diff --git a/lib/datasets/synthetic_example.py b/lib/datasets/synthetic_example.py
new file mode 100644
index 0000000..55e0fa4
--- /dev/null
+++ b/lib/datasets/synthetic_example.py
@@ -0,0 +1,27 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+
+from .math_base_funcs import DynamicQuadraticFunc
+from .synthetic_utils import ConstantGenerator, SinGenerator
+from .synthetic_env import SyntheticDEnv
+
+
+def create_example_v1(timestamps=50, num_per_task=5000):
+    mean_generator = SinGenerator(num=timestamps)
+    std_generator = SinGenerator(num=timestamps, min_amplitude=0.5, max_amplitude=0.5)
+    std_generator.set_transform(lambda x: x + 1)
+    dynamic_env = SyntheticDEnv(
+        [mean_generator], [[std_generator]], num_per_task=num_per_task
+    )
+    function = DynamicQuadraticFunc()
+    function_param = dict()
+    function_param[0] = SinGenerator(
+        num=timestamps, num_sin_phase=4, phase_shift=1.0, max_amplitude=1.0
+    )
+    function_param[1] = ConstantGenerator(constant=0.9)
+    function_param[2] = SinGenerator(
+        num=timestamps, num_sin_phase=5, phase_shift=0.4, max_amplitude=0.9
+    )
+    function.set(function_param)
+    return dynamic_env, function
diff --git a/notebooks/TOT/synthetic-env.ipynb b/notebooks/TOT/synthetic-env.ipynb
index 26dd110..e735313 100644
--- a/notebooks/TOT/synthetic-env.ipynb
+++ b/notebooks/TOT/synthetic-env.ipynb
@@ -37,7 +37,8 @@
     "    sys.path.insert(0, str(lib_dir))\n",
     "\n",
     "from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
-    "from datasets import DynamicQuadraticFunc"
+    "from datasets import DynamicQuadraticFunc\n",
+    "from datasets.synthetic_example import create_example_v1"
    ]
   },
   {
@@ -46,16 +47,9 @@
    "id": "detected-second",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SyntheticDEnv(20/20 elements, ndim=1, num_per_task=100)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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UCAAAAADYTbOzs3H9+vXY2tqKnHNsbW3F9evXY3Z2tuhqMLbee++9ewbZb21txXvvvVdQIxhvzWYzlpaWYn5+PjY2NmJ+fj6Wlpai2WwWXQ3GkpX0AMC2z3zmMyPlAAAA3Ovs2bOxsbEROefY2NgweAd2QafTiUajEbOzs9FoNKLT6RRdCQAAYKz4XAW76/333x8pBwAAAADGy5UrV0bKgUfr9Xoj5cCjNZvNWF5ejtXV1VheXjZ4B55CpegCAMDeMT09HT/96U/vmwMAAAAUodPpxKlTp+LWrVsxHA7j0qVLcerUqYgIXxICAAA8hk6nE4uLi5FzjsnJyVhZWYnFxcWI8LkKnlTOeaQcAAAAABgvGxsbI+XAow0Gg5FyAHieki9797aFhYV88eLFomsAcEDMzs5GSinW1tZiMBhEuVyO6enpyDnH6upq0fUAAACAA6her8cHH3xwT37s2LHodrvPvxAAAM9cSumBj7nOBUbXaDRiZWUlarXadtbr9WJ+fj6Wl5eLKwZjzO8qAAAAYK9xvgJ2l/cU7L5SqXTf909KKYbDYQGNADhoUko/zDkv3O+xyvMuAwDsXfV6PVZWVmJubm47u3PRJQAAAEARPvzww4jYeUFLznk7BwAA4OHuDC69fv369iYsU1NTBpoCAAAAAADw3FQqlej3+/fNAaBopaILAAB7R6vVipRS9Hq9yDlHr9eLlFK0Wq2iqwEAAAAH2Kd3u7F7FAAAwOObnZ2NGzduxGAwiIiIwWAQN27ciNnZ2WKLAQAAAAAAcGBUKpUdm/BFfLIpn+E7AOwFhu8AANuazWYsLS3F/Px8bGxsxPz8fCwtLUWz2Sy6GgAAAHBAHTt2LFJK2wN3cs6RUopjx44V3AwAAGA83H0R84NuA6OZm5sbKQcAAAAAgIPu+PHjMTU1tT2Ep1KpxNTUVBw/frzoajC2Op1ONBqNmJ2djUajEZ1Op+hKMLYM3wEAdmg2m7G8vByrq6uxvLxs8A4AAABQqHPnzsX09HSUSqUYDodRKpVieno6zp07V3Q1AACAsXDt2rWYmZmJcrkcOecol8sxMzMT165dK7oajK233347JiYmdmQTExPx9ttvF9QIAAAAANhNlUplpBx4tFarFRMTEzEzMxOf+9znYmZmJiYmJqLVahVdDcZSp9OJxcXFWFlZicnJyVhZWYnFxUUDeOAJGb4DAAAAAADsWc1mM86fPx8nTpyImZmZOHHiRJw/f97AYAAAgMdUr9ejXC7H3NxcHD16NObm5qJcLke9Xi+6GoytZrMZ3/72t+PVV1+NmZmZePXVV+Pb3/628xUAAAAAsE8YvgO7r9lsxle+8pX4+OOP4/Lly/Hxxx/HV77yFefW4Qm12+3IOUetVouUUtRqtcg5R7vdLroajCXDdwCAHTqdTjQajZidnY1Go2HKJQAAAFC4ZrMZy8vLsbq6GsvLy75sBwAAGEGr1Yr19fX48Y9/vH2sr6/bRRQAAAAAAB5gc3NzpBx4tDNnzsR3v/vdyDlHRETOOb773e/GmTNnCm4G46nb7Ua1Wt2RVavV6Ha7xRSCMWf4DgCwrdPpxOLiYqysrMTk5GSsrKzE4uKiATwAAAAAAAAAY+r73//+PYsBNjc34/vf/35BjWD8dTqdOHXqVFy6dCmuX78ely5dilOnTrm+AgAAAAAAHuA3f/M3R8qBh6vX69Hv93dk/X4/6vV6MYVgzKU70+HYmxYWFvLFixeLrgHAAdFoNGJlZSVqtdp21uv1Yn5+PpaXl4srBgAAAAAAwIGRUnrgY65zgdHVarV7LrqM+GTXw16vV0AjGH+/8Au/EO+//35EfPJ7687vp5dffjn++I//uMhqAAAAwAHl3DrsrkqlEoPB4J68XC7H1tZWAY1g/PldBbur0+nE4uJi5JyjWq1Gv9+PlFIsLS1Fs9ksuh7sSSmlH+acF+73WOl5lwEA9q5utxvVanVHVq1Wo9vtFlMIAAAAAAAAgKdyv8E7D8uBR/vggw8i57y9UODOAJ4PPvig4GYAAAAAwG44fPjwSDkAPG/NZjOWlpZifn4+NjY2Yn5+3uAdeAqVogsAAHtHvV6PlZWVqNVq21m/3496vV5cKQAAAAAAAACAPebTO/Q+bMdeAAAAAGC8rK+vj5QDjzY3NxdXr169bw48mWazadgO7JJS0QUAgL2j1WpFr9eLK1euxOXLl+PKlSvR6/Wi1WoVXQ0AAGBsnDlzJiYnJyOlFJOTk3HmzJmiKwEAAAAAu+jzn/985JxjOBxuHznn+PznP190NQAAAABgF+ScR8qBR3v77bdjYmJiRzYxMRFvv/12QY0A4E8YvgMA3JeTQQAAAKM7c+ZMfPOb34zNzc1IKcXm5mZ885vfNIAHAAAAKMyDdgu1iyg8ub/6V//qSDkAAAAAMF4qlcpIOfBozWYzvv3tb8err74aMzMz8eqrr8a3v/3taDabRVcDgEgW1u9tCwsL+eLFi0XXAOCAaDQasbKyErVabTvr9XoxPz8fy8vLxRUDAAAYE5OTk7G5uRml0p/MPR8OhzExMREbGxsFNoPx1ul0ot1uR7fbjXq9Hq1WyxfuAAD72J/9s382fvCDH9yT//Iv/3L87u/+bgGNYLzV6/V4//3378lffvnl6Ha7z78Q7AONRiP+4A/+IDY3N7eziYmJ+MVf/EXXVwAAAACFSCk98DFrSGF0pVLpvu+dlFIMh8MCGgEA8LRSSj/MOS/c9zEfnPY2w3cAeJ5mZ2djcnJyx0nXnHNsbGzE6upqccUAAADGREpp+7gj57x9AKPrdDqxuLgYOeeoVqvR7/cjpRRLS0sG8AAA7FMzMzNx48aNe/IXXnghrl+/XkAjGG+zs7MREbG+vh6DwSDK5XJMTU1FRPgeGJ7Q4cOH7ztse3JyMm7dulVAIwAAAOCgM3wHdpf3FADA/vOw4Tul+4UAwMFUr9ej3+/vyPr9ftTr9WIKAQAAjJmJiYl7vljPOcfExERBjWD8tdvtyDlHrVaLlFLUarXIOUe73S66GgAAz8j9Bu88LAcerl6vx/r6emxtbUXOOba2tmJ9fd33wPAUPn1txaNyAAAAAAAAAPYuw3cAgG2tVitSStHr9SLnHL1eL1JK0Wq1iq4GAAAwFl5//fVIKcVwOIyccwyHw0gpxeuvv150NRhb3W43qtXqjqxarUa32y2mEAAAwJip1WqxtbW1I9va2oparVZQIxh/g8FgpBwAAAAAAAB2W6fTiUajEbOzs9FoNKLT6RRdCcaW4TsAwLZmsxlLS0sxPz8fGxsbMT8/H0tLS9FsNouuBgAAMBbOnj0bX//612NiYiJyzjExMRFf//rX4+zZs0VXg7FVr9fv2TW+3+9HvV4vphAAAMCY+cEPfjBSDgAAAAAAB12lUhkpB4DnrdPpxOLiYqysrMTk5GSsrKzE4uKiATzwhAzfAQB2aDabsby8HKurq7G8vGzwDgAAwIi+/OUvxy/+4i/GzMxM/OIv/mJ8+ctfLroSjLVWqxUppej1epFzjl6vFymlaLVaRVcDAOAZOXLkyEg5ADxvFt4AAAAAwP6WUhopB4Dnrd1uR845arVapJSiVqtFzjna7XbR1WAsGb4DAOzQ6XSi0WjE7OxsNBoNUy4BAABGYAcB2H3NZjNee+21WF9fj8uXL8f6+nq89tprBgYDAOxjg8FgpBwAnrec80g5AAAAwLNmsD3srn6/P1IOAM9bt9uNarW6I6tWq9HtdospBGPO8B0AYJtFogAAAE/HDgKw+zqdTrz99tvbF670+/14++23na8AANjHhsNhRESUSqXt4+4cAIpmUBwAAACw16ytrY2UAw83MTEREfd+X3UnB55Mp9OJRqMRs7Oz0Wg0XAcIT6Fer98zFK7f70e9Xi+mEIw5w3cAgG0WiQIAADydbrcbW1tbcfXq1bh8+XJcvXo1tra27CAAT+GNN96ItbW1GA6HUSqVYjgcxtraWrzxxhtFVwMA4BlJKUVKKXLO28edDAD2gpzzSDkAAADAs+Z8Beyu119/PVJKMRwOI+ccw+EwUkrx+uuvF10Nxlan04nFxcVYWVmJycnJWFlZicXFRQN44Am1Wq3o9Xpx5cqVuHz5cly5ciV6vV60Wq2iq8FYMnwHANhmkSgAAMDTmZ2djevXr8fW1lbknGNrayuuX78es7OzRVeDsfXBBx9sL7aOiO1F2B988EHBzQAAeFaOHz++vUnEnaNWq8Xx48eLrgZj6UG78NqdF56c9xUAAAAA7G9nz56Nr3/96zExMRE555iYmIivf/3rcfbs2aKrwdhqt9vb3/2mlLa/E26320VXg7Fn4CI8PcN3AIBts7OzcePGjRgMBhERMRgM4saNGxaJAgAAPKZbt26NlAOP587gnQfdBwBgfzl58mT0er2I+JO//Xq9Xpw8ebLIWjC2fuVXfmWkHHi0B+1ubddrAAAAANg/zp49GxsbG5Fzjo2NDYN34Cl1u92oVqs7smq1Gt1ut5hCMOba7XbUarV46aWX4ud//ufjpZdeilqtZqAVPCHDdwCAbXcvXHvQbQAAAB7so48+GikHHu3zn/98RHyyK8ed4+4cAID958KFC3Ho0KGI+JPd2Q4dOhQXLlwoshaMrX/6T//pSDnwaF/+8pejUqnsyCqVSnz5y18uqBEAAABw0JXL5ZFyAHje6vV69Pv9HVm/3496vV5MIRhzBlrB7jJ8BwDYdu3atZiZmYlyuRw55yiXyzEzMxPXrl0ruhoAAMDYSClFqVTaPgw0hafz5ptvxqFDh3YM3zl06FC8+eabRVcDAOAZeffdd+P27duRUto+bt++He+++27R1WAs3bx5c6QceLTTp0/HYDDY8btqMBjE6dOni64GAAAAHFCDwWCkHACet1arFSml6PV6kXOOXq8XKaVotVpFV4OxZKAV7C7DdwCAbfV6PcrlcszNzcXRo0djbm4uyuWyP7YBAAAe0+c///mIiB1DQu7OgSdTrVajXC5HSin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e0LuT/Hrv2Ile+M1KCM+HkhzpbV+aLoAnVXW8tXZZa+3y3v6xJP935SPSBep84eleLO176bW/x3ln/wIOAAAAm1VVH+89x/mFXv/T+o5VVR3vLSy3kq/y39M9B1rZ/8LW2n7PeIab8B1gO/jjJLf17X95cvoXU1tr1+XkF39J74s5adgAAMCg9E0QeOfKULrgnWvWvuJh/iBdCnbS9Ttf0Vp7xGk+r1pr1+fkKkRJL6RUbwQAAFwIrbXPSDKTZOVF1R+rqrec6bqq+oMkt6/cJsmLW2uPPM351Vp7VpJP9g0/0KtBvwMAAJwXVXVfVd2ePPSC6EhrbVffsf+Z5LvTPfOpJJ/WWnvRqnvodwAAgK3iU5N8ZW/7LVV118oLqVW1XFVLve0PJrkrXZ+zN8k/bK39YWvtI+kWXviHK/1JVf15kvl0/U7SBZVetV4BvfeAPj+nhu8sJnoeAADg3PX1KmtmEKy811NVH08XwLPyns9jkkyuOtczniHjfyRgO3h/kv+vb/8FrbWnJd2khv4T+/avzMk/ckeS3Jqc8rIrAADAQPS+pDuS7su3sSSPb62Nnu6a1tquqjqa5Hf7hl+UXt+zMtG77/yV+z0ryfW97Q8m+XCvBr0RAABw3vRNDnhduknYu5L8XpJf2MC1K/3MXJLjve0XJXn2qnuvnD/W2/yMJE/qbb87yXsT/Q4AAHBh9FZFXV5Z8bTPLyd5X06+aPo5rbW9SdJa290b0+8AAABbwcvThd58PMn3Jg+9HHpNa+35rbUfaK39t9bau5M8J12fU0k+LckX5+Qic49NN+9txW8kOdrbflGSL0jWfN/nkt7mZyR5fG/7r5Ic7tWi5wEAAM7JJvuKd+ZkuE5ysk/xjGdICd8BtryqOpbkv/YNfVaSb+ptt4dfkSR5SroAnpVkuv9+YaoDAADYuL6XSt/ZN3xdkkvPcOlKb/PrSf6mt/0pSb65tXZZVZ3o+3IvK6sQJfmqdC+9JsmtVfUXqyc1AAAAnIvW2u6qWm6tvTrJZ/aGP5rkJ6rq7g3cYqXf+c0kb+1t70/y7a21x/Xu/dBz7apabK1dmuTr++7xN1X1TisEAQAAF1Nv8YQTSd7WN3xNkpVnNvodAABgS2itXZEuQKelm0/2U621v2yt3Z/kI0nemORfJ5lK8qlZ+12dxXQhO0s59Z3E1yd5c297NMl3ttaeUVXVf3FVfbK1dk2Sl/QNv7WqFsxpAwAABmAxyR19+49LHgoSXQnL8YxniPgfCtgu/izJ7/a2L0v3gumzVie99VK39yX5sSQn0n3h92tJHryYxQIAAKxjZZLAW/vGJpJccbqL+nqfP0ryK32HvirJT/TOOZ50X/S11q5orX1Pkq9IN7H7WJJf6p13yqQGAACAs9V70fR4a+3vJHlpTvY8/6Gq/mwj91jpd3rn/2LfoRekm/h9xcpkhdbaaGvtyUleneTze+d9IsnP9d8LAADgIlnpQd7fN/boqrovSXrBPPodAABgK2hJPtjbvizJP0pyIOsvGvdgTr6TcyTdOzqPqarLqurHq+qhd3Sq6nCS2b5rvyDJz7TWru/1O5e01i5rrT0n3Ry2Z/TO+1CSn+rdw5w2AADgYrs0ySV9+yvv9YyY0zacdp/5FIDBq6ojrbV/le6PzuVJHpXkf7TWvjHJO9K9SDqabkXVg0me3Lv0I0l+zhdxAADAFrHSmxzqG7s2ySOTfKgv0bolJydlP3Rx1VJr7ZeSPDfJlyV5RJLvaK1dm+QPkvxNup7p+Um+Jcme3qX/q6p+9bz/NgAAwNBqrbWqOtFauyrJDyW5snfo95K8bq3zc7LXWW9CweuTfFaSlyUZS/I1SZ7SWntjkrf1PuOLk7wo3XOhJPkvVfWH5+WXAgAA2IS+heKu7RvekySttZFVvY9+BwAAGKQHk7wvyefkZM+RdO/iLCR5T7re5K+T/EWSxyX5zSSPTzfn7faqunflec8az3p+J11Azw/19j8/yVuS/H66d34ek64n+sze/R5M8h+r6h3n99cEAAA4vd6CcyfShe08Pd1iC0fTBeYkJxdfWOEZz5Bo8iiA7WBlMkJr7RXpwnUuT/fHayTJn6f7Mu7R6RKwn9Abvy/J91TVzQMpGgAAYJXey6nVWvv0JId7wx9P8k1V9bubuM/VSf53uskIK+5J1wtdvur0m5N8b1XdfbZ1AwAArKe19hNJvr+3e3uSr62qP2+tjSY5sdmVe1prlyT5tSRf3Te8mG7iwmo/XlU/1LuuWYwBAAC4GHovm470Akk/NclfJtmX7jnNP6uq/7DOdfodAABgIHp9zHcleUmSP00XsvO2JO+tqiNrnL8/yZ+ke0dnKclsVX3XBj7nxiTfnWT3aU57IMn3VdXPbeZ3AAAAOFd9eQW7kvxUkm9PFxB6IskTqurD61znGc8QEL4DbDuttW9Il4h9Xbo/ZrvWOO3OJD8geAcAANgKWmsj6SZcp6qOt9Z2p/uibeWLma+tqrnepIVPS/LMJJ+X7ku3d6+610qAz6VJbkryJUme3HfKUpI7krw3yf9I8ptVddeF+t0AAIDh1Vp7fpL/luQRvaHvraqfXOO8S9OtjPqEJNenWy31x6vq/tPc+3uSvDjJF/WGFtOFl344yVvTrbb6J5sN9wEAANiI3oupLUmtNym6tbYvyf9K8gW9c+9M8plV9ZEz3Fu/AwAAbCm9F09XLPfmp/1eki9NN+9tLsk/rqqjp7nHyry2FyX5++leSr0k3Vy2TySZT7f49m9U1W0X5jcBAACG3Qaf8bw0yS8kWU7X8/xSkm+pqhNnuLdnPDuY8B1gW2qtPSXdH6dvTbI3yV3pvoz7UJI3JZmrqnsGVyEAAMDptdZuT3J1b3c+XW9z9arTvqGqfu0M93lauqCevUkuTbcy0F8neUdVfeJ81gwAALCitfaIJH+Y5Fm9oV9L99zmyiRPSvLEJJ+aLnBnPMkjk1yernfZk+T5VfWWldWEVt17ZYWhq3v32de778fS9U8frqpPXtjfEAAAGFattcur6r51ju1Ncm2S5yf5rnQBo0lyLMl3VtUvnuHe+h0AAGBg+haRq3Qvoj7shdC+vuW1SV6S7rnOnyR5SVV9cCVkZ7379669LN1ctivSLeJwb7r3fu7zEioAAHChtNauWCtfoBfIc2mSpyV5aZJvSDeXLUluTfJ3q+pdZ7i3Zzw7nPAdYFvrpWvvSXJVuhdM7/WHCQAA2Epaa5cn+dwkn5PkmUk+Pcl1SXanS7muJGPpkrVX+6mq+p7T3PuUiQynm9gAAABwPrXWfjLJK3Jy9Z8/TvLJJJ+SbmLCZeme4VySZHSNW/zTqnrdWuE7vfvrbwAAgIuutTaZ5DuSvDfdYnDHkhzpHb4qyVPThZA+Pd2iCstJ7k7yM0l+oqoWN/AZ+h0AAGDL6nuh9J8leXVv+O4kX1tVf3SmnkbPAwAADEJr7VOT/PMk70vy0SRH0/UySfeM59PSvdfzrHQLyJ1I8qEk311Vv7PBz9Dv7GC7B10AwLmoqhPpQnceGHQtAAAA63hxktel+3JutbFV+w8meVeStyb5myS/d7obr/7Szpd4AADAxdBauyrJc3q7x9KF63x21u57ku45zruS/FWSQ0ne1vvJequb6m8AAIABuSvdpOtvThesc0e65zmPWuf89yT5kar6b8nGJl3rdwAAgC1uZRG5dyb5oyQLSf4wJ5/t6HkAAICt6ONJrk/yjekWyb4zXZ7Kes94/jTJK6vqrRv9AP3OziZ8BwAAAODC+kC6L+2uWzX+t0nenu7F07cm+euquv0i1wYAAHA2jqabaH0gyaW9sdHev7fm1F7nHXodAABgG7kn3QIJn55kJMlj+459IsnHksyn63v+d1X9Sf/FJl0DAADbXW+R7FTV/0nyfwZcDgAAwEY9kC5E9HPThYpe3Xfs472fD6RbQO6/V9X/l2xsYQWGg/AdAAAAgAvrg0l+O8mJJG9JtwLQrVW1PNCqAAAAzt6xdL3NY5P8SbqQHb0OAACwEzyQ5N+nm3h9Sbownur9fDRd8M5CknuqamlANQIAAAAAAHCqTya5OckdSRaT3JdkKcn96RbU/nBOPuM5vnKR4B1WNP+3AAAAAAAAAAAAAAAAAAAAAADsBK21JlyHjRK+AwAAAAAAAAAAAAAAAAAAAADA0BkZdAEAAAAAAAAAAAAAAAAAAAAAAHCxCd8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAAAAAAAAAAAAAGDoCN8BAAAAAAAAAIAdrrU20Vqrvp8fGXRNAAAAAAAAAAAwaMJ3AAAAAAAAAABgQNYIxbkQPz8y6N8TAAAAAAAAAAC2IuE7AAAAAAAAAAAAAAAAAAAAAAAMHeE7AAAAAAAAAAAAAAAAAAAAAAAMnd2DLgAAAAAAAAAAAIbYQpInbPDc30jy2X37X5/kzRu47kiS/ZuqCgAAAAAAAAAAhoDwHQAAAAAAAAAAGJCqOp5kfiPnttaOrRr6aFVt6Np0ATxtw4UBAAAAAAAAAMAQGBl0AQAAAAAAAAAAAAAAAAAAAAAAcLEJ3wEAAAAAAAAAAAAAAAAAAAAAYOjsHnQBAAAAAAAAAADA9tFaG03yhUkmkjwmyX1Jbkny5qqq01y3O8nnJvn0JPuT3J3k3UneVFXHz7Gm3Uk+K8mTejWNJLkzybuSHKqq5XO5PwAAAAAAAAAAO5PwHQAAAAAAAAAA2OFaaxNJPtA39KNV9SObObe1dlmSf5nkm5Nctcal722tfXtV/eGq++1K8ook358uGGe1O1tr31dVv7zBX6f/3tclmUkylS7QZy13tdZ+LsmNVXX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       "text/plain": [
        "<Figure size 5760x2880 with 2 Axes>"
       ]
@@ -72,22 +66,10 @@
     "    dpi, width, height = 10, 800, 400\n",
     "    figsize = width / float(dpi), height / float(dpi)\n",
     "    LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
-    "    \n",
+    "\n",
     "    fig = plt.figure(figsize=figsize)\n",
-    "    \n",
-    "    timestamps = 20\n",
-    "    mean_generator = SinGenerator(num=timestamps)\n",
-    "    std_generator = SinGenerator(num=timestamps, min_amplitude=0.5, max_amplitude=0.5)\n",
-    "    std_generator.set_transform(lambda x: x + 1)\n",
-    "    dynamic_env = SyntheticDEnv([mean_generator], [[std_generator]], num_per_task=100)\n",
-    "    print(dynamic_env)\n",
-    "    \n",
-    "    function = DynamicQuadraticFunc()\n",
-    "    function_param = dict()\n",
-    "    function_param[0] = SinGenerator(num=timestamps, num_sin_phase=4, phase_shift=1.0, max_amplitude=1.0)\n",
-    "    function_param[1] = ConstantGenerator(constant=0.9)\n",
-    "    function_param[2] = SinGenerator(num=timestamps, num_sin_phase=5, phase_shift=0.4, max_amplitude=0.9)\n",
-    "    function.set(function_param)\n",
+    "\n",
+    "    dynamic_env, function = create_example_v1(num_per_task=250)\n",
     "    \n",
     "    timeaxis, xaxis, yaxis = [], [], []\n",
     "    for timestamp, dataset in dynamic_env:\n",
@@ -231,20 +213,6 @@
     "#     plt.close(\"all\")\n",
     "#     # plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "shared-envelope",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualization\n",
-    "# home_dir = Path.home()\n",
-    "# desktop_dir = home_dir / 'Desktop'\n",
-    "# print('The Desktop is at: {:}'.format(desktop_dir))\n",
-    "# visualize_syn(desktop_dir / 'tot-synthetic-v0.pdf')"
-   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/TOT/synthetic-visualize-env.ipynb b/notebooks/TOT/synthetic-visualize-env.ipynb
new file mode 100644
index 0000000..3ae2ea1
--- /dev/null
+++ b/notebooks/TOT/synthetic-visualize-env.ipynb
@@ -0,0 +1,152 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "filled-multiple",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The root path: /Users/xuanyidong/Desktop/AutoDL-Projects\n",
+      "The library path: /Users/xuanyidong/Desktop/AutoDL-Projects/lib\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import cm\n",
+    "matplotlib.use(\"agg\")\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "\n",
+    "__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
+    "root_dir = (Path(__file__).parent / \"..\").resolve()\n",
+    "lib_dir = (root_dir / \"lib\").resolve()\n",
+    "print(\"The root path: {:}\".format(root_dir))\n",
+    "print(\"The library path: {:}\".format(lib_dir))\n",
+    "assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
+    "if str(lib_dir) not in sys.path:\n",
+    "    sys.path.insert(0, str(lib_dir))\n",
+    "\n",
+    "from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
+    "from datasets import DynamicQuadraticFunc\n",
+    "from datasets.synthetic_example import create_example_v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "detected-second",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_fig(save_dir, timestamp, xaxis, yaxis):\n",
+    "    save_path = save_dir / '{:04d}'.format(timestamp)\n",
+    "    # print('Plot the figure at timestamp-{:} into {:}'.format(timestamp, save_path))\n",
+    "    dpi, width, height = 40, 1500, 1500\n",
+    "    figsize = width / float(dpi), height / float(dpi)\n",
+    "    LabelSize, LegendFontsize, font_gap = 80, 80, 5\n",
+    "\n",
+    "    fig = plt.figure(figsize=figsize)\n",
+    "    \n",
+    "    cur_ax = fig.add_subplot(1, 1, 1)\n",
+    "    cur_ax.scatter(xaxis, yaxis, color=\"k\", s=10, alpha=0.9, label=\"Timestamp={:02d}\".format(timestamp))\n",
+    "    cur_ax.set_xlabel(\"X\", fontsize=LabelSize)\n",
+    "    cur_ax.set_ylabel(\"f(X)\", rotation=0, fontsize=LabelSize)\n",
+    "    cur_ax.set_xlim(-6, 6)\n",
+    "    cur_ax.set_ylim(-40, 40)\n",
+    "    for tick in cur_ax.xaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "        tick.label.set_rotation(10)\n",
+    "    for tick in cur_ax.yaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "        \n",
+    "    plt.legend(loc=1, fontsize=LegendFontsize)\n",
+    "    fig.savefig(str(save_path) + '.pdf', dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
+    "    fig.savefig(str(save_path) + '.png', dpi=dpi, bbox_inches=\"tight\", format=\"png\")\n",
+    "    plt.close(\"all\")\n",
+    "\n",
+    "\n",
+    "def visualize_env(save_dir):\n",
+    "    save_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    dynamic_env, function = create_example_v1(100, num_per_task=500)\n",
+    "    \n",
+    "    additional_xaxis = np.arange(-6, 6, 0.1)\n",
+    "    for timestamp, dataset in dynamic_env:\n",
+    "        num = dataset.shape[0]\n",
+    "        # timeaxis = (torch.zeros(num) + timestamp).numpy()\n",
+    "        xaxis = dataset[:,0].numpy()\n",
+    "        xaxis = np.concatenate((additional_xaxis, xaxis))\n",
+    "        # compute the ground truth\n",
+    "        function.set_timestamp(timestamp)\n",
+    "        yaxis = function(xaxis)\n",
+    "        draw_fig(save_dir, timestamp, xaxis, yaxis)\n",
+    "\n",
+    "home_dir = Path.home()\n",
+    "desktop_dir = home_dir / 'Desktop'\n",
+    "vis_save_dir = desktop_dir / 'vis-synthetic'\n",
+    "visualize_env(vis_save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "rapid-uruguay",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ffmpeg -y -i /Users/xuanyidong/Desktop/vis-synthetic/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1000:1000 -vb 5000k /Users/xuanyidong/Desktop/vis-synthetic/vis.mp4\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot the data\n",
+    "cmd = 'ffmpeg -y -i {:}/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1000:1000 -vb 5000k {:}/vis.mp4'.format(vis_save_dir, vis_save_dir)\n",
+    "print(cmd)\n",
+    "os.system(cmd)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/synthetic-env.ipynb b/synthetic-env.ipynb
new file mode 100644
index 0000000..97edb85
--- /dev/null
+++ b/synthetic-env.ipynb
@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "filled-multiple",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The root path: /Users/xuanyidong\n",
+      "The library path: /Users/xuanyidong/lib\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "/Users/xuanyidong/lib does not exist",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m~/Desktop/AutoDL-Projects\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The root path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The library path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlib_dir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{:} does not exist\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: /Users/xuanyidong/lib does not exist"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import cm\n",
+    "# matplotlib.use(\"agg\")\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "\n",
+    "__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
+    "root_dir = (Path(__file__).parent / \"..\").resolve()\n",
+    "lib_dir = (root_dir / \"lib\").resolve()\n",
+    "print(\"The root path: {:}\".format(root_dir))\n",
+    "print(\"The library path: {:}\".format(lib_dir))\n",
+    "assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
+    "if str(lib_dir) not in sys.path:\n",
+    "    sys.path.insert(0, str(lib_dir))\n",
+    "\n",
+    "from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
+    "from datasets import DynamicQuadraticFunc\n",
+    "from datasets.synthetic_example import create_example_v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "detected-second",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_env():\n",
+    "    \n",
+    "    dpi, width, height = 10, 800, 400\n",
+    "    figsize = width / float(dpi), height / float(dpi)\n",
+    "    LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
+    "\n",
+    "    fig = plt.figure(figsize=figsize)\n",
+    "\n",
+    "    dynamic_env, function = create_example_v1(100, num_per_task=250)\n",
+    "    \n",
+    "    timeaxis, xaxis, yaxis = [], [], []\n",
+    "    for timestamp, dataset in dynamic_env:\n",
+    "        num = dataset.shape[0]\n",
+    "        timeaxis.append(torch.zeros(num) + timestamp)\n",
+    "        xaxis.append(dataset[:,0])\n",
+    "        # compute the ground truth\n",
+    "        function.set_timestamp(timestamp)\n",
+    "        yaxis.append(function(dataset[:,0]))\n",
+    "    timeaxis = torch.cat(timeaxis).numpy()\n",
+    "    xaxis = torch.cat(xaxis).numpy()\n",
+    "    yaxis = torch.cat(yaxis).numpy()\n",
+    "\n",
+    "    cur_ax = fig.add_subplot(2, 1, 1)\n",
+    "    cur_ax.scatter(timeaxis, xaxis, color=\"k\", linestyle=\"-\", alpha=0.9, label=None)\n",
+    "    cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
+    "    cur_ax.set_ylabel(\"X\", rotation=0, fontsize=LabelSize)\n",
+    "    for tick in cur_ax.xaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "        tick.label.set_rotation(10)\n",
+    "    for tick in cur_ax.yaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "    \n",
+    "    cur_ax = fig.add_subplot(2, 1, 2)\n",
+    "    cur_ax.scatter(timeaxis, yaxis, color=\"k\", linestyle=\"-\", alpha=0.9, label=None)\n",
+    "    cur_ax.set_xlabel(\"Time\", fontsize=LabelSize)\n",
+    "    cur_ax.set_ylabel(\"Y\", rotation=0, fontsize=LabelSize)\n",
+    "    for tick in cur_ax.xaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "        tick.label.set_rotation(10)\n",
+    "    for tick in cur_ax.yaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "    plt.show()\n",
+    "\n",
+    "visualize_env()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "supreme-basis",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def optimize_fn(xs, ys, test_sets):\n",
+    "#     xs = torch.FloatTensor(xs).view(-1, 1)\n",
+    "#     ys = torch.FloatTensor(ys).view(-1, 1)\n",
+    "    \n",
+    "#     model = SuperSequential(\n",
+    "#         SuperMLPv1(1, 10, 20, torch.nn.ReLU),\n",
+    "#         SuperMLPv1(20, 10, 1, torch.nn.ReLU)\n",
+    "#     )\n",
+    "#     optimizer = torch.optim.Adam(\n",
+    "#         model.parameters(),\n",
+    "#         lr=0.01, weight_decay=1e-4, amsgrad=True\n",
+    "#     )\n",
+    "#     for _iter in range(100):\n",
+    "#         preds = model(ys)\n",
+    "\n",
+    "#         optimizer.zero_grad()\n",
+    "#         loss = torch.nn.functional.mse_loss(preds, ys)\n",
+    "#         loss.backward()\n",
+    "#         optimizer.step()\n",
+    "        \n",
+    "#     with torch.no_grad():\n",
+    "#         answers = []\n",
+    "#         for test_set in test_sets:\n",
+    "#             test_set = torch.FloatTensor(test_set).view(-1, 1)\n",
+    "#             preds = model(test_set).view(-1).numpy()\n",
+    "#             answers.append(preds.tolist())\n",
+    "#     return answers\n",
+    "\n",
+    "# def f(x):\n",
+    "#     return np.cos( 0.5 * x + x * x)\n",
+    "\n",
+    "# def get_data(mode):\n",
+    "#     dataset = SynAdaptiveEnv(mode=mode)\n",
+    "#     times, xs, ys = [], [], []\n",
+    "#     for i, (_, t, x) in enumerate(dataset):\n",
+    "#         times.append(t)\n",
+    "#         xs.append(x)\n",
+    "#     dataset.set_transform(f)\n",
+    "#     for i, (_, _, y) in enumerate(dataset):\n",
+    "#         ys.append(y)\n",
+    "#     return times, xs, ys\n",
+    "\n",
+    "# def visualize_syn(save_path):\n",
+    "#     save_dir = (save_path / '..').resolve()\n",
+    "#     save_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    \n",
+    "#     dpi, width, height = 40, 2000, 900\n",
+    "#     figsize = width / float(dpi), height / float(dpi)\n",
+    "#     LabelSize, LegendFontsize, font_gap = 40, 40, 5\n",
