Saravanan666/pypi-org huggingface dataset:
Package: bonn-mensa
Meal plans for university canteens in BonnA python script for displaying the meal plans of the canteens of theStudierendenwerk Bonn.
The script parses the HTML response of a call to an API.
Depending on your request the API might take a few seconds to respond.InstallationTo install this script, runpipinstallbonn-mensaMacOS (using homebrew)To install the application using homebrew, run:# Add the tap to homebrewbrewtap--force-auto-updatealexanderwallau/bonn-mensahttps://github.com/alexanderwallau/bonn-mensa# Install the applicationbrewinstallbonn-mensa# Install the application from main branchbrewinstall--HEADbonn-mensaIn case you want to remove the application, run:brewuninstallbonn-mensa
brewuntapalexanderwallau/bonn-mensa
brewautoremoveNixOS (using flakes)This Repository provides a flake. If you have flakes enabled you can either runnix rungithub:alexanderwallau/bonn-mensa--<options>for one time use.
If you want to add this to your permanent configuration addbonn-mensa={url="github:alexanderwallau/bonn-mensa";inputs={nixpkgs.follows="nixpkgs";};};to your flake.nix and adjust the rest of your config accordingly if you are using Home-Manager an example can be foundhere. One could also use the flakehub route and usefh add"alexanderwallau/bonn-mensa/0.1.81"UsageTo run the script, simply runmensa. For a list of all arguments, seemensa --help$mensa--help
usage:mensa[-h][--vegan|--vegetarian][--mensa{SanktAugustin,CAMPO,Hofgarten,FoodtruckRheinbach,VenusbergBistro,CasinoZEF/ZEI,Foodtruck,Rabinstraße}][--price{Student,Staff,Guest}][--filter-categories[CATEGORY...]][--dateDATE][--lang{de,en}][--show-all-allergens][--show-additives][--no-colors][--markdown][--glutenfree]optionalarguments:-h,--helpshowthishelpmessageandexit--veganOnlyshowveganoptions--vegetarianOnlyshowvegetarianoptions--mensa{SanktAugustin,CAMPO,Hofgarten,FoodtruckRheinbach,VenusbergBistro,CasinoZEF/ZEI,Foodtruck,Rabinstraße}Thecanteentoquery.DefaultstoCAMPO.--price{Student,Staff,Guest}ThepricetodisplayonoutputdefaultstoStudent--filter-categories[CATEGORY...]Mealcategoriestohide.Defaultsto['Buffet','Dessert'].--dateDATEThedatetoqueryforinYYYY-MM-DDformat.Defaultstotoday.--lang{de,en}Thelanguageofthemealplantoquery.DefaultstoGerman.--show-all-allergensShowallallergens.Bydefault,onlyallergensrelevanttovegans(e.g.milkorfish)areshown.--show-additivesShowadditives.--glutenfreeShowonlyglutenfreemeals--no-colorsDonotuseanyANSIcolorsintheoutput.--markdownOutputinmarkdowntableformat.--verboseOutputDebugLog
Package: bonobo
Data-processing for humans.Bonobo is an extract-transform-load framework for python 3.5+ (see comparisons with other data tools).Bonobo uses plain old python objects (functions, generators and iterators), allows them to be linked together in a directed graph, and then executed using a parallelized strategy, without having to worry about the underlying complexity.Developers can focus on writing simple and atomic operations, that are easy to unit-test by-design, while the focus of the
framework is to apply them concurrently to rows of data.One thing to note: write pure transformations and you’ll be safe.Bonobo is a young rewrite of an old python2.7 tool that ran millions of transformations per day for years on production.
Although it may not yet be complete or fully stable (please, allow us to reach 1.0), the basics are there.Bonobo is under heavy development, we’re doing our best to keep the core as stable as possible while still moving forward. Please allow us to reach 1.0 stability and our sincere apologies for anything we break in the process (feel free to complain on issues, allowing us to correct breakages we did not expect)Homepage:https://www.bonobo-project.org/(Roadmap)Documentation:http://docs.bonobo-project.org/Contributing guide:http://docs.bonobo-project.org/en/latest/contribute/index.htmlIssues:https://github.com/python-bonobo/bonobo/issuesSlack:https://bonobo-slack.herokuapp.com/Release announcements:http://eepurl.com/csHFKLMade with ♥ byRomain Dorgueilandcontributors.
Package: bonobo2
Data-processing for humans.Bonobo for Baseline is an extract-transform-load framework for python 3.5+ (see comparisons with other data tools).Homepage:https://www.bonobo-project.org/(Roadmap)Documentation:http://docs.bonobo-project.org/Contributing guide:http://docs.bonobo-project.org/en/latest/contribute/index.htmlIssues:https://github.com/python-bonobo/bonobo/issuesSlack:https://bonobo-slack.herokuapp.com/Release announcements:http://eepurl.com/csHFKLMade byRomain Dorgueilandcontributors.
Package: bonobo-sqlalchemy
SQLAlchemy extension for Bonobo, bringing SQL databases to the Extract-Tranform-Load framework./!CURRENTLY REWRITING THE CODE FROM RDC.ETL, DON’T USE BEFORE AT LEAST ALPHA STATUS /!If you want to use SQLAlchemy with bonobo, this is the way to go. Note that it’s not required to connect to databases,
you can also use raw database connections (psycopg2, etc.), but it’s probably easier to use an established DBAL.Documentation:http://docs.bonobo-project.org/Release announcements:http://eepurl.com/csHFKLIssues:https://github.com/python-bonobo/bonobo-sqlalchemy/issuesRoadmap:https://www.bonobo-project.org/roadmapSlack:https://bonobo-slack.herokuapp.com/Made with ♥ byRomain Dorgueilandcontributors.
Package: bonsai
This is a module for handling LDAP operations in Python. Uses libldap2 on Unix platforms and
WinLDAP on Microsoft Windows. LDAP entries are mapped to a special Python case-insensitive
dictionary, tracking the changes of the dictionary to modify the entry on the server easily.Supports only Python 3.7 or newer, and LDAPv3.FeaturesUses LDAP libraries (OpenLDAP and WinLDAP) written in C for faster
processing.Simple pythonic design.Implements an own dictionary-like object for mapping LDAP entries
that makes easier to add and modify them.Works with various asynchronous library (like asyncio, gevent).Requirements for buildingpython3.7-dev or newerlibldap2-devlibsasl2-devlibkrb5-dev or heimdal-dev (optional)DocumentationDocumentation is availableonlinewith a simple tutorial.ExampleSimple search and modify:importbonsaiclient=bonsai.LDAPClient("ldap://localhost")client.set_credentials("SIMPLE",user="cn=admin,dc=bonsai,dc=test",password="secret")withclient.connect()asconn:res=conn.search("ou=nerdherd,dc=bonsai,dc=test",2,"(cn=chuck)")res[0]['givenname']="Charles"res[0]['sn']="Carmichael"res[0].modify()Using with asyncio:importasyncioimportbonsaiasyncdefdo():client=bonsai.LDAPClient("ldap://localhost")client.set_credentials("DIGEST-MD5",user="admin",password="secret")asyncwithclient.connect(is_async=True)asconn:res=awaitconn.search("ou=nerdherd,dc=bonsai,dc=test",2)print(res)who=awaitconn.whoami()print(who)asyncio.run(do())ChangelogThe changelog is availablehereand included in the documentation as well.ContributionAny contributions and advices are welcome. Please report any issues at
theGitHub page.
Package: bonsai-ai
A python library for integrating data sources with Bonsai BRAIN.InstallationTo install the current release version:$ pip install bonsai-aiUsageClients will subclassbonsai.Simulatorand implement the required methods.Example:#!/usr/bin/env python3

import sys
from bonsai_ai import Simulator, Brain, Config

class MySim(Simulator):
    def episode_start(self, parameters):
        initial = {"value": 1.0}
        return initial

    def simulate(self, action, objective):
        terminal = True
        state = {"value": 1.0}
        return (state, 1.0, terminal)Then, the simulator is configured and assigned a BRAIN and run.def example():
    config = Config(sys.argv)
    brain = Brain(config)
    sim = MySim(brain, 'example_simulator')
    while sim.run():
        continue
Package: bonsai-cli
A python library for making API calls to Bonsai BRAIN.Documentation for the Bonsai CLI can be found on the Bonsai Docs website.Guide to CLI installation and usage:https://docs.microsoft.com/en-us/bonsai/cli/#installationFull CLI Reference Documentation:https://docs.microsoft.com/en-us/bonsai/cli/Quick InstallationInstall the latest stable from PyPI:$ pip install bonsai-cliInstall the latest in-development version:$ pip install https://github.com/BonsaiAI/bonsai-cli
Package: bonsai-config
A python library reading and writing bonsai configuration files.InstallationInstall the latest stable from PyPI:$ pip install bonsai-configInstall the latest in-development version:$ pip install https://github.com/BonsaiAI/bonsai-configUsageOnce installed, importbonsai_configin order to access methods to
read and write configuration files used bybonsai-cliandbonsai-python.from bonsai_config import BonsaiConfig

# Read properties from the main bonsai config file:
bonsai_config = BonsaiConfig()
print(bonsai_config.username())
print(bonsai_config.access_key())

# Write properties to the main bonsai config file:
bonsai_config = BonsaiConfig()
bonsai_config.update_access_key_and_username('new_access_key', 'new_username')
Package: bonsai-gym
A python library for integrating Bonsai BRAIN with Open AI Gym environments.InstallationInstall the latest stable from PyPI:$ pip install bonsai-gymUsageOnce installed, importbonsai_gymin order to access
base classGymSimulator, which implements all of the
environment-independent Bonsai SDK integrations necessary to
train a Bonsai BRAIN to play an OpenAI Gym simulator.import gym

from bonsai_gym import GymSimulator

class CartPoleSimulator(GymSimulator):
    # Perform cartpole-specific integrations here.
Package: bonsai-gym-common
A python library for integrating Bonsai BRAIN with Open AI Gym
environments.InstallationInstall the latest stable from PyPI:$ pip install bonsai-gym-commonInstall the latest in-development version:$ pip install https://github.com/BonsaiAI/bonsai-gym-commonUsageOnce installed, importbonsai_gym_commonin order to access base
classGymSimulatorandGymImageSimulator, which implement all of
the environment-independent Bonsai SDK integrations necessary to train a
Bonsai BRAIN to play an OpenAI Gym simulator.import gym

from bonsai_gym_common import GymSimulator

class CartPoleSimulator(GymSimulator):
    # Perform cartpole-specific integrations here.
Package: bonsai-prp
Pipeline result processor (prp): A collection of parsers and data models for creation and validation of a standardized output for thejasenpipeline which is used as an input forbonsaiDependencies (latest)biopythonpydantic=2.5.3python=3.10Using prpUse the help argument for information regarding the prp's methodsprp --helpUse the method help argument for information regarding the input for each of prp's methods (create-bonsai-input,create-cdm-input,create-qc-result,print-schema,validate)prp <method> --helpCreate bonsai input from pipeline dataprp create-bonsai-input -i SAMPLE_ID -u RUN_METADATA_FILE -q QUAST_FILENAME -d PROCESS_METADATA_FILE -k KRAKEN_FILE -a AMRFINDER_FILE -m MLST_FILE -c CGMLST_FILE -v VIRULENCEFINDER_FILE -r RESFINDER_FILE -p POSTALIGNQC_FILE -k MYKROBE_FILE -t TBPROFILER_FILE [--correct_alleles] -o OUTPUT_FILE [-h]Create CDM input from pipeline dataprp create-cdm-input -q QUAST_FILENAME -c CGMLST_FILE -p POSTALIGNQC_FILE [--correct_alleles] -o OUTPUT_FILE [-h]Create QC result from bam fileprp create-qc-result -i SAMPLE_ID --b BAM_FILE [-e BED_FILE] [-a BAITS_FILE] -r REFERENCE_FILE [-c CPUS] -o OUTPUT_FILE [-h]
Package: bonsai-python
A python library for integrating data sources with Bonsai BRAIN.Installation$ pip install bonsai-pythonInstall the latest stable from PyPI:$ pip install bonsai-pythonInstall the latest in-development version:$ pip install https://github.com/BonsaiAI/bonsai-pythonUsageSubclass eitherbonsai.Simulatororbonsai.Generator,
implementing the necessary required methods.class MySimulator(bonsai.Simulator):
    # Simulator methods implementations...Run your simulator with the helper method frombonsai.BrainServerConnection.$ python3
> import bonsai
> bonsai.run_for_training_or_prediction(
.     "my_simulator", MySimulator())Note that the schema used by your simulator (or generator), as well as
the name used to identify it, should match the schemas and identifier in
the corresponding inkling file.
Package: bonsait
BonsaiT: an interface for BONSAI classificationsBonsaiT is a Python package for the standardization of classifications in Life-Cycle Assessment (LCA). It leverages NLP to aligns external classification systems with the BONSAI framework, ensuring data consistency across LCA datasets.Key components include theEncoder, which converts text to vectors via language models like Sentence Transformers and Hugging Face's BERT, and theBonsaiTransformer, which identifies the BONSAI class most similar to a given source class through vector analysis and cosine similarity.BonsaiT can be used to:Identify the BONSAI class most similar to a given source class.Translate classifications between different systems.Harmonize LCA data from different sources.InstallationTo get started with BonsaiT, install it via pip:pipinstallbonsaitQuick startHere is how to quickly implement BonsaiT in your project:frombonsaitimportBonsaiTransformersource_class="electricity from coal"# Set up the BonsaiTransformerclass_transformer=BonsaiTransformer()# Perform the transformation to find the BONSAI equivalenttransformed_class=class_transformer.transform(source_class=source_class)print(f"The source classification <{source_class}> is transformed into BONSAI classification: <{transformed_class}>")LicenseBonsaiT is released under the MIT License
Package: bonsai-tree
# Bonsai: Gradient Boosted Trees + Bayesian OptimizationBonsai is a wrapper for the XGBoost and Catboost model training pipelines that leverages Bayesian optimization for computationally efficient hyperparameter tuning.Depsite it being a very small package, it has access to nearly all of the configurable parameters in XGBoost and CatBoost as well as the Bayesian Optimization package allowing users to configure unique objectives, metrics, parameter search ranges, and search policies. This is made possible thanks to the strong similaries between both libraries.## Why use Bonsai?Grid search and random search are the most commonly used algorithms for exploring the hyperparameter space for a wide range of machine learning models. While effective for optimizing over low dimensional hyperparameter spaces (ex: few regularization terms), these methods do not scale well to tuning models with a large number of hyperparameters.Bayesian optimization on the other handdynamicallysamples from the hyperparameter space with the goal of minimizing uncertaintly about the underlying objective function. For the case of model optimization, this is consists ofiterativelybuilding a prior distribution of functions over the hyperparameter space and sampling with the goal of minimizing the posterior variance of the loss surface.## TutorialAt the moment Bonsai is solely a wrapper for XGBoost and CatBoost. Naturally, to use either library you must do the usual preprocessing so that your training data is compatible. Below are few examples that demonstrate a basic work flow with Bonsai.### EncodingsWords### Model ConfigurationBonsai provides this functionality while allowing users to easily set up gradient boosted tree models via 3 simple parameter dictionarys: model_config, cv_config, and pbounds.### Tuning and Predictionsoptimize + predict + results df display### Additional Featuresparallel coordinates + ask for contributors<div align=”center”>
<img src=”https://github.com/magi-1/bonsai/blob/199a4aa92d4ffcba98ec259671413d711ebe8f70/images/bebop1.gif” style=”max-height:50%;max-width:50%”></img>
</div>
Package: bonsancon-pyblaze
PyBlazePython package to access Blaze API resources.pipinstallbonsancon-pyblazeGames available:CrashDoubleHow to usefrompyblazeimportBlazeClientclient=BlazeClient()client.version()# Version(version='2.159.0')If you want to get data from specific game, you can:frompyblaze.gamesimportCrashclient=Crash()client.current()# CurrentResponse(crash_point=None, total_eur_bet=5454.341490738392, ...)Clients and methodsThe package have 3 clients:ClientDescriptionimportBlazeClientBlaze API Client.from pyblaze import BlazeClientCrashBlaze Crash Client.from pyblaze.games import CrashDoubleBlaze Double Client.from pyblaze.games import DoubleBlazeClientMethodDescriptionannouncement()Get current announcement.chat_rooms()Get chat rooms config.chat_room(chat_number)Get messages from a chat room.country()Get country config.currencies()Get currencies config.settings()Get Blaze platform settings.time()Get time (for what?).version()Get Blaze platform version.GamesMethodDescriptioncurrent()Get current game.get_game_by_id(id)Get game data by id.get_previous_game_hash_by_hash(hash_code)Get previous game hash by the given hash.get_previous_games_to_hash_by_hash(hash_code, hash_match)Get previous games to hash.get_previous_n_games_by_hash(hash_code, number_of_games)Get previous N games by the given hash.get_result_by_hash(game_hash)Get result by the given hash.recent()Get recent results.recent_history()Get recent results.TO DO: fix Double methods to previous games.ContributingContributions are more than welcome. Fork, improve and make a pull request.
For bugs, ideas for improvement or other, please create anissue.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: bonzo
AboutBonzo is a minimalistic SMTP Proxy built on top ofTornado.importtornado.ioloopimportemailfrombonzo.serverimportSMTPServerdefreceive_message(message):print"New received message: "print"From: "+message['from']print"Subject: "+message['subject']forlineinemail.iterators.body_line_iterator(message):printlineSMTPServer(receive_message).listen(25)tornado.ioloop.IOLoop.instance().start()InstallationYou can to usepipto install Bonzo:$ pip install bonzoOr using last source:$ pip install git+git://github.com/puentesarrin/bonzo.git
Package: booby
Booby is a standalone datamodelingandvalidationlibrary written in Python. Booby is under active development (visitthis blog postfor more info and the roadmap) and licensed under theApache2 license, so feel free tocontributeandreport errors and suggestions.UsageSee the sample code below to get an idea of the main features.fromboobyimportModel,fieldsclassToken(Model):key=fields.String()secret=fields.String()classAddress(Model):line_1=fields.String()line_2=fields.String()classUser(Model):login=fields.String(required=True)name=fields.String()email=fields.Email()token=fields.Embedded(Token,required=True)addresses=fields.Collection(Address)jack=User(login='jack',name='Jack',email='jack@example.com',token={'key':'vs7dfxxx','secret':'ds5ds4xxx'},addresses=[{'line_1':'Main Street'},{'line_1':'Main St'}])ifjack.is_valid:printjack.to_json(indent=2)else:printjson.dumps(dict(jack.validation_errors)){"email":"jack@example.com","login":"jack","token":{"secret":"ds5ds4xxx","key":"vs7dfxxx"},"name":"Jack","addresses":[{"line_1":"Main St","line_2":null},{"line_1":"Main Street","line_2":null}]}InstallationYou can install the last stable release of Booby from PyPI using pip or easy_install.$pipinstallboobyAlso you can install the latest sources from Github.$pipinstall-egit+git://github.com/jaimegildesagredo/booby.git#egg=boobyTestsTo run the Booby test suite you should install the development requirements and then run nosetests.$pipinstall-rtest-requirements.txt$noseteststests/unit$noseteststests/integrationChangesSeeChanges.
Package: boodebr
A collection of modules fromboodebr.orgfor JSON, XML, config files, SQL, locking, thread queues, and more.Packages:boodebr.ionis a serializer that implements the JSON/RFC 4627 object model, but
can read/write XML as well as JSON streams. It provides a common API (internal and external);
changing from XML to JSON (or vice versa) requires only a single parameter change. boodebr.ion implements
true object pickling on top of the JSON and XML layers, and allows pickling of binary data, while remaining 100%
compatible with RFC 4627.boodebr.configsupports configuration files similar in spirit to .INI files, but uses
either JSON or XML format, and can contain arbitrary objects. It has a much simpler API than
Python’s ConfigParser.boodebr.sqlis a light layer over pysqlite and/or APSW providing some extra convenience
and a multithread-safe wrapper (sqliteQ).boodebr.sql2is a light layer for pysqlite, APSW, and MySQL, focusing on portable semantics
and stability under high loads. (Beta quality for now; will eventually supercede boodebr.sql)boodebr.utilprovides general utility functions, include locking, thread queues, and creation
of unique IDs.boodebr.disthelperprovides a number of utilities useful for writing setup scripts,
providing replacements for some POSIX tools missing in many environments.boodebr.guiis a convenience wrapper for wxPython.See thefull documentationfor more information.
Package: booger
Eats the output of a nosetests run, and put it into a nice curses
interface, instead of letting nosetests throw a tsunami of output into
stdout after all the tests finish.SCREENSHOTSINSTALLATIONboogeris onpypi:pip install boogerUSAGETo usebooger, you can use either invoke it as a plugin, or call the
wrapping script.nosetests --booger YOUR_TESTS

booger YOUR_TESTSCommands:Move withup/downarrow keys, orn/p(next/previous) keysOn the main testcase listing, with a selected testcase, press:tfor tracebackofor stdoutlfor logging outputIn the traceback view, press:vto toggle the current frame’s variable viewOn the stdout/logging views,page up/page downalso work, as
you would expectqquits the current activity, or closes the curses interfaceMOTIVATIONIf you’ve ever threw nosetests on a big test suite, and a few of those
tests happened to be broken with a ton of logging going on (as it
should be), then you’re going to have a bad time scrolling around
trying to find the most pertinent information first.Booger is an effort to give you the best information first, with more
information easily available, quickly.To this end, booger will progressively show you which tests are
failing with the last line of the error’s traceback along with the
actual error, and make the stdout, logging, and the full traceback
with the variables at each frame available.Questions that will probably be asked frequentlyQ: Really? Toilet Humor?A: YupQ: Where can I report bugs?A: Please use theproject’s github bug trackerLICENSEThe MIT License (MIT)Copyright (c) <2012-2014> <thenoviceoof>Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.