Saravanan666/pypi-org huggingface dataset:
Package: cd-wp-api
README for cd_wp_api Package
Description
The cd_wp_api package is designed for interacting with WordPress APIs, providing a convenient way to access and manipulate WordPress data programmatically. This package simplifies the process of connecting to the WordPress API, performing CRUD operations, and handling WordPress-specific data structures.Features
WordPress API Integration: Functions for connecting to the WordPress API.
CRUD Operations: Create, read, update, and delete WordPress resources.
Data Handling: Tools to handle and parse data returned from WordPress.
(Note: Specific functions and their parameters should be listed here.)Installation
To install the cd_wp_api package, use the following command:bash
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pip install cd_wp_api
Usage
Connecting to the WordPress API
python
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from cd_wp_api import WPAPIInitialize the APIwp_api = WPAPI('your_wordpress_site_url', 'username', 'password')
Creating a New Post
python
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post_data = {
'title': 'My New Post',
'content': 'Content of the post',
'status': 'publish'
}
response = wp_api.create_post(post_data)
Scheduling a Post
python
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from datetime import datetime, timedeltaSchedule a post for 1 day laterfuture_date = datetime.now() + timedelta(days=1)
scheduled_post = {
'title': 'Future Post',
'content': 'This post is scheduled for the future.',
'status': 'future',
'date': future_date.isoformat()  # ISO 8601 format
}
response = wp_api.create_post(scheduled_post)
Adding Tags/Keywords to a Post
python
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post_data_with_tags = {
'title': 'Post with Tags',
'content': 'This is a post with tags.',
'status': 'publish',
'tags': ['tag1', 'tag2', 'tag3']  # List of tags or tag IDs
}
response = wp_api.create_post(post_data_with_tags)
Documentation
For more documentation, please go to Code Docta.Versioning
To check the current version of the package:python
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from cd_wp_api importversionprint(version)
Contribution
Contributions are welcome. Please follow the coding standards and write tests for new features.License
This package is licensed under [appropriate license, e.g., MIT].
Package: cdx
# cdxIntroductionchange derectory fast by ‘cdx bookmark’.Featuresdirpath or url saved as bookmarkcdx a directory by bookmark fastmodify, dispaly, delete bookmarks### versioncdx 1.2.6 , Jan 6 2018### setupsudo pip install cdx
if fail to setup or update, try again use ‘sudo pip3 install cdx’.### Usageusage: cdx [option] [arg]
Options and arguments:
cdx -s bookmark [dirpath|url|note1 note2 ..] # save the CURRENT dirpath or some notes as bookmark (also –save)
cdx bookmark                                 # cdx to a location or retuan notes by bookmark
cdx -l                                       # dispaly the saved bookmarks(also –list)
cdx -m old_bookmark new_bookmark             # modify a bookmark name (also –modify)
cdx -d bookmark1 bookmark2 …               # delete a bookmark (also –delete)### Example# save the current dirpath as a bookmarkjs@py:~/Documents/pycodes/myprojects$ cdx -s cdx
cdx cdx >>> /home/js/Documents/pycodes/myprojects# add a websitejs@py:~/Documents/pycodes/myprojects$ cdx -s ghhttps://github.com/ZhangLijuncn/cdxnotes gh >>>https://github.com/ZhangLijuncn/cdx# notes some infomationjs@py:~/Documents/pycodes/myprojects$ cdx -s version v1.1.2 Dec,6,2017
cdx version >>> v1.1.2 Dec,6,2017# mark a dirpathjs@py:~/Documents/pycodes/myprojects$ cdx -s doc ~/Documents/
cdx doc >>> /home/js/Documents/# modify a bookmarkjs@py:~/Documents/pycodes/myprojects$ cdx -m version v
cdx v >>> v1.1.2 Dec,6,2017# display all the bookmarksjs@py:~/Documents/pycodes/myprojects$ cdx -l
———————————————————————-
Bookmarks          Locations
———————————————————————-
doc                /home/js/Documents
v                   v1.1.2 Dec,6,2017
cdx                /home/js/Documents/pycodes/myprojects
ghhttps://github.com/ZhangLijuncn/cdx———————————————————————-# change a diretory by bookmarkjs@py:~/Documents/pycodes/myprojects$ cdx docjs@py:~/Documents$# open a url in the default web browserjs@py:~/Documents$ cdx gh# delete some bookmarksjs@py:~/Documents$ cdx -d doc v cdx
cdx: ‘doc’ was removed.
cdx: ‘v’ was removed.
cdx: ‘cdx’ was removed.
———————————————————————-
Bookmarks          Locations
———————————————————————-
ghhttps://github.com/ZhangLijuncn/cdx———————————————————————-
Package: cdxjGenerator
CDXJ GeneratorA Python script to generate CDXJ TimeMaps for testing elsewhere.InstallThis tool is published to pypi. To install it:pip install cdxjGeneratorTo use the development version, clone this repository thenpip install .UsageThese inststructions assume installation viapip.To run:cdxjGenerator [number of lines] [URI-R]For example:cdxjGenerator 12...will generate CDXJ output (to stdout by default) consisting of entries for 12 random URIs. Alternatively:cdxjGenerator 25000 memento.us...will generate 25,000 entries for the URI-Rmemento.us. This output can be written to a file like:cdxjGenerator 25000 memento.us > sample.cdxjThe resulting file will likely need to be sorted before used elsewhere. Do this via:LC_ALL=C sort sample.cdxj > sample_sorted.cdxjThis can also be performed in a single command, instead of writing to the temporary, unsortedsample.cdxjlike:cdxjGenerator 25000 memento.us | LC_ALL=C sort > sample_sorted.cdxjBackgroundTimeMaps are lists that enumerate URIs of resources that encapsulate prior states of the given resource. (RFC7089 - Memento). TimeMaps are often expressed in an extension of the Web Linking (RFC5988) format. Additional, less common formats, like JSON and CDXJ TimeMaps can also express the same information in a less rigid format.CDXJis the most flexible of the three and is used byInterPlanetary Wayback (ipwb), which sparked the initial need for this software existing.
Package: cdxj-indexer
CDXJ IndexerA command-line tool for generating CDXJ (and  CDX) indexes from WARC and ARC files.
The indexer is a new tool redesigned for fast and flexible indexing. (Based on the indexing functionality frompywb)Install withpip installcdxj-indexeror install locally withpython setup.py installThe indexer supports classic CDX index format as well as the more flexible CDXJ. With CDXJ, the indexer supports custom fields andrequestrecord access for WARC files. See the examples below and the command line-hoption for latest features. (This is a work in progress).Usage examplesGenerate CDXJ index:> cdxj-indexer /path/to/archive-file.warc.gz
com,example)/ 20170730223850 {"url": "http://example.com/", "mime": "text/html", "status": "200", "digest": "G7HRM7BGOKSKMSXZAHMUQTTV53QOFSMK", "length": "1219", "offset": "771", "filename": "example-20170730223917.warc.gz"}CDX Index (11 field):> cdxj-indexer -11 /path/to/archive-file.warc.gz
CDX N b a m s k r M S V g
com,example)/ 20170730223850 http://example.com/ text/html 200 G7HRM7BGOKSKMSXZAHMUQTTV53QOFSMK - - 1219 771 example-20170730223917.warc.gzMore advanced use cases: add additonal http headers as fields.http:prefix specifies current record headers, whilereq.http:specifies corresponding request record headers. The following adds the Date, Referer headers, and the request method to the index:> cdxj-indexer -f req.http:method,http:date,req.http:referer /path/to/archive-file.warc.gz
com,example)/ 20170801032435 {"url": "http://example.com/", "mime": "text/html", "status": "200", "digest": "A6DESOVDZ3WLYF57CS5E4RIC4ARPWRK7", "length": "1207", "offset": "834", "filename": "temp-20170801032445.warc.gz", "req.http:method": "GET", "http:date": "Tue, 01 Aug 2017 03:24:35 GMT", "referrer": "https://webrecorder.io/temp-NU34HBNO/temp/recording-session/record/http://example.com/"}
org,iana)/domains/example 20170801032437 {"url": "http://www.iana.org/domains/example", "mime": "text/html", "status": "302", "digest": "RP3Y66FDBYBZKSFYQ4VJ4RMDA5BPDJX2", "length": "675", "offset": "2652", "filename": "temp-20170801032445.warc.gz", "req.http:method": "GET", "http:date": "Tue, 01 Aug 2017 02:35:05 GMT", "referrer": "http://example.com/"}The CDXJ Indexer extends theIndexerfunctionality inwarcioand should be flexible to extend.
Package: cdxsite.databrowser
This is a through-the-web data browser forCDX, the Climate Data Exchange, an
effort of the Jet Propulsion Laboratory (JPL).  It serves as a “data portal”
for climate data.Although intended for the CDX web site, it can run on anyPlonesite.DevelopersIf you’re stuck maintaining this package, note that it depends on the CDXclientpackage.  That’s all well and dandy, except that the client itself
depends onPyHDF, which may look like a nice standard Python enhanced egg,
but believe me, it’snot.  OK, it probably is, but it depends onNumPYandHDF4, both of which are problematic.Here’s what I ended up having to do:Pick a prefix, call it $PREFIX.virtualenv-2.6 –distribute –no-site-packages –prompt=cdxcrap $PREFIXcd $PREFIXbin/pip install numpymkdir srccd srccurl -L ‘http://www.hdfgroup.org/ftp/HDF/HDF_Current/src/hdf-4.2.6.tar.bz2’ | tar xjf -cd hdf-4.2.6./configure –prefix=$PREFIX –without-szlib –disable-fortranmakemake installcd ..curl -L ‘http://sourceforge.net/projects/pysclint/files/pyhdf/0.8.3/pyhdf-0.8.3.tar.gz’ | tar xzf -cd pyhdf-0.8.3env NOSZIP=1 INCLUDE_DIRS=$PREFIX/include LIBRARY_DIRS=$PREFIX/lib $PREFIX/bin/python2.6 setup.py installYou can then use $PREFIX/bin/python2.6 with this egg:$PREFIX/bin/python2.6 bootstrap.py -dbin/buildoutbin/testTests pass, so itmustwork!cdxsite.databrowser InstallationUse zc.buildout and the plone.recipe.zope2instance:Addcdxsite.databrowserto the list of eggs to install, e.g.:[buildout]
...
eggs =
    ...
    cdxsite.databrowserTell the plone.recipe.zope2instance recipe to install a ZCML slug:[instance]
recipe = plone.recipe.zope2instance
...
zcml =
    cdxsite.databrowserRe-run buildout, e.g. with:$ ./bin/buildoutYou can skip the ZCML slug if you are going to explicitly include the package
from another package’s configure.zcml file.Changelog2.0.1 - 2011.7.1 - Plone 4.1 + Upgrade PathThis release adds the following:Compatibility with Plone 4.1.Removal of obsolete security declarations.Upgrade path.  Although currently null, the framework for future upgrades is
now in place.2.0.0 - 2011.6.27 - Infrastructure UpgradeThis release speeds things up quite a bit because now we run with Plone 4.1.0.2 - 2009.12.16 - Project UpdatesUpdates project metadata with a new download URL that collects all related
cdxsite deliverables in one location.Properly declares cdx.client as a dependency.1.0.1 - Bug fixFix an issue with path interpretation from the CDX client library.1.0.0 - FCSFirst deployment0.0.0 - UnreleasedInitial releaseCopyrightCopyright 2008-2011 by the California Institute of Technology.  ALL RIGHTS
RESERVED.The Software is owned by Caltech/JPL and is protected by United States
copyright laws and applicable international treaties and/or conventions.  The
United States Government may have prior rights to use some or all of the
Software as determined under applicable contracts and license agreements with
Caltech/JPL.This software was developed at the Jet Propulsion Laboratory, an operating
division of the California Institute of Technology and is not available for
use by any person, organization, or other entity without prior, specific
written permission.
Package: cdxsite.policy
This is the policy product for the home website of Climate Data Exchange
(CDX), a project of the Jet Propulsion Laboratory (JPL), operated by the
California Institute of Technology (Caltech) on behalf of the National
Aeronautics and Space Administration (NASA).The purpose of this policy is to turn an ordinaryPlonewebsite into the CDX
website, providing the home, software distribution, and data portal for
climate data.cdxsite.policy InstallationUse zc.buildout and the plone.recipe.zope2instance:Addcdxsite.policyto the list of eggs to install, e.g.:[buildout]
...
eggs =
    ...
    cdxsite.policyTell the plone.recipe.zope2instance recipe to install a ZCML slug:[instance]
recipe = plone.recipe.zope2instance
...
zcml =
    cdxsite.policyRe-run buildout, e.g. with:$ ./bin/buildoutYou can skip the ZCML slug if you are going to explicitly include the package
from another package’s configure.zcml file.Changelog2.0.1 - 2011.8.2 - Raiders of the Lost DocumentsThis release improves the upgrade process.2.0.0 - 2011.6.27 - SpeedyThis release of the CDX policy makes it work on Plone 4.  And now it’s
insanely fast!1.0.1 - 2009.12.16 - Dependency RepairsThis project failed to declare its dependencies, meaning that its dependent
eggs had to be present in the Python path.  This release fixes this problem
by listing all dependent eggs.Project metadata has been updated that collects all related cdxsite
deliverables in one convenient location.1.0.0 - FCSInitial release.0.0.0 - UnreleasedNot yet released.CopyrightCopyright 2008-2011 by the California Institute of Technology.  ALL RIGHTS
RESERVED.The Software is owned by Caltech/JPL and is protected by United States
copyright laws and applicable international treaties and/or conventions.  The
United States Government may have prior rights to use some or all of the
Software as determined under applicable contracts and license agreements with
Caltech/JPL.This software was developed at the Jet Propulsion Laboratory, an operating
division of the California Institute of Technology and is not available for
use by any person, organization, or other entity without prior, specific
written permission.
Package: cdxsite.theme
This is the theme product for the home website of Climate Data Exchange
(CDX), a project of the Jet Propulsion Laboratory (JPL), operated by the
California Institute of Technology (Caltech) on behalf of the National
Aeronautics and Space Administration (NASA).The purpose of this policy is to provide the theme (look and feel) of thePlonebased website that serves as the home and data portal for the CDX.cdxsite.theme InstallationUse zc.buildout and the plone.recipe.zope2instance:Addcdxsite.themeto the list of eggs to install, e.g.:[buildout]
...
eggs =
    ...
    cdxsite.themeTell the plone.recipe.zope2instance recipe to install a ZCML slug:[instance]
recipe = plone.recipe.zope2instance
...
zcml =
    cdxsite.themeRe-run buildout, e.g. with:$ ./bin/buildoutYou can skip the ZCML slug if you are going to explicitly include the package
from another package’s configure.zcml file.Changelog2.0.2 - 2011.7.28 - Layer AffinityThis release makes the CDX site theme use its own browser layer.2.0.1 - 2011.7.1 - Upgrade PathsThis release adds upgrade steps so you can progress from the previous
Plone-3–based, non-Diazo–powered, old-JPL–themed site cleanly to the new
Plone-4–based, Diazo-powered, new-JPL–themed site.2.0.0 - 2011.6.27 - CDX: The Next GenerationThis release uses the JPL Next Generation theme as a base to provide the
snazzy look and feel for the next generation of the CDX site.1.0.2 - 2009.12.16 - Download URLThis release corrects project metadata by listing a new download URL that
collects all related cdxsite packages in one place.1.0.1 - UnreleasedUnreleased.1.0.0 - FCSFirst release.0.0.0 - UnreleasedInitial release.CopyrightCopyright 2008-2011 by the California Institute of Technology.  ALL RIGHTS
RESERVED.The Software is owned by Caltech/JPL and is protected by United States
copyright laws and applicable international treaties and/or conventions.  The
United States Government may have prior rights to use some or all of the
Software as determined under applicable contracts and license agreements with
Caltech/JPL.This software was developed at the Jet Propulsion Laboratory, an operating
division of the California Institute of Technology and is not available for
use by any person, organization, or other entity without prior, specific
written permission.
Package: ce2
ce2Looking at AWS Cost Explorer data? Not sure what's costing you $$$?Explore the Cost Explorer with Cost Explorer Explorer!Install and runTry it withpipx!pipx run ce2Or install withpipxorpippipx install ce2
# or...
pip install --user ce2FeaturesGet Cost Explorer data in a format usable from the CLI.Calculate monthly averages and variance on historical spend information.
Get CE's projections for monthly spend in simple output.RoadmapLots of nice-to-have things which I haven't done yet and might never do:actual arg parsingmultiple output formats, including CSV and JSON from computed statsASCII-art graphs showing your days of highest spendthresholding to remove "negligible" cost services from the output by defaultdata trimming and normalization to remove up-front spend and other "aberrant"
data points from variance and average calculationsinteractive CLI modemulti-account data aggregation using assume-role with configured roles
Package: ceasium
CeasiumA hassle free JSON based GCC build system.IntroductionC is a great language, but tooling around it like CMake or Make are a bit
complicated. While CMake is great when it comes to being able to specify a
complicated build, its syntax has a steep learning curve and is quite cumbersome
and non-intuitive. Thus I created Ceasium.FeaturesCeasium features most of the things that are regularly needed.pkg-config to resolve C_FLAGS and LD_FLAGSAbility to specify your own C_FLAGS and LD_FLAGSBuild executables, static and dynamic libraries.c file discovery within a given directoryCaching based on modification date including dependency discoveryExclusion of certain .o files when linkingInstallationpip install ceasiumPrerequisitesPythonGCC compilerpkg-configUsageCeasium provides these commands:cs initcs build[Optional]<src.json/test.json/whatever.json>specify which json file to buildcs cleanConfigurationExample config:{"name":"game-engine","type":"exe","cflags":["-I./include","-g","-W","-Wall","-O3","-fopenmp","-fdiagnostics-color=always"],"ldflags":["-lopengl32","-fopenmp"],"libs":["glew","sdl2","glib-2.0","assimp"]}
Package: cebra
📚Documentation|💡DEMOS|🛠️ Installation|🌎 Home Page|🚨 News|🪲 Reporting IssuesWelcome! 👋CEBRAis a library for estimatingConsistentEmBeddings of high-dimensionalRecordings usingAuxiliary variables. It contains self-supervised learning algorithms implemented in PyTorch, and has support for a variety of different datasets common in biology and neuroscience.To receive updates on code releases, please 👀 watch or ⭐️ star this repository!cebrais a self-supervised method for non-linear clustering that allows for label-informed time series analysis.
It can jointly use behavioral and neural data in a hypothesis- or discovery-driven manner to produce consistent, high-performance latent spaces. While it is not specific to neural and behavioral data, this is the first domain we used the tool in. This application case is to obtain a consistent representation of latent variables driving activity and behavior, improving decoding accuracy of behavioral variables over standard supervised learning, and obtaining embeddings which are robust to domain shifts.Reference📄Publication May 2023:Learnable latent embeddings for joint behavioural and neural analysis.Steffen Schneider*, Jin Hwa Lee* and Mackenzie Weygandt Mathis. Nature 2023.📄Preprint April 2022:Learnable latent embeddings for joint behavioral and neural analysis.Steffen Schneider*, Jin Hwa Lee* and Mackenzie Weygandt MathisLicenseSince version 0.4.0, CEBRA is open source software under an Apache 2.0 license.Prior versions 0.1.0 to 0.3.1 were released for academic use only (please read the license file).
Package: cebsyldec
Cebuano-Syllable-DecoderCebuano Syllable DecoderInstallationpip install cebsyldecorinside the folder runpython setup.py installRequirementspython>=3.7Functionsget_syllables(word)Accepts a Cebuano word and returns the syllables of the word, and the sequence of Consonants and VowelsDefault Output: List of syllables and Sequence of Consonants and VowelsEx.syllabledecoder.get_syllables("tinabangay")   
   [['ti', 'na', 'ba', 'ngay'], ['CV', 'CV', 'CV', 'CVC']]syllabledecoder.get_syllables("mag-tinabangay") 
   [['mag', 'ti', 'na', 'ba', 'ngay'], ['CVC', 'CV', 'CV', 'CV', 'CVC']]How to Usefrom cebsyldec import syllabledecoder

syllabledecoder.get_syllables("mangaon")Output:
[['ma', 'nga', 'on'], ['CV', 'CV', 'VC']]ReferencesM. V. R. Bunye and E. P. Yap, Cebuano Grammar Notes. University of Hawai‘i Press, 1971. [Online]. Available:https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/75449514-a5eb-487a-aa6b-7778680179e8/contentComputational Linguistics @ Illinois. Syllable Script in Python, 2017. Available:https://www.youtube.com/watch?v=ZULS0evRLHg
Package: cebsyldec-package-EEMBERDA
Cebuano-Syllable-DecoderCebuano Syllable DecoderInstallationpip install cebsyldecorinside the folder runpython setup.py installRequirementspython>=2.7Functionsget_syllables(word)Accepts a Cebuano word and returns the syllables of the wordDefault Output: List of syllablesEx. get_syllables("nangaon")['na', 'nga', 'on']get_syllable_sequence(word)Accepts a Cebuano word and returns the syllable sequence of the wordDefault Output: Syllable sequence of the wordEx. get_syllable_sequence("nangaon")'CV-CV-VC'How to Usefrom cebsyldec import syllabledecoder

syllabledecoder.get_syllables("nangaon")

Output: 
   ['na', 'nga', 'on']


syllabledecoder.get_syllable_sequence("nangaon")

Output:
   'CV + V + CVC'Referenceshttps://www.youtube.com/watch?v=ZULS0evRLHg
Package: cec2019comp100digit
This is a Python wrapping using the C++ Implementation of the test suite for the
Special Session on Large Scale Global Optimization at 2019 IEEE Congress on
Evolutionary Computationhttp://cec2019.org/programs/competitions.html#cec-06.http://www.ntu.edu.sg/home/epnsugan/index_files/CEC2019/CEC2019.htmNoteIf you are to use any part of this code, please cite the following publications:K. V. Price, N. H. Awad, M. Z. Ali, P. N. Suganthan, “Problem Definitions and
Evaluation Criteria for the 100-Digit Challenge Special Session and
Competition on Single Objective Numerical Optimization,”  Technical Report,
Nanyang Technological University, Singapore, November 2018.http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared%20Documents/Forms/AllItems.aspx?RootFolder=%2fepnsugan%2fPublicSite%2fShared%20Documents%2fCEC%2d2019&FolderCTID=&View=%7bDAF31868%2d97D8%2d4779%2dAE49%2d9CEC4DC3F310%7dRequirementsGNU MakeGNU G++PythonCythonTesting EnvironmentDebian GNU/Linux jessie/sidGNU Make 3.81g++ (Debian 4.7.3-4) 4.7.3Python 2.7 and Python 3.2numpy 1.8.1cython 0.20.1InstalationIt is pending to submit to pip, when it is ready.Very easy,pip install cec2019comp100digit;-).You can also download fromhttps://github.com/dmolina/cec2019comp100digit, and dopython setup.py install [–user].
(the option–useris for installing the package locally, as a normal user (interesting when you want to
run the experiments in a cluster/server without administration permissions).To compile the source code in C++The source code in C++ is also available. If you want to compile only the C++
version type in ‘make’ in the root directory of source code.There are two equivalents demo executables: demo and demo2.REMEMBER: To run the C++ version the directory input_data must be available in the working directory.
In the python version, these files are included in the packages, so it is not
needed.QuickstartThe package is very simple to use. There is a package cec2019comp100digit with
three functions:init(fun_id, Dim)Init the function for the dimension selected.eval(sol)Eval the solution, when sol is a numpy (or array) of dimensionDim.end()Free resources.Init function>>> from cec2019comp100digit import cec2019comp100digit
>>> bench = cec2019comp100digit
>>> bench.init(3, 10) # Init function 3Create a random solution>>> import numpy as np
>>> sol = np.random.rand(10)Evaluate a solution>>> bench.eval(sol)
18010038.104525752Freeing resources>>> bench.end()ContactPython packageDaniel Molina @ Computer Science Deparment, University of Granada
Please feel free to contact me at <dmolina@decsai.ugr.es> for any enquiries or
suggestions.Last UpdatedC++ version
<2018-12-08>Python wrapping
<2018-12-08>