http://blog.csdn.net/pipisorry/article/details/48208433
真值测试Truth value testing
| all(a[, axis, out, keepdims]) | Test whether all array elements along a given axis evaluate to True. |
| any(a[, axis, out, keepdims]) | Test whether any array element along a given axis evaluates to True. |
只要数组中有一个值为True，则any()返回True；而只有数组的全部元素都为True，all()才返回True。
也可以直接当成array数组的方法使用。
判断numpy数组是否为空
if a.size:
print('array is not empty')
如果通过python列表，把一个列表作为一个布尔值会产生True如果有项目，False如果它是空的。
lst = []
if lst:
print "array has items"
if not lst:
print "array is empty"
判断numpy数组中是否有True
array.any()
数组内容Array contents
| isfinite(x[, out]) | Test element-wise for finiteness (not infinity or not Not a Number). |
| isinf(x[, out]) | Test element-wise for positive or negative infinity. |
| isnan(x[, out]) | Test element-wise for NaN and return result as a boolean array. |
| isneginf(x[, y]) | Test element-wise for negative infinity, return result as bool array. |
| isposinf(x[, y]) | Test element-wise for positive infinity, return result as bool array. |
numpy.isnan
numpy判断一个元素是否为np.NaN，判断某元素是否是nan
numpy.isnan(element)
Note: 不能使用array[0] == np.NaN，总是返回False!
numpy数组元素替换numpy.nan_to_num(x)
判断某元素是否是nan,inf,neginf,如果是，nan换为0，inf换为一个非常大的数，neginf换为非常小的数
numpy.nan_to_num(x)
Replace nan with zero and inf with finite numbers.
Returns an array or scalar replacing Not a Number (NaN) with zero, (positive) infinity with a very large number and negative infinity with a very small (or negative) number.
数组类型测试Array type testing
| iscomplex(x) | Returns a bool array, where True if input element is complex. |
| iscomplexobj(x) | Check for a complex type or an array of complex numbers. |
| isfortran(a) | Returns True if the array is Fortran contiguous but not C contiguous. |
| isreal(x) | Returns a bool array, where True if input element is real. |
| isrealobj(x) | Return True if x is a not complex type or an array of complex numbers. |
| isscalar(num) | Returns True if the type of num is a scalar type. |
逻辑操作Logical operations
| logical_and(x1, x2[, out]) | Compute the truth value of x1 AND x2 element-wise. |
| logical_or(x1, x2[, out]) | Compute the truth value of x1 OR x2 element-wise. |
| logical_not(x[, out]) | Compute the truth value of NOT x element-wise. |
| logical_xor(x1, x2[, out]) | Compute the truth value of x1 XOR x2, element-wise. |
两个0-1array相与操作
判断两个0-1array有多少个相同的1， 两种方式
rate = np.count_nonzero(np.logical_and(fs_predict_array, ground_truth_array))rate = np.count_nonzero(fs_predict_array * ground_truth_array)
不过fs_predict_array * ground_truth_array返回的是0-1array，而np.logical_and(fs_predict_array ,ground_truth_array)返回的是True-False array，但是都可以使用sum()得到1或者True的数目。
lz亲测下面的logical_and操作运行速度更快，没有count_nonzero会更快。
比较Comparison
| allclose(a, b[, rtol, atol, equal_nan]) | Returns True if two arrays are element-wise equal within a tolerance. |
| isclose(a, b[, rtol, atol, equal_nan]) | Returns a boolean array where two arrays are element-wise equal within a tolerance. |
| array_equal(a1, a2) | True if two arrays have the same shape and elements, False otherwise. |
| array_equiv(a1, a2) | Returns True if input arrays are shape consistent and all elements equal. |
| greater(x1, x2[, out]) | Return the truth value of (x1 > x2) element-wise. |
| greater_equal(x1, x2[, out]) | Return the truth value of (x1 >= x2) element-wise. |
| less(x1, x2[, out]) | Return the truth value of (x1 < x2) element-wise. |
| less_equal(x1, x2[, out]) | Return the truth value of (x1 =< x2) element-wise. |
| equal(x1, x2[, out]) | Return (x1 == x2) element-wise. |
| not_equal(x1, x2[, out]) | Return (x1 != x2) element-wise. |
allclose
如果两个数组在tolerance误差范围内相等，则返回True。
from: http://blog.csdn.net/pipisorry/article/details/48208433
ref: Logic functions