Saravanan666/pypi-org huggingface dataset:
Package: pyfda
pyfdaPython Filter Design Analysis Toolpyfda is a GUI based tool in Python / Qt for analysing and designing discrete time filters. Fixpoint implementations (for some filter types) can be simulated.For more info see theGithub Repoand the documentation atreadthedocs.org.PrerequisitesPython versions:3.7 ... 3.11All operating systems, no OS specific requirements.Libraries:(Py)Qt5numpyscipymatplotlib:2.1or higherOptional libraries:docutilsfor rich text in documentationxlwtand / orXlsxWriterfor exporting filter coefficients as *.xls(x) filesInstalling pyfdaSelf-executing archives are available for Windows and OS X athttps://github.com/chipmuenk/pyfda/releaseswhich do not require a Python installation. Under Linux, pyfda can be installed as a flatpak.Otherwise, installation is straight forward: There is only one version of pyfda for all supported operating systems, no compilation is required:pipInstall from PyPI using> pip install pyfdaor upgrade using> pip install pyfda -Uor install locally using> pip install -e <YOUR_PATH_TO_PYFDA>where<YOUR_PATH_TO_PYFDA>specifies the path ofsetup.pywithout includingsetup.py.
In this case, you need to have a local copy of the pyfda project, preferrably obtained using git andpip installonly creates the start script.setup.pyYou could also download the zip file from Github and extract it to a directory of your choice. Install it either to your<python>/Lib/site-packagessubdirectory using> python setup.py installor just create a link to where you have copied the python source files (for testing / development) using> python setup.py developStarting pyfdaIn any case, the start scriptpyfdaxhas been created in<python>/Scriptswhich should be in your path. So, simply start pyfda using> pyfdaxFor development and debugging, you can also run pyfda usingIn [1]: %run -m pyfda.pyfdax # IPython or
> python -m pyfda.pyfdax    # plain python interpreterAll individual files from pyfda can be run using e.g.In [2]: %run -m pyfda.input_widgets.input_pz    # IPython or 
> python -m pyfda.input_widgets.input_pz       # plain python interpreterCustomizationThe location of the following two configuration files (copied to user space) can be checked via the tabFiles -> About:Logging verbosity can be controlled via the filepyfda_log.confWidgets and filters can be enabled / disabled via the filepyfda.conf. You can also define one or more user directories containing your own widgets and / or filters.Layout and some default paths can be customized using the filepyfda/pyfda_rc.py, right now you have to edit that file at its original location.
Package: pyfdbg
PyFDbg____        _____ ____  _
|  _ \ _   _|  ___|  _ \| |__   __ _
| |_) | | | | |_  | | | | '_ \ / _` |
|  __/| |_| |  _| | |_| | |_) | (_| |
|_|    \__, |_|   |____/|_.__/ \__, |
       |___/                   |___/一个简单且强大、易于使用的调试器本项目由Python3.11环境下编写，资源在该环境下编译本项目说明文档暂时仅支持中文项目链接Github|PypiKgithub：国内Github镜像站，可能会有延迟，推送请使用Github链接推送版本稳定版本暂无最新正式版本暂无最新版本(master分支下数据不准确, 具体查看dev分支)v0.0.1-alpha1使用从源码构建准备工作安装git和make (方法自行百度)安装Python(3.9版本或者3.11版本皆可)从源码仓库克隆源代码git clone git@github.com:DashBing/phap.git或者git clone https://github.com/DashBing/phap.git国内网比较差可以尝试：git clone https://kgithub.com/DashBing/phap.git初始化打包环境make init或者尝试手动安装以下包:buildtwine以下是在Windows环境下的示例命令行:python -m pip install build
python -m pip install twine构建包make build上传包make up-pypi
Package: pyFDEMvisualizer
Metadata-Version: 2.1
Name: pyFDEMvisualizer
Version: 5.1
Summary: Wheel file of pyFDEMvisualizer modules 5.1
Home-page: UNKNOWN
Author: University of Toronto, 2022
License: UNKNOWN
Platform: UNKNOWN
Requires-Python: >=3.5,<=3.9
Description-Content-Type: text/markdownpyFDEMvisualizer - An FDEM Visualiser Post-Processing PackageV5.1University of Toronto, 2022This Python package performs transformations on FDEM models in vtk/vtu format.
Functions calculated include:Extract information within the FDEM Model based on the name of the array (e.g., Stress, Strain, Temperature, etc...) Works in 2D and 3D.Extract stress-strain information for UCS and BD Simulations (Works in 2D and 3D). Optional addition of virtual strain gauges (Limited to 2D).Plotting stress vs strain curve.Calculate the Elastic Modulus of the dataset. Eavg, Esec and Etan can be evaluated. Works in 2D and 3D.Extract information of a particular cell based on a sequence of array names. This can be extended to extracting information along a line. Works in 2D and 3D.Extract information of a threshold dataset criteria based on a sequence of array names. Works in 2D and 3D.Extract mesh information and plot rosette/polar plots. Limited to 2D.Automatic detection/ User-defined assigment of loading direction when analysing mechanical simulations, namely UCS, BD, and PLT, in both 2D and 3D simulations.How to use it - TermimalAfter installation is complete, in a terminal/cmd session where you keep your Python projects, type the following command:pythonIn terminal, import fdem-visualizer module like so:importpyFDEMvisualizerasfdSpecify the path to folder where your models (.vtk, .vtu files) are located on your machine.model=fd.Model("abs_model_path_on_machine")You are ready to start using openfdem! See the operating module for full description of functions available.Example OpenFDEM FunctionsGetting number of points in your model:model.n_pointsOutput:11904Getting the stress-strain for platen:model.complete_stress_strain(progress_bar=True)Output:>>>ScriptIdentifyingPlaten>>>PlatenMaterialIDfoundas[1]>>>Progress:|//////////////////////////////////////////////////|100.0%Complete>>>1.51secondsPlatenStressPlatenStrain00.000000e+000.00000014.825237e+000.00925929.628823e+000.01851931.441437e+010.02777841.919164e+010.037037........572.036137e-300.240741582.036137e-300.250000592.036137e-300.259259602.036137e-300.268519612.036137e-300.277778[62rowsx2columns]>>>
Package: py-fdl
py-fdlPython client forFirebase Dynamic LinksGet startedGet youWeb API KeyfromFirebase console Settings page.
Reference:Create Dynamic Links with the REST APIUsagefromfirebase_dynamic_linksimportdynamic_link_builderbuilder=dynamic_link_builder(api_key='<Web API Key>')options={'link':'https://www.karimoff.me','apn':'com.example.android','ibi':'com.example.ios'}long_link=builder.generate_long_link(app_code='karimoff',**options)# long_link# 'https://karimoff.page.link/?link=https%3A%2F%2Fwww.karimoff.me&apn=com.example.android&ibi=com.example.ios'short_link=builder.generate_short_link(app_code='karimoff',**options)# short_link# 'https://karimoff.page.link/ZhtUPPWJXLT9PKXg6'Installpipinstallpy-fdl
Package: pyfdt
The pyfdt library is aimed to facilitate manipulation of the flattened device tree in order to parse it and generate output in various formats.It is highly based on fdtdump for the dtc compiler package.Device Tree Blob (.dtb) input is actually the only input format accepted, but either Device Tree Blob (DTB) or Device Tree Structure (text DTS) is available as output format. Device Tree filesystem ‘output’ is available via the fusemount.py FUSE sample using fusepy library.Python 2.6, 2.7, 3.x supported.
Package: pyfed
PyFedPyFed is an open-source framework for federated learning. PyFed is fairly straightforward and brief in comparison to other federated learning frameworks. Furthermore, it allows running federated learning algorithms with any Tensorflow dataset on any preprocessed dataset. PyFed introduces several methods of federated learning implementation such as running multiple processes on a single machine and training on various systems. In addition, PyFed employs Tensorboard to demonstrate the history of training of each client and assess loss and accuracy of each client per round.PyFed implements FL using sockets, processes, and threads. Simply put, each client will run its particular process and tries to establish a socket connection with the server, which also has its specific process.
Once initiated, each connection will be handled by one thread of the server's process. Each thread will communicate with its respective client to receive the trained weights per round.
Once they receive the result of one round, threads will return the weights to the server's process, which will arrive at a new model using the mentioned weights. The server will send the new model to the clients using newly initiated threads.PyFed is mainly based on two classes:FL_Server: which represents the server to which clients communicate in a federated learning problem. Thetrain()function of this class handles socket connections and the FL policy.FL_Client: which represents each client in a federated learning network. An object of this class handles training procedure of any global model on any local data.PyFed can run federated learning in 2 ways:Running FL only on one system and using separate processes.Running FL on multiple systems.Tutorials and installation guides are mentioned onPyFed webpage.
Package: pyfedic
pyFEDIC : Digital Image (2D/3D) Correlation SoftwarepyFEDIC is a DIC software using global approach with finite element shape functions to describe the displacement field. It allows to perform 2D DIC and 3D DIC also called DVC (V stand for volume).This software is a pure python library and offer a simple command line to run analysis with a few parameters as input.It can be directly installed using pip:$pipinstallpyfedicFor more information, visithttps://sourcesup.renater.fr/www/pyfedic/html/index.html
Package: pyfedic_cython
pyFEDIC : Digital Image (2D/3D) Correlation SoftwarepyFEDIC is a DIC software using global approach with finite element shape functions to describe the displacement field. It allows to perform 2D DIC and 3D DIC also called DVC (V stand for volume).This software is a pure python library and offer a simple command line to run analysis with a few parameters as input.It can be directly installed using pip:$pipinstallpyfedicFor more information, visithttps://sourcesup.renater.fr/www/pyfedic/html/index.html
Package: pyfed-macos
PyFedPyFed is an open-source framework for federated learning. PyFed is fairly straightforward and brief in comparison to other federated learning frameworks. Furthermore, it allows running federated learning algorithms with any Tensorflow dataset on any preprocessed dataset. PyFed introduces several methods of federated learning implementation such as running multiple processes on a single machine and training on various systems. In addition, PyFed employs Tensorboard to demonstrate the history of training of each client and assess loss and accuracy of each client per round.PyFed implements FL using sockets, processes, and threads. Simply put, each client will run its particular process and tries to establish a socket connection with the server, which also has its specific process.
Once initiated, each connection will be handled by one thread of the server's process. Each thread will communicate with its respective client to receive the trained weights per round.
Once they receive the result of one round, threads will return the weights to the server's process, which will arrive at a new model using the mentioned weights. The server will send the new model to the clients using newly initiated threads.PyFed is mainly based on two classes:FL_Server: which represents the server to which clients communicate in a federated learning problem. Thetrain()function of this class handles socket connections and the FL policy.FL_Client: which represents each client in a federated learning network. An object of this class handles training procedure of any global model on any local data.PyFed can run federated learning in 2 ways:Running FL only on one system and using separate processes.Running FL on multiple systems.Tutorials and installation guides are mentioned onPyFed webpage.
Package: pyfeign
PyFeign - Declarative REST ClientPython implementation of Feign.Installationpipinstallpyfeign# orpoetryinstallpyfeignUsageDecorate function with appropriatepyfeign.$methoddecorator:@pyfeign.get(url='http://localhost/{id}')defget_by_id(id:str=Path())->Dict[str,Any]:"""Get by ID"""obj_dict=get_by_id('id123')ParametersPath - Argument should be used as a path template variable. Reserved variable names can be used using thenameparameter:@pyfeign.get(url='http://localhost/{id}')defget_by_id(id_val:str=Path(name='id'))->Dict[str,Any]:"""Get by ID"""Query - Argument should be used as a query parameter, and can be optionally set with a default value if not provided@pyfeign.get(url='http://localhost/{id}')defget_by_id(id_val:str=Path,summary:bool=Query(default=False,name='summary_details'))->Dict[str,Any]:"""Get by IDget_byt_id('id1', False) == http://localhost/id1?summary_details=False"""Header - Argument will be used as an HTTP HeaderCookie - Argument will be used as an HTTP CookieBody - Argument will be sent as the request body (JSON serialized)Classes@pyfeign.Pyfeign(config=Config(base_url='https://postman-echo.com'))classPostmanEcho:@pyfeign.get('/get')defget(self,foo1:str=Query(),foo2:str=Query(default='bar2'))->Dict[str,Any]:passResponsesIf the response function / method is typed withDictorList, then the response json will be parsed and returned.If return type isstrthen the response text will be returnedFor either of these responses, the return code is asserted viaResponse.raise_for_status(), and so an HTTPError will
be raised accordinglyOtherwise the fullrequests.Responseobject is returned.
Package: pyfeld
SynopsisSmall tools to Raumfeld functionalities using python.
With these simple tools you can control the following things in Raumfeld:playsettings: volume, position, pause, stop, equalizerrooms: list roomszones: list, create, dropmedia: browse and search, play in zonemacro operations: fade, wait for conditions (volume, position, title)Installationthe easiest way is to use pip3 for installationpip3 install pyfeldPlease note, it requires python3, still have to figure out that you will NOT use it with python2 and
hurting you before you use it.UsageRunning pyfeldThese examples will suppose a device calledkitchenGet some help:pyfeld--helpDiscover devices and print info of zones and rooms:pyfeld--discoverinfoSame as bevor but print:pyfeld--discover--jsoninfoSet volume of kitchen:pyfeld roomvolume 20 kitchenSet volume on a zone:pyfeld-z1-dsetvolume 50switch device on:pyfeld standby off kitchenInfo for all devices:pyfeld-dplayinfoStop a zone:pyfeld-z1 stopBrowse some stuff:pyfeld browse "0/My Music/Albums"Play one of the browsed albums:pyfeld play--zonewithroomkitchen "0/My Music/Albums/Frank%20Zappa+"Check the volume in the kitchen, then set to 30%:pyfeld--discover--zonewithroomkitchen volumepyfeld--zonewithroomkitchen setvolume 30Raise bass and treble, lower midspyfeld  roomseteq kitchen 500-100700Create new zone (pay attention to spaces, quote!):pyfeld createzone kitchen "bath room" "living room"Drop a device from the zone it is in:pyfeld drop "bath room"Pyfeld advancedYou can do operations on specific UDN, but it should not be necessary usually.Retrive UDN’s:pyfeld--discover-vinfopyfeld--udncreatezoneuuid:f7052a34-37f6-432f-b584-837466474205pyfeld--udnroomsetvolumeuuid:f7052a34-37f6-432f-b584-83746647420510
Package: pyfem
pyfempyfem是一个完全基于python语言实现的极简有限元求解器。依赖的第三方库包括numpy、scipy和meshio等，主要用于有限元方法的学习、有限元算法验证和快速建立材料本构模型的程序原型。Installation 安装支持的操作系统包括：Windows，Linux和MacOS。Recommend 推荐Use the package managerpipto install pyfem:使用pip命令安装:pipinstallpyfemIf you have no root access on Linux/MacOS, please trypython-mpipinstallpyfemUsers in China can install pyfem from mirrors such as:中国用户可以使用以下镜像:AliyunTsinghuaFrom Source 基于源代码gitclonehttps://github.com/sunwhale/pyfem.gitcdpyfem
pipinstall.or 或者gitclonehttps://github.com/sunwhale/pyfem.gitcdpyfem
pythoninstall.py采用第二种方法需要将可执行文件或批处理文件写入环境变量。Quickstart 快速开始Run in command line 在命令行运行:pyfem--helpRun the first example 执行第一个算例:当前算例文件存储目录 examples/tutorial，该算例定义了一个二维平面应变模型，材料为塑性随动强化，载荷为y方向的循环拉伸-压缩。cdexamples/tutorial
pyfem-iJob-1.toml后处理算例计算完成后将在配置文件所在目录下生成 .pvd 或 .vtu文件，可以使用开源可视化软件paraview进行查看。前处理本项目暂不提供前处理模块，基于 meshio 库，可以识别gmsh、abaqus 和 ansys等有限元软件的网格文件。Documents 帮助文档https://pyfem-doc.readthedocs.io/DevelopmentToDo list增加Neumann边界条件，支持concentrated force，distributed和pressure定义方式增加hdf5计算结果输出格式完善帮助文档完善输入文件的校检增加测试模块增加日志模块增加后台运行模式处理平面应力状态的面外应力平衡增加粘弹性力学本构模型增加晶体塑性力学本构模型增加温度场求解单元增加温度场-位移场耦合求解单元增加相场-位移场耦合求解单元增加内聚区单元增加动力学求解器建立前处理界面Bug list采用abaqus网格文件时，如果存在node不属于任何element则在计算时会导致全局刚度矩阵奇异。
Package: py-femas
py_femas中金技术api之python封装，实现接口调用。支持windows(x86/x64) linux(x64).示例#!/usr/bin/env python# -*- coding: utf-8 -*-__title__='test py ctp of se'__author__='HaiFeng'__mtime__='20190506'importsyssys.path.append('.')frompy_femas.tradeimportCtpTradefrompy_femas.quoteimportCtpQuotefrompy_femas.enumsimport*importtimeclassTestTrade(object):def__init__(self,addr:str,broker:str,user:str,pwd:str,appid:str,auth_code:str,proc:str):self.front=addrself.broker=brokerself.investor=userself.pwd=pwdself.appid=appidself.authcode=auth_codeself.proc=procself.t=CtpTrade()self.t.OnConnected=self.on_connectself.t.OnUserLogin=lambdao,x:print('Trade logon:',x)self.t.OnDisConnected=lambdao,x:print(x)self.t.OnRtnNotice=lambdaobj,time,msg:print(f'OnNotice:{time}:{msg}')self.t.OnErrRtnQuote=lambdaobj,quote,info:Noneself.t.OnErrRtnQuoteInsert=lambdaobj,o:Noneself.t.OnOrder=lambdaobj,o:print(o)self.t.OnErrOrder=lambdaobj,f,info:print(info)self.t.OnTrade=lambdaobj,o:print(o)self.t.OnInstrumentStatus=lambdaobj,inst,stat:Nonedefon_connect(self,obj):self.t.ReqUserLogin(self.investor,self.pwd,self.broker,self.proc,self.appid,self.authcode)defrun(self):self.t.ReqConnect(self.front)# self.t.ReqConnect('tcp://192.168.52.4:41205')defrelease(self):self.t.ReqUserLogout()classTestQuote(object):"""TestQuote"""def__init__(self,addr:str,broker:str,investor:str,pwd:str):""""""self.front=addrself.broker=brokerself.investor=investorself.pwd=pwdself.q=CtpQuote()self.q.OnConnected=lambdax:self.q.ReqUserLogin(self.investor,self.pwd,self.broker)self.q.OnUserLogin=lambdao,i:self.q.ReqSubscribeMarketData('IF1911')self.q.OnTick=lambdaq,t:print(f'{t.LastPrice},{t.Volume}')defrun(self):self.q.ReqConnect(self.front)defrelease(self):self.q.ReqUserLogout()if__name__=="__main__":front_trade='tcp://101.226.253.127:58002'front_quote='tcp://101.226.253.127:58005'broker='0137'investor=''pwd=''appid='client_LB_1.0'auth_code='Q5V2UZ1G6AB2QC'proc=''ifinvestor=='':investor=input('invesotr:')pwd=input('password:')appid=input('appid:')auth_code=input('auth code:')proc=input('product info:')tt=TestTrade(front_trade,broker,investor,pwd,appid,auth_code,proc)tt.run()time.sleep(5)iftt.t.logined:tt.t.ReqOrderInsert('IF1911',DirectType.Buy,OffsetType.Open,3885,3)qq=TestQuote(front_quote,broker,investor,pwd)qq.run()## time.sleep(6)# for inst in tt.t.instruments.values():#     print(inst)input()tt.release()qq.release()input()
Package: pyfembed
PyfembedScript to upload Videos to Fembed via APIRequerimentsPython >=3.6Usagepip install pyfembed

import pyfembed import Pyfembed

pyfembed = Pyfembed(client_id, client_secret)
upload_url = pyfembed.get_upload_url()
uploaded = pyfembed.upload(
    file_path=filepath, 
    url_upload=upload_url.data.url, 
    token=upload_url.data.token
)
video_id = pyfembed.get_video_id(uploaded['fingerprint'])And ready, the file is uploaded to share
Package: pyfemtet
PyFemtet is the extension package for Femtet with Python.Femtet Website:https://www.muratasoftware.com/Documentation:https://pyfemtet.github.io/pyfemtet/Source code:https://github.com/pyfemtet/pyfemtetBug reports:https://github.com/pyfemtet/pyfemtet/issuesIt provides:single and multi objective parameter optimizationtools for connect Femtet and Python by pid
Package: pyfeng
PyFENG: Python Financial ENGineeringPyFENG is the python implemention of the standard option pricing models in financial engineering.Models implementedBlack-Scholes-Merton (and displaced diffusion) modelBachelier (Normal) modelConstant-elasticity-of-variance (CEV) modelStochastic-alpha-beta-rho (SABR) modelHyperbolic normal stochastic volatility (NSVh) modelHeston modelOrnstein-Uhlenbeck driven stochastic volatility modelAbout the packageIt assumes variables arenumpyarrays. So the computations are naturally vectorized.It is purely in Python (i.e., no C, C++, cython).It is implemented with python class.It is intended for, but not limited to, academic use. By providing reference models, it saves researchers' time.InstallationpipinstallpyfengFor upgrade,pipinstallpyfeng--upgradeCode SnippetsIn [1]:importnumpyasnpimportpyfengaspfm=pf.Bsm(sigma=0.2,intr=0.05,divr=0.1)m.price(strike=np.arange(80,121,10),spot=100,texp=1.2)Out [1]:array([15.71361973,  9.69250803,  5.52948546,  2.94558338,  1.48139131])In [2]:sigma=np.array([[0.2],[0.5]])m=pf.Bsm(sigma,intr=0.05,divr=0.1)# sigma in axis=0m.price(strike=[90,95,100],spot=100,texp=1.2,cp=[-1,1,1])Out [2]:array([[ 5.75927238,  7.38869609,  5.52948546],
       [16.812035  , 18.83878533, 17.10541288]])AuthorProf.Jaehyuk Choi(Peking University HSBC Business School). Email:pyfe@eml.ccOthersSee alsoFER: Financial Engineering in Rdeveloped by the same author.
Not all models inPyFENGis implemented inFER.FERis a subset ofPyFENG.
Package: pyfenstein3d
pyfenstein3dIntroduçãoInstaçãoExecuçãoComandosImplementações FuturasIntroduçãoProjeto realizado em python com intuito educacional de tentar reproduzir o jogoWolfenstein 3dno prompt de comando.Utilizando a fonteConsolas, é possivel considerar que cada2 caracteresrepresentem1 pixel. E assim "renderizar" qualquer imagem no prompt utilizandoANSI escape codepara dar cor.Para manter o número de quadros por segundo estável, o projeto foi desenvolvido considerando uma renderização de 100x62InstalaçãoPara executar o projeto é necessario instalarpython 3Instalepiputilizando o commando:python-mensurepipExecute o comando para realizar a instalação do pyfenstein3d:python-mpipinstallpyfenstein3dExecuçãoApós instalação e configuração do prompt, execute o modulo com o comando abaixopython -m pyfenstein3dComandosComandoTeclaAndar para frenteWAndar para trásSAndar para esquerdaAAndar para direitaDVirar para esquerdaSeta esquerdaVirar para direitaSeta diretaAtirarSeta cimaAbrir portaEspaçoImplementações Futuras"Renderizar Pixel"Raycasting 2dRaycasting "3d"Textura paredeSpritesAnimaçãoPortasArmaTiroInteração itensInimigoInteligencia artificial
Package: pyfer
Pyfer ~ Encrypt and Decrypt Strings💡 What is it?Pyfer is a simple encryption and decryption tool built in Python.FeaturesWith this little library you can do the following:Encrypt and decrypt stringsUse one of three available encryption modes, each using a different ciphertext alphabetGenerate random digit keys🛠️ SetupInstall it fromPyPIby runningpip install pyfer.DependenciesThe only dependency isNumPy.🚀 ExecutionAppIf running in a virtual environment usingpyenv/virtualenv, ensure the Python version is configured fortkinterby following the instructionshere.To run the app, simply open a terminal window to the project folder and run:python app.pyCommand LineAlternatively, Pyfer can be run from the command line:python main.py <key> <input_string>For more information, have a look at the demohere!📝 DocumentationDocumentation is currently available in the form of docstrings.⚖️ LicenseThe project is licensed under the MIT license.