# Source code for dipy.workflows.nn

    from pathlib import Path
    import sys

    import numpy as np

    from dipy.core.gradients import gradient_table
    from dipy.denoise.bias_correction import bias_field_correction
    from dipy.io.gradients import read_bvals_bvecs
    from dipy.io.image import load_nifti, load_nifti_data, save_nifti
    from dipy.nn.deepn4 import DeepN4
    from dipy.nn.evac import EVACPlus
    from dipy.utils.logging import logger
    from dipy.workflows.workflow import Workflow

    class EVACPlusFlow(Workflow):

        @classmethod
        def get_short_name(cls):
            return "evacplus"

        def run(
            self,
            input_files,
            save_masked=False,
            out_dir="",
            out_mask="brain_mask.nii.gz",
            out_masked="brain_masked.nii.gz",
        ):
            """Extract brain using EVAC+.

            See :footcite:p:`Park2024` for further details about EVAC+.

            Parameters
            ----------
            input_files : string or Path
                Path to the input volumes. This path may contain wildcards to
                process multiple inputs at once.
            save_masked : bool, optional
                Save mask.
            out_dir : string or Path, optional
                Output directory.
            out_mask : string, optional
                Name of the mask volume to be saved.
            out_masked : string, optional
                Name of the masked volume to be saved.

            References
            ----------
            .. footbibliography::
            """
            io_it = self.get_io_iterator()
            empty_flag = True

            for fpath, mask_out_path, masked_out_path in io_it:
                logger.info(f"Applying evac+ brain extraction on {fpath}")

                data, affine, img, voxsize = load_nifti(
                    fpath, return_img=True, return_voxsize=True
                )
                evac = EVACPlus()
                mask_volume = evac.predict(data, affine)
                masked_volume = mask_volume * data

                save_nifti(mask_out_path, mask_volume.astype(np.float64), affine)

                logger.info(f"Mask saved as {mask_out_path}")

                if save_masked:
                    save_nifti(masked_out_path, masked_volume, affine, hdr=img.header)

                    logger.info(f"Masked volume saved as {masked_out_path}")
                empty_flag = False
            if empty_flag:
                raise ValueError(
                    "All output paths exists."
                    " If you want to overwrite "
                    "please use the --force option."
                )

            return io_it

    class BiasFieldCorrectionFlow(Workflow):

        @classmethod
        def get_short_name(cls):
            return "bias_field_correction"

        def run(
            self,
            input_files,
            bval=None,
            bvec=None,
            mask=None,
            method="auto",
            threshold=0.5,
            use_cuda=False,
            verbose=False,
            order=3,
            n_control_points=8,
            pyramid_levels="4,2,1",
            n_iter=4,
            lambda_reg=1e-3,
            robust=True,
            gradient_weighting=True,
            zero_background=False,
            out_dir="",
            out_corrected="biasfield_corrected.nii.gz",
            out_bias_field="bias_field.nii.gz",
        ):
            """Correct bias field.

            Parameters
            ----------
            input_files : string or Path
                Path to the input volumes. This path may contain wildcards to
                process multiple inputs at once.
            bval : string or Path, optional
                Path to the b-value file.
            bvec : string or Path, optional
                Path to the b-vector file.
            mask : string or Path, optional
                Path to the brain mask file. If not provided, a mask will be estimated
                from the input volume using Otsu's method for 'poly' and 'bspline' methods.
            method : string, optional
                Bias field correction method. Choose from:
                    - 'n4': DeepN4 bias field correction.
                      See :footcite:p:`Kanakaraj2024` for more details.
                    - 'poly': Legendre polynomial regression bias correction.
                    - 'bspline': Cubic B-spline regression bias correction.
                    - 'auto': Run both poly and bspline, return the one with
                      lower Coefficient of Variation within the brain mask.

