Fixed weird behaviour of bootstrap fitting for VST tests
Switched to scipy.optimize.curve_fit for this case.

 - Opening folders automatically creates a reloadable project config

- Added the possibility to get dilation data
- Replaced Savgol filter with median filter because of pick distortion
- Some minor formatting changes

The GUI Version is now fully functional and integrated into RST_Func.
To start the GUI-based picking use `python3 RST_pick_GUI.py`.
When using the default options it is equivalent to RST_main.py.

- Added more default options to the config.
-  Extended the `eval_shearstress` function by the option to review picks
- Changed the convention of DocStrings from single to double quotes.
- Added DocStrings to some functions.

Will allow to change parameters and easier opening of files.

Added some more files to .gitignore

This is more consistent with the rest of the code.

- Changed from Pandas dataframes to numpy arrays or native python types
- Replaced savgol-filter with scipy.signal.savgol_filter (250% faster)
- Improved peak-detection (2000% faster)
- Optimized the mutual linear regression with list comprehensions
- Added a bootstrap linear regression function
- Replaced hardcoded axis scaling by ratio (x1.125 instead of +2000)
- Removed all r'strings' for a more consistent layout, using \\ for TeX
- The timeseries plot is now flexible for experiments with less runs
- The saving functions were adjusted to match the new data types
- Some variables have been renamed
- Cleaned and reformatted code to match pep8 style

Added Manual Picking\n  - Currently independent from the rest of the scripts\n  - Manual selection of peaks, for cross-checking the automatic picks

* Changed the format of the code to more or less comply with PEP8 
standard (e.g. 80 chars per line, whitespaces around operators, etc.). 
Some too long lines still remain, maybe changed later.
* Removed spelling errors where found
* Fixed #1

- Replaced ELIF statements with dictionary
- Now uses separate input functions that generates a dictionary 
depending on the data file