Tibeuleu/FOC_Reduction
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small improvement and bugfix

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This commit is contained in:
Thibault Barnouin
2023-07-03 11:34:06 +02:00
parent 724f2c1f42
commit fb1f075078
4 changed files with 22 additions and 39 deletions
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2
src/lib/query.py
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@@ -137,7 +137,7 @@ def get_product_list(target=None, proposal_id=None):
for prod in products:
prod['target_name'] = observations['target_name'][observations['obsid']==prod['obsID']][0]
tab = unique(products, ['target_name', 'proposal_id'])
if np.all(tab['target_name']==tab['target_name'][0]):
if len(tab)>1 and np.all(tab['target_name']==tab['target_name'][0]):
target = tab['target_name'][0]
products["Obs"] = [np.argmax(np.logical_and(tab['proposal_id']==data['proposal_id'],tab['target_name']==data['target_name']))+1 for data in products]

35
src/lib/reduction.py
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@@ -575,8 +575,7 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
if instr == 'FOC':
HST_aper = 2400. # HST aperture in mm
Dxy_arr = np.ones((data_array.shape[0],2))
for i, enum in enumerate(list(zip(data_array, error_array, headers))):
image, error, header = enum
for i, (image, error, header) in enumerate(list(zip(data_array, error_array, headers))):
# Get current pixel size
w = WCS(header).deepcopy()
new_header = deepcopy(header)
@@ -592,8 +591,7 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
raise ValueError("'{0:s}' invalid scale for binning.".format(scale))
new_shape = np.ceil(min(image.shape/Dxy_arr,key=lambda x:x[0]+x[1])).astype(int)
for i, enum in enumerate(list(zip(data_array, error_array, headers))):
image, error, header = enum
for i, (image, error, header) in enumerate(list(zip(data_array, error_array, headers))):
# Get current pixel size
w = WCS(header).deepcopy()
new_header = deepcopy(header)
@@ -617,21 +615,12 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
if operation.lower() in ["mean", "average", "avg"]:
new_error = np.sqrt(bin_ndarray(error**2,
new_shape=new_shape, operation='average'))
#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 = np.sqrt(bin_ndarray(error**2,
new_shape=new_shape, operation='sum'))
#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
#nw = w.slice((np.s_[::Dxy[0]], np.s_[::Dxy[1]]))
nw = w.deepcopy()
nw.wcs.cdelt *= Dxy
nw.wcs.crpix /= Dxy
@@ -762,21 +751,20 @@ def align_data(data_array, headers, error_array=None, background=None,
# Initialize rescaled images to background values
rescaled_error[i] *= 0.01*background[i]
# Get shifts and error by cross-correlation to ref_data
shift, error, phase_diff = phase_cross_correlation(ref_data/ref_data.max(), image/image.max(),
upsample_factor=upsample_factor)
if do_shift:
shift, error, _ = phase_cross_correlation(ref_data/ref_data.max(), image/image.max(),
upsample_factor=upsample_factor)
else:
shift = pol_shift[headers[i]['filtnam1'].lower()]
error = sigma_shift[headers[i]['filtnam1'].lower()]
# Rescale image to requested output
rescaled_image[i,res_shift[0]:res_shift[0]+shape[1],
res_shift[1]:res_shift[1]+shape[2]] = deepcopy(image)
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
if do_shift:
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])
else:
shift = pol_shift[headers[i]['filtnam1'].lower()]
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])
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)
@@ -792,9 +780,6 @@ def align_data(data_array, headers, error_array=None, background=None,
#sum quadratically the errors
rescaled_error[i] = np.sqrt(rescaled_error[i]**2 + error_shift**2)
#if i==1:
#np.savetxt("output/s_shift.txt",error_shift)
shifts.append(shift)
errors.append(error)