def calcCRB(self, caller=None):
from PYME.Analysis import cramerRao
from PYME.Analysis.PSFGen import fourierHNA
#print 'b'
import numpy as np
try:
d = self.image.data[:, :, :, 0].squeeze()
I = d[:, :, int(d.shape[2] / 2)].sum()
vs = self.image.voxelsize_nm
#print 'fi'
FI = cramerRao.CalcFisherInformZn2(d * (2e3 / I) + self.background,
100,
voxelsize=vs)
#print 'crb'
self.crb = cramerRao.CalcCramerReoZ(FI)
#print 'crbd'
z_ = np.arange(d.shape[2]) * vs.z
self.z_ = z_ - z_.mean()
ps_as = fourierHNA.GenAstigPSF(self.z_, dx=vs.x, strength=2)
I = ps_as[:, :, int(ps_as.shape[2] / 2)].sum()
self.crb_as = (cramerRao.CalcCramerReoZ(
cramerRao.CalcFisherInformZn2(ps_as * 2000 / I +
self.background,
500,
voxelsize=vs)))
self.draw()
except np.linalg.linalg.LinAlgError:
# don't hang if we can't compute the CRLB
import traceback
traceback.print_exc()
def OnCalcCRB3DvsBG(self, event):
from PYME.Analysis import cramerRao
from PYME.Analysis.PSFGen import fourierHNA
#print 'b'
import numpy as np
vs = 1e3 * np.array([
self.image.mdh['voxelsize.x'], self.image.mdh['voxelsize.y'],
self.image.mdh['voxelsize.z']
])
zf = self.image.data.shape[2] / 2
dz = 500 / vs[2]
d = self.image.data[:, :, (zf - dz):(zf + dz + 1)]
I = d[:, :, d.shape[2] / 2].sum()
bgv = np.logspace(-1, 2)
z_ = np.arange(d.shape[2]) * vs[2]
z_ = z_ - z_.mean()
ps_as = fourierHNA.GenAstigPSF(z_, vs[0], 2)
Ias = ps_as[:, :, ps_as.shape[2] / 2].sum()
crb3D = []
crb3Das = []
for bg in bgv:
FI = cramerRao.CalcFisherInformZn2(d * (2e3 / I) + bg,
100,
voxelsize=vs)
crb = cramerRao.CalcCramerReoZ(FI)
crb_as = (cramerRao.CalcCramerReoZ(
cramerRao.CalcFisherInformZn2(ps_as * 2000 / Ias + bg,
500,
voxelsize=vs)))
crb3D.append(np.sqrt(crb.sum(1)).mean())
crb3Das.append(np.sqrt(crb_as.sum(1)).mean())
import pylab
pylab.figure()
pylab.plot(bgv, crb3Das, label='Theoretical PSF')
pylab.plot(bgv, crb3D, label='Measured PSF')
pylab.legend()
pylab.xlabel('Background [photons]')
pylab.ylabel('Average CRB 3D')
pylab.title('Cramer-Rao bound vs Background')
def OnCalcCRB(self, event):
#print 'f'
from PYME.Analysis import cramerRao
from PYME.Analysis.PSFGen import fourierHNA
#print 'b'
import numpy as np
d = self.image.data[:, :, :]
I = d[:, :, d.shape[2] / 2].sum()
vs = 1e3 * np.array([
self.image.mdh['voxelsize.x'], self.image.mdh['voxelsize.y'],
self.image.mdh['voxelsize.z']
])
#print 'fi'
FI = cramerRao.CalcFisherInformZn2(d * (2e3 / I), 100, voxelsize=vs)
#print 'crb'
crb = cramerRao.CalcCramerReoZ(FI)
#print 'crbd'
import pylab
z_ = np.arange(d.shape[2]) * self.image.mdh['voxelsize.z'] * 1.0e3
z_ = z_ - z_.mean()
print('p')
pylab.figure()
pylab.plot(z_, np.sqrt(crb[:, 0]), label='x')
pylab.plot(z_, np.sqrt(crb[:, 1]), label='y')
pylab.plot(z_, np.sqrt(crb[:, 2]), label='z')
pylab.legend()
pylab.xlabel('Defocus [nm]')
pylab.ylabel('Std. Dev. [nm]')
pylab.title('Cramer-Rao bound for 2000 photons')
ps_as = fourierHNA.GenAstigPSF(z_, vs[0], 2)
I = ps_as[:, :, ps_as.shape[2] / 2].sum()
crb_as = np.sqrt(
cramerRao.CalcCramerReoZ(
cramerRao.CalcFisherInformZn2(ps_as * 2000 / I,
500,
voxelsize=vs)))
pylab.plot(z_, crb_as[:, 0], 'b:')
pylab.plot(z_, crb_as[:, 1], 'g:')
pylab.plot(z_, crb_as[:, 2], 'r:')