def detect_burr(data,
pv,
left=None,
right=None,
method=0,
minimum_peak_distance=100):
titles = data.columns
titleList = titles.values.tolist()
if pv in titleList:
pvn = titleList.index(pv)
sta = DisplayData.showStatistic(data)
print("statistic data:")
print(sta)
# use boxplot define threshold
iqr = sta.loc['75%'][titles[pvn]] - sta.loc['25%'][titles[pvn]]
if left is None:
left = sta.loc['25%'][titles[pvn]] - 1.5 * iqr
if right is None:
right = sta.loc['75%'][titles[pvn]] + 1.5 * iqr
print('min edge:', left, 'max edge:', right)
burrdata = data[((data[titles[pvn]]) < left) |
((data[titles[pvn]]) > right)]
LoadData.df2other(burrdata, 'csv', 'newfile.csv')
y = data[titles[pvn]].values
if method == 0:
# find_peaks by scipy signal
peaks, _ = signal.find_peaks(y, height=right)
plt.plot(y, 'b', lw=1)
plt.plot(peaks, y[peaks], "+", mec='r', mew=2, ms=8)
plt.plot(np.zeros_like(y) + right, "--", color="gray")
plt.title("find_peaks min_height:%7f" % right)
plt.show()
if method == 1:
detect_peaks(y, mph=right, mpd=minimum_peak_distance, show=True)
if method == 2:
print('Detect peaks with minimum height and distance filters.')
# thres=right/max(y)
indexes = peakutils.peak.indexes(np.array(y),
thres=right / max(y),
min_dist=minimum_peak_distance)
print('Peaks are: %s' % (indexes))
plt.plot(y, 'b', lw=1)
for i in indexes:
plt.plot(i, y[i], "+", mec='r', mew=2, ms=8)
plt.plot(np.zeros_like(y) + right, "--", color="gray")
plt.title("peakutils.peak thres:%f ,minimum_peak_distance:%d" %
(right, minimum_peak_distance))
plt.show()
else:
print("Wrong PV name, not in ", titleList)
