import nibabel as nib
import os
import os.path as op
from time import time
import numpy as np
import pandas as pd
import logging
import pimms
from AFQ.tasks.decorators import as_file
from AFQ.tasks.utils import get_fname, with_name, str_to_desc
from AFQ.recognition.recognize import recognize
from AFQ.utils.path import drop_extension, write_json
import AFQ.utils.streamlines as aus
from AFQ.tasks.utils import get_default_args
import AFQ.utils.volume as auv
from AFQ._fixes import gaussian_weights
try:
from trx.io import load as load_trx
from trx.io import save as save_trx
from trx.trx_file_memmap import TrxFile
except ModuleNotFoundError:
has_trx = False
from dipy.io.streamline import load_tractogram, save_tractogram
from dipy.io.stateful_tractogram import Space
from dipy.stats.analysis import afq_profile
from dipy.tracking.streamline import set_number_of_points, values_from_volume
[docs]logger = logging.getLogger('AFQ')
@pimms.calc("bundles")
@as_file('_tractography', include_track=True, include_seg=True)
[docs]def segment(data_imap, mapping_imap,
tractography_imap, segmentation_params):
"""
full path to a trk/trx file containing containing
segmented streamlines, labeled by bundle
Parameters
----------
segmentation_params : dict, optional
The parameters for segmentation.
Default: use the default behavior of the seg.Segmentation object.
"""
bundle_dict = data_imap["bundle_dict"]
reg_template = data_imap["reg_template"]
streamlines = tractography_imap["streamlines"]
if streamlines.endswith(".trk") or streamlines.endswith(".tck"):
tg = load_tractogram(
streamlines, data_imap["dwi"], Space.VOX,
bbox_valid_check=False)
is_trx = False
elif streamlines.endswith(".trx"):
is_trx = True
trx = load_trx(streamlines, data_imap["dwi"])
trx.streamlines._data = trx.streamlines._data.astype(np.float32)
tg = trx.to_sft()
indices_to_remove, _ = tg.remove_invalid_streamlines()
if len(indices_to_remove) > 0:
logger.warning(f"{len(indices_to_remove)} invalid streamlines removed")
start_time = time()
bundles, bundle_meta = recognize(
tg,
data_imap["dwi"],
mapping_imap["mapping"],
bundle_dict,
reg_template,
**segmentation_params)
seg_sft = aus.SegmentedSFT(bundles, Space.VOX)
if len(seg_sft.sft) < 1:
raise ValueError("Fatal: No bundles recognized.")
if is_trx:
seg_sft.sft.dtype_dict = {'positions': np.float16,
'offsets': np.uint32}
tgram = TrxFile.from_sft(seg_sft.sft)
tgram.groups = seg_sft.bundle_idxs
meta = {}
else:
tgram, meta = seg_sft.get_sft_and_sidecar()
seg_params_out = {}
for arg_name, value in segmentation_params.items():
if isinstance(value, (int, float, bool, str)):
seg_params_out[arg_name] = value
elif isinstance(value, (list, tuple)):
seg_params_out[arg_name] = [str(v) for v in value]
elif isinstance(value, dict):
for k, v in value.items():
seg_params_out[k] = str(v)
else:
seg_params_out[arg_name] = str(value)
meta["source"] = streamlines
meta["Recognition Parameters"] = seg_params_out
meta["Bundle Parameters"] = bundle_meta
meta["Timing"] = time() - start_time
return tgram, meta
@pimms.calc("indiv_bundles")
[docs]def export_bundles(base_fname, output_dir,
bundles,
tracking_params,
segmentation_params):
"""
dictionary of paths, where each path is
a full path to a trk file containing the streamlines of a given bundle.
"""
is_trx = tracking_params.get("trx", False)
if is_trx:
extension = ".trx"
else:
extension = ".trk"
bundles_dir = op.join(output_dir, "bundles")
os.makedirs(bundles_dir, exist_ok=True)
seg_sft = aus.SegmentedSFT.fromfile(bundles)
for bundle in seg_sft.bundle_names:
fname = op.split(
get_fname(
base_fname,
f'_desc-{str_to_desc(bundle)}'
f'_tractography{extension}',
tracking_params=tracking_params,
segmentation_params=segmentation_params))
fname = op.join(bundles_dir, fname[1])
bundle_sft = seg_sft.get_bundle(bundle)
if len(bundle_sft) > 0:
logger.info(f"Saving {fname}")
if is_trx:
seg_sft.sft.dtype_dict = {
'positions': np.float16,
'offsets': np.uint32}
trxfile = TrxFile.from_sft(bundle_sft)
save_trx(trxfile, fname)
else:
save_tractogram(
bundle_sft, fname,
bbox_valid_check=False)
else:
logger.info(f"No bundle to save for {bundle}")
meta = dict(
source=bundles,
params=seg_sft.get_bundle_param_info(bundle))
meta_fname = drop_extension(fname) + '.json'
write_json(meta_fname, meta)
return bundles_dir
@pimms.calc("sl_counts")
@as_file('_desc-slCount_dwi.csv', include_track=True, include_seg=True)
[docs]def export_sl_counts(bundles):
"""
full path to a JSON file containing streamline counts
"""
sl_counts = []
seg_sft = aus.SegmentedSFT.fromfile(bundles)
for bundle in seg_sft.bundle_names:
sl_counts.append(len(
seg_sft.get_bundle(bundle).streamlines))
sl_counts.append(len(seg_sft.sft.streamlines))
counts_df = pd.DataFrame(
data=dict(
n_streamlines=sl_counts),
index=seg_sft.bundle_names + ["Total Recognized"])
return counts_df, dict(source=bundles)
@pimms.calc("median_bundle_lengths")
@as_file(
'_desc-medianBundleLengths_dwi.csv',
include_track=True, include_seg=True)
[docs]def export_bundle_lengths(bundles):
"""
full path to a JSON file containing median bundle lengths
"""
med_len_counts = []
seg_sft = aus.SegmentedSFT.fromfile(bundles)
for bundle in seg_sft.bundle_names:
these_lengths = seg_sft.get_bundle(
bundle)._tractogram._streamlines._lengths
if len(these_lengths) > 0:
med_len_counts.append(np.median(
these_lengths))
else:
med_len_counts.append(0)
med_len_counts.append(np.median(
seg_sft.sft._tractogram._streamlines._lengths))
counts_df = pd.DataFrame(
data=dict(
median_len=med_len_counts),
index=seg_sft.bundle_names + ["Total Recognized"])
return counts_df, dict(source=bundles)
@pimms.calc("density_maps")
@as_file('_desc-density_dwi.nii.gz', include_track=True, include_seg=True)
[docs]def export_density_maps(bundles, data_imap):
"""
full path to 4d nifti file containing streamline counts per voxel
per bundle, where the 4th dimension encodes the bundle
"""
seg_sft = aus.SegmentedSFT.fromfile(
bundles)
entire_density_map = np.zeros((
*data_imap["data"].shape[:3],
len(seg_sft.bundle_names)))
for ii, bundle_name in enumerate(seg_sft.bundle_names):
bundle_sl = seg_sft.get_bundle(bundle_name)
bundle_density = auv.density_map(bundle_sl).get_fdata()
entire_density_map[..., ii] = bundle_density
return nib.Nifti1Image(
entire_density_map, data_imap["dwi_affine"]), dict(
source=bundles, bundles=list(seg_sft.bundle_names))
@pimms.calc("profiles")
@as_file('_desc-profiles_dwi.csv', include_track=True, include_seg=True)
[docs]def tract_profiles(bundles,
scalar_dict, data_imap,
profile_weights="gauss",
n_points_profile=100):
"""
full path to a CSV file containing tract profiles
Parameters
----------
profile_weights : str, 1D array, 2D array callable, optional
How to weight each streamline (1D) or each node (2D)
when calculating the tract-profiles. If callable, this is a
function that calculates weights. If None, no weighting will
be applied. If "gauss", gaussian weights will be used.
If "median", the median of values at each node will be used
instead of a mean or weighted mean.
Default: "gauss"
n_points_profile : int, optional
Number of points to resample each streamline to before
calculating the tract-profiles.
Default: 100
"""
if not (profile_weights is None
or isinstance(profile_weights, str)
or callable(profile_weights)
or hasattr(profile_weights, "__len__")):
raise TypeError(
"profile_weights must be string, None, callable, or"
+ "a 1D or 2D array")
if isinstance(profile_weights, str):
profile_weights = profile_weights.lower()
if isinstance(profile_weights, str) and\
profile_weights != "gauss" and profile_weights != "median":
raise TypeError(
"if profile_weights is a string,"
+ " it must be 'gauss' or 'median'")
bundle_names = []
node_numbers = []
profiles = np.empty((len(scalar_dict), 0)).tolist()
this_profile = np.zeros((len(scalar_dict), n_points_profile))
reference = nib.load(scalar_dict[list(scalar_dict.keys())[0]])
seg_sft = aus.SegmentedSFT.fromfile(
bundles,
reference=reference)
seg_sft.sft.to_rasmm()
for bundle_name in seg_sft.bundle_names:
this_sl = seg_sft.get_bundle(bundle_name).streamlines
if len(this_sl) == 0:
continue
if profile_weights == "gauss":
# calculate only once per bundle
bundle_profile_weights = gaussian_weights(
this_sl,
n_points=n_points_profile)
for ii, (scalar, scalar_file) in enumerate(scalar_dict.items()):
if isinstance(scalar_file, str):
scalar_file = nib.load(scalar_file)
scalar_data = scalar_file.get_fdata()
if isinstance(profile_weights, str):
if profile_weights == "gauss":
this_prof_weights = bundle_profile_weights
elif profile_weights == "median":
# weights bundle to only return the mean
def _median_weight(bundle):
fgarray = set_number_of_points(
bundle, n_points_profile)
values = np.array(
values_from_volume(
scalar_data,
fgarray,
data_imap["dwi_affine"]))
weights = np.zeros(values.shape)
for ii, jj in enumerate(
np.argsort(values, axis=0)[
len(values) // 2, :]):
weights[jj, ii] = 1
return weights
this_prof_weights = _median_weight
else:
this_prof_weights = profile_weights
this_profile[ii] = afq_profile(
scalar_data,
this_sl,
data_imap["dwi_affine"],
weights=this_prof_weights,
n_points=n_points_profile)
profiles[ii].extend(list(this_profile[ii]))
nodes = list(np.arange(this_profile[0].shape[0]))
bundle_names.extend([bundle_name] * len(nodes))
node_numbers.extend(nodes)
profile_dict = dict()
profile_dict["tractID"] = bundle_names
profile_dict["nodeID"] = node_numbers
for ii, scalar in enumerate(scalar_dict.keys()):
profile_dict[scalar] = profiles[ii]
profile_dframe = pd.DataFrame(profile_dict)
meta = dict(source=bundles,
parameters=get_default_args(afq_profile),
scalars=list(scalar_dict.keys()),
bundles=list(seg_sft.bundle_names))
return profile_dframe, meta
@pimms.calc("scalar_dict")
[docs]def get_scalar_dict(data_imap, mapping_imap, scalars=["dti_fa", "dti_md"]):
"""
dicionary mapping scalar names
to their respective file paths
Parameters
----------
scalars : list of strings and/or scalar definitions, optional
List of scalars to use.
Can be any of: "dti_fa", "dti_md", "dki_fa", "dki_md", "dki_awf",
"dki_mk". Can also be a scalar from AFQ.definitions.image.
Default: ["dti_fa", "dti_md"]
"""
# Note: some scalars preprocessing done in plans, before this step
scalar_dict = {}
for scalar in scalars:
if isinstance(scalar, str):
sc = scalar.lower()
scalar_dict[sc] = data_imap[f"{sc}"]
elif f"{scalar.get_name()}" in mapping_imap:
scalar_dict[scalar.get_name()] = mapping_imap[
f"{scalar.get_name()}"]
return {"scalar_dict": scalar_dict}
[docs]def get_segmentation_plan(kwargs):
if "segmentation_params" in kwargs\
and not isinstance(kwargs["segmentation_params"], dict):
raise TypeError(
"segmentation_params a dict")
segmentation_tasks = with_name([
get_scalar_dict,
export_sl_counts,
export_bundle_lengths,
export_bundles,
export_density_maps,
segment,
tract_profiles])
default_seg_params = get_default_args(recognize)
if "segmentation_params" in kwargs:
for k in kwargs["segmentation_params"]:
default_seg_params[k] = kwargs["segmentation_params"][k]
kwargs["segmentation_params"] = default_seg_params
return pimms.plan(**segmentation_tasks)
