Saravanan666/pypi-org huggingface dataset:
Package: michaelPanSqlLib
数据库公共方法库(Database Common Method Library)1. 简介(abstract)数据库公共方法库目前包括对MySQL，MongoDB，Redis，PostgreSQL四种数据库的增删改查，具体的使用说明和使用示例可以查看类或者方法的说明，如果有任何bug或建议，请联系我进行修改完善。
The public database method library currently includes CRUD operations (Create, Read, Update, Delete) for four types of databases: MySQL, MongoDB, Redis, and PostgreSQL. For specific usage instructions and examples, please refer to the class or method descriptions. If you encounter any bugs or have suggestions, please do not hesitate to contact me for improvements and modifications.2.作者及联系方式# ***************************************************************
# Maintainers:
#     chuntong pan <panzhang1314@gmail.com>
# Date:
#     2023.8
# ***************************************************************
Package: michaelPanThematicLib
绘制专题图公共库1. 简介该库为风云国际项目绘制专题图功能的优化版，使用一个数据字典，完成对专题图的绘制，同时只使用'pillow', 'michaelPanPrintLib', 'numpy', 'pydantic'四个库，降低了外部库的使用数量，同时边界线上是直接使用图片，提升了绘图速度2.具体使用详情见风云国际项目说明文档或者方法说明3.作者及联系方式# ***************************************************************
# Maintainers:
#     chuntong pan <panzhang1314@gmail.com>
# Date:
#     2023.8
# ***************************************************************
Package: michaelPanUtilsLib
杂项功能公共库1. 简介该库为常用功能的集合库，涵盖了工作生活中很多常用的方法。2.具体使用该库导入时会提示有哪些方法的关键词，使用库中的select_method方法可以查询需要的方法名称，方法中也有相关的使用介绍。
注：
	1.本库的所有权归作者本人，任何组织或个人未经授权禁止复制和窃取。
	2.如果使用过程中有任何bug，请通过以下方式联系作者
	3.欢迎对方法进行补充完善3.作者及联系方式# ***************************************************************
# Maintainers:
#     chuntong pan <panzhang1314@gmail.com>
# Date:
#     2024.02
# ***************************************************************
Package: michel
Michel is your friendly mate that helps you managing your todo list. Itcan push/pull flat text files to google tasks.Usage=====Commands--------Michel keeps it stupid simple. It only has two commands:michel pullwhich prints the default todo list on the standard outputmichel push <TODO.txt>which replaces the default todo list with the content of TODO.txtNon features------------Michel aims at being simple: it does not handle due dates nor notes.Syntax------One line is one task. tab-indented lines (with the real tab character)are subtasks of the "parent" line.Suggestion----------Here is how michel can be used. A crontask pulls every 15 minutes thedefault TODO list, and another one displays a notification during 10seconds every hour (requires notify-send).*/15 * * * * michel pull > /tmp/TODO && mv /tmp/TODO ~/.TODO0 * * * * DISPLAY=":0.0" notify-send -t 10000 TODO "$(cat ~/.TODO)"After you modify your TODO list, don't forget to push it!michel push .TODOIf this trick is not working, it is probably because the variable PATHdoes not contains /usr/local/bin in crontab. You might want to set itmanually. See 'man 5 crontab'.Installing==========install python-xdg, then runeasy_install michelorpip install michelAbout=====Author/License--------------- License: Public Domain- Original author: Christophe-Marie Duquesne ([blog post](http://blog.chmd.fr/releasing-michel-a-flat-text-file-to-google-tasks-uploader.html))Contributing------------As usual, patches are welcome.
Package: michell-raytracer
Michell's RaytracerSimple relativistic nonlinear raytracer named after John Michell,
the first man to think about existence of black holes (he called
them "dark stars").It uses Schwarzschild's metric to implement calculations of rays
paths near black hole.Here are example of how to use this package:import matplotlib.pyplot as plt
import numpy as np
from michell.raytracer import Raytracer
from michell.scene import Camera, Sphere, Ring, Scene
from michell.textures import ColorTexture, ImageTexture

blackhole_texture = ColorTexture(np.array([0, 0, 0]))
background_texture = ImageTexture("space.jpg")
ring_texture = ImageTexture("rings.jpeg")

blackhole = Sphere(np.array([0, 0, 0]), 1, blackhole_texture)
background = Sphere(np.array([0, 0, 0]), 8, background_texture, inverse=True)
ring = Ring(np.array([0, 0, 0]), (1.5, 4), np.array([0, 1, 0]), ring_texture)

camera = Camera(np.array([-7,0.5,0]), np.array([0, 0, 0]), np.array([0, 1, 0]), np.pi / 3)

scene = Scene(camera)

scene.push_entity(blackhole)
scene.push_entity(ring)
scene.push_entity(background)

raytracer = Raytracer(scene, (300, 200), (0.16, 8), curvature=3)

plt.imsave('output.png', raytracer.render())Firstly we create textures for scene entities. Then we create scene entities.
Next step is to create camera and scene. The last one before making raytracer
is to push entities created earlier to scene's list of entities. And finally
we create raytracer for rendering the scene we created few lines ago.REMEMBERto put sphere in <0, 0, 0> to correctly render black hole.
Package: michie
michieDistributed high-throughput pythonic framework for multi-agent simulationsInstallationmichieis available on PyPI. To install itpip install michieUsageInmichieeach object has its ownstateand there is oneglobal stateThere arethreedistinct types of operations inmichie:GlobalStateMappersCanreadandwriteevery stateCanreadandwitetheglobal stateAre executedsynchronouslyfrom theMaster processStateMappersCanreadandwriteone stateCanonly readtheglobal stateAre executedasynchronouslyfrom theWorker processesTransitionsCanreadandwriteone stateCannot readtheglobal stateAre executedasynchronouslyfrom theWorker processesDistributed ModelmichieexecutesTransitionsandStateMapperson remoteWorkers.
It uses the fastest JSON python serializerorjsonto serialize jobs and results.
Before sending the job to the workers it runs therequirementsfunction to check if all the needed fields are available in the current state and uses thestate_mapandglobal_state_map(only forStateMappers) to map the whole state in a smallermapped_stateto be serialized (reducing the communication overhead to the minimum)ExamplesYou can find some exampleshereContributions and licenseThe code is distributed as Free Software under theGNU/GPLv3license.
It is not only possible, but also encouraged, to copy, adapt, and republish it.If you have any further questions, please contact me atfederico.galatolo@ing.unipi.itor on Telegram@galatolo.
Package: michina
MichinaFrom the quechua word for "pasture".Unit testing and integration testing for LLMs.Features[openai] Can run in a Github Actions for workflowTodoAdd monitoringVersionsv0.2.1 -> v0.2.2Fixing typo.v0.1.1 -> v0.2.0This change allows you to configure the OpenAI model version to use.The API was like thisfromsrc.promptsimportcheck_for_political_content,respond_to_customerfrommichina.checksimportToneCheck,ConsistencyCheck"""This is a consistency check. It tests whether your prompt's outputis consistent with the goal of the test itself."""deftest_check_for_political_content_consistent():message="I want to buy a campaign poster for Obama."statement=check_for_political_content(message)response=ConsistencyCheck.check(message,statement)assertresponse.judgment>0.5and now it's like thisfromsrc.promptsimportcheck_for_political_content,respond_to_customerfrommichina.checksimportToneCheck,ConsistencyCheckfromosimportenvironasenvmichina_config={'model':'gpt-3.5-turbo','temperature':0,'openai_api_key':env["OPENAI_API_KEY"],}consistency=ConsistencyCheck(**michina_config)deftest_check_for_political_content_consistent():message="I want to buy a campaign poster for Obama."statement=check_for_political_content(message)response=consistency.check(message,statement)assertresponse.judgment>0.5
Package: mickjagger
Notre moduleLe package $mickjagger$ a été réalisé par nous Diagana Adja, Afangnibo Kokou Jérôme, Clinton Maradonna, Houri Yasmine et Preda Ioana, étudiants en 2e année à l'ENSAE Paris. Il a été réalisé dans le cadre de notre cours de Python pour le datascientist/l'économiste. Il vise à représenter un graphe ou une carte dans le but de suivre l'évolution temporelle d'une variable donnée.Idéalement, tout devrait être fait avec $matplotlib$ mais certains modules comme $cartopy$ , $imageio$ , $networkx$ nous ont été utiles. Et nous proposons une représentation avec $folium$ en vue d'alternatives à matplotlib. Folium propose quant à lui des cartes avec une affichage semblable à celle de Google Map. Nous avons notamment une fonction qui retourne un fichier GIF enregristré dans le répertoire de travail.Par ailleurs, une autre fonction, dont nous détaillons le fonctionnement dans le notebook $example$ propose un graphe pour aller plus loint dans la représentation et dans le suivi de la série temporelle.*Le module porte le nom de $mickjagger$ en référence à une tactique utilisée par Deckard Shaw(Jason Statham) et sa soeur dans le film "Fast and Furious: Hobbs & Shaw" qui a plu à un de nos membres.µ
Package: mico
Mico is a tool-toy to manage a number of hosts deployed in cloud services
(currently only support Amazon AWS), and also allows you to deploy new hosts
with specified template or create autoscaling groups and manage them easily.InstallationAs usual, mico is available frompypi, and can be installed usingpiporeasy_install:pip install micoOr:easy_install micoConfigure MicoMico just need an AWS key ID and AWS secret key to run. By default mico just
take this variables from the OS environment:export AWS_ACCESS_KEY_ID="*foo*"
export AWS_SECRET_ACCESS_KEY="*bar*"Creating templatesMico works using the concept of template. A template is just a python code
(with steroids which we calllibraries), the template can implements
a number of functions. Here are a simple and stupid template (stupid.py)defhello():print"Hello world!"defbye():print"Bye cruel world!"defhola(args):print"Hola%s"%argsOnce, your template is created, you need to put it into a mico template path
(by default uses/etc/micoand~/.config/mico/, and the current
working directory.Then you can just run mico:mico -H my_new_host stupid:hello
Hello world!

mico -H my_new_host stupid:bye
Bye cruel world!

mico stupid:hola everyone
Hola everyoneYou can see more complex (and useful!) templates inexamples directory.
Package: micodag
micodag is a Python package for learning Bayesian network using mixed integer convex programming.Related paper: Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian ModelsAuthors: Tong Xu, Armeen Taeb, Simge Kucukyavuz, Ali ShojaieCode for reproducing experiments in the paper is available onGithubSource code:https://github.com/AtomXT/micodagInstall$ pip install micodagSimple examplePlease download the following test files:data,true graph,
andmoral graph.import micodag as mic
import pandas as pd
import numpy as np

data = pd.read_csv("data_3bowling_n_500_iter_1.csv", header=None)
moral = pd.read_table('Sparse_Moral_edges_9_500_3.txt', delimiter=",", header=None)
true_B = pd.read_table('Sparse_Original_edges_9_500_3.txt', delimiter=",", header=None)
n, p = data.shape
lam = 12*np.log(p)/n
_, B, _ = mic.optimize(data, moral, lam)
B_arcs = pd.DataFrame([[i+1, j+1] for i in range(p) for j in range(p) if B[i, j] != 0])
print(B_arcs)
print(true_B)
Package: micom
WelcomeMICOMis a Python package for metabolic modeling of microbial
communities currently developed in theGibbons Labat theInstitute for Systems
Biologyand theHuman Systems Biology Groupof
Prof. Osbaldo Resendis Antonio at theNational Institute of Genomic
Medicine Mexico.MICOMallows you to construct a community model from a list on input
COBRA models and manages exchange fluxes between individuals and individuals
with the environment. It explicitly accounts for different abundances of
individuals in the community and can thus incorporate data from biomass quantification,
cytometry, ampliconsequencing, or metagenomic shotgun sequencing.It identifies a relevant flux space by incorporating an ecological model for the trade-off
between individual taxa growth and community-wide growth that shows good agreement with
experimental data.AttributionMICOM is published inMICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota
Christian Diener, Sean M. Gibbons, Osbaldo Resendis-Antonio
mSystems 5:e00606-19
https://doi.org/10.1128/mSystems.00606-19Please cite this publication when referencing MICOM. Thanks :smile:InstallationMICOMis available on PyPi and can be installed viapipinstallmicomFor more info on the installation or setting up the solvers please see thedocumentation.Getting startedDocumentation can be found athttps://micom-dev.github.io/micom.Getting helpGeneral questions on usage can be asked in Github Discussionshttps://github.com/micom-dev/micom/discussionsWe are also available on the cobrapy Gitter channelhttps://gitter.im/opencobra/cobrapyQuestions specific to the MICOM Qiime2 plugin (q2-micom) can also be asked on the Qiime2 forumhttps://forum.qiime2.org/c/community-plugin-support/
Package: micomputing
MIComputingIntroductionPackagemicomputingis the medical image processing package under projectPyCAMIA. It handles medical image read write, image interpolation, transformation, registration and so on. This package works underPyCAMIAand usebatorch.Tensoras its basic data format.InstallationThis package can be installed bypip install micomputingor moving the source code to the directory of python libraries (the source code can be downloaded ongithuborPyPI).pipinstallmicomputingAcknowledgment@Yuncheng Zhou: Developer
Package: micpeek
micpeekListen to a pulseaudio mic and plot a live spectrum in the terminal.Install$pipxinstallmicpeekor$pipxinstallmicpeek@git+https://github.com/pteromys/micpeek.gitUsePassed a fire truck going the other way and it's making you anxious?
No problem—ssh into the laptop on your desk and just typemicpeekto
reassure yourself that the fire alarm is not sounding. The gif at the
top of the page is a guy saying some words and whistling some scales,
whereas this is a fire alarm being tested:Test it out with your own lovely voice before you use it remotely, so
that you can learn baselines for an empty room or open windows or kids
playing video games. But if you have to, you could validate it remotely
by turning up the volume inalsamixerand playing some music.Acknowledgementsmicpeekis built on thehenrikschnor/pasimplepulseaudio python bindings.Animations were recorded usingasciinemainComic Mono, converted to
gif withasciinema/agg, and
compressedwith this ffmpeg filter:split[s0][s1];[s0]palettegen=max_colors=256[p];[s1][p]paletteuse=dither=noneStalking risk ecosystem impactLow, I hope. While this example lowers the barrier to entry for hooking
up a laptop mic to arbitrary code, it can't do speech recognition;
and there's creepier stuff out there that's easier to install and use.Please don't prove me wrong about that, but if you did, please let me
know. :(
Package: micral
Micral (MICROstructure anALysis)Description :The aim of this tool is to gather information about microstructure.Installation :pip install micralUsage :In a python file or directly in the interpreter, type :import micralmicral.analyse(<your image>)Input :<your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]Note that the image must be in the same directory than the python file (or in the current directory of the command)Output :The output will be an Python dictionnary, and each entry is a dictionnary how gather the differents informations on the image.
Package: micral-classify
Micral (MICROstructure anALysis)Classification moduleDescription :The aim is to distinguish between martensite, spheroidite and pearlite microstructure
This tools output the category, and some others details as the accuracy.Installation :pip install micral_classifyUsage :In a python file or directly in the interpreter, type :import micral_classify
print(micral_classify.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-classify-core
Micral (MICROstructure anALysis)Classification module - core moduleDescription :The aim is to distinguish between martensite, spheroidite and pearlite microstructure
This tools output the category, and some others details as the accuracy.
This tools doesn’t plot anything, it just return raw data that could be get by an user, or use with the graphic moduleInstallation :pip install micral_classify_coreUsage :In a python file or directly in the interpreter, type :import micral_classify_core
print(micral_classify_core.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-core
Micral (MICROstructure anALysis) - core moduleDescription :The aim is to gather information about microstructure.
It provide raw infomation.Installation :pip install micral_coreUsage :In a python file or directly in the interpreter, type :import micral_core
print(micral_core.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-grain
Micral (MICROstructure anALysis)Grain separation moduleDescription :The aim is to distinguish between coarse and ultrafine graine microstructure.
This tools output the pourcentage of coarse grain.Installation :pip install micral_grainUsage :In a python file or directly in the interpreter, type :import micral_grain
print(micral_grain.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-grain-core
Micral (MICROstructure anALysis)Grain separation module - core moduleDescription :The aim is to distinguish between coarse and ultrafine graine microstructure.
This tools output the pourcentage of coarse grain.
This tools doesn’t plot anything, it just return raw data that could be get by an user, or use with the graphic moduleInstallation :pip install micral_grain_coreUsage :In a python file or directly in the interpreter, type :import micral_grain_core
print(micral_grain_core.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-harmonic
Micral (MICROstructure anALysis)Harmonic measurement moduleDescription :The aim is to figure out in what extend a given microstrucure could be considered like an harmonic structure or not.Installation :pip install micral_harmonicUsage :In a python file or directly in the interpreter, type :import micral_harmonic
print(micral_harmonic.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-harmonic-core
Micral (MICROstructure anALysis)Harmonic measurement module - core moduleDescription :The aim is to figure out in what extend a given microstrucure could be considered like an harmonic structure or not.
This module don’t plot any result, but only raw result to be use in others applicationsInstallation :pip install micral_harmonic_coreUsage :In a python file or directly in the interpreter, type :import micral_harmonic_core
print(micral_harmonic_core.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-name
Micral (MICROstructure anALysis)Name moduleDescription :The aim is to give a name between 7 available
This tools output the name of the sample, and some others details as the accuracy.Installation :pip install micral_nameUsage :In a python file or directly in the interpreter, type :import micral_name
print(micral_name.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-name-core
Micral (MICROstructure anALysis)Name module - core moduleDescription :The aim is to give a name between 7 available
This tools output the name of the sample, and some others details as the accuracy.Installation :pip install micral_nameUsage :In a python file or directly in the interpreter, type :import micral_name
print(micral_name.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)
Package: micral-summary
Micral (MICROstructure anALysis)Classification module - core moduleDescription :The aim is to distinguish between martensite, spheroidite and pearlite microstructure
This tools output the category, and some others details as the accuracy.
This tools doesn’t plot anything, it just return raw data that could be get by an user, or use with the graphic moduleInstallation :pip install micral_classify_coreUsage :In a python file or directly in the interpreter, type :import micral_classify_core
print(micral_classify_core.analyse(<your image>))Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc…))
The output is an unique value.It’s also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, …]
And the return will be also a list of all the resultsNote that the image must be in the same directory than the python file (or in the current directory of the command)