Saravanan666/pypi-org huggingface dataset:
Package: mac-app-factory
Installation$[sudo]pipinstallmac-app-factory$[sudo]npmi-gmac-app-factoryHow it works<name>.app
├── Contents
│   ├── MacOS
│   │   └── executable
│   ├── Resources
│   │   └── Icon.icns
│   └── Info.plist
│   executable          # edit this file. shebang required
│   <image>.png         # copy any png image
│   reveal.sh           # helper for reveal folder from your IDEFeaturessuper fast (pure shell)safe (native apps ignored)Examples$mac-app-factorypath/to/my-finder-toolbar-appspath/to/my-dock-apps$mac-app-factorypath/to/name1.apppath/to/name2.appreadme42.com
Package: mac-app-generator
Installation$[sudo]pipinstallmac-app-generator$[sudo]npmi-gmac-app-generatorFeaturesshell (mini) and python (full) versionsExamplescreate app from shell script$mac-app-generatorscript.shname.app
$mac-app-generatorscript.shname.appIcon.pngcreate app from python script>>>mac_app.App(app_script="file.py",app_path="name.app").create_app()create app from a python classimportmac_app_generatorclassMyApp(mac_app_generator.App):defrun(self):passif__name__=="__main__":MyApp().run()>>>MyApp().create_app()Relatedcommands-generator- shell commands generatorlaunchd-generator- launchd.plist generatorreadme-generator-README.mdgeneratorsetupcfg-generator-setup.cfggeneratortravis-generator-.travis.ymlgeneratorreadme42.com
Package: macapype
Quick InstallTo use it:pipinstallmacapypeTo develop:gitclonehttps://github.com/Macatools/macapype.gitcdmacapype
pythonsetup.pydevelopDocker imagedockerpullmacatools/macapype/latestDocumentationhttps://macatools.github.io/macapype/Related to project:A guide for PNH MRI processing, and in particular, the session onstructural MRI processingOpen Science Room OSR 2020 of OHBM congress presentation of macapypeForum macapype_usersThe macapype_users can be be foundhere(requires a framateam/framagit account, but should be accessible with a github or bitbucket account)
Package: macaque
MacaqueStability testing tool based on fastbot transformation.Local accessfrommacaque.coreimportprepareprepare(udid='192.168.0.119:5555',package='com.panda.app.wid',duration=2,throttle=800,whitelist='com.panda.app.wid.panda_main.activity.MainActivity',widget='[{"bounds": "0.1,0.87,1,0.95"}]',ime=False)Thankshttps://github.com/bytedance/Fastbot_Android
Package: macarna
Macarna: MAC Vendor LookupVendor/Manufacturer lookup by Mac Address. This library depends onWireshark manuf.Installationpip install macarnaBasic Usagefrom macarna import mac_lookup
print(mac_lookup("FC:FF:AA:00:00:00"))Command Linepython3 -m macarna fc:ff:aa:11:11:11Aliasalias maclookup='python3 -m macarna '
Package: macaron
Macaron is a small object-relational mapper (ORM) for SQLite on Python.
It is distributed as a single file module which has no dependencies other
than the Python Standard Library.Macaron provides easy access way to SQLite database as standalone. And also
it can work in Bottle web framework through the plugin mechanism.Example:>>> import macaron
>>> macaron.macaronage("members.db")
>>> team = Team.create(name="Houkago Tea Time")
>>> team.members.append(name="Ritsu", part="Dr")
<Member object 1>
>>> mio = team.members.append(name="Mio", part="Ba")
>>> print mio
<Member 'Mio : Ba'>
>>> for member in team.members: print member
...
<Member 'Ritsu : Dr'>
<Member 'Mio : Ba'>
>>> macaron.bake()
>>> macaron.cleanup()
Package: macaroonbakery
A Python library for working with macaroons.InstallationThe easiest way to install macaroonbakery is via pip:$ pip install macaroonbakerymacaroonbakery was developed around pymacaroons. On ubuntu, you
can get libsodium from a ppa:$ sudo add-apt-repository ppa:yellow/ppa -y
$ apt-get install libsodium13UsageInteracting with a protected url, you can use the BakeryAuth provided to deal
with the macaroon bakery>>> from macaroonbakery import httpbakery
>>> jar = requests.cookies.RequestsCookieJar()
>>> resp = requests.get('some protected url',
                        cookies=jar,
                        auth=httpbakery.BakeryAuth(cookies=jar))
>>> resp.raise_for_status()You can use any cookie storage you’d like so next subsequent calls the macaroon
saved in the cookie jar will be directly used and will not require
any other authentication (for example, cookielib.FileCookieJar).
Package: macast
MacastA menu bar application using mpv asDLNA Media Renderer. You can push videos, pictures or musics from your mobile phone to your computer.InstallationMacOS || Windows || DebianDownload link:Macast release latestPackage managerpipinstallmacast
macast-gui# or macast-cliPlease see our wiki for more information(likeaursupport):#package-managerLinux users may have problems installing using pip. Two additional libraries that I have modified need to be installed:pipinstallgit+https://github.com/xfangfang/pystray.git
pipinstallgit+https://github.com/xfangfang/pyperclip.gitBuild from sourcePlease refer to:Macast DevelopmentUsageFor ordinary usersAfter opening this app, a small icon will appear in themenubar/taskbar/desktop panel, then you can push your media files from a local DLNA client to your computer.For advanced usersBy loading theMacast-plugins, Macast can support third-party players like IINA and PotPlayer.For more information, see:#how-to-use-third-party-player-plug-inYou can modify the shortcut keys or configuration of the default mpv player by yourself, see:#how-to-set-personal-configurations-to-mpvFor developerYou can use a few lines of code to add support for other players like IINA and PotPlayer or even add additional features, like downloading media files while playing videos.Tutorials and examples are shown in:Macast/wiki/Custom-Renderer.Fell free to submit a pull request toMacast-plugins.FAQIf you have any questions about this application, please check:Macast/wiki/FAQ.If this does not solve your problem, please open a new issue to notify us, we are willing to help you solve the problem.ScreenshotsYou can copy the video link after the video is casted：Or select a third-party player plug-inRelevant linksUPnP™ Device Architecture 1.1UPnP™ ResourcesUPnP™ ContentDirectory:1 serviceUPnP™ MediaRenderer:1 deviceUPnP™ AVTransport:1 serviceUPnP™ RenderingControl:1 servicepython-upnp-ssdp-example
Package: macauff
macauffmacauff, the python package for Matching Across Catalogues using the Astrometric Uncertainty Function and Flux, is a package for cross-matching photometric catalogues. Using the positions, uncertainties, and flux measurements of sources, as well as modelling of the level to which objects are affected by hidden, blended contaminants, macauff provides posterior probabilities of "many-to-many" matches and non-matches between the two catalogues being counterparts to one another.InstallationFor installation instructions, check thedocumentation.CitationsIf you have made use of macauff in your research, please citeWilson & Naylor (2017, MNRAS, 468, 2517)Wilson & Naylor (2018a, MNRAS, 473, 5570)Wilson & Naylor (2018b, MNRAS, 481, 2148)Wilson (2022, RNAAS, 6, 60)Licensemacauff is licensed under a 3-clause BSD style license - see theLICENSEfile.
Package: macaulay2-jupyter-kernel
M2JK — Jupyter kernel for Macaulay2Beta Testing!You can now useJupyter(Notebook or Lab) as a front-end forMacaulay2.See thedemofor sample use and an outline of the kernel-specific features.
For bugs or requests, open an issue.
For recent changes, see thechangelog.RequirementsYou need a recent version of Python andpip.
Python 3 is recommended for build installs and necessary for source installs.
You can install Jupyter directly from PyPI bypip3install--upgradepip
pip3installjupyterMacaulay2 needs to be installed and on your path.
If you are using Emacs as your front-end, it already is, but you can test it bywhich M2.
Otherwise, you can achieve that by runningsetup()from within an M2 session.
Alternatively, you can configure M2JK to use a specific binary.InstallationYou can install the latest release version directly from PyPI by$pip3installmacaulay2-jupyter-kernel
$python3-mm2_kernel.installAlternatively, you can install the latest development version from source by$gitclonehttps://github.com/radoslavraynov/macaulay2-jupyter-kernel.git
$cdmacaulay2-jupyter-kernel
$pip3install.
$python3-mm2_kernel.installRunning the notebookOnce the installation is complete, you need to start (or restart) Jupyter by$jupyternotebook&This shoud fire up a browser for you. If not, copy the output URL into one.
Then select File ⇨ New Notebook ⇨ M2 from the main menu.LicenseThis software is not part of Macaulay2 and is released under the MIT License.
Package: mac-brave
Installation$[sudo]pipinstallmac-brave$[sudo]npmi-gmac-braveConfig$exportBRAVE_TIMEOUT=10# 5 by defaultExamplesurl(s)$braveurls
https://github.com/
https://google.com/
$braveurl
https://github.com/open/refresh$braveopen"https://github.com/""https://google.com/"$braverefresh"https://github.com/"fullscreen$bravefullscreen-enter
$bravefullscreen-detect1$bravefullscreen-exitfrontmost (1or0)$bravefrontmost0Brave Browser.appprocess$bravepid42$braveisready5# timeout 5 seconds1$bravekillreadme42.com
Package: maccarone
Maccarone: AI-managed code blocks in Python ⏪⏩Maccarone lets youdelegatesections of your Python program to AI ownership.Here's what it looks like inthe VS Code extension:ExampleYou might write some code like this:defmain(path:str):#<<filenames = a list of filenames under path>>forfninfilenames:#<<size = size of fn in bytes>>print(fn,size)#<<use argparse and call main>>Maccarone then fills in the sections you've delegated:defmain(path:str):#<<filenames = list of filenames under path; no dirs>>importosfilenames=[fforfinos.listdir(path)ifos.path.isfile(os.path.join(path,f))]#<</>>forfninfilenames:#<<size = size of fn in bytes>>size=os.path.getsize(os.path.join(path,fn))#<</>>print(fn,size)#<<use argparse and call main>>importargparseparser=argparse.ArgumentParser()parser.add_argument("path",type=str)args=parser.parse_args()main(args.path)#<</>>Make a change in your code, like adding anextensionparameter tomain, and Maccarone keeps its sections up to date:defmain(path:str,extension:str|None=None):#<<filenames = list of filenames under path; no dirs>>…ifextension:filenames=[fforfinfilenamesiff.endswith(extension)]#<</>>…#<<use argparse and call main>>…parser.add_argument("--extension",type=str,default=None)args=parser.parse_args()main(args.path,args.extension)#<</>>QuickstartPrerequisitesPython 3.8+OpenAI API key with GPT-4 (export OPENAI_API_KEY)Easy Mode - VS Code ExtensionEasy mode is the free extension fromthe VS Code marketplace.Install it in VS Code and you're done (if you have the prerequisites above).Other Option - Command LineIf you don't use VS Code, you can still install Maccarone directly from PyPI:pip install maccaroneThen runmaccaroneto generate code and update your source file:$maccarone--rewriteexamples/file_sizes.pyUsage notesRunningmaccaroneon a directoryMaccarone can rewrite all files in a directory:$maccarone--rewrite--suffix.pyexamples/Be careful! You should probably run this only on files in source control, for example.Related workhttps://github.com/bsilverthorn/vernacFAQsIt needs my OpenAI API key?Maccarone prompts GPT-4 to write code. It will make OpenAI API calls using your key and youwill be chargedby OpenAI.API calls are made every time Maccarone preprocesses a new version of a source file.The number of tokens consumed is proportional to the size of your completed code. You cannot accurately predict that number in advance. A small source module might cost $0.01–0.10 to preprocess.What prevents my program from behaving differently after each preprocessing run?The strength of your faith in GPT-4.What about non-English languages?They are likely to work, but less likely than English.What does "maccarone" mean?https://en.wikipedia.org/wiki/Macaronic_language
Package: macchiato
macchiatoData-driven nearest neighbor models for predicting experimental results on
silicon lithium-ion battery anodes.RequirementsYou need Python 3.8+ to run macchiato.InstallationYou can install the most recent stable release of macchiato withpippython -m pip install -U pip
python -m pip install -U macchiatoUsageThe Jupyter Notebook pipeline in thepaper folderis presented to reproduce the results of the published article.CitationFernandez, F., Otero, M., Oviedo, M. B., Barraco, D. E., Paz, S. A., & Leiva,
E. P. M. (2023). NMR, x-ray, and Mössbauer results for amorphous Li-Si alloys
using density functional tight-binding method. Physical Review B, 108(14), 144201.BibTeX entry:@article{fernandez2023nmr,title={NMR, x-ray, and M{\"o}ssbauer results for amorphous Li-Si alloys using density functional tight-binding method},author={Fernandez, F and Otero, M and Oviedo, MB and Barraco, DE and Paz, SA and Leiva, EPM},journal={Physical Review B},volume={108},number={14},pages={144201},year={2023},publisher={APS}}ContactYou can contact me if you have any questions atffernandev@gmail.com
Package: mac-chrome
Installation$[sudo]pipinstallmac-chrome$[sudo]npmi-gmac-chromeConfig$exportCHROME_TIMEOUT=10# 5 by defaultExamplesurl(s)$chromeurls
https://github.com/
https://google.com/
$chromeurl
https://github.com/open/refresh$chromeopen"https://github.com/""https://google.com/"$chromerefresh"https://github.com/"fullscreen$chromefullscreen-enter
$chromefullscreen-detect1$chromefullscreen-exitfrontmost (1or0)$chromefrontmost0Google Chrome.appprocess$chromepid42$chromeisready5# timeout 5 seconds1$chromekillreadme42.com
Package: mac-cleanup
mac-cleanup-py🧹 Python cleanup script for macOSmac-cleanup-pyis a powerful cleanup script for macOS.This project is a rewrite of the originalmac-cleanup-shrewritten in Python.🚀 Featuresmac-cleanup-pyhelps you:Empty TrashDelete unnecessary logs & filesClear cache📦 Default Modulesadobe- ClearsAdobecache filesandroid- ClearsAndroidcachesbrew- ClearsHomebrewcachecacher- ClearsCacherlogschrome- ClearsGoogle Chromecachecomposer- Clearscomposercachedns_cache- ClearsDNScachedocker- Cleanup danglingDockerImages and stopped containersdropbox- ClearsDropboxcachegem- Cleanup any old versions ofGemsgo- ClearsGocachegoogle_drive- ClearsGoogle Drivecachesgradle- ClearsGradlecachesinactive_memory- PurgeInactive Memoryios_apps- CleanupiOS Applicationsios_backups- RemovesiOS Device Backupsjava_cache- RemovesJava head dumpsfrom home directoryjetbrains- Removes logs fromPhpStorm,PyCharmetckite- DeletesKitelogslunarclient- RemovesLunar Clientlogs and cachemicrosoft_teams- RemoveMicrosoft Teamslogs and cacheminecraft- RemoveMinecraftlogs and cachenpm- CleanupnpmCachepnpm- CleanuppnpmCachepod- CleanupCocoaPodsCache Filespoetry- ClearsPoetrycachepyenv- CleanupPyenv-VirtualEnvCachesteam- RemoveSteamlogs and cachesystem_caches- ClearSystem cachesystem_log- ClearSystem LogFilestrash- Empty theTrashon All Mounted Volumes and the Main HDDwget_logs- RemoveWgetlogs and hostsxcode- CleanupXcode Derived DataandArchivesxcode_simulators- ResetiOS simulatorsyarn- CleanupyarnCache📥 InstallationUsing Homebrewbrewtapmac-cleanup/mac-cleanup-py
brewinstallmac-cleanup-pyUsing pippip3installmac-cleanup🗑️ UninstallationUsing Homebrewbrewuninstallmac-cleanup-py
brewuntapmac-cleanup/mac-cleanup-pyUsing pippip3uninstallmac-cleanup💡 Usage OptionsHelp menu:$ mac-cleanup -h

usage: mac-cleanup [-h] [-n] [-u] [-c] [-p]

    A Mac Cleanup Utility in Python
    3.0.4
    https://github.com/mac-cleanup/mac-cleanup-py    

options:
  -h, --help         show this help message and exit
  -n, --dry-run      Dry run without deleting stuff
  -u, --update       Update HomeBrew on cleanup
  -c, --configure    Configure default and custom modules
  -p, --custom-path  Specify path for custom modules🌟 ContributingContributions are always welcome!If you have any ideas, suggestions, or bug reports, feel free to submit an issue or open a pull request.📝 LicenseThis project is licensed under theApache-2.0 License.👏 AcknowledgementsThis project is developed using tools provided by theJetBrains OSS Development Program.Find out more about their program and how they support open sourcehere.
