solweig 0.1.0b32

High-performance SOLWEIG urban microclimate model (Rust + Python)

SOLWEIG

High-performance urban microclimate model for computing Mean Radiant Temperature (Tmrt) and thermal comfort indices (UTCI, PET).

Rust + Python performance-critical algorithms with GPU and tiled processing support.

This package is currently in testing as a proof of concept. Please open an issue if you have any feedback or suggestions.

Documentation

• Quick Start Guide - Detailed tutorial
• API Reference - Full documentation site (MkDocs, 25 pages)
• Physics Specifications - Scientific documentation (10 specs)
• ROADMAP.md - Development priorities

Original Code

This package is adapted from the GPLv3-licensed UMEP-processing by Fredrik Lindberg, Ting Sun, Sue Grimmond, Yihao Tang, and Nils Wallenberg.

Licensed under GNU General Public License v3.0. See LICENSE for details.

Citation:

Adapted from UMEP (Urban Multi-scale Environmental Predictor) by Fredrik Lindberg, Sue Grimmond, and contributors. If you use this plugin in research, please cite:

Lindberg F, Grimmond CSB, Gabey A, Huang B, Kent CW, Sun T, Theeuwes N, Järvi L, Ward H, Capel-Timms I, Chang YY, Jonsson P, Krave N, Liu D, Meyer D, Olofson F, Tan JG, Wästberg D, Xue L, Zhang Z (2018) Urban Multi-scale Environmental Predictor (UMEP) - An integrated tool for city-based climate services. Environmental Modelling and Software 99, 70-87 doi:10.1016/j.envsoft.2017.09.020

Installation

# Clone and install
git clone https://github.com/UMEP-dev/solweig.git
cd solweig
uv sync                  # Install Python dependencies
maturin develop          # Build Rust extension

Quick Start

import solweig
from datetime import datetime

# Create surface from DSM array
surface = solweig.SurfaceData(dsm=my_dsm_array, pixel_size=1.0)

# Define location and weather
location = solweig.Location(latitude=57.7, longitude=12.0)
weather = solweig.Weather(
    datetime=datetime(2024, 7, 15, 12, 0),
    ta=25.0,        # Air temperature (°C)
    rh=50.0,        # Relative humidity (%)
    global_rad=800.0  # Global radiation (W/m²)
)

# Calculate Tmrt
result = solweig.calculate(surface, location, weather)
print(f"Tmrt: {result.tmrt.mean():.1f}°C")

Loading from GeoTIFFs

import solweig

# Load and prepare surface data (auto-computes walls/SVF)
surface = solweig.SurfaceData.prepare(
    dsm="data/dsm.tif",
    working_dir="cache/",       # Walls/SVF cached here
    cdsm="data/cdsm.tif",       # Optional: vegetation
)

# Load weather from EPW file
weather_list = solweig.Weather.from_epw(
    "data/weather.epw",
    start="2023-07-01",
    end="2023-07-03",
)

# Calculate timeseries
results = solweig.calculate_timeseries(
    surface=surface,
    weather_series=weather_list,
    output_dir="output/",
)

Post-Processing (UTCI/PET)

Thermal comfort indices can be computed directly from results:

# Single timestep: compute directly from result
result = solweig.calculate(surface, location, weather)
utci = result.compute_utci(weather)  # Fast polynomial
pet = result.compute_pet(weather)    # Slower iterative solver

# Batch processing: from saved Tmrt files
solweig.compute_utci(tmrt_dir="output/", weather_series=weather_list, output_dir="utci/")
solweig.compute_pet(tmrt_dir="output/", weather_series=weather_list, output_dir="pet/")

Input Validation

Validate inputs before expensive calculations:

try:
    warnings = solweig.validate_inputs(surface, location, weather)
    for w in warnings:
        print(f"Warning: {w}")
    result = solweig.calculate(surface, location, weather)
except solweig.GridShapeMismatch as e:
    print(f"Grid mismatch: {e.field}")
except solweig.MissingPrecomputedData as e:
    print(f"Missing data: {e}")

Demos

Complete working examples are in the demos/ folder:

• demos/athens-demo.py - Full workflow with GeoTIFFs
• demos/solweig_gbg_test.py - Gothenburg test data

QGIS Plugin

SOLWEIG is available as a QGIS Processing plugin for interactive spatial analysis:

1. Open QGIS → Plugins → Manage and Install Plugins
2. Go to Settings tab → Check "Show also experimental plugins"
3. Search for "SOLWEIG" in the All tab
4. Click Install Plugin

See qgis_plugin/ for source code and development details.

Build & Test

maturin develop          # Build Rust extension
pytest tests/            # Run all 353 tests
poe verify_project       # Full verification (format, lint, test)

---

Development

Project Structure

pysrc/solweig/              # Python source (modular architecture)
  api.py                    # Public API re-exports
  models/                   # Dataclass package (~3,080 lines)
  components/               # Modular component functions
  computation.py            # Core orchestration logic
  timeseries.py             # Batch time series processing
  tiling.py                 # Large raster tiling support
rust/                       # Rust extensions via maturin
qgis_plugin/                # QGIS Processing plugin
tests/                      # 353 tests (100% pass rate)
  golden/                   # Reference data validation
  spec/                     # Physical property tests
docs/                       # MkDocs documentation site
specs/                      # Markdown specifications

Development Setup

# Install dependencies
uv sync

# Build Rust extension for development
maturin develop

# Run tests with coverage
pytest tests/ --cov=pysrc/solweig

# Format and lint
ruff format pysrc/ tests/
ruff check pysrc/ tests/ --fix

# Type checking
ty pysrc/

# Full verification pipeline
poe verify_project

Building Documentation

# Serve docs locally
mkdocs serve

# Build static site
mkdocs build

QGIS Plugin Bundle Preparation

The easiest way is to let GitHub Actions build the package — push a version tag (e.g. v0.1.0) or trigger the workflow manually. Download the universal ZIP from the release artifacts and upload it to the QGIS Plugin Repository.

The plugin source lives in qgis_plugin/solweig_qgis/ and is built using the qgis_plugin/build_plugin.py script, which compiles the Rust extension, bundles it with the Python library, and creates a distributable ZIP.

1. Update Plugin Metadata

Edit qgis_plugin/solweig_qgis/metadata.txt and increment the version.

2. Build the Plugin

# Build for current platform (builds Rust extension + bundles Python library)
python qgis_plugin/build_plugin.py

# Build and create a distributable ZIP for the current platform
python qgis_plugin/build_plugin.py --package

# Create a universal multi-platform ZIP from pre-built wheels in dist/
# (this is what GitHub Actions uses)
python qgis_plugin/build_plugin.py --universal

3. Test Plugin Locally

# Symlink for development (macOS)
ln -s "$(pwd)/qgis_plugin/solweig_qgis" ~/Library/Application\ Support/QGIS/QGIS3/profiles/default/python/plugins/solweig_qgis

Then:

1. Open QGIS
2. Plugins → Manage and Install Plugins → Installed
3. Enable SOLWEIG
4. Test in Processing Toolbox → SOLWEIG

4. Upload to QGIS Plugin Repository

1. Register at https://plugins.qgis.org/
2. Log in → My Plugins → Upload a plugin
3. Select the ZIP from qgis_plugin/solweig-qgis-*-universal.zip
4. Check "Experimental" for pre-release versions
5. Add changelog/release notes
6. Click Upload

Release Checklist

• Version incremented in qgis_plugin/solweig_qgis/metadata.txt
• All tests passing (pytest tests/)
• Documentation updated
• CHANGELOG.md updated
• Plugin tested in QGIS locally
• ZIP bundle created and validated
• Uploaded to plugins.qgis.org

Tooling Preferences

Tool	Use For	Instead Of
uv	Package management	pip, poetry, pipenv
ruff	Linting and formatting	black, isort, flake8, pylint
ty	Type checking	mypy, pyright

Code Metrics

• api.py: 403 lines (simplified from 3,976)
• models/ package: ~3,080 lines (6 modules)
• Component functions: All ≤ 455 lines
• Test count: 353 tests (100% pass rate)
• Legacy code removed: 6,100 lines