Merge branch 'master' of github.com:MIT-LCP/wfdb-python

cx1111 · cx1111 · commit 366f894c58c3 · 2017-08-14T18:54:05.000-04:00
diff --git a/README.rst b/README.rst
@@ -106,6 +106,7 @@ file with 'rdann'.
 
 The attributes of the Annotation object give information about the annotation as specified
 by https://www.physionet.org/physiotools/wag/annot-5.htm:
+
 - ``annsamp``: The annotation location in samples relative to the beginning of the record.
 - ``anntype``: The annotation type according the the standard WFDB codes.
 - ``subtype``: The marked class/category of the annotation.
@@ -628,10 +629,10 @@ Example Usage:
     max_bpm = 350
     min_gap = fs*60/min_bpm
     max_gap = fs*60/max_bpm
-    new_indexes = wfdb.processing.correct_peaks(x=sig[:,0], peak_indexes=peak_indexes, min_gap=min_gap, max_gap=max_gap, smooth_window=150)
+    new_indexes = wfdb.processing.correct_peaks(x=sig[:,0], peaks_indexes=peak_indexes, min_gap=min_gap, max_gap=max_gap, smooth_window=150)
 
 Input arguments:
-peaks_indexes, min_gap, max_gap, smooth_window
+
 - ``x`` (required): The signal.
 - ``peaks_indexes`` (required): The location of the peaks.
 - ``min_gap`` (required): The minimum gap in samples between two peaks.
diff --git a/wfdb/processing/peaks.py b/wfdb/processing/peaks.py
@@ -39,42 +39,6 @@ def find_peaks(x):
     soft_peaks = numpy.asarray(soft_peaks)+1
     return hard_peaks, soft_peaks
 
-def find_peaks(x):
-    # Definitions:
-    # * Hard peak: a peak that is either /\ or \/
-    # * Soft peak: a peak that is either /-*\ or \-*/ (In that cas we define the middle of it as the peak)
-
-    # Returns two numpy arrays:
-    # * hard_peaks contains the indexes of the Hard peaks
-    # * soft_peaks contains the indexes of the Soft peaks
-
-    if len(x) == 0:
-        return numpy.empty([0]), numpy.empty([0])
-
-    tmp = x[1:]
-    tmp = numpy.append(tmp, [x[-1]])
-    tmp = x-tmp
-    tmp[numpy.where(tmp>0)] = +1
-    tmp[numpy.where(tmp==0)] = 0
-    tmp[numpy.where(tmp<0)] = -1
-    tmp2 = tmp[1:]
-    tmp2 = numpy.append(tmp2, [0])
-    tmp = tmp-tmp2
-    hard_peaks = numpy.where(numpy.logical_or(tmp==-2,tmp==+2))[0]+1
-    soft_peaks = []
-    for iv in numpy.where(numpy.logical_or(tmp==-1,tmp==+1))[0]:
-        t = tmp[iv]
-        i = iv+1
-        while True:
-            if i==len(tmp) or tmp[i] == -t or tmp[i] == -2 or tmp[i] == 2:
-                break
-            if tmp[i] == t:
-                soft_peaks.append(int(iv+(i-iv)/2))
-                break
-            i += 1
-    soft_peaks = numpy.asarray(soft_peaks)+1
-    return hard_peaks, soft_peaks
-
 
 def correct_peaks(x, peaks_indexes, min_gap, max_gap, smooth_window):
     N = x.shape[0]
