Tibeuleu/FOC_Reduction
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add transmittance correction to Stokes fluxes computation

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Thibault Barnouin
2024-06-18 16:37:30 +02:00
parent 97479f90fb
commit 7ab87b50d1
2 changed files with 49 additions and 36 deletions
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63
package/lib/reduction.py
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@@ -1026,7 +1026,7 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None, scale=
return polarizer_array, polarizer_cov, pol_headers
def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale='pixel', smoothing='combine', transmitcorr=False):
def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale='pixel', smoothing='combine', transmitcorr=True):
"""
Compute the Stokes parameters I, Q and U for a given data_set
----------
@@ -1060,9 +1060,9 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
Defaults to 'combine'. Won't be used if FWHM is None.
transmitcorr : bool, optional
Weither the images should be transmittance corrected for each filter
along the line of sight. Latest calibrated data products (.c0f) does
along the line of sight. Latest calibrated data products (.c1f) does
not require such correction.
Defaults to False.
Defaults to True.
----------
Returns:
I_stokes : numpy.ndarray
@@ -1132,8 +1132,8 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
pol_eff[(i+2) % 3]*np.cos(2.*globals()["theta"][(i+2) % 3]))*2./transmit[i]
# Normalization parameter for Stokes parameters computation
A = (coeff_stokes[0, :]*transmit/2.).sum()
coeff_stokes = coeff_stokes/A
N = (coeff_stokes[0, :]*transmit/2.).sum()
coeff_stokes = coeff_stokes/N
I_stokes = np.zeros(pol_array[0].shape)
Q_stokes = np.zeros(pol_array[0].shape)
U_stokes = np.zeros(pol_array[0].shape)
@@ -1177,29 +1177,40 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
s_Q2_stat = np.sum([coeff_stokes[1, i]**2*sigma_flux[i]**2 for i in range(len(sigma_flux))], axis=0)
s_U2_stat = np.sum([coeff_stokes[2, i]**2*sigma_flux[i]**2 for i in range(len(sigma_flux))], axis=0)
pol_flux_corr = np.array([pf*2./t for (pf, t) in zip(pol_flux, transmit)])
coeff_stokes_corr = np.array([cs*t/2. for (cs, t) in zip(coeff_stokes.T, transmit)]).T
# Compute the derivative of each Stokes parameter with respect to the polarizer orientation
dI_dtheta1 = 2.*pol_eff[0]/A*(pol_eff[2]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0])*(pol_flux[1]-I_stokes) -
pol_eff[1]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1])*(pol_flux[2]-I_stokes))
dI_dtheta2 = 2.*pol_eff[1]/A*(pol_eff[0]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1])*(pol_flux[2]-I_stokes) -
pol_eff[2]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2])*(pol_flux[0]-I_stokes))
dI_dtheta3 = 2.*pol_eff[2]/A*(pol_eff[1]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2])*(pol_flux[0]-I_stokes) -
pol_eff[0]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0])*(pol_flux[1]-I_stokes))
dI_dtheta1 = 2.*pol_eff[0]/N*(pol_eff[2]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0])*(pol_flux_corr[1]-I_stokes) -
pol_eff[1]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1])*(pol_flux_corr[2]-I_stokes) +
coeff_stokes_corr[0, 0]*(np.sin(2.*globals()["theta"][0])*Q_stokes-np.cos(2*globals()["theta"][0])*U_stokes))
dI_dtheta2 = 2.*pol_eff[1]/N*(pol_eff[0]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1])*(pol_flux_corr[2]-I_stokes) -
pol_eff[2]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2])*(pol_flux_corr[0]-I_stokes) +
coeff_stokes_corr[0, 1]*(np.sin(2.*globals()["theta"][1])*Q_stokes-np.cos(2*globals()["theta"][1])*U_stokes))
dI_dtheta3 = 2.*pol_eff[2]/N*(pol_eff[1]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2])*(pol_flux_corr[0]-I_stokes) -
pol_eff[0]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0])*(pol_flux_corr[1]-I_stokes) +
coeff_stokes_corr[0, 2]*(np.sin(2.*globals()["theta"][2])*Q_stokes-np.cos(2*globals()["theta"][2])*U_stokes))
dI_dtheta = np.array([dI_dtheta1, dI_dtheta2, dI_dtheta3])
dQ_dtheta1 = 2.*pol_eff[0]/A*(np.cos(2.*globals()["theta"][0])*(pol_flux[1]-pol_flux[2]) - (pol_eff[2]*np.cos(-2.*globals()
