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LAP level1 nypipe
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wmfilt_nipype_jm
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"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"============== LAP WMFILT level1 nipype =============="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os # system functions\n",
"\n",
"import nipype.interfaces.io as nio # Data i/o\n",
"import nipype.interfaces.fsl as fsl # fsl\n",
"import nipype.interfaces.utility as util # utility\n",
"import nipype.pipeline.engine as pe # pypeline engine\n",
"import nipype.algorithms.modelgen as model # model generation\n",
"import nipype.algorithms.rapidart as ra # artifact detection"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fsl.FSLCommand.set_default_output_type('NIFTI_GZ')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"set up model workflow"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"modelfit = pe.Workflow(name='modelfit')\n",
"\n",
"#Use nipype.algorithms.modelgen.SpecifyModel to generate design information.\n",
"modelspec = pe.Node(interface=model.SpecifyModel(), name=\"modelspec\")\n",
"\n",
"#Use nipype.interfaces.fsl.Level1Design to generate a run specific fsf file for analysis\n",
"level1design = pe.Node(interface=fsl.Level1Design(), name=\"level1design\")\n",
"\n",
"#Use nipype.interfaces.fsl.FEATModel to generate a run specific mat file for use by FILMGLS\n",
"modelgen = pe.MapNode(interface=fsl.FEATModel(), name='modelgen',\n",
" iterfield = ['fsf_file', 'ev_files'])\n",
"\n",
"#Use nipype.interfaces.fsl.FILMGLS to estimate a model specified by a mat file and a functional run\n",
"modelestimate = pe.MapNode(interface=fsl.FILMGLS(smooth_autocorr=True,\n",
" mask_size=5,\n",
" threshold=1000),\n",
" name='modelestimate',\n",
" iterfield = ['design_file','in_file'])\n",
"\n",
"#Use nipype.interfaces.fsl.ContrastMgr to generate contrast estimates\n",
"conestimate = pe.MapNode(interface=fsl.ContrastMgr(), name='conestimate',\n",
" iterfield = ['tcon_file','param_estimates',\n",
" 'sigmasquareds', 'corrections',\n",
" 'dof_file'])\n",
"\n",
"#connect workflow components\n",
"modelfit.connect([\n",
" (modelspec,level1design,[('session_info','session_info')]),\n",
" (level1design,modelgen,[('fsf_files', 'fsf_file'),\n",
" ('ev_files', 'ev_files')]),\n",
" (modelgen,modelestimate,[('design_file','design_file')]),\n",
" (modelgen,conestimate,[('con_file','tcon_file')]),\n",
" (modelestimate,conestimate,[('param_estimates','param_estimates'),\n",
" ('sigmasquareds', 'sigmasquareds'),\n",
" ('corrections','corrections'),\n",
" ('dof_file','dof_file')]),\n",
" ])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Specify the location of the data.\n",
"data_dir = os.path.abspath('/gablab/p/LAP/Analysis/wmfilt/bips/results/preproc/')\n",
" #LAP_2016/preproc/output/bandpassed/fwhm_6.0\n",
"\n",
"\n",
"# Specify the subject directories\n",
"subject_list = ['LAP_2016'] #, 's3']\n",
"\n",
"# Map field names to individual subject runs.\n",
"info = dict(func=[['subject_id', 'subject_id', [0, 1, 2, 3]]])\n",
" #struct=[['subject_id','struct']])\n",
"\n",
"infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),\n",
" name=\"infosource\")\n",
"\n",
"infosource.iterables = ('subject_id', subject_list)\n",
"\n",
"\n",
"#Now we create a nipype.interfaces.io.DataSource object and fill in the information \n",
"#from above about the layout of our data. The nipype.pipeline.NodeWrapper module wraps \n",
"#the interface object and provides additional housekeeping and pipeline specific functionality.\n",
"\n",
"datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],\n",
" outfields=['func'], sort_filelist=True),\n",
" name = 'datasource')\n",
"datasource.inputs.base_directory = data_dir\n",
"datasource.inputs.template = '%s/preproc/output/bandpassed/fwhm_6.0/%s_r%02d_fsl_bandpassed.nii.gz'\n",
"datasource.inputs.template_args = info"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Setup a function that returns subject-specific information about the experimental paradigm. \n",
"def subjectinfo(subject_id):\n",
" import os\n",
" from nipype.interfaces.base import Bunch\n",
" from copy import deepcopy\n",
" import numpy as np\n",
" print \"Subject ID: %s\\n\"%str(subject_id)\n",
" subject_no = int(subject_id.split('_')[-1])\n",
" output = []\n",
" conditions_all = [\"inst_dist_c\", \"inst_nodist_c\", \"delay_2R_c\", \"delay_4RY_c\", \"delay_DRY_c\", \"probe_c\",\n",
" \"inst_dist_m\", \"inst_nodist_m\", \"delay_2R_m\", \"delay_4RY_m\", \"delay_DRY_m\", \"probe_m\"]\n",
" for r in [1,2,3,4]:\n",
" conditions = []\n",
" onsets = []\n",
" durations = []\n",
" amplitudes = []\n",
" for cond in conditions_all:\n",
" fname = \"/gablab/p/LAP/Analysis/wmfilt/bips/onsets/wmfilt_run%d/%d_%s.txt\" % (r, subject_no, cond)\n",
" with open(fname, 'r') as fid:\n",
" tmp_data = fid.read().strip()\n",
" if len(tmp_data) == 0:\n",
" # we don't have any onsets, don't include this condition\n",
" continue\n",
" data = np.genfromtxt(fname)\n",
" # check if there is only one trial\n",
" if data.shape == (3,):\n",
" data = data[None, :]\n",
" else:\n",
" this_onsets = map(float, data[:, 0])\n",
" this_durations = map(float, data[:, 1])\n",
" this_amp = map(float, data[:, 2])\n",
" conditions.append(cond)\n",
" onsets.append(this_onsets) \n",
" durations.append(this_durations)\n",
" amplitudes.append(this_amp)\n",
" \n",
" output.insert(r-1,\n",
" Bunch(conditions=deepcopy(conditions),\n",
" onsets=deepcopy(onsets),\n",
" durations=deepcopy(durations),\n",
" amplitudes=deepcopy(amplitudes),\n",
" tmod=None,\n",
" pmod=None,\n",
" regressor_names=None,\n",
" regressors=None))\n",
" return output\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#def getcontrasts(subject_id):\n",
"c1 = ('inst_filt', 'T', ['inst_dist_c','inst_nodist_c'],[1,-1])\n",
"c2 = ('load', 'T', ['delay_4RY_c','delay_2R_c'],[1,-1])\n",
"c3 = ('leaky-load', 'T', ['delay_DRY_c','delay_2R_c'],[1,-1])\n",
"c4 = ('inst_dist_vs_baseline', 'T', ['inst_dist_c'],[1])\n",
"c5 = ('inst_nodist_vs_baseline','T', ['inst_nodist_c'],[1])\n",
"c6 = ('delay_2R_vs_baseline','T', ['delay_2R_c'],[1])\n",
"c7 = ('delay_4R_vs_baseline','T', ['delay_4r_c'],[1])\n",
"c8 = ('delay_DRY_vs_baseline', 'T', ['delay_DRY_c'],[1])\n",
"c9 = ('probe_vs_baseline', 'T', ['delay_DRY_c'],[1])\n",
"c10 = ('all_vs_baseline','T', ['inst_dist_c', 'inst_nodist_c', 'delay_2R_c', 'delay_4RY_c', 'delay_DRY_c', 'probe_c'],[.1666,.16666,.16666,.16666,.16666,.16666])\n",
"contrasts = [c1,c2,c3]\n",
" #c4,c5,c6,c7,c8,c9,c10]\n",
"#return contrasts\n",
"\n",
"\n",
"\n",
"modelfit.inputs.modelspec.input_units = 'secs'\n",
"modelfit.inputs.modelspec.time_repetition = 2.0\n",
"modelfit.inputs.modelspec.high_pass_filter_cutoff = -1\n",
"\n",
"modelfit.inputs.level1design.interscan_interval = 2.0\n",
"modelfit.inputs.level1design.bases = {'dgamma':{'derivs': False}}\n",
"modelfit.inputs.level1design.contrasts = contrasts\n",
"modelfit.inputs.level1design.model_serial_correlations = True\n",
"\n",
"\n",
"#Set up complete workflow\n",
"l1pipeline = pe.Workflow(name= \"level1\")\n",
"l1pipeline.base_dir = os.path.abspath('../results/')\n",
"l1pipeline.config = {\"execution\": {\"crashdump_dir\":os.path.abspath('../errors/level1')}}\n",
"\n",
"l1pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]),\n",
" (infosource, modelfit, [(('subject_id', subjectinfo), 'modelspec.subject_info')]),\n",
" (datasource, modelfit, [('func', 'modelspec.functional_runs')]),\n",
" (datasource, modelfit, [('func', 'modelestimate.in_file')]),\n",
" ])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l1pipeline.write_graph()\n",
"l1pipeline.run()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:44,748 workflow INFO:\n",
"\t Converting dotfile: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/graph.dot to png format\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:44,808 workflow INFO:\n",
"\t ['check', 'execution', 'logging']\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Subject ID: LAP_2016\n",
"\n",
"131105-10:36:44,992 workflow INFO:\n",
"\t Running serially.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:44,994 workflow INFO:\n",
"\t Executing node datasource.a0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/_subject_id_LAP_2016/datasource\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:45,533 workflow INFO:\n",
"\t Executing node modelspec.a0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelspec\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:45,540 workflow INFO:\n",
"\t Collecting precomputed outputs\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:46,130 workflow INFO:\n",
"\t Executing node level1design.a0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/level1design\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:46,171 workflow INFO:\n",
"\t Collecting precomputed outputs\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:46,924 workflow INFO:\n",
"\t Executing node modelgen.a0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:48,568 workflow INFO:\n",
"\t Executing node modelestimate.a0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelestimate\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:48,600 workflow INFO:\n",
"\t Executing node _modelestimate0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelestimate/mapflow/_modelestimate0\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:48,625 workflow INFO:\n",
"\t Running: film_gls -ms 5 -rn results -sa /gablab/p/LAP/Analysis/wmfilt/bips/results/preproc/LAP_2016/preproc/output/bandpassed/fwhm_6.0/LAP_2016_r00_fsl_bandpassed.nii.gz /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen0/run0.mat 1000.000000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:49,698 interface INFO:\n",
"\t stdout 2013-11-05T10:36:49.698505:Log directory is: results\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:52,842 interface INFO:\n",
"\t stdout 2013-11-05T10:36:52.842208:paradigm.getDesignMatrix().Nrows()=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:52,843 interface INFO:\n",
"\t stdout 2013-11-05T10:36:52.842208:paradigm.getDesignMatrix().Ncols()=9\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:52,843 interface INFO:\n",
"\t stdout 2013-11-05T10:36:52.842208:sizeTS=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:52,844 interface INFO:\n",
"\t stdout 2013-11-05T10:36:52.842208:numTS=609\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:52,847 interface INFO:\n",
"\t stdout 2013-11-05T10:36:52.847607:Calculating residuals...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:54,290 interface INFO:\n",
"\t stdout 2013-11-05T10:36:54.290726:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:54,291 interface INFO:\n",
"\t stdout 2013-11-05T10:36:54.290726:Estimating residual autocorrelation...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:54,381 interface INFO:\n",
"\t stdout 2013-11-05T10:36:54.381300:Calculating raw AutoCorrs... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:54,382 interface INFO:\n",
"\t stdout 2013-11-05T10:36:54.382587:mode = 1217.44\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:54,383 interface INFO:\n",
"\t stdout 2013-11-05T10:36:54.382587:sig = 56\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:36:54,383 interface INFO:\n",
"\t stdout 2013-11-05T10:36:54.382587:Spatially smoothing auto corr estimates\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:14,822 interface INFO:\n",
"\t stdout 2013-11-05T10:37:14.822625:.............\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:14,823 interface INFO:\n",
"\t stdout 2013-11-05T10:37:14.822625:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:14,824 interface INFO:\n",
"\t stdout 2013-11-05T10:37:14.824605:Tukey M = 14\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:14,828 interface INFO:\n",
"\t stdout 2013-11-05T10:37:14.828632:Tukey estimates... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:14,830 interface INFO:\n",
"\t stdout 2013-11-05T10:37:14.828632:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:14,830 interface INFO:\n",
"\t stdout 2013-11-05T10:37:14.828632:Prewhitening and Computing PEs...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:14,831 interface INFO:\n",
"\t stdout 2013-11-05T10:37:14.828632:Percentage done:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:17,74 interface INFO:\n",
"\t stdout 2013-11-05T10:37:17.074545:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:17,75 interface INFO:\n",
"\t stdout 2013-11-05T10:37:17.074545:Saving results... \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:21,54 interface INFO:\n",
"\t stdout 2013-11-05T10:37:21.054408:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:21,906 workflow INFO:\n",
"\t Executing node _modelestimate1 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelestimate/mapflow/_modelestimate1\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:21,929 workflow INFO:\n",
"\t Running: film_gls -ms 5 -rn results -sa /gablab/p/LAP/Analysis/wmfilt/bips/results/preproc/LAP_2016/preproc/output/bandpassed/fwhm_6.0/LAP_2016_r01_fsl_bandpassed.nii.gz /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen1/run1.mat 1000.000000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:22,752 interface INFO:\n",
"\t stdout 2013-11-05T10:37:22.752242:Log directory is: results\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:25,820 interface INFO:\n",
"\t stdout 2013-11-05T10:37:25.820431:paradigm.getDesignMatrix().Nrows()=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:25,821 interface INFO:\n",
"\t stdout 2013-11-05T10:37:25.820431:paradigm.getDesignMatrix().Ncols()=9\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:25,822 interface INFO:\n",
"\t stdout 2013-11-05T10:37:25.820431:sizeTS=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:25,822 interface INFO:\n",
"\t stdout 2013-11-05T10:37:25.820431:numTS=609\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:25,823 interface INFO:\n",
"\t stdout 2013-11-05T10:37:25.823496:Calculating residuals...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:27,247 interface INFO:\n",
"\t stdout 2013-11-05T10:37:27.247125:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:27,248 interface INFO:\n",
"\t stdout 2013-11-05T10:37:27.247125:Estimating residual autocorrelation...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:27,326 interface INFO:\n",
"\t stdout 2013-11-05T10:37:27.325966:Calculating raw AutoCorrs... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:27,327 interface INFO:\n",
"\t stdout 2013-11-05T10:37:27.327409:mode = 1217.44\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:27,328 interface INFO:\n",
"\t stdout 2013-11-05T10:37:27.327409:sig = 56\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:27,328 interface INFO:\n",
"\t stdout 2013-11-05T10:37:27.327409:Spatially smoothing auto corr estimates\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:47,663 interface INFO:\n",
"\t stdout 2013-11-05T10:37:47.663806:.............\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:47,664 interface INFO:\n",
"\t stdout 2013-11-05T10:37:47.663806:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:47,665 interface INFO:\n",
"\t stdout 2013-11-05T10:37:47.665691:Tukey M = 14\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:47,669 interface INFO:\n",
"\t stdout 2013-11-05T10:37:47.669596:Tukey estimates... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:47,670 interface INFO:\n",
"\t stdout 2013-11-05T10:37:47.669596:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:47,670 interface INFO:\n",
"\t stdout 2013-11-05T10:37:47.669596:Prewhitening and Computing PEs...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:47,671 interface INFO:\n",
"\t stdout 2013-11-05T10:37:47.669596:Percentage done:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:49,951 interface INFO:\n",
"\t stdout 2013-11-05T10:37:49.950959:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:49,953 interface INFO:\n",
"\t stdout 2013-11-05T10:37:49.950959:Saving results... \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:53,901 interface INFO:\n",
"\t stdout 2013-11-05T10:37:53.901472:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:54,749 workflow INFO:\n",
"\t Executing node _modelestimate2 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelestimate/mapflow/_modelestimate2\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:54,774 workflow INFO:\n",
"\t Running: film_gls -ms 5 -rn results -sa /gablab/p/LAP/Analysis/wmfilt/bips/results/preproc/LAP_2016/preproc/output/bandpassed/fwhm_6.0/LAP_2016_r02_fsl_bandpassed.nii.gz /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen2/run2.mat 1000.000000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:55,586 interface INFO:\n",
"\t stdout 2013-11-05T10:37:55.585771:Log directory is: results\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:58,704 interface INFO:\n",
"\t stdout 2013-11-05T10:37:58.703881:paradigm.getDesignMatrix().Nrows()=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:58,704 interface INFO:\n",
"\t stdout 2013-11-05T10:37:58.703881:paradigm.getDesignMatrix().Ncols()=10\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:58,705 interface INFO:\n",
"\t stdout 2013-11-05T10:37:58.703881:sizeTS=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:58,706 interface INFO:\n",
"\t stdout 2013-11-05T10:37:58.703881:numTS=609\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:37:58,707 interface INFO:\n",
"\t stdout 2013-11-05T10:37:58.707139:Calculating residuals...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:00,282 interface INFO:\n",
"\t stdout 2013-11-05T10:38:00.282800:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:00,284 interface INFO:\n",
"\t stdout 2013-11-05T10:38:00.282800:Estimating residual autocorrelation...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:00,362 interface INFO:\n",
"\t stdout 2013-11-05T10:38:00.362289:Calculating raw AutoCorrs... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:00,363 interface INFO:\n",
"\t stdout 2013-11-05T10:38:00.363666:mode = 1217.44\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:00,364 interface INFO:\n",
"\t stdout 2013-11-05T10:38:00.363666:sig = 56\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:00,364 interface INFO:\n",
"\t stdout 2013-11-05T10:38:00.363666:Spatially smoothing auto corr estimates\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:20,680 interface INFO:\n",
"\t stdout 2013-11-05T10:38:20.679921:.............\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:20,681 interface INFO:\n",
"\t stdout 2013-11-05T10:38:20.679921:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:20,681 interface INFO:\n",
"\t stdout 2013-11-05T10:38:20.681763:Tukey M = 14\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:20,684 interface INFO:\n",
"\t stdout 2013-11-05T10:38:20.684679:Tukey estimates... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:20,685 interface INFO:\n",
"\t stdout 2013-11-05T10:38:20.684679:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:20,686 interface INFO:\n",
"\t stdout 2013-11-05T10:38:20.684679:Prewhitening and Computing PEs...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:20,687 interface INFO:\n",
"\t stdout 2013-11-05T10:38:20.684679:Percentage done:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:23,194 interface INFO:\n",
"\t stdout 2013-11-05T10:38:23.194386:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:23,195 interface INFO:\n",
"\t stdout 2013-11-05T10:38:23.194386:Saving results... \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:27,522 interface INFO:\n",
"\t stdout 2013-11-05T10:38:27.522357:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:29,45 workflow INFO:\n",
"\t Executing node _modelestimate3 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelestimate/mapflow/_modelestimate3\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:29,79 workflow INFO:\n",
"\t Running: film_gls -ms 5 -rn results -sa /gablab/p/LAP/Analysis/wmfilt/bips/results/preproc/LAP_2016/preproc/output/bandpassed/fwhm_6.0/LAP_2016_r03_fsl_bandpassed.nii.gz /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen3/run3.mat 1000.000000\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:29,994 interface INFO:\n",
"\t stdout 2013-11-05T10:38:29.993967:Log directory is: results\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:32,986 interface INFO:\n",
"\t stdout 2013-11-05T10:38:32.986138:paradigm.getDesignMatrix().Nrows()=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:32,987 interface INFO:\n",
"\t stdout 2013-11-05T10:38:32.986138:paradigm.getDesignMatrix().Ncols()=9\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:32,987 interface INFO:\n",
"\t stdout 2013-11-05T10:38:32.986138:sizeTS=204\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:32,988 interface INFO:\n",
"\t stdout 2013-11-05T10:38:32.986138:numTS=609\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:32,988 interface INFO:\n",
"\t stdout 2013-11-05T10:38:32.988934:Calculating residuals...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:34,413 interface INFO:\n",
"\t stdout 2013-11-05T10:38:34.413747:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:34,414 interface INFO:\n",
"\t stdout 2013-11-05T10:38:34.413747:Estimating residual autocorrelation...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:34,499 interface INFO:\n",
"\t stdout 2013-11-05T10:38:34.499072:Calculating raw AutoCorrs... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:34,500 interface INFO:\n",
"\t stdout 2013-11-05T10:38:34.499072:mode = 1217.44\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:34,500 interface INFO:\n",
"\t stdout 2013-11-05T10:38:34.499072:sig = 56\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:34,501 interface INFO:\n",
"\t stdout 2013-11-05T10:38:34.501807:Spatially smoothing auto corr estimates\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:58,814 interface INFO:\n",
"\t stdout 2013-11-05T10:38:58.814696:.............\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:58,815 interface INFO:\n",
"\t stdout 2013-11-05T10:38:58.814696:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:58,816 interface INFO:\n",
"\t stdout 2013-11-05T10:38:58.816320:Tukey M = 14\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:58,820 interface INFO:\n",
"\t stdout 2013-11-05T10:38:58.820633:Tukey estimates... Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:58,821 interface INFO:\n",
"\t stdout 2013-11-05T10:38:58.820633:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:58,822 interface INFO:\n",
"\t stdout 2013-11-05T10:38:58.820633:Prewhitening and Computing PEs...\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:38:58,822 interface INFO:\n",
"\t stdout 2013-11-05T10:38:58.820633:Percentage done:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:01,94 interface INFO:\n",
"\t stdout 2013-11-05T10:39:01.094035:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:01,95 interface INFO:\n",
"\t stdout 2013-11-05T10:39:01.094035:Saving results... \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:05,56 interface INFO:\n",
"\t stdout 2013-11-05T10:39:05.055906:Completed\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:09,992 workflow INFO:\n",
"\t Executing node conestimate.a0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:10,39 workflow INFO:\n",
"\t Executing node _conestimate0 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate0\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:10,79 workflow INFO:\n",
"\t Running: contrast_mgr /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate0 /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen0/run0.con\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:12,844 workflow INFO:\n",
"\t Executing node _conestimate1 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate1\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:12,886 workflow INFO:\n",
"\t Running: contrast_mgr /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate1 /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen1/run1.con\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:15,542 workflow INFO:\n",
"\t Executing node _conestimate2 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate2\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:15,582 workflow INFO:\n",
"\t Running: contrast_mgr /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate2 /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen2/run2.con\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:18,447 workflow INFO:\n",
"\t Executing node _conestimate3 in dir: /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate3\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"131105-10:39:18,497 workflow INFO:\n",
"\t Running: contrast_mgr /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/conestimate/mapflow/_conestimate3 /gablab/p/LAP/Analysis/wmfilt/bips/results/level1/modelfit/_subject_id_LAP_2016/modelgen/mapflow/_modelgen3/run3.con\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"<networkx.classes.digraph.DiGraph at 0x7bba3d0>"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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