Tibeuleu/FOC_Reduction
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plots clean-up, correct crop, prepare analysis

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Thibault Barnouin
2022-04-04 11:15:25 +02:00
parent 1ddff076b8
commit c4311bbb4b
399 changed files with 256 additions and 90 deletions
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35
src/lib/fits.py
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@@ -12,7 +12,7 @@ prototypes :
import numpy as np
from astropy.io import fits
from astropy import wcs
from lib.convex_hull import image_hull
from lib.convex_hull import image_hull, clean_ROI
def get_obs_data(infiles, data_folder="", compute_flux=False):
@@ -78,33 +78,6 @@ def get_obs_data(infiles, data_folder="", compute_flux=False):
return data_array, headers
def clean_ROI(image):
H,J = [],[]
shape = np.array(image.shape)
row, col = np.indices(shape)
for i in range(0,shape[0]):
r = row[i,:][image[i,:]>0.]
c = col[i,:][image[i,:]>0.]
if len(r)>1 and len(c)>1:
H.append((r[0],c[0]))
H.append((r[-1],c[-1]))
H = np.array(H)
for j in range(0,shape[1]):
r = row[:,j][image[:,j]>0.]
c = col[:,j][image[:,j]>0.]
if len(r)>1 and len(c)>1:
J.append((r[0],c[0]))
J.append((r[-1],c[-1]))
J = np.array(J)
xmin = np.min([H[:,1].min(),J[:,1].min()])-1
xmax = np.max([H[:,1].max(),J[:,1].max()])+1
ymin = np.min([H[:,0].min(),J[:,0].min()])-1
ymax = np.max([H[:,0].max(),J[:,0].max()])+1
return np.array([xmin,xmax,ymin,ymax])
def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
s_P_P, PA, s_PA, s_PA_P, headers, data_mask, filename, data_folder="",
return_hdul=False):
@@ -145,10 +118,10 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
exp_tot = np.array([header['exptime'] for header in headers]).sum()
new_wcs = wcs.WCS(ref_header).deepcopy()
vertex = clean_ROI(1.-data_mask)
vertex = clean_ROI(data_mask)
shape = vertex[1::2]-vertex[0::2]
new_wcs.array_shape = shape
new_wcs.wcs.crpix -= vertex[0::2]
new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2]
header = new_wcs.to_header()
header['instrume'] = (ref_header['instrume'], 'Instrument from which data is reduced')
@@ -179,7 +152,7 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
new_Stokes_cov = np.zeros((3,3,shape[0],shape[1]))
for i in range(3):
for j in range(3):
Stokes_cov[i,j][data_mask] = 0.
Stokes_cov[i,j][(1-data_mask).astype(bool)] = 0.
new_Stokes_cov[i,j] = Stokes_cov[i,j][vertex[0]:vertex[1],vertex[2]:vertex[3]]
Stokes_cov = new_Stokes_cov