notebooks/monkey/06_MONKEY_Decoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Figure 4 Monkey Decoding\n",
    "### Decoding Samples Monkey behav | neural activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import os\n",
    "import pickle\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "from maskedvae.utils.utils import load_individual_pickle, save_as_pickle\n",
    "from maskedvae.plotting.plotting import cm2inch, make_col_dict_monkey, get_title_dict\n",
    "\n",
    "\n",
    "\n",
    "fontsize=10\n",
    "plt.rcParams.update({'font.size': fontsize})\n",
    "plt.rcParams.update({'axes.spines.right': False})\n",
    "plt.rcParams.update({'axes.spines.top': False})\n",
    "plt.rcParams.update({'xtick.labelsize': fontsize})\n",
    "plt.rcParams.update({'ytick.labelsize': fontsize})\n",
    "plt.rcParams.update({'axes.titlesize': fontsize})\n",
    "plt.rcParams.update({'axes.labelsize': fontsize})\n",
    "plt.rcParams.update({'legend.fontsize': fontsize})\n",
    "  \n",
    "# reload the developing code \n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# specify the directory here where runs are stored\n",
    "temp_dir = \"../../runs/monkey/\"\n",
    "\n",
    "maskedloss=True\n",
    "new_date=False\n",
    "\n",
    "\n",
    "# now read it in\n",
    "with open(\"./R_runs_xam_poisson_xby_gnll_beta_1.txt\", \"r\") as f:\n",
    "    run_dirs = f.readlines()\n",
    "run_dirs = [x.strip() for x in run_dirs] # remove \\n\n",
    "\n",
    "col_dict = make_col_dict_monkey()\n",
    "title_dict = get_title_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set up dictionaries and select runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count_masked: 16, count_naive: 16\n"
     ]
    }
   ],
   "source": [
    "dimwise = True  # dimwise scaling\n",
    "cross_loss = False  # no cross loss\n",
    "beta_val = 1  # vae beta\n",
    "nll_beta = 0.3  # gnll beta\n",
    "\n",
    "data_directory_fig_4 = \"../../data/monkey/decoding/\" \n",
    "local_dir = \"../../data/monkey/\"\n",
    "\n",
    "# create the dictioniaries make dires if they dont exist\n",
    "if not os.path.exists(local_dir):\n",
    "    os.makedirs(local_dir)\n",
    "if not os.path.exists(local_dir + \"figures/\"):\n",
    "    os.makedirs(local_dir + \"figures/\")\n",
    "if not os.path.exists(data_directory_fig_4):\n",
    "    os.makedirs(data_directory_fig_4)\n",
    "\n",
    "\n",
    "# check how many masked and all obs runs there are\n",
    "count_masked = 0\n",
    "count_naive = 0\n",
    "for run in run_dirs:\n",
    "    count_masked += 1 if \"masked\" in run else 0\n",
    "    count_naive += 1 if \"all_obs\" in run else 0\n",
    "print(f\"count_masked: {count_masked}, count_naive: {count_naive}\")\n",
    "\n",
    "if count_naive > count_masked:\n",
    "    run_dirs = run_dirs[: 2 * count_masked]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### read in all the files for plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading from file...\n",
      "all_xb_d\n",
      "all_xb_y\n",
      "run_names\n",
      "session_xb_d_gt\n",
      "session_xb_y_gt\n",
      "all_mean_decoding\n",
      "model_tags\n",
      "all_calibs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_xb_d = []\n",
    "all_xb_y = []\n",
    "run_names = []\n",
    "session_xb_d_gt = []\n",
    "session_xb_y_gt = []\n",
    "all_mean_decoding = []\n",
    "model_tags = []\n",
    "all_calibs = []\n",
    "\n",
    "dict_names = [\n",
    "    \"all_xb_d\",\n",
    "    \"all_xb_y\",\n",
    "    \"run_names\",\n",
    "    \"session_xb_d_gt\",\n",
    "    \"session_xb_y_gt\",\n",
    "    \"all_mean_decoding\",\n",
    "    \"model_tags\",\n",
    "    \"all_calibs\",\n",
    "]\n",
    "\n",
    "load_from_scratch = False\n",
    "if load_from_scratch:\n",
    "    for q, run in enumerate(run_dirs):\n",
    "        # load calibration dict\n",
    "        with open(os.path.join(temp_dir, run, \"calibration_dict.pkl\"), \"rb\") as f:\n",
    "            calibration_dict = pickle.load(f)\n",
    "        with open(os.path.join(temp_dir, run, \"samples.pkl\"), \"rb\") as f:\n",
    "            samples = pickle.load(f)\n",
    "        with open(os.path.join(temp_dir, run, \"session_data_0.pkl\"), \"rb\") as f:\n",
    "            session_data = pickle.load(f)\n",
    "        with open(os.path.join(temp_dir, run, \"LogL_mean_decoding.pkl\"), \"rb\") as f:\n",
    "            logL_mean_decoding = pickle.load(f)\n",
    "        with open(os.path.join(temp_dir, run, \"mean_decoding.pkl\"), \"rb\") as f:\n",
    "            all_mean_decoding.append(pickle.load(f))\n",
    "\n",
    "        run_names.append(run)\n",
    "\n",
    "        session_xb_d_gt.append(session_data[\"xb_d\"].detach().cpu().numpy())\n",
    "        session_xb_y_gt.append(session_data[\"xb_y\"].detach().cpu().numpy())\n",
    "        all_xb_d.append(samples[\"xb_d\"])\n",
    "        all_xb_y.append(samples[\"xb_y\"])\n",
    "        reduced_calib = {}\n",
    "        reduced_calib[\"xb_y\"] = calibration_dict[\"xb_y\"]\n",
    "        all_calibs.append(reduced_calib)\n",
    "\n",
    "        if \"all_obs\" in run:\n",
    "            model_tags.append(\"naive\")\n",
    "        elif \"masked\" in run:\n",
    "            model_tags.append(\"masked\")\n",
    "\n",
    "        # delete to save memory\n",
    "        del samples\n",
    "        del calibration_dict\n",
    "        del session_data\n",
    "        gc.collect()\n",
    "\n",
    "    gc.collect()\n",
    "\n",
    "    # save all datav\n",
    "    for file_name in dict_names:\n",
    "        data = globals().get(file_name)\n",
    "        if data is not None:\n",
    "            save_as_pickle(data, file_name, data_directory_fig_4)\n",
    "        del data\n",
    "        gc.collect()\n",
    "else:\n",
    "    print(\"Loading from file...\")\n",
    "    # Loading all files into a dictionary\n",
    "    loaded_data = {\n",
    "        file_name: load_individual_pickle(file_name, data_directory_fig_4)\n",
    "        for file_name in dict_names\n",
    "    }\n",
    "\n",
    "    # Dynamically create global variables from the keys of the dictionary\n",
    "    for key, value in loaded_data.items():\n",
    "        print(key)\n",
    "        globals()[key] = value\n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 4 Panel D, E, F\n",
    "### Plot mean across model seeds, samples from the model and calibration of decoded behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 669.291x216.535 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blues = plt.get_cmap(\"Blues\")(np.linspace(0.2, 0.95, int(len(run_dirs) / 2)))\n",
    "reds = plt.get_cmap(\"Reds\")(np.linspace(0.2, 0.95, int(len(run_dirs) / 2)))\n",
    "shift_m = {}\n",
    "shift_m[\"masked\"] = 0\n",
    "shift_m[\"naive\"] = 0\n",
    "# which condition mask setting to plot (xb_yd -> behav | neural activity)\n",
    "mask_string = \"xb_yd\"\n",
    "# which behavioral variable to plot (xb_d -> speed,  xb_y -> position)\n",
    "behav_var = \"xb_y\"\n",
    "# which session to plot (if only one session 0 only possible)\n",
    "session_ = 0\n",
    "\n",
    "\n",
    "# specify the time steps to be plotted\n",
    "t_min = 2600\n",
    "t_max = 3000\n",
    "fps = 15.625\n",
    "\n",
    "# read in scale_mu and scale_std\n",
    "with open(data_directory_fig_4 + \"scale_mu.pkl\", \"rb\") as f:\n",
    "    scale_mu = pickle.load(f)\n",
    "\n",
    "with open(data_directory_fig_4 + \"scale_std.pkl\", \"rb\") as f:\n",
    "    scale_std = pickle.load(f)\n",
    "\n",
    "time = np.arange(t_min, t_max) / fps\n",
    "\n",
    "# specify ylims according to the behavioral variable\n",
    "ylims = {}\n",
    "if behav_var == \"xb_d\":\n",
    "    ylims[\"x\"] = [-7.5, 4]\n",
    "    ylims[\"y\"] = [-2.5, 4.2]\n",
    "elif behav_var == \"xb_y\":\n",
    "    ylims[\"x\"] = [-35, 85]\n",
    "    ylims[\"y\"] = [-35, 90]\n",
    "\n",
    "# specify the index of the subplot\n",
    "dict_index = {\n",
    "    \"xnaivemean\": [1, 0],\n",
    "    \"ynaivemean\": [0, 0],\n",
    "    \"xmaskedmean\": [1, 1],\n",
    "    \"ymaskedmean\": [0, 1],\n",
    "    \"xnaivesample\": [1, 2],\n",
    "    \"ynaivesample\": [0, 2],\n",
    "    \"xmaskedsample\": [1, 3],\n",
    "    \"ymaskedsample\": [0, 3],\n",
    "}\n",
    "\n",
    "# ------------------ Set up the figure  ------------------\n",
    "\n",
    "fig, axes = plt.subplots(2, 5, figsize=cm2inch((17, 5.5)), sharex=False)\n",
    "plt.rcParams.update({\"font.family\": \"Arial\"})\n",
    "# Manually link the x-axes of subplots, excluding axes[0, 4] and axes[1, 4]\n",
    "for i in range(2):  # Loop over rows\n",
    "    for j in range(4):  # Loop over the first four columns\n",
    "        axes[i, j].sharex(axes[i, 0])\n",
    "\n",
    "\n",
    "# share y axis only rowwise for these plots\n",
    "for j in range(4):  # Loop over the first four columns\n",
    "    axes[0, j].sharey(axes[0, 0])\n",
    "    axes[1, j].sharey(axes[1, 0])\n",
    "# --------------------------------------------------------\n",
    "\n",
    "\n",
    "# plot the mean over many seeds \n",
    "count_idx = 0\n",
    "for x_index, xx in enumerate(\n",
    "    [\n",
    "        \"x\",\n",
    "        \"y\",\n",
    "    ]\n",
    "):\n",
    "    for tag in [\"naive\", \"masked\"]:\n",
    "        axs = axes[dict_index[xx + tag + \"mean\"][0], dict_index[xx + tag + \"mean\"][1]]\n",
    "        naiveflag = True\n",
    "        for mask_key in [\n",
    "            \"xb_yd\",\n",
    "        ]:\n",
    "            for q, mean_decoding in enumerate(all_mean_decoding):\n",
    "                if model_tags[q] == tag:\n",
    "                    axs.plot(\n",
    "                        time,\n",
    "                        scale_mu[xx]\n",
    "                        + np.array(\n",
    "                            mean_decoding[behav_var][model_tags[q]][mask_key][session_]\n",
    "                        )[0, x_index, t_min:t_max]\n",
    "                        / scale_std[xx],\n",
    "                        alpha=0.7,\n",
    "                        color=(\n",
    "                            blues[q - int(len(run_dirs) / 2)]\n",
    "                            if model_tags[q] == \"naive\"\n",
    "                            else reds[q]\n",
    "                        ),\n",
    "                        label=str(q),\n",
    "                    )\n",
    "            if behav_var == \"xb_d\":\n",
    "                axs.plot(\n",
    "                    time,\n",
    "                    session_xb_d_gt[q][0, x_index, t_min:t_max],\n",
    "                    color=\"k\",\n",
    "                    label=\"true\",\n",
    "                )\n",
    "            elif behav_var == \"xb_y\":\n",
    "                axs.plot(\n",
    "                    time,\n",
    "                    scale_mu[xx]\n",
    "                    + session_xb_y_gt[q][0, x_index, t_min:t_max] / scale_std[xx],\n",
    "                    color=\"k\",\n",
    "                    label=\"true\",\n",
    "                )\n",
    "            axs.spines[\"left\"].set_position((\"outward\", 4))\n",
    "            axs.spines[\"bottom\"].set_position((\"outward\", 4))\n",
    "            axs.set_ylim(ylims[xx])\n",
    "            count_idx += 1\n",
    "\n",
    "\n",
    "# plot the samples for a selected seed\n",
    "for x_index, xx in enumerate([\"x\", \"y\"]):\n",
    "    for q, samples_xb in enumerate(all_xb_d if behav_var == \"xb_d\" else all_xb_y):\n",
    "        for mask_key in [\n",
    "            \"xb_yd\",\n",
    "        ]:\n",
    "            if q == 3 or q == 29:\n",
    "\n",
    "                scaled_samples = (\n",
    "                    scale_mu[xx]\n",
    "                    + (\n",
    "                        np.array(samples_xb[model_tags[q]][mask_key][session_])[\n",
    "                            :, x_index, t_min:t_max\n",
    "                        ].T\n",
    "                    )\n",
    "                    / scale_std[xx]\n",
    "                )\n",
    "                # compute confidence interval per time step\n",
    "                conf_int = np.zeros((scaled_samples.shape[0], 3))\n",
    "\n",
    "                for t in range(scaled_samples.shape[0]):\n",
    "                    conf_int[t, 0] = np.quantile(scaled_samples[t, :], 0.025)\n",
    "                    conf_int[t, 1] = np.quantile(scaled_samples[t, :], 0.975)\n",
    "                    conf_int[t, 2] = np.quantile(scaled_samples[t, :], 0.5)  # median\n",
    "\n",
    "                axs = axes[\n",
    "                    dict_index[xx + model_tags[q] + \"sample\"][0],\n",
    "                    dict_index[xx + model_tags[q] + \"sample\"][1],\n",
    "                ]\n",
    "\n",
    "                axs.plot(\n",
    "                    time,\n",
    "                    scale_mu[xx]\n",
    "                    + (\n",
    "                        np.array(samples_xb[model_tags[q]][mask_key][session_])[\n",
    "                            :, x_index, t_min:t_max\n",
    "                        ].T\n",
    "                    )\n",
    "                    / scale_std[xx],\n",
    "                    \"-\",\n",
    "                    alpha=0.1,\n",
    "                    lw=0.5,\n",
    "                    color=\"steelblue\" if model_tags[q] == \"naive\" else \"darkred\",\n",
    "                    label=\"samples\",\n",
    "                )\n",
    "                axs.plot(\n",
    "                    time,\n",
    "                    scale_mu[xx]\n",
    "                    + np.array(\n",
    "                        all_mean_decoding[q][behav_var][model_tags[q]][mask_key][\n",
    "                            session_\n",
    "                        ]\n",
    "                    )[0, x_index, t_min:t_max]\n",
    "                    / scale_std[xx],\n",
    "                    alpha=1,\n",
    "                    color=(\n",
    "                        blues[q - int(len(run_dirs) / 2)]\n",
    "                        if model_tags[q] == \"naive\"\n",
    "                        else reds[q]\n",
    "                    ),\n",
    "                    label=\"mean recon\",\n",
    "                )\n",
    "                if behav_var == \"xb_d\":\n",
    "                    axs.plot(\n",
    "                        time,\n",
    "                        session_xb_d_gt[q][0, x_index, t_min:t_max],\n",
    "                        color=\"k\",\n",
    "                        label=\"true\",\n",
    "                    )\n",
    "                elif behav_var == \"xb_y\":\n",
    "                    axs.plot(\n",
    "                        time,\n",
    "                        scale_mu[xx]\n",
    "                        + session_xb_y_gt[q][0, x_index, t_min:t_max] / scale_std[xx],\n",
    "                        color=\"k\",\n",
    "                        label=\"true\",\n",
    "                    )\n",
    "                axs.spines[\"left\"].set_position((\"outward\", 4))\n",
    "                axs.spines[\"bottom\"].set_position((\"outward\", 4))\n",
    "                # only take unique labels\n",
    "                handles, labels = plt.gca().get_legend_handles_labels()\n",
    "                axs.set_ylim(ylims[xx])\n",
    "\n",
    "\n",
    "axes[1, 1].set_xlabel(\"time [s]\")\n",
    "axes[1, 0].set_ylabel(\"x [mm]\")\n",
    "axes[0, 0].set_ylabel(\"y [mm]\")\n",
    "\n",
    "# plot calibration\n",
    "percentile_label = [95, 90, 80, 60]\n",
    "# Loop through mask keys\n",
    "for mask_key in [mask_string]:\n",
    "    for q, calib in enumerate(all_calibs):\n",
    "        for percentile in percentile_label:\n",
    "            rand = 1 * np.random.rand()\n",
    "            for vid, x_index in enumerate([1, 0]):\n",
    "                axes[vid, 4].plot(\n",
    "                    shift_m[model_tags[q]] + q * 0.1 + rand + percentile,\n",
    "                    100\n",
    "                    * all_calibs[q][behav_var][model_tags[q]][mask_key][session_][\n",
    "                        percentile\n",
    "                    ][x_index],\n",
    "                    \".\",\n",
    "                    ms=4,\n",
    "                    alpha=0.8,\n",
    "                    color=(\n",
    "                        blues[q - int(len(run_dirs) / 2)]\n",
    "                        if model_tags[q] == \"naive\"\n",
    "                        else reds[q]\n",
    "                    ),\n",
    "                )\n",
    "\n",
    "                # Customize the ax4 subplot\n",
    "                # send all the way to the back\n",
    "                axes[vid, 4].plot(percentile_label, percentile_label, \"grey\", zorder=-1)\n",
    "                axes[vid, 4].spines[\"left\"].set_position((\"outward\", 4))\n",
    "                axes[vid, 4].spines[\"bottom\"].set_position((\"outward\", 4))\n",
    "                axes[vid, 4].spines[\"top\"].set_visible(False)\n",
    "                axes[vid, 4].spines[\"right\"].set_visible(False)\n",
    "                axes[vid, 4].set_xticks(percentile_label)\n",
    "                axes[vid, 4].set_yticks([20, 60, 100])\n",
    "\n",
    "plt.tight_layout(pad=0.5, w_pad=0.1, h_pad=0.1)\n",
    "file_name = f\"Fig_4_traces_monkey_decoding_all_seeds_mean_decoding_mask_{mask_key}_{behav_var}_{tag}_samples_and_calibration_small\"\n",
    "plt.savefig(f\"{local_dir}/figures/{file_name}.pdf\", bbox_inches=\"tight\")\n",
    "plt.savefig(f\"{local_dir}/figures/{file_name}.png\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 4 Panel B & C\n",
    "### Raches over the 8x8 grid and distribution of spike counts per neuron per time bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eeoqSkhLy8/MpKysjODiY2NhYiW+Lj48PO3fupLKykmvXrokCOHPmDEuWLOHixYt0d3fj7+9PYmIiGo2GCxcukJqaSmJiIiEhIRQVFZGVlWW2PxYWFsTHxxMYGIhKpeLQoUPU19eTnJxMVVXVhNeWmZl51ykLwPbt23Fzc6Ouro5Lly6Jg380oz8nODiYRYsWcezYMQBJ5NtEGI3GOR0nPGsCuHXrFn19fdja2rJ//36qqqp444036O/vF4+PxtyjejR/+ctfePvtt++5P9HR0Rw9epSsrCxWrVpFZGQkpaWlXL9+na+++oqAgABiY2Px8PAARp5KISEhBAUFkZ+fT2ZmJlVVVVRVVWFlZcWuXbskfv6PPPIISqWS9PR00tPTJ+yL0Wjk6tWr4p3XxPHjxyd8n1KpxMXFBYPBMCaKDEYc8kwbZ05OTlhbW2Nvb09ISIho5hyNi4sLOp0OS0tLtFotmZmZwIj1Z7LObv39/eOuq+YCsyaAgIAAMQWHQqEgJiYGo9HICy+8wPr168cM9oGBgQm/yNdee40dO3aIfxcXF0t82O+GVqslMTGRS5cuUV1dzcKFC8Wf8vJy8vLyOHz4MO7u7ixYsIDQ0FBsbW2xsLAgJiZGHBwAQ0NDHDhwgJdeekkUskKhICEhgZCQENrb2/Hz88PBwYGLFy+O2fyCEXt7W1sbt27dmvQ16PV68SloYWGBj48PDg4O9Pf3U11dLQ5+gE8//VS8qdja2hIUFERVVZWkvaGhITw8PGhoaJA8Vby9vSflEjI0NITBYJAFMB533kUiIiLQ6/V4enqKHo4mRpsgzeHl5TXh65Nh0aJFaLVaioqKyM/PJycnB39/fyIiInjyySepra2lpKSEy5cvk5GRQWBgIAsXLpRYe0ZvkH388cdERUURHx+PpaUlCoUCT09PPD09xfPXrVvHypUrycnJobCwUHyvlZWVuPOr0WhwdXU1K5TxMBqNYqY3Nzc3yd3fxMDAAIGBgaxevRqlUsl7770nOa+7u5vu7u4x/j23b98mJSWF8PBwwsPDx53imI7P5QCZWYsIO3XqFG5ubpKY2Ly8PNzc3FizZg2XL18WB4MgCFy6dImVK1fOeL9Mn//CCy+wevVqent7OXnyJF9++SWdnZ1s2rSJPXv2sGzZMtra2vjmm284ceKE+P7NmzdL2svPz+eTTz4Zc3cdja2tLatWreLJJ58Uj2VlZWFlZUVISAhDQ0OUlpZKglmSk5P5wQ9+wN69e8Vos6ioKLP+N21tbWMGv4nq6mr27dsnmjFv3ryJi4sLzzzzDN7e3gDiv6O9TRsaGrhw4YKYQMsclpaWqFSqcV045gKzZgbt7u4mIiKCtWvX8otf/ILKykpeffVVfvzjH/P6668TGhrKc889x2uvvcZf/vIX9u/fT0VFxaT3AaYzJrilpYWioiI+/fRTOjo6uHjxIk1NTWIC2cLCQskAc3Z2NjswXF1d2bZtm2RKcOfXf+nSJYmZEkZ8fSIjI3Fzc+Pjjz8GICUlhZMnTzI0NERRURFpaWns3r0bpVJJeXm5ZEo2EaaNMUCy0ejh4YGnpyc3b94ULVOjc/+YUKlULF++nIULF0puWCZOnDiBXq9n586dc3IxPKv7ADdu3OAnP/kJGRkZODo68tprr/Hv//7vKBQKMjMzef311ykuLiYqKop33nmH2NjYSbc93UHxCoUCNzc31q5di7u7O9evXycjI0P8T01JSRmTUcHKympMihDTgMvNzaW5uZnm5mZx06ympoa8vDzRDGkiPDycVatWkZeXx40bN/jVr37F+vXrUSgUdHR0oNFoxMGrUCiwsLAgKCiIBQsW4O7uTlFRETk5OWNcHkYP/hs3bqBWqyXrqIkwmVszMjI4cOAAzc3NXL58WYxJNg2r27dvc+LECXbs2IGnpyfZ2dlUVVXxxBNP3Ldr+XQwq2uAyMhIzpw5Y/a15cuXm03JN5u0tbVx+PBhoqOjiY+P5/jx46xfvx57e3uioqJEAezevZvKykrKysrGzZEzWszvvfee2Xny2rVrUSgUXLlyRfS/Wbp0Ke3t7QiCgLW19Rhb/OrVqwkJCZFkoIuNjcXJyYmzZ8+Kx6yspP/1JrOznZ0dGo0GvV4vDmZzmPYakpKS+N73vkdCQgI7d+6krKxM8vQxeaUWFBRgaWkpis6cu/dsMDd68R1CEATy8vI4fvw4HR0dHDx4kJqaGgICAoiOjgZGfJxiYmJ4/vnn2bp164R5gyorKyUB8aO5ePEigiBI0owMDg7y5JNPjkmxaJr7+/v7S17T6/WcOXNGMvgBSRIvk5coSF25J+Kzzz4TvUobGhr4+uuvSUtLw9/fn2eeeYa8vDyGh4dRKBRiBJlpr2H37t1zZjok5wW6C93d3TQ2NtLY2MiPfvQj0VJjiry6cOECNTU1eHh4sGTJEnGgaTQatmzZIi5c+/v7+dvf/gaYn3eHhIQwPDw8rqv1nWRnZ9Pc3ExdXR2vvvqqeHz16tUsXLiQ+vp6zpw5I/HtCQwMJDo6Gnt7ezH9YXZ2Nh988AEqlYrPP/+c2tracT9z9K73aFasWMGaNWvo7u7G1taWc+fOcePGDZycnFi5ciWWlpbiHsayZcvu+v8hCAKtra1UVVXh6el5V/+m+0EWwP9z9epViouLCQkJEefWnZ2d4n+4o6Oj6A69bNkyvLy8xIF669YtCgoKJA5sdnZ22NjYkJycLFppOjo6OHny5JSsIgkJCVy+fNnsayqVCpVKRWtr64RtREREEBMTg7W1NQUFBRQVFUn2Wezs7Ni8eTMeHh4IgkBTUxOXL1++a7t34unpydq1a1Gr1XR0dHD48OExnqWj4xfg75VocnNzxT0PUxC+ra0t8fHxLFq0aEr9mAqyAP6f0dYXFxcX8cfNzQ1PT0+USiV1dXVcvXpVHBimhZ0JUzY3GAmgqaysZHh4mK1bt4p7Hnfm0gGoqanh1KlT03Pho9i1axfu7u7o9Xry8/PF6zMNynXr1olOeDCyVoiLi5MkvKqrq7vrDvSd+Pr6Mjw8bDbN4rZt29DpdKSlpY37/iVLluDv74+Xl9eMrxVkAfw/Q0NDHDp0CAsLC3bu3DmuhcJ0xz916hRarZbHH39c8vrorAvPPPMMqampdHd3s2nTpgkLZQiCwPvvvz+jm0bBwcH4+flx4cIF4uPjiY2NJS8vb4zJVKvVEhYWhrOzM319fZw/f170dO3o6Jjyk+FumDY+IyMjSUxMnNa274YsgFG0tbVx6NAhtFotGo0GBwcHHB0dxX9HLy4rKio4d+4cGzduJCQkRNLO0aNHaWxsJDo6mtjYWFJTU6mvr2fNmjUsWLDA7GcXFxeTlpZGRETEuM5ur7zyCu3t7bS2ttLa2jqlnWFzKBQK7OzsMBgMk87obHKPvl+USiWbNm3C09OT8+fPU1NTw7PPPivZ7HsQyCGRo3Bzc2PDhg2UlJRQXV1NT0/PmKAWBwcH8Qfg7NmztLS0EBMTg62tLQqFgujoaBobGykoKGDFihVs2bKFS5cuiTun8fHxY6ZA6enpREZGkpCQMK4APvzwQzQaDb6+vhIfIdOceaqYotZgZOMrICAAJycnsrKyzLowa7Va7O3t77tu2LZt28jMzCQlJUU8tnHjxgc++EEWwBhCQkLEO7ppgPT09NDd3S35t76+XlwE5+fnk5+fj5WVlZhmBEZ8YIqLi3F1dWXp0qU4OTmRmZmJTqdj/fr1WFlZUVNTQ2pqKgEBASQkJNy1fy0tLWM2yiYa/KbF+90wbcpNxOj4g6myZMkS0eP0m2++GeMHNjAwIEnX/qCQp0D3gUkgX3/9Nf39/cTHx4uCqampGXP+ndMHk1lRqVSSkJDA0NAQzs7OCIIgLqbvRkhICCqVSuJINxpTyGR0dLSYzTojI0McjA+ynFF8fPy4MRAmTE/R0ekfR6NSqXjyySenLcRyyk+Al156ieeee85sHvqHndF3J9Pdyt7enu3bt7N//35UKpW4w9vW1sbBgwdxdnYmKSlJ8vT44x//KE6h4uLi0Ov1nDt3btzP1Wg0KJVKSR1f017CtWvXSEhIEGuSFRcXi2WZSktLxXhhOzs76urqqKmpkbRz7do18XfTnoSVlZUY93C32GRHR0d6enrMtnMndxv8gMTka66dvr6+aa1AM2UBODk5sXfvXgwGA7t27eLZZ58V/VIeZiYqTKdWqwkMDOTatWsEBASgVCrF+F9TrLGFhQVubm6sX7+e6OhoQkNDgZFB8eKLL1JSUoKXl5dZ0+GdU57RG2kKhYJPP/2U/fv3i2uHqqqqMQvk0QH8piS5o9spKiri2LFj1NbWSq51eHiY999/f9zvpbu7W9KOqd24uDjUarUkrbo53NzccHNzw8XFhe9///tiwt3R7Zii6GYisH7KRtY//vGP1NbW8tVXX2Ftbc2ePXvw8fERndrmK4mJiQwPD3Py5Emam5sl+TW/+uorTpw4QUpKCrt37xYHP4z4xJjy9oxXnuhuBAcHc+DAATFk9M5Isjsxt8AtLCwU1zWjMWeWNT0d7sbdBr9SqRRTK1ZUVEgGv4lnnnmGyMjIGcsqcU+7DAqFgnXr1vGnP/2J0tJS9u7dy7vvvktiYiLBwcH813/917hzuIeVrq4uHBwcaG5u5vDhwxO6I+fn53P+/HkuX748JhGtOaysrAgKChJ3lCfD3QLWYWTz76OPPqK9vZ2tW7dKrDCDg4Pcvn2bzz//XPIeR0dHs6IoLi6+6yL6TvR6Pbm5uZSUlEhCOLOysjhx4gQffPDBjOcXvadFcE9PDykpKRw4cIBTp07h4+PDM888wzPPPENDQwP/8i//goeHBydPnpyJPk+KmVgE37kGMLkv3806YponC4JAUFAQe/bsoaenhxdffHHCZF+vvPIK1tbW6PV6zp49K0nHMp4f/2QxLcizs7Pp7u7G0dGRuLg4YmNjaWhoGBORZyI8PJyKigrs7OwICQkRE3a98847BAYGSlyzp4K9vT27du2aMFZiJpiyAB5//HHOnDmDi4sLTz/9NM8999yYWlkHDhxg7969s5oOYyatQIIg0NjYKLFjA4SGhuLt7Y1Wq+XWrVvk5uaKi9AdO3ZQUVEx6QqKWq1W9Lefa8TFxeHi4kJTU5MkmN7V1ZX29nYsLS3Zvn27mBTg/PnzknSSJuzs7FizZg11dXXcuHGD4OBg1q5dO8bTdSaZ8iJYq9XyzTffTLjwXbNmzaQjkr6LHD9+XLwbe3h40NzcLGZRM+XljIyMJDw8XPQAPXr06JQ+o6mp6b7s7jNJbm4uRqNRkjwsLi6OuLg4Tpw4gYWFBR4eHvT393PixAl0Oh2bN2/GycmJAwcOiO+xsbHB0tKSxMREvL29uXDhAocOHSIpKWncJGLTzZQFYC5j8Z3cGfj9sBEdHS0KwMnJiYSEBGpqamhsbCQ7O5uhoSEsLCymNGd/0NjZ2bFgwQL8/f2xtrYmJSVFUoppIoxGI9HR0cTFxXHgwAG6u7spLS0lJiYGBwcHqqqq6Ojo4PTp0xgMBjEHkSl5744dO7C1teXSpUucOHGC4OBgEhIS2LVrF6mpqRw+fFiMvGttbcXBweG+Ex6Mh7wRdo+YEntlZWUxODhIVFQU0dHRWFpaUl9fz9mzZyfMYzRT2NvbExYWRltbm8Tef79s3rxZrA1cWFjIrVu3sLCwEGuKdXZ2snbtWhobG8XoNUdHR5KTk8WFbFNTE0eOHBG9VAVBoLi4WMyTZIpwM7f3sHfv3hnZd5JdIe4RhULBwoULCQ4OJi8vj+vXr5OTkyNOhSwsLPD19UWv10+79+RE9Pb2TsqyNBUSExMJCAgARrLi+fj4oNPpyMnJoaysTDR3Xrx4URL99sQTT0jMl6bvoaWlhYKCAlpbWyWm0sHBQerr6yW+TcuXLycgIGDGNl1lAdwnNjY2LFiwQLzjmu76w8PDE0ZXzVWWLl06Jha7sLAQf39/yZTOycmJsLAwysrKSEpKoqioiPr6ejH9S3BwsGTw63Q6Ll26BEBaWhoajQYvLy+xfoBaraa5uZm0tDR0Oh3x8fHExMTM+AarPAW6T9rb2/nqq6+AkcDyO1OazBR+fn4EBgaKgSUbN24UF5UWFhZYWVlhaWk5JgJtPN8fhUKBSqWit7d3jAOdjY0Nw8PDLF68mPj4eDFIxZTVeu/evVhYWLB//376+vrEgBuTj1NBQYG4m61QKNizZ8+42eKGh4fJyckhNzeXkJAQ1q1bNyaAfzSmzHb3ivwEmAJ37gMAkjxFkx3892vDd3R05Omnn6anpwcPDw9+/etf09jYOCaYxBS7vGTJEpqammhubsbV1ZWkpCSUSiXHjx8X92pOnjxJdXU1jY2NlJeXS6rKGAwG0eZvaWkp7tj29PRgaWlJY2Mj27ZtY8WKFZJrMufjtHbt2glTJZoGe1BQEP/2b//GsWPHePTRR1EqlbS0tFBRUUFJSYnEqXCi1PB3Q34CTBJzj2JBEOjo6JCY9u7GnX4zAwMDPPXUU+PWHIARd4mAgABxemFqo6CggGvXrhEaGspPfvITYmNjWbp0qThf7urq4ssvvxRzgEZERJCQkCC+vnz5cmJjY7l9+za+vr5kZmaSm5uLQqGY0AfIz88PKysrSX8GBwcZGBjAwcGBuro6Pv74Y44fPy7GVJu8Up988slxE+uO/o4dHBxITEwcU0HoThYsWCCmj7kX5CfAPWJlZUVmZuZ9LzhtbW0nHPwwYnYMDw9n48aNXL9+XRTAkiVLiIyMpL6+Hnt7e3Jzc6murmbdunV4eHiIm1QDAwM88sgjEh8kGNkN7u7u5uTJkyxbtowVK1aQnp7O6tWrJR6Xo6vQwEiyK5Nlx8bGhi+//BKdTocgCPzgBz8QHdxefPFFuru7uXbtmphCpaWlBbVaLRbs0Ol0YrWcNWvWEBgYOG5gTEhICAsXLkSr1U44LZoKsgDuAVPwiinZ01RDBDs6OiSp0yeDKWj+zjtdVVUVSqVSjOwyZWMYzY4dO2hvb+fdd9/FwsKC73//+1hYWNDY2EhUVBQODg5kZWVhNBqJi4ujsbGRRYsWoVAo8PDwMLshZ9rlj4+PF9NArlq1Cvh7mSt7e3vs7OyIjo4WBXDhwgVJIP5oIiIixN97enrIysriz3/+M2VlZaSlpbFx48YpfWeTQRbAJDH5/5tigHfu3ElCQgIHDx40e/54OXRM07LRf0+1H6PbMBgMfPLJJ9jb21NfX09JSckY9+nRu9Du7u7iIraoqIif/OQnuLu74+Pjww9/+EOGh4cJDAwULTbj7UabZs6XLl3iq6++4vTp05Jr+vTTT82We7KwsJA409nb27N48WIqKytZvnw5v/3tb6mqqmJ4eNhsBo3pRl4DTAHTRo6jo6PoAmGyliiVSpKSkvDy8qK3t5erV6+Om5F5qpir3Hi/bNq0CVdXV7788kvJcaVSOW2evLGxsTg7O2Nra8upU6ck0zC9Xk9paSmVlZUSwd6ZNwigpKSEixcv8g//8A/TLgb5CTAFjEYj9vb2WFlZ0d/fj6enJytXriQwMBCFQoHRaKSgoGBcB7ZNmzYhCAKpqanASD2CuznH2dvb4+HhgUajwc3NDbVajdFoZGBgYMxUZyqMl5P1Xga/ra0trq6utLa28vLLL1NbW8uJEycICgrC3d1d3JEe7aKtVCrRarV3vUn09vZSUFBgtmjhdCALYAp4eXmxZ88es6+NTn14J48++iiBgYHi335+fty+fZuysjJWrFiBm5ubmHzK0dGRmJgYfH19aWtrE82Xubm5DA4OolAocHV1NVsCaSbx9fVl48aNos399OnT9Pf309TUxMDAAA0NDaIXp4+PD3Z2dmKl+ZaWFrFMbG9vLzqdjuvXr3Pr1i08PDzYvn07FRUVY1wguru7OXbsGIIgsG3bthmZCs1pAej1en70ox9x8OBB7Ozs+NnPfsZPf/rT2e6WiCAIDA4Oms10FhYWRmtrKx0dHTg4OKDT6cSEtO3t7Tg7O+Pr60tZWZnoJAYjnrSmAnSOjo6icARBoLa2lnPnzj2Qwb98+XKWLFkyrgtCZ2cnvr6+rF27VjQDGwwGmpub0Wq1uLu7iwvllpYWBgYG+OKLL8ZkqFixYoVY+M/Kyorh4WEsLS3p6Ojg+PHjWFlZsW3btvn5BHjzzTe5du0a586do6amhpdeeomAgABJJZXZ5Jtvvhk3aNxk9QD4+uuvzZ5jrojGhQsXCAgIwNraGpVKRVdXFyUlJTOWMS44OBgHBwdsbGwkge2ZmZlkZmai0Whwd3cXp2EuLi4IgoBOp0OtVuPi4sIrr7zCgQMH6Onp4caNG2i1WpRKpTjYTRkyPDw8SEhIYHBwUNwgS0lJQaVSsXjxYiIiIrC0tKS1tZXjx4+jUqlITk6e0Rpjc1YAvb29vPfee5w4cYKlS5eydOlSbty4wZ/+9Kc5I4AVK1aQmZkp+gOZFnzW1tY0NzeTkpLCqlWr0Gq1DA4OMjg4SGZmJp2dnZKs0SbUajV2dnaUl5dPy6LXXGVNLy8v/Pz88PPzw9XVFUEQOH369JhifL6+vgQEBNDS0kJjYyMlJSUIgoBSqcTe3l4sf2qqU/D888+TlpZGVVWVmOrFtLhdvHgxGo0GjUZDQUEBZWVlWFpaEhoaSnh4OJ6enmJbpaWlXLlyBWdnZ5KTk+/LzWEyzFkBXL9+ncHBQUmyqNWrV/PrX/8ao9E4JwosaDQasTj2nZju2IGBgRInMlM94kuXLlFbW4ufnx/Lly8XA0AOHDggGfzOzs4sXrwYlUpFdnY27e3tk+7fnYPf3d1drKfc2tpKe3v7mMB1Nzc3du/ePaatwcFBWlpaqK+vF53l0tLSyMvLw9vbGx8fHyIjI6mqqiI9PR13d3dR3AkJCZSWlnLw4EFsbW2JjY0lIiJCsuHV1tZGeno6TU1NhIaGsnr16gcSGTZnBdDQ0IC7u7vkS9Bqtej1etra2sYEfd9PoeyZwCTQ0VMXo9Eouk2rVCo2b94suhmbCAgIwGAwsGnTJjQaDUajkYyMDNEufy/4+vpSW1srccsend4wLCyMrq4uWlpaxt1ssra2xtvbG29vb6ytrbl69SqbN2+mrq6Ouro6SRqWqqoqDAYDAwMDGI1GMjMzyc/PZ+HChSQmJkrWFQaDgezsbAoLC1Gr1Tz22GNiUb4HwZwVwHjFsmHsnQ3uv1D2dHOnADo7Ozl//jytra1ERUURFxdndjt/+fLlxMfHU19fP+Ea469//au4vxEXF4erqyvx8fF4e3ujUCg4ePAgXV1dqNXqcd2yt2/fztDQEN9++y0KhYLk5GTUavVdr62zsxONRkNAQIAoYHt7e3x8fFi8eDFbtmwR3TsqKyvJz89n1apVLFmyRGxDEAQqKyu5cuUKBoOB5cuXs3jx4geebG3OCkCpVJotlg2YXRTdb6Hs6Wa0AG7fvk1qair29vY8/vjj4+bVMRqNlJWVcfHixXHbXbFiBTExMcDI08LNzY3XXntN9Pxsbm7myJEj4vmmKY6npyfNzc0YjUY2bdpEQEAAWVlZXL9+HV9fXzZs2DDp5LQGg0FyczKZJ8vLyykvL6egoIA333wTQFzsmmqQmfqUnp5OfX09QUFBrFq1asasPHdjzgrAx8eH1tZWhoaGxDtlY2MjdnZ2Zu9S01EoezoxCaC6upqcnBz8/PzYuHGjpDaXCaPRSHp6usQF2RzPP/+8ZKAMDQ1RXl6Os7Mz2dnZlJeXSzJxuLi4sHTpUvz8/LCwsOCDDz4QLTQpKSk0NzezYsUKoqKipmxjn+j8iooKnn76afbv3y8ea2trQ61Wk5ubS35+Pg4ODmzdulVSe3g2mLMCMJX0ycjIYPXq1QCkp6dLAjLmMibRZmdn4+fnx+bNm80OmtOnT0tShvj5+bFy5UpcXFzYt28ffX19+Pj4kJycLHm/nZ0dWq1WLGaRn59PcHAwa9asEadBozHN+ZuamkhJScHR0ZEdO3ag1WqnfG2T8Z4ZvXZzdHSktbWVM2fO0N/fT2xsLDExMXMit+ycFYBKpeKll17i9ddf58MPP6Suro7f/e53fPjhh7PdtUnh5OSEk5MTOp2OiIiIceMJTG4C5u7E27dvB/5eAbKzs5PGxkaOHTsmcXD753/+ZwICAiZ0EQ4KCuL5559naGiI4eFhnJyczD6NJstoEdyZ1tyUMt6E0WgkLS0NPz8/HnvsMTE36VxgzgoA4Pe//z0//OEP2bBhA87Ozrz99tt3DZCYK5jSR6akpEwYALJ3795x2zAN/NFhlwDe3t5s2bKFlpYWtm/fPqmpn0KhmLZ5tpOT05hEV6MFMTw8TEFBASEhIWKl+U2bNok+U3OJOS0AlUrFxx9/zMcffzzbXbknvLy87suD0eQDNLrWwAsvvEBHRwcnTpwgIiJiVvIvOTg40NvbO25Bi7KyMvr6+oiLiyM+Ph47O7v7etrMJHNaAA8D9zL4GxoayM3Npba2FrVazfr16wkNDcXCwoKOjg7OnDmDt7c3iYmJs3JHdXNzY3h4mMrKyjH10Ux7HYGBgZMyqc42sgDmCIIgiLusDQ0NuLq6snHjRoKDg1EoFAwMDFBWViZaUJKSkmbNGODp6UlQUBDnzp2jvLwcLy8vhoaGMBqNNDU1odPpSEpKmpW+TRVZAHOA0W4AGo2GzZs3i9XRGxsbKSoqorq6GkEQxHDMB5lA9k5MkXFlZWWUlpaSnZ2NIAhYWlqi1WrFtIbfBWQBzCLDw8Pk5uaSl5eHs7MzW7duFV2ha2trycnJoampCWdnZ5YtW0Z4ePisVFI0h4WFBQsXLmThwoWA+QLg3wVkAcwSzc3NYtlUk13cwsKC27dvk5OTQ3NzMx4eHmzZsgU/P785P7Dmev/GQxbAA2ZoaIisrCwKCwtxd3dn165duLi4cOvWLXJycmhpaUGr1ZKcnIyPj893dmB9V5AF8ACpr6/n4sWL9Pb2ihFXNTU1nD9/nra2Njw9Pdm2bZvZnVyZmUEWwANAEASuXbtGbm4uXl5ebNmyhfb2dg4dOkRbWxve3t6T3tCSmV5kAcwwBoOB8+fPc+vWLeLi4lCpVJw6dYquri58fHzkgT/LyAKYQYxGI8eOHaO1tRVPT0+KiorQ6/UEBgayfv36e3JEk5leZAHMIKWlpZKiEOHh4URFRc146U+ZySMLYAZxcHDA3d0dPz8/Fi9ePGds+DJ/RxbADGLKviAzd5n7kSUyMjOILACZeY0sAJl5zUObHr2vr4+SkhIWLlw4o6n1ZL7bPLQCkJGZDPIUSGZeIwtAZl4zr/YBTOsCmfnBZNZ/80oAJSUl91SYWua7yWTqw82rRfBcfAKYcpju27dPUib0YeRBX6v8BLgDlUo1bRUjp5uIiIg527fpZi5dq7wIlpnXyAKQmdfIAphlvLy8+MUvfjEvosLm4rXOq0WwjMydyE8AmXmNLACZeY0sAJl5jSyAWUKv17N3717UajVeXl78z//8z2x3aVo5dOgQCoVC8mMqcJ6bm8uKFStQqVTEx8eTnZ09a/2UBTBLvPnmm1y7do1z587x5z//mbfffltSBea7TlFREdu3bxfrNzc0NPDee+/R29tLcnIya9asITs7m4SEBLZt20Zvb+/sdFSQeeD09PQISqVSOH/+vHjsP//zP4V169bNWp+mmz179gg///nPxxx///33haCgIMFoNAqCIAhGo1EIDQ0VPvzwwwfcwxHkJ8AscP36dQYHB0lISBCPrV69mqtXr0oqy3+XKSoqIjw8fMxxU9VPU+5ThUJBYmIiV65cedBdBOQp0KzQ0NCAu7u7pMiFVqtFr9fT1tY2iz2bHgRBoLS0lFOnThEeHk5ISAhvvfUWBoOBhoYGvL29Jedrtdpxq9nPNPPKGW6u0NfXJ6m0Doh/DwwMzEaXppVbt26J17h//36qqqp444036O/vH/faZ+u6ZQHMAkqlcsx/uOnvhyGAPyAggLa2NlxcXFAoFMTExGA0GnnhhRdYv3692WufreuWp0CzgI+PD62trQwNDYnHGhsbsbOz+05UVpwMrq6ukhoHERER6PV6PD09aWxslJzb2Ng4a/5BsgBmgZiYGKytrcnIyBCPpaenEx8fP2uVH6eTU6dO4ebmRl9fn3gsLy8PNzc31qxZw+XLl8WaYoIgcOnSJVauXDk7nZ0V25OM8NprrwmRkZFCZmamcOjQIcHJyUk4ePDgbHdrWtDpdIKPj4/w3HPPCSUlJcLx48cFb29v4b//+7+Frq4uQaPRCG+88YZw48YN4Y033hA8PT2Fnp6eWemrLIBZore3V/je974n2NvbC97e3sL//u//znaXppXCwkIhKSlJcHBwELy8vIT/+I//EG3/V69eFWJjYwWlUiksX75cyMnJmbV+yu7QMvOa7/6EU0bmPpAFIDOvkQUgM6+RBSAzr5EFIDOvkQUgM6+RBSAzr5EFIDOvkQUgM6+RBSAzr5EFIDOvkQXwkPL+++9ja2tLRUUFMFIcRKlUcuTIkVnu2dxCdoZ7SBEEgQ0bNuDo6MjRo0dZt24dvr6+fPbZZ7PdtTmFLICHmLKyMqKjo9m1axepqancuHEDd3f32e7WnEKeAj3EhIeH89Zbb/HZZ5/xu9/9Th78ZpAF8JBz/fp1LC0tOXfu3Gx3ZU4iC+Ah5siRI5w6dYpjx47x6aefyiIwg7wGeEjp7u5m0aJF/OM//iM///nP+elPf8rRo0cpKChAqVTOdvfmDLIAHlL+6Z/+iVOnTlFYWIiNjQ3d3d0sWLCAl19+md/85jez3b05gywAmXmNvAaQmdfIApCZ18gCkJnXyAKQmdfIApCZ18gCkJnXyAKQmdfIApCZ18gCkJnXyAKQmdfIApCZ1/wf3LIHrBgwwrAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 157.48x118.11 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 118.11x118.11 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load\n",
    "data_dir = \"../../data/monkey/\"\n",
    "with open(data_dir + \"/PD_test.pkl\", \"rb\") as f:\n",
    "    PD_test = pickle.load(f)\n",
    "\n",
    "# plot the continuous reaches\n",
    "\n",
    "plt.figure(figsize=cm2inch((4, 3)))\n",
    "plt.plot(\n",
    "    PD_test.target_pos[0][0, :],\n",
    "    PD_test.target_pos[0][1, :],\n",
    "    \"o\",\n",
    "    ms=2,\n",
    "    color=\"black\",\n",
    "    alpha=0.8,\n",
    ")\n",
    "plt.plot(\n",
    "    PD_test.cursor_pos[0][0, :],\n",
    "    PD_test.cursor_pos[0][1, :],\n",
    "    color=\"grey\",\n",
    "    alpha=0.8,\n",
    "    lw=1,\n",
    ")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "\n",
    "# move axes outwards\n",
    "plt.gca().spines[\"left\"].set_position((\"outward\", 4))\n",
    "plt.gca().spines[\"bottom\"].set_position((\"outward\", 4))\n",
    "plt.gca().spines[\"right\"].set_visible(False)\n",
    "plt.gca().spines[\"top\"].set_visible(False)\n",
    "\n",
    "plt.savefig(f\"{local_dir}/figures/reaches_test.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.savefig(f\"{local_dir}/figures/reaches_test.pdf\", dpi=300, bbox_inches=\"tight\")\n",
    "\n",
    "# plot the spike count distribution\n",
    "top = 8\n",
    "plt.figure(figsize=cm2inch((3, 3)))\n",
    "bins = np.linspace(-0.5, top - 1.5, top)\n",
    "plt.hist(\n",
    "    PD_test.get_session(0)[\"xa_m\"][0, :, :].flatten(),\n",
    "    bins=bins,\n",
    "    color=\"grey\",\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(\"spike count\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.xticks(np.arange(0, top - 1, 1))\n",
    "# get rid of right and top spines\n",
    "plt.gca().spines[\"right\"].set_visible(False)\n",
    "plt.gca().spines[\"top\"].set_visible(False)\n",
    "plt.gca().spines[\"left\"].set_position((\"outward\", 4))\n",
    "plt.gca().spines[\"bottom\"].set_position((\"outward\", 4))\n",
    "plt.xlim([-0.5, 6])\n",
    "\n",
    "plt.savefig(f\"{local_dir}/figures/spike_count_dist.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.savefig(f\"{local_dir}/figures/spike_count_dist.pdf\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nbc",
   "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": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}