diff --git a/scripts/actor_bench/time/notebook.ipynb b/scripts/actor_bench/time/notebook.ipynb
new file mode 100644
index 0000000..3a348b5
--- /dev/null
+++ b/scripts/actor_bench/time/notebook.ipynb
@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array([5, 10, 20, 30, 40, 50, 60, 70])\n",
+    "y_tmp = np.array([124524, 40162, 12673, 5561, 2796, 1365, 774, 449])\n",
+    "y = y_tmp/720"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert to pandas dataframe\n",
+    "d = {'x' : x, 'y' :y}\n",
+    "data = pd.DataFrame(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot using lineplot\n",
+    "sns.set(style='darkgrid')\n",
+    "plot = sns.lineplot(x='x', y='y', data=data)\n",
+    "plot.set_xlabel('MIN_ACTORS value')\n",
+    "plot.set_ylabel('Time (minutes)')\n",
+    "plt.savefig('actors_time.png', dpi=300)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.7 64-bit",
+   "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": "3.9.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/movie_bench/time/notebook.ipynb b/scripts/movie_bench/time/notebook.ipynb
new file mode 100644
index 0000000..7443cd1
--- /dev/null
+++ b/scripts/movie_bench/time/notebook.ipynb
@@ -0,0 +1,89 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array([500, 1000, 5000, 10000, 25000, 50000, 75000, 100000])\n",
+    "y_tmp = np.array([34933, 18122, 3916, 2125, 940, 533, 366, 269])\n",
+    "y = y_tmp/720 # Dividing by 60 (results in minutes) x 12 (number of threads)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert to pandas dataframe\n",
+    "d = {'x' : x, 'y' :y}\n",
+    "data = pd.DataFrame(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot using lineplot\n",
+    "sns.set(style='darkgrid')\n",
+    "plot = sns.lineplot(x='x', y='y', data=data)\n",
+    "plot.set_xlabel(' value')\n",
+    "plot.set_ylabel('Time (minutes)')\n",
+    "plt.savefig('movies_time.png', dpi=300)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.7 64-bit",
+   "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": "3.9.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}