Saravanan666/pypi-org huggingface dataset:
Package: nh5
nh5nh5is a Python package to support a new file format, .nh5, which stands for "not hdf5". This project aims to simplify and improve the loading of data in web applications, particularly for visualization purposes.This package has anvery smallcodebase. Feel free to explore!See theaccompanying JavaScript library.Motivation behind nh5The creation of this format was driven by specific challenges encountered with the hdf5 format, particularly in the context of web-based applications. While hdf5 is efficient for packaging large volumes of data, including data arrays, it falls short in terms of efficiency and ease when it comes to reading data from a web browser or from remote files. This inefficiency is primarily due to the scattered nature of metadata within the hdf5 file, necessitating multiple HTTP requests for access.Simplified Data Access:Unlike hdf5, where metadata can be dispersed throughout the file, nh5 starts with a straightforward JSON text header. This header contains all the necessary attributes and metadata about the data arrays and can be loaded in one shot by the browser.Predefined Data Structure:While hdf5 allows appending to an existing file, nh5 assumes that data is known in advance and needs to be packaged for visualization in web applications.InstallationYou can install nh5 using pippipinstallnh5UsageTo convert an hdf5 file to an nh5 file, use the h5_to_nh5 function:fromnh5importh5_to_nh5h5_to_nh5("path_to_your_hdf5_file.h5","path_for_the_output_nh5_file.nh5")To convert an nh5 file to an hdf5 file, use the nh5_to_h5 function:fromnh5importnh5_to_h5nh5_to_h5("path_to_your_nh5_file.nh5","path_for_the_output_hdf5_file.h5")LimitationsLimited data types: Does not support all hdf5 data types.No compression support: Currently, the nh5 format does not support data compression.ContributingContributions are welcome! If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.LicensingThe code in this project is licensed under Apache License 2.0.AuthorThe package and format were created by Jeremy Magland
Package: nhanes-dl
nhanes-dlPython Library to download Continuous NHANES codebooks into ready to use panda dataframes for all years!Current Supported Features:Downloading a specific CodebookCombining CodebooksDownload the linked Mortality dataCombining Linked Mortality data with CodebooksFuture Features:Better Documentation for download functionsDownload All codebooks for a year without specifying codebooksChecking if codebooks entered to download are valid for that yearBetter error messages if a download failsLinking to other NHANES link datasets
Package: nh-awssamlpy2
As part of AWS Governance to enhance the security with the accounts and IAM users, Federated API access is recommended for AWS resources instead of hard-coded IAM AcessID and SecretKeys in the config file.# Steps:If this is the first time install of this python package, use below command:For Python2.x version, pip install awssamlpy2
For Python3.x version, pip install awssamlpy3To upgrade this python package to latest version, use below command:For Python2.x version, pip install awssamlpy2 –upgrade
For Python3.x version, pip install awssamlpy3 –upgradeCreate a ‘awssaml.properties’ (~/awssaml.properties) file under your user home directory like below -[UserProp]
aws-region=us-east-1
aws-outputformat=json
idpurl=https://<Your Company AWS SAML Domain>/adfs/ls/IdpInitiatedSignOn.aspx?loginToRp=urn:amazon:webservicesPlease refer to Mesh DOC-111675 for the idpurlCreate a ‘configure’ file under ~/.aws directory without providing the values for access and secret keys. Dont worry if haven’t yet; the package will automatically create one if this file is not present. You may just choose the output and region fields as per your need.[default]
output = json
region = us-east-1
aws_access_key_id =
aws_secret_access_key =We have taken care to package the required modules. But if there are any additional packages required, install the missing modules based on the error encountered like below:On Linux, pip install <module>Eg: pip install requestsOn Windows, easy_install <module>Eg: easy_install requestsWhenever you need SAML access to your AWS services, just the command:aws-samlThis does the following:Verifies your ~/.aws/configure file to set the approriate region; OR creates one if its not presentPrompts the user for AD username/password and does SAML auth with our ADFSNOTE: Username has to be in the format <domain><networkID>Based on SAML response, prompts the user to choose the roles available on AWS for that userThen, stores the temporarily created credentials (using Amazon STS service) for the user in the ~/.aws/credentials file along with STS tokenUse API calls to work on AWS resourcesSample API call used in the script is for listing the S3 buckets, which is in Boto2.x format
Package: nhaystack
Version:0.0.6Author:Noveo Group (http://noveogroup.com/)Improvements, optimizations and back-ports for Haystack (with Elastic biases).Build statusRequirementsDjango: targeting LTS releases (1.6, 1.8),
tested against 1.6.11 only at the moment.Haystack: tested against 2.4.1Elastic: tested against 1.7.0elasticsearch-py: tested against 1.9.0elasticstack: tested against 0.4.1Features and goalsTODOWhy not just add stuff to upstream (Haystack or Elasticstack)?This project aims to solve problems and reuse code patterns between Noveo Group
projects. Parts of this work, once finalized, tested and proven, may become
suitable to be pushed to upstream.
