def peakfind_2D(self, subpixel=False, peak_width=10, medfilt_radius=5,
maxpeakn=30000):
"""Find peaks in a 2D array (peaks in an image).
Function to locate the positive peaks in a noisy x-y data set.
Returns an array containing pixel position of each peak.
Parameters
---------
subpixel : bool (optional)
default is set to True
peak_width : int (optional)
expected peak width. Affects subpixel precision fitting window,
which takes the center of gravity of a box that has sides equal
to this parameter. Too big, and you'll include other peaks.
default is set to 10
medfilt_radius : int (optional)
median filter window to apply to smooth the data
(see scipy.signal.medfilt)
if 0, no filter will be applied.
default is set to 5
maxpeakn : int (optional)
number of maximum detectable peaks
default is set to 30000
"""
from peak_char import two_dim_findpeaks
if len(self.data.shape)==2:
self.peaks=two_dim_findpeaks(self.data, subpixel=subpixel,
peak_width=peak_width,
medfilt_radius=medfilt_radius)
elif len(self.data.shape)==3:
# preallocate a large array for the results
self.peaks=np.zeros((maxpeakn,2,self.data.shape[2]))
for i in xrange(self.data.shape[2]):
tmp=two_dim_findpeaks(self.data[:,:,i],
subpixel=subpixel,
peak_width=peak_width,
medfilt_radius=medfilt_radius)
self.peaks[:tmp.shape[0],:,i]=tmp
trim_id=np.min(np.nonzero(np.sum(np.sum(self.peaks,axis=2),axis=1)==0))
self.peaks=self.peaks[:trim_id,:,:]
elif len(self.data.shape)==4:
# preallocate a large array for the results
self.peaks=np.zeros((maxpeakn,2,self.data.shape[0],self.data.shape[1]))
for i in xrange(self.data.shape[0]):
for j in xrange(self.data.shape[1]):
tmp=two_dim_findpeaks(self.data[i,j,:,:],
subpixel=subpixel,
peak_width=peak_width,
medfilt_radius=medfilt_radius)
self.peaks[:tmp.shape[0],:,i,j]=tmp
trim_id=np.min(np.nonzero(np.sum(np.sum(np.sum(self.peaks,axis=3),axis=2),axis=1)==0))
self.peaks=self.peaks[:trim_id,:,:,:]
def locate_peaks(self):
peaks=[]
progress = ProgressDialog(title="Peak finder progress", message="Finding peaks on %s images"%self.numfiles, max=self.numfiles, show_time=True, can_cancel=False)
progress.open()
for idx in xrange(self.numfiles):
self.controller.set_active_index(idx)
self.data = self.controller.get_active_image()[:]
self.CC = cv_funcs.xcorr(self.template, self.data)
# peak finder needs peaks greater than 1. Multiply by 255 to scale them.
pks=pc.two_dim_findpeaks(self.CC*255, peak_width=self.peak_width, medfilt_radius=None)
pks[:,2]=pks[:,2]/255.
peaks.append(pks)
progress.update(idx+1)
#ipdb.set_trace()
self.peaks=peaks
self.redraw_plots()
def peakfind_2D(self,
subpixel=False,
peak_width=10,
medfilt_radius=5,
maxpeakn=30000):
"""Find peaks in a 2D array (peaks in an image).
Function to locate the positive peaks in a noisy x-y data set.
Returns an array containing pixel position of each peak.
Parameters
---------
subpixel : bool (optional)
default is set to True
peak_width : int (optional)
expected peak width. Affects subpixel precision fitting window,
which takes the center of gravity of a box that has sides equal
to this parameter. Too big, and you'll include other peaks.
default is set to 10
medfilt_radius : int (optional)
median filter window to apply to smooth the data
(see scipy.signal.medfilt)
if 0, no filter will be applied.
default is set to 5
maxpeakn : int (optional)
number of maximum detectable peaks
default is set to 30000
"""
from peak_char import two_dim_findpeaks
if len(self.data.shape) == 2:
self.peaks = two_dim_findpeaks(self.data,
subpixel=subpixel,
peak_width=peak_width,
medfilt_radius=medfilt_radius)
elif len(self.data.shape) == 3:
# preallocate a large array for the results
self.peaks = np.zeros((maxpeakn, 2, self.data.shape[2]))
for i in xrange(self.data.shape[2]):
tmp = two_dim_findpeaks(self.data[:, :, i],
subpixel=subpixel,
peak_width=peak_width,
medfilt_radius=medfilt_radius)
self.peaks[:tmp.shape[0], :, i] = tmp
trim_id = np.min(
np.nonzero(np.sum(np.sum(self.peaks, axis=2), axis=1) == 0))
self.peaks = self.peaks[:trim_id, :, :]
elif len(self.data.shape) == 4:
# preallocate a large array for the results
self.peaks = np.zeros(
(maxpeakn, 2, self.data.shape[0], self.data.shape[1]))
for i in xrange(self.data.shape[0]):
for j in xrange(self.data.shape[1]):
tmp = two_dim_findpeaks(self.data[i, j, :, :],
subpixel=subpixel,
peak_width=peak_width,
medfilt_radius=medfilt_radius)
self.peaks[:tmp.shape[0], :, i, j] = tmp
trim_id = np.min(
np.nonzero(
np.sum(np.sum(np.sum(self.peaks, axis=3), axis=2), axis=1)
== 0))
self.peaks = self.peaks[:trim_id, :, :, :]
