diff --git a/Losningsforslag/Oving1/Avrundingsfeil - subtraksjon lf.ipynb b/Losningsforslag/Oving1/Avrundingsfeil - subtraksjon lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..45ee8bf4a4aa34eb556b4786c067e93eec658981
--- /dev/null
+++ b/Losningsforslag/Oving1/Avrundingsfeil - subtraksjon lf.ipynb	
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d8e68450",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a27abec5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def a_pluss_b_ganger_a_minus_b(a, b):\n",
+    "    return (a + b) * (a - b)\n",
+    "\n",
+    "\n",
+    "def a_opphoyd_i_annen_minus_b_opphoyd_i_annen(a, b):\n",
+    "    return (a ** 2 - b**2)\n",
+    "    \n",
+    "def avvik(a, b):\n",
+    "     return a_pluss_b_ganger_a_minus_b(a, b) - a_opphoyd_i_annen_minus_b_opphoyd_i_annen(a, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45cb676c",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "474d0f14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def areal1(a, b, c):\n",
+    "    s_verdi = s(a, b, c)\n",
+    "    return np.sqrt(s_verdi * (s_verdi - a) * (s_verdi - b) * (s_verdi - c))\n",
+    "\n",
+    "def areal2(a, b, c):\n",
+    "    return np.sqrt(((a + (b + c)) * (c - (a - b)) * (c + (a -b)) * (a + (b - c))))/4"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Losningsforslag/Oving1/Avrundingsfeil - summering lf.ipynb b/Losningsforslag/Oving1/Avrundingsfeil - summering lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7da4c64bdfa1645c1b8dcf4a789b9dcdb45b7141
--- /dev/null
+++ b/Losningsforslag/Oving1/Avrundingsfeil - summering lf.ipynb	
@@ -0,0 +1,68 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "30801ef4",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ddd558ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Standard sum(): 310000000000.0024\n",
+      "Numpy sum(): 310000000000.00134\n",
+      "Math fsum(): 310000000000.0012\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import math\n",
+    "\n",
+    "T = 3.1e11 # et stort tall\n",
+    "t = 3.1e-5 # et lite tall\n",
+    "\n",
+    "# Liste med stort tall fremst, bruker Pythons standard sum-funksjon\n",
+    "L = [T,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t,t]\n",
+    "print(f'Standard sum(): {sum(L)}')\n",
+    "\n",
+    "# Numpy array, stort tall fremst, np.sum() funksjon\n",
+    "A = np.array(L)\n",
+    "print(f'Numpy sum(): {np.sum(A)}')\n",
+    "\n",
+    "# math.fsum() funksjonen\n",
+    "print(f'Math fsum(): {math.fsum(L)}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Losningsforslag/Oving1/Geometri lf.ipynb b/Losningsforslag/Oving1/Geometri lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0041d98801881a64738d2fb155db54f814e6fe49
--- /dev/null
+++ b/Losningsforslag/Oving1/Geometri lf.ipynb	
@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def omkrets_sirkel(r):\n",
+    "    return 2 * np.pi * r\n",
+    "\n",
+    "def areal_sirkel(r):\n",
+    "    return np.pi * r**2\n",
+    "\n",
+    "def areal_sylinder(r, h):\n",
+    "    return 2 * areal_sirkel(r) + 2 * np.pi * r * h\n",
+    "\n",
+    "def volum_sylinder(r, h):\n",
+    "    return areal_sirkel(r) * h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S1+5N8Ng3Yf0SKJsG5/8WBo0NuiqRqCnAJf0cnHzquR95p77P/C+omAcZ0VygSqT3UIBLetn5rjf51OaXYcx0OO/XUKh56iU5KcAlPYSa4cWb4X9+Dtl5cMEfYMIcTT4lSU0BLqlv6xve5FPbVsH4C2Dmf0L/jwRdlUiPKcAldTUfgP+5CV78DfQbBF+4x5s9UCRFKMAlNVW95E0+9cE6mPglOOs/ILco6KpEYkoBLqmlsQ6e+zG89ifv4OSXH4ExpwddlUhcKMAldbz3HDz+TdhTDSddCWf8EPr0D7oqkbhRgEvyq6+Bv33fO6Ny0Edh3jNQqqv9SepTgEvycg7eedSbw6ShFj7xHe9PVp+gKxNJCAW4JKe6bfDEt2HN41ByAnz5YRh6fNBViSSUAlySi3Ow4h545gfQ0ghn3ghTr4JM/ZQl/ehXL8mjdqN3hZwNS2HkKXDeb2DQUUFXJRKYTgPczEqBu4EhgAMWOOduNrNi4AGgHNgIzHbO1cavVElb4RC8ugCW3AiWCef+Ek68VJNPSdqL5m9AC/Bt59x4YCpwlZmNB64FljjnxgJL/McisbVjDdwxA56+1tvrvuplmKyZA0Ugij1w59xWYKt/v87MVgPDgVnAJ/3VFgJLge/GpUpJP6Fm+Mev4e+/gJz+8Nk/wfGf1+RTIhG6NAZuZuXAROAVYIgf7gDb8IZY2nrNfGA+QFmZpu2UKLy/Ah69Gra/Bcd+Fs75BfQfHHRVIr1O1AFuZv2BvwDfdM7ttYg9IeecMzPX1uuccwuABQAVFRVtriMCeJc0W/ozWPZb6D8E5vwZxp0bdFUivVZUAW5m2Xjhfa9z7q/+4u1mVuKc22pmJcCOeBUpKc45eOcRbw6T2n/CpLlee2BuYdCVifRq0XShGHA7sNo598uIpxYDc4Gb/NtH41KhpLaNL8Kz18OWSvjIePjKYhh9WtBViSSFaPbATwG+DKwys5X+su/jBfeDZjYPqAJmx6VCSU071njXpHz3KcgfBrNuhQkXQUZm0JWJJI1oulD+AbR36H96bMuRlLd3Kyz9qXc2ZU5/mH4DnHQF5OQFXZlI0tGZmJIYB/bCst/Aslsg3AJTvuZNPNVvYNCViSQtBbjEV0sTLL/Lu5hw/S447nNwxnVQPCroykSSngJc4uNgZ8mSG6FmA5R/3OssGT4p6MpEUoYCXGIvsrNk8DFw8UMw9kydRSkSYwpwiZ2da73OkrVPqrNEJAEU4NJze7d6Z1Cu+G91logkkAJcuk+dJSKBUoBL16mzRKRXUIBL9A5eRHjJjyM6S34Mw08MujKRtKQAl+hULYNnrlNniUgvogCXjqmzRKTXUoBL247oLLkeTrpSnSUivYgCXA53sLPkpVu9y5qps0Sk11KAiyfU7HWWLL1JnSUiSUIBnu7UWSKStBTg6UydJSJJTQGejlp3lpx/C5xwsTpLRGIsFHa8u72Oyqpa5kwuJTszI6bvrwBPJ3XbvM6S1+9WZ4lIHBxoDrFy826WV9Xy2sYallfVUnegBYATRhRy/IiCmH6eAjwdNNbBi7+Bl25RZ4lIDNXsb6JyYw2VfmC/tWUPzSEHwNFD+nPehGFUjCxicnkxI4pyY/75CvBU1rqz5NjPwvTroHh00JWJJB3nHFUf1PPaxhoqN9ZSWVXD+p37AcjJzOBjIwqYd+poJpcXceLIIgrzcuJekwI8FR3qLLkRatbDyFPhrBvVWSLSBc2hMKu37uW1jbVUbqzhtY217NrXCEBBbjYVI4u48MRSJpcXcdzwAvpmJ/4YkgI81VQt866GU/2a31nyIIw9S50lIp3Y19jCik21hwJ7xabdNDSHACgtzuXjYwdRUe4Nhxw1uD8ZGcH/neo0wM3sDuDTwA7n3HH+smLgAaAc2AjMds7Vxq9M6dTOtfDcj2HtE5Bfos4SkU5s33vgsOGQd97fS9hBhsExJQP4wuRSKsqLqBhZzNCCvkGX26Zo9sDvAm4B7o5Ydi2wxDl3k5ld6z/+buzLk05FdpZk91NniUgbwmHH+p37PhwOqaphc00DALnZmUwsK+TqM8YyubyIE0oLye+bHXDF0ek0wJ1zfzez8laLZwGf9O8vBJaiAE+sIzpL5vudJYOCrkwkcI0tIVZV7zkU2JVVtexpaAZgUP8cKkYWM3daOZPLixk/bEDM+7MTpbtj4EOcc1v9+9uAIe2taGbzgfkAZWVl3fw4OUSdJSJH2F3fxPKqWiqrvMB+o3oPTS1hAEYP7seMY4ceGr8eOTAPS5FjQj0+iOmcc2bmOnh+AbAAoKKiot31pBPOwerF3ji3OkskjTnnqK5toLKq5tAe9rvb9wGQlWEcP6KAS04u58SRRVSMLGJg/z4BVxw/3Q3w7WZW4pzbamYlwI5YFiWtqLNE0lgo7Fi9de+hoZDKjbVs23sAgPw+WUwaWcT5E4ZRUV7MhBGF5Oakz4H77gb4YmAucJN/+2jMKpIPqbNE0lB9UwsrN++mcqN3duOKTbvZ1+idjl5S0Jcpo4qZXF5ERXkxRw/JJ7MXtPMFJZo2wvvwDlgOMrNq4Aa84H7QzOYBVcDseBaZdnas9g5Orvyz11lyxnUw9evqLJGUtLOukeURwyFvv7+XlrDDDD46JJ/PTBzutfOVFzO8MPanoyezaLpQLmrnqekxriW9OQfrlsDLt8L65yErV50lknLCYceGXft53Z87pLKqln/u8k5H75OVwYTSQr522mgqyouZVFZEQW5ytPMFRWdiBq25Ad64H17+PexaC/2HenvcFZdBXnHQ1Yl0294DzazZWseabXtZvXUvq7fWsXZb3aGzG4vysqkoL+aiKaVUlBdz3LACcrKSs50vKArwoNRtg1f/BJV3QEMNlEyAzyyAYz8DWfGfBEckVsJhR1VNPau37mXN1r2844d2dW3DoXUKcrM5piSfOVNKOaZkAJPKihgzuF/KtPMFRQGeaFvfgJd+B2/9BcItMO5cb3x75MnqKpFeb++BZtZuq/P3qI/cq84wGD24PxPLirhoShnjSwYwriSfoQP6KqzjQAGeCOEQvPu0F9xV//AOTFZcBid9DQaOCbo6kSN0ea966ACOKRnA2CH9A5mVL10pwOOpcR+svNcb3679JxSUwpn/DpO+ArmFQVcnArTeq/Zu29qrPqG0UHvVvYwCPB72VMMrf4TlC6FxD4yY7E0ydcz5kKlNLsHQXnXqUZrEUnUlvHSrdzEFgPHnw9SroHRysHVJ2unqXvUxJfkcUzJAe9VJRgHeU6EWWPOYN75d/Sr0KYBpX/euO1lYGnR1kuK6slf9hcmljC/RXnUqUYB314E93hzcryyAPZugaBSc8wvvVPc++UFXJylIe9XSmgK8q2o2eOPbK+6Bpn3erIDn3ARHz9AcJRITB/eq1/itetqrlvYowKPhHGx6yRvfXvOEF9THfc7r3x52QtDVSRLb09DMu9s73qseNaif9qqlTQrwjrQ0wTuPeMG9dSXkFsHHvwWTvwoDSoKuTpJAY0uILbUNbK5tYHNNPZtr673bmgY219azu7750Lqt96rHleRz9JB87VVLuxTgbamvgeV3eqe6122FQUfDp38FH5ujGQHlMOGwY3vdAS+Q/YDeVFNPtR/Q2/YewEVcxiQnM4PhRbmMKMrlYyNKKC3O4+gh/Rk3dAAlBdqrlq5RgEfa9R68/DtYeR+0NMDo0+H838KY6ZChSXbS1Z76ZjZF7D179xuorqmnuraBplD40LpmMCS/L2XFeUwbM5DSojxKi/MoLcqlbGAeQ/L7kpHG81dLbCnAnYMNS73gfu8ZyOwDH5vtjW8PGR90dZIAB5pDVNd+OKxxKKT9x3UHWg5bvyA3m7LiPMaV5HPm+CGMKM6jzA/p4UW59MnSkIckRvoGePMBWPWQd5r7jreh32D45Pe9OUr6Dw66OomhUNixdU/DoUCujtiL3lxTz466xsPW75OVcWivuaK86MO96OJcSovzGNBXc1RL75B+Ab5vB7x2O1TeDvt3wpDjYNbv4PgLISt1L36aypxz1OxvavdA4fu7G2gOfTgQnWFQUpBLaXEupx09mNKDe9DFuZQW5TGofx8Nc0hSSJ8A3/62d7bkqgch1ARjz/bOmBx1mqZxTQL1TS1HHCjcXNPgD33Us78pdNj6A/vlMKI4j+OHFzDz+BJ/iMML6WGFuWRn6piGJL/UDvBwGNY967UB/vN/IDsPJn4Zpl4Jg8YGXZ34nHPsPdBCzf4m3t/dcNiBws019VTX1rNrX9Nhr8nLyTwUyFNHD/T3oD/ci+7XJ7V/2iKQqgHeVA9v3OeNb3/wHuQPg+k3wImX6DJlceacY39TiNr9TdTsb6KmvunQ/dr6Jmr2N3uP/eW19U3U1jcTCrvD3icrwxhW6A1zfOqYIX44e+PSpcV5DOyXo5Y7SXupFeB73/d6t5ffCQ21MGwifPY2OPYCyNSBp+5oaAr5wdv04e3+JmrqDw/ig8/X7m8+rK0uUmaGUZSXTVFeDkX9chgzuD9F/XIo7ucvy8uhpLAvpUV5lBT0JUvDHCIdSo0Af3+FN7799l/Bhf3LlF0FZVM1vh2hsSXE7vrmiBA+GL7N7YR0Ewea2w5jMyjMzfYCOC+H0uI8JowoPCyQi/vlHHq+qF8O+X2ydHBQJIaSN8DDIVj7pBfcm5ZBTj5Mme9dpqyoPOjq4q4lFKa2vtnf640YnmgVwB/eNrOvsaXd98vvm+UFbl4OQwb0ZdzQAV4QRwTwweeL++VQkJtNpsJYJFA9CnAzmwHcDGQCtznnbopJVR1prPNmAnzlD1C7EQrL4OyfwsQvQd+CuH98tJxzhMKOplCYppbwh7ctYZpDzl8Woqnlw3WaI9ZpCoWpO9DSdiDvb2LvgfbDuF9O5mGBO3pwfz94jwzkQn9IQ10ZIsmn2wFuZpnArcCZQDXwmpktds69E6viDrN7kzeN6+t3Q+NeKJ0KZ95I+OiZNLkML/z2NdIUCtPc4mgKhWiMCMuD4dgYGZShdpYfvB8KR7xH6LDwbQyFaY4I5tbh2xQKHzYHRnflZGUwMGLPd0RRHsV52UfsER+8LczL1uRHImmiJ3vgU4B1zrkNAGZ2PzALiHmAv/LbuVR8sBgHLLFp/Ledy+tVY2jeEKY59ExMPysrw8jJyiA7M4OcrAxyWt1mZ3rPF+Rkk5MZse6h5zPok5VxxHtkZ2XQJ7PVcv/9+mRlkJOZSXaWHfZ5/ftmkZudqW4LEWlTTwJ8OLA54nE1cFLrlcxsPjAfoKysrFsfFBpQyvPh2bw06HPU9x3K6KwMxvmh2DpgjwxfIycz87DwbXfdzAwdZBORpBH3g5jOuQXAAoCKiopuDSqcPPc/AG+sRkREPD05crUFiLxq7wh/mYiIJEBPAvw1YKyZjTKzHGAOsDg2ZYmISGe6PYTinGsxs6uBv+G1Ed7hnHs7ZpWJiEiHejQG7px7EngyRrWIiEgX6OwNEZEkpQAXEUlSCnARkSSlABcRSVLmYjFhR7QfZrYTqOrmywcBu2JYTqyorq5RXV2juromVesa6Zw74mrrCQ3wnjCzSudcRdB1tKa6ukZ1dY3q6pp0q0tDKCIiSUoBLiKSpJIpwBcEXUA7VFfXqK6uUV1dk1Z1Jc0YuIiIHC6Z9sBFRCSCAlxEJEn1igA3sxlmttbM1pnZtW0838fMHvCff8XMyiOe+56/fK2ZnZ3gur5lZu+Y2ZtmtsTMRkY8FzKzlf6fmE6zG0Vdl5jZzojPvzziublm9p7/Z26C6/pVRE3vmtnuiOfisr3M7A4z22Fmb7XzvJnZb/ya3zSzSRHPxXNbdVbXF/16VpnZMjObEPHcRn/5SjOrTHBdnzSzPRHf1fURz3X4/ce5ru9E1PSW/3sq9p+L5/YqNbMX/Bx428