:py:mod:`AFQ.models.dki` ======================== .. py:module:: AFQ.models.dki Module Contents --------------- Functions ~~~~~~~~~ .. autoapisummary:: AFQ.models.dki.fit_dki AFQ.models.dki.predict .. py:function:: fit_dki(data_files, bval_files, bvec_files, mask=None, min_kurtosis=-1, max_kurtosis=3, out_dir=None, b0_threshold=50) Fit the DKI model, save files with derived maps :Parameters: **data_files** : str or list Files containing DWI data. If this is a str, that's the full path to a single file. If it's a list, each entry is a full path. **bval_files** : str or list Equivalent to `data_files`. **bvec_files** : str or list Equivalent to `data_files`. **mask** : ndarray, optional Binary mask, set to True or 1 in voxels to be processed. Default: Process all voxels. **min_kurtosis** : float, optional The minimal plausible value of kurtosis. Default: -1. **max_kurtosis** : float, optional The maximal plausible value of kurtosis. Default: 3. **out_dir** : str, optional A full path to a directory to store the maps that get computed. Default: maps get stored in the same directory as the last DWI file in `data_files`. **b0_threshold** : float .. :Returns: **file_paths** : a dict with the derived maps that were computed and full-paths .. to the files containing these maps. .. .. !! processed by numpydoc !! .. py:function:: predict(params_file, gtab, S0_file=None, out_dir=None) Create a signal prediction from DKI params params_file : str Full path to a file with parameters saved from a DKI fit gtab : GradientTable object The gradient table to predict for S0_file : str 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. .. !! processed by numpydoc !!