fixed input/output types of ants interfaces

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

@@ -5,11 +5,11 @@
 #
 # Docs
 # ----
-# 
+#
 #     A replacement for nipype.interfaces.ants.resampling.ApplyTransforms that
 #     fixes the resampled image header to match the xform of the reference
 #     image
-#     
+#
 task_name: FixHeaderApplyTransforms
 nipype_name: FixHeaderApplyTransforms
 nipype_module: niworkflows.interfaces.fixes
@@ -19,14 +19,14 @@ inputs:
   rename:
   # dict[str, str] - fields to rename in the Pydra interface
   types:
-  # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
-  # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
-  # 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.
-    input_image: generic/file
+    # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
+    # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
+    # 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.
+    input_image: medimage/itk-image+list-of,medimage/itk-image
     # type=file|default=<undefined>: image to apply transformation to (generally a coregistered functional)
-    reference_image: generic/file
+    reference_image: medimage/itk-image+list-of,medimage/itk-image
     # type=file|default=<undefined>: reference image space that you wish to warp INTO
   callable_defaults:
   # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py`
@@ -39,74 +39,74 @@ outputs:
   rename:
   # dict[str, str] - fields to rename in the Pydra interface
   types:
-  # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
-  # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
-  # 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.
-    output_image: generic/file
+    # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
+    # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
+    # 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.
+    output_image: medimage/itk-image+list-of,medimage/itk-image
     # type=file: Warped image
     # type=str|default='': output file name
   callables:
   # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py`
   # to set to the `callable` attribute of output fields
   templates:
-  # dict[str, str] - `output_file_template` values to be provided to output fields
+    # dict[str, str] - `output_file_template` values to be provided to output fields
     output_image: output_image
     # type=file: Warped image
     # type=str|default='': output file name
   requirements:
   # dict[str, list[str]] - input fields that are required to be provided for the output field to be present
 tests:
-- inputs:
-  # dict[str, str] - values to provide to inputs fields in the task initialisation
-  # (if not specified, will try to choose a sensible value)
-    transforms:
-    # type=inputmultiobject|default=[]: transform files: will be applied in reverse order. For example, the last specified transform will be applied first.
-    dimension:
-    # type=enum|default=2|allowed[2,3,4]: This option forces the image to be treated as a specified-dimensional image. If not specified, antsWarp tries to infer the dimensionality from the input image.
-    input_image_type:
-    # type=enum|default=0|allowed[0,1,2,3]: Option specifying the input image type of scalar (default), vector, tensor, or time series.
-    input_image:
-    # type=file|default=<undefined>: image to apply transformation to (generally a coregistered functional)
-    output_image:
-    # type=file: Warped image
-    # type=str|default='': output file name
-    out_postfix:
-    # type=str|default='_trans': Postfix that is appended to all output files (default = _trans)
-    reference_image:
-    # type=file|default=<undefined>: reference image space that you wish to warp INTO
-    interpolation:
-    # type=enum|default='Linear'|allowed['BSpline','CosineWindowedSinc','Gaussian','HammingWindowedSinc','LanczosWindowedSinc','Linear','MultiLabel','NearestNeighbor','WelchWindowedSinc']: 
-    interpolation_parameters:
-    # type=traitcompound|default=None: 
-    invert_transform_flags:
-    # type=inputmultiobject|default=[]: 
-    default_value:
-    # type=float|default=0.0: 
-    print_out_composite_warp_file:
-    # type=bool|default=False: output a composite warp file instead of a transformed image
-    float:
-    # type=bool|default=False: Use float instead of double for computations.
-    num_threads:
-    # type=int|default=1: Number of ITK threads to use
-    args:
-    # type=str|default='': Additional parameters to the command
-    environ:
-    # type=dict|default={}: Environment variables
-  imports:
-  # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item
-  # consisting of 'module', 'name', and optionally 'alias' keys
-  expected_outputs:
-  # dict[str, str] - expected values for selected outputs, noting that tests will typically
-  # be terminated before they complete for time-saving reasons, and therefore
-  # these values will be ignored, when running in CI
-  timeout: 10
-  # int - the value to set for the timeout in the generated test, 
-  # after which the test will be considered to have been initialised 
-  # successfully. Set to 0 to disable the timeout (warning, this could
-  # lead to the unittests taking a very long time to complete)
-  xfail: true
-  # bool - whether the unittest is expected to fail or not. Set to false
-  # when you are satisfied with the edits you have made to this file
+  - inputs:
+      # dict[str, str] - values to provide to inputs fields in the task initialisation
+      # (if not specified, will try to choose a sensible value)
+      transforms:
+      # type=inputmultiobject|default=[]: transform files: will be applied in reverse order. For example, the last specified transform will be applied first.
+      dimension:
+      # type=enum|default=2|allowed[2,3,4]: This option forces the image to be treated as a specified-dimensional image. If not specified, antsWarp tries to infer the dimensionality from the input image.
+      input_image_type:
+      # type=enum|default=0|allowed[0,1,2,3]: Option specifying the input image type of scalar (default), vector, tensor, or time series.
