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

10
package/lib/reduction.py
View File

@@ -446,9 +446,9 @@ def get_error(
return_background=False, return_background=False,
): ):
""" """
Look for sub-image of shape sub_shape that have the smallest integrated Estimate background intensity level from either fitting the intensity histogram
flux (no source assumption) and define the background on the image by the or by looking for the sub-image of smallest integrated intensity (no source assumption)
standard deviation on this sub-image. and define the background on the image by the standard deviation on this sub-image.
---------- ----------
Inputs: Inputs:
data_array : numpy.ndarray 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 '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 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 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. Defaults to None.
subtract_error : float or bool, optional subtract_error : float or bool, optional
If float, factor to which the estimated background should be multiplied 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 Updated list of headers corresponding to the reduced images accounting
for the new orientation angle. for the new orientation angle.
""" """
# Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
old_center = np.array(data_array[0].shape) / 2 old_center = np.array(data_array[0].shape) / 2
shape = np.fix(np.array(data_array[0].shape) * np.sqrt(2.5)).astype(int) shape = np.fix(np.array(data_array[0].shape) * np.sqrt(2.5)).astype(int)
new_center = np.array(shape) / 2 new_center = np.array(shape) / 2