# Copyright (c) 2021. Lucas G. S. Jeub
#
# 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.
from pathlib import Path
import numpy as np
from dask import delayed
from dask.distributed import secede, rejoin
from local2global.utils import SVDAlignmentProblem, local_error
from local2global_embedding.run.scripts.utils import num_nodes, num_patches
@delayed
def last_patch_error(patches, scale, use_median):
prob = SVDAlignmentProblem(patches)
if use_median:
me = prob.align_patches(scale=scale).median_embedding()
else:
me = prob.align_patches(scale=scale).mean_embedding()
patch = prob.patches[-1]
return patch.nodes, local_error(patch, me)
[docs]
def windowed_align_errors(patches, output_file, window=14, scale=True, use_median=True):
output_file = Path(output_file)
if not output_file.is_file():
n_nodes = num_nodes(patches).compute()
n_patches = num_patches(patches).compute()
workfile = output_file.with_suffix('.tmp.npy')
try:
errors = np.lib.format.open_memmap(workfile, mode='w+', dtype=float, shape=(n_nodes, n_patches))
errors[:, :] = np.nan
patch_errors = [last_patch_error(patches[i-window:i], scale, use_median).persist()
for i in range(window, n_patches+1)]
secede()
for i, err in zip(range(window-1, n_patches), patch_errors):
nodes, patch_err = err.compute()
errors[nodes, i] = patch_err
rejoin()
errors.flush()
except Exception as e:
workfile.unlink(missing_ok=True)
raise e
workfile.replace(output_file)
return output_file