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@@ -0,0 +1,59 @@
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+import numpy as np
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+
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+
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+"""
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+ Extract data fom dictionaries for specific organ
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+
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+ Input:
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+ organ: string in ["lung", "bowel", "thyroid"]
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+ perc: int, percentiles [1, ... , 100]
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+ suv_dict: dict containing percentiles of suv
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+ flags_dict: dict containing states
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+"""
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+
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+def get_data(organ, perc, suv_dict, flags_dict, nr_patient = 58):
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+ fname = "get_data"
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+
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+ # get concrete data set
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+ suv = suv_dict[organ + '_SUVperc_COMBINED'][:nr_patient,:,:] # suv percentiles
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+
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+ # index of percentile
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+ perc_idx = perc -1
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+
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+ # computing max SUV percentile per patient, ignoring nans
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+ x = np.nanmax(suv[:,:,perc_idx], axis = 1)
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+
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+ # determining index in the flags based on organ
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+ match organ:
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+ case "lung":
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+ flags_idx = 3
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+ case "bowel":
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+ flags_idx = 1
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+ case "thyroid":
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+ flags_idx = 5
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+ case _:
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+ assert False, f"{fname}::this organ {organ = } is not supported"
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+
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+ # getting state of patients: 0 == NC, 1 == AE
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+ y = flags_dict['flags'][:nr_patient, flags_idx]
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+
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+ return x, y
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+
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+
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+"""
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+ Check if a vector lies within the specified bounds for each dimension.
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+
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+ Parameters:
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+ - vector (np.ndarray): 1D array representing the point to check. Shape: (n,)
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+ - bounds (np.ndarray): 2D array of shape (n, 2), where each row is (min, max) for a dimension.
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+
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+ Returns:
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+ - bool: True if the vector is within bounds in all dimensions, False otherwise.
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+"""
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+def within_bounds(vector: np.ndarray, bounds: np.ndarray) -> bool:
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+
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+ if vector.shape[-1] != bounds.shape[0]:
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+ raise ValueError("Dimension mismatch: vector length and bounds rows must be equal.")
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+
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+ return np.apply_along_axis(lambda x: np.all((x >= bounds[:, 0]) & (x <= bounds[:, 1])), -1, vector)
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+
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