Saravanan666/pypi-org huggingface dataset:
Package: openmqttgateway-ble-decoder
OpenMqttGateway BLE decoderThis is a really quick and REALLY DIRTY implementation
of custom decoder for OpenMqttGateway, specifically for
Xiaomi BLE devices with encrypted packets.This was hacked together from a couple of different scripts
found on github.It expects OpenMqttGateway device to runmy modified firmware.Publishes decoded packets intohome/mqttgateway_ble_decoder/{{MAC_WITHOUT_:}}topic.UsageUsage: openmqttgateway_ble_decoder [OPTIONS]

Options:
  --mqtt TEXT               MQTT server ip  [required]
  --mqtt-port INTEGER       MQTT server port
  --mqtt-keepalive INTEGER  MQTT connection keepalive param
  --mqtt-username TEXT      MQTT username
  --mqtt-password TEXT      MQTT password
  --device <TEXT TEXT>...   Macs and decryption keys  [required]
  --help                    Show this message and exit.
Package: openmsistream
Applications for laboratory, analysis, and computational materials data streaming usingApache KafkaAvailable on PyPI athttps://pypi.org/project/openmsistreamand GitHub athttps://github.com/openmsi/openmsistreamOfficial documentation athttps://openmsistream.readthedocs.io/en/latest/Developed for Open MSI (NSF DMREF award #1921959); published in theJournal of Open Source Software
Package: openmt
SYSTMA perception system of tracking and motion understanding.InstallationWe currently support Python 3.7 and 3.8.You can install the package dependency via vanilla python and pip:python3-mpipinstall-rscripts/requirements.txt# This may not work for you. Please look up pytorch website for your configurationspython3-mpipinstalltorchtorchvisiontorchaudio# Install fresh detectron2python3-mpipinstall'git+https://github.com/facebookresearch/detectron2.git'If you're using conda, run the following commands:condacreate--nameopenmtpython=3.8
condaactivateopenmt
pipinstall-rscripts/requirements.txt
condainstallpytorchtorchvisiontorchaudiocudatoolkit=11.0-cpytorch
pipinstall'git+https://github.com/facebookresearch/detectron2.git'You can also usepythonandcondascripts on CPU Linux installation.More information about torch and detectron2 installationPytorchDetectron2To installopenmtpackage,python3setup.pyinstallUsageTraining detectorpython3tools/detect.pytrain--config<config_path><maybeotherarguments>Generate detection prediction resultspython3tools/detect.pypredict--config<config_path><maybeotherarguments>
Package: openmultimedia.headband
Table of ContentsLife, the Universe, and EverythingDon’t PanicScreenshotsMostly HarmlessInstallationShare and EnjoyChangelog1.0b1 (2013-01-25)Life, the Universe, and EverythingThis package defines an editable viewlet to add an image headband for the
site.Don’t PanicConfigureGo to ‘Site Setup’.Select ‘openmultimedia.headband settings’.Upload an image.You will see the image on top of the site.ScreenshotsControl PanelControl PanelMostly HarmlessGot an idea? Found a bug? Let us know byopening a support ticket.InstallationTo enable this product in a buildout-based installation:Edit your buildout.cfg and addopenmultimedia.headbandto the list
of eggs to install:[buildout]
...
eggs =
    openmultimedia.headbandAfter updating the configuration you need to run ‘’bin/buildout’’, which will
take care of updating your system.Go to the ‘Site Setup’ page in a Plone site and click on the ‘Add-ons’ link.Check the box next toopenmultimedia.headbandand click the ‘Activate’
button.Note: You may have to empty your browser cache and save your resource
registries in order to see the effects of the product installation.Share and Enjoyopenmultimedia.headbandwould not have been possible without the
contribution of the following people:Juan Pablo Giménez, original authorHéctor VelardeDevelopment sponsored by OpenMultimedia.ChangelogBecause you have to know where your towel is.1.0b1 (2013-01-25)Initial release.
Package: opennem
OpenNEM Energy Market Data AccessThe OpenNEM project aims to make the wealth of public National Electricity Market (NEM) data more accessible to a wider audience.This client library for Python enables accessing the Opennem API and data sets.Project homepage athttps://opennem.org.auDeveloper documentation athttps://developers.opennem.org.au/Currently supporting the following energy networks:Australia NEM:https://www.nemweb.com.au/Australia WEM (West Australia):http://data.wa.aemo.com.au/APVI rooftop solar data for Australia1. RequirementsPython 3.8+ (see.python-versionwithpyenv)Docker anddocker-composeif you want to run the local dev stack2. Quickstart$pipinstallopennem>>> import opennem3. Development3.1 Auto setup and installFor contributions and development of this repository you need to install all the requirements. There
are some helper scripts in thescripts/folder.$./scripts/init.shBy default the venv is installed in the user local cache folder and not in the project path. To link the venv
so that it can be found automatically by the shell or editors run the helper script$./scripts/link_venv.sh
Created.venv3.2 Manual Setup3.2.1 PrerequisitesFor MacOS and Linux requirepyenvandpoetrypyenv homepage- simple install withbrew install pyenvpoetry install(don't install poetry with brew - seethis issue)3.2.2 Initialize pythonWe usepyenvfor python versioning as it allows a system to run multiple version of python. The version for this project is specified in the.python-versionfile in the root of the repository.To install the locally required python version$pyenvinstall`cat.python-version`To initialize and use the local python version$pyenvversionlocal3.9.6(setby/Users/user/Projects/Opennem/opennempy/.python-version)To test the install is correct❯python-V
Python3.9.6
❯whichpython
/Users/n/.pyenv/shims/python3.2.3 Install with poetryTo manually setup the local development environment, simply create the virtual environment, link it and setup
the PYTHONPATH$poetryinstall
$ln-s`poetryenvinfo-p`.venv
$source.venv/bin/activate
$pwd>.venv/lib/python3.9/site-packages/local.pthAlternatively to actiavate the virtual environmentpoetryhas a shell command:$poetryshell
Spawningshellwithin/Users/n/Library/Caches/pypoetry/virtualenvs/opennem-pFt2SfpM-py3.9
$whichpython
/Users/n/Library/Caches/pypoetry/virtualenvs/opennem-pFt2SfpM-py3.9/bin/python3.2.4 Install with venvAlternatively if you do not wish to usepoetryyou can setup a simple venv in the local folder and activate it.$python-mvenv.venv
$source.venv/bin/activate
$pipinstall-rrequirements.txt3.3 Test InstallYou should be able to run a Python REPL (likeiPython) and import theopennemmodule$ipython
Python3.9.6(default,Jun282021,19:24:41)Type'copyright','credits'or'license'formoreinformation
IPython7.23.0--AnenhancedInteractivePython.Type'?'forhelp.

In[1]:importopennem

In[2]:
Package: openneuro-py
openneuro-pyA Python client for accessingOpenNeurodatasets.Installationvia condacondainstall-cconda-forgeopenneuro-pyvia pippipinstallopenneuro-pyJupyter and IPython supportTo get basic support for Jupyter Lab, Jupyter Notebook, IPython interactive
sessions, and VS Code's interactive Jupyter interface, you will also need to
installipywidgets:via condacondainstall-cconda-forgeipywidgetsvia pippipinstallipywidgetsBasic usage – Python interfaceimportopenneuroasonon.download(dataset='ds000246',target_dir='data/bids')Basic usage – command line interfaceDownload an entire datasetopenneuro-pydownload--dataset=ds000246Specify a target directoryTo store the downloaded files in a specific directory, use the--target_dirswitch. The directory will be created if it doesn't exist
already.openneuro-pydownload--dataset=ds000246\--target_dir=data/bidsContinue an interrupted downloadInterrupted downloads will resume where they left off when you run the command
again.Advanced usage – command line interfaceExclude a directory from the downloadopenneuro-pydownload--dataset=ds000246\--exclude=sub-emptyroomDownload only a single fileopenneuro-pydownload--dataset=ds000246\--include=sub-0001/meg/sub-0001_coordsystem.jsonNote that a few essential BIDS files arealwaysdownloaded in addition.Download or exclude multiple files--includeand--excludecan be passed multiple times:openneuro-pydownload--dataset=ds000246\--include=sub-0001/meg/sub-0001_coordsystem.json\--include=sub-0001/meg/sub-0001_acq-LPA_photo.jpgUse an API token to log inTo download private datasets, you will need an API key that grants you access
permissions. Go to OpenNeuro.org, My Account → Obtain an API Key. Copy the key,
and run:openneuro-pyloginPaste the API key and press return.
Package: openni
Python-bindings forOpenNI2andNiTE2.This package provides only the Python bindings; be sure to install OpenNI2 (and optionally NiTE2) first.Example:from openni import openni2

