procpgn.py: start digesting data and producing statistical results (PST-like)
data.c: get rid of "conventional" PSTs, and start developing new ones.
procpgn.py: correctly parse en passant captures
data.c: add level 1 correlation (very preliminary)
procpgn.py: swap knight's and bishop's indices (2 <-> 3)
eval.c: added preliminary level-1 correlation table.