Saravanan666/pypi-org huggingface dataset:
Package: lying
This small project create a fake terminal, run fake commands and display fake
results. So, yes it’s lying.InstallFrom source:pip install git+https://github.com/axju/lying.gitFrom Pypi:pip install lyingDo not forget to use a virtual environment.How to useRelay simple, just create the instruction file and then run:lying run examples/instruction.jsonTo create the instruction file, take a look at the example folder. There is
also a setup function to interactively create the file:lying setup filename.jsonIf you want to record the result, you can use ffmpeg. The following commands
can help you:ffmpeg -video_size 1920x1080 -framerate 25 -f x11grab -i :0.0+0,0 ext/video.mp4 -yCreate a gif:ffmpeg -i ext/video.mp4 -filter_complex "[0:v] palettegen" palette.png -y
ffmpeg -i ext/video.mp4 -i palette.png -filter_complex "[0:v][1:v] paletteuse" ext/video.gifDevelopmentSome information for crazy developers. Virtual environment windows:python -m venv venv
venv\Scripts\activateVirtual environment linux:python3 -m venv venv
source venv/bin/activateSetup project:python -m pip install --upgrade pip wheel setuptools twine tox flake8 pylint coverage rstcheck
python setup.py developRun some test:tox
coverage run --source lying setup.py test
coverage report -mCreate package:python setup.py sdist bdist_wheelPublish package:rstcheck README.rst
python setup.py --version
python setup.py check
python setup.py sdist bdist_wheel
twine upload dist/*
git push origin 0.1.1
Package: lyingOn
📦 setup.py (for humans)这是一个为LyingOnFramework框架服务的项目，在日常自动化测试中，我们的测试人员往往没有一个合适的架构框架，仅仅采用selenium进行杂乱无章的测试，且重复使用的
基础方法缺少封装，换了项目再继续copy使用。同时，对于不同的公司，自动化测试框架也不一样，甚至有些公司没有自动化测试框架，譬如我们公司☺...
为解决我们可爱的，经常给我们开发提出各种各样问题，经常不改完不让开发下班的，我们亲爱的测试人员的令人头大的问题（听君一席话，如听一席话），本项目封装了自动化
测试中常用的方法，且封装了日志以及企业微信消息通知，便于测试跟随项目发布，若自动化测试未通过，则报警发送至企业微信，便于监控项目。若项目年久失修或出现问题，请联系作者邮箱：chuqisky@gmail.com
Package: lykability
Using empythy to score likability based on sentiment analysis of recent tweets about a given personPurposeTo piggyback off of the empythy natural languare classifier package to analyze average sentiment of tweets related to a particular person to calculate a ‘likability score’ for that person. Useful in tracking sentiment changes across a certain period of time, i.e. the likability score of a celebrity before and after a concert.InstructionsOpen terminal. Make sure you havepython3andpipdownloaded.pip install likabilityCreate a csv file with the names of the people you’d like to analyze for likability. Name this file name.csv in the current directory.Determine how many recent tweets you’d like to query for each person. This will be used in the script below asnum_tweets.Make sure you have Twitter API keys and access tokens. If you do not, go to [Twitter Apps](https://apps.twitter.com/), create an app, and find the required keys and tokens under Applications Settings -> Consumer Key (API Key) -> manage keys and access tokens.Run Python 3 by typingpythoninto the terminal.Enter script below to run the LikabilityAnalyzer module.from likability import LikabilityAnalyzerfilepath = 'name.csv'num_tweets = 100sentimentScore = LikabilityAnalyzer.analyzer(filepath,num_tweets)When prompted, enter in your Twitter API keys. This will allow likability to access the Twitter API to query the tweets needed to complete the sentiment analysis.Wait for script to run to completion. Please note, due to Twitter API Rate Limiting, querying more than 15 names will lead to longer wait times. Please allow 1 minute per name for lists greater than 15 names.Upon completion, open the newly created Sentiment.csv in the current directory to access the likability scores for each person.Possible UsageScore top fantasy football players to see what the Twittersphere thinks about each player pre-draftInstead of names of people, use product names to track customer sentiment in real-timeSolve the question: who is more likable, Justin Timberlake or Jimmy Fallon
Package: lykkellesysmail
This is package that contains all modules needed to run
the database and other configurations
Contents:
1. Lykkelle system email sender
2. parameters are as follows:
sendername="name of sender"
sender_email="sender email"
receivername="Deb Mishra"
receivers=[{"email":"","name":"sender name"}]
subject="Email subject. Not needed if template id 2 is there"
message="Message body for plain test message"
template = possible values- jobname,jobdetails,firstname,password
templateid= possible values - 2,3
Package: lymboy-lstm
Updates2023-03-16Support multivariate time forcast2023-03-08AddCNN_BiLSTM_Attentionmodel中文文档Introducelymboy-lstmcontains several commonly used LSTM models for time series forecasting. Currently only univariate time series forecasting is supported.
Currently built-in models are:LSTMBiLSTMCNN_LSTMCNN_BiLSTMCNN_BiLSTM_AttentionOther models are under study... (CNN_BiLSTM_Attention, Encoder-Decoder Model,Multivariate Time Prediction Support) Please look forward to it~Packaging methodpython./setup.pysdistbdist_wheel
pipinstalldist/lymboy-lstm-[latest-version].tar.gz# Upload to pypi# pip install twine# twine upload dist/*How to install?pipinstalllymboy-lstmHow to use?Taking LSTM model to predict power consumption as an exampleImport libimportpandasaspdimportnumpyasnpfromlstmimportLSTMfromlstm.utilimportplotRead datasetfile='./dataset/power/power_consumption_A.csv'df=pd.read_csv(file,index_col=0)sequence=df.loadModeling# Use the data of the past 96 times to predict the data of the next 10 times in the futuremodel=LSTM(n_steps=96,n_output=10)# Process sequence data as model input, specifying a test set ratio of 20%model.createXY(sequence,test_size=0.2)model.fit(epochs=500,verbose=True)print(model.score())plot(model.y_hat[:,0],model.y_test[:,0])CNN_BiLSTM predicts transformer oil temperatureImport libimportpandasaspdimportnumpyasnpfromlstmimportLSTM,BiLSTM,CNN_BiLSTMfromlstm.utilimportplotRead datasetfile='./dataset/ETT/ETTh1.csv'df=pd.read_csv(file,index_col=0)sequence=df.OTModelingmodel=CNN_BiLSTM(n_steps=96,n_output=24,n_seq=6)model.createXY(sequence)model.fit(epochs=500,verbose=True)print(model.score())Prediction results of LSTM model on multiple data sets (multivariate, multi-step prediction)Parameter Descriptionn_steps: training step size, representing the step size of historical data, intn_output: predicted output length, intn_seq: subsequence, int (note that n_seq should be divisible by n_steps, the minimum is 1)learning_rate: learning rate for Adm, floatOther parameters are consistent withtensorflowError feedbackalayama@163.com
Package: lymph
Lymphlymph is an opinionated framework for Python services. Its features areDiscovery: pluggable service discovery (e.g. backed by ZooKeeper)RPC: request-reply messaging (via ZeroMQ + MessagePack)Events: pluggable and reliable pub-sub messaging (e.g. backed by RabbitMQ)Process ManagementThere’sdocumentationon readthedocs.org.Installation (as a dependency)# py-monotime requires python headers, and gevent and cython require build-essential
$ sudo apt-get install build-essential python-dev$ pip install https://github.com/deliveryhero/lymph.git#egg=lymphDevelopment (of lymph itself)$ git clone https://github.com/deliveryhero/lymph.git
$ cd lymph
$ pip install -r requirements/dev.txtRun tests withtox, build documentation withfab docs.Running servicesTo run the example services, you can use the example node config inconf/sample-node.yml. You’ll also need a local installation
ofZooKeeper(with the configuration provided in theGetting Started Guide) andRabbitMQ:$ export PYTHONPATH=examples
$ cp conf/sample-node.yml .lymph.yml
$ lymph nodeYou can then discover running services:$ lymph discoverand send requests to them from the commandline:$ lymph request echo.upper '{"text": "transform me"}'To see the log output of a running service, try:$ lymph tail echo -l DEBUG
Package: lyncs
A python API for Lattice QCD applicationsLyncs is a Python API for Lattice QCD applications currently under development with a first
released version expected by the end of Q2 of 2020. Lyncs aims to bring several popular
libraries for Lattice QCD under a common framework. Lyncs will interface with libraries for
GPUs and CPUs in a way that can accommodate additional computing architectures as these
arise, achieving the best performance for the calculations while maintaining the same high-
level workflow. Lyncs is one of 10 applications supported by PRACE-6IP, WP8 "Forward
Looking Software Solutions".Lyncs distributes calculations using Dask, with bindings to the libraries performed
automatically via Cppyy. Multiple distributed tasks can be executed in parallel and different
computing units can be used at the same time to fully exploit the machine allocation. The data
redistribution is efficiently managed by the API. We expect this model of distributing tasks to
be well suited for modular architectures, allowing to flexibly distribute
work between the different modules.
While Lyncs is designed to quite generally allow linking to multiple libraries, we will
focus on a set of targeted packages that include tmLQCD, DDalphaAMG, PLEGMA and QUDA.Installation:The package can be installed viapip:pip install [--user] lyncsSub-modules and pluginsSub-modules and plugins can also be installed viapipwith:pip install [--user] lyncs[NAME]where NAME is the name of the sub-module. Hereafter the list of the available sub-modules.Groupsall: installs all the plugins enabling all Lyncs' functionalities (e.g. hmc, visualization, etc..).
Note it does not install libraries with strong dependencies like MPI, GPUs, etc.
Simple CPUs libraries may be installed.mpi: installs all MPI libraries.cuda: installs all NVIDIA GPUs libraries.io: installs all IO libraries for full support of IO formats (clime, HDF5, etc..).LQCD librairesDDalphaAMG: multigrid solver library for Wilson and Twisted mass fermions.QUDA: NVIDIA GPUs library for LQCD.clime: IO library for c-lime format.tmLQCD: legacy code of the Extended Twisted Mass collaboration.Goals:Include several Lattice QCD libraries under a single frameworkProvide crosschecks and benchmarks of different libraries' implementationsHandle memory distribution and mappingAllow for multitasking parallelization and unequal distributionDependencies:Python utils:numpy: Multidimensional arrays in pythondask: Utility for sceduling distributed taskscppyy: Automatic binding to C/C++ libraries(optional) dask-mpi, mpi4py: MPI for python(under consideration) numba: JIT compilation of python codeothers: xmltodict,LQCD libraries:QUDA: Lattice QCD operators and solvers on GPUsDDalphaAMG: Multigrid solver on CPUstmLQCD: HMC routines on CPUsPLEGMA: contraction kernels on GPUsExtras requirements:Jupyter notebook/lab: for visualizing and running the avaialble notebooksdask-labextension: utils for profiling the task execution in Jupyter labFundings:PRACE-6IP, Grant agreement ID: 823767, Project name: LyNcs.Authors:Simone Bacchio, The Cyprus Institute
Package: lyncs-clime
A Python interface to c-lime for LyncsLyncs.clime is a faithful Python interface to c-lime.c-limeis a C-API for creating, reading, writing, and manipulating LIME files
and offers a small set of utilities for examining, packing and unpacking LIME files.LIME (which stands for Lattice QCD Interchange Message Encapsulation or more generally,
Large Internet Message Encapsulation) is a simple packaging scheme for combining records
containing ASCII and/or binary data. Its ancestors are the Unix cpio and tar formats and
the Microsoft Corporation DIME (Direct Internet Message Encapsulation) format.It is simpler and allows record sizes up to $2^{63}$ bytes, making chunking unnecessary
for the foreseeable future. Unlike tar and cpio, the records are not associated with Unix files.
They are identified only by a record-type (LIME type) character string, analogous to the familiar
MIME application type.InstallationThe package can be installed viapip:pip install [--user] lyncs_climeDocumentationThe following classes are available in lyncs_clime: Reader, ...ContributingWhen contributing to the package, clone the source fromgithub:git clone https://github.com/Lyncs-API/lyncs.clime
cd lyncs.climeinstall the package in development mode:pip install -e .[all]and run the test-suite for checking the correctness of the installation:pytest -vIf everything goes well, you should see all the tests passed and obtain a coverage report.A main implementation requirement is anhigh code-coverage.
If you are going to implement something new, please, also add the respective
test files or functions in thetest/directory.Another implementation requirement is toformat the codeviablackand to usepylintfor improving the code standard.These packages can be installed via pip:pip install black pylintBefore any commit, run black from the source directory:black .When you are done with the implementation, try to resolve as many comments/warnings/errors
as possible brought up bypylint:pylint lyncs_climeNOTE:pylint and black are incompatible in few formatting assumptions. Please, ignore
the comments C0303 and C0330 of pylint. If they show up in the files you have edited/added,
please, add the following line after the documentation string at the beginning of the respective files:# pylint: disable=C0303,C0330
Package: lyncs-cppyy
cppyy wrapper for LyncsIn this package we provide some additional features tocppyy.InstallationThe package can be installed viapip:pip install [--user] lyncs_cppyyDocumentationThe main documentation ofcppyyis available athttps://cppyy.readthedocs.io/en/latest/.cppyy is an automatic, run-time, Python-C++ bindings generator, for calling C++ from Python [...]Lyncs_cppyy is an additional layer between cppyy and the interfaces to libraries provided by Lyncs.
In the following we give an overview of the additional functionalities.Lib classIn cppyy, libraries are loaded and used as follow:>>>importcppyy>>>cppyy.include('zlib.h')# bring in C++ definitions>>>cppyy.load_library('libz')# load linker symbols>>>cppyy.gbl.zlibVersion()# use a zlib API'1.2.11'>>>In lyncs_cppyy we have define the class Lib for holding the information on a library. It is used as follow>>>fromlyncs_cppyyimportLib>>>zlib=Lib(header='zlib.h',library='libz')>>>zlib.zlibVersion()'1.2.11'>>>Lib returns a variable that represents the library.
The library is loaded at the first usage of the variable.
In most of the cases, accessing attributes of the variable is like accessing attributed ofcppyy.gbl.
Exceptions are made formacros defined in the headers, e.g.zlib.ZLIB_VERSION -> '1.2.11'functions defined under a namespace which can be directly accessed specifying a namespaceand the following attributes of Lib, e.g.zlib.header -> ['zlib.h'].The list of attributes and initialization options ofLibare:header: string(or list) of the header(s) to be included.library: string(or list) of the shared library(ies) to be loaded.
A library can also be an instance of theLibclass.c_include: if the library is a c library (True) or a c++ library (False, default)namespace: in case of a c++ library, a string(or list) of the namespace(s) to be used.
Equivalent tousing namespace ...in c++. Without, symbols should be access aslib.namespace.*.path: string(or list) of the path(s) to the base directory of the library.
Headers will be searched inPATH/includeand libraries inPATH/lib.include: string(or list) of the directory(ies) to include. Equivalent to-Iused at compile time.defined: dictionary of aliases that act as defined macros.Other functionsloaded_libraries(): Returns the list of loaded librariesto_pointer(ptr, ctype="void *"): Converts an integer (ptr) to cppyy pointerCppType(ctype): Generates a base class that allows for automatic type conversion calling
the look-up method__cppyy__. See its doc for more details.array_to_pointers(arr): Creates a c-style array of pointers to be used for accessing the elements
of a numpy-like array.
Package: lyncs-DDalphaAMG
A Python interface to the DDalphaAMG multigrid solver libraryThis package provides a Python interface to DDalphaAMG.DDalphaAMGis a solver library for inverting Wilson Clover and Twisted Mass fermions from lattice QCD.
It provides an implementation of an adaptive aggregation-based algebraic multigrid ($\alpha$AMG) method.InstallationNOTE: lyncs_DDalphaAMG requires a working MPI installation.
This can be installed viaapt-get:sudoapt-getinstalllibopenmpi-devopenmpi-binOR usingconda:condainstall-canacondampi4pyThe package can be installed viapip:pipinstall[--user]lyncs_DDalphaAMGDocumentationThe functions provided by theDDalphaAMG APIare available in the Solver classlyncs_DDalphaAMG.Solver.
Please, usehelp(Solver)to see an overview of the functionalities.
In the following we present some examples on the usage of the package.fromlyncs_DDalphaAMGimportSolver# Creating the solversolver=Solver(global_lattice=[4,4,4,4],kappa=0.125)# Reading the configurationsconf=solver.read_configuration("test/conf.random")plaq=solver.set_configuration(conf)print("Plaquette:",plaq)# Computing the solution of a random vectorvector=solver.random()result=solver.solve(vector)The above example is run locally and all the fields are numpy arrays.The Solver class is also Dask compatible and by giving a distributed communicator,
as in the following example, the solver instance is now distributed remotely on
the dask workers and all the fields are Dask arrays.fromlyncs_mpiimportClient# Creating a client with 4 workersclient=Client(num_workers=4)comm=client.create_comm()procs=[2,2,1,1]comm=comms.create_cart(procs)solver=Solver(global_lattice=[4,4,4,4],comm=comm,kappa=0.125)# Continues as above
Package: lyncs-mpi
Utils for interfacing to MPI libraries using mpi4py and daskThis package provides tools for interfacing to MPI libraries based onmpi4pyanddask:mpi4pyis a complete Python API of the MPI standard.daskis a flexible library for parallel computing in Python.
Particurally, we use the following sub-modules of the latter:dask.distributedfor distributed computing,dask.arrayfor distributed numpy arrays anddask_mpifor interfacing to MPI.InstallationNOTE: lyncs_mpi requires a working MPI installation.
This can be installed viaapt-get:sudo apt-get install libopenmpi-dev openmpi-binOR usingconda:conda install -c anaconda mpi4pyThe package can be installed viapip:pip install [--user] lyncs_mpiDocumentationIn this package we implement several low-level tools for supporting classes distributed over MPI.
These are described in thisguidefor developers. In the following we describe the high-level tools
provided in this package.ClientThe Client is a wrapper ofdask.distributed.Clientmade MPI compatible following the instructions
ofdask-mpidocumentation.fromlyncs_mpiimportClientclient=Client(num_workers=4)If the above script is run in a interactive shell, the Client will start an MPI server in the background
running overnum_workers+1processes. The workers are the effective processes involved in the calculation.
The extra process (+1) is the scheduler that will manage the task scheduling.The client, the interactive shell in this example, will proceed processing the script: submitting tasks to
the scheduler that will run them on the workers.The same script can be run directly viampirun. In this case one needs to executempirun-n$((num_workers+2))pythonscript.pythat will run onnum_workers+2processes (as above +1 for the scheduler and +1 for the client that processes the script).CommunicatorsAnother feature that makelyncs_mpi.ClientMPI compatible is the support of MPI communicators.comm=client.commcomm1=client.create_comm(num_workers=2)comm2=client.create_comm(exclude=comm1.workers)In the example,comm = client.commis the main MPI communicator involving all the workers.
The secondcomm1and thirdcomm2communicators, instead, are communicators over 2 workers each.
The first two workers have been optimally chosen by the client, the other two instead are the remaining
one excluding the workers ofcomm1.Another kind of communicators are Cartesian MPI communicators. They can be initialized doingcart=comm.create_cart([2,2])where[2,2]are the dimensions of the multi-dimensional grid where the processes are distributed.Cartesian communicators directly supportDask arraysand e.g.cart.zeros([4,4,3,2,1])instantiates a distributed Dask array assigned to the workers
of the communicator with local shape (chunks)(2,2,3,2,1).
Package: lyncs-setuptools
Setup tools for Lyncs and CMake supportIn this package we provide a wrap around the standard setutools to be used in Lyncs projects.InstallationThe package can be installed viapip:pip install [--user] lyncs_setuptoolsUsageLyncs setuptools automizes the deduction of many setup.py options.Its use in asetup.pyfile is the followingfromlyncs_setuptoolsimportsetupsetup(package_name,**kwargs)where package_name is the name of the package and kwargs are a list of arguments additional or replacement of the one automatically deduced.NOTE:For seeing the list of the automatically deduced options, runlyncs_setuptoolsfrom command line in the directory containing the filesetup.py.NOTE:for correctly installing your package viapip, you need to add a file namedpyproject.tomlwith the following content.[build-system]
requires = ["lyncs_setuptools", ]Add"cmake"to the requirements list if you use the following CMake extension.CMakeExensionBased onhttps://www.benjack.io/2017/06/12/python-cpp-tests.htmlwe provide a CMakeExtension to support CMake files.A CMakeExtension can be added as followfromlyncs_setuptoolsimportsetup,CMakeExtensionext=CMakeExtension(install_dir,source_dir='.',cmake_args=[])setup(package_name,ext_modules=[ext])Setup parametersThe following are the parameter used by default in the setupAutomatically deduced:author: (git) author of first commitauthor_email:(git) email of author of first commitversion:(python) value of__version__defined in__init__.pyurl:(git) remote address of originproject_urls:(git) defines Source, Documentation, Tracker, Downloaddescription:(file) first title of the READMElong_description:(file) content of the READMElong_description_content_type:(file) type of README (md/rst)classifiers:(partially)version:Planning(<0.1.0),Pre-Alpha(<0.3.0),Alpha(<0.6.0),Beta(<1.0.0),Production/Stable(<3.0.0),Mature(>=3.0.0)license: TODOkeywords:(>3 chars or capital) words in descriptionDefaulted valuesclassifiers:python 3-only, science
Package: lyncs-tmLQCD
A Python interface to tmLQCD for LyncstmLQCDis the simulation library used by the Extended Twisted Mass Collaboration (ETMC).
tmLQCD is a freely available software suite providing a set of tools to be used in lattice QCD
simulations. This is mainly a (P/R)HMC implementation for Wilson and Wilson twisted mass fermions
and inverter for different versions of the Dirac operator.The code is fully parallelised and ships with optimisations for various modern architectures,
such as commodity PC clusters and the Blue Gene family.InstallationThe package can be installed viapip:pip install [--user] lyncs_tmLQCDExternal dependenciesFor compiling tmLQCD, a fortran compiler, flex, openblas and lapack are required.These can be installed viaapt:apt install -y flex libopenblas-dev liblapack-dev gfortranOR usingconda:conda install -c anaconda openblas
conda install -c conda-forge flex lapack fortran-compilerDocumentation
Package: lyngdorf
lyngdorfAutomation Library for Lyngdorf receiversInstallationUse pip:$ pip install lyngdorfor$ pip install --use-wheel lyngdorfUsagePrimarily designed to be used by Home Assistant, this library can be used as follows:>>> from lyngdorf.mp60 import LyngdorfMP60Client
>>> mp60 = LyngdorfMP60Client("192.168.16.16")
>>> await mp60.async_connect()
>>> mp60.power_on(True)
>>> print(mp60.volume)
-36.5
>>> mp60.volume=-22.5
>>> print(mp60.volume)
--22.5
Package: lynkz
A python wrapper for thehttps://lynkz.meapiUsageimportlynkzoutput=lynkz.shorten("https://your.website/here")# output now is an array that looks like this:# ["https://lynkz.me/ID-HERE", "YOUR DELETE KEY"]# To delete the shortend link:lynkz.delete("https://lynkz.me/ID-HERE","YOUR DELETE KEY")# do NOT put a slash after the link id# ORlynkz.delete(output[0],output[1])# if you still have the origional array