Saravanan666/pypi-org huggingface dataset:
Package: marlowe-ui
Takaaki AOKI (aoki.takaaki@nagoya-u.jp)About Malouwe User InterfaceMarlowe User Interface is support program for marlowe (a binary collision simulator,http://www.oecd-nea.org/tools/abstract/detail/psr-0137/)
to desgin input data, as well as parse output .lst data.Programs and scriptsmui (mui.py, mui.exe):GUI program to construct marlowe input fileml_post_ui (ml_post_ui.exe or ml_post_ui.py):GUI program to parse output data.ml_post (ml_post.exe or ml_post.py):Command line program to parse output data.Project URLshttps://bitbucket.org/marlowe_ui/marlowe_ui/Documentshttps://bitbucket.org/marlowe_ui/marlowe_ui/wiki/Windows Binaryhttps://bitbucket.org/marlowe_ui/marlowe_ui/downloads/(downloadmarlowe_ui-(version)-win-amd64.exe)PyPIhttps://pypi.python.org/pypi/marlowe_ui
Package: marm
Supportswindowed/resumabletranscoding of recorded media codec packets by:Reading stored/archived media packets (e.g.pcap’drtp)Reconstructs media frames (e.g. depacketized encoded frame fragmented by network protocol)Synchronizing frame streams (e.g. paired audio and video)Muxing frame stream(s) to a container (e.g.mkvfile) usinglibav*Detectingstitchinginformation used to seamlessly resume transcoding…depsInstall devellibav*, e.g.:gitclonegit://source.ffmpeg.org/ffmpeg.gitcdffmpeg./configure\--enable-gpl\--enable-version3\--enable-nonfree\--enable-gpl\--enable-libass\--enable-libfaac\--enable-libfdk-aac\--enable-libmp3lame\--enable-libopus\--enable-libtheora\--enable-libvorbis\--enable-libvpx\--enable-libx264makesudomakeinstallandlibpcap, e.g.:sudoapt-getinstalllibpcap-devif you need them.installIf you just want to use it:pipinstallmarmbut if you are developing then get it:gitclonegit@github.com:mayfieldrobotics/marm.git~/code/marmcd~/code/marmcreate avenv:mkvirtualenvmarmworkonmarmpipinstallCythonpipinstall-e.[test]and test it:py.testtest/--covmarm--cov-reportterm-missing--pep8docstodoreleaseTests pass:py.testtest/--covmarm--cov-reportterm-missing--pep8so update__version__inmarm/__init__.py. Commit and tag it:gitcommit-am"release v{version}"gittag-av{version}-m"release v{version}"gitpush--tagsandtraviswill publish it topypi.
Package: marmalade
# Marmalade - A Python Client for [This Is My Jam](http://thisismyjam.com/)* Tastes Great* Good on Toast* Easy to use!## INSTALL$ easy_install -U marmalade## GETTING STARTEDimport marmalademe = marmalade.TIMJUser('tylerbw')def print_friends(user):print "Followers:"for i, follower in enumerate(user.get_followers(sort='affinity')):print '\t',i,':',follower.get_full_name()print_friends(me)def find_slacker_friends(user):return [friend for friend in user.get_followers() if not friend.has_current_jam()]print find_slacker_friends(me)a_jam = marmalade.Jam.from_user('flaneur')print a_jam.get_num_plays()def find_most_popular_follower(user):return sorted((friend.get_num_followers(),friend) for friend in user.get_followers())[-1][1]print find_most_popular_follower(me)import randomdef random_walk_generator(user, steps_away):yield userfollowers = user.get_followers()for _ in xrange(steps_away):new_user = random.choice(followers)yield new_useruser = new_userfollowers = user.get_followers()print " ==> ".join(u.id for u in random_walk_generator(me, 4))### ADVANCEDLook at the source or [test.py](https://github.com/tylerwilliams/marmalade/blob/master/test.py) for more examples.## YUM![alt text](https://raw.github.com/tylerwilliams/marmalade/master/marmalade.jpg "mmmmm (from: http://www.flickr.com/photos/wendycopley/4451578552/sizes/n/in/photostream/)")
Package: marmee
marmeeExecute safely Google Earth Engine algorithms through a tunnel like a marmotFree software: MIT licenseDocumentation:https://marmee.readthedocs.io.FeaturesTODOSTAC ParserIt can translate an object from GEE to aSTACschema representation.frommarmee.utils.parserimportStacimgAsset='projects/fao-wapor/L1_AET/L1_AET_0910'item=Stac(imgAsset).parse()printitem.jsonWhich produces the JSON below as a STAC Item:{"assets":{"L1_AET_0910":{"href":"L1_AET_0910","name":"L1_AET_0910"}},"links":[{"href":"projects/fao-wapor/L1_AET","rel":"ImageCollection","hreflang":"EN"}],"geometry":{"type":"LinearRing","coordinates":[[9.33536203029916,40.00558427004632],[-1.7982352104060524,40.0055842949407],[-14.416312088575413,40.00558426248278],[-30.0062673066322,40.00557958159156],[-30.0062673066322,-40.00557958159156],[-17.756391286901717,-40.0055842747424],[-4.396074577361316,-40.00558421517503],[15.644400466056124,-40.00558425647372],[32.3447962745126,-40.005584280349574],[47.56071249632567,-40.0055842595196],[65.00626724904829,-40.00557956740052],[65.00626724904829,40.00557956740052],[51.27191157635778,40.00558426297113],[38.653834709547986,40.00558426359986],[30.860316653195245,40.00558428056272],[17.87111989480795,40.00558425744303],[9.33536203029916,40.00558427004632]]},"properties":{"provider":"","ext_properties":{"system:index":"L1_AET_0910","level":1,"system:asset_size":255491726,"area":"AfNE","days_in_dk":10,"id_no":"L1_AET_0910","system:time_start":1238544000000,"system:footprint":{"type":"LinearRing","coordinates":[[9.33536203029916,40.00558427004632],[-1.7982352104060524,40.0055842949407],[-14.416312088575413,40.00558426248278],[-30.0062673066322,40.00557958159156],[-30.0062673066322,-40.00557958159156],[-17.756391286901717,-40.0055842747424],[-4.396074577361316,-40.00558421517503],[15.644400466056124,-40.00558425647372],[32.3447962745126,-40.005584280349574],[47.56071249632567,-40.0055842595196],[65.00626724904829,-40.00557956740052],[65.00626724904829,40.00557956740052],[51.27191157635778,40.00558426297113],[38.653834709547986,40.00558426359986],[30.860316653195245,40.00558428056272],[17.87111989480795,40.00558425744303],[9.33536203029916,40.00558427004632]]}},"license":"","datetime":"2009-04-01T00:00:00+00:00"},"bbox":[-30.0062673066322,-40.005584280349574,65.00626724904829,40.0055842949407],"id":"projects/fao-wapor/L1_AET/L1_AET_0910"}CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-02-15)First release on PyPI.0.2.4 (2018-11-08)Update of dependencies.
