Fix more explanation of CI and also add a tiny comment to stop puzzling about why it works.

Author: smoia

phys2bids/cli/run.py

@@ -154,9 +154,10 @@ def _get_parser():
         help="The Confidence Interval (CI) to use in the estimation of the trigger clusters. "
         "The cluster algorithm considers triggers with duration (in samples) within this "
         "CI as part of the same group, thus the same. If CI is an integer, it will consider "
-        "that amount of triggers. If CI is a float and < 1, it will consider that "
-        "percentage of the trigger duration. CI cannot be a float > 1. Default is 1. "
-        "Change to .25 if there is a CMRR DWI sequence or when recording sub-triggers.",
+        "that amount of samples around the distance. If CI is a float and < 1, it will "
+        "consider that percentage of the trigger duration. CI cannot be a float > 1. "
+        "Default is 1. Change to .25 if there is a CMRR DWI sequence or when recording "
+        "sub-triggers.",
         default=1,
     )
     optional.add_argument(

phys2bids/slice4phys.py

@@ -23,7 +23,7 @@ def estimate_ntp_and_tr(phys_in, thr=None, ci=1):
         Confidence Interval (CI) to use in the estimation of the trigger clusters. The
         cluster algorithm considers triggers with duration (in samples) within this CI
         as part of the same group, thus the same. If CI is an integer, it will consider
-        that amount of triggers. If CI is a float and < 1, it will consider that
+        that amount of samples. If CI is a float and < 1, it will consider that
         percentage of the trigger duration. CI cannot be a float > 1. Default is 1.
         Change to .25 if there is a CMRR DWI sequence or when recording sub-triggers.
 
@@ -67,6 +67,7 @@ def estimate_ntp_and_tr(phys_in, thr=None, ci=1):
     k = 0
     current_group = [unique_isi[0]]
 
+    # np.unique returns sorted elements → unique_isi[0] == min(unique_isi), so THIS WORKS.
     for n, i in enumerate(range(1, len(unique_isi))):
         if unique_isi[i] <= upper_ci_isi[n]:
             current_group.append(unique_isi[i])