Normalize / Translate ndarray - Numpy / Python
You could use np.ptp1 (peak to peak) in conjunction with np.min to do this in the general case:
new_arr = (a - a.min())/np.ptp(a)example:
>>> a = np.array([[-1., 0, 1], [0, 2, 1]])>>> np.ptp(a)3.0>>> aarray([[-1., 0., 1.], [ 0., 2., 1.]])>>> (a - a.min())/np.ptp(a)array([[ 0. , 0.33333333, 0.66666667], [ 0.33333333, 1. , 0.66666667]])Of course, this still would give an error if a consists of entirely zeros -- But the problem isn't well posed in that case.
1IIRC, np.ptp calls np.max and np.min. If performance is really critical, you might what to create your own ptp and save np.min to a temporary variable so you don't calculate it twice.