Saravanan666/pypi-org huggingface dataset:
Package: openi-test
OpenIPYPI package for 启智AI协作平台。使用说明安装启智平台提供的Python工具包，使用户能在本地上传数据集。用户可以获取镜像内存放代码，数据集，模型，输出结果的路径可以使用公共函数实现数据集的拷贝，模型的上传等使用说明：使用前请在平台个人设置中获取token：点击跳转token获取界面当前版本为了解决用户上传数据集的需求，建议在本地使用。后续版本将适配代码仓配置、隐藏token及云脑任务。安装适配python3.6及以上版本PYPI package for 启智 AI 协作平台。安装适配 python3.6 及以上版本pip3install-Uopeni_test
pipinstallopeni-test==0.5.1-ihttps://pypi.tuna.tsinghua.edu.cn/simple本地上传数据集示例dataset.upload_file(file, username, repository, token, cluster = "NPU")平台数据集的上传与下载提供命令行与代码使用具体请参考帮助文档-API参考filestr, 必填：文件路径(包含文件名，支持linux与mac路径，也支持windows文件路径如d:\xxx)usernamestr, 必填：上传数据集所属项目owner用户名，可以是用户或组织repositorystr, 必填：数据集所属项目路径，此为仓库地址中的名字，更改过名称的项目需填写仓库地址中的路径tokenstr, 必填：用户启智上获取的令牌token，并对该数据集有权限clusterstr, 选填, 默认"NPU"：可填入GPU或NPU，上传至不同的存储集群fromopeni.datasetimportupload_fileupload_file(file="",# 必填，文件路径(包含文件名)username="",# 必填，数据集所属项目用户名repository="",# 必填，数据集所属项目名token="",#必填，用户启智上获取的令牌token，并对该项目数据集有权限cluster="",# 选填，可填入GPU或NPU，不填写后台默认为NPUapp_url=""#选填, 默认为平台地址，用户不用填写，开发测试用file="",username="",repository="",token="",cluster="")界面fromopeni.datasetimportdownload_filedownload_file(file="my_data.zip",repo_id="user1/repo1",cluster="gpu",save_path="local_path/",)""" outputComplete( my_data.zip)(gpu): 100%|██████████████████████████████████████████| 22.0MB/22.0MB [00:01<00:00, 15.9MB/s]""">>>openi
usage:openi{login,whoami,dataset,...}[<args>][-h]OpenIcommandlinetool启智AI协作平台命令行工具

commands:{login,logout,whoami,dataset,d,model,m}login使用令牌登录启智并保存到本机logout登出当前用户并删除本地令牌文件whoami查询当前登录用户dataset(d){upload,download}上传/下载启智AI协作平台的数据集model(m){upload,download}上传/下载启智AI协作平台的模型>>>openilogin██████╗██████╗███████╗███╗██╗██████╗██╔═══██╗██╔══██╗██╔════╝████╗██║██╔═╝██║██║██████╔╝█████╗██╔██╗██║██║██║██║██╔═══╝██╔══╝██║╚██╗██║██║╚██████╔╝██║███████╗██║╚████║██████╗╚═════╝╚═╝╚══════╝╚═╝╚═══╝╚═════╝


点击链接获取令牌并复制粘贴到下列输入栏https://openi.pcl.ac.cn/user/settings/applications[WARNING]若本机已存在登录令牌，本次输入的令牌会将其覆盖粘贴前请先按退格键⇦删除多余空格🔒token:云脑资源初始化与上传，获取路径示例：#导入包
from openi.context import prepare, upload_openi

#初始化导入数据集和预训练模型到容器内
openi_context = prepare()

#获取数据集路径，预训练模型路径，输出路径
dataset_path = openi_context.dataset_path
pretrain_model_path = openi_context.pretrain_model_path
you_must_save_here = openi_context.output_path

#回传结果到openi
upload_openi()
Package: openitesthi
OpenI PyPiPYPI package for 启智AI协作平台。使用说明安装启智平台提供的Python工具包，使用户能在本地上传数据集。用户可以获取镜像内存放代码，数据集，模型，输出结果的路径可以使用公共函数实现数据集的拷贝，模型的上传等使用说明：使用前请在平台个人设置中获取token：点击跳转token获取界面当前版本为了解决用户上传数据集的需求，建议在本地使用。后续版本将适配代码仓配置、隐藏token及云脑任务。安装适配python3.6及以上版本pipinstall-Uopeni-test本地上传数据集示例dataset.upload_file(file, username, repository, token, cluster = "NPU")上传本地单个文件到启智平台数据集，支持断点续传filestr, 必填：文件路径(包含文件名，支持linux与mac路径，也支持windows文件路径如d:\xxx)usernamestr, 必填：上传数据集所属项目owner用户名，可以是用户或组织repositorystr, 必填：数据集所属项目路径，此为仓库地址中的名字，更改过名称的项目需填写仓库地址中的路径tokenstr, 必填：用户启智上获取的令牌token，并对该数据集有权限clusterstr, 选填, 默认"NPU"：可填入GPU或NPU，上传至不同的存储集群fromopeni.datasetimportupload_fileupload_file(file="",# 必填，文件路径(包含文件名)username="",# 必填，数据集所属项目用户名repository="",# 必填，数据集所属项目名token="",#必填，用户启智上获取的令牌token，并对该项目数据集有权限cluster="",# 选填，可填入GPU或NPU，不填写后台默认为NPUapp_url=""#选填, 默认为平台地址，用户不用填写，开发测试用file="",username="",repository="",token="",cluster="")界面如下：GPU，GCU资源获取路径函数及示例：#公共函数
from openi.path import (
    get_code_path,
    get_data_path,
    get_pretrain_model_path,
    get_output_path,
    push_output_to_openi,
)
#使用方法：
code_path = get_code_path()
data_path = get_data_path()
pretrain_model_path = get_pretrain_model_path()
output_path = get_output_path()
push_output_to_openi = push_output_to_openi()NPU资源使用公共函数及示例#公共函数
from openi.cloudbrain import (
    openi_multidataset_to_env  启智集群任务将数据集拷贝到训练镜像
    c2net_multidataset_to_env  智算集群任务将数据集拷贝到训练镜像
    pretrain_to_env            将预训练模型文件拷贝到训练镜像
    env_to_openi               将训练镜像的输出结果拷贝回启智平台
    obs_copy_file              通过mox拷贝文件
    obs_copy_folder 	   通过mox拷贝文件夹
)
#使用方法：
openi_multidataset_to_env(args.multi_data_url, data_dir)
c2net_multidataset_to_env(args.multi_data_url, data_dir)
pretrain_to_env(args.pretrain_url, pretrain_dir)
env_to_openi(train_dir,args.train_url)
obs_copy_file(obs_path_file,local_path_file)
obs_copy_folder(obs_path,local_path)test
Package: openj
openjKanban for Lean manufacturing (also called
just-in-time manufacturing, abbreviated JIT).InstallPyPIInstall and update using pip:pipinstall-UopenjRepositoryWhen using git, clone the repository and change your
present working directory.gitclonehttp://github.com/mcpcpc/openjcdopenj/Create and activate a virtual environment.python3-mvenvvenvsourcevenv/bin/activateInstall LibreHTF to the virtual environment.pipinstall-e.DeploymentFlaskNon-production WSGI via waitress for development and
debugging.flask--appopenjrun--debugWaitressProduction WSGI via waitress.pipinstallwaitress
waitress-serve--callopenj:create_appTestpython3-munittestRun with coverage report.coveragerun-munittest
coveragereport
coveragehtml# open htmlcov/index.html in a browserResourceshttps://en.m.wikipedia.org/wiki/Kanban
Package: open-jarvis-sdk
Jarvis SDKThis Jarvis Software Development Kit contains methods for various AI assistant tasks including Intent ParsingDocumentationSkillWhenever Jarvis captures a Skill with Intent in a spoken phrase or chat platform, you can execute some code and provide an appropriate response.from jarvis_sdk import Skill

@Skill.on("Weather", "getWeather")
def Weather_getWeather(captured_data):
    # captured_data is an instance of [`CapturedIntentData`](#CapturedIntentData)
    # process the query in here and return a list of possible responses. The Jarvis AI will try to  
    # pick the best reply based on mood, talkactiveness and much more
    return [
        "It's sunny",
        "It's really sunny, temperatures reaching 34°C"
    ]

@Skill.on("Weather", "*")
def Weather_all(captured_data):
    # you can also attach a listener for all intents on all skill.
    # Make sure you return None or nothing, because everything else
    # is being treated as response and is presented to the user
    return None
Package: open-journal
open-journalA simple, private, open-source journal for Linux. No user data is collected.EncryptionJournals can be optionally encrypted using a uniquely generated encryption key. Thecryptographylibrary used is built on AES. The encryption key only needs to be generated once. Export a copy of it from the program and store it in a safe place.The encryption key must be stored in the same location asmain.py. It is placed here automatically upon generation.Installation and UsageInstall Open Journal via pip. Preferably in a new pip or conda environment for easy management. Usepip3if necessary.$ pip install open-journalTo run, execute the following from a terminal, in same environment it was installed.$ openjournalTo remove, simply run:$ pip uninstall open-journalScreenshotsLicenseCopyright (C) 2020 Stuart ClaytonThis 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.
Package: openjudge
OpenJudgeRefactored!OpenJudge has been refactored to be faster and easier to set up! This
repository was about quick contests for small groups of 20-30 people. Somewhere
along the way, I faltered and it became bloated. It's back now in a form which
is fast and light.ScreenshotsUsageFirst install MongoDB by following the instructionsin their docssudoapt-getinstallpython3-dev
pipinstall--userpipenv
pipenvinstallopenjudge--python3.5
pipenvshell

mkdirquestionscdquestions

mkdir1cd1# Create these filestouchstatementi0o0i1o0# go back to where the questions directory residescd../../To run openjudge two commands need to be issued.openjudgeandopenjudge --judgein two separate terminals. The first is the interface and the second is the "judge".In case you have a lot of people sending in code and you deem that the code check is too slow, you just have to start another judge process withopenjudge --judge.
Package: openkongqi
Fetch outdoor air quality data from multiple resources, the system is made of
the following components:fetching: fetches resources to create the raw material for air
quality data. That material is saved to create a history and possibility to
fix errors after the fact.data extraction: extract the data from the raw material to create data feeds.data reliability: determine which data source is the most reliable at any
given time.ArchitectureThe data flow from online resource to reliable data point is broken down as
follow:sources: online resources that provide the data, polled at regular interval
and cached for archive purposefeeds: extracted data from sources, feeds are saved as time series in a
databasedatapoint: extracted from feeds using an election algorithm to provide the
most reliable dataConfigurationThe default local configuration file isokqconfig.py, this can be changed
using the environment variableOKQ_CONFMODULE. Theokqconfig.pyfile
should define a dict calledsettings, this dict can be used to overwrite
any configuration option defined in theglobal_settingsofopenkongqi.conf.Station UUIDEach station needs to be attached to a unique identier, a station identifier is
of the following formats:CC/STATION
CC/CITY/STATION
CC/STATE/CITY/STATIONThe following fragments compose the UUID:CC: country code - a two letter country code as defined by ISO 3166-1
alpha-2, mandatory.STATE: state name - state nameCITY: city name - city nameSTATION: stations name - station name, mandatory.All names have to be expressed in Uppercase or lowercase or numeric latin letters.InstallMake sure thelxmlpackage is installed, on debian:$apt-getinstallpython-lxmlInstall the code:$pipinstall-Upip$pipinstallpip-tools$pip-syncrequirements.txtDevelopmentInstall development environment:$pipinstall-rrequirements-dev.txtCreate necessary database & tables:(openkongqi)$python-c"import openkongqi.bin; openkongqi.bin.okq_init()"Run celery:(openkongqi)$pythonopenkongqi/bin.pyworker--loglevel=debug--concurrency=1--autoreload-BTestTest the package:$python-munittestdiscoverAutomatic testing in various environments:$toxVersioningOpenkongqi tries to follow thePEP 440/Semverconventions as defined byPyPATo update the version of the package, use the following command:$bumpr-b-pPackagingCreate packages:$pythonsetup.pysdistbdist_wheelPush package:$twineuploaddist/*$twineupload-rpypidist/*Name originKōngqì (空气) is the Chinese word for air/atmosphere.LicenseThis software is licensed under the Apache License 2.0. See the LICENSE file in the top distribution directory for the full license text.
Package: openkorpos-dic
openkorpos-dic-pyThis is a version ofOpenKorPOS-dicpackaged for use in Python withpymecab-ko.OpenKorPOS-dic is developed bySangwhan Moon,Won Ik Cho,Hyejoo Han,Naoaki Okazaki, andNam Soo Kimfor a comparison target of a new method claimed intheir paperUsageTo install:pipinstallopenkorpos-dicTo initialize with pymecab-ko:>>>importmecab_koasMeCab>>>importopenkorpos_dic>>>tagger=MeCab.Tagger(openkorpos_dic.MECAB_ARGS)>>>print(tagger.parse("아버지가방에들어가신다"))아버지NNG,*,F,아버지,*,*,*,*가JKS,*,F,가,*,*,*,*방NNG,*,T,방,*,*,*,*에JKB,*,F,에,*,*,*,*들어가VV,*,F,들어가,*,*,*,*신다EP+EF+VCP,*,F,신다,Inflect,EP,VCP,시/EP/*+ᆫ다/EF/*+이/VCP/*EOSVersion08/31/2022:The version of the original dictionary is not defined yet.Therefore, I defined the version like the version of other mecab compatible dictionaries. And I added the commit hash id of the original repository to make it distinguishable.>>>openkorpos_dic.VERSION'1.0.0-20220620-c72cb7d'LicenseOpenKorPOS-dic is available under the GPL-2.0 license,see here.
Package: openkts
OPENKTSAn m of n key sharing solution.Features:Customizable split/join functionalityPassword protection on individual shares (uses Fernet)Usage:Depending on your dependencies/setup you might have to changepythontopython3below.Install Cryptography on the active environmentpython -m pip install cryptographyInstall openkts to a targeted locationpip install openkts --target="C:\tmp\openkts"Go to the targeted directory before running openktscd "C:\tmp\openkts"Confirm that openkts is foundpython openkts -hSplit :python openkts split -f "file_path" -m 3 -n 5 -ePropertyTypeDescription-fSTRINGFilepath to file to be split into shares.-mINTMinimum number of shares needed to reconstruct the file.-nINTTotal number of shares to split the file into.-eNONE[OPTIONAL]Password encrypt shares when provided.Join :python openkts join -s "share_1" -s "share_2" -s "share_3" -o "out_file" -dPropertyTypeDescription-sSTRING[REPEATABLE]Filepath to one of the shares.-oINT[OPTIONAL]Output file path.-dINT[OPTIONAL]Password decrypt shares when provided.
Package: openla
OpenLA: open-source library for e-book log analysisIntroductionOpenLA is an open-source library for e-book log analysis.This Python library helps reduce redundant development when preprocessing e-book logs:
calculating reading times for each learner, counting up operations, page-wise summary of student behavior, etc.API conceptFour preprocessing processes are essential and common in e-book log analysis: getting course information, converting the log into a form suitable for analysis, extracting the required information, and visualizing the data.In order to improve efficiency and reduce reiteration in these processes, OpenLA provides the corresponding four modules: Course Information, Data Conversion, Data Extraction, and Data Visualization.InstallationOpenLA isavailable on PyPi. You can install it withpip.pipinstallOpenLAOpenLA works on python 3.7, 3.8, 3.9 and 3.10.Below are the versions of OpenLA's main dependencies we use for testing, but you probably do not need to worry about this.python 3.7: matplotlib 3.5.2, numpy 1.21.6, pandas 1.3.5python 3.8 or newer: matplotlib 3.5.2, numpy 1.22.3, pandas 1.4.2Datasets for OpenLAThe dataset used in this library has the same structure as the open source ones used to conductData Challenge Workshops in LAK19 and LAK20.We targetBookRolllogs, but logs from other systems can be adapted.The dataset may include up to four types of files.Course_#_EventStream.csvData of the logged activity data from learners' interactions with the BookRoll systemColumns:userid,contentsid,operationname,pageno,marker,memo_length,devicecode, andeventtimeCourse_#_LectureMaterial.csvInformation about the length of the lecture materials usedColumns:lecture,contentsid, andpagesCourse_#_LectureTIme.csvInformation about the schedule of the lecturesColumns:lecture,starttime, andendtimeCourse_#_QuizScore.csvData on the final score for each studentColumns:useridandscoreNote: from version 0.2.1, OpenLA can treat grading data, which was not present in the LAK19 and LAK20 datasets.Course_#_GradePoint.csvData on the final grade for each studentColumns:useridandgradeWhere#is the course id. BookRoll is an e-book system to record learning activities.If you need a sample dataset, please contactopenla@limu.ait.kyushu-u.ac.jp.DocumentationRead the docsfor detailed information about all the modules, and for code examples.For more information about BookRoll and the learning analytics platform on which the data was collected, please refer to the following:Brendan Flanagan, Hiroaki Ogata, Integration of Learning Analytics Research and Production Systems While Protecting Privacy, Proceedings of the 25th International Conference on Computers in Education (ICCE2017), pp.333-338, 2017.Hiroaki Ogata, Misato Oi, Kousuke Mohri, Fumiya Okubo, Atsushi Shimada, Masanori Yamada, Jingyun Wang, and Sachio Hirokawa, Learning Analytics for E-Book-Based Educational Big Data in Higher Education, In Smart Sensors at the IoT Frontier, pp.327-350, Springer, Cham, 2017.
Package: openlabs_address_autocomplete_au
This tryton module enhances the user experience in filling in addresses.
The module tries to autocomplete addresses from the partially filled in
street information or pin (zip) codes.NoteThe autocompletion requires Australia Post web service.InstallationThe module can be installed from PyPI:pip install openlabs_address_autocomplete_auor from the sources:git clone git@github.com:openlabs/trytond-address-autocomplete-au.git
cd trytond-address-autocomplete-au
python setup.py installConfigurationAfter installating the module, the API key needs to be set in theParty
configuration. The API key can be obtained fromAustralia Post Website.NoteIf the API key is not set the autocompletion fails silently with a log
warning.Authors and ContributorsThis module was built atOpenlabs.Professional SupportThis module is professionally supported byOpenlabs.
If you are looking for on-site teaching or consulting support, contact oursalesandsupportteams.
Package: openlabs_email_queue
This module implements an email queue which acts as a transaction safe
buffer for tryton modules to send emails.Source code:https://github.com/openlabs/email-queueWhy should I use this module ?Do you send emails from your tryton module ? Then you most probably need
this module. Here’s why:Transaction SafetyConsider the case where you are sending an Order confirmation email when
you click the confirm button. The transaction could roll back for a
variety of reasons after the email has been sent out to the customer. This
could be specially annoying if you sent an order number or other
information which could change, the second time you save the record - in
addition to the second time the second email that would spam the user.This module solves the problem by buffering the email to a database table
within the same transaction. Later, a cron task clears out the email. If
the transaction was rolled back the would not be saved in the buffer too
and you would not have to do anything separate.PerformanceDepending on how your SMTP server is setup and the bandwidth of your
servers, the sending of an email takes way more time than a database
write. This creates blocking operations resulting in poor user experience
and your app would now need more workers to handle more such requests.
This module works around the problem by first buffering the email and then
having a separate cron task which clears out the email by actually sending
it.ScalabilityIf you desire to be webscale like most of the internet aspires to be, you
would probably want to perform network bound operations like sending
emails from separate servers and scale using a message queue. This module
offers an API which lets you easily scale by subclassing the email.queue
model and changing thesend_allimplementation.How do I install this module ?Install from PyPI:pip install openlabs_email_queueInstall from source:git clone git@github.com:openlabs/email-queue
python setup.py installYou can then install the module in your database.How do I use this functionality in my modules ?The module provides a convenient method which has the same signature as
python’ssmtplib.SMTP.sendmailmethod. This makes it easy to update your existing email sending code.msg=MIMEText('This is the body')msg['Subject']='An important email'msg['From']='me@me.com'msg['To']='you@you.com'EmailQueue=Pool().get('email.queue')EmailQueue.queue_mail(me,[you],msg.as_string())If your transaction was successful, the email would be queued to be sent
and the mail would be sent out through the SMTP server when the cron runs
next time.The cron runs every 1 minute and you can change the frequency from cron
settingsHow do I configure the SMTP Server ?By default the emails are sent out using the smtp client provided by
Tryton. You can configure the settings for the same on your tryton
configuration file.Authors and ContributorsThis module was built atOpenlabs.Professional SupportThis module is professionally supported byOpenlabs.
If you are looking for on-site teaching or consulting support, contact oursalesandsupportteams.
Package: openlabs_nereid_rest
Nereid Rest is a tryton module which adds RESTful API toNereid.InstallationGet the latest code from github and install:pip install git+ssh://git@github.com/openlabs/nereid-rest.git@developUsageAfter installing nereid-rest, access data of any model just by accessing/rest/modelfollowed by the model name in the URL, which gives you model records as JSON:GET, POST/rest/model/<mode.name>To get a specific record from a model:GET, PUT, DELETE/rest/model/<mode.name>/<record_id>For exampleTo get all the records ofparty.party/rest/model/party.partyGet data of record with id 5 fromparty.partymodel:/rest/model/party.party/5By default you only get theidandrec_nameif there’s noserializemethod in the model.But, if there’s a serialize method in the model, nereid-rest will return whateverserializemethod returns.
Package: openlabs_nereid_s3
Nereid-S3 helps to upload files to amazon-s3 from tryton layer. Files are
stored in a folder. You can create multiple folders. Folder could be public or
private.You need to specify following options under nereid_s3 section in your tryton
configuration file:[nereid_s3]
access_key =
secret_key =
bucket =
cloudfront =Internally, private folder has key prefixed with_private. To
make this work, following bucket policy should be applied:{
  "Version": "2008-10-17",
        "Statement": [
                {
                        "Sid": "AddPerm",
                        "Effect": "Allow",
                        "Principal": {
                                "AWS": "*"
                        },
                        "Action": "s3:GetObject",
                        "NotResource": [
                                "arn:aws:s3:::bucket_name/_private",
                                "arn:aws:s3:::bucket_name/_private/*"
                        ]
                }
        ]
}Authors and ContributorsThis module was built atOpenlabs.Professional SupportThis module is professionally supported byOpenlabs.
If you are looking for on-site teaching or consulting support, contact oursalesandsupportteams.
Package: openlabs_nereid_webshop
Full Webshop based on Tryton NereidInstallationSetting this module up is similar to the setup of any other tryton module.Step 1: Create a virtualenvvirtualenv webshopYou can now activate the virtualenvcd webshop
source bin/activateStep 2: Clone and Setup the modulegit clone git@github.com:openlabs/nereid-webshop.git
cd nereid-webshop
python setup.py installThis command would install all the required dependencies for the module to
function.Step 3: Setup DatabaseThe module should now be available on the modules list and can be
installed into any database. Setup a website as shown below:You will have to create a guest user for nereid. The guest user would be
the user which would be available in the context when there are no users
logged into the website.Ensure that you have the following too:A pricelistA payment_termStep 4: Create an application scriptCreate anapplication.pyscript which could lauch the application. A
reference is provided in the web folder (application-example.py).In most cases the only changes you may need are:the DATABASE_NAME which should be the name of the database (from step 3).the TRYTON_CONFIG which should be the location of the tryton config
file.You should now be able to run the development server by running the
application using:python application.pyOn pointing the browser tolocalhost:5000you
should be able to see the home page.Step 5: Production DeploymentTODOStep 6: CustomizationFor base customization in webshop you have to inherit base.jinja as follow:{% extends "webshop/base.jinja" %}6.1: Favicon and logoSet custom favicon by setting icon path inSHOP_FAVICONvariable before extendingwebshop/base.jinjaas follows:{% set SHOP_FAVICON = "<path to custom favicon>" %}
{% extends "webshop/base.jinja" %}
Package: openlabs_nereid_webshop_elastic_search
Migration to 3.4In version 3.4, the module changed from usingproduct_attributecore
module from tryton community toproduct_attribute_strict. Theproduct_attribute_strictmodule includes a script which helps to
migrate fromproduct_attributetransparently.Authors and ContributorsThis module was built atOpenlabs.Professional SupportThis module is professionally supported byOpenlabs.
If you are looking for on-site teaching or consulting support, contact oursalesandsupportteams.