Saravanan666/pypi-org huggingface dataset:
Package: dpgen
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy modelsDP-GEN (Deep Potential GENerator) is a software written in Python, delicately designed to generate a deep learning based model of interatomic potential energy and force field. DP-GEN is dependent onDeePMD-kit. With highly scalable interface with common softwares for molecular simulation, DP-GEN is capable to  automatically prepare scripts and maintain job queues on HPC machines (High Performance Cluster) and analyze results.If you use this software in any publication, please cite:Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, and Weinan E, DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models, Computer Physics Communications, 2020, 253, 107206.Highlighted featuresAccurate and efficient: DP-GEN is capable to sample more than tens of million structures and select only a few for first principles calculation. DP-GEN will finally obtain a uniformly accurate model.User-friendly and automatic: Users may install and run DP-GEN easily. Once successfully running, DP-GEN can dispatch and handle all jobs on HPCs, and thus there's no need for any personal effort.Highly scalable: With modularized code structures, users and developers can easily extend DP-GEN for their most relevant needs. DP-GEN currently supports for HPC systems (Slurm,PBS, LSF and cloud machines), Deep Potential interface with DeePMD-kit, MD interface withLAMMPS,Gromacs,AMBER, Calypso andab-initiocalculation interface withVASP,PWSCF,CP2K,SIESTA,Gaussian, Abacus,PWmat, etc. We're sincerely welcome and embraced to users' contributions, with more possibilities and cases to use DP-GEN.Download and InstallDP-GEN only supports Python 3.9 and above. You can use one of the following methods to install DP-GEN:Install via pip:pip install dpgenInstall via conda: `conda install -c conda-forge dpgen``Install from source code:git clone https://github.com/deepmodeling/dpgen && pip install ./dpgenTo test if the installation is successful, you may executedpgen-hWorkflows and usageDP-GEN contains the following workflows:dpgen run: Main process of Deep Potential Generator.Init: Generating initial data.dpgen init_bulk: Generating initial data for bulk systems.dpgen init_surf: Generating initial data for surface systems.dpgen init_reaction: Generating initial data for reactive systems.dpgen simplify: Reducing the amount of existing dataset.dpgen autotest: Autotest for Deep Potential.For detailed usage and parameters, readDP-GEN documentation.Tutorials and examplesTutorials: basic tutorials for DP-GEN.Examples: input files inJSONformat.Publications: Published research articles using DP-GEN.User guide: frequently asked questions listed in troubleshooting.LicenseThe project dpgen is licensed underGNU LGPLv3.0.ContributingDP-GEN is maintained byDeepModeling's developers. Contributors are always welcome.
Package: dpgui
DP-GUIA web-based GUI for user inputs.For usersWebsiteVisit the app fromhttps://deepmodeling.org/dpgui.Python packageTo have arguments from other installed Python packages, install the DP-GUI Python package:pipinstalldpgui
dpguiFor developersVue appInstall the app using yarn:yarninstallPreview the app:yarnserveBuild the app:yarnbuildAdd arguments from other packages to DP-GUIAdd entry pointsdpguiinpyproject.toml:[project.entry-points."dpgui"]"DPDispatcher Machine"="dpdispatcher.arginfo:machine_dargs""DPDispatcher Resources"="dpdispatcher.arginfo:resources_dargs""DPDispatcher Task"="dpdispatcher.arginfo:task_dargs"where the entry points value (such asmachine_dargs) should be a function that returnsdargs.Argumentorlist[dargs.Argument].
Package: dphoyes.libssh2
== Depenendencies ==Python (2.4 or later)libssh2 (1.2.2 or later)zlib (1.2.x or later)== Installation ==python setup.py build
python setup.py install (as root)== License ==This program is free software: you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License as published by the Free
Software Foundation.This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.== Authors ==Sebastian Noack <sebastian.noack@gmail.com>
Vincent Jaulin <vincent.jaulin@keyphrene.com>
Package: dpi-maps
DPI MapsRetrieve maps and species reports from the Department of Prime IndustriesSetuppip install dpi-mapsTo login you will need to provide either an environment variable, or use the flags.Environment variables which can be set to login:DPI_USERNAMEandDPI_PASSWORD.Alternatively, use--usernameand--passwordflag for each invocation of the command.By default, all maps and report are downloaded to/tmp/dpi. Use--directoryorDPI_DIRECTORYto override this behaviour.UsageTo view the available commands rundpi-mapsRetrieve all mapsdpi-maps scrapewill retrieve all the most recent maps available.This will get bothkmzandpdf. Use--map-type=pdfor--map-type=kmzto get only that type.Get the latest species reportdpi-maps reportswill collect the latest species report.
Package: dpimport_test_2
DPimport: A command line glob importer for DPdashDPimport is a command line tool for importing files into DPdash using a
simpleglobexpression.Table of contentsInstallationConfigurationUsageMongoDBInstallationJust usepippipinstallhttps://github.com/AMP-SCZ/dpimport.gitConfigurationDPimport requires a configuration file in YAML format, passed as a command
line argument with-c|--config, for establishing a MongoDB database
connection. You will find an example configuration file in theexamplesdirectory within this repository.UsageThe main command line tool isimport.py. You can use this tool to import any
DPdash-compatible CSV files or metadata files using the direct path to a file
or a glob expression (use single quotes to avoid shell expansion)import.py-cconfig.yml'/PHOENIX/GENERAL/STUDY_A/SUB_001/DATA_TYPE/processed/*.csv'You may also now use the**recursive glob expression, for example:import.py-cconfig.yml'/PHOENIX/**/*.csv'orimport.py-cconfig.yml'/PHOENIX/GENERAL/**/processed/*.csv'and so on.Details about the pattern /**/directory/*/*.csvmatches onlydirectory/[subdirectory]/[filename].csv. With arecursive glob pattern,directory/**/*.csvwill additionally match:directory/[filename].csv(no subdirectory)directory/[subdirectory1]/[subdirectory2]/[filename].csv(sub-subdirectory)and so on, for as many levels deep as exist in the directory tree.MongoDBThis tool requires MongoDB to be running and accessible with the credentials you
supply in theconfig.ymlfile. For tips on MongoDB as it is used in DPdash and DPimport,
seethe DPdash wiki.
Package: dpiper
PiperPiperis a python package to help simplify data wrangling tasks withpandas. It provides a set of wrapper functions or
'verbs' that provide a simpler interface to standard Pandas functions.Piper functions accept and receive pandas dataframe objects. They can be used as
standalone functions but are more powerful when used together in aJupyternotebook cell to form a data pipeline.Instead of the traditional the 'dot notation' or method calling technique from
within an object, piper receives and passes on the dataframe object between
functions using the piper magic command link operator (that is'>>') within
a cell. So, in traditional pandas, to see the first 5 rows of a dataframe:df.head()The equivalent in piper would be:%%piperdf>>head()InstallationTo install the package, enter the following:pip install dpiperDocumentationPiper API documentation available atreadthedocsQuick startExample #1A dataframe consisting of two columns A and B.importpandasaspdimportnumpyasnpnp.random.seed(42)df=pd.DataFrame({'A':np.random.randint(10,1000,10),'B':np.random.randint(10,1000,10)})df.head()AB0112476144522428703403280468411697Piper equivalentfrompiper.defaultsimport*%%piperdf>>assign(C=lambdax:x.A+x.B,D=lambdax:x.C<1000)>>where("~D")ABCD28703401210False86246731297FalseExample #2Suppose you need the following function to trim columnar text data.deftrim_columns(df):''' Trim blanks for given dataframe '''str_cols=df.select_dtypes(include='object').columnsforcolinstr_cols:df[col]=df[col].str.strip()returndfimportpandasaspdfrompiper.factoryimportsample_datadf=sample_data()# Select all columns EXCEPT 'dates'subset_cols=['order_dates','regions','countries','values_1','values_2']criteria1=~df['countries'].isin(['Italy','Portugal'])criteria2=df['values_1']>40criteria3=df['values_2']<25df2=(df[subset_cols][criteria1&criteria2&criteria3].pipe(trim_columns).sort_values('countries',ascending=False))df2.head()Piper equivalentUsing the%%pipermagic function, piper verbs can be combined with standard
python functions.frompiper.defaultsimport*%%pipersample_data()>>trim_columns()>>select('-dates')>>where(""" ~countries.isin(['Italy', 'Portugal']) &values_1 > 40 &values_2 < 25 """)>>order_by('-countries')>>head(5)Result:datesorder_datescountriesidsvalues_1values_22020-03-032020-03-09SwedenE194202020-05-022020-05-08SwedenD322142020-01-202020-01-26SpainA183202020-02-012020-02-07NorwayD344212020-05-062020-05-12NorwayB13521
Package: dpipes
dPipes - Pythonic Data PipelinesAboutdPipesis a Python package for creatingreusable, modular, and composabledata pipelines.
It's small project that came out of the desire to turn this:importpandasaspddata=(data.pipe(func_1).pipe(func_2).pipe(func_3))into this:fromdpipes.processorimportPipeProcessorps=PipeProcessor(funcs=[func_1,func_2,func_3])data=ps(data)Now, arguably, there is not much functional difference between the two implementations. They both
accomplish the same task with roughly the same amount of code.But, what happens if you want to apply the same pipeline of functions to a different data
object?Using the first method, you'd need to re-write (copy/paste) your method-chaining pipeline:new_data=(new_data.pipe(func_1).pipe(func_2).pipe(func_3))Using the latter method,you'd only need to pass in a different objectto the pipeline:new_data=ps(new_data)Under the HooddPipesuses two functions from Python'sfunctoolsmodule:reduceandpartial. Thereducefunction enables function composition; thepartialfunction enables use of arbitrarykwargs.GeneralizationAlthoughdPipesinitially addressedpd.DataFrame.pipemethod-chaining, it's extensible to any
API that implements a pandas-likeDataFrame.pipemethod (e.g. Polars). Further, thedpipes.pipelineextends this composition to any arbitrary Python function.That is, this:result=func_3(func_2(func_1(x)))or this:result=func_1(x)result=func_2(result)result=func_3(result)becomes this:fromdpipes.pipelineimportPipelinepl=Pipeline(funcs=[func_1,func_2,func_3])result=pl(x)which is, arguably, more readable and, once again, easier to apply to other objects.InstallationdPipesis can be installed viapip:pipinstalldpipesWe recommend setting up a virtual environment with Python >= 3.8.BenefitsReusable PipelinesAs you'll see in thetutorials,
one of the key benefits of usingdPipesis the reusable pipeline object that can be called on
multiple datasets (provided their schemas are similar):fordsin[split_1,split_2,split_3]:result_b=ps(ds)pd.testing.assert_frame_equal(result_a,result_b)Modular PipelinesAnother is the ability to create modularized pipelines that can easily be imported and used
elsewhere in code:"""My pipeline module."""fromdpipes.processorimportPipeProcessordeftask_1(...):...deftask_2(...):...deftask_3(...):...deftask_4(...):...my_pipeline=PipeProcessor([task_1,task_2,task_3,task_4])frommy_moduleimportmy_pipelinemy_pipeline(my_data)Composable PipelinesFinally, you can compose large, complex processing pipelines using an arbitrary number of sub-pipelines:ps=PipeProcessor([task_1,task_2,task_3,task_4,])col_ps_single=ColumnPipeProcessor(funcs=[task_5,task_6],cols="customer_id")col_ps_multi=ColumnPipeProcessor(funcs=[task_7,task_8],cols=["customer_id","invoice"])col_ps_nested=ColumnPipeProcessor(funcs=[task_9,task_10],cols=[["quantity","price"],["invoice"],])pipeline=PipeProcessor([ps,col_ps_single,col_ps_multi,col_ps_nested,])result=pipeline(data)
Package: dpi-textract
Librería Python para Extracción de Datos del DPI en Imágenes mediante TextractEsta librería Python ha sido desarrollada como parte del trabajo de graducación llevado a cabo en la Universidad de San Carlos de Guatemala, en la Facultad de Ingeniería, Escuela de Ingeniería en Ciencias y Sistemas. La librería ha sido diseñada y desarrollada para permitir una fácil integración con el servicio Textract de AWS. Esto garantiza que los usuarios puedan aprovechar la potencia de Textract para la extracción de datos de DPI de manera sencilla. Se realizaron adaptaciones específicas en el servicio Textract para garantizar que la identificación de datos en los DPI sea precisa y eficiente. El proyecto ha sido desarrollado por Luis Manuel Morales López, bajo la asesoría de Msc. Lic. Mariano Mackenzie Asturias Miranda.ObjetivoEl objetivo principal de esta librería es proporcionar una herramienta que permita la identificación simplificada de datos en documentos DPI, con alta precisión y confiabilidad. Esto tiene aplicaciones en una variedad de ámbitos donde la extracción de datos de DPI sea necesaria, como la automatización de procesos, validación de identidad y más.InstalaciónPuedes instalar esta librería a través de PyPI utilizando el siguiente comando:pipinstalldpi-textractUsoEjemplo 1importdpi_textractasdpidpi.client(aws_access_key_id="your_id",aws_secret_access_key="your_key")mi_dpi=dpi.analyze_dpi(dpi_path_front="your path")print("CUI:")print(dpi.cui())print(dpi.name())# > CUI:# > 9876 54321 0123# > Luis ManuelEjemplo 2importdpi_textractasdpiimportjsondpi.client(aws_access_key_id="you_id",aws_secret_access_key="your_key")mi_dpi=dpi.analyze_dpi(dpi_path_front="you_path_front",dpi_path_back="your_path_back")mi_json=json.dumps(mi_dpi,indent=4)print(mi_json)# > {# >     "CUI": "9876 54321 0123",# >     "PAIS DE NACIMIENTO": "GTM",# >     "FECHA DE NACIMIENTO": "21DIC2000",# >     "SEXO": "MASCULINO",# >     "NACIONALIDAD": "GTM",# >     "APELLIDOS": "TUS APELLIDOS",# >     "NOMBRES": "TUS NOMBRES",# >     "EMISION": "21DIC2000",# >     "VERSION": "001",# >     "EXPIRACION": "21DIC2000",# >     "ESTADO CIVIL": "SOLTERO",# >     "SERIE": "0000000000001",# >     "COMUNIDAD": "ESPAÑOL",# >     "VECINDAD": "GUATEMALA GUATEMALA",# >     "LUGAR DE NACIMIENTO": "GUATEMALA GUATEMALA"# > }ContribucionesSe agradecen las contribuciones de la comunidad para mejorar esta librería.ContactoSi tienes alguna pregunta o comentario, no dudes en ponerte en contacto con el desarrollador:Email:soy.lmml@gmail.com¡Gracias por utilizar la librería! Espero que sea una herramienta útil para tus proyectos.
Package: dpivsoft
DPIVSoft pythonDPIVSoft is an open PIV algorithm developed by Thomas Leweke and Patrice Munier. The original source of DPIVSoft for Matlab enviorement is accesible at the following link:https://www.irphe.fr/~meunier/. This project tries to migrate the original source to a python enviorement and also add the capabilites of GPU computing using OpenCL.DPIVSoft consist in Python and OpenCL modules for scripting and executing the analysis of 2D PIV image pairs. At current state some Python knowledge is needed to use this software. A graphic user interface is planned to be added to make the software more accesible to people without programming skills.WarningThe DPIVSoft Python version is still in it'sbetastate. This means that it can have some bugs and the API may change. However, testing and contributing is very welcome.InstallingNote: DPIVSoft is only compatible with Python 3.7, 3.8 & 3.9Using PYPIDPIVSoft can be installed using PyPI from:https://pypi.org/project/dpivsoft/. In order to install just need to use the following command line:$pipinstalldpivsoftBuild from sourceAlternatively DPIVSoft can ge installed from source. In orther to do that, be sure you already have installed the package setuptools, otherwise install it using:$pipinstallsetuptoolsClone the GitLab reposistory using:$gitclonehttps://gitlab.com/jacabello/dpivsoft_python.gitFor the global installation, first step is go to the source folder and generate an installable using the following command:$pythonsetup.pysdistIf everithing is fine, a folder named "dist" must have been created. Install the installable generated inside "dist" folder by using:$pipinstallInstallable_NameLearning to use DPIVSoftThere are tutorials and examples of how to compute PIV included with this package on source_folder/ExamplesDeveloping DPIVSOFTNote: In this early state, there is not any test available yet.To install dpivsoft, along with the tools that you need to develop and run test, run the following line in your virtualenv:$pipinstall-e.[dev]ContributorsJorge Aguilar-CabelloAkcnowledgmentPatrice MeunierThomas LewekeRaul Infante-SainzLuis ParrasCarlos del PinoHow to cite this workhttps://doi.org/10.1016/j.softx.2022.101256
Package: dpixels
dpixelsA(nother) wrapper for the Python Discord Pixel API.FeaturesProper ratelimite handeling.Saves ratelimits in a json file, so restarting scripts won't trigger cooldowns.Supports all Pixel API endpoints.Supports autodrawing of images.ExamplesGet the canvas:client=dpixels.Client(token="your token")canvas=awaitclient.get_canvas()# this also caches the canvas, so later you can do:canvas=client.canvasGet a specific pixel:pixel=canvas[0,0]# get the pixel at 0,0pixel=awaitclient.get_pixel(0,0)# fetch the pixel at 0, 0pixel.hex# the hex valuepixel.int# the int valuepixel.rgb# the rgb valueSetting a pixel:awaitclient.set_pixel(0,0,dpixels.Color(255,255,255))# set the pixel at 0,0 to whiteAutodrawing an image:fromPILimportImageim=Image.open("path_to_image.ext")source=dpixels.Source.from_image((0,0),im)awaitclient.draw_sources([source])# draw the sourceCreditsThis library is heavily based onArtemis21/dpypx
Package: dpixiv
dpixivTool to simple use pixiv api of siteInstallpip install dpixivExample of usingfromdpixivimportDPixivIllustspix=DPixivIllusts(session=None,proxy=None)### Attributes: ###pix.is_auth#(True|False)pix.session#sessionpix.proxy#http proxy 'http://{ip}:{port}'### Methods:    #### Authpix.auth(login,password,captcha_token)# To get capthca_token go to https://accounts.pixiv.net/login# and write in browser console command:# document.getElementById('recaptcha-v3-token').value# Do it fast and save pix.session# Get lists of ids that recommended to user by pixivpictures_ids=pix.recommender(sample_illusts=None,count=100)# Get list(or not) of short information of pictures (fast speed)short_pictures_info=pix.illust_list(illusts)# Get full information of picture (not so fast)full_picture_info=pix.info(id,token=False)# Use next method for a lot of pictures(much faster then one by one)# Get list of full information of picture (medium speed)full_pictures_info=pix.info_packs(ids,token=False)# Get lists of ids of pictures that similar to picture with id (fast speed)similar_pictures_ids=pix.similar(id,limit=10)# Get list of bookmarksbookmarks_ids=pix.bookmarks(page=None,from_page=1,to_page=1000000,step_count=10)# Use page to show only one page# Get list of followingfollowing_ids=pix.new_work_following(page=None,from_page=1,to_page=1000000,step_count=10)# Use page to show only one page# Search by namesearch_ids=pix.search(word,page=None,from_page=1,to_page=1000000,step_count=10)# Use page to show only one page# Add tag (set token=True to info and get by 'token' key)response=pix.add_tag(pic_id,tag,token)# Delete tagresponse=pix.del_tag(pic_id,tag,token)
Package: dpkg
dpkgdpkg is a package that collects functions frequently used in healthcare analysis.installpip install dpkgapkComputes the average precision at k. This function computes the average prescision at k between two lists of items.Parametersactual : listA list of elements that are to be predicted (order doesn't matter)predicted : listA list of predicted elements (order does matter)k : int, optionalThe maximum number of predicted elementsReturnsscore : double The average precision at k over the input listsSimple examplefromdpkg.apkimportApkScoreApkScore(actual,predicted,k)mapkMean Average Precision (mAP) is commonly used to analyze the performance of object detection and segmentation systems.Parametersapk_list : listA list of apk scoresReturnsscore : Mean average precision score of input apk listsSimple examplefromdpkg.mapkimportMapkScoreMapkScore(apk_list)datasetDataset is a package that loads data sets related to food, bio, and healthcare.Datasetkhane : Korean Health and Nutrition Survey dataset(2016)main : main dataset including meta informationffq : food frequency questionnaires dataset including meta information24rc : dietary assessment by 24-hour dataset including meta informationoe : oral examination dataset including meta informationParameterskhane datasetdata type : 'main', 'ffq', '24rc', 'oe'Simple examplefromdpkg.datasetimportKhanemain_ds,main_meta=Khane().load_data('main')
Package: dplearn
Why Should I Use This?This is a Python package for data analysis which contains some very useful functions.Installation$pipinstalldplearnOr, you can download the source and$pythonsetup.pyinstallAdd sudo in the beginning if you met problem.ModulestoolsmathquantextraUsageUse As A Python Modulefromdplearn.toolsimporttick_start,tick_endtick_start("<JOB TITLE>")<DOSOMETHIGNHERE>tick_end()DocumentationCheck out the latestdplearndocumentation atRead the DocsContributingPlease send pull requests, very much appriciated.on Github.Make a branch off of master and commit your changes to it.Create a Pull Request with your contribution.
Package: dp-learning-ff
DescriptionRepository of differentially-private methods for learning from features.UsageInstallationpip install dp-learning-ffPrivate Prototype CalculationSet your total privacy budget in (0,rho)-zCDP.Divide your total budget into a budget per step. Good starting values are listed below. The algorithm will performlen(Ps)many steps.Ps=[rho]# One iterationPs=[5/64*rho,7/64*rho,52/64*rho]# Three iterationsCall the private prototype calculation using yourtrain_preds,train_targetsandPsas outlined above.num_samples=100dimensionality=10num_classes=5train_preds=np.random.normal(0,1,(num_samples,dimensionality))train_targets=np.random.randint(num_classes,size=(num_samples))# Supports unbalanced classesprivate_prototypes=dp_learning_ff.give_private_prototypes(train_preds,train_targets,Ps)>>>private_prototypes.shape(5,10)>>>private_prototypesarray([[-1.08239659,0.50517265,0.46295668,0.11665974,-0.41539363,0.46428818,-0.30204127,-0.20684517,-0.11748276,-0.44173581],[-0.97640077,0.73495051,-0.3125302,0.39733846,-0.15187237,-0.09618042,0.50008494,0.06408969,-0.59472233,0.56000984],[-0.07384877,-0.04661516,0.83469973,-0.18854654,-0.3631222,-0.56158617,0.03480437,-0.17749153,-0.25144926,0.99669163],[0.02682651,-0.57948712,-0.33462215,0.32657359,-0.13894844,-1.16420508,0.84630674,-0.0343235,-0.38193481,0.29124748],[1.12158357,0.17188541,-0.2582887,-0.2140504,0.06759121,-0.26975582,-0.46858189,0.67295234,0.64183255,-0.10445519]])AcknowledgementsPrototype Calculations use the coinpress algorithm from the paperCoinPress: Practical Private Mean and Covariance Estimation, authored bySourav Biswas,Yihe Dong,Gautam Kamath,Jonathan Ullman.
Code contributed by all four authors.@incollection{BiswasDKU20,
  title         = {CoinPress: Practical Private Mean and Covariance Estimation},
  author        = {Biswas, Sourav and Dong, Yihe and Kamath, Gautam and Ullman, Jonathan},
  booktitle = {Advances in Neural Information Processing Systems 33},
  url       = {arXiv preprint arXiv:2006.06618},
  year          = {2020}
}
Package: dplib-py
Data Package LibraryPython implementation of the Data Package standard and various tools for working with data.PurposeThe Data Package Library is a lightweight Data Package Standard implementation in Python providing Pydantic data models and various metadata converters. At the moment, the main purpose of this library is to be used as an underlying component of Data Package based integrations.DocumentationPlease visit the documentation portal:https://frictionlessdata.github.io/dplib-py/