Package: nhc2-coco
pynhc2License: MITUsageCreate a NHC2 objectNHC2(address, username, password, port, ca_path, switches_as_lights)address- IP or host of the connected controllerusername- The UUID of the profilepassword- The passwordport- (optional) The MQTT port. Default = 8883ca_path- (optional) Path of the CA file. Default = included CA file.switches_as_lights- (optional) socket and switched-generic show up as lights.example:coco = NHC2('192.168.1.2', 'abcdefgh-ijkl-mnop-qrst-uvwxyz012345', 'secret_password')What is supported?light, socket, switched-generic, dimmerWhat now?TODO - write doc.TODO - refactor into logical groups that match niko documentation (NHC Relay Action, NHC Dimmer Action, etc)What can you do to help?Contribute to this project with constructive issues, suggestions, PRs, etc.Help me in any way to get support for more entities (eg heating)
Package: nhc2-coco-2
# pynhc2License: MIT## Usage### Create a NHC2 object` NHC2(address, username, password, port, ca_path, switches_as_lights) `__address__ - IP or host of the connected controller__username__ - The UUID of the profile__password__ - The password__port__ - (optional) The MQTT port. Default = 8883__ca_path__ - (optional) Path of the CA file. Default = included CA file.__switches_as_lights__ - (optional) socket and switched-generic show up as lights.example:` coco =NHC2('192.168.1.2','abcdefgh-ijkl-mnop-qrst-uvwxyz012345','secret_password') `### What is supported?
light, socket, switched-generic, dimmer### What now?TODO - write doc.TODO - refactor into logical groups that match niko documentation (NHC Relay Action, NHC Dimmer Action, etc)## What can you do to help?Contribute to this project with constructive issues, suggestions, PRs, etc.Help me in any way to get support for more entities (eg heating)
Package: nhc2-coco3
pynhc2License: MITUsageCreate a NHC2 objectNHC2(address, username, password, port, ca_path, switches_as_lights)address- IP or host of the connected controllerusername- The UUID of the profilepassword- The passwordport- (optional) The MQTT port. Default = 8883ca_path- (optional) Path of the CA file. Default = included CA file.switches_as_lights- (optional) socket and switched-generic show up as lights.example:coco = NHC2('192.168.1.2', 'abcdefgh-ijkl-mnop-qrst-uvwxyz012345', 'secret_password')What is supported?light, socket, switched-generic, dimmerWhat now?TODO - write doc.TODO - refactor into logical groups that match niko documentation (NHC Relay Action, NHC Dimmer Action, etc)What can you do to help?Contribute to this project with constructive issues, suggestions, PRs, etc.Help me in any way to get support for more entities (eg heating)
Package: nh-currency
A simple currency module to:Retrive various info about currencyFormat currency priceConvert between currenciesRequirementspython3Internet connection (optional)Installation$pipinstallnh-currencyUsageimportcurrencyGet currency namecurrency.name('USD')currency.name('USD',plural=True)'US Dollar''US dollars'Symbolcurrency.symbol('CAD')currency.symbol('CAD',native=False)currency.symbol('NOK')currency.symbol('NOK',native=False)'$''CA$''kr''Nkr'Number of decimal digitscurrency.decimals('USD')currency.decimals('JPY')20Round to the maximum decimal digitscurrency.rounding(100.115735,'USD')currency.rounding(2253.12309,'ISK')100.122253Currency increment used for roundingcurrency.roundto('USD')currency.roundto('CHF')00.05Format currencycurrency.pretty(10050000.2394,'USD')currency.pretty(10050000.2394,'USD',trim=True)currency.pretty(10050000.2394,'USD',abbrev=False)'$10,050,000.2394''$10,050,000.24''10,050,000.2394 USD'Convert to other currencycurrency.convert('USD','EUR')currency.convert('USD','EUR',2)currency.convert('JPY','AUD',100)0.8157971.6315941.1759Currency infocurrency.info('USD')# Output has been formatted for representation purpose{'symbol':'$','name':'US Dollar','symbol_native':'$','decimal_digits':2,'rounding':0,'code':'USD','name_plural':'US dollars'}Testing$cd.../py-currency$python-munittestRelated WorkcoinifyResourceshttps://gist.github.com/Fluidbyte/2973986https://www.wikipedia.org/
Package: nhdnet
U.S. National Hydrography Dataset Network & Barrier Analysis ToolsThis library was used to help perform network connectivity analysis for theSoutheast Aquatic Barrier Prioritization Tool. Seehttps://github.com/astutespruce/sarp-connectivityfor more information about specific processing for that project.This library is intended to provide more general processing utilities to assist with analyzing connectivity using the National Hydrography Dataset (NHD) - High Resolution Plus version.We do not currently intend to add support for the NHD - Medium Resolution dataset. Pull requests are welcome to add this functionality.Due to the large size of NHD data, it may be possible to only process a single region at a time, or a group of regions. The key limits are based on the amount of available memory (RAM) and the file sizes of the outputs (shapefiles are limited to 2 GB in size).Key features:preprocessing utilities to prepare NHD data for analysis within this librarymerging of NHD flowlines between adjacent basins or regionsautomatic snapping barriers to nearest flowlines, including heuristics to aid with manual QA/QCcutting of NHD flowlines at barriersconstruction of functional upstream networks from a barrier to the next upstream barriers or origins of a stream networknetwork statisticsoptimized data I/O using thefeatherfile format for intermediate data products and customized serialization / deserialization of spatial dataNotes:reading / writing shapefiles using Geopandas can be very slow. We preferred to use thefeatherformat for intermediate files (geofeatherprovides spatial compatibility).data from NHD are downloaded as ArcGIS File Geodatabases. While these formats can be read (usually) using Geopandas, it is not possible to write this format, so shapefile outputs are generally the only option for use in GIS.Installationpip install nhdnetThis project usesGeoPandas,Pandas,rtree, andshapelyin Python 3.6+.We do not intend to support Python < 3.6.Due to the complexity of these libraries, installation instructions for your platform may vary from the following.rtreefirst requires the separate installation oflibspatialindex.
On MacOS:brew install spatialindexPython dependencies and virtual environment are managed usingpipenv.pipenv installIf you do not wish to usepipenv, see thePipfilefor the list of dependencies.Source DataNHD High Resolution data are downloaded by HUC4 fromNHD Data Distribution Site.DevelopmentThis project usesblackfor autoformatting andpylintfor linting.NotesKnown issues:flowlines that cross HUC4 or region boundaries need to be specifically handled as part of network analyses. Seehttps://github.com/astutespruce/sarp-connectivity/tree/master/analysis/networkfor more information about network analysis based on this library.CreditsThis project was made possible in partnership with theSoutheast Aquatic Resources Partnershipas part of a larger project to develop a comprehensive inventory of aquatic barriers in the Southeastern US and assess impacts of these barriers on aquatic systems.The results of this project are available in theSoutheast Aquatic Barrier Prioritization Tool.
Package: nhdpy
nhdpyA python port of thenhdRpackage for querying, downloading, and networking theNational
Hydrography Dataset (NHD)dataset.Installationcondaenvcreate-nnhdpy-fenvironment.yml# local install# pip install -e  .# development installpipinstallgit+git://github.com/jsta/nhdpy.git# development upgrade# pip install --upgrade git+git://github.com/jsta/nhdpy.gitUsageimportnhdpynhdpy.nhd_get(state="DC")nhdpy.nhd_list(state="DC")dc_waterbodies=nhdpy.nhd_load("DC","NHDWaterbody")# import matplotlib.pyplot as plt# dc_waterbodies.iloc[1:2].plot()# plt.show()
Package: nheatmap
nheatmapInspired by pheatmap in R, this plotting tool aims to enable multi-level heatmap with the option to perform hierarchical clustering. The goal is to develop a python plotting package that is both intuitive in usage and extensive in plotting configuration.Requirementspython >= 3.5matplotlib >= 3.0.3scipy >= 1.3.1numpy >= 1.17.2pandas >= 0.25.1InstallationOption 1 via pip:pipinstallnheatmapOption 2 via github:gitclonehttps://github.com/xuesoso/nheatmapInclude the cloned directory as part of the python pathUsageCheck herefor a tutorial on usage.To-doDocumentation of the codeCutting of heatmapBottom panel plotsEnable usage of AnnData structureHighlight individual rows / columnsOther plotting toolsDensityPlot
Package: nhelper
NheLPerNheLPeris Python package designed to easebehavioral testingof Natural Language Processing models to identify
possible capability failures.1. About the projectBehavioral tests are intended to test a model against some input data while treating as a black box. The aim is to
observe the model's reaction against some perturbations that might occur once the model is productionized. For a more
detailed explanation on behavioral testing of NLP models I encourage you to read the insightful
paper:Beyond Accuracy: Behavioral Testing of NLP models with CheckListNLPtestprovides helper objects for three different aspects:easily generate text samplestest some specific behaviors of your modelaggregate the tests outcomes of your model2. Getting started2.1. InstallationYou can directly installNheLPerusingpypi:pip3 install nhelper2.2. UsageTo help you get the hang of the library we provide three different Notebooks to the user, accessible from
theexamples/folder:Samples_generation.ipynb: shows you how to easily generate texts using theGeneratorobject.Please_Behave.ipynb: getting familiar with theBehaviorobject.End2End_tests.ipynb: how to run tests and get an overview of your model behavior.ReferencesBelow, you can find resources that were used for the creation ofNLPtestas well as relevant resources about
behavioral testing.MadeWithMLCheckListBeyond Accuracy: Behavioral Testing of NLP models with CheckList
Package: nhentaidevs
NhentaiDevs - Nhentai Scraper - 1.1.3This is my basic Nhentai Scraper for downloading images and fetching
information fromnhentai.netusing bs4, requests and other required modules.Installation and Single Code Use (replace pip and python with pip3 and python3 for linux):pipinstall--upgradenhentaidevs
python-mnhentaidevsyour_codeFor Multi-Code UseFor Windows and Linux (replace python with python3 for Linux):python-mnhentaidevsinitThis will create CodesHere.code file, where you can put all
your codes for multi-download purposes.[Nhentai]codes=[]# Insert your code/s in the 'codes' block (Use ',' for multi-codes)# Example:# codes=[177013, 228922, 4299]Finally, download your codes in the CodesHere.code file by simply running:For Windows and Linux (replace python with python3 for Linux):python-mnhentaidevsNote: Please update to the latest version as possible :>
Package: nhentai-doujin-downloader
This is a simple program that allows you to download Doujins from nHentai. It will save the Doujins as JPEG files, and will store them in the current directory. It will log to the console the page numbers that have been downloaded whilst the process is running, with a final message once the process has terminated successfully.Usage:—import importlibnh = importlib.import_module(“nHentaiDoujinDownloader”)nh.downloadDoujin(177013, 20) # (177013 is the ID of the Doujin, 20 is the number of pages.)—Change Log1.0.1 (16/06/2021)Initial release. Hope you like it!1.0.2 (16/06/2021)Edited README1.0.3 (16/06/2021)Got rid of unneccessary comments that would fill up your terminal screen.1.0.4 (16/06/2021)Minor bug fix.1.0.5 (16/06/2021)Edited README ( I really need to get better at Markdown )1.0.6 (16/06/2021)Minor bug fix.1.0.7 (16/06/2021)Minor bug fix.1.0.8 (16/06/2021)Edited README ( I really need to get better at Markdown )1.0.9 (16/06/2021)Minor bug fix.
Package: nhentai.py
nhentai.pyAPInhentai.get_doujin(id:int) -> DoujinReturns a doujin by given /g/id.nhentai.get_random_id() -> intReturns a random and valid /g/id.nhentai.get_homepage(page:int) -> List[Doujin]Returns a list of the most recently uploaded doujins on the homepage.nhentai.search(query:str, page:int, sort_by:str) -> List[Doujin]Returns a list of doujins related to a givenkeyword.nhentai.search_tagged(tag_id:int, page:int, sort_by:str) -> List[Doujin]Returns a list of doujins that have the given tag id.ExamplesGetting doujin using a /g/id>>> d = nhentai.get_doujin(123)
>>>
>>> # URL to first page's image.
>>> d[0].url 
'https://i.nhentai.net/galleries/635/1.jpg'
>>>
>>> d.__dict__
{'id': 123, 
'media_id': '635',
'thumbnail': 'https://t.nhentai.net/galleries/635/thumb.jpg',
'cover': 'https://t.nhentai.net/galleries/635/cover.jpg'}
'titles': {'english': '(C69) [Nakayohi Mogudan (Mogudan)] Omakehon 2005 (Neon Genesis Evangelion)', 'japanese': '(C69) [なかよひモグダン (モグダン)] おまけ本 2005 (新世紀エヴァンゲリオン)', 'pretty': 'Omakehon 2005'},
'tags': [Tag(id=17202, type='artist', name='mogudan', url='/artist/mogudan/', count=213)
...Getting a random doujin>>> id = nhentai.get_random_id()
>>> id
118927
>>>
>>> nhentai.get_doujin(id).tags
[Tag(id=17249, type='language', name='translated', url='/language/translated/', count=109734), 
...]
Package: nheri-simcenter
NHERI SimCenter Python DependenciesThenheri_simcenterPython package facilitates the installation of the Python dependencies required to run the backend applications used by the NHERI SimCenter. It is available at the Python Package Index (PyPI). With the help of this package, all of those dependencies can be installed usingpipas follows:pip install nheri_simcenterOn Mac computers, use the command below to ensure that the dependencies are installed under Python3:pip3 install nheri_simcenterLicensenheri_simcenteris distributed under the BSD 3-Clause license, see LICENSE.AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No. 1612843. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.ContactAdam Zsarnóczay, NHERI SimCenter, Stanford University,adamzs@stanford.edu
Package: nhk-api
Python client for NHK API.RequirementsPython 3.3Installation$ pip install nhk-apiUsagefrom datetime import date
from nhk import ProgramGuide


client = ProgramGuide(api_key='YOUR_API_KEY')

# Get program list
# http://api-portal.nhk.or.jp/doc_list-v1_con
program_list = client.pg_list('130', 'g1', date.today())
# or
# program_list = client.pg_list('東京', 'ＮＨＫ総合１', date.today())

# Get program list by genre
# http://api-portal.nhk.or.jp/doc_genre-v1_con
program_list_by_genre = client.pg_genre('130', 'g1', '0700', date.today())
# or
# program_list_by_genre = client.pg_genre('東京', 'ＮＨＫ総合１', 'アニメ／特撮(国内アニメ)', date.today())

# Get program information
# http://api-portal.nhk.or.jp/doc_info-v1_con
program_info = client.pg_info('130', 'g1', '2014021499999')
# or
# program_info = client.pg_info('東京', 'ＮＨＫ総合１', '2014021499999')

# Get information of program that is broadcasting now
# http://api-portal.nhk.or.jp/doc_now-v1_con
program_now = client.pg_now('130', 'g1')
# or
# program_now = client.pg_now('東京', 'ＮＨＫ総合１')
Package: nhkv
NHKVNHKV (no hassle key-value) is a library for on-disk key-value storage. The use case is primarily directed towards storing large objects. The goal was to make it lightweight and without external dependencies. Was created primarily for storing datasets for machine learning.InstallationpipinstallnhrvQuick StartfromnhkvimportKVStorestorage=KVStore("path/to/storage/location")# a folder is createdkey=0value="python serializable object"storage[key]=valuestorage.save()Available Storage ClassesDbDictData is stored in Sqlite3 database. THe idea id similar toSqliteDict. Functionality ofDbDictis inferior toSqliteDict, but overhead is also smaller.fromnhkvimportDbDictstorage=DbDict("path/to/storage/location",key_type=str)# a database file is created# can also use int keysstorage["string key"]="python serializable object"storage.save()# database transaction is not completed until saved explicitly# frequent saving affects performanceCompactKeyValueStoreThe data is kept in mmap file. The index is kept in memory, can become very large if many objects are stored. Mmap files are split in shards.fromnhkvimportCompactKeyValueStorestorage=CompactKeyValueStore("path/to/storage/location",# folder is createdserializer=lambdastring_:string_.encode("utf8"),# optional serializerdeserializer=lambdabytes_:bytes_.decode("utf8"),# optional deserializershard_size=1048576# optional shard size in bytes)# key is any type that can be used with `dict`storage["string key"]="python serializable object"storage.save()# save to ensure transaction is complete# frequent saving affects performancestorage.close()storage=CompactKeyValueStore.load("path/to/storage/location")KVStoreThe data is kept in mmap file. The index is kept either in sqlite or shelve database.fromnhkvimportKVStorestorage=KVStore("path/to/storage/location",# folder is createdindex_backend='sqlite',# possible options: sqlite | shelveserializer=lambdastring_:string_.encode("utf8"),# optional serializerdeserializer=lambdabytes_:bytes_.decode("utf8"),# optional deserializershard_size=1048576# optional shard size in bytes)# sqlite uses int keys# shelve uses str keysstorage[100]="python serializable object"storage.save()# save to ensure transaction is complete# frequent saving affects performancestorage.close()storage=KVStore.load("path/to/storage/location")AlternativesNHKV is closely related to libraries such asShelve- has non-zero probability of key collision, very slowSqliteDict- slower reads and writes, but more functionality (eg. multiprocessing)DiskCache- slower reads and writes, but more functionality (eg. cache features)LimitationStorage classes in this library are better suited for the batch writes and consecutive batch reads. This represents the intended use case: storing datasets for machine learning. Alternating many reads and writes will result in reduced performance.