error_bar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pl 2    Pl 3    Pl 4\n",
      "0  0.5142  0.7258  0.9303\n",
      "1  0.5058  0.6831  0.9273\n",
      "2  0.5197  0.5263  0.9868\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modulation Recognition: NEU\n",
    "\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[51.42, 72.58, 93.03],[50.58, 68.31, 92.73],[51.97, 52.63, 98.68]])    # gps,img,lidar,all \n",
    "d= d*0.01\n",
    "\n",
    "\n",
    "\n",
    "# stds = np.array([[3.018676862,2.356217307,2.333125943,2.222917002],[7.523796618,3.856220948,2.206801607,3.303879201],[3.735202508,1.765655056,1.719134149,3.285939744],[5.101632092,3.987449756,4.257772109,2.846566938],[4.426380638,1.597294936,1.497114261,1.380016506]])\n",
    "# stds = stds *0.01\n",
    "\n",
    "# df = pd.DataFrame(d, columns=['IQ','CSP','Fusion'])\n",
    "df = pd.DataFrame(d, columns=['Pl 2','Pl 3','Pl 4'])\n",
    "#stds_f = pd.DataFrame(stds, columns=['IQ','CSP','Fusion'])\n",
    "print(df)\n",
    "# print(stds_f)\n",
    "ax = plt.figure(figsize=(5,3)).add_subplot(111)\n",
    "\n",
    "plots = df.plot(ax=ax, kind='bar',width=0.8,color=['#FFA07A','#9FE2BF','#CCCCFF'],edgecolor='black',legend=False)\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    ######################################\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() ,\n",
    "                    bar.get_height()), ha='right', va='center',\n",
    "                   size=12, xytext=(0, 5),  weight='bold',\n",
    "                   textcoords='offset points')\n",
    "    ######################################\n",
    "    \n",
    "# ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=14)\n",
    "ax.set_xticklabels(('131072', '262144', '524288'))\n",
    "plt.xticks(fontsize=20,rotation=0)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0.4, 1.1])\n",
    "\n",
    "###############################\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "##############################\n",
    "\n",
    "plt.savefig('t-SNE/modulation_accuracy_NEU.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pl 1    Pl 2    Pl 3    Pl 4\n",
      "0  0.6138  0.9981  0.9981  0.9981\n",
      "1  0.6930  0.9960  1.0000  1.0000\n",
      "2  0.8019  0.9238  0.9910  0.9910\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anomaly Detection: NEU\n",
    "\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[61.38, 99.81, 99.81, 99.81],[69.30, 99.6, 100, 100],[80.19,92.38, 99.1, 99.1]])    # gps,img,lidar,all \n",
    "d= d*0.01\n",
    "# stds = np.array([[3.018676862,2.356217307,2.333125943,2.222917002],[7.523796618,3.856220948,2.206801607,3.303879201],[3.735202508,1.765655056,1.719134149,3.285939744],[5.101632092,3.987449756,4.257772109,2.846566938],[4.426380638,1.597294936,1.497114261,1.380016506]])\n",
    "# stds = stds *0.01\n",
    "\n",
    "# df = pd.DataFrame(d, columns=['IQ','CSP','Fusion'])\n",
    "df = pd.DataFrame(d, columns=['Pl 1', 'Pl 2','Pl 3','Pl 4'])\n",
    "#stds_f = pd.DataFrame(stds, columns=['IQ','CSP','Fusion'])\n",
    "print(df)\n",
    "# print(stds_f)\n",
    "ax = plt.figure(figsize=(5,3)).add_subplot(111)\n",
    "plots = df.plot(ax=ax, kind='bar',width=0.8,color=['#6495ED', '#FFA07A','#9FE2BF','#CCCCFF'],edgecolor='black',legend=False)\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    ######################################\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() ,\n",
    "                    bar.get_height()), ha='right', va='center',\n",
    "                   size=10, xytext=(0, 5),  weight='bold',\n",
    "                   textcoords='offset points')\n",
    "    ######################################\n",
    "    \n",
    "# ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=14)\n",
    "ax.set_xticklabels(('131072', '262144', '524288'))\n",
    "plt.xticks(fontsize=20,rotation=0)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0.2, 1.1])\n",
    "###############################\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "##############################\n",
    "plt.savefig('t-SNE/anomaly_accuracy_NEU.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       P2      P3      P4\n",
      "0  0.6217  0.6616  0.7500\n",
      "1  0.6517  0.6852  0.6852\n",
      "2  0.6752  0.7058  0.7058\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modulation Recignition: NWRA\n",
    "\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[62.17, 66.16, 75],[65.17, 68.52, 68.52],[67.52, 70.58,  70.58]])    # gps,img,lidar,all \n",
    "d= d*0.01\n",
    "\n",
    "\n",
    "# stds = np.array([[3.018676862,2.356217307,2.333125943,2.222917002],[7.523796618,3.856220948,2.206801607,3.303879201],[3.735202508,1.765655056,1.719134149,3.285939744],[5.101632092,3.987449756,4.257772109,2.846566938],[4.426380638,1.597294936,1.497114261,1.380016506]])\n",
    "# stds = stds *0.01\n",
    "\n",
    "# df = pd.DataFrame(d, columns=['IQ','CSP','Fusion'])\n",
    "df = pd.DataFrame(d, columns=['P2','P3','P4'])\n",
    "#stds_f = pd.DataFrame(stds, columns=['IQ','CSP','Fusion'])\n",
    "print(df)\n",
    "# print(stds_f)\n",
    "ax = plt.figure(figsize=(5,3)).add_subplot(111)\n",
    "plots = df.plot(ax=ax, kind='bar',width=0.8,color=['#FFA07A','#9FE2BF','#CCCCFF'],edgecolor='black',legend=False)\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    ######################################\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() ,\n",
    "                    bar.get_height()), ha='right', va='center',\n",
    "                   size=12, xytext=(0, 5),  weight='bold',\n",
    "                   textcoords='offset points')\n",
    "    ######################################\n",
    "    \n",
    "ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=20)\n",
    "ax.set_xticklabels(('131072', '262144', '524288'))\n",
    "plt.xticks(fontsize=20,rotation=0)\n",
    "# plt.yticks(fontsize=20)\n",
    "# plt.ylabel('Accuracy', fontsize=20)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0.4, 1.1])\n",
    "###############################\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "##############################\n",
    "\n",
    "plt.savefig('t-SNE/modulation_accuracy_NWRA.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pl 1    Pl 2    Pl 3    Pl 4\n",
      "0  0.7580  0.9675  0.8062  0.9895\n",
      "1  0.8177  0.9375  0.8116  0.9796\n",
      "2  0.8322  0.9750  0.8470  0.9825\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anomaly Detection: NWRA\n",
    "\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[75.80, 96.75, 80.62, 98.95],[81.77, 93.75, 81.16, 97.96],[83.22, 97.5, 84.7, 98.25]])    # gps,img,lidar,all \n",
    "d= d*0.01\n",
    "# stds = np.array([[3.018676862,2.356217307,2.333125943,2.222917002],[7.523796618,3.856220948,2.206801607,3.303879201],[3.735202508,1.765655056,1.719134149,3.285939744],[5.101632092,3.987449756,4.257772109,2.846566938],[4.426380638,1.597294936,1.497114261,1.380016506]])\n",
    "# stds = stds *0.01\n",
    "\n",
    "# df = pd.DataFrame(d, columns=['IQ','CSP','Fusion'])\n",
    "df = pd.DataFrame(d, columns=['Pl 1', 'Pl 2','Pl 3','Pl 4'])\n",
    "#stds_f = pd.DataFrame(stds, columns=['IQ','CSP','Fusion'])\n",
    "print(df)\n",
    "# print(stds_f)\n",
    "ax = plt.figure(figsize=(5,3)).add_subplot(111)\n",
    "plots = df.plot(ax=ax, kind='bar',width=0.8,color=['#6495ED', '#FFA07A','#9FE2BF','#CCCCFF'],edgecolor='black',legend=False)\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    ######################################\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() ,\n",
