Saravanan666/pypi-org huggingface dataset:
Package: naro-laser
Naro-LaserThis is a simple example package. You can useGithub-flavored Markdownto write your content.setup.py, setup.cfg 방식을 혼합 사용한다.개발 절차빌드python3 -m pip install --upgrade build
python3 -m build업로드python3 -m pip install --upgrade twine
python3 -m twine upload --repository testpypi dist/*배치python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps packaging-tutorial-nash
Package: narou-api
narou_apiunofficial syosetu.com api libraryInstallingInstall and update using pippip install -U narou-apiA Simple Exampleimportnarou_apir=narou_api.get(st=1,lim=5,order="new")print(r.text)CLInarou_apigetst=1lim=5order=newFunctionDescriptiongetなろう小説APIget_rankなろう小説ランキングAPIget_rankinなろう殿堂入りAPIget_userなろうユーザ検索APIgetxなろうR18小説APILinksOfficial API Documet:https://dev.syosetu.com/man/api/PyPI Releases:https://pypi.org/project/narou-api/
Package: narration
narrationLogging is easy, but setup is messy and boring in multi processes contexts, due to the various way python can spawn
processes (fork, spawn, forkserver) via explicit or implicit spawning context.Narration handles the following logging setup boiler plate:main process’s logger writes log record emitted by the process itself or its child processeschild process’s logger’s handler forward log records to the main process onlyInstallationInstall python virtual envs needed by tox/IDEbash make.sh --setup-python-dev-envspip install narrationUsageCreate your loggers as you like (eg:logging.basicConfig(…))On the master process, setup the logger to act as server:importnarrationimportmultiprocessingimportloggingctx=mutiprocessing.get_context()m=ctx.Manager()logger=logging.getLogger()client_handler_settings=narration.setup_server_handlers(logger=logger,ctx=ctx,ctx_manager=m)logger.debug("Main process ready")pool=multiprocessing.pool.Pool(10,child_process_worker_function,[client_handler_settings],2,ctx)On the child process, setup the logger to act as ‘emitting’ client:importnarrationimportmultiprocessingimportlogging# Child process must receive the 'client_handler_settings' payload when starting the process# Re-create a logger, replacing handlers/formatting (if any) with handler to forrward records to the master processlogger=logging.getLogger()narration.setup_client_handlers(logger=logger,handler_name_to_client_handler_settings=client_handler_settings)logger.debug("Child process ready")Child process log records will be sent to the master process’s logger’s original handlers for procesing.Main process ready
Child process ready
Package: narrative
narrativeA small Python package for splitting text into dialogue and narrative.Installationnarrativeis available on PyPI. Simply install it withpip:pipinstallnarrativeUsagenarrativesplits a piece of prose into narrative and dialogue components. The
main functionsplit()will return a dict containing bothnarrativeanddialoguecomponents:>>>importnarrative>>>text='"Hello," he said. "How are you today?"'>>>narrative.split(text){'dialogue':['"Hello,"','"How are you today?"'],'narrative':['',' he said. ','']}There are two other helper functions as well.get_dialogue()returns only the dialogue components:>>>narrative.get_dialogue(text)['"Hello,"','"How are you today?"']get_narrative()returns only the narrative components:>>>narrative.get_narrative(text)['',' he said. ','']Note: The empty strings are a feature of Python'ssplit()function. SeeWhy are empty strings returned in split() results?for an explanation.British StyleEach function accepts a second parameter of a regular expression used to parse
out the dialogue. This defaults tonarrative.DIALOGUE_RE, which follows the
American standard of using double quotes for initial quotes.narrativenow
includes a second regular expression,narrative.BRITISH_DIALOGUE_RE, which
follows the British style of using single quotes for initial quotes. Simply use
it as the second parameter for any function:>>>importnarrative>>>narrative.split(text,narrative.BRITISH_DIALOGUE_RE)>>>…>>>narrative.get_dialogue(text,narrative.BRITISH_DIALOGUE_RE)>>>…>>>narrative.get_narrative(text,narrative.BRITISH_DIALOGUE_RE)>>>…
Package: narrow
