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Node extractfromresultdict failed to run on host #637

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SchefflerF opened this issue Oct 23, 2023 · 5 comments
Open

Node extractfromresultdict failed to run on host #637

SchefflerF opened this issue Oct 23, 2023 · 5 comments
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@HippocampusGirl
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bug Something isn't working

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@SchefflerF
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What happened?
I am trying to run the models but it crashes.

Is there an error message?
Node: nipype.models_wf.aggregateSeedCorrAcrossRuns_wf.extractfromresultdict
Working directory: /ext/scratch/freda/PASS/working/nipype/models_wf/aggregateSeedCorrAcrossRuns_wf/extractfromresultdict

Node inputs:

indict =

Traceback (most recent call last):
File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/plugins/multiproc.py", line 292, in _send_procs_to_workers
num_subnodes = self.procs[jobid].num_subnodes()
File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 1309, in num_subnodes
self._check_iterfield()
File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 1334, in _check_iterfield
% iterfield
ValueError: Input indict was not set but it is listed in iterfields.

When creating this crashfile, the results file corresponding
to the node could not be found.freda@srvcnthpc126 /scratch/freda/PASS/working$ clc
bash: clc: command not found
freda@srvcnthpc126 /scratch/freda/PASS/working$ clear

freda@srvcnthpc126 /scratch/freda/PASS/working$ cat crash-20231023-094114-freda-extractfromresultdict-a1010300-627c-4e4d-b4a6-c5436530f0c8.txt
Node: nipype.models_wf.aggregateSeedCorrAcrossRuns_wf.extractfromresultdict
Working directory: /ext/scratch/freda/PASS/working/nipype/models_wf/aggregateSeedCorrAcrossRuns_wf/extractfromresultdict

Node inputs:

indict =

Traceback (most recent call last):
File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/plugins/multiproc.py", line 292, in _send_procs_to_workers
num_subnodes = self.procs[jobid].num_subnodes()
File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 1309, in num_subnodes
self._check_iterfield()
File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 1334, in _check_iterfield
% iterfield
ValueError: Input indict was not set but it is listed in iterfields.

When creating this crashfile, the results file corresponding
to the node could not be found.

What is the exact command that you used to run Halfpipe?
singularity run --containall --bind /:/ext /opt/exp_soft/singularity-containers/mindandbrain/pipeline/halfpipe_latest.sif --only-model-chunk

Which settings did you use?
{
"halfpipe_version": "1.1.1",
"schema_version": "3.0",
"timestamp": "2023-10-04_10-07",
"global_settings": {
"slice_timing": true,
"skull_strip_algorithm": "ants",
"run_mriqc": false,
"run_fmriprep": true,
"run_halfpipe": true,
"fd_thres": 0.5,
"anat_only": false,
"write_graph": false,
"hires": false,
"run_reconall": false,
"t2s_coreg": false,
"medial_surface_nan": false,
"bold2t1w_dof": 9,
"fmap_bspline": true,
"force_syn": false,
"longitudinal": false,
"regressors_all_comps": false,
"regressors_dvars_th": 1.5,
"regressors_fd_th": 0.5,
"skull_strip_fixed_seed": false,
"skull_strip_template": "OASIS30ANTs",
"aroma_err_on_warn": false,
"aroma_melodic_dim": -200,
"sloppy": false
},
"files": [
{
"suffix": "T1w",
"extension": ".nii.gz",
"datatype": "anat",
"path": "/ext/scratch/freda/PASS/input/{sub}/anat/{sub}{run}T1w.nii.gz",
"tags": {}
},
{
"suffix": "bold",
"extension": ".nii.gz",
"datatype": "func",
"path": "/ext/scratch/freda/PASS/input/{sub}/func/{sub}{task}{run}{dir}.nii.gz",
"metadata": {},
"tags": {}
},
{
"suffix": "seed",
"extension": ".nii.gz",
"datatype": "ref",
"path": "/ext/scratch/freda/PASS/masks/yeo2011_7networks{desc}.nii.gz",
"metadata": {
"space": "MNI152NLin2009cAsym"
},
"tags": {}
},
{
"datatype": "spreadsheet",
"path": "/ext/scratch/freda/PASS/working/PASSCovariates_Oct2023.xlsx",
"metadata": {
"variables": [
{
"name": "id",
"type": "id"
},
{
"name": "AgeYrs",
"type": "continuous"
},
{
"name": "Sex",
"levels": [
"2",
"1"
],
"type": "categorical"
},
{
"name": "Group",
"levels": [
"1",
"2"
],
"type": "categorical"
},
{
"name": "TotalStdDrinksLMP",
"type": "continuous"
},
{
"name": "TotalStdDrinksT1",
"type": "continuous"
},
{
"name": "TotalStdDrinksT2",
"type": "continuous"
},
{
"name": "TotalStdDrinksT3",
"type": "continuous"
},
{
"name": "TotalStdDrinksPreg",
"type": "continuous"
},
{
"name": "CigsPerDayPreg",
"type": "continuous"
},
{
"name": "CigsPerDayLMP",
"type": "continuous"
},
{
"name": "CigsPerDayT1",
"type": "continuous"
},
{
"name": "CigsPerDayT2",
"type": "continuous"
},
{
"name": "CigsPerDayT3",
"type": "continuous"
}
]
}
}
],
"settings": [
{
"ica_aroma": true,
"smoothing": {
"fwhm": 6.0
},
"grand_mean_scaling": {
"mean": 10000.0
},
"bandpass_filter": {
"high": 0.1,
"low": 0.01,
"type": "frequency_based"
},
"confounds_removal": [
"(trans|rot)[xyz]",
"a_comp_cor_0[0-4]"
],
"name": "seedCorrSetting",
"filters": [],
"output_image": false
},
{
"ica_aroma": true,
"smoothing": {
"fwhm": 6.0
},
"grand_mean_scaling": {
"mean": 10000.0
},
"bandpass_filter": {
"high": 0.1,
"low": 0.01,
"type": "frequency_based"
},
"confounds_removal": [
"(trans|rot)[xyz]",
"a_comp_cor_0[0-4]"
],
"name": "preproc",
"filters": [],
"output_image": true
}
],
"features": [
{
"seeds": [
"dorsal_attention",
"frontoparietal",
"dmn",
"ventral_attention"
],
"type": "seed_based_connectivity",
"name": "seedCorr",
"min_seed_coverage": 0.8,
"setting": "seedCorrSetting"
}
],
"models": [
{
"name": "aggregateSeedCorrAcrossRuns",
"inputs": [
"seedCorr"
],
"type": "fe",
"across": "run"
},
{
"name": "model1",
"inputs": [
"aggregateSeedCorrAcrossRuns"
],
"filters": [
{
"cutoff": 0.5,
"action": "exclude",
"field": "fd_mean",
"type": "cutoff"
},
{
"cutoff": 0.1,
"action": "exclude",
"field": "fd_perc",
"type": "cutoff"
},
{
"action": "exclude",
"variable": "AgeYrs",
"type": "missing"
},
{
"action": "exclude",
"variable": "Sex",
"type": "missing"
},
{
"action": "exclude",
"variable": "Group",
"type": "missing"
},
{
"action": "exclude",
"variable": "TotalStdDrinksLMP",
"type": "missing"
},
{
"action": "exclude",
"variable": "TotalStdDrinksT1",
"type": "missing"
},
{
"action": "exclude",
"variable": "TotalStdDrinksT2",
"type": "missing"
},
{
"action": "exclude",
"variable": "TotalStdDrinksT3",
"type": "missing"
},
{
"action": "exclude",
"variable": "TotalStdDrinksPreg",
"type": "missing"
},
{
"action": "exclude",
"variable": "CigsPerDayPreg",
"type": "missing"
},
{
"action": "exclude",
"variable": "CigsPerDayLMP",
"type": "missing"
},
{
"action": "exclude",
"variable": "CigsPerDayT1",
"type": "missing"
},
{
"action": "exclude",
"variable": "CigsPerDayT2",
"type": "missing"
},
{
"action": "exclude",
"variable": "CigsPerDayT3",
"type": "missing"
}
],
"type": "lme",
"across": "sub",
"algorithms": [
"flame1",
"mcartest",
"heterogeneity"
],
"spreadsheet": "/ext/scratch/freda/PASS/working/PASSCovariates_Oct2023.xlsx",
"contrasts": [
{
"variable": [
"AgeYrs"
],
"type": "infer"
},
{
"variable": [
"Sex"
],
"type": "infer"
},
{
"variable": [
"Group"
],
"type": "infer"
},
{
"variable": [
"TotalStdDrinksLMP"
],
"type": "infer"
},
{
"variable": [
"TotalStdDrinksT1"
],
"type": "infer"
},
{
"variable": [
"TotalStdDrinksT2"
],
"type": "infer"
},
{
"variable": [
"TotalStdDrinksT3"
],
"type": "infer"
},
{
"variable": [
"TotalStdDrinksPreg"
],
"type": "infer"
},
{
"variable": [
"CigsPerDayPreg"
],
"type": "infer"
},
{
"variable": [
"CigsPerDayLMP"
],
"type": "infer"
},
{
"variable": [
"CigsPerDayT1"
],
"type": "infer"
},
{
"variable": [
"CigsPerDayT2"
],
"type": "infer"
},
{
"variable": [
"CigsPerDayT3"
],
"type": "infer"
}
]
}
]
@SchefflerF SchefflerF added the bug Something isn't working label Oct 23, 2023
@HippocampusGirl
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Hi @SchefflerF,

This error means that the intermediate files in the nipype folder could not be found. Could you check if there are any files there?

Alternatively, you could use the new group-level command that I have been building. It will work even without these files, it just needs the derivatives folder.

The new command is documented here, but you can leave out the "export" options.

I will be back from vacation next week and will be able to respond in more detail.

@SchefflerF
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SchefflerF commented Oct 31, 2023 via email

@HippocampusGirl
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HippocampusGirl commented Oct 31, 2023 •
edited

Hi Freda,

I have not documented this yet, but the options are:

  --export-atlas NAME TYPE IMAGE_PATH LABEL_PATH
                        for all images, export region signals based on the atlas defined by the image/label files

where TYPE can be one of z for the z-statistic), effect for the parameter estimates, standardizedEffect for the standardized parameter estimates (scaled like a correlation from -1 to 1) and cohensD for the effect size.

However, if you're just interested in group analysis, you can leave out the atlases :-)

Hope this helps!

Best,
Lea

@SchefflerF
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SchefflerF commented Nov 6, 2023 via email

1 similar comment
@SchefflerF
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SchefflerF commented Nov 28, 2023 via email

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