[Numpy-discussion] strange behavior of np.minimum and np.maximum
Hello, a, b, c = np.array([10]), np.array([2]), np.array([7]) min_val = np.minimum(a, b, c) min_val array([2]) max_val = np.maximum(a, b, c) max_val array([10]) min_val array([10]) (I'm using numpy 1.4, and I observed the same behavior with numpy 2.0.0.dev8600 on another machine). I'm quite surprised by this behavior (It took me quite a long time to figure out what was happening in a script of mine that wasn't giving what I expected, because of np.maximum changing the output of np.minimum). Is it a bug, or am I missing something? Cheers, Emmanuelle ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] strange behavior of np.minimum and np.maximum
a, b, c = np.array([10]), np.array([2]), np.array([7]) min_val = np.minimum(a, b, c) min_val array([2]) max_val = np.maximum(a, b, c) max_val array([10]) min_val array([10]) (I'm using numpy 1.4, and I observed the same behavior with numpy 2.0.0.dev8600 on another machine). I'm quite surprised by this behavior (It took me quite a long time to figure out what was happening in a script of mine that wasn't giving what I expected, because of np.maximum changing the output of np.minimum). Is it a bug, or am I missing something? Read the documentation for numpy.minimum and numpy.maximum: they give you element-wise minimum values from two arrays passed as arguments. E.g.: numpy.minimum([1,2,3],[3,2,1]) array([1, 2, 1]) The optional third parameter to numpy.minimum is an out array - an array to place the results into instead of making a new array for that purpose. (This can save time / memory in various cases.) This should therefore be enough to explain the above behavior. (That is, min_val and max_val wind up being just other names for the array 'c', which gets modified in-place by the numpy.minimum and numpy.maximum.) If you want the minimum value of a sequence of arbitrary length, use the python min() function. If you have a numpy array already and you want the minimum (global, or along a particular axis), use the min() method of the array, or numpy.min(arr). Zach ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] strange behavior of np.minimum and np.maximum
Hi Emmanuelle, a, b, c = np.array([10]), np.array([2]), np.array([7]) min_val = np.minimum(a, b, c) min_val array([2]) max_val = np.maximum(a, b, c) max_val array([10]) min_val array([10]) (I'm using numpy 1.4, and I observed the same behavior with numpy 2.0.0.dev8600 on another machine). I'm quite surprised by this behavior (It took me quite a long time to figure out what was happening in a script of mine that wasn't giving what I expected, because of np.maximum changing the output of np.minimum). Is it a bug, or am I missing something? you're just missing that np.minimum/np.maximum are _binary ufuncs_ with syntax np.minimum(X, Y, out=None) i.e. you were telling np.minimum to store it's output in array c and then return min_val, obviously as a reference, not a copy of it. Thus when storing the output of np.maximum in c as well, the contents of c also changed again. Being binary ufuncs, I think you'll have to apply them consecutively if you need the min/max of several arrays. See also np.info(np.minimum) HTH, Derek ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] strange behavior of np.minimum and np.maximum
Hi Zach and Derek, thank you very much for your quick and clear answers. Of course the third parameter is the out array, I was just being very stupid! (I had read the documentation though, but somehow it didn't make it to my brain :-) Sorry... Read the documentation for numpy.minimum and numpy.maximum: they give you element-wise minimum values from two arrays passed as arguments. E.g.: numpy.minimum([1,2,3],[3,2,1]) array([1, 2, 1]) The optional third parameter to numpy.minimum is an out array - an array to place the results into instead of making a new array for that purpose. (This can save time / memory in various cases.) Thanks again, Emmanuelle ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
