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motion_correction: Remove some dead code, more docs cleanups
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@pgunn
pgunn committed Dec 10, 2024
1 parent 6aa2309 commit 54e1952
Showing 1 changed file with 25 additions and 45 deletions.
70 changes: 25 additions & 45 deletions caiman/motion_correction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,7 @@ class implementing motion correction operations
def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig=1, splits_rig=14, num_splits_to_process_rig=None,
strides=(96, 96), overlaps=(32, 32), splits_els=14, num_splits_to_process_els=None,
upsample_factor_grid=4, max_deviation_rigid=3, shifts_opencv=True, nonneg_movie=True, gSig_filt=None,
use_cuda=False, border_nan=True, pw_rigid=False, num_frames_split=80, var_name_hdf5='mov',is3D=False,
use_cuda=False, border_nan=True, pw_rigid=False, num_frames_split=80, var_name_hdf5='mov', is3D=False,
indices=(slice(None), slice(None))):
"""
Constructor class for motion correction operations
Expand All @@ -95,11 +95,11 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig
max_shifts: tuple
maximum allow rigid shift
niter_rig':int
niter_rig:int
maximum number of iterations rigid motion correction, in general is 1. 0
will quickly initialize a template with the first frames
splits_rig': int
splits_rig: int
for parallelization split the movies in num_splits chunks across time
num_splits_to_process_rig: list,
Expand All @@ -112,13 +112,10 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig
overlaps: tuple
overlap between patches (size of patch strides+overlaps)
pw_rigig: bool, default: False
flag for performing motion correction when calling motion_correct
splits_els':list
for parallelization split the movies in num_splits chunks across time
num_splits_to_process_els: UNUSED
num_splits_to_process_els: UNUSED, deprecated
Legacy parameter, does not do anything
upsample_factor_grid:int,
Expand All @@ -133,6 +130,9 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig
nonneg_movie: bool
make the SAVED movie and template mostly nonnegative by removing min_mov from movie
gSig_filt: list
(UNDOCUMENTED)
use_cuda : bool (DEPRECATED)
cuda is no longer supported for motion correction; this kwarg will be removed in a future version of Caiman
Expand All @@ -141,17 +141,20 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig
TODO: make this just the bool, and make another variable called
border_strategy to hold the how
pw_rigid: bool, default: False
flag for performing motion correction when calling motion_correct
num_frames_split: int, default: 80
Number of frames in each batch. Used when constructing the options
through the params object
var_name_hdf5: str, default: 'mov'
If loading from hdf5, name of the variable to load
is3D: bool, default: False
is3D: bool, default: False
Flag for 3D motion correction
indices: tuple(slice), default: (slice(None), slice(None))
indices: tuple(slice), default: (slice(None), slice(None))
Use that to apply motion correction only on a part of the FOV
Returns:
Expand Down Expand Up @@ -477,7 +480,6 @@ def apply_shifts_movie(self, fname, rigid_shifts:bool=None, save_memmap:bool=Fal
resize_sk(shiftZ.astype(np.float32), dims) + z_grid), axis=0),
order=3, mode='constant')
for img, shiftX, shiftY, shiftZ in zip(Y, shifts_x, shifts_y, shifts_z)]
# borderValue=add_to_movie) # borderValue=add_to_movie)
else:
xy_grid = [(it[0], it[1]) for it in sliding_window(Y[0], self.overlaps, self.strides)]
dims_grid = tuple(np.max(np.stack(xy_grid, axis=1), axis=1) - np.min(
Expand Down Expand Up @@ -801,7 +803,7 @@ def motion_correct_online(movie_iterable, add_to_movie, max_shift_w=25, max_shif
init_mov = movie_iterable[slice(0, init_frames_template, 1)]

dims = (len(movie_iterable),) + movie_iterable[0].shape # TODO: Refactor so length is either tracked separately or is last part of tuple
logger.debug("dimensions:" + str(dims))
logger.debug(f"dimensions: {dims}")

if use_median_as_template:
template = bin_median(movie_iterable)
Expand Down Expand Up @@ -1034,9 +1036,6 @@ def bin_median(mat, window=10, exclude_nans=True):
Returns:
img:
median image
Raises:
Exception 'Path to template does not exist:'+template
"""

T, d1, d2 = np.shape(mat)
Expand Down Expand Up @@ -1066,9 +1065,6 @@ def bin_median_3d(mat, window=10, exclude_nans=True):
Returns:
img:
median image
Raises:
Exception 'Path to template does not exist:'+template
"""

T, d1, d2, d3 = np.shape(mat)
Expand Down Expand Up @@ -1153,9 +1149,6 @@ def motion_correct_parallel(file_names, fr=10, template=None, margins_out=0,
Returns:
base file names of the motion corrected files:list[str]
Raises:
Exception
"""
logger = logging.getLogger("caiman")
args_in = []
Expand Down Expand Up @@ -1267,15 +1260,13 @@ def _upsampled_dft(data, upsampled_region_size,
upsampled_region_size = [upsampled_region_size, ] * data.ndim
else:
if len(upsampled_region_size) != data.ndim:
raise ValueError("shape of upsampled region sizes must be equal "
"to input data's number of dimensions.")
raise ValueError("shape of upsampled region sizes must be equal to input data's number of dimensions.")

if axis_offsets is None:
axis_offsets = [0, ] * data.ndim
else:
if len(axis_offsets) != data.ndim:
raise ValueError("number of axis offsets must be equal to input "
"data's number of dimensions.")
raise ValueError("number of axis offsets must be equal to input data's number of dimensions.")

col_kernel = np.exp(
(-1j * 2 * np.pi / (data.shape[1] * upsample_factor)) *
Expand Down Expand Up @@ -1388,13 +1379,11 @@ def register_translation_3d(src_image, target_image, upsample_factor = 1,

# images must be the same shape
if src_image.shape != target_image.shape:
raise ValueError("Error: images must really be same size for "
"register_translation_3d")
raise ValueError("Error: images must really be same size for register_translation_3d")

# only 3D data makes sense right now
if src_image.ndim != 3 and upsample_factor > 1:
raise NotImplementedError("Error: register_translation_3d only supports "
"subpixel registration for 3D images")
raise NotImplementedError("Error: register_translation_3d only supports subpixel registration for 3D images")

# assume complex data is already in Fourier space
if space.lower() == 'fourier':
Expand All @@ -1409,13 +1398,11 @@ def register_translation_3d(src_image, target_image, upsample_factor = 1,
src_freq = np.fft.fftn(src_image_cpx)
target_freq = np.fft.fftn(target_image_cpx)
else:
raise ValueError("Error: register_translation_3d only knows the \"real\" "
"and \"fourier\" values for the ``space`` argument.")
raise ValueError('Error: register_translation_3d only knows the "real" and "fourier" values for the ``space`` argument.')

shape = src_freq.shape
image_product = src_freq * target_freq.conj()
cross_correlation = np.fft.ifftn(image_product)
# cross_correlation = ifftn(image_product) # TODO CHECK why this line is different
new_cross_corr = np.abs(cross_correlation)

CCmax = cross_correlation.max()
Expand Down Expand Up @@ -1583,13 +1570,11 @@ def register_translation(src_image, target_image, upsample_factor=1,
"""
# images must be the same shape
if src_image.shape != target_image.shape:
raise ValueError("Error: images must really be same size for "
"register_translation")
raise ValueError("Error: images must really be same size for register_translation")

# only 2D data makes sense right now
if src_image.ndim != 2 and upsample_factor > 1:
raise NotImplementedError("Error: register_translation only supports "
"subpixel registration for 2D images")
raise NotImplementedError("Error: register_translation only supports subpixel registration for 2D images")

# assume complex data is already in Fourier space
if space.lower() == 'fourier':
Expand All @@ -1607,8 +1592,7 @@ def register_translation(src_image, target_image, upsample_factor=1,
target_freq = np.array(
target_freq, dtype=np.complex128, copy=False)
else:
raise ValueError("Error: register_translation only knows the \"real\" "
"and \"fourier\" values for the ``space`` argument.")
raise ValueError('Error: register_translation only knows the "real" and "fourier" values for the ``space`` argument.')

# Whole-pixel shift - Compute cross-correlation by an IFFT
shape = src_freq.shape
Expand Down Expand Up @@ -2108,7 +2092,6 @@ def tile_and_correct(img, template, strides, overlaps, max_shifts, newoverlaps=N
m_reg = cv2.remap(img, cv2.resize(shift_img_y.astype(np.float32), dims[::-1]) + x_grid,
cv2.resize(shift_img_x.astype(np.float32), dims[::-1]) + y_grid,
cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
# borderValue=add_to_movie)
total_shifts = [
(-x, -y) for x, y in zip(shift_img_x.reshape(num_tiles), shift_img_y.reshape(num_tiles))]
return m_reg - add_to_movie, total_shifts, None, None
Expand Down Expand Up @@ -2225,7 +2208,7 @@ def tile_and_correct_3d(img:np.ndarray, template:np.ndarray, strides:tuple, over
4) stiching back together the corrected subpatches
Args:
img: ndaarray 3D
img: ndarray 3D
image to correct
template: ndarray
Expand Down Expand Up @@ -2369,7 +2352,7 @@ def tile_and_correct_3d(img:np.ndarray, template:np.ndarray, strides:tuple, over
(-x, -y, -z) for x, y, z in zip(shift_img_x.reshape(num_tiles), shift_img_y.reshape(num_tiles), shift_img_z.reshape(num_tiles))]
return m_reg - add_to_movie, total_shifts, None, None

# create automatically upsample parameters if not passed
# create automatically upsampled parameters if not passed
if newoverlaps is None:
newoverlaps = overlaps
if newstrides is None:
Expand Down Expand Up @@ -2400,7 +2383,7 @@ def tile_and_correct_3d(img:np.ndarray, template:np.ndarray, strides:tuple, over

num_tiles = np.prod(dim_new_grid)

# what dimension shear should be looked at? shearing for 3d point scanning happens in y and z but no for plane-scanning
# what dimension shear should be looked at? shearing for 3d point scanning happens in y and z but not for plane-scanning
max_shear = np.percentile(
[np.max(np.abs(np.diff(ssshh, axis=xxsss))) for ssshh, xxsss in itertools.product(
[shift_img_x, shift_img_y], [0, 1])], 75)
Expand Down Expand Up @@ -2706,8 +2689,6 @@ def motion_correct_batch_rigid(fname, max_shifts, dview=None, splits=56, num_spl
if is3D:
# TODO - motion_correct_3d needs to be implemented in movies.py
template = caiman.motion_correction.bin_median_3d(m) # motion_correct_3d has not been implemented yet - instead initialize to just median image
# template = caiman.motion_correction.bin_median_3d(
# m.motion_correct_3d(max_shifts[2], max_shifts[1], max_shifts[0], template=None)[0])
else:
if not m.flags['WRITEABLE']:
m = m.copy()
Expand Down Expand Up @@ -2860,7 +2841,7 @@ def motion_correct_batch_pwrigid(fname, max_shifts, strides, overlaps, add_to_mo
else:
logger.debug(f'saving mmap of {fname}')

if isinstance(fname, tuple):
if isinstance(fname, tuple): # TODO Switch to using standard path functions
base_name=os.path.splitext(os.path.split(fname[0])[-1])[0] + '_els_'
else:
base_name=os.path.splitext(os.path.split(fname)[-1])[0] + '_els_'
Expand Down Expand Up @@ -2920,7 +2901,6 @@ def tile_and_correct_wrapper(params):
# todo todocument
logger = logging.getLogger("caiman")


try:
cv2.setNumThreads(0)
except:
Expand Down

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