step2_CoContraction-analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Co-contraction Analysis\n",
    "\n",
    "## NOTE: Data is not currently shared. This code is only provided for reference.\n",
    "\n",
    "Co-contraction analysis uses the pre-processed coactivations from `step1` and used `SMART` toolbox to smoothly downsample the results to get only a handful datapoints.\n",
    "\n",
    "This script re-produces Figures 4 and 5 of the manuscript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/torch-beta/lib/python3.9/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n",
      "2022-11-11 17:18:30,519\tINFO worker.py:1509 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n"
     ]
    }
   ],
   "source": [
    "# Import libraries and setup values\n",
    "import pickle as pkl\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pingouin as pg\n",
    "import plotly.graph_objects as go\n",
    "import ray\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import to_rgb as rgb\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "import EssentialEMGFuncs as ess\n",
    "import lineNdots as lnd\n",
    "import SMART_Funcs as SF\n",
    "\n",
    "ray.init(dashboard_host='127.0.0.1', ignore_reinit_error=True)\n",
    "save_fig, save_stats, save_data = False, False, False\n",
    "plot_fig = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup variables\n",
    "SUBJS = {'young': [\"PS04\", \"PS05\", \"PS06\", \"PS07\", \"PS15\", \"PS16\", \"PS17\", \"PS18\", \"PS19\", \"PS20\", \"PS21\", \"PS22\",\n",
    "                   \"PS23\", \"PS24\", \"PS25\", \"PS26\", \"PS27\"],\n",
    "         'old': [\"PS08\", \"PS09\", \"PS10\", \"PS28\", \"PS29\", \"PS31\", \"PS32\", \"PS33\", \"PS34\", \"PS35\", \"PS36\"]\n",
    "         }\n",
    "CONDS = ['LME', 'LEI', 'RME', 'REI']\n",
    "BLOCKS = {'pre': [0], 'pert': [1, 3], 'catch': [2], 'post': [4]}\n",
    "DATA_PATH = './data/emg/'\n",
    "RESMAPLED_DATA = 'gaussian-resmped_young-old_raw-data.pickle'\n",
    "COACTIVATION_DATA = 'coactivation_young-and-old_resampled_perturb-recovery-steps.pickle'\n",
    "STEP_DATA = 'stepping_time_young-and-old.pickle'\n",
    "TESTS = {'perturbed': ['start', 'other'], 'recovery': ['other', 'end']}\n",
    "TARGET_PAIR = {\n",
    "    'left_step': {\n",
    "        'LTA-LSO': ['LSO', 'LTA'],\n",
    "        'LRF-LST': ['LRF', 'LST'],\n",
    "        'LAD-LPD': ['LPD', 'LAD'],\n",
    "        'RTA-RSO': ['RTA', 'RSO'],\n",
    "        'RRF-RST': ['RST', 'RRF'],\n",
    "        'RAD-RPD': ['RAD', 'RPD']\n",
    "    },\n",
    "    'right_step': {\n",
    "        'LTA-LSO': ['LTA', 'LSO'],\n",
    "        'LRF-LST': ['LST', 'LRF'],\n",
    "        'LAD-LPD': ['LAD', 'LPD'],\n",
    "        'RTA-RSO': ['RSO', 'RTA'],\n",
    "        'RRF-RST': ['RRF', 'RST'],\n",
    "        'RAD-RPD': ['RPD', 'RAD']\n",
    "    }\n",
    "}\n",
    "TP_NAMES = list(TARGET_PAIR['left_step'])\n",
    "\n",
    "# load the step times\n",
    "step_time = pkl.load(open(DATA_PATH + 'group_response/' + STEP_DATA, 'rb'))\n",
    "# load coactivation data from step 1\n",
    "coactivation = pkl.load(open(DATA_PATH + 'group_response/' + COACTIVATION_DATA, 'rb'))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## convert the co-contraction data to the SMART format\n",
    "Using the SMART toolbox and working with the kernel and step size we can find the suitable outputs for our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the coactivation format to SMART short format\n",
    "smart_cols = ['subj_no', 'time'] + [p for p in TP_NAMES]\n",
    "metric_short = {t: {a: {c: {b: pd.DataFrame(columns=smart_cols) for b in BLOCKS} for c in CONDS} for a in SUBJS}\n",
    "                for t in TESTS}\n",
    "\n",
    "for t in TESTS:\n",
    "    for a in SUBJS:\n",
    "        for c in CONDS:\n",
    "            for b in BLOCKS:\n",
    "                for s in SUBJS[a]:\n",
    "                    try:\n",
    "                        strides = coactivation[t][c][s][b].index.tolist()\n",
    "                        temp_dict = {'subj_no': s, 'time': step_time[c][s].loc[strides, 'start'].values,\n",
    "                                     **{p: coactivation[t][c][s][b][p].values for p in TP_NAMES}}\n",
    "                        metric_short[t][a][c][b] = pd.concat(\n",
    "                            (metric_short[t][a][c][b], pd.DataFrame([temp_dict])), ignore_index=True)\n",
    "                    except Exception:\n",
    "                        print(f'there was a problem in {a}:{s}_{c}_{b}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we use `Ray` to make the processing parallel. If you choose to cahgne the `step_size` to a smaller number, it can take up to a couple of minutes (based on your computation power), to run the following steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's do the SMART thing\n",
    "time_var = 'time'  # short format file spec\n",
    "# min and max time stamps for the different blocks\n",
    "time_lim = {'pre': [0, 120], 'pert': [120, 480], 'catch': [120, 480], 'post': [480, 600]}\n",
    "kernel_size, step_size = 16, 60  # smoothing params\n",
    "n_perms, baseline = 1000, 1  # permutation params\n",
    "sig_level = 0.05  # stats params\n",
    "l_color = {'pre': rgb('black'), 'pert': rgb('green'), 'catch': rgb('darkviolet'), 'post': rgb('dodgerblue')}\n",
    "# plotting params. \"l\": line, \"m\": marker, \"hist_res\": histogram resolution for permutation the distribution plot.\n",
    "l_width, ma_offset, m_size, label_size, hist_res = 2, 0.75, 10, 20, 100\n",
    "\n",
    "# pre-allocate variables\n",
    "no_subjs = {a: {c: {b: len(metric_short[t][a][c][b]) for b in BLOCKS} for c in CONDS} for a in SUBJS}\n",
    "all_times = {a: {c: {b: np.hstack(metric_short[t][a][c][b][time_var]) for b in BLOCKS} for c in CONDS} for a in SUBJS}\n",
    "time_vect = {b: np.arange(time_lim[b][0], time_lim[b][1], step_size) for b in BLOCKS}\n",
    "timeVect_ray = {b: ray.put(time_vect[b]) for b in BLOCKS}\n",
    "\n",
    "# smoothed data and weights.\n",
    "sm_data, sm_weights = [{t: {a: {c: {b: {p: np.zeros((no_subjs[a][c][b], len(time_vect[b]))) for p in TP_NAMES}\n",
    "                                    for b in BLOCKS} for c in CONDS} for a in SUBJS} for t in TESTS}\n",
    "                       for _ in range(2)]\n",
    "\n",
    "# permutation data weights.\n",
    "perm_d1, perm_w1, perm_d2, perm_w2 = [{t: {a: {c: {b: {p: np.zeros((no_subjs[a][c][b], len(time_vect[b]), n_perms))\n",
    "                                                      for p in TP_NAMES} for b in BLOCKS} for c in CONDS}\n",
    "                                          for a in SUBJS} for t in TESTS} for _ in range(4)]\n",
    "\n",
    "# ray smoothing and permute identifiers.\n",
    "sm_id, pm_id = [{t: {a: {c: {b: {p: {i: [] for i in np.arange(no_subjs[a][c][b])} for p in TP_NAMES} for b in BLOCKS}\n",
    "                         for c in CONDS} for a in SUBJS} for t in TESTS} for _ in range(2)]\n",
    "\n",
    "par_smooth = ray.remote(SF.gaussSmooth)\n",
    "par_permute = ray.remote(SF.permute)\n",
    "# run smoothing and permutation\n",
    "for t in TESTS:\n",
    "    for a in SUBJS:\n",
    "        for c in CONDS:\n",
    "            for b in BLOCKS:\n",
    "                for p in TP_NAMES:\n",
    "                    for i in np.arange(no_subjs[a][c][b]):\n",
    "                        # Sanity Check\n",
    "                        # sm_data[t][a][c][b][p][i,:], sm_weights[t][a][c][b][p][i, :] = SF.gaussSmooth(\n",
    "                        #     metric_short[t][a][c][b][time_var][i], metric_short[t][a][c][b][p][i], time_vect[b],\n",
    "                        #     kernel_size)\n",
    "                        # (perm_d1[t][a][c][b][p][i, :, :], perm_w1[t][a][c][b][p][i, :, :],\n",
    "                        #  perm_d2[t][a][c][b][p][i, :, :], perm_w2[t][a][c][b][p][i, :, :]) = SF.permute(\n",
    "                        #     metric_short[t][a][c][b][time_var][i], metric_short[t][a][c][b][p][i], newX=time_vect[b],\n",
    "                        #     sigma=kernel_size, nPerms=n_perms, baseline=baseline)\n",
    "\n",
    "                        sm_id[t][a][c][b][p][i] = par_smooth.remote(\n",
    "                            metric_short[t][a][c][b][time_var][i], metric_short[t][a][c][b][p][i], timeVect_ray[b],\n",
    "                            kernel_size)\n",
    "                        pm_id[t][a][c][b][p][i] = par_permute.remote(metric_short[t][a][c][b][time_var][i],\n",
    "                            metric_short[t][a][c][b][p][i], newX=timeVect_ray[b], sigma=kernel_size, nPerms=n_perms,\n",
    "                            baseline=baseline)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Ray results\n",
    "for t in TESTS:\n",
    "    for a in SUBJS:\n",
    "        for c in CONDS:\n",
    "            for b in BLOCKS:\n",
    "                for p in TP_NAMES:\n",
    "                    for i in np.arange(no_subjs[a][c][b]):\n",
    "                        try:\n",
    "                            sm_id[t][a][c][b][p][i] = ray.get(sm_id[t][a][c][b][p][i])\n",
    "                            pm_id[t][a][c][b][p][i] = ray.get(pm_id[t][a][c][b][p][i])\n",
    "                            sm_data[t][a][c][b][p][i, :], sm_weights[t][a][c][b][p][i, :] = [\n",
    "                                sm_id[t][a][c][b][p][i][k] for k in range(2)]\n",
    "                            (perm_d1[t][a][c][b][p][i, :, :], perm_w1[t][a][c][b][p][i, :, :],\n",
    "                             perm_d2[t][a][c][b][p][i, :, :], perm_w2[t][a][c][b][p][i, :, :]) = [\n",
    "                                pm_id[t][a][c][b][p][i][k] for k in range(4)]\n",
    "                        except Exception:\n",
    "                            print(f'smoothing failed for {a}_subject{i}_{c}_{b}_{t}')\n",
    "\n",
    "del sm_id, pm_id  # deleting is required to free up space from RAY processing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quantify weighted coactivation average and the CI\n",
    "weighted_average, weight_sum, sigInd_inClust, ensemTval_inClust, conf95, perm_distr, sig_ther, stat_id = [{t: {a: {c:\n",
    "    {b: [] for b in BLOCKS} for c in CONDS} for a in SUBJS} for t in TESTS} for _ in range(8)]\n",
    "stats = True  # whether to run stats or not. Needed if you want to have rugplots\n",
    "\n",
    "\n",
    "@ray.remote\n",
    "def run_stats(sm_data, sm_weights, analysis_type, sig_level, stats=False, baseline=0, perm_d1=None, perm_d2=None,\n",
    "              perm_w1=None, perm_w2=None):\n",
    "    weighted_average, weights, sigInd_inClust, ensemTval_inClust, conf95, perm_distr, sig_ther = [\n",
    "        {t: [] for t in analysis_type} for _ in range(7)]\n",
    "    for t in analysis_type:\n",
    "        weighted_average[t], weights[t] = np.average(sm_data[t], weights=sm_weights[t], axis=0, returned=True)\n",
    "        conf95[t] = ess.bootstrap_confidence_interval(\n",
    "            sm_data[t], weights=sm_weights[t], stat_fun='weighted_average', ci=1 - sig_level)\n",
    "        if stats:\n",
    "            sigInd_inClust[t], ensemTval_inClust[t] = SF.clusterStat_oneSamp(\n",
    "                sm_data[t], sm_weights[t], baseline, sig_level)\n",
    "            perm_distr[t] = SF.permuteClusterStat(perm_d1[t], perm_d2[t], perm_w1[t], perm_w2[t], sig_level)\n",
    "            sig_ther[t] = np.percentile(perm_distr[t], 100 - (sig_level * 100))\n",
    "    if not stats:\n",
    "        return weighted_average, weights, conf95\n",
    "    else:\n",
    "        return weighted_average, weights, conf95, sigInd_inClust, ensemTval_inClust, perm_distr, sig_ther\n",
    "\n",
    "\n",
    "for t in TESTS:\n",
    "    for a in SUBJS:\n",
    "        for c in CONDS:\n",
    "            for b in BLOCKS:\n",
    "                if not stats:\n",
    "                    stat_id[t][a][c][b] = run_stats.remote(\n",
    "                        sm_data[t][a][c][b], sm_weights[t][a][c][b], TP_NAMES, sig_level)\n",
    "                else:\n",
    "                    stat_id[t][a][c][b] = run_stats.remote(\n",
    "                        sm_data[t][a][c][b], sm_weights[t][a][c][b], TP_NAMES,\n",
    "                        sig_level, stats=stats, baseline=1,\n",
    "                        perm_d1=perm_d1[t][a][c][b],\n",
    "                        perm_d2=perm_d2[t][a][c][b],\n",
    "                        perm_w1=perm_w1[t][a][c][b],\n",
    "                        perm_w2=perm_w2[t][a][c][b])\n",
    "for t in TESTS:\n",
    "    for a in SUBJS:\n",
    "        for c in CONDS:\n",
    "            for b in BLOCKS:\n",
    "                stat_id[t][a][c][b] = ray.get(stat_id[t][a][c][b])\n",
    "                if not stats:\n",
    "                    weighted_average[t][a][c][b], weight_sum[t][a][c][b], conf95[t][a][c][b] = [\n",
    "                        stat_id[t][a][c][b][i] for i in range(3)]\n",
    "                else:\n",
    "                    (weighted_average[t][a][c][b], weight_sum[t][a][c][b], conf95[t][a][c][b],\n",
    "                     sigInd_inClust[t][a][c][b], ensemTval_inClust[t][a][c][b], perm_distr[t][a][c][b],\n",
    "                     sig_ther[t][a][c][b]) = [stat_id[t][a][c][b][i] for i in range(7)]\n",
    "\n",
    "del stat_id"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make heatmap with the CI index.\n",
    "CI index deviation from \"one\" is the most important feature of the CI. We can summarize it in a heatmap with rows that are representative of the muscle pairs and steps and the columns that are representative of time. Similar to Iturralde and Torres-Oviedo 2019 eNeuro. Here, each column is a minute.\n",
    "To get per-minute data, we just need to change the kernel size in the SMART section of the code above. After that, the following lines are a shear reorganization of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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   "source": [
    "NUM_COL = 10  # one column per minute\n",
    "ci_maps = {a: {c: np.zeros([len(TP_NAMES) * len(TESTS), NUM_COL]) for c in CONDS} for a in SUBJS}\n",
    "DES_POINTS = {'pre': 2, 'pert': 6, 'catch': 6, 'post': 2}\n",
    "\n",
    "for a in SUBJS:\n",
    "    for c in CONDS:\n",
    "        for i, p in enumerate([(x, y) for x in TESTS for y in TP_NAMES]):\n",
    "            count = 0\n",
    "            for b in BLOCKS:\n",
    "                if b != 'catch':\n",
    "                    ci_maps[a][c][i, count: count + DES_POINTS[b]] = weighted_average[p[0]][a][c][b][p[1]]\n",
    "                    count += DES_POINTS[b]\n",
    "\n",
    "# A similar approach is needed for the significance.\n",
    "# The difference is that the significance does not have the raw array. So we need to first make it.\n",
    "significance = {t: {a: {c: {b: {p: np.zeros_like(weighted_average[t][a][c][b][p]) for p in TP_NAMES} for b in BLOCKS}\n",
    "                        for c in CONDS} for a in SUBJS} for t in TESTS}\n",
    "\n",
    "for t in TESTS:\n",
    "    for a in SUBJS:\n",
    "        for c in CONDS:\n",
    "            for b in BLOCKS:\n",
    "                for p in TP_NAMES:\n",
    "                    for sdx, s in enumerate(\n",
    "                            sigInd_inClust[t][a][c][b][p]):  # type: ignore\n",
    "                        if ensemTval_inClust[t][a][c][b][p][sdx] > sig_ther[t][a][c][b][p]:  # type: ignore\n",
    "                            significance[t][a][c][b][p][s] = 1\n",
    "\n",
    "sig_maps, sigmaps_num = [{a: {c: np.zeros([len(TP_NAMES) * len(TESTS), NUM_COL]) for c in CONDS} for a in SUBJS}\n",
    "                         for _ in range(2)]\n",
    "\n",
    "for a in SUBJS:\n",
    "    for c in CONDS:\n",
    "        for i, p in enumerate([(x, y) for x in TESTS for y in TP_NAMES]):\n",
    "            count = 0\n",
    "            for b in BLOCKS:\n",
    "                if b != 'catch':\n",
    "                    sig_maps[a][c][i, count: count + DES_POINTS[b]] = significance[p[0]][a][c][b][p[1]]\n",
    "                    count += DES_POINTS[b]\n",
    "        sigmaps_num[a][c] = sig_maps[a][c].copy()\n",
    "        sig_maps[a][c] = np.char.array(np.uint(sig_maps[a][c]), unicode=True, copy=True)\n",
    "        sig_maps[a][c][sig_maps[a][c] == '1'] = '•'\n",
    "        sig_maps[a][c][sig_maps[a][c] == '0'] = ' '\n",
    "\n",
    "# it is easier to plot the dots w/ plotly\n",
    "fig = make_subplots(len(CONDS), len(SUBJS), subplot_titles=[f'{x}_{y}' for x in CONDS for y in SUBJS])\n",
    "y = [f'{r}_{t}' for r in ['p', 'r'] for t in TP_NAMES]\n",
    "y.reverse()\n",
    "for i, c in enumerate(CONDS):\n",
    "    for j, a in enumerate(SUBJS):\n",
    "        trace = go.Heatmap(\n",
    "            z=np.flipud(ci_maps[a][c]),\n",
    "            y=y, x=list(range(1, 11)),\n",
    "            colorscale='rdbu', reversescale=True, zmin=.7, zmax=1.3, xgap=2, ygap=2,\n",
    "            text=np.flipud(sig_maps[a][c]), texttemplate=\"%{text}\",\n",
    "            textfont={\"size\": 20, \"color\": \"black\"}\n",
    "        )\n",
    "        fig.add_trace(trace, row=i + 1, col=j + 1)\n",
    "fig.update_layout(height=1600, width=1000, title_text='co-contraction index', font_family=\"Open Sans\")\n",
    "if plot_fig:\n",
    "    fig.show()\n",
    "if save_fig:\n",
    "    fig.write_image(DATA_PATH + 'group_response/coContraction_index_heatmap.pdf')\n",
    "\n",
    "# Seaborn plotting, not the easiest to put the dots on heatmap though.\n",
    "# fig, ax = plt.subplots(len(CONDS), len(SUBJS), figsize=(9, 15), dpi=400, facecolor=None, )\n",
    "# for i, c in enumerate(CONDS):\n",
    "#     for j, a in enumerate(SUBJS):\n",
    "#         temp_df = pd.DataFrame(ci_maps[a][c], index=tp_names+tp_names, columns=range(1,11))\n",
    "#         sns.heatmap(temp_df,vmin=0.8, vmax=1.2, cmap='vlag', linewidths=0.5, ax= ax[i,j],\n",
    "#                    cbar_kws={'orientation':'horizontal','location':'top','shrink':0.3,\n",
    "#                   'ticks':[0.8,1,1.2],'anchor':(0,0)})\n",
    "#         ax[i,j].set_title(f'{c}_{a}')\n",
    "\n",
    "# fig.suptitle('co-contraction index')\n",
    "# plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the young vs. old CI\n",
    "# Two ways:\n",
    "# One is resistive CI to all KCI (resistive are not significant though).\n",
    "# Two, the percentage of the driving muscles over time (number of driving muscle-pairs per minute).\n",
    "\n",
    "# This is only for average heatmap seen above and to provide some insight.\n",
    "# We can only look on the significant time-frames:\n",
    "grp_sig_driving, grp_CI = [{a: {c: [] for c in CONDS} for a in SUBJS} for _ in range(2)]\n",
    "mean_driving_index, mean_CI = [{a: {c: 0 for c in CONDS} for a in SUBJS} for _ in range(2)]\n",
    "sig_driving_index = pd.DataFrame()\n",
    "for i, c in enumerate(CONDS):\n",
    "    for a in SUBJS:\n",
    "        grp_sig_driving[a][c] = np.count_nonzero(sigmaps_num[a][c], axis=0)\n",
    "        mean_driving_index[a][c] = np.mean(grp_sig_driving[a][c])\n",
    "        temp_map = ci_maps[a][c].copy()\n",
    "        grp_CI[a][c] = np.sum(temp_map, axis=0) / 12\n",
    "        mean_CI[a][c] = np.mean(grp_CI[a][c])\n",
    "\n",
    "# to get the stats and plots for the driving muscle pairs, replace grp_CI below to grp_sig_driving\n",
    "    sig_driving_index = pd.concat(\n",
    "        [sig_driving_index, pg.ttest(grp_CI['young'][c], grp_CI['old'][c], alternative='less')],\n",
    "        ignore_index=True\n",
    "    )\n",
    "    sig_driving_index.loc[i, 'condition'] = c\n",
    "\n",
    "if save_stats:\n",
    "    with pd.ExcelWriter(DATA_PATH + 'group_response/number_of_driving_pairs_per_min.xlsx') as writer:\n",
    "        sig_driving_index.to_excel(writer, sheet_name='unpaired_ttest')\n",
    "\n",
    "fig, ax = plt.subplots(1, len(CONDS), sharey=True, figsize=(15, 5))\n",
    "for i, c in enumerate(CONDS):\n",
    "    box_data = pd.DataFrame(grp_CI['young'][c], columns=['average CI'])\n",
    "    box_data['age'] = 'young'\n",
    "    box_data = pd.concat(\n",
    "        [box_data, pd.DataFrame(grp_CI['old'][c], columns=['average CI'])], ignore_index=True)\n",
    "    box_data.loc[box_data['age'] != 'young', 'age'] = 'old'\n",
    "    lnd.lnd(data=box_data, y='average CI', x='age', ax=ax[i], palette='Set2', adtnl_space=0.2,\n",
    "            mean_size=0.3, size=10, lw=4)\n",
    "    ax[i].set_title(c)\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.suptitle('average CI per minute', y=1.02)\n",
    "if not plot_fig:\n",
    "    plt.close(fig)\n",
    "if save_fig:\n",
    "    fig.savefig(DATA_PATH + 'group_response/averageCI_per_min.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weight normalization\n",
    "# Part of the smoothing was to find the weight of each data point, so, we cant' just add them together and make an\n",
    "#  average. First we need to embed the weights into the CI values.\n",
    "\n",
    "normal_average = {a: {c: np.zeros([len(SUBJS[a]), len(TP_NAMES) * len(TESTS), NUM_COL]) for c in CONDS}\n",
    "                  for a in SUBJS}\n",
    "\n",
    "\n",
    "def normalize_by_weight(data, weights, groups, axis=0):\n",
    "    for g in groups:\n",
    "        data[g] = data[g] * weights[g] / np.tile(\n",
    "            np.sum(weights[g], axis=axis), [data[g].shape[axis], 1]) * data[g].shape[axis]\n",
    "    return data\n",
    "\n",
    "\n",
    "for k, t in enumerate(TESTS):\n",
    "    for a in SUBJS:\n",
    "        for c in CONDS:\n",
    "            count = 0\n",
    "            for b in BLOCKS:\n",
    "                if b != 'catch':\n",
    "                    temp_average = normalize_by_weight(\n",
    "                        sm_data[t][a][c][b], sm_weights[t][a][c][b], groups=TP_NAMES, axis=0)\n",
    "                    for i, p in enumerate(TP_NAMES):\n",
    "                        for j in range(temp_average[p].shape[0]):\n",
    "                            normal_average[a][c][j, i + len(TP_NAMES) * k,\n",
    "                                                 count: count + DES_POINTS[b]] = temp_average[p][j, :]\n",
    "                    count += DES_POINTS[b]\n",
    "            normal_average[a][c][normal_average[a][c] == 0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/torch-beta/lib/python3.9/site-packages/numpy/core/shape_base.py:65: UserWarning:\n",
      "\n",
      "Warning: converting a masked element to nan.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Collapse over muscles (and then over time)\n",
    "# We can try to find the resiting ratio for 1- each time-frame and then 2- for the overall. This way, we only collapse\n",
    "# the muscles, not the time\n",
    "resisting_ratio = {a: {c: np.zeros((normal_average[a][c].shape[0], normal_average[a][c].shape[2])) for c in CONDS}\n",
    "                   for a in SUBJS}\n",
    "accum_ratio = {a: {c: [] for c in CONDS} for a in SUBJS}\n",
    "for c in CONDS:\n",
    "    for a in SUBJS:\n",
    "        resisting_ratio[a][c] = np.count_nonzero(normal_average[a][c] > 1, axis=1) / (\n",
    "            np.ones_like(resisting_ratio[a][c]) * normal_average[a][c].shape[1])\n",
    "        accum_ratio[a][c] = np.nanmean(resisting_ratio[a][c], axis=1)\n",
    "\n",
    "accum_ratio_stats = pd.DataFrame()  # also crushed time, only one value per subject\n",
    "for i, c in enumerate(CONDS):\n",
    "    accum_ratio_stats = pd.concat([accum_ratio_stats, pg.ttest(\n",
    "        accum_ratio['young'][c], accum_ratio['old'][c], alternative='less', correction=False)], ignore_index=True\n",
    "    )\n",
    "    accum_ratio_stats.loc[i, 'condition'] = c\n",
    "\n",
    "ratio_stats = pd.DataFrame()\n",
    "for i, c in enumerate(CONDS):\n",
    "    for n in range(NUM_COL):\n",
    "        temp_test = pg.ttest(\n",
    "            resisting_ratio['young'][c][:, n], resisting_ratio['old'][c][:, n], alternative='less', correction=False)\n",
    "        ratio_stats = pd.concat([ratio_stats, temp_test], ignore_index=True)\n",
    "        ratio_stats.loc[i * NUM_COL + n, 'condition'] = f\"{c}_{n}\"\n",
    "\n",
    "if save_stats:\n",
    "    with pd.ExcelWriter(DATA_PATH + 'group_response/resistance_ratio_stats.xlsx') as writer:\n",
    "        accum_ratio_stats.to_excel(writer, sheet_name='accum_test')\n",
    "        ratio_stats[ratio_stats['p-val'] < 0.1].to_excel(writer, sheet_name='perminute_test')\n",
    "\n",
    "\n",
    "# %% plot it\n",
    "# plot the accumulated stats\n",
    "box_data = pd.DataFrame(accum_ratio['young'])\n",
    "box_data['age'] = 'young'\n",
    "box_data = pd.concat([box_data, pd.DataFrame(accum_ratio['old'])], ignore_index=True)\n",
    "box_data.loc[box_data['age'] != 'young', 'age'] = 'old'\n",
    "adtnl_space = 0.1\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "lnd.lnd(\n",
    "    data=box_data.melt(id_vars=['age'], value_vars=CONDS, var_name='condition', value_name='resistance ratio'),\n",
    "    x='condition', y='resistance ratio', hue='age', palette='Set2', size=3, ax=ax\n",
    ")\n",
    "ax.set_title('resisitance ratio of young and older adults')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1))\n",
    "if not plot_fig:\n",
    "    plt.close(fig)\n",
    "if save_fig:\n",
    "    fig.savefig(DATA_PATH + 'group_response/accumulated_resistance_ratio_by_age.jpg', bbox_inches='tight')\n",
    "\n",
    "# plotly implementation (harder to implement subplots)\n",
    "# fig = px.box(\n",
    "#     box_data.melt(id_vars=['age'], value_vars=CONDS, var_name='condition', value_name='resistance ratio'),\n",
    "#     x='condition', y='resistance ratio', color='age',\n",
    "#     title='accumulated resistance ratio by age')\n",
    "# if plot_fig:\n",
    "#     fig.show()\n",
    "# if save_fig:\n",
    "#     fig.write_image(DATA_PATH + 'group_response/accumulated_resistance_ratio_by_age.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the temporal comparison.\n",
    "l_color = {a: [] for a in SUBJS}\n",
    "l_color['young'] = {'pre': rgb('lime'), 'pert': rgb('dodgerblue'), 'catch': rgb('deeppink'), 'post': rgb('darkgreen')}\n",
    "l_color['old'] = {'pre': rgb('orange'), 'pert': rgb('royalblue'), 'catch': rgb('darkviolet'),\n",
    "                  'post': rgb('darkgoldenrod')}\n",
    "plot_colors = {'young':\n",
    "               np.concatenate((\n",
    "                   np.tile(l_color['young']['pre'], (2, 1)),\n",
    "                   np.tile(l_color['young']['pert'], (6, 1)),\n",
    "                   np.tile(l_color['young']['post'], (2, 1))), axis=0),\n",
    "               'old':\n",
    "               np.concatenate((\n",
    "                   np.tile(l_color['old']['pre'], (2, 1)),\n",
    "                   np.tile(l_color['old']['pert'], (6, 1)),\n",
    "                   np.tile(l_color['old']['post'], (2, 1))), axis=0)}\n",
    "\n",
    "box_data = {c: [] for c in CONDS}\n",
    "fig, ax = plt.subplots(len(CONDS), 1, sharey=True, figsize=(10, 16))\n",
    "for i, c in enumerate(CONDS):\n",
    "    box_data[c] = pd.DataFrame(resisting_ratio['young'][c])\n",
    "    box_data[c]['age'] = 'young'\n",
    "    box_data[c] = pd.concat([box_data[c], pd.DataFrame(resisting_ratio['old'][c])], ignore_index=True)\n",
    "    box_data[c].loc[box_data[c]['age'] != 'young', 'age'] = 'old'\n",
    "    lnd.lnd(\n",
    "        data=box_data[c].melt(id_vars=['age'], value_vars=list(range(NUM_COL)), var_name='minute', value_name='R-R'),\n",
    "        x='minute', y='R-R', hue='age', palette='Set2', ax=ax[i], size=5, lw=2, colors=plot_colors\n",
    "    )\n",
    "    sns.move_legend(ax[i], 'upper left', bbox_to_anchor=(1, 1))\n",
    "    ax[i].set_title(c)\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].get_legend().set_visible(False)\n",
    "    if i == 0:\n",
    "        ax[i].get_legend().set_visible(True)\n",
    "fig.tight_layout()\n",
    "fig.suptitle('resistance ratio over time', y=1.02)\n",
    "if not plot_fig:\n",
    "    plt.close(fig)\n",
    "if save_fig:\n",
    "    fig.savefig(DATA_PATH + 'group_response/resistance_ratio_by_time_and_age.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch-beta",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7cc401fccc55e2825ef583b7b43bb1f971ba34a72aecd6f049901fe5d37b7bbc"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}