def find_all_thres(data,thres,msep,find_segs=False):
"""
Peak pick a spectrum using a threshhold-minimum distance algorithm.
Find peaks (local maxima) in a arbitrary dimensional NMR spectra above a
set threshold with a minimal distance between peaks. When the spectrum is
small and multiple copies can fit into RAM use the _fast version of this
function. Segments are found by finding the first point in each direction
along each dimension which is below the threshold.
Parameters:
* data N-dimensional array.
* thres Threshold value for minimum peak height
* msep N-tuple of minimum peak seperations along each axis
* find_segs True or False to return a list of slices for the segments.
Returns: locations,seg_slices
* locations List of indicies of peak locations
* seg_slices List of slices which extract a region around each peak.
"""
locations = [] # create an empty list of peak locations
wsize = tuple([2*i+1 for i in msep]) #window size is 2*seperation+1
# loop over the windows
for idx,s in ndwindow_index(data.shape,wsize):
max = data[s].max()
if max == data[idx] and max > thres:
locations.append(idx)
if find_segs:
seg_slices = find_pseg_slices(data,locations,thres)
return locations,seg_slices
else:
return locations
def find_all_nthres(data,thres,msep,find_segs=False):
"""
Peak pick a spectrum using a threshhold-minimum distance algorithm.
Identical to find_all_thres except local minima are found below the
given threshold. See find_all_thres for a description of the algorithm and
parameters.
"""
locations = [] # create an empty list of peak locations
wsize = tuple([2*i+1 for i in msep]) #window size is 2*seperation+1
# loop over the windows
for idx,s in ndwindow_index(data.shape,wsize):
min = data[s].min()
if min == data[idx] and min < thres:
locations.append(idx)
if find_segs:
seg_slices = find_pseg_slices(data,locations,thres)
return locations,seg_slices
else:
return locations