Saravanan666/pypi-org huggingface dataset:
Package: noglobal
noglobalThis python package provides the@noglobaldecorator which prohibits the use of global variables from the function scope.InstallpipinstallnoglobalUsageimportos.pathasospfromnoglobalimportNoGlobalnoglobal=NoGlobal(globals()).noglobalfoo=3defbar():return"bar"@noglobaldeff():print(osp.join("foo","bar"))print(bar())print(foo)f()foo/bar
bar
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [1], in <cell line: 16>()
     13     print(bar())
     14     print(foo)
---> 16 f()

Input In [1], in f()
     12 print(osp.join("foo", "bar"))
     13 print(bar())
---> 14 print(foo)

NameError: name 'foo' is not definedFAQHow all this works?The function with@noglobaldecorator are re-created as atype.FunctionTypeobject, with no global variables.All globals are affected?No. Althogh this package name is "noglobal", the globals below are not affected:modulescallable objects (which means the objects which have__call__method)Thus you can not bother importing already imported modules everytime, and so on for the funcsions or classes.Can we use explicit global variables?No. Functions with the@noglobaldecorator also disallow the explicit use of global variables. That is, if you update a variable bound by aglobaldeclaration within a function, the original global variable will not be affected.
The following code prints10on the standard output.bar=10@noglobaldeffoo():globalbarbar=11foo()print(bar)What happens for the nested functions?Nested functions in the function decorated with@noglobalare also affected.
For example, the code below returns error:buzz=11@noglobaldeffoo():defbar():print(buzz)bar()foo()---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [8], line 1
----> 1 foo()

Cell In [7], line 5, in foo()
      3 def bar():
      4     print(buzz)
----> 5 bar()

Cell In [7], line 4, in foo.<locals>.bar()
      3 def bar():
----> 4     print(buzz)

NameError: name 'buzz' is not definedAttributionBased on the source codes:originally posted in StackOverflow: (CC BY-SA 3.0)skyking:https://stackoverflow.com/questions/31023060/disable-global-variable-lookup-in-python/31047259#31047259Glenn Maynard:https://stackoverflow.com/questions/4858100/how-to-list-imported-modules/4858123#4858123later modified byAxel Huebl:https://gist.github.com/ax3l/59d92c6e1edefcef85ac2540eb056da3yoshipon:https://gist.github.com/yoshipon/3adba8cc5d7daac6c3256c9163f48920raven38:https://gist.github.com/raven38/4e4c3c7a179283c441f575d6e375510c
Package: noglobal-magic
noglobal-magicFor Jupyter Notebook user's,noglobal-magicfind global variables in a local scope.With:No need to wait executing a functionflake8style ignoring-error annotation (# noqa)no_globalmagic command makes raise error, andwarn_globaltells a just warningInstallationMake sure you've thisnoglobal-magic(And the Python packagepyflakes).pipinstallnoglobal-magicHow to useIn a cell on Jupyter Notebook, load and activate this extension:%load_ext noglobal_magic
%no_globalYou've ready to enjoy coding.Let's see incolabhow it works.
Package: no-global-nonlocal-vars
no_global_nonlocal_varsFunction decorator that ensures that no global and no nonlocal variables are used, making Jupyter notebooks much saferInstallingInstall:pip install no_global_nonlocal_varsUsage# Correct Output
@no_global_nonlocal_vars
def test_no_global_nonlocal_vars_GOOD(x, repeat):
    return np.array([x] * repeat)


# Error from typo
@no_global_nonlocal_vars
def test_no_global_nonlocal_vars_typo_GOOD(x_typo, repeat_typo):
    return np.array([x] * repeat)


# Nested function works
@no_global_nonlocal_vars
def test_no_global_nonlocal_vars_nested_GOOD(x, repeat=10):

    @no_global_nonlocal_vars
    def helper(x, repeat):
        return np.array([x] * repeat)

    return helper(x, repeat)


# Error from nonlocal variable typo
@no_global_nonlocal_vars
def test_no_global_nonlocal_vars_nested_typo_GOOD(x, repeat=10):

    @no_global_nonlocal_vars
    def helper(x_typo, repeat_typo):
        return np.array([x] * repeat)

    return helper(x, repeat)NotesThe errors show up only upon first time running the function, not at function definition timeRelated Workhttps://github.com/tillahoffmann/localscopehttps://github.com/diazona/localscope
Package: nohands
Look ma no hands!A little library and command line tool for effortless demos.Example - fully scripted demoWhen you have a short demo that can be fully scripted and requires no
interaction, you can scrip the entire demo in one python module.Seeexample.pyfor details.Example - interactive demoWhen you need an interactive demo - run one command, discuss the command
and the output, then run the next command, you can create a demo yaml
and interact with the demo with thenhtool.Example - navigating an interactive demoYou can navigate in an interactive demo using the--reset,--skip,
and--previousoptions.LicenseThis work is licensed under MIT license. SeeLICENSES/MIT.txtfor more
info.
Package: noicelink
Noicelink.pyThe simple, easy to use python wrapper fornoice.linkQuickstartIn your ternimal, with pip installed, run the following command to install:pip install noicepyOr with git:pip install git+https://github.com/JohnjiRomanji/noicepy.gitAfter that you should be good to import noicepy and use it like in the docs.importnoicepyLOOK AT THE DOCSPLEASE LOOK AT THE DOCShttps://johnjiromanji.github.io/noicepyQuestions, Problems, or Suggestions? Report them to the noice.link discord server
Package: noid-mint
NOID-mint Python PackageInstallationpip install noid-mintorgit clone git@github.com:vt-digital-libraries-platform/NOID-mint.git
python setup.py installUsageGenerate noidnoid -f local.ymlTestingnosetestsAuthorsVirginia Tech Libraries - Digital Libraries Development developersYinlin ChenTingting JiangLee HunterSee also the list ofcontributorswho participated in this project.ThanksThis tool was heavily influenced frompynoid
Package: noiftimer
noiftimerSimple timer class to track elapsed time.Install with:pip install noiftimerUsage:>>> from noiftimer import Timer, time_it
>>> import timeTimer object>>> def very_complicated_function():
...     time.sleep(1)
...
>>> timer = Timer()
>>> for _ in range(10):
...     timer.start()
...     very_complicated_function()
...     timer.stop()
...
>>> print(timer.stats)
elapsed time: 1s 1ms 173us
average elapsed time: 1s 912us
>>> timer.elapsed
1.001173496246338
>>> timer.elapsed_str
'1s 1ms 173us'
>>> timer.average_elapsed
1.0009121656417848
>>> timer.average_elapsed_str
'1s 912us'time_it decorator (executes the decorated function 10 times)>>> @time_it(10)
... def very_complicated_function():
...     time.sleep(1)
...
>>> very_complicated_function()
very_complicated_function average execution time: 1s 469us
Package: noimport
noimport v0.0.1A simple package to prevent the abusive use of theimportstatement in Python.InstallationTo install, just run:pip install noimportUsageAfter importing thenoimportpackage, any attempts of importation would result in an error>>>importnoimport>>>importtime# Built in modules won't work, ...Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>ModuleNotFoundError:importoftimehalted;Noneinsys.modules>>>importfoo# ... nor local modules, ...Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>ModuleNotFoundError:Nomodulenamed'foo'>>>importPIL# ... nor pip installed packagesTraceback(mostrecentcalllast):File"<stdin>",line1,in<module>ModuleNotFoundError:Nomodulenamed'PIL'LicenseThis project is licensed under theMIT licensePython without modules and packages, how bad can it be?
Package: no-init
no_initTool that checks that there are no__init__.pyin your modules.It's opinionated tool, sometimes__init__.pyfiles are useful. But often if you use them you end up in a circular import error mess.Some packages (eg mypy) works better with__init__.py, for this case you can pass--allow-emptyparameter.# installpipinstallno_init# usage (you can also use python -m no_init)no_initmy_module# require empty __init__.py for each all submodules (recursively)no_init--require-emptymy_module# require __version__ variable in  my_module/__init__.py (no other content is allowed)no_init--require-versionmy_modulepre-commit hook</code></pre><h2>dev install</h2><pre lang="sh"><code>pip install -e .[dev]
Package: noip-api-CodeGuy3
noip_apiA little noip ddns api for python.RequirementssocketrequestscryptographyBeautifulSoup4Descriptionloginfunction logs you in with your username and passwordsaveLoginInfofunction save your username and password encrypted in noip.config fileloadLoginInfofunction loads your username and password from noip.config filegetCurrentfunction returns current ip saved in your hostnamesetDNSfunction sets given ip to your hostnamegetMyIPfunction returns your current public ip addresstestOpenPortfunction returns if your port is open, closed or filtered(when the routers firewall is blocking it)Example 1importnoip_apiusername=""password=""hostname=""DDNS=noip_api.noip(hostname)DDNS.login(username=username,password=password)print(DDNS.getMyIP())print(DDNS.getCurrent())DDNS.setDNS(ip=DDNS.getMyIP())Example 2importnoip_apiusername=""password=""hostname=""DDNS=noip_api.noip(hostname)DDNS.saveLoginInfo(username=username,password=password)Example 3importnoip_apiusername=""password=""hostname=""DDNS=noip_api.noip(hostname)DDNS.loadLoginInfo()DDNS.setDNS(ip=DDNS.getMyIP())
Package: noipclient
noipclient is a noip.com dynamic DNS update client.InstallationDependenciesnoipclient depends on the Python development package. For example, to install it on Ubuntu:$ sudo apt-get install python-devOr on CentOS:$ sudo yum install python-develInstalling from pypiYou can installnoipclienteither via the Python Package Index (PyPI)
or from source.To install usingpip,:$ pip install noipclientTo install usingeasy_install,:$ easy_install noipclientDownloading and installing from sourceAlternatively, you can download the latest version ofnoipclientfromhttp://pypi.python.org/pypi/noipclient/You can install it by doing the following,:$ tar xvfz noipclient-0.0.0.tar.gz
$ cd noipclient-0.0.0
$ sudo python setup.py installUsing the development versionYou can clone the git repository by doing the following:$ git clone git://github.com/acordiner/noipclient.gitUsing noipclientTo start noipclient, run:$ noipclient startThe first time you run the client, you will be prompted to enter your noip.com account details:Config file /home/fbar/.noipclient.cfg not found. Create one now? [Yn]
no-ip.com username: fbar
no-ip.com password:
no-ip.com hostname (e.g. myhost.no-ip.org): foobar.no-ip.org
Starting noipclient ... OKYou can then start, stop, restart and check the status of noipclient:$ noipclient status
noipclient -- pid: 23842, status: sleeping, uptime: 0m, %cpu: 0.0, %mem: 0.1
$ noipclient restart
Stopping noipclient ... OK
Starting noipclient ... OK
$ noipclient stop
Stopping noipclient ... OKBug trackerIf you have any suggestions, bug reports or annoyances please report them
athttp://github.com/acordiner/noipclient/issues/LicenseThis software is licensed under theGPL v2 License. See theLICENSEfile in the top distribution directory for the full license text.
Package: noise
Perlin noise is ubiquitous in modern CGI. Used for procedural texturing,
animation, and enhancing realism, Perlin noise has been called the “salt” of
procedural content. Perlin noise is a type of gradient noise, smoothly
interpolating across a pseudo-random matrix of values.The noise library includes native-code implementations of Perlin “improved”
noise and Perlin simplex noise. It also includes a fast implementation of
Perlin noise in GLSL, for use in OpenGL shaders. The shader code and many of
the included examples require Pyglet (http://www.pyglet.org), the native-code
noise functions themselves do not, however.The Perlin improved noise functions can also generate fBm (fractal Brownian
motion) noise by combining multiple octaves of Perlin noise. Shader functions
for convenient generation of turbulent noise are also included.1.2.2 AppVeyor support for Windows builds (Thanks to Federico Tomassetti)1.2.1 Fixes MSVC compatibility (Thanks to Christoph Gohlke)1.2.0 adds 4D simplex noise, tiling for 2D simplex noise,
and parameterized lacunaritySee CHANGES.txt for more details
Package: noisecut
NoiseCutnoisecutis an easy-to-use Python package for the implementation of
tree-structured functional networks (FNs) as a model class for the
classification of binary data with prior knowledge on input features.
FNs can be viewed as modular neural networks, where the structure of
the links between the modules and the information flow from input variables
to the output variable is pre-determined. Here, each module of the FN is
simply represented as a black-box module. The identification of an FN, i.e.,
learning the input-output function of the FN, is then decomposed to the
identification of the individual interior black-box modules.noisecutcan be used for any tree-structured FNs which has the below
criteria. It should havetwo hidden layer,arbitrary number of black-box modules in the first hidden layer,only one black-box module in the second hidden layer,each input feature goes only to one black-box module (tree structure).InstallationDependenciesPython (>=3.9)numpypandasscipycplexdocplexUser installationBofore you can use NoiseCut package, you need to installnoisecutusing pip:$pipinstallnoisecutSimple demoCode snippet shown below summarizes a complete workflow, starting with
the generation of synthetic data, proceeding to the division of data into
training and testing sets, and concluding with model fitting and result
evaluation.fromnoisecut.model.noisecut_coderimportMetricfromnoisecut.model.noisecut_modelimportNoiseCutfromnoisecut.tree_structured.data_manipulatorimportDataManipulatorfromnoisecut.tree_structured.sample_generatorimportSampleGenerator# Synthetic data generationgen_dataset=SampleGenerator([4,4,4],allowance_rand=True)# [4,4,4] determines the number of inputs to each black box of the FN modelX,y=gen_dataset.get_complete_data_set()# Add noise in data labeling. Train and test set split.x_noisy,y_noisy=DataManipulator().get_noisy_data(X,y,percentage_noise=10)x_train,y_train,x_test,y_test=DataManipulator().split_data(x_noisy,y_noisy,percentage_training_data=50)# Trainingmdl=NoiseCut(n_input_each_box=[4,4,4])# 'n_input_each_box' should fit to the generated datamdl.fit(x_train,y_train)# Evaluationy_pred=mdl.predict(x_test)accuracy,recall,precision,F1=Metric.set_confusion_matrix(y_test,y_pred)UsageVarious use cases of the useful functions ofnoisecutpackage are provided
as jupyter notebooks:Usage example of NoiseCutSynthetic data generationNoise-tolerant classificationClassification with reduced training dataExamples show how to use the package to fit the model and investigate
the predicted results in score, probability or simple binary output format.Licensenoisecutwas created by Hedieh Mirzaieazar and Moein E. Samadi. It is
licensed under the terms of the GPLv3 license.
Package: noiseflow
NoiseFlowPrerequisitesNoiseFlow now supportsClangandGCCcompiler in MacOS and Linux system separately, and all installation processes are under thecondaenvironment, and we recommend to use miniconda. Make sure to install the following pre-packages before installing noiseflow:If you useClangin Mac, please installOpenMPviabrewas following:brewinstallopenmpAnd usepipandcondato install the following packages:pipinstalljoblib

condainstall-cconda-forgenumpyscipymatplotlibcondainstall-cconda-forgeobspy
condainstall-cconda-forgefftw(动态库)condainstall-cconda-forgepybind11(头文件)condainstall-cconda-forgextensorxsimdxtlxtensor-blasxtensor-python(可能是静态库)condainstall-cconda-forgextensor-fftw#(usually failed at most time)Thextensor-fftwandKFRneed to be installed from source, first download them:gitclonehttps://github.com/OUCyf/noiseflow.gitcdnoiseflow
gitsubmoduleinit
gitsubmoduleupdateNote thextensor-fftwdo not support M1 chip, and if it is failed to install via conda, you can install it from source into conda environment as$CONDA_PREFIXcd./external/xtensor-fftw
mkdirbuild&&cdbuild
cmake..-DCMAKE_INSTALL_PREFIX=$CONDA_PREFIXmakeinstallTheKFRpackage is C++ DSP framework, should be installed in./extern/kfrfrom sourcecd./external/kfr
mkdirbuild&&cdbuild
cmake..-DCMAKE_INSTALL_PREFIX=$CONDA_PREFIXmakeinstallInstallationNow you can installNoiseFlow. If you useMacOS, please make sure to use Clang as the complierexportCXX=clang++# unset CXXpythonsetup.pyinstallIf you useLinux, please use GCC as the compilerexportCXX=g++-13
pythonsetup.pyinstallIf you useHPCwithmoduletool, you can use both Clang and GCC, for example using NOTS in Rice University.# use gccmoduleloadGCC/13.1.0exportCXX=g++
pythonsetup.pyinstallINCLUDE_CMAKE_EXTENSION=1pipinstall.# use clangmoduleloadGCCcore/11.2.0
moduleloadClang/13.0.1exportCXX=clang++
pythonsetup.pyinstallcondainstall-cconda-forgestockwellNOISEFLOW_USE_CPP=1pipinstall--no-binary:all:noiseflow--no-cache-dir

gitsubmoduleaddhttps://gitclone.com/github.com/kfrlib/kfr.gitextern/kfrwLicenseNoiseflow is dual-licensed, available under both commercial and apache license.If you want to use noiseflow in a commercial product or a closed-source project, you need to purchase a Commercial License.stockwell: 'numpy>=1.18', scipy, fftw, ['cp36-', 'cp37-', 'cp38-', 'cp39-', 'cp310-*']INSTALL (NOETS)conda:brewinstallfftw
condainstall-cconda-forgefftw(没用)condainstall-cconda-forgepybind11(头文件)condainstall-cconda-forgextensorxsimdxtlxtensor-blasxtensor-python(可能是静态库)cd./external/xtensor-fftw
mkdirbuild&&cdbuildmyenv=$(condaenvlist|grep' \* '|awk'{print $1}')conda_prefix=$(condainfo--base)exportCMAKE_PREFIX_PATH="${conda_prefix}/envs/${myenv}"cmake..-DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX-DCMAKE_PREFIX_PATH=$CONDA_PREFIXmakeinstall[build-system]cmake = '^3.18.0'
fftw = '^3.3.10'
pybind11 = '^2.10.4'
xtensor = '^0.24.6'
xsimd = '8.0.5' # when the version is larger than 8.0.5, it will got error in compiling stage.
xtl = '^0.7.5'
xtensor_blas = '^0.20.0'
xtensor_python = '^0.26.1'
xtensor_fftw = '^0.2.6' # not show in conda env
kfr = '^5.0.2' # not show in conda env[optional-for-mac]libomp = '^16.0.6' # via brew
Package: noise-img-generator
noise-image-generatorCLI tool to generate noise png file to be used in game design.Installpip3installnoise-img-generatorUsagenoise_img_generator[-h][--gentypeGENTYPE][--sizeSIZE][--filenameFILENAME][--seedSEED][--clusterCLUSTER][--imgsIMGS][--normsNORMS][--dimsDIMS][--octavesOCTAVES][--tileTILE[TILE...]]Generatenoisepngfiles.

optionalarguments:-h,--helpshowthishelpmessageandexit--gentypeGENTYPEspecifyanoisegeneratortouse[random,perlin](default:random)--sizeSIZEsizeofoutputimage(default:512)--filenameFILENAMEnameoffilecreated(default:noise.png)--seedSEEDseedtouseforgeneration(default:None)--clusterCLUSTERwidthofkernaltoaverageneighbouringpixels(default:0)--imgsIMGSnumberofrandomgeneratedimagestocombine(default:1)--normsNORMSnumberoftimestonormalizeimage(default:1)--dimsDIMSnumberofdimensionstousewithperlin(default:2)--octavesOCTAVESnumberofoctavestousewithperlin,recommendedmax4(default:1)--tileTILE[TILE...]connectedtilingstobeused[spacedimensions](default:11)
Package: noisemaker
NoiseMakerOlsen Noise algorithm for infinite scoped deterministic fractal noise. The noise is deterministic so it's perfectly for tiling and returning to the same postion and having the same regenerated noise. The algorithm is infinite and scoped so only enough values as needed are generated.fromPILimportImagefromnoisemakerimportnoiseImage.fromarray(noise((250,100),(0,0),transpose=True)).save("noise0.png")Image.fromarray(noise((250,100),(0,100),transpose=True)).save("noise1.png")Image.fromarray(noise((250,100),(0,200),transpose=True)).save("noise2.png")Thenoise()algorithm requires only the first shape parameter. Thetransposeflag is to covert it into an image ready format.TilingNote these are three different images. Simply adjacent in the requested space, so they stack.Box Kernel (Default)fromnoisemakerimportnoisefromPILimportImageImage.fromarray(noise(500,500,iteration=5)).save("noise-5.png")Gaussian KernelThere is also a gaussian kernel availible, which is about a third the blur factor of the default box blur:fromnoisemakerimportnoise,GAUSSIANfromPILimportImageImage.fromarray(noise((500,500),iteration=5,kernel=GAUSSIAN)).save("noise-5g.png")
Package: noisepy-seis-io
noisepy-seis-ioThis project was automatically generated using the LINCC-Frameworkspython-project-template.A repository badge was added to show that this project uses the python-project-template, however it's up to
you whether or not you'd like to display it!For more information about the project template see thedocumentation.Dev Guide - Getting StartedBefore installing any dependencies or writing code, it's a great idea to create a
virtual environment. LINCC-Frameworks engineers primarily usecondato manage virtual
environments. If you have conda installed locally, you can run the following to
create and activate a new environment.>> conda create env -n <env_name> python=3.10
>> conda activate <env_name>Once you have created a new environment, you can install this project for local
development using the following commands:>> pip install -e .'[dev]'
>> pre-commit install
>> conda install pandocNotes:The single quotes around'[dev]'may not be required for your operating system.pre-commit installwill initialize pre-commit for this local repository, so
that a set of tests will be run prior to completing a local commit. For more
information, see the Python Project Template documentation onpre-commitInstallpandocallows you to verify that automatic rendering of Jupyter notebooks
into documentation for ReadTheDocs works as expected. For more information, see
the Python Project Template documentation onSphinx and Python Notebooks
Package: noise-randomized
Perlin noise is ubiquitous in modern CGI. Used for procedural texturing,
animation, and enhancing realism, Perlin noise has been called the “salt” of
procedural content. Perlin noise is a type of gradient noise, smoothly
interpolating across a pseudo-random matrix of values.The noise library includes native-code implementations of Perlin “improved”
noise and Perlin simplex noise. It also includes a fast implementation of
Perlin noise in GLSL, for use in OpenGL shaders. The shader code and many of
the included examples require Pyglet (http://www.pyglet.org), the native-code
noise functions themselves do not, however.The Perlin improved noise functions can also generate fBm (fractal Brownian
motion) noise by combining multiple octaves of Perlin noise. Shader functions
for convenient generation of turbulent noise are also included.1.2.3 Adds ability to randomize permutation table to the C implementations1.2.2 AppVeyor support for Windows builds (Thanks to Federico Tomassetti)1.2.1 Fixes MSVC compatibility (Thanks to Christoph Gohlke)1.2.0 adds 4D simplex noise, tiling for 2D simplex noise,
and parameterized lacunaritySee CHANGES.txt for more details
Package: noisi
Python package to simulate ambient seismic noise cross-correlations and sensitivity kernels to noise sources for noise source optimization. Earth’s oceans, wind, human activity and other sources create ambient seismic vibrations that are picked up by seismometers and used by seismologists to study the interior of the Earth and the interactions of solid Earth, oceans and atmosphere. The noisi tool provides a framework for the convenient simulation of auto- and cross-correlations of the ambient noise. Find out more at:https://github.com/lermert/noisi,https://github.com/jigel/noisi_inv