Wrong error curve fitting

[Gabriel-p]

A couple of issues with error handling:

• In the err_accpt_rejct function, if the manual mode was used, the max error value (stored in clp['err_max']) could be different from the one in the params file (stored in pd['err_max']). Yet, the err_range_avrg function uses the pd['err_max'] value rather than the possibly updated one.
• If the manual mode is used and all stars are kept, the code stores the string all in clp['err_max']. This produces a crash later on when the synthetic clusters are produced.
• The err_range_avrg function uses the raw error data read from file, instead of the cleaned data after the max error rejection performed in err_accpt_rejct (this appears to be a widespread issue)
• The get_m_c_errors function (in err_range_avrg) uses err_max as a fitting point in the last resort fit ("2P exponential error function fit failed") This produces unrealistic error curves such as this one: