almost working

hippunfold/workflow/Snakefile

@@ -117,9 +117,9 @@ work = os.path.join(config["root"], "work")
 
 
 include: "rules/common.smk"
+include: "rules/downloads.smk"
 include: "rules/preproc_t1.smk"
 # include rules only as they are needed..
-include: "rules/downloads.smk"
 
 
 if config["modality"] == "segT1w" or config["modality"] == "segT2w":

hippunfold/workflow/rules/downloads.smk

@@ -1,6 +1,6 @@
 rule download_atlas:
     params:
-        url=config["atlas_links_url"][config["atlas"]],
+        url=expand(config["atlas_links_url"][config["atlas"]]),
     output:
         model_zip=os.path.join(download_dir, config["atlas"] + ".zip"),
     container:
@@ -31,52 +31,35 @@ rule download_template_shape:
         "wget https://{params.url} -O {output.model_zip}"
 
 
-def atlas_outs():
-    outs = []
-    for fn in config["atlas_files"][config["atlas"]]:
-        for hemi in ["L", "R"]:
-            outs.append(
-                download_dir
-                + "/"
-                + expand(config["atlas_files"][config["atlas"]][fn], hemi=hemi)[0]
-            )
-    return list(dict.fromkeys(outs))
+def atlas_outs(wildcards):
+    outs = {}
+    for fn in config["atlas_files"][wildcards.atlas]:
+        outs[fn] = download_dir + "/" + expand(config["atlas_files"][wildcards.atlas][fn], hemi=wildcards.hemi)[0]
+    return outs
 
 
-def template_outs():
-    outs = []
+def template_outs(wildcards):
+    outs = {}
     for fn in config["template_files"][config["template"]]:
-        for hemi in ["L", "R"]:
-            for dir in ["AP", "PD", "IO"]:
-                outs.append(
-                    download_dir
-                    + "/"
-                    + expand(
-                        config["template_files"][config["template"]][fn],
-                        hemi=hemi,
-                        dir=dir,
-                    )[0]
-                )
-    return list(dict.fromkeys(outs))
-
-
-def template_shape_outs():
-    outs = []
+        outs[fn] = download_dir + "/" + expand(
+                config["template_files"][config["template"]][fn],
+                hemi=hemi,
+                dir=dir,
+                modality=modality
+                )[0]
+    return outs
+
+
+def template_shape_outs(wildcards):
+    outs = {}
     for fn in config["template_files"][config["inject_template"]]:
-        for hemi in ["L", "R"]:
-            for dir in ["AP", "PD", "IO"]:
-                for autotop in ["hipp", "dentate"]:
-                    outs.append(
-                        download_dir
-                        + "/"
-                        + expand(
-                            config["template_files"][config["inject_template"]][fn],
-                            hemi=hemi,
-                            dir=dir,
-                            autotop=autotop,
-                        )[0]
-                    )
-    return list(dict.fromkeys(outs))
+        outs[fn] = download_dir + "/" + expand(
+                config["template_files"][config["inject_template"]][fn],
+                hemi=wildcards.hemi,
+                dir=wildcards.dir,
+                autotop=wildcards.autotop,
+                )[0]
+    return outs
 
 
 rule unzip_template:
@@ -85,18 +68,18 @@ rule unzip_template:
     params:
         dir=os.path.join(download_dir, config["template"]),
     output:
-        template_outs(),
+        unpack(template_outs),
     shell:
         "unzip {input.model_zip} -d {params.dir}"
 
 
 rule unzip_atlas:
     input:
-        model_zip=os.path.join(download_dir, config["atlas"] + ".zip"),
+        model_zip=os.path.join(download_dir, config[atlas] + ".zip"),
     params:
-        dir=os.path.join(download_dir, config["atlas"]),
+        dir=os.path.join(download_dir, config[atlas]),
     output:
-        atlas_outs(),
+        unpack(atlas_outs),
     shell:
         "unzip {input.model_zip} -d {params.dir}"
 
@@ -107,6 +90,6 @@ rule unzip_template_shape:
     params:
         dir=os.path.join(download_dir, config["inject_template"]),
     output:
-        template_shape_outs(),
+        unpack(template_shape_outs)
     shell:
         "unzip {input.model_zip} -d {params.dir}"

hippunfold/workflow/rules/gifti.smk

@@ -439,6 +439,7 @@ rule calculate_thickness_from_surface1:
 rule metric_to_nii:
     """converts metric .gii files to .nii for use in ANTs"""
     input:
+        expand(rules.unzip_atlas.output),
         metric_gii=bids(
             root=work,
             datatype="surf",
@@ -462,10 +463,6 @@ rule metric_to_nii:
             label="hipp",
             **config["subj_wildcards"]
         ),
-        refflatnii=os.path.join(
-            download_dir,
-            config["atlas_files"]["multihist7"]["label_nii"],
-        ),
     params:
         interp="-nearest-vertex 1",
     output:
@@ -485,13 +482,14 @@ rule metric_to_nii:
     group:
         "subj"
     shell:
-        "wb_command -metric-to-volume-mapping {input.metric_gii} {input.unfoldedsurf} {input.refflatnii} {output.metric_nii} {params.interp}"
+        "wb_command -metric-to-volume-mapping {input.metric_gii} {input.unfoldedsurf} {input.label_nii} {output.metric_nii} {params.interp}"
 
