Convert binary (0|1) numpy to integer or binary-string?

Question

Is there a shortcut to Convert binary (0|1) numpy array to integer or binary-string ? F.e.

b = np.array([0,0,0,0,0,1,0,1])   
  => b is 5

np.packbits(b)

works but only for 8 bit values ..if the numpy is 9 or more elements it generates 2 or more 8bit values. Another option would be to return a string of 0|1 ...

What I currently do is :

    ba = bitarray()
    ba.pack(b.astype(np.bool).tostring())
    #convert from bitarray 0|1 to integer
    result = int( ba.to01(), 2 )

which is ugly !!!

Answer 1

One way would be using dot-product with 2-powered range array -

b.dot(2**np.arange(b.size)[::-1])

Sample run -

In [95]: b = np.array([1,0,1,0,0,0,0,0,1,0,1])

In [96]: b.dot(2**np.arange(b.size)[::-1])
Out[96]: 1285

Alternatively, we could use bitwise left-shift operator to create the range array and thus get the desired output, like so -

b.dot(1 << np.arange(b.size)[::-1])

If timings are of interest -

In [148]: b = np.random.randint(0,2,(50))

In [149]: %timeit b.dot(2**np.arange(b.size)[::-1])
100000 loops, best of 3: 13.1 µs per loop

In [150]: %timeit b.dot(1 << np.arange(b.size)[::-1])
100000 loops, best of 3: 7.92 µs per loop

---

Reverse process

To retrieve back the binary array, use np.binary_repr alongwith np.fromstring -

In [96]: b = np.array([1,0,1,0,0,0,0,0,1,0,1])

In [97]: num = b.dot(2**np.arange(b.size)[::-1]) # integer

In [98]: np.fromstring(np.binary_repr(num), dtype='S1').astype(int)
Out[98]: array([1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1])

Answer 2

My timeit results:

b.dot(2**np.arange(b.size)[::-1])
100000 loops, best of 3: 2.48 usec per loop

b.dot(1 << np.arange(b.size)[::-1])
100000 loops, best of 3: 2.24 usec per loop

# Precompute powers-of-2 array with a = 1 << np.arange(b.size)[::-1]
b.dot(a)
100000 loops, best of 3: 0.553 usec per loop

# using gmpy2 is slower
gmpy2.pack(list(map(int,b[::-1])), 1)
100000 loops, best of 3: 10.6 usec per loop

So if you know the size ahead of time, it's significantly faster to precompute the powers-of-2 array. But if possible, you should do all computations simultaneously using matrix multiplication like in Geoffrey Anderson's answer.