Tibeuleu/FOC_Reduction
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change histogram binning to numpy function

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This commit is contained in:
Thibault Barnouin
2025-03-19 16:38:03 +01:00
parent 44256ba9a6
commit c94d646b02
1 changed files with 9 additions and 23 deletions
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32
package/lib/background.py
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@@ -278,7 +278,7 @@ def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, save
return n_data_array, n_error_array, headers, background
def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, display=False, savename=None, plots_folder=""):
def bkg_hist(data, error, mask, headers, n_bins=None, subtract_error=True, display=False, savename=None, plots_folder=""):
"""
----------
Inputs:
@@ -333,29 +333,15 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
for i, image in enumerate(data):
# Compute the Count-rate histogram for the image
n_mask = np.logical_and(mask, image > 0.0)
if sub_type is not None:
if isinstance(sub_type, int):
n_bins = sub_type
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 ["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: # 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
if not isinstance(n_bins, int) and n_bins not in ["auto", "fd", "doane", "scott", "stone", "rice", "sturges", "sqrt"]:
match n_bins.lower():
case "square-root" | "squareroot":
n_bins = "sqrt"
case "freedman-diaconis" | "freedmandiaconis":
n_bins = "fd"
case _:
n_bins = "scott"
hist, bin_edges = np.histogram(np.log(image[n_mask]), bins=n_bins)
histograms.append(hist)
binning.append(np.exp(bin_centers(bin_edges)))