JoVe - 6 months ago 26

Python Question

I have a numpy masked array of data:

`data = masked_array(data = [7 -- 7 1 8 -- 1 1 -- -- 3 -- -- 3 --],`

mask = [False True False False False True False False True True False True True False True])

I have a flag of a specific type of data, which is a boolean masked array:

`flag = masked_array(data = [True False False True -- -- -- False -- True -- -- -- -- True],`

mask = [False False False False True True True False True False True True True True False])

I want to do something like

`data[flag]`

`output_wanted = [7 1 -- --]`

which corresponds to the data elements where the flag is True. Instead I get this:

`output_real = [7 -- 7 1 8 -- 1 1 -- -- 3 -- -- 3 --]`

I did not copied the masks of the outputs for better clarity.

I dont mind having an output with the size of the flag as long as it selects the data I want (the one corresponding to the True values of the flag). But I cannot figure out why it gives theses values in the real output !

Answer

If I reconstruct your arrays with:

```
In [28]: d=np.ma.masked_equal([7,0,7,1,8,0,1,1,0,0,3,0,0,3,0],0)
In [29]: f=np.ma.MaskedArray([True,False,False,True, False,False,False,False,True,True,True,True,True,True,True],[False, False, False, False, True, True, True, False, True, False, True, True, True, True, False])
In [30]: d
Out[30]:
masked_array(data = [7 -- 7 1 8 -- 1 1 -- -- 3 -- -- 3 --],
mask = [False True False False False True False False True True False True
True False True],
fill_value = 0)
In [31]: f
Out[31]:
masked_array(data = [True False False True -- -- -- False -- True -- -- -- -- True],
mask = [False False False False True True True False True False True True
True True False],
fill_value = True)
```

The masked displays match, but I'm guessing at what the masked values are.

```
In [32]: d[f]
Out[32]:
masked_array(data = [7 1 -- -- 3 -- -- 3 --],
mask = [False False True True False True True False True],
fill_value = 0)
In [33]: d[f.data]
Out[33]:
masked_array(data = [7 1 -- -- 3 -- -- 3 --],
mask = [False False True True False True True False True],
fill_value = 0)
```

Indexing the `f`

is the same as indexing with its `data`

attribute. Its mask does nothing. Evidently my masked values are different from yours.

But if I index with a `filled`

array, I get the desired array:

```
In [34]: d[f.filled(False)]
Out[34]:
masked_array(data = [7 1 -- --],
mask = [False False True True],
fill_value = 0)
```

`filled`

is used a lot in `np.ma`

code, with differing fill values depending on the `np`

operation (e.g. 0 for sum v 1 for product). Masked arrays don't usually iterate over their values skipping the masked ones; instead they convert the masked ones to innocuous values, and use regular numpy operations. The other strategy is to remove the masked values with `compressed`

.

`indices = np.where(flag.filled(False))`

is mentioned in another answer, but plain boolean form works just as well.

A masked array has a `data`

and `mask`

attribute. Masking does not change the `data`

values directly. That task is left to methods like `filled`

.