plots/event_count_viz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scienceplots\n",
    "# plt.style.use(['science', 'grid'])\n",
    "plt.style.use(['science'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2250x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(7.5, 4), sharex=True, sharey=True, dpi=300)\n",
    "\n",
    "ax = axs[0]\n",
    "x = np.load('../../data/bin_10ms/A1.npy', allow_pickle=True)\n",
    "# evts = np.concatenate(x[:10])\n",
    "x = [e for e in x if e['t'].shape[0] > 5e3]\n",
    "evts = x[1]\n",
    "n_bins = 1_000\n",
    "data = np.histogram(evts['t'] - evts['t'].min(), bins=n_bins)\n",
    "ax.set_title('Particles measured for the present experiment')\n",
    "ax.bar(data[1][:-1] / 1e4, data[0] / data[0].max(), width=(data[1][1]-data[1][0])/1e4)\n",
    "\n",
    "ax = axs[1]\n",
    "x = np.load('Event_count_YAR.npy')\n",
    "ax.set_title('Biological cells measured for a forthcoming experiment')\n",
    "ax.bar(np.arange(0, 1, 1./len(x)), x/x.max(), width=1/len(x))\n",
    "\n",
    "axs[1].set_xlabel('Time (normalized)')\n",
    "for ax in axs:\n",
    "    # ax.grid(False)\n",
    "    # ax.set_ylabel('Normalized event count')\n",
    "    # ax.set_xlabel('Time (normalized)')\n",
    "    ax.set_ylim(-0.05, 1.05)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('event_count_viz_.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2250x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(7.5, 4), sharex=True, sharey=True, dpi=300)\n",
    "\n",
    "ax = axs[0]\n",
    "x = np.load('../../data/bin_10ms/A1.npy', allow_pickle=True)\n",
    "x = [e for e in x if e['t'].shape[0] > 5e3]\n",
    "evts = x[1]\n",
    "n_bins = 1_000\n",
    "data = np.histogram(evts['t'] - evts['t'].min(), bins=n_bins)\n",
    "ax.set_title('Particles measured for the present experiment')\n",
    "ax.bar(data[1][:-1], data[0] / data[0].max(), width=data[1][1]-data[1][0])\n",
    "\n",
    "ax = axs[1]\n",
    "x = np.load('Event_count_YAR.npy')\n",
    "ax.set_title('Biological cells measured for a forthcoming experiment')\n",
    "ax.bar(np.arange(0, 1e4, 1e4/len(x)), x/x.max(), width=1e4/len(x))\n",
    "\n",
    "axs[1].set_xlabel('Time (us)')\n",
    "for ax in axs:\n",
    "    # ax.grid(False)\n",
    "    # ax.set_ylabel('Normalized event count')\n",
    "    ax.set_ylim(-0.05, 1.05)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('event_count_viz.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# up_data = np.histogram(evts[evts['p'] == 1]['t'], bins=n_bins)\n",
    "# down_data = np.histogram(evts[evts['p'] == 0]['t'], bins=n_bins)\n",
    "# plt.bar(up_data[1][:-1], up_data[0], width=up_data[1][1]-up_data[1][0])\n",
    "# plt.bar(down_data[1][:-1], -1*down_data[0], width=down_data[1][1]-down_data[1][0])\n",
    "# plt.hist(x[0]['t'], bins=100)/\n",
    "# plt.plot(up_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dvsflow",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}