def test_mfit_9():
"""
Test peak significance calculation.
"""
height = 10.0
sigma = 1.5
x_size = 100
y_size = 120
background = numpy.zeros((x_size, y_size)) + 10.0
image = dg.drawGaussians((x_size, y_size),
numpy.array([[50.0, 30.0, height, sigma, sigma],
[50.0, 50.0, 2.0*height, sigma, sigma],
[50.0, 70.0, 3.0*height, sigma, sigma]]))
image += background
mfit = daoFitC.MultiFitter2D(sigma_range = [1.0, 2.0])
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(background)
peaks = {"x" : numpy.array([30.0, 50.0, 70.0]),
"y" : numpy.array([50.0, 50.0, 50.0]),
"z" : numpy.array([0.0, 0.0, 0.0]),
"sigma" : numpy.array([sigma, sigma, sigma])}
mfit.newPeaks(peaks, "finder")
if False:
with tifffile.TiffWriter("test_mfit_9.tif") as tf:
tf.save(image.astype(numpy.float32))
sig = mfit.getPeakProperty("significance")
sig = sig/sig[0]
assert(numpy.allclose(sig, numpy.arange(1,4)))
def test_mfit_10():
"""
Test 'pre-specified' peak locations addition (hdf5).
"""
image = numpy.ones((40,40))
mfit = daoFitC.MultiFitter2D(sigma_range = [1.0, 2.0])
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(image)
# Add peaks.
peaks = {"background" : numpy.array([10.0, 20.0]),
"height" : numpy.array([11.0, 21.0]),
"x" : numpy.array([12.0, 22.0]),
"xsigma" : numpy.array([1.0, 2.0]),
"y" : numpy.array([14.0, 24.0]),
"ysigma" : numpy.array([3.0, 4.0]),
"z" : numpy.array([16.0, 26.0])}
mfit.newPeaks(peaks, "hdf5")
# Round trip verification.
for pname in peaks:
pvals = peaks[pname]
mvals = mfit.getPeakProperty(pname)
for i in range(pvals.size):
assert(abs(pvals[i] - mvals[i]) < 1.0e-6)
mfit.cleanup(verbose = False)
def test_mfit_3():
"""
Test error peak removal.
"""
image = numpy.ones((40, 40))
mfit = daoFitC.MultiFitter2D()
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(image)
# Add good peaks.
n_peaks = 2
peaks = {
"x": numpy.ones(n_peaks) * 20.0,
"y": numpy.ones(n_peaks) * 20.0,
"z": numpy.zeros(n_peaks),
"sigma": numpy.ones(n_peaks)
}
mfit.newPeaks(peaks, "testing")
# Check that no peaks are removed.
mfit.removeErrorPeaks()
assert (mfit.getNFit() == 2)
# Add error peaks.
n_peaks = 2
peaks = {
"x": numpy.zeros(n_peaks),
"y": numpy.zeros(n_peaks),
"z": numpy.zeros(n_peaks),
"sigma": numpy.ones(n_peaks) * 2.0
}
mfit.newPeaks(peaks, "testing")
assert (mfit.getNFit() == 4)
# Add more good peaks.
n_peaks = 2
peaks = {
"x": numpy.ones(n_peaks) * 20.0,
"y": numpy.ones(n_peaks) * 20.0,
"z": numpy.zeros(n_peaks),
"sigma": numpy.ones(n_peaks)
}
mfit.newPeaks(peaks, "testing")
assert (mfit.getNFit() == 6)
# Check that two peaks are removed.
mfit.removeErrorPeaks()
assert (mfit.getNFit() == 4)
# Check that the right peaks were removed.
w = mfit.getPeakProperty("xsigma")
for i in range(w.size):
assert (abs(w[i] - 1.0) < 1.0e-6)
mfit.cleanup(verbose=False)
def test_mfit_12():
"""
Test RQE correction is as expected.
"""
height = 10.0
rqe_term = 0.7
sigma = 1.5
x_size = 60
y_size = 120
# RQE corrected image.
background = numpy.zeros((x_size, y_size)) + 10.0
image = dg.drawGaussians((x_size, y_size),
numpy.array([[30.0, 30.0, height, sigma, sigma],
[30.0, 60.0, height, sigma, sigma],
[30.0, 90.0, height, sigma, sigma]]))
image += background
# Variable RQE.
rqe = numpy.ones_like(image)
rqe[:,:60] = rqe_term
mfit = daoFitC.MultiFitter2D(rqe = rqe,
sigma_range = [1.0, 2.0])
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(background)
peaks = {"x" : numpy.array([30.0, 60.0, 90.0]),
"y" : numpy.array([30.0, 30.0, 30.0]),
"z" : numpy.array([0.0, 0.0, 0.0]),
"sigma" : numpy.array([sigma, sigma, sigma])}
mfit.newPeaks(peaks, "finder")
# All localizations should have the same RQE.
sig = mfit.getPeakProperty("significance")
sig = sig/sig[0]
assert(numpy.allclose(sig, numpy.ones_like(sig)))
# Fit image should be RQE corrected.
fit_image = mfit.getFitImage() + background
assert(numpy.allclose(image, fit_image, atol = 0.2))
if False:
with tifffile.TiffWriter("test_mfit_12.tif") as tf:
tf.save(image.astype(numpy.float32))
tf.save(fit_image.astype(numpy.float32))
mfit.cleanup(verbose = False)
def test_mfit_5():
"""
Test initialization and fitting.
"""
height = 20.0
sigma = 1.5
x_size = 100
y_size = 120
background = numpy.zeros((x_size, y_size)) + 10.0
image = dg.drawGaussians((x_size, y_size),
numpy.array([[50.0, 50.0, height, sigma, sigma],
[50.0, 54.0, height, sigma, sigma]]))
image += background
mfit = daoFitC.MultiFitter2D()
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(background)
peaks = {
"x": numpy.array([50.0, 54.0]),
"y": numpy.array([50.0, 50.0]),
"z": numpy.array([0.0, 0.0]),
"sigma": numpy.array([sigma, sigma])
}
mfit.newPeaks(peaks, "finder")
mfit.doFit()
if False:
with tifffile.TiffWriter("test_mfit_5.tif") as tf:
tf.save((image - background).astype(numpy.float32))
tf.save(mfit.getFitImage().astype(numpy.float32))
# Check peak x,y.
x = mfit.getPeakProperty("x")
y = mfit.getPeakProperty("y")
for i in range(x.size):
assert (abs(x[i] - peaks["x"][i]) < 1.0e-2)
assert (abs(y[i] - peaks["y"][i]) < 1.0e-2)
# Check peak w.
w = mfit.getPeakProperty("xsigma")
for i in range(w.size):
assert (abs((w[i] - sigma) / sigma) < 0.02)
mfit.cleanup(verbose=False)
def test_ia_util_3():
"""
Test agreement with fitting regarding orientation.
"""
height = 20.0
sigma = 1.5
x_size = 100
y_size = 120
background = numpy.zeros((x_size, y_size)) + 10.0
image = dg.drawGaussians((x_size, y_size),
numpy.array([[20.0, 40.0, height, sigma, sigma]]))
image += background
# Configure fitter.
mfit = daoFitC.MultiFitter2D()
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(background)
# Configure finder.
z_values = [0.0]
mxf = iaUtilsC.MaximaFinder(margin=1,
radius=2 * sigma,
threshold=background[0, 0] + 0.5 * height,
z_values=z_values)
# Find peaks.
[x, y, z] = mxf.findMaxima([image])
sigma = numpy.ones(x.size) * sigma
peaks = {"x": x, "y": y, "z": z, "sigma": sigma}
# Pass peaks to fitter.
mfit.newPeaks(peaks, "finder")
# Check height.
h = mfit.getPeakProperty("height")
for i in range(h.size):
assert (abs(h[i] - height) / height < 0.1)
# Check background.
bg = mfit.getPeakProperty("background")
for i in range(bg.size):
assert (abs(bg[i] - 10.0) < 1.0e-6)
mfit.cleanup(verbose=False)
def test_mfit_7():
"""
Test removing RUNNING peaks.
"""
image = numpy.ones((40, 40))
mfit = daoFitC.MultiFitter2D()
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(image)
# Add good peaks.
n_peaks = 2
peaks = {
"x": numpy.ones(n_peaks) * 20.0,
"y": numpy.ones(n_peaks) * 20.0,
"z": numpy.zeros(n_peaks),
"sigma": numpy.ones(n_peaks) * 1.0
}
peaks["sigma"][1] = 2.0
mfit.newPeaks(peaks, "testing")
# Mark peaks as converged.
status = mfit.getPeakProperty("status")
status[:] = iaUtilsC.CONVERGED
mfit.setPeakStatus(status)
# Check that no peaks are removed.
mfit.removeRunningPeaks()
assert (mfit.getNFit() == 2)
# Mark the first peak as running.
status = mfit.getPeakProperty("status")
status[0] = iaUtilsC.RUNNING
mfit.setPeakStatus(status)
# Check that one peak was removed.
mfit.removeRunningPeaks()
assert (mfit.getNFit() == 1)
# Check that the right peak was removed.
w = mfit.getPeakProperty("xsigma")
for i in range(w.size):
assert (abs(w[i] - 2.0) < 1.0e-6)
mfit.cleanup(verbose=False)
def test_mfit_4():
"""
Test height and background initialization.
"""
height = 20.0
sigma = 1.5
x_size = 100
y_size = 120
background = numpy.zeros((x_size, y_size)) + 10.0
image = dg.drawGaussians((x_size, y_size),
numpy.array([[50.0, 50.0, height, sigma, sigma],
[50.0, 54.0, height, sigma, sigma]]))
image += background
mfit = daoFitC.MultiFitter2D()
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(background)
peaks = {
"x": numpy.array([50.0, 54.0]),
"y": numpy.array([50.0, 50.0]),
"z": numpy.array([0.0, 0.0]),
"sigma": numpy.array([sigma, sigma])
}
mfit.newPeaks(peaks, "finder")
if False:
with tifffile.TiffWriter("test_mfit_4.tif") as tf:
tf.save((image - background).astype(numpy.float32))
tf.save(mfit.getFitImage().astype(numpy.float32))
# Check height.
h = mfit.getPeakProperty("height")
for i in range(h.size):
assert (abs(h[i] - height) / height < 0.1)
# Check background.
bg = mfit.getPeakProperty("background")
for i in range(bg.size):
assert (abs(bg[i] - 10.0) < 1.0e-6)
mfit.cleanup(verbose=False)
def test_mfit_2():
"""
Test initialization & growing with error peak removal.
"""
image = numpy.ones((40, 40))
mfit = daoFitC.MultiFitter2D()
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(image)
# Create ERROR peaks (too close to edge).
n_peaks = 5
peaks = {
"x": numpy.zeros(n_peaks),
"y": numpy.zeros(n_peaks),
"z": numpy.zeros(n_peaks),
"sigma": numpy.ones(n_peaks)
}
# Add peaks & check size.
mfit.newPeaks(peaks, "testing")
assert (mfit.getNFit() == 5)
assert (mfit.getNFitMax() == 500)
# Add good peaks. ERROR peaks should be removed.
n_peaks = 500
peaks = {
"x": numpy.ones(n_peaks) * 20.0,
"y": numpy.ones(n_peaks) * 20.0,
"z": numpy.zeros(n_peaks),
"sigma": numpy.ones(n_peaks) * 2.0
}
mfit.newPeaks(peaks, "testing")
assert (mfit.getNFit() == n_peaks)
assert (mfit.getNFitMax() == 1000)
# Check for correct peak values.
w = mfit.getPeakProperty("xsigma")
assert (abs(w[0] - 2.0) < 1.0e-6)
mfit.cleanup(verbose=False)
def test_mfit_1():
"""
Test initialization & growing peak array size.
"""
image = numpy.ones((40, 40))
mfit = daoFitC.MultiFitter2D()
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(image)
# Check correct initialization.
assert (mfit.getNFit() == 0)
assert (mfit.getNFitMax() == 0)
# Create peaks in the center of the image.
n_peaks = 1020
peaks = {
"x": numpy.ones(n_peaks) * 20.0,
"y": numpy.ones(n_peaks) * 20.0,
"z": numpy.zeros(n_peaks),
"sigma": numpy.ones(n_peaks)
}
# Add peaks & check size.
mfit.newPeaks(peaks, "testing")
assert (mfit.getNFit() == n_peaks)
assert (mfit.getNFitMax() == 1500)
# Add again & check size.
peaks["sigma"][:] = 2.0
mfit.newPeaks(peaks, "testing")
assert (mfit.getNFit() == 2 * n_peaks)
assert (mfit.getNFitMax() == 2500)
# Check some peak values.
w = mfit.getPeakProperty("xsigma")
assert (abs(w[0] - 1.0) < 1.0e-6)
assert (abs(w[n_peaks] - 2.0) < 1.0e-6)
mfit.cleanup(verbose=False)
def test_mfit_6():
"""
Test marking peak status.
"""
image = numpy.ones((40, 40))
mfit = daoFitC.MultiFitter2D(sigma_range=[1.0, 2.0])
mfit.initializeC(image)
mfit.newImage(image)
mfit.newBackground(image)
# Add good peaks.
n_peaks = 5
peaks = {
"x": numpy.ones(n_peaks) * 20.0,
"y": numpy.ones(n_peaks) * 20.0,
"z": numpy.zeros(n_peaks),
"sigma": 0.1 * numpy.arange(n_peaks) + 1.0
}
mfit.newPeaks(peaks, "testing")
# Check that no peaks are removed.
mfit.removeErrorPeaks()
assert (mfit.getNFit() == 5)
# Mark every other peak as bad.
status = mfit.getPeakProperty("status")
status[1] = iaUtilsC.ERROR
status[3] = iaUtilsC.ERROR
mfit.setPeakStatus(status)
# Check that two peaks were removed.
mfit.removeErrorPeaks()
assert (mfit.getNFit() == 3)
# Check that the right peaks were removed.
w = mfit.getPeakProperty("xsigma")
assert numpy.allclose(w, numpy.array([1.0, 1.2, 1.4]))
mfit.cleanup(verbose=False)
def initFindAndFit(parameters):
"""
Return the appropriate type of finder and fitter.
"""
fmodel = parameters.getAttr("model")
# Create peak finder.
finder = fitting.PeakFinderGaussian(parameters = parameters)
# Initialize Z fitting parameters.
wx_params = None
wy_params = None
min_z = None
max_z = None
if (parameters.getAttr("model", "na") == "Z"):
[wx_params, wy_params] = parameters.getWidthParams(for_mu_Zfit = True)
[min_z, max_z] = parameters.getZRange()
# Check for camera calibration (this function is also used by sCMOS analysis).
rqe = None
variance = None
if parameters.hasAttr("camera_calibration"):
# Load variance, scale by gain.
#
# Offset is in units of ADU.
# Variance is in units of ADU*ADU.
# Gain is ADU/photo-electron.
# RQE is dimensionless, it should be around 1.0.
#
[offset, variance, gain, rqe] = analysisIO.loadCMOSCalibration(parameters.getAttr("camera_calibration"))
variance = variance/(gain*gain)
# Set variance in the peak finder, this method also pads the
# variance to the correct size.
variance = finder.setVariance(variance)
# Pad relative quantum efficiency array to the correct size.
rqe = finder.padArray(rqe)
# Create C fitter object.
kwds = {'roi_size' : finder.getROISize(),
'als_fit' : (parameters.getAttr("anscombe", 0) != 0),
'rqe' : rqe,
'scmos_cal' : variance,
'wx_params' : wx_params,
'wy_params' : wy_params,
'min_z' : min_z,
'max_z' : max_z}
if (fmodel == '2dfixed'):
mfitter = daoFitC.MultiFitter2DFixed(**kwds)
elif (fmodel == '2d'):
sigma = parameters.getAttr("sigma")
kwds['sigma_range'] = [0.5 * sigma, 5.0 * sigma]
mfitter = daoFitC.MultiFitter2D(**kwds)
elif (fmodel == '3d'):
sigma = parameters.getAttr("sigma")
kwds['sigma_range'] = [0.5 * sigma, 5.0 * sigma]
mfitter = daoFitC.MultiFitter3D(**kwds)
elif (fmodel == 'Z'):
mfitter = daoFitC.MultiFitterZ(**kwds)
else:
raise Exception("Unknown fitting model " + fmodel)
# Create peak fitter.
fitter = fitting.PeakFitter(mfitter = mfitter,
parameters = parameters)
# Specify which properties we want from the analysis.
properties = ["background", "error", "height", "iterations", "significance", "sum", "x", "y"]
if (fmodel == "2dfixed") or (fmodel == "2d"):
properties.append("xsigma")
elif (fmodel == "3d"):
properties.append("xsigma")
properties.append("ysigma")
elif (fmodel == "Z"):
properties.append("xsigma")
properties.append("ysigma")
properties.append("z")
return fitting.PeakFinderFitter(peak_finder = finder,
peak_fitter = fitter,
properties = properties)
def __init__(self, parameters):
SCMOSPeakFitter.__init__(self, parameters)
self.mfitter = daoFitC.MultiFitter2D(self.scmos_cal, self.wx_params,
self.wy_params, self.min_z,
self.max_z)
