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amap-distance-matrix
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distance_matrixname = "amap_distance_matrix"version = "0.4.4"description = "amap distance matrix service based on Redis/Cluster Redis and MySQL"authors = ["[email protected]"]license = "The MIT LICENSE"readme = "README.md"homepage = "https://github.com/Euraxluo/distance_matrix"repository = "https://github.com/Euraxluo/distance_matrix"installpip install amap-distance-matrixNotethis project is only a pilot project
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amap-holydragon
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AMAPIntroductionAMAP is a command line interface tool similar to the famous NMAP. It scans an IP address or a domain name to find active ports and returns related information.It uses FOFA AMAP API to implement the function.Installationpipinstallamap-holydragonUsage & OptionsUsage:amap [-h | --help]amap <ip_address_or_domain_name> [-n | --nmap] [-x <filename> | --xml=<filename>]Options:-h --help Show help information-n --nmap Use nmap to scan-x <filename> --xml=<filename> Output XML fileInformationPython version >= 3.7 is required to runsubprocess.run()Packagedocoptis required as a CLI builder
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amap-poi
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No description available on PyPI.
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amaptor
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This library is an abstraction layer, with its own classes, to access mapping functions, regardlessof which version of ArcGIS you are using. Think of it as the “six” package, but for arcpy.mapping/arcpy.mp. If you
write your mapping code against this pacakge, it will work no matter which version of ArcGIS your end users are
running. In general, the API adheres closely to the ArcGIS Pro api, since that has a cleaner, object-oriented design,
but it may included differences. Further, methods are lowercased and underscored instead of camelcased, partially to
make it easy to see at a glance that it’s not the same code, and partially due to author preference.Documentation can be found athttp://amaptor.readthedocs.io
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amaptt
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amapAmap api for home-assistant
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amar
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No description available on PyPI.
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amara3.iri
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Core of the Amara3 project, which provides a variety of data processing tools. Core is mostly a library for handling Internationalized Resource Identifiers (IRIs)
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amara3.xml
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Amara 3 XMLPython 3 tools for processingMicroXML, a simplification of XML. Amara 3 XML implements the MicroXML data model, and allows you to parse into this from tradiional XML and MicroXML.Themicroxcommand line tool is especially useful for quick query and processing of XML.InstallRequires Python 3.4+. Just run:pip install amara3.xmlUseThough Amara 3 is focused on MicroXML rather than full XML, the reality is that
most of the XML-like data you’ll be dealing with is full XML
1.0. his package provides capabilities to parse legacy XML and reduce it to
MicroXML. In many cases the biggest implication of this is that
namespace information is stripped. As long as you know what you’re doing
you can get pretty far by ignoring this, but make sure you know what
you’re doing.from amara3.uxml import xml
MONTY_XML = """<monty xmlns="urn:spam:ignored">
<python spam="eggs">What do you mean "bleh"</python>
<python ministry="abuse">But I was looking for argument</python>
</monty>"""
builder = xml.treebuilder()
root = builder.parse(MONTY_XML)
print(root.xml_name) #"monty"
child = next(root.xml_children)
print(child) #First text node: ""
child = next(root.xml_children)
print(child.xml_value) #"What do you mean "bleh""
print(child.xml_attributes["spam"]) #"eggs"There are some utilities to make this a bit easier as well.from amara3.uxml import xml
from amara3.uxml.treeutil import *
MONTY_XML = """<monty xmlns="urn:spam:ignored">
<python spam="eggs">What do you mean "bleh"</python>
<python ministry="abuse">But I was looking for argument</python>
</monty>"""
builder = xml.treebuilder()
root = builder.parse(MONTY_XML)
py1 = next(select_name(root, "python"))
print(py1.xml_value) #"What do you mean "bleh""
py2 = next(select_attribute(root, "ministry", "abuse"))
print(py2.xml_value) #"But I was looking for argument"Experimental MicroXML parserFor this parser the input truly must be MicroXML. Basics:>>> from amara3.uxml.parser import parse
>>> events = parse('<hello><bold>world</bold></hello>')
>>> for ev in events: print(ev)
...
(<event.start_element: 1>, 'hello', {}, [])
(<event.start_element: 1>, 'bold', {}, ['hello'])
(<event.characters: 3>, 'world')
(<event.end_element: 2>, 'bold', ['hello'])
(<event.end_element: 2>, 'hello', [])
>>>Or…And now for something completely different!…Incremental parsing.>>> from amara3.uxml.parser import parsefrags
>>> events = parsefrags(['<hello', '><bold>world</bold></hello>'])
>>> for ev in events: print(ev)
...
(<event.start_element: 1>, 'hello', {}, [])
(<event.start_element: 1>, 'bold', {}, ['hello'])
(<event.characters: 3>, 'world')
(<event.end_element: 2>, 'boldImplementation notesSwitched to a hand-crafted parser because:Worried about memory consumption of the needed PLY lexerLack of incremental feed parse for PLYInspiration from James Clark's JS parserhttps://github.com/jclark/microxml-js/blob/master/microxml.jsAuthor:Uche [email protected]
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amaraapi
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# DESCRIPTIONA wrapper around amara api# REQUIREMENTSMethods accept a headers dictionaries` amara =Amara({'X-api-username':'<yourusername>','X-api-key':'<your-api-kei>'})`
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amaranth
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No description available on PyPI.
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amaranth-boards
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No description available on PyPI.
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amaranth-soc
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No description available on PyPI.
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amaranth-stdio
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No description available on PyPI.
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amaranth-yosys
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Amaranthis a Python-based hardware description language that usesYosysas a backend to emit Verilog.The Amaranth HDL Yosys wheels provide a specializedWebAssemblybased build of Yosys that runs viawasmtime-pyif there is no system-wide Yosys installation, or if that installation is too old. This build is aggressively optimized for binary size and startup latency, and only includes features required by Amaranth’s Verilog and CXXRTL backends; it is not useful for any other purpose.Although this package is platform-independent, it depends on wasmtime-py wheels, which are currently available only for x86_64 Windows, Linux, and macOS. This is expected to improve in the future.VersioningThe version of this package (which is derived from the upstream Yosys package version) is comprised of five parts in aX.Y.Z.N.postMformat:X: Yosys major versionY: Yosys minor versionZ: Yosys patch version; only present for some Yosys releases, zero if not presentN: Yosys node version; only present for unreleased Yosys snapshots (where it matches theNin theX.Y+Nupstream version), zero for releasespostM: package version, monotonically incrementing from the initial commitBuildingThe primary build environment for this repository is theubuntu-latestGitHub CI runner; packages are built on every push and automatically published from thereleasebranch to PyPI.To reduce maintenance overhead, the only development environment we will support for this repository is x86_64 Linux.LicenseThis package is covered by theISC license, which is the same as theYosys license.
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amarese-hello
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No description available on PyPI.
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amaretto
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AMArETTo - Azure MAnagEmenT by The1bitChange log - version 0.0.2.11StorageBugfix in copyFile function. SAS token related copy return value expandation.Change log - version 0.0.2.10StorageGenerate SAS Token for StorageAccountSAS token length check has been modifiedChange log - version 0.0.2.9Coremodule: Contains functions for login to Azure and set default subscriptionRestoremodule: Contains full support of unmanaged and managed disk based VMs’DISKrestore.Storagemodule: Can upload files to a storage account and store them according to their versions.RequirementsLinux OSYou have to install before the first usage the followings:Python (2.7 or 3.4)Azure-Cli 2.0Basic installTo use the tools you merely follow the following steps:>>> import pip
>>> pip.main(['install', '--user', 'amaretto'])
>>> import amaretto(install without cache: pip install amaretto –no-cache-dir )Note:
After the update please execute the following command from shell ‘pip show amaretto’ If you can see that not the latest version is installed, please execute ‘pip uninstall amaretto’ to unistall it.Core moduleSteps for include restore module>>> import amaretto
>>> from amaretto import amarettocore
>>> amaretto.amarettocore.azureLogin()You can find the detailed documentation inhttps://github.com/the1bit/amaretto/blob/master/amaretto/amarettocore.md.Restore moduleSteps for include restore module>>> import amaretto
>>> from amaretto import amarettorestore
>>> amaretto.amarettorestore.storageUriFromCloud('AzureCloud')You can find the detailed documentation inhttps://github.com/the1bit/amaretto/blob/master/amaretto/amarettorestore.md.Storage moduleSteps for include storage module>>> import amaretto
>>> from amaretto import amarettostorage
>>> amaretto.amarettostorage.uploadAllFiles(fileVersion = '1.0.0.0', storageaccountName = <your storage account name>, sasToken = <sasToken for your storage account>, storageKey = <storageKey for your storage account>, filePath = <local path of flies>, modificationLimitMin = <1440 means you upload files which are older than one day>)You can find the detailed documentation inhttps://github.com/the1bit/amaretto/blob/master/amaretto/amarettostorage.md.Please read the license related information.LICENSECopyright (c) 2018 Tibor KissPermission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the “Software”), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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amarettopy
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AmarettoPyPython library to control Buildit servo actuators (only for python3)InstallWindows$ pip3 install amarettopyUbuntu$ sudo apt-get -y install python3-tk
$ pip3 install amarettopyUsageamarettopy libraryライブラリについてのドキュメントhttps://smartrobotics.github.io/AmarettoPy-doc/amarettopy.html初期化>>> from amarettopy import *
>>> amaretto = AmarettoPy(port="/dev/ttyXXXX", timeout_ms=3000) #for Linux
>>> amaretto = AmarettoPy(port="COMX", timeout_ms=3000) #for Win
>>> deviceId = 1現在の位置や速度といった情報の取得>>> (position, velocity, current, referenceValue, temperature, faults) = amaretto.query_servo_status(deviceId)
>>> print(state2str(amaretto.state()))速度制御と位置制御の方法>>> #amaretto.clear_fault(deviceId)
>>> amaretto.ready(deviceId)
>>> amaretto.set_ref_velocity(deviceId, fromRPM(42.5))
>>> amaretto.set_ref_position(deviceId, fromDegree(180))amrtctlamrtctl は AmarettoPy の各メソッドをコマンドラインから呼び出す為のツールです。
アクチュエーターの状態を確認する場合は以下のように実行します。$ amrtctl query-servo-status -d 1 -p /dev/ttyUSB0
state: STATE_HOLD
pos: -51636
vel: 0
cur: 0
ref: 0
temp: 26
faults: NO_FAULTS速度制御を行う場合は以下のように実行します。$ amrtctl ready -d 1 -p /dev/ttyUSB0
$ amrtctl set-ref-velocity 2500 -d 1 -p /dev/ttyUSB0 # 指定速度の単位は [rpm/100]
0位置制御を行う場合は以下のように実行します。$ amrtctl stop -d 1 -p /dev/ttyUSB0
$ amrtctl set-ref-position 2500 -d 1 -p /dev/ttyUSB0 # 指定位置の単位は [360/65536 度]
-39775amrtct-guiamrtctl は AmarettoPy の各メソッドをコマンドラインから呼び出す為のツールです。$ amrtctl-guiポートを選択し、Connectボタンを押した後、各種ボタンを使ってデバイスを操作することが出来ます。
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amargan
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=============About Amargan=============.. image:: https://img.shields.io/badge/Author:%20francis%20horsman-Available-brightgreen.svg?style=plastic:target: https://www.linkedin.com/in/francishorsman.. image:: https://img.shields.io/pypi/v/amargan.svg:target: https://pypi.python.org/pypi/amargan:alt: PyPi version.. image:: https://img.shields.io/travis/sys-git/amargan.svg:target: https://travis-ci.org/sys-git/amargan:alt: CI Status.. image:: https://coveralls.io/repos/github/sys-git/amargan/badge.svg:target: https://coveralls.io/github/sys-git/amargan:alt: Coverage Status.. image:: https://badge.fury.io/py/amargan.svg:target: https://badge.fury.io/py/amargan.. image:: https://img.shields.io/pypi/l/amargan.svg:target: https://img.shields.io/pypi/l/amargan.svg.. image:: https://img.shields.io/pypi/wheel/amargan.svg:target: https://img.shields.io/pypi/wheel/amargan.svg.. image:: https://img.shields.io/pypi/pyversions/amargan.svg:target: https://img.shields.io/pypi/pyversions/amargan.svg.. image:: https://img.shields.io/pypi/status/amargan.svg:target: https://img.shields.io/pypi/status/amargan.svg.. image:: https://readthedocs.org/projects/amargan/badge/?version=latest:target: https://amargan.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/sys-git/amargan/shield.svg:target: https://pyup.io/repos/github/sys-git/amargan/:alt: Updates.. code-block:: python:linenos:>>> print('Anagrams for everyone')Reasons to use amargan----------------------* A simple but powerful pythonic interface.. code-block:: python:linenos:>>> from amargan import Amargan...... with open('words.txt') as iterator:... anagrams = Amargan(iterator)... anagrams['hello']set(['elloh' 'hello' 'lehol'])* A powerful command-line tool.. code-block:: bash:linenos:$ find_anagrams -i words.txt helloelloh hello lehol$ amargan -i words.txt helloelloh hello lehol* Extensive configuration options- Case (in)sensitivity- Exclusion of word from results- Output formatting (one per line, multiple-per-line, custom seperator)- Output to a file- Read from a file- Use existing dictionary of words* Time complexity: O(1)* Space complexity: O(n) where n = number of words in dictionary.* Memory efficient, uses iterators extensively.* Add and remove word(s) from the dictionary.* Extensively tested with excellent code and branch coverage.* Extensive error checking with a rich set of checked exceptions.* Uses `Certifiable <https://pypi.python.org/pypi/certifiable>`_ if available to catch runtime type and parameter validation errors.* JSON serializable and reconstitutable.* Fully documented* Free software: MIT license* Documentation: https://amargan.readthedocs.io=======History=======
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amari
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Amari.py is an async, easy to use API wrapper for the AmariBot.InstallationEnter any of these commands to install the library:pipinstallamari.pypython-mpipinstallamari.pyKey featuresUtilizes the whole Amari APIAsynchronous builtAnti 429s (rate limiting)SupportIf you have any questions or require support with the API wrapper, visit theAmariBot Support Serverand ask in#amari.py.BugsIf you find any bugs with the library, you can report it by creating a bug issue atAmari.py’s Github repo.DocsVisit theAmari.py docsfor usage of the library.
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amarillo
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AmarilloCRUD for carpool offersAn Amarillo is ayellow-dressed personhelping others to find a car pool in Cuba.SetupPython 3.9.2 with pippython3-venvCreate a virtual environmentpython3 -m venv venv.Activate the environment and install the dependenciespip install -r requirements.txt.Runuvicorn amarillo.main:app.In development, you can use--reload.Environment VariablesenvADMIN_TOKENSecurityAll endpoints are protected by an API-Key in the HTTP header.
There is a specialadminuser.
For this user, the API-Key must be passed in as an environment variable when
Amarillo is started.The admin can create additional API-Keys in the/agencyconfendpoint. This
endpoint is always available but not always shown in/docs, especially not
when running in production.
The Swagger docs for/agencyconfcan be seen on the MFDZ demo server.Permissions work this waythe admin is allowed to call all operations on all resources. Only the admin
can create new API-Keys by POSTing anAgencyConfJSON object to/agencyconf.API-Keys for agencies are allowed to POST/PUT/GET/DELETE their own
resources and GET some public resources.DevelopmentGTFS-RT python bindingsIn case you modify or update the proto-files in amarillo/proto, you'll need to regenerate the python bindings. First, create the python files:$cdamarillo/proto
$protoc--version
libprotoc3.21.6
$protoc--proto_path=.--python_out=../services/gtfsrtgtfs-realtime.protorealtime_extension.proto
$sed's/import gtfs_realtime_pb2/import amarillo.services.gtfsrt.gtfs_realtime_pb2/g'../services/gtfsrt/realtime_extension_pb2.py|sponge../services/gtfsrt/realtime_extension_pb2.pyTestingIn the top directory, runpytest amarillo/tests.DockerBased ontiangolo/uvicorn-gunicorn:python3.9-slimbuilddocker build -t amarillo --build-arg="PLUGINS=amarillo-metrics" .rundocker run --rm --name amarillo -p 8000:80 -e MAX_WORKERS="1" -e ADMIN_TOKEN=$ADMIN_TOKEN -e RIDE2GO_TOKEN=$RIDE2GO_TOKEN -e TZ=Europe/Berlin -v $(pwd)/data:/app/data amarillo
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amarillo-metrics
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amarillo-metricsAmarillo plugin that provides Prometheus metrics
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amari.py
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Amari.py is an async, easy to use API wrapper for the AmariBot.InstallationEnter any of these commands to install the library:pipinstallamari.pypython-mpipinstallamari.pyKey featuresUtilizes the whole Amari APIAsynchronous builtAnti 429s (rate limiting)SupportIf you have any questions or require support with the API wrapper, visit theAmariBot Support Serverand ask in#amari.py.BugsIf you find any bugs with the library, you can report it by creating a bug issue atAmari.py’s Github repo.DocsVisit theAmari.py docsfor usage of the library.
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amari-python
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Amari Python LibraryAmari connects your OpenAI calls to the internet. It automatically understands when your OpenAI calls need relevant real-time information from the internet and, augments your calls with them.InstallationYou can install this package by runningpipinstallamari-pythonExamplefromamariimportopenaiopenai.amari_api_key="..."openai.api_key="sk-..."chat_completion=openai.ChatCompletion.create(model="gpt-3.5-turbo",messages=[{"role":"user","content":"What's the weather in San Francisco today?"}],temperature=0,)print(chat_completion.choices[0].message.content)# The current weather in San Francisco is 69°F with mostly sunny conditions.
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amarium
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AmariumTable of ContentAmarium1. Why2. What3. Usage4. Dev Notes1. WhyWe found ourselves constantly writing files of some sort, always having to face the same problems and writing the same code to solve these problems. So we decided at some point to seperate the most common function as package out.2. WhatSmall package with a collection of functions frequently used in handling the filesystem.This package is really for perfectionists. It is one of the few occasions this bad habit makes sense.These functions have to be rock-solid!They are tested, and tested to the bone - verified over many projects, and evaluated with engineered automated testing. Why?Because these functions have to be reliableHow can you satisfy your craving for perfection with this package? Read theDevsection.3. UsagePlease refer to thetests/directory for examples of the functions and their usage4. Dev NotesTo develop here, we want you to understand that this package is only about creating code of the highest quality.Take the download numbers as a reminder for your responsibility.For example, in a very early stage we changed the naming in the package and lost users (naturally). Thus here,no mistakesare allowed. When we sayno mistakes, we mean absolutetlyzero,nada.Of course, we know it is impossible. To somewhat come close to it, we do:NoPEPviolations and checking with lintingAutomated testing aiming for 100% coverageUsage of CI on the repo and locally!Writing documentationWriting typed codeThus, we encourage to use out new packageliafrequently.
Install lia viapipinstallspawn-liaLiathen helps you to keep your code nice withliahealpackage_name/Everytime you write a new function, you should check it locally usingLia, to ensure you are not diverging from the quality constraints. Again, we remind you:If you are not having any sense for perfection, this is not a place for you to develop!
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amarna
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AmarnaAmarna is a static-analyzer and linter for the Cairo programming language.FeaturesFinds code-smells and potential vulnerabilities in Cairo codeCompiler-identical parsing of Cairo code and StarkNet contractsSupports creating local and global rulesExports the parsed AST of a Cairo fileExports static-analysis results to theSARIFformat.Basic UsageAnalyze a Cairo project in the current directory and export the SARIF results to a file:amarna.-oout.sarifAnalyze a single filefile.cairoand export the SARIF results to a file:amarnafile.cairo-oout.sarifAnalyze a single filefile.cairoand print a summary of the results:amarnafile.cairo-sIntegrationFor GitHub action integration, useamarna-action.Currently supported rules#RuleWhat it findsImpactPrecision1Arithmetic operationsAll uses of arithmetic operations +, -, *, and /InfoHigh2Unused argumentsFunction arguments that are not used in the functions in which they appearWarningHigh3Unused importsUnused importsInfoHigh4Mistyped decoratorsMistyped code decoratorsInfoHigh5Unused functionsFunctions that are never calledInfoMedium6Error codesFunction calls that have return values that must be checkedInfoHigh7Inconsistent assert usageAsserts that use the same constant in different ways, e.g.,assert_le(amount, BOUND)andassert_le(amount, BOUND - 1)WarningHigh8Dead storesVariables that are assigned values but not used before a return statementInfoMedium9Unchecked overflowsFunction calls that ignore the returned overflow flags, e.g.,uint256_addWarningHigh10Caller address return valueFunction calls to theget_caller_addressfunction.InfoHigh11Storage variable collisionMultiple@storage_varwith the same name. (deprecated)WarningHigh12Implicit function importFunction with decorator@external, @view, @l1_handlerthat is being implicitly imported. (deprecated)InfoHigh13Unenforced view functionState modification within a@viewfunctionInfoHigh14Uninitialized variableLocal variables that are never initialized.InfoHighUsageAnalyze a Cairo project in the current directory and export results to a file:amarna.-oout.sarifAnalyze a single filedeleverage.cairoand export results to a file:amarnadeleverage.cairo-odeleverage.sarifAnalyze a single filecode.cairoand print a summary of the results:amarnacode.cairo-sParse a Cairo file and output the recovered AST inpng:amarnafile.cairo-pngAnalyze a Cairo file with the unused_import rule:amarnafile.cairo--rules=unused-importsAnalyze a Cairo file using all rules except the arithmetic-add rule:amarnafile.cairo--except-rules=arithmetic-addThe full help menu is:usage: amarna [-h] [-p] [-o OUTPUT] [-s] [-png] [-rules RULES] [-exclude-rules EXCLUDE_RULES] [-show-rules] [-disable-inline] -f
Amarna is a static-analyzer for the Cairo programming language.
positional arguments:
-f the name of the .cairo file or directory with .cairo files to analyze
optional arguments:
-h, --help show this help message and exit
-p, --print print output
-o OUTPUT, --output OUTPUT
file to write the output results in sarif format
-s, -summary, --summary
output summary
-png, --png save a png with the AST of a file
-rules RULES, --rules RULES
Only run this set of rules. Enter rule names comma-separated, e.g., dead-store,unused-arguments
-exclude-rules EXCLUDE_RULES, --exclude-rules EXCLUDE_RULES
Exclude these rules from the analysis. Enter rule names comma-separated, e.g., dead-store,unused-arguments
-show-rules, --show-rules
Show all supported rules and descriptions.
-disable-inline, --disable-inline
Disable rules with inline comments. The comments should be the first line and of the form: # amarna: disable=rulename1,rulename2SARIF file formatTheSARIFfile format is a standard format for static-analysis tools and can be viewed in vscode with theofficial extension.InstallationpipinstallamarnaHow the rules workThe static-analysis rules can be:local rules, which analyse each file independently.gatherer rules, which analyse each file independently and gather data to be used in post-process rules.post-process rules, which run after all files were analyzed can use the data gathered in the gatherer rules.Examples of these are:local rules: find all arithmetic operations in a filegatherer rules: gather all declared functions, and called functionspost-process rules: find unused functions using the gathered data, i.e., functions that were declared but never called.Rule allowlist, denylist and inline commentsRule namesObtain the names of the currently implemented rules with:amarna--show-rulesRule allowlistRun amarna with a defined set of rules usingamarna--rules=rule1,rule2.The following command will only run theunused-importsrule and print the summary resultamarna--rules=unused-imports.-sRule denylistRun amarna with all rules except a defined set of rules usingamarna--exclude-rules=arithmetic-add,arithmetic-sub.-sInline rule disabling commentsYou can change the first line of a cairo file to disable a specific rule set on that file.
For example, adding the line// amarna: disable=arithmetic-div,arithmetic-sub,arithmetic-mul,arithmetic-addas the first line offile.cairoand running amarna withamarnadirectory/--disable-inline-swill not report any arithmetic rule to thefile.cairofile.
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amarokHola
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UNKNOWN
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amarpdf
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This is the homepage of our project.
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amarps
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Amazon Review Profile ScraperDescriptionA very basic tool to scrape the user reviews of a product on Amazon and the
profiles that created those reviews.It is intended to be used for research to analyze the quality of a user review
based on other information belonging to the user.UsageInstall this toolpip install amarps.Runpython -m amarps --helpto check the usageRun e.g.python -m amarps https://www.amazon.com/product-reviews/B07ZPL752N/
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amarthyas-demo-package
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No description available on PyPI.
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amaryllis
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AmaryllisThis module is that calculate insurance value.Getting startedYou can install Amaryllis by using pip.pip install amaryllisHow to use・interest・calc_base・population・pension・single
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amas
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AMASAlignment manipulation and summary statisticsIf you are using this program, please citethis publication:Borowiec, M.L. 2016. AMAS: a fast tool for alignment manipulation and computing of summary statistics. PeerJ 4:e1660.InstallationYou can download this repository zipped (button on the right-hand side of the screen) and useAMAS.pyin theamasdirectory as a stand-alone program or clone it if you havegit installedon your system.If your system doesn't have a Python version 3.4 or newer (AMASwill work under Python 3.0 but you may noy be able to use it with multiple cores), you will need todownload and install it. On Linux-like systems (including Ubuntu) you can install it from the command line usingsudo apt-get install python3To useAMASas a Python module you can get it withpipfrom thePython Package Index:pip install amasSee below for the instructions on how to use this program as a Python module.Command line interfaceAMAScan be run from the command line. Here is the general usage (you can view this in your command line withpython3 AMAS.py -h):usage: AMAS <command> [<args>]
The AMAS commands are:
concat Concatenate input alignments
convert Convert to other file format
replicate Create replicate data sets for phylogenetic jackknife
split Split alignment according to a partitions file
summary Write alignment summary
remove Remove taxa from alignment
translate Translate DNA alignment into protein alignment
Use AMAS <command> -h for help with arguments of the command of interest
positional arguments:
command Subcommand to run
optional arguments:
-h, --help show this help message and exitTo show help for individual commands, useAMAS.py <command> -horAMAS.py <command> --help.ExamplesFor everyAMAS.pyrun on the command line you need to specify action withconcat,convert,replicate,split, orsummaryfor the input to be processed.
Additionally, you need to provide three arguments required for all commands. The order in which the arguments are given does not matter:input file name(s) with-i(or in long version:--in-files),format with-f(--in-format),and data type with-d(--data-type).The options available for the format arefasta,phylip,nexus(sequential),phylip-int, andnexus-int(interleaved). Data types areaafor protein alignments anddnafor nucleotide alignments.For example:python3 AMAS.py concat -i gene1.nex gene2.nex -f nexus -d dnaIf you have many files that you want to input in one run, you can use multiple cores of your computer to process them in parallel. Thesummarycommand supports-cor--coreswith which you can specify the number of cores to be used:python3 AMAS.py summary -f phylip -d dna -i *phy -c 12In the above, we specified 12 cores. Note that this won't improve computing time if you're working with only one or very few files. The parallel processing is only used for the file parsing step and calculating alignment summaries.In addition to overall alignment summaries, you can also print statistics calculated on a sequence (taxon) by sequence basis. Use-sor--by-taxonflag to turn it on.AMASin this mode will print out one file with overall alignment summaries and a file with taxon summaries for each input alignment.IMPORTANT!AMASis fast and powerful, but be careful: it assumes you know what you are doing and will not prevent you overwriting a file. It will, however, print out a warning if this has happened.AMASwas also written to work with aligned data and some of the output generated from unaligned sequences won't make sense. Because of computing efficiencyAMASby default does not check if input sequences are aligned. You can turn this option on with-eor--check-align.Concatenating alignmentsFor example, if you want to concatenate all DNA phylip files in a directory and all of them have the.phyextension, you can run:python3 AMAS.py concat -f phylip -d dna -i *phyBy default the output will be written to two files:partitions.txt, containing partitions from which your new alignment was constructed, andconcatenated.outwith the alignment itself in the fasta format. You can change the default names for these files with-p(--concat-part) and-t(--concat-out), respectively, followed by the desired name. The output format is specified by-u(--out-format) and can also be any of the following:fasta,phylip,nexus(sequential),phylip-int, ornexus-int(interleaved).Below is a command specifying the concatenated file output format as nexus with-u nexus:python3 AMAS.py concat -f fasta -d aa -i *fas -u nexusAlignments to be concatenated need not have identical sets of taxa before processing: the concatenated alignment will be populated with missing data where a given locus is missing a taxon. However, if every file to be concatenated includes only unique names (for example species name plus sequence name:D_melanogaster_NW_001845408.1in one alignment,D_melanogaster_NW_001848855.1in other alignment etc.), you will first need to trim those names so that sequences from one taxon have equivalents in all files.In addition to the name, you can also specify the format of the partitions output file. By default, the format is the following:AA = 1-605
AK = 606-1200
28S = 1201-1800RAxML:python3 AMAS.py concat -f phylip -d dna -i *phy --part-format raxmlDNA, AA = 1-605
DNA, AK = 606-1200
DNA, 28S = 1201-1800Nexus:python3 AMAS.py concat -f phylip -d dna -i *phy --part-format nexus#NEXUS
Begin sets;
charset AA = 1-605;
charset AK = 606-1200;
charset 28S = 1201-1800;
End;Getting alignment statisticsThis is an example of how you can summarize two protein fasta alignments by running:python3 AMAS.py summary -f fasta -d aa -i my_aln.fasta my_aln2.fastaBy defaultAMASwill write a file with the summary of the alignment insummary.txt. You can change the name of this file with-oor--summary-out. You can also summarize a single or multiple sequence alignments at once.The statistics calculated include the number of taxa, alignment length, total number of matrix cells, overall number of undetermined characters, percent of missing data, AT and GC contents (for DNA alignments), number and proportion of variable sites, number and proportion of parsimony informative sites, and counts of all characters present in the relevant alphabet.Converting among formatsTo convert all nucleotide fasta files with a.fasextension in a directory to nexus alignments, you could use:python3 AMAS.py convert -d dna -f fasta -i *fas -u nexusIn the above, the required options are combined withconvertcommand to convert the input files and-u nexuswhich indicates the output format.AMASwill not overwrite over input here but will create new files instead, automatically appending appropriate extensions to the input file's name:-out.fas,-out.phy,-out.int-phy,-out.nex, or-out.int-nex.Splitting alignment by partitionsIf you have a partition file, you can split a concatenated alignment and write a file for each partition:python3 AMAS.py split -f nexus -d dna -i concat.nex -l partitions.txt -u nexusIn the above one input fileconcat.nexwas provided for splitting withsplitand partitions filepartitions.txtwith-l(same as--split-by). For splitting you should only use one input and one partition file at a time. This is an example partition file:AApos1&2 = 1-604\3, 2-605\3
AApos3 = 3-606\3
28SAutapoInDels=7583, 7584, 7587, 7593If this was thepartitions.txtfile from the example command above,AMASwould write three output files calledconcat_AApos1&2.nex,concat_AApos3.nex, andconcat_28SautapoInDels.nex. The partitions file will be parsed correctly as long as there is no text prior to the partition name (CHARSET AApos1&2orDNA, AApos1&2will not work) and commas separate ranges or individual sites in each partition.Sometimes after splitting you will have alignments with taxa that have only gaps-or missing data?. If you want to these to not be included in the output , add-jor--remove-emptyto the command line.Translating a DNA alignment into aligned protein sequencesYou can translate a nucleotide alignment to amino acids with AMAS using one of theNCBI translation tables. For example, to correctly translate an insect mitochondrial gene alignment that begins at a second codon position:python3 AMAS.py translate -f nexus -d dna -i concat.nex --code 5 --reading-frame 2 --out-format phylip--codeand--reading-frameare the same as-band-kand are both set to 1 (the standard genetic code and the first character of the alignment corresponds to the first codon position) by default. When translating,AMASwill contract gaps-and missing?, such that---becomes-in the translated alignment. A warning will be printed if stop codons are found and these are indicated as asterisks*in the output. SeeAMAS.py translate -hfor more info.Creating replicate data setsWithAMASyou can create concatenated alignments from a proportion of randomly chosen alignments that can be used for, for example, a phylogenetic jackknife analysis. Say you have 1000 phylip files, each containing a single aligned locus, and you want to create 200 replicate phylip alignments, each built from 100 loci randomly chosen from all the input files. You can do this by specifyingreplicatecommand and following it with-ror--rep-alnfollowed by the number of replicates (in this case200) and number of alignments (100). Remember to supply the output format with-uif you want it to be other thanfasta:python3 AMAS.py replicate -r 200 100 -d dna -f phylip -i *phy -u phylipRemoving taxa/sequences from alignmentIt is possible to remove taxa from alignments:python3 AMAS.py remove -x species1 species2 -d dna -f nexus -i *nex -u nexus-int -g no_species12_The above will process allnexusfiles in the directory and remove taxa calledspecies1andspecies2. The argument-x(the same as--taxa-to-remove) is followed by the names of sequences to be removed. Note thatAMASconverts spaces into underscores and strips any quotes present in input sequence names before processing, so you may need to modify your names to remove accordingly. The argument-g(the same as--out-prefix) specifies a prefix to be added to output file names. The default prefix is 'reduced_'. You may want to realign your files after taxon removal.Checking if input is alignedBy specifying optional argument-e(--check-align), you can makeAMAScheck if your input files contain only aligned sequences. This option is disabled by default because it can substantially increase computation times in files with many taxa. Enabling this option also provides an additional check against misspecified input file format.AMAS as a Python moduleUsingAMASinside your Python pipeline gives you much more flexibility in how the input and output are being processed. All the major functions of the command line interface can recreated usingAMASas a module. Following installation frompipuse:pydoc amas.AMASTo access detailed documentation for the classes and functions available.You can importAMASto your script with:fromamasimportAMASThe class used to manipulate alignments inAMASisMetaAlignment. This class has to be instantiated with the same, named arguments as on the command line:in_files,data_type,in_format. You also need to supply the number of cores to be used withcores. MetaAlignment holds one or multiple alignments and itsin_filesoption must be a list, even if only one file is being read.meta_aln=AMAS.MetaAlignment(in_files=["gene1.phy"],data_type="dna",in_format="phylip",cores=1)Creating MetaAlignment with multiple files is easy:multi_meta_aln=AMAS.MetaAlignment(in_files=["gene1.phy","gene1.phy"],data_type="dna",in_format="phylip",cores=2)Now you can call the various methods on your alignments..get_summaries()method will compute summaries for your alignments and produce headers for them as atuple with first element being the header and the second element a list of lists with the statistics:summaries=meta_aln.get_summaries()The header is different for nucleotide and amino acid data. You may choose to skip it and print only the second element of the tuple, that is a list of summary statistics:statistics=summaries[1].get_parsed_alignments()returns a list of dictionaries where each dictionary is an alignment and where taxa are the keys and sequences are the values. This allows you to, for example, print only taxa names in each alignment or do other manipulation of the sequence data:# get parsed dictionatiesaln_dicts=multi_meta_aln.get_parsed_alignments()# print only taxa names in the alignments:foralignmentinaln_dicts:fortaxon_nameinalignment.keys():print(taxon_name)Similar to the above example, it is also easy to get translated amino acid alignment as a list of dictionaries (one per input alignment):# get parsed dictionatiesaln_dicts=multi_meta_aln.get_translated(2,1)# 2: vertebrate mitochondrial genetic code and 1: reading frame starting at first characterTo split alignment use.get_partitioned("your_partitions_file")on aMetaAlignmentwith a single input file..get_partitioned()returns a list of dictionaries of dictionaries, with{ partition_name : { taxon : sequence } }structure for each partition:partitions=meta_aln.get_partitioned("partitions.txt")AMASuses.get_partitions("your_partitions_file")to parse the partition file:parsed_parts=meta_aln.get_partitions("partitions.txt")print(parsed_parts).get_replicate(no_replicates, no_loci)gives a list of parsed alignments (dictionaries), each a replicate constructed from the specified number of loci:replicate_sets=multi_meta_aln.get_replicate(2,2)To concatenate multiple alignments first parse them with.get_parsed_alignments(), then pass to.get_concatenated(your_parsed_alignments). This will return a tuple where the first element is the{ taxon : sequence }dictionary
of concatenated alignment and the second element is the partitions dict with{ name : range }.parsed_alns=multi_meta_aln.get_parsed_alignments()concat_tuple=multi_meta_aln.get_concatenated(parsed_alns)concatenated_alignments=concat_tuple[0]concatenated_partitions=concat_tuple[1]Removing taxa from alignments is very easy:spp_to_remove=["taxon1","taxon2","taxon3"]reduced_alns=multi_meta_aln.remove_taxa(spp_to_remove)To print to file or convert among file formats use one of the.print_format(parsed_alignment)methods called with a parsed dictionary as an argument. These methods include.print_fasta(),.print_nexus(),.print_nexus_int(),print_phylip(), and.print_phylip_int(). They return an apporpriately formatted string.foralignmentinconcatenated_alignments:nex_int_string=meta_aln.print_nexus_int(alignment)print(nex_int_string)
|
amasel
|
No description available on PyPI.
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amass
|
amassVendor libraries from cdnjs
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amatak-shop
|
Amatak Online Shopwas created and is currently maintained and developed by Amatak the full documentation can be found inamatak.orgJoin ourStack OverflowTo clone this project fromGitHub.Httpshttps://github.com/amatak-org/[email protected]:amatak-org/AmatakOnlineShop.gitGITHUB CLIgh repo clone amatak-org/AmatakOnlineShopHere is briefly descript about this online shopping.Amatak Online Shop is a small online store shopping that everyone can be owned.To get started head up toDjangoProjectpage and follow all the way of tutorial thereThis is the best way to get started twith this project if you are not familiar with the models.Install from py library:pip install amatak-shopInstalled App:add (amatak_shop) in Django App setting.INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'amatak_shop',
]Current Contents.features:Categories:Women FashionMen FashionPhones & TelecommunicationsComputer,Office & SecurityConsumer & ElectronicsJewelry & WatchesHome,pet & AppliancesBags & ShoesToys, Kids & BabiesOutdoor Fun & SportsBeauty, Health & HairAutomobiles & MotorcyclesTools & Home ImprovementStore' Models:AddressCategoryItemOrderOrder ItemPaymentRefundUser ProfileUser Receiver ProfileVender StoreSeller DasboardCustomer ReviewsShipping IntegratePayment' Gateway:StripePaypalFrontend:GetWebBootrapBulmaMdbootrapVuesJsBackend:DjangoHomepage ScreenClient Checkout With CouponClient Checkout Views
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amaterasu-j2
|
amaterasuIt is wandbox additional ofkamidanaof jinja2 cliamaterasu cli is kamidana wrapper, Adds additional option automatically.UsageList Example$pipinstallamaterasu-j2
$amaterasusample/wandbox.j2
*cpython-3**cpython-3.10.2*cpython-3.9.3*cpython-3.8.9*cpython-3.7.10*cpython-3.6.12sample/wandbox.j2* cpython-3*
{%- set compilers = wandbox_list() | wandbox_fnmatch_compilers("cpython-3*") %}
{%- for compiler in compilers %}
* {{ compiler.name }}
{%- endfor %}Compile Example$pipinstallamaterasu-j2
$amaterasusample/wandbox-run.j2```# This file is a "Hello, world!" in Python language by CPython for wandbox.importsys
print("Hello, world!")iflen(sys.argv)>1:print(sys.argv[1])# CPython references:# https://www.python.org/``````Hello,world!``````Hello,world!
Test```sample/wandbox-run.j2{%- set compilers = wandbox_list() | wandbox_fnmatch_compilers("cpython-3*") %}
{%- set compiler = compilers[0].name %}
{%- set src = fread("sample/main.py") %}
```
{{ src }}
```
```
{{ wandbox_run(compiler, src).program_message }}
```
```
{{ wandbox_run(compiler, src, runtime_option="Test").program_message }}
```Use kamidana$pipinstallamaterasu-j2
$kamidana-a=amaterasu.amaterasusample/wandbox.j2
*cpython-3**cpython-3.10.2*cpython-3.9.3*cpython-3.8.9*cpython-3.7.10*cpython-3.6.12FeaturesGlobalnameusagedetailwandbox_list{{ wandbox_list() }}return wandbox compilers list jsonwandbox_languages{{ wandbox_languages() }}return wandbox language list arraywandbox_run{{ wandbox_run(compiler, src, [options], [compier_option], [runtime_option] ) }}return wandbox compile resultFilternameusagedetailwandbox_fnmatch_compilers{{ wandbox_list() | wandbox_fnmatch_compilers("clang-3*c") }}filter compiler name by fnmatchwandbox_language_compilers{{ wandbox_list() | wandbox_language_compilers("C++") }}filter by languageUtilitiesutils.pymarkdown_link_pairurlencodeurl_quoteurl_quote_plusreplace_url_quote
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aMath
|
aMath packageThe aMath package consists of basic arithmatic operations.Installationpip install -ihttps://test.pypi.org/simple/aMathPython version= python3
|
amathon
|
UNKNOWN
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amatiCalpackage
|
Failed to fetch description. HTTP Status Code: 404
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amatino
|
Amatino PythonAmatino is a double-entry accounting system. It provides double entry accounting as a service via an HTTP API. Amatino Python is a library for interacting with the Amatino API from within a Python application. By using Amatino Python, a Python developer can utilise Amatino services without needing to deal with raw HTTP requests.About AmatinoAmatino gives you a full set of tools to store, organise and retrieve financial information. You don't need to set up databases or write any of your own double-entry accounting logic. All you need is this library, anAmatino account (Try free for two weeks!), and you are off and running.Under constructionRight now, the Amatino API offers a full range of accounting services via HTTP requests. However, this Amatino Python library is in an 'Alpha' state. Its capabilities are limited. A subset of full Amatino features are available.To see what proportion of Amatino features are ready in Amatino Python, check out theDocumentationpage. Linked classes are available, un-linked ones are still under construction.InstallationAmatino Python may be installed viaPIP.$pipinstallamatinoTo use Amatino Python, you will need an active Amatino subscription. You can start a free trial athttps://amatino.io/subscribe.Example UsageThe first step is to login to Amatino by creating aSessioninstance. That Session then becomes your key to using Amatino classes.fromamatinoimportSessionsession=Session.create_with_email(email='[email protected]',secret='uncrackable epic passphrase!')Amatino stores financial data inside discreteEntities. An Entity might describe a person, project, company, or some other entity which you wish to describe with financial data.fromamatinoimportEntitymega_corporation=Entity.create(session=session,# Created abovename='Mega Corporation')Entities are structured as a hierarchical tree ofAccounts. You might wish to create a chart of Accounts that mirror the real-world structure of the Entity you are describing.fromamatinoimportAccountrevenue=Account.create(entity=mega_corporation,# Created abovedescription='Revenue from world domination',am_type=AMType.revenue,# An AMType enumeration optiondenomination=USD# A GlobalUnit)The real fun begins withTransactions, where debits and credits come into playfromamatinoimportTransaction,Entry,SidefromdatetimeimportdatetimefromdecimalimportDecimalrevenue_recognition=Transaction.create(entity=mega_corporation,time=datetime.utcnow(),entries=[Entry(Side.debit,Decimal(10),cash),Entry(Side.credit,Decimal(5),revenue),Entry(Side.credit,Decimal(5),customer_deposits)]denomination=USD)Check out the full range of available classes, including Ledgers, in theAmatino Python documentationAPI stability & versioningAmatino Python obeys theSemantic Versionconvention. Until v1.0.0, the Python API (not to be confused with the Amatino HTTP API) should be considered unstable and liable to change at any time.Watch out! API currently unstable!You can see available versionsin GitHub's releases sectionorin PyPi's release history section.Tell us what your think/want/like/hatePlease join us on theAmatino discussion forumsand give us your feedback. We would love to hear from you. Amatino is in its earliest stages of development, and your feedback will influence the direction it moves in.Pull requests, comments, issues, forking, and so on are alsomost welcome on Github!Useful linksAmatino homeDevelopment blogDevelopment newsletterDiscussion forumMore Amatino client librariesHTTP DocumentationPython DocumentationBilling and account managementAbout Amatino Pty LtdGet in contactTo quickly speak to a human about Amatino,email [email protected] at him on Twitter (@hugh_jeremy).
|
amauri
|
TWBlog ReaderEsse é uma simples CLI (Command Line Interface) para ler os artigos do blog daTreinaWeb. Esse projeto foi desenvolvido para o artigoEmpacotando e publicando pacotes Python.InstalaçãoPara instalar o pacote, basta executar o comando abaixo:pipinstalltwblog-readerUsoApós a instalação do pacote será disponibilizado o comandotwblogna linha de comando. Para ver a lista do dezesseis últimos artigos, basta executar o comando abaixo:twblogA saída será parecida com a abaixo:Últimos artigos do blog da TreinaWeb:
0 - Desenvolvedor mobile: por que investir nessa carreira?
1 - ASP.NET - Conhecendo a Minimal API
2 - Como se preparar para entrevistas técnicas
3 - Seletores avançados do CSS
4 - Criando o primeiro CRUD com NestJS
5 - Seletores Básicos do CSS
6 - Orientação a objetos em Dart
7 - 5 dicas para entrevistas na área de TI
8 - Novos recursos do ECMAScript 2022
9 - O que preciso conhecer antes de iniciar estudos em uma linguagem de programação?
10 - Criando o primeiro CRUD com FastAPI
11 - Unidades de medidas no CSS
12 - Orientação a objetos no C#
13 - Guia da linguagem C#
14 - Estruturas condicionais e de repetição no C#
15 - Conhecendo variáveis e constantes no C#Para ver o conteúdo de um artigo específico, basta executar o comando abaixo:twblog[id]Onde[id]é o identificador do artigo que você deseja ver. Por exemplo, para ver o artigo de identificador 5, basta executar o comando abaixo:twblog5LicençaEsse projeto está sob a licença MIT. Veja o arquivoLICENSEpara mais detalhes.
|
ama-util
|
No description available on PyPI.
|
amaxa
|
IntroductionAmaxa is a new data loader and ETL (extract-transform-load) tool for Salesforce, designed to support the extraction and loading of complex networks of records in a single operation. For example, an Amaxa operation can extract a designated set of Accounts, their associated Contacts and Opportunities, their Opportunity Contact Roles, and associated Campaigns, and then load all of that data into another Salesforce org while preserving the connections between the records.Amaxa is designed to replace complex, error-prone workflows that manipulate data exports with spreadsheet functions likeVLOOKUP()to maintain object relationships.Amaxa is free and open source software, distributed under the BSD License. Amaxa is byDavid Reed, (c) 2019-2020.Documentation for Amaxa is available onReadTheDocs. The project is developed onGitHub.What Does Amaxa Mean?ἄμαξαis the Ancient Greek word for a wagon.
|
amaz
|
Failed to fetch description. HTTP Status Code: 404
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amaz3dpy
|
Introductionamaz3dpy is a Python SDK to interact with Amaz3d Web APIs.
This SDK is built by three main component:Object primitives for dealing with Authentication, Projects and OptimizationsA simple Client that simplifies interaction with Web APIs providing authentication, projects handling (visualization, creation, etc.), optimizations handling (creation and download)A cli command for using Amaz3D from a terminal sessionPlease consider that an Amaz3d account is required for using all functionalitiesFor further information visit:Adapta Studio WebsiteObject primitivesamaz3dpy provides primitives for interacting with Amaz3d functionalities.
Here you can find the import statements for the main objects:fromamaz3dpy.authimportAuthfromamaz3dpy.projectsimportProjectsfromamaz3dpy.modelsimportLoginInput,Optimization,OptimizationNormalBakingParamsInput,OptimizationOutputFormat,OptimizationParams,OptimizationPreset,Projectfromamaz3dpy.optimizationsimportProjectOptimizationsfromamaz3dpy.customer_walletimportCustomerWalletfromamaz3dpy.optimization_templatesimportOptimizationTemplatesfromamaz3dpy.termsimportTermsClientThe client encapsulates object primitives to follow the application business logic:fromamaz3dpy.clientsimportAmaz3DClientfromamaz3dpy.modelsimportOptimizationOutputFormat,OptimizationParams,OptimizationPresetamaz3dclient=Amaz3DClient()amaz3dclient.login(email="[email protected]",password="mypass")my_project={"name":"My project","file_path":"/path/to/file"}amaz3dclient.create_project(**my_project)amaz3dclient.load_projects()print(amaz3dclient.projects()amaz3dclient.select_a_project(id="....id.....")amaz3dclient.load_optimizations()print(amaz3dclient.optimizations())format=OptimizationOutputFormat['format_orig']params=OptimizationParams()params.face_reduction=0.5params.feature_importance=0params.preserve_hard_edges=False....optimization=amaz3dclient.create_optimization("my optimization",format,params=params)amaz3dclient.select_an_optimization(id=".....id......")amaz3dclient.download_selected_optimization(dst_path="/my/path")CliIt is possible to run a simple interactive CLI application for creating projects and optimizations.
Open your terminal and run:amaz3dLoginRun thelogincommand, and insert your credentialsProjects managementIt is possible to load and view projects by runningload_projectsandprojectsThe first command loads projects page by page.The second command instead prints all projects loaded.create_projectis used to create a new project and to upload an object that could be optimized afterwardsOptimizations managementBefore creating or visualizing optimization it is necessary to select a project withselect_projectcommand.Optimization visualization follows the same logic of projects: useload_optimizationsandoptimizationscommandscreate_optimizationis used to create an optimization for the selected project. It is possible to create optimization by selecting a preset or by tweaking advanced parametersDownlaod an optimizationTo download an optimization it is necessary to select it first using theselect_optimizationcommand.
After that an optimization is selected it is possible to download the result with thedownload_selected_optimizationcommand.HelpType help to get a list of available commands
|
amazarashi
|
No description available on PyPI.
|
amaze
|
UNKNOWN
|
amazed
|
amazedMaze generation & solver in one. Simply put, justa maze(d).Still in active development!Capable of both generating mazes of any rectangular size and solving them using various algorithms.GenerationThere are multiple ways of creating a maze:Depth First Search using recursionHunt and KillBinary TreeRandom KruskalAldous BroderRandom CarvingBoulder (also named Cinnamon Bun)SolversDepth First SearchDepth First Search RandomizedDepth First Search HeuristicLeeA*VisualizationEach generated maze can be exported either as a simple image (all possible formats frompillow) or as a GIF (will be availablev0.0.3), where the carving process can be observed. For solvers, each one can be exported either as an image or as a GIF, with a red line (the color is configurable) displaying the chosen path.
|
amazed-adipeterca
|
Failed to fetch description. HTTP Status Code: 404
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amazedb
|
amazedbIt is a file based NoSQL database management system written in python.All the databases are stored in thedbsub-directory of the current directory. This behaviour can be manipulated as we'll see later on.How to useYou can install amazedb through pip.$ python -m pip install amazedbExample usagefromamazedbimportdbms# Open a databasemydb=dbms.db("mydatabase")# Create a group inside itusers=mydb.createGroup("users")# Or access it viausers=mydb.getGroup("users")# Add some data to our usersusers.insert({"name":"Jalaj","role":"admin","email":"[email protected]","age":17})# Or add multiple documents simultaneouslyusers.insert_many({"name":"ABCD","age":10},{"name":"EFGH","age":20},{"name":"IJKL","age":30})# Get the user with the name ABCDabcd=user.get_one({"name":"ABCD"})# Or use some advanced search filtersabcd=users.get_one({"name":{"__re":".*A.*"}# All users with A in their name,"age":{"__gt":10}# All users with age more than 10})# Or get them sorted# Get all users sorted by ageabcd=users.get({},sortby="age")# Update some valuesupdated=users.update({"name":"ABCD"# Find the user with the name ABCD},{"email":"[email protected]"# And update its email field})# Use advanced search in update functionupdated=users.update({# All users with age greater than or equal to 10"age":{"__gte":10}},{"age":5})# Update the firs occurence of search filterupdate_one=users.update_one({# You can also use custom functions for search"name":{"__cf":lambdaval:Trueiflen(val)>10elseFalse}},{"name":"newName"})# Delete some documentsdelete=users.remove({"name":"ABCD"})# Delete only one documentdel_one=users.remove_one({"name":"EFGH"})# Delete the groupusers.drop()# Delete the databasemydb.drop()For detailed usage instructions, refer toUSAGE.mdContributingContributions to our project through newissuesandpull requestsare always welcome.
We would love to see other people contribute to the project and make it better.Although, before you contribute, you should follow ourissue templates.
Any PR or issue not following one of the templates will be ignored and closed immediately.
And yes, before opening a issue and don't forget to follow thecode of conductLicenseThis project is licensed underMIT.
|
amaze-dict
|
Example Package一个便捷的访问多层级dict的方法像操作类属性一样访问dict的属性，支持多级访问针对场景：针对复杂的多级dict的属性访问，如 user_info = {"name": "xxx", "address": {"city": "xxx", "country": "xxx"}}老的方式：user_info={"name":"xxx","address":{"city":"xxx","country":"xxx"}}# 方式一 （当user_info没有address或address不为dict时，后面get会报错）：user_info.get("address").get("city")# 方式二（当user_info没有address或address不为dict时，后面get会报错）：user_info['address']['city']# 方式三 (增加多级判断)(user_info.get("address")or{}).get("city")使用amaze_dict：fromamaze_dictimportwrap_valueuser_dict={"name":"xiaoming","age":30,"address":{"city":"beijing","country":"china"}}user_amaze_dict=wrap_value(user_dict)print(user_amaze_dict.address.city)>>output:beijingprint(user_amaze_dict.address.country)>>output:chinaprint(user_amaze_dict.contact.phone_num)>>output:<amaze_dict.amaze_dict.LB_Noneobjectat0x7f8664882cd0>多级访问直接用属性递进查找，不受中间属性没有影响。 可直接对需要的属性进行访问和判断。使用方法fromamaze_dictimportwrap_valueuser_dict={"name":"xiaoming","age":30,"address":{"city":"beijing","country":"china"}}user_amaze_dict=wrap_value(user_dict)访问一级属性：>>>print(user_amaze_dict.name)xiaoming
>>>>print(user_amaze_dict.age)30访问多级属性：>>>print(user_amaze_dict.address.city)beijing
>>>>print(user_amaze_dict.address.country)china条件判断：判断属性是否存在:
>>>ifuser_amaze_dict.address.community:
...print(user_amaze_dict.address.city)beijing
|
aMAZEing
|
Failed to fetch description. HTTP Status Code: 404
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amazeing123
|
mazeSolverquick maze solving program to teach students basic programming
|
amazing
|
UNKNOWN
|
amazing-printer
|
Amazing PrinterIt's an attempt to port the Ruby'sawesome_printto Python.Installpipinstallamazing-printerUsagefromamazing_printerimportapap(object,options={})# Options:# sort_keys - boolean, set to sort the dictionary keys, default is False.# indent - integer, set the width of indentation, default is 4 spaces.# multiple_lines - boolean, set to print the fields for dict or list in multiple lines, default is True.# color - dict, set the color for different literals, default is# {# 'str': 'yellow',# 'list': 'blue',# 'tuple': 'green',# 'none': 'red',# }# supported colors: 'yellow', 'blue', 'green', 'red', 'black', 'magenta', 'cyan', 'white'ContributingFork & clone the [email protected]:<your-github-handle>/amazing_printer.git
$cdamazing_printerCreate a virtual environment and activate it'
This project usesPipenv. Follow the instructionhereto install.$pipenvshell--threeInstall dependencies# Install dependencies for development purpose.$pipenvinstall--devMake your awesome changesRun test and coverage# Run test(env)$pytest# Run test with coverage report(env)$pytest--cov=amazing_printeramazing_printer/tests/# Run specific test(env)$pytest-k"TestFormatter"Run test for multiple environments
Make sure you have installed multiple versions of Python. Recommended way of managing Python version usingPyenv(env)$toxMake your pull requestLicenseCopyright (c) 2019 Kher Yee, TingReleased under the MIT license. SeeLICENSEfile for details.
|
amazon-ad
|
No description available on PyPI.
|
amazonadapi
|
# amazonadapiClient for the Amazon Ad API# Notes:## The Amazon Ad API pulls only the IDs for each ad tech object.# E.G. client.get_orders('AD_ID') just returns the order IDs.# Take the array of order IDs and call client.get_order('ORDER_ID').#order_ids = client.get_orders('AD_ID')for order_id in order_ids:order = client.get_order(order_id)# FOR ETL or automated job servers, e.g. SMP, use the following to initialize:from amazonadapi import AmazonClientimport osclient = AmazonClient()client.refresh_token = os.environ['AMZN_REFRESH_TOKEN']client.auto_refresh_token()client.set_region()client.get_profiles()client.profile_id = 'BE_SURE_TO_SET_THIS_FOR_YOUR_ORGANIZATION'client.get_advertisers()client.get_orders('AD_ID')client.get_order('ORDER_ID')# Sample code for using browser and command-line to auth:from amazonadapi import AmazonClientclient = AmazonClient()client.cli_auth_dance()client.get_profiles()client.profile_id = 'BE_SURE_TO_SET_THIS'# create an orderorder = AmazonOrder()order.advertiserId = '3678742709207'order.name = 'amazon api test {}'.format(time.time())order.startDateTime = 1511909961000 # unix time * 1000order.endDateTime = 1512514761000 # unix time * 1000hash_order = {"object": {"advertiserId": {"value": order.advertiserId},"name": order.name,"startDateTime": order.startDateTime,"endDateTime": order.endDateTime,"deliveryActivationStatus": order.status}}created_order = client.create_order(hash_order)# create a line itemline_item = AmazonLineItem()line_item.orderId = 'ORDER_ID'line_item.advertiserId = 'AD_ID'line_item.name = 'Test API Line Item Creation'# types: NON_GUARANTEED_DISPLAY,NON_GUARANTEED_MOBILE_APP,NON_GUARANTEED_VIDEOline_item.type = 'NON_GUARANTEED_DISPLAY'line_item.startDateTime = 1506873006000line_item.endDateTime = 1507045806000line_item.status = 'INACTIVE'line_item.budget['amount'] = 100line_item.budget['deliveryProfile'] = 'FRONTLOADED'line_item.budget['deliveryBuffer'] = 1# recurrenceTypes: DAILY, MONTHLY, LIFETIMEline_item.deliveryCaps.append({'amount': 0.9, 'recurrenceType': 'DAILY'})hashline_item = {"object": {"orderId": {"value": line_item.orderId},"advertiserId": {"value": line_item.advertiserId},"name": line_item.name,"type": line_item.type,"startDateTime": line_item.startDateTime,"endDateTime": line_item.endDateTime,"deliveryActivationStatus": line_item.status,"budget" : line_item.budget,"deliveryCaps" : line_item.deliveryCaps}}result = client.create_line_item(hashline_item)client.create_line_item(hashline_item)
|
amazon-ad-sdk
|
No description available on PyPI.
|
amazon-advertising
|
simple Amazon Advertising API client
|
amazon-affiliate-scraper
|
Amazon Affiliate - Scraper ToolQuickstartCreating Affiliate URL:fromamazon_affiliateimportAmazonAffiliatedefmain():amazon_affiliate=AmazonAffiliate(cookies_file_path="path/to/amazon_cookies.json",# Path to Amazon site cookiesorigin_url="https://www.amazon.com.br/",# Amazon root URL in your countrywebdriver_file_path="path/to/webdriver.exe",# Path to browser webdriverheadless=True# If the browser will appear)# Create an affiliate URL like: https://amzn.to/3qAp1rZaffiliate_url=amazon_affiliate.create_affiliate_url("https://www.amazon.com.br/Introdu%C3%A7%C3%A3o-Programa%C3%A7%C3%A3o-com-Python-Algoritmos/dp/8575227181/?_encoding=UTF8&pd_rd_w=cLgOh&content-id=amzn1.sym.142cf30d-cd8e-4f67-a147-740799e27bf0&pf_rd_p=142cf30d-cd8e-4f67-a147-740799e27bf0&pf_rd_r=Q5SHBAZH69HSPBRR2MGS&pd_rd_wg=98FtW&pd_rd_r=ffa375c6-01c9-4e9c-9f87-2aef974a89af&ref_=pd_gw_ci_mcx_mr_hp_atf_m")print(affiliate_url)if__name__=="__main__":main()
|
amazon-affiliate-url
|
amazon_affiliate_urlInstallpipinstallamazon_affiliate_url
or
pip3installamazon_affiliate_urlUsagefromamazon_affiliate_urlimportAmazonAffiliateUrl,CountryASIN='SOME_ASIN'TAG='YOUR_AFFILIATE_TAG'BITLY_TOKEN='YOUR_BITLY_TOKEN'print(AmazonAffiliateUrl.url_cls(asin_or_url=ASIN,affiliate_tag=TAG,# bitly_token=BITLY_TOKEN,# shorten_url=True,country=Country.Netherlands))
|
amazon-appflow-custom-connector-sdk
|
Amazon AppFlow Custom Connector SDK Development GuideAmazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, SAP, Zendesk, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. With AppFlow, you can run data flows at enterprise scale at the frequency you choose - on a schedule, in response to a business event, or on demand. You can configure data transformation capabilities like filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps. AppFlow automatically encrypts data in motion, and it allows users to restrict data from flowing over the public internet for SaaS applications that are integrated with AWS PrivateLink, reducing exposure to security threats. For more details about AppFlow, seehttps://aws.amazon.com/appflow/.The AppFlow Custom Connector SDK enables customers and third party developers to build custom source and/or destination connectors for the AppFlow service. With the SDK, you can connect to private APIs, on-premise proprietary systems, and other cloud services by adding to AppFlow's library of connectors (https://aws.amazon.com/appflow/integrations/).The Amazon AppFlow Custom Connector SDK simplifies connector development through the use of a normalized interface and data model. It provides authentication, pagination, throttling, error handling, deployment scripts, and a test framework. Connectors developed with the SDK can be used privately, shared within your organization or partners, or published on AWS Marketplace (https://aws.amazon.com/marketplace).This document is a guide for developing AppFlow custom connectors. The SDK includes a complete working example of a sample connector in Python/Java.What is an AppFlow 'Connector'?A connector translates the normalized AppFlow requests (read and write calls) to the requests compatible for the underlying application. The translation includes protocol, API format, query format, data model etc. Connectors support queries, partial queries (paginated) and write calls (insert, update, upsert and delete operations).AppFlow transfers data between source and destination connectors using an incremental query model. It constructs a query based on the Flow configuration and uses that to pull a configurable number of records from the source connector. For subsequent runs, AppFlow pulls only the records that have changed or were created since the last run. For structured data, the queries are based on entities (e.g. Accounts, Orders, Contact, Benefit, Report etc) as declared by a given connector.Connector ConfigurationAppFlow manages authentication information for each connector, and it makes those credentials available to the connectors with the request for data reads and writes. AppFlow depends on the connector to provide the authentication scheme(s) the connectors support and the corresponding configuration parameters. It then gives an option to the AppFlow user to pick the desired authentication scheme from the list of supported schemes for that connector. Apart from authentication configuration, connector can also declare the implementation specific runtime settings. For these settings, input from AppFlow users is required during the ConnectorProfile creation or Flow creation. AppFlow stores the user credentials in user’s security manager. The authentication credentials ARN and the runtime settings are stored along with the ConnectorProfile state, and they are passed along with every request to the connector.Entity MetadataAppFlow support dynamic discovery of schema and depends on the connectors to provide the required metadata. Therefore,
AppFlow depends on two metadata queries from the connectors:listEntities. This API is recursive in nature and provides a hierarchical entity listing based on entityPath. If thelistEntitiesresponse hashasChildren=true, that that indicates that there are more entities in the next level.describeEntity. This API provides field-level details of a given entity.
After retrieving the metadata from the connector, AppFlow caches the metadata as per the ttl value returned by the connector
along with the metadata response in the CacheControl structure. If no ttl value is specified, AppFlow defaults to
1 hour.SDK InterfacesInterfacesDescriptionConfigurationHandlerDefines the functionality to be implemented for configurations, credentials related operationsMetadataHandlerDefines the functionality to be implemented for metadata operationsRecordHandlerDefines the functionality to be implemented for record related CRUD operationsIn the next section, we take a closer look at these interfaces. AppFlow uses these interfaces to interact with connectors
for fetching data from the source connector or pushing data into the destination connector. It also uses these interfaces
for the connector registration using the configuration returned by the connector and also to retrieve
metadata dynamically. Therefore, along with the data interfaces, the connector developers are required to implement
the interface to declare supported authentication types, configuration, and also the metadata interface
which helps the connector declare the supported entities and their shape.The connector developers therefore are essentially writing the wiring code to translate to/from from vendor’s API to
the generic AppFlow custom connector interface. The connector code needs to handle the following:Necessary protocol and data model transformations.Normalization to standard error-codes.If the token was found to be expired, the connector can propagate the exception. Then AppFlow can handle
the exception and refresh the token and reissue the request.Pagination support.Vendors specific versioning differences.ConfigurationHandler DetailsAllows the connector to declare connector runtime settings and authentication config etc using the
describeConnectorConfiguration method.It also includes the implementation for the following callbacks:validateConnectorRuntimeSettings: to validate user input for connector runtime settings.validateCredentials: to validate user credentials entered by the AppFlow user.classConfigurationHandler(metaclass=abc.ABCMeta):"""This abstract base class defines the functionality to be implemented by custom connectors for configurations,credentials related operations."""@abc.abstractmethoddefdescribe_connector_configuration(self,request:requests.DescribeConnectorConfigurationRequest)->\responses.DescribeConnectorConfigurationResponse:"""Describes the Connector Configuration supported by the connector.Parameters:request (DescribeConnectorConfigurationRequest)Return:DescribeConnectorConfigurationResponse"""[email protected]_connector_runtime_settings(self,request:requests.ValidateConnectorRuntimeSettingsRequest)->\responses.ValidateConnectorRuntimeSettingsResponse:"""Validates the user inputs corresponding to the connector settings for a given ConnectorRuntimeSettingScopeParameters:request (ValidateConnectorRuntimeSettingsRequest)Return:ValidateConnectorRuntimeSettingsResponse"""[email protected]_credentials(self,request:requests.ValidateCredentialsRequest)->\responses.ValidateCredentialsResponse:"""Validates the user provided credentials.Parameters:request (ValidateCredentialsRequest)Return:ValidateCredentialsResponse"""passMetadataHandler DetailsAllows the connector to declare the entity metadata for the underlying applicationlistEntitiesdescribeEntityclassMetadataHandler(metaclass=abc.ABCMeta):"""This abstract base class defines the functionality to be implemented by custom connectors for metadataoperations."""@abc.abstractmethoddeflist_entities(self,request:requests.ListEntitiesRequest)->responses.ListEntitiesResponse:"""Lists all the entities available in a paginated fashion. This API is recursive in natureand provides a heretical entity listing based on entityPath. If the ListEntitiesResponsereturns has_children=true, that indicates that there are more entities in the next level.Parameters:request (ListEntitiesRequest)Return:ListEntitiesResponse"""[email protected]_entity(self,request:requests.DescribeEntityRequest)->responses.DescribeEntityResponse:"""Describes the entity definition with its field level metadata.Parameters:request (DescribeEntityRequest)Return:DescribeEntityResponse"""passRecordHandler DetailsThis is where the main functionality of data exchange is implemented:retrieveData: lookup records against a batch of idsqueryData: queries the underlying application by translating the incoming filterExpress that follows a specific
AppFlow DSL (SDK contains a parser to help parse the filterExpression)writeData: writes a batch of records to the underlying applicationclassRecordHandler(metaclass=abc.ABCMeta):"""This abstract base class defines the functionality to be implemented by custom connectors for record relatedoperations."""@abc.abstractmethoddefretrieve_data(self,request:requests.RetrieveDataRequest)->responses.RetrieveDataResponse:"""Retrieves the batch of records against a set of identifiers from the source application.Parameters:request (RetrieveDataRequest)Return:RetrieveDataResponse"""[email protected]_data(self,request:requests.QueryDataRequest)->responses.QueryDataResponse:"""Writes batch of records to the destination application.Parameters:request (QueryDataResponse)Return:WriteDataResponse"""[email protected]_data(self,request:requests.WriteDataRequest)->responses.WriteDataResponse:"""Writes batch of records to the destination application.Parameters:request (WriteDataRequest)Return:WriteDataResponse"""passFilter Expression DSL and ParserAppFlow uses an incremental query model to fetch data from the source. The initial pull queries records modified/created in
the past 30 days (configurable). In the next flow execution onwards, it runs incremental queries to pull data modified/created since the
last execution. The AppFlow query engine takes the user input for filtering from the flow definition and translates it to
a normalized DSL and passes it on to connectors. Connectors then take that normalized expression and translate it to
application-specific format. The domain specific language (DSL) that defines the filter expressions grammar is AppFlow
specific. For example: “price > 100 and accountName contains 'Alexa'". The query expression DSL is a normalized DSL
and not specific to DSLs used by specific underlying applications. The connector's job is to provide the necessary
translation from AppFlow DSL to application-specific DSL or other syntax the underlying application requires. In order
to simplify the work needed to parse the filter expression the parser has been included. Please see the README.md under
thecustom_connector_queryfiltermodule for the details.AppFlow Custom Connector – End-to-End FlowThe following is a sample end-to-end processing flow to help understand how AppFlow interacts with the custom connectors using
the above interfaces:Step 1: Connector implementation declares the supported authentication types using the ConfigurationHandler class.
AppFlow usesdescribeConnectorConfigurationfunction to retrieve the connector configuration. TheDescribeConnectorConfigurationResponseincludesAuthenticationConfigandConnectorRuntimeSettingsdata structures.
AuthenticationConfig defines the auth scheme supported by the connector (OAuth2/BASIC etc) and
ConnectorRuntimeSettings represents the setting that the connector needs at runtime, input for which are provided by
the AppFlow user.(e.g. timeout values or any other implementation dependent inputs)Step 2: AppFlow customer deploys this connector Lambda stack into their account and registers aConnectoragainst the
Lambda using theregister_connectorAppFlow public API.Step 3: AppFlow customer creates a newConnectorProfileagainst this newly registered connector and picks an
authentication type (for this example, let’s assume they picked OAuth2)Step 4: AppFlow Console presents them with the corresponding authentication flow and AppFlow takes user inputs and
invokesvalidateCredentialsfunction on the connector. If that succeeds, the console presents the screen to take user input for
the "connector runtime settings" declared by the connector. When user inputs the values corresponding to the settings (example:
var1=10, var2=“Foo”). AppFlow invokesvalidateConnectorRuntimeSettingsfunction on the connector so that it can validate the
user input. Upon successful validation the credentials and connector runtime settings are stored along with the newly createdConnectorProfile.Step 5: AppFlow user then uses thatConnectorProfileto createFlowdefinitions. During the flow creation AppFlow
invokeslistEntityanddescribeEntityfunctions on the connector to retrieve the list of supported entities and the entity
detail of the specific entity AppFlow user selected on the console / API.Step 6: When the flow is started AppFlow invokes the connector to execute the query constructed using the user input
during the AppFlow definition using thequeryDatafunction.Step 7: For incremental flows AppFlow adds the timestamp based filter on the query and passes it on the connector to
invoke it again for incremental data pulls using the query_data function.Step 8: If the destination connector is also a custom connector, AppFlow processes the incoming records as per the
task definitions and prepares the payload as per the entity shape of the destination connector and hands it over to the
destination connector by invoking thewriteDatafunction.Connector Developer ExperienceClone thecustom-connector-examplemodule and modify the implementation to fit the underlying application you are
writing the connector for.Edit the implementations for the three lambda handlers:ConfigurationHandler,MetadataHandlerandRecordHandler.Add unit tests.This SDK productivity scripts under thecustom-connector-toolsmodule, to help build and export the package into
S3 using AWS CloudFormation template and then deploy the Lambda stack into your account.Login into the AppFlow console and register aConnectoragainst the new Lambda.Create aConnectorProfileagainst the newly registeredConnector.Create a flow using the newConnectorandConnectorProfile.Execute the flow.Getting Started GuideDocumentationThe Amazon AppFlow documentation is located here:https://docs.aws.amazon.com/appflow/index.html. The API reference guide can be found here:https://docs.aws.amazon.com/appflow/1.0/APIReference/API_Operations.html.Downloading the SDKThe Amazon AppFlow Custom Connector SDK is available for both Java and Python on github at the following link:https://github.com/orgs/awslabs/teams/amazon-appflow/repositoriesJava SDK - You can find the Java SDK inAppFlowJavaSdk/Python SDK - You can find the Python SDK .whl file inAppFlowPythonSDK/Using the SDKFor Java SDK Users:Once you have the jar, create the Amazon AppFlow client instance and invoke the AppFlow APIs.AmazonAppflow appflowClient = AmazonAppflowClientBuilder.standard()
.withRegion(region)
.build();For Python Users:Once you have the python whl file, install the whl files using pip3 install. Create the AppFlow client using the boto3 and call the AppFlow APIs.Commands to install the Python SDK:python3 -m pip install botocore-1.21.25-py3-none-any.whl
python3 -m pip install boto3-1.18.25-py3-none-any.whlimport boto3
appflowClient = boto3.client('appflow')Lambda Permissions and Resource Policy Must be Manually UpdatedIn order for AppFlow to invoke the custom connector Lambda function while executing customer flows, the following permissions need to be added manually (or by simply using thedeploy.shscript provided in the custom-connector-tools package):{
"Version": "*2012-10-17*",
"Id": "default",
"Statement": [{
"Sid": "*appflow-example-01*",
"Effect": "Allow",
"Principal": {
"Service": "appflow.amazonaws.com"
},
"Action": "lambda:InvokeFunction",
"Resource": "arn:aws:lambda:*us-east-1*:123456789012:function:*my-function*",
"Condition": {
"StringEquals": {
"AWS:SourceAccount": "*123456789012*"
},
"ArnLike": {
"AWS:SourceArn": "arn:aws:appflow:*us-east-1*:*123456789012*:*"
}
}
}]
}source account: the customers accountsource arn: all of the Amazon AppFlow resources present in the customers accountThe Source Account and the Source ARN condition helps to prevent the “confused deputy” security vulnerability. The allow-listed service principal allows all of the AWS accounts under the “appflow” service principal to access the customer Lambda.Registering your Custom ConnectorFor Console Users:i) Go to the Amazon AppFlow console and select “Connectors” on the left-side menu.ii) Click on the “Register New Connector” button on the console.iii) Your custom connector Lambda function will be available on this webpage. If not, ensure that the appropriate Lambda permissions are set, as described in the section “Updating Lambda Permissions and Resource Policy Required."iv) Provide the label for your connector. The label must be unique per account per region. Click on the “Register” button.v) Once registered, you’ll see the connector in the list of custom connectors.For API Users:Invoke theregisterConnectorpublic API endpoint with the following request payload:{
"connectorLabel":"TestCustomConnector", [The unique Label]
"connectorProvisioningType":"LAMBDA", [The provisioning type of the connector.
Currently only supported value is LAMBDA]
"connectorProvisioningConfig":{
"lambda":{
"lambdaArn":"arn:aws:lambda:us-west-2:364320160620:function:
banvipulCustomConnectorBugBash" [The arn of the deployed lambda]
}
}
}Creating the Connector ProfileFor Console Users:i) Go to the “Connections” link from the left menu and select the registered connector from the list.ii) Once selected click on the ‘Create Connection’ button.iii) A pop-up appears asking for the required details to create a connection. Please fill in the details and scroll down to click on the ‘Continue’ button.iv) Your connection now appears in the list.For API Users:For custom connectors, a new parameter has been added in the request object for this API.{
.............................
.............................
.............................
"connectorLabel":"TestCustomConnector", [The Label provided while registering the
connector]
.............................
.............................
.............................
}Please follow the sample API request herehttps://docs.aws.amazon.com/appflow/1.0/APIReference/API_CreateConnectorProfile.htmlCreating FlowsFor Console Users:i) Click on the Flow tab in the left menu, select Create Flow, then follow the standard AppFlow flow configuration steps.For API Users:Follow the sample API request here:https://docs.aws.amazon.com/appflow/1.0/APIReference/API_CreateFlow.htmlIn addition to this, for the custom connectors we have added the following parameters in the request object for this API:Finding and Listing Custom ConnectorsFor Console Users:Go the Amazon AppFlow console and click on “Connectors” on the left menu. Select “Custom connectors” in the drop down list.For API Users:Call the listConnectors API with the following request format, this will list all the custom connectors registered in your account:{
"maxResults" : 100 // default is 20 and accepted values are between 1 to 100.
}Obtaining Details about the Registered ConnectorFor Console Users:Go the Amazon AppFlow console, click on “Connectors” on the left menu, then click “View details” on the connector of interest.For API Users:Please call describeConnector API. Please follow the sample API request here:{
"connectorType" : `"CustomConnector",
"connectorLabel": <YourConnectorLabel>`
}Testing the connectorUsing AWS Lambda consoleAfter your connector is deployed, you can access the connector through the AWS Lambda console.You can go to the test tab, and define the payloads as per the request format and then invoke the Lambda to test this.To the request payload, you can build the request using the Request model defined as SDK and then convert it to JSON string and then use that on console.You can also use our Testing Tools incustom-connector-testsandcustom-connector-integ-testto test your connector.Additional details:Please refer to the below READMEs to learn more about the SDK:You can find QueryFilter README incustom-connector-queryfilterYou can find Example README incustom-connector-exampleYou can find Deployment Tools README incustom-connector-toolsYou can find Testing Tools README incustom-connector-testsandcustom-connector-integ-test
|
amazon-bedrock-haystack
|
amazon-bedrock-haystackTable of ContentsInstallationContributingLicenseInstallationpip install amazon-bedrock-haystackContributinghatchis the best way to interact with this project, to install it:pipinstallhatchWithhatchinstalled, to run all the tests:hatch run testNote: there are no integration tests for this project.To run the lintersruffandmypy:hatch run lint:allLicenseamazon-bedrock-haystackis distributed under the terms of theApache-2.0license.
|
amazon-braket-algorithm-library
|
Amazon Braket Algorithm LibraryThe Braket Algorithm Library provides Amazon Braket customers with pre-built implementations of prominent quantum algorithms and experimental workloads as ready-to-run example notebooks.Currently, Braket algorithms are tested on Linux, Windows, and Mac.Running notebooks locally requires additional dependencies located innotebooks/textbook/requirements.txt. See notebooks/textbook/README.md for more information.Installing the Amazon Braket Algorithm LibraryThe Amazon Braket Algorithm Library can be installed from source by cloning this repository and running a pip install command in the root directory of the repository.gitclonehttps://github.com/amazon-braket/amazon-braket-algorithm-library.gitcdamazon-braket-algorithm-library
pipinstall.To run the notebook examples locally on your IDE, first, configure a profile to use your account to interact with AWS. To learn more, seeConfigure AWS CLI.After you create a profile, use the following command to set theAWS_PROFILEso that all future commands can access your AWS account and resources.exportAWS_PROFILE=YOUR_PROFILE_NAMEConfigure your AWS account with the resources necessary for Amazon BraketIf you are new to Amazon Braket, onboard to the service and create the resources necessary to use Amazon Braket using theAWS console.SupportIssues and Bug ReportsIf you encounter bugs or face issues while using the algorithm library, please let us know by posting
the issue on ourGitHub issue tracker.For other issues or general questions, please ask on theQuantum Computing Stack Exchangeand add the tag amazon-braket.Feedback and Feature RequestsIf you have feedback or features that you would like to see on Amazon Braket, we would love to hear from you!GitHub issuesis our preferred mechanism for collecting feedback and feature requests, allowing other users
to engage in the conversation, and +1 issues to help drive priority.LicenseThis project is licensed under the Apache-2.0 License.
|
amazon-braket-build-tools
|
Braket Build ToolsInstalling the Amazon Braket Build ToolsThe Amazon Braket Build Tools can be installed with pip as follows:pipinstallamazon-braket-build-toolsYou can also install from source by cloning this repository and running a pip install command in the root directory of the repository:gitclonehttps://github.com/aws/amazon-braket-build-tools.gitcdamazon-braket-build-tools
pipinstall.Flake8 Checkstyle PluginThis tool checks python source code for Braket checkstyle standards as a Flake8 plugin.
Installing amazon-braket-build-tools automatically registers this plugin with flake8 and
will be run by default whenever flake8 is used.Testing The InstallationTo test if the extension has been installed run:flake8--enable-extensions=BCS--helpAt the bottom of the output you should see braket checkstyle among your installed plugins:Installed plugins: braket.flake8_plugins.braket_checkstyle_plugin: 0.1.0RunningTo run with the extension enabled, just run flake8 on the module of your choice.
For example:flake8--enable-extensions=BCSsrcSecuritySeeCONTRIBUTINGfor more information.LicenseThis project is licensed under the Apache-2.0 License.
|
amazon-braket-default-simulator
|
Amazon Braket Default SimulatorThe Amazon Braket Default Simulator is a Python open source library that provides an implementation of a quantum simulator
that you can run locally. You can use the simulator to test quantum tasks that you construct for theAmazon Braket SDKbefore you submit them to the Amazon Braket service for execution.Setting up Amazon Braket Default Simulator PythonYou must have theAmazon Braket SDKinstalled to use the local simulator.
Follow the instructions in theREADMEfor setup.Checking the version of the default simulatorYou can check your currently installed version ofamazon-braket-default-simulatorwithpip show:pipshowamazon-braket-default-simulatoror alternatively from within Python:>>> from braket import default_simulator
>>> default_simulator.__version__UsageThe quantum simulator implementationsStateVectorSimulatorandDensityMatrixSimulatorplug into theLocalSimulatorinterface inAmazon Braket SDK, with thebackendparameters as"braket_sv"and"braket_dm", respectively.
Alternatively, to useStateVectorSimulator, you can instantiateLocalSimulatorwith no arguments or withbackend="default":Executing a circuit using the default simulatorfrombraket.circuitsimportCircuitfrombraket.devicesimportLocalSimulatordevice=LocalSimulator()bell=Circuit().h(0).cnot(0,1)print(device.run(bell,shots=100).result().measurement_counts)DocumentationDetailed documentation, including the API reference, can be found onRead the DocsTo generate the API Reference HTML in your local environmentFirst, install tox:pipinstalltoxTo generate the HTML, first change directories (cd) to position the cursor in theamazon-braket-default-simulator-pythondirectory. Then, run the following command to generate the HTML documentation files:tox-edocsTo view the generated documentation, open the following file in a browser:../amazon-braket-default-simulator-python/build/documentation/html/index.htmlTestingIf you want to contribute to the project, be sure to run unit tests and get a successful result
before you submit a pull request. To run the unit tests, first install the test dependencies using the following command:pipinstall-e"amazon-braket-default-simulator-python[test]"To run the unit tests:tox-eunit-testsYou can also pass in various pytest arguments to run selected tests:tox-eunit-tests--your-argumentsFor more information, please seepytest usage.To run linters and doc generators and unit tests:toxTo run the performance tests:tox-eperformance-testsThese tests will compare the performance of a series of simulator executions for your changes against the latest commit on the main branch.Note: The execution times for the performance tests are affected by the other processes running on the system.
In order to get stable results, stop other applications when running these tests.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-default-simulator-kshitijc
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Amazon Braket Default SimulatorThe Amazon Braket Default Simulator is a Python open source library that provides an implementation of a quantum simulator
that you can run locally. You can use the simulator to test quantum tasks that you construct for theAmazon Braket SDKbefore you submit them to the Amazon Braket service for execution.Setting up Amazon Braket Default Simulator PythonYou must have theAmazon Braket SDKinstalled to use the local simulator.
Follow the instructions in theREADMEfor setup.Checking the version of the default simulatorYou can check your currently installed version ofamazon-braket-default-simulatorwithpip show:pipshowamazon-braket-default-simulatoror alternatively from within Python:>>> from braket import default_simulator
>>> default_simulator.__version__UsageThe quantum simulator implementationsStateVectorSimulatorandDensityMatrixSimulatorplug into theLocalSimulatorinterface inAmazon Braket SDK, with thebackendparameters as"braket_sv"and"braket_dm", respectively.
Alternatively, to useStateVectorSimulator, you can instantiateLocalSimulatorwith no arguments or withbackend="default":Executing a circuit using the default simulatorfrombraket.circuitsimportCircuitfrombraket.devicesimportLocalSimulatordevice=LocalSimulator()bell=Circuit().h(0).cnot(0,1)print(device.run(bell,shots=100).result().measurement_counts)DocumentationDetailed documentation, including the API reference, can be found onRead the DocsTo generate the API Reference HTML in your local environmentFirst, install tox:pipinstalltoxTo generate the HTML, first change directories (cd) to position the cursor in theamazon-braket-default-simulator-pythondirectory. Then, run the following command to generate the HTML documentation files:tox-edocsTo view the generated documentation, open the following file in a browser:../amazon-braket-default-simulator-python/build/documentation/html/index.htmlTestingIf you want to contribute to the project, be sure to run unit tests and get a successful result
before you submit a pull request. To run the unit tests, first install the test dependencies using the following command:pipinstall-e"amazon-braket-default-simulator-python[test]"To run the unit tests:tox-eunit-testsYou can also pass in various pytest arguments to run selected tests:tox-eunit-tests--your-argumentsFor more information, please seepytest usage.To run linters and doc generators and unit tests:toxTo run the performance tests:tox-eperformance-testsThese tests will compare the performance of a series of simulator executions for your changes against the latest commit on the main branch.Note: The execution times for the performance tests are affected by the other processes running on the system.
In order to get stable results, stop other applications when running these tests.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-ocean-plugin
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Amazon Braket Ocean PluginThe Amazon Braket Ocean Plugin is an open source library in Python that provides a framework that you can use to interact with Ocean tools on top of Amazon Braket.PrerequisitesBefore you begin working with the Amazon Braket Ocean Plugin, make sure that you've installed or configured the following prerequisites.Python 3.7.2 or greaterDownload and install Python 3.7.2 or greater fromPython.org.
If you are using Windows, chooseAdd Python to environment variablesbefore you begin the installation.Amazon Braket SDKMake sure that your AWS account is onboarded to Amazon Braket, as per the instructions in theREADME.Ocean toolsDownload and installOcean tools.pipinstalldwave-ocean-sdkInstall the Amazon Braket Ocean PluginThe Amazon Braket Ocean Plugin can be installed with pip as follows:pipinstallamazon-braket-ocean-pluginYou can also install from source by cloning this repository and running a pip install command in the root directory of the repository:gitclonehttps://github.com/aws/amazon-braket-ocean-plugin-python.gitcdamazon-braket-ocean-plugin-python
pipinstall.You can check your currently installed version ofamazon-braket-ocean-pluginwithpip show:pipshowamazon-braket-ocean-pluginor alternatively from within Python:>>> from braket import ocean_plugin
>>> ocean_plugin.__version__DocumentationDetailed documentation, including the API reference, can be found onRead the Docs.To generate the API Reference HTML in your local environmentFirst, you must have tox installed.pipinstalltoxThen, you can run the following command with tox to generate the documentation:tox-edocsTo view the generated documentation, open the following file in a browser:BRAKET_OCEAN_PLUGIN_ROOT/build/documentation/html/index.htmlUsageThis package provides samplers which use Braket solvers. These samplers extend abstract base classes provided in Ocean's dimod and thus have the same interfaces as other samplers in Ocean.BraketSampleris a structured sampler that uses Braket-formatted parameters and properties. For example, instead ofanswer_mode, which is used for D-Wave QPU samplers, Braket usesresultFormatinstead.Linkedis a sample example of solving theminimum vertex coverproblem usingBraketSampler.BraketDWaveSampleris a structured sampler that uses D-Wave-formatted parameters and properties. It is interchangeable with D-Wave'sDWaveSampler.Linkedis the same example as above of solving the minimum vertex cover problem. Only the parameter inputs to the solver have been changed to be D-Wave formatted (e.g.answer_modeinstead ofresultFormat).These usage examples can be found as python scripts in theBRAKET_OCEAN_PLUGIN_ROOT/examples/folder.Debugging LogsTasks sent to QPUs don't always complete right away. To view task status, you can enable debugging logs. An example of how to enable these logs is included in the repo:BRAKET_OCEAN_PLUGIN_ROOT/examples/debug_*. These examples enable task logging so that status updates are continuously printed to terminal after a quantum task is executed. The logs can also be configured to save to a file or output to another stream. You can use the debugging example to get information on the tasks you submit, such as the current status, so that you know when your task completes.Install Additional Packages for TestingMake sure to install test dependencies first:pipinstall-e"amazon-braket-ocean-plugin-python[test]"Unit TestsTo run the unit tests:tox-eunit-testsYou can also pass in various pytest arguments to run selected tests:tox-eunit-tests--your-argumentsFor more information, please seepytest usage.To run linters and doc generators and unit tests:toxIntegration TestsSet theAWS_PROFILE, as instructed in the amazon-braket-sdk-pythonREADME.exportAWS_PROFILE=YOUR_PROFILE_NAMERunning the integration tests will create an S3 bucket in the same account as theAWS_PROFILEwith the following naming conventionamazon-braket-ocean-plugin-integ-tests-{account_id}.Run the tests:tox-einteg-testsAs with unit tests, you can also pass in various pytest arguments:tox-einteg-tests--your-argumentsLicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-pennylane-plugin
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The Amazon Braket PennyLane plugin offers four Amazon Braket quantum devices to work with PennyLane:braket.aws.qubitfor running with the Amazon Braket service’s quantum devices, both QPUs and simulatorsbraket.local.qubitfor running the Amazon Braket SDK’s local simulator where you can optionally specify the backend (“default”, “braket_sv”, “braket_dm” etc)braket.aws.ahsfor running with the Amazon Braket service’s analog Hamiltonian simulation QPUsbraket.local.ahsfor running analog Hamiltonian simulation on Amazon Braket SDK’s local simulatorTheAmazon Braket Python SDKis an open source
library that provides a framework to interact with quantum computing hardware
devices and simulators through Amazon Braket.PennyLaneis a machine learning library for optimization and automatic
differentiation of hybrid quantum-classical computations.The plugin documentation can be found here:https://amazon-braket-pennylane-plugin-python.readthedocs.io/en/latest/.FeaturesProvides four devices to be used with PennyLane:Two gate-based devices,braket.aws.qubitfor running on the Amazon Braket service,
andbraket.local.qubitfor running on the Amazon Braket SDK’s local simulator.Two analog Hamiltonian simulation devices,braket.aws.ahsfor running on QPU via the Amazon Braket service,
andbraket.local.ahsfor running on the Amazon Braket SDK’s local simulator.Combines Amazon Braket with PennyLane’s automatic differentiation and optimization.For the gate-based devices:Both devices support most core qubit PennyLane operations.All PennyLane observables are supported.Provides custom PennyLane operations to cover additional Braket operations:ISWAP,PSWAP, and many more.
Every custom operation supports analytic differentiation.For the analog Hamiltonian simulation devices:The devices supportParametrizedEvolutionoperators created via thePennyLane pulse programmingmodule.PennyLane observables in the measurement (Z) basis are supportedProvides translation of user-defined pulse level control to simulation and hardware implementationInstallationBefore you begin working with the Amazon Braket PennyLane Plugin, make sure
that you installed or configured the following prerequisites:Download and installPython 3.9or greater.
If you are using Windows, choose the optionAdd Python to environment variablesbefore you begin the installation.Make sure that your AWS account is onboarded to Amazon Braket, as per the instructionshere.Download and installPennyLane:pipinstallpennylaneYou can then install the latest release of the PennyLane-Braket plugin as follows:pipinstallamazon-braket-pennylane-pluginYou can also install the development version from source by cloning this repository and running a
pip install command in the root directory of the repository:gitclonehttps://github.com/amazon-braket/amazon-braket-pennylane-plugin-python.gitcdamazon-braket-pennylane-plugin-pythonpipinstall.You can check your currently installed version ofamazon-braket-pennylane-pluginwithpip show:pipshowamazon-braket-pennylane-pluginor alternatively from within Python:frombraketimportpennylane_pluginpennylane_plugin.__version__TestsMake sure to install test dependencies first:pipinstall-e"amazon-braket-pennylane-plugin-python[test]"Unit testsRun the unit tests using:tox-eunit-testsTo run an individual test:tox-eunit-tests---k'your_test'To run linters and unit tests:toxIntegration testsTo run the integration tests, set theAWS_PROFILEas explained in the amazon-braket-sdk-pythonREADME:exportAWS_PROFILE=Your_Profile_NameRunning the integration tests creates an S3 bucket in the same account as theAWS_PROFILEwith the following naming conventionamazon-braket-pennylane-plugin-integ-tests-{account_id}.Run the integration tests with:tox-einteg-testsTo run an individual integration test:tox-einteg-tests---k'your_test'DocumentationTo build the HTML documentation, run:tox-edocsThe documentation can then be found in thedoc/build/documentation/html/directory.ContributingWe welcome contributions - simply fork the repository of this plugin, and then make apull requestcontaining your contribution.
All contributers to this plugin will be listed as authors on the releases.We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects
or applications built with the plugin.SupportSource Code:https://github.com/amazon-braket/amazon-braket-pennylane-plugin-pythonIssue Tracker:https://github.com/amazon-braket/amazon-braket-pennylane-plugin-python/issuesGeneral Questions:https://quantumcomputing.stackexchange.com/questions/ask(add the tag amazon-braket)PennyLane Forum:https://discuss.pennylane.aiIf you are having issues, please let us know by posting the issue on our Github issue tracker, or
by asking a question in the forum.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-qrack-cuda-simulator
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Amazon Braket (SDK) Qrack SimulatorThis is an Amazon Braket SDK back end for the open-sourceunitaryfund/qracksimulator. (PyQrackprovides pure Python language bindings from C++ Qrack.) This simulator can be run locally, via either a (vendor-agnostic) OpenCL implementation or a (NVIDIA-specific) CUDA implementation. (CPU-only local simulation is also supported, when compiling and installing Qrack from source.) You can use the simulator to test quantum tasks that you construct for theAmazon Braket SDKbefore you submit them to the Amazon Braket service for execution.Setting up Amazon Braket Qrack Simulator PythonInstalling this package will install theAmazon Braket SDK, necessary to use the local simulator.
For manual installation of the SDK, follow the instructions in theREADMEfor setup.Checking the version of the Qrack simulatorYou can check your currently installed version ofamazon-braket-qrack-simulatorwithpip show:pipshowamazon-braket-qrack-simulatoror alternatively from within Python:>>> from braket import qrack_simulator
>>> qrack_simulator.__version__UsageThe quantum simulator implementationBraketQrackSimulatorplugs into theLocalSimulatorinterface inAmazon Braket SDK, with thebackendparameter as"qrack".Executing a circuit using the default simulatorfrombraket.circuitsimportCircuitfrombraket.devicesimportLocalSimulatordevice=LocalSimulator(backend="qrack")bell=Circuit().h(0).cnot(0,1)print(device.run(bell,shots=100).result().measurement_counts)DocumentationBraketQrackSimulatorfollows a subset of theAmazon Braket SDKdefault simulator input and output interfaces. Detailed documentation for the default simulator, including the API reference, can be found onRead the Docs.To generate the API Reference HTML in your local environmentFirst, install tox:pipinstalltoxTo generate the HTML, first change directories (cd) to position the cursor in theamazon-braket-qrack-simulator-pythondirectory. Then, run the following command to generate the HTML documentation files:tox-edocsTo view the generated documentation, open the following file in a browser:../amazon-braket-qrack-simulator-python/build/documentation/html/index.htmlTestingIf you want to contribute to the project, be sure to run unit tests and get a successful result
before you submit a pull request. To run the unit tests, first installpytest, if necessary:pipinstallpytestWith pytest installed, use this command in theamazon-braket-qrack-simulator-pythondirectory:pytest.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-qrack-simulator
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Amazon Braket (SDK) Qrack SimulatorThis is an Amazon Braket SDK back end for the open-sourceunitaryfund/qracksimulator. (PyQrackprovides pure Python language bindings from C++ Qrack.) This simulator can be run locally, via either a (vendor-agnostic) OpenCL implementation or a (NVIDIA-specific) CUDA implementation. (CPU-only local simulation is also supported, when compiling and installing Qrack from source.) You can use the simulator to test quantum tasks that you construct for theAmazon Braket SDKbefore you submit them to the Amazon Braket service for execution.Setting up Amazon Braket Qrack Simulator PythonInstalling this package will install theAmazon Braket SDK, necessary to use the local simulator.
For manual installation of the SDK, follow the instructions in theREADMEfor setup.Checking the version of the Qrack simulatorYou can check your currently installed version ofamazon-braket-qrack-simulatorwithpip show:pipshowamazon-braket-qrack-simulatoror alternatively from within Python:>>> from braket import qrack_simulator
>>> qrack_simulator.__version__UsageThe quantum simulator implementationBraketQrackSimulatorplugs into theLocalSimulatorinterface inAmazon Braket SDK, with thebackendparameter as"qrack".Executing a circuit using the default simulatorfrombraket.circuitsimportCircuitfrombraket.devicesimportLocalSimulatordevice=LocalSimulator(backend="qrack")bell=Circuit().h(0).cnot(0,1)print(device.run(bell,shots=100).result().measurement_counts)DocumentationBraketQrackSimulatorfollows a subset of theAmazon Braket SDKdefault simulator input and output interfaces. Detailed documentation for the default simulator, including the API reference, can be found onRead the Docs.To generate the API Reference HTML in your local environmentFirst, install tox:pipinstalltoxTo generate the HTML, first change directories (cd) to position the cursor in theamazon-braket-qrack-simulator-pythondirectory. Then, run the following command to generate the HTML documentation files:tox-edocsTo view the generated documentation, open the following file in a browser:../amazon-braket-qrack-simulator-python/build/documentation/html/index.htmlTestingIf you want to contribute to the project, be sure to run unit tests and get a successful result
before you submit a pull request. To run the unit tests, first installpytest, if necessary:pipinstallpytestWith pytest installed, use this command in theamazon-braket-qrack-simulator-pythondirectory:pytest.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-schemas
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Amazon Braket Python SchemasAmazon Braket Python Schemas is an open source library that contains the schemas for Braket, including:intermediate representations (IR) for Amazon Braket quantum tasks and offers serialization and deserialization of those IR payloads. Think of the IR as the contract between the Amazon Braket SDK and Amazon Braket API for quantum programs.schemas for the S3 results of each quantum taskschemas for the device capabilities of each deviceInstallationPrerequisitesPython 3.9+StepsThe preferred way to get Amazon Braket Python Schemas is by installing theAmazon Braket Python SDK, which will pull in the schemas.
Follow the instructions in theREADMEfor setup.However, if you only want to use the schemas, it can be installed on its own as follows:pipinstallamazon-braket-schemasYou can install from source by cloning this repository and running a pip install command in the root directory of the repository:gitclonehttps://github.com/amazon-braket/amazon-braket-schemas-python.gitcdamazon-braket-schemas-python
pipinstall.You can check your currently installed version ofamazon-braket-schemaswithpip show:pipshowamazon-braket-schemasor alternatively from within Python:>>> import braket._schemas as braket_schemas
>>> braket_schemas.__version__UsageOpenQASM (Open Quantum Assembly Language) is one type of IR. See below for its usage.Serializing python structuresfrombraket.ir.openqasmimportProgramasOpenQASMProgramprogram=OpenQASMProgram(source="OPENQASM 3.0; cnot $0, $1;")print(program.json(indent=2))"""{"braketSchemaHeader": {"name": "braket.ir.openqasm.program","version": "1"},"source": "OPENQASM 3.0; cnot $0, $1;","inputs": null}"""Deserializing into python structuresfrombraket.ir.openqasmimportProgramasOpenQASMProgramopenqasm_string="""{"braketSchemaHeader": {"name": "braket.ir.openqasm.program","version": "1"},"source": "OPENQASM 3.0; cnot $0, $1;"}"""program=OpenQASMProgram.parse_raw(openqasm_string)print(program)"""braketSchemaHeader=BraketSchemaHeader(name='braket.ir.openqasm.program', version='1') source='OPENQASM 3.0; cnot $0, $1;' inputs=None"""DocumentationDetailed documentation, including the API reference, can be found onRead the Docs.You can also generate the docs from source. First, install tox:pipinstalltoxTo build the Sphinx docs, run the following command in the root repo directory:tox-edocsYou can then find the generated HTML files inbuild/documentation/html.TestingMake sure to install test dependencies first:pipinstall-e"amazon-braket-schemas-python[test]"To run the unit tests:tox-eunit-testsYou can also pass in various pytest arguments to run selected tests:tox-eunit-tests--your-argumentsTo run linters and doc generators and unit tests:toxFor more information, please seepytest usage.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-sdk
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Amazon Braket Python SDKThe Amazon Braket Python SDK is an open source library that provides a framework that you can use to interact with quantum computing hardware devices through Amazon Braket.PrerequisitesBefore you begin working with the Amazon Braket SDK, make sure that you've installed or configured the following prerequisites.Python 3.9 or greaterDownload and install Python 3.9 or greater fromPython.org.GitInstall Git fromhttps://git-scm.com/downloads. Installation instructions are provided on the download page.IAM user or role with required permissionsAs a managed service, Amazon Braket performs operations on your behalf on the AWS hardware that is managed by Amazon Braket. Amazon Braket can perform only operations that the user permits. You can read more about which permissions are necessary in the AWS Documentation.The Braket Python SDK should not require any additional permissions aside from what is required for using Braket. However, if you are using an IAM role with a path in it, you should grant permission for iam:GetRole.To learn more about IAM user, roles, and policies, seeAdding and Removing IAM Identity Permissions.Boto3 and setting up AWS credentialsFollow the installationinstructionsfor Boto3 and setting up AWS credentials.Note:Make sure that your AWS region is set to one supported by Amazon Braket. You can check this in your AWS configuration file, which is located by default at~/.aws/config.Configure your AWS account with the resources necessary for Amazon BraketIf you are new to Amazon Braket, onboard to the service and create the resources necessary to use Amazon Braket using theAWS console.Installing the Amazon Braket Python SDKThe Amazon Braket Python SDK can be installed with pip as follows:pipinstallamazon-braket-sdkYou can also install from source by cloning this repository and running a pip install command in the root directory of the repository:gitclonehttps://github.com/amazon-braket/amazon-braket-sdk-python.gitcdamazon-braket-sdk-python
pipinstall.Check the version you have installedYou can view the version of the amazon-braket-sdk you have installed by using the following command:pipshowamazon-braket-sdkYou can also check your version ofamazon-braket-sdkfrom within Python:>>> import braket._sdk as braket_sdk
>>> braket_sdk.__version__Updating the Amazon Braket Python SDKYou can update the version of the amazon-braket-sdk you have installed by using the following command:pipinstallamazon-braket-sdk--upgrade--upgrade-strategyeagerUsageRunning a circuit on an AWS simulatorimportboto3frombraket.awsimportAwsDevicefrombraket.circuitsimportCircuitdevice=AwsDevice("arn:aws:braket:::device/quantum-simulator/amazon/sv1")bell=Circuit().h(0).cnot(0,1)task=device.run(bell,shots=100)print(task.result().measurement_counts)The code sample imports the Amazon Braket framework, then defines the device to use (the SV1 AWS simulator). It then creates a Bell Pair circuit, executes the circuit on the simulator and prints the results of the hybrid job. This example can be found in../examples/bell.py.Running multiple quantum tasks at onceMany quantum algorithms need to run multiple independent circuits, and submitting the circuits in parallel can be faster than submitting them one at a time. In particular, parallel quantum task processing provides a significant speed up when using simulator devices. The following example shows how to run a batch of quantum tasks on SV1:circuits=[bellfor_inrange(5)]batch=device.run_batch(circuits,shots=100)print(batch.results()[0].measurement_counts)# The result of the first quantum task in the batchRunning a hybrid jobfrombraket.awsimportAwsQuantumJobjob=AwsQuantumJob.create(device="arn:aws:braket:::device/quantum-simulator/amazon/sv1",source_module="job.py",entry_point="job:run_job",wait_until_complete=True,)print(job.result())whererun_jobis a function in the filejob.py.The code sample imports the Amazon Braket framework, then creates a hybrid job with the entry point being therun_jobfunction. The hybrid job creates quantum tasks against the SV1 AWS Simulator. The hybrid job runs synchronously, and prints logs until it completes. The complete example can be found in../examples/job.py.Available SimulatorsAmazon Braket provides access to two types of simulators: fully managed simulators, available through the Amazon Braket service, and the local simulators that are part of the Amazon Braket SDK.Fully managed simulators offer high-performance circuit simulations. These simulators can handle circuits larger than circuits that run on quantum hardware. For example, the SV1 state vector simulator shown in the previous examples requires approximately 1 or 2 hours to complete a 34-qubit, dense, and square circuit (circuit depth = 34), depending on the type of gates used and other factors.The Amazon Braket Python SDK includes an implementation of quantum simulators that can run circuits on your local, classic hardware. For example the braket_sv local simulator is well suited for rapid prototyping on small circuits up to 25 qubits, depending on the hardware specifications of your Braket notebook instance or your local environment. An example of how to execute the quantum task locally is included in the repository../examples/local_bell.py.For a list of available simulators and their features, consult theAmazon Braket Developer Guide.Debugging logsQuantum tasks sent to QPUs don't always run right away. To view quantum task status, you can enable debugging logs. An example of how to enable these logs is included in repo:../examples/debug_bell.py. This example enables quantum task logging so that status updates are continuously printed to the terminal after a quantum task is executed. The logs can also be configured to save to a file or output to another stream. You can use the debugging example to get information on the quantum tasks you submit, such as the current status, so that you know when your quantum task completes.Running a Quantum Algorithm on a Quantum ComputerWith Amazon Braket, you can run your quantum circuit on a physical quantum computer.The following example executes the same Bell Pair example described to validate your configuration on a Rigetti quantum computer.importboto3frombraket.circuitsimportCircuitfrombraket.awsimportAwsDevicedevice=AwsDevice("arn:aws:braket:::device/qpu/rigetti/Aspen-8")bell=Circuit().h(0).cnot(0,1)task=device.run(bell)print(task.result().measurement_counts)When you execute your task, Amazon Braket polls for a result. By default, Braket polls for 5 days; however, it is possible to change this by modifying thepoll_timeout_secondsparameter inAwsDevice.run, as in the example below. Keep in mind that if your polling timeout is too short, results may not be returned within the polling time, such as when a QPU is unavailable, and a local timeout error is returned. You can always restart the polling by usingtask.result().task=device.run(bell,poll_timeout_seconds=86400)# 1 dayprint(task.result().measurement_counts)To select a quantum hardware device, specify its ARN as the value of thedevice_arnargument. A list of available quantum devices and their features can be found in theAmazon Braket Developer Guide.ImportantQuantum tasks may not run immediately on the QPU. The QPUs only execute quantum tasks during execution windows. To find their execution windows, please refer to theAWS consolein the "Devices" tab.Sample NotebooksSample Jupyter notebooks can be found in theamazon-braket-examplesrepo.Braket Python SDK API Reference DocumentationThe API reference, can be found onRead the Docs.To generate the API Reference HTML in your local environmentTo generate the HTML, first change directories (cd) to position the cursor in theamazon-braket-sdk-pythondirectory. Then, run the following command to generate the HTML documentation files:pipinstalltox
