{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detector simulation\n",
    "In this notebook we load a track dataset generated by `edep-sim` and we calculate the ADC counts corresponding to each pixel. The result is exported to a HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is need so you can import larndsim without doing python setup.py install\n",
    "import os,sys,inspect\n",
    "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parentdir = os.path.dirname(currentdir)\n",
    "sys.path.insert(0,parentdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import ceil\n",
    "from time import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm, colors\n",
    "import mpl_toolkits.mplot3d.art3d as art3d\n",
    "\n",
    "import numpy as np\n",
    "import cupy as cp\n",
    "import fire\n",
    "import h5py\n",
    "\n",
    "from numba import cuda\n",
    "from numba.cuda.random import create_xoroshiro128p_states\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 100\n",
    "\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from larndsim import consts\n",
    "import importlib\n",
    "importlib.reload(consts)\n",
    "consts.load_properties(\"../larndsim/detector_properties/ndlar-module.yaml\",\"../larndsim/pixel_layouts/multi_tile_layout-3.0.40.yaml\")\n",
    "\n",
    "from larndsim import quenching, drifting, detsim, pixels_from_track, fee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from larndsim.consts import detector, physics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = cp.load('../larndsim/bin/response_38.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset import\n",
    "First of all we load the `edep-sim` output. For this sample we need to invert $z$ and $y$ axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('lbnfSpillLAr.edep.h5', 'r') as f:\n",
    "    tracks = np.array(f['segments'])\n",
    "    \n",
    "selected_tracks = tracks[:100]\n",
    "    \n",
    "x_start = np.copy(selected_tracks['x_start'] )\n",
    "x_end = np.copy(selected_tracks['x_end'])\n",
    "x = np.copy(selected_tracks['x'])\n",
    "\n",
    "selected_tracks['x_start'] = np.copy(selected_tracks['z_start'])\n",
    "selected_tracks['x_end'] = np.copy(selected_tracks['z_end'])\n",
    "selected_tracks['x'] = np.copy(selected_tracks['z'])\n",
    "\n",
    "selected_tracks['z_start'] = x_start\n",
    "selected_tracks['z_end'] = x_end\n",
    "selected_tracks['z'] = x\n",
    "# selected_tracks['eventID'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.lib.recfunctions as rfn\n",
    "\n",
    "if 'n_photons' not in selected_tracks.dtype.names:\n",
    "    n_photons = np.zeros(selected_tracks.shape[0], dtype=[('n_photons', 'f4')])\n",
    "    selected_tracks = rfn.merge_arrays((selected_tracks, n_photons), flatten=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quenching and drifting\n",
    "We calculate the number of electrons after recombination (`quenching` module) and the position and number of electrons after drifting (`drifting` module)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext wurlitzer\n",
    "importlib.reload(drifting)\n",
    "TPB = 256\n",
    "BPG = ceil(selected_tracks.shape[0] / TPB)\n",
    "quenching.quench[BPG,TPB](selected_tracks, physics.BIRKS)\n",
    "drifting.drift[BPG,TPB](selected_tracks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find the pixels intersected by the projection of the tracks on the anode plane using the Bresenham's algorithm. We also take into account the neighboring pixels, due to the transverse diffusion of the charges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(pixels_from_track)\n",
    "unique_eventIDs = cp.unique(selected_tracks['eventID'])\n",
    "\n",
    "longest_pix = ceil(max(selected_tracks[\"dx\"])/detector.PIXEL_PITCH)\n",
    "max_radius = ceil(max(selected_tracks[\"tran_diff\"])*5/detector.PIXEL_PITCH)\n",
    "\n",
    "TPB = 128\n",
    "BPG = ceil(selected_tracks.shape[0] / TPB)\n",
    "\n",
    "max_pixels = np.array([0])\n",
