Saravanan666/pypi-org huggingface dataset:
Package: nbgitpuller
nbgitpullernbgitpullerlets you distribute content in a git repository to your students
by having them click a simple link.Automatic
mergingensures that your students are never exposed togitdirectly. It is primarily
used with a JupyterHub, but can also work on students' local computers.Seethe documentationfor more
information.InstallationpipinstallnbgitpullerExampleThis example shows how to use thenbgitpuller link generatorto create an nbgitpuller link, which a user then clicks.The nbgitpuller link generator GUI is used to create a
link.This link is clicked, and the content is pulled into a live Jupyter session.
Package: nbgitpuller-downloader-dropbox
nbgitpuller - dropbox download pluginDropbox file/folder names add the dl=0 URL query parameter to their URLs.This plugin expects the Dropbox URL included in the nbgitpuller link to look like this:https://www.dropbox.com/s/qou3g7hf41vq6sw/materials-sp20-external.zip?dl=0This plugin replaces dl=0 with dl=1 and then downloads the file using the
nbgitpuller's handle_files_helper function.Please note that the file(compressed archive) must have permissions set so that anyone
with the link can view the file.Installationpython3-mpipinstallnbgitpuller-downloader-dropbox
Package: nbgitpuller-downloader-generic-web
nbgitpuller - generic web download pluginThis plugin is used by nbgitpuller to download archives
from any publicly available URL.This plugin expects the Dropbox URL included in the nbgitpuller link to look like this:https://www.example.com/materials-sp20-external.zipIn this plugin, there is no special handling of the URL so it must point directly to
the archive and the archive must by accessible to anyone with the link.Installationpython3-mpipinstallnbgitpuller-downloader-generic-web
Package: nbgitpuller-downloader-googledrive
nbgitpuller - google drive download pluginGoogle Drive uses a uniquely formatted URL to identify files and folders. As a result,
programmatically downloading from Google Drive requires special handling. The
implementation of the download plugin for Google Drive handles these requirements.The plugin is expecting a Google Drive URL in this format:https://drive.google.com/file/d/1p3m0h5UGWdLkVVP0SSJH6j1HpG2yeDlU/view?usp=sharingPlease note that the file(compressed archive) must have permissions set so that anyone
with the link can view the file.Installationpython3-mpipinstallnbgitpuller-downloader-googledrive
Package: nbgrader
nbgraderBuild:Forum:Cite:A system for assigning and grading Jupyter notebooks.Documentation can be found on Read the Docs.Highlights of nbgraderInstructor toolbar extension for Jupyter notebooksThe nbgrader toolbar extension for Jupyter notebooks guides the instructor through
assignment and grading tasks using the familiar Jupyter notebook interface.Instructor "formgrader" extension for Jupyter notebooksThe formgrader extension for the Jupyter notebook allows instructors to use
the core functionality of nbgrader---generating the student version of an
assignment, releasing assignments to students, collecting assignments,
autograding submissions, and manually grading submissions.Student assignment list extension for Jupyter notebooksUsing the assignment list extension, students may conveniently view, fetch,
submit, and validate their assignments.The command line tools of nbgraderCommand line toolsoffer an efficient way for the instructor to generate, assign, release, collect,
and grade notebooks.SciPy 2017 overview talk (click to view on YouTube)InstallationFor detailed instructions on installing nbgrader and the nbgrader extensions
for Jupyter notebook, please seeInstallationsection in the User Guide.ContributingPlease see thecontributing guidelines and documentation.If you want to develop features for nbgrader, please follow thedevelopment installation instructions.
Package: nbgrader-unittest-sebastian-stigler
nbgrader_unittestDrop-in replacement for the unittest module to use them in tests fornbgraderassignments.You can use this module just like theunittestmodule. Just don't
call themainfunction to run the tests. Use therun_nbgrader_testfunction instead.If your tests all pass, no output will be generated and nbgrader will assign
full marks to this cell.  If one of your tests fail, the usual unittest output
will be created and printed tosys.stderrand no marks will be assigned to
this cell in nbgrader.ExampleYou prepare a new jupyter notebook assignment with parts, that should be
autograded withnbgrader.Your assignment could be something like
this:Write a function that returns a list of numbers, such that $x_i=i^2$, for
$1 \leq i \leq n$. Make sure it handles the case where $n<1$ by raising aValueError.Your code cell with the "Autograded answer" will look like this:defsquares(n):"""Compute the squares of numbers from 1 to n, such that theith element of the returned list equals i^2."""### BEGIN SOLUTIONifn<1:raiseValueError("n must be greater than or equal to 1")return[x**2forxinrange(1,n+1)]### END SOLUTIONIn a new code cell you put your "Autograder test":importnbgrader_unittestclassSquaresTests(nbgrader_unittest.TestCase):deftest_square_of_one(self):self.assertEqual(squares(1),[1])deftest_squares_up_to_ten(self):self.assertEqual(squares(10),[1,4,9,16,25,36,49,64,81,100])deftest_negative_argument(self):withself.assertRaises(ValueError):squares(-4)nbgrader_unittest.run_nbgrader_test(SquaresTests)
Package: nb-hdr-plotter
NB HDR plotterA tool to plotHDR histogramdata (andhistostatsoutput) fromNoSQLBench.SetupPre-requisitesPython 3.8+ is required.InstallationInstall withpip install nb-hdr-plotterQuickstartAssuming you have anHDRhistogram file to plot:nb_hdr_plotter                          \
    hdr_data.log                        \
    -b -c -s                            \
    -p SampleData                       \
    -d SampleDump                       \
    -m cqlkeyvalue_default_main.resultAssuming you have ahistostatsoutput file to plot:histostats_plotter                      \
    histostats.log                      \
    -p HistostatsSample                 \
    -m cqlkeyvalue_default_main.resultThe Github repository containssample plotsobtained with these tools.More optionsnb_hdr_plottercan dump to atxttable file (as opposed to a PNG image);it can selectively plot histogram, percentiles, stability plot;it can interactively help you select the metric to plot;it can optionally refrain from time unit conversion and overwrite files.Check out the online help:$> nb_hdr_plotter -h
usage: nb_hdr_plotter [-h] [-i] [-m METRICTAG] [-b] [-c] [-s] [-p PLOTFILEROOT] [-d DUMPFILEROOT] [-f] [-r] filename

Manipulate HDR data generated in NoSQLBench.

positional arguments:
  filename              HDR input data

optional arguments:
  -h, --help            show this help message and exit
  -i, --inspect         Detailed input breakdown

Analysis tasks:
  -m METRICTAG, --metric METRICTAG
                        Work on the specified metric tag (interactive choice if not provided)
  -b, --baseplot        Create standard distribution plot
  -c, --percentiles     Create percentile analysis
  -s, --stability       Perform stability analysis (per-slice plots)

Output control:
  -p PLOTFILEROOT, --plot PLOTFILEROOT
                        Create plot images (with given file root)
  -d DUMPFILEROOT, --dump DUMPFILEROOT
                        Dump to data files (with given file root)
  -f, --force           Overwrite existing file(s) if necessary
  -r, --raw             Keep raw values found in histograms (no unit conversions)histostats_plottercan include/exclude plotting the "max" alongside the other percentiles;it offers interactive selection of the metric to plot;it optionally overwrites existing files:$> histostats_plotter -h
usage: histostats_plotter [-h] [-m METRICTAG] [-p PLOTFILEROOT] [--include-max] [-f] filename

Quickly plot "--log-histostats" output from NoSQLBench to an image.

positional arguments:
  filename              Histostats input file

optional arguments:
  -h, --help            show this help message and exit
  -m METRICTAG, --metric METRICTAG
                        Work on the specified metric tag (interactive choice if not provided)
  -p PLOTFILEROOT, --plot PLOTFILEROOT
                        Create plot image (with given file root), automatic if not provided
  --include-max         Include "max" to plotting
  -f, --force           Overwrite existing file if necessary
Package: nbHint
nbHintInstallationnbHint can be installed via pippipinstallnbhintExample#import pacakgefromnbHintimportnbHint#parse Jupyter Notebooknb_path="PATH/TO/YOUR/NB.ipynb"nb_cells=nbhint.Parser(nbPath=nb_path)#exract cell of interest using the unique cell idcurr_cell=nb_cells.get_cell_by_id('cell-4119246836a38e04')#conduct different checkscurr_cell.check_for()curr_cell.check_if(only_if=True)For other use cases have a look at theexamples\examples.ipynb.Note: If you change the content of a cell, the notebook hast to be saved before conducting a check.
Package: nbhood-static-pages
Polls is a simple Django app to conduct Web-based polls. For each
question, visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'nbhood-static-pages',
]Include the polls URLconf in your project urls.py like this:url(r'^polls/', include('polls.urls')),Runpython manage.py migrateto create the polls models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll.
Package: nbhtml
Thisexperimentalproject tries to save Jupyter notebooks as HTML and read them back.To try it:python3 -m nbhtml.write Sample.ipynb
python3 -m nbhtml.read Sample.ipynb.html

diff from_html.ipynb Sample.ipynbThis could one day be combined with a project likeipymdto save notebooks in two complementary
formats:‘Complete’ HTML notebooks, including images and outputs, which can easily be
shared with people who may not have the Notebook application installed.‘Clean’ notebooks containing only the input (code and markdown) and metadata,
convenient for version control (e.g. in git), making diffing and merging easy.
Package: nb-http-client
nb_http_client 是 python 史上性能最强的http客户端0. 安装 pip install nb_http_client1.测试比较环境为 win10 + python3.7 + 2.5ghz主频 + 单进程 + 线程池e5 2678v3 主频很差，cpu架构很落后，如果用最新的13代i9超频最高可以达到6ghz，测试指标有可能可以提高100%。
但是不同的请求客户端都是跑在同一个2.5ghz主频上，所以是公平的比较。2.不同的请求客户端，请求同一个server服务端，测试结果线程池 + requests  390次每秒
线程池 + requests 同一个session 420次每秒
线程池 + urllib3    1070次每秒
线程池 + nb_http_client  2500次每秒
asyncio + aiohttp    1480次每秒
线程池 + pycurl 30次每秒3.测试代码3.1服务端fromaiohttpimportweb"""主要是用来测试http客户端池的最牛性能。不能让服务端成为客户端测试性能的瓶颈。"""routes=web.RouteTableDef()@routes.get('/')asyncdefhello(request):returnweb.Response(text="Hello, aio")app=web.Application()app.add_routes(routes)web.run_app(app,port=5678)3.2.1 线程池 + 各种请求客户端importtypingfromhttp.clientimportHTTPConnectionimporttimeimportdecorator_libsimportrequestsimporturllib3importpycurlfromioimportBytesIOfromnb_http_clientimportObjectPool,HttpOperatorfromthreadpool_executor_shrink_ableimportBoundedThreadPoolExecutorhttp_pool=ObjectPool(object_type=HttpOperator,object_pool_size=50,object_init_kwargs=dict(host='127.0.0.1',port=5678),max_idle_seconds=30)requests_session=requests.session()urllib3_pool=urllib3.PoolManager(100)thread_pool=BoundedThreadPoolExecutor(50)deftest_by_requets():# 这个连接池是requests性能5倍。 headers = {'Connection':'close'} 为了防止频繁报错 OSError: [WinError 10048] 通常每个套接字地址(协议/网络地址/端口)只允许使用一次。resp=requests.get('http://127.0.0.1:5678/',headers={'Connection':'close'})print(resp.text)deftest_by_requests_session():resp=requests_session.get('http://127.0.0.1:5678/',headers={'Connection':'close'})print(resp.text)deftest_by_urllib3():resp=urllib3_pool.request('get','http://127.0.0.1:5678/',headers={'Connection':'close'})# urllib3 第二快，次代码手动实现的http池是第一快。print(resp.data)deftest_by_pycurl():# 这个号称c库，性能是最差的buffer=BytesIO()c=pycurl.Curl()c.setopt(c.URL,'http://127.0.0.1:5678/')c.setopt(c.WRITEDATA,buffer)c.perform()c.close()body=buffer.getvalue()print(body.decode())deftest_by_nb_http_client():withhttp_pool.get()asconn:# type: typing.Union[HttpOperator,HTTPConnection]  # http对象池的请求速度暴击requests的session和直接requests.getr1=conn.request_and_getresponse('GET','/')print(r1.text[:10],)if__name__=='__main__':withdecorator_libs.TimerContextManager():forxinrange(30000):# time.sleep(5)  # 这是测试是否是是智能节制新建对象，如果任务不密集，不需要新建那么多对象。thread_pool.submit(test_by_pycurl,)# TOOD 这里换成不同的函数来测试，然后在控制台搜索时分秒就能看到每一秒的响应个数了。thread_pool.shutdown()time.sleep(10000)# 这个可以测试nb_http_client的连接长时间不使用，能自动摧毁
Package: nbhub
NbHub 💫A command-line application for sharing jupyter notebooks to the web.Simple, minimal, hacky.This is alpha! Check theroadmapand don't blow up my server.InstallationNbHub requires pyhton 3.6 or newer.$pipinstallnbhubI actually recommend you usepipxfor installing CLI tools.UsageOnce installed, simply run:$nbhubmy_cool_notebook.ipynbAfter a couple of prompts you will receive a linklike this one.ContributingNote: If you want to develop new features and not sure where to start, justemail me.Start by forking this repo.Install the development dependencies (you probably want to do this in avirtual environment):$pipinstall-rrequirements-dev.txtMake sure the tests run:$pytestBugs and other issuesshould be reportedright here.RoadmapOpen source the web server.Date limit on notebooks to be destroyed.Ability to set password on link creation for private notebooks.Improvements to CLI like only accepting notebook format.Web server improvements (auto clean ups, encryption, etc.)
Package: nbib
nbibA parser of the nbib citation format exported by PubMed & other NCBI tools.Aboutnbibis opinionated in what data it parses and how it is structured, with the aim of supporting the most common use
cases. Unlike other parsers, which produce "flat" data (i.e. string key-value pairs),nbib:Parses strings into their correct data type, as possibleCreates hierarchical and list objects when appropriateInstallInstall the latest production fromPyPi:pipinstallnbibTo install the latest dev version:pipinstallgit+https://github.com/holub008/nbib.gitUsingExampleimportnbibrefs=nbib.read("""PMID- 1337\nTI  - `nbib` Rocks!\n\n""")Generalnbibdoes:Provide parsing of both nbib files (read_file()) and strings (read())Guarantee that the output format will remain backwards compatible, within a major releaseThe type of an attribute will never change within a major releaseAn attribute will never change name within a major releaseNew attributes may be added with a minor releaseGuarantee that the order of output refs matches the incoming order. Moreover, this holds for all list attributes
(e.g. authors)nbibdoes not:Allow users to customize parsing methodsnbibopines that performing the "obvious" parsing covers 99% of use cases, so don't push this work onto the
clientPlay nicely with improperly formatted files - exceptions are aggressively thrown for unexpected inputsGiven PubMed is effectively the sole producer of these files, the risk is minimalPlease report any issues encountered!Have great run time performanceAs of writing, a 10,000 ref file (PubMed max export size) of 829K lines took 9.2 seconds on a standard laptop.
For comparison, the ris parsing packagerispy, which produces flat string data, took 2.2 seconds for 10K refs
(670K lines).If your use case needs faster performance, please file an issue!DevelopingIssues and pull requests are always welcome.TestingTo set up the project:pipenvinstall--devTo run tests:pipenvrunpython-mpytest
Package: nbic
nbicDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nnbic--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallnbicnbdev# activate conda environment$condaactivatenbic# make sure the nbic package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the nbic package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/nbic.gitor fromconda$condainstall-cdsm-72nbicor frompypi$pipinstallnbicDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: nbindex-jupyter
Index for Jupyter notebooksnbindexis a small package containing several Javascript additions for Jupyter
notebooks like a Table of Content or a Code Hider. These additions are designed
to be executed in a notebook cell so that the added HTML and Javascript functions
will remain hidden in the cell output and can be exported by the regular HTML
export of Jupyter.Installationpip install nbindex-jupyterUsageTable of Content and Code Hiderfrom nbindex import inline
inline.tableofcontent()
inline.codehider()from nbindex import floating
floating.tableofcontent()
floating.codehider()Numbered objects (Figures, ...)from nbindex import numbered
numbered.Figure()
numbered.Figure("This is a nice Figure.")
numbered.object("Figure")
Package: nbi-stat
Statistical tools for teaching at NBIThis package extends some of the existing tools inNumPyandSciPywith
some useful features designed to make life easier for the students at
the Niels Bohr Institute.TopicsReporting scientific results, including proper roundingTabulation of data useful in Jupyter NotebooksVisualisation of data in 1 and many dimensionsRobust calculations of sample means, variances, and covariances, for
unweighted and weighted samples.  For weighted samples, bothfrequencyandnon-frequency weights are supported.HistogrammingSampling of arbitrary PDFsCurve fitting usingLinear least squaresNon-linear least squaresMaximum likelihood estimatesExtendedBinnedRepresentation of fit confidence contoursHyppthesis testingConfidence intervalsTemplate fittingSimultaneous fitting over regions (channels)Likelihood calculationsExamples of useThis
notebookgives examples of use.Book on Statistics with PythonThe bookStatistics Overview - With
Pythonlays out much of the theoretical foundation for the tools available.Some other notes on statistics is available from the same site, includingPrinciple Component Analysisas a more robust alternative to boosted decision treesBootstrap and Jackknifeand why you should be careful with these estimatesCoefficent of determinationand why you shouldn't use itApplication Programming Interface DocumentationThe API isdocumented.2019 ©Christian Holm Christensen
Package: nbit
==================================================================NBit: Node! Build IT!==================================================================NBit: Build tool for node.js projectsThis is simple build tool allows you to make one js file from sources listed in template.Installation------------$ pip install nbitUsage------ Create template file with sources listed at JsDoc with tag @src, as follows:```/*** @src** path/to/src/1.js* path/to/src/1.js* path/to/src/1.js*/```- Run nbit:$ nbit path/to/templte.jsalso you can specify output direcroty (defauld: ./bin):$ nbit path/to/template.js --out path/to/out/dirif you want to build many templates, you can list them all:$ nbit path/to/template1.js path/to/template2.jsnbit names output files with names of templates- You can use it with npm. Just add nbit into scripts to your package.json:```...,"scripts": {"build": "nbit path/to/template.js"},...```
Package: nbkickoff
nbkickoffLaunch an IPython Notebook from a template notebook file.RequirementsTested with:Jupyter Notebook 5.5 (or higher)Python 3.6Should also work with other Python 3.x versions.InstallationFor general use, you can installnbkickoffusingpip:pip install nbkickoffTo upgrade to the latest version:pip install -U nbkickoffFor local development:git clone https://github.com/lumicks/nbkickoff.git
cd nbkickoff
pip install -e .UsageInvoke using either the full:python -m nbkickoff TEMPLATEFILE TARGETFILE [VAR=VALUE [VAR2=VALUE ...]]Or, if the corresponding entry point script is on thePATH, the shorter version:nbkickoff TEMPLATEFILE TARGETFILE [VAR=VALUE [VAR2=VALUE ...]]Here,TEMPLATEFILEis an IPython Notebook file that functions as a template. This file is copied toTARGETFILE(which must not exist yet), after which all variables in the notebook's cells are substituted with their corresponding values.So, if your template notebook contains a cell with the text:my_python_var = '{{ foo }}'and you invokenbkickoffusing:nbkickoff template.ipynb my_notebook.ipynb foo=barThen your shiny new notebook will have a cell containing:my_python_var = 'bar'If a suitable Jupyter server process is already running,nbkickoffopens the new notebook in the existing server. Otherwise, a new Jupyter server process is started. This behavior is based on that of thenbopentool, with the notable change thatnbkickoffalways returns immediately; any newly spawned Jupyter server process is detached.
Package: nbkit
nbkitDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nnbkit--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallnbkitnbdev# activate conda environment$condaactivatenbkit# make sure the nbkit package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the nbkit package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/nbkit.gitor fromconda$condainstall-cdsm-72nbkitor frompypi$pipinstallnbkitDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.