# If you publish results that make use of this software or the Birth Annotator
# for Budding Yeast algorithm, please cite:
# Julian M J Pietsch, Alán Muñoz, Diane Adjavon, Ivan B N Clark, Peter S
# Swain, 2021, Birth Annotator for Budding Yeast (in preparation).
#
#
# The MIT License (MIT)
#
# Copyright (c) Julian Pietsch, Alán Muñoz and Diane Adjavon 2021
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
import typing as t
import numpy as np
# Calculate barycentre
[docs]def calc_barycentre(centres, weights=None, **kwargs):
"""
:centres: ndarray containing the (x,y) centres of each cell
:weights: (optional) list of weights to consider for each cell
"""
if weights is None:
weights = np.ones_like(centres)
barycentre = np.average(centres, axis=0, weights=weights)
return barycentre
# Calculate distance to center
[docs]def calc_barydists(centres, bary, **kwargs):
"""
Calculate distances to the barycentre
:centre: int (2,) tuple. Centre of cell
:bary: float (2,) tuple. Barycentre of image
"""
vec2bary = centres - bary
dists = np.sqrt(np.sum(vec2bary**2, axis=1))
return dists
# Calculate angle to center
[docs]def calc_baryangles(centres, bary, areas=None, **kwargs):
"""
Calculate angle using centre of cell and barycentre
:centre: int (2,) tuple. Centre of cell
:bary: float (2,) tuple. Barycentre of image
:anchor_cell: int Cell id to use as angle 0.
"""
angles = []
vec2bary = centres - bary
angles = np.apply_along_axis(lambda x: np.arctan2(*x), 1, vec2bary)
if areas is not None:
anchor_cell = np.argmax(areas)
angles -= angles[anchor_cell]
return angles
[docs]def pick_baryfun(key):
baryfuns = {"barydist": calc_barydists, "baryangle": calc_baryangles}
return baryfuns[key]
## Tracking benchmark utils
[docs]def lol_to_adj(cell_ids: t.List[t.List[int]]):
"""
Convert a series list of lists with cell ids into a matrix
representing a graph.
Note that information is lost in the process, and a matrix can't be
turned back into a list of list by itself.
input
:lol: list of lists with cell ids
returns
:adj_matrix: (n, n) ndarray where n is the number of cells
"""
n = len([y for x in cell_ids for y in x])
adj_mat = np.zeros((n, n))
prev = None
cur = 0
for c_ids_single_lst in cell_ids:
if not prev:
prev = c_ids_single_lst
else:
for i, el in enumerate(c_ids_single_lst):
prev_idx = prev.index(el) if el in prev else None
if prev_idx is not None:
adj_mat[cur + len(prev) + i, cur + prev_idx] = True
cur += len(c_ids_single_lst)
return adj_mat
[docs]def compare_pred_truth_lols(prediction, truth):
"""
input
:prediction: list of lists with predicted cell ids
:truth: list of lists with real cell ids
returns
number of diferences between equivalent truth matrices
"""
adj_pred = lol_to_adj(prediction)
adj_truth = lol_to_adj(truth)
return int(((adj_pred - adj_truth) != 0).sum())