>>> x = 1.000001 >>> y = 1.000002 >>> np.testing.assert_almost_equal(x, y) AssertionError: Arrays are not almost equal to 7 decimals ACTUAL: 1.000001 DESIRED: 1.000002 >>> np.testing.assert_almost_equal(x, y, 5) >>> np.testing.assert_almost_equal([x, x, x], [y, y, y]...
def assert_duckarray_equal(x, y, err_msg="", verbose=True): """ Like `np.testing.assert_array_equal`, but for duckarrays """ __tracebackhide__ = True 40 xarray/tests/test_testing.py @@ -1,3 +1,5 @@ import warnings import numpy as np import pytest @@ -127,3 +129,41 ...
inassert_array_equalverbose=verbose,header='Arrays are not equal')File"/opt/anaconda/lib/python2.7/site-packages/numpy/testing/utils.py",line737,inassert_array_compareraiseAssertionError(msg)AssertionError:Arraysarenotequal(mismatch100.0%)x:MaskedArray([1.1,nan,3.])y:MaskedArray([nan,2.2,3.1])...
>>>np.testing.assert_array_equal([1.0,2.33333,np.nan],...[np.exp(0),2.33333, np.nan]) 断言失败,浮点数不精确: >>>np.testing.assert_array_equal([1.0,np.pi,np.nan],...[1, np.sqrt(np.pi)**2, np.nan]) Traceback (most recent call last): ... AssertionError: Arrays arenoteq...
numpy.testing.assert_array_equal(x, y, err_msg='', verbose=True)[source] 如果两个array_like对象不相等,则引发AssertionError。 给定两个array_like对象,检查形状是否相等以及这些对象的所有元素是否相等(但是请参阅注释,了解标量的特殊处理)。当形状不匹配或值冲突时引发异常。与numpy中的标准用法相反,将nan...
numpy.testing.assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True)[source] 如果两个对象不等于期望的精度,则引发AssertionError。 注意:为了更一致的浮点比较,建议使用assert_allclose,assert_array_almost_equal_nulp或assert_array_max_ulp之一来代替此函数。
numpy.testing.assert_array_almost_equal assert_array_almost_equal(x,y,decimal=6,err_msg=' ',verbose=True) 如果x,y不满足一定精度的误差的话,会报错,decimal默认为6。 精度的设置是:abs(desired-actual) < 1.5 * 10**(-decimal),而且是逐个元素的比较。
檢查左右 ExtensionArrays 是否相等。參數: left, right:擴展數組 要比較的兩個數組。 check_dtype:布爾值,默認為真 是否檢查 ExtensionArray dtypes 是否相同。 index_values:numpy.ndarray,默認無 可選索引(左右共享),用於輸出。 check_less_precise:bool 或 int,默認為 False 指定比較精度。僅在 check_exact ...
xp_assert_equal(np.array(42), np.array(21)*2)should be as True asxp_assert_equal(np.array([42]), np.array([21])*2). ==is just an implementation detail: my knee-jerk implementation would rely on==returning a scalar not numpy 0D arrays. Maybe it's too brittle, and the assertion...
NumPy arrays are nicely generic over their dimension, which can range from 0 to NPY_MAXDIMS (==32). NumPy also has its own scalars to complement the Python-native ones (e.g. np.int64(...) is obviously limited in range but has constant footprint, whereas int(...) is unbounded in ...