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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Behavioral and Decoding Analyses"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook contains all of the top-level code underlying the statistical analyses reported in the paper, although much of the heavy lifting occurs elsewhere, e.g. in the [lyman](http://stanford.edu/~mwaskom/software/lyman/) package. All of the code to draw the data plots is also here.\n",
      "\n",
      "The structure of the notebook should correspond pretty directly to the structure of the paper. All of the analysis is written in specific functions to try and keep the namespace clean and avoid bugs."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h2>Contents</h2>\n",
      "<ul class=ult>\n",
      "  <li><a href=\"#Imports,-definitions,-and-project-specific-functions\">Imports, definitions and project-specific functions</a></li>\n",
      "  <li><a href=\"#Behavioral-results\">Behavioral Results</a></li>\n",
      "  <li><a href=\"#Context-decoding-in-lateral-PFC\">Context decoding in lateral PFC</a></li>\n",
      "  <li><a href=\"#Control-signals-in-posterior-neocortex\">Control signals in posterior neocortex</a></li>\n",
      "  <li><a href=\"#Effects-of-control-demands-on-context-representation\">Effects of control demands on context representation</a></li>\n",
      "</ul>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Imports, definitions, and project-specific functions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, import external packages that we'll use in this notebook"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "import os.path as op\n",
      "import itertools\n",
      "import numpy as np\n",
      "import scipy as sp\n",
      "import pandas as pd\n",
      "import nibabel as nib\n",
      "from scipy import stats\n",
      "import statsmodels.api as sm\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt\n",
      "from sklearn.linear_model import LogisticRegression\n",
      "from sklearn.metrics import r2_score"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now my personal libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import lyman\n",
      "from lyman import mvpa, evoked\n",
      "import seaborn as sns\n",
      "import moss"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set up display options for figures and tables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "sns.set(context=\"paper\", style=\"ticks\", font=\"Arial\")\n",
      "mpl.rcParams.update({\"xtick.major.width\": 1, \"ytick.major.width\": 1, \"savefig.dpi\": 150})\n",
      "pd.set_option('display.precision', 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Activate 3D plotting for matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits import mplot3d\n",
      "from mpl_toolkits.mplot3d import Axes3D"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set a random seed so code that uses randomness is reproducible. This mostly affects the bootstrapping that underlies error bars in the plots -- the permutation code for classifier analysis is seeded separately."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(sum(map(ord, reversed(\"DKsort\"))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the IPython magic extension to use R within this notebook and import external R packages we'll use."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rmagic\n",
      "%R library(lme4)\n",
      "%R library(multcomp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Loading required package: Matrix\n",
        "Loading required package: lattice\n",
        "\n",
        "Attaching package: \u2018lme4\u2019\n",
        "\n",
        "The following objects are masked from \u2018package:stats\u2019:\n",
        "\n",
        "    AIC, BIC\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Loading required package: mvtnorm\n",
        "Loading required package: survival\n",
        "Loading required package: splines\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Connect to an IPython cluster (this must be started separately) and get two direct view references -- one to all engines (we expect to run this on my 8-core Mac Pro) and one to only 4 engines. We use the `dv4` cluster view with the extraction functions so we can extract data in parallel without killing things by running too many parallel i/o processes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client, TimeoutError\n",
      "try:\n",
      "    dv = Client()[:]\n",
      "    dv4 = Client()[:4]\n",
      "except (IOError, TimeoutError):\n",
      "    dv = None\n",
      "    dv4 = None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/mwaskom/anaconda/envs/dksort/lib/python2.7/site-packages/IPython/parallel/client/client.py:444: RuntimeWarning: \n",
        "            Controller appears to be listening on localhost, but not on this machine.\n",
        "            If this is true, you should specify Client(...,sshserver='you@10.37.129.2')\n",
        "            or instruct your controller to listen on an external IP.\n",
        "  RuntimeWarning)\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas 0.12 prints a very annoying deprecation warning every time an HTML representation is created for a DataFrame. We're going to silence those here:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Control saving figures globally with this function so it is easy to switch between file types and possibly other options."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def save_figure(fig, figname):\n",
      "    fig.savefig(\"figures/%s.pdf\" % figname, dpi=300)\n",
      "    fig.savefig(\"figures/%s.tiff\" % figname, dpi=300)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the list of subject IDs for all the analyses."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subjects = pd.Series(lyman.determine_subjects(), name=\"subj\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get root paths for lyman outputs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "project = lyman.gather_project_info()\n",
      "anal_dir = project[\"analysis_dir\"]\n",
      "data_dir = project[\"data_dir\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set some colors globally to use in all figures."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rule_colors = dict(dim=[\"#EE6F25\", \"#4BC75B\", \"#9370DB\"], dec=[\"#6495EB\", \"#FF6347\"])\n",
      "roi_colors = dict(IFS=\"#CC3333\", aMFG=\"#3380CC\", pMFG=\"#33CC80\", pSFS=\"#CC8732\", IFG=\"#6464D8\",\n",
      "                  aIns=\"#F08080\", FPC=\"#CCCC33\", IPS=\"#2A4380\", OTC=\"#24913C\")\n",
      "roi_colors.update({\"aIFS\": \"#863D3D\", \"pIFS\": \"#7A5252\",\n",
      "                   \"lh-IFS\": \"#D19494\", \"rh-IFS\": \"#C2A3A3\"})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define an R function to facilitate reporting of likelihood ratio test of nested LMEMs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "lr_test = function(m1, m2, name){\n",
      "    out = anova(m1, m2)\n",
      "    chi2 = out$Chisq[2]\n",
      "    dof = out$\"Chi Df\"[2]\n",
      "    p = out$\"Pr(>Chisq)\"[2]\n",
      "    test_str = \"Likelihood ratio test for %s:\\n  Chisq(%d) = %.2f; p = %.3g\"\n",
      "    writeLines(sprintf(test_str, name, dof, chi2, p))\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Behavioral/design information"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read each subject's master behavioral file and build two group DataFrames -- one with all trials (`behav_full`) and one with only trials considered in the fMRI analyses (`behav_df`). The latter exludes incorrect trials as well as trials identified as artifacts in the fMRI preprocessing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_behav_join():\n",
      "    behav_temp = op.join(data_dir, \"%s/behav/behav_data.csv\")\n",
      "    behav_data = []\n",
      "    for subj in subjects:\n",
      "        df = pd.read_csv(behav_temp % subj, index_col=\"trial\")\n",
      "        df[\"subj\"] = np.repeat(subj, len(df))\n",
      "        behav_data.append(df)\n",
      "    behav_full = pd.concat(behav_data)\n",
      "    behav_df = behav_full[behav_full[\"clean\"] & behav_full[\"correct\"]]\n",
      "    return behav_full, behav_df\n",
      "    \n",
      "behav_full, behav_df = dksort_behav_join()\n",
      "%Rpush behav_df\n",
      "%Rpush behav_full"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "fMRI Parameters and Functions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define some parameters shared across fMRI analyses below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_rois = [\"IFS\", \"aMFG\", \"pMFG\", \"FPC\", \"IFG\", \"aIns\", \"pSFS\"]\n",
      "net_rois = [\"IFS\", \"IPS\", \"OTC\"]\n",
      "all_rois = pd.Series(pfc_rois + net_rois[1:], name=\"roi\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "other_rois = [\"lh-IFS\", \"rh-IFS\", \"aIFS\", \"pIFS\", \"AllROIs\"]\n",
      "other_masks = [\"lh.yeo17_ifs\", \"rh.yeo17_ifs\", \"yeo17_aifs\", \"yeo17_pifs\", \"dksort_all_pfc\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frames = np.arange(-1, 5)\n",
      "timepoints = frames * 2 + 1\n",
      "up_timepoints = (np.linspace(0, 12, 25) - 1)[:-1]\n",
      "model = LogisticRegression()\n",
      "n_shuffle = 1000\n",
      "peak = slice(2, 4)\n",
      "shuffle_seed = sum(map(ord, \"DKsort\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define several functions to perform the same sequence of analyses on different sets of ROIs/events."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_decode(rois, rule):\n",
      "    \"\"\"Do the all the main decoding steps across sets of ROIs.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    rois: list of strings\n",
      "        list of ROI names that can be easily mapped to a mask name\n",
      "    rule: string\n",
      "        name of rule type corresponding to design file in data dir\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    dictionary with the following entries\n",
      "        rois: list of roi names\n",
      "        accs: DataFrame with decoding accuracy. Index is hierarchical with\n",
      "              (ROI, subject), and columns are timepoint.\n",
      "        chance: DataFrame in same shape as `accs` with the mean value from\n",
      "                the shuffled null distribution for each test.\n",
      "        peak: DataFrame with decoding accuracy from data averaged over 3s\n",
      "              and 5s. Index is subject id and column is ROI.\n",
      "        null: DataFrame with maximum shuffled accuracy across timepoints.\n",
      "              Index is hierarchical with (subj, iteration) and columns\n",
      "              are ROIS.\n",
      "        ttest: DataFrame with ROIs in the index and (t, p, max_tp) for the\n",
      "               group average test against empirical chance in the\n",
      "               columns. `t` is the t statistic for the peak accuracy value,\n",
      "               and `p` is the corresponding p value corrected for multiple\n",
      "               comparisons across region and timepoint. max_tp is in seconds\n",
      "               relative to stimulus onset.\n",
      "        signif: DataFrame with the null distribution percentiles corresponding\n",
      "                to the best observed accuracy for each subject/ROI. Index is\n",
      "                hierarchical with (correction, subject) where correction can be\n",
      "                `time` or `omni` for the space of tests the percentile is corrected\n",
      "                against. columns are ROIs.\n",
      "                \n",
      "    \"\"\"    \n",
      "    # Set up the DataFrames to hold the persisent outputs\n",
      "    roi_index = moss.product_index([rois, subjects], [\"ROI\", \"subj\"])\n",
      "    columns = pd.Series(timepoints, name=\"timepoints\")\n",
      "    accs = pd.DataFrame(index=roi_index, columns=columns, dtype=float)\n",
      "\n",
      "    null_index = moss.product_index([subjects, np.arange(n_shuffle)], [\"subj\", \"iter\"])\n",
      "    null = pd.DataFrame(index=null_index, columns=pd.Series(rois, name=\"ROI\"), dtype=float)\n",
      "\n",
      "    peak_df = pd.DataFrame(index=subjects, columns=pd.Series(rois, name=\"ROI\"))\n",
      "    chance = pd.DataFrame(index=roi_index, columns=timepoints, dtype=float)\n",
      "    \n",
      "    # For each ROI load the data, decode, and simulate the null distribution\n",
      "    for roi in rois:\n",
      "        mask = \"yeo17_\" + roi.lower()\n",
      "        \n",
      "        # Load the dataset and do the basic time-resolved decoding\n",
      "        ds = mvpa.extract_group(rule, roi, mask, frames, confounds=\"rt\", dv=dv4)\n",
      "        roi_accs = mvpa.decode_group(ds, model, dv=dv)\n",
      "        accs.loc[roi, :] = roi_accs\n",
      "        \n",
      "        # Now do the shuffling and save the partially transformed null distribution\n",
      "        roi_null = mvpa.classifier_permutations(ds, model, n_iter=n_shuffle,\n",
      "                                                random_seed=shuffle_seed, dv=dv)\n",
      "        chance.loc[roi, :] = roi_null.mean(axis=1)\n",
      "        null[roi] = roi_null.max(axis=-1).ravel()\n",
      "\n",
      "        # Finally re-load the data averaging over the peak timepoints and decode\n",
      "        peak_ds = mvpa.extract_group(rule, roi, mask, frames, peak, \"rt\", dv=dv4)\n",
      "        peak_df[roi] = mvpa.decode_group(peak_ds, model, dv=dv)\n",
      "\n",
      "    # Do the group t tests\n",
      "    wide_accs = accs.unstack(level=\"ROI\")\n",
      "    wide_chance = chance.unstack(level=\"ROI\")\n",
      "    mus = wide_accs.mean(axis=0)\n",
      "    sds = wide_accs.std(axis=0)\n",
      "    ts, ps = moss.randomize_onesample(wide_accs, h_0=wide_chance)\n",
      "\n",
      "    # Build the t test output\n",
      "    t_df = pd.DataFrame(dict(t=ts, p=ps, mu=mus, sd=sds),\n",
      "                        index=wide_accs.columns, columns=[\"mu\", \"sd\", \"t\", \"p\"])\n",
      "    ttest = t_df.groupby(level=\"ROI\").apply(lambda x: x.loc[x.mu.idxmax()])\n",
      "    ttest[\"tp\"] = t_df.groupby(level=\"ROI\").mu.apply(lambda x: x.idxmax()[0])\n",
      "\n",
      "    # From the null distribution, find the percentile corresponding to the observed score\n",
      "    signif_index = moss.product_index([[\"time\", \"omni\"], subjects], [\"correction\", \"subj\"])    \n",
      "    signif = pd.DataFrame(index=signif_index, columns=rois, dtype=float)\n",
      "    for roi, roi_accs in accs.max(axis=1).groupby(level=\"ROI\"):\n",
      "        for subj, score in roi_accs.groupby(level=\"subj\"):\n",
      "            signif.loc[(\"time\", subj), roi] = stats.percentileofscore(null[roi], score[0])\n",
      "            signif.loc[(\"omni\", subj), roi] = stats.percentileofscore(null.max(axis=1), score[0])    \n",
      "\n",
      "    return dict(rois=rois, accs=accs, chance=chance, peak=peak_df,\n",
      "                null=null, ttest=ttest, signif=signif)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_timecourse_figure(ax, data, ytick_args, err_style=\"ci_band\", legend=True, err_kws=None, **kwargs):\n",
      "    \"\"\"Plot time-resolved decoding accuracies for multiple ROIs\"\"\"\n",
      "    # Represent chance empirically based on the null distribution\n",
      "    chance = np.array(data[\"chance\"].mean(axis=0))\n",
      "    ax.plot(timepoints, chance, \"k--\")\n",
      "\n",
      "    # Plot the stimulus onset\n",
      "    ax.plot([0, 0], ytick_args[:2], ls=\":\", c=\"k\")\n",
      "    \n",
      "    # Draw the accuracy timecourse for each ROI\n",
      "    colors = [roi_colors[roi] for roi in data[\"rois\"]]\n",
      "    interpolate = not err_style == \"ci_bars\"\n",
      "    accs = pd.melt(data[\"accs\"].reset_index(), [\"subj\", \"ROI\"], value_name=\"acc\")\n",
      "    sns.tsplot(accs, time=\"timepoints\", unit=\"subj\", condition=\"ROI\", value=\"acc\",\n",
      "               color=colors, err_style=err_style, interpolate=interpolate, legend=legend,\n",
      "               err_kws=err_kws, ax=ax, **kwargs)\n",
      "\n",
      "    # Adjust the axis scales and tick labels\n",
      "    ylim = ytick_args[:2]\n",
      "    yticks = np.linspace(*ytick_args)\n",
      "    \n",
      "    ax.set_xlim(timepoints.min(), timepoints.max())\n",
      "    ax.set_ylim(ylim)\n",
      "    ax.set_yticks(yticks)\n",
      "\n",
      "    # Label the axes\n",
      "    ax.set_xlabel(\"Time relative to stimulus onset (s)\")\n",
      "    ax.set_ylabel(\"Cross-validated decoding accuracy\")\n",
      "    \n",
      "    # Make a legend with the ROIs\n",
      "    ax.legend(frameon=False, loc=\"upper right\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_point_figure(ax, data, chance, ytick_args, yaxis_label):\n",
      "    \"\"\"Plot a point estimate and CI for the 3-5s accuracy by ROI.\"\"\"\n",
      "    # Plot the chance line\n",
      "    ax.axhline(chance, color=\"k\", ls=\"--\")\n",
      "\n",
      "    # Plot each accuracy and confidence interval\n",
      "    for i, roi in enumerate(data.columns):\n",
      "        color = roi_colors[roi]\n",
      "        accs = data[roi].values\n",
      "        ci = moss.ci(moss.bootstrap(accs), 68)\n",
      "        ax.plot(i, accs.mean(), \"o\", color=color, ms=5, mew=0, mec=color)\n",
      "        ax.plot([i, i], ci, color=color, lw=2, solid_capstyle=\"round\")\n",
      "\n",
      "    # Set the axis limits\n",
      "    ax.set_xlim(-.5, data.shape[1] - .5)\n",
      "    ax.set_ylim(*ytick_args[:2])\n",
      "    yticks = np.linspace(*ytick_args)\n",
      "    ax.set_yticks(yticks)\n",
      "    ax.set_yticklabels([\"%.2f\" % t for t in yticks])\n",
      "    ax.xaxis.grid(False)\n",
      "    \n",
      "    # Set the axis labels\n",
      "    ax.set_xticks(np.arange(len(data.columns)))\n",
      "    ax.set_xticklabels(data.columns, rotation=60)\n",
      "    if yaxis_label:\n",
      "        ax.set_ylabel(\"Cross-validated decoding accuracy\")\n",
      "    else:\n",
      "        ax.set_yticklabels([])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_significance_figure(ax, data):\n",
      "    \"\"\"Plot the percentile of the null for each ROI by subject.\"\"\"\n",
      "    width = 2 / len(data)\n",
      "    xbase = np.arange(0, 1, width)\n",
      "    rois = data.columns\n",
      "    \n",
      "    # Plot percentile across ROI/time and time correction\n",
      "    for kind, kind_df in data.groupby(level=\"correction\"):\n",
      "        if kind == \"time\": continue\n",
      "        for i, roi in enumerate(rois):\n",
      "            height = kind_df[roi]\n",
      "            color = sns.desaturate(roi_colors[roi], .75)\n",
      "            edgecolor = sns.desaturate(color, .5)\n",
      "            alpha = .25 if kind == \"time\" else .7\n",
      "            \n",
      "            ax.bar(xbase + i, np.sort(height), width=width,\n",
      "                   color=color, edgecolor=edgecolor, lw=0.1, alpha=alpha)\n",
      "            if i:\n",
      "                ax.axvline(i, lw=.5, color=\"#555555\")\n",
      "\n",
      "    # Set the other plot aesthetics\n",
      "    ax.set_ylim(0, 100)\n",
      "    ax.axhline(95, c=\"#444444\", ls=\":\")\n",
      "    ax.set_xticks(np.arange(data.shape[1]) + .5)\n",
      "    ax.set_xticklabels(rois, rotation=60)\n",
      "    ax.xaxis.grid(False)\n",
      "    ax.set_ylabel(\"Percentile in null distribution\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Behavioral results"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Speed and accuracy summary"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Summarize overall accuracy and reaction time across subjects. Take medians for RT within subject, but report the group mean over these medians."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_behav_summary():\n",
      "    accs = behav_full.groupby(\"subj\").correct.mean()\n",
      "    print \"Accuracy across subjects: Mean %.2g; Std %.2g; Min: %.2g\" % (accs.mean(), accs.std(), accs.min())\n",
      "    rts = behav_df.groupby(\"subj\").rt.median() * 1000\n",
      "    print \"Median RT across subjects (ms) - Mean %.3g; Std %.3g\" % (rts.mean(), rts.std()) \n",
      "    iqrs = behav_df.groupby(\"subj\").rt.apply(moss.iqr) * 1000\n",
      "    print \"RT IQRs across subjects (ms) - Mean: %.3g; Std %.3g\" % (iqrs.mean(), iqrs.std())\n",
      "\n",
      "dksort_behav_summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy across subjects: Mean 0.95; Std 0.033; Min: 0.87\n",
        "Median RT across subjects (ms) - Mean 826; Std 209\n",
        "RT IQRs across subjects (ms) - Mean: 307; Std 101\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Speed as a function of rule"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, test whether the task rules influence the speed of responses. We'll perform these analyses in R using linear mixed effects models (LMEMs), which account for both within- and between-subjects variance. To test the significance of the models, we'll use likelihood ratio tests ([Barr et al. 2013](http://idiom.ucsd.edu/~rlevy/papers/barr-etal-2013-jml.pdf)). Also following this paper, we will use random effects structures that model random slopes for all main effect terms considered in the fixed effects structure.\n",
      "\n",
      "First, we fit both an interactive model and additive model (betwen rulesets) predicting RT and use a likelihood ratio test to see whether the interaction is significant."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R \n",
      "m.rt.int = lmer(rt ~ dim_rule * dec_rule + (dim_rule + dec_rule | subj), behav_df, REML=F)\n",
      "m.rt.add = lmer(rt ~ dim_rule + dec_rule + (dim_rule + dec_rule | subj), behav_df, REML=F)\n",
      "lr_test(m.rt.int, m.rt.add, \"interaction\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for interaction:\n",
        "  Chisq(2) = 0.57; p = 0.752\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Print the additive model coefficients and standard errors"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R print(m.rt.add, corr=FALSE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Linear mixed model fit by maximum likelihood \n",
        "Formula: rt ~ dim_rule + dec_rule + (dim_rule + dec_rule | subj) \n",
        "   Data: behav_df \n",
        "   AIC  BIC logLik deviance REMLdev\n",
        " 925.4 1020 -447.7    895.4   920.1\n",
        "Random effects:\n",
        " Groups   Name            Variance   Std.Dev. Corr                 \n",
        " subj     (Intercept)     0.04205506 0.205073                      \n",
        "          dim_rulepattern 0.00140533 0.037488 -0.350               \n",
        "          dim_ruleshape   0.00094827 0.030794 -0.130  0.974        \n",
        "          dec_rulesame    0.00291850 0.054023 -0.022  0.174  0.179 \n",
        " Residual                 0.07148692 0.267370                      \n",
        "Number of obs: 3961, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "                 Estimate Std. Error t value\n",
        "(Intercept)      0.923318   0.053624  17.218\n",
        "dim_rulepattern -0.009415   0.014222  -0.662\n",
        "dim_ruleshape   -0.016024   0.013077  -1.225\n",
        "dec_rulesame    -0.083386   0.016337  -5.104\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Report the omnibus test for the additive model and likelihood ratio tests for each main effect."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "print(anova(m.rt.add))\n",
      "m.rt.dim = lmer(rt ~ dec_rule + (dim_rule + dec_rule | subj), behav_df, REML=F)\n",
      "m.rt.dec = lmer(rt ~ dim_rule + (dim_rule + dec_rule | subj), behav_df, REML=F)\n",
      "lr_test(m.rt.add, m.rt.dim, \"dimension rule\")\n",
      "lr_test(m.rt.add, m.rt.dec, \"decision rule\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Analysis of Variance Table\n",
        "         Df  Sum Sq Mean Sq F value\n",
        "dim_rule  2 0.05954 0.02977  0.4164\n",
        "dec_rule  1 1.86241 1.86241 26.0524\n",
        "Likelihood ratio test for dimension rule:\n",
        "  Chisq(2) = 1.52; p = 0.467\n",
        "Likelihood ratio test for decision rule:\n",
        "  Chisq(1) = 15.09; p = 0.000103\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It's possible that the dimension rules may influence RTs differently for each participant and wash out in the group analysis. To examine this possiblitly, perform a one-way ANOVA across each ruleset within each participant and count the number of subjects with a trending or significant effect."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_rt_anova():\n",
      "    subj_grouped = behav_df.groupby(\"subj\")\n",
      "    dim = subj_grouped.apply(moss.df_oneway, \"dim_rule\", \"rt\", False)\n",
      "    dec = subj_grouped.apply(moss.df_oneway, \"dec_rule\", \"rt\", False)\n",
      "    f_scores = pd.concat([dim, dec], keys=[\"dimension\", \"decision\"], names=[\"rules\", \"subj\"])\n",
      "    print \"Median F score: %.2f\" % f_scores.loc[\"dimension\"][\"F\"].median()\n",
      "    f_scores[\"p < 0.05\"] = f_scores.p < 0.05\n",
      "    f_scores[\"p < 0.1\"] = f_scores.p < 0.1\n",
      "    print f_scores.groupby(level=\"rules\")[[\"p < 0.05\", \"p < 0.1\"]].sum()\n",
      "    return subj_grouped, f_scores\n",
      "\n",
      "subj_grouped, f_scores = dksort_rt_anova()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Median F score: 0.67\n",
        "           p < 0.05  p < 0.1\n",
        "rules                       \n",
        "dimension         2        2\n",
        "decision          8       10\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Also, to get a measurement of the slowing associated with the 'different' rule, take the difference in medians across the rules within subjects, then report the mean and bootstrapped confidence interval for the mean across subjects."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_rule_rt_effect():\n",
      "    pivot = pd.pivot_table(behav_df, \"rt\", \"subj\", \"dec_rule\", np.median)\n",
      "    cost = np.diff(pivot, axis=1) * 1000\n",
      "    boots = moss.bootstrap(cost)\n",
      "    ci_low, ci_high = moss.ci(boots, 95)\n",
      "    print \"'Different' rule effect (ms): Mean %.3g, CI: [%.3g, %.3g]\" % (boots.mean(), ci_low, ci_high)\n",
      "\n",
      "dksort_rule_rt_effect()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "'Different' rule effect (ms): Mean -94.4, CI: [-123, -66.2]\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Speed as a function of attended feature matching and decision rule"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next we'll ask whether RT is modulated by whether the attended features match"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.match = lmer(rt ~ attend_match + (attend_match | subj), behav_df, REML=FALSE)\n",
      "m.match.drop = lmer(rt ~ (attend_match | subj), behav_df, REML=FALSE)\n",
      "print(m.match, corr=FALSE)\n",
      "lr_test(m.match, m.match.drop, \"attended matching\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Linear mixed model fit by maximum likelihood \n",
        "Formula: rt ~ attend_match + (attend_match | subj) \n",
        "   Data: behav_df \n",
        "  AIC  BIC logLik deviance REMLdev\n",
        " 1028 1066 -508.2     1016    1028\n",
        "Random effects:\n",
        " Groups   Name             Variance   Std.Dev. Corr  \n",
        " subj     (Intercept)      0.04046794 0.201166       \n",
        "          attend_matchTRUE 0.00014153 0.011897 0.208 \n",
        " Residual                  0.07422838 0.272449       \n",
        "Number of obs: 3961, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "                 Estimate Std. Error t value\n",
        "(Intercept)      0.866011   0.052302  16.558\n",
        "attend_matchTRUE 0.013382   0.009189   1.456\n",
        "Likelihood ratio test for attended matching:\n",
        "  Chisq(1) = 1.99; p = 0.159\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll also ask whether this interacts with the decision rule."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.match.int = lmer(rt ~ attend_match * dec_rule + (attend_match + dec_rule | subj), behav_df, REML=FALSE)\n",
      "m.match.add = lmer(rt ~ attend_match + dec_rule + (attend_match + dec_rule | subj), behav_df, REML=FALSE)\n",
      "print(m.match.int, corr=FALSE)\n",
      "lr_test(m.match.int, m.match.add, \"interaction\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Linear mixed model fit by maximum likelihood \n",
        "Formula: rt ~ attend_match * dec_rule + (attend_match + dec_rule | subj) \n",
        "   Data: behav_df \n",
        "   AIC   BIC logLik deviance REMLdev\n",
        " 842.3 911.4 -410.1    820.3   844.7\n",
        "Random effects:\n",
        " Groups   Name             Variance   Std.Dev. Corr          \n",
        " subj     (Intercept)      0.03968419 0.199209               \n",
        "          attend_matchTRUE 0.00017723 0.013313  0.322        \n",
        "          dec_rulesame     0.00299747 0.054749  0.010 -0.782 \n",
        " Residual                  0.07028425 0.265112               \n",
        "Number of obs: 3961, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "                               Estimate Std. Error t value\n",
        "(Intercept)                    0.870299   0.052129  16.695\n",
        "attend_matchTRUE               0.088947   0.012443   7.148\n",
        "dec_rulesame                  -0.007847   0.018508  -0.424\n",
        "attend_matchTRUE:dec_rulesame -0.150621   0.016855  -8.936\n",
        "Likelihood ratio test for interaction:\n",
        "  Chisq(1) = 79.04; p = 6.08e-19\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Response accuracy as a function of rule"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now perform a similar sequence of analyses on response accuracy, here using mixed effects logistic regression models."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.acc.int = lmer(correct ~ dim_rule * dec_rule +\n",
      "                 (dim_rule + dec_rule | subj), behav_full, family=binomial)\n",
      "m.acc.add = lmer(correct ~ dim_rule + dec_rule +\n",
      "                 (dim_rule + dec_rule | subj), behav_full, family=binomial)\n",
      "lr_test(m.acc.int, m.acc.add, \"interaction\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for interaction:\n",
        "  Chisq(2) = 0.15; p = 0.927\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R print(m.acc.add, corr=FALSE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Generalized linear mixed model fit by the Laplace approximation \n",
        "Formula: correct ~ dim_rule + dec_rule + (dim_rule + dec_rule | subj) \n",
        "   Data: behav_full \n",
        "  AIC  BIC logLik deviance\n",
        " 1696 1785 -833.8     1668\n",
        "Random effects:\n",
        " Groups Name            Variance Std.Dev. Corr                 \n",
        " subj   (Intercept)     0.46217  0.67983                       \n",
        "        dim_rulepattern 0.01600  0.12649  -1.000               \n",
        "        dim_ruleshape   0.22851  0.47802  -0.250  0.250        \n",
        "        dec_rulesame    0.15589  0.39483  -0.285  0.285 -0.347 \n",
        "Number of obs: 4320, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "                Estimate Std. Error z value Pr(>|z|)    \n",
        "(Intercept)       3.0981     0.2304  13.449   <2e-16 ***\n",
        "dim_rulepattern  -0.3197     0.1775  -1.801   0.0716 .  \n",
        "dim_ruleshape    -0.1417     0.2229  -0.636   0.5249    \n",
        "dec_rulesame      0.4053     0.1805   2.246   0.0247 *  \n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.acc.dim = lmer(correct ~ dec_rule + (dim_rule + dec_rule | subj), behav_full, family=binomial)\n",
      "m.acc.dec = lmer(correct ~ dim_rule + (dim_rule + dec_rule | subj), behav_full, family=binomial)\n",
      "lr_test(m.acc.add, m.acc.dim, \"dimension rules\")\n",
      "lr_test(m.acc.add, m.acc.dec, \"decision rules\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for dimension rules:\n",
        "  Chisq(2) = 2.64; p = 0.267\n",
        "Likelihood ratio test for decision rules:\n",
        "  Chisq(1) = 3.85; p = 0.0498\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_rule_acc_effect():\n",
      "    pivot = pd.pivot_table(behav_full, \"correct\", \"subj\", \"dec_rule\")\n",
      "    cost = np.diff(pivot, axis=1) * 100\n",
      "    boots = moss.bootstrap(cost)\n",
      "    low, high = moss.ci(boots, 95)\n",
      "    args = cost.mean(), low, high\n",
      "    print \"'Different' rule response accuracy cost: %.2g%%; CI: [%.2g%%, %.2g%%]\" % args\n",
      "\n",
      "dksort_rule_acc_effect()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "'Different' rule response accuracy cost: 1.9%; CI: [0.28%, 3.7%]\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Rule Switching Analyses"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now consider how shifting back and forth between different rulesets influences response speed.\n",
      "\n",
      "First, analyze it as a function of how many trials have elapsed since the rule switch. We'll fit this in two separate models as the lag is frequently identical for each ruleset."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.lag.dim = lmer(rt ~ dim_shift_lag + (dim_shift_lag | subj), behav_df, REML=FALSE)\n",
      "m.lag.dec = lmer(rt ~ dec_shift_lag + (dec_shift_lag | subj), behav_df, REML=FALSE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R print(m.lag.dim, corr=FALSE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Linear mixed model fit by maximum likelihood \n",
        "Formula: rt ~ dim_shift_lag + (dim_shift_lag | subj) \n",
        "   Data: behav_df \n",
        "  AIC  BIC logLik deviance REMLdev\n",
        " 1031 1068 -509.3     1019    1033\n",
        "Random effects:\n",
        " Groups   Name          Variance   Std.Dev.  Corr  \n",
        " subj     (Intercept)   4.0091e-02 0.2002269       \n",
        "          dim_shift_lag 1.0637e-06 0.0010314 1.000 \n",
        " Residual               7.4303e-02 0.2725866       \n",
        "Number of obs: 3961, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "               Estimate Std. Error t value\n",
        "(Intercept)   0.8714653  0.0520818  16.733\n",
        "dim_shift_lag 0.0005944  0.0022995   0.258\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R print(m.lag.dec, corr=FALSE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Linear mixed model fit by maximum likelihood \n",
        "Formula: rt ~ dec_shift_lag + (dec_shift_lag | subj) \n",
        "   Data: behav_df \n",
        "  AIC  BIC logLik deviance REMLdev\n",
        " 1022 1059 -504.9     1010    1024\n",
        "Random effects:\n",
        " Groups   Name          Variance   Std.Dev.  Corr   \n",
        " subj     (Intercept)   4.4698e-02 0.2114198        \n",
        "          dec_shift_lag 1.5458e-05 0.0039316 -0.830 \n",
        " Residual               7.4109e-02 0.2722290        \n",
        "Number of obs: 3961, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "               Estimate Std. Error t value\n",
        "(Intercept)    0.885098   0.054989  16.096\n",
        "dec_shift_lag -0.004589   0.002155  -2.129\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.lag.nodim = lmer(rt ~ 1 + (dim_shift_lag | subj), behav_df, REML=FALSE)\n",
      "m.lag.nodec = lmer(rt ~ 1 + (dec_shift_lag | subj), behav_df, REML=FALSE)\n",
      "lr_test(m.lag.dim, m.lag.nodim, \"dimension rules\")\n",
      "lr_test(m.lag.dec, m.lag.nodec, \"decision rules\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for dimension rules:\n",
        "  Chisq(1) = 0.07; p = 0.797\n",
        "Likelihood ratio test for decision rules:\n",
        "  Chisq(1) = 3.88; p = 0.0487\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, directly test switch costs by analyzing RT on the first trial of each block as a function of rule switches. We can do this in one model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "shift_subset = behav_df$block_pos == 0\n",
      "m.shift.add = lmer(rt ~ dim_shift + dec_shift + (dim_shift + dec_shift | subj),\n",
      "                   behav_df, subset=shift_subset, REML=FALSE)\n",
      "m.shift.int = lmer(rt ~ dim_shift * dec_shift + (dim_shift + dec_shift | subj), behav_df,\n",
      "                   subset=shift_subset, REML=FALSE)\n",
      "lr_test(m.shift.add, m.shift.int, \"interaction\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for interaction:\n",
        "  Chisq(1) = 0.61; p = 0.436\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R print(m.shift.add, corr=FALSE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Linear mixed model fit by maximum likelihood \n",
        "Formula: rt ~ dim_shift + dec_shift + (dim_shift + dec_shift | subj) \n",
        "   Data: behav_df \n",
        " Subset: shift_subset \n",
        " AIC   BIC logLik deviance REMLdev\n",
        " 397 448.6 -188.5      377   393.7\n",
        "Random effects:\n",
        " Groups   Name          Variance   Std.Dev. Corr          \n",
        " subj     (Intercept)   0.04266668 0.206559               \n",
        "          dim_shiftTRUE 0.00013579 0.011653 -1.000        \n",
        "          dec_shiftTRUE 0.00056459 0.023761  1.000 -1.000 \n",
        " Residual               0.07488755 0.273656               \n",
        "Number of obs: 1290, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "              Estimate Std. Error t value\n",
        "(Intercept)    0.86474    0.05606  15.426\n",
        "dim_shiftTRUE  0.01119    0.01728   0.648\n",
        "dec_shiftTRUE  0.01977    0.01668   1.185\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.shift.nodim = lmer(rt ~ dec_shift + (dim_shift + dec_shift | subj),\n",
      "                     behav_df, subset=shift_subset, REML=FALSE)\n",
      "m.shift.nodec = lmer(rt ~ dim_shift + (dim_shift + dec_shift | subj),\n",
      "                     behav_df, subset=shift_subset, REML=FALSE)\n",
      "lr_test(m.shift.add, m.shift.nodim, \"dimension rule\")\n",
      "lr_test(m.shift.add, m.shift.nodec, \"decision rule\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for dimension rule:\n",
        "  Chisq(1) = 0.42; p = 0.517\n",
        "Likelihood ratio test for decision rule:\n",
        "  Chisq(1) = 1.39; p = 0.239\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Behavioral analysis figures"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is the main behavioral analysis figure. There is a lot of information packed into this plot, so it is complicated to draw."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_figure_2():\n",
      "    # Set up variables shared across subplots\n",
      "    dfs = [behav_df, behav_full]\n",
      "    measures = [\"rt\", \"correct\"]\n",
      "    agg_funcs = [np.median, np.mean]\n",
      "    ci = 68\n",
      "    xlabels = [\"Dimension rules\", \"Attended features\", \"Trials since rule switch\"]\n",
      "    ylabels = [\"Reaction time (s)\", \"Response accuracy\"]\n",
      "    xticks = [range(3), range(2), range(1, 7)]\n",
      "    xticklabels = [[\"Shape\", \"Color\", \"Pattern\"], [\"Mismatch\", \"Match\"], range(1, 7)]\n",
      "    xlims = [[-.5, 2.5], [-.5, 1.5], [.25, 6.75]]\n",
      "    ylims = [[.67, 1.02], [.8, 1.02]]\n",
      "    yticks = [[.7, 1, 4], [.8, 1, 3]]\n",
      "    ms, mew, lw = 3.5, 1.2, 1.2\n",
      "    err_kws = dict(linewidth=lw, mew=mew)\n",
      "    lag_colors = dict(Dimension=\"black\", Decision=\"lightslategray\")\n",
      "    text_offset = (0.01, -0.01)\n",
      "    text_size = 11\n",
      "    \n",
      "    # Draw each plot\n",
      "    f, axes = plt.subplots(2, 3, figsize=(4.48, 3))\n",
      "    for i in range(2):\n",
      "\n",
      "        # First analyze RT/accuracy analysis sorted by rule types\n",
      "        pivots = []\n",
      "        for subj, df_subj in dfs[i].groupby(\"subj\"):\n",
      "            pivot = pd.pivot_table(df_subj, measures[i], \"dim_rule\", \"dec_rule\", agg_funcs[i])\n",
      "            pivots.append(np.array(pivot))\n",
      "        pivots = np.array(pivots, float)\n",
      "        means = pivots.mean(axis=0)\n",
      "        cis = moss.ci(moss.bootstrap(pivots, axis=0), ci, axis=0)\n",
      "    \n",
      "        # Plot the above results in plot columns\n",
      "        ax = axes[i, 0]\n",
      "        for dim in range(3):\n",
      "            color = rule_colors[\"dim\"][dim]\n",
      "            for dec in range(2):\n",
      "                x = dim + (dec - .5) / 3.5\n",
      "                y = means[dim, dec]\n",
      "                ci_x = cis[:, dim, dec]\n",
      "                mfc = ax.get_axis_bgcolor() if dec else color\n",
      "\n",
      "                # Plotting calls\n",
      "                ax.plot([x, x], ci_x, lw=lw, color=color)\n",
      "                ax.plot(x, y, \"o\", ms=ms, mew=mew, mfc=mfc, mec=color)\n",
      "    \n",
      "        # Draw two plots out of the axis range to support the legend\n",
      "        for j, rule in enumerate([\"Same\", \"Different\"]):\n",
      "\n",
      "            # Plotting calls\n",
      "            ax.plot(-1, -1, \"o\", color=\"#444444\", mec=\"#444444\", ms=ms,\n",
      "                    mfc=[\"none\", \"#444444\"][j], mew=mew, label=\"'%s' rule\" % rule)\n",
      "    \n",
      "        # Next plot the decision rule by feature matching analysis\n",
      "        pivots = []\n",
      "        for subj, df_subj in dfs[i].groupby(\"subj\"):\n",
      "            pivot = pd.pivot_table(df_subj, measures[i], \"attend_match\", \"dec_rule\", agg_funcs[i])\n",
      "            pivots.append(np.array(pivot))\n",
      "        pivots = np.array(pivots, float)\n",
      "        means = pivots.mean(axis=0)\n",
      "        cis = moss.ci(moss.bootstrap(pivots, axis=0), ci, axis=0)\n",
      "        \n",
      "        ax  = axes[i, 1]\n",
      "        for k, rule in enumerate([\"Same\", \"Different\"]):\n",
      "            color = rule_colors[\"dec\"][k]\n",
      "            x = np.array([0, 1])\n",
      "\n",
      "            # Plotting calls\n",
      "            sns.tsplot(pivots[..., 1 - k], time=x, err_style=\"ci_bars\", ci=ci,\n",
      "                       color=color, marker=\"o\", mec=color, label = \"'%s' rule\" % rule,\n",
      "                       ms=ms, mfc=color, mew=mew, lw=lw, err_kws=err_kws, ax=ax)\n",
      "    \n",
      "        # Now do the analysis as a function of rule switching\n",
      "        for j, ruleset in enumerate([\"Dimension\", \"Decision\"]):\n",
      "            color = lag_colors[ruleset]\n",
      "            pivot_cols = ruleset[:3].lower() + \"_shift_lag\"\n",
      "            lag_pivot = pd.pivot_table(dfs[i], measures[i], \"subj\", pivot_cols, agg_funcs[i])\n",
      "            ts_kws =  dict(err_style=\"ci_bars\", color=color, ci=ci,\n",
      "                           lw=lw, err_kws=err_kws, ax=axes[i, 2], marker=\"o\", ms=3)\n",
      "\n",
      "            # Plotting calls\n",
      "            sns.tsplot(lag_pivot.loc[:, :2].values, time=[1, 2, 3], **ts_kws)\n",
      "            sns.tsplot(lag_pivot.loc[:, (2, 3)].values, time=[3, 4], linestyle=\":\", **ts_kws)\n",
      "            sns.tsplot(lag_pivot.loc[:, 3:5].values, time=[4, 5, 6], label=ruleset + \" rules\", **ts_kws)\n",
      "    \n",
      "        # Finally, iterate through the plots and set more supporting variables\n",
      "        for j in range(3):\n",
      "            ax = axes[i, j]\n",
      "            yticks_ = np.linspace(*yticks[i])\n",
      "            ax.set_xticks(xticks[j])\n",
      "            ax.set_yticks(yticks_)\n",
      "            ax.set_xlim(*xlims[j])\n",
      "            ax.set_ylim(ylims[i])\n",
      "            if i:\n",
      "                ax.set_xlabel(xlabels[j])\n",
      "                ax.set_xticklabels(xticklabels[j])\n",
      "            else:\n",
      "                ax.set_xticklabels([])\n",
      "            if not j:\n",
      "                ax.set_ylabel(ylabels[i])\n",
      "                ax.set_yticklabels(yticks_)\n",
      "            else:\n",
      "                ax.set_yticklabels([])\n",
      "            if i:\n",
      "                ax.legend(loc=\"lower left\")\n",
      "            \n",
      "    f.subplots_adjust(wspace=.08, hspace=.08, left=.1, bottom=.13, top=.97, right=.97) \n",
      "    \n",
      "    # Label the facets\n",
      "    for ax, s in zip(f.axes, \"ACEBDF\"):\n",
      "        (x, _), (_, y) = ax.bbox.transformed(f.transFigure.inverted()).get_points()\n",
      "        x += text_offset[0]\n",
      "        y += text_offset[1]\n",
      "        f.text(x, y, s, size=text_size, ha=\"left\", va=\"top\")\n",
      "    \n",
      "    sns.despine()\n",
      "    save_figure(f, \"figure_2\")\n",
      "\n",
      "dksort_figure_2()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CpYjou+fiY3Dz8MTBUZ2/Q+T/Fs5iwUfvmL33RlwiANU9PXB0KHxziV+27sVe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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dee92d0>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"width:585px;padding-left:120px\" align=\"justify\">\n",
      "**Main behavioral results.** Mean within-subject median reaction time (top row) and mean within-subject proportion correct responses (bottom row) sorted by the two rule sets (A and B), by the decision rule and whether the attended features matched or differed (C and D), and by the number of since the rule switch plotted separately with respect to the dimension and decision rules (E and F). The timecourses in E and F are dashed between trials 3 and 4 to indicate passage through a mini-block boundary. Error bars on all facets indicate bootstrapped standard errors of the aggregate values across subjects. Reaction times are plotted only for trials included in the imaging analyses.\n",
      "</div>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now the supplemental behavioral figure (not used in the JNeuro submission, but keeping this around)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_supplemental_figure_1():\n",
      "    f, axes = plt.subplots(2, 1, figsize=(6.85, 6.3))\n",
      "    \n",
      "    positions = dict(dim=[.25, .5, .75], dec=[.33, .66])\n",
      "    widths = dict(dim=.18, dec=.25)\n",
      "    rules = dict(dim=[\"shape\", \"color\", \"pattern\"], dec=[\"same\", \"different\"])\n",
      "    \n",
      "    text_offset = (.01, -.015)\n",
      "    text_size = 12\n",
      "    \n",
      "    subj_medians = subj_grouped.rt.median()\n",
      "    subj_sorted = subjects[np.argsort(subj_medians)]\n",
      "    for ruleset, ax in zip([\"dimension\", \"decision\"], axes):\n",
      "        rs = ruleset[:3]\n",
      "        for i, subj in enumerate(subj_sorted):\n",
      "            df_i = behav_df[behav_df[\"subj\"] == subj]\n",
      "            for j, rule in enumerate(rules[rs]):\n",
      "                df_rule = df_i[df_i[rs + \"_rule\"] == rule]\n",
      "                pos = np.array([i + positions[rs][j]])\n",
      "                rule_rt = np.array(df_rule[\"rt\"])\n",
      "                color = sns.desaturate(rule_colors[rs][j], .7)\n",
      "                sns.boxplot([rule_rt], color=color, positions=pos, fliersize=2,\n",
      "                            whis=1.75, linewidth=1, widths=widths[rs], ax=ax)\n",
      "        ax.set_xlim(0, 15)\n",
      "        ax.set_xticks(np.arange(15) + 0.5)\n",
      "        rule_fs = f_scores[\"F\"][ruleset].reindex(subj_sorted)\n",
      "        ax.set_xticklabels([\"%.2g\" % _f for _f in rule_fs])\n",
      "        ax.set_ylabel(\"Reaction time (s)\")\n",
      "        ax.set_xlabel(\"One-way F score over rules\")\n",
      "        indiv_rules = rules[ruleset[:3]]\n",
      "        n_rules = len(indiv_rules)\n",
      "        ax.legend(ax.artists[:n_rules], indiv_rules, loc=4,\n",
      "                  ncol=n_rules)\n",
      "    sns.despine()\n",
      "    f.tight_layout()\n",
      "    \n",
      "    f.text(.02, .96, \"A\", size=text_size)\n",
      "    f.text(.02, .47, \"B\", size=text_size)\n",
      "    \n",
      "    save_figure(f, \"supplemental_figure_1\")\n",
      "\n",
      "dksort_supplemental_figure_1()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAAAghrnX+ZdNZfuBPCemqqpqwM+Fe/h8Ph133HEyDINOnDxx3f9yNBrVjTfeqAsuuEDX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n79+iEsLAwXLlzAsmXL8NNPP+GDDz7AE088YfZ7enh4YObMmXj11Vfh4eGBmJgY3L59GzNmzMCV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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x126f9ec50>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Descriptive statistics about ROIs and artifacts"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "ROI sizes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For each subject and ROI, load the mask file and count the number of voxels included in the mask."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_roi_sizes():\n",
      "    roi_sizes = pd.DataFrame(columns=all_rois, index=subjects, dtype=float)\n",
      "    mask_template = op.join(data_dir, \"%s/masks/yeo17_%s.nii.gz\")\n",
      "    for roi in roi_sizes:\n",
      "        for subj in subjects:\n",
      "            vox_count = nib.load(mask_template % (subj, roi.lower())).get_data().sum()\n",
      "            roi_sizes.loc[subj, roi] = vox_count\n",
      "    return roi_sizes.describe().loc[[\"mean\", \"std\"]].astype(int)\n",
      "dksort_roi_sizes()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>roi</th>\n",
        "      <th>IFS</th>\n",
        "      <th>aMFG</th>\n",
        "      <th>pMFG</th>\n",
        "      <th>FPC</th>\n",
        "      <th>IFG</th>\n",
        "      <th>aIns</th>\n",
        "      <th>pSFS</th>\n",
        "      <th>IPS</th>\n",
        "      <th>OTC</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 626</td>\n",
        "      <td> 573</td>\n",
        "      <td> 468</td>\n",
        "      <td> 595</td>\n",
        "      <td> 534</td>\n",
        "      <td> 356</td>\n",
        "      <td> 277</td>\n",
        "      <td> 409</td>\n",
        "      <td> 1865</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  83</td>\n",
        "      <td>  77</td>\n",
        "      <td>  68</td>\n",
        "      <td>  70</td>\n",
        "      <td>  63</td>\n",
        "      <td>  37</td>\n",
        "      <td>  61</td>\n",
        "      <td>  52</td>\n",
        "      <td>  218</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "roi   IFS  aMFG  pMFG  FPC  IFG  aIns  pSFS  IPS   OTC\n",
        "mean  626   573   468  595  534   356   277  409  1865\n",
        "std    83    77    68   70   63    37    61   52   218"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Mean signal"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now look at the mean signal across each ROI in the original (preprocessed) timecourse."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_roi_signal():\n",
      "    roi_signal = pd.DataFrame(columns=all_rois, index=subjects, dtype=float)\n",
      "    for roi in all_rois:\n",
      "        signal = evoked.extract_group(\"yeo17_\" + roi.lower(), dv=dv4)\n",
      "        signal = [np.concatenate(d[\"data\"]).mean() for d in signal]\n",
      "        roi_signal[roi] = signal\n",
      "    return (roi_signal / 100).describe().loc[[\"mean\", \"std\"]]\n",
      "dksort_roi_signal()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>roi</th>\n",
        "      <th>IFS</th>\n",
        "      <th>aMFG</th>\n",
        "      <th>pMFG</th>\n",
        "      <th>FPC</th>\n",
        "      <th>IFG</th>\n",
        "      <th>aIns</th>\n",
        "      <th>pSFS</th>\n",
        "      <th>IPS</th>\n",
        "      <th>OTC</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 116.36</td>\n",
        "      <td> 118.42</td>\n",
        "      <td> 117.22</td>\n",
        "      <td> 101.45</td>\n",
        "      <td> 99.08</td>\n",
        "      <td> 117.86</td>\n",
        "      <td> 116.66</td>\n",
        "      <td> 138.02</td>\n",
        "      <td> 117.85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   2.51</td>\n",
        "      <td>   5.29</td>\n",
        "      <td>   4.75</td>\n",
        "      <td>   7.78</td>\n",
        "      <td>  6.01</td>\n",
        "      <td>   2.75</td>\n",
        "      <td>   3.80</td>\n",
        "      <td>   5.81</td>\n",
        "      <td>   5.37</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "roi      IFS    aMFG    pMFG     FPC    IFG    aIns    pSFS     IPS     OTC\n",
        "mean  116.36  118.42  117.22  101.45  99.08  117.86  116.66  138.02  117.85\n",
        "std     2.51    5.29    4.75    7.78   6.01    2.75    3.80    5.81    5.37"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Functional artifacts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_artifact_counts():\n",
      "    artifacts = []\n",
      "    for subj in subjects:\n",
      "        subj_artifacts = 0\n",
      "        for run in range(1, 5):\n",
      "            art = pd.read_csv(op.join(anal_dir, \"dksort/%s/preproc/run_%d/artifacts.csv\" % (subj, run)))\n",
      "            art = art.max(axis=1)\n",
      "            subj_artifacts += art.sum()\n",
      "        artifacts.append(subj_artifacts)\n",
      "    print \"Mean number of artifacts: %d\" % np.mean(artifacts)\n",
      "    print \"Percent artifact scans: %.1f%%\" % (np.mean(artifacts) / 2328 * 100)\n",
      "dksort_artifact_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean number of artifacts: 23\n",
        "Percent artifact scans: 1.0%\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Context decoding in lateral PFC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we turn to the central fMRI analyses: multivariate decoding of task variables from voxel populations in our cortical ROIs."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Decoding of dimension rules"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First try to decode the Dimension Rules -- that is, whether subjects should attend to the color, shape, or pattern of the multidimensional stimuli. We will fit separate models for each of the six prefrontal ROIs at each of six timepoints from one TR before stimulus onset (during which time there was an anticipatory cue that signlaed only an oncoming stimulus) through five TRs following stimulus onset.\n",
      "\n",
      "We defined a function above that does the heavy lifting on the analysis. We'll run that and then unpack the results over the next few cells."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_dimension = dksort_decode(pfc_rois, \"dimension\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We calculate a t statistic for each region and timepoint by subtracting a shuffled chance estimate from each subject's accuracy and then using a randomization test against the null hypothesis that the group mean is 0. We then report the t statiatisc for the peak accuracy across time for each region, along with a p value that is corrected for multiple comparisons across time and region."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_dimension[\"ttest\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mu</th>\n",
        "      <th>sd</th>\n",
        "      <th>t</th>\n",
        "      <th>p</th>\n",
        "      <th>tp</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROI</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>FPC</th>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 3.31</td>\n",
        "      <td> 7.12e-02</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IFG</th>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 2.44</td>\n",
        "      <td> 2.96e-01</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IFS</th>\n",
        "      <td> 0.46</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 7.95</td>\n",
        "      <td> 1.00e-04</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aIns</th>\n",
        "      <td> 0.37</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 4.48</td>\n",
        "      <td> 8.70e-03</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aMFG</th>\n",
        "      <td> 0.39</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 6.60</td>\n",
        "      <td> 5.00e-04</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pMFG</th>\n",
        "      <td> 0.38</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 8.69</td>\n",
        "      <td> 0.00e+00</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pSFS</th>\n",
        "      <td> 0.38</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 5.92</td>\n",
        "      <td> 7.00e-04</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "        mu    sd     t         p  tp\n",
        "ROI                                 \n",
        "FPC   0.36  0.03  3.31  7.12e-02   7\n",
        "IFG   0.36  0.04  2.44  2.96e-01   3\n",
        "IFS   0.46  0.06  7.95  1.00e-04   3\n",
        "aIns  0.37  0.03  4.48  8.70e-03   3\n",
        "aMFG  0.39  0.04  6.60  5.00e-04   5\n",
        "pMFG  0.38  0.02  8.69  0.00e+00   5\n",
        "pSFS  0.38  0.03  5.92  7.00e-04   5"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We also use the shuffled null distribution to assess the significance of the individual subject decoding models. Report here how many subjects have at least one model over time for each region reach \"significance\", where signficiance is defined as p < 0.05, one-tailed, corrected for multiple comparisons either across regions and time or only across time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_dimension[\"signif\"].groupby(level=\"correction\").apply(lambda x: (x > 95).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>IFS</th>\n",
        "      <th>aMFG</th>\n",
        "      <th>pMFG</th>\n",
        "      <th>FPC</th>\n",
        "      <th>IFG</th>\n",
        "      <th>aIns</th>\n",
        "      <th>pSFS</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>correction</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>omni</th>\n",
        "      <td> 11</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>time</th>\n",
        "      <td> 13</td>\n",
        "      <td> 4</td>\n",
        "      <td> 6</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 3</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "            IFS  aMFG  pMFG  FPC  IFG  aIns  pSFS\n",
        "correction                                       \n",
        "omni         11     2     0    3    1     1     1\n",
        "time         13     4     6    4    4     3     4"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Perform a paired T-test on the \"peak\" decoding accuracy between each PFC region. Use a Bonferroni correction on the P values for the 15-way comparisons.\n",
      "\n",
      "We don't actually use this figure, but the function presents a nice table."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_pfc_paired_ttests():\n",
      "    pfc_paired_t = pd.DataFrame(index=pfc_rois, columns=pfc_rois, dtype=float)\n",
      "    pfc_paired_p = pd.DataFrame(index=pfc_rois, columns=pfc_rois, dtype=float)\n",
      "    for roi_i, roi_j in itertools.product(pfc_rois, pfc_rois):\n",
      "        peak_i = pfc_dimension[\"peak\"][roi_i]\n",
      "        peak_j = pfc_dimension[\"peak\"][roi_j]\n",
      "        t, p = stats.ttest_rel(peak_i, peak_j)\n",
      "        pfc_paired_t.loc[roi_i, roi_j] = t\n",
      "        pfc_paired_p.loc[roi_i, roi_j] = p\n",
      "    pfc_paired_p *= 15\n",
      "    sns.symmatplot(pfc_paired_t.values, pfc_paired_p.values,\n",
      "                   names=pfc_rois, cmap=\"RdPu_r\", cmap_range=(-10, 0))\n",
      "\n",
      "dksort_pfc_paired_ttests()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8+HAhSZL47bffSuyYuTNYyWQy/j08RX7Xdu7gxMWLF+e7T25SfOrUKVOEaFEGDhwoJEkS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hYtWqS1a9e6Xv/DDz8oIiJCkydPltlsdgUOoLhh0XPh8KsAAACQj8lkUk5Ojnbu3CnDMHTo0CFt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\ntybfsGGDO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yjNCAwAAAAA3CIwAAAAAHCLwAAAAADALQIDAAAAALcIDAAAAADcIjAAAAAA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\naHj1V1UqOwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aed32d0>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Dimension rule decoding in PFC with other ROI definitions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The next set of analyses support the localization claims about dimension context representation in the IFS. This is a bit of a hodepodge, but we a) break up the IFS two ways (lateralization and anterior/posterior division), b) test the IFS against an agglomerative region with all PFC ROIs together, and c) test decoding after averaging the IFS signal across the voxels.\n",
      "\n",
      "We do all of these analyses on the \"peak\" data, which averages the BOLD data between the 3s and 5s timepoints before fitting the decoding models."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_peak_decode(problem, rois, masks, comparison):\n",
      "    rois = pd.Series(rois, name=\"ROI\")\n",
      "    accs = pd.DataFrame(columns=rois, index=subjects, dtype=np.float)\n",
      "    ttest = pd.DataFrame(columns=rois, index=[\"t\", \"p\"],  dtype=np.float)\n",
      "    for roi, mask in zip(rois, masks):\n",
      "        ds = mvpa.extract_group(problem, roi, mask, frames, peak, \"rt\", dv=dv4)\n",
      "        roi_accs = mvpa.decode_group(ds, model, dv=dv).squeeze()\n",
      "        accs[roi] = roi_accs\n",
      "        ttest[roi] = stats.ttest_rel(roi_accs, comparison)\n",
      "    return dict(accs=accs, ttest=ttest, descrip=accs.describe())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ifs_subrois = dksort_peak_decode(\"dimension\", other_rois, other_masks, pfc_dimension[\"peak\"][\"IFS\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_ifs_mean_decode():\n",
      "    mean_ds = mvpa.extract_group(\"dimension\", \"IFS\", \"yeo17_ifs\", frames, peak, \"rt\")\n",
      "\n",
      "    for ds in mean_ds:\n",
      "        ds[\"X\"] = ds[\"X\"].mean(axis=1).reshape(-1, 1)\n",
      "        ds[\"roi_name\"] = \"IFS_mean\"\n",
      "        \n",
      "    accs = mvpa.decode_group(mean_ds, model, dv=dv).squeeze()\n",
      "    null = mvpa.classifier_permutations(mean_ds, model, n_shuffle,\n",
      "                                        random_seed=shuffle_seed, dv=dv).squeeze()\n",
      "    thresh = moss.percentiles(null, 95, axis=1)\n",
      "    \n",
      "    ifs_subrois[\"accs\"][\"IFS_mean\"] = accs\n",
      "    ifs_subrois[\"ttest\"][\"IFS_mean\"] = stats.ttest_rel(accs, pfc_dimension[\"peak\"][\"IFS\"])\n",
      "    \n",
      "    return dict(accs=accs, null=null, thresh=thresh)\n",
      "\n",
      "ifs_mean = dksort_ifs_mean_decode()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look at the summary statistics for these ROIs across subjects."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ifs_subrois[\"accs\"].describe().T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>mean</th>\n",
        "      <th>std</th>\n",
        "      <th>min</th>\n",
        "      <th>25%</th>\n",
        "      <th>50%</th>\n",
        "      <th>75%</th>\n",
        "      <th>max</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROI</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>lh-IFS</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0.45</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.37</td>\n",
        "      <td> 0.41</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 0.48</td>\n",
        "      <td> 0.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rh-IFS</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0.44</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.38</td>\n",
        "      <td> 0.39</td>\n",
        "      <td> 0.45</td>\n",
        "      <td> 0.48</td>\n",
        "      <td> 0.52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aIFS</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0.45</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.40</td>\n",
        "      <td> 0.45</td>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pIFS</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0.45</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.39</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 0.45</td>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AllROIs</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0.44</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.40</td>\n",
        "      <td> 0.44</td>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IFS_mean</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.30</td>\n",
        "      <td> 0.32</td>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.37</td>\n",
        "      <td> 0.39</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "          count  mean   std   min   25%   50%   75%   max\n",
        "ROI                                                      \n",
        "lh-IFS       15  0.45  0.05  0.37  0.41  0.43  0.48  0.57\n",
        "rh-IFS       15  0.44  0.05  0.38  0.39  0.45  0.48  0.52\n",
        "aIFS         15  0.45  0.06  0.35  0.40  0.45  0.50  0.55\n",
        "pIFS         15  0.45  0.04  0.39  0.43  0.45  0.47  0.53\n",
        "AllROIs      15  0.44  0.05  0.36  0.40  0.44  0.47  0.54\n",
        "IFS_mean     15  0.35  0.03  0.30  0.32  0.36  0.37  0.39"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ifs_subrois[\"accs\"].apply(lambda d: pd.Series(moss.ci(moss.bootstrap(d), 95), index=[2.5, 97.5]), axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>ROI</th>\n",
        "      <th>lh-IFS</th>\n",
        "      <th>rh-IFS</th>\n",
        "      <th>aIFS</th>\n",
        "      <th>pIFS</th>\n",
        "      <th>AllROIs</th>\n",
        "      <th>IFS_mean</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2.5 </th>\n",
        "      <td> 0.42</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 0.42</td>\n",
        "      <td> 0.43</td>\n",
        "      <td> 0.41</td>\n",
        "      <td> 0.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97.5</th>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.48</td>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.47</td>\n",
        "      <td> 0.36</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "ROI   lh-IFS  rh-IFS  aIFS  pIFS  AllROIs  IFS_mean\n",
        "2.5     0.42    0.42  0.42  0.43     0.41      0.34\n",
        "97.5    0.47    0.47  0.48  0.47     0.47      0.36"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look at the paired T test for each \"other\" ROI against the original IFS data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ifs_subrois[\"ttest\"].T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>t</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROI</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>lh-IFS</th>\n",
        "      <td>-4.25</td>\n",
        "      <td> 8.11e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rh-IFS</th>\n",
        "      <td>-5.31</td>\n",
        "      <td> 1.10e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aIFS</th>\n",
        "      <td>-3.82</td>\n",
        "      <td> 1.87e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pIFS</th>\n",
        "      <td>-4.14</td>\n",
        "      <td> 1.01e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AllROIs</th>\n",
        "      <td>-4.71</td>\n",
        "      <td> 3.33e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IFS_mean</th>\n",
        "      <td>-9.24</td>\n",
        "      <td> 2.46e-07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "             t         p\n",
        "ROI                     \n",
        "lh-IFS   -4.25  8.11e-04\n",
        "rh-IFS   -5.31  1.10e-04\n",
        "aIFS     -3.82  1.87e-03\n",
        "pIFS     -4.14  1.01e-03\n",
        "AllROIs  -4.71  3.33e-04\n",
        "IFS_mean -9.24  2.46e-07"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Directly test for laterality."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_ifs_lateralization_test():\n",
      "    diff = ifs_subrois[\"accs\"][\"lh-IFS\"] - ifs_subrois[\"accs\"][\"rh-IFS\"]\n",
      "    low, high = moss.ci(moss.bootstrap(diff), 95)\n",
      "    t, p = stats.ttest_1samp(diff, 0)\n",
      "    args = (diff.mean(), low, high, t, p)\n",
      "    print \"Test for laterality: mean difference = %.3f; 95%% CI: %.3f, %.3f, t = %.2f; p = %.3g \" % args\n",
      "\n",
      "dksort_ifs_lateralization_test()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Test for laterality: mean difference = 0.004; 95% CI: -0.023, 0.028, t = 0.29; p = 0.775 \n"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Directly test for a rostro-caudal difference in decoding performance."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_ifs_rostral_test():\n",
      "    diff = ifs_subrois[\"accs\"][\"aIFS\"] - ifs_subrois[\"accs\"][\"pIFS\"]\n",
      "    low, high = moss.ci(moss.bootstrap(diff), 95)\n",
      "    t, p = stats.ttest_1samp(diff, 0)\n",
      "    print \"Test for rosto-caudal organization: mean difference = %.3f; \" % diff.mean(),\n",
      "    print \"95%% CI: %.3f, %.3f, t = %.2f; p = %.3g \" % (low, high, t, p)\n",
      "\n",
      "dksort_ifs_rostral_test()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Test for rosto-caudal organization: mean difference = 0.002;  95% CI: -0.022, 0.024, t = 0.13; p = 0.896 \n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Test the reduced dimensionality dataset against chance."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_ifs_mean_test():\n",
      "    low, high = moss.ci(moss.bootstrap(ifs_mean[\"accs\"]), 95)\n",
      "    chance = ifs_mean[\"null\"].mean()\n",
      "    delta = ifs_mean[\"accs\"] - chance\n",
      "    t, p = moss.randomize_onesample(delta)\n",
      "    signif = ifs_mean[\"accs\"] > ifs_mean[\"thresh\"]\n",
      "        \n",
      "    print \"IFS_Mean ROI:\"\n",
      "    print \"  mean accuracy = %.3f; 95%% CI: %.3f, %.3f;\" % (ifs_mean[\"accs\"].mean(), low, high),\n",
      "    print \"chance = %.3f; t = %.2f; p = %.3g \" % (chance, t, p)\n",
      "    print \"  %d/15 significant IFS_Mean models\" % signif.sum()\n",
      "    \n",
      "dksort_ifs_mean_test()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "IFS_Mean ROI:\n",
        "  mean accuracy = 0.350; 95% CI: 0.336, 0.364; chance = 0.339; t = 1.52; p = 0.0729 \n",
        "  2/15 significant IFS_Mean models\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we have all of the information we need to make the figure about decoding dimension rules in PFC"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_figure_4():\n",
      "    f = plt.figure(figsize=(4.48, 5.5))\n",
      "    \n",
      "    ax_ts = f.add_axes([.12, .53, .86, .45])\n",
      "    dksort_timecourse_figure(ax_ts, pfc_dimension, (.32, .48, 5))\n",
      "    \n",
      "    ax_sig = f.add_axes([.12, .08, .34, .34])\n",
      "    dksort_significance_figure(ax_sig, pfc_dimension[\"signif\"])\n",
      "    \n",
      "    ax_pfc_peak = f.add_axes([.59, .08, .22, .34])\n",
      "    dksort_point_figure(ax_pfc_peak, pfc_dimension[\"peak\"], .33, (.32, .5, 7), True)\n",
      "    \n",
      "    ax_sub_peak = f.add_axes([.86, .08, .12, .34])\n",
      "    dksort_point_figure(ax_sub_peak, ifs_subrois[\"accs\"].filter(regex=\"IFS$\"), .33, (.32, .5, 7), False)\n",
      "    \n",
      "    f.text(.01, .97, \"A\", size=12)\n",
      "    f.text(.01, .42, \"B\", size=12)\n",
      "    f.text(.48, .42, \"C\", size=12)\n",
      "    f.text(.825, .42, \"D\", size=12)\n",
      "\n",
      "    sns.despine()\n",
      "    save_figure(f, \"figure_4\")\n",
      "\n",
      "dksort_figure_4()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KHzlx4oTUcRWactps2IuKPN5tX/DRRzjLylD8/Ii6zEdijYVfZCR+119P2PXX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\nEie7E6d3O7K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VEldsbLqpjUALgcznEY6Dlcrh9e0xaa4MBsOWpTE1rRRCCKwOSo9HUcTR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65Dhw69p5nUpUuXOjAaIURZYTJkc+TjqVisNjz0ClA6r8isW7eOQYMG5Svxl0en0xV7qcDB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11e76aa90>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"width:585px;padding-left:120px\" align=\"justify\">\n",
      "**Decoding results for the dimensions rules in prefrontal cortex.** (A) Time-resolved decoding results. Solid lines indicate mean decoding accuracy within each time bin, and error bands denote bootstrapped standard error across subjects. The horizontal dashed line shows the empirical measure of chance performance, derived from the permutation analysis. The vertical dashed line is placed at the onset of the first stimulus. (B) The height of each bar shows the percentile in the shuffled null distribution corresponding to the observed accuracy for each subject and region. The horizontal dashed line demarcates the criterion of significance at a (corrected) alpha = 0.05. Bars are sorted by height within region. (C and D) Decoding accuracies fit to data averaged across the timepoints at 3 and 5 seconds following stimulus onset. Points and error bars represent mean and bootstrapped standard error across subjects, respectively.\n",
      "</div>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Dimension rule decoding during cue period"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now test whether we can decode the dimension rules during the cue period. This is both a) possibly interesting on its own and b) serves as a control against certain interpretations of our main results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "pfc_cue = dksort_decode([\"IFS\"], \"dimension_cue\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Take the mean accuracy across subjects and look at the maximum over time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_cue_test():\n",
      "    accs = pfc_cue[\"accs\"].mean(axis=0)\n",
      "    peak_tp = np.argmax(accs)\n",
      "    max_accs = pfc_cue[\"accs\"].values[:, peak_tp]\n",
      "    low, high = moss.ci(moss.bootstrap(max_accs), 95)\n",
      "    peak_time = pfc_cue[\"accs\"].columns[peak_tp]\n",
      "    args = max_accs.mean(), peak_time, low, high\n",
      "    print \"Max accuracy: %.3f at %ds; 95%% CI: %.3f, %.3f\" % args\n",
      "\n",
      "dksort_cue_test()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Max accuracy: 0.363 at 5s; 95% CI: 0.338, 0.389\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now look at the maximum t statistic across time. The p value is corrected for multiple comparisons, here just across time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_cue[\"ttest\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mu</th>\n",
        "      <th>sd</th>\n",
        "      <th>t</th>\n",
        "      <th>p</th>\n",
        "      <th>tp</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROI</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>IFS</th>\n",
        "      <td> 0.36</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 2.12</td>\n",
        "      <td> 0.12</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "       mu    sd     t     p  tp\n",
        "ROI                            \n",
        "IFS  0.36  0.05  2.12  0.12   5"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Decision rule decoding in PFC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next we turn to the Decision Rules (i.e. whether \"yes\" means \"same\" or \"different\"). We just repeat the main steps from above. First load up all the data and do the heavy lifting on the analysis side of things."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_decision = dksort_decode(pfc_rois, \"decision\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Print the results of the group t test against chance."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_decision[\"ttest\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mu</th>\n",
        "      <th>sd</th>\n",
        "      <th>t</th>\n",
        "      <th>p</th>\n",
        "      <th>tp</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROI</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>FPC</th>\n",
        "      <td> 0.49</td>\n",
        "      <td> 0.06</td>\n",
        "      <td>-0.97</td>\n",
        "      <td> 1.00</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IFG</th>\n",
        "      <td> 0.48</td>\n",
        "      <td> 0.06</td>\n",
        "      <td>-1.45</td>\n",
        "      <td> 1.00</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IFS</th>\n",
        "      <td> 0.51</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.40</td>\n",
        "      <td> 0.94</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aIns</th>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.24</td>\n",
        "      <td> 0.96</td>\n",
        "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>aMFG</th>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.06</td>\n",
        "      <td>-0.32</td>\n",
        "      <td> 1.00</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pMFG</th>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.11</td>\n",
        "      <td> 0.98</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pSFS</th>\n",
        "      <td> 0.50</td>\n",
        "      <td> 0.03</td>\n",
        "      <td>-0.02</td>\n",
        "      <td> 0.99</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "        mu    sd     t     p  tp\n",
        "ROI                             \n",
        "FPC   0.49  0.06 -0.97  1.00   1\n",
        "IFG   0.48  0.06 -1.45  1.00   1\n",
        "IFS   0.51  0.06  0.40  0.94   5\n",
        "aIns  0.50  0.05  0.24  0.96  -1\n",
        "aMFG  0.50  0.06 -0.32  1.00   5\n",
        "pMFG  0.50  0.05  0.11  0.98   5\n",
        "pSFS  0.50  0.03 -0.02  0.99   3"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now also look at whether any individual models were significant."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pfc_decision[\"signif\"].groupby(level=\"correction\").apply(lambda x: (x > 95).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>IFS</th>\n",
        "      <th>aMFG</th>\n",
        "      <th>pMFG</th>\n",
        "      <th>FPC</th>\n",
        "      <th>IFG</th>\n",
        "      <th>aIns</th>\n",
        "      <th>pSFS</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>correction</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>omni</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>time</th>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "            IFS  aMFG  pMFG  FPC  IFG  aIns  pSFS\n",
        "correction                                       \n",
        "omni          1     0     0    0    0     1     0\n",
        "time          3     1     1    0    0     4     1"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "other_decision = dksort_peak_decode(\"decision\", other_rois, other_masks, pfc_decision[\"peak\"][\"IFS\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can plot the decision rule figure"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_figure_5():\n",
      "    f = plt.figure(figsize=(4.48, 5.5))\n",
      "\n",
      "    ax_ts = f.add_axes([.12, .53, .86, .45])\n",
      "    dksort_timecourse_figure(ax_ts, pfc_decision, (.43, .67, 7))\n",
      "\n",
      "    ax_sig = f.add_axes([.12, .08, .34, .34])\n",
      "    dksort_significance_figure(ax_sig, pfc_decision[\"signif\"])\n",
      "\n",
      "    ax_pfc_peak = f.add_axes([.59, .08, .22, .34])\n",
      "    dksort_point_figure(ax_pfc_peak, pfc_decision[\"peak\"], .5, (.43, .67, 7), True)\n",
      "\n",
      "    ax_sub_peak = f.add_axes([.86, .08, .12, .34])\n",
      "    dksort_point_figure(ax_sub_peak, other_decision[\"accs\"].filter(regex=\"IFS$\"), .5, (.43, .67, 7), False)\n",
      "\n",
      "    f.text(.01, .97, \"A\", size=12)\n",
      "    f.text(.01, .42, \"B\", size=12)\n",
      "    f.text(.48, .42, \"C\", size=12)\n",
      "    f.text(.825, .42, \"D\", size=12)\n",
      "\n",
      "    sns.despine()\n",
      "    save_figure(f, \"figure_5\")\n",
      "\n",
      "dksort_figure_5()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GDHln/x4eHu88b29vj/Pnz6u1Hz16FAUFr38hOHjwIGbPnl3CJxMWpwoQERERlaNbt26h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\nfOPUBiMlRnp4JuRFV1wGyJpfY81iWp01WmYQrBkaIj1fkFejdVYIa22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2pX1KbYRqvkmo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xlZtJCmhTKt0M/bU1OKbbhEoXBxE1+a8308M9SkokaWn7b0fHLG8JkZS16+7cjB7W3FK9Y1s3sF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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12b759290>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"width:585px;padding-left:120px\" align=\"justify\">\n",
      "Plot conventions are identical to those in Figure 4. The y-axis is scaled to span similar binomial probabilities as in Figure 4.\n",
      "</div>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Control signals in posterior neocortex"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now return to the dimension rules and build decoding models to predict these rules from patterns in posterior cortex ROIS (specifically IPS and OTC). We'll carry forward the IFS ROI as we are interested in how the profile of information in this region is similar or different to the other regions. Lacking a good name for this collection of ROIs, we'll call them `net` as they form a sort of goal-directed attention network."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Spatiotemporal decoding in IPS and visual cortex"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "net_dimension = dksort_decode(net_rois, \"dimension\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net_dimension[\"signif\"].groupby(level=\"correction\").apply(lambda x: (x > 95).sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>IFS</th>\n",
        "      <th>IPS</th>\n",
        "      <th>OTC</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>correction</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>omni</th>\n",
        "      <td> 11</td>\n",
        "      <td> 15</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>time</th>\n",
        "      <td> 13</td>\n",
        "      <td> 15</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "            IFS  IPS  OTC\n",
        "correction               \n",
        "omni         11   15   15\n",
        "time         13   15   15"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net_dimension[\"ttest\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mu</th>\n",
        "      <th>sd</th>\n",
        "      <th>t</th>\n",
        "      <th>p</th>\n",
        "      <th>tp</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROI</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>IFS</th>\n",
        "      <td> 0.46</td>\n",
        "      <td> 0.06</td>\n",
        "      <td>  7.95</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IPS</th>\n",
        "      <td> 0.56</td>\n",
        "      <td> 0.10</td>\n",
        "      <td>  8.87</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>OTC</th>\n",
        "      <td> 0.61</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 14.76</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "       mu    sd      t  p  tp\n",
        "ROI                          \n",
        "IFS  0.46  0.06   7.95  0   3\n",
        "IPS  0.56  0.10   8.87  0   3\n",
        "OTC  0.61  0.07  14.76  0   5"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Spatial scale of context information"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are interested in dissociating the information within this network. First we inquire about its spatial resolution. We do so by computing the first-order spatial autocorrelation on the maps of learned coefficients. Basically, what happens is that you extract the coefficients and put them in the native image space. Then, shift the map one voxel along each axis and compute the correlation with itself (ignoring the voxels that get pushed outside of the mask). Because there are three dimension rules, the logistic regression model actually has three binary models with associated weights. So we end up with correlation value for each axis/model, which we average to obtain a score for each subject and ROI."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_calculate_ar1():\n",
      "    # Set up the dataframe\n",
      "    roi_index = moss.product_index([net_rois, subjects], [\"roi\", \"subj\"])\n",
      "    ar1s = pd.Series(index=roi_index, name=\"ar1\", dtype=float)\n",
      "    shifters = [(0, 0, 1), (0, 1, 0), (1, 0, 0)]\n",
      "    \n",
      "    for roi in net_rois:\n",
      "        mask_name = \"yeo17_\" + roi.lower()\n",
      "        ds = mvpa.extract_group(\"dimension\", roi, mask_name, frames, peak, \"rt\")\n",
      "        coefs = mvpa.model_coefs(ds, model, flat=False)\n",
      "        \n",
      "        for subj, coef_s in zip(subjects, coefs):\n",
      "            ar1_vals = []\n",
      "            \n",
      "            # Average over the three underlying models\n",
      "            for coef_c in coef_s.transpose(3, 0, 1, 2):\n",
      "                mask = ~np.isnan(coef_c)\n",
      "                orig = coef_c[mask]\n",
      "                locs = np.argwhere(mask)\n",
      "                \n",
      "                # Average over shifts in the x, y, and z direction\n",
      "                for shifter in shifters:\n",
      "                    x, y, z = (locs + shifter).T\n",
      "                    shifted = coef_c[x, y, z]\n",
      "                    notnan = ~np.isnan(shifted)\n",
      "                    r, p = stats.pearsonr(orig[notnan], shifted[notnan])\n",
      "                    ar1_vals.append(r)\n",
      "                    \n",
      "            ar1s.loc[(roi, subj)] = np.mean(ar1_vals)\n",
      "    \n",
      "    # Print descriptive statistics about the autocorrelation\n",
      "    for roi in net_rois:\n",
      "        m, sd = ar1s.loc[roi].describe()[[\"mean\", \"std\"]]\n",
      "        print \"%s Spatial AR1: M = %.3g (SD = %.2g)\" % (roi, m, sd)\n",
      "    \n",
      "    # Perform pairwise tests for each ROI combination\n",
      "    for roi_a, roi_b in itertools.combinations(net_rois, 2):\n",
      "        t, p = stats.ttest_rel(ar1s.loc[roi_a], ar1s.loc[roi_b])\n",
      "        dof = len(ar1s.loc[roi_a]) - 1\n",
      "        print \"Test for %s vs. %s: t(%d) = %.3f; p = %.3g\" % (roi_a, roi_b, dof, t, p)   \n",
      "    \n",
      "    ar1_df = ar1s.reset_index()\n",
      "    return ar1_df\n",
      "\n",
      "ar1_df = dksort_calculate_ar1()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "IFS Spatial AR1: M = 0.153 (SD = 0.035)\n",
        "IPS Spatial AR1: M = 0.151 (SD = 0.043)\n",
        "OTC Spatial AR1: M = 0.249 (SD = 0.034)\n",
        "Test for IFS vs. IPS: t(14) = 0.133; p = 0.896\n",
        "Test for IFS vs. OTC: t(14) = -8.835; p = 4.23e-07\n",
        "Test for IPS vs. OTC: t(14) = -6.498; p = 1.41e-05\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Perform an omnibus test over the ROI factor (this actually comes before the pairwise tests above)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i ar1_df\n",
      "m.ar1 = lmer(ar1 ~ roi + (roi | subj), ar1_df, REML=FALSE)\n",
      "m.ar1.nest = lmer(ar1 ~ 1 + (roi | subj), ar1_df, REML=FALSE)\n",
      "print(anova(m.ar1))\n",
      "lr_test(m.ar1, m.ar1.nest, \"ROI effect\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Analysis of Variance Table\n",
        "    Df   Sum Sq  Mean Sq F value\n",
        "roi  2 0.036324 0.018162  42.595\n",
        "Likelihood ratio test for ROI effect:\n",
        "  Chisq(2) = 28.48; p = 6.53e-07\n"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Temproal profile of context decoding"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next we ask about temporal dissociations between the regions. This is hypothesis-driven: we expect that the IFS and IPS will have information about the attended dimensions earlier than the OTC. We're going to operationalize \"time of information\" with \"time of peak information\". There are other measures that might be insightful in different ways, but I think this is the best measurement given the type of data we have.\n",
      "\n",
      "For a potentially more precise test, we are going to use cubic spline interpolation to upsample the *original* timeserieses to 500 ms bins. Then, we'll repeat the decoding analysis on these new timepoints. Finally, we'll fit gamma probability density functions to the high-res timecourse data and use those to infer on the time of peak information."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_upsample():\n",
      "    if dv is not None:\n",
      "        dv[\"up_timepoints\"] = up_timepoints\n",
      "    roi_index = moss.product_index([net_rois, subjects], [\"ROI\", \"subj\"])\n",
      "    columns = pd.Series(up_timepoints, name=\"timepoints\")\n",
      "    up_accs = pd.DataFrame(index=roi_index, columns=columns, dtype=float)\n",
      "    up_hrf_info = pd.DataFrame(index=roi_index, columns=[\"peak\", \"r2\"], dtype=float)\n",
      "    up_hrfs = {}\n",
      "    bounds = dict(shape=(2, 8), coef=(0, None), loc=(-2.5, 2.5), scale=(0, 3))\n",
      "    \n",
      "    for roi in net_rois:\n",
      "        mask = \"yeo17_\" + roi.lower()\n",
      "        ds = mvpa.extract_group(\"dimension\", roi + \"_500ms\", mask, frames,\n",
      "                                upsample=4, confounds=\"rt\", dv=dv4)\n",
      "        roi_accs = mvpa.decode_group(ds, model, dv=dv)\n",
      "        up_accs.loc[roi, :] = roi_accs\n",
      "        hrfs = [moss.GammaHRF(loc=-1.5, bounds=bounds) for s in subjects]\n",
      "        mapper = map if dv is None else dv.map_sync\n",
      "        hrfs_ = mapper(lambda h, a: h.fit(up_timepoints, a), hrfs, roi_accs)\n",
      "        up_hrfs[roi] = hrfs_\n",
      "        for subj, hrf in zip(subjects, hrfs_):\n",
      "            up_hrf_info.loc[(roi, subj)] = hrf.peak_time_, hrf.fit_r2_\n",
      "    \n",
      "    return up_hrfs, up_hrf_info, up_accs\n",
      "\n",
      "up_hrfs, up_hrf_info, up_accs = dksort_upsample()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Report main information about the fits."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_peak_report():\n",
      "    for roi, info in up_hrf_info.groupby(level=\"ROI\"):\n",
      "        peak_time = info[\"peak\"].mean()\n",
      "        low, high = moss.ci(moss.bootstrap(info[\"peak\"]), 95)\n",
      "        print roi + \"\\n---\"\n",
      "        print \" Mean peak: %.2fs;  95%% CI: (%.2f, %.2f)\" % (peak_time, low, high)\n",
      "        r2 = info[\"r2\"].median()\n",
      "        low, high = moss.ci(moss.bootstrap(info[\"r2\"], func=np.median), 95)\n",
      "        print \" Median R^2: %.2f;  95%% CI: (%.2f, %.2f)\\n\" % (r2, low, high)\n",
      "\n",
      "dksort_peak_report()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "IFS\n",
        "---\n",
        " Mean peak: 3.71s;  95% CI: (3.47, 3.95)\n",
        " Median R^2: 0.77;  95% CI: (0.72, 0.89)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "IPS\n",
        "---"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " Mean peak: 3.47s;  95% CI: (3.22, 3.74)\n",
        " Median R^2: 0.89;  95% CI: (0.78, 0.92)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "OTC\n",
        "---"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " Mean peak: 4.42s;  95% CI: (4.19, 4.64)\n",
        " Median R^2: 0.95;  95% CI: (0.92, 0.96)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a short function to compute them mean and 95% CI for the pairwise differences and apply it to eaach pair."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def up_peak_test(info, roi_a, roi_b):\n",
      "    peaks = np.array(info[\"peak\"].unstack(level=\"ROI\")[[roi_a, roi_b]])\n",
      "    diff = np.diff(peaks, axis=1)\n",
      "    mean_diff = np.mean(diff)\n",
      "    diff_ci = moss.ci(moss.bootstrap(diff), 95)\n",
      "    t, p = stats.ttest_rel(peaks[:, 0], peaks[:, 1])\n",
      "    print \"%s - %s difference:\" % (roi_a, roi_b)\n",
      "    print \"  mean = %.2fs;\" % mean_diff, \n",
      "    print \"95%% CI: %.2f, %.2f\" % tuple(diff_ci)\n",
      "    print \"  t(%d) = %.3f; p = %.3g\" % (len(diff) - 1, t, p)\n",
      "    print \"  %d subjects with positive difference\" % (diff > 0).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "up_peak_test(up_hrf_info, \"IFS\", \"OTC\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "IFS - OTC difference:\n",
        "  mean = 0.71s; 95% CI: 0.41, 0.99\n",
        "  t(14) = -4.670; p = 0.000361\n",
        "  13 subjects with positive difference\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "up_peak_test(up_hrf_info, \"IPS\", \"OTC\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "IPS - OTC difference:\n",
        "  mean = 0.95s; 95% CI: 0.77, 1.11\n",
        "  t(14) = -10.358; p = 6.03e-08\n",
        "  15 subjects with positive difference\n"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "up_peak_test(up_hrf_info, \"IFS\", \"IPS\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "IFS - IPS difference:\n",
        "  mean = -0.24s; 95% CI: -0.53, 0.04\n",
        "  t(14) = 1.577; p = 0.137\n",
        "  3 subjects with positive difference\n"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Now we can plot the figure corresponding to these analyses"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_figure_6():\n",
      "    f = plt.figure(figsize=(4.48, 5.5))\n",
      "\n",
      "    net_colors = [roi_colors[roi] for roi in net_rois]\n",
      "    \n",
      "    # Set up the axes\n",
      "    ax_ts = f.add_axes([.12, .53, .855, .44])\n",
      "    ax_peak = f.add_axes([.12, .07, .16, .35])\n",
      "    ax_ar1 = f.add_axes([.37, .29, .61, .13])\n",
      "    ax_time = f.add_axes([.37, .07, .61, .13])\n",
      "    \n",
      "    # Time-resolved decoding accuracy figure\n",
      "    # Plot the original-resolution accuracy as point estimate and CI\n",
      "    dksort_timecourse_figure(ax_ts, net_dimension, (.3, .66, 5),\n",
      "                             \"ci_bars\", False, {\"linewidth\": 2}, ms=5)\n",
      "    \n",
      "    # Plot the Gamma HRF modeled-accuracy as a smooth curve\n",
      "    xx = np.linspace(-2, 10, 500)\n",
      "    net_rois_r = list(reversed(net_rois))\n",
      "    fits = [[hrf.predict(xx) for hrf in up_hrfs[roi]] for roi in net_rois_r]\n",
      "    fits = np.transpose(fits, (1, 2, 0))\n",
      "    sns.tsplot(fits, time=xx, condition=net_rois_r, err_style=None, linewidth=1.75,\n",
      "               color=reversed(net_colors), ax=ax_ts)\n",
      "    \n",
      "    # Remove the old chance line that doesn't span the whole plot\n",
      "    ax_ts.lines[0].remove()\n",
      "    ax_ts.plot([-2, 10], [1 / 3, 1 / 3], ls=\"--\", c=\"k\")\n",
      "\n",
      "    # Peak average decoding accuracy\n",
      "    dksort_point_figure(ax_peak, net_dimension[\"peak\"], .33, (.3, .66, 5), True)\n",
      "\n",
      "    # Spatial autocorrelation of model parameters\n",
      "    box_colors = [sns.set_hls_values(c, l=.5, s=.3) for c in net_colors]\n",
      "    ar1_vals = [df.ar1.values for _, df in ar1_df.groupby(\"roi\")]\n",
      "    sns.boxplot(ar1_vals, vert=False, color=box_colors, linewidth=1, widths=.6, ax=ax_ar1)\n",
      "    ax_ar1.set_yticklabels(net_rois)\n",
      "    ax_ar1.set_xlim(.05, .35)\n",
      "    ax_ar1.set_xticks([.1, .2, .3])\n",
      "    ax_ar1.xaxis.grid(True)\n",
      "    ax_ar1.set_xlabel(\"Spatial autocorrelation of feature weights (r)\")\n",
      "\n",
      "    # Pairwise differences in peak decoding time\n",
      "    time_vals = [up_hrf_info.loc[roi, \"peak\"] - up_hrf_info.loc[\"OTC\", \"peak\"] for roi in [\"IFS\", \"IPS\"]]\n",
      "    sns.boxplot(time_vals, vert=False, ax=ax_time, linewidth=1, widths=.5, color=box_colors[:2])\n",
      "    ax_time.set_xlim(-2.2, .7)\n",
      "    ax_time.xaxis.grid(True)\n",
      "    ax_time.set_yticklabels(net_rois[:2])\n",
      "    ax_time.axvline(0, ls=\":\", c=\"#444444\")\n",
      "    ax_time.set_xlabel(\"Time of peak decoding relative to OTC (s)\")\n",
      "\n",
      "    # Panel labels\n",
      "    text_size = 12\n",
      "    f.text(.01, .97, \"A\", size=text_size)\n",
      "    f.text(.01, .43, \"B\", size=text_size)\n",
      "    f.text(.3, .43, \"C\", size=text_size)\n",
      "    f.text(.3, .20, \"D\", size=text_size)\n",
      "\n",
      "    sns.despine()\n",
      "    save_figure(f, \"figure_6\")\n",
      "\n",
      "dksort_figure_6()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/mwaskom/anaconda/envs/dksort/lib/python2.7/site-packages/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n",
        "  warnings.warn(\"No labeled objects found. \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qLT5wJVHp1KBBA0RHR8PLywvTpk1TOZaVlYUHDx6gYcOGEkWnPqJMztqwYQN6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MG9e/fg7++vKuNwOODxeCgqKh44nZSUhIyMDIwYMYJ1bPfu3aGvr4/Tp083Sv9J04mLi8OgQYMw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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aede350>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"width:585px;padding-left:120px\" align=\"justify\">\n",
      "**Decoding results for the dimension rules in posterior neocortex.** (A) Points and error bars show the mean and bootstrapped standard error for decoding accuracy in the original time bins. The solid traces show the gamma PDF models used to derive temporal information, averaged across subjects. Plot conventions are otherwise as in Figure 4A. (B) Plot conventions are as in Figure 4C. (C) Boxplots showing the distribution of model coefficient autocorrelation across subjects, sorted by region. (D) Boxplots  showing the distribution of relative differences in the time of peak decoding accuracy for the IFS and IPS models relative to the OTC models. Negative numbers indicate later peaks in the OTC.\n",
      "</div>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Frontoparietal influence on visual selection"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The next set of analyses focus on the question of influence, or at least interactions, within this network. Our question is, when the representation is stronger in frontoparietal areas (as measured by higher probabilistic prediction for the correct class), is that trial more likely to be classified correctly in OTC? I've actually done this with both binary and continuous DVs; the latter is actually a stronger effect, but the former provides a more interpreteable plot, with probabiltiy of correct prediction as the DV."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_model_logits():\n",
      "    logits = dict()\n",
      "    preds = dict()\n",
      "    bins = np.arange(-14.5, 14.5, 1)\n",
      "    for roi in net_rois:\n",
      "        mask = \"yeo17_\" + roi.lower()\n",
      "        ds = mvpa.extract_group(\"dimension\", roi, mask, frames, peak, \"rt\", dv=dv4)\n",
      "        logits_ = mvpa.decode_group(ds, model, logits=True, trialwise=True, dv=dv)\n",
      "        logits[roi] = np.concatenate(logits_)\n",
      "        preds_ = mvpa.decode_group(ds, model, trialwise=True, dv=dv)\n",
      "        preds[roi] = np.concatenate(preds_)\n",
      "    \n",
      "    logit_df = behav_df[[\"subj\"]]\n",
      "    for roi in net_rois:\n",
      "        logit_df[roi] = logits[roi]\n",
      "        logit_df[roi + \"_acc\"] = preds[roi]\n",
      "        logit_df[roi + \"_bin\"] = bins[np.digitize(logits[roi], bins)] - 1\n",
      "\n",
      "    return logit_df.reset_index(drop=True)\n",
      "logit_df = dksort_model_logits()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set up the main mixed model and the nested models for likelihood ratio tests."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i logit_df\n",
      "m.logits = lmer(OTC_acc ~ IFS + IPS + (IFS + IPS | subj), logit_df, family=binomial)\n",
      "m.logits.noifs = lmer(OTC_acc ~ IPS + (IFS + IPS | subj), logit_df, family=binomial)\n",
      "m.logits.noips = lmer(OTC_acc ~ IFS + (IFS + IPS | subj), logit_df, family=binomial)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Report on the model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "print(m.logits, corr=FALSE)\n",
      "lr_test(m.logits, m.logits.noifs, \"IFS effect\")\n",
      "lr_test(m.logits, m.logits.noips, \"IPS effect\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Generalized linear mixed model fit by the Laplace approximation \n",
        "Formula: OTC_acc ~ IFS + IPS + (IFS + IPS | subj) \n",
        "   Data: logit_df \n",
        "  AIC  BIC logLik deviance\n",
        " 5062 5118  -2522     5044\n",
        "Random effects:\n",
        " Groups Name        Variance   Std.Dev.  Corr          \n",
        " subj   (Intercept) 4.7257e-02 0.2173867               \n",
        "        IFS         3.0514e-05 0.0055239 -1.000        \n",
        "        IPS         2.4432e-03 0.0494288 -0.353  0.353 \n",
        "Number of obs: 3961, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "            Estimate Std. Error z value Pr(>|z|)    \n",
        "(Intercept)  0.51027    0.06630   7.697 1.40e-14 ***\n",
        "IFS          0.08242    0.01289   6.396 1.59e-10 ***\n",
        "IPS          0.17603    0.01924   9.151  < 2e-16 ***\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
        "Likelihood ratio test for IFS effect:\n",
        "  Chisq(1) = 24.49; p = 7.47e-07\n",
        "Likelihood ratio test for IPS effect:\n",
        "  Chisq(1) = 29.10; p = 6.88e-08\n"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It looks like the regression coefficient for the IPS is larger than the IFS. Is this statistically significant?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "C = matrix(c(0, -1, 1), 1, 3)\n",
      "rownames(C) = \"IPS-IFS\"\n",
      "print(summary(glht(m.logits, C)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "\t Simultaneous Tests for General Linear Hypotheses\n",
        "\n",
        "Fit: glmer(formula = OTC_acc ~ IFS + IPS + (IFS + IPS | subj), data = logit_df, \n",
        "    family = binomial)\n",
        "\n",
        "Linear Hypotheses:\n",
        "             Estimate Std. Error z value Pr(>|z|)    \n",
        "IPS-IFS == 0  0.09361    0.02453   3.816 0.000136 ***\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
        "(Adjusted p values reported -- single-step method)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now collect and add the motion estimates. We'll take the average relative displacement metric on the two TRs that the BOLD data were taking from on each trial."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_collect_motion_info():\n",
      "    all_motion = []\n",
      "    motion_template = op.join(anal_dir, \"dksort/%s/preproc/run_%d/realignment_params.csv\")\n",
      "    for subj in subjects:\n",
      "        subj_motion = []\n",
      "        for run in range(1, 5):\n",
      "            motion = pd.read_csv(motion_template % (subj, run))[\"displace_rel\"]\n",
      "            stim_onsets = behav_df.loc[(behav_df.subj == subj) & (behav_df.run == run), \"stim_time\"]\n",
      "            stim_onsets = stim_onsets.values.astype(int) / 2\n",
      "            first_tr = stim_onsets + 2\n",
      "            second_tr = stim_onsets + 3\n",
      "            run_motion = np.mean([motion[first_tr], motion[second_tr]], axis=0)\n",
      "            subj_motion.append(run_motion)\n",
      "        subj_motion = np.concatenate(subj_motion)\n",
      "        all_motion.append(subj_motion)\n",
      "    logit_df[\"motion\"] = stats.zscore(np.concatenate(all_motion))\n",
      "dksort_collect_motion_info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i logit_df\n",
      "m.logits.motion = lmer(OTC_acc ~ IFS + IPS + motion + (IFS + IPS + motion | subj), logit_df, family=binomial)\n",
      "print(m.logits.motion)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Generalized linear mixed model fit by the Laplace approximation \n",
        "Formula: OTC_acc ~ IFS + IPS + motion + (IFS + IPS + motion | subj) \n",
        "   Data: logit_df \n",
        "  AIC  BIC logLik deviance\n",
        " 5061 5149  -2516     5033\n",
        "Random effects:\n",
        " Groups Name        Variance   Std.Dev.  Corr                 \n",
        " subj   (Intercept) 4.7142e-02 0.2171227                      \n",
        "        IFS         1.4172e-05 0.0037646 -1.000               \n",
        "        IPS         2.9784e-03 0.0545748 -0.308  0.308        \n",
        "        motion      2.9775e-02 0.1725535 -0.464  0.464 -0.700 \n",
        "Number of obs: 3961, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "            Estimate Std. Error z value Pr(>|z|)    \n",
        "(Intercept)  0.50026    0.06663   7.508 6.02e-14 ***\n",
        "IFS          0.08159    0.01291   6.320 2.61e-10 ***\n",
        "IPS          0.17660    0.02019   8.747  < 2e-16 ***\n",
        "motion      -0.07409    0.05872  -1.262    0.207    \n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
        "\n",
        "Correlation of Fixed Effects:\n",
        "       (Intr) IFS    IPS   \n",
        "IFS     0.070              \n",
        "IPS    -0.167 -0.136       \n",
        "motion -0.273  0.056 -0.364\n"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a figure corresponding to these analyses"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_figure_7():\n",
      "    \n",
      "    # Fit a logistic regression for each subject and save predictions\n",
      "    xx = np.linspace(-10, 6, 100)\n",
      "    x_pred = sm.add_constant(xx, prepend=True)\n",
      "    pred_rois = [\"IFS\", \"IPS\"]\n",
      "    models = {roi: np.empty((subjects.size, xx.size)) for roi in pred_rois}\n",
      "    xlim = [-6, 4]\n",
      "\n",
      "    models = dict()\n",
      "    for roi in pred_rois:\n",
      "        x_fit = sm.add_constant(logit_df[roi].values, prepend=True)\n",
      "        fit = sm.GLM(logit_df.OTC_acc.values, x_fit, family=sm.families.Binomial()).fit()\n",
      "        models[roi] = fit.predict(x_pred)\n",
      "    \n",
      "    f = plt.figure(figsize=(3.34, 5.5))\n",
      "    axr = f.add_axes([.15, .38, .8, .58])\n",
      " \n",
      "    # Plot the data for each ROI\n",
      "    for i, roi in enumerate(pred_rois):\n",
      "        color = roi_colors[roi]\n",
      "        \n",
      "        histcolor = sns.set_hls_values(color, l=.5, s=.3)\n",
      "        sns.tsplot(models[roi], time=xx, color=color, label=roi,\n",
      "                   linewidth=1.25, err_style=None, ax=axr)\n",
      "        roi_pivot = pd.pivot_table(logit_df, \"OTC_acc\", \"subj\", roi + \"_bin\").values\n",
      "        bins = np.sort(logit_df[roi + \"_bin\"].unique())\n",
      "        sns.tsplot(roi_pivot, time=bins, err_style=\"ci_bars\", color=color, interpolate=False,\n",
      "                   estimator=stats.nanmean, err_kws={\"linewidth\": 2}, markersize=6, ax=axr)\n",
      "        axh = f.add_axes([.15, .23 - i * .15, .8, .12])\n",
      "        bins = np.linspace(*xlim, num=xlim[1] - xlim[0] + 1)\n",
      "        axh.hist(logit_df[roi], bins, rwidth=.95,\n",
      "                 color=histcolor, linewidth=0, label=roi)\n",
      "        axh.set_yticks(np.linspace(0, 900, 3))\n",
      "        axh.set_ylabel(\"Trials\")\n",
      "        if i:\n",
      "            axh.set_xlabel(\"Classifier evidence for target class\")\n",
      "        else:\n",
      "            axh.set_xticklabels([])\n",
      "        #axh.legend(loc=\"upper left\")\n",
      "    \n",
      "    # Set the regession axis properties\n",
      "    axr.set_xlim(*xlim)\n",
      "    axr.set_ylim(.25, .85)\n",
      "    axr.set_xticklabels([])\n",
      "    axr.axhline(.33, c=\"k\", ls=\"--\")\n",
      "    axr.set_ylabel(\"OTC classifier accuracy\")\n",
      "\n",
      "    # Label the traces and histograms\n",
      "    axr.text(-3, .6, \"IFS\", color=roi_colors[\"IFS\"], ha=\"center\")\n",
      "    axr.text(-1.5, .48, \"IPS\", color=roi_colors[\"IPS\"], ha=\"center\")\n",
      "    f.axes[1].text(-4, 600, \"IFS evidence\", color=roi_colors[\"IFS\"], ha=\"center\")\n",
      "    f.axes[2].text(-4, 600, \"IPS evidence\", color=roi_colors[\"IPS\"], ha=\"center\")\n",
      "\n",
      "    # Label the panels\n",
      "    textsize = 11\n",
      "    f.text(.02, .96, \"A\", size=textsize)\n",
      "    f.text(.02, .35, \"B\", size=textsize)\n",
      "    f.text(.02, .20, \"C\", size=textsize)\n",
      "    \n",
      "    sns.despine()\n",
      "    save_figure(f, \"figure_7\")\n",
      "\n",
      "dksort_figure_7()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RYQ4w0ImIyAACVx7OMczTnbn0AIErM9+SB4B9x26g59drcf+RdmnTCmUdsWPpUPTzbmIU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\nOAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12d747590>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"width:585px;padding-left:120px\" align=\"justify\">\n",
      "**Relationship between frontoparietal classifier evidence and visual classifier performance.** (A) Points show the mean OTC classifier accuracy sorted by binned IFS and IPS classifier evidence (the logit-transformed probability of the target class). Error bars represent the bootstrapped standard error across subjects. Solid traces show the predictions of a logistic regression model fit to all datapoints. Horizontal dashed line shows chance performance. (B and C) Histograms showing the distribution of classifier evidence in IFS and IPS for all trials and subjects.\n",
      "</div>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Effects of control demands on context representation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we ask how the context representation adapts in the task structure. What happens when we sort the accuracy of classifier predictions by position relative to the most recent rule switch?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_rule_shift():    \n",
      "    shift_dfs = []\n",
      "    for roi in net_rois:\n",
      "        mask = \"yeo17_\" + roi.lower()\n",
      "        ds = mvpa.extract_group(\"dimension\", roi, mask, frames, peak, \"rt\")\n",
      "        accs = mvpa.decode_group(ds, LogisticRegression(), trialwise=True)\n",
      "        roi_df = behav_df[[\"subj\", \"dim_shift\", \"dim_shift_lag\"]]\n",
      "        roi_df[\"roi\"] = roi\n",
      "        roi_df[\"acc\"] = np.concatenate(accs)\n",
      "        shift_dfs.append(roi_df)\n",
      "    shift_df = pd.concat(shift_dfs, ignore_index=True)\n",
      "    shift_df[\"IFS_logit\"] = np.tile(logit_df.IFS.values, 3)\n",
      "    shift_df[\"IPS_logit\"] = np.tile(logit_df.IPS.values, 3)\n",
      "    \n",
      "    shift_df = shift_df[shift_df.dim_shift_lag < 6]\n",
      "    shift_df[\"log_shift_lag\"] = np.log(shift_df.dim_shift_lag + 1)\n",
      "\n",
      "    return shift_df\n",
      "\n",
      "shift_df = dksort_rule_shift()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i shift_df\n",
      "m.shift.int = lmer(acc ~ roi * log_shift_lag + (roi + log_shift_lag | subj), shift_df, family=binomial)\n",
      "m.shift.add = lmer(acc ~ roi + log_shift_lag + (roi + log_shift_lag | subj), shift_df, family=binomial)\n",
      "lr_test(m.shift.int, m.shift.add, \"ROI X Lag interaction\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for ROI X Lag interaction:\n",
        "  Chisq(2) = 2.18; p = 0.336\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "print(m.shift.add, corr=FALSE)\n",
      "m.shift.nolag = lmer(acc ~ roi + (roi + log_shift_lag | subj), shift_df, family=binomial)\n",
      "lr_test(m.shift.add, m.shift.nolag, \"Lag main effect\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Generalized linear mixed model fit by the Laplace approximation \n",
        "Formula: acc ~ roi + log_shift_lag + (roi + log_shift_lag | subj) \n",
        "   Data: shift_df \n",
        "   AIC   BIC logLik deviance\n",
        " 15282 15384  -7627    15254\n",
        "Random effects:\n",
        " Groups Name          Variance  Std.Dev. Corr                 \n",
        " subj   (Intercept)   0.0163745 0.12796                       \n",
        "        roiIPS        0.0986636 0.31411  -0.579               \n",
        "        roiOTC        0.0685217 0.26177  -0.709  0.530        \n",
        "        log_shift_lag 0.0012838 0.03583   0.173  0.441  0.477 \n",
        "Number of obs: 11250, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "              Estimate Std. Error z value Pr(>|z|)    \n",
        "(Intercept)    0.11064    0.05411   2.044 0.040907 *  \n",
        "roiIPS         0.33510    0.09361   3.580 0.000344 ***\n",
        "roiOTC         0.47687    0.08229   5.795 6.84e-09 ***\n",
        "log_shift_lag -0.19760    0.03421  -5.776 7.65e-09 ***\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
        "Likelihood ratio test for Lag main effect:\n",
        "  Chisq(1) = 18.71; p = 1.52e-05\n"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now ask what happens when we add in the IFS and IPS logits as predictors"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.shift.top = lmer(acc ~ log_shift_lag + IFS_logit + IPS_logit + (log_shift_lag + IFS_logit + IPS_logit | subj),\n",
      "                   shift_df, family=binomial, subset=shift_df$roi == \"OTC\")\n",
      "print(m.shift.top, corr=FALSE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Generalized linear mixed model fit by the Laplace approximation \n",
        "Formula: acc ~ log_shift_lag + IFS_logit + IPS_logit + (log_shift_lag +      IFS_logit + IPS_logit | subj) \n",
        "   Data: shift_df \n",
        " Subset: shift_df$roi == \"OTC\" \n",
        "  AIC  BIC logLik deviance\n",
        " 4792 4879  -2382     4764\n",
        "Random effects:\n",
        " Groups Name          Variance   Std.Dev. Corr                 \n",
        " subj   (Intercept)   0.10354262 0.321780                      \n",
        "        log_shift_lag 0.02410617 0.155262 -0.835               \n",
        "        IFS_logit     0.00023268 0.015254 -0.637  0.956        \n",
        "        IPS_logit     0.00220897 0.047000 -0.168 -0.221 -0.399 \n",
        "Number of obs: 3750, groups: subj, 15\n",
        "\n",
        "Fixed effects:\n",
        "              Estimate Std. Error z value Pr(>|z|)    \n",
        "(Intercept)    0.59512    0.10384   5.731 9.98e-09 ***\n",
        "log_shift_lag -0.09953    0.07211  -1.380    0.168    \n",
        "IFS_logit      0.08510    0.01373   6.196 5.78e-10 ***\n",
        "IPS_logit      0.17476    0.01918   9.113  < 2e-16 ***\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.shift.top.noifs = lmer(acc ~  IPS_logit + log_shift_lag + (log_shift_lag + IFS_logit + IPS_logit | subj),\n",
      "                         shift_df, family=binomial, subset=shift_df$roi == \"OTC\")\n",
      "m.shift.top.noips = lmer(acc ~  IFS_logit + log_shift_lag + (log_shift_lag + IFS_logit + IPS_logit | subj),\n",
      "                         shift_df, family=binomial, subset=shift_df$roi == \"OTC\")\n",
      "lr_test(m.shift.top, m.shift.top.noifs, \"IFS main effect\")\n",
      "lr_test(m.shift.top, m.shift.top.noips, \"IPS main effect\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for IFS main effect:\n",
        "  Chisq(1) = 21.91; p = 2.86e-06\n",
        "Likelihood ratio test for IPS main effect:\n",
        "  Chisq(1) = 28.83; p = 7.89e-08\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "m.shift.top.nolag = lmer(acc ~ IFS_logit + IPS_logit + (log_shift_lag + IFS_logit + IPS_logit | subj),\n",
      "                         shift_df, family=binomial, subset=shift_df$roi == \"OTC\")\n",
      "lr_test(m.shift.top, m.shift.top.nolag, \"Lag main effect\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Likelihood ratio test for Lag main effect:\n",
        "  Chisq(1) = 1.81; p = 0.179\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_figure_8():\n",
      "    f, axes = plt.subplots(1, 3, sharey=True, figsize=(4.48, 3.5))\n",
      "    text_pos = [.41, .48, .54]\n",
      "    data = shift_df.groupby([\"roi\", \"subj\", \"dim_shift_lag\"]).acc.mean().unstack()\n",
      "    for i, roi in enumerate(net_rois):\n",
      "        ax = axes[i]\n",
      "        color = roi_colors[roi]\n",
      "        roi_data = data.loc[roi].values\n",
      "        x = range(1, 7)\n",
      "        xx = np.linspace(.7, 6.3, 100)\n",
      "        sns.tsplot(roi_data, time=x, err_style=\"ci_bars\",\n",
      "                   interpolate=False, color=color, ms=5, mec=color,\n",
      "                   err_kws=dict(linewidth=1.5), ax=ax)\n",
      "        y = roi_data.mean(axis=0)\n",
      "        fit = np.polyfit(np.log(x), y, 1)\n",
      "        r2 = r2_score(y, np.polyval(fit, np.log(x)))\n",
      "        yy = np.polyval(fit, np.log(xx))\n",
      "        a, b = 3.1, 3.9\n",
      "        block_break = (xx >=a) & (xx <= b)\n",
      "        lw=1.25\n",
      "        ax.plot(xx[xx < a], yy[xx < a], lw=lw, color=color, label=roi)\n",
      "        ax.plot(xx[block_break], yy[block_break], lw=lw, ls=\":\", color=color)\n",
      "        ax.plot(xx[xx > b], yy[xx > b], lw=lw, color=color)\n",
      "        \n",
      "        ax.text(1.6, text_pos[i], \"$R^2 =\\/%.2f$\" % r2, size=8)\n",
      "        ax.text(2.8, yy[30] + .03, roi, size=9, color=color)\n",
      "        ax.xaxis.grid(False)\n",
      "        ax.set_xlim(.7, 6.3)\n",
      "        ax.set_ylim(.3, .7)\n",
      "        ax.set_yticks(np.linspace(.3, .7, 5))\n",
      "        ax.axhline(.33, ls=\"--\", c=\"#444444\")\n",
      "        if i == 1:\n",
      "            ax.set_xlabel(\"Trials since rule switch\")\n",
      "        if not i:\n",
      "            ax.set_ylabel(\"Cross-validated decoding accuracy\")\n",
      "    \n",
      "    f.subplots_adjust(wspace=.05, bottom=.12, top=.97, left=.1, right=.98)\n",
      "    sns.despine()\n",
      "    save_figure(f, \"figure_8\")\n",
      "\n",
      "dksort_figure_8()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2kt1c/ucOn3+9nHGDWqfbkZonjbtIgRws/+VDZi7aweZdp9n591kio2LIntWL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\nu/Rfht38CuFh+u0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10af0dbd0>"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"width:450px;padding-left:120px\" align=\"justify\">\n",
      "**Relationship between control demands and dimension rule decoding.** Points and error bars show mean and bootstrapped standard error across subjects. Solid traces show the predictions of a log-linear fit to all datapoints. Traces are dashed between trials 3 and 4 to indicate passage through a mini-block boundary. Horizontal dashed line shows chance performance.\n",
      "</div>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Schematic figure about influence of control demands"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We finally plot a cartoon figure illustrating our perspective on how control demands influence context representation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def make_dataset(offset):\n",
      "    a = np.random.randn(50, 3) + [-offset, 0, offset * .5] \n",
      "    b = np.random.randn(50, 3) + [offset, 0, -offset * .5]\n",
      "    return a.T, b.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_plane(ax, normal, color=\"#666666\", grid=False):\n",
      "    point = np.array([0, 0, 0])\n",
      "    d = -point.dot(normal)\n",
      "    steps = np.linspace(-2.5, 2.5, 5) if grid else [-2.5, 2.5]\n",
      "    x, y = np.meshgrid(steps, steps)\n",
      "    a, b, c = normal\n",
      "    z = (-a * x - b * y - d) / c\n",
      "    ax.plot_surface(x, y, z, alpha=.25, shade=False, color=color, rstride=20, cstride=20)\n",
      "    ax.plot_wireframe(x, y, z, color=\"#555555\", linewidths=.15)\n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dksort_figure_9():\n",
      "    o, g, p = rule_colors[\"dim\"]\n",
      "    f = plt.figure(figsize=(6.85, 2))\n",
      "    for i, offset in enumerate([.5, 1, 2], 1):\n",
      "        ax = f.add_subplot(1, 3, i, projection=\"3d\", axisbg=\"white\")\n",
      "        a, b = make_dataset(offset)\n",
      "        ax.scatter3D(*a, color=g, s=8)\n",
      "        ax.scatter3D(*b, color=p, s=8)\n",
      "        plot_plane(ax, [-1, 0, .5], \"#999989\", True)\n",
      "        for axis in [\"x\", \"y\", \"z\"]:\n",
      "            getattr(ax, \"set_%slim\" % axis)(-4, 4)\n",
      "            getattr(ax, \"set_%sticks\" % axis)([])\n",
      "            getattr(ax, \"set_%sticklabels\" % axis)([])\n",
      "        ax.set_title([\"Low\", \"Moderate\", \"High\"][i - 1] + \" control demands\", size=8)\n",
      "    plt.tight_layout()\n",
      "    save_figure(f, \"figure_9\")\n",
      "\n",
      "dksort_figure_9()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WgnjaOsYYY4wxxhhjLARxhZ4xxhhjjDHGGAtBPCgeuyBEBKfTCavVColEIv6T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JRO0cBvFPdPUVRBMKKyOr/u0GeSpavtQgiSsfWU+uZOgzt9Gaycm1Jae0gjFbdZmFgPdNQd0jh2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\n/Yh2oidoZ3C4emykIgc4dOZkm0JC2y/rG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       "text": [
        "<matplotlib.figure.Figure at 0x12caeed90>"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 96
    }
   ],
   "metadata": {}
  }
 ]
}