openni2.initialize()     # can also accept the path of the OpenNI redistribution

dev = openni2.Device.open_any()
print dev.get_device_info()

depth_stream = dev.create_depth_stream()
depth_stream.start()
frame = depth_stream.read_frame()
frame_data = frame.get_buffer_as_uint16()
depth_stream.stop()

openni2.unload()NoteRefer to the OpenNI2/NiTE2 C API for complete documentation
Package: opennms-client
==============opennms_client==============.. image:: https://travis-ci.org/mvillarejo/opennms_client.png?branch=master:target: https://travis-ci.org/mvillarejo/opennms_client.. image:: https://readthedocs.org/projects/opennms_client/badge/?version=latest:target: http://opennms_client.readthedocs.org/en/latest/?badge=latestopennms_client is a simple package to manage openNMS.Usage=====.. code-block:: python>>> import opennms_client>>> c = opennms_client.OpenNMSClient("http://demo.opennms.org/opennms", "demo", "demo")>>> print chttp://demo.opennms.org/opennms (demo) <Response [200]>>>> len(c.get_nodes())10>>> len(c.get_nodes(limit=0))56>>> c.get_services(){u'Amazon': 35,u'BingSearch': 38,u'DNS': 12,u'Dell-OpenManage': 24,u'FTP': 10,u'GoogleSearch': 36,...>>> c.get_node("www.amazon.com"){ ...u'createTime': 1355967495052,u'foreignId': u'1355756384535',u'foreignSource': u'Minimal Detectors',u'id': u'53',u'label': u'www.amazon.com',u'labelSource': u'U',u'lastCapsdPoll': 1447528445225,u'type': u'A'}>>> c.get_node_ipinterfaces("www.amazon.com")[{u'hostName': u'205.251.242.54',u'id': u'66463',u'ifIndex': None,u'ipAddress': u'205.251.242.54',u'isDown': False,u'isManaged': u'M',u'lastCapsdPoll': 1447528445225,u'monitoredServiceCount': 1,u'nodeId': 53,u'snmpPrimary': u'P'}]>>> In [21]: c.get_node_services("www.amazon.com")[{u'applications': [],u'down': False,u'lastFail': None,u'lastGood': None,u'notify': None,u'qualifier': None,u'serviceType': {u'id': 35, u'name': u'Amazon'},u'source': None,u'status': u'N',u'statusLong': u'Not Monitored'}]>>> c.get_node_services_list("www.amazon.com")[u'Amazon']>>> c.set_node_service("www.amazon.com ", "ICMP")<Response [200]>>>> c.delete_node("www.amazon.com")<Response [200]>Requirements============.. code-block:: bashpip install -r requirements.txtErrors======You might experience some errors tyring ot build ssl support, try this before installing:Mac.. code-block:: bashexport LDFLAGS="-L$(brew --prefix openssl)/lib"export CFLAGS="-I$(brew --prefix openssl)/include"Linux.. code-block:: bashyum install gcc python-devel libffi-devel openssl-devel -yReleases========Download pre-built releases on the [releases](https://github.com/mvillarejo/opennms_client/releases) pageContributors============[Manuel Villarejo](https://github.com/mvillarejo) - Core DevelopmentLicense=======MIT License.. :changelog:Changelog---------0.1 (2015-11-20)++++++++++++++++* client: version 0.10.1.1 (2015-11-20)++++++++++++++++++* client: added set_service(hostname, service_name)0.2 (2015-11-24)++++++++++++++++* Package renamed from opennms-client.client to opennms_client.opennms_client* Fixed installation of dependencies using pip* readthedocs.org documentation integrated now: http://opennms-client.readthedocs.org/en/latest/0.2.1 (2015-11-24)++++++++++++++++++* delete_node(hostname)* get_node_services,_list()* delete_node_service(hostname, service_name)0.2.2 (2016-03-9)+++++++++++++++++* set_node_service now has a index argument to set the service under interface you want to0.2.3 (2016-03-9)+++++++++++++++++* added instructions to avoid SSL errors in Mac and Linux0.3 (2016-07-18)+++++++++++++++++* added alarms to the class
Package: opennpi-scraper
