{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of key generation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:00:25.518989Z",
     "start_time": "2019-03-17T23:00:24.501601Z"
    }
   },
   "outputs": [],
   "source": [
    " %matplotlib notebook \n",
    "import numpy as np\n",
    "from scipy.stats import describe\n",
    "from scipy.stats import norm as norm_dist\n",
    "from scipy.stats.mstats import mquantiles\n",
    "from math import log, sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import ticker, colors, gridspec\n",
    "from copy import deepcopy\n",
    "from utils import plot_hist, moving_average, hw\n",
    "from binascii import unhexlify\n",
    "from IPython.display import display, HTML\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "import tabulate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings\n",
    "Enter your input below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:06:29.704432Z",
     "start_time": "2019-03-17T23:06:29.694540Z"
    }
   },
   "outputs": [],
   "source": [
    "# File name with output from ECTesterReader or ECTesterStandalone signatures.\n",
    "fname = \"filename.csv\"\n",
    "\n",
    "# The amount of entries skipped from the beginning of the file, as they are usually outliers.\n",
    "skip_first = 10\n",
    "\n",
    "# Whether to plot things in logarithmic scale or not.\n",
    "log_scale = False\n",
    "\n",
    "# Whether to trim the time data outside the 1 - 99 percentile range (adjust below). Quite useful.\n",
    "trim = True\n",
    "\n",
    "# How much to trim? Either a number in [0,1] signifying a quantile, or an absolute value signifying a threshold\n",
    "trim_low = 0.01\n",
    "trim_high = 0.99\n",
    "\n",
    "# Graphical (matplotlib) style name\n",
    "style = \"ggplot\"\n",
    "\n",
    "# Color map to use, and what color to assign to \"bad\" values (necessary for log_scale)\n",
    "color_map = plt.cm.plasma\n",
    "color_map_bad = \"black\"\n",
    "\n",
    "# What function to use to calculate number of histogram bins of time\n",
    "# one of \"sqrt\", \"sturges\", \"rice\", \"scott\" and \"fd\" or a number specifying the number of bins\n",
    "hist_size = \"sturges\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:06:30.551732Z",
     "start_time": "2019-03-17T23:06:30.545202Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setup plot style\n",
    "\n",
    "plt.style.use(style)\n",
    "\n",
    "cmap = deepcopy(color_map)\n",
    "cmap.set_bad(color_map_bad)\n",
    "\n",
    "# Normalization, linear or log.\n",
    "if log_scale:\n",
    "    norm = colors.LogNorm()\n",
    "else:\n",
    "    norm = colors.Normalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:00:38.023486Z",
     "start_time": "2019-03-17T23:00:27.178465Z"
    }
   },
   "outputs": [],
   "source": [
    "# Read the header line.\n",
    "\n",
    "with open(fname, \"r\") as f:\n",
    "    header = f.readline()\n",
    "header_names = header.split(\";\")\n",
    "if len(header_names) != 9:\n",
    "    print(\"Bad data?\")\n",
    "    exit(1)\n",
    "\n",
    "# Load the data\n",
    "\n",
    "hx = lambda x: int(x, 16)\n",
    "data = np.genfromtxt(fname, delimiter=\";\", skip_header=1, converters={3: unhexlify, 4: unhexlify,\n",
    "                                                                      5: hx, 6: unhexlify, 7: hx,\n",
    "                                                                      8: lambda b: bool(int(b))},\n",
    "                     dtype=np.dtype([(\"index\", \"u4\"), (\"sign_time\", \"u4\"), (\"verify_time\", \"u4\"),\n",
    "                                     (\"data\", \"O\"), (\"pub\", \"O\"), (\"priv\", \"O\"), (\"signature\", \"O\"),\n",
    "                                    (\"nonce\", \"O\"), (\"valid\", \"b\")]))\n",
    "\n",
    "    \n",
    "sign_unit = \"ms\"\n",
    "verify_unit = \"ms\"\n",
    "# Setup the datatrimmed = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:00:38.465677Z",
     "start_time": "2019-03-17T23:00:38.025692Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setup the data\n",
    "\n",
    "# Skip first (outliers?)\n",
    "\n",
    "data = data[skip_first:]\n",
    "\n",
    "# If in nanoseconds, scale to microseconds\n",
    "if header_names[1].endswith(\"[nano]\") and sign_unit == \"ms\":\n",
    "    sign_unit = r\"$\\mu s$\"\n",
    "    np.floor_divide(data[\"sign_time\"], 1000, out=data[\"sign_time\"])\n",
    "\n",
    "if header_names[2].endswith(\"[nano]\") and verify_unit == \"ms\":\n",
    "    verify_unit = r\"$\\mu s$\"\n",
    "    np.floor_divide(data[\"verify_time\"], 1000, out=data[\"verify_time\"])\n",
    "\n",
    "# Trim times\n",
    "quant_low_bound = trim_low if 0 <= trim_low <= 1 else 0.01\n",
    "quant_high_bound = trim_high if 0 <= trim_high <= 1 else 0.95\n",
    "quantiles_sign = mquantiles(data[\"sign_time\"], prob=(quant_low_bound, 0.25, 0.5, 0.75, quant_high_bound))\n",
    "if trim:\n",
    "    low_bound = quantiles_sign[0] if 0 <= trim_low <= 1 else trim_low\n",
    "    high_bound = quantiles_sign[4] if 0 <= trim_high <= 1 else trim_high\n",
    "    data_trimmed = data[np.logical_and(data[\"sign_time\"] >= low_bound,\n",
    "                                       data[\"sign_time\"] <= high_bound)]\n",
    "    quantiles_sign_trim = mquantiles(data_trimmed[\"sign_time\"], prob=(quant_low_bound, 0.25, 0.5, 0.75, quant_high_bound))\n",
    "else:\n",
    "    low_bound = None\n",
    "    high_bound = None\n",
    "    data_trimmed = data\n",
    "    quantiles_sign_trim = quantiles_sign\n",
    "\n",
    "description_sign = describe(data[\"sign_time\"])\n",
    "description_sign_trim = describe(data_trimmed[\"sign_time\"])\n",
    "\n",
    "max_sign_time = description_sign.minmax[1]\n",
    "min_sign_time = description_sign.minmax[0]\n",
    "bit_size = len(bin(max(data[\"priv\"]))) - 2\n",
    "byte_size = (bit_size + 7) // 8\n",
    "bit_size = byte_size * 8\n",
    "\n",
    "if hist_size == \"sqrt\":\n",
    "    hist_size_func = lambda n, xmin, xmax, var, xlower, xupper: int(sqrt(n)) + 1\n",
    "elif hist_size == \"sturges\":\n",
    "    hist_size_func = lambda n, xmin, xmax, var, xlower, xupper: int(log(n, 2)) + 1\n",
    "elif hist_size == \"rice\":\n",
    "    hist_size_func = lambda n, xmin, xmax, var, xlower, xupper: int(2 * n**(1/3))\n",
    "elif hist_size == \"scott\":\n",
    "    hist_size_func = lambda n, xmin, xmax, var, xlower, xupper: (xmax - xmin) // int((3.5 * sqrt(var)) / (n**(1/3)))\n",
    "elif hist_size == \"fd\":\n",
    "    hist_size_func = lambda n, xmin, xmax, var, xlower, xupper: (xmax - xmin) // int(2 * (xupper - xlower) / (n**(1/3)))\n",
    "else:\n",
    "    hist_size_func = lambda n, xmin, xmax, var, xlower, xupper: hist_size\n",
    "\n",
    "hist_size_sign_time = hist_size_func(description_sign.nobs, min_sign_time, max_sign_time, description_sign.variance, quantiles_sign[1], quantiles_sign[3])\n",
    "hist_size_sign_time_trim = hist_size_func(description_sign_trim.nobs, description_sign_trim.minmax[0], description_sign_trim.minmax[1], description_sign_trim.variance, quantiles_sign_trim[1], quantiles_sign_trim[3])\n",
    "\n",
    "if hist_size_sign_time < 30:\n",
    "    hist_size_sign_time = max_sign_time - min_sign_time\n",
    "if hist_size_sign_time_trim < 30:\n",
    "    hist_size_sign_time_trim = description_sign_trim.minmax[1] - description_sign_trim.minmax[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:00:39.540701Z",
     "start_time": "2019-03-17T23:00:39.511019Z"
    }
   },
   "outputs": [],
   "source": [
    "display(\"Raw\")\n",
    "desc = [(\"N\", \"min, max\", \"mean\", \"variance\", \"skewness\", \"kurtosis\"),\n",
    "        description_sign]\n",
    "display(HTML(tabulate.tabulate(desc, tablefmt=\"html\")))\n",
    "display(\"Trimmed\")\n",
    "desc = [(\"N\", \"min, max\", \"mean\", \"variance\", \"skewness\", \"kurtosis\"),\n",
    "        description_sign_trim]\n",
    "display(HTML(tabulate.tabulate(desc, tablefmt=\"html\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selected quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:00:40.974497Z",
     "start_time": "2019-03-17T23:00:40.953755Z"
    }
   },
   "outputs": [],
   "source": [
    "tbl = [(quant_low_bound, \"0.25\", \"0.5\", \"0.75\", quant_high_bound),\n",
    "       list(map(lambda x: \"{} {}\".format(x, sign_unit), quantiles_sign))]\n",
    "display(HTML(tabulate.tabulate(tbl, tablefmt=\"html\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:00:41.961541Z",
     "start_time": "2019-03-17T23:00:41.949385Z"
    }
   },
   "outputs": [],
   "source": [
    "display(\"Bitsize:\", bit_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nonce MSB vs signature time heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:06:34.030472Z",
     "start_time": "2019-03-17T23:06:33.761991Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_nonce = plt.figure(figsize=(10.5, 8), dpi=90)\n",
    "axe_nonce = fig_nonce.add_subplot(1, 1, 1)\n",
    "nonce_msb = np.array(list(map(lambda x: x >> (bit_size - 8), data_trimmed[\"nonce\"])), dtype=np.dtype(\"u1\"))\n",
    "max_msb = max(nonce_msb)\n",
    "min_msb = min(nonce_msb)\n",
    "heatmap, xedges, yedges = np.histogram2d(nonce_msb, data_trimmed[\"sign_time\"],\n",
    "                                         bins=[max_msb - min_msb + 1, hist_size_sign_time_trim])\n",
    "extent = [min_msb, max_msb, yedges[0], yedges[-1]]\n",
    "im = axe_nonce.imshow(heatmap.T, extent=extent, aspect=\"auto\", cmap=cmap, origin=\"low\",\n",
    "                   interpolation=\"nearest\", norm=norm)\n",
    "axe_nonce.set_xlabel(\"nonce key MSB value\")\n",
    "axe_nonce.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
    "fig_nonce.colorbar(im, ax=axe_nonce)\n",
    "\n",
    "del nonce_msb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nonce Hamming Weight vs signature time heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:05:26.804859Z",
     "start_time": "2019-03-17T23:05:18.214110Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_nonce_hist = plt.figure(figsize=(10.5, 12), dpi=90)\n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[2.5, 1])\n",
    "axe_nonce_hist = fig_nonce_hist.add_subplot(gs[0])\n",
    "axe_nonce_hist_hw = fig_nonce_hist.add_subplot(gs[1], sharex = axe_nonce_hist)\n",
    "nonce_hw = np.array(list(map(hw, data_trimmed[\"nonce\"])), dtype=np.dtype(\"u2\"))\n",
    "h, xe, ye = np.histogram2d(nonce_hw, data_trimmed[\"sign_time\"], bins=[max(nonce_hw) - min(nonce_hw), hist_size_sign_time_trim])\n",
    "im = axe_nonce_hist.imshow(h.T, origin=\"low\", cmap=cmap, aspect=\"auto\", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=norm)\n",
    "axe_nonce_hist.axvline(x=bit_size//2, alpha=0.7, linestyle=\"dotted\", color=\"white\", label=str(bit_size//2) + \" bits\")\n",
    "axe_nonce_hist.set_xlabel(\"nonce Hamming weight\")\n",
    "axe_nonce_hist.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
    "axe_nonce_hist.legend(loc=\"best\")\n",
    "\n",
    "plot_hist(axe_nonce_hist_hw, nonce_hw, \"nonce Hamming weight\", log_scale, True, True)\n",
    "\n",
    "param = norm_dist.fit(nonce_hw)\n",
    "pdf_range = np.arange(min(nonce_hw), max(nonce_hw))\n",
    "norm_pdf = norm_dist.pdf(pdf_range, *param[:-2], loc=param[-2], scale=param[-1]) * description_sign_trim.nobs\n",
    "axe_nonce_hist_hw.plot(pdf_range, norm_pdf, label=\"fitted normal distribution\")\n",
    "axe_nonce_hist_hw.legend(loc=\"best\")\n",
    "fig_nonce_hist.colorbar(im, ax=[axe_nonce_hist, axe_nonce_hist_hw])\n",
    "\n",
    "display(HTML(\"<b>Nonce Hamming weight fitted with normal distribution:</b>\"))\n",
    "display(HTML(tabulate.tabulate([(\"Mean\", \"Variance\"), param], tablefmt=\"html\")))\n",
    "\n",
    "del nonce_hw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Signature time histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:05:32.395983Z",
     "start_time": "2019-03-17T23:05:32.068823Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_sig_hist = plt.figure(figsize=(10.5, 8), dpi=90)\n",
    "axe_hist_full = fig_sig_hist.add_subplot(2, 1, 1)\n",
    "axe_hist_trim = fig_sig_hist.add_subplot(2, 1, 2)\n",
    "plot_hist(axe_hist_full, data[\"sign_time\"], \"signature time ({})\".format(sign_unit), log_scale, hist_size_sign_time);\n",
    "plot_hist(axe_hist_trim, data_trimmed[\"sign_time\"], \"signature time ({})\".format(sign_unit), log_scale, hist_size_sign_time_trim);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verification time histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:05:33.358613Z",
     "start_time": "2019-03-17T23:05:32.963791Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_vrfy_hist = plt.figure(figsize=(10.5, 5), dpi=90)\n",
    "axe_hist_full = fig_vrfy_hist.add_subplot(1, 1, 1)\n",
    "plot_hist(axe_hist_full, data[\"verify_time\"], \"verification time ({})\".format(verify_unit), log_scale, hist_size_sign_time);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moving averages of signature and verification times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:05:33.971385Z",
     "start_time": "2019-03-17T23:05:33.732857Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_avg = plt.figure(figsize=(10.5, 8), dpi=90)\n",
    "axe_sign_avg = fig_avg.add_subplot(2, 1, 1)\n",
    "axe_vrfy_avg = fig_avg.add_subplot(2, 1, 2, sharex=axe_sign_avg)\n",
    "avg_sign_100 = moving_average(data[\"sign_time\"], 100)\n",
    "avg_sign_1000 = moving_average(data[\"sign_time\"], 1000)\n",
    "axe_sign_avg.plot(avg_sign_100, label=\"window = 100\")\n",
    "axe_sign_avg.plot(avg_sign_1000, label=\"window = 1000\")\n",
    "if low_bound is not None:\n",
    "    axe_sign_avg.axhline(y=low_bound, alpha=0.7, linestyle=\"dotted\", color=\"green\", label=\"Low trim bound = {}\".format(low_bound))\n",
    "if high_bound is not None:\n",
    "    axe_sign_avg.axhline(y=high_bound, alpha=0.7, linestyle=\"dotted\", color=\"orange\", label=\"Hight trim bound = {}\".format(high_bound))\n",
    "axe_sign_avg.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
    "axe_sign_avg.set_xlabel(\"index\")\n",
    "axe_sign_avg.legend(loc=\"best\")\n",
    "\n",
    "avg_vrfy_100 = moving_average(data[\"verify_time\"], 100)\n",
    "avg_vrfy_1000 = moving_average(data[\"verify_time\"], 1000)\n",
    "axe_vrfy_avg.plot(avg_vrfy_100, label=\"window = 100\")\n",
    "axe_vrfy_avg.plot(avg_vrfy_1000, label=\"window = 1000\")\n",
    "axe_vrfy_avg.set_ylabel(\"verification time ({})\".format(verify_unit))\n",
    "axe_vrfy_avg.set_xlabel(\"index\")\n",
    "axe_vrfy_avg.legend(loc=\"best\")\n",
    "\n",
    "del avg_sign_100, avg_sign_1000, avg_vrfy_100, avg_vrfy_1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nonce MSB and LSB histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:05:36.256032Z",
     "start_time": "2019-03-17T23:05:35.302194Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_nonce_hists = plt.figure(figsize=(10.5, 8), dpi=90)\n",
    "nonce_msb = np.array(list(map(lambda x: x >> (bit_size - 8), data[\"nonce\"])), dtype=np.dtype(\"u1\"))\n",
    "nonce_lsb = np.array(list(map(lambda x: x & 0xff, data[\"nonce\"])), dtype=np.dtype(\"u1\"))\n",
    "axe_msb_n_hist = fig_nonce_hists.add_subplot(2, 1, 1)\n",
    "axe_lsb_n_hist = fig_nonce_hists.add_subplot(2, 1, 2)\n",
    "plot_hist(axe_msb_n_hist, nonce_msb, \"nonce MSB\", log_scale, False, False)\n",
    "plot_hist(axe_lsb_n_hist, nonce_lsb, \"nonce LSB\", log_scale, False, False)\n",
    "\n",
    "del nonce_msb, nonce_lsb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nonce bit length histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T23:05:45.320760Z",
     "start_time": "2019-03-17T23:05:44.951189Z"
    }
   },
   "outputs": [],
   "source": [
    "fig_bl = plt.figure(figsize=(10.5, 12), dpi=90)\n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[2.5, 1])\n",
    "axe_bl_heat = fig_bl.add_subplot(gs[0])\n",
    "axe_bl_hist = fig_bl.add_subplot(gs[1], sharex=axe_bl_heat)\n",
    "bl_data = np.array(list(map(lambda x: x.bit_length(), data_trimmed[\"nonce\"])), dtype=np.dtype(\"u2\"))\n",
    "\n",
    "h, xe, ye = np.histogram2d(bl_data, data_trimmed[\"sign_time\"], bins=[max(bl_data) - min(bl_data), hist_size_sign_time_trim])\n",
    "im = axe_bl_heat.imshow(h.T, origin=\"low\", cmap=cmap, aspect=\"auto\", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=norm)\n",
    "axe_bl_heat.set_xlabel(\"nonce bit length\")\n",
    "axe_bl_heat.set_ylabel(\"signature time ({})\".format(sign_unit))\n",
    "\n",
    "plot_hist(axe_bl_hist, bl_data, \"nonce bit length\", log_scale, align=\"right\")\n",
    "fig_bl.colorbar(im, ax=[axe_bl_heat, axe_bl_hist])\n",
    "\n",
    "del bl_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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