Saravanan666/pypi-org huggingface dataset:
Package: aaa2.1.1
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pip install roc2.1.2


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    python 
            from ROC import roc
            roc.roc("truth","test","result","roc")
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            from ROC import conRoc
            conRoc.conRoc("pre_file","image")
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            from transform import txt2xml
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Package: aaaa
aaaaHave you ever been creating an animated plot by saving a series of images and joining them together? The obvious way to name these frames would be using numbers:foriinrange(15):plt.savefig('{}.png'.format(i))But many operating systems will order these files as follows:0.png1.png10.png11.png12.png13.png14.png2.png3.png4.png5.png6.png7.png8.png9.pngThereby screwing up the order of your animation.This is whereaaaa(pronounced like a scream) comes in!aaaahas a single class which creates an iterator that yields an alphabetical sequenceaa,ab,ac, etc. So we can change our above example:fromaaaaimportaaaafname=aaaa()foriinrange(15):plt.savefig('{}.png'.format(next(fname)))Which will preserve the order of the files in your operating system:aa.png
ab.png
ac.png
ad.png
ae.png
af.png
ag.png
ah.png
ai.png
aj.png
ak.png
al.png
am.png
an.png
ao.pngFile sorting can differ between operating systems, sometimes withserious results.Installationaaaahas no dependences and is tested on Python 3.5 through 3.8 and it can be installed via pip:pipinstallaaaaUsageThere are two ways to useaaaa. Firstly, you can loop directly over anaaaainstance, until there are no items left (in this case the last value of name will bezz):fromaaaaimportaaaanames=aaaa()fornameinnames:print(name)Or, you can use thenext()operator to only get as many values as you need:fromaaaaimportaaaanames=aaaa()foriinrange(10):print(next(names))The default length of an output fromaaaais 2 characters long - this will allow you to order 676 files. If you need a larger or smaller amount of characters you can specify this as an argument toaaaa:fromaaaaimportaaaanames_short=aaaa(1)names_long=aaaa(4)print(next(names_short),next(names_long))# Outputs --> a aaaaBeyond four or five characters in length, initializingaaaabecomes prohibitively slow. Note that four characters gives you456976permutations - so you should never need more than this.ContributeFor my purposes this package is feature complete, but if you find a bug, or have a feature request, open an issue, or create a pull request!LicenseThe project is licensed under the MIT license.
Package: aaa-approx
The AAA algorithm for rational approximationThis is a Python implementation of the AAA algorithm for rational approximation
described in the paper "The AAA Algorithm for Rational Approximation" by Yuji
Nakatsukasa, Olivier Sète, and Lloyd N. Trefethen, SIAM Journal on Scientific
Computing 2018 40:3, A1494-A1522.(doi)A MATLAB implementation of this algorithm is contained inChebfun.
The present Python version is a more or less direct port of the MATLAB version.The "cleanup" feature for spurious poles and zeros is not currently implemented.InstallationThe implementation is in pure Python and requires only numpy and scipy as
dependencies. Install it using pip:pip install aaa-approxUsageHere's an example of how to approximate a function in the interval [0,1]:import numpy as np
from aaa import aaa

Z = np.linspace(0.0, 1.0, 1000)
F = np.exp(Z) * np.sin(2*np.pi*Z)

r = aaa(F, Z, mmax=10)Instead of the maximum number of termsmmax, it's also possible to specify
the error tolerancetol.  Both arguments work exactly as in the MATLAB
version.The returned objectris an instance of the classaaa.BarycentricRationaland can
be called like a function. For instance, you can compute the error onZlike this:err = F - r(Z)
print(np.linalg.norm(err, np.inf))If you are interested in the poles and residues of the computed rational function,
you can query them likepol,res = r.polres()and the zeroes usingzer = r.zeros()Finally, the nodes, values and weights used for interpolation (calledzj,fjandwjin the original implementation) can be accessed as properties:r.nodes
r.values
r.weights
Package: aaa-features
NOTE: This code is a extract fromhttps://github.com/malfante/AAA.This code calculates the feature vector from a MSEED file to used it later for training and classification purposes.Automatic Analysis ArchitectureWelcome to this automatic classification scheme! Please carefully read the following before asking questions :)If used, the software should be credited as follow:Automatic Analysis Architecture, M. MALFANTE, J. MARS, M. DALLA MURAand the original paper for which the code was developped should be cited:Malfante, M., Dalla Mura, M., Metaxian, J. P., Mars, J. I., Macedo, O., & Inza, A. (2018). Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives. IEEE Signal Processing Magazine, 35(2), 20-30.We thank you for the respect of the authors work.Set up and requierements needed to run the codeThis code was developed under Python 3, and needs the following libraries. .obspy>=1.1python_speech_featuressympyCreate and activate your working environment (in a terminal session):conda create -n aaa_features python=3.9conda activate aaa_featuresClone the repository and installgit clone https://github.com/awacero/aaa_features.gitcd aaa_featurespip install .Install the package aaa_features from PYPI:pip install aaa_featuresConfiguration filesthe feature setting file, contained inconfig_sample/features*.jsonMSEED data samplethe folder data_sample contains a MSEED file from the station EC RETU SHZ 2012-06-28Run the code to get the featurespython call_aaa_features.pyMore infoIf you still have questions, try running and exploring the code.
The playground files are relatively easy to play with.If you still have question, fell free to ask !Contact:marielle.malfante@gmail.com
Package: aa_airtable
Airtable integration for Django-based projectsThis project’s target is to make the Airtable import easier in Django-based applications.InstallationAddaa_airtableto your app’sINSTALLED_APPS, and also setAIRTABLE_SETTINGSin project settings.
After all please migrate the app (./manage.py migrate aa_airtable).SettingsAPI_KEY - Airtable API key.DATABASES - Database settings eg.("Media", "tests.parsers.MediaParser")whereMediais table name andtests.parsers.MediaParseris path to table parserENDPOINT_URL - Airtable API endpoint url.DATA_DIRECTORY - Folder with json data backups.FILES_DIRECTORY - Folder with uploaded files to airtable.SAVE_FILES - Should library save uploaded files (default: True)Example Parserfrom aa_airtable.parser import AbstractParser
class ArticleParser(AbstractParser):
    model = Article
    raw_fields = [
        "Name",
        ("custom_name", "Title"),
        "Description",
    ]
    related_fields = [
        ("gallery", "Gallery", Media),
    ]
    file_fields = [
        "NY Logo"
    ]SupportDjango 1.11Python 3.4-3.6
Package: aa-algorithms-utils
READMEThis package contains all the common functions used to run the Advanced Analytics algorithms. We provide a brief explanation of each of the modules and the corresponding functions:athena_data_providercontains the classAthenaDataProvider, which is initialized by giving the parametersaws_access_key_idaws_secret_access_keys3_staging_dirregion_name.The functionread_queryreturns a DataFrame with the information requested by the query passed as argument.loggercontains the classLogger, which logs important messages and prints them to the terminal or CloudWatch in AWS.metricscontains the classMetrics, which creates the metrics and pushes them to Datadog.s3_clientcontains the classS3Client, that is initialized using boto3.client and boto3.resource and the environment variable RESULT_BUCKET.The functionupload_filesuploads the DataFrame returned by the algorithm (output_df) to the given path (output_path) in AWS, for the chosen variant (variant– typicallyproductionor the name of the experiment, if testing new features).To use these modules in the experiments do the following:
Package: aa-alumni
Alliance Auth - AlumniFeaturesIntegration with Alliance Auth's State System, creates and maintains an Alumni State for past members of an Alliance and/or Corporation.InstallationStep 1 - Prepare AuthRemove/Promote any state with a priority of1, Alumni is considered slightly better than the built in Guest State.Step 2 - Install from pippipinstallaa-alumniStep 3 - Configure Auth settingsConfigure your Auth settings (local.py) as follows:Add'alumni'toINSTALLED_APPSAdd below lines to your settings file:## Settings for AA-Alumni# TasksCELERYBEAT_SCHEDULE['alumni_run_alumni_check_all']={'task':'alumni.tasks.run_alumni_check_all','schedule':crontab(minute=0,hour=0,day_of_week=4),}CELERYBEAT_SCHEDULE['alumni_run_update_all_models']={'task':'alumni.tasks.update_all_models','schedule':crontab(minute=0,hour=0,day_of_week=3),}Step 4 - Update AA's State systempythonmyauth/manage.pyalumni_stateStep 5 - Maintain Alliance AuthRun migrationspython manage.py migrateGather your staticfilespython manage.py collectstaticRestart your projectsupervisorctl restart myauth:Step 6 - Configure FurtherIn the Admin interface, visitalumni > config > addor<AUTH-URL>/admin/alumni/config/add/Select the Alliances and/or Corporations for which characters with historical membership are AlumniContributingMake sure you have signed theLicense Agreementby logging in athttps://developers.eveonline.combefore submitting any pull requests. All bug fixes or features must not include extra superfluous formatting changes.
Package: aaanalysis
Welcome to the AAanalysis documentation!AAanalysis(Amino Acid analysis) is a Python framework for interpretable sequence-based protein prediction.
Its foundation are the following algorithms:CPP: Comparative Physicochemical Profiling, a feature engineering algorithm comparing two sets of protein
sequences to identify the set of most distinctive features.dPULearn: deterministic Positive-Unlabeled (PU) Learning algorithm to enable training on
unbalanced and small datasets.AAclust: k-optimized clustering wrapper framework to select redundancy-reduced sets of numerical scales
(e.g., amino acid scales).In addition, AAanalysis provide functions for loading various protein benchmark datasets (load_data), amino acid
scales (load_scales), and their two-level classification (AAontology). We combinedCPPwith the explainable
AISHAPframework to explain sample level predictions with
single-residue resolution.If you are looking to make publication-ready plots with a view lines of code, see ourPlotting Prelude.You can find the official documentation atRead the Docs.InstallAAanalysiscan be installed either fromPyPiorconda-forge:pipinstall-uaaanalysisorcondainstall-cconda-forgeaaanalysisContributingWe appreciate bug reports, feature requests, or updates on documentation and code. For details, please refer toContributing Guidelines. These include specifics about AAanalysis and also notes on Test
Guided Development (TGD) using ChatGPT. For further questions or suggestions, please emailstephanbreimann@gmail.com.CitationsIf you use AAanalysis in your work, please cite the respective publication as follows:AAclust:Breimann and Frishman (2024a),AAclust: k-optimized clustering for selecting redundancy-reduced sets of amino acid scales,bioRxiv.AAontology:Breimannet al.(2024b),AAontology: An ontology of amino acid scales for interpretable machine learning,bioRxiv.CPP:Breimann and Kampet al.(2024c),Charting γ-secretase substrates by explainable AI,
.. # Link if availabledPULearn:Breimann and Kampet al.(2024c),Charting γ-secretase substrates by explainable AI,
.. # Link if available
Package: aaapi
aaapiAnother Audio API - Collection of audio and music processing API with massive amount of dependenciesFree software: MIT licenseDocumentation:https://aaapi.readthedocs.io.FeaturesAudioUtilsloading wav filessplitting audio based on volumn peaksextracting audio features of MFCC and spectral entropyf0 estimation using CREPEMidiUtilsconverting frequencies to musical notesconverting frequencies to musical labelconverting pandas pd into midi filesgrouping consecutive notes into one notegrouping multiple notes into weighted-averaged noteMusicUtilsidentifying the scale of the musical notesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-12-09)First release on PyPI.
Package: aaargs
Aaargs ...I'm not a huge fan of theargparselibrary that ships with Python.
Personally, I much prefertyperorclick.
Butargparseis often used so this is my approach in bringing at leastattributeautocompletion to theargparse library.Let us take a look at the official documentation and use their examples:importargparseparser=argparse.ArgumentParser(prog='ProgramName',description='What the program does',epilog='Text at the bottom of help')parser.add_argument('filename')# positional argumentparser.add_argument('-c','--count')# option that takes a valueparser.add_argument('-v','--verbose',action='store_true')# on/off flagargs=parser.parse_args()print(args.filename,args.count,args.verbose)Why isn't theargparse.ArgumentParsera container class, like a dataclass?So my approach tosolvethis looks like this:fromaaargsimportArgumentParser,ArgumentclassMyParser(ArgumentParser):rog="ProgramName"description="What the program does"epilog="Text at the bottom of help"# You can define arguments directlyfilename=Argument(positional=True)# positional argumentencoding=Argument()# keyword argument '--encoding'# or pass the 'name_or_flags' argumentcount=Argument("-c","--count")verbose=Argument("-v","--verbose",action="store_true")# annotations are also supported for boolean argumentsdebug:bool=Argument()# --debug with action="store_true"parser:argparse.ArgumentParser=MyParser.get_parser()args:MyParser=MyParser.parse_args()You can also print the parser just like the original:args=MyParser.parse_args(["README.md","--encoding","utf-8","-c","3","--debug"])print(args)>>>MyParser(count='3',debug=True,encoding='utf-8',filename='README.md',verbose=False)print(args.encoding)# this will autocomplete 🎉>>>"utf-8"You can also create a Parser using keyword arguments if you prefer (I don't):fromaaargsimportArgumentParserclassMyParser(ArgumentParser,prog="ProgramName",description="What the program does",epilog="Text at the bottom of help",):...
Package: aaaworld2023
OverviewThis is my Linux Kernel project, which aims to develop and customize a Linux kernel for specific hardware or use cases.FeaturesCustomized kernel configurationDevice driver developmentPerformance optimizationSecurity enhancementsKernel module developmentGetting StartedTo get started with this project, follow these steps:Clone the repository:git clonehttps://github.com/your_username/your_project.gitBuild the Linux kernel:make ARCH=your_architectureInstall the kernel:make ARCH=your_architecture installCustomize the kernel configuration:make ARCH=your_architecture menuconfigModify and develop the kernel code according to your requirements.ContributingContributions are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.LicenseThis project is licensed under the MIT License. See theLICENSEfile for more details.ContactFor any questions or inquiries, please contact me atyour_email@example.com.
Package: aabbtree
AABBTree - Axis-Aligned Bounding Box TreesRepositoryDocumentationPyPIAABBTree is a pure Python implementation of a static d-dimensional
axis aligned bounding box (AABB) tree. It is inspired byIntroductory Guide to AABB Tree Collision DetectionfromAzure From The Trenches.Left: An AABB tree, leaves numbered by insertion order.
Right: The AABBs and their bounding boxes.InstallationAABBTree is available through PyPI and can be installed by running:pip install aabbtreeTo test that the package installed properly, run:python -c "import aabbtree"Alternatively, the package can be installed from source by downloading the
latest release from theAABBTree repositoryon GitHub. Extract the source
and, from the top-level directory, run:pip install -e .The--userflag may be needed, depending on permissions.ExampleThe following example shows how to build an AABB tree and test for overlap:>>> from aabbtree import AABB
>>> from aabbtree import AABBTree
>>> tree = AABBTree()
>>> aabb1 = AABB([(0, 0), (0, 0)])
>>> aabb2 = AABB([(-1, 1), (-1, 1)])
>>> aabb3 = AABB([(4, 5), (2, 3)])
>>> tree.add(aabb1, 'box 1')
>>> tree.does_overlap(aabb2)
True
>>> tree.overlap_values(aabb2)
['box 1']
>>> tree.does_overlap(aabb3)
False
>>> tree.add(aabb3)
>>> print(tree)
AABB: [(0, 5), (0, 3)]
Value: None
Left:
  AABB: [(0, 0), (0, 0)]
  Value: box 1
  Left: None
  Right: None
Right:
  AABB: [(4, 5), (2, 3)]
  Value: None
  Left: None
  Right: NoneDocumentationDocumentation for the project is available athttps://aabbtree.readthedocs.io.ContributingContributions to the project are welcome.
Please visit theAABBTree repositoryto clone the source files,
create a pull request, and submit issues.PublicationIf you use AABBTree in you work, please consider including this citation
in your bibliography:K. A. Hart and J. J. Rimoli, Generation of statistically representative
microstructures with direct grain geomety control,Computer Methods in Applied Mechanics and Engineering, 370 (2020), 113242.
(BibTeX)
(DOI)The incremental insertion method is discussed in section 2.2.2 of the paper.License and Copyright NoticeCopyright © 2019-2021, Georgia Tech Research CorporationAABBTree is open source and freely available under the terms of
the MIT license.
Package: aabc
aabc: Android App Bundle Checkeraabc is a utility that checks whether Android apps on a device were built using Android
App Bundles (AAB) or whether they're monolithic APKs. This script calls ADB to gather
this information and can output lists of apps built with AAB.RequirementsAndroid Debug Bridge (adb)is required
to run aabc. ADB is a part ofAndroid SDK Platform-Tools.UsageIf you find that the usage instructions below are unclear or inaccurate, pleaseopen an issue.Connecting Android Device(s) with adbEnable developer options and USB debugging on your Android device(s).Connect Android device(s) to your computer via USB.Runadb devicesand note the serial of the target device (the output in the left column).A pop-up may appear on your device asking you to allow the connection. Allow it.Running aabcSpecify the devices you want to check by passing their serials (from step 3 above) to aabc:# Check device with serial "FOBAR1234"aabcFOOBAR1234# Check multiple devices by passing multiple serialsaabcFOOBAR1234HELLOWORLD12By default, aabc will list apps that are built using Android App Bundles. This preference can be
specified explicitly by passing-a(this does the same as the above example):aabc-aFOOBAR1234You can do the opposite and output apps that don't use Android App Bundles (monolithic apps) with-m:aabc-mFOOBAR1234To filter out system apps that you might not care about checking, pass-3to look at third-party
apps only:aabc-3FOOBAR1234See an overview of available options and usage information:$aabc-h

usage:aabc[-h][-3][-a|-m]device_serial[device_serial...]OutputlistofAndroidappsinstalledondevicesthatuseAndroidAppBundles(default)oraremonolithic.

