Saravanan666/pypi-org huggingface dataset:
Package: pymitt
pymittA minimal Python wrapper for Keymitt webhooksUsage is super simple. Import the script, and initialize as many Keymitt locks or push buttons as you like by providing the device's ID and token.
Needs an event loop to run.Minimal example to lock and unlock a Keymitt lock from a simple script:import asyncio

from pymitt import pymitt

loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)

k = pymitt.KeymittLock("YOUR_LOCK_ID", "YOUR_TOKEN")

# Lock the lock
loop.run_until_complete(k.lock())

# Unlock the lock
loop.run_until_complete(k.unlock())
Package: pymitv
A Python 3 based control of the Mi Tv 3(s)The Mi TV 3s 65-inchIntroductionThis package was developed to interface with the Xiaomi TV 3 series
through their local HTTP API using Python. The package has both the
ability to discover TVs and control them. The TV lineup in question is
thisone.It should be noted, that all the TV logic and hardware is
in the soundbar. Thus, if you have a soundbar that looks identical to
the one in the picture, you should be golden with this library.Supported modelsMi TV 3s (all sizes)Not sure if supported modelsMi TV 4A (all sizes)DocumentationYou can find the documentation on Githubhere.
Package: pymiwifi
pyMiWiFiA python API to the Xiaomi Mi WiFi Router web interface(Tested on the Mi Router 3C)InstallPyPIpip install pymiwifiLicense: MITPython >= 3.6Useimportpymiwifimiwifi=pymiwifi.MiWiFi()miwifi.login('admin_pass')miwifi.status(){'dev': [{'mac': 'xx:xx:xx:xx:xx:xx',
   'maxdownloadspeed': '933178',
   'upload': '28777897',
   'upspeed': '339',
   'downspeed': '194',
   'online': '1320',
   'devname': 'android-xx',
   'maxuploadspeed': '672600',
   'download': '69654908'},
  {'mac': 'xx:xx:xx:xx:xx:xx',
   'maxdownloadspeed': '1239976',
   'upload': '1249682170',
   'upspeed': '431417',
   'downspeed': '8283',
   'online': '5531',
   'devname': 'xx-pc',
   'maxuploadspeed': '744959',
   'download': '42672896'},
  {'mac': 'xx:xx:xx:xx:xx:xx',
   'maxdownloadspeed': '1074270',
   'upload': '1745315',
   'upspeed': '63',
   'downspeed': '84',
   'online': '5392',
   'devname': 'xy-pc',
   'maxuploadspeed': '37234',
   'download': '19197169'},
  {'mac': 'xx:xx:xx:xx:xx:xx',
   'maxdownloadspeed': '0',
   'upload': '0',
   'upspeed': '0',
   'downspeed': '0',
   'online': '5531',
   'devname': '5C:CF:7F:33:BD:41',
   'maxuploadspeed': '0',
   'download': '0'}],
 'code': 0,
 'mem': {'usage': 0.38, 'total': '64 M', 'hz': '800MHz', 'type': 'DDR2'},
 'temperature': 0,
 'count': {'all': 4, 'online': 3},
 'hardware': {'mac': 'xx:xx:xx:xx:xx:xx',
  'platform': 'R3L',
  'version': '2.8.50',
  'channel': 'release',
  'sn': '15516/20172849'},
 'upTime': '5573.27',
 'cpu': {'core': 1, 'hz': '575MHz', 'load': 0.3267},
 'wan': {'downspeed': '9023',
  'maxdownloadspeed': '1264691',
  'history': '460239,437753,431551,444594,443864,444399,437105,425708,444010,443786,443956,443522,437202,425671,444129,448279,443948,437086,429470,440070,444176,444169,444589,436990,424927,443799,446999,447205,431575,430753,467157,444888,444520,443385,432773,429452,443798,444853,445282,436298,425354,444444,444744,444884,442913,429838,434359,444254,444070,445424',
  'devname': 'eth0.2',
  'upload': '1296497767',
  'upspeed': '436401',
  'maxuploadspeed': '751567',
  'download': '136487233'}}AdvancedIf there's an endpoint that's not available in the API, you can just -miwifi.get_api_endpoint('xqsystem/wifi_macfilter_info')(Use chrome dev tools' "Network" Tab to see these endpoints)
Package: pymix
PyMix - The Python mixture package
Author: Alexander Mendez
Original Author: Benjamin Georgi
Contact:georgi@molgen.mpg.deInstallation Instructions:Required PackagesPython (version 2.5+ recommended, tested with 2.7)Numpy  (version 1.6?, tested with 1.6)GSL GNU Scientific libraryOptional PackagesGHMM for mixtures of HMMspylab for plotting functions in plotMixture.pyHow to install ?Extract the tarball to some directory of your choice.
Change into the directory and run:python setup.py build
python setup.py install –prefix=/some/whereIf you wish to pull down the github version you can use pip:pip install git+https://github.com/ajmendez/PyMix.gitIt is now part of pypi:pip install pymixAfter the installation is completed I would recommend to run
mixtureunittests.py to check whether everything is in order.To get the GSL on OSX I recomend using homebrew to build the universal
package so that it works with both the i386 version in EPD:brew install gsl –universalDocumentation:Example code for most aspects of the library can be found in
the pymix/examples subdirectory and mixtureunittest.py.
Automatically generated documentation for the module is available
on the Pymix home page www.pymix.org.
Package: pymixbox
Mixbox: Pigment-Based Color MixingUsageimportmixboxrgb1=(0,33,133)# bluergb2=(252,211,0)# yellowt=0.5# mixing ratiorgb_mix=mixbox.lerp(rgb1,rgb2,t)print(rgb_mix)Mixing Multiple Colorsz1=mixbox.rgb_to_latent(rgb1)z2=mixbox.rgb_to_latent(rgb2)z3=mixbox.rgb_to_latent(rgb3)z_mix=[0]*mixbox.LATENT_SIZEforiinrange(len(z_mix)):# mix together:z_mix[i]=(0.3*z1[i]+#   30% of rgb10.6*z2[i]+#   60% of rgb20.1*z3[i])#   10% of rgb3rgb_mix=mixbox.latent_to_rgb(z_mix)Pigment ColorsPigmentRGBFloat RGBLinear RGBCadmium Yellow254, 236, 00.996, 0.925, 0.00.991, 0.839, 0.0Hansa Yellow252, 211, 00.988, 0.827, 0.00.973, 0.651, 0.0Cadmium Orange255, 105, 01.0, 0.412, 0.01.0, 0.141, 0.0Cadmium Red255, 39, 21.0, 0.153, 0.0081.0, 0.02, 0.001Quinacridone Magenta128, 2, 460.502, 0.008, 0.180.216, 0.001, 0.027Cobalt Violet78, 0, 660.306, 0.0, 0.2590.076, 0.0, 0.054Ultramarine Blue25, 0, 890.098, 0.0, 0.3490.01, 0.0, 0.1Cobalt Blue0, 33, 1330.0, 0.129, 0.5220.0, 0.015, 0.235Phthalo Blue13, 27, 680.051, 0.106, 0.2670.004, 0.011, 0.058Phthalo Green0, 60, 500.0, 0.235, 0.1960.0, 0.045, 0.032Permanent Green7, 109, 220.027, 0.427, 0.0860.002, 0.153, 0.008Sap Green107, 148, 40.42, 0.58, 0.0160.147, 0.296, 0.001Burnt Sienna123, 72, 00.482, 0.282, 0.00.198, 0.065, 0.0LicenseCopyright (c) 2022, Secret Weapons. All rights reserved.Mixbox is provided under the CC BY-NC 4.0 license for non-commercial use only.If you want to obtain commercial license, please contact:mixbox@scrtwpns.com
Package: pymixel
PymixelPymixelis a simple and easy way to deal with the Dynamixel X Series. It's fast and based on theDynamixelSDK.Features:fast and easy to understandsupport for group writesmost of the common features implementedDynamixelSDKas dependencyLicense:Python & Pypi:Install & Upgrade:# install via pypipip3installpymixel# upgradepip3install--upgradepymixelImport:frompymixelimportDynamixelUsage:Examples:frompymixelimportDynamixelDXL_ID_ONE=1# dynamixel idVELOCITY_MODE=1# velocity modedynamixel=Dynamixel()# create instancedynamixel.open()# open serial communicationdynamixel.set_operating_mode(DXL_ID_ONE,VELOCITY_MODE)# set dynamixel modedynamixel.set_torque(DXL_ID_ONE,True)# enable torquedynamixel.set_led(DXL_ID_ONE,True)# enable leddynamixel.set_goal_velocity(DXL_ID_ONE,100)# start moving...Author:👨🏼‍💻Vinzenz Weist
Package: pymixer
PyMixerOverviewIt is a library for mixing input MIDI and/or WAV files to output WAV files. To convert MIDI to WAV,[Sine]thesizerandFluidSynthare used.To start with, there is atutorialwhere this library is used for generating polyphonic audio from MIDI representation of a demo fugue.To get more information, one can read docstrings from the source code. These docstrings are informative enough.InstallationTo install a stable version, run:pipinstallpymixerAbove command also installssinethesizerPython package as a dependency, but it does not installfluidsynth(which is not a Python package). Please install it according to instructions from itsofficial website.
Package: pymixin
PyMixinPyMixin is a python library designed to help inject code in a compatible way.
It is heavily inspired by the SpongePowered Mixin project.FeaturesRedirect function calls>>>frommixinimport*>>>frommathimportlog,log10>>>>>>deftest_function(n:int)->int:...returnlog(n)...>>>@redirect(method=test_function,at=At(value=AtValue.INVOKE,target=log))...defreal_input(n:int)->int:...# Call log10 instead of log...returnlog10(n)...>>>test_function(10)1.0Change constants>>>frommixinimport*>>>fromrandomimportrandint>>>>>>defpercent(n:int)->float:...returnn/100...>>>@modify_const(method=percent,at=At(value=AtValue.LOAD,target=100))...defrandom_denominator():...returnrandint(0,100)...>>>percent(10)0.2777777777777778>>>percent(10)0.35714285714285715Inject callbacks>>>frommixinimport*>>>>>>definternal_message_handler(data:bytes):...return# Dummy implementation...>>>@inject(method=internal_message_handler,at=At(value=AtValue.HEAD))...deflog_message(data:bytes,callback_info:CallbackInfo):...print("Received data:",data)...>>>internal_message_handler(b"Hello world")Receiveddata:b'Hello world'Cancel functions>>>frommixinimport*>>>>>>defprocess(body:str):...ifbody=="Hello":...print("World")...else:...print("Invalid body")...>>>@inject(method=process,at=At(value=AtValue.HEAD),cancellable=True)...defcancel_if_bad(body:str,callback_info:CallbackInfo):...ifbody!="Hello":...callback_info.cancel()...>>>process("Hello")World>>>process("World")>>>Modify returned value>>>frommixinimport*>>>>>>defreturn_n_squared(n):...returnn*n...>>>@inject(method=return_n_squared,at=At(value=AtValue.RETURN),cancellable=True)# Warning: injects at EVERY return by default...defreturn_n_cubed_instead(callback_info:CallbackInfo):...n=(callback_info.return_value**0.5)...callback_info.set_return(n**3)...>>>return_n_squared(10)1000.0Overwrite functions>>>frommixinimport*>>>>>>defspam_a():...whileTrue:...print("a")...>>>@overwrite(method=spam_a)...defreplacement():...print("b")...>>>spam_a()bA note on decoratorsOften in python, a function is wrapped with a decorator. This means the value of a function is no longer the same.
To resolve this, we addedmixin.unwrap, to get the original function back (assumingfunctools.wraps) was used.>>>frommixinimport*>>>fromfunctoolsimportwraps>>>>>>defwith_print(func):...@wraps(func)...definner(*args,**kwargs):...print("args",args,kwargs)...returnfunc(*args,**kwargs)...returninner...>>>@with_print...deftest(n):...returnn*2...>>>@inject(method=unwrap(test),at=At(value=AtValue.HEAD))...deflog_n(n,callback_info):...print("N:",n)...>>>test(10)args(10,){}N:1020InstallingTo install PyMixin, you can just use pip:pipinstallpymixinLicensePyMixin is licensed under MIT.
Package: pymixing
pymixingpymixing is a python audio processing / analysis library based on soundfile, numpy, librosa, and pyloudnorm.Inspired by traditional console and digital audio workstations, it aims to provide audio experts with flexible assistance to deal with the complex audio processing / detection tasks.
Currently, pymixing can not only supports the mono files in wav format, but also the stereo files. Besides，it is already possible to export the corresponding 48k / 24bit or 44.1k/16bit wav format mono/stereo audio files as required.
Package: pymixxx
This project contains some commmand line tools and python libraries tocontrol Mixxx dj software.Installing the modules requires musa music library tools for album, songand playlist processing. Musa is not otherwise used (no DB import or musiclibrary merging is implemented).The scripts include:mixxx-automix- List tracks in automix list, add or remove tracks by pathmixxx-crates- List crates, list song paths in cratesmixxx-lookup- Search songs from mixxx database matching artist, album, title, comment or genrefieldsmixxx-playlists- List, import, export or remove mixxx playlists. Import/export requires m3u listsand musa playlists configuration done correctly for nowmixxx-trackinfo- Show relevant database fields from mixxx database for given tracks
Package: pymization
pymizationSimple solver for optimization problems.CDS implementedInstallationpipinstallpymizationExampleCode to use CDS given a matrix where rows are jobs and columns are machines.importpymizationaspymsolver=pym.Solver("CDS")solver.load_data_from_matrix(matrix)solver.run()If you want to use some functions developed on CDS you can use as the followimportpymizationaspymsolver=pym.Solver("CDS")solver.load_data_from_matrix(matrix)solver.load_model()solver.model.get_sets()# Retrieves order sets to Johnson's algorithmsolver.model.min_makespan(job_sequence)# Given a Johnson's ordered jobs calculate min_makespan and actualize objective_functionsolver.model.final_sequencesolver.model.objective_functionWork in progressAny inquirements:Nicolas Camus:ncamusf@gmail.comMaurice Poirrier:maurice@merkenlabs.com
Package: pymklist
pymklistLDraw mklist in PythonOnce installed withpip install pymklist, it should be an almost drop-in replacement for the make-list bash file found inhttps://github.com/nathaneltitane/ldrawor the Windows executable mklist.exe found in LDraw.org complete.zip
you can start it by just doingmkliston the command-line.You can instruct mklist to generate the parts.lst with a sort on the description (--description) or the code (--number),
usemklist--helpfor usage descriptionpymklist has been tested on Python 2.7, 3.4, 3.5 and 3.6You can also get the parts list from Python code, just dofrom mklist.generate import get_parts_lstand call theget_parts_lstfunction to get a listorfrom mklist.generate import generate_parts_lstto generate a parts.lstFree software: GNU General Public License v3FeaturesTODOHistory0.1.0 (2018-02-28)First release on PyPI.
Package: pymkup
pymkuppymkup is a Python library for viewing markups lists and property data in PDFs created by Bluebeam Revu.AboutThis is a reverse-engineered unofficial API for accessing data generated in Bluebeam Revu authored PDFs. Once a file is loaded, it can be scraped for some information.InstallationUse the package managerpipto install pymkup.pipinstallpymkupUsagefrompymkupimportPymkupx=Pymkup("link to your Revu PDF")x.spaces()# Returns JSON dictionary of document spaces.x.markups()# Returns JSON dictionary of markups.Data export with custom columns exampleFirst, you should identify the columns that are accessible in your file:x.get_columns().values()Second, you should review the extended columns that can also be added:x.extended_columns()Lastly, you can build the custom columns that you want to see returned:columns=['Subject','Label','Date','UUID','Space']x.markups(column_list=columns)Requirementspdfreader for browsing the PDF tree.matplotlib to build Spaces, convert colors.tox to run virtualenv tests.pytest for unit testing.Dev TestingpipinstalltoxOnce installed, run 'tox' from project root directory, and it will run unit tests on multiple python installation. Tests are files within the /test folder.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: pymlab
MLAB’s I2C bus modules binding. Should be used for data readout from MLAB (http://wiki.mlab.cz/) electronic modules.Software DependenciesPython-smbusCython-hidapi (https://pypi.python.org/pypi/hidapi/) (optional, required with USBI2C module)libusb, hidapi and libudev on Linux (optional, required with USBI2C module)Supported devicesCurrently supported sensors are:ALTIMET01A (MPL3115A2)LTS01A    (MAX31725)MAG01A    (HMC5883L)SHT25v01A (Sensirion SHT25)Currently supported devices are:ACOUNTER02ACLKGEN01B   (Silicon Labs Si570 oscillator)I2CHUB02A (TCA9548A)Currently supported interfaces are:Kernel I2C interfacing subsystemUSBI2C01A (USB HID interface with Silicon Labs cp2112)
Package: pymlask
pymlaskpymlask is a Python version of ML-Ask (eMotive eLement and Expression Analysis system)For details about ML-Ask, Seehttp://arakilab.media.eng.hokudai.ac.jp/~ptaszynski/repository/mlask.htmSee alsohttp://qiita.com/yukinoi/items/ef6fb48b5e3694e9659c(in Japanese)Contributions are welcome!InstallationModified dictionary versionpip install pymlaskML-Ask Original dictionary version (same as Ptaszynski’s Perl version)pip install git+https://github.com/ikegami-yukino/pymlask@originalExamplefrommlaskimportMLAskemotion_analyzer=MLAsk()emotion_analyzer.analyze('彼のことは嫌いではない！(;´Д`)')# => {'text': '彼のことは嫌いではない！(;´Д`)',#     'emotion': defaultdict(<class 'list'>,{'yorokobi': ['嫌い*CVS'], 'suki': ['嫌い*CVS']}),#     'orientation': 'POSITIVE',#     'activation': 'NEUTRAL',#     'emoticon': ['(;´Д`)'],#     'intension': 2,#     'intensifier': {'exclamation': ['！'], 'emotikony': ['´Д`', 'Д`', '´Д', '(;´Д`)']},#     'representative': ('yorokobi', ['嫌い*CVS'])#     }LICENSEThe BSD 3-Clause LicenseCopyrightML-Ask Python: The BSD 3-Clause License
(c) 2017 Yukino Ikegami. All Rights Reserved.ML-Ask (original): The BSD 3-Clause License
(c) 2007-2013, Michal Ptaszynski, Pawel Dybala, Rafal Rzepka, Kenji Arakii. All Rights Reserved.CHANGES0.3.2 (2019-07-09)Fix bugs about emotion pattern matching (thanks @brunotoshio)Fix bug about activation count (thanks @brunotoshio)0.3.1 (2019-05-22)Use MeCab.Tagger().parse() instead of MeCab.Tagger().parseToNode0.3 (2019-05-17)The 712 emotional words from Kanjou kotoba erabi jiten (感情ことば選び辞典) are newly addedSupport Python 3.7Unsupport Python 2.6 and 3.30.2.5 (2017-09-14)Fix bugs about MeCab (thanks @Kensuke-Mitsuzawa)Delete install_requires0.2.4 (2017-03-01)Fix many bugsAdd some emotional wordsDelete invalid wordsCorrect typo0.2.1 (2017-02-23)Add 67 emotional words0.2 (2017-02-22)Support Python 2.XAdd 52 emotional wordsFix bug0.1.1 (2017-02-15)Delete debug print (thanks @ssirai)0.1 (2017-02-10)First release.
Package: pymlbenchmark
pymlbenchmark: benchmark around machine learningThis project started with my first attempt
to bring a modification toscikit-learnThe template was reused to measure various
implementations or models and to make them
shorter but with probably less understanding
of the whole script. It evolved into providing
classes to compareONNXruntimes such asonnxruntimetoscikit-learn.GitHub/pymlbenchmarkdocumentationBlogHistorycurrent - 2021-01-05 - 0.00Mb12: Support double when converting to onnx (2019-12-21)0.2.141 - 2019-12-13 - 0.04Mb11: Fix dimension when calling convert_sklearn (2019-10-07)8: implements OnnxRuntimeBenchPerfTestRegression (2019-07-15)6: use assume_finite=True whever possible (2019-07-15)4: update colors in graph, use similar colors but not equal (2019-05-23)5: run profiler on some calls and saves the results (2019-04-23)3: add mechanism to dump error, data to investigate later when it happens (2019-03-12)1: move benchmark from _benchmarks (2019-03-12)0.1.30 - 2019-03-07 - 0.02Mb2: first commit (2019-03-05)
Package: pymlconf
pymlconfAboutpymlconf(Python YAML Configuration Library) helps to easily manage
and access to your application configurations which was already Written
inYAMLlanguage.CheckoutDocumentationfor more info.InstallationpipinstallpymlconfDevelopmentcdpath/to/pymlconf
pipinstall-e.
