Saravanan666/pypi-org huggingface dataset:
Package: noisify
NoisifyNoisify is a simple light weight library for augmenting and modifying data by adding realistic noise.IntroductionAdd some human noise (typos, things in the wrong boxes etc.)>>> from noisify.recipes import human_error
>>> test_data = {'this': 1.0, 'is': 2, 'a': 'test!'}
>>> human_noise = human_error(5)
>>> print(list(human_noise(test_data)))
[{'a': 'tset!', 'this': 2, 'is': 1.0}]
>>> print(list(human_noise(test_data)))
[{'a': 0.0, 'this': 'test!', 'is': 2}]Add some machine noise (gaussian noise, data collection interruptions etc.)>>> from noisify.recipes import machine_error
>>> machine_noise = machine_error(5)
>>> print(list(machine_noise(test_data)))
[{'this': 1.12786393038729, 'is': 2.1387080616716307, 'a': 'test!'}]If you want both, just add them together>>> combined_noise = machine_error(5) + human_error(5)
>>> print(list(combined_noise(test_data)))
[{'this': 1.23854334573554, 'is': 20.77848220943227, 'a': 'tst!'}]Add noise to numpy arrays>>> import numpy as np
>>> test_array = np.arange(10)
>>> print(test_array)
[0 1 2 3 4 5 6 7 8 9]
>>> print(list(combined_noise(test_array)))
[[0.09172393 2.52539794 1.38823741 2.85571154 2.85571154 6.37596668
                  4.7135771  7.28358719 6.83600156 9.40973018]]Read an image>>> from PIL import Image
>>> test_image = Image.open(noisify.jpg)
>>> test_image.show()And now with noise>>> from noisify.recipes import human_error, machine_error
>>> combined_noise = machine_error(5) + human_error(5)
>>> for out_image in combined_noise(test_image):
...     out_image.show()Noisifyallows you to build flexible data augmentation pipelines for arbitrary objects.
All pipelines are built from simple high level objects, plugged together like lego.
Use noisify to stress test application interfaces, verify data cleaning pipelines, and to make your ML algorithms more
robust to real world conditions.InstallationPrerequisitesNoisify relies on Python 3.5+Installation from pipy$ pip install noisifyAdditional InformationFull documentation is available at TODO ReadTheDocs Link.LicenceDstl (c) Crown Copyright 2019Noisify is released under the MIT licence
Package: noisy
# Noisy[Noise](https://github.com/FRidh/noisy) is a Python module for generating noise with different colors.## InstallationThe latest release can be found on PyPI and installed withpip install noisy.## DocumentationThe latest version of the documentation can be found athttp://fridh.github.io/noisy/.## LicenseThe BSD 2-Clause License applies to the code.
Package: noisy-cifar-owaix2quzq
Noisy CIFAR-10/CIFAR-100Quickstartfrom noisy_cifar import NoisyCIFAR10
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor

root = '/datasets/CIFAR-10'
train_dataset = NoisyCIFAR10(root, 'symmetric', 0.2, transform=ToTensor())
val_dataset = CIFAR10(root, train=False, transform=ToTensor())Installpipinstallnoisy-cifar-owaix2quzqArgumentsroot: path to the original datasetnoise_type: symmetric / asymmetric /humannoise_level:(float) [0.0 ~ 1.0] for symmetric & asymmetric(str) {aggregate, random1, random2, random3, worst} for humanrandom_seed: default0transform: defaultNonetarget_transform: defaultNonedownload: defaultFalse
Package: noisydatacleaner
NoisyDataCleanerPython classes that identify and correct/remove noise in datasetsThese models leverage on monte carlo simulation to approximate the correctness of a given label. The correction of the label builds on from the noise detection model.Install:pipinstallnoisydatacleanerModels:NoiseRemover
Identifies and then removes the noise from the dataset. Random Forest is used for smaller datasets as it yields better results. Whereas for larger datasets, k-Nearest Neighbors is much more efficient.LabelClassificationCorrector
Corrects the labels for classification datasets. Instead of only using 1 model likeNoiseRemover, this model uses 5 different models:models={'Random Forest':RandomForestClassifier(n_estimators=128),'Extra Trees':ExtraTreesClassifier(n_estimators=128),'Linear Discriminant':LinearDiscriminantAnalysis(),'Logistic Regression':LogisticRegression(max_iter=128),'Neural Network':MLPClassifier(hidden_layer_sizes=(128,64,32))}All of which comes from the sklearn library
Package: noisyduck
Noisy DuckA python tool for computing eigenmode decompositions of duct flows.Free software: BSD licenseDocumentation:https://noisyduck.readthedocs.io.FeaturesAnnular duct mode decompositionCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-02-02)First release on PyPI.0.1.5 (2018-03-12)New governing equationsTests for uniform axial mean flowNew API + documentation
Package: noisyenv
noisyenv: Simple Noisy Environment Augmentation for Reinforcement LearningThis package contains a set of generic wrappers designed to augment RL environments with noise and encourage agent exploration and improve training data diversity which are applicable to a broad spectrum of RL algorithms and environments. For more details, please refer to our paper:https://arxiv.org/abs/2305.02882.Note that this package has been developed for the new step and reset API introduced inOpenAI Gym v26andGymnasium v26. Use thegymnasium.wrappers.EnvCompatibilitywrapper to update old environments for compatibility.InstallationpipinstallnoisyenvUsageimportgymnasiumasgymfromnoisyenv.wrappersimportRandomUniformScaleRewardbase_env=gym.make("HalfCheetah-v2")env=RandomUniformScaleReward(env=base_env,noise_rate=0.01,low=0.9,high=1.1)# And just use as you would normallyobservation,info=env.reset(seed=333)for_inrange(100):action=env.action_space.sample()observation,reward,terminated,truncated,info=env.step(action)ifterminatedortruncated:observation,info=env.reset()env.close()Citing noisyenvIf you usenoisyenvin your work, please cite our paper:@misc{khraishi2023simple,title={Simple Noisy Environment Augmentation for Reinforcement Learning},author={Raad Khraishi and Ramin Okhrati},year={2023},eprint={2305.02882},archivePrefix={arXiv},primaryClass={cs.LG}}
Package: noisytest
NoisyTestNoisyTest is a command-line tool for noise-based system tests of (robotic) systems. It uses scalar noise (vibration) data from
a simulation environment or experiments to detect software flaws via machine learning. The main objective behind this tool is the improvement
of virtual system tests to reduce the time required for real-world experiments. Especially in the robotics domain,
real-world experiments are rather costly and too many flaws remain undetected in simulation.NoisyTest is used for virtual system testing of the humanoid robotLola.
In general, the tool may be used on any noise data to detect and characterize failures.
Feel free to use NoisyTest for your purposes and let me know when you find it useful.InstallThis is as easy aspip install noisytest.UsageThe install of NoisyTest registers an entry pointnoisytest, which works as a command line interface. It has a
built-in usage help:$ noisytest --help
usage: noisytest [-h] [--pipeline NOISYTEST_PIPELINE]
                 [--config NOISYTEST_CONFIG] [-v]
                 {train,run} ...

This is NoisyTest 0.0.1

positional arguments:
  {train,run}
    train               Train a model from given data using default parameters
    run                 Run test on specified noise file

optional arguments:
  -h, --help            show this help message and exit
  --pipeline NOISYTEST_PIPELINE
                        the noisytest pipeline to use. (default:
                        default.noisy)
  --config NOISYTEST_CONFIG
                        noisytest config file name (default: noisytest-
                        config.json)
  -v, --verbosity       console output verbosity (default: None)Train a modelTo train a model, we first need some noise-based training / validation data set. We use the dataset from the humanoid robot LOLA:$gitclonehttps://github.com/am-lola/noisytest-data-lola.gitdataWe may then use noisytest to train a model:$noisytest--configdata/noisytest-config.jsontrainThe trained, self-contained pipeline (model + preprocessor) is written to disk after successful training: 'default.noisy'.:warning: NoisyTest usespicklefor serialization.
Don't load pipelines of untrustworthy origin!Test noise dataTo actually test a noise file for failures / flaws you run a test on a noise file:$noisytestrundata/validation/earlycontacttoe.log
WARNING:possibleoscillationsintimeregion0.0001-0.4
WARNING:possibleoscillationsintimeregion6.7-7.1
...Data formatsNoise data is read from whitespace-separated files with two columns. One is the actual or simulated time in seconds,
the other the scalar noise pressure estimate. Annotation data for the training and validation sets uses TOML files
to mark individual time-frames.Disclaimer & ContributingThis project started as a mere proof of concept.  Although it already reached a state which can in general be used
productively, it currently lacks some features - and more important - a solid test base. This will be fixed as soon
as I find time. In the meantime be warned of possible errors ;)Feel free to contribute to this project.
Package: noita-save-manager
Noita save managerSmall save-game manager written for usage with Noita works in Linux or Windows.ScreenshotFeaturesCheck to see if Noita is running (currently commented out, to work with noita together)Backup active saveRestore from backupNon destructiveNon DestructiveCurrently you cannot use this tool to delete / remove saves or backups.
It will always create a backup in the Noita folder prior to restoring. So it won't overwrite / delete anything.Installing with Pip, Pipenv or Poetrypipinstallnoita-save-manager
pipenvinstallnoita-save-manager
poetryaddnoita-save-managerRunningnoita_save_managerPre-Built Windows clientYou can grab the latest releasehttps://github.com/mcgillij/noita_save_manager/releases/download/0.1.0/save_manager_0.1.0.zipBuilding from sourceIf you want to build your own binary/ source distribution / wheel, you can use the following steps. Usespoetryfor dependency management.poetryinstall
poetryrunpyinstaller-F--noconsolesrc/noita_save_manager/save_manager.pyThis will plop out a binary for you in thedist/folder.
Package: noize
NoizeNoize is a tool and a library to apply different kind of noise methods to images.
Various noises with parameters can be applied for both grayscale and RGB images.Here is an example, from left to right, original, salt and pepper, gaussian, periodic versions of an image.Noize requiers Python 3.8 or newer versions, however some or all methods can be used older Python 3 versions.Some supported methods:Periodic NoiseSalt and Pepper NoiseNoises with Probability Density FunctionsInstallationInstall Noize from PyPI:$pipinstallnoizeCli UsageHere is an example to apply salt and pepper noise, usenoize --helpfor more:$noizesalt-and-pepperlenna.png-p0.01--seed25-ooutput.pngLib UsageimportnumpyasnpfromPILimportImagefromnoizeimportnoise# load imageim=Image.open("doc/lenna.png")# apply noise (input should be np.ndarray)out=noise.salt_and_pepper(np.array(im),0.1,25)# save resultout_im=Image.fromarray(out)out_im.save("output.png")Checkout the noise moduledocumentationfor more.LicenseMIT License

Copyright (c) 2021 mcemilg

Permission 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: noizy
noizyLog function calls: from noizy import noizyWARNING: This repo is in development. It was automatically generated withmkpylib. If you're reading this message, it means that I use this repo for my own purposes right now. It might not do anything at all; the default functionality isprint('TODO').If you really want to try it out, feel free. I recommend reading through the code and commit history to see if it does what you need, orask mefor status updates.Stay tuned!Installpython3 -m pip install -U noizyUsageimportnoizyprint('TODO')LicenseMITContactA library byShawn Presser. If you found it useful, please considerjoining my patreon!My Twitter DMs are always open; you shouldsend me one! It's the best way to reach me, and I'm always happy to hear from you.Twitter:@theshawwnPatreon:https://www.patreon.com/shawwnHN:sillysaurusxWebsite:shawwn.com
Package: noizze-crawler
NOIZZE CrawlerA web page crawler PyPI Package which returns (title(og, head), image(og, meta), description(og, meta)).DependencyBeautifulSoup4InstallationRun the folowing to install:pipinstallnoizze-crawlerUsageimportnoizze_crawlerasncimportsysif__name__=='__main__':url='https://dvdprime.com/g2/bbs/board.php?bo_table=comm&wr_id=20525678'try:(title,desc,image_url,html)=nc.crawler(url)exceptnc.HostNotFoundase:print("Host Not Found")sys.exit(1)exceptnc.HTTPErrorase:print("HTTP{}".format(e))sys.exit(1)print(title,desc,image_url)# htmlChangeLogv11: Fixed bugs #3 #8v10: Fixed bugsv9: Youtube crawler with Google API #4v8: Changed PyPI dependency - bs4v7: PEP8 passed codesv6: HostNotFound, HTTPError exceptions
Package: nojazz
nojazzA collection of one-off functions I use for various tasks as a Data Scientist-- if it ain't broke, don't fix it.Name inspired by my favorite Pearls Before Swine comicDisclaimer: I also like jazzInstallationnojazzis hosted on PyPIand can be installed with a simplepip install nojazzFeaturesAs mentioned above, I finally sat down to make this package after noticing that I was copying a lot of the same helper methods from project to project. To that end,nojazzis hardly comprehensive (nor necessarily related, submodule to submodule) but has a few, small "figured it out once and packaged it for reuse" utilities for:sqlite3My first and biggest re-use culprit.nojazzprovides a simple context manager to allow that handles all of the connection/commit/closing of sqlite database functions.withconnect_to_db('test.db')ascursor:cursor.execute('some sql')Note, this will generate anojazz_conf.pyat the root of your project when calling. The library uses this to read thedirectorylocation for all of these database connections (i.e. on another drive, perhaps)pandasThere are all kinds of tricks to this library. For now,nojazzprovides the following helper methodsfill_time_series_nullsFor a given dataset with rows: agents and columns: observations, there are applications where want to considerNULLvalues as0,but only after the user has shown up for the first time. This function does this, at the row-level and returns a copy of the original DataFrame. For example0    1     2     3     4
0  1   NaN   NaN    1    NaN
1 NaN  NaN   1     NaN    1transforms to0    1     2     3     4
0  1    0     0     1    NaN
1 NaN  NaN    1     0     1realign_nonnull_dataSimilar tofill_time_series_nulls, this function is used when you want to line all of your records up to the same starting period. Like so0     1     2    3
0  NaN   NaN   NaN   1
1  NaN   NaN    1    1
2  NaN    1     1    1transforms to0     1     2    3
0   1    NaN   NaN   NaN
1   1     1    NaN   NaN
2   1     1     1    NaNNote, this likely means needing to rename the columns to something more appropriate. Furthermore, we maintained the shape of the original DataFrame by padding all extra fields withNaN
Package: nojsoncomments
nojsoncommentsnojsoncomments is a Cython extension to remove js-style comments from JSON. Trailing comma is also supported. It is a spin-off of the jstyleson package by Lin Jackson (linjackson): The comment removal code is exactly the same as in jstyleson, but being compiled as a Cython extension, it's over twice as fast. There is also no convenience wrapper. Parsing the JSON is left to the user: this package only does one thing and tries to do it well.JSON by standard does not allow comments and trailing comma, and the python standard json module does not offer options to parse such informal JSON.importjsoninvalid_json_str="""{// Single-line comment"foo": "bar", // behold that trailing comma/*Multi-line comment*/}"""json.loads(invalid_json_str)# raises json.decoder.JSONDecodeErrornojsoncomments supports removal of:single-line commentmulti-line commentinline commenttrailing commaInstallationpip install nojsoncommentsUsageSimply like this:importnojsoncommentsvalid_json_str=nojsoncomments.clear(invalid_json_str)Testingpython setup.py testNote that for the extension compilation to succeed, you may have to do something like this in your shell:export ARCHFLAGS="-arch x86_64"LicenseThe MIT License (MIT)
Copyright (c) 2016 linjacksonPermission 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: nokcut
NokCutThai Word Segmentation using TCC + Bidirectional RNNsCredit code fromA Beginner's Guide to Deep NLP with PyTorch - Dr. Prachya BoonkwanColab Notebook :https://colab.research.google.com/drive/1WS08VsjlZGAmCGsoI7AlRm-Do3zo-b-gTrain by BEST I Corpus Training set. (90% training , 10% test)ep 6
loss: 0.017879242024514966
f1 : 98.47012481095481F1 From BEST I Corpus Test setF-measure: 96.94929
Recall: 122271.00000/125850.00000 = 97.15614

Precision: 122271.00000/126387.00000 = 96.74333

Number of incorrect : 3579.00000 wordsMr. Wannaphong Phatthiyaphaibunwannaphong@kkumail.com
Package: nokia1830pss
A ssh library for CLI/root shell on Nokia 1830-PSS Network ElementsInstallpip install nokia1830pssUsageCLI interactionfrom pss1830ssh.pss1830cli import PSS1830Cli

cli = PSS1830Cli('10.0.0.1', 22, 'admin', 'admin')
cli.open()
cli.execute('show version')
cli.execute('show card inv *')
cli.close()Root interactionfrom pss1830ssh.pss1830root import PSS1830Root

# Execute command on the master shelf, active EC
root = PSS1830Root('10.0.0.1', 22, 'root', 'root')
root.open()
root.execute('ps -ef')

# Execute command on standby EC of the master shelf
root.login_to_stdby()
root.execute('ps -ef')
root.logout_from_stdby()

# Execute command on the active EC of a shelf without knowing which one
root.login_to_shelf(shelf=2)
root.execute('ps -ef')
root.logout_from_shelf()

# Execute command on the active EC of a shelf assuming the active EC is EC 2/1
root.login_to_shelf(shelf=2, ec=1)
root.execute('ps -ef')
root.logout_from_shelf()

# Execute command on a specific EC of a shelf
root.login_to_shelf(shelf=3, ec=18, act=False)
root.execute('ps -ef')
root.logout_from_shelf()

# Execute command on a slot
root.login_to_slot(shelf=1, slot=3)
root.execute('ps -ef')
root.logout_from_slot()

root.close()
Package: nokia-ont-port-finder
nokia_ont_port_finderAutomate port finding based on the provided slot/port numberExample of slot/port number: "1/3" for slot with 8 ports, 16 ports and 16 plus 4 portsExample of slot/port number: "1" for slot with 4 portsHow to use the tool: read the example.txt file for more infopip install nokia-ont-port-finder==0.0.1from OLT_ONT_PORTFINDER.portfinder import PortFinder
Package: nolds
Nolds is a small numpy-based library that provides an implementation and a learning resource for nonlinear measures for dynamical systems based on one-dimensional time series.
Currently the following measures are implemented:sample entropy(sampen)Measures the complexity of a time-series, based on approximate entropycorrelation dimension(corr_dim)A measure of thefractal dimensionof a time series which is also related to complexity.Lyapunov exponent(lyap_r,lyap_e)Positive Lyapunov exponents indicate chaos and unpredictability.
Nolds provides the algorithm of Rosenstein et al. (lyap_r) to estimate the largest Lyapunov exponent and the algorithm of Eckmann et al. (lyap_e) to estimate the whole spectrum of Lyapunov exponents.Hurst exponent(hurst_rs)The hurst exponent is a measure of the “long-term memory” of a time series.
It can be used to determine whether the time series is more, less, or equally likely to increase if it has increased in previous steps.
This property makes the Hurst exponent especially interesting for the analysis of stock data.detrended fluctuation analysis (DFA)(dfa)DFA measures the Hurst parameterH, which is very similar to the Hurst exponent.
The main difference is that DFA can be used for non-stationary processes (whose mean and/or variance change over time).Exampleimport nolds
import numpy as np

rwalk = np.cumsum(np.random.random(1000))
h = nolds.dfa(rwalk)RequirementsNolds supports Python 2 (>= 2.7) and 3 (>= 3.4) from one code source. It requires the packagenumpy.These are the only hard requirements, but some functions will need other packages:If you want to use the RANSAC algorithm for line fitting, you will also need the packagesklearn.For the true random numbers generated bynolds.qrandomyou need the packagequantumrandom.The plotting functions innolds.examplesrequire the packagematplotlib.InstallationNolds is available through PyPI and can be installed using pip:pip install noldsYou can test your installation by running some sample code with:python-mnolds.exampleslyapunov-logisticAlternatively, if you do not havematplotlibinstalled, you can run the unittests with:python-munittest nolds.test_measuresDocumentationNolds is designed as a learning resource for the measures mentioned above.
Therefore the corresponding functions feature extensive documentation that not only explains the interface but also the algorithm used and points the user to additional reference code and papers.
The documentation can be found in the code, but it is also available asHTML-Versionand onRead the Docs.The relevant measures can be found in the filenolds/measures.py.Contact informationIf you have any questions, suggestions or corrections, you can find my contact
information on myblog.