def process_atlas_files(filenames: List[str],
pipeline_sub_dir) -> List[MincAtom]:
suffixes = ["_average.mnc", "_mask.mnc", "_labels.mnc"
] + [".mnc"] # N.B.: order matters ...
d = defaultdict(dict) # TODO: rename `d`
for filename in filenames:
suffix = find_by(filename.endswith, suffixes)
base = filename[:-len(suffix)] # :-l
d[base][suffix] = filename
grouped_atlas_files = {}
for group, files in d.items():
img_file = files.get("_average.mnc") or files.get(
".mnc") # FIXME: is this OK when just _mask, _labels exist?
mask_file = files.get("_mask.mnc")
label_file = files.get("_labels.mnc")
if len(files) > 3 or not (img_file and mask_file and label_file):
raise ValueError(
"atlas filename conventions are wrong for atlas '%s'" % group)
else:
# TODO: screw around with filenames/directories as usual
grouped_atlas_files[group] = MincAtom(
name=img_file,
mask=MincAtom(name=mask_file,
pipeline_sub_dir=pipeline_sub_dir),
labels=MincAtom(name=label_file,
pipeline_sub_dir=pipeline_sub_dir),
pipeline_sub_dir=pipeline_sub_dir)
return pd.Series(list(grouped_atlas_files.values()))
def mincblob(op : str, grid : MincAtom, subdir : str = "tmp") -> Result[MincAtom]:
"""
Low-level mincblob wrapper with the one exception being the determinant option. By
default the inner clockwork of mincblob subtracts 1 from all determinant values that
are being calculated. As such, 1 needs to be added to the result of the mincblob call.
We will do that here, because it makes most sense here.
>>> stages = mincblob('determinant', MincAtom("/images/img_1.mnc", pipeline_sub_dir="/tmp")).stages
>>> [s.render() for s in stages]
['mincblob -clobber -determinant /images/img_1.mnc /tmp/img_1/img_1_determinant.mnc']
"""
if op not in ["determinant", "trace", "translation", "magnitude"]:
raise ValueError('mincblob: invalid operation %s' % op)
# if we are calculating the determinant, the first file produced is a temp file:
if op == "determinant":
out_file = grid.newname_with_suffix("_temp_det", subdir=subdir)
else:
out_file = grid.newname_with_suffix('_' + op, subdir=subdir)
stage = CmdStage(inputs=(grid,), outputs=(out_file,),
cmd=['mincblob', '-clobber', '-' + op, grid.path, out_file.path])
s = Stages([stage])
# now create the proper determinant if that's what was asked for
if op == "determinant":
result_file = s.defer(mincmath(op='add',
const=1,
vols=[out_file],
subdir=subdir,
new_name=grid.filename_wo_ext + "_det"))
else:
result_file = out_file
return Result(stages=s, output=result_file)
def NLIN_pipeline(options):
if options.application.files is None:
raise ValueError("Please, some files! (or try '--help')") # TODO make a util procedure for this
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
# TODO this is tedious and annoyingly similar to the registration chain and MBM and LSQ6 ...
processed_dir = os.path.join(output_dir, pipeline_name + "_processed")
nlin_dir = os.path.join(output_dir, pipeline_name + "_nlin")
resolution = (options.registration.resolution # TODO does using the finest resolution here make sense?
or min([get_resolution_from_file(f) for f in options.application.files]))
imgs = [MincAtom(f, pipeline_sub_dir=processed_dir) for f in options.application.files]
# determine NLIN settings by overriding defaults with
# any settings present in protocol file, if it exists
# could add a hook to print a message announcing completion, output files,
# add more stages here to make a CSV
initial_target_mask = MincAtom(options.nlin.target_mask) if options.nlin.target_mask else None
initial_target = MincAtom(options.nlin.target, mask=initial_target_mask)
full_hierarchy = get_nonlinear_configuration_from_options(nlin_protocol=options.nlin.nlin_protocol,
flag_nlin_protocol=next(iter(options.nlin.flags_.nlin_protocol)),
reg_method=options.nlin.reg_method,
file_resolution=resolution)
s = Stages()
nlin_result = s.defer(nlin_build_model(imgs, initial_target=initial_target, conf=full_hierarchy, nlin_dir=nlin_dir))
# TODO return these?
inverted_xfms = [s.defer(invert_xfmhandler(xfm)) for xfm in nlin_result.output]
if options.stats.calc_stats:
# TODO: put the stats part behind a flag ...
determinants = [s.defer(determinants_at_fwhms(
xfm=inv_xfm,
inv_xfm=xfm,
blur_fwhms=options.stats.stats_kernels))
for xfm, inv_xfm in zip(nlin_result.output, inverted_xfms)]
return Result(stages=s,
output=Namespace(nlin_xfms=nlin_result,
avg_img=nlin_result.avg_img,
determinants=determinants))
else:
# there's no consistency in what gets returned, yikes ...
return Result(stages=s, output=Namespace(nlin_xfms=nlin_result, avg_img=nlin_result.avg_img))
def get_imgs(options):
if options.csv_file and options.files:
raise ValueError("both --csv-file and --files specified ...")
if options.csv_file:
try:
csv = pd.read_csv(
options.csv_file, index_col=False
) # because this is just weird, and may cause 'wrong' cols to be read!
# TODO turn on index_col=False in other read_csv calls!
except:
warnings.warn(
"couldn't read csv ... did you supply `file` column?")
raise
# FIXME check `file` column is present ...
# TODO check for zero length file ...
csv_base = os.path.dirname(
options.csv_file) if not options.csv_paths_relative_to_wd else ""
if hasattr(csv, 'mask_file'):
masks = [
MincAtom(os.path.join(csv_base, mask.strip()),
pipeline_sub_dir=os.path.join(
options.output_directory, options.pipeline_name +
"_processed")) if isinstance(mask, str) else
None # better way to handle missing (nan) values?
for mask in csv.mask_file
]
else:
masks = [None] * len(csv.file)
imgs = [
MincAtom(os.path.join(csv_base, name.strip()),
mask=mask,
pipeline_sub_dir=os.path.join(
options.output_directory,
options.pipeline_name + "_processed"))
# TODO does anything break if we make imgs a pd.Series?
for name, mask in zip(csv.file, masks)
]
elif options.files:
imgs = [
MincAtom(name,
pipeline_sub_dir=os.path.join(
options.output_directory,
options.pipeline_name + "_processed"))
for name in options.files
]
else:
raise ValueError("No images supplied")
return imgs
def reconstitute_laplacian_grid(cortex : MincAtom,
grid : MincAtom,
midline : MincAtom) -> Result[MincAtom]:
output_grid = grid.newname_with_suffix("_reconstituted")
stage = CmdStage(inputs=(cortex, grid, midline), outputs=(output_grid,),
cmd=["reconstitute_laplacian_grid", midline.path, cortex.path, grid.path, output_grid.path])
return Result(stages=Stages([stage]), output=output_grid)
def tamarack_pipeline(options):
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
#processed_dir = os.path.join(output_dir, pipeline_name + "_processed")
first_level_dir = os.path.join(output_dir, pipeline_name + "_first_level")
s = Stages()
with open(options.application.csv_file, 'r') as f:
files_df = (pd.read_csv(
filepath_or_buffer=f,
usecols=['group', 'filename']).assign(file=lambda df: df.apply(
axis="columns",
func=lambda r: MincAtom(r.filename.strip(),
pipeline_sub_dir=os.path.join(
first_level_dir, "%s_processed" % r
.group.strip())))))
check_MINC_input_files(files_df.file.apply(lambda img: img.path))
#grouped_files_df = pd.DataFrame({'file' : pd.concat([imgs])}).assign(group=lambda df: df.index)
tamarack_result = s.defer(tamarack(files_df, options=options))
tamarack_result.first_level_results.applymap(maybe_deref_path).to_csv(
"first_level_results.csv", index=False)
tamarack_result.resampled_determinants.applymap(maybe_deref_path).to_csv(
"resampled_determinants.csv", index=False)
tamarack_result.overall_determinants.applymap(maybe_deref_path).to_csv(
"overall_determinants.csv", index=False)
return Result(stages=s, output=tamarack_result)
def LSQ12_pipeline(options):
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
# TODO this is tedious and annoyingly similar to the registration chain and MBM and LSQ6 ...
processed_dir = os.path.join(output_dir, pipeline_name + "_processed")
lsq12_dir = os.path.join(output_dir, pipeline_name + "_lsq12")
resolution = (
options.registration.
resolution # TODO does using the finest resolution here make sense?
or min(
[get_resolution_from_file(f) for f in options.application.files]))
imgs = [
MincAtom(f, pipeline_sub_dir=processed_dir)
for f in options.application.files
]
# determine LSQ12 settings by overriding defaults with
# any settings present in protocol file, if it exists
# could add a hook to print a message announcing completion, output files,
# add more stages here to make a CSV
return lsq12_pairwise(imgs,
lsq12_conf=options.lsq12,
lsq12_dir=lsq12_dir,
resolution=resolution)
def get_imgs(options):
if options.files:
raise ValueError("you used --files; please use --csv-file")
csv = pd.read_csv(options.csv_file, dtype='str', index_col=False)
csv_base = os.path.dirname(options.csv_file)
csv = csv.assign(anatomical_MincAtom = lambda df: df["anatomical"].apply(lambda file:
MincAtom(os.path.join(csv_base, file.strip()),
pipeline_sub_dir=os.path.join(options.output_directory,
options.pipeline_name + "_processed"))),
count_MincAtom = lambda df: df["count"].apply(lambda file:
MincAtom(os.path.join(csv_base, file.strip()),
pipeline_sub_dir=os.path.join(options.output_directory,
options.pipeline_name + "_processed"))))
return csv
def atlases_from_csv(atlas_csv: str, pipeline_sub_dir: str) -> pd.Series:
d = os.path.dirname(atlas_csv)
df = (pd.read_csv(atlas_csv, usecols=[
"file", "mask_file", "label_file"
]).apply(axis=1,
func=lambda row: MincAtom(
name=os.path.join(d, row.file),
pipeline_sub_dir=pipeline_sub_dir,
mask=MincAtom(os.path.join(d, row.mask_file),
pipeline_sub_dir=pipeline_sub_dir),
labels=MincAtom(os.path.join(d, row.label_file),
pipeline_sub_dir=pipeline_sub_dir))))
#if np.isnan(df).any().any():
# raise ValueError("missing values in atlas CSV, currently not supported")
return df
def smooth_vector(source: MincAtom, fwhm: float) -> Result[MincAtom]:
outf = source.newname_with_suffix(
"_smooth_fwhm%s" % fwhm, subdir="tmp") # TODO smooth_displacement_?
cmd = [
'smooth_vector', '--clobber', '--filter',
'--fwhm=%s' % fwhm, source.path, outf.path
]
stage = CmdStage(inputs=(source, ), outputs=(outf, ), cmd=cmd)
return Result(stages=Stages([stage]), output=outf)
def reconstitute_laplacian_grid(cortex: MincAtom, grid: MincAtom,
midline: MincAtom) -> Result[MincAtom]:
output_grid = grid.newname_with_suffix("_reconstituted")
stage = CmdStage(inputs=(cortex, grid, midline),
outputs=(output_grid, ),
cmd=[
"reconstitute_laplacian_grid", midline.path,
cortex.path, grid.path, output_grid.path
])
return Result(stages=Stages([stage]), output=output_grid)
def minclaplace(input_grid : MincAtom,
extra_args : List[str] = [],
solution_vertices : Optional[FileAtom] = None,
create_surface : bool = True,) -> Result[FileAtom]:
# TODO the ambiguity of the return type is slightly annoying ...
# best to create separate minclaplace_at_vertices for the case when `--solve-at-vertices` is used?
solved = input_grid.newname_with_suffix("_solved", ext=".txt" if solution_vertices else ".mnc")
if create_surface:
out_surface = input_grid.newname_with_suffix("_surface", ext=".obj")
stage = CmdStage(inputs=(input_grid,), outputs=(solved,) + ((out_surface,) if create_surface else ()),
cmd=["minclaplace"]
+ (["--solve-at-vertices=%s" % solution_vertices.path]
if solution_vertices is not None else [])
+ (["--create-surface=%s" % out_surface.path] if create_surface else [])
+ extra_args
+ [input_grid.path, solved.path])
return Result(stages=Stages([stage]),
output=Namespace(solved=solved, surface=out_surface)
if create_surface else Namespace(solved=solved))
def mincblob(op: str, grid: MincAtom, subdir: str = "tmp") -> Result[MincAtom]:
"""
Low-level mincblob wrapper with the one exception being the determinant option. By
default the inner clockwork of mincblob subtracts 1 from all determinant values that
are being calculated. As such, 1 needs to be added to the result of the mincblob call.
We will do that here, because it makes most sense here.
>>> stages = mincblob('determinant', MincAtom("/images/img_1.mnc", pipeline_sub_dir="/tmp")).stages
>>> [s.render() for s in stages]
['mincblob -clobber -determinant /images/img_1.mnc /tmp/img_1/img_1_determinant.mnc']
"""
if op not in ["determinant", "trace", "translation", "magnitude"]:
raise ValueError('mincblob: invalid operation %s' % op)
# if we are calculating the determinant, the first file produced is a temp file:
if op == "determinant":
out_file = grid.newname_with_suffix("_temp_det", subdir=subdir)
else:
out_file = grid.newname_with_suffix('_' + op, subdir=subdir)
stage = CmdStage(
inputs=(grid, ),
outputs=(out_file, ),
cmd=['mincblob', '-clobber', '-' + op, grid.path, out_file.path])
s = Stages([stage])
# now create the proper determinant if that's what was asked for
if op == "determinant":
result_file = s.defer(
mincmath(op='add',
const=1,
vols=[out_file],
subdir=subdir,
new_name=grid.filename_wo_ext + "_det"))
else:
result_file = out_file
return Result(stages=s, output=result_file)
def minclaplace(
input_grid: MincAtom,
extra_args: List[str] = [],
solution_vertices: Optional[FileAtom] = None,
create_surface: bool = True,
) -> Result[FileAtom]:
# TODO the ambiguity of the return type is slightly annoying ...
# best to create separate minclaplace_at_vertices for the case when `--solve-at-vertices` is used?
solved = input_grid.newname_with_suffix(
"_solved", ext=".txt" if solution_vertices else ".mnc")
if create_surface:
out_surface = input_grid.newname_with_suffix("_surface", ext=".obj")
stage = CmdStage(
inputs=(input_grid, ),
outputs=(solved, ) + ((out_surface, ) if create_surface else ()),
cmd=["minclaplace"] +
(["--solve-at-vertices=%s" %
solution_vertices.path] if solution_vertices is not None else []) +
(["--create-surface=%s" % out_surface.path] if create_surface else [])
+ extra_args + [input_grid.path, solved.path])
return Result(stages=Stages([stage]),
output=Namespace(solved=solved, surface=out_surface)
if create_surface else Namespace(solved=solved))
def marching_cubes(in_volume : MincAtom,
min_threshold : float = None,
max_threshold : float = None,
threshold : float = None):
if not xor(threshold is None, min_threshold is None and max_threshold is None):
raise ValueError("specify either threshold or min and max thresholds")
out_volume = FileAtom(in_volume.newname_with(NotImplemented)) # forget MINCy fields # FIXME this coercion doesn't work
stage = CmdStage(inputs=(in_volume,), outputs=(out_volume,),
cmd=["marching_cubes", in_volume.path, out_volume.path]
+ ([str(threshold)] if threshold is not None else [str(min_threshold), str(max_threshold)]))
return Result(stages=Stages([stage]), output=out_volume)
def voxel_vote(label_files: List[MincAtom],
output_dir: str,
name: str = "voted"): # TODO too stringy ...
if len(label_files) == 0:
raise ValueError("can't vote with 0 files")
out = MincAtom(name=os.path.join(output_dir, "%s.mnc" % name),
output_sub_dir=output_dir) # FIXME better naming
s = CmdStage(cmd=["voxel_vote", "--clobber"] +
[l.path for l in sorted(label_files)] + [out.path],
inputs=tuple(label_files),
outputs=(out, ))
return Result(stages=Stages([s]), output=out)
def marching_cubes(in_volume: MincAtom,
min_threshold: float = None,
max_threshold: float = None,
threshold: float = None):
if not xor(threshold is None, min_threshold is None
and max_threshold is None):
raise ValueError("specify either threshold or min and max thresholds")
out_volume = FileAtom(
in_volume.newname_with(NotImplemented)
) # forget MINCy fields # FIXME this coercion doesn't work
stage = CmdStage(
inputs=(in_volume, ),
outputs=(out_volume, ),
cmd=["marching_cubes", in_volume.path, out_volume.path] +
([str(threshold)] if threshold is not None else
[str(min_threshold), str(max_threshold)]))
return Result(stages=Stages([stage]), output=out_volume)
def NLIN_pipeline(options):
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
# TODO this is tedious and annoyingly similar to the registration chain and MBM and LSQ6 ...
processed_dir = os.path.join(output_dir, pipeline_name + "_processed")
nlin_dir = os.path.join(output_dir, pipeline_name + "_nlin")
resolution = (
options.registration.
resolution # TODO does using the finest resolution here make sense?
or min(
[get_resolution_from_file(f) for f in options.application.files]))
imgs = get_imgs(options.application)
initial_target_mask = MincAtom(
options.nlin.target_mask) if options.nlin.target_mask else None
initial_target = MincAtom(options.nlin.target, mask=initial_target_mask)
nlin_module = get_nonlinear_component(reg_method=options.nlin.reg_method)
nlin_build_model_component = get_model_building_procedure(
options.nlin.reg_strategy, reg_module=nlin_module)
nlin_conf = (nlin_build_model_component.parse_build_model_protocol(
options.nlin.nlin_protocol, resolution=resolution)
if options.nlin.nlin_protocol is not None else
nlin_build_model_component.get_default_build_model_conf(
resolution=resolution))
s = Stages()
nlin_result = s.defer(
nlin_build_model_component.build_model(
imgs=imgs,
initial_target=initial_target,
conf=nlin_conf,
nlin_dir=nlin_dir,
use_robust_averaging=options.nlin.use_robust_averaging,
nlin_prefix=""))
inverted_xfms = [
s.defer(invert_xfmhandler(xfm)) for xfm in nlin_result.output
]
if options.stats.calc_stats:
determinants = s.defer(
determinants_at_fwhms(xfms=inverted_xfms,
inv_xfms=nlin_result.output,
blur_fwhms=options.stats.stats_kernels))
return Result(stages=s,
output=Namespace(nlin_xfms=nlin_result,
avg_img=nlin_result.avg_img,
determinants=determinants))
else:
# there's no consistency in what gets returned, yikes ...
return Result(stages=s,
output=Namespace(nlin_xfms=nlin_result,
avg_img=nlin_result.avg_img))
def common_space(mbm_result, options):
s = Stages()
# TODO: the interface of this function (basically a destructive 'id' function) is horrific
# TODO: instead, copy the mbm_result here ??
if not options.mbm.common_space.common_space_model:
raise ValueError("No common space template provided!")
if not options.mbm.common_space.common_space_mask:
warnings.warn(
"No common space mask provided ... might be OK if your consensus average mask is OK"
)
# TODO allow lsq6 registration as well ...
common_space_model = MincAtom(
options.mbm.common_space.common_space_model,
# TODO fix the subdirectories!
mask=MincAtom(options.mbm.common_space.common_space_mask,
pipeline_sub_dir=os.path.join(
options.application.output_directory,
options.application.pipeline_name + "_processed"))
if options.mbm.common_space.common_space_mask else None,
pipeline_sub_dir=os.path.join(
options.application.output_directory,
options.application.pipeline_name + "_processed"))
# TODO allow different lsq12/nlin config params than the ones used in MBM ...
# full_hierarchy = get_nonlinear_configuration_from_options(nlin_protocol=options.mbm.nlin.nlin_protocol,
# reg_method=options.mbm.nlin.reg_method,
# file_resolution=options.registration.resolution)
# WEIRD ... see comment in lsq12_nlin code ...
# nlin_conf = full_hierarchy.confs[-1] if isinstance(full_hierarchy, MultilevelANTSConf) else full_hierarchy
# also weird that we need to call get_linear_configuration_from_options here ... ?
# nlin_build_model_component = model_building_with_initial_target_generation(
# final_model_building_component=nlin_build_model_component,
# prelim_model_building_component=prelim_nlin_build_model_component)
# TODO don't use name 'x_module' for something that's technically not a module ... perhaps unit/component?
nlin_component = get_nonlinear_component(
reg_method=options.mbm.nlin.reg_method)
lsq12_conf = get_linear_configuration_from_options(
conf=options.mbm.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=options.registration.resolution)
# N.B.: options.registration.resolution has been *updated* correctly by mbm( ). sigh ...
model_to_common = s.defer(
lsq12_nlin(
source=mbm_result.avg_img,
target=common_space_model,
lsq12_conf=lsq12_conf,
nlin_module=nlin_component,
resolution=options.registration.resolution,
nlin_options=options.mbm.nlin.nlin_protocol, # =nlin_conf,
resample_source=True))
model_common = s.defer(
mincresample_new(img=mbm_result.avg_img,
xfm=model_to_common.xfm,
like=common_space_model,
postfix="_common"))
overall_xfms_to_common = [
s.defer(concat_xfmhandlers([rigid_xfm, nlin_xfm, model_to_common]))
for rigid_xfm, nlin_xfm in zip(mbm_result.xfms.rigid_xfm,
mbm_result.xfms.lsq12_nlin_xfm)
]
overall_xfms_to_common_inv = [
s.defer(invert_xfmhandler(xfmhandler)) for xfmhandler in [
s.defer(concat_xfmhandlers([rigid_xfm, nlin_xfm, model_to_common]))
for rigid_xfm, nlin_xfm in zip(mbm_result.xfms.rigid_xfm,
mbm_result.xfms.lsq12_nlin_xfm)
]
]
xfms_to_common = [
s.defer(concat_xfmhandlers([nlin_xfm, model_to_common]))
for nlin_xfm in mbm_result.xfms.lsq12_nlin_xfm
]
mbm_result.xfms = mbm_result.xfms.assign(
xfm_to_common=xfms_to_common,
overall_xfm_to_common=overall_xfms_to_common)
if options.mbm.stats.calc_stats:
log_nlin_det_common, log_full_det_common = ([
dets.map(lambda d: s.defer(
mincresample_new(img=d,
xfm=model_to_common.xfm,
like=common_space_model,
postfix="_common")))
for dets in (mbm_result.determinants.log_nlin_det,
mbm_result.determinants.log_full_det)
])
overall_determinants = s.defer(
determinants_at_fwhms(xfms=overall_xfms_to_common_inv,
blur_fwhms=options.mbm.stats.stats_kernels))
mbm_result.determinants = \
mbm_result.determinants.assign(log_nlin_det_common=log_nlin_det_common,
log_full_det_common=log_full_det_common,
log_nlin_overall_det_common=overall_determinants.log_nlin_det,
log_full_overall_det_common=overall_determinants.log_full_det
)
mbm_result.model_common = model_common
return Result(stages=s, output=mbm_result)
def map_to_MincAtom(row):
return MincAtom(name=row.file,
pipeline_sub_dir=pipeline_sub_dir,
mask=MincAtom(name=row.mask_file,
pipeline_sub_dir=pipeline_sub_dir))
def smooth_vector(source : MincAtom, fwhm : float) -> Result[MincAtom]:
outf = source.newname_with_suffix("_smooth_fwhm%s" % fwhm, subdir="tmp") # TODO smooth_displacement_?
cmd = ['smooth_vector', '--clobber', '--filter', '--fwhm=%s' % fwhm,
source.path, outf.path]
stage = CmdStage(inputs=(source,), outputs=(outf,), cmd=cmd)
return Result(stages=Stages([stage]), output=outf)
def img():
return MincAtom('/images/img_1.mnc')
def imgs():
return [MincAtom('/images/img_%d.mnc' % i) for i in range(1, 4)]
def tissue_vision_pipeline(options):
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
csv = original_csv = get_imgs(options.application)
# check_MINC_input_files([img.path for img in imgs])
s = Stages()
s.defer(create_quality_control_images(imgs=csv['anatomical_MincAtom'].tolist(), montage_dir=output_dir,
montage_output=os.path.join(output_dir, pipeline_name + "_resampled",
"input_montage"),
auto_range=True,
message="input_mincs"))
#############################
# Step 1: Run MBM.py to create a consensus average
#############################
mbm_result = s.defer(mbm(imgs=csv['anatomical_MincAtom'].tolist(), options=options,
prefix=options.application.pipeline_name,
output_dir=output_dir,
with_maget=False))
#TODO remove
transforms = mbm_result.xfms.assign(native_file=lambda df: df.rigid_xfm.apply(lambda x: x.source),
lsq6_file=lambda df: df.lsq12_nlin_xfm.apply(lambda x: x.source),
lsq6_mask_file=lambda df:
df.lsq12_nlin_xfm.apply(lambda x: x.source.mask if x.source.mask else ""),
nlin_file=lambda df: df.lsq12_nlin_xfm.apply(lambda x: x.resampled),
nlin_mask_file=lambda df:
df.lsq12_nlin_xfm.apply(lambda x: x.resampled.mask if x.resampled.mask else ""))\
.applymap(maybe_deref_path)
determinants = mbm_result.determinants.drop(["full_det", "nlin_det"], axis=1)\
.applymap(maybe_deref_path)
csv = csv.assign(anatomical_lsq6_MincAtom=mbm_result.xfms.lsq12_nlin_xfm.apply(lambda xfm: xfm.source),
mbm_lsq6_XfmAtom=mbm_result.xfms.rigid_xfm.apply(lambda x: x.xfm),
mbm_lsq12_nlin_XfmAtom=mbm_result.xfms.lsq12_nlin_xfm.apply(lambda x: x.xfm),
mbm_full_XfmAtom=mbm_result.xfms.overall_xfm.apply(lambda x: x.xfm))
# x.assign(count_lsq6_MincAtom=lambda df: [x + y for x, y in zip(df["x"], df["y"])])
csv = csv.assign(count_lsq6_MincAtom = lambda df:
[s.defer(mincresample_new(img = img,
xfm = xfm,
like = like))
for img, xfm, like in zip(df["count_MincAtom"],
df["mbm_lsq6_XfmAtom"],
df["anatomical_lsq6_MincAtom"])])
#############################
# Step 2: Register consensus average to ABI tissuevision Atlas
#############################
lsq12_conf = get_linear_configuration_from_options(conf=options.mbm.lsq12,
transform_type=LinearTransType.lsq12,
file_resolution=options.registration.resolution)
nlin_component = get_nonlinear_component(reg_method=options.mbm.nlin.reg_method)
atlas_target = MincAtom(name=options.consensus_to_atlas.atlas_target,
orig_name=options.consensus_to_atlas.atlas_target,
mask=MincAtom(name=options.consensus_to_atlas.atlas_target_mask,
orig_name=options.consensus_to_atlas.atlas_target_mask))
atlas_target_label = MincAtom(name=options.consensus_to_atlas.atlas_target_label,
orig_name=options.consensus_to_atlas.atlas_target_label,
mask=MincAtom(name=options.consensus_to_atlas.atlas_target_mask,
orig_name=options.consensus_to_atlas.atlas_target_mask))
lsq12_nlin_result = s.defer(lsq12_nlin(source=mbm_result.avg_img,
target=atlas_target,
lsq12_conf=lsq12_conf,
nlin_module=nlin_component,
nlin_options=options.mbm.nlin.nlin_protocol,
resolution=options.registration.resolution,
resample_source=False
))
#############################
# Step 3: Resample count volumes to ABI tissuevision Atlas space and vice versa
#############################
csv = csv.assign(lsq6_to_atlas_XfmAtom = lambda df: df['mbm_lsq12_nlin_XfmAtom'].apply(lambda xfm:
s.defer(xfmconcat([xfm, lsq12_nlin_result.xfm]))))
csv = csv.assign(
anatomical_targetspace_MincAtom=lambda df:
[s.defer(mincresample_new(img=img, xfm=xfm, like=atlas_target))
for img, xfm in zip(df["anatomical_lsq6_MincAtom"], df["lsq6_to_atlas_XfmAtom"])],
count_targetspace_MincAtom=lambda df:
[s.defer(mincresample_new(img=img, xfm=xfm, like=atlas_target))
for img, xfm in zip(df["count_lsq6_MincAtom"], df["lsq6_to_atlas_XfmAtom"])],
atlas_lsq6space_MincAtom=lambda df:
[s.defer(mincresample_new(img=atlas_target_label, xfm=xfm, like=like, invert=True,
interpolation=Interpolation.nearest_neighbour,
extra_flags=('-keep_real_range',)))
for xfm, like in zip( df["lsq6_to_atlas_XfmAtom"], df["count_lsq6_MincAtom"])]
)
csv.applymap(maybe_deref_path).to_csv("analysis.csv",index=False)
s.defer(create_quality_control_images(imgs=csv.count_targetspace_MincAtom.tolist(), montage_dir=output_dir,
montage_output=os.path.join(output_dir, pipeline_name + "_resampled",
"count_montage"),
auto_range=True,
message="count_mincs"))
return Result(stages=s, output=())
def tv_recon_pipeline(options):
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
s = Stages()
slices_df = pd.read_csv(options.application.csv_file,
dtype={"brain_name": str, "brain_directory": str, "slice_directory": str})
if "qc" not in slices_df.columns:
slices_df["qc"] = False
step = int(1 / options.deep_segment.qc_fraction)
slices_df.qc[0::step] = True
#############################
# Step 1: Run deep_segment.py
#############################
#TODO surely theres a way around deep_segment_result=""?
slices_df = slices_df.assign(
deep_segment_result="",
segmentation_directory = lambda df: df.apply(
lambda row: os.path.join(output_dir, pipeline_name + "_deep_segmentation", row.brain_name), axis=1)
)
for index, row in slices_df.iterrows():
slices_df.at[index,"deep_segment_result"] = s.defer(deep_segment(image = FileAtom(row.slice,
output_sub_dir = row.segmentation_directory),
deep_segment_pipeline = FileAtom(options.deep_segment.deep_segment_pipeline),
anatomical_suffix = options.deep_segment.anatomical_name,
count_suffix = options.deep_segment.count_name,
outline_suffix = options.deep_segment.outline_name if row.qc else None,
cell_min_area = options.deep_segment.cell_min_area,
cell_mean_area = options.deep_segment.cell_mean_area,
cell_max_area = options.deep_segment.cell_max_area,
temp_dir = options.deep_segment.temp_dir
))
#hacky solution requires deep_segment() returns in that order
#https://stackoverflow.com/questions/35491274/pandas-split-column-of-lists-into-multiple-columns
slices_df[["anatomical_result", "count_result", "outline_result"]] = \
pd.DataFrame(slices_df.deep_segment_result.values.tolist())
#############################
# Step 2: Run stacks_to_volume.py
#############################
#This is annoying... If I add anything to slices_df, I will have to delete it here as well
mincs_df = slices_df.drop(['slice', 'deep_segment_result', "anatomical_result", "count_result", "outline_result", "qc"], axis=1) \
.drop_duplicates().reset_index(drop=True)\
.assign(
anatomical_list = slices_df.groupby("brain_name")['anatomical_result'].apply(list).reset_index(drop=True),
count_list=slices_df.groupby("brain_name")['count_result'].apply(list).reset_index(drop=True),
#the above is so hacky...
stacked_directory=lambda df: df.apply(
lambda row: os.path.join(output_dir, pipeline_name + "_stacked", row.brain_name), axis=1),
)
mincs_df = mincs_df.assign(
anatomical_stacked_MincAtom=lambda df: df.apply(
lambda row: MincAtom(
os.path.join(row.stacked_directory,
row.brain_name + "_" + options.deep_segment.anatomical_name + "_stacked.mnc")
), axis=1
),
count_stacked_MincAtom=lambda df: df.apply(
lambda row: MincAtom(
os.path.join(row.stacked_directory,
row.brain_name + "_" + options.deep_segment.count_name + "_stacked.mnc")
), axis=1
)
)
if not options.stacks_to_volume.manual_scale_output:
mincs_df["scale_output"] = mincs_df.interslice_distance/mincs_df.interslice_distance.min()
for index, row in mincs_df.iterrows():
s.defer(stacks_to_volume(
slices = row.anatomical_list,
output_volume = row.anatomical_stacked_MincAtom,
z_resolution = row.interslice_distance,
stacks_to_volume_options=options.stacks_to_volume,
uniform_sum=False
))
s.defer(stacks_to_volume(
slices=row.count_list,
output_volume=row.count_stacked_MincAtom,
z_resolution=row.interslice_distance,
stacks_to_volume_options=options.stacks_to_volume,
scale_output = row.scale_output,
uniform_sum=True
))
#############################
# Step 3: Run autocrop to resample to isotropic
#############################
for index, row in mincs_df.iterrows():
mincs_df.at[index,"anatomical_isotropic_result"] = s.defer(autocrop(
img = row.anatomical_stacked_MincAtom,
isostep = options.stacks_to_volume.plane_resolution,
suffix = "isotropic"
))
mincs_df.at[index, "count_isotropic_result"] = s.defer(autocrop(
img=row.count_stacked_MincAtom,
isostep=options.stacks_to_volume.plane_resolution,
suffix="isotropic",
nearest_neighbour = True
))
#############################
slices_df = slices_df.assign(
anatomical_slice = lambda df: df.apply(lambda row: row.anatomical_result.path, axis=1),
count_slice=lambda df: df.apply(lambda row: row.count_result.path, axis=1),
outline_slice=lambda df: df.apply(lambda row: row.outline_result.path if row.outline_result else None, axis=1),
)
slices_df.drop(slices_df.filter(regex='.*_directory.*|.*_result.*'), axis=1)\
.to_csv("TV_processed_slices.csv", index=False)
mincs_df = mincs_df.assign(
anatomical=lambda df: df.apply(lambda row: row.anatomical_isotropic_result.path, axis=1),
count=lambda df: df.apply(lambda row: row.count_isotropic_result.path, axis=1),
)
mincs_df.drop(mincs_df.filter(regex='.*_result.*|.*_list.*|.*_MincAtom.*'), axis=1)\
.to_csv("TV_mincs.csv", index=False)
#TODO overlay them
# s.defer(create_quality_control_images(imgs=reconstructed_mincs, montage_dir = output_dir,
# montage_output=os.path.join(output_dir, pipeline_name + "_stacked", "reconstructed_montage"),
# message="reconstructed_mincs"))
#TODO
# s.defer(create_quality_control_images(imgs=all_anatomical_pad_results, montage_dir=output_dir,
# montage_output=os.path.join(output_dir, pipeline_name + "_stacked",
# "%s_montage" % anatomical),
# message="%s_mincs" % anatomical))
# s.defer(create_quality_control_images(imgs=all_count_pad_results, montage_dir=output_dir,
# montage_output=os.path.join(output_dir, pipeline_name + "_stacked",
# "%s_montage" % count),
# auto_range=True,
# message="%s_mincs" % count))
return Result(stages=s, output=())
def two_level_pipeline(options: TwoLevelConf):
def relativize_path(fp):
#this annoying function takes care of the csv_paths_relative_to_wd flag.
return os.path.join(os.path.dirname(options.application.csv_file),fp) \
if not options.application.csv_paths_relative_to_wd \
else fp
first_level_dir = options.application.pipeline_name + "_first_level"
if options.application.files:
warnings.warn("Got extra arguments: '%s'" % options.application.files)
with open(options.application.csv_file, 'r') as f:
try:
files_df = (pd.read_csv(
filepath_or_buffer=f,
usecols=['group', 'file'],
index_col=False).assign(file=lambda df: df.apply(
axis="columns",
func=lambda r: MincAtom(relativize_path(r.file).strip(),
pipeline_sub_dir=os.path.join(
first_level_dir,
"%s_processed" % r.group,
)))))
except AttributeError:
warnings.warn(
"Something went wrong ... does your .csv file have `group` and `file` columns?"
)
raise
# TODO is it actually sufficient that *within-study* filenames are distinct, as follows??
for name, g in files_df.groupby("group"): # TODO: collect the outputs
check_MINC_input_files(g.file.map(lambda x: x.path))
#check_MINC_input_files(files_df.file.map(lambda x: x.path))
pipeline = two_level(grouped_files_df=files_df, options=options)
# TODO write these into the appropriate subdirectory ...
overall = (pipeline.output.overall_determinants.drop(
'inv_xfm', axis=1).applymap(maybe_deref_path))
overall.to_csv("overall_determinants.csv", index=False)
resampled = (pipeline.output.resampled_determinants.drop(
['inv_xfm', 'full_det', 'nlin_det'],
axis=1).applymap(maybe_deref_path))
resampled.to_csv("resampled_determinants.csv", index=False)
# rename/drop some columns, bind the dfs and write to "analysis.csv" as it should be.
# deprecate the two csvs next release.
analysis = pd.read_csv(options.application.csv_file).assign(
native_file=lambda df: df.file.apply(relativize_path))
overall = (overall.drop(["full_det", "nlin_det"],
axis=1).rename(columns={"overall_xfm": "xfm"}))
resampled = resampled.rename(
columns={
"first_level_log_full_det": "log_full_det",
"first_level_log_nlin_det": "log_nlin_det",
"first_level_xfm": "xfm",
"first_level_log_full_det_resampled": "resampled_log_full_det",
"first_level_log_nlin_det_resampled": "resampled_log_nlin_det"
})
(analysis.merge(pd.concat([resampled, overall],
axis=1)).to_csv("analysis.csv", index=False))
return pipeline
def saddle_recon_pipeline(options):
output_dir = options.application.output_directory
pipeline_name = options.application.pipeline_name
fid_input_dir = options.saddle_recon.varian_recon.fid_input_directory
# TODO check that the varian recon "output_file_name" doesn't name a directory, or if it does, that it matches the output directory
#The input directory should contain a host of fid files that will be used for the reconstruction of the mnc files
# TODO check that there are fid files in this directory
# TODO check that all mandatory inputs are provided
#Make list of input fid files, with location, and create a "FileAtom" type
varian_fid_files = [
fid_input_dir + "/fid" + str(num_fid)
for num_fid in range(0, options.saddle_recon.varian_recon.num_fids)
]
fids = [FileAtom(name) for name in varian_fid_files]
# Varian recon will spit out images of the format <output_file_name>.<coil#>.<rep#>.mnc
# TODO If .mnc isn't provided at the end of the output_file_name, then there is no "." before the coil number. Need to check and correct for this.
# Al/ the files created will be spit out to the output_dir
# Create list of files we expect to be produced by varian recon
coil_list_0based = [
int(x) for x in options.saddle_recon.varian_recon.mouse_list.split(',')
]
coil_list = [x + 1 for x in coil_list_0based]
file_name_base = os.path.splitext(
options.saddle_recon.varian_recon.output_file_name)[0]
varian_mnc_output = [
os.path.join(
output_dir,
file_name_base + "." + str(coil) + "_" + str(rep) + ".mnc")
for coil in coil_list
for rep in range(0, options.saddle_recon.varian_recon.num_reps)
]
varian_coil_output = [
str(coil) for coil in coil_list
for rep in range(0, options.saddle_recon.varian_recon.num_reps)
]
recon_sub_dir = [
file_name[:-6] + "_processed" for file_name in varian_mnc_output
]
imgs = [
MincAtom(varian_mnc_output[k], pipeline_sub_dir=recon_sub_dir[k])
for k in range(0, len(varian_mnc_output))
]
s = Stages()
#############################
# Step 1: Run varian_recon.py
#############################
varian_recon_results = s.defer(
varian_recon_ge3dmice_saddle(
fids=fids,
imgs=imgs,
varian_recon_options=options.saddle_recon.varian_recon,
output_dir=output_dir))
# Hold results obtained in the loop
all_lsq6_results = []
all_dc_results = []
all_crop_results = []
# Loop through all the coils
for icoil in coil_list:
icoil_imgs = numpy.array(imgs)[numpy.where(
numpy.array(varian_coil_output) == str(icoil))[0]]
icoil_varian_mnc_output = numpy.array(varian_mnc_output)[numpy.where(
numpy.array(varian_coil_output) == str(icoil))[0]]
###########################
# Step 2: lsq6 registration
###########################
# TODO change functionality of get_resolution_from_file so that it can be deferred
lsq6_dir = os.path.join(output_dir,
file_name_base + "." + str(icoil) + "_lsq6")
target_dir = os.path.join(
output_dir, file_name_base + "." + str(icoil) + "_target_file")
resolution = options.registration.resolution
#resolution = (options.registration.resolution or
# get_resolution_from_file(icoil_varian_mnc_output[0]))
options.registration = options.registration.replace(
resolution=resolution)
target_file = MincAtom(name=icoil_varian_mnc_output[0],
pipeline_sub_dir=target_dir)
targets = RegistrationTargets(registration_standard=target_file,
xfm_to_standard=None,
registration_native=None)
lsq6_result = s.defer(
lsq6_nuc_inorm(imgs=icoil_imgs,
resolution=resolution,
registration_targets=targets,
lsq6_dir=lsq6_dir,
lsq6_options=options.saddle_recon.lsq6))
all_lsq6_results.append(lsq6_result)
###########################
# Step 3: distortion correct lsq6 output image
###########################
lsq6_file = MincAtom(os.path.join(lsq6_dir, "average.mnc"),
pipeline_sub_dir=lsq6_dir)
dc_lsq6_file = MincAtom(os.path.join(lsq6_dir,
"average.aug2015_dist_corr.mnc"),
pipeline_sub_dir=lsq6_dir)
dc_result = s.defer(
dist_corr_saddle(img=lsq6_file, dc_img=dc_lsq6_file))
all_dc_results.append(dc_result)
###########################
# Step 4: crop distortion corrected lsq6 output image
###########################
cropped_dc_lsq6_file = MincAtom(os.path.join(
lsq6_dir, "average.aug2015_dist_corr.cropped.mnc"),
pipeline_sub_dir=lsq6_dir)
crop_result = s.defer(
crop_to_brain(
img=dc_lsq6_file,
cropped_img=cropped_dc_lsq6_file,
crop_to_brain_options=options.saddle_recon.crop_to_brain))
all_crop_results.append(crop_result)
return Result(stages=s,
output=Namespace(varian_output=varian_recon_results,
lsq6_output=all_lsq6_results,
dc_output=all_dc_results,
crop_output=all_crop_results))