pipinstall-rrequirements-dev.txtRunning testspytestCoveragepytest--cov=pymlconfDocumentationcdsphinx
makedoctestmakehtml
or
makelivehtml
Package: py-mld
MLDThis repository stores the implementation of MLDv1 router-side state machines. This can be used to detect multicast interest of directly connected hosts.The goal of this repository/module is to facilitate maintainability of this MLD implementation since its code is used by other Python projects/modules:HPIM-DMPIM-DMRequirementsLinux machinePython3 (we have written all code to be compatible with at least Python v3.3)pip (to install all dependencies)Installationpip3 install py-mldHow to use it?# import modulefrommldimportInterfaceMLDintf=InterfaceMLD(interface_name="eth0")intf.enable()# start receiving MLD packets# get information from a given multicast groupmulticast_group_obj=intf.interface_state.get_group_state(group_ip="ff05::10:11:12")interest=multicast_group_obj.has_members()# boolean that informs if there is# multicast interest in this groupgroup_state=multicast_group_obj.state.print_state()# get string identifying# the state in which this# group is at# get notified of interest changes on this groupclassMulticastGroupNotifier:defnotify_membership(self,has_members):print(has_members)notifier=MulticastGroupNotifier()multicast_group_obj.add_multicast_routing_entry(notifier)# when there is a change of multicast interest (for example group ff05::10:11:12# gets interested receivers), the object associated to this object is notified# through "notify_membership" method with has_members=True# if you no longer want to monitor the interest of ff05::10:11:12, remove the# notifier from the groupmulticast_group_obj.remove_multicast_routing_entry(notifier)intf.remove()# stop receiving MLD packets
Package: pyml-ensemble
Python Machine Learning EnsembleA library used to create and manage adaptable machine learning ensembles in Python.The ensemble is intended to use deep learning models as the models. However, rather than
including these models with the package there are ANN and LSTM models in the provided
examples (examples/models/). This was to remove the package's dependency on the
tensorflow package since many ensembles can be created without this package. There are
some other, smaller dependencies still to make the Ensemble usable out of the box.Under ConstructionThe initial version of PyML Ensemble is currently being developed. Feel free to use and contribute to the project but don't expect too much functionality. Additionally, the PyPi package has not been released yet.
Package: pymlfs
pymlfspymlfs: Machine Learning From Scratchpymlfs implements classical machine learning models from scratch. Implementations are presented as simply as possible, as the library is designed for educational purposes.Installation$pipinstallpymlfsContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensepymlfswas created by Youssef Achenchabe. It is licensed under the terms of the MIT license.
Package: pymlimageutils
mlutilsThis package is useful for checking image data quality, separating image and label files, and splitting datasets.RequirementsThe package requires the following dependencies to be installed:osPIL (Python Imaging Library)cv2 (OpenCV)randomshutilInstallationyou can install mlutils via pip:pip install mlutilsUsageCheck for corrupted imagesUse the check_corrupted_images_in_directory(dir_in) function to check for corrupted images in a directory. It returns the number of corrupted images found.frommlutilsimportcheck_corrupted_images_in_directorycheck_corrupted_images_in_directory("/path/to/images/folder")#####Check image naming conventions
Use the check_naming_conventions(dir_in) function to check and fix image naming conventions in a directory. This function renames images with upper-case file extensions to lower-case and changes "jpeg" to "jpg" file extensions.frommlutilsimportcheck_naming_conventionscheck_naming_conventions("/path/to/images/folder")####Detect and fix images with a premature ending
Use the detect_and_fix_premature_ending(dir_path) function to detect and fix images with a premature ending in a directory. It returns the number of fixed images.frommlutilsimportdetect_and_fix_premature_endingdetect_and_fix_premature_ending("/path/to/images/folder")####Separate and copy files
Use the separate_and_copy_files(folders, dest_main_folder) function to separate image and label files from multiple folders and copy them to a new destination folder.frommlutilsimportseparate_and_copy_filesfolders=["/path/to/1st/images/folder","/path/to/2nd/images/folder"]dest_main_folder="/path/to/destination/folder"separate_and_copy_files(folders,dest_main_folder)####Split a dataset
Use the split_dataset(images_path, labels_path, img_val_path, label_val_path, split_fraction) function to split a dataset into training and validation sets. The function moves a fraction of images and their corresponding label files to validation folders.frommlutilsimportsplit_datasetimages_path="/path/to/training/images"labels_path="/path/to/training/labels"img_val_path="/path/to/validation/images"label_val_path="/path/to/validation/labels"split_fraction=0.2# 20% of the data will be moved to validation setsplit_dataset(images_path,labels_path,img_val_path,label_val_path,split_fraction)