Saravanan666/pypi-org huggingface dataset:
Package: microftp
microftpPython module to handle FTP protocol (client side only). It is modified original ftplib to handle broken FTP servers in embedded world.RequirementsIt should work with both python2 and python3 with simple pip commands:pip install microftpmicroftpcmd examplesFew microftpcmd examples:microftpcmd --host 192.168.4.1 ls
microftpcmd --host 192.168.4.1 -d -v ls
microftpcmd --host 192.168.4.1 put local.txt remote.txt
microftpcmd --host 192.168.4.1 put local.txt remote.txt
microftpcmd --host 192.168.4.1 get remote-file.txt local-file.txt
microftpcmd --host 192.168.4.1 rm file-to-delete.txtNote that you can also specify basic parameters using environment variables:export MICROFTP_HOST=127.0.0.1
export MICROFTP_USER=user
export MICROFTP_PASSWORD=password
export MICROFTP_DIR=/somethingand then you can just specify command:microftpcmd lsAll options are listed using --help:microftpcmd --helpRequirementsIt should work with both python2 and python3 with simple pip commands:sudo apt-get update
sudo apt-get install -y python3 python3-pip
sudo pip3 install microftpExamplesSimple example to get devices and its current status:import microftp

ftp = microftp.microFTP("127.0.0.1")
ftp.set_pasv(True)
ftp.login()
ftp.set_debuglevel(9999)
ftp.cwd(args.dir)
print(ftp.raw_retrlines('LIST'))
ftp.quit()Manualusage: microftpcmd [-h] [--host HOST] [--port PORT] [--delay DELAY]
                   [--block BLOCK] [--verbose] [--debug] [--user USER]
                   [--password PASSWORD] [--site SITE] [--siteafter SITEAFTER]
                   [--dir DIR]
                   CMD [CMD ...]

microftp - connect to broken or embedded FTP servers

positional arguments:
  CMD                   commands for ftp

optional arguments:
  -h, --help            show this help message and exit
  --host HOST, -i HOST
  --port PORT, -P PORT
  --delay DELAY
  --block BLOCK
  --verbose, -v
  --debug, -d
  --user USER, -u USER
  --password PASSWORD, -p PASSWORD
  --site SITE, -S SITE
  --siteafter SITEAFTER, -A SITEAFTER
  --dir DIR, -D DIR

microftpcmd --host 192.168.4.1 ls
microftpcmd --host 192.168.4.1 get remote-file.txt local-file.txt
microftpcmd --host 192.168.4.1 put local-file.txt remote-file.txt
microftpcmd --host 192.168.4.1 -S mount -D sd -A umount ls
microftpcmd --host 192.168.4.1 -S mount -D sd -A site /sd/blink.bit -A umount put blink.bit blink.bitulx3sSome specific ulx3s examples:microftpcmd --host 192.168.5.7 --delay 0.3 --block 32 -v -d --user root put ~/wget /root/wget
microftpcmd --host 192.168.4.1 -S mount -D sd -A umount ls
microftpcmd --host 192.168.4.1 -S mount -D sd -A site /sd/blink.bit -A umount put blink.bit blink.bit
Package: microfuse
This is a file system plus console plus MQTT plus take-your-pick
multiplexer for MicroPython.On the embedded device, a small single-client server runs in the
background. It receives file system commands, REPL input, OTA updates,
MQTT messages, and/or any other commands you might want to hook up. It
sends MQTT messages, REPL output, and any replies your commands generate.The other side (Linux, for now) starts a multiplexer which connects to that
mini-server and exposes a messaging socket and a bidirectional channel for
raw REPL data. It also connects to MQTT.A FUSE driver links the messaging socket to a convenient place in the
file system; it’s also possible to send file system commands directly.A terminal program (based on pyserial-miniterm) is included.TODOOTA updates.File system commands from the command line / an async Python program.The msgpack implementation needs improvement; it is crazy inefficient.
Sending a console lineshouldeat 50 bytes (one three-element dict to be
transmitted) or zero bytes (if we hand-roll the message), not 1700.There is not yet any attempt at reconnecting. (Should there be?)There are occasional MicroPython crashes:assertion "ATB_GET_KIND(block) == AT_HEAD" failed: file "…/micropython/py/gc.c", line 591, function: gc_freeThis definitely looks like a MicroPython bug. The problem might be related to bytearray slicing.
Package: microgen
Microgen is a simple python library that helps to generate and mesh microstructures.Here are the main features:Entirely written in Python 3 and based on CadQuery and PyVista libraries.It allows to generate simple reinforcement geometries (spheres, cylinder, ellipsoids, …) to generate virtual composites microstructures.Three-dimensional Voronoi tessellation rallons to simulate the response granular materials and polycrystalline metals.Regular mesh and periodic mesh are implemented using Gmsh, remeshing using Mmg is also implemented.DocumentationProviderStatusRead the DocsInstallationWith conda:conda install -c conda-forge -c cadquery -c set3mah microgenWith pip (Linux only):pip install microgenYou may need to install dependencies mentioned in the requirements.txt filepip install -r requirements.txtTo modify the sources, clone this repository and set up the following environment:Create a conda environment with all the required dependenciesconda env create -f environment.yml -n microgen-dev
conda activate microgen-devThen install microgen:python setup.py installExamplesClick on the image to be redirected to the corresponding example on Microgen's documentationBasic shapesRepeated cellsTriply Periodic Minimal Surfaces (TPMS)3D operationsMesh
Package: micrograd
microgradA tiny Autograd engine (with a bite! :)). Implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API. Both are tiny, with about 100 and 50 lines of code respectively. The DAG only operates over scalar values, so e.g. we chop up each neuron into all of its individual tiny adds and multiplies. However, this is enough to build up entire deep neural nets doing binary classification, as the demo notebook shows. Potentially useful for educational purposes.Example usageBelow is a slightly contrived example showing a number of possible supported operations:frommicrograd.engineimportValuea=Value(-4.0)b=Value(2.0)c=a+bd=a*b+b**3c+=c+1c+=1+c+(-a)d+=d*2+(b+a).relu()d+=3*d+(b-a).relu()e=c-df=e**2g=f/2.0g+=10.0/fprint(f'{g.data:.4f}')# prints 24.7041, the outcome of this forward passg.backward()print(f'{a.grad:.4f}')# prints 138.8338, i.e. the numerical value of dg/daprint(f'{b.grad:.4f}')# prints 645.5773, i.e. the numerical value of dg/dbTraining a neural netThe notebookdemo.ipynbprovides a full demo of training an 2-layer neural network (MLP) binary classifier. This is achieved by initializing a neural net frommicrograd.nnmodule, implementing a simple svm "max-margin" binary classification loss and using SGD for optimization. As shown in the notebook, using a 2-layer neural net with two 16-node hidden layers we achieve the following decision boundary on the moon dataset:Tracing / visualizationFor added convenience, the notebooktrace_graph.ipynbproduces graphviz visualizations. E.g. this one below is of a simple 2D neuron, arrived at by callingdraw_doton the code below, and it shows both the data (left number in each node) and the gradient (right number in each node).frommicrogradimportnnn=nn.Neuron(2)x=[Value(1.0),Value(-2.0)]y=n(x)dot=draw_dot(y)
Package: micrograd2023
micrograd2023IntroductionI have done several projectsHow to installThemicrograd2023package was
uploaded toPyPIand can be easily installed using
the below command.pip install micrograd2023Developer installIf you want to developmicrograd2023yourself, please use an editable
installation.git clone https://github.com/hdocmsu/micrograd2023.gitpip install -e "micrograd2023[dev]"You also need to use an editable installation ofnbdev,fastcore, andexecnb.Happy Coding!!!How to useHere are examples of using micrograd2023.# import necessary objects and functionsfrommicrograd2023.engineimportValuefrommicrograd2023.nnimportNeuron,Layer,MLPfrommicrograd2023.utilsimportdraw_dotimportrandom# inputs xs, weights ws, and bias bw1=Value(1.1)x1=Value(0.5)w2=Value(0.12)x2=Value(1.7)b=Value(0.34)# pre-activations=w1*x1+x2*w2+b# activationy=s.tanh()# automatic differentiationy.backward()# show the computation graph of the perceptrondraw_dot(y)# added random seed for reproducibilityrandom.seed(1234)n=Neuron(3)x=[Value(0.15),Value(-0.21),Value(-0.91)]y=n(x)y.backward()draw_dot(y)You can usemicrograd2023to train a MLP and learn fundamental
concepts such as overfilling, optimal learning rate, etc.Good trainingOverfittingTestingsTo perform unit testing, using terminal to navigate to the directory,
which containstestsfolder, then simply typepython -m pyteston
the terminal. Note that,PyTorchis needed for the
test to run since derivatives calculated usingmicrograd2023are
compared against those calculated usingPyTorchas references.python -m pytest
Package: microgrids
The Microgrids.py package allows simulating the energetic operation of an isolated microgrid,
returning economic and operation indicators.Installation withpip:pip install -U microgridsDocumentationSee theMicrogrid_py_PV_BT_DG.ipynbnotebook example which walks through:the main data structure to describe a Microgrid projectthe main function to simulate it and display the resultsYou can have a live demo of this notebook right in your browser:Microgrid_py_PV_BT_DG.ipynb web demo(fromMicrogrids.webrepository).AcknowledgementsThe development of Microgrids.jl (sibling package in Julia) was first led by
Evelise de Godoy Antunes. She was financed in part by
the Coordenação de Aperfeiçoamento de Pessoal de Nı́vel Superior - Brasil (CAPES) – Finance Code 001,
by Conselho Nacional de Desenvolvimento Cientı́fico e Tecnológico - Brasil (CNPq)
and by the grant “Accélérer le dimensionnement des systèmes énergétiques avec
la différentiation automatique” fromGdR SEEDS (CNRS, France).Other microgrids-related packages in PythonFound by searching for"microgrid"on PyPI or from personal knowledge:PyEPLAN: a free software toolbox for designing resilient mini-grids in developing countries. From Leeds, CUT, ICL.pymgrid(PYthon MicroGRID): a python library to generate and simulate a large number of microgrids.OpenModelica Microgrid Gym(OMG):
a software toolbox for the simulation and control optimization of microgrids
based on energy conversion by power electronic converters.“The main characteristics of the toolbox are the plug-and-play grid design and simulation in OpenModelica as well as the ready-to-go approach of intuitive reinforcement learning (RL) approaches through a Python interface.”“The OMG toolbox is built upon the OpenAI Gym environment definition framework. Therefore, the toolbox is specifically designed for running reinforcement learning algorithms to train agents controlling power electronic converters in microgrids. Nevertheless, also arbritary classical control approaches can be combined and tested using the OMG interface.”CVXMG: Planning of Microgrids considering Demand Side Management Strategies using Disciplined Convex Deterministic and Stochastic Programming
Package: microgue
AWS Microservice Bootstrap CodeThis project contains bootstrap code to speed up the development of AWS based microservicesThis includes:Redis CacheEventBridgeDynamoDB Models, Tables, and ObjectsSQS QueuesSecrets ManagerS3 StorageThis project also includes base code for:Logger DefinitionsService ConnectionsFlask App DefinitionsAdditional UtilitiesWorking On MicrogueRepo:https://bitbucket.org/michaelhudelson/microgue/src/masterClone:git clone https://bitbucket.org/michaelhudelson/microgue/src/masterRequirementsPython 3.7+Pre-SetupMake sure you have an IAM user created with correct permissions in your AWS accountCreate an Access Key on that userInstall awsclipip install awscliAdd that Access Key withaws configureVerify you are using the correct Access Key withaws configure listYou can also verify by looking at the file~/.aws/credentialsInstall Microguepip install microgueFlask Setup (Optional)Put the following code in the app.py file in the root of the projectimportloggingfrommicrogue.abstract_appimportAbstractApplogging.basicConfig()classApp(AbstractApp):passapp=App().appIn the terminal run the following commandsexport PYTHONUNBUFFERED=1
export FLASK_DEBUG=1
flask runGEThttp://127.0.0.1:5000Distribution# update version in setup.py

