How to get boolean or of two numpy masks with Python

Question

Given two numpy array masks, created with the 3rd and 4th columns of data of 7 columns total:

exp_mask = np.repeat(data[:,2]>7., data.shape[1])
loggf_mask = np.repeat(data[:,3]<-7., data.shape[1])

How can I mask data which are masked by either exp_mask or loggf_mask?

The logic of what I am trying to describe is:

mask = exp_mask or loggf_mask

Roy Shahaf · Accepted Answer · 2015-07-13 20:35:31Z

2

I believe you are looking for a bitwise or, which is |.

answered Jul 13, 2015 at 20:35

Roy Shahaf

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Comments

user3692741 · Accepted Answer · 2015-07-13 20:34:55Z

1

You can use np.any() to evaluate boolean or on masks:

mask = np.any([exp_mask,loggf_mask],axis=0)

answered Jul 13, 2015 at 20:34

user3692741

tom10 · Accepted Answer · 2015-07-14 00:22:46Z

1

You can use either bitwise_or, which also has the | shorthand, or logical_or. Both will work since your array will be of type bool:

mask = exp_mask | loggf_mask

answered Jul 13, 2015 at 21:31

tom10

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