maxi = ((y[x[crit]] - y[x[crit] + window] > 0) & \
       (y[x[crit]] - y[x[crit] - window] > 0) * 1)
mini = ((y[x[crit]] - y[x[crit] + window] < 0) & \
       (y[x[crit]] - y[x[crit] - window] < 0) * 1)
maxi = maxi.astype(float)

# find trend on closing price
trendy.minitrends(ohlc.c, window=30, charts=True)

    143     # Find whether max's or min's
--> 144     maxi = (y[x[crit]] - y[x[crit] + window] > 0) & \
    145            (y[x[crit]] - y[x[crit] - window] > 0) * 1

python 3.6.9
numpy 1.16.4

# Find the indexes of these maxima in the data
segments = int(segments)
maxima = np.ones(segments)
minima = np.ones(segments) 
x_maxima = np.ones(segments)
x_minima = np.ones(segments)
segsize = int(len(y)/segments)
for i in range(1, segments+1):
    ind2 = i*segsize
    ind1 = ind2 - segsize
    seg = y[ind1:ind2]
    maxima[i-1] = max(seg)
    minima[i-1] = min(seg)
    x_maxima[i-1] = ind1 + (np.where(seg == maxima[i-1])[0][0])
    x_minima[i-1] = ind1 + (np.where(seg == minima[i-1])[0][0])