def test_hdf5_to_image_4():
"""
Test category (using tracks).
"""
tracks = {
"category": [1, 2],
"x": numpy.array([10.0, 20.0]),
"y": numpy.array([20.0, 10.0])
}
h5_name = storm_analysis.getPathOutputTest("test_hdf5_to_image.hdf5")
storm_analysis.removeFile(h5_name)
# Write data (as peaks).
with saH5Py.SAH5Py(h5_name, is_existing=False, overwrite=True) as h5:
h5.setMovieInformation(40, 30, 1, "")
h5.addTracks(tracks)
# Render image.
image = hdf5ToImage.render2DImage(h5_name, category=1, scale=1)
assert (image.shape[0] == 30)
assert (image.shape[1] == 40)
assert (image[10, 20] == 0)
assert (image[20, 10] == 1)
def test_hdf5_to_image_3():
"""
Test simple 2D image versus gaussian image.
"""
tracks = {"x": numpy.array([10]), "y": numpy.array([20])}
h5_name = storm_analysis.getPathOutputTest("test_hdf5_to_image.hdf5")
storm_analysis.removeFile(h5_name)
# Write data (as peaks).
with saH5Py.SAH5Py(h5_name, is_existing=False, overwrite=True) as h5:
h5.setMovieInformation(40, 30, 1, "")
h5.addTracks(tracks)
# Render image.
im_grid = hdf5ToImage.render2DImage(h5_name, scale=1)
im_gauss = hdf5ToImage.render2DImage(h5_name, scale=1, sigma=0.25)
assert (numpy.allclose(im_grid, im_gauss, atol=1.0e-3, rtol=1.0e-3))
def test_hdf5_to_image_3():
"""
Test simple 2D image versus gaussian image.
"""
tracks = {"x" : numpy.array([10]),
"y" : numpy.array([20])}
h5_name = storm_analysis.getPathOutputTest("test_hdf5_to_image.hdf5")
storm_analysis.removeFile(h5_name)
# Write data (as peaks).
with saH5Py.SAH5Py(h5_name, is_existing = False, overwrite = True) as h5:
h5.setMovieInformation(40,30,1,"")
h5.addTracks(tracks)
# Render image.
im_grid = hdf5ToImage.render2DImage(h5_name, scale = 1)
im_gauss = hdf5ToImage.render2DImage(h5_name, scale = 1, sigma = 0.25)
assert(numpy.allclose(im_grid, im_gauss, atol = 1.0e-3, rtol = 1.0e-3))
def collate():
# We assume that there are two directories, the first with xy correction
# only and the second with xyz correction.
#
# Make 2D images of the drift corrected data.
if False:
for adir in ["test_01/", "test_02/"]:
im = hdf5ToImage.render2DImage(adir + "test.hdf5", sigma=0.5)
tifffile.imsave(adir + "test_im_2d.tif", im.astype(numpy.float32))
# Make 3D images of the drift corrected data.
if False:
z_edges = numpy.arange(-0.5, 0.55, 0.1)
for adir in ["test_01/", "test_02/"]:
images = hdf5ToImage.render3DImage(adir + "test.hdf5",
z_edges,
sigma=0.5)
with tifffile.TiffWriter(adir + "test_im_3d.tif") as tf:
for elt in images:
tf.save(elt.astype(numpy.float32))
# Measure error in drift measurements.
if True:
print("Drift correction RMS error (nanometers):")
for i, elt in enumerate(["drift_xy.txt", "drift_xyz.txt"]):
print(" ", elt)
ref = numpy.loadtxt(elt)
exp = numpy.loadtxt("test_{0:02}/test_drift.txt".format(i + 1))
max_len = exp.shape[0]
for j, elt in enumerate(["X", "Y", "Z"]):
refv = ref[:max_len, j]
expv = exp[:, j + 1]
# Correct for DC offset.
expv += numpy.mean(refv - expv)
# Calculate error (in nanometers).
if (elt == "Z"):
print(" ", elt,
"{0:.3f}".format(numpy.std(refv - expv) * 1.0e+3))
else:
print(
" ", elt, "{0:.3f}".format(
numpy.std(refv - expv) * settings.pixel_size))
print()
def collate():
# We assume that there are two directories, the first with xy correction
# only and the second with xyz correction.
#
# Make 2D images of the drift corrected data.
if False:
for adir in ["test_01/", "test_02/"]:
im = hdf5ToImage.render2DImage(adir + "test.hdf5", sigma = 0.5)
tifffile.imsave(adir + "test_im_2d.tif", im.astype(numpy.float32))
# Make 3D images of the drift corrected data.
if False:
z_edges = numpy.arange(-0.5, 0.55, 0.1)
for adir in ["test_01/", "test_02/"]:
images = hdf5ToImage.render3DImage(adir + "test.hdf5", z_edges, sigma = 0.5)
with tifffile.TiffWriter(adir + "test_im_3d.tif") as tf:
for elt in images:
tf.save(elt.astype(numpy.float32))
# Measure error in drift measurements.
if True:
print("Drift correction RMS error (nanometers):")
for i, elt in enumerate(["drift_xy.txt", "drift_xyz.txt"]):
print(" ", elt)
ref = numpy.loadtxt(elt)
exp = numpy.loadtxt("test_{0:02}/test_drift.txt".format(i+1))
max_len = exp.shape[0]
for j, elt in enumerate(["X", "Y", "Z"]):
refv = ref[:max_len,j]
expv = exp[:,j+1]
# Correct for DC offset.
expv += numpy.mean(refv - expv)
# Calculate error (in nanometers).
if (elt == "Z"):
print(" ", elt, "{0:.3f}".format(numpy.std(refv - expv) * 1.0e+3))
else:
print(" ", elt, "{0:.3f}".format(numpy.std(refv - expv) * settings.pixel_size))
print()
def test_hdf5_to_image_1():
"""
Test simple 2D image (using localizations).
"""
peaks = {"x": numpy.array([10.0]), "y": numpy.array([20.0])}
h5_name = storm_analysis.getPathOutputTest("test_hdf5_to_image.hdf5")
storm_analysis.removeFile(h5_name)
# Write data (as peaks).
with saH5Py.SAH5Py(h5_name, is_existing=False, overwrite=True) as h5:
h5.setMovieInformation(40, 30, 1, "")
h5.addLocalizations(peaks, 0)
# Render image.
image = hdf5ToImage.render2DImage(h5_name, scale=1)
assert (image.shape[0] == 30)
assert (image.shape[1] == 40)
assert (image[10, 20] == 0)
assert (image[20, 10] == 1)
def test_hdf5_to_image_2():
"""
Test simple 2D image (using tracks).
"""
tracks = {"x" : numpy.array([10.0]),
"y" : numpy.array([20.0])}
h5_name = storm_analysis.getPathOutputTest("test_hdf5_to_image.hdf5")
storm_analysis.removeFile(h5_name)
# Write data (as peaks).
with saH5Py.SAH5Py(h5_name, is_existing = False, overwrite = True) as h5:
h5.setMovieInformation(40,30,1,"")
h5.addTracks(tracks)
# Render image.
image = hdf5ToImage.render2DImage(h5_name, scale = 1)
assert(image.shape[0] == 30)
assert(image.shape[1] == 40)
assert(image[10,20] == 0)
assert(image[20,10] == 1)