    "                    bar.get_height()), ha='right', va='center',\n",
    "                   size=10, xytext=(0, 5),  weight='bold',\n",
    "                   textcoords='offset points')\n",
    "    ######################################\n",
    "    \n",
    "ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=20)\n",
    "ax.set_xticklabels(('131072', '262144', '524288'))\n",
    "plt.xticks(fontsize=20,rotation=0)\n",
    "# plt.yticks(fontsize=20)\n",
    "# plt.ylabel('Accuracy', fontsize=20)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0.2, 1.1])\n",
    "###############################\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "##############################\n",
    "plt.savefig('t-SNE/anomaly_accuracy_NWRA.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pl 2    Pl 3    Pl 4\n",
      "0  0.6667  0.5218  0.6667\n",
      "1  0.6964  0.4719  0.9285\n",
      "2  0.5104  0.6250  0.9062\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Modulation Recognition: POWDER\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[66.67, 52.18, 66.67],[69.64, 47.19, 92.85],[51.04, 62.5, 90.62]])    # gps,img,lidar,all \n",
    "d= d*0.01\n",
    "# stds = np.array([[3.018676862,2.356217307,2.333125943,2.222917002],[7.523796618,3.856220948,2.206801607,3.303879201],[3.735202508,1.765655056,1.719134149,3.285939744],[5.101632092,3.987449756,4.257772109,2.846566938],[4.426380638,1.597294936,1.497114261,1.380016506]])\n",
    "# stds = stds *0.01\n",
    "\n",
    "\n",
    "\n",
    "# df = pd.DataFrame(d, columns=['IQ','CSP','Fusion'])\n",
    "df = pd.DataFrame(d, columns=['Pl 2','Pl 3','Pl 4'])\n",
    "#stds_f = pd.DataFrame(stds, columns=['IQ','CSP','Fusion'])\n",
    "print(df)\n",
    "# print(stds_f)\n",
    "ax = plt.figure(figsize=(5,3)).add_subplot(111)\n",
    "plots = df.plot(ax=ax, kind='bar',width=0.8,color=['#FFA07A','#9FE2BF','#CCCCFF'],edgecolor='black',legend=False)\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    ######################################\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() ,\n",
    "                    bar.get_height()), ha='right', va='center',\n",
    "                   size=12, xytext=(0, 5),  weight='bold',\n",
    "                   textcoords='offset points')\n",
    "    ######################################\n",
    "    \n",
    "# ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=18)\n",
    "ax.set_xticklabels(('131072', '262144', '524288'))\n",
    "plt.xticks(fontsize=20,rotation=0)\n",
    "# plt.yticks(fontsize=20)\n",
    "# plt.ylabel('Accuracy', fontsize=20)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0.4, 1.1])\n",
    "###############################\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "##############################\n",
    "plt.savefig('t-SNE/modulation_accuracy_POWDER.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pl 1  Pl 2    Pl 3  Pl 4\n",
      "0  0.5068   1.0  0.6201   1.0\n",
      "1  0.3256   1.0  0.8068   1.0\n",
      "2  0.2232   1.0  0.8333   1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anomaly Detection: POWDER\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[50.68, 100, 62.01, 100],[32.56, 100, 80.68, 100],[22.32, 100, 83.33, 100]])    # gps,img,lidar,all \n",
    "d= d*0.01\n",
    "# stds = np.array([[3.018676862,2.356217307,2.333125943,2.222917002],[7.523796618,3.856220948,2.206801607,3.303879201],[3.735202508,1.765655056,1.719134149,3.285939744],[5.101632092,3.987449756,4.257772109,2.846566938],[4.426380638,1.597294936,1.497114261,1.380016506]])\n",
    "# stds = stds *0.01\n",
    "\n",
    "# df = pd.DataFrame(d, columns=['IQ','CSP','Fusion'])\n",
    "df = pd.DataFrame(d, columns=['Pl 1', 'Pl 2','Pl 3','Pl 4'])\n",
    "#stds_f = pd.DataFrame(stds, columns=['IQ','CSP','Fusion'])\n",
    "print(df)\n",
    "# print(stds_f)\n",
    "ax = plt.figure(figsize=(5,3)).add_subplot(111)\n",
    "plots = df.plot(ax=ax, kind='bar',width=0.8,color=['#6495ED', '#FFA07A','#9FE2BF','#CCCCFF'],edgecolor='black',legend=False)\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    ######################################\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() ,\n",
    "                    bar.get_height()), ha='right', va='center',\n",
    "                   size=10, xytext=(0, 5),  weight='bold',\n",
    "                   textcoords='offset points')\n",
    "    ######################################\n",
    "    \n",
    "# ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=14)\n",
    "ax.set_xticklabels(('131072', '262144', '524288'))\n",
    "plt.xticks(fontsize=20,rotation=0)\n",
    "# plt.yticks(fontsize=20)\n",
    "# plt.ylabel('Accuracy', fontsize=20)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0.2, 1.1])\n",
    "###############################\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "##############################\n",
    "plt.savefig('t-SNE/anomaly_accuracy_POWDER.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PL 1     Pl 2    Pl 3     Pl 4\n",
      "0   1.0  0.09300  0.0900  0.18000\n",
      "1   1.0  0.03370  0.0340  0.06270\n",
      "2   1.0  0.00337  0.0034  0.00627\n",
      "   PL 1     Pl 2      Pl 3      Pl 4\n",
      "0   0.2  0.04440  0.044480  0.046370\n",
      "1   0.2  0.02440  0.024480  0.026370\n",
      "2   0.2  0.00244  0.002448  0.002637\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparision of Inference Times\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[1, 0.093, 0.09, 0.18],[1, 0.0337, 0.034, 0.0627],[1, 0.00337, 0.0034, 0.00627]])  # gps,img,lidar,all \n",
    "#d= d*0.01\n",
    "stds = np.array([[0.2,0.0444, 0.04448,0.04637],[0.2,0.0244, 0.02448,0.02637],[0.2,0.00244, 0.002448,0.002637]])\n",
    "#stds = stds *0.01\n",
    "\n",
    "df = pd.DataFrame(d, columns=['PL 1', 'Pl 2', 'Pl 3', 'Pl 4'])\n",
    "stds_f = pd.DataFrame(stds, columns=['PL 1', 'Pl 2', 'Pl 3', 'Pl 4'])\n",
    "print(df)\n",
    "print(stds_f)\n",
    "ax = plt.figure(figsize=(10,3)).add_subplot(111)\n",
    "df.plot(ax=ax, kind='bar',width=0.8,color=['#FBEAE9','#FAE4D8','#FBD6AB','#F1AF79'],edgecolor='black',yerr=stds_f,legend=False,error_kw=dict(lw=2, capsize=5, capthick=2.5,color='red'))\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    \n",
    "ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=14)\n",
    "ax.set_xticklabels(('Jetson TX2', '2080Ti', 'A100'))\n",
    "plt.xticks(fontsize=15,rotation=0)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.ylabel('Inference Time (s)', fontsize=15)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,0.2])\n",
    "plt.savefig('inference_time_error_bar.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PL 1       Pl 2      Pl 3       Pl 4\n",
      "0   1.0  34.556298  0.000361  64.363173\n",
      "1   1.0   0.283577  0.043313   0.292869\n",
      "2   1.0   0.028348  0.000435   0.038493\n",
      "   PL 1       Pl 2      Pl 3       Pl 4\n",
      "0   0.2  30.152995  0.000038  43.966934\n",
      "1   0.2   0.012391  0.029435   0.013643\n",
      "2   0.2   0.002903  0.000045   0.007510\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inference times: Jul 23 (mean and variance)\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[1, 34.55629767, 0.0003614666667, 64.36317347],[1, 0.28357663, 0.04331340327, 0.292869072],[1, 0.02834833233, 0.0004350284333, 0.03849287933]])  \n",
    "stds = np.array([[0.2,30.15299529, 0.00003787892994, 43.96693391],[0.2,0.01239078493, 0.02943489396, 0.01364347705],[0.2,0.002903403384, 0.0000447171152, 0.007509752109]])\n",
    "#stds = stds *0.01\n",
    "\n",
    "\n",
    "\n",
    "df = pd.DataFrame(d, columns=['PL 1', 'Pl 2', 'Pl 3', 'Pl 4'])\n",
    "stds_f = pd.DataFrame(stds, columns=['PL 1', 'Pl 2', 'Pl 3', 'Pl 4'])\n",
    "print(df)\n",
    "print(stds_f)\n",
    "ax = plt.figure(figsize=(10,3)).add_subplot(111)\n",
    "df.plot(ax=ax, kind='bar',width=0.8,color=['#FBEAE9','#FAE4D8','#FBD6AB','#F1AF79'],edgecolor='black',yerr=stds_f,legend=False,error_kw=dict(lw=2, capsize=5, capthick=2.5,color='red'))\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "\n",
    "# plt.scatter(x, y, c=colors)    \n",
    "    \n",
    "ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=14)\n",
    "ax.set_xticklabels(('Intel i7 CPU', '2080Ti', 'A100'))\n",
    "plt.xticks(fontsize=15,rotation=0)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.ylabel('Inference Time (s)', fontsize=15)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,1])\n",
    "plt.savefig('inference_time_error_bar.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PL 1       Pl 2      Pl 3       Pl 4\n",
      "0   1.0  34.556298  0.000361  64.363173\n",
      "1   1.0   0.283577  0.043313   0.292869\n",
      "2   1.0   0.028348  0.000435   0.038493\n",
      "   PL 1       Pl 2      Pl 3       Pl 4\n",
      "0   0.2  30.152995  0.000038  43.966934\n",
      "1   0.2   0.012391  0.029435   0.013643\n",
      "2   0.2   0.002903  0.000045   0.007510\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inference times: Jul 23 (mean and variance)\n",
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[1, 34.55629767, 0.0003614666667, 64.36317347],[1, 0.28357663, 0.04331340327, 0.292869072],[1, 0.02834833233, 0.0004350284333, 0.03849287933]])  \n",
    "stds = np.array([[0.2,30.15299529, 0.00003787892994, 43.96693391],[0.2,0.01239078493, 0.02943489396, 0.01364347705],[0.2,0.002903403384, 0.0000447171152, 0.007509752109]])\n",
    "#stds = stds *0.01\n",
    "\n",
    "\n",
    "\n",
    "df = pd.DataFrame(d, columns=['PL 1', 'Pl 2', 'Pl 3', 'Pl 4'])\n",
    "stds_f = pd.DataFrame(stds, columns=['PL 1', 'Pl 2', 'Pl 3', 'Pl 4'])\n",
    "print(df)\n",
    "print(stds_f)\n",
    "ax = plt.figure(figsize=(10,3)).add_subplot(111)\n",
    "df.plot(ax=ax, kind='bar',width=0.8,color=['#FBEAE9','#FAE4D8','#FBD6AB','#F1AF79'],edgecolor='black', legend=False,error_kw=dict(lw=2, capsize=5, capthick=2.5,color='red'))\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "\n",
    "# plt.scatter(x, y, c=colors)    \n",
    "    \n",
    "ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=14)\n",
    "ax.set_xticklabels(('Intel i7 CPU', '2080Ti', 'A100'))\n",
    "plt.xticks(fontsize=15,rotation=0)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.ylabel('Inference Time (s)', fontsize=15)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,0.2])\n",
    "plt.savefig('inference_time_error_bar.png',bbox_inches='tight',dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Coordinate    Image    LiDAR   Fusion\n",
      "0     0.16651  0.22896  0.19730  0.23374\n",
      "1     0.30644  0.29218  0.23362  0.28498\n",
      "2     0.20826  0.24294  0.20910  0.24730\n",
      "3     0.31145  0.27870  0.23983  0.26979\n",
      "4     0.24537  0.25702  0.21792  0.25387\n",
      "   Coordinate     Image     LiDAR    Fusion\n",
      "0    0.030187  0.023562  0.023331  0.022229\n",
      "1    0.075238  0.038562  0.022068  0.033039\n",
      "2    0.037352  0.017657  0.017191  0.032859\n",
      "3    0.051016  0.039874  0.042578  0.028466\n",
      "4    0.044264  0.015973  0.014971  0.013800\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'figs/top1_accuracy_error_bar.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-2b9b416e0d19>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figs/top1_accuracy_error_bar.png'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 723\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    724\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2201\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2203\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2103\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2104\u001b[0m                     \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2105\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2106\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2107\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    533\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    536\u001b[0m                 _png.write_png(renderer._renderer, fh, self.figure.dpi,\n\u001b[1;32m    537\u001b[0m                                metadata={**default_metadata, **metadata})\n",
      "\u001b[0;32m/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mopen_file_cm\u001b[0;34m(path_or_file, mode, encoding)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;34mr\"\"\"Pass through file objects and context-manage `.PathLike`\\s.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m     \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_filehandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopened\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mto_filehandle\u001b[0;34m(fname, flag, return_opened, encoding)\u001b[0m\n\u001b[1;32m    401\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m         \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'seek'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'figs/top1_accuracy_error_bar.png'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt    \n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "d= np.array([[16.651,22.896,19.73,23.374],[30.644,29.218,23.362,28.498],[20.826,24.294,20.91,24.73],[31.145,27.87,23.983,26.979],[24.537,25.702,21.792,25.387]])    # gps,img,lidar,all \n",
    "d= d*0.01\n",
    "stds = np.array([[3.018676862,2.356217307,2.333125943,2.222917002],[7.523796618,3.856220948,2.206801607,3.303879201],[3.735202508,1.765655056,1.719134149,3.285939744],[5.101632092,3.987449756,4.257772109,2.846566938],[4.426380638,1.597294936,1.497114261,1.380016506]])\n",
    "stds = stds *0.01\n",
    "\n",
    "df = pd.DataFrame(d, columns=['Coordinate','Image','LiDAR','Fusion'])\n",
    "stds_f = pd.DataFrame(stds, columns=['Coordinate','Image','LiDAR','Fusion'])\n",
    "print(df)\n",
    "print(stds_f)\n",
    "ax = plt.figure(figsize=(10,3)).add_subplot(111)\n",
    "df.plot(ax=ax, kind='bar',width=0.8,color=['#FBEAE9','#FAE4D8','#FBD6AB','#F1AF79'],edgecolor='black',yerr=stds_f,legend=False,error_kw=dict(lw=2, capsize=5, capthick=2.5,color='red'))\n",
    "\n",
    "bars = ax.patches\n",
    "hatches = ''.join(h*len(df) for h in 'xo\\.')\n",
    "for bar, hatch in zip(bars, hatches):\n",
    "    bar.set_hatch(hatch)\n",
    "    \n",
    "ax.legend(loc='center right', bbox_to_anchor=(1.01, 1.2), ncol=6,fontsize=14)\n",
    "ax.set_xticklabels(('Category 1', 'Category 2', 'Category 3', 'Category 4','Combined'))\n",
    "plt.xticks(fontsize=15,rotation=0)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.ylabel('Top-1 Accuracy', fontsize=15)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,0.5])\n",
    "plt.savefig('figs/top1_accuracy_error_bar.png',bbox_inches='tight',dpi=400)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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