[Draft] ENH: Proper restarting during optimisations/MDs

Related to #1454 @AndrewRosen @flokno

There are probably a lot of things to say about the current code in this MR but it fixes problems that I would say are important:

• Current restarting mechanisms do not allow for complete results reproducibility, this is especially true for Dynamics types that are stochastic; the state of the random number generator has to be kept between restarts. This is fixed here by printing a pickle RestartFile which is put as an observer.

• The current restart mechanism always performs one more calculation (at the beginning of each restart), resulting in increased computational cost, we fix it by introducing a restart method in the Dynamics classes

Andersen MD, first a run with a restart mid-way, then the complete run, this was not possible before:

Time[ps]      Etot[eV]     Epot[eV]     Ekin[eV]    T[K]
0.0000           2.0024       2.0024       0.0000     0.0
0.0010           2.0419       1.9718       0.0701   180.8
0.0020           2.0374       1.9085       0.1289   332.5
0.0030           2.0065       1.8813       0.1252   322.9
0.0040           1.9986       1.8932       0.1053   271.6
0.0050           1.9847       1.9197       0.0650   167.6
Time[ps]      Etot[eV]     Epot[eV]     Ekin[eV]    T[K]
0.0060           1.9848       1.9210       0.0638   164.5
0.0070           1.9782       1.8992       0.0790   203.7
0.0080           1.9330       1.8822       0.0508   130.9
0.0090           1.9415       1.8868       0.0547   141.0
0.0100           1.9438       1.9156       0.0282    72.6
Time[ps]      Etot[eV]     Epot[eV]     Ekin[eV]    T[K]
0.0000           2.0024       2.0024       0.0000     0.0
0.0010           2.0419       1.9718       0.0701   180.8
0.0020           2.0374       1.9085       0.1289   332.5
0.0030           2.0065       1.8813       0.1252   322.9
0.0040           1.9986       1.8932       0.1053   271.6
0.0050           1.9847       1.9197       0.0650   167.6
0.0060           1.9848       1.9210       0.0638   164.5
0.0070           1.9782       1.8992       0.0790   203.7
0.0080           1.9330       1.8822       0.0508   130.9
0.0090           1.9415       1.8868       0.0547   141.0
0.0100           1.9438       1.9156       0.0282    72.6

Other, less important problems that would be nice to address:

• Be compatible with the new Numpy random Generator's class, this is done here by changing from random_sample() to random() for generating random numbers

• There should be only one correct way to do things: if there is an attach mechanism for observers, we should delete the trajectory and logfile keywords in the constructors, especially since they do not allow as much flexibility

• Similarly, fixcm or fix_com should probably be discarded since there is already a whole module to deal with constraints?

I think this is a nice introduction to discuss these issues, it does not have to be using the current code

Checklist

• I am familiar with ASE's contribution guidelines.
• Doc strings in code changed in this MR are up to date.
• Unit tests have been added for new or changed code.
• Issue is resolved via "closes #XXXX" if applicable.