Saravanan666/pypi-org huggingface dataset:
Package: landscape-api-py3
Landscape API (Python 3)Client for the Landscape API (Python 3)Free software: MIT licenseDocumentation:https://landscape-api-py3.readthedocs.io.Featureseasy installation fromPyPI(you can usepipenv,pip,pipex,Chocolatey, …)working onWindows(pipxcreatelandscape-api.exeshim)working withPython>=v3.5(easilyfrom landscape_api_py3.base import API)for quick use can be installed withpipx install landscape_api_py3Quick startCheck if you have installedPython v3.5and above.To install Landscape API (Python 3), run this command in your terminal:On Linux:$pipinstalllandscape-api-py3$python-mlandscape-api--urihttps://your-uri-to-ls-api/api--key<yourAPIkey>--secret<yourAPIsecret>--jsonget-computers--limit1On Windows:C:\> pip install landscape-api-py3
C:\> python -m landscape-api --uri https://your-uri-to-ls-api/api --key <your API key> --secret <your API secret> --json get-computers --limit 1or you can usepipx(virtual environment will be created automatically):On Linux:$pipinstall--userpipx$pipxensurepath$exec$SHELL# Restart your shell to reload PATH$pipxinstalllandscape-api-py3$landscape-api--urihttps://your-uri-to-ls-api/api--key<yourAPIkey>--secret<yourAPIsecret>--jsonget-computers--limit1On Windows:C:\> pip install --user pipx
C:\> pipx ensurepath
C:\> REM Restart console window to reload PATH
C:\> pipx install landscape-api-py3
C:\> landscape-api --uri https://your-uri-to-ls-api/api --key <your API key> --secret <your API secret> --json get-computers --limit 1Known issuesnone (issues with dependencies resolved in v0.3.0)CreditsBased on packagelandscape-apifromCanonical Ltd.This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.LicenseHistoryv0.7.1 (2021-01-27)fixed documentationv0.7.0 (2021-01-27)fix #245: CreateScript endpoint raises exception (solution suggested and tested)fix #246: CreateScriptAttachment endpoint raises exception (solution suggested and tested)minor format changeadd pyproject.tomlv0.6.1 (2020-06-17)fix #19minor updatesv0.5.0 (2020-06-11)added support for Python 3.5, 3.6, 3.7, 3.8 and abovefixed minor bugsupdated documentation (Installation, Usage, Quick start)v0.4.2 (2020-06-10)fixed documentation import bugfixed default CA cert bugv0.4.1 (2020-06-10)fixed bug with imports - now it’s compatible with Canonicallandscape-apiv0.3.5 (2020-06-10)minor fixesv0.3.4 (2020-06-09)replacedpycurl–>requestsv0.2.0alpha (2020-06-08) - First pre-release of landscape-api package🎉 firstALPHA non-productionversion release oflandscape-apiported toPython v3.8Known issues (v0.2.0alpha):onWindowsdownload CA certificate file fromhttps://curl.haxx.se/ca/cacert.pemand use–ssl-ca-fileorLANDSCAPE_API_SSL_CA_FILE(see Landscape API documentationhere)onLinuxdepends ongnutlsandlibssl(requirepycurlpackage for installation)Before installation of the package (v0.2.0alpha):onUbuntu 16.04(Xenial Xerus) usesudo apt-get install -y libgnutls-devonUbuntu 20.04(Focal Fossa) usesudo apt-get install -y libgnutls28-dev libcurl4-openssl-dev libssl-devonWindows 10simply usepipx install landscape_api_py30.1.3 (2020-06-07)first release on PyPI
Package: landscapelaser
landscapelaserImplementation of thelandscapelaserPyPi package.Calculates sharpness of the loss landscape of a model.UseTo use the package, do:from landscapelaser import LandscapeLaser
ll = LandscapeLaser()

import numpy as np
arr = np.random.random((20,20))

sharpness, mean_loss = ll.calculate(values=arr)
print(
    f'The sharpness measure of the loss landscape is `{sharpness}`'
    f' with mean loss over the landscape equal to `{mean_loss}`'
)Here, arr is a 2d numpy array containing the loss values in the landscape. To calculate this, refer toloss-landscapes package.InstallationThe package is available on PyPI. Install using:pip install landscapelaser
Package: landscape-pdf
landscape-pdfUtility to convert a pdf for viewing in landscape mode usingPyPDF2package. This makes
it easier to read papers and other documents meant to a4 or
letter format on ereader devices like the remarkable2 which
do not make it easy to read content in landscape. Each
page is converted to two overlapping pages covering the
top and bottom of the original page.beforeafterInstallationCan be installed using pip withpip install landscape_pdfRunningInstallation places a script calledlandscape-pdfin the path which takes two arguments, the name of the pdf file to convert and the name of the output file. For examplelandscape-pdf 2012.13391.pdf 2012.13391_landscape.pdf
Package: landsifier
IntroductionLandsifieris a Python based library to estimate likely triggers of mapped landslides.
The Beta version of library consitute three machine learning based method for finding the trigger of Landslide inventories.Geometric feature based methodTopological feature based methodImage based methodSample output of each lmethodThe below plot shows the probability of each landslide polygons in testing inventory belonging to earthquake and rainfall-induced class. The majority trigger of
landslides is the final trigger of the testing inventory.(1) Geometric feature based methodThis method is based on using 2D landslide polygon geometric properties for classification. This method calculates various geometric properties of landslide polygon and these geometric properties are used as a feature space for machine learning based algorithm.Sample landslide polygonsThe below plot shows the sample landslide polygons of earthquake and rainfall-induced inventories.Geometric properties of landslide polygonThe geometric properties of landslide polygons used are:-Area (A) of landslide PolygonPerimetre (P) of Landslide PolygonRatio of Area (A) to Perimetre(P)Convex hull based measures (Ratio of area of polygon to area of convex hull fitted to polygon)Width of minimum area bounding box fitted to polygonEccentricity of ellipse fitted to polygon having area A and perimetre Pminor-axis of ellipse fitted to polygon having area A and perimetre PThe below plot shows the various geometric properties of landslide polygon(2) Topological feature based methodThis method convert 2D landslide polygon to 3D landslide shape by including elevation information.Landsifierlibrary compute topological
features of 3D landslide shape use topological data analysis. These topological features can be used in machine learning algorithm for landslide triggers
classification.Sample 3D landslide shapeThe below plot shows the sample 3D landslide shape of earthquake and rainfall-induced inventories.Coversion of 2D landslide polygons to 3D landslide shapeThe below plot shows the method for coversion of 2d landslide polygon to 3D shape.(3) Image based methodThis method convert landslide polygon data to landslide polygon Images. These converted landslide images are used as a input to
Convolutional Neural Networks for landslide classification.Sample Polygon ImagesThe below plot shows the sample landslide polygon Images.Coversion of 2D landslide polygons to ImagesThe below plot shows the method for coversion of 2d landslide polygon to grayscale binary images.
Package: landspout
Landspout is a static website generation tool, usingTornado Template. Create
your template structure, and your content, and point landspout at it.Landspout has three operational modes:Single run build of the siteWatch for changes in the source or template directory, rendering on changeRun a HTTP server while watching for changes and rendering themUsageSee theexampledirectory for template and content usage examples.usage: Static website generation tool

positional arguments:
  {build,watch,serve}   The command to run (default: build)

optional arguments:
  -h, --help            show this help message and exit
  -s SOURCE, --source SOURCE
                        Source content directory (default: content)
  -d DEST, --destination DEST
                        Destination directory for built content (default:
                        build)
  -t TEMPLATE DIR, --templates TEMPLATE DIR
                        Template directory (default: templates)
  -b BASE_URI_PATH, --base-uri-path BASE_URI_PATH
  --whitespace {all,single,oneline}
                        Compress whitespace (default: all)
  -n NAMESPACE, --namespace NAMESPACE
                        Load a JSON file of values to inject into the default
                        rendering namespace. (default: None)
  -i INTERVAL, --interval INTERVAL
                        Interval in seconds between file checks while watching
                        or serving (default: 3)
  --port PORT           The port to listen on when serving (default: 8080)
  --debug               Extra verbose debug logging (default: False)
  -v, --version         output version information, then exit
Package: landtransportsg
This is an unofficial Python package for interacting with APIs available atLTA DataMall.Installing the packageInstall the package usingpip:pip install landtransportsgUsing the packagePre-requisite:API key from LTA.Request for API access.The main steps are:Import a class.Instantiate an object from the class.Call a function on that object.For more information,refer to the documentation.Usage overviewInteracting withLTA DataMall’s API is done through one of three clients,
where each client corresponds with a set of endpoints.The four clients are:ActiveMobility,Geospatial,PublicTransportandTraffic.Each client contains several public functions, one function per endpoint. A
function’s name is the same as its corresponding endpoint’s ending path.Some functions accept named arguments, where an argument corresponds with a
parameter that the endpoint accepts.Why have separate clients instead of one single client?Without knowing howLTA DataMall’s API will evolve, and noticing that
the endpoints were themselves already partitioned into sets, it seemed like
a good idea to keep each set of endpoints in its own contextual client. This
allows for each set of endpoints to be customised on their own, e.g. thePublicTransportpassenger volume-related endpoints allow for a string
to be returned, whereas the other endpoints return a list.ReferenceLTA DataMall’s Developer Guide
Package: lanelet2anchors
lanelet2anchorsThis package enables the generation of diverse map-based anchor paths for a given lanelet or vehicle pose. It was developed as part of our paperLanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-based Anchor Paths. For details check ourproject page.InstallationUse Python >= 3.8 andpipto installpipinstalllanelet2anchorsUsageCheck thenotebooksin thedocumentationfor details and examples.Generate anchors for given vehiclefromlanelet2anchorsimportAnchorGeneratorlanelet_map=AnchorGenerator(osm_file=osm_file,origin_latitude=49.00345654351,origin_longitude=8.42427590707)anchors=lanelet_map.create_anchors_for_vehicle(vehicle_pose=X,anchor_length=100,num_anchors:5,)Generate anchors for a given Laneletfromlanelet2anchorsimportAnchorGeneratorlanelet_map=AnchorGenerator(osm_file=osm_file,origin_latitude=49.00345654351,origin_longitude=8.42427590707)anchors=lanelet_map.create_anchors_for_lanelet(lanelet_id=lanelet_map.lanelet_ids[0],anchor_length=100)CitationIf you use this code for scientific research, please consider citing@InProceedings{naumannHertleinLanelet2NuScenes,author={Naumann, Alexander and Hertlein, Felix and Grimm, Daniel and Zipfl, Maximilian and Thoma, Steffen and Rettinger, Achim and Halilaj, Lavdim and Luettin, Juergen and Schmid, Stefan and Caesar, Holger},title={Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-Based Anchor Paths},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},month={June},year={2023},pages={3247-3256}}LicenseThis project is licensed underCC-BY-NC-SA. Development byFZI Forschungszentrum Informatikand all rights reserved byRobert Bosch GmbH.
Package: lanelet2-parser
lanelet2-parser: Parser for Lanelet2TutorialCreating Lanelet2Parser instance>>> from lanelet2_parser import Lanelet2Parser
>>> lanelet2_parser = Lanelet2Parser()Parse OSM File>>> osm_data = lanelet2_parser.parse(osm_map_file_path)OSM Data FormatOSM
- version: Float
- nodes: List<Node>
- ways: List<Way>
- relations: List<Relation>

Node
- id: Int
- lat: Decimal
- lon: Decimal
- ele: Decimal
- tags: List<Tag>
- visible: Bool

Way
- id: Int
- nodes: List<Node>
- tags: List<Tag>
- visible: Bool

Relation
- id: Int
- members: List<Member>
- tags: List<Tag>
- visible: Bool

Tag
- key: String
- value: String

Member
- type: String
- ref: Int
- role: StringConvert Lanelet2 format from OSM>>> lanelet2_data = lanelet2_parser.convert_to_lanelet2(osm_data)Lanelet2 Data FormatLanelet2
- version: Float
- points: List<Node>
- line_strings: List<LineString>
- lanelets: List<Lanelet>
- areas: List<Area>
- polygons: List<Polygon>
- regulatory_elements: List<RegulatoryElement>
- line_string_relations: Dict<line_string_id: LineStringRelation>
- lanelet_relations: Dict<lanelet_id: LaneletRelation>

Point
- id: Int
- lat: Decimal
- lon: Decimal
- ele: Decimal
- x: Decimal
- y: Decimal
- z: Decimal
- attibutes: List<Attribute>

LineString
- id: Int
- points: List<Point>
- attibutes: List<Attribute>

Lanelet
- id: Int
- left_bound: LineString
- right_bound: LineString
- center_line: LineString
- attibutes: List<Attribute>
- regulatory_element: List<RegulatoryElement>

RegulatoryElement
- id: Int
- subtype: String
- attibutes: List<Attribute>
- parameters: List<Parameter>

Parameter
- refers: LineString or Lanelet
- ref_line: LineString
- right_of_way: Lanelet
- param_yield: Lanelet
- cancels: Lanelet
- cancel_line: LineString

LineStringRelation
- prev_ids: List<Int>
- next_ids: List<Int>

LaneletRelation
- prev_ids: List<Int>
- next_ids: List<Int>
Package: lang
Lang - Python Language Constraints=============================Lang is a Python module that allows enforcing programming language constraints. Lang was built using a Java like mindset, so many of the constraints that are supported are mirrors of constraints in the Java programming language.Features:- Access Modifiers- Private & Protected modifiers- Protect variables, properties, getters and setters- Class decorators, Meta classes, Base classes to enforce constraints-  Interface classes (Only signatures & class members)- Abstract classes, methods, properties- Final classes (& methods WIP)Examples:--------------**Protected/Private members***Private variables can only be accessed by the same class while protected variables can also be accessed by subclasses. *class Box(object):__metaclass__ = EnforceProtectedMetadef __init__(self):self._cant_touch_this = 7or@enforce_protectedclass Box(object):def __init__(self):self._cant_touch_this = 7orclass Box(EnforceProtected):def __init__(self):self._cant_touch_this = 7Private is that same as protected but exchange 'protected' with 'private'.  Private variables are variables that begin with an '\_' and end with an '\_',  for example:  \_i_am_private_for_this_class\_**Protected/Private properties**class Box(object):@protected_propertydef size(self):...@size.setterdef size(self, value):...Protected setter/getter onlyclass Box(object):@protected_setter_onlydef size(self):...@size.setterdef size(self):...**Interface Classes**An interface is a class that can only implement function signatures and class members.class IBox(Interface):# or __metaclass__ = EnforceInterfaceMetaDEFAULT_HEIGHT = 10def size(self):passdef calculate_volume(self):passIf a function is implemented that it's source is not only 'pass' then an exception is thrown.**Abstract Classes**This exists already in python  (abc module) so I just wrapped it to be in the same package.class Box(Abstract):# or __metaclass__ = EnforceAbstractMeta@abstract_methoddef i_am_abstract(self):...@abstract_propertydef size(self):...**Final Classes**Final classes are classes that cannot be subclassedclass TheBestBox(object):__metaclass__ = FinalClassMetaInstallation----------------Either install from pip:pip install langor clone the git repo and run:python setup.py installLicense----------This module is under [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0) license.Contact Me----------------If you have any ideas or suggestions or bugs, please do contact me!LinkedIn: Amit AssarafEmail: amit@helpi.me
Package: langacore.kit.common
WarningTHIS LIBRARY IS DEPRECATED AND SUPERSEDED BYlck.common.This library consists of various simple common routines and language
constructs that are so useful they ten to be rewritten in every subsequent
project I’m working on. Each function, decorator or module on its own is too
simple to dedicate an entire PyPI package for it.  Together however, this
library represents a Swiss army knife for everyday needs (YMMV). Among the
things you might find inside:robust memoizationsome less obvious collections (e.g.orderedset)a@synchronizeddecorator (with threading or lockfile backends)file finder (searching locations commonly used for storing app data)Thesource code repositoryandissue
trackerare maintained onGitHub.Note:Since 0.2.0langacore.kit.commondoes requirePython
2.7because one of the virtues in the creation of this library is to
make the code beautiful.
Package: langacore.kit.django
WarningTHIS LIBRARY IS DEPRECATED AND SUPERSEDED BYlck.django.This library consists of various Django-related routines that extend or modify
the behaviour of the framework:extensions forsettings.py(current directory resolution, namespace
package support, settings profile support)Choices: an enum implementation for Django forms and models (with
predefined classes for languages, countries, etc.)typical filters, template tags, models, form fields, etc.Complete documentation for the package can be found here:http://packages.python.org/langacore.kit.django/Thesource code repositoryandissue
trackerare maintained onGitHub.This package bundles some royalty free static images that are useful in almost
every Django project:Silk icons 1.3 by FamFamFam- requires attributing the authorSilk Companion 1 by Damien Guard- requires
attributing the authorCountry Flags by SenojFlags.com- requires
using the following HTML:<a href="http://www.senojflags.com">Country flag</a> image from <a href="http://www.senojflags.com">Flags of all Countries</a>Note:Since 0.2.0langacore.kit.djangorequiresPython 2.7because
it’s using theabsolute_imports,division,print_functionandunicode_literalsfutures. One of the virtues in the creation of this library
is to make the code beautiful. These switches give us a useful transitional
state between the old Python 2.x and the new Python 3.x. You should use them as
well.
Package: langacore.thingsweforget
langacore.thingsweforgetThis is a simple script that synchronizes all post-it images fromhttp://thingsweforget.blogspot.comA couple of reasons why you would want to keep a separate copy of all these pictures:they are highly motivationalyou don’t always have Internet accessyou might want to create anawesome screensaverby simply pointing it to the directory
where you store those post-itsyou can shuffle the post-its and e-mail yourself daily one of themanything creative of the sorts, maybe you want to do a massive collage of the images for your next
yellow t-shirt?RequirementsYou need Python 3.1 withdistribute. Instructions on installing Python can be found on theofficial
Website, as fordistribute, download thedistribute_setup.pyfile fromhttp://python-distribute.organd run it:python3.1 distribute_setup.pyInstallationThe best bet will probably to use a stable package fromPyPIwhich you
can install by simply issuing:python3.1 -m easy_install langacore.thingsweforgetIt will download the package and hopefully successfully build its depedencies (it’slxmlfor its
awesome XML/HTML querying abilities).Alternatively, you candownload the source from GitHuband install it manually:git clone git://github.com/LangaCore/thingsweforget-fetcher.git
cd thingsweforget-fetcher
python3.1 setup.py installUsageFor now this comes to running this command:python3.1 -m langacore.thingsweforgetwhich will download all first images from every post on the website to the current folder.Need more details?The code is rather thoroughly documented, you can request more documentation from me
directly:lukasz@langa.pl.
Package: langai-client
# langai-python [![travis](https://api.travis-ci.com/lang-ai/langai-python.svg?branch=master)](https://travis-ci.com/lang-ai/langai-python)A LangAI API client written in Python.Check the [API Docs](https://docs.lang.ai) and the [Developer site](https://lang.ai/developers) for more info on how to use and integrate the API.> To work with the LangAI API, you must have a token. If you don't have a token already, [request an invite](https://lang.ai/developers).## InstallationInstall [`langai-python`](https://www.npmjs.com/package/langai-python) in your python project with pip.```pip install -u langai```See the example below on how to import and use the LangAI API to analyze intents in new texts. A projectId is required. Check the [`workflow`](https://docs.lang.ai/#workflow) documentation to get an overview of the process.## Usage```pythonfrom langai import LangAiClientlang = LangAiClient("your_token")params = {text: 'your-text',projectId: 'your-projectId',}result = lang.analyze(**params)print(result)```
Package: langassist
AssistantsDeveloping language assistants with LLMs in a low-code way.Quick InstallInstall using pip:pipinstalllangassistThe framework requires a database to register the assistants. The default database used is MongoAtlas. Future versions of the framework will support additional database types. Currently, there is a MongoDB collection named assistants which registers each assistant as shown below:_id : 6556db6cf114addad09420c6
assistant_name: "test_assist"
assistant_id: "asst_52TLl1OdJW6ulmLCEsDlizz8"
file_id: "file-lZUBJ3ZnxtkTfqsuE0WYMXDI"What are Assistants?Assistants is a framework for developing language-based assistants using micro agents, akin to the microservices concept. Each micro agent, like a microservice, can be independently developed and deployed. The framework adheres to the Single Responsibility Principle (SRP), allowing these assistants to be combined to address complex business processes through workflows.Capabilities of the FrameworkIn time, the framework will include built-in assistants for immediate deployment within OpenAI environments. It will also support the development of custom assistants.ContributionContributions are welcome in any form, be it through new features or improved documentation.Example UsageThe repository includes various examples demonstrating framework usage.# This is an example that shows how to use and deploy the summarization agent
# Deploy the agent first
# and then run the assistant
import os

from assistants.langassist.summarize_assistant import SummarizeAssistant

from dotenv import load_dotenv

from assistants.langregister.mongo_db_atlas import MongoAtlas

load_dotenv()
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
MONGO_PASSWORD = os.getenv("MONGO_PASSWORD")

mongo_client = MongoAtlas()
mongo_client.uri = "mongodb+srv://rajib76:{MONGO_PASSWORD}@cluster0.cp3rxai.mongodb.net/?retryWrites=true&w=majority".format(
    MONGO_PASSWORD=MONGO_PASSWORD)

assistant_name = "summarize_assistant_03"
file = "gen_ai.pdf"
def deploy_assistant():
    sc = SummarizeAssistant(file=file,assistant_name=assistant_name)
    fileobj, assistantobj = sc.deploy_assistant(mongo_client)
    print(fileobj,assistantobj)

def run_assistant():
    sc = SummarizeAssistant(assistant_name=assistant_name)
    question = "Summarize the content in 300 words. Please ensure all points are covered"
    print(sc.run_assistant(question,client=mongo_client))



if __name__ == "__main__":
    # First deploy
    # deploy_assistant()

    #Then run
    run_assistant()