Implement some thinning methods

[KseniaBastrakova]

1. Leveling thinning: At the beginning, this method calculates the average weight of particulates W_avg(x) . Then if the particle weight W_i(x) is less than coeff * W_avg then this particle with probability W_i /(*coeff * W_avg) stays in the ensemble with a new weight coeff * W_avg
   Thus, the procedure gets rid of particles with low weight.
   The procedure preserves the distribution functions, but leads to local and global deviations from the laws of conservation of charge, energy, and momentum.

2. NumberConservative thinning In this method, the choice of K particles with repetition occurs, and the choice of a single particle occurs with probability W_i /W , where W -- sum particles weight. After this, particles that have not been selected once are removed from the ensemble, while the rest receive new weights. k_iW/K, where k_i -- the amount of deposition in the procedure for choosing a given particle. However, it does not save other invariants, such as total energy.

3. EnergyConservative thinning there is a selection of K particles with repetition, while the choice of a single particle occurs with probability e_i * W_i/E. Where E is the total energy of the particles, and e_i - energy of a current particle. After this, particles that have not been selected once are removed from the ensemble, while the rest get new weights k_i* E/ (K * e_i) The method saves distribution functions and total energy of all macroparticles.

4. MergeAverage First there is a clustering of particles using the k-means algorithm. After that, within each obtained cluster, instead of all particles belonging to it in the ensemble, a particle with an average value of coordinates and momentum is added, and the weight of this particle is equal to the sum of weights of all particles in this cluster.