Add doc.

Dubrzr · Dubrzr · commit d515911613b2 · 2017-06-26T15:57:24.000Z
diff --git a/README.rst b/README.rst
@@ -561,8 +561,8 @@ Input arguments:
 - ``window_size`` (required): The smoothing window width.
 
 
-GQRS detector
-~~~~~~~~~~~~~
+Peak detection
+~~~~~~~~~~~~~~
 
 **gqrs_detect** - The GQRS detector function
 
@@ -579,8 +579,7 @@ Example Usage:
     tf = 20000
     sig, fields = wfdb.srdsamp('sampledata/100', sampfrom=t0, sampto=tf, channels=[0])
     record = wfdb.rdsamp("sampledata/100", sampfrom=t0, sampto=tf, channels=[0], physical=False)
-
-    peak_indexes = wfdb.processing.gqrs_detect(x=x, freq=fields['fs'], gain=record.adcgain[0], adczero=record.adczero[0], threshold=1.0)
+    peak_indexes = wfdb.processing.gqrs_detect(x=sig[:,0], freq=fields['fs'], gain=record.adcgain[0], adczero=record.adczero[0], threshold=1.0)
 
 Input arguments:
 
@@ -601,6 +600,40 @@ Input arguments:
 - ``QRSamin`` (default=130): Minimum QRS peak-to-peak amplitude, in microvolts.
 
 
+**correct_peaks** - A post-processing algorithm to correct peaks position.
+
+See code comments for details about the algorithm.
+
+
+::
+
+  correct_peaks(x, peaks_indexes, min_gap, max_gap, smooth_window)
+
+Example Usage:
+
+::
+
+    import wfdb
+    t0 = 10000
+    tf = 20000
+    sig, fields = wfdb.srdsamp('sampledata/100', sampfrom=t0, sampto=tf, channels=[0])
+    record = wfdb.rdsamp("sampledata/100", sampfrom=t0, sampto=tf, channels=[0], physical=False)
+    peak_indexes = wfdb.processing.gqrs_detect(x=sig[:,0], freq=fields['fs'], gain=record.adcgain[0], adczero=record.adczero[0], threshold=1.0)
+    fs = fields['fs']
+    min_bpm = 10
+    max_bpm = 350
+    min_gap = fs*60/min_bpm
+    max_gap = fs*60/max_bpm
+    y_idxs = wfdb.processing.correct_peaks(x=sig[:,0], peak_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.
+- ``max_gap`` (required): The maximum gap in samples between two peaks.
+- ``smooth_window`` (required): The size of the smoothing window.
+
 Based on the original WFDB software package specifications
 ----------------------------------------------------------
 
diff --git a/tests/test_processing.py b/tests/test_processing.py
@@ -67,7 +67,6 @@ def test_7(self):
         sig, fields = wfdb.srdsamp('sampledata/100', channels = [0, 1])
         ann = wfdb.rdann('sampledata/100', 'atr')
         fs = fields['fs']
-        trained_fs = 360
         min_bpm = 10
         max_bpm = 350
         min_gap = fs*60/min_bpm
diff --git a/wfdb/processing/peaks.py b/wfdb/processing/peaks.py
@@ -76,8 +76,8 @@ def find_peaks(x):
     return hard_peaks, soft_peaks
 
 
-def correct_peaks(signal, peaks_indexes, min_gap, max_gap, smooth_window):
-    N = signal.shape[0]
+def correct_peaks(x, peaks_indexes, min_gap, max_gap, smooth_window):
+    N = x.shape[0]
 
     rpeaks = numpy.zeros(N)
     rpeaks[peaks_indexes] = 1.0
@@ -97,23 +97,23 @@ def correct_peaks(signal, peaks_indexes, min_gap, max_gap, smooth_window):
             if rpeaks[i] == 1:
                 tmp_idx = i
 
-    smoothed = smooth(signal, smooth_window)
+    smoothed = smooth(x, smooth_window)
 
     # Compute signal's peaks
-    hard_peaks, soft_peaks = find_peaks(x=signal)
+    hard_peaks, soft_peaks = find_peaks(x=x)
     all_peak_idxs = numpy.concatenate((hard_peaks, soft_peaks)).astype('int64')
 
     # Replace each range of ones by the index of the best value in it
     tmp = set()
     for rp_range in rpeaks_ranges:
         r = numpy.arange(rp_range[0], rp_range[1]+1, dtype='int64')
-        vals = signal[r]
+        vals = x[r]
         smoothed_vals = smoothed[r]
         p = r[numpy.argmax(numpy.absolute(numpy.asarray(vals)-smoothed_vals))]
         tmp.add(p)
 
     # Replace all peaks by the peak within x-max_gap < x < x+max_gap which have the bigget distance from smooth curve
-    dist = numpy.absolute(signal-smoothed) # Peak distance from the smoothed mean
+    dist = numpy.absolute(x-smoothed) # Peak distance from the smoothed mean
     rpeaks_indexes = set()
     for p in tmp:
         a = max(0, p-max_gap)
@@ -135,7 +135,7 @@ def correct_peaks(signal, peaks_indexes, min_gap, max_gap, smooth_window):
         if len(r) == 1:
             continue
         rr = r.astype('int64')
-        vals = signal[rr]
+        vals = x[rr]
         smoo = smoothed[rr]
         the_one = r[numpy.argmax(numpy.absolute(vals-smoo))]
         for i in r:
