Saravanan666/pypi-org huggingface dataset:
Package: nmmn
nmmnpackageTools for astronomy, data analysis, time series, numerical simulations, gamma-ray astronomy and more! These are modules I wrote which I find useful—for whatever reason—in my research.List of modules available (more info here):astro: astronomydsp: signal processinglsd: misc. operations on arrays, lists, dictionaries and setsstats: statistical methodssed: spectral energy distributionsplots: custom plotsfermi: Fermi LAT analysis methodsbayes: Bayesian tools for dealing with posterior distributionsgrmhd: tools for dealing with GRMHD numerical simulationsVery basicdocumentationfor the package. Generated with Sphinx.InstallationYou have a couple of options to install the module:1. Install usingpip:pip install nmmn2. Install the module on the system’s python library path:git clone https://github.com/rsnemmen/nmmn.git
cd nmmn
python setup.py install3. Install the package with a symlink, so that changes to the source files will be immediately available:git clone https://github.com/rsnemmen/nmmn.git
cd nmmn
python setup.py developThis last method is preferred if you want the latest, bleeding-edge updates in the repo. You may need to run the last command withsudo.UpdatingIf you installed withpip(method 1), to upgrade the package to the latest stable version usepip install --upgrade nmmnIf you installed with thesetup.pyscript and thedevelopoption (method 3), usecd /path/to/nmmn
git pullUsageFirst import the specific module that you want to use:import nmmn.lsdThen call the method you need. For example, to remove allnanandinfelements from anumpyarray:importnumpyasnp# generates some array with nan and infx=np.array([1,2,np.nan,np.inf])# removes strange elementsxok=nmmn.lsd.delweird(x)For more examples, please refer to theexamples doc.TODOneed more examples of how to use the modulesadd IFU data cubes method (refer toifscube)LicenseSeeLICENSEfile.If you have suggestions of improvements, by all means please contribute with a pull request!  :)The MIT License (MIT). Copyright (c) 2020Rodrigo NemmenVisit the author's web pageand/or follow him on twitter (@nemmen).
Package: nmodels
Ngram ModelThis is a basic python module that creates and then pads bigrams from a given sentence.Python RequirementsAny Python version 3.6 and above is supported.InstallInstall this pip package by typing the following into the command prompt:pip install nmodelsNmodelsTo import the module function, proceed as follows:>>> import ngrams
>>> from nmodels.gram import simple_bigram
>>> string = "Hello world, how are you" 
>>> [('<START>', 'Hello'), ('Hello', 'world,'), ('world,', 'how'),
 ('how', 'are'), ('are', 'you'), ('you', '<END>')]
Package: nmodl-preprocessor
nmodl_preprocessorThis program optimizes NMODL files for the NEURON simulator.It scans all of your project's files to perform aggressive whole program optimization.It performs the following optimizations to ".mod" files:Hardcode the parametersHardcode the temperatureHardcode any assigned variables with constant valuesInline all functions and proceduresConvert assigned variables into local variablesThese optimizations can improve run-time performance and memory usage by between
5% and 15%.InstallationPrerequisitesPythonandpipThe NMODL Frameworkpip install nmodl_preprocessorUsage$ nmodl_preprocessor [-h] project_dir [model_dir ...]

positional arguments:
  project_dir  root directory of all simulation files
  model_dir    input directory of nmodl files

options:
  -h, --help   show this help message and exitTipsAlways check your results for accuracy and correctness.Do not use this tool with neuron's graphical user interface "nrngui".Keep your projects in separate directories.Use unique and descriptive variable names.Remove unnecessary VERBATIM statements.
Package: nMOLDYN
nMOLDYN is an interactive program for the analysis of Molecular
Dynamics simulations. It is especially designed for the computation
and decomposition of neutron scattering spectra. The structure and
dynamics of the simulated systems can be characterized in terms of
various space and time correlation functions. To analyze the dynamics
of complex systems, rigid-body motions of arbitrarily chosen molecular
subunits can be studied.
Package: nmongo
MongoDB 3.x client for CPython and MicroPython.It has mongo shell like APIs.Support platformCPython 3.5+https://www.python.orgMicroPython (unix)http://docs.micropython.org/en/latest/unix/(I haven’t tried any other platforms.)Support databaseMongoDB 3.2, 3.4, 3.6Azure CosmosDB (MongoDB API)InstallCPython$ python3 -m pip install nmongoMicroPythonif you use MicroPython patch datetime.pyhttps://github.com/nakagami/nmongo/blob/master/tzinfo.patchhttps://github.com/micropython/micropython-lib/pull/338$ micropython -m upip install micropython-time micropython-datetime
$ patch --directory=$HOME/.micropython/lib < tzinfo.patch
$ micropython -m upip install nmongoExampleConnect to Database>>> import nmongo
>>> db = nmongo.connect('servername', 'somewhatdatabase')
>>>SSL connection>>> import nmongo
>>> db = nmongo.connect('servername', 'somewhatdatabase', use_ssl=True)
>>>or>>> import nmongo
>>> db = nmongo.connect('servername', 'somewhatdatabase', use_ssl=True, ssl_ca_certs='/path/to/something-cert.crt)
>>>User Authentication>>> import nmongo
>>> db = nmongo.connect('servername', 'somewhatdatabase', user='user', password='password')
>>>Connect to Azure CosmosDB (MongoDB API)>>> import nmongo
>>> db = nmongo.connect('xxx.mongo.cosmos.azure.com', 'somewhatdatabase', user='xxx', password='password', port=10255, use_ssl=True)
>>>Create>>> db.fruits.insert({'name': 'apple', 'price': 200})
1
>>> db.fruits.insert([{'name': 'orange', 'price': 100}, {'name': 'banana', 'price': 50}])
2
>>> db.fruits.count()
3
>>>Read>>> cur = db.fruits.find({'name': 'banana'})
>>> cur.fetchone()
{'price': 50, '_id': ObjectId("5826b2313d28909ce9f6ea63"), 'name': 'banana'}
>>> cur = db.fruits.find()
>>> cur.fetchall()
[{'price': 200, '_id': ObjectId("5826b2273d28909ce9f6ea61"), 'name': 'apple'}, {'price': 100, '_id': ObjectId("5826b2313d28909ce9f6ea62"), 'name': 'orange'}, {'price': 50, '_id': ObjectId("5826b2313d28909ce9f6ea63"), 'name': 'banana'}]
>>>Update>>> db.fruits.update({'name': 'banana'}, {'$inc': {'price': 20}})
{'nModified': 1, 'ok': 1, 'n': 1}
>>> cur = db.fruits.find({'name': 'banana'})
>>> cur.fetchone()
{'_id': ObjectId("5823dd6d3d28909ce9f6e99c"), 'name': 'banana', 'price': 70}
>>>Delete>>> db.fruits.remove({'name': 'banana'})
1
>>> db.fruits.count()
2Count each collection records>>> for collection in db.getCollections():
...     print(collection.name, collection.count())
...
fruits 3
>>> db.getCollection('fruits').count()
3
>>>See also mongo Shell Methods (Collection and Database sections).https://docs.mongodb.com/manual/reference/method/#collectionhttps://docs.mongodb.com/manual/reference/method/#databaseFeatures Not ImplementedGridFS
Package: nmoo
noisy-mooA wrapper-based framework forpymooproblem modification
and algorithm benchmarking. Initially developed to testKNN-averaging[^quatic21].InstallationSimply runpipinstallnmooGetting startedIn a notebookSeeexample.ipynbfor a quick example.For larger benchmarksFor larger benchmarks, you may want to use nmoo's CLI. First, create a module,
sayexample.py,
containing your benchmark factory (a function that returns yourbenchrmark),
saymake_benchmark(). Then, run it usingpython-mnmoorun--verbose10example:make_benchmarkRefer topython-mnmoo--helpfor more information.Main submodules and classesnmoo.benchmark.Benchmark: ABenchmarkobject represents... a benchmark
🤔. At construction, you can specify problems and algorithms to run, how many
times to run them, what performance indicators to compute, etc. Refer tonmoo.benchmark.Benchmark.__init__for more details.nmoo.wrapped_problem.WrappedProblem: The main idea ofnmoois to wrap
problems in layers. Each layer should redefinepymoo.Problem._evaluateto
intercept calls to the wrapped problem. It is then possible to apply/remove
noise, keep a call history, log, etc.nmoo.denoisers: Sublasses ofnmoo.wrapped_problem.WrappedProblemthat
implement denoising algorithms. In a simple scenario, a synthetic problem
would be wrapped in a noise layer, and further wrapped in a denoising layer
to test the performance of the latter.nmoo.noises: Sublasses ofnmoo.wrapped_problem.WrappedProblemthat apply
noise.ContributingDependenciespython3.8or newer;requirements.txtfor runtime dependencies;requirements.dev.txtfor development dependencies (optional);make(optional).Simply runvirtualenvvenv-ppython3.8
../venv/bin/activate
pipinstall-rrequirements.txt
pipinstall-rrequirements.dev.txtDocumentationSimply runmakedocsThis will generate the HTML doc of the project, and the index file should be atdocs/index.html. To have it directly in your browser, runmakedocs-browserCode qualityDon't forget to runmaketo format the code followingblack,
typecheck it usingmypy, and check it against coding
standards usingpylint.[^quatic21]: Klikovits, S., Arcaini, P. (2021). KNN-Averaging for Noisy
Multi-objective Optimisation. In: Paiva, A.C.R., Cavalli, A.R., Ventura
Martins, P., Pérez-Castillo, R. (eds) Quality of Information and
Communications Technology. QUATIC 2021. Communications in Computer and
Information Science, vol 1439. Springer, Cham.https://doi.org/10.1007/978-3-030-85347-1_36
Package: nmpyc
nMPyCnMPyCis a Python library for solving optimal control problems via model predictive control (MPC).nMPyCcan be understood as a blackbox method. The user can only enter the desired optimal control problem without having much knowledge of the theory of model predictive control or its implementation in Python. Nevertheless, for an advanced user, there is the possibility to adjust all parameters.This library supports a variety of discretization methods and optimizers includingCasADiandSciPysolvers.In summary,nMPyCsolves nonlinear finite horizon optimal control problemssolves nonlinear optimal control problems with model predicitve control (MPC)uses algorithmic differentation viaCasADican chose between different discretization methodscan chose between different solvers for nonlinear optimization (depending on the problem)supports time-varying optimal control problemssupports the special structure of linear-quadratic optimal control problemssupports discounted optimal control problemscan save and load the simulation resultsThenMPyCsoftware is Python based and works therefore on any OS with a Python distribution.nMPyChas been developed by Jonas Schießl and Lisa Krügel under the supervision of Prof. Lars Grüne at theChair of Applied Mathematicof University of Bayreuth.nMPyCis a further devolpement in Python of theMatlab codethat was implemented for theNMPC Bookfrom Lars Grüne and Jürgen Pannek.InstallationThe easiest way to install thenMPyCpackage is using PIP by runningpip install nmpycThis command will automatically add the package to the Python default path and install the required Python packages and their dependencies which areCasADi,osqp,NumPy,SciPy,matplotlibanddill.For further informations about the Python library please visit thenMPyC Documentation.
Package: nmqn
nmqnサイトの変更をCSSの差分でレポーティングするためのツールです。Installationpandocに依存しています。# Macの場合brewinstallpandoc
pipinstallnmqnUsage設定ファイルの仕様は、GitHubリポジトリのsample.yamlを確認してください。# sample.yamlの設定を基にクロールnmqncrawl-csample.yaml# 前日分と比較し、HTMLのレポートを生成nmqncompare-csample.yaml
Package: nmr
🔢nmr: name all canonical things 🔢Convert each canonical thing into a number, and then that number into a unique,
non-repeating name from a short list of common, short English words… or use a
word list of your choice.Installs both a module namednmrand an executable callednmr.pyEXAMPLEimportnmrassertnmr(0)==['the']assertnmr(2718281828)==['the','race','tie','hook']foriinrange(-2,3):print(i,':',*nmr(i))# Prints#   -2 : to#   -1 : of#   0 : the#   1 : and#   2 : a
Package: nmraspecds
Welcome! This is the documentation for nmraspecds, a Python package for reading in, manipulating and plotting NMR spectra with the ultimate goal to document all steps and make the history of the processed spectrum reproducible.FeaturesA list of features:Dataset containing main information on the dataset (not yet completed).And to make it even more convenient for users and future-proof:Open source project written in Python (>= 3.7)Developed fully test-drivenExtensive user and API documentationWarningnmraspecds is currently under active development and still considered in Alpha development state. Therefore, expect frequent changes in features and public APIs that may break your own code. Nevertheless, feedback as well as feature requests are highly welcome.InstallationTo install the nmraspecds package on your computer (sensibly within a Python virtual environment), open a terminal (activate your virtual environment), and type in the following:pipinstallnmraspecdsLicenseThis program is free software: you can redistribute it and/or modify it under the terms of theBSD License.
Package: nmrbox-iptables
Utility for setting ip table rulesUsagePackage is undergoing development. There is a command line utility, addrule, which accepts a YAML file.
See ./lib/python3.8/site-packages/example/multitest.yaml for an example.from nmrbox_iptables import ManagerIt is best used as a context manager.addrule add <yaml file>addrule remove <yaml file>
Package: nmr-classifier
# nmr-classifierJisun Kangnmr-classifier is the Python implementation of the nuclear norm regression for classification.
This package uses facial structural information to recognize facial images.## Installation### User installation
The easiest way to install scikit-learn is using`{Python} pip install nmr_classifier `## Resource
-https://github.com/riverKangg/nmr_classifier.git## Recommendations
- Python3
Package: nmrespy
NMR-EsPy:NuclearMagneticResonanceEstimation inPython is a Python package developed for estimating the parameters that
describe NMR data.DocumentationFor installation, help on getting started, API reference etcHTML:https://foroozandehgroup.github.io/NMR-EsPy/PDF:https://github.com/foroozandehgroup/NMR-EsPy/blob/gh-pages/nmr-espy.pdfExample of 1D EstimationExample of 2DJ Estimation
Package: nmrglue
What is nmrglue?nmrglue is a module for working with NMR data in Python. When used with the
numpy, scipy, and matplotlib packages nmrglue provides a robust interpreted
environment for processing, analyzing, and inspecting NMR data.Important LinksLanding page:http://www.nmrglue.comDocumentation:http://nmrglue.readthedocs.org/en/latest/index.htmlExamples:http://nmrglue.readthedocs.org/en/latest/examples/index.htmlMailing List:https://groups.google.com/forum/#!forum/nmrglue-discussSource code:https://github.com/jjhelmus/nmrglueWhat can nmrglue do?nmrglue has the ability to read, write and convert between a number of common
NMR file formats including Varian, Bruker, NMRPipe, and Sparky files. The
files, which are represented in python as dictionaries of spectral parameters
and Numpy array objects, can be easily examined, modified and processed as
desired.nmrglue provides a number of common functions for processing NMR data such as
apodization, spectral shifting, Fourier and other transformations, baseline
smoothing and flattening, and linear prediction. In addition new processing
schemes can be implemented easily using the nmrglue provided functions and the
multitude of numerical routines provided by the Numpy and Scipy packages.When used in conjunction with the matplotlib (or other) python plotting
library nmrglue can be used to create publication quality figures of NMR
spectrum or examine data interactively.nmrglue can be used to analyze NMR data, with routines to perform peak
picking, multidimensional lineshape fitting (peak fitting), and peak
integration. New analysis methods can be rapidly developed and tested in
Python or by integrating Fortran and C/C++ code.To get started, see our online documentation:http://nmrglue.readthedocs.org/en/latest/index.htmlCiting nmrglueIf you find nmrglue useful in your research please cite the package as:J.J. Helmus, C.P. Jaroniec, Nmrglue: An open source Python package for the
analysis of multidimensional NMR data, J. Biomol. NMR 2013, 55, 355-367,http://dx.doi.org/10.1007/s10858-013-9718-x.
Package: nmrgnn-data
Data for NMR GNNThis contains the parsing scripts and data used for ourGNN chemical shift predictor model.Installpipinstallnmrgnn-dataWorking in PythonHere's an example of how to load and work with data in python. The records
are loaded as a tensorflow dataset (read more here), but can be used in a for loop as shown below.importnmrdatadataset=nmrdata.load_records('data/metabolite-records.tfrecord')forrecordindataset:# get single recordbreakprint(record.keys())output:dict_keys(['natoms', 'nneigh', 'features', 'nlist', 'positions', 'peaks', 'mask', 'name', 'class', 'index'])Access positions as a numpy arrayrecord['positions'].numpy()output:array([[ 0.83740795,  0.09760247,  0.2959486 ],
       [-0.562893  ,  0.00262405, -0.00434441],
       [-1.0725924 , -0.37873718,  0.9061929 ],
       [-0.75536764, -0.72710234, -0.8159687 ],
       [-1.0367495 ,  0.9557108 , -0.27988592],
       [ 1.2855262 , -0.8334997 ,  0.10487328],
       [ 1.3046683 ,  0.8834019 , -0.20681578]], dtype=float32)Get chemical shiftsrecord['peaks'].numpy()array([0.  , 0.  , 2.59, 2.59, 2.59, 0.  , 0.  ], dtype=float32)Numpy ErrorIf you see this error:ValueError:numpy.ndarraysizechanged,mayindicatebinaryincompatibility.Expected88fromCheader,got80fromPyObjectTry re-install numpypipuninstall-ynumpy&&pipinstallnumpyParsing ScriptsTo install with the parsing functionality, use thiscondainstall-comniaopenmm
pipinstallnmrgnn-data[parse]Working with DataAll commands below can have additional information printed using the--helpargument.Find pairsFind pairs of atoms with chemical shifts that are neighbors and sort them based on distance.nmrdatafind-pairsstructure-test.tfrecords-data.tfrecordALA-HALA-NCount NamesGet class/atom name counts:nmrdatacount-namesstructure-test.tfrecords-data.tfrecordValidateCheck that records are consistent with embeddingsnmrdatavalidate-embeddingsstructure-test.tfrecords-data.tfrecordCheck that neighbor lists are consistent with embeddingsnmrdatavalidate-nliststructure-test.tfrecords-data.tfrecordCheck that peaks are reasonable (no nans, no extreme values, no bad masks)nmrdatavalidate-peaksstructure-test.tfrecords-data.tfrecordOutput LablesTo extract labels ordered by PDB and residue:nmrdatawrite-peak-labelstest-structure-shift-data.tfrecordtest-structure-shift-record-info.txtlabels.txtMaking New DataSee commandsnmrparse shiftml,nmrparse metabolites,nmrparse shiftxwhich are parsers for various databases.From RefDB FilesThis requires a pickled python object calleddata.pbto be in the directory. It is
a list ofdicts containingpdb_file(path to PDB),pdb(PDB ID),corr(path to.corrfile), andchain(which chain).chaincan be_to indicate use first chain.nmrparseparse-refdbdirectoryname--pdb_filterexclude_ids.txtCitationPlease citePredicting Chemical Shifts with Graph Neural Networks@article{yang2021predicting,title={Predicting chemical shifts with graph neural networks},author={Yang, Ziyue and Chakraborty, Maghesree and White, Andrew D},journal={Chemical science},volume={12},number={32},pages={10802--10809},year={2021},publisher={Royal Society of Chemistry}}
Package: nmrml2isa
Overviewnmrml2isa is a Python3 program that can be used to generate an ISA-Tab structured
investigation out of nmrML files, providing the backbone of a study that can then be
edited with an ISA editing tool (seeMetaboLights pre-packaged
ISA Creator)InstallSee theInstallation pageof
theonline documentation.UseSee theUsage pageand
theExamples pagefor
more detailed descriptions of usage and examples.CLIThe parser comes with a simple one-liner:mzml2isa-i/path/to/nmrml_files/-o/path/to/out_folder-sname_of_studyModuleIt possible to importnmrml2isaas a Python module:fromnmrml2isaimportparsingin_dir="/path/to/nmrml_files/"out_dir="/path/to/out_folder/"study_identifier_name="name_of_study"parsing.full_parse(in_dir,out_dir,study_identifier_name)Meta extractionIf you just want to extract the meta information without writing them
in ISA-Tab files, it is possible to do so either by running thenmrml2isa.nmrmlmodule as main:python-mnmrml2isa.nmrml/path/to/your/file.nmrML# this will produce a JSON dict of all extracted metadataOr within a python program with thenmrmlsubmodule:fromnmrml2isaimportnmrmlnmrml_path="/path/to/file.nmrML"nmrml_meta=nmrml.nmrMLmeta(nmrml_path)# python dictionnaryprint(nmrml_meta.meta)# json dictionnaryprint(nmrml_meta.meta_json)
Package: nmrml2isa-qt
nmrml2isa-qt============A PyQt interface for nmrml2isa parser'''''''''''''''''''''''''''''''''''''Overview--------This program is a Graphical User Interface for the`nmrml2isa <https://github.com/althonos/nmrml2isa>`__ parser. It providesan easy-to-use interface to convert nmrML files to an ISA-Tab Study. Itwas made with Python3 and PyQt5.Install-------With PIP~~~~~~~~If ``pip`` is present on your system (comes along most of Python install/ releases), it can be used to install the program and its dependencies:.. code:: bashpip3 install nmrml2isa-qtWithout PIP~~~~~~~~~~~Once dependencies installed, clone the **nmrml2isa-qt** repository to afolder with writing permissions:.. code:: bashgit clone git://github.com/althonos/nmrml2isa-qtAfter that, either run the GUI directly:.. code:: bashpython3 run.pyOr install it locally to run with ``nmrmlisa-qt`` command:.. code:: bashcd nmrml2isa-qt && python3 setup.py installUse---Open the GUI with the ``nmrml2isa-qt`` command. To simply parse **.nmrML**files to **ISA**, select the directory containing your files. Withdefault settings, the program will create the new ISA files in thatfolder, assuming the folder's name is the study identifier (*MTBSLxxx*for instance for MetaboLights studies). This can be changed by untickingthe ``Export result to directory of each study`` box. Once parametersare set up, click the ``Convert`` button to start the parser.MetaboLights------------Generating a study to upload on MetaboLights requires pieces ofinformation the parser cannot guess from the mzML file alone. To providemore metadata to your final ISA-Tab files, use the ``Add Metadata``button to open a new window and update details about your study. Still,even with all the required fields filled, **the generated ISA needs tobe enhanced after the end of the parsing** (using for instance`Metabolight pre-packaged ISACreator <http://www.ebi.ac.uk/metabolights/>`__ to add missing fields).Missing information required for MetaboLights upload are at the moment:- Study Factors (sample dependent, must be added to the *study* fileand to the *investigation* file)- Metabolite Assignment Files- Study DesignsTODO-----  Either add a ``metabolite assignment file`` field to main window orchange the **nmrml2isa** parser behaviour so that it successfullydetects metabolite assignment files and add them to the study file.License-------GPLv3