Saravanan666/pypi-org huggingface dataset:
Package: dpline
_____||____|(_)_______/_|_\|||_\/_\|(_|||_)||||||__/\____|__/|_|_|_||_|\___||_|________________________________________________________AboutCLI tool to remove duplicate lines from text file.Installation$tarxvfdpline-<version>.tar.gz$cddpline-<version>$pythonsetup.pyinstallorviapip:$pipinstalldpline--upgradeuninstall:$pipuninstalldplineCommand Line Tool UsageUsage:dpline[OPTION]<file>[--ignore-blank,[--case-ins],[--number]]dplineistooltoremoveduplicatelinesfromfileOptionalarguments:-h,--helpPrintthishelpmessageandexit-v,--versionPrintprogramversionandexit-d,--displayDisplayremovedlines-p,--previewPreviewduplicatelinesbeforeremoval--ignore-blankIgnoreblanklinesfromremove--case-insMatchingupper-andlowercaseletters--numberViewduplicatelinenumberCopyrightCopyright © Dimitris Zlatanidis
Package: dplot
Installationpip3 install dplotUsage examplescrysoloutputdplot"out/crysol.png"--other"in/crysol.int"\--headers"Q,Intensity,Scattering (in vacuo),Scattering (excluded volume),Convex border layer"\--skip1-x"Q"--logordplot"out/crysol.png"--crysol"in/crysol.int"debyeroutputdplot"out/debyer.png"--other"in/debyer.dat"--skip2--headers"Q,Intensity"-x"Q"--logordplot"out/debyer.png"--debyer"in/debyer.dat"psaxsoutputdplot"out/psaxs.png"--other"in/psaxs.tsv"--headers"Q,Intensity"-x"Q"--logordplot"out/psaxs.png"--psaxs"in/psaxs.tsv"Multiple sources./dplot.py"out/combined.png"--psaxs"in/psaxs.tsv"--crysol"in/crysol.int"--debyer"in/debyer.dat"Mixing manual plots with presets works as well./dplot.py"out/combined.png"--other"in/psaxs.tsv"--headers"Q,Intensity"-x"Q"--crysol"in/crysol.int"--debyer"in/debyer.dat"WindowProvide-instead of a file name to open a windowdplot---psaxs"in/psaxs.tsv"Possible optionsUnfortunately--helpwon't work.Use--verboseto see possible switches.DevelopmentDependenciespandasseabornmatplotlibPython 3.6 - 3.9Withpyenvorcondapython3 -m pip install pandas seaborn matplotlibClonegitclonegit@gitlab.com:cbjh/plotting/dplot.gitInstallcddplot
python3-mpipinstall-e.Files indplotfolder can be edited without need for re-installation.Re-installation still needs to be done if the folder is moved somewhere elsepython3-mpipuninstalldplotcddplot
python3-mpipinstall-e.Testssudoaptinstallimagemagickpython3-mpipinstall-e.
./test.shKeep in mind that this overrides your locally installeddplot.PackagingPrerequisitessudoapt-getinstallpython3-venv
python3-mpipinstall--upgradebuildtwinePublishingReviewsetup.py, in particular change the version.python3-mbuild
python3-mtwineupload--repositorypypidist/*(it asks for PyPI credentials)
Package: dploy
# DployDploy is a tool for creating symbolic links similarly to [GNU
Stow](https://www.gnu.org/software/stow/). It is provided as a CLI tool and
Python 3.3+ module and supports Windows, Linux, and OSX.Dploy’s commandstowcreates symbolic links to the contents of source
directories or packages in a specified destination directory. Repeating thestowcommand with the same arguments will confirm that the contents of the
package have been symbolically linked.Dploy’s commandunstowremoves symbolic links that resulted fromstowcommands. Repeating theunstowcommand with the same arguments will confirm
that the links to stowed packages have been removed.## Installation
* Latest Release:pip install dploy* Development Version:pip install git+https://github.com/arecarn/dploy.git## Basic CLI Usage
*dploy stow <source-directory>… <destination-directory>*dploy unstow <source-directory>… <destination-directory>*dploy –help## Rational
Dploy started out as simple Python script to create symbolic links to my
dotfiles for Windows, Mac, and Linux. Over time I keep improving and tweaking my
script to suit my needs, but I was running into a problem.  Keeping all the
files I wanted to link in a config file was becoming a real pain in the neck.I started looking for another solution to solve my problem, and found many
alternatives but none of them seemed to be a good fit. The solution that seemed
the most promising was using GNU Stow. It seemed like the most simple elegant
solution to the problem. The only issue was that it didn’t support Windows.Then I thought to myself, why can’t I just create my own version of Stow that
work on Windows, Linux and OSX. So after that my I started morphing
simple python script into what would become Dploy and learned a lot more about
python in the process.## How does it compare with GNU Stow?
Below are just a few few major points of comparison between GNU stow and Dploy.Like GNU Stow Dploy runs in two passes. First by collecting the actions
required to complete the command and verifying that the command can
completed without any issues. If no issues are detected then the second
pass executes these actions are execute to complete the command. Otherwise
Dploy will exit and indicate why the command can not be completed. This way a
stow or unstow operation is atomic and never partially done.Like Stow, Dploy supports tree folding and tree unfolding.Unlike Stow, Dploy requires an explicit source(s) and a destination
directory.Unlike Stow, Dploy does not have any concept of ownership, but will only
operate on symbolic links and the creation or removal of directories for these
symbolic links.
Package: dploy-kickstart
dploy-kickstartExpose your Python functions via an HTTP API.dploy-kickstartis designed with a focus of making deployment of machine learning models as easy as possible with as clean of a contract as possible. This means that you will find configurations options that are less relevant outside of this domain.Installationpip install dploy_kickstartWhen installed it provides akickstartexecutable. Have a look at the help functionality:kickstart --help.Usagedploy_kickstartis a helper utility that can expose your Python functions as HTTP endpoints. Based on comment annotations it wraps your chosen functions to be served as API endpoints. It is meant to be used via thekickstartCLI, hence, it would generally not be used programmatically.Let's say you have ascript.pywith@dploycomment annotations:# @dploy endpoint echodefecho_func(payload):returnpayloadServe this function with:kickstartserve-escript.pyLet's try to call the endpoint viacurl.$ curl -d '{"foo":"bar"}' -H "Content-Type: application/json" -X POST http://localhost:8080/echo/

{"foo":"bar"}AnnotationsFollowing annotation options are currently available.# @dploy endpoint {endpoint_path}Will expose the annotated function on path {endpoint_path} with trailing slash (and redirect without trailing slash).# @dploy request_method {method}Specify allowed request method.default:postavailable: any method supported by Flask# @dploy json_to_kwargsWill map json payload's keys to function kwargs if supplied.Endpoint utilitiesFollowing paths are generated automatically:GET /healthz: that always returns a 200GET /openapi.yaml: autogenerated OpenAPI Spec (WIP)Current dependency treeAim is to keep this as small as possible to not clutter the user space.$ poetry show --no-dev --tree

apispec 3.3.0 A pluggable API specification generator. Currently supports the OpenAPI Specification (f.k.a. the Swagger specification).
click 7.1.2 Composable command line interface toolkit
flask 1.1.2 A simple framework for building complex web applications.
├── click >=5.1
├── itsdangerous >=0.24
├── jinja2 >=2.10.1
│   └── markupsafe >=0.23 
└── werkzeug >=0.15
paste 3.4.0 Tools for using a Web Server Gateway Interface stack
└── six >=1.4.0
waitress 1.4.3 Waitress WSGI serverPackage namingIsn't using underscores in package names discouraged? The reasoning behind thedploy_kickstartnaming is that this utility is not meant to be used programmatically. Its meant to be used as a sidecar to user code that should be unaware of its existence, therefore, a longer name has a lower chance of clashing with user code. Yes, this is quite subjective.ContributingQuestions, ideas, suggestions? Feel free to create issue or PR.
Package: dpm
Introductiondpm (data package manager) is a command line tool and python library for
working withData Packagesand interacting with data repositories likethe Data Hub.For more information visit the documentation at:http://readthedocs.org/docs/dpm/InstallationSee doc/install.rst or the online documentation.LicenseSee doc/license.rstFor DevelopersBuilding the documentation.You need sphinx (python-sphinx) to do this. Now you can build the docs:python setup.py build_sphinxRunning tests. We suggest you use nose:nosetests dpm/tests/To exclude tests depend on access to the internet:nosetests -a \!__external__ dpm/tests
Package: dpm360-lightsaber
Welcome to lightsaberOverviewlightsaberis designed ground up to provide asimple,composible, and unified
model training framework. It has been designed based on state-of-the-art open source
tools and extended to support the common use cases for disease progression modeling (DPM).lightsaberprovides four main components:Data ingestion modulesModel TrainersDPM problem specific model evaluationModel tracking and support for post-hoc model evaluation.Each of these components are designed such that a user should be able to pick some
or all of the modules and embed these seamlessly with their current workflow.
Futhermore, when used in the recommended manner,lightsaberprovides abatteries includedapproach allowing the modeler to focus only on developing the logic of their model and
lettinglightsaberhandle the rest.Currently, we support the following DPM use cases:classification: one or multi-classAlso, we support and extend the following frameworks:scikit-learncompliant models: for classical modelspytorchcompliant models: for general purpose models, including deep learning models.To summarize, it is thus anopinionatedtake on how DPM should be conducted providing with a
unified core to abstract and standardize out the engineering, evaluation, model training, and model tracking
to support:(a) reproducible research, (b) accelarate model development, and (c) standardize model deployment.Installation InstructionsLightsaberis installable as a python package.It can be installed usingcondaas:conda install -c conda-forge dpm360-lightsaberor frompypias:pip install dpm360-lightsaberIt can also be installed from source using pip as follows:barebones install ofLightsaber:pip install .with doc support:pip install .[doc]with time-to-event modeling (T2E) support:pip install .[t2e]full install with all components:pip install .[full]
Package: dpmdtools
DPToolsDeepPotentialTools(DPTools) provides a command-line interface and python library to simplify training and deployingDeePMD-kitmachine learning potentials (MLPs), also known as ML force fields. The primary goal of DPTools is to condense workflows for training DP MLPs and running atomistic simulations withLAMMPSon HPC systems into a handful of intuitive CLI commands. It is intended for scientists with knowledge of quantum mechanics-basedab-initiosimulation methods who are interested in effortlessly transitioning to ML-based approaches to greatly increase computational throughput. It requires no prior experience with DeePMD-kit or LAMMPS software, only familiarity with the popularAtomic Simulation Environment (ASE)python package is needed.Main FeaturesSetup deepmd-kit training sets from VASP output or common ASE formatsTrain ensemble of DP modelsGenerate parity plots to assess accuracy of MLP energy and force predictionsIntelligently sample and select new training configurations from DPMD trajectoriesEasily setup and run different atomistic simulations in LAMMPS:Single point energy calculationsStructure geometry optimizationsStructure unit cell optimizationsMolecular dynamics (NVT and NPT ensembles)Equations of State and bulk moduli calculationsVibratrional/phonon modes using the finite differences approachOther common simulation methods available upon requestSupports Slurm job submission on HPC systemsSetup and run simulations on thousands of structures with a single commandDocumentationFor detailed descriptions on setting up and using DPTools, visit theofficial documentation.Quick InstallThe current stable version (1.0.1) of DPTools can be installed usingpipwith the following command:pip install dpmdtoolsTo verify that the installation was completed successfully, run the command:dptools --versionSupportIf you are having issues with DPTools, create an issuehere. For more assistance, new feature requests, or general inquiries, feel free to contact Ty attsours@ucdavis.edu.
Package: dpmenu
=====dpmenu=====Django app.Detailed documentation is in the "docs" directory.Quick start-----------1. Add "dpmenu" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [...'dpmenu',]2. Include the polls URLconf in your project urls.py like this::url(r'^polls/', include('polls.urls')),3. Run `python manage.py migrate` to create the dpmenu models.4. Start the development server and visit http://127.0.0.1:8000/admin/to create a poll (you'll need the Admin app enabled).5. Visit http://127.0.0.1:8000/polls/ to participate in the poll.
Package: dpmfa
DPMFAIf you run into any problems please use create a new issue at theupstream repository.Usage exampleFirst, clone the repository:$gitclone<url-of-this-repo>[Optional] Create a virtual environment for the project:$mkvirtualenvdpmfaInstall the required dependencies:$pipinstall-rrequirements.txtThen, run the example:$pythonexample/plots.pyYou should now have a directoryexample_output/with the results of the
example simulation.DevelopmentFirst, clone the repo:$gitclone<url-of-this-repo>[Optional] Create a virtual environment for the project:$mkvirtualenvdpmfaInstall the required dependencies:$pipinstall-rrequirements.txt-rrequirements-dev.txtInstall the git hooks with pre-commit:$pre-commitinstallRunning tests:$py.test
Package: dpmfa-simulator
Author: <a href="mailto:nikolaus.bornhoeft (et) empa.ch">Nikolaus Bornhoeft</a>Institution: Empa St. Gallen,  <a href="http://www.empa.ch/plugin/template/empa/*/6692/---/l=1">Environmental Risk Assessment and Management Group</a>The dpmfa framework supports the generation and use of dynamic probabilistic material flow models.It enables to model of time dynamic flow models on a period base.Incomplete knowledge about the true values of a system parameter about the absolute inflows to the system over time and therelative transfer coefficients for the flows between the system compartments is represented as Bayesian probability distribution.It is propagated to dependent model variables using Monte-Carlo simulation, while ensuring mass-balance in every element of the Monte-Carlo sample.The dpmfa_simulator package holds a simulation infrastructure providing a ready-to-use simulator class and a set of model components that need to be implemented and assembled to represent system specific knowledge.Also an example package is included that contains a small example model and a runner file to illustrate the use of the dpmfa simulator.We are currently preparing a publication about the development and application of the simulation framework. It will be linked as soon, as it is published.Its main aim is to provide means for environmental exporsure assessment modeling of anthropogenic pollutants. The possibilities and limitations of existing tools is discussed <a href="http://www.merlin.uzh.ch/publication/show/8351">here </a>.The dpmfa-framework was created within the <a href="http://www.marina-fp7.eu/">"Marina - Managing the Risk of Nanomaterials" </a> Framework of the European Union
Package: dpmix
dpmix is a library for understanding posterior distributions for
Dirichlet and heirarchical Dirichlet mixtures of normal distributions
represented by truncated stick breaking.RequirementsNumPySciPyCythonPyCUDAcyarmacyrandscikits.cudagpustatsmpi4pyInstallation and testingInstall viapython setup.py installTo test, run the scripts in the “test” subfolder.UsageCheck out the class docstrings for more info.MPIThe multigpu facilities are developed using MPI. Therefore,
using multiple machines is possible. However, note that the
machines must be configured the same way. (Python)Running the code on multiple machines requiresmpiexec:mpiexec -hostfile my_hosts -np 1 python tests/test_dpmix.py --gpu MPIWhere themy_hostsfile looks likehost1 slots=3
host2 slots=2I’m assuming here that the master instance of python is running on host1
and that host1 and host2 have 2 GPUs each. Note, an extra slot needs to be
reserved for the master on host1. Furthermore, we need to specify which
devices to use on each host. Thegpuargument in the class constructors
must be a dictionary likegpu={'host1': [0,1], 'host2': [0,1]}The keys must match the result of a call toos.uname()to get the
host string.
Package: dpmkit
dpmkitA simple tool for creating DPMKs.UsageFirst of all, you will need to find the downstream kernel. If you don't have the source tree, this tool is useless.Once you find the source tree, download it with dpmkit (replace the URL here with the one for your source tree):python3 -m dpmkit fetch-downstream https://github.com/lowendlibre/mt8168-linux-merged/archive/refs/tags/4.14.186-20230124.tar.gzOnce you fetch the downstream kernel, dpmkit will suggest a command to download the matching mainline version (referred to asoldmainline), which you will need to run.Then, download the latest mainline version:python3 -m dpmkit fetch-mainline https://cdn.kernel.org/pub/linux/kernel/v6.x/linux-6.4.3.tar.xzOnce you have downloaded all of the kernels needed, you can then create the patch:python3 -m dpmkit patchAnd finally, to apply the patch to mainline:python3 -m dpmkit applyYou will quickly noticea lotof changes that can't be applied. You will need to fix those manually.What is a DPMK?A downstream patched mainline kernel, or DPMK, is a mainline kernel that has downstream vendor patches applied to it.Why should I use a DPMK?Normally, you shouldn't. If your device has good enough mainline support, you should just use the mainline kernel.However, for devices which use an outdated kernel version for their downstream branch and do not have proper mainline support, creating a DPMK would be the easiest way to get the latest security updates.DPMKs are also a useful starting point for mainlining.Why don't you just use git to merge the kernel branches?Device vendors rarely publish their git branches, so this quickly becomes a painful task since you have to create commits with the correct parent commit to avoid conflicts.On the other hand, dpmkit is specifically designed for this task.
Package: dpmmlearn
dpmmlearn is a algorithms for Dirichlet Process Mixture Model.DependenciesThe required dependencies to use dpmmlearn are,scikit-learnnumpyscipyYou also need matplotlib, seaborn to run the demo and pytest to run the tests.installpipinstalldpmmlearnUSAGEWe have posted a usage example in the github’s demo folder.LicenseThis code is licensed under MIT License.Testpythonsetup.pytest
Package: dpmmpythonStreaming
DPMMSubClustersStreamingThis package is a Python wrapper for theDPMMSubClustersStreaming.jlJulia package.(for our paperSampling in Dirichlet Process Mixture Models for Clustering Streaming Data, AISTATS 2022.).InstallationInstall Julia from:https://julialang.org/downloads/platformAdd our DPMMSubClusterStreaming package from within a Julia terminal via Julia package manager:] add DPMMSubClustersStreamingAdd our dpmmpythonStreaming package in python: pip install dpmmpythonStreamingAdd Environment Variables:On Linux:Add to the "PATH" environment variable the path to the Julia executable (e.g., in .bashrc add: export PATH =$PATH:$HOME/julia/julia-1.6.0/bin).On Windows:Add to the "PATH" environment variable the path to the Julia executable (e.g., C:\Users<USER>\AppData\Local\Programs\Julia\Julia-1.6.0\bin).Install PyJulia from within a Python terminal:import julia;julia.install();Usage Example:from julia.api import Julia
jl = Julia(compiled_modules=False)
from dpmmpythonStreaming.dpmmwrapper import DPMMPython
from dpmmpythonStreaming.priors import niw
import numpy as np
data,gt = DPMMPython.generate_gaussian_data(10000, 2, 10, 100.0)
batch1 = data[:,0:5000]
batch2 = data[:,5000:]
prior = DPMMPython.create_prior(2, 0, 1, 1, 1)
model= DPMMPython.fit_init(batch1,100.0,prior = prior,verbose = True, burnout = 5, gt = None, epsilon = 0.0000001)
labels = DPMMPython.get_labels(model)
model = DPMMPython.fit_partial(model,1, 2, batch2)
labels = DPMMPython.get_labels(model)
print(labels)MiscFor any questions:dinari@post.bgu.ac.ilContributions, feature requests, suggestion etc.. are welcomed.If you use this code for your work, please cite the following:@inproceedings{dinari2022streaming,
  title={Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data},
  author={Dinari, Or and  Freifeld, Oren},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2022}
}
Package: dpmt
dpmdpm is a TUI frontend to MPDFeaturesThe feature set, at present, is somewhat limited. Right now, it can:browse your librarysearch for mediamanage playlist settingsshuffle your libraryview the current playlistLimitationsbrowsing works only with a 3-level directory structure (i.e. artist -> album -> songs)managing playlist settings does not include managing the queueIssuesif left idle for too long, the app loses connection to MPDInstallationTODORequirementsdpm requires a working installation of MPD.For now, if your MPD setup has a different hostname and port number, change it inclient.py. The default hostname is"localhost"and port number is6600.DependenciesRich (install withpip install rich)UsageNavigationCtrl+c is used to navigate backwards. For example, when browsing the library, inputtingCtrl+cwill take you to the parent directory.Main MenuSimply input the index corresponding to the desired task. For example, if browsing the library is at index 1, input a 1.Browsing the libraryThe app will present a list of folders. To navigate, input the letter 'v' followed by the corresponding index. For example, if the artist "Kraftwerk" is at index 20, input "v 20" to view its subdirectories.Adding to the queue followes the same procedure. To add an artist, album or track to the queue, input 'a' followed by the corresponding index. For example, if the album '13' is at index 1, input "a 20" to add it to the queue.SearchingInput a search query. The app will return a table of search results. To add something to the queue, input 'a' followed by the corresponding index, as detailed above.Playlist OptionsSimple input the index corresponding to the desired operation. For example, if shuffling the playlist is at index 6, simply input a 6.ScreenshotsMain MenuBrowsing the LibrarySearching the LibraryPlaylist OptionsCurrently Playing
Package: dpnewman
dpnewmanDP-Newman Graph Generative ModelAuthors & ContributorsShunsuke Aihara -http://argmax.jpLicenseThis code is licensed under MIT License.ReferencesS. Kuwata, N. Ueda and T. Yamada, Graph Clustering based on the Nonparametric Bayes Model (in Japanese),Technical report of IEICE. PRMU, 2007.