Saravanan666/pypi-org huggingface dataset:
Package: eqxvision
EqxvisionEqxvision is a package of popular computer vision model architectures built usingEquinox.InstallationUse the package managerpipto install eqxvision.pipinstalleqxvisionrequires:python>=3.7optional:torch, only ifpretrainedmodels are required.DocumentationAvailable athttps://eqxvision.readthedocs.io/en/latest/.UsagePicking a model and doing a forward pass is as simple as ...importjaximportjax.randomasjrimportequinoxaseqxfromeqxvision.modelsimportalexnetfromeqxvision.utilsimportCLASSIFICATION_URLS@eqx.filter_jitdefforward(net,images,key):keys=jax.random.split(key,images.shape[0])output=jax.vmap(net,axis_name=('batch'))(images,key=keys)...net=alexnet(torch_weights=CLASSIFICATION_URLS['alexnet'])images=jr.uniform(jr.PRNGKey(0),shape=(1,3,224,224))output=forward(net,images,jr.PRNGKey(0))What's New?FCN,DeepLabV3andLRASPPadded as new image segmentation models.Backward incompatible changes tov0.2.0for loading apretrainedmodel.Almost all image classification models are ported fromtorchvision.New tutorial for generatingadversarial examplesand others coming soon.Get Started!Start with any one of these easy to followtutorials.TipsBetter to use@equinox.filter_jitinstead of@jax.jit.Usejax.{v,p}mapwithaxis_name='batch'when using models that use batch normalisation.Don't forget to switch toinferencemode for evaluations. (model = eqx.tree_inference(model))Initialise Optax optimisers asoptim.init(eqx.filter(net, eqx.is_array)). (See here.)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Development ProcessIf you plan to modify the code or documentation, please follow the steps below:Fork the repository and create your branch fromdev.If you have modified the code (new feature or bug-fix), please add unit tests.If you have changed APIs, update the documentation. Make sure the documentation builds.mkdocs serveEnsure the test suite passes.pytest tests -vvvMake sure your code passes the formatting checks. Automatically checked with apre-commithook.AcknowledgementsEquinoxPatrick KidgerTorchvisionLicenseMIT
Package: era
Sane date and time tools for Python.[![Build Status](https://travis-ci.org/WiFast/era.svg?branch=master)](https://travis-ci.org/WiFast/era)What Is It?Era is a collection of tools for managing dates/times with associated timezone information. Some
fine points:Era assumes UTC for all timezones when not specified.Naive datetimes (those without timezones) are treated as being in the local timezone.You should be using UTC for all the things. See [Problems with Localtime][2].Documentation is inline in era.py.What’s In It?Imports so that you don’t have to import fifteen different modules.calendartzlocaltimezone - from pytzutc - from pytzdatedatetimetimetimedeltatzinfoUseful constants:utc - The UTC timezone.localtz - The local timezone.epoch - The Unix Epoch. 1 January 1970 in UTC.Day of week constants.mon = 0tue = 1wed = 2thu = 3fri = 4sat = 5sun = 6Time units. Each unit is a number of (or fraction of) seconds. These are naive in that they do not
account for differences in months (a month is 30 days here), leap years (a year is 365 days), leap
seconds, or any other real life scenario that would make the measurement incorrect.second = 1millisecond = second / 1000.0microsecond = millisecond / 1000.0minute = 60 * secondhour = 60 * minuteday = 24 * hourweek = 7 * daymonth = 30 * dayyear = 365 * dayDefined functions:is_time - Check if a value is a time.is_date - Check if a value is a date.is_datetime - Check if a value is a datetime.to_datetime - Convert a value to a datetime.settz - Set a timezone without accounting for a time change.totz - Convert a time from one timezone to another.localize - Convert a time to the local timezone.now - Get the current time in a particular timezone.localnow - Get the current time in the local timezone.daysago - Get a date from n days ago.timeago - Get a time from n time ago.ts - Get the Unix timestamp for a value (seconds since the epoch).tsms - Get the Javascript timestamp for a value (milliseconds since the epoch).fromts - Get a time from a Unix timestamp.fromtsms - Get a time from a Javascript timestamp.truncate - Reduce the precision of a date/time.weekday - Find the date for the given day of the week.prevweekday - Find the date for the most recent day of the week.nextweekday - Find the date for the next week day.LicenseCopyright (c) 2014 WiFast, Inc. This project and all of its contents is licensed under the
BSD-derived license as found in the included [LICENSE][1] file.[1]:https://github.com/WiFast/era/blob/master/LICENSE“LICENSE”
[2]:http://pytz.sourceforge.net/#problems-with-localtime“Problems with Localtime”
Package: era5cli
A command line interface to download ERA5 data from theCopernicus Climate Data Store.Withera5cliyou can:Download meteorological data in GRIB/NetCDF, including ERA5 data from the preliminary back extension, and ERA5-Land data.List and retrieve information on available variables and pressure levelsSelect multiple variables for several months and yearsSplit outputs by years (and optionally months), producing a separate files instead of merging them in one fileDownload multiple files at onceExtract data for a sub-region of the globeFree software: Apache Software License 2.0Documentation:https://era5cli.readthedocs.io
Package: eradicate
eradicateremoves commented-out code from Python files.IntroductionWith modern revision control available, there is no reason to save
commented-out code to your repository.eradicatehelps cleans up
existing junk comments. It does this by detecting block comments that
contain valid Python syntax that are likely to be commented out code.
(It avoids false positives like the sentencethis is not good,
which is valid Python syntax, but is probably not code.)Example$eradicate--in-placeexample.pyBefore runningeradicate.#import os# from foo import junk#a = 3a=4#foo(1, 2, 3)deffoo(x,y,z):# print('hello')print(x,y,z)# This is a real comment.#return TruereturnFalseAfter runningeradicate.a=4deffoo(x,y,z):print(x,y,z)# This is a real comment.returnFalseWhitelistingFalse positives can happen so there is a whitelist feature to fix them shorthand.
You can either add entries to the default whitelist with--whitelist-extendor overwrite the default with--whitelist.
Both arguments expect a string of#separated regex strings (whitespaces are preserved). E.g.eradicate--whitelist"foo#b a r" filenameThose regex strings are matched case insensitive against the start of the comment itself.For the default whitelist please seeeradicate.py.RelatedThere are different tools, plugins, and integrations foreradicateusers:flake8-eradicate- Flake8 plugin to find commented out or dead code.
Package: erajp
|pypi| |pypi| |GitHub license| |travis| |coveralls|erajp: Convert datetime to Japanese era=======================================Requirements-------------  Python 2.7+-  Python 3.2+Installation------------Install erajp via PyPI:::pip install erajpHow to use----------.. code:: python>>> strjpftime()'H27.08.05' # now>>> strjpftime(datetime.datetime(1989, 1, 8))'H1.01.08'>>> strjpftime(datetime.datetime(1989, 1, 8), u"%O%E年")'平成元年'New available date format~~~~~~~~~~~~~~~~~~~~~~~~~-  %o : alpabet era-  %O : Chinese charactor era-  %E : era yearMain Project Website.---------------------https://github.com/recruit-mtl/erajpLicense-------MIT License.. |pypi| image:: https://img.shields.io/pypi/dm/erajp.svg:target: https://pypi.python.org/pypi/erajp.. |pypi| image:: https://img.shields.io/pypi/v/erajp.svg:target: https://pypi.python.org/pypi/erajp.. |GitHub license| image:: https://img.shields.io/github/license/recruit-mtl/erajp.svg:target: https://github.com/recruit-mtl/erajp.. |travis| image:: https://img.shields.io/travis/recruit-mtl/erajp.svg:target: https://travis-ci.org/recruit-mtl/erajp.. |coveralls| image:: https://img.shields.io/coveralls/recruit-mtl/erajp.svg:target: https://coveralls.io/github/recruit-mtl/erajp
Package: eralchemy2
Entity relation diagrams generatoreralchemy2 generates Entity Relation (ER) diagram (like the one below) from databases or from SQLAlchemy models.
Works with SQLAlchemy < 1.4 but also with versions greater than 1.4ExampleExample for NewsMemeQuick StartInstallTo install eralchemy2, just do:$ pip install eralchemy2eralchemy2requiresGraphVizto generate the graphs and Python. Both are available for Windows, Mac and Linux.Usage from Command LineFrom a database$ eralchemy2 -i sqlite:///relative/path/to/db.db -o erd_from_sqlite.pdfThe database is specified as aSQLAlchemydatabase url.From a markdown file.$ curl 'https://raw.githubusercontent.com/maurerle/eralchemy2/main/example/newsmeme.er' > markdown_file.er
$ eralchemy2 -i 'markdown_file.er' -o erd_from_markdown_file.pdfFrom a Postgresql DB to a markdown file excluding tables namedtempandaudit$ eralchemy2 -i 'postgresql+psycopg2://username:password@hostname:5432/databasename' -o filtered.er --exclude-tables temp auditFrom a Postgresql DB to a markdown file excluding columns namedcreated_atandupdated_atfrom all tables$ eralchemy2 -i 'postgresql+psycopg2://username:password@hostname:5432/databasename' -o filtered.er --exclude-columns created_at updated_atFrom a Postgresql DB to a markdown file for the schemaschema$ eralchemy2 -i 'postgresql+psycopg2://username:password@hostname:5432/databasename' -s schemaUsage from Pythonfromeralchemy2importrender_er## Draw from SQLAlchemy baserender_er(Base,'erd_from_sqlalchemy.png')## Draw from databaserender_er("sqlite:///relative/path/to/db.db",'erd_from_sqlite.png')ArchitectureThanks to it's modular architecture, it can be connected to other ORMs/ODMs/OGMs/O*Ms.ContributeEvery feedback is welcome on theGitHub issues.To run the tests, use :$ py.testor$ tox.
Some tests require a local postgres database with a schema named test in a database
named test all owned by a user named eralchemy with a password of eralchemy.
This can be easily set up using docker-compose with:docker-compose up -d.All tested PR are welcome.Noteseralchemy2 is a fork of its predecessorERAlchemyby @Alexis-benoist, which is not maintained anymore and does not work with SQLAlchemy > 1.4.
If it is maintained again, I'd like to push the integrated changes upstream.ERAlchemy was inspired byerd, though it is able to render the ER diagram directly
from the database and not just only from theERmarkup language.Released under an Apache License 2.0Initial Creator: Alexis BenoistAlexis_Benoist
Package: eralegis
EraLegisConfigurando o ambiente$python3-mvenvvenv
$sourcevenv/bin/activate(venv)$Instalando via gitgitclonehttps://github.com/tiagocordeiro/eralegis.gitInstalando via pippipinstalleralegisImportando o módulo$python
>>>fromeralegisimportthelemicdate
>>>thelemicdate.now()'☉ in ... ☽ in ... Dies ... Anno ... æræ novæ'>>>thelemicdate.in_day(1905,4,8,12,30)'☉ in 18º ♈ ☽ in 28º ♉ Dies Saturnii Anno I:0 æræ novæ'
Package: erasehate
Erase Hate Python Library: Library for the Erase Hate API!This is the official Python library for interfacing with the EraseHateApp.com API. Erase Hate App allows users to perform Natural Language Processing and classification of text as ‘hateful’, ‘offensive’, or ‘neither’ through the use of ML and neural network deep learning. If users disagree with the model’s analysis, they may then reclassify an item choosing the classification they think is the best fit. Their reclassification data is then used in further continuous model training. Use this library to easily execute hate speech classification and perform many of the same functions available at EraseHateApp.com . Built for the purpose of allowing external Developers to integrate NLP into their own projects. Developers are strongly encouraged to submit any user reclassified data to the Erase Hate server, for use in model retraining.Need data?The Erase Hate Library also wraps the Twitter API, using the tweepymashup twitter library, to  allow users the ability to source text data from Twitter keyword or user timeline searches.When using the erasehateapp.com API, it is highly recommended to use this libraryData sent to the classification server, its results and any reclassification results are stored by erasehateapp.com for the purposes of improving the classification model. No personal information, or identifying information is stored.InstallationThe easiest way to install the latest version
is by using pip/easy_install to pull it from PyPI:pip install erasehateYou may also use Git to clone the repository from
GitHub and install it manually:git clonegit@github.com:oblockton/Erase_Hate_Python_Library.gitcd Erase_Hate_Python_Librarypython setup.py installPython 3.5, 3.6, & 3.7 are supported.
Package: erasure
erasure.pyPython client for the erasure protocolErasure Protocol version:1.2.0Setup:Create & activate virtulenv (python 3.6+)Install the library using pypi.pip install erasureUsage:The client is built to replicate the usagehere.Update thesettingsby configuring the appropriate enviornment variables.Initializea client:erasure_client = ErasureClient(w3, mode, version)Createa feed:feed = Feed(erasure_client=erasure_client, feed_address=FEED_ADDRESS)Submita post:receipt = feed.create_post(raw_data, key=key)Note: The encryption keys, and data for this post will be saved inERASURE_KEY_STORE/<proof_hash>Reveala post:key_cid, data_cid = post.reveal()Development:Clone & enter the repo.git clone https://github.com/ankitchiplunkar/erasure.py.gitInstall required libraries.pip install -r requirements.txtTesting:Launch a local version of erasure protocol viaganacheInstall, initlalize and run the ipfs daemon locallyipfs daemonRun the tests locallypytest -vv tests/
Package: eratools
eratoolsA package that extends xarray with functionality to work with ECMWF ERA data.IntroductioneratoolsloadsECMWFERA5 or ERA-Interim data files that contain model level data of temperature, relative humidity, surface pressure, and surface geopotential.
It uses these variables to calculate pressure and temperature data on pressure levels and altitude levels.Also included is a utility for downloading daily files of ERA5 or ERA-Interim data.Further readingECMWF IFS dosumentationCy38r1 Part IIIECMWF referencedpaperExamplecodeon ECMWF's wikiInstallationTo create a conda environmen and install run:conda env create -f environment.yml
source activate eratools
pip install eratoolsOptional DependenciesIf you wish to use the ERA downloading utilities, you will need to follow the instructionshereSupported ProductsERA5ERA-Interim
Package: erbium
erbiumShort blurb about what your product does.One to two paragraph statement about your product and what it does.InstallationOS X & Linux:pip3installerbiumWindows:pipinstallerbiumUsage exampleA few motivating and useful examples of how your product can be used. Spice this up with code blocks and potentially more screenshots.For more examples and usage, please refer to theWiki.Development setupDescribe how to install all development dependencies and how to run an automated test-suite of some kind. Potentially do this for multiple platforms.pipinstall--editableerbiumDocumentationRead the DocsMetaStephen R A King :stephen.ra.king@gmail.comDistributed under the MIT License. SeeLICENSEfor more information.https://github.com/Stephen-RA-King/erbium
Package: erc1820-predeployed
erc1820-predeployedDescriptionA tool for generating predeployed ERC1820Registry smart contractInstallationpip install erc1820-predeployedUsage examplefromerc1820_predeployedimportERC1820RegistryGenerator,ERC1820_REGISTRY_ADDRESSerc1820_registry_generator=ERC1820RegistryGenerator()genesis={# genesis block parameters'alloc':{**erc1820_registry_generator.generate_allocation(contract_address=ERC1820_REGISTRY_ADDRESS)}}
Package: erc721
Project DescriptionA simple interface to interact with the ethereum ETL data stored in public Google BigQuery datasets. Requires a GCP Project to interact with the data.Setup and InstallationFirstly you must have set up a GCP project to interact with the public google BigQuery datasets using Python. To do this follow the instructions located under quick setuphere.Next, install this module from PyPi.pip install erc721That's it, once this is setup you should be able to interact with the provided functions.InteractionsThere are a few simple interactions provided:get_all_collection_transfers_sql(nft_token_address)This fetches all of the transfers and sales for a single ERC721 token. For example to get all transfers for BAYC, pass the token address "0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d" as a string to the function.This will extract all of the sales and transfer data for that desired collection and return it as a pandas dataframe.Additionally a highwatermark (of the blocknumber) can be passed to this function to allow for faster batch processing from past that point only. The function will return a second value which is a new highwatermark of the highest block number returned.get_all_sales(all_data=None,collection_address=None)This fetches all of the sales data and returns it as a pandas dataframe. This can either be called alone by being passed a collection address (for BAYC: collection_address="0xbc4ca0eda7647a8ab7c2061c2e118a18a936f13d") or if you have already extracted data using get_all_collection_transfers, the result of this can be passed.get_all_transfers(all_data=None,collection_address=None)Working in the same way as the above function, this function returns all of the transfers (without any payment).get_all_sales_and_transfers(all_data=None,collection_address=None)Working in the same way as the two above functions, this function returns two arguments all sales and all transfers respectively as two separate pandas dataframes.
Package: er-calvin
CalvinWhat is this?Calvin is an application environment that lets things talk to things. It comprises of both a development framework for IoT application developers, and a runtime environment which handles the running application. Calvin is based on the fundamental idea that application development should be simple and fun. There should be no unnecessary impediments between an idea and its implementation, and an app developer should not have to worry about communication protocols or hardware specifics (but will not stop you from doing it if you want to.)Getting StartedGo to theCalvin Wikifor instructions on how to install and configure Calvin, and how to write and deploy applications.For the really impatient, the following may work:At a prompt, execute:pip install er-calvinStart a Calvin runtime:csruntime --host localhost --gui-mock-devicesPoint your browser tohttp://localhost:8000If you encounter problems have a look at the wiki. If all else fails, post an issue describing the problem.ContactThis is a community project that was started by a team in Ericsson Research. If you have questions or problems,report an issueand we will get back go you as soon as we can.RelatedOther members of the Calvin family includecalvin-constrained, a smaller runtime aimed at supporting devices with constrained or otherwise limited resources available.Open issuesFewer than before.
Package: ercotutils
ERCOT Python UtilityThis library aims to ease the use of the ERCOT MIS system.How to installpip install ercotutilsHow to usefromercotutilsimportmisutil# Get a list of all the ICE documents for a given report typeice_doc_list=misutil.get_ice_doc_list('13499')....# Get the file contents of a zipped csv documentbyte_content=misutil.get_zipped_file_contents('4433444')LicenseMIT License
Package: erdammer
Simple tool to generate database schema documentation and export in svg, dot, rst or csvInstallpipinstallerdammerUsageRunerdammer –helpfor a list of options.Things to note, to connect to the database you will have to supply a connection string uri.
For exampleerdammer--uri"mysql+mysqlconnector://user:password@server/dbname"-output="db-schema/"-format=svgBy default svg export creates a file callederd.svgin the directory you specified in output, in this
case it is./db-schema.If on the other hand you executeerdammer--uri"mysql+mysqlconnector://user:password@server/dbname"-output="db-schema/"-format=rstOne ReStructured file per table will be created, with table name as filename.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: erdantic
erdantic: Entity Relationship Diagramserdanticis a simple tool for drawingentity relationship diagrams (ERDs)for Python data model classes. Diagrams are rendered using the venerableGraphvizlibrary. Supported data modeling frameworks are:Pydantic V2Pydantic V1 legacydataclassesfrom the Python standard libraryFeatures include a convenient CLI, automatic native rendering in Jupyter notebooks, and easy extensibility to other data modeling frameworks. Docstrings are even accessible as tooltips for SVG outputs. Great for adding a simple and clean data model reference to your documentation.Installationerdantic's graph modeling depends onpygraphvizandGraphviz, an open-source C library. If you are on Linux or macOS, the easiest way to install everything together is to use conda and conda-forge:condainstallerdantic-cconda-forgeIf not using conda, Graphviz must be installed first (before you can install pygraphviz). For recommended options and installation troubleshooting, see thepygraphviz docs. Then to install erdantic and its Python dependencies from PyPI:pipinstallerdanticDevelopment versionYou can get the development version from GitHub with:pipinstallgit+https://github.com/drivendataorg/erdantic.git#egg=erdanticQuick UsageThe fastest way to produce a diagram like the above example is to use the erdantic CLI. Simply specify the full dotted path to your data model class and an output path. The rendered format is interpreted from the filename extension.erdanticerdantic.examples.pydantic.Party-odiagram.pngYou can also import the erdantic Python library and use its functions.importerdanticaserdfromerdantic.examples.pydanticimportParty# Easy one-linererd.draw(Party,out="diagram.png")# Or create a diagram object that you can inspect and do stuff withdiagram=erd.create(Party)diagram.models#> [PydanticModel(Adventurer), PydanticModel(Party), PydanticModel(Quest), PydanticModel(QuestGiver)]diagram.draw("diagram.png")Check out the "Usage Examples" section of ourdocsto see more.
Package: erdc-quest
# Environmental Simulator QuestGitter | Linux/Mac | Windows | ReadTheDocs | Test Coverage |——— | ——— | ——— | ————- | ————- |[![Join the chat athttps://gitter.im/Quest-Development/Quest](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/Quest-Development/Quest) | [![Build Status](https://travis-ci.org/erdc/quest.svg?branch=master)](https://travis-ci.org/erdc/quest) | [![Build Status](https://ci.appveyor.com/api/projects/status/e20arxcfrcmb2ylm/branch/master?svg=true)](https://ci.appveyor.com/project/dharhas/quest) | [![Documentation Status](https://readthedocs.org/projects/quest/badge/?version=latest)](https://quest.readthedocs.io/en/latest/?badge=latest) | [![Coverage Status](https://coveralls.io/repos/github/erdc/quest/badge.svg)](https://coveralls.io/github/erdc/quest) |### Project Description
Quest is a Python library that provides the ability to search, publish and download data (both geographical and non-geographical) from multiple data sources, including local repositories and web-based data providers. Quest also provides a suite of tools for manipulating and transforming data once it is downloaded.### Project Links
- Here is a live link for the Quest Documentation:https://quest.readthedocs.io/en/latest/Quest was designed to be extensible and has three types of plugins (io, tool, and provider). Provider plugins allow Quest to search for data from remote and local data providers. Tool plugins alloww Quest to perform different data manipulations. I/O plugins allows Quest to read and write different file formats.Here is a link to an example Quest Plugin:https://github.com/quest-dev/quest_ncep_provider_plugin