def compute_image(self, idx, NX, NY, rtmin, rtmax, mzmin, mzmax):
if rtmin >= rtmax or mzmin >= mzmax:
smoothed = np.zeros((1, 1))
else:
# optimized:
# one additional row / col as we loose one row and col during smoothing:
# sample_image only works on level1, therefore we have to use getDominatingPeakmap()
pm = self.peakmaps[idx].getDominatingPeakmap()
data = sample_image(pm, rtmin, rtmax, mzmin, mzmax, NX + 1, NY + 1)
smoothed = smooth(data, mzmax, mzmin)
# enlarge single pixels depending on the mz range in the image:
# turn up/down
smoothed = smoothed[::-1, :]
imin = self.imin
imax = self.imax
if self.is_log:
smoothed = np.log(1.0 + smoothed)
imin = np.log(1.0 + imin)
imax = np.log(1.0 + imax)
# set values out of range to imin, later this is scaled to 0, also black:
smoothed[smoothed < imin] = imin
smoothed[smoothed > imax] = imin
smoothed -= imin
# scale to 1.0
maxd = np.max(smoothed)
if maxd:
smoothed /= maxd
# apply gamma
smoothed = smoothed ** (self.gamma) * 255
return smoothed.astype(np.uint8)
def compute_image(self, idx, NX, NY, rtmin, rtmax, mzmin, mzmax):
if rtmin >= rtmax or mzmin >= mzmax:
smoothed = np.zeros((1, 1))
else:
# optimized:
# one additional row / col as we loose one row and col during smoothing:
# sample_image only works on level1, therefore we have to use getDominatingPeakmap()
pm = self.peakmaps[idx].getDominatingPeakmap()
data = sample_image(pm, rtmin, rtmax, mzmin, mzmax, NX + 1, NY + 1)
smoothed = smooth(data, mzmax, mzmin)
# enlarge single pixels depending on the mz range in the image:
# turn up/down
smoothed = smoothed[::-1, :]
imin = self.imin
imax = self.imax
if self.is_log:
smoothed = np.log(1.0 + smoothed)
imin = np.log(1.0 + imin)
imax = np.log(1.0 + imax)
# set values out of range to imin, later this is scaled to 0, also black:
smoothed[smoothed < imin] = imin
smoothed[smoothed > imax] = imin
smoothed -= imin
# scale to 1.0
maxd = np.max(smoothed)
if maxd:
smoothed /= maxd
# apply gamma
smoothed = smoothed**(self.gamma) * 255
return smoothed.astype(np.uint8)
def compute_image(self, idx, NX, NY, rtmin, rtmax, mzmin, mzmax):
if rtmin >= rtmax or mzmin >= mzmax:
dilated = np.zeros((1, 1))
else:
# optimized:
# one additional row / col as we loose one row and col during smoothing:
# sample_image only works on level1, therefore we have to use getDominatingPeakmap()
pm = self.peakmaps[idx].getDominatingPeakmap()
data = sample_image(pm, rtmin, rtmax, mzmin, mzmax, NX + 1, NY + 1)
imin = self.imin
imax = self.imax
if self.is_log:
data = np.log(1.0 + data)
imin = np.log(1.0 + imin)
imax = np.log(1.0 + imax)
# set values out of range to black:
overall_max = np.max(data)
data[data < imin] = 0
data[data > imax] = 0
data /= overall_max
# enlarge peak pixels depending on the mz range in the image:
dilated = dilate(data, mzmax, mzmin)
# turn up/down
dilated = dilated[::-1, :]
# apply gamma
dilated = dilated**(self.gamma) * 255
return dilated.astype(np.uint8)
def compute_image(self, idx, NX, NY, rtmin, rtmax, mzmin, mzmax):
if rtmin >= rtmax or mzmin >= mzmax:
dilated = np.zeros((1, 1))
else:
# optimized:
# one additional row / col as we loose one row and col during smoothing:
# sample_image only works on level1, therefore we have to use getDominatingPeakmap()
pm = self.peakmaps[idx].getDominatingPeakmap()
data = sample_image(pm, rtmin, rtmax, mzmin, mzmax, NX + 1, NY + 1)
imin = self.imin
imax = self.imax
if self.is_log:
data = np.log(1.0 + data)
imin = np.log(1.0 + imin)
imax = np.log(1.0 + imax)
# set values out of range to black:
overall_max = np.max(data)
data[data < imin] = 0
data[data > imax] = 0
data /= overall_max
# enlarge peak pixels depending on the mz range in the image:
dilated = dilate(data, mzmax, mzmin)
# turn up/down
dilated = dilated[::-1, :]
# apply gamma
dilated = dilated ** (self.gamma) * 255
return dilated.astype(np.uint8)