def test_nonintersecting_window_index():
"""See gh-2378"""
t = from_origin(0, 0, 1, 1)
w = from_bounds(-3, -3, -1, -1, t)
data = np.arange(25).reshape(5, 5)
selection = data[window_index(w, height=5, width=5)]
assert selection.shape == (2, 0)
assert selection.flatten().tolist() == []
def test_window_index():
idx = window_index(((0, 4), (1, 12)))
assert len(idx) == 2
r, c = idx
assert r.start == 0
assert r.stop == 4
assert c.start == 1
assert c.stop == 12
arr = np.ones((20, 20))
assert arr[idx].shape == (4, 11)
def test_window_index():
idx = window_index(((0, 4), (1, 12)))
assert len(idx) == 2
r, c = idx
assert r.start == 0
assert r.stop == 4
assert c.start == 1
assert c.stop == 12
arr = np.ones((20, 20))
assert arr[idx].shape == (4, 11)
def main(cadastre_shp_filepath, agglom_extent_filepath, dst_filepath, dst_res,
dst_nodata, dst_dtype):
logger = logging.getLogger(__name__)
cadastre_gdf = gpd.read_file(cadastre_shp_filepath,
bbox=(WEST, SOUTH, EAST, NORTH))
# rasterize the cadastre
cadastre_arr, cadastre_transform = rasterize_cadastre(
cadastre_gdf, dst_res, dst_nodata, dst_dtype)
logger.info("rasterized cadastre vector LULC dataset to shape %s",
str(cadastre_arr.shape))
# TODO; crop it to the extent
agglom_extent_geom_nodata = 0
agglom_extent_geom = gpd.read_file(
agglom_extent_filepath)['geometry'].iloc[:1]
agglom_extent_mask = features.rasterize(agglom_extent_geom,
out_shape=cadastre_arr.shape,
fill=agglom_extent_geom_nodata,
transform=cadastre_transform)
# get window and transform of valid data points, i.e., the computed extent
extent_window = windows.get_data_window(agglom_extent_mask,
nodata=agglom_extent_geom_nodata)
extent_transform = windows.transform(extent_window, cadastre_transform)
dst_arr = np.where(agglom_extent_mask, cadastre_arr,
dst_nodata)[windows.window_index(extent_window)]
# dump it
with rio.open(
dst_filepath,
'w',
driver='GTiff',
width=extent_window.width,
height=extent_window.height,
count=1,
crs=CRS, # cadastre_gdf.crs
transform=extent_transform,
dtype=dst_dtype,
nodata=dst_nodata) as dst:
dst.write(dst_arr, 1)
logger.info("dumped rasterized dataset to %s", dst_filepath)
def window_index(*args, **kwargs):
from rasterio.windows import window_index
warnings.warn("Deprecated; Use rasterio.windows instead", FutureWarning)
return window_index(*args, **kwargs)
def window_index(*args, **kwargs):
from rasterio.windows import window_index
warnings.warn("Deprecated; Use rasterio.windows instead", FutureWarning)
return window_index(*args, **kwargs)
def main(cadastre_filepath, dst_tif_filepath, dst_shp_filepath, dst_res,
num_patches, kernel_radius, urban_threshold, buffer_dist, dst_nodata):
logger = logging.getLogger(__name__)
logger.info("preparing raster agglomeration LULC from %s",
cadastre_filepath)
cadastre_arr, cadastre_transform = utils.rasterize_cadastre(
cadastre_filepath, dst_res, dst_nodata)
logger.info("rasterized cadastre vector LULC dataset to shape %s",
str(cadastre_arr.shape))
# get the urban extent mask according to the criteria used in the "Atlas
# of Urban Expansion, The 2016 Edition" by Angel, S. et al.
uf = ufp.UrbanFootprinter(cadastre_arr,
urban_classes=utils.URBAN_CLASSES,
res=dst_res)
urban_mask = uf.compute_footprint_mask(kernel_radius,
urban_threshold,
num_patches=num_patches,
buffer_dist=buffer_dist)
logger.info(
"obtained extent of the %d largest urban cluster(s) (%d pixels)",
num_patches, np.sum(urban_mask))
# exclude lake
# TODO: arguments to customize `LULC_WATER_VAL` and `SIEVE_SIZE`
label_arr = ndi.label(cadastre_arr == utils.LULC_WATER_VAL,
ndi.generate_binary_structure(2, 2))[0]
cluster_label = np.argmax(np.unique(label_arr,
return_counts=True)[1][1:]) + 1
largest_cluster = np.array(label_arr == cluster_label, dtype=np.uint8)
urban_mask = features.sieve(
np.array(urban_mask.astype(bool) & ~largest_cluster.astype(bool),
dtype=urban_mask.dtype), SIEVE_SIZE)
# get window and transform of valid data points, i.e., the computed extent
extent_window = windows.get_data_window(urban_mask, nodata=0)
extent_transform = windows.transform(extent_window, cadastre_transform)
dst_arr = np.where(urban_mask, cadastre_arr,
dst_nodata)[windows.window_index(extent_window)]
# dump it
# ACHTUNG: use hardcoded CRS string (for the same CRS) to avoid issues
with rio.open(
dst_tif_filepath,
'w',
driver='GTiff',
width=extent_window.width,
height=extent_window.height,
count=1,
crs=utils.CRS, # cadastre_gdf.crs
transform=extent_transform,
dtype=np.uint8,
nodata=dst_nodata) as dst:
dst.write(dst_arr, 1)
logger.info("dumped rasterized dataset to %s", dst_tif_filepath)
if dst_shp_filepath:
# save the geometry extent
# get the urban mask geometry
# urban_mask_geom = uf.compute_footprint_mask_shp(
# kernel_radius,
# urban_threshold,
# largest_patch_only=largest_patch_only,
# buffer_dist=buffer_dist,
# transform=extent_transform)
urban_mask_geom = geometry.shape(
max([(geom, val) for geom, val in features.shapes(
np.array(dst_arr != dst_nodata, dtype=np.uint8),
transform=extent_transform) if val == 1],
key=lambda geom: len(geom[0]['coordinates']))[0])
# get the window and transform of the lake extent
lake_mask = features.sieve(largest_cluster, SIEVE_SIZE)
extent_window = windows.get_data_window(lake_mask, nodata=0)
extent_transform = windows.transform(extent_window, cadastre_transform)
lake_mask = lake_mask[windows.window_index(extent_window)]
# get the lake mask geometry
lake_mask_geom = geometry.shape(
max([(geom, val) for geom, val in features.shapes(
lake_mask, transform=extent_transform) if val == 1],
key=lambda geom: len(geom[0]['coordinates']))[0])
# ACHTUNG: use hardcoded CRS string (for the same CRS) to avoid issues
gpd.GeoSeries([urban_mask_geom, lake_mask_geom],
crs=utils.CRS).to_file(dst_shp_filepath)
logger.info("dumped extent geometry to %s", dst_shp_filepath)
