Hi Yao,
What you probably care about is the `conditional_arrivals` and/or the `conditional_fluxes` data sets. `conditional arrivals` should be equivalent to the `conditional_fluxes` dataset, normalized with the summed weight from the `labeled_populations` dataset
from all bins. The normalization should be very close to 1, since you ran with recycling. If you did cumulative averaging, you need to do that to the `labeled_population` dataset yourself (though since it's closed to 1 due to recycling, it shouldn't matter
too much).
Now looking at the included drawing, to get the "successful" trajectories, you need to get all the (1)s, but not (2-4). `assignments` will give you extra points because it might have included points like (2). To easily find all the successful trajectories,
I suggest using LPATH's extract step. (
https://github.com/chonglab-pitt/lpath contains the paper and installation instructions)
Running something like the following (assuming you're going from unbound to bound, but if not, swap the 0 and 1)
lpath extract -we -W west.h5 -A ANALYSIS/TEST/assign.h5 --source-state 0 \
--target-state 1 --extract-output output.pickle --out-dir succ_traj
The `output.pickle` file should be nested list of list, containing all the successful trajectories. The first dimension is all of the successful pathways, and each would contain as list of lists which indicate the `iter_id/seg_id/state_id/pcoord_or_auxdata/frame#/weight`.
The last frame of each list would be the frame it hits the target. The first frame would be the last time it exited the source.
Best,
Jeremy L.
---
Jeremy M. G. Leung
PhD Candidate, Chemistry
Graduate Student Researcher, Chemistry (Chong Lab)
University of Pittsburgh | 219 Parkman Avenue, Pittsburgh, PA 15260
jml...@pitt.edu | [He, Him, His]