diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/core/tests/test_umath.py | 48 |
1 files changed, 26 insertions, 22 deletions
diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index bfbdd00cd..5b2a8f0f9 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -745,16 +745,26 @@ class TestDivisionOverflows: import operator result_type = namedtuple('result_type', ['nocast', 'casted']) + helper_lambdas = { + 'zero': lambda dtype: 0, + 'min': lambda dtype: np.iinfo(dtype).min, + 'neg_min': lambda dtype: -np.iinfo(dtype).min, + 'min-zero': lambda dtype: (np.iinfo(dtype).min, 0), + 'neg_min-zero': lambda dtype: (-np.iinfo(dtype).min, 0), + } overflow_results = { - np.remainder: result_type('0', '0'), - np.fmod: result_type('0', '0'), - operator.mod: result_type('0', '0'), - operator.floordiv: result_type('np.iinfo(dividend_dtype).min', - '-np.iinfo(dividend_dtype).min'), - np.floor_divide: result_type('np.iinfo(dividend_dtype).min', - '-np.iinfo(dividend_dtype).min'), - np.divmod: result_type('(np.iinfo(dividend_dtype).min, 0)', - '(-np.iinfo(dividend_dtype).min, 0)') + np.remainder: result_type( + helper_lambdas['zero'], helper_lambdas['zero']), + np.fmod: result_type( + helper_lambdas['zero'], helper_lambdas['zero']), + operator.mod: result_type( + helper_lambdas['zero'], helper_lambdas['zero']), + operator.floordiv: result_type( + helper_lambdas['min'], helper_lambdas['neg_min']), + np.floor_divide: result_type( + helper_lambdas['min'], helper_lambdas['neg_min']), + np.divmod: result_type( + helper_lambdas['min-zero'], helper_lambdas['neg_min-zero']) } @pytest.mark.parametrize("dividend_dtype", @@ -787,34 +797,28 @@ class TestDivisionOverflows: dividend_dtype(np.iinfo(dividend_dtype).min), divisor_dtype(-1) ) - assert result == eval(self.overflow_results[operation].nocast) # Arrays - with pytest.raises(FloatingPointError): - for a in arrays: - # In case of divmod, we need to flatten the result - # column first as we get a column vector of quotient and - # remainder and a normal flatten of the expected result. + for a in arrays: + # In case of divmod, we need to flatten the result + # column first as we get a column vector of quotient and + # remainder and a normal flatten of the expected result. + with pytest.raises(FloatingPointError): result = np.array(operation(a, divisor)).flatten('f') - expected_array = np.array( - [eval( - self.overflow_results[operation].nocast - )]*len(a)).flatten() - assert_array_equal(result, expected_array) else: # Scalars result = operation( dividend_dtype(np.iinfo(dividend_dtype).min), divisor_dtype(-1) ) - assert result == eval(self.overflow_results[operation].casted) + assert result == self.overflow_results[operation].casted(dividend_dtype) # Arrays for a in arrays: # See above comment on flatten result = np.array(operation(a, divisor)).flatten('f') expected_array = np.array( - [eval(self.overflow_results[operation].casted)]*len(a) + [self.overflow_results[operation].casted(dividend_dtype)]*len(a) ).flatten() assert_array_equal(result, expected_array) |