https://github.com/idlesign/narrowWork in progress. Stay tuned.DescriptionNaive throughput measurements for Python web apps and serversThis tries to measure a throughput for various Python web apps and serversBenchmark report sample:https://idlesign.github.io/narrow/Stands:nginx_ssl_static: Nginx static response using SSLnginx_ssl_tcp_uwsgi: Nginx -> UWSGI -> TCP socket -> uwsgi -> app response. Using SSLnginx_ssl_unix_uwsgi: Nginx -> UWSGI -> Unix socket -> uwsgi -> app response. Using SSLnginx_static: Nginx static responsenginx_tcp_uwsgi: Nginx -> UWSGI -> TCP socket -> uwsgi -> app responsenginx_unix_uwsgi: Nginx -> UWSGI -> Unix socket -> uwsgi -> app responseuwsgi: uwsgi HTTP router -> app responseuwsgi_ssl: uwsgi HTTP router -> app response. Using SSLApps/frameworks:bottle: Bottle framework applicationcherrypy: CherryPy framework applicationdjango: Django framework applicationflask: Flask framework applicationpy: Pure wsgi application – defaultBenchers:h2load: h2load from nghttp2weighttp: weighttp (no SSL support)RequirementsBasics:Python 3.4+nginxuwsgiBenchmarking tools (any):h2loadweighttpRunningRun benchmarks, dump and plot the results:$narrow--help$narrowlist_stands$narrowlist_apps$narrowlist_benchers$narrowrunlocal--plot$narrow--verboserunlocal--log--standuwsgi--appflask--bencherweighttp$narrowstats_plot
Package: narrow-down
Narrow Down - Efficient near-duplicate searchNarrow Down offers a flexible but easy-to-use Python API to finding duplicates or similar documents also in very large datasets. It reduces the O(n²) problem of comparing all strings with each other to linear scale by using approximation algorithms like Locality Sensitive Hashing.GitHub repo:https://github.com/chr1st1ank/narrow-down.gitDocumentation:https://chr1st1ank.github.io/narrow-downFeaturesDocument indexing and search based on the Minhash LSH algorithmHigh performance thanks to a native extension module in RustEasy-to-use API with automated parameter tuningWorks with exchangeable storage backends. Currently implemented:In-MemoryCassandra / ScyllaDBSQLiteUser defined backends (by implementing a small interface)Native asyncio interfaceInstallationThe Python package can be installed withpip:pipinstallnarrow-downExtrasSome of the heavier functionality is available asextra:pipinstallnarrow-down[scylladb]# Cassandra / ScyllaDB storage backendSimilar projectspylshoffers a good implementation of the classic Minhash LSH scheme in Python and Cython. If you only need this and you don't need a database backend it can be a good choice.Datasketchimplements an interesting collection of different data sketching algorithms for similarity matching, cardinality estimation and k-nearest-neighbour search. The implementation is not highly optimized but very well usable, the documentation rich and multiple database backends can be used for some of the sketchesMilvusoffers a full database stack for vector search, a different approach for fast searching. It can also be applied to text search when an embedding like Word2Vec or Bert is used to vectorize the text.CreditsThis package was created withCookiecutterand thefedejaure/cookiecutter-modern-pypackageproject template.
Package: narsil
# NarsilA package built for analyzing time-lapse microscopy data of prokaryotes.
Most time-lapse data analysis involves extracting information about the cells in the data.The primary goal of this package is to build primitives that helps in building your own pipelines for image analysis. The basic primitives involve segmentation, tracking and operations on tracks.As a start, the pipeline is built to work with mother-machine datasets containing multiple-species of prokaryotes growing.The secondary goal is to evolve this pipeline to be able to work in cooperation with microscopes to run the analysis while the experimental data is being acquired. We aim to close the gap between experiments and analysis to be able to run closed-loop experiments in the future.The algorithms we use for segmentation and tracking are mainly based on deep learning. The package has functions to help train your own networks as well as run ours.The advent of mother-machine like devices in microbiology promotes the need for standardization of analysis tools i.e., make things like segmenting a tiff stack, or tracking cells in a tiff stack as easy as possible.### To install the packageFirst clone the repository and run the following commands.` pip install . `or` python3 setup.py install `### Package structure
Package: nart
