def test_align_psfs_1():
"""
Test alignment of multiple PSFs to each other.
"""
pos = [5.0, 6.0, 7.0]
maxd = 3.0
psfs = []
for i in range(7):
psfs.append(
dg.drawGaussiansXYZ((12, 13, 14),
numpy.array([pos[0] + int(i / maxd)]),
numpy.array([pos[1] + int(i / maxd)]),
numpy.array([pos[2] + int(i / maxd)])))
[average_psf, i_score] = mPSFUtils.alignPSFs(psfs)
assert (i_score > 2.1)
if False:
with tifffile.TiffWriter("psf.tif") as tf:
tf.save(mPSFUtils.averagePSF(psfs)[6, :, :].astype(numpy.float32))
tf.save(average_psf[6, :, :].astype(numpy.float32))
def measurePSF(zstack_name,
zfile_name,
psf_name,
pixel_size=0.1,
refine=False,
z_range=0.75,
z_step=0.050,
normalize=False):
"""
zstack_name - The name of the file containing the 2x up-sampled z-stacks.
zfile_name - The text file containing the z offsets (in microns) for each frame.
psf_name - The name of the file to save the measured PSF in (as a pickled Python dictionary).
pixel_size - The pixel size in microns.
refine - Align the measured PSF for each bead to the average PSF.
z_range - The range the PSF should cover in microns.
z_step - The z step size of the PSF.
normalize - If true, normalize the PSF to unit height.
"""
# Create z scaling object.
z_sclr = measurePSFUtils.ZScaler(z_range, z_step)
max_z = z_sclr.getMaxZ()
# Load z-stacks.
zstacks = numpy.load(zstack_name)
x_size = zstacks[0].shape[0]
y_size = zstacks[0].shape[1]
# Load z-offsets.
z_offset_data = numpy.loadtxt(zfile_name, ndmin=2)
is_valid = z_offset_data[:, 0]
z_offsets = z_offset_data[:, 1]
# Check if the z-stack has fewer frames than the offset file.
n_frames = z_offsets.size
if (n_frames > zstacks[0].shape[2]):
print("Warning! z stack has", n_frames - zstacks[0].shape[2],
"fewer frames than the z offset file.")
n_frames = zstacks[0].shape[2]
# Average stacks in z.
psfs = []
for i in range(len(zstacks)):
psf = numpy.zeros((max_z, x_size, y_size))
totals = numpy.zeros(max_z, dtype=numpy.int)
for j in range(n_frames):
# 0.0 = invalid, 1.0 = valid.
if (is_valid[j] > 1.0e-3):
zi = z_sclr.convert(z_offsets[j])
if z_sclr.inRange(zi):
psf[zi, :, :] += zstacks[i][:, :, j]
totals[zi] += 1
# Check that we got at least one valid measurement. Totals
# is expected to be the same for each PSF.
if (i == 0):
assert (numpy.max(totals) > 0)
# Normalize each PSF z plane by the total counts in the plane.
for j in range(max_z):
if (i == 0):
print("z plane {0:0d} has {1:0d} samples".format(j, totals[j]))
if (totals[j] > 0):
psf[j, :, :] = psf[j, :, :] / float(totals[j])
# Append to list of PSFs.
psfs.append(psf)
# Align the PSFs to each other. This should hopefully correct for
# any small errors in the input locations, and also for fields of
# view that are not completely flat.
#
if refine:
print("Refining PSF alignment.")
[average_psf, i_score] = measurePSFUtils.alignPSFs(psfs)
else:
average_psf = measurePSFUtils.averagePSF(psfs)
# Normalize to unity height, if requested.
if normalize and (numpy.amax(average_psf) > 0.0):
print("Normalizing PSF.")
average_psf = average_psf / numpy.amax(average_psf)
if not (numpy.amax(average_psf) > 0.0):
print("Warning! Measured PSF maxima is zero or negative!")
# Save PSF.
#
# At least for now the PSFs use nanometers, not microns.
#
z_range = z_range * 1.0e+3
z_step = z_step * 1.0e+3
cur_z = -z_range
z_vals = []
for i in range(max_z):
z_vals.append(cur_z)
cur_z += z_step
psf_dict = {
"maximum": numpy.amax(average_psf),
"psf": average_psf,
"pixel_size": pixel_size,
"type": "3D",
"version": 2.0,
"zmin": -z_range,
"zmax": z_range,
"zvals": z_vals
}
with open(psf_name, 'wb') as fp:
pickle.dump(psf_dict, fp)
# Save (normalized) z_stack as tif for inspection purposes.
if (numpy.amax(average_psf) > 0.0):
average_psf = average_psf / numpy.amax(average_psf)
average_psf = average_psf.astype(numpy.float32)
with tifffile.TiffWriter(os.path.splitext(psf_name)[0] + ".tif") as tf:
for i in range(max_z):
tf.save(average_psf[i, :, :])
def measurePSF(zstack_name, zfile_name, psf_name, pixel_size = 0.1, refine = False, z_range = 0.75, z_step = 0.050, normalize = False):
"""
zstack_name - The name of the file containing the z-stacks.
zfile_name - The text file containing the z offsets (in microns) for each frame.
psf_name - The name of the file to save the measured PSF in (as a pickled Python dictionary).
pixel_size - The pixel size in microns.
refine - Align the measured PSF for each bead to the average PSF.
z_range - The range the PSF should cover in microns.
z_step - The z step size of the PSF.
normalize - If true, normalize the PSF to unit height.
"""
# Create z scaling object.
z_sclr = measurePSFUtils.ZScaler(z_range, z_step)
max_z = z_sclr.getMaxZ()
# Load z-stacks.
zstacks = numpy.load(zstack_name)
x_size = zstacks[0].shape[0]
y_size = zstacks[0].shape[1]
# Load z-offsets.
z_offset_data = numpy.loadtxt(zfile_name, ndmin = 2)
is_valid = z_offset_data[:,0]
z_offsets = z_offset_data[:,1]
# Check if the z-stack has fewer frames than the offset file.
n_frames = z_offsets.size
if (n_frames > zstacks[0].shape[2]):
print("Warning! z stack has", n_frames - zstacks[0].shape[2], "fewer frames than the z offset file.")
n_frames = zstacks[0].shape[2]
# Average stacks in z.
psfs = []
for i in range(len(zstacks)):
psf = numpy.zeros((max_z, x_size, y_size))
totals = numpy.zeros(max_z, dtype = numpy.int)
for j in range(n_frames):
# 0.0 = invalid, 1.0 = valid.
if (is_valid[j] > 1.0e-3):
zi = z_sclr.convert(z_offsets[j])
if z_sclr.inRange(zi):
psf[zi,:,:] += zstacks[i][:,:,j]
totals[zi] += 1
# Check that we got at least one valid measurement. Totals
# is expected to be the same for each PSF.
if (i == 0):
assert (numpy.max(totals) > 0)
# Normalize each PSF z plane by the total counts in the plane.
for j in range(max_z):
if (i == 0):
print("z plane {0:0d} has {1:0d} samples".format(j, totals[j]))
if (totals[j] > 0):
psf[j,:,:] = psf[j,:,:]/float(totals[j])
# Append to list of PSFs.
psfs.append(psf)
# Align the PSFs to each other. This should hopefully correct for
# any small errors in the input locations, and also for fields of
# view that are not completely flat.
#
if refine:
print("Refining PSF alignment.")
[average_psf, i_score] = measurePSFUtils.alignPSFs(psfs)
else:
average_psf = measurePSFUtils.averagePSF(psfs)
# Normalize to unity height, if requested.
if normalize and (numpy.amax(average_psf) > 0.0):
print("Normalizing PSF.")
average_psf = average_psf/numpy.amax(average_psf)
if not (numpy.amax(average_psf) > 0.0):
print("Warning! Measured PSF maxima is zero or negative!")
# Save PSF.
#
# At least for now the PSFs use nanometers, not microns.
#
z_range = z_range * 1.0e+3
z_step = z_step * 1.0e+3
cur_z = -z_range
z_vals = []
for i in range(max_z):
z_vals.append(cur_z)
cur_z += z_step
psf_dict = {"maximum" : numpy.amax(average_psf),
"psf" : average_psf,
"pixel_size" : pixel_size,
"type" : "3D",
"version" : 2.0,
"zmin" : -z_range,
"zmax" : z_range,
"zvals" : z_vals}
with open(psf_name, 'wb') as fp:
pickle.dump(psf_dict, fp)
# Save (normalized) z_stack as tif for inspection purposes.
if (numpy.amax(average_psf) > 0.0):
average_psf = average_psf/numpy.amax(average_psf)
average_psf = average_psf.astype(numpy.float32)
with tifffile.TiffWriter(os.path.splitext(psf_name)[0] + ".tif") as tf:
for i in range(max_z):
tf.save(average_psf[i,:,:])
def measurePSFBeads(movie_name,
zfile_name,
beads_file,
psf_name,
aoi_size=12,
pixel_size=0.1,
refine=False,
z_range=0.6,
z_step=0.05):
"""
movie_name - The name of the movie, presumably a z stack for PSF measurement.
zfile_name - The text file containing the z offsets (in microns) for each frame.
beads_file - The text file containing the locations of the beads.
psf_name - The name of the file to save the measured PSF in (as a pickled Python dictionary).
aoi_size - The AOI of interest in pixels. The final AOI is 2x this number.
pixel_size - The pixel size in microns.
refine - Align the measured PSF for each bead to the average PSF.
z_range - The range the PSF should cover in microns.
z_step - The z step size of the PSF.
"""
# Load the z-offset information for the dax file.
#
# This is a text file with one line per frame that contains the
# z-offset (in microns) for that frame. Each line is a space separated
# valid, z_pos pair. If valid if 0 the frame will be ignored,
# otherwise it will be used.
#
z_offsets = numpy.loadtxt(zfile_name)
# Create array specifying what frame corresponds to what
# Z slice in the PSF.
#
z_index = measurePSFUtils.makeZIndexArray(z_offsets, z_range, z_step)
# Load the locations of the beads.
#
# This is a text file the contains the locations of the beads that
# will be used to construct the PSF. Each line is a space separated
# x, y pair of bead locations (in pixels).
#
# One way to create this file is to look at the bead movie with
# visualizer.py and record the center positions of several beads.
#
data = numpy.loadtxt(beads_file, ndmin=2)
bead_x = data[:, 1] + 1
bead_y = data[:, 0] + 1
# Create a reader of the movie.
#
# We assume that the bead stack was measured with a camera
# that does not have a large pixel to pixel variation in
# gain and offset. The offset and magnitude are not that
# important at we will estimate and subtract the offset
# and normalize 1.
#
# Movie (frame) reader.
frame_reader = analysisIO.FrameReaderStd(movie_file=movie_name,
camera_gain=1.0,
camera_offset=0.0)
# Measure PSFs for each bead.
#
total_samples = None
psfs = []
for i in range(bead_x.size):
[psf, samples] = measurePSFUtils.measureSinglePSFBeads(frame_reader,
z_index,
aoi_size,
bead_x[i],
bead_y[i],
zoom=2)
# Verify that we have at least one sample per section, because if
# we don't this almost surely means something is wrong.
if (i == 0):
for j in range(samples.size):
assert (samples[i] > 0), "No data for PSF z section " + str(i)
# Normalize by the number of sample per z section.
#for j in range(samples.size):
# psf[j,:,:] = psf[j,:,:]/samples[j]
# Keep track of total number of samples.
if total_samples is None:
total_samples = samples
else:
total_samples += samples
psfs.append(psf)
# Set the PSF to have zero average on the X/Y boundaries. We are
# matching the behavior of spliner.measure_psf here.
#
sum_psf = measurePSFUtils.sumPSF(psfs)
for i in range(sum_psf.shape[0]):
mean_edge = measurePSFUtils.meanEdge(sum_psf[i, :, :])
for j in range(len(psfs)):
psfs[j][i, :, :] -= mean_edge / float(len(psfs))
# Align the PSFs to each other. This should hopefully correct for
# any small errors in the input locations, and also for fields of
# view that are not completely flat.
#
if refine:
print("Refining PSF alignment.")
# Normalize each PSF by the number of z sections.
for psf in psfs:
for i in range(samples.size):
psf[i, :, :] = psf[i, :, :] / samples[i]
[average_psf, i_score] = measurePSFUtils.alignPSFs(psfs)
else:
average_psf = measurePSFUtils.averagePSF(psfs)
# Normalize PSF.
#
# This normalizes the PSF so that sum of the absolute values
# of each section is 1.0. This only makes sense if the AOI is
# large enough to capture all the photons, which might not be
# true. Not clear how important this is as Spliner will fit
# for the height anyway.
#
for i in range(average_psf.shape[0]):
print("z plane {0:0d} has {1:0d} samples".format(i, total_samples[i]))
section_sum = numpy.sum(numpy.abs(average_psf[i, :, :]))
# Do we need this test? We already check that we have at
# least one sample per section.
if (section_sum > 0.0):
average_psf[i, :, :] = average_psf[i, :, :] / section_sum
# Normalize to unity maximum height.
if (numpy.max(average_psf) > 0.0):
average_psf = average_psf / numpy.max(average_psf)
else:
print("Warning! Measured PSF maxima is zero or negative!")
# Save PSF (in image form).
if True:
tif_name = os.path.splitext(psf_name)[0]
with tifffile.TiffWriter(tif_name + "_beads.tif") as tf:
for i in range(average_psf.shape[0]):
tf.save(average_psf[i, :, :].astype(numpy.float32))
# Save PSF.
#
# For historical reasons all the PSF z values are in nanometers.
# At some point this should be fixed.
#
z_range = 1.0e+3 * z_range
z_step = 1.0e+3 * z_step
cur_z = -z_range
z_vals = []
for i in range(average_psf.shape[0]):
z_vals.append(cur_z)
cur_z += z_step
psf_dict = {
"psf": average_psf,
"pixel_size": 0.5 * pixel_size,
"type": "3D",
"version": 1.0,
"zmin": -z_range,
"zmax": z_range,
"zvals": z_vals
}
pickle.dump(psf_dict, open(psf_name, 'wb'))
def measurePSFBeads(movie_name, zfile_name, beads_file, psf_name, aoi_size = 12, pixel_size = 0.1, refine = False, z_range = 0.6, z_step = 0.05):
"""
movie_name - The name of the movie, presumably a z stack for PSF measurement.
zfile_name - The text file containing the z offsets (in microns) for each frame.
beads_file - The text file containing the locations of the beads.
psf_name - The name of the file to save the measured PSF in (as a pickled Python dictionary).
aoi_size - The AOI of interest in pixels. The final AOI is 2x this number.
pixel_size - The pixel size in microns.
refine - Align the measured PSF for each bead to the average PSF.
z_range - The range the PSF should cover in microns.
z_step - The z step size of the PSF.
"""
# Load the z-offset information for the dax file.
#
# This is a text file with one line per frame that contains the
# z-offset (in microns) for that frame. Each line is a space separated
# valid, z_pos pair. If valid if 0 the frame will be ignored,
# otherwise it will be used.
#
z_offsets = numpy.loadtxt(zfile_name)
# Create array specifying what frame corresponds to what
# Z slice in the PSF.
#
z_index = measurePSFUtils.makeZIndexArray(z_offsets, z_range, z_step)
# Load the locations of the beads.
#
# This is a text file the contains the locations of the beads that
# will be used to construct the PSF. Each line is a space separated
# x, y pair of bead locations (in pixels).
#
# One way to create this file is to look at the bead movie with
# visualizer.py and record the center positions of several beads.
#
data = numpy.loadtxt(beads_file, ndmin = 2)
bead_x = data[:,1] + 1
bead_y = data[:,0] + 1
# Create a reader of the movie.
#
# We assume that the bead stack was measured with a camera
# that does not have a large pixel to pixel variation in
# gain and offset. The offset and magnitude are not that
# important at we will estimate and subtract the offset
# and normalize 1.
#
# Movie (frame) reader.
frame_reader = analysisIO.FrameReaderStd(movie_file = movie_name,
camera_gain = 1.0,
camera_offset = 0.0)
# Measure PSFs for each bead.
#
total_samples = None
psfs = []
for i in range(bead_x.size):
[psf, samples] = measurePSFUtils.measureSinglePSFBeads(frame_reader,
z_index,
aoi_size,
bead_x[i],
bead_y[i],
zoom = 1)
# Verify that we have at least one sample per section, because if
# we don't this almost surely means something is wrong.
if (i == 0):
for j in range(samples.size):
assert(samples[j] > 0), "No data for PSF z section " + str(j)
# Normalize by the number of sample per z section.
#for j in range(samples.size):
# psf[j,:,:] = psf[j,:,:]/samples[j]
# Keep track of total number of samples.
if total_samples is None:
total_samples = samples
else:
total_samples += samples
psfs.append(psf)
# Set the PSF to have zero average on the X/Y boundaries. We are
# matching the behavior of spliner.measure_psf here.
#
sum_psf = measurePSFUtils.sumPSF(psfs)
for i in range(sum_psf.shape[0]):
mean_edge = measurePSFUtils.meanEdge(sum_psf[i,:,:])
for j in range(len(psfs)):
psfs[j][i,:,:] -= mean_edge/float(len(psfs))
# Align the PSFs to each other. This should hopefully correct for
# any small errors in the input locations, and also for fields of
# view that are not completely flat.
#
if refine:
print("Refining PSF alignment.")
# Normalize each PSF by the number of z sections.
for psf in psfs:
for i in range(samples.size):
psf[i,:,:] = psf[i,:,:]/samples[i]
[average_psf, i_score] = measurePSFUtils.alignPSFs(psfs)
else:
average_psf = measurePSFUtils.averagePSF(psfs)
# Normalize PSF.
#
# This normalizes the PSF so that sum of the absolute values
# of each section is 1.0. This only makes sense if the AOI is
# large enough to capture all the photons, which might not be
# true. Not clear how important this is as Spliner will fit
# for the height anyway.
#
for i in range(average_psf.shape[0]):
print("z plane {0:0d} has {1:0d} samples".format(i, total_samples[i]))
section_sum = numpy.sum(numpy.abs(average_psf[i,:,:]))
# Do we need this test? We already check that we have at
# least one sample per section.
if (section_sum > 0.0):
average_psf[i,:,:] = average_psf[i,:,:]/section_sum
# Normalize to unity maximum height.
if (numpy.max(average_psf) > 0.0):
average_psf = average_psf/numpy.max(average_psf)
else:
print("Warning! Measured PSF maxima is zero or negative!")
# Save PSF (in image form).
if True:
tif_name = os.path.splitext(psf_name)[0]
with tifffile.TiffWriter(tif_name + "_beads.tif") as tf:
for i in range(average_psf.shape[0]):
tf.save(average_psf[i,:,:].astype(numpy.float32))
# Save PSF.
#
# For historical reasons all the PSF z values are in nanometers.
# At some point this should be fixed.
#
z_range = 1.0e+3 * z_range
z_step = 1.0e+3 * z_step
cur_z = -z_range
z_vals = []
for i in range(average_psf.shape[0]):
z_vals.append(cur_z)
cur_z += z_step
psf_dict = {"psf" : average_psf,
"pixel_size" : pixel_size,
"type" : "3D",
"version" : 2.0,
"zmin" : -z_range,
"zmax" : z_range,
"zvals" : z_vals}
pickle.dump(psf_dict, open(psf_name, 'wb'))