_rd_dat_signals: always honor the smooth_frames parameter.

This function can return signal data in either of two formats:

- "smooth" (a two-dimensional array, where x[t,s] is sample t of
  signal s)

- "non-smooth" (a list of one-dimensional arrays, where x[s][t] is
  sample t of signal s)

Previously, _rd_dat_signals would use "smooth" format if
'sum(samps_per_frame) == n_sig', regardless of 'smooth_frames'.  This
makes little sense, since the caller needs to know what type of return
value to expect.  Instead, the format should be determined solely by
the 'smooth_frames' parameter.

(In this case, the only caller of _rd_dat_signals is _rd_segment, and
the only caller of _rd_segment is wfdb.io.record.rdrecord.)

Author: Benjamin Moody

wfdb/io/_signal.py

@@ -1089,7 +1089,7 @@ def _rd_dat_signals(file_name, dir_name, pn_dir, fmt, n_sig, sig_len,
     sampto : int
         The final sample number to be read from the signals.
     smooth_frames : bool
-        Whether to smooth channels with multiple samples/frame.
+        Whether to return the result as a two-dimensional array.
     no_file : bool, optional
         Used when using this function with just an array of signal data
         and no associated file to read the data from.
@@ -1101,9 +1101,8 @@ def _rd_dat_signals(file_name, dir_name, pn_dir, fmt, n_sig, sig_len,
     -------
     signal : ndarray, list
         The signals read from the dat file(s). A 2d numpy array is
-        returned if the signals have uniform samples/frame or if
-        `smooth_frames` is True. Otherwise a list of 1d numpy arrays
-        is returned.
+        returned if `smooth_frames` is True. Otherwise a list of 1d
+        numpy arrays is returned.
 
     Notes
     -----
@@ -1212,7 +1211,7 @@ def _rd_dat_signals(file_name, dir_name, pn_dir, fmt, n_sig, sig_len,
     # required for storing the final digital samples.
 
     # No extra samples/frame. Obtain original uniform numpy array
-    if tsamps_per_frame == n_sig:
+    if smooth_frames and tsamps_per_frame == n_sig:
         # Reshape into multiple channels
         signal = sig_data.reshape(-1, n_sig)
         # Skew the signal