Saravanan666/pypi-org huggingface dataset:
Package: live-parking-norwich
Live car parking spaces in Norwich, UKThis package provides real-time information about available parking spaces in car parks and park & ride sites around Norwich. The data is sourced from anXML feedwhich is maintained byNorfolk County Counciland refreshed every 5 minutes.Installationpython3-mvenvvenvsourcevenv/bin/activate
pip3installlive_parking_norwichUsagefromlive_parking_norwichimportLiveParkingNorwich# Create new Usage objectusage=LiveParkingNorwich()# Returns a list of CarPark objectscar_parks=usage.refresh()# Check for successifusage.success:print(f"Last updated:{usage.last_updated}")# Iterate through each CarPark objectforcar_parkincar_parks:print("---")print(f"{car_park.code}|{car_park.name}")print(f"{car_park.occupancy}% full |{car_park.status}")print(f"{car_park.occupied_spaces}used")print(f"{car_park.remaining_spaces}remaining")print(f"{car_park.total_capacity}in total")else:print(usage.error_message)print(usage.traceback)Last updated: 2024-02-18 15:37:24
---
CPN0017 | Chantry Place, Chapelfield Road,
53.0% full | enoughSpacesAvailable
520 used
455 remaining
975 in total
---
CPN0016 | ThickThorn, Norwich Road, Norwich
4.0% full | enoughSpacesAvailable
33 used
693 remaining
726 in total
---
CPN0015 | Harford, Ipswich Road, Norwich
0.0% full | carParkClosed
0 used
798 remaining
798 in total
...
...
...CarPark attributesAttributeTypecodeStringnameStringoccupancyStringstatusStringoccupied_spacesIntegerremaining_spacesIntegertotal_capacityIntegerCarPark.status valuesStatusDescriptioncarParkClosedThe specified car park is closed.allCarParksFullAll car parks are full within a specified area.carParkFacilityFaultyThe specified car parking facility is not operating normally.carParkFullA specified car park is completely occupied.carParkStatusUnknownThe status of the specified car park(s) is unknown.enoughSpacesAvailableSpecified car parks have car-parking spaces available.multiStoryCarParksFullMulti level car parks are fully occupied.noMoreParkingSpacesAvailableSpecified car parks are fully occupied.noParkAndRideInformationNo park and ride information will be available until the specified time.noParkingAllowedNo parking allowed until the specified time.noParkingInformationAvailableCar parking information is not available until a specified time.normalParkingRestrictionsLiftedThe parking restrictions that normally apply in the specified location have been temporarily lifted.onlyAFewSpacesAvailableSpecified car parks have 95% or greater occupancy.parkAndRideServiceNotOperatingPark and ride services are not operating until the specified time.parkAndRideServiceOperatingPark and ride services are operating until the specified time.specialParkingRestrictionsInForceParking restrictions, other than those that normally apply, are in force in a specified area.LicenceOpen Government Licence
Package: liveplots
This is a python package which serves a (so far very minimalistic) plotting API through a xmlrpc server. The plots are generated by Gnuplot.This is a python package which serves a (so far very minimalistic) plotting API through a xmlrpc server. The plots are generated by Gnuplot.The purpose of this package is to monitor long running computations without interfering with it, i.e. slowing it down.Multiple plot servers can be started, each one on its separate daemon process.a simple example can be found here:https://github.com/fccoelho/liveplots/blob/master/Examples/simple.pyA file system monitor is also offered in the package. Example usage is below.https://github.com/fccoelho/liveplots/blob/master/Examples/monitor.py
Package: liveprofiler
[![CircleCI](https://circleci.com/gh/fieldaware/liveprofiler.svg?style=svg)](https://circleci.com/gh/fieldaware/liveprofiler)[![PyPI version](https://badge.fury.io/py/liveprofiler.svg)](https://badge.fury.io/py/liveprofiler)# Liveprofiler visualizer and collectorThis is a service that collects and presents samples generated by [livepofier_sampler](https://github.com/fieldaware/liveprofiler_sampler).It is follow up work based on https://github.com/nylas/nylas-perftools. So most of the credit goes to: nylas.comTo really understand what is going on here check this great blog post: https://www.nylas.com/blog/performance# Overview```web clientservice one<----------------+                           +|                           |+                           vservice two<-----------+collector+--------+         visualizer+             |             ^|             v             |service three<--------------+          database+--------+```* each service runs liveprofiler_sampler middleware* collector pulls metrics from services and saves them to python dbm database* visualizer reads dbm database and plots the results per host# CollectorCollector is responsible for gathering samples for configured hosts. Because sampler is protected with secret header, it has to be configured in collector as well. Collector should be called periodically (every few seconds). If you are planning to run collector with uWSGI (recommended) see running with uWSGI section. If not, you can set up cron to call `collector/` endpoint. It will return number of collected samples from all hosts.```[collector]secret_header = {SECRET HEADER}hosts= {LIST OF HOSTS SEPARATED BY COMMA (host1,host2,host3)}```# VisualizerUses flame graph to plot stacks and their counts.![visualized samples](visualizer.png "Example visualization")# ExampleIf you provide sampler configuration you can plot samples for the visualizer itself - it is a nice self-contained demo.This is a snippet from the `example/config.ini````[global]dbpath = /tmp/liveprofiler/[collector]secret_header = samplers3cr3thosts=localhost:9999[sampler]enabled = 1interval = 0.01secret_header = samplers3cr3t```* `secret_header` is the same on the both sides.* If sampler configuration is not provided or if the sampler is not enabled no metrics will be collectedTo run the example, create virtualenv and install dependencies from requirements.txt (`pip install -r requirements.txt`)To run the server:```> uwsgi example/uwsgi.ini```Then visit `http://localhost:9999/`.# uWSGI integration* Use at least 2 workers (processes) to run visualizer - one for visualizer and one for collector* Visualizer uses [uWSGI decorators](http://uwsgi-docs.readthedocs.io/en/latest/PythonDecorators.html) to schedule collector calls - therefore `master = true` has to be enabled in uWSGI configuration* `app_config` is a path to the visualizer configuration file - it must be set and it recommend it be an absolute path* if you are running visualizer with sampler enabled please check required uWSGI configuration to make it running
Package: liveprofiler_sampler
[![CircleCI](https://circleci.com/gh/fieldaware/liveprofiler_sampler.svg?style=svg)](https://circleci.com/gh/fieldaware/liveprofiler_sampler)[![PyPI version](https://badge.fury.io/py/liveprofiler_sampler.svg)](https://badge.fury.io/py/liveprofiler_sampler)# Object and middleware for profiling live servicesThis is a package that should be used to profile python services on production. This is fallow up work based on https://github.com/nylas/nylas-perftools. So most of the credit goes to: nylas.comTo really understand what is going on here check this great blog post: https://www.nylas.com/blog/performance# UsageFor wsgi applications you can this library as any other middleware. Here we have a little flask example:```from flask import Flaskapp = Flask(__name__)import liveprofiler_samplerapp.wsgi_app = liveprofiler_sampler.ProfilingMiddleware(app, interval=0.05, secret_header='secret')app.run()> curl -v localhost:5000/liveprofiler*   Trying 127.0.0.1...* Connected to localhost (127.0.0.1) port 5000 (#0)> GET /liveprofiler HTTP/1.1> Host: localhost:5000> User-Agent: curl/7.43.0> Accept: */*>* HTTP 1.0, assume close after body< HTTP/1.0 403 YOU SHALL NOT PASS!!!!!!!!!!!!1111oneoneonelephant< Connection: close< Server: Werkzeug/0.11.10 Python/2.7.8< Date: Thu, 30 Jun 2016 15:30:21 GMT<* Closing connection 0```Our tiny app denied the request, because we should not expose our profiling data to anyone. The `secret_header` param passed to `ProfilingMiddleware` constructor is the secret value that protects our stacks. Passing the secret token will return a json ready to consume and visualise.```> curl curl --header "PROFILER_TOKEN: secret" -v localhost:5000/liveprofiler* Rebuilt URL to: curl/* Could not resolve host: curl* Closing connection 0curl: (6) Could not resolve host: curl*   Trying 127.0.0.1...* Connected to localhost (127.0.0.1) port 5000 (#1)> GET /liveprofiler HTTP/1.1> Host: localhost:5000> User-Agent: curl/7.43.0> Accept: */*> PROFILER_TOKEN: secret>* HTTP 1.0, assume close after body< HTTP/1.0 200 OK< Connection: close< Server: Werkzeug/0.11.10 Python/2.7.8< Date: Thu, 30 Jun 2016 15:31:39 GMT<{"granularity": 0.05, "stacks": [{"count": 9, "frame": "<module>(__main__);start_ipython(IPython);launch_instance(traitlets.config.application);start(IPython.terminal.ipapp);mainloop(IPython.terminal.interactiveshell);interact(IPython.terminal.interactiveshell);run_cell(IPrt(IP...........```To visualize the data please check: https://github.com/fieldaware/liveprofiler# InstallationTo install the package simply type:```pip install liveprofiler_sampler```# uWSGI integrationBecause sampler is implemented based on signals, you have to be aware of that when running your service under uWSGI.In order to gather any samples you need to add following configuration to your uwsgi.ini```py-call-osafterfork = true  # enable child processes running cpython to trap OS signalslazy-apps = true  # load apps in each worker instead of the master```
Package: liveproxy
LiveProxyLiveProxy can redirect Livestreams to your favorite player on a lot of devices.Issue Tracker:https://github.com/back-to/liveproxy/issuesGithub:https://github.com/back-to/liveproxyINSTALLATIONpip as user# Latest pip version:python3-mpipinstall--upgradeliveproxy# Latest dev version:python3-mpipinstall--upgradegit+https://github.com/back-to/liveproxy.gitpip as root# Latest pip version:sudo-Hpython3-mpipinstall--upgradeliveproxy# Latest dev version:sudo-Hpython3-mpipinstall--upgradegit+https://github.com/back-to/liveproxy.gitsourcegit clone https://github.com/back-to/liveproxy.git
cd liveproxy
python3 setup.py installURL-GUIDETutorialFirst, start LiveProxy on your system.$ liveproxy
[main][INFO] For LiveProxy support visit https://github.com/back-to/liveproxy
[main][INFO] Starting server: 127.0.0.1 on port 53422host and port can be changed with--host/--port$ liveproxy --host 0.0.0.0 --port 12345
[main][INFO] For LiveProxy support visit https://github.com/back-to/liveproxy
[main][INFO] Starting server: 0.0.0.0 on port 12345Now that LiveProxy is running, you will have to create a valid URL.For the examples here,53422is used asdefault port.Base64You will need to base64 encode your used commands.Streamlinkhttp://127.0.0.1:53422/base64/STREAMLINK-COMMANDS/Example forstreamlink https://www.youtube.com/user/france24/live besthttp://127.0.0.1:53422/base64/c3RyZWFtbGluayBodHRwczovL3d3dy55b3V0dWJlLmNvbS91c2VyL2ZyYW5jZTI0L2xpdmUgYmVzdA==/Youtube-DLhttp://127.0.0.1:53422/base64/YOUTUBE-DL-COMMANDS/Example foryoutube-dl https://www.youtube.com/user/france24/livehttp://127.0.0.1:53422/base64/eW91dHViZS1kbCBodHRwczovL3d3dy55b3V0dWJlLmNvbS91c2VyL2ZyYW5jZTI0L2xpdmU=/LiveProxy-CommandLiveProxy can create this URL automatically.Create a new file with your commands.#EXTM3U
#EXTINF:-1,Arte FR
streamlink https://www.arte.tv/fr/direct/ 720p,720p_alt,best
#EXTINF:-1,France24
streamlink https://www.youtube.com/user/france24/live best
#EXTINF:-1 tvg-id="EuroNews" tvg-name="EuroNews",Euronews
streamlink https://www.euronews.com/live best
#EXTINF:-1,France24
youtube-dl https://www.youtube.com/user/france24/liveFor this example the filename isexample.m3uliveproxy --file example.m3uIt will create a new fileexample.m3u.newwith valid URLs,
only lines withstreamlink,youtube-dloryoutube_dlat the start will be changed.#EXTM3U
#EXTINF:-1,Arte FR
http://127.0.0.1:53422/base64/c3RyZWFtbGluayBodHRwczovL3d3dy5hcnRlLnR2L2ZyL2RpcmVjdC8gNzIwcCw3MjBwX2FsdCxiZXN0/
#EXTINF:-1,France24
http://127.0.0.1:53422/base64/c3RyZWFtbGluayBodHRwczovL3d3dy55b3V0dWJlLmNvbS91c2VyL2ZyYW5jZTI0L2xpdmUgYmVzdA==/
#EXTINF:-1 tvg-id="EuroNews" tvg-name="EuroNews",Euronews
http://127.0.0.1:53422/base64/c3RyZWFtbGluayBodHRwczovL3d3dy5ldXJvbmV3cy5jb20vbGl2ZSBiZXN0/
#EXTINF:-1,France24
http://127.0.0.1:53422/base64/eW91dHViZS1kbCBodHRwczovL3d3dy55b3V0dWJlLmNvbS91c2VyL2ZyYW5jZTI0L2xpdmU=/You can also use--file-outputfor a specified new file,
but be careful don't overwrite any important files.liveproxy --file example.m3u --file-output my_file.m3u
Package: liver
Live scheduleRSetupSettings:PLAYOUTSERVER = {}
PLAYOUTSERVER["token"] = "mytoken"
PLAYOUTSERVER["prefix"] = "http://127.0.0.1:1935/vod/_definst_/smil:storage/"
PLAYOUTSERVER["jwplayer_key"] = "jwplayer_key"Recording scripts:liver-do-jobs   -t 3fa16564-3850-11e4-83cd-2120fb613c75 --loglevel 10 -H 172.25.14.31 --storagebasedir=/storage/
liver-get-mo-to-delete  -t 3fa16564-3850-11e4-83cd-2120fb613c75 --loglevel 10 -H 172.25.14.31 --storagebasedir=/storage/Cronjobs:(server)
40 3 * * * liver-delete-jobs -k 90 --projectpath=/opt/comoda/ > /dev/null
40 4 * * * liver-delete-recordings --projectpath=/opt/comoda/ > /dev/null

(agent)
0 */6 * * *liver-mo-status -t 3fa16564-3850-11e4-83cd-2120fb613c75 --loglevel 10 -H 172.25.14.31 --storagebasedir=/storage/Librariesdjangopython-dateutilhttplib2simplejsonslugify
Package: liveramp-automation
liveramp-automationan automation framework helps you quickly author scripts for any automation testingBackgroundAre you eager to embark on your journey of creating a remarkable test project? 
But, you find yourself grappling with the complexities of environment configuration, 
puzzled by the plethora of testing frameworks to choose from, 
and unsure about integration and execution strategies. 

Moreover, you feel helpless when it comes to presenting test reports 
or recording test results effectively in the long term.

Fret not!

We have a perfect solution for you. 
Our team offers an exceptional open-source and user-friendly testing framework designed 
to empower QE/DEV professionals and anyone enthusiastic about automation testing. 

With our solution, you can effortlessly kickstart your automation endeavors, 
exploring various types of tests with ease and confidence.1. Descprition of the repositoryThis repository is to build a base autoamtion framework. 
    We would like to integrate the common helpers and libraries here and then build a lib.
    Later the only thing is to add the cases according to the features. 
    Since most of the common helpers and libraries have been integrated to the lib, 
    you only need to import that lib and methods.2. Things need to follow:Create a python repositoryCreate a file namedrequirements.txtand only input to this fileliveramp-automation, you can ignore the versionCopyconftest.pyandpytest.inito your repository's root directoryAuthor scriptsDone
Package: liveramp-clean-room
Liveramp clean roomThis library provides high level functions for interacting with liveramp's clean room API. Whilst users can directly interact with the API, we highly recommend the use of this client as it provides the following benefits:Automatic fetching and efficient caching of access tokenAutomatic fetching of query status with exponential backoffProvides a simple interface for fetching query result as a pandas dataframeProvides user friendly interfaces for interacting with assets and associated permissionsProvides clear and intuitive error messagesPre-RequisitesCredentials FileUse of the client requires an account configured with access to an organization within Data Hub. LiveRamp customers must use a service account for that purpose. A service account credential, in the form of a JSON file, can be generated by a LiveRamp employee and shared securely.InstallationThe Client is hosted onhttps://pypi.org/project/liveramp-clean-room/and thus can be installed viapipinstallliveramp_clean_roomOnce installed the client be instantiated and added to your code as the example below shows:fromliveramp_clean_roomimportdatahub## Initialize a clientorg_id="00XXX0XXX0XXX00X0XXX000XXX"credentials_file="Path to service account file"client=datahub.Client(org_id,credentials_file)Whereorg_idis the id of an organization within DataHub andcredentials_fileis the JSON file location of your service accountRunning QueriesTo run a query utilize the run_query function. DataHub supports the following query types:Data Manipulation Language (DML)SELECT queryData Definition Language (DDL)CREATE OR REPLACE FUNCTION/DROP FUNCTION), used to create or drop UDFs, a createdAssetID will be returned in the response.Example: Run a query and get resultsfromliveramp_clean_roomimportdatahuborg_id="00XXX0XXX0XXX00X0XXX000XXX"credentials_file="Path to service account file"client=datahub.Client(org_id,credentials_file)query_id=client.run_query("select * from schema.mytable")## Fetch result as a pandas dataframedataframe=client.get_result(query_id)Note that the client returns a Pandas Dataframe
Package: liver-ct-segmentation-package
liver-ct-segmentation-packagePrediction package for reproducible U-Net models trained on the LiTS dataset. These models are trained using the mlf-core framework (https://github.com/mlf-core/liver-ct-segmentation/).Free software: MITDocumentation:https://liverctsegmentationpackage.readthedocs.io.FeaturesModel download CLI: liver-ct-seg-model-dlPrediction CLI: liver-ct-seg-predUncertainty of prediction CLI: liver-ct-seg-uncertInput feature importance (Guided Grad-CAM) CLI: liver-ct-seg-feat-ggcamCreditsThis package was created withmlf-coreusingcookiecutter.ChangelogThis project adheres toSemantic Versioning.1.6.0 (2021-11-01)AddedIntroduced command to compute uncertainty of prediction, via the monte-carlo dropout procedure.Introduced command to compute input feature importance via the guided grad-cam algorithm, as implemented by the captum lib.FixedDependenciesDeprecated1.5.0 (2021-09-01)AddedFirst working prototypeFixedDependenciesDeprecated0.1.0-SNAPSHOT (2021-07-28)AddedCreated the project using mlf-coreFixedDependenciesDeprecated
Package: livereloadx
LiveReload XThis is afolkoflepture/python-livereloadfor fixingforeverdelayissue#125.
Future features are not guaranteed.Reload webpages on changes, without hitting refresh in your browser.InstallationLiveReload is for web developers who know Python. It is available onPyPI.$ pip install livereloadxDocumentationLiveReload's documentation is hosted on ReadTheDocs.Security ReportTo report a security vulnerability, please use theTidelift security contact.
Tidelift will coordinate the fix and disclosure.
Package: livermask
livermaskAutomatic liver parenchyma and vessel segmentation in CT using deep learninglivermaskwas developed by SINTEF Medical Technology to provide an open tool to accelerate research.Installpip install livermaskAlternatively, to install from source do:pip install git+https://github.com/andreped/livermask.gitAs TensorFlow 2.4 only supports Python 3.6-3.8, so does livermask. Software
is also compatible with Anaconda. However, best way of installing livermask is usingpip, which
also works for conda environments.(Optional) To add GPU inference support for liver vessel segmentation (which uses Chainer and CuPy), you need to installCuPy. This can be easily done by addingcupy-cudaX, whereXis the CUDA version you have installed, for instancecupy-cuda110for CUDA-11.0:pip install cupy-cuda110(DEBUG) If you experience issues with numpy, after installing CuPy, try reinstalling CuPy with this extension:pip install 'cupy-cuda110>=7.7.0,<8.0.0'Program has been tested using Python 3.7 on Windows, macOS, and Ubuntu Linux 18.04.Usage:livermask --input path-to-input --output path-to-outputcommanddescription--inputthe full path to the input data. Could be nifti file or directory (if directory is provided as input)--outputthe full path to the output data. Could be either output name or directory (if directory is provided as input)--cputo disable the GPU (force computations on CPU only)--verboseto enable verbose--vesselsto segment vessels--extensionof the output file (default: .nii)Using code directly:If you wish to use the code directly (not as a CLI and without installing), you can run this command:python -m livermask.livermask --input path-to-input --output path-to-outputDICOM/NIfTI formatPipeline assumes input is in the NIfTI format, and output a binary volume in the same format (.nii or .nii.gz).
DICOM can be converted to NIfTI using the CLIdcm2niix, as such:dcm2niix -s y -m y -d 1 "path_to_CT_folder" "output_name"Note that "-d 1" assumed that "path_to_CT_folder" is the folder just before the set of DICOM scans you want to import and convert. This can be removed if you want to convert multiple ones at the same time. It is possible to set "." for "output_name", which in theory should output a file with the same name as the DICOM folder, but that doesn't seem to happen...TroubleshootingYou might have issues downloading the model when using VPN. If any issues are observed, try to disable VPN and try again.If the program struggles to install, attempt to install using:pip install --force-reinstall --no-deps git+https://github.com/andreped/livermask.gitAcknowledgementsIf you found this tool helpful in your research, please, consider citing it:@software{andre_pedersen_2022_7448865,
  author       = {André Pedersen},
  title        = {andreped/livermask: v1.4.0},
  month        = dec,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v1.4.0},
  doi          = {10.5281/zenodo.7448865},
  url          = {https://doi.org/10.5281/zenodo.7448865}}Information on how to cite can be foundhere.The model was trained on the LITS dataset. The dataset is openly accessible and can be downloaded fromhere.
Package: livescript
python-livescriptlets you call LiveScript code from PythonExample>>> from livescript import lseval
>>> print lseval('''
      require! 'fs'
      return fs.readdirSync('.')
    ''')Installation$ pip install livescriptReturning valuesReturning will pass back the output back to Python. Any datatype supported by JSON can be passed back. If you want to return the last statement, you can omit the return statement.NPM modulesTo use modules, simply install them withnpmand ensure that thenode_modulesdirectory they are installed to is your current working directory. You can use them as usual with require.LicenseMIT