{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0f84133",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ded9836e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def load_data(filename):\n",
    "    m = np.array(pd.read_csv(filename, header=None))\n",
    "    return np.tril(m) + np.tril(m).transpose()\n",
    "\n",
    "\n",
    "def show_heapmap(\n",
    "    m, title=None, subtitle=None, vmin=None, vmax=None, yticks=True, figsize=None\n",
    "):\n",
    "    vmin = np.nanmin(m) if vmin is None else vmin\n",
    "    vmax = np.nanmax(m) if vmax is None else vmax\n",
    "    black_at = (vmin + 3 * vmax) / 4\n",
    "    subtitle = \"Core-to-core latency\" if subtitle is None else subtitle\n",
    "\n",
    "    isnan = np.isnan(m)\n",
    "\n",
    "    plt.rcParams[\"xtick.bottom\"] = plt.rcParams[\"xtick.labelbottom\"] = False\n",
    "    plt.rcParams[\"xtick.top\"] = plt.rcParams[\"xtick.labeltop\"] = True\n",
    "\n",
    "    figsize = np.array(m.shape) * 0.3 + np.array([6, 1]) if figsize is None else figsize\n",
    "    fig = plt.figure(figsize=figsize, dpi=130)\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    fig.patch.set_facecolor(\"w\")\n",
    "    ax.set_facecolor(\"w\")\n",
    "\n",
    "    ax.imshow(np.full_like(m, 0.7), vmin=0, vmax=1, cmap=\"gray\")  # for the alpha value\n",
    "    im = ax.imshow(m, cmap=plt.colormaps.get_cmap(\"viridis\"), vmin=vmin, vmax=vmax)\n",
    "\n",
    "    fontsize = 9 if vmax >= 100 else 10\n",
    "\n",
    "    for i, j in np.ndindex(m.shape):\n",
    "        t = \"\" if isnan[i, j] else f\"{m[i,j]:.1f}\" if vmax < 10.0 else f\"{m[i,j]:.0f}\"\n",
    "        c = \"w\" if m[i, j] < black_at else \"k\"\n",
    "        ax.text(j, i, t, ha=\"center\", va=\"center\", color=c, fontsize=fontsize)\n",
    "\n",
    "    ax.set_xticks(np.arange(m.shape[1]))\n",
    "    ax.set_xticklabels([f\"{i+1}\" for i in range(m.shape[1])], fontsize=9)\n",
    "    if yticks:\n",
    "        ax.set_yticks(np.arange(m.shape[0]))\n",
    "        ax.set_yticklabels([f\"CPU {i+1}\" for i in range(m.shape[0])], fontsize=9)\n",
    "    else:\n",
    "        ax.set_yticks([])\n",
    "\n",
    "    ax.xaxis.tick_top()\n",
    "    ax.xaxis.set_label_position(\"top\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    display(Markdown(f\"## {title}\"))\n",
    "    ax.set_title(\n",
    "        f\"{title}\\n\"\n",
    "        + f\"{subtitle}\\n\"\n",
    "        + f\"Min={vmin:0.1f}ns Median={np.nanmedian(m):0.1f}ns Max={vmax:0.1f}ns\",\n",
    "        fontsize=11,\n",
    "        pad=20,\n",
    "    )\n",
    "    display(fig)\n",
    "    plt.close(fig)\n",
    "\n",
    "\n",
    "# Get all CSV files in the current directory\n",
    "csv_files = [f for f in os.listdir(\".\") if f.endswith(\".csv\")]\n",
    "\n",
    "for fname in csv_files:\n",
    "    cpu_name = os.path.splitext(fname)[0]\n",
    "    m = load_data(fname)\n",
    "\n",
    "    show_heapmap(m, title=cpu_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e06994",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Run this scripts in python jupyter notes and you will see all history results.\\n\"*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25c2db29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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