{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9dbe513",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib widget\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import mat73\n",
    "import pickle\n",
    "\n",
    "import MARBLE\n",
    "from MARBLE import plotting\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import neo\n",
    "from elephant.statistics import instantaneous_rate\n",
    "from elephant.kernels import GaussianKernel\n",
    "from quantities import ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fd6f954",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data compiled into matlab cell array\n",
    "!wget -nc https://dataverse.harvard.edu/api/access/datafile/6963157 -O data/conditions_spiking_data.mat\n",
    "spiking_data = mat73.loadmat('data/conditions_spiking_data.mat')['all_results']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c578b742",
   "metadata": {},
   "outputs": [],
   "source": [
    "trial = 1\n",
    "session = 9\n",
    "\n",
    "spikes, colors = [], []\n",
    "for cond in range(7):\n",
    "    st = spiking_data[session][cond][trial][0][:,:]\n",
    "    spikes += [np.where(st[ch,:])[0] for ch in range(24)]\n",
    "    colors += [cm.viridis(cond / 6) for _ in range(24)]\n",
    "\n",
    "_, ax = plt.subplots(figsize=(5,4))\n",
    "ax.eventplot(spikes, color=colors)\n",
    "\n",
    "_, ax = plt.subplots(figsize=(5,4))\n",
    "gk = GaussianKernel(100 * ms) # increase this for smoother signals (previously used auto)\n",
    "\n",
    "for sp in spikes[:24]:\n",
    "    st = neo.SpikeTrain(sp, units='ms', t_stop=1200)\n",
    "                        \n",
    "    inst_rate = instantaneous_rate(st, kernel=gk, sampling_period=1 * ms).magnitude\n",
    "    ax.plot(inst_rate, 'C0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4424eb17",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget -nc https://dataverse.harvard.edu/api/access/datafile/7062086 -O data/raw_data_session_9_3D.pkl\n",
    "pos, vel, time, _ = pickle.load(open('data/raw_data_session_9_3D.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d8a958a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "n_traj=10\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from mpl_toolkits.mplot3d.art3d import Line3DCollection\n",
    "\n",
    "for i,cond in enumerate([1,4,6]):\n",
    "    ax = fig.add_subplot(int('13{}'.format(i+1)), projection='3d')\n",
    "    ax.view_init(elev=10., azim=90)\n",
    "    starts = np.where(time[cond]==0)[0]\n",
    "    for j in range(n_traj):\n",
    "        t = range(starts[j], starts[j+1]-1)\n",
    "        p = pos[cond][t]\n",
    "        segments = np.stack([p[:-1], p[1:]], axis=1)\n",
    "        \n",
    "        colors = [(0, 0, 0), cm.viridis(cond/6)] # first color is black, last is red\n",
    "        cmap = LinearSegmentedColormap.from_list(\"Custom\", colors, N=len(time[cond][t]))\n",
    "        r = cmap(np.linspace(0,1,len(time[cond][t])))\n",
    "        \n",
    "        ax.add_collection(Line3DCollection(segments,colors=list(r)))\n",
    "        ax.set_xlim([min(pos[cond][:,0]), max(pos[cond][:,0])])\n",
    "        ax.set_ylim([min(pos[cond][:,1]), max(pos[cond][:,1])])\n",
    "        ax.set_zlim([min(pos[cond][:,2]), max(pos[cond][:,2])])\n",
    "        \n",
    "        ax.scatter(pos[cond][starts[j],0],pos[cond][starts[j],1],pos[cond][starts[j],2],color=colors[0])\n",
    "        ax.scatter(pos[cond][starts[j+1]-1,0],pos[cond][starts[j+1]-1,1],pos[cond][starts[j+1]-1,2],color=colors[1])\n",
    "        ax.get_xaxis().set_ticks([])\n",
    "        ax.get_yaxis().set_ticks([])\n",
    "        ax.get_zaxis().set_ticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b0b713",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = MARBLE.construct_dataset(pos, features=vel, graph_type='cknn', k=10, stop_crit=0.05, local_gauges=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb29a0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_plot = data.to_data_list()\n",
    "for i in [1,4,6]:\n",
    "    d = data_plot[i]\n",
    "    c = [(0, 0, 0), cm.viridis(i/6)] # first color is black, last is Ci\n",
    "    cmap = LinearSegmentedColormap.from_list(\"Custom\", c, N=140)\n",
    "    ind = np.linspace(0,1,140)\n",
    "    colors = cmap(ind[time[i][d.sample_ind].astype(int)])\n",
    "    plotting.fields([d], view=(10,90), figsize=(3,3), scale=2, width=7., color=colors, axes_visible=False)\n",
    "    plt.axis('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43d0816b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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