Tibeuleu/FOC_Reduction
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move alignement before rebinning, before background computation

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This commit is contained in:
Thibault Barnouin
2022-04-12 17:17:34 +02:00
parent 7bbd2bc2e8
commit 3770a78940
52 changed files with 269 additions and 187 deletions
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src/lib/reduction.py
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@@ -43,13 +43,13 @@ import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.patches import Rectangle
from datetime import datetime
from scipy.ndimage import rotate as sc_rotate
from scipy.ndimage import shift as sc_shift
from scipy.ndimage import rotate as sc_rotate, shift as sc_shift
from scipy.signal import convolve2d
from astropy.wcs import WCS
from astropy import log
log.setLevel('ERROR')
import warnings
from lib.deconvolve import deconvolve_im, gaussian_psf
from lib.deconvolve import deconvolve_im, gaussian_psf, gaussian2d, zeropad
from lib.convex_hull import image_hull, clean_ROI
from lib.plots import plot_obs
from lib.cross_correlation import phase_cross_correlation
@@ -189,7 +189,7 @@ def bin_ndarray(ndarray, new_shape, operation='sum'):
return ndarray
def crop_array(data_array, headers, error_array=None, step=5, null_val=None,
def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, null_val=None,
inside=False, display=False, savename=None, plots_folder=""):
"""
Homogeneously crop an array: all contained images will have the same shape.
@@ -299,6 +299,7 @@ def crop_array(data_array, headers, error_array=None, step=5, null_val=None,
savename=savename+'_crop_region',plots_folder=plots_folder)
plt.show()
crop_headers = deepcopy(headers)
crop_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1]))
crop_error_array = np.zeros((data_array.shape[0],new_shape[0],new_shape[1]))
for i,image in enumerate(data_array):
@@ -306,11 +307,14 @@ def crop_array(data_array, headers, error_array=None, step=5, null_val=None,
crop_array[i] = image[v_array[0]:v_array[1],v_array[2]:v_array[3]]
crop_error_array[i] = error_array[i][v_array[0]:v_array[1],v_array[2]:v_array[3]]
#Update CRPIX value in the associated header
curr_wcs = deepcopy(WCS(headers[i]))
curr_wcs.wcs.crpix = curr_wcs.wcs.crpix - np.array([v_array[0], v_array[2]])
headers[i].update(curr_wcs.to_header())
return crop_array, crop_error_array, headers
curr_wcs = deepcopy(WCS(crop_headers[i]))
curr_wcs.wcs.crpix = curr_wcs.wcs.crpix - np.array([v_array[2], v_array[0]])
crop_headers[i].update(curr_wcs.to_header())
if not data_mask is None:
crop_mask = data_mask[v_array[0]:v_array[1],v_array[2]:v_array[3]]
return crop_array, crop_error_array, crop_mask, crop_headers
else:
return crop_array, crop_error_array, crop_headers
def deconvolve_array(data_array, headers, psf='gaussian', FWHM=1., scale='px',
@@ -355,7 +359,7 @@ def deconvolve_array(data_array, headers, psf='gaussian', FWHM=1., scale='px',
for i,header in enumerate(headers):
# Get current pixel size
w = WCS(header).deepcopy()
pxsize[i] = np.round(w.wcs.cdelt/3600.,5)
pxsize[i] = np.round(w.wcs.cdelt/3600.,15)
if (pxsize != pxsize[0]).any():
raise ValueError("Not all images in array have same pixel size")
FWHM /= pxsize[0].min()
@@ -377,7 +381,7 @@ def deconvolve_array(data_array, headers, psf='gaussian', FWHM=1., scale='px',
return deconv_array
def get_error(data_array, headers, sub_shape=(15,15), display=False,
def get_error(data_array, headers, error_array=None, data_mask=None, sub_shape=None, display=False,
savename=None, plots_folder="", return_background=False):
"""
Look for sub-image of shape sub_shape that have the smallest integrated
@@ -391,7 +395,7 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
Headers associated with the images in data_array.
sub_shape : tuple, optional
Shape of the sub-image to look for. Must be odd.
Defaults to (15,15).
Defaults to 10% of input array.
display : boolean, optional
If True, data_array will be displayed with a rectangle around the
sub-image selected for background computation.
@@ -423,8 +427,15 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
Only returned if return_background is True.
"""
# Crop out any null edges
data, error, headers = crop_array(data_array, headers, step=5, null_val=0., inside=False)
if error_array is None:
error_array = np.zeros(data_array.shape)
if not data_mask is None:
data, error, mask = data_array, error_array, data_mask#_ = crop_array(data_array, headers, error_array, data_mask, step=5, null_val=0., inside=False)
else:
data, error, _ = crop_array(data_array, headers, error_array, step=5, null_val=0., inside=False)
mask = np.ones(data[0].shape, dtype=bool)
if sub_shape is None:
sub_shape = (np.array(data_array.shape[1:])/10).astype(int)
sub_shape = np.array(sub_shape)
# Make sub_shape of odd values
if not(np.all(sub_shape%2)):
@@ -433,16 +444,19 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
shape = np.array(data.shape)
diff = (sub_shape-1).astype(int)
temp = np.zeros((shape[0],shape[1]-diff[0],shape[2]-diff[1]))
error_array = np.ones(data_array.shape)
error_bkg = np.ones(data_array.shape)
rectangle = []
background = np.zeros((shape[0]))
for i,image in enumerate(data):
# Find the sub-image of smallest integrated flux (suppose no source)
#sub-image dominated by background
fmax = np.finfo(np.double).max
img = deepcopy(image)
img[1-mask] = fmax/(diff[0]*diff[1])
for r in range(temp.shape[1]):
for c in range(temp.shape[2]):
temp[i][r,c] = image[r:r+diff[0],c:c+diff[1]].sum()
temp[i][r,c] = np.where(mask[r,c], img[r:r+diff[0],c:c+diff[1]].sum(), fmax/(diff[0]*diff[1]))
minima = np.unravel_index(np.argmin(temp.sum(axis=0)),temp.shape[1:])
@@ -451,8 +465,8 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
# 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
error = np.sqrt(np.sum((sub_image-sub_image.mean())**2)/sub_image.size)
error_array[i] *= error
bkg = np.sqrt(np.sum((sub_image-sub_image.mean())**2)/sub_image.size)
error_bkg[i] *= bkg
data_array[i] = np.abs(data_array[i] - sub_image.mean())
# Quadratically add uncertainties in the "correction factors" (see Kishimoto 1999)
@@ -466,7 +480,7 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
#estimated to less than 3%
err_flat = data_array[i]*0.03
error_array[i] = np.sqrt(error_array[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
error_array[i] = np.sqrt(error_array[i]**2 + error_bkg[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
background[i] = sub_image.sum()
if (data_array[i] < 0.).any():
@@ -550,7 +564,7 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
def rebin_array(data_array, error_array, headers, pxsize, scale,
operation='sum'):
operation='sum', data_mask=None):
"""
Homogeneously rebin a data array to get a new pixel size equal to pxsize
where pxsize is given in arcsec.
@@ -631,9 +645,13 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
if operation.lower() in ["mean", "average", "avg"]:
new_error[mask] = np.sqrt(bin_ndarray(error**2*image,
new_shape=new_shape, operation='average')[mask]/sum_image[mask])
#new_error[mask] = np.sqrt(bin_ndarray(error**2,
# new_shape=new_shape, operation='average')[mask])
else:
new_error[mask] = np.sqrt(bin_ndarray(error**2*image,
new_shape=new_shape, operation='sum')[mask]/sum_image[mask])
#new_error[mask] = np.sqrt(bin_ndarray(error**2,
# new_shape=new_shape, operation='sum')[mask])
rebinned_error.append(np.sqrt(rms_image**2 + new_error**2))
# Update header
@@ -641,12 +659,16 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
header['NAXIS1'],header['NAXIS2'] = w.array_shape
header.update(w.to_header())
rebinned_headers.append(header)
if not data_mask is None:
data_mask = bin_ndarray(data_mask,new_shape=new_shape,operation='average') > 0.80
rebinned_data = np.array(rebinned_data)
rebinned_error = np.array(rebinned_error)
return rebinned_data, rebinned_error, rebinned_headers, Dxy
if data_mask is None:
return rebinned_data, rebinned_error, rebinned_headers, Dxy
else:
return rebinned_data, rebinned_error, rebinned_headers, Dxy, data_mask
def align_data(data_array, headers, error_array=None, upsample_factor=1.,
@@ -744,7 +766,7 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
# Create a rescaled null array that can contain any rotation of the
#original image (and shifted images)
shape = data_array.shape
res_shape = int(np.ceil(np.sqrt(2.5)*np.max(shape[1:])))
res_shape = int(np.ceil(np.sqrt(2.)*np.max(shape[1:])))
rescaled_image = np.zeros((shape[0],res_shape,res_shape))
rescaled_error = np.ones((shape[0],res_shape,res_shape))
rescaled_mask = np.zeros((shape[0],res_shape,res_shape),dtype=bool)
@@ -758,7 +780,7 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
# Initialize rescaled images to background values
rescaled_error[i] *= background[i]
# Get shifts and error by cross-correlation to ref_data
shift, error, phase_diff = phase_cross_correlation(ref_data, image,
shift, error, phase_diff = phase_cross_correlation(ref_data/ref_data.max(), image/image.max(),
upsample_factor=upsample_factor)
# Rescale image to requested output
rescaled_image[i,res_shift[0]:res_shift[0]+shape[1],
@@ -766,9 +788,9 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
rescaled_error[i,res_shift[0]:res_shift[0]+shape[1],
res_shift[1]:res_shift[1]+shape[2]] = deepcopy(error_array[i])
# Shift images to align
rescaled_image[i] = sc_shift(rescaled_image[i], shift, cval=0.)
rescaled_error[i] = sc_shift(rescaled_error[i], shift, cval=background[i])
curr_mask = sc_shift(res_mask, shift, cval=False)
rescaled_image[i] = sc_shift(rescaled_image[i], shift, order=1, cval=0.)
rescaled_error[i] = sc_shift(rescaled_error[i], shift, order=1, cval=background[i])
curr_mask = sc_shift(res_mask, shift, order=1, cval=False)
mask_vertex = clean_ROI(curr_mask)
rescaled_mask[i,mask_vertex[2]:mask_vertex[3],mask_vertex[0]:mask_vertex[1]] = True
@@ -789,17 +811,19 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
errors = np.array(errors)
# Update headers CRPIX value
added_pix = (np.array(rescaled_image.shape[1:]) - np.array(data_array.shape[1:]))/2
headers_wcs = [deepcopy(WCS(header)) for header in headers]
new_crpix = np.array([wcs.wcs.crpix for wcs in headers_wcs]) + shifts + added_pix
new_crpix = np.array([wcs.wcs.crpix for wcs in headers_wcs]) + shifts[:,::-1] + res_shift[::-1]
for i in range(len(headers_wcs)):
headers_wcs[i].wcs.crpix = new_crpix.mean(axis=0)
headers_wcs[i].wcs.crpix = new_crpix[0]
headers[i].update(headers_wcs[i].to_header())
data_mask = rescaled_mask.all(axis=0)
data_array, error_array, data_mask, headers = crop_array(rescaled_image, headers, rescaled_error, data_mask)
if return_shifts:
return rescaled_image, rescaled_error, headers, rescaled_mask.any(axis=0), shifts, errors
return data_array, error_array, headers, data_mask, shifts, errors
else:
return rescaled_image, rescaled_error, headers, rescaled_mask.any(axis=0)
return data_array, error_array, headers, data_mask
def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
@@ -869,29 +893,29 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
# Catch expected "OverflowWarning" as we overflow values that are not in the image
with warnings.catch_warnings(record=True) as w:
g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*data_array.shape[0])
# Apply weighted combination
smoothed[r,c] = np.where(data_mask[r,c], np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc), 0.)
error[r,c] = np.where(data_mask[r,c], np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc), 0.)
# Apply weighted combination
smoothed[r,c] = np.where(data_mask[r,c], np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc), 0.)
error[r,c] = np.where(data_mask[r,c], np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc), 0.)
# Nan handling
error[np.isnan(smoothed)] = 0.
smoothed[np.isnan(smoothed)] = 0.
error[np.isnan(error)] = 0.
elif smoothing.lower() in ['gauss','gaussian']:
elif smoothing.lower() in ['weight_gauss','weighted_gaussian','gauss','gaussian']:
# Convolution with gaussian function
smoothed = np.zeros(data_array.shape)
error = np.zeros(error_array.shape)
for i,image in enumerate(data_array):
xx, yy = np.indices(image.shape)
for r in range(image.shape[0]):
for c in range(image.shape[1]):
dist_rc = np.where(data_mask, np.sqrt((r-xx)**2+(c-yy)**2), fmax)
# Catch expected "OverflowWarning" as we overflow values that are not in the image
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] = np.where(data_mask[r,c], np.sum(image*g_rc), 0.)
error[i][r,c] = np.where(data_mask[r,c], np.sqrt(np.sum(error_array[i]**2*g_rc**2)), 0.)
x, y = np.meshgrid(np.arange(-image.shape[1]/2,image.shape[1]/2),np.arange(-image.shape[0]/2,image.shape[0]/2))
weights = np.ones(error_array[i].shape)
if smoothing.lower()[:6] in ['weight']:
weights = 1./error_array[i]**2
weights /= weights.sum()
kernel = gaussian2d(x, y, stdev)
kernel /= kernel.sum()
smoothed[i] = convolve2d(image*weights/image.sum(),kernel,'same')*image.sum()
error[i] = np.sqrt(convolve2d((error_array[i]/error_array[i].sum())**2*weights**2,kernel**2,'same'))*error_array[i].sum()
# Nan handling
error[i][np.isnan(smoothed[i])] = 0.
@@ -989,16 +1013,25 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
else:
# Sum on each polarization filter.
print("Exposure time for polarizer 0°/60°/120° : ",
np.sum([header['exptime'] for header in headers0]),
np.sum([header['exptime'] for header in headers60]),
np.sum([header['exptime'] for header in headers120]))
pol0_t = np.sum([header['exptime'] for header in headers0])
pol60_t = np.sum([header['exptime'] for header in headers60])
pol120_t = np.sum([header['exptime'] for header in headers120])
for i in range(pol0_array.shape[0]):
pol0_array[i] *= headers0[i]['exptime']/pol0_t
err0_array[i] *= headers0[i]['exptime']/pol0_t
for i in range(pol60_array.shape[0]):
pol60_array[i] *= headers60[i]['exptime']/pol60_t
err60_array[i] *= headers60[i]['exptime']/pol60_t
for i in range(pol120_array.shape[0]):
pol120_array[i] *= headers120[i]['exptime']/pol120_t
err120_array[i] *= headers120[i]['exptime']/pol120_t
pol0 = pol0_array.sum(axis=0)
pol60 = pol60_array.sum(axis=0)
pol120 = pol120_array.sum(axis=0)
pol_array = np.array([pol0, pol60, pol120])
# Propagate uncertainties quadratically
err0 = np.sqrt(np.sum(err0_array**2,axis=0))
err60 = np.sqrt(np.sum(err60_array**2,axis=0))
@@ -1145,33 +1178,22 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
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()
A = (coeff_stokes[0,:]*transmit/2.).sum()
coeff_stokes = coeff_stokes/A
I_stokes = np.zeros(pol_array[0].shape)
Q_stokes = np.zeros(pol_array[0].shape)
U_stokes = np.zeros(pol_array[0].shape)
Stokes_cov = np.zeros((3,3,I_stokes.shape[0],I_stokes.shape[1]))
for i in range(I_stokes.shape[0]):
for j in range(I_stokes.shape[1]):
I_stokes[i,j], Q_stokes[i,j], U_stokes[i,j] = np.dot(coeff_stokes, pol_flux[:,i,j]).T
Stokes_cov[:,:,i,j] = np.dot(coeff_stokes, np.dot(pol_cov[:,:,i,j], coeff_stokes.T))
I_stokes = np.sum([coeff_stokes[0,i]*pol_flux[i] for i in range(3)], axis=0)
Q_stokes = np.sum([coeff_stokes[1,i]*pol_flux[i] for i in range(3)], axis=0)
U_stokes = np.sum([coeff_stokes[2,i]*pol_flux[i] for i in range(3)], axis=0)
# Remove nan
I_stokes[np.isnan(I_stokes)]=0.
Q_stokes[I_stokes == 0.]=0.
U_stokes[I_stokes == 0.]=0.
Q_stokes[np.isnan(Q_stokes)]=0.
U_stokes[np.isnan(U_stokes)]=0.
mask = (Q_stokes**2 + U_stokes**2) > I_stokes**2
if mask.any():
print("WARNING : found {0:d} pixels for which I_pol > I_stokes".format(I_stokes[mask].size))
#Stokes covariance matrix
Stokes_cov = np.zeros((3,3,I_stokes.shape[0],I_stokes.shape[1]))
Stokes_cov[0,0] = coeff_stokes[0,0]**2*pol_cov[0,0]+coeff_stokes[0,1]**2*pol_cov[1,1]+coeff_stokes[0,2]**2*pol_cov[2,2] + 2*(coeff_stokes[0,0]*coeff_stokes[0,1]*pol_cov[0,1]+coeff_stokes[0,0]*coeff_stokes[0,2]*pol_cov[0,2]+coeff_stokes[0,1]*coeff_stokes[0,2]*pol_cov[1,2])
Stokes_cov[1,1] = coeff_stokes[1,0]**2*pol_cov[0,0]+coeff_stokes[1,1]**2*pol_cov[1,1]+coeff_stokes[1,2]**2*pol_cov[2,2] + 2*(coeff_stokes[1,0]*coeff_stokes[1,1]*pol_cov[0,1]+coeff_stokes[1,0]*coeff_stokes[1,2]*pol_cov[0,2]+coeff_stokes[1,1]*coeff_stokes[1,2]*pol_cov[1,2])
Stokes_cov[2,2] = coeff_stokes[2,0]**2*pol_cov[0,0]+coeff_stokes[2,1]**2*pol_cov[1,1]+coeff_stokes[2,2]**2*pol_cov[2,2] + 2*(coeff_stokes[2,0]*coeff_stokes[2,1]*pol_cov[0,1]+coeff_stokes[2,0]*coeff_stokes[2,2]*pol_cov[0,2]+coeff_stokes[2,1]*coeff_stokes[2,2]*pol_cov[1,2])
Stokes_cov[0,1] = Stokes_cov[1,0] = coeff_stokes[0,0]*coeff_stokes[1,0]*pol_cov[0,0]+coeff_stokes[0,1]*coeff_stokes[1,1]*pol_cov[1,1]+coeff_stokes[0,2]*coeff_stokes[1,2]*pol_cov[2,2]+(coeff_stokes[0,0]*coeff_stokes[1,1]+coeff_stokes[1,0]*coeff_stokes[0,1])*pol_cov[0,1]+(coeff_stokes[0,0]*coeff_stokes[1,2]+coeff_stokes[1,0]*coeff_stokes[0,2])*pol_cov[0,2]+(coeff_stokes[0,1]*coeff_stokes[1,2]+coeff_stokes[1,1]*coeff_stokes[0,2])*pol_cov[1,2]
Stokes_cov[0,2] = Stokes_cov[2,0] = coeff_stokes[0,0]*coeff_stokes[2,0]*pol_cov[0,0]+coeff_stokes[0,1]*coeff_stokes[2,1]*pol_cov[1,1]+coeff_stokes[0,2]*coeff_stokes[2,2]*pol_cov[2,2]+(coeff_stokes[0,0]*coeff_stokes[2,1]+coeff_stokes[2,0]*coeff_stokes[0,1])*pol_cov[0,1]+(coeff_stokes[0,0]*coeff_stokes[2,2]+coeff_stokes[2,0]*coeff_stokes[0,2])*pol_cov[0,2]+(coeff_stokes[0,1]*coeff_stokes[2,2]+coeff_stokes[2,1]*coeff_stokes[0,2])*pol_cov[1,2]
Stokes_cov[1,2] = Stokes_cov[2,1] = coeff_stokes[1,0]*coeff_stokes[2,0]*pol_cov[0,0]+coeff_stokes[1,1]*coeff_stokes[2,1]*pol_cov[1,1]+coeff_stokes[1,2]*coeff_stokes[2,2]*pol_cov[2,2]+(coeff_stokes[1,0]*coeff_stokes[2,1]+coeff_stokes[2,0]*coeff_stokes[1,1])*pol_cov[0,1]+(coeff_stokes[1,0]*coeff_stokes[2,2]+coeff_stokes[2,0]*coeff_stokes[1,2])*pol_cov[0,2]+(coeff_stokes[1,1]*coeff_stokes[2,2]+coeff_stokes[2,1]*coeff_stokes[1,2])*pol_cov[1,2]
# 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.*theta[2]+2.*theta[0])*(pol_flux[1]-I_stokes) - pol_eff[1]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes))
dI_dtheta2 = 2.*pol_eff[1]/A*(pol_eff[0]*np.cos(-2.*theta[0]+2.*theta[1])*(pol_flux[2]-I_stokes) - pol_eff[2]*np.cos(-2.*theta[1]+2.*theta[2])*(pol_flux[0]-I_stokes))
@@ -1198,13 +1220,14 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
Stokes_cov[1,1] += s_Q2_axis
Stokes_cov[2,2] += s_U2_axis
if not(FWHM is None) and (smoothing.lower() in ['gaussian_after','gauss_after']):
if not(FWHM is None) and (smoothing.lower() in ['weighted_gaussian_after','weight_gauss_after','gaussian_after','gauss_after']):
smoothing = smoothing.lower()[:-6]
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)])
Stokes_headers = headers[0:3]
Stokes_array, Stokes_error = smooth_data(Stokes_array, Stokes_error,
headers=Stokes_headers, FWHM=FWHM, scale=scale)
Stokes_array, Stokes_error = smooth_data(Stokes_array, Stokes_error, data_mask,
headers=Stokes_headers, FWHM=FWHM, scale=scale, smoothing=smoothing)
I_stokes, Q_stokes, U_stokes = Stokes_array
Stokes_cov[0,0], Stokes_cov[1,1], Stokes_cov[2,2] = Stokes_error**2
@@ -1215,12 +1238,12 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
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))
I_diluted_err = np.sqrt(np.sum(Stokes_cov[0,0][mask]))
Q_diluted_err = np.sqrt(np.sum(Stokes_cov[1,1][mask]))
U_diluted_err = np.sqrt(np.sum(Stokes_cov[2,2][mask]))
IQ_diluted_err = np.sqrt(np.sum(Stokes_cov[0,1][mask]**2))
IU_diluted_err = np.sqrt(np.sum(Stokes_cov[0,2][mask]**2))
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)
@@ -1391,33 +1414,41 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
#Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
alpha = ang*np.pi/180.
new_I_stokes = 1.*I_stokes
new_Q_stokes = np.cos(2*alpha)*Q_stokes + np.sin(2*alpha)*U_stokes
new_U_stokes = -np.sin(2*alpha)*Q_stokes + np.cos(2*alpha)*U_stokes
#Compute new covariance matrix on rotated parameters
new_Stokes_cov = deepcopy(Stokes_cov)
new_Stokes_cov[1,1] = np.cos(2.*alpha)**2*Stokes_cov[1,1] + np.sin(2.*alpha)**2*Stokes_cov[2,2] + 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
new_Stokes_cov[2,2] = np.sin(2.*alpha)**2*Stokes_cov[1,1] + np.cos(2.*alpha)**2*Stokes_cov[2,2] - 2.*np.cos(2.*alpha)*np.sin(2.*alpha)*Stokes_cov[1,2]
new_Stokes_cov[0,1] = new_Stokes_cov[1,0] = np.cos(2.*alpha)*Stokes_cov[0,1] + np.sin(2.*alpha)*Stokes_cov[0,2]
new_Stokes_cov[0,2] = new_Stokes_cov[2,0] = -np.sin(2.*alpha)*Stokes_cov[0,1] + np.cos(2.*alpha)*Stokes_cov[0,2]
new_Stokes_cov[1,2] = new_Stokes_cov[2,1] = np.cos(2.*alpha)*np.sin(2.*alpha)*(Stokes_cov[2,2] - Stokes_cov[1,1]) + (np.cos(2.*alpha)**2 - np.sin(2.*alpha)**2)*Stokes_cov[1,2]
mrot = np.array([[1., 0., 0.],
[0., np.cos(2.*alpha), np.sin(2.*alpha)],
[0, -np.sin(2.*alpha), np.cos(2.*alpha)]])
old_center = np.array(I_stokes.shape)/2
shape = np.fix(np.array(I_stokes.shape)*np.sqrt(2.5)).astype(int)
new_center = np.array(shape)/2
I_stokes = zeropad(I_stokes, shape)
Q_stokes = zeropad(Q_stokes, shape)
U_stokes = zeropad(U_stokes, shape)
data_mask = zeropad(data_mask, shape)
Stokes_cov = zeropad(Stokes_cov, [*Stokes_cov.shape[:-2],*shape])
new_I_stokes = np.zeros(shape)
new_Q_stokes = np.zeros(shape)
new_U_stokes = np.zeros(shape)
new_Stokes_cov = np.zeros((*Stokes_cov.shape[:-2],*shape))
for i in range(shape[0]):
for j in range(shape[1]):
new_I_stokes[i,j], new_Q_stokes[i,j], new_U_stokes[i,j] = np.dot(mrot, np.array([I_stokes[i,j], Q_stokes[i,j], U_stokes[i,j]])).T
new_Stokes_cov[:,:,i,j] = np.dot(mrot, np.dot(Stokes_cov[:,:,i,j], mrot.T))
#Rotate original images using scipy.ndimage.rotate
new_I_stokes = sc_rotate(new_I_stokes, ang, order=5, reshape=False, cval=0.)
new_Q_stokes = sc_rotate(new_Q_stokes, ang, order=5, reshape=False, cval=0.)
new_U_stokes = sc_rotate(new_U_stokes, ang, order=5, reshape=False, cval=0.)
new_data_mask = sc_rotate(data_mask.astype(float)*10., ang, order=5, reshape=False, cval=0.)
new_I_stokes = sc_rotate(new_I_stokes, ang, order=1, reshape=False, cval=0.)
new_Q_stokes = sc_rotate(new_Q_stokes, ang, order=1, reshape=False, cval=0.)
new_U_stokes = sc_rotate(new_U_stokes, ang, order=1, reshape=False, cval=0.)
new_data_mask = sc_rotate(data_mask.astype(float)*10., ang, order=1, reshape=False, cval=0.)
new_data_mask[new_data_mask < 2] = 0.
new_data_mask = new_data_mask.astype(bool)
for i in range(3):
for j in range(3):
new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang,
new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang, order=1,
reshape=False, cval=0.)
new_Stokes_cov[i,i] = np.abs(new_Stokes_cov[i,i])
old_center = np.array(I_stokes.shape)/2
new_center = np.array(new_I_stokes.shape)/2
#Update headers to new angle
new_headers = []
@@ -1445,7 +1476,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
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.crpix = np.dot(mrot, new_wcs.wcs.crpix - old_center) + new_center
new_wcs.wcs.crpix = np.dot(mrot, new_wcs.wcs.crpix - old_center[::-1]) + new_center[::-1]
new_wcs.wcs.set()
new_header.update(new_wcs.to_header())
@@ -1467,12 +1498,12 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
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))
I_diluted_err = np.sqrt(np.sum(new_Stokes_cov[0,0][mask]))
Q_diluted_err = np.sqrt(np.sum(new_Stokes_cov[1,1][mask]))
U_diluted_err = np.sqrt(np.sum(new_Stokes_cov[2,2][mask]))
IQ_diluted_err = np.sqrt(np.sum(new_Stokes_cov[0,1][mask]**2))
IU_diluted_err = np.sqrt(np.sum(new_Stokes_cov[0,2][mask]**2))
QU_diluted_err = 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)
@@ -1518,19 +1549,26 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
#Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
alpha = ang*np.pi/180.
old_center = np.array(data_array[0].shape)/2
shape = np.fix(np.array(data_array[0].shape)*np.sqrt(2.5)).astype(int)
new_center = np.array(shape)/2
data_array = zeropad(data_array, [data_array.shape[0],*shape])
error_array = zeropad(error_array, [error_array.shape[0],*shape])
data_mask = zeropad(data_mask, shape)
#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, order=5, reshape=False,
new_data_array.append(sc_rotate(data_array[i], ang, order=1, reshape=False,
cval=0.))
new_error_array.append(sc_rotate(error_array[i], ang, order=1, reshape=False,
cval=0.))
new_error_array.append(sc_rotate(error_array[i], ang, order=5, reshape=False,
cval=error_array.mean()))
new_data_array = np.array(new_data_array)
new_data_mask = sc_rotate(data_mask*10., ang, order=5, reshape=False, cval=0.)
new_error_array = np.array(new_error_array)
new_data_mask = sc_rotate(data_mask*10., ang, order=1, reshape=False, cval=0.)
new_data_mask[new_data_mask < 2] = 0.
new_data_mask = new_data_mask.astype(bool)
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.
@@ -1562,6 +1600,7 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
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.crpix = np.dot(mrot, new_wcs.wcs.crpix - old_center[::-1]) + new_center[::-1]
new_wcs.wcs.set()
new_header.update(new_wcs.to_header())