Merge in new changes to AntPost branch

.github/workflows/python-testing.yml

@@ -35,7 +35,7 @@ jobs:
     needs: ["quality"]
     strategy:
       matrix:
-        python-version: ["3.7", "3.8", "3.9"]
+        python-version: ["3.8", "3.9"]
 
     steps:
       - name: Install non-python dependencies

Dockerfile

@@ -1,4 +1,4 @@
-FROM khanlab/autotop_deps:v0.4.4-fix
+FROM khanlab/hippunfold_deps:v0.5.1
 
 MAINTAINER [email protected]
 

docs/contributing/contributing.md

@@ -75,6 +75,10 @@ from the `hippunfold` folder that contains the
     cd hippunfold
     snakemake -np
 
+## Wet-run testing your workflow:
+
+Opensource data has been made available for wet-run testing any changes to HippUnfold on OSF [here](https://osf.io/k2nme/). These are real opensource data meant ot span a wide array of HippUnfold use cases with various modalities, resolutions, developmental stages, and species. Please note that some changes to hippUnfold will impact all worflows and should be run and then visually inspected for all these cases. Other changes may impact only one workflow (e.g. adding a new template) and so the recommended run parameters should be adjusted accordingly. 
+
 ## Instructions for Compute Canada
 
 This section provides an example of how to set up a `pip installed` copy

hippunfold/config/snakebids.yml

@@ -128,7 +128,7 @@ parse_args:
   --version:
     help: 'Print the version of HippUnfold'
     action: version
-    version: "1.5.0-pre.47"
+    version: "1.5.2-pre.2"
 
 
   --modality:
@@ -384,21 +384,25 @@ template_based_segmentation:
     hemi:
       - R
       - L
-  CIVM:
+  dHCP:
     hemi:
       - R
       - L
+
   MBMv2:
     hemi:
       - R
   MBMv3:
     hemi:
       - R
-  ABAv3:
+  CIVM:
     hemi:
       - R
       - L
-  dHCP:
+  upenn:
+    hemi:
+      - R
+  ABAv3:
     hemi:
       - R
       - L

hippunfold/pipeline_description.json

@@ -5,7 +5,7 @@
     "GeneratedBy": [
         {
             "Name": "hippunfold",
-            "Version": "1.5.0-pre.47",
+            "Version": "1.5.2-pre.2",
             "CodeURL": "https://github.com/khanlab/hippunfold",
             "Author": "Jordan DeKraker & Ali Khan",
             "AuthorEmail": "[email protected]"

hippunfold/workflow/rules/common.smk

@@ -1,3 +1,5 @@
+from appdirs import AppDirs
+
 # take mean of all scans if >1, otherwise just copy the one scan
 def get_avg_or_cp_scans_cmd(wildcards, input, output):
     if len(input) > 1:

hippunfold/workflow/rules/gifti.smk

@@ -978,7 +978,7 @@ rule normalize_curvature2:
             space="{space}",
             hemi="{hemi}",
             desc="unnorm",
-            label="hipp",
+            label="{autotop}",
             **config["subj_wildcards"]
         ),
     output:

hippunfold/workflow/rules/nnunet.smk

@@ -1,34 +1,36 @@
 import re
-from appdirs import AppDirs
 
 
 def get_nnunet_input(wildcards):
-    if config["modality"] == "T2w":
-        nii = (
-            bids(
-                root=work,
-                datatype="anat",
-                **config["subj_wildcards"],
-                suffix="T2w.nii.gz",
-                space="corobl",
-                desc="preproc",
-                hemi="{hemi}",
-            ),
-        )
+    T1w_nii = bids(
+        root=work,
+        datatype="anat",
+        **config["subj_wildcards"],
+        suffix="T1w.nii.gz",
+        space="corobl",
+        desc="preproc",
+        hemi="{hemi}",
+    )
+    T2w_nii = bids(
+        root=work,
+        datatype="anat",
+        **config["subj_wildcards"],
+        suffix="T2w.nii.gz",
+        space="corobl",
+        desc="preproc",
+        hemi="{hemi}",
+    )
+    if (config["modality"] == "T1w" or config["modality"] == "T2w") and config[
+        "force_nnunet_model"
+    ] == "T1T2w":
+        return (T1w_nii, T2w_nii)
+
+    elif config["modality"] == "T2w":
+        return T2w_nii
     elif config["modality"] == "T1w":
-        nii = (
-            bids(
-                root=work,
-                datatype="anat",
-                **config["subj_wildcards"],
-                suffix="T1w.nii.gz",
-                space="corobl",
-                desc="preproc",
-                hemi="{hemi}",
-            ),
-        )
+        return T1w_nii
     elif config["modality"] == "hippb500":
-        nii = bids(
+        return bids(
             root=work,
             datatype="dwi",
             hemi="{hemi}",
@@ -38,7 +40,6 @@ def get_nnunet_input(wildcards):
         )
     else:
         raise ValueError("modality not supported for nnunet!")
-    return nii
 
 
 def get_model_tar():
@@ -95,14 +96,27 @@ def parse_trainer_from_tar(wildcards, input):
     return trainer
 
 
+def get_cmd_copy_inputs(wildcards, input):
+    in_img = input.in_img
+    if isinstance(in_img, str):
+        # we have one input image
+        return f"cp {in_img} tempimg/temp_0000.nii.gz"
+    else:
+        cmd = []
+        # we have multiple input images
+        for i, img in enumerate(input.in_img):
+            cmd.append(f"cp {img} tempimg/temp_{i:04d}.nii.gz")
+        return " && ".join(cmd)
+
+
 rule run_inference:
     """ This rule uses either GPU or CPU .
     It also runs in an isolated folder (shadow), with symlinks to inputs in that folder, copying over outputs once complete, so temp files are not retained"""
     input:
         in_img=get_nnunet_input,
         model_tar=get_model_tar(),
     params:
-        temp_img="tempimg/temp_0000.nii.gz",
+        cmd_copy_inputs=get_cmd_copy_inputs,
         temp_lbl="templbl/temp.nii.gz",
         model_dir="tempmodel",
         in_folder="tempimg",
@@ -149,7 +163,7 @@ rule run_inference:
         # run inference
         #copy from temp output folder to final output
         "mkdir -p {params.model_dir} {params.in_folder} {params.out_folder} && "
-        "cp {input.in_img} {params.temp_img} && "
+        "{params.cmd_copy_inputs} && "
         "tar -xf {input.model_tar} -C {params.model_dir} && "
         "export RESULTS_FOLDER={params.model_dir} && "
         "export nnUNet_n_proc_DA={threads} && "