diff options
| -rw-r--r-- | utils/sddm/plot_energy.py | 20 |
1 files changed, 16 insertions, 4 deletions
diff --git a/utils/sddm/plot_energy.py b/utils/sddm/plot_energy.py index 7852412..efbaa36 100644 --- a/utils/sddm/plot_energy.py +++ b/utils/sddm/plot_energy.py @@ -86,7 +86,7 @@ def retrigger_cut(ev): """ Cuts all retrigger events. """ - return ev[ev.dt > 500] + return ev[(ev.dt > 500) & (ev.gtid_diff == 0)] def breakdown_follower_cut(ev): """ @@ -459,7 +459,13 @@ def get_events(filenames, merge_fits=False, nhit_thresh=None, apply_nhit_trigger # First, remove orphans since they won't have a 50 MHz clock and so could # screw up the 50 MHz clock unwrapping - ev = ev[(ev.gtid == 0) & (ev.gtr == 0)] + ev = ev[~((ev.gtid == 0) & (ev.gtr == 0) & (ev.trg_type == 0))] + + # Calculate the expected next GTID for all GTIDs. The algorithm here is + # taken from the builder's eb_make_entry() function in eb_queue_ph.c. + next_gtid = np.roll(np.arange(0x1000000),-1) + next_gtid[(next_gtid & 0xffff) == 0xffff] += 1 + next_gtid[(next_gtid & 0xff0000) == 0xff0000] = 0 # We need the events to be in time order here in order to calculate the # delta t between events. It's not obvious exactly how to do this. You @@ -498,13 +504,19 @@ def get_events(filenames, merge_fits=False, nhit_thresh=None, apply_nhit_trigger # Warn about GTID jumps since we could be missing a potential flasher # and/or breakdown, and we need all the events in order to do a # retrigger cut - if np.count_nonzero(np.diff(ev_run.gtid) != 1): - print_warning("Warning: %i GTID jumps in run %i" % (np.count_nonzero(np.diff(ev_run.gtid) != 1),run)) + jumps = ev_run.gtid.values[1:] - next_gtid[ev_run.gtid[:-1].values] + if np.count_nonzero(jumps != 0): + print(ev_run[jumps != 0][['run','gtid','gtid_diff']]) + print_warning("Warning: %i GTID jumps in run %i" % (np.count_nonzero(jumps != 0),run)) # calculate the time difference between each event and the previous event # so we can tag retrigger events ev['dt'] = ev.groupby(['run'],group_keys=False)['gtr'].transform(lambda x: np.concatenate(([1e9],np.diff(x.values)))) + # calculate the time difference between each event and the previous event + # so we can tag retrigger events + ev['gtid_diff'] = ev.groupby(['run'],group_keys=False)['gtid'].transform(lambda x: np.concatenate(([0],x.values[1:]-next_gtid[x[:-1].values]))) + # Calculate the approximate Ockham factor. # See Chapter 20 in "Probability Theory: The Logic of Science" by Jaynes # |