Error when masking 2d numpy array

Question

I'm not sure what the correct terminology is here but I'm trying to mask out some values in a numpy array using multiple conditions from several arrays. For example, I want to find and mask out the areas in X where arrays t/l,lat2d,x, and m meet certain criteria. All the arrays are of the same shape: (250,500). I tried this:

cs[t < 274.0 | 
   l > 800.0 |
   lat2d > 60 |
   lat2d < -60 | 
   (x > 0 & m > 0.8) |
   (x < -25 & m < 0.2)] = np.nan

ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''.

I replaced the &,| with and/or and got the error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

any idea how to do this in Python 3?

are the arrays l, t, m, x, lat2d fixed?

kmario23
– kmario23

2017-03-27 07:36:04 +00:00
Commented Mar 27, 2017 at 7:36 — kmario23
– kmario23, Commented Mar 27, 2017 at 7:36
Yes, the arrays are fixed.

Shejo284
– Shejo284

2017-03-27 07:49:51 +00:00
Commented Mar 27, 2017 at 7:49 — Shejo284
– Shejo284, Commented Mar 27, 2017 at 7:49

Paul Panzer · Accepted Answer · 2017-03-27 07:50:30Z

1

This is just a matter of operator precedence:

cs[(t < 274.0) | 
   (l > 800.0) |
   (lat2d > 60) |
   (lat2d < -60) |
   ((x > 0) & (m > 0.8)) |
   ((x < -25) & (m < 0.2))] = np.nan

should work

answered Mar 27, 2017 at 7:50

Paul Panzer

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6 Comments

kmario23 Over a year ago

This is pretty neat!

Shejo284 Over a year ago

So the parenthesis were the issue? Can you explain a bit more how your () are different than mine?

kmario23 Over a year ago

@Shejo284 I think it boils down to remembering the precedence rule PEMDAS in that order. So, using parenthesis will always make thing explicit and of higher precedence (i.e. it evaluates first)

Paul Panzer Over a year ago

The problem is that for certain technical reasons numpy abuses bitwise operators where in an ideal world logical operators would be used. These bitwise operators have higher operator precedence than their logical cousins, so for example without the additional parentheses your condition is interpreted like t < (274.0 | l) > (800.0 | ... which, of course, doesn't make much sense. For starters bitwise or is not defined on floats.

Shejo284 Over a year ago

@PaulPanzer I see. Thanks for the solution and explanation. I'm a bit wiser today, pun intended :-)

|

kmario23 · Accepted Answer · 2017-03-27 07:53:52Z

0

You could do it using with a python function and then applying that function on the array.

def cond(x):
    if (np.all(t < 274.0) or np.all(l > 800.0) or np.all(lat2d > 60) or \
        np.all(lat2d < -60) or (np.all(x > 0) and np.all(m > 0.8)) or  \
        (np.all(x < -25) and np.all(m < 0.2))):
        return np.nan

Then apply this function on the array:

cs[:] = np.apply_along_axis(cond, 0, cs)

edited Mar 27, 2017 at 7:53

answered Mar 27, 2017 at 7:38

kmario23

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1 Comment

Shejo284 Over a year ago

This is an interesting idea, but the other suggested solution works and with less coding. Appreciate the tips though :-)

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