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

add code to compare to Kishimoto's pipeline output

Browse Source
This commit is contained in:
Tibeuleu
2022-10-24 16:53:51 +02:00
parent 3e315783f2
commit 23d802cabb
4 changed files with 123 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
src/lib/reduction.py
View File

@@ -318,6 +318,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5,
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())
crop_headers[i]['naxis1'], crop_headers[i]['naxis2'] = crop_array[i].shape
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
@@ -508,6 +509,11 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
background[i] = sub_image.sum()
if (data_array[i] < 0.).any():
print(data_array[i])
if i==0:
np.savetxt("output/s_bg.txt",error_bkg[i])
np.savetxt("output/s_wav.txt",err_wav)
np.savetxt("output/s_psf.txt",err_psf)
np.savetxt("output/s_flat.txt",err_flat)
if display:
plt.rcParams.update({'font.size': 10})
@@ -770,7 +776,7 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
if error_array is None:
_, error_array, headers, background = get_error(data_array, headers, return_background=True)
else:
_, _, headers, background = get_error(data_array, headers, return_background=True)
_, _, headers, background = get_error(data_array, headers, error_array=error_array, return_background=True)
# Crop out any null edges
#(ref_data must be cropped as well)
@@ -836,6 +842,9 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
error_shift = np.abs(rescaled_image[i] - shifted_image)/2.
#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)
@@ -1075,17 +1084,6 @@ 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])
# Update headers
for header in headers:
if header['filtnam1']=='POL0':
list_head = headers0
elif header['filtnam1']=='POL60':
list_head = headers60
else:
list_head = headers120
header['exptime'] = np.sum([head['exptime'] for head in list_head])/len(list_head)
headers_array = [headers0[0], headers60[0], headers120[0]]
if not(FWHM is None) and (smoothing.lower() in ['gaussian','gauss']):
# Smooth by convoluting with a gaussian each polX image.
pol_array, polerr_array = smooth_data(pol_array, polerr_array,
@@ -1093,6 +1091,17 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
pol0, pol60, pol120 = pol_array
err0, err60, err120 = polerr_array
# Update headers
for header in headers:
if header['filtnam1']=='POL0':
list_head = headers0
elif header['filtnam1']=='POL60':
list_head = headers60
elif header['filtnam1']=='POL120':
list_head = headers120
header['exptime'] = np.sum([head['exptime'] for head in list_head])#/len(list_head)
pol_headers = [headers0[0], headers60[0], headers120[0]]
# Get image shape
shape = pol0.shape
@@ -1109,7 +1118,7 @@ def polarizer_avg(data_array, error_array, data_mask, headers, FWHM=None,
polarizer_cov[1,1] = err60**2
polarizer_cov[2,2] = err120**2
return polarizer_array, polarizer_cov
return polarizer_array, polarizer_cov, pol_headers
def compute_Stokes(data_array, error_array, data_mask, headers,
@@ -1172,7 +1181,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
# Routine for the FOC instrument
if instr == 'FOC':
# Get image from each polarizer and covariance matrix
pol_array, pol_cov = polarizer_avg(data_array, error_array, data_mask,
pol_array, pol_cov, pol_headers = polarizer_avg(data_array, error_array, data_mask,
headers, FWHM=FWHM, scale=scale, smoothing=smoothing)
pol0, pol60, pol120 = pol_array
@@ -1246,6 +1255,9 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
s_I2_axis = np.sum([dI_dtheta[i]**2 * sigma_theta[i]**2 for i in range(len(sigma_theta))],axis=0)
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)
np.savetxt("output/sI_dir.txt", np.sqrt(s_I2_axis))
np.savetxt("output/sQ_dir.txt", np.sqrt(s_Q2_axis))
np.savetxt("output/sU_dir.txt", np.sqrt(s_U2_axis))
# Add quadratically the uncertainty to the Stokes covariance matrix
Stokes_cov[0,0] += s_I2_axis