Tibeuleu/FOC_Reduction
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remove axis error computation on polarization components

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This commit is contained in:
Thibault Barnouin
2022-03-23 15:09:46 +01:00
parent b0075ff6fc
commit 0d8048240d
10 changed files with 14 additions and 58 deletions
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62
src/lib/reduction.py
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@@ -66,16 +66,6 @@ globals()['theta'] = np.array([180.*np.pi/180., 60.*np.pi/180., 120.*np.pi/180.]
# Uncertainties on the orientation of the polarizers' axes taken to be 3deg (see Nota et. al 1996, p36; Robinson & Thomson 1995)
globals()['sigma_theta'] = np.array([3.*np.pi/180., 3.*np.pi/180., 3.*np.pi/180.])
def princ_angle(ang):
"""
Return the principal angle in the 0-180° quadrant.
"""
while ang < 0.:
ang += 180.
while ang > 180.:
ang -= 180.
return ang
def get_row_compressor(old_dimension, new_dimension, operation='sum'):
"""
@@ -1068,7 +1058,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
# Routine for the FOC instrument
if instr == 'FOC':
# Get image from each polarizer and covariance matrix
pol_array, pol_cov_m = polarizer_avg(data_array, error_array, data_mask,
pol_array, pol_cov = polarizer_avg(data_array, error_array, data_mask,
headers, FWHM=FWHM, scale=scale, smoothing=smoothing)
pol0, pol60, pol120 = pol_array
@@ -1103,15 +1093,14 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
# Orientation and error for each polarizer
fmax = np.finfo(np.float64).max
pol_flux = 2.*np.array([pol0/transmit[0], pol60/transmit[1], pol120/transmit[2]])#np.array([pol0, pol60, pol120])#
pol_cov = np.array([[4.*pol_cov_m[i,j]/(transmit[i]*transmit[j]) for j in range(pol_cov_m.shape[1])] for i in range(pol_cov_m.shape[0])])
pol_flux = np.array([pol0, pol60, pol120])
coeff_stokes = np.zeros((3,3))
# Coefficients linking each polarizer flux to each Stokes parameter
for i in range(3):
coeff_stokes[0,i] = pol_eff[(i+1)%3]*pol_eff[(i+2)%3]*np.sin(-2.*theta[(i+1)%3]+2.*theta[(i+2)%3])#*2./transmit[i]
coeff_stokes[1,i] = (-pol_eff[(i+1)%3]*np.sin(2.*theta[(i+1)%3]) + pol_eff[(i+2)%3]*np.sin(2.*theta[(i+2)%3]))#*2./transmit[i]
coeff_stokes[2,i] = (pol_eff[(i+1)%3]*np.cos(2.*theta[(i+1)%3]) - pol_eff[(i+2)%3]*np.cos(2.*theta[(i+2)%3]))#*2./transmit[i]
coeff_stokes[0,i] = pol_eff[(i+1)%3]*pol_eff[(i+2)%3]*np.sin(-2.*theta[(i+1)%3]+2.*theta[(i+2)%3])*2./transmit[i]
coeff_stokes[1,i] = (-pol_eff[(i+1)%3]*np.sin(2.*theta[(i+1)%3]) + pol_eff[(i+2)%3]*np.sin(2.*theta[(i+2)%3]))*2./transmit[i]
coeff_stokes[2,i] = (pol_eff[(i+1)%3]*np.cos(2.*theta[(i+1)%3]) - pol_eff[(i+2)%3]*np.cos(2.*theta[(i+2)%3]))*2./transmit[i]
# Normalization parameter for Stokes parameters computation
A = coeff_stokes[0,:].sum()
@@ -1157,24 +1146,6 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
dU_dtheta3 = 2.*pol_eff[2]/A*(np.sin(2.*theta[2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*theta[1]+2.*theta[2]) - pol_eff[0]*np.cos(-2.*theta[2]+2.*theta[0]))*U_stokes)
dU_dtheta = np.array([dU_dtheta1, dU_dtheta2, dU_dtheta3])
# Compute the derivative of each polarization component with respect to the polarizer orientation
mask = I_stokes > 0.
dP_dtheta1 = np.zeros(I_stokes.shape)*fmax
dP_dtheta2 = np.zeros(I_stokes.shape)*fmax
dP_dtheta3 = np.zeros(I_stokes.shape)*fmax
dP_dtheta1[mask] = 1./(I_stokes[mask]*np.sqrt(Q_stokes[mask]**2 + U_stokes[mask]**2))*(Q_stokes[mask]*dQ_dtheta1[mask] + U_stokes[mask]*dQ_dtheta1[mask]) - 1./I_stokes[mask]**2 * dI_dtheta1[mask]
dP_dtheta2[mask] = 1./(I_stokes[mask]*np.sqrt(Q_stokes[mask]**2 + U_stokes[mask]**2))*(Q_stokes[mask]*dQ_dtheta2[mask] + U_stokes[mask]*dQ_dtheta2[mask]) - 1./I_stokes[mask]**2 * dI_dtheta2[mask]
dP_dtheta3[mask] = 1./(I_stokes[mask]*np.sqrt(Q_stokes[mask]**2 + U_stokes[mask]**2))*(Q_stokes[mask]*dQ_dtheta3[mask] + U_stokes[mask]*dQ_dtheta3[mask]) - 1./I_stokes[mask]**2 * dI_dtheta3[mask]
dP_dtheta = np.array([dP_dtheta1, dP_dtheta2, dP_dtheta3])
dPA_dtheta1 = np.zeros(I_stokes.shape)*fmax
dPA_dtheta2 = np.zeros(I_stokes.shape)*fmax
dPA_dtheta3 = np.zeros(I_stokes.shape)*fmax
dPA_dtheta1[mask] = 90./np.pi*1./(U_stokes[mask]**2 + Q_stokes[mask]**2)*(Q_stokes[mask]*dU_dtheta1[mask] - U_stokes[mask]*dQ_dtheta1[mask])
dPA_dtheta2[mask] = 90./np.pi*1./(U_stokes[mask]**2 + Q_stokes[mask]**2)*(Q_stokes[mask]*dU_dtheta2[mask] - U_stokes[mask]*dQ_dtheta2[mask])
dPA_dtheta3[mask] = 90./np.pi*1./(U_stokes[mask]**2 + Q_stokes[mask]**2)*(Q_stokes[mask]*dU_dtheta3[mask] - U_stokes[mask]*dQ_dtheta3[mask])
dPA_dtheta = np.array([dPA_dtheta1, dPA_dtheta2, dPA_dtheta3])
# Compute the uncertainty associated with the polarizers' orientation (see Kishimoto 1999)
s_I2_axis = np.sum([dI_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
s_Q2_axis = np.sum([dQ_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
@@ -1196,10 +1167,10 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
I_stokes, Q_stokes, U_stokes = Stokes_array
Stokes_cov[0,0], Stokes_cov[1,1], Stokes_cov[2,2] = Stokes_error**2
return I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dtheta
return I_stokes, Q_stokes, U_stokes, Stokes_cov
def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dtheta, headers):
def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
"""
Compute the polarization degree (in %) and angle (in deg) and their
respective errors from given Stokes parameters.
@@ -1260,14 +1231,6 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dtheta,
# Propagate previously computed errors
s_P[mask] = (1/I_stokes[mask])*np.sqrt((Q_stokes[mask]**2*Stokes_cov[1,1][mask] + U_stokes[mask]**2*Stokes_cov[2,2][mask] + 2.*Q_stokes[mask]*U_stokes[mask]*Stokes_cov[1,2][mask])/(Q_stokes[mask]**2 + U_stokes[mask]**2) + ((Q_stokes[mask]/I_stokes[mask])**2 + (U_stokes[mask]/I_stokes[mask])**2)*Stokes_cov[0,0][mask] - 2.*(Q_stokes[mask]/I_stokes[mask])*Stokes_cov[0,1][mask] - 2.*(U_stokes[mask]/I_stokes[mask])*Stokes_cov[0,2][mask])
s_PA[mask] = (90./(np.pi*(Q_stokes[mask]**2 + U_stokes[mask]**2)))*np.sqrt(U_stokes[mask]**2*Stokes_cov[1,1][mask] + Q_stokes[mask]**2*Stokes_cov[2,2][mask] - 2.*Q_stokes[mask]*U_stokes[mask]*Stokes_cov[1,2][mask])
# Compute the uncertainty associated with the polarizers' orientation (see Kishimoto 1999)
s_P_axis = np.sqrt(np.sum([dP_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))], axis=0))
s_PA_axis = np.sqrt(np.sum([dPA_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))], axis=0))
# Add axis uncertainties quadratically
with warnings.catch_warnings(record=True) as w:
s_P[mask] = np.sqrt(s_P[mask]**2 + s_P_axis[mask]**2)
s_PA[mask] = np.sqrt(s_PA[mask]**2 + s_PA_axis[mask]**2)
s_P[np.isnan(s_P)] = fmax
s_PA[np.isnan(s_PA)] = fmax
@@ -1303,7 +1266,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dtheta,
return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dtheta, data_mask, headers, ang, SNRi_cut=None):
def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, ang, SNRi_cut=None):
"""
Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation
matrix to rotate Q, U of a given angle in degrees and update header
@@ -1386,13 +1349,6 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dthet
new_Stokes_cov[i,i] = np.abs(new_Stokes_cov[i,i])
center = np.array(new_I_stokes.shape)/2
#Compute new derivatives of P, PA on rotated parameters
new_dP_dtheta = deepcopy(dP_dtheta)
new_dPA_dtheta = deepcopy(dPA_dtheta)
for i in range(len(dP_dtheta)):
new_dP_dtheta[i] = sc_rotate(dP_dtheta[i], ang, reshape=False, cval=0.)
new_dPA_dtheta[i] = sc_rotate(dPA_dtheta[i], ang, reshape=False, cval=0.)
#Update headers to new angle
new_headers = []
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
@@ -1435,7 +1391,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, dP_dtheta, dPA_dthet
new_U_stokes[np.isnan(new_U_stokes)] = 0.
new_Stokes_cov[np.isnan(new_Stokes_cov)] = fmax
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_dP_dtheta, new_dPA_dtheta, new_headers, new_data_mask
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers, new_data_mask
def rotate_data(data_array, error_array, data_mask, headers, ang):
"""