"""Some pcraster functions to support older models."""
import glob
import logging
import os
import tempfile
from os.path import basename, dirname, isdir, isfile, join
from pathlib import Path
import numpy as np
import pandas as pd
import rasterio
import xarray as xr
from hydromt.io import open_mfraster
from pyflwdir import core_conversion, core_d8, core_ldd
from pyproj import CRS
try:
import pcraster as pcr
HAS_PCRASTER = True
except ImportError:
HAS_PCRASTER = False
logger = logging.getLogger(__name__)
# specify pcraster map types
# NOTE non scalar (float) data types only
PCR_VS_MAP = {
"wflow_ldd": "ldd",
"wflow_river": "bool",
"wflow_streamorder": "ordinal",
"wflow_gauges": "nominal", # to avoid large memory usage in pcraster.aguila
"wflow_subcatch": "nominal", # idem.
"wflow_landuse": "nominal",
"wflow_soil": "nominal",
"wflow_reservoirareas": "nominal",
"wflow_reservoirlocs": "nominal",
"wflow_lakeareas": "nominal",
"wflow_lakelocs": "nominal",
"wflow_glacierareas": "nominal",
}
GDAL_DRIVER_CODE_MAP = {
"asc": "AAIGrid",
"blx": "BLX",
"bmp": "BMP",
"bt": "BT",
"dat": "ZMap",
"dem": "USGSDEM",
"gen": "ADRG",
"gif": "GIF",
"gpkg": "GPKG",
"grd": "NWT_GRD",
"gsb": "NTv2",
"gtx": "GTX",
"hdr": "MFF",
"hf2": "HF2",
"hgt": "SRTMHGT",
"img": "HFA",
"jpg": "JPEG",
"kro": "KRO",
"lcp": "LCP",
"map": "PCRaster",
"mbtiles": "MBTiles",
"mpr/mpl": "ILWIS",
"ntf": "NITF",
"pix": "PCIDSK",
"png": "PNG",
"pnm": "PNM",
"rda": "R",
"rgb": "SGI",
"rst": "RST",
"rsw": "RMF",
"sdat": "SAGA",
"sqlite": "Rasterlite",
"ter": "Terragen",
"tif": "GTiff",
"vrt": "VRT",
"xpm": "XPM",
"xyz": "XYZ",
}
GDAL_EXT_CODE_MAP = {v: k for k, v in GDAL_DRIVER_CODE_MAP.items()}
def write_clone(tmpdir, gdal_transform, wkt_projection, shape):
"""Write pcraster clone file to a tmpdir using gdal."""
from osgeo import gdal
gdal.AllRegister()
driver1 = gdal.GetDriverByName("GTiff")
driver2 = gdal.GetDriverByName("PCRaster")
fn = join(tmpdir, "clone.map")
# create temp tif file
fn_temp = join(tmpdir, "clone.tif")
TempDataset = driver1.Create(fn_temp, shape[1], shape[0], 1, gdal.GDT_Float32)
TempDataset.SetGeoTransform(gdal_transform)
if wkt_projection is not None:
TempDataset.SetProjection(wkt_projection)
# TODO set csr
# copy to pcraster format
driver2.CreateCopy(fn, TempDataset, 0)
# close and cleanup
TempDataset = None
return fn
def write_map(
data,
raster_path,
nodata,
transform,
crs=None,
clone_path=None,
pcr_vs="scalar",
**kwargs,
):
"""Write pcraster map files using pcr.report functionality.
A PCRaster clone map is written to a temporary directory if not provided.
For PCRaster types see https://www.gdal.org/frmt_various.html#PCRaster
Parameters
----------
data : ndarray
Raster data
raster_path : str
Path to output map
nodata : int, float
no data value
transform : affine transform
Two dimensional affine transform for 2D linear mapping
clone_path : str, optional
Path to PCRaster clone map, by default None
pcr_vs : str, optional
pcraster type, by default "scalar"
**kwargs:
not used in this function, mainly here for compatability reasons.
crs:
The coordinate reference system of the data.
Raises
------
ImportError
pcraster package is required
ValueError
if invalid ldd
"""
import tempfile
with tempfile.TemporaryDirectory() as tmpdir:
# deal with pcr clone map
if clone_path is None:
clone_path = write_clone(
tmpdir,
gdal_transform=transform.to_gdal(),
wkt_projection=None if crs is None else CRS.from_user_input(crs).wkt,
shape=data.shape,
)
elif not isfile(clone_path):
raise IOError(f'clone_path: "{clone_path}" does not exist')
pcr.setclone(clone_path)
if nodata is None and pcr_vs != "ldd":
raise ValueError("nodata value required to write PCR map")
# write to pcrmap
if pcr_vs == "ldd":
# if d8 convert to ldd
data = data.astype(np.uint8) # force dtype
if core_d8.isvalid(data):
data = core_conversion.d8_to_ldd(data)
elif not core_ldd.isvalid(data):
raise ValueError("LDD data not understood")
mv = int(core_ldd._mv)
ldd = pcr.numpy2pcr(pcr.Ldd, data.astype(int), mv)
# make sure it is pcr sound
# NOTE this should not be necessary
pcrmap = pcr.lddrepair(ldd)
elif pcr_vs == "bool":
pcrmap = pcr.numpy2pcr(pcr.Boolean, data.astype(bool), np.bool_(nodata))
elif pcr_vs == "scalar":
pcrmap = pcr.numpy2pcr(pcr.Scalar, data.astype(float), float(nodata))
elif pcr_vs == "ordinal":
pcrmap = pcr.numpy2pcr(pcr.Ordinal, data.astype(int), int(nodata))
elif pcr_vs == "nominal":
pcrmap = pcr.numpy2pcr(pcr.Nominal, data.astype(int), int(nodata))
pcr.report(pcrmap, raster_path)
# set crs (pcrmap ignores this info from clone ??)
if crs is not None:
with rasterio.open(raster_path, "r+") as dst:
dst.crs = crs
[docs]
def read_staticmaps_pcr(
root: Path | str, crs: int = 4326, obj: object = None, **kwargs
):
"""Read and staticmaps at <root/staticmaps> and parse to xarray."""
da = None
fn = join(root, "staticmaps", "*.map")
fns = glob.glob(fn)
if len(fns) == 0:
logger.warning(f"No staticmaps found at {fn}")
return
_staticmaps = open_mfraster(fns, **kwargs)
path = join(root, "staticmaps", "clim", "LAI*")
if len(glob.glob(path)) > 0:
ds_lai = open_mfraster(
path, concat=True, concat_dim="time", logger=logger, **kwargs
)
lai_key = list(ds_lai.data_vars)[0]
_staticmaps["LAI"] = ds_lai[lai_key]
# reorganize c_0 etc maps
da_c = []
list_c = [v for v in _staticmaps if str(v).startswith("c_")]
if len(list_c) > 0:
for i, v in enumerate(list_c):
da_c.append(_staticmaps[f"c_{i:d}"])
da = xr.concat(
da_c, pd.Index(np.arange(len(list_c), dtype=int), name="layer")
).transpose("layer", ...)
_staticmaps = _staticmaps.drop_vars(list_c)
_staticmaps["c"] = da
_staticmaps = _staticmaps.rename({"x": "lon", "y": "lat"})
# add maps to staticmaps
if obj is not None:
obj.set_grid(_staticmaps)
if obj.crs is None:
if crs is None:
crs = 4326 # default to 4326
obj.set_crs(crs)
return _staticmaps
[docs]
def write_staticmaps_pcr(
staticmaps: xr.Dataset,
root: Path | str,
):
"""Write staticmaps at <root/staticmaps> in PCRaster maps format."""
root = os.path.join(root, "staticmaps")
if not isdir(root):
os.makedirs(root)
profile_kwargs = {}
mask = True
ds_out = staticmaps
if "LAI" in ds_out.data_vars:
ds_out = ds_out.rename_vars({"LAI": "clim/LAI"})
if "c" in ds_out.data_vars:
for layer in ds_out["layer"]:
ds_out[f"c_{layer.item():d}"] = ds_out["c"].sel(layer=layer)
ds_out[f"c_{layer.item():d}"].raster.set_nodata(ds_out["c"].raster.nodata)
ds_out = ds_out.drop_vars(["c", "layer"])
logger.info("Writing (updated) staticmap files.")
# add datatypes for maps with same basenames, e.g. wflow_gauges_grdc
pcr_vs_map = PCR_VS_MAP.copy()
for var_name in ds_out.raster.vars:
base_name = "_".join(var_name.split("_")[:-1]) # clip _<postfix>
if base_name in PCR_VS_MAP:
pcr_vs_map.update({var_name: PCR_VS_MAP[base_name]})
# ds_out.raster.to_mapstack(
# root=join(root, "staticmaps"),
# mask=True,
# driver="PCRaster",
# pcr_vs_map=pcr_vs_map,
# logger=logger,
# )
ext = GDAL_EXT_CODE_MAP.get("PCRaster")
with tempfile.TemporaryDirectory() as tmpdir:
clone_path = write_clone(
tmpdir,
gdal_transform=ds_out.raster.transform.to_gdal(),
wkt_projection=None
if ds_out.raster.crs is None
else ds_out.raster.crs.to_wkt(),
shape=ds_out.raster.shape,
)
profile_kwargs.update({"clone_path": clone_path})
for var in ds_out.raster.vars:
if "/" in var:
# variables with in subfolders
folders = "/".join(var.split("/")[:-1])
if not isdir(join(root, folders)):
os.makedirs(join(root, folders))
var0 = var.split("/")[-1]
raster_path = join(root, folders, f"{var0}.{ext}")
else:
raster_path = join(root, f"{var}.{ext}")
profile_kwargs.update({"pcr_vs": pcr_vs_map.get(var, "scalar")})
da_out = ds_out[var].copy()
for k in ["height", "width", "count", "transform"]:
if k in profile_kwargs:
msg = f"{k} will be set based on the DataArray, remove the argument"
raise ValueError(msg)
if "nodata" in profile_kwargs:
da_out.raster.set_nodata(profile_kwargs.pop("nodata"))
nodata = da_out.raster.nodata
if nodata is not None and not np.isnan(nodata):
da_out = da_out.fillna(nodata)
elif nodata is None:
logger.warning(f"nodata value missing for {raster_path}")
if mask and "mask" in da_out.coords and nodata is not None:
da_out = da_out.where(da_out.coords["mask"] != 0, nodata)
if "crs" in profile_kwargs:
da_out.raster.set_crs(profile_kwargs.pop("crs"))
# check dimensionality
dim0 = da_out.raster.dim0
count = 1
if dim0 is not None:
count = da_out[dim0].size
da_out = da_out.sortby(dim0)
# write
for i in range(count):
if dim0:
bname = basename(raster_path).split(".")[0]
bname = f"{bname[:8]:8s}".replace(" ", "0")
raster_path = join(dirname(raster_path), f"{bname}.{i+1:03d}")
data = da_out.isel({dim0: i}).load().squeeze().data
else:
data = da_out.load().data
write_map(
data,
raster_path,
crs=da_out.raster.crs,
transform=da_out.raster.transform,
nodata=nodata,
**profile_kwargs,
)