 
 rule unfolded_registration:
     """performs 2D registration in unfolded space as in [reference paper]
     this is done in a shadow directory to get rid of the tmp files generated by ants."""
     input:
+        expand(rules.unzip_atlas.output),
         thickness=bids(
             root=work,
             datatype="anat",
@@ -525,18 +523,6 @@ rule unfolded_registration:
             label="hipp",
             **config["subj_wildcards"]
         ),
-        refthickness=lambda wildcards: os.path.join(
-            download_dir,
-            config["atlas_files"][wildcards.atlas]["thick"],
-        ),
-        refcurvature=lambda wildcards: os.path.join(
-            download_dir,
-            config["atlas_files"][wildcards.atlas]["curv"],
-        ),
-        refgyrification=lambda wildcards: os.path.join(
-            download_dir,
-            config["atlas_files"][wildcards.atlas]["gyr"],
-        ),
     params:
         antsparams="-d 2 -t so",
         outsuffix="tmp",
@@ -589,7 +575,7 @@ rule unfolded_registration:
         mem_mb=16000,
         time=10,
     shell:
-        "antsRegistrationSyNQuick.sh {params.antsparams} -f {input.refthickness} -f {input.refcurvature} -f {input.refgyrification} -m {input.thickness} -m {input.curvature} -m {input.gyrification} -o {params.outsuffix} &> {log} && "
+        "antsRegistrationSyNQuick.sh {params.antsparams} -f {input.thick} -f {input.curv} -f {input.gyr} -m {input.thickness} -m {input.curvature} -m {input.gyrification} -o {params.outsuffix} &> {log} && "
         "cp {params.warpfn} {output.warp} && "
         "cp {params.invwarpfn} {output.invwarp}"
 
@@ -1053,9 +1039,8 @@ rule calculate_thickness_from_surface2:
 rule resample_atlas_to_refvol:
     """this is just done in case the atlas has a different unfolded config than the current run"""
     input:
-        atlas=os.path.join(
-            download_dir, config["atlas_files"][config["atlas"]]["label_nii"]
-        ),
+        expand(rules.unzip_atlas.output),
+        unpack(atlas_outs),
         refvol=bids(
             root=root,
             space="unfold",
@@ -1090,11 +1075,12 @@ rule resample_atlas_to_refvol:
     group:
         "subj"
     shell:
-        "antsApplyTransforms -d 3 -n MultiLabel -i {input.atlas} -r {input.refvol} -o {output.label_nii} -v &> {log}"
+        "antsApplyTransforms -d 3 -n MultiLabel -i {input.label_nii} -r {input.refvol} -o {output.label_nii} -v &> {log}"
 
 
 rule nii_to_label_gii:
     input:
+        expand(rules.unzip_atlas.output),
         label_nii=bids(
             root=work,
             datatype="anat",
@@ -1112,10 +1098,6 @@ rule nii_to_label_gii:
             "unfold_template_hipp",
             "tpl-avg_space-unfold_den-{density}_midthickness.surf.gii",
         ),
-        label_list=lambda wildcards: os.path.join(
-            download_dir,
-            config["atlas_files"][wildcards.atlas]["label_list"],
-        ),
     params:
         structure_type=lambda wildcards: hemi_to_structure[wildcards.hemi],
     output:

hippunfold/workflow/rules/nnunet.smk

@@ -198,28 +198,9 @@ rule unflip_nnunet_nii:
         " {input.unflip_ref} -push FLIPPED -copy-transform -o {output.nnunet_seg} "
 
 
-def get_f3d_ref(wildcards):
-    if config["modality"] == "T2w":
-        nii = (
-            os.path.join(
-                download_dir,
-                config["template_files"][config["template"]]["crop_ref"],
-            ),
-        )
-    elif config["modality"] == "T1w":
-        nii = (
-            os.path.join(
-                download_dir,
-                config["template_files"][config["template"]]["crop_refT1w"],
-            ),
-        )
-    else:
-        raise ValueError("modality not supported for nnunet!")
-    return nii
-
-
 rule qc_nnunet_f3d:
     input:
+        expand(rules.unzip_template.output),
         img=(
             bids(
                 root=work,
@@ -240,7 +221,6 @@ rule qc_nnunet_f3d:
             space="corobl",
             hemi="{hemi}"
         ),
-        ref=get_f3d_ref,
     output:
         cpp=bids(
             root=work,
@@ -283,12 +263,13 @@ rule qc_nnunet_f3d:
     group:
         "subj"
     shell:
-        "reg_f3d -flo {input.img} -ref {input.ref} -res {output.res} -cpp {output.cpp} &> {log} && "
-        "reg_resample -flo {input.seg} -cpp {output.cpp} -ref {input.ref} -res {output.res_mask} -inter 0 &> {log}"
+        "reg_f3d -flo {input.img} -ref {input.crop_ref} -res {output.res} -cpp {output.cpp} &> {log} && "
+        "reg_resample -flo {input.seg} -cpp {output.cpp} -ref {input.crop_ref} -res {output.res_mask} -inter 0 &> {log}"
 
 
 rule qc_nnunet_dice:
     input:
+        expand(rules.unzip_template.output),
         res_mask=bids(
             root=work,
             datatype="anat",
@@ -298,10 +279,6 @@ rule qc_nnunet_dice:
             space="template",
             hemi="{hemi}"
         ),
-        ref=os.path.join(
-            download_dir,
-            config["template_files"][config["template"]]["Mask_crop"],
-        ),
     params:
         hipp_lbls=[1, 2, 7, 8],
     output: