Saravanan666/pypi-org huggingface dataset:
Package: asset_bender
## Usage:First, make sure you've installed asset_bender: https://github.com/HubSpot/asset_bender/tree/masterSecondly, make sure that `PROJ_NAME`, `PROJ_DIR`, `BENDER_S3_DOMAIN`, and `BENDER_CDN_DOMAIN` are set in yoursettings. `PROJ_NAME` should match the name in static_conf.json and `PROJ_DIR` needs to point to the pythonmodule path (via something like `PROJ_DIR = dirname(realpath(__file__))`).`BENDER_S3_DOMAIN` is the domain that points to your S3 bucket and `BENDER_CDN_DOMAIN` is the CDN domain infront of S3 (if you have one).Thirdly, make sure that you've included these lines in your Manifest.in:global-include static_conf.jsonglobal-include prebuilt_recursive_static_conf.jsonNext, in your app's context processor do:```pythonfrom django.template import RequestContextfrom asset_bender.bundling import BenderAssetsdef my_context_processor(request):context = RequestContext(request)bender_assets = BenderAssets(['my_project/static/js/my_project_bundle.js','my_project/static/css/my_project_bundle.css','some_library/static/js/some_library_bundle.js','some_library/static/css/some_library_bundle.js'... etc ...], request.GET)context.update(bender_assets.generate_context_dict())return context```And lastly, in your base template you'll need to include these templates:```html<head>...{% include "asset_bender/scaffold/head.html" %}...</head><body>...{% include "asset_bender/scaffold/end_of_body.html" %}</body>```To manually include a particular static asset in your HTML, use the template tag:```{% load asset_bender_tags %}{% bender_url "project_name/static/js/my-file.js" %}```The tag will output a full url with the proper domain and version number (as specified by this projects's dependencies).
Package: asset-extraction-framework
assetsis a reverse-engineering framework for extracting multimedia assets from legacy software.Unlikemrcrowbarand other reverse-engineering packages,
this library is not designed for editing data in-place.assetsinstead focuses on extraction.You write the code that converts the data from your application into a standard format (pixels and PCM audio).assetsthen provides the following:A command-line extractor interfaceUses Pillow and ffmpeg to export images/audio in a wide variety of formats in various configurations.For real-world examples, see myassets-based projects:Tonka...and more coming soon!
Package: assetic-mapinfo
This package provides tools that can be used to assist with integration
between Assetic and MapInfo.
The tools may be included in the MapInfo python add-in, or in scripts run
outside of the MapInfo desktop environment.Changelog** Release 1.3.0.1**Added boolean parameter for creation of Functional Location when checking for it’s existence during asset association** Release 1.3.0.0**Added support for Component and Dimension ‘upsert’ where the Component and or Dimension is created as part of
the Asset update if the Component/Dimension does not already exist.
Requires assetic SDK 2023.16.1.2 minimum, and configuration setting to enable** Release 1.2.0.2**Added functionality to bulk update asset spatial dataFixed bug with bulk update having incorrect parametersFixed create assets issue where the skip defaults XML config was applied** Release 1.2.0.1**Improve capture of unhandled exceptions** Release 1.0.2.0**Improve functionality/loggingUtilises shared functionality of base assetic python sdkRelease 1.0.0.5Include Mapinfo libraries.  If not in a MapInfo environment these
libraries will initiated by emptyRelease 1.0.0.0First Release
Package: assetic-qgis
Descriptionassetic-qgisis a library to allow interoperability between Assetic Cloud services and
QGIS.Installation (recommended)To installassetic-qgisrun:pip install assetic-qgisManual InstallationCan be installed manually but dependencies will also require installing
Can optionally use a zip file available from Assetic if pip cannot be usedInstructions for useIntroduction to QGIS, Quickstart documentation, etc. can be found on the Assetic
Knowledge Base (login required):https://assetic.zendesk.com/hc/en-us/articles/360001933756-QGIS-Integration-IntroductionReporting issuesLog issues via Assetic SupportChangelogRelease 2.1.0.2Fixed a bug when trying to access a feature’s geometry for bulk spatial updates via the Data ExchangeRelease 2.1.0.1Added boolean parameter for creation of Functional Location when checking for it’s existence during asset associationRelease 2.1.0.0Added support for Component and Dimension ‘upsert’ where the Component and or Dimension is created as part of
the Asset update if the Component/Dimension does not already exist.
Requires assetic SDK 2023.16.1.2 minimum, and configuration setting to enableWhere invalid polygon orientation provided for multipolyon the orientation will be corrected
where possibleRelease 2.0.1.8Fixed an issue when committing changes to a layer where editing mode was disabled and only partial Assetic IDs
were written back.Fixed an issue with bulk updates using the Data Exchange was not properly handling QGIS specific types (e.g QDate)Release 2.0.1.7Fixed issue where feature fields with ‘falsy’ numeric values were treated as null and not included in API payloadsFixed an issue with the handling of empty layer fields mapped to asset addressRelease 2.0.1.6Option to Buffer update of layer with Assetic ID on asset creation (default option) or apply feature updates directly (set using use_buffered_edit=False)Release 2.0.1.5Improve functional location configImprove reporting of unhandled exceptionsRelease 2.0.1.4Fixed bug that was preventing dimension info being explictly added to each
QGIS rowPartial asset creation now being correctly reported to loggerQueries now working correctly when creating/updating functional locations
from layerRemoved QGIS specific geometry fields from base python-sdk in to qgis packageRelease 2.0.1.3Fixes for initialisation crash in some circumstances where logging failedRelease 2.0.1.2Added functionality to bulk update assets via data-exchangeUpdated assetic dependencyRelease 2.0.1.1Updated required version of assetic package after discovering bugRelease 2.0.1.0Fixed issue where features of type Point and MultiPoint where not
being created in Assetic CloudRelease 2.0.0.1Updated read me with some nicer formatting + extra informationRelease 2.0.0.0Supports create and update by selection in QGIS GUIUses consolidated Assetic SDK functionality, sharing code with other GIS repositoriesRelease 1.0.0.0Initial Build
Package: assetid
# assetid  [![Build Status](https://travis-ci.org/axiom-data-science/assetid.svg)](https://travis-ci.org/axiom-data-science/assetid)An ocean data asset ID parser developed and used by [Axiom Data Science](http://axiomdatascience.com)## Installation##### Stablepip install assetid##### Developmentpip install git+https://github.com/axiom-data-science/assetid.git### IOOS URNs[More Information](https://geo-ide.noaa.gov/wiki/index.php?title=IOOS_Conventions_for_Observing_Asset_Identifiers)###### URN NormalizationExamples:```pythonfrom assetid.urn import IoosUrnu = IoosUrn(asset_type='station', authority='axiom', label='station1')print(u.__dict__)# {'asset_type': 'station',#  'authority': 'axiom',#  'component': None,#  'label': 'station1',#  'discriminant': None}print(u.urn)# 'urn:ioos:station:axiom:station1'``````pythonfrom assetid.urn import IoosUrnu = IoosUrn.from_string('urn:ioos:station:axiom:station1')print(u.__dict__)# {'asset_type': 'station',#  'authority': 'axiom',#  'component': None,#  'label': 'station1',#  'discriminant': None}print(u.urn)# 'urn:ioos:station:axiom:station1'``````pythonfrom assetid.urn import IoosUrn# NetCDF variable attributes from a "sensor" urnprint(IoosUrn.from_string('urn:ioos:sensor:axiom:station1').attributes())# {'standard_name': 'wind_speed'}print(IoosUrn.from_string('urn:ioos:sensor:axiom:foo:lwe_thickness_of_precipitation_amount#cell_methods=time:mean,time:variance;interval=pt1h').attributes())# {'standard_name': 'lwe_thickness_of_precipitation_amount',#  'cell_methods': 'time: mean time: variance (interval: PT1H)'}# URN from `dict` of variable attributesattributes = {'standard_name': 'wind_speed','cell_methods': 'time: mean (interval: PT24H)'}print(IoosUrn.from_dict('authority', 'label', attributes).urn)# 'urn:ioos:sensor:authority:label:wind_speed#cell_methods=time:mean;interval=pt24h'# URN from a `netCDF4` Variable objectnc = netCDF4.Dataset('http://thredds45.pvd.axiomalaska.com/thredds/dodsC/grabbag/USGS_CMG_WH_OBS/WFAL/9001rcm-a.nc')print(IoosUrn.from_dict('authority', 'label', nc.variables['T_28'].dict).urn)# 'urn:ioos:sensor:authority:label:sea_water_temperature'```
Package: assetman
# Assetman[![Latest Version](https://pypip.in/v/assetman/badge.png)](https://pypi.python.org/pypi/assetman/)[![Build Status](https://travis-ci.org/petermelias/assetman.png?branch=master)](https://travis-ci.org/petermelias/assetman)[![Montly Downloads](https://pypip.in/d/assetman/badge.png?month)](https://pypi.python.org/pypi/assetman)[![Download format](https://pypip.in/format/assetman/badge.png)](https://pypi.python.org/pypi/assetman/)[![Coverage Status](https://coveralls.io/repos/petermelias/assetman/badge.png?branch=master)](https://coveralls.io/r/petermelias/assetman?branch=master)[![License](https://pypip.in/license/assetman/badge.png)](https://pypi.python.org/pypi/assetman/)## Usage```pythonfrom assetman import (init,create_blob)manager = init(aws_key, aws_secret, my_bucket)some_data = 'my awsome string / image binary / html / markdown / national secrets'asset = create_blob(some_data)asset.public = True # makes the asset publicly accessible over S3manager.save_asset(asset)print asset.uuid # returns a 16 character UUID which you can save for later# ... somewhere over the Mediterranean sea...my_saved_asset = manager.get_asset(uuid)print my_saved_asset.data'my awsome string / image binary / html / markdown / national secrets'```It is also important to note that there are some convenience Asset Types built in for convenient handlingof meta data and mimetypes.For example, the ```Image``` asset type will track format and dimensions for you. See tests for example.## TestingTo install with testing support: ``` pip install -e .[test] ```This will install with the optional dependencies required for testing. (wand, for example.)``` nosetests ```
Package: assetra
The ASSET Lab Resource adequacy package (assetra) is a light-weight, open-source energy system resource adequacy package maintained by the University of Michigan ASSET Lab.Free software: MIT licenseDocumentation:https://assetra.readthedocs.io.FeaturesProbabilistic Monte Carlo state-sampling simulation framework, supporting:Time-varying forced outage rates in thermal unitsSequential storage unit dispatchUser-defined energy unit typesResource adequacy calculation:Expected unserved energy (EUE)Loss of load hours (LOLH)Loss of load days (LOLD)Loss of load frequency (LOLF)Resource contribution calculation:Effective load-carrying capability (ELCC)Object-oriented interface to manage energy units within energy systemsInternal computation stored inxarraydatasetsFuture WorkRegional interchange and transmissionParallelized computationCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: assetreload
AssetReload uses WebSockets to broadcast asset changes to the browser.## InstallationInstall with pip:pip install assetreloadIf you don’t have it installed, useeasy_installinstead.## UsageOnce setup,cdinto a directory you would like to monitor for changes and start the server by runningassetreload.There are optional arguments:pathdefines the directory to monitor (defaults to current directory)portthe port on which to start the server (defaults to 8888)E.g.assetreload –path=~/mysite/ –port=8889### In the browserMake a copy ofassetreload.jsin your project, and include the following:<script id=”assetreload” src=”assetreload.js”></script>Changes to the current document or JavaScript trigger a page reload, but images and CSS can happen without. Configure your port to match the above by usingdata-porton thescripttag.You can automatically generateassetreload.jsinto a directory of your choosing by running:assetreload genscript## Changelog### Version 1.1Added: error handling for incorrectly configured portsRemoved: need to specify a list of files to watch - files are now handled automatically
Package: assets2036py
Assets2036PyHelper lib for consuming and providing assets2036 assets.Usagefromassets2036pyimportAssetManager# for local MQTT broker with default portmgr=AssetManager("localhost",1883,"my_namespace","endpoint_name")my_lamp=mgr.create_asset("my_lamp","https://raw.githubusercontent.com/boschresearch/assets2036-submodels/master/light.json")my_lamp.light.light_on.value=TrueFor further examples take a look at thetests.
Package: assets_angular
What’s that?Is a filter for generating html templates as javascript includes in your bundles of django_assetsHow to useAdd the application to your INSTALLED_APPSINSTALLED_APPS=(...'assets_angular','django_assets',...)Using itfromdjango_assetsimportBundle,registertemplates=Bundle('tpl/template1.html','tpl/template1.html',filters='angular',output="templates.js")app_js=Bundle(models,controllers,directives,filters,services,templates,filters='uglifyjs',output='gen/app.js')register('app',app_js)
Package: asset-scanner
Asset ScannerLegacy Documentation:https://cedadev.github.io/asset-scanner/Documentation:https://asset-scanner.readthedocs.io/en/latest/index.htmlThe asset scanner provides the framework and access to shared tools.
The framework allows you to build extractors to get metadata from file objects using plugins to change the source of the
files, the output of the metadata and the processing chain which extracts the metadata.
The framework leverages a modular, plugin architecture to allow users to modify the workflow to fit their needs.
Package: assetvaluation
Functions to support assets valuation for finance, accounting, and modelling.AcknowledgmentsThe module was kick-started with the support ofLipartner AG.InstallationThe module can be installed usingpipeither by downloading areleaseor asnapshot of the repository, or by cloning the repository.To install runpip install<path-to-module>, where<path-to-module>indicates the path to the module:development:pip install https://gitlab.wemod.ch/kakila/assetvaluation/PyPI version:pip install assetvaluationIf you have cloned(downloaded) the repository<path-to-module>is the full path to the folder
where you have the clone(archived file).DocumentationThe html documentation is hosted athttps://kakila.wemod.info/assetvaluationCompiling the documentationThe sources can be found in the folderdocof the repository.To compile the documentation you will needsphinxin addition
to the runtime dependencies of the module.
See therequirements.txtfile for details.Once you have sphinx, you can compile the documentation by running the commandmake htmlin thedocfolder.
Package: assher
ASSHerAsynchronous SSH/SFTP runner.Installationpython3-mpipinstallassherSome examples of usage as command-line utilityYou can see help by runningpython3-massher--helpExample of running on a couple of networks (using bash sequence expression)python3-massher-L50--hosts192.168.22{5..6}.{10..254}-c"hostname -s"\-Uroot-i~/.ssh/very_secret_privkeyThe same with overriding global connection parametes for second subnetpython-massher-L10\--hosts{192.168.225.{10..254},user:password@192.168.226.{10.254}:2222}\-c"uptime"-Uroot-Panotherpass
Package: as-shinchan
Usage of this ModuleCreate a new file with any name you want!!then,first import this as_shinchan modulethen,use below functions to draw shinchanshort()legs()shirt()head()hands()mouth()eyebrows()eyes()robo()Sample Programimport as_shinchan as s
s.short()
s.legs()
s.shirt()
s.head()
s.hands()
s.mouth()
s.eyebrows()
s.eyes()
s.robo()
s.ht()
s.done()For more info:https://www.youtube.com/channel/UCfthEj3oCLiFakXtSzhZ7Dw
Package: assign
# Assign---------`assign` is a black magic module for supporting `obj.__assign__`:### How to use it:#### 1. magic patchSuppose that there is a `test.py````pythona = 1class T():def __assign__(self, v):print('called with %s' % v)b = T()c = b```It just works as:```pythonPython 3.6.0 (default, Mar  6 2017, 15:44:48)[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)] on darwinType "help", "copyright", "credits" or "license" for more information.>>> import magic>>> import testcalled with c```#### 2. manually patch```from assign.patch import patch_moduleimport testpatch_module(test)```### Installjust:`pip install assign`### Notes* Tested with `Py2.7` and `Py3.6`### Known Issues* 1. Won't work under `REPL`* 2. May slow import operation.* 3. May failed when patch some modules like `os` and `sys`
Package: assignbot
This project aims to automatically assign unassigned pending
merge-requests on a gitlab/heptapod instance.Each member of a project can be randomly assigned to a pending
merge-requests.Assignbot assigns reviews to users, based on their preferences. Their
preferences are store in a simple yaml file. The history of the
assignment is stored in a CSV file, in a S3 storage.The preferences fileThe users define their “review preferences” in a preferences file,
formatted in yaml.The “preferences file” is a yaml file constructed as follow :username_1:
    max_auto_review_per_week: XXX
    max_auto_review_per_day: YYY
username_2:
    max_auto_review_per_week: ZZZ
    max_auto_review_per_day: WWWwhereusername_1is the username of a gitlab user, andmax_auto_review_per_week(max_auto_review_per_day) is the maximum
of automatically assigned review per week (per day).The S3 storageAssignbot uses a CSV file to keep track of users assignments. This CSV
file is stored in an S3 storage. When you execute the bot, you must
provide the following environment variables:AWS_ACCESS_KEY_ID: your S3 key IDAWS_SECRET_ACCESS_KEY: your S3 secret access keyS3_ENDPOINT_URL: your S3 endpoint urlS3_BUCKET_NAME: the S3 bucket to be usedExecuting the botOnce assignbot installed, you can run it as follow :$GITLAB_URL="https://your.forge.org"\GITLAB_TOKEN="XXX-an-api-token"\S3_ENDPOINT_URL="https://your.s3.storage.fr"\S3_BUCKET_NAME="you_bucket_name"\AWS_SECRET_ACCESS_KEY="your_secret_access_key"\AWS_ACCESS_KEY_ID="your_access_key_id"\python3-massignbot./users_preferences.ymland the bot should start assigning merge requests to the users.Use case exampleTo use this bot, you can add a new repository on your forge with the following.gitlab-ci.yml:assign:stage:assignonly:-schedulesvariables:# there is a gitlab bug in the validation of AWS variables. We work# around it while waiting for the correction.# see: https://gitlab.com/gitlab-org/gitlab/-/issues/215927AWS_ACCESS_KEY_ID:"$_AWS_ACCESS_KEY_ID"AWS_SECRET_ACCESS_KEY:"$_AWS_SECRET_ACCESS_KEY"script:-pip install assignbot-python -m assignbot ./users_preferences.ymlThis job assumes that you have defined the appropriate environment variables,
and that you have ausers_preferences.ymlfile at the root of this
repository.Then, you can create a new “schedule job” in gitlab, to call this job
periodically.
Package: assigning-high-impact-pairs
Assigning High Impact PairsThis package makes it easy to assign pairs to experts using logic based on fields from the unified dataset.
The aim is that engineers will only have to worry about the logic of assigning pairs, rather than the humdrum work of
getting the data in and out of tamr.It's workflow is:Load the unified dataset (UD) for the mastering project. A csv file of the UD is saved locally, along with a version number.
If the version number of the UD in Tamr differs to the saved version, the latest version will be downloaded and saved.Get the high impact pairs (HIP) out of Tamr.Join the HIP to the UD{Custom user logic to assign HIP to experts}Push the assigned pairs back to tamr.
Package: assignment
# MinimumPriceBookScraper-> This project browses the given book in given sites and extracts the price of the book from the sites using Scrapy.
-> After extracting the prices, it writes to a file and stores uniquely in unique files.
-> The python code then compares the prices from the files and list the minimal price of the book.# Clone the repository:->https://github.com/ptyma/MinimumPriceBookScraper# Activate your virtual environment of Scrapy:-> source virtual_env_name/bin/activate# Enter to project directory:-> cd MinimumPriceBookScraper# Run the code on terminal:-> python main.py# The minimum value is listed (answer).
