"""
scale_factor = 3
mean = numpy.average(samples)
- rms = scale_factor*((numpy.sum((samples-mean)**2)/len(samples))**.5)
+ rms = numpy.max([scale_factor*((numpy.sum((samples-mean)**2)/len(samples))**.5), .1])
min = mean - rms
max = mean + rms
return min, max
#autorange
if self[AUTORANGE_KEY] and time.time() - self.autorange_ts > AUTORANGE_UPDATE_RATE:
bounds = [common.get_min_max(samples) for samples in sampleses]
- y_min = numpy.min(*[bound[0] for bound in bounds])
- y_max = numpy.max(*[bound[1] for bound in bounds])
+ y_min = numpy.min([bound[0] for bound in bounds])
+ y_max = numpy.max([bound[1] for bound in bounds])
#adjust the y per div
y_per_div = common.get_clean_num((y_max-y_min)/self[Y_DIVS_KEY])
if y_per_div != self[Y_PER_DIV_KEY]: self.set_y_per_div(y_per_div)