+      input_image:
+      # type=file|default=<undefined>: image to apply transformation to (generally a coregistered functional)
+      output_image:
+      # type=file: Warped image
+      # type=str|default='': output file name
+      out_postfix:
+      # type=str|default='_trans': Postfix that is appended to all output files (default = _trans)
+      reference_image:
+      # type=file|default=<undefined>: reference image space that you wish to warp INTO
+      interpolation:
+      # type=enum|default='Linear'|allowed['BSpline','CosineWindowedSinc','Gaussian','HammingWindowedSinc','LanczosWindowedSinc','Linear','MultiLabel','NearestNeighbor','WelchWindowedSinc']:
+      interpolation_parameters:
+      # type=traitcompound|default=None:
+      invert_transform_flags:
+      # type=inputmultiobject|default=[]:
+      default_value:
+      # type=float|default=0.0:
+      print_out_composite_warp_file:
+      # type=bool|default=False: output a composite warp file instead of a transformed image
+      float:
+      # type=bool|default=False: Use float instead of double for computations.
+      num_threads:
+      # type=int|default=1: Number of ITK threads to use
+      args:
+      # type=str|default='': Additional parameters to the command
+      environ:
+      # type=dict|default={}: Environment variables
+    imports:
+    # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item
+    # consisting of 'module', 'name', and optionally 'alias' keys
+    expected_outputs:
+    # dict[str, str] - expected values for selected outputs, noting that tests will typically
+    # be terminated before they complete for time-saving reasons, and therefore
+    # these values will be ignored, when running in CI
+    timeout: 10
+    # int - the value to set for the timeout in the generated test,
+    # after which the test will be considered to have been initialised
+    # successfully. Set to 0 to disable the timeout (warning, this could
+    # lead to the unittests taking a very long time to complete)
+    xfail: true
+    # bool - whether the unittest is expected to fail or not. Set to false
+    # when you are satisfied with the edits you have made to this file
 doctests: []

nipype-auto-conv/specs/interfaces/fix_n4_bias_field_correction.yaml

@@ -15,20 +15,20 @@ inputs:
   rename:
   # dict[str, str] - fields to rename in the Pydra interface
   types:
-  # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
-  # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
-  # 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.
+    # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
+    # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
+    # 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.
     bias_image: Path
     # type=file: Estimated bias
     # type=file|default=<undefined>: Filename for the estimated bias.
-    input_image: generic/file
+    input_image: medimage/itk-image+list-of,medimage/itk-image
     # type=file|default=<undefined>: input for bias correction. Negative values or values close to zero should be processed prior to correction
-    mask_image: generic/file
+    mask_image: medimage/itk-image+list-of,medimage/itk-image
     # type=file|default=<undefined>: image to specify region to perform final bias correction in
-    weight_image: generic/file
-    # type=file|default=<undefined>: image for relative weighting (e.g. probability map of the white matter) of voxels during the B-spline fitting. 
+    weight_image: medimage/itk-image+list-of,medimage/itk-image
+    # type=file|default=<undefined>: image for relative weighting (e.g. probability map of the white matter) of voxels during the B-spline fitting.
   callable_defaults:
   # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py`
   # to set as the `default` method of input fields
@@ -40,81 +40,81 @@ outputs:
   rename:
   # dict[str, str] - fields to rename in the Pydra interface
   types:
-  # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
-  # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
-  # 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.
-    bias_image: generic/file
+    # dict[str, type] - override inferred types (use "mime-like" string for file-format types,
+    # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred
+    # 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.
+    bias_image: medimage/itk-image+list-of,medimage/itk-image
     # type=file: Estimated bias
     # type=file|default=<undefined>: Filename for the estimated bias.
-    output_image: generic/file
+    output_image: medimage/itk-image+list-of,medimage/itk-image
     # type=file: Warped image
     # type=str|default='': output file name
   callables:
-  # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py`
-  # to set to the `callable` attribute of output fields
+    # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py`
+    # to set to the `callable` attribute of output fields
     negative_values: negative_values_callable
     # type=bool: Indicates whether the input was corrected for nonpositive values by adding a constant offset.
   templates:
   # dict[str, str] - `output_file_template` values to be provided to output fields
   requirements:
   # dict[str, list[str]] - input fields that are required to be provided for the output field to be present
 tests:
-- inputs:
-  # dict[str, str] - values to provide to inputs fields in the task initialisation
-  # (if not specified, will try to choose a sensible value)
-    dimension:
-    # type=enum|default=3|allowed[2,3,4]: image dimension (2, 3 or 4)
-    input_image:
-    # type=file|default=<undefined>: input for bias correction. Negative values or values close to zero should be processed prior to correction
-    mask_image:
-    # type=file|default=<undefined>: image to specify region to perform final bias correction in
-    weight_image:
-    # type=file|default=<undefined>: image for relative weighting (e.g. probability map of the white matter) of voxels during the B-spline fitting. 
-    output_image:
-    # type=file: Warped image
-    # type=str|default='': output file name
-    bspline_fitting_distance:
-    # type=float|default=0.0: 
-    bspline_order:
-    # type=int|default=0: 
-    shrink_factor:
-    # type=int|default=0: 
-    n_iterations:
-    # type=list|default=[]: 
-    convergence_threshold:
-    # type=float|default=0.0: 
-    save_bias:
-    # type=bool|default=False: True if the estimated bias should be saved to file.
-    bias_image:
-    # type=file: Estimated bias
-    # type=file|default=<undefined>: Filename for the estimated bias.
-    copy_header:
-    # type=bool|default=False: copy headers of the original image into the output (corrected) file
-    rescale_intensities:
-    # type=bool|default=False: [NOTE: Only ANTs>=2.1.0] At each iteration, a new intensity mapping is calculated and applied but there is nothing which constrains the new intensity range to be within certain values. The result is that the range can "drift" from the original at each iteration. This option rescales to the [min,max] range of the original image intensities within the user-specified mask.
-    histogram_sharpening:
-    # type=tuple|default=(0.15, 0.01, 200): Three-values tuple of histogram sharpening parameters (FWHM, wienerNose, numberOfHistogramBins). These options describe the histogram sharpening parameters, i.e. the deconvolution step parameters described in the original N3 algorithm. The default values have been shown to work fairly well.
-    num_threads:
-    # type=int|default=1: Number of ITK threads to use
-    args:
-    # type=str|default='': Additional parameters to the command
-    environ:
-    # type=dict|default={}: Environment variables
-  imports:
-  # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item
-  # consisting of 'module', 'name', and optionally 'alias' keys
-  expected_outputs:
-  # dict[str, str] - expected values for selected outputs, noting that tests will typically
-  # be terminated before they complete for time-saving reasons, and therefore
-  # these values will be ignored, when running in CI
-  timeout: 10
-  # int - the value to set for the timeout in the generated test, 
-  # after which the test will be considered to have been initialised 
-  # successfully. Set to 0 to disable the timeout (warning, this could
-  # lead to the unittests taking a very long time to complete)
-  xfail: true
-  # bool - whether the unittest is expected to fail or not. Set to false
-  # when you are satisfied with the edits you have made to this file
+  - inputs:
+      # dict[str, str] - values to provide to inputs fields in the task initialisation
+      # (if not specified, will try to choose a sensible value)
+      dimension:
+      # type=enum|default=3|allowed[2,3,4]: image dimension (2, 3 or 4)
+      input_image:
+      # type=file|default=<undefined>: input for bias correction. Negative values or values close to zero should be processed prior to correction
+      mask_image:
+      # type=file|default=<undefined>: image to specify region to perform final bias correction in
+      weight_image:
+      # type=file|default=<undefined>: image for relative weighting (e.g. probability map of the white matter) of voxels during the B-spline fitting.
+      output_image:
+      # type=file: Warped image
+      # type=str|default='': output file name
+      bspline_fitting_distance:
+      # type=float|default=0.0:
+      bspline_order:
+      # type=int|default=0:
+      shrink_factor:
+      # type=int|default=0:
+      n_iterations:
+      # type=list|default=[]:
+      convergence_threshold:
+      # type=float|default=0.0:
+      save_bias:
+      # type=bool|default=False: True if the estimated bias should be saved to file.
+      bias_image:
+      # type=file: Estimated bias
+      # type=file|default=<undefined>: Filename for the estimated bias.
+      copy_header:
+      # type=bool|default=False: copy headers of the original image into the output (corrected) file
+      rescale_intensities:
+      # type=bool|default=False: [NOTE: Only ANTs>=2.1.0] At each iteration, a new intensity mapping is calculated and applied but there is nothing which constrains the new intensity range to be within certain values. The result is that the range can "drift" from the original at each iteration. This option rescales to the [min,max] range of the original image intensities within the user-specified mask.
+      histogram_sharpening:
+      # type=tuple|default=(0.15, 0.01, 200): Three-values tuple of histogram sharpening parameters (FWHM, wienerNose, numberOfHistogramBins). These options describe the histogram sharpening parameters, i.e. the deconvolution step parameters described in the original N3 algorithm. The default values have been shown to work fairly well.
+      num_threads:
+      # type=int|default=1: Number of ITK threads to use
+      args:
+      # type=str|default='': Additional parameters to the command
+      environ:
+      # type=dict|default={}: Environment variables
+    imports:
+    # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item
+    # consisting of 'module', 'name', and optionally 'alias' keys
+    expected_outputs:
+    # dict[str, str] - expected values for selected outputs, noting that tests will typically
+    # be terminated before they complete for time-saving reasons, and therefore
+    # these values will be ignored, when running in CI
+    timeout: 10
+    # int - the value to set for the timeout in the generated test,
+    # after which the test will be considered to have been initialised
+    # successfully. Set to 0 to disable the timeout (warning, this could
+    # lead to the unittests taking a very long time to complete)
+    xfail: true
+    # bool - whether the unittest is expected to fail or not. Set to false
+    # when you are satisfied with the edits you have made to this file
 doctests: []