EM mixture model fit tweaks

- Fix: fit() now has default value for nontarget angles. Fixed doctext as well.
- Fix: load_data_simult can now fit the mixture model
Compute precision from samples

- Add: function to compute the precision from samples.
- Add: function to sample random angles.
Support for label legend in plot_mean_std_area

- Add: now passes label= to plot, which allow for clever legend plotting.
- Add: Doctext for hist_angular_data
Script checking precision dependence on randomness

- Add: Precision seemed sensitive to random outliers, so check the strength of this effect. Assume a von mises with random samples, of chosen probability. See how the estimated precision evolves with that. It is INCREDIBLY sensitive to random samples, only 2-3% of samples displaced can reduce by 2 the estimated precision, especially for high kappa… This is bad. Fitting a two mixture model to the samples, on the other hand, recovers the original kappa more reliably (but check [Awh] for comments on number of samples required). Means we cannot use precision to compare our model to experimental data, too sensitive.
Memory curve fits using kappa

- Added: Now chooses parameters based on fit between model and experimental kappa. Fits GREAT. Finally. Similar parameters than with loglikelihood. Done for mixed, should do for the others.