Package: assignment-tool-sebastian-stigler
Assignment ToolThis tool is used to lead students through the assignments of the
"Strukturierte Programmierung" course at Aalen Univerity.Installation GuidInstall OS dependencies (for Ubuntu 20.04), create a virtual env and activate it:$sudo-i# apt install python3.8-venv python3-wheel# python3.8 -m venv /opt/assignment_tool_venv# source /opt/assignment_tool_venv/bin/activateThen install Kivy-Garden (0.1.4) without building a wheel, as it can't build a wheel.(assignment_tool_venv)# pip install kivy-garden==0.1.4 --no-binary :all:Now you can install the actual package(assignment_tool_venv)# pip install assignment-tool-sebastian-stiglerFinally, you create aassignment_tool.desktopfile (adapt theExecandIconpath if your
virtual env has a different name).[Desktop Entry]Version=1.0Name=Assignment ToolExec=/opt/assignment_tool_venv/bin/assignment_toolIcon=/opt/assignment_tool_venv/lib/python3.8/site-packages/assignment_tool/assets/logo.pngTerminal=falseType=ApplicationCategories=TextEditor;Development;IDE;To integrate it into the desktop call:# desktop-file-install assignment_tool.desktop
Package: assign-reviewers
assign-reviewersVery simple script to randomly assign reviewers and create scoring sheets.Installationpipinstallassign-reviewersHow to useStart from an Excel or Google sheet. Each row corresponds to a submission. Columns are organized as perthis example
CSV file.Export the sheet into a CSV file. For this example, let's usethis example CSV file.Run:assign-reviewers-cform.csv-rAnna-rElsa-rChristophe-rSven-n2This will generate four CSV files, each containing an additional column with the name of the reviewer.Send individual CSV file to the corresponding reviewer.Each reviewer uploads the CSV on Google Sheet:File > Import, then click "Upload", drag & drop the CSV file,
and click "Import data". The listed submissions can then be graded/ranked as shown below:Each reviewer shares their Google Sheet with you, and you can simply copy the reviewer column and append it to the
main Google Sheet document. Then, a row-wise summation can easily be done.More complex usageThe software can also ignore reviewers if their affiliation match that of a submitter. Below is an example:assign-reviewers-cform.csv-rAnna-a"Chiang Mai"-rElsa-aUBC"British Columbia"In this example, the reviewerElsahas the affiliations {'UBC', 'British Columbia'}, and if these are found in the
affiliation of the submitter, then this reviewer will not be assigned.Got any problem?Pleaseopen an issue
Package: assimp-py
assimp-pyMinimal Python Bindings for ASSIMP Library using C-APIInstallationpip install assimp-pyfrom sourcegit clone https://github.com/ranjian0/assimp_py.git
cd assimp_py
python setup.py installcmake>=3.18 is required for building from source[Optional] Run tests to make sure everything works finepip install pytest
pytest testsExample Programimportassimp_py# -- loading the sceneprocess_flags=(assimp_py.Process_Triangulate|assimp_py.Process_CalcTangentSpace)scene=assimp_py.ImportFile("models/planet/planet.obj",process_flags)# -- getting dataforminscene.meshes:# -- getting vertex data# vertices are guaranteed to existverts=m.vertices# other components must be checked for Nonenormals=[]orm.normalstexcoords=[]orm.texcoordstangents=[]orm.tangentsbitangent=[]orm.bitangents# -- getting materials# mat is a dict consisting of assimp material propertiesmat=scene.materials[m.material_index]# -- getting colordiffuse_color=mat["COLOR_DIFFUSE"]# -- getting texturesdiffuse_tex=mat["TEXTURES"][assimp_py.TextureType_DIFFUSE]Supported Mesh FormatsAMF 3DS AC ASE ASSBIN B3D BVH COLLADA DXF CSM HMP IRRMESH IRR LWO LWS M3D MD2 MD3 MD5 MDC MDL NFF NDO OFF OGRE OPENGEX PLY MS3D COB BLEND IFC XGL FBX Q3D Q3BSP RAW SIB SMD STL TERRAGEN 3D X X3D GLTF 3MF MMD OBJ