do_test_success_stochastic_e2_bat_fb_all.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from get_mmap import get_mmap\n",
    "import random\n",
    "\n",
    "# Setup the necessary paths and parameters\n",
    "fmap = 'e2_bat_fb_beta_raw_s_0_3071.raw'\n",
    "data_title = 'Stochastic A2 BAT FB'\n",
    "path_data = 'results/'\n",
    "name_data = 'a2_bat_fb_stochastic_f9_all_dlinear_n200r_slr_g1000_r10.pkl'\n",
    "rand_iter = 10\n",
    "n_profile = 200 # ensure that: n_profile + n_attack < nr_blocks\n",
    "nr_traces_vec = list(range(1, 11)) + list(range(20, 101, 10)) + [200, 500, 1000]\n",
    "\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapping data for profile...\n",
      "Done mapping data\n"
     ]
    }
   ],
   "source": [
    "# Load file\n",
    "print('Mapping data...')\n",
    "mmap_data, metadata = get_mmap(fmap)\n",
    "print('Done mapping data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Assuming 'metadata' is a dictionary or an object with 'nr_groups' and 'nr_trials' as attributes\n",
    "nr_groups = metadata['nr_groups']\n",
    "nr_blocks = int(np.floor(metadata['nr_trials'] / nr_groups))\n",
    "idx = np.arange(1, nr_blocks + 1)\n",
    "\n",
    "# Profile and attack indices\n",
    "idx_profile = np.union1d(np.where(idx % 3 == 1)[0], np.where(idx % 3 == 2)[0])\n",
    "idx_attack = np.where(idx % 3 == 0)[0]\n",
    "\n",
    "# Construct idx_profile_all\n",
    "idx_profile_all = np.concatenate([(np.arange((p-1) * nr_groups, p * nr_groups) + 1) for p in idx_profile])\n",
    "\n",
    "# # Convert to zero-based indexing for Python (optional, depending on your use case)\n",
    "# idx_profile_all -= 1\n",
    "# idx_attack -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  -255   -254   -253 ... 785918 785919 785920]\n",
      "[   2    5    8 ... 3065 3068 3071]\n"
     ]
    }
   ],
   "source": [
    "print(idx_profile_all)\n",
    "print(idx_attack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[None, None, None, None, None, None]]\n"
     ]
    }
   ],
   "source": [
    "# Select basis\n",
    "base = 'F9'\n",
    "\n",
    "# Select different factors for n_profile\n",
    "# nvec = [1, nr_groups, int(np.ceil(nr_groups**(1/2))), int(np.ceil(nr_groups**(1/3))), int(np.ceil(nr_groups**(1/4)))]\n",
    "nvec = [nr_groups]\n",
    "\n",
    "results = [[None for _ in range(6)] for _ in range(len(nvec))] # TODO this is wrong\n",
    "# results = np.full((len(nvec), 6), None)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running run_stochastic_attack()...\n",
      "Computing Stochastic coefficients...\n",
      "Computing raw covariance...\n",
      "Computing between-groups matrix B...\n",
      "Computing Fishers LDA parameters...\n",
      "Computing Stochastic coefficients...\n",
      "Computing covariance...\n",
      "Computing attack data...\n",
      "Computing success info...\n",
      "Computing success info for group size 1\n",
      "Computing success info for group size 2\n",
      "Computing success info for group size 3\n",
      "Computing success info for group size 4\n",
      "Computing success info for group size 5\n",
      "Computing success info for group size 6\n",
      "Computing success info for group size 7\n",
      "Computing success info for group size 8\n",
      "Computing success info for group size 9\n",
      "Computing success info for group size 10\n",
      "Computing success info for group size 20\n",
      "Computing success info for group size 30\n",
      "Computing success info for group size 40\n",
      "Computing success info for group size 50\n",
      "Computing success info for group size 60\n",
      "Computing success info for group size 70\n",
      "Computing success info for group size 80\n",
      "Computing success info for group size 90\n",
      "Computing success info for group size 100\n",
      "Computing success info for group size 200\n",
      "Computing success info for group size 500\n",
      "Computing success info for group size 1000\n",
      "Running run_stochastic_attack()...\n",
      "Computing Stochastic coefficients...\n",
      "Computing PCA parameters...\n",
      "Computing Stochastic coefficients...\n",
      "Computing covariance...\n",
      "Computing attack data...\n",
      "Computing success info...\n",
      "Computing success info for group size 1\n",
      "Computing success info for group size 2\n",
      "Computing success info for group size 3\n",
      "Computing success info for group size 4\n",
      "Computing success info for group size 5\n",
      "Computing success info for group size 6\n",
      "Computing success info for group size 7\n",
      "Computing success info for group size 8\n",
      "Computing success info for group size 9\n",
      "Computing success info for group size 10\n",
      "Computing success info for group size 20\n",
      "Computing success info for group size 30\n",
      "Computing success info for group size 40\n",
      "Computing success info for group size 50\n",
      "Computing success info for group size 60\n",
      "Computing success info for group size 70\n",
      "Computing success info for group size 80\n",
      "Computing success info for group size 90\n",
      "Computing success info for group size 100\n",
      "Computing success info for group size 200\n",
      "Computing success info for group size 500\n",
      "Computing success info for group size 1000\n",
      "Running run_stochastic_attack()...\n",
      "Computing Stochastic coefficients...\n",
      "Computing attack data...\n",
      "Computing success info...\n",
      "Computing success info for group size 1\n",
      "Computing success info for group size 2\n",
      "Computing success info for group size 3\n",
      "Computing success info for group size 4\n",
      "Computing success info for group size 5\n",
      "Computing success info for group size 6\n",
      "Computing success info for group size 7\n",
      "Computing success info for group size 8\n",
      "Computing success info for group size 9\n",
      "Computing success info for group size 10\n",
      "Computing success info for group size 20\n",
      "Computing success info for group size 30\n",
      "Computing success info for group size 40\n",
      "Computing success info for group size 50\n",
      "Computing success info for group size 60\n",
      "Computing success info for group size 70\n",
      "Computing success info for group size 80\n",
      "Computing success info for group size 90\n",
      "Computing success info for group size 100\n",
      "Computing success info for group size 200\n",
      "Computing success info for group size 500\n",
      "Computing success info for group size 1000\n",
      "Running run_stochastic_attack()...\n",
      "Computing Stochastic coefficients...\n",
      "Computing attack data...\n",
      "Computing success info...\n",
      "Computing success info for group size 1\n",
      "Computing success info for group size 2\n",
      "Computing success info for group size 3\n",
      "Computing success info for group size 4\n",
      "Computing success info for group size 5\n",
      "Computing success info for group size 6\n",
      "Computing success info for group size 7\n",
      "Computing success info for group size 8\n",
      "Computing success info for group size 9\n",
      "Computing success info for group size 10\n",
      "Computing success info for group size 20\n",
      "Computing success info for group size 30\n",
      "Computing success info for group size 40\n",
      "Computing success info for group size 50\n",
      "Computing success info for group size 60\n",
      "Computing success info for group size 70\n",
      "Computing success info for group size 80\n",
      "Computing success info for group size 90\n",
      "Computing success info for group size 100\n",
      "Computing success info for group size 200\n",
      "Computing success info for group size 500\n",
      "Computing success info for group size 1000\n",
      "Running run_stochastic_attack()...\n",
      "Computing Stochastic coefficients...\n",
      "Computing attack data...\n",
      "Computing success info...\n",
      "Computing success info for group size 1\n",
      "Computing success info for group size 2\n",
      "Computing success info for group size 3\n",
      "Computing success info for group size 4\n",
      "Computing success info for group size 5\n",
      "Computing success info for group size 6\n",
      "Computing success info for group size 7\n",
      "Computing success info for group size 8\n",
      "Computing success info for group size 9\n",
      "Computing success info for group size 10\n",
      "Computing success info for group size 20\n",
      "Computing success info for group size 30\n",
      "Computing success info for group size 40\n",
      "Computing success info for group size 50\n",
      "Computing success info for group size 60\n",
      "Computing success info for group size 70\n",
      "Computing success info for group size 80\n",
      "Computing success info for group size 90\n",
      "Computing success info for group size 100\n",
      "Computing success info for group size 200\n",
      "Computing success info for group size 500\n",
      "Computing success info for group size 1000\n",
      "Running run_stochastic_attack()...\n",
      "Computing Stochastic coefficients...\n",
      "Computing attack data...\n",
      "Computing success info...\n",
      "Computing success info for group size 1\n",
      "Computing success info for group size 2\n",
      "Computing success info for group size 3\n",
      "Computing success info for group size 4\n",
      "Computing success info for group size 5\n",
      "Computing success info for group size 6\n",
      "Computing success info for group size 7\n",
      "Computing success info for group size 8\n",
      "Computing success info for group size 9\n",
      "Computing success info for group size 10\n",
      "Computing success info for group size 20\n",
      "Computing success info for group size 30\n",
      "Computing success info for group size 40\n",
      "Computing success info for group size 50\n",
      "Computing success info for group size 60\n",
      "Computing success info for group size 70\n",
      "Computing success info for group size 80\n",
      "Computing success info for group size 90\n",
      "Computing success info for group size 100\n",
      "Computing success info for group size 200\n",
      "Computing success info for group size 500\n",
      "Computing success info for group size 1000\n"
     ]
    }
   ],
   "source": [
    "from run_stochastic_attack import run_stochastic_attack\n",
    "\n",
    "results = np.full((len(nvec), 6), None)\n",
    "\n",
    "for k in range(len(nvec)):\n",
    "    # Select traces for stochastic model\n",
    "    idx_profile_sel = np.random.choice(idx_profile_all, size=n_profile * nvec[k], replace=True)\n",
    "\n",
    "    # Run attack for LDA\n",
    "    atype = 'templatelda'\n",
    "    aparams = {\n",
    "        'lda_threshold': 0.95,\n",
    "        'lda_dimensions': 4\n",
    "    }\n",
    "    discriminant = 'linearnocov'\n",
    "    eparams = {}\n",
    "\n",
    "    results[k, 0] = run_stochastic_attack(\n",
    "        mmap_data, metadata, idx_profile_sel, base,\n",
    "        mmap_data, metadata, idx_attack,\n",
    "        atype, aparams, discriminant,\n",
    "        rand_iter, nr_traces_vec, eparams\n",
    "    )\n",
    "\n",
    "    # Run attack for PCA\n",
    "    atype = 'templatepca'\n",
    "    aparams = {\n",
    "        'pca_threshold': 0.95,\n",
    "        'pca_alternate': 0,\n",
    "        'pca_dimensions': 4\n",
    "    }\n",
    "    discriminant = 'linear'\n",
    "    eparams = {}\n",
    "    results[k, 1] = run_stochastic_attack(\n",
    "        mmap_data, metadata, idx_profile_sel, base,\n",
    "        mmap_data, metadata, idx_attack,\n",
    "        atype, aparams, discriminant,\n",
    "        rand_iter, nr_traces_vec, eparams\n",
    "    )\n",
    "    \n",
    "    # Run attack for classic, 1ppc\n",
    "    atype = 'same_profile'\n",
    "    aparams = {\n",
    "        'signal': 'dom',\n",
    "        'sel': '1ppc',\n",
    "        'p1': 240,\n",
    "        'p2': 0.95\n",
    "    }\n",
    "    discriminant = 'linear'\n",
    "    eparams = {}\n",
    "    results[k, 2] = run_stochastic_attack(\n",
    "        mmap_data, metadata, idx_profile_sel, base,\n",
    "        mmap_data, metadata, idx_attack,\n",
    "        atype, aparams, discriminant,\n",
    "        rand_iter, nr_traces_vec, eparams\n",
    "    )\n",
    "    \n",
    "    # Run attack for classic, 3ppc\n",
    "    atype = 'same_profile'\n",
    "    aparams = {\n",
    "        'signal': 'dom',\n",
    "        'sel': '3ppc',\n",
    "        'p1': 240,\n",
    "        'p2': 0.95\n",
    "    }\n",
    "    discriminant = 'linear'\n",
    "    eparams = {}\n",
    "    results[k, 3] = run_stochastic_attack(\n",
    "        mmap_data, metadata, idx_profile_sel, base,\n",
    "        mmap_data, metadata, idx_attack,\n",
    "        atype, aparams, discriminant,\n",
    "        rand_iter, nr_traces_vec, eparams\n",
    "    )\n",
    "    \n",
    "    # Run attack for classic, 20ppc\n",
    "    atype = 'same_profile'\n",
    "    aparams = {\n",
    "        'signal': 'dom',\n",
    "        'sel': '20ppc',\n",
    "        'p1': 240,\n",
    "        'p2': 0.95\n",
    "    }\n",
    "    discriminant = 'linear'\n",
    "    eparams = {}\n",
    "    results[k, 4] = run_stochastic_attack(\n",
    "        mmap_data, metadata, idx_profile_sel, base,\n",
    "        mmap_data, metadata, idx_attack,\n",
    "        atype, aparams, discriminant,\n",
    "        rand_iter, nr_traces_vec, eparams\n",
    "    )\n",
    "    \n",
    "    # Run attack for classic, allap\n",
    "    atype = 'same_profile'\n",
    "    aparams = {\n",
    "        'signal': 'dom',\n",
    "        'sel': 'allap',\n",
    "        'p1': 0.95,\n",
    "        'p2': None\n",
    "    }\n",
    "    discriminant = 'linear'\n",
    "    eparams = {}\n",
    "    results[k, 5] = run_stochastic_attack(\n",
    "        mmap_data, metadata, idx_profile_sel, base,\n",
    "        mmap_data, metadata, idx_attack,\n",
    "        atype, aparams, discriminant,\n",
    "        rand_iter, nr_traces_vec, eparams\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All done, saving data...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "# Save data to disk so you can prevent wasting time\n",
    "print('All done, saving data...\\n')\n",
    "os.makedirs(path_data, exist_ok=True)\n",
    "\n",
    "with open(os.path.join(path_data, name_data), 'wb') as f:\n",
    "    pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "from do_show_results_templates_a2_bat_fb import get_ge_from_success_info, get_line_properties_templates, make_figures_ge\n",
    "from get_uid_cmethod import get_uid_cmethod\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "with open(os.path.join(path_data, name_data), 'rb') as f:\n",
    "    data = pickle.load(f)\n",
    "\n",
    "# fdata = f'results/{name_data}.pkl'\n",
    "rpath = 'figures/'\n",
    "font_size = 12\n",
    "options = 'gp'\n",
    "# yrange = [0, 6.5]\n",
    "yrange = [0, 7]\n",
    "nr_traces_vec = list(range(1, 11)) + list(range(20, 101, 10)) + [200, 500, 1000]\n",
    "len_na_vec = len(nr_traces_vec)\n",
    "\n",
    "# Reset environment\n",
    "plt.close('all')\n",
    "\n",
    "nump = len(data[0][0]['idx_profile'])\n",
    "rand_iter = data[0][0]['rand_iter']\n",
    "nr_exp_ab = len(data[0])\n",
    "L = [None] * nr_exp_ab\n",
    "G = np.zeros((nr_exp_ab, len_na_vec))\n",
    "slines_ab = [None] * nr_exp_ab\n",
    "style = 'fancy'\n",
    "\n",
    "for k in range(nr_exp_ab):\n",
    "    if data[0][k]['atype'] == 'templatelda':\n",
    "        L[k] = 'A2 BAT FB, DLINEAR, S-LDA'\n",
    "    elif data[0][k]['atype'] == 'templatepca':\n",
    "        L[k] = 'A2 BAT FB, DLINEAR, S-PCA'\n",
    "    else:\n",
    "        L[k] = 'A2 BAT FB, DLINEAR, SELECTION'\n",
    "\n",
    "    g = get_ge_from_success_info(data[0][k]['success_info'], nr_traces_vec)\n",
    "    G[k, :] = g['joint']\n",
    "    # uid = get_uid_cmethod(cmethod, results[k]['cparams'])\n",
    "    slines_ab[k] = get_line_properties_templates(k, style)\n",
    "\n",
    "# Plot results\n",
    "title_results = \"\" #f\"A2D AB, BOFFSET, BAT FB, nump={nump}\"\n",
    "rprefix = f\"a2d_ab_bat_fb_boffset_dlinear_n{nump}r_ls_r{rand_iter}_\"\n",
    "make_figures_ge(G, nr_traces_vec, rpath, rprefix, title_results, L, font_size, slines_ab, options, yrange)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}