Expose information about segments in info and "print_metadata" (#117)

xdf-modules · Oct 21, 2024 · 13ab943 · 13ab943
1 parent 44190d3
commit 13ab943
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Showing 2 changed files with 11 additions and 2 deletions.
diff --git a/pyxdf/examples/print_metadata.py b/pyxdf/examples/print_metadata.py
@@ -16,14 +16,15 @@ def main(fname: str):
 
     print("Found {} streams:".format(len(streams)))
     for ix, stream in enumerate(streams):
-        msg = "Stream {}: {} - type {} - uid {} - shape {} at {} (effective {}) Hz"
+        msg = "Stream {}: {} - type {} - uid {} - shape {} in {} segments at {} (effective {}) Hz"
         print(
             msg.format(
                 ix + 1,
                 stream["info"]["name"][0],
                 stream["info"]["type"][0],
                 stream["info"]["uid"][0],
                 (int(stream["info"]["channel_count"][0]), len(stream["time_stamps"])),
+                len(stream["info"]["segments"]),
                 stream["info"]["nominal_srate"][0],
                 stream["info"]["effective_srate"],
             )

diff --git a/pyxdf/pyxdf.py b/pyxdf/pyxdf.py
@@ -370,6 +370,10 @@ def load_xdf(
         )
 
     # perform jitter removal if requested
+    for tmp in temp.values():
+        # initialize segment list in case jitter_removal was not selected
+        tmp.segments = [(0, len(stream.time_series) - 1)]  # inclusive
+
     if dejitter_timestamps:
         logger.info("  performing jitter removal...")
         temp = _jitter_removal(
@@ -397,6 +401,7 @@ def load_xdf(
             )
         stream["info"]["stream_id"] = k
         stream["info"]["effective_srate"] = tmp.effective_srate
+        stream["info"]["segments"] = tmp.segments
         stream["time_series"] = tmp.time_series
         stream["time_stamps"] = tmp.time_stamps
         stream["clock_times"] = tmp.clock_times
@@ -646,7 +651,8 @@ def _jitter_removal(streams, threshold_seconds=1, threshold_samples=500):
             # 0th sample is a segment start and last sample is a segment stop
             seg_starts = np.hstack(([0], break_inds))
             seg_stops = np.hstack((break_inds - 1, nsamples - 1))  # inclusive
-
+            for a, b in zip(seg_starts, seg_stops):
+                stream.segments.append((a, b))
             # Process each segment separately
             for start_ix, stop_ix in zip(seg_starts, seg_stops):
                 # Calculate time stamps assuming constant intervals within each
@@ -666,6 +672,8 @@ def _jitter_removal(streams, threshold_seconds=1, threshold_samples=500):
                     stream.time_stamps[seg_stops] + stream.tdiff
                 ) - stream.time_stamps[seg_starts]
                 stream.effective_srate = np.sum(counts) / np.sum(durations)
+        else:
+            stream.segments = [0, nsamples - 1]
 
         srate, effective_srate = stream.srate, stream.effective_srate
         if srate != 0 and np.abs(srate - effective_srate) / srate > 0.1: