Update trigger detection tutorial

docs/heuristic.rst

@@ -85,7 +85,7 @@ Once you modified your heuristic file or created a new one, you can save it anyw
 
 .. code-block:: shell
 
-    phys2bids -in tutorial_file.txt -chtrig 1 -outdir /home/arthurdent/physio_bids -ntp 158 -tr 1.2 -thr 0.735 -heur /home/arthurdent/git/phys2bids/phys2bids/heuristics/heur_tutorial.py -sub 006 -ses 01
+    phys2bids -in tutorial_file.txt -indir /home/arthurdent/git/phys2bids/phys2bids/tests/data/ -chtrig 1 -ntp 158 -tr 1.2 -outdir /home/arthurdent/physio -heur /home/arthurdent/git/phys2bids/phys2bids/heuristics/heur_tutorial.py -sub 006 -ses 01
 
 Remember to **specify the full path** to the heuristic file. A copy of the heuristic file will be saved in the site folder.
 

phys2bids/phys2bids.py

@@ -276,8 +276,8 @@ def phys2bids(filename, info=False, indir='.', outdir='.', heur_file=None,
         viz.plot_trigger(phys_in.timeseries[0], phys_in.timeseries[chtrig],
                          plot_path, tr, phys_in.thr, num_timepoints_expected, filename)
     else:
-        LGR.info('Not plotting trigger. If you want the trigger to be'
-                 ' plotted enter -tr or -ntp, preferably both.')
+        LGR.warning('Skipping trigger pulse count. If you want to run it, '
+                    'call phys2bids using "-ntp" and "-tr" arguments')
 
     # The next few lines remove the undesired channels from phys_in.
     if chsel:

phys2bids/physio_obj.py

@@ -269,13 +269,19 @@ def check_trigger_amount(self, chtrig=1, thr=None, num_timepoints_expected=0, tr
         # Use the trigger channel to find the TRs,
         # comparing it to a given threshold.
         trigger = self.timeseries[chtrig]
+        flag = 0
         if thr is None:
             thr = np.mean(trigger) + 2 * np.std(trigger)
+            flag = 1
         timepoints = trigger > thr
         num_timepoints_found = len([is_true for is_true, _ in groupby(timepoints,
                                     lambda x: x != 0) if is_true])
-        LGR.info(f'The number of timepoints according to the std_thr method '
-                 f'is {num_timepoints_found}. The computed threshold is {thr}')
+        if flag == 1:
+            LGR.info(f'The number of timepoints according to the std_thr method '
+                     f'is {num_timepoints_found}. The computed threshold is {thr}')
+        else:
+            LGR.info(f'The number of timepoints found with the manual threshold of {thr} '
+                     f'is {num_timepoints_found}')
         time_offset = self.timeseries[0][timepoints.argmax()]
 
         if num_timepoints_expected: