plotting/imcoh-MedOffOn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Plot MED OFF vs. ON ImCoh (Figure 3a & 3b).\"\"\"\n",
    "\n",
    "import os\n",
    "import sys\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import copy\n",
    "import pandas as pd\n",
    "import pte_stats\n",
    "import pte_decode\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.stats import sem\n",
    "\n",
    "cd_path = Path(os.getcwd()).absolute().parent\n",
    "sys.path.append(os.path.join(cd_path, \"coherence\"))\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.rc('xtick', labelsize=6)\n",
    "matplotlib.rc('ytick', labelsize=6)\n",
    "matplotlib.rc('legend', fontsize=6)\n",
    "matplotlib.rc(\"font\", size=6, family=\"Arial\")\n",
    "matplotlib.rc('axes', labelsize=7)\n",
    "matplotlib.rc('axes', titlesize=7)\n",
    "matplotlib.rcParams[\"pdf.fonttype\"] = 42\n",
    "matplotlib.rcParams[\"ps.fonttype\"] = 42\n",
    "\n",
    "prop_cycle = plt.rcParams[\"axes.prop_cycle\"]\n",
    "\n",
    "n_perm = 100_000\n",
    "two_sided = True\n",
    "alpha = 0.05\n",
    "\n",
    "FOLDERPATH_ANALYSIS = \"Path_to\\\\Project\\\\Analysis\"\n",
    "FOLDERPATH_FIGURES = os.path.join(os.path.dirname(os.getcwd()), \"figures\")\n",
    "\n",
    "lfreq = 4.0\n",
    "hfreq = 40.0\n",
    "\n",
    "fband_func = np.mean\n",
    "\n",
    "fbands = {\n",
    "    \"alpha\": (8, 12),\n",
    "    \"low_beta\": (12, 20),\n",
    "    \"high_beta\": (20, 30)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WHOLE CORTEX <-> STN ImCoh (Figure 3b)\n",
    "data = pd.read_pickle(os.path.join(\n",
    "    FOLDERPATH_ANALYSIS, \"task-Rest_acq-multi_run-multi_con_imcoh_whole-MedOffOn_multi_sub.pkl\")\n",
    ")\n",
    "freqs = copy.deepcopy(data[\"frequencies\"][0])\n",
    "freqs_delta = np.unique(np.subtract(freqs[1:], freqs[:-1]))[0]\n",
    "data[\"frequencies\"] = [freqs] * len(data[\"seed_types\"])\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "\n",
    "imcoh_off = np.array(data[\"connectivity-imcoh\"][data[\"med\"] == \"Off\"].to_list())\n",
    "imcoh_on = np.array(data[\"connectivity-imcoh\"][data[\"med\"] == \"On\"].to_list())\n",
    "\n",
    "np.random.seed(44)\n",
    "\n",
    "lfreq_i = freqs.index(lfreq)\n",
    "hfreq_i = freqs.index(hfreq)\n",
    "plot_freqs = freqs[lfreq_i:hfreq_i+1]\n",
    "\n",
    "imcoh_off = imcoh_off[:, lfreq_i:hfreq_i+1]\n",
    "imcoh_on = imcoh_on[:, lfreq_i:hfreq_i+1]\n",
    "\n",
    "imcoh_off_sem = sem(imcoh_off)\n",
    "imcoh_on_sem = sem(imcoh_on)\n",
    "\n",
    "fig, axis = pte_decode.lineplot_prediction_compare(\n",
    "    x_1=imcoh_off.T,\n",
    "    x_2=imcoh_on.T,\n",
    "    times=np.arange(lfreq, hfreq+0.5, 0.5),\n",
    "    data_labels=[\"OFF therapy\", \"ON levodopa\"],\n",
    "    x_label=\"Frequency (Hz)\",\n",
    "    y_label=\"Absolute imaginary coherency (A.U.)\",\n",
    "    two_tailed=two_sided,\n",
    "    paired_x1x2=True,\n",
    "    n_perm=n_perm,\n",
    "    correction_method=\"cluster_pvals\",\n",
    "    title=\"Cortex <--> STN\",\n",
    "    colour=[\"#DF4A4A\", \"#376EB4\"]\n",
    ")\n",
    "\n",
    "for fband_name, fband_lims in fbands.items():\n",
    "    stat, pval = pte_stats.permutation_onesample(\n",
    "        data_a=fband_func(imcoh_off[:, freqs.index(fband_lims[0]):freqs.index(fband_lims[1])+1], 1),\n",
    "        data_b=fband_func(imcoh_on[:, freqs.index(fband_lims[0]):freqs.index(fband_lims[1])+1], 1),\n",
    "        n_perm=n_perm, two_tailed=two_sided\n",
    "    )\n",
    "    if pval < alpha:\n",
    "        axis.axvspan(fband_lims[0], fband_lims[1], color=[0.8, 0.8, 0.8], alpha=0.3, linewidth=0)\n",
    "        prefix = \"[SIGNIFICANT]\"\n",
    "    else:\n",
    "        prefix = \"\"\n",
    "    print(f\"{prefix} {fband_name}: stat={stat}; p-val={pval}\")\n",
    "\n",
    "axis.spines['top'].set_visible(False)\n",
    "axis.spines['right'].set_visible(False)\n",
    "axis.set_box_aspect(0.8)\n",
    "axis.legend(handlelength=0.5, handletextpad=0.5, labelspacing=0, borderaxespad=0, loc=\"upper left\")\n",
    "leg = axis.get_legend()\n",
    "leg.legend_handles[0].set_linewidth(1.5)\n",
    "leg.legend_handles[1].set_linewidth(1.5)\n",
    "leg.legend_handles[2].set_color([0.5, 0.5, 0.5])\n",
    "leg.set_frame_on(False)\n",
    "axis.set_xticks(np.arange(5, 45, 5))\n",
    "axis.tick_params(\"x\", pad=2, size=2)\n",
    "axis.tick_params(\"y\", pad=2, size=2)\n",
    "axis.xaxis.labelpad = 0\n",
    "axis.yaxis.labelpad = 1\n",
    "\n",
    "fig.set_size_inches(2, 2)\n",
    "\n",
    "fig.savefig(os.path.join(FOLDERPATH_FIGURES, \"Manuscript_ImCoh_Med_whole_cortex-STN.pdf\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WHOLE CORTEX <-> STN MIC (Figure 3a inset)\n",
    "data = pd.read_pickle(os.path.join(\n",
    "    FOLDERPATH_ANALYSIS, \"task-Rest_acq-multi_run-multi_con_mim_mic_whole-MedOffOn_multi_sub.pkl\")\n",
    ")\n",
    "freqs = copy.deepcopy(data[\"frequencies\"][0])\n",
    "freqs_delta = np.unique(np.subtract(freqs[1:], freqs[:-1]))[0]\n",
    "data[\"frequencies\"] = [freqs] * len(data[\"seed_types\"])\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "\n",
    "mic_off = np.array(data[\"connectivity-mic\"][data[\"med\"] == \"Off\"].to_list())\n",
    "mic_on = np.array(data[\"connectivity-mic\"][data[\"med\"] == \"On\"].to_list())\n",
    "\n",
    "np.random.seed(44)\n",
    "\n",
    "lfreq = 5\n",
    "hfreq = 35\n",
    "\n",
    "lfreq_i = freqs.index(lfreq)\n",
    "hfreq_i = freqs.index(hfreq)\n",
    "plot_freqs = freqs[lfreq_i:hfreq_i+1]\n",
    "\n",
    "mic_off = mic_off[:, lfreq_i:hfreq_i+1]\n",
    "mic_on = mic_on[:, lfreq_i:hfreq_i+1]\n",
    "\n",
    "mic_off_sem = sem(mic_off)\n",
    "mic_on_sem = sem(mic_on)\n",
    "\n",
    "fig, axis = plt.subplots(1, 1)\n",
    "\n",
    "axis.plot(plot_freqs, mic_off.mean(0), color=\"#DF4A4A\", label=\"OFF therapy\")\n",
    "axis.fill_between(plot_freqs, mic_off.mean(0)-mic_off_sem, mic_off.mean(0)+mic_off_sem, color=\"#DF4A4A\", alpha=0.3)\n",
    "axis.plot(plot_freqs, mic_on.mean(0), color=\"#376EB4\", label=\"ON levodopa\")\n",
    "axis.fill_between(plot_freqs, mic_on.mean(0)-mic_on_sem, mic_on.mean(0)+mic_on_sem, color=\"#376EB4\", alpha=0.3)\n",
    "\n",
    "xlim = axis.get_xlim()\n",
    "axis.axvspan(4, 20, color=\"white\", alpha=0.6, edgecolor=\"none\", linewidth=0, zorder=99)\n",
    "axis.axvspan(30, 40, color=\"white\", alpha=0.6, edgecolor=\"none\", linewidth=0, zorder=99)\n",
    "axis.set_xlim(xlim)\n",
    "\n",
    "axis.spines['top'].set_visible(False)\n",
    "axis.spines['right'].set_visible(False)\n",
    "axis.set_xticks(np.arange(10, 36, 10))\n",
    "axis.tick_params(\"x\", pad=2, size=2)\n",
    "axis.tick_params(\"y\", pad=2, size=2)\n",
    "axis.set_xlabel(\"Frequency (Hz)\")\n",
    "axis.set_ylabel(\"Abs. imag.\\ncoherency (A.U.)\")\n",
    "fig.suptitle(\"Cortex <--> STN\")\n",
    "axis.xaxis.labelpad = 0.1\n",
    "axis.yaxis.labelpad = 0\n",
    "\n",
    "fig.set_size_inches(1, 1)\n",
    "\n",
    "fig.savefig(os.path.join(FOLDERPATH_FIGURES, \"Manuscript_MIC_Med_full_hbhighlight_whole_cortex-STN.pdf\"))"
   ]
  }
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