def test_ia_util_12():
"""
Test removeNeighbors().
"""
x = numpy.array([1.0, 2.0, 4.0])
y = numpy.ones(x.size)
[px, py] = iaUtilsC.removeNeighbors(x, y, 0.5)
assert (px.size == 3)
assert (py.size == 3)
[px, py] = iaUtilsC.removeNeighbors(x, y, 1.5)
assert (px.size == 1)
assert (py.size == 1)
def psfLocalizations(i3_filename, mapping_filename, frame = 1, aoi_size = 8, movie_filename = None):
# Load localizations.
i3_reader = readinsight3.I3Reader(i3_filename)
# Load mapping.
mappings = {}
if os.path.exists(mapping_filename):
with open(mapping_filename, 'rb') as fp:
mappings = pickle.load(fp)
else:
print("Mapping file not found, single channel data?")
# Try and determine movie frame size.
i3_metadata = readinsight3.loadI3Metadata(i3_filename)
if i3_metadata is None:
if movie_filename is None:
raise Exception("I3 metadata not found and movie filename is not specified.")
else:
movie_fp = datareader.inferReader(movie_filename)
[movie_y, movie_x] = movie_fp.filmSize()[:2]
else:
movie_data = i3_metadata.find("movie")
# FIXME: These may be transposed?
movie_x = int(movie_data.find("movie_x").text)
movie_y = int(movie_data.find("movie_y").text)
# Load localizations in the requested frame.
locs = i3_reader.getMoleculesInFrame(frame)
print("Loaded", locs.size, "localizations.")
# Remove localizations that are too close to each other.
in_locs = numpy.zeros((locs["x"].size, util_c.getNPeakPar()))
in_locs[:,util_c.getXCenterIndex()] = locs["x"]
in_locs[:,util_c.getYCenterIndex()] = locs["y"]
out_locs = util_c.removeNeighbors(in_locs, 2 * aoi_size)
xf = out_locs[:,util_c.getXCenterIndex()]
yf = out_locs[:,util_c.getYCenterIndex()]
#
# Remove localizations that are too close to the edge or
# outside of the image in any of the channels.
#
is_good = numpy.ones(xf.size, dtype = numpy.bool)
for i in range(xf.size):
# Check in Channel 0.
if (xf[i] < aoi_size) or (xf[i] + aoi_size >= movie_x):
is_good[i] = False
continue
if (yf[i] < aoi_size) or (yf[i] + aoi_size >= movie_y):
is_good[i] = False
continue
# Check other channels.
for key in mappings:
if not is_good[i]:
break
coeffs = mappings[key]
[ch1, ch2, axis] = key.split("_")
if (ch1 == "0"):
if (axis == "x"):
xm = coeffs[0] + coeffs[1]*xf[i] + coeffs[2]*yf[i]
if (xm < aoi_size) or (xm + aoi_size >= movie_x):
is_good[i] = False
break
elif (axis == "y"):
ym = coeffs[0] + coeffs[1]*xf[i] + coeffs[2]*yf[i]
if (ym < aoi_size) or (ym + aoi_size >= movie_y):
is_good[i] = False
break
#
# Save localizations for each channel.
#
gx = xf[is_good]
gy = yf[is_good]
basename = os.path.splitext(i3_filename)[0]
with writeinsight3.I3Writer(basename + "_c1_psf.bin") as w3:
w3.addMoleculesWithXY(gx, gy)
index = 1
while ("0_" + str(index) + "_x" in mappings):
cx = mappings["0_" + str(index) + "_x"]
cy = mappings["0_" + str(index) + "_y"]
#cx = mappings[str(index) + "_0" + "_x"]
#cy = mappings[str(index) + "_0" + "_y"]
xm = cx[0] + cx[1] * gx + cx[2] * gy
ym = cy[0] + cy[1] * gx + cy[2] * gy
with writeinsight3.I3Writer(basename + "_c" + str(index+1) + "_psf.bin") as w3:
w3.addMoleculesWithXY(xm, ym)
index += 1
#
# Print localizations that were kept.
#
print(gx.size, "localizations were kept:")
for i in range(gx.size):
print("ch0: {0:.2f} {1:.2f}".format(gx[i], gy[i]))
index = 1
while ("0_" + str(index) + "_x" in mappings):
cx = mappings["0_" + str(index) + "_x"]
cy = mappings["0_" + str(index) + "_y"]
xm = cx[0] + cx[1] * gx[i] + cx[2] * gy[i]
ym = cy[0] + cy[1] * gx[i] + cy[2] * gy[i]
print("ch" + str(index) + ": {0:.2f} {1:.2f}".format(xm, ym))
index += 1
print("")
print("")
def measurePSF():
# Create sparse random localizations for PSF measurement.
#
print("Creating random localization.")
sim_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call(["python", sim_path + "emitters_uniform_random.py",
"--bin", "sparse_random.hdf5",
"--density", "0.0002",
"--margin", str(settings.margin),
"--sx", str(settings.x_size),
"--sy", str(settings.y_size)])
# Create sparser grid for PSF measurement.
#
print("Creating data for PSF measurement.")
