add axis support to cwt

[grlee77]

Users requested batched operation for cwt in #445. This can be done by adding an axis argument as in this PR. This PR allows the input data to be n-dimensional with batched operation over all axes aside from the specified cwt axis. For 1D data, the behavior is unchanged from before.

The final shape of the output for n-dimensional data becomes:
(len(scales),) + data.shape (i.e. the scales dimension is always first as it was for the 1D case previously)

For the 'conv' case implementation is via a simple for loop, but for the 'fft' case we do not have to repeat the FFT of the wavelet filter for each item in the batch, so there is a performance benefit to batched operation.

a subset of benchmark results.
first, for non-batch cases

              -------------------------------------------- ---------------------
                n     wavelet   max_scale       dtype         conv       fft    
              ====== ========= =========== =============== ========== ==========

               128      mexh        16      numpy.float32   2.01±0ms   5.13±0ms 
               128      mexh        16      numpy.float64   4.32±0ms   5.99±0ms 

               2048     shan       256      numpy.float32   397±0ms    68.0±0ms 
               2048     shan       256      numpy.float64   864±0ms    100±0ms  

a few batch (n_batch=5) cases

               128      mexh        16      numpy.float32   6.68±0ms   6.67±0ms 
               128      mexh        16      numpy.float64   5.46±0ms   7.39±0ms 

               2048     shan       256      numpy.float32   1.87±0s    178±0ms  
               2048     shan       256      numpy.float64   4.32±0s    291±0ms

Summary for the 2048/shan/float32 case:

with conv: n_batch=5 batch takes 4.7 times longer than the non-batch case

with fft:  n_batch=5 batch takes 1.8 times longer than the non-batch case

for the non-batch case, mode 'fft' is 5.8 times faster than 'conv'

for the n_batch=5 batch case, mode 'fft' is 10.5 times faster than 'conv'

closes #445

[grlee77]

I should note that the FFT results in the benchmarks above are for single-threaded scipy.fftpack as the fft backend. It is likely multi-threaded FFT backend would give additional benefit in batched operation.

[rgommers]

Backwards-compatible, and seems like a nice usability improvement.

I mostly just reviewed the tests and description. axis=-1 is the right default. Going to merge this, looks good. Thanks @grlee77