Merge pull request #15 from nipype/develop

Bug fixes

Author: tclose

example-specs/task/nipype/afni/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: medimage-afni/threed
+    in_file: medimage-afni/three-d
     # type=file|default=<undefined>: input file to 3dAFNItoNIFTI
     out_file: Path
     # type=file: output file

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

@@ -38,7 +38,7 @@ 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_files: medimage-afni/oned+list-of
+    in_files: medimage-afni/one-d+list-of
     # type=list|default=[]: 
     out_file: Path
     # type=file: output file
@@ -59,7 +59,7 @@ 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: medimage-afni/oned
+    out_file: medimage-afni/one-d
     # type=file: output file
     # type=file|default='catout.1d': output (concatenated) file name
   callables:

example-specs/task/nipype/afni/cat_matvec.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: medimage-afni/oned
+    out_file: medimage-afni/one-d
     # type=file: output file
     # type=file|default=<undefined>: File to write concattenated matvecs to
   callables:

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 0x107b12910>, <traits.trait_types.Float object at 0x107b12a10>, <traits.trait_types.Float object at 0x107b12a90>): 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=(<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.
     local_ijk:
     # type=bool|default=False: Output values as (i,j,k) in local orientation
     roi_vals:

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

@@ -81,7 +81,7 @@ outputs:
     # type=file|default=<undefined>: output statistics file
     reml_script: generic/file
     # type=file: automatically generated script to run 3dREMLfit
-    x1D: medimage-afni/oned
+    x1D: medimage-afni/one-d
     # type=file: save out X matrix
     # type=file|default=<undefined>: specify name for saved X matrix
   callables:

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 0x107b18fd0>, <traits.trait_types.Float object at 0x107b18ed0>): Means to further restrict the voxels from 'mset' so thatonly those mask values within this range (inclusive) willbe used.
+    # 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.
     demean:
     # type=bool|default=False: Remove the mean from each volume prior to computing the correlation
     docor:

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

@@ -39,9 +39,9 @@ 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: medimage-afni/oned
+    in_file_a: medimage-afni/one-d
     # type=file|default=<undefined>: input file to 1deval
-    in_file_b: medimage-afni/oned
+    in_file_b: medimage-afni/one-d
     # type=file|default=<undefined>: operand file to 1deval
     in_file_c: generic/file
     # type=file|default=<undefined>: operand file to 1deval
@@ -66,7 +66,7 @@ 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: medimage-afni/oned
+    out_file: medimage-afni/one-d
     # type=file: output file
     # type=file|default=<undefined>: output image file name
   callables:

example-specs/task/nipype/afni/fim.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.
-    ideal_file: medimage-afni/oned
+    ideal_file: medimage-afni/one-d
     # type=file|default=<undefined>: ideal time series file name
     in_file: medimage/nifti1
     # type=file|default=<undefined>: input file to 3dfim+

example-specs/task/nipype/afni/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: medimage-afni/oned
+    in_file: medimage-afni/one-d
     # type=file|default=<undefined>: input file to OneDTool
     out_file: Path
     # type=file: output of 1D_tool.py
@@ -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: medimage-afni/oned
+    out_file: medimage-afni/one-d
     # type=file: output of 1D_tool.py
     # type=file|default=<undefined>: write the current 1D data to FILE
   callables:
@@ -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 0x107b49590>, <nipype.interfaces.base.traits_extension.File object at 0x107b49650>): Tuple of motion limit and outfile prefix. need to also set set_nruns -r set_run_lengths
+    # 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
     censor_prev_TR:
     # type=bool|default=False: for each censored TR, also censor previous
     show_trs_uncensored:

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

@@ -109,13 +109,13 @@ 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.
-    base_file: medimage/nifti1
+    base_file: medimage/nifti1,medimage/nifti-gz
     # type=file|default=<undefined>: Base image (opposite phase encoding direction than source image).
     emask: generic/file
     # type=file|default=<undefined>: Here, 'ee' is a dataset to specify a mask of voxelsto EXCLUDE from the analysis -- all voxels in 'ee'that are NONZERO will not be used in the alignment.The base image always automasked -- the emask isextra, to indicate voxels you definitely DON'T wantincluded in the matching process, even if they areinside the brain.
     gridlist: generic/file
     # type=file|default=<undefined>: This option provides an alternate way to specify the patch grid sizes used in the warp optimization process. 'gl' is a 1D file with a list of patches to use -- in most cases, you will want to use it in the following form: ``-gridlist '1D: 0 151 101 75 51'``  * Here, a 0 patch size means the global domain. Patch sizes   otherwise should be odd integers >= 5. * If you use the '0' patch size again after the first position,   you will actually get an iteration at the size of the   default patch level 1, where the patch sizes are 75% of   the volume dimension.  There is no way to force the program   to literally repeat the sui generis step of lev=0.  
-    in_file: medimage/nifti1
+    in_file: medimage/nifti1,medimage/nifti-gz
     # type=file|default=<undefined>: Source image (opposite phase encoding direction than base image).
     iniwarp: medimage-afni/head+list-of
     # type=list|default=[]: A dataset with an initial nonlinear warp to use.  * If this option is not used, the initial warp is the identity. * You can specify a catenation of warps (in quotes) here, as in   program 3dNwarpApply. * As a special case, if you just input an affine matrix in a .1D   file, that will work also -- it is treated as giving the initial   warp via the string "IDENT(base_dataset) matrix_file.aff12.1D". * You CANNOT use this option with -duplo !! * -iniwarp is usually used with -inilev to re-start 3dQwarp from   a previous stopping point.  
@@ -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 0x107a43190>, <traits.trait_types.Float object at 0x107a431d0>): 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=(<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.  
     out_weight_file:
     # type=file|default=<undefined>: Write the weight volume to disk as a dataset
     blur: