Tibeuleu/FOC_Reduction
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add optimal_binning to plotting

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sugar_jo
2024-07-15 19:39:21 +08:00
parent 8e5f439259
commit 62aef1b1c4
4 changed files with 291 additions and 201 deletions
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6
package/lib/background.py
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@@ -235,7 +235,7 @@ 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])*subtract_error))
bkg = np.sum(weights*(coeff[:, 1]+np.abs(coeff[:, 2]) * 0.01)) # why not just use 0.01
error_bkg[i] *= bkg
@@ -342,7 +342,7 @@ def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, dis
# popt, pcov = curve_fit(gausspol, binning[-1], hist, p0=p0)
popt, pcov = curve_fit(gauss, binning[-1], hist, p0=p0)
coeff.append(popt)
bkg = popt[1]+np.abs(popt[2])*subtract_error
bkg = popt[1]+np.abs(popt[2]) * 0.01 # why not just use 0.01
error_bkg[i] *= bkg
@@ -443,7 +443,7 @@ def bkg_mini(data, error, mask, headers, sub_shape=(15, 15), subtract_error=True
# Compute error : root mean square of the background
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)*subtract_error 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)*0.01 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)