Saravanan666/pypi-org huggingface dataset:
Package: autotagger
Tag.mp3and.m4aaudio files from iTunes data automatically.There are various standards in audio file tagging, and the fields they contain
are huge mess, but since most of them are not being used by us normal people,
to make things simpler, I chose 8 essential and common fields pragmatically:TitleAlbumArtistAlbum ArtistGenreRelease DateTrack NumberDisc NumberBy default, autotagger will only work with these 8 fields, anything not included will be
ignored.autotagger is currently tested under Mac OS X with Python 2.7.10, if you find anything wrong
with this program, feel free to submit an issue.TODO:[ ] append album artwork in tracksInstallationpip install autotaggerUsageautotagger combines audio files with iTunes data,
it uses track number (and disc number, if exists) to identify a song and
match it with iTunes data. In short, the progress could be summarized in three steps:Find the url of your album on iTunes, forexample.Make sure every song you want to tag has track number (and disc number,
if its a multi-CD album) set properly. You can use a ID3 tagging tool likemetaon OS X ormp3tagon Windows to achieve that.Runautotaggercommand with data above.autotagger--helpto see detailed information about command line options.Input/Paste songs manuallyUse-uto indicate the iTunes album url:autotagger -u https://itunes.apple.com/us/album/schole-compilation-vol.-1/id251480659After running this command, autotagger will ask you to enter the file paths,
you can copy them by right click on songs in finder and chooseCopy PathThen paste them in the terminal, and hit enter to continue.You can also album id instead of url to make it clearer:autotagger -i 251480659Pass songs from pipelineIf you can get the song names from other command’s output, you can use
pipeline mode to feed the input, add-poption to enable this feature:find album -type f -name '*.mp3' | autotagger -i 251480659 -pClear other tagsIf you want the songs to be tagged just the 8 fields other than anything else,
add-cto enable that. By adding this option, only the 8 fields
will be contained in the processed songs, any other fields will be removed.autotagger -i 251480659 -cDownload artworkAdd-aoption to download artwork, note this option will make the command stop tagging songs.autotagger -i 251480659 -aScreenshotsautotagger-i251480659find album-typef-name'*.mp3'| autotagger-i251480659-p
Package: auto_tagify
Auto Tagify is a simple auto tagging module that uses NLTK to generate tags out of a selection of text. Any
text that is less than 3 characters long or matches a particular POS (part-of-speech) will be ignored.There are two operations Auto Tagify performs - one returns the selection of text with links embedded in the
string and the other returns a list of all the taggable words as the stem word (using lemmatization).For the first operation, everything is optional, but it is most effective to enter some text. Optional
parameters you can set are the paths for tag links and the css classes for link. For instance, if you set
your tag routing to a relative path such as /tags/<tagged_word> and want to use the css class named “tagged”:from auto_tagify import AutoTagifyt = AutoTagify()t.text = “This is the text to display!”t.link = “/tags”t.css = “tagged”t.generate()The result will be: This is the <a href=”/tags/text” class=”tagged”>text</a> to <a href=”/tags/display” class=”tagged”>display!</a>If no link is set, the default path is “/<tagged word>”, such as “/text”.For the second operation, you will only receive a list of all your taggable words from the text. You can call it like so:t.text = “This text is tagged kittens”t.tag_list()The result will be a list: [‘text’, ‘tag’, ‘kitten’]By default, generate() and tag_list() will be in strict mode, which means all special characters will be stripped and
lemmatization will be enforced. If generate(strict=False) or tag_list(strict=False) is set, then special characters will be url encoded
and lemmatization will be ignored.These two operations are sufficient for you to maintain tag counts and tag references to text in your application.
Package: auto-tagify2
Auto Tagify2 is a simple auto tagging module that uses NLTK to generate tags out of a selection of text. Any
text that is less than 3 characters long or matches a particular POS (part-of-speech) will be ignored.There are two operations Auto Tagify performs - one returns the selection of text with links embedded in the
string and the other returns a list of all the taggable words as the stem word (using lemmatization).For the first operation, everything is optional, but it is most effective to enter some text. Optional
parameters you can set are the paths for tag links and the css classes for link. For instance, if you set
your tag routing to a relative path such as /tags/<tagged_word> and want to use the css class named “tagged”:from auto_tagify2 import AutoTagifyt = AutoTagify()t.text = “This is the text to display!”t.link = “/tags”t.css = “tagged”t.generate()The result will be: This is the <a href=”/tags/text” class=”tagged”>text</a> to <a href=”/tags/display” class=”tagged”>display!</a>If no link is set, the default path is “/<tagged word>”, such as “/text”.For the second operation, you will only receive a list of all your taggable words from the text. You can call it like so:t.text = “This text is tagged kittens”t.tag_list()The result will be a list: [‘text’, ‘tag’, ‘kitten’]By default, generate() and tag_list() will be in strict mode, which means all special characters will be stripped and
lemmatization will be enforced. If generate(strict=False) or tag_list(strict=False) is set, then special characters will be url encoded
and lemmatization will be ignored.These two operations are sufficient for you to maintain tag counts and tag references to text in your application.
Package: autotech-sdk
autotech-sdk: software development kit for autotech companyautotech-sdkis a library for common task in cloud development such as: MongoDB setup, Confluent-kafka setup.Mongo setup example:from autotech_sdk.database.mongo import BaseMongDB
    from pymongo import IndexModel        

    class UserModel(BaseMongoDB):
        indexes = [
            IndexModel(
                keys="username",
                unique=True,
                background=True
            ),
        ]Confluent-kafka consumer example:from autotech_sdk.kafka import ConfluentConsumerConfig, ConfluentConsumer
    
    class GetInforConsumer(ConfluentConsumer):
        def process_data(self, data):
            print(data)
        
        def process_data_error(self, msg, err):
            passIn short, autotech_sdk can be used to:MongoDBsetup connection.Kafkasetup connection.Get It Now$ pip install -U autotech-sdkRequirementsPython >= 3.9
Package: autotestreg
AutoTest!RegAutomatically test your functions to see if you have changed their behavior by mistake!View Demo·Report BugAbout The ProjectHave you ever spent hours reworking someone else's code, trying not to break anything, only to realize at the last moment that something doesn't work anymore, but not knowing at which step you introduced the bug? So frustrating!AutoTest!Reg allows you to automatically track the code you are working on and not introduce any regression, without writing a single test!Getting StartedUsing AutoTest!Reg requires no effort at all!Install AutoTest!Reg with pip :pipinstallautotestregImport AutoTest!Reg into your tests, adding this line:importautotestregTo follow a functionmy_func, this code is all you need:fromautotestregimportautotest_funcdefmy_func():...autotest_func(my_func)To track an entire modulemypackage, with all the functions and methods it contains:fromautotestregimportautotest_moduleimportmypackageautotest_module(mypackage)NextIf no regression was introduced when you modified the code, your tests will pass.Otherwise, AutoTest!Reg will give you the function whose behavior was changed.(back to top)Contributing(Section in english)I want to add a lot of functionnalities to this project, but I don't have much time to work on it. Contributions are welcome!Roadmap/todoTaskImportanceDifficultyContributor on itDescription./5./5NOBODYe.g.: ...Non-Code contribution :TaskImportanceDifficultyContributor on itDescription./5./5NOBODYe.g.: ...For every todo, just click on the link to find the discussion where I describe how I would do it.See theopen issuesfor a full list of proposed features (and known issues).(back to top)How to contributeContributing is an awesome way to learn, inspire, and help others. Any contributions you make aregreatly appreciated, even if it's just about styling and best practices.If you have a suggestion that would make this project better, please fork the repo and create a pull request.Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/YourAmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestAuthorsThis library was created byNicolas MICAUX.
Package: autoth
Autoth: Automatic optimzie hyper parametersAutoth is a Python toolbox to automatically optimize hyper parameters to maximize scores. For example, Autoth can optimize hyper parameters to maximize F1 score in a classification task. In practice, Autoth numerically calculate gradients of scores over hyper parameters. Then, the hyper parameters are updated according to the gradients iteratively. Please see [1] for details.Installpip install autothExamplepython3 example.pyResults------ Manually selected hyper parameters ------
Hyper parameters: [0.3, 0.3, 0.3]
Score: 0.5556

------ Automatic optimized hyper parameters ------
Optimizing hyper parameters ...
learning rate: 0.010, total epochs: 10
    Hyper parameters: [0.3, 0.31, 0.29], score: 0.5556
    Epoch: 0, Time: 0.0181 s
    Hyper parameters: [0.3, 0.3197, 0.2801], score: 0.5556
    Epoch: 1, Time: 0.0178 s
    Hyper parameters: [0.3, 0.3237, 0.2702], score: 0.5714
    Epoch: 2, Time: 0.0212 s
    Hyper parameters: [0.3, 0.3245, 0.263], score: 0.6099
    Epoch: 3, Time: 0.0144 s
    Hyper parameters: [0.3, 0.3232, 0.2548], score: 0.6099
    Epoch: 4, Time: 0.0142 s
    Hyper parameters: [0.3, 0.3204, 0.2464], score: 0.6099
    Epoch: 5, Time: 0.0151 s
    Hyper parameters: [0.3, 0.3164, 0.2382], score: 0.6099
    Epoch: 6, Time: 0.0159 s
    Hyper parameters: [0.3, 0.316, 0.2302], score: 0.5940
    Epoch: 7, Time: 0.0143 s
    Hyper parameters: [0.3, 0.318, 0.2226], score: 0.5940
    Epoch: 8, Time: 0.0150 s
    Hyper parameters: [0.3, 0.3186, 0.2152], score: 0.6099
    Epoch: 9, Time: 0.0177 s

Optimized hyper parameters: [0.3, 0.3186, 0.2152]
Score: 0.6099Cite[1] Kong, Qiuqiang, Yong Xu, Wenwu Wang, and Mark D. Plumbley. "Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization." arXiv preprint arXiv:1912.04761 (2019).
Package: autoth-qiuqiangkong
Autoth: Automatic optimzie hyper parametersAutoth is a Python toolbox to automatically optimize hyper parameters to maximize scores. For example, Autoth can optimize hyper parameters to maximize F1 score in a classification task. In practice, Autoth numerically calculate gradients of scores over hyper parameters. Then, the hyper parameters are updated according to the gradients iteratively. Please see [1] for details.Installpip install autothExamplepython3 example.pyResults------ Manually selected hyper parameters ------
Hyper parameters: [0.3, 0.3, 0.3]
Score: 0.5556

------ Automatic optimized hyper parameters ------
Optimizing hyper parameters ...
learning rate: 0.010, total epochs: 10
    Hyper parameters: [0.3, 0.31, 0.29], score: 0.5556
    Epoch: 0, Time: 0.0181 s
    Hyper parameters: [0.3, 0.3197, 0.2801], score: 0.5556
    Epoch: 1, Time: 0.0178 s
    Hyper parameters: [0.3, 0.3237, 0.2702], score: 0.5714
    Epoch: 2, Time: 0.0212 s
    Hyper parameters: [0.3, 0.3245, 0.263], score: 0.6099
    Epoch: 3, Time: 0.0144 s
    Hyper parameters: [0.3, 0.3232, 0.2548], score: 0.6099
    Epoch: 4, Time: 0.0142 s
    Hyper parameters: [0.3, 0.3204, 0.2464], score: 0.6099
    Epoch: 5, Time: 0.0151 s
    Hyper parameters: [0.3, 0.3164, 0.2382], score: 0.6099
    Epoch: 6, Time: 0.0159 s
    Hyper parameters: [0.3, 0.316, 0.2302], score: 0.5940
    Epoch: 7, Time: 0.0143 s
    Hyper parameters: [0.3, 0.318, 0.2226], score: 0.5940
    Epoch: 8, Time: 0.0150 s
    Hyper parameters: [0.3, 0.3186, 0.2152], score: 0.6099
    Epoch: 9, Time: 0.0177 s

Optimized hyper parameters: [0.3, 0.3186, 0.2152]
Score: 0.6099Cite[1] Kong, Qiuqiang, Yong Xu, Wenwu Wang, and Mark D. Plumbley. "Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization." arXiv preprint arXiv:1912.04761 (2019).
Package: autothread
AutothreadParallelization made easy.Autothread contains a collection of decorators that make it as easy as possible to add
threading or multiprocessing to your projects. Autothread has two types of decorators: blocking and non-blocking.Non-blockingAutothreads non-blocking decorators are the easiest way to add threading/multiprocessing to your
project. You just need to add a single decorator, which changes your function to calculate in
the background instead of blocking the script.importautothreadimporttimefromtimeimportsleepasheavyworkload@autothread.async_threaded()# <-- This is all you need to adddefexample(x,y)->int:heavyworkload(1)returnx*ystart=time.time()results=[]foriinrange(5):results.append(example(i,10))# the thread is startedprint(results)# autothread waits for the thread to end and gives you the resultprint("Time expired: ",time.time()-start)>>>[0,10,20,30,40]Timeexpired:1.002363681793213autothread.async_processedworks in the same way but uses multiprocessing instead of threading.
More info can be found in thenon-blocking README.BlockingThe blocking decorators of autothread change the function slightly, but give you more control
over when the function is executed:importautothreadimporttimefromtimeimportsleepasheavyworkload@autothread.multithreaded()# <-- This is all you need to adddefexample(x:int,y:int):heavyworkload(1)returnx*y@autothread.multiprocessed()# <-- Or to use multiprocessingdefexample2(x:int,y:int):heavyworkload(1)returnx*yNow, instead of integers, your function can take lists of integers. The function will
be repeated or each item in your list on a separate thread/process:start=time.time()result=example([1,2,3,4,5],10)print(result)print("Time expired: ",time.time()-start)>>>[10,20,30,40,50]Timeexpired:1.0041766166687012More info can be found in theblocking README.InstallingYou can install autothread using:pip install autothreadOr by cloning the source:git clone https://github.com/Basdbruijne/autothread.git
cd autothread
pip install -e .Known issuesNone at the moment, please open a bug if you run into an issue.
Package: autotimeseries
autotimeseriesNixtla SDK. Time Series Forecasting pipeline at scale.autotimeseriesis a python SDK to consume the APIs developed inhttps://github.com/Nixtla/nixtla.InstallPyPIpip install autotimeseriesHow to useCheck the following examples for a full pipeline:M5 state-of-the-art reproduction.M5 state-of-the-art reproduction in ColabBasic usageimportosfromautotimeseries.coreimportAutoTSautotimeseries=AutoTS(bucket_name=os.environ['BUCKET_NAME'],api_id=os.environ['API_ID'],api_key=os.environ['API_KEY'],aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'])Upload dataset to S3train_dir='../data/m5/parquet/train'# File with target variablesfilename_target=autotimeseries.upload_to_s3(f'{train_dir}/target.parquet')# File with static variablesfilename_static=autotimeseries.upload_to_s3(f'{train_dir}/static.parquet')# File with temporal variablesfilename_temporal=autotimeseries.upload_to_s3(f'{train_dir}/temporal.parquet')Each time series of the uploaded datasets is defined by the columnitem_id. Meanwhile the time column is defined bytimestampand the target column bydemand. We need to pass this arguments to each call.columns=dict(unique_id_column='item_id',ds_column='timestamp',y_column='demand')Send the job to make forecastsresponse_forecast=autotimeseries.tsforecast(filename_target=filename_target,freq='D',horizon=28,filename_static=filename_static,filename_temporal=filename_temporal,objective='tweedie',metric='rmse',n_estimators=170,**columns)Download forecastsautotimeseries.download_from_s3(filename='forecasts_2021-10-12_19-04-32.csv',filename_output='../data/forecasts.csv')
Package: auto-timewatch
AutoWatchSetup:pip install ..env example:COMPANY_NUMBER=123
EMPLOYEE_NUMBER=10
MY_EMPLOYEE_NUMBER=10
PASSWORD=whatever
START_TIME=0900
END_TIME=1800
AUTOMATIC_TIME_FRAME=True
FILL_MONTH=7
FILL_YEAR=2022Information about environment variablesMY_EMPLOYEE_NUMBER - your own employee number, a safety measure for the case where EMPLOYEE_NUMBER is different because you filled hours for a colleague.START_TIME - the time you came to the office (default is 09:00)END_TIME - the time you left the office (default is 18:00)AUTOMATIC_TIME_FRAME - if True, it will fill in 9 hours from START_TIME (unless it's a half day, then it'll fill in 5 hours). If False, it'll just use END_TIMEFILL_YEAR, FILL_MONTH - optional variables for if you need to fill a specific month and not the current monthExecution:python -m autowatch.app
Package: autotm
AutoTMAutomatic parameters selection for topic models (ARTM approach) using evolutionary algorithms.
