Tibeuleu/FOC_Reduction
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bug fix and small improvements

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This commit is contained in:
Thibault Barnouin
2023-05-30 18:07:24 +02:00
parent 8e19cc74f1
commit b1fcad9e25
7 changed files with 138 additions and 97 deletions
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43
src/lib/background.py Normal file → Executable file
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@@ -119,7 +119,7 @@ def sky_part(img):
# Intensity range
sky_med = np.median(rand_pix)
sig = np.min([img[img<sky_med].std(),img[img>sky_med].std()])
sky_range = [sky_med-2.*sig, sky_med+sig]
sky_range = [sky_med-2.*sig, np.max([sky_med+sig,7e-4])] #Detector background average FOC Data Handbook Sec. 7.6
sky = img[np.logical_and(img>=sky_range[0],img<=sky_range[1])]
return sky, sky_range
@@ -170,7 +170,7 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
savename : str, optional
Name of the figure the map should be saved to. If None, the map won't
be saved (only displayed). Only used if display is True.
Defaults to None.
Defaults to None.CNRS-Unistra Labo ObsAstroS
plots_folder : str, optional
Relative (or absolute) filepath to the folder in wich the map will
be saved. Not used if savename is None.
@@ -212,18 +212,7 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
error_bkg[i] *= bkg
# Quadratically add uncertainties in the "correction factors" (see Kishimoto 1999)
#wavelength dependence of the polariser filters
#estimated to less than 1%
err_wav = data[i]*0.01
#difference in PSFs through each polarizers
#estimated to less than 3%
err_psf = data[i]*0.03
#flatfielding uncertainties
#estimated to less than 3%
err_flat = data[i]*0.03
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
#Substract background
if subtract_error>0:
@@ -327,18 +316,7 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
error_bkg[i] *= bkg
# Quadratically add uncertainties in the "correction factors" (see Kishimoto 1999)
#wavelength dependence of the polariser filters
#estimated to less than 1%
err_wav = data[i]*0.01
#difference in PSFs through each polarizers
#estimated to less than 3%
err_psf = data[i]*0.03
#flatfielding uncertainties
#estimated to less than 3%
err_flat = data[i]*0.03
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
#Substract background
if subtract_error > 0:
@@ -435,18 +413,7 @@ def bkg_mini(data, error, mask, headers, sub_shape=(15,15), subtract_error=True,
bkg = np.sqrt(np.sum(sub_image**2)/sub_image.size)*subtract_error if subtract_error>0 else np.sqrt(np.sum(sub_image**2)/sub_image.size)
error_bkg[i] *= bkg
# Quadratically add uncertainties in the "correction factors" (see Kishimoto 1999)
#wavelength dependence of the polariser filters
#estimated to less than 1%
err_wav = data[i]*0.01
#difference in PSFs through each polarizers
#estimated to less than 3%
err_psf = data[i]*0.03
#flatfielding uncertainties
#estimated to less than 3%
err_flat = data[i]*0.03
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
#Substract background
if subtract_error>0.: