Saravanan666/pypi-org huggingface dataset:
Package: marimo-cloud
marimo-cloudCloud CLI formarimo.Installationpipinstallmarimo-cloudThis installs a CLI tool calledmarimo-cloud(which is also aliased tomcloud, for short).Usage❯marimo-cloud--help
╭─Commands───────────────────────────────────────────────────────────────────────────╮
│login│
│logout│
│publish│
│version│
╰──────────────────────────────────────────────────────────────────────────────────────╯
Package: marina
marinaMarina is a tool for building docker images with a focus on separating
compile-time dependencies from run-time dependencies in order to keep
the shipped images small and secure.Installationpip install marinaUsagemarina -vvv build examples/shootoutApp Configname: dummy

compile:
  base_image: ubuntu:14.04
  commands:
    - dd if=/dev/urandom of=/srv/dummy bs=50kB count=1
  files:
    - /srv/dummy

run:
  base_image: ubuntu:14.04Running Testspip install marina[testing]
py.test0.4.3 (2020-03-19)Always try to pull images before building.0.4.2 (2020-01-28)Avoid blocking the main thread moar so that SIGINT works better.0.4.1 (2020-01-28)Avoid blocking the main thread so that SIGINT works better.0.4.0 (2019-03-25)Requiredocker >= 3.0.0.3.0 (2017-03-10)Update to usedockerpackage instead of the now-defunctdocker-pypackage.0.2.0 (2016-10-11)Pin todocker-py< 1.10until a bug is fixed.
Seehttps://github.com/docker/docker-py/issues/1211[build] The cache volume is now created as a docker volume instead of a
data container. It can be controlled usingdocker volumecommands.[build] Properly exit with an error if there was an issue reading
from the container stdout/stderr while attached.[build] Thebusyboximage is no longer required. The archive will
be pulled directly from the build container.0.1.1 (2016-10-07)[build] Fix an issue when the system does not already have the busybox
image installed.0.1.0 (2016-09-14)[build] Fix some unicode issues when running on Python 2.0.0.9 (2016-07-25)[build] Add another workaround for docker-py when pulling a new image.
Seehttps://github.com/docker/docker-py/issues/11340.0.8 (2016-07-22)Support Python 3.[build] Stop using an API that was removed in docker 1.12.[build] Allow the BUILD_CONTEXT (cwd in scripts) to be writeable.
Previously it was mounted readonly.[build] Search for the ssh identity file in known paths.[build] Add a workaround for a bug in docker-py causing the runner image
to fail to build. Seehttps://github.com/docker/docker-py/issues/11340.0.7 (2016-02-18)[build] Flush stdout to keep container messages from pausing.0.0.6 (2016-01-08)[build] Delete any volumes associated with a container.[build] Fix usage of ExposedPorts from runner base image.0.0.5 (2015-08-25)[build] Exit with an error code upon failure instead of 0.0.0.4 (2015-08-24)Turn off docker hostname verification to enable connections with
docker-machine instances over SSL untilhttps://github.com/docker/docker-py/issues/731is resolved.[build] Avoid detaching from the archive container before the tarfile
has been fully written to disk.[build] Add--skip-cleanupoption for keeping images/containers/files
around after the build.0.0.3 (2014-11-19)Support docker 1.3.x and its TLS requirements.0.0.2 (2014-07-12)Support--quietfor suppressing output.[build] Add--envoption for specifying credentials and other
configurable build-time settings.[build] Ensure thebusyboximage is around.0.0.1 (2014-07-03)Initial release.First cut at “marina build” to generate a working docker container.
Package: marine
MARINE:Multi-task leaRnIng-based JapaNese accentEstimationmarineis a tool kit for building the Japanese accent estimation model proposed inour paper.For academic use, please cite the following paper (IEEE Xplore).@inproceedings{park22b_interspeech,author={Byeongseon Park and Ryuichi Yamamoto and Kentaro Tachibana},title={{A Unified Accent Estimation Method Based on Multi-Task Learning for Japanese Text-to-Speech}},year=2022,booktitle={Proc. Interspeech 2022},pages={1931--1935},doi={10.21437/Interspeech.2022-334}}NoticeThe model included in this package is trained usingJSUT corpus, which is not the same as the dataset inour paper. Therefore, the model's performance is also not equal to the performance introduced in our paper.Get startedInstall$pipinstallmarineFor develop$pipinstall-e".[dev]"Quick demoIn[1]:frommarine.predictimportPredictorIn[2]:nodes=[...:{...:"surface":"こんにちは",...:"pos":"感動詞:*:*:*",...:"pron":"コンニチワ",...:"c_type":"*",...:"c_form":"*",...:"accent_type":0,...:"accent_con_type":"-1",...:"chain_flag":-1...:}...:]In[3]:predictor=Predictor()In[4]:predictor.predict([nodes])Out[4]:{'mora':[['コ','ン','ニ','チ','ワ']],'intonation_phrase_boundary':[[0,0,0,0,0]],'accent_phrase_boundary':[[0,0,0,0,0]],'accent_status':[[0,0,0,0,0]]}In[5]:predictor.predict([nodes],accent_represent_mode="high_low")Out[5]:{'mora':[['コ','ン','ニ','チ','ワ']],'intonation_phrase_boundary':[[0,0,0,0,0]],'accent_phrase_boundary':[[0,0,0,0,0]],'accent_status':[[0,1,1,1,1]]}Build model your selfComing soonLICENSEmarine: Apache_2.0 license (LICENSE)JSUT: CC-BY-SA 4.0 license (Please checkjsut-label/LICENCE.txt)
Package: marinedebrisdetector
Large Scale Detection of Marine Debris with Sentinel-2Rußwurm M, Venkatesa J. S., Tuia D. (2023; in preparation) Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2Getting StartedWe provide a python package for easy installation and model predictionsInstallation of the Python Packagepip install git+https://github.com/marccoru/marinedebrisdetectororgit clone https://github.com/MarcCoru/marinedebrisdetector.git
cd marinedebrisdetector
pip install .You now have amarinedebrisdetectorprogram to make predictions of Sentinel-2 tif images on the console.
Checkmarindebrisdetector --helpfor information on parameters and options.Predict the qualitative test images from the paper with this command.marinedebrisdetector --plot-qualitativepredict a Sentinel-2 scene (Durban) with these commands# download example scene (~300 MB) to ("durban_20190424.tif")
marinedebrisdetector --download-durban

# predict the durban example scene
marinedebrisdetector durban_20190424.tifthe prediction should be in the same folder nameddurban_20190424_prediction.tif. You can inspect the prediction scene in any image viewer,
but best in a GIS programQGISorArcGISthat allows you inspect and overlay other georeferenced data.The tested prediction times of the scene prediction above are:11 sec on NVIDIA GeForce RTX 309015 sec on NVIDIA GeForce GTX 97011 minutes on Macbook Pro CPUPre-trained ModelsWe provide pre-trained weights for 12-channel Sentinel-2 imagery.
A detailed list of weights can be found on themodels pagepretrained segmentation models can be loaded via the torch hub in pythonimporttorchtorch.hub.load("marccoru/marinedebrisdetector","unetpp")torch.hub.load("marccoru/marinedebrisdetector","unet")# trained without label refinement (can lead to thinner more fine-grained predictions)torch.hub.load("marccoru/marinedebrisdetector","unetpp",label_refinement=False)Themarinedebrisdetectordefaults to theunetpp/unet++.DatasetsWe trained the underlyingUnet++ segmentation model(implementation fromPytorch Segmentation Models) on an aggregated a combination of existing datasets for trainingFloatingObjects Dataset (Mifdal et al., 2020),Marine Debris Archive (Kikaki et al., 2022),S2Ships (Ciocarlan et al., 2021),
and newly annotated a refinedFloatingObjects dataset and Sentinel-2 images of thePlastic Litter Projects (Papageorgiou et al., 2022; under review)More details in a dedicateddata page.
Executing the training script (see this "model training" page) with--downloadwill automatically download and uncompress the required datasets (116 GB (uncompressed)).Model TrainingWe provide aa training scriptpowered byPytorch LightningandWeights and Biasesto train new models and reproduce our results.
More details on training commands and ablations in a dedicated page formodel training
Package: mariner
MarinerNavigate torrents in CLI with Mariner. It offers a simple interface for streamlined experience. No more annoying ads and pop-up windows.It is currently under heavy development, so expect breaking changes. Currently only works in Linux, but any contributions in this regard are welcome.FeaturesRuns on Linux and Windows.Automatically get a working proxy for trackers that have them.Download torrent files and copy magnet links to clipboard.Open torrents in your default torrent application.Show torrent details.Asynchronous I/O for better responsiveness.Supports the following trackers:Archive.orgDistrowatchEtreeLimeTorrentsLinuxtrackerNyaaNyaaPantsuTokyoToshoInstallationMariner requires Python 3.6 or newer to run.Python packageYou can easily install Mariner using pip. This is the preferred method:pip3 install marinerManualAlternatively, to get the latest development version, you can clone this repository and then manually install it:gitclonegit@gitlab.com:radek-sprta/mariner.gitcdmariner
poetrybuild
pipinstalldist/*.whlUsageMariner supports both interactive and non-interactive modes. To see the list of commands, simply type:mariner helpIn order to start Mariner in interactive mode, run it without any arguments:marinerThen search for Ubuntu torrents:(mariner) search Ubuntu -t linuxtrackerand download the first result on the list:(mariner) download 0Alternatively, copy the magnet link to clipboard:(mariner) magnet 0Or open it in your torrent application:(mariner) open 0And quit the program:(mariner) quitFor more information, check thedocumentation.ContributingFor information on how to contribute to the project, please check theContributor's GuideDisclaimerI do not encourage anyone to act in conflict with their local laws and I do not endorse any illegal activity. Some content in the search results provided be Mariner might be illegal in your country and it is up to you to check your local laws before using it. Neither I, nor Mariner can be held liable for any action taken against you as the result of using it.Contactmail@radeksprta.euincoming+radek-sprta/mariner@gitlab.comAcknowledgementsMariner uses many excellent open-source libraries. But I would particularly like to mention the following, as without them, Mariner might not have been possible:AiohttpBeautifulSoupCliffTinyDBLicenseGNU General Public License v3.0
Package: marinvaders
The MarINvaders ToolkitThe MarINvaders Toolkit is a Python 3 module to assess the native and alien (non-native) distribution of marine species.It can be used to find the native and alien distribution of a given species or to get an overview of all alien and native species found in onemarine ecoregion.To do so, MarINvaders cross-references and harmonizes distribution maps fromseveral databasesto find all occurrences of a given species and to gather information on its native and alien status per location recording.
You can findthe full documentation here.Where to get itThe full source code and all required local data is availableat the MarINvaders GitLab repository..MarINvaders is registered at PyPI and at conda-forge for installation within a conda environment.
To install usepip install MarINvaders --upgradeand when using conda:conda install -c conda-forge MarINvadersWe recommend to install the package in avirtual environmentorconda environment. Seehere for further information (including how to make the environment discoverable in your JupyterLab..Getting started in five linesInstall the package as explained above and start your preferred Python interpreterImport the packageimportmarinvadersGet the species AphiaID you are interested in from theWoRMS - marine species database. Here we use * Amphibalanus amphitrite * (Darwin, 1854), aka thestriped barnaclewhich has the AphiaID 421137.Now we can get the species data from this barnacle withspecies_data=marinvaders.Species(aphia_id=421137)and list all occurrencesspecies_data.all_occurrencesas well as the alien distribution of the barnacle withspecies_data.reported_as_alienThese can also be easily plotted withspecies_data.plot()In addition, MarINvaders includes API functions for analyzing all species within an ecoregion.For a full overview of the capabilities see theexample/tutorial notebook.
This can also be run in the cloud throughBinder:CitationsReleases of MarINvaders are deposited atthe Zenodo research repositoryand can be cited by their DOI: 10.5281/zenodo.4621393Communication, issues, bugs and enhancementsPlease use theissue trackerfor documenting bugs, proposing enhancements and all other communication related to marinvaders.
Seethe Contribution section of the docsfor further information on code contributions.License and data terms of useThis project is licensed under TheGNU GPL v3This license only applies to the source code, for the licenses of the data processed by the modules seeRequired data sources - terms of use:WoRMS/marinespecies:http://marinespecies.org/about.php#termsOBIS:https://obis.org/manual/policy/NatCon:https://www.conservationgateway.org/ConservationPractices/Marine/Pages/marineinvasives.aspxseehttps://www.conservationgateway.org/Pages/Terms-of-Use.aspxIUCN/optional data - terms of useThis data is not allowed to redistributed and most be downloaded manually. For more informationsee the documentationICUN GISD:http://www.iucngisd.org/gisd/legal.phpICUN Red List:https://www.iucnredlist.org/terms/terms-of-use
Package: mario-64-super-star-battle
Mario 64 Super-Star BattleNote: this is still a work in progress!__▒▒▒▒▒—-▒▒▒▒▒▒▒▒▒—–▓▓▓░░▓░—▓░▓░░░▓░░░—▓░▓▓░░░▓░░░—▓▓░░░░▓▓▓▓——░░░░░░░░—-▓▓▒▓▓▓▒▓▓–▓▓▓▒▓▓▓▒▓▓▓▓▓▓▓▒▒▒▒▒▓▓▓▓░░▓▒░▒▒▒░▒▓░░░░░▒▒▒▒▒▒▒░░░░░▒▒▒▒▒▒▒▒▒░░—-▒▒▒ ——▒▒▒–▓▓▓———-▓▓▓▓▓▓▓———-▓▓▓▓The ultimate competition between Mario 64 superstars! Fight in a best
of "X" over who can six-star complete more levels, faster!RulesThis is for two players. One player is randomly selected to be the first
player. That player selects a course, and, timed, completes all 6 stars
for that course (not including the 100 coins star—but you can include it
if you wish) while the other player observes. Then the next player gets
timed performing the same run.After that round, the player with the quickest time gets a point. The
player who went second last round now goes first for the next round.Repeat until one superstar emerges victorious.If the players get to the last round, they must collectively decide
which of the remaining stages to play. The best way to do this is with a
ban system, but that's for you to decide.InstallationEither run this from the source code directly with./run_mario64superstarbattle.pyor install this with pip usingpip3 install mario-64-super-star-battleand run it withm64ssb
Package: mario-addons
Contributed commands forMarioInstallationpip install mario-addonsDocumentationMario:https://python-mario.readthedocs.io/Mario addons:https://mario-addons.readthedocs.io/ChangelogChanges for the upcoming release can be found in the“changelog.d” directoryin our repository.0.1.29 (2019-07-30)No significant changes.0.1.26 (2019-07-29)Backward-incompatible Changeswalk-tno longer usesevalon the left-hand side of the equal sign. That value can only refer to a type by name.#470.1.0 (2019-07-17)ChangesFirst release on PyPI.—
Package: mariocard.py
mariocard.pyThis is simple maker for cards in discord bot in discord.py or pycord.InstallingPython 3.8 or higher is required# Linux/macOSpip3install-Umariocard.py# Windowspipinstall-Umariocard.pyLevel Card Examplefromdiscord.extimportcommandsfrommariocardimport*client=commands.Bot(command_prefix=".")@client.command()asyncdefcard(ctx):#creating levelcard objectlevelcard=LevelCard()#setting avatar url for imagelevelcard.avatar=ctx.author.avatar_url#setting member namelevelcard.name=ctx.author#setting xp for bar on cardlevelcard.xp=10#setting required xp for bar on cardlevelcard.required_xp=20#setting level to text on cradlevelcard.level=2#sending image to discord channelawaitctx.send(file=awaitlevelcard.create())client.run("token")Generated Level CardWelcome Card Examplefromdiscord.extimportcommandsfrommariocardimport*client=commands.Bot(command_prefix=".")@client.command()asyncdefcard(ctx):#creating levelcard objectlevelcard=LevelCard()#setting avatar url for imagelevelcard.avatar=ctx.author.avatar_url#setting member namelevelcard.name=ctx.author#setting server namelevelcard.server=ctx.guild.name#sending image to discord channelawaitctx.send(file=awaitlevelcard.create())client.run("token")Generated Welcome CardLinksDocumentationGithubYoutube ChannelMy Discord ServerDownloadChange Logversion 1.0.0: -first versionversion 1.0.1: -added documentation
Package: marionette_extension
marionette-extensionThis repository contains Marionette in gecko extension form, intended for use
with FxOS devices. It includes an installation script which you can use,
provided your phone is connected via ADB:sourceinstall_marionette_extension.shVERSIONWhere VERSION is a supported version number, one of: 1.3, 1.4, 2.0, 2.1, 2.2, or 2.5.If you need to install adb, seehttps://developer.mozilla.org/en-US/Firefox_OS/Debugging/Installing_ADBFirefoxOS v1.3 through v2.5 are currently supported.Python PackageThis is also the ‘marionette_extension’ python package. Once you install this
package, you will have access to a command line program called
install_marionette which you can use like so:marionette_extension--helpWhich will show you what installation options you have available to you.If you need an example, this is how you can install Marionette on a v1.3 device:marionette_extension--install1.3You can also import the marionette_extension package into your python script
and use its install function to install marionette via a script. Example:frommarionette_extensionimportinstallinstall(FXOS_VERSION,adb=ADBPATH)# Replace FXOS_VERSION with a supported version, and you may use ADBPATH to specify your adb path. If not specified, it will assume 'adb' is on the $PATHHow to add a new version?Create a new folder indicating your new gecko versionhgclonehttps://hg.mozilla.org/mozilla-central/Starting now, please reference on the files and folder structure of previous marionette-extension versionGo to mozilla-central folder and find /testing/specialpowers(mozilla-central) and copy them intospecial-powers@mozilla.orgfolderCopy all the .js files in /testing/marionette(mozilla-central) tomarionette@mozilla.org/chrome/contentCopy chrome.manifest and install.rdf from previous version of marionette-extension tomarionette@mozilla.orgfolderCopy testing/marionette/components/marionettecomponent.js(mozilla-central) to/marionette@mozilla.org/componentsand rename it to marionetteextensioncomponent.jsDo this to marionetteextensioncomponent.js:replace MARIONETTE_CONTRACTID value with “@mozilla.org/marionetteextension;1”change the profile-after-change listener to always start Marionette and not be dependent on build flags or command linesthis.enabled=true;this.logger.info("marionette enabled via extension");//Wewanttosuppressthemodaldialogthat'sshown//whenstartingupinsafe-modetoenabletesting.if(Services.appinfo.inSafeMode){this.observerService.addObserver(this,"domwindowopened",false);}change the component name from MarionetteComponent to MarionetteExtensionComponentrequest of putting this on pypi for faster installation
Package: marionette-harness
Marionette is an automation driver for Mozilla’s Gecko engine. It can remotely
control either the UI or the internal JavaScript of a Gecko platform, such as
Firefox. It can control both the chrome (i.e. menus and functions) or the
content (the webpage loaded inside the browsing context), giving a high level
of control and ability to replicate user actions. In addition to performing
actions on the browser, Marionette can also read the properties and attributes
of the DOM.The marionette_harness package contains the test runner for Marionette, and
allows you to run automated tests written in Python for Gecko based
applications. Therefore it offers the necessary testcase classes, which are
based on the unittest framework.For more information and the repository please checkout:home and docs:https://developer.mozilla.org/en-US/docs/Mozilla/QA/MarionetteExampleThe following command will run the tests as specified via a manifest file, or
test path, or test folder in Firefox:marionette –binary %path_to_firefox% [manifest_file | test_file | test_folder]To get an overview about all possible option runmarionette –help.
Package: marionette-predeployed
marionette-predeployedDescriptionA tool for generating predeployed marionette smart contractInstallationpip install marionette-predeployedUsage examplefrommarionette_predeployedimportUpgradeableMarionetteGenerator,MARIONETTE_ADDRESS,MARIONETTE_IMPLEMENTATION_ADDRESSPROXY_ADMIN_ADDRESS='0xd200000000000000000000000000000000000000'MAINNET_OWNER_ADDRESS='0xd200000000000000000000000000000000000001'SCHAIN_OWNER_ADDRESS='0xD200000000000000000000000000000000000002'MESSAGE_PROXY_FOR_SCHAIN_ADDRESS='0xd200000000000000000000000000000000000003'marionette_generator=UpgradeableMarionetteGenerator()genesis={# genesis block parameters'alloc':{**marionette_generator.generate_allocation(contract_address=MARIONETTE_ADDRESS,implementation_address=MARIONETTE_IMPLEMENTATION_ADDRESS,proxy_admin_address=PROXY_ADMIN_ADDRESS,schain_owner=MAINNET_OWNER_ADDRESS,marionette=MARIONETTE_ADDRESS,owner=SCHAIN_OWNER_ADDRESS,ima=MESSAGE_PROXY_FOR_SCHAIN_ADDRESS,)}}
Package: marionette-transport
Marionetteis a Mozilla project to enable remote automation in Gecko-based
projects, including desktop Firefox, mobile Firefox, and Firefox OS. It is
inspired bySelenium Webdriver.This package defines the transport layer used by a Marionette client to
communicate with the Marionette server embedded in Gecko.  It has no entry
points; rather it’s designed to be used by a Marionette client implementation.
Package: mariqt
We strive to make marine image data FAIR. We develop FDOs for images to establish a common language for marine imagery, we develop best-practice operating procedures for handling marine images and we develop the MarIQT software to apply the iFDOs and procedures to marine imagery. MarIQT stands for Image Quality control / quality assurance and curation Tools (IQT) conceptualised and developed by the MareHub working group on Videos/Images (part of the DataHub, a research data manangement initiative by the Helmholtz association).
Package: mariqt-widgets
# mariqt_widgetsContains widgets commonly used by MarIQT Notebooks.Free software: GNU General Public License v3Documentation:https://datahub.pages.hzdr.de/marehub/ag-videosimages/mariqt-notebook-widgets/# FeaturesTODO# CreditsThis package was created with
[Cookiecutter](https://github.com/audreyr/cookiecutter) and the
[open-source/toolbox/cookiecutter-pypackage](https://git.geomar.de/open-source/toolbox/cookiecutter-pypackage)
project template.# History0.3.8exceptions while trying to get info from osis are now caughtfixed tests for current iFDO version0.1.0 (2023-05-09)First release