Patch pathlib incompatibility issue

Open Dhananjhay opened this issue 2 months ago • 0 comments
Hippunfold fails to run inside an environment with python 3.13 because of pathlib package incompatibility. It works fine with Python versions 3.11 and 3.12. It's possible the error might be stemming from snakebids. As a quick fix, this PR pins python v3.11 when creating the conda env and reduces the upper limit of python from <4.0 to <3.13.
Error:
Traceback (most recent call last):
  File "/local/scratch/test-lowresMRI-3.13/.snakemake/scripts/tmph4225s8_.dice.py", line 2, in <module>
    import sys;sys.path.extend(['/home/UWO/dbansal7/miniconda3/envs/snakebids-env/lib/python3.13/site-packages', '/local/scratch/hippunfold/hippunfold/workflow/rules', '/local/scratch/hippunfold/hippunfold/workflow', '/local/scratch/hippunfold/hippunfold', '/home/UWO/dbansal7/miniconda3/envs/snakebids-env/lib/python3.13', '/home/UWO/dbansal7/miniconda3/envs/snakebids-env/lib/python3.13/lib-dynload', '/home/UWO/dbansal7/miniconda3/envs/snakebids-env/lib/python3.13/site-packages', '/localscratch/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpbs3coo2s/file/local/scratch/hippunfold/hippunfold/workflow/rules/../scripts', '/local/scratch/hippunfold/hippunfold/workflow/rules/../scripts']);import pickle;from snakemake import script;script.snakemake = pickle.loads(b'\x80\x04\x95\xb7P\x00\x00\x00\x00\x00\x00\x8c\x10snakemake.script\x94\x8c\tSnakemake\x94\x93\x94)\x81\x94}\x94(\x8c\x05input\x94\x8c\x0csnakemake.io\x94\x8c\nInputFiles\x94\x93\x94)\x81\x94(\x8c=sub-01/anat/sub-01_hemi-R_space-template_desc-f3d_mask.nii.gz\x94\x8c0/localscratch/.cache/hippunfold/template/CITI168\x94e}\x94(\x8c\x06_names\x94}\x94(\x8c\x08res_mask\x94K\x00N\x86\x94\x8c\x0ctemplate_dir\x94K\x01N\x86\x94u\x8c\x12_allowed_overrides\x94]\x94(\x8c\x05index\x94\x8c\x04sort\x94eh\x15h\x06\x8c\x0eAttributeGuard\x94\x93\x94)\x81\x94}\x94\x8c\x04name\x94h\x15sbh\x16h\x18)\x81\x94}\x94h\x1bh\x16sbh\x0fh\nh\x11h\x0bub\x8c\x06output\x94h\x06\x8c\x0bOutputFiles\x94\x93\x94)\x81\x94\x8c-sub-01/qc/sub-01_hemi-R_desc-unetf3d_dice.tsv\x94a}\x94(h\r}\x94\x8c\x04dice\x94K\x00N\x86\x94sh\x13]\x94(h\x15h\x16eh\x15h\x18)\x81\x94}\x94h\x1bh\x15sbh\x16h\x18)\x81\x94}\x94h\x1bh\x16sbh%h"ub\x8c\r_params_store\x94h\x06\x8c\x06Params\x94\x93\x94)\x81\x94(]\x94(K\x01K\x02K\x07K\x08e\x8c\x0epathlib._local\x94\x8c\tPosixPath\x94\x93\x94\x8c0/localscratch/.cache/hippunfold/template/CITI168\x94\x8c&Mask_300umCoronalOblique_hemi-R.nii.gz\x94\x86\x94R\x94e}\x94(h\r}\x94(\x8c\thipp_lbls\x94K\x00N\x86\x94\x8c\x03ref\x94K\x01N\x86\x94uh\x13]\x94(h\x15h\x16eh\x15h\x18)\x81\x94}\x94h\x1bh\x15sbh\x16h\x18)\x81\x94}\x94h\x1bh\x16sbh:h0h<h7ub\x8c\r_params_types\x94}\x94\x8c\twildcards\x94h\x06\x8c\tWildcards\x94\x93\x94)\x81\x94(\x8c\x0201\x94\x8c\x01R\x94e}\x94(h\r}\x94(\x8c\x07subject\x94K\x00N\x86\x94\x8c\x04hemi\x94K\x01N\x86\x94uh\x13]\x94(h\x15h\x16eh\x15h\x18)\x81\x94}\x94h\x1bh\x15sbh\x16h\x18)\x81\x94}\x94h\x1bh\x16sb\x8c\x07subject\x94hI\x8c\x04hemi\x94hJub\x8c\x07threads\x94K\x01\x8c\tresources\x94h\x06\x8c\tResources\x94\x93\x94)\x81\x94(K\x01K\x01\x8c\x04/tmp\x94e}\x94(h\r}\x94(\x8c\x06_cores\x94K\x00N\x86\x94\x8c\x06_nodes\x94K\x01N\x86\x94\x8c\x06tmpdir\x94K\x02N\x86\x94uh\x13]\x94(h\x15h\x16eh\x15h\x18)\x81\x94}\x94h\x1bh\x15sbh\x16h\x18)\x81\x94}\x94h\x1bh\x16sbh`K\x01hbK\x01hdh]ub\x8c\x03log\x94h\x06\x8c\x03Log\x94\x93\x94)\x81\x94}\x94(h\r}\x94h\x13]\x94(h\x15h\x16eh\x15h\x18)\x81\x94}\x94h\x1bh\x15sbh\x16h\x18)\x81\x94}\x94h\x1bh\x16sbub\x8c\x06config\x94}\x94(\x8c\x17plugins.version.version\x94\x8c\x07unknown\x94\x8c\x0fanalysis_levels\x94]\x94(\x8c\x0bparticipant\x94\x8c\x12group_create_atlas\x94\x8c\x05group\x94e\x8c\x19targets_by_analysis_level\x94}\x94(\x8c\x0bparticipant\x94]\x94\x8c\x03all\x94a\x8c\x12group_create_atlas\x94]\x94\x8c\x10all_create_atlas\x94a\x8c\x05group\x94]\x94\x8c\rall_group_tsv\x94au\x8c\rpybids_inputs\x94}\x94(\x8c\x03T2w\x94}\x94(\x8c\x07filters\x94}\x94(\x8c\x06suffix\x94\x8c\x03T2w\x94\x8c\textension\x94\x8c\x07.nii.gz\x94\x8c\x08datatype\x94\x8c\x04anat\x94u\x8c\twildcards\x94]\x94(\x8c\x07subject\x94\x8c\x07session\x94\x8c\x0bacquisition\x94\x8c\x03run\x94eu\x8c\x08hippb500\x94}\x94(\x8c\x07filters\x94}\x94(\x8c\x06suffix\x94\x8c\x04b500\x94\x8c\textension\x94\x8c\x07.nii.gz\x94\x8c\x08datatype\x94\x8c\x03dwi\x94u\x8c\twildcards\x94]\x94(\x8c\x07subject\x94\x8c\x07session\x94eu\x8c\x03T1w\x94}\x94(\x8c\x07filters\x94}\x94(\x8c\x06suffix\x94\x8c\x03T1w\x94\x8c\textension\x94\x8c\x07.nii.gz\x94\x8c\x08datatype\x94\x8c\x04anat\x94u\x8c\twildcards\x94]\x94(\x8c\x07subject\x94\x8c\x07session\x94\x8c\x0bacquisition\x94\x8c\x03run\x94eu\x8c\ndsegtissue\x94}\x94(\x8c\x07filters\x94}\x94(\x8c\x06suffix\x94\x8c\x04dseg\x94\x8c\textension\x94\x8c\x07.nii.gz\x94\x8c\x08datatype\x94\x8c\x04anat\x94\x8c\x04desc\x94\x8c\x06tissue\x94u\x8c\twildcards\x94]\x94(\x8c\x07subject\x94\x8c\x07session\x94\x8c\x04hemi\x94\x8c\x0bacquisition\x94\x8c\x03run\x94eu\x8c\rdsegsubfields\x94}\x94(\x8c\x07filters\x94}\x94(\x8c\x06suffix\x94\x8c\x04dseg\x94\x8c\textension\x94\x8c\x07.nii.gz\x94\x8c\x08datatype\x94\x8c\x04anat\x94\x8c\x04desc\x94\x8c\tsubfields\x94u\x8c\twildcards\x94]\x94(\x8c\x07subject\x94\x8c\x07session\x94\x8c\x04hemi\x94\x8c\x0bacquisition\x94\x8c\x03run\x94euu\x8c\nparse_args\x94}\x94(\x8c\n--modality\x94}\x94(\x8c\x04help\x94X\'\x01\x00\x00Type of image to run hippunfold on. For dsegtissue and be sure to match the label scheme applied in the look up table (lut) online. For best performance, please also make sure the input is approximately aligned to the template space, with separate hemi-L|R in the name(s). (default: %(default)s)\x94\x8c\x08required\x94\x88\x8c\x07choices\x94]\x94(\x8c\x03T1w\x94\x8c\x03T2w\x94\x8c\x08hippb500\x94\x8c\ndsegtissue\x94eu\x8c\n--template\x94}\x94(\x8c\x07choices\x94]\x94(\x8c\x07CITI168\x94\x8c\x04dHCP\x94\x8c\x05MBMv2\x94\x8c\x05MBMv3\x94\x8c\x04CIVM\x94\x8c\x05ABAv3\x94\x8c\x08bigbrain\x94e\x8c\x07default\x94\x8c\x07CITI168\x94\x8c\x04help\x94X\xb5\x01\x00\x00Set the template to use for registration to coronal oblique (and optionally for template-based segmentation if --use-template-seg is enabled). CITI168 is for adult human data, dHCP is for neonatal human data, MBMv2 is for ex vivo marmoset data, MBMv3 is for in vivo marmoset data, CIVM is for in vivo macaque data, and ABAv3 is for mouse data. When using a non-human template, consider using a corresponding atlas. (default: %(default)s)\x94u\x8c\x11--inject_template\x94}\x94(\x8c\x07choices\x94]\x94\x8c\x05upenn\x94a\x8c\x07default\x94\x8c\x05upenn\x94\x8c\x04help\x94\x8cCSet the template to use for shape injection. (default: %(default)s)\x94u\x8c\x12--use_template_seg\x94}\x94(\x8c\x04help\x94XL\x01\x00\x00Use template-based segmentation for hippocampal tissue *instead of* nnUnet and shape injection. This is only to be used if nnUnet models are not trained for the data you are using, e.g. for non-human primate data with the MBMv2 (ex vivo marmoset), MBMv3 (in vivo marmoset), or CIVM (ex vivo macaque) template. (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c\x1f--template_seg_smoothing_factor\x94}\x94(\x8c\x04help\x94\x8c\xc7Scales the default smoothing sigma for gradient and warp in greedy registration for template-based segmentation. Using a value higher than 1 will use result in a smoother warp. (default: %(default)s)\x94\x8c\x07default\x94G@\x00\x00\x00\x00\x00\x00\x00u\x8c\x0e--skip_preproc\x94}\x94(\x8c\x04help\x94\x8c]Set this flag if your inputs (e.g. T2w, dwi) are already pre-processed (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c\x0c--skip_coreg\x94}\x94(\x8c\x04help\x94\x8cgSet this flag if your inputs (e.g. T2w, dwi) are already registered to T1w space (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c\x1d--skip_inject_template_labels\x94}\x94(\x8c\x04help\x94\x8c\x98Set this flag to skip post-processing template injection into CNN segmentation. Note this will disable generation of DG surfaces. (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c"--inject_template_smoothing_factor\x94}\x94(\x8c\x04help\x94Xo\x01\x00\x00Scales the default smoothing sigma for gradient and warp in template shape injection. Using a value higher than 1 will use result in a smoother warp, and greater capacity to patch larger holes in segmentations. Try setting to 2 if nnunet segmentations have large holes. Note: the better solution is to re-train network on the data you are using (default: %(default)s)\x94\x8c\x07default\x94G?\xf0\x00\x00\x00\x00\x00\x00u\x8c\x14--rigid_reg_template\x94}\x94(\x8c\x04help\x94\x8cxUse rigid instead of affine for registration to template. Try this if your images are reduced FOV (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c\x11--no_reg_template\x94}\x94(\x8c\x04help\x94\x8cDUse if input data is already in space-CITI168 (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c\x11--t1_reg_template\x94}\x94(\x8c\x04help\x94\x8c\x99Use T1w to register to template space, instead of the segmentation modality. Note: this was the default behavior prior to v1.0.0.  (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c\n--crop_res\x94}\x94(\x8c\x04help\x94\x8c\xedSets the bounding box resolution for the crop original modality (e.g. cropT1w space). Under the hood, hippUnfold operates at higher resolution than the original image, so this tries to preserve some of that detail. 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Note: the segmentation will always be subsequently cropped around the segmentation, with a padding of 5 voxels.  (default: %(default)s)\x94\x8c\x07default\x94Nu\x8c\x19--laminar_coords_res_hipp\x94}\x94(\x8c\x04help\x94\x8c\xd3Set the resolution that the laminar (IO) coords will be computed with for hippocampus. This is implemented by resampling the segmentation to this resolution prior to generating the coords. (default: %(default)s)\x94\x8c\x07default\x94\x8c\r0.3x0.3x0.3mm\x94u\x8c\x1c--laminar_coords_res_dentate\x94}\x94(\x8c\x04help\x94\x8c\xd9Set the resolution that the laminar (IO) coords will be computed with for the dentate gyrus. This is implemented by resampling the segmentation to this resolution prior to generating the coords. (default: %(default)s)\x94\x8c\x07default\x94\x8c\r0.1x0.1x0.1mm\x94u\x8c\x11--no_unfolded_reg\x94}\x94(\x8c\x04help\x94\x8c\xbaDo not perform unfolded space (2D) registration based on surface metrics (e.g. thickness, curvature, and gyrification) for closer alignment to the reference atlas. (default: %(default)s)\x94\x8c\x07default\x94\x89\x8c\x06action\x94\x8c\nstore_true\x94u\x8c\x15--generate_myelin_map\x94}\x94(\x8c\x04help\x94\x8c\x9dGenerate myelin map using T1w divided by T2w, and map to surface with ribbon approach. Requires both T1w and T2w images to be present. 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script;from snakemake.logging import logger;from snakemake.script import snakemake;__real_file__ = __file__; __file__ = '/local/scratch/hippunfold/hippunfold/workflow/scripts/dice.py';
ModuleNotFoundError: No module named 'pathlib._local'; 'pathlib' is not a package
Oct 04 '25 06:10 Dhananjhay