Improve continuous wavelet transform

[OlgaVorokh]

This PR improves CWT, which was added in #25 and recently improved directly in Gammapy master.

On this PR the follow work was done:

• quality of CWT code was improved
• all the data images and information about using kernels was been separated from CWT class into CWTData and CWTKernels classes
• the tests were added for all the created classes
• docs was been modernized
• ipython tutorial was written for CWT algorithm (see here)

[cdeil]

The most important thing to add is the link to http://adsabs.harvard.edu/abs/2001ICRC....7.2923T so that others can quickly find what publication exactly you are referencing.
The description can be shortened to only contain the key information like this:

R. Terrier et al (2001) "Wavelet analysis of EGRET data"
See http://adsabs.harvard.edu/abs/2001ICRC....7.2923T

[OlgaVorokh]

OK, I'll fixed it now.

[OlgaVorokh]

@cdeil @adonath I prefer to implement the follow style of using CWT:

cwt = CWT(... set parameters... )
data = .. some data, maybe CWTData ...
cwt.analyze(data)

So we firstly setup the parameters and than start to analyze data and after computing we can use created CWT for analyzing other data.

What are you thinking about it?

[cdeil]

No strong opinion on this, maybe small +1 to what you propose, to use the sklearn style of passing data only later to run or whatever it's called and not already to __init__.

[cdeil]

I think my preference would be to split out the scales and kernels into a separate class.
Mostly to have it nicely grouped, not necessarily for code re-use from other applications, although that might be possible later.

class CWTKernels:
    def __init__(self, scales):
        scales = np.array(scales)
        # compute kernels here, like previously done in `CWT.__init__`

    @classmethod
    def from_scale_grid(cls, n, min, step):
        scales = [min * step ** _ for _ in range(n)]
        return cls(scales)

    def __str__():
        # print useful info about the grid of scales, can be added later

We will be able to re-use that class for other multi-scale algorithms, so I think it's good to split it out now. To use it for CWT:

from gammapy.detect import CWTKernels, CWT

kernels = CWTKernels.from_scale_grid(... parameters ...)
print(scales) # for debugging
cwt = CWT(scales)
cwt.run(data)

OK to split out the scale / kernel handling this way, or do you prefer to keep the scales / kernel handling in CWT.__init__?

[OlgaVorokh]

@cdeil I moved this method here, but as you can see it will be not work, because we don't have some information about the scales in that class. Maybe it will be better to create kernel class, but I don't know

[OlgaVorokh]

If I understand correct, this method should look like this. As I understand, maximal image should be created on the same way as we create data.transform_2d in the CWT._inverse_transform.

[cdeil]

Merging this no.

@OlgaVorokh - Thank you for this big contribution and your patience with review / merging on this PR!