Saravanan666/pypi-org huggingface dataset:
Package: cr-download
Overviewcr_download checks recent Critical Role Twitch VODs for videos with
titles looking like Critical Role episode titles, and prompts the user
to download each one. The file is downloaded as video using thestreamlinkAPI, and converted to
audio usingffmpeg.Optionally, cr_download can use theChromaprintmusic fingerprinting
algorithm to attempt to detect the soundtracks Critical Role plays
before/after the show, in the opening credits, and during the
break. If a good enough set of transition points is found in the
episode audio, cr_download will recut the audio to leave out
pre/post-show segments, intermission, and (optionally) the
announcement section of the episode.In addition, cr_download can also upload the audio files for the
episode to a fixed Google drive folder.SetupInstallffmpegif you haven't already.If installing via pip (recommended, if I've actually uploaded the
package to PyPI by the time you read this), runpip install cr-download.Otherwise, download and extract the repository, and runpython setup.py installfrom the directory you extracted it to.Runstreamlink --twitch-oauth-authenticateto authorize
streamlink to access your Twitch account. Copy the provided oauth
token into your config file, located at~/.config/cr_download/config.yaml(it will be automatically created
the first time you run the program).UsageRuncritrole_download(it should be added to your path after
installation) to display a list of recent Critical Role VODs. You may
choose one (or more) to download and convert to audio.To run the automatic audio editor on the downloaded files, runcritrole_download -a.You can also runautocut_vod FILENAME [FILENAME ...]to run the
autocutting tool on a local audio/video file.To see a full list of options for each command, runcritrole_download -horautocut_vod -h. You can also change the behavior of the
program by editing options in the configuration file located at~/.config/cr_download/config.yaml.
Package: crdp
crdpCython implementation of Ramer-Douglas-Peucker algorithm.Usagepip install crdpIn[1]:fromcrdpimportrdpIn[2]:rdp([[1,1],[2,3],[3,3],[4,4]],0.8)Out[2]:[[1,1],[4,4]]In[3]:rdp([[1,1],[2,3],[3,3],[4,4]],0.7)Out[3]:[[1,1],[2,3],[4,4]]Performance10k random points can be processed in a few milliseconds.
Over 100x faster than python native implementation.$ python3 benchmark.py
rdp 100 points, epsilon=0, duration 28.990745544433594 ms
crdp 100 points, epsilon=0, duration 0.0 ms
rdp 100 points, epsilon=0.01, duration 35.98904609680176 ms
crdp 100 points, epsilon=0.01, duration 0.0 ms
rdp 100 points, epsilon=0.1, duration 35.98833084106445 ms
crdp 100 points, epsilon=0.1, duration 0.0 ms
rdp 100 points, epsilon=0.5, duration 21.99268341064453 ms
crdp 100 points, epsilon=0.5, duration 0.0 ms
rdp 100 points, epsilon=1, duration 3.9980411529541016 ms
crdp 100 points, epsilon=1, duration 0.0 ms
rdp 1000 points, epsilon=0, duration 489.84360694885254 ms
crdp 1000 points, epsilon=0, duration 1.0001659393310547 ms
rdp 1000 points, epsilon=0.01, duration 513.8356685638428 ms
crdp 1000 points, epsilon=0.01, duration 0.0 ms
rdp 1000 points, epsilon=0.1, duration 470.84927558898926 ms
crdp 1000 points, epsilon=0.1, duration 0.9999275207519531 ms
rdp 1000 points, epsilon=0.5, duration 497.84135818481445 ms
crdp 1000 points, epsilon=0.5, duration 0.0 ms
rdp 1000 points, epsilon=1, duration 42.986392974853516 ms
crdp 1000 points, epsilon=1, duration 0.9999275207519531 ms
rdp 10000 points, epsilon=0, duration 7042.751789093018 ms
crdp 10000 points, epsilon=0, duration 5.997896194458008 ms
rdp 10000 points, epsilon=0.01, duration 6574.901103973389 ms
crdp 10000 points, epsilon=0.01, duration 4.998445510864258 ms
rdp 10000 points, epsilon=0.1, duration 6856.811046600342 ms
crdp 10000 points, epsilon=0.1, duration 4.998683929443359 ms
rdp 10000 points, epsilon=0.5, duration 6256.002902984619 ms
crdp 10000 points, epsilon=0.5, duration 3.998994827270508 ms
rdp 10000 points, epsilon=1, duration 407.8691005706787 ms
crdp 10000 points, epsilon=1, duration 1.9996166229248047 ms
Package: crdp-gcp
crdp-gcpCython implementation of Ramer-Douglas-Peucker algorithm, identical tocrdpbut modified to run as a google cloud function.Usagepip install crdp-gcpIn[1]:fromcrdpimportrdpIn[2]:rdp([[1,1],[2,3],[3,3],[4,4]],0.8)Out[2]:[[1,1],[4,4]]In[3]:rdp([[1,1],[2,3],[3,3],[4,4]],0.7)Out[3]:[[1,1],[2,3],[4,4]]All credit toBi Ran, author of the original code
Package: creache
CreacheA poorly named toolPurposeThe goal of this tool is to convertSwiftstructtypes intoclasstypes that can be used withRealm Swift.Convert files formatted like this:structUser{varname:String!varage:IntvarisHappy:BoolenumCodingKeys:String,CodingKey{casename,agecaseisHappy="is_happy"}init(fromdecoder:Decoder)throws{letcontainer=trycontainer.decode(keyedBy:CodingKeys.self)name=trycontainer.decode(String.self,forKey:.name)age=trycontainer.decode(Int.self,forKey:.age)isHappy=trycontainer.decode(Bool.self,forKey:.isHappy)}}to thisclassUser_Entity{@dynamicvarname:String!=''@dynamicvarage:Int!=0@dynamicvarisHappy:Bool!=false}FeaturesAutomatically renamestructtoclassand append `Entityto the new typeAutomatically generate a new entity file in the directy in which this script is calledAutomatically strip everything away thaat isn't a variable declaration
Package: creacli
# CreaPy for CreaSwiss army knife for interacting with the Crea blockchain.## Quick-StartA quickstart guide can be found on Crea: [library.creas.io](https://library.creas.io/creacli/)## Installation` pip3 install creacli `## DocumentationThe full-length documentation of the cli tool can be found on [CreaCLI Python Docs](https://library.creas.io/creacli/).
API documentation of the library is located at [Crea Python Lib Docs](https://library.creas.io/crea-python-lib/).## Public APIThe public API node atd.creas.ioserves as an experimental
endpoint.Use it for prototyping of your toolsUse it for testingDo not expect it to be reliableDo not spam it with unnecessary load# IMPORTANT NOTETHE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Package: crea-graphenelib
Python 3 library for Graphene 2.0!InstallationInstall withpip:$ sudo apt-get install libffi-dev libssl-dev python-dev
$ pip3 install crea-graphenelibManual installation:$ git clonehttps://github.com/creativechain/crea-python-graphenelib/$ cd crea-python-graphenelib
$ python3 setup.py install –userDependenciesSome dependencies are not required for parts of the library to run
properly. However these modules require some additional libraries to be
present:graphenebase.bip38pycrypto==2.6.1scrypt==0.7.1(to speedup scrypt hashing)graphenebase.memopycrypto==2.6.1Upgrade$ pip install –user –upgrade crea-graphenelibDocumentationThanks to readthedocs.io, the documentation can be viewed
[online](https://docs.creas.io/graphene)Documentation is written with the help of sphinx and can be compile to
html with:cd docs
make htmlLicenceMIT, seeLICENCE.txt
Package: creak-sense
Testing whether a sentence is consistent with the CREAK datasetThis framework is trained on theCREAK dataset.Installpip install creak-senseExamplefromcreak_senseimportCreakSensesense=CreakSense("fractalego/creak-sense")claim="Bananas can be found in a grocery list"sense.make_sense(claim)with output "True".Example with explanationfromcreak_senseimportCreakSensesense=CreakSense("fractalego/creak-sense")claim="Bananas can be found in a grocery list"sense.get_reason(claim)with output "Bananas are a staple food".
Package: crea-lib
# CreaLib (Python)This library is unmaintained, do not build productive buisness with it!
Please not the disclaimer in the license file!Python Library for CreaPython 3 library for Crea!InstallationInstall withpip3:$ sudo apt-get install libffi-dev libssl-dev python-dev python3-pip
$ pip3 install crea-libManual installation:$ git clonehttps://github.com/creativechain/crea-python-lib/$ cd crea-python-lib
$ python3 setup.py install –userUpgrade$ pip3 install crea –user –upgradeAdditional dependenciescreaapi.creaasyncclient:asyncio==3.4.3pyyaml==3.11DocumentationDocumentation is written with the help of sphinx and can be compile to
html with:cd docs
make html
Package: cream-python
Cream🍦: Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language ModelsOfficial Implementation of Cream |Paper|Slide|PosterIntroductionCream (Contrastive Reading Model) is a language-image understanding module designed to enhance the visually-situated natural language understanding capability in Large Language Models (LLMs). The primary goal of Cream is to effectively capture intricate details in images (e.g., texts), ensuring accurate responses in various visual langauge precessing applications.Our academic paper, which describes our method in detail and provides full experimental results and analyses, can be found here:Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models.Geewook Kim,Hodong Lee,Daehee Kim,Haeji Jung,Sanghee Park,Yoonsik Kim,Sangdoo Yun,Taeho Kil,Bado Lee,Seunghyun Park. In EMNLP 2023.Updates2024-01-16Fix minor errors/typos. Release the PyPi package (pip install cream-python). Further updates will follow shortly.2024-01-02Fix minor errors/typos. Further updates will follow shortly.2023-12-06First commit with a codebase.Software Installationpipinstallcream-pythonor clone this repository and install the dependencies:gitclonehttps://github.com/naver-ai/cream.gitcdcream/
condacreate-ncream_officialpython=3.8
condaactivatecream_official
pipinstall.If you want to runtrain.pyortest.py, please also install other dependencies:pipinstall-rrequirements.txtCitationIf you find our work useful in your work, please consider citing our paper:@inproceedings{kim2023cream,
      title={Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models}, 
      author={Geewook Kim and Hodong Lee and Daehee Kim and Haeji Jung and Sanghee Park and Yoonsik Kim and Sangdoo Yun and Taeho Kil and Bado Lee and Seunghyun Park},
      booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
      year={2023},
}LicenseMIT license

Copyright (c) 2023-present NAVER Cloud Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Package: creasepattern
creasepatternBasic tools to work with Origami Crease Patterns.UsageInstall with pippip install creasepatternThis installs thecreaseexecutable:Usage:
        crease creasepattern.cp image.png
        crease creasepattern.cp image.svgcp2png(infile, outfile, size=2048, margin=20, aa_scale=4)Convert.cpfiles to.pngSize of the output file is scaled to the given size, either horizonally or vertically, whichever is larger.aa_scaleis used to anti alias. Set to 1 to disable anti aliasing.fromcreasepatternimportcp2pngcp2png('file.cp','file.png')cp2svg(infile, outfile, margin=10)Convert.cpfiles to.svgfromcreasepatternimportcp2svgcp2svg('file.cp','file.svg')opx2png(infile, outfile, size=2048, margin=20, aa_scale=4)Convert Oripa.opxto.pngopx2svg(infile, outfile, margin=10)Convert Oripa.opxto.svgopx2cp(infile, outfile)Convert Oripa.opxto.cporh2png(infile, outfile, size=2048, margin=20, aa_scale=4)Convert Orihime.orhto.pngorh2svg(infile, outfile, margin=10)Convert Orihime.orhto.svg.Only supports exporting normal lines, so no folded bases, aux circles and non-interfering aux lines.orh2cp(infile, outfile)Convert Orihime.orhto.cp.from_cp(infile: str) -> CpConvert.cpto aCpobject.from_cp_str(lines: list[str]) -> CpConvert string lines in.cpformat to aCpobject.from_opx(infile: str) -> CpConvert Oripa.opxto aCpobject.from_orh(infile: str) -> CpConvert Orihime.orhto aCpobjectto_svg(cp: Cp) -> strConvert aCpobject to an svg stringto_png(cp: Cp) -> ImageConvert aCpobject to a PIL Imageto_cp(cp : Cp) -> str`Convert aCpobject to a.cpfile string.Roadmap(Must) Make size of output configurable(Must) Make edge types configurable(Could) Support more filetypes
Package: creassessment
# Creassessment
An App Inventor app creativity assessment package
# Using creassessment
## Import the package and choose an aia file to analyze`py from creassessment.controler.app import App file_path = 'HealthyPlants.aia' app = App(file_path) `## Assess creativity with results in a pandas dataframe or printed results on console`py app.grade_creativity() app.grade_creativity_to_string() `## Get xls files for an app`py app.do_all() `## Grade many apps at once`py dir_apps_path ='D:\AppInventorAIAs'batch_caller = Batch_Caller() batch_caller.analyze(dir_apps_path, App.grade_creativity) `
Package: create-a-cli
create-a-cliCreate basic command line interfaces using PythonInstallation[python | python3] -m [pip | pip3] install create-a-cliUsagefromcliimportInterfacecli=Interface("Example CLI")@cli.command()defhello():print("Hello World")cli.run()Command Line > python path/to/file.py
>>> hello
Hello World
Package: create-a-cli-tool
A way to make simple python CLIs.🏠HomepageInstallpipinstall-Ucreate-a-cli-toolFeaturesEasy-To-UseMaintained CLI Tool MakerBuilt-In Help CommandQuick Examplefromcli.cliimportCLImy_cli=CLI("My Nice CLI tool!")@my_cli.command(name="hi",description="Say hello!")defhi():print("Hello World!")my_cli.run()Author👤SengoldaGithub:@Sengolda🤝 ContributingContributions, issues and feature requests are welcome!Feel free to checkissues
page.Show your supportGive a ⭐️ if this project helped you!
Package: createadmin
Create Django Admin Default CredentialsThecreateadminpackage provides a Django management command,createadmin, that facilitates the creation of superuser default login credentials for the Django Admin Interface. This command is useful for setting up initial access to the admin interface during project development, packaging & deployment.InstallationTo install the package, use the following pip command:pipinstallcreateadminAfter installation, add‘createadmin’to theINSTALLED_APPSlist in your project’ssettings.pyfile:INSTALLED_APPS=[# ...'createadmin',# ...]Usage### Default CredentialsTo create a superuser with the default credentials (username: admin, password: admin, email:admin@example.com), run the following command in the terminal:pythonmanage.pycreateadmin### Custom CredentialsYou can also create a superuser with custom credentials by adding a dictionary block to your project’ssettings.pyfile:# settings.pyCREATE_ADMIN={'username':'your_custom_username','password':'your_custom_password','email':'your_custom_email@example.com',}Then, run the following command in the terminal:pythonmanage.pycreateadminNote:Ensure that the‘createadmin’app is included in yourINSTALLED_APPSlist before running the command.ExampleHere’s an example of using custom credentials in thesettings.pyfile:# settings.pyINSTALLED_APPS=[# ...'createadmin',# ...]CREATE_ADMIN={'username':'yash','password':'yash','email':'ychaudhari124@gmail.com',}Run the following command in the terminal:pythonmanage.pycreateadminThis will create a superuser with the specified custom credentials.Note:It is recommended to change the default or custom credentials once the initial setup is complete for security reasons.—This package simplifies the process of creating Django Admin default credentials, making it more efficient for project setup, development, packaging & deployment.
Package: createai
CreateAI v0.1.2.1It's easy tool to create ai in python easy and fast.
You can look at source code of CreateAI onCreate AI's githubWARNING!On my PC python can't find this module.
I am using source code from github.Dependenciesnumpyknowledge of pythonour documentationyour motivationyour brainAuthors@R0fael- main programmerFeaturesCustom function activationsNeuron creating in one line of codeabsolutly open sourseInstallationInstalling project with pipWindows:pipinstallcreateaiMacOS:pip3installcreateaiLinux:sudopipinstallcreateaiOr if you don't have pip :
download sourse code and editing examples filesExample# importing CreateAIfromcreateaiimport*n=Neuron(weights(3,1),inputs([[0,0,1],[1,1,1],[1,0,1],[0,1,1]]),outputs([[0,1,1,0]]),save_path="my_save.cns")# Neuron creating with saving#.cns -> file extension -> neuron weight saven.learn(20_000)# Neuron learningprint(n.process([1,1,0]))# Neuron testprint(n.process([0,1,0]))# Neuron testChange log-0.1.0 - first version-0.1.1 - speed up and little bug fix-0.1.2 - without numpy - error-0.1.2.1 - big bugfixPlans-0.1.3 - More metods of learning-0.1.4 - Multi neurons update
Package: create-aio-app
Create aio appThe tool that lets you bootstrap aiohttp application with best practices ready for development.Here is a screenshot of our interfacesInstallationRequires python3.6 - python3.7 and docker-composepipinstallcreate-aio-appUsagecreate-aio-appmy_projectIf you want to use interactive mode enter the next command:create-aio-appThis will create a new directory calledmy_project.
To start you new project run the next commands:cdmy_project

makerun# start your projectHere is a link to all the make commands.Then, navigate in your browser tohttp://localhost:8080/Salient Featuresaiohttp- the best python framework :)mypy- optional static typingpytest- unit testsflake8- linterblack- code formattertrafaret- data validationaio devtools- developer toolsaiohttp debug toolbar- tool for debuggingpostgres- storagealembic- database migration toolsqlAlchemy- ormsphinx- docsdocker-compose- tool for defining and running multi-container Docker applicationspy-spy- Sampling profiler for Python programsOptions--without-postgres- remove postgres and all of its requirements--redis- add redis to the template--uvloop- uvloop event loop for aiohttpContributingcreate-aio-appis a boilerplate from aiohttp community for aiohttp
community. Feel free to make any suggestions on the issues or
create a pull request. We will be very happy 😀.
SeeCONTRIBUTING.mdfor more information about
how to contribute tocreate-aio-app.LicenseCreate aio App is an open source softwareavailable under the MIT license.
Package: create-api-django
create-api-djangoDjango Rest Framework Boilerplate to easily create APIsFree software: MIT licenseDocumentation:https://create-api-django.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-10-06)First release on PyPI.0.2.0 (2018-10-14)Second release on PyPI0.3.0 (2018-10-14)Fixed command line argument0.4.0 (2018-10-15)A new django project is now created
Package: create-argparse-project
# Create Argparse ProjectCLI Python application generator## Usage`bashcreate-argparse-projectProject Name > Create Argparse Project Package Name > createargparseproject Author Name > Hibikine Kage Author Email > mail@hibikine.me Repository URL >https://github.com/HibikineKage/create-argparse-projectDescription > CLI Python application generator Command Name >create-argparse-project`## Install### RequirementPython3pip`bash pip installcreate-argparse-project`## LicenseMIT
Package: create-brandon
create-brandonOverviewDocumentation can be foundhere.This is a smallcommand line applicationto help with the creation of directories for experiments or data science use.By callingcreate-brandonin the command line, the user will be prompted to answer a few questions.brandonwill create a directory named accordingly based on the answers to the questions. The directory will be named based on the date and time of runningcreate-brandonand
will containdata/andscripts/folders as well as a jupyter notebook. If the user callscreate-brandon -vthey will meet Brandon and be welcomed to the matrix.The application is intened for those who work with quantum devies in dilution refridgerators. Hence the questions such asName of the deviceandName of the fridge, users can leave these blank by just hitting the <ENTER/Return> key.Installation$pipinstallcreate-brandonUsageEnter in the command line/terminal:$create-brandonTo have a conversation withbrandon, pass the arguments-vor-verbose, for example:$create-brandon-vor similarly$create-brandon-verboseN.B.Commands also work withcreate_brandonusing an underscore instead of a dash. The package name iscreate_brandon.OutputFor Example:$ create-brandon

Name of the fridge: triton1
Name of the device: sige_heterostructure 1
Name of the experiment: fine_tuning
Creating directories...
Experiment directory made at: 20220308_154655_triton1_sige_heterostructure_1_fine_tuningDirectory structure is as follows:20220308_154655_triton1_sige_heterostructure_1_fine_tuning/
├── 20220308_154655_triton1_sige_heterostructure_1_fine_tuning.ipynb
├── data
└── scriptsContributionsMIT license, so feel free to fork submit PRs and contribute, inluding help with docs 😎.Developingcreate-brandonTo installcreate-brandon, along with the tools you need to develop and run tests, run the following in your virtual env:$pipinstall-e.[dev]Main requirment ispytest==6.2.5
Package: create-brandon-brandonseverin
create_brandonOverviewThis is a smallcommand line applicationto help with the creation of directories for experiments or data science use.By callingcreate_brandonin the command line, the user will be prompted to answer a few questions.brandonwill create a directory named accordingly based on the answers to the questions. The directory will be named based on the date and time of runningcreate_brandonand
will containdata/andscripts/folders as well as a jupyter notebook. If the user callscreate_brandon -vthey will meet Brandon and be welcomed to the matrix.The application is intened for those who work with quantum devies in dilution refridgerators. Hence the questions such asName of the deviceandName of the fridge, users can leave these blank by just hitting the <ENTER/Return> key.Installation$pipinstallcreate_brandonUsageEnter in the command line/terminal:$create_brandonTo have a conversation withbrandon, pass the arguments-vor-verbose, for example:$create_brandon-vor similarly$create_brandon-verboseCommands also work withcreate-brandonusing a dash instead of an underscore.OutputFor Example:$ create_brandon

Name of the fridge: triton1
Name of the device: sige_heterostructure 1
Name of the experiment: fine_tuning
Creating directories...
Experiment directory made at: 20220308_154655_triton1_sige_heterostructure_1_fine_tuningDirectory structure is as follows:20220308_154655_triton1_sige_heterostructure_1_fine_tuning/
├── 20220308_154655_triton1_sige_heterostructure_1_fine_tuning.ipynb
├── data
└── scriptsContributionsMIT license, so feel free to fork submit PRs and contribute, inluding help with docs 😎.Developingcreate_brandonTo installcreate_brandon, along with the tools you need to develop and run tests, run the following in your virtual env:$pipinstall-e.[dev]Main requirment ispytest==6.2.5