def _print_distribution_stats(self):
'Print a summary table of distribution stats.'
self._print_title('Distribution stats')
print 'Number of values: %d' % len(self.readings_array)
print 'Minimum: %0.3f' % self.readings_array.min()
print 'Maximum: %0.3f' % self.readings_array.max()
print 'Mean: %0.3f' % self.readings_array.mean()
print 'Deviation: %0.3f' % self.readings_array.std()
print 'Median: %0.3f' % percentile(self.readings_array, 50)
print '80th Percentile: %0.3f' % percentile(self.readings_array, 80)
print
def _compute_stats_function(values):
stats = None
if len(values)>1:
stats = {}
stats['min'] = min(values)
stats['max'] = max(values)
stats['mean'] = mean(values)
stats['median'] = median(values)
stats['1st-quartile'] = percentile(values,25)
stats['3rd-quartile'] = percentile(values,75)
stats['std-error'] = std(values)
return stats
def _print_zoom_histograms(self):
'Generates zoom histograms based on user specified intervals'
if not self.app.pargs.zoom or not self.app.pargs.zoom[0]:
return
interval_start = 0
# set the initial minimum to less than the input minimum
# makes the condition match easier (min < x <= max)
min_value = self.readings_array.min() - 1
for interval_width in self.app.pargs.zoom:
if not interval_width:
self.app.log.info(
'Skipping zero width interval at %dth percentile' % (interval_start))
continue
interval_end = interval_start + interval_width
max_value = percentile(self.readings_array, interval_end)
title = 'Distribution of values in percentile range %d - %d' % (
interval_start, interval_end)
def value_in_interval(value):
if min_value < value <= max_value:
return value
self._print_histogram(title, condition=value_in_interval)
interval_start = interval_end
min_value = max_value
def get_focus_measure(src, kind):
if not isinstance(src, ndarray):
src = asarray(src)
dst = laplace(src.astype(float))
d = dst.flatten()
d = percentile(d, 99)
return d.mean()
def get_focus_measure(src, kind):
if not isinstance(src, ndarray):
src = asarray(src)
dst = laplace(src.astype(float))
d = dst.flatten()
d = percentile(d, 99)
return d.mean()
def get_focus_measure(src, kind):
if not isinstance(src, ndarray):
src = asarray(src)
# w, h = GetSize(pychron)
# dst = CreateMat(w, h, CV_16SC1)
# Laplace(pychron, dst)
# d = asarray(dst).flatten()
dst = laplace(src.astype(float))
d = dst.flatten()
d = percentile(d, 99)
return d.mean()
def get_focus_measure(src, kind):
if not isinstance(src, ndarray):
src = asarray(src)
# w, h = GetSize(pychron)
# dst = CreateMat(w, h, CV_16SC1)
# Laplace(pychron, dst)
# d = asarray(dst).flatten()
dst = laplace(src.astype(float))
d = dst.flatten()
d = percentile(d, 99)
return d.mean()
