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Add a check for Epoch being monotonically increasing in xarray_to_cdf #238

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Mar 15, 2024
Merged

Add a check for Epoch being monotonically increasing in xarray_to_cdf #238

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e9fdb5b
Add a check for Epoch being monotonically increasing
bryan-harter Mar 15, 2024
f782e0a
Fixing python linting stuff
bryan-harter Mar 15, 2024
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14 changes: 8 additions & 6 deletions cdflib/cdfwrite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -858,7 +858,9 @@ def _write_var_attrs(self, f: io.BufferedWriter, varNum: int, var_attrs: Dict[st
offset = self.attrsinfo[attrNum][2]

if entry is None:
logger.warning(f"Attribute: {attr}" + " is None type, which does not have an equivalent in CDF... Skipping attribute.")
logger.warning(
f"Attribute: {attr}" + " is None type, which does not have an equivalent in CDF... Skipping attribute."
)
continue

# Check if dataType was provided
Expand Down Expand Up @@ -2542,14 +2544,14 @@ def _checklistofstrs(obj: Any) -> bool:

@staticmethod
def _checklistofNums(obj: Any) -> bool:
'''
"""
This method checks if a list is ready to be immediately converted to binary format,
or if any pre-processing needs to occur. Numbers and datetime64 objects can be immediately converted.
'''
or if any pre-processing needs to occur. Numbers and datetime64 objects can be immediately converted.
"""
if hasattr(obj, "__len__"):
return bool(all(obj)) and all((isinstance(elem, numbers.Number) or isinstance(elem, np.datetime64)) for elem in obj)
return bool(all(obj)) and all((isinstance(elem, numbers.Number) or isinstance(elem, np.datetime64)) for elem in obj)
else:
return (isinstance(obj, numbers.Number) or isinstance(obj, np.datetime64))
return isinstance(obj, numbers.Number) or isinstance(obj, np.datetime64)

def _md5_compute(self, f: io.BufferedWriter) -> bytes:
"""
Expand Down
2 changes: 1 addition & 1 deletion cdflib/xarray/cdf_to_xarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -616,7 +616,7 @@ def _generate_xarray_data_variables(
var_data = _convert_fillvals_to_nan(var_data, var_atts, var_props)

# Finally, create the new variable
created_vars[var_name] = xr.Variable(var_dims, var_data, attrs=var_atts) # type: ignore[no-untyped-call]
created_vars[var_name] = xr.Variable(var_dims, var_data, attrs=var_atts)

return created_vars, depend_dimensions

Expand Down
24 changes: 20 additions & 4 deletions cdflib/xarray/xarray_to_cdf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -319,7 +319,14 @@ def _epoch_checker(dataset: xr.Dataset, dim_vars: List[str], terminate_on_warnin
epoch_found = False
for d in depend_0_list:
if d.lower().startswith("epoch"):
epoch_found = True
monotonically_increasing = _verify_monotonically_increasing(dataset[d].data)
if monotonically_increasing:
epoch_found = True
else:
_warn_or_except(
f"Variable {d} was determined to be an ISTP 'Epoch' variable, but it is not monotonically increasing.",
terminate_on_warning,
)

if not epoch_found:
_warn_or_except(
Expand All @@ -330,6 +337,10 @@ def _epoch_checker(dataset: xr.Dataset, dim_vars: List[str], terminate_on_warnin
return depend_0_list, time_varying_dimensions


def _verify_monotonically_increasing(epoch_data: npt.NDArray) -> np.bool_:
return np.all(epoch_data[1:] > epoch_data[:-1])


def _add_depend_variables_to_dataset(
dataset: xr.Dataset,
dim_vars: List[str],
Expand Down Expand Up @@ -402,8 +413,10 @@ def _variable_attribute_checker(dataset: xr.Dataset, epoch_list: List[str], term
# Ensure None of the attributes are given a type of "None"
for key, value in d[var].attrs.items():
if value is None:
_warn_or_except(f"CDF Warning: {key} was given a type of None for variable {var}. CDF does not allow None types, so {key} will be skipped.", terminate_on_warning)

_warn_or_except(
f"CDF Warning: {key} was given a type of None for variable {var}. CDF does not allow None types, so {key} will be skipped.",
terminate_on_warning,
)

# Check for VAR_TYPE
if "VAR_TYPE" not in d[var].attrs:
Expand Down Expand Up @@ -817,7 +830,10 @@ def xarray_to_cdf(
var_data = _datetime_to_tt2000(d[var].data)
elif datetime64_to_cdftt2000:
if d[var].dtype.type != np.datetime64:
_warn_or_except(f"datetime64_to_cdftt2000 is set, but datetime64 is not used in the {var} variable", terminate_on_warning)
_warn_or_except(
f"datetime64_to_cdftt2000 is set, but datetime64 is not used in the {var} variable",
terminate_on_warning,
)
else:
unixtime_from_datetime64 = d[var].data.astype("datetime64[ns]").astype("int64") / 1000000000
var_data = _unixtime_to_tt2000(unixtime_from_datetime64)
Expand Down
26 changes: 13 additions & 13 deletions tests/test_xarray_reader_writer.py
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Expand Up @@ -314,10 +314,10 @@ def test_build_from_scratch():
pytest.importorskip("xarray")
var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
var_dims = ["epoch", "direction"]
data = xr.Variable(var_dims, var_data) # type: ignore[no-untyped-call]
data = xr.Variable(var_dims, var_data)
epoch_data = [1, 2, 3]
epoch_dims = ["epoch"]
epoch = xr.Variable(epoch_dims, epoch_data) # type: ignore[no-untyped-call]
epoch = xr.Variable(epoch_dims, epoch_data)
ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
xarray_to_cdf(ds, "hello.cdf")
os.remove("hello.cdf")
Expand All @@ -337,14 +337,14 @@ def test_build_from_scratch():
"Logical_source": ":)",
"Logical_source_description": ":(",
}
data = xr.Variable(var_dims, var_data) # type: ignore[no-untyped-call]
epoch = xr.Variable(epoch_dims, epoch_data) # type: ignore[no-untyped-call]
data = xr.Variable(var_dims, var_data)
epoch = xr.Variable(epoch_dims, epoch_data)
ds = xr.Dataset(data_vars={"data": data, "epoch": epoch}, attrs=global_attributes)
xarray_to_cdf(ds, "hello.cdf")
os.remove("hello.cdf")
dir_data = [1, 2, 3]
dir_dims = ["direction"]
direction = xr.Variable(dir_dims, dir_data) # type: ignore[no-untyped-call]
direction = xr.Variable(dir_dims, dir_data)
ds = xr.Dataset(data_vars={"data": data, "epoch": epoch, "direction": direction}, attrs=global_attributes)
xarray_to_cdf(ds, "hello.cdf")
os.remove("hello.cdf")
Expand All @@ -361,10 +361,10 @@ def test_datetime64_conversion():
pytest.importorskip("xarray")
var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
var_dims = ["epoch", "direction"]
data = xr.Variable(var_dims, var_data) # type: ignore[no-untyped-call]
data = xr.Variable(var_dims, var_data)
epoch_data = [np.datetime64(1, "s"), np.datetime64(2, "s"), np.datetime64(3, "s")]
epoch_dims = ["epoch"]
epoch = xr.Variable(epoch_dims, epoch_data) # type: ignore[no-untyped-call]
epoch = xr.Variable(epoch_dims, epoch_data)
ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
xarray_to_cdf(ds, "hello.cdf", datetime64_to_cdftt2000=True)
x = cdf_to_xarray("hello.cdf", to_datetime=True)
Expand All @@ -378,10 +378,10 @@ def test_datetime64_no_conversion():
pytest.importorskip("xarray")
var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
var_dims = ["epoch", "direction"]
data = xr.Variable(var_dims, var_data) # type: ignore[no-untyped-call]
data = xr.Variable(var_dims, var_data)
epoch_data = [np.datetime64(1, "s"), np.datetime64(2, "s"), np.datetime64(3, "s")]
epoch_dims = ["epoch"]
epoch = xr.Variable(epoch_dims, epoch_data) # type: ignore[no-untyped-call]
epoch = xr.Variable(epoch_dims, epoch_data)
ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
xarray_to_cdf(ds, "hello.cdf")
x = cdf_to_xarray("hello.cdf")
Expand All @@ -396,10 +396,10 @@ def test_datetime64_conversion_odd_units():
pytest.importorskip("xarray")
var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
var_dims = ["epoch", "direction"]
data = xr.Variable(var_dims, var_data) # type: ignore[no-untyped-call]
data = xr.Variable(var_dims, var_data)
epoch_data = [np.datetime64("2000-01-01"), np.datetime64("2000-01-02"), np.datetime64("2000-01-03")]
epoch_dims = ["epoch"]
epoch = xr.Variable(epoch_dims, epoch_data) # type: ignore[no-untyped-call]
epoch = xr.Variable(epoch_dims, epoch_data)
ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
xarray_to_cdf(ds, "hello.cdf", datetime64_to_cdftt2000=True)
x = cdf_to_xarray("hello.cdf", to_datetime=True)
Expand All @@ -413,10 +413,10 @@ def test_numpy_string_array():
pytest.importorskip("xarray")
var_data = ["a", "b", "c"]
var_dims = ["epoch"]
data = xr.Variable(var_dims, var_data) # type: ignore[no-untyped-call]
data = xr.Variable(var_dims, var_data)
epoch_data = [np.datetime64("2000-01-01"), np.datetime64("2000-01-02"), np.datetime64("2000-01-03")]
epoch_dims = ["epoch"]
epoch = xr.Variable(epoch_dims, epoch_data) # type: ignore[no-untyped-call]
epoch = xr.Variable(epoch_dims, epoch_data)
ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
xarray_to_cdf(ds, "hello.cdf", datetime64_to_cdftt2000=True)
x = cdf_to_xarray("hello.cdf", to_datetime=True)
Expand Down
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