def test_unordered_categorical_disallowed(self):
"""Test which verifies rt.nanmin raises an exception if called with an unordered Categorical."""
# Create an unordered Categorical.
cat = rt.Categorical(["PA", "NY", "NY", "AL", "LA", "PA", "CA", "IL", "IL", "FL", "FL", "LA"], ordered=False)
assert not cat.ordered
with pytest.raises(ValueError):
rt.nanmin(cat)
def test_nan_awareness(self, arr, func_type):
"""
Check how :func:`rt.nanmin` handles NaN values by comparing it against :func:`rt.min`.
Call :func:`rt.nanmin` with an array, then remove any NaNs from the array and call
:func:`np.min` with the 'clean' array. The results should match.
"""
# Get the function implementation based on how we want to call it.
if func_type == 'module':
test_func = lambda x: rt.nanmin(x)
elif func_type == 'member':
test_func = lambda x: x.nanmin()
else:
raise ValueError(
f"Unhandled value '{func_type}' specified for the function type."
)
# Get the nan-unaware version of the function.
nan_unaware_func = lambda x: rt.min(x)
# Wrap the input as a FastArray to ensure we'll get the riptable implementation of the function.
arr = rt.FA(arr)
# Call the test implementation.
NanAwareTestImpl.test_nan_awareness(test_func, nan_unaware_func, arr)
def test_allnans(self, arg):
# Call rt.nanmin with the test input.
# It should raise a RuntimeWarning when given an input which
# has all NaNs **on the specified axis**.
with pytest.warns(RuntimeWarning):
result = rt.nanmin(arg)
# If given a scalar or 1D array (or some collection converted to such)
# the result should be a NaN; for higher-rank arrays, the result should
# be an array where one of the dimensions was collapsed and if there were
# all NaNs along the selected axis there'll be a NaN there in the result.
# TODO: Need to fix this to assert correctly for when rt.nanmin called with a higher-rank array.
assert rt.isnan(result)
def test_ordered_categorical_returns_scalar(self):
"""
Test which verifies rt.nanmin returns a scalar (Python object or numpy scalar) representing the min Category given an ordered Categorical.
"""
# Create an ordered Categorical (aka 'ordinal').
cat = rt.Categorical(
["PA", "NY", "", "NY", "AL", "LA", "PA", "", "CA", "IL", "IL", "FL", "FL", "LA"], ordered=True)
assert cat.ordered
result = rt.nanmin(cat)
# The result should either be a Python string, a numpy string scalar, or a Categorical scalar (if we implement one).
is_py_str = isinstance(result, (bytes, str))
is_np_scalar = isinstance(result, np.str)
is_rt_cat = isinstance(result, rt.Categorical)
assert is_py_str or is_np_scalar or is_rt_cat
# Check the result is correct.
assert result == "PA"
def test_empty(self, arg):
# Call rt.nanmin with an empty input -- it should raise a ValueError.
with pytest.raises(ValueError):
rt.nanmin(arg)