Saravanan666/pypi-org huggingface dataset:
Package: dappy
# dappypackage to allow defining an API declaratively## ExampleAll the API calls below (".get()", ".search()") return a dict parsed with json.loads()```pythonfrom dappy import API, EndpointItunesAPI = API('itunes.apple.com', [Endpoint('search', '/search',query_map={ 'search_string': 'term' },  # Map input query params to what the API actually expectsdefault_query={ 'entity': 'podcast' }  # Default query params to send with every request),Endpoint('get', '/lookup')], scheme='https')  # scheme defaults to 'https'ItunesAPI.search(query={'search_string': 'Hello, World'  # 'search_string" will get mapped to 'term' before we send the request})  # 'entity=podcast' gets added to the query without us providing it hereItunesAPI.get(query={ 'id': '656270845' })```See https://dappy.readthedocs.io/en/latest/usage.html for more usage info.* Free software: MIT license* Documentation: https://dappy.readthedocs.io.=======History=======2.0.1 (2019-01-03)------------------* Readme change to clarify the new API expectations2.0.0 (Who can remember)------------------* API now behaves differently.* no more kwargs on endpoint calls, use `query={}` to pass query params and `params={}` to pass post params1.0.0 (2017-09-13)------------------* First release on PyPI.
Package: dapr
Dapr is a portable, serverless, event-driven runtime that makes it easy for developers to
build resilient, stateless and stateful microservices that run on the cloud and edge and
embraces the diversity of languages and developer frameworks.Dapr codifies the best practices for building microservice applications into open,
independent, building blocks that enable you to build portable applications with the language
and framework of your choice. Each building block is independent and you can use one, some,
or all of them in your application.
Package: dapr-api-wrapper
Dapr Api WrapperThis is a simple Interface built overMicrosoft Dapr APIsfor easy transition from libraries like requests and aiohttp to dapr's grpc interface without additional changes in code.RequirementsKubernetes cluster (eg.Azure Kubernetes)Dapr Setup in KubernetesSetupStep:1Copy the contents of configuration yml fromdapr_api_wrapper > setup > setup.ymlStep:2Paste them in a new dapr_setup.yml file and add references to passwords (recommended) or passwords directly.
Change namespace if required.Step:3Apply changes to your kubernetes cluster by running:kubectl apply -f path/to/your/dapr_setup.ymlStep:4In your kubernetes pod deployment.yml configuration add dapr annotations as referencedhere.apiVersion: apps/v1
kind: Deployment
metadata:
labels:
    app: myawesome-svc
name: myawesome-svc-deployment
spec:
replicas: 1
selector:
    matchLabels:
    app: myawesome-svc

# Add the below lines
# Remove '#' to uncomment any annotation

template:
    metadata:
        annotations:
            dapr.io/app-id: myawesome-svc
            dapr.io/app-port: "<myawesome-svc-port>"
            dapr.io/app-protocol: http # http | grpc
            dapr.io/config: appconfig
            # dapr.io/enable-metrics: "true"
            # dapr.io/enable-profiling: "true" # Setting this paramater to true starts the Dapr profiling server on port 7777
            dapr.io/enabled: "true"
            # dapr.io/http-max-request-size: 64 # Increasing max size of request body http and grpc servers parameter in MB to handle uploading of big files. Default is 4 MB
            # dapr.io/log-as-json: "false"
            dapr.io/log-level: debug # info | warn | error
            # dapr.io/metrics-port: "9090"
            # dapr.io/sidecar-cpu-limit: 1
            # dapr.io/sidecar-memory-limit: 500Mi

        labels:
            app: myawesome-svc
            redis-client: "true"
# Add the above lines
    # Further configuration
    spec:
    containers:Understanding the requestGet the request arguments from the docstring of below mentioned class.fromdapr_api_wrapper.requestsimportDaprRequestAPI Usagefromdapr_api_wrapperimportrequestsasdapr_requestsfromdatetimeimportdatetimerequest_body={"id":"12345","api_key":"1234567","updated_at":str(datetime.now()),"num":1234567,"is_auth":True,}resp=dapr_requests.post(app_id="myawesome-svc",url_path="http://myawesome-svc-svc/test",data=request_body,content_type="application/json",headers={"key1":"value1"},http_query_params={"querykey1":"queryvalue1"},)resp_json=resp.json()resp_text=resp.textresp_bytes=resp.bytes()print(f"\n{resp_json= }\n{resp_text= }\n{resp_bytes= }",flush=True)
Package: dapr-client
Python client for dapr.Example codeA client can be created as follows:fromdaprimportdapr_pb2asmessagesfromdaprimportdapr_pb2_grpcasservicesimportgrpcfromgoogle.protobuf.any_pb2importAnychannel=grpc.insecure_channel('localhost:50001')client=services.DaprStub(channel)You can find a complete example athttps://github.com/dapr/python-sdk
Package: dap.responses.html
This is a simple HTML response, building a form to download data
in ASCII format. The response builds the HTML page and redirects
the user to the ASCII response when a POST is done.Even though pydap uses Cheetah for templating, I decided to use a
templating engine calledtemplessfor this response. Templess
is lightweight (~25k) and fun to work with, justifying the choice.A nice thing about the response is that the redirection to the ASCII
response and the error message when no variable is selected are
both done by raising exceptions. These exceptions arenotcaptured
by the server, that allows them to be captured by the Paste#httpexceptions
middleware.If you use this response, don’t forget to edit the template file
and add a link pointing to the HTML response when clicking a filename.The latest version is available in aSubversion repository.
Package: dapr-ext-fastapi
This is the FastAPI extension for Dapr.Dapr is a portable, serverless, event-driven runtime that makes it easy for developers to
build resilient, stateless and stateful microservices that run on the cloud and edge and
embraces the diversity of languages and developer frameworks.Dapr codifies the best practices for building microservice applications into open,
independent, building blocks that enable you to build portable applications with the language
and framework of your choice. Each building block is independent and you can use one, some,
or all of them in your application.
Package: dapr-ext-grpc
This is the gRPC extension for Dapr.Dapr is a portable, serverless, event-driven runtime that makes it easy for developers to
build resilient, stateless and stateful microservices that run on the cloud and edge and
embraces the diversity of languages and developer frameworks.Dapr codifies the best practices for building microservice applications into open,
independent, building blocks that enable you to build portable applications with the language
and framework of your choice. Each building block is independent and you can use one, some,
or all of them in your application.
Package: dapr-ext-workflow
This is the Workflow authoring extension for Dapr.Dapr is a portable, serverless, event-driven runtime that makes it easy for developers to
build resilient, stateless and stateful microservices that run on the cloud and edge and
embraces the diversity of languages and developer frameworks.Dapr codifies the best practices for building microservice applications into open,
independent, building blocks that enable you to build portable applications with the language
and framework of your choice. Each building block is independent and you can use one, some,
or all of them in your application.
Package: dapr-microservice-cli
服务初始化模板命令行脚手架设计要点命令行设计projectcreate-f --framework django/fastapi/flask/gin-p --path project path, default current pathproject nameother项目模板目录设计templatespythonfastapidjangoflaskgogindapr_grpcPython FastAPI 项目模板config 配置文件目录dev.yaml 开发配置prod.yaml 生产配置doc 文档目录，包括：http请求文档service 服务代码目录main.py 服务app模块文件deps.py 共享依赖模块文件models.py 模型模块文件，可以将其升级为models目录，拆分代码为多个文件routers.py 路由模块文件，可以将其升级为routers目录，拆分代码为多个文件schemas.py 校检模块文件，同上middlewares.py 中间件模块，同上tests 服务测试代码目录.gitignore git忽略文件设置文件Dockerfile dockerfile文件Makefile make运行命令README.md 说明文档requirements.txt python依赖包生成文件pip包发布发布环境pip install build pip setuptools wheel twine构建pip包python3 -m build步骤：切换到打包根目录执行build命令检查是否成功构建至dist目录测试pip包pip install -e .步骤：安装本地构建的软件包安装后，检查当前环境是否可以检测到dapr-microservice-cli命令执行dapr-microservice-cli进行项目创建测试发布pip包twine upload dist/*参考FastAPI 如何组织大型项目结构
Package: daproli
daproliA small data processing library that attempts to make data transformation more declarative.InstallationYou can install daproli with PyPi:python -m pip install daproliExamplesLet's first import daproli.>>>importdaproliasdpThe library provides basic data transformation methods. In default mode, all transformations are single-threaded and silent. You can specify the amount of jobs withn_jobs, provide further parameters likebackendfor thejoblibmodule and increase the verbosity level withverbose.>>>names=['John','Susan','Mike']>>>numbers=range(10)>>>even_numbers=range(0,10,2)>>>odd_numbers=range(1,10,2)>>>dp.map(str.lower,names)['john','susan','mike']>>>dp.filter(lambdan:len(n)%2==0,names)['John','Mike']>>>dp.split(lambdax:x%2==0,numbers)[[1,3,5,7,9],[0,2,4,6,8]]>>>dp.expand(lambdax:(x,x**2),numbers)[[0,1,2,3,4,5,6,7,8,9],[0,1,4,9,16,25,36,49,64,81]]>>>dp.combine(lambdax,y:(x,y),even_numbers,odd_numbers)[(0,1),(2,3),(4,5),(6,7),(8,9)]>>>dp.join(lambdax,y:y-x==3,even_numbers,odd_numbers)[(0,3),(2,5),(4,7),(6,9)]daproli implements basic data manipulation functions.>>>dp.windowed(numbers,2,step=2)[[0,1],[2,3],[4,5],[6,7],[8,9]]>>>dp.flatten([[0,1],[2,3],[4,5],[6,7],[8,9]])[0,1,2,3,4,5,6,7,8,9]Additionally, it provides a data transformation pipeline framework. All transformation and manipulation procedures have respective transformers with the same arguments. There are also utility transformers likeUnionorManipulatorthat help to connect transformers or make global changes to the data container.>>>dp.Pipeline(dp.Splitter(lambdax:x%2==1),dp.Union(dp.Mapper(lambdax:x**2),dp.Mapper(lambdax:x**3),),dp.Combiner(lambdax1,x2:(x1,x2))).transform(numbers)[(0,1),(4,27),(16,125),(36,343),(64,729)]>>>dp.Pipeline(dp.Filter(lambdax:x>1),dp.Filter(lambdax:all(x%idx!=0foridxinrange(2,x))),).transform(numbers)[2,3,5,7]You can find more exampleshere.
Package: daps
A toolbox to generate random data and create a stack state for the data based on grammars specified
by lex and yacc files.Detailed instructions and usage exist in the files themselves as well.This is the README file for the project.Authors: Rice DSP - Joshua Michalenko, ameeshshah4 files exist in this package: Two are used for the random data generation process
(createYAMLFromYacc, generateDataFromYAML) and two are used for creating the stack state
based on that data (makeStates.sh and its subprocess, processStackOutput.py).Please direct any questions tojjm7@rice.eduorars7@rice.edu.
Package: dapyto
DaPyTo (Daniel's Python Tools) is a very small but hopefully growing collection of command-line tools that aim to ease your Python developer life.InstallInstall and update usingpip:pip install -U dapytoToolsdpt-scan: Scans for Python virtual environments.UsageCall tools directly:dpt-scan -h

dpt-scan ./example-folder

dpt-scan ./example-folder -s false
Package: daqmx
PurposeTo create a python API for working with National Instruments NIDAQmx.  Check
thedocumentationfor
details.Project MaturityThe project is somewhat immature, but works well for where items are
implemented.  Currently, it has been tested on a single model device; however
there is no reason to suspect that the project wouldn't work on any device
that supports digital IO, analog inputs, and analog outputs.  There is
approximately ~80% test coverage and a pretty good start on thedocumentation.Installationpython -m pip install daqmxUsageYou must first import the package:from ni import NIDAQmxInstrumentYou can then allocate the hardware without any further specifiers.  Note that,
if there is more than one DAQmx instrument available on your PC, the hardware
allocated may not be the one you are expecting!  Be sure to specify the device
name, model number, or serial number to make the hardware acquisition process
more deterministic.daq = NIDAQmxInstrument()  # hardware with no specifiers

daq = NIDAQmxInstrument(device_name='Dev3')  # hardware specified by the device name

daq = NIDAQmxInstrument(model_number='USB-6001')  # hardware specified by model number

daq = NIDAQmxInstrument(serial_number=1234)  # hardware specified by serial numberOnce you have theNIDAQmxInstrumentinstance, then you can use it to operate
the instrument.  See theexamplesdirectory for complete examples.Some snippets to demonstrate common usages:daq = NIDAQmxInstrument()  # automatic acquisition of hardware

daq.ao0 = 2.7V  # set the analog out 0 to 2.7V
daq.ao1 = 1.3V  # set the analog out 1 to 1.3V

print(f'daq.ai0.value: {daq.ai0.value:.3f}V')  # print a single sample 
                                               # from analog input 0

values = daq.ai1.capture(
    sample_count=10, rate=100,
    max_voltage=10.0, min_voltage=-10.0,
    mode='differential', timeout=3.0
)  # capture 10 samples from ai1 at a rate of 100Hz in differential mode
print(values)

daq.port0.line2 = True  # set the daq.<port>.<line> to True or False to write
print(daq.port0.line3)  # read the daq.<port>.<line> to read state of lineContribution GuidelinesSince I don't have access to unlimited instruments, I have to trust that
contributions that say they work do actually work.  As a result, any new
instruments that are contributed should have the following:An issue associated with the instrumentA test in thetestdirectory which describes the necessary
connections and implements testingPull requests are welcome!
Package: daqp
DAQPDAQP is a dual active-set solver that solves convex quadratic programs of the formminimize        0.5 x' H x + f' x

subject to      l  <=  x  <= u
		bl <=  Ax <= bu.Binary constraints of the form $A x \in \lbrace b_l, b_u \rbrace$ are also supported, allowing for mixed-integer quadratic programs to be solved.The code is written in C and islibrary free. DAQP can be interfaced to C, Julia, MATLAB, and Python.SeeDocumentationfor an installation guide and basic use of the interfaces.Citing DAQP@article{arnstrom2022dual,
  author={Arnström, Daniel and Bemporad, Alberto and Axehill, Daniel},
  journal={IEEE Transactions on Automatic Control},
  title={A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive {LDL}$^{T}$ Updates},
  year={2022},
  volume={67},
  number={8},
  pages={4362-4369},
  doi={10.1109/TAC.2022.3176430}
}
Package: daquiri
daquiri := DAQ + UI generation + Reactivity + InstrumentsYou should be spending your time designing and running experiments,
not your DAQ software.DAQuiri is a nuts and bolts included framework for scientific data acquisition (DAQ),
designed for rapid prototyping and the challenging DAQ environment of angle resolved
photoemission spectroscopy. If you specify how to sequence motions and data collection,
daquiri can manage the user interface, talking to and managing instruments,
plotting interim data, data collation, and IO for you.DAQuiri also has logging and notification support built in and can let you know
over email or Slack when your experiment finishes (successfully or not!).If DAQuiri doesn’t do exactly what you need, get in contact with us or
check out the examples. There’s a good chance that if it isn’t built in,
DAQuiri is flexible enough to support your use case.RequirementsPython 3.7 overNoArchFeaturesAutomated DAQDAQuiri wraps instruments and data sources in a uniform interface, if you specify how
to sequence motion and acquisition, DAQuiri handles async collection, IO, and visualizing
your data as it is acquired.UI GenerationDAQuiri using PyQt and Qt5 to generate UIs for your experiments. It also
provides simple bindings (daquiri.ui) that make making managing the day to day
of working on PyQt simpler, if you need to do UI scripting of your own.It also ships with a window manager that you can register your windows against,
making it seamless to add extra functionality to your experiments.The DAQuiri UI bindings are wrapped to publish as RxPY observables, making it easier
to integrate your PyQT UI into a coherent asynchronous application.Installation$ pip install daquiriUsageFor usage examples, explore the scripts in the examples folder. You can run them with$ python -m daquiri.examples.[example_name]replacing [example_name] with one of:minimal_appplot_datasimple_actorswrapping_instrumentsscanning_experimentscanning_experiment_revisitedChangeLog0.0.2 (2019-09-10)Added scan modes + publish examples through the examples module.0.0.1 (2019-09-09)Essentially working for basic applications
Package: daqview
DAQviewDAQview is a desktop application for viewing live and historic DAQ data.It connects to a DAQd server which can stream it live data from a DAQ system,
or make a list of historic datasets available for download and viewing.Written in Python using PyQt5 and pyqtgraph.Licensed under the GPL 3.0 license.InstallationThe recommended way to install is to usepipxto install from the published
version on PyPI:pipx install daqviewTo run after installation:daqviewDevelopment EnvironmentFirst ensure poetry is installed:pip install --user poetryThen you should be able to install all dependencies using:poetry installRun using:poetry run python -m daqviewRun tests with:poetry run pytestRun linters with:poetry run flake8 daqview
poetry run pylint --rcfile=pylint.rc daqviewGenerally flake8 should always pass cleanly, while pylint is much
harsher and its output should be checked over for any useful suggestions.ReleaseWhen ready to cut a release, ensuredaqview/__init__.pyhas the correct
version number at the top, and then make a commit to master. Tag the commit
withrelease-VERSION, e.g.release-0.1.0, and push the commit and the
tag to GitLab, which will trigger a release build.
Package: dara-components
Dara ComponentsBuild decision apps in PythonTap into the power of causality by transforming data into interactive graphs and applicationsDara is a dynamic application framework designed for creating interactive web apps with ease, all in pure Python. Over the past two years, Dara has fueled the development of hundreds of apps, now widely used and appreciated by both our customers and our in-house teams!QuickstartTo install the package, run:pipinstalldara-componentsPackage contentsThis package contains the following modules:dara_components.common- common dashboarding components for building Dara appsdara_components.graph- components for displaying graphsdara_components.plotting- components for displaying plots with Bokeh, Plotly and Matplotlib or Seaborndara_components.smart- smart components such asDataSlicerCheck out ourDocumentationfor more details.
Package: dara-core
Dara Application FrameworkBuild decision apps in PythonTap into the power of causality by transforming data into interactive graphs and applicationsDara is a dynamic application framework designed for creating interactive web apps with ease, all in pure Python. Over the past two years, Dara has fueled the development of hundreds of apps, now widely used and appreciated by both our customers and our in-house teams!QuickstartTo get started with Dara, you can use thecreate-dara-appCLI tool to create a new app.pipinstallcreate-dara-appYou can also usepipxto install the CLI tool in an isolated environment.Then simply run the following command to create a new app.create-dara-appBy default the CLI will attempt to scaffold your project withpoetrybut will fall back topipifpoetryis not present. This can be overriden with--packaging pipor--packaging poetryflag.After the project is created, you can run it with:cdmy-dara-app# with poetry installationpoetryrundarastart# with pip installation make sure to activate the new virtual environmentsource.venv/bin/activate
darastartNote:pipinstallation usesPEP 660pyproject.toml-based editable installs which requirepip >= 21.3andsetuptools >= 64.0.0. You can upgrade both with:python-mpipinstall--upgradepip
pipinstall--user--upgradesetuptoolsFor more details check out ourDocumentation.Dara App examplesExplore some of our favorite apps - a great way of getting started and getting to know the framework!Dara AppDescriptionDemonstrates how to use incorporate a LLM chat box into your decision app to understand model insightsDemonstrates how to enable the user to interact with plots, trigger actions based on clicks, mouse movements and other interactions withBokehorPlotlyplotsDemonstrates how to use theCausalGraphViewercomponent to display your graphs or networks, customising the displayed information through colors and tooltips, and updating the page based on user interaction.Check out ourApp Galleryfor more inspiration!Repository introductionThis repository covers the Dara Application Framework first-party packages.dara-core: The core of the Dara framework, this includes the core framework code for creating applications.dara-components: Components for the Dara Framework.create-dara-app: A CLI tool for creating new Dara applications.More information on the repository structure can be found in theCONTRIBUTING.mdfile.LicenseDara is open-source and licensed under theApache 2.0 License.
Package: daraja
Daraja - Easy to use wrapper around M-Pesa Daraja APIThis module provides functions around the Mpesa Daraja API to simplify the whole integration process(problem).This is what you need to do to get things running:Fill out theconfig.jsonfile with your credentials from Daraja dashboard.Install daraja withpip install darajaImport thempesamodule fromdarajalike so:fromdarajaimportMpesaInstantiate a Mpesa object with only two parameters, the config.json file and your environment(either "dev" or "prod"); dev being a development environment and prod being a production environment. This value is used to determine the API endpoints that will be used....mpesa=Mpesa(config_file="config.json",env="prod")Use the Mpesa object to do stuff, like so:...response=mpesa.stk(receiver="254791500264",amount=50)print(response)I am working on extending this to cover all the remaining features the Daraja API has to offer. If you can help with it,contactme.
Package: daraja-mpesa
.pympesa.DescriptionThis is an unofficial wrapper providing convenient access to the Safaricom MPESA Daraja API for applications written in Python.It has been tested with Python 2 & 3Setup and Installationpipinstalldaraja-mpesaYou can also clone or download the library package and install it using setuptools:gitclonehttps://github.com/TralahM/pympesa.gitcdpympesa
pythonsetup.pyinstallTestsThe library comes with simple integration tests with Safaricom's sandbox APIs. Due to factors beyond my control, the tests are structured to pass even when a specific Daraja API is under maintenance. To run the tests, simply execute pytest from the library's root directory:pytestUsagefrommpesa.api.<API>import<APIClass>APIThe following APIs are supported:transaction_statusmpesa_expressreversalbalanceauthb2cc2bb2bAPI ClassThe following are the corresponding API classes:TransactionStatusMpesaExpressReversalBalanceMpesaBaseB2BC2BB2CDocumentationRead the DocsDependenciesHow to InstallBuilding from Source for Developersgitclonehttps://github.com/TralahM/pympesa.gitcdpympesa
pythonsetupybdist_wheel
pythonsetupyinstallContributingSee the Contributing FileSee the Pull Request FileSupportLICENCERead the license hereAcknowledgements
Package: daraja-py
daraja-pyImplementing Daraja API servicesImplemented FeaturesLipa na mpesa onlineC2BB2CInstallationTo install the library using pip, run;pip install daraja-pyRequired parametersLipa na Mpesaphone_numberamountBusinessShortCodeCallBackURLC2Bregister urlConfirmationUrlValidationUrlsimulate transactionphone_numberamountUsagefromdarajapyimportlipanampesa,c2blmn=lipanampesa.lipa_na_mpesa('07XXXXXXXX','500','324XXXX','https://www.somecallbackurl.com')# The register transaction is only run oncec2b_register=c2b.register_url('https://www.someconfirmationurl.com','https://www.somevalidationurl.com')c2b_simulate_transaction=c2b.simulate_c2b_transaction('07XXXXXXXX','500')