Saravanan666/pypi-org huggingface dataset:
Package: auto-modelling
auto-modellingAuto-modelling is a convenient library to train and tune machine models automatically.Its main features include the following:preprocessing columns in all datatypes. (numeric, categorical, text)train machine models and tune parameters automatically.return top n best models with optimized parameters.Applystackingtechnique to combine the n best models returned by the repo or self-determined fitted models together to get an even better result.The machine learning models include the following:Classification:ExtraTreesClassifierRandomForestClassifierKNeighborsClassifierLogisticRegressionXGBClassifierRegression:ExtraTreesRegressorGradientBoostingRegressorAdaBoostRegressorDecisionTreeRegressorRandomForestRegressorXGBRegressorStack:for classify: LogisticRegressionfor regression: LinearRegressionreference:https://github.com/EpistasisLab/tpot/blobInstallationpip install auto-modellingUsage Examplefrom auto_modelling.classification import GoClassify
from auto_modelling.regression import GoRegress
from auto_modelling.preprocess import DataManager
from auto_modelling.stack import Stack

# preprocessing data
dm = DataManager(directory = 'preprocess_tools')
train, test = dm.drop_sparse_columns(x_train, x_test)
train, test = dm.process_data(x_train, x_test)
# the encoders are stored in the directory called data_process_tools.

# use the same processing tools to process new data
predict_data = dm.process_predict_data(predict_x)
# predict_x should have the same format as x_train/x_test

# classification
clf = GoClassify(n_best=1)
best = clf.train(x_train, y_train)
y_pred = best.predict(x_test)

# regression
reg = GoRegress(n_best=1)
best = reg.train(x_train, y_train)
y_pred = best.predict(x_test)

# get top 3 best models
clf = GoClassify(n_best=3)
bests = clf.train(x_train, y_train)
y_preds = [m.predict(x_test) for m in bests]

# Stack top 3 best models
stack = Stack(n_models = 3)
level_0_models, level_1_model = stack.train(x_train, y_train, x_test, y_test)There are examplestest.pyandsample.pyin the root directory of this package. runpython test.py/python sample.py.Development GuideClone the repoCreate the virtual environmentmkvirtualenv auto
workon auto
pip install requirements.txtif you have issues in installingxgboostrefrence:https://xgboost.readthedocs.io/en/latest/build.html#https://www.ibm.com/developerworks/community/blogs/jfp/entry/Installing_XGBoost_on_Mac_OSX?lang=enNoteTO DO: Feature selection, evaluation metricssThoughtsIdeally, any dataframe being throw into this repo, it should be processed.pre-processingdrop column that have too many null(Done)fill na for both numeric and non-numeric values(Done)encoded for non-numeric values(Done)scale values if neededbalance the dataset if neededmodel-trainingmode =classification,regression,auto(Done)split data-settuning parameters and model selection (Done)feature selectionreturn a model with parameters, columns and a script to process x_test(Done)stacking with customized fitted models (Done)model-evualationOther referencePackaging your project
Package: automodel-server
AutoModel ServerO que é?É o software do AutoModel responsável por aguardar as conexões de novos clientes do AutoModel e gerenciar suas modelagens.Iniciando o Servidor do AutoModelPara iniciar o Servidor do AutoModel basta entrar na pasta do AutoModel Server e executar o seguinte comando:user@automodel-server$  sh serverup.shlogo após isso o Servidor esta pronto para receber as conexões dos clientes do AutoModel.Mais informaçõesQualquer dúvida entre em contato com João Luiz pelo e-mailjlalmeidaf@hotmail.com.
Package: automodinit
automodinit v0.16 5th March 2017:-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-Niall Douglas http://www.nedproductions.biz/See http://pypi.python.org/pypi/automodinit for latest versionGo to http://github.com/ned14/automodinit to report bugsThis package fixes a small problem which has been bugging me throughoutyears of python development: forgetting to keep a module's __init__.pyup to date with new files added. This causes the following, irritatingproblems:1. Test suites don't find docstring tests.2. Static analysis tools don't see some module content in __all__.3. Things which scan themselves for plugins mismatch what os.listdir()returns as against what the module import table has.4. I waste time over something which should take care of itself.5. os.listdir() based solutions tend to fail when freezed intoan executable binary because they don't understand running frominside a ZIP archive.So here's how to make the problem go away forever:1. Include the automodinit package into your setup.py dependencies.2. Replace all __init__.py files like this:__all__ = ["I will get rewritten"]# Don't modify the line above, or this line!import automodinitautomodinit.automodinit(__name__, __file__, globals())del automodinit# Anything else you want can go after here, it won't get modified.3. That's it! From now on importing a module will set __all__ toa list of .py[co] files in the module and will also import eachof those files as though you had typed:for x in __all__: import xTherefore the effect of "from M import *" matches exactly "import M".Customising:-=-=-=-=-=-=automodinit can take the following additional parameters:filter: This is a callable which will be passed a list of tuples(loader, modulename, ispkg) which is the output ofpkgutil.iter_modules() for the calling module. Return onlythose which you want to be imported.importFindings: Defaults to True. Set to False to not auto-importthe contents of __all__.Version history:-=-=-=-=-=-=-=-=* v0.16 5th Mar 2017* Fixed stripping of __init__.py file encoding. Thanks to wtyerogersfor reporting this.* Removed suggestion this is the smallest package on pypi. Thanks toasl97 for reporting this.* Tell PyPi we are under the MIT licence. Thanks to njwhite forreporting this.* v0.13 9th Feb 2013* Fixed a bug where the source distribution would fail to install dueto not including distribute_setup.py. Thanks to kanzure for reporting this.* v0.12 5th Mar 2012* Fixed a bug where isinstance would occasionally fail. Turns out thepkgutil loading mechanism doesn't check to see if the module is alreadyloaded, so it was loading duplicates whose types wouldn't compare.* v0.11 5th Mar 2012* Fixed some typos in Readme.txt* Typically what worked before packaging did not work after. Fixed!* v0.10 5th Mar 2012First release
Package: automol
Library: AutoChemAndreas V. Copan, Kevin B. Moore III, Sarah N. Elliott, and Stephen J. KlippensteinAutochem is a library of molecular toolkit packages, including automol, phydat, and transformations.Installation>>>condainstallautomol-cauto-mechPackage: phydatDescriptionphysical constants and dataUsageOur pytest tests serve as an example for building filesystemsPackage: transformationsDescriptionThe Python package "transformations" is developed externally by Christoph Gohlke.
A README is provided with this package. Further copyright information is provided in
_transformations.pyUsagePackage: automolDescriptiongraphgeomzmatinchismilesreacrotor
Externalopenbabelrdkit
Package: automongodatabase
# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.# difference between requirements_dev.txt and requirements.txtrequirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.# tox.ini
We use if for the testing in the python package testing against different version of the python## how tox works tox enviornment creation
1. Install depedencies and packages
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec# Testing python applicationtypes of testing1. Automated testing
2. Manual testingMode of testing1. Unit testing
2. Integration testsTesting frameworkspytestunittestrobotframeworkseleniumbehavedoctest# check with the code style formatting and syntax(coding standard)pylintflake8(it is best because it containt 3 library pylint pycodestyle mccabe)pycodestyle
Package: automonisaur
Automonisaur: Core Librariesabout|integrations|install|docker|unittest locally|codecov|pypiAboutThis library adds some easier-to-use wrappers around common services for data
science and threat intelligence.Provides easier clients and configuration options, as well as any additional
helpers to get things up and running.Github issues and feature requests welcomed.IntegrationsCategoryLibraryAPIflaskChatslackData Scrapingbeautifulsoupfacebook groupsinstagramscrapyDatabaseselasticsearchneo4jsplunkData StoreminioswiftDevicessnmpGoogle Cloudgoogle auth apigoogle people apigoogle sheets apiLoggingsentryiomacOSairportmacchangerPythonloggingrequestsReconnmapTest AutomationseleniumRequirespython >= 3.8Note: install requirements.txt to use all integrationsinstall core library/bin/bashinstall.shinstall integration libraries# shell script/bin/bashinstall-requirements.sh# pippython3-mpipinstall-U-rrequirements.txt# pippython3-mpipinstall-U-rhttps://raw.githubusercontent.com/TheShellLand/automonisaur/master/requirements.txt# master branchpython3-mpipinstall--upgradegit+https://github.com/TheShellLand/automonisaur.git@master#eggunittest locally/bin/bashunittests.sh
Package: automower-ble
AutoMower-BLEThis is an unofficial reverse engineered Husqvarna Automower Connect BLE library. This allows connecting and controlling an Automower without any accounts, cloud or network connection.<a href="https://liberapay.com/alistair23/donate"><img alt="Donate using Liberapay" src="https://liberapay.com/assets/widgets/donate.svg"></a>This library is written with the intent of integrating into Home Assistant, but it can be used independently as well.Details on how this was developed are available at:https://www.alistair23.me/2024/01/06/reverse-engineering-automower-bleThis was developed and tested against a Automower 305, but it should work on all Automowers. If you are able to test on different models please do and report any results back.Testing RequestsYou can run the request unit tests withpython3./request.pyTesting ResponsesYou can run the response unit tests withpython3./response.pyTesting ConnectionsYou can test querying data and sending commands with the followingpython3./mower.py--addressD8:B6:73:40:07:37You can uncomment parts ofasync def main(mower)to send commands
Package: automoxapi
automoxapi=======A thin python wrapper for the Automox APIUsage-----Log into Automox, navigate to settings and locate and copy the Access Key.In your python environment, install automoxapi:.. code:: bashpip install automoxapiIn your python project import and create an Automox instance:.. code:: pythonfrom automoxapi import Automoxautomox = Automox('thisis-not-areal-accesskey')for org in automox.get_organizations():for server in automox.get_servers(org['id']):print(server['display_name'])For details on interacting with the API visit the website:https://docs.automox.com/api/
Package: automsr
AutoMSRDescriptionAutoMSRis an automation tool created for educational purpose,
intending to show how to use Selenium as an automation driver
for the Microsoft Rewards service.What it doesThis tool is intended to show how to collect automatically daily points for
Microsoft Rewards.What is included:Daily promotions completed.Other activities completed.Free punchcards completed.Searches with a desktop User Agent completed.Searches with a mobile User Agent completed.WarningBefore using this software, read carefully theMicrosoft Terms of Service,
sectionMicrosoft Rewards.TL;DR: the usage of AutoMSR could lead in a ban.SetupInstallation$pipinstallautomsr# this will install the binary `automsr`ConfigurationAutoMSR behaviour can be configured using aconfig.yamlfile.An example with every input described in detail is found here:link.ChromedriverDownload the correctChromedrivermatching your Chrome version.UsageHelp$automsr--helpExecute AutoMSR$automsrrun# config.yaml is somewhere else than current directory$automsrrun--configpath/to/a/config.yamlGenerate automatically a Config file$automsrinitRetrieve local Chrome profiles$automsrprofiles# ChromeProfile(displayed_name='yourProfileName', path=Path('generic/profile/dir'))$automsrprofiles--formatpretty-json# [#     {#         "displayed_name": "yourProfileName",#         "path": generic/profile/dir"#     }# ]Check that email sending works$automsremail
Package: automua
automua: Email client configuration made easyautomua makes configuring email accounts easy. It unites methods for automated mailbox configuration from Apple'sMobileconfig, Microsoft'sAutodiscoverand Mozilla'sAutoconfigin one tool.
automua is a fork of automx2. It accepts code contributions.Copyrightautomua is a fork ofautomx2.
Check per-file copyright notices to see the sole modifications and the new files of the fork automua that are Copyright © 2022 Gaspard d'Hautefeuille.
automx2 and most of the files of the fork are Copyright © 2019-2022 Ralph Seichter.automua is licensed under the GNU General Public License V3 or later. The project is hosted on GitHub in theHLFH/automuarepository. It is published onPyPI.organdArch Linux AUR.Advantagesautomua as a fork of automx2 has a few advantages:automua is workingbetter with Autodiscover;automua implementsPEP 517 & PEP 518;automua supportsArch Linux;automua supports the last version ofFlask.DocumentationDetailed documentation is available inHTML,PDFandother formats.Dev notesMobileconfig mail payloadUseApple Configuratorto go into details.MS-OXDSCLI Autodiscover Publishing and Lookup ProtocolMozilla Thunderbird is looking up withAutodiscoverbefore Autoconfig.But itdoes not support the Encryption element, only the SSL element.ContributingThis project accepts code contributions.Furthermore, submitting ideas, suggestions, requests and bug reports in the form ofGitHub issuesis welcome.SponsorshipIt is possible to sponsor specific new features. Please contact me via email to discuss this in detail.
Package: automutualinformation
Auto Mutual InformationAuto Mutual Information (Sequential Mutual Information) for temporal data.Auto mutual information can be treated as the equivalent of autocorrelation for symbolic data.InstallationThe python package is installable via pip.pip install automutualinformationQuick Startfromautomutualinformationimportsequential_mutual_informationassmi(MI,MI_var),(shuff_MI,shuff_MI_var)=smi([signal],distances=np.arange(1,100))Run an example notebook in Colab:DocumentationDocumentation and usage information is currently available in jupyter notebooks in the notebooks folder.CitationIf you use this package, please cite the following paper:@article {NBC2020,
    author = {Sainburg, Tim and Mai, Anna and Gentner, Timothy Q.},
    title = {Long-range sequential dependencies precede complex syntactic production in language acquisition},
    journal = {Proceedings of the Royal Society B},
    doi = {https://dx.doi.org/10.1098/rspb.2021.2657},
    year = 2022,
    }TODOmake pypi packagecreate tests/travisciadd additional parameters exampleFor more info references see:Mutual information functions versus correlation functions. W Li. (1990). Journal of Statistical PhysicsCritical Behavior in Physics and Probabilistic Formal Languages. HW Lin, M Tegmark (2017) EntropyParallels in the sequential organization of birdsong and human speech. T Sainburg, B Thielman, M Thielk, TQ Gentner, (2019) Nature CommunicationsLong-range sequential dependencies precede complex syntactic production in language acquisition. T Sainburg, A Mai, TQ Gentner. Proceedings of the Royal Society B
Package: automvs
MVS Automation Python LibraryThis library allows the use of MVS/CE in an automated fashion. This can
be used for deploying XMI files, pushing out updates, building software,
creating custom MVS deployments.Using this library requires a recent version of hercules SDL and MVS/CE.Example:IPL MVS/CE and submit JCL and check the results, attach a device
and send an operator command::fromautomvsimportautomationcwd=os.getcwd()build=automation()build.ipl(clpa=False)try:print("Submitting{}/jcl/upload.jcl".format(cwd))withopen("{}/jcl/upload.jcl".format(cwd),"r")asjcl:build.submit(jcl.read())build.wait_for_string("HASP250 UPLOAD   IS PURGED")build.check_maxcc("UPLOAD")build.send_herc('devinit 170 tape/zdlib1.het')build.wait_for_string("IEF238D SMP4P44 - REPLY DEVICE NAME OR 'CANCEL'.")build.send_reply('170')build.send_oper("$DU")finally:build.quit_hercules()
Package: automx2
automx2: Email client configuration made easyautomx2 makes configuring email accounts easy. It unites methods for automated mailbox configuration from Apple'sMobileconfig, Microsoft'sAutodiscoverand Mozilla'sAutoconfigin one tool.
automx2 is successor to automx. It has been designed to be easier to setup, easier to configure and easier to use.Copyrightautomx2 is Copyright © 2019-2024 Ralph Seichter.
automx2 is licensed under the GNU General Public License V3 or later. The project is hosted on GitHub in therseichter/automx2repository and published onPyPI.org.DocumentationDetailed documentation is available inHTML,PDFandother formats.ContributingTo avoid copyright issues, I do not currently accept code contributions. I hope that this will change at some point
in the future, but for now, please refrain from opening merge requests.However, submitting ideas, suggestions and bug reports in the form ofGitHub issuesis welcome. Please refer tothis blog articlefor an explanation on why issues
are a good method to share and discuss ideas.SponsorshipIt is possible to sponsor specific new features. Please contact me via email to discuss this in detail.
Package: autoname
autonamean enumAutoNamefrompython docswith multiple stringcase options.Get Started$pipinstallautonamefromautonameimportAutoNamefromenumimportauto# an enum classclassGameType(AutoName):INDIE=auto()print(GameType.INDIE.value)# "INDIE"# could be alternative in pydantic instead of literalfrompydanticimportBaseModelclassGame(BaseModel):type:GameTypeAlso have others stringcases coverterAutoNameLower- convert name value to lowercaseAutoNameUpper- convert name value to uppercasee.g.fromautonameimportAutoNameLowerfromenumimportautoclassGameType(AutoNameLower):INDIE=auto()print(GameType.INDIE.value)# "indie"You could also bring your own case convertion algorithm.fromautonameimportAutoName,transformfromenumimportauto@transform(function=str.lower)classGameType(AutoName):INDIE=auto()print(GameType.INDIE.value)# "indie"If theautonameis not a sound variable name. there are alias too.StrEnum=AutoNameLowerStrEnum=AutoNameLowerUpperStrEnum=AutoNameUppere.g.fromautonameimportStrEnum,transformfromenumimportautoclassGameType(StrEnum):INDIE=auto()print(GameType.INDIE.value)# "INDIE"AlternativeStrEnumfromfastapi-utils
Package: auto-name-enum
A utility for producing enums with automatic namesThis package provides an extension of python Enum objects that automatically
assigns values to members. This uses theauto()feature to assign text values
to the enums instead of having to manually set them.Specifying your enumFor example, you might create an enum with this like so:class Pets(AutoNameEnum):
    DOG = auto()
    CAT = auto()
    PIG = auto()Getting valuesUsing the class, verify the value ofDOGwould be 'dog':>>> print(Pets.DOG.value)
'DOG'You may also get the same value by just using the name of the item:>>> print(Pets.DOG)
'DOG'IteratingPython enums may be iterated over:for pet in Pets:
    print(f"name: {pet.name}, value: {pet.value}")For more information on enums (and the auto method), see [the official docs]
(https://docs.python.org/3/library/enum.html)
Package: autonav
A Python package for simulating UAV Navigation in Satellite-Less Environments. The package contains two algorithms the GTRS[1]and WLS[2]whose goal is to estimate and navigate a UAV.InstallationInstall from PyPI:pipinstall--upgradepip
pipinstallautonavFirst StepsAfter installing the package one can import the algorithms and necessary dependencies as follows:importmatplotlib.pyplotaspltfromautonavimportgtrs,wlsfromautonav.file_handlersimportreadpathfilefromautonav.plotsimportplot_trajectoriesfromnumpyimportarrayAfterwards, one can create the necessary values to run the algorithms:b=200n=8a_i=array([[0,0,0],[0,b,0],[b/2,0,0],[b/2,b,0],[0,0,b/8],[0,b,b/8],[b/2,0,b/8],[b/2,b,b/8],]).Tk=50sigma=1initial_uav_position=[10,10,5]destinations=readpathfile("docs/docs/examples/Path.csv")Finally, run the GTRS or WLS algorithm and plot the trajectories:[estimated_trajectory,true_trajectory]=gtrs(a_i,n,k,sigma,destinations,initial_uav_position)plot_trajectories(destinations,[estimated_trajectory],a_i,['GTRS'])plt.show()References[1] J. P. Matos-Carvalho, R. Santos, S. Tomic and M. Beko, "GTRS-Based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry," in IEEE Access, vol. 9, pp. 89120-89132, 2021, doi: 10.1109/ACCESS.2021.3089900.https://ieeexplore.ieee.org/document/9456863[2] R. Santos, J. P. Matos-Carvalho, S. Tomic and M. Beko, "WLS algorithm for UAV navigation in satellite‐less environments," in IET Wireless Sensor Systems, 2022, 12, (3-4), p. 93-102, DOI: 10.1049/wss2.12041
IET Digital Library,https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/wss2.12041LicenseMIT License
Package: autonbox
The Auton Lab TA1 primitivesThis repository contains additional Auton Lab TA1 primitives for the D3M program.Iterative Labeling- Blackbox based iterative labeling for semi-supervised learningVideo featurizer- Video Feature Extraction for Action Classification With 3D ResNetInstallationTo install primitives, run:pipinstall-UautonboxVideo featurizerrequires a static file, for pre-trained model weights.
To download it, run:mkdir-p/tmp/cmu/pretrained_files
python3-md3mindexdownload-o/tmp/cmu/pretrained_files# requires d3m coreVideo featurizerThe primitive outputs a data frame of size N x M, where N is the number of videos and M is 2024 features of type float.It supports running on GPUs.
Package: autoneo
AutoneoAutoneo makes it easy to build complex graphs in Neo4j.This tool allows users to represent their graph structure in a JSON schema, with the JSON containing
the properties to load into Neo4j but also how to extract the information from a data source.Using autoneoPrequisitesSet up Neo4j to allow imports from external sources. This allows data from CSV files to be loaded into Neo4j (which is how this tool does its job!).Go to the config file for your instance. (You can find this location by starting neo4j from the command line. There will be a line of output showing the location of theconfigdirectory. The config file inside it. )Comment out this line: dbms.directories.import=import (Add a #)Uncomment this line: #dbms.security.allow_csv_import_from_file_urls=true (Remove the #)Install autoneopip install autoneoUsing autoneoIn CodeYou can useautoneolike:from autoneo import builder

builder.build(
    "path/to/config.json", 
    "path/to/data_dir", 
    "path/to/query_dir", 
    "path/to/csv_dir")Seeconfig.jsonfor an example of how to define a graph. Currently we support extraction from sqlite3 databases and CSV files. Support for other databases formats is on the way.Data directory will contain.dbfiles. Query dir will be where all scripts for retrieving data are placed. CSV dir is where you can drop a CSV for when you don't need a Database file/Query File. If you provide those, then a CSV will be generated by autoneo and placed in this directory.Working on the projectTo build:python setup.py sdist bdist_wheelTo upload:python -m twine upload dist/*(Token name: pypi_all_purpose)
To run tests:nosetests(to run all tests)