Saravanan666/pypi-org huggingface dataset:
Package: ludibrio
Welcome to ludibrioAuthor:Gustavo Rezende <nsigustavo@gmail.com>Contributors:Diego Pinheiro <me@dmpinheiro.net>Hugo Lopes <hltbra@gmail.com>Rodrigo Manhães <rmanhaes@gmail.com>Rebeca Motta <becacmotta@gmail.com>InstallLudibrio is registered with PyPi. If you have pip, setuptools or Distribute you can install mock with:$ sudo easy_install ludibrioDownloadThe latest official version is 3.0.2. Here’s how to get it:http://pypi.python.org/pypi/ludibrio/3.0.2DocumentationSee thehttp://ludibriar.appspot.com/for the current documentation.Getting involved !Ludibrio’s development may be viewed and followed on github:http://github.com/nsigustavo/ludibrioRetrieve the source code using ‘git’:$ git clone git://github.com/nsigustavo/ludibrio.gitInstall package in ‘development mode’ and run tests with doctestcommand:$ sudo easy_install doctestcommand
$ git clone git://github.com/nsigustavo/ludibrio.git
$ cd ludibrio
$ sudo python setup.py develop
$ cd ludibrio
$ doctestIn a nutshellTest doubles are fake objects that simulate the behavior of a real object for testing purposes.MockMocks are objects pre-programmed with expectations which form a specification of the calls they are expected to receive:>>> from ludibrio import Mock
>>> with Mock() as MySQLdb:
...     con = MySQLdb.connect('server', 'user', 'XXXX')
...     con.select_db('DB') >> None
...     cursor = con.cursor()
...     cursor.execute('select * from numbers') >> None
...     cursor.fetchall() >> [1,2,3,4,5]>>> con = MySQLdb.connect('server', 'user', 'XXXX')
>>> con.select_db('DB')
>>> cursor = con.cursor()
>>> cursor.execute('select * from numbers')
>>> cursor.fetchall()
[1, 2, 3, 4, 5]

>>> MySQLdb.validate() #passedStubStubs provide pre-defined answers to method calls made during a test:>>> from ludibrio import Stub
>>> with Stub() as x:
...     x.anything() >> 'response'>>> x.anything()
'response'Trivial mocking or stubing for any external moduleLudibrio also offers a replace mode, which basically means if a “from … import …” statement is defined into a ‘with’ scope, the replay mechanism will return a mock object to replace the original object in namespace of the whole Python interpreter (including any modules, etc). There’s a simple example below to illustrate how use it:>>> from ludibrio import Stub

>>> with Stub() as time:
...     from time import time
...     time() >> 171>>> from time import time
>>> time()
171ProxyTwo Ludibrio’s powerful features that aren’t found in other mocking systems is the ability of proxying existing objects, or patching a real instance or class.When an object is proxied, Ludibrio create a Test Double object holding a reference to the real object, allowing expressions passthrough to it(mocked or not, and by default or on request):>>> from os.path import splitext
>>> with Stub(proxy=splitext) as splitext:
...     splitext('ludibrio/stubed.py') >> ('/temp/temp','.temp')>>> splitext('mock.py')
('mock', '.py')
>>> splitext('ludibrio/stubed.py')
('/temp/temp', '.temp')
Package: ludicrousdns
ludicrousdnsLudicrously speedy, infectious with the async.ludicrousdnsis designed to be a cleaner, more accurate and more rate-limited version ofmassdns.Installationpip install ludicrousdnsUsageludicrousdnscan be used both as a library and a binary:fromludicrousdnsimportResolverPoolr=ResolverPool()r.resolve_hosts(["example.com","google.com"])orecho-e"example.com\ngoogle.com">hosts.txt
ludicrousdnsresolve-dhosts.txtor simplyecho-e"example.com\ngoogle.com"|ludicrousdnsresolveFeaturesRate-limitedDetects wildcard DNSLudicrously speedyTODOAdd benchmark to measure CPU- and network usageAdd benchmark to measure overall speed (use randomized subdomains to avoid effects of caching)Add timeout to connections, for example withasync_timeoutAdd option to adjust rate-limiting
Package: ludivine
M1-2022-gitIntroductionThis repository is a fork from the previous repository.
The bugs in the converter have been fixed and now we want to publish our work as a publicly available python package.Getting startedYou should be working fromyour own repositorythat is a fork of M1-2022-git-workflow.
In order to run this project, you will have to follow these steps.Clone this projectmove into the project foldercdM1-2022-git-workflowSet up a virtual environmentCreate the virtual environmentpython-mvenvmyvenvActivate venv.venv/bin/activateInstall pytestpipinstall-UpytestExecute main scriptpythonmain.pyInstructionsMerge feature branch into devNow that the converter has no more bugs, we want to merge our branch feature into the dev branch.
We will do a pull request (on your own repo). On GitHub, go to the "Pull requests tab" and click "New pull request".
Selectbaseandcompare. Remember : we want to mergedecimal-binary-converterintodev.
Then,Create the pull request. Add some description of the work you have done and publish the Pull Request.Note: We could have just mergedecimal-binary-converterintodevfrom the command line. However, when working on a real project, you probably won't have the rights to do so. Remember : dev is a public branch. So all the work going down there should be carefully reviewed and tested.Publish your workWe now have an awesome binary/decimal two ways converter ! It's time to publish it so the rest of the world can enjoy it !
We will publish it on PyPI (Python Package Index) which is a public repository of softwares for Python.First, you have to create an account onPypi.orgThen, in your repository, you will find a.github directoryInside this directory, you will find .yml files that defines workflows (using GitHub actions)Open the yml files and take time to understand them. Do not hesitate to ask the teacher about themHands on !We want to create a workflow that will, on publish, test our code and if tests succeed, then publish our package on PyPIIn GitHub, click on theActionstab, then click onnew workflowChoose among the suggestions the template that best suits your use-case and clickConfigureYou may find some useful informations in thePython documentationWhen creating a release on GitHub, do not forget to create a tag corresponding to that releasePull requestOnce the workflow is setup and tested (you have published your package using it) you can open a pull request to theoriginal repository
Package: lu-dj-utils
LookUP.cl’s open source utilities for use in Django projects.LicenseThis software is licensed under the OSI-approved “BSD License”. To avoid
confusion with the original BSD license from 1990, the FSF refers to it as
“Modified BSD License”. Other names include “New BSD”, “revised BSD”, “BSD-3”,
or “3-clause BSD”.See the included LICENSE.txt file.Run testsAt the project root directory run:python setup.py testTo test the project in all the supported Python environments run:tox
Package: ludolph
Monitoring Jabber Bot withZabbixsupport, completely written in Python.FeaturesSimple and modular designAlerts from ZabbixMulti-User Chat (XEP-0045)Colorful messages (XEP-0071)Attention (XEP-0224)Avatars (XEP-0084)Roster management and ACL configurationWebhooks and cron jobsPlugins and commands:* ludolph.plugins.zabbix
    * ack - acknowledge event with optional note
    * alerts - show a list of current or previous zabbix alerts
    * duty - show a list of users in duty user group
    * groups - show a list of host groups
    * hosts - show a list of hosts
    * outage - show, create or delete maintenance periods
    * zabbix-version - show version of Zabbix API

* ludolph.plugins.base
    * about - details about this project
    * at - list, add, or delete jobs for later execution
    * attention - send XMPP attention to user/room
    * avatar - list available avatars or set an avatar for Ludolph (admin only)
    * broadcast - sent private message to every user in roster (admin only)
    * help - show this help
    * message - send new XMPP message to user/room
    * remind - list, add, or delete reminders
    * roster - list and manage users on Ludolph's roster (admin only)
    * status - set Ludolph's status (admin only)
    * uptime - show Ludolph uptime
    * version - display version of Ludolph or registered plugin

* ludolph.plugins.muc
    * invite - invite user or yourself to multi-user chat room (room admin only)
    * kick - kick user from multi-user chat room (room admin only)
    * motd - show, set or remove message of the day
    * topic - set room subject (room admin only)

* ludolph.plugins.commands
    * os-uptime - display system uptimeInstallationInstall the latest released version using pip:pip install ludolphOr install the latest development version:pip install https://github.com/erigones/ludolph/zipball/masterMake sure all dependencies (listed below) are installed (done automatically when installing via pip)Create and edit the configuration file:cp /usr/lib/python2.7/site-packages/ludolph/ludolph.cfg.example /etc/ludolph.cfgTheludolphcommand should be installed somewhere in yourPATH.Init scripts for Debian and RHEL based distributions are also available:https://github.com/erigones/Ludolph/tree/master/init.dSeethe complete install guideandZabbix integration guidefor more info.Dependencies:ludolph-zabbix(1.5+)dnspython(1.13.0+)sleekxmpp(1.1.11+)bottle(0.12.7+)LinksWiki:https://github.com/erigones/Ludolph/wikiBug Tracker:https://github.com/erigones/Ludolph/issuesContribution guide:https://github.com/erigones/Ludolph/wiki/Contribution-guideGoogle+ Community:https://plus.google.com/u/0/communities/112192048027134229675Twitter:https://twitter.com/erigonesLicenseFor more information see theLICENSEfile.Avatars have been designed byFreepik.com.
Package: ludolph-doorman
Ludolph: Doorman pluginPlugin for Ludolph to be able to useRPi.doormanlibrary on Raspberry Pi.InstallationInstall the latest released version using pip:pip install ludolph-doormanAdd new plugin section into Ludolph configuration file:[ludolph_doorman.serve]Update Ludolph to run as a default user (pi), as it needs access to GPIOReload Ludolph:sudo systemctl restart ludolph.serviceDependencies:Ludolph(0.6.0+)RPi.doorman(0.1.0+)LicenseFor more information see theLICENSEfile.
Package: ludolph-erigones
Ludolph: Erigones SDDC API pluginInstallationInstall the latest released version using pip:pip install ludolph-erigonesAdd new plugin section into Ludolph configuration file:[ludolph_erigones.erigones_api]
api_url = https://my.erigones.com/apiReload Ludolph:service ludolph reloadDependencies:Ludolph(0.6.0+)Erigones SDDC API Python Library(1.0+)LinksErigones SDDC API Documentation:https://my.erigones.com/docs/api/Bug Tracker:https://github.com/erigones/ludolph-es/issuesGoogle+ Community:https://plus.google.com/u/0/communities/112192048027134229675Twitter:https://twitter.com/erigonesLicenseFor more information see theLICENSEfile.
Package: ludolph-skeleton
Ludolph: Skeleton pluginSample plugin that provides directory structure for Ludolph plugins and basic command examples.InstallationInstall the latest released version using pip:pip install ludolph-skeletonAdd new plugin section into Ludolph configuration file:[ludolph_skeleton.hello_world]Reload Ludolph:service ludolph reloadDependencies:Ludolph(0.6.0+)LinksWiki:https://github.com/erigones/Ludolph/wiki/How-to-create-a-plugin#create-3rd-party-pluginBug Tracker:https://github.com/erigones/ludolph-skeleton/issuesGoogle+ Community:https://plus.google.com/u/0/communities/112192048027134229675Twitter:https://twitter.com/erigonesLicenseFor more information see theLICENSEfile.
Package: ludolph-vcs
Ludolph: vcs pluginVCS plugin that provides notifications for Ludolph from GitHub and GitLab. Ludolph has to be on a public IP (URL) where VCS provider will POST data to aLudolph’s webhook. Make sure Ludolph’s webserver can receive this message. We do recommend using a proxy server in front of Ludolph’s webserver in order not to expose it to public internet.InstallationInstall the latest released version using pip:pip install ludolph-vcsAdd new plugin section into Ludolph configuration file:[ludolph_vcs.gitlab]
secret_token = secret_key_gitlab_gave_you

[ludolph_vcs.github]
sectet_token = secret_key_you_defined_on_githubReload Ludolph:service ludolph reloadDependencies:Ludolph(0.6.0+)LinksWiki:https://github.com/erigones/Ludolph/wiki/How-to-create-a-plugin#create-3rd-party-pluginBug Tracker:https://github.com/erigones/ludolph-vcs/issuesGoogle+ Community:https://plus.google.com/u/0/communities/112192048027134229675Twitter:https://twitter.com/danubecloudLicenseFor more information see theLICENSEfile.
Package: ludolph-weather
Ludolph: Weather pluginSimple plugin that displays current weather information or weather forecast.
Although it currently uses theOpenWeatherMapservice it can be easily extended to support other weather forecast providers.InstallationInstall the latest released version using pip:pip install ludolph-weatherAdd new plugin section into Ludolph configuration file:[ludolph_weather.open_weather_map]
api_key = xxxxxxxxxxxxxxxxxxxxxxxxx
default_location = Bratislava,SK
timezone = Europe/BratislavaReload Ludolph:service ludolph reloadDependencies:Ludolph(0.6.0+)requestspytzLinksWiki:https://github.com/erigones/Ludolph/wiki/How-to-create-a-plugin#create-3rd-party-pluginBug Tracker:https://github.com/erigones/ludolph-weather/issuesGoogle+ Community:https://plus.google.com/u/0/communities/112192048027134229675Twitter:https://twitter.com/erigonesLicenseFor more information see theLICENSEfile.
Package: ludolph-zabbix
Ludolph: Zabbix API pluginInstallationInstall the latest released version using pip:pip install ludolph-zabbixAdd new plugin section into Ludolph configuration file:[ludolph_zabbix.zapi]
# Zabbix server URI
server = https://zabbix.example.com/zabbix
# ssl_verify = true

# Zabbix credentials
username = ludolph
password =

# HTTP authetication
#httpuser =
#httppasswd =Reload Ludolph:service ludolph reloadDependencies:Ludolph(0.9.0+)zabbix-api-erigones(1.2.2+)LinksWiki:https://github.com/erigones/Ludolph/wiki/How-to-configure-Zabbix-to-work-with-LudolphBug Tracker:https://github.com/erigones/ludolph-zabbix/issuesGoogle+ Community:https://plus.google.com/u/0/communities/112192048027134229675Twitter:https://twitter.com/erigonesLicenseFor more information see theLICENSEfile.The Ludolph Zabbix plugin is inspired byDante.
Package: ludoSim-jaib1
This repository contains code to create a simple ludo simulator in Python to settle a bet between Pip and Anna.The root folder contains thisREADME.md, aLICENSE, andsetup.py, for configuring this repository as a distributable package (on PyPI and Anaconda's conda-forge channel). Thetestsfolder contains unit and integration tests for confirming the source code is functioning as expected. (These tests are to be run in the pytest package testing framework: to run these tests, follow the instructions in therunTest.pyfile.) TheludoSimfolder contains the source code package and a README detailing its contents. Thebuildfolder contains the latest build of theludoSimsource code package, and thedistfolder contains distributions of this build in different compressed file formats.Running theludoSim/analysis/gameAnalysis.pyscript runs 10000 games and displays results to determine the outcome of thebet. Running this script took less than a minute on a Windows10 PC with an Intel core i5-6500 CPU with 16 GB DDR4-2133 RAM.To run a game of ludo, navigate to the local folder where you have cloned or installed this repository (unnecessary if you have installed viapip install ludoSim-jaib1orconda install ludoSim-jaib1 -c conda-forge), launch python, and run:from ludoSim import *
b = Board() # look at the optional input args to set the board however you'd like
b.playGame()Note, this package was created using the Anaconda (5.3) package manager distribution (running Python 3.7), and imports some packages native to Anaconda. For best results, download and installAnaconda, navigate to the local folder where you have cloned or installed this ludoSim repository (unnecessary if you have installed viaconda install ludoSim-jaib1 -c conda-forge), and in your conda terminal run:conda activate ludoSim_envto activate the environment with the appropriate package dependencies which were used at the time this package was created
Package: ludovico
LudovicoAn opinionated DataFrame-to-TeX table generator.What's this?Ludovico is a small tool to generate valid TeX tables from data contained within Pandas DataFrames. It's a personal tool that I'm developing to assist myself in the process of generating tables for my Computer Science undergraduate senior thesis, which requires making a considerable amount of tables from data that is currently handled as dataframes.Note that this codebase is wildly unstable, messy and caffeine-ridden. Contributions welcome!
Package: lue
The LUE Python package allows you to perform I/O to theLUE Scientific Database, which is anHDF5binary data format.The LUE database lets you efficiently store huge amounts of attribute values of huge amounts of objects. These objects can be located in time and/or in space, and the attribute values can be n-dimensional values. The LUE database isdevelopedby thePCRasterR&D Team, to be useful in the domain of agent-based and field-based modeling. But the data format is general enough to allow it to be used in other domains as well.LUE can be used for storing information that is related to agents (AKA objects, individuals, items), e.g.:the location of housesGPS tracks of birdselevation of a mountain rangeThe number of agents for which information is stored is at least 1 (e.g.: a single research area, country, economy, planet) and is at most limited by the available computing resources.At the core, LUE manages item properties, where items are the indiviuals (sometimes named objects or agents) that can have zero or more properties, where all of these properties can have very different characteristics (vary through time or not, vary through space or not, etc). Also, the items can be linked to each other to allow the representation of networks, like social or physical networkѕ (e.g. family relations, road networks).The LUE data model is capable of representing many kinds of data. Some interesting aspects of LUE data are:The data can optionally vary through time and/or spaceThe data can be optionally discretized through time and/or space.The position in space of data can optionally vary through time.Combining these aspects results in many different concrete types of data that LUE can represent. Here are some examples:Spatial grids (rasters), e.g.:digital elevation models (DEMs)…Feature data, e.g.:RoadsCountries……Links:Documentation:http://lue.readthedocs.ioSource code:https://github.com/pcraster/lue
Package: luftdaten
Python client for interacting withsensor.community(previously known asluftdaten.info.This module is not official, developed, supported or endorsed by sensor.community/luftdaten.info.InstallationThe module is available from thePython Package Index.$pip3installluftdatenOn a Fedora-based system or on a CentOS/RHEL machine with has EPEL enabled.$sudodnf-yinstallpython3-luftdatenFor Nix or NixOS ispre-packed moduleavailable. The lastest release is usually present in theunstablechannel.$nix-env-iAnixos.python39Packages.luftdatenUsageThe fileexample.pycontains an example about how to use this module.Licensepython-luftdatenis licensed under MIT, for more details check LICENSE.
Package: lug
LugLugis an open source package that allows you to move the execution of specific Python functions to different
environments on each call. This means that instead of deploying a function permanently to a specific environment
(e.g. your local computer or a cloud-based server), you can choose where you want to run the function at the time of
execution. This flexibility allows you to optimize your resource consumption and cost for the needs of the specific
function.Lug is particularly useful for computational science, where researchers and scientists often have programs that are
challenging to install and run. Lug automatically detects and packages pip-installed dependencies and local modules,
and you can even attach a sidecar Docker image for the command-line programs that need their own image – making it
simple to debug and scale your code.Check out the full docs atlug.devHighlights📦 Automatic dependency detection and propagation🐳 Attach function-scoped sidecar Docker images☁️ Execute on your computer, the cloud, or on your own serversPrerequisitesmacOS or LinuxDocker Engine, if you're using Lug Docker Sidecar functionsInstallInstall Docker Engine and the CLI, if you don't have it.With pip:pip install lugGet startedThere are two ways to use Lug:Creating a Lug Hybrid function that executes on your computer or in the cloud.Creating a Lug Docker Sidecar function, which is the same as a Lug Hybrid function but with a function-scoped Docker
image.On this readme, we'll just create a short Lug Hybrid function, but there's more detail in thedocs.Quick start:Let's create a simple Python function that tells us how many CPUs our system has:importlugimportmultiprocessing@lug.hybrid(cloud=False)defnum_cpus():returnmultiprocessing.cpu_count()print(num_cpus())This function will execute locally and print the number of CPUs on the system. To run on the cloud, firstgenerate a
Toolchest API keyand setkeyto your Toolchest API key andcloud=True:importlugimportmultiprocessing@lug.hybrid(cloud=True,key="YOUR_KEY_HERE")defnum_cpus():returnmultiprocessing.cpu_count()print(num_cpus())This function will execute on the cloud and print the number of CPUs on the cloud-based server.For more detail, check out the docs atlug.devOpen-source roadmapRun a Python function in a local containerMaintain Python major.version in function and in containerSerialize and deserialize Python function and Python dependenciesos.system(),subprocess.run(), andsubprocess.Popen()redirect to a user-specified containerLocal files passed to as input go to./input/in remote Docker containerRemote files written to./output/in the container are written to local output PathRuns locallyRun in the cloud withToolchestStream livestdoutduring remote cloud executionpip-based environment propagationconda-based environment propagation (help needed)LicenseLug is licensed under the Apache License 2.0