Tibeuleu/FOC_Reduction
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correction on principal angle computation

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Tibeuleu
2022-10-28 15:17:54 +02:00
parent 700cc7e4df
commit da805b71f1
27 changed files with 38 additions and 38 deletions
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2
src/lib/fits.py
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@@ -13,6 +13,7 @@ import numpy as np
from astropy.io import fits
from astropy import wcs
from lib.convex_hull import image_hull, clean_ROI
from lib.plots import princ_angle
import matplotlib.pyplot as plt
@@ -66,6 +67,7 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
new_wcs.wcs.cdelt = new_cdelt
for key, val in new_wcs.to_header().items():
header[key] = val
header['orientat'] = princ_angle(float(header['orientat']))
if compute_flux:
for i in range(len(infiles)):

12
src/lib/plots.py
View File

@@ -51,12 +51,12 @@ from astropy.io import fits
def princ_angle(ang):
"""
Return the principal angle in the 0-180° quadrant.
Return the principal angle in the -180° to 180° quadrant.
"""
while ang < 0.:
ang += 180.
while ang <= -180.:
ang += 360.
while ang > 180.:
ang -= 180.
ang -= 360.
return ang
@@ -1808,8 +1808,8 @@ class pol_map(object):
P_reg = np.sqrt(Q_reg**2+U_reg**2)/I_reg
P_reg_err = np.sqrt((Q_reg**2*Q_reg_err**2 + U_reg**2*U_reg_err**2 + 2.*Q_reg*U_reg*QU_reg_err)/(Q_reg**2 + U_reg**2) + ((Q_reg/I_reg)**2 + (U_reg/I_reg)**2)*I_reg_err**2 - 2.*(Q_reg/I_reg)*IQ_reg_err - 2.*(U_reg/I_reg)*IU_reg_err)/I_reg
PA_reg = princ_angle((90./np.pi)*np.arctan2(U_reg,Q_reg))
PA_reg_err = (90./(np.pi*(Q_reg**2+U_reg**2)))*np.sqrt(U_reg**2*Q_reg_err**2 + Q_reg**2*U_reg_err**2 - 2.*Q_reg*U_reg*QU_reg_err)
PA_reg = np.degrees((1./2.)*np.arctan2(U_reg,Q_reg))
PA_reg_err = princ_angle(np.degrees((1./(2.*(Q_reg**2+U_reg**2)))*np.sqrt(U_reg**2*Q_reg_err**2 + Q_reg**2*U_reg_err**2 - 2.*Q_reg*U_reg*QU_reg_err)))
if hasattr(self, 'cont'):
for coll in self.cont.collections:

18
src/lib/reduction.py
View File

@@ -488,11 +488,13 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
rectangle.append([minima[1], minima[0], sub_shape[1], sub_shape[0], 0., 'r'])
# Compute error : root mean square of the background
sub_image = image[minima[0]:minima[0]+sub_shape[0],minima[1]:minima[1]+sub_shape[1]]
#error = np.std(sub_image) # Previously computed using standard deviation over the background
#bkg = np.std(sub_image) # Previously computed using standard deviation over the background
bkg = np.sqrt(np.sum((sub_image-sub_image.mean())**2)/sub_image.size)
error_bkg[i] *= bkg
#Substract background
data_array[i] = np.abs(data_array[i] - sub_image.mean())
# Quadratically add uncertainties in the "correction factors" (see Kishimoto 1999)
#wavelength dependence of the polariser filters
#estimated to less than 1%
@@ -1087,7 +1089,7 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
if not(FWHM is None) and (smoothing.lower() in ['gaussian','gauss']):
# Smooth by convoluting with a gaussian each polX image.
pol_array, polerr_array = smooth_data(pol_array, polerr_array,
data_mask, headers_array, FWHM=FWHM, scale=scale)
data_mask, headers, FWHM=FWHM, scale=scale)
pol0, pol60, pol120 = pol_array
err0, err60, err120 = polerr_array
@@ -1294,8 +1296,8 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
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)
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 = np.degrees((1./2.)*np.arctan2(U_diluted,Q_diluted))
PA_diluted_err = princ_angle(np.degrees((1./(2.*(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')
@@ -1470,7 +1472,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
for i,head in enumerate(headers):
ang[i] = -head['orientat']
ang = ang.mean()
alpha = ang*np.pi/180.
alpha = np.radians(ang)
mrot = np.array([[1., 0., 0.],
[0., np.cos(2.*alpha), np.sin(2.*alpha)],
[0, -np.sin(2.*alpha), np.cos(2.*alpha)]])
@@ -1553,8 +1555,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
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)
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 = np.degrees((1./2.)*np.arctan2(U_diluted,Q_diluted))
PA_diluted_err = princ_angle(np.degrees((1./(1.*(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 new_headers:
header['P_int'] = (P_diluted, 'Integrated polarization degree')