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Maintain raw precision instead of down casting to float32 #395

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Maintain raw precision instead of down casting to float32 #395

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598baf3
cast to float64
dmey Aug 14, 2022
93e7f62
Determine dtype from grib messages
juntyr Jul 30, 2024
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16 changes: 13 additions & 3 deletions cfgrib/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -345,11 +345,11 @@ class OnDiskArray:
)
missing_value: float
geo_ndim: int = attr.attrib(default=1, repr=False)
dtype = np.dtype("float32")
dtype: np.dtype = attr.attrib(default=np.dtype("float32"), repr=False)

def build_array(self) -> np.ndarray:
"""Helper method used to test __getitem__"""
array = np.full(self.shape, fill_value=np.nan, dtype="float32")
array = np.full(self.shape, fill_value=np.nan, dtype=self.dtype)
for header_indexes, message_ids in self.field_id_index.items():
# NOTE: fill a single field as found in the message
message = self.index.get_field(message_ids[0]) # type: ignore
Expand All @@ -363,7 +363,7 @@ def __getitem__(self, item):
header_item_list = expand_item(item[: -self.geo_ndim], self.shape)
header_item = [{ix: i for i, ix in enumerate(it)} for it in header_item_list]
array_field_shape = tuple(len(i) for i in header_item_list) + self.shape[-self.geo_ndim :]
array_field = np.full(array_field_shape, fill_value=np.nan, dtype="float32")
array_field = np.full(array_field_shape, fill_value=np.nan, dtype=self.dtype)
for header_indexes, message_ids in self.field_id_index.items():
try:
array_field_indexes = [it[ix] for it, ix in zip(header_item, header_indexes)]
Expand Down Expand Up @@ -595,12 +595,22 @@ def build_variable_components(
extra_coords_data[coord_name][header_value] = coord_value
offsets[tuple(header_indexes)] = message_ids
missing_value = data_var_attrs.get("missingValue", messages.MISSING_VAUE_INDICATOR)
if len(offsets) > 0:
# Infer the dtype from the first data message
header_indexes, message_ids = next(iter(offsets.items()))
message = index.get_field(message_ids[0])
values = get_values_in_order(message, np.empty(shape)[header_indexes].shape)
dtype = values.dtype
else:
# Fall back to a reasonable default dtype
dtype = np.dtype("float32")
on_disk_array = OnDiskArray(
index=index,
shape=shape,
field_id_index=offsets,
missing_value=missing_value,
geo_ndim=len(geo_dims),
dtype=dtype,
)

if "time" in coord_vars and "step" in coord_vars:
Expand Down