def collateRQE(dirs, settings):
"""
Results collation for RQE correction.
"""
for a_dir in dirs:
print("Processing", a_dir)
t_locs = saH5Py.loadLocalizations("grid_list.hdf5", fields=["x", "y"])
t_locs_found = numpy.zeros_like(t_locs["x"])
n_frames = 0
with saH5Py.SAH5Py(os.path.join(a_dir, "test.hdf5")) as h5:
for i in range(h5.getMovieLength()):
n_frames += 1
m_locs = h5.getLocalizationsInFrame(i, fields=["x", "y"])
dist = iaUtilsC.peakToPeakDistAndIndex(t_locs['x'],
t_locs['y'],
m_locs['x'],
m_locs['y'],
max_distance=3)[0]
for j in range(dist.size):
if (dist[j] > -0.1):
t_locs_found[j] += 1
# Check results against the binomial distribution.
p = numpy.sum(t_locs_found) / (n_frames * t_locs["x"].size)
print(" Mean P found : {0:.3f}".format(p))
print(" Expected variance: {0:.3f}".format(n_frames * p * (1 - p)))
print(" Actual variance : {0:.3f}".format(numpy.var(t_locs_found)))
print()
def fitzTracks(h5_name, cutoff, wx_params, wy_params, z_min, z_max, z_step):
"""
This processes the tracked localizations.
Note: Localizations whose wx/wy values are too far from the calibration
curve will be given a z value that is less than z_min and also
assigned to category 9.
"""
zfit_data = c_fitz.initialize(numpy.ascontiguousarray(wx_params),
numpy.ascontiguousarray(wy_params),
z_min * 1000.0, z_max * 1000.0,
z_step * 1000.0, cutoff)
# Fit tracked localizations & save z value (in microns).
with saH5Py.SAH5Py(h5_name) as h5:
pixel_size = h5.getPixelSize()
for index, locs in enumerate(h5.tracksIterator()):
z_vals = numpy.zeros(locs["xsigma"].size, dtype=numpy.float64)
for i in range(locs["xsigma"].size):
wx = pixel_size * 2.0 * locs["xsigma"][i] / locs[
"track_length"][i]
wy = pixel_size * 2.0 * locs["ysigma"][i] / locs[
"track_length"][i]
z_vals[i] = c_fitz.findBestZ(zfit_data, wx, wy) * 1.0e-3
h5.addTrackData(z_vals, index, "z")
c_fitz.cleanup(zfit_data)
def loadWxWyZData(h5_name, zfile_name):
"""
h5_name - The name of the HDF5 localization file.
zfile_name - The name of the text file containing z offset data.
"""
# This file contains two columns, the first is whether or not
# the data in this frame should be used (0 = No, 1 = Yes) and
# the second contains the z offset in microns.
#
# For movies acquired using storm-control this can be created
# from the .off file using storm_analysis/spliner/offset_to_z.py.
#
z_data = numpy.loadtxt(zfile_name, ndmin=2)
# Create arrays with wx, wy, z data.
wx = None
wy = None
z = None
with saH5Py.SAH5Py(h5_name) as h5:
pixel_size = h5.getPixelSize()
for curf, locs in h5.localizationsIterator(
fields=["xsigma", "ysigma"]):
if (int(z_data[curf, 0]) == 0):
continue
if wx is None:
wx = 2.0 * locs["xsigma"]
wy = 2.0 * locs["ysigma"]
z = numpy.ones(wx.size) * z_data[curf, 1]
else:
wx = numpy.concatenate((wx, 2.0 * locs["xsigma"]))
wy = numpy.concatenate((wy, 2.0 * locs["ysigma"]))
z = numpy.concatenate(
(z, numpy.ones(locs["xsigma"].size) * z_data[curf, 1]))
return [wx, wy, z, pixel_size]
def test_fiducials_4():
"""
Test no localizations in reference frame.
"""
peaks = {
"x": numpy.array([1.0, 2.0, 3.0]),
"y": numpy.array([1.0, 1.0, 1.0])
}
filename = "test_fiducials.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
for i in range(3):
h5.addLocalizations(peaks, i)
h5.addMovieInformation(FakeReader(n_frames=5))
# Track fiducials..
okay = False
try:
fiducials.trackFiducials(h5_name, radius=0.1, reference_frame=3)
except fiducials.FiducialException:
okay = True
assert okay
def test_sa_h5py_14():
"""
Test gridding tracks with dx, dy.
"""
tracks = {
"x": numpy.array([10, 20, 30]),
"y": numpy.array([10, 10, 10]),
"z": numpy.array([-0.2, 0.0, 0.2])
}
dx = 1
dy = 2
h5_name = storm_analysis.getPathOutputTest("test_sa_hdf5.hdf5")
# Tracks.
with saH5Py.SAH5Py(h5_name, is_existing=False, overwrite=True) as h5:
h5.setMovieInformation(40, 40, 1, "")
h5.addTracks(tracks)
with saH5Py.SAH5Grid(filename=h5_name, scale=1, z_bins=3) as h5g:
im_2d = h5g.gridTracks2D(dx=dx, dy=dy)
im_3d = h5g.gridTracks3D(-0.201, 0.201, dx=dx, dy=dy)
for i in range(tracks["x"].size):
assert (im_2d[int(tracks["x"][i] + dx),
int(tracks["y"][i] + dy)] == 1)
assert (im_3d[int(tracks["x"][i] + dx),
int(tracks["y"][i] + dy), i] == 1)
def overlayImage(movie_name, locs_name, frame_number, sx=8, sy=8):
"""
Create an image of a frame with the localizations overlaid.
"""
frame = datareader.inferReader(movie_name).loadAFrame(frame_number).astype(
numpy.float64)
with saH5Py.SAH5Py(locs_name) as h5:
locs = h5.getLocalizationsInFrame(0)
frame = frame - numpy.min(frame)
frame = frame / numpy.max(frame)
fig = pyplot.figure(figsize=(sx, sy))
ax = fig.add_subplot(1, 1, 1)
ax.imshow(frame, interpolation='nearest', cmap="gray")
for i in range(locs["x"].size):
width = 10
height = 10
if "xsigma" in locs:
width = height = 5.0 * locs["xsigma"][i]
if "ysigma" in locs:
height = 5.0 * locs["ysigma"][i]
ellipse = patches.Ellipse((locs["x"][i], locs["y"][i]),
width,
height,
facecolor='none',
edgecolor='g',
linewidth=2)
ax.add_artist(ellipse)
#ax.scatter(locs["x"], locs["y"], s = 200,
ax.set_title("Overlay Image")
pyplot.show()
def test_cl_sa_h5py_2():
"""
Test basic cluster file mechanics (using tracks).
"""
tracks = {
"x": numpy.arange(11, dtype=numpy.float),
"y": numpy.arange(11, dtype=numpy.float)
}
filename = "test_clusters_sa_h5py.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write track data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.setMovieInformation(1, 1, 2, "")
h5.addTracks(tracks)
# Write clustering data for tracks.
cluster_id = numpy.remainder(numpy.arange(11), 3)
cluster_data = {
"track_id": numpy.zeros(11, dtype=numpy.int),
"loc_id": numpy.arange(11)
}
cl_size = [0, 4, 4, 3]
with clSAH5Py.SAH5Clusters(h5_name) as cl_h5:
cl_h5.addClusters(cluster_id, cluster_data)
assert (cl_h5.getNClusters() == (len(cl_size) - 1))
for index, cluster in cl_h5.clustersIterator(skip_unclustered=False):
for field in cluster:
assert (cluster[field].size == cl_size[index])
def test_fiducials_7():
"""
Iterator test.
"""
peaks = {
"x": numpy.array([1.0, 2.0, 3.0]),
"y": numpy.array([1.0, 1.0, 1.0])
}
filename = "test_fiducials.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
for i in range(3):
temp = {}
for elt in peaks:
temp[elt] = peaks[elt][i:]
h5.addLocalizations(temp, i)
h5.addMovieInformation(FakeReader(n_frames=4))
# Track fiducials..
fiducials.trackFiducials(h5_name, radius=0.1, reference_frame=2)
# Check.
with fiducials.SAH5Fiducials(h5_name) as h5:
for fdcl in h5.fiducialsIterator():
assert (numpy.allclose(fdcl["frame"], numpy.arange(3)))
def test_cl_sa_h5py_5():
"""
Test getting all of the localizations for clustering.
"""
locs = {
"category": numpy.arange(4, dtype=numpy.int32),
"x": numpy.arange(4, dtype=numpy.float),
"y": numpy.arange(4, dtype=numpy.float)
}
filename = "test_clusters_sa_h5py.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write localization data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.setMovieInformation(1, 1, 5, "")
h5.setPixelSize(100.0)
h5.addLocalizations(locs, 1)
h5.addLocalizations(locs, 3)
# Test getting all the localization data.
with clSAH5Py.SAH5Clusters(h5_name) as cl_h5:
[x, y, z, c, cl_dict] = cl_h5.getDataForClustering()
assert (numpy.allclose(x, cl_dict['loc_id']))
assert (numpy.allclose(y, cl_dict['loc_id']))
assert (numpy.allclose(z, numpy.zeros(x.size)))
assert (numpy.allclose(c, cl_dict['loc_id']))
assert (numpy.allclose(cl_dict['frame'],
numpy.array([1, 1, 1, 1, 3, 3, 3, 3])))
def test_cl_sa_h5py_6():
"""
Test getting all of the tracks for clustering.
"""
tracks = {
"category": numpy.arange(4, dtype=numpy.int32),
"x": numpy.arange(4, dtype=numpy.float),
"y": numpy.arange(4, dtype=numpy.float),
"z": numpy.arange(4, dtype=numpy.float)
}
filename = "test_clusters_sa_h5py.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write tracks data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.setMovieInformation(1, 1, 2, "")
h5.setPixelSize(100.0)
h5.addTracks(tracks)
h5.addTracks(tracks)
# Test getting all the tracking data.
with clSAH5Py.SAH5Clusters(h5_name) as cl_h5:
[x, y, z, c, cl_dict] = cl_h5.getDataForClustering()
assert (numpy.allclose(x, cl_dict['loc_id']))
assert (numpy.allclose(y, cl_dict['loc_id']))
assert (numpy.allclose(z, cl_dict['loc_id']))
assert (numpy.allclose(c, cl_dict['loc_id']))
assert (numpy.allclose(cl_dict['track_id'],
numpy.array([0, 0, 0, 0, 1, 1, 1, 1])))
def test_cl_sa_h5py_4():
"""
Test cluster info string round trip.
"""
locs = {
"x": numpy.arange(10, dtype=numpy.float),
"y": numpy.arange(10, dtype=numpy.float)
}
filename = "test_clusters_sa_h5py.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write localization data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.setMovieInformation(1, 1, 2, "")
h5.addLocalizations(locs, 1)
# Write clustering data for localizations.
cluster_id = numpy.remainder(numpy.arange(10), 3)
cluster_data = {
"frame": numpy.ones(10, dtype=numpy.int),
"loc_id": numpy.arange(10)
}
info_string = "dbscan,eps,10.0,mc,5"
with clSAH5Py.SAH5Clusters(h5_name) as cl_h5:
cl_h5.addClusters(cluster_id, cluster_data)
cl_h5.setClusteringInfo(info_string)
assert (cl_h5.getClusteringInfo() == info_string)
def checkAnalysis(dir_name):
# Find all the job*.xml files.
job_xml_files = glob.glob(os.path.join(dir_name, "job*.xml"))
# Sort job files.
job_xml_files = sorted(
job_xml_files,
key=lambda x: int(
os.path.splitext(os.path.basename(x))[0].split("_")[1]))
# Check for corresponding HDF5 files.
incomplete = None
for i in range(len(job_xml_files)):
if ((i % 20) == 0):
print("Checking", job_xml_files[i])
h5_name = os.path.join(dir_name, "p_" + str(i + 1) + ".hdf5")
if os.path.exists(h5_name):
with saH5Py.SAH5Py(h5_name) as h5:
if h5.isAnalysisFinished():
continue
print("Job", job_xml_files[i], "is incomplete.")
if incomplete is None:
incomplete = str(i + 1)
else:
incomplete += "," + str(i + 1)
if incomplete is not None:
print("suggested job array string:")
print(incomplete)
def fitTilt(h5_name, start=0, stop=1):
"""
h5_name - The name of the HDF5 localization file (must be fit for z).
Use the localizations in the range start <= frame number < stop. This
should be a region of the movie where the stage is near zero and also
not moving.
"""
# Load localizations.
with saH5Py.SAH5Py(h5_name) as h5:
locs = h5.getLocalizationsInFrameRange(start,
stop,
fields=["x", "y", "z"])
# Find the best fit plane through x,y,z.
def fitfn(p):
zf = p[0] + p[1] * locs["x"] + p[2] * locs["y"]
return locs["z"] - zf
params = [scipy.mean(locs["z"]), 0.0, 0.0]
[results, success] = scipy.optimize.leastsq(fitfn, params)
if (success < 1) or (success > 4):
raise ZCalibrationException("fitTilt: fit failed!")
return results
def zCheck(h5_name, parameters):
"""
Mark all locations outside of the specified z range as category 9.
"""
[min_z, max_z] = parameters.getZRange()
with saH5Py.SAH5Py(h5_name) as h5:
# Localizations.
if h5.hasLocalizationsField("z"):
for fnum, locs in h5.localizationsIterator(fields = ["category", "z"]):
if((fnum%2000)==0):
print(" frame", fnum)
cat = locs["category"]
z_mask = (locs["z"] < min_z) | (locs["z"] > max_z)
cat[z_mask] = 9
h5.addLocalizationData(cat, fnum, "category")
# Tracks.
if h5.hasTracks():
for index, locs in enumerate(h5.tracksIterator(fields = ["category", "z"])):
if((index%5)==0):
print(" track group", index)
cat = locs["category"]
z_mask = (locs["z"] < min_z) | (locs["z"] > max_z)
cat[z_mask] = 9
h5.addTrackData(cat, index, "category")
def test_fiducials_8():
"""
Gap test.
"""
peaks = {
"x": numpy.array([1.0, 2.0, 3.0]),
"y": numpy.array([1.0, 1.0, 1.0])
}
filename = "test_fiducials.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
for i in [0, 1, 3]:
h5.addLocalizations(peaks, i)
h5.addMovieInformation(FakeReader(n_frames=4))
# Track fiducials..
fiducials.trackFiducials(h5_name, radius=0.1, max_gap=1)
# Check.
with fiducials.SAH5Fiducials(h5_name) as h5:
expected = numpy.array([0, 1, 3])
for fdcl in h5.fiducialsIterator():
assert (numpy.allclose(fdcl["frame"], expected))
def test_fitz_c_4():
"""
Test that tracks with wx, wy values that are not near the calibration
curve are assigned z values less than z minimum.
Their category remains unchanged as this is done in a separate step.
"""
# Load 3D parameters.
settings = storm_analysis.getData("test/data/test_3d_3d.xml")
parameters = params.ParametersDAO().initFromFile(settings)
[wx_params, wy_params] = parameters.getWidthParams()
[min_z, max_z] = parameters.getZRange()
pixel_size = parameters.getAttr("pixel_size")
# Calculate widths.
z_vals = numpy.arange(-250.0, 251.0, 50)
[sx, sy] = fitzC.calcSxSy(wx_params, wy_params, z_vals)
# Create HDF5 file with these widths.
track_length = numpy.ones(sx.size)
track_length[:2] = 2
tracks = {
"category": numpy.ones(sx.size, dtype=numpy.int32),
"track_length": track_length,
"x": numpy.zeros(sx.size),
"xsigma": track_length * (sx / pixel_size + numpy.ones(sx.size)),
"ysigma": track_length * (sy / pixel_size + numpy.ones(sx.size))
}
h5_name = storm_analysis.getPathOutputTest("test_sa_hdf5.hdf5")
storm_analysis.removeFile(h5_name)
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.setMovieInformation(256, 256, 10, "XYZZY")
h5.setPixelSize(pixel_size)
h5.addTracks(tracks)
# Calculate Z values.
fitzC.fitzTracks(h5_name, 1.5, wx_params, wy_params, min_z, max_z, 1.0e-3)
# Check Z values.
with saH5Py.SAH5Py(h5_name) as h5:
for tracks in h5.tracksIterator():
assert (numpy.allclose(tracks["z"],
min_z * numpy.ones(sx.size) - 1.0e-3))
assert (numpy.allclose(tracks["category"], numpy.ones(sx.size)))
def test_drift_correction_8():
"""
Test XY offset determination & correction with offset.
"""
n_locs = 500
peaks = {
"x": numpy.random.normal(loc=10.0, scale=0.2, size=n_locs),
"y": numpy.random.normal(loc=10.0, scale=0.2, size=n_locs)
}
h5_name = storm_analysis.getPathOutputTest("test_dc_hdf5.hdf5")
# Save peaks.
t_dx = 3.0
t_dy = 1.0
with saH5Py.SAH5Py(h5_name, is_existing=False, overwrite=True) as h5:
h5.setMovieInformation(20, 20, 2, "")
h5.addLocalizations(peaks, 0)
peaks["x"] += t_dx
peaks["y"] += t_dy
h5.addLocalizations(peaks, 1)
scale = 2
with driftUtils.SAH5DriftCorrection(filename=h5_name, scale=scale) as h5d:
h5d.setFrameRange(0, 1)
im1 = h5d.grid2D()
h5d.setFrameRange(1, 2)
im2 = h5d.grid2D()
# Check that both images have the same number localizations.
assert (numpy.sum(im1) == numpy.sum(im2))
# Measure offset.
[corr, dx, dy, success
] = imagecorrelation.xyOffset(im1,
im2,
scale,
center=[-t_dx * scale, -t_dy * scale])
# Test that it succeeded.
assert (success)
# Test that we got the right answer.
dx = dx / scale
dy = dy / scale
assert (numpy.allclose(numpy.array([dx, dy]),
numpy.array([-t_dx, -t_dy]),
atol=1.0e-6))
# Test that we are correcting in the right direction.
h5d.setDriftCorrectionXY(dx, dy)
im2 = h5d.grid2D(drift_corrected=True)
[corr, dx, dy, success] = imagecorrelation.xyOffset(im1, im2, scale)
dx = dx / scale
dy = dy / scale
assert (numpy.allclose(numpy.array([dx, dy]),
numpy.array([0.0, 0.0]),
atol=1.0e-6))
def verifyNumberLocalizations(h5_name):
"""
Return the number of localizations in a HDF5 file.
"""
n_locs = None
with saH5Py.SAH5Py(h5_name) as h5:
n_locs = h5.getNLocalizations()
return n_locs
def verifyZWasCalculated(h5_name):
"""
Return the true if all the Z values are not exactly identical.
"""
locs = None
with saH5Py.SAH5Py(h5_name) as h5:
locs = h5.getLocalizations(fields=["z"])
return (numpy.std(locs["z"]) > 1.0e-6)
def test_sa_h5py_1():
"""
Test metadata round trip.
"""
metadata = "<xml><field1><data1>data</data1></field></xml>"
filename = "test_sa_hdf5.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write metadata.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.addMetadata(metadata)
# Read metadata.
with saH5Py.SAH5Py(h5_name) as h5:
assert (metadata == h5.getMetadata())
def psfZStack(movie_name, h5_filename, zstack_name, scmos_cal = None, aoi_size = 8, driftx = 0.0, drifty = 0.0):
"""
movie_name - The movie file containing the z stack.
h5_filename - The HDF5 file containing the localizations to use for the PSF measurement.
zstack_name - The name of the file to save the zstack in.
scmos_cal - The sCMOS calibration file.
aoi_size - The AOI size in pixels.
driftx, drifty are in units of pixels per frame, (bead x last frame - bead x first frame)/n_frames.
"""
# Create appropriate reader.
if scmos_cal is None:
fr_reader = datareader.inferReader(movie_name)
else:
fr_reader = analysisIO.FrameReaderSCMOS(movie_file = movie_name,
calibration_file = scmos_cal)
[movie_x, movie_y, movie_len] = fr_reader.filmSize()
# Load localizations.
with saH5Py.SAH5Py(h5_filename) as h5:
locs = h5.getLocalizations()
x = locs["y"] + 1
y = locs["x"] + 1
# Measure Z stacks.
z_stacks = []
for i in range(x.size):
z_stacks.append(numpy.zeros((4*aoi_size, 4*aoi_size, movie_len)))
for i in range(movie_len):
if((i%50)==0):
print("Processing frame {0:0d}".format(i))
# Load the frame. This also handles gain and offset correction.
#
frame = fr_reader.loadAFrame(i)
# Subtract estimated background. This assumes that the image is
# mostly background and that the background is uniform.
#
frame = frame - numpy.median(frame)
for j in range(x.size):
xf = x[j] + driftx * float(i)
yf = y[j] + drifty * float(i)
z_stacks[j][:,:,i] = measurePSFUtils.extractAOI(frame, aoi_size, xf, yf)
# Save z_stacks.
numpy.save(zstack_name + ".npy", z_stacks)
# Save a (normalized) z_stack as tif for inspection purposes.
z_stack = z_stacks[0]
z_stack = z_stack/numpy.amax(z_stack)
z_stack = z_stack.astype(numpy.float32)
with tifffile.TiffWriter(zstack_name + ".tif") as tf:
for i in range(movie_len):
tf.save(z_stack[:,:,i])
def test_tracker_2():
"""
Test descriptor.
"""
peaks = {
"x": numpy.array([1.0, 2.0, 3.0]),
"y": numpy.array([1.0, 1.0, 1.0]),
"sum": numpy.array([4.0, 4.0, 4.0])
}
filename = "test_sa_hdf5.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
for i in range(4):
temp = {}
for elt in peaks:
temp[elt] = peaks[elt][:i]
if (len(temp["x"]) > 0):
h5.addLocalizations(temp, i)
h5.addMovieInformation(FakeReader(n_frames=4))
# Track.
tracker.tracker(h5_name, descriptor="1212", radius=0.1)
# Tracking.
with saH5Py.SAH5Py(h5_name) as h5:
assert (h5.getNTracks() == 3)
for t in h5.tracksIterator():
assert (numpy.allclose(peaks["x"], t["x"]))
assert (numpy.allclose(peaks["y"], t["y"]))
assert (numpy.allclose(numpy.array([1, 0, 1]), t["category"]))
# Check localization categories.
for fnum, locs in h5.localizationsIterator(fields=["category"]):
if (fnum == 1):
assert (numpy.allclose(numpy.array([1]), locs["category"]))
if (fnum == 2):
assert (numpy.allclose(numpy.array([0, 0]), locs["category"]))
if (fnum == 3):
assert (numpy.allclose(numpy.array([1, 1, 1]),
locs["category"]))
def __init__(self, filename=None, **kwds):
super(MoleculeListHDF5, self).__init__(**kwds)
self.fields = [
"x", "y", "z", "background", "error", "height", "sum", "xsigma",
"ysigma", "category", "iterations", "significance"
]
self.reader = saH5Py.SAH5Py(filename)
def emittersInClusters(h5_name, ncl, nlocs, dev, sigma = 1.5, sx = 256, sy = 256, z_start = -0.5, z_stop = 0.5):
"""
h5_name - The name of the HDF5 file to save the emitter locations, etc.
ncl - The number of clusters.
nlocs - The number of localizations per cluster.
dev - Cluster standard deviation in pixels.
sigma - The sigma for the localizatoins, default is 1.5 pixels.
sx - Image x size in pixels, default is 256.
sy - Image y size in pixels, default is 256.
z_start - Starting value for z position in microns, default is -0.5um.
z_stop = Stopping value for z position in microns, default is 0.5um.
"""
# First, create a list of cluster centers.
cl_centers = []
while (len(cl_centers) < ncl):
cx = random.uniform(0.0, sx)
cy = random.uniform(0.0, sy)
cz = random.uniform(z_start, z_stop)
# Don't keep the cluster if it is too close to the edge of the image.
if (cx < 2.0) or (cx > (sx - 2.0)):
continue
if (cy < 2.0) or (cy > (sy - 2.0)):
continue
cl_centers.append([cx, cy, cz])
# Next, create localizations for each cluster.
xp = None
yp = None
zp = None
for clc in cl_centers:
if xp is None:
xp = numpy.random.normal(scale = dev, size = nlocs) + clc[0]
yp = numpy.random.normal(scale = dev, size = nlocs) + clc[1]
# Z is in microns, we'll assume a 100nm pixel size.
zp = numpy.random.normal(scale = dev * 0.1, size = nlocs) + clc[2]
else:
xp = numpy.append(xp, numpy.random.normal(scale = dev, size = nlocs) + clc[0])
yp = numpy.append(yp, numpy.random.normal(scale = dev, size = nlocs) + clc[1])
zp = numpy.append(zp, numpy.random.normal(scale = dev * 0.1, size = nlocs) + clc[2])
# Create a molecule list structure & save it.
peaks = {}
peaks["x"] = xp
peaks["y"] = yp
peaks["z"] = zp
peaks["xsigma"] = sigma*numpy.ones(xp.size)
peaks["ysigma"] = sigma*numpy.ones(yp.size)
# Save localizations.
with saH5Py.SAH5Py(h5_name, is_existing = False, overwrite = True) as h5:
h5.setMovieInformation(sx, sy, 1, "")
h5.addLocalizations(peaks, 0)
def verifyNumberLocalizations(h5_name):
"""
Return the number of localizations in a HDF5 file.
"""
n_locs = None
with saH5Py.SAH5Py(h5_name) as h5:
assert (h5.isAnalysisFinished())
n_locs = h5.getNLocalizations()
return n_locs
def test_tracker_1():
"""
Basic tracking test.
"""
peaks = {
"x": numpy.array([1.0, 2.0, 3.0]),
"y": numpy.array([1.0, 1.0, 1.0]),
"sum": numpy.array([4.0, 4.0, 4.0])
}
filename = "test_sa_hdf5.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write data.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
for i in range(3):
temp = {}
for elt in peaks:
temp[elt] = peaks[elt][i:]
h5.addLocalizations(temp, i)
h5.addMovieInformation(FakeReader(n_frames=3))
# Track.
tracker.tracker(h5_name, radius=0.1)
# Tracking.
with saH5Py.SAH5Py(h5_name) as h5:
# Check that we have the right number of tracks.
assert (h5.getNTracks() == 3)
# Check tracks.
for t in h5.tracksIterator():
assert (numpy.allclose(peaks["x"], t["x"]))
assert (numpy.allclose(peaks["y"], t["y"]))
assert (numpy.allclose(numpy.array([0, 0, 0]), t["frame_number"]))
assert (numpy.allclose(numpy.array([1, 2, 3]), t["track_length"]))
# Check that the localizations 'track_id' field is correct.
for fnum, locs in h5.localizationsIterator(fields=["track_id"]):
assert (numpy.allclose(
numpy.array([0, 1, 2])[fnum:], locs["track_id"]))
def mergeHDF5(hdf5_files, results_file):
"""
Note: This only merges the tracks not the localizations.
"""
with saH5Py.SAH5Py(results_file, is_existing = False) as h5_out:
for i, h5_name in enumerate(hdf5_files):
with saH5Py.SAH5Py(h5_name) as h5_in:
if (i == 0):
[mx, my] = h5_in.getMovieInformation()[:2]
h5_out.setMovieInformation(mx, my, 0, "")
h5_out.setPixelSize(h5_in.getPixelSize())
h5_out.addMetadata(h5_in.getMetadata())
for tracks in h5_in.tracksIterator():
sys.stdout.write(".")
sys.stdout.flush()
h5_out.addTracks(tracks)
sys.stdout.write("\n")
def test_std_analysis_1():
"""
Test zCheck.
"""
# Load 3D parameters.
settings = storm_analysis.getData("test/data/test_3d_3d.xml")
parameters = params.ParametersDAO().initFromFile(settings)
[min_z, max_z] = parameters.getZRange()
assert (abs(min_z + 0.5) < 1.0e-6)
assert (abs(max_z - 0.5) < 1.0e-6)
# Create HDF5 file with localizations and tracks.
zvals = numpy.arange(-1.0, 1.05, 0.2)
peaks = {
"category": numpy.ones(zvals.size, dtype=numpy.int32),
"x": numpy.zeros(zvals.size),
"z": zvals
}
h5_name = storm_analysis.getPathOutputTest("test_sa_hdf5.hdf5")
storm_analysis.removeFile(h5_name)
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.setMovieInformation(256, 256, 10, "XYZZY")
h5.addLocalizations(peaks, 1)
h5.addTracks(peaks)
# Run z check on the file.
stdAnalysis.zCheck(h5_name, parameters)
# Check track and localization categories.
category = numpy.ones(zvals.size, dtype=numpy.int32)
z_mask = (zvals < min_z) | (zvals > max_z)
category[z_mask] = 9
with saH5Py.SAH5Py(h5_name) as h5:
for fnum, locs in h5.localizationsIterator(fields=["category"]):
assert (numpy.allclose(locs["category"], category))
for tracks in h5.tracksIterator(fields=["category"]):
assert (numpy.allclose(tracks["category"], category))
def collate():
dirs = sorted(glob.glob("test*"))
if (len(dirs) == 0):
print("No test directories found.")
return
# Adjust z positions in the channel 0 reference.
for a_dir in dirs:
with saH5Py.SAH5Py(a_dir + "/test_c1_ref.hdf5") as h5_in:
with saH5Py.SAH5Py(a_dir + "/test_ref.hdf5",
is_existing=False,
overwrite=True) as h5_out:
h5_out.setMovieInformation(*h5_in.getMovieInformation())
for fnum, locs in h5_in.localizationsIterator():
locs["z"] -= settings.z_planes[0]
h5_out.addLocalizations(locs, fnum)
collateResults.collateDAO(dirs, settings, calc_width_error=False)
def test_voronoi_clustering_1():
numpy.random.seed(1)
filename = "test_clustering_sa_h5py.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Write tracks data.
category = numpy.zeros(10, dtype=numpy.int32)
x = 10.0 * numpy.arange(10)
y = 10.0 * numpy.arange(10)
z = numpy.zeros(10)
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
h5.setMovieInformation(1, 1, 2, "")
h5.setPixelSize(1.0)
for i in range(100):
tracks = {
"category": category,
"x": x + numpy.random.normal(scale=0.1, size=10),
"y": y + numpy.random.normal(scale=0.1, size=10),
"z": z + numpy.random.normal(scale=0.1, size=10)
}
h5.addTracks(tracks)
# Cluster data with voronoi.
voronoiAnalysis.findClusters(h5_name, 0.1, 10, verbose=False)
# Check clustering results.
with clSAH5Py.SAH5Clusters(h5_name) as cl_h5:
assert (cl_h5.getNClusters() == 10)
for index, cluster in cl_h5.clustersIterator(skip_unclustered=True,
fields=["x", "y", "z"]):
for elt in ['x', 'y', 'z']:
dev = numpy.std(cluster[elt])
assert (dev > 0.07)
assert (dev < 0.12)
# Calculate common cluster statistics.
stats_name = dbscanAnalysis.clusterStats(h5_name, 50, verbose=False)
# Check statistics.
stats = numpy.loadtxt(stats_name, skiprows=1)
index = numpy.argsort(stats[:, 3])
assert (stats.shape[0] == 10)
assert (numpy.allclose(stats[:, 0], numpy.arange(10) + 1))
assert (numpy.allclose(stats[:, 1], numpy.zeros(10)))
assert (numpy.count_nonzero(
numpy.greater(stats[:, 2], 80.0 * numpy.ones(10))) == 10)
assert (numpy.allclose(stats[index, 3], x, rtol=0.2, atol=2.0))
assert (numpy.allclose(stats[index, 4], y, rtol=0.2, atol=2.0))
assert (numpy.allclose(stats[index, 5], z, rtol=0.2, atol=20.0))
