Tibeuleu/FOC_Reduction
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high definition map for NGC1068 and recomputation for IC5063

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This commit is contained in:
Thibault Barnouin
2022-03-01 17:03:56 +01:00
parent 72f6d33221
commit d700f46738
19 changed files with 88 additions and 19 deletions
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32
src/FOC_reduction.py
View File

@@ -17,11 +17,11 @@ from lib.convex_hull import image_hull
def main():
##### User inputs
## Input and output locations
# globals()['data_folder'] = "../data/NGC1068_x274020/"
# infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
# 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
# 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
# globals()['plots_folder'] = "../plots/NGC1068_x274020/"
globals()['data_folder'] = "../data/NGC1068_x274020/"
infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
globals()['plots_folder'] = "../plots/NGC1068_x274020/"
# globals()['data_folder'] = "../data/NGC1068_x14w010/"
# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -81,13 +81,13 @@ def main():
# infiles = ['x25d0401t_c0f.fits','x25d0402t_c0f.fits','x25d0403t_c0f.fits']
# globals()['plots_folder'] = "../plots/PictorA_x25d040/"
globals()['data_folder'] = "../data/3C273_x0u20/"
infiles = ['x0u20101t_c0f.fits','x0u20102t_c0f.fits','x0u20103t_c0f.fits','x0u20104t_c0f.fits','x0u20105t_c0f.fits','x0u20106t_c0f.fits','x0u20201t_c0f.fits','x0u20202t_c0f.fits','x0u20203t_c0f.fits','x0u20204t_c0f.fits','x0u20205t_c0f.fits','x0u20206t_c0f.fits','x0u20301t_c0f.fits','x0u20302t_c0f.fits','x0u20303t_c0f.fits','x0u20304t_c0f.fits','x0u20305t_c0f.fits','x0u20306t_c0f.fits']
globals()['plots_folder'] = "../plots/3C273_x0u20/"
# globals()['data_folder'] = "../data/3C273_x0u20/"
# infiles = ['x0u20101t_c0f.fits','x0u20102t_c0f.fits','x0u20103t_c0f.fits','x0u20104t_c0f.fits','x0u20105t_c0f.fits','x0u20106t_c0f.fits','x0u20201t_c0f.fits','x0u20202t_c0f.fits','x0u20203t_c0f.fits','x0u20204t_c0f.fits','x0u20205t_c0f.fits','x0u20206t_c0f.fits','x0u20301t_c0f.fits','x0u20302t_c0f.fits','x0u20303t_c0f.fits','x0u20304t_c0f.fits','x0u20305t_c0f.fits','x0u20306t_c0f.fits']
# globals()['plots_folder'] = "../plots/3C273_x0u20/"
## Reduction parameters
# Deconvolution
deconvolve = True
deconvolve = False
if deconvolve:
psf = 'gaussian' #Can be user-defined as well
psf_FWHM = 0.15
@@ -97,12 +97,12 @@ def main():
# Cropping
display_crop = False
# Error estimation
error_sub_shape = (75,75)
error_sub_shape = (150,150)
display_error = False
# Data binning
rebin = True
if rebin:
pxsize = 0.15
pxsize = 0.05
px_scale = 'arcsec' #pixel or arcsec
rebin_operation = 'sum' #sum or average
# Alignement
@@ -110,16 +110,16 @@ def main():
display_data = False
# Smoothing
smoothing_function = 'combine' #gaussian_after, gaussian or combine
smoothing_FWHM = 0.15 #If None, no smoothing is done
smoothing_FWHM = 0.10 #If None, no smoothing is done
smoothing_scale = 'arcsec' #pixel or arcsec
# Rotation
rotate_stokes = True #rotation to North convention can give erroneous results
rotate_data = False #rotation to North convention can give erroneous results
# Polarization map output
figname = '3C273_FOC' #target/intrument name
figtype = '_combine_FWHM015_deconvolved' #additionnal informations
SNRp_cut = 5. #P measurments with SNR>3
SNRi_cut = 20. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
figname = 'NGC1068_FOC' #target/intrument name
figtype = '_combine_FWHM020' #additionnal informations
SNRp_cut = 50. #P measurments with SNR>3
SNRi_cut = 350. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
##### Pipeline start

75
src/lib/reduction.py
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@@ -626,7 +626,7 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
def align_data(data_array, headers, error_array=None, upsample_factor=1.,
ref_data=None, ref_center=None, return_shifts=True):
ref_data=None, ref_center=None, return_shifts=False):
"""
Align images in data_array using cross correlation, and rescale them to
wider images able to contain any rotation of the reference image.
@@ -851,7 +851,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*data_array.shape[0])
# Apply weighted combination
smoothed[r,c] = (1.-data_mask[r,c])*np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
error[r,c] = (1.-data_mask[r,c])*np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
# Nan handling
error[np.isnan(smoothed)] = 0.
@@ -871,7 +871,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
with warnings.catch_warnings(record=True) as w:
g_rc = np.exp(-0.5*(dist_rc/stdev)**2)/(2.*np.pi*stdev**2)
smoothed[i][r,c] = (1.-data_mask[r,c])*np.sum(image*g_rc)
error[i][r,c] = np.sqrt(np.sum(error_array[i]*g_rc**2))
error[i][r,c] = (1.-data_mask[r,c])*np.sqrt(np.sum(error_array[i]*g_rc**2))
# Nan handling
error[i][np.isnan(smoothed[i])] = 0.
@@ -1440,3 +1440,72 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
new_Stokes_cov[np.isnan(new_Stokes_cov)] = fmax
return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_headers
def rotate_data(data_array, error_array, data_mask, headers, ang):
"""
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
orientation keyword.
----------
Inputs:
data_array : numpy.ndarray
Array of images (2D floats) to be rotated by angle ang.
error_array : numpy.ndarray
Array of error associated to images in data_array.
headers : header list
List of headers corresponding to the reduced images.
ang : float
Rotation angle (in degrees) that should be applied to the Stokes
parameters
----------
Returns:
new_data_array : numpy.ndarray
Updated array containing the rotated images.
new_error_array : numpy.ndarray
Updated array containing the rotated errors.
new_headers : header list
Updated list of headers corresponding to the reduced images accounting
for the new orientation angle.
"""
#Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
alpha = ang*np.pi/180.
#Rotate original images using scipy.ndimage.rotate
new_data_array = []
new_error_array = []
for i in range(data_array.shape[0]):
new_data_array.append(sc_rotate(data_array[i], ang, reshape=False,
cval=0.))
new_error_array.append(sc_rotate(error_array[i], ang, reshape=False,
cval=error_array.mean()))
new_data_array = np.array(new_data_array)
new_data_mask = sc_rotate(data_mask, ang, reshape=False, cval=True)
new_error_array = np.array(new_error_array)
for i in range(new_data_array.shape[0]):
new_data_array[i][new_data_array[i] < 0.] = 0.
#Update headers to new angle
new_headers = []
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
[np.sin(-alpha), np.cos(-alpha)]])
for header in headers:
new_header = deepcopy(header)
new_header['orientat'] = header['orientat'] + ang
new_wcs = WCS(header).deepcopy()
if new_wcs.wcs.has_cd(): # CD matrix
del new_wcs.wcs.cd
keys = ['CD1_1','CD1_2','CD2_1','CD2_2']
for key in keys:
new_header.remove(key, ignore_missing=True)
new_wcs.wcs.cdelt = 3600.*np.sqrt(np.sum(new_wcs.wcs.get_pc()**2,axis=1))
elif new_wcs.wcs.has_pc(): # PC matrix + CDELT
newpc = np.dot(mrot, new_wcs.wcs.get_pc())
new_wcs.wcs.pc = newpc
new_wcs.wcs.set()
new_header.update(new_wcs.to_header())
new_headers.append(new_header)
return new_data_array, new_error_array, new_data_mask, new_headers