Package: marmoolak
installpip install marmoolakUsageimport marmoolak
marmoolak.REDIS_HOST = '192.168.99.100'
marmoolak.REDIS_PORT = 6379
machine = marmoolak.Machine

def onpanic(e):
    print('panic! ' + e.msg)
def oncalm(e):
    print('thanks to ' + e.msg + ' done by ' + e.args[0])
def ongreen(e):
    print('green')
def onyellow(e):
    print('yellow')
def onred(e):
    print('red')


fsm = machine('myname', 'version1' , {'initial': 'green',
             'events': [
                 {'name': 'warn', 'src': 'green', 'dst': 'yellow'},
                 {'name': 'panic', 'src': 'yellow', 'dst': 'red'},
                 {'name': 'panic', 'src': 'green', 'dst': 'red'},
                 {'name': 'calm', 'src': 'red', 'dst': 'yellow'},
                 {'name': 'clear', 'src': 'yellow', 'dst': 'green'}],
             'callbacks': {
                 'onpanic': onpanic,
                 'oncalm': oncalm,
                 'ongreen': ongreen,
                 'onyellow': onyellow,
                 'onred': onred }})



fsm.panic(msg='killer bees', url="http://appido.ir/appido/api/getBooks.json")
fsm.calm('bob', msg='sedatives in the honey pots')creditsI used fysom and redis for achiving this functionality. So most of the
credit goes to redis and fysom developers.Contact meFeel free to drop me a mail atrodmena@me.com
Package: marmot-agents
Marmot is an extension of_simpythat makes it easier to build agent-based process models. Marmot allows a user
to model the actions of discrete agents acting within an environment.Version:0.2.5Authors:Jake NunemakerDocumentation:Coming soon!Installationpip installmarmot-agentsGetting StartedfrommarmotimportEnvironment,Agent# An agent must be created and registered to an environmentenv=Environment()agent=Agent("Test Agent")env.register(agent)# The registered agent can now be scheduled to perform tasksagent.task("Run",10)# The agent is scheduled to run for 10 units of time# The simulation must be ranenv.run()print(env.now)>>>10# Logs of all tasks are keptenv.logs>>>[{"agent":"Test Agent","action":"Run","duration":10,"time":10}]
Package: maro-auth
Quick start-----------1. Add `git+https://github.com/Maronato/maro_auth.git` to your `requirements.txt`2. Add `maro_auth` to the _end_ of your INSTALLED_APPS setting like this::```PythonINSTALLED_APPS = [...'maro_auth',]```3. Include the URLconf in your project urls.py like this::`url(r'^auth/', include('maro_auth.urls', namespace='maro_auth')),`4. Run `python manage.py migrate` to create the auth models.5. Add the following settings to your `settings.py` to configure the app:```PythonINDEX_URL_NAME = 'index'  # name of your project's index url. Used on redirectsPROFILE_URL_NAME = 'profile'  # name of your project's profile url. Used on redirectsSITE_URL = 'http://localhost:8000'  # your site's url, without the '/' at the end. Used on emailsPROJECT_NAME = 'Projeto Genérico'  # your project's name. Used on emailsLIMIT_USERS = True  # Whether or not to limit your users to DAC users only. Used during signup evaluationPROFILE_APP_NAME = 'auth'  # (optional) the name of the app containing your Profile modelPROFILE_MODEL_NAME = 'User'  # (optional) the name of the Profile model you'll want to use and associate with your UsersFIELDS = ('first_name', 'email', 'last_name')  # (optional) the Profile fields that you want to include in your signup formEXCLUDE = ('username', 'password1', 'password2')  # (optional) the Profile fields that you want to exclude from your signup form```And also don't forget to setup your email sending protocols:```PythonDEFAULT_FROM_EMAIL = 'example@example.com'EMAIL_HOST_USER = 'example@example.com'EMAIL_HOST_PASSWORD = 'super_s3cret'EMAIL_USE_TLS = TrueEMAIL_HOST = 'smtp.gmail.com'EMAIL_PORT = 587SERVER_EMAIL = 'example@example.com'```And, if you want to receive error logs:`ADMINS = [('Admin', 'admin@example.com'), ]`Customizing the templates-------------------------Add a folder called `maro_auth` to your app's templates, as in```|my_app/|---- templates/|---- maro_auth/|---- email/|---- change_email.html|---- change_email.txt|---- reset_password.html|---- reset_password.txt|---- welcome_email.html|---- welcome_email.txt|---- login.html|---- reset_password.html|---- reset_password_complete.html|---- reset_password_confirm.html|---- reset_password_done.html|---- set_password.html|---- signup.html```You can now modify all the default templates at will
Package: marol
# Disclaimer* theScore does not use this in production. This library was built when AWS Lambda was stuck at 2.7 for a long time and we wanted to use Python 3. Two days after this idea succeeded, AWS announced support for 3.6 :). However, this may still be of use to those who want to use versions other than the official version# MarolRun any Python 3 version instead of the standard AWS Lambda Python version.## Python 3 version support* Tested on 3.6.1, 3.6.2, 3.6.3## InstructionsYou pass in your handler file(e.g. `my_handler.py`) and it returns back:* `handler_python3.py`. `my_handler.py` is renamed to `handler_python3.py` and code is added to make it callable from a python2 handler* `handler.py`. This is the Python2 module that is called which delegates to the Python3 handler file* path to `marol_venv` folder. This is the folder which contains all the executables for the Python version that you chose.To use Marol, your `my_handler.py` will have the following constraints:* The handler function should be named `handler`* The file cannot be called `handler.py`(This is reserved for the Python2 handler which will execute the Python3 handler* There should be no `main` statement in the handler file* One should not have functions with the following names:1. `load`2. `read_execution_uuid`## Installation### Pre-requisites* Ensure `docker` is installed and running on the machine where Marol is executed.### Mac OS X* Open the terminal and run `/Applications/<Python Version>/Install Certificates.command`## UsageYour deployment scripts for lambda should specify `handler.py` and `handler` as the starting point for your Lambda function. This is the Python2 handler returned by Marol which calls your modified Python3 handler.```pythonimport marolmy_project_site_packages = ...my_deploy_lambda_function(lambda_name,marol.get_lambda_files('~/mypath/mypy3handler.py', '3.6.1') + my_project_site_packages,'lambda_s3_exec_role',128,300,lambda_description='Test for Python 3',handler_name="handler",handler_file="handler.py",region_name='us-east-1')````get_lambda_files` will check `MAROL_HOME` if `marol_venv` exists for the particular version that you want. If it exists, you will get back that path. If it does not exist,`marol` will* download the python source* build the binaries using a [docker image](https://github.com/lambci/docker-lambda) which mirrors the AWS Lambda environment* create a basic `marol_venv` and store it at `MAROL_HOME/staging/<python_version>`## Future Work* Some of the context attributes are not supported yet.* Ensure that the context object is updated for future changes## Notes* This adds an additional 2-5 seconds to execution time.* This only provides the core Python libraries.* Default Marol home is `<HOME>/.marol`.* It will create `marol_venv` in `<HOME>/.marol/staging/<python_version>`## Notes for those thinking about security* This uses an unofficial [docker image](https://github.com/lambci/docker-lambda)
Package: maroon
maroonMaroon is a package that manages music for Roon.Featuresconvertwavandcuefiles into flac tracks,split tracks into folders according to disc numberadding genre info into metadatamaroonformat<something.cue>sample run:Nameofthealbum(default:i):菊花夜行军
Genreofthealbum(splitby;sign):Chinese;Folk;Acoustic;ChineseFolk;AvantFolk;FolkRock
SplittingCDImage:100%|██████████|10/10[00:07<00:00,1.37it/s]Split10tracks.Installationinstall from PyPI:pipinstallmaroonbuild from source:gitclonehttps://github.com/trojblue/marooncdmaroon
poetryinstall
poetrybuild
pipinstalldist/<build_version>.whl
Package: marops-cli
MarOps CLIPublicly available onPyPifor convenience but if you don't work at Greenroom Robotics, you probably don't want to use this.InstallFor development:pip install -e ./libs/marops_configpip install -e ./tools/marops_cliFor production:pip install marops-cliYou may also need toexport PATH=$PATH:~/.local/binif you don't have~/.local/binin your pathInstall autocomplete:bash:echo 'eval "$(_MAROPS_COMPLETE=bash_source marops)"' >> ~/.bashrczsh:echo 'eval "$(_MAROPS_COMPLETE=zsh_source marops)"' >> ~/.zshrc(this is much nicer)If using zsh, the git checker plugin make terminal slow. Suggest you rungit config oh-my-zsh.hide-info 1in the zsh terminal inside the repoUsagemarops --helpto get help with the CLIDev modeMarOps CLI can be ran in dev mode. This will happen if it is installed withpip install -e ./tools/maropsor if the environment variableMAROPS_CLI_DEV_MODEis set totrue.
Package: marops-config
MarOps ConfigMarOps Config is used to load config stored inside the~/.config/greenroomfolder.Installpip install -e ./libs/marops_configUsageReading configfrommarops_configimportreadconfig=read()Writing configfrommarops_configimportwrite,MarOpsConfigconfig=MarOpsConfig()write(config)Generating schemasAfter changing the dataclasses, you can generate the schemas with:python3-mmarops_config.generate_schemas
Package: marpledata
Marple SDKA simple SDK to upload data to MarpleInstallationInstall the Marple SDK using:pip install marpledataImport the package using:import marpleUsageSetupCreate a Marple connection using:frommarpleimportMarplem=Marple(ACCESS_TOKEN)You can generate access tokens in the app, underSettings->Tokens.If you are using Marple on-premise, you can add the custom URL:m = Marple(ACCESS_TOKEN, api_url='https://marple.company.com/api/v1')To check your connection, use:m.check_connection()Calling endpointsCall endpoints by their METHOD:m.get('/version')m.post('/sources/info',json={'id':98})Upload data filesIf your data is already in a file format, use this function to upload the data to Marple.source_id = m.upload_data_file(file_path, marple_folder='/', metadata={})file_path: the file_path of your data setmarple_folder: in what folder you would like to upload the datametadata: dictionary with metadata. Example:{'Pilot': 'John Doe'}'Note that the metadata fields need to be added to your workspace before you will see them.Upload a dataframeIf your data is in a pandas dataframe, use this function to upload the data to Marple.source_id = m.upload_dataframe(dataframe, target_name, marple_folder='/', metadata={})target_name: the target name for the dataset, this is how it will appear in Marple.Add and send dataYou can also use the Marple SDK to add data piece by piece and send it to Marple.
First use:m.add_data(data_dict)data_dict= dictionary with signal, value pairs in it.
Example:{'time': 2, 'signal 1': 0, 'signal 2': 5}Once all the data has been added, use:source_id = m.send_data(target_name, marple_folder, metadata={})Get a link to the dataYou can generate a link to Marple that opens the data and a project immediately. This can be very useful to see the results of a simulation directly.link = m.get_link(source_id, project_name)source_id: identifier for the data set, is returned by all above functions.project_name: name of the project that you want to open the data in.
Package: marppy
This is a python implementation of conversions to and from the Modified Apex - Rotated Pole (MARP) coordinate system.  MARP is based on the Apex coordinate system (Richmond, 1995), but performs a  transformation to rotate the pole of the coordinate system to an arbitrary location.  This is advantageous for doing calculations at high latitudes because it removes some of the complications
associated with performing calculations near the pole of the coordinate system.This code is heavily based onapexpy.InstallationThe easiest way to install marppy is from PyPI:pip install marppyNote: apexpy must already be installed for this to work.SeeInstallationfor more details.UsageAll functionality is available through the marppy.Marp class. Instantiate the class with the date and the MARP null island (“origin”) coordinates that dictate how the coordinate system will be rotated, then use the various conversion routines.>>>frommarppyimportMarp>>>M=Marp(date=2022.5,lam0=80.,phi0=30.)>>># geo to marp>>>M.geo2marp(50.6,27.6,300.)(-4.537915474007271,39.96811884264296)>>># marp to apex>>>M.marp2apex(5.8,10.2)(79.06102883542528,98.18833972513148)>>># apex to marp - entering the original rotation coordinates should return (0,0)>>>M.apex2marp(80.,30.)(0.0,0.0)SeeUsagefor more details.DocumentationFull documentation is available onreadthedocs.
Package: marpyter
marpyter - Marp for JupyterLabmarpyteris a JupyterLab extension allowing you to create interactive slide decks using Marp directly within JupyterLab. This extension provides a convenient way for previewing Markdown-based slides in real time.How to useTo render a markdown file withmarpyter, from the file browser panel of JupyterLab, users can right-click on the markdown file ->Open With->Marp Preview.
A new panel will be opened with the rendered content of the Marp slides.RequirementsJupyterLab >= 4.0.0InstallYou can installmarpyterusing mamba, pip or conda:#Using mambamambainstallmarpyter#Using condacondainstallmarpyter#Using pippipinstallmarpyterUninstallTo remove the extension, execute:#Using mambamambauninstallmarpyter#Using condacondauninstallmarpyter#Using pippipuninstallmarpyterContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the marpyter directory# Install package in development modepipinstall-e"."# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallmarpyterIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedmarpyterwithin that folder.Packaging the extensionSeeRELEASE
Package: marquetry
MarquetryMarquetry meansYosegi-zaiku, a traditional Japanese woodworking technique, in Japan.
It is a beautiful culture craft originated in Japan, which is a box or ornament or so by small wooden pieces.
The design is UNIQUE, it depends on the arrangement of the wood tips.
I believe Deep Learning is similar with the concept.
Deep Learning models are constructed through the combination of the layers or functions.
Just as a slight variation in arrangement can result in a completely distinct model.
I want you can enjoy the deep/machine learning journey like
you craft aMarquetryfrom combination of various wood tips.About this FrameworkYou can use this framework for help your learningMachine/Deep Learning.
This framework is written onlyPython, so you can understand the implementation easily if you are python engineer.
For simplify the construct, there are un-efficiency implementation.
I develop this framework to enjoy learning the construction of the machine/machine learning notPractical Usage.
I hope to enjoy your journey!
Package: marquez-dbt-bigquery
dbt-bigquery-openlineagePlugin fordbt(data build tool) for automatic metadata and lineage collection.This integration is experimental, and there is high possibility that it will change in the future,
as together with dbt developers we're figuring what's the best way to extract metadata from it.RequirementsPython 3.6+
sqlparse>=0.3.1Installation$ pip install marquez-dbt-bigqueryConfigurationYou need to configure dbt'sprofiles.yamlfile.
This plugin introduces additional type: openlineage_bigquery.It handles the same configuration flags as original bigquery plugin, with the addition of OpenLineage related ones:openlineage_url-setsURLofOpenLineageserverwhereeventswillbepushedopenlineage_timeout-optional-setstimeoutforconnectingtoOpenLineageserver.Bydefaultit'ssetto5seconds.openlineage_api_key-optional-setsapikeyforOpenLineageserver
Package: marquez-dbt-snowflake
dbt-snowflake-openlineagePlugin fordbt(data build tool) for automatic metadata and lineage collection.This integration is experimental, and there is high possibility that it will change in the future,
as together with dbt developers we're figuring what's the best way to extract metadata from it.RequirementsPython 3.6+
sqlparse>=0.3.1Installation$ pip install marquez-dbt-snowflakeConfigurationYou need to configure dbt'sprofiles.yamlfile.
To use this plugin you need to use type: openlineage_snowflake.It handles the same configuration flags as original snowflake plugin, with the addition of OpenLineage related ones:openlineage_url-setsURLofOpenLineageserverwhereeventswillbepushedopenlineage_timeout-optional-setstimeoutforconnectingtoOpenLineageserver.Bydefaultit'ssetto5seconds.openlineage_api_key-optional-setsapikeyforOpenLineageserver