positionalarguments:device_serialSerial(s)ofdevice(s)tocheck(from"adb devices"output)optionalarguments:-h,--helpshowthishelpmessageandexit-3,--third-party-apps-onlyOnlycheckifthird-partyappsuseAndroidAppBundles-a,--output-aabOutputlistofpackagesthatuseAndroidAppBundles-m,--output-monolithicOutputlistofpackagesthataremonolithic(notusingAndroidAppBundles)
Package: aablobwrapper
aablobwrapperFor convenience, this module provide some wrappers for the Azure append blob writer. It implements the write function of the object and a context manager, allowing for you to easily use things like the CVS package, or simply appending to an append blob directly.InstallationpipinstallaablobwrapperExamplesimportcsvfromaablobwrapperimportSyncBlobAppenderconnection_string="https://<storageaccount>.blob.core.windows.net/<container_name>"credential="<key>"container_name="<container_name>"blob_name="<blobname>"appender=SyncBlobAppender(connection_string,credential,container_name,blob_name,binary=False)csv_writer=csv.writer(appender)csv_writer.writerow(["write","some","data"])for more examples, for example, to see how to use context managers or how to use the async version, see the examples folder.
Package: aa-blueprints
AA BlueprintsThis is an blueprints library app forAlliance Auth(AA) that can be used to list blueprints for your corporation or alliance.OverviewFeaturesLists all blueprints owned by corporation or alliance (configurable with permissions)Manage user requests for making copies of blueprintsHintBlueprints sends Auth notifications to inform users about blueprint requests. If you like to get those as direct messages on Discord please consider also installingDiscord Notify.ScreenshotsLibraryView BlueprintCreate a RequestMy RequestsOpen RequestsManage BlueprintsInstallationRequirementsAA Blueprints needs the appdjango-eveuniverseto function. Please make sure it is installed before before continuing.StepsStep 1 - Install the PackageMake sure you are in the virtual environment (venv) of your Alliance Auth installation. Then install the newest release from PyPI:pip install aa-blueprintsStep 2 - Configure AAAdd 'blueprints' toINSTALLED_APPSinsettings/local.py.Add the following automated task definition:CELERYBEAT_SCHEDULE['blueprints_update_all_blueprints']={'task':'blueprints.tasks.update_all_blueprints','schedule':crontab(minute=0,hour='*/3'),}CELERYBEAT_SCHEDULE['blueprints_update_all_industry_jobs']={'task':'blueprints.tasks.update_all_industry_jobs','schedule':crontab(minute=0,hour='*'),}CELERYBEAT_SCHEDULE['blueprints_update_all_locations']={'task':'blueprints.tasks.update_all_locations','schedule':crontab(minute=0,hour='*/12'),}Step 3 - Finalize App installationRun migrations & copy static files:pythonmanage.pymigrate
pythonmanage.pycollectstaticRestart your supervisor services for AuthStep 4 - Update EVE Online API ApplicationUpdate the Eve Online API app used for authentication in your AA installation to include the following scopes:esi-assets.read_assets.v1esi-assets.read_corporation_assets.v1esi-characters.read_blueprints.v1esi-corporations.read_blueprints.v1esi-industry.read_character_jobs.v1esi-industry.read_corporation_jobs.v1esi-universe.read_structures.v1Step 5 - Data importLoad EVE Online type data from ESI:pythonmanage.pyblueprints_load_typesPermissionsIDDescriptionNotesbasic_accessCan access this apprequest_blueprintsCan request blueprintsmanage_requestsCan review and accept blueprint requestsadd_personal_blueprint_ownerCan add personal blueprint ownersadd_corporate_blueprint_ownerCan add corporate blueprint owners:warning: Should only be given to directors or the CEO.view_alliance_blueprintsCan view alliance's blueprintsview_industry_jobsCan view details about running industry jobs:warning: This permission will let someone seeallindustry job information.UpgradingSeeUPGRADING.md.AuthorsThe main authors (in alphabetical order):Erik KalkokenRebecca Claire Murphy, aka MyrheaPeter Pfeufer, aka Rounon Dax