Tibeuleu/FOC_Reduction
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default background estimation to Scott statistics

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This commit is contained in:
Thibault Barnouin
2025-03-14 14:22:18 +01:00
parent 8da199880b
commit 749a08eae0
2 changed files with 15 additions and 17 deletions
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22
package/lib/background.py
View File

@@ -294,7 +294,7 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
sub_type : str or int, optional
If str, statistic rule to be used for the number of bins in counts/s.
If int, number of bins for the counts/s histogram.
Defaults to "Freedman-Diaconis".
Defaults to "Scott".
subtract_error : float or bool, optional
If float, factor to which the estimated background should be multiplied
If False the background is not subtracted.
@@ -336,25 +336,25 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
if sub_type is not None:
if isinstance(sub_type, int):
n_bins = sub_type
elif sub_type.lower() in ["sqrt"]:
elif sub_type.lower() in ["square-root", "squareroot", "sqrt"]:
n_bins = np.fix(np.sqrt(image[n_mask].size)).astype(int) # Square-root
elif sub_type.lower() in ["sturges"]:
n_bins = np.ceil(np.log2(image[n_mask].size)).astype(int) + 1 # Sturges
elif sub_type.lower() in ["rice"]:
n_bins = 2 * np.fix(np.power(image[n_mask].size, 1 / 3)).astype(int) # Rice
elif sub_type.lower() in ["scott"]:
n_bins = np.fix((image[n_mask].max() - image[n_mask].min()) / (3.5 * image[n_mask].std() / np.power(image[n_mask].size, 1 / 3))).astype(
int
) # Scott
else:
elif sub_type.lower() in ["freedman-diaconis", "freedmandiaconis", "freedman", "diaconis"]:
n_bins = np.fix(
(image[n_mask].max() - image[n_mask].min())
/ (2 * np.subtract(*np.percentile(image[n_mask], [75, 25])) / np.power(image[n_mask].size, 1 / 3))
).astype(int) # Freedman-Diaconis
else:
n_bins = np.fix(
(image[n_mask].max() - image[n_mask].min()) / (2 * np.subtract(*np.percentile(image[n_mask], [75, 25])) / np.power(image[n_mask].size, 1 / 3))
).astype(int) # Freedman-Diaconis
else: # Fallback
n_bins = np.fix((image[n_mask].max() - image[n_mask].min()) / (3.5 * image[n_mask].std() / np.power(image[n_mask].size, 1 / 3))).astype(
int
) # Scott
else: # Default statistic
n_bins = np.fix((image[n_mask].max() - image[n_mask].min()) / (3.5 * image[n_mask].std() / np.power(image[n_mask].size, 1 / 3))).astype(
int
) # Scott
hist, bin_edges = np.histogram(np.log(image[n_mask]), bins=n_bins)
histograms.append(hist)

10
package/lib/reduction.py
View File

@@ -446,9 +446,9 @@ def get_error(
return_background=False,
):
"""
Look for sub-image of shape sub_shape that have the smallest integrated
flux (no source assumption) and define the background on the image by the
standard deviation on this sub-image.
Estimate background intensity level from either fitting the intensity histogram
or by looking for the sub-image of smallest integrated intensity (no source assumption)
and define the background on the image by the standard deviation on this sub-image.
----------
Inputs:
data_array : numpy.ndarray
@@ -468,7 +468,7 @@ def get_error(
If 'auto', look for optimal binning and fit intensity histogram with au gaussian.
If str or None, statistic rule to be used for the number of bins in counts/s.
If int, number of bins for the counts/s histogram.
If tuple, shape of the sub-image to look for. Must be odd.
If tuple, shape of the sub-image of lowest intensity to look for.
Defaults to None.
subtract_error : float or bool, optional
If float, factor to which the estimated background should be multiplied
@@ -1801,8 +1801,6 @@ def rotate_data(data_array, error_array, data_mask, headers):
Updated list of headers corresponding to the reduced images accounting
for the new orientation angle.
"""
# Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
old_center = np.array(data_array[0].shape) / 2
shape = np.fix(np.array(data_array[0].shape) * np.sqrt(2.5)).astype(int)
new_center = np.array(shape) / 2