Saravanan666/pypi-org huggingface dataset:
Package: ncconvert
nc-convertConvert netCDF files to other formatsUsagepipinstall"ncconvert[cli]"ncconvertto_csvdata/*.nc--output-diroutput_data/--verboseFormats other than csv are also supported. To see more information about supported formats, runncconvert--helpA python API is also available for each format, e.g.:importxarrayasxrfromncconvertimportto_csvds=xr.open_dataset("netcdf-file.nc")to_csv(ds,"output_folder/filename.csv")DevelopingCreate a python environment using at least python 3.8, then install the requirements:pipinstall".[dev,cli]"Runmake coverageto generate a report of test coverage.Releasing the package is as simple as creating a tagged release in GitHub. Make sure to create a new tag usingvX.Y.Zformat, whereXis the major version,Yis the minor version, andZis the micro version. Set the release title
toX.Y.Z.
Package: nccsv
nccsvnccsv - Ncurses CSV Editor written in PythonInstall Dev (on windows)$ python3 -m venv venv$ . venv/bin/activate(venv) $ pip install -r windows-cursesInstall Prod$ pip install nccsvUsageDev:(venv) $ python run.py [Filename]Prod:python -m nccsvScreenshotsTODO:ArchitectureWireframesHome / MenuFile selectorOpenCloseEditorLayout GridEntryMultiple viewsHome / MenuFile selectorEditorCustom widgetsLayout GridEntry (text editor with box)Scroll down / right[.] Delete contents of cell when you hit "DEL"Row / Col bars (A,B,C, 1,2,3)[.] Actually handle CSVsColorDocumentationPublish to pipTestsMisc folder is for miscellaneous test files to add features (pad scrolling, etc)NotesHow the Textbox should work:Is an ObjectConstr:Has x, y coord on the master screen?Do we want this?Can we make it have it's own coordinates, so we don't have to worry about the master screen?Has size definitionCustom callback if wantedDefault text (to load in)Props:Text to displayIs it highlightedInput:When calling edit_text(), it will edit the text and return it back on EnterWhen calling clear_text(), it will remove the text that is rendered + savedFeatures:Editable text fieldBox surrounding text field so we can see itHighlight-able (on hover)Easy to get the text programmaticallyHow the grid should work:Is an ObjectConstr:Has x, y coord on the master screenHas rows / colsProps:2D Matrix of TextboxesKeeps track of which box is selectedInput:Can scroll up/down/left/rightOn Enter, send input to Textbox object to handleOn Del, clear the input of the TextboxOn Ctrl + S, it savesOn Q, it quitsFeatures:Load a 2D matrix from a file (csv)Save a 2D matrix from a file (csv)Add rows and columns if user scrolls past what currently existsLICENSEMIT© 2020 Sebastian Safari
Package: ncdistribute
Provides the script ncdistributer.py that will package up a python script together with any non-standard libraries into a zip file
that can be executed on a computer with a python interpreter installed.It is similar to tools such as pyzzer, but aims to be even more straight-forward to use.Unlike projects such as Cython and Nuitka, the this aim of this tool is not to distribute a binary file, but to distribute
a python package that will work on standard installations of python.Usagencdistributer.py <filenames>…Feedback tonicholas.cole@gmail.comis welcome.
Package: ncdjango
ncdjangoNcdjango turnsDjangoprojects into map servers backed byNetCDFdatasets. It can be added Django project
to provide various web interfaces to NetCDF data and geoprocessing tools written in Python which operate on NetCDF data.Why?This project grew out of a need for a map server capable of delivering time-series raster data from NetCDF data, with
enough extensibility to support different web APIs for the same map service. The result is a Django app which adds a
range of map service capabilities to a Django project. Currently, ncdjango includes a partial implementation of theArcGIS REST APIwith the added feature of per-request styling.
It also includes a data interface which can provide summary information about service data and generate class breaks
(equal, quantile, or natural breaks) based on the service data.Ncdjango provides an admin API for creating and managing map services, and a geoprocessing framework which allows
clients to execute processing jobs against NetCDF result. Job results can be automatically published as new services,
meaning that a web client could call a geoprocessing job, and upon its completion, show the processed results in a map.Use casesNcdjango is used to provide map services of NetCDF data forData Basin. Data Basin users can
upload NetCDF datasets and view and share them in a web map, all with no programming or server coniguration. Example:NARCCAP Monthly Average Maximum Daily TemperatureIt is also used in theSeedlot Selection Toolboth to provide map services
of NetCDF data, and to implement the geoprocessing needs for the tool and map services of the results.DocumentationFull documentation availablehere.
Package: nc-dnsapi
nc_dnsapiA simple API wrapper for the netcup DNS APIfromnc_dnsapiimportClient,DNSRecordcustomer=123456api_key="your-personal-api-key"api_password="your-private-api-password"withClient(customer,api_key,api_password)asapi:# fetch recordsrecords=api.dns_records("example.com")forrecordinrecords:print(record)# fetch zone detailszone=api.dns_zone("example.com")print(zone)# update single recordapi.update_dns_record("example.com",DNSRecord("my-hostname","A","127.0.0.2",id=108125))# update list of recordsapi.update_dns_record("example.com",[DNSRecord("my-hostname","A","127.0.0.2",id=108125),DNSRecord("my-hostname2","A","127.0.0.2",id=108126)])# delete recordapi.delete_dns_record("example.com",DNSRecord("my-hostname","A","127.0.0.2",id=108125))# add recordapi.add_dns_record("example.com",DNSRecord("another-host","AAAA","::1"))# update zonezone=api.dns_zone("example.com")zone.refresh=3600api.update_dns_zone("example.com",zone)
Package: ncdr
ncdr es una aplicación DjangoComienzo rápidoAgregar la aplicaciónncdr` a su archivo de configuración ``settings.py, en la variableINSTALLED_APPSasi:INSTALLED_APPS = (
    ...
    "ncdr",
)Incluir la aplicación de encuestas en la URLconf de su proyecto
urls.py de este modo:(r'^ncdr/', include('ncdr.urls')),Ejecutar el comandopython manage.py syncdbpara crear los
modelos de las encuestas.Inicia el servidor y visitar la paginahttp://127.0.0.1:8000/admin/para crear una encuesta (Necesitas habilitar la interfaz administrativa
para poder crear encuestas).
Package: ncdu-compare
ncdu-compareCompare ncdu export files and shows differences to find out what consumes most space.Installation# from pypi
pip3 install ncdu-compare

# ... or from git
pip3 install git+https://github.com/yaroslaff/ncdu-compare.gitExample usage# measure current /var/log$ncdu/var/log/-0o/tmp/1# create extra 1M file... and just wait$sudoddif=/dev/zeroof=/var/log/zzz.logbs=1Mcount=11+0recordsin1+0recordsout1048576bytes(1.0MB,1.0MiB)copied,0.00229604s,457MB/s# measure /var/log again$ncdu/var/log/-0o/tmp/2# see top usage$ncdu-compare/tmp/1/tmp/2|sort-n|tail-n5575FILE/var/log/auth.log(273797>274372)12209FILE/var/log/daemon.log(62452009>62464218)12286FILE/var/log/syslog(63463377>63475663)1048576FILE/var/log/zzz.log(0>1048576)1074015DIR/var/log(3895153251>3896227266)Invalid unicode filenamesSometimes ncdu can produce invalid JSON files (seehttps://dev.yorhel.nl/ncdu/jsonfmt). When loading ncdu files ncdu-compare uses (default) 'replace' mode to handle unicode errors. You can  override it with-e/--erroroptions, e.g.--error ignore. More at python doc toopen().See alsoMy other projectPlus Sizeto detect changes in short-time (e.g. fast growing log files)ncdu-diffC + Python ncdu fork that can compare and diff results
Package: ncdu-dropbox
ncdu-dropboxGeneratesncducompatible JSON file for
Dropbox.You can use this script (with ncdu) to visualize which files and folders
are taking up all the space in your dropbox.UsageFirst, generate an oauth token from Dropbox’sapp
console.$ncdu-dropbox--token$TOKEN-odropbox.json$ncdu-fdropbox.jsonCaution: Due to limitations in Dropbox API, it takes about 10s to fetch
the information of ~5,000 files and folders. So, if you have millions of
files in your Dropbox, this script may be running for hours.Related projectsSeencdu-s3.
Package: ncdump-rich
FeaturesThis project provides an easy way of previewingnetCDFfiles with nicely
formatted text in your terminal. The information extracted from the.ncfiles are obtained in a similar way tothis example, with some
modifications. The source code used on the website can be downloaded asnetcdf_example.pywith:$curl-Ohttp://schubert.atmos.colostate.edu/~cslocum/code/netcdf_example.pyTo make the output more readable it is formatted using the python libraryrich.RequirementsThe project depends on the python packagesclick,netCDF4andrich.
Installation viapiporpipxensures that all dependencies are installed correctly.InstallationYou can installRich NcDumpviapipfromPyPI:$pipinstallncdump-richor perhaps even better viapipx:$pipxinstallncdump-richUsagePlease see theCommand-line Referencefor details.ExamplesUse the program as a previewer for.ncfiles, for example throughstpv.My own
forkprovides additional support for previewing.ncfiles using this project.Preview inlfSimilarly you can get preview of.ncfiles innnnby including an option for the
extensionncin thepreview-tuiplugin.nc) fifo_pager ncdump-rich "$1" ;;Preview innnnContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theGPL 3.0 license,Rich NcDumpis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: ncem
Featuresncemis a model repository in a single python package for the manuscriptFischer, D. S., Schaar, A. C. and Theis, F. Learning cell communication from spatial
graphs of cells. 2021.(preprint)InstallationYou can installncemviapipfromPyPI:$pipinstallncemCreditsThis package was created withcookietempleusingCookiecutterbased onHypermodern_Python_Cookiecutter.
Package: ncephes
NcephesThis package provides a python interface for theCepheslibrary.
It also supportsNumbaand itsnopythonmode.Usagefromncephesimportcprobprint(cprob.incbet(1.,3.,0.3))prints0.657.You can also call them inside a numba functionfromncephesimportcprobfromnumbaimportjit@jitdefnumba_incbet(a,b,x):returncprob.incbet(a,b,x)print(numba_incbet(1.,3.,0.3))and with nopython mode and nogil enabledfromncephesimportcprobfromnumbaimportjitincbet=cprob.incbet@jit(nogil=True,nopython=True)defnumba_incbet(a,b,x):returnincbet(a,b,x)print(numba_incbet(1.,3.,0.3))One can also statically link the compiled Cephes librariesncprobandncellf. Please, have a peek at theexamples/prj_namefor a minimalistic
example.InstallThe recommended way of installing it is viacondacondainstall-cconda-forgencephesAn alternative way would be via pippipinstallncephesRunning the testsAfter installation, you can test itpython-c"import ncephes; ncephes.test()"as long as you havepytest.AuthorsDanilo Horta-https://github.com/HortaLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: ncflag
NetCDF Flag Wrapper (ncflag)So... you want to inspect CF Compliant NetCDF flag variables?CF Compliant NetCDF Flag variables are integer flags associated with, or having:flag_valuesflag_meaningsflag_masks (optionally)Read theCF Conventions on flagsfor more information.TL;DRInstall the utility with with pip:pip install ncflagOn the command line, usencflag:Usage: ncflag [OPTIONS] NCFILE FLAG

Options:
  -v, --version                   Show the version and exit.
  --show_flags PATH               Print the flags this tool can inspect.
  --use_time_var TEXT
  -l [DEBUG|INFO|WARNING|ERROR|CRITICAL]
                                  log level
  --help                          Show this message and exit.Notes:Use --show_flags to discover what flags in a given file can be inspected.Limitation: can only inspect flags of at most one dimension. See details below for dealing with multidimensional flags.The nominal output with --use_time_var specified is shown below. Without use_time_var, the index along the
dimension will be printed instead of a iso 8601 timestamp.2017-11-27T21:07:41.543778: [u'data_quality_error']
2017-11-27T21:07:42.543812: [u'good_data']
2017-11-27T21:07:43.543807: [u'good_data']
2017-11-27T21:07:44.543802: [u'good_data']Multidimensional FlagsOccasionally, by some poor misfortune, you may encounter multidimensional flag variables. These are currently not
supported by the Command Line Interface (CLI), however, the FlagWrap class can still be used in code, or through an
interactive (IPython) session. TheFlagWrap.get_flags_set_at_indexcan be passed a tuple of indicies to get the
flags set in a multidimensional flag variable. Below is an example.fromncflagimportFlagWrapimportnetCDF4asncwithnc.Dataset("somenetcdf.nc")asnc_in:v=nc_in.variables["mutidim_variable"]print(v.shape)# --> (2, 10), is multidim.w=FlagWrap.init_from_netcdf(v)print(w.get_flags_set_at_index((0,0)))# --> ["good_quality_qf"]API and DocumentationTo use the FlagWrap in your own code, see the example
above for multidimensional flags.For documentation, please readflag_wrapper.py. It is one file
and is documented with comprehensive docstrings. The functions are
named descriptively. A following functions are available from a FlagWrap instance.- get_flag(self, flag_meaning)
- reduce(self, exclude_mask, axis=-1)
- get_flag_at_index(self, flag_meaning, i)
- get_flags_set_at_index(self, i, exit_on_good=False)
- find_flag(self, options)
- set_flag(self, flag_meaning, should_be_set, zero_if_unset=True)
- set_flag_at_index(self, flag_meaning, i)
- get_value_for_meaning(self, flag_meaning)
- get_mask_for_meaning(self, flag_meaning)TestingThere are tests, using both synthetic flags, as well as some more serious tests
for some fairly complex "in the wild" flags taken from a sample GOES-16 EXIS-L1b-SFXR product file.test/test_theoretical.pyis actually a very thourough read to help anyone really understand
what's possible and what's going on with these flags.Deploy to pip, after testing with python2 and python3:rm-rdist/
pythonsetup.pybdist_wheel--universal
twineuploaddist/*
Package: ncfp
README.md -ncfpThis repository contains code for a script that identifies and writes the corresponding nucleotide sequences for each protein in an input multiple sequence file to be used, for example, in backthreading coding sequences onto protein alignments for phylogenetic analyses.ncfpuses the NCBI accession or UniProt gene name (as appropriate) to identify source nucleotide sequences in the NCBI databases, download them, and write them to a file.Quickstart:ncfpat the command-lineProviding an input file of protein sequences as<INPUT>.fasta, and writing output to the directory<OUTPUT>, while specifying a user email to NCBI of<EMAIL>will generate two files:<OUTPUT>/ncfp_aa.fastaand<OUTPUT>/ncfp_nt.fasta.ncfp<INPUT>.fasta<OUTPUT><EMAIL>The file<OUTPUT>/ncfp_aa.fastacontains the amino acid sequences for all input proteins for which a corresponding nucleotide coding sequence could be identified, in FASTA format.The file<OUTPUT>/ncfp_nt.fastacontains nucleotide coding sequences, where they could be found, for all the input proteins, in FASTA format.Any input protein sequences for which a corresponding nucleotide sequence cannot be recovered, for any reason, are placed in the file<OUTPUT>/skipped.fas.To find out more about whatncfpcan do, tryncfp--helpat the command-lineDocumentationFor more detailed information aboutncfpas a program, or using the underlyingncbi_cds_from_proteinPython module, please see the stable version documentation athttps://ncfp.readthedocs.io/en/stable/LicenseUnless otherwise indicated, all code is licensed under the MIT license and subject to the following agreement:(c) The James Hutton Institute 2017-2019
(c) The University of Strathclyde 2019-2020
Author: Leighton Pritchard

Contact: leighton.pritchard@strath.ac.uk

Address:
Leighton Pritchard,
Strathclyde Institute for Pharmacy and Biomedical Sciences,
Cathedral Street,
Glasgow,
G4 0RE,
Scotland,
UKThe MIT LicenseCopyright (c) 2017-2019 The James Hutton Institute
Copyright (c) 2019-2020 The University of StrathclydePermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Package: ncfs
Neigbhorhood Component Feature SelectionThis is a Python implementation of Neighborhood Component Feature Selection,
originally introduced inYang et al. 2012.
NCFS is an embedded feature selection method that learns feature weights by
maximizing prediction accuracy in a leave-one-out KNN classifier.InstallationThe package can be with pip using the following command:pip install ncfsExamplefromNCFSimportNCFSX,y=NCFS.toy_dataset()feature_select=NCFS.NCFS()feature_select.fit(X,y)print(sum(feature_select.coef_>1))TestsTo compare results to the original paper run the following commandpython tests/generate_results.pyTo perform unit tests ensuring accurate distance calculations, run:python tests/test_distances.pyComparison with Original ResultsDistance metricThe original paper uses the Manhattan distance when calculating distances
between samples/features. While this implementation defaults to using this
distance, weights comparable with published results were only found using the
euclidean distance. However, while exact weights differed between distance
metrics, the selected features did not. Unfortunately, the original paper
did not link to the code used, and I've been unable to find a public
implementation of the aglorithm.Numerical stabilityNCFS uses the original kernel function when calculating probabilities; however, with
a large number of features, distance values can easily approach a large enough
value such that the negative exponent rounds to zero. This leads to division by
zero issues, and fitting fails. To get around this, small pseudocounts are added
to distances when a division by zero would otherwise occur. To keep distances
small, features should be scaled between 0 and 1 (enforced by NCFS).
Package: ncgocr
# NCGOCR[![](https://img.shields.io/travis/jeroyang/ncgocr.svg)](https://travis-ci.org/jeroyang/ncgocr)[![](https://img.shields.io/pypi/v/ncgocr.svg)](https://pypi.python.org/pypi/ncgocr)- Named Concept Gene Ontology Concept Recognition- Automatic recognize Gene Ontology (GO) concepts from context.## InstallationUsing 'pip' to install the Python module```bash$ pip install -U ncgocr```## Usage```pythonfrom ncgocr import Craft, GoData, NCGOCR, Corpus, evaluatecraft = Craft('data')corpus = craft.get_corpus()goldstandard = craft.get_goldstandard()print('Loading GO...')godata = GoData('data/craft-1.0/ontologies/GO.obo')print('Initiating NCGOCR...')ncgocr = NCGOCR(godata)print('Training the model...')ncgocr.train(corpus, goldstandard)print('Loading the testing corpus...')corpus_name = 'testing corpus'testing_corpus = Corpus.from_dir('data/craft-1.0/articles/txt/', corpus_name)print('predicting the results...')result = ncgocr.process(testing_corpus)print('Show the first 10 results...')print(result.to_list()[:10])print('Evaluate the results...')report = evaluate(result, goldstandard, 'Using the training corpus as the testing corpus')print(report)```## License* Free software: MIT license
Package: nChain
nChainnChain is a Python package that provides a flexible and efficient implementation to create LLM bots over extensible dataset.InstallationYou can install nChain directly from PyPI using pip:pipinstallnChainAlternatively, you can clone the repository and install from source:gitclonehttps://github.com/jamesliu/nChain.gitcdnChain
pipinstall.UsageHere are examples of how to use nChain to search ArXiv paper using embedding.nchainaddhttps://arxiv.org/abs/1409.0473
nchainaddhttps://arxiv.org/abs/2010.14701
nchainaddhttps://arxiv.org/abs/2203.02155
nchainaddhttps://arxiv.org/abs/2302.01318v1
nchainaddhttps://arxiv.org/abs/2309.17453
nchainaddhttps://arxiv.org/abs/2310.02304

nchainquery"Show me the Scaling Laws for Autoregressive Generative Modeling."nchainquery"Show me the algorithm about Large Lanuage Model Decoding with Speculative Sampling."nchainquery"How to handle streaming apps efficiently in LLM?"DocumentationFull documentation is availablehere.ContributingWe welcome contributions to nChain! If you're interested in contributing, please see ourcontribution guidelinesandcode of conduct.LicensenChain is licensed under the Apache License 2.0. See theLICENSEfile for more details.SupportFor support, questions, or feature requests, please open an issue on ourGitHub repositoryor contact the maintainers.ChangelogSee thereleasespage for a detailed changelog of each version.
Package: nchainz
nchainz - Automatic Method Chaining in PythonA metaclass for automatic method chaining. (Only for methods that returnNone, obviously)fromnchainzimportChainzclassA(metaclass=Chainz):defhas_return_value(self):return4defthis_is_chainable(self):print("hello")# implicitly returns selfa=A()asserta=a.this_is_chainable().this_is_chainable()asserta.has_return_value()==4What is Method Chaining?Method chainingdescribes the Syntax of not having to assign objects
between methods which are changing the state.For example, in JS one can just chain the array transformations like[1,2,3,4,5,6].filter(x=>x%2==0).map(x=>x*x).find(x=>x>30)How to do it manually?Pretty easy. You just returnself. Here is an example:classMyNum:def__init__(self,x):self.x=xdefinc(self):self.x+=1returnselfthree=MyNum(3)six=three.inc().inc().inc()assertthree.x+3==six.xInstallpip install nchainzUseJust use theChainzmetaclass:fromnchainzimportChainzclassMyClass(metaclass=Chainz):...Why? Like seriously, Why?I had my 5 minutes, I am sorry.Further readingIt's such a great read, you should really read it. (Written for Python 2)Meta-classes Made Easy