def test_matched_filter2():
"""
Test recovering the original height from the convolved image.
FIXME: Not sure this test is relevant anymore as we no longer use
this approach for initializing peak heights.
"""
x_size = 80
y_size = 90
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0,:] = [x_size/2, y_size/2, 1.0, 2.0, 2.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf_norm = psf/numpy.sum(psf)
flt = matchedFilterC.MatchedFilter(psf_norm)
rescale = 1.0/numpy.sum(psf * psf_norm)
# Create object with height 10 and the same shape as the filter.
height = 10.0
objects[0,:] = [x_size/2, y_size/2, height, 2.0, 2.0]
image = dg.drawGaussians((x_size, y_size), objects)
# Apply filter.
conv = flt.convolve(image)
# Verify that final height is 'close enough'.
assert (abs(numpy.amax(conv) * rescale - height)/height < 1.0e-2)
flt.cleanup()
def test_matched_filter2():
"""
Test recovering the original height from the convolved image.
FIXME: Not sure this test is relevant anymore as we no longer use
this approach for initializing peak heights.
"""
x_size = 80
y_size = 90
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0,:] = [x_size/2, y_size/2, 1.0, 2.0, 2.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf_norm = psf/numpy.sum(psf)
flt = matchedFilterC.MatchedFilter(psf_norm)
rescale = 1.0/numpy.sum(psf * psf_norm)
# Create object with height 10 and the same shape as the filter.
height = 10.0
objects[0,:] = [x_size/2, y_size/2, height, 2.0, 2.0]
image = dg.drawGaussians((x_size, y_size), objects)
# Apply filter.
conv = flt.convolve(image)
# Verify that final height is 'close enough'.
assert (abs(numpy.amax(conv) * rescale - height)/height < 1.0e-2)
def getPSFs(self, h5_data):
x = h5_data['x']
y = h5_data['y']
a = h5_data['sum']
sx = h5_data['ysigma']
sy = h5_data['xsigma']
h = 0.5 * a / (2.0 * numpy.pi * sx * sy)
h5_data['height'] = h
angle = numpy.pi * 0.9 * ((h5_data['z'] - self.z_min) /
(self.z_max - self.z_min))
dx = 2.0 * sx * numpy.cos(angle)
dy = 2.0 * sy * numpy.sin(angle)
x1 = x + dx
y1 = y + dy
x2 = x - dx
y2 = y - dy
x = numpy.concatenate((x1, x2))
y = numpy.concatenate((y1, y2))
h = numpy.concatenate((h, h))
sx = numpy.concatenate((sx, sx))
sy = numpy.concatenate((sy, sy))
return dg.drawGaussians((self.x_size, self.y_size),
numpy.stack((x, y, h, sx, sy), axis=1),
res=5)
def test_matched_filter4():
"""
Verify that fftw_estimate has no effect on the results.
"""
x_size = 80
y_size = 90
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0,:] = [x_size/2, y_size/2, 1.0, 1.0, 1.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf = psf/numpy.sum(psf)
flt1 = matchedFilterC.MatchedFilter(psf, fftw_estimate = False)
flt2 = matchedFilterC.MatchedFilter(psf, fftw_estimate = True)
for i in range(1,5):
image = numpy.zeros((x_size, y_size))
image[int(x_size/2), int(y_size/2)] = float(i)
conv1 = flt1.convolve(image)
conv2 = flt2.convolve(image)
assert(numpy.allclose(conv1, conv2))
flt1.cleanup()
flt2.cleanup()
def test_matched_filter5():
"""
Verify that the filter results are correct.
"""
x_size = 80
y_size = 90
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0,:] = [x_size/2, y_size/2, 1.0, 1.0, 1.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf = psf/numpy.sum(psf)
flt = matchedFilterC.MatchedFilter(psf)
# Make test image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2), int(y_size/2)] = float(100)
mf_conv = flt.convolve(image)
t1 = numpy.fft.fft2(recenterPSF.recenterPSF(psf))
t2 = numpy.fft.fft2(image)
np_conv = numpy.real(numpy.fft.ifft2(t1*t2))
assert(numpy.allclose(mf_conv, np_conv))
flt.cleanup()
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_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")
assert(abs((sig[1] - sig[0]) - (sig[2] - sig[1])) < 0.1)
def getPSFs(self, h5_data):
x = h5_data['x']
y = h5_data['y']
a = h5_data['sum']
sx = h5_data['ysigma']
sy = h5_data['xsigma']
h = 0.5*a/(2.0 * numpy.pi * sx * sy)
h5_data['height'] = h
angle = numpy.pi * 0.9 * ((h5_data['z'] - self.z_min)/(self.z_max - self.z_min))
dx = 2.0 * sx * numpy.cos(angle)
dy = 2.0 * sy * numpy.sin(angle)
x1 = x + dx
y1 = y + dy
x2 = x - dx
y2 = y - dy
x = numpy.concatenate((x1, x2))
y = numpy.concatenate((y1, y2))
h = numpy.concatenate((h, h))
sx = numpy.concatenate((sx, sx))
sy = numpy.concatenate((sy, sy))
return dg.drawGaussians((self.x_size, self.y_size),
numpy.stack((x, y, h, sx, sy), axis = 1),
res = 5)
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_matched_filter3():
"""
Test memoization.
"""
x_size = 40
y_size = 50
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0,:] = [x_size/2, y_size/2, 1.0, 2.0, 2.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf_norm = psf/numpy.sum(psf)
flt = matchedFilterC.MatchedFilter(psf_norm, memoize = True, max_diff = 0.1)
# Convolve first image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.0
res1 = flt.convolve(image)
# Repeat with approximately the same image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.05
res2 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) < 1.0e-12)
# Repeat with a different image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.1
res2 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) > 1.0e-12)
# Repeat with original image to verify update.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.0
res1 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) > 1.0e-12)
# Repeat with exactly the same image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.0
res2 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) < 1.0e-12)
flt.cleanup()
def test_matched_filter3():
"""
Test memoization.
"""
x_size = 40
y_size = 50
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0,:] = [x_size/2, y_size/2, 1.0, 2.0, 2.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf_norm = psf/numpy.sum(psf)
flt = matchedFilterC.MatchedFilter(psf_norm, memoize = True, max_diff = 0.1)
# Convolve first image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.0
res1 = flt.convolve(image)
# Repeat with approximately the same image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.05
res2 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) < 1.0e-12)
# Repeat with a different image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.1
res2 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) > 1.0e-12)
# Repeat with original image to verify update.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.0
res1 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) > 1.0e-12)
# Repeat with exactly the same image.
image = numpy.zeros((x_size, y_size))
image[int(x_size/2),int(y_size/2)] = 1.0
res2 = flt.convolve(image)
assert(abs(numpy.max(res2 - res1)) < 1.0e-12)
flt.cleanup()
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 getPSFs(self, h5_data):
x = h5_data['x']
y = h5_data['y']
a = h5_data['sum']
sx = h5_data['ysigma']
sy = h5_data['xsigma']
h = a / (2.0 * numpy.pi * sx * sy)
h5_data['height'] = h
return dg.drawGaussians(
(self.x_size, self.y_size),
numpy.concatenate(
(x[:, None], y[:, None], h[:, None], sx[:, None], sy[:, None]),
axis=1),
res=5)
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_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(sigma_range = [1.0, 2.0])
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 getPSFs(self, i3_data):
x = i3_data['x'] - 1.0
y = i3_data['y'] - 1.0
a = i3_data['a']
ax = i3_data['ax']
w = i3_data['w']
sx = 0.5 * numpy.sqrt(w * w / ax) / self.nm_per_pixel
sy = 0.5 * numpy.sqrt(w * w * ax) / self.nm_per_pixel
h = a / (2.0 * numpy.pi * sx * sy)
i3_data['h'] = h
return dg.drawGaussians(
(self.x_size, self.y_size),
numpy.concatenate(
(x[:, None], y[:, None], h[:, None], sx[:, None], sy[:, None]),
axis=1))
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 getPSFs(self, h5_data):
x = h5_data['x']
y = h5_data['y']
a = h5_data['sum']
sx = h5_data['ysigma']
sy = h5_data['xsigma']
h = a/(2.0 * numpy.pi * sx * sy)
h5_data['height'] = h
return dg.drawGaussians((self.x_size, self.y_size),
numpy.concatenate((x[:,None],
y[:,None],
h[:,None],
sx[:,None],
sy[:,None]),
axis = 1),
res = 5)
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(sigma_range = [1.0, 2.0])
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_matched_filter1():
"""
Verify that the filter results are normalized correctly.
"""
x_size = 80
y_size = 90
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0, :] = [x_size / 2, y_size / 2, 1.0, 1.0, 1.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf = psf / numpy.sum(psf)
flt = matchedFilterC.MatchedFilter(psf)
for i in range(1, 5):
image = numpy.zeros((x_size, y_size))
image[int(x_size / 2), int(y_size / 2)] = float(i)
conv = flt.convolve(image)
assert (abs(numpy.sum(image) - numpy.sum(conv)) < 1.0e-6)
def test_matched_filter1():
"""
Verify that the filter results are normalized correctly.
