Metadata-Version: 2.4 Name: ObjectNat Version: 2.0.0 Summary: ObjectNat is an open-source library created for geospatial analysis created by IDU team Author-email: DDonnyy <63115678+DDonnyy@users.noreply.github.com> License-Expression: BSD-3-Clause License-File: LICENSE.txt Requires-Python: <3.13,>=3.11 Requires-Dist: geopandas<2.0.0,>=1.1.0 Requires-Dist: iduedu<3.0.0,>=2.0.0 Requires-Dist: loguru<0.8.0,>=0.7.3 Requires-Dist: numpy<3.0.0,>=2.4.0 Requires-Dist: pandas<4.0.0,>=3.0.0 Requires-Dist: scipy<2.0.0,>=1.17.0 Requires-Dist: tqdm<5.0.0,>=4.66.2 Description-Content-Type: text/x-rst ObjectNat ========= Object-oriented Network Analysis Tools -------------------------------------- .. |badge-black| image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/psf/black :alt: Code style: black .. |badge-pypi| image:: https://img.shields.io/pypi/v/objectnat.svg :target: https://pypi.org/project/objectnat/ :alt: PyPI version .. |badge-ci| image:: https://github.com/IDUclub/ObjectNat/actions/workflows/quality.yml/badge.svg :target: https://github.com/IDUclub/ObjectNat/actions/workflows/quality.yml :alt: Tests and coverage .. |badge-codecov| image:: https://codecov.io/gh/IDUclub/ObjectNat/graph/badge.svg?token=K6JFSJ02GU :target: https://codecov.io/gh/IDUclub/ObjectNat :alt: Coverage .. |badge-license| image:: https://img.shields.io/badge/license-BSD--3--Clause-blue.svg :target: https://opensource.org/licenses/BSD-3-Clause :alt: License .. |badge-docs| image:: https://img.shields.io/badge/docs-latest-4aa0d5?logo=readthedocs :target: https://iduclub.github.io/ObjectNat/ :alt: Docs |badge-black| |badge-pypi| |badge-ci| |badge-codecov| |badge-license| |badge-docs| `РИДМИ (Russian) `__ .. image:: https://raw.githubusercontent.com/IDUclub/ObjectNat/master/docs/_static/ONlogo.svg :align: center :width: 400 :alt: ObjectNat logo **ObjectNat** is an open-source library developed by the **IDU** team for spatial and network analysis in urban studies. The library provides tools for analyzing **accessibility**, **visibility**, **noise propagation**, and **service provision**. ---- Key Features ------------ Each feature includes a **Jupyter Notebook example** and **full documentation**. 1. **Isochrones and Transport Accessibility** Isochrones represent areas reachable from an origin point within a specified time along a transport network. This feature allows the analysis of transport accessibility using pedestrian, road, public transport, or multimodal ``UrbanGraph`` objects prepared by IduEdu. The library supports several methods for building isochrones: - **Basic isochrones**: display a single zone reachable within a specified time. - **Step isochrones**: divide the accessibility area into time intervals (e.g., 3, 5, 10 minutes). 📘 `Example `__ 🔗 `Documentation `__ 2. **Graph Coverage Zones from Points** A function for generating **coverage areas** from a set of origin points using a transport network. It computes the area reachable from each point by **travel time** or **distance**, then builds polygons using **Voronoi diagrams** and clips them by a given boundary if specified. 📘 `Example `__ 🔗 `Documentation `__ 3. **Service Provision Analysis** A function to evaluate how well residential buildings and their populations are provided with services (e.g., schools, clinics) that have limited **capacity** and a defined **accessibility threshold** (in minutes or meters). The function models the **balance between supply and demand**, assessing how well services meet the needs of nearby buildings within an acceptable time. ObjectNat 2.0 returns a structured ``ProvisionResult`` with a sparse flow matrix and materialization helpers for buildings, services, and link geometries. 📘 `Example `__ 🔗 `Documentation `__ 4. **Visibility Analysis** A function for evaluating visibility from a given point or set of points to nearby buildings within a given radius. It is used to assess visual accessibility in urban environments. The unified ``get_visibility`` API supports accurate and simplified methods, including parallel execution for batches of observer points. 📘 `Example `__ 🔗 `Documentation `__ 5. **Noise Simulation & Noise Frame** Simulation of noise propagation from sources, taking into account **obstacles**, **vegetation**, and **environmental factors**. 📘 `Example `__ 🔗 `Documentation `__ 🧠 `Detailed theory `__ ---- City Graphs via *IduEdu* ------------------------ For optimal performance, **ObjectNat** is recommended to be used with graphs created by the `IduEdu `_ library. Graph-based ObjectNat methods consume ``iduedu.UrbanGraph`` directly; ObjectNat does not build NetworkX graphs internally. **IduEdu** is an open-source Python library designed for building and processing complex urban networks based on OpenStreetMap data. **IduEdu** can be installed via ``pip``:: pip install IduEdu Example usage:: from iduedu import get_4326_boundary, get_intermodal_graph poly = get_4326_boundary(osm_id=1114252) urban_graph = get_intermodal_graph(territory=poly, clip_by_territory=True) ---- Installation ------------ **ObjectNat** can be installed via ``pip``:: pip install ObjectNat ---- Configuration ------------- You can adjust logging and progress bar output using the config module:: from objectnat import config config.change_logger_lvl("INFO") # mute debug logs config.set_enable_tqdm(False) # disable tqdm progress bars Migration to ObjectNat 2.0 -------------------------- ObjectNat 2.0 replaces NetworkX-based graph inputs with ``iduedu.UrbanGraph``, renames accessibility functions, and returns ``ProvisionResult`` from service provision. See the migration guide in the documentation: `Migration 1.x to 2.0 `__. ---- Contacts -------- - `NCCR `_ — National Center for Cognitive Research - `IDU `_ — Institute of Design and Urban Studies - `Natalya Chichkova `_ — Project Manager - `Danila Oleynikov (Donny) `_ — Lead Software Engineer ---- Publications ------------ Coming soon.