made radprof helper memoizable
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@ -10,7 +10,7 @@ def calc_radial_integration(results, data, pixel_mask_pf, center, rad, norm):
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rad = norm = None
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if rad is None or norm is None:
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rad, norm = prepare_radial_profile(data, center, keep_pixels=pixel_mask_pf)
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rad, norm = prepare_radial_profile(data.shape, center, pixel_mask_pf)
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r_min = min(rad)
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r_max = max(rad) + 1
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@ -27,7 +27,7 @@ def calc_radial_integration(results, data, pixel_mask_pf, center, rad, norm):
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if threshold_max > threshold_min:
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data[data > threshold_max] = np.nan
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rp = radial_profile(data, rad, norm, keep_pixels=pixel_mask_pf)
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rp = radial_profile(data, rad, norm, pixel_mask_pf)
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silent_min = results.get("radial_integration_silent_min", None)
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silent_max = results.get("radial_integration_silent_max", None)
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@ -51,8 +51,8 @@ def calc_radial_integration(results, data, pixel_mask_pf, center, rad, norm):
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return center, rad, norm
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def prepare_radial_profile(data, center, keep_pixels=None):
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y, x = np.indices(data.shape)
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def prepare_radial_profile(shape, center, keep_pixels):
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y, x = np.indices(shape)
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x0, y0 = center
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rad = np.sqrt((x - x0)**2 + (y - y0)**2)
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if keep_pixels is not None:
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@ -62,7 +62,7 @@ def prepare_radial_profile(data, center, keep_pixels=None):
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return rad, norm
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def radial_profile(data, rad, norm, keep_pixels=None):
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def radial_profile(data, rad, norm, keep_pixels):
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if keep_pixels is not None:
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data = data[keep_pixels]
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data = data.ravel()
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