def bandpass_filtering(sphere_data, bpf):
# apply bandpass filter to all the data sets
# allocate space for the image data set
bp_data_complex = np.zeros((num_test, res, res, 2))
bp_data_intensity = np.zeros((num_test, res, res, 1))
cnt = 0
# merge the 2-channel dataset into one channel as complex images
complex_im = sphere_data[..., 0] + sphere_data[..., 1] * 1j
# for each image
for i in range(num_test):
# apply bandpass filtering
filtered_im_complex = ms.apply_filter(simRes, simFov,
complex_im[i, ...], bpf)
# crop the field after bandpass filtering
cropped_im_complex = ms.crop_field(res, filtered_im_complex)
# saving them individually
bp_data_complex[i, :, :, 0] = np.real(cropped_im_complex)
bp_data_complex[i, :, :, 1] = np.imag(cropped_im_complex)
bp_data_intensity[i, :, :, 0] = np.abs(cropped_im_complex)**2
# print progress
cnt += 1
sys.stdout.write('\r' + str(cnt / num_test * 100) + ' %')
sys.stdout.flush() # important
return bp_data_complex, bp_data_intensity
def bandpass_filtering(bpf):
# apply bandpass filter to all the data sets
# allocate space for the image data set
bp_data_complex = np.zeros((num_test, res, res, 2))
bp_data_intensity = np.zeros((num_test, res, res, 1))
cnt = 0
# band pass and crop
for h in range(num):
sphere_dir = data_dir + '\X_test_%3.3d' % (h) + '.npy'
sphere_data = np.load(sphere_dir)
complex_im = sphere_data[..., 0] + sphere_data[..., 1] * 1j
for i in range(num_test_in_group):
filtered_im_complex = ms.apply_filter(res, fov, complex_im[i, ...],
bpf)
bp_data_complex[h * num_test_in_group + i, :, :,
0] = np.real(filtered_im_complex)
bp_data_complex[h * num_test_in_group + i, :, :,
1] = np.imag(filtered_im_complex)
bp_data_intensity[h * num_test_in_group + i, :, :,
0] = np.abs(filtered_im_complex)**2
# print progress
cnt += 1
sys.stdout.write('\r' + str(cnt / num_test * 100) + ' %')
sys.stdout.flush() # important
return bp_data_complex, bp_data_intensity
nr = np.linspace(nr_min, nr_max, num)
ni = np.linspace(ni_min, ni_max, num)
return a, nr, ni
#%%
# set the size and resolution of both planes
fov = 16 # field of view
res = 128 # resolution
lambDa = 1 # wavelength
k = 2 * math.pi / lambDa # wavenumber
padding = 3 # padding
# simulation resolution
# in order to do fft and ifft, expand the image use padding
simRes, simFov = ms.pad(res, fov, padding)
ps = [0, 0, 0] # position of the sphere
k_dir = [0, 0, -1] # propagation direction of the plane wave
E = [1, 0, 0] # electric field vector
working_dis = ms.get_working_dis(padding)
# scale factor of the intensity
scale_factor = ms.get_scale_factor(simRes, simFov, working_dis)
# define feature space
num = 20
num_samples = num**3
a_min = 1.0
a_max = 2.0
nr_min = 1.1
nr_max = 2.0
noise_pc = np.linspace(noise_pc_min, noise_pc_max, num_bp)
return a, nr, ni, noise_pc
#%%
# set the size and resolution of both planes
fov = 32 # field of view
res = 256 # resolution
lambDa = 1 # wavelength
k = 2 * math.pi / lambDa # wavenumber
padding = 2 # padding
simRes, simFov = ms.pad(res, fov, padding)
working_dis = ms.get_working_dis(padding)
scale_factor = ms.get_scale_factor(res, fov, working_dis)
line_size = int(simRes / 2)
ps = [0, 0, 0] # position of the sphere
k_dir = [0, 0, -1] # propagation direction of the plane wave
E = [1, 0, 0] # electric field vector
# define feature space
num = 10
num_bp = 100
num_samples = num**3 * num_bp
# position of the sphere ps = [0, 0, 0] # resolution of the cropped image res = 128 # in and out numerical aperture of the bandpass optics NA_in = 0 NA_out = 0 # wave length lambDa = 1 # wavenumber k = 2 * np.pi / lambDa # padding size padding = 2 # field of view fov = 16 simRes, simFov = ms.pad(res, fov, padding) # set ranges of the features of the data set # refractive index n_r_min = 1.1 n_r_max = 2.0 # attenuation coefficient n_i_min = 0.01 n_i_max = 0.05 # sphere radius a_min = 5 a_max = 10 # the z position of the visiulization plane z_max = a_max # the maximal order l = ms.get_order(a_max, lambDa)
#%%
# set parameters
fov = 32 # field of view
res = 256 # resolution
a = 2 # radius of the spere
lambDa = 1 # wavelength
n = 1.4 + 1j * 0.02 # refractive index
k = 2 * math.pi / lambDa # wavenumber
padding = 0 # padding
working_dis = 10000 * (2 * padding + 1) # working distance
scale_factor = working_dis * 2 * math.pi * res/fov # scale factor of the intensity
NA_in = 0.0
NA_out = 1
simRes, simFov = ms.pad(res, fov, padding)
ps = [0, 0, 0] # position of the sphere
k_dir = [0, 0, -1] # propagation direction of the plane wave
E = [1, 0, 0] # electric field vector
E0 = 1
#%%
# get 1-D far field
E_far_line = ms.far_field(simRes, simFov, working_dis, a, n,
lambDa, k_dir, scale_factor, dimension=1)
# get 1-D near line without bandpass filtering
E_near_line, E_near_x = ms.idhf(simRes, simFov, E_far_line)
# get 1-D bandpass filter
bpf_line = ms.bandpass_filter(simRes, simFov, NA_in, NA_out, dimension=1)
# get 1-D near field and its sample index
import numpy as np import math import matplotlib.pyplot as plt import chis.MieScattering as ms #%% # set parameters fov = 16 # field of view res = 128 # resolution a = 1 # radius of the spere lambDa = 1 # wavelength n = 1.5 + 1j*0.01 # refractive index k = 2 * math.pi / lambDa # wavenumber padding = 1 # padding simRes, simFov = ms.pad(res, fov, padding) working_dis = 10000 * (2 * padding + 1) # working distance scale_factor = working_dis * 2 * math.pi * res/fov # scale factor of the intensity NA_in = 0.3 NA_out = 0.6 ps = [0, 0, 0] # position of the sphere k_dir = [0, 0, -1] # propagation direction of the plane wave E = [1, 0, 0] # electric field vector E0 = 1 #%% # get far field E_far = ms.far_field(simRes, simFov, working_dis, a, n, lambDa, scale_factor) # get near field E_near = ms.far2near(E_far) + E0
# for the meanings of these parameters
# by keep the cursor in the function and hit Ctrl+I
k = [0, 0, -1]
res = 256
numSample = 1000
NA_in = 0
NA_out = 0
numFrames = 70
option = 'Horizontal'
n0 = 1.2
fov = 8
padding = 0
a = 1
pp = 0
ps = [0, 0, 0]
simRes, simFov = ms.pad(res, fov, padding)
#%%
# calculate the horizontal simulation
Et_h, B0, Emask0, rVecs = traditional_mie.getTotalField(
k, k, n0, res, a, ps, pp, padding, fov, numSample, NA_in, NA_out, option)
# calculate the vertical simulation
Et_v, B1, Emask1, rVecs_v = traditional_mie.getTotalField(
k, k, n0, res, a, ps, pp, padding, fov, numSample, NA_in, NA_out,
'Vertical')
# propagate the horizontal simulation
# the field can be propagated along the k direction for d distance
d = 1
Et_0_p = ms.propagate_2D(simRes, simFov, Et_h, d)
raise ValueError('Invalid Value for dimension')
return E_crop
#%%
# specify parameters
fov = 32 # field of view
res = 256 # resolution
a = 1 # radius of the spere
lambDa = 1 # wavelength
n = 1.5 + 1j * 0.01 # refractive index
k = 2 * np.pi / lambDa # wavenumber
padding = 1 # padding
simRes, simFov = ms.pad(res, fov, padding)
working_dis = ms.get_working_dis(padding)
scale_factor = ms.get_scale_factor(
res, fov, working_dis) # scale factor of the intensity
NA_in = 0.0
NA_out = 1.0
ps = [0, 0, 0] # position of the sphere
k_dir = [0, 0, -1] # propagation direction of the plane wave
E = [1, 0, 0] # electric field vector
E0 = 1
shift1 = 0
shift2 = 99
#%%
# get 1-D far field
#%%
# load data set
raw_im = np.load(
r'D:\irimages\irholography\CNN\data_v9_far_field\raw_data\im_data010.npy')
raw_im = np.reshape(raw_im, (640, 640, 2, 400))
raw_im = np.swapaxes(raw_im, 0, -1)
raw_im = np.swapaxes(raw_im, -2, -1)
data_dir = r'D:\irimages\irholography\CNN\data_v9_far_field'
#%%
num_test = 400
lambDa = 1
NA_in = 0
NA_out = 0.25
res = 128
fov = 16
padding = 2
simRes, simFov = ms.pad(res, fov, padding)
# create a bandpass filter
bpf = ms.bandpass_filter(simRes, simFov, NA_in, NA_out, dimension=2)
# apply bandpass filtering to all the images
im_bp_comp, im_bp_inten = bandpass_filtering(raw_im, bpf)
#%%
# create an animation
animation.anime(im_bp_inten, 20, data_dir, 'intensityBP0.25', 'Real')
# crop the image
E_crop = E[y_lower: y_upper, x_left: x_right]
return E_crop
#%%
# specify parameters
fov = 16 # field of view
res = 256 # resolution
a = 1 # radius of the spere
lambDa = 1 # wavelength
n = 1.5 + 1j*0.01 # refractive index
k = 2 * np.pi / lambDa # wavenumber
padding = 1 # padding
simRes, simFov = ms.pad(res, fov, padding)
working_dis = ms.get_working_dis(padding)
scale_factor = ms.get_scale_factor(res, fov, working_dis) # scale factor of the intensity
NA_in = 0.0
NA_out = 1.0
ps = [0, 0, 0] # position of the sphere
k_dir = [0, 0, -1] # propagation direction of the plane wave
E = [1, 0, 0] # electric field vector
E0 = 1
shift1 = 0
shift2 = int(simRes/8)
#%%
# get far field
E_far = ms.far_field(simRes, simFov, working_dis, a, n, lambDa, k_dir, scale_factor)
n_i_min = 0.01
n_i_max = 0.05
a_min = 5
a_max = 10
nb_nr = 25
nb_ni = 25
nb_a = 25
nr = np.linspace(n_r_min, n_r_max, nb_nr)
ni = np.linspace(n_i_min, n_i_max, nb_ni)
a = np.linspace(a_min, a_max, nb_a)
l = ms.get_order(a_max)
im_data = np.zeros((res, res, 2, nb_nr, nb_ni, nb_a))
sphere_data = np.zeros((3, nb_nr, nb_ni, nb_a))
B_data_real = np.zeros((len(l), nb_nr, nb_ni, nb_a))
B_data_imag = np.zeros((len(l), nb_nr, nb_ni, nb_a))
cnt = 0
for i in range(nb_nr):
for j in range(nb_ni):
for h in range(nb_a):
n0 = nr[i] + ni[j] * 1j
a0 = a[h]
#position of the visualization plane, along z axis
pp = a0
intensity_CNN = load_model(
r'D:\irimages\irholography\CNN\CNN_v12_far_field_a_n\intensity\intensity_model30.h5'
)
# parent directory of the data set
data_dir = r'D:\irimages\irholography\CNN\data_v10_far_field\split_data\test'
# allocate space for complex and intensity accuracy
complex_error = np.zeros((nb_NA, 3), dtype=np.float64)
intensity_error = np.zeros((nb_NA, 3), dtype=np.float64)
# for each NA to be tested
for NA_idx in range(nb_NA):
# calculate the band pass filter
bpf = ms.bandpass_filter(simRes, simFov, NA_in, NA_list[NA_idx])
# print some info about the idx of NA
print('Banbpassing the ' + str(NA_idx + 1) + 'th filter \n')
im_data_complex, im_data_intensity = bandpass_filtering(bpf)
print('Evaluating complex model \n')
# handle complex model first
complex_error[NA_idx, :] = calculate_error(im_data_complex,
option='complex')
print('Evaluating intensity model \n')
# handle intensity model second
intensity_error[NA_idx, :] = calculate_error(im_data_intensity,
option='intensity')
S_noise = S + eta_r + 1j * eta_i
return S_noise
# set the size and resolution of both planes
fov = 16 # field of view
res = 128 # resolution
lambDa = 1 # wavelength
k = 2 * math.pi / lambDa # wavenumber
padding = 2 # padding
simRes, simFov = ms.pad(res, fov, padding)
working_dis = ms.get_working_dis(padding)
scale_factor = ms.get_scale_factor(res, fov, working_dis)
line_size = int(simRes / 2)
ps = [0, 0, 0] # position of the sphere
k_dir = [0, 0, -1] # propagation direction of the plane wave
E = [1, 0, 0] # electric field vector
# define feature space
num = 20
num_samples = num**4
a_min = 1.0
# allocate space for data set
sphere_data = np.zeros((3, num, num, num, num_bp))
im_data = np.zeros((line_size, 2, num, num, num, num_bp))
im_dir = r'D:\irimages\irholography\CNN\data_v12_far_line\more_bandpass_HD\im_data'
# get all the bandpass filters
bpfs = []
for b in range(num_bp):
bp0 = bp[b]
bpf = get_bpf(simFov, simRes, 0, bp0)
bpf_line = bpf[0, :int(simRes/2)+1]
bpf_line0 = ms.bandpass_filter(simRes, simFov, 0, bp0, dimention=1)
bpfs.append(bpf_line)
cnt = 0
for h in range(num):
for i in range(num):
for j in range(num):
a0 = a[h]
n0 = nr[i] + 1j*ni[j]
B = coeff_b(l, k, n0, a0)
# integrate through all the orders to get the farfield in the Fourier Domain
E_scatter_fft = np.sum(scatter_matrix * B, axis = -1) * scale_factor