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import attrs
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import numpy as np
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from typing import Tuple
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from dnafiber.postprocess.skan import trace_skeleton
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@attrs.define
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class Bbox:
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x: int
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y: int
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width: int
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height: int
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@property
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def bbox(self) -> Tuple[int, int, int, int]:
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return (self.x, self.y, self.width, self.height)
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@bbox.setter
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def bbox(self, value: Tuple[int, int, int, int]):
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self.x, self.y, self.width, self.height = value
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@attrs.define
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class Fiber:
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bbox: Bbox
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data: np.ndarray
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@attrs.define
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class FiberProps:
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fiber: Fiber
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fiber_id: int
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red_pixels: int = None
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green_pixels: int = None
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category: str = None
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@property
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def bbox(self):
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return self.fiber.bbox.bbox
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@bbox.setter
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def bbox(self, value):
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self.fiber.bbox = value
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@property
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def data(self):
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return self.fiber.data
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@data.setter
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def data(self, value):
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self.fiber.data = value
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@property
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def red(self):
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if self.red_pixels is None:
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self.red_pixels, self.green_pixels = self.counts
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return self.red_pixels
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@property
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def green(self):
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if self.green_pixels is None:
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self.red_pixels, self.green_pixels = self.counts
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return self.green_pixels
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@property
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def length(self):
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return sum(self.counts)
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@property
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def counts(self):
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if self.red_pixels is None or self.green_pixels is None:
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self.red_pixels = np.sum(self.data == 1)
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self.green_pixels = np.sum(self.data == 2)
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return self.red_pixels, self.green_pixels
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@property
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def fiber_type(self):
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if self.category is not None:
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return self.category
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red_pixels, green_pixels = self.counts
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if red_pixels == 0 or green_pixels == 0:
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self.category = "single"
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else:
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self.category = estimate_fiber_category(self.data)
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return self.category
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@property
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def ratio(self):
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return self.green / self.red
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@property
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def is_valid(self):
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return (
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self.fiber_type == "double"
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or self.fiber_type == "one-two-one"
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or self.fiber_type == "two-one-two"
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)
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def scaled_coordinates(self, scale: float) -> Tuple[int, int]:
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"""
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Scale down the coordinates of the fiber's bounding box.
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"""
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x, y, width, height = self.bbox
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return (
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int(x * scale),
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int(y * scale),
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int(width * scale),
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int(height * scale),
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)
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def estimate_fiber_category(fiber: np.ndarray) -> str:
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"""
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Estimate the fiber category based on the number of red and green pixels.
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"""
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coordinates = trace_skeleton(fiber > 0)
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coordinates = np.asarray(coordinates)
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values = fiber[coordinates[:, 0], coordinates[:, 1]]
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diff = np.diff(values)
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jump = np.sum(diff != 0)
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n_ccs = jump + 1
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if n_ccs == 2:
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return "double"
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elif n_ccs == 3:
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if values[0] == 1:
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return "one-two-one"
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else:
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return "two-one-two"
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else:
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return "multiple"
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