analyze_previous_opts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from matplotlib import rcParams\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.backends.backend_pdf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '/home/ws/zx5016/Dropbox/KIT/xfel/ocelot_data'\n",
    "# data_dir = '/home/ws/zx5016/Dropbox/KIT/xfel/ocelot_data_2'\n",
    "plot_dir = '/home/ws/zx5016/Dropbox/KIT/xfel'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_plot(data_dict, keys_to_remove, title):\n",
    "    fig, ax = plt.subplots(figsize=(15, 8))\n",
    "\n",
    "    ax2 = ax.twinx() \n",
    "    ax2.plot(data_dict['obj_values'], color='black', linewidth=3, label=data_dict['function'].split('=')[1] )\n",
    "    ax2.axhline(np.mean([data_dict['ref_sase'][0][0],\n",
    "                data_dict['ref_sase'][1][0]]), color='black', linewidth=3)\n",
    "    ax2.set_ylabel('Objective')\n",
    "\n",
    "    for kr in keys_to_remove:\n",
    "        del data_dict[kr]\n",
    "\n",
    "    for k in data_dict.keys():\n",
    "        ax.plot(data_dict[k], label=k)\n",
    "        ax.legend(framealpha=1)\n",
    "\n",
    "    ax.set_xlabel('Steps')\n",
    "    ax.grid(True)\n",
    "    plt.title(title)\n",
    "    plt.legend(framealpha=1)\n",
    "\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_to_remove = ['method', 'dev_times', 'obj_times', 'maximization', 'nreadings', 'function', 'beam_energy', 'wavelength', 'obj_values', 'std', 'ref_sase', 'charge']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot single file\n",
    "# f = '2022-11-15 17-37-53.json'\n",
    "# data_file = open(os.path.join(data_dir, f))\n",
    "# data = json.load(data_file)\n",
    "# timestamp = f.strip('.json')\n",
    "# if data['charge'][0] is not None:\n",
    "#     title = timestamp + ' / ' + data['method'] + ' ' + str(\n",
    "#                     data['nreadings'][0]) + ' / charge = ' + '{:.2E}'.format(data['charge'][0])\n",
    "# else:\n",
    "#     title = timestamp + ' / ' + \\\n",
    "#                     data['method'] + ' ' + str(data['nreadings'][0])\n",
    "\n",
    "# get_plot(data, keys_to_remove, title)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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      "/tmp/ipykernel_438378/3928825392.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig, ax = plt.subplots(figsize=(15, 8))\n"
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       "<Figure size 1080x576 with 0 Axes>"
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       "<Figure size 1080x576 with 0 Axes>"
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       "<Figure size 1080x576 with 0 Axes>"
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       "<Figure size 1080x576 with 0 Axes>"
      ]
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    },
    {
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       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
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    },
    {
     "data": {
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       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
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    }
   ],
   "source": [
    "pdf = matplotlib.backends.backend_pdf.PdfPages(os.path.join(plot_dir, 'ocelot_optim.pdf'))\n",
    "for subdir, dirs, files in os.walk(data_dir):\n",
    "    for f in sorted(files):\n",
    "        # print(f)\n",
    "        data_file = open(os.path.join(data_dir, f))\n",
    "        data = json.load(data_file)\n",
    "        if data['maximization']:\n",
    "            timestamp = f.strip('.json')\n",
    "            try:\n",
    "                if data['charge'][0] is not None:\n",
    "                    title = timestamp + ' / ' + data['method'] + ' ' + str(data['nreadings'][0]) + ' / charge = ' + '{:.2E}'.format(data['charge'][0])\n",
    "                else:\n",
    "                    title = timestamp + ' / ' + data['method'] + ' ' + str(data['nreadings'][0])\n",
    "                fig = get_plot(data, keys_to_remove, title)\n",
    "                pdf.savefig(fig, bbox_inches='tight')\n",
    "                plt.clf()\n",
    "            except:\n",
    "                print('>> some error', f)\n",
    "\n",
    "pdf.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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