                'n4' method is recommended for T1-weighted images. 'poly' and 'bspline'
                methods are recommended for diffusion-weighted images.
            threshold : float, optional
                Threshold for cleaning the final correction field in DeepN4 method.
            use_cuda : bool, optional
                Use CUDA for DeepN4 bias field correction.
            verbose : bool, optional
                Print verbose output.
            order : int, optional
                Maximum Legendre polynomial degree (used with method='poly').
            n_control_points : int, optional
                Control grid size per axis (used with method='bspline').
            pyramid_levels : string, optional
                Comma-separated downsampling factors for coarse-to-fine pyramid,
                e.g. '4,2,1' (used with method='poly' or 'bspline').
            n_iter : int, optional
                Reweighting iterations per pyramid level (poly/bspline methods).
            lambda_reg : float, optional
                Ridge regularization strength (poly/bspline methods).
            robust : bool, optional
                Apply Tukey biweight robust reweighting (poly/bspline methods).
            gradient_weighting : bool, optional
                Apply gradient-based edge suppression (poly/bspline methods).
            zero_background : bool, optional
                If True, set the saved bias field to 1.0 outside the brain mask,
                suppressing extrapolation artifacts in the background
                (poly/bspline methods).
            out_dir : string or Path, optional
                Output directory.
            out_corrected : string, optional
                Name of the corrected volume to be saved.
            out_bias_field : string, optional
                Name of the bias field volume to be saved (poly/bspline methods).

            References
            ----------
            .. footbibliography::

            """
            io_it = self.get_io_iterator()

            if method.lower() not in ["n4", "poly", "bspline", "auto"]:
                logger.error(
                    "Unknown bias field correction method. "
                    "Choose from 'n4', 'poly', 'bspline', 'auto'."
                )
                sys.exit(1)

            prefix = "t1" if method.lower() == "n4" else "dwi"
            for i, name in enumerate(self.flat_outputs):
                if str(name).endswith("biasfield_corrected.nii.gz"):
                    self.flat_outputs[i] = Path(name).parent / Path(
                        "biasfield_corrected.nii.gz"
                    ).with_name(f"{prefix}_biasfield_corrected.nii.gz")

            if io_it:
                self.update_flat_outputs(self.flat_outputs, io_it)
            else:
                if isinstance(self.last_generated_outputs, dict):
                    self.last_generated_outputs = dict(
                        zip(self.last_generated_outputs.keys(), self.flat_outputs)
                    )
                else:
                    self.last_generated_outputs = self.flat_outputs
            for fpath, corrected_out_path, obf in io_it:
                logger.info(f"Applying bias field correction on {fpath}")

                data, affine, img, voxsize = load_nifti(
                    fpath, return_img=True, return_voxsize=True
                )

                corrected_data = None
                if method.lower() == "n4":
                    deepn4_model = DeepN4(verbose=verbose, use_cuda=use_cuda)
                    deepn4_model.fetch_default_weights()
                    corrected_data = deepn4_model.predict(data, affine, threshold=threshold)
                elif method.lower() in ["poly", "bspline", "auto"]:
                    bvals, bvecs = read_bvals_bvecs(bval, bvec)
                    gtab = gradient_table(bvals, bvecs=bvecs)
                    levels = tuple(int(x.strip()) for x in pyramid_levels.split(","))
                    n_ctrl = (int(n_control_points),) * 3
                    mask_arr = load_nifti_data(mask) if mask is not None else None
                    corrected_data, bias = bias_field_correction(
                        data,
                        gtab,
                        mask=mask_arr,
                        method=method.lower(),
                        order=int(order),
                        n_control_points=n_ctrl,
                        pyramid_levels=levels,
                        n_iter=int(n_iter),
                        lambda_reg=float(lambda_reg),
                        robust=bool(robust),
                        gradient_weighting=bool(gradient_weighting),
                        return_bias_field=True,
                        zero_background=bool(zero_background),
                    )
                    save_nifti(str(obf), bias.astype(np.float32), affine)
                    logger.info(f"Bias field saved as {obf}")

                save_nifti(corrected_out_path, corrected_data, affine, hdr=img.header)
                logger.info(f"Corrected volume saved as {corrected_out_path}")

            return io_it