pyQES

Version: 2.4.0 · Python: ≥ 3.10 · License: GPL-3.0-only

Python bindings for the Quick Environmental Simulation (QES) C++ suite — QES-Winds, QES-Plume, and QES-Fire.

The C++ core lives in the qes-core git submodule (fork of UtahEFD/QES-Public, branch develop_pyqes). This repository ships the pybind11 wrappers, the pyQES package, examples, and GitHub Actions for wheels / PyPI.

GPU: NVIDIA GPU with Compute Capability 7.0+ (CUDA wheel on Linux). CPU builds work without CUDA.

Features
Compatibility
Installation
Build from source
Quick start
Package layout
Examples
Development
Continuous integration
Updating the QES core
Links
License

Features

Run QES-Winds from Python (config / XML / JSON), with optional DEM / buildings preprocessing and vegetation (Homogeneous / IsolatedTree / tree shapefile)
Export wind magnitude to georeferenced GeoTIFF (pywinds.to_tif)
Export u/v arrow seeds as GeoJSON for MapLibre (pywinds.to_streamlines)
Export RK4 streamlines as GeoJSON LineStrings for MapLibre (pywinds.to_flowlines)
Run QES-Plume on winds + turbulence NetCDF fields
Run coupled QES-Fire (optional smoke plume)
Pydantic v2 config models, XML/JSON I/O, geospatial helpers
Prebuilt CPU wheels for Linux, macOS (arm64), and Windows; optional Linux CUDA wheel

Compatibility


Package version	`2.4.0`
Python	3.10 · 3.11 · 3.12 · 3.13
OS (CPU wheels)	Linux `x86_64` (manylinux_2_28) · macOS `arm64` (deployment target 14.0+) · Windows `AMD64`
GPU wheel	Linux CUDA (cp312), NVIDIA CC ≥ 7.0
Build system	scikit-build-core ≥ 1.0 · CMake ≥ 3.18 · C++17 · pybind11 ≥ 2.12

Runtime dependencies

Extra	Packages	Role
(core)	`pydantic≥2`, `numpy`	Config models & arrays
`geo`	`rasterio`, `pyproj`, `geopandas`	DEM / buildings preprocessing, GeoTIFF & GeoJSON export
`io`	`netCDF4`	NetCDF helpers (incl. `windsOut.nc` → GeoTIFF / GeoJSON)

Native libraries (from-source builds)

Boost (program-options, date-time, property-tree, optional), NetCDF-C++, GDAL — install via system packages or the included vcpkg manifest (vcpkg.json).

Installation

From PyPI

pip install pyqes

# optional extras
pip install "pyqes[geo]"      # geospatial preprocessing / GeoTIFF / GeoJSON
pip install "pyqes[io]"       # NetCDF helpers
pip install "pyqes[geo,io]"   # both (needed for to_tif / to_streamlines / to_flowlines)

# with uv
uv add pyqes
uv add "pyqes[geo,io]"

Verify:

import pyQES
print(pyQES.__version__)  # e.g. 2.4.0

From source

git clone --recursive https://github.com/rupeelab17/pyQES.git
cd pyQES

Native deps (Boost, NetCDF-C++, GDAL) — pick one:

# macOS (Homebrew)
brew install boost netcdf-cxx gdal

# Linux (Ubuntu / Debian)
sudo apt install libboost-all-dev libnetcdf-dev libnetcdf-c++4-dev \
  libnetcdf-cxx-legacy-dev libgdal-dev netcdf-bin cmake

# Debian Trixie (e.g. python:3.13-slim-trixie): libnetcdf-cxx-legacy-dev
# is not packaged — use only libnetcdf-dev and libnetcdf-c++4-dev:
# sudo apt install libboost-all-dev libnetcdf-dev libnetcdf-c++4-dev \
#   libgdal-dev netcdf-bin cmake

# Windows (vcpkg — recommended; needs Visual Studio / MSVC)
git clone https://github.com/microsoft/vcpkg.git
.\vcpkg\bootstrap-vcpkg.bat
$env:VCPKG_ROOT = (Resolve-Path .\vcpkg).Path
# CMAKE_ARGS is picked up by scikit-build-core / uv sync
$env:CMAKE_ARGS = "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT/scripts/buildsystems/vcpkg.cmake"

Alternatively on any OS, use the repo vcpkg.json manifest (same toolchain as the CI wheels).

# Editable install + extras ([uv](https://docs.astral.sh/uv/))
uv sync --extra geo --extra io

Always clone or pull with submodules:

git pull --recurse-submodules
# or
git submodule update --init --recursive

Build from source

Requirements beyond Python:

C++17 compiler
CMake ≥ 3.18
Git with submodule support
Native libs: Boost, NetCDF-C++, GDAL (or vcpkg via the repo manifest)

# after clone --recursive
uv sync --extra geo --extra io
# or
pip install -e ".[geo,io]"

Wheels are built with cibuildwheel (see Continuous integration).

Quick start

From a QES XML file

from pyQES import pywinds

result = pywinds.run(
    xml="examples/umep_workflow/qes/umep_larochelle.xml",
    dem="examples/umep_workflow/DEM_clip.tif",
    buildings_src="examples/umep_workflow/buildings.shp",
    buildings_mask="examples/umep_workflow/mask.shp",
    solver="cpu",
    work_dir="/tmp/qes_out",
)
print(result.winds_out)

Sensor paths referenced in the XML (e.g. sensor_umep.xml) are resolved relative to the XML directory. Set auto_preprocess=False to use the file as-is without DEM / buildings preprocessing.

From a Python config

from pyQES import pywinds
from pyQES.util.config import (
    WindsParameters,
    SensorParameters,
    TimeSeries,
    Homogeneous,
    IsolatedTree,
    VegetationParameters,
)

params = WindsParameters()
params.simulation_parameters.dem = "path/to/DEM.tif"
params.simulation_parameters.cell_size = (2.0, 2.0, 0.5)
params.simulation_parameters.halo_x = 40.0
params.simulation_parameters.halo_y = 40.0
params.simulation_parameters.domain_rotation = 0.0  # must be 0
params.buildings_params.shp_file = "path/to/buildings.shp"

# Optional vegetation — Homogeneous block (Cionco; rectangle or xVertex/yVertex)
params.vegetation_params = VegetationParameters(
    homogeneous=[
        Homogeneous(
            attenuation_coefficient=1.0,
            height=10.0,
            base_height=0.0,
            x_start=80.0,
            y_start=60.0,
            length=40.0,
            width=80.0,
            canopy_rotation=0.0,
        )
    ]
)
# Or IsolatedTree blocks (QES domain coords, metres):
# params.vegetation_params = VegetationParameters(
#     isolated_trees=[
#         IsolatedTree(
#             attenuation_coefficient=3.0,
#             height=15.0,
#             base_height=0.0,
#             z_max_lai=0.7,
#             x_center=20.0,
#             y_center=100.0,
#             width=10.0,
#         )
#     ]
# )
# Or trees from a point shapefile (fields H, D, LAI):
# params.vegetation_params = VegetationParameters(
#     shp_file="path/to/trees.shp", shp_tree_layer="trees"
# )

sensor = SensorParameters(
    time_series=[TimeSeries(speed=3.0, direction=270.0, height=10.0, site_z0=0.24)]
)
result = pywinds.run(config=params, sensor=sensor, solver="cpu", work_dir="/tmp/qes_out")
print(result.winds_out)

# optional GeoTIFF of |V| at 1.5 m AGL (needs pyqes[geo,io])
tif = pywinds.to_tif(z=1.5)
print(tif)

# optional GeoJSON arrows (bearing + speed) for MapLibre icon-rotate
arrows = pywinds.to_streamlines(z=1.5, stride=4)
print(arrows)

# optional RK4 streamlines (LineStrings) for MapLibre symbol-placement:line
flowlines = pywinds.to_flowlines(z=1.5, seed_stride=8)
print(flowlines)

pywinds.run accepts exactly one of config, xml, or json (or none, with keyword overrides). Solvers: "cpu" / "gpu" (or the QES integer code).

Package layout

Module	Role
`pyQES.pywinds`	QES-Winds — `run(...)`, `to_tif(...)`, `to_streamlines(...)`, `to_flowlines(...)`
`pyQES.pyplume`	QES-Plume — `run(xml=..., winds_file=..., turb_file=...)`
`pyQES.pyfire`	Coupled QES-Fire — `run(...)` (+ optional `plume_xml`)
`pyQES.util`	Pydantic config, XML/JSON I/O, paths, geo helpers, NetCDF→GeoTIFF / GeoJSON
`pyQES._winds` / `_plume` / `_fire` / `_util`	Compiled pybind11 extensions

Config models live in pyQES.util.config: WindsParameters, SimulationParameters, SensorParameters, TimeSeries, VegetationParameters, Homogeneous, IsolatedTree, etc.

Examples

Sample La Rochelle / UMEP inputs: examples/umep_workflow/ (DEM, buildings, mask, QES XML).

uv run python examples/run_winds_demo.py
uv run python examples/run_winds_demo.py --speed 5 --direction 180
uv run python examples/run_vegetation_demo.py
uv run python examples/run_vegetation_demo.py --canopy isolated
uv run python examples/umep_workflow/run_qeswinds.py
uv run python examples/umep_workflow/run_qeswinds_args.py --speed 5 --direction 180

Vegetation demo (examples/run_vegetation_demo.py): flat domain with one Homogeneous rectangle (default), --canopy isolated for IsolatedTree, or --trees-shp for a point shapefile (H, D, LAI).

From a WGS84 bbox (pymdurs / IGN)

Script: examples/pymdurs_workflow/run_from_bbox.py.

Downloads DEM, buildings, LiDAR trees, and mask via pymdurs (France + network), then runs QES-Winds. Tree tops (H, D, LAI) are buffered to crown polygons for QES. Default cell size is 2.5 2.5 1 (finer grids on large bboxes may segfault). Optional flags: --to-tif (|V| GeoTIFF), --to-streamlines (arrow Points), --to-flowlines (RK4 LineStrings), --no-trees (skip vegetation), --lai / --trees-resolution / --min-tree-height.

uv sync --extra geo --extra io
uv pip install pymdurs
uv run python examples/pymdurs_workflow/run_from_bbox.py --to-tif
uv run python examples/pymdurs_workflow/run_from_bbox.py \
  --bbox=-1.152704,46.181627,-1.139893,46.18699 --to-tif --to-streamlines --to-flowlines
# reuse previous IGN / LiDAR downloads in examples/pymdurs_workflow/output/
uv run python examples/pymdurs_workflow/run_from_bbox.py --skip-fetch --to-tif --to-flowlines
# buildings + DEM only (no LiDAR trees)
uv run python examples/pymdurs_workflow/run_from_bbox.py --no-trees --to-tif

Development

uv sync --extra geo --extra io

# lint / typecheck
uv run ruff check pyQES tests examples
uv run mypy

# fast unit tests (no full solver run)
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 uv run pytest tests -m "not slow"

# end-to-end winds run (compiled extension + example data)
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 uv run pytest tests -m slow

Dev tools (via [dependency-groups] dev): pytest, pytest-cov, ruff, mypy, plus the geo / io extras.

Continuous integration

Workflow	Role
`ci.yml`	Ruff, mypy, pure-Python pytest
`wheels.yml`	CPU wheels — Linux / macOS / Windows · cp310–cp313 · sdist
`cuda-build.yml`	Linux CUDA wheel (cp312)
`publish.yml`	Publish to PyPI on `v*` tags

Updating the QES core

git submodule update --remote qes-core
git add qes-core
git commit -m "Bump qes-core"

Links


Homepage / source	https://github.com/rupeelab17/pyQES
PyPI	https://pypi.org/project/pyqes/
QES documentation	https://qes-documentation.readthedocs.io/en/latest
QES core (submodule)	https://github.com/rupeelab17/QES-Public
Upstream QES	https://github.com/UtahEFD/QES-Public

License

GPL-3.0-only — see LICENSE.

QES core: see qes-core/LICENSE.

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Release history Release notifications | RSS feed

2.5.1

Jul 27, 2026

This release

2.5.0

Jul 27, 2026

2.4.0

Jul 27, 2026

2.3.2

Jul 27, 2026

2.3.1

Jul 27, 2026

pyqes 2.5.0

pyQES

Table of contents

Features

Compatibility

Runtime dependencies

Native libraries (from-source builds)

Installation

From PyPI

From source

Build from source

Quick start

From a QES XML file

From a Python config

Package layout

Examples

From a WGS84 bbox (pymdurs / IGN)

Development

Continuous integration

Updating the QES core

Links

License

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

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