AutoTM provides necessary tools to preprocess english and russian text datasets and tune topic models.What is AutoTM?Topic modeling is one of the basic methods for EDA of unlabelled text data. While ARTM (additive regularization
for topic models) approach provides the significant flexibility and quality comparative or better that neural
approaches it is hard to tune such models due to amount of hyperparameters and their combinations.To overcome the tuning problems AutoTM presents an easy way to represent a learning strategy to train specific models for input corporas.Optimization procedure is done by genetic algorithm which operators are specifically tuned for
the task. To speed up the procedure we also implemented surrogate modeling that, for some iterations,
approximate fitness function to reduce computation costs on training topic models.Installation! Note: The functionality of topic models training is available only for linux distributions.Via pip:pip install autotmFrom source:pip install -r requirements.txtpython -m spacy download en_core_web_smexport PYTHONPATH="${PYTHONPATH}:/path/to/src"QuickstartThe notebook with an example is available inexamplesfolder.Running from the command lineTo fit a model:autotmctl --verbose fit --config conf/config.yaml --in data/sample_corpora/sample_dataset_lenta.csvTo predict with a fitted model:autotmctl predict --in data/sample_corpora/sample_dataset_lenta.csv --model model.artmBacklog:Add testsAdd new multi-stageCitation@article{10.1093/jigpal/jzac019,author={Khodorchenko, Maria and Butakov, Nikolay and Sokhin, Timur and Teryoshkin, Sergey},title="{ Surrogate-based optimization of learning strategies for additively regularized topic models}",journal={Logic Journal of the IGPL},year={2022},month={02},issn={1367-0751},doi={10.1093/jigpal/jzac019},url={https://doi.org/10.1093/jigpal/jzac019},}
Package: autotoloka
AutoTolokaAutoToloka is a Python library for hosting and controlling projects of the Yandex.Toloka serviceInstallationTo install the latest version from PyPI:pipinstall-UautotolokaGetting startedSee video demo hereDocs pageQuickstartColabOAuth TokenFor authorization, you need to get an OAuth token from your Yandex.Toloka profile.
Go to:
Profile -> External Services Integration -> Get OAuth tokenProxyIf you want to use Yandex.Disk as a storage (recommended) for your images, you need a proxy. You can add proxy on your Yandex.Toloka profile.
Go to:
Profile -> External Services Interation -> Yandex.Disk Integration -> Add ProxyExamplefromautotolokaimportTolokaProjectHandlerfromautotoloka.json_dataimportjson_dataOAUTH_TOKEN='your_token'PROJECT_CONFIG=json_data['project_name']POOL_ID='pool_id'# ID of the created poolPROJECT_ID='project_id'# ID of the project that the pool was created forSUITE_ID='suite_id'# ID of the created suitehandler=TolokaProjectHandler(OAUTH_TOKEN)# Creates Toloka project by configuration in a given filehandler.create_toloka_project(PROJECT_CONFIG)# Updates the project, obtained by the handlerhandler.update_toloka_project(PROJECT_CONFIG)# Prints out project parametershandler.get_project_params()# Creates Toloka pool by dictionary-stored or file-based configurationshandler.create_toloka_pool()# Updates Toloka pool by given parametershandler.update_pool(POOL_ID)# Prints and returns all available pools' parametershandler.get_pools_params(less_info=True,only_current_project=True)# Opens or closes the required poolhandler.open_close_pool(handler.get_pools_params())# also you can close pool, then write 'close'# Creates either a Toloka task or a Toloka task-suite by dictionary-stored input valuesinput_values=[{'key_1':'value_1','key_2':'value_2'},{'key_1':'value_3','key_2':'value_4'}]handler.create_task_suite(POOL_ID,input_values)# Creates either a Toloka task or a Toloka task-suite with data from Ya.Disk proxy-folderhandler.create_task_suite_from_yadisk_proxy(POOL_ID,OAUTH_TOKEN,'test-photos/test1/',tasks_on_suite=1)# Prints all available tasks or task-suites in the projecthandler.get_toloka_tasks_suites(POOL_ID)# Archives the given object by its ID and typehandler.archive_object('project',PROJECT_ID)# also you use archive_object for pools, then you need ('pool', POOL_ID)# Changes the overlap of either the task or the task-suite, also is able to set infinite overlaphandler.change_task_suite_overlap(SUITE_ID,overlap=1)# Sends the signal to stop showing the task-suite by its IDhandler.stop_showing_task_suite(SUITE_ID)handler.get_answers(POOL_ID)AuthorsThe library created bySHIFTLab CFT
Package: autotools-language-server
autotools-language-serverLanguage server forautotools.Supports:configure.ac:autoconfMakefile.am:automakeMakefile:makeFeatures:Goto DefinitionfunctionvariabletargetFind ReferencesfunctionvariabletargetDiagnosticDocument FormattingHoverdefinitiondocumentCompletiondefinitiondocumentOther features:pre-commit-hookslinterformatterScreenshotsDiagnosticHoverCompletionReadto know more.Similar Projectscmake-language-server