def median_bounds(im, peak, shared=True):
"""
@Summary: Calculates the median of the left and right bounds found
for each apexing peak mass
@param im: The originating IntensityMatrix object
@type im: pyms.GCMS.Class.IntensityMatrix
@param peak: The Peak object
@type peak: pyms.Peak.Class.Peak
@param shared: Include shared ions shared with neighbouring peak
@type shared: BooleanType
@return: median left and right boundary offset in points
@rtype: TupleType
@author: Andrew Isaac
"""
mat = im.get_matrix_list()
ms = peak.get_mass_spectrum()
rt = peak.get_rt()
apex = im.get_index_at_time(rt)
# check if RT based index is simmilar to stored index
tmp = peak.get_pt_bounds()
if is_list(tmp) and apex-1 < tmp[1] and tmp[1] < apex+1:
apex = tmp[1]
# get peak masses with non-zero intensity
mass_ii = [ ii for ii in xrange(len(ms.mass_list)) \
if ms.mass_spec[ii] > 0 ]
# get stats on boundaries
left_list = []
right_list = []
for ii in mass_ii:
# get ion chromatogram as list
ia = [ mat[scan][ii] for scan in xrange(len(mat)) ]
area, left, right, l_share, r_share = ion_area(ia, apex)
if shared or not l_share:
left_list.append(left)
if shared or not r_share:
right_list.append(right)
# return medians
# NB if shared=True, lists maybe empty
l_med = 0
r_med = 0
if len(left_list) > 0:
l_med = median(left_list)
if len(right_list) > 0:
r_med = median(right_list)
return l_med, r_med
def median_bounds(im, peak, shared=True):
"""
@Summary: Calculates the median of the left and right bounds found
for each apexing peak mass
@param im: The originating IntensityMatrix object
@type im: pyms.GCMS.Class.IntensityMatrix
@param peak: The Peak object
@type peak: pyms.Peak.Class.Peak
@param shared: Include shared ions shared with neighbouring peak
@type shared: BooleanType
@return: median left and right boundary offset in points
@rtype: TupleType
@author: Andrew Isaac
"""
mat = im.get_matrix_list()
ms = peak.get_mass_spectrum()
rt = peak.get_rt()
apex = im.get_index_at_time(rt)
# check if RT based index is simmilar to stored index
tmp = peak.get_pt_bounds()
if is_list(tmp) and apex - 1 < tmp[1] and tmp[1] < apex + 1:
apex = tmp[1]
# get peak masses with non-zero intensity
mass_ii = [ ii for ii in xrange(len(ms.mass_list)) \
if ms.mass_spec[ii] > 0 ]
# get stats on boundaries
left_list = []
right_list = []
for ii in mass_ii:
# get ion chromatogram as list
ia = [mat[scan][ii] for scan in xrange(len(mat))]
area, left, right, l_share, r_share = ion_area(ia, apex)
if shared or not l_share:
left_list.append(left)
if shared or not r_share:
right_list.append(right)
# return medians
# NB if shared=True, lists maybe empty
l_med = 0
r_med = 0
if len(left_list) > 0:
l_med = median(left_list)
if len(right_list) > 0:
r_med = median(right_list)
return l_med, r_med
def info(self, print_scan_n=False):
"""
@summary: Prints some information about the data
@param print_scan_n: If set to True will print the number
of m/z values in each scan
@type print_scan_n: BooleanType
@author: Vladimir Likic
"""
# print the summary of simply attributes
print " Data retention time range: %.3f min -- %.3f min" % \
(self.__min_rt/60.0, self.__max_rt/60)
print " Time step: %.3f s (std=%.3f s)" % ( self.__time_step, \
self.__time_step_std )
print " Number of scans: %d" % ( len(self.__scan_list) )
print " Minimum m/z measured: %.3f" % ( self.__min_mass )
print " Maximum m/z measured: %.3f" % ( self.__max_mass )
# calculate median number of m/z values measured per scan
n_list = []
for ii in range(len(self.__scan_list)):
scan = self.__scan_list[ii]
n = len(scan)
n_list.append(n)
if print_scan_n: print n
mz_mean = mean(n_list)
mz_median = median(n_list)
print " Mean number of m/z values per scan: %d" % ( mz_mean )
print " Median number of m/z values per scan: %d" % ( mz_median )
def __median_window(ia, wing_length):
"""
@summary: Applies median-window averaging on the array of intensities.
@param ia: Intensity array
@type ia: nympy.core.ndarray
@param wing_length: An integer value representing the number of
points on either side of a point in the ion chromatogram
@type wing_length: IntType
@return: Smoothed intensity array
@rtype: nympy.core.ndarray
@author: Vladimir Likic
"""
#print " -> Window smoothing (median): the wing is %d point(s)" % (wing_length)
ia_denoise = numpy.repeat([0], ia.size)
index = 0
end = ia.size - 1
while index <= end:
left = index - wing_length
right = index + wing_length + 1
if left < 0: left = 0
slice = ia[left:right]
ia_denoise[index] = median(slice)
index = index + 1
return ia_denoise
def info(self, print_scan_n=False):
"""
@summary: Prints some information about the data
@param print_scan_n: If set to True will print the number
of m/z values in each scan
@type print_scan_n: BooleanType
@author: Vladimir Likic
"""
# print the summary of simply attributes
print " Data retention time range: %.3f min -- %.3f min" % \
(self.__min_rt/60.0, self.__max_rt/60)
print " Time step: %.3f s (std=%.3f s)" % ( self.__time_step, \
self.__time_step_std )
print " Number of scans: %d" % ( len(self.__scan_list) )
print " Minimum m/z measured: %.3f" % ( self.__min_mass )
print " Maximum m/z measured: %.3f" % ( self.__max_mass )
# calculate median number of m/z values measured per scan
n_list = []
for ii in range(len(self.__scan_list)):
scan = self.__scan_list[ii]
n = len(scan)
n_list.append(n)
if print_scan_n: print n
mz_mean = mean(n_list)
mz_median = median(n_list)
print " Mean number of m/z values per scan: %d" % ( mz_mean )
print " Median number of m/z values per scan: %d" % ( mz_median )
def __median_window(ia, wing_length):
"""
@summary: Applies median-window averaging on the array of intensities.
@param ia: Intensity array
@type ia: nympy.core.ndarray
@param wing_length: An integer value representing the number of
points on either side of a point in the ion chromatogram
@type wing_length: IntType
@return: Smoothed intensity array
@rtype: nympy.core.ndarray
@author: Vladimir Likic
"""
#print " -> Window smoothing (median): the wing is %d point(s)" % (wing_length)
ia_denoise = numpy.repeat([0], ia.size)
index = 0
end = ia.size - 1
while index <= end:
left = index - wing_length
right = index + wing_length + 1
if left < 0: left = 0
slice = ia[left:right]
ia_denoise[index] = median(slice)
index = index + 1
return ia_denoise
