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Correct Unexpected floats when reading LI L2 LFL #2998

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Correct Unexpected floats when reading LI L2 LFL #2998

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e9732c8
fix : Makes sure that the apply_fill_value method is not applyed if …
ClementLaplace Nov 22, 2024
c48d54f
feat : Converts computed latitude and longitude to a float32 and add …
ClementLaplace Dec 5, 2024
a47690e
typo: Erase whitespace as requested in codefactor
ClementLaplace Dec 5, 2024
0821afb
correction: Aplly corrections asked https://github.com/pytroll/satpy/…
ClementLaplace Dec 5, 2024
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13 changes: 4 additions & 9 deletions satpy/readers/li_base_nc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -306,7 +306,6 @@ def get_projection_config(self):
"""Retrieve the projection configuration details."""
# We retrieve the projection variable name directly from our swath settings:
proj_var = self.swath_coordinates["projection"]

geos_proj = self.get_measured_variable(proj_var, fill_value=None)
# cast projection attributes to float/str:
major_axis = float(geos_proj.attrs["semi_major_axis"])
Expand Down Expand Up @@ -355,9 +354,9 @@ def generate_coords_from_scan_angles(self):
# Finally, we should store those arrays as internal variables for later retrieval as
# standard datasets:
self.internal_variables[lon_name] = xr.DataArray(
da.asarray(lon), dims=["y"], attrs={"standard_name": "longitude"})
da.asarray(lon), dims=["y"], attrs={"standard_name": "longitude"}).astype(np.float32)
self.internal_variables[lat_name] = xr.DataArray(
da.asarray(lat), dims=["y"], attrs={"standard_name": "latitude"})
da.asarray(lat), dims=["y"], attrs={"standard_name": "latitude"}).astype(np.float32)

def inverse_projection(self, azimuth, elevation, proj_dict):
"""Compute inverse projection."""
Expand Down Expand Up @@ -439,12 +438,11 @@ def get_measured_variable(self, var_paths, fill_value=np.nan):
# Also handle fill value here (but only if it is not None, so that we can still bypass this
# step if needed)
arr = self.apply_fill_value(arr, fill_value)

return arr

def apply_fill_value(self, arr, fill_value):
"""Apply fill values, unless it is None."""
if fill_value is not None:
"""Apply fill values, unless it is None and when _FillValue is provided in the array attributes."""
if fill_value is not None and arr.attrs.get("_FillValue") is not None:
if np.isnan(fill_value):
fill_value = np.float32(np.nan)
arr = arr.where(arr != arr.attrs.get("_FillValue"), fill_value)
Expand Down Expand Up @@ -597,9 +595,7 @@ def apply_use_rescaling(self, data_array, ds_info=None):
# TODO remove scaling_factor fallback after issue in NetCDF is fixed
scale_factor = attribs.setdefault("scale_factor", attribs.get("scaling_factor", 1))
add_offset = attribs.setdefault("add_offset", 0)

data_array = (data_array * scale_factor) + add_offset

# rescale the valid range accordingly
if "valid_range" in attribs.keys():
attribs["valid_range"] = attribs["valid_range"] * scale_factor + add_offset
Expand Down Expand Up @@ -742,7 +738,6 @@ def get_dataset(self, dataset_id, ds_info=None):
# Retrieve default infos if missing:
if ds_info is None:
ds_info = self.get_dataset_infos(dataset_id["name"])

# check for potential error:
if ds_info is None:
raise KeyError(f"No dataset registered for {dataset_id}")
Expand Down
4 changes: 1 addition & 3 deletions satpy/readers/li_l2_nc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -158,11 +158,9 @@ def get_array_on_fci_grid(self, data_array: xr.DataArray):
data_2d = da.map_blocks(_np_add_at_wrapper, data_2d, (rows, cols), data_array,
dtype=data_array.dtype,
chunks=(LI_GRID_SHAPE[0], LI_GRID_SHAPE[1]))
data_2d = da.where(data_2d > 0, data_2d, np.nan)

data_2d = da.where(data_2d > 0, data_2d, np.nan).astype(np.float32)
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This would prevent one unnecessary upcasting:

Suggested change
data_2d = da.where(data_2d > 0, data_2d, np.nan).astype(np.float32)
data_2d = da.where(data_2d > 0, data_2d, np.float32(np.nan))

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@pnuu casting a nan who is a float32 is not enough to convert all the arrays into a float32 . For example if data_2d is an int32 the method where will convert it to a float64. To prevent it I have used the method astype(np.float32).

xarr = xr.DataArray(da.asarray(data_2d, CHUNK_SIZE), dims=("y", "x"))
xarr.attrs = attrs

return xarr


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