["theta"][2]+2.*globals()["theta"][0]) - pol_eff[1]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1]))*Q_stokes)
dQ_dtheta2 = 2.*pol_eff[1]/A*(np.cos(2.*globals()["theta"][1])*(pol_flux[2]-pol_flux[0]) - (pol_eff[0]*np.cos(-2.*globals()
["theta"][0]+2.*globals()["theta"][1]) - pol_eff[2]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2]))*Q_stokes)
dQ_dtheta3 = 2.*pol_eff[2]/A*(np.cos(2.*globals()["theta"][2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*globals()
["theta"][1]+2.*globals()["theta"][2]) - pol_eff[0]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0]))*Q_stokes)
dQ_dtheta1 = 2.*pol_eff[0]/N*(np.cos(2.*globals()["theta"][0])*(pol_flux_corr[1]-pol_flux_corr[2]) - (pol_eff[2]*np.cos(-2.*globals()
["theta"][2]+2.*globals()["theta"][0]) - pol_eff[1]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1]))*Q_stokes +
coeff_stokes_corr[1, 0]*(np.sin(2.*globals()["theta"][0])*Q_stokes-np.cos(2*globals()["theta"][0])*U_stokes))
dQ_dtheta2 = 2.*pol_eff[1]/N*(np.cos(2.*globals()["theta"][1])*(pol_flux_corr[2]-pol_flux_corr[0]) - (pol_eff[0]*np.cos(-2.*globals()
["theta"][0]+2.*globals()["theta"][1]) - pol_eff[2]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2]))*Q_stokes +
coeff_stokes_corr[1, 1]*(np.sin(2.*globals()["theta"][1])*Q_stokes-np.cos(2*globals()["theta"][1])*U_stokes))
dQ_dtheta3 = 2.*pol_eff[2]/N*(np.cos(2.*globals()["theta"][2])*(pol_flux_corr[0]-pol_flux_corr[1]) - (pol_eff[1]*np.cos(-2.*globals()
["theta"][1]+2.*globals()["theta"][2]) - pol_eff[0]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0]))*Q_stokes +
coeff_stokes_corr[1, 2]*(np.sin(2.*globals()["theta"][2])*Q_stokes-np.cos(2*globals()["theta"][2])*U_stokes))
dQ_dtheta = np.array([dQ_dtheta1, dQ_dtheta2, dQ_dtheta3])
dU_dtheta1 = 2.*pol_eff[0]/A*(np.sin(2.*globals()["theta"][0])*(pol_flux[1]-pol_flux[2]) - (pol_eff[2]*np.cos(-2.*globals()
["theta"][2]+2.*globals()["theta"][0]) - pol_eff[1]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1]))*U_stokes)
dU_dtheta2 = 2.*pol_eff[1]/A*(np.sin(2.*globals()["theta"][1])*(pol_flux[2]-pol_flux[0]) - (pol_eff[0]*np.cos(-2.*globals()
["theta"][0]+2.*globals()["theta"][1]) - pol_eff[2]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2]))*U_stokes)
dU_dtheta3 = 2.*pol_eff[2]/A*(np.sin(2.*globals()["theta"][2])*(pol_flux[0]-pol_flux[1]) - (pol_eff[1]*np.cos(-2.*globals()
["theta"][1]+2.*globals()["theta"][2]) - pol_eff[0]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0]))*U_stokes)
dU_dtheta1 = 2.*pol_eff[0]/N*(np.sin(2.*globals()["theta"][0])*(pol_flux_corr[1]-pol_flux_corr[2]) - (pol_eff[2]*np.cos(-2.*globals()
["theta"][2]+2.*globals()["theta"][0]) - pol_eff[1]*np.cos(-2.*globals()["theta"][0]+2.*globals()["theta"][1]))*U_stokes +
coeff_stokes_corr[2, 0]*(np.sin(2.*globals()["theta"][0])*Q_stokes-np.cos(2*globals()["theta"][0])*U_stokes))
dU_dtheta2 = 2.*pol_eff[1]/N*(np.sin(2.*globals()["theta"][1])*(pol_flux_corr[2]-pol_flux_corr[0]) - (pol_eff[0]*np.cos(-2.*globals()
["theta"][0]+2.*globals()["theta"][1]) - pol_eff[2]*np.cos(-2.*globals()["theta"][1]+2.*globals()["theta"][2]))*U_stokes +
coeff_stokes_corr[2, 1]*(np.sin(2.*globals()["theta"][1])*Q_stokes-np.cos(2*globals()["theta"][1])*U_stokes))
dU_dtheta3 = 2.*pol_eff[2]/N*(np.sin(2.*globals()["theta"][2])*(pol_flux_corr[0]-pol_flux_corr[1]) - (pol_eff[1]*np.cos(-2.*globals()
["theta"][1]+2.*globals()["theta"][2]) - pol_eff[0]*np.cos(-2.*globals()["theta"][2]+2.*globals()["theta"][0]))*U_stokes +
coeff_stokes_corr[2, 2]*(np.sin(2.*globals()["theta"][2])*Q_stokes-np.cos(2*globals()["theta"][2])*U_stokes))
dU_dtheta = np.array([dU_dtheta1, dU_dtheta2, dU_dtheta3])
# Compute the uncertainty associated with the polarizers' orientation (see Kishimoto 1999)
@@ -1228,12 +1239,10 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
QU_diluted_err = np.sqrt(np.sum(Stokes_cov[1, 2][mask]**2))
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted **
2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted, Q_diluted))
PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err **
2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
for header in headers:
header['P_int'] = (P_diluted, 'Integrated polarization degree')