Add warning for quantized variables. This lets a user know that the v…

…ariable in question is stored as a quantized integer, but loaded as a float, which will cause the calculation of the bitinformation to yield bogus results depending on the data.

tests/test_get_bitinformation.py

@@ -5,6 +5,7 @@
 import numpy as np
 import pytest
 import xarray as xr
+import warnings
 from numpy.testing import assert_allclose, assert_equal
 from xarray.core import formatting
 from xarray.core.dataarray import DataArray
@@ -258,3 +259,17 @@ def test_implementations_agree(ds, dim, axis, request):
         masked_value=None,
     )
     bitinfo_assert_allclose(bi_python, bi_julia, rtol=1e-4)
+
+
+@pytest.mark.parametrize("implementation", ["python", "julia"])
+@pytest.mark.parametrize("dataset_name", ["air_temperature", "eraint_uvz"])
+def test_warn_on_quantized_variables(dataset_name, implementation):
+    ds_quantized = xr.tutorial.load_dataset(dataset_name)
+    ds_raw = xr.tutorial.load_dataset(dataset_name, mask_and_scale=False)
+
+    with pytest.warns(UserWarning):
+        _ = xb.get_bitinformation(ds_quantized, implementation=implementation)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        _ = xb.get_bitinformation(ds_raw, implementation=implementation)

xbitinfo/xbitinfo.py

@@ -1,6 +1,7 @@
 import json
 import logging
 import os
+import warnings
 
 import numpy as np
 import xarray as xr
@@ -233,6 +234,15 @@ def get_bitinformation(  # noqa: C901
         pbar = tqdm(ds.data_vars)
         for var in pbar:
             pbar.set_description(f"Processing var: {var} for dim: {dim}")
+
+            if _quantized_variable_is_scaled(ds, var):
+                loaded_dtype = ds[var].dtype
+                quantized_storage_dtype = ds[var].encoding["dtype"]
+                warnings.warn(
+                    f"Variable {var} is quantized as {quantized_storage_dtype}, but loaded as {loaded_dtype}. Consider reopening using `mask_and_scale=False` to get sensible results",
+                    category=UserWarning
+                )
+
             if implementation == "julia":
                 info_per_bit_var = _jl_get_bitinformation(ds, var, axis, dim, kwargs)
                 if info_per_bit_var is None:
@@ -260,6 +270,17 @@ def get_bitinformation(  # noqa: C901
     return info_per_bit
 
 
+def _quantized_variable_is_scaled(ds: xr.DataArray, var: str) -> bool:
+    loaded_dtype = ds[var].dtype
+    quantized_storage_dtype = ds[var].encoding["dtype"]
+    has_scale_or_offset = any(["add_offset" in ds[var].encoding, "scale_factor" in ds[var].encoding])
+
+    if has_scale_or_offset and quantized_storage_dtype != loaded_dtype:
+        return True
+    else:
+        return False
+
+
 def _jl_get_bitinformation(ds, var, axis, dim, kwargs={}):
     X = ds[var].values
     Main.X = X