{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluation of fMRIPrep: assessing robustness and quality of results\n", "\n", "This notebook is a supplemental material to the paper: [doi here]\n", "\n", "### 0. Setting up" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#%matplotlib inline\n", "import os\n", "import json\n", "import pandas as pd\n", "import glob\n", "import numpy as np\n", "from pathlib import Path\n", "import matplotlib as mpl\n", "mpl.use('pgf')\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "OUTPUT_PATH = Path(os.getenv('FMRIPREP_FIGURES', os.getcwd()))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Evolution of quality from 1.0.0 to 1.0.7\n", "\n", "### Read data in" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | overall | \n", "t1_reconall | \n", "t1_seg_brainmask | \n", "t1_t1_2_mni | \n", "bold_rois | \n", "bold_bbr | \n", "bold_syn_sdc | \n", "
---|---|---|---|---|---|---|---|
count | \n", "109.000000 | \n", "109.000000 | \n", "109.000000 | \n", "109.000000 | \n", "106.000000 | \n", "106.000000 | \n", "103.000000 | \n", "
mean | \n", "2.528287 | \n", "2.727064 | \n", "2.611621 | \n", "2.821101 | \n", "2.653057 | \n", "2.834710 | \n", "2.558120 | \n", "
std | \n", "0.500028 | \n", "0.464655 | \n", "0.516350 | \n", "0.448882 | \n", "0.359703 | \n", "0.242025 | \n", "0.319758 | \n", "
min | \n", "0.500000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "1.812500 | \n", "2.000000 | \n", "1.875000 | \n", "
25% | \n", "2.500000 | \n", "2.500000 | \n", "2.500000 | \n", "3.000000 | \n", "2.462500 | \n", "2.750000 | \n", "2.333333 | \n", "
50% | \n", "2.500000 | \n", "3.000000 | \n", "2.750000 | \n", "3.000000 | \n", "2.750000 | \n", "2.943750 | \n", "2.500000 | \n", "
75% | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "2.833333 | \n", "
max | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "3.000000 | \n", "