Saravanan666/pypi-org huggingface dataset:
Package: loading-display
Loading DisplaySimple loading bar and spinner for the terminal.spinnerPrints unicode frames for a spinning animation.The icons used as frames in the spinner can be changed using theiconsparameter:>>>fromloading_displayimportspinner>>>s=spinner(icons=['🌑','🌒','🌓','🌔','🌕','🌖','🌗','🌘'])>>>while1:...next(s)Loading barPrints a progress bar.Usage:fromloading_displayimportloading_barwhileloading:loading_bar(current_progress,total=total_size,bar_length=10,show_percentage=True)Default appearance:████████████████████ 100.0 %The appearance can be customized with theiconparameter.
Package: loadingpy
loadingpyIn this repository, we provide a custom made progress bar for python iterables. This library can be used as is or modified for any purposes (see licence).for deep learningThere is now a new progress bar available for deep learning purposes (I guess it could be leveraged for other stuff as well...). Say, you want to train a model using a dataset $D$ over $e$ epochs. UsingTrainBaryou can get a double progress bar (first for the epochs and second for the steps in the current epoch) on a single line. you can check thetestor this simple example:fromloadingpyimportTrainBarfordatainTrainBar(trainloader,num_epochs=e,base_str="training",):inputs,labels=dataExampleYou can install with pippip install loadingpyand use as followsfromloadingpyimportPyBarloss=0.0accuracy=0.0forinputs,labelsinPyBar(dataset,monitoring=[loss,accuracy],naming=["loss","accuracy"],base_str="training"):# do whatever you pleaseloss+=0.0# update monitoring variables in placeaccuracy+=0.0# update monitoring variables in placeFor a more detailed exampel (in torch) check thistutorial. You can use a global argument in order to disable the verbatim from the loading bars as follows:fromloadingpyimportBarConfigBarConfig["disable loading bar"]=TrueArgumentsHere is a list of the arguments and their descriptionargumentdescriptiontypeiterablepython object that can be iterated overcan be a list, tuple, range, np.ndarray, torch.Tensor, dataset,...monitoringa python object (or list of python objects) that will be printed after each iteration using the following format f'{monitoring}'. IF they are updated during the loop, make sure to update inplace, in order to see the changesan be a tensor, float or list of thesenamingif you want to add a descritpion prefix to the monitoring variablesstr or list of strtotal_stepsnumber of iterations to perform (if you set it to a lower value than the length of the iterable, then the process will stop after the given total_steps)intbase_strprefix description of the loop we are iterating overstrcolorwhich color to use for the loading barstr
Package: loadingspinner
loading-spinnerAn incredibly simple “loading spinner” for python projectsUsage:NOTE: Please do NOT import spinner. You MUST use loadingspinner.import loadingspinner
loadingspinner.setText(“Loading…”)
loadingspinner.start()
<lines of code>
loadingspinner.stop()Customizing the spinner:Changing the spinner animation:loadingspinner.setFrames(<list of characters to display in order from left to right>)
Ex. loadingspinner.setFrames([“|”,”/”,”-“,”"])Changing the text color (requires Colorama to be installed):loadingspinner.setTextColor(color)Changing the spinner color (Incomplete. Also requires Colorama):loadingspinner.setSpinnerColor(color)
Package: loading-wrapper
Loading WrapperLoading Wrapper is a Dash component library.
Component to get whether Loading of Dash Core Components is Loading or not.Installation$ pip install loading_wrapperPropertieschildren (dash component)id (string)className (string)targetClassName (string; require)Class of target to be monitored for appearance or non-appearance.interval (number; default: 1000)Monitoring interval.ignores (list[string])List of data-loading_ignore attributes of the Loading component to be excluded from monitoring.is_loading (bool)loading or not loading.is_stop (bool; default: false)Or discontinue surveillance.
