Tibeuleu/FOC_Reduction
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correction on principal angle computation

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Tibeuleu
2022-10-28 15:17:54 +02:00
parent 700cc7e4df
commit da805b71f1
27 changed files with 38 additions and 38 deletions
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src/comparison_Kishimoto.py
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@@ -80,12 +80,8 @@ print('From my pipeline :\n', "P = {0:.2f} ± {1:.2f} %\n".format(data_S['P_dil'
print("From Kishimoto's pipeline :\n", "P = {0:.2f} ± {1:.2f} %\n".format(data_K['P_dil']*100.,data_K['sP_dil']*100.), "PA = {0:.2f} ± {1:.2f} °".format(data_K['PA_dil'],data_K['sPA_dil']))
#compare different types of error
xx, yy = np.indices(data_S['mask'].shape)
mask_ind = np.array([[y,x] for y,x in zip(yy[data_S['mask']],xx[data_S['mask']])])
index = mask_ind[np.random.randint(len(mask_ind))]
print("My pipeline : sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_S['sI'][index[0],index[1]]/data_S['I'][index[0],index[1]]),np.mean(data_S['sQ'][index[0],index[1]]/data_S['Q'][index[0],index[1]]),np.mean(data_S['sU'][index[0],index[1]]/data_S['U'][index[0],index[1]]),np.mean(data_S['sP'][index[0],index[1]]/data_S['P'][index[0],index[1]])))
print("Kishimoto's pipeline : sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_K['sI'][index[0],index[1]]/data_K['I'][index[0],index[1]]),np.mean(data_K['sQ'][index[0],index[1]]/data_K['Q'][index[0],index[1]]),np.mean(data_K['sU'][index[0],index[1]]/data_K['U'][index[0],index[1]]),np.mean(data_K['sP'][index[0],index[1]]/data_K['P'][index[0],index[1]])))
print("For random pixel in cut at {}".format(index))
print("My pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_S['sI'][data_S['mask']]/data_S['I'][data_S['mask']]),np.mean(data_S['sQ'][data_S['mask']]/data_S['Q'][data_S['mask']]),np.mean(data_S['sU'][data_S['mask']]/data_S['U'][data_S['mask']]),np.mean(data_S['sP'][data_S['mask']]/data_S['P'][data_S['mask']])))
print("Kishimoto's pipeline : average sI/I={0:.2f} ; sQ/Q={1:.2f} ; sU/U={2:.2f} ; sP/P={3:.2f}".format(np.mean(data_K['sI'][data_S['mask']]/data_K['I'][data_S['mask']]),np.mean(data_K['sQ'][data_S['mask']]/data_K['Q'][data_S['mask']]),np.mean(data_K['sU'][data_S['mask']]/data_K['U'][data_S['mask']]),np.mean(data_K['sP'][data_S['mask']]/data_K['P'][data_S['mask']])))
for d,i in zip(['I','Q','U','P','PA','sI','sQ','sU','sP','sPA'],[0,1,2,5,8,(3,0,0),(3,1,1),(3,2,2),6,9]):
data_K[d] = np.loadtxt(path_join(root_dir_K,d+'.txt'))
with fits.open(path_join(root_dir_data_S,'NGC1068_K_FOC_bin10px.fits')) as f: