Saravanan666/pypi-org huggingface dataset:
Package: pyrebrickable
This is pyrebrickable, tools for thewww.rebrickable.comwebsiteIt provides:an auto-generated rebrickable API (in rebrickable.api)a CLI wrapper around that API (in rebrickable.cli)To use the CLI, first register your API key withrebrickable registerThen login withrebrickable users loginAfterards, you can use the LEGO API throughrebrickable lego, to access
data about sets, parts, etcTo use the user API, userebrickable user, to access and modify data about
user sets, set lists ,etca SQLalchemy wrapper around the monthly database dumps (in rebrickable.data)It uses the monthly data dumps (https://www.rebrickable.com/downloads) and provides
SQLAlchemy models to query the data inside. Data is stored in a local SQLite file
in your user directory (inside %APPDATA% on Windows, ~/.local/share on Linux, ~/Library/Application Support/ on MacOS)rebrickable data downloadto download csv files from rebrickablerebrickable data importto import them in a databaserebrickable data reset-dbto reset the database
#EXPERIMENTAL#rebrickable data download-extrato download data from rebrickable that are not in the dumps (e.g. MOCs data)see some examples in examples folderThe full documentation reference is athttps://rienafairefr.github.io/pyrebrickable
Package: pyrebrickable-cli
It supports the v3 API through it’s openAPI specification.https://rebrickable.com/api/v3/swagger/?format=openapiIt uses the rebrickable_api package, which can be found independently in pyrebrickable_api
The rebrickable_api package was auto-generated by Swagger Codegenhttps://github.com/swagger-api/swagger-codegen)Models for Part, Set, etc. have been manually added to provide meaningful results from HTTP responsesSome endpoints might not work, don’t hesitate to file an issue
Home-page:https://rienafairefr.github.io/pyrebrickable/Author: UNKNOWN
Author-email:rienafairefr@gmail.comLicense: UNKNOWN
Description: UNKNOWN
Keywords: LEGO,Rebrickable,CLI
Platform: UNKNOWN
Package: pyrebrickable-data
It uses the monthly data dumps (http://www.rebrickable.com/downloads) and provides
SQLAlchemy models to query the data insideYou gain access to the rebrickable-data cli :rebrickable-data downloadto download csv files from rebrickablerebrickable-data importto import them in a databasesee examples in examples folderHome-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
License: UNKNOWN
Description: UNKNOWN
Keywords: LEGO,Rebrickable,SQL
Platform: UNKNOWN
Package: pyrec
Recordis a datatype similar to the Cstruct: an ordered collection of data members with predefined names. It’s like the Pythonnamedtuplebut with mutable attributes and some extra conveniences.>>> from pyrec import Record

>>> Point = Record('Point', 'x y z')

>>> p = Point(1, 2, 3)
>>> p.z = 9
>>> p
<Record object Point(x=1, y=2, z=9)>For the full documentation, seehttps://github.com/bintoro/pyrec.
Package: pyrecdp
RecDP - one stop toolkit for AI data processWe provide intel optimized solution forAuto Feature Engineering-  Provides an automatical way to generate new features for any tabular dataset which containing numericals, categoricals and text features. It only takes 3 lines of codes to automatically enrich features based on data analysis, statistics, clustering and multi-feature interacting.LLM Data Preparation- Provides a parallelled easy-to-use data pipeline for LLM data processing. It supports multiple data source such as jsonlines, pdfs, images, audio/vides. Users will be able to perform data extraction, deduplication(near dedup, rouge, exact), splitting, special_character fixing, types of filtering(length, perplexity, profanity, etc), quality analysis(diversity, GPT3 quality, toxicity, perplexity, etc). This tool also support to save output as jsonlines, parquets, or insertion into VectorStores(FaissStore, ChromaStore, ElasticSearchStore).How it worksInstall this tool through pip.DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jre graphviz
pip install pyrecdp[all] --preRecDP - Tabularlearn moreAuto Feature Engineering PipelineOnly3lines of codes to generate new features for your tabular data. Usually 5x new features can be found with up to 1.2x accuracy boostfrom pyrecdp.autofe import AutoFE

pipeline = AutoFE(dataset=train_data, label=target_label, time_series = 'Day')
transformed_train_df = pipeline.fit_transform()High Performance on Terabyte Tabular data processingRecDP - LLMlearn moreLow-code Fault-tolerant Auto-scaling Parallel Pipelinefrom pyrecdp.primitives.operations import *
from pyrecdp.LLM import ResumableTextPipeline

pipeline = ResumableTextPipeline()
ops = [
    UrlLoader(urls, max_depth=2),
    DocumentSplit(),
    ProfanityFilter(),
    PIIRemoval(),
    ...
    PerfileParquetWriter("ResumableTextPipeline_output")
]
pipeline.add_operations(ops)
pipeline.execute()LICENSEApache 2.0DependencySpark 3.4.*python 3.*Ray 2.7.*
Package: pyre-check
Pyre is a performant type checker for Python compliant withPEP 484. Pyre can analyze codebases with millions of lines of code incrementally – providing instantaneous feedback to developers as they write code. You can try it out on examples inthe Pyre Playground.Pyre ships withPysa, a security focused static analysis tool we've built on top of Pyre that reasons about data flows in Python applications. Please refer to ourdocumentationto get started with our security analysis.Pysa is also available on theGitHub Marketplace as a Github ActionRequirementsTo get started, you needPython 3.8 or laterandwatchmanworking on your system. OnMacOSyou can get everything withhomebrew:$brewinstallpython3watchmanOnUbuntu,Mint, orDebian; useapt-getandhomebrew:$sudoapt-getinstallpython3python3-pippython3-venv
$brewinstallwatchmanWe tested Pyre onUbuntu 18.04.5 LTS,CentOS 7, as well asOSX 10.11and later.Setting up a ProjectWe start by creating an empty project directory and setting up a virtual environment:$mkdirmy_project&&cdmy_project
$python3-mvenv~/.venvs/venv
$source~/.venvs/venv/bin/activate(venv)$pipinstallpyre-checkNext, we teach Pyre about our new project:(venv)$pyreinitThis command will set up a configuration for Pyre (.pyre_configuration) as well as watchman (.watchmanconfig) in your project's directory. Accept the defaults for now – you can change them later if necessary.Running PyreWe are now ready to run Pyre:(venv)$echo"i: int = 'string'">test.py(venv)$pyreƛFound1typeerror!
test.py:1:0Incompatiblevariabletype[9]:iisdeclaredtohavetype`int`butisusedastype`str`.This first invocation will start a daemon listening for filesystem changes – type checking your project incrementally as you make edits to the code. You will notice that subsequent invocations ofpyrewill be faster than the first one.For more detailed documentation, seehttps://pyre-check.org.Join the Pyre communitySeeCONTRIBUTING.mdfor how to help out.LicensePyre is licensed under the MIT license.
Package: pyre-check-nightly
Pyre is a performant type checker for Python compliant withPEP 484. Pyre can analyze codebases with millions of lines of code incrementally – providing instantaneous feedback to developers as they write code. You can try it out on examples inthe Pyre Playground.Pyre ships withPysa, a security focused static analysis tool we've built on top of Pyre that reasons about data flows in Python applications. Please refer to ourdocumentationto get started with our security analysis.Pysa is also available on theGitHub Marketplace as a Github ActionRequirementsTo get started, you needPython 3.8 or laterandwatchmanworking on your system. OnMacOSyou can get everything withhomebrew:$brewinstallpython3watchmanOnUbuntu,Mint, orDebian; useapt-getandhomebrew:$sudoapt-getinstallpython3python3-pippython3-venv
$brewinstallwatchmanWe tested Pyre onUbuntu 18.04.5 LTS,CentOS 7, as well asOSX 10.11and later.Setting up a ProjectWe start by creating an empty project directory and setting up a virtual environment:$mkdirmy_project&&cdmy_project
$python3-mvenv~/.venvs/venv
$source~/.venvs/venv/bin/activate(venv)$pipinstallpyre-checkNext, we teach Pyre about our new project:(venv)$pyreinitThis command will set up a configuration for Pyre (.pyre_configuration) as well as watchman (.watchmanconfig) in your project's directory. Accept the defaults for now – you can change them later if necessary.Running PyreWe are now ready to run Pyre:(venv)$echo"i: int = 'string'">test.py(venv)$pyreƛFound1typeerror!
test.py:1:0Incompatiblevariabletype[9]:iisdeclaredtohavetype`int`butisusedastype`str`.This first invocation will start a daemon listening for filesystem changes – type checking your project incrementally as you make edits to the code. You will notice that subsequent invocations ofpyrewill be faster than the first one.For more detailed documentation, seehttps://pyre-check.org.Join the Pyre communitySeeCONTRIBUTING.mdfor how to help out.LicensePyre is licensed under the MIT license.
Package: pyrecipepuppy
pyrecipepuppyA simple Python wrapper for theRecipe Puppy APIMakes requests to the Recipe Puppy API to retrieve the recipes containing a given list
of ingredients and/or a given search term. It returns the list of all the
recipes returned by the API. For each recipe the following data might
be available:titlelink to a web page containing the entire recipethe list of ingredients used in the recipelink to a thumbnail photo with the result of the recipeFor more information on the API, please visit theRecipe Puppy API web page <http://www.recipepuppy.com/about/api/>_Installationpip install pyrecipepuppyUsage::import pprint
from pyrecipepuppy import RecipePuppy

rp = RecipePuppy()
recipes = rp.get_recipes(ingredients='salmon,onions', search_query='omelet')
pprint.pprint(recipes)
Package: pyrecipes
PyrecipesRecipes fromPython Cookbook, Third Edition, by David Beazley and Brian K. Jones. Copyright © 2013 David Beazley and Brian Jones. Published by O'Reilly Media, Inc. Used with permission.This project implements a simple CLI tool to list, run and view these recipes.Special thanks to O'Reilly Media, Inc and the Authors for permission to use their recipes.Check out Author David Beazley's website:https://www.dabeaz.com/Add this amazing book to your bookshelfhereInstallationpip install pyrecipesExample UsageShow recipes help and subcommandsrecipesList all chaptersrecipes chaptersList all recipesrecipes lsList all recipes in a specific chapterrecipes ls 1List all recipes in a specific chapter with a short descriptionrecipes ls 1 -dShow recipe coderecipes show 1 3Run the recipe as a scriptrecipes run 1 3Search for recipes containing a patternRegEx is supported.recipes search 'itertools'
recipes search 'itertools' --color green
recipes search 'event' --ignore-case
recipes search 'functools' -c
recipes search '[a-z]\d[^\s]'
Package: pyrecoil
pyrecoilprovides Python bindings toRECOIL, the Retro Computer Image Library, allowing you to open over 500 image file formats from retro computers.Installationpip install pyrecoilpyrecoilis most useful when installed in conjunction withPillow, the active fork of the Python Image Library:pip install PillowUsagefrom recoil import RecoilImage

# load image from disk and decode it:
img = RecoilImage("foo.lbm")

# load image from an existing file handle:
# (filename must still be provided, as a hint to the decoder)
img = RecoilImage("foo.lbm", f)

# inspecting properties:
img.colors  # number of colors used in the image
> 16

img.frames  # number of alternating frames (used for 'interlace' / 'gigascreen' effects)
> 1

img.size  # return a tuple of (width, height)
> (320, 200)

img.original_size  # for images that have been resized to account for non-square pixels
> (320, 200)

img.platform
> 'Amiga'

# Retrieving pixel data -
# returns a bytearray consisting of three bytes (r, g, b) for each pixel,
# in left-to-right, top-to-bottom order
pixels = img.get_pixels()

# convert to a PIL / Pillow image object:
pil_image = img.to_pil()
pil_image.save('foo.png')Build notesThe RECOIL library itself is implemented in theFusion Programming Language. For ease of distribution, the generated C output (recoil.candrecoil.h) from thefuttranspiler is included in the package - this, along withrecoil_interface.c, forms the Python extension module.AuthorMatt Westcott -matt@west.co.tt,http://twitter.com/gasmanic
Package: py-recommendation
IntroductionThis library provides a pure Python interface for a Generalized Recommendation system.Recommender SystemsRecommender Systems are algorithms aimed at suggesting relevant items to users (items being products to buy, movies to watch, text to read or anything else depending on industries). There exist two major categories of methodsContent based methodsCollaborative filtering methodsContent Based RecommendationContent based approaches use additional information about users and/or items. The major idea of content based methods is to try to build a model, based on the available “features”, that explain the observed user-item interactions.Collaborative Filtering MethodsCollaborative methods for recommender systems are methods that are based solely on the past interactions recorded between users and items in order to produce new recommendations.Installingpip install py-recommendationUsageModule Importimportpy_recommendationasprcFetch the Input DataGenerate input data object which can be used for various types of recommendations availableItems DataitemsInitialise=prc.ItemData()Configure Data# Reffer from sample data given below:sample_items_data={"title":['Place-A','Place-B','Place-C','Place-D','Place-E','Place-F','Place-G'],"tags":['party & dance, nightlife, food, bar & restaurant, enjoy family','Historic old monument, landmarks & enjoy$ beautiful','Beach party, bar & nightlife @water, Beautiful','Old Historic Temple, peaceful & rest, Family vacation','Nature waterfall & green, beauty water','Beautiful Himalaya hills, nature snow, water & peace','Sights & Landmarks, Monuments & Statues']}itemData=itemsInitialise.setData(data=sample_items_data,itemNameField='title',itemTagField='tags',)Or else set data from file,itemData=itemsInitialise.setData_file(filePath='path to the data file',itemName_col='column name carrying the item name/id',itemTag_col='column name carrying the item features',fileType='type of file (xlsx/csv)')Users DatausersInitialise=prc.UsersData()Configure Data# Reffer from sample data given below:sample_users_data={"names":['Professor','Lisbon','Berlin','Nairobi','Helsinki','Rio'],"visited":[['Place-B','Place-D'],['Place-D','Place-E'],['Place-B'],['Place-C','Place-E'],['Place-F'],['Place-A','Place-F']]}usersData=usersInitialise.setData(data=sample_users_data,userNameField='names',triedItemField='visited',)Recommeder based on Similar ItemsThis is used to find other items similar to the given item based on the features.similarItemInit=prc.SimilarItem(itemData)recommendation=similarItemInit.similarItem('Place-D')Content-based Recommeder for specific users based on items triedcontentRecommendInit=prc.UserContent(itemData=itemData,usersData=usersData)recommendation=contentRecommendInit.contentRecomend('Lisbon')recommendationcarries a list with each recommended items and their recommendation score on 100.recommendationmight have items with negative score which implies the item is already tried. This scenario occurs only if you have less number of items in 'ItemData'
Package: pyrecon
# Recon File FormatThe goal of thereconfile format (or more precisely, suite of file
formats) is to provide a compact format for storing (typically
time-series oriented) simulation data in a network friendly way.
Along the way, we’ve added support for embedding metadata at the file,
table, object and signal level as well.Obviously compactness is important to keep the size of files down.
Disk space is less an issue these days than it has been in the past.
Nevertheless, one our requirements was to be competitive in terms of
how much disk space is required to store signals.In trying to develop a network friendly format, we quickly found
ourselves facing two conflicting goals.  On the one hand, we wanted
the ability to stream data from simulations to files.  This meant,
essentially, having a format that was “append friendly”.  On the other
hand, we wanted a format where data for an individual signal could be
accomplished with a “single” read (i.e. single request over the
network).  Note, our definition of “single” has some caveats.We recognized that we could not accomplish this goal with a single
format (at least not without making the implementation extremely
complicated and involving lots of extra writes).  So we opted to
specify to file formats that could easily be converted.## DependenciesThis package requires an implementation ofmsgpack.  However, there
are several implementation in Python.  During development, themsgpack-pythonpackage was used.  It is possible that other packages
might work as well.## The “Wall” FormatThe wall format is the “append friendly” format.  You can think of the
wall format as a series of “bricks” being laid down.  Each “brick”
represents some data being added to the file.  One of the nice
advantages of this format is that it allows concurrent writing to
multiple tables of data.  So in a simulation where different results
are reported at different intervals, this file format can be used to
append different results to different tables.  In other words, it
supports appending to multiple tables, not just one.## The “Meld” FormatThe meld format is mainly an archival format.  It rearranges the data
so that individual signals can be easily extracted.  This is what
enables data to be extracted with a minimal number of “reads” from the
data source.  The key issue with reads is the case where the data is
being read over a network.As simulation moves to cloud based systems, it will be come
increasingly cumbersome to move entire files back and forth between
the cloud and the desktop/browser.  Having a format that supports
“pulling” just the information that is required on demand facilitates
utilizing cloud/remote storage solutions which will lead to more
responsive interfaces and better data management practices and
capacity.  The meld format is designed for this use case.
Package: pyReconcile
pyReconcilepyReconcile is a library providing a reconciliation loop system for enabling declarative configuration of arbitrary systems.You bring the systems, the state representations, and the actions to modify them, and this brings the glue to put it all together.InstallaionUsageSee two-lists.py for an example of a built around mutating one list into another.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMozilla Public License, v2.0
Package: py-reconciliation-service-api
Python Reconciliation Service APIThis is a python implementation of theReconciliation Service API v0.2. It isavailable on pypi.It does not support all methods or utilities of the API including authentication.UsageHere is a basic usage examplefrompy_reconciliation_service_apiimportbuild_reconciliation_service# the standard location of the wikidata implementation of this APISERVICE_URL="https://wikidata.reconci.link/en/api"service=build_reconciliation_service(SERVICE_URL)# get 6 candidates for the string "sf"candidates=service.reconcile({"query":"sf","limit":6})assertlen(candidates.result)==6# the top result should be called San Franciscoassertcandidates.result[0].name=="San Francisco"
Package: pyrecord
Arecord(aka
“struct” in C) is a pre-defined collection of values where each is accessed
by a unique name. Depending on the nature of the data, records may be a
superior alternative to dictionaries and instances of custom classes.PyRecordallows you to use records in CPython v2.7, CPython v3.3+, PyPy
and PyPy v3, and can be thought of as an improvednamedtuple.The documentation is available athttps://pythonhosted.org/pyrecord/
Package: pyrecorder
You can find the detailed documentation here:https://anyoptimization.com/projects/pyrecorder/InstallationThe framework is available at the PyPi Repository:pipinstall-UpyrecorderUsageIt’s as simple as it should be. Initialize aRecorderobject with aWriterand store plots by callingrecord().importmatplotlib.pyplotaspltimportnumpyasnpfrompyrecorder.recorderimportRecorderfrompyrecorder.writers.videoimportVideo# create a writer object (here, mp4)writer=Video("video.mp4")# use the with statement to close the recorder when donewithRecorder(writer)asrec:# record 10 different snapshotsfortinrange(10):# create the plot (here, using matplotlib)X=np.random.random((50,2))plt.scatter(X[:,0],X[:,1],facecolor="none",edgecolor="red")# use the record to store the current plotrec.record()ContactFeel free to contact me if you have any question:Julian Blank (blankjul [at] egr.msu.edu)
Michigan State University
Computational Optimization and Innovation Laboratory (COIN)
East Lansing, MI 48824, USA
Package: pyrecsys
PyRecSys=======Python Collaborative FilteringOverview--------Features--------Installation------------To install from [PyPI](https://pypi.python.org/pypi/recsys/), use pip:$ pip install recsysOr clone the repo:$ git clone https://github.com/vlarine/pyrecsys.git$ cd pyrecsys$ pip install -r requirements.txt$ pip install .Getting Started---------------License-------Released under the MIT LicenseReferences----------Collaborative Filtering for Implicit Feedback Datasets.Yifan Hu. AT&T Labs – Research. Florham Park, NJ 07932.Yehuda Koren. Yahoo! Research.[http://yifanhu.net/PUB/cf.pdf](http://yifanhu.net/PUB/cf.pdf)Ben Frederickson. Fast Python Collaborative Filteringfor Implicit Datasets.[https://github.com/benfred/implicit](https://github.com/benfred/implicit)Evgeny Frolov, Ivan Oseledets. Fifty Shades of Ratings: How to Benefitfrom a Negative Feedback in Top-N Recommendations Tasks.[https://arxiv.org/abs/1607.04228](https://arxiv.org/abs/1607.04228)[https://github.com/Evfro/polara](https://github.com/Evfro/polara)
Package: pyrecursive
pyrecursiveHave you ever needed to transform awhatever python objectin depth? So this one is for you. =]Installation:pipinstallpyrecursiveUsage:frompyrecursiveimportpyrecursivedefdouble(obj):returnx*2pyrecursive(42,double)# result: 84pyrecursive([1,2,3,[4,5,[6],7],8,9,'x'],double)# result: [2, 4, 6, [8, 10, [12], 14], 16, 18, 'xx']pyrecursive({'hamster':[1,2,3,{'test':42}]},double)# result: {'hamster': [2, 4, 6, {'test': 84}]}# In order to transform dict keys in depth: set transform_dict_keys variable to True (default: False).pyrecursive({'hamster':[1,2,3,{'test':42}]},double,transform_dict_keys=True)# result: {'hamsterhamster': [2, 4, 6, {'testtest': 84}]}# In order not to transform dict values in depth: set transform_dict_values variable to False (default: True).pyrecursive({'hamster':[1,2,3,{'test':42}]},double,transform_dict_values=False)# result: {'hamster': [2, 4, 6, {'test': 42}]}# In order to define custom rules for each object type in depth: set custom_rules variable.pyrecursive([1,2,3,'banana'],double,custom_rules={int:lambdax:x*5})# result: [5, 10, 15, 'bananabanana'] (multiplied by 5 when int, multiplied by 2 otherwise)ContributeDid you think in some interesting feature, or have you found a bug? Please let me know!Of course you can also download the project and send me somepull requests. Please keep in mind that (1) we do follow pep8, (2) our line-length is 120 chars and (3) your changes must be compatible with Python 2.7 and Python 3.6+.You can send your suggestions byopening issues.You can contact me directly as well. Take a look at my contact information athttp://filwaitman.github.io/(email is preferred rather than mobile phone).
Package: py-redact
py_redactDocument redaction library in Python.Install Requirementspip install py_redactExample UsageRedact Microsoft Word Documentfrompy_redact.docx_redactorimportDocxRedactorreplace_char='*'regexes=[r"""\d{3}-\d{2}-\d{4}""",r"""(([a-zA-Z0-9_\.+-]+)@([a-zA-Z0-9-]+)\.[a-zA-Z0-9-\.]+)"""]redactor=DocxRedactor(input_file,regexes,replace_char)redactor.redact(output_file_path)Redact Microsoft Power Point Slidefrompy_redact.pptx_redactorimportPptxRedactorreplace_char='*'regexes=[r"""\d{3}-\d{2}-\d{4}""",r"""(([a-zA-Z0-9_\.+-]+)@([a-zA-Z0-9-]+)\.[a-zA-Z0-9-\.]+)"""]redactor=PptxRedactor(input_file,regexes,replace_char)redactor.redact(output_file_path)