sim_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call(["python", sim_path + "emitters_on_grid.py",
"--bin", "sparse_grid.hdf5",
"--nx", "8",
"--ny", "3",
"--spacing", "40"])
# Create text files for PSF measurement.
#
locs = saH5Py.loadLocalizations("sparse_random.hdf5")
[xf, yf] = iaUtilsC.removeNeighbors(locs["x"], locs["y"], 2.0 * ((settings.psf_size/2)+1))
numpy.savetxt("sparse_random.txt", numpy.transpose(numpy.vstack((xf, yf))))
locs = saH5Py.loadLocalizations("sparse_grid.hdf5")
numpy.savetxt("sparse_grid.txt", numpy.transpose(numpy.vstack((locs['x'], locs['y']))))
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-settings.psf_z_range, settings.psf_z_range + 0.001, 0.010)
drift_data = numpy.zeros((dz.size, 3))
drift_data[:,2] = dz
numpy.savetxt("drift.txt", drift_data)
# Also create the z-offset file.
#
z_offset = numpy.ones((dz.size, 2))
z_offset[:,1] = dz
numpy.savetxt("z_offset.txt", z_offset)
z_offset[:,0] = 0
numpy.savetxt("z_offset_none_valid.txt", z_offset)
# Create simulated data for PSF measurement.
#
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = 10)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, 100.)
drift_f = lambda s, x, y, i3 : drift.DriftFromFile(s, x, y, i3, "drift.txt")
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, 20000.0)
psf_f = lambda s, x, y, i3 : psf.PupilFunction(s, x, y, i3, 100.0, settings.zmn)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
drift_factory = drift_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = settings.x_size,
y_size = settings.y_size)
if True:
sim.simulate("sparse_grid.dax", "sparse_grid.hdf5", dz.size)
sim.simulate("sparse_random.dax", "sparse_random.hdf5", dz.size)
# Measure the PSF using spliner/measure_psf_beads.py and multiplane/measure_psf.py
#
diff_detected = False
# Grid.
if True:
print("Measuring PSF (beads).")
spliner_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
subprocess.call(["python", spliner_path + "measure_psf_beads.py",
"--movie", "sparse_grid.dax",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--beads", "sparse_grid.txt",
"--psf", "sparse_grid_beads.psf",
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5, with zoffset).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_grid.dax",
"--bin", "sparse_grid_ref.hdf5",
"--psf", "sparse_grid_hdf5_zo.psf",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_grid.dax",
"--bin", "sparse_grid_ref.hdf5",
"--psf", "sparse_grid_hdf5.psf",
"--zoffset", "",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
multiplane_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/multi_plane/"
print("Measure PSF (multiplane).")
subprocess.call(["python", multiplane_path + "psf_zstack.py",
"--movie", "sparse_grid.dax",
"--bin", "sparse_grid.hdf5",
"--zstack", "sparse_grid_zstack",
"--aoi_size", str(int(settings.psf_size/2)+1)])
subprocess.call(["python", multiplane_path + "measure_psf.py",
"--zstack", "sparse_grid_zstack.npy",
"--zoffsets", "z_offset.txt",
"--psf_name", "sparse_grid_hdf5_mp_zo.psf",
"--z_range", str(settings.psf_z_range),
"--z_step", str(settings.psf_z_step),
"--normalize", "True"])
# Check that the PSFs are the same.
psf_beads = numpy.load("sparse_grid_beads.psf")["psf"]
psf_hdf5_zo = numpy.load("sparse_grid_hdf5_zo.psf")["psf"]
psf_hdf5 = numpy.load("sparse_grid_hdf5.psf")["psf"]
psf_hdf5_mp_zo = numpy.load("sparse_grid_hdf5_mp_zo.psf")["psf"]
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_zo)
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5)
# Here we are only checking they are close.
if (settings.psf_size >= 20):
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_mp_zo, atol = 0.17, rtol = 0.17)
# Grid, no valid z offsets.
if True:
print("Measuring PSF (beads).")
spliner_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
try:
subprocess.check_output(["python", spliner_path + "measure_psf_beads.py",
"--movie", "sparse_grid.dax",
"--zoffset", "z_offset_none_valid.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--beads", "sparse_grid.txt",
"--psf", "sparse_grid_beads.psf",
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
except subprocess.CalledProcessError:
pass
else:
assert False, "spliner.measure_psf_beads did not fail!"
print("Measuring PSF (HDF5, with zoffset).")
try:
subprocess.check_output(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_grid.dax",
"--bin", "sparse_grid_ref.hdf5",
"--psf", "sparse_grid_hdf5_zo.psf",
"--zoffset", "z_offset_none_valid.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
except subprocess.CalledProcessError:
pass
else:
assert False, "spliner.measure_psf did not fail!"
multiplane_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/multi_plane/"
print("Measure PSF (multiplane).")
try:
subprocess.check_output(["python", multiplane_path + "psf_zstack.py",
"--movie", "sparse_grid.dax",
"--bin", "sparse_grid.hdf5",
"--zstack", "sparse_grid_zstack",
"--aoi_size", str(int(settings.psf_size/2)+1)])
subprocess.check_output(["python", multiplane_path + "measure_psf.py",
"--zstack", "sparse_grid_zstack.npy",
"--zoffsets", "z_offset_none_valid.txt",
"--psf_name", "sparse_grid_hdf5_mp_zo.psf",
"--z_range", str(settings.psf_z_range),
"--z_step", str(settings.psf_z_step),
"--normalize", "True"])
except subprocess.CalledProcessError:
pass
else:
assert False, "multiplane PSF measurement did not fail!"
# Random.
if True:
print("Measuring PSF (beads).")
spliner_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
subprocess.call(["python", spliner_path + "measure_psf_beads.py",
"--movie", "sparse_random.dax",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--beads", "sparse_random.txt",
"--psf", "sparse_random_beads.psf",
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5, with zoffset).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_random.dax",
"--bin", "sparse_random_ref.hdf5",
"--psf", "sparse_random_hdf5_zo.psf",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_random.dax",
"--bin", "sparse_random_ref.hdf5",
"--psf", "sparse_random_hdf5.psf",
"--zoffset", "",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
psf_beads = numpy.load("sparse_random_beads.psf")["psf"]
psf_hdf5_zo = numpy.load("sparse_random_hdf5_zo.psf")["psf"]
psf_hdf5 = numpy.load("sparse_random_hdf5.psf")["psf"]
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_zo)
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5)
if diff_detected:
print("Difference detected in PSF measurements!")
else:
print("No differences detected, all good.")
if False:
with tifffile.TiffWriter("psf_diff.tif") as tf:
for i in range(psf_beads.shape[0]):
tf.save((psf_beads[i,:,:] - psf_hdf5_zo[i,:,:]).astype(numpy.float32))
# Select localizations in current frame & not near the edges.
mask = (i3_data['fr'] == curf) & (i3_data['x'] > aoi_size) & (
i3_data['x'] < (dax_y - aoi_size - 1)) & (i3_data['y'] > aoi_size) & (
i3_data['y'] < (dax_x - aoi_size - 1))
xr = i3_data['x'][mask]
yr = i3_data['y'][mask]
ht = i3_data['h'][mask]
# Remove localizations that are too close to each other.
in_peaks = numpy.zeros((xr.size, util_c.getNResultsPar()))
in_peaks[:, util_c.getXCenterIndex()] = xr
in_peaks[:, util_c.getYCenterIndex()] = yr
in_peaks[:, util_c.getHeightIndex()] = ht
out_peaks = util_c.removeNeighbors(in_peaks, aoi_size)
print(curf, in_peaks.shape, out_peaks.shape)
# Use remaining localizations to calculate spline.
image = dax_data.loadAFrame(curf - 1).astype(numpy.float64)
xr = out_peaks[:, util_c.getXCenterIndex()]
yr = out_peaks[:, util_c.getYCenterIndex()]
ht = out_peaks[:, util_c.getHeightIndex()]
for i in range(xr.size):
xf = xr[i]
yf = yr[i]
xi = int(xf)
yi = int(yf)
def measurePSF(movie_name,
zfile_name,
movie_mlist,
psf_name,
want2d=False,
aoi_size=12,
z_range=750.0,
z_step=50.0):
"""
The actual z range is 2x z_range (i.e. from -z_range to z_range).
"""
# Load dax file, z offset file and molecule list file.
dax_data = datareader.inferReader(movie_name)
z_offsets = None
if os.path.exists(zfile_name):
try:
z_offsets = numpy.loadtxt(zfile_name, ndmin=2)[:, 1]
except IndexError:
z_offsets = None
print("z offsets were not loaded.")
i3_data = readinsight3.loadI3File(movie_mlist)
if want2d:
print("Measuring 2D PSF")
else:
print("Measuring 3D PSF")
#
# Go through the frames identifying good peaks and adding them
# to the average psf. For 3D molecule z positions are rounded to
# the nearest 50nm.
#
z_mid = int(z_range / z_step)
max_z = 2 * z_mid + 1
average_psf = numpy.zeros((max_z, 4 * aoi_size, 4 * aoi_size))
peaks_used = 0
totals = numpy.zeros(max_z)
[dax_x, dax_y, dax_l] = dax_data.filmSize()
for curf in range(dax_l):
# Select localizations in current frame & not near the edges.
mask = (i3_data['fr'] == curf + 1) & (i3_data['x'] > aoi_size) & (
i3_data['x'] <
(dax_x - aoi_size - 1)) & (i3_data['y'] >
aoi_size) & (i3_data['y'] <
(dax_y - aoi_size - 1))
xr = i3_data['x'][mask]
yr = i3_data['y'][mask]
# Use the z offset file if it was specified, otherwise use localization z positions.
if z_offsets is None:
if (curf == 0):
print("Using fit z locations.")
zr = i3_data['z'][mask]
else:
if (curf == 0):
print("Using z offset file.")
zr = numpy.ones(xr.size) * z_offsets[curf]
ht = i3_data['h'][mask]
# Remove localizations that are too close to each other.
in_peaks = numpy.zeros((xr.size, util_c.getNPeakPar()))
in_peaks[:, util_c.getXCenterIndex()] = xr
in_peaks[:, util_c.getYCenterIndex()] = yr
in_peaks[:, util_c.getZCenterIndex()] = zr
in_peaks[:, util_c.getHeightIndex()] = ht
out_peaks = util_c.removeNeighbors(in_peaks, 2 * aoi_size)
#out_peaks = util_c.removeNeighbors(in_peaks, aoi_size)
print(curf, "peaks in", in_peaks.shape[0], ", peaks out",
out_peaks.shape[0])
# Use remaining localizations to calculate spline.
image = dax_data.loadAFrame(curf).astype(numpy.float64)
xr = out_peaks[:, util_c.getXCenterIndex()]
yr = out_peaks[:, util_c.getYCenterIndex()]
zr = out_peaks[:, util_c.getZCenterIndex()]
ht = out_peaks[:, util_c.getHeightIndex()]
for i in range(xr.size):
xf = xr[i]
yf = yr[i]
zf = zr[i]
xi = int(xf)
yi = int(yf)
if want2d:
zi = 0
else:
zi = int(round(zf / z_step) + z_mid)
# check the z is in range
if (zi > -1) and (zi < max_z):
# get localization image
mat = image[xi - aoi_size:xi + aoi_size,
yi - aoi_size:yi + aoi_size]
# zoom in by 2x
psf = scipy.ndimage.interpolation.zoom(mat, 2.0)
# re-center image
psf = scipy.ndimage.interpolation.shift(
psf, (-2.0 * (xf - xi), -2.0 * (yf - yi)), mode='nearest')
# add to average psf accumulator
average_psf[zi, :, :] += psf
totals[zi] += 1
# Force PSF to be zero (on average) at the boundaries.
for i in range(max_z):
edge = numpy.concatenate((average_psf[i, 0, :], average_psf[i, -1, :],
average_psf[i, :, 0], average_psf[i, :, -1]))
average_psf[i, :, :] -= numpy.mean(edge)
# Normalize the PSF.
if want2d:
max_z = 1
for i in range(max_z):
print(i, totals[i])
if (totals[i] > 0.0):
average_psf[i, :, :] = average_psf[i, :, :] / numpy.sum(
numpy.abs(average_psf[i, :, :]))
average_psf = average_psf / numpy.max(average_psf)
# Save PSF (in image form).
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
dxw = daxwriter.DaxWriter(
os.path.join(os.path.dirname(psf_name), "psf.dax"),
average_psf.shape[1], average_psf.shape[2])
for i in range(max_z):
dxw.addFrame(1000.0 * average_psf[i, :, :] + 100)
dxw.close()
# Save PSF.
if want2d:
psf_dict = {"psf": average_psf[0, :, :], "type": "2D"}
else:
cur_z = -z_range
z_vals = []
for i in range(max_z):
z_vals.append(cur_z)
cur_z += z_step
psf_dict = {
"psf": average_psf,
"pixel_size": 0.080, # 1/2 the camera pixel size in nm.
"type": "3D",
"zmin": -z_range,
"zmax": z_range,
"zvals": z_vals
}
with open(psf_name, 'wb') as fp:
pickle.dump(psf_dict, fp)
[dax_x, dax_y, dax_l] = dax_data.filmSize()
while (curf < dax_l) and (peaks_used < min_peaks):
# Select localizations in current frame & not near the edges.
mask = (i3_data['fr'] == curf) & (i3_data['x'] > aoi_size) & (i3_data['x'] < (dax_y - aoi_size - 1)) & (i3_data['y'] > aoi_size) & (i3_data['y'] < (dax_x - aoi_size - 1))
xr = i3_data['x'][mask]
yr = i3_data['y'][mask]
ht = i3_data['h'][mask]
# Remove localizations that are too close to each other.
in_peaks = numpy.zeros((xr.size,util_c.getNResultsPar()))
in_peaks[:,util_c.getXCenterIndex()] = xr
in_peaks[:,util_c.getYCenterIndex()] = yr
in_peaks[:,util_c.getHeightIndex()] = ht
out_peaks = util_c.removeNeighbors(in_peaks, aoi_size)
print(curf, in_peaks.shape, out_peaks.shape)
# Use remaining localizations to calculate spline.
image = dax_data.loadAFrame(curf-1).astype(numpy.float64)
xr = out_peaks[:,util_c.getXCenterIndex()]
yr = out_peaks[:,util_c.getYCenterIndex()]
ht = out_peaks[:,util_c.getHeightIndex()]
for i in range(xr.size):
xf = xr[i]
yf = yr[i]
xi = int(xf)
yi = int(yf)
def measurePSF(movie_name, zfile_name, movie_mlist, psf_name, want2d = False, aoi_size = 12, z_range = 750.0, z_step = 50.0):
"""
The actual z range is 2x z_range (i.e. from -z_range to z_range).
"""
# Load dax file, z offset file and molecule list file.
dax_data = datareader.inferReader(movie_name)
z_offsets = None
if os.path.exists(zfile_name):
try:
z_offsets = numpy.loadtxt(zfile_name, ndmin = 2)[:,1]
except IndexError:
z_offsets = None
print("z offsets were not loaded.")
i3_data = readinsight3.loadI3File(movie_mlist)
if want2d:
print("Measuring 2D PSF")
else:
print("Measuring 3D PSF")
#
# Go through the frames identifying good peaks and adding them
# to the average psf. For 3D molecule z positions are rounded to
# the nearest 50nm.
#
z_mid = int(z_range/z_step)
max_z = 2 * z_mid + 1
average_psf = numpy.zeros((max_z,4*aoi_size,4*aoi_size))
peaks_used = 0
totals = numpy.zeros(max_z)
[dax_x, dax_y, dax_l] = dax_data.filmSize()
for curf in range(dax_l):
# Select localizations in current frame & not near the edges.
mask = (i3_data['fr'] == curf+1) & (i3_data['x'] > aoi_size) & (i3_data['x'] < (dax_x - aoi_size - 1)) & (i3_data['y'] > aoi_size) & (i3_data['y'] < (dax_y - aoi_size - 1))
xr = i3_data['x'][mask]
yr = i3_data['y'][mask]
# Use the z offset file if it was specified, otherwise use localization z positions.
if z_offsets is None:
if (curf == 0):
print("Using fit z locations.")
zr = i3_data['z'][mask]
else:
if (curf == 0):
print("Using z offset file.")
zr = numpy.ones(xr.size) * z_offsets[curf]
ht = i3_data['h'][mask]
# Remove localizations that are too close to each other.
in_peaks = numpy.zeros((xr.size,util_c.getNPeakPar()))
in_peaks[:,util_c.getXCenterIndex()] = xr
in_peaks[:,util_c.getYCenterIndex()] = yr
in_peaks[:,util_c.getZCenterIndex()] = zr
in_peaks[:,util_c.getHeightIndex()] = ht
out_peaks = util_c.removeNeighbors(in_peaks, 2*aoi_size)
#out_peaks = util_c.removeNeighbors(in_peaks, aoi_size)
print(curf, "peaks in", in_peaks.shape[0], ", peaks out", out_peaks.shape[0])
# Use remaining localizations to calculate spline.
image = dax_data.loadAFrame(curf).astype(numpy.float64)
xr = out_peaks[:,util_c.getXCenterIndex()]
yr = out_peaks[:,util_c.getYCenterIndex()]
zr = out_peaks[:,util_c.getZCenterIndex()]
ht = out_peaks[:,util_c.getHeightIndex()]
for i in range(xr.size):
xf = xr[i]
yf = yr[i]
zf = zr[i]
xi = int(xf)
yi = int(yf)
if want2d:
zi = 0
else:
zi = int(round(zf/z_step) + z_mid)
# check the z is in range
if (zi > -1) and (zi < max_z):
# get localization image
mat = image[xi-aoi_size:xi+aoi_size,
yi-aoi_size:yi+aoi_size]
# zoom in by 2x
psf = scipy.ndimage.interpolation.zoom(mat, 2.0)
# re-center image
psf = scipy.ndimage.interpolation.shift(psf, (-2.0*(xf-xi), -2.0*(yf-yi)), mode='nearest')
# add to average psf accumulator
average_psf[zi,:,:] += psf
totals[zi] += 1
# Force PSF to be zero (on average) at the boundaries.
for i in range(max_z):
edge = numpy.concatenate((average_psf[i,0,:],
average_psf[i,-1,:],
average_psf[i,:,0],
average_psf[i,:,-1]))
average_psf[i,:,:] -= numpy.mean(edge)
# Normalize the PSF.
if want2d:
max_z = 1
for i in range(max_z):
print(i, totals[i])
if (totals[i] > 0.0):
average_psf[i,:,:] = average_psf[i,:,:]/numpy.sum(numpy.abs(average_psf[i,:,:]))
average_psf = average_psf/numpy.max(average_psf)
# Save PSF (in image form).
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
dxw = daxwriter.DaxWriter(os.path.join(os.path.dirname(psf_name), "psf.dax"),
average_psf.shape[1],
average_psf.shape[2])
for i in range(max_z):
dxw.addFrame(1000.0 * average_psf[i,:,:] + 100)
dxw.close()
# Save PSF.
if want2d:
psf_dict = {"psf" : average_psf[0,:,:],
"type" : "2D"}
else:
cur_z = -z_range
z_vals = []
for i in range(max_z):
z_vals.append(cur_z)
cur_z += z_step
psf_dict = {"psf" : average_psf,
"pixel_size" : 0.080, # 1/2 the camera pixel size in nm.
"type" : "3D",
"zmin" : -z_range,
"zmax" : z_range,
"zvals" : z_vals}
pickle.dump(psf_dict, open(psf_name, 'wb'))
def measurePSF():
# Create sparse random localizations for PSF measurement.
#
print("Creating random localization.")
sim_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call(["python", sim_path + "emitters_uniform_random.py",
"--bin", "sparse_random.hdf5",
"--density", "0.0002",
"--margin", str(settings.margin),
"--sx", str(settings.x_size),
"--sy", str(settings.y_size)])
# Create sparser grid for PSF measurement.
#
print("Creating data for PSF measurement.")
sim_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call(["python", sim_path + "emitters_on_grid.py",
"--bin", "sparse_grid.hdf5",
"--nx", "8",
"--ny", "3",
"--spacing", "40"])
# Create text files for PSF measurement.
#
locs = saH5Py.loadLocalizations("sparse_random.hdf5")
[xf, yf] = iaUtilsC.removeNeighbors(locs["x"], locs["y"], 2.0 * ((settings.psf_size/2)+1))
numpy.savetxt("sparse_random.txt", numpy.transpose(numpy.vstack((xf, yf))))
locs = saH5Py.loadLocalizations("sparse_grid.hdf5")
numpy.savetxt("sparse_grid.txt", numpy.transpose(numpy.vstack((locs['x'], locs['y']))))
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-settings.psf_z_range, settings.psf_z_range + 0.001, 0.010)
drift_data = numpy.zeros((dz.size, 3))
drift_data[:,2] = dz
numpy.savetxt("drift.txt", drift_data)
# Also create the z-offset file.
#
z_offset = numpy.ones((dz.size, 2))
z_offset[:,1] = dz
numpy.savetxt("z_offset.txt", z_offset)
z_offset[:,0] = 0
numpy.savetxt("z_offset_none_valid.txt", z_offset)
# Create simulated data for PSF measurement.
#
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = 10)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, 100.)
drift_f = lambda s, x, y, i3 : drift.DriftFromFile(s, x, y, i3, "drift.txt")
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, 20000.0)
psf_f = lambda s, x, y, i3 : psf.PupilFunction(s, x, y, i3, 100.0, settings.zmn)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
drift_factory = drift_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = settings.x_size,
y_size = settings.y_size)
if True:
sim.simulate("sparse_grid.tif", "sparse_grid.hdf5", dz.size)
sim.simulate("sparse_random.tif", "sparse_random.hdf5", dz.size)
# Measure the PSF using spliner/measure_psf_beads.py and multiplane/measure_psf.py
#
diff_detected = False
# Grid.
if True:
# Remove old results.
for elt in ["sparse_grid_beads.psf", "sparse_grid_hdf5_zo.psf",
"sparse_grid_hdf5.psf", "sparse_grid_hdf5_mp_zo.psf"]:
if os.path.exists(elt):
os.remove(elt)
print("Measuring PSF (beads).")
spliner_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
subprocess.call(["python", spliner_path + "measure_psf_beads.py",
"--movie", "sparse_grid.tif",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--beads", "sparse_grid.txt",
"--psf", "sparse_grid_beads.psf",
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5, with zoffset).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_grid.tif",
"--bin", "sparse_grid_ref.hdf5",
"--psf", "sparse_grid_hdf5_zo.psf",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_grid.tif",
"--bin", "sparse_grid_ref.hdf5",
"--psf", "sparse_grid_hdf5.psf",
"--zoffset", "",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
multiplane_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/multi_plane/"
print("Measure PSF (multiplane).")
