ENH: Enable smoothing at any analysis level

Dockerfile

@@ -1,10 +1,10 @@
 # Generated by Neurodocker version 0.4.3-6-g12a2b6f
 # Timestamp: 2019-02-28 22:10:25 UTC
-# 
+#
 # Thank you for using Neurodocker. If you discover any issues
 # or ways to improve this software, please submit an issue or
 # pull request on our GitHub repository:
-# 
+#
 #     https://github.com/kaczmarj/neurodocker
 
 FROM neurodebian@sha256:5fbbad8c68525b588a459092254094436aae9dc1f3920f8d871a03053b10377c
@@ -100,7 +100,7 @@ RUN bash -c "source activate neuro \
     && sync
 
 RUN bash -c "source activate neuro \
-    &&   pip install --no-cache-dir -e \
+    &&   pip install --no-use-pep517 --no-cache-dir -e \
              '/src/fitlins[all]'" \
     && rm -rf ~/.cache/pip/* \
     && sync \

fitlins/cli/run.py

@@ -96,10 +96,11 @@ def get_parser():
                         help="use BOLD files with the provided description label")
 
     g_prep = parser.add_argument_group('Options for preprocessing BOLD series')
-    g_prep.add_argument('-s', '--smoothing', action='store', metavar="TYPE:FWHM",
+    g_prep.add_argument('-s', '--smoothing', action='store', metavar="TYPE:FWHM:LEVEL",
                         help="Smooth BOLD series with FWHM mm kernel prior to fitting. "
                              "Valid types: iso (isotropic); "
-                             "e.g. `--smothing iso:5` will use an isotropic 5mm FWHM kernel")
+                             "e.g. `--smoothing iso:5:1` will use an isotropic 5mm"
+                             "FWHM at the first level.")
 
     g_perfm = parser.add_argument_group('Options to handle performance')
     g_perfm.add_argument('--n-cpus', action='store', default=0, type=int,

fitlins/interfaces/nistats.py

@@ -44,7 +44,6 @@ class FirstLevelModelInputSpec(BaseInterfaceInputSpec):
     smoothing_fwhm = traits.Float(desc='Full-width half max (FWHM) in mm for smoothing in mask')
 
 
-
 class FirstLevelModelOutputSpec(TraitedSpec):
     contrast_maps = traits.List(File)
     contrast_metadata = traits.List(traits.Dict)
@@ -127,6 +126,7 @@ class SecondLevelModelInputSpec(BaseInterfaceInputSpec):
     stat_files = traits.List(traits.List(File(exists=True)), mandatory=True)
     stat_metadata = traits.List(traits.List(traits.Dict), mandatory=True)
     contrast_info = traits.List(traits.Dict, mandatory=True)
+    smoothing_fwhm = traits.Float(desc='Full-width half max (FWHM) in mm for smoothing in mask')
 
 
 class SecondLevelModelOutputSpec(TraitedSpec):
@@ -151,7 +151,11 @@ class SecondLevelModel(NistatsBaseInterface, SimpleInterface):
     output_spec = SecondLevelModelOutputSpec
 
     def _run_interface(self, runtime):
-        model = level2.SecondLevelModel()
+        smoothing_fwhm = self.inputs.smoothing_fwhm
+        if not isdefined(smoothing_fwhm):
+            smoothing_fwhm = None
+
+        model = level2.SecondLevelModel(smoothing_fwhm=smoothing_fwhm)
         contrast_maps = []
         contrast_metadata = []
 

fitlins/workflows/base.py

@@ -57,17 +57,18 @@ def init_fitlins_wf(bids_dir, derivatives, out_dir, analysis_level, space,
         name='getter')
 
     if smoothing:
-        smoothing_params = smoothing.split(':', 1)
+        smoothing_params = smoothing.split(':', 2)
         if smoothing_params[0] != 'iso':
             raise ValueError(f"Unknown smoothing type {smoothing_params[0]}")
         smoothing_fwhm = float(smoothing_params[1])
+        smoothing_level = int(smoothing_params[1])
+        if smoothing_level < 0:
+            smoothing_level = len(model_dict['Steps']) - smoothing_level
 
     l1_model = pe.MapNode(
         FirstLevelModel(),
         iterfield=['session_info', 'contrast_info', 'bold_file', 'mask_file'],
         name='l1_model')
-    if smoothing:
-        l1_model.inputs.smoothing_fwhm = smoothing_fwhm
 
     # Set up common patterns
     image_pattern = 'reports/[sub-{subject}/][ses-{session}/]figures/[run-{run}/]' \
@@ -159,6 +160,9 @@ def init_fitlins_wf(bids_dir, derivatives, out_dir, analysis_level, space,
 
         level = 'l{:d}'.format(ix + 1)
 
+        if smoothing and smoothing_level == ix:
+            model.inputs.smoothing_fwhm = smoothing_fwhm
+
         # TODO: No longer used at higher level, suggesting we can simply return
         # entities from loader as a single list
         select_entities = pe.Node(