cneuromod_validation.c.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cneuromod validation\n",
    "\n",
    "https://docs.cneuromod.ca/en/latest/DATASETS.html#hcptrt\n",
    "\n",
    "This cneuromod dataset is called HCP test-retest (hcptrt), because participants repeated 15 times the functional localizers developed by the Human Connectome Project, for a total of approximately 10 hours of functional data per subject."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n",
      "  warn(\"Fetchers from the nilearn.datasets module will be \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "nib.imageglobals.logger.setLevel(40)\n",
    "\n",
    "np.set_printoptions(suppress=True)\n",
    "from glob import glob\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from lib.plotting import plot_29k\n",
    "import hcp_utils as hcp \n",
    "\n",
    "from collections import defaultdict\n",
    "def mydefault():\n",
    "  return defaultdict(mydefault);\n",
    "\n",
    "from IPython.display import clear_output\n",
    "from lib.plotting import plot_gen_surf2, image_grid, fig2tightPIL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-09-04 17:42:31\n"
     ]
    }
   ],
   "source": [
    "\"\"\"for name, value in os.environ.items():\n",
    "    if \"SLURM\" in name: \n",
    "      print(\"{0}: {1}\".format(name, value))\"\"\"\n",
    "\n",
    "import os\n",
    "from datetime import datetime\n",
    "ts = int(os.environ[\"SLURM_JOB_END_TIME\"])\n",
    "print(datetime.utcfromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**default params**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subjects: ['sub-01' 'sub-02' 'sub-03' 'sub-04' 'sub-05' 'sub-06']\n",
      "Tasks: ['EMOTION', 'GAMBLING', 'LANGUAGE', 'MOTOR', 'RELATIONAL', 'SOCIAL', 'WM']\n",
      "Contrasts: ['LANGUAGE_STORY-MATH' 'WM_2BK-0BK' 'MOTOR_CUE-AVG' 'EMOTION_FACES-SHAPES'\n",
      " 'SOCIAL_TOM-RANDOM' 'RELATIONAL_REL-MATCH' 'GAMBLING_REWARD-PUNISH'\n",
      " 'WM_FACE-AVG' 'MOTOR_T-AVG']\n"
     ]
    }
   ],
   "source": [
    "# Find all subjects\n",
    "\"\"\"subj_dirs = glob(f\"{dataset_dir}/fmriprep/hcptrt/sub-*/\")\n",
    "subj_dirs.sort();\n",
    "cneuro_subjs = [p.split(\"/\")[-2] for p in subj_dirs]\n",
    "np.savetxt(\"data/subjs_cneuro6.txt\", cneuro_subjs, fmt=\"%s\")\"\"\"\n",
    "cneuro_subjs=np.loadtxt(\"data/subjs_cneuro6.txt\", dtype=str)\n",
    "print(\"Subjects:\", cneuro_subjs)\n",
    "\n",
    "from lib.default_config import  contrast_info\n",
    "tasks = np.unique([c[0] for c in contrast_info]).tolist()\n",
    "print(\"Tasks:\",tasks)\n",
    "\n",
    "unique_contrasts = np.loadtxt(\"data/task_contrasts_47_unique_names.txt\", dtype=str);\n",
    "contrast_ids_to_show = [8, 38, 16, 2, 27, 24, 5, 44, 21]\n",
    "contrast_names = unique_contrasts[contrast_ids_to_show]\n",
    "tnames_mid = [\"Lang S-M\", \"WM2-0\", \"Motor Cue-Avg\", \"Emoti Face-Shape\", \"Social TOM-Rand\", \"Rel-Match\", \"Gamble Rew-Pun\" ,\"WM Face-Av\", \"Motor T-Avg\"]\n",
    "print(\"Contrasts:\", contrast_names)\n",
    "\n",
    "dataset_dir= \"/scratch/users/robert.scholz2/cneuroprep\"\n",
    "\n",
    "cneuro_subjs_x = ['sub-01', 'sub-03', 'sub-05']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Download Cneuromod data\n",
    "\n",
    "common issue: S3 bucket does not allow public access. Then set the access keys in your `bash` terminal everytime you want to download data:\n",
    "\n",
    "`export AWS_ACCESS_KEY_ID=<s3_access_key>`\n",
    "\n",
    "`AWS_SECRET_ACCESS_KEY=<s3_secret_key>`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# run directly on the cluster to get the git repository and file metadata\n",
    "\"\"\"\n",
    "cd /scratch/users/robert.scholz2/cneuroprep\n",
    "datalad get -n $scratch/cneuroprep/fmriprep/hcptrt/\n",
    "datalad get -n smriprep\"\"\";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download task run data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/fmriprep/hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-emotion_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "/fmriprep/hcptrt/sub-01/ses-003/func/sub-01_ses-003_task-emotion_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "/fmriprep/hcptrt/sub-01/ses-004/func/sub-01_ses-004_task-emotion_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "/fmriprep/hcptrt/sub-01/ses-005/func/sub-01_ses-005_task-emotion_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "/fmriprep/hcptrt/sourcedata/hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-emotion_run-01_events.tsv\n",
      "/fmriprep/hcptrt/sourcedata/hcptrt/sub-01/ses-003/func/sub-01_ses-003_task-emotion_run-01_events.tsv\n",
      "/fmriprep/hcptrt/sourcedata/hcptrt/sub-01/ses-004/func/sub-01_ses-004_task-emotion_run-01_events.tsv\n",
      "/fmriprep/hcptrt/sourcedata/hcptrt/sub-01/ses-005/func/sub-01_ses-005_task-emotion_run-01_events.tsv\n"
     ]
    }
   ],
   "source": [
    "# list sample files\n",
    "subj = cneuro_subjs[0]\n",
    "task = tasks[0].lower()\n",
    "cifti_files = glob(f\"{dataset_dir}/fmriprep/hcptrt/{subj}/ses-0*/func/*{task}_run-1*.dtseries.nii\")\n",
    "event_files = glob(f\"{dataset_dir}/fmriprep/hcptrt/sourcedata/hcptrt/{subj}/ses-0*/func/*{task}_run-01*events.tsv\")\n",
    "event_files.sort(); cifti_files.sort();\n",
    "for f in cifti_files[:4]: print(f[len(dataset_dir):])\n",
    "for f in event_files[:4]: print(f[len(dataset_dir):])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sub-01' 'ses-002' 'task-emotion' 'run-1']\n",
      "['sub-01' 'ses-002' 'task-emotion' 'run-1']\n"
     ]
    }
   ],
   "source": [
    "# function to make sure we have corresponding mri data and events files\n",
    "import re\n",
    "\n",
    "def get_all_attr(string, attr=[\"sub\", \"ses\", \"task\", \"run\"], ret_slice = 0):\n",
    "  data = []\n",
    "  for a in attr: \n",
    "    res = re.findall(a + r'-[a-zA-Z0-9]+', string)\n",
    "    data.append(res[ret_slice].replace(\"run-0\", \"run-\"))  # with hotfix\n",
    "  return np.array(data);\n",
    "\n",
    "print(get_all_attr(cifti_files[0]))\n",
    "print(get_all_attr(event_files[0]))\n",
    "assert np.all(get_all_attr(cifti_files[0]) ==get_all_attr(event_files[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " sub-01: EMOTION 14 14, GAMBLING 15 15, LANGUAGE 14 14, MOTOR 15 15, RELATIONAL 14 14, SOCIAL 15 15, WM 15 15, \n",
      " sub-02: EMOTION 15 15, GAMBLING 15 15, LANGUAGE 15 15, MOTOR 15 15, RELATIONAL 15 15, SOCIAL 15 15, WM 15 15, \n",
      " sub-03: EMOTION 15 15, GAMBLING 15 15, LANGUAGE 15 15, MOTOR 15 15, RELATIONAL 15 15, SOCIAL 15 15, WM 15 15, \n",
      " sub-04: EMOTION 14 14, GAMBLING 14 14, LANGUAGE 14 14, MOTOR 14 14, RELATIONAL 14 14, SOCIAL 14 14, WM 14 14, \n",
      " sub-05: EMOTION 14 14, GAMBLING 14 14, LANGUAGE 14 14, MOTOR 14 14, RELATIONAL 14 14, SOCIAL 14 14, WM 14 14, \n",
      " sub-06: EMOTION 18 18, GAMBLING 17 17, LANGUAGE 18 18, MOTOR 18 18, RELATIONAL 17 17, SOCIAL 14 14, WM 18 18, "
     ]
    }
   ],
   "source": [
    "runs, events, attr = [defaultdict(mydefault) for i in range(3)]\n",
    "\n",
    "for subj in cneuro_subjs:\n",
    "  print(\"\\n\", subj, end=\": \");\n",
    "  for task in tasks:\n",
    "    cifti_files = glob(f\"{dataset_dir}/fmriprep/hcptrt/{subj}/ses-0*/func/*{task.lower()}*.dtseries.nii\")    \n",
    "    event_files = glob(f\"{dataset_dir}/fmriprep/hcptrt/sourcedata/hcptrt/{subj}/ses-0*/func/*{task.lower()}*events.tsv\")\n",
    "    event_files.sort(); cifti_files.sort();\n",
    "    run_attrs = [get_all_attr(cifti_files[i]) for i in range(4)];\n",
    "    evn_attrs = [get_all_attr(cifti_files[i]) for i in range(4)];\n",
    "    print(task, len(cifti_files), len(event_files), end=\", \")\n",
    "    assert np.all(np.array(run_attrs) == np.array(evn_attrs))\n",
    "    runs[subj][task]   = cifti_files\n",
    "    events[subj][task] = event_files\n",
    "    attr[subj][task]   = run_attrs\n",
    "    \n",
    "np.save(\"data/cneuromod_task_run_info.npy\", dict(runs=runs, events=events, attr=attr, subjs = cneuro_subjs, tasks= tasks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**now to the actual download using datalad**\n",
    "\n",
    "* cd /scratch/users/robert.scholz2/cneuroprep\n",
    "* datalad get .../sub-01/ses-002/func/sub-01_ses-002_task-language_run-01_events.tsv\n",
    "* datalad get fmriprep/hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-language_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
    "* cat fmriprep/hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-language_run-1_space-fsLR_den-91k_bold.json\n",
    "  + { \"DelayTime\": 0.11749999999999994,  \"RepetitionTime\": 1.49 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cd /scratch/users/robert.scholz2/cneuroprep\n",
      "fmriprep/hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-emotion_run-1_space-fsLR_den-91k_bold.dtseries.nii fmriprep/hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-emotion_run-2_space-fsLR_den-91k_bold.dtseries.nii fmriprep/hcptrt/sub-01/ses-003/func/sub-01_ses-003_task-emotion_run-1_space-fsLR_den-91k_bold.dtseries.nii fmriprep/hcptrt/sub-01/ses-004/func/sub-01_ses-004_task-emotion_run-1_space-fsLR_den-91k_bold.dtseries.nii fmriprep/hcptrt/sub-01/ses-005/func/sub-01_ses-005_task-emotion_run-1_sp ...\n"
     ]
    }
   ],
   "source": [
    "cifti_files = []\n",
    "event_files = []\n",
    "\n",
    "n_runs=8\n",
    "\n",
    "for subj in cneuro_subjs:\n",
    "  for task in tasks:\n",
    "    cifti_files = cifti_files + runs[subj][task][:n_runs]\n",
    "    event_files = event_files + events[subj][task][:n_runs]\n",
    "    #print(attr[subj][task])\n",
    "\n",
    "dataladstr = \"\"#\"datalad get \"\n",
    "for f in cifti_files: dataladstr += f[len(dataset_dir)+1:] + \" \"\n",
    "for f in event_files: dataladstr += f[len(dataset_dir)+1:] + \" \"\n",
    "print(\"cd /scratch/users/robert.scholz2/cneuroprep\")\n",
    "print(dataladstr[:500], \"...\")\n",
    "\n",
    "# writing to a file\n",
    "text_file = open(\"data/cneuro_taskrun_downloads.txt\", \"w\")\n",
    "text_file.write(dataladstr)\n",
    "text_file.close()\n",
    "\n",
    "# full data can be downloaded using datalad\n",
    "# datalad get $(<$HOME/code/dist_model/data/cneuro_taskrun_downloads.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True 0 \t hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-language_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "True 1 \t hcptrt/sub-01/ses-002/func/sub-01_ses-002_task-language_run-2_space-fsLR_den-91k_bold.dtseries.nii\n",
      "True 2 \t hcptrt/sub-01/ses-003/func/sub-01_ses-003_task-language_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "True 3 \t hcptrt/sub-01/ses-004/func/sub-01_ses-004_task-language_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "True 4 \t hcptrt/sub-01/ses-005/func/sub-01_ses-005_task-language_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "True 5 \t hcptrt/sub-01/ses-005/func/sub-01_ses-005_task-language_run-2_space-fsLR_den-91k_bold.dtseries.nii\n",
      "True 6 \t hcptrt/sub-01/ses-006/func/sub-01_ses-006_task-language_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "True 7 \t hcptrt/sub-01/ses-006/func/sub-01_ses-006_task-language_run-2_space-fsLR_den-91k_bold.dtseries.nii\n",
      "----\n",
      "8 of 90\n"
     ]
    }
   ],
   "source": [
    "# make sure the download was successful\n",
    "\n",
    "import os\n",
    "n_existing_runs = 0;\n",
    "for i, f in enumerate(cifti_files):\n",
    "  exists= os.path.exists(f);\n",
    "  if exists: \n",
    "    n_existing_runs+=1\n",
    "    print(exists, i,  \"\\t\", f[50:])\n",
    "\n",
    "print(f\"----\\n{n_existing_runs} of {len(cifti_files)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download resting state runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True \t sub-01 5 \t hcptrt/sub-01/ses-001/func/sub-01_ses-001_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-01 5 \t hcptrt/sub-01/ses-003/func/sub-01_ses-003_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-01 5 \t hcptrt/sub-01/ses-004/func/sub-01_ses-004_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-01 5 \t hcptrt/sub-01/ses-007/func/sub-01_ses-007_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-01 5 \t hcptrt/sub-01/ses-009/func/sub-01_ses-009_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-02 5 \t hcptrt/sub-02/ses-001/func/sub-02_ses-001_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-02 5 \t hcptrt/sub-02/ses-003/func/sub-02_ses-003_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-02 5 \t hcptrt/sub-02/ses-005/func/sub-02_ses-005_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-02 5 \t hcptrt/sub-02/ses-007/func/sub-02_ses-007_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-02 5 \t hcptrt/sub-02/ses-009/func/sub-02_ses-009_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-03 5 \t hcptrt/sub-03/ses-002/func/sub-03_ses-002_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-03 5 \t hcptrt/sub-03/ses-004/func/sub-03_ses-004_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-03 5 \t hcptrt/sub-03/ses-005/func/sub-03_ses-005_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-03 5 \t hcptrt/sub-03/ses-007/func/sub-03_ses-007_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-03 5 \t hcptrt/sub-03/ses-010/func/sub-03_ses-010_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-04 5 \t hcptrt/sub-04/ses-001/func/sub-04_ses-001_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-04 5 \t hcptrt/sub-04/ses-003/func/sub-04_ses-003_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-04 5 \t hcptrt/sub-04/ses-006/func/sub-04_ses-006_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-04 5 \t hcptrt/sub-04/ses-008/func/sub-04_ses-008_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-04 5 \t hcptrt/sub-04/ses-010/func/sub-04_ses-010_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-05 4 \t hcptrt/sub-05/ses-002/func/sub-05_ses-002_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-05 4 \t hcptrt/sub-05/ses-003/func/sub-05_ses-003_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-05 4 \t hcptrt/sub-05/ses-006/func/sub-05_ses-006_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-05 4 \t hcptrt/sub-05/ses-009/func/sub-05_ses-009_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-06 6 \t hcptrt/sub-06/ses-001/func/sub-06_ses-001_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-06 6 \t hcptrt/sub-06/ses-004/func/sub-06_ses-004_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-06 6 \t hcptrt/sub-06/ses-007/func/sub-06_ses-007_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-06 6 \t hcptrt/sub-06/ses-008/func/sub-06_ses-008_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-06 6 \t hcptrt/sub-06/ses-010/func/sub-06_ses-010_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n",
      "False \t sub-06 6 \t hcptrt/sub-06/ses-013/func/sub-06_ses-013_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii\n"
     ]
    }
   ],
   "source": [
    "rs_runs = defaultdict(mydefault)\n",
    "\n",
    "for subj in cneuro_subjs:\n",
    "   rs_files = glob(f\"{dataset_dir}/fmriprep/hcptrt/{subj}/ses-*/func/*restingstate*.dtseries.nii\")\n",
    "   rs_files.sort()\n",
    "   rs_runs[subj] = rs_files;\n",
    "   for f in rs_files:\n",
    "    print(os.path.exists(f), \"\\t\", subj, len(rs_files), \"\\t\", f[50:])\n",
    "    \n",
    "#np.save(\"data/cneuromod_rs_run_info.npy\", dict(rs_runs=rs_runs, subjs = cneuro_subjs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fmriprep/hcptrt/sub-01/ses-001/func/sub-01_ses-001_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii fmriprep/hcptrt/sub-01/ses-003/func/sub-01_ses-003_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii fmriprep/hcptrt/sub-01/ses-004/func/sub-01_ses-004_task-restingstate_run-1_space-fsLR_den-91k_bold.dtseries.nii fmriprep/hcptrt/sub-01/ses-007/func/sub-01_ses-007_task-restings'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataladstr = \"\"\n",
    "for subj in cneuro_subjs:\n",
    "  for f in rs_runs[subj][:4]:\n",
    "     dataladstr += f[len(dataset_dir)+1:] + \" \"\n",
    "\n",
    "# writing to a file\n",
    "text_file = open(\"data/cneuro_rsrun_downloads.txt\", \"w\")\n",
    "text_file.write(dataladstr)\n",
    "text_file.close()\n",
    "\n",
    "dataladstr[:400]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download individual surfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datalad get smriprep/sub-01/anat/sub-01_hemi-L_midthickness.surf.gii smriprep/sub-02/anat/sub-02_hemi-L_midthickness.surf.gii smriprep/sub-03/anat/sub-03_hemi-L_midthickness.surf.gii smriprep/sub-04/anat/sub-04_hemi-L_midthickness.surf.gii \n"
     ]
    }
   ],
   "source": [
    "surf_files = glob(f\"{dataset_dir}/smriprep/sub-*/anat/*L_midthicknes*.gii\")\n",
    "surf_files.sort()\n",
    "dataladstr = \"datalad get \"\n",
    "stub = \"/scratch/users/robert.scholz2/cneuroprep/\"\n",
    "for f in surf_files[:4]: dataladstr += f[len(stub):] + \" \"\n",
    "print(dataladstr)\n",
    "# [S3 bucket does not allow public access; Set both AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY to use S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation / Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.glm.contrasts import compute_contrast\n",
    "from nilearn.glm.first_level import make_first_level_design_matrix\n",
    "from nilearn.glm.first_level import run_glm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict(runs=runs, events=events, attr=attr, subjs = cneuro_subjs, tasks= tasks)\n",
    "p = np.load(\"data/cneuromod_task_run_info.npy\",allow_pickle=1).item()\n",
    "runs,events, attrs, subx, taskx = p[\"runs\"], p[\"events\"], p[\"attr\"], p[\"subjs\"], p[\"tasks\"]\n",
    "assert np.all(subx == cneuro_subjs)\n",
    "assert np.all(taskx == tasks)\n",
    "\n",
    "p = np.load(\"data/cneuromod_rs_run_info.npy\",allow_pickle=1).item()\n",
    "rs_runs, subx = p[\"rs_runs\"], p[\"subjs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "sub-01: ... RS 4/5\t EMOTION 8/14 + 8, GAMBLING 8/15 + 8, LANGUAGE 8/14 + 8, MOTOR 8/15 + 8, RELATIONAL 8/14 + 8, SOCIAL 8/15 + 8, WM 8/15 + 8, \n",
      "sub-02: ... RS 0/5\t EMOTION 0/15 + 0, GAMBLING 0/15 + 0, LANGUAGE 0/15 + 0, MOTOR 0/15 + 0, RELATIONAL 0/15 + 0, SOCIAL 0/15 + 0, WM 0/15 + 0, \n",
      "sub-03: ... RS 4/5\t EMOTION 8/15 + 8, GAMBLING 8/15 + 8, LANGUAGE 8/15 + 8, MOTOR 8/15 + 8, RELATIONAL 8/15 + 8, SOCIAL 8/15 + 8, WM 8/15 + 8, \n",
      "sub-04: ... RS 0/5\t EMOTION 0/14 + 0, GAMBLING 0/14 + 0, LANGUAGE 0/14 + 0, MOTOR 0/14 + 0, RELATIONAL 0/14 + 0, SOCIAL 0/14 + 0, WM 0/14 + 0, \n",
      "sub-05: ... RS 4/4\t EMOTION 8/14 + 8, GAMBLING 8/14 + 8, LANGUAGE 8/14 + 8, MOTOR 8/14 + 8, RELATIONAL 8/14 + 8, SOCIAL 8/14 + 8, WM 8/14 + 8, \n",
      "sub-06: ... RS 0/6\t EMOTION 0/18 + 0, GAMBLING 0/17 + 0, LANGUAGE 0/18 + 0, MOTOR 0/18 + 1, RELATIONAL 0/17 + 0, SOCIAL 0/14 + 0, WM 0/18 + 0, "
     ]
    }
   ],
   "source": [
    "# check how much of each is available\n",
    "\n",
    "for subj in cneuro_subjs:\n",
    "    print(f\"\\n{subj}\", end=\": ... \");\n",
    "    \n",
    "    n = np.sum([os.path.exists(f) for f in rs_runs[subj]])\n",
    "    print(f\"RS {n}/{len(rs_runs[subj])}\", end=\"\\t \")\n",
    "    \n",
    "    for task in tasks:\n",
    "        n = np.sum([os.path.exists(f) for f in runs[subj][task]])\n",
    "        m = np.sum([os.path.exists(f) for f in events[subj][task]])\n",
    "        print(f\"{task} {n}/{len(runs[subj][task])} + {m}\", end=\", \")\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resting state gradients (predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from brainspace.gradient.alignment import ProcrustesAlignment\n",
    "from tqdm.auto import tqdm\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 91282)\n"
     ]
    }
   ],
   "source": [
    "#def load_cneuro_ts_data(file_path, subj, session, v=False, bma_slice=slice(0, 29696, None), zscore=False):\n",
    "#  file_path = file_path.format(**{\"session\":session, \"subj\" : subj})\n",
    "#  if v: print(file_path)\n",
    "subj = \"sub-01\"\n",
    "nimg = nib.load(rs_runs[subj][1])\n",
    "fsdata = nimg.get_fdata()\n",
    "print(fsdata.shape)  # (600, 91282), aka we need 8 runs to get to 4800 timepoints\n",
    "\n",
    "#[:, bma_slice]\n",
    "#return scipy.stats.zscore(fsdata, axis=0) if zscore else fsdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b9ebeb3de50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(fsdata, aspect=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b9ebef5ef40>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(scipy.stats.zscore(fsdata, axis=0), aspect=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_29k(np.nan_to_num(scipy.stats.zscore(fsdata, axis=0))[30, :29696])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_cneuro_ts_data(file_path, v=False, bma_slice=slice(0, 29696, None), zscore=False):\n",
    "  nimg = nib.load(file_path)\n",
    "  fsdata = nimg.get_fdata()[:, bma_slice]\n",
    "  if v: print(fsdata.shape)  # (600, 91282), aka we need 8 runs to get to 4800 timepoints\n",
    "  return scipy.stats.zscore(fsdata, axis=0) if zscore else fsdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b44d60bed90>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(lh_full_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b44d15e57f0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.nan_to_num(lh_full_ts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_grads_idvpro = {}\n",
    "expl_vars_indv = np.zeros((len(cneuro_subjs), 200))\n",
    "indv_pcas =[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [04:37<00:00, 92.34s/it] \n"
     ]
    }
   ],
   "source": [
    "#cneuro_subjs=np.loadtxt(\"data/subjs_cneuro_xxx\").astype(int).astype(str);\n",
    "group_comps = np.load(\"data/group_components.pca200c.npy\") # hcp reference group components\n",
    "\n",
    "n_comps = 200\n",
    "alignment = ProcrustesAlignment(verbose=False, n_iter = 10)\n",
    "\n",
    "for i, subj in tqdm(enumerate(cneuro_subjs_x), total=len(cneuro_subjs_x)):\n",
    "  print(subj, end=\" ... \")\n",
    "  if subj in all_grads_idvpro.keys(): continue;\n",
    "\n",
    "  lh_full_ts = np.concatenate([load_cneuro_ts_data(file, zscore=True, bma_slice= slice(0, 29696, None)) for file in rs_runs[subj][:4]], axis=0)\n",
    "  \n",
    "  print(lh_full_ts.shape, end=\" ... \")\n",
    "\n",
    "  icmat = np.nan_to_num(np.corrcoef(lh_full_ts.T))\n",
    "  print(icmat.shape, end=\" ... \")\n",
    "\n",
    "  pca_indv = PCA(n_components=n_comps)\n",
    "  indiv_comps = pca_indv.fit_transform(icmat)\n",
    "  print(indiv_comps.shape, pca_indv.explained_variance_ratio_.sum().round(3))\n",
    "\n",
    "  indiv_comps_aligned = alignment.fit([indiv_comps], group_comps).aligned_[0]\n",
    "  #icomps_zgr = pca_zgr.transform(fishers_z(icmat*0.999999999))\n",
    "  #icomps_gr = pca_gr.transform(icmat)\n",
    "  \n",
    "  expl_vars_indv[i,:] = pca_indv.explained_variance_ratio_\n",
    "  indv_pcas.append(pca_indv)\n",
    "  \n",
    "\n",
    "  #icomps = scipy.stats.zscore(icomps, axis=0)\n",
    "  #all_grads_zgr[subj] = icomps_zgr.astype(np.float32);\n",
    "  #all_grads_gr[subj] = icomps_gr.astype(np.float32);\n",
    "  all_grads_idvpro[subj] = indiv_comps_aligned.astype(np.float32);\n",
    "  clear_output(wait=True)  \n",
    "    \n",
    "  \n",
    "  fn = \"/scratch/users/robert.scholz2/acc_dists/all_grads_6_cneuro.L.corrcoef.procrAlignGr.npy\"\n",
    "  np.save(fn, all_grads_idvpro)\n",
    "  np.save(\"/scratch/users/robert.scholz2/acc_dists/all_grads_6_cneuro.L.corrcoef.procrAlignGr.pca_info.npy\", \\\n",
    "          {\"pca_indvs\": indv_pcas, \"var_expl_ratios\" : expl_vars_indv, \"subjs\":cneuro_subjs[:i+1]});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"/scratch/users/robert.scholz2/acc_dists/all_grads_6_cneuro.L.corrcoef.procrAlignGr.pca_info.npy\", \\\n",
    "          {\"pca_indvs\": indv_pcas, \"var_expl_ratios\" : expl_vars_indv, \"subjs\":cneuro_subjs_x});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68M /scratch/users/robert.scholz2/acc_dists/all_grads_6_cneuro.L.corrcoef.procrAlignGr.npy\n"
     ]
    }
   ],
   "source": [
    "!ls -ash /scratch/users/robert.scholz2/acc_dists/all_grads_6_cneuro.L.corrcoef.procrAlignGr.npy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9971324504278433"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_indv.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b44d5498af0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/jpeg": 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5ldCKysREQBxjv/vUIx7juKUj903B6f8As1OiXK59xVtqzG9jqPArHNv6f29p38564qdiZuR2/rXa+BBh4B/1HtO/9r1xk33hx261pD4mdzdqcPmRRH52x6U5vuEDoD/Skj6N9KcR8p+v/sprV7kN6iof3nPoKn3EBQBx/wDXqCP/AFx/CrRACr/nvWU3qYVHqiJT+9c98c1IpwOAaaAPtEn0qcqNucVEpJESZAGIXgd6d0jPXqaUgBOR/FinBQIj1PzGhtCbOl+GP/Icm+tr/wCl1tXLb2GeB/rM/jiuq+Gf/Icm+tr/AOlttXJnq3XO/wD9lpvVs7a38GHz/NFmOV/KQALtC9TSpJIC2FXof5GoF/1SYJAxT0zuPJ+6f5Gs3FanDbUGd/NyQucdKaGOTx2NNyftHUninoOo/wBnrVWSQ2rDNzfZ0BAx/wDWqFG4X8amA/0aPr1/pUarwnFXG2patqe8fs9/8kr0/wD6+77/ANLJq9Hrzn9nz/kllh/1933/AKWTV6NXBLdn39L4F6BRRRSLCiiigAooooAKbL/qn+hp1Nl/1T/Q0AfJPggkfC2Pp/rLD/0RPU1nIomUscAL/Rah8EqD8LY/eWw/9ET0lumZF5I4/oK62k4u58Jivj+/82WJZAElwQSeOv0p8MxSUshGTu71BJGFEnP+eKfbxAvyT0b+tZuMeU4yxJP5jYkwOwP4n9Kq2zlXlAxyGHJ46n/D+VPmiAZcHqcf+hf4UyxjWR3BOODj82/wpJRjBtbFLYnaVS7HJ3ndx25FQTtlMkr1HSntCBcsM8CormLZH1PUf1pwUbqxK3HM2FAXB5H/AKARUvmFIwDjIH8P+fb+VRCIfJkk/MBz/uE1LNCqoCPQfyNKXLdIb6EVy27zCMZZyarykj0xuNTTRgM3Jxu/oKhlUBRW9O1lYcXqTSPlIgcZyaC4BbaeSP8ACmTIAIjmlWMbmyfX+lKysLQkmZTnBPfqahi7ZxnbzmldeSMn/JpqJubqfu/0pxSURq1ictyvIyCf5Col6N9zGR3pzR/MvJ7/AMqYkeVPJ60o2sJWFJG4ZIzx/OooT8pxjO/Iz9aeUzKgyeSv8zUdsuev97H61orWNElykuTiQHbnn6U+U/umJ29e1NaMbpB6A0+aICFjnvis7q6I00I5zlmxjPNTwuA/B7Hr+P8AjVeZNrsMnAzUkEYLgEn7pP6GiSXIHQkZgfNAIwc9fXFLO7Oh+5jcO/P3RTREMTc/dJp0kKiInPQj+QrP3U0IiDYhbdjO3t/vCliOF4K43DvTGQeUxz2/qKdHGCnXuK0drMbtY6rwKctB6/29p3/teuKuM+bxjGPx612fgMfNb8/8x7Tv/a9cbMoBBz2rSGkjtlZU6fz/ADIojy3TGPxpxPyfiOv+6aSMDBPtS4zHn3H/AKCTWz3Ie7FU/vjj0FWQSVXO3/Jqsg/ekfSrQjGwc+1ZTsY1LXRGGPnycLnFTB2xjC1Bs/0mUegqwYl9aiTWhE7aEe5ivIXrnk0odjGRhep60hjGz8aEQFD9TRoLQ6j4Zf8AIcmzjra/+l1tXLZBZuf+Wnf0xXU/DIY1yb62v/pdbVygX5mGT97/ANlpu12dtb+DD5/mieMoIE3Fs7e1SKyZb5m5Ugfkaq4/dIQTT1Xk/MehP6GolFa6nE0KSnnjBPQdelKp+YnIxtPfnvUAGbjbk1MqAr1OdtNpIGkhN4+zIM9/6VErHCdOppwT9xGcnr/SkCjC+9UrIrRHu/7Pf/JLNP8A+vu+/wDSyavRq85/Z7/5JZYf9fd9/wClk1ejVwy3Z+gUvgXoFFFFIsKK4f4l6NrurXGhtofnyW1vLK95bxatNpxmUx4QeZFluGwcYxxXLaPqP/CVaxoPhqC51zSrK3tr+a/hOoytdG4gnji8prncXZQZWbIbkbO3FAHsNFeM6fquq6qNC8LXWrXyxnXNRsLi/hlMdzcQWodkXzFwQx+QMy4J2NyMmi41XVbVL7wpHq1+Y18T2+kpqDSlrlLWW3S4ZPMPJcZaMOctgjuM0AezU2X/AFT/AENcR4Ha507xj4o8OtfXl7YWUVpd2z3k7TyxCYShozIxLMAYtw3EkbsdMVu+I9ZvtMZEsvDuqasroS0lnJbKsZ9D5sqHP0BoA+YvBH/JLo/+uth/6InpbYnzFA9P8Kb4KJHwtTgf63T/AP0RPSQkhgQO3+FdttGfCYte99/5ssT8+ZnsaWLO/wCm4VFI5IfIAJNKrNk8dcmo5Xy2OO2hNJ98c9Ofx5qG0JV2I64J/Vv8TQzHfyPao4iSSFGeCP1P/wBf/PUUfdaZSTsy7wZOT1FQ3WTHlum4f1pm47+nNI7ExFXUeoIqYwaaZK3JCSVXPqD/AOOGnSOSgDeg/lUEm4AcY5H/AKCaHzgZGOO1VyJ2G9bCzEnd/vf4VFL057mnOTtIx3zUch/Rq1giooll+7Hz1Jp3Jkb8f6VFITiPjoaXcQ7cf54pcugraD3z81MiJBGP7v8ASlZzhuKbGc4wM8DrQloCWhO2fl/H+QqJGYK31FPLMOdo49vaoxnB+Veo7f8A16UVoCQ7JEiH3X+ZqK2OQcf3/wCtSHJK8DPH8zUUAKg4x17/AFq1sy1blZYfKmQjrg06bPkN6ZqFixZuBnnjHFOlLGFgQuPpWdtiLbCT5y341JHncD3wf61E+ScEetORj6dj1+tU17tgexNzuuPxpZCdhA6Fh/IVCS26XgdyeKGLBDgDr6ewrPl1FYQ58l/896kiyUH1H9KgJPktx/nNOQsFHAq3HQbWh1ngQ/Nb/wDYe07/ANr1xk7HcAR2rsfAfLW//Ye07/2vXFzA7wcDGK0gveZ3SX7uHzGxnIYdsU7OI+OxH/oJpkfBJ7YpeqkD1/oa1ZDWo9OZj+FWgSFGPUGqiEiQ+vFWAWKjhfyrOaMKi1EPNxJ64qdice//ANeqwJEz9M4qQlsdB+VRJbEyWwjMQnHrSx/dOPU1Hzt6A80qEhDgDr3qmtAa0Os+GR/4nk31tf8A0utq5UZ3MR/f/wDZa6n4Y/8AIcm472v/AKXW1coCcnj+L+lK2rOusv3MPn+aJFH7hBnjFSIDk8/wn+RqNXHlrwcY9aVXwx4PQ/yNQ09Tidxoz9oDZ5Ap65w3oFphb94MDkjpQGIBAHG2m1cb1AE+Qn1/pTV6J9aaCfJTjof6U0E7Vq0i7Hvf7Pf/ACSyw/6+77/0smr0avOf2e/+SV6f/wBfd9/6WTV6NXny3Z99S+BegUUUUizH8Q6LPq5t2ttb1XSWh3AmxaL94GxwwkRwcY4IAIyfWsQ/DrS4rHT49OvNT0+9sZJpY9RgmVrl2mOZi7OrK+84Jyp5AxjArs6KAOSfwDpQ0DT9MtZ760ksLhry3v4ZR9pWdt2+QswIYv5j7gwIO48dMIvgHSv+Edn0qSe+kkmuxqD6g0w+1G6BBWYOAAGG1QABtwAMY4rrqKAMLwx4at9Aa+mW6vL+/vpFkub28dWllKrtUfKqqFA4AVQOT3Jrbl/1T/Q06my/6p/oaAPkjwSP+LWx/wDXWw/9ET0W5bzBgc4/wo8EDPwtj/662H/oienWykzDHTb/AIV2N6M+Exfxff8Amx0oYiTI6c06IM0nTpu/rSuOJsnGF49zxTrYfvn3HA+es3L3Tj6DJFYMOPf+dR2ine5AzgE/+hf41anUbuGz8p/PmobMczc87Gx9f85/KhSvBjW1hxRw+COKhmLFencCrr7Q2N4zzn8qpODkbuBuFFOVxLcl+aTaCMDIP/jh/wAakkjcqoA4IH8v/r1Gn3QWOP8A93/+urlt5bRx+bIEGOc9uB/9f8qzqScdUU10M6YHLgjo2D+lQyjI+pqxdqVnnHUeYcH14qF/c1003dJjWg6VWCx8cGlwzO2R2/wp0wASLn1oUfM+T/D/AIUk9CbjHDc5A5pkZKsMDgL/AEqWYEVEoORtz92qi7oa2JmJyvHUn+VRKxwxx3FSOD8n1Of0qBA+1uD2oitAS0JNxEigD0/nUUOdv1b+tSHPmD/gP86bGPk4P8Y6fWq6FLYewIDkDsafLnymBHemkfuZMctzx+FOlB2t6Z/pUdUSRzE7myPWpItzP07H+v8AhUdwPnkHXmpYQS3/AAFv5NRL4Lj6DyDmckeoP5UkoYRkMOrD+QpW+7Pzzz+PFOu1IXjJG4dPoKyT95L+uhKKrA+U2B/nIqSMEqDjuKbt/dH6Z/8AHhToR8ozx8wFat6Dex1HgTIa3B/6D2nf+164yblhkdq7TwOPnt8f9B7Tv/a9cVNnzORxirhrJndL+HT+f5jI+d2PSl6J7Z/9lNJH95vTFOP+r4/vf+y1szN7ioCZT68VYGQo496gT/W++BUzZwMZI/8Ar1nPcxnuhmCbiT3FWCpx0qDH7+TntVjHyA57VnN7ETexEwITp3xQqkIfqaJQfLGMn5qcV/dHB7076B0On+GPGuTfW1/9LrauT5DN/vf0rq/hl/yHJvra/wDpdbVyjZ3tjn5/6U+rOyt/Ch8/zQ9NwhQAcYp6l8nj+E/yNNUERJ6baeP4sf3T/KobOJvUiy32gHAyAKcueeOdv9aaQfN98CnqMyH02nn86p7DZGN3kJx1P9KRV+VAKkUfuEJPf+gpox8mOmTTuO57t+z3/wAkssP+vu+/9LJq9Grzn9nz/kllh/1+X3/pZNXo1cEt2ff0vgj6BRRRSNAooooAKKKKACmy/wCqf6GnU2X/AFT/AENAHyN4L/5JYn/XWw/9ET1HGeRz2qXwV/yStP8ArrYf+iJ6ijBMg47f4V3x2Z8Nifi+/wDNjnPB5pVJzwexpxXiTI6D/CiFfnbj1ourHJ0GsTkc9/8AGm2+S7fj/NqmkXk8dif51HCMF8D+E/5/Wi/ujT91gc+YelJLwvX/ADzUzL+96VCwynTuKE7iTu0K4+7znkfyNLKPl6+n9aEycZH+dtOIJA4/zii9mDdmCgs23Pr/ACpJYyOv1pUysjHHRsfpTnOVQEVN2mK9mQTDGz8/0pQDuPI7/wBKfdKcRcHoP5CkjHzNn0P9KpS925V9BhJ3EZ/zmlAIIyeo/pQwwQaJPvcf3aoNxSeTzzQvK9e9BHzLx61EhO0/hQldDSuiQgb159KbCBg49aR8+Yv1WiDOOncU7aDt7o8D96+emDT5hiM89zTJOAxA/hp8/KHj/PNR1RPVCMvzvz0pqkhsZ9aWXId+O1IgJOcev9aa2BbCgH95z608g+U5z0/+tSKDiTjuae4wjDHUf4VLepLepBICIc574/lTo+R19KJRmL/PtSwj5V4qr+6Vf3TrPAQ+aDn/AJj2nf8AteuMmHTvxXZeBeHt/wDsPad/7Xri5Sd5B9KcPiZ2S/h0/n+YiDg+uDSgfIM+v/sppIvvEUsn+rOP739K062M+thFH7w/hVhOUz71DF98Z9BUx4XgdqmfYyqb2EOBNIPQU8jBpn/Ld+O1St06dqh9CJdCIcLzSRnIok/1Y4/ipVGF6d6roPodZ8Mv+Q5N9bX/ANLbauRX77An+L/2Wut+GP8AyHJfra/+l1tXIHIY/wC8P5UluzsqfwYfP80OJPlIcincg9R0/wAaQr8iDtinEfyNNs5Gxq8zAZ9Kk2kAc9VBqEDDk1OOmMdE/pUyFIjAzGnT/IpwjOxfpSID5URx3/oKkY8Lx2/xobaFJ2Pc/wBnz/kllh/1933/AKWTV6NXnP7Pn/JLbD/r8vv/AEsmr0auCW7Pv6XwR9AooopGgUUUUAFFFFABTZf9U/0NOpsv+qf6GgD5H8En/i1if9dLD/0RPTYfvjjt/hT/AARj/hV0f/XWw/8ARE9FuCZVwB93/Cu29kz4XFP3vv8AzYEna+fTFIhIZsAdTU0qgJLkcACmxgbn3Du38qSkrHGnoNLEnoOlRxtjf/umrBUZ+UfwE1FCB8+R/C1NNWGnoK7HOQPWoix25OOoq2yjd0+XH9KrSAeV+I/lRCSYRYmSFH+f4acHIVeKdgbVz/n5KUYKrxQ2gbInz85Hd6YzMGGankA+fPTzMVFKBuHrmqi7lRZKzBlQN74NNww3YHOP8KdJ9yLb0OanRULuJOF28Y/Co5lFEbFdwfSoQDty3pVyZGGcgVXIwgx02/0qoSuhxeghOSBx3qNSNmOO1TH7y4HrUKH5T65FWti1sKfvA8dv502N8rx2P9aefvr6ZX+ZqK3IKtn+9z+dNbFJe7cl3Eq+cdKe7HaScYB5pPlCuV/un+tPm2+U+PWob1M+pDKxLN6k1JCCAMj1/rTJ8bm9MmrEQIHTsf60Sfujb91ELk7ZMdTTpHLZxjGf6CnkAibPrxT5RHg7c5z/AEFRzK+xNytyYsH0/rSxkgADHUVIoHlHH+eaIgPl3evNU5aMd9GdR4EyTbn117Tv5z1xkvD4briu18C9Lb0/t7Tv5z1xlyQJWB64/rVQfvM7ZP8Adw+f5kUfJIHWg58sE+v9KW3wXI9qH4g/4EP/AEGtupH2rCp/rB7gVPnIHT/JqOMDzFz6Cpjt2jHt/MVnJ6mM3qMB/eP609W9aQbfPk/z61INnbr/APXqJPyM5MhyNoyR1pQw2cHvQxAQYHOaF27Dx3NUM6r4YnOuTfW1/wDS62rkiDk8fx4/Sut+GWP7blx62v8A6XW1csQdx4/5af0pXtJnZV0pQ+f5iquVTIyMUBW547Ht7VIiMYosDjBp4RvnwBjaf5VDmcN9SsVO88dqBuDE+2KkKt5jZA70qgc/Q1TloPmIckQpj1poZjtz71MP+PePI78/kKbgZGPemmVc92/Z7/5JZp//AF933/pZNXo1ecfs9/8AJK9P/wCvu+/9LJq9Hrgluz7+l8C9AqhrGs6XolsLjWtSstPtydolu51iUn0yxAq/XG/GKG0k+GHit7qOB5I9IvDC0iglW8h/u56H6UizpNT1jTNKsBfapqNnZWRwBcXE6xxnPT5mIHNLcavptvpX9p3GoWcWm7BJ9reZVh2no28nGDnrmvPryS0l8dfD9tReB7D+y7sW5kIKfa9sGOvG7y/Nx3xurlNF+y/aPDXneT/wiv8Awl+pfZd2PI+5N5OO23zd+3tnbjtQB7ZDq+mz6V/acGoWcmm7DJ9rSZTDtHVt4OMe+aTTdY03VLA32majZ3lkM5uLedZI+OvzKSOK8U1X7L9o8QeT5P8Awin/AAmdh9oxjyP9VD52e2zz9m7tu3Z70/x35Hn/ABL/ALL2f2V5Gk/2h5P3N3nN9pzjjP2fbu9sZoA9n0fWtL1uF5tG1Ky1CGNtjvaTpKqt6EqTg+1XZf8AVP8AQ159pf2L/hc0n9ifZ/s//CPr9s+zY2bvPHkbtvGdvnY9vaun8R23iGdkOganpdlEEPmLeafJcsx9QVmjwPbBoA+YPBOf+FWof+mmn/8Aomen20qq4J/u4/lSeCUB+Fqf9dLA/wDkCemQpuI4HT/Cu3Rp3PhcXbm+/wDNk0sgMMgB6kUwSAlseppZIwAwI70JACeR1z/WkuVI5FawqSYJyeqkVErfex6H+dSNCFfA55x/OmW8W5iPr/Nv8KacUmxq1myRpOMd8H+VQs37upfKHmEdqJogkZ474/Q0RcUJWGM2QMf54NJuIAqSSIYXIxyBx7qTRLAFH4A/zpqUR6DXbchA6l81FMCXGB/ET+tSsm3OB0J5pkg5z05qo26Di9dBWYhYs9s1K75Y4/ugfyqKVANnpnFOI/eNkAYz/SpstxOzLUkysjKfwNVD8iBWGOKGGN3ApYsyqEYDgZB79KmMVBabCSsND88+/wDKowRjI9RVp7cK2APao0gG0nHIIq1ONrjUokRYYHrx/M1HD91vdv61YEfzooAxkfzNMt4wc+hOP1quZWZakrMQZwwPof60rk+XID1NS+UFlkx2Bp88Krbs3qcVLmronmVytIctjuM5qxHIAv51E67XY0saZIBHUGiVmhOzQhf731pcnqemQf0p3lDdMMdM08xARMRz2/QUuaIrojVtkRz/AJ5pFbOCPXP86HGITwD3/WljTge2BRpZselrnWeBTkWw7/29p38564m5yZifb+tdt4BPzQDg/wDE+0//ANr1xk/JBOASO1VDSbO1u1OHz/MhgBEhPtSt/qefUfypU4BPoKBygHbP/sprZ73Ib1uKp+ZcegqQZ2jPt/So0GZSD2wKuLEuxcdzWc5JGM2kVhnzX+gpy7s/59afsU3EgPpU3kqOcnP/ANeplNIiUkVQDgdKATsPNTLCuwHJ64pqQLsJy2cmjnQ+ZHUfDD/kOS/W1/8AS62rlw4/8i5/Suq+GSga5Nj1tf8A0utq5NR8xwBnf/7LQ7Ns661nRh8/zRbWUBI8/wB2kSYANnuD/Kq3/LJMKMdKcMk/dHQ+voaz9mjh5SR5FLHHvTM8H3BNNyfO27V5xTlXIJI/hqrJBZIZu/cIAaYA3f3pwX9yh75/pSDlVOTVovY94/Z6/wCSV6f/ANfd9/6VzV6PXnH7Pf8AySvT/wDr7vv/AEsmr0evPluz7+n8C9ArO1vQtI163jg1zS7DUoI23pHeW6TKrYxkBgQDgnmtGikWYq+E/Di6OdJXQNIGlF/NNmLKPyd/97Zt2598VduNI0240r+zLjT7OXTdgj+yPCrQ7R0XYRjAx0xV2igClDpGmwaV/ZkGn2cem7DH9kSFRDtPVdgGMe2KTTdH0zS7A2OmadZ2dkc5t7eBY4+evyqAOavUUAUNH0XS9EheHRdNstPhkbe6WkCRKzepCgZPvV2X/VP9DTqbL/qn+hoA+SfBPHwtT/rpYf8Aoiekt2IIOO3+FL4MP/FrI8f39P8A/RE9RKSOldqV0fCYpXl9/wCbJ5GyG9zTw5B6ev8AWq5ZiAP9rP607cSR9P8AGlyaHJYkZ/3g/wB4fzNR27Ycn6/zamsWDr7OD/Om25PmfRv6v/jT5fdZaj7rJvM/eGnTvmI59f6Gq3zec3H+cinzkmMjn/OaHBXQuXVEsznABH8Q/wDQSKJ5DtwfQf1qKQlmX/eH8jS3eTuxzwP5GhRV0gS2HSMdh98moZWyD/vGnsTtA/2TUco4P+8auCsOCJJGz5Y96Gf52/z6UyTIZPrSEksfr/hQogokrNndSWbfNx/d/pURdvm4p1sSr9+lDj7rG42iy8z5J46H+lMwQpOOpFRM5y3HWrKyhlAJwOP5VztOK0MbWK+7DLx6fzNRW+AD/vZ/WnykrLt689RUURw3Tqf8K2S0NEtCXd++k98/0qSd827D/a/pVfJFw5/z2p8pPlY9zScdUFtUDncenXNPiY7s49ajXO/kdKRGIkA+tNrSwraEjSfNN75pWfMR/wB7+gqIZJfj1p3Iib6/0FLlQWGs37g/T+tOVuPxFQvkRED0/rT1JIP1FW46FNaHW+AT80H/AGHtO/8Aa9cbMclT7V2Pw/zvgz/0HdO/9r1xzkFEz1xTj8bOyekIfMjXhT9KVTwP8/wmk/gOKEGMf5/hNavqR3HIf3zfUVdQ/Kg9/wCtUFP71z7j+tXYmwi5/vVlVRhWWwwEGeQ+q1MGzVcf65vpUucH8aiSIkh0XIx7/wBaFGIz06n+dJC23nPqP1psZO3qO/8AOk07slo6n4Zf8h2b62v/AKW21coh6n/b/wDZa6z4Z8a9cf71r/6W21ckhILD/a/9kFV1Z3Vf4MPn+g8EiJVx6URuc9Oo/oajLMI1PuKRCyuOD/nNPlumcnLoyUn/AEkHHYUBuD9P61ErsbleD2qQ8RKO+0Gk1ayBxtYaW/cJ/ntUSN8i+1O5MKD/AD0NIi/Iv0q1ZF6JHvX7Pf8AySvT/wDr7vv/AEsmr0evOP2e/wDklen/APX3ff8ApZNXo9edLdn3tL4F6BRRRSLCiiigAooooAKbL/qn+hp1Nl/1T/Q0AfIvgz/klqf9dNP/APRE9CEZH40/wWufhbH7yaf/AOiJ6hj5A6V3x1R8LitZff8Amx79PxpSeR9P8aJAAeMdcfypQuQOnI/xourXOXoNzh/+Bf40yI/Ofqf5tUjL8493A/nSQJvce/8A8U4/pTbSTLS91h6+tOJ6/T+hpuzM5T6fqRUkyeXEX464/Rv8KltbEW2G3WCBjsf8aR+VOeeB/WllTlQMckDj3BNF0nlEjjgD+R/woi1ohrohrH5fwNMm5Z/941JsBAzjlT/T/GmSADDetVFq44vUR+Nv1okPzcf56UsuAye5o2gyEcdT/SqQLowzw/0p1ueFz/npTRg7hxSwAZI46Z/rSezB7Mkz8rn2FGeevYfypWT5XHHIH+NOCDZ24IFZXRnoCEbMH+7UYTAf/e/rSgAvGvHzFR+ZP+FLZOspIb2x+YoeibHZpXIo/wDXMSe5/pTn5ibnv/hUkkQS8kQ8Ac8/hTpowtqX96fOm0+9ht6oiQ4f6GkDcn8f50EAPn8abEo4HHINVZbhZbit958f3f6Ujf6tue/9BTxHuaYDtkU5Iw0TH/PQUuZIL2IH/wBQf896cvJH4f1omA8hsdR/jT40G0E+mapvQbfunWeAOsP/AGHtP/8Aa9cVPyy89v8AGuz+HxBeH313Tv8A2vXHSAbEPtRHSbOyekIfMiXjP0NKh4H+f4TSrgxk/wCe9NQdP8/wmtn1I3uL/wAtX+oqeE8DnvUKAGZvqKuxRjZk/wB6s6kklqY1WloVyD5jn/ZpT1HNS/L57r6LTpIwG6cZA/Wo5yHLuV0bA/CmoflP41IifKOn+c0kC7gc+9XdalNrVnV/DH/kPTfW1/8AS22rlIzy3+8f/QRXWfDIf8T2b62v/pbbVyMY69Ov/sgqd2zqq60YfP8AQep/dLn2pQw3D6f41Ex+VTx1ApEGWXp0/wAarl6nLy6NlkkeYv0WmMeR9P61GGBn28dqlVQYi3+zUW5SbcpH1iH+e1Rr9xKcP9TH7n+lKFGwfStNjTY95/Z7/wCSV6f/ANfd9/6VzV6PXnH7Pf8AySvT/wDr7vv/AEsmr0evNluz7yl8C9AooopFhRRRQAUUUUAFNl/1T/Q06my/6p/oaAPknwSmfhbHyeZLD/0RPUMJ5U4HI6Y+lTeCTj4Wx/8AXSw/9ET1HABuX6f4V3R2Z8Jifi+/82Sy/LuOAd3H06c0iEgkgDnP4daWUg7s/wCelC4GfxxUr4TkvoDNlxlRyQv0znn9KbanzGIwFyCMj6sM/wDjv/jxpxxvH1/xplucNx6H+bf/AF6Gvddi01yvQmL/AL3btXKgHOOe3+P6UyYbItx+bDDg9OhP9KUsPNyOvH9KSU5gIbpuH8jSStYlboGGxVxzkgZ9Pk6/p+tLOfLAOBJgD7w65B/w/WkkbhfXI/8AQTSTnI+b0H9f/r0Jaq476oRl3MwzjOTx246VE+R745qfOASPU4/SopT0zjHStIsIvUWXGE4HFO24dsc8n+lJMQRGKdu+dvx/pS1sLoMIBLDgY9qSBNx5ODtJ/IZp5xlsU2A/nt/pTu+V2GnoSuOcegJz60yOINHne3JA/Wntzk9//rU1CQh+oqFexKegxBunjBOMsozjpyaS0AfgDb8w5H1p+cPGe4Kn9TTLPABI67h/OrezNL+6WhEJZp4WYjYpIc9eO361JewCK3LbifmIwRxxxVfzCJ5SOGIIqa7lL2zBv7xrnampR7EX2Kky4dhnuf6URc4HHQnP506c/MfXmiLqPXBre/ujv7o/nNxg469KcybombJXB6DvwKYW+afHcmn7z5TAYxn+grN36EsryDFuW6nHf60+EfIOc9P6U1zm3Yf560+E4A9K0exTfunU+AV3PB2/4nunHj/tvXHTQhVV95Pyg47V2PgE8wY/6D2n/wDteuOnY8AnjFVG/Ozsm37OFvP8yNUwhfJ6dPzpqfOqk8bjk/8AfBP9KdGeD6YpB9wY9f8A2U1rrqR3uPjGJiv0FaEdurxKxdlO4LjH6/pWfEcTE/T+dXkkIiAB4yKwrc2ljCq9SPylF3MnJ2gnPc9anaAcjc3BAz+NQbh9qlbPJFSNKST9R/Os5c2hnLcjWEMgBYjv/wDWpkI3qeNvJ6UBiUGP89aSE4U49TWmtmVrZnV/DNca5Nyetr/6XW1ckibmYZx82M/8ABrrfhn/AMhyb62v/pbbVyS/eYj+/wD+y1Wt2dtR/uYfP80Ju/dI21eeMGlVsnovKnt7H/CnYHlx+lKgGfwP8jQ2rHJdEYwZ9mFHvipY1yCc8Ffu9h2puB5/HWpIz8pHbbSk9NBSfYhUfuU4HX+lLt+RT7Uo/wBRH9f6UgbKr06Uxts92/Z7/wCSV6f/ANfd9/6WTV6PXnH7Pf8AySvT/wDr7vv/AEsmr0euCW7Pv6XwL0CsXxV4o0jwpYfbdduXt7b5juSCSYgKMsSEViAAMk4xW1XD/GzV9O0r4X+Jk1K9t7VrzTLu2txNIFM0rQPtRc9WPoKRZta74u0fQ1043810W1AMbVLaynuXlAUMcJEjMMAg8inXnirSLHQYdYvZ57aynYRxCa1lSZ3JICCEr5hY4OF25PXGK4fU9W8Ba3ovhq/1TxPFbC2jdLS6tNSa3/ebEWRRIhHzAFRtz36Gsi11O8j0vwP4g8RzXMujadrF6pvruMq/2Zo54ra5mGBjIKAsQPvhjjNAHpkXjHQJfDk+ujUY10uBjHLK6MjRuGC7GQgOHyQNpG7JHHIpdO8X6FqGk3+pQX6x2lhn7WbmN4Gt8LuPmJIFZeCDyBkdK8ovpFvTrPimzSSXw1/wldhf+YkZKyQQwxRS3CgD5kEg3bh1EZNL4odfE6+PtZ8Oq2oaQYNKQyWyl1uzb3DSziPH38RMF4zk8dqAPWfDfirR/EjXCaRdPJLbhWlimgkgkVWztbZIqttODhsYODg1sy/6p/oa878ParY+J/is+r+HrhL3TLXRDaz3cPMbTPOrpHu7sqq5I/h3jOM11XiPw1Y64yS3s+qRtEhVRZ6nc2qkdeRE6hj7nNAHzB4MH/FrExn/AFmn/wDoiempu3ArUngn/klqf9dNP/8ARE9PtseaMkfd/wAK7eayPhMW/e+/82Qndg5HBPNOG7Pc5GfpU78LKBzSoBubB5yann0OTm0KxLbuAeTjp9aZDksQcjqP1P8Ah+tXW4bk9Qf51DEN2/nHyt/P/P501PRjUtCLc28gjge3NK+Qh6n2P41adTu/OoWGI+Dk5HFCmmCdyKUHjOTyB9OD/n8aJflzjLYA6/jUxXgYbPT/ANBpShwu054p84XsRAcfOTggnHoabNtGe5DVLN9xxu5381DLnIwc/MTTjrqOOokpJKYBxR827nJqdvuw57E0FeXwe3+FNS0DmK534Of5U5OgC+lWZSMHn1qCLp8x/hoUrq4c10B3YIyenX8KVU9zzjmpTjjnuf5VD8pU8+lSncSYPnIAPHFMiIGRtI57VIwGQM+n8zUUGdrZGPm/rVrYpbC9ZWGDj1p8hPlHAP0oYcNzx6091/dPzzxxSb2FfYEKsxV+nb2pg3BiAcjJ5H1om4PXuc1ZtSpG0nj5ufzqZPlXMLZXKoyS+cjOeaUg+WxIYHtx7CpXT/W/NwDRIpH3j36fgKOYLlZ8+Sduf8mnR546mpEH7k5Pb/2alQccHuKrm0aG5aWOp8ActBngf29p/wD7Xri5hhgeScV2ngTrb5/6D2nfznriph+9654qofEztl/Dh8xqYBJ9qXgr+P8A7KabF99j7U5v9Wf94fyrV7kPcFJEjfgKsR8rzkc1DGP3n4CrGOF5/wA5FZzZjUetiPP71/lP3etKRluA1Ko/eOPQdakVff8AzmpbsQ3YgVNw70RqcHIIFSjAUZPendY/xP8AOm5Mbmzp/hl/yHZh72v/AKW21cmgJZskY3f+y11nwx/5Dk31tf8A0utq5U4wRnnzP6UurOqt/Bh8/wAxDvESkYz6U7LbuCORUykbUPfHSiPGWyR0P8jUOfkcXN5FcFjIoOO3NOIwAR3XkU9iPMx2x1pFHLc84PFO/UdyLP7tOOP/AK1Iu3ap71IRm2j9c/0qJQfl565q07l7nvX7Pf8AySyw/wCvu+/9LJq9Grzn9nv/AJJZp/8A1933/pZNXo1efLdn31L4F6BRRRSLCiiigAooooAKbL/qn+hp1Nl/1T/Q0AfJPgn/AJJbFgf8tbD/ANET0kCgup9v6Cl8ENj4Wx4A/wBbYf8AoiemwSYdeAeP8K7FezsfCYr4vv8AzZNKSPM2r14+g4p0K/vOBksGJGOnX/CmySHEnQZ9qIZiHJAXPzdutRZ8uhydB8ud4yuOdvTsd1R2XzyOrDAwVz7fNz+g/OnSTMzDIHr0+vH61FbyFWc4ByD1+p/z+Ao5XyNDWxYZiZjlMY/+tUVyojTCcncD/OnNK3nE8c+1RzSlo/uqDkcgfWiEXdCitSQgIFIGRkD8PLPP6D86klGEVkGSAO3sf8B+dQeaWUDaoGR0H+xj/P0pzzsFAUKuB2Ht/n8qXLK6GxJVAd8jnccj04FQy/KPlGSKlkkLs5ZRuZySfXgVFIemBitYX0uC3Hzk7Y/l6f4U9R+8kAGcg/0pkzsViBxT1kPmOQAMg/0qdeUXQjbqeOlNjALDtlf6VI5znimA7OcA/L3+lWthrYkccgfWmRqpVhngEDNK0uWGQMc8YqON8IcKuMjtSSdgSdh4AaZPqv8AOo7ckDJX+ID9aUuRIhAHVf5mmW7sQc8/MD+tXZ2Ls+UsN87SgjCgHn6VJcDELfLzkj8qh3kGTgEEHIp88rNC3yqDknIFZOLuiLbDLgDzH4xTosK4VeRtPP1BqOdyZHyB70+J+QQo+6Rj8DVNPkHbQsoFIugwwDnnHSpLqBo4GeRNpDYA9flH+NVRKf3+QuGJzxVq5u5Z4Ckqqfm3A45Hygf0rmlGammtuv4C0tqUnGIDx24P41JBjYoAzyOfypkhPk4IGMYH50tu5VV2gdQfyrdpuIuh1HgQfPBkYP8Ab2ncf9/642dcEEeldn4FJLwEjn+3tO/nPXGXBJYcAcVpC/Md0/4dP5/mRR8KxI7ZpQAyDPGT/wCymki5yDQThAR69/8AdrZkdWPT5ZiAM4wKtD5kBIwcgVVjP73PrirO87RkA4x/OsqhhU3E3MtxKAuQo4qY5HReP/r1Bub7RIfUdPSpvMbkYGPp71nJPQzkRMDsHHfNEZYocrxk0hc7BwOvpSpIxQjg8k1VnYqzsdT8M/8AkOTfW1/9LrauTABdh2D/APstdX8MyTrk2fW1/wDS62rk8/MeB9//ANlqurOyr/Bh8/zQqtmBHx83pTwFyf8AdP8AI0gwIox7dacGGeg+6f5GkzjfkRDm5CnhTjJqZAMdOSnI9OcVGG/fFsdO3apEbAJAG7b1pSuEhi/6hOOh/pQANqHHWkRz5EYGOT/SjnCjHSmNnu37Pf8AySyw/wCvu+/9LJq9Grzn9nv/AJJZp/8A1933/pZNXo1cMt2foFL4F6BRRRSLCiiigAooooAKbL/qn+hp1Nl/1T/Q0AfI/go4+Fsf/XXT/wD0RPSQ5DjHp/hWp4E0LV5fhZa+VpV+/nGwlj227neggm+ZeOR8y8j1HrT4fDPiAMM6JqfTvaSe3tXamrM+IxVKblpF9fzZmSsdr/lSISCffNbMnhrXiHxomp+v/HpJ/hRH4a18Mc6Jqff/AJdJP8KlSXKcnsalvhf3GOWJbp7fzqOBjub6Efqa3n8Na9v/AOQJqf8A4CSe/tUUXhnXgzZ0PU+h62knqfb3oUlZjVGpZ+6/uMpmO8nHGKZIf3Z/z2rebw1rvmZ/sTU8f9ekn+FRyeGteZcDQ9THI/5dJPf2ojJaCVGp/K/uMZzt2/X+hockKPpWz/wjWvEc6HqfGP8Al0k/u/SnN4Z15wP+JJqQ4/59JP8ACjmQ3Rqfyv7jFJ4JP94n+VRzN8w9jittvDOv5Yf2JqeMn/l0k9PpTH8La+SP+JJqf/gJJ/hVRaGqNTrF/cZEjEiOpNxDN9P8K1ZfDGv4TGiamcelpJ/hSjwzr+9s6Jqff/l0k9valdWF7Gpb4X9xksxw2f8APNMJygHHT+lbB8Na/wA/8STVP/AST/Cmjwxr4/5gmp9P+fST/CmrAqFT+V/cZHf/AD6U1BgY962z4a1/A/4keqZx/wA+kn+FMTw1r+w50LVM8f8ALpJ/hTUtBqjUt8L+4yMElT9P60y3bAbjv/Wtz/hGdf3p/wASPVMDGf8ARJPX6UQeF9dRfm0PVOo/5dJPX6U+ZWK9lUt8L+4xt2Xb0INOkbML1sP4Z10l2Gh6p7D7JJ/hT5PDOumEqND1PJ/6dJPT6VPMtCPY1Lr3X9xhyHdIenNOjyOPY1rS+F9f8x8aJqfB7Wkn+FEfhrxAAM6HqmcH/l0k9/ai6cR+xqW+F/cZWf8AWA+tPL5Rs/T9BWt/wjWvfvv+JJqfOcf6JJ/hUknhrXQh/wCJJqR+lpJ6D2qHJXJ9jU/lf3GEzZhI9/60sR2ba1pPDGveSQNE1PJ9LST1+lPt/Deu7Ar6JqYPHW0k4/Sm2lEPY1LfC/uNHwGcmAn/AKD2n/znrjLg4f8ACvRfBugavayxi40u+jC61YyHfbuBsUzbm5H3RkZPQZFcjN4X8QEj/iRaoeOotJP8KuDXNc7ZUp8kPdfX8zDhOCfpSnmL8R/6DWxH4W8QDP8AxItV/wDAOT/Cg+FvEG0f8SLVev8Az5yf3fpWzkrkOlO/wv7jIU/vPwFTo2VFaSeF/EAkydC1XHH/AC5yf4VYHhnXgP8AkBap1H/LpJ6/SonJGVSlU/lZib/30nHYU/f83+fWtn/hGNd85z/YeqYx/wA+kn+FK3hnXc8aHqfb/l0k9fpWfMiHSn/K/uMDOVFEbYB/Gtk+GPEGwY0LVOv/AD6Sf4UsfhjXwvzaHqn/AICSf4VfMrFOjUt8LNL4ZHOuS/W1/wDS62rlRncf9/8A9lrvvh5oOr2msSvdaTfwoTbYaS2dRxeW7HkjsFY/QE9q5geGfEGT/wASPVPvZ/49JP7v0pXV2dVWlN0oWi+v6GWCTFHj0pwz7dD/ACNaY8M+IAiY0TVOB/z6Sf4U7/hGfEAP/IE1P7p/5dJPT6VLscboVP5X9xkc+YRxzTt2B/wHFaf/AAjHiDzM/wBiapjj/l0k/wAKkXwxr2Sp0TU/udfsknX8qG0DoVP5X9xhqSIY/Y5/SgPnbWwvhfX/ACkB0TVODz/oknp9KB4X17C/8SPVP/AST/CqvEp0Z/yv7j179nv/AJJXp/8A1933/pZNXo9eefAKKSD4ZWcUyPHLHe36ujjDKReTAgjsa9Drhluz7un8C9AooopFhRRRQAUUUUAFNl/1T/Q06my/6p/oaAOV+EX/ACSrwd/2B7T/ANErXWV4T8OPih/Z/wAKtG/4lHmf2fa2Vl/x848zML/N9zj/AFXTn73XjnQ/4Xfx/wAi/wD+Tv8A9rrVUJvZHG8fh4uzl+D/AMj2aivG/wDhdvT/AIp//wAnf/tdH/C7v+pf/wDJ3/7XT+r1OxP9pYb+b8H/AJHslFeNH43c/wDIv98f8fv/ANrpR8bc/wDMv/8Ak79f+mftR9Xqdg/tHDfzfg/8j2SivGv+F3fMR/wj/wD5O/8A2ulPxt/6l/8A8nf/ALXR9Xqdg/tLDfzfg/8AI9korxv/AIXb/wBQD/yd/wDtdDfG3H/MA/8AJ3/7XR9Xqdg/tLDfzfg/8j2SivG/+F28gf8ACP8AbP8Ax+//AGugfG3P/Mv9s/8AH7/9ro+r1Owf2lhv5vwf+R7JRXjZ+NuM/wDEg6f9Pv8A9ro/4Xb/ANS//wCTv/2uj6vU7B/aWG/m/B/5HslFeNf8Lu5/5F//AMnf/tdKPjbk/wDIv/8Ak7/9ro+r1Owf2lhv5vwf+R7JRXjZ+NoH/MA/8nf/ALXTh8a8gH+wOv8A0+//AGuj2FTsL+08N/N+D/yPYqK8b/4XbyP+Kf8A/J3/AO107/hdfI/4kH/k7/8Aa6PYVOw/7Twy+1+D/wAj2KivHR8a/mI/sDp/0+//AGug/GvGP+JB6/8AL7/9rpewn2F/aeF/m/B/5HsVFeN/8Lt5/wCQB/5O/wD2ukHxvyP+Rf8A/J3/AO10/q9TsP8AtHDfzfg/8j2WivG/+F29f+Kf/wDJ3/7XS/8AC7OP+QB/5O//AGuj2FTsH9pYb+b8H/kex0V423xt2rn/AIR//wAnf/tdA+NuR/yL/wD5O/8A2uj6vU7B/aWG35vwf+R7JRXmvh/4o/2uYx/Y/k77+2sv+PrdjzvM+b7g6bOnfPUVkD41fKD/AGB1/wCnz/7XS9jPaw5Zjh4pNy38n/kew0V46/xr2jP9gZ/7ff8A7XTR8bc4/wCKf6/9Pv8A9rp+wqdhLMsM9eb8H/keyUV43/wu3k/8U/0/6ff/ALXTh8a+P+QB/wCTv/2uj2FTsJ5nhl9r8H/kexUV47/wuv8A6gH/AJO//a6P+F1/9QD/AMnf/tdL2E+wf2nhf5vwf+R7FRXjo+Nef+YB/wCTv/2ukHxs/wCoB/5O/wD2un7Cp2D+08L/ADfg/wDI9jorzXwv8Uf7dv3tv7H8jb5Xzfat2d88UXTYOnm5/DHfIxB8bsk/8U//AOTvtn/nnSVCb0sW8ww6SlzaPyf+R7LRXjJ+N+P+Zf74/wCP3/7XSf8AC8On/FPf+Tv/ANrqvq1TsL+0MP8Azfg/8j2eivGv+F3/APUv/wDk7/8Aa6UfG3OP+Kf7Z/4/f/tdL6vU7C/tHDfzfg/8j2SivGh8bs4/4p//AMnf/tdOPxs4/wCRf/8AJ3/7XR9Xqdg/tLDfzfg/8jtPhZ/yLV5/2GtV/wDS+4rr64T4KXv9peAlvvL8r7VqWpTbN27buvZzjPfGa7usXodqaaugrnvEep+ILG4xougW+o26Q+bJLNqAt+cn5EXY2WwAcnaORz1x0NeffEz/AISPUL610fT9C1G88OTRF9RuNPubeOabkj7MPNlQopAyzjJIO0YySAZZfx61/Z+Gz4Z0s6hfa7aG/hguJ/sywwKqFnkcK+OZEUAKck+gzUa/EE3Oj6W+naRJLrt/ezacumyziMQzw7/O3yAEBFEbHcAc5XA5qPUrDVLLW/DniTRPD0rrbadNptxpCzQRzQxO0TpsO/yvlMQBAfGG4zjFY9n4U1/S4NG19bCO61iDVr3UrvTYp1B8u6DgxpIxCl0Bj6kA7W55FAG5/wALBKaJcyT6RIuvQakmjnTFnBDXLhWTEuAPLKMH3kDC54yMUp+IJsdL8QPrukvaaroxhWSyt5xOJzOQsHlyELkOx28gYIOawpfCmv3NreeIGsI49bk8QQ61HpjTrnyooVgERkGVEhjDNnJUMQM45pdT8Ka94jt/FWrTWKabqV62ntYWNxOjsBZy+cvmMhZQXdmHBOBg0Add4b8TXt7rt1omvaSml6rFbpeRpFdfaY5oWYrkPtU7lYYII7jBINdNMyrG+5gOD1NcX4cstY1PxzceJNY0p9Hii05dPt7WWeOWVyZPMkcmNmULwgAzngk46VueIvC3h/xAyTa7ommalLEhWN7u1SYoOuAWBxQB8t+Dcf8ACq1z/wA9dP8A/RE9QCNCQpzjrU3g0f8AFqx/120//wBET1CPvZ9q9SGx8ZiLqWnn+bHMideeKQInfPHFB70AdfxqjnV7bgY0OOvHzfl/+ukRUUknPHP8/wDGlPH5YpByD9KNQu2tx3lR7s880jBGXPf/AD/jTjjpTMYFCBN9WLtQ46+v6UMqP+P+f60g7UKMYoD5jgqZzzxxQUUHjvxSDqfrmg9qQte4Oqds80ojTPf1prdh+FOBo1DW24nlICSM0FEUA85xQWzQxyKeo1zdWBCE/WhXAAHpTcYxTduR/wDXp2GknuPwoYH3FEZB69vSkI6D6UiL1osO2mpKCquz+o/z/Khym04zkUxhxj2oK8GlYiy3uOIUMSKQRoPXjikalznNA9bbgFT5uvtTvkwRzTOx9qXHOaQreYrKjLjmhQnGe9J0/OgDIFMOm52XgEKDbEf9B7Tf/a9cc7gDYM4A9K7DwFz9mH/Ue03/ANr1xjcsTjrURXvO50zV6cL+f5jlYOvlt0puAig914/T/wCvSR8MTTnwYyPetOpjs7dBAFLZOecGpQUA7+v+fzqIdh6ClIpPUlq+4/KF264pcpnoaj28mlxSsKyF3KoBwc9KRSo7UxxkfjS4yKdirKx2XwwI/tyUj/p1/wDS62rjgqgn64/TFdh8Lh/xO5f+3X/0utq45uT+NTH4mdFT+FC3mBgQqvXPXrS/Z4x68D1/z60u7pz2pS3B/GqvI5uafcaIkDFuaeEQcc8Cm7uoo6mjUHd7sAibV9T1p21enPpTRjAo70hO579+z3/ySvT/APr7vv8A0smr0evOf2e/+SV6f/1933/pZNXo1eXLdn3NL4F6BRRRSLCiiigAooooAKbL/qn+hp1Nl/1T/Q0AfIvguPPwrXjrLYf+iJ6rKoJHPar3gkgfCyPP/PSw/wDRE9Voyo257CvQpydj4fESfN9/5sRo/vfShYgSfxqeVgc4/wA9KRSMdfX+tPmdjlU3YheMA9/85pI0z+X+P+FTMw3D2IP86S2YI2T6H+b/AONNydiuZ8rYwxjzMc5prRgID74qwWHnk+w/pSTMGgwOu4fyNJSeglJ3RAIxx3//AGc0rRgYqdnU7MdiP/QSKS6cNwPQfyNClJtBzNtEAQAn60hUYHHarAZc5/z2qNyCQB6U1JjUncbIgGPyoVBuYYJxmpZCOB707cpZsep/pS5nYnmdisV5xg0iruyOmBmp8ruY06AqAT/sH+RFNyaVyud2ITFhhTVi+TOe9WmZS5PtSxuojH1B/Wp52kT7SVip5YLqM9SB+pohj3dx1FTqwWeInsyn9TRZcNyO4P61Tm7MtzfKRtFhnHoKdLCFQn3qyJQt1OSBgrgD8qku5Ve0IAGdxNZe0ldaEc7ujNkQAmhFBA69Cf51NKwMhx6n+lELL8v0P8jW3M+W5fO+UjKACT/ZJp5i+UnHapN6/vvcn+dSq6+U474J/Ss3ORm5souuEzTo48qDUsuPII7/AP16dCRtX6VTm7FOb5Tp/AI5tx6a9pv/ALXrjmTjPtXa+ACN0P8A2HdO/wDa9cjMwMagegoUnzs6qsn7OHzK6LwTTf4R2z/hUqH92Qfeo14RPb/4g1rfczT3HRLucL7VYa3IJB68VDCQZ8+4/nWrA6+XtbBO8H9awrVJQ2MakmnoZ4tyZXHpTzbH09KuooN1Kw4DDA/WlMi+d1wNy/zrF15X0M3N33MswnaOO+KRISRnFWkkG1fmPf8AmaigYBeST1rfnlZl88rM6n4Yrt1uX62v/pbbVxu3k/UfyzXa/DT/AJDk31tP/S22rkFKhmJ/vj/0AU4yd2dk5P2MH6/miHHC9QaMA+vQ1IcbIxnnNA2gjnsf5GruYcxGFG9hzUipkHjtmlGPPY9qmQqI/fbSlJkzmyqqjYp9/wClO2+3anqR5Ef1/pSgjaB7UNsHJnvH7Pf/ACSzT/8Ar7vv/SyavRq84/Z7/wCSV6f/ANfd9/6VzV6PXmy3Z95S+BegUUUUiwooooAKKKKACmy/6p/oadTZf9U/0NAHyV4KXPwrj/66WH/oieqq4JWr3gX/AJJZH0+/Y/8AoieqcR+6fb/Cu6m9z4XEP3vv/NkzqAzAdhn+VNjQMWH1/lUjty59Rj+VELYdjxzu/kaV3ynInoRSIAw/z602FQ3/AHyf5tU07Elc46gf+hU20OWYcdCP1anzPluXd8tweMfaNv0/pTZFxCD/ALQH6GpWkJuycjj/AOtTJz+67feH8jQm9CU3dIaEyB07f+gZomj5H0H8qmVsbMY6gf8AkOluDtx0GAP5UlN3Q22noQBBkimFOVPtmpWPzt05Of0qNjj8BVpsE2OlUKVz1zSqgLvjHBP9KWY529OtOVsO+Mck/wBKm7sK7sQYGSKWIDDDj7hNKT8xp1tzI2cf6tv5Gqb0bK3QrKA2PUH+dEYzCDx1H86lmP8ApIHHBP8AOmwqPKAwPvD+dZ83u3IvoQ4/fRj1ZR+pp1mNzc98U0f8fMfT7y/zpLQkMvP8Q61pLWLNGvdLDx4uJuRwuf0FPuo9luTx1P8AKkDbpbgHH3P6CpL1iYO332/lXPeXNFf1sZ9UUZRiRvr/AIUkCggfRj+hpZjmVx7/AOFOtjyOn3W/9Baul3ULmn2RdgxP04J/nUxiAjY8D5T/ACqMtxP05J/nViRswk8dP6CsZN6GcnqVJgBD/n1p1sgKKeOn9KSY/uR9P60sLYjH4Vo78o9eU6rwDxJD/wBh3Tv/AGvXISL8i4I6D+Vdh4B5kg/7Dunf+165GUfu1PHQfyoT9866r/d0/mRoPkOaixwn1/8AZTUg4hJ+v9ajU5VPc/8AsprZdSI9SW3UNcY+n8600jCjPHBH86zIW23H0I/nWq53Rxn5ecD9W/wrlxDd0YVdya2RftDo2PlP+NMNsFmGcY3J/OoEfF3MdwHX+TVqRSb0CnZjK8+nNcNVypu66pEpLYw1TYqE4xkj9aZAny9Mda2BFvijGF++Tx9ap4xCOnLGumNfmuv66ibZv/DUY1yb/etP/S22rjhgZH+0P/QBXY/DX/kOTD/atP8A0ttq48csf94f+gCuqO7O+f8AAh8/zQ1iP3efWm/Lkf7p/kaex+WM+9Ip5H0P9a06GC2DI81xU8agof8AcqAH/SG/CrEZwn1UVE9iJ6IhQA28X1/pTwnH4f401Di3j9j/AEp2flH0odwle57v+z5/ySyw/wCvu+/9LJq9Grzn9nv/AJJZp/8A1933/pZNXo1efLdn31L4F6BRRRSLCiiigAooooAKbL/qn+hp1Nl/1T/Q0AfJvgYZ+Fa/71j/AOiJ6qIowB7VZ8ESBPhUmCPv2IP/AH4nqsjAEfSu2nfU+FxF+b7/AM2ShMq3PTn+VEaZYgH1/lSeYAGxjnj8Kckiq2RjnOab5rHJqNlTBXJ7g/qabCuC3OMc/qf8afLKGK9OoH4c0yFxkgng5B/M/wCAp68pWvKBT9+3Pc/zpJB8hx6j+tOaTM5PGKSZl8v5euc/zpq+g1e6FUA7cHuP/QTT7hdzcH0/lTFcLt28DI6/7pFOllA+6QcAfy/+sKWvMJp3ViOQYLc8g4qNh1we5FSuykk9ycmo2IA4q43KjclZfmXPdv8AGmgfM2D/AJ4pZJeV6dc0gYBm98/0qVclXsI6gE80ttgSNk/wN/I00sCelEJAclu6kfpVNPlZeti3Mo+14zwXb/0I0xCNi+mRSNKplDehJ/XNCNHsxkdaxs0tTJpkDcTfQ02PO5fqKfIV80c55HP402Fhkbj0IrdbGy2JF4aUknoP5U+XpjJ+838qYXBeQZG0j+lOlddmRjdk1nrdGet0QEfvjUtsoLDns3/oJpjbS55p8LhGH0I/MEf1q5X5dC29BrLw/Pc/zqaRcR8k/wCRUZZSJB2yTUjyB0JYjI/wFQ73RDuQuuY15/g/rSKvy8H+JaV2Hl+4GB+dKjDZjPcGr1sVd2Ot8AjDwf8AYd07/wBr1xzn5VAPaux8AnLwE/8AQd03/wBr1xzkAL64pL4mdVT+HD5jP+WZ/wA+tMHG36/0NPU/IfWm9hn/ADwa1IXUcg/en6itCTgQAntn/wAeaqEZxL7cVcaVXC5IyvH6k1jVTbRjV3I48+afx/rWhaDK5Ldif51nq4EzcjHarEdwEGFIx0rKtGUlZGUlqa1lswgY5HmFf/HqqXkOy2TkffI/U1VS8IUDK8OW/HNL9t82EJIVwCSD+dcsaFSM+bz/AMxvbY6H4Z/8h6f62v8A6W21cgFGP+BD/wBBFdh8NCP7cn5H3rX/ANLbauPJBz/vD/0Gu9bs7an8GHz/ADQrqNq+uajVRkc/55p5bKqSMmkXg8jt/jVq6Rzq6QgA80/hUgHA5/hFRgjzjxxUqldnXnHShilcj2/uE+v+NIB8o57f1pc/uUHcH+lC9ByOlA7s96/Z7/5JXp//AF933/pZNXo9ec/s9/8AJLNP/wCvu+/9LJq9GrzZbs+8pfAvQK5Px1Hf21pc6sniyTQdOs7Yu4S0hkUuM/M5kViQcqAq7ST0OTx1lcT418Jazr/iHTL+z1yxt7GwXzI9PvNOa5iNxk4mO2aPJUYCg5CnJ64wizC1vU/Gt5ovgu7gt9YspLq0abWItIt7Z5o5DGhVcXIKgbi3HXtzirEniW71TTfCGneHdauvO1meeKbU7q2jFzEsCO0qmLYEWXeoTlcDk4NdTqGn+J5rWyFjr+n210kZW5d9LMkczHGGRfOBTHPBZutYy/D77LoukxaZq0sOtadeTagmpTQiXzZpi/neZGCoKv5jfKCMYXB4oAwpfFev21reeH2v45Nbj8QQ6LHqbQLnypYVnEpjGFMgjLLjAXcAcY4pdS8V694ct/FWkzXyalqVk2nrYX1xAiEi8l8lfMVAqko6seAMjArb/wCFfF9EuY59XkbXp9STWDqawABblAqpiLJHlhFCbCTlc85OaU/D832l+IE13Vnu9V1kwtJe28AgWAwENB5cZZsBGG7ljkk0ASeHL3WNM8c3HhvWNVfWIpdOXUbe6lgjikQiTy5EIjVVK8oQcZ5IOetbfiPxLY6GyRXsGqSNKhZTZ6Zc3SgdOTEjBT7HFUvDfhm9stdutb17Vk1TVZbdLON4rX7NFDCrFsBNzHczHJJPYYAArp5f9U/0NAHmHwt/4RD/AIVV4Z+1/wBgZ/s6z+0+b5P+u8njfn+P/Wdefve9dAP+Ff8Ab/hFf/JevnbwUufhdH/100//ANEz0QRghciun2ate583XzX2bt7NP/h2fRR/4QDv/wAIt/5L0f8AFAf9Sr/5L188SxAQggfxgUwoMDjt/jQoJ9WY/wBs6fw0fRX/ABb/AP6lX/yXpB/wr/t/wiv/AJL187pGC6gj+Mf1qO3QNIQf7x/m/wD9an7NWerH/bGjfs0fRv8Axb/OP+KV/wDJegj4f9/+EV/8l6+cxGMscdv8KdcRgISPT+ho9mr2uL+2Vf8Aho+iv+Lf/wDUq/8AkvQf+Ffjr/wio/8AAevnWaMALj+8B+jUlxGoQkeg/rQqa7sazjb92j6L/wCLf/8AUq/+S9J/xb//AKlX/wAl6+dnjGwcc8/0qKdNrMPRiKcaSfUI5wn/AMu0fR5/4V/3/wCEV/8AJej/AIoD/qVf/JevnEpgJx1zT5IgGGB/nil7Jdw/thf8+0fRf/FAf9Sr/wCS9H/Fv/8AqVf/ACXr53aIAPx/nNMhiGxSe4/pS5Fa92L+2Va/s0fRePh//wBSr/5L0bfAHp4V/wDJevnYoMtTkRSQM/xChwS6sX9tf9O0fQ3/ABb/AP6lX/yXoA+H/YeFf/JevnUoN1QRDIbJ/ix+tUqV+rLWcXX8NH0ljwBnGPCuf+3egjwBjn/hFf8AyXr5ygQNNJk8DPP5U6WMCJj3yaXs1e1xPONbezR9F/8AFvz/ANCr/wCS9H/Fvz/0Kv8A5L185mMCQj0zTljG/GKPZruJ5yv+faPon/i3/P8AyKv/AJL0uPAHp4W/8l6+dBGNzil8seWxPUMP5Cj2a7h/bK/59o+iv+Lfgf8AMq4/7d6B/wAK/wC3/CK/+S9fOez9wTjn/wCuKFQdvUUezXcf9sf9O0fTFh/wh/y/2f8A2B/r49vkeT/rvm8vGP4/vbe/XHeqZHw/6keFf/JevIvAKf8AHuT/ANB3T/086uLuV24H+z/jRGleVrmzzT3Yv2a1ufSH/Fvuv/FKf+S9J/xb7H/MqY/7d6+a4xlfX5f8aIx+7Unjkf8AoBNaOhb7TF/am/7tH0r/AMW+z/zKmf8At3pceAPTwt/5L181MMSn8KswICik9dwqZUbK9yJZtZX9mj6Mx8P89PCuf+3ejHgD/qVv/Jevm9lxNIPRRTwvzH/Pel7HzE83t/y7R9G48AHt4V/8l6APh/2HhX/yXr5zgUHrxwaZEMg/U/zo9l5i/tjf92j6a0v/AIQ/zz/Zf/CP+d8mfs3k7vvrt+7/ALezH+1t74qpj4f+nhX/AMl68d+GQxrs/wBbX/0utq5qCNSWB4xKR/44KlwUb6m0805acZezWtz6H/4t+P8AoVf/ACXoz8P/AF8K/wDkvXzx5I+yI5Xk/wCFCwDcfl7D+tLlj3Zh/bK/59o+h/8Ai3+f+ZVz/wBu9H/FAf8AUq/+S9fOyxA3AGOOKDEAAPb+tPkXdh/bK/59o+if+Lf4/wCZVx/270n/ABb/AP6lX/yXr50MY8peP84qNY/kU+uapUl3K/thf8+0fRfwg+z/APCHS/YvK+y/2tqfleTjZs+3T7duOMYxjFdrXnH7PX/JKtP/AOvu+/8ASuavR65WrOx9NB3imFFFFIoKKKKACiiigApsv+qf6GnU2X/VP9DQB8jeCT/xa5eR/rdP/wDRM9XLTaJ8MBjZ/hVLwSB/wq5c/wDPXT//AETPVq0K/aPm/uf4V1VPhZ8JjPj+/wDNhIV+yS9MhlI/Wmtjc444ZgKdIF+zy/7wx+Rpjbcvz/EaUf6/A4+hJFt3OT2UkfXiqqEDeRwcN/P/AOuasxbMv/uH+lVsLtbPHyt/OqhuyolohPsoP8Xzc/gar53RNkjoP6VO237OB25/kargDyTg5P8A9YUoAh05+UZI9f0NEZDRruIPH9aSbG0ZOeP8aSPbsXHPH9RV20DoSTEeU+McSnH0zUN2QZX5/wCWh/rUsu3yH5/5af1qG5wZD3/eH+tFNalxJMr5UJyM/Nn9KnuNu7j/AJ5r+fFV8L5cP/As/pU0+3d/wBf6VL3Xz/Mhk9zs2ttA79vequcQR/TH6VZn2mNse9VVwYU59KikvdJWwKwLDp1P/oNQ78M3I6ip0C5HPc/+g1XZRljnuK3ja5pGxM3Kk8dM1STIjk5AO8/zq6wHl8HjA/mapxLlJM5Hzn+dXDZl09E/kSI2Ic5G4hv60+RsxS8jqKYFXysD0bn86e6qIZiDzxQ7X/ruDtzfP9R789Dyc/1q020NIeOG/rVRwNq49/61bG0iQn1P/oQrCp0MWQSEBmwR0H/oNRu2NwBGN4/kKfJt3Nz0x/KoyFyef4h/IVcS4kkBBiOcHr/MU3PzryOSP60sOPKJP+eaTCl157/40dWLqzr/AANxHbY/6D+n/wA5q4a9JF9IucAA8f8AAjXceBv9Xbf9h7Tv5zVw93zeOc54P/oRqqS99nfG3s4f9vfmMtTm4YZ42f0qSTi3bHZh/wCg1Haj/SGJ/u/0p8v/AB7t/vD+VbS+L7jOXx/cP43r9FpythFwR2/mKaoBdM/3RTgq7V57D+YqGYu3UZnMshyOgpyNzyw/yaQIPMk9MClVVz/n1puw20AfC9RRuAQ4I6n+dJgbaXClDx6/zpaC0Ot+F5zrcvPe1/8AS62rnmwJW4H/AB8N27V0PwvAGtyfW1/9LraucbBfP/TY1jP4jfEfwYfM1LXynsolZRnymOcd+KeyRjzsrzjjj2H+NQW5UQRLgf6ojH5U9XUiUH0OPyrzJRak7f1qcTZBIqhwwHY9vrVSQjzCMjHP9atzjBPoAcH86puAXbPXLf1rspakoGI8hf8APYVXjP3Oe5/pUxx5C+v/ANaq6Yyv1NdMFoax2Z71+z1/ySrT/wDr7vv/AErmr0evOP2ev+SV6f8A9fd9/wClc1ej1wy3Z9/T+BegUUUUiwooooAKKKKACmy/6p/oadTZf9U/0NAHyL4K/wCSXL/110//ANEz1NBjzAe+P8Kj8FEf8KtTj/lpp/8A6InpIX+cZJ6en0rtauj4TGay+/8ANkkh/csB3YUj4y31OaHZdjcN6ikDjJyDwSBQkciWgqcMfoQfpUS85DdOR+p/wFS7l3dG9R9ajiYAsQDnBP45P+Jpra5S2ZISTFjjv/KmgAI23+73+lOJQHgN7imO4MZBByeMj6UkhK466xhdvA6H/wAe/wDrUwABBtHbv9aV5NwBbJxz+h/xNK7B15yAR0H+frTV0rFdEEmPLI/2if1qO5H718f3zipGcAFcHhiKjlcM2CD1yfenG9wje4v8Mf41LMRn/gIH8qjfGI8AjGc04kEkkEjH+FHmJk0jfI4/z1qJMGFB/ntSsyjI55qNHwoIz049qmMdBJaD1AAb/PaoyAWPXHH8qfvHoaasp25wM5HNUrjVyQAeUeP4f8arIDsk6feP86lMhyAMjp/WmQsArZBOW/rTimkyo3SYkQ7HpzTmH7qTHfFKCNzlQQQD9KdKR5TDB5NNvUG9RR1Hsf8AGpSQPMHbP9RUEj4Y4zxxTkfPJyTz2qJRb1Iae41wNzfQH9KawGGPfcMfkKezgeYeTilLAKw2kgnnP0FNNopNoanEJ+v9aB95cf560FgYTwf8mljYHG4E4o11Ya6s6/wH923B6/29p/8AOauHuwPtBI7j+prtfAZwYMZ/5D2ndf8AtvXGT5L5PIxTp6TZ2PSnD5/mRQcSE99tPbmHB/vD/wBBNJEOTkcYoP8AqxjuR/6DWz3IbvIXo49gKkjxhc/56VGuPM+bngVYUgKMLUSdjKbsQ9Hf0wKVRg/59aeNvmuSpPFP4z905/8Ar0uYlyIFAxQB8p/H+dSA/KMDmhCAv3fWncfMdV8MP+Q5L/26/wDpdbVzakFsdvNY/pXTfDH/AJDk3He1/wDS62rlA5DHHXf1/wCA1nJXbOivrRh8/wAy9E5EEbfxbKduALcdqprIvloACFx0pRIBk5bgHt7HFYulucLiy277gFONpH9TVSX759eSf1ppl/ecE5HSlEgI2MDwOD3qowcQSaGn/VD6f0qBB90+pP8ASpsYhQHOQeffimLjC8HvWyNFoj3j9nr/AJJXp/8A1933/pXNXo9ecfs9/wDJK9P/AOvu+/8ASyavR68+W7Pv6fwL0CiiikWFFFFABRRRQAU2X/VP9DTqbL/qn+hoA+RvBI/4tan/AF10/wD9ET0sDgSrkcbf8Kj8GSKvwsTLdJbD/wBET1Ek0asCWGP/ANVd6jdHw+Kg3L7/AM2W5CPLcUxSCW+rVE86MCNw6U1ZkBPzjvTUHY5VB22LWQDz/dNRQc7x/smmGeMH74/zmmwzIpYlh0PT8aOV2YKDsy42Mbe+Cc/hVdjhB9R/Kgzx5PzjOPX2qJ5UYABuc96IxaCMGT4BUYGP/wBmnKNqLUAmTA+bp6/7tO89Co+cD68UOLBwl2JmG9nUDkuTTJE+YHpnmmLcIJD83cn9KV7hG6MOAKSUkw5ZJ7EsvyrHnrgmncHOOPl/wqrLMjBfmHAx+lOFxHk/MKOR2FyO2xYkKnIA9aiOFjUd9tRG4jLfeFI86EKNwyBjimoNaDVOXYlzyB9ajQ5HHqKaZkyPm7GmpKoHU9c9KtLQtQaRMSNwGOeB+tRxZKt/vf1oMqbwd3pSI6quC3Vs0W0GlZEyqdrjqcE0+VSqv9agEyjed3UEYHJp8s6spAbqalp3IcZXHT/ecY5Bp6MMnjH+TUMkyM0hDdaaJ492dwx/+ujlbQcja2LBUESEClkQ4Y+hB/lUHnoBJ8wpzToFcbuvT9KnllcnllcevyxZP0/UUkYyQfcVE86eVt3Drn+VEcyDHzDtVcrsyuSVrnZ+Bf8Al29f7e07+c9cVOv71j0GP612PgOVCbb5h/yHtO/9r1xssisSQ3BGKIJqTOuSapw+f5iQA7j6YpW/1A/3h/6DTY3Vclj2xSmRfKC7hnI6f7uK0e5lrzDhy6/QVLg7B+H86riRd4+YdBUglULjd+VTJMiUWP5Esoz2FPCkH/PrURkUyyNu+8MCnGZc53cVLTIaYhyqD60LkrjPc1G0q7QMk89hmhHVerflVW0K5XY7H4Yj/idy59bX/wBLrauUJG8jH/LT+ldV8MpE/tyX5h1tf/S62rkDMgkJ3ZBbPHPapteTOmrFulD5/mi0rqII84PykZp+9AH+Ucg/yqiJ4/JRSwyAaka4iwfnXp61LpnG6bvsSl1LnilA+fPqM/zqsJo/MJ3DFSfaI8gbv4abi1sDg1sidWDQRr6nH6CmmErtz7moRPGFUbhwf6CpRdIQoZumanlkthOMlsj3X9nv/klmn/8AX3ff+lk1ejV5z+z5/wAkssP+vu+/9LJq9Griluz9ApfBH0CiiikWFFFFABRRRQAU2Rd0bLjOQRTqwfGPiE+HrG0aCza+v766Sys7USCMSysCeWIO1QqsxODwp4NAHMeC/hX4XsfCGiWmt+E/D82qwWUMV3K1jFIXlCAOSxX5ssDz3rZ/4Vr4G/6E7w7/AOC2H/4mrXhLxFNrM+qWOpWA07V9MlWK5t1m85MOgdHR8LuUg9wCCCCOK6KgDk/+Fa+Bv+hO8O/+C2H/AOJo/wCFa+Bv+hO8O/8Agth/+JrrKKAOT/4Vr4G/6E7w7/4LYf8A4mj/AIVr4G/6E7w7/wCC2H/4musrl/iJ4qPhDQ7e/W3tZzNeQ2g+1Xf2WKPzGxveTa21R1PFAEf/AArXwN/0J3h3/wAFsP8A8TWJ4u+FXhe80qCLR/Cfh+G5W+s5XZbGJCYUuY2lXIXoY1cY75x3q8/ja8j0LTbgadpt7qeq3n2PT7fTdU+0QTfIXLtOY12qqpIThWxtHUnFMPxBNvo2pPfaRJFrtjfxaY2mRzhxLPNsMWyUgAowkU7iBgBsjigDQ/4Vr4G/6E7w7/4LYf8A4mj/AIVr4G/6E7w7/wCC2H/4mqB+IB06x8QnxJpTWGo6NDFcSW1vOLhbhJciIxOVTJZ1ZMFRgj05rQ8OeJ7+78QS6H4g0dNK1L7KL6FYrsXMcsW7Y3zbVwysVBGMfMME0AJ/wrXwN/0J3h3/AMFsP/xNH/CtfA3/AEJ3h3/wWw//ABNdZRQByf8AwrXwN/0J3h3/AMFsP/xNH/CtfA3/AEJ3h3/wWw//ABNdZXJ6F4ov7zxhqmharo8enNaWkd7FMt4JvNjeSRAWAUBT+6Jxk9RQAf8ACtfA3/QneHf/AAWw/wDxNYl38KvC7+MtMu4fCfh8aTFY3UVxF9hiAaZpIDExXbgkKkvPbcfWrGifEKfUJ9FurrQ2tNA1uYwadf8A2oO7sVZozJFtGwOqEqQzdQDjNIPiHOZlvv7EP/CLtqX9ljU/tQ3+Z5vk+Z5O3/Veb8m7fnvtxQBqf8K18Df9Cd4d/wDBbD/8TR/wrXwN/wBCd4d/8FsP/wATWXrPxDnsJtZu7fRGufD2i3AttQv/ALUEdGAUyGOLad6oHG4ll6NgHFWPEXje9sdT1e30XQjqsGiwJPqMv2oQsm5S4SJSp8x9g3YJUcgZyaALn/CtfA3/AEJ3h3/wWw//ABNH/CtfA3/QneHf/BbD/wDE10mm3sGo6da31m/mW1zEs0T/AN5GAIP5EVYoA5P/AIVr4G/6E7w7/wCC2H/4mj/hWvgb/oTvDv8A4LYf/ia6PUprm3sJ5bG1F3dKuY4DII/MPpuPA+tcO3xDuIfhzrPia70eKG60y6ntJbL7blC8U/kk+bs4GQTnbQBq/wDCtfA3/QneHf8AwWw//E1iaB8KvC9vqviKXUPCfh+S2ub5ZbJTYxMI4RbQqVA2/KPMWQ4989609N8bf8Unq/iHWF0cadYRNL5mj6n/AGgrhVJYZ8tAG6ADnOe1JpfjS7j1P7D4r0ZdElksZNRgZbsXCtFGV81WIVdsi70JUbhg8E4oAs/8K18Df9Cd4d/8FsP/AMTR/wAK18Df9Cd4d/8ABbD/APE1R8O+OrzUNQ0RNV0FtNstdjaTTJ/tQld8J5gWVAo8tjGCwALdCCQah0v4hzXk2l3kuiND4b1W8NlZaj9qDO7ksEZ4dvyo5UhTuJ5XIGaANT/hWvgb/oTvDv8A4LYf/iaP+Fa+Bv8AoTvDv/gth/8Aia6yigDk/wDhWvgb/oTvDv8A4LYf/iaP+Fa+Bv8AoTvDv/gth/8Aia1PEN7rVqbddB0e31FpNxka4vfsyRAYxyEcknJ6LjjkjiucX4g+f4d026s9Jll1i/v5NLi0551UC4iaQS7pQCNiiKQ7gDkAcZOKAL//AArXwN/0J3h3/wAFsP8A8TWJ4R+FXhez0qeLWPCfh+a5a+vJUZrGJyIXuZGiXJXoI2QY7Yx2q2fiCbfRtSe+0iSLXbG/i0xtMjnDiWebYYtkpABRhIp3EDADZHFKfiAdOsfEJ8SaU1hqOjQxXEltbzi4W4SXIiMTlUyWdWTBUYI9OaAL/wDwrXwN/wBCd4d/8FsP/wATR/wrXwN/0J3h3/wWw/8AxNVbTxxNZ3WpWvi7Sl0e4s9ObVh5N0LpJLdOJDu2rhlOMrjHzDBNL4d8Z395rGmWOu6EdJ/ta2e509xdCcuEClo5AFHlybWDYBYcHnigCz/wrXwN/wBCd4d/8FsP/wATR/wrXwN/0J3h3/wWw/8AxNdZRQByf/CtfA3/AEJ3h3/wWw//ABNH/CtfA3/QneHf/BbD/wDE1F4w8Uaz4ci1LUD4diudB0+Pz57r+0FSZowoZ2ji2EHbzwzqTt47VQ8WfEGXR/FVpolnbaKWuLFb1JtV1f7AG3OVCIPKfc3Ge1AEXjP4V+F77whrdponhPw/Dqs9lNFaSrYxRlJShCEMF+XDEc9q10+GngcIoPg7w7nHP/Eth/8Aiafr3ifULLU9N0fSdIi1DXLq2e7kha88mGCJCqsxl2EnLOFUBeeScYrPX4gm80nRH0fSXuNZ1Wea2TT55xCIHgLCfzJAGwEKkZAJJK4HNAF//hWvgb/oTvDv/gth/wDiaP8AhWvgb/oTvDv/AILYf/iaz/8AhYJOhCUaRJ/bp1T+xv7M88Y+1Y3Y83GNnl/vN2M7e2eK2vCXiKbWZ9UsdSsBp2r6ZKsVzbrN5yYdA6Oj4XcpB7gEEEEcUAVf+Fa+Bv8AoTvDv/gth/8AiaP+Fa+Bv+hO8O/+C2H/AOJrrKKAOT/4Vr4G/wChO8O/+C2H/wCJo/4Vr4G/6E7w7/4LYf8A4musooAp6Rpdho1hHY6RZW1jZRklILaIRxrkknCjgZJJ/GrlFFABRRRQAUUUUAFFFFABXJ/ELStQvotE1DR7dbq90fUUvltTIIzOnlyROgY8BtspIzgZAGRmusooA4/wRpupf254i1/WLI6dNqskCQ2TSpI8UUKbQXZCV3MzOcAkAY5NdhRRQAUUUUAFZPiWe4trKKS10OTWmEo3W8ckSMowfnHmsqkg4GMjrWtRQB49b+EtfttVbxTaaNFbXEetf2hFoSXEYPktaG2lO4HyxK27fjO35cFskmrN14U1/UrfV/ED2EdtrM2s2eqW2mSTqT5VsqII3kUlQ7qJDkEgFl54NesUUAeUax4U13xbH4r1O7sV0i9vLS0tdOtLmZJGBtpWnDSmMsoDSNjgnAGe+K3tAtNa1bx2viLWdIfRobTTXsIbeW4jmkkeSRHkfMZICjykAycnJyBXc0UAFFFFABXm1uNem+KeoX9x4T1GLSbvTodL+1G6tSF2TTsZSolLbSJVxxu68V6TRQB5NoPhzxI1h4N8Nalpa21j4buY5ZdT+0Rsl0kCMsQjQHeC2ULbgoGDjPFMHhjxF/YaeCjpQ/spdXF2dX+0R7Dai7+1bdmd/m/8s/u7e+7tXrlFAHkuu+G/EY07xf4Y0/S1uLDxFdyzR6p9ojVLWOdVEokQneWUhyu0EHK5I5q9rOk+ItI1bxYNA0canb+IIYvJm+0xxLaTLAID5oYglNqo2UDHqMdDXplFAGb4Z0saJ4b0rSVfzFsLSK1D4xu2IFz+laVFFAFfUJ5baxnmt7WW8mjQslvEyq0pH8ILEKCfcgV5x4H/AOEh03Q9dg1LwVes82p3WoRQy3dowmWa5MgQYlIDqrZ+bAyuAelen0UAeTXngzVfEy+NZ302Pw6ms6ZFZQWryRu7zxmRhPL5RZB99V4LHC89hV6bRdc8Z63Fc+INIbQ7W10m7sArXEczTTXIRXZdjH92qx8bsMS3QYr0uigDzDQNI8SX994Mtdb0hdPt/DSl5rr7THIl5KLdoE8pVJYKQ7Od4UjAGD1qlo3hjxEmleF/CV1pYh03QtQiuG1X7RG0dxDA5eIIgO8OxCBtwAGGwTxXrlFABRRRQBxvxLufEy2VnZeFdMvLhLt2W9vLOaBJrWIAf6sTOgLtkgNk7cE4JwKyLnQryLRfCV94e8OT2U3h68aRdIuLiHzZYXikifDq7IXPmbwWfkg5IJr0migDye68Ka/qVvq/iB7CO21mbWbPVLbTJJ1J8q2VEEbyKSod1EhyCQCy88Gl1jwprvi2PxXqd3YrpF7eWlpa6daXMySMDbStOGlMZZQGkbHBOAM98V6vRQB5fqPhzWvG9/q9zrOmNoMMugXGjQRzTxzO0k5UvJ+7JAVfLQDJycnIFW9FsPEWseJfDN3rujDSYdBtpg7m5jlF1O8Yj/dhCSIwu85faeQMda9FooAKKKKAPM/HC+IdW8UiwuvC2pah4StPLlCWdzaAahNw2JRJMjCND/Bj5mGT8oAO34vlvp1mtYfBLaylza7PNkntljBOcxy723BRnqobqcV2NFAHl+neHNf8JS+Fb+2tG8QT2WhjRr6KGdI5CwKMsiGUqrKCrA5IOCDzyKg0/wAKa9ocHhzW47FL/Vra81C6v9PgnRSVvXMjLG7kKSh2DkgEBueler0UAeTjwpr62S699gjOt/8ACRnXTpnnrnyjB9m8nzPub/Lw2c7d3Gcc11HgjTdS/tzxFr+sWR06bVZIEhsmlSR4ooU2guyEruZmc4BIAxya7CigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//2Q==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(icmat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Preprocess structural maps "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Convert from maps from fs-native to indiv-fslr32k**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create proper gifti files for the surfaces\n",
    "#mris_convert \"$FreeSurferFolder\"/surf/\"$hemisphere\"h.\"$Surface\" \"$T1wFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$Surface\".native.surf.gii\n",
    "mris_convert FreeSurferFolder/surf/lh.pial T1wFolder/NativeFolder/sub-001.L.pial.native.surf.gii\n",
    "mris_convert FreeSurferFolder/surf/lh.white T1wFolder/NativeFolder/sub-001.L.white.native.surf.gii\n",
    "#wb_command -set-structure \"$T1wFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$Surface\".native.surf.gii ${Structure} -surface-type $Type$Secondary\n",
    "#wb_command -surface-apply-affine \"$T1wFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$Surface\".native.surf.gii \"$FreeSurferFolder\"/mri/c_ras.mat \"$T1wFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$Surface\".native.surf.gii\n",
    "#wb_command -surface-apply-warpfield \"$T1wFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$Surface\".native.surf.gii \"$InverseAtlasTransform\".nii.gz \"$AtlasSpaceFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$Surface\".native.surf.gii -fnirt \"$AtlasTransform\".nii.gz\n",
    "\n",
    "## Create midthickness mesh by averaging white and pial surfaces and use it to make inflated surfacess\n",
    "# https://github.com/Washington-University/HCPpipelines/blob/master/PostFreeSurfer/scripts/FreeSurfer2CaretConvertAndRegisterNonlinear.sh#L327C5-L327C100\n",
    "#wb_command -surface-average \"$Folder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".midthickness.native.surf.gii -surf \"$Folder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".white.native.surf.gii -surf \"$Folder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".pial.native.surf.gii\n",
    "wb_command -surface-average NativeFolder/sub-001.L.midthickness.native.surf.gii \n",
    " -surf NativeFolder/sub-001.L.white.native.surf.gii -surf NativeFolder/sub-001.L.pial.native.surf.gii\n",
    "#wb_command -set-structure \"$Folder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".midthickness.native.surf.gii ${Structure} -surface-type ANATOMICAL -surface-secondary-type MIDTHICKNESS\n",
    "\n",
    "## Convert also the microstrcuture maps into proper gifti metric shape files\n",
    "## so far, everything is still in freesurfer subject-native space\n",
    "for Map in sulc@sulc@Sulc thickness@thickness@Thickness curv@curvature@Curvature ; do\n",
    "  fsname, wbname, mapname = Map[1:3]\n",
    "  #mris_convert -c \"$FreeSurferFolder\"/surf/\"$hemisphere\"h.\"$fsname\" \"$FreeSurferFolder\"/surf/\"$hemisphere\"h.white \"$AtlasSpaceFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$wbname\".native.shape.gii\n",
    "  mris_convert -c FreeSurferFolder/surf/lh.sulc FreeSurferFolder/surf/lh.white NativeFolder/sub-001.L.sulc.native.shape.gii\n",
    "  #wb_command -set-structure \"$AtlasSpaceFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$wbname\".native.shape.gii ${Structure}\n",
    "  \n",
    "  # inverse the map?\n",
    "  # wb_command -metric-math \"var * -1\" \"$AtlasSpaceFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$wbname\".native.shape.gii -var var \"$AtlasSpaceFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$wbname\".native.shape.gii\n",
    "  wb_command -metric-math \"var * -1\" NativeFolder/sub-001.L.sulc.native.shape.gii -var var NativeFolder/sub-001.L.sulc.native.shape.gii\n",
    "  #wb_command -set-map-names \"$AtlasSpaceFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$wbname\".native.shape.gii -map 1 \"$Subject\"_\"$Hemisphere\"_\"$mapname\"\n",
    "  #wb_command -metric-palette \"$AtlasSpaceFolder\"/\"$NativeFolder\"/\"$Subject\".\"$Hemisphere\".\"$wbname\".native.shape.gii MODE_AUTO_SCALE_PERCENTAGE -pos-percent 2 98 -palette-name Gray_Interp -disp-pos true -disp-neg true -disp-zero true\n",
    "\n",
    "## Do the actual resampling from native to fslr32k space\n",
    "# for that we need the registered spheres ...\n",
    "RegSphere= AtlasSpaceFolder/NativeFolder/sub-001.L.sphere.MSMSulc.native.surf.gii\n",
    "# nativefolder is always in the atlasspacefolder\n",
    "\n",
    "wb_command -metric-resample NativeFolder/sub-001.L.sulc.native.shape.gii $RegSphere \n",
    "  AtlasSpaceFolder/fsaverage_LR32k/sub-001.L.sphere.32k_fs_LR.surf.gii \n",
    "  ADAP_BARY_AREA \n",
    "  AtlasSpaceFolder/fsaverage_LR32k/sub-001.L.sulc.32k_fs_LR.shape.gii \n",
    "  -area-surfs T1W/NativeFolder/sub-001.L.midthickness.native.surf.gii\n",
    "  AtlasSpaceFolder/fsaverage_LR32k/sub-001.L.midthickness.32k_fs_LR.surf.gii \n",
    "  -current-roi AtlasSpaceFolder/NativeFolder/sub-001.L.roi.native.shape.gii\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Minimal sample \n",
    "\n",
    "mris_convert -c anat.freesurfer/sub-01/surf/lh.sulc anat.freesurfer/sub-01/surf/sub-001.L.sulc.native.shape.gii\n",
    "mris_convert FreeSurferFolder/surf/lh.sphere.reg anat.freesurfer/sub-01/surf/sub-001.L.sphere.reg.native.surf.gii\n",
    "\n",
    "$RegSphere=anat.freesurfer/sub-01/surf/sub-001.L.sphere.reg.native.surf.gii\n",
    "$FS32kSphere= fsaverage_LR32k/sub-001.L.sulc.32k_fs_LR.shape.gii \n",
    "wb_command -metric-resample anat.freesurfer/sub-01/surf/sub-001.L.sulc.native.shape.gii $RegSphere $FS32kSphere ADAP_BARY_AREA\n",
    "  output/sub-001.L.sulc.32k_fs_LR.shape.gii "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Compute task maps (prediction targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Sample First Level Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Load event and mri-run files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['presentation_math' 'presentation_story' 'question_math' 'question_story'\n",
      " 'response_math' 'response_story']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trial_type</th>\n",
       "      <th>onset</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>presentation_story</td>\n",
       "      <td>0.300</td>\n",
       "      <td>18.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>question_story</td>\n",
       "      <td>19.647</td>\n",
       "      <td>4.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>response_story</td>\n",
       "      <td>23.947</td>\n",
       "      <td>0.998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           trial_type   onset  duration\n",
       "0  presentation_story   0.300    18.834\n",
       "1      question_story  19.647     4.281\n",
       "2      response_story  23.947     0.998"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# event dataframe probably only needs \"trial_type\", \"onset\" and \"duration\"\n",
    "events_raw = pd.read_table(event_files[0])\n",
    "print(np.unique(events_raw[\"trial_type\"]))\n",
    "events = events_raw[[\"trial_type\", \"onset\", \"duration\"]]\n",
    "events[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trial_type</th>\n",
       "      <th>stim_file</th>\n",
       "      <th>ntrial</th>\n",
       "      <th>onset</th>\n",
       "      <th>duration</th>\n",
       "      <th>stim_file_question1</th>\n",
       "      <th>stim_file_question2</th>\n",
       "      <th>question_option1</th>\n",
       "      <th>question_option2</th>\n",
       "      <th>onset_that_was_about</th>\n",
       "      <th>onset_question_option1</th>\n",
       "      <th>onset_or</th>\n",
       "      <th>onset_question_option2</th>\n",
       "      <th>correct_answer</th>\n",
       "      <th>answer</th>\n",
       "      <th>response_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>presentation_story</td>\n",
       "      <td>language/story/Story22.wav</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.300</td>\n",
       "      <td>18.834</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>question_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.647</td>\n",
       "      <td>4.281</td>\n",
       "      <td>language/story/taking.wav</td>\n",
       "      <td>language/story/looking.wav</td>\n",
       "      <td>taking</td>\n",
       "      <td>looking</td>\n",
       "      <td>19.647</td>\n",
       "      <td>20.964</td>\n",
       "      <td>21.730</td>\n",
       "      <td>23.113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>response_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.947</td>\n",
       "      <td>0.998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option1</td>\n",
       "      <td>option1</td>\n",
       "      <td>0.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level10-130-Q.wav</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.396</td>\n",
       "      <td>5.601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34.213</td>\n",
       "      <td>3.683</td>\n",
       "      <td>language/math/math-level10-130-1.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.912</td>\n",
       "      <td>2.984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option1</td>\n",
       "      <td>option1</td>\n",
       "      <td>2.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level10-16-Q.wav</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41.062</td>\n",
       "      <td>5.485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46.812</td>\n",
       "      <td>3.385</td>\n",
       "      <td>language/math/math-level10-16-2.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.212</td>\n",
       "      <td>2.985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option2</td>\n",
       "      <td>option2</td>\n",
       "      <td>2.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level10-30-Q.wav</td>\n",
       "      <td>4.0</td>\n",
       "      <td>53.412</td>\n",
       "      <td>5.964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>59.545</td>\n",
       "      <td>3.063</td>\n",
       "      <td>language/math/math-level10-30-1.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>62.628</td>\n",
       "      <td>2.980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option1</td>\n",
       "      <td>option1</td>\n",
       "      <td>2.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>presentation_story</td>\n",
       "      <td>language/story/Story25.wav</td>\n",
       "      <td>5.0</td>\n",
       "      <td>67.227</td>\n",
       "      <td>17.541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>question_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85.310</td>\n",
       "      <td>3.888</td>\n",
       "      <td>language/story/planting.wav</td>\n",
       "      <td>language/story/hunting.wav</td>\n",
       "      <td>planting</td>\n",
       "      <td>hunting</td>\n",
       "      <td>85.310</td>\n",
       "      <td>86.726</td>\n",
       "      <td>87.760</td>\n",
       "      <td>88.476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>response_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>89.209</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option2</td>\n",
       "      <td>option2</td>\n",
       "      <td>0.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>presentation_story</td>\n",
       "      <td>language/story/Story2.wav</td>\n",
       "      <td>6.0</td>\n",
       "      <td>92.345</td>\n",
       "      <td>16.733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>question_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109.575</td>\n",
       "      <td>4.633</td>\n",
       "      <td>language/story/running.wav</td>\n",
       "      <td>language/story/falling.wav</td>\n",
       "      <td>running</td>\n",
       "      <td>falling</td>\n",
       "      <td>109.575</td>\n",
       "      <td>110.860</td>\n",
       "      <td>112.210</td>\n",
       "      <td>112.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>response_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>114.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option2</td>\n",
       "      <td>option2</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>presentation_story</td>\n",
       "      <td>language/story/Story1.wav</td>\n",
       "      <td>7.0</td>\n",
       "      <td>117.374</td>\n",
       "      <td>14.034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>question_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>131.907</td>\n",
       "      <td>3.771</td>\n",
       "      <td>language/story/fairness.wav</td>\n",
       "      <td>language/story/happiness.wav</td>\n",
       "      <td>fairness</td>\n",
       "      <td>happiness</td>\n",
       "      <td>131.907</td>\n",
       "      <td>133.190</td>\n",
       "      <td>134.123</td>\n",
       "      <td>134.823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>response_story</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>135.690</td>\n",
       "      <td>0.231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option1</td>\n",
       "      <td>option1</td>\n",
       "      <td>0.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level11-45-Q.wav</td>\n",
       "      <td>8.0</td>\n",
       "      <td>140.192</td>\n",
       "      <td>5.774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>146.206</td>\n",
       "      <td>3.602</td>\n",
       "      <td>language/math/math-level11-45-2.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>149.822</td>\n",
       "      <td>2.986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option2</td>\n",
       "      <td>option2</td>\n",
       "      <td>2.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level11-99-Q.wav</td>\n",
       "      <td>9.0</td>\n",
       "      <td>153.022</td>\n",
       "      <td>5.924</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>159.155</td>\n",
       "      <td>3.691</td>\n",
       "      <td>language/math/math-level11-99-1.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>162.872</td>\n",
       "      <td>2.974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option1</td>\n",
       "      <td>option1</td>\n",
       "      <td>2.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level11-55-Q.wav</td>\n",
       "      <td>10.0</td>\n",
       "      <td>167.321</td>\n",
       "      <td>5.665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>173.221</td>\n",
       "      <td>2.715</td>\n",
       "      <td>language/math/math-level11-55-1.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>175.954</td>\n",
       "      <td>2.982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option1</td>\n",
       "      <td>option1</td>\n",
       "      <td>2.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level12-100-Q.wav</td>\n",
       "      <td>11.0</td>\n",
       "      <td>179.104</td>\n",
       "      <td>6.574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>185.837</td>\n",
       "      <td>3.620</td>\n",
       "      <td>language/math/math-level12-100-2.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>189.470</td>\n",
       "      <td>2.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option2</td>\n",
       "      <td>option2</td>\n",
       "      <td>2.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level12-12-Q.wav</td>\n",
       "      <td>12.0</td>\n",
       "      <td>192.670</td>\n",
       "      <td>5.606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>198.453</td>\n",
       "      <td>3.525</td>\n",
       "      <td>language/math/math-level12-12-2.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>201.986</td>\n",
       "      <td>2.992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option2</td>\n",
       "      <td>option2</td>\n",
       "      <td>2.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level12-21-Q.wav</td>\n",
       "      <td>13.0</td>\n",
       "      <td>205.189</td>\n",
       "      <td>5.857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>211.236</td>\n",
       "      <td>3.241</td>\n",
       "      <td>language/math/math-level12-21-2.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>214.485</td>\n",
       "      <td>2.992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option2</td>\n",
       "      <td>option2</td>\n",
       "      <td>2.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>presentation_math</td>\n",
       "      <td>language/math/math-level13-22-Q.wav</td>\n",
       "      <td>14.0</td>\n",
       "      <td>217.718</td>\n",
       "      <td>7.117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>question_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>225.118</td>\n",
       "      <td>3.698</td>\n",
       "      <td>language/math/math-level13-22-1.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>response_math</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>228.835</td>\n",
       "      <td>2.981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>option1</td>\n",
       "      <td>option1</td>\n",
       "      <td>2.981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            trial_type                             stim_file  ntrial    onset  \\\n",
       "0   presentation_story            language/story/Story22.wav     1.0    0.300   \n",
       "1       question_story                                   NaN     1.0   19.647   \n",
       "2       response_story                                   NaN     1.0   23.947   \n",
       "3    presentation_math  language/math/math-level10-130-Q.wav     2.0   28.396   \n",
       "4        question_math                                   NaN     2.0   34.213   \n",
       "5        response_math                                   NaN     2.0   37.912   \n",
       "6    presentation_math   language/math/math-level10-16-Q.wav     3.0   41.062   \n",
       "7        question_math                                   NaN     3.0   46.812   \n",
       "8        response_math                                   NaN     3.0   50.212   \n",
       "9    presentation_math   language/math/math-level10-30-Q.wav     4.0   53.412   \n",
       "10       question_math                                   NaN     4.0   59.545   \n",
       "11       response_math                                   NaN     4.0   62.628   \n",
       "12  presentation_story            language/story/Story25.wav     5.0   67.227   \n",
       "13      question_story                                   NaN     5.0   85.310   \n",
       "14      response_story                                   NaN     5.0   89.209   \n",
       "15  presentation_story             language/story/Story2.wav     6.0   92.345   \n",
       "16      question_story                                   NaN     6.0  109.575   \n",
       "17      response_story                                   NaN     6.0  114.225   \n",
       "18  presentation_story             language/story/Story1.wav     7.0  117.374   \n",
       "19      question_story                                   NaN     7.0  131.907   \n",
       "20      response_story                                   NaN     7.0  135.690   \n",
       "21   presentation_math   language/math/math-level11-45-Q.wav     8.0  140.192   \n",
       "22       question_math                                   NaN     8.0  146.206   \n",
       "23       response_math                                   NaN     8.0  149.822   \n",
       "24   presentation_math   language/math/math-level11-99-Q.wav     9.0  153.022   \n",
       "25       question_math                                   NaN     9.0  159.155   \n",
       "26       response_math                                   NaN     9.0  162.872   \n",
       "27   presentation_math   language/math/math-level11-55-Q.wav    10.0  167.321   \n",
       "28       question_math                                   NaN    10.0  173.221   \n",
       "29       response_math                                   NaN    10.0  175.954   \n",
       "30   presentation_math  language/math/math-level12-100-Q.wav    11.0  179.104   \n",
       "31       question_math                                   NaN    11.0  185.837   \n",
       "32       response_math                                   NaN    11.0  189.470   \n",
       "33   presentation_math   language/math/math-level12-12-Q.wav    12.0  192.670   \n",
       "34       question_math                                   NaN    12.0  198.453   \n",
       "35       response_math                                   NaN    12.0  201.986   \n",
       "36   presentation_math   language/math/math-level12-21-Q.wav    13.0  205.189   \n",
       "37       question_math                                   NaN    13.0  211.236   \n",
       "38       response_math                                   NaN    13.0  214.485   \n",
       "39   presentation_math   language/math/math-level13-22-Q.wav    14.0  217.718   \n",
       "40       question_math                                   NaN    14.0  225.118   \n",
       "41       response_math                                   NaN    14.0  228.835   \n",
       "\n",
       "    duration                   stim_file_question1  \\\n",
       "0     18.834                                   NaN   \n",
       "1      4.281             language/story/taking.wav   \n",
       "2      0.998                                   NaN   \n",
       "3      5.601                                   NaN   \n",
       "4      3.683  language/math/math-level10-130-1.wav   \n",
       "5      2.984                                   NaN   \n",
       "6      5.485                                   NaN   \n",
       "7      3.385   language/math/math-level10-16-2.wav   \n",
       "8      2.985                                   NaN   \n",
       "9      5.964                                   NaN   \n",
       "10     3.063   language/math/math-level10-30-1.wav   \n",
       "11     2.980                                   NaN   \n",
       "12    17.541                                   NaN   \n",
       "13     3.888           language/story/planting.wav   \n",
       "14     0.419                                   NaN   \n",
       "15    16.733                                   NaN   \n",
       "16     4.633            language/story/running.wav   \n",
       "17     0.000                                   NaN   \n",
       "18    14.034                                   NaN   \n",
       "19     3.771           language/story/fairness.wav   \n",
       "20     0.231                                   NaN   \n",
       "21     5.774                                   NaN   \n",
       "22     3.602   language/math/math-level11-45-2.wav   \n",
       "23     2.986                                   NaN   \n",
       "24     5.924                                   NaN   \n",
       "25     3.691   language/math/math-level11-99-1.wav   \n",
       "26     2.974                                   NaN   \n",
       "27     5.665                                   NaN   \n",
       "28     2.715   language/math/math-level11-55-1.wav   \n",
       "29     2.982                                   NaN   \n",
       "30     6.574                                   NaN   \n",
       "31     3.620  language/math/math-level12-100-2.wav   \n",
       "32     2.987                                   NaN   \n",
       "33     5.606                                   NaN   \n",
       "34     3.525   language/math/math-level12-12-2.wav   \n",
       "35     2.992                                   NaN   \n",
       "36     5.857                                   NaN   \n",
       "37     3.241   language/math/math-level12-21-2.wav   \n",
       "38     2.992                                   NaN   \n",
       "39     7.117                                   NaN   \n",
       "40     3.698   language/math/math-level13-22-1.wav   \n",
       "41     2.981                                   NaN   \n",
       "\n",
       "             stim_file_question2 question_option1 question_option2  \\\n",
       "0                            NaN              NaN              NaN   \n",
       "1     language/story/looking.wav           taking          looking   \n",
       "2                            NaN              NaN              NaN   \n",
       "3                            NaN              NaN              NaN   \n",
       "4                            NaN              NaN              NaN   \n",
       "5                            NaN              NaN              NaN   \n",
       "6                            NaN              NaN              NaN   \n",
       "7                            NaN              NaN              NaN   \n",
       "8                            NaN              NaN              NaN   \n",
       "9                            NaN              NaN              NaN   \n",
       "10                           NaN              NaN              NaN   \n",
       "11                           NaN              NaN              NaN   \n",
       "12                           NaN              NaN              NaN   \n",
       "13    language/story/hunting.wav         planting          hunting   \n",
       "14                           NaN              NaN              NaN   \n",
       "15                           NaN              NaN              NaN   \n",
       "16    language/story/falling.wav          running          falling   \n",
       "17                           NaN              NaN              NaN   \n",
       "18                           NaN              NaN              NaN   \n",
       "19  language/story/happiness.wav         fairness        happiness   \n",
       "20                           NaN              NaN              NaN   \n",
       "21                           NaN              NaN              NaN   \n",
       "22                           NaN              NaN              NaN   \n",
       "23                           NaN              NaN              NaN   \n",
       "24                           NaN              NaN              NaN   \n",
       "25                           NaN              NaN              NaN   \n",
       "26                           NaN              NaN              NaN   \n",
       "27                           NaN              NaN              NaN   \n",
       "28                           NaN              NaN              NaN   \n",
       "29                           NaN              NaN              NaN   \n",
       "30                           NaN              NaN              NaN   \n",
       "31                           NaN              NaN              NaN   \n",
       "32                           NaN              NaN              NaN   \n",
       "33                           NaN              NaN              NaN   \n",
       "34                           NaN              NaN              NaN   \n",
       "35                           NaN              NaN              NaN   \n",
       "36                           NaN              NaN              NaN   \n",
       "37                           NaN              NaN              NaN   \n",
       "38                           NaN              NaN              NaN   \n",
       "39                           NaN              NaN              NaN   \n",
       "40                           NaN              NaN              NaN   \n",
       "41                           NaN              NaN              NaN   \n",
       "\n",
       "    onset_that_was_about  onset_question_option1  onset_or  \\\n",
       "0                    NaN                     NaN       NaN   \n",
       "1                 19.647                  20.964    21.730   \n",
       "2                    NaN                     NaN       NaN   \n",
       "3                    NaN                     NaN       NaN   \n",
       "4                    NaN                     NaN       NaN   \n",
       "5                    NaN                     NaN       NaN   \n",
       "6                    NaN                     NaN       NaN   \n",
       "7                    NaN                     NaN       NaN   \n",
       "8                    NaN                     NaN       NaN   \n",
       "9                    NaN                     NaN       NaN   \n",
       "10                   NaN                     NaN       NaN   \n",
       "11                   NaN                     NaN       NaN   \n",
       "12                   NaN                     NaN       NaN   \n",
       "13                85.310                  86.726    87.760   \n",
       "14                   NaN                     NaN       NaN   \n",
       "15                   NaN                     NaN       NaN   \n",
       "16               109.575                 110.860   112.210   \n",
       "17                   NaN                     NaN       NaN   \n",
       "18                   NaN                     NaN       NaN   \n",
       "19               131.907                 133.190   134.123   \n",
       "20                   NaN                     NaN       NaN   \n",
       "21                   NaN                     NaN       NaN   \n",
       "22                   NaN                     NaN       NaN   \n",
       "23                   NaN                     NaN       NaN   \n",
       "24                   NaN                     NaN       NaN   \n",
       "25                   NaN                     NaN       NaN   \n",
       "26                   NaN                     NaN       NaN   \n",
       "27                   NaN                     NaN       NaN   \n",
       "28                   NaN                     NaN       NaN   \n",
       "29                   NaN                     NaN       NaN   \n",
       "30                   NaN                     NaN       NaN   \n",
       "31                   NaN                     NaN       NaN   \n",
       "32                   NaN                     NaN       NaN   \n",
       "33                   NaN                     NaN       NaN   \n",
       "34                   NaN                     NaN       NaN   \n",
       "35                   NaN                     NaN       NaN   \n",
       "36                   NaN                     NaN       NaN   \n",
       "37                   NaN                     NaN       NaN   \n",
       "38                   NaN                     NaN       NaN   \n",
       "39                   NaN                     NaN       NaN   \n",
       "40                   NaN                     NaN       NaN   \n",
       "41                   NaN                     NaN       NaN   \n",
       "\n",
       "    onset_question_option2 correct_answer   answer  response_time  \n",
       "0                      NaN            NaN      NaN            NaN  \n",
       "1                   23.113            NaN      NaN            NaN  \n",
       "2                      NaN        option1  option1          0.998  \n",
       "3                      NaN            NaN      NaN            NaN  \n",
       "4                      NaN            NaN      NaN            NaN  \n",
       "5                      NaN        option1  option1          2.984  \n",
       "6                      NaN            NaN      NaN            NaN  \n",
       "7                      NaN            NaN      NaN            NaN  \n",
       "8                      NaN        option2  option2          2.985  \n",
       "9                      NaN            NaN      NaN            NaN  \n",
       "10                     NaN            NaN      NaN            NaN  \n",
       "11                     NaN        option1  option1          2.980  \n",
       "12                     NaN            NaN      NaN            NaN  \n",
       "13                  88.476            NaN      NaN            NaN  \n",
       "14                     NaN        option2  option2          0.419  \n",
       "15                     NaN            NaN      NaN            NaN  \n",
       "16                 112.925            NaN      NaN            NaN  \n",
       "17                     NaN        option2  option2          0.000  \n",
       "18                     NaN            NaN      NaN            NaN  \n",
       "19                 134.823            NaN      NaN            NaN  \n",
       "20                     NaN        option1  option1          0.231  \n",
       "21                     NaN            NaN      NaN            NaN  \n",
       "22                     NaN            NaN      NaN            NaN  \n",
       "23                     NaN        option2  option2          2.986  \n",
       "24                     NaN            NaN      NaN            NaN  \n",
       "25                     NaN            NaN      NaN            NaN  \n",
       "26                     NaN        option1  option1          2.974  \n",
       "27                     NaN            NaN      NaN            NaN  \n",
       "28                     NaN            NaN      NaN            NaN  \n",
       "29                     NaN        option1  option1          2.982  \n",
       "30                     NaN            NaN      NaN            NaN  \n",
       "31                     NaN            NaN      NaN            NaN  \n",
       "32                     NaN        option2  option2          2.987  \n",
       "33                     NaN            NaN      NaN            NaN  \n",
       "34                     NaN            NaN      NaN            NaN  \n",
       "35                     NaN        option2  option2          2.992  \n",
       "36                     NaN            NaN      NaN            NaN  \n",
       "37                     NaN            NaN      NaN            NaN  \n",
       "38                     NaN        option2  option2          2.992  \n",
       "39                     NaN            NaN      NaN            NaN  \n",
       "40                     NaN            NaN      NaN            NaN  \n",
       "41                     NaN        option1  option1          2.981  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(159, 91282)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not sure if I should be doing smoothing too\n",
    "run = nib.load(cifti_files[0])\n",
    "print(run.shape)\n",
    "plot_29k(run.get_fdata()[100, :29696])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Create design matrix and run GLM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.12, 1.61, 3.1 , 4.59, 6.08]), 3.93, 'min')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr = 1.49\n",
    "n_scans = run.shape[0]\n",
    "frame_times = 0.1175 + np.arange(n_scans) * tr\n",
    "frame_times.round(2)[:5], (frame_times[-1]/60).round(2), \"min\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:label='conditions', ylabel='scan number'>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 445x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Surface-based autocorrelation estimate smoothing was incorporated into FSL’s FILM at a sigma of 5mm\n",
    "\n",
    "# using pretty much the defaults, except for \"spm + derivative\", as \"glover\" is default\n",
    "design_matrix = make_first_level_design_matrix(frame_times, events, hrf_model= \"glover + derivative\",\n",
    "  drift_model=\"Cosine\",high_pass= 0.005) #, slice_time_ref = 0.0, high_pass=0.01 default; 200s highpass for hcp\n",
    "\n",
    "from nilearn.plotting import plot_design_matrix\n",
    "plot_design_matrix(design_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting a GLM\n"
     ]
    }
   ],
   "source": [
    "#from nilearn.glm.first_level import FirstLevelModel\n",
    "print(\"Fitting a GLM\")\n",
    "#fmri_glm = FirstLevelModel()\n",
    "# fmri_glm = FirstLevelModel(mask_img=data['mask'], minimize_memory=True)\n",
    "#fmri_glm = fmri_glm.fit(run.get_fdata(), design_matrices=[design_matrix])\n",
    "\n",
    "labels, estimates = run_glm(run.get_fdata(), design_matrix.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define and estimate contrasts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['presentation_math', 'presentation_math_derivative', 'presentation_story', 'presentation_story_derivative', 'question_math', 'question_math_derivative', 'question_story', 'question_story_derivative', 'response_math', 'response_math_derivative', 'response_story', 'response_story_derivative', 'drift_1', 'drift_2', 'constant'])\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 600x266 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contrast_matrix = np.eye(design_matrix.shape[1])\n",
    "bc = {  column: contrast_matrix[i] for i, column in enumerate(design_matrix.columns) }\n",
    "print(bc.keys())\n",
    "\n",
    "bc[\"story\"] = bc['presentation_story'] + bc['question_story'] + bc['response_story']\n",
    "bc[\"math\"] = bc['presentation_math'] + bc['question_math'] + bc['response_math']\n",
    "\n",
    "contrasts = {\n",
    "    \"story-math\": bc[\"story\"] - bc[\"math\"],\n",
    "}\n",
    "\n",
    "from nilearn.plotting import plot_contrast_matrix\n",
    "for key, values in contrasts.items():\n",
    "    plot_contrast_matrix(values, design_matrix=design_matrix)\n",
    "    plt.suptitle(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tcontrast id: story-math\n",
      "(91282,)\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for contrast_id, contrast_val in contrasts.items():\n",
    "  print(f\"\\tcontrast id: {contrast_id}\")\n",
    "  # compute the contrasts\n",
    "  contrast = compute_contrast(labels, estimates, contrast_val, contrast_type='t')\n",
    "  z_score = contrast.z_score()\n",
    "  x_score = contrast.stat()\n",
    "  print(z_score.shape)\n",
    "  plot_29k(x_score[:29696])  \n",
    "  #plot_29k(z_score[:29696])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**compare with reference contrasts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn = \"/scratch/users/robert.scholz2/acc_dists/all_10_tasks_254_full_unrelated.raw.npy\"\n",
    "yfull_task_data =np.load(fn, allow_pickle=True).item()\n",
    "print(yfull_task_data[\"208327\"].shape)\n",
    "plot_29k(yfull_task_data[\"208327\"][:,6])\n",
    "plot_29k(yfull_task_data[\"525541\"][:,6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-Run task contrast analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.glm.contrasts import _compute_fixed_effect_contrast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# multiple runs:\n",
    "\n",
    "def estimate_mutli_run_first_level(cifti_files, event_files, verbose=False, tr=None):\n",
    " #n_runs= len(cifti_files)#4\n",
    " labels_ = []; results_ = []; design_mats = []\n",
    "\n",
    " for run_idx, run_file in enumerate(cifti_files):\n",
    "   events = pd.read_table(event_files[run_idx])[[\"trial_type\", \"onset\", \"duration\"]]\n",
    "   #frame_times = 0.1175 + np.arange(n_scans) * tr\n",
    "   run_data = nib.load(run_file).get_fdata();\n",
    "   if verbose: print(f\"Run#{run_idx} {run_data.shape}, n_events={len(events)}, \"+ \\\n",
    "         f\"file: {event_files[run_idx].split('/')[-1]}\")\n",
    "   n_scans = run_data.shape[0]\n",
    "   if tr is None:\n",
    "        tr = nib.load(run_file).header.get_axis(0).step;\n",
    "   frame_times = np.arange(n_scans) * tr\n",
    "   design_matrix = make_first_level_design_matrix(\\\n",
    "      frame_times, events, hrf_model= \"glover + derivative\", drift_model=\"Cosine\",high_pass= 0.005)\n",
    "   design_mats.append(design_matrix)\n",
    "   \n",
    "   labels, results = run_glm(run_data, design_matrix.values)\n",
    "   labels_.append(labels)\n",
    "   results_.append(results)\n",
    "\n",
    " # make sure all the runs have the same events\n",
    " for dmat in design_mats[1:]:\n",
    "   assert dmat.columns.tolist() == design_mats[0].columns.tolist()\n",
    "\n",
    " return labels_, results_, design_mats;\n",
    "\n",
    "def compute_contrast_from_multi_first_level(labels_, results_, contrast_defs):\n",
    "  contrasts = []\n",
    "  n_runs = len(labels_)\n",
    "  for contrast_id, contrast_val in contrast_defs.items():\n",
    "   contrast = _compute_fixed_effect_contrast(labels_, results_, [contrast_val]*n_runs, contrast_type='t')\n",
    "   contrasts.append(contrast)\n",
    "  return contrasts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**single subject lanugage example**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "subj = \"sub-01\"\n",
    "task= \"LANGUAGE\"\n",
    "cifti_files = runs[subj][task]\n",
    "event_files = events[subj][task]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_, results_, design_mats = estimate_mutli_run_first_level(cifti_files[:1], event_files[:1], verbose=0, tr=1.49)\n",
    "contrast_matrix = np.eye(design_mats[0].shape[1])\n",
    "bc = {  column: contrast_matrix[i] for i, column in enumerate(design_mats[0].columns) }\n",
    "bc[\"story\"] = bc['presentation_story'] + bc['question_story'] + bc['response_story']\n",
    "bc[\"math\"] = bc['presentation_math'] + bc['question_math'] + bc['response_math']\n",
    "contrast_defs = {\"story-math\": bc[\"story\"] - bc[\"math\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_, results_, design_mats = estimate_mutli_run_first_level(cifti_files[:1], event_files[:1], verbose=0, tr=1.49)\n",
    "contrast_matrix = np.eye(design_mats[0].shape[1])\n",
    "bc = {  column: contrast_matrix[i] for i, column in enumerate(design_mats[0].columns) }\n",
    "contrast_defs = {\"story-math\": bc[\"presentation_story\"] - bc[\"presentation_math\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run#0 (159, 91282), n_events=42, file: sub-01_ses-002_task-language_run-01_events.tsv\n",
      "Run#1 (159, 91282), n_events=36, file: sub-01_ses-003_task-language_run-01_events.tsv\n",
      "Run#2 (159, 91282), n_events=33, file: sub-01_ses-005_task-language_run-01_events.tsv\n",
      "Run#3 (159, 91282), n_events=30, file: sub-01_ses-006_task-language_run-01_events.tsv\n",
      "(91282,)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_, results_, design_mats = estimate_mutli_run_first_level(cifti_files[:8:2], event_files[:8:2], verbose=1)\n",
    "contrasts = compute_contrast_from_multi_first_level(labels_, results_, contrast_defs)\n",
    "a_score = contrasts[0].stat()\n",
    "print(a_score.shape) \n",
    "plot_29k(a_score[:29696]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run#0 (159, 91282), n_events=36, file: sub-01_ses-002_task-language_run-02_events.tsv\n",
      "Run#1 (159, 91282), n_events=33, file: sub-01_ses-004_task-language_run-01_events.tsv\n",
      "Run#2 (159, 91282), n_events=30, file: sub-01_ses-005_task-language_run-02_events.tsv\n",
      "Run#3 (159, 91282), n_events=33, file: sub-01_ses-006_task-language_run-02_events.tsv\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_, results_, design_mats = estimate_mutli_run_first_level(cifti_files[1:8:2], event_files[1:8:2], verbose=1)\n",
    "contrasts = compute_contrast_from_multi_first_level(labels_, results_, contrast_defs)\n",
    "b_score = contrasts[0].stat()\n",
    "plot_29k(b_score[:29696]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.92333952],\n",
       "       [0.92333952, 1.        ]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(a_score[:29696], b_score[:29696])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### > All Task multirun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " sub-01: \n",
      " sub-01 EMOTION dict_keys(['countdown', 'countdown_derivative', 'new_bloc_face', 'new_bloc_face_derivative', 'new_bloc_shape', 'new_bloc_shape_derivative', 'response_face', 'response_face_derivative', 'response_shape', 'response_shape_derivative', 'drift_1', 'constant'])\n",
      "\n",
      " sub-01 GAMBLING dict_keys(['Feedback', 'Feedback_derivative', 'Neutral', 'Neutral_derivative', 'Punishment', 'Punishment_derivative', 'Reward', 'Reward_derivative', 'countdown', 'countdown_derivative', 'drift_1', 'constant'])\n",
      "\n",
      " sub-01 LANGUAGE dict_keys(['presentation_math', 'presentation_math_derivative', 'presentation_story', 'presentation_story_derivative', 'question_math', 'question_math_derivative', 'question_story', 'question_story_derivative', 'response_math', 'response_math_derivative', 'response_story', 'response_story_derivative', 'drift_1', 'drift_2', 'constant'])\n",
      "\n",
      " sub-01 MOTOR dict_keys(['countdown', 'countdown_derivative', 'cross_fixation', 'cross_fixation_derivative', 'new_bloc_left_foot', 'new_bloc_left_foot_derivative', 'new_bloc_left_hand', 'new_bloc_left_hand_derivative', 'new_bloc_right_foot', 'new_bloc_right_foot_derivative', 'new_bloc_right_hand', 'new_bloc_right_hand_derivative', 'new_bloc_tongue', 'new_bloc_tongue_derivative', 'response_left_foot', 'response_left_foot_derivative', 'response_left_hand', 'response_left_hand_derivative', 'response_right_foot', 'response_right_foot_derivative', 'response_right_hand', 'response_right_hand_derivative', 'response_tongue', 'response_tongue_derivative', 'drift_1', 'drift_2', 'constant'])\n",
      "\n",
      " sub-01 RELATIONAL dict_keys(['Control', 'Control_derivative', 'Relational', 'Relational_derivative', 'countdown', 'countdown_derivative', 'new_bloc_control', 'new_bloc_control_derivative', 'new_bloc_relational', 'new_bloc_relational_derivative', 'drift_1', 'constant'])\n",
      "\n",
      " sub-01 SOCIAL dict_keys(['countdown', 'countdown_derivative', 'mental', 'mental_derivative', 'random', 'random_derivative', 'drift_1', 'drift_2', 'constant'])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " sub-01 WM dict_keys(['0-Back', '0-Back_derivative', '2-Back', '2-Back_derivative', 'countdown', 'countdown_derivative', 'drift_1', 'drift_2', 'drift_3', 'constant'])\n"
     ]
    }
   ],
   "source": [
    "# List all the available events first level columns\n",
    "for subj in cneuro_subjs[:1]:\n",
    "  for task in tasks:\n",
    "    labels_, results_, design_mats = estimate_mutli_run_first_level(runs[subj][task][:1], events[subj][task][:1], verbose=0)\n",
    "    contrast_matrix = np.eye(design_mats[0].shape[1])\n",
    "    bc = {  column: contrast_matrix[i] for i, column in enumerate(design_mats[0].columns) }\n",
    "    print(\"\\n\", subj, task, bc.keys())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(unique_contrasts[contrast_ids_to_show])\n",
    "# ['LANGUAGE_STORY-MATH' 'WM_2BK-0BK' 'MOTOR_CUE-AVG' 'EMOTION_FACES-SHAPES' 'SOCIAL_TOM-RANDOM' 'RELATIONAL_REL-MATCH' 'GAMBLING_REWARD-PUNISH' 'WM_FACE-AVG' 'MOTOR_T-AVG']\n",
    "\n",
    "def get_contrast_defs_for_task(design_mats, task):\n",
    "    contrast_defs = {}\n",
    "    task=task.upper();\n",
    "    \n",
    "    contrast_matrix = np.eye(design_mats[0].shape[1])\n",
    "    bc = {  column: contrast_matrix[i] for i, column in enumerate(design_mats[0].columns) }\n",
    "    \n",
    "    if task == 'LANGUAGE':\n",
    "      contrast_defs = {\"LANGUAGE_STORY-MATH\": bc[\"presentation_story\"] - bc[\"presentation_math\"]}\n",
    "    \n",
    "    if task == 'WM':\n",
    "      \"\"\"bc[\"face\"] =  ????\n",
    "      bc[\"avg\"] =   ????\n",
    "      contrast_defs = {\"WM_FACE-AVG\": bc[\"face\"] - bc[\"avg\"]}\"\"\"\n",
    "      contrast_defs = {\"WM_2BK-0BK\": bc[\"2-Back\"] - bc[\"0-Back\"]}\n",
    "    \n",
    "    \"\"\"if task == 'MOTOR':\n",
    "      ### ??????????????????????????????\n",
    "      bc[\"avg\"] =   ????\n",
    "      bc[\"t\"] =   ????\n",
    "      contrast_defs = {\"MOTOR_CUE-AVG\": bc[\"cross_fixation\"] -bc[\"avg\"]  }\n",
    "      contrast_defs.update({\"MOTOR_T-AVG\": bc[\"t\"]-  bc[\"avg\"] })\"\"\"\n",
    "    \n",
    "    if task == 'EMOTION':\n",
    "      contrast_defs = {\"EMOTION_FACES-SHAPES\": bc[\"response_face\"] - bc[\"response_shape\"]}\n",
    "    \n",
    "    if task == 'SOCIAL':\n",
    "      contrast_defs = {\"SOCIAL_TOM-RANDOM\": bc[\"mental\"] - bc[\"random\"]}\n",
    "    \n",
    "    if task == 'RELATIONAL':\n",
    "      contrast_defs = {\"RELATIONAL_REL-MATCH\": bc[\"Relational\"] - bc[\"Control\"]}\n",
    "    \n",
    "    if task == 'GAMBLING':\n",
    "      contrast_defs = {\"GAMBLING_REWARD-PUNISH\": bc[\"Reward\"] - bc[\"Punishment\"]}\n",
    "    \n",
    "    return contrast_defs;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub-01: EMOTION 1 t test, EMOTION 1 t retest, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/hemodynamic_models.py:299: UserWarning: Some stimulus onsets are earlier than -24.0 in the experiment and are thus not considered in the model\n",
      "  warnings.warn(('Some stimulus onsets are earlier than %s in the'\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAMBLING 1 t test, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/hemodynamic_models.py:299: UserWarning: Some stimulus onsets are earlier than -24.0 in the experiment and are thus not considered in the model\n",
      "  warnings.warn(('Some stimulus onsets are earlier than %s in the'\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAMBLING 1 t retest, LANGUAGE 1 t test, LANGUAGE 1 t retest, RELATIONAL 1 t test, RELATIONAL 1 t retest, SOCIAL 1 t test, SOCIAL 1 t retest, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WM 1 t test, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WM 1 t retest, sub-03: EMOTION 1 t test, EMOTION 1 t retest, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/hemodynamic_models.py:299: UserWarning: Some stimulus onsets are earlier than -24.0 in the experiment and are thus not considered in the model\n",
      "  warnings.warn(('Some stimulus onsets are earlier than %s in the'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAMBLING 1 t test, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/hemodynamic_models.py:299: UserWarning: Some stimulus onsets are earlier than -24.0 in the experiment and are thus not considered in the model\n",
      "  warnings.warn(('Some stimulus onsets are earlier than %s in the'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAMBLING 1 t retest, LANGUAGE 1 t test, LANGUAGE 1 t retest, RELATIONAL 1 t test, RELATIONAL 1 t retest, SOCIAL 1 t test, SOCIAL 1 t retest, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WM 1 t test, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WM 1 t retest, sub-05: EMOTION 1 t test, EMOTION 1 t retest, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/hemodynamic_models.py:299: UserWarning: Some stimulus onsets are earlier than -24.0 in the experiment and are thus not considered in the model\n",
      "  warnings.warn(('Some stimulus onsets are earlier than %s in the'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAMBLING 1 t test, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/hemodynamic_models.py:299: UserWarning: Some stimulus onsets are earlier than -24.0 in the experiment and are thus not considered in the model\n",
      "  warnings.warn(('Some stimulus onsets are earlier than %s in the'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAMBLING 1 t retest, LANGUAGE 1 t test, LANGUAGE 1 t retest, RELATIONAL 1 t test, RELATIONAL 1 t retest, SOCIAL 1 t test, SOCIAL 1 t retest, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WM 1 t test, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/glm/first_level/experimental_paradigm.py:117: UserWarning: Duplicated events were detected. Amplitudes of these events will be summed. You might want to verify your inputs.\n",
      "  warnings.warn(\"Duplicated events were detected. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WM 1 t retest, "
     ]
    }
   ],
   "source": [
    "task_contrasts = defaultdict(mydefault)\n",
    "retest_contrasts = defaultdict(mydefault)\n",
    "\n",
    "for subj in cneuro_subjs_x:\n",
    "  print(subj, end=\": \");\n",
    "  for task in tasks:\n",
    "    if task in [\"MOTOR\"]: continue; # not implemented\n",
    "    \n",
    "    #for trt, slc in enumerate([slice(0,4),slice(4, None)]):\n",
    "    for trt, slc in enumerate([slice(0,8,2),slice(1,8,2)]):\n",
    "        labels_, results_, design_mats = estimate_mutli_run_first_level(runs[subj][task][:4], events[subj][task][:4], verbose=0)\n",
    "        contrast_defs= get_contrast_defs_for_task(design_mats, task)\n",
    "        contrasts = compute_contrast_from_multi_first_level(labels_, results_, contrast_defs)\n",
    "        print(task, len(contrasts), contrasts[0].contrast_type, (\"test\" if trt==0 else \"retest\"), end =\", \")\n",
    "        \n",
    "        for i in range(len(contrasts)):\n",
    "           cname= list(contrast_defs.keys())[i]\n",
    "           if trt ==0:\n",
    "             task_contrasts[subj][cname] = contrasts[0].stat();\n",
    "           else: \n",
    "             retest_contrasts[subj][cname] = contrasts[0].stat();\n",
    "                \n",
    "#https://github.com/nilearn/nilearn/blob/4f4730163097457cf9ddb5674ffd158ee8fa822e/nilearn/glm/first_level/experimental_paradigm.py#L199"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26M data/cneuromod_task_contrasts_3subjs.npy\n"
     ]
    }
   ],
   "source": [
    "p = dict(task_contrasts=task_contrasts, retest_contrasts=retest_contrasts, trt_slices=[slice(0,8,2),slice(1,8,2)], \\\n",
    "         events=events, runs=runs, subjs=cneuro_subjs_x)\n",
    "np.save(\"data/cneuromod_task_contrasts_3subjs.npy\",p)\n",
    "!ls -ash data/cneuromod_task_contrasts_3subjs.npy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['EMOTION_FACES-SHAPES', 'GAMBLING_REWARD-PUNISH', 'LANGUAGE_STORY-MATH', 'RELATIONAL_REL-MATCH', 'SOCIAL_TOM-RANDOM', 'WM_2BK-0BK'])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_contrasts['sub-01'].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison with task contrasts from HCP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = \"/scratch/users/robert.scholz2/dist_model/results/retest_hcp40.task_maps47.zscored.npy\"\n",
    "payload = np.load(fn, allow_pickle=1).item()\n",
    "tdata = payload[\"test_data\"] #rdata = payload[\"retest_data\"]\n",
    "mean_test_maps = tdata.mean(axis=0); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = {\"img1\": hcp.left_cortex_data, \"img2\": hcp.left_cortex_data, \"img3\": hcp.left_cortex_data}\n",
    "pcf = [\"img1_0_180_6.4\", \"img2_0_180_6.4\", \"img3_0_180_6.4\"]\n",
    "stub = '/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/brainspace/datasets/surfaces/'\n",
    "surfs = [stub+\"conte69_32k_lh.gii\"]*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=5380x7404>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lib.plotting import plot_gen_surf2, image_grid, fig2tightPIL\n",
    "\n",
    "v = np.absolute(mean_test_maps).max()\n",
    "full_retest_imgs=[]\n",
    "\n",
    "for contrast_name in task_contrasts['sub-01'].keys():\n",
    "  tn = list(unique_contrasts).index(contrast_name)\n",
    "  data0 = mean_test_maps[:,tn]\n",
    "  data1 = tdata[0,:, tn]\n",
    "  data2 = scipy.stats.zscore(task_contrasts['sub-01'][contrast_name])\n",
    "  plot_gen_surf2([data0, data1, data2], transform=transform, surfs=surfs, plot_conf=pcf, vmin=-v, vmax=v, cmap='coolwarm');\n",
    "  plt.gcf().suptitle(f\"Subject-01, {unique_contrasts[tn]} - [0] mean, [1] hcp sample individual, [2] CNeuroSub1\", x=0.06, y=0.82, ha='left', va='top', weight='bold', size=14)\n",
    "  pil_img = fig2tightPIL(close_fig=1, dpi=300)\n",
    "  full_retest_imgs.append(pil_img)\n",
    "  #display(pil_img)\n",
    "\n",
    "rtst2subjimg=image_grid(full_retest_imgs, len(task_contrasts['sub-01'].keys()),1, pad=0.015)\n",
    "#rtst2subjimg.save(\"retest_lang_examples.png\")\n",
    "rtst2subjimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06'],\n",
       "      dtype='<U6')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cneuro_subjs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/home/mpg02/MLSC/robert.scholz2/.local/lib/python3.7/site-packages\")\n",
    "from sklearn.utils import Bunch\n",
    "import scipy\n",
    "from lib.linear_models import score\n",
    "import sklearn\n",
    "from lib.linear_models import train_test_split\n",
    "from lib.data_loading import load_xy_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subjects n=3: ['sub-01', 'sub-03', 'sub-05']\n",
      "Contrasts n=6: ['LANGUAGE_STORY-MATH', 'EMOTION_FACES-SHAPES', 'WM_2BK-0BK', 'SOCIAL_TOM-RANDOM', 'RELATIONAL_REL-MATCH', 'GAMBLING_REWARD-PUNISH']\n"
     ]
    }
   ],
   "source": [
    "# Prediction targets\n",
    "\n",
    "p = np.load(\"data/cneuromod_task_contrasts_3subjs.npy\", allow_pickle=1).item()\n",
    "p.keys()\n",
    "task_contrasts = p[\"task_contrasts\"]\n",
    "subjs = p[\"subjs\"]\n",
    "subj=subjs[0]\n",
    "\n",
    "cneuromod_contrasts = list(task_contrasts[subjs[0]].keys())\n",
    "# reorder\n",
    "cneuromod_task_names = [\"Language\", \"Emotion\", \"WM\", \"Social\", \"Relational\", \"Gambling\"]\n",
    "\n",
    "cneuromod_contrasts = ['LANGUAGE_STORY-MATH', 'EMOTION_FACES-SHAPES', 'WM_2BK-0BK', 'SOCIAL_TOM-RANDOM', 'RELATIONAL_REL-MATCH','GAMBLING_REWARD-PUNISH',]\n",
    "z_score_predictors_along_dim = 1# 1 ~ along spatial axis; 0~ across subjects\n",
    "\n",
    "\n",
    "print(f\"Subjects n={len(subjs)}:\", subjs[:5])\n",
    "print(f\"Contrasts n={len(cneuromod_contrasts)}:\", cneuromod_contrasts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictors:\t 3 (29696, 200)\n",
      "Targets:\t 3 (29696, 6)\n"
     ]
    }
   ],
   "source": [
    "# Predictors (RS-PCA)\n",
    "fn = \"/scratch/users/robert.scholz2/acc_dists/all_grads_6_cneuro.L.corrcoef.procrAlignGr.npy\"\n",
    "predictors = np.load(fn, allow_pickle=1).item()\n",
    "predictors = {subj : predictors[subj] for subj in subjs}\n",
    "print(\"Predictors:\\t\", len(predictors), predictors[subj].shape)\n",
    "yfull_task_data = {subj : np.array( [task_contrasts[subj][contr][:29696] for contr in cneuromod_contrasts]).T for subj in subjs}\n",
    "print(\"Targets:\\t\", len(yfull_task_data), yfull_task_data[subj].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 missing subjects:  []\n",
      "z-scoring task maps along the spatial dimension\n",
      "All 3 have task data attached to them\n",
      "z-scoring predictors along d= 1\n",
      "['sub-01', 'sub-03', 'sub-05'] (3, 29696, 200) (3, 29696, 6)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "\n",
    "isubjs, xdata, ydata = load_xy_data(subjs, \"\", predictors, yfull_task_data)\n",
    "if not(z_score_predictors_along_dim is None):\n",
    "    print(\"z-scoring predictors along d=\", z_score_predictors_along_dim)\n",
    "    xdata = scipy.stats.zscore(xdata, axis= z_score_predictors_along_dim)\n",
    "    \n",
    "print(isubjs, xdata.shape, ydata.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['RS-PCA200', 'FS-StructuralMaps'])\n"
     ]
    }
   ],
   "source": [
    "# HCP results for comparison\n",
    "modality = \"RS-PCA200\"\n",
    "fn = \"results/scores/linear_regression_scores_and_info_v3.yfull.aa.extended.small.npy\"\n",
    "regr_data_L = np.load(fn, allow_pickle=1).item();\n",
    "print(regr_data_L.keys())\n",
    "coeffs_L = regr_data_L[modality][\"skr\"].coef_ # shape (10, 224)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply exisitng model (fit on HCP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((47, 200), (47,))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading of the HCP model\n",
    "fn = \"results/scores/linear_regression_scores_and_info_v3.yfull.aa.npy\"\n",
    "pl = np.load(fn, allow_pickle=1).item()['RS-PCA200']\n",
    "skr = pl[\"skr\"]\n",
    "skr.coef_.shape, skr.intercept_.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ids for overlapping contrasts: [8, 2, 38, 27, 24, 5]\n",
      "(3, 29696, 6) (3, 29696, 6)\n",
      "['LANGUAGE_STORY-MATH', 'EMOTION_FACES-SHAPES', 'WM_2BK-0BK', 'SOCIAL_TOM-RANDOM', 'RELATIONAL_REL-MATCH', 'GAMBLING_REWARD-PUNISH']\n",
      "Correlation: [ 0.426  0.217  0.214  0.009  0.232 -0.013]\n",
      "Differentiability: [ 0.164  0.028  0.021 -0.047  0.052 -0.011]\n"
     ]
    }
   ],
   "source": [
    "cneuro_ctr_ids = [list(unique_contrasts).index(ctr) for ctr in cneuromod_contrasts]\n",
    "print(\"Ids for overlapping contrasts:\",cneuro_ctr_ids)\n",
    "# Reorder them to correspond to our usual way of plotting\n",
    "#cneuro_ctr_ids = [8, 2, 38, 27, 24, 5];\n",
    "\n",
    "\n",
    "x      = xdata.reshape((-1, xdata.shape[-1]))\n",
    "y_pred = np.dot(x, skr.coef_[cneuro_ctr_ids,:].T) + skr.intercept_[cneuro_ctr_ids]\n",
    "y_pred = y_pred.reshape(ydata.shape)\n",
    "print(ydata.shape, y_pred.shape)\n",
    "scoresd, diff_scores_test = score(y_pred, ydata);\n",
    "\n",
    "print(cneuromod_contrasts)\n",
    "print(\"Correlation:\", scoresd.corr.mean(0).round(3))\n",
    "print(\"Differentiability:\", diff_scores_test.round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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mPxRCiiiu480KKKKAPs3wF/yIvhz/ALBtt/6KWvCv2k/+R6sf+wbH/wCjZa918Bf8iL4c/wCwbbf+ilrwr9pP/kerH/sGx/8Ao2WvMw38Z/M9nF/7uvkeT0UUV6Z4wUUUUAd18Dv+So6L/wBt/wD0RJX1hXyf8Dv+So6L/wBt/wD0RJX1hXmY3+IvQ9nLv4T9f8j4Vooor0zxgooooAK+kv2bP+RFvv8AsJSf+ioq+ba+kv2bP+RFvv8AsJSf+ioq5cX/AAztwH8YP2k/+RFsf+wlH/6Klr5tr6S/aT/5EWx/7CUf/oqWvm2jCfwxY/8AjBRRRXUcYVu+Av8AkevDn/YStv8A0atYVbvgL/kevDn/AGErb/0atTP4WXT+JH2bRRXk/jj4wf8ACLeKb3Rv7D+1fZtn777Xs3bkV/u7Dj72OvavFhTlUdon0NSrGkrzdj1iivCv+GgP+pa/8n//ALXR/wANAf8AUtf+T/8A9rrX6rV7fkY/XaP834M91orwr/hoD/qWv/J//wC10f8ADQH/AFLX/k//APa6PqtXt+QfXaP834M91orhfhf8QP8AhOv7T/4ln2D7F5X/AC383fv3/wCyuMbPfrXdVjKLg+WW5vCcakeaOxyGmf8AJWvEf/YF03/0de119chpn/JWvEf/AGBdN/8AR17XX1JZk+IvD2meIreGLVrdpRBJ5sMkcrwyRPgjKSIQynBI4IyDUWn+FNF07+yvsNisH9liUWoSR8J5n+sJGfmLdSWyc5PU1t0UAc3ZeCPDtlrn9r22nBL0SPMn76QxxyPne6RFtiM2TllUE5PPJpF8DeHxrC6m9nNNcpcG6RZ7uaWKOYknesTOY1bJJBCjHaulooAK5DTP+SteI/8AsC6b/wCjr2uvrkNM/wCSteI/+wLpv/o69oA6+vk/44/8lR1r/th/6Ijr6wr5P+OP/JUda/7Yf+iI67MF/EfocGY/wl6/5nC0UUV6Z4wUUUUAesfs2f8AI9X3/YNk/wDRsVfSVfNv7Nn/ACPV9/2DZP8A0bFX0lXlYv8AiHt4D+CFFFFcp2hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcL8cf+SXa1/wBsP/R8dd1XC/HH/kl2tf8AbD/0fHWlL+JH1RlX/hS9GfJ9FFFe2fOBRRRQAV91V8K191V5+O+z8z1Mt+18v1CiiiuA9QKKKKACiiigArkNT/5K14c/7Aupf+jrKuvrkNT/AOSteHP+wLqX/o6yoA6+iiigAooooAKKKKACiiigDkPBH/Iy+Pf+w1H/AOkFpXX1yHgj/kZfHv8A2Go//SC0rr6APk/44/8AJUda/wC2H/oiOuFruvjj/wAlR1r/ALYf+iI64Wvbpfw4+iPnK/8AFl6sKKKK0Mgr3X9l/wD5mX/t2/8AateFV7r+y/8A8zL/ANu3/tWufFfwn/XU6sF/Gj8/yPWPHv8AyIviP/sG3P8A6KavjKvs3x7/AMiL4j/7Btz/AOimr4yrHBfCzozH4ohRRRXceaFFFFAH2b4C/wCRF8Of9g22/wDRS14V+0n/AMj1Y/8AYNj/APRste6+Av8AkRfDn/YNtv8A0UteFftJ/wDI9WP/AGDY/wD0bLXmYb+M/mezi/8Ad18jyeiiivTPGCiiigDuvgd/yVHRf+2//oiSvrCvk/4Hf8lR0X/tv/6Ikr6wrzMb/EXoezl38J+v+R8K0UUV6Z4wUUUUAFfSX7Nn/Ii33/YSk/8ARUVfNtfSX7Nn/Ii33/YSk/8ARUVcuL/hnbgP4wftJ/8AIi2P/YSj/wDRUtfNtfSX7Sf/ACItj/2Eo/8A0VLXzbRhP4Ysf/GCiiiuo4wrd8Bf8j14c/7CVt/6NWsKt3wF/wAj14c/7CVt/wCjVqZ/Cy6fxI+za+T/AI4/8lR1r/th/wCiI6+sK+T/AI4/8lR1r/th/wCiI687BfxH6HrZj/CXr/mcLRRRXpnjBRRRQB7r+y//AMzL/wBu3/tWvda8K/Zf/wCZl/7dv/ate615GK/iv+uh72C/gx+f5nIaZ/yVrxH/ANgXTf8A0de119chpn/JWvEf/YF03/0de119c51BRRRQAUUUUAFchpn/ACVrxH/2BdN/9HXtdfXIaZ/yVrxH/wBgXTf/AEde0AdfXyf8cf8AkqOtf9sP/REdfWFfJ/xx/wCSo61/2w/9ER12YL+I/Q4Mx/hL1/zOFooor0zxgooooA9Y/Zs/5Hq+/wCwbJ/6Nir3nxrNLb+Ddent5HimjsLh0kRirIwjYggjkEHvXg37Nn/I9X3/AGDZP/RsVe6+Pf8AkRfEf/YNuf8A0U1eXif433Hs4P8A3d/M+T/+Ex8Tf9DHrP8A4HS//FUf8Jj4m/6GPWf/AAOl/wDiqwqK9Lkj2PI55dzd/wCEx8Tf9DHrP/gdL/8AFUf8Jj4m/wChj1n/AMDpf/iqwqKOSPYOeXc3f+Ex8Tf9DHrP/gdL/wDFV9aeCppbjwboM9xI8s0lhbu8jsWZ2Makkk8kk96+Lq+zfAX/ACIvhz/sG23/AKKWuLGpKKsejl8m5Suy/qmsaZpPlf2rqNlZebny/tM6x78YzjcRnGR+Yqh/wmPhn/oY9G/8Dov/AIqvJ/2oP+Za/wC3n/2lXhVRRwqqQUmzSvjZUqjglsfZv/CY+Gf+hj0b/wADov8A4qj/AITHwz/0Mejf+B0X/wAVXxlRWn1KPcx/tGX8p9m/8Jj4Z/6GPRv/AAOi/wDiqnsfEuhahdJbWGtaZc3L52RQ3Ubu2Bk4AOTwCfwr4rruvgd/yVHRf+2//oiSpng4xi5X2Lp4+U5qNtz6wooorgPUCiiigAooooAK4X44/wDJLta/7Yf+j467quF+OP8AyS7Wv+2H/o+OtKX8SPqjKv8AwpejPk+iiivbPnAooooAK+6q+Fa+6q8/HfZ+Z6mW/a+X6hRRRXAeoFFFFABRRRQAVyGp/wDJWvDn/YF1L/0dZV19chqf/JWvDn/YF1L/ANHWVAHX0UUUAFZPiLxFpnh23hl1a4aITyeVDHHE80kr4JwkaAsxwCeAcAVrV5/47vLfRPH3hLXNYlW30aC3vrWS6k4jt5pBCULt0UERyLk8ZOO9AHZaJq1jrmmQ6hpVylzZzA7JFyOhIIIPIIIIIPIIINZNl438O3uuf2RbaiHvTI8KfuZBHJImd6JKV2Oy4OVViRg8cGud+F+qWcQvvMkaFfEGsX17pcbxspnhXbucccAkFxnGQwPeuM8P3cE/hTwH4RhYnxRperwPe2m0+ZbiGRmmlf0Vl3Ybo3mDGc0Aeqy+N/DsWvf2NJqIF95y2x/cyGITMMiIy7fLDnIwhbdyOK6SvBr67gTwdrfgx2P/AAltz4hkeK02nzZQ9+JkuAP7gjIYv0G0jORiva9asbjULEwWmp3emSlgftFqsTOAO2JEdcH6UAc94I/5GXx7/wBhqP8A9ILSuvrhLHwDf2NzqFxbeOPEiS38wuLhvKsTvcRpGDzbcfLGg4wOPXJq5/wies/9D74k/wC/Fh/8jUAfP3xx/wCSo61/2w/9ER1wtfSGt/BGw1zVJtR1XxT4jnvJtvmSf6Iu7ChRwsAHQDtVH/hnvQv+hj8R/wDfdt/8Zr0YYuEYpNPQ8mpgKk5uSa1Z8+0V7T4z+Bum6N4Q1vU9O1zxDPe2dlNcQRN9nYPIiFlUgQgnJAGAc1sJ+z5oZRSfEXiPJH962/8AjNV9dh2ZH9nVO6/r5Hz9Xuv7L/8AzMv/AG7f+1au/wDDPehf9DH4j/77tv8A4zW74Y+E6+F/tP8AYXjDxJa/advm/LZvu25x963OPvHp61lWxUKkHFI2w+CnSqKba0Ou8e/8iL4j/wCwbc/+imr4yr60v/BGpX9jcWd3468SSW1xG0MqeTYDcjDBGRbZHBPSuN/4Z70L/oY/Ef8A33bf/GajD140k1I1xeGnWacXsfPtFfQX/DPehf8AQx+I/wDvu2/+M1i3fwO06Lxlpmlx654haxubG6uZZv8AR8rJHJAqKD5OACJXOCMnaMdDXR9dh2Zyf2dU7r+vkeL0V9Bf8M96F/0MfiP/AL7tv/jNH/DPehf9DH4j/wC+7b/4zR9dh2Yf2dU7r+vkel+Av+RF8Of9g22/9FLXhX7Sf/I9WP8A2DY//RsteuWHgjUrCxt7O08deJI7a3jWGJPJsDtRRgDJtsngDrWF4k+Dlt4lvkvNb8W+JLq5SMQq+LNMICSBhbcDqx/OuOjVUKnMz0K9GVSlyLc+aKK+gv8AhnvQv+hj8R/9923/AMZo/wCGe9C/6GPxH/33bf8Axmuz67Dszz/7Oqd1/XyPn2ivaPCPwO07VtKnuNQ1zxDBMl9eWyqv2dQY4rmSNGwYTyURTnoc5HFbX/DPehf9DH4j/wC+7b/4zR9dh2Yf2dU7r+vkeb/A7/kqOi/9t/8A0RJX1hXkuifBGw0PVIdR0rxT4jgvId3lyf6I23KlTw0BHQntXV/8InrP/Q++JP8AvxYf/I1ceIqqrK6PQwtGVGDjLufIlFfQX/DPehf9DH4j/wC+7b/4zR/wz3oX/Qx+I/8Avu2/+M12fXYdmef/AGdU7r+vkfPtFe0a/wDA7TrDVfDtva654hkhv75ra5Y/ZyY4xbTSBgRDwd8aDJyPmx1Ira/4Z70L/oY/Ef8A33bf/GaPrsOzD+zqndf18j59r6S/Zs/5EW+/7CUn/oqKs/8A4Z70L/oY/Ef/AH3bf/Ga6Tw38MpfDVi9nonjPxJa2zyGZk2WT5cgAnLW5PRR+VY18TGpDlSOjDYOdKfNJoy/2k/+RFsf+wlH/wCipa+ba+pvEnwyl8S2KWet+M/El1bJIJlTZZJhwCAcrbg9GP51zf8Awz3oX/Qx+I/++7b/AOM0UMTGnDlYYnBzqz5otHz7RX0F/wAM96F/0MfiP/vu2/8AjNYugfA7Tr/VfEVvda54hjhsL5ba2YfZwZIzbQyFiTDyd8jjIwPlx1Brb67Dszn/ALOqd1/XyPF63fAX/I9eHP8AsJW3/o1a9l/4Z70L/oY/Ef8A33bf/GansPgNpNhfW95aeJvEcdzbyLNE+bU7XU5BwYMHkDrSljINNWZUcvqJp3X9fI9hr5P+OP8AyVHWv+2H/oiOvoH/AIRPWf8AoffEn/fiw/8AkauU1v4I2GuapNqOq+KfEc95Nt8yT/RF3YUKOFgA6Adq5MPVVKV2d2KoyrQUY9z5vor6C/4Z70L/AKGPxH/33bf/ABmsbxn8DdN0bwhrep6drniGe9s7Ka4gib7OweRELKpAhBOSAMA5rs+uw7M8/wDs6p3X9fI8Wor6BT9nzQyik+IvEeSP71t/8Zpf+Ge9C/6GPxH/AN923/xmj67Dsw/s6p3X9fIpfsv/APMy/wDbt/7Vr3WvNPDHwnXwv9p/sLxh4ktftO3zfls33bc4+9bnH3j09a3P+ET1n/offEn/AH4sP/kauGtNVJuSPTw9N0qag+gaZ/yVrxH/ANgXTf8A0de119c34a8LNo2sahqd1rWpate3kENu0l6sC7EiaRlCiKNB1lfqDXSVkbGT4i8RaZ4dt4ZdWuGiE8nlQxxxPNJK+CcJGgLMcAngHAFWNE1ax1zTIdQ0q5S5s5gdki5HQkEEHkEEEEHkEEGuN8d3lvonj7wlrmsSrb6NBb31rJdScR280ghKF26KCI5FyeMnHeofhfqlnEL7zJGhXxBrF9e6XG8bKZ4V27nHHAJBcZxkMD3oA6Ky8b+Hb3XP7IttRD3pkeFP3MgjkkTO9ElK7HZcHKqxIweODRL438Oxa9/Y0mogX3nLbH9zIYhMwyIjLt8sOcjCFt3I4ryrw/dwT+FPAfhGFifFGl6vA97abT5luIZGaaV/RWXdhujeYMZzRfXcCeDtb8GOx/4S258QyPFabT5soe/EyXAH9wRkMX6DaRnIxQB7zXIaZ/yVrxH/ANgXTf8A0de102pW8t3YzQQXk9lLIuFuIAheM+qh1Zc/UGuNi8A38Wr3Opp448SC9uIYreSTyrH5kjZ2QY+zY4Mr9BnnnPGADu6+T/jj/wAlR1r/ALYf+iI6+gf+ET1n/offEn/fiw/+Rq5TW/gjYa5qk2o6r4p8Rz3k23zJP9EXdhQo4WADoB2rfD1VSldnNiqMq0FGPc+b6K+gv+Ge9C/6GPxH/wB923/xmsbxn8DdN0bwhrep6drniGe9s7Ka4gib7OweRELKpAhBOSAMA5rs+uw7M8/+zqndf18jxaivoFP2fNDKKT4i8R5I/vW3/wAZpf8AhnvQv+hj8R/9923/AMZo+uw7MP7Oqd1/XyOV/Zs/5Hq+/wCwbJ/6Nir3Xx7/AMiL4j/7Btz/AOimrifDfwctvDV895oni3xJa3LxmFnxZvlCQSMNbkdVH5Vu3/gjUr+xuLO78deJJLa4jaGVPJsBuRhgjItsjgnpXHVqqdTnR6FCjKnS5HufJdFfQX/DPehf9DH4j/77tv8A4zR/wz3oX/Qx+I/++7b/AOM12fXYdmef/Z1Tuv6+R8+0V7Rd/A7TovGWmaXHrniFrG5sbq5lm/0fKyRyQKig+TgAiVzgjJ2jHQ1tf8M96F/0MfiP/vu2/wDjNH12HZh/Z1Tuv6+R8+19m+Av+RF8Of8AYNtv/RS15p/wz3oX/Qx+I/8Avu2/+M12Vh4I1KwsbeztPHXiSO2t41hiTybA7UUYAybbJ4A61z4ivGqkonXhMNOi25M4D9qD/mWv+3n/ANpV4VX0/wCJ/hOvij7N/bvjDxJdfZt3lfLZpt3Yz923Gfujr6Vhf8M96F/0MfiP/vu2/wDjNXRxUKcFFoyxGCnVqOaa1Pn2ivoL/hnvQv8AoY/Ef/fdt/8AGaxfCPwO07VtKnuNQ1zxDBMl9eWyqv2dQY4rmSNGwYTyURTnoc5HFa/XYdmY/wBnVO6/r5Hi9d18Dv8AkqOi/wDbf/0RJXpH/DPehf8AQx+I/wDvu2/+M1e0T4I2Gh6pDqOleKfEcF5Du8uT/RG25UqeGgI6E9qmeLhKLST1Lp4CpCak2tGetUVyH/CJ6z/0PviT/vxYf/I1H/CJ6z/0PviT/vxYf/I1ecesdfRXIf8ACJ6z/wBD74k/78WH/wAjVi+LtG8SaTpUFxp/jfxFPM99Z2zK1tYsBHLcxxu2BbDkI7HPQYyeKAPSaK5D/hE9Z/6H3xJ/34sP/kaj/hE9Z/6H3xJ/34sP/kagDr64X44/8ku1r/th/wCj46t/8InrP/Q++JP+/Fh/8jVR1v4e3euaXNp2q+N/Ek9nNt8yPy7Fd2GDDlbYHqB3qoS5ZJvoRUi5wcV1R8pUV9Bf8M96F/0MfiP/AL7tv/jNH/DPehf9DH4j/wC+7b/4zXo/XYdmeT/Z1Tuv6+R8+0V7RafA7TpfGWp6XJrniFbG2sbW5im/0fLSSSTq6k+TggCJDgDI3HPUVtf8M96F/wBDH4j/AO+7b/4zR9dh2Yf2dU7r+vkfPtfdVeL/APDPehf9DH4j/wC+7b/4zXc/8InrP/Q++JP+/Fh/8jVzYmtGrbl6HbhMPKhfm6nX0VyH/CJ6z/0PviT/AL8WH/yNR/wies/9D74k/wC/Fh/8jVynYdfRXHv4U1kIxHj3xJkD/nhYf/I1Y/gzRfEes+ENE1PUfG/iKC9vLKG4niW2sVCSOgZlANsSMEkYJzQB6RRXIf8ACJ6z/wBD74k/78WH/wAjUf8ACJ6z/wBD74k/78WH/wAjUAdfXIan/wAla8Of9gXUv/R1lR/wies/9D74k/78WH/yNUuj+EZrLxHBrOoeItX1e5gtZbWJLxLZURZGjZiPKiQk5iTqTQB1VFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFeQfGXTrx9esdSk0jU9Vs7c2L2f2KJp/ss0d6sk7mNectCAA2D9xhxu57P4dxXrW2tX97Z3FjFqWpSXdta3IxLFEY41+ZcnaWZHfb1G/nnNAHWUUUUAFFFFABUE15bQvsmuYY3/ALrOAanrwjw14egtNHgg174OT6nqSZ8++nXT5pblsn967PLnc3UjJAJwDigD3eiiigAooooAKqXGqWFvK0VxfWsUq9UeZVI79Cat14Z8T9M8DzeNL6a3i+0+NGWP7TALSG6iPyKE84XP7qMbNvR0YjB5oA9stL21vN/2S5gn2Y3eVIGxnpnH0NT1xnwtsbOz0BXGm+GtP1iUD7cmhRosRwzeXnbznaTwScEtgnrXZ0AFFFFABVXU9RstKsZL3VLy2srOLHmT3EqxxpkgDLMQBkkD6mrVR3EEVxE0VxEksTdUdQwPfoaAOc/4WF4L/wChv8O/+DOH/wCKrorW4hu7WG5tJo57eZBJFLEwZHUjIZSOCCDkEV4trGta/da7Jb+H9N8HWlozailuNQs3d3azkRG3MrqF3lnYcHCrkk549X8H339p+EtEv/3f+lWME/7uLyl+aNW+VMnaOeBk46ZNAGvRRRQAUUUUAFFcv8QNF8N3+jzan4r0+O8tdJt5rnLZyiBdz4AIzkIPyryXRE0218TWjr8MLHT4rXVbS0e9fViXtZZVikjYoEOT+8QYBI3cZ70AfQVFFFABRRRQAVlat4k0PR7hbfV9Z02xnZBIsd1dJExUkjcAxBxkEZ9jWrXnfjXSLyPxnFrw8M2niWz+wLaLA8kKS2riRmLr5uFIcMoPII2D1NAHZ6TrukayXGkapYX5j5f7LcJLt+u0nFaNeVeC/A9za61p+ujTtL0iRdSuriS1tXDNDayW+wW+5F2tmUCUjO0HpmvVaACiiigAooooAKKKKACiiigAooooAKKKKACiivK/j7o97q2g4j0291WwWxvozaWil2F28QFtKYwcuFIccA4Lq2OMgA9UoriPADXmoa5rmtSaffadp91DaQQQ30ZilkeISb5TGeVB3oozgny846V29ABRRRQAUUUUAQz3VvAwE88UZPIDuBn86lRldFZGDKwyCDkEV4qNBjj1vXn134VT+IZptRnli1KYWMzTRM5KDEsuVVRhVH90AkAkivX9FRI9HsEhsf7OjWCNVssKPs4CjEeEJUbenykjjjigC5RRRQAUUUUAVrrULK0kEd1eW8DkbgskqqSPXBNFrqFldyGO1vLedwNxWOVWIHrgGvKfjJp/ga58Q2cuuea/isWoW2jt7dLl2h3tjfHMrQhNxf5n29wGFbHwg060trSae60jwrYa0SyK2kQQxytbfLjzRGWAbcOQrFeF+gAPR6KKKACiiigCK6uILO1mubuaOC2hQySyysFRFAyWYngAAZJNc7/wsLwX/wBDf4d/8GcP/wAVXSyIksbRyKrowKsrDIIPUEV5F4t1TVF8Vf2X4c0zwva2qajFpbTajZNKzTSWrXAYBGQBfuIOpLMfTBAPVdM1Gy1WxjvdLvLa9s5c+XPbyrJG+CQcMpIOCCPqKtVy/wAM9RuNU8F2NzfLYrd+ZPDMtjEYoA8czo2wFm4yp5zz14zgdRQAUUUUAFFFcv8AEDRfDd/o82p+K9PjvLXSbea5y2cogXc+ACM5CD8qAOoor590RNNtfE1o6/DCx0+K11W0tHvX1Yl7WWVYpI2KBDk/vEGASN3Ge9fQVABRRRQAUUUUAZWreJND0e4W31fWdNsZ2QSLHdXSRMVJI3AMQcZBGfY1JpOu6RrJcaRqlhfmPl/stwku367ScVxnjXSLyPxnFrw8M2niWz+wLaLA8kKS2riRmLr5uFIcMoPII2D1NZ3gvwPc2utafro07S9IkXUrq4ktbVwzQ2slvsFvuRdrZlAlIztB6ZoA9VooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPJ/iQ1nF49t5PEuu+IdD0T+zlEE1heTwW80/mPuWQx8KwXYR0LbjydoFU/hxDrFvrlleafqfiXUtCu9RubYNqrySrJaCDfHP+8GUPnDyweA684PWtL4r634gs7+6i0nV30axsLS1vJ54raOV5EkuvLmOZFYBYo1LnAzlhk4FbPwk1O61PR9WN3rzeIUttSkgg1MJEqTxhIyNnlqFIBZlJ5yytzjAAB3NFFFABRRRQAV8+/2W/wDY3k/2F4q/4WZnH9p7J/J+07v9Z5+fJ8j/AGM42/LtzXtviS907SrGPVdZ1H+z7GxlErzNN5cZ3AxhZPVcuOD/ABBT2rwy28T+DrrwhFqVt428Rza1LZrPHosfiKVp2nKZEAwM7tx252/hQB9EUUUUAFFFFABXjHxG03Rk8X3t4NW8M3OqyiNn0jUNBj1GYYRVH+pAnAIAI3buvHGBXs9eY+M9Im1DxLdtongm5fUTsEmtrqv9mLIdgwd8TGWTaMLymPlx0oAt/CBrpodU+1+CNP8ADABjCXFnEIVvh83PllFkTb6OP4zjvXodcd8OdG8VaRDfDxbrsWprKUNrAibvsoG7cDKQrSZyvLDjb712NABRRRQAVBem6EA+wrC03mJkTMVXZvG85APO3djsTjOBzU9FAHlviTStW19NQik0T4fajo9veSyZu7iQlHUkFpAIiFkx97n1HSvRtEcyaNYO32Tc1vGT9jbdBnaP9WcDKehwOMV5NrOtXPhPWNU8O2PinwdbQ6hdTXSf2ncOlzZtcOXYGMfK43OxXcycEDnrXqvh3TItE8P6ZpVs7SQWNrFaxu3VlRAoJ98CgDQooooAKKKKAMzxRLYW/hnV5taTfpcdnM92uCd0IQlxgf7Oa82tNLuNW8T6Xq0vgPWrWAyW7s0utRCP93gRzTQCQ73QYIzlvlHUgV6X4kW1fw7qqajcG1sWtZRPOuMxR7DuYZBHAyeQRx0NeS+FJIbK/wBEjl1T4lWemSTRRWc2q+R9muDkbI3wpkUPwBvC5zjIJFAHtdFFFABRRRQAV5X8RNJW78eW9zqvgq+8WaUNOWONEEDxW03mOWKpLIoLMpXJxwFXBOSB6pXj2p2Go6l9k+0eAvGa/ZbdLaPyfFEcWUXOC227G5ueWbLHuaAO6+HtrYWmizR6X4Vl8Lwm4ZjZyRQxl22r+8xE7LyMDJOfl6dK6euW+HVnJY6JPFLpOraUxuGbydT1L7dK3yr8wfzJMLxjbkcgnHOT1NABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXmvxcMaat4bk1TVNc0jQV+0fa7zS7iaECTEflpKY+in5zuI4KgZG459Krz74s6p4htYYrPwxdtY3L2F9eidLdZneSCNDHCocFRvLkngnCHGOtAHHeGo78a/LqfhLXPFWraNHqFjDE99PNcQzxyOVuVAkH3I02uJBj5iRk4r3KvNfhDrdzq2o69E/ip/FFjFHaSwXYigRIjIshaL90oBcbVLZ6BlGAc59KoAKKKKACiiqOuNbR6XPPfXb2dpbYuZZ0l8vYkbCQ7m/u4XDDupI70AeO61p1mdS17/hKPD/AIr1HxDJdztp93YJcOghLH7OIZYz5cO1doO4r8wYnOa9e8Mx6hF4b0qPWnEmqpaRLduCCGmCDeeP9rNeM2/ivwHrJv8AULr4g6vp8zXlyFtYtfcAqszqrIo6KygMqgcBgOa9h8HzvdeEtEuJormGWWxgdorqQySoTGpKu5ALMOhJAycmgDXooooAKKKKAPKfizpujS69a3l7rfh+31EWyxx6fqmkQ6g0yh3IZU4nxkkfKccdM5yvwla6Ou3Yl8B6To9utsdmtWNk1l5/zL+68mSNZQD97kkfKO+K1PiFpwv9ag+x+CrnV9SW3AXVIr5bAQrubEfnqwl4OWwqkDdnuaf8PNA8Y6VqlxP4k1yKfS3hKw6YJGumhfcCHNy6q7YAIwQRzntQB31FFFABRRRQBBfG5FjcGwWFrwRt5KzMVjL4+UMQCQucZIBOK4PxXp+s65f6tptnpHgrU9PYxfaIr+eTziQgK+aqxsAQcleemDXodeU+Mdam8D+K9RuLPXvCtuutGO4ktdauXhkikSNYvMTYG3IVjTgheQfm5oA7rwQvl+FrCMRaRCI1aMR6Q++1QKxACHA6Ac8cHIrcrA8B6YukeFLK2W+i1AuZLp7qIARzPNI0rsgBOFLOccnjFb9ABRRRQAVmeKJbC38M6vNrSb9Ljs5nu1wTuhCEuMD/AGc1p1neJFtX8O6qmo3BtbFrWUTzrjMUew7mGQRwMnkEcdDQB5paaXcat4n0vVpfAetWsBkt3ZpdaiEf7vAjmmgEh3ugwRnLfKOpAr1yvFPCkkNlf6JHLqnxKs9Mkmiis5tV8j7NcHI2RvhTIofgDeFznGQSK9roAKKKKACiiigDyv4iaSt348t7nVfBV94s0oacscaIIHitpvMcsVSWRQWZSuTjgKuCckDrfh7a2Fpos0el+FZfC8JuGY2ckUMZdtq/vMROy8jAyTn5enSuF1Ow1HUvsn2jwF4zX7LbpbR+T4ojiyi5wW23Y3NzyzZY9zXcfDqzksdEnil0nVtKY3DN5Op6l9ulb5V+YP5kmF4xtyOQTjnJAOpooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOQ8YLqjajGLDxhpuiQmEBra6s45mc5b58tIpwRgYx/Cah+FOpXl/pOqw6hqVnqb2GoyWiXVlbrDC6BI2GwKSDjeQeeG3DnGTi+O7K71zx8mmaboHg/UZINNjuJrjW7UySRq0sioqkA5UlWOO3PqKZ4D8U6za61BoGs6Rodpa/bZtMQ6QzKkNxHD9o27GGCjRksCCCDwRQB6lRRRQAUUUUAFeG2ninVrPwNF4mtPE+lQeUo8vwxFZxLGmDtFmMHzBKPudcbv4ccV7lXjui2mt69NF4mt4/AOmalMS2J9Jaa8tmzgpJKJUPmDo3AwQaAPYqKKKACiiigAooooAKKKKACiiigAqK6uIbS1mubmRYoIUMkkjHAVQMkn2AFS1m+JtKXXfDeq6S8hiW/tJbUyAZKCRCufwzQB5D4k1ufwRa6nYSW2gXum+I7i5vre9uZpQ3lyncxnhSFyyR7wN2QNoUHbXrnhSxTTPC+j2Ed19sS1s4YFuc584KgUP+OM/jXK6F4e8RG91TV/EK6O2pnTF0yygtJHaHau5mdyyAjexTIAOAg5NdT4S0t9E8K6NpU0glksbKG1Zx0YpGFJ/HFAGrRRRQAUUUUAY3imTTptJv8ATdXS4e0urC4M6wwSSZhChZACgPzYcYX7zc7QcHHluj39vqeq6Np+reKfEWp2FvdwvbW0vhm4tjJKjgxGebygCFYKxOEGQCeM16J8SbXxDfeDtStPCLWqancQSQh55XiKBo2G6Nl6SBiuCcAetcoNV8QanqOn6ZqniPwdZKl7bvKmn38jXUnlyq/khSR98qEI5yGIwc0AepUUUUAFFFFABXiXiXVrDxH4vim1/wAI+MdS0JLIJDatpNwI4bgOxd2TgMWUoAedu09M5r22vLPiTrFtaeNLSx1zxLq2kaZLYebbw6TkyvMJGDtLsRnC7dm08LkPk5xQBr/B3zRoOpj7Fq2n2A1GQWFnqkciTQQbI8L8/JXdvYckANt/hrvK5b4cz6bPok7aPq+ratbi4YNPqfmeYrbVyo8xVO3GD0xkmupoAKKKKACiiigAooooAKKKKACiiigAooooAK5rxl/aH+h/2b4nsdB+/v8AtVqk3nfdxjc64xz0z94V0ted/FCG41HXfDmk2Gi+GdUu7lLmX/ieW5kWGNPK3MhAOCSygjHPHYGgCX4aajqEmu+JNL1DW9P1lLL7PJFPYWqQIvmCQsrbWbL5UZHYFT/Fgd/XkPh/xDrvhfXpNF1HQvDNtp0V1aQzDRd8Xl/a3McMoUjB+dSrDgjg8ivXqACiiigAooooA8gbWdQkHiW9tvF+k+GotLvrqMaY1nCU+R2PmTliHJlP7zKleHHU5Nem+GdQm1bw3pWo3Vuba4vLSK4kgPWJnQMV/AnH4V5veWmp+J/EWoXy23gezn0+8ktYf7U0trq8VY2wsjP5ibQww6gD7rDmvU9P+0fYLb7bLDNdeUvmyQKVjd8DcVBJIUnOASeO5oAnooooAKKKKACiiigAooooAKKKKACvIfEPiCTwhqOp+KdOh0jWNJ8RSRLFI9zJHMsiRCPy1VIpDJHiMv8AKMglzjBzXr1eceC/But6bqmhprUunPpfh20ltNONszmWcvtUSyhlARhGpXALZLscjpQBufCy0is/AWlJb3drdxSiS5WW0BEI82RpNkYPIRd+0A4OFGQOldXWB4F0i50Pw3FZXxhNyZ7i4cQklFMs8ku1SQOBvx0HSt+gAooooAKxvFMmnTaTf6bq6XD2l1YXBnWGCSTMIULIAUB+bDjC/ebnaDg42a5j4k2viG+8HalaeEWtU1O4gkhDzyvEUDRsN0bL0kDFcE4A9aAPO9Hv7fU9V0bT9W8U+ItTsLe7he2tpfDNxbGSVHBiM83lAEKwVicIMgE8Zr2uvLRqviDU9R0/TNU8R+DrJUvbd5U0+/ka6k8uVX8kKSPvlQhHOQxGDmvUqACiiigAooooA8S8S6tYeI/F8U2v+EfGOpaElkEhtW0m4EcNwHYu7JwGLKUAPO3aemc12Pwd80aDqY+xatp9gNRkFhZ6pHIk0EGyPC/PyV3b2HJADbf4ayPiTrFtaeNLSx1zxLq2kaZLYebbw6TkyvMJGDtLsRnC7dm08LkPk5xXVfDmfTZ9EnbR9X1bVrcXDBp9T8zzFbauVHmKp24wemMk0AdTRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeZ/Ea1j1vxRb6bYeGDqurWlmLh7watJphhikdlVBLEC7ZMbnbjAwD3rf8AAegpY6TAl74b0/R7i0nke3jguzeH51AaUysitvbLA5ycDqc4HKfFqbQbXxHaXV4ni241aO2jhCaDeNB5UUs/loXPmIo3yEL1JO30XI6T4UXdvd6De/ZofEUBhvZIZIteuvPuVdVTIzvcqvTAJHc4wQSAdpRRRQAUUUUARzzxW8Ye4ljiQsqBnYKNzEKoye5JAA7kgV4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      "text/plain": [
       "<Figure size 800x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cmasher as cmr\n",
    "corr_CNEURO = scoresd.corr.mean(0)[:];\n",
    "diff_CNEURO = diff_scores_test[:];\n",
    "\n",
    "corr_HCP = regr_data_L[modality][\"scoresd\"][\"corr\"].mean(0)[cneuro_ctr_ids]\n",
    "diff_HCP =  regr_data_L[modality][\"diff_scores_test\"][cneuro_ctr_ids];\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(2,2, figsize=(8,3*2+1))\n",
    "axs = np.array(axs).flatten()\n",
    "titles=[\"HCP Accuracy\", \"CNeuroMod Accuracy\", \"HCP Discriminability\", \"CNeuroMod Discriminability\"]\n",
    "for i,data in enumerate([corr_HCP, corr_CNEURO, diff_HCP, diff_CNEURO]):\n",
    "  axs[i].bar(np.arange(len(cneuro_ctr_ids)), data, color=cmr.pride(0.18))\n",
    "  axs[i].set_xticks(np.arange(len(cneuromod_task_names)));\n",
    "  axs[i].set_xticklabels( np.array(cneuromod_task_names), rotation=90);\n",
    "  axs[i].set_title(titles[i])\n",
    "    \n",
    "for i in [0,1]: axs[i].set_ylim(0, 0.8)\n",
    "for i in [2,3]: axs[i].set_ylim(0, 0.23)\n",
    "plt.suptitle(\"Applying the HCP RSPCA model to CNeuroMod-Data (3 Subjs)\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.42577234,  0.21685098,  0.21421586,  0.00920233,  0.23167547,\n",
       "       -0.01286513])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_CNEURO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# HCP\n",
      "---- Accuracy (Correlation) ---- (6,)\n",
      "           Language  Emotion     WM  Social  Relational  Gambling\n",
      "RS-PCA200     0.688     0.42  0.456   0.417       0.318     0.102\n",
      "\n",
      "---- Discrimanability ---- (6,)\n",
      "           Language  Emotion     WM  Social  Relational  Gambling\n",
      "RS-PCA200     0.196    0.105  0.159   0.122       0.094     0.033\n",
      "\n",
      "\n",
      "# Cneuromod\n",
      "---- Accuracy (Correlation) ---- (6,)\n",
      "           Language  Emotion     WM  Social  Relational  Gambling\n",
      "RS-PCA200     0.426    0.217  0.214   0.009       0.232    -0.013\n",
      "\n",
      "---- Discrimanability ---- (6,)\n",
      "           Language  Emotion     WM  Social  Relational  Gambling\n",
      "RS-PCA200     0.164    0.028  0.021  -0.047       0.052    -0.011\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "print(\"# HCP\")\n",
    "print(\"---- Accuracy (Correlation) ----\",data.shape)\n",
    "print(pd.DataFrame([corr_HCP.round(3)], index=[modality], columns=cneuromod_task_names))\n",
    "print(\"\\n---- Discrimanability ----\",data.shape)\n",
    "print(pd.DataFrame([diff_HCP.round(3)], index=[modality], columns=cneuromod_task_names))\n",
    "\n",
    "print(\"\\n\\n# Cneuromod\")\n",
    "print(\"---- Accuracy (Correlation) ----\",data.shape)\n",
    "print(pd.DataFrame([corr_CNEURO.round(3)], index=[modality], columns=cneuromod_task_names))\n",
    "print(\"\\n---- Discrimanability ----\",data.shape)\n",
    "print(pd.DataFrame([diff_CNEURO.round(3)], index=[modality], columns=cneuromod_task_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit new model on one subject and predict the other two "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LANGUAGE_STORY-MATH', 'EMOTION_FACES-SHAPES', 'WM_2BK-0BK', 'SOCIAL_TOM-RANDOM', 'RELATIONAL_REL-MATCH', 'GAMBLING_REWARD-PUNISH']\n",
      "Correlation: [ 0.493  0.223  0.144 -0.05   0.06  -0.012]\n",
      "Differentiability: [ 0.235  0.116  0.056 -0.006 -0.072 -0.004]\n"
     ]
    }
   ],
   "source": [
    "# Fit model\n",
    "\n",
    "#y_train, y_train_pred, y_test, y_test_pred, skr = predict_y_from_x(xdata, ydata, lmodel=lmodel, verbose=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(xdata, ydata, test_size=0.6, shuffle=False)\n",
    "x = x_train.reshape((-1, x_train.shape[-1]))\n",
    "\n",
    "y = y_train.reshape((-1, y_train.shape[-1]))\n",
    "skr = sklearn.linear_model.LinearRegression()\n",
    "skr.fit(x, y)\n",
    "xt = x_test.reshape((-1, x_test.shape[-1]))\n",
    "y_test_pred =skr.predict(xt).reshape(y_test.shape)\n",
    "\n",
    "scoresd, diff_scores_test = score(y_test_pred, y_test);\n",
    "\n",
    "print(cneuromod_contrasts)\n",
    "print(\"Correlation:\", scoresd.corr.mean(0).round(3))\n",
    "print(\"Differentiability:\", diff_scores_test.round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Cneuromod: newly fit\n",
      "---- Accuracy (Correlation) ---- (6,)\n",
      "           Language  Emotion     WM  Social  Relational  Gambling\n",
      "RS-PCA200     0.493    0.223  0.144   -0.05        0.06    -0.012\n",
      "\n",
      "---- Discrimanability ---- (6,)\n",
      "           Language  Emotion     WM  Social  Relational  Gambling\n",
      "RS-PCA200     0.235    0.116  0.056  -0.006      -0.072    -0.004\n"
     ]
    }
   ],
   "source": [
    "print(\"# Cneuromod: newly fit\")\n",
    "print(\"---- Accuracy (Correlation) ----\",data.shape)\n",
    "print(pd.DataFrame([scoresd.corr.mean(0).round(3)], index=[modality], columns=cneuromod_task_names))\n",
    "print(\"\\n---- Discrimanability ----\",data.shape)\n",
    "print(pd.DataFrame([diff_scores_test.round(3)], index=[modality], columns=cneuromod_task_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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SBnvisLSfA2u29voOg3dxpp8M6JfLeW80bObqZY2ZoYnQrtXaSuWDHOwcDNADdN8b6tP41utN1LUdK0y0TVHsraC40i5DXSDGNlyZRFvbkAYPToa9C1nV9N0SyN5rN/aafaBgpmupliQE9BuYgZrkvE2ieKPE7LpeoLotpoi38VybmCaWS5eOKZZUURlAqMdigtvbHOBXdkAjBAI96AOF+GepWOr6t44vtKvLe9spdZTy57eQSRvixtAcMMg4II+oruq5DwOAPEnj0AAD+2o+n/XhaV19AHyf8cf+So61/wBsP/REdcLXdfHH/kqOtf8AbD/0RHXC17dL+HH0R85X/iy9WFFFFaGQV7r+y/8A8zL/ANu3/tWvCq91/Zf/AOZl/wC3b/2rXPiv4T/rqdWC/jR+f5HrHj3/AJEXxH/2Dbn/ANFNXxlX2b49/wCRF8R/9g25/wDRTV8ZVjgvhZ0Zj8UQoooruPNCiiigD7N8Bf8AIi+HP+wbbf8Aopa8K/aT/wCR6sf+wbH/AOjZa918Bf8AIi+HP+wbbf8Aopa8K/aT/wCR6sf+wbH/AOjZa8zDfxn8z2cX/u6+R5PRRRXpnjBRRRQB3XwO/wCSo6L/ANt//RElfWFfJ/wO/wCSo6L/ANt//RElfWFeZjf4i9D2cu/hP1/yPhWiiivTPGCiiigAr6S/Zs/5EW+/7CUn/oqKvm2vpL9mz/kRb7/sJSf+ioq5cX/DO3Afxg/aT/5EWx/7CUf/AKKlr5tr6S/aT/5EWx/7CUf/AKKlr5town8MWP8A4wUUUV1HGFbvgL/kevDn/YStv/Rq1hVu+Av+R68Of9hK2/8ARq1M/hZdP4kfZtFFFeEfShRRRQAUUUUAFFFFAHIaZ/yVrxH/ANgXTf8A0de119chpn/JWvEf/YF03/0de119AHKeL9c1S21vRdC8PrZLqWpLPOZ7xGkighhC7mKKyliWkQAbh1J7VY8C69c67pd3/aUMMOpafeTWF2sBJjMkZ+8medrKVYA8jOOcZqv4v0PVLnW9F13w+1k2paas8BgvHaOKeGYLuUuqsVIaNCDtPQjvmofB+ha3oWzz5dOnOoXlzf6s67wVkkx5awDuqgBSW5IGe+KAMLSPHWu3EGg69d22mjwzrd8tnbwxq4uoVkZlhldy21txC5UKMbxycU3TfG+rT+NbrTdS1HStMtE1R7K2guNIuQ10gxjZcmURb25AGD06GnaT4G123t9B0G7uNNPhnRL5by3mjZzdTLGzNDE6Fdq7SVywY52DgZrT8TaJ4o8Tsul6gui2miLfxXJuYJpZLl44pllRRGUCox2KC29sc4FAHZ6jfWmm2U15qNzDa2kK7pJp3CIg9Sx4AriPCGt6Vr/xP8S3Wh6lZajbLpOnRtNaTrKgcTXhKkqSM4IOPcV35AIwRkVx+lAL8WfEYUAD+xtN6f8AXa9oA7Cvk/44/wDJUda/7Yf+iI6+sK+T/jj/AMlR1r/th/6IjrswX8R+hwZj/CXr/mcLRRRXpnjBRRRQB6x+zZ/yPV9/2DZP/RsVe6+Pf+RF8R/9g25/9FNXhX7Nn/I9X3/YNk/9GxV7r49/5EXxH/2Dbn/0U1eZif4y+R7OE/3d/M+MqKKK9M8YKKKKACvs3wF/yIvhz/sG23/opa+Mq+zfAX/Ii+HP+wbbf+ilrhxvwo9HLvikcn8ZPHep+Cv7I/sqCym+2ed5n2lGbGzZjG1h/eP6V5t/wvbxN/z46N/35l/+OVu/tQf8y1/28/8AtKvCqrD0YSpptCxVepCq4xen/APWP+F7eJv+fHRv+/Mv/wAco/4Xt4m/58dG/wC/Mv8A8cryeitvq9Psc31qt/Mesf8AC9vE3/Pjo3/fmX/45XSfDn4sa74m8Z6dpF/aaZHbXHmb2hjkDjbGzDBLkdVHavA67r4Hf8lR0X/tv/6IkqKlCmoNpdDWjiasqkU5dUfWFFFFeSe4FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFchqf/JWvDn/AGBdS/8AR1lXX1yGp/8AJWvDn/YF1L/0dZUAdfRRRQAUUUUAFFFFABRRRQByHgj/AJGXx7/2Go//AEgtK6+uQ8Ef8jL49/7DUf8A6QWldfQB8n/HH/kqOtf9sP8A0RHXC13Xxx/5KjrX/bD/ANER1wte3S/hx9EfOV/4svVhRRRWhkFe6/sv/wDMy/8Abt/7Vrwqvdf2X/8AmZf+3b/2rXPiv4T/AK6nVgv40fn+R6x49/5EXxH/ANg25/8ARTV8ZV9m+Pf+RF8R/wDYNuf/AEU1fGVY4L4WdGY/FEKKKK7jzQooooA+zfAX/Ii+HP8AsG23/opa8K/aT/5Hqx/7Bsf/AKNlr3XwF/yIvhz/ALBtt/6KWvCv2k/+R6sf+wbH/wCjZa8zDfxn8z2cX/u6+R5PRRRXpnjBRRRQB3XwO/5Kjov/AG3/APRElfWFfJ/wO/5Kjov/AG3/APRElfWFeZjf4i9D2cu/hP1/yPhWiiivTPGCiiigAr6S/Zs/5EW+/wCwlJ/6Kir5tr6S/Zs/5EW+/wCwlJ/6Kirlxf8ADO3Afxg/aT/5EWx/7CUf/oqWvm2vpL9pP/kRbH/sJR/+ipa+baMJ/DFj/wCMFFFFdRxhW74C/wCR68Of9hK2/wDRq1hVu+Av+R68Of8AYStv/Rq1M/hZdP4kfZteI/Eb4sa74Z8Z6jpFhaaZJbW/l7GmjkLndGrHJDgdWPavbq+T/jj/AMlR1r/th/6Ijry8JCM5tSXQ9nHVJU6acXbU3v8Ahe3ib/nx0b/vzL/8co/4Xt4m/wCfHRv+/Mv/AMcryeivQ+r0+x5X1qt/Mesf8L28Tf8APjo3/fmX/wCOUf8AC9vE3/Pjo3/fmX/45Xk9FH1en2D61W/mPqH4N+O9T8a/2v8A2rBZQ/Y/J8v7MjLnfvzncx/uj9a9Jrwr9l//AJmX/t2/9q17rXmYiKjUaR7OFk50lKW//BOQ0z/krXiP/sC6b/6Ova6+uQ0z/krXiP8A7Aum/wDo69rr6xOgKKKKACiiigArkNM/5K14j/7Aum/+jr2uvrkNM/5K14j/AOwLpv8A6OvaAOvr5P8Ajj/yVHWv+2H/AKIjr6wr5P8Ajj/yVHWv+2H/AKIjrswX8R+hwZj/AAl6/wCZwtFFFemeMFFFFAHrH7Nn/I9X3/YNk/8ARsVe6+Pf+RF8R/8AYNuf/RTV4V+zZ/yPV9/2DZP/AEbFXuvj3/kRfEf/AGDbn/0U1eZif4y+R7OE/wB3fzPjKiiivTPGCiiigAr7N8Bf8iL4c/7Btt/6KWvjKvs3wF/yIvhz/sG23/opa4cb8KPRy74pHk/7UH/Mtf8Abz/7Srwqvdf2oP8AmWv+3n/2lXhVbYX+Ev66mGN/jS+X5BRRRXQcoV3XwO/5Kjov/bf/ANESVwtd18Dv+So6L/23/wDRElZ1f4cvRmtD+LH1R9YUUUV4h9GFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFchqf/JWvDn/YF1L/ANHWVdfXIan/AMla8Of9gXUv/R1lQB19FFFABWT4i8RaZ4dt4ZdWuGiE8nlQxxxPNJK+CcJGgLMcAngHAFa1ef8Aju8t9E8feEtc1iVbfRoLe+tZLqTiO3mkEJQu3RQRHIuTxk470AdlomrWOuaZDqGlXKXNnMDskXI6Eggg8ggggg8ggg1k2Xjfw7e65/ZFtqIe9Mjwp+5kEckiZ3okpXY7Lg5VWJGDxwa534X6pZxC+8yRoV8QaxfXulxvGymeFdu5xxwCQXGcZDA964zw/dwT+FPAfhGFifFGl6vA97abT5luIZGaaV/RWXdhujeYMZzQB6rL438Oxa9/Y0mogX3nLbH9zIYhMwyIjLt8sOcjCFt3I4rpK8GvruBPB2t+DHY/8Jbc+IZHitNp82UPfiZLgD+4IyGL9BtIzkYr2vWrG41CxMFpqd3pkpYH7RarEzgDtiRHXB+lAHPeCP8AkZfHv/Yaj/8ASC0rr64Sx8A39jc6hcW3jjxIkt/MLi4byrE73EaRg823HyxoOMDj1yauf8InrP8A0PviT/vxYf8AyNQB8/fHH/kqOtf9sP8A0RHXC19Ia38EbDXNUm1HVfFPiOe8m2+ZJ/oi7sKFHCwAdAO1Uf8AhnvQv+hj8R/9923/AMZr0YYuEYpNPQ8mpgKk5uSa1Z8+0V7T4z+Bum6N4Q1vU9O1zxDPe2dlNcQRN9nYPIiFlUgQgnJAGAc1sJ+z5oZRSfEXiPJH962/+M1X12HZkf2dU7r+vkfP1e6/sv8A/My/9u3/ALVq7/wz3oX/AEMfiP8A77tv/jNbvhj4Tr4X+0/2F4w8SWv2nb5vy2b7tucfetzj7x6etZVsVCpBxSNsPgp0qim2tDrvHv8AyIviP/sG3P8A6KavjKvrS/8ABGpX9jcWd3468SSW1xG0MqeTYDcjDBGRbZHBPSuN/wCGe9C/6GPxH/33bf8Axmow9eNJNSNcXhp1mnF7Hz7RX0F/wz3oX/Qx+I/++7b/AOM1i3fwO06Lxlpmlx654haxubG6uZZv9HyskckCooPk4AIlc4Iydox0NdH12HZnJ/Z1Tuv6+R4vRX0F/wAM96F/0MfiP/vu2/8AjNH/AAz3oX/Qx+I/++7b/wCM0fXYdmH9nVO6/r5HpfgL/kRfDn/YNtv/AEUteFftJ/8AI9WP/YNj/wDRsteuWHgjUrCxt7O08deJI7a3jWGJPJsDtRRgDJtsngDrWF4k+Dlt4lvkvNb8W+JLq5SMQq+LNMICSBhbcDqx/OuOjVUKnMz0K9GVSlyLc+aKK+gv+Ge9C/6GPxH/AN923/xmj/hnvQv+hj8R/wDfdt/8Zrs+uw7M8/8As6p3X9fI+faK9o8I/A7TtW0qe41DXPEMEyX15bKq/Z1BjiuZI0bBhPJRFOehzkcVtf8ADPehf9DH4j/77tv/AIzR9dh2Yf2dU7r+vkeb/A7/AJKjov8A23/9ESV9YV5LonwRsND1SHUdK8U+I4LyHd5cn+iNtypU8NAR0J7V1f8Awies/wDQ++JP+/Fh/wDI1ceIqqrK6PQwtGVGDjLufIlFfQX/AAz3oX/Qx+I/++7b/wCM0f8ADPehf9DH4j/77tv/AIzXZ9dh2Z5/9nVO6/r5Hz7RXtGv/A7TrDVfDtva654hkhv75ra5Y/ZyY4xbTSBgRDwd8aDJyPmx1Ira/wCGe9C/6GPxH/33bf8Axmj67Dsw/s6p3X9fI+fa+kv2bP8AkRb7/sJSf+ioqz/+Ge9C/wChj8R/9923/wAZrpPDfwyl8NWL2eieM/ElrbPIZmTZZPlyACctbk9FH5VjXxMakOVI6MNg50p80mjL/aT/AORFsf8AsJR/+ipa+ba+pvEnwyl8S2KWet+M/El1bJIJlTZZJhwCAcrbg9GP51zf/DPehf8AQx+I/wDvu2/+M0UMTGnDlYYnBzqz5otHz7RX0F/wz3oX/Qx+I/8Avu2/+M1i6B8DtOv9V8RW91rniGOGwvltrZh9nBkjNtDIWJMPJ3yOMjA+XHUGtvrsOzOf+zqndf18jxet3wF/yPXhz/sJW3/o1a9l/wCGe9C/6GPxH/33bf8Axmp7D4DaTYX1veWnibxHHc28izRPm1O11OQcGDB5A60pYyDTVmVHL6iad1/XyPYa+T/jj/yVHWv+2H/oiOvoH/hE9Z/6H3xJ/wB+LD/5GrlNb+CNhrmqTajqvinxHPeTbfMk/wBEXdhQo4WADoB2rkw9VUpXZ3YqjKtBRj3Pm+ivoL/hnvQv+hj8R/8Afdt/8ZrG8Z/A3TdG8Ia3qena54hnvbOymuIIm+zsHkRCyqQIQTkgDAOa7PrsOzPP/s6p3X9fI8Wor6BT9nzQyik+IvEeSP71t/8AGaX/AIZ70L/oY/Ef/fdt/wDGaPrsOzD+zqndf18il+y//wAzL/27f+1a91rzTwx8J18L/af7C8YeJLX7Tt835bN923OPvW5x949PWtz/AIRPWf8AoffEn/fiw/8AkauGtNVJuSPTw9N0qag+gaZ/yVrxH/2BdN/9HXtdfXN+GvCzaNrGoanda1qWrXt5BDbtJerAuxImkZQoijQdZX6g10lZGxk+IvEWmeHbeGXVrhohPJ5UMccTzSSvgnCRoCzHAJ4BwBVjRNWsdc0yHUNKuUubOYHZIuR0JBBB5BBBBB5BBBrjfHd5b6J4+8Ja5rEq2+jQW99ayXUnEdvNIIShduigiORcnjJx3qH4X6pZxC+8yRoV8QaxfXulxvGymeFdu5xxwCQXGcZDA96AOisvG/h291z+yLbUQ96ZHhT9zII5JEzvRJSux2XByqsSMHjg0S+N/DsWvf2NJqIF95y2x/cyGITMMiIy7fLDnIwhbdyOK8q8P3cE/hTwH4RhYnxRperwPe2m0+ZbiGRmmlf0Vl3Ybo3mDGc0X13Ang7W/Bjsf+EtufEMjxWm0+bKHvxMlwB/cEZDF+g2kZyMUAe81yGmf8la8R/9gXTf/R17XTalby3djNBBeT2Usi4W4gCF4z6qHVlz9Qa42LwDfxavc6mnjjxIL24hit5JPKsfmSNnZBj7Njgyv0Geec8YAO7r5P8Ajj/yVHWv+2H/AKIjr6B/4RPWf+h98Sf9+LD/AORq5TW/gjYa5qk2o6r4p8Rz3k23zJP9EXdhQo4WADoB2rfD1VSldnNiqMq0FGPc+b6K+gv+Ge9C/wChj8R/9923/wAZrG8Z/A3TdG8Ia3qena54hnvbOymuIIm+zsHkRCyqQIQTkgDAOa7PrsOzPP8A7Oqd1/XyPFqK+gU/Z80MopPiLxHkj+9bf/GaX/hnvQv+hj8R/wDfdt/8Zo+uw7MP7Oqd1/XyOV/Zs/5Hq+/7Bsn/AKNir3Xx7/yIviP/ALBtz/6KauJ8N/By28NXz3mieLfElrcvGYWfFm+UJBIw1uR1UflW7f8AgjUr+xuLO78deJJLa4jaGVPJsBuRhgjItsjgnpXHVqqdTnR6FCjKnS5HufJdFfQX/DPehf8AQx+I/wDvu2/+M0f8M96F/wBDH4j/AO+7b/4zXZ9dh2Z5/wDZ1Tuv6+R8+0V7Rd/A7TovGWmaXHrniFrG5sbq5lm/0fKyRyQKig+TgAiVzgjJ2jHQ1tf8M96F/wBDH4j/AO+7b/4zR9dh2Yf2dU7r+vkfPtfZvgL/AJEXw5/2Dbb/ANFLXmn/AAz3oX/Qx+I/++7b/wCM12Vh4I1KwsbeztPHXiSO2t41hiTybA7UUYAybbJ4A61z4ivGqkonXhMNOi25M4D9qD/mWv8At5/9pV4VX0/4n+E6+KPs39u+MPEl19m3eV8tmm3djP3bcZ+6OvpWF/wz3oX/AEMfiP8A77tv/jNXRxUKcFFoyxGCnVqOaa1Pn2ivoL/hnvQv+hj8R/8Afdt/8ZrF8I/A7TtW0qe41DXPEMEyX15bKq/Z1BjiuZI0bBhPJRFOehzkcVr9dh2Zj/Z1Tuv6+R4vXdfA7/kqOi/9t/8A0RJXpH/DPehf9DH4j/77tv8A4zV7RPgjYaHqkOo6V4p8RwXkO7y5P9EbblSp4aAjoT2qZ4uEotJPUungKkJqTa0Z61RXIf8ACJ6z/wBD74k/78WH/wAjUf8ACJ6z/wBD74k/78WH/wAjV5x6x19Fch/wies/9D74k/78WH/yNWL4u0bxJpOlQXGn+N/EU8z31nbMrW1iwEctzHG7YFsOQjsc9BjJ4oA9JorkP+ET1n/offEn/fiw/wDkaj/hE9Z/6H3xJ/34sP8A5GoA6+iuQ/4RPWf+h98Sf9+LD/5Go/4RPWf+h98Sf9+LD/5GoA6+iuQ/4RPWf+h98Sf9+LD/AORqP+ET1n/offEn/fiw/wDkagDr6K82tNG8SS+MtT0uTxv4iWxtrG1uYpvs1jlpJJJ1dSfs2CAIkOAMjcc9RW1/wies/wDQ++JP+/Fh/wDI1AHX0VyH/CJ6z/0PviT/AL8WH/yNR/wies/9D74k/wC/Fh/8jUAdfRXIf8InrP8A0PviT/vxYf8AyNR/wies/wDQ++JP+/Fh/wDI1AHX0Vx7+FNZCMR498SZA/54WH/yNWP4M0XxHrPhDRNT1Hxv4igvbyyhuJ4ltrFQkjoGZQDbEjBJGCc0AekUVyH/AAies/8AQ++JP+/Fh/8AI1H/AAies/8AQ++JP+/Fh/8AI1AHX1yGp/8AJWvDn/YF1L/0dZUf8InrP/Q++JP+/Fh/8jVLo/hGay8RwazqHiLV9XuYLWW1iS8S2VEWRo2YjyokJOYk6k0AdVRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRXkHxl068fXrHUpNI1PVbO3Ni9n9iiaf7LNHerJO5jXnLQgANg/cYcbuez+HcV61trV/e2dxYxalqUl3bWtyMSxRGONfmXJ2lmR329Rv55zQB1lFFFABRRRQAVBNeW0L7JrmGN/wC6zgGp68I8NeHoLTR4INe+Dk+p6kmfPvp10+aW5bJ/euzy53N1IyQCcA4oA93ooooAKKKKACqlxqlhbytFcX1rFKvVHmVSO/QmrdeGfE/TPA83jS+mt4vtPjRlj+0wC0huoj8ihPOFz+6jGzb0dGIweaAPbLS9tbzf9kuYJ9mN3lSBsZ6Zx9DU9cZ8LbGzs9AVxpvhrT9YlA+3JoUaLEcM3l52852k8EnBLYJ612dABRRRQAVV1PUbLSrGS91S8trKzix5k9xKscaZIAyzEAZJA+pq1UdxBFcRNFcRJLE3VHUMD36GgDnP+FheC/8Aob/Dv/gzh/8Aiq6K1uIbu1hubSaOe3mQSRSxMGR1IyGUjggg5BFeLaxrWv3WuyW/h/TfB1paM2opbjULN3d2s5ERtzK6hd5Z2HBwq5JOePV/B99/afhLRL/93/pVjBP+7i8pfmjVvlTJ2jngZOOmTQBr0UUUAFFFFABRXL/EDRfDd/o82p+K9PjvLXSbea5y2cogXc+ACM5CD8q8l0RNNtfE1o6/DCx0+K11W0tHvX1Yl7WWVYpI2KBDk/vEGASN3Ge9AH0FRRRQAUUUUAFZWreJND0e4W31fWdNsZ2QSLHdXSRMVJI3AMQcZBGfY1q153410i8j8Zxa8PDNp4ls/sC2iwPJCktq4kZi6+bhSHDKDyCNg9TQB2ek67pGslxpGqWF+Y+X+y3CS7frtJxWjXlXgvwPc2utafro07S9IkXUrq4ktbVwzQ2slvsFvuRdrZlAlIztB6Zr1WgAooooAKKKKACiiigAooooAKKKKACiiigAooryv4+6Pe6toOI9NvdVsFsb6M2lopdhdvEBbSmMHLhSHHAOC6tjjIAPVKK4jwA15qGua5rUmn32nafdQ2kEEN9GYpZHiEm+UxnlQd6KM4J8vOOldvQAUUUUAFFFFAEM91bwMBPPFGTyA7gZ/OpUZXRWRgysMgg5BFeKjQY49b159d+FU/iGabUZ5YtSmFjM00TOSgxLLlVUYVR/dAJAJIr1/RUSPR7BIbH+zo1gjVbLCj7OAoxHhCVG3p8pI444oAuUUUUAFFFFAFa61CytJBHdXlvA5G4LJKqkj1wTRa6hZXchjtby3ncDcVjlViB64Bryn4yaf4GufENnLrnmv4rFqFto7e3S5dod7Y3xzK0ITcX+Z9vcBhWx8INOtLa0mnutI8K2GtEsitpEEMcrW3y480RlgG3DkKxXhfoAD0eiiigAooooAiuriCztZrm7mjgtoUMkssrBURQMlmJ4AAGSTXO/8LC8F/8AQ3+Hf/BnD/8AFV0siJLG0ciq6MCrKwyCD1BFeReLdU1RfFX9l+HNM8L2tqmoxaW02o2TSs00lq1wGARkAX7iDqSzH0wQD1XTNRstVsY73S7y2vbOXPlz28qyRvgkHDKSDggj6irVcv8ADPUbjVPBdjc3y2K3fmTwzLYxGKAPHM6NsBZuMqec89eM4HUUAFFFFABRRXL/ABA0Xw3f6PNqfivT47y10m3muctnKIF3PgAjOQg/KgDqKK+fdETTbXxNaOvwwsdPitdVtLR719WJe1llWKSNigQ5P7xBgEjdxnvX0FQAUUUUAFFFFAGVq3iTQ9HuFt9X1nTbGdkEix3V0kTFSSNwDEHGQRn2NSaTrukayXGkapYX5j5f7LcJLt+u0nFcZ410i8j8Zxa8PDNp4ls/sC2iwPJCktq4kZi6+bhSHDKDyCNg9TWd4L8D3NrrWn66NO0vSJF1K6uJLW1cM0NrJb7Bb7kXa2ZQJSM7QemaAPVaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDyf4kNZxePbeTxLrviHQ9E/s5RBNYXk8FvNP5j7lkMfCsF2EdC248naBVP4cQ6xb65ZXmn6n4l1LQrvUbm2Daq8kqyWgg3xz/vBlD5w8sHgOvOD1rS+K+t+ILO/uotJ1d9GsbC0tbyeeK2jleRJLry5jmRWAWKNS5wM5YZOBWz8JNTutT0fVjd683iFLbUpIINTCRKk8YSMjZ5ahSAWZSecsrc4wAAdzRRRQAUUUUAFfPv9lv8A2N5P9heKv+FmZx/aeyfyftO7/WefnyfI/wBjONvy7c17b4kvdO0qxj1XWdR/s+xsZRK8zTeXGdwMYWT1XLjg/wAQU9q8MtvE/g668IRalbeNvEc2tS2azx6LH4iladpymRAMDO7cdudv4UAfRFFFFABRRRQAV4x8RtN0ZPF97eDVvDNzqsojZ9I1DQY9RmGEVR/qQJwCACN27rxxgV7PXmPjPSJtQ8S3baJ4JuX1E7BJra6r/ZiyHYMHfExlk2jC8pj5cdKALfwga6aHVPtfgjT/AAwAYwlxZxCFb4fNz5ZRZE2+jj+M4716HXHfDnRvFWkQ3w8W67FqaylDawIm77KBu3AykK0mcryw42+9djQAUUUUAFQXpuhAPsKwtN5iZEzFV2bxvOQDzt3Y7E4zgc1PRQB5b4k0rVtfTUIpNE+H2o6Pb3ksmbu4kJR1JBaQCIhZMfe59R0r0bRHMmjWDt9k3Nbxk/Y23QZ2j/VnAynocDjFeTazrVz4T1jVPDtj4p8HW0OoXU10n9p3Dpc2bXDl2BjHyuNzsV3MnBA5616r4d0yLRPD+maVbO0kFjaxWsbt1ZUQKCffAoA0KKKKACiiigDM8US2Fv4Z1ebWk36XHZzPdrgndCEJcYH+zmvNrTS7jVvE+l6tL4D1q1gMlu7NLrUQj/d4Ec00AkO90GCM5b5R1IFel+JFtX8O6qmo3BtbFrWUTzrjMUew7mGQRwMnkEcdDXkvhSSGyv8ARI5dU+JVnpkk0UVnNqvkfZrg5GyN8KZFD8Abwuc4yCRQB7XRRRQAUUUUAFeV/ETSVu/Hlvc6r4KvvFmlDTljjRBA8VtN5jliqSyKCzKVyccBVwTkgeqV49qdhqOpfZPtHgLxmv2W3S2j8nxRHFlFzgttuxubnlmyx7mgDuvh7a2Fpos0el+FZfC8JuGY2ckUMZdtq/vMROy8jAyTn5enSunrlvh1ZyWOiTxS6Tq2lMbhm8nU9S+3St8q/MH8yTC8Y25HIJxzk9TQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV5r8XDGmreG5NU1TXNI0FftH2u80u4mhAkxH5aSmPop+c7iOCoGRuOfSq8++LOqeIbWGKz8MXbWNy9hfXonS3WZ3kgjQxwqHBUby5J4JwhxjrQBx3hqO/Gvy6n4S1zxVq2jR6hYwxPfTzXEM8cjlblQJB9yNNriQY+YkZOK9yrzX4Q63c6tqOvRP4qfxRYxR2ksF2IoESIyLIWi/dKAXG1S2egZRgHOfSqACiiigAooqjrjW0elzz3129naW2LmWdJfL2JGwkO5v7uFww7qSO9AHjutadZnUte/4Sjw/wCK9R8QyXc7afd2CXDoISx+ziGWM+XDtXaDuK/MGJzmvXvDMeoReG9Kj1pxJqqWkS3bgghpgg3nj/azXjNv4r8B6yb/AFC6+IOr6fM15chbWLX3AKrM6qyKOisoDKoHAYDmvYfB873XhLRLiaK5hllsYHaK6kMkqExqSruQCzDoSQMnJoA16KKKACiiigDyn4s6bo0uvWt5e634ft9RFsscen6ppEOoNModyGVOJ8ZJHynHHTOcr8JWujrt2JfAek6PbrbHZrVjZNZef8y/uvJkjWUA/e5JHyjvitT4hacL/WoPsfgq51fUltwF1SK+WwEK7mxH56sJeDlsKpA3Z7mn/DzQPGOlapcT+JNcin0t4SsOmCRrpoX3Ahzcuqu2ACMEEc57UAd9RRRQAUUUUAQXxuRY3BsFha8EbeSszFYy+PlDEAkLnGSATiuD8V6frOuX+rabZ6R4K1PT2MX2iK/nk84kICvmqsbAEHJXnpg16HXlPjHWpvA/ivUbiz17wrbrrRjuJLXWrl4ZIpEjWLzE2BtyFY04IXkH5uaAO68EL5fhawjEWkQiNWjEekPvtUCsQAhwOgHPHByK3KwPAemLpHhSytlvotQLmS6e6iAEczzSNK7IAThSznHJ4xW/QAUUUUAFZniiWwt/DOrza0m/S47OZ7tcE7oQhLjA/wBnNadZ3iRbV/DuqpqNwbWxa1lE864zFHsO5hkEcDJ5BHHQ0AeaWml3GreJ9L1aXwHrVrAZLd2aXWohH+7wI5poBId7oMEZy3yjqQK9crxTwpJDZX+iRy6p8SrPTJJoorObVfI+zXByNkb4UyKH4A3hc5xkEiva6ACiiigAooooA8r+Imkrd+PLe51XwVfeLNKGnLHGiCB4rabzHLFUlkUFmUrk44CrgnJA634e2thaaLNHpfhWXwvCbhmNnJFDGXbav7zETsvIwMk5+Xp0rhdTsNR1L7J9o8BeM1+y26W0fk+KI4soucFtt2Nzc8s2WPc13Hw6s5LHRJ4pdJ1bSmNwzeTqepfbpW+VfmD+ZJheMbcjkE45yQDqaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDkPGC6o2oxiw8YabokJhAa2urOOZnOW+fLSKcEYGMfwmofhTqV5f6TqsOoalZ6m9hqMlol1ZW6wwugSNhsCkg43kHnhtw5xk4vjuyu9c8fJpmm6B4P1GSDTY7ia41u1MkkatLIqKpAOVJVjjtz6imeA/FOs2utQaBrOkaHaWv22bTEOkMypDcRw/aNuxhgo0ZLAggg8EUAepUUUUAFFFFABXhtp4p1az8DReJrTxPpUHlKPL8MRWcSxpg7RZjB8wSj7nXG7+HHFe5V47otprevTReJrePwDpmpTEtifSWmvLZs4KSSiVD5g6NwMEGgD2KiiigAooooAKKKKACiiigAooooAKiuriG0tZrm5kWKCFDJJIxwFUDJJ9gBUtZvibSl13w3qukvIYlv7SW1MgGSgkQrn8M0AeQ+JNbn8EWup2EltoF7pviO4ub63vbmaUN5cp3MZ4Uhcske8DdkDaFB21654UsU0zwvo9hHdfbEtbOGBbnOfOCoFD/jjP41yuheHvERvdU1fxCujtqZ0xdMsoLSR2h2ruZncsgI3sUyADgIOTXU+EtLfRPCujaVNIJZLGyhtWcdGKRhSfxxQBq0UUUAFFFFAGN4pk06bSb/AE3V0uHtLqwuDOsMEkmYQoWQAoD82HGF+83O0HBx5bo9/b6nqujafq3inxFqdhb3cL21tL4ZuLYySo4MRnm8oAhWCsThBkAnjNeifEm18Q33g7UrTwi1qmp3EEkIeeV4igaNhujZekgYrgnAHrXKDVfEGp6jp+map4j8HWSpe27ypp9/I11J5cqv5IUkffKhCOchiMHNAHqVFFFABRRRQAV4l4l1aw8R+L4ptf8ACPjHUtCSyCQ2raTcCOG4DsXdk4DFlKAHnbtPTOa9tryz4k6xbWnjS0sdc8S6tpGmS2Hm28Ok5MrzCRg7S7EZwu3ZtPC5D5OcUAa/wd80aDqY+xatp9gNRkFhZ6pHIk0EGyPC/PyV3b2HJADbf4a7yuW+HM+mz6JO2j6vq2rW4uGDT6n5nmK21cqPMVTtxg9MZJrqaACiiigAooooAKKKKACiiigAooooAKKKKACua8Zf2h/of9m+J7HQfv7/ALVapN533cY3OuMc9M/eFdLXnfxQhuNR13w5pNhovhnVLu5S5l/4nluZFhjTytzIQDgksoIxzx2BoAl+Gmo6hJrviTS9Q1vT9ZSy+zyRT2FqkCL5gkLK21my+VGR2BU/xYHf15D4f8Q674X16TRdR0LwzbadFdWkMw0XfF5f2tzHDKFIwfnUqw4I4PIr16gAooooAKKKKAPIG1nUJB4lvbbxfpPhqLS766jGmNZwlPkdj5k5YhyZT+8ypXhx1OTXpvhnUJtW8N6VqN1bm2uLy0iuJID1iZ0DFfwJx+Feb3lpqfifxFqF8tt4Hs59PvJLWH+1NLa6vFWNsLIz+Ym0MMOoA+6w5r1PT/tH2C2+2ywzXXlL5skClY3fA3FQSSFJzgEnjuaAJ6KKKACiiigAooooAKKKKACiiigAryHxD4gk8IajqfinTodI1jSfEUkSxSPcyRzLIkQj8tVSKQyR4jL/ACjIJc4wc169XnHgvwbrem6poaa1Lpz6X4dtJbTTjbM5lnL7VEsoZQEYRqVwC2S7HI6UAbnwstIrPwFpSW93a3cUokuVltARCPNkaTZGDyEXftAODhRkDpXV1geBdIudD8NxWV8YTcme4uHEJJRTLPJLtUkDgb8dB0rfoAKKKKACsbxTJp02k3+m6ulw9pdWFwZ1hgkkzCFCyAFAfmw4wv3m52g4ONmuY+JNr4hvvB2pWnhFrVNTuIJIQ88rxFA0bDdGy9JAxXBOAPWgDzvR7+31PVdG0/VvFPiLU7C3u4XtraXwzcWxklRwYjPN5QBCsFYnCDIBPGa9rry0ar4g1PUdP0zVPEfg6yVL23eVNPv5GupPLlV/JCkj75UIRzkMRg5r1KgAooooAKKKKAPEvEurWHiPxfFNr/hHxjqWhJZ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      "text/plain": [
       "<Figure size 800x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cmasher as cmr\n",
    "corr_CNEURO2 = scoresd.corr.mean(0)[:];\n",
    "diff_CNEURO2 = diff_scores_test[:];\n",
    "\n",
    "corr_HCP = regr_data_L[modality][\"scoresd\"][\"corr\"].mean(0)[cneuro_ctr_ids]\n",
    "diff_HCP =  regr_data_L[modality][\"diff_scores_test\"][cneuro_ctr_ids];\n",
    "\n",
    "fig, axs = plt.subplots(2,2, figsize=(8,3*2+1))\n",
    "axs = np.array(axs).flatten()\n",
    "titles=[\"HCP Accuracy\", \"CNeuroMod Accuracy\", \"HCP Discriminability\", \"CNeuroMod Discriminability\"]\n",
    "for i,data in enumerate([corr_HCP, corr_CNEURO2, diff_HCP, diff_CNEURO2]):\n",
    "  axs[i].bar(np.arange(len(cneuro_ctr_ids)), data, color=cmr.pride(0.18))\n",
    "  axs[i].set_xticks(np.arange(len(cneuromod_task_names)));\n",
    "  axs[i].set_xticklabels( np.array(cneuromod_task_names), rotation=90);\n",
    "  axs[i].set_title(titles[i])\n",
    "    \n",
    "for i in [0,1]: axs[i].set_ylim(0, 0.8)\n",
    "for i in [2,3]: axs[i].set_ylim(0, 0.3)\n",
    "plt.suptitle(\"Applying the HCP RSPCA model to CNeuroMod-Data (3 Subjs)\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 1200x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cmasher as cmr\n",
    "corr_CNEURO2 = scoresd.corr.mean(0)[:];\n",
    "diff_CNEURO2 = diff_scores_test[:];\n",
    "\n",
    "corr_HCP = regr_data_L[modality][\"scoresd\"][\"corr\"].mean(0)[cneuro_ctr_ids]\n",
    "diff_HCP =  regr_data_L[modality][\"diff_scores_test\"][cneuro_ctr_ids];\n",
    "\n",
    "fig, axs = plt.subplots(2,3, figsize=(8*1.5,3*2+1))\n",
    "axs = np.array(axs).flatten()\n",
    "titles=[\"HCP Model Accuracy on HCP (n=51)\", \"HCP Model Accuracy on CneuroMod (n=3)\", \"CNM Model Accuracy on CNM (n=2)\", \\\n",
    "        \"HCP Model Discriminability on HCP\", \"HCP Model Discriminability on CneuroMod\", \"CNM Model Discriminability on CNM\"]\n",
    "for i,data in enumerate([corr_HCP, corr_CNEURO, corr_CNEURO2, diff_HCP, diff_CNEURO, diff_CNEURO2]):\n",
    "  axs[i].bar(np.arange(len(cneuro_ctr_ids)), data, color=cmr.pride(0.18))\n",
    "  axs[i].set_xticks(np.arange(len(cneuromod_task_names)));\n",
    "  axs[i].set_xticklabels( np.array(cneuromod_task_names), rotation=90);\n",
    "  axs[i].set_title(titles[i])\n",
    "    \n",
    "for i in [0,1,2]: axs[i].set_ylim(0, 0.8)\n",
    "for i in [3,4,5]: axs[i].set_ylim(0, 0.3)\n",
    "plt.suptitle(\"Validation using CNeuroMod (CNM)\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**looking at the weights**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmasher as cmr\n",
    "cmap_coeffs = \"cmr.prinsenvlag_r\" #cmc.vik #\"PiYG\"\n",
    "n_comps = 30;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"cneuro_single_subj_coeffs.npy\", skr.coef_[:,:n_comps])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 800x585 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,1, figsize=(8,3.9*1.5)) # 4 plots in a row\n",
    "data1 = coeffs_L[cneuro_ctr_ids,:n_comps]\n",
    "data2 = skr.coef_[:,:n_comps]\n",
    "\n",
    "\n",
    "titles=[f\"HCP Model Weights\", \"Single Subj CNeurMod Model weights\"];\n",
    "for i, data in enumerate([data1, data2]):\n",
    "    v = np.absolute(np.concatenate((data))).max()\n",
    "    aspr=(data.shape[1]/data.shape[0])/2.4\n",
    "    im = axs[i].matshow(data, cmap = cmap_coeffs, vmin=-v, vmax=v, aspect=aspr); \n",
    "    plt.colorbar(im,fraction=0.020, pad=0.02, ax=axs[i])\n",
    "    axs[i].set_yticks(np.arange(len(cneuromod_task_names))); axs[i].set_yticklabels( cneuromod_task_names);\n",
    "    axs[i].set_xlabel(titles[i]); axs[i].xaxis.set_label_position('top') \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 30)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs_CN = skr.coef_[:,:n_comps]\n",
    "HCP = coeffs_L[cneuro_ctr_ids,:n_comps]\n",
    "\n",
    "from brainspace.gradient.alignment import ProcrustesAlignment\n",
    "\n",
    "alignment = ProcrustesAlignment(verbose=False, n_iter = 10)\n",
    "\n",
    "coeffsCN2HCP = alignment.fit([coeffs_CN], HCP).aligned_[0]\n",
    "coeffsCN2HCP.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ten biggest coeffs for Language               at components:\t [ 1  0  2  8 23  6  9 18 13 20]\n",
      "ten biggest coeffs for Emotion                at components:\t [ 7 23  5 11 15 25  6  2 19 12]\n",
      "ten biggest coeffs for WM                     at components:\t [ 1  8  5  7 10  4 21  0  6 18]\n",
      "ten biggest coeffs for Social                 at components:\t [ 1  5  6  8  4 14 20 23 11 15]\n",
      "ten biggest coeffs for Relational             at components:\t [ 0  6  5  2 16  7  9 24 21 18]\n",
      "ten biggest coeffs for Gambling               at components:\t [ 2  0 14  3 22 16 17 10  9 15]\n",
      "----\n",
      "ten biggest coeffs for Language      at components:\tc1 -2.222 | c0 0.964 | c2 0.757 | c8 -0.739 | c23 -0.561 | c6 0.476 | c9 -0.314 | c18 0.28 | c13 -0.267 | \n",
      "ten biggest coeffs for Emotion       at components:\tc7 -1.187 | c23 0.713 | c5 0.622 | c11 0.603 | c15 -0.6 | c25 -0.462 | c6 -0.437 | c2 -0.436 | c19 -0.394 | \n",
      "ten biggest coeffs for WM            at components:\tc1 1.456 | c8 1.356 | c5 1.049 | c7 0.934 | c10 0.684 | c4 -0.678 | c21 -0.673 | c0 0.624 | c6 -0.615 | \n",
      "ten biggest coeffs for Social        at components:\tc1 0.793 | c5 0.674 | c6 0.666 | c8 0.409 | c4 -0.386 | c14 0.346 | c20 -0.244 | c23 0.229 | c11 0.224 | \n",
      "ten biggest coeffs for Relational    at components:\tc0 1.273 | c6 -0.822 | c5 0.717 | c2 0.653 | c16 -0.435 | c7 0.348 | c9 -0.334 | c24 0.333 | c21 0.299 | \n",
      "ten biggest coeffs for Gambling      at components:\tc2 -1.467 | c0 -0.953 | c14 -0.763 | c3 0.584 | c22 0.458 | c16 0.416 | c17 0.405 | c10 -0.377 | c9 0.37 | \n"
     ]
    }
   ],
   "source": [
    "# for CNEURO\n",
    "\n",
    "coeffs_CNEURO = coeffsCN2HCP#skr.coef_[:,:n_comps]\n",
    "\n",
    "idxs = np.argsort(np.abs(coeffs_CNEURO), axis=1)\n",
    "for n in range(len(cneuromod_task_names)):\n",
    "  tn = contrast_ids_to_show[n]\n",
    "  txt = f\"ten biggest coeffs for {cneuromod_task_names[n]} \";\n",
    "  print(txt, (44-len(txt))*\" \", \"at components:\\t\", idxs[n][::-1][:10])\n",
    "\n",
    "print(\"----\")\n",
    "idxs = np.argsort(np.abs(coeffs_CNEURO), axis=1)\n",
    "for n in range(len(cneuromod_task_names)):\n",
    "  txt = f\"ten biggest coeffs for {cneuromod_task_names[n]} \";\n",
    "  tn = contrast_ids_to_show[n]\n",
    "  print(txt, (35-len(txt))*\" \", \"at components:\\t\", end=\"\")\n",
    "  for c in idxs[n][::-1][:9]:\n",
    "     print(f\"c{c}\", coeffs_CNEURO[n, c].round(3), end=\" | \")\n",
    "  print(\"\")"
   ]
  }
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