Saravanan666/pypi-org huggingface dataset:
Package: micromed-io
A library to read, emulate, and forward Micromed data in standard formats. Seeonline doc.Main features:simulate online data from a trc filepush online tcp data to LSL serverconvert trc to mne formatrename trc files to include the recording datetimeInstall$pipinstallmicromed-ioConvert a Micromed (.trc) file to MNE (.fif) formatfrommicromed_io.to_mneimportcreate_mne_from_micromed_recordingmne_raw=create_mne_from_micromed_recording("path/to/file.TRC")Emulate TRC to TCP & read/forward to LSL serverSee details in next sectionsEmulate Online Micromed TCP from.trcfile$mmio_emulate_trc--file=../data/sample.TRC--address=localhost--port=5123Emulate the online data stream of Micromed to test your real-time
platform. See all the arguments and adapt them:$mmio_emulate_trc--help# to see all argumentsNoteNew: not only data, but also markers and notes are send through
TCP, exactly as Micromed doesRead TCP and push to LSL Stream$mmio_tcp_to_lsl--address=localhost--port=5123While receiving online data throug tcp, this command forward the data to
3 LSL stream outlets:Micromed_EEG: the eeg data infloat32format[n_channels, n_samples]Micromed_Markers: markers if any inint32format[sample, marker](2 channels)Micromed_Notes: notes if any instringformat[sample, note](2 channels)You can easily change the LSL parameters:$mmio_tcp_to_lsl--help# to see all argumentsRead TRC filefrommicromed_io.trcimportMicromedTRCmmtrc=MicromedTRC("sample.TRC")Then you have access to thetrcdata:mmtrc.get_header()mmtrc.get_markers()mmtrc.get_data()mmtrc.get_notes()NoteNote:get_data()might take times because it loads the brain
dataRead and parse Micromed TCP live dataDownloadtcp_to_lsl.pyfrom thegithub
repoinscripts/$pythontcp_to_lsl.py--address=localhost--port=5123Note: Micromed TCP behaves as a client. If you want to try the
emulate/read TCP script, launch the reader first that acts as server,
then the emulator.Read online Micromed TCP in a sliding window bufferIf you plan to use the Micromed data as input of a decoder, you probably
want epochs of format(n_channels, n_samples). Then theMicromedBufferclass is for you. The scripttcp_to_epoch.pyshows you how to use it (see thePROCESS HEREcomment). It uses abufferthat mimics thesliding windowand triggers each time it
is filled.frommicromed_io.bufferimportMicromedBuffermicromed_buffer=MicromedBuffer(epoch_duration=5,epoch_overlap=2.5)To run the example, you can download and run thetcp_to_epoch.pyscript inextern/folder:$pythontcp_to_epoch.py--epoch-size=5--overlap=2.5NoteTry it withemulated datafrom a TRC file (cfemulate TRC)Rename TRC files with recording datetime$mmio_rename_trc--dirpath=./--format=%Y%m%d-%H%M%SRename the TRC files of the given folder to include the recording date
in the filename. Output is :<filename>__<recording_date>.TRC. The
format must be compliant with pythonstrftime format
codesmmio_rename_trc--help# to see helpLocal installDownload the repo and:$condaenvcreate-fenvironment.yml$condaactivatemmio$poetryinstallPlease feel free to reach me if you want to contribute.
Package: micromelon
MicromelonPython ModuleThis module provides an API for connecting and controllingMicromelon Roversand simulated rovers in theMicromelon Robot Simulator.The API is equivalent to what is available in the Micromelon Code Editor application.Installationpip install micromelonUsageAfter import you will need a reference to the RoverControllerfrommicromelonimport*rc=RoverController()The RoverController object contains all the functionality for connection and control of the robot's state.At the start of any script that controls the rover you will need to connect and put the rover in a running state.
Once complete you will need to return the rover to the idle state and optionally you can end the Python program.rc.connectBLE(1)# Connects over Bluetooth to rover with ID of 1rc.startRover()# Puts in running state# Body of script controlling rover# eg. Motors.write(20, 20, 1)rc.stopRover()# Returns to idle staterc.end()# Disconnects and end Python programFull code examples available in the examples folder.DocumentationGenerating DocumentationFull API documentation available athttps://micromelon-robotics.github.io/mm-pymodule/Documentation is generated through the python module pdoc3.Full documentation on pdocavailable here.1. Installing pdocInstall pdoc through pip.Requires python 3.pip3 install pdoc32. Generating HTMLpdoc--html--template-dirdocs/pdoc_templates-odocsmicromelon-f3. Running docs locallyYou can run the documentation as a webserver on your local machine with:pdoc --http : micromelonThen openhttp://localhost:8080/in a browser.BuildingGet Python build toolpython -m pip install --upgrade buildBuild packagepython -m buildUploadingInstall twinepython -m pip install --upgrade twineUploadingpython -m twine upload dist/*
Package: micromentor-django-pattern-library
micromentor-django-pattern-libraryThis is a MicroMentor specific fork of:django-pattern-libraryUI pattern libraries for Django templates. Try ouronline demo.FeaturesThis package automates the maintenance of UI pattern libraries or styleguides for Django projects, and allows developers to experiment with Django templates without having to create Django views and models.Create reusable patterns by creating Django templates files as usual.All patterns automatically show up in the pattern library’s interface.Define data as YAML files for the templates to render with the relevant Django context.Override Django templates tags as needed to mock the template’s dependencies.Document your patterns with Markdown.Why you need thisPattern libraries will change your workflow for the better:They help separate concerns, both in code, and between members of a development team.If needed, they make it possible for UI development to happen before models and views are created.They encourage code reuse – defining independent UI components, that can be reused across apps, or ported to other projects.It makes it much simpler to test UI components – no need to figure out where they’re used across a site or app.Learn more by watching our presentation –Reusable UI components: A journey from React to Wagtail.Online demoThe pattern library is dependent on Django for rendering – but also supports exporting as a static site if needed. Try out our online demo:For a component,accordion.htmlFor a page-level template,person_page.htmlDocumentationDocumentation is available attorchbox.github.io/django-pattern-library, with source files in thedocsdirectory.Getting startedGuidesDefining template contextOverriding template tagsCustomizing template renderingUsage tipsReferenceAPI & settingsKnown issues and limitationsContributingSee anything you like in here? Anything missing? We welcome all support, whether on bug reports, feature requests, code, design, reviews, tests, documentation, and more. Please have a look at ourcontribution guidelines.If you want to set up the project on your own computer, the contribution guidelines also contain all of the setup commands.Nightly buildsTo try out the latest features before a release, we also create builds from every commit tomaster. Note we make no guarantee as to the quality of those pre-releases, and the pre-releases are overwritten on every build so shouldn’t be relied on for reproducible builds.Download the latestdjango_pattern_library-0.0.0.dev0-py3-none-any.whl.CreditsView the full list ofcontributors.BSDlicensed.Project logo fromFxEmoji. Documentation website built withMkDocs, and hosted inGitHub Pages.
Package: micromenu
micromenuA minimalistic command line menu for Python, with a title and optional message bars and dividers. Callable functions are attached to each menu item.╭─── This is a title ────────────────────────╮
│ Message that shows on top                  │
╞════════════════════════════════════════════╡
│ 1: Menu item 1                             │
│ 2: Menu item 2                             │
│ 3: Menu item 3                             │
├────────────────────────────────────────────┤
│ 0: Exit                                    │
├────────────────────────────────────────────┤
│ Bottom message row 1                       │
│ Bottom message row 2                       │
╰────────────────────────────────────────────╯
Action number:Set the parametercycle=Falseif the menu should not loop back after an item has been executed.menu.show()will returnTrueafter terminating.add_function_itemhas an optional parameteruidwhich can be used to address the menu options without using the index numbers.Installationpython3-mpipinstallmicromenuExample usageimportmicromenumenu=micromenu.Menu("test","this is a message","and a bottom message",min_width=25)menu.add_function_item("item1",lambdax:print(x),{'x':"testparam"})menu.add_divider()menu.add_function_item("item2",lambdax:print(x),{'x':"testparam"}))menu.add_message_bottom_row("bottom msg")menu.show()
Package: micromez
Overview========This is the Python library to support the micromez add-on board that is part of the 96Boards Mezzanine Community effort:https://github.com/96boards/mezzanine-community/tree/master/boards/other/micromezDocumentation=============https://micromez.readthedocs.io/en/latest/overview.htmlBuilding mraa=============This library has a dependancy on the mraa GPIO library which can be installed with Python bindings as follows:```sudo apt-get install git build-essential swig3.0 python3-dev nodejs-dev cmake libjson-c-devgit clone https://github.com/intel-iot-devkit/mraa.gitcd mraamkdir buildcd buildcmake -DBUILDSWIG=ON \-DBUILDSWIGPYTHON=ON \-DBUILDSWIGPYTHON3=ON \-DBUILDSWIGNODE=OFF \-DBUILDSWIGJAVA=OFF \..makesudo make install```Load additional Fonts=====================If more fonts are required in addition to the three provided, the following will need to be done:```sudo apt-get install python3-pipsudo pip3 install setuptoolssudo pip3 install freetype-pysudo apt-get install libfreetype6-dev./font_to_py.py /usr/share/fonts/opentype/ipafont-gothic/ipag.ttf 16 myfont.py```
Package: micromind
This is the official repo ofmicromind, a toolkit that aims at bridging two communities: artificial intelligence and embedded systems.micromindis based onPyTorchand provides exportability for the supported models in ONNX, Intel OpenVINO, and TFLite.💡 Key featuresSmooth flow from research to deployment;Support for multimedia analytics recipes (image classification, sound event detection, etc);Detailed API documentation;Tutorials for embedded deployment;🛠️️ InstallationUsing PipFirst of all, installPython 3.8 or later. Open a terminal and run:pip install micromindfor the basic install. To installmicromindwith the full exportability features, runpip install micromind[conversion]From sourceFirst of all, installPython 3.9 or later.
Clone or download and extract the repository, navigate to<path-to-repository>, open a
terminal and run:pip install -e .for the basic install. To installmicromindwith the full exportability features, runpip install -e .[conversion]Training networks with recipesAfter the installation, get started looking at the examples and the docs!Export your model and run it on your MCUCheck outthistutorial and have fun deploying your network on MCU!📧 Contactfrancescopaissan@gmail.com
Package: micromodel
MicromodelStatic and runtime dictionary validation.Install$pipinstallmicromodelWhyWe had a HUGE Python code base which was usingpydanticto provide a validation layer for MongoDB operations. The code was all messy but it worked fine, until we decided to switch the type checker config from "basic" to "strict", then over a thousand of dictionary-related errors popped up, not to mention the annoying conversions fromdictto classes that were going on on every validation and that should be checked everytime.We then decided to make this validation in-loco using a more vanilla approach with onlyTypedDicts. Now our dictionaries containing MongoDB documents are consistently dicts that match with the static typing.UsageimporttypingfrommicromodelimportmodelAnimal=typing.TypedDict('Animal',{'name':str,'specie':list[typing.Literal['dog','cat','bird']]})# even another TypedDicts can be used!Person=typing.TypedDict('Person',{'name':str,'age':int,'animal':Animal})m=model(Person,{'Animal':Animal})old_validate=m.validatedefnew_validate(target:Person):new=target.copy()new['name']=new['name'].capitalize()returnvalidate(Person,typing.cast(typing.Any,new))# hooks can be implemented using monkeypatchingm.validate=new_validateresult=m.validate({'name':'joao','animal':{'name':'thor','specie':['dog',# 'turtle' (this would produce both static and runtime errors)]}})"""{"name": "Joao","animal": {"name": "thor","specie": ["dog"]}}"""print(result)LicenseThis library isMIT licensed.
Package: micromouse-api
MicroMouseThis is the main repository for the MicroMouse API Project.IntroductionHaving recently heard about theMicroMouse Comepetition,
I have acquired an interest in developing an online version for the same.
This project aims to provide a virtual maze with an local API to access the maze,
and the contest will be to make a standalone mouse programs, that can use various techniques
such as:Flood fill algorithmQ LearningNEAT AlgorithmA* Path Finding AlgorithmAnd many more such algorithms, to get their mouse to the finish line as fast as possible.ComponentsThe Components involved in this repository are:Maze Generator APIMouse Example (Not Completed)Documentation (Not Completed)UsageClone the repository with submodules# Instructions for installation here.Use the Documentation to get started.ContributingContributingInstructionshere.(For now, you can just fork the repo and suggest changes, as it is in early development)Thanks!
Package: micron
micronMinimal replacement for cron in containersmicronis made to easily create scheduled-tasks containers,
with the following limits:1 task per container1 schedule per task (cron-compatible)one execution at a time
(if the first execution is not finished in time
for the next schedule, execution is skipped)Installationpip install micronUsageusage: micron [-h] [-x] [-q] schedule program [program ...]

positional arguments:
  schedule             a cron-like schedule
  program              the command line to run

optional arguments:
  -h, --help           show this help message and exit
  -x, --exit-on-error  exit if the program return-code is non-zero
  -q, --quiet          do not log micron actionsExamplemicron '0 0 * * *' -- find /var/log -name '*.log' -mtime +30 -delete
Package: micronets
MicronetsMicronets is a python framework for the simple implementation and training of deep neural networks. The project consists of a small auto-gradient engine module which implements back propagation through a DAG (Directed Acyclic Graph) and a module resembling the PyTorch/Tensorflow APIs to create and train dense neural networks on top of it.The package also provides additional functionalities such as plotting of the training curve, make predictions and visualisation of computational graphs after trainingInstallationpipinstallmicronetsUsage1. Building Computational Graphsfrommicronets.autograd_engineimportValuefrommicronets.graphimportdraw#inputsx1=Value(2.0,label='x1')x2=Value(4.0,label='x2')#weightsw1=Value(-3.0,label='w1')w2=Value(1.0,label='w2')#biasb=Value(3.3,label='b')x1w1=x1*w1;x1w1.label='x1*w1'x2w2=x2*w2;x2w2.label='x2*w2'x1w1x2w2=x1w1+x2w2;x1w1x2w2.label='x1w1 + x2w2'z=x1w1x2w2+b;z.label='z'output=z.tanh();output.label='output'output.backward()draw(output)2. Neural Net Implementationfrommicronets.nnimportDNN# Dense Neural Networkfrommicronets.lossesimportbinary_cross_entropyxs=[[2.0,3.0,-1.0],[3.0,-1.0,0.5],[-0.5,-1.0,-1.0],[-1.0,-1.0,0.5]]ys=[1.0,0.0,1.0,0.0]model=DNN(input_features=3,layers=[2,2,1],activations=['relu','relu','sigmoid'])# creates model architecturehistory,graph=model.train(X=xs,Y=ys,iterations=500,loss_function=binary_cross_entropy,learning_rate=0.1)# trains network and stores loss function history and computational graphpredictions=model.predict([# outputs predictions[2.0,-1.0,3.5],[-1.0,-3.0,0.5]])model.plot(history)# plots learning curveAdditionally theplayground.ipynbfile demonstrates functionality through a more complex example along with its outputs.Lastly, a big thank you toAndrej Karpathyfor inspiring this project :)LicenseMIT
Package: micronuclAI-labeling
micronuclAI labeling toolInstallationpip install micronuclAI-labelingRequirementsPython 3.10UsageTo launch micronuclAI labeling tool, run the following command:python -m micronuclai_labeling --input path/to/image --mask path/to/mask --out path/to/outfile.csvOnce the tool is launched, you can use the following keyboard shortcuts:The tool works by pressing any of the keys from 0-9:The key 0 for no micro-nuclei present.The keys 1-9 for the number of observed micro-nuclei in the image.The key r is used to go back one image (in case any labeling mistake occurs).NOTE:Labeling doesn't have to be done in one session, it can be resumed later on.NOTE:Once labeling is complete the tool will not initiate.NOTE:Output, Input and Mask paths are required and must be the same in order to resume labeling.
Package: microparcel
microparcel-pythonSerialize and deserialize structured data.microparcel impementation in Python.Free software: MIT licenseProvides three differents entities:A Message is the payload, with methods to access specifics bitfields on the data bufferThe Frame encapsulates the Message between a Start Of Frame and a CheckSumAnd the Parser is used to Parse bytes into Message and encodes Messages in FrameSee Documentation:https://microparcel-python.readthedocs.io/en/latest/CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-09-24)First release on PyPI.
Package: microparcel-tools
Microparcel ToolsCode generation tool for microparcelFree software: MIT licenseDocumentation:https://microparcel-tools.readthedocs.io.FeaturesGenerates a Communication protocol from a schema.Generates C++ and python codeBased on microparcel, suitable for embedded systemsCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2019-02-28)First release on PyPI.
Package: microperf
MicroPerfWarningThis is a work in progress.perfI recommend the following commands to build this project on Ubuntu.
Most packages are needed to enable perf features, some may not be necessary.
They may be named differently on other distributions.% sudo apt update

% sudo apt install -y         \
        binutils-dev          \
        bison                 \
        flex                  \
        g++                   \
        git                   \
        libdw-dev             \
        libbabeltrace-ctf-dev \
        libtraceevent-dev     \
        libcap-dev            \
        libelf-dev            \
        libiberty-dev         \
        liblzma-dev           \
        libnuma-dev           \
        libperl-dev           \
        libpfm4-dev           \
        libslang2-dev         \
        libssl-dev            \
        libunwind-dev         \
        libzstd-dev           \
        make                  \
        openjdk-11-jdk        \
        pkg-config            \
        python3-dev           \
        python3-setuptools    \
        systemtap-sdt-dev

% python3 microperf/build-perf.pyPrestoA Docker image is provided to run Presto with this suite of tools.
It differs slightly from the official image, for example removing the-Xmx1GJVM flag to allow processing of larger profiles.% docker image build microperf/presto --compress --tag perf-presto
% docker run -d -p 8080:8080 --name perf-presto perf-presto
Package: micropie
MicropieMicropie is a asynchronous micro framework to build microservices inspired bynamekofocused on simplicity configurability and scalability.InstallingTo installmicropiejust runpip install micropieQuick startDefine your service classclassPerson:defget_full_name(self):# your function body here ...passAssuming that your class is in file calledperson.pyrun your service as followmicropie person:PersonOr frompythoncode asmicropie.run(Person)An output like this is showed if all was okay=======================
     MICROPIE v0.0.1
=======================
 * pid: 147563
 * standalone: True
 * service: person
 * description: 
 * instance: 8dea5f67bc2849ff9ec5e7934f5b1ad5
 * transport: tcp
 * loop: auto
 * logs: INFO
2020-09-23 18:07:09,063 (micropie.service) [INFO]: STARTED listen on: tcp://0.0.0.0:39523 (Press CTRL+C to quit)To read full documentation visitmicropie docs
Package: micropip
micropipA lightweight Python package installer for the webInstallationInPyodide, you can install micropip,either implicitly by importing it in the REPLor explicitly viapyodide.loadPackage('micropip'). You can also install by URL from PyPI for instance.Usageimportmicropipawaitmicropip.install(<list-of-packages>)For more information see thedocumentation.Licensemicropip uses theMozilla Public License Version
2.0.
Package: micropipelines
READMEThis README would normally document whatever steps are necessary to get your application up and running.What is this repository for?Quick summaryVersionLearn MarkdownHow do I get set up?Summary of set upConfigurationDependenciesDatabase configurationHow to run testsDeployment instructionsContribution guidelinesWriting testsCode reviewOther guidelinesWho do I talk to?Repo owner or adminOther community or team contact
Package: microplink
microplinkThis class provides a minimal and fast interface for retrieving genotypes
from Plinks's BED/BIM/FAM file trios. The BED file must fit in memory.Requirementsnumpy,pandas, andnumba. These are included with many Python
distributions.InstallationRunpip install microplinkor clone this repository and runpython setup.py installfrom the repository directory.UsageTypical usage:import microplink as mic

# Equivalent to `pf = mic.PlinkFiles('test.bed', 'test.bim', 'test.fam')`
pf = mic.PlinkFiles('test')

# fetch by chromosome and position
pf.get_genotypes(chromosome=1, position=10000)

# fetch by variant name (often RSID)
pf.get_genotypes(snp='rs1235123')

# fetch by SNP integer index
pf.get_genotypes(snp_index=0)