Updates since last week

• Neural networks: Tried different classification networks for IROC and OROC's separately, but no significant performance increase could be observed
• Training data production: Added transformations from (row, pad, time) to (X, Y, Z) (details below)

Momentum vector estimation via track propagation (below: exaggerated view by scaling up the momentum vectors)

To add this to the training data:

1. Run the tracking on ideal clusters
2. Read back tracking clusters (subset of ideal clusters) and tracks
3. Run regular chain of training data (digit-ideal assignment, occupancy tagging, etc.)
4. After assignment is done: 
   1. For every tracking cluster check in the tpc sector map if this ideal cluster can be found again (which should always happen)
   2. Save association of the tracking cluster with the ideal cluster that was matched
5. When creating training data get assigned momentum data of the ideal cluster

This is a major step: I can now assign e.g. track momentum (but also e.g. track position when crossing a pad-row) to the digit maxima. This can easily also be applied on real data (instead of ideal clusters)! Like this we can also train the networks (in the future) on real data using the position where a track passes a pad-row (once I get the transformation right).