peaks = peak_map[somaDiameter]
# Measure intensity over time, for every peak
# by averaging the signal within a radius of each peak.
measurement_radius_px = [
(somaDiameter / calibration[d]) * 0.66 for d in xrange(3)
] # NOTE: DIVIDING BY 3, not 2, to make the radius a bit smaller
spheres = [
ClosedWritableEllipsoid([peak.getFloatPosition(d)
for d in xrange(3)], measurement_radius_px)
for peak in peaks
]
insides = [
Regions.iterable(
Views.interval(Views.raster(Masks.toRealRandomAccessible(sphere)),
Intervals.largestContainedInterval(sphere)))
for sphere in spheres
]
count = float(Regions.countTrue(insides[0])) # same for all
def measurePeaks(filename, retry=0):
img3D = klb.readFull(os.path.join(srcDir, filename))
"""
mean_intensities = []
print Intervals.dimensionsAsLongArray(img3D)
for inside, peak in zip(insides, peaks):
samples = Regions.sample(inside, img3D)
#print type(samples)
#print Intervals.dimensionsAsLongArray(samples)
def measureFluorescence(series_name, img4D, mask=None):
csv_fluorescence = os.path.join(srcDir,
"%s_fluorescence.csv" % series_name)
if not os.path.exists(csv_fluorescence):
# Generate projection over time (the img3D) and extract peaks with difference of Gaussian using the params
# (Will check if file for projection over time exists and just load it)
img3D_filepath = os.path.join(
srcDir, "%s_4D-to-3D_max_projection.zip" % series_name)
img3D, peaks, spheresRAI, impSpheres = findNucleiByMaxProjection(
img4D, params, img3D_filepath, show=True)
comp = showAsComposite([wrap(img3D), impSpheres])
# Measure intensity over time, for every peak
# by averaging the signal within a radius of each peak.
measurement_radius = somaDiameter / 3.0
spheres = [
ClosedWritableSphere([peak.getFloatPosition(d)
for d in xrange(3)], measurement_radius)
for peak in peaks
]
insides = [
Regions.iterable(
Views.interval(
Views.raster(Masks.toRealRandomAccessible(sphere)),
Intervals.largestContainedInterval(sphere)))
for sphere in spheres
]
count = float(Regions.countTrue(insides[0])) # same for all
measurements = []
with open(csv_fluorescence, 'w') as csvfile:
w = csv.writer(csvfile,
delimiter=",",
quotechar='"',
quoting=csv.QUOTE_NONNUMERIC)
# Header: with peak coordinates
w.writerow(["timepoint"] + [
"%.2f::%.2f::%.2f" %
tuple(peak.getFloatPosition(d) for d in xrange(3))
for peak in peaks
])
# Each time point
for t in xrange(img4D.dimension(3)):
img3D = Views.hyperSlice(img4D, 3, t)
mean_intensities = array(
((sum(t.get()
for t in Regions.sample(inside, img3D)) / count)
for inside in insides), 'f')
w.writerow([t] + mean_intensities.tolist())
measurements.append(mean_intensities)
else:
# Parse CSV file
with open(csv_fluorescence, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
header = reader.next()
# Parse header, containing peak locations
peaks = [
RealPoint.wrap(map(float, h.split("::")))
for h in islice(header, 1, None)
]
# Parse rows
measurements = [map(float, islice(row, 1, None)) for row in reader]
return peaks, measurements
