satra

satra

@prefix daniel: <http://mayor2.dia.fi.upm.es/oeg-upm/files/dgarijo/motifAnalysis/> .
@prefix dc: <http://purl.org/dc/terms/> .
@prefix ore: <http://www.openarchives.org/ore/terms/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ro: <http://purl.org/wf4ever/ro#> .
@prefix schema: <http://schema.org/> .
@prefix xhv: <http://www.w3.org/1999/xhtml/vocab#> .
@prefix xml: <http://www.w3.org/XML/1998/namespace> .

satra

def _laplacian_dense2(graph, normalization=None, return_diag=False,
                      max_iters=100, tol=1e-5):
    """Compute different graph Laplacians
    """

    dd = np.sum(graph, axis=0)
    dd_idx = np.nonzero(dd)[0]
    dd_inv = dd
    dd_inv[dd_idx] = np.power(dd_inv[dd_idx], -1.)

satra

This gist describes a flexible dicom converter for organizing brain imaging data into structured directory layouts.

• it allows customizable directory layouts and naming schemes through heuristic file
• it only converts the necessary dicoms, not everything in a directory
• you can keep links to dicom files in the participant layout
• it's faster than parsesdicomdir or mri_convert if you use dcm2nii option
• it tracks the provenance of the conversion from dicom to nifti in w3c prov format
• the example shows a conversion to openfmri layout structure

outputs:

satra

antsRegistration --float --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 1 --dimensionality 3 --initial-moving-transform [ $FIXED, $MOVING, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Rigid[ 0.1 ] --metric Mattes[ $FIXED, $MOVING, 1, 32, Regular, 0.3 ] --convergence [ 10000x111110x11110, 1e-08, 20 ] --smoothing-sigmas 4.0x2.0x1.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 0 --transform Affine[ 0.1 ] --metric Mattes[ $FIXED, $MOVING, 1, 32, Regular, 0.3 ] --convergence [ 10000x111110x11110, 1e-08, 20 ] --smoothing-sigmas 4.0x2.0x1.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 0 --transform SyN[ 0.2, 3.0, 0.0 ] --metric Mattes[ $FIXED, $MOVING, 0.5, 32 ] --metric CC[ $FIXED, $MOVING, 0.5, 4 ] --convergence [ 100x50x30, -0.01, 5 ] --smoothing-sigmas 1.0x0.5x0.0vox --shrink-factors 4x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 -

satra

from copy import deepcopy

from dcmstack import DcmMetaExtension
import nibabel as nb

def embed_nifti(dcmfiles, niftifile, force=False):
    stack = ds.parse_and_stack(dcmfiles, force=force).values()
    if len(stack) > 1:
        raise ValueError('Found multiple series')
    stack = stack[0]

satra

from surfer import Brain
lh = Brain('fs404', 'lh', 'pial')
rh = Brain('fs404', 'rh', 'pial', figure=lh._f)
lh.add_annotation('aparc', borders=False, alpha=0)
rh.add_annotation('aparc', borders=False, alpha=0.5)
"""
lh._geo_surf.actor.property.opacity = 0.3
rh._geo_surf.actor.property.opacity = 0.3
lh._geo_surf.contour.contours = [0.5]
lh._geo_surf.actor.mapper.progress = 1.0

satra

import argparse
import os

from nipype.pipeline.engine import Workflow, Node, MapNode
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.fsl.utils import ImageMaths
from nipype.interfaces.fsl.utils import Split
from nipype.interfaces.fsl.dti import XFibres
from nipype.interfaces.fsl.utils import Merge
from nipype.interfaces.fsl.dti import MakeDyadicVectors

satra

Step 0: recon-all -subjid H0351.1015 -all -i /data/allen/H0351.1015/T1.nii.gz -openmp 8 -notalairach

Step 1: recon-all -subjid H0351.1015 -T2 /data/allen/H0351.1015/T2.nii.gz -openmp 8

Step 2: recon-all -subjid H0351.1015 -T2pial -openmp 8

WARNING: tcsh v6.17.06 has an exit code bug! Please update tcsh!

Subject Stamp: freesurfer-Linux-centos6_x86_64-stable-pub-v5.3.0
Current Stamp: freesurfer-Linux-centos6_x86_64-stable-pub-v5.3.0

satra