def makeData():
index = 1
# Create HDF5 files for each plane.
#
for elt in ["grid_list.hdf5", "random_storm.hdf5"]:
locs = saH5Py.loadLocalizations(elt)
locs["color"] = numpy.random.randint(4, size=locs["x"].size)
zo = locs["z"].copy()
locs["z"][:] = zo + 1.0e-3 * settings.z_planes[0]
saH5Py.saveLocalizations("sim_input_c1_" + elt, locs)
for i in range(1, 4):
locs["x"] += settings.dx
locs["y"] += settings.dy
locs["z"][:] = zo + 1.0e-3 * settings.z_planes[i]
saH5Py.saveLocalizations("sim_input_c" + str(i + 1) + "_" + elt,
locs)
if True:
# Create a movie for each plane.
for [bg, photons] in settings.photons:
# Adjust photons by the number of planes.
photons = photons / float(len(settings.z_planes))
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
for i in range(4):
bg_f = lambda s, x, y, i3: background.UniformBackground(
s, x, y, i3, photons=bg)
cam_f = lambda s, x, y, i3: camera.SCMOS(
s, x, y, i3, "calib.npy")
pp_f = lambda s, x, y, i3: photophysics.AlwaysOnMC(
s, x, y, i3, color=i, photons=photons)
psf_f = lambda s, x, y, i3: psf.PupilFunction(
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,
x_size=settings.x_size,
y_size=settings.y_size)
sim.simulate(wdir + "/test_c" + str(i + 1) + ".dax",
"sim_input_c" + str(i + 1) + "_grid_list.hdf5",
settings.n_frames)
index += 1
def makeData():
index = 1
# Ideal camera movies.
#
if True:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3: background.UniformBackground(
s, x, y, i3, photons=bg)
cam_f = lambda s, x, y, i3: camera.Ideal(s, x, y, i3, settings.
camera_offset)
pp_f = lambda s, x, y, i3: photophysics.AlwaysOn(
s, x, y, i3, photons)
psf_f = lambda s, x, y, i3: psf.PupilFunction(
s, x, y, i3, settings.pixel_size, settings.zmn)
sim = simulate.Simulate(background_factory=bg_f,
camera_factory=cam_f,
photophysics_factory=pp_f,
psf_factory=psf_f,
x_size=settings.x_size,
y_size=settings.y_size)
sim.simulate(wdir + "/test.dax", "grid_list.hdf5",
settings.n_frames)
index += 1
# Create "peak_locations" file if needed.
#
if hasattr(settings, "peak_locations") and (settings.peak_locations
is not None):
with saH5Py.SAH5Py("test_01/test_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
if settings.peak_locations.endswith(".hdf5"):
saH5Py.saveLocalizations(settings.peak_locations, locs)
else:
numpy.savetxt(
settings.peak_locations,
numpy.transpose(
numpy.vstack((locs['x'], locs['y'], locs['height'],
locs['background']))))
def makePeakFile(settings):
# Create "peak_locations" file if needed.
#
if hasattr(settings, "peak_locations") and (settings.peak_locations
is not None):
with saH5Py.SAH5Py("test_01/test_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
if settings.peak_locations.endswith(".hdf5"):
saH5Py.saveLocalizations(settings.peak_locations, locs)
else:
numpy.savetxt(
settings.peak_locations,
numpy.transpose(
numpy.vstack((locs['x'], locs['y'], locs['height'],
locs['background']))))
def makeData():
index = 1
# Ideal camera movies.
#
if True:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, photons)
psf_f = lambda s, x, y, i3 : psf.PupilFunction(s, x, y, i3, settings.pixel_size, settings.zmn)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(wdir + "/test.dax", "grid_list.hdf5", settings.n_frames)
index += 1
# Create "peak_locations" file if needed.
#
if hasattr(settings, "peak_locations") and (settings.peak_locations is not None):
with saH5Py.SAH5Py("test_01/test_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
if settings.peak_locations.endswith(".hdf5"):
saH5Py.saveLocalizations(settings.peak_locations, locs)
else:
numpy.savetxt(settings.peak_locations,
numpy.transpose(numpy.vstack((locs['x'],
locs['y'],
locs['height'],
locs['background']))))
def createMovie(n_frames):
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, signal)
psf_f = lambda s, x, y, i3 : psf.GaussianPSF(s, x, y, i3, pixel_size)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = x_size,
y_size = y_size)
sim.simulate("test.tif", "sim_locs.hdf5", n_frames)
# Also create file to use for peak locations.
with saH5Py.SAH5Py("test_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
saH5Py.saveLocalizations("peak_locs.hdf5", locs)
def createMovie(n_frames):
bg_f = lambda s, x, y, i3: background.UniformBackground(
s, x, y, i3, photons=bg)
cam_f = lambda s, x, y, i3: camera.Ideal(s, x, y, i3, camera_offset)
pp_f = lambda s, x, y, i3: photophysics.AlwaysOn(s, x, y, i3, signal)
psf_f = lambda s, x, y, i3: psf.GaussianPSF(s, x, y, i3, pixel_size)
sim = simulate.Simulate(background_factory=bg_f,
camera_factory=cam_f,
photophysics_factory=pp_f,
psf_factory=psf_f,
x_size=x_size,
y_size=y_size)
sim.simulate("test.tif", "sim_locs.hdf5", n_frames)
# Also create file to use for peak locations.
with saH5Py.SAH5Py("test_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
saH5Py.saveLocalizations("peak_locs.hdf5", locs)
def emittersOnGrid(h5_name, nx, ny, sigma, spacing, zrange, zoffset, seed=0):
if seed is not None:
random.seed(seed)
if (nx * ny > 1):
curz = -zrange
z_inc = 2.0 * zrange / (nx * ny - 1)
else:
curz = 0.0
z_inc = 0.0
peaks = {
"id": numpy.zeros(nx * ny, dtype=numpy.int32),
"x": numpy.zeros(nx * ny),
"y": numpy.zeros(nx * ny),
"z": numpy.zeros(nx * ny),
"xsigma": sigma * numpy.ones(nx * ny),
"ysigma": sigma * numpy.ones(nx * ny)
}
curx = spacing
for i in range(nx):
cury = spacing
for j in range(ny):
k = i * ny + j
peaks['x'][k] = curx + random.random() - 0.5
peaks['y'][k] = cury + random.random() - 0.5
peaks['z'][k] = curz + zoffset
# Record emitter id in the 'id' field.
peaks['id'][k] = k
cury += spacing
curz += z_inc
curx += spacing
saH5Py.saveLocalizations(h5_name, peaks)
def emittersOnGrid(h5_name, nx, ny, sigma, spacing, zrange, zoffset, seed = 0):
if seed is not None:
random.seed(seed)
if (nx*ny > 1):
curz = -zrange
z_inc = 2.0 * zrange/(nx*ny - 1)
else:
curz = 0.0
z_inc = 0.0
peaks = {"id" : numpy.zeros(nx*ny, dtype = numpy.int32),
"x" : numpy.zeros(nx*ny),
"y" : numpy.zeros(nx*ny),
"z" : numpy.zeros(nx*ny),
"xsigma" : sigma * numpy.ones(nx*ny),
"ysigma" : sigma * numpy.ones(nx*ny)}
curx = spacing
for i in range(nx):
cury = spacing
for j in range(ny):
k = i*ny+j
peaks['x'][k] = curx + random.random() - 0.5
peaks['y'][k] = cury + random.random() - 0.5
peaks['z'][k] = curz + zoffset
# Record emitter id in the 'id' field.
peaks['id'][k] = k
cury += spacing
curz += z_inc
curx += spacing
saH5Py.saveLocalizations(h5_name, peaks)
def configure(psf_model, no_splines):
# Create parameters file for analysis.
#
print("Creating XML file.")
params = testingParameters(psf_model)
params.toXMLFile("multiplane.xml")
# Create localization on a grid file.
#
print("Creating gridded localization.")
sim_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call([
"python", sim_path + "emitters_on_grid.py", "--bin", "grid_list.hdf5",
"--nx",
str(settings.nx), "--ny",
str(settings.ny), "--spacing", "20", "--zrange",
str(settings.test_z_range), "--zoffset",
str(settings.test_z_offset)
])
# Create randomly located localizations file.
#
print("Creating random localization.")
subprocess.call([
"python", sim_path + "emitters_uniform_random.py", "--bin",
"random_list.hdf5", "--density", "1.0", "--margin",
str(settings.margin), "--sx",
str(settings.x_size), "--sy",
str(settings.y_size), "--zrange",
str(settings.test_z_range)
])
# 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", "psf_list.hdf5",
"--nx", "6", "--ny", "3", "--spacing", "40"
])
# Create sCMOS camera calibration files.
#
numpy.save("calib.npy", [
numpy.zeros(
(settings.y_size, settings.x_size)) + settings.camera_offset,
numpy.ones(
(settings.y_size, settings.x_size)) * settings.camera_variance,
numpy.ones((settings.y_size, settings.x_size)) * settings.camera_gain,
numpy.ones((settings.y_size, settings.x_size)), 2
])
# Create mapping file.
with open("map.map", 'wb') as fp:
pickle.dump(settings.mappings, fp)
if no_splines:
return
multiplane_path = os.path.dirname(
inspect.getfile(storm_analysis)) + "/multi_plane/"
# Create pupil functions for 'pupilfn'.
if (psf_model == "pupilfn"):
pupilfn_path = os.path.dirname(
inspect.getfile(storm_analysis)) + "/pupilfn/"
print("Creating pupil functions.")
for i in range(len(settings.z_planes)):
subprocess.call([
"python", pupilfn_path + "make_pupil_fn.py", "--filename",
"c" + str(i + 1) + "_pupilfn.pfn", "--size",
str(settings.psf_size), "--pixel-size",
str(settings.pixel_size), "--zmn",
str(settings.pupil_fn), "--z-offset",
str(-settings.z_planes[i])
])
# Both 'spline' and 'psf_fft' need measured PSFs.
else:
# Create localization files for PSF measurement.
#
locs = saH5Py.loadLocalizations("psf_list.hdf5")
for i, z_offset in enumerate(settings.z_planes):
cx = settings.mappings["0_" + str(i) + "_x"]
cy = settings.mappings["0_" + str(i) + "_y"]
locs_temp = {
"x": locs["x"].copy(),
"y": locs["y"].copy(),
"z": locs["z"].copy()
}
xi = locs_temp["x"]
yi = locs_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
locs_temp["x"] = xf
locs_temp["y"] = yf
locs_temp["z"][:] = z_offset
saH5Py.saveLocalizations("c" + str(i + 1) + "_psf.hdf5", locs_temp)
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-settings.spline_z_range,
settings.spline_z_range + 0.001, 0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5: drift.DriftFromFile(
s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5: psf.PupilFunction(
s, x, y, h5, settings.pixel_size, settings.pupil_fn)
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)
for i in range(len(settings.z_planes)):
sim.simulate("c" + str(i + 1) + "_zcal.dax",
"c" + str(i + 1) + "_psf.hdf5", dz.size)
# Measure the PSF.
#
print("Measuring PSFs.")
psf_fft_path = os.path.dirname(
inspect.getfile(storm_analysis)) + "/psf_fft/"
spliner_path = os.path.dirname(
inspect.getfile(storm_analysis)) + "/spliner/"
for i in range(len(settings.z_planes)):
subprocess.call([
"python", multiplane_path + "psf_zstack.py", "--movie",
"c" + str(i + 1) + "_zcal.dax", "--bin",
"c" + str(i + 1) + "_psf.hdf5", "--zstack",
"c" + str(i + 1) + "_zstack", "--scmos_cal", "calib.npy",
"--aoi_size",
str(int(settings.psf_size / 2) + 1)
])
# Measure PSF and calculate spline for Spliner.
#
if (psf_model == "spline"):
# PSFs are independently normalized.
#
if settings.independent_heights:
for i in range(len(settings.z_planes)):
subprocess.call([
"python", multiplane_path + "measure_psf.py", "--zstack",
"c" + str(i + 1) + "_zstack.npy", "--zoffsets",
"z_offset.txt", "--psf_name",
"c" + str(i + 1) + "_psf_normed.psf", "--z_range",
str(settings.spline_z_range), "--normalize", "True"
])
# PSFs are normalized to each other.
#
else:
for i in range(len(settings.z_planes)):
subprocess.call([
"python", multiplane_path + "measure_psf.py", "--zstack",
"c" + str(i + 1) + "_zstack.npy", "--zoffsets",
"z_offset.txt", "--psf_name",
"c" + str(i + 1) + "_psf.psf", "--z_range",
str(settings.spline_z_range)
])
norm_args = [
"python", multiplane_path + "normalize_psfs.py", "--psfs",
"c1_psf.psf"
]
for i in range(len(settings.z_planes) - 1):
norm_args.append("c" + str(i + 2) + "_psf.psf")
subprocess.call(norm_args)
# Measure the spline for Spliner.
#
print("Measuring Spline.")
for i in range(len(settings.z_planes)):
subprocess.call([
"python", spliner_path + "psf_to_spline.py", "--psf",
"c" + str(i + 1) + "_psf_normed.psf", "--spline",
"c" + str(i + 1) + "_psf.spline", "--spline_size",
str(settings.psf_size)
])
# Measure PSF and downsample for PSF FFT.
#
elif (psf_model == "psf_fft"):
# PSFs are independently normalized.
#
if settings.independent_heights:
for i in range(len(settings.z_planes)):
subprocess.call([
"python", multiplane_path + "measure_psf.py", "--zstack",
"c" + str(i + 1) + "_zstack.npy", "--zoffsets",
"z_offset.txt", "--psf_name",
"c" + str(i + 1) + "_psf_normed.psf", "--z_range",
str(settings.psf_z_range), "--z_step",
str(settings.psf_z_step), "--normalize", "True"
])
# PSFs are normalized to each other.
#
else:
for i in range(len(settings.z_planes)):
subprocess.call([
"python", multiplane_path + "measure_psf.py", "--zstack",
"c" + str(i + 1) + "_zstack.npy", "--zoffsets",
"z_offset.txt", "--psf_name",
"c" + str(i + 1) + "_psf.psf", "--z_range",
str(settings.psf_z_range), "--z_step",
str(settings.psf_z_step)
])
norm_args = [
"python", multiplane_path + "normalize_psfs.py", "--psfs",
"c1_psf.psf"
]
for i in range(len(settings.z_planes) - 1):
norm_args.append("c" + str(i + 2) + "_psf.psf")
subprocess.call(norm_args)
# Downsample the PSF to 1x for PSF FFT.
print("Downsampling PSF.")
for i in range(len(settings.z_planes)):
subprocess.call([
"python", psf_fft_path + "downsample_psf.py", "--spliner_psf",
"c" + str(i + 1) + "_psf_normed.psf", "--psf",
"c" + str(i + 1) + "_psf_fft.psf", "--pixel-size",
str(settings.pixel_size)
])
# Calculate Cramer-Rao weighting.
#
print("Calculating weights.")
subprocess.call([
"python", multiplane_path + "plane_weighting.py", "--background",
str(settings.photons[0][0]), "--photons",
str(settings.photons[0][1]), "--output", "weights.npy", "--xml",
"multiplane.xml", "--no_plots"
])
def configure():
# Get relevant paths.
mm_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/micrometry/"
mp_path = os.path.dirname(
inspect.getfile(storm_analysis)) + "/multi_plane/"
sp_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
# Create analysis XML files.
#
print("Creating XML files.")
params = testingParametersSCMOS()
params.toXMLFile("scmos.xml")
params = testingParametersMC()
params.toXMLFile("multicolor.xml")
# Useful variables
aoi_size = int(settings.psf_size / 2) + 1
# Create sCMOS data and HDF5 files we'll need for the simulation.
#
if True:
# Create sCMOS camera calibration files.
#
numpy.save("calib.npy", [
numpy.zeros(
(settings.y_size, settings.x_size)) + settings.camera_offset,
numpy.ones(
(settings.y_size, settings.x_size)) * settings.camera_variance,
numpy.ones(
(settings.y_size, settings.x_size)) * settings.camera_gain, 1
])
# Create localization on a grid file.
#
print("Creating gridded localizations.")
sim_path = os.path.dirname(
inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call([
"python", sim_path + "emitters_on_grid.py", "--bin",
"grid_list.hdf5", "--nx",
str(settings.nx), "--ny",
str(settings.ny), "--spacing", "20", "--zrange",
str(settings.test_z_range), "--zoffset",
str(settings.test_z_offset)
])
# Create randomly located localizations file (for STORM movies).
#
print("Creating random localizations.")
subprocess.call([
"python", sim_path + "emitters_uniform_random.py", "--bin",
"random_storm.hdf5", "--density", "1.0", "--margin",
str(settings.margin), "--sx",
str(settings.x_size), "--sy",
str(settings.y_size), "--zrange",
str(settings.test_z_range)
])
# Create randomly located localizations file (for mapping measurement).
#
print("Creating random localizations.")
subprocess.call([
"python", sim_path + "emitters_uniform_random.py", "--bin",
"random_map.hdf5", "--density", "0.0003", "--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",
"psf_list.hdf5", "--nx", "6", "--ny", "3", "--spacing", "40"
])
## This part makes / tests measuring the mapping.
##
if True:
print("Measuring mapping.")
# Make localization files for simulations.
#
locs = saH5Py.loadLocalizations("random_map.hdf5")
locs["z"][:] = 1.0e-3 * settings.z_planes[0]
saH5Py.saveLocalizations("c1_random_map.hdf5", locs)
for i in range(1, 4):
locs["x"] += settings.dx
locs["y"] += settings.dy
locs["z"][:] = settings.z_planes[i]
saH5Py.saveLocalizations("c" + str(i + 1) + "_random_map.hdf5",
locs)
# Make localization files for simulations.
#
locs = saH5Py.loadLocalizations("random_map.hdf5")
locs["z"][:] = 1.0e-3 * settings.z_planes[0]
saH5Py.saveLocalizations("c1_random_map.hdf5", locs)
for i in range(1, 4):
locs["x"] += settings.dx
locs["y"] += settings.dy
locs["z"][:] = settings.z_planes[i]
saH5Py.saveLocalizations("c" + str(i + 1) + "_random_map.hdf5",
locs)
# Make simulated mapping data.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 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,
x_size=settings.x_size,
y_size=settings.y_size)
for i in range(4):
sim.simulate("c" + str(i + 1) + "_map.dax",
"c" + str(i + 1) + "_random_map.hdf5", 1)
# Analyze simulated mapping data
#
for i in range(4):
scmos.analyze("c" + str(i + 1) + "_map.dax",
"c" + str(i + 1) + "_map.hdf5", "scmos.xml")
# Measure mapping.
#
for i in range(3):
subprocess.call([
"python", mm_path + "micrometry.py", "--locs1", "c1_map.hdf5",
"--locs2", "c" + str(i + 2) + "_map.hdf5", "--results",
"c1_c" + str(i + 2) + "_map.map", "--no_plots"
])
# Merge mapping.
#
subprocess.call([
"python", mm_path + "merge_maps.py", "--results", "map.map",
"--maps", "c1_c2_map.map", "c1_c3_map.map", "c1_c4_map.map"
])
# Print mapping.
#
if True:
print("Mapping is:")
subprocess.call([
"python", mp_path + "print_mapping.py", "--mapping", "map.map"
])
print("")
# Check that mapping is close to what we expect (within 5%).
#
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
for i in range(3):
if not numpy.allclose(mappings["0_" + str(i + 1) + "_x"],
numpy.array(
[settings.dx * (i + 1), 1.0, 0.0]),
rtol=0.05,
atol=0.05):
print("X mapping difference for channel", i + 1)
if not numpy.allclose(mappings["0_" + str(i + 1) + "_y"],
numpy.array(
[settings.dy * (i + 1), 0.0, 1.0]),
rtol=0.05,
atol=0.05):
print("Y mapping difference for channel", i + 1)
## This part measures / test the PSF measurement.
##
if True:
# 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 + 0.05,
0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5: drift.DriftFromFile(
s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5: psf.PupilFunction(s, x, y, h5, settings.
pixel_size, [])
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:
for i in range(4):
sim.simulate("c" + str(i + 1) + "_zcal.dax",
"c" + str(i + 1) + "_random_map.hdf5", dz.size)
# Get localizations to use for PSF measurement.
#
subprocess.call([
"python", mp_path + "psf_localizations.py", "--bin",
"c1_map_ref.hdf5", "--map", "map.map", "--aoi_size",
str(aoi_size)
])
# Create PSF z stacks.
#
for i in range(4):
subprocess.call([
"python", mp_path + "psf_zstack.py", "--movie",
"c" + str(i + 1) + "_zcal.dax", "--bin",
"c1_map_ref_c" + str(i + 1) + "_psf.hdf5", "--zstack",
"c" + str(i + 1) + "_zstack", "--scmos_cal", "calib.npy",
"--aoi_size",
str(aoi_size)
])
# Measure PSF.
#
for i in range(4):
subprocess.call([
"python", mp_path + "measure_psf.py", "--zstack",
"c" + str(i + 1) + "_zstack.npy", "--zoffsets", "z_offset.txt",
"--psf_name", "c" + str(i + 1) + "_psf_normed.psf",
"--z_range",
str(settings.psf_z_range), "--normalize"
])
## This part creates the splines.
##
if True:
print("Measuring Splines.")
for i in range(4):
subprocess.call([
"python", sp_path + "psf_to_spline.py", "--psf",
"c" + str(i + 1) + "_psf_normed.psf", "--spline",
"c" + str(i + 1) + "_psf.spline", "--spline_size",
str(settings.psf_size)
])
## This part measures the Cramer-Rao weights.
##
if True:
print("Calculating weights.")
subprocess.call([
"python", mp_path + "plane_weighting.py", "--background",
str(settings.photons[0][0]), "--photons",
str(settings.photons[0][1]), "--output", "weights.npy", "--xml",
"multicolor.xml", "--no_plots"
])
def configure():
# Get relevant paths.
mm_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/micrometry/"
mp_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/multi_plane/"
sp_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
# Create analysis XML files.
#
print("Creating XML files.")
params = testingParametersSCMOS()
params.toXMLFile("scmos.xml")
params = testingParametersMC()
params.toXMLFile("multicolor.xml")
# Useful variables
aoi_size = int(settings.psf_size/2)+1
# Create sCMOS data and HDF5 files we'll need for the simulation.
#
if True:
# Create sCMOS camera calibration files.
#
numpy.save("calib.npy", [numpy.zeros((settings.y_size, settings.x_size)) + settings.camera_offset,
numpy.ones((settings.y_size, settings.x_size)) * settings.camera_variance,
numpy.ones((settings.y_size, settings.x_size)) * settings.camera_gain,
numpy.ones((settings.y_size, settings.x_size)),
2])
# Create localization on a grid file.
#
print("Creating gridded localizations.")
sim_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call(["python", sim_path + "emitters_on_grid.py",
"--bin", "grid_list.hdf5",
"--nx", str(settings.nx),
"--ny", str(settings.ny),
"--spacing", "20",
"--zrange", str(settings.test_z_range),
"--zoffset", str(settings.test_z_offset)])
# Create randomly located localizations file (for STORM movies).
#
print("Creating random localizations.")
subprocess.call(["python", sim_path + "emitters_uniform_random.py",
"--bin", "random_storm.hdf5",
"--density", "1.0",
"--margin", str(settings.margin),
"--sx", str(settings.x_size),
"--sy", str(settings.y_size),
"--zrange", str(settings.test_z_range)])
# Create randomly located localizations file (for mapping measurement).
#
print("Creating random localizations.")
subprocess.call(["python", sim_path + "emitters_uniform_random.py",
"--bin", "random_map.hdf5",
"--density", "0.0003",
"--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", "psf_list.hdf5",
"--nx", "6",
"--ny", "3",
"--spacing", "40"])
## This part makes / tests measuring the mapping.
##
if True:
print("Measuring mapping.")
# Make localization files for simulations.
#
locs = saH5Py.loadLocalizations("random_map.hdf5")
locs["z"][:] = 1.0e-3 * settings.z_planes[0]
saH5Py.saveLocalizations("c1_random_map.hdf5", locs)
for i in range(1,4):
locs["x"] += settings.dx
locs["y"] += settings.dy
locs["z"][:] = settings.z_planes[i]
saH5Py.saveLocalizations("c" + str(i+1) + "_random_map.hdf5", locs)
# Make localization files for simulations.
#
locs = saH5Py.loadLocalizations("random_map.hdf5")
locs["z"][:] = 1.0e-3 * settings.z_planes[0]
saH5Py.saveLocalizations("c1_random_map.hdf5", locs)
for i in range(1,4):
locs["x"] += settings.dx
locs["y"] += settings.dy
locs["z"][:] = settings.z_planes[i]
saH5Py.saveLocalizations("c" + str(i+1) + "_random_map.hdf5", locs)
# Make simulated mapping data.
#
bg_f = lambda s, x, y, h5 : background.UniformBackground(s, x, y, h5, photons = 10)
cam_f = lambda s, x, y, h5 : camera.SCMOS(s, x, y, h5, "calib.npy")
pp_f = lambda s, x, y, h5 : photophysics.AlwaysOn(s, x, y, h5, 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,
x_size = settings.x_size,
y_size = settings.y_size)
for i in range(4):
sim.simulate("c" + str(i+1) + "_map.dax", "c" + str(i+1) + "_random_map.hdf5", 1)
# Analyze simulated mapping data
#
for i in range(4):
scmos.analyze("c" + str(i+1) + "_map.dax", "c" + str(i+1) + "_map.hdf5", "scmos.xml")
# Measure mapping.
#
for i in range(3):
subprocess.call(["python", mm_path + "micrometry.py",
"--locs1", "c1_map.hdf5",
"--locs2", "c" + str(i+2) + "_map.hdf5",
"--results", "c1_c" + str(i+2) + "_map.map",
"--no_plots"])
# Merge mapping.
#
subprocess.call(["python", mm_path + "merge_maps.py",
"--results", "map.map",
"--maps", "c1_c2_map.map", "c1_c3_map.map", "c1_c4_map.map"])
# Print mapping.
#
if True:
print("Mapping is:")
subprocess.call(["python", mp_path + "print_mapping.py",
"--mapping", "map.map"])
print("")
# Check that mapping is close to what we expect (within 5%).
#
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
for i in range(3):
if not numpy.allclose(mappings["0_" + str(i+1) + "_x"], numpy.array([settings.dx*(i+1), 1.0, 0.0]), rtol = 0.05, atol = 0.05):
print("X mapping difference for channel", i+1)
if not numpy.allclose(mappings["0_" + str(i+1) + "_y"], numpy.array([settings.dy*(i+1), 0.0, 1.0]), rtol = 0.05, atol = 0.05):
print("Y mapping difference for channel", i+1)
## This part measures / test the PSF measurement.
##
if True:
# 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 + 0.05, 0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5 : background.UniformBackground(s, x, y, h5, photons = 10)
cam_f = lambda s, x, y, h5 : camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5 : drift.DriftFromFile(s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5 : photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5 : psf.PupilFunction(s, x, y, h5, settings.pixel_size, [])
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:
for i in range(4):
sim.simulate("c" + str(i+1) + "_zcal.dax",
"c" + str(i+1) + "_random_map.hdf5",
dz.size)
# Get localizations to use for PSF measurement.
#
subprocess.call(["python", mp_path + "psf_localizations.py",
"--bin", "c1_map_ref.hdf5",
"--map", "map.map",
"--aoi_size", str(aoi_size)])
# Create PSF z stacks.
#
for i in range(4):
subprocess.call(["python", mp_path + "psf_zstack.py",
"--movie", "c" + str(i+1) + "_zcal.dax",
"--bin", "c1_map_ref_c" + str(i+1) + "_psf.hdf5",
"--zstack", "c" + str(i+1) + "_zstack",
"--scmos_cal", "calib.npy",
"--aoi_size", str(aoi_size)])
# Measure PSF.
#
for i in range(4):
subprocess.call(["python", mp_path + "measure_psf.py",
"--zstack", "c" + str(i+1) + "_zstack.npy",
"--zoffsets", "z_offset.txt",
"--psf_name", "c" + str(i+1) + "_psf_normed.psf",
"--z_range", str(settings.psf_z_range),
"--normalize"])
## This part creates the splines.
##
if True:
print("Measuring Splines.")
for i in range(4):
subprocess.call(["python", sp_path + "psf_to_spline.py",
"--psf", "c" + str(i+1) + "_psf_normed.psf",
"--spline", "c" + str(i+1) + "_psf.spline",
"--spline_size", str(int(settings.psf_size/2))])
## This part measures the Cramer-Rao weights.
##
if True:
print("Calculating weights.")
subprocess.call(["python", mp_path + "plane_weighting.py",
"--background", str(settings.photons[0][0]),
"--photons", str(settings.photons[0][1]),
"--output", "weights.npy",
"--xml", "multicolor.xml",
"--no_plots"])
fdecon.decon(parameters.getAttr("fista_iterations"),
parameters.getAttr("fista_lambda"),
verbose = True)
# Save results.
fx = fdecon.getXVector()
print(numpy.min(fx), numpy.max(fx))
with tifffile.TiffWriter(args.output) as tf:
tf.save(image.astype(numpy.float32))
for i in range(fx.shape[2]):
tf.save(fx[:,:,i].astype(numpy.float32))
# Find peaks in the decon data.
peaks = fdecon.getPeaks(parameters.getAttr("fista_threshold"), 5)
saH5Py.saveLocalizations(args.output[:-4] + ".hdf5", peaks)
# Clean up.
fdecon.cleanup()
#
# The MIT License
#
# Copyright (c) 2016 Zhuang Lab, Harvard University
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
def configure(psf_model, no_splines):
# Create parameters file for analysis.
#
print("Creating XML file.")
params = testingParameters(psf_model)
params.toXMLFile("multiplane.xml")
# Create localization on a grid file.
#
print("Creating gridded localization.")
sim_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/simulator/"
subprocess.call(["python", sim_path + "emitters_on_grid.py",
"--bin", "grid_list.hdf5",
"--nx", str(settings.nx),
"--ny", str(settings.ny),
"--spacing", "20",
"--zrange", str(settings.test_z_range),
"--zoffset", str(settings.test_z_offset)])
# Create randomly located localizations file.
#
print("Creating random localization.")
subprocess.call(["python", sim_path + "emitters_uniform_random.py",
"--bin", "random_list.hdf5",
"--density", "1.0",
"--margin", str(settings.margin),
"--sx", str(settings.x_size),
"--sy", str(settings.y_size),
"--zrange", str(settings.test_z_range)])
# 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", "psf_list.hdf5",
"--nx", "6",
"--ny", "3",
"--spacing", "40"])
# Create sCMOS camera calibration files.
#
numpy.save("calib.npy", [numpy.zeros((settings.y_size, settings.x_size)) + settings.camera_offset,
numpy.ones((settings.y_size, settings.x_size)) * settings.camera_variance,
numpy.ones((settings.y_size, settings.x_size)) * settings.camera_gain,
numpy.ones((settings.y_size, settings.x_size)),
2])
# Create mapping file.
with open("map.map", 'wb') as fp:
pickle.dump(settings.mappings, fp)
if no_splines:
return
multiplane_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/multi_plane/"
# Create pupil functions for 'pupilfn'.
if (psf_model == "pupilfn"):
pupilfn_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/pupilfn/"
print("Creating pupil functions.")
for i in range(len(settings.z_planes)):
subprocess.call(["python", pupilfn_path + "make_pupil_fn.py",
"--filename", "c" + str(i+1) + "_pupilfn.pfn",
"--size", str(settings.psf_size),
"--pixel-size", str(settings.pixel_size),
"--zmn", str(settings.pupil_fn),
"--z-offset", str(-settings.z_planes[i])])
# Both 'spline' and 'psf_fft' need measured PSFs.
else:
# Create localization files for PSF measurement.
#
locs = saH5Py.loadLocalizations("psf_list.hdf5")
for i, z_offset in enumerate(settings.z_planes):
cx = settings.mappings["0_" + str(i) + "_x"]
cy = settings.mappings["0_" + str(i) + "_y"]
locs_temp = {"x" : locs["x"].copy(),
"y" : locs["y"].copy(),
"z" : locs["z"].copy()}
xi = locs_temp["x"]
yi = locs_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
locs_temp["x"] = xf
locs_temp["y"] = yf
locs_temp["z"][:] = z_offset
saH5Py.saveLocalizations("c" + str(i+1) + "_psf.hdf5", locs_temp)
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-settings.spline_z_range, settings.spline_z_range + 0.001, 0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5 : background.UniformBackground(s, x, y, h5, photons = 10)
cam_f = lambda s, x, y, h5 : camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5 : drift.DriftFromFile(s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5 : photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5 : psf.PupilFunction(s, x, y, h5, settings.pixel_size, settings.pupil_fn)
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)
for i in range(len(settings.z_planes)):
sim.simulate("c" + str(i+1) + "_zcal.dax",
"c" + str(i+1) + "_psf.hdf5",
dz.size)
# Measure the PSF.
#
print("Measuring PSFs.")
psf_fft_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/psf_fft/"
spliner_path = os.path.dirname(inspect.getfile(storm_analysis)) + "/spliner/"
for i in range(len(settings.z_planes)):
subprocess.call(["python", multiplane_path + "psf_zstack.py",
"--movie", "c" + str(i+1) + "_zcal.dax",
"--bin", "c" + str(i+1) + "_psf.hdf5",
"--zstack", "c" + str(i+1) + "_zstack",
"--scmos_cal", "calib.npy",
"--aoi_size", str(int(settings.psf_size/2)+1)])
# Measure PSF and calculate spline for Spliner.
#
if (psf_model == "spline"):
# PSFs are independently normalized.
#
if settings.independent_heights:
for i in range(len(settings.z_planes)):
subprocess.call(["python", multiplane_path + "measure_psf.py",
"--zstack", "c" + str(i+1) + "_zstack.npy",
"--zoffsets", "z_offset.txt",
"--psf_name", "c" + str(i+1) + "_psf_normed.psf",
"--z_range", str(settings.spline_z_range),
"--normalize", "True"])
# PSFs are normalized to each other.
#
else:
for i in range(len(settings.z_planes)):
subprocess.call(["python", multiplane_path + "measure_psf.py",
"--zstack", "c" + str(i+1) + "_zstack.npy",
"--zoffsets", "z_offset.txt",
"--psf_name", "c" + str(i+1) + "_psf.psf",
"--z_range", str(settings.spline_z_range)])
norm_args = ["python", multiplane_path + "normalize_psfs.py",
"--psfs", "c1_psf.psf"]
for i in range(len(settings.z_planes)-1):
norm_args.append("c" + str(i+2) + "_psf.psf")
subprocess.call(norm_args)
# Measure the spline for Spliner.
#
print("Measuring Spline.")
for i in range(len(settings.z_planes)):
subprocess.call(["python", spliner_path + "psf_to_spline.py",
"--psf", "c" + str(i+1) + "_psf_normed.psf",
"--spline", "c" + str(i+1) + "_psf.spline",
"--spline_size", str(int(settings.psf_size/2))])
# Measure PSF and downsample for PSF FFT.
#
elif (psf_model == "psf_fft"):
# PSFs are independently normalized.
#
if settings.independent_heights:
for i in range(len(settings.z_planes)):
subprocess.call(["python", multiplane_path + "measure_psf.py",
"--zstack", "c" + str(i+1) + "_zstack.npy",
"--zoffsets", "z_offset.txt",
"--psf_name", "c" + str(i+1) + "_psf_normed.psf",
"--z_range", str(settings.psf_z_range),
"--z_step", str(settings.psf_z_step),
"--normalize", "True"])
# PSFs are normalized to each other.
#
else:
for i in range(len(settings.z_planes)):
subprocess.call(["python", multiplane_path + "measure_psf.py",
"--zstack", "c" + str(i+1) + "_zstack.npy",
"--zoffsets", "z_offset.txt",
"--psf_name", "c" + str(i+1) + "_psf.psf",
"--z_range", str(settings.psf_z_range),
"--z_step", str(settings.psf_z_step)])
norm_args = ["python", multiplane_path + "normalize_psfs.py",
"--psfs", "c1_psf.psf"]
for i in range(len(settings.z_planes)-1):
norm_args.append("c" + str(i+2) + "_psf.psf")
subprocess.call(norm_args)
# Calculate Cramer-Rao weighting.
#
print("Calculating weights.")
subprocess.call(["python", multiplane_path + "plane_weighting.py",
"--background", str(settings.photons[0][0]),
"--photons", str(settings.photons[0][1]),
"--output", "weights.npy",
"--xml", "multiplane.xml",
"--no_plots"])
def makeSampleData(mappings = None):
# Create sample bead data for mapping measurement.
#
# Create randomly located localizations file (for STORM movies).
#
print("Creating random localizations.")
emittersUniformRandom.emittersUniformRandom("random.hdf5", density, margin, x_size, y_size, 0.0)
# Create mapping, if not specified.
#
if mappings is None:
mappings = {"0_0_x" : numpy.array([0.0, 1.0, 0.0]),
"0_0_y" : numpy.array([0.0, 0.0, 1.0]),
"0_1_x" : numpy.array([2.0, 1.0, 0.0]),
"0_1_y" : numpy.array([5.0, 0.0, 1.0]),
"1_0_x" : numpy.array([-2.0, 1.0, 0.0]),
"1_0_y" : numpy.array([-5.0, 0.0, 1.0])}
# Figure out number of planes in the mapping.
#
n_planes = 0
for elt in mappings:
[i, j] = map(int, elt.split("_")[:2])
if (i > n_planes):
n_planes = i
n_planes += 1
print(n_planes)
# Create localization files for PSF measurement.
#
locs = saH5Py.loadLocalizations("random.hdf5")
for i in range(n_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
locs_temp = {"x" : locs["x"].copy(),
"y" : locs["y"].copy(),
"z" : locs["z"].copy()}
xi = locs_temp["x"]
yi = locs_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
locs_temp["x"] = xf
locs_temp["y"] = yf
saH5Py.saveLocalizations("c" + str(i+1) + "_map.hdf5", locs_temp)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5 : background.UniformBackground(s, x, y, h5, photons = 10)
cam_f = lambda s, x, y, h5 : camera.SCMOS(s, x, y, h5, "calib.npy")
pp_f = lambda s, x, y, h5 : photophysics.AlwaysOn(s, x, y, h5, 10000.0)
psf_f = lambda s, x, y, h5 : psf.PupilFunction(s, x, y, h5, pixel_size, [])
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = x_size,
y_size = y_size)
for i in range(n_planes):
sim.simulate("c" + str(i+1) + "_map.dax", "c" + str(i+1) + "_map.hdf5", 2)
def makeData():
index = 1
if True:
# Create .bin files for each plane.
h5_locs = saH5Py.loadLocalizations("grid_list.hdf5")
# Load channel to channel mapping file.
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
for i, z_plane in enumerate(settings.z_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
xi = h5_locs["x"].copy()
yi = h5_locs["y"].copy()
zi = h5_locs["z"].copy()
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
zf = zi + z_plane
h5_temp = {"x": xf, "y": yf, "z": zf}
saH5Py.saveLocalizations("sim_input_c" + str(i + 1) + ".hdf5",
h5_temp)
# Create a movie for each plane.
for [bg, photons] in settings.photons:
# Adjust photons by the number of planes.
photons = photons / float(len(settings.z_planes))
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3: background.UniformBackground(
s, x, y, i3, photons=bg)
cam_f = lambda s, x, y, i3: camera.SCMOS(s, x, y, i3, "calib.npy")
pp_f = lambda s, x, y, i3: photophysics.AlwaysOn(
s, x, y, i3, photons)
psf_f = lambda s, x, y, i3: psf.PupilFunction(
s, x, y, i3, settings.pixel_size, settings.pupil_fn)
sim = simulate.Simulate(background_factory=bg_f,
camera_factory=cam_f,
photophysics_factory=pp_f,
psf_factory=psf_f,
x_size=settings.x_size,
y_size=settings.y_size)
for i in range(len(settings.z_planes)):
sim.simulate(wdir + "/test_c" + str(i + 1) + ".dax",
"sim_input_c" + str(i + 1) + ".hdf5",
settings.n_frames)
index += 1
# Create "peak_locations" file if needed.
#
if hasattr(settings, "peak_locations") and (settings.peak_locations
is not None):
with saH5Py.SAH5Py("test_01/test_c1_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
if settings.peak_locations.endswith(".hdf5"):
saH5Py.saveLocalizations(settings.peak_locations, locs)
else:
numpy.savetxt(
settings.peak_locations,
numpy.transpose(
numpy.vstack((locs['x'], locs['y'], locs['height'],
locs['background']))))
def configure():
# Create analysis XML files.
#
print("Creating XML files.")
params = testingParametersSCMOS()
params.toXMLFile("scmos.xml")
params = testingParametersMC()
params.toXMLFile("multicolor.xml")
# Useful variables
aoi_size = int(settings.psf_size / 2) + 1
# Create sCMOS data and HDF5 files we'll need for the simulation.
#
if True:
# Create sCMOS camera calibration files.
#
numpy.save("calib.npy", [
numpy.zeros(
(settings.y_size, settings.x_size)) + settings.camera_offset,
numpy.ones(
(settings.y_size, settings.x_size)) * settings.camera_variance,
numpy.ones(
(settings.y_size, settings.x_size)) * settings.camera_gain,
numpy.ones((settings.y_size, settings.x_size)), 2
])
# Create localization on a grid file.
#
print("Creating gridded localizations.")
emittersOnGrid.emittersOnGrid("grid_list.hdf5", settings.nx,
settings.ny, 1.5, 20,
settings.test_z_range,
settings.test_z_offset)
# Create randomly located localizations file (for STORM movies).
#
print("Creating random localizations.")
emittersUniformRandom.emittersUniformRandom("random_storm.hdf5", 1.0,
settings.margin,
settings.x_size,
settings.y_size,
settings.test_z_range)
# Create randomly located localizations file (for mapping measurement).
#
print("Creating random localizations.")
emittersUniformRandom.emittersUniformRandom("random_map.hdf5", 0.0003,
settings.margin,
settings.x_size,
settings.y_size,
settings.test_z_range)
# Create sparser grid for PSF measurement.
#
print("Creating data for PSF measurement.")
emittersOnGrid.emittersOnGrid("psf_list.hdf5", 6, 3, 1.5, 40, 0.0, 0.0)
## This part makes / tests measuring the mapping.
##
if True:
print("Measuring mapping.")
# Make localization files for simulations.
#
locs = saH5Py.loadLocalizations("random_map.hdf5")
locs["z"][:] = 1.0e-3 * settings.z_planes[0]
saH5Py.saveLocalizations("c1_random_map.hdf5", locs)
for i in range(1, 4):
locs["x"] += settings.dx
locs["y"] += settings.dy
locs["z"][:] = settings.z_planes[i]
saH5Py.saveLocalizations("c" + str(i + 1) + "_random_map.hdf5",
locs)
# Make localization files for simulations.
#
locs = saH5Py.loadLocalizations("random_map.hdf5")
locs["z"][:] = 1.0e-3 * settings.z_planes[0]
saH5Py.saveLocalizations("c1_random_map.hdf5", locs)
for i in range(1, 4):
locs["x"] += settings.dx
locs["y"] += settings.dy
locs["z"][:] = settings.z_planes[i]
saH5Py.saveLocalizations("c" + str(i + 1) + "_random_map.hdf5",
locs)
# Make simulated mapping data.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 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,
x_size=settings.x_size,
y_size=settings.y_size)
for i in range(4):
sim.simulate("c" + str(i + 1) + "_map.dax",
"c" + str(i + 1) + "_random_map.hdf5", 1)
# Analyze simulated mapping data
#
for i in range(4):
h5_name = "c" + str(i + 1) + "_map.hdf5"
if os.path.exists(h5_name):
os.remove(h5_name)
scmos.analyze("c" + str(i + 1) + "_map.dax", h5_name, "scmos.xml")
# Measure mapping.
#
for i in range(3):
micrometry.runMicrometry("c1_map.hdf5",
"c" + str(i + 2) + "_map.hdf5",
"c1_c" + str(i + 2) + "_map.map",
min_size=5.0,
max_size=100.0,
max_neighbors=20,
tolerance=1.0e-2,
no_plots=True)
# Merge mapping and save results.
#
merged_map = mergeMaps.mergeMaps(
["c1_c2_map.map", "c1_c3_map.map", "c1_c4_map.map"])
with open("map.map", 'wb') as fp:
pickle.dump(merged_map, fp)
# Print mapping.
#
if True:
print("Mapping is:")
printMapping.printMapping("map.map")
print("")
# Check that mapping is close to what we expect (within 5%).
#
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
for i in range(3):
if not numpy.allclose(mappings["0_" + str(i + 1) + "_x"],
numpy.array(
[settings.dx * (i + 1), 1.0, 0.0]),
rtol=0.05,
atol=0.05):
print("X mapping difference for channel", i + 1)
if not numpy.allclose(mappings["0_" + str(i + 1) + "_y"],
numpy.array(
[settings.dy * (i + 1), 0.0, 1.0]),
rtol=0.05,
atol=0.05):
print("Y mapping difference for channel", i + 1)
## This part measures / test the PSF measurement.
##
if True:
# 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 + 0.05,
0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5: drift.DriftFromFile(
s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5: psf.PupilFunction(s, x, y, h5, settings.
pixel_size, [])
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:
for i in range(4):
sim.simulate("c" + str(i + 1) + "_zcal.dax",
"c" + str(i + 1) + "_random_map.hdf5", dz.size)
# Get localizations to use for PSF measurement.
#
psfLocalizations.psfLocalizations("c1_map_ref.hdf5",
"map.map",
aoi_size=aoi_size)
# Create PSF z stacks.
#
for i in range(4):
psfZStack.psfZStack("c" + str(i + 1) + "_zcal.dax",
"c1_map_ref_c" + str(i + 1) + "_psf.hdf5",
"c" + str(i + 1) + "_zstack",
aoi_size=aoi_size)
# Measure PSF.
#
for i in range(4):
mpMeasurePSF.measurePSF("c" + str(i + 1) + "_zstack.npy",
"z_offset.txt",
"c" + str(i + 1) + "_psf_normed.psf",
z_range=settings.psf_z_range,
normalize=True)
## This part creates the splines.
##
if True:
print("Measuring Splines.")
for i in range(4):
psfToSpline.psfToSpline("c" + str(i + 1) + "_psf_normed.psf",
"c" + str(i + 1) + "_psf.spline",
int(settings.psf_size / 2))
## This part measures the Cramer-Rao weights.
##
if True:
print("Calculating weights.")
planeWeighting.runPlaneWeighting("multicolor.xml",
"weights.npy",
[settings.photons[0][0]],
settings.photons[0][1],
no_plots=True)
def configure(psf_model, no_splines):
# Create parameters file for analysis.
#
print("Creating XML file.")
params = testingParameters(psf_model)
params.toXMLFile("multiplane.xml")
# Create localization on a grid file.
#
print("Creating gridded localization.")
emittersOnGrid.emittersOnGrid("grid_list.hdf5", settings.nx, settings.ny,
1.5, 20, settings.test_z_range,
settings.test_z_offset)
# Create randomly located localizations file.
#
print("Creating random localization.")
emittersUniformRandom.emittersUniformRandom("random_list.hdf5", 1.0,
settings.margin,
settings.x_size,
settings.y_size,
settings.test_z_range)
# Create sparser grid for PSF measurement.
#
print("Creating data for PSF measurement.")
emittersOnGrid.emittersOnGrid("psf_list.hdf5", 6, 3, 1.5, 40, 0.0, 0.0)
# Create sCMOS camera calibration files.
#
numpy.save("calib.npy", [
numpy.zeros(
(settings.y_size, settings.x_size)) + settings.camera_offset,
numpy.ones(
(settings.y_size, settings.x_size)) * settings.camera_variance,
numpy.ones((settings.y_size, settings.x_size)) * settings.camera_gain,
numpy.ones((settings.y_size, settings.x_size)), 2
])
# Create mapping file.
with open("map.map", 'wb') as fp:
pickle.dump(settings.mappings, fp)
if no_splines:
return
# Create pupil functions for 'pupilfn'.
if (psf_model == "pupilfn"):
print("Creating pupil functions.")
for i in range(len(settings.z_planes)):
makePupilFn.makePupilFunction("c" + str(i + 1) + "_pupilfn.pfn",
settings.psf_size,
settings.pixel_size * 1.0e-3,
settings.pupil_fn,
z_offset=-settings.z_planes[i])
# Both 'spline' and 'psf_fft' need measured PSFs.
else:
# Create localization files for PSF measurement.
#
locs = saH5Py.loadLocalizations("psf_list.hdf5")
for i, z_offset in enumerate(settings.z_planes):
cx = settings.mappings["0_" + str(i) + "_x"]
cy = settings.mappings["0_" + str(i) + "_y"]
locs_temp = {
"x": locs["x"].copy(),
"y": locs["y"].copy(),
"z": locs["z"].copy()
}
xi = locs_temp["x"]
yi = locs_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
locs_temp["x"] = xf
locs_temp["y"] = yf
locs_temp["z"][:] = z_offset
saH5Py.saveLocalizations("c" + str(i + 1) + "_psf.hdf5", locs_temp)
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-settings.spline_z_range,
settings.spline_z_range + 0.001, 0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5: drift.DriftFromFile(
s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5: psf.PupilFunction(
s, x, y, h5, settings.pixel_size, settings.pupil_fn)
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)
for i in range(len(settings.z_planes)):
sim.simulate("c" + str(i + 1) + "_zcal.dax",
"c" + str(i + 1) + "_psf.hdf5", dz.size)
# Measure the PSF.
#
print("Measuring PSFs.")
for i in range(len(settings.z_planes)):
psfZStack.psfZStack("c" + str(i + 1) + "_zcal.dax",
"c" + str(i + 1) + "_psf.hdf5",
"c" + str(i + 1) + "_zstack",
aoi_size=int(settings.psf_size / 2 + 1))
# Measure PSF and calculate spline for Spliner.
#
if (psf_model == "spline"):
# PSFs are independently normalized.
#
if settings.independent_heights:
for i in range(len(settings.z_planes)):
mpMeasurePSF.measurePSF("c" + str(i + 1) + "_zstack.npy",
"z_offset.txt",
"c" + str(i + 1) + "_psf_normed.psf",
z_range=settings.spline_z_range,
normalize=True)
# PSFs are normalized to each other.
#
else:
for i in range(len(settings.z_planes)):
mpMeasurePSF.measurePSF("c" + str(i + 1) + "_zstack.npy",
"z_offset.txt",
"c" + str(i + 1) + "_psf.psf",
z_range=settings.spline_z_range)
norm_args = ["c1_psf.psf"]
for i in range(len(settings.z_planes) - 1):
norm_args.append("c" + str(i + 2) + "_psf.psf")
normalizePSFs.normalizePSFs(norm_args)
# Measure the spline for Spliner.
#
print("Measuring Spline.")
for i in range(len(settings.z_planes)):
psfToSpline.psfToSpline("c" + str(i + 1) + "_psf_normed.psf",
"c" + str(i + 1) + "_psf.spline",
int(settings.psf_size / 2))
# Measure PSF and downsample for PSF FFT.
#
elif (psf_model == "psf_fft"):
# PSFs are independently normalized.
#
if settings.independent_heights:
for i in range(len(settings.z_planes)):
mpMeasurePSF.measurePSF("c" + str(i + 1) + "_zstack.npy",
"z_offset.txt",
"c" + str(i + 1) + "_psf_normed.psf",
z_range=settings.spline_z_range,
normalize=True)
# PSFs are normalized to each other.
#
else:
for i in range(len(settings.z_planes)):
mpMeasurePSF.measurePSF("c" + str(i + 1) + "_zstack.npy",
"z_offset.txt",
"c" + str(i + 1) + "_psf.psf",
z_range=settings.spline_z_range)
norm_args = ["c1_psf.psf"]
for i in range(len(settings.z_planes) - 1):
norm_args.append("c" + str(i + 2) + "_psf.psf")
normalizePSFs.normalizePSFs(norm_args)
# Calculate Cramer-Rao weighting.
#
print("Calculating weights.")
planeWeighting.runPlaneWeighting("multiplane.xml",
"weights.npy", [settings.photons[0][0]],
settings.photons[0][1],
no_plots=True)
def makeSampleData():
# Create sample bead data for PSF measurement.
#
# Create sparser grid for PSF measurement.
#
print("Creating data for PSF measurement.")
emittersOnGrid.emittersOnGrid("psf_locs.hdf5", 6, 3, 1.5, 40, 0.0, 0.0)
# Create localization files for PSF measurement.
#
locs = saH5Py.loadLocalizations("psf_locs.hdf5")
for i, z_offset in enumerate(z_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
locs_temp = {"x" : locs["x"].copy(),
"y" : locs["y"].copy(),
"z" : locs["z"].copy()}
xi = locs_temp["x"]
yi = locs_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
locs_temp["x"] = xf
locs_temp["y"] = yf
locs_temp["z"][:] = z_offset
saH5Py.saveLocalizations("c" + str(i+1) + "_psf.hdf5", locs_temp)
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-spline_z_range, spline_z_range + 0.001, 0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5 : background.UniformBackground(s, x, y, h5, photons = 10)
cam_f = lambda s, x, y, h5 : camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5 : drift.DriftFromFile(s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5 : photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5 : psf.PupilFunction(s, x, y, h5, pixel_size, [])
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 = x_size,
y_size = y_size)
for i in range(len(z_planes)):
sim.simulate("c" + str(i+1) + "_zcal.dax",
"c" + str(i+1) + "_psf.hdf5",
dz.size)
def makeSampleData():
# Create sample bead data for PSF measurement.
#
# Create sparser grid for PSF measurement.
#
print("Creating data for PSF measurement.")
emittersOnGrid.emittersOnGrid("psf_locs.hdf5", 6, 3, 1.5, 40, 0.0, 0.0)
# Create localization files for PSF measurement.
#
locs = saH5Py.loadLocalizations("psf_locs.hdf5")
for i, z_offset in enumerate(z_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
locs_temp = {
"x": locs["x"].copy(),
"y": locs["y"].copy(),
"z": locs["z"].copy()
}
xi = locs_temp["x"]
yi = locs_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
locs_temp["x"] = xf
locs_temp["y"] = yf
locs_temp["z"][:] = z_offset
saH5Py.saveLocalizations("c" + str(i + 1) + "_psf.hdf5", locs_temp)
# Create drift file, this is used to displace the localizations in the
# PSF measurement movie.
#
dz = numpy.arange(-spline_z_range, spline_z_range + 0.001, 0.01)
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)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
drift_f = lambda s, x, y, h5: drift.DriftFromFile(s, x, y, h5, "drift.txt")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 20000.0)
psf_f = lambda s, x, y, h5: psf.PupilFunction(s, x, y, h5, pixel_size, [])
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=x_size,
y_size=y_size)
for i in range(len(z_planes)):
sim.simulate("c" + str(i + 1) + "_zcal.dax",
"c" + str(i + 1) + "_psf.hdf5", dz.size)
def makeSampleData(mappings=None):
# Create sample bead data for mapping measurement.
#
# Create randomly located localizations file (for STORM movies).
#
print("Creating random localizations.")
emittersUniformRandom.emittersUniformRandom("random.hdf5", density, margin,
x_size, y_size, 0.0)
# Create mapping, if not specified.
#
if mappings is None:
mappings = {
"0_0_x": numpy.array([0.0, 1.0, 0.0]),
"0_0_y": numpy.array([0.0, 0.0, 1.0]),
"0_1_x": numpy.array([2.0, 1.0, 0.0]),
"0_1_y": numpy.array([5.0, 0.0, 1.0]),
"1_0_x": numpy.array([-2.0, 1.0, 0.0]),
"1_0_y": numpy.array([-5.0, 0.0, 1.0])
}
# Figure out number of planes in the mapping.
#
n_planes = 0
for elt in mappings:
[i, j] = map(int, elt.split("_")[:2])
if (i > n_planes):
n_planes = i
n_planes += 1
print(n_planes)
# Create localization files for PSF measurement.
#
locs = saH5Py.loadLocalizations("random.hdf5")
for i in range(n_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
locs_temp = {
"x": locs["x"].copy(),
"y": locs["y"].copy(),
"z": locs["z"].copy()
}
xi = locs_temp["x"]
yi = locs_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
locs_temp["x"] = xf
locs_temp["y"] = yf
saH5Py.saveLocalizations("c" + str(i + 1) + "_map.hdf5", locs_temp)
# Create simulated data for PSF measurements.
#
bg_f = lambda s, x, y, h5: background.UniformBackground(
s, x, y, h5, photons=10)
cam_f = lambda s, x, y, h5: camera.SCMOS(s, x, y, h5, "calib.npy")
pp_f = lambda s, x, y, h5: photophysics.AlwaysOn(s, x, y, h5, 10000.0)
psf_f = lambda s, x, y, h5: psf.PupilFunction(s, x, y, h5, pixel_size, [])
sim = simulate.Simulate(background_factory=bg_f,
camera_factory=cam_f,
photophysics_factory=pp_f,
psf_factory=psf_f,
x_size=x_size,
y_size=y_size)
for i in range(n_planes):
sim.simulate("c" + str(i + 1) + "_map.dax",
"c" + str(i + 1) + "_map.hdf5", 2)
def makeData():
index = 1
if True:
# Create localization files for each plane.
h5_locs = saH5Py.loadLocalizations("grid_list.hdf5")
# Load channel to channel mapping file.
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
for i, z_plane in enumerate(settings.z_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
xi = h5_locs["x"].copy()
yi = h5_locs["y"].copy()
zi = h5_locs["z"].copy()
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
zf = zi + z_plane
h5_temp = {"x" : xf,
"y" : yf,
"z" : zf}
saH5Py.saveLocalizations("sim_input_c" + str(i+1) + ".hdf5", h5_temp)
# Create a movie for each plane.
for [bg, photons] in settings.photons:
# Adjust photons by the number of planes.
photons = photons/float(len(settings.z_planes))
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.SCMOS(s, x, y, i3, "calib.npy")
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, photons)
psf_f = lambda s, x, y, i3 : psf.PupilFunction(s, x, y, i3, settings.pixel_size, settings.pupil_fn)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = settings.x_size,
y_size = settings.y_size)
for i in range(len(settings.z_planes)):
sim.simulate(wdir + "/test_c" + str(i+1) + ".dax",
"sim_input_c" + str(i+1) + ".hdf5",
settings.n_frames)
index += 1
# Create "peak_locations" file if needed.
#
if hasattr(settings, "peak_locations") and (settings.peak_locations is not None):
with saH5Py.SAH5Py("test_01/test_c1_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
if settings.peak_locations.endswith(".hdf5"):
saH5Py.saveLocalizations(settings.peak_locations, locs)
else:
numpy.savetxt(settings.peak_locations,
numpy.transpose(numpy.vstack((locs['x'],
locs['y'],
locs['height'],
locs['background']))))
else:
curz = 0.0
z_inc = 0.0
peaks = {
"id": numpy.zeros(nx * ny, dtype=numpy.int32),
"x": numpy.zeros(nx * ny),
"y": numpy.zeros(nx * ny),
"z": numpy.zeros(nx * ny),
"xsigma": 1.5 * numpy.ones(nx * ny),
"ysigma": 1.5 * numpy.ones(nx * ny)
}
curx = spacing
for i in range(nx):
cury = spacing
for j in range(ny):
k = i * ny + j
peaks['x'][k] = curx + random.random() - 0.5
peaks['y'][k] = cury + random.random() - 0.5
peaks['z'][k] = curz + args.zoffset
# Record emitter id in the 'id' field.
peaks['id'][k] = k
cury += spacing
curz += z_inc
curx += spacing
saH5Py.saveLocalizations(args.hdf5, peaks)
def makeData():
# Create .bin files for each plane.
h5_locs = saH5Py.loadLocalizations("grid_list.hdf5")
# Load channel to channel mapping file.
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
# Add z offset to reference localizations.
x = h5_locs["x"].copy()
y = h5_locs["y"].copy()
z = h5_locs["z"].copy() + settings.z_planes[0]
h5_temp = {"x" : x,
"y" : y,
"z" : z}
saH5Py.saveLocalizations("sim_input_c1.hdf5", h5_temp)
# Create a movie for first plane.
[bg, photons] = settings.photons
# Adjust photons by the number of planes.
photons = photons/float(len(settings.z_planes))
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.SCMOS(s, x, y, i3, "calib.npy")
pp_f = lambda s, x, y, i3 : photophysics.SimpleSTORM(s, x, y, i3,
photons = photons,
on_time = settings.on_time,
off_time = settings.off_time)
psf_f = lambda s, x, y, i3 : psf.PupilFunction(s, x, y, i3, settings.pixel_size, settings.pupil_fn)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(os.path.join(settings.wdir, "test_c1.dax"),
"sim_input_c1.hdf5",
settings.n_frames)
# Create other movies.
for i in range(1, len(settings.z_planes)):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
z_offset = settings.z_planes[i] - settings.z_planes[0]
pp_f = lambda s, x, y, i3 : photophysics.Duplicate(s, x, y, i3,
h5_name = os.path.join(settings.wdir, "test_c1_ref.hdf5"),
cx = cx, cy = cy, z_offset = z_offset)
sim = simulate.Simulate(background_factory = bg_f,
camera_factory = cam_f,
photophysics_factory = pp_f,
psf_factory = psf_f,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(os.path.join(settings.wdir, "test_c" + str(i+1) + ".dax"),
"sim_input_c1.hdf5", # This is not actually used.
settings.n_frames)
# Remove any old XML files.
for elt in glob.glob(os.path.join(settings.wdir, "job*.xml")):
os.remove(elt)
# Make analysis XML files.
splitAnalysisXML.splitAnalysisXML(settings.wdir, "multiplane.xml", 0, settings.n_frames, settings.divisions)
# Load channel to channel mapping file.
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
for i, z_plane in enumerate(settings.z_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
xi = h5_locs["x"].copy()
yi = h5_locs["y"].copy()
zi = h5_locs["z"].copy()
xf = cx[0] + cx[1] * xi + cx[2] * yi
yf = cy[0] + cy[1] * xi + cy[2] * yi
zf = zi + z_plane
h5_temp = {"x": xf, "y": yf, "z": zf}
saH5Py.saveLocalizations("sim_input_c" + str(i + 1) + ".hdf5", h5_temp)
# Create a movie for each plane.
for [bg, photons] in settings.photons:
# Adjust photons by the number of planes.
photons = photons / float(len(settings.z_planes))
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3: background.UniformBackground(
s, x, y, i3, photons=bg)
cam_f = lambda s, x, y, i3: camera.SCMOS(s, x, y, i3, "calib.npy")
def makeData(dither = False):
index = 1
# Gaussian PSF, uniform background.
if True:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, photons)
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 = dither,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(wdir + "/test.dax", "grid_list.hdf5", settings.n_frames)
index += 1
# Pupil Function PSF.
if False:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, photons)
psf_f = lambda s, x, y, i3 : psf.PupilFunction(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,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(wdir + "/test.dax", "grid_list.hdf5", settings.n_frames)
index += 1
# Gaussian non-uniform background, always on.
if False:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.GaussianBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, photons)
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,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(wdir + "/test.dax", "grid_list.hdf5", settings.n_frames)
index += 1
# Uniform background, STORM.
if False:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.UniformBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.SimpleSTORM(s, x, y, i3, photons)
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,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(wdir + "/test.dax", "random_list.hdf5", settings.n_frames)
index += 1
# Gaussian non-uniform background, STORM.
if False:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.GaussianBackground(s, x, y, i3, photons = bg)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.SimpleSTORM(s, x, y, i3, photons)
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,
x_size = settings.x_size,
y_size = settings.y_size)
sim.simulate(wdir + "/test.dax", "random_list.hdf5", settings.n_frames)
index += 1
# Sloped non-uniform background, always on.
if False:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.SlopedBackground(s, x, y, i3, slope = 0.4, offset = 10)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, photons)
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(wdir + "/test.dax", "grid_list.hdf5", settings.n_frames)
index += 1
# Sine non-uniform background, always on.
if False:
for [bg, photons] in settings.photons:
wdir = "test_{0:02d}".format(index)
print(wdir)
if not os.path.exists(wdir):
os.makedirs(wdir)
bg_f = lambda s, x, y, i3 : background.SineBackground(s, x, y, i3, photons = bg, period = 45)
cam_f = lambda s, x, y, i3 : camera.Ideal(s, x, y, i3, settings.camera_offset)
pp_f = lambda s, x, y, i3 : photophysics.AlwaysOn(s, x, y, i3, photons)
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(wdir + "/test.dax", "grid_list.hdf5", settings.n_frames)
index += 1
# Create "peak_locations" file if needed.
#
if hasattr(settings, "peak_locations") and (settings.peak_locations is not None):
with saH5Py.SAH5Py("test_01/test_ref.hdf5") as h5:
locs = h5.getLocalizationsInFrame(0)
if settings.peak_locations.endswith(".hdf5"):
saH5Py.saveLocalizations(settings.peak_locations, locs)
else:
numpy.savetxt(settings.peak_locations,
numpy.transpose(numpy.vstack((locs['x'],
locs['y'],
locs['height'],
locs['background']))))
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("")
