"""
Class representation of template matching data.
Copyright (c) 2023 European Molecular Biology Laboratory
Author: Valentin Maurer <valentin.maurer@embl-hamburg.de>
"""
import warnings
from typing import Tuple, List, Optional, Generator, Dict
import numpy as np
from . import Density
from .filters import Compose
from .backends import backend as be
from .types import BackendArray, NDArray
from .matching_utils import compute_parallelization_schedule
__all__ = ["MatchingData"]
[docs]
class MatchingData:
"""
Contains data required for template matching.
Parameters
----------
target : np.ndarray or :py:class:`tme.density.Density`
Target data.
template : np.ndarray or :py:class:`tme.density.Density`
Template data.
target_mask : np.ndarray or :py:class:`tme.density.Density`, optional
Target mask data.
template_mask : np.ndarray or :py:class:`tme.density.Density`, optional
Template mask data.
invert_target : bool, optional
Whether to invert the target before template matching.
rotations: np.ndarray, optional
Template rotations to sample. Can be a single (d, d) or a stack (n, d, d)
of rotation matrices where d is the dimension of the template.
Examples
--------
The following achieves the minimal definition of a :py:class:`MatchingData` instance.
>>> import numpy as np
>>> from tme.matching_data import MatchingData
>>> target = np.random.rand(50,40,60)
>>> template = target[15:25, 10:20, 30:40]
>>> matching_data = MatchingData(target=target, template=template)
"""
def __init__(
self,
target: NDArray,
template: NDArray,
template_mask: NDArray = None,
target_mask: NDArray = None,
invert_target: bool = False,
rotations: NDArray = None,
):
self.target = target
self.target_mask = target_mask
self.template = template
if template_mask is not None:
self.template_mask = template_mask
self.rotations = rotations
self._invert_target = invert_target
self._translation_offset = tuple(0 for _ in range(len(target.shape)))
self._set_matching_dimension()
@staticmethod
def _shape_to_slice(shape: Tuple[int]) -> Tuple[slice]:
return tuple(slice(0, dim) for dim in shape)
@classmethod
def _slice_to_mesh(cls, slice_variable: Tuple[slice], shape: Tuple[int]) -> NDArray:
if slice_variable is None:
slice_variable = cls._shape_to_slice(shape)
ranges = [range(slc.start, slc.stop) for slc in slice_variable]
indices = np.meshgrid(*ranges, sparse=True, indexing="ij")
return indices
@staticmethod
def _load_array(arr: BackendArray) -> BackendArray:
"""Load ``arr``, if ``arr`` type is a :obj:`numpy.memmap`, reload from disk."""
if isinstance(arr, np.memmap):
return np.memmap(arr.filename, mode="r", shape=arr.shape, dtype=arr.dtype)
return arr
[docs]
def subset_array(
self,
arr: NDArray,
arr_slice: Tuple[slice],
padding: NDArray,
invert: bool = False,
) -> NDArray:
"""
Extract a subset of the input array according to the given slice and
apply padding. If the padding exceeds the array dimensions, the
padded regions are filled by reflection of the boundaries. Otherwise,
the values in ``arr`` are used.
Parameters
----------
arr : NDArray
The input array from which a subset is extracted.
arr_slice : tuple of slice
Defines the region of the input array to be extracted.
padding : NDArray
Padding values for each dimension.
invert : bool, optional
Whether the returned array should be inverted.
Returns
-------
NDArray
Subset of the input array with padding applied.
"""
padding = be.to_numpy_array(padding)
padding = np.maximum(padding, 0).astype(int)
slice_start = np.array([x.start for x in arr_slice], dtype=int)
slice_stop = np.array([x.stop for x in arr_slice], dtype=int)
# We are deviating from our typical right_pad + mod here
# because cropping from full convolution mode to target shape
# is defined from the perspective of the origin
right_pad = np.divide(padding, 2).astype(int)
left_pad = np.add(right_pad, np.mod(padding, 2))
data_voxels_left = np.minimum(slice_start, left_pad)
data_voxels_right = np.minimum(
np.subtract(arr.shape, slice_stop), right_pad
).astype(int)
arr_start = np.subtract(slice_start, data_voxels_left)
arr_stop = np.add(slice_stop, data_voxels_right)
arr_slice = tuple(slice(*pos) for pos in zip(arr_start, arr_stop))
arr_mesh = self._slice_to_mesh(arr_slice, arr.shape)
# Note different from joblib memmaps, the memmaps created by
# Density are guaranteed to only contain the array of interest
if isinstance(arr, Density):
if isinstance(arr.data, np.memmap):
arr = Density.from_file(arr.data.filename, subset=arr_slice).data
else:
arr = np.asarray(arr.data[*arr_mesh])
else:
arr = np.asarray(arr[*arr_mesh])
padding = tuple(
(left, right)
for left, right in zip(
np.subtract(left_pad, data_voxels_left),
np.subtract(right_pad, data_voxels_right),
)
)
# The reflections are later cropped from the scores
arr = np.pad(arr, padding, mode="reflect")
if invert:
arr = -arr
return arr
[docs]
def subset_by_slice(
self,
target_slice: Tuple[slice] = None,
template_slice: Tuple[slice] = None,
target_pad: NDArray = None,
template_pad: NDArray = None,
invert_target: bool = False,
) -> "MatchingData":
"""
Subset class instance based on slices.
Parameters
----------
target_slice : tuple of slice, optional
Target subset to use, all by default.
template_slice : tuple of slice, optional
Template subset to use, all by default.
target_pad : BackendArray, optional
Target padding, zero by default.
template_pad : BackendArray, optional
Template padding, zero by default.
Returns
-------
:py:class:`MatchingData`
Newly allocated subset of class instance.
Examples
--------
>>> import numpy as np
>>> from tme.matching_data import MatchingData
>>> target = np.random.rand(50,40,60)
>>> template = target[15:25, 10:20, 30:40]
>>> matching_data = MatchingData(target=target, template=template)
>>> subset = matching_data.subset_by_slice(
>>> target_slice=(slice(0, 10), slice(10,20), slice(15,35))
>>> )
"""
if target_slice is None:
target_slice = self._shape_to_slice(self._target.shape)
if template_slice is None:
template_slice = self._shape_to_slice(self._template.shape)
if target_pad is None:
target_pad = np.zeros(len(self._target.shape), dtype=int)
if template_pad is None:
template_pad = np.zeros(len(self._template.shape), dtype=int)
target_mask, template_mask = None, None
target_subset = self.subset_array(
self._target, target_slice, target_pad, invert=self._invert_target
)
template_subset = self.subset_array(
arr=self._template, arr_slice=template_slice, padding=template_pad
)
if self._target_mask is not None:
mask_slice = zip(target_slice, self._target_mask.shape)
mask_slice = tuple(x if t != 1 else slice(0, 1) for x, t in mask_slice)
target_mask = self.subset_array(
arr=self._target_mask, arr_slice=mask_slice, padding=target_pad
)
if self._template_mask is not None:
mask_slice = zip(template_slice, self._template_mask.shape)
mask_slice = tuple(x if t != 1 else slice(0, 1) for x, t in mask_slice)
template_mask = self.subset_array(
arr=self._template_mask, arr_slice=mask_slice, padding=template_pad
)
ret = self.__class__(
target=target_subset,
template=template_subset,
template_mask=template_mask,
target_mask=target_mask,
rotations=self.rotations,
invert_target=self._invert_target,
)
# Deal with splitting offsets
mask = np.subtract(1, self._template_batch).astype(bool)
target_offset = np.zeros(len(self._output_target_shape), dtype=int)
target_offset[mask] = [x.start for x in target_slice]
mask = np.subtract(1, self._target_batch).astype(bool)
template_offset = np.zeros(len(self._output_template_shape), dtype=int)
template_offset[mask] = [x.start for x in template_slice]
ret._translation_offset = tuple(x for x in target_offset)
ret.target_filter = self.target_filter
ret.template_filter = self.template_filter
ret.set_matching_dimension(
target_dim=getattr(self, "_target_dim", None),
template_dim=getattr(self, "_template_dim", None),
)
return ret
[docs]
def to_backend(self):
"""
Transfer and convert types of internal data arrays to the current backend.
Examples
--------
>>> matching_data.to_backend()
"""
backend_arr = type(be.zeros((1), dtype=be._float_dtype))
for attr_name, attr_value in vars(self).items():
converted_array = None
if isinstance(attr_value, np.ndarray):
converted_array = be.to_backend_array(attr_value.copy())
elif isinstance(attr_value, backend_arr):
converted_array = be.to_backend_array(attr_value)
else:
continue
current_dtype = be.get_fundamental_dtype(converted_array)
target_dtype = be._fundamental_dtypes[current_dtype]
# Optional, but scores are float so we avoid casting and potential issues
if attr_name in ("_template", "_template_mask", "_target", "_target_mask"):
target_dtype = be._float_dtype
if target_dtype != current_dtype:
converted_array = be.astype(converted_array, target_dtype)
setattr(self, attr_name, converted_array)
[docs]
def set_matching_dimension(self, target_dim: int = None, template_dim: int = None):
"""
Sets matching dimensions for target and template.
Parameters
----------
target_dim : int, optional
Target batch dimension, None by default.
template_dim : int, optional
Template batch dimension, None by default.
Examples
--------
>>> matching_data.set_matching_dimension(target_dim=0, template_dim=None)
Notes
-----
If target and template share a batch dimension, the target will take
precendence and the template dimension will be shifted to the right. If target
and template have the same dimension, but target specifies batch dimensions,
the leftmost template dimensions are assumed to be collapse dimensions.
"""
target_ndim = len(self._target.shape)
_, target_dims = self._compute_batch_dims(target_dim, ndim=target_ndim)
template_ndim = len(self._template.shape)
_, template_dims = self._compute_batch_dims(template_dim, ndim=template_ndim)
target_ndim -= len(target_dims)
template_ndim -= len(template_dims)
if target_ndim != template_ndim:
raise ValueError(
f"Dimension mismatch: Target ({target_ndim}) Template ({template_ndim})."
)
self._set_matching_dimension(
target_dims=target_dims, template_dims=template_dims
)
def _set_matching_dimension(
self, target_dims: Tuple[int] = (), template_dims: Tuple[int] = ()
):
self._target_dim, self._template_dim = target_dims, template_dims
target_ndim, template_ndim = len(self._target.shape), len(self._template.shape)
batch_dims = len(target_dims) + len(template_dims)
target_measurement_dims = target_ndim - len(target_dims)
collapse_dims = max(
template_ndim - len(template_dims) - target_measurement_dims, 0
)
matching_dims = target_measurement_dims + batch_dims
target_shape = np.full(shape=matching_dims, fill_value=1, dtype=int)
template_shape = np.full(shape=matching_dims, fill_value=1, dtype=int)
template_batch = np.full(shape=matching_dims, fill_value=1, dtype=int)
target_batch = np.full(shape=matching_dims, fill_value=1, dtype=int)
target_index, template_index = 0, 0
for k in range(matching_dims):
target_dim = k - target_index
template_dim = k - template_index
if target_dim in target_dims:
target_shape[k] = self._target.shape[target_dim]
template_batch[k] = 0
if target_index == len(template_dims) and collapse_dims > 0:
template_shape[k] = self._template.shape[template_dim]
collapse_dims -= 1
template_index += 1
continue
if template_dim in template_dims:
template_shape[k] = self._template.shape[template_dim]
target_batch[k] = 0
target_index += 1
continue
target_batch[k] = template_batch[k] = 0
if target_dim < target_ndim:
target_shape[k] = self._target.shape[target_dim]
if template_dim < template_ndim:
template_shape[k] = self._template.shape[template_dim]
batch_mask = np.logical_or(target_batch, template_batch)
self._output_target_shape = tuple(int(x) for x in target_shape)
self._output_template_shape = tuple(int(x) for x in template_shape)
self._batch_mask = tuple(int(x) for x in batch_mask)
self._template_batch = tuple(int(x) for x in template_batch)
self._target_batch = tuple(int(x) for x in target_batch)
output_shape = np.add(
self._output_target_shape,
np.multiply(self._template_batch, self._output_template_shape),
)
output_shape = np.subtract(output_shape, self._template_batch)
self._output_shape = tuple(int(x) for x in output_shape)
@staticmethod
def _compute_batch_dims(batch_dims: Tuple[int], ndim: int) -> Tuple:
"""
Computes a mask for the batch dimensions and the validated batch dimensions.
Parameters
----------
batch_dims : tuple of int
A tuple of integers representing the batch dimensions.
ndim : int
The number of dimensions of the array.
Returns
-------
Tuple[ArrayLike, tuple of int]
Mask and the corresponding batch dimensions.
Raises
------
ValueError
If any dimension in batch_dims is not less than ndim.
"""
mask = np.zeros(ndim, dtype=int)
if batch_dims is None:
return mask, ()
if isinstance(batch_dims, int):
batch_dims = (batch_dims,)
for dim in batch_dims:
if dim < ndim:
mask[dim] = 1
continue
raise ValueError(f"Batch indices needs to be < {ndim}, got {dim}.")
return mask, batch_dims
@staticmethod
def _batch_shape(shape: Tuple[int], mask: Tuple[int], keepdims=True) -> Tuple[int]:
if keepdims:
return tuple(x if y == 0 else 1 for x, y in zip(shape, mask))
return tuple(x for x, y in zip(shape, mask) if y == 0)
@staticmethod
def _batch_iter(shape: Tuple[int], mask: Tuple[int]) -> Generator:
def _recursive_gen(current_shape, current_mask, current_slices):
if not current_shape:
yield current_slices
return
if current_mask[0] == 1:
for i in range(current_shape[0]):
new_slices = current_slices + (slice(i, i + 1),)
yield from _recursive_gen(
current_shape[1:], current_mask[1:], new_slices
)
else:
new_slices = current_slices + (slice(None),)
yield from _recursive_gen(
current_shape[1:], current_mask[1:], new_slices
)
return _recursive_gen(shape, mask, ())
@staticmethod
def _batch_axis(mask: Tuple[int]) -> Tuple[int]:
return tuple(i for i in range(len(mask)) if mask[i] == 0)
[docs]
def target_padding(self, pad_target: bool = False) -> Tuple[int]:
"""
Computes the padding of the target to the full convolution
shape given the registered template.
Parameters
----------
pad_target : bool, optional
Whether to pad the target, defaults to False.
Returns
-------
tuple of int
Padding along each dimension.
Examples
--------
>>> matching_data.target_padding(pad_target=True)
"""
padding = np.zeros(len(self._output_target_shape), dtype=int)
if pad_target:
padding = np.subtract(self._output_template_shape, 1)
if hasattr(self, "_target_batch"):
padding = np.multiply(padding, np.subtract(1, self._target_batch))
if hasattr(self, "_template_batch"):
padding = tuple(x for x, i in zip(padding, self._template_batch) if i == 0)
return tuple(int(x) for x in padding)
@staticmethod
def _fourier_padding(
target_shape: Tuple[int],
template_shape: Tuple[int],
pad_fourier: bool,
batch_mask: Tuple[int] = None,
) -> Tuple[Tuple, Tuple, Tuple, Tuple]:
fourier_pad = template_shape
fourier_shift = np.zeros_like(template_shape)
if batch_mask is None:
batch_mask = np.zeros_like(template_shape)
batch_mask = np.asarray(batch_mask)
if not pad_fourier:
fourier_pad = np.ones(len(fourier_pad), dtype=int)
fourier_pad = np.multiply(fourier_pad, 1 - batch_mask)
fourier_pad = np.add(fourier_pad, batch_mask)
pad_shape = np.maximum(target_shape, template_shape)
ret = be.compute_convolution_shapes(pad_shape, fourier_pad)
conv_shape, fast_shape, fast_ft_shape = ret
template_mod = np.mod(template_shape, 2)
if not pad_fourier:
fourier_shift = 1 - np.divide(template_shape, 2).astype(int)
fourier_shift = np.subtract(fourier_shift, template_mod)
shape_diff = np.multiply(
np.subtract(target_shape, template_shape), 1 - batch_mask
)
shape_mask = shape_diff < 0
if np.sum(shape_mask):
shape_shift = np.divide(shape_diff, 2)
offset = np.mod(shape_diff, 2)
if pad_fourier:
offset = -np.subtract(
offset,
np.logical_and(np.mod(target_shape, 2) == 0, template_mod == 1),
)
else:
warnings.warn(
"Template is larger than target and padding is turned off. Consider "
"swapping them or activate padding. Correcting the shift for now."
)
shape_shift = np.multiply(np.add(shape_shift, offset), shape_mask)
fourier_shift = np.subtract(fourier_shift, shape_shift).astype(int)
fourier_shift = tuple(np.multiply(fourier_shift, 1 - batch_mask).astype(int))
return tuple(conv_shape), tuple(fast_shape), tuple(fast_ft_shape), fourier_shift
[docs]
def fourier_padding(
self, pad_fourier: bool = False
) -> Tuple[Tuple, Tuple, Tuple, Tuple]:
"""
Computes efficient shape four Fourier transforms and potential associated shifts.
Parameters
----------
pad_fourier : bool, optional
If true, returns the shape of the full-convolution defined as sum of target
shape and template shape minus one, False by default.
Returns
-------
Tuple[tuple of int, tuple of int, tuple of int, tuple of int]
Tuple with convolution, forward FT, inverse FT shape and corresponding shift.
Examples
--------
>>> conv, fwd, inv, shift = matching_data.fourier_padding(pad_fourier=True)
"""
return self._fourier_padding(
target_shape=be.to_numpy_array(self._output_target_shape),
template_shape=be.to_numpy_array(self._output_template_shape),
batch_mask=be.to_numpy_array(self._batch_mask),
pad_fourier=pad_fourier,
)
[docs]
def computation_schedule(
self,
matching_method: str = "FLCSphericalMask",
max_cores: int = 1,
use_gpu: bool = False,
pad_fourier: bool = False,
pad_target_edges: bool = False,
analyzer_method: str = None,
available_memory: int = None,
max_splits: int = 256,
) -> Tuple[Dict, Tuple]:
"""
Computes a parallelization schedule for a given template matching operation.
Parameters
----------
matching_method : str
Matching method to use, default "FLCSphericalMask".
max_cores : int, optional
Maximum number of CPU cores to use, default 1.
use_gpu : bool, optional
Whether to utilize GPU acceleration, default False.
pad_fourier : bool, optional
Apply Fourier padding, default False.
pad_target_edges : bool, optional
Apply padding to target edges, default False.
analyzer_method : str, optional
Method used for score analysis, default None.
available_memory : int, optional
Available memory in bytes. If None, uses all available system memory.
max_splits : int, optional
Maximum number of splits to consider, default 256.
Returns
-------
target_splits : dict
Optimal splits for each axis of the target tensor
schedule : tuple
(n_outer_jobs, n_inner_jobs_per_outer) defining the parallelization schedule
"""
if available_memory is None:
available_memory = be.get_available_memory() * be.device_count()
_template = self._output_template_shape
shape1 = np.broadcast_shapes(
self._output_target_shape,
self._batch_shape(_template, np.subtract(1, self._template_batch)),
)
shape2 = tuple(0 for _ in _template)
if pad_fourier:
shape2 = np.multiply(_template, np.subtract(1, self._batch_mask))
padding = tuple(0 for _ in self._output_target_shape)
if pad_target_edges:
padding = tuple(
x if y == 0 else 1 for x, y in zip(_template, self._template_batch)
)
return compute_parallelization_schedule(
shape1=shape1,
shape2=shape2,
shape1_padding=padding,
max_cores=max_cores,
max_ram=available_memory,
matching_method=matching_method,
analyzer_method=analyzer_method,
backend=be._backend_name,
float_nbytes=be.datatype_bytes(be._float_dtype),
complex_nbytes=be.datatype_bytes(be._complex_dtype),
integer_nbytes=be.datatype_bytes(be._int_dtype),
split_only_outer=use_gpu,
split_axes=self._target_dim if len(self._target_dim) else None,
max_splits=max_splits,
)
@property
def rotations(self):
"""Return stored rotation matrices."""
return self._rotations
@rotations.setter
def rotations(self, rotations: BackendArray):
"""
Set :py:attr:`MatchingData.rotations`.
Parameters
----------
rotations : BackendArray
Rotations matrices with shape (d, d) or (n, d, d).
"""
if rotations is None:
print("No rotations provided, assuming identity for now.")
rotations = np.eye(len(self._target.shape))
if rotations.ndim not in (2, 3):
raise ValueError("Rotations have to be a rank 2 or 3 array.")
elif rotations.ndim == 2:
print("Reshaping rotations array to rank 3.")
rotations = rotations.reshape(1, *rotations.shape)
self._rotations = rotations.astype(np.float32)
@staticmethod
def _get_data(
attribute,
output_shape: Tuple[int],
reverse: bool = False,
axis: Tuple[int] = None,
):
if isinstance(attribute, Density):
attribute = attribute.data
if attribute is not None:
if reverse:
rev_axis = tuple(i for i in range(attribute.ndim) if i not in axis)
attribute = be.reverse(attribute, axis=rev_axis)
attribute = attribute.reshape(tuple(int(x) for x in output_shape))
return attribute
@property
def target(self) -> BackendArray:
"""Return the target."""
return self._get_data(self._target, self._output_target_shape, False)
@property
def target_mask(self) -> BackendArray:
"""Return the target mask."""
target_mask = getattr(self, "_target_mask", None)
if target_mask is None:
return None
_output_shape = self._output_target_shape
if be.size(target_mask) != np.prod(_output_shape):
_output_shape = self._batch_shape(_output_shape, self._target_batch, True)
return self._get_data(target_mask, _output_shape, False)
@property
def template(self) -> BackendArray:
"""Return the reversed template."""
_output_shape = self._output_template_shape
return self._get_data(self._template, _output_shape, True, self._template_dim)
@property
def template_mask(self) -> BackendArray:
"""Return the reversed template mask."""
template_mask = getattr(self, "_template_mask", None)
if template_mask is None:
return None
_output_shape = self._output_template_shape
if np.prod([int(i) for i in template_mask.shape]) != np.prod(_output_shape):
_output_shape = self._batch_shape(_output_shape, self._template_batch, True)
return self._get_data(template_mask, _output_shape, True, self._template_dim)
@target.setter
def target(self, arr: NDArray):
"""
Set :py:attr:`MatchingData.target`.
Parameters
----------
arr : NDArray
Array to set as the target.
"""
self._target = arr
@template.setter
def template(self, arr: NDArray):
"""
Set :py:attr:`MatchingData.template` and initializes
:py:attr:`MatchingData.template_mask` to an to an uninformative
mask filled with ones if not already defined.
Parameters
----------
arr : NDArray
Array to set as the template.
"""
self._template = arr
if getattr(self, "_template_mask", None) is None:
self._template_mask = np.full(
shape=arr.shape, dtype=np.float32, fill_value=1
)
@staticmethod
def _set_mask(mask, shape: Tuple[int]):
if mask is not None:
if np.broadcast_shapes(mask.shape, shape) != shape:
raise ValueError("Mask and data shape need to be broadcastable.")
return mask
@target_mask.setter
def target_mask(self, arr: NDArray):
"""
Set :py:attr:`MatchingData.target_mask`.
Parameters
----------
arr : NDArray
Array to set as the target_mask.
"""
self._target_mask = self._set_mask(mask=arr, shape=self._target.shape)
@template_mask.setter
def template_mask(self, arr: NDArray):
"""
Set :py:attr:`MatchingData.template_mask`.
Parameters
----------
arr : NDArray
Array to set as the template_mask.
"""
self._template_mask = self._set_mask(mask=arr, shape=self._template.shape)
@staticmethod
def _set_filter(composable_filter) -> Optional[Compose]:
if composable_filter is None:
return None
if not isinstance(composable_filter, Compose):
warnings.warn(
"Custom filters are not sanitized and need to be correctly shaped."
)
return composable_filter
@property
def template_filter(self) -> Optional[Compose]:
"""
Returns the template filter.
Returns
-------
:py:class:`tme.preprocessing.compose.Compose` | BackendArray | None
Composable filter, a backend array or None.
"""
return getattr(self, "_template_filter", None)
@property
def target_filter(self) -> Optional[Compose]:
"""
Returns the target filter.
Returns
-------
:py:class:`tme.preprocessing.compose.Compose` | BackendArray | None
Composable filter, a backend array or None.
"""
return getattr(self, "_target_filter", None)
@template_filter.setter
def template_filter(self, template_filter):
self._template_filter = self._set_filter(template_filter)
@target_filter.setter
def target_filter(self, target_filter):
self._target_filter = self._set_filter(target_filter)
def _split_rotations_on_jobs(self, n_jobs: int) -> List[NDArray]:
"""
Split the rotation matrices into parts based on the number of jobs.
Parameters
----------
n_jobs : int
Number of jobs for splitting.
Returns
-------
list of NDArray
List of split rotation matrices.
"""
nrot_per_job = self.rotations.shape[0] // n_jobs
rot_list = []
for n in range(n_jobs):
init_rot = n * nrot_per_job
end_rot = init_rot + nrot_per_job
if n == n_jobs - 1:
end_rot = None
rot_list.append(self.rotations[init_rot:end_rot])
return rot_list
def _free_data(self):
"""
Dereference data arrays owned by the class instance.
"""
attrs = ("_target", "_template", "_template_mask", "_target_mask")
for attr in attrs:
setattr(self, attr, None)