Tibeuleu/FOC_Reduction
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Move P_int, PA_int computation in pipeline, replot NGC1068, MKN463, IC5063

Browse Source
This commit is contained in:
Thibault Barnouin
2022-03-28 10:42:38 +02:00
parent 453afca32c
commit 8d4c33603a
156 changed files with 86 additions and 138 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
src/lib/fits.py
View File

@@ -126,6 +126,10 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
header['targname'] = (ref_header['targname'], 'Target name')
header['orientat'] = (ref_header['orientat'], 'Angle between North and the y-axis of the image')
header['filename'] = (filename, 'Original filename')
header['P_int'] = (ref_header['P_int'], 'Integrated polarization degree')
header['P_int_err'] = (ref_header['P_int_err'], 'Integrated polarization degree error')
header['PA_int'] = (ref_header['PA_int'], 'Integrated polarization angle')
header['PA_int_err'] = (ref_header['PA_int_err'], 'Integrated polarization angle error')
#Create HDUList object
hdul = fits.HDUList([])

40
src/lib/plots.py
View File

@@ -233,7 +233,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
#Get image mask
if data_mask is None:
data_mask = np.ones(stkI.shape).astype(bool)
data_mask = np.zeros(stkI.shape).astype(bool)
#Plot Stokes parameters map
if display is None or display.lower() == 'default':
@@ -353,43 +353,15 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
ax.add_patch(Rectangle((x, y), width, height, angle=angle,
edgecolor=color, fill=False))
# Compute integrated parameters and associated errors for pixels in the cut
n_pix = mask.size
I_int = stkI.data[mask].sum()
Q_int = stkQ.data[mask].sum()
U_int = stkU.data[mask].sum()
I_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0][mask]))
Q_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1][mask]))
U_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2][mask]))
IQ_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1][mask]**2))
IU_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2][mask]**2))
QU_int_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,2][mask]**2))
P_int = np.sqrt(Q_int**2+U_int**2)/I_int
P_int_err = (1./I_int)*np.sqrt((Q_int**2*Q_int_err**2 + U_int**2*U_int_err**2 + 2.*Q_int*U_int*QU_int_err)/(Q_int**2 + U_int**2) + ((Q_int/I_int)**2 + (U_int/I_int)**2)*I_int_err**2 - 2.*(Q_int/I_int)*IQ_int_err - 2.*(U_int/I_int)*IU_int_err)
PA_int = princ_angle((90./np.pi)*np.arctan2(U_int,Q_int))
PA_int_err = (90./(np.pi*(Q_int**2 + U_int**2)))*np.sqrt(U_int**2*Q_int_err**2 + Q_int**2*U_int_err**2 - 2.*Q_int*U_int*QU_int_err)
# Compute integrated parameters and associated errors for all pixels
#Get integrated values from header
n_pix = stkI.data[data_mask].size
I_diluted = stkI.data[data_mask].sum()
Q_diluted = stkQ.data[data_mask].sum()
U_diluted = stkU.data[data_mask].sum()
I_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,0][data_mask]))
Q_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,1][data_mask]))
U_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[2,2][data_mask]))
IQ_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,1][data_mask]**2))
IU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[0,2][data_mask]**2))
QU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(stk_cov.data[1,2][data_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 = np.sqrt(2/n_pix)*100.
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 = P_diluted_err/(2.*P_diluted)*180./np.pi
P_diluted = Stokes[0].header['P_int']
P_diluted_err = Stokes[0].header['P_int_err']
PA_diluted = Stokes[0].header['PA_int']
PA_diluted_err = Stokes[0].header['PA_int_err']
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(pivot_wav,sci_not(I_diluted*convert_flux,I_diluted_err*convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_diluted*100.,P_diluted_err*100.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_diluted,PA_diluted_err), color='white', fontsize=16, xy=(0.01, 0.92), xycoords='axes fraction')

123
src/lib/reduction.py
View File

@@ -67,6 +67,17 @@ globals()['theta'] = np.array([180.*np.pi/180., 60.*np.pi/180., 120.*np.pi/180.]
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'):
"""
Return the matrix that allows to compress an array from an old dimension of
@@ -454,19 +465,9 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
#flatfielding uncertainties
#estimated to less than 3%
err_flat = data_array[i]*0.03
if i==0:
pr = data_array[i] > 0.
print("Background error = {0:2.2f}%".format(np.median(error_array[i][pr]/data_array[i][pr]*100.)))
print("Wavelength polarizer dependence error = {0:2.2f}%".format(np.median(err_wav[pr]/data_array[i][pr]*100.)))
print("PSF polarizer difference error = {0:2.2f}%".format(np.median(err_psf[pr]/data_array[i][pr]*100.)))
print("Flatfield polarizer difference error = {0:2.2f}%".format(np.median(err_flat[pr]/data_array[i][pr]*100.)))
error_array[i] = np.sqrt(error_array[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
if i==0:
pr = data_array[i] > 0.
print("Total estimated error = {0:2.2f}%".format(np.median(error_array[i][pr]/data_array[i][pr]*100.)))
background[i] = sub_image.sum()
if (data_array[i] < 0.).any():
print(data_array[i])
@@ -610,11 +611,6 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
new_error[mask] = np.sqrt(bin_ndarray(error**2*image,
new_shape=new_shape, operation='sum')[mask]/sum_image[mask])
rebinned_error.append(np.sqrt(rms_image**2 + new_error**2))
if i==0:
pr = rebin_data > 0.
print("Rebin RMS error = {0:2.2f}%".format(np.median(rms_image[pr]/rebin_data[pr]*100.)))
print("Rebin weigthed sum squarred error = {0:2.2f}%".format(np.median(new_error[pr]/rebin_data[pr]*100.)))
print("Total rebin error = {0:2.2f}%".format(np.median(rebinned_error[0][pr]/rebin_data[pr]*100.)))
# Update header
w = w.slice((np.s_[::Dxy[0]], np.s_[::Dxy[1]]))
@@ -759,18 +755,8 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
shifted_image = sc_shift(rescaled_image[i], prec_shift, cval=0.)
error_shift = np.abs(rescaled_image[i] - shifted_image)/2.
#sum quadratically the errors
if i==0:
pr = rescaled_image[0] > 0.
print("Rescaled (aligned) error = {0:2.2f}%".format(np.median(rescaled_error[0][pr]/rescaled_image[0][pr]*100.)))
print("Shift error = {0:2.2f}%".format(np.median(error_shift[pr]/rescaled_image[0][pr]*100.)))
rescaled_error[i] = np.sqrt(rescaled_error[i]**2 + error_shift**2)
if i==0:
pr = rescaled_image[0] > 0.
print("Total align error = {0:2.2f}%".format(np.median(rescaled_error[0][pr]/rescaled_image[0][pr]*100.)))
#rescaled_error[i][1-rescaled_mask[i]] = 0.
shifts.append(shift)
errors.append(error)
@@ -890,9 +876,6 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
else:
raise ValueError("{} is not a valid smoothing option".format(smoothing))
pr = smoothed > 0.
print("Smoothed error = {0:2.2f}%".format(np.median(error[pr]/smoothed[pr]*100.)))
return smoothed, error
@@ -997,9 +980,6 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
err120 = np.sqrt(np.sum(err120_array**2,axis=0))
polerr_array = np.array([err0, err60, err120])
pr = pol0 > 0.
print("Summed POL0 error = {0:2.2f}%".format(np.median(err0[pr]/pol0[pr]*100.)))
# Update headers
for header in headers:
if header['filtnam1']=='POL0':
@@ -1034,9 +1014,6 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
polarizer_cov[1,1] = err60**2
polarizer_cov[2,2] = err120**2
pr = pol0 > 0.
print("Total POL0 error = {0:2.2f}%".format(np.median(err0[pr]/pol0[pr]*100.)))
return polarizer_array, polarizer_cov
@@ -1191,25 +1168,11 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
s_Q2_axis = np.sum([dQ_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
s_U2_axis = np.sum([dU_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
prI = I_stokes > 0.
print("Propagated I_stokes error = {0:2.2f}%".format(np.median(np.sqrt(Stokes_cov[0,0][prI])/I_stokes[prI]*100.)))
print("Axis I_stokes error = {0:2.2f}%".format(np.median(np.sqrt(s_I2_axis[prI])/I_stokes[prI]*100.)))
prQ = Q_stokes > 0.
print("Propagated Q_stokes error = {0:2.2f}%".format(np.median(np.sqrt(Stokes_cov[1,1][prQ])/Q_stokes[prQ]*100.)))
print("Axis Q_stokes error = {0:2.2f}%".format(np.median(np.sqrt(s_Q2_axis[prQ])/Q_stokes[prQ]*100.)))
prU = U_stokes > 0.
print("Propagated U_stokes error = {0:2.2f}%".format(np.median(np.sqrt(Stokes_cov[2,2][prU])/U_stokes[prU]*100.)))
print("Axis U_stokes error = {0:2.2f}%".format(np.median(np.sqrt(s_U2_axis[prU])/U_stokes[prU]*100.)))
# Add quadratically the uncertainty to the Stokes covariance matrix
Stokes_cov[0,0] += s_I2_axis
Stokes_cov[1,1] += s_Q2_axis
Stokes_cov[2,2] += s_U2_axis
print("Total I_stokes error = {0:2.2f}%".format(np.median(np.sqrt(Stokes_cov[0,0][prI])/I_stokes[prI]*100.)))
print("Total Q_stokes error = {0:2.2f}%".format(np.median(np.sqrt(Stokes_cov[1,1][prQ])/Q_stokes[prQ]*100.)))
print("Total U_stokes error = {0:2.2f}%".format(np.median(np.sqrt(Stokes_cov[2,2][prU])/U_stokes[prU]*100.)))
if not(FWHM is None) and (smoothing.lower() in ['gaussian_after','gauss_after']):
Stokes_array = np.array([I_stokes, Q_stokes, U_stokes])
Stokes_error = np.array([np.sqrt(Stokes_cov[i,i]) for i in range(3)])
@@ -1220,6 +1183,31 @@ 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
#Compute integrated values for P, PA before any rotation
mask = (1-data_mask).astype(bool)
n_pix = I_stokes[mask].size
I_diluted = I_stokes[mask].sum()
Q_diluted = Q_stokes[mask].sum()
U_diluted = U_stokes[mask].sum()
I_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(Stokes_cov[0,0][mask]))
Q_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(Stokes_cov[1,1][mask]))
U_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(Stokes_cov[2,2][mask]))
IQ_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(Stokes_cov[0,1][mask]**2))
IU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(Stokes_cov[0,2][mask]**2))
QU_diluted_err = np.sqrt(n_pix)*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)
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)
for header in headers:
header['P_int'] = (P_diluted, 'Integrated polarization degree')
header['P_int_err'] = (P_diluted_err, 'Integrated polarization degree error')
header['PA_int'] = (PA_diluted, 'Integrated polarization angle')
header['PA_int_err'] = (PA_diluted_err, 'Integrated polarization angle error')
return I_stokes, Q_stokes, U_stokes, Stokes_cov
@@ -1317,11 +1305,6 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
s_P_P[np.isnan(s_P_P)] = fmax
s_PA_P[np.isnan(s_PA_P)] = fmax
prP = P > 0.
prPA = PA > 0.
print("Propagated P error = {0:2.2f}%".format(np.median(s_P[prP]/P[prP]*100.)))
print("Propagated PA error = {0:2.2f}%".format(np.median(s_PA[prPA]/PA[prPA]*100.)))
return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P
@@ -1450,12 +1433,31 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
new_U_stokes[np.isnan(new_U_stokes)] = 0.
new_Stokes_cov[np.isnan(new_Stokes_cov)] = fmax
prI = new_I_stokes > 0.
prQ = new_Q_stokes > 0.
prU = new_U_stokes > 0.
print("Propagated rotated I_stokes error = {0:2.2f}%".format(np.median(np.sqrt(new_Stokes_cov[0,0][prI])/new_I_stokes[prI]*100.)))
print("Propagated rotated Q_stokes error = {0:2.2f}%".format(np.median(np.sqrt(new_Stokes_cov[1,1][prQ])/new_Q_stokes[prQ]*100.)))
print("Propagated rotated U_stokes error = {0:2.2f}%".format(np.median(np.sqrt(new_Stokes_cov[2,2][prU])/new_U_stokes[prU]*100.)))
#Compute updated integrated values for P, PA
mask = (1-new_data_mask).astype(bool)
n_pix = new_I_stokes[mask].size
I_diluted = new_I_stokes[mask].sum()
Q_diluted = new_Q_stokes[mask].sum()
U_diluted = new_U_stokes[mask].sum()
I_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(new_Stokes_cov[0,0][mask]))
Q_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(new_Stokes_cov[1,1][mask]))
U_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(new_Stokes_cov[2,2][mask]))
IQ_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(new_Stokes_cov[0,1][mask]**2))
IU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(new_Stokes_cov[0,2][mask]**2))
QU_diluted_err = np.sqrt(n_pix)*np.sqrt(np.sum(new_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)
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)
for header in new_headers:
header['P_int'] = (P_diluted, 'Integrated polarization degree')
header['P_int_err'] = (P_diluted_err, 'Integrated polarization degree error')
header['PA_int'] = (PA_diluted, 'Integrated polarization angle')
header['PA_int_err'] = (PA_diluted_err, 'Integrated polarization angle error')
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_headers, new_data_mask
@@ -1496,9 +1498,6 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
cval=0.))
new_error_array.append(sc_rotate(error_array[i], ang, order=5, reshape=False,
cval=error_array.mean()))
if i==0:
pr = new_data_array[0] > 0.
print("Rotated data error = {0:2.2f}%".format(np.median(new_error_array[0][pr]/new_data_array[0][pr]*100.)))
new_data_array = np.array(new_data_array)
new_data_mask = sc_rotate(data_mask, ang, order=5, reshape=False, cval=True)
new_error_array = np.array(new_error_array)

6
src/lib/test_overplot.py
View File

@@ -3,7 +3,7 @@ from astropy.io import fits
import numpy as np
from plots import overplot_maps
Stokes_UV = fits.open("../../data/IC5063_x3nl030/IC5063_FOC_combine_FWHM020_pol_wae.fits")
Stokes_UV = fits.open("../../data/IC5063_x3nl030/IC5063_FOC_combine_FWHM020_pol.fits")
Stokes_18GHz = fits.open("../../data/IC5063_x3nl030/radio/IC5063.18GHz.fits")
Stokes_24GHz = fits.open("../../data/IC5063_x3nl030/radio/IC5063.24GHz.fits")
@@ -12,9 +12,9 @@ levelsMorganti = np.array([1.,2.,3.,8.,16.,32.,64.,128.])
#levels18GHz = np.array([0.6, 1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_18GHz[0].data.max()
levels18GHz = levelsMorganti*0.28*1e-3
A = overplot_maps(Stokes_UV, Stokes_18GHz)
A.plot(levels=levels18GHz, SNRp_cut=10.0, SNRi_cut=100.0, savename='../../plots/IC5063_x3nl030/18GHz_overplot_forced_maxUV.png')
A.plot(levels=levels18GHz, SNRp_cut=10.0, SNRi_cut=100.0, savename='../../plots/IC5063_x3nl030/18GHz_overplot.png')
#levels24GHz = np.array([1.,1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_24GHz[0].data.max()
levels24GHz = levelsMorganti*0.46*1e-3
B = overplot_maps(Stokes_UV, Stokes_24GHz)
B.plot(levels=levels24GHz, SNRp_cut=10.0, SNRi_cut=100.0, savename='../../plots/IC5063_x3nl030/24GHz_overplot_forced_maxUV.png')
B.plot(levels=levels24GHz, SNRp_cut=10.0, SNRi_cut=100.0, savename='../../plots/IC5063_x3nl030/24GHz_overplot.png')