Saravanan666/pypi-org huggingface dataset:
Package: d3m-common-primitives
Common D3M primitivesA common set of primitives for D3M project, maintained together.
It contains example primitives, various glue primitives, and other primitives performers
contributed.InstallationThis package works on Python 3.6+ and pip 19+.This package additional dependencies which are specified in primitives' metadata,
but if you are manually installing the package, you have to first run, for Ubuntu:$ apt-get install libopenblas-dev ffmpeg
$ pip3 install python-prctlTo install common primitives from inside a cloned repository, run:$ pip3 install -e .When cloning a repository, clone it recursively to get also git submodules:$ git clone --recursive https://gitlab.com/datadrivendiscovery/common-primitives.gitChangelogSeeHISTORY.mdfor summary of changes to this package.Repository structuremasterbranch contains latest code of common primitives made against the latest stable
release of thed3mcore package(itsmasterbranch).develbranch contains latest code of common primitives made against the
future release of thed3mcore package (itsdevelbranch).Releases aretaggedbut they are not done
regularly. Each primitive has its own versions as well, which are not related to package versions.
Generally is the best to just use the latest code available inmasterordevelbranches (depending which version of the core package you are using).Testing locallyFor each commit to this repository, tests run automatically in theGitLab CI.If you don't want to wait for the GitLab CI test results and run the tests locally,
you can install and use theGitLab runnerin your system.With the local GitLab runner, you can run the tests defined in the.gitlab-ci.ymlfile of this repository, such as:$ gitlab-runner exec docker style_check
$ gitlab-runner exec docker type_checkYou can also just try to run tests available under/testsby running:$ python3 run_tests.pyContributeFeel free to contribute more primitives to this repository. The idea is that we build
a common set of primitives which can help both as an example, but also to have shared
maintenance of some primitives, especially glue primitives.All primitives are written in Python 3 and are type checked usingmypy, so typing annotations are required.About Data Driven Discovery ProgramDARPA Data Driven Discovery (D3M) Program is researching ways to get machines to build
machine learning pipelines automatically. It is split into three layers:
TA1 (primitives), TA2 (systems which combine primitives automatically into pipelines
and executes them), and TA3 (end-users interfaces).
Package: d3m-interface
D3M Interface LibraryLibrary to use D3M AutoML systems. This repository contains an implementation to integrate
D3M AutoML systems with Jupyter Notebooks.Documentation is available here.InstallationThis package works with Python 3.6 through 3.8 in Linux, Mac and Windows.You can install the latest stable version of this library fromPyPI:$ pip install d3m-interfaceTo install the latest development version:$ pip install git+https://gitlab.com/ViDA-NYU/d3m/d3m_interface.git@devel#egg=d3m_interface
Package: d3nb
This is a really thin shiv that lets you run static d3js scripts to produce
SVG / HTML outputs in your Jupyter notebooks. Underneath the covers,
it usesd3-node.Why is this?Because it makes writing my dissertation easier. I have some dynamic
visualizations that are d3-based. But, for the write-up, I also have
static snapshots as illustrations. This is my DRY hack. It let’s me reuse
the code in a way that isn’t terribly intrusive. (Well, that, and I don’t
like out-of-Jupyter-core JS in my notebooks.)InstallationAssuming you havenodeandyarn,pipinstalld3nbUsageThe following snippet runs the given cell source withmy_node_project_diras the working directory, then outputs
the SVG. If that directory doesn’t exist, you can run the
initial cell magic line as,%%d3nb_svg my_node_project_dir--initto set it up.%%d3nb_svgmy_node_project_dirvarD3Node=require('d3-node')vard3=require('d3')vard3n=newD3Node()varsvg=d3n.createSVG()// See: https://www.npmjs.com/package/d3-node// ...console.log(d3n.svgString())
Package: d3net-spleeterweb
D3Net (Music Source Separation) for Spleeter WebThis is a modified version of theofficial D3Net repomade to be compatible withSpleeter Web!This is inference code for D3Net based music source separation.Quick Music Source Separation Demo by D3NetFrom the Colab link below, you can try using D3Net to generate and listen to separated audio sources of your audio music file. Please give it a try!Getting startedPrerequisitesnnablalibrosapydubnumpysoundfileyamlInference: Music source separation with pretrained modelDownload the pre-trained D3Net model for Music Source Separationhere.Run the below inference command for a sample audio filetest.wavin current directory:python./separate.py-i./test.wav-ooutput/-md3net-mss.h5-ccudnnArguments:-i : Input files. (Any audio format files supported by FFMPEG.)-o : Output directory. (Output folder path to save separated instruments)-m : Model file. (Pre-trained model)-c : Context. (Extension modules :cpuorcudnn)Training: Train the music source separation model from scratch (coming soon)
Package: d3plus
d3plusd3plusis Python packageStar this repo if you like it! ⭐️Blog/Documentationd3plus documentation pages (Sphinx)Notebook with examplesRead more details and usage about d3plus in this blog!d3plus documentation pagesNotebook exampleBlog: A step-by-step guide for clustering imagesContentsInstallationContributeCitationMaintainersLicenseInstallationInstall d3plus from PyPI (recommended). d3plus is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.A new environment can be created as following:condacreate-nenv_d3pluspython=3.8
condaactivateenv_d3pluspipinstalld3plus# normal installpipinstall--upgraded3plus# or update if neededAlternatively, you can install from the GitHub source:# Directly install from github sourcepipinstall-egit://github.com/erdogant/d3plus.git@0.1.0#egg=master
pipinstallgit+https://github.com/erdogant/d3plus#egg=master
pipinstallgit+https://github.com/erdogant/d3plus# By cloninggitclonehttps://github.com/erdogant/d3plus.gitcdd3plus
pipinstall-U.Import d3plus packageimportd3plusasd3plusExample:df=pd.read_csv('https://github.com/erdogant/hnet/blob/master/d3plus/data/example_data.csv')model=d3plus.fit(df)G=d3plus.plot(model)Referenceshttps://github.com/erdogant/d3plusCitationPlease cite in your publications if this is useful for your research (see citation).MaintainersErdogan Taskesen, github:erdogantContributeAll kinds of contributions are welcome!If you wish to buy me aCoffeefor this work, it is very appreciated :)LicenceSeeLICENSEfor details.
Package: d3tree
d3treed3treeis a Python package used to visualize file paths using D3.js. The package is inspired byDirtree, a similar library written in Ruby.InstallationInstalld3treein your virtual environment of choice using:python-mpipinstalld3treeUsaged3treeis a command-line utility that can be used as follows:Usage:d3tree[OPTIONS][PATH]Options:-v,--versionPrintversion-o,--outputTEXTSpecifyfilepathtowriteHTMLoutput-t,--template[tree|circles|flame|treemap]Specifytemplate--helpShowthismessageandexit.ExamplesVisualize current directory:ls|d3tree-ooutput.htmlOr use a file that contains the filepaths:catfilepaths.txt|d3tree-ooutput.htmlFeatures not implementedThe following features are not implemented (yet):shell completionscreenshot featureusing local JS dependenciesFeel free to create a PR for these or other features.
Package: d3wordcloud
d3wordcloudA simple Jupyter (Lab/Notebook) port of Jason Davies d3 JS wordcloud generatorhttps://www.jasondavies.com/wordcloud/UsageDisplays the interactive SVG wordcloud right in your Jupyter Notebook (or Jupyter Lab).importd3wordcloudasd3wcd3wc.display_wordcloud(sample_data,show_settings=False)sample_datais a space-separated string, e.g."energy fossil fuel EU"show_settingsshows the original settings for interactive control in your notebook, just like in theoriginal version
Package: d42
d42d42package offers several tools to define, generate, validate, and substitute data based on models defined using the district42 data description language.Installation$pip3installd42ComponentsThed42package comprises the following components:district42— a data description language for defining data modelsblahblah— a fake data generator tailored for the district42 schemavalera— a validator designed for the district42 schemarevolt— a value substitutor compatible with the district42 schemaUsagefromd42importschema,fake,validate_or_fail# Define a schema with a string "banana"sch=schema.str("banana")# Generate a fake value based on the schema and validate itassertvalidate_or_fail(sch,fake(sch))For a more comprehensive guide and detailed information, check out theofficial documentation.
Package: d4data
D4DataData Engineered with pythonProof of concept project for python data engineering. Envisioned use cases:Data access and sharing with data defined as code.Data catologing and discovery.Data transfer and partitioning for distributed computing.Go from remote data sources to model training with simple and expressive python.Installationpipinstalld4dataExample API:Define data as codefromd4data.storage_clientsimportFTPStorageClientfromd4data.sourcesimportCSVDataSourceclassNIHChromosomeSNPS38(CSVDataSource):def__init__(self,chromosome,output_path):# define data that is specific to your data sourceself.chromosome=chromosome# give your data source a name, file name, local paths to save to and uriself.name="NIH_Chromose_{}_SNPS38".format(self.chromosome)self.file_name="bed_chr_{}.bed.gz".format(self.chromosome)self.uri="https://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/BED/"+self.file_nameself.local_paths=[os.path.join(output_path,self.file_name)]# assign a storage clientself.client=FTPStorageClient()Download data programmaticallydata=NIHChromosomeSNPS38(chromosome=1,local_path="./datasources")# calls client.download(uri=self.uri)data.to_disk()Process datadataset=data.to_dataset()foriinrange(len(dataset)):some_func(dataset[i])Compose DataSources dynamically with a DataStrategy:fromd4data.storage_clientsimportHTTPStorageClientfromd4data.coreimportDataStrategy,CompositeDataSource# Define the DataSourceclassHaploRegSource(CSVDataSource):def__init__(self,population,local_path):self.name="LD_{}".format(population.upper())self.file_name=self.name+".tsv.gz"self.uri="https://pubs.broadinstitute.org/mammals/haploreg/data/"+self.file_nameself.local_paths=[os.path.join(local_path,self.file_name)]self.client=HTTPStorageClient()# Define the DataStrategy# Data Strategies contain logic for building data sources from some higher level data about the data, e.g list of s3 urls.# Data Strategies can also contain a partition strategy where logic for partitioning data sources can be implemented- you may want to partition based on compute resources available.classHaploRegStrategy(DataStrategy):def__init__(self,populations,local_path):self.populations=populationsself.local_path=local_pathself._sources={"haplo_reg":HaploRegSource}defcreate_sources(self):comp_source=CompositeDataSource()source=self._sources["haplo_reg"]forpopulationinself.populations:ds=source(population,self.local_path)comp_source.add(ds)returncomp_sourcepops=["afr","eur","amr]haplo_strategy=HaploRegStrategy(pops,local_path="./data_sources")comp_source=haplo_strategy.create_sources()forsourceincomp_source:# Download sources to in-memory file systemd=s.to_memfs()Prefect Integration: TODOPytorch Integration: TODOFree software: Apache Software License 2.0Documentation:https://d4data.readthedocs.io.FeaturesTODO
Package: d4m
d4mA cross-platform Project Diva MegaMix+ mod managerFeaturesCross-platformSimple usage, everything you need and nothing you don't (no bloat!)Automatically determine game install directoryInstall and update DivaModLoaderEasily enable/disable modsSearch and Install mods fromGameBananaandDivaModArchiveInstall mods from a local archive (.7z,.rar,.zip, etc)Update installed mods easilyMigrate metadata from DivaModManagerAutomatic self-update checkingTUI or GUI: your choice! (TUI is Linux only)InstallationRequirements:python >= 3.9,libarchiveLinuxd4m is now available in the AUR!Before installingd4m, you first need to install libarchive.For Ubuntu, this can be installed withsudo apt install libarchive-devAfter installing libarchive, installd4mvia pip:python -m pip install d4mFor Windows (Bundled)Download the latest d4m installer from thereleases page.From Source*Note: libarchive must be installedgitclonehttps://github.com/Brod8362/d4mcdd4m
python-mpipinstallbuild
python-mbuild
python-mpipinstalldist/*.whlFrom Source (Development)*Note: libarchive must be installedgitclonehttps://github.com/Brod8362/d4mcdd4m
gitcheckoutunstable
python-mpipinstallbuild
python-mpipinstall-e.Flatpak (pre-built binary)Download the latest .flatpak from thereleasessection and install it viaflatpak install. (e.g,flatpak install d4m-VERSION.flatpak)Flatpak (building)gitclonehttps://github.com/Brod8362/d4mcdd4m/flatpak
./build.sh--repo=REPO_PATH--force-clean
flatpakbuild-bundleREPO_PATHd4m-VERSION.flatpakpw.byakuren.d4m
flatpakinstalld4m.flatpak
rmd4m.flatpak# optionalRunningLinux (via pip)d4m(TUI)d4m-gui(GUI)Linux (via AUR or Flatpak)d4mshould have been added as a desktop entry. This will run the GUI.For AUR, you can also use thed4mord4m-guicommands.If you're having issues with the Flatpak or AUR distributions,please open an issue.Demo (TUI)Demo (GUI)FAQWhy is DivaModLoader installation not supported on my platform?As of d4m 0.3.1,libarchiveisrequiredfor installations on all platforms.As of d4m 0.3.0,libarchiveshould be bundled with all Windows installations.Currently, automatic DivaModLoader installation/updating depends onlibarchive. Most Windows users likely don't have this installed, and I don't want to make it a hard dependency of the application.libarchiveis needed because the packager of DivaModLoader creates archives using BCJ2, which is not supported bypy7zr.If you manually installlibarchiveand the corresponding pip package (libarchive), you should then be able to auto install/update DML.ConfigurationYou can override some parameters of d4m via environment variables.Environment VariablePurposeDefaultD4M_INSTALL_DIRMegaMix+ install directoryAuto-determined via steam
Package: d4-pyclient
Main featuresd4-pyclientis python implemention of theD4 encapsulation
protocol.It is a low-barrier entry for anyone interested into tinkering with the D4
protocol or embedding a d4 client into another project. It supports both regular
types and types defined by meta-header.Installpipinstalld4-pyclientLaunching./d4_client.py-h
usage:d4_client.py[-h]-cCONFIG[-cc]optionalarguments:-h,--helpshowthishelpmessageandexit-cCONFIG,--configCONFIGconfigdirectory-cc,--check_certificatecheckservercertificateConfiguration Filesof the client configuration can be stored in folder containing the following files:key: your Pre-Shared-Keysnaplen: default is 4096source: stdin or d4serverdestination: stdout, [fe80::ffff:ffff:ffff:a6fb]:4443, 127.0.0.1:4443type: D4 packet type, seetypesuuid: generated automatically if emptyversion: protocol versionrootCA.crt: optional : CA certificate to check the server certificatemetaheader.json: optional : a json file describing feed's meta-typetypes
Package: d4train
Dataset Preparation for Transformers Fine-tuningTheDataset Prep Transformerspackage simplifies the process of preparing datasets for fine-tuning or training various large language models available in the Hugging Face Transformers library. Whether you're using a model from the Hugging Face repository or have your own dataset, this package streamlines the data integration for a seamless training experience.FeaturesEasily integrate your dataset with Hugging Face Transformers models for training or fine-tuning.Specify the model repository ID and dataset from the Hugging Face library to automatically fetch and configure the data.Seamlessly incorporate your custom dataset by providing it as input to the package.InstallationYou can install the package using pip:pipinstalld4train
Package: d6
Information:http://rpg-1d6.sf.netChanges:d6 tipreduced the size to 60x30, so it runs on simple terminals.shuffle the fighters before action: Fairness.FIX: it stopped at 1 or not at allleave it running for 10 frames after the end.no more flickeringthe armored ones don’t win all the time anymore.cinteresting battles: exp 6, 3, 1.active battle: swordmasters 17 skill, armored 14 armor, killers 16 weapon.more room for fighting (visually) and exp for winning without wound again.random groups againkiller against armored is about equal, toonow it’s about equal…armored and swordmasters equal: can’t harm each other except for crits.FIX: Breakage and infinite loop when using floats for experience (sub 1).stronger swordmasters.FIX: attvalue2cost was not used. Also added faster implementation of attvalue2costadded mean powerstats.charbase: more efficient version of bonus2cost().more experience!termctl addition of light colors.slightly faster still by avoiding the passing of a fat object.asciibattle: performance optimizations and a counter :)added a safe (deepcopy) yaml loader cache for faster loading of many similar characters.asciibattle basically works.added simple termctl script, borrowed from evolve-keybourd-layout:http://bitbucket.org/ArneBab/evolve-keyboard-layout/better readable attack function.better readable return statement.minor comments.Characters can now attack each other and get wounds and damage.characters can now compete against each other.improve now only shows stats which actually changed instead of showing all stats which were given marks.char.improve() belongs to char.py: It is something another programmer may want to use.factored the basic character stuff out into charbase.py, so char.py shows more directly what you can do.more package import stuff.next version will be 0.2Added signature for changeset 0a2f373d47c5Added tag 0.1 for changeset c112b93bbaced6 0.1skillvalue: always use the higher value from skill with basevalue and without base value (necessary when the base skill is low - i.e. 3).improve now returns a list with all the changes it did.correct in-package import handling.classifier: python 3no scripts yet.status: Alpha, target: devs.added info: for python 3added plan for chars: status: activewounds give a malus on rolls.next version will be 0.1FIX: char.py is now recognized as script.FIX: changelog generation took only the last two revs.Added signature for changeset 046449e70a4fAdded tag 0.0 for changeset 398b1e1cb820
Package: d64
d64This Python module enables access to disk image files (.d64, .d71, .d80, .d81, .d82) used by various Commodore microcomputer emulators and tools.It provides familiar interfaces for developers to read and write program and data files within an image.In addition it contains tools useful to inspect and modify images, for example a script to check the integrity of an image.ExamplesClasses and functions reside in thed64module, the whole module may be imported or just those definitions referenced by the user.Displaying an image contentsTo perform a directory listfromd64importDiskImagewithDiskImage('squadron.d64')asimage:forlineinimage.directory():print(line)This prints out0 "SQUADRON        " Q9 2A
15   "SQUADRON PAL"     PRG
15   "SQUADRON NTSC"    PRG
634 BLOCKS FREE.Copying a file from an imageTo extract, for example, a BASIC program and write it to a filefromd64importDiskImagewithDiskImage('test.d64')asimage:withimage.path(b'METEOR').open()asin_f:withopen('meteor.prg','wb')asout_f:out_f.write(in_f.read())TODOdetailed documentationbetter docstringsmore examples
Package: d64gfx
This project (d64gfx) contains a few graphics routines associated with my Commodore 64 disk image utilities (d64py). They're used by my DiskWrangler (q.v.), and are in a separate library because they're Qt6-specific.getting the icon from a GEOS file as a QPixmapfont previews (including mega-fonts) as a QPixmapgeoPaint previews as a QPixmapYou can find the API docshere, and the changeloghere.
