manually fixed nipype2pydra converter issue where formatters are assigned as strings

.github/workflows/auto-release.yaml

@@ -33,4 +33,4 @@ jobs:
               "draft": false,
               "prerelease": false
             }' \
-            "https://api.github.com/repos/nipype/pydra-#PACKAGE#/releases"
+            "https://api.github.com/repos/nipype/pydra-tasks-#PACKAGE#/releases"

.github/workflows/ci-cd.yaml

@@ -129,10 +129,10 @@ jobs:
     - name: Install task package
       run: |
         pip install "./related-packages/fileformats[test]" "./related-packages/fileformats-extras[test]"
-        python -c "import fileformats.medimage_afni as m; print(f'{m.__name__} {m.__version__} @ {m.__file__}')"
+        python -c "import fileformats.vendor.afni.medimage as m; print(f'{m.__name__} {m.__version__} @ {m.__file__}')"
     - name: Test fileformats with pytest
       run: >-
-        pytest ./related-packages -sv --cov fileformats.medimage_afni
+        pytest ./related-packages -sv --cov fileformats.vendor.afni.medimage
         --cov fileformats.extras.medimage_afni --cov-report xml .
 
   deploy-fileformats:

docs/conf.py

@@ -17,7 +17,7 @@
 
 # -- Project information -----------------------------------------------------
 
-project = "pydra-afni"
+project = "pydra-tasks-afni"
 copyright = "2020, Xihe Xie"
 author = "Xihe Xie"
 

nipype-auto-conv/requirements.txt

@@ -6,6 +6,6 @@ PyYAML>=6.0
 fileformats >=0.8
 fileformats-medimage >=0.4
 fileformats-datascience >= 0.1
-fileformats-medimage-afni
+fileformats-vendor-afni
 traits
 nipype2pydra

nipype-auto-conv/specs/interfaces/afn_ito_nifti.yaml

@@ -36,7 +36,7 @@ inputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    in_file: fileformats.medimage_afni.ThreeD
+    in_file: fileformats.vendor.afni.medimage.ThreeD
     # type=file|default=<undefined>: input file to 3dAFNItoNIFTI
   callable_defaults:
   # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py`

nipype-auto-conv/specs/interfaces/calc.yaml

@@ -72,7 +72,7 @@ outputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    out_file: fileformats.medimage_afni.All1
+    out_file: fileformats.vendor.afni.medimage.All1
     # type=file: output file
     # type=file|default=<undefined>: output image file name
   callables:

nipype-auto-conv/specs/interfaces/cat.yaml

@@ -56,7 +56,7 @@ outputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    out_file: fileformats.medimage_afni.OneD
+    out_file: fileformats.vendor.afni.medimage.OneD
     # type=file: output file
     # type=file|default='catout.1d': output (concatenated) file name
   callables:

nipype-auto-conv/specs/interfaces/convert_dset.yaml

@@ -54,7 +54,7 @@ outputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    out_file: fileformats.medimage_afni.Dset
+    out_file: fileformats.vendor.afni.medimage.Dset
     # type=file: output file
     # type=file|default=<undefined>: output file for ConvertDset
   callables:

nipype-auto-conv/specs/interfaces/deconvolve.yaml

@@ -75,7 +75,7 @@ outputs:
     # type=file|default=<undefined>: output statistics file
     reml_script: generic/file
     # type=file: automatically generated script to run 3dREMLfit
-    x1D: fileformats.medimage_afni.OneD
+    x1D: fileformats.vendor.afni.medimage.OneD
     # type=file: save out X matrix
     # type=file|default=<undefined>: specify name for saved X matrix
   callables:

nipype-auto-conv/specs/interfaces/eval.yaml

@@ -39,7 +39,7 @@ inputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    in_file_a: fileformats.medimage_afni.OneD
+    in_file_a: fileformats.vendor.afni.medimage.OneD
     # type=file|default=<undefined>: input file to 1deval
     in_file_b: generic/file
     # type=file|default=<undefined>: operand file to 1deval
@@ -63,7 +63,7 @@ outputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    out_file: fileformats.medimage_afni.OneD
+    out_file: fileformats.vendor.afni.medimage.OneD
     # type=file: output file
     # type=file|default=<undefined>: output image file name
   callables:

nipype-auto-conv/specs/interfaces/notes.yaml

@@ -36,7 +36,7 @@ inputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    in_file: fileformats.medimage_afni.Head
+    in_file: fileformats.vendor.afni.medimage.Head
     # type=file|default=<undefined>: input file to 3dNotes
   callable_defaults:
   # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py`

nipype-auto-conv/specs/interfaces/one_d_tool_py.yaml

@@ -31,7 +31,7 @@ inputs:
   # from the nipype interface, but you may want to be more specific, particularly
   # for file types, where specifying the format also specifies the file that will be
   # passed to the field in the automatically generated unittests.
-    in_file: fileformats.medimage_afni.OneD
+    in_file: fileformats.vendor.afni.medimage.OneD
     # type=file|default=<undefined>: input file to OneDTool
     show_cormat_warnings: generic/file
     # type=file|default=<undefined>: Write cormat warnings to a file

nipype-auto-conv/specs/interfaces/remlfit.yaml

@@ -51,7 +51,7 @@ inputs:
     # type=file|default=<undefined>: filename of 3D mask dataset; only data time series from within the mask will be analyzed; results for voxels outside the mask will be set to zero.
     matim: generic/file
     # type=file|default=<undefined>: read a standard file as the matrix. You can use only Col as a name in GLTs with these nonstandard matrix input methods, since the other names come from the 'matrix' file. These mutually exclusive options are ignored if 'matrix' is used.
-    matrix: fileformats.medimage_afni.OneD
+    matrix: fileformats.vendor.afni.medimage.OneD
     # type=file|default=<undefined>: the design matrix file, which should have been output from Deconvolve via the 'x1D' option
     slibase: generic/file+list-of
     # type=inputmultiobject|default=[]: similar to 'addbase' in concept, BUT each specified file must have an integer multiple of the number of slices in the input dataset(s); then, separate regression matrices are generated for each slice, with the first column of the file appended to the matrix for the first slice of the dataset, the second column of the file appended to the matrix for the first slice of the dataset, and so on. Intended to help model physiological noise in FMRI, or other effects you want to regress out that might change significantly in the inter-slice time intervals. This will slow the program down, and make it use a lot more memory (to hold all the matrix stuff).

nipype-auto-conv/specs/interfaces/retroicor.yaml

@@ -58,7 +58,7 @@ inputs:
     # type=file|default=<undefined>: Filename for 1D cardiac phase output
     in_file: medimage/nifti1
     # type=file|default=<undefined>: input file to 3dretroicor
-    resp: fileformats.medimage_afni.OneD
+    resp: fileformats.vendor.afni.medimage.OneD
     # type=file|default=<undefined>: 1D respiratory waveform data for correction
     respphase: generic/file
     # type=file|default=<undefined>: Filename for 1D resp phase output

nipype-auto-conv/specs/interfaces/volreg.yaml

@@ -74,7 +74,7 @@ outputs:
     md1d_file: generic/file
     # type=file: max displacement info file
     # type=file|default=<undefined>: max displacement output file
-    oned_file: fileformats.medimage_afni.OneD
+    oned_file: fileformats.vendor.afni.medimage.OneD
     # type=file: movement parameters info file
     # type=file|default=<undefined>: 1D movement parameters output file
     oned_matrix_save: generic/file

pydra/tasks/afni/__init__.py

@@ -15,7 +15,7 @@
     from ._version import __version__
 except ImportError:
     raise RuntimeError(
-        "pydra-afni has not been properly installed, please run "
+        "pydra-tasks-afni has not been properly installed, please run "
         f"`pip install -e {str(pkg_path)}` to install a development version"
     )
 if "nipype" not in __version__:

pydra/tasks/afni/v25/model/deconvolve.py

@@ -1,7 +1,7 @@
 import attrs
 from fileformats.generic import File
 from fileformats.medimage import Nifti1
-from fileformats.medimage_afni import OneD
+from fileformats.vendor.afni.medimage import OneD
 import logging
 from pydra.tasks.afni.v25.nipype_ports.utils.filemanip import fname_presuffix
 import os
@@ -132,7 +132,7 @@ class Deconvolve(shell.Task["Deconvolve.Outputs"]):
 
     >>> from fileformats.generic import File
     >>> from fileformats.medimage import Nifti1
-    >>> from fileformats.medimage_afni import OneD
+    >>> from fileformats.vendor.afni.medimage import OneD
     >>> from pathlib import Path
     >>> from pydra.tasks.afni.v25.model.deconvolve import Deconvolve
 
@@ -312,7 +312,7 @@ class Deconvolve(shell.Task["Deconvolve.Outputs"]):
     gltsym: list[str] = shell.arg(
         help="general linear tests (i.e., contrasts) using symbolic conventions (e.g., '+Label1 -Label2')",
         position=-2,
-        formatter="gltsym_formatter",
+        formatter=gltsym_formatter,
     )
     glt_label: list[ty.Any] = shell.arg(
         help="general linear test (i.e., contrast) labels",

pydra/tasks/afni/v25/model/remlfit.py

@@ -1,7 +1,7 @@
 import attrs
 from fileformats.generic import File
 from fileformats.medimage import Nifti1
-from fileformats.medimage_afni import OneD
+from fileformats.vendor.afni.medimage import OneD
 import logging
 import os
 from pathlib import Path
@@ -138,7 +138,7 @@ class Remlfit(shell.Task["Remlfit.Outputs"]):
 
     >>> from fileformats.generic import File
     >>> from fileformats.medimage import Nifti1
-    >>> from fileformats.medimage_afni import OneD
+    >>> from fileformats.vendor.afni.medimage import OneD
     >>> from pathlib import Path
     >>> from pydra.tasks.afni.v25.model.remlfit import Remlfit
 

pydra/tasks/afni/v25/model/tests/test_remlfit.py

@@ -1,6 +1,6 @@
 from fileformats.generic import File
 from fileformats.medimage import Nifti1
-from fileformats.medimage_afni import OneD
+from fileformats.vendor.afni.medimage import OneD
 import logging
 from nipype2pydra.testing import PassAfterTimeoutWorker
 from pydra.tasks.afni.v25.model.remlfit import Remlfit

pydra/tasks/afni/v25/preprocess/qwarp.py

@@ -294,7 +294,7 @@ class Qwarp(shell.Task["Qwarp.Outputs"]):
     allineate_opts: str = shell.arg(
         help="add extra options to the 3dAllineate command to be run by 3dQwarp.",
         requires=["allineate"],
-        formatter="allineate_opts_formatter",
+        formatter=allineate_opts_formatter,
     )
     nowarp: bool = shell.arg(help="Do not save the _WARP file.", argstr="-nowarp")
     iwarp: bool = shell.arg(

pydra/tasks/afni/v25/preprocess/qwarp_plus_minus.py

@@ -237,7 +237,7 @@ class QwarpPlusMinus(shell.Task["QwarpPlusMinus.Outputs"]):
     allineate_opts: str = shell.arg(
         help="add extra options to the 3dAllineate command to be run by 3dQwarp.",
         requires=["allineate"],
-        formatter="allineate_opts_formatter",
+        formatter=allineate_opts_formatter,
     )
     nowarp: bool = shell.arg(help="Do not save the _WARP file.", argstr="-nowarp")
     iwarp: bool = shell.arg(

pydra/tasks/afni/v25/preprocess/retroicor.py

@@ -1,7 +1,7 @@
 import attrs
 from fileformats.generic import File
 from fileformats.medimage import Nifti1
-from fileformats.medimage_afni import OneD
+from fileformats.vendor.afni.medimage import OneD
 import logging
 from pathlib import Path
 from pathlib import Path
@@ -35,7 +35,7 @@ class Retroicor(shell.Task["Retroicor.Outputs"]):
 
     >>> from fileformats.generic import File
     >>> from fileformats.medimage import Nifti1
-    >>> from fileformats.medimage_afni import OneD
+    >>> from fileformats.vendor.afni.medimage import OneD
     >>> from pathlib import Path
     >>> from pydra.tasks.afni.v25.preprocess.retroicor import Retroicor
 
@@ -53,7 +53,7 @@ class Retroicor(shell.Task["Retroicor.Outputs"]):
 
     executable = "3dretroicor"
     in_file: Nifti1 = shell.arg(
-        help="input file to 3dretroicor", position=-1, formatter="in_file_formatter"
+        help="input file to 3dretroicor", position=-1, formatter=in_file_formatter
     )
     card: File = shell.arg(
         help="1D cardiac data file for cardiac correction",

pydra/tasks/afni/v25/preprocess/roi_stats.py

@@ -102,7 +102,7 @@ class ROIStats(shell.Task["ROIStats.Outputs"]):
     )
     stat: MultiInputObj = shell.arg(
         help="Statistics to compute. Options include:\n\n * mean       =   Compute the mean using only non_zero voxels.\n                  Implies the opposite for the mean computed\n                  by default.\n * median     =   Compute the median of nonzero voxels\n * mode       =   Compute the mode of nonzero voxels.\n                  (integral valued sets only)\n * minmax     =   Compute the min/max of nonzero voxels\n * sum        =   Compute the sum using only nonzero voxels.\n * voxels     =   Compute the number of nonzero voxels\n * sigma      =   Compute the standard deviation of nonzero\n                  voxels\n\nStatistics that include zero-valued voxels:\n\n * zerominmax =   Compute the min/max of all voxels.\n * zerosigma  =   Compute the standard deviation of all\n                  voxels.\n * zeromedian =   Compute the median of all voxels.\n * zeromode   =   Compute the mode of all voxels.\n * summary    =   Only output a summary line with the grand\n                  mean across all briks in the input dataset.\n                  This option cannot be used with nomeanout.\n\nMore that one option can be specified.",
-        formatter="stat_formatter",
+        formatter=stat_formatter,
     )
 
     class Outputs(shell.Outputs):

pydra/tasks/afni/v25/preprocess/t_corr_map.py

@@ -216,7 +216,7 @@ class TCorrMap(shell.Task["TCorrMap.Outputs"]):
         help="", argstr="-Sexpr {sum_expr[0]} {sum_expr[1]}"
     )
     histogram_bin_numbers: int = shell.arg(help="")
-    histogram: Path = shell.arg(help="", formatter="histogram_formatter")
+    histogram: Path = shell.arg(help="", formatter=histogram_formatter)
     num_threads: int = shell.arg(help="set number of threads", default=1)
     outputtype: ty.Any = shell.arg(help="AFNI output filetype")
     out_file: Path = shell.arg(

pydra/tasks/afni/v25/preprocess/t_shift.py

@@ -140,11 +140,11 @@ class TShift(shell.Task["TShift.Outputs"]):
     )
     tpattern: ty.Any | None = shell.arg(
         help="use specified slice time pattern rather than one in header",
-        formatter="tpattern_formatter",
+        formatter=tpattern_formatter,
     )
     slice_timing: ty.Any | None = shell.arg(
         help="time offsets from the volume acquisition onset for each slice",
-        formatter="slice_timing_formatter",
+        formatter=slice_timing_formatter,
     )
     slice_encoding_direction: ty.Any = shell.arg(
         help="Direction in which slice_timing is specified (default: k). If negative,slice_timing is defined in reverse order, that is, the first entry corresponds to the slice with the largest index, and the final entry corresponds to slice index zero. Only in effect when slice_timing is passed as list, not when it is passed as file.",

pydra/tasks/afni/v25/preprocess/tests/test_retroicor.py

@@ -1,6 +1,6 @@
 from fileformats.generic import File
 from fileformats.medimage import Nifti1
-from fileformats.medimage_afni import OneD
+from fileformats.vendor.afni.medimage import OneD
 import logging
 from nipype2pydra.testing import PassAfterTimeoutWorker
 from pydra.tasks.afni.v25.preprocess.retroicor import Retroicor

pydra/tasks/afni/v25/preprocess/volreg.py

@@ -63,7 +63,7 @@ class Volreg(shell.Task["Volreg.Outputs"]):
     )
     in_weight_volume: ty.Any = shell.arg(
         help="weights for each voxel specified by a file with an optional volume number (defaults to 0)",
-        formatter="in_weight_volume_formatter",
+        formatter=in_weight_volume_formatter,
     )
     basefile: Nifti1 = shell.arg(
         help="base file for registration", argstr="-base {basefile}", position=-6

pydra/tasks/afni/v25/utils/afn_ito_nifti.py

@@ -1,4 +1,4 @@
-from fileformats.medimage_afni import ThreeD
+from fileformats.vendor.afni.medimage import ThreeD
 import logging
 import os
 from pathlib import Path
@@ -20,7 +20,7 @@ class AFNItoNIFTI(shell.Task["AFNItoNIFTI.Outputs"]):
     Examples
     -------
 
-    >>> from fileformats.medimage_afni import ThreeD
+    >>> from fileformats.vendor.afni.medimage import ThreeD
     >>> from pathlib import Path
     >>> from pydra.tasks.afni.v25.utils.afn_ito_nifti import AFNItoNIFTI
 

pydra/tasks/afni/v25/utils/bucket.py

@@ -43,7 +43,7 @@ class Bucket(shell.Task["Bucket.Outputs"]):
     in_file: list[ty.Any] = shell.arg(
         help="List of tuples of input datasets and subbrick selection strings\nas described in more detail in the following afni help string\nInput dataset specified using one of these forms:\n``prefix+view``, ``prefix+view.HEAD``, or ``prefix+view.BRIK``.\nYou can also add a sub-brick selection list after the end of the\ndataset name.  This allows only a subset of the sub-bricks to be\nincluded into the output (by default, all of the input dataset\nis copied into the output).  A sub-brick selection list looks like\none of the following forms::\n\n    fred+orig[5]                     ==> use only sub-brick #5\n    fred+orig[5,9,17]                ==> use #5, #9, and #17\n    fred+orig[5..8]     or [5-8]     ==> use #5, #6, #7, and #8\n    fred+orig[5..13(2)] or [5-13(2)] ==> use #5, #7, #9, #11, and #13\n\nSub-brick indexes start at 0.  You can use the character '$'\nto indicate the last sub-brick in a dataset; for example, you\ncan select every third sub-brick by using the selection list\n``fred+orig[0..$(3)]``\nN.B.: The sub-bricks are output in the order specified, which may\nnot be the order in the original datasets.  For example, using\n``fred+orig[0..$(2),1..$(2)]``\nwill cause the sub-bricks in fred+orig to be output into the\nnew dataset in an interleaved fashion. Using ``fred+orig[$..0]``\nwill reverse the order of the sub-bricks in the output.\nN.B.: Bucket datasets have multiple sub-bricks, but do NOT have\na time dimension.  You can input sub-bricks from a 3D+time dataset\ninto a bucket dataset.  You can use the '3dinfo' program to see\nhow many sub-bricks a 3D+time or a bucket dataset contains.\nN.B.: In non-bucket functional datasets (like the 'fico' datasets\noutput by FIM, or the 'fitt' datasets output by 3dttest), sub-brick\n``[0]`` is the 'intensity' and sub-brick [1] is the statistical parameter\nused as a threshold.  Thus, to create a bucket dataset using the\nintensity from dataset A and the threshold from dataset B, and\ncalling the output dataset C, you would type::\n\n    3dbucket -prefix C -fbuc 'A+orig[0]' -fbuc 'B+orig[1]\n\n",
         position=-1,
-        formatter="in_file_formatter",
+        formatter=in_file_formatter,
     )
     num_threads: int = shell.arg(help="set number of threads", default=1)
     outputtype: ty.Any = shell.arg(help="AFNI output filetype")

pydra/tasks/afni/v25/utils/calc.py

@@ -66,7 +66,7 @@ class Calc(shell.Task["Calc.Outputs"]):
 
     executable = "3dcalc"
     in_file_a: Nifti1 = shell.arg(
-        help="input file to 3dcalc", formatter="in_file_a_formatter", position=1
+        help="input file to 3dcalc", formatter=in_file_a_formatter, position=1
     )
     in_file_b: File = shell.arg(
         help="operand file to 3dcalc", argstr="-b {in_file_b}", position=2

pydra/tasks/afni/v25/utils/cat.py

@@ -1,6 +1,6 @@
 import attrs
 from fileformats.generic import File
-from fileformats.medimage_afni import OneD
+from fileformats.vendor.afni.medimage import OneD
 import logging
 from pydra.tasks.afni.v25.nipype_ports.utils.filemanip import split_filename
 from pathlib import Path
@@ -44,7 +44,7 @@ class Cat(shell.Task["Cat.Outputs"]):
     -------
 
     >>> from fileformats.generic import File
-    >>> from fileformats.medimage_afni import OneD
+    >>> from fileformats.vendor.afni.medimage import OneD
     >>> from pathlib import Path
     >>> from pydra.tasks.afni.v25.utils.cat import Cat