Saravanan666/pypi-org huggingface dataset:
Package: nas-bench-graph
NAS-Bench-GraphThis repository provides the official codes and all evaluated architectures for NAS-Bench-Graph, a tailored benchmark for graph neural architecture search.UsageFirst, read the benchmark of a certain dataset by specifying the name. The nine supported datasets are: cora, citeseer, pubmed, cs, physics, photo, computers, arxiv, and proteins. For example, for the Cora dataset:from readbench import lightread
bench = lightread('cora')The data is stored as adictin Python.Then, an architecture needs to be specified by its macro space and operations.
We consider the macro space as a directed acyclic graph (DAG) and constrain the DAG to have only one input node for each intermediate node. Therefore, the macro space can be specificed by a list of integers, indicating the input node index for each computing node (0 for the raw input, 1 for the first computing node, etc.). Then, the operations can be specified by a list of strings with the same length. For example, we provide the code to specify the architecture in the following figure:from architecture import Arch
arch = Arch([0, 1, 2, 1], ['gcn', 'gin', 'fc', 'cheb'])
# 0 means the inital computing node is connected to the input node
# 1 means the next computing node is connected to the first computing node
# 2 means the next computing node is connected to the second computing node 
# 1 means there is another computing node connected to the first computing nodeNotice that we assume all leaf nodes (i.e., nodes without descendants) are connected to the output, so there is no need to specific the output node.Besides, the list can be specified in any order, e.g., the following code can specific the same architecture:arch = Arch([0, 1, 1, 2], ['gcn', 'cheb', 'gin', 'fc'])The benchmark data can be obtained by a look-up table. In this repository, we only provide the validation and test performance, the latency, and the number of parameters as follows:info = bench[arch.valid_hash()]
info['valid_perf']   # validation performance
info['perf']         # test performance
info['latency']      # latency
info['para']         # number of parametersFor the complete benchmark, please downloadfromhttps://figshare.com/articles/dataset/NAS-bench-Graph/20070371, which contains the training/validation/testing performance at each epoch. Since we run each dataset with three random seeds, each dataset has 3 files, e.g.,from readbench import read
bench = read('cora0.bench')   # cora1.bench and cora2.benchThe full metric for any epoch can be obtained as follows.info = bench[arch.valid_hash()]
epoch = 50
info['dur'][epoch][0]   # training performance
info['dur'][epoch][1]   # validation performance
info['dur'][epoch][2]   # testing performance
info['dur'][epoch][3]   # training loss
info['dur'][epoch][4]   # validation loss
info['dur'][epoch][5]   # testing loss
info['dur'][epoch][6]   # best performanceExample usage of NNI and AutoGLNAS-Bench-Graph can be used together with other libraries such AutoGL and NNI.For the usage ofAutoGL, please refer to theagnn branch.You can also refer torunnni.pyto use the benchmark together withNNI.
Package: nasbench-pytorch
NASBench-PyTorchNASBench-PyTorch is a PyTorch implementation of the search spaceNAS-Bench-101including the training of the networks**. The original
implementation is written in TensorFlow, and this projects contains
some files from the original repository (in the directorynasbench_pytorch/model/).Important:if you want to reproduce the original results, please refer to theReproducibilitysection.OverviewA PyTorch implementation oftrainingof NAS-Bench-101 dataset:NAS-Bench-101: Towards Reproducible Neural Architecture Search.
The dataset contains 423,624 unique neural networks exhaustively generated and evaluated from a fixed graph-based search space.UsageYou need to have PyTorch installed.You can install the package by runningpip install nasbench_pytorch. The second possibility is to install from source code:Clone this repogit clone https://github.com/romulus0914/NASBench-PyTorch
cd NASBench-PyTorchInstall the projectpip install -e .The filemain.pycontains an example training of a network. To see
the different parameters, run:python main.py --helpTrain a network by hashTo train a network whose architecture is queried from NAS-Bench-101
using its unique hash, install the originalnasbenchrepository. Follow the instructions in the README, note that you
need to install TensorFlow. If you need TensorFlow 2.x, installthis forkof the
repository instead.Then, you can get the PyTorch architecture of a network like this:fromnasbench_pytorch.modelimportNetworkasNBNetworkfromnasbenchimportapinasbench_path='$path_to_downloaded_nasbench'nb=api.NASBench(nasbench_path)net_hash='$some_hash'# you can get hashes using nasbench.hash_iterator()m=nb.get_metrics_from_hash(net_hash)ops=m[0]['module_operations']adjacency=m[0]['module_adjacency']net=NBNetwork((adjacency,ops))Then, you can train it just like the example network inmain.py.ArchitectureExample architecture (picture from the original repository)ReproducibilityThe code should closely match the TensorFlow version (including the hyperparameters), but there are some differences:RMSProp implementation in TensorFlow and PyTorch isdifferentFor more information refer tohereandhere.Optionally, you can install pytorch-image-models where aTensorFlow-like RMSPropis implementedpip install timmThen, pass--optimizer rmsprop_tftomain.pyto use itYou can turn gradient clipping off by setting--grad_clip_off TrueThe original training was on TPUs, this code enables only GPU and CPU trainingInput data augmentation methods are the same, but due to randomness they are not applied in the same mannerCause: Batches and images cannot be shuffled as in the original TPU training, and the augmentation seed is also differentResults may still differ due to TensorFlow/PyTorch implementation differencesRefer to thisissuefor more information and for comparison with API results.DisclaimerModified fromNASBench: A Neural Architecture Search Dataset and Benchmark.graph_util.pyandmodel_spec.pyare directly copied from the original repo. Original license can be foundhere.**Please note that this repo is only used to train one possible architecture in the search space, not to generate all possible graphs and train them.
Package: nascam-imager-readfile
NASCAM All-Sky Imager Raw PGM Data ReadfilePython library for reading NASCAM All-Sky Imager (ASI) stream0 raw PNG-file data. The data can be found athttps://data.phys.ucalgary.ca.Supported DatasetsNASCAM ASI raw:stream0.pngPNG filesInstallationThe nascam-imager-readfile library is available on PyPI:$python3-mpipinstallnascam-imager-readfileSupported Python Versionsnascam-imager-readfile officially supports Python 3.8+.ExamplesExample Python notebooks can be found in the "examples" directory. Further, some examples can be found in the "Usage" section below.UsageImport the library usingimport nascam_imager_readfileWarning: On Windows, be sure to put anyreadcalls into amain()method. This is because we utilize the multiprocessing library and the method of forking processes in Windows requires it. Note that if you're using Jupyter or other IPython-based interfaces, this is not required.Read a single file>>>importnascam_imager_readfile>>>filename="path/to/data/2010/01/01/atha_nascam-iccd04/ut06/20100101_0600_atha_nascam-iccd04.png.tar">>>img,meta,problematic_files=nascam_imager_readfile.read(filename)Read multiple files>>>importnascam_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2010/01/01/atha_nascam-iccd04/ut06/*.tar")>>>img,meta,problematic_files=nascam_imager_readfile.read(file_list)Read using multiple worker processes>>>importnascam_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2010/01/01/atha_nascam-iccd04/ut06/*.tar")>>>img,meta,problematic_files=nascam_imager_readfile.read(file_list,workers=4)Read with no output>>>importnascam_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2010/01/01/atha_nascam-iccd04/ut06/*.tar")>>>img,meta,problematic_files=nascam_imager_readfile.read(file_list,workers=4,quiet=True)DevelopmentClone the repository and install dependencies using Poetry.$gitclonehttps://github.com/ucalgary-aurora/nascam-imager-readfile.git$cdnascam-imager-readfile/python$makeinstallTesting$maketest[ or do each test separately ]$maketest-flake8$maketest-pylint$maketest-pytest
Package: nasdaq-stock
Nasdaq Stock: Stock data retriever=========================How to use--------------->>>from nasdaq_stock import nasdaq_stock as nsor>>>from nasdaq_stock import nasdaq_stock>>>nasdaq_stock.stock(‘spyd’)or>>>spyd = nasdaq_stock.stock(‘spyd’)This will return string data and a dictionary of that data.Current data fields are:ticker: The stock or id of that ticker.date: The date when the stock was queried.time: The time when the stock was taken. This can be helpful if you are trying to do minute by minute data gatheringprice: The current price or bid priceask: The current ask pricehigh: The high of the daylow: The low of the dayprevious_close: The previous closing form the last day. Not to be confused with last ask pricevolume: Volume of shares for the current day.market_cap: Market cap of the stock. i.e. dollar amount currently vested in stock
Package: nasdaq-stock-quote
Copyright (c) 2017 Andrew PorterPermission 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.Download-URL:https://github.com/AndrewRPorter/nasdaq-stock-quote/releasesDescription: UNKNOWN
Platform: UNKNOWN
Package: nashbanksync
Nasheq Bank Sync Standardized A.P.I.s Python IntegrationsQuick GuideBelow are the steps on how to get the web app up and runningInstall Dependencies:pipinstallrequestspipinstalldjango-compression-middlewarepipinstalldjangorestframeworkInstall Nash Sync ERP Library:pipinstallnashbanksyncView get started documentation:https://nashbanksuite.readme.io/v1.2.0/docsStart Coding
Package: nashledger
Nasheq E.R.P. Standardized A.P.I.s python integrationsQuick GuideBelow are the steps on how to get the web app up and runningInstall Dependencies:pipinstallrequestspipinstalldjango-compression-middlewarepipinstalldjangorestframeworkInstall Nash Sync ERP Library:pipinstallnashledgerView get started documentation:https://nashledger.readme.io/docsStart Coding
Package: nashpy
Nashpy: a python library for 2 player games.Nashpy is:Anexcellently documented library:TheNashpy game theory text bookaims
to be a course text on the background theory.Thecontributor
documentationaims to be a text on research software development and helpfirst timeopen
source software contributions.Astate of the art developed code
basewhich
aims to use the best of available tools to ensure the code is correct,
readable and robust.Feature rich, the following are implemented:Support enumerationHow to docs 🐍-Theory docs 📘Vertex enumerationHow to docs 🐍-Theory docs 📘Lemke-Howson algorithmHow to docs 🐍-Theory docs 📘Fictitious playHow to docs 🐍-Theory docs 📘Stochastic fictitious playHow to docs 🐍-Theory docs 📘Replicator dynamicsHow to docs 🐍-Theory docs 📘Replicator-mutation dynamicsHow to docs 🐍-Theory docs 📘Asymmetric replicator dynamicsHow to docs 🐍-Theory docs 📘Moran processesHow to docs 🐍Generate games from repeated gamesHow to docs 🐍-Theory docs 📘Moran processes on interaction graphsHow to docs 🐍Moran processes on replication graphsHow to docs 🐍DocumentationFull documentation is available here:http://nashpy.readthedocs.io/Installation$python-mpipinstallnashpyTo install Nashpy on Fedora, use:$dnfinstallpython3-nashpyUsageCreate bi matrix games by passing two 2 dimensional arrays/lists:>>>importnashpyasnash>>>A=[[1,2],[3,0]]>>>B=[[0,2],[3,1]]>>>game=nash.Game(A,B)>>>foreqingame.support_enumeration():...print(eq)(array([1.,0.]),array([0.,1.]))(array([0.,1.]),array([1.,0.]))(array([0.5,0.5]),array([0.5,0.5]))>>>game[[0,1],[1,0]]array([3,3])Other game theoretic softwareGambitis a library with a python api and
support for more algorithms and more than 2 player games.Game theory explorera web interface to
gambit useful for teaching.Axelroda research library aimed
at the study of the Iterated Prisoners dilemmaDevelopmentClone the repository and create a virtual environment:$gitclonehttps://github.com/drvinceknight/nashpy.git
$cdnashpy
$python-mvenvenvActivate the virtual environment and installtox:$sourceenv/bin/activate
$python-mpipinstalltoxMake modifications.To run the tests:$python-mtoxTo build the documentation. First install the software which also installs the
documentation build requirements.$python-mpipinstallflit
$python-mflitinstall--symlinkThen:$cddocs
$makehtmlFull contribution documentation is available athttps://nashpy.readthedocs.io/en/latest/contributing/index.htmlPull requests are welcome.Code of conductIn the interest of fostering an open and welcoming environment, all
contributors, maintainers and users are expected to abide by the Python code of
conduct:https://www.python.org/psf/codeofconduct/
Package: nashvegas
The purpose of this app is to enable a plug and play method for managing
database changes.Database migrations is a large topic with a lot of different approaches.  This
approach worked well for my needs and maybe it will for you as well.DocumentationYou can find the documentation in thedocs/folder of the repo or online at:http://nashvegas.readthedocs.orgInstallationInstallation is simple:$ pip install nashvegasThen addnashvegasto your INSTALLED_APPS in your Djangosettings.pyfile.SupportYou can either log issues on the Github issue tracker for this project or pop
into #nashvegas on Freenode.
Package: nasp
.. image:: https://landscape.io/github/TGenNorth/NASP/tests/landscape.svg?style=flat:target: https://landscape.io/github/TGenNorth/NASP/tests:alt: Code Health.. |copy|   unicode:: U+000A9 .. COPYRIGHT SIGNThe Northern Arizona SNP Pipeline (NASP)========================================OVERVIEW:---------NASP is a pipeline for analysis of genomic data. It is a suite of toolsmeant to collect and report on statistically-relevant high-confidencepositions in a collection of genomes, with emphasis on variantpositions, especially single nucleotide polymorphisms (SNPs). NASPexpects some combination of files in FASTA, FASTQ, SAM, BAM, and VCFformat as input, and will produce output files also inthose formats. As NASP is a pipeline, it expects to link a set ofexternal tools (usually installed separately) to complete specificanalysis tasks.USAGE:------Usage depends upon the installation method used on your system, and theuser interface you select. For standard installations with thecommand-line interface, you would collect (or symbolically link) all ofyour input files into a folder, and then invoke the command-lineinterface from that folder. Expected usage for the command-lineinterface is:`nasp.py [output\_folder]`You will then be prompted to answer a few questions about your analysis.Optionally, if you are re-running a previous analysis with the same (orsimilar) options, you can pass in an xml-based configuration file (thisis written out to your output\_folder after running the command-lineinterface) using the format:`nasp.py --config`INSTALLATION:-------------See the main page for documentation (http://tgennorth.github.io/NASP/).DEPENDENCIES:-------------For information about external tools that are required, or can beutilized, and those versions that have been tested to work with NASP,refer to the main NASP page (http://tgennorth.github.io/NASP/)LICENSE:--------Copyright |copy| The Translational Genomics Research Institute See theincluded "LICENSE" document.PUBLICATION:--------Please read our paper for more information:Jason W. Sahl,  Darrin Lemmer,  Jason Travis,  James M. Schupp,  John D. Gillece,  Maliha Aziz,  Elizabeth M. Driebe,  Kevin P. Drees,  Nathan D. Hicks,  Charles Hall Davis Williamson,  Crystal M. Hepp,  David Earl Smith,  Chandler Roe,  David M. Engelthaler,  David M. Wagner, Paul Keim"NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats". Published Ahead of Print: 21 June, 2016 Microbial Genomics doi: 10.1099/mgen.0.000074(http://mgen.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000074)CONTACT:--------| TGen North| 3051 W Shamrell Blvd Ste 106| Flagstaff, AZ 86001-9435| Darrin Lemmer| dlemmer@tgen.org| +1-928-226-6374History=======1.0.0 (2014-06-05)------------------* First release on PyPI.0.9.6 (2014-06-05)------------------* First release on PyPI.
Package: nastran-aeroelasticity
nastran-aeroelasticityThis project is intended to analyze the Supersonic Panel Flutter using the NASTRAN routines.
It has pre and post processing routines to generate the NASTRAN models and parse the results.The project uses the pyNastran and the python scientific packages (i.e., scipy, numpy, matplotlib).Currently, the focus is to use the aerodynamic Piston Theory, available on NASTRAN with the CAERO5 element.
But it can be extended to use with any aerodynamic element.This software is result of a research project of the Department of Mechanical Engineering
at the Federal University of Minas Gerais (UFMG).Please,cite usif you use this project.What is Panel Flutter?Panel Flutter is a specific aeroelastic phenomena studied in the aerospace engineering field. It is a dynamic instability resulted from the interaction of aerodynamic, elastic and inertial forces (and thermal stresses). It usually can happen at supersonic speeds (M > 1.2) mostly because the needed energy to provide instability in such structure (a panel) is high. When we say "dynamic instability", this means that response (or amplitude) in displacement of the given panel will grow exponentially. In fact, the growth is limited by the non-linear effects (normally structural), but induces very high cyclic stresses in the structure, and in consequence reduces its fatigue life.What is NASTRAN?NASTRAN (acronym for NAsa STRuctural ANalysis) is a software originally made by NASA in FORTRAN for structural analysis. It has become public domain since (??), and after that CAE companies had developed the code further, but closely.
Today -- actually since the 70's -- it have many capabilities, including aeroelastic analysis.Some efforts in the FOSS version of Nastran are placed in theMYSTRAN.UseSome examples of utilization are placed on thenotebooksdirectory. Please refer to them. Some documentation shall be made soon.Here are some resulting plots made with the package.ContributingPlease, make a fork of the project, a PR and be clear in the intends and modifications made.
Thank you for the interest!
Package: nastran-aero-flutter
nastran-aero-flutterThis project is intended to analyze the Supersonic Panel Flutter using the NASTRAN routines.
It has pre and post processing routines to generate the NASTRAN models and parse the results.The project uses the pyNastran and the python scientific packages (i.e., scipy, numpy, matplotlib).Currently, the focus is to use the aerodynamic Piston Theory, available on NASTRAN with the CAERO5 element.
But it can be extended to use with any aerodynamic element.This software is result of a research project of the Department of Mechanical Engineering
at the Federal University of Minas Gerais (UFMG).Please,cite usif you use this project.What is Panel Flutter?Panel Flutter is a specific phenomena studied in the aerospace engineering field. It is a dynamic instability resulted from the interaction of aerodynamic, elastic and inertial forces (and thermal stresses). It mostly happens at supersonic speeds (M > 1.2) mostly because the needed energy to provide instability in such structure (a panel) is high.What is NASTRAN?NASTRAN (acronym for NAsa STRuctural ANalysis) is a software originally made by NASA in FORTRAN for structural analysis. It has become public domain since (??), and after that CAE companies had developed the code further, but closely.
Today -- actually since the 70's -- it have many capabilities, including aeroelastic analysis.Some efforts in the FOSS version of Nastran are placed in theMYSTRAN.UseSome examples of utilization are placed on thenotebooksdirectory. Please refer to them. Some documentation shall be made soon.Here are some resulting plots made with the package.ContributingPlease, make a fork of the project, a PR and be clear in the intends and modifications made.
Thank you for the interest!
Package: nastran_pch_reader
parser of NASTRAN punch files.Module currently support the following outputs:solution sequences: SOL101, SOL111requests: MPCF, SPCF, ACCELERATION, DISPLACEMENTS, FORCE (only for CBUSH and CELAS2)sorting: SORT1 and SORT2representation for frequency response: real/imaginary and magnitude/phaseExample:import nastran_pch_reader
parser = nastran_pch_reader.PchParser('test-data/sol101.pch')
# summary of the forces at the elements
for element in [3000, 3001]:
        print('Element force at %d' % element)
        for subcase in parser.get_subcases():
                forces = parser.get_forces(subcase)
                fx, fy, fz, mx, my, mz = forces[element]
                print('\t subcase',subcase, '=>', (fx, fy, fz))