subprocess.call(["python", multiplane_path + "psf_zstack.py",
"--movie", "sparse_grid.tif",
"--bin", "sparse_grid.hdf5",
"--zstack", "sparse_grid_zstack",
"--aoi_size", str(int(settings.psf_size/2)+1)])
subprocess.call(["python", multiplane_path + "measure_psf.py",
"--zstack", "sparse_grid_zstack.npy",
"--zoffsets", "z_offset.txt",
"--psf_name", "sparse_grid_hdf5_mp_zo.psf",
"--z_range", str(settings.psf_z_range),
"--z_step", str(settings.psf_z_step),
"--normalize"])
# Check that the PSFs are the same.
psf_beads = numpy.load("sparse_grid_beads.psf", allow_pickle = True)["psf"]
psf_hdf5_zo = numpy.load("sparse_grid_hdf5_zo.psf", allow_pickle = True)["psf"]
psf_hdf5 = numpy.load("sparse_grid_hdf5.psf", allow_pickle = True)["psf"]
psf_hdf5_mp_zo = numpy.load("sparse_grid_hdf5_mp_zo.psf", allow_pickle = True)["psf"]
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_zo)
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5)
# Here we are only checking they are close.
if (settings.psf_size >= 20):
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_mp_zo, atol = 0.17, rtol = 0.17)
# Grid, no valid z offsets. These are supposed to fail.
#
if True:
print("Measuring PSF (beads).")
spliner_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
try:
subprocess.check_output(["python", spliner_path + "measure_psf_beads.py",
"--movie", "sparse_grid.tif",
"--zoffset", "z_offset_none_valid.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--beads", "sparse_grid.txt",
"--psf", "sparse_grid_beads.psf",
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
except subprocess.CalledProcessError:
pass
else:
assert False, "spliner.measure_psf_beads did not fail!"
print("Measuring PSF (HDF5, with zoffset).")
try:
subprocess.check_output(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_grid.tif",
"--bin", "sparse_grid_ref.hdf5",
"--psf", "sparse_grid_hdf5_zo.psf",
"--zoffset", "z_offset_none_valid.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
except subprocess.CalledProcessError:
pass
else:
assert False, "spliner.measure_psf did not fail!"
multiplane_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/multi_plane/"
print("Measure PSF (multiplane).")
try:
subprocess.check_output(["python", multiplane_path + "psf_zstack.py",
"--movie", "sparse_grid.tif",
"--bin", "sparse_grid.hdf5",
"--zstack", "sparse_grid_zstack",
"--aoi_size", str(int(settings.psf_size/2)+1)])
subprocess.check_output(["python", multiplane_path + "measure_psf.py",
"--zstack", "sparse_grid_zstack.npy",
"--zoffsets", "z_offset_none_valid.txt",
"--psf_name", "sparse_grid_hdf5_mp_zo.psf",
"--z_range", str(settings.psf_z_range),
"--z_step", str(settings.psf_z_step),
"--normalize"])
except subprocess.CalledProcessError:
pass
else:
assert False, "multiplane PSF measurement did not fail!"
# Random.
if True:
# Remove old results.
for elt in ["sparse_random_beads.psf", "sparse_random_hdf5_zo.psf", "sparse_random_hdf5.psf"]:
if os.path.exists(elt):
os.remove(elt)
print("Measuring PSF (beads).")
spliner_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
subprocess.call(["python", spliner_path + "measure_psf_beads.py",
"--movie", "sparse_random.tif",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--beads", "sparse_random.txt",
"--psf", "sparse_random_beads.psf",
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5, with zoffset).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_random.tif",
"--bin", "sparse_random_ref.hdf5",
"--psf", "sparse_random_hdf5_zo.psf",
"--zoffset", "z_offset.txt",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
print("Measuring PSF (HDF5).")
subprocess.call(["python", spliner_path + "measure_psf.py",
"--movie", "sparse_random.tif",
"--bin", "sparse_random_ref.hdf5",
"--psf", "sparse_random_hdf5.psf",
"--zoffset", "",
"--aoi_size", str(int(settings.psf_size/2)+1),
"--zrange", str(settings.psf_z_range),
"--zstep", str(settings.psf_z_step)])
psf_beads = numpy.load("sparse_random_beads.psf", allow_pickle = True)["psf"]
psf_hdf5_zo = numpy.load("sparse_random_hdf5_zo.psf", allow_pickle = True)["psf"]
psf_hdf5 = numpy.load("sparse_random_hdf5.psf", allow_pickle = True)["psf"]
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_zo)
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5)
if diff_detected:
print("Difference detected in PSF measurements!")
else:
print("No differences detected, all good.")
if False:
with tifffile.TiffWriter("psf_diff.tif") as tf:
for i in range(psf_beads.shape[0]):
tf.save((psf_beads[i,:,:] - psf_hdf5_zo[i,:,:]).astype(numpy.float32))
def measurePSF():
# Create sparse random localizations for PSF measurement.
#
print("Creating random localization.")
emittersUniformRandom.emittersUniformRandom("sparse_random.hdf5", 0.0002,
settings.margin,
settings.x_size,
settings.y_size, 0.0)
# Create sparser grid for PSF measurement.
#
print("Creating data for PSF measurement.")
emittersOnGrid.emittersOnGrid("sparse_grid.hdf5", 8, 3, 1.5, 40, 0.0, 0.0)
# Create text files for PSF measurement.
#
locs = saH5Py.loadLocalizations("sparse_random.hdf5")
[xf, yf] = iaUtilsC.removeNeighbors(locs["x"], locs["y"],
2.0 * ((settings.psf_size / 2) + 1))
numpy.savetxt("sparse_random.txt", numpy.transpose(numpy.vstack((xf, yf))))
locs = saH5Py.loadLocalizations("sparse_grid.hdf5")
numpy.savetxt("sparse_grid.txt",
numpy.transpose(numpy.vstack((locs['x'], locs['y']))))
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-settings.psf_z_range, settings.psf_z_range + 0.001,
0.010)
drift_data = numpy.zeros((dz.size, 3))
drift_data[:, 2] = dz
numpy.savetxt("drift.txt", drift_data)
# Also create the z-offset file.
#
z_offset = numpy.ones((dz.size, 2))
z_offset[:, 1] = dz
numpy.savetxt("z_offset.txt", z_offset)
z_offset[:, 0] = 0
numpy.savetxt("z_offset_none_valid.txt", z_offset)
# Create simulated data for PSF measurement.
#
bg_f = lambda s, x, y, i3: background.UniformBackground(
s, x, y, i3, photons=10)
cam_f = lambda s, x, y, i3: camera.Ideal(s, x, y, i3, 100.)
drift_f = lambda s, x, y, i3: drift.DriftFromFile(s, x, y, i3, "drift.txt")
pp_f = lambda s, x, y, i3: photophysics.AlwaysOn(s, x, y, i3, 20000.0)
psf_f = lambda s, x, y, i3: psf.PupilFunction(s, x, y, i3, 100.0, settings.
zmn)
sim = simulate.Simulate(background_factory=bg_f,
camera_factory=cam_f,
drift_factory=drift_f,
photophysics_factory=pp_f,
psf_factory=psf_f,
x_size=settings.x_size,
y_size=settings.y_size)
if True:
sim.simulate("sparse_grid.tif", "sparse_grid.hdf5", dz.size)
sim.simulate("sparse_random.tif", "sparse_random.hdf5", dz.size)
# Measure the PSF using spliner/measure_psf_beads.py and multiplane/measure_psf.py
#
diff_detected = False
# Grid.
if True:
# Remove old results.
for elt in [
"sparse_grid_beads.psf", "sparse_grid_hdf5_zo.psf",
"sparse_grid_hdf5.psf", "sparse_grid_hdf5_mp_zo.psf"
]:
if os.path.exists(elt):
os.remove(elt)
print("Measuring PSF (beads).")
measurePSFBeads.measurePSFBeads("sparse_grid.tif",
"z_offset.txt",
"sparse_grid.txt",
"sparse_grid_beads.psf",
aoi_size=int(settings.psf_size / 2 +
1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
print("Measuring PSF (HDF5, with zoffset).")
spMeasurePSF.measurePSF("sparse_grid.tif",
"z_offset.txt",
"sparse_grid_ref.hdf5",
"sparse_grid_hdf5_zo.psf",
aoi_size=int(settings.psf_size / 2 + 1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
print("Measuring PSF (HDF5).")
spMeasurePSF.measurePSF("sparse_grid.tif",
"",
"sparse_grid_ref.hdf5",
"sparse_grid_hdf5.psf",
aoi_size=int(settings.psf_size / 2 + 1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
multiplane_path = os.path.dirname(
inspect.getfile(storm_analysis)) + "/multi_plane/"
print("Measure PSF (multiplane).")
psfZStack.psfZStack("sparse_grid.tif",
"sparse_grid.hdf5",
"sparse_grid_zstack",
aoi_size=int(settings.psf_size / 2 + 1))
mpMeasurePSF.measurePSF("sparse_grid_zstack.npy",
"z_offset.txt",
"sparse_grid_hdf5_mp_zo.psf",
z_range=settings.psf_z_range,
z_step=settings.psf_z_step,
normalize=True)
# Check that the PSFs are the same.
psf_beads = numpy.load("sparse_grid_beads.psf",
allow_pickle=True)["psf"]
psf_hdf5_zo = numpy.load("sparse_grid_hdf5_zo.psf",
allow_pickle=True)["psf"]
psf_hdf5 = numpy.load("sparse_grid_hdf5.psf", allow_pickle=True)["psf"]
psf_hdf5_mp_zo = numpy.load("sparse_grid_hdf5_mp_zo.psf",
allow_pickle=True)["psf"]
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_zo)
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5)
# Here we are only checking they are close.
if (settings.psf_size >= 20):
diff_detected = diff_detected or psfDiffCheck(
psf_beads, psf_hdf5_mp_zo, atol=0.17, rtol=0.17)
# Grid, no valid z offsets. These are supposed to fail.
#
if True:
print("Measuring PSF (beads).")
try:
measurePSFBeads.measurePSFBeads(
"sparse_grid.tif",
"z_offset_none_valid.txt",
"sparse_grid.txt",
"sparse_grid_beads.psf",
aoi_size=int(settings.psf_size / 2 + 1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
except AssertionError:
pass
else:
assert False, "spliner.measure_psf_beads did not fail!"
print("Measuring PSF (HDF5, with zoffset).")
try:
spMeasurePSF.measurePSF("sparse_grid.tif",
"z_offset_none_valid.txt",
"sparse_grid_ref.hdf5",
"sparse_grid_hdf5_zo.psf",
aoi_size=int(settings.psf_size / 2 + 1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
except AssertionError:
pass
else:
assert False, "spliner.measure_psf did not fail!"
print("Measure PSF (multiplane).")
try:
psfZStack.psfZStack("sparse_grid.tif",
"sparse_grid.hdf5",
"sparse_grid_zstack",
aoi_size=int(settings.psf_size / 2 + 1))
mpMeasurePSF.measurePSF("sparse_grid_zstack.npy",
"z_offset_none_valid.txt",
"sparse_grid_hdf5_mp_zo.psf",
z_range=settings.psf_z_range,
z_step=settings.psf_z_step,
normalize=True)
except AssertionError:
pass
else:
assert False, "multiplane PSF measurement did not fail!"
