def test_measure_psf_2D():
movie = storm_analysis.getData("test/data/test.dax")
mlist = storm_analysis.getData("test/data/test_ref.hdf5")
psf = storm_analysis.getPathOutputTest("test_spliner_psf_2d.psf")
storm_analysis.removeFile(psf)
from storm_analysis.spliner.measure_psf import measurePSF
measurePSF(movie, "", mlist, psf, want2d=True, aoi_size=5)
def test_measure_psf():
movie = storm_analysis.getData("test/data/test_spliner.dax")
mlist = storm_analysis.getData("test/data/test_spliner_ref.hdf5")
psf = storm_analysis.getPathOutputTest("test_spliner_psf.psf")
storm_analysis.removeFile(psf)
from storm_analysis.spliner.measure_psf import measurePSF
measurePSF(movie, "", mlist, psf)
def create2DSpline():
movie = storm_analysis.getData("test/data/test.dax")
mlist = storm_analysis.getData("test/data/test_ref.hdf5")
psf = storm_analysis.getPathOutputTest("test_spliner_psf_2d.psf")
spline = storm_analysis.getPathOutputTest("test_spliner_psf_2d.spline")
storm_analysis.removeFile(psf)
storm_analysis.removeFile(spline)
measurePSF.measurePSF(movie, "", mlist, psf, want2d=True, aoi_size=5)
psfToSpline.psfToSpline(psf, spline, 4)
def create2DSpline():
movie = storm_analysis.getData("test/data/test.dax")
mlist = storm_analysis.getData("test/data/test_ref.hdf5")
psf = storm_analysis.getPathOutputTest("test_spliner_psf_2d.psf")
spline = storm_analysis.getPathOutputTest("test_spliner_psf_2d.spline")
storm_analysis.removeFile(psf)
storm_analysis.removeFile(spline)
measurePSF.measurePSF(movie, "", mlist, psf, want2d = True, aoi_size = 5)
psfToSpline.psfToSpline(psf, spline, 4)
def create3DSpline():
movie = storm_analysis.getData("test/data/test_spliner.dax")
mlist = storm_analysis.getData("test/data/test_spliner_ref.hdf5")
psf = storm_analysis.getPathOutputTest("test_spliner_psf.psf")
spline = storm_analysis.getPathOutputTest("test_spliner_psf.spline")
storm_analysis.removeFile(psf)
storm_analysis.removeFile(spline)
measurePSF.measurePSF(movie, "", mlist, psf, aoi_size=6)
psfToSpline.psfToSpline(psf, spline, 5)
def create3DSpline():
movie = storm_analysis.getData("test/data/test_spliner.dax")
mlist = storm_analysis.getData("test/data/test_spliner_ref.hdf5")
psf = storm_analysis.getPathOutputTest("test_spliner_psf.psf")
spline = storm_analysis.getPathOutputTest("test_spliner_psf.spline")
storm_analysis.removeFile(psf)
storm_analysis.removeFile(spline)
measurePSF.measurePSF(movie, "", mlist, psf, aoi_size = 6)
psfToSpline.psfToSpline(psf, spline, 5)
def configure(no_splines, cal_file = None):
# Create sCMOS calibration file if requested.
#
if cal_file is not None:
offset = numpy.zeros((settings.y_size, settings.x_size)) + settings.camera_offset
variance = numpy.ones((settings.y_size, settings.x_size))
gain = numpy.ones((settings.y_size, settings.x_size)) * settings.camera_gain
rqe = numpy.ones((settings.y_size, settings.x_size))
numpy.save(cal_file, [offset, variance, gain, rqe, 2])
# Create parameters file for analysis.
#
print("Creating XML file.")
params = testingParameters(cal_file = cal_file)
params.toXMLFile("spliner.xml")
# Create localization on a grid file.
#
print("Creating gridded localization.")
emittersOnGrid.emittersOnGrid("grid_list.hdf5",
settings.nx,
settings.ny,
1.5,
20,
0.0,
0.0)
# Create randomly located localizations file.
#
print("Creating random localization.")
emittersUniformRandom.emittersUniformRandom("random_list.hdf5",
1.0,
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_list.hdf5",
6,
3,
1.5,
40,
0.0,
0.0)
if no_splines:
return
# Create beads.txt file for spline measurement.
#
with saH5Py.SAH5Py("sparse_list.hdf5") as h5:
locs = h5.getLocalizations()
numpy.savetxt("beads.txt", numpy.transpose(numpy.vstack((locs['x'], locs['y']))))
# 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.)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, 20000.0)
psf_f = lambda s, x, y, i3 : psf.GaussianPSF(s, x, y, i3, settings.pixel_size)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
dither = True,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate("spline_2d.tif", "sparse_list.hdf5", 5)
# Measure the PSF.
#
print("Measuring PSF.")
psf_name = "psf.psf"
measurePSF.measurePSF("spline_2d.tif",
"na",
"sparse_list.hdf5",
psf_name,
want2d = True,
aoi_size = int(settings.spline_size + 1),
pixel_size = settings.pixel_size * 1.0e-3)
# Measure the Spline.
#
if True:
print("Measuring Spline.")
psfToSpline.psfToSpline(psf_name, "psf.spline", settings.spline_size)
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))