"""
x_size = 80
y_size = 90
objects = numpy.zeros((1, 5))
# Make filter with unit sum.
objects[0,:] = [x_size/2, y_size/2, 1.0, 1.0, 1.0]
psf = dg.drawGaussians((x_size, y_size), objects)
psf = psf/numpy.sum(psf)
flt = matchedFilterC.MatchedFilter(psf)
for i in range(1,5):
image = numpy.zeros((x_size, y_size))
image[int(x_size/2), int(y_size/2)] = float(i)
conv = flt.convolve(image)
assert(abs(numpy.sum(image) - numpy.sum(conv)) < 1.0e-6)
def getPSFs(self, i3_data):
x = i3_data['x'] - 1.0
y = i3_data['y'] - 1.0
a = i3_data['a']
ax = i3_data['ax']
w = i3_data['w']
sx = 0.5*numpy.sqrt(w*w/ax)/self.nm_per_pixel
sy = 0.5*numpy.sqrt(w*w*ax)/self.nm_per_pixel
h = a/(2.0 * numpy.pi * sx * sy)
i3_data['h'] = h
return dg.drawGaussians((self.x_size, self.y_size),
numpy.concatenate((x[:,None],
y[:,None],
h[:,None],
sx[:,None],
sy[:,None]),
axis = 1))
if (__name__ == "__main__"):
import tifffile
import storm_analysis.simulator.draw_gaussians_c as dg
x_size = 200
y_size = 300
objects = numpy.zeros((1, 5))
# PSF of shape 1.
objects[0,:] = [x_size/2, y_size/2, 1.0, 1.0, 1.0]
psf1 = dg.drawGaussians((x_size, y_size), objects)
psf1 = psf1/numpy.sum(psf1)
flt1 = MatchedFilter(psf1)
# PSF of shape 2.
objects[0,:] = [x_size/2, y_size/2, 1.0, 2.0, 0.7]
psf2 = dg.drawGaussians((x_size, y_size), objects)
psf2 = psf2/numpy.sum(psf2)
flt2 = MatchedFilter(psf2)
result1 = flt1.convolve(10000.0 * psf2)
result2 = flt2.convolve(10000.0 * psf2)
print("Match to 1:", numpy.max(result1))
print("Match to 2:", numpy.max(result2))
if (__name__ == "__main__"):
import tifffile
import storm_analysis.simulator.draw_gaussians_c as dg
x_size = 200
y_size = 300
objects = numpy.zeros((1, 5))
# PSF of shape 1.
objects[0,:] = [x_size/2, y_size/2, 1.0, 1.0, 1.0]
psf1 = dg.drawGaussians((x_size, y_size), objects)
psf1 = psf1/numpy.sum(psf1)
flt1 = MatchedFilter(psf1)
# PSF of shape 2.
objects[0,:] = [x_size/2, y_size/2, 1.0, 2.0, 0.7]
psf2 = dg.drawGaussians((x_size, y_size), objects)
psf2 = psf2/numpy.sum(psf2)
flt2 = MatchedFilter(psf2)
result1 = flt1.convolve(10000.0 * psf2)
result2 = flt2.convolve(10000.0 * psf2)
print("Match to 1:", numpy.max(result1))
print("Match to 2:", numpy.max(result2))
y_size) * random.random()
z_off = 0.8 * (random.random() - 0.4)
z_vals[j] = z_off * 1000.0
# On a grid.
if 1:
x_vals[j] = 20.0 + 40.0 * (j % 6)
y_vals[j] = 20.0 + 40.0 * math.floor(float(j) / 6.0)
z_off = -0.75 + 1.5 * float(j) / float(num_objects - 1)
z_vals[j] = z_off * 1000.0
# Generate objects.
objects = PSF.PSF(x_vals, y_vals, z_vals, h_vals)
# Draw the image.
image = dg.drawGaussians([x_size, y_size], objects, background=50, res=5)
#image = dg.drawGaussians([x_size, y_size], objects, background = 0, res = 5)
#image[0:(x_size/2),:] += 50
# Add poisson noise and baseline.
image = numpy.random.poisson(image) + 100.0
# Save the image.
dax_data.addFrame(image)
# Save the molecule locations.
a_vals = PSF.PSFIntegral(z_vals, h_vals)
ax = numpy.ones(num_objects)
ww = 2.0 * 160.0 * numpy.ones(num_objects)
if (PSF.psf_type == "astigmatic"):
y_vals[j] = 0.05 * float(y_size) + 0.9 * float(y_size) * random.random()
z_off = 0.8*(random.random() - 0.4)
z_vals[j] = z_off * 1000.0
# On a grid.
if 1:
x_vals[j] = 20.0 + 40.0*(j%6)
y_vals[j] = 20.0 + 40.0*math.floor(float(j)/6.0)
z_off = -0.75 + 1.5*float(j)/float(num_objects - 1)
z_vals[j] = z_off * 1000.0
# Generate objects.
objects = PSF.PSF(x_vals, y_vals, z_vals, h_vals)
# Draw the image.
image = dg.drawGaussians([x_size, y_size], objects, background = 50, res = 5)
#image = dg.drawGaussians([x_size, y_size], objects, background = 0, res = 5)
#image[0:(x_size/2),:] += 50
# Add poisson noise and baseline.
image = numpy.random.poisson(image) + 100.0
# Save the image.
dax_data.addFrame(image)
# Save the molecule locations.
a_vals = PSF.PSFIntegral(z_vals, h_vals)
ax = numpy.ones(num_objects)
ww = 2.0 * 160.0 * numpy.ones(num_objects)
if (PSF.psf_type == "astigmatic"):