| Name | pc-rasterize JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Rasterize point cloud data in parallel |
| upload_time | 2024-09-19 19:04:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License |
| keywords |
point
cloud
raster
spatial
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# PC Rasterize: Rasterize Point Clouds in Parallel
---
## How to use:
```python
import pc_rasterize as pcr
import glob
files = sorted(glob.glob("../data/points/*.laz"))
# Create a GeoBox grid specification with a 100m buffer around data
geobox = pcr.build_geobox(files, resolution=0.50, crs="5070", buffer=100)
# Build a lazy CHM raster
chm = pcr.rasterize(
files,
geobox,
cell_func="max",
# Set custom dask chunk-size
chunksize=(500, 500),
nodata=np.nan,
# Filter out points over 100m
pdal_filters=[
{
"type": "filters.expression",
"expression": "Z < 100"
}
],
)
```
### Saving with default dask scheduling:
```python
# Use rioxarray to save to disk
chm.rio.to_raster("points_chm.tiff", tiled=True)
```
### Saving with dask's more advanced scheduling:
Dask's more advanced 'distributed' scheduling also provides a dashboard at
[http://localhost:8787/status](http://localhost:8787/status) for viewing
progress in your browser.
```python
from dask.distributed import Client, LocalCluster, Lock
with LocalCluster() as cluster, Client(cluster) as client:
chm.rio.to_raster("points_chm.tiff", tiled=True, lock=Lock("rio"))
```
Raw data
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