Bugfixes with package generation

.github/workflows/ci-cd.yml

@@ -113,3 +113,40 @@ jobs:
       with:
         user: __token__
         password: ${{ secrets.PYPI_API_TOKEN }}
+
+
+  trigger_post_release:
+    needs: [deploy]
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+          repo:
+          - afni
+          - ants
+          - fsl
+          - freesurfer
+    env:
+      SECRET: ""
+    steps:
+    - name: Get secret for corresponding repo
+      id: get_secret
+      run: |
+        if [ "${{ matrix.repo }}" == "afni" ]; then
+          SECRET="${{ secrets.AFNI_PAT }}"
+        elif [ "${{ matrix.repo }}" == "ants" ]; then
+          SECRET="${{ secrets.ANTS_PAT }}"
+        elif [ "${{ matrix.repo }}" == "fsl" ]; then
+          SECRET="${{ secrets.FSL_PAT }}"
+        elif [ "${{ matrix.repo }}" == "freesurfer" ]; then
+          SECRET="${{ secrets.FREESURFER_PAT }}"
+        fi
+        echo "SECRET=$SECRET" >> $GITHUB_ENV
+
+    - name: Trigger post-release on downstream repos
+      if: github.event_name == 'release' && env.SECRET
+      run: |
+        curl -XPOST -u "${{ env.SECRET }}" -H "Accept: application/vnd.github.everest-preview+json" \
+        "https://api.github.com/repos/nipype/pydra-${{ matrix.repo }}/dispatches" \
+        -d '{"event_type": "create-post-release"}'
+      env:
+        PAT: ${{ env.SECRET }}

conftest.py

@@ -58,7 +58,7 @@ def pytest_internalerror(excinfo):
         raise excinfo.value
 
     def pytest_configure(config):
-        config.option.capture = 'no'  # allow print statements to show up in the console    
+        config.option.capture = "no"  # allow print statements to show up in the console
         config.option.log_cli = True  # show log messages in the console
         config.option.log_level = "INFO"  # set the log level to INFO
 
@@ -70,8 +70,3 @@ def pytest_configure(config):
 @pytest.fixture
 def catch_cli_exceptions():
     return CATCH_CLI_EXCEPTIONS
-
-
-def show_cli_trace(result):
-    "Used in testing to show traceback of CLI output"
-    return "".join(traceback.format_exception(*result.exc_info))

example-specs/task/nipype/afni/center_mass.yaml

@@ -91,7 +91,7 @@ tests:
     automask:
     # type=bool|default=False: Generate the mask automatically
     set_cm:
-    # type=tuple|default=(<traits.trait_types.Float object at 0x1150ec450>, <traits.trait_types.Float object at 0x1150ec550>, <traits.trait_types.Float object at 0x1150ec5d0>): After computing the center of mass, set the origin fields in the header so that the center of mass will be at (x,y,z) in DICOM coords.
+    # type=tuple|default=None: After computing the center of mass, set the origin fields in the header so that the center of mass will be at (x,y,z) in DICOM coords.
     local_ijk:
     # type=bool|default=False: Output values as (i,j,k) in local orientation
     roi_vals:

example-specs/task/nipype/afni/dot.yaml

@@ -85,7 +85,7 @@ tests:
     mask:
     # type=file|default=<undefined>: Use this dataset as a mask
     mrange:
-    # type=tuple|default=(<traits.trait_types.Float object at 0x1150eeb90>, <traits.trait_types.Float object at 0x1150eea90>): Means to further restrict the voxels from 'mset' so thatonly those mask values within this range (inclusive) willbe used.
+    # type=tuple|default=None: Means to further restrict the voxels from 'mset' so thatonly those mask values within this range (inclusive) willbe used.
     demean:
     # type=bool|default=False: Remove the mean from each volume prior to computing the correlation
     docor:

example-specs/task/nipype/afni/one_d_tool_py.yaml

@@ -82,7 +82,7 @@ tests:
     show_censor_count:
     # type=bool|default=False: display the total number of censored TRs  Note : if input is a valid xmat.1D dataset, then the count will come from the header.  Otherwise the input is assumed to be a binary censorfile, and zeros are simply counted.
     censor_motion:
-    # type=tuple|default=(<traits.trait_types.Float object at 0x115116ed0>, <nipype.interfaces.base.traits_extension.File object at 0x115116f90>): Tuple of motion limit and outfile prefix. need to also set set_nruns -r set_run_lengths
+    # type=tuple|default=None: Tuple of motion limit and outfile prefix. need to also set set_nruns -r set_run_lengths
     censor_prev_TR:
     # type=bool|default=False: for each censored TR, also censor previous
     show_trs_uncensored:

example-specs/task/nipype/afni/qwarp.yaml

@@ -193,7 +193,7 @@ tests:
     wball:
     # type=list|default=[]: "``-wball x y z r f`` Enhance automatic weight from '-useweight' by a factor of 1+f\*Gaussian(FWHM=r) centered in the base image at DICOM coordinates (x,y,z) and with radius 'r'. The goal of this option is to try and make the alignment better in a specific part of the brain. Example:  -wball 0 14 6 30 40 to emphasize the thalamic area (in MNI/Talairach space).  * The 'r' parameter must be positive! * The 'f' parameter must be between 1 and 100 (inclusive). * '-wball' does nothing if you input your own weight   with the '-weight' option. * '-wball' does change the binary weight created by   the '-noweight' option. * You can only use '-wball' once in a run of 3dQwarp.  **The effect of '-wball' is not dramatic.** The example above makes the average brain image across a collection of subjects a little sharper in the thalamic area, which might have some small value.  If you care enough about alignment to use '-wball', then you should examine the results from 3dQwarp for each subject, to see if the alignments are good enough for your purposes.
     wmask:
-    # type=tuple|default=(<nipype.interfaces.base.traits_extension.File object at 0x115018350>, <traits.trait_types.Float object at 0x1150183d0>): Similar to '-wball', but here, you provide a dataset 'ws' that indicates where to increase the weight.  * The 'ws' dataset must be on the same 3D grid as the base dataset. * 'ws' is treated as a mask -- it only matters where it   is nonzero -- otherwise, the values inside are not used. * After 'ws' comes the factor 'f' by which to increase the   automatically computed weight.  Where 'ws' is nonzero,   the weighting will be multiplied by (1+f). * As with '-wball', the factor 'f' should be between 1 and 100.  
+    # type=tuple|default=None: Similar to '-wball', but here, you provide a dataset 'ws' that indicates where to increase the weight.  * The 'ws' dataset must be on the same 3D grid as the base dataset. * 'ws' is treated as a mask -- it only matters where it   is nonzero -- otherwise, the values inside are not used. * After 'ws' comes the factor 'f' by which to increase the   automatically computed weight.  Where 'ws' is nonzero,   the weighting will be multiplied by (1+f). * As with '-wball', the factor 'f' should be between 1 and 100.  
     out_weight_file:
     # type=file|default=<undefined>: Write the weight volume to disk as a dataset
     blur:

example-specs/task/nipype/afni/qwarp_plus_minus.yaml

@@ -129,7 +129,7 @@ tests:
     wball:
     # type=list|default=[]: "``-wball x y z r f`` Enhance automatic weight from '-useweight' by a factor of 1+f\*Gaussian(FWHM=r) centered in the base image at DICOM coordinates (x,y,z) and with radius 'r'. The goal of this option is to try and make the alignment better in a specific part of the brain. Example:  -wball 0 14 6 30 40 to emphasize the thalamic area (in MNI/Talairach space).  * The 'r' parameter must be positive! * The 'f' parameter must be between 1 and 100 (inclusive). * '-wball' does nothing if you input your own weight   with the '-weight' option. * '-wball' does change the binary weight created by   the '-noweight' option. * You can only use '-wball' once in a run of 3dQwarp.  **The effect of '-wball' is not dramatic.** The example above makes the average brain image across a collection of subjects a little sharper in the thalamic area, which might have some small value.  If you care enough about alignment to use '-wball', then you should examine the results from 3dQwarp for each subject, to see if the alignments are good enough for your purposes.
     wmask:
-    # type=tuple|default=(<nipype.interfaces.base.traits_extension.File object at 0x115018350>, <traits.trait_types.Float object at 0x1150183d0>): Similar to '-wball', but here, you provide a dataset 'ws' that indicates where to increase the weight.  * The 'ws' dataset must be on the same 3D grid as the base dataset. * 'ws' is treated as a mask -- it only matters where it   is nonzero -- otherwise, the values inside are not used. * After 'ws' comes the factor 'f' by which to increase the   automatically computed weight.  Where 'ws' is nonzero,   the weighting will be multiplied by (1+f). * As with '-wball', the factor 'f' should be between 1 and 100.  
+    # type=tuple|default=None: Similar to '-wball', but here, you provide a dataset 'ws' that indicates where to increase the weight.  * The 'ws' dataset must be on the same 3D grid as the base dataset. * 'ws' is treated as a mask -- it only matters where it   is nonzero -- otherwise, the values inside are not used. * After 'ws' comes the factor 'f' by which to increase the   automatically computed weight.  Where 'ws' is nonzero,   the weighting will be multiplied by (1+f). * As with '-wball', the factor 'f' should be between 1 and 100.  
     out_weight_file:
     # type=file|default=<undefined>: Write the weight volume to disk as a dataset
     blur:

example-specs/task/nipype/afni/t_corr_map.yaml

@@ -164,7 +164,7 @@ tests:
     polort:
     # type=int|default=0: 
     bandpass:
-    # type=tuple|default=(<traits.trait_types.Float object at 0x114ff9c90>, <traits.trait_types.Float object at 0x114ff9f90>): 
+    # type=tuple|default=None: 
     regress_out_timeseries:
     # type=file|default=<undefined>: 
     blur_fwhm: