docs/changes/newsfragments/409.bugfix

@@ -0,0 +1 @@
+Use correct file output suffices for AFNI-based markers by `Synchon Mandal`_

docs/changes/newsfragments/409.enh

@@ -0,0 +1 @@
+Ease asset storage for AFNI-based markers by `Synchon Mandal`_

junifer/markers/falff/_afni_falff.py

@@ -50,7 +50,7 @@ class AFNIALFF(metaclass=Singleton):
     @lru_cache(maxsize=None, typed=True)
     def compute(
         self,
-        data: "Nifti1Image",
+        input_path: Path,
         highpass: float,
         lowpass: float,
         tr: Optional[float],
@@ -59,8 +59,8 @@ class AFNIALFF(metaclass=Singleton):
 
         Parameters
         ----------
-        data : 4D Niimg-like object
-            Images to process.
+        input_path : pathlib.Path
+            Path to the input data.
         highpass : positive float
             Highpass cutoff frequency.
         lowpass : positive float
@@ -82,19 +82,17 @@ class AFNIALFF(metaclass=Singleton):
         """
         logger.debug("Creating cache for ALFF computation via AFNI")
 
-        # Create component-scoped tempdir
-        tempdir = WorkDirManager().get_tempdir(prefix="afni_alff+falff")
-
-        # Save target data to a component-scoped tempfile
-        nifti_in_file_path = tempdir / "input.nii"  # needs to be .nii
-        nib.save(data, nifti_in_file_path)
+        # Create element-scoped tempdir
+        element_tempdir = WorkDirManager().get_element_tempdir(
+            prefix="afni_lff"
+        )
 
         # Set 3dRSFC command
-        alff_falff_out_path_prefix = tempdir / "alff_falff"
+        lff_out_path_prefix = element_tempdir / "output"
         bp_cmd = [
             "3dRSFC",
-            f"-prefix {alff_falff_out_path_prefix.resolve()}",
-            f"-input {nifti_in_file_path.resolve()}",
+            f"-prefix {lff_out_path_prefix.resolve()}",
+            f"-input {input_path.resolve()}",
             f"-band {highpass} {lowpass}",
             "-no_rsfa -nosat -nodetrend",
         ]
@@ -104,49 +102,48 @@ class AFNIALFF(metaclass=Singleton):
         # Call 3dRSFC
         run_ext_cmd(name="3dRSFC", cmd=bp_cmd)
 
-        # Create element-scoped tempdir so that the ALFF and fALFF maps are
-        # available later as nibabel stores file path reference for
-        # loading on computation
-        element_tempdir = WorkDirManager().get_element_tempdir(
-            prefix="afni_alff_falff"
-        )
-
+        # Read header to get output suffix
+        niimg = nib.load(input_path)
+        header = niimg.header
+        sform_code = header.get_sform(coded=True)[1]
+        if sform_code == 4:
+            output_suffix = "tlrc"
+        else:
+            output_suffix = "orig"
+        # Set params suffix
         params_suffix = f"_{highpass}_{lowpass}_{tr}"
 
         # Convert alff afni to nifti
-        alff_afni_to_nifti_out_path = (
-            element_tempdir / f"alff{params_suffix}_output.nii"
+        alff_nifti_out_path = (
+            element_tempdir / f"output_alff{params_suffix}.nii"
         )  # needs to be .nii
         convert_alff_cmd = [
             "3dAFNItoNIFTI",
-            f"-prefix {alff_afni_to_nifti_out_path.resolve()}",
-            f"{alff_falff_out_path_prefix}_ALFF+orig.BRIK",
+            f"-prefix {alff_nifti_out_path.resolve()}",
+            f"{lff_out_path_prefix}_ALFF+{output_suffix}.BRIK",
         ]
         # Call 3dAFNItoNIFTI
         run_ext_cmd(name="3dAFNItoNIFTI", cmd=convert_alff_cmd)
 
         # Convert falff afni to nifti
-        falff_afni_to_nifti_out_path = (
-            element_tempdir / f"falff{params_suffix}_output.nii"
+        falff_nifti_out_path = (
+            element_tempdir / f"output_falff{params_suffix}.nii"
         )  # needs to be .nii
         convert_falff_cmd = [
             "3dAFNItoNIFTI",
-            f"-prefix {falff_afni_to_nifti_out_path.resolve()}",
-            f"{alff_falff_out_path_prefix}_fALFF+orig.BRIK",
+            f"-prefix {falff_nifti_out_path.resolve()}",
+            f"{lff_out_path_prefix}_fALFF+{output_suffix}.BRIK",
         ]
         # Call 3dAFNItoNIFTI
         run_ext_cmd(name="3dAFNItoNIFTI", cmd=convert_falff_cmd)
 
         # Load nifti
-        alff_data = nib.load(alff_afni_to_nifti_out_path)
-        falff_data = nib.load(falff_afni_to_nifti_out_path)
-
-        # Delete tempdir
-        WorkDirManager().delete_tempdir(tempdir)
+        alff_data = nib.load(alff_nifti_out_path)
+        falff_data = nib.load(falff_nifti_out_path)
 
         return (
             alff_data,
             falff_data,
-            alff_afni_to_nifti_out_path,
-            falff_afni_to_nifti_out_path,
-        )  # type: ignore
+            alff_nifti_out_path,
+            falff_nifti_out_path,
+        )

junifer/markers/falff/_junifer_falff.py

@@ -47,7 +47,7 @@ class JuniferALFF(metaclass=Singleton):
     @lru_cache(maxsize=None, typed=True)
     def compute(
         self,
-        data: "Nifti1Image",
+        input_path: Path,
         highpass: float,
         lowpass: float,
         tr: Optional[float],
@@ -56,8 +56,8 @@ class JuniferALFF(metaclass=Singleton):
 
         Parameters
         ----------
-        data : 4D Niimg-like object
-            Images to process.
+        input_path : pathlib.Path
+            Path to the input data.
         highpass : positive float
             Highpass cutoff frequency.
         lowpass : positive float
@@ -80,9 +80,10 @@ class JuniferALFF(metaclass=Singleton):
         logger.debug("Creating cache for ALFF computation via junifer")
 
         # Get scan data
-        niimg_data = data.get_fdata().copy()
+        niimg = nib.load(input_path)
+        niimg_data = niimg.get_fdata().copy()
         if tr is None:
-            tr = float(data.header["pixdim"][4])  # type: ignore
+            tr = float(niimg.header["pixdim"][4])  # type: ignore
             logger.info(f"`tr` not provided, using `tr` from header: {tr}")
 
         # Bandpass the data within the lowpass and highpass cutoff freqs
@@ -120,19 +121,17 @@ class JuniferALFF(metaclass=Singleton):
         # Calculate ALFF
         alff = numerator / np.sqrt(niimg_data.shape[-1])
         alff_data = nimg.new_img_like(
-            ref_niimg=data,
+            ref_niimg=niimg,
             data=alff,
         )
         falff_data = nimg.new_img_like(
-            ref_niimg=data,
+            ref_niimg=niimg,
             data=falff,
         )
 
-        # Create element-scoped tempdir so that the ALFF and fALFF maps are
-        # available later as nibabel stores file path reference for
-        # loading on computation
+        # Create element-scoped tempdir
         element_tempdir = WorkDirManager().get_element_tempdir(
-            prefix="junifer_alff+falff"
+            prefix="junifer_lff"
         )
         output_alff_path = element_tempdir / "output_alff.nii.gz"
         output_falff_path = element_tempdir / "output_falff.nii.gz"

junifer/markers/falff/falff_base.py

@@ -146,7 +146,7 @@ class ALFFBase(BaseMarker):
             estimator = JuniferALFF()
         # Compute ALFF + fALFF
         alff, falff, alff_path, falff_path = estimator.compute(  # type: ignore
-            data=input_data["data"],
+            input_path=input_data["path"],
             highpass=self.highpass,
             lowpass=self.lowpass,
             tr=self.tr,

junifer/markers/falff/tests/test_falff_spheres.py

@@ -69,7 +69,9 @@ def test_ALFFSpheres(caplog: pytest.LogCaptureFixture, tmp_path: Path) -> None:
             # Fit transform marker on data
             output = marker.fit_transform(element_data)
 
-            assert "Creating cache" in caplog.text
+            # Tests for ALFFParcels run before this with the same data and that
+            # should create the cache
+            assert "Calculating ALFF and fALFF" in caplog.text
 
             # Get BOLD output
             assert "BOLD" in output

junifer/markers/reho/_afni_reho.py

@@ -50,7 +50,7 @@ class AFNIReHo(metaclass=Singleton):
     @lru_cache(maxsize=None, typed=True)
     def compute(
         self,
-        data: "Nifti1Image",
+        input_path: Path,
         nneigh: int = 27,
         neigh_rad: Optional[float] = None,
         neigh_x: Optional[float] = None,
@@ -65,8 +65,8 @@ class AFNIReHo(metaclass=Singleton):
 
         Parameters
         ----------
-        data : 4D Niimg-like object
-            Images to process.
+        input_path : pathlib.Path
+            Path to the input data.
         nneigh : {7, 19, 27}, optional
             Number of voxels in the neighbourhood, inclusive. Can be:
 
@@ -128,19 +128,17 @@ class AFNIReHo(metaclass=Singleton):
         """
         logger.debug("Creating cache for ReHo computation via AFNI")
 
-        # Create component-scoped tempdir
-        tempdir = WorkDirManager().get_tempdir(prefix="afni_reho")
-
-        # Save target data to a component-scoped tempfile
-        nifti_in_file_path = tempdir / "input.nii"  # needs to be .nii
-        nib.save(data, nifti_in_file_path)
+        # Create element-scoped tempdir
+        element_tempdir = WorkDirManager().get_element_tempdir(
+            prefix="afni_reho"
+        )
 
         # Set 3dReHo command
-        reho_out_path_prefix = tempdir / "reho"
+        reho_out_path_prefix = element_tempdir / "output"
         reho_cmd = [
             "3dReHo",
             f"-prefix {reho_out_path_prefix.resolve()}",
-            f"-inset {nifti_in_file_path.resolve()}",
+            f"-inset {input_path.resolve()}",
         ]
         # Check ellipsoidal / cuboidal volume arguments
         if neigh_rad:
@@ -164,28 +162,28 @@ class AFNIReHo(metaclass=Singleton):
         # Call 3dReHo
         run_ext_cmd(name="3dReHo", cmd=reho_cmd)
 
-        # Create element-scoped tempdir so that the ReHo map is
-        # available later as nibabel stores file path reference for
-        # loading on computation
-        element_tempdir = WorkDirManager().get_element_tempdir(
-            prefix="afni_reho"
-        )
+        # Read header to get output suffix
+        niimg = nib.load(input_path)
+        header = niimg.header
+        sform_code = header.get_sform(coded=True)[1]
+        if sform_code == 4:
+            output_suffix = "tlrc"
+        else:
+            output_suffix = "orig"
+
         # Convert afni to nifti
-        reho_afni_to_nifti_out_path = (
+        reho_nifti_out_path = (
             element_tempdir / "output.nii"  # needs to be .nii
         )
         convert_cmd = [
             "3dAFNItoNIFTI",
-            f"-prefix {reho_afni_to_nifti_out_path.resolve()}",
-            f"{reho_out_path_prefix}+orig.BRIK",
+            f"-prefix {reho_nifti_out_path.resolve()}",
+            f"{reho_out_path_prefix}+{output_suffix}.BRIK",
         ]
         # Call 3dAFNItoNIFTI
         run_ext_cmd(name="3dAFNItoNIFTI", cmd=convert_cmd)
 
         # Load nifti
-        output_data = nib.load(reho_afni_to_nifti_out_path)
+        output_data = nib.load(reho_nifti_out_path)
 
-        # Delete tempdir
-        WorkDirManager().delete_tempdir(tempdir)
-
-        return output_data, reho_afni_to_nifti_out_path  # type: ignore
+        return output_data, reho_nifti_out_path

junifer/markers/reho/_junifer_reho.py

@@ -48,15 +48,15 @@ class JuniferReHo(metaclass=Singleton):
     @lru_cache(maxsize=None, typed=True)
     def compute(
         self,
-        data: "Nifti1Image",
+        input_path: Path,
         nneigh: int = 27,
     ) -> tuple["Nifti1Image", Path]:
         """Compute ReHo map.
 
         Parameters
         ----------
-        data : 4D Niimg-like object
-            Images to process.
+        input_path : pathlib.Path
+            Path to the input data.
         nneigh : {7, 19, 27, 125}, optional
             Number of voxels in the neighbourhood, inclusive. Can be:
 
@@ -89,7 +89,8 @@ class JuniferReHo(metaclass=Singleton):
         logger.debug("Creating cache for ReHo computation via junifer")
 
         # Get scan data
-        niimg_data = data.get_fdata()
+        niimg = nib.load(input_path)
+        niimg_data = niimg.get_fdata().copy()
         # Get scan dimensions
         n_x, n_y, n_z, _ = niimg_data.shape
 
@@ -119,7 +120,7 @@ class JuniferReHo(metaclass=Singleton):
         # after #299 is merged
         # Calculate whole brain mask
         mni152_whole_brain_mask = nmask.compute_brain_mask(
-            target_img=data,
+            target_img=niimg,
             threshold=0.5,
             mask_type="whole-brain",
         )
@@ -227,7 +228,7 @@ class JuniferReHo(metaclass=Singleton):
 
         # Create new image like target image
         output_data = nimg.new_img_like(
-            ref_niimg=data,
+            ref_niimg=niimg,
             data=reho_map,
             copy_header=False,
         )

junifer/markers/reho/reho_base.py

@@ -125,7 +125,7 @@ class ReHoBase(BaseMarker):
             estimator = JuniferReHo()
         # Compute reho
         reho_map, reho_map_path = estimator.compute(  # type: ignore
-            data=input_data["data"],
+            input_path=input_data["path"],
             **reho_params,
         )