Tibeuleu/FOC_Reduction
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add subtract_bkg funcition

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Allow subtracting the bkg simpler
This commit is contained in:
sugar_jo
2024-07-14 15:46:22 +08:00
parent a4e8f51c50
commit 8e5f439259
4 changed files with 86 additions and 33 deletions
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51
package/lib/background.py
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@@ -239,19 +239,22 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
error_bkg[i] *= bkg
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
# 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
# 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.].std()
background[i] = bkg
if 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:
display_bkg(data, background, std_bkg, headers, histograms=histograms, binning=binning, coeff=coeff, savename=savename, plots_folder=plots_folder)
return n_data_array, n_error_array, headers, background
return n_data_array, n_error_array, headers, background, error_bkg
def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, display=False, savename=None, plots_folder=""):
@@ -343,19 +346,22 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
error_bkg[i] *= bkg
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
# 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
# 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.].std()
background[i] = bkg
if 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:
display_bkg(data, background, std_bkg, headers, histograms=histograms, binning=binning, coeff=coeff, savename=savename, plots_folder=plots_folder)
return n_data_array, n_error_array, headers, background
return n_data_array, n_error_array, headers, background, error_bkg
def bkg_mini(data, error, mask, headers, sub_shape=(15, 15), subtract_error=True, display=False, savename=None, plots_folder=""):
@@ -440,16 +446,31 @@ 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
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
# 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
# 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.].std()
background[i] = bkg
if 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:
display_bkg(data, background, std_bkg, headers, rectangle=rectangle, savename=savename, plots_folder=plots_folder)
return n_data_array, n_error_array, headers, background
return n_data_array, n_error_array, headers, background, error_bkg
def subtract_bkg(data, error, mask, background, error_bkg):
assert data.ndim == 3, "Input data must have more than 1 image."
n_data_array, n_error_array = deepcopy(data), deepcopy(error)
for i in range(data.shape[0]):
n_data_array[i][mask] = n_data_array[i][mask] - background[i]
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3 * background[i])] = 1e-3 * background[i]
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2)
return n_data_array, n_error_array, background, error_bkg