Saravanan666/pypi-org huggingface dataset:
Package: dieboldmariano
dieboldmarianoThis package provides a simple, pure-Python implementation of the Diebold-Mariano statistical test. It has no dependencies outside the Python standard library.What is the Diebold-Mariano test?Assume we have a real-valued timeseries $T_{n \ge 0}$, and two forecasters
$F$, $G$ who each produce predictions $F_{n \ge 0}$, $G_{n \ge 0}$. Which
one is better? Is the difference statistically significant?The cost function for errors can be specified arbitrarily and does not
need to be symmetric (for example, one might want to penalize errors in one
direction more).An usual choice (the default in this implementation if no cost function
is passed explicitly) is the square error.The test produces the DM statistic and the corresponding p-value.A more detailed and very accessible description is available at the link
in the references section.Code example>>> from dieboldmariano import dm_test
>>> T = [10, 20, 30, 40, 50]
>>> F = [11, 21, 29, 42, 53]
>>> G = [13, 26, 24, 40, 59]

>>> dm_test(T, F, G, one_sided=True)
(-2.2229922805746782, 0.04515565862099125)ReferencesDiebold, F.X. and Mariano, R.S. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253-263.Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of forecasting, 13(2), 281-291.https://www.real-statistics.com/time-series-analysis/forecasting-accuracy/diebold-mariano-test/
Package: diebold-mariano-test
Diebold Mariano Test 工具包特点通过DM检验的结果，可以判别任意两类算法的预测结果是否存在差异性。安装pipinstalldiebold-mariano-test更新pipinstall--upgradediebold-mariano-test使用说明>>># 导入包>>>fromdiebold_mariano_testimport*

>>>ori_list=[3,5,4,1]# 原始时间序列数据>>>a1_list=[2,3,4,2]# 预测算法一得到的时间序列数据>>>a2_list=[3,2,2,4]# 预测算法二得到的时间序列数据>>># 使用MSE方法计算 Diebold-Mariano Test.>>>d_t_list=cul_d_t(MSE,ori_list,a1_list,a2_list)>>>print(d_t_list)# 输出中间变量d_t的数值。[1,-4,-8,-16]>>>cul_DM(d_t_list)# 输出结果-2.254898764780173
>>>cul_P(d_t_list)# 输出伴随的P值0.024139683878213303

>>># 使用MAE方法计算 Diebold-Mariano Test.>>>d_t_list=cul_d_t(MAE,ori_list,a1_list,a2_list)>>>print(d_t_list)# 输出中间变量d_t的数值。[1,0,-2,-4]>>>cul_DM(d_t_list)# 输出结果-1.4213381090374029
>>>cul_P(d_t_list)# 输出伴随的P值0.155218489684684说明项目发布于PyPI：单击访问本模块尚不完善，有任何相关问题或建议，欢迎在本项目的github页面中提交issue:单击访问LicenseGNU General Public License v3.0
Package: diecutter
diecutteris a web application around file generation:templates are the resources ;the most common operation is toPOST data to templates in order to retrieve
generated files.diecuttercan render single files and directories. Directories are rendered
as archives.ExampleGETraw content of a template:$ curl -X GET http://diecutter.io/api/greetings.txt
{{ greetings|default('Hello') }} {{ name }}!POSTdata to the template and retrieve generated content:$ curl -X POST -d name=world http://diecutter.io/api/greetings.txt
Hello world!Project statusAlthough under active development,diecutteralready works, sogive it a
try!.Checkmilestonesandvisionfor details about the future.Also notice thatdiecutteris part of an ecosystem:piecutteris the core Python API. It provides stuff like template engines
or template loaders.diecutterimplements a WSGI application and REST interface on top ofpiecutter.diecutter-indexis a
proof-of-concept project for an online template registry.http://diecutter.iois the SAAS platform runningdiecutterecosystem.See alsoalternatives and related projectssection in documentation.ResourcesOnline demo:http://diecutter.ioDocumentation:http://diecutter.readthedocs.orgPyPI page:http://pypi.python.org/pypi/diecutterBugtracker:https://github.com/diecutter/diecutter/issuesChangelog:https://diecutter.readthedocs.org/en/latest/about/changelog.htmlRoadmap:https://github.com/diecutter/diecutter/issues/milestonesCode repository:https://github.com/diecutter/diecutterContinuous integration:https://travis-ci.org/diecutter/diecutterIRC Channel:irc://irc.freenode.net/#diecutter
Package: diefpy
Philipp D. Rohde, Nikoleta ThemeliotoudiefpyPython package for computing diefficiency metricsdief@tanddief@k.The metricsdief@tanddief@kallow for measuring the diefficiency during
an elapsed time periodtor whilekanswers are produced, respectively.dief@tanddief@krely on the computation of the area under the curve (AUC) of
answer traces, and thus capturing the answer rate concentration over a time interval.This fork of theoriginal diefpy repoby Maribel Acosta provides a complete Python3 version.DescriptionFigure 1: Overview of Result Plots.Fig. 1 gives an overview of the result plots that can be produced using the package.
Firstly, the overallExecution Timefor all the tests and approaches (NotAdaptive, Random and Selective) in the metrics
data can be created as a bar plot.
For evaluating the input tests an answer trace of each approach (NotAdaptive, Random and Selective) can be created which shows how many answers were produced.
Finally, two Radar Plots can be created. The Radar Plot on the left comparesdief@twith other benchmark metrics in a specific test. The other benchmark metrics being conventional metrics liketotal execution time,time for the first tuple,throughput, andnumber of answers produced.
The Radar Plot on the right comparesdief@kat different answer completeness percentages in a specific test by measuring the continuous efficiency of approaches when producing
the first 25%, 50%, 75%, and 100% of the answers.InstallationYou can build and install diefpy from sourcegitclonegit@github.com:SDM-TIB/diefpy.gitcddiefpy
python-mpipinstall-e.or downloading it from PyPI:python-mpipinstalldiefpyNotice:Most likely you want to install diefpy into a virtual environment for the experiments you were running.UsageWe refer the user to thedocumentationof the library for a detailed explanation of the implemented functionality.
The page also includes someexamples.
Additionally, there is an iPython notebook in theexamplefolder that demonstrates the use of the diefpy library.Publications[1] Maribel Acosta, Maria-Esther Vidal, York Sure-Vetter. Diefficiency Metrics: Measuring the Continuous Efficiency of Query Processing Approaches. In Proceedings of the International Semantic Web Conference, 2017. Nominated to Best Paper Award at the Resource Track.https://doi.org/10.1007/978-3-319-68204-4_1[2] Maribel Acosta, Maria-Esther Vidal. Measuring the Performance of Continuous Query Processing Approaches with dief@t and dief@k. In  the International Semantic Web Conference, Posters and Demos, 2017.online
Package: dielectric
A Python package to assist in the study of sedimentation phenomena in
colloidal suspensions. The code is being developed byPaul J.M. van
Kanfor the MUDNET group at Delft
Technical University and DELTARES, The Netherlands. Thedielectricpackage
can be used under the conditions of the GPLv3 license.FeaturesBasic classes for binary electrolytes.Basic classes for colloidal systems with particles.Basic functions for DLVO theory.basic functions for calculation of ion vibration potential (IVP).InstallationTo use the packagedielectric, install it in a Python environment:(env)pip install dielectricor(env)conda install dielectricThis should
automatically install the dependency packagesmatplotlib,scipyandpandas, if they haven’t been installed already. If you are
installing by hand, ensure that these packages are installed as well.Example useimportnumpyasnpfromdielectric.constantsimport*fromdielectric.electrolytesimportElectrolytefromdielectric.particlesimportParticlefromdielectric.file_utilsimportload_configimportmatplotlibimportmatplotlib.pyplotaspltfrompathlibimportPathmatplotlib.use("Qt5Agg")DATADIR=Path(__file__).parentel=Electrolyte.from_yaml(str(DATADIR/"cegm.yaml"))el.calc_kappa()param=load_config(str(DATADIR/"cegm.yaml"))part=Particle.from_dict(param["particles"][0])zeta_zero=BOLTZ_T/(el.z_plus*E_CHARGE)print(f"zeta_zero:{1000*zeta_zero}mV")psi_zero=0.5*zeta_zerodielectric pagesPyPi: dielectric Python packageBitBucket: dielectric source codeReadTheDocs: dielectric documentationAuthor and licenseAuthor: Paul J.M. van KanContact:pjm@vankanscientific.nlLicense:GPLv3References…
Package: diem
The official Diem Client SDK for Python.API ReferencePypi packagehttps://pypi.org/project/diem/Examples>>>fromdiemimportjsonrpc,testnet>>>client=jsonrpc.Client(testnet.JSON_RPC_URL)>>>client.get_metadata()version:3300304timestamp:1601492912847973chain_id:2You can find more examples under theexamplesdirectory:Create Child VASP accountAll Types Peer To Peer TransferIntent IdentifierNote:make testruns all examples too, see the Makefile for details.Off-chain service exampleCheckoutMiniWalletimplementation for off-chain service implementation example.MiniWallet and MiniWallet Test SuiteSeemini_wallet.mdBuild & Testmake init
make testrun specific test:make test t=<test file / test name match pattern>run with local docker testnet (requires initializing diem submodule):make test t=<test file / test name match pattern> dt=1Re-generate diem_types, stdlib, jsonrpc response data structuresgit submodule update --init diem
cd diem
git pull origin main
cd ..
make genModules OverviewSPEC = specificationDIP-X = Diem Improvement ProtocolRoot module name:diemSub-modules:jsonrpc: diem JSON-RPC APIs client and API response types.SPECstdlib: generated code, move stdlib script utils for constructing transaction script playload.diem_types: generated code, Diem on-chain data structure types for encoding and decodingBCSdata.utils: utility functions, account address utils, currency code, hashing, hex encoding / decoding, transaction utils.AuthKey|auth_key: auth key utilsidentifier: Diem Account Identifier and Diem Intent Identifier.DIP-5txnmetadata: utils for creating peer to peer transaction metadata.DIP-4testnet: Testnet utility, minting coins, create Testnet client, chain id, Testnet JSON-RPC URL.testing: Testing utility, MiniWallet application, MiniWallet test suites,LocalAccountfor managing local account keys and generating random local account.chain_ids: list of static chain ids
Package: diematic-client
diematic_clientA Python module to consume information from diematic_server.Software requirementsA running instance of diematic_server. In my case I've it running in a Raspberry PI 3 connected to the DeDietrich boiler using the RS-485 port. See the documentation for the diematic_server repositoryThe documentation for the diematic_server project is in a separate repository and it is also a pipy package available to download and use.This package has been created to isolate the functionality to access to the server and allow for the integration of components inside home assistant.Installationpip install diematic_clientIf you are installing the home assistant components (At this time, they are WIP) they should install this package automaticallyReferencesThis project has been created following the original structure of the IPP printer module
Package: diepvries
diepvriesWelcome todiepvries, a Python library to generateData VaultSQL statements.What does it do?diepvriestakes as input a Data Vault model (a list of hubs, links and satellites tables),
and generates SQL statements to load data in those tables. For that purpose, it relies
on naming conventions for tables and columns. There are 2 ways to feed a Data Vault
model todiepvries:Either declaratively, by enumerating tables and columns;Or automatically, by building these structures using the database metadata.At the moment,diepvriesis only compatible withSnowflake.diepvriesworks with Python 3.8, 3.9, and 3.10.Getting starteddiepvriesis distributed as a Python wheel on PyPI. In a virtual environment, you can
grab the latest version by running:pipinstalldiepvriesand in a Python console:fromdiepvries.hubimportHubhelp(Hub)If you see the help page for theHubclass, you're all set! :rocket:Continue the journeyThe best way to start usingdiepvriesis by readingits documentation website. You'll find a tutorial, a
list of naming conventions, and more!ContributingWant to fix a bug, improve the docs, or add a new feature? That's awesome! Please read
thecontributing document.ChangelogYou can find the changelog of this package inCHANGELOG.md.
Package: diesel
diesel is a framework for easily writing reliable and scalable network
applications in Python.  It uses the greenlet library layered atop
asynchronous socket I/O in Python to achieve benefits of both
the threaded-style (linear, blocking-ish code flow) and evented-style
(no locking, low overhead per connection) concurrency paradigms.  It’s
design is heavily inspired by the Erlang/OTP platform.It contains high-quality buffering, queuing and synchronization primitives,
procedure supervision and supervision trees, connection pools, seamless
thread integration, and more.An HTTP/1.1+WSGI+WebSockets implementation is included, as well as tight
integration with the Flask web framework.Other bundled protocols include MongoDB, Riak, and Redis client libraries.
Package: diet
DIETDash Import Export ToolInstallationpipinstall--user-UdietUsageDash Import and Export Tool

positional arguments:
  {import,export}     Action to perform
  {users-orgs,users}  The target of the import/export
  file                Files to import

optional arguments:
  -h, --help          show this help message and exitImport users into the systemdietimportusers./user-records.csvConfigurationThis file must be present for the application to operateAll configuration settings are stored in ~/.diet/config.json{"auth":{"client_id":"","client_secret":"","host":"","verify_ssl":true},"api":{"client_id":"","client_secret":"","host":"","verify_ssl":true}}The top level property determines the service to configure, currently there are two services,apiandauth. Api is the main application services and Auth is Sporga's Authenication service.SettingDescriptionclient_idThe client id to authenticate withclient_secretThe client secret to authenticate withhostThe url of the service to import/exportverify_sslEnable/disable SSL certificate verifcationImport file structureSupported file typesCSVCSV StructureThe CSV file must include a header row that matches the field's name you wish to importexactlyUser ImportfirstNamesurnameemailgenderaddresssuburbstatemobilePhoneNumberpasswordpostcodeTestAdministratorcsss@sporga.com.auFemale20 resolution DrCaringbahNSW61410100100*******2229
Package: dieterpy
DIETERpy is electricity market model developed by the research groupTransformation of the Energy EconomyatDIW Berlin(German Institute of Economic Research).The open-source power sector optimization model“Dispatch and Investment Evaluation Tool with Endogenous Renewables” (DIETER)has been developed to investigate the role of electricity storage and sector coupling options in future scenarios with high shares of renewable energy sources.DIETERpy is a Python-based tool that enables an easy pre- and post-processing of the model data, sophisticated scenario analysis, and visualization of results. The optimization routine of DIETERpy is based on the General Algebraic Modeling System (GAMS) of DIETER, which is now maintained separately as a GAMS-only version calledDIETERgms.InstallationDIETERpy is distributed onPyPIand can installed withpip:$pipinstalldieterpyRead the documentation to learn how to properlyinstallDIETERpy.Configuration and running the modelPlease consult our fulldocumentationon how to configure and run DIETERpy.LinksDocumentation:https://diw-evu.gitlab.io/dieter_public/dieterpySource code:https://gitlab.com/diw-evu/dieter_public/dieterpyIssues:https://gitlab.com/diw-evu/dieter_public/dieterpy/issuesPyPI releases:https://pypi.org/project/dieterpyLicense:http://opensource.org/licenses/MITAuthorsThe developers areCarlos Gaete-Morales (lead), Alexander Roth and Martin Kittel, and Wolf-Peter Schill, in collaboration with Alexander Zerrahn.ApplicationsDIETER has been used for numerous publications, both by DIW and external researchers. Please have a look at ourApplicationssection of our documentation for a full list ofDIWas well asexternalpapers and projects. There, we provide detailed descriptions and the DIETER version used in the respective projects.LicenseDIETERpy is an open source tool which code may be freely used and modified by anyone. The code is licensed under the MIT License and availablehttps://gitlab.com/diw-evu/dieter_public/dieterpy.DIETER is an open source model which may be freely used and modified by anyone. The code is licensed under the MIT License. Input data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License and available underhttp://www.diw.de/dieter.
Package: diet-video
Video dietThis project aims to reduce the spaces of your videos and audios encoding it onhevc.Why video-diet?The answer is easy. I have a lot old-movies/videos and music/audios taking a lot of space in the hard-drive.
So I'm always short on disk space, the by accident discover dehevccodec. when i need to shrink a video of3GBto upload it toTelegram, the convertion take my 3GB movie and returned a 300 MB with the same quality 😱. So I
decided that I would convert all my video and audio files , but they are a lot, so I build this tool for it.More info abouthevc:https://en.wikipedia.org/wiki/High_Efficiency_Video_CodingInstallation$pipinstallvideo-dietFFMPEGIn order to run the project you must installffmpeg.For LinuxIn any linux machine you can get it from your favorite package manager.For arch:sudo pacman -S ffmpegFor Debian/Ubuntu:sudo apt-get install ffmpegFor WindowsDownload a windows ffmpeg build herehttps://ffmpeg.org/download.html. Unzip it and change the folder name toFFmpeg. Copy the folder intoC:\and then add the pathC:\FFmpeg\binto the enviroment variables of the system.To check correct instalation open a new instance of cmd and type:ffmpeg --version`.ExampleFor a filevideo-dietfiletest.mp4This option conserve the original fileFor a foldervideo-dietfolder~/VideosThis option replaces the original file for the converted filesIgnoring files on the foldervideo-dietfolder~/Videos--ignore-extension.mp4This option ignores all the .mp4 files on ~/Videosvideo-dietfolder~/Videos--ignore-path~/Videos/subfolderThis option ignores all the files on ~/Videos/subfolderAV1 SupportTo use AV1 instead of hevc add --codec av1 to the commnads above and that will use libaom-av1 instad of libx265 to encode the filesNoteThe video conversion can take some time. Depending on the original video properties; the conversion time can be longer than the video.For developersYou must first installpoetryPoetry provides a custom installer that will installpoetryisolated from the rest of your system by vendorizing its dependencies. This is the recommended way of installingpoetry.osx / linux / bashonwindows install instructionscurl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | pythonwindows powershell install instructions(Invoke-WebRequest -Uri https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py -UseBasicParsing).Content | pythonThe installer installs thepoetrytool to Poetry'sbindirectory. On Unix it is located at$HOME/.poetry/binand on Windows at%USERPROFILE%\.poetry\bin.This directory will be in your$PATHenvironment variable, which means you can run them from the shell without further configuration.Then you need to configure the environmentInside the project makepoetry installand afterpoetry shellfor start the virtualenv.For testing the code runvideo-diet.SeeCONTRIBUTING.mdfor more details.Good luck 😉.Project Structure based on awesome tutorial by @tiangolo athttps://typer.tiangolo.com/tutorial/package
Package: difai
Did I forget any imports?DIFAI searches for import statements for all the python and jupyter notebook files in the current directory. It then usespip freezeto get your installed versions andpip-compileto generate arequirements.txtfile containing all of your dependencies and their depdendencies including hashes for a reproducible build.RunSimply calldifaiin the current folder.
You can change the input (where the.pyand.ipynbfiles are read) and output (where therequirement.inandrequirements.txtfiles are written) folders using--in-pathand--out-pathrespectively.
In order to exclude certain packages from the search, you can use the--excludeoption.Pipelinegraph TB
    A[glob] --> B
    A --> C
    B[.py] --> D
    C[.ipynb] -->|nbconvert| B
    D[AST]  --> E
    X[pip freeze] --> E
    E[requirements.in] -->|pip tools| F
    F[requirements.txt]
Package: difconnect
:purple_square: Getting StartedDifconnectis the Service that parse website for data and connect to Diffusion Server to extract data from it.Requirementsasyncioanddiffusionlibraries are required.InstallingInstall viapippip install difconnect:purple_square: UsageRun service with arguments:getto get information from serveradd arguments to run for specific url, race etc:purple_square: DeploymentSet up.envfile for parametersSERVER_URL=wss://diffusion.some_site.com
BOOKMAKERS=AA|BB|CC
RACE_URL=https://www.some_site.com/Edit Dockerfile and spicify arguments to run serviceBuild imagedocker build -t difconnect .Run imagedocker run -it --env-file .env difconnect:purple_square: VersioningUsingSemVerfor versioning. For the versions available, see thetags on this repository.:purple_square: AuthorsZakhar Bengart-Initial work-ZeSee also the list ofcontributorswho participated in this project.:purple_square: LicenseThis 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 - see theLICENSEfile for details
Package: difdyf
difdyfcalculate differential geometry contentdifdyf是一个计算不同度规场，坐标系下各种张量的库更多功能正在添加中使用方式import difdyf as df
g,symb=df.sphereMetric()## 生成球坐标度规场g，symb=【'r','theta','phi'】
T=df.getChristSymb(g,symb)## 生成球坐标下克氏算符
R=df.getRiemannCurvature(T,symb)## 生成球坐标表示的R3空间黎曼曲率张量，和正交坐标表示的R3曲率一样，为0
##输入度规场g可任意指定，需要是一个2维字符串值的字典，例如g['r']['r']='1'内部函数df.sphereMetric()## 生成球坐标度规场g，symb=【'r','theta','phi'】
df.orthMetric(n)## 生成n维正交度规场g，symb=【'x0','x1',... ,'xn'】
df.getChristSymb(g,symb)## 生成度规场下的克氏算符
df.getRiemannCurvature(T,symb)## 生成黎曼曲率
df.getRici(T,symb)##里奇张量
df.coord2MetricFromOrth(n,coordList,varlist)## 生成不同坐标系的度规场
                                               n为空间维度（整数）
                                               coordList为坐标表示式 i.e.['r*cos(theta)*cos(phi)','r*cos(theta)*sin(phi)','r*sin(theta)']
                                                    需要是一个n维列表，列表每一个字符串依次代表x0，x1,..., xn在新坐标系中的表示式
                                               varlist为新坐标名字 i.e. ['r','theta','phi']
df.plotFromCoord(coord,symb,ranges)# 生成指定坐标系（3维）在正交坐标下的象
                                    coord=['r*cos(theta)*cos(phi)','r*cos(theta)*sin(phi)','r*sin(theta)']
                                    var=['r','theta','phi']
                                    ranges：坐标范围
                       i.e. df.plotFromCoord(coord,var,[[1],np.linspace(-1.57,1.57,40),np.linspace(0,6.28,40)])安装$ pip install difdyf
Package: diff-1c
Утилита для сравненияepf-,erf-,ert- иmd-файловЧто делаетФайлы разбираются с помощью пакетаparse-1c-buildв каталоги, которые затем сравниваются указанной в аргументах
командной строки утилитой сравнения. Поддерживаются AraxisMerge, ExamDiff, KDiff3, WinMerge.При установке пакета в каталоге скриптов интерпретатора Python создаётся исполняемый файлdiff1c.exe.ТребованияWindowsPython 3.7 и выше. Каталоги интерпретатора и скриптов Python должны быть прописаны в переменной окружения PathПакетparse-1c-buildс необходимыми настройками