using numpy.unravel_index

Question

Hi I have a 2x4 array called mi_reshaped. I used the argmax to find out the indeces of the largest elements in my array. Now I want to convert these indeces to x,y coordinates. So I used the numpy.unravel_index. I get this error:

Traceback (most recent call last):
File "CAfeb.py", line 273, in <module>
analyzeCA('full', im)
File "CAfeb.py", line 80, in analyzeCA
bg_params = parameterSearch( im, [3, 2], roi, ew, hist_sz, w_data);
File "CAfeb.py", line 185, in parameterSearch
ix = np.unravel_index(max_ix, mi_reshaped.shape)#(mi.size)
File "/usr/lib/pymodules/python2.7/numpy/lib/index_tricks.py", line 64, in unravel_index
if x > _nx.prod(dims)-1 or x < 0:
ValueError: The truth value of an array with more than one element isambiguous.   
  a.any() or a.all()
mi_reshaped=mi.reshape(2,4)
max_ix = np.argmax(mi_reshaped, axis=1)
ix = np.unravel_index(max_ix, mi_reshaped.shape)#(mi.size)

Thank you

Answer 1

You should skip the axis=1 for this. If you do a numpy.argmax(array) it will look for max in the flattened array, and then you can do the unravel_index with the array shape to find the actual index. When you pass the axis, numpy will look for the maximum for that axis for each entry in the array. For example:

>>>data = numpy.array(range(8)).reshape(2, 4)
>>>data
array([[0, 1, 2, 3],
       [4, 5, 6, 7]])
>>>max_ix = numpy.argmax(data, axis=1)
>>>max_ix
array([3, 3])  
>>>numpy.unravel_index(max_ix, data.shape)
(array([0, 0]), array([3, 3]))

Now if you skip the axis:

>>>max_ix = numpy.argmax(data)
>>>max_ix
7
>>>numpy.unravel_index(max_ix, data.shape)
(1, 3)

Now what happened is you told numpy to give you the index for maximums on the dimension 1 and it finds the maximums '3' and '7' with indexes [3, 3]. Still you should't get an error with your code, just the wrong final result.

Answer 2

np.unravel_index expects an integer as its first argument. max_ix is an array.

Moreover, each value in max_ix is an index with respect to the second axis (axis = 1) of mi.

Try instead:

ix = [(row, ix) for row, ix in enumerate(max_ix)]

For example,

In [89]: mi_reshaped = np.array(range(8)).reshape(2, 4)
In [90]: mi_reshaped
Out[90]: 
array([[0, 1, 2, 3],
       [4, 5, 6, 7]])

In [91]: max_ix = np.argmax(mi_reshaped, axis=1)

In [92]: max_ix
Out[92]: array([3, 3])

In [93]: ix = [(row, ix) for row, ix in enumerate(max_ix)]

In [94]: ix
Out[94]: [(0, 3), (1, 3)]