+    "    \n",
+    "#     fig = plt.figure(figsize=figsize)\n",
+    "    \n",
+    "#     times, xs, ys = get_data(None)\n",
+    "    \n",
+    "#     def draw_ax(cur_ax, xaxis, yaxis, xlabel, ylabel,\n",
+    "#                 alpha=0.1, color='k', linestyle='-', legend=None, plot_only=False):\n",
+    "#         if legend is not None:\n",
+    "#             cur_ax.plot(xaxis[:1], yaxis[:1], color=color, label=legend)\n",
+    "#         cur_ax.plot(xaxis, yaxis, color=color, linestyle=linestyle, alpha=alpha, label=None)\n",
+    "#         if not plot_only:\n",
+    "#             cur_ax.set_xlabel(xlabel, fontsize=LabelSize)\n",
+    "#             cur_ax.set_ylabel(ylabel, rotation=0, fontsize=LabelSize)\n",
+    "#             for tick in cur_ax.xaxis.get_major_ticks():\n",
+    "#                 tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "#                 tick.label.set_rotation(10)\n",
+    "#             for tick in cur_ax.yaxis.get_major_ticks():\n",
+    "#                 tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "    \n",
+    "#     cur_ax = fig.add_subplot(2, 1, 1)\n",
+    "#     draw_ax(cur_ax, times, xs, \"time\", \"x\", alpha=1.0, legend=None)\n",
+    "\n",
+    "#     cur_ax = fig.add_subplot(2, 1, 2)\n",
+    "#     draw_ax(cur_ax, times, ys, \"time\", \"y\", alpha=0.1, legend=\"ground truth\")\n",
+    "    \n",
+    "#     train_times, train_xs, train_ys = get_data(\"train\")\n",
+    "#     draw_ax(cur_ax, train_times, train_ys, None, None, alpha=1.0, color='r', legend=None, plot_only=True)\n",
+    "    \n",
+    "#     valid_times, valid_xs, valid_ys = get_data(\"valid\")\n",
+    "#     draw_ax(cur_ax, valid_times, valid_ys, None, None, alpha=1.0, color='g', legend=None, plot_only=True)\n",
+    "    \n",
+    "#     test_times, test_xs, test_ys = get_data(\"test\")\n",
+    "#     draw_ax(cur_ax, test_times, test_ys, None, None, alpha=1.0, color='b', legend=None, plot_only=True)\n",
+    "    \n",
+    "#     # optimize MLP models\n",
+    "# #     [train_preds, valid_preds, test_preds] = optimize_fn(train_xs, train_ys, [train_xs, valid_xs, test_xs])\n",
+    "# #     draw_ax(cur_ax, train_times, train_preds, None, None,\n",
+    "# #             alpha=1.0, linestyle='--', color='r', legend=\"MLP\", plot_only=True)\n",
+    "# #     import pdb; pdb.set_trace()\n",
+    "# #     draw_ax(cur_ax, valid_times, valid_preds, None, None,\n",
+    "# #             alpha=1.0, linestyle='--', color='g', legend=None, plot_only=True)\n",
+    "# #     draw_ax(cur_ax, test_times, test_preds, None, None,\n",
+    "# #             alpha=1.0, linestyle='--', color='b', legend=None, plot_only=True)\n",
+    "\n",
+    "#     plt.legend(loc=1, fontsize=LegendFontsize)\n",
+    "\n",
+    "#     fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
+    "#     plt.close(\"all\")\n",
+    "#     # plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "shared-envelope",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualization\n",
+    "# home_dir = Path.home()\n",
+    "# desktop_dir = home_dir / 'Desktop'\n",
+    "# print('The Desktop is at: {:}'.format(desktop_dir))\n",
+    "# visualize_syn(desktop_dir / 'tot-synthetic-v0.pdf')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/synthetic-visualize-env.ipynb b/synthetic-visualize-env.ipynb
new file mode 100644
index 0000000..03f0db5
--- /dev/null
+++ b/synthetic-visualize-env.ipynb
@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "filled-multiple",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The root path: /Users/xuanyidong\n",
+      "The library path: /Users/xuanyidong/lib\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "/Users/xuanyidong/lib does not exist",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m~/Desktop/AutoDL-Projects\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The root path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The library path: {:}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlib_dir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{:} does not exist\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlib_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAssertionError\u001b[0m: /Users/xuanyidong/lib does not exist"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys\n",
