Saravanan666/pypi-org huggingface dataset:
Package: abrain
Artificial Brains (ABrain) for PythonC++/Python implementation of fully evolvable Artificial Neural Networks.
Uses the ES-HyperNEAT algorithms toindirectlyencode ANNs with bio-mimetic
patterns (repetitions, symmetry...), large number of neurons and relative
robustness to input/output variations between generations.
The API is served in Python and computations are performed in C++.tested on latestDevelopmentOptional dependencies:Graphviz (dot)To generate directed graphs for the genomes.
Can only be fetched by system installer (apt-get, yum, ...).
Seehttps://graphviz.org/download/for instructionsKaleidoTo generate non-interactive images of ANN (through plotly).
Due to inconsistent support, left as an optional dependency.
Usepip install abrain[...,kaleido]to get itTodo list:Functionalities:Order-independent ANN evaluation (with back buffer)?Crossover / historical markingsActually needed?MANN IntegrationEasy extractionbuilt-in testingC++ wrapperVisuMisc:DocumentationAdvanced usagemove to scikit/poetry/... ?CI/CDRecent install givesno loadimage plugion for "svg:cairo"for pdf output
Package: abrconnection
# abrconnection
An interface between Python and Autonomous Battle Royale
<br>
<br>
Documentation (ABR v0.0.3): <br><br>Coordinate system is left-handed, with x being east, y being upwards, and z being north.RobotConnection(): class which handles connection to the game. Should be instantiated at beginning of script, and methodconnect()should be called immediately after.disconnect()ends connection.RobotConnection.set_tire_torque(tire_name, torque): sets torque of tiretire_nametotorque. Current tire names are “BackLeft”, “BackRight”, “FrontLeft”, and “FrontRight.”RobotConnection.set_tire_steering(tire_name, bering): sets tiretire_nametobering. All angles/berings are clockwise off of vertical (unity’s coordinate system is left-handed).RobotConnection.state_dict: Dictionary/Hashtable containing information about the state of the robot.Vectors are stored as dictionaries with keys“x”,“y”, and“z”.state_dict[“gps”]: Sensor containing position information of the robot.state_dict[“gps”][“position”]: Vector containing current position of robot relative to starting point.state_dict[“gyroscope”]: Sensor containing rotation information of the robot:state_dict[“gyroscope”][“right”]: Unit vector pointing right RELATIVE to the robot. For example, if the robot was facing in the default direction, its right vector would be <1, 0, 0> because its right direction is east. If the robot turned 90 degees counterclockwise, its right vector would be <0, 0, 1>. If the robot was facing a bering of 45 degrees and was climbing a 20 degree grade, its right vector would be <cos(45), sin(20), sin(45)> / sqrt(cos(45)^2 + sin(20)^2 + sin(45)^2).state_dict[“gyroscope”][“up”]: Unit vector pointing up RELATIVE to the robot. Same idea as before.state_dict[“gyroscope”][“forward”]: Unit vector pointing up RELATIVE to the robot. Same idea as before.state_dict[“lidar”]: Array containing distance to any object at 1 degree increments.state_dict[“lidar”][0]would describe how many meters of clearance the robot has in front of itself,state_dict[“lidar”][90]would describe its clearance to the right, and so on. If the robot has more than 100 meters of clearance in a particular direction, the value will capped at 100. In future updates, lidar upgrades might include an increase in range or density for in-game currency. Vertical FOV will be coming soon.
Package: abreai
InstallPypipip install abreaiGitHubpip install git+https://github.com/1Marcuth/encurtanet-py.gitSimple use examplefromabreaiimportAbreAishortener=AbreAi()url_info=shortener.shorten(url="https://google.com",# Your urlalias="url-alias"# Alias of the url)shortened_url=url_info.get_shortened_url()print(url_info.get_shortened_url())
Package: abrechnung
AbrechnungTheAbrechnung(German forreckoning,settlement,revenge) aims to be a versatile and user-centricpayment,transactionandbookkeepingmanagement tool for human groups and events.You can simplytryourdemo instance!Abrechnung is a tool to trackmoney,purchases(and its items) anddebtorsfor:Group lifeEventsTravellingFlat share roommatesCooking revelriesHoliday tripsYour HackerspaceLAN partiesBusiness tripsFamily lifeRegular partiesAdventures.........All this is possible through thebasic blocks:Purchase & transaction trackingAccounts with balance compensationInvoice handling + optional position specificationAssignment (by fractions or counts) of positions and invoicesClearing accounts for merging transactionsMultitenant accessDocumentationTo help you set up your instance or understand the inner workings:Read the documentation!Technical foundationTechnologyComponentPythonBackend logicReactWeb UI frameworkPostgresSQLDatabaseHomo SapiensMagic sauceContributingIf there isthat featureyou really want to see implemented, you found abugor would like to help in some other way, the project of course benefits from your contributions!Contribution guideIssue trackerCode contributionsDevelopment roadmapContactTo directly reach developers and other users, we have chat rooms.
For questions, suggestions, problem support, please join and just ask!ContactWhere?Issue TrackerSFTtech/abrechnungMatrix Chat#sfttech:matrix.orgSupport usLicenseReleased under theGNU Affero General Public Licenseversion 3 or later, seeCOPYINGandLICENSEfor details.
Package: abridge
abridgeEffortlessly shorten videos.Aboutabridgecan automatically shorten video files by removing parts from the video
where not much happens. This is great for making timelapse videos more engaging
and removes the need for manual editing to cut these dead spots from the videos.Installationpip install abridgeabridgemakes use ofmoviepy, which releys onffmpeg.ffmpegshould be installed
when the package is installed, but this may not work on some systems.Dockeradbridgecan be run as a docker image, which gaurentees it will run
on all systems.docker pull freshollie/abridge:latestdocker run freshollie/abridgeUsageusage: abridge [-h] [-w workers] [-o outdir] [-t diff-threshold]
               [-r repetition-threshold]
               clip [clip ...]

Effortlessly shorten videos

positional arguments:
  clip                  Clip to cut or glob group

optional arguments:
  -h, --help            show this help message and exit
  -w workers            Number of clip processors
  -o outdir
  -t diff-threshold     Difference threshold required between frames for a
                        frames to be considered different
  -r repetition-threshold
                        Number of frames in a row required to make a cutApifromabridgeimportabridge_clipabridge_clip("/path/to/clip")DevelopingTheabridgeproject is managed and packaged bypoetryUsepoetry installto download the required packages for developmentpoetry run pre-commit installshould be run to install the pre-commit
scripts which help with ensuring code is linted before push.TestsTests are written withpytestand can be run withmake testLintingabridgeis linted withpylintand formatted withblackandisortmypyis used throughout the project to ensure consitent types.make lintwill check linting, code formatting, and typesmake formatwill format code to required standardsTODO:Test coverage on processorLicenseMIT
Package: abroca
abroca"abroca" is a python package to provide basic functionality for computing and visualizing the Absolute Between-ROC Area (ABROCA).InstallThe source code is currently hosted ongithuband Python package atPyPi# PyPIpipinstallabrocaExampleYou can find the .ipynb file under example folder. It is a basic example which demonstrates the use of theabrocapackage to compute the ABROCA for a simple logistic regression classifier.#Compute Abrocaslice=compute_abroca(df_test,pred_col='pred_proba',label_col='returned',protected_attr_col='Gender',compare_type='binary',n_grid=10000,plot_slices=True)The plot is automatically saved to a file and is displayed on-screen. The link to download the data is given in the comments in the example file. Parameters are self explainatory through the example file. Parameter details below.df - dataframe containing colnames matching pred_col, label_col and protected_attr_colpred_col - name of column containing predicted probabilities (string)label_col - name of column containing true labels (should be 0,1 only) (string)protected_attr_col - name of column containing protected attribute (string)compare_type - comparison group being 'overall', 'multiple' (compare one majority with all classes) or 'binary'majority_protected_attr_val(optional) - name of 'majority' group with respect to protected attribute (string)n_grid (optional) - number of grid points to use in approximation (numeric) (default of 10000 is more than adequate for most cases)plot_slices (optional) - if TRUE, ROC slice plots are generated and saved to file_name (boolean)lb (optional) - Lower limit of integration (use -numpy.inf for -infinity) Default is 0ub (optional) - Upper limit of integration (use -numpy.inf for -infinity) Default is 1limit (optional) - An upper bound on the number of subintervals used in the adaptive algorithm.Default is 1000Reference Paper:
Josh Gardner, Christopher Brooks, and Ryan Baker (2019). Evaluating the
Fairness of Predictive Student Models Through Slicing Analysis.
Proceedings of the 9th International Conference on Learning Analytics
and Knowledge (LAK19); March 4-8, 2019; Tempe, AZ, USA.https://doi.org/10.1145/3303772.3303791Getting HelpIf you encounter a clear bug, please file a minimal reproducible example ongithub, or contact the package maintainers directly (see the package documentation).
Package: abrox
![Logo](abrox/gui/icons/readme_logo.png)# Approximate Bayes rocks!`ABrox` is a python package for Approximate Bayesian Computation accompanied by a user-friendly graphical interface.## Features* Model comparison via approximate Bayes factors+ rejection+ random forest* Parameter inference+ rejection+ MCMC* Cross-validation## InstallationNote that `ABrox`only works with Python 3.`ABrox` can be installed via pip. Simply open a terminal and type:```bashpip install abrox```It might take a few seconds since there are several dependencies that you might have to install as well.### MacPortsIf you installed Python via MacPorts, the `abrox-gui` command after installation of `abrox` does not work.You can alternatively start the GUI via (assuming Python version 3.5):```bashcd /Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/abrox/gui/python3.5 main.py```### WindowsUnfortunately, the installation under Windows is a bit cumbersome. We explain the relevant steps below.If not already done, install a Python3 version from [here](https://www.python.org/).Check the version of Python that is installed by typing `python` into the console.![Python on Windows](abrox/gui/icons/python_windows2.png)Now, install Visual Studio Build Tools from:1. [here](http://landinghub.visualstudio.com/visual-cpp-build-tools)Now visit the following page to install the Scipy wheel. Choose the link that fitsyour Python version (see picture above). `cp` should be followed by the actual version (e.g. `cp36`) whilethe last part of the link should match the bit-version (e.g. `win32`).2. [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy)After the installation, open a console in the directory the wheel has been downloaded into and type:```bashpython -m pip install #name_of_the_whl_file```Repeat the same steps for the Numpy wheel:3. [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy)Now, open a terminal and type:```bashpython -m pip install abrox```You are now ready to use `ABrox`!## ABrox using the GUIAfter `ABrox` has been installed, you can start the user interface by typing `abrox-gui`.We provide several templates in order to get more familiar with the GUI.## ABrox using PythonIf you are more comfortable with plain Python, you can run your project once from the GUI andcontinue working with the Python-file that has been generated in the output folder.## TemplatesWe provide a few example project files so you can see how `ABrox` works ([here](https://github.com/mertensu/ABrox/tree/master/project_files)).Currently, we provide:* Two-sample t-test* Levene-Test### Contributors* [Ulf Mertens](http://www.psychologie.uni-heidelberg.de/ae/meth/team/mertens/)* Stefan Radev
Package: abs2rel
abs2relA python script that traverses a whole package turning local absolute imports into relative ones.It can reduce substantialy the time needed to convert local absolute imports from a Python package into local relative imports. Additionally, because the conversion is automated, it is less error-prone than having someone make the changes manually.This command only recognizes local absolute imports which are in thefrom ... import ...format, where thefromandimportkeywords are on the same line.This script was originally tested on thenodezator appat commitbe4c17f, managing to convert all 1685 existing absolute local imports across 270 python files into relative imports successfully, without needing any additional manual work.If you want to try this yourself, just clone nodezator, checkout the mentioned commit and, inside the nodezator/nodezator folder, execute theabs2relcommand. Then, go up one folder level and launch the app withpython -m nodezatorand you'll see that the local relative imports work properly, launching the app without problems.Installationpipinstallabs2relUsageAfter installing, just go to your package top-level directory and execute theabs2relcommand. Before actually changing the files, the script presents the number of imports and files that are about to be changed and asks the user to confirm in order to proceed. Remember to make a backup of your package before executing this script, just in case.Licenseabs2rel is dedicated to the public domain withThe Unlicense.
Package: absarsnewer
Getting Started with Zype-V1IntroductionTODO: Add a descriptionInstall the PackageThe package is compatible with Python versions2 >=2.7.9and3 >=3.4.
Install the package from PyPi using the following pip command:pipinstallabsarsnewer==2.1You can also view the package at:https://pypi.python.org/pypi/absarsnewerTest the SDKYou can test the generated SDK and the server with test cases.unittestis used as the testing framework andnoseis used as the test runner. You can run the tests as follows:Navigate to the root directory of the SDK and run the following commandspip install -r test-requirements.txt
nosetestsInitialize the API ClientNote:Documentation for the client can be foundhere.The following parameters are configurable for the API Client:ParameterTypeDescriptionhttp_client_instanceHttpClientThe Http Client passed from the sdk user for making requestsoverride_http_client_configurationboolThe value which determines to override properties of the passed Http Client from the sdk usertimeoutfloatThe value to use for connection timeout.Default: 60max_retriesintThe number of times to retry an endpoint call if it fails.Default: 0backoff_factorfloatA backoff factor to apply between attempts after the second try.Default: 2retry_statusesArray of intThe http statuses on which retry is to be done.Default: [408, 413, 429, 500, 502, 503, 504, 521, 522, 524]retry_methodsArray of stringThe http methods on which retry is to be done.Default: ['GET', 'PUT']The API client can be initialized as follows:fromzypev1.zypev_1_clientimportZypev1Clientfromzypev1.configurationimportEnvironmentclient=Zypev1Client(environment=Environment.PRODUCTION,)List of APIsVideosPlaylistsManaging Playlist RelationshipsCategoriesAppsDevicesDevice CategoriesPlayersMaster ManifestsSegmentsVideo ImportsVideo SourcesCategory Content RulesPlaylist Content RulesVideo Content RulesConsumersDevice LinkingO AuthVideo EntitlementsPlaylist EntitlementsSubscription EntitlementsSubscriptionsPlansTransactionsRedemption CodesAd TagsRevenue ModelsVideo FavoritesZobject TypesZobjectClasses DocumentationUtility ClassesHttpResponseHttpRequest
Package: absarv3testing8
Getting Started with RESTAPI SDKInstall the PackageThe package is compatible with Python versions2 >=2.7.9and3 >=3.4.
Install the package from PyPi using the following pip command:pipinstallabsarv3testing8==1.1.1You can also view the package at:https://pypi.python.org/pypi/absarv3testing8Test the SDKYou can test the generated SDK and the server with test cases.unittestis used as the testing framework andnoseis used as the test runner. You can run the tests as follows:Navigate to the root directory of the SDK and run the following commandspip install -r test-requirements.txt
nosetestsInitialize the API ClientNote:Documentation for the client can be foundhere.The following parameters are configurable for the API Client:ParameterTypeDescriptioncontent_typestringbody content type for post requestDefault:'application/json'accept_languagestringresponse formatDefault:'application/json'base_urlstringDefault:'https://localhost:443'environmentEnvironmentThe API environment.Default:Environment.PRODUCTIONhttp_client_instanceHttpClientThe Http Client passed from the sdk user for making requestsoverride_http_client_configurationboolThe value which determines to override properties of the passed Http Client from the sdk usertimeoutfloatThe value to use for connection timeout.Default: 60max_retriesintThe number of times to retry an endpoint call if it fails.Default: 0backoff_factorfloatA backoff factor to apply between attempts after the second try.Default: 2retry_statusesArray of intThe http statuses on which retry is to be done.Default: [408, 413, 429, 500, 502, 503, 504, 521, 522, 524]retry_methodsArray of stringThe http methods on which retry is to be done.Default: ['GET', 'PUT']The API client can be initialized as follows:fromrestapisdk.restapisdk_clientimportRestapisdkClientfromrestapisdk.configurationimportEnvironmentclient=RestapisdkClient(content_type='application/json',accept_language='application/json',environment=Environment.PRODUCTION,base_url='https://localhost:443',)AuthorizationThis API usesOAuth 2 Bearer token.List of APIsAPISessionUserGroupMetadataDatabaseConnectionClasses DocumentationUtility ClassesHttpResponseHttpRequest
Package: absbox
AbsBoxa structured finance cashflow engine wrapper for structured credit professionals:transparency -> open source for both wrapper and backend engine.human readable waterfall -> no more coding/scripting, justlistsandmapsin Python !easy interaction with Python numeric libraries as well as databases/Excel to accomodate daily work.installationpip install absboxDocumentationEnglish ->https://absbox-doc.readthedocs.ioChinese ->https://absbox.readthedocs.ioGoalStructuringEasy way to create different pool assets/deal capital structures and waterfallsUser can tell how key variables(service fee/bond WAL/bond cashflow etc) changes in different structure of transaction.InvestorGiven powerful modeling language to build cashflow model , user can price bonds of transaction after setting pool performance assumptionWhat it doesProvide building blocks to create cashflow models for ABS/MBSAdapt to multiple asset classesResidential Mortgage / AdjustRateMortgage / Auto LoansCorp LoansConsumer CreditLeaseFix AssetFeaturesSensitivity Analysis on different scenarios or deal structuressensitivity analysis on pool performance assumptionssensitivity analysis on capital structures or any deal componentsBond Cashflow/Pool Cashflow Forecast, PricingData flowCommunity & SupportDiscussionMiscProposed Rule regarding Asset-Backed Securities: File No. S7-08-10
Package: abscab
Accurate Biot-Savart routines with Correct Asymptotic BehaviourThis library can be used to compute the magnetic field and the magnetic vector potential
of filamentary current carriers in the form of a circular loop and straight segments.
Arbitrary geometries of conductors can be approximated by a polygon along its contour
and the connecting segments between the polygon vertices are modeled by straight segments.
Finite-width conductors can be approximated by arranging multiple filaments
throughout the cross section of the current carrier.Please consider leaving a GitHub star if you like this software.If you use this software for scientific work,
we kindly ask you to citethe corresponding article:@article{abscab_2023,title={{Biot-Savart routines with minimal floating point error}},author={Jonathan Schilling and Jakob Svensson and Udo Höfel and Joachim Geiger and Henning Thomsen},journal={Computer Physics Communications},pages={108692},year={2023},issn={0010-4655},doi={10.1016/j.cpc.2023.108692}}This is the Python implementation ofABSCAB.descriptionlink to filemain implementationabscab.pyunit teststest_abscab.pydemo codedemo_abscab.pyparallelized:heavy_multiplication_x:
Package: absdataset
SUMMARY:Just some datasets from KaggleINSTALLATION:$pipinstall-UabsdatasetUSAGE:importabsdatasetabsdataset.help()LICENSE:MIT License

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Package: abses
An Agent-Based computational framework makes modeling artificialSocial-ecological systemseasier.WhyABSESpy?Agent-based model (ABM) is essential for social-ecological systems (SES) research.ABSESpyis designed for modelingcouples humans and nature systemsby:Architectural Elegance for Modular Socio-Ecological Systems Modeling.Unveiling Human Decision Dynamics in SES Modeling by an advanced human behavior simulating framework.Mastering Time in SES Modeling with Real-world Precision and Dynamic Updates.Basic Usage & DocumentsInstall with pip or your favorite PyPI package manager.pipinstallabsesAccess theDocumentation here.Get in touchFor enthusiastic developers and contributors, all contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.SES researchersare welcome to use this package in social-ecological system (SES) studies. It would be appreciated if you contribute a published model to our gallery.If you need any help when usingABSESpy, don't hesitate to get in touch with us through:Ask usage questions ("How to do?") onGitHubDiscussions.Report bugs, suggest features, or view the source codeonGitHubIssues.Use themailing listfor less well-defined questions or ideas or to announce other projects of interest toABSESpyusers.LicenseCopyright 2023,ABSESpyShuang SongLicensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License athttps://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.ABSESpybundles portions ofMesa,mesa-geo,pandas,NumPy, andXarray; the full text of these licenses are included in the licenses directory.Thanks to all contributors