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Merge pull request #949 from arokem/small_doc_fixes
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Small documentation fixes, to improve auto-rendering of API docs.
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@arokem
arokem committed Feb 6, 2023
2 parents dffb3da + 656e3b0 commit afcc50c
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153 changes: 77 additions & 76 deletions AFQ/api/bundle_dict.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -62,88 +62,89 @@ def append_l_r(bundle_list, no_lr_list):


class BundleDict(MutableMapping):
"""
Create a bundle dictionary, needed for the segmentation.
Parameters
----------
bundle_info : list or dict, optional
A list of the bundles to be used, or a dictionary defining
custom bundles. See `Defining Custom Bundle Dictionaries`
in the `usage` section of pyAFQ's documentation for details.
Default: AFQ.api.bundle_dict.BUNDLES
seg_algo: One of {"afq", "reco", "reco16", "reco80"}
The bundle segmentation algorithm to use.
"afq" : Use waypoint ROIs + probability maps, as described
in [Yeatman2012]_
"reco" / "reco16" : Use Recobundles [Garyfallidis2017]_
with a 16-bundle set.
"reco80": Use Recobundles with an 80-bundle set.
resample_to : Nifti1Image or bool, optional
If there are bundles in bundle_info with the 'space' attribute
set to 'template', or with no 'space' attribute,
their images (all ROIs and probability maps)
will be resampled to the affine and shape of this image.
If None, the MNI template will be used.
If False, no resampling will be done.
Default: None
resample_subject_to : Nifti1Image or bool, optional
If there are bundles in bundle_info with the 'space' attribute
set to 'subject', their images (all ROIs and probability maps)
will be resampled to the affine and shape of this image.
If False, no resampling will be done.
Default: None
keep_in_memory : bool, optional
Whether, once loaded, all ROIs and probability maps will stay
loaded in memory within this object. By default, ROIs are loaded
into memory on demand and no references to ROIs are kept, other
than their paths. The default 18 bundles use ~6GB when all loaded.
Default: False
Examples
--------
# import OR ROIs and create a custom bundle dict
# from them
import AFQ.data.fetch as afd
or_rois = afd.read_or_templates()
bundles = BundleDict({
"L_OR": {
"include": [
or_rois["left_OR_1"], # these can be paths to Nifti files
or_rois["left_OR_2"]], # or they can Nifti images
"exclude": [
or_rois["left_OP_MNI"],
or_rois["left_TP_MNI"],
or_rois["left_pos_thal_MNI"]],
"start": or_rois['left_thal_MNI'],
"end": or_rois['left_V1_MNI'],
"cross_midline": False,
},
"R_OR": {
"include": [
or_rois["right_OR_1"],
or_rois["right_OR_2"]],
"exclude": [
or_rois["right_OP_MNI"],
or_rois["right_TP_MNI"],
or_rois["right_pos_thal_MNI"]],
"start": or_rois['right_thal_MNI'],
"end": or_rois['right_V1_MNI'],
"cross_midline": False
}
})
"""

def __init__(self,
bundle_info=BUNDLES,
seg_algo="afq",
resample_to=None,
resample_subject_to=False,
keep_in_memory=False):
"""
Create a bundle dictionary, needed for the segmentation
Parameters
----------
bundle_info : list or dict, optional
A list of the bundles to be used, or a dictionary defining
custom bundles. See `Defining Custom Bundle Dictionaries`
in the `usage` section of pyAFQ's documentation for details.
Default: AFQ.api.bundle_dict.BUNDLES
seg_algo: One of {"afq", "reco", "reco16", "reco80"}
The bundle segmentation algorithm to use.
"afq" : Use waypoint ROIs + probability maps, as described
in [Yeatman2012]_
"reco" / "reco16" : Use Recobundles [Garyfallidis2017]_
with a 16-bundle set.
"reco80": Use Recobundles with an 80-bundle set.
resample_to : Nifti1Image or bool, optional
If there are bundles in bundle_info with the 'space' attribute
set to 'template', or with no 'space' attribute,
their images (all ROIs and probability maps)
will be resampled to the affine and shape of this image.
If None, the MNI template will be used.
If False, no resampling will be done.
Default: None
resample_subject_to : Nifti1Image or bool, optional
If there are bundles in bundle_info with the 'space' attribute
set to 'subject', their images (all ROIs and probability maps)
will be resampled to the affine and shape of this image.
If False, no resampling will be done.
Default: None
keep_in_memory : bool, optional
Whether, once loaded, all ROIs and probability maps will stay
loaded in memory within this object. By default, ROIs are loaded
into memory on demand and no references to ROIs are kept, other
than their paths. The default 18 bundles use ~6GB when all loaded.
Default: False
Examples
--------
# import OR ROIs and create a custom bundle dict
# from them
import AFQ.data.fetch as afd
or_rois = afd.read_or_templates()
bundles = BundleDict({
"L_OR": {
"include": [
or_rois["left_OR_1"], # these can be paths to Nifti files
or_rois["left_OR_2"]], # or they can Nifti images
"exclude": [
or_rois["left_OP_MNI"],
or_rois["left_TP_MNI"],
or_rois["left_pos_thal_MNI"]],
"start": or_rois['left_thal_MNI'],
"end": or_rois['left_V1_MNI'],
"cross_midline": False,
},
"R_OR": {
"include": [
or_rois["right_OR_1"],
or_rois["right_OR_2"]],
"exclude": [
or_rois["right_OP_MNI"],
or_rois["right_TP_MNI"],
or_rois["right_pos_thal_MNI"]],
"start": or_rois['right_thal_MNI'],
"end": or_rois['right_V1_MNI'],
"cross_midline": False
}
})
"""
if not (isinstance(bundle_info, dict)
or isinstance(bundle_info, list)):
raise TypeError((
Expand Down
21 changes: 15 additions & 6 deletions AFQ/models/dti.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ def _fit(gtab, data, mask=None, sigma=None):
def fit_dti(data_files, bval_files, bvec_files, mask=None,
out_dir=None, file_prefix=None, b0_threshold=50):
"""
Fit the DTI model using default settings, save files with derived maps
Fit the DTI model using default settings, save files with derived maps.
Parameters
----------
Expand All @@ -107,11 +107,12 @@ def fit_dti(data_files, bval_files, bvec_files, mask=None,
Returns
-------
file_paths : a dict with the derived maps that were computed and full-paths
to the files containing these maps.
file_paths : dict
A dict with the derived maps that were computed and full-paths
to the files containing these maps.
Note
----
Notes
-----
Maps that are calculated: FA, MD, AD, RD
"""
img, data, gtab, mask = ut.prepare_data(data_files, bval_files,
Expand Down Expand Up @@ -147,8 +148,10 @@ def fit_dti(data_files, bval_files, bvec_files, mask=None,

def predict(params_file, gtab, S0_file=None, out_dir=None):
"""
Create a signal prediction from DTI params
Create a signal prediction from DTI params.
Parameters
----------
params_file : str
Full path to a file with parameters saved from a DKI fit
Expand All @@ -159,6 +162,12 @@ def predict(params_file, gtab, S0_file=None, out_dir=None):
Full path to a nifti file that contains S0 measurements to incorporate
into the prediction. If the file contains 4D data, the volumes that
contain the S0 data must be the same as the gtab.b0s_mask.
Returns
-------
fname : str
The name of the nifti file with saved predictions.
"""
if out_dir is None:
out_dir = op.join(op.split(params_file)[0])
Expand Down

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