Saravanan666/pypi-org huggingface dataset:
Package: ccalgo
BasicAlgo1. ObjectiveThe objective of this project is to be familiar with python language and data structures and algorithms.2. ScopeThe scope of this project is basic data structures and algorithms used in day-to-day life.3. Introduction to libraryThis library is intended to the users who want to use basic algorithms in python. The basic algorithms includes:Kadane's AlgorithmNext permutationLongest consecutive sequenceFractional knapsackN-QueensSudoku solvingM colouring problemPrint all permutations of a stringWord breakPalindrome partitioningSubset sumKth permutation sequenceCheck for balanced parenthesesNext greater elementLargest rectangle in histogramReverse words in stringLongest Common prefixRabin KarpPrefix function / Z-functionKMP AlgorithmBinary tree, traversals and viewsHeight of binary treeLCA in binary treeMaximum sum pathBinary tree from inorder and preorderBinary tree from inorder and postorderMST using Prim's AlgorithmMST using Kruskal's AlgorithmMax product subarrayLongest increasing subsequenceLongest common subsequence0/1 Knapsack ProblemCoin ChangeSubset sumEgg droppingPalindrome partitioning2. Usage of libraryimport basic_algo
from basic_algo import Stack
from basic_algo.Stack import CheckForBalancedParentheses as cfbp


obj = cfbp.CheckForBalancedParentheses()
if obj.CheckForBalancedParentheses("((()))"):
    print("Yes")
else:
    print("No")3. Contact for updatesSiddharth Marvania: 17bit046@nirmauni.ac.in
Jenil Mehta: 17bit048@nirmauni.ac.in
Package: ccall
Uses cffi to build a simple interface for calling functions in a C file. Thispackage is designed for convenience and should work only in the very simplecases. It is used in an educational setting in a mixed Python/C programmingcourse.Usage=====It is very simple. Say you have a "libfuncs.c" in the working directory.. code-block::c#include<stdio.h>int double(int x) {return x + x;}We can call the `double` function by relying in the implicit import in the:mod:`ccall` module.>>> import ccall as c>>> c.libfuncs.double(21)42Limitations===========This software is beta. Use with care!
Package: ccalogging
CCA LoggingEasy python logging moduleInstallingInstall from pypi# install into users system site-packages
pip install ccalogging --user# install into a virtual environment
pip install ccalogging# as part of your own package, add to setup.py
setup(
    ...,
    install_requires=[
        '...',
        'ccalogging',
        '...',
        ],
    ...,
)UsageImport this module, set logging output, optionally set logging level,
set a script level variable pointing to this moduleslogvariable,
then issue log.debug() etc.You only need to set the output destination and logging level once, so
if you have multiple files in your project, set the output destination
and the logging level in the first one.  In all the others just point a
script level variable toccalogging.log.When using a log file, if the file already exists, new log messages are appended
to it.The default logging level is WARNING.# first (or only) project file

import ccalogging          # import the module

ccalogging.setConsoleOut() # set global logging destination
ccalogging.setDebug()      # set global log level
log = ccalogging.log       # point a script-level variable to the logger

log.info("Logging has been started")# subsequent project files

import ccalogging    # import the module

log = ccalogging.log # point a script-level variable to the logger

log.warning("continuing to log to the same location as setup in the first file")OptionsIt is possible to log to the console and/or to a file. When logging to the
console logs go tostderrby default.setDebug()Sets the global loglevel to be logging.DEBUGsetInfo()Sets the global loglevel to be logging.INFOsetWarn()Sets the global loglevel to be logging.WARNING.
This is the default level, if you don't set a level.setError()Sets the global loglevel to be logging.ERRORsetLogFile(fqfn)Sends log output to a fileparameters:fqfn: the fully qualified path name of the log filefformat: message format - defaults to:"%(asctime)s [%(levelname)-5.5s]  %(message)s"datefmt: date format - defaults to:"%d/%m/%Y %H:%M:%S"rotation: default: None - set this to the number of days to keep the log file for
older logs will be compressed and numbered (up to this value)setConsoleOut()Sends log output to the consoleparameters:STDOUT: ifTruesend tostdout- defaults to False (stderr)cformat: message format - defaults to:"%(asctime)s [%(levelname)-5.5s]  %(message)s"datefmt: date format - defaults to:"%d/%m/%Y %H:%M:%S"toggle()This toggles between DEBUG and INFO modes
Package: ccanvas
Coordinate CanvasInteractive canvas that allows you to draw 2D coordinates in a plane and output their corresponding coordinates to a JSON file.[!NOTE]
All instructions in this README assume your Python 3.11.6+ installation is in your PATH and is aliased underpython. If this is not the case, you will need to replacepythonwith the alias or path that points to the correct Python executable.Installationpython-mpipinstallccanvas# Use dash instead of underscore!UsageYou can display the help message by running the following command:python-mccanvas--help# Use underscore instead of dash!Once the canvas has been opened, you will be able to click on any part of it and add a new coordinate. Lines can be switched using the numeric pad on the keyboard, as explained on the header of the window.Once you have added all the desired coordinates, just press the "Escape" or "Q" keys or close the window. A JSON file will be generated containing all the coordinates you added. This is how the JSON structure looks like:{"line_1":{"x":[3.064516129032258,5.510752688172044,10.45698924731183,14.045698924731184,...],"y":[8.837828837828837,13.18015318015318,13.126873126873129,8.03862803862804,...]},"line_2":{...},...}Data retrievalJSON data can easily be retrieved with a few lines of code.importjson# Assuming the script is located in the same directory where the program is#   being executed (if not, modify the path below):withopen("coordinates.json",mode="r",encoding="utf-8")asfp:data=json.load(fp)# Loads all data in a dictionary.# Ways to retrieve data:line_1=data["line_1"]line_1_x=data["line_1"]["x"]line_1_y=data["line_1"]["y"]line_1_xy=[(x,y)forx,yinzip(data["line_1"].values())]# Recommended!ContributingIf you are planning on contributing to the repository, take a look at thecontribution guidelines.
Package: ccapi
ccapiA Python Library to interact with theCell CollectiveAPI v3ccapiis a Python Package that provides a simple interface toCell Collectivefor modelling and analysis of
biological networks.Table of ContentsFeaturesInstallationUsageLicenseFeaturesccapiattempts to be an all-in-one toolbox for modelling biological systems.Reading public and private models fromCell Collective.A rich knowledge-base for each model species.Importing models using an SBML file format.Exporting models into SBML qual, Boolean Expressions, Truth Tables, Interaction Matrix and GML formats.Querying for models.Visualizing models graphically.ccapiofficially supports Python 2.7+ and 3.4+.Installation$pipinstallhttps://github.com/cellcollective/ccapi.gitDocumentationVisithttps://ccapi.readthedocs.orgfor documentation.LicenseThis repository has been released under theMIT License.Made with ❤️ usingboilpy.
Package: ccard
ccardFake credit-card generator
is a Python 3 Package for TESTING PURPOSES AND DATA VERIFICATION ONLY!
Instead of using a real credit card
this tool will help you generate a vaild credit card numbers
Giving you the option to generate the following type of credit cardVisaDiscoverMaster CardAmerican ExpressInstallationThe script isavailable on PyPI.  To install with pip:pip install ccardUsagecredit-card generator can be used as a command line utility or imported as a Python package.Command Line UsageTo use the script from the command line:
the defualt value is Visa
.. code-block:: bash$ ccard
4916232538105515Python Package UsageHere are examples of all current features:>>>importccard>>>ccard.visa()4485610834415848>>>ccard.discover()6011017754491148>>>ccard.mastercard()5179703192884719>>>ccard.americanexpress()374300139079794LicenseThis project is released under an MIT License.ContributingPlease file bugs to theGithub issue tracker.  Pull requests are welcome.Hacking and Pull RequestsOpening your pull request sooner will allow others to comment on it sooner.A checklist of things to remember when making a feature:Write tests if applicableUpdate theREADMEfile if neededUpdate the documentation if neededAdd yourself to theAUTHORSfile
Package: ccaugmentation
CCAugmentationData preprocessing and augmentation framework that is designed for working with crowd counting datasets.
It supports multitude of simple as well as advanced transformations
and is based on pipelines which allow a flexible flow of data between loaders, transformations and outputs.
Deep learning framework-independent, though works best with PyTorch.Current capabilitiesEach data preprocessing procedure is defined in form of a pipeline that consists of a data loader and
a list of operations to sequentially execute on the data. Each of the operations may be of the following types:Transformation- Returns transformed data on output, does not have side effectsOutput- Returns unmodified data on output, has side effects that, for example, write data to filesOperation- Performs any other functions, not qualifying for any of the aforementioned typesAvailable transformations are:CropScaleDownscaleRotateStandardizeSizeNormalizeNormalizeDensityMapFlipLRToGrayscaleLambdaTransformationCutoutAvailable outputs are:DemonstrateSaveImagesToFilesSaveImagesToBinaryFileSaveDensityMapsToCSVFilesSaveDensityMapsToBinaryFileAvailable operations are:DuplicateDropoutRandomArgsOptimizeBatchAvailable loaders are:BasicImageFileLoaderImageFileLoaderBasicGTPointsMatFileLoaderGTPointsMatFileLoaderBasicDensityMapCSVFileLoaderDensityMapCSVFileLoaderVariableLoaderConcatenatingLoaderCombinedLoaderFor more information about the specific topics, please refer to the related comments in the code.How to useLoading the data from ShanghaiTech dataset and taking crops with 1/4 size:import CCAugmentation as cca
import CCAugmentation as ccat


train_data_pipeline = cca.Pipeline(
    cca.examples.loading.SHHLoader("/data/ShanghaiTech/", "train", "B"),
    [
        ccat.Crop(None, None, 1/4, 1/4)
    ]
)

train_img, train_dm = train_data_pipeline.execute_collect()
# you can also use execute_generate() to create a generator

print(len(train_img), len(train_dm))To see more examples of usage, please see ourexperiment environment repository.
Package: cca-zoo
CCA-ZooUnlock the hidden relationships in multiview data.IntroductionIn today's data-driven world, revealing hidden relationships across multiview datasets is critical.CCA-Zoois your go-to library, featuring a robust selection of linear, kernel, and deep canonical correlation analysis methods.Designed to be user-friendly, CCA-Zoo is inspired by the ease of use inscikit-learnandmvlearn. It provides a seamless programming experience with familiarfit,transform, andfit_transformmethods.📖 Table of ContentsQuick StartPerformance HighlightsDetailed DocumentationHow to CiteContributeAcknowledgments🚀 Quick StartInstallationWhether you're apipenthusiast or apoetryaficionado, installing CCA-Zoo is a breeze:pipinstallcca-zoo# For additional featurespipinstallcca-zoo[probabilistic,visualisation,deep]For Poetry users:poetryaddcca-zoo# For extra featurespoetryaddcca-zoo[probabilistic,visualisation,deep]Note thatdeeprequirestorchandlightningwhich may be better installed separately following thePyTorch installation guide.probabilisticrequiresnumpyrowhich may be better installed separately following theNumPyro installation guide.visualisationrequiresmatplotlibandseaborn🏎️ Performance HighlightsCCA-Zoo shines when it comes to high-dimensional data analysis. It significantly outperforms scikit-learn, particularly as dimensionality increases. For comprehensive benchmarks, see ourscriptand the graph below.📚 Detailed DocumentationEmbark on a journey through multiview correlations with ourcomprehensive guide.🙏 How to CiteYour support means a lot to us! If CCA-Zoo has been beneficial for your research, there are two ways to show your appreciation:Star our GitHub repository.Cite our research paper in your publications.For citing our work, please use the following BibTeX entry:@software{Chapman_CCA-Zoo_2023,author={Chapman, James and Wang, Hao-Ting and Wells, Lennie and Wiesner, Johannes},doi={10.5281/zenodo.4382739},month=aug,title={{CCA-Zoo}},url={https://github.com/jameschapman19/cca_zoo},version={2.3.0},year={2023}}Or check out our JOSS paper:📜 Chapman et al., (2021). CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework. Journal of Open Source Software, 6(68), 3823,Link.👩‍💻 ContributeEvery idea, every line of code adds value. Check out ourcontribution guideand help CCA-Zoo soar to new heights!🙌 AcknowledgmentsSpecial thanks to the pioneers whose work has shaped this field. Explore their work:Regularised CCA/PLS:MATLABSparse PLS:MATLAB SPLSDCCA/DCCAE:Keras DCCA,Torch DCCA
Package: cc-backend-lib
CC Backend LibraryThis library contains several classes and data models that are useful when
writing services that interact with other services in Conflict Cartographer. In
particular, the modulesapi_clientandschemarespectively provide classes
for retrieving and modelling data from APIs.Data retrievalData retrieval is offered via thecc_backend_lib.dal.Dalclass. This class is
instantiated by passing several API clients:from cc_backend_lib.clients import predictions_client, scheduler_client, users_client, countries_client
from cc_backend_lib import dal

cc_dal = dal.Dal(
      predictions = predictions_client.PredictionsClient(...),
      scheduler = scheduler_client.SchedulerClient(...),
      users = users_client.UsersClient(...),
      countries = countries_client.CountriesClient(...),
   )The class has several methods that offer access to data and summaries. Seehelp(Dal).  The.participation_summarymethod caches results for past
(shift < 0) time-periods for efficiency.CachingA powerful caching decorator is provided that lets you decorate both sync and
async functions for caching. For using different kinds of backends or for other
kinds of customization (LRU, etc.), the caching decorator lets you pass a
custom cache class. There are some provided cache classes, including one that
uses Redis:from cc_backend_lib.cache import redis_cache, cache

@cache.cache(redis_cache.RedisBackend, host = "...", expiry_time = 100)
def my_slow_function(a,b,c):
   ...

# First time slow
a = my_slow_function(1,2,3)

# Second time fast
b = my_slow_function(1,2,3)

assert a == b
Package: ccb-essentials
Python EssentialsGeneral-purpose tools for Python 3.Development EnvironmentFirst,install Poetry.Set uppoetry install --sync
poetry check
poetry showMaintenanceCode testpoetry run pytestCode lintpoetry run bin/lint.shBuild artifactspoetry buildPublishpoetry version [major|minor|patch]
V=v`poetry version -s` && git add pyproject.toml && git commit -m $V && git tag -a -m $V $V
poetry publish --build
Package: ccb-extras
Python ExtrasGeneral-purpose tools for Python 3.Development EnvironmentFirst,install Poetry.Set uppoetry install --sync
poetry check
poetry showMaintenanceCode testpoetry run pytestCode lintpoetry run bin/lint.shBuild artifactspoetry buildPublishpoetry version [major|minor|patch]
V=v`poetry version -s` && git add pyproject.toml && git commit -m $V && git tag -a -m $V $V
poetry publish --build
Package: ccbuild
ccbuildccbuild将python开发的项目、流程或脚本文件进行编译成C的动态共享库*.so文件，不影响项目运行，可用于某些场景下python源码保护。依赖LinuxPython >=2.7.10, <=3.10cython安装git repo with the development versionpip install git+https://github.com/yodeng/ccbuild.gitPypipip install ccbuild使用$ ccbuild -h相关参数解释如下：参数描述-h/--help打印帮助并退出-p/--python编译后运行项目使用的python解释器路径，非绝对路径会从$PATH中查找-i/--input需要编译的py文件或项目目录-o/--output编译后的项目或文件输出路径-t/--threadscpu核数，默认5-c/--compile-continue若某个文件编译失败，是否跳过，继续编译，默认编译失败退出程序--exclude-dir跳过的编译目录，默认"__pycache__"会被跳过, 多个输入空白隔开，支持简单shell匹配--exclude-file跳过的编译文件，默认"__init__.py"会被跳过，多个输入空白隔开，支持简单shell匹配-v/--version打印版本并退出-p/--python指定的解释器为编译后运行项目的解释器，相同版本的解释器可以通用说明编译后的输出目录，使用方式不变，只是项目中的py文件变成了so二进制文件。有可能存在编译失败的情况，原因是由于python代码中有cython识别不了的未导入的模块，cython和python并不是百分百兼容，需要调整python代码即可通过编译。
Package: ccbuilder
ccbuildercan build and installllvmandgcc.Basic Usage:ccbuilder build [compiler] [revision]compilermust be eitherllvmorgccandrevisioneither a commit hash
or a tag.ccbuilderrelies on clones the GCC and LLVM git repos and can build most of their recent revisions/commits/tags, e.g.,ccbuilder build gcc releases/gcc-13.1.0will build GCC 13.1.0.The built compilers are installed underccbuilder's compiler store (a filesystem directory).
After building a compiler, its installation location is printed, e.g.:% ccbuilder build llvm main 
/path/to/ccbuilder_compiler_store/clang-ff7d2fabe074d95cefb683d4a742eec172bd36d5
% /path/to/ccbuilder_compiler_store/clang-ff7d2fabe074d95cefb683d4a742eec172bd36d5/bin/clang -v
clang version 18.0.0 (https://github.com/llvm/llvm-project.git ff7d2fabe074d95cefb683d4a742eec172bd36d5)
...ccbuilderstores everything under$HOME/.cache, the default locations can
be overriden via command line flags, e.g.,ccbuilder build llvm master --compiler-store-path /some/other/pathTo update the compiler git repos useccbuilder --pull.For more options checkccbuilder -h.Installation:pip install [-U] ccbuilder
Package: cc.buildout_reports
ContentsXXX/TODO ReportHistoryDownloadcc.buildout_reports provides recipe[s] for generating developer reports
using zc.buildout.XXX/TODO ReportThe cc.bulidout_reports:xxx recipe can be used to scan a project for comments
flagged with a specifed pattern (XXX|TODO by default).  The recipe does not
scan temporary files (*~) or traverse intoeggand.svndirectories.The recipe supports two optional parameters:patternThe pattern to scan for; if not specified, defaults to(XXX|TODO).report_fileThe file to save the report to; if not specified, defaults
toXXXreport.html, stored in the buildout directory.  If specified
as a relative path, it is interpreted as relative to the buildout
directory.History0.1Initial public release.Download
Package: ccc007
# ccc007[![PyPI 版本](https://badge.fury.io/py/ccc007.svg)](https://pypi.org/project/ccc007/)
[![许可证：GPL 3.0](https://img.shields.io/badge/License-GPL%203.0-blue.svg)](https://opensource.org/licenses/GPL-3.0)这是我的工具合集## 安装使用软件包管理器 [pip](https://pip.pypa.io/en/stable/) 安装您的包名称。
Package: cccalculator
cccalculatorcalculate the cyclomatic complexity of python source file
Use M = E − N + 2 to calculate the complexity .
The output is the McCabe complexity of each python source file under the path.Examplecd into the cccalculate directory$ python calculate.py /path_to_your_python_file_directory/The output will be like this/path_to_your_python_file_directory/test_file4.pyfun:7----------6/path_to_your_python_file_directory/test_file1.pyclass:1--fun:2----------1class:1--fun:5----------5fun:26----------5
Package: cccconverter-ostrichb
CCCConverter基於RIME-Cantonese及CCC粵拼轉換表的香港政府粵語拼音轉換器。支援範圍本程式的粵拼對照表來自RIME-Cantonese中收錄的單字，包括正體中文及常用的粵字共計 27087 字。簡化字可能需要先轉換成正體中文方可正常轉換，為了確保用字的準確，本程式將不會支援簡化字的粵拼轉換。使用轉換單句：使用cccconverter.convert中的convert_sentence方法可以將一句話的中文字轉換為對應的港府粵語拼法，如下：>>>convert_sentence("慈雲圩")'tsz/tszi/tszu/tsi/tsee/chz/chzi/chzu/chi/chee wan/wun/wen hui||yu/yue||wai/wei'因為 CCC 拼法中，同一個字，甚至是同一個音都有眾多的拼法，預設的情況下，轉換的結果會保留所有的拼法，其中，同一個音的不同的拼法會使用斜槓（/）相隔，不同音會用（||）相隔。若僅需要寫出一個拼法，可以加上only_keep_first_spell=True引數：>>>convert_sentence("慈雲圩",only_keep_first_spell=True)'tsz wan hui||yu||wai'在上述基礎下，若僅需要一個讀音，可以再加上only_keep_first_pronunciation=True引數：>>>convert_sentence("慈雲圩",only_keep_first_spell=True,only_keep_first_pronunciation=True)'tsz wan hui'（兩個引數之間不衝突，可據實際情況組合使用）若需要使生成結果作為列表顯示，可以使用convert_sentence_map函數，引數同上：>>>convert_sentence_map("慈雲圩")[('慈','tsz/tszi/tszu/tsi/tsee/chz/chzi/chzu/chi/chee'),('雲','wan/wun/wen'),('圩','hui||yu/yue||wai/wei')]If you have questions or better ideas about this package, please feel free to open an issue or request a PR!
Package: cccd
c3d - create & commentcsvdata filesA small library for post-processing of data (currently only csv) files:generating metadata and documentation of included data setsUsage exampleA minimal working example looks like that:importosimportcccdascdfds=cdf.DataSet("Data","The description of the data set.")ds.append("Value_1","The description of the value nr. 1.",42)ds.append("Value_2","The description of the second value.",69)# we decide to save all of our results hereresults_f="example_results"ifnotos.path.exists(results_f):os.mkdir(results_f)cdf.write_ds(ds,f"{results_f}/ex1.csv")Here, we have a dataset calledDatawith a description. We also have two
values that belong to the dataset. We also provide a description of the values.
Finally, we save it all inex1.csvfile that looks as follows:# Data:
#    The description of the data set.
#
# Value_1:
#    The description of the value nr. 1.
#
# Value_2:
#    The description of the second value.
#
Value_1,Value_2
42,69A more involved example (including metadata and some customisation) below:importosimportcccdascdfimportpolarsasplif__name__=="__main__":# ingest the raw datatest_data=pl.read_csv("examples/test.csv")data_set=cdf.DataSet("This is the dataset nr. 1","The description of the data set and each field can be long. It will"" be wrapped anyway. I could even put Lorem Ipsum here.",)# lets describe the raw data that we havedata_set.append("f1",# name"This data describes f1 and is very meaningful!",# descriptiontest_data["first"],# value, currently it should be a Series or something like that)# We can create the DataDescription here and pass it to our dataset. This# is not very convienent as we have to also make sure that the second arg# is a dataframe.data_set.append_data_raw(cdf.DataDescription("f2","Second data set. Not very meanigful."),pl.DataFrame({"f2":test_data["second"]}),)# we decide to save all of our results hereresults_f="example_results"ifnotos.path.exists(results_f):os.mkdir(results_f)# We will write the described dataset, including metadata.mdata=cdf.metadata(author="James Bond",time_stamp=True,count_lines=True)# We will also overwrite the comment sign to `--` like in `lua`.cdf.write_ds(data_set,f"{results_f}/ex2.csv",meta_data=mdata,comment_text="--",)
Package: ccc-model-manager
Machine Learning model managerInstallTo install latest version:pip install --index-url https://wcp-docker-ci.artifactory.eng.vmware.com/api/pypi/pypi-virtual/simple ccc-model-managerUseTo use BERTModel:from model_manager.models.bert_model import BERTModelPublish new versionInstall pythonbuildpackage:pip install buildMake your changes in model_manager packageTest your changes by runningmake testBump version number inccc-model-manager/pyproject.tomlBump version number indeploy/images/ccc-pipeline-base/Dockerfileand inference apps that use the library.Build and Upload to repo by running./scripts/build_publish.sh
Package: cc-connector-cli
CC Connector CLICC Connector CLI is part of the Curious Containers project.For more information please refer to the Curious Containersdocumentation.AcknowledgementsThe Curious Containers software is developed atCBMI(HTW Berlin - University of Applied Sciences). The work is supported by the German Federal Ministry of Economic Affairs and Energy (ZIM project BeCRF, grant number KF3470401BZ4), the German Federal Ministry of Education and Research (project deep.TEACHING, grant number 01IS17056 and project deep.HEALTH, grant number 13FH770IX6) and HTW Berlin Booster.
Package: cc-core
CC-CoreCC-Core is part of the Curious Containers project. It contains shared code of the CC-FAICE and CC-Agency packages.For more information please refer to the Curious Containersdocumentation.AcknowledgementsThe Curious Containers software is developed atCBMI(HTW Berlin - University of Applied Sciences). The work is supported by the German Federal Ministry of Economic Affairs and Energy (ZIM project BeCRF, grant number KF3470401BZ4), the German Federal Ministry of Education and Research (project deep.TEACHING, grant number 01IS17056 and project deep.HEALTH, grant number 13FH770IX6) and HTW Berlin Booster.