oesteban

"""Check node duration in a Nipype workflow."""
from pathlib import path
import numpy as np
import pandas as pd

data = {'node': [], 'duration': []}
for pth in Path().glob("**/report.rst"):
    rst = pth.read_text()
    info = [l.split(":")[-1].strip() for l in rst.splitlines() if "duration" in l or "Hierarchy" in l]
    data['node'].append(info[0])

oesteban

Problem

A lot of GitHub projects need to have pretty math formulas in READMEs, wikis or other markdown pages. The desired approach would be to just write inline LaTeX-style formulas like this:

$e^{i \pi} = -1$

Unfortunately, GitHub does not support inline formulas. The issue is tracked here.

Investigation

oesteban

# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
A tool to sample OpenfMRI datasets using a datalad installation

Please run this first: ::

    # install openfmri dataset
    datalad install -r ///openfmri

oesteban

oesteban

# Makefile for Sphinx documentation
#

# You can set these variables from the command line.
SPHINXOPTS    =
SPHINXBUILD   = sphinx-build
PAPER         =
BUILDDIR      = _build

# User-friendly check for sphinx-build

oesteban

from os import getenv
from pathlib import Path
from json import load
import urllib.request, json
import pandas as pd

def get_rating(checksum):
    """
    Grab the latest rating found for a given MD5 checksum.
    

oesteban

antsRegistration -d 3 -m MI[sub-EMV00017_run-01_T2w_preproc.nii.gz,time2_hmc_avg_inu.nii.gz,1.0,32,Random,0.2] -t Affine[0.01] -c [100x100x100,1.0e-8,25] -s 0.6x0.4x0.2mm -f 2x1x1 -l -x [sub-EMV00017_run-01_T2w_brainmask.nii.gz] -u -v -o ts2_epi0 -a 1
antsRegistration -d 3 -m MI[sub-EMV00017_run-01_T2w_preproc.nii.gz,time2_hmc_avg_inu.nii.gz,0.5,32,Random,0.2] -m CC[sub-EMV00017_run-01_T2w_preproc.nii.gz,time2_hmc_avg_inu.nii.gz,0.5,5,Random,0.2] -t SyN[1,2.0,1.75] -c [200x50,1.0e-8,5] -s 0.4x0mm -f 2x1 -m MI[sub-EMV00017_run-01_T2w_preproc.nii.gz,time2_hmc_avg_inu.nii.gz,0.5,32,Random,0.2] -m CC[sub-EMV00017_run-01_T2w_preproc.nii.gz,time2_hmc_avg_inu.nii.gz,0.5,5,Random,0.2] -t SyN[1,1.0,0.75] -c [200x50,1.0e-8,5] -s 0.6x0.2mm -f 2x1 -l -x [sub-EMV00017_run-01_T2w_brainmask.nii.gz] -l -x [sub-EMV00017_run-01_T2w_brainmask.nii.gz] -u -v -o ts2_epiA -a 1 -r ts2_epi0Composite.h5 &> ts2.log
antsApplyTransforms -d 3 -i ~/Data/LC_dfMRI/tpl-Schwarz/tpl-Schwarz_rois.nii.gz -r time2_hmc_avg_inu.nii.gz -o time2_rois.

oesteban

# Find empty folders starting with ds
for dir in ds*; do [ -z "`find $dir -maxdepth 1 -type f`" ] && echo "$dir is empty"; done

oesteban

#!/bin/bash
#
#SBATCH -J fmriprep
#SBATCH --array=1-257%7
#SBATCH --time=24:00:00
#SBATCH -n 1
#SBATCH --cpus-per-task=16
#SBATCH --mem-per-cpu=4G
#SBATCH -p russpold,owners,normal

oesteban

import nibabel as nb
from nipype.utils.filemanip import fname_presuffix
def center_surface(in_gifti, newpath=None,
                   offset_from=['VolGeomC_R', 'VolGeomC_A', 'VolGeomC_S']):
    gii = nb.load(in_gifti)
    offset = [float(gii.darrays[0].meta.metadata[v]) for v in offset_from]
    data = gii.darrays[0].data + offset
    meta = gii.darrays[0].meta.metadata
    for k in offset_from:
        meta[k] = '%f' % 0.0