NLi10Me - 1 year ago 106

Python Question

I have a 2D NumPy array and would like to replace all values in it greater than or equal to a threshold T with 255.0. To my knowledge, the most fundamental way would be:

`shape = arr.shape`

result = np.zeros(shape)

for x in range(0, shape[0]):

for y in range(0, shape[1]):

if arr[x, y] >= T:

result[x, y] = 255

- What is the most concise and pythonic way to do this?
- Is there a faster (possibly less concise and/or less pythonic) way to do this?

This will be part of a window/level adjustment subroutine for MRI scans of the human head. The 2D numpy array is the image pixel data.

Answer

I think both the fastest and most concise way to do this is to use Numpy's builtin indexing. If you have a `ndarray`

named `arr`

you can replace all elements `>255`

with a value `x`

as follows:

```
arr[arr > 255] = x
```

I ran this on my machine with a 500 x 500 random matrix, replacing all values >0.5 with 5, and it took an average of 7.59ms.

```
In [1]: import numpy as np
In [2]: A = np.random.rand(500, 500)
In [3]: timeit A[A > 0.5] = 5
100 loops, best of 3: 7.59 ms per loop
```

Source (Stackoverflow)