Add 1 sigma region to the best fit synthetic cluster

[Gabriel-p]

This is important to avoid people focusing too much on the plotted isochrone, which is not correct.

The correct approach would be as follows:

1. Randomly sample 1000 synthetic clusters from the 1-sigma regions
2. Combine them all into a single large 1-sigma synth cluster
3. Generate a histogram from this data
4. Color (grey) the observed CMD with it (as done with the Hess diagram) using the same bins used to perform the match
5. Repeat with a 2-sigma region, and color (pink?) only those bins that are not already colored in the 1 sigma region

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Old approach

This is also not a trivial issue. After some investigating, the answer seems to be related to alpha-shapes:

• Alpha Shapes in Python
• Drawing Boundaries In Python (uses Shapely)
• Estimating an area of an image generated by a set of points (Alpha shapes??) (uses Shapely)

(Old issue: #344)