Multiple conditions using 'or' in numpy array
Question:
So I have these conditions:
A = 0 to 10 OR 40 to 60
B = 20 to 50
and I have this code:
area1 = N.where((A>0) & (A<10)),1,0)
area2 = N.where((B>20) & (B<50)),1,0)
My question is: how do I do ‘OR‘ condition in numpy?
Answers:
If numpy overloads & for boolean and you can safely assume that | is boolean or.
area1 = N.where(((A>0) & (A<10)) | ((A>40) & (A<60))),1,0)
There’s numpy.logical_or
http://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_or.html
numpy logical_and and logical_or are the ufuncs that you want (I think)
Note that & is not logical and, it is bitwise and. This still works for you because (a>10) returns a logical array (e.g. 1’s and 0’s) as does your second condition. So, in this case, “logical and” and “bitwise and” are equivalent (same with logical and bitwise or). But in other cases, the bitwise operations may yield surprising results (mostly because python’s & and | operators have lower precedence than expected in these contexts).
So I have these conditions:
A = 0 to 10 OR 40 to 60
B = 20 to 50
and I have this code:
area1 = N.where((A>0) & (A<10)),1,0)
area2 = N.where((B>20) & (B<50)),1,0)
My question is: how do I do ‘OR‘ condition in numpy?
If numpy overloads & for boolean and you can safely assume that | is boolean or.
area1 = N.where(((A>0) & (A<10)) | ((A>40) & (A<60))),1,0)
There’s numpy.logical_or
http://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_or.html
numpy logical_and and logical_or are the ufuncs that you want (I think)
Note that & is not logical and, it is bitwise and. This still works for you because (a>10) returns a logical array (e.g. 1’s and 0’s) as does your second condition. So, in this case, “logical and” and “bitwise and” are equivalent (same with logical and bitwise or). But in other cases, the bitwise operations may yield surprising results (mostly because python’s & and | operators have lower precedence than expected in these contexts).