Saravanan666/pypi-org huggingface dataset:
Package: demoshell
demoshellis a simplified shell for live demonstrations. It always
shows the command prompt at the top of the screen and pushes command
output down instead of letting iscroll up.Huh?POSIX shells print their output in such a way that it scrolls up and
off the top of the screen because they are using tty semantics, which
are based on hardware that used to literally print everything on a
roll of paper that moved up through the machine and over the top.It’s the 21st century. We don’t use paper-based terminals any
more. While continuing to pretend we do is fine for day-to-day work,
when we are giving live presentations it is not ideal because the most
interesting thing you are doing is probably at the bottom of your
screen during a live demo. That is the hardest part of the screen for
people at the back of the room to see, because it is often blocked by
other people’s heads.demoshellavoids this problem by always keeping the command prompt
at the top of the screen and showing the output of commands below,
pushing older commands off of the bottom of the screen to make space
for newer text.Using demoshellInstall the shell withpip3(it works best under Python 3):$ pip3 install demoshellRundemoshell:$ demoshellRun any shell command at the prompt:$ ls

ls
AUTHORS
ChangeLog
LICENSE
README.rst
demoshell
demoshell.egg-info
dist
requirements.txt
setup.cfg
setup.py
testUseexitorCtrl-Dto leave the shell.Useclearto clear the screen.Config File for DemoShell are in following location:If running on Mac OS:
~/Library/Application Support/DemoShell/demoshell.iniIf running on Linux:
~/.local/share/DemoShell/demoshell.iniIf running on Windows:
C:Documents and Settings<User>Application DataLocal SettingsDoug HellmanDemoShelldemoshell.ini
OR:
C:Documents and Settings<User>Application DataDoug HellmanDemoShelldemoshell.iniTo add Aliases:
Open config file in a text editor
Edit “Aliases” section as per example below.
alias = alias command. It may look something like below.[Aliases]
ll = ls -laResourcesGitHub:https://github.com/dhellmann/demoshellBugs:https://github.com/dhellmann/demoshell/issuesDocumentation:Help wanted!
Package: demostat
DemostatStatusseite für als nächstes anstehende Demonstrationen in Erfurt.Die Software steckt noch in der tiefsten Anfangsphase und ist extrem unvollständig! (Das hält hier aber keinen davon ab, sie trotzdem zu nutzen :P)Wie, gestern war wieder eine Demo? Warum weiß ich davon nix?Leider ist diese Frage hier viel zu oft vorgekommen. Das wollen wir ändern und habe diese Website geschaffen. Auf einem Blick alle Demos der nächsten vier Wochen. Dazu gleich eine Übersichtskarte, wo sich getroffen wird und Links zu offiziellen Aufrufen, Livetickern und anderweitiges Material.Findet die Demo erst in weiter Zukunft statt oder ist schon lange vorbei? Dann lohnt sich ein Blick in das Archiv. Sortiert nach Jahr, Monat und Tag und (blad) filterbar.Wer steckt hinter der Demo? Auch das lässt sich ganz einfach herausfinden. Zu jeder Organisation gibt es eine kleine Zusammenfassung mit Links zur Homepage.InhalteStartseiteAlle Demos der nächsten vier Wochen aufgelistet nach Datum.ArchivAlle Demos, die eingetragen wurden, sortiert nach Jahr, Monat und Tag.DemoJeder Demo wir ein Ort und eine Organisation zugeordnet. Neben Name und kurzer Beschreibung ist noch eine Linkliste vorhanden, bei der z.B. auf den offiziellen Aufruf oder Social-Media Kanäle verwiesen werden können.OrganisationJede Organisation hat einen Namen, eine kurze Beschreibung und einen Link zur Homepage.Dokumentation & Installationhttps://docs.demostat.deSelber bauenpip3 install sphinx
cd docs/
make htmlDie fertigen HTML sind dann unterdocs/_build/html/verfügbar.LizenzDiese Software hatnochkeine Lizenz. Das bedeutet, dass das deutsche Urherberrecht gilt.Wir arbeiten fleißig daran, dass schnellstmöglich eine passende Lizenz gefunden und diese Projekt darunter veröffentlicht wird.
Package: demosys-py
demosys-pyA Python 3 cross platform OpenGL 3.3+ core framework based onModernGLOriginally made for for non-interactive real time graphics combined with music
("real time music videos", seedemoscene)
but can of course be used almost any purpose.Made for people who enjoy playing around with modern OpenGL without spending lots of
time creating all the tooling to get things up and running. UsingModernGLalso makes working with OpenGL a breeze accomplishing a lot with very few lines of code.A high priority for this project is also to help improvingModernGL.DocumentationModernGL DocumentationExamplesEffect Templatesdemosys-py on Githubdemosys-py on PyPiDiscord ServerCreating a project with a spinning cube can be done in less than a minute.pipinstalldemosys-py
demosys-admincreateprojectmyproject
demosys-admincreateeffectmyproject/cubeNow run the effectpythonmanage.pyruneffectmyproject.cubeFeaturesA simple effect system based on python packagesSupports most modern OpenGL features throughModernGLResource loading system for shader programs, textures, scenes and misc dataLoading GLTF 2.0 and wavefront obj files/scenesSupports the rocket sync-tracker system to create interesting keyframe data (Usingpyrocket)Runtime re-loading shader programs (press R)Supports headless rendering and various visible windows such as pyGLFW, PyQt5, PySDL2 and Pyglet.Management commands for creting new projects and effects including the ability to make custom commandsA highly pluggable framework with customizable timers, resource loaders, timelines and moreA geometry module for quick creation of common mesh/VAO typesExperimental audio playback supportRequirementsIn order to use the framework it's an advantage to know the following:Basic or intermediate PythonBasic glslBasic matrix mathIf you are missing any of these requirements the framework can definitely be used to learn. 0 lines of code are needed to generate a project with a spinning cube and you can star poking at things.Local DevelopmentInstalling the project in development mode (in a virtualenv):pythonsetup.pydevelop# PyQt5 doesn't support develop and have to manually be force-reinstalled installed afterpipinstall-IPyQt5Running tests:# All testspipinstall-rrequirements-test.txt
pythonmanage.pytest# Single tests modulepythonmanage.pytesttests/test_effect.pyBuilding docs:pipinstall-rdocs/requirements.txt
pythonsetup.pybuild_sphinxContributorsEinar ForselvArttu TamminenbinaryfDependenciesModernGLPyOpenGL replacementPIL/Pillowfor texture loadingPyrrrfor math (uses numpy)PyQt5is default for window/context creation (works out of the box on most platforms)Optional:pyGLFWfor window/context creationpygletfor window/context creation (Does not work on OS X)pygameusing the mixer module for musicpython-vlcfor audio playbackMentionsAlso consider supportingRead the Docsby becoming a
gold member though a one time ($5 or more) donation for an ad-free experience.
Package: demotivate
demotivateA simple script to print random demotivations.Inspired by depression and the simple scriptmotivate, which was inspired byforture, of which you have ill.But seriously,don't let the intrusive thoughts win, if you or someone you know is experiencing suicidal thoughts or
a crisis, please reach out immediately to your local crisis helpline orfind a helpline.FeaturesSupportsbash,zshand probably others, but I couldn't be bothered to test.import demotivate; demotivate.demotivate()will give you some sad text, or something.A bunch of quotes, add more, or don't.Getting StartedInstall$pipinstalldemotivateRun$demotivateLife. What a fucking nighmare.Profit?LicenseLicensed underDO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE v2. IDGAF :smiley_cat:
Package: demo-ue4-docker
Unreal Engine 4 Docker ContainersContinuous Integration•Cloud Rendering•UE4-Powered MicroservicesLooking for a place to start? Check out theUnreal Containers community hubfor implementation-agnostic information on using the Unreal Engine inside Docker containers, and then head to thecomprehensive ue4-docker documentationto view details specific to using the ue4-docker project.The ue4-docker Python package contains a set of Dockerfiles and accompanying build infrastructure that allows you to build Docker images for Epic Games'Unreal Engine 4. The images also incorporate the infrastructure fromue4cli,conan-ue4cli, andue4-ci-helpersto facilitate a wide variety of use cases.Key features include:Unreal Engine 4.20.0 and newer is supported.Both Windows containers and Linux containers are supported.Building and packaging UE4 projects is supported.Running automation tests is supported.Running built UE4 projects with offscreen rendering is supported via NVIDIA Docker under Linux.Resources:Documentation:https://docs.adamrehn.com/ue4-docker/GitHub repository:https://github.com/adamrehn/ue4-dockerPackage on PyPI:https://pypi.org/project/ue4-docker/Related articles:https://adamrehn.com/articles/tag/Unreal%20Engine/Unreal Containers community hub:https://unrealcontainers.com/Development channel on the Unreal Containers Community Discord server:https://discord.gg/46CJg9fyJ9ContributingSee the fileCONTRIBUTING.mdfor guidelines on how to contribute to the development of ue4-docker.LegalCopyright © 2018 - 2021, Adam Rehn. Licensed under the MIT License, see the fileLICENSEfor details.Unreal and its logo are Epic Games' trademarks or registered trademarks in the US and elsewhere.Docker and the Docker logo are trademarks or registered trademarks of Docker in the United States and other countries.
Package: demo-webapp
This application is written in Python and based onFlask. The template usesBootstrap framework. It only show some
content fordemo purpose.In its initial release, it is mainly a wrapper of a
Single-page-Application (SPA) stored on ExoscaleSimple Object Storage
(SOS)but can evolve
differently in the future.From the release of August 1st, it fetch a random advice fromadviceslip.com Open API.
Package: demo-widgets
demo_widgets[![Build Status](https://travis-ci.org/Training 4 Developers LLC/demo_widgets.svg?branch=master)](https://travis-ci.org/Training4 Developers LLC/demo_widgets)
[![codecov](https://codecov.io/gh/Training 4 Developers LLC/demo_widgets/branch/master/graph/badge.svg)](https://codecov.io/gh/Training4 Developers LLC/demo_widgets)A Custom Jupyter Widget LibraryInstallationYou can install usingpip:pipinstalldemo_widgetsIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]demo_widgetsDevelopment InstallationCreate a dev environment:condacreate-ndemo_widgets-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivatedemo_widgets-devInstall the python. This will also build the TS package.pipinstall-e".[test, examples]"When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
yarn run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py demo_widgets
jupyter nbextension enable --sys-prefix --py demo_widgetsNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript:If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
terminals to watch for changes in the extension's source and automatically rebuild the widget.# Watch the source directory in one terminal, automatically rebuilding when neededyarnrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.Updating the versionTo update the version, install tbump and use it to bump the version.
By default it will also create a tag.pipinstalltbump
tbump<new-version>
Package: dempy
Simon Filhol, October 2017InstallationpipinstalldempyWARNING: Use at your own risk.ObjectiveThis package is a collection of useful tool to work with geospatial type
of data within Python. Tools for working with rasters, shapefile,
pointcloud data, and more advanced dem analysis tool such as fourier
decomposition.Some of the tools included are:geoTIFF importDEM detrend with linear planeDEM filtering (FFT or Convolution)Generating random DEM with diamondSquare (implemented in Cython)Derive morphometric dataplotting DEMderive Blowing snow terrain parameter:Tabler’s drift profile (Tabler, R.D., 1975. Predicting Profiles of
Snowdrifts in Topographic Catchments. InProceedings of the 43rd
Annual Western Snow Conference. San Diego, CA, pp. 87–97.)Winstral Sx (Winstral, A., Elder, K. & Davis, R.E., 2002. Spatial
Snow Modeling of Wind-Redistributed Snow Using Terrain-Based
Parameters.Journal of Hydrometeorology, 3(5), pp.524–538.)RequirementsRequires a few libraries such as laspy, gdal, cython, geopandas …
Package: demquery
demqueryWrapper around rasterio to query points on a Digital Elevation Model.FeaturesUse multiple raster files without having to merge them into a new fileQuery many points at onceOptional 2D interpolation (linear, cubic, or quintic)Reasonably performant by reading the minimal data required from rasterInstallI recommend first installing dependencies with Conda, then installing demquery
itself with pip.conda install gdal rasterio numpy scipy -c conda-forgepip install demqueryCLI Script> demquery --help
Usage: demquery [OPTIONS] FEATURES...

  Assign elevations to GeoJSON

Options:
  -d, --dem PATH          Paths to DEM files.  [required]
  -g, --dem-glob TEXT     Glob expression for DEM paths if folder is provided.
  -b, --band INTEGER      Band of rasters to use  [default: 1]
  -i, --interp-kind TEXT  either None, "linear", "cubic", or "quintic". None
                          will do no interpolation and choose the value in the
                          DEM closest to the provided point. linear creates a
                          3x3 grid and runs linear interpolation; cubic
                          creates a 5x5 grid and runs cubic interpolation;
                          quintic creates a 7x7 grid and runs quintic
                          interpolation.
  --help                  Show this message and exit.echo\'{"type":"Feature","properties":{"name": "Glacier Peak"},"geometry":{"type":"Point","coordinates":[-121.2436843,48.0163834]}}'\|demquery-d/path/to/dem/filesOutputs:{"type":"FeatureCollection","features":[{"type":"Feature","geometry":{"type":"Point","coordinates":[-121.243684,48.016383,1431.5755615234375]},"properties":{"name":"Glacier Peak"}}]}DocumentationfromdemqueryimportQuerydem_paths=['dem1.tif','dem2.tif']query=Query(dem_paths)# Points must be in longitude, latitude order!# These points are in Manhattan, not Antarcticapoints=[(-73.985564,40.757965),(-73.968520,40.778912)]elevations=query.query_points(points,interp_kind='linear')Data DownloadFor a great visual tool to download worldwide SRTM data, check out these sites:30m resolution:http://dwtkns.com/srtm30m/90m resolution:http://dwtkns.com/srtm/ReleasingTo upload a new release to PyPIpythonsetup.pysdist
twineuploaddist/demquery-0.3.0.tar.gzChangelog[0.3.1] - 2020-08-19No changes: try to get conda-forge package to work correctly[0.3.0] - 2020-01-28Add CLI script[0.2.1] - 2019-12-04Include requirements.txt and requirements_dev.txt in manifest bundle[0.2.0] - 2019-12-02Fix virtual raster issues.[0.1.0] - 2019-11-27Initial release on PyPI
Package: demregpy
DEMREG-PYThis is a python implementation of Hannah & Kontar (2012)'s regularised inversion method. The code is tightly based on the IDL mapping version of the DEM reg-inv code found athttps://github.com/ianan/demregin addition, the code enforces a positivity constraint on the DEM (hence pos)The philosophy was to produce as similar a piece of software as the original version and as such, this python version has been shown to recover the same DEM as the IDL version (to within approximately 4 significant figures). It is likely this philosophy has lead to performance hits and I plan to go back and address the more hacky parts of the code at a later date.To calculate a DEM you first need:Data and associated error for a range of channels: e.g. in dn/s/px or counts/sTemperature dependent channel response: How sensitive are your channels to plasma of each temperature?To use: simply call dn2dem_pos with either a single pixel, 1d slice or 2d map of DN values as a function of filter (and associated error on DN), an array of temperatures over which to perform the DEM analysis and a temperature response for those filters.For large datasets, beyond 200 pixels, the code switches to a parallel execution providing significant speedups.
Package: demset
This module provides an implementation of the *set* and *frozenset* types whichwere introduced in Python 2.4, but which work under older versions (Python 2.2or 2.3).  It is a standalone module written entirely in Python, and can easilybe used as a substitute for the built-in types when runing under older Pythons.Care was taken to try to provide nearly 100% compatibility with Python 2.4'sbehavior.
Package: demultipart
ContentsOverviewUsageTODOUnpack a MIME message into a directory of files.OverviewMIME messages can be useful as cheap archives: unlike zipfile/tarfile standard
modules, email.mime.multipart does not require a full-functional file to
create an archive. MIME messages lack the ability to seek to a given file
efficiently (much like tar), they have no standard compression (but it can be
handled independently anyway), and lack a standard tool to extract them.Inspired from python’semail examples.UsageExtract to directory$PWD/some$ demultipart /path/to/some.multipartAny number of multipart files can be specified, they will be extracted in
provided order in their own directories.Tries to prevent escaping extraction directory, but not tested on Windows.Skips input file if extraction directory already exists.TODOProvide compatibility with standard CLI archive manipulation tools (tar,
unzip…) so GUI can wrap this command too.Maybe add multipart creation support.
Package: demultiplex
Versatile NGS demultiplexer with the following features:Support for FASTA and FASTQ files.Support for gzip and bzip2 compressed files.Support for multiple reads per fragment, e.g., paired-end.Handles barcodes in the header and in the reads.Handles barcodes atunknownlocations in reads (e.g., PacBio or Nanopore
barcodes).Support for selection of part of a barcode.Allows for mismatches, insertions and deletions.Barcode guessing by frequency or fixed amount.Handles large numbers (over one million) of barcodes.Please seeReadTheDocsfor the latest documentation.
Package: demure-captcha
DEMURE_CAPTCHASimple liight-weight package for generate image and auidio captchaSUMMARYInstallpython3-mpipinstalldemure_captchaConsolepython3-mdemure_captcha--length10--useIntegersTrue--width300--height300--formatfile# Created files with audio and video>>>%AppData%\Local\Temp\0c7e4ddf-885b-4651-905e-b7efd758fc8d.png
>>>%AppData%\Local\Temp\0c7e4ddf-885b-4651-905e-b7efd758fc8d.mp3

python3-mdemure_captcha--length10--useIntegersTrue--width300--height300--formatjson# Base64 strings>>>{"mp3":"rewwgwrrrwggrwrg...","png":"wfweewggewegwg..."}Pakagefromdemure_captchaimportgeneratecaptcha=generate(width=300,height=300)captcha.image.show()print(captcha.save())print(captcha.save('tmp/captcha'))Example captcha
Package: demystify
DemystifyPen and paper puzzles like Sudoku, Futoshiki and Skyscrapers are hugely
popular. Solving such puzzles can be a trivial task for modern AI systems.
However, most AI systems solve problems using a form of backtracking, while
people try to avoid backtracking as much as possible. This means that
existing AI systems do not output explanations about their reasoning that
are meaningful to people.Demystify is a tool which allows puzzles to be expressed in a high-level
constraint programming language and uses MUSes to automatically produce
descriptions of steps in the puzzle solving.Install InstructionsInstall conjure (in conjure git checkout):git clone https://github.com/conjure-cp/conjure && cd conjure && make && make solvers && make installInstall some python packages:pip3 install python-sat z3-solver numpy sortedcontainersThen try:python3 demystify --eprime eprime/binairo.eprime --eprimeparam eprime/binairo-1.paramVisualizerDemystify also has a visual interface, which you can find in a separate repositoryhereImplemented PuzzlesBinairoFutoshikiSudokuJigsaw SudokuX-SudokuMiracle SudokuKakuroSkyscrapersStar BattleTents and TreesThermometersAll the models can be found in theeprime
directory. If you
are interested in a puzzle that is not implemented, there is a guide for
modelling your own puzzleshere.PublicationsUsing Small MUSes to Explain How to Solve Pen and Paper Puzzles
Package: dencam
OverviewThis repo contains control code for Mini DenCam, a polar bear maternal
den observation device.  The project is a collaboration between the
Conservation Technology Lab at the San Diego Zoo Wildlife Alliance and
Polar Bears International.HardwareThe target hardware is a Raspberry Pi 4 Model B single board computer
with a Picamera-style camera and the AdaFruit PiTFT screen (2.8"
resistive touch model with 4 GPIO-connected buttons)
attached. Typically several storage devices are connected via the USB
ports on the Pi (e.g. uSD card readers). This hardware is typically
integrated into a larger assembly that includes a weatherproof
enclosure, batteries, charger controller, and an external solar
panels.
Package: dendrify
Introducing dendrites to spiking neural networksAlthough neuronal dendrites play a crucial role in shaping how individual
neurons process synaptic information, their contribution to network-level
functions has remained largely unexplored. Current spiking neural networks
(SNNs) often oversimplify dendritic properties or overlook their essential
functions. On the other hand, circuit models with morphologically detailed
neuron representations are computationally intensive, making them impractical
for simulating large networks.In an effort to bridge this gap, we present Dendrify—a freely available,
open-source Python package that seamlessly integrates with theBrian 2 simulator. Dendrify,
through simple commands, automatically generates reduced compartmental neuron
models with simplified yet biologically relevant dendritic and synaptic
integrative properties. These models offer a well-rounded compromise between
flexibility, performance, and biological accuracy, enabling us to investigate
the impact of dendrites on network-level functions.If you use Dendrify for your published research, we kindly ask you to cite our article:Pagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the Dendrify framework
for incorporating dendrites to spiking neural networks.
Nature Communications, 14(1), 131.https://doi.org/10.1038/s41467-022-35747-8Documentation for Dendrify can be found athttps://dendrify.readthedocs.io/en/latest/The project presentation for the INCF/OCNS Software Working Group is availableon google drive.