openNPI ScraperIntroductionTheopennpi-scraperPython package enables efficient access to the OpenNPI database, facilitating detailed queries on healthcare providers and offering a streamlined interface for extracting National Provider Identifier (NPI) records. This tool is invaluable for healthcare research and data analysis, offering advanced search capabilities and detailed information retrieval.FeaturesAdvanced Search Functionality:Perform detailed searches using a variety of filters such as name, specialty, and location.Detailed Information Retrieval:Access comprehensive details about individual healthcare providers and doctors.User-Friendly Interface:Simplifies the extraction process with an easy-to-use API, designed for developers and researchers alike.InstallationTo installopennpi-scraper, run the following command in your terminal:pipinstallopennpi-scraperUsageImporting the ModuleStart by importing theopenNPIclass from theopennpipackage:fromopennpiimportopenNPICreating an InstanceCreate an instance of theopenNPIclass:scraper=openNPI.Scraper()Fetching DetailsRetrieve detailed information about healthcare providers and doctors by their NPI numbers.Providersprovider_info=scraper.get_provider_detail(NPI="1629848031")print(provider_info)Doctorsdoctor_info=scraper.get_doctor_detail(NPI="8224486550")print(doctor_info)Returns:A dictionary with the provider's or doctor's name and additional details extracted from their respective pages.SearchConduct searches for healthcare providers or doctors based on specific criteria.Providers Search ParametersParameterTypeDescriptionquerystrGeneral search term.taxonomystrFilter by the provider's specialty.statestrState abbreviation.citystrCity name.zipstrPostal code.pageintPage number of results to query.Returns:A dictionary withdoctorskey containing a list of matching providers andnextPageindicating if more results are available.advanced_results=scraper.search_providers(query="John Doe",state="CA")print(advanced_results)Doctors Search ParametersParameterTypeDescriptionquerystrGeneral search term.specialtystrMedical specialty of the doctor.schoolstrMedical school or university of the doctor.organizationstrOrganization or medical practice the doctor belongs to.statestrState abbreviation.citystrCity name.zipstrPostal code.pageintPage number of results to query.Returns:A dictionary withdoctorskey containing a list of matching doctors andnextPageindicating if more results are available.advanced_results=scraper.search_doctors(school="Wayne State University School of Medicine",state='MI')print(advanced_results)LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.
Package: openNPL
openNPLis a Python / Django powered platform for working with detailed (loan level) data with a particular emphasis on capturing non-performing loan information.The source code is available in theOpen Risk Repository.Author:Open RiskLicense: MITCode Documentation:Read The DocsDomain Documentation:Open Risk Manual Loan DataDevelopment Website:GithubProject Discussion:Open Risk CommonsDocker Image:DockerTraining:Open Risk Academy
Package: open-nsfw-python3
latest version 0.0.5What is this?The open_nsfw_python3 is a python3 library based on the open NSFW developed by yahoo which is a convolutional machine learning model. Using this lib you'll be able to recognize images which have sexual content by a score from 0 to 1.RequirementscaffeInstallingpip install open-nsfw-python3We are going to need to install the caffe lib. This repository has a Dockerfile which can tell you how to build a Linux environment with caffe step by step.After all the libraries and their dependencies are installed, you just need to import the NSFWClassifier class in your python3, create a new instance of the class, and start to predict!Using Example:from open_nsfw_python3 import NSFWClassifier

classifier = NSFWClassifier()
score = classifier.get_score('image.jpg')

print(score)
0.8041712045669556Copyright 2016, Yahoo Inc.Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: opennsfw-standalone
opennsfw-standaloneA small library for intergratingyahoo/open_nsfwmodel directly into an application.This library uses theONNX Runtimeto run inference against Open NSFW. The conversion from the original Caffe model provided byyahoo/open_nsfwwas done as following:Convert from Caffe to Tensorflow usingethereon/caffe-tensorflow.Convert from Tensorflow to ONNX usingonnx/tensorflow-onnx.The image pre-processing routine was approximated and is implemented usingPillow.PrerequisitesONNX Runtime 1.xNumPy 1.xInstallation$ pip install opennsfw-standaloneUsagePythonimportsysfromopennsfw_standaloneimportOpenNSFWInferenceRunnerinference_runner=OpenNSFWInferenceRunner.load()forimage_filenameinsys.argv[1:]:withopen(image_filename,"rb")asfp:nsfw_score=inference_runner.infer(fp.read())print(image_filename,nsfw_score)CLI$python-mopennsfw_standalonemyimage.jpegmysecondimage.jpeg
myimage.jpg0.9997739
mysecondimage.jpg0.9984438LicenseAlthough this repository is MIT licensed, the model itself is subject to theBSD 2-Clause licenseYahoo provided.
Package: opennucleome
OpenNucleomeOpenNucleome is an open-source software designed for conducting molecular dynamics (MD) simulations of the human nucleus. This software streamlines the process of setting up whole nucleus simulations through just a few lines of Python scripting. OpenNucleome can unveil intricate, high-resolution structural and dynamic chromosome arrangements at a 100 KB resolution. It empowers researchers to track the kinetics of condensate formation and fusion while also exploring the influence of chemical modifications on condensate stability. Furthermore, it facilitates the examination of nuclear envelope deformation's impact on genome organization. The software's modular architecture enhances its adaptability and extensibility. Leveraging the power of OpenMM, a GPU-accelerated MD engine, OpenNucleome ensures efficient simulations.CitationPlease cite the following paper if you use opennucleome package:"OpenNucleome for high resolution nuclear structural and dynamical modeling", eLife, doi: 10.1101/2023.10.16.562451EnvironmentWe recommend using openmm 7.5.1 together with openNucleome, which can be installed with the following command:conda install -c conda-forge openmm=7.5.1Other required packages: numpy, pandas, scikit-learn, MDAnalysis, mdtraj.TutorialsDetailed instructions for performing simulations of a human nucleus can be found at: tutorials/HFF_100KB. We also provide a tutorial for parameter optimization to develop new nucleus models at: tutorials/optimization.ManualDetailed documentation of the source code can be found at:https://zhanggroup-mitchemistry.github.io/OpenNucleome/
Package: opennyai
Opennyai : An efficient NLP Pipeline for Indian Legal documentsOpennyai is a python library for natural language preprocessing on Indian legal texts.This library provides unified access to the following 3 pre-trained AI models developed by OpenNyAI which focus on
Indian court
judgments:Named Entity Recognition (NER):GitHub,paperJudgment Structuring using Sentence Rhetorical
Roles:GitHub,paperExtractive Summarizer:GitHubThis library is mainly for running the pretrained models on your custom input judgments text. For more details about
data and model training, please refer to individual git repo links.🔧 1. InstallationTo get started using opennyai first create a new python virtual environment usingconda:Supports python 3.8, 3.9, 3.10condacreate-nopennyaipython=3.8
condaactivateopennyaiInstall it using pip by running the following line in your terminalpipinstall-UopennyaiFor GPU supportIf you want to utilize spacy with GPU please installCupyandcudatoolkitdependency of appropriate version.condainstallcudatoolkit==<your_cuda_version>#### E.g. cudatoolkit==11.2pipinstallcupy-cuda<your_cuda_version>##### E.g. cupy-cuda112In case of any issue with installation please refer tospacy installation with cupyRemember you need spacy of 3.2.4 version for models to work perfectly.📖 2. DocumentationPlease refer to theDocumentationfor more details.👩‍💻 3. UsageTo run the 3 OpenNyAI models on judgment texts of your choice please run following python codefromopennyaiimportPipelinefromopennyai.utilsimportDataimporturllib# Get court judgment texts on which to run the AI modelstext1=urllib.request.urlopen('https://raw.githubusercontent.com/OpenNyAI/Opennyai/master/samples/sample_judgment1.txt').read().decode()text2=urllib.request.urlopen('https://raw.githubusercontent.com/OpenNyAI/Opennyai/master/samples/sample_judgment2.txt').read().decode()# you can also load your text files directly into thistexts_to_process=[text1,text2]# create Data object for data  preprocessing before running ML modelsdata=Data(texts_to_process)# If you have access to GPU then set this to True else Falseuse_gpu=True# Choose which of the AI models you want to run from the 3 models 'NER', 'Rhetorical_Role','Summarizer'. E.g. If just Named Entity is of interest then just select 'NER'pipeline=Pipeline(components=['NER','Rhetorical_Role','Summarizer'],use_gpu=use_gpu,verbose=True)results=pipeline(data)The output of each model is present in following keys of each element of the outputresults[0]['annotations']## shows the result of model at sentence level, each entry will have entities, rhetorical role, and other detailsresults[0]['summary']## shows Summary for each of the Rheorical Role for first judgment textFor more details on usage please refer to thedocumentationGoogle Colab NotebookWe encourage you to use the local machine installation of opennyai library instead of Google colab as it works with python 3.8. Since Google colab works with higher versions of python, opennyai installation does not work.DescriptionLinkRun InferenceVisualization of outputsWe encourage users to useour webappfor visualizing the results for a
judgment of your choice.