tox-edocsTo view the generated documentation, open the following file in a browser:../amazon-braket-sdk-python/build/documentation/html/index.htmlTestingThis repository has both unit and integration tests.To run the tests, make sure to install test dependencies first:pipinstall-e"amazon-braket-sdk-python[test]"Unit TestsTo run the unit tests:tox-eunit-testsYou can also pass in various pytest arguments to run selected tests:tox-eunit-tests--your-argumentsFor more information, please seepytest usage.To run linters and doc generators and unit tests:toxIntegration TestsFirst, configure a profile to use your account to interact with AWS. To learn more, seeConfigure AWS CLI.After you create a profile, use the following command to set theAWS_PROFILEso that all future commands can access your AWS account and resources.exportAWS_PROFILE=YOUR_PROFILE_NAMETo run the integration tests for local hybrid jobs, you need to have Docker installed and running. To install Docker follow these instructions:Install DockerRun the tests:tox-einteg-testsAs with unit tests, you can also pass in various pytest arguments:tox-einteg-tests--your-argumentsSupportIssues and Bug ReportsIf you encounter bugs or face issues while using the SDK, please let us know by posting
the issue on ourGithub issue tracker.For other issues or general questions, please ask on theQuantum Computing Stack Exchange.Feedback and Feature RequestsIf you have feedback or features that you would like to see on Amazon Braket, we would love to hear from you!Github issuesis our preferred mechanism for collecting feedback and feature requests, allowing other users
to engage in the conversation, and +1 issues to help drive priority.Code contributorsLicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-sdk-kshitijc
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Amazon Braket Python SDKThe Amazon Braket Python SDK is an open source library that provides a framework that you can use to interact with quantum computing hardware devices through Amazon Braket.PrerequisitesBefore you begin working with the Amazon Braket SDK, make sure that you've installed or configured the following prerequisites.Python 3.7.2 or greaterDownload and install Python 3.7.2 or greater fromPython.org.GitInstall Git fromhttps://git-scm.com/downloads. Installation instructions are provided on the download page.IAM user or role with required permissionsAs a managed service, Amazon Braket performs operations on your behalf on the AWS hardware that is managed by Amazon Braket. Amazon Braket can perform only operations that the user permits. You can read more about which permissions are necessary in the AWS Documentation.The Braket Python SDK should not require any additional permissions aside from what is required for using Braket. However, if you are using an IAM role with a path in it, you should grant permission for iam:GetRole.To learn more about IAM user, roles, and policies, seeAdding and Removing IAM Identity Permissions.Boto3 and setting up AWS credentialsFollow the installationinstructionsfor Boto3 and setting up AWS credentials.Note:Make sure that your AWS region is set to one supported by Amazon Braket. You can check this in your AWS configuration file, which is located by default at~/.aws/config.Configure your AWS account with the resources necessary for Amazon BraketIf you are new to Amazon Braket, onboard to the service and create the resources necessary to use Amazon Braket using theAWS console.Installing the Amazon Braket Python SDKThe Amazon Braket Python SDK can be installed with pip as follows:pipinstallamazon-braket-sdkYou can also install from source by cloning this repository and running a pip install command in the root directory of the repository:gitclonehttps://github.com/aws/amazon-braket-sdk-python.gitcdamazon-braket-sdk-python
pipinstall.Check the version you have installedYou can view the version of the amazon-braket-sdk you have installed by using the following command:pipshowamazon-braket-sdkYou can also check your version ofamazon-braket-sdkfrom within Python:>>> import braket._sdk as braket_sdk
>>> braket_sdk.__version__Updating the Amazon Braket Python SDKYou can update the version of the amazon-braket-sdk you have installed by using the following command:pipinstallamazon-braket-sdk--upgrade--upgrade-strategyeagerUsageRunning a circuit on an AWS simulatorimportboto3frombraket.awsimportAwsDevicefrombraket.circuitsimportCircuitdevice=AwsDevice("arn:aws:braket:::device/quantum-simulator/amazon/sv1")bell=Circuit().h(0).cnot(0,1)task=device.run(bell,shots=100)print(task.result().measurement_counts)The code sample imports the Amazon Braket framework, then defines the device to use (the SV1 AWS simulator). It then creates a Bell Pair circuit, executes the circuit on the simulator and prints the results of the job. This example can be found in../examples/bell.py.Running multiple tasks at onceMany quantum algorithms need to run multiple independent circuits, and submitting the circuits in parallel can be faster than submitting them one at a time. In particular, parallel task processing provides a significant speed up when using simulator devices. The following example shows how to run a batch of tasks on SV1:circuits=[bellfor_inrange(5)]batch=device.run_batch(circuits,shots=100)print(batch.results()[0].measurement_counts)# The result of the first task in the batchRunning a hybrid jobfrombraket.awsimportAwsQuantumJobjob=AwsQuantumJob.create(device="arn:aws:braket:::device/quantum-simulator/amazon/sv1",source_module="job.py",entry_point="job:run_job",wait_until_complete=True,)print(job.result())whererun_jobis a function in the filejob.py.The code sample imports the Amazon Braket framework, then creates a hybrid job with the entry point being therun_jobfunction. The hybrid job creates quantum tasks against the SV1 AWS Simulator. The job runs synchronously, and prints logs until it completes. The complete example can be found in../examples/job.py.Available SimulatorsAmazon Braket provides access to two types of simulators: fully managed simulators, available through the Amazon Braket service, and the local simulators that are part of the Amazon Braket SDK.Fully managed simulators offer high-performance circuit simulations. These simulators can handle circuits larger than circuits that run on quantum hardware. For example, the SV1 state vector simulator shown in the previous examples requires approximately 1 or 2 hours to complete a 34-qubit, dense, and square circuit (circuit depth = 34), depending on the type of gates used and other factors.The Amazon Braket Python SDK includes an implementation of quantum simulators that can run circuits on your local, classic hardware. For example the braket_sv local simulator is well suited for rapid prototyping on small circuits up to 25 qubits, depending on the hardware specifications of your Braket notebook instance or your local environment. An example of how to execute the task locally is included in the repository../examples/local_bell.py.For a list of available simulators and their features, consult theAmazon Braket Developer Guide.Debugging logsTasks sent to QPUs don't always run right away. To view task status, you can enable debugging logs. An example of how to enable these logs is included in repo:../examples/debug_bell.py. This example enables task logging so that status updates are continuously printed to the terminal after a quantum task is executed. The logs can also be configured to save to a file or output to another stream. You can use the debugging example to get information on the tasks you submit, such as the current status, so that you know when your task completes.Running a Quantum Algorithm on a Quantum ComputerWith Amazon Braket, you can run your quantum circuit on a physical quantum computer.The following example executes the same Bell Pair example described to validate your configuration on a Rigetti quantum computer.importboto3frombraket.circuitsimportCircuitfrombraket.awsimportAwsDevicedevice=AwsDevice("arn:aws:braket:::device/qpu/rigetti/Aspen-8")bell=Circuit().h(0).cnot(0,1)task=device.run(bell)print(task.result().measurement_counts)When you execute your task, Amazon Braket polls for a result. By default, Braket polls for 5 days; however, it is possible to change this by modifying thepoll_timeout_secondsparameter inAwsDevice.run, as in the example below. Keep in mind that if your polling timeout is too short, results may not be returned within the polling time, such as when a QPU is unavailable, and a local timeout error is returned. You can always restart the polling by usingtask.result().task=device.run(bell,poll_timeout_seconds=86400)# 1 dayprint(task.result().measurement_counts)To select a quantum hardware device, specify its ARN as the value of thedevice_arnargument. A list of available quantum devices and their features can be found in theAmazon Braket Developer Guide.ImportantTasks may not run immediately on the QPU. The QPUs only execute tasks during execution windows. To find their execution windows, please refer to theAWS consolein the "Devices" tab.Sample NotebooksSample Jupyter notebooks can be found in theamazon-braket-examplesrepo.Braket Python SDK API Reference DocumentationThe API reference, can be found onRead the Docs.To generate the API Reference HTML in your local environmentTo generate the HTML, first change directories (cd) to position the cursor in theamazon-braket-sdk-pythondirectory. Then, run the following command to generate the HTML documentation files:pipinstalltox
tox-edocsTo view the generated documentation, open the following file in a browser:../amazon-braket-sdk-python/build/documentation/html/index.htmlTestingThis repository has both unit and integration tests.To run the tests, make sure to install test dependencies first:pipinstall-e"amazon-braket-sdk-python[test]"Unit TestsTo run the unit tests:tox-eunit-testsYou can also pass in various pytest arguments to run selected tests:tox-eunit-tests--your-argumentsFor more information, please seepytest usage.To run linters and doc generators and unit tests:toxIntegration TestsFirst, configure a profile to use your account to interact with AWS. To learn more, seeConfigure AWS CLI.After you create a profile, use the following command to set theAWS_PROFILEso that all future commands can access your AWS account and resources.exportAWS_PROFILE=YOUR_PROFILE_NAMETo run the integration tests for local jobs, you need to have Docker installed and running. To install Docker follow these instructions:Install DockerRun the tests:tox-einteg-testsAs with unit tests, you can also pass in various pytest arguments:tox-einteg-tests--your-argumentsSupportIssues and Bug ReportsIf you encounter bugs or face issues while using the SDK, please let us know by posting
the issue on ourGithub issue tracker.For issues with the Amazon Braket service in general, please use theDeveloper Forum.Feedback and Feature RequestsIf you have feedback or features that you would like to see on Amazon Braket, we would love to hear from you!Github issuesis our preferred mechanism for collecting feedback and feature requests, allowing other users
to engage in the conversation, and +1 issues to help drive priority.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-braket-strawberryfields-plugin
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This plugin provides aBraketEngineclass for running photonic quantum circuits created in Strawberry Fields on the Amazon Braket service.TheAmazon Braket Python SDKis an open source
library that provides a framework to interact with quantum computing hardware
devices and simulators through Amazon Braket.Strawberry Fieldsis an open source library for writing
and running programs for photonic quantum computers.The plugin documentation can be found here:https://amazon-braket-strawberryfields-plugin-python.readthedocs.io/en/latest/.FeaturesThis plugin provides the classesBraketEnginefor submitting photonic circuits to Amazon Braket andBraketJobfor tracking the status of the Braket task.BraketEngineandBraketJobhave the same interfaces asRemoteEnginein Strawberry Fields andJobin the Xanadu Cloud Client, respectively, and can be used as drop-in replacements:frombraket.strawberryfields_pluginimportBraketEngineeng=BraketEngine("arn:aws:braket:us-east-1::device/qpu/xanadu/Borealis")result=eng.run(prog,shots=1000)# Synchronous, returns sf.Resultjob=eng.run_async(prog,shots=1000)# Asychronous, returns BraketJobprint(job.status)InstallationBefore you begin working with the Amazon Braket Strawberry Fields Plugin, make sure
that you installed or configured the following prerequisites:Download and installPython 3.7.2or greater.
If you are using Windows, choose the optionAdd Python to environment variablesbefore you begin the installation.Make sure that your AWS account is onboarded to Amazon Braket, as per the instructionshere.Download and installStrawberry Fields:pipinstallstrawberryfieldsYou can then install the latest release of the Strawberry Fields-Braket plugin as follows:pipinstallamazon-braket-strawberryfields-pluginYou can also install the development version from source by cloning this repository and running a
pip install command in the root directory of the repository:gitclonehttps://github.com/aws/amazon-braket-strawberryfields-plugin-python.gitcdamazon-braket-strawberryfields-plugin-pythonpipinstall.You can check your currently installed version ofamazon-braket-strawberryfields-pluginwithpip show:pipshowamazon-braket-strawberryfields-pluginor alternatively from within Python:frombraketimportstrawberryfields_pluginstrawberryfields_plugin.__version__TestsMake sure to install test dependencies first:pipinstall-e"amazon-braket-strawberryfields-plugin-python[test]"Unit testsRun the unit tests using:tox-eunit-testsTo run an individual test:tox-eunit-tests---k'your_test'To run linters and unit tests:toxIntegration testsTo run the integration tests, set theAWS_PROFILEas explained in the amazon-braket-sdk-pythonREADME:exportAWS_PROFILE=Your_Profile_NameRun the integration tests with:tox-einteg-testsTo run an individual integration test:tox-einteg-tests---k'your_test'DocumentationTo build the HTML documentation, run:tox-edocsThe documentation can then be found in thedoc/build/documentation/html/directory.ContributingWe welcome contributions - simply fork the repository of this plugin, and then make apull requestcontaining your contribution.
All contributers to this plugin will be listed as authors on the releases.We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects
or applications built with the plugin.SupportSource Code:https://github.com/aws/amazon-braket-strawberryfields-plugin-pythonIssue Tracker:https://github.com/aws/amazon-braket-strawberryfields-plugin-python/issuesGeneral Questions:https://quantumcomputing.stackexchange.com/questions/ask(add the tag amazon-braket)Strawberry Fields Forum:https://discuss.strawberryfields.aiIf you are having issues, please let us know by posting the issue on our Github issue tracker, or
by asking a question in the forum.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-buddy
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amazon_buddyDescriptionAmazon scraper.Installpipinstallamazon_buddy# orpip3installamazon_buddyUsagefromamazon_buddyimportProduct,AmazonBuddy,Category,SortTypeab=AmazonBuddy(debug=True,user_agent='ADD_USER_AGENT')# # products = ab.search_products(# # 'face wash',# # sort_type=SortType.PRICE_HIGH_TO_LOW,# # min_price=0,# # category=Category.BEAUTY_AND_PERSONAL_CARE,# # max_results=20# # )# # print(len(products))asin='B07RZV9LB7'# # reviews = ab.get_reviews(asin=asin)# # print(reviews)ab.get_product_details(asin).save('{}.json'.format(asin))# ab.get_product_details(asin).jsonprint()# Product.load('/Users/kristofk/github/py_amazon_buddy/B0758GYJK2.json').jsonprint()
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amazoncaptcha
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The motivation behind the creation of this library is taking its start from the genuinely simple idea: "I don't want to use pytesseract or some other non-amazon-specific OCR services, nor do I want to install some executables to just solve a captcha. I desire to get a solution with 2 lines of code without any heavy add-ons, using a pure Python."Pure Python, lightweight,Pillow-based solver forAmazon's text captcha.Recent NewsMay 5, 2023: tested and approved compatibility with Pillow 9.5.0January 25, 2022: tested and approved compatibility with Python 3.10January 25, 2022: dropped support for Python 3.6InstallationYou can simply install the library fromPyPiusingpip. For more methods check thedocs.pipinstallamazoncaptchaQuick SnippetAn example of the constructor usage. Scroll a bit down to see some tasty class methods.For consistency across different devices, it is highly recommended to usefromlinkclass method.fromamazoncaptchaimportAmazonCaptchacaptcha=AmazonCaptcha('captcha.jpg')solution=captcha.solve()# Or: solution = AmazonCaptcha('captcha.jpg').solve()StatusUsage and Class MethodsBrowsing Amazon usingseleniumand stuck on captcha? The class method below will do all the dirty work of extracting an image from the webpage for you. Practically, it takes a screenshot from your webdriver, crops the captcha and stores it into bytes array which is then used to create anAmazonCaptchainstance. This also means avoiding any local savings.For consistency across different devices, it is highly recommended to usefromlinkclass method instead offromdriver.fromamazoncaptchaimportAmazonCaptchafromseleniumimportwebdriverdriver=webdriver.Chrome()# This is a simplified exampledriver.get('https://www.amazon.com/errors/validateCaptcha')captcha=AmazonCaptcha.fromdriver(driver)solution=captcha.solve()If you are not usingseleniumor the previous method is not just the case for you, it is possible to use a captcha link directly. This class method will request the url, check the content type and store the response content into bytes array to create an instance ofAmazonCaptcha.fromamazoncaptchaimportAmazonCaptchalink='https://images-na.ssl-images-amazon.com/captcha/usvmgloq/Captcha_kwrrnqwkph.jpg'captcha=AmazonCaptcha.fromlink(link)solution=captcha.solve()In addition, if you are a machine learning or neural network developer and are looking for some training data, checkthisrepository, which was created to store images and other non-script data for the solver.Help the DevelopmentIf you are willing to help the development, consider settingkeep_logsargument of thesolvemethod toTrue. Here is the example, if you are usingfromdriverclass method. If set toTrue, all the links of the unsolved captcha will be stored so that later you canopen the issue and send the logs.fromamazoncaptchaimportAmazonCaptchafromseleniumimportwebdriverdriver=webdriver.Chrome()# This is a simplified exampledriver.get('https://www.amazon.com/errors/validateCaptcha')captcha=AmazonCaptcha.fromdriver(driver)solution=captcha.solve(keep_logs=True)If you have any suggestions or ideas of additional instances and methods, which you would like to see in this library, please, feel free to contact the owner via email or fork'n'pull to repository. Any contribution is highly appreciated!AdditionalIf you want to see theHistory of Changes,Code of Conduct,Contributing Policy, orLicense, use these inline links to navigate based on your needs.If you are facing any errors, please, report your situation via an issue.This project is for educational and research purposes only. Any actions and/or activities related to the material contained on this GitHub Repository is solely your responsibility. The author will not be held responsible in the event any criminal charges be brought against any individuals misusing the information in this GitHub Repository to break the law.Amazon is the registered trademark of Amazon.com, Inc. Amazon name used in this project is for identification purposes only. The project is not associated in any way with Amazon.com, Inc. and is not an official solution of Amazon.com, Inc.
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amazon-captcha-0.1
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The motivation behind the creation of this library is taking its start from the genuinely simple idea: "I don't want to use pytesseract or some other non-amazon-specific OCR services, nor do I want to install some executables to just solve a captcha. I desire to get a solution with 2 lines of code without any heavy add-ons, using a pure Python."Pure Python, lightweight,Pillow-based solver forAmazon's text captcha.Recent NewsJanuary 19, 2023: tested and approved compatibility with Pillow 9.4.0January 25, 2022: tested and approved compatibility with Python 3.10January 25, 2022: dropped support for Python 3.6InstallationYou can simply install the library fromPyPiusingpip. For more methods check thedocs.pipinstallamazoncaptchaQuick SnippetAn example of the constructor usage. Scroll a bit down to see some tasty class methods.For consistency across different devices, it is highly recommended to usefromlinkclass method.fromamazoncaptchaimportAmazonCaptchacaptcha=AmazonCaptcha('captcha.jpg')solution=captcha.solve()# Or: solution = AmazonCaptcha('captcha.jpg').solve()StatusUsage and Class MethodsBrowsing Amazon usingseleniumand stuck on captcha? The class method below will do all the dirty work of extracting an image from the webpage for you. Practically, it takes a screenshot from your webdriver, crops the captcha and stores it into bytes array which is then used to create anAmazonCaptchainstance. This also means avoiding any local savings.For consistency across different devices, it is highly recommended to usefromlinkclass method instead offromdriver.fromamazoncaptchaimportAmazonCaptchafromseleniumimportwebdriverdriver=webdriver.Chrome()# This is a simplified exampledriver.get('https://www.amazon.com/errors/validateCaptcha')captcha=AmazonCaptcha.fromdriver(driver)solution=captcha.solve()If you are not usingseleniumor the previous method is not just the case for you, it is possible to use a captcha link directly. This class method will request the url, check the content type and store the response content into bytes array to create an instance ofAmazonCaptcha.fromamazoncaptchaimportAmazonCaptchalink='https://images-na.ssl-images-amazon.com/captcha/usvmgloq/Captcha_kwrrnqwkph.jpg'captcha=AmazonCaptcha.fromlink(link)solution=captcha.solve()In addition, if you are a machine learning or neural network developer and are looking for some training data, checkthisrepository, which was created to store images and other non-script data for the solver.Help the DevelopmentIf you are willing to help the development, consider settingkeep_logsargument of thesolvemethod toTrue. Here is the example, if you are usingfromdriverclass method. If set toTrue, all the links of the unsolved captcha will be stored so that later you canopen the issue and send the logs.fromamazoncaptchaimportAmazonCaptchafromseleniumimportwebdriverdriver=webdriver.Chrome()# This is a simplified exampledriver.get('https://www.amazon.com/errors/validateCaptcha')captcha=AmazonCaptcha.fromdriver(driver)solution=captcha.solve(keep_logs=True)If you have any suggestions or ideas of additional instances and methods, which you would like to see in this library, please, feel free to contact the owner via email or fork'n'pull to repository. Any contribution is highly appreciated!AdditionalIf you want to see theHistory of Changes,Code of Conduct,Contributing Policy, orLicense, use these inline links to navigate based on your needs.If you are facing any errors, please, report your situation via an issue.This project is for educational and research purposes only. Any actions and/or activities related to the material contained on this GitHub Repository is solely your responsibility. The author will not be held responsible in the event any criminal charges be brought against any individuals misusing the information in this GitHub Repository to break the law.Amazon is the registered trademark of Amazon.com, Inc. Amazon name used in this project is for identification purposes only. The project is not associated in any way with Amazon.com, Inc. and is not an official solution of Amazon.com, Inc.
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amazoncaptcha-api
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DescriptionSimpleanti-captcha.comcompatable API (createTask/getTaskResult) to solve the Amazon's text captcha for free.InstallationUse the package managerpipto install amazoncaptcha-apipip3installamazoncaptcha-apiorgitclonehttps://github.com/melehin/amazoncaptcha-apicdamazoncaptcha-api
pip3install.RunUnder flask development server (bind local ip)python3-mamazoncaptcha_apiUnder supervisor and gunicorn (bind external ip)Put this snippet to/etc/supervisor/conf.d/amazoncaptcha_api.conf[program:amazoncaptcha_api]
directory=/tmp/
command=gunicorn amazoncaptcha_api:app -b 0.0.0.0:5000
autostart=true
autorestart=true
stderr_logfile=/var/log/amazoncaptcha_api.err.log
stdout_logfile=/var/log/amazoncaptcha_api.out.logAnd runsupervisorctlreloadContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
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amazon-codeguru-jupyterlab-extension
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Amazon CodeGuru JupyterLab ExtensionAmazon CodeGuru extension forJupyterLabandSageMaker Studio.
This extension runs scans on your notebook files and provides security recommendations and quality improvements to your code.RequirementsJupyterLab >= 3.0Installpipinstallamazon_codeguru_jupyterlab_extensionUninstallpipuninstallamazon_codeguru_jupyterlab_extensionDevelopmentPrerequisitesEnsure the following dependencies are available in your environment.Python >= 3.8JupyterLab >= 3.0NodeJS >= 18Alternatively, you can create a conda virtual environment with the following commands:condaenvupdate--filebinder/environment.yml
condaactivateamazon-codeguru-extension-demoManual SetupInstall the Python package in development mode.pipinstall-e.Link the extension with JupyterLab.jupyterlabextensiondevelop.--overwriteBuild the Typescript source.jlpmbuild# orjlpmwatch# automatically rebuild changesStart the JupyterLab serverjupyterlabQuick SetupRun the following command to quickly build and install the extension.python3binder/postBuildReleaseThis extension can be distributed as a Python package.
First, install build dependencies:pipinstallbuildtwinehatchBump the version usinghatch. By default this will create a tag.hatchversion<new-version>To generate a new Python source package (.tar.gz) and the binary package (.whl) in thedist/directory, run the following command:python-mbuildThen to upload the package to PyPI, run the following command:twineuploaddist/*SecuritySeeSECURITYfor more information.LicenseThis project is licensed under the Apache-2.0 License.
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amazon-codewhisperer-jupyterlab-ext
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Amazon CodeWhisperer for JupyterLabAmazon CodeWhisperer is an AI coding companion which provides developers with real-time code suggestions in JupyterLab. Individual developers can use CodeWhisperer for free in JupyterLab and AWS SageMaker Studio.RequirementsIn order to use CodeWhisperer in JupyterLab, you must have a version of JupyterLab >= 4 installed. The previous major version of CodeWhisperer 1.x extension supports JupyterLab >= 3.5, <4. You will also need a freeAWS Builder IDaccount to access CodeWhisperer. (You can set that up the first time you log in.)In order to use CodeWhisperer in SageMaker Studio, you must have set up a SageMaker Studio notebook instance, along with an execution role with the appropriate IAM Permissions.Getting StartedInstallJupyterLabon your computer or if you already have JupyterLab installed, check it’s version by running the following command.pip show jupyterlabNote the version in the response, and follow the use the corresponding directions in one of the following sections.Installation Using Pip for Jupyter Lab version >= 4.0You can install and enable the CodeWhisperer extension for JupyterLab 4 with the following commands.# JupyterLab 4
pip install amazon-codewhisperer-jupyterlab-extInstallation Using Pip for Jupyter Lab version >= 3.6 and < 4.0You can install and enable the CodeWhisperer 1.x extension for JupyterLab 3 with the following commands.# JupyterLab 3
pip install amazon-codewhisperer-jupyterlab-ext~=1.0
jupyter server extension enable amazon_codewhisperer_jupyterlab_extOnce installed, chooseStart CodeWhispererfrom the CodeWhisperer panel at the bottom of the window. This will enable to you log in toAWS Builder IDto access CodeWhisperer. Refer toSetting up CodeWhisperer with JupyterLabfor detailed setup instructions.SageMaker StudioTo setup the CodeWhisperer extension with a SageMaker Studio notebook instance, you must add IAM Permissions forcodewhisperer:GenerateRecommendationsfor your user profile. Then you must install and enable the extension with the following commands.conda activate studio
pip install amazon-codewhisperer-jupyterlab-ext~=1.0
jupyter server extension enable amazon_codewhisperer_jupyterlab_ext
conda deactivate
restart-jupyter-serverAfter you complete installation and refresh your browser, a CodeWhisperer panel will appear at the bottom of the window. Refer toSetting up CodeWhisperer with SageMaker Studiofor detailed setup instructions.FeaturesCode CompletionCodeWhisperer for JupyterLab provides AI powered suggestions as ghost text with the following default keybindings. These can be modified in the settings.ActionKey BindingManually trigger CodeWhispererAlt C (Window) / ⌥ C (Mac)Accept a recommendationTabNext recommendationRight arrowPrevious recommendationLeft arrowReject a recommendationESCPython is the only supported programming language for now. Users can start or pause suggestions by toggling the menu item in the CodeWhisperer panel that will appear at the bottom of the window.Code ReferencesWith the reference log, you can view references to code recommendations. You can also update and edit code recommendations suggested by CodeWhisperer.To view Code References for accepted suggestions, chooseOpen Code Reference Logfrom the CodeWhisperer panel at the bottom of the window. Users can also turn off code suggestions with code references in Settings.More ResourcesCodeWhisperer User GuideSetting up Amazon CodeWhisperer with JupyterLabSetting up CodeWhisperer with Amazon SageMaker StudioChange Log2.0.1Improved handling when Jupyter has no access to internet.Migrated network call to be made asynchronously.2.0.0Initial release - Adoption of JupyterLab 4
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amazon-connect-decryption-lib
|
RequiredPython 3.8+https://github.com/aws/aws-encryption-sdk-python/boto3Install$ pip install amazon-connect-decryption-libUsagefromamazon_connect_decryption_libimportdecryptssm_parameter_name='CONNECT_INPUT_DECRYPTION_KEY'encrypted_text='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'plain_text=decrypt(ssm_parameter_name,encrypted_text)print(plain_text)# output is 112344567789Usage with AWS Lambdafromamazon_connect_decryption_libimportdecryptdeflambda_handler(event,context):ssm_parameter_name='CONNECT_INPUT_DECRYPTION_KEY'encrypted_text=event['Details']['ContactData']['Attributes']['EncryptedNumber']# EncryptedNumber is your contact attribute name.plain_text=decrypt(ssm_parameter_name,encrypted_text)print(plain_text)return{'DecryptResult':plain_text}Test(for Developer)$ python -m unittest
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amazon-dash
|
Amazon-dash is still aliveAmazon has abandoned the Amazon-dash buttons, but this project wants to keep them alive. However, we are looking
for alternatives. If you have suggestions you are welcome to open an incident. All suggestions are welcome.
You can open an issue with your suggestions.Python Amazon DashHack your Amazon Dash to run what you want. Without welders. For the entire family.This program written in Python runs in daemon mode waiting for someone in the same
network to press a configured Amazon Dash button. It is not necessary to know
programming to use this program. Amazon-Dash executescommands by command line,
calls a url and more. This program works well on aRaspberry PIor on computers
with few resources.InstallAmazon Dash:$sudopip3installamazon-dash# and after:$sudopython3-mamazon_dash.installAlso available onAURandFreeNAS. You can also usepip2andpython2if your system only has Python2, but Python 3 is the recommended version. See other installation
methodsin the documentation.Note:It may also be necessary to installtcpdumpon your system (in Debianapt install tcpdump).Usediscovery modeto know the mac of your Dash(Run the program, and then press any button):$sudoamazon-dashdiscoveryEditconfig file(/etc/amazon-dash.yml):# amazon-dash.yml# ---------------settings:delay:10devices:0C:47:C9:98:4A:12:# Command examplename:Herouser:nekmocmd:spotifyAC:63:BE:75:1B:6F:# SSH examplename:Tassimocmd:door --openssh:192.168.1.23:2222AC:63:BE:67:B2:F1:# Url Webhook examplename:Kit Katurl:'http://domain.com/path/to/webhook'method:postcontent-type:jsonbody:'{"mac":"AC:63:BE:67:B2:F1","action":"toggleLight"}'confirmation:send-tg40:B4:CD:67:A2:E1:# Home Assistant examplename:Fairyhomeassistant:hassio.localevent:toggle_kitchen_light18:74:2E:87:01:F2:# OpenHAB examplename:Doritosopenhab:192.168.1.140item:open_doorstate:"ON"confirmation:send-pb44:65:0D:75:A7:B2:# IFTTT examplename:Pompadourifttt:cdxxx-_gEJ3wdU04yyyzzzevent:pompadour_buttondata:{"value1":"Pompadourbutton"}confirmations:send-tg:service:telegramtoken:'402642618:QwGDgiKE3LqdkNAtBkq0UEeBoDdpZYw8b4h'to:24291592is_default:falsesend-pb:service:pushbullettoken:'o.BbbPYjJizbPr2gSWgXGmqNTt6T9Rew51'is_default:falseUPGRADEfromprevious versionsThe following execution methods are supported with your Amazon Dash button with this program:System commandCall urlHomeassistantOpenHABIFTTTAmazon-dash also allows you tosend a confirmationafter pressing a button. You will also receive a message in
case of failure.TelegramandPushbulletare supported.For more information seethe documentation of the configuration file.Run thedaemon:If you use aSystemdsystem(Debian 8+, Fedora 15+, Ubuntu 15.04+, Arch Linux 2012+, OpenSUSE 12.1+, and more)execute:$sudosystemctlstartamazon-dashTo run Amazon-dash atstartup:$sudosystemctlenableamazon-dashTo run Amazon-dash manually look atthe documentation.Avoid a connection to Amazon serversDecember 31 is the last day to block requests from your Amazon-dash buttons to Amazon servers. In 2020 your buttons
can be bricked in an update from Amazon servers. To continue using your buttons you must configure your router
to block Internet connections from the buttons. Alternatively you can block these domains:dash-button-na-aws-opf.amazon.com,0.amazon.pool.ntp.org,1.amazon.pool.ntp.org,2.amazon.pool.ntp.org,3.amazon.pool.ntp .org. However, it is recommended to block all requests from the buttons.DockerUsing Amazon Dash within docker is easy! First, pull the Docker image:$dockerpullnekmo/amazon-dash:latestThen, create a container and run Amazon Dash itself:$dockerrun-it--network=host\-v</full/path/path/to/amazon-dash.yml>:/config/amazon-dash.yml\nekmo/amazon-dash:latest\amazon-dashrun--ignore-perms--root-allowed\--config/config/amazon-dash.ymlJoin the communityDo you need ideas on how to use Amazon Dash? See what the community does with this project. Some examples:The Simpsons Random EpisodeShopping list in Google KeepPlay a audioSee all the examplesin the community.
|
amazondata
|
amazondataA python package to get amazon product and search data in json form. The package does not require any API keys as it works by scraping the amazon page.Reference:How To Scrape Amazon Product Details and Pricing using PythonInstallpip install amazondataUsageTo get Amazon product details from the url, use the following function.get_product_from_url(url)fromamazondata.product_details_extractorimportProductDetailsExtractorproduct_details_extractor=ProductDetailsExtractor()data=product_details_extractor.get_product_from_url('https://www.amazon.in/dp/B09JSYVNZ2')print(data)To get Amazon product details from the ASIN (Amazon Standard Identification Number) code, use the following function.get_product_from_asin_code(asin_code)fromamazondata.product_details_extractorimportProductDetailsExtractorproduct_details_extractor=ProductDetailsExtractor()data=product_details_extractor.get_product_from_asin_code('B09JSYVNZ2')print(data)To get the list of products from search query use the following functionsearch(query, page)fromamazondata.search_result_extractorimportSearchResultExtractorsearch_result_extractor=SearchResultExtractor()data=search_result_extractor.search('perfume for men',3)print(data)NOTE: Optionally, you can pass customheadersto all these functions. The default headers value is:headers={"Accept":"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8","Sec-Fetch-Site":"none","Host":"www.amazon.in","Accept-Language":"en-IN,en-GB;q=0.9,en;q=0.8","Sec-Fetch-Mode":"navigate","Accept-Encoding":"gzip, deflate, br","User-Agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.0 Safari/605.1.15","Connection":"keep-alive","Upgrade-Insecure-Requests":"1","Sec-Fetch-Dest":"document","Priority":"u=0, i",}In case the the scraper gets blocked from Amazon, you can fetch the html code using selenium and pass the html code to the following functiondata=extract_search_results(html_code)data=extract_product_details(html_code)
|
amazon-dax-client
|
The Amazon DAX Client for Python is used to accessAmazon DAXclusters from
Python. It is nearly source-compatible with Boto3, with only a small change
needed to the client initialization to use DAX instead of DynamoDB.
Creating a connection to your DAX cluster requires using the cluster discovery endpoint URL returned
in the DescribeClusters response as the endpoint.InstallationInstall Amazon DAX Client using pip:$pipinstallamazon-dax-clientQuick StartBoto3 has two different interfaces, theresource interfaceand the botocoreclient interface. Both are supported by the Amazon DAX client, with slightly
different client initialization.For the resource API, change from:ddb=boto3.resource('dynamodb')todax=AmazonDaxClient.resource(endpoint_url=<cluster_discovery_endpoint_url>)All otherboto3.resource()arguments are accepted.For the botocore client API, change from:session=botocore.session.get_session()ddb=session.create_client('dynamodb',...)tosession=botocore.session.get_session()dax=AmazonDaxClient(session,...)For Boto3 client API, change from:ddb=boto3.client('dynamodb')todax=AmazonDaxClient(endpoint_url=<cluster_discovery_endpoint_url>)The Boto3 and botocore client APIs are exactly the same.Hostname Verification for TLS connections is enabled by default when making requests from the client to the cluster
and has no effect for unencrypted clusters. This can be turned off using the client API, be sure you understand the
implication of turning it off, which is the inability to authenticate the cluster that you are connecting to.Unsupported FeaturesThe Amazon DAX client does not support table operations. Any table manipulation
operations must use the regular Boto3 or botocore DynamoDB client.Paginators are not currently supported for DAX.DocumentationOnce created, the interface is the same as the Boto3/botocore DynamoDB clients.Boto3 DynamoDB resource APIbotocore DynamoDB client APIFor acomplete example, follow the guide tocreate a sample app.Getting HelpPlease use these community resources for getting help.Ask a question onStackOverflowand tag it withamazon-dynamodb-daxAsk a question onthe AWS DynamoDB forumOpen a support ticket withAWS SupportChangesv2.0.3Fixes strict antlr4-python3-runtime dependencyv2.0.1Retry on NoMoreData errorFixes erroneous validation error on duplicate attribute name values in ExpressionAttributeNamesFixes botocore dependencyv2.0.0Encryption in transit featureDrop support for python versions below 3.6v1.1.8Retry logic bug fixesv1.1.7Minor bug fixesv1.1.6Linting fixesUpdated logging to log on Logger objectsv1.1.5Add jitter and backoff for retryable exceptionsv1.1.4Fixes bug starting with clusters of less than 3 nodes.v1.1.3Fixes and improvements to service discovery logic.Add support for Python 3.8.Fix'RetryHandler' object has no attribute '_cluster': AttributeErrorproblem reported in the AWS Forums.Fix problems found by static analysis.v1.1.2Fixes a bug that can result in a failure to update the roster when it
changes, which can later result in aNoRouteException.v1.1.1Fixes a bug that can result in an infinite loop on node failure.Update TransactWriteItem test item limit to 25.v1.1.0Adds support for transact-get-items and transact-write-items APIs for DyanamoDB transactions.Improved efficiency of connection pooling.v1.0.7Fix scheduling of background tasks.v1.0.6Properly de-anonymize UnprocessedItems results from BatchWrite.Raise a proper error if no backends are available.v1.0.5Fix UpdateItem result parsing with a subset of attributes changedv1.0.4Use user-specified timeoutsFix issue with update response if the item is not changedFix error decoding BatchWrite UnprocessedItems.v1.0.3Fix AmazonDaxClient.resource() when using batch_get_items or batch_write_items.v1.0.2Fix Python 2 encoding issuesFix decoding of ConsumedCapacity, ItemCollectionMetrics in batch operationsv1.0.1Initial release
|
amazon-denseclus
|
Amazon DenseClusDenseClus is a Python module for clustering mixed type data usingUMAPandHDBSCAN. Allowing for both categorical and numerical data, DenseClus makes it possible to incorporate all features in clustering.Installationpython3-mpipinstallamazon-denseclusQuick StartDenseClus requires a Panda's dataframe as input with both numerical and categorical columns.
All preprocessing and extraction are done under the hood, just call fit and then retrieve the clusters!fromdenseclusimportDenseClusfromdenseclus.utilsimportmake_dataframedf=make_dataframe()clf=DenseClus(df)clf.fit(df)scores=clf.evaluate()print(scores[0:10])UsagePredictionDenseClus uses apredictmethod whenumap_combine_methodis set toensemble.
Results are return in 2d array with the first part being the labels and the second part the probabilities.fromdenseclusimportDenseClusfromdenseclus.utilsimportmake_dataframeRANDOM_STATE=10df=make_dataframe(random_state=RANDOM_STATE)train=df.sample(frac=0.8,random_state=RANDOM_STATE)test=df.drop(train.index)clf=DenseClus(random_state=RANDOM_STATE,umap_combine_method='ensemble')clf.fit(train)predictions=clf.predict(test)print(predictions)# labels, probabilitiesOn Combination MethodFor a slower but morestableresults selectintersection_union_mapperto combine embedding layers via a third UMAP, which will provide equal weight to both numerics and categoriel columns. By default, you are setting the random seed which eliminates the ability for UMAP to run in parallel but will help circumevent some ofthe randomnessof the algorithm.clf=DenseClus(umap_combine_method="intersection_union_mapper",)To Use with GPU with EnsembleTo use with gpu first haverapids installed.
You can do this as setup by providing cuda verision.pip install denseclus[gpu-cu12]Then to run:clf=DenseClus(umap_combine_method="ensemble",use_gpu=True)Advanced UsageFor advanced users, it's possible to select more fine-grained control of the underlying algorithms by passing
dictionaries intoDenseClusclass for either UMAP or HDBSCAN.For example:fromdenseclusimportDenseClusfromdenseclus.utilsimportmake_dataframeumap_params={"categorical":{"n_neighbors":15,"min_dist":0.1},"numerical":{"n_neighbors":20,"min_dist":0.1},}hdbscan_params={"min_cluster_size":10}df=make_dataframe()clf=DenseClus(umap_combine_method="union",umap_params=umap_params,hdbscan_params=hdbscan_params,random_state=None)# this will run in parallelclf.fit(df)ExamplesNotebooksA hands-on example with an overview of how to use is currently available in the form of aExample Jupyter Notebook.Should you need to tune HDBSCAN, here is an optional approach:Tuning with HDBSCAN NotebookShould you need to validate UMAP emeddings, there is an approach to do so in theValidation for UMAP NotebookBlogsAWS Blog: Introducing DenseClus, an open source clustering package for mixed-type dataTDS Blog: How To Tune HDBSCANTDS Blog: On the Validation of UMAPReferences@article{mcinnes2018umap-software,title={UMAP: Uniform Manifold Approximation and Projection},author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas},journal={The Journal of Open Source Software},volume={3},number={29},pages={861},year={2018}}@article{mcinnes2017hdbscan,title={hdbscan: Hierarchical density based clustering},author={McInnes, Leland and Healy, John and Astels, Steve},journal={The Journal of Open Source Software},volume={2},number={11},pages={205},year={2017}}
|
amazon-ec2-best-instance
|
Amazon EC2 Best Instance (amazon-ec2-best-instance)Amazon EC2 Best Instance (amazon-ec2-best-instance) allows you to choose the most optimal and cheap EC2 instance type for on-demand and spot and with a less reclaimed rate for a spot instance.Prerequisitespython3pip3boto3AWS AccountAWS CredentialsInstallpip install amazon-ec2-best-instanceOptionsvcpuRequired. Float. Describes the vCPU configurations for the instance type.memory_gbRequired. Float. Describes the memory for the instance type in GiB.usage_classOptional. String. Indicates whether the instance type is offered for spot or On-Demand. Default: 'on-demand'. Values: 'spot'|'on-demand'.burstableOptional. Boolean. Indicates whether the instance type is a burstable performance instance type.architectureOptional. String. The architectures supported by the instance type. Default: 'x86_64'. Values: 'i386'|'x86_64'|'arm64'|'x86_64_mac'product_descriptionsOptional. List. The operating system that you will use on the virtual machine. Default: ['Linux/UNIX']. Values: Linux/UNIX | Red Hat Enterprise Linux | SUSE Linux | Windows | Linux/UNIX (Amazon VPC) | Red Hat Enterprise Linux (Amazon VPC) | SUSE Linux (Amazon VPC) | Windows (Amazon VPC)is_current_generationOptional. Boolean. Use the latest generation or not.has_gpuOptional. Boolean. Indicating whether the instance is equipped with a GPU. Default: Nonegpu_memoryOptional. Integer. Amount of GPU Memory in Gigabytes (GiB). Only activated when 'has_gpu' is set to True. Default: Noneis_instance_storage_supportedOptional. Boolean. Use instance types with instance store supportmax_interruption_frequencyOptional. Integer (%). Max spot instance frequency interruption in percent. Note: If you specify >=21, then the '>20%' rate is applied. It is used only if 'usage_class' == 'spot' and 'is_best_price' == Trueavailability_zonesOptional. List. Availability zones. E.g. ['us-east-1a', 'us-east-1b']final_spot_price_determination_strategyOptional. String. Default: "min". Valid values: "min"|"max"|"average"UsageSimplefrom amazon_ec2_best_instance import Ec2BestInstance
ec2_best_instance = Ec2BestInstance()
# It returns all available instance types, including those with over-provisioning resources (CPU, memory, etc.).
response = ec2_best_instance.get_best_instance_types({
'vcpu': 1,
'memory_gb': 2
})
print(response)
'''
[{'instance_type': 'c5n.2xlarge'}, ... , {'instance_type': 'x2iedn.8xlarge'}]
'''Advancedimport boto3
from botocore.config import Config
import logging
from amazon_ec2_best_instance import Ec2BestInstance
ec2_client_config = Config(
retries={
'max_attempts': 20,
'mode': 'adaptive'
}
)
pricing_client_config = Config(
retries={
'max_attempts': 10,
'mode': 'standard'
}
)
ec2_client = boto3.Session().client('ec2', config=ec2_client_config)
pricing_client = boto3.Session().client('pricing', config=pricing_client_config)
# Optional.
options = {
# Optional. Default: us-east-1
'region': 'us-east-1',
# Optional. Default: 10
'describe_spot_price_history_concurrency': 20,
# Optional. Default: 10
'describe_on_demand_price_concurrency': 20,
'clients': {
'ec2': ec2_client,
'pricing': pricing_client
},
# Optional. Integer. Default: 120. It limits the lifetime of cache data.
'cache_ttl_in_minutes': 60
}
logging.basicConfig(level=logging.INFO, format='%(asctime)s: %(levelname)s: %(message)s')
# Optional.
logger = logging.getLogger()
ec2_best_instance = Ec2BestInstance(options, logger)
response = ec2_best_instance.get_best_instance_types({
# Required.
'vcpu': 1,
# Required.
'memory_gb': 2,
# Optional. Default: 'on-demand'. Values: 'spot'|'on-demand'
'usage_class': 'spot',
# Optional.
'burstable': False,
# Optional. Default: 'x86_64'. Values: 'i386'|'x86_64'|'arm64'|'x86_64_mac'
'architecture': 'x86_64',
# Optional. Default: ['Linux/UNIX'].
# Values: Linux/UNIX | Red Hat Enterprise Linux | SUSE Linux | Windows | Linux/UNIX (Amazon VPC) |
# Red Hat Enterprise Linux (Amazon VPC) | SUSE Linux (Amazon VPC) | Windows (Amazon VPC)
'product_descriptions': ['Linux/UNIX'],
# Optional.
'is_current_generation': True,
# Optional. If this parameter is set to True, the method will return the instance type with the best price.
'is_best_price': True,
# Optional. If this parameter is set to True, the method will return the instance type with the instance storage.
'is_instance_storage_supported': True,
# Optional. Integer. Max spot instance frequency interruption in percent.
'max_interruption_frequency': 10,
# Optional. List<String>. The availability zones.
'availability_zones': ['us-east-1a', 'us-east-1b']
})
print(response)
'''
[{'instance_type': 'c5d.large', 'price': '0.032700', 'interruption_frequency': {'min': 0, 'max': 5, 'rate': '<5%'}}, ...]
'''SpotIf you need to get a spot instance with minimal price and minimal frequency of interruption you can use 'is_best_price' and/or 'max_interruption_frequency' input parameterfrom amazon_ec2_best_instance import Ec2BestInstance
ec2_best_instance = Ec2BestInstance()
response = ec2_best_instance.get_best_instance_types({
# Required. Float
'vcpu': 31.2,
# Required. Float
'memory_gb': 100.5,
# Optional. String. Default: 'on-demand'. Values: 'spot'|'on-demand'
'usage_class': 'spot',
# Optional. Boolean.
# If this parameter is set to True, the method will return the instance type with the best price.
'is_best_price': True,
# Optional. Integer. Max spot instance frequency interruption in percent.
# Note: If you specify >=21, then the '>20%' rate is applied
# It is used only if 'usage_class' == 'spot' and 'is_best_price' == True
'max_interruption_frequency': 10
})
print(response)
'''
[{'instance_type': 'm6id.8xlarge', 'price': '0.642600', 'interruption_frequency': {'min': 6, 'max': 10, 'rate': '5-10%'}}, ...]
'''from amazon_ec2_best_instance import Ec2BestInstance
ec2_best_instance = Ec2BestInstance()
response = ec2_best_instance.get_best_instance_types({
# Required. Float
'vcpu': 31.2,
# Required. Float
'memory_gb': 100.5,
# Optional. String. Default: 'on-demand'. Values: 'spot'|'on-demand'
'usage_class': 'spot',
# Optional. Boolean.
# If this parameter is set to True, the method will return the instance type with the best price.
'is_best_price': True
})
print(response)
'''
[{'instance_type': 'r5a.8xlarge', 'price': '0.578100'}, ...]
'''
|
amazoned
|
Python script to change an original Amazon link to an affiliate Amazon link.
|
amazon-efs
|
Amazon EFS (amazon-efs)Amazon EFS (amazon-efs) allows programmatically manipulate EFS data (create, read, delete, list files) from any machine.Prerequisitespythonpipboto3AWS AccountAWS CredentialsInstallpip install amazon-efsWarningEFS should have at least one mount target in a Private subnetLimitsLambda compute envlist_files,upload,download,deleteactions are limited by 15 minutes execution time (AWS Lambda works under the hood)Batch compute envlist_files,upload,downloadactions are not implemented yetBasicsSupported compute environments:Lambda (Default)BatchLambda compute environment (by default):efs = Efs('<file_system_id>')Lambda compute environment:efs = Efs('<file_system_id>', compute_env_name='lambda')Batch compute environment:The "batch_queue" option is requiredefs = Efs('<file_system_id>', {
'batch_queue': '<batch_queue>',
}, compute_env_name='batch')Lambda compute environmentThis computing environment is used for lightweight operations (lasting no more than 15 minutes).from amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
# Deploying required underlying resources
efs.init()
# Actions (e.g. list_files, upload, download, delete)
files_list = efs.list_files()
# Don't forget to destroy underlying resources at the end of the session
efs.destroy()ActionsList filesfrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
efs.init()
files_list = efs.list_files()
print(files_list)
files_list = efs.list_files('dir1')
print(files_list)
files_list = efs.list_files('dir1/dir2')
print(files_list)
efs.destroy()Uploadfrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
efs.init()
efs.upload('file.txt')
efs.upload('file.txt', 'dir1/new_file.txt')
efs.upload('file.txt', 'dir1/dir2/new_file.txt')
efs.upload('file.txt', 'dir1/dir3/new_file.txt')
efs.upload('file.txt', 'dir2/dir3/new_file.txt')
efs.upload('file.txt', 'dir2/dir4/new_file.txt')
efs.destroy()Downloadfrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
efs.init()
efs.download('dir1/dir3/new_file.txt', 'file1.txt')
efs.destroy()DeleteDelete filefrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
efs.init()
efs.delete('dir2/dir3/new_file.txt')
efs.destroy()Delete folderfrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
efs.init()
efs.delete('dir1/dir2/*')
efs.delete('dir1/*')
efs.destroy()Async deleteUse it if you want to schedule deletion and monitor progress yourself.from amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
state = efs.init()
http_response_status_code = efs.delete('dir2/dir3/new_file.txt')Then, after the job is completed, destroy the infrastructure.efs = Efs(efs_id, { 'state': state })
efs.destroy()Batch compute environmentThis computing environment is used for heavy operations (lasting more than 15 minutes).ActionsDeleteThe "batch_queue" option is requiredDelete filefrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
batch_queue = '<batch_queue>'
efs = Efs(efs_id, {
'batch_queue': batch_queue,
}, compute_env_name='batch')
efs.init()
efs.delete('dir2/dir3/new_file.txt')
efs.destroy()Delete folderfrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
batch_queue = '<batch_queue>'
efs = Efs(efs_id, {
'batch_queue': batch_queue,
}, compute_env_name='batch')
efs.init()
efs.delete('dir1/dir2/*')
efs.delete('dir1/*')
efs.destroy()Async deleteUse it if you want to schedule deletion and monitor progress yourself.from amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
batch_queue = '<batch_queue>'
efs = Efs(efs_id, {
'batch_queue': batch_queue,
}, compute_env_name='batch')
state = efs.init()
batch_job_arn = efs.delete('dir2/dir3/new_file.txt')Then, after the job is completed, destroy the infrastructure.efs = Efs(efs_id, { 'state': state }, compute_env_name='batch')
efs.destroy()StateYou can destroy underlying infrastructure even after destroying EFS object from RAM if you saved thestatefrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id)
state = efs.init()
# Destroy object
del efs
efs = Efs(efs_id, { 'state': state })
files_list = efs.list_files()
print(files_list)
efs.destroy()TagsYou can add custom tags to underlying resourcesfrom amazon_efs import Efs
efs_id = 'fs-0d74736bfc*******'
efs = Efs(efs_id, {
'tags': {
'k1': 'v1',
'k2': 'v2'
}
})
efs.init()
files_list = efs.list_files()
print(files_list)
efs.destroy()Loggingfrom amazon_efs import Efs
import logging
fs_id = 'fs-0d74736bfc*******'
logger = logging.getLogger()
logging.basicConfig(level=logging.ERROR, format='%(asctime)s: %(levelname)s: %(message)s')
efs = Efs(fs_id, logger=logger)
|
amazonenvirosensors
|
NewkitonI bought a temperature senson on Amazon (https://www.amazon.com/Newkiton-NK-01B-Thermometer-Temperature-Compatible/dp/B07W53L4RD)
I wanted to integrate it into a Graphana dashboard and to do so I needed to figure out how to pull the data from itWith that in mind, I reverse engineered the protocol. At the moment I know the basic on how to get the temperatire readings
from the device and not much more.I am not that familar with BLE but I know wireshark so I used that to reverse engineer it. The I just copied the
protocol and came up with thisThe device seems to store the data on a NVM with a simple address scheme. I presume it wraps around but I haven't
run it for long enough just yet.To use this, just instanciate a device and call the temperature method. The temperature is only updated every 10 minutes
so call it more frequently will only return a cached versionfromNewkitonimportNewkitonimporttimesensor=Newkiton.Newkiton(deviceAddr="8e:f9:00:00:00:ed")whileTrue:print("Temp={}".format(sensor.temperature()))time.sleep(60*10)ThermoBeaconThis is another similar device from Amazon which I also bought. I based
this code fromhttps://github.com/rnlgreen/thermobeaconfromThermoBeaconimportThermoBeaconimporttimesensor=ThermoBeacon.ThermoBeacon(deviceAddr="8e:f9:00:00:00:ed")whileTrue:print("Temp={}".format(sensor.temperature()))time.sleep(60*10)MiTemperatureThis is another similar device from Amazon which I also bought. I based
this code fromhttps://github.com/JsBergbau/MiTemperature2https://www.amazon.fr/gp/product/B082B4X4B2/ref=ppx_yo_dt_b_asin_title_o00_s00?ie=UTF8&psc=1
|
amazon-fba-automation
|
Failed to fetch description. HTTP Status Code: 404
|
amazon-fees
|
No description available on PyPI.
|
amazon-fresh-pkg
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Failed to fetch description. HTTP Status Code: 404
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amazon-frustration-free-setup-certification-tool
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Python-based automation test scripts which can help Non-Amazon device partners to launch their devices with FFS with shorten self-certification time. The document will describe how to run those scripts to complete the certification tests, especially the provisioning performance test.Frustration Free Setup OverviewSeeUnderstanding Frustration-Free Setupfor more information.Frustration Free Setup Certification ProcessSeeProvisionee Certification Guidefor more information.Requirements and Getting started1 Android phone in USB debugging modeAlexa App installed and login with your Amazon accountThe phone is connected with your test machine (Windows, MacOS or Linux) with USB connection.1 Provisioner device, which is registered to your Amazon account, see recommended devices fromUnderstanding Frustration-Free Setup - Testing Your Device1 Provisionee device, your device under test, called DUT in this document.2 Control devices,Amazon Smart Plugsare recommendedPlease register both to your Amazon account.Please plug DUT on top of one smart plug, and turn that plugONif DUT has been registered to your Amazon account, orOFFif DUT is in factory reset mode.Please plug the provisioner device on top of the other smart plug, and turn itON.Please submit your DUT to pre-register our device setup service viaSubmit Test DevicesRun Test ScriptsSeeSource on GitHubfor more detailsLicenseThis project is licensed under the Apache-2.0 License.
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amazonian
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AmazonianAmazonianis aPythonlibrary for interacting easily with Amazon S3 and Redshift.Installationpip install amazonianUsageS3fromamazonianimportS3s3=S3(key=None,secret=None,iam_role=None,root='s3://',spark=spark)# get list of files:s3.ls(path='s3://bucket/directory/subdirectory')# get a tree representation of folder structures3.tree(path='s3://bucket/directory/subdirectory')# get file size:s3.get_size(path='some_file')# save a Spark DataFrame as a Parquets3.save_parquet(data=my_data,path='s3://bucket/directory/subdirectory/name.parquet')# load a Parquet into a Spark DataFramemy_data=s3.load_parquet(path='s3://bucket/directory/subdirectory/name.parquet')
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amazonify
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# python-amazonifyThe simplest way to build Amazon Affiliate links, in Python.## InstallTo install ``python-amazonify``, simply run``pip install amazonify`` and you'll get the latest version installedautomatically.## UsageUsing ``amazonify`` is really easy. All you do is pass it the Amazon URL you'dlike to make into an affiliate link, and your Amazon affiliate tag.``` python>>> from amazonify import amazonify>>>>>> # Your Amazon affiliate tag:>>> affiliate_tag = 'rdegges-20'>>>>>> # Some non-affiliate Amazon URLs:>>> urls = [... 'http://www.amazon.com/Canon-21-1MP-Frame-Digital-Camera/dp/B001G5ZTLS/ref=sr_1_1?ie=UTF8&qid=1337148615&sr=8-1',... 'http://www.amazon.com/Transcend-Compact-Flash-Card-400X/dp/B002WE4H8I/ref=pd_bxgy_p_img_b',... 'http://www.amazon.com/Canon-LP-E6-Battery-Digital-Cameras/dp/B001KELVS0/ref=pd_bxgy_e_img_b',... 'http://www.amazon.com/Canon-50mm-1-8-Camera-Lens/dp/B00007E7JU/ref=sr_1_1?ie=UTF8&qid=1337148688&sr=8-1',... 'http://www.amazon.com/Canon-70-300mm-4-5-6-Lens-Cameras/dp/B0007Y794O/ref=sr_1_3?ie=UTF8&qid=1337148688&sr=8-3',... ]>>> affiliate_urls = [amazonify(u, tag) for u in urls]>>> affiliate_urls['http://www.amazon.com/Canon-21-1MP-Frame-Digital-Camera/dp/B001G5ZTLS/ref=sr_1_1?tag=rdegges-20','http://www.amazon.com/Transcend-Compact-Flash-Card-400X/dp/B002WE4H8I/ref=pd_bxgy_p_img_b?tag=rdegges-20','http://www.amazon.com/Canon-LP-E6-Battery-Digital-Cameras/dp/B001KELVS0/ref=pd_bxgy_e_img_b?tag=rdegges-20','http://www.amazon.com/Canon-50mm-1-8-Camera-Lens/dp/B00007E7JU/ref=sr_1_1?tag=rdegges-20','http://www.amazon.com/Canon-70-300mm-4-5-6-Lens-Cameras/dp/B0007Y794O/ref=sr_1_3?tag=rdegges-20']```**NOTE**: If the URL you try to ``amazonify`` is invalid, ``amazonify`` will return ``None``.## Confused?Have no idea what I'm talking about? See[Amazon's Affiliate Program](https://affiliate-program.amazon.com/gp/associates/network/main.html).Or...[Shop on Amazon!](http://www.amazon.com/?_encoding=UTF8&tag=rdegges-20&linkCode=ur2&camp=1789&creative=390957)## Tests[](http://travis-ci.org/rdegges/python-amazonify)Want to run the tests? No problem:``` bash$ git clone git://github.com/rdegges/python-amazonify.git$ cd python-amazonify$ python setup.py develop...$ pip install -r requirements.txt # Install test dependencies.$ nosetests.............----------------------------------------------------------------------Ran 13 tests in 0.166sOK```## Changelogv0.1: 5-16-2012- Initial release!
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amazon-invoice-downloader
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Amazon Invoice DownloaderTable of ContentsInstallationLicenseWhat it doesThis programamazon-invoice-downloader.pyis a utility script that uses thePlaywrightlibrary to spin up a Chromium instance and automate the process of downloading invoices for Amazon purchases within a specified date range. The script logs into Amazon using the provided email and password, navigates to the "Returns & Orders" section, and retrieves invoices for the specified year or date range.The user can provide their Amazon login credentials either through command line arguments (--email= --password=) or as environment variables ($AMAZON_EMAIL and $AMAZON_PASSWORD).The script accepts the date range either as a specific year (--year=) or as a date range (--date-range=). If no date range is provided, the script defaults to the current year.Once the invoices are retrieved, they are saved as PDF files in a local "downloads" directory. The filename of each PDF is formatted asYYYYMMDD_<total>_amazon_<orderid>.pdf, where YYYYMMDD is the date of the order, total is the total amount of the order (with dollar signs and commas removed), and orderid is the unique Amazon order ID.The program has a built-in "human latency" function, sleep(), to mimic human behavior by introducing random pauses between certain actions. This can help prevent the script from being detected and blocked as a bot by Amazon.The script will skip downloading a file if it already exists in the./downloadsdirectory.Installationpip install amazon-invoice-downloaderplaywright installUsageWhen running this program, Amazon may detect you are automation and introduce CAPTCHA's or make you login again. Just do so, and once successfully logged in, the script will continue.$amazon-invoice-downloader-hAmazon Invoice DownloaderUsage:amazon-invoice-downloader.py \[--email=<email> --password=<password>] \[--year=<YYYY> | --date-range=<YYYYMMDD-YYYYMMDD>]amazon-invoice-downloader.py (-h | --help)Login Options:--email=<email> Amazon login email [default: $AMAZON_EMAIL].--password=<password> Amazon login password [default: $AMAZON_PASSWORD].Date Range Options:--date-range=<YYYYMMDD-YYYYMMDD> Start and end date range--year=<YYYY> Year, formatted as YYYY [default: <CUR_YEAR>].Options:-h --help Show this screen.Examples:amazon-invoice-downloader.py --year=2022 # This uses env vars $AMAZON_EMAIL and $AMAZON_PASSWORDamazon-invoice-downloader.py --date-range=20220101-20221231amazon-invoice-downloader.py [email protected] --password=secret # Defaults to current yearamazon-invoice-downloader.py [email protected] --password=secret --year=2022amazon-invoice-downloader.py [email protected] --password=secret --date-range=20220101-20221231Licenseamazon-invoice-downloaderis distributed under the terms of theMITlicense.
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amazon.ion
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No description available on PyPI.
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amazon-kclpy
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No description available on PyPI.
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amazon_kclpy-ext
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No description available on PyPI.
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amazon-kclpy-test
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UNKNOWN
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