@@ -253,15 +253,6 @@ def _set_up_cyclical_iterator(dists: dict[str, int]) -> Iterator[str]:
253253 return cycle (value_list )
254254
255255
256- # def _get_pointer_count_negbinom_distributions(num_of_patients: int, pointers_per_px: float) -> cycle:
257- # dispersion = 2 # lower = more variance; higher = closer to Poisson
258- # p = dispersion / (dispersion + pointers_per_px)
259- # n = dispersion
260-
261- # p_count_distr = np.random.negative_binomial(n=n, p=p, size=num_of_patients)
262- # return cycle(p_count_distr)
263-
264-
265256def _get_pointer_count_poisson_distributions (
266257 num_of_patients : int , pointers_per_px : float
267258) -> Iterator [int ]:
@@ -281,21 +272,5 @@ def _set_up_custodian_iterators(
281272 return custodian_iters
282273
283274
284- # def _set_up_count_iterator(pointers_per_px: float) -> iter:
285- # """
286- # Given a target average number of pointers per patient,
287- # generates a distribution of counts per individual patient.
288- # """
289- #
290- # extra_per_hundred = int(
291- # (pointers_per_px - 1.0) * 100
292- # ) # no patients can have zero pointers
293- # counts = {}
294- # counts["3"] = extra_per_hundred // 10
295- # counts["2"] = extra_per_hundred - 2 * counts["3"]
296- # counts["1"] = 100 - counts[2] - counts[3]
297- # return _set_up_cyclical_iterator(counts)
298-
299-
300275if __name__ == "__main__" :
301276 fire .Fire (_populate_seed_table )
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