Saravanan666/pypi-org huggingface dataset:
Package: arson
ARSON: A Restructured Object NotationARSON is JSON, with a little bit of sugar: Comments, Commas, and Tags.For example:{
    "numbers": +0123.0,       # Can have leading zeros
    "octal": 0o10,            # Oh, and comments too
    "hex": 0xFF,              #
    "binary": 0b1000_0001,    # Number literals can have _'s 

    "lists": [1,2,3,],        # Lists can have trailing commas

    "strings": "At least \x61 \u0061 and \U00000061 work now",
    "or": 'a string',         # both "" and '' work.

    "records": {
        "a": 1,               # Must have unique keys
        "b": 2,               # and the order must be kept
    },
}Along with some sugar atop JSON, ARSON supports tagging literals to represent types outside of JSON:@datetime "2017-11-22T23:32:07.100497Z", a tagged RFC 3339 datestamp@duration 60(a duration in seconds, float or int)@base64 "...==", a base64 encoded bytestring@set,@dict,@complex,@bytestringQuickstartUsepip install arsonto get the latest copy of the python library. Thearsonlibrary
has two methods,parseanddump.import arson

print(arson.parse(arson.dump([1,2,3])))If you want to use your own tagged object types, you can create a customCodec:import arson

class Example:
    def __init__(self, value):
        self.value = value
    def __repr__(self):
        return f"Example {self.value}"

def object_to_tagged(obj):
    if isinstance(obj, Example):
        return "Example", {"value": obj.value}
    else:
        raise NotImplementedError()

def tagged_to_object(name, value):
    if name == "Example":
        return Example(value['value'])
    else:
        raise NotImplementedError()
        
codec = arson.Codec(object_to_tagged, tagged_to_object)

