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FIX: Avoid loss of precision when casting in packet loading #781

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FIX: Avoid loss of precision when casting in packet loading #781

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28 changes: 22 additions & 6 deletions imap_processing/tests/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,10 +97,26 @@ def test_packet_file_to_datasets(use_derived_value, expected_mode):
np.testing.assert_array_equal(data["mode"], [expected_mode] * len(data["mode"]))


def test__create_minimum_dtype_array():
@pytest.mark.parametrize(
("arr", "dtype", "expected_dtype"),
[
# Expected basic case
([1, 2, 3], "uint8", "uint8"),
# We shouldn't go lower than requested either
([1, 2, 3], "uint16", "uint16"),
# Can't cast negative, fallback to default
([-1, 2, 3], "uint8", int),
# Small signed ints should be good
([-1, 2, 3], "int8", "int8"),
# Can't cast strings to ints, fallback to default
(["a", "b", "c"], "uint8", "<U1"),
# Can't cast floats to ints, fallback to default
([1, 2.5, 3], "uint8", float),
# Can't cast larger ints, fallback to default
([1, 1000, 2000], "uint8", int),
],
)
def test__create_minimum_dtype_array(arr, dtype, expected_dtype):
"""Test expected return types for minimum data types."""
result = utils._create_minimum_dtype_array([1, 2, 3], "uint8")
assert result.dtype == np.dtype("uint8")
# fallback to a generic array if the requested dtype can't be satisfied
result = utils._create_minimum_dtype_array(["a", "b", "c"], "uint8")
assert result.dtype == np.dtype("<U1")
result = utils._create_minimum_dtype_array(arr, dtype)
assert result.dtype == np.dtype(expected_dtype)
12 changes: 10 additions & 2 deletions imap_processing/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -299,10 +299,18 @@ def _create_minimum_dtype_array(values: list, dtype: str) -> npt.NDArray:
array : np.array
The array of values.
"""
# Create an initial array and then try to safely cast it to the desired dtype
x = np.asarray(values)
try:
return np.array(values, dtype=dtype)
# ValueError: when trying to cast strings (enum states) to ints
y = x.astype(dtype, copy=False)
# We need to compare the arrays to see if we trimmed any values by
# casting to a smaller datatype (e.g. float64 to uint8, 2.1 to 2)
if np.array_equal(x, y):
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This is all somewhat unfortunate in how many "arrays" we'll be creating here. If anyone has ideas for a better way to do this I'm interested in hearing it.

We are potentially creating 3 separate arrays:

  1. initial asarray from a list -> creates an int64
  2. Trying to cast to a smaller dtype -> creates a uint8
  3. Doing this array comparison in a higher-order dtype space.

They are all happening in numpy c-level functions so shouldn't be a huge hit, but it would be nice to not do this array comparison just to check loss of precision.

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My thought would be to modify the _get_minimum_numpy_datatype function to take the derived value logic into account and return the appropriate datatype.

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Really good suggestion, thanks.

I opened up: #786
with an alternative approach, which is actually able to get rid of the numpy try/except array creation too. The one downside is there isn't a great way to test all of the XTCE stuff currently :-/ I did test it on the new SWE XTCE file that was failing and it looks good, but I don't have a great way to parameterize that PR like we do with this one.

return y
except ValueError:
return np.array(values)
pass
return x


def packet_file_to_datasets(
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