Tibeuleu/FOC_Reduction
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Update background.py

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sugar_jo
2024-07-16 22:06:46 +08:00
parent fa4dce398f
commit a72b799713
1 changed files with 6 additions and 33 deletions
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39
package/lib/background.py
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@@ -251,23 +251,13 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
weights = 1 / chi2**2
weights /= weights.sum()
bkg = np.sum(weights*(coeff[:, 1]+np.abs(coeff[:, 2]) * 0.01)) # why not just use 0.01
bkg = np.sum(weights*(coeff[:, 1]+np.abs(coeff[:, 2]) * subtract_error))
error_bkg[i] *= bkg
# n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
# Substract background
# if subtract_error > 0:
# n_data_array[i][mask] = n_data_array[i][mask] - bkg
# n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3*bkg)] = 1e-3*bkg
std_bkg[i] = image[np.abs(image - bkg) / bkg < 1.0].std()
background[i] = bkg
if subtract_error > 0:
if np.abs(subtract_error) > 0:
n_data_array, n_error_array, background, error_bkg = subtract_bkg(n_data_array, n_error_array, mask, background, error_bkg)
if display:
@@ -366,22 +356,13 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
popt, pcov = curve_fit(gauss, binning[-1], hist, p0=p0)
coeff.append(popt)
bkg = popt[1]+np.abs(popt[2]) * 0.01 # why not just use 0.01
bkg = popt[1] + np.abs(popt[2]) * subtract_error
error_bkg[i] *= bkg
# n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
# Substract background
# if subtract_error > 0:
# n_data_array[i][mask] = n_data_array[i][mask] - bkg
# n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3*bkg)] = 1e-3*bkg
std_bkg[i] = image[np.abs(image - bkg) / bkg < 1.0].std()
background[i] = bkg
if subtract_error > 0:
if np.abs(subtract_error) > 0:
n_data_array, n_error_array, background, error_bkg = subtract_bkg(n_data_array, n_error_array, mask, background, error_bkg)
if display:
@@ -469,21 +450,13 @@ def bkg_mini(data, error, mask, headers, sub_shape=(15, 15), subtract_error=True
sub_image = image[minima[0] : minima[0] + sub_shape[0], minima[1] : minima[1] + sub_shape[1]]
# bkg = np.std(sub_image) # Previously computed using standard deviation over the background
bkg = np.sqrt(np.sum(sub_image**2)/sub_image.size)*0.01 if subtract_error > 0 else np.sqrt(np.sum(sub_image**2)/sub_image.size)
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
# n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
# Substract background
# if subtract_error > 0.:
# n_data_array[i][mask] = n_data_array[i][mask] - bkg
# n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3*bkg)] = 1e-3*bkg
std_bkg[i] = image[np.abs(image - bkg) / bkg < 1.0].std()
background[i] = bkg
if subtract_error > 0:
if np.abs(subtract_error) > 0:
n_data_array, n_error_array, background, error_bkg = subtract_bkg(n_data_array, n_error_array, mask, background, error_bkg)
if display: