def createDefocusPSFOversampTest():
""""""
#size = [6.2, 6.2, 6.4]
size = [1.6, 1.6, 3.2]
resolution = [0.1, 0.1, 0.1]
sig = .05
zi = 800.
K = 100.
psf_defocus_params = {'sigma': sig, 'zi': zi, 'K': K}
f_ident = 'psf_defocus_{:0>3}.tif'
path = util.ptjoin(
util.SIM_PSF,
'defocussing_sig-{:.2f}_zi-{:.0f}_K-{:.0f}'.format(sig, zi, K),
'res_[{0[0]:.1f},{0[1]:.1f},{0[2]:.1f}]'.format(resolution))
psf_simu = pg.PSF_Generator(size,
resolution,
psf_type='positive',
psf_model='defocus',
psf_oddity='odd',
name='DefocusPSF',
comment='SecondIteration',
psf_params=psf_defocus_params)
psf_simu.createPSF(oversampling=3)
psf_simu2 = pg.PSF_Generator(size,
resolution,
psf_type='positive',
psf_model='defocus',
psf_oddity='odd',
name='DefocusPSF',
comment='SecondIteration',
psf_params=psf_defocus_params)
psf_simu2.createPSF(oversampling=5)
psf_diff = psf_simu2.out - psf_simu.out
print(psf_simu.out.shape)
print(psf_simu2.out.shape)
#psf_simu.initSaveParameters(path, f_ident, overwrite=True)
#psf_simu.saveSolution()
#[fig, ani] = v_util.imshow3D_ani(psf_simu2.out, scale_log=False)
#[fig, ani] = v_util.imshow3D_ani(psf_simu.out, scale_log=False)
[fig, ani] = v_util.imshow3D_ani(psf_diff, scale_log=False)
#plt.figure()
#plt.subplot(1,2,1)
#plt.plot(psf_simu.out[:,32,128])
#plt.plot(psf_simu2.out[:,32,128])
#plt.plot(psf_diff[:,32,128])
plt.show()
def createGaussSimpleBatch():
""""""
size = [1.6, 1.6, 1.6]
#resolution = [[0.1, 0.1, 0.1],[0.2,0.2,0.2]]
resolution = [[0.4, 0.4, 0.4], [0.8, 0.8, 0.8]]
sig_list = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]
f_ident = 'psf_gauss_simple_{:0>3}.tif'
for res in resolution:
for sig in sig_list:
path = util.ptjoin(
util.SIM_PSF, 'odd', 'gauss_simple_sig-{:.2f}'.format(sig),
'res_[{0[0]:.1f},{0[1]:.1f},{0[2]:.1f}]'.format(res))
psf_gauss_simple = {'sigma': sig}
psf_simu = pg.PSF_Generator(size,
res,
psf_type='positive',
psf_model='gaussian_simple',
psf_oddity='odd',
name='GaussianPSF',
comment='FifthIteration',
psf_params=psf_gauss_simple)
psf_simu.createPSF(oversampling=5)
psf_simu.initSaveParameters(path, f_ident, overwrite=True)
psf_simu.saveSolution()
def createGaussSimple():
""""""
size = [1.6, 1.6, 1.6]
resolution = [0.1, 0.1, 0.1]
sig = 0.3
psf_gauss_simple = {'sigma': sig}
f_ident = 'psf_gauss_simple_{:0>3}.tif'
path = util.ptjoin(
util.SIM_PSF, 'gauss_simple_sig-{:.2f}'.format(sig),
'res_[{0[0]:.1f},{0[1]:.1f},{0[2]:.1f}]'.format(resolution))
psf_simu = pg.PSF_Generator(size,
resolution,
psf_type='positive',
psf_model='gaussian_simple',
psf_oddity='even',
name='GaussianPSF',
comment='SecondIteration',
psf_params=psf_gauss_simple)
psf_simu.createPSF()
psf_simu.initSaveParameters(path, f_ident, overwrite=True)
psf_simu.saveSolution()
[fig, ani] = v_util.imshow3D_ani(psf_simu.out, scale_log=True)
plt.show()
def createDefocusPSF():
""""""
#size = [6.2, 6.2, 6.4]
size = [1.6, 1.6, 6.4]
resolution = [0.1, 0.1, 0.1]
sig = .05
zi = 2000.
K = 400.
psf_defocus_params = {'sigma': sig, 'zi': zi, 'K': K}
f_ident = 'psf_defocus_{:0>3}.tif'
path = util.ptjoin(
util.SIM_PSF,
'defocussing_sig-{:.2f}_zi-{:.0f}_K-{:.0f}'.format(sig, zi, K),
'res_[{0[0]:.1f},{0[1]:.1f},{0[2]:.1f}]'.format(resolution))
psf_simu = pg.PSF_Generator(size,
resolution,
psf_type='positive',
psf_model='defocus',
psf_oddity='odd',
name='DefocusPSF',
comment='ThirdIteration',
psf_params=psf_defocus_params)
psf_simu.createPSF(oversampling=5)
psf_simu.initSaveParameters(path, f_ident, overwrite=True)
psf_simu.saveSolution()
def createGaussPSF():
""""""
size = [3.2, 3.2, 3.2]
resolution = [0.1, 0.1, 0.1]
sig0 = 0.15
sig1 = 0.
sig2 = 0.22
psf_gauss_params = {'sigma_0': sig0, 'sigma_1': sig1, 'sigma_2': sig2}
f_ident = 'psf_gauss_{:0>3}.tif'
path = util.ptjoin(
util.SIM_PSF,
'gauss_sig0-{:.2f}_sig1-{:.2f}_sig2-{:.2f}'.format(sig0, sig1, sig2),
'res_[{0[0]:.1f},{0[1]:.1f},{0[2]:.1f}]'.format(resolution))
psf_simu = pg.PSF_Generator(size,
resolution,
psf_type='positive',
psf_model='gaussian',
psf_oddity='odd',
name='GaussianPSF',
comment='SecondIteration',
psf_params=psf_gauss_params)
psf_simu.createPSF()
psf_simu.initSaveParameters(path, f_ident, overwrite=True)
psf_simu.saveSolution()
[fig, ani] = v_util.imshow3D_ani(psf_simu.out, scale_log=True)
plt.show()
def createGaussBatch():
""""""
size = [3.2, 3.2, 3.2]
#resolution = [[0.1, 0.1, 0.1],[0.2,0.2,0.2]]
resolution = [[0.4, 0.4, 0.4], [0.8, 0.8, 0.8]]
sig0_list = [0.05, 0.10, 0.15, 0.2, 0.3, 0.4]
sig1_list = [0]
sig2_list = [0.15, 0.2, 0.25, 0.3, 0.4]
f_ident = 'psf_gauss_{:0>3}.tif'
for res in resolution:
for sig0 in sig0_list:
for sig1 in sig1_list:
for sig2 in sig2_list:
psf_gauss_params = {
'sigma_0': sig0,
'sigma_1': sig1,
'sigma_2': sig2
}
path = util.ptjoin(
util.SIM_PSF, 'odd',
'gauss_sig0-{:.2f}_sig1-{:.2f}_sig2-{:.2f}'.format(
sig0, sig1, sig2),
'res_[{0[0]:.1f},{0[1]:.1f},{0[2]:.1f}]'.format(res))
psf_simu = pg.PSF_Generator(size,
res,
psf_type='positive',
psf_model='gaussian',
psf_oddity='odd',
name='GaussianPSF',
comment='FifthIteration',
psf_params=psf_gauss_params)
psf_simu.createPSF(oversampling=5)
psf_simu.initSaveParameters(path, f_ident, overwrite=True)
psf_simu.saveSolution()
def createDefocusBatch():
""""""
size = [3.2, 3.2, 6.4]
#resolution = [[0.1,0.1,0.1],[0.2,0.2,0.2]]
resolution = [[0.4, 0.4, 0.4], [0.8, 0.8, 0.8]]
sig_list = [.05, 0.1, 0.2, 0.3]
zi_list = [800, 1000, 2000]
K_list = [100, 200, 300, 400]
f_ident = 'psf_defocus_{:0>3}.tif'
for res in resolution:
for sig in sig_list:
for zi in zi_list:
for K in K_list:
psf_defocus_params = {'sigma': sig, 'zi': zi, 'K': K}
path = util.ptjoin(
util.SIM_PSF, 'odd',
'defocussing_sig-{:.2f}_zi-{:.0f}_K-{:.0f}'.format(
sig, zi, K),
'res_[{0[0]:.1f},{0[1]:.1f},{0[2]:.1f}]'.format(res))
psf_simu = pg.PSF_Generator(size,
res,
psf_type='positive',
psf_model='defocus',
psf_oddity='odd',
name='DefocusPSF',
comment='FifthIteration',
psf_params=psf_defocus_params)
psf_simu.createPSF(oversampling=5)
psf_simu.initSaveParameters(path, f_ident, overwrite=True)
psf_simu.saveSolution()
def testConvolutionIdea():
""""""
orig_stack = np.ones((256, 256, 64))
orig_stack[128:130, 128:130, 30:32] = 0.6
orig_stack = orig_stack * 255
psf = psfg.PSF_Generator([3.2, 3.2, 3.2], [0.1, 0.1, 0.1],
psf_oddity='odd',
psf_params={
'sigma': 0.2,
'zi': 1000,
'K': 200
})
psf.createPSF(oversampling=3)
#invert orig_stack
orig_max = orig_stack.max()
orig_min = orig_stack.min()
orig_stack = (-1) * (orig_stack - orig_min) + (orig_max - orig_min)
print(psf.out.max())
print(psf.out.min())