# commit and push changes
git add .
git commit -m "v1.0.X"
git push origin master

# tag the commit and push
git tag -a v1.0.X -m "Release v1.0.X"
git push --tags

# package with:
python setup.py sdist bdist_wheel

# https://pypi.org/project/microgue/
# upload to pypi with:
python -m twine upload dist/*

# OPTIONAL
# https://test.pypi.org/project/microgue/
# upload to test pypi with:
twine upload --repository-url https://test.pypi.org/legacy/ dist/*Backlogget_all accept sk_operation ["eq", "begins_with", "between", "lt", "lte", "gt", "gte"]Operation.begins_with = lambda sk, sk_value: Key(sk).begins_with(sk_value)
Package: microhackaton-service-discovery-py
Python library to interact with other microservices athttp://microhackaton.github.io/2014/InstallationYou can get it fromhttps://pypi.python.org/pypi/microhackaton-service-discovery-py$pipinstallmicrohackaton-service-discovery-pyUsagefromservice_discoveryimportServiceDiscoverysd=ServiceDiscovery('/pl/pl/microhackaton','zookeeper.microhackathon.pl:2181')How to register your service?instance_id=sd.register('something-collector','12.34.56.78',8080)How to find instances of other service?urls_as_list=sd.get_instances('blog-collector')How to find random service instance?url_as_str=sd.get_instance('blog-collector')
Package: microhapdb
MicroHapDBNBFAC, 2018-2023https://github.com/bioforensics/microhapdbMicroHapDBis a comprehensive catalog of human microhaplotype variation.
The database integrates marker and population frequency data from numerous published research articles.
Allele frequency estimates and allelic diversity statistics ($A_e$) are computed for 26 global populations so that markers can be ranked and evaluated for various applications.MicroHapDB is designed to be user-friendly for researchers, technology developers, and forensic practitioners, supporting a range of access methods from browsing and simple text queries to complex queries and full programmatic access via a Python API.
It is designed as a community resource requiring minimal infrastructure to use and maintain: the entire contents of the database are distributed in plain text with each copy of MicroHapDB.InstallationSeethis pagefor complete installation instructions.
If this isn't your first rodeo, the commands below are provided as a quick reference.conda install -c bioconda microhapdb pytest
pytest --pyargs microhapdb --doctest-modulesUsageMicroHapDB provides several methods to access the contents of a locally installed database.
TheMicroHapDB documentationincludes a"Getting started" guideas well as a comprehensive reference for running MicroHapDB on the command line.A particularly intrepid and curious user may also be so bold as to download the core database tables directly fromGitHub.MicroHapDB includesstatistics for ranking markers, tools forpanel design, and instructions foradding markers to a private local copy of the database.CitationIf you use this database, please cite our work.Standage DS,  Mitchell RN (2020) MicroHapDB: A Portable and Extensible Database of All Published Microhaplotype Marker and Frequency Data.Frontiers in Genetics11:781,doi:10.3389/fgene.2020.00781.MicroHapDB was created and is maintained by the Bioinformatics Group at the National Bioforensic Anaylsis Center (NBFAC).Additional references are availableon this page.
Package: microhapulator
MicroHapulatorDaniel Standage, 2018-2019https://github.com/bioforensics/microhapulatorMicroHapulatoris a package for analyzing and interpreting microhaplotype sequence data for forensic analysis.
It provides tools for genotyping as well as deterministic and probabilistic interpretation of simple and complex samples.
It also provides tools for constructing mock genotypes and simulating MPS sequencing of single- and multiple-contributor samples.
MicroHapulator relies on microhap marker definitions and population allele frequencies fromMicroHapDBand the MPS error models included withInSilicoSeq.InstallationInstallation with bioconda is recommended.conda install -c bioconda microhapulatorNOTE: If you'd prefer to install with pip, see.travis.ymlandsetup.pyfor hints.Interactive demoClick the badge below to launch a quick interactive demo of MicroHapulator.Usageusage: mhpl8r [-h] [-v] [-l F] [--tee] subcmd ...

≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠
  __  __ _            _  _                _      _
 |  \/  (_)__ _ _ ___| || |__ _ _ __ _  _| |__ _| |_ ___ _ _
 | |\/| | / _| '_/ _ \ __ / _` | '_ \ || | / _` |  _/ _ \ '_|
 |_|  |_|_\__|_| \___/_||_\__,_| .__/\_,_|_\__,_|\__\___/_|
                               |_|
≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠≠
Invoke `mhpl8r <subcmd> --help` and replace `<subcmd>` with one of the
subcommands listed below to see instructions for that operation.

Subcommands:
  subcmd             contain, contrib, diff, dist, mix, prob, seq, sim, type,
                     unite

Global arguments:
  -h, --help         show this help message and exit
  -v, --version      show program's version number and exit
  -l F, --logfile F  log file for diagnostic messages, warnings, and errors
  --tee              write diagnostic output to logfile AND terminal (stderr)ContributingWe welcome contributions from the community!
Feel free to ask questions, make suggestions, or report bugs using theissue tracker.
If you are interested in submitting patches,docs/DEVEL.mdcontains a few suggestions for a development setup.
All contributors are expected to abide by the project'sCode of Conduct.
Package: microhomie-node-bme280
homie.node.bme280================Microhomie node for the BME280 sensor to provide *temperature*, *humidity* and *pressure* data.Install------->>> import upip>>> upip.install('microhomie-node-bme280')You'll also need the mpy_bm280 library, which you can find here:https://github.com/catdog2/mpy_bme280_esp8266Uplaod it as bme.pyWiring-----------------.. image:: bme280.png
Package: microhorario-dl
Microhorario DownloaderBiblioteca destinada ao download e utilização de dados provenientes doMicrohorarioda PUC-Rio.Essa biblioteca permite baixar todos os dados, e disponibiliza modelos para a utilização destes.A biblioteca não tem poder de alterar nenhum dado, somente baixar os dados e manipular localmente.Além disso, é possível baixar as ementas automaticamente de todas as disciplinas disponíveis.FuncionamentoO microhorário não disponibiliza nenhuma API para poder fazer um download automático das disciplinas. Por isso,
é necessário fazer requests específicos para conseguir simular um usuário baixando os dados.Para baixar os dados, um usuário precisa carregar a página inicial, fazer uma consulta sem nenhum filtro, e depois
baixar os dados no formato CSV. Essa biblioteca simula essas três simulações atráves de um GET e dois POSTS.Para poder baixar o CSV, é preciso fazer um POST enviando os dados do formulário, assim como variáveis de ASP.NET. Essas
variáveis são adquiridas ao fazer uma consulta sem filtro, por isso é necessário fazer um POST usando os dados do formulário
para fazer essa consulta. Porém, essa consulta também utiliza variáveis de ASP.NET e um id de sessão adquiridos ao entrar
na página. Por isso, é necessário fazer um GET na página inicial para poder adquirir essas variáveis e esse id.Portanto, são feitos três requests, nomeadosconsulta_inicial,consulta_intermediaria, econsulta_final:consulta inicial (GET https://puc-rio.br/microhorario):
    recebe:
        cookies
        id da sessao
        nomes e códigos dos departamentos
        nomes e códigos dos destinos
        __VIEWSTATEGENERATOR, __VIEWSTATE e __EVENTVALIDATION

consulta intermediaria (POST /WebMicroHorarioConsulta/MicroHorarioConsulta.aspx):
    envia:
        formulario pedindo uma consulta sem filtro
        cookies
        id da sessao
        __VIEWSTATEGENERATOR, __VIEWSTATE e __EVENTVALIDATION

    recebe:
        cookies
        id da sessao
        __VIEWSTATEGENERATOR, __VIEWSTATE e __EVENTVALIDATION

consulta final (POST /WebMicroHorarioConsulta/MicroHorarioConsulta.aspx):
    envia:
        formulario pedindo para baixar os resultados de uma consulta sem filtro
        cookies
        id da sessao
        __VIEWSTATEGENERATOR, __VIEWSTATE e __EVENTVALIDATION

    recebe:
        arquivo csv em utf-16UsoInstale a biblioteca:pipinstallmicrohorario-dlImporte e baixe os dados:>>>frommicrohorario_dlimportMicrohorario>>>micro=Microhorario.download()<Microhorario object at ...>>>>micro.disciplinas[<Disciplina [ACN1000]>, <Disciplina[ACN1002]>, ...]Baixe as ementas e prerequisitos:>>>micro.coletar_extra(verbose=True)[0/ 2000] Baixando ACN1000...  DONE (...)[1/ 2000] Baixando ACN1002...  DONE (...)[2/ 2000] Baixando ACN1004......Exporte para um json:>>>importjson>>>print(json.dumps(micro.as_json(),indent=2)){"periodo": "20221","emissao": "05/04/2022 22:57 h",# ..."disciplinas": [# ...]}
Package: microhttp_ratelimit
Rate limit extension for microhttpBased LeakyBucket method on RedisIP throttler decoratorInstallpipinstallmicrohttp-ratelimitNote: required to installRedis.Configurationrate_limit:namespace_prefix:myAppredis:host:localhostport:6379ip_rules:default:period:900# 15 Minuteslimit:180slow:period:86400# 24 Hourslimit:50very_slow:period:86400limit:10
Package: microjoy
comuinitfrom j.joy import Joy

j = Joy(35)

while True:
    print(j.read())escalafrom j.joy import Joy

j=Joy(35,escala=(-10,10))

while True:
    print(j.read())inverter o valorfrom j.joy import Joy

j=Joy(35, invert=True)

while True:
    print(j.read())
Package: microjson
MicroJSONMicroJSON is a JSON-based format inspired byGeoJSON, designed to encode a variety of data structures related to microscopy images. It can handle representations of reference points, regions of interest, and other annotations, making it an ideal solution for conveying complex microscopy data in a straightforward, easy-to-use format.FeaturesMicroJSON offers a range of features designed to meet the needs of microscopy data representation:Flexible Data Structures:MicroJSON can represent diverse data structures, including geometries (such as points, multipoints, linestrings, polygons), features (individual entities with specific properties), feature collections (groups of features), and coordinate systems.Standardized Format:MicroJSON uses the widely adopted JSON format, ensuring compatibility with a wide range of programming languages and tools.Extensibility:MicroJSON can handle additional properties associated with specific features, such as metadata relating to microscopy images.Additional FunctionsThere are two additional functionalities added which supports binary images.BinaryMicrojsonModel:Converts objects in a binary image into polygon coordinates (rectangle, encoding) and save them in json file format using microjson package.MicrojsonBinaryModel:Reconstruct binary images using polygon coordinates from json file.UsageMicroJSON can be used with any application or tool that can process JSON data. Due to its design, it is particularly suited to applications related to the analysis, visualization, and manipulation of microscopy images.External ResourcesThe GeoJSON test files are copied from theGeoJSON Schema GitHub repository, and are Copyright (c) 2018 Tim Schaub under MIT License.ContributionWe welcome contributions to the development and enhancement of MicroJSON. Whether you're reporting bugs, suggesting enhancements, or contributing to the code, your input is highly appreciated.LicenseMicroJSON is licensed under MIT License, (c) 2023 Polus AI & Nextonic Solutions LLC.AuthorsBengt Ljungquistbengt.ljungquist@nextonicsolutions.comThis project is maintained by Polus AI. For any queries or further discussion, please contact the author on email address above.