Saravanan666/pypi-org huggingface dataset:
Package: engutils
AboutModule that contains commonly used utilities for solving arbitrary engineering
problems.AuthorMatthew C. Jones <matt.c.jones.aoe@gmail.com>InstallationInstall with:pipinstall<path/to/engutils>UsageIn an ipython notebook or a Python script, import my utilities with:fromengutilsimport*LicenseEngutils is licensed under the MIT LICENSE. See the LICENSE document.Dependenciesambiance: package for Standard Atmosphere calculations.pint: package for dealing with units.numpy: package for broad scientific computing.
Package: enhaaancedQueues
The 'EnhaaancedQueues' library contains the 'EnhQueue' class -
coming withextended / enhanced (in particular portability) capabilities.The 'EnhQueue' class is inherited from 'multiprocessing.queues.JoinableQueue' -
which after that is enhancedto work properly on all main desktop platforms('Windows', 'macOS' and 'Linux').Windows:Windows processes do not share memory, which means, shared objects
have to be 'pickled' and transmitted to the respective subprocess
via pipe - when given as parameters to (multiprocessing) process calls.
The 'EnhQueue' class has been made pickable (have a look at it's
'__ reduce __' method).macOS:The 'qsize' method of the 'multiprocessing.queues.JoinableQueue' does not
work under some 'macOS'es (and maybe other *nixes); it might lead to a
'NotImplementedError'.The 'EnhQueue' class wraps 'multiprocessing.queues.JoinableQueue' to correct
this aspect. The additional lock used for that might also have a positive
effect to the reliability of the methods 'empty' and 'full'.Miscellaneous:Shutting down an application with several processes properly is a delicate
thing, if some of said processes crashed / got stuck. The 'EnhQueue' class
has been equipped with a semi-blocking 'join' method - a 'join' with a timeout.Due to the parallel nature of multiprocessing, emptying a queue reliable in
a setup, where several processes could put another element in said queue during
or after said emptying is a delicate thing too.The 'EnhQueue' has been equipped with a 'clear' method emptying the queue in
the most reliable and least blocking way possible - as known to the author yet.
It in particular is designed to be useful during cleaning up when shutting down
an application properly.Further infomations and links can be found on my homepagehttps://www.blackward.deHave Fun!
Package: enhanced
enhancedEnhanced interactions for interactions.pyJoin theDiscord serverto ask questions, get help, or to discuss the project!Installationpipinstall-UenhancedTable of ContentsInstallationTable of ContentsWhat is this library?GuideDocumentationWhat is this library?This isenhanced, a library forinteractions.pywhich modifies interactions, and adds useful helper functions.It simplifies the process of creating commands, and provides an easy and organized way to make subcommands. Enhanced callbacks are also implemented, which allow for more advanced functionality. Enhanced components are also implemented, which allow for better looking code. A cooldown system is also implemented, which allows for commands to have a cooldown.GuideHomeEnhanced commandsEnhanced componentsEnhanced callbacksCooldownDocumentation
Package: enhanced-coreapi-client
Enhanced CoreAPI clientWrapper aroundcoreapi.Clientfor more convenient usage.UsageInitialize client:fromenhanced_coreapi_clientimportClientconf={'SCHEMA_URL':'https://example.com/api/schema/','AUTH_USERNAME':'client-example','AUTH_PASSWORD':'password-example',}client=Client(conf)Access API endpoints in accordance with the schema, e.g.:users=client.api.users.list()project=client.api.users.projects.read(id=7)new_project=client.api.users.projects.create(name='xxx',user_id=3)1.3.0 (2021-09-21)Decouple with Django.1.2.0 (2021-03-19)A more general and flexible way of configuring the client.1.1.0 (2017-08-20)Loosening Django version.1.0.1 (2017-08-11)Performance improvement.1.0.0 (2017-08-11)Releasing v1.0.0 to PyPi.0.2.1 (2017-08-11)README.0.2.0 (2017-08-11)Syntactic sugar.0.1.0 (2017-08-10)Django CoreAPI client v0.1.0 released.
Package: enhanced-dpy
A modern, easy to use, feature-rich, and async ready API wrapper for Discord written in Python.
Credits to theoriginal lib by RapptzWARNING: This is not the official discord.py lib! This is a custom version to which I add some features that might be useful or just makes things easier for the lazy people. See below which features have been added. This lib will also be kept updated with the BETA version of the original lib! So things may be unstable, please keep that in mind.Custom FeaturesMoved to:Custom FeaturesKey FeaturesModern Pythonic API usingasyncandawait.Proper rate limit handling.100% coverage of the supported Discord API.Optimised in both speed and memory.InstallingPython 3.5.3 or higher is requiredTo install the library without full voice support, you can just run the following command:# Linux/macOSpython3-mpipinstall-Uenhanced-dpy# Windowspy-3-mpipinstall-Uenhanced-dpyTo install the development version, do the following:$gitclonehttps://github.com/iDutchy/discord.py$cddiscord.py$python3-mpipinstall-U.[voice]Optional PackagesPyNaCl (for voice support)Please note that on Linux installing voice you must install the following packages via your favourite package manager (e.g.apt,dnf, etc) before running the above commands:libffi-dev (orlibffi-develon some systems)python-dev (e.g.python3.6-devfor Python 3.6)Quick ExampleimportdiscordclassMyClient(discord.Client):asyncdefon_ready(self):print('Logged on as',self.user)asyncdefon_message(self,message):# don't respond to ourselvesifmessage.author==self.user:returnifmessage.content=='ping':awaitmessage.channel.send('pong')client=MyClient()client.run('token')Bot Exampleimportdiscordfromdiscord.extimportcommandsbot=commands.Bot(command_prefix='>')@bot.command()asyncdefping(ctx):awaitctx.send('pong')bot.run('token')You can find more examples in the examples directory.LinksDocumentationOfficial Discord ServerDiscord API
Package: enhanced-enums
EnhancedEnumis a generic, enhanced enumeration class that extends
the functionality of Python’s standardEnumclass. It provides
additional convenience methods for a more user-friendly approach to
handling enumerations. This class utilizes Python’s generics to ensure
type safety and clarity.FeaturesUser-Friendly String Representation: Override the__str__method to return the name of the enum member.Listing Members: Easily list all members of the enumeration.Validation: Check if a given value is part of the enumeration.Tuple Conversion: Convert enum members into a tuple.Navigation: Navigate to the next or previous enum members in
sequence.Creation from String: Instantiate an enum member from its string
name.Creation from Value: Instantiate an enum member from its value.InstallationYou can copy theEnhancedEnumclass code into your project, or
import it if you’ve saved it as a separate Python file.UsageBelow is an example of how you might define and use theEnhancedEnum:fromenumimportEnum,autofromenhanced_enumimportEnhancedEnumclassColor(EnhancedEnum):RED=auto()GREEN=auto()BLUE=auto()# List all membersprint(Color.list_all())# Validate a memberprint(Color.validate(Color.RED))# Get next and previous membersprint(Color.RED.next())print(Color.RED.previous())# Create a member from a stringprint(Color.from_string('RED'))# Create a member from a valueprint(Color.from_value(1))Methods__str__: Return a more user-friendly string representation of the
enum member.list_all: Return a list of all enum members.validate: Check if a given value is a valid member of the enum.as_tuple: Return the enum members as a tuple.next: Return the next enum member in sequence.previous: Return the previous enum member in sequence.from_string: Return the corresponding enum member for a given
name.from_value: Return the corresponding enum member for a given
numeric value.ContributingContributions, issues, and feature requests are welcome. Feel free to
checkissues
pageif you want
to contribute.LicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactMassimo Ghiani -m.ghiani@gmail.comProject Link:https://github.com/m-ghiani/ENHANCED_ENUMS
Package: enhanced-google-search
enhanced_google_searchAn enhanced google search libraryThe New PyPi Project:https://pypi.org/project/enhanced-google-search/Fixed the language issue in the original repo:https://github.com/cj-praveen/googlesearch.pyso you can search with any language google.com supportpipinstallenhanced-google-searchExamples:fromenhanced_google_searchimportsearchresults=search("بايثون",lang="ar")print(results)fromenhanced_google_searchimportsearchresults=search(query="Python")print(results)fromenhanced_google_searchimportsearchresults=search("Python")print(results)fromenhanced_google_searchimportsearchresults=search("Python",num=2)# Number of resultsprint(results)Discord: imacoolhuman
Package: enhanced-icm20948
Enhanced-ICM20948 Python Library1. IntroductionBased onlibmraa, this library aims to provide a tool for generic Linux boards to read data fromICM-20948 9-Axis MotionTracking deviceat a high speed(200Hz+ for accelerometer,gyroscope and 100Hz for magnetometer).The core of the library is written in C++, with the help ofPybind11providing Python bindings. The library also supports reading data from multiple ICM-20948 simultaneously from multiple I2C Bus(Works with single I2C Bus as long as there is no address conflict. However, limited by I2C Bus Frequency, the speed of reading data may be restricted)Besides, relative 3D orientation estimation between different sensors is supported as well. Based onEigen3, Extended Kalman Filters(EKF) is implemented and applied with considerable efficiency.The library is a part of two students’ Undergraduate Final Year Project conducted in NUS(Suzhou) Research Institute, Zhejiang University and University of Electronic Science and Technology of China.At present, the library is under development.If you have problems with the library or find any bugs during usage, feel free to contact with us:flyinghorse0510@zju.edu.cnor2019021412017@std.uestc.edu.cn2. Installation2.1 Install DependenciesFor Ubuntu-based systems, you can install dependencies by the following commands:sudoaptupdate
sudoaptinstallgitbuild-essentialswig3.0libnode-devcmakelibjson-c-devpython3-pippython3-dev2.2 Install libmraaIf your currently used boards have official supports forlibmraa, it will be highly recommended that you install libmraa through the corresponding package manager or under tutorials provided by boards’ manufacturers.If not, following instructions may help you to some extent:2.2.1 Get Source Codegitclonehttps://github.com/eclipse/mraa.git2.2.2 Compile libmraacdmraa
mkdirbuildcdbuild############################################### if you use ARM-based boards ######cmake..-DBUILDSWIGPYTHON=OFF-DCMAKE_INSTALL_PREFIX:PATH=/usr-DBUILDARCH=arm###### if you use X86-based boards ######cmake..-DBUILDSWIGPYTHON=OFF-DCMAKE_INSTALL_PREFIX:PATH=/usr#########################################make-j2.2.3 Install Compiled Filessudomakeinstall
sudoldconfig2.3 Install Enhanced-ICM20948python3 -m pip install enhanced-icm20948==0.1.03. Usage Examples
Package: enhancedminidom
This module enhances minidom and will allow you to have ordered
attributes instead of having them sorted automaticaly.It is relevent in the fact that you may want to script the
loading/modification/saving of xml files and then print out a diff.
with the normal minidom since attributes are sorted automaticaly you can
have an unwanted diff for each unchaged line just because the order of
the attributes changed.once you load this module, just use minidom normaly.example:>>> from xml.dom import minidom
>>> import enhancedminidomthen use minidom normaly except that:Document and Element classes have a ‘getElementsByAttributeName’
method which can be verry handy.‘toxml’ and ‘toprettyxml’ will print attributes in the order they are
created/loaded if ‘minidom.SORTEDATTRIBUTES = False’ (default)example to get all nodes having a “foo” attribute:>>> document = minidom.parse('yourfile.xml')
>>> nodes_with_foo = document.getElementsByAttributeName('foo')example to get all nodes having a “foo” attribute if they are in a “fum” tag:>>> document = minidom.parse('yourfile.xml')
>>> nodes_with_foo = document.getElementsByAttributeName('foo', 'fum')Release Notes :Release 0.1:First VersionRelease 0.2:Fixed a typo in this doc
Package: enhanced-multiprocessing
A wrapper around Python’s multiprocessing, providing support for tqdm
progress bars and shared arguments.Provides simple, familiar interface with addiitonal superpowers.Installationpipinstallenhanced_multiprocessingExample usagefromenhanced_multiprocessingimportPooldefadd_n(x,n):returnx+n# the number of processes will be set to number of cores - 1 by defaultp=Pool()# will apply add_n to the element list of length three with n=5, showing a nice progress bar alongresult=p.imap(add_n,[1,2,3],shared_args=(5,))assertlist(result)==[6,7,8]HistoryOriginally published atkn-bibs/pathways-analysis,
then further developed forkrassowski/drug-disease-profile-matching.
Package: enhancedocs
EnhanceDocs CLIEnhanceDocs Command Line Interface (CLI)RequirementsPython 3.11.2Getting StartedYou can install and configure EnhanceDocs using this command:pipinstall-UenhancedocsRunning EnhanceDocs CLI on Github ActionsSet up your GitHub Actions workflow with a specific version of EnhanceDocs CLI.https://github.com/marketplace/actions/setup-enhancedocs
Package: enhanced-political-optimizer
How to use the package.To use the package you need to provide four things to the PO methodsOptimization function, which the is function that you are trying to optimize. Our method can cater to constraints as well by using static penalty. An example is given below.Number of dimensions of the problemLower boundaryUpper boundaryNoteThe boundaries can be scalars or arrays. The example below uses arrays because the
boundaries are different for different variables, if they are same, use scalar values.import numpy as np
import matplotlib.pyplot as plt


## optimization function.
def TCSD(x: np.ndarray):
    x1 = x[0]
    x2 = x[1]
    x3 = x[2]
    z = (x3 + 2) * x2 * (x1 * x1)

    c = [
        1 - ((x2 * x2 * x2 * x3)) / (71785 * x1 * x1 * x1 * x1),
        ((4 * x2 * x2 - (x1 * x2)) / (12566 * (x2 * x1 * x1 * x1 - (x1 ** 4))))
        + (1 / (5108 * x1 * x1))
        - 1,
        1 - ((140.45 * x1) / (x2 * x2 * x3)),
        ((x1 + x2) / 1.5) - 1,
    ]
    cmaxes = [x if x > 0 else 0 for x in c]
    c_np_array = np.array(cmaxes)
    c_np_array = np.power(c_np_array, 2)
    c_np_array = (10 ** 10) * c_np_array
    sumofarray = np.sum(c_np_array)
    return z + sumofarray


scores = [0 for x in range(RUNS)]

for i in range(RUNS):
    # matlab's rng("shuffle") seeds with the system's default time, python's random.seed has that as default val
    np.random.seed(1)
    # implement political optimizer.

    objectiveFunction = TCSD
    dim = 3 # dimentions
    lb = [0.05, 0.25, 2.00] # upper boundary
    ub = [2.00, 1.30, 15.00] # lower boundary
    (leaderScore, leaderPosition, convergenceCurve) = PO(lb, ub, dim, objectiveFunction)
    print(leaderScore)
    print(leaderPosition)
Package: enhanced-str
EnhancedStrA Python library for prettier, fully customizable console outputs.Code Examplefromenhanced_strimportes,clearConsoledefmain()->None:clearConsole()es("You have a new message!").mixedGradient(0xFF00FF,0xFFFFFF,2).underline().out()print("\n")print(es("Cool forward color transition!").gradient(0x000000,0xFFFFFF))print(es("Cool reverse color transition!").gradient(0xFFFFFF,0x000000))print("\n")es("Error!").hexColor(0xFF0000).bold().out()es("Warning!").hexColor(0xFFFF00).bold().out()es("Success!").hexColor(0x00FF00).bold().out()print("\n")es("Alert: Something might be going wrong.").hexColor(0xFF7F7F).bold().outBlink(0.2,5)print("\n")es("Hello World! This is a step by step character demonstration.").gradient(0x3D1E6D,0xFFABFF).bold().outSBS(0.02)while1:...if__name__=="__main__":main()
Package: enhanceml
enhancemlenhanceml (Enhance Machine Learning model) is a Python library to enhance the prediction of model using various statistical technique. This contains various sub-models which can be fitted over data to predict outcome.PBOB (Probability based Best Out of Best outcome): This method is used to enhance the prediction based on probability.InstallationUse the package managerpipto install enhanceml.pipinstallenhancemlorhttps://github.com/sumitc29/enhanceml.gitUsagefromenhancemlimportenhancerknn=KNeighborsClassifier()gnb=GaussianNB()pbob_model=enhancer.PBOB(knn,gnb)pbob_model.fit(x_train,y_train)predictions=pbob_model.predict(x_test)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: enhance-okta-jwt
Okta JwtFork Okta JWT Access Token verifier for Python using cached JWKs.Link to PyPi - clickhereInstallationpipinstallokta_jwtUsageTo generate a token, you need to pass inissuer,client_id,client_secret,usernameandpasswordas parameters>>>fromokta_jwt.jwtimportgenerate_token>>>generate_token(issuer,client_id,client_secret,username,password)This generates and returns Okta Access Token.To Validate the Access Token, you need to pass in theaccess_token,issuer,audienceandclient_idsas parameters. You can pass in multiple Client IDs>>>fromokta_jwt.jwtimportvalidate_token>>>validate_token(access_token,issuer,audience,client_ids)If the token is valid then it will return the payload.Running the testsTo run the unit tests, run$ python -m unittestBreak down into testsThe unit tests pretty much covers all the main functionality of the package, like generating the token, Validating the token and Verifying Claims.ContributingBug reports and Pull Requests(PR's) are welcome on GitHub athttps://github.com/adithyasampatoor/okta_jwt. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to theContributor Covenantcode of conduct.AuthorsAdithya SampatoorLicenseThe Library is available as open source under the terms of theMIT License
Package: enhancesa
enhancesaEnhancesa is a collection of tools for a better and more simplified statistical analysis in Python. It primarily aids in manual analysis and prediction tasks that use packages likeStatsmodelsandScikit-learnin their workflow.For example, Enhancesa provides answers to questions like: Which subset of features gives me the lowest error rate in an ordinary least squares model? What are estimates of population mean and standard deviation using bootstrap resampling? And etc.Upcoming featuresPartial least squares (PLS) regressionPrincipal components regression (PCR)Subset selection plotsAdditional test statistics in bootstrap resamplingMotivationEnhancesa is a result of solutions to exercises in the bookIntroduction to Statistical Learningby the Tibshirani et al. When going through the exercises, I found Python, unlike R, lacking in providing convenient functionalities.At this stage, this package is simply a collection of functions I used in my solutions to exercises in the book.InstallationEnhancesa can be installed from thePyPIpackage repository.$ pip install enhancesaQuick glimpse>>>importnumpyasnp>>>importenhancesaasesa>>># Create some dummy data>>>x=np.random.normal(size=100)>>># Compute test statistics with bootstrap resampling>>>esa.bootstrap(x,iters=1000)Estimatedmean:-0.025309EstimatedSE:0.095531dtype:float64Find out more about the full set of features in thedocumentation.Issues & improvementsPossible to further reduce dependencies.boostrapmethod can be improved by adding estimates of more test statistics of interest.UsePoetryfor package and dependency management, which usespyproject.tomlrecommended byPEP 518.enhancesa.SubsetSelectwill giveNotImplementederror ifXinput is a Numpy array.LicenseThis package is licensed under anMITlicense.
Package: en-hobbit
This is a spaCy package for working with Middle Earth Data.Feature | Description |— | — |Name|en_hobbit|Version|0.0.5|spaCy|>=3.6.1,<3.7.0|Default Pipeline|span_ruler,merge_spans,find_relations|Components|span_ruler,merge_spans,find_relations|Vectors| 0 keys, 0 unique vectors (0 dimensions) |Sources| n/a |License|MIT|Author| [W.J.B. Mattingly](https://github.com/wjbmattingly/hobbit-spacy) |### Label Scheme<details><summary>View label scheme (11 labels for 1 components)</summary>Component | Labels |— | — |`span_ruler`|AINUR,CVT,DWARF,ELF,HOBBIT,MAN,MOUNTAIN,REALM,RIVER,ROAD,WEAPON|</details>
Package: enhsp-wrapper
ENHSP WrapperThis is a simple wrapper for the ENHSP planner. For most use cases of ENHSP in Python, you should look atunified-planninginstead. This package does not automatically stay updated with the latest version of ENHSP. The version of ENHSP used in this package is 20-0.10.0 (this number may not even be correct if I forget to update it!), and is likely out-dated when you read this file.If, for some reason, unified-planning does not work for you, then you may find this ENHSP wrapper useful. Unlike unified-planning, this wrapper does not try to parse the problem before passing it onto ENHSP. This is likely only useful if you are using a PDDL syntax that ENHSP supports, but unified-planning does not.InstallationRunpipinstallenhsp-wrapperto install the package. This will include the ENHSP jar files this package depends on, which in turn depend on Java 17. It may not work if you do not have Java 17 installed.Example Usagefromenhsp_wrapper.enhspimportENHSP,PlanningStatus# pass ENHSP its command line argumentsplanner=ENHSP('-s gbfs -h hadd -timeout 10')# pass in the domain and problem PDDL filesresult=planner.solve('d.pddl','p.pddl')assertresult.status==PlanningStatus.SUCCESSprint('\n'.join(result.plan))ContributeTo contribute to this project, installpoetry, clone the repository, then runpeotryshell
poetryinstall