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force convention on WCS (unitary PCi_ja for given initial cdelt)

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Thibault Barnouin
2022-03-11 17:31:22 +01:00
parent d700f46738
commit da15353318
27 changed files with 152 additions and 144 deletions
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119
src/lib/reduction.py
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@@ -331,23 +331,7 @@ 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()
if w.wcs.has_cd():
del w.wcs.cd
keys = list(w.to_header().keys())+['CD1_1','CD1_2','CD2_1','CD2_2']
for key in keys:
header.remove(key, ignore_missing=True)
w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
if (w.wcs.cdelt == np.array([1., 1.])).all() and \
(w.array_shape in [(512, 512),(1024,512),(512,1024),(1024,1024)]):
# Update WCS with relevant information
HST_aper = 2400. # HST aperture in mm
f_ratio = header['f_ratio']
px_dim = np.array([25., 25.]) # Pixel dimension in µm
if header['pxformt'].lower() == 'zoom':
px_dim[0] = 50.
w.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
header.update(w.to_header())
pxsize[i] = np.round(w.wcs.cdelt,5)
pxsize[i] = np.round(w.wcs.cdelt/3600.,5)
if (pxsize != pxsize[0]).any():
raise ValueError("Not all images in array have same pixel size")
FWHM /= pxsize[0].min()
@@ -570,31 +554,17 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
image, error, header = enum
# Get current pixel size
w = WCS(header).deepcopy()
if w.wcs.has_cd():
del w.wcs.cd
keys = list(w.to_header().keys())+['CD1_1','CD1_2','CD2_1','CD2_2']
for key in keys:
header.remove(key, ignore_missing=True)
w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
if (w.wcs.cdelt == np.array([1., 1.])).all() and \
(w.array_shape in [(512, 512),(1024,512),(512,1024),(1024,1024)]):
# Update WCS with relevant information
f_ratio = header['f_ratio']
px_dim = np.array([25., 25.]) # Pixel dimension in µm
if header['pxformt'].lower() == 'zoom':
px_dim[0] = 50.
w.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
header.update(w.to_header())
# Compute binning ratio
if scale.lower() in ['px', 'pixel']:
Dxy = np.array([pxsize,]*2)
elif scale.lower() in ['arcsec','arcseconds']:
Dxy = np.floor(pxsize/w.wcs.cdelt).astype(int)
Dxy = np.floor(pxsize/w.wcs.cdelt/3600.).astype(int)
else:
raise ValueError("'{0:s}' invalid scale for binning.".format(scale))
if (Dxy <= 1.).any():
print(Dxy, pxsize, w.wcs.cdelt*3600.)
raise ValueError("Requested pixel size is below resolution.")
new_shape = (image.shape//Dxy).astype(int)
@@ -605,9 +575,6 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
# Propagate error
rms_image = np.sqrt(bin_ndarray(image**2, new_shape=new_shape,
operation='average'))
#std_image = np.sqrt(bin_ndarray(image**2, new_shape=new_shape,
# operation='average') - bin_ndarray(image, new_shape=new_shape,
# operation='average')**2)
new_error = np.sqrt(Dxy[0]*Dxy[1])*bin_ndarray(error,
new_shape=new_shape, operation='average')
rebinned_error.append(np.sqrt(rms_image**2 + new_error**2))
@@ -806,23 +773,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
for i,header in enumerate(headers):
# Get current pixel size
w = WCS(header).deepcopy()
if w.wcs.has_cd():
del w.wcs.cd
keys = list(w.to_header().keys())+['CD1_1','CD1_2','CD2_1','CD2_2']
for key in keys:
header.remove(key, ignore_missing=True)
w.wcs.cdelt = 3600.*np.sqrt(np.sum(w.wcs.get_pc()**2,axis=1))
if (w.wcs.cdelt == np.array([1., 1.])).all() and \
(w.array_shape in [(512, 512),(1024,512),(512,1024),(1024,1024)]):
# Update WCS with relevant information
HST_aper = 2400. # HST aperture in mm
f_ratio = header['f_ratio']
px_dim = np.array([25., 25.]) # Pixel dimension in µm
if header['pxformt'].lower() == 'zoom':
px_dim[0] = 50.
w.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
header.update(w.to_header())
pxsize[i] = np.round(w.wcs.cdelt,4)
pxsize[i] = np.round(w.wcs.cdelt*3600.,4)
if (pxsize != pxsize[0]).any():
raise ValueError("Not all images in array have same pixel size")
FWHM /= pxsize[0].min()
@@ -1176,46 +1127,6 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
#Stokes_cov[1,1] += s_Q2_axis
#Stokes_cov[2,2] += s_U2_axis
# s_I_I = np.sqrt(Stokes_cov[0,0])/I_stokes*100.
# s_I_axis_I = np.sqrt(s_I2_axis)/I_stokes*100.
# s_Q_Q = np.sqrt(Stokes_cov[1,1])/Q_stokes*100.
# s_Q_axis_Q = np.sqrt(s_Q2_axis)/Q_stokes*100.
# s_U_U = np.sqrt(Stokes_cov[2,2])/U_stokes*100.
# s_U_axis_U = np.sqrt(s_U2_axis)/U_stokes*100.
#
# fig, ax = plt.subplots(3,3,figsize=(15,15))
# im = ax[0,0].imshow(s_I_I, origin='lower')
# ax[0,0].set_title(r"$\frac{\sigma_{I}}{I}$")
# fig.colorbar(im, ax=ax[0,0])
# im = ax[0,1].imshow(s_I_axis_I, origin='lower')
# ax[0,1].set_title(r"$\frac{\sigma_{I}^{axis}}{I}$")
# fig.colorbar(im, ax=ax[0,1])
# im = ax[0,2].imshow(s_I_axis_I/s_I_I, origin='lower')
# ax[0,2].set_title(r"$\frac{\sigma_{I}^{axis}}{\sigma_{I}}$")
# fig.colorbar(im, ax=ax[0,2])
# im = ax[1,0].imshow(s_Q_Q, origin='lower')
# ax[1,0].set_title(r"$\frac{\sigma_{Q}}{Q}$")
# fig.colorbar(im, ax=ax[1,0])
# im = ax[1,1].imshow(s_Q_axis_Q, origin='lower')
# ax[1,1].set_title(r"$\frac{\sigma_{Q}^{axis}}{Q}$")
# fig.colorbar(im, ax=ax[1,1])
# im = ax[1,2].imshow(s_Q_axis_Q/s_Q_Q, origin='lower')
# ax[1,2].set_title(r"$\frac{\sigma_{Q}^{axis}}{\sigma_{Q}}$")
# fig.colorbar(im, ax=ax[1,2])
# im = ax[2,0].imshow(s_U_U, origin='lower')
# ax[2,0].set_title(r"$\frac{\sigma_{U}}{U}$")
# fig.colorbar(im, ax=ax[2,0])
# im = ax[2,1].imshow(s_U_axis_U, origin='lower')
# ax[2,1].set_title(r"$\frac{\sigma_{U}^{axis}}{U}$")
# fig.colorbar(im, ax=ax[2,1])
# im = ax[2,2].imshow(s_U_axis_U/s_U_U, origin='lower')
# ax[2,2].set_title(r"$\frac{\sigma_{U}^{axis}}{\sigma_{U}}$")
# fig.colorbar(im, ax=ax[2,2])
# plt.show()
# print("s_I/I = {}% ; s_I_axis/I = {}%".format(np.mean(s_I_I[I_stokes > 0.]), np.mean(s_I_axis_I[I_stokes > 0.])))
# print("s_Q/Q = {}% ; s_Q_axis/Q = {}%".format(np.mean(s_Q_Q[Q_stokes > 0.]), np.mean(s_Q_axis_Q[Q_stokes > 0.])))
# print("s_U/U = {}% ; s_U_axis/U = {}%".format(np.mean(s_U_U[U_stokes > 0.]), np.mean(s_U_axis_U[U_stokes > 0.])))
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)])
@@ -1416,11 +1327,20 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
new_wcs = WCS(header).deepcopy()
if new_wcs.wcs.has_cd(): # CD matrix
# Update WCS with relevant information
HST_aper = 2400. # HST aperture in mm
f_ratio = header['f_ratio']
px_dim = np.array([25., 25.]) # Pixel dimension in µm
if ref_header['pxformt'].lower() == 'zoom':
px_dim[0] = 50.
new_cdelt = 206.3/3600.*px_dim/(f_ratio*HST_aper)
old_cd = new_wcs.wcs.cd
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(w.wcs.get_pc()**2,axis=1))
new_wcs.wcs.pc = np.dot(mrot, np.dot(old_cd, np.diag(1./new_cdelt)))
new_wcs.wcs.cdelt = new_cdelt
elif new_wcs.wcs.has_pc(): # PC matrix + CDELT
newpc = np.dot(mrot, new_wcs.wcs.get_pc())
new_wcs.wcs.pc = newpc
@@ -1495,11 +1415,20 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
new_wcs = WCS(header).deepcopy()
if new_wcs.wcs.has_cd(): # CD matrix
# Update WCS with relevant information
HST_aper = 2400. # HST aperture in mm
f_ratio = ref_header['f_ratio']
px_dim = np.array([25., 25.]) # Pixel dimension in µm
if ref_header['pxformt'].lower() == 'zoom':
px_dim[0] = 50.
new_cdelt = 206.3/3600.*px_dim/(f_ratio*HST_aper)
old_cd = new_wcs.wcs.cd
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))
new_wcs.wcs.pc = np.dot(mrot, np.dot(old_cd, np.diag(1./new_cdelt)))
new_wcs.wcs.cdelt = new_cdelt
elif new_wcs.wcs.has_pc(): # PC matrix + CDELT
newpc = np.dot(mrot, new_wcs.wcs.get_pc())
new_wcs.wcs.pc = newpc