# Random.
if True:
# Remove old results.
for elt in [
"sparse_random_beads.psf", "sparse_random_hdf5_zo.psf",
"sparse_random_hdf5.psf"
]:
if os.path.exists(elt):
os.remove(elt)
print("Measuring PSF (beads).")
measurePSFBeads.measurePSFBeads("sparse_random.tif",
"z_offset.txt",
"sparse_random.txt",
"sparse_random_beads.psf",
aoi_size=int(settings.psf_size / 2 +
1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
print("Measuring PSF (HDF5, with zoffset).")
spMeasurePSF.measurePSF("sparse_random.tif",
"z_offset.txt",
"sparse_random_ref.hdf5",
"sparse_random_hdf5_zo.psf",
aoi_size=int(settings.psf_size / 2 + 1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
print("Measuring PSF (HDF5).")
spMeasurePSF.measurePSF("sparse_random.tif",
"",
"sparse_random_ref.hdf5",
"sparse_random_hdf5.psf",
aoi_size=int(settings.psf_size / 2 + 1),
z_range=settings.psf_z_range,
z_step=settings.psf_z_step)
psf_beads = numpy.load("sparse_random_beads.psf",
allow_pickle=True)["psf"]
psf_hdf5_zo = numpy.load("sparse_random_hdf5_zo.psf",
allow_pickle=True)["psf"]
psf_hdf5 = numpy.load("sparse_random_hdf5.psf",
allow_pickle=True)["psf"]
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5_zo)
diff_detected = diff_detected or psfDiffCheck(psf_beads, psf_hdf5)
if diff_detected:
print("Difference detected in PSF measurements!")
else:
print("No differences detected, all good.")
if False:
with tifffile.TiffWriter("psf_diff.tif") as tf:
for i in range(psf_beads.shape[0]):
tf.save((psf_beads[i, :, :] - psf_hdf5_zo[i, :, :]).astype(
numpy.float32))
def psfLocalizations(h5_filename, mapping_filename, frame = 0, aoi_size = 8, min_height = 0.0):
# Load localizations & movie size.
with saH5Py.SAH5Py(h5_filename) as h5:
locs = h5.getLocalizationsInFrame(frame)
assert bool(locs), "No localizations found in frame " + str(frame)
[movie_x, movie_y] = h5.getMovieInformation()[:2]
# Load mapping.
mappings = {}
if os.path.exists(mapping_filename):
with open(mapping_filename, 'rb') as fp:
mappings = pickle.load(fp)
else:
print("Mapping file not found, single channel data?")
# Remove localizations that are too dim.
mask = (locs["height"] > min_height)
locs_mask = {}
for elt in ["x", "y"]:
locs_mask[elt] = locs[elt][mask]
# Remove localizations that are too close to each other.
[xf, yf] = iaUtilsC.removeNeighbors(locs_mask["x"], locs_mask["y"], 2.0 * aoi_size)
# Remove localizations that are too close to the edge or
# outside of the image in any of the channels.
#
is_good = numpy.ones(xf.size, dtype = numpy.bool)
for i in range(xf.size):
# Check in Channel 0.
if (xf[i] < aoi_size) or (xf[i] + aoi_size >= movie_x):
is_good[i] = False
continue
if (yf[i] < aoi_size) or (yf[i] + aoi_size >= movie_y):
is_good[i] = False
continue
# Check other channels.
for key in mappings:
if not is_good[i]:
break
coeffs = mappings[key]
[ch1, ch2, axis] = key.split("_")
if (ch1 == "0"):
if (axis == "x"):
xm = coeffs[0] + coeffs[1]*xf[i] + coeffs[2]*yf[i]
if (xm < aoi_size) or (xm + aoi_size >= movie_x):
is_good[i] = False
break
elif (axis == "y"):
ym = coeffs[0] + coeffs[1]*xf[i] + coeffs[2]*yf[i]
if (ym < aoi_size) or (ym + aoi_size >= movie_y):
is_good[i] = False
break
#
# Save localizations for each channel.
#
gx = xf[is_good]
gy = yf[is_good]
basename = os.path.splitext(h5_filename)[0]
saH5Py.saveLocalizations(basename + "_c1_psf.hdf5", {"x" : gx, "y" : gy})
index = 1
while ("0_" + str(index) + "_x" in mappings):
cx = mappings["0_" + str(index) + "_x"]
cy = mappings["0_" + str(index) + "_y"]
xm = cx[0] + cx[1] * gx + cx[2] * gy
ym = cy[0] + cy[1] * gx + cy[2] * gy
saH5Py.saveLocalizations(basename + "_c" + str(index+1) + "_psf.hdf5", {"x" : xm, "y" : ym})
index += 1
#
# Print localizations that were kept.
#
print(gx.size, "localizations were kept out of", xf.size)
for i in range(gx.size):
print("ch0: {0:.2f} {1:.2f}".format(gx[i], gy[i]))
index = 1
while ("0_" + str(index) + "_x" in mappings):
cx = mappings["0_" + str(index) + "_x"]
cy = mappings["0_" + str(index) + "_y"]
xm = cx[0] + cx[1] * gx[i] + cx[2] * gy[i]
ym = cy[0] + cy[1] * gx[i] + cy[2] * gy[i]
print("ch" + str(index) + ": {0:.2f} {1:.2f}".format(xm, ym))
index += 1
print("")
print("")
