Saravanan666/pypi-org huggingface dataset:
Package: opencopilot-db
# opencopilot_db`opencopilot_db`is a Python library that provides database schemas common for opencopilot main app and workers application. It defines SQLAlchemy models for working with databases, making it easy to integrate and manage your database operations.## InstallationYou can install`opencopilot_db`using pip:

```bash
pip install opencopilot_dbUsageTo create pool of connections use the followingfromshared.modelsimportcreate_database_schemafromdotenvimportload_dotenvimportosload_dotenv("../llm-server/.env")create_database_schema()Importing the Modelsfromopencopilot_db.modelsimportPdfDataSource,ChatbotCreating and Managing Database Records# Create a PdfDataSourcepdf_data_source=PdfDataSource(chatbot_id=1,files=['file1.pdf','file2.pdf'],folder_name='pdf_folder')session.add(pdf_data_source)session.commit()# Query PdfDataSourcespdf_data_sources=session.query(PdfDataSource).all()forpdf_data_sourceinpdf_data_sources:print(f"PdfDataSource ID:{pdf_data_source.id}, Chatbot ID:{pdf_data_source.chatbot_id}, Files:{pdf_data_source.files}")Working with the Chatbot Model# Create a Chatbotchatbot=Chatbot(name="My Chatbot",website="https://example.com")session.add(chatbot)session.commit()# Query Chatbotschatbots=session.query(Chatbot).all()forchatbotinchatbots:print(f"Chatbot ID:{chatbot.id}, Name:{chatbot.name}, Website:{chatbot.website}")ContributingIf you'd like to contribute toopencopilot_db, please follow these guidelines:Fork the repository.Create a new branch for your feature or fix.Make your changes and ensure that tests pass.Submit a pull request to themainbranch of the repository.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.AcknowledgmentsSpecial thanks to the SQLAlchemy project for making it easy to work with databases in Python.Publish guidePublishing Your LibraryIncrement the version of the library in setup.py
Use the publish scriptpublish.shto publish the library
Package: opencopilot-utils
Project DescriptionIn yourREADME.mdfile, you can provide a detailed project description. Based on your provided description, it would look like this:# OpenCopilot Utility LibraryThe OpenCopilot Utility Library contains a collection of common utility functions, types, and interfaces used across the entire OpenCopilot application. It also provides essential embeddings for your project.## Features-A comprehensive set of utility functions.-Common types and interfaces that are widely used within the application.-Pre-configured embeddings for seamless integration.## Getting StartedTo start using the utility library, you can initialize your vector store with the provided embeddings using the following code:

```python
from opencopilot_utils import get_embeddings, init_vector_store, StoreOptions

embeddings = get_embeddings()
init_vector_store(docs, embeddings, StoreOptions(namespace=bot_id))InstallationYou can install the library via pip:pipinstallopencopilot-utilsUsageTo use the utility functions and types, simply import them in your Python code:fromopencopilot_utilsimportsome_utility_function,SomeCommonTypeFor more details and examples, please refer to the official documentation.ContributingWe welcome contributions from the community. If you have suggestions, bug reports, or would like to contribute to the project, please check ourcontribution guidelines.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.Publishing Your LibraryIncrement the version of the library in setup.py
Use the publish scriptpublish.shto publish the library
Package: opencore
========opencore========opencore facilitates collaboration and communication between people united for acommon goal.Currently, opencore powers the community organizing site OpenPlans[http://www.openplans.org] and the livable streets activism website NYCStreets[http://www.nycstreets.org]License=======Unless otherwise noted::GPLv3 (see COPYING.txt)Installation============Following the quickstart guide is the trouble free way to get going::see: Getting Started with Opencore<http://www.openplans.org/projects/opencore/getting-started>Developer Resources===================* `The Opencore Project http://www.openplans.org/projects/opencore* `#openplans irc://irc.freenode.net/openplansBrought to you by=================`The Open Planning Project [http://topp.openplans.org] and many fineopen source software projects.
Package: opencore-fassembler_projects
Contains fassembler projects for building components of an OpenCore stack.Changes0.5Use pip for frontend project.0.4Add “frontend” templates directly into source distribution,
rather than pulling them from a separate SVN repository.0.3The frontend project now looks for thenum_extra_zopessetting in
build.ini [general] section.This setting is now used to determine the default value ofopencore_remote_uriif that parameter is not set.Ifnum_extra_zopesis set, the default value ofopencore_remote_uriwill now be a space-separated list of URLs for
all the Zope instances built (assumed to be on their default ports,
e.g. 10001, 10011, 10021 etc)0.2Added support for Twirlip in the frontend project.If Twirlip is used (which is the default), the frontend will host
Twirlip at the path /notifications and will also filter the entire
application with Transcluder, since the UI for Twirlip is transcluded
into pages.To disable Twirlip support, build the frontend project with the
settinguse_twirlip=0.Also included in this release is an experimental zine project;
this is incomplete and should not be used in production builds.
Package: opencpaas
OpenCpaas Python SDKOpenCpaas allows customers to switch between CpaaS providers without having to rewrite code.Currently supports:BandwidthTwilioSee ourfull documentation.RequirementsPython3.5+InstallationpipinstallopencpaasGetting StartedClient InitializationBegin by initializing a new CpaaS client. This example uses the BandwidthClient for initialization. Specifics for each client can be found in each client's repository.importopencpaasauth={'api_token':'789','api_secret':'1011','account_id':'myaccount','application_id':'myapplication','user':'username','pass':'password','site_id':'12345'}client=BandwidthClient(auth)Each type of CpaaS client will require different authentications. Refer to our documentation for a complete list.Send an SMSmessage=client.send_sms(to="+12223334444"from_="+12223334445"text="Hello there!")print(message['id'])Send an MMSmessage=client.send_mms(to="+12223334444"from_="+12223334445"text="Take a look at this!"media_url="www.media.com/cute-cat")print(message['id'])Order a phone numberresult=client.order_number(phone_number="+12223334444")print(result['order_status'])Order multiple phone numbersresult=client.order_numbers(area_code="410"quantity=10)print(result['order_status'])Make an outgoing phone callparameters={'url':"http://mycallbackurl.com/123",'status_callback':"http://mystatuscallbackurl.com/123",'answer_url':"http://myanswerurl.com/123"}call_id=client.create_call(to="+12223334444",from_="+12223334445",**parameters)print(call_id)CpaaS clients require different callback urls. Please check our documentation for specific information on these urls.
Package: opencpaas-bandwidth
opencpaas-bandwidthThis is the Bandwidth implementation of OpenCpaas.See ourfull documentation.RequirementsPython3.5+Installationpipinstallopencpaas-bandwidthTestingRun the following command in the project directory:python-munittestdiscoverGetting StartedClient InitializationBegin by initializing a new Bandwidth client.fromopencpaas_bandwidthimportBandwidthClientauth={'api_token':'789','api_secret':'1011','account_id':'myaccount','application_id':'myapplication','user':'username','pass':'password','site_id':'12345'}client=BandwidthClient(auth)Bandwidth requires several different authorizations in order to perform voice, messaging, or number functions.
In order to initialize a new Bandwidth Client you MUST include an account_id or a CpaasAuthenticationException will be thrown.
Package: opencpaas-twilio
opencpaas-twilioThis is the Twilio implementation of OpenCpaas.See ourfull documentation.RequirementsPython3.5+Installationpipinstallopencpaas-twilioTestingRun the following command in the project directory:python-munittestdiscoverGetting StartedClient InitializationBegin by initializing a new Twilio client.fromopencpaas_twilioimportTwilioClientauth={'account_sid':"12345"'auth_token':"678910"}client=TwilioClient(auth)In order to initialize a new Twilio Client you MUST include an account_sid and auth_token a CpaasAuthenticationException will be thrown.
Package: open-cravat
Open-CRAVAT is a python package that performs genomic variant interpretation including variant impact, annotation, and scoring.
It has a modular architecture with a wide variety of analysis modules that are developed both by
the CRAVAT team and the broader variant analysis community. Open-CRAVAT is a product of theKarchin Labat Johns Hopkins University with funding provided by the National
Cancer Institute’sITCRprogram.Please see:Open-CRAVAT Docsfor installation instructions and software documentation.
Package: open-cravat-multiuser
open-cravat-server enables OpenCRAVAT to support multiple users in its web server.OpenCRAVAT is a python package that performs genomic variant interpretation including variant impact, annotation, and scoring.  It has a modular architecture with a wide variety of analysis modules that are developed both by the CRAVAT team and the broader variant analysis community. OpenCRAVAT is a product of theKarchin Labat Johns Hopkins University in collaboration withIn Silico Solutionswith funding provided by the National Cancer Institute’sITCRprogram.Please see:OpenCRAVAT Docsfor installation instructions and software documentation for OpenCRAVAT.
Package: opencroplib
Python Library for simulating physiological processes in plants.
The aim of this project is to implement the energy balance model
developed in Berni et al. 2009 that allows to estimate canopy conductance
from a combination of canopy temperature and basic micro-metheorological
observations.The different components of the model can also be used in the simulation and
estimation of physical processes such as aerodynamic modeling, calculation
of solar radiation, etc.# Background
Calculation of ETo and ETa# Installation`pip install opencropib `# Examples# Terms of use and citation
This library is licensed under the MIT license.Copyright 2021 Jose A. Jimenez-BerniLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.# References
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop
water requirements. Roma: Food and Agriculture Organization of the United Nations Available at:https://appgeodb.nancy.inra.fr/biljou/pdf/Allen_FAO1998.pdf.Berni, J. A. J., Zarco-Tejada, P. J., Sepulcre-CantÃ³, G., Fereres, E., and Villalobos, F. (2009). Mapping canopy
conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sens. Environ. 113,
2380â€“2388. doi:10.1016/j.rse.2009.06.018.https://github.com/woodcrafty/PyETo
Package: opencrypt
InstallationYou can grab the latest .whl file from repository and install it via pip or just simply type:pip install opencryptIf you want build from source code, then.git clone https://github.com/EbryxLabs/opencrypt
cd opencrypt/
python setup.py installYou will have a package namedopencryptinstalled for your python.Encryption / DecryptionYou can encrypt / decrypt data usingebcrypttool.(AES-256 Encryption)For example, to encrypt you can do following:ebcrypt <my-file.json> -e --new
# encrypts myfile.json using newly created crypto-secure key. Keys will be written to _keys file.

ebcrypt <my-file.json> -e
# encrypts using keys in AES_KEY, AES_IV environment variable.For decryption, you'll need to haveAES_KEYandAES_IVkeys in your environment variables.ebcrypt <my-file.json> -dThis is an optional step in case you want to decode your encrypted data explicitly for manual checking. Program decrypts your encrypted config file automatically.You can get detailed help on crypto tool by typing.ebcrypt -hOpenSSL compatibilityEncryption done by openssl utility can be decrypted byebcryptutility and vice versa. For example, you can encrypt using openssl as follows.openssl aes-256-cbc -a -e -K <hex-key> -iv <hex-iv> -in <input-file> -out <output-file>hex-keyandhex-ivshould be replaced with actual keys in hex format. Using the same keys you used in openssl, you can decrypt usingebcryptas follows.ebcrypt <encrypted-file> -d
# AES_KEY environment variable should hold <hex-key>.
# AES_IV environment variable should hold <hex-iv>.Similarly, you can encrypt usingebcryptand decrypt the content using openssl, given that you're using the same keys.Code UsageYou can also use it in your own code by importing the appropriate module.import opencrypt
opencrypt.encrypt_file(filename, new_keys=False)
opencrypt.decrypt_file(filename, write_to_file=True, is_ciphertext=False)
Package: openct
openctConfiguration backup and analysis* tools for devices running pfSense* and RouterOS.* aspirationalInstallationRun asrootmkdir/opt/openctcd/opt/openct
aptupdate
aptinstallpython3.11python3.11-venv
python3.11-mvenv.venv
..venv/bin/activate
pipinstallpip--upgrade
python-mpipinstallopenctSetupSSH KeysCreate a key pair in the project root folderCopy the public key to the target routersAdd the private key to theconfig.ymlfile belowCreating a configuration file:python-mopenct.setupEdit the configuration file:nanoconfig.ymlDatastoremkdir-pdatastore
touchdatastore/datastore.yml
mkdir-plogsAdd the target routers to the datastore in the following format:-"0.0.0.0"-"10.10.10.10"-"192.168.0.1Create a script to make running easierFor example inrun-openct.py:#!/opt/openct/.venv/bin/pythonimportrunpyrunpy.run_module(mod_name='openct')Make it executable:chmod+xrun-openct.pyThen run it:./run-openct.pyContributingSubmit bugs and feature requests, and help us verify as they are checked inReviewsource code changesFeedbackFile an issueLicenseCopyright (c) Weehooey Inc. All rights reserved.Licensed under theGNU GPL v3.0license.
Package: openctp-ctp
openctp-ctpExampleDemofromopenctp_ctpimportmdapi,tdapimd_api=mdapi.CThostFtdcMdApi.CreateFtdcMdApi("market")td_api=tdapi.CThostFtdcTraderApi.CreateFtdcTraderApi('user_id')Licenseopenctp-ctpis distributed under the terms of
theBSD-3-Clauselicense.
Package: openctp-tts
openctp-ttsInstallationpip install openctp-tts==6.3.15.*pip install openctp-tts==6.3.19.*pip install openctp-tts==6.5.1.*pip install openctp-tts==6.6.1.*pip install openctp-tts==6.6.7.*pip install openctp-tts==6.6.9.*pip install openctp-tts==6.7.0.*pip install openctp-tts==6.7.1.*pip install openctp-tts==6.7.2.*ExampleDemofromopenctp_ttsimportmdapi,tdapimd_api=mdapi.CThostFtdcMdApi.CreateFtdcMdApi("market")td_api=tdapi.CThostFtdcTraderApi.CreateFtdcTraderApi('user_id')Licenseopenctp-ttsis distributed under the terms of
theBSD-3-Clauselicense.
Package: openctr
OpenCTRClick-through rate (CTR) prediction is an important task in many industrial applications such as online advertising, recommender systems, and sponsored search. OpenCTR builds an open-source library for benchmarking existing CTR prediction models.Model ListCTR prediction models currently available:PublicationModelPaperAvailableWWW'07LRPredicting Clicks: Estimating the Click-Through Rate for New Ads[Microsoft]:heavy_check_mark:ICDM'10FMFactorization Machines:heavy_check_mark:CIKM'15CCPMA Convolutional Click Prediction Model:heavy_check_mark:RecSys'16FFMField-aware Factorization Machines for CTR Prediction[Criteo]:heavy_check_mark:RecSys'16YoutubeDNNDeep Neural Networks for YouTube Recommendations[Google]:heavy_check_mark:DLRS'16Wide&DeepWide & Deep Learning for Recommender Systems[Google]:heavy_check_mark:ECIR'16FNNDeep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction[RayCloud]:heavy_check_mark:ICDM'16IPNNProduct-based Neural Networks for User Response Prediction:heavy_check_mark:KDD'16DeepCrossDeep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features[Microsoft]:heavy_check_mark:NIPS'16HOFMHigher-Order Factorization Machines:heavy_check_mark:IJCAI'17DeepFMDeepFM: A Factorization-Machine based Neural Network for CTR Prediction, [Huawei]:heavy_check_mark:SIGIR'17NFMNeural Factorization Machines for Sparse Predictive Analytics:heavy_check_mark:IJCAI'17AFMAttentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks:heavy_check_mark:ADKDD'17DCNDeep & Cross Network for Ad Click Predictions[Google]:heavy_check_mark:WWW'18FwFMField-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising[Oath, TouchPal, LinkedIn, Ablibaba]:heavy_check_mark:KDD'18xDeepFMxDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[Microsoft]:heavy_check_mark:KDD'18DINDeep Interest Network for Click-Through Rate Prediction[Alibaba]:heavy_check_mark:CIKM'19FiGNNFiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction:heavy_check_mark:CIKM'19AutoInt+AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks:heavy_check_mark:RecSys'19FiBiNETFiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[Sina Weibo]:heavy_check_mark:WWW'19FGCNNFeature Generation by Convolutional Neural Network for Click-Through Rate Prediction[Huawei]:heavy_check_mark:AAAI'19HFM+Holographic Factorization Machines for Recommendation:heavy_check_mark:NeuralNetworks'20ONNOperation-aware Neural Networks for User Response Prediction:heavy_check_mark:AAAI'20AFN+Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions:heavy_check_mark:AAAI'20LorentzFMLearning Feature Interactions with Lorentzian Factorization:heavy_check_mark:WSDM'20InterHAtInterpretable Click-through Rate Prediction through Hierarchical Attention:heavy_check_mark:DLP-KDD'20FLENFLEN: Leveraging Field for Scalable CTR Prediction[Tencent]:heavy_check_mark:
Package: opencv-camcalib
相机标定器一个基于OpenCV的自动化相机数据采集和标定程序。观看演示•报告错误•功能需求English•简体中文喜欢这个项目吗？请考虑捐赠（微信|支付宝），以帮助它改善！演示测试相机 RTSP 地址：rtsp://admin:a12345678@y52t229909.zicp.vip功能数据采集棋盘标定畸变矫正圆孔标定安装命令界面pipinstallopencv-camcalib图像界面WindowsmacOSLinux用法命令界面$ opencv-camcalib数据采集opencv-camcalibcapturertsp://admin:a12345678@y52t229909.zicp.vip棋盘标定opencv-camcalibcalibrate--data_dir="/path/to/data"--rows=9--cols=6畸变纠正opencv-camcalibundistort--data_dir="/path/to/data"图形界面构建命令界面poetrybuild图像界面Windowspyinstaller-w-Fopencv_camcalib/app.py-iimages/favicon_256x256.ico--add-data"images/favicon_256x256.ico;images"macOS | Linuxpyinstaller-w-Fopencv_camcalib/app.py-iimages/favicon_256x256.ico--add-data"images/favicon_256x256.ico:images"文档安装gitclonehttps://github.com/XavierJiezou/OpenCV-CamCalib.gitcdOpenCV-CamCalib/
pipinstall-rdocs/requirements.txt构建mkdocsbuild部署mkdocsserve贡献gitclonehttps://github.com/XavierJiezou/OpenCV-CamCalib.gitcdOpenCV-CamCalib/
pipinstallpoetry
poetryinstall日志见CHANGELOG.md证书MIT License依赖生产依赖开发依赖参考Displaying webcam feed using OpenCV and Python+PySide.OpenCV Face Detection Example
Package: opencv_cffi
RequirementsYou’ll need a copy of OpenCV 3.0.x to link against, and the Haar Cascade
files from OpenCV 2.4.10 to do recognition, since the newer versions
changed the format but the C API seems to not have been updated to be
able to read them.You can grab both versions from:http://opencv.org/downloads.htmlBuild the 3.0 version (viacmake . && make). Install it if you
wish, otherwise follow below to just tellopencv-cffiwhere you’ve
downloaded it.On OS X, you can also get OpenCV 3.0 by running:$ brew edit homebrew/science/opencvremove the, branch => '2.4', and then$ brew install –HEAD –without-python homebrew/science/opencv(We do not need the Python bindings, you’re lookin’ at Python bindings).BuildingE.g.:$ opencv=~/Development/opencv-3.0.0 \
  LD_LIBRARY_PATH=$opencv/lib/ \
  DYLD_LIBRARY_PATH=$LD_LIBRARY_PATH \
  C_INCLUDE_PATH=$opencv/include/:$opencv/modules/core/include/:$opencv/modules/hal/include:$opencv/modules/imgproc/include:$opencv/modules/photo/include:$opencv/modules/video/include:$opencv/modules/objdetect/include:$opencv/modules/videoio/include/:$opencv/modules/highgui/include/:$opencv/modules/imgcodecs/include \
  LDFLAGS=-L$opencv/lib\
  pypy opencv_cffi/build.py
Package: opencv-compare
OpenCV CompareA simple image comparison tool, in OpenCVInstallationpipinstallopencv-compareUsagepython -m opencv_compare path/to/images/importcv2fromopencv_compareimportcompareimage1=cv2.imread("image1.png")image2=cv2.imread("image2.png")image3=cv2.imread("image3.png")compare([image1,image2,image3])CommandsKey InputActionESCexitCcapture cropped imagesAdecrease the crop sizeSincrease the crop sizeZdecrease the zoom sizeXincrease the zoom sizeLicenseMIT
Package: opencv-cython
This wrapper is a new opencv wrapper.
Designed to be easier to use.
It can can do on the fly encapsulation / decapsulation of numpy arrays into
various types of OpenCv datatypes, such as images, matrices or C array.
Even if it has a small overload cost because of the encapsulation /
decapsulation of the image, the image data is not recopied, and the wrapper is
still efficient