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7 changes: 7 additions & 0 deletions odc/stats/plugins/_base.py
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
from typing import Mapping, Optional, Sequence, Tuple

import xarray as xr
import numpy as np
from datacube.model import Dataset
from datacube.utils.geometry import GeoBox
from odc.algo import to_rgba
Expand Down Expand Up @@ -47,6 +48,12 @@ def measurements(self) -> Tuple[str, ...]:
pass

def native_transform(self, xx: xr.Dataset) -> xr.Dataset:
for var in xx.data_vars:
if (
xx[var].attrs.get("nodata") is None
and np.dtype(xx[var].dtype).kind == "f"
):
xx[var].attrs["nodata"] = xx[var].dtype.type(None)
return xx

def fuser(self, xx: xr.Dataset) -> xr.Dataset:
Expand Down
26 changes: 22 additions & 4 deletions odc/stats/plugins/lc_level3.py
Original file line number Diff line number Diff line change
Expand Up @@ -21,16 +21,22 @@ def measurements(self) -> Tuple[str, ...]:
_measurements = ["level3_class"]
return _measurements

def fuser(self, xx: xr.Dataset) -> xr.Dataset:
return xx

def reduce(self, xx: xr.Dataset) -> xr.Dataset:

# Cultivated pipeline applies a mask which feeds only terrestrial veg (110) to the model
# Just exclude no data (255) and apply the cultivated results
# Just exclude no data (255 or nan) and apply the cultivated results
# 255: load with product definition; nan: load without
# hence accormmodate both

res = expr_eval(
"where(a<nodata, a, b)",
"where((a!=a)|(a>=nodata), b, a)",
{"a": xx.cultivated_class.data, "b": xx.classes_l3_l4.data},
name="mask_cultivated",
dtype="float32",
**{"nodata": NODATA},
**{"nodata": xx.cultivated_class.attrs.get("nodata")},
)

# Mask urban results with bare sfc (210)
Expand All @@ -42,10 +48,22 @@ def reduce(self, xx: xr.Dataset) -> xr.Dataset:
"b": xx.urban_classes.data,
},
name="mark_urban",
dtype="uint8",
dtype="float32",
**{"_u": 210},
)

# Mark nodata to 255 in case any nan

res = expr_eval(
"where(a==a, a, nodata)",
{
"a": res,
},
name="mark_nodata",
dtype="uint8",
**{"nodata": NODATA},
)

attrs = xx.attrs.copy()
attrs["nodata"] = NODATA
dims = xx.classes_l3_l4.dims[1:]
Expand Down
14 changes: 7 additions & 7 deletions odc/stats/plugins/lc_veg_class_a1.py
Original file line number Diff line number Diff line change
Expand Up @@ -94,9 +94,6 @@ def measurements(self) -> Tuple[str, ...]:
_measurements = ["classes_l3_l4", "water_seasonality"]
return _measurements

def native_transform(self, xx):
return xx

def fuser(self, xx):
return xx

Expand Down Expand Up @@ -175,12 +172,16 @@ def l3_class(self, xx: xr.Dataset):
)
elif b == "canopy_cover_class":
# aquatic_veg: (mangroves > 0) & (mangroves != nodata)
# mangroves.nodata = 255 or nan
l3_mask = expr_eval(
"where((a>0)&(a<nodata), m, b)",
"where((a>0)&((a<nodata)|(nodata!=nodata)), m, b)",
{"a": xx[b].data, "b": l3_mask},
name="mark_mangroves",
dtype="uint8",
**{"nodata": NODATA, "m": self.output_classes["aquatic_veg"]},
**{
"nodata": xx[b].attrs["nodata"],
"m": self.output_classes["aquatic_veg"],
},
)

# all unmarked values (0) is terretrial veg
Expand All @@ -199,10 +200,9 @@ def l3_class(self, xx: xr.Dataset):

# Mask nans with NODATA
l3_mask = expr_eval(
"where((a!=a)|(b>=nodata), nodata, e)",
"where((a!=a), nodata, e)",
{
"a": si5,
"b": xx.veg_frequency.data,
"e": l3_mask,
},
name="mark_nodata",
Expand Down
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