trying to mask 2D numpy arrays based on values in one column

Question

I have the following array:

[[    6.           105.             2.             8.09841881]
[    6.           105.             4.             9.34220351]
[    6.           105.             6.             9.97663435]
[    6.          1001.             2.             9.57108242]
[    6.          1001.             4.            12.22355794]
[    6.          1001.             6.            13.57295942]
[   12.          1001.             2.            12.37474466]
[   12.          1001.             4.            17.45334004]
[   12.          1001.             6.            19.88499289]
[   18.          1007.             2.            16.09076561]
[   18.          1007.             4.            23.43742275]
[   18.          1007.             6.            27.73041646]]

And I have tried to extract only the rows with the first element being a six via

print ma.MaskedArray(a, mask=(np.ones_like(a)*(a[:,0]==6.0)).T)

which I got from the question " mask a 2D numpy array based on values in one column ". However, I get

File "./Prova.py", line 170, in <module>
print ma.MaskedArray(a, mask=(np.ones_like(a)*(a[:,0]==6.0)).T)
ValueError: operands could not be broadcast together with shapes (12,4) (12)

do you have a clue of why this doesn't work?

The question might be stupid, but please bear with me since I just started programming python. :-)

Answer 1

Setting up some test data to work on:

>>> a = np.arange(12*4).reshape((12,4))

First, we "allocate" space for our mask array:

>>> mask = np.empty(a.shape,dtype=bool)

Now we can't assign into it from the first column of a == 6 directly because they're not the proper shape:

>>> mask[:,:] = a[:,0] == 6
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: operands could not be broadcast together with shapes (12,4) (12) 

But we can broadcast our first column of a up to the correct shape by simply inserting a newaxis so that it becomes a 2-D array:

>>> mask[:,:] = (a[:,0] == 6)[:,np.newaxis]

As we can see, our mask is now correct.

>>> mask
array([[ True,  True,  True,  True],
       [ True,  True,  True,  True],
       [ True,  True,  True,  True],
       [ True,  True,  True,  True],
       [ True,  True,  True,  True],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False],
       [False, False, False, False]], dtype=bool)

Now we just make our masked array and sit back and enjoy :).

>>> ma.MaskedArray(a,mask=mask)
masked_array(data =
 [[-- -- -- --]
 [-- -- -- --]
 [-- -- -- --]
 [-- -- -- --]
 [-- -- -- --]
 [20 21 22 23]
 [24 25 26 27]
 [28 29 30 31]
 [32 33 34 35]
 [36 37 38 39]
 [40 41 42 43]
 [44 45 46 47]],
             mask =
 [[ True  True  True  True]
 [ True  True  True  True]
 [ True  True  True  True]
 [ True  True  True  True]
 [ True  True  True  True]
 [False False False False]
 [False False False False]
 [False False False False]
 [False False False False]
 [False False False False]
 [False False False False]
 [False False False False]],
       fill_value = 999999)