+    "import torch\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import cm\n",
+    "matplotlib.use(\"agg\")\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "\n",
+    "__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
+    "root_dir = (Path(__file__).parent / \"..\").resolve()\n",
+    "lib_dir = (root_dir / \"lib\").resolve()\n",
+    "print(\"The root path: {:}\".format(root_dir))\n",
+    "print(\"The library path: {:}\".format(lib_dir))\n",
+    "assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
+    "if str(lib_dir) not in sys.path:\n",
+    "    sys.path.insert(0, str(lib_dir))\n",
+    "\n",
+    "from datasets import ConstantGenerator, SinGenerator, SyntheticDEnv\n",
+    "from datasets import DynamicQuadraticFunc\n",
+    "from datasets.synthetic_example import create_example_v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "detected-second",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_fig(save_dir, timestamp, xaxis, yaxis):\n",
+    "    save_path = save_dir / '{:04d}'.format(timestamp)\n",
+    "    # print('Plot the figure at timestamp-{:} into {:}'.format(timestamp, save_path))\n",
+    "    dpi, width, height = 40, 1500, 1500\n",
+    "    figsize = width / float(dpi), height / float(dpi)\n",
+    "    LabelSize, LegendFontsize, font_gap = 80, 80, 5\n",
+    "\n",
+    "    fig = plt.figure(figsize=figsize)\n",
+    "    \n",
+    "    cur_ax = fig.add_subplot(1, 1, 1)\n",
+    "    cur_ax.scatter(xaxis, yaxis, color=\"k\", s=10, alpha=0.9, label=\"Timestamp={:02d}\".format(timestamp))\n",
+    "    cur_ax.set_xlabel(\"X\", fontsize=LabelSize)\n",
+    "    cur_ax.set_ylabel(\"f(X)\", rotation=0, fontsize=LabelSize)\n",
+    "    cur_ax.set_xlim(-6, 6)\n",
+    "    cur_ax.set_ylim(-40, 40)\n",
+    "    for tick in cur_ax.xaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "        tick.label.set_rotation(10)\n",
+    "    for tick in cur_ax.yaxis.get_major_ticks():\n",
+    "        tick.label.set_fontsize(LabelSize - font_gap)\n",
+    "        \n",
+    "    plt.legend(loc=1, fontsize=LegendFontsize)\n",
+    "    fig.savefig(str(save_path) + '.pdf', dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
+    "    fig.savefig(str(save_path) + '.png', dpi=dpi, bbox_inches=\"tight\", format=\"png\")\n",
+    "    plt.close(\"all\")\n",
+    "\n",
+    "\n",
+    "def visualize_env(save_dir):\n",
+    "    save_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    dynamic_env, function = create_example_v1(100, num_per_task=500)\n",
+    "    \n",
+    "    additional_xaxis = np.arange(-6, 6, 0.1)\n",
+    "    for timestamp, dataset in dynamic_env:\n",
+    "        num = dataset.shape[0]\n",
+    "        # timeaxis = (torch.zeros(num) + timestamp).numpy()\n",
+    "        xaxis = dataset[:,0].numpy()\n",
+    "        xaxis = np.concatenate((additional_xaxis, xaxis))\n",
+    "        # compute the ground truth\n",
+    "        function.set_timestamp(timestamp)\n",
+    "        yaxis = function(xaxis)\n",
+    "        draw_fig(save_dir, timestamp, xaxis, yaxis)\n",
+    "\n",
+    "home_dir = Path.home()\n",
+    "desktop_dir = home_dir / 'Desktop'\n",
+    "vis_save_dir = desktop_dir / 'vis-synthetic'\n",
+    "visualize_env(vis_save_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "greatest-pepper",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the data\n",
+    "cmd = 'ffmpeg -y -i {:}/%04d.png -pix_fmt yuv420p -vf fps=2 -vf scale=1000:1000 -vb 5000k {:}/vis.mp4'.format(vis_save_dir, vis_save_dir)\n",
+    "print(cmd)\n",
+    "os.system(cmd)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}