Tibeuleu/FOC_Reduction
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Change some error handling for better smoothing

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This commit is contained in:
Thibault Barnouin
2021-06-17 23:15:31 +02:00
parent 44a060e2ae
commit d9c45870e6
70 changed files with 31 additions and 29 deletions
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30
src/FOC_reduction.py
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@@ -17,11 +17,11 @@ import lib.plots as proj_plots #Functions for plotting data
def main():
##### User inputs
## Input and output locations
globals()['data_folder'] = "../data/NGC1068_x274020/"
infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
globals()['plots_folder'] = "../plots/NGC1068_x274020/"
# globals()['data_folder'] = "../data/NGC1068_x274020/"
# infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
# 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
# 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
# globals()['plots_folder'] = "../plots/NGC1068_x274020/"
# globals()['data_folder'] = "../data/NGC1068_x14w010/"
# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
@@ -44,13 +44,13 @@ def main():
# infiles = ['x3mc0101m_c0f.fits','x3mc0102m_c0f.fits','x3mc0103m_c0f.fits']
# globals()['plots_folder'] = "../plots/3C109_x3mc010/"
# globals()['data_folder'] = "../data/MKN463_x2rp030/"
# infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits',
# 'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits',
# 'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits',
# 'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits',
# 'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits']
# globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
globals()['data_folder'] = "../data/MKN463_x2rp030/"
infiles = ['x2rp0201t_c0f.fits', 'x2rp0202t_c0f.fits', 'x2rp0203t_c0f.fits',
'x2rp0204t_c0f.fits', 'x2rp0205t_c0f.fits', 'x2rp0206t_c0f.fits',
'x2rp0207t_c0f.fits', 'x2rp0301t_c0f.fits', 'x2rp0302t_c0f.fits',
'x2rp0303t_c0f.fits', 'x2rp0304t_c0f.fits', 'x2rp0305t_c0f.fits',
'x2rp0306t_c0f.fits', 'x2rp0307t_c0f.fits']
globals()['plots_folder'] = "../plots/MKN463_x2rp030/"
# globals()['data_folder'] = "../data/PG1630+377_x39510/"
# infiles = ['x3990201m_c0f.fits', 'x3990205m_c0f.fits', 'x3995101r_c0f.fits',
@@ -108,10 +108,10 @@ def main():
rotate_stokes = True #rotation to North convention can give erroneous results
rotate_data = False #rotation to North convention can give erroneous results
# Polarization map output
figname = 'NGC1068_FOC' #target/intrument name
figname = 'MKN463_FOC' #target/intrument name
figtype = '_combine_FWHM020_rot' #additionnal informations
SNRp_cut = 20 #P measurments with SNR>3
SNRi_cut = 130 #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
SNRp_cut = 3 #P measurments with SNR>3
SNRi_cut = 30 #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
##### Pipeline start

30
src/lib/reduction.py
View File

@@ -800,7 +800,7 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
raise ValueError("Not all images in array have same pixel size")
FWHM /= pxsize[0].min()
#Define gaussian stdev
# Define gaussian stdev
stdev = FWHM/(2.*np.sqrt(2.*np.log(2)))
fmax = np.finfo(np.float64).max
@@ -821,15 +821,16 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
g_rc = np.array([np.exp(-0.5*(dist_rc/stdev)**2),]*len(data_array))
# Apply weighted combination
if data_mask[r,c]:
smoothed[r,c] = 0.
error[r,c] = 1.
else:
smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
smoothed[r,c] = (1.-data_mask[r,c])*np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
# Nan handling
error[np.isnan(smoothed)] = 0.
smoothed[np.isnan(smoothed)] = 0.
error[np.isnan(error)] = 0.
elif smoothing.lower() in ['gauss','gaussian']:
#Convolution with gaussian function
# Convolution with gaussian function
smoothed = np.zeros(data_array.shape)
error = np.zeros(error_array.shape)
for i,image in enumerate(data_array):
@@ -838,12 +839,13 @@ def smooth_data(data_array, error_array, data_mask, headers, FWHM=1.,
for c in range(image.shape[1]):
dist_rc = np.where(data_mask, fmax, np.sqrt((r-xx)**2+(c-yy)**2))
g_rc = np.exp(-0.5*(dist_rc/stdev)**2)/(2.*np.pi*stdev**2)
if data_mask[r,c]:
smoothed[r,c] = 0.
error[r,c] = 1.
else:
smoothed[r,c] = np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
error[r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
smoothed[i][r,c] = (1.-data_mask[r,c])*np.sum(data_array*weight*g_rc)/np.sum(weight*g_rc)
error[i][r,c] = np.sqrt(np.sum(weight*g_rc**2))/np.sum(weight*g_rc)
# Nan handling
error[i][np.isnan(smoothed)] = 0.
smoothed[i][np.isnan(smoothed)] = 0.
error[i][np.isnan(error)] = 0.
else:
raise ValueError("{} is not a valid smoothing option".format(smoothing))