Saravanan666/pypi-org huggingface dataset:
Package: dpaste-magic
dpaste_magicMagic function that pushes or pulls code snippets out of pastebins.
dpaste.org is currently supported.Pushing code & cells%dpaste answer = 42https://dpaste.org/WXYZpushes line content & print the dpaste url.%%dpaste
  answer = 42https://dpaste.org/WXYZpushes cell content & print the dpaste url.%%dpaste {-1x -1h -1d -1w}
  answer = 42https://dpaste.org/WXYZreturns a dpaste url with expires duration:-1x: just for one read-1h: one hour (default)-1d: one day-1w: one weekThese are the supported expiration time supported by dpaste.org.Options:-o: return URL as value (for storage in a variable)-s: silent mode (URL not printed)my_url = %dpaste -o answer = 42push code on line, prints & stores url inmy_urlvariable.my_url = %dpaste -o -s answer = 42push code on line & stores url inmy_urlvariable (silent mode, no print).Getting back the paste%getdpaste WXYZretrieves snippet from WXYZ dpaste hash or URL and changes cell to:answer = 42%getdpaste -u WXYZadd retrieved URL as a Python comment in first line:#https://dpaste.org/WXYZ/raw

  answer = 42With%dpaste -gget option:%dpaste -g WXYZretrieves snippet from WXYZ dpaste hash or URL and changes cell to:answer = 42InstallationUse:pip install dpaste-magicto install the magic command.First load the magic in a cell:%load_ext dpaste_magicand then use the function in your cell to dpaste its content.%%dpaste,%dpasteor%getdpaste.TO DOincrease tests rangecode unload functioncatch HTTPError exception 404 to display friendly message
Package: dpauth
=====dpauth=====Django app.Detailed documentation is in the "docs" directory.Quick start-----------1. Add "dpauth" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [...'dpauth',]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 dpauth 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: dpay-graphenelib
Python 3 library for Graphene 2.0!InstallationInstall withpip:$ sudo apt-get install libffi-dev libssl-dev python-dev
$ pip3 install dpay-graphenelibManual installation:$ git clonehttps://github.com/dpays/dpay-python-graphenelib/$ cd dpay-python-graphenelib
$ python3 setup.py install –userDependenciesSome dependencies are not required for parts of the library to run
properly. However these modules require some additional libraries to be
present:graphenebase.bip38pycrypto==2.6.1scrypt==0.7.1(to speedup scrypt hashing)graphenebase.memopycrypto==2.6.1Upgrade$ pip install –user –upgrade dpay-graphenelibDocumentationThanks to readthedocs.io, the documentation can be viewed
[online](https://docs.dpays.io/graphene)Documentation is written with the help of sphinx and can be compile to
html with:cd docs
make htmlLicenceMIT, seeLICENCE.txt
Package: dpay-lib
# dPayLib (Python)This library is unmaintained, do not build productive buisness with it!
Please not the disclaimer in the license file!Python Library for dPayPython 3 library for dPay!InstallationInstall withpip3:$ sudo apt-get install libffi-dev libssl-dev python-dev python3-pip
$ pip3 install dpay-libManual installation:$ git clonehttps://github.com/dpays/dpay-python-lib/$ cd dpay-python-lib
$ python3 setup.py install –userUpgrade$ pip3 install dpay –user –upgradeAdditional dependenciesdpayapi.dpayasyncclient:asyncio==3.4.3pyyaml==3.11DocumentationDocumentation is written with the help of sphinx and can be compile to
html with:cd docs
make html
Package: dpayocr
DPayOcr介绍dpayocr可以识别支付宝微信收款码信息。例如支付类型，支付金额，支付链接。安装$pipinstalldpayocr使用命令行使用dpayocr 66.JPG输出PayQrCode(pay_type=1, price=66.0, url='https://qr.alipay.com/xxx')接口>>>fromdpayocrimportparse_pay_qr_code>>>fp='66.JPG'# 可以传入文件名或者二进制IO>>>parse_pay_qr_code(fp)PayQrCode(pay_type=1,price=66.0,url='https://qr.alipay.com/xxx')问题在虚拟环境找不到 Tesseract 命令# If you don't have tesseract executable in your PATH, include the following:
pytesseract.pytesseract.tesseract_cmd = r'<full_path_to_your_tesseract_executable>'
# Example tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract'参考链接tesseractpytesseractpyzbarA comprehensive guide to OCR with Tesseract, OpenCV and Python
Package: dPCA
dPCA is a linear dimensionality reduction technique that automatically discovers and highlights the essential features of complex population activities. The population activity is decomposed into a few demixed components that capture most of the variance in the data and that highlight the dynamic tuning of the population to various task parameters, such as stimuli, decisions, rewards, etc.@article{kobak2016dpca,
   title={Demixed principal component analysis of neural population data},
   volume={5},
   ISSN={2050-084X},
   url={http://dx.doi.org/10.7554/eLife.10989},
   DOI={10.7554/elife.10989},
   journal={eLife},
   publisher={eLife Sciences Publications, Ltd},
   author={Kobak, Dmitry and Brendel, Wieland and Constantinidis, Christos and Feierstein, Claudia E and Kepecs, Adam and Mainen, Zachary F and Qi, Xue-Lian and Romo, Ranulfo and Uchida, Naoshige and Machens, Christian K},
   year={2016},
   month={Apr}
}🚀 Quickstartpipinstalldpca🎉 Use dPCASimple example code for surrogate data can be found in [dpca_demo.ipynb](http://nbviewer.ipython.org/github/wielandbrendel/dPCA/blob/master/python/dPCA_demo.ipynb) anddpca_demo.m.API of dPCA is similar to sklearn. To use dPCA, you should first import dPCA and initialize it before callling the fitting function,fromdpcaimportdPCAdpca=dPCA(labels,n_components,regularizer)Z=dpca.fit_transform(X)The required initialization parameters are:X:A multidimensional array containing the trial-averaged data. E.g. X[n,t,s,d] could correspond to the mean response of then-th neuron at timetin trials with stimulussand decisiond. The observable (e.g. neuron index) needs to come first.labels:Optional; list of characters with which to describe the parameter axes, e.g. ‘tsd’ to denote time, stimulus and decision axis. All marginalizations (e.g. time-stimulus) are refered to by subsets of those characters (e.g. ‘ts’).n_components:Dictionary or integer; if integer use the same number of components in each marginalization, otherwise every (key,value) pair refers to the number of components (value) in a marginalization (key).More detailed documentation, and additional options, can be found indpca.py.
Package: dpcanvas
=====dpcanvas=====Django app.Detailed documentation is in the "docs" directory.Quick start-----------1. Add "dpcanvas" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [...'dpcanvas',]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 dpcanvas 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: dpcgeneration
Differentially Private Release of High-Dimensional Datasets using the Gaussian CopulaAsghar, Hassan; Ding, Ming; Rakotoarivelo, Thierry; M'rabet, Sirine; Kaafar, Dali. Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula. Journal of Privacy and Confidentiality. 2020; 10(2):1-38.http://hdl.handle.net/102.100.100/366470?index=1Introductiondpc is a library to efficiently release differentially private synthetic versions of high-dimensional datasets with high utility.generate differantial private datase.  The core technique in our mechanism is the use of copulas, which are functions representing dependencies among random variables with a multivariate distribution.This library includes three models:dpc-onehot : categorical attributes are converted to one hot binning. Gaussian copula is uded to define dependencies of attributes in the input dataset. Synthetic records are sampled through this copula.dpc : catagorical attributes are considered as discrete data in wich the order is chosen arbiterary. Gaussian copula is uded to define dependencies of attributes in the input dataset. Synthetic records are sampled through this copula.dpc-cond : run Baysesian network to capture the joint probability distribution of the attributes. Select n attributes having highest correlation and run dpc algorithm to create a skeleton. Synthetic records are sampled through a conditionnal algorithm using the skeleton.Installation# Requires the latest pip
pip install --upgrade pip

# Current statble release
pip install -i https://test.pypi.org/simple/ dpcgenerationDpc Library  is tested and supported on the following 64-bit systems:Python 3.6Ubuntu 16.04 or laterMac Os 10.15 or laterGet startedHow to multiply one number by another with this lib:fromdpcgenerationimportDPCopulaimportpandasaspdimportnumpyasnpfromdpcgenerationimportcalculate_oneWay,calculate_print,calculate_twoWay# Create testfile with ramdom 0 and 1df=pd.DataFrame(np.random.randint(0,2,size=(100,4)),columns=list('ABCD'))# Instantiate a Multiplication objectcopula=DPCopula()# Run dpc_bin algorith with epsilon=1df_res=copula.rundpc_bin(data=df,withNoise=True,epslimit=1,path_fnl='../out/',labels=['dpc-bin'],convert_bin=False,verbose=True)#plot one-way and two-way absolute error between original and generated datasetdf.columns=df.columns.astype(str)df_res=df_res.astype(int)#One way errordf_oneway_orig=calculate_oneWay(df,df.columns.tolist()[5:10])df_oneway_gen=calculate_oneWay(df_res,df_res.columns.tolist())calculate_print([df_oneway_orig],[df_oneway_gen],'CDF of One Way',labels=['5 onehot encoding attributes'],figname='../out/'+'mnist0-oneway',printgraph=False,savedata='../out2/'+'mnist0-oneway_gen')#Two eay errordf_twoway_orig=calculate_twoWay(df,df.columns.tolist()[5:10])df_twoway_gen=calculate_twoWay(df_res,df_res.columns.tolist())calculate_print([df_twoway_orig],[df_twoway_gen],'CDF of Two Way',labels=['5 onehot encoding attributes'],figname='../out/'+'mnist0-twoway',printgraph=False,savedata='../out2/'+'mnist0-twoway_gen')
Package: dpcluster
Descriptiondpcluster is a package for grouping together (clustering) vectors. It automatically chooses the number of clusters that fits the data best. Specifically, it models the data as a Dirichlet Process mixture in the exponential family. For a tutorial see“Dirichlet Process” by Y.W. Teh (2010). Currently the only distribution implemented is the multivariate Gaussian with a Normal-Inverse-Wishart conjugate prior but extensions to other distributions are possible.Two inference algorithms are implemented:Variational inference as described in“Variational Inference for Dirichlet Process Mixtures” by Blei et al. (2006). This is a batch algorithm that requires storing all data in memory.An experimental on-line inference algorithm that requires only O(log(n)) memory where n is the total number of observations.To install locally run:python setup.py install --userUsageHere is a simple example to demonstrate clustering a number of random points in the plane:>>> from dpcluster import *
>>> n = 10
>>> data = np.random.normal(size=2*n).reshape(-1,2)
>>> vdp = VDP(GaussianNIW(2))
>>> vdp.batch_learn(vdp.distr.sufficient_stats(data))
>>> plt.scatter(data[:,0],data[:,1])
>>> vdp.plot_clusters(slc=np.array([0,1]))
>>> plt.show()Running this might produce 2-3 clusters depending on the randomly generated data. The adaptive nature of the Dirichlet Process mixture model becomes apparent when we increase the number of data points fromn = 10ton = 500. In this case the clustering algorithm will likely explain the data using only one cluster.ToDoImplement more clustering algorithms e.g. based on Gibbs sampling, expectation propagation, stochastic gradient descent.Implement more clustering distributions.Re-implement algorithms to take advantage of multi-core or GPU computing.
Package: dpctl
Data Parallel ControlData Parallel Control ordpctlis a Python library that allows users
tocontrolthe execution placement of acompute
kernelon anXPU.The compute kernel can be a code:written by the user, e.g., usingnumba-dpexthat is part of a library, such as oneMKLThedpctllibrary is built upon theSYCL
standard. It implements Python
bindings for a subset of the standardruntime
classesthat allow users to:query platformsdiscover and represent devices and sub-devicesconstruct contexts and queuesdpctlfeatures classes forSYCL Unified Shared Memory
(USM)management and implements a tensorarray
API.The library helps authors of Python native extensions written
in C, Cython, or pybind11 to accessdpctlobjects representing SYCL
devices, queues, memory, and tensors.Dpctlis the core part of a larger family ofdata-parallel Python
libraries and toolsto program on XPUs.InstallingYou can install the library usingcondaorpippackage managers. It is also available in theIntel(R)
Distribution for
Python(IDP).Intel(R) oneAPIYou can find the most recent release ofdpctlevery quarter as part of the Intel(R) oneAPI releases.To get the library from the latest oneAPI release, follow the
instructions from Intel(R)oneAPI installation
guide.NOTE:You need to install the Intel(R) oneAPI AI Analytics Tookit to get
IDP anddpctl.CondaTo installdpctlfrom the Intel(R) channel on Anaconda
cloud, use the following command:condainstalldpctl-cintelPipThedpctlcan be installed usingpipobtaining wheel packages either from PyPi or from Intel(R) channel on Anaconda.
To installdpctlwheel package from Intel(R) channel on Anaconda, run the following command:python-mpipinstall--index-urlhttps://pypi.anaconda.org/intel/simpledpctlInstalling the bleeding edgeTo try out the current master, install it from our
development channel on Anaconda cloud:condainstalldpctl-cdppy/label/devBuildingRefer to ourDocumentationfor more information on
setting up a development environment and buildingdpctlfrom the source.ExamplesOur examples are located in theexamples/folder and are organized in sub-folders. Examples
in thePython/folder demonstrate how to inspect the heterogeneous platform,
select a device, create an execution queue, and how to control device memory allocation and
execution placement.Examples inCython/,C/, andPybind11folders
demonstrate creation of SYCL-powered native Python extensions. Please refer to each folder's README
document for directions on how to build and use each example.Running TestsTests are located in folderdpctl/tests.To run the tests, use:pytest--pyargsdpctlRunning full test suite requires working C++ compiler. To run the test suite without one, use:pytest--pyargsdpctl-k"not test_cython_api"
Package: dpdata-abinit
dpdata_abinitAbinit plugin for dpdataInstallpip install .Usage:Prepare the abinit histfile which contains a set of structures
and the corresponding energies, forces, and stresses. Here we call this file
Abinit_hist_file.abihistWrite a python script like this.#!/usr/bin/env python3importdpdata# load data from the file Abinit_hist_file.abihistd=dpdata.LabeledSystem("Abinit_hist_file.abihist",format='abihist')# write to raw filed.to_deepmd_raw('00.data')# You can also write to npy file.  The data set is splitted into multiple npy files, each containing set_size of samples.d.to_deepmd_npy('00.data',set_size=2638)Run the script
Package: dpdata-ani
dpdata_aniThedpdataplugin for ANI.Featuresani/1xformat to loadANI-1x datasetani/1x,ani/2x, andani/1ccxdriver to predict using ANI models with automatic batch sizeaiqm1driver forAIQM1which is based on ANIInstallationpipinstalldpdata-aniTo enable AIQM1,pipinstalldpdata-ani[aiqm1]UsageFirst, download ANI-1x dataset:wgethttps://figshare.com/ndownloader/files/18112775-Oani1x.hdf5Load the ANI-1x dataset, and predict it with ANI-2x models:importdpdataani=dpdata.MultiSystems().from_ani_1x("ani1x.hdf5")predict=ani.predict(driver="ani/2x")predict.to_deepmd_hdf5("predict.hdf5")The inferred data will be stored inpredict.hdf5.
Package: dpdata-qdpi
dpdata_qdpidpdataplugin forQDπ.FeaturesqdpidriverInstallationIf you have installedDeePMD-kit,pipinstalldpdata_qdpiOtherwise, addgpuorcputo install DeePMD-kit:pipinstalldpdata_qdpi[gpu]# or for the CPU version of TensorFlowpipinstalldpdata_qdpi[cpu]At this time, you need to install eitherAMBERToolsSQM (sqm) orDFTB+(dftb+).condainstallambertools-cconda-forge# ORcondainstalldftbplus-cconda-forgeUsageDownload the QDπ model fromRutgersLBSR/qdpi.fromdpdata_qdpiimportQDPiDriverqdpi=QDPiDriver(model="qdpi-1.0.pb",charge=0,backend="sqm",)backendcan be eithersqmordftb+.Assume you have an XYZ filech4.xyz5

C          0.92334        0.06202        0.01660
H          2.01554        0.06202        0.01660
H          0.55927        1.09164        0.03247
H          0.55927       -0.46653        0.90033
H          0.55927       -0.43903       -0.88301Load the structure:fromdpdataimportSystemch4=System("ch4.xyz")Perform single point calculation using the QDπ model:p=ch4.predict(driver=qdpi)print("Energies:",p['energies'][0])print("Forces:",p['forces'][0])Energies: -1102.0472189112793
Forces: [[-4.92853860e-05  3.71129259e-04 -1.00154387e-04]
 [ 2.07637527e-02 -1.98691092e-06 -7.85158242e-07]
 [-6.81949398e-03  1.93688568e-02  3.32209598e-04]
 [-6.96972976e-03 -1.01335356e-02  1.69318148e-02]
 [-6.92524354e-03 -9.60446352e-03 -1.71630849e-02]]Or do an optimization:fromdpdata.plugins.aseimportASEMinimizerlbfgs=ASEMinimizer(driver=qdpi,)p=ch4.minimize(minimizer=lbfgs)print("Coordinates:",p['coords'][0])print("Energies:",p['energies'][0])print("Forces:",p['forces'][0])Coordinates: [[ 0.92333966  0.06202338  0.01659862]
 [ 2.0161223   0.06202041  0.0165999 ]
 [ 0.55907714  1.0921887   0.03247964]
 [ 0.559075   -0.46681303  0.9008036 ]
 [ 0.5590758  -0.43929946 -0.88349175]]
Energies: -1102.0472
Forces: [[-1.0836746e-04  9.8321143e-05 -2.5905898e-05]
 [ 6.7257555e-05  8.3519126e-06 -2.5666191e-06]
 [ 4.4526685e-05 -5.3852447e-05  1.7676884e-05]
 [-9.4877823e-06 -1.9283394e-05  2.8249622e-05]
 [ 6.0710017e-06 -3.3537217e-05 -1.7453991e-05]]Readdpdata's documentationfor more usage of dpdata.
Package: dpdispatcher
DPDispatcherDPDispatcher is a Python package used to generate HPC (High-Performance Computing) scheduler systems (Slurm/PBS/LSF/Bohrium) jobs input scripts, submit them to HPC systems, and poke until they finish.
​
DPDispatcher will monitor (poke) until these jobs finish and download the results files (if these jobs are running on remote systems connected by SSH).For more information, check thedocumentation.InstallationDPDispatcher can be installed bypip:pipinstalldpdispatcherTo addBohriumsupport, executepipinstalldpdispatcher[bohrium]UsageSeeGetting Startedfor usage.ContributingDPDispatcher is maintained by Deep Modeling's developers and welcomes other people.
SeeContributing Guideto become a contributor! 🤓ReferencesDPDispatcher is derivated from theDP-GENpackage. To mention DPDispatcher in a scholarly publication, please read Section 3.3 in theDP-GEN paper.
Package: dpdktest
This is a small tool that tests if you have NICs (Network Interface Cards)
that are supported byhttp://dpdk.org/framework.It works only on python 3:$ pip3 install dpdktestIf you’re lucky, you’ll see something like this:$ dpdktest
NICs supported by DPDK: ['eth0', 'eth1']
$ echo $?
0Otherwise:$ dpdktest
You don't have NICs supported by DPDK
$ echo $?
255How It Works?First of all supported devices vedor:device ID list is constructed fromrte_pci_dev_ids.h.Thendpdktestfind all network interfaces in your computer, gets
vendor:device ID pairs for every interfaces and checks if ID is indpdktest/supported_devices.txt.
Package: dpdtrack
myDPD Python ClientThis is a Python client for the myDPD Austria (https://mydpd.at) tracker. It allows you to track your shipments.It currentlyonlysupports DPD Austria. If you want to add support for other countries, feel free to open a pull request. Tracking for DPD shipments in other countriesmaywork, but it is not guaranteed.InstallationpipinstalldpdtrackUsagefromdpdtrackimportDPDapi=DPD()# Realtime trackingtracking=api.tracking("YOUR_SHIPMENT_NUMBER")# Optionally pass the recipient's postal code to get more accurate resultstracking=api.tracking("YOUR_SHIPMENT_NUMBER","RECIPIENT_POSTAL_CODE")LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: dpeaDPi
DPEA_DPiDPEA Pi Family of Circuit Boards Developed by Stan Reifel 2022This repo is for all of the DPi files to be added to a python package.This package is on PyPi under the dpea account
To maintain, clone the repository, edit files, recreate wheelspython3 setup.py sdist bdist_wheelupload the new files to testPyPi first then finally to PyPi with twine.twine upload --skip-existing dist/*Note:You will need to have the dpea PyPi API token. When it asks for a username, input__token__and the API key as the password.Harlow has the token in LastPassMore information:https://gist.github.com/arsho/fc651bfadd8a0f42be72156fd21bd8a9
Package: dpea-odrive
dpea-odriveDISCLAIMERThis library is meant to replaceRPI_ODriveand be used for the 2022-2023 school year.Aboutdpea-odrive is a helper library for DPEA projects that use the ODrive motor controller. You can think
of this library as a wrapper to existing odrive functions to streamline the programming processs. For
more detailed information, consider visiting the below links and digging around.ODrive Getting Started PageOfficial ODrive Github RepoUsageTo get your ODrive up and running, please navigate to the following site --DPEA ODrive Getting StartedBuild InstructionsTo build the PyPI project, run the following:Install python build packagepython3 -m pip install --upgrade buildInstall twinepython3 -m pip install --upgrade twineBuild package (make sure to change the version in pyproject.toml to provent version collision!):python3 -m buildPush the project to PyPi:python3 -m twine upload dist/*Twine will prompt you for a password. UseUsername: __token__
Password: YOUR_API_TOKEN
Package: dpeeg
dpeegDpeeg provides a complete workflow for deep learning decoding EEG tasks, including basic datasets (datasets
can be easily customized), basic network models, model training, rich experiments, and detailed experimental
result storage.Each module in dpeeg is decoupled as much as possible to facilitate the use of separate modules.UsageInstallation dependencies are not written yet, please install as follows:Create a new virtual environment named "dpeeg" with python3.10 using Anaconda3 and activate it：condacreate--namedpeegpython=3.10
condaactivatedpeegInstall environment dependenciescondainstallpytorchpytorch-cuda=11.8-cpytorch-cnvidia
condainstall-canacondaipythonipykernelpandasscikit-learn
condainstall-cconda-forgetorchinfomne-basetensorboardtorchmetricsseaborn
pipinstallmoabb==0.5.0
pipinstalldpeeg
Package: dpern
DPERN - Describe PERsian NumbersInstallationpipinstalldpernUsageIf you want to use it in a module, you need to import it like this:fromdpernimportdpernAnd use it like this:print(dpern.describe(53523))# پنجاه و سه هزار و پانصد و بیست و سهprint(dpern.describe("35"))# سی و پنجprint(dpern.describe("۹۹۹"))# نهصد و نود و نهIf you want to use it in the command line, make sure to install it
with--userflag:pipinstalldpern--userThen you can run it like this:$python-mdpern10ده

$python-mdpern۱۲۸صدوبیستوهشت