Metadata-Version: 2.4
Name: rusterize
Version: 0.8.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Dist: numpy>=2.0.0
Requires-Dist: geopandas>=1.0.0 ; extra == 'all'
Requires-Dist: pandas>=2.0.0 ; extra == 'all'
Requires-Dist: pyarrow>=18.1.0 ; extra == 'all'
Requires-Dist: polars>=1.19.0 ; extra == 'all'
Requires-Dist: polars-st>=0.4.3 ; extra == 'all'
Requires-Dist: rioxarray>=0.18.2 ; extra == 'all'
Requires-Dist: xarray>=2025.1.1 ; extra == 'all'
Requires-Dist: xarray>=2025.1.1 ; extra == 'xarray'
Requires-Dist: rioxarray>=0.18.2 ; extra == 'xarray'
Provides-Extra: all
Provides-Extra: xarray
License-File: LICENSE
Summary: High performance rasterization tool for Python built in Rust
Keywords: rust,fast,raster,geometry,geopandas,xarray
Requires-Python: >=3.11
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: repository, https://github.com/ttrotto/rusterize

<p align="center">
  <img src="img/logo.png" width="620"/>
</p>

<br>

High performance rasterization tool for Python built in Rust, inspired by the [fasterize](https://github.com/ecohealthalliance/fasterize.git) package with lots of useful improvements (see [API](#API)).

**rusterize** is designed to work on _all_ shapely geometries, even when they are nested inside complex geometry collections. Functionally, it supports four input types:

- [geopandas](https://geopandas.org/en/stable/) GeoDataFrame
- [polars-st](https://oreilles.github.io/polars-st/) GeoDataFrame
- Python list of geometries in WKB or WKT format
- Numpy array of geometries in WKB or WKT format

It returns a [xarray](https://docs.xarray.dev/en/stable/), a [numpy](https://numpy.org/), or a sparse array in COOrdinate format.

### Installation

`rusterize` comes with numpy as the only required dependency and is distributed in different flavors. A `core` library that performs the rasterization and returns a bare `numpy` array, a `xarray` flavor that returns a georeferenced `xarray` (requires `xarray` and `rioxarray` and is the recommended flavor), or an `all` flavor with dependencies for all supported inputs.

Install the current version with pip:

```shell
# core library
pip install rusterize

# xarray capabilities
pip install 'rusterize[xarray]'

# support all input types
pip install 'rusterize[all]'
```

### Contributing

Any contribution is welcome! You can install **rusterize** directly from this repo using [maturin](https://www.maturin.rs/) as an editable package. For this to work, you’ll need to have [Rust](https://www.rust-lang.org/tools/install) and [cargo](https://doc.rust-lang.org/cargo/getting-started/installation.html) installed. To run the tests you need to have `gdal` installed as well as the `rusterize[all]` flavor.

```shell
# clone repo
git clone https://github.com/<username>/rusterize.git
cd rusterize

# install Rust nightly toolchain
rustup toolchain install nightly-2026-01-09

# install maturin
pip install maturin

# install editable version with optmized code
maturin develop --profile dist-release

# test the new contribution
pytest
```

### API

**rusterize** has a simple API consisting of a single function `rusterize()`:

```python
from rusterize import rusterize

rusterize(
    data,
    like=None,
    res=(30, 30),
    out_shape=(10, 10),
    extent=(0, 10, 10, 20),
    field="field",
    by="by",
    burn=None,
    fun="sum",
    background=0,
    encoding="xarray",
    all_touched=False,
    tap=False,
    dtype="uint8"
)
```

- **data** : `geopandas.GeoDataFrame`, `polars.DataFrame`, `list`, `numpy.ndarray` <br>
  Input data to rasterize.
  - If `polars.DataFrame`, it must be have a "geometry" column with geometries stored in WKB or WKT format.
  - If `list` or `numpy.ndarray`, geometries must be in WKT, WKB, or shapely formats (EPSG is not inferred and defaults to None).

- **like** : `xarray.DataArray` or `xarray.Dataset` (default: None) <br>
  Template array used as a spatial blueprint (resolution, shape, extent). Mutually exclusive with `res`, `out_shape`, and `extent`. Requires xarray and rioxarray.

- **res** : `tuple` or `list` (default: None) <br>
  Pixel resolution defined as (xres, yres).

- **out_shape** : `tuple` or `list` (default: None) <br>
  Output raster dimensions defined as (nrows, ncols).

- **extent** : `tuple` or `list` (default: None) <br>
  Spatial bounding box defined as (xmin, ymin, xmax, ymax).

- **field** : `str` (default: None) <br>
  Column name to use for pixel values. Mutually exclusive with `burn`. Not considered when input is list or numpy.ndarray.

- **by** : `str` (default: None) <br>
  Column used for grouping. Each group is rasterized into a distinct band in the output. Not considered when input is list or numpy.ndarray.

- **burn** : `int` or `float` (default: None) <br>
  A static value to apply to all geometries. Mutually exclusive with `field`.

- **fun** : `str` (default: "last") <br>
  Pixel function to use when burning geometries. Available options: `sum`, `first`, `last`, `min`, `max`, `count`, or `any`.

- **background** : `int` or `float` (default: numpy.nan) <br>
  Value assigned to pixels not covered by any geometry.

- **encoding** : `str` (default: "xarray") <br>
  The format of the returned object: `"xarray"`, `"numpy"`, or `"sparse"`.

- **all_touched** : `bool` (default: False) <br>
  If True, every pixel touched by a geometry is burned.

- **tap** : `bool` (default: False) <br>
  Target Aligned Pixel: aligns the extent to the pixel resolution.

- **dtype** : `str` (default: "float64") <br>
  Output data type (e.g., `uint8`, `int32`, `float32`).

Note that control over the desired extent is not as strict as for resolution and shape. That is, when resolution, output shape, and extent are specified, priority is given to resolution and shape. So, extent is not guaranteed, but resolution and shape are. If extent is not given, it is taken from the polygons and is not modified, unless you specify a resolution value. If you only specify an output shape, the extent is maintained. This mimics the logics of `gdal_rasterize`.

### Encoding

`rusterize` offers three encoding options for the rasterization output. You can return a `xarray/numpy` with the rasterized geometries, or a new `SparseArray` structure. This `SparseArray` structure stores the band/row/column triplets of where the geometries should be burned onto the final raster, as well as their corresponding values before applying any pixel function. This can be used as an intermediate output to avoid allocating memory before materializing the final raster, or as a final product. `SparseArray` has three convenience functions: `to_xarray()`, `to_numpy()`, and `to_frame()`. The first two return the final `xarray/numpy` with the appropriate pixel function, the last returns a `polars` dataframe with only the coordinates and values of the rasterized geometries. Note that `SparseArray` avoids allocating memory for the array during rasterization until it's actually needed (e.g. calling `to_xarray()`). See below for an example.

### Usage

```python
from rusterize import rusterize
import geopandas as gpd
from shapely import wkt
import matplotlib.pyplot as plt

# construct geometries
geoms = [
    "POLYGON ((-180 -20, -140 55, 10 0, -140 -60, -180 -20), (-150 -20, -100 -10, -110 20, -150 -20))",
    "POLYGON ((-10 0, 140 60, 160 0, 140 -55, -10 0))",
    "POLYGON ((-125 0, 0 60, 40 5, 15 -45, -125 0))",
    "MULTILINESTRING ((-180 -70, -140 -50), (-140 -50, -100 -70), (-100 -70, -60 -50), (-60 -50, -20 -70), (-20 -70, 20 -50), (20 -50, 60 -70), (60 -70, 100 -50), (100 -50, 140 -70), (140 -70, 180 -50))",
    "GEOMETRYCOLLECTION (POINT (50 -40), POLYGON ((75 -40, 75 -30, 100 -30, 100 -40, 75 -40)), LINESTRING (60 -40, 80 0), GEOMETRYCOLLECTION (POLYGON ((100 20, 100 30, 110 30, 110 20, 100 20))))"
]

# create a GeoDataFrame with shapely geometries from WKT
gdf = gpd.GeoDataFrame({'value': range(1, len(geoms) + 1)}, geometry=wkt.loads(geoms), crs='EPSG:32619')

# rusterize to "xarray" -> return a xarray with the burned geometries and spatial reference (default)
# will raise a ModuleNotFoundError if xarray and rioxarray are not found
output = rusterize(
    gdf,
    res=(1, 1),
    field="value",
    fun="sum",
).squeeze()

# plot it
fig, ax = plt.subplots(figsize=(12, 6))
output.plot.imshow(ax=ax)
plt.show()

# rusterize to "sparse" -> custom structure storing the coordinates and values of the rasterized geometries
output = rusterize(
    gdf,
    res=(1, 1),
    field="value",
    fun="sum",
    encoding="sparse"
)
output
# SparseArray:
# - Shape: (131, 361)
# - Extent: (-180.5, -70.5, 180.5, 60.5)
# - Resolution: (1.0, 1.0)
# - EPSG: 32619
# - Estimated size: 378.33 KB

# materialize into xarray or numpy
array = output.to_xarray()
array = output.to_numpy()

# get only coordinates and values
output.to_frame()
# shape: (29_340, 3)
# ┌─────┬─────┬──────┐
# │ row ┆ col ┆ data │
# │ --- ┆ --- ┆ ---  │
# │ u32 ┆ u32 ┆ f64  │
# ╞═════╪═════╪══════╡
# │ 6   ┆ 40  ┆ 1.0  │
# │ 6   ┆ 41  ┆ 1.0  │
# │ 6   ┆ 42  ┆ 1.0  │
# │ 7   ┆ 39  ┆ 1.0  │
# │ 7   ┆ 40  ┆ 1.0  │
# │ …   ┆ …   ┆ …    │
# │ 64  ┆ 258 ┆ 1.0  │
# │ 63  ┆ 259 ┆ 1.0  │
# │ 62  ┆ 259 ┆ 1.0  │
# │ 61  ┆ 260 ┆ 1.0  │
# │ 60  ┆ 260 ┆ 1.0  │
# └─────┴─────┴──────┘
```

