def show(title, nuclei, radius, bounds, scale=1.0):
points = [RealPoint.wrap([c * scale for c in coords]) for coords in nuclei]
interval = FinalInterval([int(b * scale) for b in bounds[0]],
[int(b * scale) for b in bounds[1]])
img = virtualPointsRAI(points, radius * scale, interval)
imp = showStack(img, title=title)
return imp, img, points
def withIcospheres(time_window=None):
with open(os.path.join(baseDir, csvFilename), 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
header = reader.next()
peaks = [RealPoint.wrap(imap(float, peak.split('::'))) for peak in islice(header, 1, None)]
# Template icosahedron
ico = MeshMaker.createIcosahedron(2, radius)
# Share lists of Point3f across all timepoints
icos = [MeshMaker.copyTranslated(ico, peak.getFloatPosition(0), peak.getFloatPosition(1) , peak.getFloatPosition(2)) for peak in peaks]
#
univ = Image3DUniverse(512, 512)
instants = TreeMap()
#
rows = reader if time_window is None else islice(reader, time_window[0], time_window[1])
for row in rows:
# Values mapped to 0-1: Color3f takes 3 values in domain [0, 1].
# So: ensure the value is inside [minimum, maximum] range, then rezero by subtracting minimum, and divide by span (maximum - minimum)
values = ((min(max(float(v), minimum), maximum) - minimum) / span for v in islice(row, 1, None))
meshes = [CustomTriangleMesh(mesh, Color3f(v, v, v), 0)
for v, mesh in izip(values, icos)]
ci = ContentInstant(str(row[0]))
ci.display(CustomMultiMesh(meshes)) # each mesh has its color
ci.setLocked(True)
print row[0]
instants.put(int(row[0]), ci)
print "n instants:", instants.size()
univ.addContent(Content("deltaF/F", instants, False))
univ.show()
univ.updateStartAndEndTime(0, len(instants) -1)
def withVirtualStack(time_window=None, subsample=None):
with open(os.path.join(baseDir, csvFilename), 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='"')
header = reader.next()
peaks = [
RealPoint.wrap(imap(float, peak.split('::')))
for peak in islice(header, 1, None)
]
frames = [
virtualPointsRAI(peaks,
radius,
interval,
inside=to8bitRange(
map(float, islice(row, 1, None))))
for row in reader
]
if time_window:
first, last = time_window
frames = frames[first:last + 1]
img4D = Views.stack(frames)
# Scale by a factor of 'subsample' in every dimension by nearest neighbor, sort of:
if subsample:
img4D = Views.subsample(img4D, subsample)
imp = ImagePlus("deltaF/F", ImageJVirtualStackUnsignedByte.wrap(img4D))
imp.setDimensions(1, img4D.dimension(2), img4D.dimension(3))
imp.setDisplayRange(0, 255)
com = CompositeImage(imp, CompositeImage.GRAYSCALE)
com.show()
univ = Image3DUniverse(512, 512)
univ.show()
univ.addVoltex(com)
"calibration": calibration,
"minPeakValue": 0.2, # determined by hand: the bright peaks
"sigmaSmaller": somaDiameter / 4.0, # in calibrated units: 1/4 soma
"sigmaLarger": somaDiameter / 2.0, # in calibrated units: 1/2 soma
}
csv_path = os.path.join(
tgtDir, "peaks_somaDiameter=%0.1f_minPeakValue=%0.3f.csv" %
(somaDiameter, params["minPeakValue"]))
if os.path.exists(csv_path):
print "Parsing CSV file %s" % csv_path
with open(csv_path, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar="\"")
reader.next() # skip first line: the header
peaks = [RealPoint.wrap(map(float, coords)) for coords in reader]
else:
peaks = doGPeaks(max_projection, params)
print "Detected %i peaks with somaDiameter %f" % (len(peaks),
somaDiameter)
if len(peaks) > 0:
with open(csv_path, 'w') as csvfile:
w = csv.writer(csvfile,
delimiter=",",
quoting=csv.QUOTE_NONNUMERIC)
w.writerow(["x", "y", "z"]) # header
for peak in peaks:
w.writerow([peak.getFloatPosition(d) for d in xrange(3)])
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
# Test whether the first nearest neighbor is the point itself from net.imglib2 import RealPoint, KDTree from net.imglib2.neighborsearch import RadiusNeighborSearchOnKDTree points = [RealPoint.wrap([1.0, 1.0, float(i)]) for i in xrange(10)] tree = KDTree(points, points) search = RadiusNeighborSearchOnKDTree(tree) radius = 3 search.search(RealPoint.wrap([1.0, 1.0, 1.0]), 3, False) # unordered for i in xrange(search.numNeighbors()): print "Point " + str(i), search.getSampler(i).get() # Prints: # Point 0 (1.0,1.0,3.0) # Point 1 (1.0,1.0,4.0) # Point 2 (1.0,1.0,0.0) # Point 3 (1.0,1.0,1.0) # Point 4 (1.0,1.0,2.0) # So yes: the first point is always the query point.
import sys
sys.path.append("/home/albert/lab/scripts/python/imagej/IsoView-GCaMP/")
from lib.io import readN5
from lib.dogpeaks import createDoG
from lib.synthetic import virtualPointsRAI
from lib.ui import showStack
from net.imglib2 import RealPoint, FinalInterval
points = [RealPoint.wrap([255, 255, 255]),
RealPoint.wrap([255, 255, 0]),
RealPoint.wrap([128, 384, 128])]
rai = virtualPointsRAI(points, 70, FinalInterval([512, 512, 512]))
imp = showStack(rai, title="test virtualPointsRAI")