Package: load-m3u8
load-m3u8Importif__name__=='__main__':fromload_m3u8.generate.resolveimportCreateM3U8# Make sure the D:/test/test.ts video file existsCreateM3U8('D:/test/test.ts',hls_enc_key='5dd0a99887d8c801').run()fromload_m3u8.load.resolveimportLoadM3U8LoadM3U8("D:/test//test.ts.m3u8",video_path="D:/test/load/test_load.ts").run()# Download from the ServerLoadM3U8("http://127.0.0.1/m3u8/test.m3u8").run()Usageinstall load-m3u8pip--no-cache-dirinstallload-m3u8Decrypt and Encrypt# Make sure the D:/test/test.ts video file existsgen-m3u8D:/test/test.ts-key5dd0a99887d8c801# Download the video and decrypt itload-m3u8"D:/test//test.ts.m3u8"-o"D:/test/load"Specifying a aes key server# Make sure the http://127.0.0.1/enc has accessgen-m3u8D:/test/test.ts-key5dd0a99887d8c801-iv5dd0a99887d8c801-t120-base"D:/test/segment"-m3u8"D:/test/m3u8/test.m3u8"-seg"test_stream"-url"http://127.0.0.1/enc"-methodPOST-d# Download the video and decrypt itload-m3u8"D:/test/m3u8/test.m3u8"-o"D:/test/load"-d-tw2OR

load-m3u8"D:/test/m3u8/test.m3u8"-o"D:/test/load"-d-pw2
Package: load-modules
py_load_modulesDescriptionpy_load_modules is a python3 library which takes a class, and a list of
directories.  It will search within those directories (without any recursive
searching) for classes which implement the argument class. It will return a
list of instances of those classes for use.Installationpython3 -m pip install  git+https://github.com/rhhayward/py_load_modules.git@masterUsageAssuming a directory structure like this:from load_modules import load_modules

modules = load_modules.load(SuperClass, ['/path/to/py_files/', 'relative_path/to/py_files'])
for module in modules:
    module.class_method()
Package: loado
NAME
loadoDESCRIPTION
Easy, modern, and beginner friendly loading animations.ruby COLORS: RED GREEN BLUE RESET```javascript
TYPES:
    BAR1: █,
    BAR2: ▒,
    BAR3: ▬,
    BAR4: ━,
    BAR5: =,
    BAR6: ○,
    BAR7: ●,

    SPIN1: ['|', '/', '-', '\\', '|', '/', '-', '\\', '|', '/', '-', '\\', '|', '/', '-', '\\', '|', '/', '-', '\\', '|', '/', '-', '\\'],
    SPIN2: ['◜', '◠', '◝', '◞', '◡', '◟', '◜', '◠', '◝', '◞', '◡', '◟', '◜', '◠', '◝', '◞', '◡', '◟', '◜', '◠', '◝', '◞', '◡', '◟', '◜', '◠', '◝', '◞', '◡', '◟', '◜', '◠', '◝', '◞', '◡', '◟'],
    SPIN3: ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏', '⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏', '⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏', '⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏', '⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏'],
    SPIN4: ['⣾', '⣽', '⣻', '⢿', '⡿', '⣟', '⣯', '⣷', '⣾', '⣽',
    '⣻', '⢿', '⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿', '⡿', '⣟', '⣯',
    '⣷', '⣾', '⣽', '⣻', '⢿', '⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿',
    '⡿', '⣟', '⣯', '⣷'],
    SPIN5: ['⠁', '⠂', '⠄', '⡀', '⢀', '⠠', '⠐', '⠈', '⠁', '⠂',
    '⠄', '⡀', '⢀', '⠠', '⠐', '⠈', '⠁', '⠂', '⠄', '⡀', '⢀', '⠠', '⠐',
    '⠈', '⠁', '⠂', '⠄', '⡀', '⢀', '⠠', '⠐', '⠈', '⠁', '⠂', '⠄', '⡀',
    '⢀', '⠠', '⠐', '⠈'],
    SPIN6: ['⣀', '⡀', '⠄', '⡈', '⠐', '⠈', '⠄', '⡀', '⣀', '⡀',
    '⠄', '⡈', '⠐', '⠈', '⠄', '⡀', '⣀', '⡀', '⠄', '⡈', '⠐', '⠈', '⠄',
    '⡀', '⣀', '⡀', '⠄', '⡈', '⠐', '⠈', '⠄', '⡀', '⣀', '⡀', '⠄', '⡈',
    '⠐', '⠈', '⠄', '⡀'],
    SPIN7: ['▔', '▕', '▖', '▗', '▘', '▙', '▚', '▛', '▜', '▝',
    '▞', '▟', '▔', '▕', '▖', '▗', '▘', '▙', '▚', '▛', '▜', '▝', '▞',
    '▟', '▔', '▕', '▖', '▗', '▘', '▙', '▚', '▛', '▜', '▝', '▞', '▟']

    WAVE1: ['▁', '▂', '▃', '▄', '▅', '▆', '▇', '█'],
    WAVE2: ['▏', '▎', '▍', '▌', '▋', '▊', '▉', '█'],
    WAVE3: ['▔', '▕', '▖', '▗', '▘', '▙', '▚', '▛', '▜', '▝',
    '▞', '▟'],
    WAVE4: ['⣀', '⡀', '⠄', '⡈', '⠐', '⠈', '⠄', '⡀'],
    WAVE5: ['⣠', '⣤', '⣶', '⣾', '⣿'],# Basic example
```python
import loado as ld

# Bar
ld.bar(10, BAR1, 'A bar').run().stop()

# Spin
spinner = ld.spin(10, SPIN1, 'A spin')
spinner.run()
spinner.stop()

# Wave
with ld.wave(10, WAVE1, 'A wave') as wave:
    wave.run()
    wave.stop()
    print('Finished wave!')

# Timed
with ld.timed(10, 'A timed') as timed:
    timed.run()
    timed.stop()
    print('Finished timed!')You can use the variable way or the with statement way.DATAall= ['bar', 'spin', 'wave', 'timed']copyright= 'Copyright (c) 2023 loado'license= 'MIT'VERSION
5.8.0AUTHOR
loado
Package: loads
WarningLoads is under heavy development. Don’t use it.Loadsis a framework for load testing an HTTP service.by Juan Pablo BravoInstallation:$ bin/pip install loadsSee the docs athttp://loads.readthedocs.orgCHANGES0.3 - 2014-05-22Agents id are now unique across boxesDisabling heartbeat on busy agentsBusy agents cannot be killed that easilymoved from a REP/ROUTER/REQ pattern to a REP/ROUTER/ROUTERRefactored Funkload outputRefactored external runner interfaceMany more things0.2 - 2013-09-27improved test coverage, distributed stability and documentationrefactored the code in cleaner & smaller modules.15% speedup for the brokerDatabase backends are now plugins. We provide a pure Python disk-based one
and a Redis one.extended the database API - now used in the loads-web project to display
running load tests.the console can now be detached and also displays tracebacks.added several options: –duration, –ping-broker, –check-cluster,
–no-patchingFixed the DNS resolving issues.Added support for multiple runs in external runnersAdded an experimental Funkload output0.1 - 2013-08-02Initial release.
Package: loadsave
DatastoreCurrently supported file structures: CSV WAV JSON PICKLEPython module which loads and saves data structures based on the file extension.DependenciesPython3 (and native libraries)jsoncsvwavestructimportlibospickleInstallationpip install loadsaveUsageimport loadsave

data = loadsave.load(filename)
... manipulating data ...
loadsave.save(data, filename)Saving data can be a bit unintuitive.csvrequires data to be a list of dictionarieswavrequires data to be a tuple or list of the form(y,sr)exactly how it would be returned byload('example.wav')ObjectiveThis module attempts to follow ageneric procedure for reading and writing common data storage formats using only the file extension for guidance.The motivation here is as follows; Most of the time, the most generic approach to opening a file will work. It's may not be the most computationaly efficient way, but if loading time is not a bottleneck for your workflow, you might as well spend your time manipulating the data as opposed figuring out how to load it into python. The goal of Datastore is to make implementing these generic approaches as fast as possible. If it doesn't work, well at least you didn't waste much time. Otherwise, you just skipped a monotonous task and can get on with real work.Exampleimport loadsave

d = [{'id': [1,2,3,4],'otherID':[4,3,2,1]}]

# save this dict as a pickle
loadsave.save(d, 'test.pkl')'test.pkl'can be replaced with'test.json', or'test.csv'. Attempting to save it to'test.wav'will return an error, becausedis not an acceptable format for an audio waveform.However, ifd = [0,1,2,3,4]you could callloadsave.save((d,44100),'test.wav').csvs require a list of dictionaries, so trying to savethisas a csv will failFile TypesCSVThis is just a wrapper for csv.DictReader() / csv.DictWritter()The data must be a list of dictionariesWAVRestricted to 16-bit audioCan handle stereo/multi-channel waveformsData must be a list or tuple of the form (waveform, samplerate), just like it is provided fromloadsave.load('example.wav')PICKLEThis should work with any data structure in python.JSONCan hanledict,list,str,int,float,bool, andNone
Package: load-simulator
Load SimulatorOverviewThe Load Simulator is designed to simulate a variety of mathematical operations. It offers different levels of complexities and maintains an operation rate limiting feature.UpdateThe Load Simulator package is now available on PyPI. You can easily install it using pip:pipinstallload-simulator==0.1.1Modulesconstants.pyContains constants and enumerations.models.pyDefines data models for operation details, a list of operations, and simulator configuration.operation_executor.pyContains theOperationExecutorclass to execute mathematical operations.utilities.pyUtility functions and context managers for logging and other functionalities.load_simulator.pyContains theLoadSimulatorclass andrun_simulatorfunction for simulating the load for mathematical operations.InstallationPyPI MethodInstall the packagepipinstallload-simulator==0.1.1Manual MethodClone the repositorygitclonehttps://github.com/yourkin/load-simulator.gitNavigate to the project directorycdload_simulatorInstall dependencies using PoetrypoetryinstallActivate the virtual environmentpoetryshellUsageTherun_simulatorfunction can be imported to run the simulation:fromload_simulator.load_simulatorimportrun_simulatorrun_simulator(seconds_to_run=10,complexity="medium",max_ops_per_sec=5)Parameters forrun_simulatorseconds_to_run: Number of seconds to run the simulation.complexity: Complexity level for operations. Options are "easy", "medium", and "complicated".max_ops_per_sec: Maximum number of operations per second.TestingRun the following command to execute the tests:pytestContributingFork the repository and make changes as you'd like. Pull requests are warmly welcome.For commit messages, conform toConventional Commits.LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: loadtest
loadtestLoad tester and benchmark tool for Python programmers.Installation:pipinstallloadtestBasic usagedefrequest_func():importrequestsresponse=requests.get("https://jsonplaceholder.typicode.com/todos/1")ifresponse.status_code!=200:print(response.status_code)else:sample={"userId":1,"id":1,"title":"delectus aut autem","completed":False}ifresponse.json()!=sample:print(response.json())returnresponsefromloadtestimportLoadTesterload_tester=LoadTester(n_jobs=10,worker=request_func,count=100)load_tester.start()
Package: load-test
load-testQuick StartDefine by implementingTestBase, run withRunnerimportload_testclassMyTest(load_test.TestBase):def_run_task(self):requests.get("https://google.com")defmain():load_test.Runner([MyTest]).run()SeeexamplesGet It NowRunpip install load_testHow It WorksPreconfigured amount of processes will run a test case over and over again until timeout. You can send more than one
test case and then after each test finishes, it will continue to the next one (after some delay).Each process gathers statistics, when all tests finish the main process will create a single result file.Runner OptionsOptionDescriptionDefaultprocessesHow many processes will run the test case in parallel1durationHow many seconds each process will run the test case over and over again3results_dirThe directory of the final results, the filename is the timestamp of the run"results"runsThe limit amount for each process to run the test definition (Nonefor no limit)NonedelayThe delay in seconds between each test case (Nonefor no delay)NoneloggerLogger for general log messages (Nonefor no logger)None
Package: loadtime
LoadTimeEnglish|日本語LoadTimeis a Python package designed specifically
to tackle the challenge of long waiting times associated
with loading large-scale pretrained language models,
such as HuggingFace models, into GPU or CPU memory.WithLoadTime, instead of waiting in uncertainty,
you can visualize the progress of your loading process.Of course, it can also be used for other long-term operations.InstallationYou can install LoadTime via pip:pipinstallloadtimeKey FeaturesReal-time tracking: LoadTime provides real-time tracking of the loading process. No more staring at a static screen!Progress Bar: The package displays a progress bar, showing you how much of the process has been completed and how much is still remaining. It takes the guesswork out of waiting!Past Loading Time Cache: One unique feature of LoadTime is its ability to remember the time it took to load a model in the past. The package automatically caches the total loading time of your operations. The next time you load the same model, LoadTime uses this cached information to provide an even more accurate progress bar.Customizable Display: LoadTime allows you to customize the progress display with your own messages. You can tailor the tool to fit your personal needs.Optimized for HuggingFace Models: LoadTime has been optimized for loading HuggingFace models, with special handling of the download progress display when the model is not cached locally.Basic UsageHere is a simple example of how to use the LoadTime package:importtorchfromtransformersimportAutoTokenizer,AutoModelForCausalLMfromloadtimeimportLoadTimemodel_path="togethercomputer/RedPajama-INCITE-Chat-3B-v1"model=LoadTime(name=model_path,fn=lambda:AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.float16))()tokenizer=AutoTokenizer.from_pretrained(model_path)# important: load tokenizer after loading modelInitial ParametersParameterDescriptionnameName of the long-term process. For loading HuggingFace models, specify the model name.messageSpecify the message to be displayed. If omitted, the default message is used.pbarSet to True to display the progress bar and percentage.dirnameDirectory name for cache storage.hfSet to True to use for time display for loading HuggingFace models. If the model data has not yet been downloaded to the disk, HuggingFace's loader displays the download progress, so this library does not display it.fnFunction to execute the long-term process.fn_printFunction to perform the display. If omitted, it will be output to the console.Take control of your loading times with LoadTime!
Package: loadwright
✨ LoadwrightWe want toMake it really easy to load test data apps in Python and get useful insightsWe provideALoadTestRunnerthat can record complexUserinteractions with your data appALoadTestViewerthat can show you the results of the recording.The framework is based onPlaywrightandPanel.You can install and use the package as simple as.pipinstallloadwrightSee thetestsfolder for examples or thePanel test operating capacity howto.Please note this project is at avery early stage an the api and functionality will change!Why not LocustI love Locust. But Locust and Playwright just does not work well for me. Seelocust-plugins #101. And this gave me an oppportunity to play with Panel+Async+Streaming.⭐ SupportPlease supportPanelandawesome-panelby giving the projects a star on Github:holoviz/panel.awesome-panel/awesome-panel.Thanks❤️ ContributeIf you are looking to contribute to this project you can find ideas in theissue tracker. To get started check out theDEVELOPER_GUIDE.I would love to support and receive your contributions. Thanks..Monitor
Package: load-xl
load_xl- A Python Configuration Parser Libraryload_xl is a simple and easy to use library for parsing environment variable and configuration files inPython. It provides a convenient way to load environment variables from .envi files and parse unique syntax configuration files.
InstallationThe package can be installed using pip.pipinstallload-xlUsageHere's how to use load_xl in your Python code:importload_xl# Load environment variables from .envi fileload_xl.load_envi_file('.envi')# Read configuration fileconfig=load_xl.read_config_file('.configx')Theload_envi_filefunction takes a file path to a .envi file and loads the defined environment variables to the environment.Theread_config_filefunction takes a file path to a unique syntax configuration file and returns a dictionary of all the keys and values defined in the file.
.envi file formatThe .envi file should contain one environment variable per line in the following format:# Key and ValueKEY=VALUELines starting with a # symbol will be ignored as comments.
Unique syntax configuration file formatThe unique syntax configuration file should follow the following rules:Lines starting with a - symbol are treated as environment variables and split on the : sign.Lines starting with & or % are ignored as comments.Only values that start with $ are taken into consideration.Example# This is a comment line, ignored by the parser&Thisisacommentline&,ignoredbytheparser
%Thisisacommentline%,ignoredbytheparser# Keys and Values$key1:value1# This line follows the correct syntax for key-value pair$key2:value2
-key3:value3# This line follows the correct syntax for environment variableError and Exception handlingThe library provides a comprehensive error and exception handling mechanism to ensure the stability and reliability of your code.Change Log==============0.1.0 (06/02/2023)initial release0.1.1 (06/02/2023)Fixed README.md0.2.0 (07/02/2023)Fixed Some BugsNow you can load any.ini,.yamland.jsonfile directelyChecktestsfolder for example usageContributingIf you want to contribute to load_xl, please reach out to the maintainers. We welcome contributions, bug reports, and feedback.Licenseload_xl is released under the MIT License.
Package: loaf
🍞 LoafSo bland yet so good!™Effortlessly access your SQL servers and procedures, plus some other utilities.Install$ pip install loafExamplesImporting Into Your ProjectfromloafimportLoafSetting Up Credentials# Setup your credentials with a single line.loaf=Loaf(port=6969,db="pizzeria")# Or load your credentials from a file.loaf=Loaf(file="creds.ini")# Or use a local SQLite file instead.loaf=Loaf(file="pizzeria.db")Executing Queries# Make queries easily.toppings=loaf.query("SELECT * from toppings")# Load your quieries directly from files.clients=loaf.query(file="getHappyClients.sql")# Prevent disasters by executing multiple queries.pepperoni_id,client_name=loaf.multi(["SELECT id FROM toppings WHERE name='Pepperoni'","SELECT name FROM clients WHERE id=6"])Printing# Display info using built-in tables!loaf.print(pepperoni_id)loaf.print(client_name)loaf.print(toppings)┏━━━━┓┃id┃┡━━━━┩│1│└────┘┏━━━━━━━━━━━┓┃name┃┡━━━━━━━━━━━┩│'Alfonso'│└───────────┘┏━━━━┳━━━━━━━━━━━━━┳━━━━━━━┓┃id┃name┃price┃┡━━━━╇━━━━━━━━━━━━━╇━━━━━━━┩│1│'Pepperoni'│1.49││2│'Mushrooms'│1.99││3│'Onions'│0.99│└────┴─────────────┴───────┘Data Manipulation# Manipulate your data with dictionaries, as God intended.fortoppingintoppings:print(topping['name'])PepperoniMushroomsOnionsUtilities# Not lazy enough? Try some of the pre-built queires.# Equivalent of: SELECT name FROM client WHERE name='Marco' LIMIT 1result=loaf.select("name","clients","name='Marco'",limit=1)# Get all values from a table.result=loaf.all("toppings")# Got stored procedures? No problemo!result=loaf.call("ProcedureFindClient",1)⚠️ Syntax for the package has changed heavily since version 0.2.0, if your project depends on Loaf and is using an inferior version, I heavily suggest that you use the previous stable version:$ pip install loaf==0.1.30
Package: loafang
LoafangA JSON based RESTapi Query Language. It's mainly for small to intermediate sized project.MotivationI'm bored.I needed a smaller version of graphql.It's fun to do something like thisWhy name it LoafangIf you google the synonyms for 'rest' one of the result is 'loaf' or 'loafing'. Now, where do you see the word 'rest', 'REST API' and it's more of a language kind of thing so
'loafang' makes senseAn example Schema{"GET:get-user:pe":{"user --name adwaith --age 10":["username","email_address"]},"PUT:up-user":{"user --name some-user":{"email":"new_email@address.com"},"after":"get-user"}}For more docs visit:https://adwaith-rajesh.github.io/loafang/Road MapThings kind of worksMake an easier API for the user to use.More tests