CODEWITHSUNDEEP
pythonnumpynan
PEBKAC
PEBKAC

Reputation: 788

Create a mask both for nan and inf values in an array

I have to remove both nan and inf values from two arrays. I found this post useful https://stackoverflow.com/a/48591908/7541421 for removing nan. Is there any similar solution when I can create a mask to remove both nan and inf values?

The example below is just illustrative, I have arrays of large dimensions (400 elements)

import numpy as np
from numpy import nan, inf

a = np.asarray([0.5, 6.2, np.nan, 4.5, np.inf])
b = np.asarray([np.inf, np.inf, 0.3, np.nan, 0.5])

bad = ~np.logical_or(np.isnan(a), np.isnan(b))

X = np.compress(bad, a)  
Y = np.compress(bad, b) 

BIAS = np.nanmean(X - Y)
RMSE = np.sqrt(np.nanmean((X - Y)**2))
CORR = np.corrcoef(X, Y)

I need this in order to get both the statistics and plots correctly

Upvotes: 7

Views: 5734

Answers (3)

Fronza
Fronza

Reputation: 11

It works fine to me.

I created it with this to solve the problem in merge NaN and masked arrays:

masked_red_diff = masked_red_diff[np.isfinite(masked_red_diff)]
masked_red_diff.mean()

Upvotes: 1

John Zwinck
John Zwinck

Reputation: 249243

You can use np.isfinite(). It will return a boolean mask with True wherever the values are neither infinite nor NAN.

You can get the finite values this way:

a = np.asarray(a)
a = a[np.isfinite(a)]

Or for both arrays together:

mask = np.isfinite(a) | np.isfinite(b)
a = a[mask]
b = b[mask]

Upvotes: 14

YSelf
YSelf

Reputation: 2711

np.isfinite

Test element-wise for finiteness (not infinity or not Not a Number).

Upvotes: 3

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