query_window_by_polygon[source]

query_window_by_polygon(input_raster:Union[str, DatasetReader, PosixPath], output_path:str, geometry:Polygon, mask=False)

Query a cloud-optimized geotiff based on polygon and save to disk Returns a subset of a rasterio dataset based on a window defined by (left,right,bottom,top) coordinates. This function assumes that the CRS of the bounds is based on the input dataset

Args: input_raster (str, DatasetReader, PosixPath): Input rasterio dataset, which can be specified by a local filename or an open rasterio dataset output_path (str): Path to save output file geometry (polygon): polygon to crop Returns: None

Cloud optimized geotiffs are also supported, which allows you to query a geotiff hosted in an HTTP server.

query_window_by_gdf[source]

query_window_by_gdf(input_raster:Union[str, DatasetReader, PosixPath], output_folder:str, gdf, name_col=None, mask=False)

Query a subset of a geotiff based on a window and save to disk Returns a subset of a rasterio dataset based on a window defined by (left,right,bottom,top) coordinates. This function assumes that the CRS of the bounds is based on the input dataset

Cloud optimized geotiffs are also supported, which allows you to query a geotiff hosted in an HTTP server.

Args: input_raster (str, DatasetReader, PosixPath): Input rasterio dataset, which can be specified by a local filename or an open rasterio dataset output_folder (str): Path to save output file gdf, name_col (str): column name to base output filepath on. If none, outputs will be named sequentially as output_0.tif Returns: None

Download raster

input_image = "../data/phl_ppp_2020_constrained.tif"

raster = rio.open(input_image)
raster.meta

{'driver': 'GTiff',
 'dtype': 'float32',
 'nodata': -99999.0,
 'width': 11613,
 'height': 19781,
 'count': 1,
 'crs': CRS.from_epsg(4326),
 'transform': Affine(0.0008333333299750276, 0.0, 116.927916214,
        0.0, -0.0008333333300136493, 21.070416784)}

show(raster.read(1), cmap="plasma", transform=raster.transform)

<AxesSubplot:>

Define bounding box

bbox = (120.888062, 14.394778, 121.199112, 14.705822)

circle_gdf

	lat	lon	geometry
0	14.599512	120.984222	POLYGON ((121.98422 14.59951, 121.97941 14.501...

print(circle_gdf.crs)

epsg:4326

fig, ax = plt.subplots(1, 1, figsize=(4, 8))
show(raster.read(1), cmap="viridis", ax=ax, transform=raster.transform)
circle_gdf.plot(ax=ax, facecolor="none", edgecolor="yellow")

ax

<AxesSubplot:>

Crop a raster without masking

output_folder = Path("../data")

query_window_by_gdf(input_image, output_folder, circle_gdf, mask=False)

../data/output_0.tif

with rio.open(output_folder / "output_0.tif") as dst:
    fig, ax = plt.subplots(1, 1, figsize=(4, 4))

    show(dst.read(1), cmap="viridis", ax=ax, transform=dst.transform)
    circle_gdf.plot(facecolor="none", edgecolor="yellow", ax=ax)
    print(dst.read(1))
ax

[[-99999. -99999. -99999. ... -99999. -99999. -99999.]
 [-99999. -99999. -99999. ... -99999. -99999. -99999.]
 [-99999. -99999. -99999. ... -99999. -99999. -99999.]
 ...
 [-99999. -99999. -99999. ... -99999. -99999. -99999.]
 [-99999. -99999. -99999. ... -99999. -99999. -99999.]
 [-99999. -99999. -99999. ... -99999. -99999. -99999.]]

<AxesSubplot:>

Crop a raster with masking

Crop on multiple geometries at once

grid_generator = grids.SquareGridGenerator(100_000)

grid_gdf = grid_generator.generate_grid(circle_gdf)
grid_gdf["name"] = (
    "gridxy-" + grid_gdf["x"].astype(str) + "-" + grid_gdf["y"].astype(str)
)

grid_gdf

	x	y	geometry	name
0	0	0	POLYGON ((119.98422 13.59951, 120.88254 13.599...	gridxy-0-0
1	0	1	POLYGON ((119.98422 14.47100, 120.88254 14.471...	gridxy-0-1
2	0	2	POLYGON ((119.98422 15.33908, 120.88254 15.339...	gridxy-0-2
3	1	0	POLYGON ((120.88254 13.59951, 121.78085 13.599...	gridxy-1-0
4	1	1	POLYGON ((120.88254 14.47100, 121.78085 14.471...	gridxy-1-1
5	1	2	POLYGON ((120.88254 15.33908, 121.78085 15.339...	gridxy-1-2
6	2	0	POLYGON ((121.78085 13.59951, 122.67917 13.599...	gridxy-2-0
7	2	1	POLYGON ((121.78085 14.47100, 122.67917 14.471...	gridxy-2-1

fig, ax = plt.subplots(1, 1, figsize=(4, 8))
show(raster.read(1), cmap="viridis", ax=ax, transform=raster.transform)
grid_gdf.plot(ax=ax, facecolor="none", edgecolor="yellow")

ax

<AxesSubplot:>

query_window_by_gdf(input_image, output_folder, grid_gdf, name_col="name", mask=False)

../data/gridxy-0-0.tif
../data/gridxy-0-1.tif
../data/gridxy-0-2.tif
../data/gridxy-1-0.tif
../data/gridxy-1-1.tif
../data/gridxy-1-2.tif
../data/gridxy-2-0.tif
../data/gridxy-2-1.tif

for name in grid_gdf["name"]:
    image_path = output_folder / (name + ".tif")
    with rio.open(image_path) as dst:
        fig, ax = plt.subplots(1, 1, figsize=(4, 4))
        ax.set_title(image_path)
        show(dst.read(1), cmap="viridis", ax=ax, transform=dst.transform)

Crop on cloud optimized geotiff