print("Reclassifying data...")
# Remap the raw values to priorities and categories
table = read_dataframe(
src_dir / "NaturesNetwork_conservdesign_180625.tif.vat.dbf",
columns=["Value", "Priority", "Descrpt"],
read_geometry=False,
)
table = table.loc[table.Value > 0].copy()
table.Priority = table.Priority.astype("uint8")
table["category"] = table.Descrpt.str[0].astype("uint8")
remap_table = table[["Value", "Priority"]].values.astype("uint8")
priority_data = remap(data, remap_table, nodata=nodata)
write_raster(outfilename, priority_data, transform, DATA_CRS, nodata=nodata)
print("Adding overviews and masks...")
add_overviews(outfilename)
create_lowres_mask(
outfilename,
str(outfilename).replace(".tif", "_mask.tif"),
factor=MASK_FACTOR,
ignore_zero=False,
)
### Process indicators
# Extract cores from the categories
table["aquatic_cores"] = table.Descrpt.str.contains("Aquatic")
print("Reclassifying TNC data...")
table = read_dataframe(
src_dir / "Resilient_and_Connected20180308.tif.vat.dbf",
read_geometry=False,
columns=["Value"],
)
for i, row in table.iterrows():
if i == row.Value:
continue
data[data == row.Value] = i
write_raster(tnc_outfilename,
data,
transform=transform,
crs=DATA_CRS,
nodata=255)
# Reclassify to merge with below
print("Reclassifying TNC data for merge...")
# map TNC values to merged NatureScape / TNC values
tnc_values = {
# 0:0, # noop
2: 4,
3: 0,
# 4: 4, # noop
11: 4,
12: 2,
13: 4,
14: 4,
# if masks are all false for this window, we can skip this step
if not np.asarray(masks).sum():
continue
# stack mask bits and layer_bits along inner dimension so that we have a shape of
# (height, width, total_bits)
data_bits = np.dstack(masks + layer_bits)
# packbits must be in little order to read the whole array properly in JS
packed = np.squeeze(np.packbits(data_bits, axis=-1, bitorder="little"))
window_shape = (window.height, window.width)
# fill remaining bytes up to dtype bytes
fill = np.zeros(shape=window_shape, dtype="uint8")
encoded = np.dstack([packed] + ([fill] * (num_bytes - packed.shape[-1])))
out[window.toslices()] = encoded.view(dtype).reshape(window_shape)
# determine the window where data are available, and write out a smaller output
print("Calculating data window...")
data_window = get_data_window(out, nodata=0)
out = out[data_window.toslices()]
transform = blueprint.window_transform(data_window)
print("Writing GeoTIFF...")
outfilename = out_dir / f"indicators_{i}.tif"
write_raster(outfilename, out, transform=transform, crs=blueprint.crs, nodata=0)
add_overviews(outfilename)
print("Extracting Florida inland input area mask...")
mask, transform, window = get_input_area_mask("fl")
### Extract FL Blueprint
print("Reading and warping Florida Blueprint...")
with rasterio.open(src_dir / "HubsData&Blueprint/Blueprint_V_1_3.tif") as src:
nodata = int(src.nodata)
data = extract_window(src, window, transform, nodata=nodata)
# fill with 0 where not 1 or 2
data[data == nodata] = 0
# apply mask
data = np.where(mask == 1, data, nodata).astype("uint8")
write_raster(outfilename, data, transform=transform, crs=DATA_CRS, nodata=nodata)
add_overviews(outfilename)
create_lowres_mask(
outfilename,
str(outfilename).replace(".tif", "_mask.tif"),
factor=MASK_FACTOR,
ignore_zero=False,
)
### Extract FL indicators from CLIP v4
indicators = {
"clip4_aggregate": "Priority_CLIP4.tif",
"clip4_fnai_habitat": "fhab_clip41.tif",
"clip4_landscape_resource": "landscape_rp_CLIP4.tif",
width=window.width,
height=window.height,
nodata=nodata,
transform=transform,
crs=DATA_CRS,
resampling=Resampling.nearest,
)
raw_data = vrt.read()[0]
raw_data = raw_data.astype("uint16")
write_raster(
src_dir / "midse_raw.tif",
raw_data,
transform=transform,
crs=DATA_CRS,
nodata=nodata,
)
print("Reclassifying data...")
table = read_dataframe(src_dir / "MidSE_2020_CVI.img.vat.dbf")
table["priority"] = table.M_SEBcode.astype("uint8")
remap_table = table[["Value", "priority"]].values.astype("uint16")
data = remap(raw_data, remap_table, nodata=nodata)
print("Done reclassifying values")
# coerce down to uint8, set nodata to smaller value
data[data == nodata] = 255