print(codec.parse(codec.dump(Example(1))))Supported DatatypesThis library supports serializing and deserializing the following typesstr,int,boolfloat(including Infinity and Nan)list,tuple(but will always return a list)dict,set,OrderedDict(but will use dicts on 3.7+)datetime,timedelta(but will convert times to UTC)bytestring,bytearray(but will return a bytearray)complexThe fixed width numeric types are currently unsupported (Sorry).ARSON in a NutshellFile MUST be utf-8, not cesu-8/utf-16/utf-32, without surrogate pairs.Use#.... <end of line>for commentsByte Order Mark is treated as whitespace (along with\x09,\x0a,\x0d,\x20)ARSON Document is any ARSON Object, (i.e1is a valid ARSON file).Lists are[],[obj],[obj,],[obj, obj]... (trailing comma optional)Records are{"key": value}, keys must be unique, order must be preserved.Built-ins:true,false,null"unicode strings"with escapes\" \\ \/ \b \f \n \r \t \uFFFF \UFFFFFFFF, no control codes unecaped, and''can be used instead of"".int/float numbers (unary plus or minus, allowleading zeros, hex, octal, and binary integer liters)Tagged literals:@name [1,2,3]for any other type of value.Errors are fatal. A record with duplicate keys, or a string too long, or a number too big to represent MUST cause the parse to fail outright.
Package: arSort
a library for lazy people who are too lazy to write their own sortingthere are sorting:1 bubble sortingimportarSortarray=[0,6,8,4]sort=arSort.Sort(array)print(sort.bubbleSort("<"))#bubble sorting from smaller to largerprint(sort.bubbleSort(">"))#bubble sorting of more to lessinput:[0, 4, 6, 8]
[8, 6, 4, 0]2 sorting by shakerimportarSortarray=[0,6,8,4]sort=arSort.Sort(array)print(sort.shakerSort("<"))#sorting the shaker from smaller to largerprint(sort.shakerSort(">"))#sorting the shaker from bigger to smallerinput:[0, 4, 6, 8]
[8, 6, 4, 0]3 sorting combimportarSortarray=[0,6,8,4]sort=arSort.Sort(array)print(sort.hairbrushSort("<"))#sorting with a comb from smaller to largerprint(sort.hairbrushSort(">"))#sorting with a comb from more to lessinput:[0, 4, 6, 8]
[8, 6, 4, 0]4 sorting by insertsimportarSortarray=[0,6,8,4]sort=arSort.Sort(array)print(sort.insertsSort("<"))#sorting by inserts from smaller to largerprint(sort.insertsSort(">"))#sorting by inserts from larger to smallerinput:[0, 4, 6, 8]
[8, 6, 4, 0]5 sorting dwarvesimportarSortarray=[0,6,8,4]sort=arSort.Sort(array)print(sort.dwarvesSort("<"))#sorting dwarves from smaller to largerprint(sort.dwarvesSort(">"))#sorting dwarves from bigger to smallerinput:[0, 4, 6, 8]
[8, 6, 4, 0]6 sorting the bongo clown (insane)importarSortarray=[0,6,8,4]sort=arSort.Sort(array)#we strongly advise against doing this,#because the worst indicator of the time of this sorting is infinity of seconds.print(sort.bogoSort("<"))#sorting the bongo clown from smaller to largerprint(sort.bogoSort(">"))#sorting the bongo clown from bigger to smallerinput:[0, 4, 6, 8]
[8, 6, 4, 0]
Package: arsr
arbitrary-srIt's about arbitrary scale image super-resolution.UsageInstallationpipinstallHow to usefromarsrimportsrscale=2.4sr0=sr('edsr')up_img=sr.upsample(numpy_img,scale)# numpy_img {'type':numpy, 'dimension':(H,W,C) }methodensembleMetaSRImplicit representationUltraSR (Implicit representation + Spatial encoding)
Package: arst
Quick project generation.InstallationpipinstallarstCreating a new projectTwo shortcuts are installed:ars, andarst. These keys are
consecutive if you’re using a Colemak keyboard layout.arsgenerateproject-typeor if a project is already created, and we want to reaply the templates,
but with a tree diff for all the conflicting files. This will use the
program specified in theARS_DIFF_TOOLor in case the variable is
not definedvimdiff:arstgenerateproject-typeThis copies all the resources from the~/.projects/project-typeinto
the current folder. Files that have the.hbsextension will be used
as templates, and copied with the extension removed.The project type is sent asNAMEinto the handlebars templates.Thus if you have a structure such as:.projects/project-type
├── package.json.hbs
└── static
    └── index.htmlAfter thearsproject-typecommand you will have in your current
folder:.├──package.json└──static└──index.htmlThe package.json file will be parsed as expected.If the file name from the project ends with.KEEPon subsequent
calls from the same folder, it won’t be overwritten.ParametersParameters can be also passed to the templates themselves. In case a
parameter does not have a value,truewill be set instead.arsgeneratepackage-typename1=valuename2name3=3This will generate apackage-typeproject with the following
parameters sent into the handlebars template:{"templates":["package-type"],"name1":"value","name2":true,"name3":"3","arg0":"name1","arg1":"name2","arg2":"name3"}Since the templating also happens to the file names themselves, so a
file named{{name1}}.txtwill be installed asvalue.txt. This is
particularly useful in conjunction with the positional argument names,
making possible scenarios such as:arsgeneratenew-modelUserIf in our project we have:{{arg0}}.html.hbsand{{arg0}}.js.hbs, they will be expanded as:User.htmlandUser.js.Extra CommandsCommandDescriptiondiffDiff a file against the templateeditEdit a file from the projectgenerateGenerate or update the project
sourcesllsList a folder from the projectlsList the project folderpushPush a file into the templatepwdDisplay the project locationtreeDisplay the project treeversionPrint the current application
versionTable: Extra CommandsConfigurationIf you store your project files into a different folder, you can use theARS_PROJECTS_FOLDERenvironment variable to point to the absolute
path of it.Implicitly when creating a new project, an.arsfile will be created
with the current settings, so if the project is changed, you can reaplly
your project template. If you want not to have this file created, just
add a.noarsfile in the project template.
Package: arstecnica.raccoon.autobahn
author:Lele Gaifaxcontact:lele@metapensiero.itlicense:GNU General Public License version 3 or laterChanges0.4 (2015-10-31)Internal tweak, forward the debug flags to the transport factory0.3 (2015-10-23)Fix issue with the asyncio loop and txaio0.2 (2015-09-25)Minor packaging tweaks0.1 (2015-09-25)Renamed to arstecnica.raccoon.autobahn (to workaroundhttps://github.com/pypa/pip/issues/3)0.0 (unreleased)Initial effort.
Package: arstecnica.sqlalchemy.async
Theaiopgandaiomysqlprojects allow to operate asynchronously withPostgresQLandMySQL, respectively, even thruSQLAlchemy. Their
approach isn’t ideal, though, because they reimplement a considerable
amount of SA low level stuff with undesirable glitches.TheTwistedbasedAlchimiaway is much lighter and even if maybe
slightly less performant it does not introduce unexpected surprises.UsageBasically the module wraps a minimal set of SA classes (currently justEngine,Connection,TransactionandResultProxy) into
asyncronous counterparts, and you work on them like the following
example:from asyncio import coroutine
from arstecnica.sqlalchemy.async import create_engine

@coroutine
def do_something(db_url):
    engine = create_engine(db_url)
    with (yield from engine.connect()) as conn:
        with (yield from conn.begin()) as trans:
            yield from conn.execute(users.insert()
                                    .values(id=42, name="Async",))

        res = yield from conn.execute(users.select()
                                      .where(users.c.id == 42))
        rows = yield from res.fetchall()

        res = yield from conn.execute(users.delete()
                                      .where(users.c.id == 42))
        assert res.rowcount == 1If you are using Python 3.5 or better, the above should be written as:from arstecnica.sqlalchemy.async import create_engine

async def do_something(db_url):
    engine = create_engine(db_url)
    async with await engine.connect() as conn:
        async with await conn.begin() as trans:
            await conn.execute(users.insert()
                               .values(id=42, name="Async",))

        res = await conn.execute(users.select()
                                 .where(users.c.id == 42))
        rows = await res.fetchall()

        res = await conn.execute(users.delete()
                                 .where(users.c.id == 42))
        assert res.rowcount == 1TestsTo run the unit tests, you should:create a Python virtual environment and install this package in
development mode:python3 -m venv env
source env/bin/activate
python setup.py developinstallpytestand eitherpsycopg2orpymysql:pip install pytest psycopg2 pymysqlcreate a test database, for example withcreatedb testdbexecute thepy.testrunner with an environment variable with
the SA URL of the db:TEST_DB_URL="postgresql://localhost/testdb" py.test src
TEST_DB_URL="mysql+pymysql://localhost/testdb" py.test srcChanges0.4 (2015-09-25)Packaging tweaks0.3 (2015-09-23)Support Python 3.5 asynchronous context managers0.2 (2015-09-09)First (usable) distribution on PyPI0.1 (private)Works reasonably well!0.0 (private)Initial effort.
Package: artan
ArtanModel-parallel online latent state estimation with Apache Spark.OverviewDownloadDocs and ExamplesUsageOverviewThis library provides supports for model-parallel latent state estimation with Apache Spark, with a focus on online
learning compatible with structured streaming. Mainly developed for time series estimation of latent variables of many small
scale systems, this library could fit to your use case if you're looking for:Model-parallelism.Model-parallelism is the main mode of parallelism, such as training multiple similar time series
models from online measurements/multiple sensors, or same models with different priors/hyperparameters etc,..Online learning.Model parameters are updated sequentially with measurements with a single pass. The state used
by the algorithms are bounded with #models and model parameters.Latent state estimation.Focusing on methods for hidden state estimation, implemented methods include solutions for
filtering (Kalman filters, EKF, UKF, Multiple-Model Adaptive filters, etc..) problems, smoothing (RTS) problems,
finite mixture models (Multivariate Gaussian, Poisson, Bernoulli, etc,..).Artan requires Scala 2.12, Spark 3.0+ and Python 3,6+DownloadThis project has been published to the Maven Central Repository. When submitting jobs on your cluster, you can usespark-submitwith--packagesparameter to download all required dependencies including python packages.spark-submit --packages='com.github.ozancicek:artan_2.12:0.5.1'For SBT:libraryDependencies += "com.github.ozancicek" %% "artan" % "0.5.1"For python:pip install artanNote that pip will only install the python dependencies. To submit pyspark jobs,--packages='com.github.ozancicek:artan_2.12:0.5.1'argument should be specified in order to download necessary jars.Docs and ExamplesVisitdocsto get started and seeexamplesfor all sample scripts.Structured streaming examplesLocal linear trend filtering with Linear Kalman Filter (python,scala)Recursive least squares (python,scala)Nonlinear estimation with Extended Kalman Filter (scala)Nonlinear estimation with Unscented Kalman Filter (scala)Multiple-Model Adaptive estimation (scala)Online Gaussian Mixture Model (python,scala)
Package: artblog
artblogStatic site generator for art blogs written with markdown and lots of images.Goals:Simplicity (minimal configurability)Posts source data from multiple directories (e.g. multiple Git repositories)Limitations:No e-commerce featuresEasy To UseInstall:python -m pip install artblogGenerate config.yml template:artblog path/to/config.ymlConfigure: Edit thepath/to/config.ymlfileGenerate:artblog path/to/config.ymlCopy to your web host:scp -r output/* hosting_site:~/www/.Theconfig.ymlfile is where you specify the information about your site's URL, locations of your content, etc. In step 2 above, you specify where you want to create the template forconfig.yml. After that, you edit this file.Steps 4 and 5 are repeated as you add articles over time.
Package: artd-customer
ArtD CustomerArt Customer is a package that makes it possible to manage customers, tags, addresses and additional fields.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'artd_location', 'artd_partner', 'django-json-widget' 'artd_customer' ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_cities
Package: artd-infobip
ArtD InfobipArt Infobip is a package that makes it possible to integrate Infobip to send messages.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'artd_location', 'artd_partner', 'django-json-widget' 'artd_infobip' ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_cities
Package: artdirector
artdirectorSet a focal point and "artdirector" crop your image to different resolutions without missing the subject. Ideal to create images for mixed or responsive media.ExampleOriginal Image (by me)Variantsartdirector --focus-x 260 --focus-y 440 --height 600 --width 600 --zoom 0.0 --edge 3.0 example.jpeg test-1.jpeg
artdirector --focus-x 260 --focus-y 440 --height 600 --width 300 --zoom 0.2 --edge 3.0 example.jpeg test-2.jpeg
artdirector --focus-x 260 --focus-y 440 --height 600 --width 600 --zoom 0.7 --edge 3.0 example.jpeg test-3.jpegInstallationpip3 install artdirectorUsageUse it as a command-line tool:$ artdirector --help
usage: artdirector [-h] [--width WIDTH] [--height HEIGHT] [--focus-x FOCUS_X] [--focus-y FOCUS_Y] [--zoom ZOOM]
                      INPUT_FILE OUTPUT_FILE

positional arguments:
  INPUT_FILE         Input image
  OUTPUT_FILE        Output image

options:
  -h, --help         show this help message and exit
  --width WIDTH      Crop width
  --height HEIGHT    Crop height
  --focus-x FOCUS_X  Focal point
  --focus-y FOCUS_Y  Focal point
  --zoom ZOOM        Zoom between 0.0 - 1.0 (0.0. Default)
  --edge EDGE        Edge (size/n) around the focal target areaAs a Python modulefrom artdirector import ArtDirector

ad = ArtDirector()
ad.load('input.jpg')
ad.crop([400, 400], focus=[600, 300], zoom=0.3)
ad.save('output.jpg')
ad.filter_blur().filter_bw()
ad.save('output-blur-bw.jpg')

print(ad.image) # PIL Image
Package: artd-location
ArtD LocationArtD location is a package that makes it possible to have countries, regions and cities with their respective coding, by default we have all the regions and cities of Colombia.Add "artD_ocation" to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'artd_location', ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_cities
Package: artd-nequi
=================
Nequi IntegrationA Django app to create Nequi integration.Quick startAdd "artd_nequi" to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
     ...
     "django_json_widget",
     "artd_location",
     "artd_partner",
     "artd_nequi",
 ]Runpython manage.py migrateto create the nequi models.Runpython manage.py create_countriesto create countries.Runpython manage.py create_colombian_regionsto create colombian regions.Runpython manage.py create_colombian_citiesto create colombian cities.Start the development server and visithttp://127.0.0.1:8000/admin/
Package: artd-openpay
ArtD Open PayArtD Open Pay It is a package that contains the Open Pay payment gateway for electronic commerce from the BBVA bank.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'artd_location', 'artd_partner', 'django-json-widget' 'artd_customer' 'artd_openpay' ]Run python manage.py migrate to create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_cities
Package: artd-order
ArtD OrderArt Order is a package that makes it possible to manage orders.Add to your INSTALLED_APPS setting like this::INSTALLED_APPS = [ ... 'django_json_widget', 'artd_location', 'artd_partner', 'artd_customer', 'artd_product', 'artd_price_list', 'artd_stock', 'artd_order', ]Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_citiespython manage.py create_taxespython manage.py create_order_statuses
Package: artd-partner
ArtD PartnerArt Partner is a package that makes it possible to manage headquarters, locations, co-workers, etc.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'artd_location', 'artd_partner', ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_cities
Package: artd-price-list
ArtD Price ListArt Product is a package that makes it possible to manage price list and price list changes.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'django-json-widget' 'artd_location', 'artd_partner', 'artd_product', 'artd_price_list', ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_citiespython manage.py create_taxes
Package: artd-product
ArtD ProductArt Product is a package that makes it possible to manage categories, products, taxes, brands, etc.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'artd_location', 'artd_partner', 'django-json-widget' ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_citiespython manage.py create_taxes
Package: artd-promotion
ArtD PromotionArt promotion is a package that makes it possible to manage promotions and rules based on customers, categories and products.Add to your INSTALLED_APPS setting like this::INSTALLED_APPS = [ ... 'django-json-widget' 'artd_location', 'artd_partner', 'artd_customer' 'artd_product', 'artd_promotion', ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_citiespython manage.py create_taxes
Package: artd-shopify
ArtD ShopifyArtD Shopify is a package that connects with a store developed in Shopify and extracts products, their variants, prices, stock and images and other data.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'django-json-widget' 'artd_location', 'artd_partner', 'artd_product', 'artd_product_price', 'artd_stock', ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_citiespython manage.py create_taxesAfter you've installed the migrations and set up login details, you can import the information from Shopifypython manage.py import_shopify_product <<partner_slug>>
Package: artd-stock
ArtD StockArt Product is a package that makes it possible to manage stock and stock changes.Add to your INSTALLED_APPS setting like this:INSTALLED_APPS = [ ... 'django-json-widget' 'artd_location', 'artd_partner', 'artd_product', 'artd_stock', ]Runpython manage.py migrateto create the models.Run the seeder data:python manage.py create_countriespython manage.py create_colombian_regionspython manage.py create_colombian_citiespython manage.py create_taxes
Package: arte
# arte: Arcetri Random sTuff collEctionA collection of routines and utilities
developed by the Arcetri Astrophysical Observatory’s AO group.## DocumentationSee readthedocshttps://arte.readthedocs.io## Installation
__arte__ is tested on python 3.6+ onlyFrom pip (typically old version):`pip install arte`From source (latest, check that Python Package test is passing):` git clonehttps://github.com/ArcetriAdaptiveOptics/arte.gitcd arte pip install-e. `![Python package](https://github.com/ArcetriAdaptiveOptics/arte/workflows/Python%20package/badge.svg)
[![Coverage Status][codecov]][codecovlink]
[![Documentation Status](https://readthedocs.org/projects/arte/badge/?version=latest)](https://arte.readthedocs.io/en/latest/?badge=latest)
[![PyPI version][pypiversion]][pypiversionlink]
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