Saravanan666/pypi-org huggingface dataset:
Package: ezmote-cmdserver
ConfigurationExample config# commands can have defaults added via the defaults item
# if icon is not supplied then it is defaulted to <item name>.ico
# command title must be a zone name if it is played by jriver
commands:
  defaults:
    exe: 'x:\mc_scripts\ezmote.exe'
    volume: 0.40
    stopAll: true
  close:
    args: ['CloseAll']
    # icons prefixed with mi are special cased to be a material-ui icon 
    icon: 'mi/close'
    # idx is the order in which the commands will be listed in the ui
    idx: 0
    title: 'Close'
  music:
    args: ['jriver', 'Music']
    icon: 'mi/library_music'
    idx: 1
    title: 'Music'
    # sets the top appbar to the jriver selector which is based on MCWS browseChildren 
    control: 'jriver'
    # nodeId is the start point to navigate through MCWS browseChildren
    nodeId: 1
    stopAll: false
  video:
    args: ['jriver', 'Film']
    icon: 'mi/movie'
    idx: 2
    title: 'Films'
    control: 'jriver'
    nodeId: 3
    stopAll: false
  netflix:
    args: ['netflix']
    idx: 3
    title: 'Netflix'
    playingNowId: 5000
    # if zoneId is provided, the specified zone will be activated when the command is sent 
    zoneId: 10009
  virgin:
    args: ['tivo']
    idx: 4
    title: 'Virgin'
    control: 'tivo'
    playingNowId: 6000
    zoneId: 10009
  iplayer:
    args: ['iplayer']
    idx: 5
    title: 'BBC iPlayer'
    playingNowId: 1000
    zoneId: 10009
  amazon:
    args: ['amazon']
    idx: 6
    title: 'Amazon Video'
    playingNowId: 4000
    zoneId: 10009
  all4:
    args: ['all4']
    idx: 7
    title: 'All4'
    playingNowId: 3000
    zoneId: 10009
  itv:
    args: ['itv']
    idx: 8
    title: 'ITV'
    playingNowId: 2000
    zoneId: 10009
  radio:
    args: ['jriver', 'Radio']
    icon: 'mi/radio'
    idx: 9
    title: 'Radio'
    control: 'jriver'
    nodeId: 1000
    stopAll: false
  playlists:
    args: ['jriver', 'Playlist']
    icon: 'mi/playlist play'
    idx: 10
    title: 'Playlists'
    control: 'jriver'
    nodeId: 4
    stopAll: false
iconPath: 'x:\mc_scripts\icons'
playingNowExe: 'x:\mc_scripts\getPlayingNow.exe'
debug: false
debugLogging: true
host: megatron
port: 53199
# use for debug
webappPath: 'C:\Users\mattk\github\ezmote\build'
Package: ezmse
EasyMSE is a Python package for creating Magic: The Gathering cards from code or the command line. It interfaces with an installation of Magic Set Editor 2 and its built in CLI to make generating custom cards easier.InstallationWith Python and PIP installed, open a terminal and enterpip install ezmseYou must haveMagic Set Editor 2installed. Your working directory must contain the .exe and .com versions of MSE as well as its data and resource folder. You must also have theMagic - M15 fontsinstalled on your computer in order for the cards to render properly.UsageGenerating a CardfromezmseimportCardmyCard=Card()myCard.name="Cheese, The Destroyer"myCard.color="red,black"myCard.castingCost="2RB"myCard.type="Legendary Creature - Cat"myCard.rarity="Rare"myCard.superType="Legendary"myCard.power=5myCard.toughness=6myCard.illustrator="Kev Walker"myCard.setCode="XXX-XX"myCard.text=r"Indestructible\n<sym>T</sym>: Draw one BILLION cards"myCard.image="cheese.jpg"myCard.export("cheese-the-destroyer.png")Developing EasyMSEClone the repository, navigate to the root folder, and enterpip install -e .[dev]Features that need added:A more genericized Card class that can generate other types of cards (Planeswalker, Saga, etc.)Flavor textMore unit tests! (I'm fairly new to them)A way to make pyfakefs a development-only dependency (only including it in the dev section of setup.py makes the unit tests error out upon deployment)
Package: ezmsg
ezmsgMessaging and Multiprocessing.ezmsgis a pure-Python implementation of a directed acyclic graph (DAG) pub/sub messaging pattern based onlabgraphwhich is optimized and intended for use in constructing real-time software.ezmsgimplements much of thelabgraphAPI (with a few notable differences), and owes a lot of its design to thelabgraphdevelopers/project.ezmsgis very fast and uses Python'smultiprocessing.shared_memorymodule to facilitate efficient message passing without C++ or any compilation/build tooling.Installationpip install ezmsgDependenciesDue to reliance onmultiprocessing.shared_memory,ezmsgrequiresminimum Python 3.8.typing_extensionsTestingezmsgrequires:pytestpytest-covpytest-asyncionumpySetup (Development)$python3-mvenvenv
$sourceenv/bin/activate(env)$pipinstall--upgradepip(env)$pipinstall-e".[test]"(env)$python-mpytesttests# Optionally, Perform testsDocumentationhttps://ezmsg.readthedocs.io/en/latest/ezmsgis very similar tolabgraph, so you might get a primer with their documentation and examples. Additionally, there are many examples provided in the examples/tests directories strewn throughout this repository.ExtensionsSee the extension directory for more detailsezmsg-sigproc-- Timeseries signal processing modulesezmsg-websocket-- Websocket server and client nodes forezmsggraphsezmsg-zmq-- ZeroMQ pub and sub nodes forezmsggraphs... More to come!
Package: ezmsg-sigproc
ezmsg.sigprocTimeseries signal processing implementations for ezmsgInstallationpip install ezmsg-sigprocDependenciesezmsgnumpyscipySetup (Development)Installezmsgeither usingpip install ezmsgor set up the repo for development as described in theezmsgreadme.cdto this directory (ezmsg-sigproc) and runpip install -e .Signal processing modules are available underimport ezmsg.sigproc
Package: ezmsg-vispy
ezmsg-vispyezmsg-vispyis an extension to theezmsgframework, crafted to enhance and expedite the development
of real-time data visualizations. By leveraging the power of PyQt6 and the Vispy plotting
library,ezmsg-vispyoffers a fluid interface for high-performance
visualizations within systems developed withezmsg.
It's an ideal tool for researchers and developers who require rapid prototyping
of graphical interfaces for complex datasets. Withezmsg-vispy, creating
interactive visualizations that can integrate seamlessly into message-driven
architectures is more intuitive than ever.InstallationTo installezmsg-vispy, simply run:pipinstallezmsg-vispy
Package: ezmsg-websocket
ezmsg.websocketWebsocket server and client units for ezmsgInstallationpip install ezmsg-websocketDependencieswebsocketsSetup (Development)Installezmsgeither usingpip install ezmsgor set up the repo for development as described in theezmsgreadme.cdto this directory (ezmsg-websocket) and runpip install -e .Signal processing modules are available underimport ezmsg.websocket
Package: ezmsg-zmq
ezmsg.zmqZero-MQ pub/sub units for ezmsgInstallationpip install ezmsg-sigprocDependenciesezmsgpyzmqSetup (Development)Installezmsgeither usingpip install ezmsgor set up the repo for development as described in theezmsgreadme.cdto this directory (ezmsg-zmq) and runpip install -e .Signal processing modules are available underimport ezmsg.zmq
Package: eznf
EZNFThis library is designed to speed-up prototyping encodings, especiall for combinatorial problems.ModelerThe main abstraction of the library is the so-calledmodeler.Therefore we will always start with:importmodelerZ=modeler.Modeler()Then, we will use theZmodeler variable tomodelthe problem.For example, we can useZ.add_var(f'v_{i}',f'vertex{i}is selected for the CLIQUE')
Package: eznlp
Easy Natural Language ProcessingOverparameterized neural networks are lazy (Chizat et al., 2019), so we design structures and objectives that can be easily optimized.eznlpis aPyTorch-based package for neural natural language processing, currently supporting the following tasks:Text Classification (Experimental Results)Named Entity Recognition (Experimental Results)Relation Extraction (Experimental Results)Attribute ExtractionMachine TranslationImage CaptioningThis repository also maintains the code of our papers:Check thislinkfor "Deep Span Representations for Named Entity Recognition" accepted to Findings of ACL 2023.Check thislinkfor "Boundary Smoothing for Named Entity Recognition" in ACL 2022.Check thislinkfor the annotation scheme described in "A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text".InstallationCreate an environment$condacreate--nameeznlppython=3.8
$condaactivateeznlpInstall dependencies$condainstallnumpy=1.18.5pandas=1.0.5xlrd=1.2.0matplotlib=3.2.2$condainstallpytorch=1.7.1torchvision=0.8.2torchtext=0.8.1{cpuonly|cudatoolkit=10.2|cudatoolkit=11.0}-cpytorch$pipinstall-rrequirements.txtInstalleznlpFrom source (recommended)$pythonsetup.pysdist
$pipinstalldist/eznlp-<version>.tar.gz--no-depsWithpip$pipinstalleznlp--no-depsRunning the CodeText classification$pythonscripts/text_classification.py--dataset<dataset>[options]Entity recognition$pythonscripts/entity_recognition.py--dataset<dataset>[options]Relation extraction$pythonscripts/relation_extraction.py--dataset<dataset>[options]Attribute extraction$pythonscripts/attribute_extraction.py--dataset<dataset>[options]CitationIf you find our code useful, please cite the following papers:@article{zhu2022deep-span,
  title={Deep Span Representations for Named Entity Recognition},
  author={Zhu, Enwei and Liu, Yiyang and Li, Jinpeng},
  journal={arXiv preprint arXiv:2210.04182},
  year={2022}
}@inproceedings{zhu2022boundary,
  title={Boundary Smoothing for Named Entity Recognition},
  author={Zhu, Enwei and Li, Jinpeng},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month={may},
  year={2022},
  address={Dublin, Ireland},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/2022.acl-long.490},
  pages={7096--7108}
}@article{zhu2021framework,
  title={A Unified Framework of Medical Information Annotation and Extraction for {C}hinese Clinical Text},
  author={Zhu, Enwei and Sheng, Qilin and Yang, Huanwan and Li, Jinpeng},
  journal={arXiv preprint arXiv:2203.03823},
  year={2021}
}ReferencesChizat, L., Oyallon, E., and Bach, F. On lazy training in differentiable programming. InNeurIPS 2019.
Package: eznorm
eznorm下の方に日本語の説明がありますOverviewEasy to use normalization tool for machine learning.Normalization is performed on test data as well as on training parameters to prevent leakage.Automatically prevents division by zero for features with a standard deviation of zeroUsageimporteznormtrain_x=[[1,-10,0.3],[2,-5,0.1],[1,-10,0.5],]test_x=[[2,-5,0.2],[1,-7,0.3]]# Normalize training datanorm_params=eznorm.fit(train_x)# Fit the data to the normalization parameters (returns normalization parameters) [eznorm]norm_train_x=eznorm.normalize(train_x,norm_params)# Normalize the data [eznorm]"""norm_train_x:[[-0.70710678 -0.70710678  0.        ][ 1.41421356  1.41421356 -1.22474487][-0.70710678 -0.70710678  1.22474487]]"""# Normalize test datanorm_test_x=eznorm.normalize(test_x,norm_params)# Normalize the data [eznorm]"""norm_test_x:[[ 1.41421356  1.41421356 -0.61237244][-0.70710678  0.56568542  0.        ]]"""概要機械学習の正規化処理を簡単に実施するツールテストデータに対してもにも学習時のパラメータで正規化を実施することでリーケージを防止標準偏差が0の特徴量に対してのゼロ割りを自動的に防止使用例importeznormtrain_x=[[1,-10,0.3],[2,-5,0.1],[1,-10,0.5],]test_x=[[2,-5,0.2],[1,-7,0.3]]# 学習データの正規化norm_params=eznorm.fit(train_x)# 学習データへの適合 (正規化パラメータを返す) [eznorm]norm_train_x=eznorm.normalize(train_x,norm_params)# データの正規化 [eznorm]"""norm_train_x:[[-0.70710678 -0.70710678  0.        ][ 1.41421356  1.41421356 -1.22474487][-0.70710678 -0.70710678  1.22474487]]"""# テストデータの正規化norm_test_x=eznorm.normalize(test_x,norm_params)# データの正規化 [eznorm]"""norm_test_x:[[ 1.41421356  1.41421356 -0.61237244][-0.70710678  0.56568542  0.        ]]"""
Package: ezntfs
ezNTFSezNTFS is an easy-to-use wrapper for NTFS-3G on macOS.ezNTFS can be used as a menu bar app, or via the CLI in the terminal.InstallationTo use ezNTFS, you will needPython 3,FUSE for macOS (osxfuse),
andNTFS-3Ginstalled in your system.To install NTFS-3G, you may compile and install it yourself
(recommended, see instructions at the end),
or you may also install itvia nixorvia brew.To install ezNTFS, run:$ pip3 install ezntfsIt should be accessible from the terminal afterwards.To configure the menu bar app, run the command after installing ezNTFS:$ sudo ezntfs-app installThis command addsntfs-3gto a sudoers file,
and automatically launches the app on start-up.
The app will also be launched right after install.NOTE:The app icon will only show up if there are NTFS volumes plugged in.
You may also need to grant Python access to removable volumes for this to work.Usage (CLI)Mount all read-only NTFS volumes usingntfs-3g(root privileges are needed for mounting):$ sudo ezntfs allMount a specific NTFS volume (runezntfs listto find the disk id):$ sudo ezntfs <disk id>AlternativesFormat as exFATNatively supported by macOSexFAT is less reliable (no journaling), only use for flash drivesAuto-mount scriptSetup requires disabling System Integrity Protection (SIP) or Sealed System Volume (SSV)Mounts with NTFS-3G even when not needed, which may slow down readsMountyVolumes won't show up on FinderWriting via Apple's native NTFS driver is not officially supportedTuxera NTFS driverNot freeMature (same developers as NTFS-3G)Kernel driver, should be faster than FUSEParagon NTFS driverNot free,except for Seagate external drivesMatureKernel driver, should be faster than FUSECompiling and installing NTFS-3GWe recommend compiling and installing NTFS-3G yourself for security.
This allows installing it as root, which keeps things secure when added to sudoers.Instructions# Clone the git repo (you may want to clone a newer version if available)
git clone --depth 1 --branch 2021.8.22 https://github.com/tuxera/ntfs-3g.git
cd ntfs-3g

# Install the dependencies needed for compilation
brew install autoconf automake libgcrypt libtool pkg-config gettext

# Compile and install ntfs-3g
export LDFLAGS=-lintl
./autogen.sh
./configure \
    --disable-debug \
    --disable-dependency-tracking \
    --with-fuse=external \
    --prefix=/usr/local \
    --exec-prefix=/usr/local \
    --mandir=/usr/local/share/man \
    --sbindir=/usr/local/sbin
make
sudo make install

# Delete the cloned git repo
rm -r .
Package: ezomero
ezomeroA module with convenience functions for writing Python code that interacts with OMERO.InstallationJustpip install ezomeroand you should be good to go! The repo contains the specific package versions we testezomerowith insetup.py, but any Python>=3.8 and latestomero-pyandnumpyshouldwork -  note that this package is in active development!If you want to useget_tableandpost_tableto/from Pandas dataframes, you need to installezomero[tables]- that install an optionalpandasdependency. Installing ezomero without this will defaultget_tableandpost_tableto use lists of row lists as their default.UsageIn general, you will need to create aBlitzGatewayobject usingezomero.connect(), then pass theconnobject to most of these helper functions along with function-specific parameters.DocumentationDocumentation is available athttps://thejacksonlaboratory.github.io/ezomero/
Package: ez-openai
Ez OpenAIMy opinion of theopenaiPython library is best illustrated by the fact that if you
ask ChatGPT about it, it will usually hallucinate a more reasonable API. So, I wrote
this library, because if I had to manually poll for a tool update again I would
instigate the robot uprising myself.InstallationRun this somewhere:pip install ez-openaiUsageBasic usageUsing Ez OpenAI is (hopefully) straightforward, otherwise I've failed at the one thing
I've set out to make:fromez_openaiimportAssistant# To use a previously-created assistant:ass=Assistant.get("asst_someassistantid")# To create a new one:ass=Assistant.create(name="Weatherperson",instructions="You are a helpful weatherperson.",)# You can store the ID for later.assistant_id=ass.id# Delete it when you're done.ass.delete()Function callingNo more wizardry, just plain Python functions:fromez_openaiimportAssistant,openai_functionfromenumimportEnum@openai_function(descriptions={"city":"The city to get the weather for.","unit":"The temperature unit , either `c` or `f`.",})defget_weather(city:str,unit:Enum("unit",["c","f"])):"""Get the weather for a given city, and in the given unit."""# ...do some magic here to get the weather...print(f"I'm getting the weather for{city}woooooo")return{"temperature":26,"humidity":"60%"}ass=Assistant.create(name="Weatherperson",instructions="You are a helpful weatherperson.",functions=[get_weather])# Or, if you already have one, you can fetch it (but still# need to specify the functions).ass=Assistant.get("asst_O5ZAsccgOOtgjrcgHhUMloSA",functions=[get_weather])conversation=ass.conversation.create()# Similarly, you can store the ID to fetch later:old_conversation=ass.conversation.get(old_conversation.id)# The library will handle all the background function calls itself:conversation.ask("Hi, what's the weather like in Thessaloniki and Athens right now?")>I'm getting the weather for Thessaloniki woooooo>I'm getting the weather for Athens woooooo>"The weather today in both Thessaloniki and Athens is quite similar, with atemperatureof26°Candahumiditylevelat60%.Enjoyapleasantandcomfortableday!"Because assistants change (eg if you want to add some more functions), and it's tedious
to create new ones every time, there's a helper method that will update an assistant
with new functions/instructions:fromez_openaiimportAssistantass=Assistant.get_and_modify(id="asst_someassistantid",name="Weatherperson",instructions="These are your new instructions.",functions=[get_weather,some_new_function])gg ez
Package: ezpackage
ezpackageWhat is ezpackage?ezpackageis a Python package for building and sharing your python package in an easy way.Where to get ezpackageThe source code is currently hosted at GitHub:https://github.com/datanooblol/ezpackageThe latest version is available atPython Package Index (PyPI)# pippipinstallezpackageHow to use ezconfigurationSteps to follow:# import EzPackagefromezpackage.package_managerimportEzPackage# initialize EzPackage objectezpkg=EzPackage(template_package_name='your_package',template_version='v0.1.0',template_author_name='your_name',template_author_email='your-email@something.com',template_url='https://github.com/your-git-account/your-package',template_description='A package summary.',template_install_requires=['dependency1','dependency2'],template_keywords=['tag1','tag2'],template_include_package_data=True,template_python_requires='>=3.0')# create setup.py at the same level of your packageezpkg.create_setup_py(package_parent_dir='your-package-parent-directory')# build packages including: build, dist, <your package>.egg-infoezpkg.build_package()# upload your package in /your-package-parent-directory/dist/* to pypiezpkg.upload_to_pypi(username='your-pypi-username',password='your-pypi-password')# remove built packages from your-package-parent-directoryezpkg.remove_built_package()
Package: ezpacket
Ez-PacketA byte manipulation tool for IoT and network communications.This module aims to simplify writing protocols for I/O by allowing declarative byte parsing.UsageSince this module is not published yet it only works with relative import, so python console needs to run on the same level as this module's folderfromezpacketimportPacket,Sectionpacket=Packet([Section(0x1100),Section(value=0x11,byte_size=2)])packet.to_bytes()# 0x11000011response=Packet([Section.Template(2),Section.Template(byte_size=3)])raw_data=bytes([1,2,3,4,5])response.decode(raw_data)response[0]# 0x0102response[-1]# 0x030405FeaturesSimple and familiar API:Each Section has two parameters: value and byte size.Packet object is just a section array withto_bytes,to_intanddecodefunctions added, which means its easier to readSection.Templatemakes easier to read without adding extra complexity. This function just sets thebyte_sizeand returns a regular Section for its value to be filled during parsingExpandableSections can parse data as long as they desire, so they are not limited with their byte size. This allows expanded section implementationsFastPacket is making use of integer and byte shifting logic with some loops, so no string parsing and decoding going on speeding up the processSupports trailing or leading zeros!Packet(trail_or_lead=Zeros.LEADING)orSection(trail_or_lead=Zeros.LEADING)SectionsSectionSimple section with a value and byte size. Used for sections in byte array protocols with fixed lengthExpanding SectionWhen this section is added to packet, it consumes all remaining bytes and sets its own length. Used for sections that require custom parsing with custom logic, returned value can be parsed manually after parsingDynamic SectionThis section sets its own first byte as the length byte. Used for transmitting and decoding protocols with flexible lengths
Package: ez-parse
ez-parseA Python library that scrapes essential information from PDFs of LinkedIn profiles.Project BoardOverviewThis is a parser that extracts important information from a LinkedIn profile PDF. It converts the PDF to a list of strings, and then uses LinkedIn's headers to create a dictionary that maps said headers to string values that contain the most relevant parts of a candidate's profile.InstallationInstall the library's dependencies and build the library using:pip install ez-parseAccessing LinkedIn PDFsVisit the LinkedIn profile that you would like to parse. Under the individual's basic profile information, there is a button labeled "More". Click on this button, and then click on "Save to PDF".UsageIn your code, begin by importing the package:from ez-parse import parserYou can extract the text data from the PDF like so:data = parser.extract_pdf(<path_to_linkedin_pdf>)This parsed data can also be stored in a dictionary:res = parser.get_many(data)ExamplesBelow are some minimal examples of how the helper functions for each header work. Note how each helper function is designed to exclude information after encountering an irrelevant section header:get_contact>>> li = ["999-999-9999", "Email", "URL", "Top Skills"]>>> print(get_contact(li, -1)[0])["999-999-9999", "Email", "URL"]get_skills>>> li = ["Python", "Java", "C++", "Certifications"]>>> print(get_skills(li, -1)[0])["Python", "Java", "C++"]get_certifications>>> li = ["QuickBooks", "CPR", "Bartending", "Honors-Awards"]>>> print(get_certifications(li, -1)[0])["QuickBooks", "CPR", "Bartending"]get_honors>>> li = ["USACO Gold", "USAMO Silver", "USACO Bronze"]>>> print(get_honors(li, -1)[0])["USACO Gold", "USAMO Silver", "USACO Bronze"]get_summary>>> li = ["A", "mysterious", "person.", "Languages"]>>> print(get_summary(li, -1)[0])["A", "mysterious", "person."]get_languages>>> li = ["English", "Spanish", "Latin"]>>> print(get_languages(li, -1)[0])["English", "Spanish", "Latin"]For a more in-depth example that extracts text from the PDF and relies on all of these helper functions, please see thedocumentation.
Package: ezpickle
ezpickleHelper functions to simplify pickling and unpickling objects to/from files.pickle_data(data, filepath: Path | str, overwrite: bool = False): Pickle an object (such as a dictionary) and write it to a fileunpickle_data(filepath: Path | str, dict_keys: str | Iterable | None = None): Load a pickled object from a fileReleases1.0, 2021-10-26Initial releaseLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.
Package: ezpkl
EZPKL: lovely pickling🥒 for context manager hater👊컨텍스트 매니저를 열고 닫는 것이 귀찮아서 만들었습니다.설치패키지 관리자pip를 사용하여 ezpkl를 설치합니다.pipinstallezpkl사용법객체 저장하기fromezpklimportsave_pkla=[1,2,3,4,5]# 현재 폴더에 'a.pkl'이 저장됩니다.save_pkl(var=a)# 현재 폴더에 'a_list_temp.pkl'이 저장됩니다.save_pkl(var=a,file_name='a_list_temp')객체 불러오기fromezpklimportload_pkla=load_pkl('a.pkl')라이선스MIT