Copyright (c) 2019 Keunhyun OhPermission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THESOFTWARE.Description: # nart[![PyPI version shields.io](https://img.shields.io/pypi/v/ansicolortags.svg)](https://pypi.org/project/nart/)[![PyPI pyversions](https://img.shields.io/pypi/pyversions/ansicolortags.svg)](https://pypi.org/project/nart/)[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)NART: NAver RealTime keywords# Install```consolefoo@bar:~$ pip install --upgrade nart```# API```pythonfrom nart.nart import Nartfrom nart.writer.builtins.csvwriter import CSVWriteroutrepos = [CSVWriter(path='/tmp/test.csv')]nart = Nart(outrepos)nart.write_nart()nart.run_with_hour(interval=1)```You can define custom outrepositories and datasources by using inheriting OutRepository and DataSource# Console```consoleusage: nart [-h] [-u TIMEUNIT] [-iv INTERVAL] [-csv CSVOUT] [-v] [-on]NART needs argumentsoptional arguments:-u, --timeunit              Input schedule time unit. (hour, min, or sec)-h, --help            show this help message and exit-iv INTERVAL, --interval INTERVALInverval time between collecting realtime keywords.Timeunit is based on timeunit-csv CSVOUT, --csvout CSVOUTSet the csvfilepath, if you want to write csvfile-v, --verbose         Set the flag, if you want for results to be printed-on, --startontime    Set the flag, collecting data start on the hour or onthe min```For example```consolefoo@bar:~$ nart -u hour -csv /tmp/test.csv -v -iv 1```Platform: anyClassifier: Topic :: Software Development :: Libraries :: Python ModulesClassifier: Environment :: MacOS XClassifier: Intended Audience :: Information TechnologyClassifier: Environment :: ConsoleClassifier: Operating System :: MacOS :: MacOS XClassifier: Intended Audience :: DevelopersClassifier: Programming Language :: PythonClassifier: Topic :: Office/Business :: News/DiaryClassifier: Programming Language :: Python :: 3.6Classifier: Programming Language :: Python :: 3.7Classifier: License :: OSI Approved :: MIT LicenseClassifier: Operating System :: Microsoft :: WindowsClassifier: Intended Audience :: Science/ResearchClassifier: Operating System :: POSIX :: LinuxRequires-Python: >=3.6
Package: naruno
NarunoUpgrade to new internet !Explore the website »..........IntroducingNaruno is a lightning-fast, secure, and scalable blockchain that is able to create transaction proofs and verification via raw data and timestamp. We remove the archive nodes and lazy web3 integrations. With Naruno everyone can get the proof (5-10MB) of their transactions via their nodes and after everyone can use in another node for verification the raw data and timestamp. Also you can integrate your web3 applications with 4 code lines (just python for now) via our remote app system.World Wide First In Node Level Transaction Proof Mechanism (Revulation to archive nodes)FBA Consensus Protocol7 Second Block Time (Reducible to 3s)10k Potential TPSTotaly New Infrastructure (100% Python)Already Automated Workflows100% Code Coverage with unit, functional and stability testsAlready distribute CLI, API and GUI in Android, Windows, MacOS and LinuxMobile Master Node SupportTwo Web3 Integration way as Embedded App (Naruno App Engine) and Remote App (Naruno API)And more...*Some features will be adjusted in stable release.The software is written inPEP8format andonlyneeds dependent libraries for GUI panel. Naruno software, designed with a focus on thecommunity on the Github platform, In addition,Stability,FunctionalandUnittests of all systems are ready and automated. It also has various structures forautomatic setup of test environments. It is fully compatible withGit code management systemandAgile mindset.DocumentsOur documents are available in thedocsfolder andthiswebsite.ContributeNaruno is a completely community driven project, you can contribute in any way if you follow theguidelinesandterms.Analysis && StatisticsGithub Statistics...SponsorsContributors
Package: naruno-scan
Naruno ScanThis is a simple website that build withjfletandKOTmodules. It's show the situation of Naruno's network.ContributingContributions to Scan are welcome! If you have any suggestions or find a bug, please open an issue on the GitHub repository. If you want to contribute code, please fork the repository and create a pull request.LicenseScan is released under the MPL-2.0 License.
Package: naruto
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.This is the README file for the project.The file should use UTF-8 encoding and be written using ReStructured Text. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: nasa-api
NASA-APInasa-apiis an async wrapper for The Official NASA API.Note: Currently in development.Make sure to generate an API key fromhttps://api.nasa.gov/Available APIsAPODStands forAstronomy Picture of the Day.importnasaimportasyncioasyncdefmain():response=awaitnasa.APOD().apod('API-KEY-HERE',**kwargs)print(response)asyncio.get_event_loop().run_until_complete(main())Parameters:ParameterTypeExampleDescriptiondatestr"YYYY-MM-DD","2021-06-11"The date of the APOD image to retrievedate_rangestr"START_DATE, END_DATE","YYYY-MM-DD, YYYY-MM-DD","2021-06-11, 2021-07-30"A date range, when requesting date for a range of dates. Cannot be used withdate.countint1If this is specified then count randomly chosen images will be returned. Cannot be used withdateordate_range.thumbsstr"True","False"Return the URL of video thumbnail. If an APOD is not a video, this parameter is ignored.api_keystr"DEMO_KEY"a api.nasa.gov key. This is a positional argument
Package: nasaoutgassingjir
Example PackageThis program automatically downloads the json file from the public databasehttps://data.nasa.gov/Applied-Science/Outgassing-Db/r588-f7pr(located inhttps://data.nasa.gov/resource/r588-f7pr.json)
and generates some statistics for the variable total mass loss (tml):Prints on terminal tml min, max and meanPlot a boxplot for the first two elements with more samples
Package: nasa-power
NASA PowerA thin wrapper around the NASA Power API.Installationpython -m pip install nasa-powerUsageCurrently only the points API has been implemented. The point method accepts six named parameters (coordinates, parameters, start, end, resolution, and community).importnasapowerdf=nasapower.point(coordinates=(52.09,5.12),parameters=['WS50M','CLRSKY_SFC_SW_DWN'],start=datetime.date(2020,01,01),end=datetime.date(2020,12,31))LicensingThe code in this project is licensed under theMIT license.
Package: nasa.py
nasa.pyAn easy to use, async ready wrapper for the NASA Open API's.Key FeaturesIntuitive Syntax deriving from API NamesProper Exception Handling100% coverage of each supported APIInstallingThere are a few ways which you can install the Library. To install the stable version, simply run the following commands:# Linux/macOSpython3-mpipinstall-Unasa.py# Windowspy-3-mpipinstall-Unasa.pyTo install the Library with its http speedups, simply run the same commands but with the following alterations:# Linux/macOSpython3-mpipinstall-U"nasa.py[speedups]"# Windowspy-3-mpipinstall-Unasa.py[speedups]You can install the development version, you can do the following:$gitclonehttps://github.com/quiktea/nasa.py
$cdnasa.py
$python3-mpipinstall-U.[speedups]ExamplesAll of the library's syntax is heavily inspired by the NASA Open API names. For example, to fetch theapodapi, you can simply run theawait .apodmethod.Astronomy Picture of the Day (APOD)fromnasaimportClientclient=Client("api_key")#providing an invalid api key or no api key will raise an Invalid API_KEY Errorapod_result=awaitclient.apod()video=apod_result.media.videoprint(video)print(video.url)This will return:>>><Videourl='https://www.youtube.com/embed/badXkJ6BnOs?rel=0'>
>>>https://www.youtube.com/embed/badXkJ6BnOs?rel=0Getting an API KeyTo get an API Key, simply visit theNasa Open APIwebsite, fill out the form and get your api key!Currently Supported API'sAPOD (Astronomy Picture of the Day)
Package: nasa-scrub
SCRUBSCRUB is an orchestration and aggregation platform for static code analysis tools.SCRUB allows users to run multiple static code analysis tools, collect the results, and export them to external tools. The results from each analyzer are post-processed to a standard warning format that provides information about the location of the warning as well as a brief description of the warning. These warnings can then reviewed and assessed by experienced developers to determine their merit.A full description of the origins of SCRUB, written by the original author (Gerard Holzmann), can befound here.SCRUB Documentation
Package: nasasync
nasa.pyWork in progressto run the docs usesphinx-autobuild -a docs/source docs/_build/html --watch nasa --open-browserDemoAPODEPICInstallationCurrently the package is not available on pypi, this means that you can't install it using justpip.
To installNasa.pyyou need to installgitand to run this command.. code-block:: textpip install git+https://github.com/Snipy7374/nasa.pyRequirements.. code-block:: textaiohttp
aiofiles
requestsBasic usageCreate a client object.. code-block:: python3import typing as t
from nasa import NasaSyncClient

if t.TYPE_CHECKING:
    from nasa import AstronomyPicture

client = NasaSyncClient(token="TOKEN_HERE")get the todays astronomy picture.. code-block:: python3astronomy_picture: AstronomyPicture = client.get_astronomy_picture()save an image.. code-block:: python3astronomy_picture.image.save("image.png")This library also supports Async requests.. code-block:: python3import typing as t
from nasa import NasaAsyncClient

if t.TYPE_CHECKING:
    from nasa import AstronomyPicture

client = NasaAsyncClient(token="TOKEN_HERE")

async def main():
    async with client:
        astronomy_picture: AstronomyPicture = await client.get_astronomy_picture()
        await astronomy_picture.image.save("image.png")Currently supported NASA API endpointsAPOD (Astronomy picture of the day) -/planetary/apod- (all query parameters)EPIC (Earth Polychromatic Imaging Camera) -/EPIC/api- (all)TODOAddis_videoproperty on AstronomyPicture (based onmedia_type)Add support for thecountquery parameter on/planetary/apodendpoint to get multiple random imageSolve typing issues withtyping.overloads onclient.pySupport other endpointsMars rover photosNASA image and video libraryAdd async client & methodsAdd loggingAdd docs :)Add developing toolsnoxpyrightblackflake8pre-commitAdd workflows on githubtyping checkflake8blackCreate the first releaseAdd package configuration to publish on pypi (pyproject.toml)Upload the project on PyPiCustomize the docsadd custom colorsAdd examplesupload on githubdocs: improve the structure of payloads and add Dev docsdocs: add contributing section
Package: nasa-tle-loader
SpaceTrackApi clientSmall TLE loader from NASA.Requirementsrequests >= 2.14.2Installingpip install nasa-tle-loaderGetting startedTo retrieve something from Space-Track:# -*- coding: utf-8 -*-from__future__importunicode_literals,print_functionimportjsonfromnasa_tle_loaderimportNasaTLELoaderdefmain():# Loader initializingloader=NasaTLELoader()# Getting list `nasa_tle_loader.TLE`(namedtuple like) objectstle_list=loader()# Print result as JSONprint(json.dumps([tle.as_dict()fortleintle_list[:3]],indent=2))if__name__=='__main__':main()Result:[
  {
    "EPOCH": "2017-05-17 13:16:58",
    "EPOCH_MICROSECONDS": "124064",
    "NORAD_CAT_ID": "25544",
    "TLE_LINE0": "ISS",
    "TLE_LINE1": "1 25544U 98067A   17137.55345051  .00016717  00000-0  10270-3 0  9004",
    "TLE_LINE2": "2 25544  51.6389 191.0057 0005051 169.7469 190.3787 15.54030000 16987"
  },
  {
    "EPOCH": "2017-05-17 22:32:35",
    "EPOCH_MICROSECONDS": "151072",
    "NORAD_CAT_ID": "25544",
    "TLE_LINE0": "ISS",
    "TLE_LINE1": "1 25544U 98067A   17137.93929573  .00016717  00000-0  10270-3 0  9014",
    "TLE_LINE2": "2 25544  51.6398 189.0848 0005258 166.2909 193.8387 15.53887043 17040"
  },
  {
    "EPOCH": "2017-05-18 01:37:47",
    "EPOCH_MICROSECONDS": "963136",
    "NORAD_CAT_ID": "25544",
    "TLE_LINE0": "ISS",
    "TLE_LINE1": "1 25544U 98067A   17138.06791624  .00016717  00000-0  10270-3 0  9024",
    "TLE_LINE2": "2 25544  51.6394 188.4430 0005111 170.0057 190.1198 15.53888284 17061"
  }
]Source codeThe latest developer version is available in a github repository:https://github.com/nkoshell/nasa-tle-loader
Package: nasbench301
NAS-Bench-301This repository containts code for the paper:"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".The surrogate models can be downloaded on figshare. This includes the models forv0.9andv1.0as well as thedatasetthat was used to train the surrogate models. We also provide thefull training logsfor all architectures, which include learning curves on the train, validation and test sets. These can
be automatically downloaded, please seenasbench301/example.py.To install all requirements (this may take a few minutes), run$catrequirements.txt|xargs-n1-L1pipinstall
$pipinstallnasbench301If installing directly from github$gitclonehttps://github.com/automl/nasbench301
$cdnasbench301
$catrequirements.txt|xargs-n1-L1pipinstall
$pipinstall.To run the example$python3nasbench301/example.pyTo fit a surrogate model run$python3fit_model.py--modelgnn_gin--nasbench_dataPATH_TO_NB_301_DATA_ROOT--data_config_pathconfigs/data_configs/nb_301.json--log_dirLOG_DIRNOTE: This codebase is still subject to changes. Upcoming updates include improved versions of the surrogate models and code for all experiments from the paper. The API may still be subject to changes.