plotting/power-MedOffOn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Plot MED OFF vs. ON power (Figures 2 & S2).\"\"\"\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",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\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",
    "alpha = 0.05\n",
    "two_sided = True\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",
    "fbands = {\n",
    "    \"alpha\": (8, 12),\n",
    "    \"low_beta\": (12, 20),\n",
    "    \"high_beta\": (20, 30)\n",
    "}\n",
    "\n",
    "fband_func = np.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WHOLE CORTEX POWER (Figure 2a; also Figure S2 when adjusting the data loaded)\n",
    "data = pd.read_pickle(os.path.join(\n",
    "    FOLDERPATH_ANALYSIS, \"task-Rest_acq-multi_run-multi_pow_standard_whole-MedOffOn_multi_sub.pkl\")\n",
    ")\n",
    "freqs = copy.deepcopy(data[\"freqs\"][0])\n",
    "freqs_delta = np.unique(np.subtract(freqs[1:], freqs[:-1]))[0]\n",
    "data[\"freqs\"] = [freqs] * len(data[\"ch_types\"])\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "\n",
    "pow_off = np.array(data[\"power-multitaper\"][(data[\"med\"] == \"Off\") & (data[\"ch_regions\"] == \"cortex\")].to_list())\n",
    "pow_on = np.array(data[\"power-multitaper\"][(data[\"med\"] == \"On\") & (data[\"ch_regions\"] == \"cortex\")].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",
    "pow_off = pow_off[:, lfreq_i:hfreq_i+1]\n",
    "pow_on = pow_on[:, lfreq_i:hfreq_i+1]\n",
    "\n",
    "fig, axis = pte_decode.lineplot_prediction_compare(\n",
    "    x_1=pow_off.T,\n",
    "    x_2=pow_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=\"Power (% total)\",\n",
    "    two_tailed=True,\n",
    "    paired_x1x2=True,\n",
    "    n_perm=n_perm,\n",
    "    correction_method=\"cluster_pvals\",\n",
    "    title=\"Cortex\",\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(pow_off[:, freqs.index(fband_lims[0]):freqs.index(fband_lims[1])+1], 1),\n",
    "        data_b=fband_func(pow_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, zorder=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=\"lower 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(1.6, 1.6)\n",
    "\n",
    "fig.savefig(os.path.join(FOLDERPATH_FIGURES, \"Manuscript_Power_Med_whole_cortex.pdf\"), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STN POWER (Figure 2a; also Figure S2 when adjusting the data loaded)\n",
    "data = pd.read_pickle(os.path.join(\n",
    "    FOLDERPATH_ANALYSIS, \"task-Rest_acq-multi_run-multi_pow_standard_whole-MedOffOn_multi_sub.pkl\")\n",
    ")\n",
    "freqs = copy.deepcopy(data[\"freqs\"][0])\n",
    "freqs_delta = np.unique(np.subtract(freqs[1:], freqs[:-1]))[0]\n",
    "data[\"freqs\"] = [freqs] * len(data[\"ch_types\"])\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "\n",
    "pow_off = np.array(data[\"power-multitaper\"][(data[\"med\"] == \"Off\") & (data[\"ch_regions\"] == \"STN\")].to_list())\n",
    "pow_on = np.array(data[\"power-multitaper\"][(data[\"med\"] == \"On\") & (data[\"ch_regions\"] == \"STN\")].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",
    "pow_off = pow_off[:, lfreq_i:hfreq_i+1]\n",
    "pow_on = pow_on[:, lfreq_i:hfreq_i+1]\n",
    "\n",
    "fig, axis = pte_decode.lineplot_prediction_compare(\n",
    "    x_1=pow_off.T,\n",
    "    x_2=pow_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=\"Power (% total)\",\n",
    "    two_tailed=True,\n",
    "    paired_x1x2=True,\n",
    "    n_perm=n_perm,\n",
    "    correction_method=\"cluster_pvals\",\n",
    "    title=\"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(pow_off[:, freqs.index(fband_lims[0]):freqs.index(fband_lims[1])+1], 1),\n",
    "        data_b=fband_func(pow_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.set_ylim((0.2, 3.1))\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=\"lower 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",
    "axis.set_yticks([1, 2, 3])\n",
    "\n",
    "fig.set_size_inches(1.58, 1.58)\n",
    "\n",
    "fig.savefig(os.path.join(FOLDERPATH_FIGURES, \"Manuscript_Power_Med_STN.pdf\"), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STN POWER for EL008 (Figure 2c inset)\n",
    "FOLDERPATH_ANALYSIS_EL008 = \"Path_to\\\\Project\\\\Analysis\\\\Results\\\\BIDS_01_Berlin_Neurophys\\\\sub-EL008\\\\ses-multi\"\n",
    "\n",
    "data = pd.read_pickle(\n",
    "    os.path.join(\n",
    "        FOLDERPATH_ANALYSIS_EL008,\n",
    "        \"task-Rest_acq-multi_run-1_pow_standard_channels-MedOffOn_single_sub.pkl\",\n",
    "    )\n",
    ")\n",
    "freqs = copy.deepcopy(data[\"freqs\"][0])\n",
    "freqs_delta = np.unique(np.subtract(freqs[1:], freqs[:-1]))[0]\n",
    "data[\"freqs\"] = [freqs] * len(data[\"ch_types\"])\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "\n",
    "off_idcs = data.index[(data[\"med\"] == \"Off\") & (data[\"ch_regions\"] == \"STN\")]\n",
    "\n",
    "ch_names = data[\"ch_names\"][off_idcs]\n",
    "\n",
    "ch_nums = [str(num) for num in range(1, 17)]\n",
    "ch_nums.pop(ch_nums.index(\"13\"))\n",
    "for num_idx, num in enumerate(ch_nums):\n",
    "    if len(num) == 1:\n",
    "        ch_nums[num_idx] = f\"0{num}\"\n",
    "\n",
    "ch_data = {num: [] for num in ch_nums}\n",
    "for key in ch_data.keys():\n",
    "    for name_idx, name in enumerate(ch_names):\n",
    "        if key in name:\n",
    "            ch_data[key].append(data[\"power-multitaper\"][off_idcs[name_idx]])\n",
    "    ch_data[key] = np.mean(ch_data[key], axis=0)\n",
    "\n",
    "pow_off = np.array(list(ch_data.values()))\n",
    "\n",
    "\n",
    "patterns = pd.DataFrame.from_dict(\n",
    "    pd.read_pickle(\n",
    "        os.path.join(\n",
    "            FOLDERPATH_ANALYSIS,\n",
    "            \"task-Rest_acq-multi_run-multi_pow_ssd_topography_raw-MedOffOn_multi_sub.pkl\",\n",
    "        )\n",
    "    )\n",
    ")\n",
    "off_idcs = patterns.index[\n",
    "    (patterns[\"med\"] == \"Off\")\n",
    "    & (patterns[\"ch_regions\"] == \"STN\")\n",
    "    & (patterns[\"band_names\"] == \"low_beta\")\n",
    "    & (patterns[\"component_numbers\"] == 1)\n",
    "    & (patterns[\"sub\"] == \"008\")\n",
    "]\n",
    "ch_names = [\n",
    "    \"01\", \"02\", \"03\",\n",
    "    \"04\", \"05\", \"06\",\n",
    "    \"07\", \"08\", \"09\",\n",
    "    \"10\", \"11\", \"12\",\n",
    "          \"14\", \"15\",\n",
    "          \"16\",\n",
    "]\n",
    "ch_data = []\n",
    "ch_data_med = []\n",
    "patterns = patterns.loc[off_idcs]\n",
    "for ch_name in ch_names:\n",
    "    ch_data.append([])\n",
    "    for row_i in patterns.index:\n",
    "        if ch_name in patterns[\"ch_names\"].loc[row_i]:\n",
    "            ch_data[-1].append(patterns[\"ssd_topographies\"].loc[row_i])\n",
    "for val_i, val in enumerate(ch_data):\n",
    "    ch_data[val_i] = np.mean(val)\n",
    "\n",
    "lfreq_i = freqs.index(9)\n",
    "hfreq_i = freqs.index(31)\n",
    "plot_freqs = freqs[lfreq_i : hfreq_i + 1]\n",
    "\n",
    "pow_off = pow_off[:, lfreq_i : hfreq_i + 1]\n",
    "\n",
    "fig, axis = plt.subplots(1, 1)\n",
    "\n",
    "prop_cycle = plt.rcParams[\"axes.prop_cycle\"]\n",
    "def_colors = prop_cycle.by_key()[\"color\"]\n",
    "\n",
    "n_colours = 256\n",
    "colours = plt.get_cmap(\"Reds\")(range(n_colours))\n",
    "cmap = LinearSegmentedColormap.from_list(name=\"red_alpha\", colors=colours)\n",
    "\n",
    "cmap = plt.get_cmap(\"viridis\")\n",
    "\n",
    "normalise = matplotlib.colors.Normalize(\n",
    "    vmin=np.min(ch_data), vmax=np.max(ch_data)\n",
    ")\n",
    "colours = cmap(normalise(ch_data))\n",
    "\n",
    "for ch_idx, colour in enumerate(colours):\n",
    "    if ch_idx not in [6, 7, 8]:\n",
    "        axis.plot(plot_freqs, pow_off[ch_idx], color=colour, linewidth=1)\n",
    "\n",
    "for ch_idx, colour in enumerate(colours):\n",
    "    if ch_idx in [6, 7, 8]:\n",
    "        axis.plot(plot_freqs, pow_off[ch_idx], color=colour, linewidth=1)\n",
    "\n",
    "axis.set_ylim(0.5, 2.5)\n",
    "axis.set_xlabel(\"Frequency (Hz)\")\n",
    "axis.set_ylabel(\"Power (% total)\")\n",
    "axis.spines[\"top\"].set_visible(False)\n",
    "axis.spines[\"right\"].set_visible(False)\n",
    "axis.set_xticks(np.arange(10, 31, 10))\n",
    "axis.set_yticks([0.5, 2.5])\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 = -8\n",
    "axis.set_title(\"Subject EL008\")\n",
    "\n",
    "fig.set_size_inches(1.1, 1.1)\n",
    "\n",
    "fig.savefig(\n",
    "    os.path.join(FOLDERPATH_FIGURES, \"Manuscript_Power_EL008_MedOFF_STN.pdf\"), dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WHOLE CORTEX SSD POWER (Figure 2b inset)\n",
    "data = pd.read_pickle(os.path.join(\n",
    "    FOLDERPATH_ANALYSIS, \"task-Rest_acq-multi_run-multi_pow_ssd_noavg-MedOffOn_multi_sub.pkl\")\n",
    ")\n",
    "freqs = copy.deepcopy(data[\"frequencies\"][0])\n",
    "del data[\"frequencies\"]\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "\n",
    "fband_label = \"high_beta\"\n",
    "lfreq = 19\n",
    "hfreq = 31\n",
    "\n",
    "power_off = np.array(data[\"power-ssd\"][(data[\"med\"] == \"Off\") & (data[\"transformed_ch_regions\"] == \"cortex\")\n",
    "                        & (data[\"band_names\"] == fband_label) & (data[\"component_numbers\"] == 1)].tolist())\n",
    "power_on = np.array(data[\"power-ssd\"][(data[\"med\"] == \"On\") & (data[\"transformed_ch_regions\"] == \"cortex\")\n",
    "                        & (data[\"band_names\"] == fband_label) & (data[\"component_numbers\"] == 1)].tolist())\n",
    "\n",
    "lfreq_i = freqs.index(lfreq)\n",
    "hfreq_i = freqs.index(hfreq)\n",
    "plot_freqs = freqs[lfreq_i:hfreq_i+1]\n",
    "power_off = power_off[:, lfreq_i:hfreq_i+1]\n",
    "power_on = power_on[:, lfreq_i:hfreq_i+1]\n",
    "\n",
    "power_off_sem = sem(power_off, axis=0)\n",
    "power_on_sem = sem(power_on, axis=0)\n",
    "\n",
    "fig, axis = plt.subplots(1, 1)\n",
    "\n",
    "axis.plot(plot_freqs, power_off.mean(0), color=\"#DF4A4A\", label=\"OFF therapy\")\n",
    "axis.fill_between(plot_freqs, power_off.mean(0)-power_off_sem, power_off.mean(0)+power_off_sem, color=\"#DF4A4A\", alpha=0.3)\n",
    "axis.plot(plot_freqs, power_on.mean(0), color=\"#376EB4\", label=\"ON levodopa\")\n",
    "axis.fill_between(plot_freqs, power_on.mean(0)-power_on_sem, power_on.mean(0)+power_on_sem, color=\"#376EB4\", alpha=0.3)\n",
    "\n",
    "\n",
    "axis.spines['top'].set_visible(False)\n",
    "axis.spines['right'].set_visible(False)\n",
    "axis.set_xticks(np.arange(20, 31, 5))\n",
    "axis.set_yticks([0, 2])\n",
    "axis.set_ylim(0, 2)\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(\"Power (dB)\")\n",
    "fig.suptitle(\"Cortex\")\n",
    "axis.xaxis.labelpad = 0\n",
    "axis.yaxis.labelpad = -5\n",
    "\n",
    "fig.set_size_inches(1, 1)\n",
    "\n",
    "fig.savefig(os.path.join(FOLDERPATH_FIGURES, \"Manuscript_Power_SSD_spectrum_Med_cortex.pdf\"), dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WHOLE STN SSD POWER (Figure 2b inset)\n",
    "data = pd.read_pickle(os.path.join(\n",
    "    FOLDERPATH_ANALYSIS, \"task-Rest_acq-multi_run-multi_pow_ssd_noavg-MedOffOn_multi_sub.pkl\")\n",
    ")\n",
    "freqs = copy.deepcopy(data[\"frequencies\"][0])\n",
    "del data[\"frequencies\"]\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "\n",
    "fband_label = \"low_beta\"\n",
    "lfreq = 10\n",
    "hfreq = 22\n",
    "\n",
    "power_off = np.array(data[\"power-ssd\"][(data[\"med\"] == \"Off\") & (data[\"transformed_ch_regions\"] == \"STN\")\n",
    "                        & (data[\"band_names\"] == fband_label) & (data[\"component_numbers\"] == 1)].tolist())\n",
    "power_on = np.array(data[\"power-ssd\"][(data[\"med\"] == \"On\") & (data[\"transformed_ch_regions\"] == \"STN\")\n",
    "                        & (data[\"band_names\"] == fband_label) & (data[\"component_numbers\"] == 1)].tolist())\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",
    "power_off = power_off[:, lfreq_i:hfreq_i+1]\n",
    "power_on = power_on[:, lfreq_i:hfreq_i+1]\n",
    "\n",
    "power_off_sem = sem(power_off, axis=0)\n",
    "power_on_sem = sem(power_on, axis=0)\n",
    "\n",
    "fig, axis = plt.subplots(1, 1)\n",
    "\n",
    "axis.plot(plot_freqs, power_off.mean(0), color=\"#DF4A4A\", label=\"OFF therapy\")\n",
    "axis.fill_between(plot_freqs, power_off.mean(0)-power_off_sem, power_off.mean(0)+power_off_sem, color=\"#DF4A4A\", alpha=0.3)\n",
    "axis.plot(plot_freqs, power_on.mean(0), color=\"#376EB4\", label=\"ON levodopa\")\n",
    "axis.fill_between(plot_freqs, power_on.mean(0)-power_on_sem, power_on.mean(0)+power_on_sem, color=\"#376EB4\", alpha=0.3)\n",
    "\n",
    "axis.spines['top'].set_visible(False)\n",
    "axis.spines['right'].set_visible(False)\n",
    "axis.set_xticks(np.arange(12, 21, 4))\n",
    "axis.set_yticks([0, 2])\n",
    "axis.set_ylim(0, 2)\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(\"Power (dB)\")\n",
    "fig.suptitle(\"STN\")\n",
    "axis.xaxis.labelpad = 0\n",
    "axis.yaxis.labelpad = -5\n",
    "\n",
    "fig.set_size_inches(1, 1)\n",
    "\n",
    "fig.savefig(os.path.join(FOLDERPATH_FIGURES, \"Manuscript_Power_SSD_spectrum_Med_STN.pdf\"), dpi=300)"
   ]
  }
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