipinstallcoarUsageUsage is displayed on association rules mined usingCleverminerusing modified version ofCleverMiner quickstart example. You need to installcleverminerfirst.pipinstallcleverminerMining association rules usingcleverminer:# importsimportjsonimportpandasaspdfromcleverminerimportcleverminer# getting the source filedf=pd.read_csv('https://www.cleverminer.org/hotel.zip',encoding='cp1250',sep='\t')# selecting the columnsdf=df[['VTypeOfVisit','GState','GCity']]# mining association rulesclm=cleverminer(df=df,proc='4ftMiner',quantifiers={'conf':0.6,'Base':50},ante={'attributes':[{'name':'GState','type':'subset','minlen':1,'maxlen':1},{'name':'GCity','type':'subset','minlen':1,'maxlen':1},],'minlen':1,'maxlen':2,'type':'con'},succ={'attributes':[{'name':'VTypeOfVisit','type':'subset','minlen':1,'maxlen':1}],'minlen':1,'maxlen':1,'type':'con'},)# saving rules to filewithopen('rules.json','w')assave_file:save_file.write(json.dumps(clm.rulelist))Clustering rules usingcoar:# importsimportjsonimportpandasaspdfromcoar.clusterimportagglomerative_clustering,cluster_representative# loading rulesrule_file=open('rules.json')rule_list=json.loads(rule_file.read())# creating dataframedf=pd.DataFrame.from_records([{'antecedent':set(attrforattrinrule['cedents_str']['ante'].split(' & ')),'succedent':set(attrforattrinrule['cedents_str']['succ'].split(' & ')),'support':rule['params']['rel_base'],'confidence':rule['params']['conf']}forruleinrule_list])# clusteringclustering=agglomerative_clustering(df,abs_ante_attr_diff_threshold=1,abs_succ_attr_diff_threshold=0,abs_supp_diff_threshold=1,abs_conf_diff_threshold=1,)# getting cluster representativesclusters_repr=cluster_representative(clustering)ContributingIf you find a bug 🐛, please open abug report.
If you have an idea for an improvement, new feature or enhancement 🚀, please open afeature request.LicenseMIT
Package: coards
A COADS compliant time parserThis module is intended to help parse time values represented using theCOARDS convention. Here is a simple example:>>>fromcoardsimportparse>>>a=[1,2,3]>>>units='days since 1998-03-01 12:00:00'>>>b=[parse(value,units)forvalueina]>>>printb[datetime.datetime(1998, 3, 2, 12, 0), datetime.datetime(1998, 3, 3, 12, 0), datetime.datetime(1998, 3, 4, 12, 0)]CreditsDistributeBuildoutmodern-package-templateNews1.0.5Release date: 20130604Create converter object.1.0.4Release date: 20130520Parse microseconds.1.0.3.2Release date: 20130218Allow origin to be expressed only as HH.1.0.3.1Release date: 20130214Fix minor typo in code.1.0.3Release date: 20130214Create parser/formatter objects to avoid repeated parsing.Happy Valentine’s!1.0.2Release date: 20120823Fixed regexp that parses the origin.1.0.1Release date: 20120818Parse RFC3339 dates.1.0Release date: 20120529Refactored code so that it works with more dates.Using modern-package-template for directory structure.
Package: coarij
CoARiJ: Corpus of Annual Reports in JapanWe organized Japanese financial reports to encourage applying NLP techniques to financial analytics.DatasetThe corpora are separated to each financial years.master version.fiscal_yearRaw file version (F)Text extracted version (E)2014.zip (9.3GB).zip (269.9MB)2015.zip (9.8GB).zip (291.1MB)2016.zip (10.2GB).zip (334.7MB)2017.zip (9.1GB).zip (309.4MB)2018.zip (10.5GB).zip (260.9MB)financial data is from決算短信情報.We use non-cosolidated data if it exist.stock data is from月間相場表（内国株式）.closeis fiscal period end andopenis 1 year before of it.Past releasev1.0Statisticsfiscal_yearnumber_of_reportshas_csr_reportshas_financial_datahas_stock_data20143,724923,5833,59520153,870963,7253,75120164,066973,9243,94120173,578893,4413,47220183,513702,8933,413File structureRaw file version (--kind F)The structure of dataset is following.chakki_esg_financial_{year}.zip
└──{year}
     ├── documents.csv
     └── docs/docsincludes XBRL and PDF file.XBRL file of annual reports (files are retrieved fromEDINET).PDF file of CSR reports (additional content).documents.csvhas metadata like following. Please refer the detail atWiki.edinet_code:E0000Xfiler_name:XXX株式会社fiscal_year:201Xfiscal_period:FYdoc_path:docs/S000000X.xbrlcsr_path:docs/E0000X_201X_JP_36.pdfText extracted version (--kind E)Text extracted version includestxtfiles that match each part of an annual report.The extracted parts are defined atxbrr.chakki_esg_financial_{year}_extracted.zip
└──{year}
     ├── documents.csv
     └── docs/ToolYou can download dataset by command line tool.pip install coarijPlease refer the usage by--(usingfire).coarij --Example command.# Download raw file version dataset of 2014.coarijdownload--kindF--year2014# Extract business.overview_of_result part of TIS.Inc (sec code=3626).coarijextractbusiness.overview_of_result--sec_code3626# Tokenize text by Janome (`janome` or `sudachi` is supported).pipinstalljanome
coarijtokenize--tokenizerjanome# Show tokenized result (words are separated by \t).head-n5data/processed/2014/docs/S100552V_business_overview_of_result_tokenized.txt1【業績等の概要】(1)業績
当連結会計年度における我が国経済は、消費税率引上げに伴う駆け込み需要の反動や海外景気動向に対する先行き懸念等から弱い動きも見られましたが、企業収益の改善等により全体...If you want to download latest dataset, please specify--version masterwhen download the data.About the parsable part, please refer thexbrr.You can useLedgerto select your necessary file from overall CoARiJ dataset.fromcoarij.storageimportStoragestorage=Storage("your/data/directory")ledger=storage.get_ledger()collected=ledger.collect(edinet_code="E00021")
Package: coarse
Copyright (c) 2019, Chris Mutel
All rights reserved.Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Description: # CoarseA parameterized Python model to calculate the energy consumption and life cycle assessment of cars.Extracted from (paper).For readthedocs, click on the info button next to the badge inhttps://readthedocs.org/projects/whatever/, and copy the markdown codeFor travis, click on the badge inhttps://travis-ci.org/BONSAMURAIS/whatever, and copy the markdown codeFor appveyor, go to the projects settings page, and then click on “badges”, and copy the markdown code## InstallationDetails on how to install the package## ContributingDetails on how to contributePlatform: UNKNOWN
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
Package: coarseclassevaluator
Project DiscriptionCoarseClassvisual:This package helps a Data scientist to reduce the number of levels inside the category to <=5 levels for a binary classification problems,
This package is the implementation of WOE based binning, and uses similar WOE to collate bin.Below are the applications of this packageExplicability: Improves explicability of the story teeling of patterns.
Instead of saying NY, SF, Seattle has more loan defaults this can logically group cities using this package and say all the hi-tech & Financial sectors are has more loan defaultsReduces Noise and sparsity: With too many levels of predictor of binary classification , It becomes too noisy and upon dummy variables increases sparsity. This package helps
one reduce the Noise and sparsity
Package: coastal-dynamics
Coastal Dynamics Open CodebookWelcome to theCoastal Dynamics Open
Codebook! This project contains
interactive exercises that
cover material of theCoastal Dynamics Open
Textbookas well as aPython package, that is designed for
creating interactive questions, complete with a simple grading mechanism.For more information regarding the course, installation and usage we refer the interested
reader to ourJupyterbook,
that is available online!UsageDetailed instructions for both the notebooks and the package are provided in our
Jupyterbookonline.Interactive teaching materialInstructions for running the tutorial notebooks are availablehere.Package installationInstall the Python package using pip:pipinstallcoastal-dynamicsQuestionsIf you have a question about the installation process or notebooks, feel free to open an
issue in theGitHub repository. If
that's your first time, have a look atthese
instructions.
We choose to use the GitHub issue-tracker because your fellow students probably have
similar problems. We will probably not troubleshoot the tutorial notebooks by email!ContributorsWe welcome and recognize all contributions. You can see a list of current contributors in
thecontributors
tab.CreditsThis project is fully created open source software! Many thanks toNUMFocusfor
supporting and maintaining all these open source initiatives. We usedcookiecutter-jupyter-book
templateas our template
for the book structure.
Package: coast-core
COAST_CORE is a tool designed for aiding the credibility assessment of online
articles. It is a collection of modules that are useful for assessing various aspects of credibility.Free software: MIT licenseDocumentation:https://coast-core.readthedocs.io.FeaturesCOAST_CORE is made up of several modules for:N-Gram extractionCitation detection and classificationClarity of writing assessmentCode detectionEvent detectionKeyword detectionNamed entity detectionPrerequisitesThe tool is built in Python 3 and tested in versions 3.5 and 3.6.There are two methods of named entity detection included as part of COAST_CORE. For
running the Stanford named entity detection, you will needJavainstalled.InstallationTo install COAST_CORE, run this command in your terminal:$pipinstallcoast_coreThis is the preferred method to install COAST_CORE, as it will always install the most recent stable release.If you don’t havepipinstalled, thisPython installation guidecan guide
you through the process.To install from source, visit ourdocumentation.HistoryDateStatusApril 2016Research on credibility begins, some initial scripts are written as part of various studiesFirst half of 2017Adrien Aucher joins UC as an intern and works with Ashley Williams on the first version of this tool. It is only used internally at this point.April 2018Yann Le Norment joins UC as an intern and works on a first public release.May 2018First public release!August 2018Version 0.1.2 released; fixing bugs and making things stable.November 2018Stefan Hall joins the project to work on the first release. Version 1.0.0 released.
Package: coastlib
coastlibcoastlibis a Python library dedicated to solving problems related to the discipline ofcoastal engineering, such as data collection (NOAA NCEI, WAVEWATCH III, etc.), extreme value analysis (EVA), data analysis and visualization, wave theories, and many more.Version:0.9.1License:GNU General Public License v3.0E-Mail:bocharovgeorgii@gmail.comDocumentation:https://coastlib.readthedocs.io/InstallationpipinstallcoastlibJupyter Notebookscoastlib.datanoaa_coops- NOAA CO-OPS data extractionnoaa_ncei- NOAA NCEI data extractionExamples
Package: coastpy
coastpyCoastal Monitoring with Remote SensingInstallation$pipinstallcoastpyUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensecoastpywas created by Floris Calkoen. It is licensed under the terms of the MIT license.Creditscoastpywas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: coastviewer
# Coastviewer
Coast viewer server side. Serves several sources as KML and geojson.Read the documentation athttp://coastviewer.readthedocs.io/# Download data
For linux/ubuntu systems:` shell cd data make `For windows systems:
Install chocolatey fromhttps://chocolatey.org/install` shell choco install make choco install wget cd data make `# Install dependencies (docker)
Install docker (for windows 7 or lower install docker terminal) fromhttps://docs.docker.com/engine/installation/.` shell docker pull openearth/coastviewer `# Install dependencies (development)Install dependencies
On linux/ubuntu systems:
- apt-get install GDAL
- apt-get install python35On windows systems:
Install GDAL, see tutorial fromhttps://sandbox.idre.ucla.edu/sandbox/tutorials/installing-gdal-for-windows/.Install python 3.5 fromhttps://www.python.org/downloads/release/python-350/.` shell pip install-e. `Install the requirements:` shell pip install-rrequirements.txt `# Run the server (docker)` shell docker run openearth/coastviewer `# Run the server (development)` shell coastviewer `# Use
Load the link [http://localhost:5000/coastviewer/1.1.0/transects/kml](transects/kml) in Google Earth (add network link).# Test
Load the [http://localhost:5000/coastviewer/1.1.0/ui](testui) in your browser. for the integration tests.
Runmake testfor the unit tests.
Runmake lintfor code formatting checks.History0.1.0 (2017-07-13)First release on PyPI.
Package: coating
coatingcreates a clickable pane, based on the standard input.or more precise:coatingreads lines from STDIN, allowing you to interactively select a value, which is then returned to STDOUT.coating enables interactive filters in piped commandsa unix-philosophy experimentbrings the mouse to the cli :)a fzf inspired toolBut, what does it do?Every non-white space becomes a tokenEvery token will be possible element for selectionAfter a selection, the token will be returned through standard outputUsageHere are some examples, after theinstallation stepThis will open the selected file/directory of the current directory in vim:vim$(ls-C|coating)Another possible usage is this:ls-C|coating|xargsxdg-openThis opens the selected file with it's standard application.For more ways to usecoatingcheck out theexamplesdirectory.Html Parserwhen run with the--tagsflag, coating will look for HTML tags (excluding semantics) and makes tag bodies clickable.this function allows for a predefinition of clickable elements, in contrast to the default case, where every non-whitespace character is clickableif the tags are nested, only the highest level of tags is clickablein the case ther are no tags in the text, every word will be tokenized and clickable.Installation[!Note]
only tested / written for linuxYou can installcoatingfrom the PyPI repositories using the following command:pip install coatingor check the realease page for a manual installation.on ubuntu first install ncurses-term:apt install ncurses-termIssues[!Important]
This tool currently supports python3.10 and upwardsOn version with a python version lower than 3.10 thecurses.BUTTON5constant is not supported.Please report possible issueshere.LicenseThis project is licensed under theGPLv3 License.Made with love by 🦝
Package: coauction
collaborative-auctionThis repository contains the source codes of our research paper in economics titled: "Addictive auctions: using lucky-draw and gambling addiction to increase participation during auctioning".Paper Title: Addictive auctions: Using lucky-draw and gambling addiction toincrease participation during auctioningAuthor:Ravin KumarPublication: 18th January 2021.Publication Journal:International Journal of Management Research and EconomicsPublication House:SvedbergOpenPublication link:https://www.svedbergopen.com/files/1612268008_(5)_IJMRE28112020MTN007_(p_68-74).pdfCite as:Ravin Kumar (2021). Addictive auctions: Using lucky-draw and gambling addiction to increase participation during auctioning.
International Journal of Management Research and Economics. 1(1),68-74.Doi:https://doi.org/10.51483/IJMRE.1.1.2021.68-74Also available on Elsevier-SSRN eJournals:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3795556Behavioral & Experimental Economics eJournal.Microeconomics: Welfare Economics & Collective Decision-Making eJournal.Microeconomics: Production, Market Structure & Pricing eJournal.Earlier Preprints:OSF Preprints :https://doi.org/10.31219/osf.io/darvsCornell University LIbrary, arXiv.org :https://arxiv.org/abs/1906.03237Steps for using the libraryimportcoauction# total_candidates : total number of participants# bidding_sequence: arrary containing timeseries data in format [[candidate_id,candidate_offer],[candidate_id,candidate_offer] ....]# relation_list: array containing relationship in form of [[sender,receiver],..] here 1 represents the person who won the bidding, 2 repreents the second last bidding candidate etc.# alpha: it determine the discount ratio for each relationship present in relation_listresults=coauction.response(total_candidates,bidding_sequence,relation_list,alpha)# results[0] contains complete list of amount each candidate gets, and results[1] contains the amount in form of a dictionary,# with candidate_id as key, and amount as value.Installing module using PyPi:pipinstallcoauctionIn our system, the candidate_id begins with 1 in the dictionary based response.Copyright(c)2019RavinKumarWebsite:https://mr-ravin.github.ioPermissionisherebygranted,freeofcharge,toanypersonobtainingacopyofthissoftwareandassociateddocumentationfiles(the“Software”),todealintheSoftwarewithoutrestriction,includingwithoutlimitationtherightstouse,copy,modify,merge,publish,distribute,sublicense,and/orsellcopiesoftheSoftware,andtopermitpersonstowhomtheSoftwareisfurnishedtodoso,subjecttothefollowingconditions:TheabovecopyrightnoticeandthispermissionnoticeshallbeincludedinallcopiesorsubstantialportionsoftheSoftware.THESOFTWAREISPROVIDED“ASIS”,WITHOUTWARRANTYOFANYKIND,EXPRESSORIMPLIED,INCLUDINGBUTNOTLIMITEDTOTHEWARRANTIESOFMERCHANTABILITY,FITNESSFORAPARTICULARPURPOSEANDNONINFRINGEMENT.INNOEVENTSHALLTHEAUTHORSORCOPYRIGHTHOLDERSBELIABLEFORANYCLAIM,DAMAGESOROTHERLIABILITY,WHETHERINANACTIONOFCONTRACT,TORTOROTHERWISE,ARISINGFROM,OUTOFORINCONNECTIONWITHTHESOFTWAREORTHEUSEOROTHERDEALINGSINTHESOFTWARE.
Package: coauthors
# coauthorsAdd coauthors to your last git commit message.## InstallTo install **coauthors**, just run:```sh$ sudo python3 -m pip install coauthors```## ConfigureFirst create a config file with your coauthors.Example ~/.coauthors.json:```javascript{"Faemino":{"name": "Juan Carlos Arroyo","email": "faemino@comedian.com"},"Cansado":{"name": "Angel Javier Pozuelo","email": "cansado@comedian.com"}}```Then you need to create an environment variable```shCOAUTHORS_FILE="/home/<user>/.coauthors.json"```## UsageYou can coauthor your commit with one friend```sh$ coauthors Faemino```or with many```sh$ coauthors Faemino Cansado```You can list your available coauthors```sh$ coauthors --list```
Package: coax
coaxPlug-n-Play Reinforcement Learning in Python withGymnasium(formerly OpenAI Gym) andJAXFor the full documentation, including many examples, go tohttps://coax.readthedocs.io/Installcoaxis built on top of JAX, but it doesn’t have an explicit dependence on thejaxpython
package. The reason is that your version ofjaxlibwill depend on your CUDA version. To install
without CUDA, simply run:$ pip install jaxlib jax coax --upgradeIf you do require CUDA support, please check out theInstallation Guide.Getting StartedHave a look at theGetting Startedpage to
train your first RL agent.