plot_signal: accept non-smooth signal data as input.

When plotting signals, in addition to allowing the signal argument to
be a one-dimensional or two-dimensional array, allow it to be a list
of one-dimensional arrays (so that each channel can have a different
length.)

This means that the array of X values (t) must be calculated
separately for each channel, as both the sampling frequency and the
number of samples may vary.  (Here, we don't require that the lengths
of the signals, in seconds, must all be the same, although they always
will be when plotting a WFDB record.)

The sig_len parameter makes no sense if the channels have different
lengths; this parameter is now unused, and marked as deprecated.

Author: Benjamin Moody

wfdb/plot/plot.py

@@ -364,10 +364,14 @@ def plot_signal(signal, sig_len, n_sig, fs, time_units, sig_style, axes,
 
     Parameters
     ----------
-    signal : ndarray
-        Tranformed expanded signal into uniform signal.
+    signal : 1d or 2d numpy array or list
+        The uniformly sampled signal or signals to be plotted.  If signal
+        is a one-dimensional array, it is assumed to represent a single
+        channel.  If it is a two-dimensional array, axes 0 and 1 must
+        represent time and channel number respectively.  Otherwise it must
+        be a list of one-dimensional arrays (one for each channel).
     sig_len : int
-        The signal length (per channel) of the dat file.
+        The signal length (per channel) of the dat file.  Deprecated.
     n_sig : int
         The number of signals contained in the dat file.
     fs : float
@@ -392,6 +396,8 @@ def plot_signal(signal, sig_len, n_sig, fs, time_units, sig_style, axes,
     N/A
 
     """
+    # Convert signal to a list if needed
+    signal = _expand_channels(signal)
     if n_sig == 0:
         return
 
@@ -402,27 +408,24 @@ def plot_signal(signal, sig_len, n_sig, fs, time_units, sig_style, axes,
     # Convert sampling_freq to a list if needed
     sampling_freq = _get_sampling_freq(sampling_freq, n_sig, fs)
 
-    if any(f != sampling_freq[0] for f in sampling_freq):
-        raise NotImplementedError(
-            'multiple sampling frequencies are not supported')
-
-    # Figure out time indices
-    if time_units == 'samples':
-        t = np.linspace(0, sig_len-1, sig_len)
-    else:
-        downsample_factor = {
-            'seconds': sampling_freq[0],
-            'minutes': sampling_freq[0] * 60,
-            'hours': sampling_freq[0] * 3600
-        }
-        t = np.linspace(0, sig_len-1, sig_len) / downsample_factor[time_units]
-
     # Plot the signals
-    if signal.ndim == 1:
-        axes[0].plot(t, signal, sig_style[0], zorder=3)
-    else:
-        for ch in range(n_sig):
-            axes[ch].plot(t, signal[:,ch], sig_style[ch], zorder=3)
+    for ch in range(n_sig):
+        ch_len = len(signal[ch])
+        ch_freq = sampling_freq[ch]
+
+        # Figure out time indices
+        if time_units == 'samples':
+            t = np.linspace(0, ch_len-1, ch_len)
+        else:
+            downsample_factor = {
+                'seconds': ch_freq,
+                'minutes': ch_freq * 60,
+                'hours': ch_freq * 3600
+            }
+            t = np.linspace(0, ch_len-1, ch_len)
+            t /= downsample_factor[time_units]
+
+        axes[ch].plot(t, signal[ch], sig_style[ch], zorder=3)
 
 
 def plot_annotation(ann_samp, n_annot, ann_sym, signal, n_sig, fs, time_units,