Source code for AFQ.recognition.preprocess

from functools import cached_property

import numpy as np

import AFQ.recognition.utils as abu



[docs]
class PreprocPlan:
    def __init__(self, tg):

[docs]
        self.tg = tg


    @cached_property

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    def fgarray(self):
        return np.asarray(abu.resample_tg(self.tg, 20), dtype=np.float32)


    @cached_property

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    def crosses(self):
        return np.logical_and(
            np.any(self.fgarray[:, :, 0] > 0, axis=1),
            np.any(self.fgarray[:, :, 0] < 0, axis=1),
        )


    @cached_property

[docs]
    def lengths(self):
        segments = np.diff(self.fgarray, axis=1)
        return np.sum(np.sqrt(np.sum(segments**2, axis=2)), axis=1)


    @cached_property

[docs]
    def endpoint_dists(self):
        return np.linalg.norm(self.fgarray[:, 0, :] - self.fgarray[:, -1, :], axis=1)