def g1(x, kimax=None, kjmax=None, cross=True):
i, j = np.meshgrid(ifreq, jfreq, indexing="ij")
x = np.asarray(x)
#add dimensions for broadcasting
x = x[..., None, None]
#expected signal
a = NUM_PARTICLES * formf**2 * AREA_RATIO
#expected variance (signal + noise)
if APPLY_DUST:
if cross:
v = a + noise_level(
(window * dust1 + window * dust2) / 2, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
else:
v = a + noise_level(
window * dust1, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
else:
v = a + noise_level(
window, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
#expected scalling factor
a = a / v
a = rfft2_crop(a, kimax, kjmax)
i = rfft2_crop(i, kimax, kjmax)
j = rfft2_crop(j, kimax, kjmax)
return a * np.exp(-D * (i**2 + j**2) * x)
def fvar(i, j):
#expected signal
a = NUM_PARTICLES * formf[i, j]**2 * AREA_RATIO
if CROSS:
v = a + noise_level(
(window * video_simulator.dust1 + window * video_simulator.dust2) /
2, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
else:
v = a + noise_level(
window * video_simulator.dust1, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
#v = a + noise_level(window,BACKGROUND)#expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
return v / (ADC_SCALE_FACTOR**2)
def g1(x, i, j):
#expected signal
a = NUM_PARTICLES * formf[i, j]**2 * AREA_RATIO
#expected variance (signal + noise)
if CROSS:
v = a + noise_level(
(window * video_simulator.dust1 + window * video_simulator.dust2) /
2, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
else:
v = a + noise_level(
window * video_simulator.dust1, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
#v = a + noise_level(window,BACKGROUND) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
#expected scalling factor
a = a / v
return a * np.exp(-D * (i**2 + j**2) * x)
def fvar(kimax=None, kjmax=None, cross=True):
#expected signal
a = NUM_PARTICLES * formf**2 * AREA_RATIO
if APPLY_DUST:
if cross:
v = a + noise_level(
(window * dust1 + window * dust2) / 2, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
else:
v = a + noise_level(
window * dust1, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
else:
v = a + noise_level(
window, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
v = v / (ADC_SCALE_FACTOR**2)
return rfft2_crop(v, kimax, kjmax)
def fdelta(i, j):
a = NUM_PARTICLES * formf[i, j]**2 * AREA_RATIO
n = noise_level(
window, BACKGROUND
) #expected abs FFT squared of gaussian noise with std of BACKGROUND**0.5
return n / (a + n)