Feature/peak picker sn performance

A new PeakPicker and Median Signal to noise estimator with a cleaner interface and improved performance

- the main idea is to use clean, STL based algorithms for peak picking and noise estimation without any dependency on OpenMS data structures. This allows potentially for easier re-use, better modularization and easier performance speedups since we can use established code
- due to using fixed windows to compute the median, the signal to noise values are not identical to the ones of the old signal to noise estimator - however the peak picker can be shown to get numerically identical results as before if the S/N threshold is adopted accordingly (see the test cases).
- for performance measurement, please use http://pastebin.com/YHC6dN1n
- on my machine I get the following performance improvements

```
86: checking Speed_no_SN ...
486:  +  line 74:  TOLERANCE_RELATIVE(1.00005)   ("1.00005")
486:  Old Peakpicker time: 12.72 for 1874300 peaks.
486:  New Peakpicker time: 5.91 for 1874300 peaks.
486: : passed
486: Warning: no subtests performed in 'Speed_no_SN' (line 109)!
486:
486: checking Speed_SN ...
486:  +  line 114:  TOLERANCE_RELATIVE(1.00005)   ("1.00005")
486:  Old Peakpicker time: 29.96 for 83900 peaks.
486:  New Peakpicker time: 2.63 for 90000 peaks.
486: : passed
486: Warning: no subtests performed in 'Speed_SN' (line 147)!
```

peak picking without signal to noise estimation (sn = 0.0) gives about a 2x speedup while when using signal to noise there is more or less a 10x performance improvement. Note that this includes the overhead of copying each spectrum to a std::vector<double> while the old code is allowed to work directly on a MSSpectrum without any copying