ValueError: Field of view of image #6 is different from reference FOV.

VisionShilin commented 4 years ago

Hi,

The fitlins can’t treat reference shape (57, 68, 43) and image shape (57, 68, 43, 1) correctly. I got the following messages.

200924-00:30:32,776 nipype.workflow WARNING: Storing result file without outputs 200924-00:30:32,780 nipype.workflow WARNING: [Node] Error on "_l2_model0" (/tmp/tmpircjz52a/fitlins_wf/l2_model/mapflow/_l2_model0) 200924-00:30:32,784 nipype.workflow WARNING: Storing result file without outputs 200924-00:30:32,787 nipype.workflow WARNING: [Node] Error on "fitlins_wf.l2_model" (/tmp/tmpircjz52a/fitlins_wf/l2_model) 200924-00:31:17,378 nipype.workflow ERROR: Node l2_model failed to run on host 79b3412349f8. 200924-00:31:17,378 nipype.workflow ERROR: Saving crash info to /work/crash-20200924-003117-neuro-l2_model-4d50a1fc-73ab-468f-88c9-d0a1f5f3f94c.txt Traceback (most recent call last): File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/multiproc.py", line 148, in init mp_context=mp_context, TypeError: init() got an unexpected keyword argument 'initializer'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/multiproc.py", line 67, in run_node result["result"] = node.run(updatehash=updatehash) File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py", line 516, in run result = self._run_interface(execute=True) File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py", line 1386, in _run_interface self.config["execution"]["stop_on_first_crash"] File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py", line 1295, in _collate_results "Subnodes of node: %s failed:\n%s" % (self.name, "\n".join(msg)) Exception: Subnodes of node: l2_model failed: Subnode 0 failed Error: Traceback (most recent call last):

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/multiproc.py", line 148, in init mp_context=mp_context,

TypeError: init() got an unexpected keyword argument 'initializer'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/utils.py", line 94, in nodelist_runner result = node.run(updatehash=updatehash)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py", line 516, in run result = self._run_interface(execute=True)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py", line 635, in _run_interface return self._run_command(execute)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py", line 741, in _run_command result = self._interface.run(cwd=outdir)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/interfaces/base/core.py", line 397, in run runtime = self._run_interface(runtime)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/fitlins/interfaces/nistats.py", line 370, in _run_interface output_type='all'

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nistats/second_level_model.py", line 483, in computecontrast Y = self.masker.transform(effect_maps)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nilearn/input_data/base_masker.py", line 177, in transform return self.transform_single_imgs(imgs, confounds)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nilearn/input_data/nifti_masker.py", line 405, in transform_single_imgs dtype=self.dtype

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/joblib/memory.py", line 352, in call return self.func(*args, **kwargs)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nilearn/input_data/nifti_masker.py", line 41, in filter_and_mask imgs = _utils.check_niimg(imgs, atleast_4d=True, ensure_ndim=4)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nilearn/_utils/niimg_conversions.py", line 260, in check_niimg return concat_niimgs(niimg, ensure_ndim=ensure_ndim, dtype=dtype)

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nilearn/_utils/niimg_conversions.py", line 459, in concat_niimgs memory=memory, memory_level=memory_level))):

File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nilearn/_utils/niimg_conversions.py", line 150, in _iter_check_niimg niimg.shape))

ValueError: Field of view of image #6 is different from reference FOV. Reference affine: array([[ 3.43700004, 0. , 0. , -96.5 ], [ 0. , 3.43799996, 0. , -132.5 ], [ 0. , 0. , 4.5999999 , -78.5 ], [ 0. , 0. , 0. , 1. ]]) Image affine: array([[ 3.43799996, 0. , 0. , -96.5 ], [ 0. , 3.43799996, 0. , -132.5 ], [ 0. , 0. , 4.5999999 , -78.5 ], [ 0. , 0. , 0. , 1. ]]) Reference shape: (57, 68, 43) Image shape: (57, 68, 43, 1)

effigies commented 4 years ago

Thanks for this. Could you provide the nib-ls outputs for all of your input images?

The real fix will probably be in nilearn, but understanding how we got to this point would be helpful.

VisionShilin commented 4 years ago

I don't know what "the nib-ls outputs" means.

VisionShilin commented 4 years ago

I used this command.

docker run --rm -it -v C:\Users\shili\Desktop\t\fitlins_data\raw:/bids:ro -v C:\Users\shili\Desktop\t\fitlins_data\prep\fmriprep:/prep:ro -v C:\Users\shili\Desktop\t\fitlins_data\analyzed:/out poldracklab/fitlins /bids /out dataset -d /prep -m /bids/models/model-ChineseArrangment_smdl.json --drop-missing

effigies commented 4 years ago

If you have nibabel installed, the nib-ls command should be available. Then you can run nib-ls C:\prep\fmriprep\*\func\*_bold.nii.gz.

VisionShilin commented 4 years ago

Here is one of subject's image data.

C:\Users\shili\Desktop\t\fitlins_data>nib-ls C:\Users\shili\Desktop\t\fitlins_data\prep\fmriprep\sub-LXY\func\sub-LXY_task-ViewChineseArrangment_space-MNI152NLin2009cAsym_boldref.nii.gz C:\Users\shili\Desktop\t\fitlins_data\prep\fmriprep\sub-LXY\func\sub-LXY_task-ViewChineseArrangment_space-MNI152NLin2009cAsym_boldref.nii.gz float32 [ 57, 68, 43] 3.44x3.44x4.60

Is this error caused by someone's image data if the out is (57, 68, 43, 1)?

effigies commented 4 years ago

I don't really know. Just trying to get a sense of what the inputs are. Can you run nib-ls on the bold.nii.gz files, rather than boldref?

Also, what version of FitLins are you using?

VisionShilin commented 4 years ago

C:\Users\shili\Desktop\t\fitlins_data>nib-ls C:\Users\shili\Desktop\t\fitlins_data\prep\fmriprep\sub-LY\func\sub-LY_task-ViewChineseArrangment_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz C:\Users\shili\Desktop\t\fitlins_data\prep\fmriprep\sub-LY\func\sub-LY_task-ViewChineseArrangment_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz float32 [ 57, 68, 43, 240] 3.44x3.44x4.60x2.00

VisionShilin commented 4 years ago

I run latest version of FitLins using docker image.

effigies commented 4 years ago

And what's your model?

VisionShilin commented 4 years ago

{
  "Name": "Chinese_Arrangment",
  "Description": "Visual Aesthetic View of Vertical and Horizental Chinese Arrangement",
  "Input": {
    "task": "ViewChineseArrangment"
  },
  "Steps": [
    {
      "Level": "run",
      "Transformations": [
        {
          "Name": "Factor",
          "Input": [
            "trial_type"
          ]
        },
        {
          "Name": "Convolve",
          "Input": ["trial_type.N", "trial_type.R","trial_type.V","trial_type.H"],
          "Model": "spm"
        }
      ],
      "Model": {
        "X": [
          "trial_type.N",
          "trial_type.R",
          "trial_type.V",
          "trial_type.H"
        ]
      },
      "Contrasts": [
        {
          "Name": "R_vs_N",
          "ConditionList": [
            "trial_type.R",
            "trial_type.N"
          ],
          "Weights": [1, -1],
          "Type": "t"
        },
        {
          "Name": "H_vs_V",
          "ConditionList": [
            "trial_type.H",
            "trial_type.V"
          ],
          "Weights": [1, -1],
          "Type": "t"
        },
        {
          "Name": "HV_vs_R",
          "ConditionList": [
            "trial_type.H",
            "trial_type.V",
            "trial_type.R"
          ],
          "Weights": [0.5, 0.5, -1],
          "Type": "t"
        },
        {
          "Name": "HV_vs_N",
          "ConditionList": [
            "trial_type.H",
            "trial_type.V",
            "trial_type.N"
          ],
          "Weights": [0.5, 0.5, -1],
          "Type": "t"
        },
        {
            "Name": "H_vs_R",
            "ConditionList": [
              "trial_type.H",
              "trial_type.R"
            ],
            "Weights": [1, -1],
            "Type": "t"
        },
        {
          "Name": "V_vs_R",
          "ConditionList": [
            "trial_type.V",
            "trial_type.R"
          ],
          "Weights": [1, -1],
          "Type": "t"
        }
      ]
    },
    {
      "Level": "dataset",
      "DummyContrasts": {
        "Conditions": ["R_gt_N", "H_vs_V", "HV_vs_R", "HV_vs_N", "H_vs_R", "V_vs_R"],
        "Type": "t"
        }
    }
  ]
}

VisionShilin commented 4 years ago

By the way, I can get the report even the command reported errors. Can I trust the report?

VisionShilin commented 4 years ago

I suggest the FitLins reports errors in the report html file like the fMRIPrep did.

effigies commented 4 years ago

The report should show valid results for the first level.

VisionShilin commented 4 years ago

Here is the content of file crash-20200926-031309-neuro-l2_model-71564ebe-b94c-4057-912b-1614d9710c3f.txt Hope it will be helpful for debugging.

Node: fitlins_wf.l2_model Working directory: /work/fitlins_wf/l2_model

Node inputs:

contrast_info = [[{'name': 'H_vs_V', 'weights': [{'H_vs_V': 1}], 'type': 't', 'entities': {'task': 'ViewChineseArrangment', 'space': 'MNI152NLin2009cAsym'}}, {'name': 'HV_vs_R', 'weights': [{'HV_vs_R': 1}], 'type': 't', 'entities': {'task': 'ViewChineseArrangment', 'space': 'MNI152NLin2009cAsym'}}, {'name': 'HV_vs_N', 'weights': [{'HV_vs_N': 1}], 'type': 't', 'entities': {'task': 'ViewChineseArrangment', 'space': 'MNI152NLin2009cAsym'}}, {'name': 'H_vs_R', 'weights': [{'H_vs_R': 1}], 'type': 't', 'entities': {'task': 'ViewChineseArrangment', 'space': 'MNI152NLin2009cAsym'}}, {'name': 'V_vs_R', 'weights': [{'V_vs_R': 1}], 'type': 't', 'entities': {'task': 'ViewChineseArrangment', 'space': 'MNI152NLin2009cAsym'}}]] effect_maps = smoothing_fwhm = stat_metadata = variance_maps =

Traceback (most recent call last): File "/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/multiproc.py", line 148, in init mp_context=mp_context, TypeError: init() got an unexpected keyword argument 'initializer'