Package: mac-colors
Installation$[sudo]pipinstallmac-colorsRequirements$brewinstalltagExamples>>>importmac_colors>>>path=["path/to/file"]>>>mac_colors.red(path)get>>>mac_colors.get(path){'path/to/file':["red"]}set multiple colors>>>mac_colors.replace(["blue","red"],path)remove colors>>>mac_colors.none(path)Linkstagreadme42.com
Package: maccorcyclingdata
Preparing MACCOR DataThemaccorcyclingdatapackage is to be used on the raw data output from theMACCORbattery cyclers and the schedule file input into theMACCORbattery cyclers. The package can also be used to validate the testdata by comparing it against the schedules.ExampleTheexample.ipynbprovides examples where all the functions are used on the given example data.
To run this example, simply open and run each cell in the example Jupyter Notebok provided.
In theexample_datadirectory, there is example testdata and an example schedule file to show how the data columns should be formatted.Requirements and InstallationThis code has been developed in Python 3.7.6. The code has been tested to run in Windows, Linux, and macOS.This code uses numpy and pandas as specified in docs/requirements.txt.The code can be run directly from a cloned GitHubrepositoryor it can also be installed as a pythonpackagethrough pip:pip install maccorcyclingdataThe functions in the package can be used after importing testdata, schedules, validate for example as follows:importmaccorcyclingdata.testdataastestdataimportmaccorcyclingdata.schedulesasschedulesimportmaccorcyclingdata.validateasvalidateCompatibilityThe raw testdata and schedule file that this package is used on must be csv files. Additionally, the first column of both files should be the header of the datatable.
The example uses data exported fromMACCORbattery cyclers.
The raw testdata should be exported with columns in the following order and with the following units (the names can be anything since theimport_maccor_datafunction will rename all columns):Cycle NumberStep NumberTotal Test Time (seconds)Step Time (seconds)Capacity (mAh)Current (mA)Voltage (V)DPt Time (seconds)Temperature (Celsius)EV Temperature (Celsius)The following columns may also exist, but must be named as the following:ACR,DCIR,Watt-hr, andnnnamed.The schedule should have columns in the following order (the names can be anything since theimport_schedulesfunction will rename all columns):Step NumberStep TypeStep Mode TypeStep Mode ValueStep Limit TypeStep Limit ValueEnd TypeOperator (=, <=, >=)End Type ValueGoto Step NumberReport TypeReport Type ValueOptionsStep NoteIf you encounter any issues running the code for any MACCOR model report an issue. Note that an example file will be needed in order to improve the code.
Package: macdaily
Platform:OS X Yosemite, OS X El Capitan, macOS Sierra
macOS High Sierra, macOS MojaveLanguage:Python (version ≥ 3.4)Environment:Console | TerminalMacDaily - macOS Automated Package ManagerPackage day-care manager on macOS.MacDailyis an all-in-one collection of console utility written in Python
with support ofPTY.
Originally works as an automated housekeeper for Mac to update all packages
outdated,MacDailyis now fully functioned and end-user oriented.MacDailycan manage packages of various distributions ofAtom,RubyGems,Homebrew,Python,Node.js, and even macOS software updates (c.f.softwareupdate(8)). Without being aware of everything about your Mac, one
can easily work around and manage packages out of no pain usingMacDaily.
Package: macdict
macdictis a command line utility and library to look up dictionary in macOS.It is built on theDictionary Services(CoreServices.framework)
andctypes. So we have a pure Python implementation without any compile-time
dependency.Installation$ pip install macdictor withpipsifor using its CLI only:$ pipsi install macdictCLI Usage$ macdict-lookup apple
apple | BrE ˈap(ə)l, AmE ˈæpəl | noun (fruit) ...API Usagefrommacdictimportlookup_worddefinition=lookup_word(u'apple')print(definition)ContributingIf you want to report bugs or request features, please feel free to open issues
or create pull requests onGitHub.
Package: mac-dock
Installation$[sudo]pipinstallmac-dock$[sudo]npmi-gmac-dockExamples$dockadd-app/Applications/iTunes.app;killallDock# arrangement:  1 - name (default), 2 - added, 3 - modification, 4 - creation, 5 - kind# displayas:    1 - folder, 2 - stack (default)# showas:       1 - beep, 2 - grid, 3 - list, 4 - auto (default)$dockadd-folder--arrangement=2--displayas=1--showas=1~/Downloads;killallDock$dockapps
/Users/username/Applications/GoogleChrome.app
/Users/username/Applications/SublimeText.app
/Applications/Utilities/Terminal.app
/Applications/iTunes.app$dockfolders
/Users/username/Downloads$dockrm/Applications/iTunes.app~/Downloads;killallDockreadme42.com
Package: mace-models
MACE modelsEffortlessly integrate pre-trained MACE models into your projects with the user-friendlymace-modelspackage.pipinstallmace-modelsLoading models is simple with a few lines of code:importmace_models# Load a default MACE modelmodel=mace_models.load()torch_model=model.get_model()ase_calculator=model.get_calculator()# get the model filemodel.get_file()Customize your model selection by loading models from various repositories or specific revisions:model=mace_models.load("<model-name>",rev="<branch-or-sha>",remote="https://github.com/<user>/<repo>")[!TIP]
If you want to persist the models locally, usegitclonehttps://github.com/RokasEl/MACE-ModelscdMACE-Models
dvcpulland usemace_models.load(remote="/path/to/MACE-Models")Ensure a seamless integration by first installing the MACE library and upgrading your PyTorch installation:pipinstall--upgradetorch--extra-index-urlhttps://download.pytorch.org/whl/cu116
pipinstallgit+https://github.com/ACEsuit/mace.gitModelsani500k_smalltraining data:https://www.nature.com/articles/sdata2017193hydromacehttps://github.com/RokasEl/hydromacemedium_spicehttps://arxiv.org/abs/2312.15211small_spicehttps://arxiv.org/abs/2312.15211Example usages with ASEimportmace_modelsfromase.buildimportmoleculemodel=mace_models.load("medium_spice")water=molecule("H2O")water.calc=model.get_calculator(dtype="float64")print(water.get_potential_energy())>>>-14.159366ReferencesModels are taken fromhttps://github.com/ACEsuit/mace/blob/docs/docs/examples/ANI_trained_MACE.zipand from@misc{kovacsMACEOFF23TransferableMachine2023,
  title = {MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules},
  author = {Kov{\'a}cs, D{\'a}vid P{\'e}ter and Moore, J. Harry and Browning, Nicholas J. and Batatia, Ilyes and Horton, Joshua T. and Kapil, Venkat and Witt, William C. and Magd{\u a}u, Ioan-Bogdan and Cole, Daniel J. and Cs{\'a}nyi, G{\'a}bor},
  year = {2023},
  number = {arXiv:2312.15211},
}MACE is described in@misc{batatiaMACEHigherOrder2022,
  title = {{{MACE}}: {{Higher Order Equivariant Message Passing Neural Networks}} for {{Fast}} and {{Accurate Force Fields}}},
  shorttitle = {{{MACE}}},
  author = {Batatia, Ilyes and Kov{\'a}cs, D{\'a}vid P{\'e}ter and Simm, Gregor N. C. and Ortner, Christoph and Cs{\'a}nyi, G{\'a}bor},
  year = {2022},
  number = {arXiv:2206.07697},
  eprint = {2206.07697},
  primaryclass = {cond-mat, physics:physics, stat},
  publisher = {{arXiv}},
  urldate = {2022-06-19},
  archiveprefix = {arxiv},
  langid = {english}
}
Package: mac-finder
Installation$[sudo]pipinstallmac-finder$[sudo]npmi-gmac-finderExamplescreate Finder alias$finder-alias~/Downloads~/Downloads2read/write Finder comment$finder-commentpath"comment value"$finder-commentpath
commentvalueset Finder icon$finder-iconpathimage.png$finder-reveal~/gitget Finder selection$finder-selection
/Users/username/Desktop
/Users/username/Downloadsexecute command from every selected Finder directory$finder-execpythonsetup.pyinstallfrontmost (1or0)$finder-frontmost0readme42.com