diff --git a/new-notebook.ipynb b/new-notebook.ipynb
index d4abac6..8c41d64 100644
--- a/new-notebook.ipynb
+++ b/new-notebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "a883dc0a-988d-4524-b5c6-3d51bc0738d3",
    "metadata": {
     "tags": []
@@ -15,46 +15,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "67f3cfed-a6c9-454a-b5a0-f2ffd9bce1aa",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'np' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m100\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "x = np.arange(100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "56eeae4f-4644-4507-aabc-e640f1252c37",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'plt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mplot(x,x\u001b[38;5;241m*\u001b[39mx,x,x\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m*\u001b[39mx)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -63,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "63792d95-a178-4ea1-87f8-77a23be2e5e9",
    "metadata": {},
    "outputs": [
@@ -71,17 +58,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_663/3399443285.py:1: RuntimeWarning: divide by zero encountered in divide\n",
+      "/tmp/ipykernel_709/3399443285.py:1: RuntimeWarning: divide by zero encountered in divide\n",
       "  plt.plot(x,1/x)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa714f46a10>]"
+       "[<matplotlib.lines.Line2D at 0x7f37a66e5db0>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },