1 files changed, 97 insertions, 58 deletions

diff --git a/util/plot_gen.ipynb b/util/plot_gen.ipynb
index 5f02a00..db618cc 100644
--- a/util/plot_gen.ipynb
+++ b/util/plot_gen.ipynb
@@ -18,7 +18,7 @@
    },
    "outputs": [],
    "source": [
-    " %matplotlib notebook \n",
+    "%matplotlib notebook\n",
     "import numpy as np\n",
     "from scipy.stats import describe\n",
     "from scipy.stats import norm as norm_dist\n",
@@ -27,7 +27,7 @@
     "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 utils import plot_hist, moving_average, hw, time_scale\n",
     "from binascii import unhexlify\n",
     "from IPython.display import display, HTML\n",
     "from ipywidgets import interact, interactive, fixed, interact_manual\n",
@@ -57,6 +57,15 @@
     "# File name with output from ECTesterReader or ECTesterStandalone key generation.\n",
     "fname = \"filename.csv\"\n",
     "\n",
+    "# The time unit used in displaying the plots. One of \"milli\", \"micro\", \"nano\".\n",
+    "# WARNING: Using nano might lead to very large plots/histograms and to the\n",
+    "#          notebook to freeze or run out of memory, as well as bad visualization\n",
+    "#          quality, due to noise and low density.\n",
+    "gen_unit = \"milli\" # Unit of key generation command\n",
+    "export_unit = \"milli\" # Unit of key export command\n",
+    "# A number which will be used to divide the time into sub-units, e.g. for 5, time will be in fifths of units\n",
+    "scaling_factor = 1\n",
+    "\n",
     "# The amount of entries skipped from the beginning of the file, as they are usually outliers.\n",
     "skip_first = 10\n",
     "\n",
@@ -111,20 +120,8 @@
     "if log_scale:\n",
     "    norm = colors.LogNorm()\n",
     "else:\n",
-    "    norm = colors.Normalize()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-03-17T19:16:42.300146Z",
-     "start_time": "2019-03-17T19:16:40.259135Z"
-    }
-   },
-   "outputs": [],
-   "source": [
+    "    norm = colors.Normalize()\n",
+    "\n",
     "# Read the header line.\n",
     "\n",
     "with open(fname, \"r\") as f:\n",
@@ -144,37 +141,19 @@
     "    data = np.genfromtxt(fname, delimiter=\";\", skip_header=1, converters={3: unhexlify, 4: hx},\n",
     "                         dtype=np.dtype([(\"index\", \"u4\"), (\"gen_time\", \"u4\"), (\"export_time\", \"u4\"),\n",
     "                                         (\"pub\", \"O\"), (\"priv\", \"O\")]))\n",
-    "    \n",
-    "gen_unit = \"ms\"\n",
-    "export_unit = \"ms\"\n",
-    "# Setup the datatrimmed = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-03-17T19:16:42.417415Z",
-     "start_time": "2019-03-17T19:16:42.302353Z"
-    }
-   },
-   "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 gen_unit == \"ms\":\n",
-    "    gen_unit = r\"$\\mu s$\"\n",
-    "    np.floor_divide(data[\"gen_time\"], 1000, out=data[\"gen_time\"])\n",
+    "# Setup the data\n",
     "\n",
-    "if len(header_names) == 5 and header_names[2].endswith(\"[nano]\") and export_unit == \"ms\":\n",
-    "    export_unit = r\"$\\mu s$\"\n",
-    "    np.floor_divide(data[\"export_time\"], 1000, out=data[\"export_time\"])\n",
+    "# Convert time data\n",
+    "orig_gen_unit = header_names[1].split(\"[\")[1][:-1]\n",
+    "gen_disp_unit = time_scale(data[\"gen_time\"], orig_gen_unit, gen_unit, scaling_factor)\n",
+    "if len(header_names) == 5:\n",
+    "    orig_export_unit = header_names[2].split(\"[\")[1][:-1]\n",
+    "    export_disp_unit = time_scale(data[\"export_time\"], orig_export_unit, export_unit, scaling_factor)\n",
     "\n",
     "# Trim gen times\n",
     "quant_low_bound = trim_low if 0 <= trim_low <= 1 else 0.01\n",
@@ -199,6 +178,7 @@
     "min_gen_time = description_gen.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",
@@ -276,7 +256,7 @@
    "outputs": [],
    "source": [
     "tbl = [(quant_low_bound, \"0.25\", \"0.5\", \"0.75\", quant_high_bound),\n",
-    "       list(map(lambda x: \"{} {}\".format(x, gen_unit), quantiles_gen))]\n",
+    "       list(map(lambda x: \"{} {}\".format(x, gen_disp_unit), quantiles_gen))]\n",
     "display(HTML(tabulate.tabulate(tbl, tablefmt=\"html\")))"
    ]
   },
@@ -312,7 +292,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Private key MSB vs time heatmap"
+    "### Private key MSB vs time heatmap\n",
+    "The heatmap should show uncorrelated variables."
    ]
   },
   {
@@ -337,7 +318,7 @@
     "im = axe_private.imshow(heatmap.T, extent=extent, aspect=\"auto\", cmap=cmap, origin=\"low\",\n",
     "                   interpolation=\"nearest\", norm=norm)\n",
     "axe_private.set_xlabel(\"private key MSB value\")\n",
-    "axe_private.set_ylabel(\"keygen time ({})\".format(gen_unit))\n",
+    "axe_private.set_ylabel(\"keygen time ({})\".format(gen_disp_unit))\n",
     "fig_private.colorbar(im, ax=axe_private)\n",
     "\n",
     "del priv_msb"
@@ -347,7 +328,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Private key Hamming Weight vs time heatmap"
+    "### Private key Hamming Weight vs time heatmap\n",
+    "The heatmap should show uncorrelated variables.\n",
+    "\n",
+    "Also contains a private key Hamming Weight histogram, which should be binomially distributed."
    ]
   },
   {
@@ -370,7 +354,7 @@
     "im = axe_priv_hist.imshow(h.T, origin=\"low\", cmap=cmap, aspect=\"auto\", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=norm)\n",
     "axe_priv_hist.axvline(x=bit_size//2, alpha=0.7, linestyle=\"dotted\", color=\"white\", label=str(bit_size//2) + \" bits\")\n",
     "axe_priv_hist.set_xlabel(\"private key Hamming weight\")\n",
-    "axe_priv_hist.set_ylabel(\"keygen time ({})\".format(gen_unit))\n",
+    "axe_priv_hist.set_ylabel(\"keygen time ({})\".format(gen_disp_unit))\n",
     "axe_priv_hist.legend(loc=\"best\")\n",
     "\n",
     "plot_hist(axe_priv_hist_hw, priv_hw, \"private key Hamming weight\", log_scale, None)\n",
@@ -409,8 +393,8 @@
     "fig_kg_hist = plt.figure(figsize=(10.5, 8), dpi=90)\n",
     "axe_hist_full = fig_kg_hist.add_subplot(2, 1, 1)\n",
     "axe_hist_trim = fig_kg_hist.add_subplot(2, 1, 2)\n",
-    "plot_hist(axe_hist_full, data[\"gen_time\"], \"keygen time ({})\".format(gen_unit), log_scale, hist_size_gen_time);\n",
-    "plot_hist(axe_hist_trim, data_trimmed[\"gen_time\"], \"keygen time ({})\".format(gen_unit), log_scale, hist_size_gen_time_trim);"
+    "plot_hist(axe_hist_full, data[\"gen_time\"], \"keygen time ({})\".format(gen_disp_unit), log_scale, hist_size_gen_time);\n",
+    "plot_hist(axe_hist_trim, data_trimmed[\"gen_time\"], \"keygen time ({})\".format(gen_disp_unit), log_scale, hist_size_gen_time_trim);"
    ]
   },
   {
@@ -436,8 +420,8 @@
     "    fig_exp_hist = plt.figure(figsize=(10.5, 8), dpi=90)\n",
     "    axe_hist_full = fig_exp_hist.add_subplot(2, 1, 1)\n",
     "    axe_hist_trim = fig_exp_hist.add_subplot(2, 1, 2)\n",
-    "    plot_hist(axe_hist_full, data[\"export_time\"], \"export time ({})\".format(export_unit), log_scale, hist_size_gen_time);\n",
-    "    plot_hist(axe_hist_trim, data_trimmed[\"export_time\"], \"export time ({})\".format(export_unit), log_scale, hist_size_gen_time_trim);"
+    "    plot_hist(axe_hist_full, data[\"export_time\"], \"export time ({})\".format(export_disp_unit), log_scale, hist_size_gen_time);\n",
+    "    plot_hist(axe_hist_trim, data_trimmed[\"export_time\"], \"export time ({})\".format(export_disp_unit), log_scale, hist_size_gen_time_trim);"
    ]
   },
   {
@@ -468,7 +452,7 @@
     "    axe_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_avg.axhline(y=high_bound, alpha=0.7, linestyle=\"dotted\", color=\"orange\", label=\"Hight trim bound = {}\".format(high_bound))\n",
-    "axe_avg.set_ylabel(\"keygen time ({})\".format(gen_unit))\n",
+    "axe_avg.set_ylabel(\"keygen time ({})\".format(gen_disp_unit))\n",
     "axe_avg.set_xlabel(\"index\")\n",
     "axe_avg.legend(loc=\"best\")\n",
     "del avg_100, avg_1000"
@@ -478,7 +462,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Private key MSB and LSB histograms"
+    "### Private key MSB and LSB histograms\n",
+    "Expected to be uniform over [0, 255]."
    ]
   },
   {
@@ -506,7 +491,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Public key coordinate MSB and LSB histograms"
+    "### Public key coordinate MSB and LSB histograms\n",
+    "Expected to be somewhat uniform over [0, 255]."
    ]
   },
   {
@@ -549,7 +535,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Private key bit length vs time heatmap"
+    "### Private key bit length vs time heatmap\n",
+    "Also contains private key bit length histogram, which is expected to be axis flipped geometric distribution with $p = \\frac{1}{2}$ peaking at the bit size of the order of the curve."
    ]
   },
   {
@@ -573,7 +560,7 @@
     "h, xe, ye = np.histogram2d(bl_data, data_trimmed[\"gen_time\"], bins=[max(bl_data) - min(bl_data), hist_size_gen_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(\"private key bit length\")\n",
-    "axe_bl_heat.set_ylabel(\"keygen time ({})\".format(gen_unit))\n",
+    "axe_bl_heat.set_ylabel(\"keygen time ({})\".format(gen_disp_unit))\n",
     "\n",
     "plot_hist(axe_bl_hist, bl_data, \"Private key bit length\", log_scale, align=\"right\")\n",
     "fig_priv_hist.colorbar(im, ax=[axe_bl_heat, axe_bl_hist])\n",
@@ -582,17 +569,69 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Validation\n",
+    "Perform some tests on the produced data and compare to expected results.\n",
+    "\n",
+    "This requires some information about the used curve, enter it below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-18T18:27:02.748493Z",
+     "start_time": "2019-03-18T18:27:01.294850Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "p_str = input(\"The prime specifying the finite field:\")\n",
+    "p = int(p_str, 16) if p_str.startswith(\"0x\") else int(p_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-18T18:27:09.351619Z",
+     "start_time": "2019-03-18T18:27:08.674272Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "r_str = input(\"The order of the curve:\")\n",
+    "r = int(r_str, 16) if r_str.startswith(\"0x\") else int(r_str)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of the following tests should pass (e.g. be true):"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "max_priv = max(data[\"priv\"])\n",
+    "display(max_priv < r)\n",
+    "display(r <= p or max_priv > p)\n",
+    "display(max_priv.bit_length() == r.bit_length())"
+   ]
   }
  ],
  "metadata": {
   "@webio": {
-   "lastCommId": "eaf134b2342a4b71afa1ac9334e37e07",
-   "lastKernelId": "95d0d16c-1dde-451e-94cb-8bd0fefb8378"
+   "lastCommId": "a0b5176ec9b441fc87297b9ed343c5b3",
+   "lastKernelId": "a6864201-5c88-44d7-8438-ea96f7295d78"
   },
   "hide_input": false,
   "kernelspec": {