![](img/plot.png)

### Benchmarks

**rusterize** is fast! Let’s try it on small and large datasets in comparison to GDAL ([benchmark_rusterize.py](benchmarks/benchmark_rusterize.py)). You can run this with [pytest](https://docs.pytest.org/en/stable/) and [pytest-benchmark](https://pytest-benchmark.readthedocs.io/en/stable/):

```
pytest <python file> --benchmark-min-rounds=10 --benchmark-time-unit='s'

-------------------------------------------------------------- benchmark: 7 tests -------------------------------------------------
Name (time in s)                  Min       Max      Mean    StdDev    Median       IQR    Outliers       OPS    Rounds  Iterations
-----------------------------------------------------------------------------------------------------------------------------------
test_water_small_f64_numpy     0.0045    0.0074    0.0051    0.0006    0.0050    0.0006       29;13  194.3373       155           1
test_water_small_f64           0.0058    0.0239    0.0110    0.0040    0.0101    0.0059        38;2   91.2137       133           1
test_water_large_f64           1.6331    2.2212    1.8923    0.2013    1.9070    0.3507         5;0    0.5285        10           1
test_water_large_f64_numpy     1.6530    2.3126    1.8641    0.2078    1.8139    0.3054         2;0    0.5365        10           1
test_water_large_gdal_f64      2.3926    2.6120    2.4650    0.0849    2.4217    0.1024         2;0    0.4057        10           1
test_roads_uint8               3.7092   17.6956    6.6547    5.5787    3.8136    1.8291         2;2    0.1503        10           1
test_roads_gdal_uint8          9.2405    9.4942    9.3018    0.0727    9.2785    0.0445         1;1    0.1075        10           1
-----------------------------------------------------------------------------------------------------------------------------------
```

And fasterize ([benchmark_fasterize.r](benchmarks/benchmark_fasterize.r)). Note that it doesn't support custom `dtype` so the returning raster is `float64`.

```
Unit: seconds
            expr              min           lq        mean       median          uq         max neval
 fasterize_small_f64   0.05764281   0.06274373   0.1286875   0.06520358   0.1128432   0.6000182    10
 fasterize_large_f64  36.91321005  37.71877265  41.0140303  40.81343803  43.9201820  46.5596799    10
```

### Comparison with other tools

While **rusterize** is fast, there are other fast alternatives out there, including `rasterio` and `geocube`. However, **rusterize** allows for a seamless, Rust-native processing with similar or lower memory footprint that doesn't require you to install GDAL and returns the geoinformation you need for downstream processing with ample control over resolution, shape, extent, and data type.

The following is a time comparison of 10 runs (median) on the same large water bodies dataset used earlier (dtype is `float64`) ([run_others.py](benchmarks/run_others.py)).

```
rusterize: 1.9 sec
rasterio:  15.2 sec
geocube:   129.2 sec
```

### Integrations

**rusterize** is integrated into the following libraries:

- [rasterix](https://github.com/xarray-contrib/rasterix)

<br>
    
<em>Disclaimer: Logo originally generated with Nano Banana Pro</em>

