Saravanan666/pypi-org huggingface dataset:
Package: apexlegendspy
IntroductionApexpy it's an asynchronous api wrapper for Apex-legend written for python 3.6+Showcaseimport asyncio
from apexpy import ApexApi


async def main():

    player = ApexApi(key='SecretApiKey')
    await player.search('DiegosaursTTV', 'pc')

    for legend in player.legends:
        print(f'{legend.name} -> {legend.stats}')

    print(player.stats)

asyncio.run(main())OutputWraith -> [{'Kills': 4329.0, 'rank': 658}, {'KillsPerMatch': 8.78, 'rank': 117}, {'DamagePerMatch': 1696.19, 'rank': 128}, {'Damage': 836222.0, 'rank': 926}, {'MatchesPlayed': 493.0, 'rank': 866}]
Mirage -> [{'Kills': 10324.0, 'rank': 1}, {'Damage': 2062356.0, 'rank': 1}, {'Headshots': 6869.0, 'rank': 4}, {'CarePackageKills': 89.0, 'rank': 2}]
Lifeline -> [{'Kills': 14.0, 'rank': None}]
Bloodhound -> [{'Kills': 139.0, 'rank': 32243}]
Caustic -> [{'Kills': 41.0, 'rank': 7054}]
Gibraltar -> [{'Kills': 102.0, 'rank': 1915}]
Bangalore -> [{'Kills': 384.0, 'rank': 27236}, {'Damage': 78092.0, 'rank': 23243}]
Pathfinder -> [{'Kills': 37.0, 'rank': 27638}, {'Specific2': 17665.0, 'rank': 12108}]
[{'Level': 392.0, 'rank': None}, {'Kills': 15370.0, 'rank': 1}, {'KillsPerMatch': 31.18, 'rank': None}, {'DamagePerMatch': 6037.87, 'rank': None}, {'Damage': 2976670.0, 'rank': 1}, {'MatchesPlayed': 493.0, 'rank': None}, {'CarePackageKills': 89.0, 'rank': None}]
Package: apex-legends-voicelines
Apex Legends voicelines[[TOC]]What is this package?I love Apex Legends voice lines so much that I wanted to use it as my window title of my text editor so I created this.You can also use it in your terminal too.Just runapex-voicelinesin the terminal and it will randomly print one voiceline.InstallThis package is available onPyPI.Recommended way to install is by usingpipx.Pipx will add isolation so that your system is always unaffected.pipxinstallapex-legends-voicelinesBut you can also install using your standardpip.python3-mpipinstallapex-legends-voicelines# or pip3 install apex-legends-voicelinesPrerequsitiesYou need Python 3.Recommended version is Python 3.8.UsageTo use just runapex-voicelinesExample:$apex-voicelines
Mysquadmustbeveryproud-MRVNakaPathfinderTODOLegend selectionMaybe some time later, I will work on specifying which Legend voicelines you need as some argument.I am thinking of doing something likeapex-voicelines --legend wraith.TODOVSCode supportI don't use VSCode much so I don't know how to make it work with it. But maybe I will look into it.If anyone figured on how to use it for some other purpose let me know, I'm excited.Using inside EmacsThese voicelines can be used inside Emacs.You can use the voicelines as the frame title.This is how I use it.As frame title on startupAdd this to your config(setqframe-title-format(shell-command-to-string"apex-voicelines"))Use interactivelyYou can also add this in your config and change the title on demand(defunchange-emacs-title-apex()(interactive)(setqframe-title-format(shell-command-to-string"apex-voicelines")))Just runM-x change-emacs-title-apexto do so.LicenseMIT License
Package: apexmf
apexmfis a set of python modules for APEX-MODFLOW model (Bailey et al., 2021) parameter estimation and uncertainty analysis with the open-source suite PEST (Doherty 2010a and 2010b, and Doherty and other, 2010).Installation>>>pipinstallapexmfBrief overview of the APIfromapexmfimportapexmf_pst_utils>>>wd="User-APEX-MODFLOW working directory">>>APEX_wd="User-APEX working directory">>>apexmf_pst_utils.init_setup(wd,APEX_wd)'apex.parm.xlsx'filecopied...passed'beopest64.exe'filecopied...passed'i64pest.exe'filecopied...passed'i64pwtadj1.exe'filecopied...passed'forward_run.py'filecopied...passed
Package: apexpy
OverviewThis is a Python wrapper for the Apex fortran library by
Emmert et al. [2010][1], which allows converting between geodetic, modified
apex, and quasi-dipole coordinates as well as getting modified apex and
quasi-dipole base vectors (Richmond [1995][2]). The geodetic system used here
is WGS84. MLT calculations are also included. The package is free software
(MIT license).Quick startInstall from PyPI usingpip:pip install apexpyThis assumes that the same version of libgfortran is installed in the same
location as when the pip wheel was built (if a wheel was used). If not, you may
have trouble importing apexpy.  If you run into trouble, try the command:pip install --no-binary :apexpy: apexpywhich requires both libgfortran and gfortran to be installed on your system.
More detailed installation instructions (and troubleshooting) is available
in thedocumentation.Conversion is done by creating anApexobject and using its methods to
perform the desired calculations. Some simple examples:from apexpy import Apex
import datetime as dt
atime = dt.datetime(2015, 2, 10, 18, 0, 0)
apex15 = Apex(date=2015.3)  # dt.date and dt.datetime objects also work

# Geodetic to apex, scalar input
mlat, mlon = apex15.convert(60, 15, 'geo', 'apex', height=300)
print("{:.12f}, {:.12f}".format(mlat, mlon))
57.477310180664, 93.590156555176

# Apex to geodetic, array input
glat, glon = apex15.convert([90, -90], 0, 'apex', 'geo', height=0)
print(["{:.12f}, {:.12f}".format(ll, glon[i]) for i,ll in enumerate(glat)])
['83.103820800781, -84.526657104492', '-74.388252258301, 125.736274719238']

# Geodetic to magnetic local time
mlat, mlt = apex15.convert(60, 15, 'geo', 'mlt', datetime=atime)
print("{:.12f}, {:.12f}".format(mlat, mlt))
56.598316192627, 19.107861709595

# can also convert magnetic longitude to mlt
mlt = apex15.mlon2mlt(120, atime)
print("{:.2f}".format(mlt))
20.90If you don’t know or use Python, you can also use the command line. See details
in the full documentation (link in the section below).Documentationhttps://apexpy.readthedocs.io/en/latestReferences[1]Emmert, J. T., A. D. Richmond, and D. P. Drob (2010),
A computationally compact representation of Magnetic-Apex
and Quasi-Dipole coordinates with smooth base vectors,
J. Geophys. Res., 115(A8), A08322, doi:10.1029/2010JA015326.[2]Richmond, A. D. (1995), Ionospheric Electrodynamics Using
Magnetic Apex Coordinates, Journal of geomagnetism and
geoelectricity, 47(2), 191–212,doi:10.5636/jgg.47.191.Badgesdocstestspackage
Package: apextras
python-argparse-extrasSome helpers for python's argparse.Currently, this package provides 3 HelpFormatter classes that you can extend and / or use with
python'sargparsemodule:Contentsformatter.WideHelpFormatterHelpFormatter which tries to use a given amount of the terminal width.The maximum line length is determines by the class attributepercentage_of_line_length.
This defaults to the full width (1.0). Override this class and set your own percentage.This usesshutil.get_terminal_size()to determine the line length.formatter.ThreeQuarterWidthHelpFormatterAWideHelpFormattersubclass with itspercentage_of_line_lengthset to three quarters of the terminal line length.formatter.ThreeQuarterWidthDefaultsHelpFormatterAThreeQuarterWidthHelpFormattersubclass which also derives fromargparse.ArgumentDefaultsHelpFormatter. This means that in addition to using 3/4 of the terminal line length, it also prints the argument defaults in the help text.Installation & UsageInstall the package with your favourite python package manager:pip install apextrasor directly into yourpipenv:pipenv install apextrasAfter that, you can use these classes in the same way as any otherargparse.HelpFormattersubclass while creating your argument parser:from apextras.formatter import WideHelpFormatter
parser = argparse.ArgumentParser(formatter_class=WideHelpFormatter)
parser.add_argument(...)
...
Package: apexxapi
This is a simple library of wrapper functions for accessing the most common Apex API 2.0
requests.
The included functions are createCardTransaction, captureCardTransaction, refundCardTransaction, cancelCardTransaction and hostedPaymentPage. Each of
These functions is associated with an API request of the same name. The parameters
listed for each API call are the same as those for the associated functions; view
The documentation for a more in-depth look at the parameters for each function.A full list of API requests can be found athttps://sandmgw.apexxfintech.com/mgw/v2/api/doc#.
Package: apeye
Handy tools for working with URLs and APIs.DocsTestsPyPIAnacondaActivityQAOtherapeyeprovides:pathlib.Path-like objects to represent URLsa JSON-backed cache decorator for functionsaCacheControladapter to limit the rate of requestsSeethe documentationfor more details.Installationapeyecan be installed from PyPI or Anaconda.To install withpip:$python-mpipinstallapeyeTo install withconda:First add the required channels$condaconfig--addchannelshttps://conda.anaconda.org/conda-forge$condaconfig--addchannelshttps://conda.anaconda.org/domdfcodingThen install$condainstallapeyeAttention!In v0.9.0 and above therate_limitermodule requires thelimiterextra to be installed:$python-mpipinstallapeye[limiter]
Package: apfake
APFAKEHave you ever thought "I should record the AP API test that's coming in a half hour" only to find out that it actually concluded a half hour AGO? I've done this several times. And if I've done it before, I have to assume others have done it as well.Taking the FINAL JSON state file of an AP API election test (e.g., what happens if you hit the test URL after a test has run but before it's zeroed out in the moments before a new test or a live election),apfakewill generate a file of zeros and thennintermediate states between the zeros and the final file with a smoothly incrementing set of votes.In short: If you give it the last file of a test,apfakewill generate all the missing files for you.CAVEATSapfakedoes not call races.apfakevotes do not properly match actual election night spikes when ballots are counted.apfakeprecincts reporting and precincts reporting percentages are probably wrong.apfakedoes not work on zeros or initialization data. It needs the format&results=rufrom the AP API.USAGEInstallingapfakeis easiest to install straight from PyPi. It requires a Python 3 environment.pip install apfakeUsingapfake -f 2018-03-20-final.json -n 10 -d /tmp/foo/ -r 2018-03-20apfakeaccepts four command-line options, two are required.-f, --final-results-pathREQUIREDThe path to the final results JSON file.-n, --numberREQUIREDThe number of files to generate.-r, --racedateOPTIONALTheYYYY-MM-DDracedate of the election. If the file specified infinal-results-pathhas a racedate in the filename, this will be used. Otherwise,apfakewill raise aValueError.-d, --data-directoryOPTIONALA directory or set of directories to write the data to.apfakewill create these directories if they do not exist, and will also create a directory named for theracedateto hold the output files. Defaults to/tmp/.DEBUGGING WITCHERYThis is the shape thatapfakeexpects your JSON to have. If you use the format&results=ruit will probably work. If you use a different format, it might work, though YMMV."""
results.keys()
dict_keys(['electionDate', 'timestamp', 'races', 'nextrequest'])

results['races'][0].keys()
dict_keys(['test', 'raceID', 'raceType', 'raceTypeID', 'officeID', 'officeName', 'party', 'seatName', 'reportingUnits'])

result['races][0]['reportingUnits'][0].keys()
dict_keys(['statePostal', 'stateName', 'level', 'lastUpdated', 'precinctsReporting', 'precinctsTotal', 'precinctsReportingPct', 'candidates'])

results['races'][0]['reportingUnits'][0]['candidates'][0].keys()
dict_keys(['first', 'last', 'party', 'incumbent', 'candidateID', 'polID', 'ballotOrder', 'polNum', 'voteCount'])
"""
Package: apf-base
Alert Processing Framework (apf)apfis a framework developed to create a dockerized pipeline to
process an alert stream, that can be easily be deployed in a local
machine or distributed usingKubernetes.First developed to processZTF datait
is capable to be used for any stream/static data processing pipeline.Installingapfapfinstallation can be done withpippip install apf_baseThis will install theapfpython package andapfcommand line
script.apfdesignapfis based onstepsconected throughApache
Kafkatopics.Eachstepis composed by aconsumerand is isolated from other
steps inside a docker container.When running, the step calls theexecute()method for eachmessageormessage batchconsumed. A step can have multiple producers and databases back-ends
plugins that can be accessed inside theexecutemethod to have a more
complex logic.This generic step greatly reduce the development of each component of
the pipeline and make it easier to test each component separately.apffeaturesAutomatic Metric Sender (KafkaMetrics)Automatic Code Generation (apf new-step <step_name>)Multiple Consumer Plugins:KafkaAVROCSVJSONProducers:KafkaCSVMetrics:KafkaapfQuickstartAquick-startguide to create a new step can be foundhere.
Package: apf-ci
组件概述组件定义面向产品配置人员提供灵活的应用配置、测试、发布的工具、流程支持。提供组件的配置组装。组件价值为支撑公司各项业务，提供快速复用已有的组件、灵活组装的技术能力；为工程院移动开发提供组件化开发框架；提供移动组件的开发、测试、发布过程中的工具化、自动化工具的支持；解决组件化开发后组装应用开发、运行过程中要解决的公共技术问题；面向产品配置人员提供灵活的应用配置、测试、发布的工具、流程支持。端支持服务端API设计文档&使用指南设计文档作为一个索引，记录已开发的特性、设计文档、使用指南设计文档使用指南API信息API测试报告功能测试报告代码质量sonarqube报告，包含单测覆盖率和静态代码检查
Package: ap-features
Action Potential featuresap_featuresis package for computing features of action potential traces. This includes chopping, background correction and feature calculations.Parts of this library is written innumbaand is therefore highly performant. This is useful if you want to do feature calculations on a large number of traces.Quick startimportmatplotlib.pyplotaspltimportnumpyasnpfromscipy.integrateimportsolve_ivpimportap_featuresasapftime=np.linspace(0,999,1000)res=solve_ivp(apf.testing.fitzhugh_nagumo,[0,1000],[0.0,0.0],t_eval=time,)trace=apf.Beats(y=res.y[0,:],t=time)print(f"Number of beats:{trace.num_beats}")print(f"Beat rates:{trace.beat_rates}")# Get a list of beatsbeats=trace.beats# Pick out the second beatbeat=beats[1]# Compute featuresprint(f"APD30:{beat.apd(30):.3f}s, APD80:{beat.apd(80):.3f}s")print(f"cAPD30:{beat.capd(30):.3f}s, cAPD80:{beat.capd(80):.3f}s")print(f"Time to peak:{beat.ttp():.3f}s")print(f"Decay time from max to 90%:{beat.tau(a=0.1):.3f}s")Number of beats: 5
Beat rates: [779.2207792207793, 769.2307692307693, 779.2207792207793, 759.493670886076]
APD30: 37.823s, APD80: 56.564s
cAPD30: 88.525s, cAPD80: 132.387s
Time to peak: 21.000s
Decay time from max to 90%: 53.618sInstallInstall the package with pippython -m pip install ap_featuresSeeinstallation instructionsfor more options.Available featuresThe list of currently implemented features are as followsAction potential duration (APD)Corrected action potential duration (cAPD)Decay time (Time for the signal amplitude to go from maximum to (1 - a) * 100 % of maximum)Time to peak (ttp)Upstroke time (time from (1-a)*100 % signal amplitude to peak)Beating frequencyAPD up (The duration between first intersections of two APD lines)Maximum relative upstroke velocityMaximum upstroke velocityAPD integral (integral of the signals above the APD line)DocumentationDocumentation is hosted at GitHub pages:https://computationalphysiology.github.io/ap_features/Note that the documentation is written usingjupyterbookand contains aninteractive demoLicenseFree software: LGPLv2.1Source Codehttps://github.com/ComputationalPhysiology/ap_features
Package: apg
APGCode generation tool, which helps you to start a project without pain.ContentsInstallationUsageInstallationpipinstallapgDependenciesmakedockerdocker-composecookiecutterClicknpm (for ReactJS projects)UsageCreate a new project in the current directory:$apginit<framework_name>Available frameworks:flask(Flaskused as basis, we added webargs, Swagger, pytest and other handy stuff)aiohttp(AIOHTTPused as basis, but we completely reorganized it to look more like flask. Also added Swagger, webargs and so on)react(Only basic functionality of ReactJS application)Once you've done that, commands listed below will be availableFlask project:$makedev# build application containers and run in developer mode$makebuild# build application containers$makeup# run application in production mode$makestop# stop application and running containers$makedb# initialize database$makemigrate# create data migration for database$makebash# run bash shell inside application container$makeshell# run pimped out python console$makedbshell# run databse console$maketest# run autotests (all project files)$maketestfile=<folder_name># to run all test files in folder$maketestfile=<folder_name>/<file_name># to run all tests in single file$maketestfile=<folder_name>/<file_name>::<test_case_name># to run single test caseAIOHTTP project:$makedev# build application containers and run in developer mode$makeshell# run pimped out python console$makecheck# check apispec$makedbshell# run database console$makemigrate# create data migration for database$makeupgrade# apply data migrations to database$maketest# run autotests (all project files)$maketestfile=<folder_name># to run all test files in folder$maketestfile=<folder_name>/<file_name># to run all tests in single file$maketestfile=<folder_name>/<file_name>::<test_case_name># to run single test caseAfter you runmake devcommand
The API documentation will be available (SwaggerUI)Flask -http://127.0.0.1:5000/api/doc/aiohttp -http://127.0.0.1:8080/api/doc/React project:$npmstart:dev# in standalone project start dev-server, otherwise compile project in dist folder and start watching it$npmbuild:prod# build productionAdd a new module to the current project (current directory):$apgmodule<name>
Package: apgl
Another Python Graph Library is a simple, fast and easy to use graph library. Here is an example of its usage:::>>> from apgl.graph import GeneralVertexList, SparseGraph>>> numVertices = 5>>> graph = SparseGraph(numVertices)>>> graph[0,1] = 1>>> graph[0,2] = 3>>> graph[1,2] = 0.1>>> graph[3,4] = 2>>> graph.setVertex(0, "abc")>>> graph.findConnectedComponents()[[0, 1, 2], [3, 4]]>>> graph.getWeightMatrix()array([[ 0. ,  1. ,  3. ,  0. ,  0. ],[ 1. ,  0. ,  0.1,  0. ,  0. ],[ 3. ,  0.1,  0. ,  0. ,  0. ],[ 0. ,  0. ,  0. ,  0. ,  2. ],[ 0. ,  0. ,  0. ,  2. ,  0. ]])>>> graph.degreeDistribution()array([0, 2, 3])>>> graph.neighbours(0)array([2, 1], dtype=int32)>>> print(graph)SparseGraph: vertices 5, edges 4, undirected, vertex list type: GeneralVertexListMore Information----------------* See the user guide at http://pythonhosted.org/apgl/* The source code is available at https://github.com/charanpald/APGL
Package: apgmx
apgmx⚠️ This project was created for internal use withinAkari, support will be minimal.A database migrations tool for asyncpg (fork of RDanny's migrations system)Key FeaturesSimple migrationsLoads database uri either from .json config or from exported environment variablesInstallingCurrently as of now, PyPi releases are not available, so this project can only be installed via git:pipinstallgit+https://github.com/No767/apgmxUsageSeeapgmx --helpfor more info and usageCreditsThanks to Danny and the contributors of RoboDanny for providing the base of this project.
Package: apgorm
apgormDocumentation|SupportAn asynchronous ORM wrapped around asyncpg. Examples can be found underexamples/. Run examples withpython -m examples.<example_name>(python -m examples.basic).Please note that this library is not for those learning SQL or Postgres. Although the basic usage of apgorm is straightforward, you will run into problems, especially with migrations, if you don't understand regular SQL well.FeaturesFairly straightforward and easy-to-use.Support for basic migrations.Protects against SQL-injection.Python-side converters and validators.Decent many-to-many support.Fully type-checked.Tested.LimitationsLimited column namespace. For example, you cannot have a column namedtablenamesince that is used to store the name of the model.Only supports PostgreSQL with asyncpg.Migrations don't natively support field/table renaming or type changes, but you can still write your own migration with raw SQL.Converters only work on instances of models and when initializing the model.Models can only detect assignments. IfUser.nicknamesis a list of nicknames, it won't detectuser.nicknames.append("new nick"). You need to douser.nicknames = user.nicknames + ["new nick"].Basic UsageDefining a model and database:classUser(apgorm.Model):username=apgorm.types.VarChar(32).field()email=apgorm.types.VarChar().nullablefield()primary_key=(username,)classDatabase(apgorm.Database):users=UserIntializing the database:db=Database(migrations_folder="path/to/migrations")awaitdb.connect(database="database name")Creating & Applying migrations:ifdb.must_create_migrations():db.create_migrations()ifawaitdb.must_apply_migrations():awaitdb.apply_migrations()Basic create, fetch, update, and delete:user=awaitUser(username="Circuit").create()print("Created user",user)assertuser==awaitUser.fetch(username="Circuit")user.email="email@example.com"awaituser.save()awaituser.delete()
Package: apgp
Asynchronously read data from Postgres, returned as a Pandas dataframe.PackagistlogoExamplefromapgpimportQueryq=Query("""SELECT * FROM github.repository""")q.execute()#         id        name                        full_name description   fork  \#  122866357  test_repo2  secretagentjamesbond/test_repo2        None  False#  122866319   test_repo   secretagentjamesbond/test_repo        None  False# homepage language default_branch          created_at  owner_id  private#     None     None         master 2018-02-25 19:02:37  36823666    False#     None     None         master 2018-02-25 19:02:15  36823666    Falseq.close()PerformanceGraphic pulled fromAsyncpggithub page.performanceSetupIn order to bypass manually connecting you will need aconfig.yamlin your project root. You can see an an example in theconfig.yaml.examplefile.database:'dbname'host:'host'port:5439user:'username'password:'password'
Package: apg-py
Python APGAPG – an ABNF Parser Generator – generates parsers directly from a superset ofABNF.
A detailed description of ABNF and the superset (SABNF) is givenhereInstallationPython APG can be installed from eitherGitHuborPyPI.
The PyPI installation will provide the generator and parsing library.
However, the GitHub installation additionally provides an extensive
set of examples, a large set of tests and the full documentation.Quick Start GuidesA quick start parser guide using the GitHub installation can be foundhere.A quick start parser guide using the PyPI installation can be foundhere.DocumentationThe full documentation is in the code and in additional documentation files.
It can be generated
withdoxygenfrom the GitHub installation. For example, using the GitHub zip download
and the Ubuntu Linux command line:unzip apg-py-main.zip
cd apg-py-main
sudo apt install graphviz
sudo apt install doxygen
doxygenThe documentation home page will now be found inhtml/index.html.
Or you can view it directly from theAPG website.