def find_peaks(histogram, thrs=5, kernel_radius=30, kernel_sigma=6.0, minima_as_boundries=True):
"""
Finds peaks within the histogram.
This is done by first applying a Gaussian filter with size
(2*kernel_radius)-1 and sigma kernel_sigma to the histogram.
Peaks are found by inspecting the gradient at each bin; once
two minima are located the maximum between them is inspected, if
its height (compared to the larger of the two minima) is greater
than the threshold (thrs) then the bin it corresponds to is
added to the returned list of bins.
If minima_as_boundries is true then this returns a pair of values:
[max_peak_bin, current_minima_bin]. This allows the minima to be
used as a boundary.
"""
# TODO re-write this for new histogram
kernel = gaussian_kernel(kernel_size=kernel_radius, sigma=kernel_sigma)
hist = convolve(histogram, kernel)
res = []
previous_y = 0 # height of previous bin
previous_gradient = 0 # gradient at previous bin
previous_max_y = 999999 # maxima's height
previous_min_y = 0 # minima's height (for thrs)
previous_max_x = 999999 # maxima's bin
for current_x, current_y in hist:
gradient = current_y - previous_y
# set the gradient for easy testing
if gradient > 0:
gradient = 1
elif gradient < 0:
gradient = -1
else:
gradient = 0
# Check if a minima or maxima has been found and if
# it's a minima check if it is to one side of a
# maxima of suitable height
if previous_gradient < 0 and gradient >= 0:
# found minima check the previous maxima
if (previous_max_y - max(previous_min_y, current_y) > thrs):
# real peak found between two minima
if minima_as_boundries:
res.append([previous_max_x, current_x])
else:
res.append(previous_max_x)
# update the minima irrespective of if it is associated with a peak
previous_min_y = current_y
elif previous_gradient > 0 and gradient <= 0:
# maxima; store then check against thrs at next minima
previous_max_y = current_y
previous_max_x = current_x
# set values for next bin
previous_y = current_y
previous_gradient = gradient
return res
def test():
print 'float_range(6)', float_range(6)
print 'float_range(6, 7)', float_range(6, 7)
print 'float_range(3, 10, 0.6)', float_range(3, 10, 0.2)
print '*'*40
h = Histogram(bins = [1,3,5,9,7,])
print "h = Histogram(bins = [1,3,5,9,7,])"
print 'h.get_bin_at(0)', h.get_bin_at(0)
print 'h.get_bin_at(10)', h.get_bin_at(10)
print 'h.get_bin_at(1.1)', h.get_bin_at(1.1)
print 'h.get_bin_at(8.9)', h.get_bin_at(8.9)
print 'h[-1]', h[-1]
print '*'*40
print 'Histogram([1,2,2,4,4,4])'
print Histogram([1,2,2,4,4,4])
print 'Histogram(bins=[1,2,3,7,])'
print Histogram(bins=[1,2,3,7,])
print 'Histogram([1,1,1,2,2,2,4,4,4], [1,2,3,7,])'
print Histogram([1,1,1,2,2,2,4,4,4], [1,2,3,7,])
print '*'*40
print 'file_to_histogram(test_hist1.txt)'
hf = file_to_histogram('test_hist1.txt')
print '*'*40
print "iteration test => for i in hf: print i"
for i in hf: print i
a = hf[0].copy()
a[0] += 1
print '*'*40
print "copy test: a = b.copy(); a[0] = b[0] + 1"
print "A", a
print "B", hf[0]
print '*'*40
print '\n file_to_histogram(test_hist1.txt, [1,4])'
hf2 = file_to_histogram('test_hist1.txt', [1,4])
for i in hf2: print "\t", i
print '\n file_to_histogram(test_hist2.txt, [1,9])'
hf3 =file_to_histogram('test_hist2.txt', [1,9])
for i in hf3: print "\t", i
# h2[2].plot()
print '\n file_to_histogram(test_hist1.txt, [1,2,3,5])',
h3 =file_to_histogram('test_hist1.txt', [1,2,3,5])
for i in hf: print "\t", i
print '*'*40
h3[1].shift_bins(-5)
print h3[1]
h3[1].add_histo(h3[1])
print h3[1]
print '*'*40
print "stress test"
hf4 = file_to_histogram('data/test_223.txt')
hf5 = file_to_histogram('data/test_209.txt') # pedestal
# hf5[0].plot()
# for i in hf4: print "\t", i
print "produce plots (smoothed and unsmoothed)"
# f = hf4[0].plot()
k = gaussian_kernel(30, 6)
hc = convolve(hf4[1], k)
hc2 = convolve(hf5[1], k)
# f2 = hc.plot()
print '*'*40
print "find peaks", find_peaks(hc)
print "find peaks2", find_peaks(hc2)
show()
