Saravanan666/pypi-org huggingface dataset:
Package: code2json
Code 2 JSONA command line utility that can insert code into JSON files and extract code from JSON to insert into a source code file.FeaturesCreate your source code in a separate file and have them be automatically written to JSON with in source markupWrite all files in a directory and it's subdirectories to the jsonSpecify the extension of the source code filesSpecify the delimiter of the identifiers in the source code used to indicate the marked up parts (useful if the language does not support the default delimiter (//)Update the source code based on the source code in the JSONPlanned featuresA safe mode that allows the user to check the made changes before committing to themRequirementsPython 3: I use print functions in the Python 3 style and I just don't feel like support Python 2 especially since it won't be supported after 2020/01/01(Optionally) Pip: The Python Package Installer can be used to easily install Code 2 JSON but this can also be done by handInstallationThere are multiple ways to install Code 2 JSON:Using pip and PyPiSimply run the following commandpip install code2jsonCloning the repository and installing with pipClone the repositorygit clone https://gitlab.com/Deathray_II/code2json.gitof if you prefer to use SSHgit clone git@gitlab.com:Deathray_II/code2json.gitgo inside the git directorycd code2jsonrun pip install on the setup filepip install .or if you wish to install it for just your userpip install --user .Cloning the repository and manually installing (on Linux)Clone repositorygit clone https://gitlab.com/Deathray_II/code2json.gitof if you prefer to use SSHgit clone git@gitlab.com:Deathray_II/code2json.gitMake accessible in your shell
There are two ways to do this either adding an alias or link it into your/usr/binfolderAdding an alias:
Got to your~/.bashrcor~/.zshrcand add the linealisas code2json=/path/to/cmd.py "$@", where/path/to/cmd.pyis the path to thecmd.pyfile in thesrc/code2json/folder in the repository.linking it into your/usr/bin: run (as a user who has access to write in/usr/bin) the commandln -s /path/to/cmd.py /usr/bin/code2json, where/path/to/cmd.pyis the path to thecmd.pyfile in thesrc/code2json/folder in the repository.UsageIf you have cloned the repository you can have a look at the test files and try some of the command on them so you get an idea of how it worksCode to JSONIf we wish to write all the code from a file (apples.c) to the corresponding JSON file we would execute the the following command from the folder that contains our source filecode2json ./apples.c .c 2jsonIf we wish to write all the .js file underneath the current directory to their JSON file we would run the following command.code2json . .js 2jsonJSON to codecode2json ./apples.c .c 2codecode2json . .js 2json
Package: code2read
# README
## Contents
1. THE README FOR THIS PROGRAM IS ALSO GENERATED BY THE CODE ITSELF.ADDS ARGUMENTSFIND THE SIDE COMMENTS AND CREATE CONTENTS LISTFORMAT THE FILE AND SAVEMAIN FUNCTION## Docstrings
>This program converts all the comments in your code to a README.md file and saves a lot of your time provided you write comments in your code. It is obvious how easy it is to actually generate the README.md file and save your time.Using the argparse module to get the file path. Note that to run the file`bash python-mcode2read.code-p<path to file>-o<path to output> #example python-mcode2read.code-p"setup.py"-o"/Users/lordvile/Downloads/" `>
For now this program only supports python.
## Side comments
- Initial fileOutput fileGENERATED README![generatedoutput](output/gen.png)INITIAL CODE![initialcode](output/code.png)
Package: code2seq
code2seqPyTorch's implementation of code2seq model.InstallationYou can easily install model through the PIP:pipinstallcode2seqUsageMinimal code example to run the model:fromargparseimportArgumentParserfromomegaconfimportDictConfig,OmegaConffrompytorch_lightningimportTrainerfromcode2seq.data.path_context_data_moduleimportPathContextDataModulefromcode2seq.modelimportCode2Seqdeftrain(config:DictConfig):# Load data moduledata_module=PathContextDataModule(config.data_folder,config.data)data_module.prepare_data()data_module.setup()# Load modelmodel=Code2Seq(config.model,config.optimizer,data_module.vocabulary,config.train.teacher_forcing)trainer=Trainer(max_epochs=config.hyper_parameters.n_epochs)trainer.fit(model,datamodule=data_module)if__name__=="__main__":__arg_parser=ArgumentParser()__arg_parser.add_argument("config",help="Path to YAML configuration file",type=str)__args=__arg_parser.parse_args()__config=OmegaConf.load(__args.config)train(__config)Navigate toconfigdirectory to see examples of configs.
If you have any questions, then feel free to open the issue.
Package: codeallyadvanced
Host common UI artifacts for various projects I am developingCommon UI artifacts for various projects I am developingWritten byHumberto A. Sanchez II(C) 2023NoteFor all kind of problems, requests, enhancements, bug reports, etc.,
please drop me an e-mail.Developer NotesThis project usesbuildlackeyfor day to day development builds== I am using GitHub under protest ==This project is currently hosted on GitHub.I urge you to read about theGive up GitHubcampaign fromthe Software Freedom Conservancy.While I do not advocate for all the issues listed there I do not like that
a company like Microsoft may profit from open source projects.I continue to use GitHub because it offers the services I need for free.  But, I continue
to monitor their terms of service.Any use of this project's code by GitHub Copilot, past or present, is done
without our permission.  We do not consent to GitHub's use of this project's
code in Copilot.
Package: codeallybasic
Host common artifacts for various projects I am developing.  This is package does not include any wxPython dependencyWritten byHumberto A. Sanchez II(C) 2023NoteFor all kind of problems, requests, enhancements, bug reports, etc.,
please drop me an e-mail.Developer NotesThis project usesbuildlackeyfor day to day development buildsI am concerned about GitHub's Copilot projectI urge you to read about theGive up GitHubcampaign fromthe Software Freedom Conservancy.While I do not advocate for all the issues listed there I do not like that
a company like Microsoft may profit from open source projects.I continue to use GitHub because it offers the services I need for free.  But, I continue
to monitor their terms of service.Any use of this project's code by GitHub Copilot, past or present, is done
without my permission.  I do not consent to GitHub's use of this project's
code in Copilot.
Package: code-analysis
code_analysisAnalyze source code dependencies and call trees in Neo4j.Free software: MIT licenseDocumentation:https://code-analysis.readthedocs.io.FeaturesThis project provides generators for Cypher code for import into Neo4J from call tree and package dependencies.
Some of these generators rely on external tools to provide their ingoing data.Static calltrees of Java code can be created with java-callgraph, which can be found on GitHub:https://github.com/gousiosg/java-callgraph.Dependencies of Java packages can be determined using JDepend, which can also be found on GitHub:https://github.com/clarkware/jdepend.Python dependencies are determined using the compiler and AST (Abstract Syntax Tree) with a tool provided in this project.Generating Cypher for a Java call treeCreate the call tree using java-callgraph and save it into a file, e.g.,java_call_tree_input.txt.
Run:java_call_tree java_call_tree_input.txt > calltree-cypher.txtcalltree-cypher.txtcontains two Cypher statements, one to insert all classes into a Neo4j database, and another to insert the call relations on method level. You can just copy each statement and paste it into the Neo4j browser.The database schema looks like this::(:Class)-[:uses]->(:Class)
(:Method)-[:calls]->(:Method)Generating Cypher code for Java dependenciesCreate dependencies using JDepend and save it in a file, e.g.,java_depend.txt.Run following command::java_dependencies java_depend.txt > java_depend.cypherNow you can copy the Cypher statements stored injava_depend.cypherand paste it into the Neo4j browser.The database schema looks like this::(:Package)-[:depends_on]->(:Package)To check for cycles you may run the query::MATCH (p:Package)-[r1:depends_on]->(q:Package)-[r2:depends_on]->(p:Package)
RETURN p, q, r1, r2It helps to switch off the default setting, which shows all relations, in the browser settings.Generating Cypher code for Python dependenciesDetermination of dependencies and generation of Cypher code are done in one step in this case::python_dependencies <path to your package> > python-deps.cypherThe tool compiles the code and walks the AST looking for import statements. Then it generates Cypher code modelling the relationships between the packages.The database schema looks like this::(:Package)-[:contains]->(:Module)
(:Module)-[:uses]->(:Module)CreditsThis package was created withCookiecutterand thestbraun/cookiecutter-pypackageproject template based onaudreyr/cookiecutter-pypackage.History0.1.0 (2021-04-04)First release on PyPI.0.1.3 (2021-04-05)Documentation enhanced.
Package: code-analyza
Code AnalyzaA code analyser package for Python.Getting StartedInstallationCode Analyza is available via PyPi.pipinstallcode-analyzaUsageRun as a script:code_analyzapath/to/file/or/directoryImport as a module:fromcode_analyza.linter.checksimportanalysebase_path=# set path/to/file/or/directoryissues=analyse(base_path)Style ConventionsCode Analyza currently implements 9 conventions as specified by thePEP 8 – Style Guide for Python Code. They include:Code Lay-outIndentationMaximum Line LengthBlank LinesCommentsInline CommentsNaming ConventionsClass, Function and Variable NamesFunction and Method ParametersSemicolonsTodosDefault Arguments🤝 ContributingContributions, issues and feature requests are welcome!SupportGive a ⭐️ if you like this project!AcknowledgementsPEP 8Ruff📝 Copyright & LicenseCopyright (c) 2023 Clifton Davies. This project is licensed underMIT. See LICENSE file for details.
Package: code-annotator
Code AnnotatorCode Annotatoris a Python package designed to automatically add comments to source code using the Mistral AI API. It enables users to quickly obtain comprehensive, context-aware comments throughout their code, facilitating better understanding and readability.InstallationTo installCode Annotator, you can use pip:pipinstallcode_annotatorUsageAs a Command Line ToolAfter installation,Code Annotatorcan be used directly from the command line.Example:code-annotator<api_token><model_name><file_path>[--temperature][--top_p][--max_tokens]api_token: Your Mistral API token.model_name: The name of the Mistral model to use.file_path: The path to the source code file you want to annotate.temperature(optional): The temperature parameter for the AI model.top_p(optional): The top_p parameter for the AI model.max_tokens(optional): The maximum number of tokens for the AI model to generate.Customizing Your AnnotationsYou can customize the behavior ofCode Annotatorby adjusting the optional command-line parameters, such as the temperature, top_p, max_tokens, etc., to fit the specific needs of your code or to tweak the behavior of the Mistral model.Output ExampleWhen you runCode Annotator, it processes your source code file and adds insightful, context-aware comments to the code. Here is an example of the annotated code output:# Example of annotated codedefexample_function(param1,param2):# This function performs a calculation using param1 and param2result=param1+param2returnresultContributingContributions, issues, and feature requests are welcome! Feel free to checkissues page.LicenseMIT
Package: codearchiver
codearchivercodearchiver is a tool for archiving version control system repositories and associated data like issues, pull requests, or releases.No documentation or detailed description exists at this time.Modulesgit: requires at least Git 2.25.0LicenseThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: code-aster-win
Code_Aster Python PackageThis Package makes the simulation software code_aster easy to install and use within python and registers also commands to use it with the CMD/Terminal. There is a version for Windows and Linux (in construction).What is Code_Aster ?Code_Aster offers a full range of multiphysical analysis and modelling methods that go well beyond the standard functions of a thermomechanical calculation code: from seismic analysis to porous media via acoustics, fatigue, stochastic dynamics, etc. Its modelling, algorithms and solvers are constantly under construction to improve and complete them (1,200,000 lines of code, 200 operators). Resolutely open, it is linked, coupled and encapsulated in numerous waysInstallationpip install code-aster-winThat's all!Test your installationimportcode_asterasca# Returns a log with all the informationlogs=ca.test_installation()# For example you can extract the output the command generatedprint(logs.stdout)It will start a test simulation and if it runs without error the installation is ok.How to use itUse of the as_run commandimportcode_asterascafrompathlibimportPath# Path to the export file# Attention use Pathlib and front slashes (/).path=Path('C:/Users/..../forma01a.export')# Returns a log with all the informationlogs=ca.as_run(path)# For example you can extract the output the command generatedprint(logs.stdout)Use of the run_astk commandimportcode_asterascafrompathlibimportPath# Returns a log with all the informationlogs=ca.run_astk()# For example you can extract the output the command generatedprint(logs.stdout)Use it directly in the Terminal/CMDpy_as_runorpy_run_astkorpy_test_installation
Package: code-attention-visualizer
Colored Tokens VisualizerA python library to color your code tokens based on certain vector of weights.It supports bothPythonandJavasource code, see these example:Quick StartOnce you have the two JSON files:example_java.jsonandexample_python.jsonyou can simply use this code to create the colored version of your tokens, using your weights.fromcodeattention.source_codeimportSourceCodefromcodeattention.tokenizerimportPythonTokenizer# read your snippetpython_code="""def hello_python():print("Hello World!")"""# prepare the tokenized code in a file of the right format (read below)pt=PythonTokenizer()pt.tokenize(python_code)pt.save_tokens("example_python.json")# inject your own application-specific weights (random here)importrandomfromrandomimportrandintrandom.seed(42)weights=[]foriinrange(len(pt.tokens)):weights.append(randint(1,10))# plotpython_sc=SourceCode("example_python.json")fig,ax=python_sc.show_with_weights(weights=weights)print(weights)Output:[2,1,5,4,4,3,2,9,2,10]Python Input FormatWith Python we might want to separate a long identifier with underscores in separate tokens. That is why we have the fieldssiandd.defhello_python():print("Hello World!")You will need to prepare your input like this JSON:example_python.json:[{"t":"def","i":0,"l":1,"c":0},{"t":"hello","i":1,"l":1,"c":4,"si":0,"d":2},{"t":"python","i":1,"l":1,"c":10,"si":1,"d":2},{"t":"(","i":2,"l":1,"c":16},...]Here the identifierhello_worldis split in two, and for the two parts you need to use the sameifield, and use an incrementalsi(subindex) field. Both should have ad(dimension) field to keep track of how many token the original identifier had.Java Input FormatGiven some Java code:classHelloWorld{publicstaticvoidmain(String[]args){System.out.println("Hello, World!");}}You can create a JSON file which contains information on the tokens you are going to color. For the given snippets you will have to provide the following JSON file:example_java.json[[{"t":"class","i":0,"l":1,"c":1},{"t":"HelloWorld","i":1,"l":1,"c":7},{"t":"{","i":2,"l":1,"c":18},{"t":"public","i":3,"l":2,"c":5},{"t":"static","i":4,"l":2,"c":12},...]Note that there are no meaningless tokens, such as whitespace, new lines, tab, etc.Warning:If you use our tool to prepare the JSON file, remember that theJavaTokenizerremoves comments whilePythonTokenizerkeeps them. Implement your own tokenizer if you want a different behavior.Which is a list of element, each representing one token. A token contains the following fields:tkey contains the string representation of the token,ikey contains the major index position of the token,lkey contains the line number of the token,ckey contains the column number of the token.sikey contains the minor index position (si = subindex),dkey contains the number of other tokens sharing the same
indexExample of java output.
Package: codebackup
codebackupA simple command line tool to backup all of your Github and Bitbucket
repositories to the specified directory.InstallingUse one of the following commands (the later works in ActivePython):$ sudo pip install codebackup

$ pypm install codebackupUsage$ codebackup --github-user=srid --bitbucket-user=srid ~/Dropbox/codebackupCreditsgithub-simple-backup, original inspirationDistributeBuildoutmodern-package-templateNews1.0b1Release date: 11-Apr-2010Initial release
Package: codebasedutils
🚀CodebasedUtilsCodebasedUtilsis a versatile CLI tool designed to supercharge your developer workflow. Born out of the necessity to efficiently navigate through codebases, this utility elegantly marries simplicity with robustness.🔍 Featurestree: Prints a directory structure while seamlessly ignoring nonessential files, giving you a clear snapshot of your project.🛠 Installationpipinstallcodebasedutils🚴‍♂️ QuickstartTo get a clean directory structure of your current directory and copy it to your clipboard:utiltree📜 LogsEncountered an anomaly? Dive deep with our comprehensive logs:catlogs/codebasedutils.log💡 ContributingContributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the projectCreate your feature branch (git checkout -b feature/AmazingFeature)Commit your changes (git commit -m 'Add some AmazingFeature')Push to the branch (git push origin feature/AmazingFeature)Open a pull request📝 LicenseDistributed under the MIT License. SeeLICENSEfor more information.🤝 ConnectAlways eager to connect with fellow developers! Find me onLinkedIn.
Package: codebear
codebear PackageThis is a simple example package. You can useGithub-flavored Markdownto write your content.源码熊 codeby yy小武酷跑 简易封装 文件
背景类
BackGround() : 传入窗口对象方法：
.load() 加载背景图片
.update() 显示到屏幕
.update (是否交替（True/False）,移动速度(int))
move_speed: 移动速度is_change=False :是否交替角色类
Role(self,image, x, y, action=False, rate=5):  管理 角色更好地 类
创建对象实例化
.x 当前的x坐标
.y 当前角色的 y 坐标
.action  bool 值 用来判断是否是动图 如果是一组图 需要设置为True
.rate 图片切换的速率一些方法update() 方法   更新下一个动作图片
get_pos() 方法  获取当前角色的x，y坐标 返回值为 [] 列表
set_speed() 方法 设置角色的移动速度  参数为speed
get_speed() 方法 获取角色当前的移动速度
setAlive() 方法 bool值 设置角色是否活着 参数为bool值
getAlive() 方法 bool值 获取角色是否活着
setBlood() 方法 给该角色设置生命值
getBlood() 方法 获取该角色的生命值
move(face="left") 方法  移动角色  参数   朝某个方向移动  up down left right
setJumpHeight(self, speed=5)方法 设置角色的跳跃高度’ 参数为角色的跳跃高度 正数
jump(floor = floor)方法 让角色调整为跳跃状态 是用之前需要设置跳跃高度  参数为初始起跳的高度
distance(self, pos, center_d2=5) 当前角色和 角色2之间的距离 参数 角色2的pos元组坐标 角色2 坐标到图中心的平均值 默认为5坐标到图中心的平均值计算方法
平均值  = (角色2 的宽除以 2 + 角色2 的高除以2) // 2给角色赋予新的能力setMyDefine(self, **kwargs):  当角色已有的属性不被满足时可以自己添加键值对的形式添加
getMyDefine(self, key) 获取自己设定属性的值
example：
# 设置得分= 5
Role.setMyDefine(point = 5)
point = Role.getMyDefine("point")v1.0.3更新 加入了雪花飘落元素 优化碰撞检测 优化自定义属性
Fall()  飘落物落下物体类
f = Fall(img, milliseconds=500):
初始化需要一个下落的图片路径和 几毫秒创建一个飘落物，
方法:
getEvent(self):
获取创建漂浮物体的事件返回一个漂浮事件创建一个飘落物体create(self, pos, crosswise_speed=-1, vertical_speed=1):
参数 pos>>>[x,y] 元组或者列表格式的x,y坐标 ，为元素出现的位置
crosswise_speed: 横向漂浮的速度
vertical_speed:  竖向漂浮的速度漂浮物落下，需要一个屏幕对象，在屏幕对象上绘制descent(self, screen):
Package: code-beatrix
code_beatrixExercices pour découvrir la programmation et les algorithmes.
Ceux-ci peuvent servir de base pour organiser uncoding goûter.
Ils ont servi de base pour la création d’ateliers
pourcodingandbricksoudigigirlzavecMicrosoft.GitHub/code_beatrixsiteBlogLe site est conçu comme la documentation d’un modulePythonqui sert à préparer et à illustrer les exercices.Historycurrent - 2021-01-02 - 0.00Mb0.0.0 - 2021-01-02 - 0.00Mb10: Fix build (2020-02-02)8: pytube3 (2020-02-01)9: Fixes #8, replace pytube by pytube3 (2020-02-01)7: update to moviepy>=1.0 (2019-02-16)6: remove use of basemap, replaces it by cartopy (2018-07-27)2: add function to manipulate frames (2018-01-17)1: add function to manipulate text and videos (2018-01-07)
Package: codebehind
Because i dont want to do the same thing all over again. If you are using Django Rest Framework, then this will might help you.FeaturesRegistration and LoginDifferent Authentication Logic (Basic , Token, HMAC Signature)Basic HelpersSince it use DRF, Browsable Rest API!Quick startAdd “codebehind” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'codebehind',
    'rest_framework',
]Runpython manage.py migrateto create the useful models.In your*settings.py:REST_FRAMEWORK = {
    # Use Django's standard `django.contrib.auth` permissions,
    # or allow read-only access for unauthenticated users.
    'DEFAULT_PERMISSION_CLASSES': (
            'rest_framework.permissions.IsAdminUser',
            'rest_framework.permissions.IsAuthenticated',
    ),

    'DEFAULT_AUTHENTICATION_CLASSES': (
       'codebehind.authentication.CodeBehindAuthentication',
       'rest_framework.authentication.BasicAuthentication',
       'rest_framework.authentication.SessionAuthentication',
    ),

    'PAGE_SIZE': 20
}inurls.py:from rest_framework import routers
from django.conf.urls import include, url
from codebehind.views import UsersViewSet, GroupViewSet

router = routers.DefaultRouter()
router.register(r'users', UsersViewSet,'users')
router.register(r'groups', GroupViewSet,'groups')

urlpatterns += [
    # add this
    url(r'^v1/', include(router.urls)),
]
Package: codebench
Automated code benchmark solution.Empower developers with tools to trace and analyze project performances.IntroductionWhat is Codebench?Codebench is a tool that runs user-defined benchmark programs, monitors system
information and generates reports. It is most powerful when using in a project
tracked by git. It runs benchmarks based on different commits and reports the
system usage difference, so that users can compare and track performance
changes of their project across commits. It can also be used in CI (Continuous
Integration) to detect recent commits that increases/decreases project
performance.What Codebench is not?Codebench itself does not provide benchmark programs. It is not smart
enough to generate a benchmark program for your code. Instead, it can be
considered as a wrapper or a supervisor for your benchmark.FeaturesNormal Modecomparison among commitsauto-generated reportCI Mode (in development)comparison with baselineauto-generated reportthreshold alarmsInstallationCodebench can be simply installed by running:pipinstallcodebenchIf you want to install from a source distribution,
extract the tarball and run the following command:pythonsetup.pyinstallUsageCommand Line InterfaceCodebench provides a command line interface. Runcodebench -hfor details.SampleSeecodebench-samplefor details.A quick demo is demonstrated below:codebench--before./before_script.sh--start./benchmark.py\--report_typechart--commitscb91b83cd96dbb1541