Package: d64py
This project is a Python library for writing programs to explore Commodore 64 disk images; my Disk Wrangler (q.v.) is based on it. It's particularly good at dissecting GEOS disks. Here's how to get started:Create a DiskImage object. Just pass a Path to the constructor; he'll detect the image type (or raise an Exception if it's not one):from pathlib import Path
from d64py.base.DiskImage import DiskImage
from d64py.base.Constants import CharSet

path = Path("/your/image/here.d64")
image = DiskImage(path)Now you can read the directory:dirEntries = image.getDirectory()
for dirEntry in dirEntries:
    print(f"{dirEntry.getDisplayFileName()}")If you have a GEOS file, you can access the fields in the file header:if dirEntry.isGeosFile():
        geosFileHeader = dirEntry.getGeosFileHeader()
        print(f"permanent name string: {geosFileHeader.getPermanentNameString()}")Say there's a geoWrite file on the disk, and you'd like to quickly see what's in it. We can ask the disk image to find the directory entry for us (if we specify CharSet.PETSCII, the filename will be translated to PETSCII first). All the routines that return lines of text return TextLine objects, which is just a helper class that includes a line of text and an error flag (for e.g. printing in red). geoWrite files return a list of pages, each of which is a list of TextLines.dirEntry = image.findDirEntry("firstBootSrc", CharSet.ASCII)
pages = image.getGeoWriteFileAsLines(dirEntry)
for page in pages:
    for line in page:
        print(line.text)Don't forget to release the image's resources when you're through with it (the entire image gets memory-mapped):image.close()To dig deeper, you can find the API docshereand the revision historyhere.
Package: d666-rpg-system
⚅⚅⚅ The d666 RPG System ⚅⚅⚅A rules-light tabletop roleplaying game by Jeremy Deram."Simplicity is the ultimate sophistication."--Leonardo DaVinciTable of ContentsIntroductionCharacter CreationHow to PlayMagicMonstersTreasuresAppendicesA.Converting Classic MonstersB.Optional RulesOnline Character Generatorhttps://jderam.github.io/d666-rpg-system/html/chargen.html?level=1&name=autoEdit query parameters in the url as follows:Updatelevelto generate a character of a higher level, e.g.level=5Updatenameto modify how name is generatedname=autowill generate a random nameUse any other value to populate a specific name of your choice, e.g.name=SteveRemove thenamequery parameter to leave name blank, so you can think about it and write one in later. Let's face it, naming is hard.LicenseThis game is released under theCC BY-SA 4.0license.⚀ ⚁ ⚂ ⚃ ⚄ ⚅
Package: d6tcollect
Much like websites, this library collects anonymous usage statistics.It ONLY collects import and function call events. It does NOT collect any of your data.Example:{'profile': 'prod', 'package': 'd6tmodule', 'module': 'd6tmodule.utils', 'classModule': 'd6tmodule.utils.MyClass', 'class': 'MyClass', 'function': 'MyClass0.myfunction_1', 'functionModule': 'd6tmodule.utils.MyClass.myfunction_1', 'event': 'call', 'params': {'args': 1, 'kwargs': 'another'}}For privacy notice seehttps://www.databolt.tech/index-terms.html#privacy