    "pixels_from_track.max_pixels[BPG,TPB](selected_tracks, max_pixels)\n",
    "\n",
    "max_neighboring_pixels = (2*max_radius+1)*max_pixels[0]+(1+2*max_radius)*max_radius*2\n",
    "active_pixels = cp.full((selected_tracks.shape[0], max_pixels[0]), -1, dtype=np.int32)\n",
    "neighboring_pixels = cp.full((selected_tracks.shape[0], max_neighboring_pixels), -1, dtype=np.int32)\n",
    "n_pixels_list = cp.zeros(shape=(selected_tracks.shape[0]))\n",
    "\n",
    "pixels_from_track.get_pixels[BPG,TPB](selected_tracks,\n",
    "                                      active_pixels,\n",
    "                                      neighboring_pixels,\n",
    "                                      n_pixels_list,\n",
    "                                      max_radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapes = neighboring_pixels.shape\n",
    "joined = neighboring_pixels.reshape(shapes[0]*shapes[1])\n",
    "unique_pix = cp.unique(joined)\n",
    "unique_pix = unique_pix[(unique_pix != -1),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Charge distribution calculation\n",
    "Here we calculate the current induced by each track on the pixels, taking into account longitudinal and transverse diffusion. The track segment is parametrized as:\n",
    "\\begin{align}\n",
    "x'(r') &=x_s + \\frac{\\Delta x}{\\Delta r}r'\\\\\n",
    "y'(r') &=y_s + \\frac{\\Delta y}{\\Delta r}r'\\\\\n",
    "z'(r') &=z_s + \\frac{\\Delta z}{\\Delta r}r',\n",
    "\\end{align}\n",
    "where $\\Delta r$ is the segment length. Here we assume $z$ as the drift direction.\n",
    "The diffused charge distribution is calculated with the following integral:\n",
    "\\begin{equation}\n",
    "\\rho(x,y,z) = \\frac{Q}{\\sqrt{(2\\pi)^3}\\sigma_x\\sigma_y\\sigma_z\\Delta r}\\exp\\left[-\\frac{(x-x_s)^2}{2\\sigma_x^2}-\\frac{(y-y_s)^2}{2\\sigma_y^2}-\\frac{(z-z_s)^2}{2\\sigma_z^2}\\right]\\int^{r'=\\Delta r}_{r'=0}dr'\\exp[-(ar'^2+br')],\n",
    "\\end{equation}\n",
    "where \n",
    "\\begin{align}\n",
    "a &= \\left[\\left(\\frac{\\Delta x}{\\Delta r}\\right)^2\\frac{1}{2\\sigma_x^2} + \\left(\\frac{\\Delta y}{\\Delta r}\\right)^2\\frac{1}{2\\sigma_y^2} + \\left(\\frac{\\Delta z}{\\Delta r}\\right)^2\\frac{1}{2\\sigma_z^2} \\right]\\\\\n",
    "b &= -\\left[\\frac{(x-x_s)}{\\sigma_x^2}\\frac{\\Delta x}{\\Delta r}+\n",
    "\\frac{(y-y_s)}{\\sigma_y^2}\\frac{\\Delta y}{\\Delta r}+\n",
    "\\frac{(z-z_s)}{\\sigma_z^2}\\frac{\\Delta z}{\\Delta r}\\right].\n",
    "\\end{align}\n",
    "\n",
    "The simmetry of the transverse diffusion along the track allows to take a slice on the $xy$ plane and solve the integral once at a fixed $z$ coordinate (e.g. at $z_{m} = (z_s+z_e)/2$) and re-use it at other $z$ coordinates away from the endpoints (where $\\rho(x,y,z)$ varies along $z$ so must be calculated at each $z$). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_length = cp.array([0])\n",
    "track_starts = cp.empty(selected_tracks.shape[0])\n",
    "threadsperblock = 128\n",
    "blockspergrid = ceil(selected_tracks.shape[0] / threadsperblock)\n",
    "detsim.time_intervals[blockspergrid,threadsperblock](track_starts, max_length, selected_tracks)\n",
    "\n",
    "signals = cp.zeros((selected_tracks.shape[0],\n",
    "                    neighboring_pixels.shape[1],\n",
    "                    cp.asnumpy(max_length)[0]), dtype=np.float32)\n",
    "threadsperblock = (1,1,64)\n",
    "importlib.reload(detsim)\n",
    "blockspergrid_x = ceil(signals.shape[0] / threadsperblock[0])\n",
    "blockspergrid_y = ceil(signals.shape[1] / threadsperblock[1])\n",
    "blockspergrid_z = ceil(signals.shape[2] / threadsperblock[2])\n",
    "blockspergrid = (blockspergrid_x, blockspergrid_y, blockspergrid_z)\n",
    "detsim.tracks_current[blockspergrid,threadsperblock](signals,\n",
    "                                                     neighboring_pixels,\n",
    "                                                     selected_tracks,\n",
    "                                                     response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a map that associates each track to the pixels where it induces a current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_index_map = cp.full((selected_tracks.shape[0], neighboring_pixels.shape[1]), -1)\n",
    "for i_ in range(selected_tracks.shape[0]):\n",
    "    compare = neighboring_pixels[i_, ..., cp.newaxis] == unique_pix\n",
    "    indices = cp.where(compare)\n",
    "    pixel_index_map[i_, indices[0]] = indices[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three-dimensional array can contain more than one signal for each pixel at different times. If we want to plot the full induced signal on the pixel, we need to join the signals corresponding to the same pixel. First, we find the start time of each signal with `time_intervals`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping between unique pixel array and track array index\n",
    "track_pixel_map = cp.full((unique_pix.shape[0], detsim.MAX_TRACKS_PER_PIXEL), -1)\n",
    "TPB = 32\n",
    "BPG = ceil(unique_pix.shape[0] / TPB)\n",
    "detsim.get_track_pixel_map[BPG, TPB](track_pixel_map, unique_pix, neighboring_pixels)\n",
    "\n",
    "threadsperblock = (1,1,64)\n",
    "blockspergrid_x = ceil(signals.shape[0] / threadsperblock[0])\n",
    "blockspergrid_y = ceil(signals.shape[1] / threadsperblock[1])\n",
    "blockspergrid_z = ceil(signals.shape[2] / threadsperblock[2])\n",
    "blockspergrid = (blockspergrid_x, blockspergrid_y, blockspergrid_z)\n",
    "pixels_signals = cp.zeros((len(unique_pix), len(detector.TIME_TICKS)))\n",
    "pixels_tracks_signals = cp.zeros((len(unique_pix),len(detector.TIME_TICKS),track_pixel_map.shape[1]))\n",
    "\n",
    "detsim.sum_pixel_signals[blockspergrid,threadsperblock](pixels_signals,\n",
    "                                                        signals,\n",
    "                                                        track_starts,\n",
    "                                                        pixel_index_map,\n",
    "                                                        track_pixel_map,\n",
    "                                                        pixels_tracks_signals)\n",
    "currents = cp.sum(pixels_signals,axis=1)*detector.TIME_SAMPLING/physics.E_CHARGE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's verify that the integral of our induced currents "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio:  0.9604030309054555\n"
     ]
    }
   ],
   "source": [
    "print(\"Ratio: \", sum(currents)/sum(selected_tracks['n_electrons']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D event display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = cm.Spectral_r\n",
    "importlib.reload(pixels_from_track)\n",
    "from matplotlib import colors\n",
    "norm = colors.Normalize(vmin=0, vmax=256)\n",
    "m = cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "\n",
    "cmap = cm.viridis\n",
    "norm_curr = colors.LogNorm(vmin=min(currents[currents>0]), vmax=max(currents))\n",
    "m_curr = cm.ScalarMappable(norm=norm_curr, cmap=cmap)\n",
    "fig = plt.figure(figsize=(7,7))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "for it,t in enumerate(selected_tracks):\n",
    "    if it == 0:\n",
    "        ax.plot((t[\"x_start\"], t[\"x_end\"]), \n",
    "            (t[\"z_start\"], t[\"z_end\"]),\n",
    "            (t[\"y_start\"], t[\"y_end\"]),\n",
    "            c='r',\n",
    "            lw=1,\n",
    "            alpha=1,\n",
    "            zorder=10,\n",
    "            label='Geant4 detector segment')\n",
    "    else:\n",
    "        ax.plot((t[\"x_start\"], t[\"x_end\"]), \n",
    "                (t[\"z_start\"], t[\"z_end\"]),\n",
    "                (t[\"y_start\"], t[\"y_end\"]),\n",
    "                c='r',\n",
    "                lw=1,\n",
    "                alpha=1,\n",
    "                zorder=9999)\n",
    "    #print(t['pixel_plane'])\n",
    "    if 0<=t['pixel_plane']<=999:\n",
    "        ax.plot((t[\"x_start\"], t[\"x_end\"]), \n",
    "            (detector.TPC_BORDERS[t['pixel_plane']][2][0], detector.TPC_BORDERS[t['pixel_plane']][2][0]),\n",
    "            (t[\"y_start\"], t[\"y_end\"]),\n",
    "            c='r',\n",
    "            lw=1,\n",
    "            ls=':',\n",
    "            alpha=1,\n",
    "            zorder=9999)\n",
    "        \n",
    "for ip, p in enumerate(unique_pix):\n",
    "    x_rect, y_rect = detsim.get_pixel_coordinates(p.get())\n",
    "    _,_,pixel_plane = pixels_from_track.id2pixel(p.get())\n",
    "    #print(pixel_plane)\n",
    "    row = int(pixel_plane // 4)\n",
    "    column = int(pixel_plane % 4)\n",
    "    if currents[ip] > 0:\n",
    "        rect = plt.Rectangle((x_rect, y_rect),\n",
    "                             detector.PIXEL_PITCH, detector.PIXEL_PITCH,\n",
    "                             linewidth=0.1, fc=m_curr.to_rgba(currents[ip].get()),\n",
    "                             edgecolor='gray')\n",
    "        ax.add_patch(rect)\n",
    "        art3d.pathpatch_2d_to_3d(rect, z=detector.TPC_BORDERS[pixel_plane][2][0], zdir=\"y\")\n",
    "        \n",
    "        \n",
    "for it in range(0,70,2):\n",
    "    anode1 = plt.Rectangle((detector.TPC_BORDERS[it][0][0], detector.TPC_BORDERS[it][1][0]),\n",
    "                            detector.TPC_BORDERS[it][0][1]-detector.TPC_BORDERS[it][0][0], \n",
    "                            detector.TPC_BORDERS[it][1][1]-detector.TPC_BORDERS[it][1][0],\n",
    "                           linewidth=1, fc='none',\n",
    "                           edgecolor='gray', label=('Pixel' if ip == 5 else ''))\n",
    "    ax.add_patch(anode1)\n",
    "    art3d.pathpatch_2d_to_3d(anode1, z=detector.TPC_BORDERS[0][2][0], zdir=\"y\")\n",
    "\n",
    "    anode2 = plt.Rectangle((detector.TPC_BORDERS[it][0][0], detector.TPC_BORDERS[it][1][0]),\n",
    "                            detector.TPC_BORDERS[it][0][1]-detector.TPC_BORDERS[it][0][0], \n",
    "                            detector.TPC_BORDERS[it][1][1]-detector.TPC_BORDERS[it][1][0],\n",
    "                           linewidth=1, fc='none',\n",
    "                           edgecolor='gray', label=('Pixel' if ip == 5 else ''))\n",
    "    ax.add_patch(anode2)\n",
    "    art3d.pathpatch_2d_to_3d(anode2, z=detector.TPC_BORDERS[it+1][2][0], zdir=\"y\")\n",
    "\n",
    "    cathode = plt.Rectangle((detector.TPC_BORDERS[it][0][0], detector.TPC_BORDERS[it][1][0]),\n",
    "                            detector.TPC_BORDERS[it][0][1]-detector.TPC_BORDERS[it][0][0], \n",
    "                            detector.TPC_BORDERS[it][1][1]-detector.TPC_BORDERS[it][1][0],\n",
    "                            linewidth=1, fc='gray', alpha=0.2,\n",
    "                            edgecolor='gray')\n",
    "    ax.add_patch(cathode)\n",
    "    z_cathode = (detector.TPC_BORDERS[it][2][0]+detector.TPC_BORDERS[it+1][2][0])/2\n",
    "    art3d.pathpatch_2d_to_3d(cathode, z=z_cathode, zdir=\"y\")\n",
    "\n",
    "    ax.plot((detector.TPC_BORDERS[it][0][0],detector.TPC_BORDERS[it][0][0]),(detector.TPC_BORDERS[it][2][0],detector.TPC_BORDERS[it+1][2][0]),\n",
    "            (detector.TPC_BORDERS[it][1][0],detector.TPC_BORDERS[it][1][0]), lw=1,color='gray')\n",
    "\n",
    "    ax.plot((detector.TPC_BORDERS[it][0][0],detector.TPC_BORDERS[it][0][0]),(detector.TPC_BORDERS[it][2][0],detector.TPC_BORDERS[it+1][2][0]),\n",
    "            (detector.TPC_BORDERS[it][1][1],detector.TPC_BORDERS[it][1][1]), lw=1,color='gray')\n",
    "\n",
    "    ax.plot((detector.TPC_BORDERS[it][0][1],detector.TPC_BORDERS[it][0][1]),(detector.TPC_BORDERS[it][2][0],detector.TPC_BORDERS[it+1][2][0]),\n",
    "            (detector.TPC_BORDERS[it][1][0],detector.TPC_BORDERS[it][1][0]), lw=1,color='gray')\n",
    "\n",
    "    ax.plot((detector.TPC_BORDERS[it][0][1],detector.TPC_BORDERS[it][0][1]),(detector.TPC_BORDERS[it][2][0],detector.TPC_BORDERS[it+1][2][0]),\n",
    "            (detector.TPC_BORDERS[it][1][1],detector.TPC_BORDERS[it][1][1]), lw=1,color='gray')\n",
    "\n",
    "# ax.set_ylim(consts.module_borders[pixel_plane][2][0],50)\n",
    "ax.set_xlim(detector.TPC_BORDERS[0][0][0],detector.TPC_BORDERS[-1][0][1])\n",
    "ax.set_ylim(detector.TPC_BORDERS[0][2][0],detector.TPC_BORDERS[-1][2][0])\n",
    "ax.set_zlim(detector.TPC_BORDERS[0][1][0],detector.TPC_BORDERS[-1][1][1])\n",
    "\n",
    "ax.set_box_aspect((4,8,4))\n",
    "ax.grid(False)\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "# ax.set_axis_off()\n",
    "ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
    "ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
    "ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
    "\n",
    "ax.view_init(azim=10)\n",
    "def rotate(angle):\n",
    "    ax.view_init(azim=angle)\n",
    "    \n",
    "from matplotlib import animation\n",
    "\n",
    "rot_animation = animation.FuncAnimation(fig, rotate, frames=np.arange(0,362,2),interval=50)\n",
    "\n",
    "ax.set_ylabel(\"z [cm]\")\n",
    "ax.set_xlabel(\"x [cm]\")\n",
    "ax.set_zlabel(\"y [cm]\")\n",
    "_ = plt.colorbar(m_curr,fraction=0.035, pad=0.05,label='Induced current integral [# electrons]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rot_animation.save('animation.gif', writer='imagemagick', fps=10, bitrate=10, dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Electronics response and digitization \n",
    "Here we simulate the electronics response (the self-triggering cycle) and the signal digitization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from larndsim.cuda_dict import CudaDict\n",
    "\n",
    "time_ticks = cp.linspace(0, len(unique_eventIDs)*detector.TIME_INTERVAL[1], pixels_signals.shape[1]+1)\n",
    "integral_list = cp.zeros((pixels_signals.shape[0], fee.MAX_ADC_VALUES))\n",
    "adc_ticks_list = cp.zeros((pixels_signals.shape[0], fee.MAX_ADC_VALUES))\n",
    "TPB = 128\n",
    "BPG = ceil(pixels_signals.shape[0] / TPB)\n",
    "rng_states = create_xoroshiro128p_states(TPB * BPG, seed=2)\n",
    "integrate = cp.zeros((pixels_signals.shape[0], time_ticks.shape[0]))\n",
    "backtracked_ids = cp.zeros((pixels_signals.shape[0], fee.MAX_ADC_VALUES, track_pixel_map.shape[1]))\n",
    "pixel_thresholds_lut = CudaDict(cp.array([fee.DISCRIMINATION_THRESHOLD]), 1, 1)\n",
    "pixel_thresholds_lut.tpb = TPB\n",
    "pixel_thresholds_lut.bpg = BPG\n",
    "orig_shape = unique_pix.shape\n",
    "pixel_thresholds = pixel_thresholds_lut[unique_pix.ravel()].reshape(orig_shape)\n",
    "fee.get_adc_values[BPG,TPB](pixels_signals,\n",
    "                            pixels_tracks_signals,\n",
    "                            time_ticks,\n",
    "                            integral_list,\n",
    "                            adc_ticks_list,\n",
    "                            0,\n",
    "                            rng_states,\n",
    "                            backtracked_ids,\n",
    "                            pixel_thresholds)\n",
    "adc_list = fee.digitize(integral_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we plot the pixel with the largest amount of deposited charge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "largest_pix = np.argmax(pixels_signals.sum(axis=1)).get()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1)\n",
    "current = pixels_signals[largest_pix][:3201].get()\n",
    "ax.plot(detector.TIME_TICKS,pixels_tracks_signals[largest_pix][:3201].get())\n",
    "ax.plot(detector.TIME_TICKS,current,label='Total track contribution',c='k',lw=2)\n",
    "ax.plot(detector.TIME_TICKS,np.sum(pixels_tracks_signals[largest_pix][:3201].get(),axis=1),c='r',ls='--',label='Sum of each track contribution')\n",
    "start_time = np.nonzero(current)[0][0]\n",
    "end_time = np.nonzero(current)[0][-1]\n",
    "ax.set_xlim(max(0,detector.TIME_TICKS[start_time]-10),detector.TIME_TICKS[end_time]+10)\n",
    "ax.set_xlabel(r\"Time [$\\mathrm{\\mu}$s]\")\n",
    "ax.set_ylabel(r\"Current [A]\")\n",
    "ax.set_ylim(-0.1e-14, max(current)*1.3)\n",
    "_=ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2D event display with induced current and ADC counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x450 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "norm = colors.Normalize(vmin=0, vmax=256)\n",
    "m = cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "\n",
    "cmap = cm.viridis\n",
    "norm_curr = colors.LogNorm(vmin=min(currents[currents>0]), vmax=max(currents))\n",
    "m_curr = cm.ScalarMappable(norm=norm_curr, cmap=cmap)\n",
    "\n",
    "fig,ax = plt.subplots(1,2,figsize=(10,4.5),sharex=True,sharey=True)\n",
    "importlib.reload(detsim)\n",
    "for ip, p in enumerate(unique_pix):\n",
    "    x_rect, y_rect = detsim.get_pixel_coordinates(p.get())\n",
    "    _,_,pixel_plane = pixels_from_track.id2pixel(p.get())\n",
    "    c = currents[ip].get()\n",
    "    if c >= 1:    \n",
    "        rect = plt.Rectangle((x_rect, y_rect),\n",
    "                             detector.PIXEL_PITCH, detector.PIXEL_PITCH,\n",
    "                             linewidth=0.2, fc=m_curr.to_rgba(c),\n",
    "                             edgecolor='grey')\n",
    "        ax[0].add_patch(rect)\n",
    "    else:\n",
    "        rect = plt.Rectangle((x_rect, y_rect),\n",
    "                             detector.PIXEL_PITCH, detector.PIXEL_PITCH,\n",
    "                             linewidth=0.2, fc='none',\n",
    "                             edgecolor='grey')\n",
    "        ax[0].add_patch(rect)\n",
    "    a = adc_list[ip][adc_list[ip]>fee.digitize(0)]\n",
    "    if len(a):    \n",
    "        rect = plt.Rectangle((x_rect, y_rect),\n",
    "                             detector.PIXEL_PITCH, detector.PIXEL_PITCH,\n",
    "                             linewidth=0.2, fc=m.to_rgba(np.sum(a.get())),\n",
    "                             edgecolor='grey')\n",
    "        ax[1].add_patch(rect)\n",
    "\n",
    "\n",
    "for it,t in enumerate(selected_tracks):\n",
    "    ax[0].plot((t[\"x_start\"], t[\"x_end\"]), \n",
    "            (t[\"y_start\"], t[\"y_end\"]),\n",
    "            c='r',\n",
    "            lw=1.25,\n",
    "            ls=':',\n",
    "            alpha=1,\n",
    "            zorder=10)\n",
    "    ax[1].plot((t[\"x_start\"], t[\"x_end\"]), \n",
    "            (t[\"y_start\"], t[\"y_end\"]),\n",
    "            c='r',\n",
    "            lw=1.25,\n",
    "            ls=':',\n",
    "            alpha=1,\n",
    "            zorder=100)\n",
    "    ax[0].scatter((t[\"x_start\"], t[\"x_end\"]), \n",
    "                  (t[\"y_start\"], t[\"y_end\"]),\n",
    "                  c='r', s=0, zorder=99999)\n",
    "    ax[1].scatter((t[\"x_start\"], t[\"x_end\"]), \n",
    "                  (t[\"y_start\"], t[\"y_end\"]),\n",
    "                  c='r', s=0, zorder=99999)       \n",
    "\n",
    "ax[0].set_aspect(\"equal\")\n",
    "ax[1].set_aspect(\"equal\")\n",
    "ax[0].set_xlabel(\"x [cm]\")\n",
    "ax[1].set_xlabel(\"x [cm]\")\n",
    "ax[0].set_ylabel(\"y [cm]\")\n",
    "from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable\n",
    "\n",
    "divider0 = make_axes_locatable(ax[1])\n",
    "cax0 = divider0.append_axes(\"right\", size=\"7%\", pad=0.07)\n",
    "fig.colorbar(m, ax=ax[1], cax=cax0, label='ADC counts sum')\n",
    "\n",
    "divider1 = make_axes_locatable(ax[0])\n",
    "cax1 = divider1.append_axes(\"right\", size=\"7%\", pad=0.07)\n",
    "fig.colorbar(m_curr, ax=ax[0], cax=cax1, label='Induced current integral [# electrons]')\n",
    "\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "fig.savefig(\"currentadc.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export result\n",
    "As a last step we backtrack the ADC counts to the Geant4 tracks and we export the result in a HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing to HDF5...: 100%|████████████████| 1941/1941 [00:00<00:00, 18163.07it/s]\n"
     ]
    }
   ],
   "source": [
    "!rm \"LAr.neutrino.100.edep.larnd-fromnotebook.h5\"\n",
    "importlib.reload(fee)\n",
    "# track_pixel_map=cp.repeat(track_pixel_map[:, cp.newaxis], 10, axis=1)\n",
    "adc_event_ids = np.full(adc_list.shape, unique_eventIDs[0].get()) # FIXME: only works if looping on a single event\n",
    "event_id_list = [adc_event_ids]\n",
    "\n",
    "pc = fee.export_to_hdf5(np.concatenate(event_id_list,axis=0),\n",
    "                        adc_list.get(),\n",
    "                        adc_ticks_list.get(),\n",
    "                        unique_pix.get(),\n",
    "                        backtracked_ids.get(),\n",
    "                        track_pixel_map.get(),\n",
    "                        \"LAr.neutrino.100.edep.larnd-fromnotebook.h5\")#,\n",
    "#                        bad_channels=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('LAr.neutrino.100.edep.larnd-fromnotebook.h5', 'a') as f:\n",
    "    f.create_dataset(\"tracks\", data=selected_tracks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the result\n",
    "Here we read back the file we just produced and compare it with the _true_ tracks. So first we create a dictionary that associates the chip key to a $(x,y)$ position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import yaml\n",
    "\n",
    "geometry_yaml = yaml.load(open(\"../larndsim/pixel_layouts/multi_tile_layout-3.0.40.yaml\"), Loader=yaml.FullLoader)\n",
    "det_yaml = yaml.load(open(\"../larndsim/detector_properties/ndlar-module.yaml\"),Loader=yaml.FullLoader)\n",
    "\n",
    "pixel_pitch = geometry_yaml['pixel_pitch']\n",
    "is_multi_tile = True\n",
    "chip_channel_to_position = geometry_yaml['chip_channel_to_position']\n",
    "tile_orientations = geometry_yaml['tile_orientations']\n",
    "tile_positions = geometry_yaml['tile_positions']\n",
    "tpc_centers = det_yaml['tpc_offsets']\n",
    "tile_indeces = geometry_yaml['tile_indeces']\n",
    "xs = np.array(list(chip_channel_to_position.values()))[:, 0] * pixel_pitch\n",
    "ys = np.array(list(chip_channel_to_position.values()))[:, 1] * pixel_pitch\n",
    "x_size = max(xs)-min(xs)+pixel_pitch\n",
    "y_size = max(ys)-min(ys)+pixel_pitch\n",
    "\n",
    "def _rotate_pixel(pixel_pos, tile_orientation):\n",
    "    return pixel_pos[0]*tile_orientation[2], pixel_pos[1]*tile_orientation[1]\n",
    "\n",
    "tile_geometry = defaultdict(int)\n",
    "geometry = {}\n",
    "io_group_io_channel_to_tile = {}\n",
    "\n",
    "for tile in geometry_yaml['tile_chip_to_io']:\n",
    "    tile_orientation = tile_orientations[tile]\n",
    "    tile_geometry[tile] = tile_positions[tile], tile_orientations[tile]\n",
    "    \n",
    "    for chip in geometry_yaml['tile_chip_to_io'][tile]:\n",
    "        io_group_io_channel = geometry_yaml['tile_chip_to_io'][tile][chip]\n",
    "        io_group = io_group_io_channel//1000\n",
    "        io_channel = io_group_io_channel % 1000\n",
    "        io_group_io_channel_to_tile[(io_group, io_channel)] = tile\n",
    "\n",
    "    for chip_channel in geometry_yaml['chip_channel_to_position']:\n",
    "        chip = chip_channel // 1000\n",
    "        channel = chip_channel % 1000\n",
    "        io_group_io_channel = geometry_yaml['tile_chip_to_io'][tile][chip]\n",
    "\n",
    "        io_group = io_group_io_channel // 1000\n",
    "        io_channel = io_group_io_channel % 1000\n",
    "        x = chip_channel_to_position[chip_channel][0] * \\\n",
    "            pixel_pitch - x_size / 2 \n",
    "        y = chip_channel_to_position[chip_channel][1] * \\\n",
    "            pixel_pitch - y_size / 2\n",
    "                \n",
    "        x, y = _rotate_pixel((x, y), tile_orientation)\n",
    "        x += tile_positions[tile][2]\n",
    "        y += tile_positions[tile][1] \n",
    "\n",
    "        geometry[(io_group, io_channel,\n",
    "                       chip, channel)] = x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can compare the digitized charge associated to packets with the number of true electrons that reach the anode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digitized charge/true charge: 0.9026745530625646\n"
     ]
    }
   ],
   "source": [
    "f = h5py.File(\"LAr.neutrino.100.edep.larnd-fromnotebook.h5\", \"r\")\n",
    "datawords = f['packets']['dataword'][f['packets']['packet_type']==0]\n",
    "print(\"Digitized charge/true charge:\", sum(datawords/fee.ADC_COUNTS*(fee.V_REF-fee.V_CM)+fee.V_CM-fee.V_PEDESTAL)/fee.GAIN/sum(f['tracks']['n_electrons']))\n",
    "selected_tracks = f['tracks']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we verify the association between the _true_ track and the hit. The color of the pixel corresponds to the color of the first associated track (a hit can be associated to more tracks, not shown here)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,tight_layout=True)\n",
    "plotted_tracks = []\n",
    "from matplotlib import colors\n",
    "from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable\n",
    "\n",
    "\n",
    "norm = colors.Normalize(vmin=0, vmax=256)\n",
    "cmap = cm.jet\n",
    "\n",
    "m = cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "module_ids = []\n",
    "\n",
    "colors = ['tab:blue','tab:orange','tab:green','tab:red','tab:purple','tab:brown']\n",
    "for ip,packet in enumerate(f['packets']):\n",
    "    \n",
    "    if packet['packet_type'] == 0:\n",
    "        io_group, io_channel, chip, channel = packet['io_group'], packet['io_channel'], packet['chip_id'], packet['channel_id']\n",
    "        module_id = (io_group-1)//4\n",
    "        if module_id not in module_ids:\n",
    "            module_ids.append(module_id)\n",
    "        io_group = io_group - (io_group-1)//4*4\n",
    "        x,y = geometry[(io_group, io_channel, chip, channel)]\n",
    "        track_ids = f['mc_packets_assn']['track_ids'][ip]\n",
    "        fractions = f['mc_packets_assn']['fraction'][ip]\n",
    "        z_offset = tpc_centers[module_id][0]*10\n",
    "        x_offset = tpc_centers[module_id][2]*10\n",
    "        y_offset = tpc_centers[module_id][1]*10\n",
    "        \n",
    "        rect = plt.Rectangle((x+x_offset, y+y_offset),\n",
    "                             detector.PIXEL_PITCH*10, detector.PIXEL_PITCH*10,\n",
    "                             linewidth=0, fc=m.to_rgba(packet['dataword']))\n",
    "\n",
    "        ax.add_patch(rect)\n",
    "\n",
    "        for trackid in track_ids:\n",
    "            if trackid >= 0 and trackid not in plotted_tracks:\n",
    "                plotted_tracks.append(trackid)\n",
    "                ax.plot((f['tracks'][trackid]['x_start']*10,f['tracks'][trackid]['x_end']*10),\n",
    "                        (f['tracks'][trackid]['y_start']*10,f['tracks'][trackid]['y_end']*10),c='r',alpha=0.75,lw=0.1)\n",
    "                       # (f['tracks'][trackid]['y_start']*10+218.236,f['tracks'][trackid]['y_end']*10+218.236),c='r',alpha=0.25)\n",
    "\n",
    "for track in selected_tracks:\n",
    "    ax.plot((track['x_start']*10,track['x_end']*10),\n",
    "            (track['y_start']*10,track['y_end']*10),c='r',alpha=0)\n",
    "\n",
    "for tpc in detector.TPC_BORDERS:\n",
    "    tpc_rect = plt.Rectangle((tpc[0][0]*10,tpc[1][0]*10), 972.8, 3040, linewidth=0.1, edgecolor='k', facecolor=cmap(0),zorder=-1)\n",
    "    ax.add_patch(tpc_rect)\n",
    "\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.set_xlabel(\"x [mm]\")\n",
    "ax.set_ylabel(\"y [mm]\")\n",
    "ax.add_patch(tpc_rect)\n",
    "ax.set_xlim(np.min(detector.TPC_BORDERS[:,0,:])*10,np.max(detector.TPC_BORDERS[:,0,:])*10)\n",
    "ax.set_ylim(np.min(detector.TPC_BORDERS[:,1,:])*10,np.max(detector.TPC_BORDERS[:,1,:])*10)\n",
    "divider0 = make_axes_locatable(ax)\n",
    "cax0 = divider0.append_axes(\"right\", size=\"5%\", pad=0.07)\n",
    "fig.colorbar(m, ax=ax, cax=cax0, label='ADC count')\n",
    "fig.savefig(\"mc.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "NumbaKernel",
   "language": "python",
   "name": "numbaenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}