def collateDAO(dirs, settings, calc_width_error = True):
"""
Results collations for 3D-DAOSTORM and sCMOS analysis.
"""
all_dx = []
all_dy = []
all_wx = []
all_wy = []
noise = 0
noise_total = 0
recall = 0
recall_total = 0
total_locs = 0
total_time = 0.0
for a_dir in dirs:
print("Processing", a_dir)
# Load timing information.
with open(a_dir + "/timing.txt") as fp:
total_time += float(fp.readline())
# Load localizations.
truth_h5 = saH5Py.SAH5Py(a_dir + "/test_ref.hdf5")
measured_h5 = saH5Py.SAH5Py(a_dir + "/test.hdf5")
total_locs += measured_h5.getNLocalizations()
# Calculate fractional recall.
[partial, total] = rfrac.recallFraction(truth_h5, measured_h5, settings.tolerance)
recall += partial
recall_total += total
# Calculate noise fraction.
[partial, total] = rfrac.noiseFraction(truth_h5, measured_h5, settings.tolerance)
noise += partial
noise_total += total
# Calculate error in fitting width.
if calc_width_error:
for i in range(truth_h5.getMovieLength()):
t_locs = truth_h5.getLocalizationsInFrame(i)
m_locs = measured_h5.getLocalizationsInFrame(i)
# Widths for truth localizations.
t_wx = t_locs["xsigma"]
t_wy = t_locs["ysigma"]
# Widths for found localizations.
if ("xsigma" in m_locs):
m_wx = m_locs["xsigma"]
else:
m_wx = 1.5*numpy.ones(m_locs['x'].size)
if ("ysigma" in m_locs):
m_wy = m_locs["ysigma"]
else:
m_wy = m_wx.copy()
p_index = iaUtilsC.peakToPeakDistAndIndex(m_locs['x'], m_locs['y'],
t_locs['x'], t_locs['y'],
max_distance = settings.tolerance)[1]
p_size = numpy.count_nonzero(p_index > -1)
d_wx = numpy.zeros(p_size)
d_wy = numpy.zeros(p_size)
k = 0
for j in range(m_locs["x"].size):
if(p_index[j] < 0):
continue
d_wx[k] = m_wx[j] - t_wx[p_index[j]]
d_wy[k] = m_wy[j] - t_wy[p_index[j]]
k += 1
all_wx.append(d_wx)
all_wy.append(d_wy)
# Calculate fitting error in XY.
max_distance = None
if True:
max_distance = 2.0 * settings.pixel_size
print("Using max_distance", max_distance, "nm for error calcuations.")
[dx, dy, dz] = ffe.findingFittingError(truth_h5,
measured_h5,
pixel_size = settings.pixel_size,
max_distance = max_distance)
if dx.size != 0:
all_dx.append([numpy.std(dx), math.sqrt(numpy.mean(dx*dx))])
all_dy.append([numpy.std(dy), math.sqrt(numpy.mean(dy*dy))])
else:
all_dx.append([0,0])
all_dy.append([0,0])
truth_h5.close()
measured_h5.close()
print()
print("Analysis Summary:")
print("Processed {0:0d} localizations in {1:.2f} seconds, {2:.2f}/sec".format(total_locs, total_time, float(total_locs)/float(total_time)))
print("Recall {0:.5f}".format(float(recall)/float(recall_total)))
print("Noise {0:.5f}".format(float(noise)/float(noise_total)))
print("XY Error Standard Deviation (nm):")
for i, a_dir in enumerate(dirs):
print(a_dir + "\t{0:.2f}\t{1:.2f}".format(all_dx[i][0], all_dy[i][0]))
print("")
print("XY RMSE (nm):")
for i, a_dir in enumerate(dirs):
print(a_dir + "\t{0:.2f}\t{1:.2f}".format(all_dx[i][1], all_dy[i][1]))
if calc_width_error:
print("")
print("XY Width Error, Mean difference with truth, Standard deviation (pixels):")
for i, a_dir in enumerate(dirs):
print(a_dir + "\t{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}".format(numpy.mean(all_wx[i]),
numpy.std(all_wx[i]),
numpy.mean(all_wy[i]),
numpy.std(all_wy[i])))
def collateSpliner(dirs, settings):
"""
Results collation for Spliner, Pupil-Function, PSF-FFT and Multiplane.
Note: The maximum distance cutoff is in XYZ, so points with Z values
that are way off will not be included in the error calculation
even though their XY values might be very good.
"""
all_dx = []
all_dy = []
all_dz = []
noise = 0
noise_total = 0
recall = 0
recall_total = 0
total_locs = 0
total_time = 0.0
for a_dir in dirs:
print("Processing", a_dir)
# Load timing information.
with open(a_dir + "/timing.txt") as fp:
total_time += float(fp.readline())
# Load localizations.
truth_h5 = saH5Py.SAH5Py(a_dir + "/test_ref.hdf5")
measured_h5 = saH5Py.SAH5Py(a_dir + "/test.hdf5")
total_locs += measured_h5.getNLocalizations()
# Calculate fractional recall.
[partial, total] = rfrac.recallFraction(truth_h5, measured_h5, settings.tolerance)
recall += partial
recall_total += total
# Calculate noise fraction.
[partial, total] = rfrac.noiseFraction(truth_h5, measured_h5, settings.tolerance)
noise += partial
noise_total += total
# Calculate fitting error in XYZ.
max_distance = None
if True:
max_distance = 2.0 * settings.pixel_size
print("Using max_distance", max_distance, "nm for error calcuations.")
[dx, dy, dz] = ffe.findingFittingError(truth_h5,
measured_h5,
pixel_size = settings.pixel_size,
max_distance = max_distance)
if dx.size != 0:
all_dx.append([numpy.std(dx), math.sqrt(numpy.mean(dx*dx))])
all_dy.append([numpy.std(dy), math.sqrt(numpy.mean(dy*dy))])
all_dz.append([numpy.std(dz), math.sqrt(numpy.mean(dz*dz))])
else:
all_dx.append([0,0])
all_dy.append([0,0])
all_dz.append([0,0])
print()
print("Analysis Summary:")
print("Processed {0:0d} localizations in {1:.2f} seconds, {2:.2f}/sec".format(total_locs, total_time, float(total_locs)/float(total_time)))
print("Recall {0:.5f}".format(float(recall)/float(recall_total)))
print("Noise {0:.5f}".format(float(noise)/float(noise_total)))
print("XYZ Error Standard Deviation (nm):")
for i, a_dir in enumerate(dirs):
print(a_dir + "\t{0:.2f}\t{1:.2f}\t{2:.2f}".format(all_dx[i][0], all_dy[i][0], all_dz[i][0]))
print("")
print("XYZ RMSE Accuracy (nm):")
for i, a_dir in enumerate(dirs):
print(a_dir + "\t{0:.2f}\t{1:.2f}\t{2:.2f}".format(all_dx[i][1], all_dy[i][1], all_dz[i][1]))
print("")