Saravanan666/pypi-org huggingface dataset:
Package: radoneye
Ecosense RadonEye CLI and API for PythonProvides simple and convinient Python API to communicate with RadonEye bluetooth devices.Supported devices:RD200 (version 1) - not testedRD200N (version 2) - testedRD200P (version 2) - should work, never testedVersion 1 support is implemented using publicly available information. I have no device to test on,
if you have old RD200 device and are willing to help to build better support (beeps, history etc),
then let me know.Usage (API)Scan for all available devices, beep, read status and history:importasyncioimportjsonfromsysimportstderrfromradoneyeimportRadonEyeClient,RadonEyeScannerasyncdefmain():fordevinawaitRadonEyeScanner.discover():print(f"Device:{dev}")asyncwithRadonEyeClient(dev)asclient:try:awaitclient.beep()print("Beep: ok")exceptException:print("Unable to beep due to error",file=stderr)try:print(f"Status:{json.dumps(awaitclient.status())}")exceptException:print("Unable to obtain status due to error",file=stderr)try:print(f"History:{json.dumps(awaitclient.history())}")exceptException:print("Unable to obtain history due to error",file=stderr)if__name__=="__main__":asyncio.run(main())Usage (CLI)$pip3installradoneye

$radoneye--help

$radoneyelist--help
$radoneyelist
70C12E8A-27F6-3AEC-0BAD-95FA94BF17A9FR:RU22201030383
3775964E-C653-C00C-7F02-7C03F9F0122DFR:RU22204180050

$radoneyebeep--help
$radoneyebeep70C12E8A-27F6-3AEC-0BAD-95FA94BF17A9
Beepingon70C12E8A-27F6-3AEC-0BAD-95FA94BF17A9

$radoneyestatus--help
$radoneyestatus70C12E8A-27F6-3AEC-0BAD-95FA94BF17A9counts_current=0counts_previous=1counts_str=0/1day_avg_bq_m3=16day_avg_pci_l=0.43latest_bq_m3=5latest_pci_l=0.14model=RD200Nmonth_avg_bq_m3=0month_avg_pci_l=0.0peak_bq_m3=32peak_pci_l=0.86serial=RU22201030383uptime_minutes=3495uptime_str=2d10h1

$radoneyehistory70C12E8A-27F6-3AEC-0BAD-95FA94BF17A9#	Bq/m3	pCi/L120.05290.243200.544100.275100.27650.14750.14830.089100.2710100.27
...NOTE: On macOS bluetooth addresses are obfuscated to UUID.Publishing# install dependencicespip3installtwine# install locallypip3install-e.# test using cliradoneye--help# buildpython3-mbuild# view what is included into wheelunzip-ldist/*.whl# check wheeltwinecheckdist/*.whl# upload to pypitwineupload--repositoryradoneyedist/*LicenseMIT
Package: radonpy
RadonPyTools to communicate with the RadonEye RD200 radon detectorRadonPy provides a Python library for communicating with theRadonEye RD200radon detector over Bluetooth Low Energy, as well as a command line program to read sensor values and configure settings.Note that devices newer than November 2021 are not currently supported. They switched from an Nordic nRF5x to an ESP32 and changed the protocol, and I don't have new hardware to test.
Package: radops
R&D OpsDocs:https://striveworks.github.io/radops/Python packageInstallpipinstallradopsDev setuppipinstall-e".[dev]"pre-commitinstallTestingUnit testsUnit tests can be run viapytesttests/unit-testsFunctional testsFunctional tests require s3 setup. This can be done locally viadockerrun-p9000:9000-p9090:9090-eMINIO_ROOT_USER=user-eMINIO_ROOT_PASSWORD=passwordquay.io/minio/minioserver/data--console-address":9090"and then the functional tests can be run using the following commands3_endpoint_url=http://127.0.0.1:9000aws_access_key_id=useraws_secret_access_key=passwordpytest-stests/functional-tests/test_data_lake.py
Package: radosgw-admin
Python REST API for the Ceph RADOS Gateway (radosgw) admin operationshttp://docs.ceph.com/docs/master/radosgw/adminops/NOTICE:The library support Ceph version >= 10.2 (Jewel).ChangesVersion 1.6: Support for Ceph Hammer or older have been dropped.Version 1.7: Functionsget_buckets()andget_users()returns an iterator, not a list anymore.Version 1.7.1: Functionget_uids()added.Version 1.7.2: Functionsget_policy(bucket, object=None,...)anddelete_usage()added. Functionset_quota(...)returnsNone.RequirementbotoInstallationThe package is available onhttps://pypi.python.org/pypi/radosgw-admin. To install it usepip:pip install radosgw-adminOr clone thisrepositoryand install it locally:python setup.py installConfiguration of the admin userTo create or modify a bucket/user in radosgw, the admin user require the followingread,writecapabilities (caps):"caps": [
   { "type": "buckets",
     "perm": "*" },
   { "type": "usage",
     "perm": "read" },
   { "type": "metadata",
     "perm": "read" },
   { "type": "users",
     "perm": "*" }
]You can use theradosgw-admincommand to add capabilities to an existing user:radosgw-admin caps add --uid <USER_ID> --caps "buckets=read,write"
radosgw-admin caps add --uid <USER_ID> --caps "users=read,write"ExamplesSee the example inexamples/radosgw-admin-example.pyHere is a simple example:importradosgwrgwadmin=radosgw.connection.RadosGWAdminConnection(host='hostname.example.org',access_key='<ADMIN_ACCESS_KEY>',secret_key='<ADMIN_SECRET_KEY>')# user operationstestuser2=rgwadmin.create_user('testuser2',display_name='A test user',email='testuser2@example.org')testuser2.update(display_name='Second test user',suspended=True)testuser1=rgwadmin.get_user('testuser1')# bucket operationsbuckets=rgwadmin.get_buckets()forbucketinbuckets:print(bucket)testuser1_buckets=testuser1.get_buckets()forbucketintestuser1_buckets:# transfer buckets to testuser2rgwadmin.link_bucket(bucket.name,bucket.id,testuser2.id)
Package: radpress
_____             _
|  __ \           | |
| |__) | __ _   __| | _ __   _ __  ___  ___  ___
|  _  / / _` | / _` || '_ \ | '__|/ _ \/ __|/ __|
| | \ \| (_| || (_| || |_) || |  |  __/\__ \\__ \
|_|  \_\\__,_| \__,_|| .__/ |_|   \___||___/|___/
                     | |
                     |_|Radpress is a simple blog application for Djangonauts. It doesn’t use a WYSIWYG
editor; the default markup syntax isreStructuredTextand you can preview
your entry simply before publishing it. Radpress now ships with aMarkdownrenderer, too!Radpress’ default theme is based onAuthor themeby Mike McAlister. Thank
you for the support.FeaturesZen mode for writing articlesDisqus support for comment and reactionsUseful sidebar widgets; tag cloud, latest postsAdding page links in navigation barListing archives by date or tagAuthor information for articlesSimple theme like in Octopress.Supported markup languagesreStructuredTextMarkdownHow to contribute?I work with Python virtualenv. After I activated my virtualenv, I install
all requires with a command:$ pip install -r requirements/development.txtThen, I install latest stable version of Node. Node is required for compiling
“.less” files. After installed Node and NPM, I install all node modules with a
command:$ fab install_node_modulesTo compile less files:$ fab compile_lessAuthorsGökmen Görgen, <gokmen[@]alageek.com>ContributorsBen Stott, <bgbnbigben[@]contextualsystems.com>LicensingRadpress is free software under terms of the MIT License. For more information,
see the LICENSE file.
Package: radproc
Radproc is an open source Python library intended to facilitate precipitation data processing and analysis for GIS-users.
It provides functions for processing, analysis and export of RADOLAN (Radar Online Adjustment) composites and rain gauge data in MR90 format.
The German Weather Service (DWD) provides the RADOLAN RW composites for free in the Climate Data Center
but the data processing represents a big challenge for many potential users.
Radproc’s goal is to lower the barrier for using these data, especially in conjunction with ArcGIS.
Therefore, radproc provides an automated ArcGIS-compatible data processing workflow based on pandas DataFrames and HDF5.
Moreover, radproc’s arcgis module includes a collection of functions for data exchange between pandas and ArcGIS.NotePlease cite radproc as Kreklow, J. (2018): Radproc - A GIS-compatible Python-Package for automated RADOLAN Composite Processing and Analysis. Zenodo.http://doi.org/10.5281/zenodo.1313701Radproc’s Main FeaturesRaw Data ProcessingSupport for the reanalyzed RADOLAN products RW (60 min), YW and RY (both 5 min. resolution)Automatically reading in all binary RADOLAN composites from a predefined directory structureOptionally clipping the composites to a study area in order to reduce data sizeDefault data structure: Monthly pandas DataFrames with full support for time series analysis and spatial location of each pixelEfficient data storage in HDF5 format with fast data access and optional data compressionEasy downsampling of time seriesReading in DWD rain gauge data in MR90 format into the same data structure as RADOLAN.Data Exchange with ArcGISExport of single RADOLAN composites or analysis results into projected raster datasets or ESRI grids for your study areaExport of all DataFrame rows into raster datasets in a new file geodatabase, optionally including several statistics rastersImport of dbf tables (stand-alone or attribute tables of feature classes) into pandas DataFramesJoining DataFrame columns to attribute tablesExtended value extraction from rasters to points (optionally including the eight surrounding cells)Extended zonal statisticsAnalysisCalculation of precipitation sums for arbitrary periods of timeHeavy rainfall analysis, e.g. identification, counting and export of rainfall intervals exceeding defined thresholdsData quality assessmentComparison of RADOLAN and rain gauge dataIn preparation: Erosivity analysis, e.g. calculation of monthly, seasonal or annual R-factorsDocumentationThe full documentation for the latest radproc version is available athttp://www.pgweb.uni-hannover.de/Most of the docs are also hosted athttps://radproc.readthedocs.iowhich will provide support for docs of older versions in future,
but unfortunately Readthedocs doesn’t seem to support sphinx autodocs for the arcpy module which is not hosted at PyPI.
Consequently, the docs for the radproc.arcgis module are missing here.
If you have any idea how to fix this issue, please let me know.
Package: radshap
radshapThis repository proposes a python tool for highlighting the contribution of different regions of interest (ROIs) to the predictions of radiomic models.
It estimates the Shapley value of the different ROIs of an image that a trained radiomic model uses to obtain a prediction.Graphical abstracta.schematic view of a generic aggregated radiomic model -b.computation of a Shapley value for a specific region.InstallInstall from sourcepip install git+https://github.com/ncaptier/radshap.gitExperimentsWe provide a jupyter notebook for an illustration with PET images and simple aggregation strategies:Classification of Non-Small Cell Lung Cancer subtype and interpretation with Shapley valuesWe provide a jupyter notebook for an illustration of a robust strategy for computing Shapley values:Robust Shapley values for explaining multi-region radiomic models with non-optional regionsExamplesExplanation with Shapley valuesimportnumpyasnpimportjoblibfromradshap.shapleyimportShapleymodel=joblib.load("trained_logistic_regression.joblib")shap=Shapley(predictor=lambdax:model.predict_proba(x)[:,1],aggregation=('mean',None))shapvalues=shap.explain(X)# X a 2D array of shape (n_instances, n_instance_features)Robust explanation with Shapley valuesimportnumpyasnpimportjoblibfromradshap.shapleyimportRobustShapleymodel=joblib.load("trained_logistic_regression.joblib")shap=RobustShapley(predictor=lambdax:model.predict_proba(x)[:,1],aggregation=('nanmean',None),background_data=Xback)# Xback a 2D array of shape (n_samples_background, n_input_features)shapvalues=shap.explain(X)# X a 2D array of shape (n_instances, n_instance_features)LicenseThis project is licensed under a custom open-source license (see theLICENSE.mdfile for more details).AcknowledgementsThis package was created as a part of the PhD project of Nicolas Captier in theLaboratory of Translational Imaging in Oncology (LITO)of Institut Curie.
Package: radssh
RadSSH is a Python package that is built with Paramiko.Documentation for the project is hosted on ReadTheDocs, athttp://radssh.readthedocs.orgFrequently Asked Questions:https://github.com/radssh/radssh/blob/master/FAQ.mdRadSSH is installable viapip, using “pip install radssh”.The RadSSH shell behaves similar to a normal ssh command line client, but instead of connecting to one host (at a time), you can connect to dozens or even hundreds at a time, and issue interactive command lines to all hosts at once. It requires very little learning curve to get started, and leverages on existing command line syntax that you already know.[paul@localhost ~]$ python -m radssh.shell huey dewey louie
Please enter a password for (paul) :
Connecting to 3 hosts...
...
RadSSH $ hostname
[huey] huey.example.org
[dewey] dewey.example.org
[louie] louie.example.org
Average completion time for 3 hosts: 0.058988s

RadSSH $ uptime
[huey]  15:21:28 up 6 days, 22:49, 17 users,  load average: 0.30, 0.43, 0.39
[louie] 15:43  up 652 days,  4:59, 0 users, load averages: 0.44 0.20 0.17
[dewey]  15:21:28 up 109 days, 23:28,  3 users,  load average: 0.27, 0.09, 0.07
Average completion time for 3 hosts: 0.044532s

RadSSH $ df -h /
[huey] Filesystem            Size  Used Avail Use% Mounted on
[huey] /dev/mapper/vg-Scientific
[huey]                        24G   22G  694M  97% /
[louie] Filesystem     Size   Used  Avail Capacity  Mounted on
[louie] /dev/disk0s3   234G   134G    99G    57%    /
[dewey] Filesystem                        Size  Used Avail Use% Mounted on
[dewey] /dev/mapper/vg_pkapp745-LogVol00   20G   17G  2.1G  89% /
Average completion time for 3 hosts: 0.036792s

RadSSH $ *exit
Shell exitingRadSSH includes a loadable plugin facility to extend the functionality of the shell with basic Python scripting, as well as a high level API that can be used to build stand alone applications for dedicated SSH control processing in a parallel environment.Interested in more?Download athttps://pypi.python.org/pypi/radsshRead the Docs athttp://radssh.readthedocs.org/en/latest/index.htmlParticipate athttps://github.com/radssh/radssh
Package: radt
radTradT (Resource Aware Data science Tracker) is an extension to MLFlow that simplifies the collection and exploration of hardware metrics of machine learning and deep learning applications. Usually, collecting and processing all the required metrics for these workloads is a hassle. In contrast, RADT is easy to deploy and use, with minimal impact on both performance and time investment. The codebase of RADT is documented and easily expandable.This work has been published at the SIGMOD workshop DEEM 2023:Data Management and Visualization for Benchmarking
Deep Learning Training SystemspipinstallradtExamplesradT on GithubradT on PyPI
Package: radtext
PurposeRadText is a high-performance Python Radiology Text Analysis System.PrerequisitesPython >= 3.6, <3.9LinuxJava# Set up environment$sudoapt-getinstallpython3-devbuild-essentialdefault-javaQuickstartThe latest radtext releases are available overpypi.Using pip, RadText releases are available as source packages and binary wheels.
It is also generally recommended installing packages in a virtual environment to
avoid modifying system state:$python-mvenvvenv
$sourcevenv/bin/activate
$pipinstall-Upipsetuptoolswheel
$pipinstall-Uradtext
$python-mspacydownloaden_core_web_sm
$radtext-download--allTo see RadText’s pipeline in action, you can launch the Python interactive
interpreter, and try the following commands:importradtextnlp=radtext.Pipeline()withopen('/PATH/TO/BIOC_FILE.xml')asfp:doc=bioc.load(fp)annotations=nlp(doc)print(annotations)RadText also supports command-line interfaces for specific NLP tasks (e.g.,
de-identification, sentence split, or named entity recognition).$radtext-deid--repl=X-i/path/to/input.xml-o/path/to/output.xml
$radtext-ssplit-i/path/to/input.xml-o/path/to/output.xml
$radext-nerspacy--radlex/path/to/Radlex4.1.xlsx-i/path/to/input.xml-o/path/to/output.xmlDocumentationYou will find complete documentation at ourRead the Docs
site.ContributingYou can find information about contributing to RadText at ourContribution
page.AcknowledgmentThis work is supported by the National Library of Medicine under Award No.
4R00LM013001 and the NIH Intramural Research Program, National Library of
Medicine.You can find Acknowledgment information at ourAcknowledgment
page.LicenseCopyright BioNLP Lab at Weill Cornell Medicine, 2022.Distributed under the terms of theMITlicense,
RadText is free and open source software.
Package: rad-tools
Spin Hamiltonians, magnons and condense matter post-processing.The package covers post-processing scenarios for the results ofQuantum Espresso,TB2JandWannier90, as well as provides some custom scripts.It is expected to be used in two ways:As Python libraryVia scripts (i.e. usage from command line)For the detailed description checkdocumentation.InstallationRequirement for RAD-tools installation are:Python (>=3.8)NumPySciPymatplotlibtqdmtermcolorRAD-tools can be installed withpipor from source.pipTo install RAD-tools, run (you may need to usepip3):pip install rad-toolsInstalling from sourceClone the project to your local computer:gitclonegit@github.com:adrybakov/rad-tools.gitChange the directory:cdrad-toolsTo install RAD-tools, run (you may need to usepip3):pip install rad-toolsAdditionally you may run the unit tests,
which requires pytest (requires Python 3.7+) to be installed:make testTo install pytest, run (you may need to usepip3):pip install pytest
Package: radtts
RADTTS libraryThis RADTTS library was changed to be used with vait library.Original code, README.md and additional information:https://github.com/NVIDIA/radttsInstalation1) Install RADTTS librarypipinstallradtts==22.12.282) Install CUDA 11.3 or 11.6pipinstall-rrequirements-cuda-11.3.txt# orpipinstall-rrequirements-cuda-11.6.txt3) Install maracasgitclonehttps://github.com/jfsantos/maracas/home/${USER}/maracascd/home/${USER}/maracas
pipinstall.cd-Usage: Trainingradtts-train-cconfig_ljs_radtts.json-ptrain_config.output_directory=outdir
radtts-train-cconfig_ljs_radtts.json-ptrain_config.output_directory=outdir_dirtrain_config.warmstart_checkpoint_path=model_path.ptmodel_config.include_modules="decatndur"Usage: Inferencingradtts-inference-cCONFIG_PATH-rRADTTS_PATH-vHG_PATH-kHG_CONFIG_PATH-tTEXT_PATH-sljs--speaker_attributesljs--speaker_textljs-oresults/
radtts-inference_voice_conversion--radtts_pathRADTTS_PATH--radtts_config_pathRADTTS_CONFIG_PATH--vocoder_pathHG_PATH--vocoder_config_pathHG_CONFIG_PATH--f0_mean=211.413--f0_std=46.6595--energy_mean=0.724884--energy_std=0.0564605--output_dir=results/-pdata_config.validation_files="{'Dummy': {'basedir': 'data/', 'audiodir':'22khz', 'filelist': 'vc_audiopath_txt_speaker_emotion_duration_filelist.txt'}}"
Package: raduga
# Raduga
## Infrastructure as Code framework for AWS![raduga](http://tuxpiperweb.s3.amazonaws.com/raduga.jpg)We are busy feeding Raduga really well, so it will soon be pouring rainbows all over you. Stay tuned!See related projects: [cloudcast](https://github.com/tuxpiper/cloudcast) and [awsom](https://github.com/tuxpiper/awsom)
Package: radware-ansible-common-alteon
IntroductionAnsible Alteon modules and Radware common module_utils.
these modules and module infrastructure consume services from the SDK, thus it must be installed prior running them
Argument specification is a construct generate during runtime from the relevant SDK Configurator metadata,
multi-choice values are directly dynamically accesses from SDK beans, therefor any update in SDK become available to the
ansible module right after updating the SDK.
this is useful for when a new attribute is introduced or a new value to current attributeall modules support Check Mode + diff display and Idempotency.
each configuration  module support ansible 'state': present, absent, read, overwrite, append
for overwrite when 'write on change' is set , an actual change will be triggered only if a change has evaluated.
furthermore they utilize two SDK configuration features:Installationpip install radware-ansible-common-alteonDesign Principles4 modules: some indexed & others are a summary5 management modules: read device info, reboot, software upload, configuration upload/download, config management (commit, save, sync, revert, etc..)3 common modules: interfacing with SDK mng & config & commonThe module execute configurator functions from the SDKCheck mode (dry_run) + diff display is supported on all modulesIdempotency: Change/ no change report + write on changes onlyRevert_on_errorException handling: SDK stacktrace copy, warn user about supported version (from SDK)Argument specification construct in runtime from SDK configurator:
-	a construct of SDK metadata: not restricted to Ansible
-	single point of change
-	choices (Enum) derive from SDK bean sub-package
-	directly become available to users after updating the SDKFunction Argument specification construct in runtime for Management services from SDKAuthorsAnsible Common & Alteon was created byLeon MeguiraCopyrightCopyright 2019 Radware LTDLicenseGNU General Public License v3.0SeeCOPYINGto see the full text.
Package: radware_os_lb_v1_kilo
This is the Radware NG driver for
OpenStack Neutron LOADBALANCER service v1, KILO release.In order to activate Radware’s lbaas provider, perform following steps:Install the radware_os_lb_v1_kilo package by executing the following command (use sudo if needed):pip install radware_os_lb_v1_kiloOpen the neutron configuration file named neutron_lbaas.conf.
Under [service_providers] section, next to Haproxy LOADBALANCER provider,
add new line, declaring the Radware LOADBALANCER provider.service_provider = LOADBALANCER:radwarev1:radware_os_lb_v1_kilo.v1_driver.RadwareLBaaSV1Driver:defaultTo keep the HAproxy provider as a default LOADBALANCER provider,
remove the attribute :default from the Radware LOADBALANCER provider line.
Otherwise, remove the :default attribute of the HAproxy LOADBALNCER provider line.Add new section called [radwarev1] at the end of neutron configuration file named neutron.conf.
Add following Radware LOADBALANCER parameters under the section:vdirect_address = < Your vDirect server IP address >For additional Radware LBaaS configuration parameters,
please see the documentationRestart the neutron-server serviceFollowing is an example of neutron_lbaas.conf file after editing:[service_providers]service_provider=LOADBALANCER:Haproxy:neutron_lbaas.drivers.haproxy.plugin_driver.HaproxyOnHostPluginDriver:default
service_provider=LOADBALANCER:radwarev1:radware_os_lb_v1_kilo.v1_driver.RadwareLBaaSV1DriverFollowing is an example of neutron.conf file after editing:[radwarev1]
vdirect_address=< Your vDirect server IP address >