yuaWOd+P3GnHOB/sGbinY9MBrIAd4Axrda5+vAH/z7c4AH/Pvj/fX7AKP898lMYF2nA3n+/SsP1uU/3hfg9roEuKWN1xYDG/zbIv9+UaLqarX+N/CmII739voEMAl4q53nZwJPAQZMBV6J97aKsq6TD34ecM7BuvzHG4FBAW2vTwKP9/T7j3VdrdY9D3g+QdurBJjk388H3m3j72PcfmO9YQ/80MWRnXNNwMGLI0eaBSz07y8CppuZ+cvvd841Ouf+Cazz3y8hdTnnXnDO1fsPX8a7KlG8RbO92nM28KxzrsY5Vws8C8wIqK6LgPti9Nntcs79HajpYJVZwN3O8zJQaGYlxHdbdVqXc26Z/7mQuN9WNNurPT35Xca6roT8tgCcc1udc6/79+uA1XjXC44Ut99Ybwjwti6O3HoDHFrHOdcC7AEGRvnaeNYVaR7ev7IH9TWzSjN72cwuiFFNXanrc/7/ri0ys4OXvusV28sfahoFPB+xOF7bqzPt1R3PbdVVrX9bDnjGzJabd9HwRJtmZm+Y2VNmdqy/rFdsLzPLwwvBv0QsTsj2Mm9odyLwSqun4vYbi/tFjdOBmX0JqABOi1g80jm3xcxGA8+b2Srn3PoElfQYcJ9zrtHMvob3fy9nJOizozEHWOScC0UsC3J79VpmdjpegJ8asfhUf1t9BHjWzNb4e6iJ8Dred7XPzGYCjwBjE/TZ0TgPeNE5F7m3HvftZWb98f7R+KZzbm8s37sjvWEPPJqLIx9ax8yygALggyhfG8+6MLNPAT8AznfONR5c7pzb4t9uAJbi/cuckLqccx9E1HIbcGK0r41nXRHm0Op/ceO4vTrTXt2BX7TbzD6G9/3Ncs59cHB5xLbaATxM7IYNO+Wc2+uc2+fffxLINrNB9ILt5evotxWX7WVm2Xjhfa9z7q9trBK/31g8Bva7eBAgC2/wfhQfHvw4ttU6V3H4QcwH/fvHcvhBzA3E7iBmNHVNxDtwM7bV8iKgj39/EPAeMTqgE2VdJRH3PwO87D48aPJPv74i/35xoury1xuHd1DJErG9/Pcsp/2Dcudy+AGmV+O9raKsqwzvmM7JrZb3A/Ij7i8DZiSwrqEHvzu8INzkb7uovv941eU/X4A3Tt4vUdvL/2+/G/h1B+vE7TcWs43bw40wE+/o7XrgB/6yG/H2agH6Ag/5P+hXgdERr/2B/7q1wDkJrus5YDuw0v+z2F9+MrDK/xGvAuYluK6fAW/7n/8CMC7itZf523EdcGki6/If/wi4qdXr4ra98PbGtgLNeGOM84ArgCv85w241a95FVCRoG3VWV23AbURv61Kf/lofzu94X/HP0hwXVdH/LZeJuIfmLa+/0TV5a9zCV5TQ+Tr4r29TsUbY38z4ruamajfmE6lFxFJUr1hDFxERLpBAS4ikqQU4CIiSUoBLiKSpBTgIiJJSgEuIpKkFOAiIknq/wDagpyH7+a+SgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "r = np.linspace(0, 2, 6)\n",
+    "h = 3\n",
+    "\n",
+    "plt.plot(r, areal_sirkel(r))\n",
+    "plt.plot(r, areal_sylinder(r, h))\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Losningsforslag/Oving1/Input og variable lf.ipynb b/Losningsforslag/Oving1/Input og variable lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..62636fd53c9fffcbb68a859e7866e27a30b58a2f
--- /dev/null
+++ b/Losningsforslag/Oving1/Input og variable lf.ipynb	
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hei, Ada\n",
+      "ITGK - interessant!\n",
+      "Ha en fin dag, Ada\n",
+      "- og lykke til med ITGK\n"
+     ]
+    }
+   ],
+   "source": [
+    "navn = 'Ada'\n",
+    "favorittfag = 'ITGK'\n",
+    "\n",
+    "print(f'Hei, {navn}')\n",
+    "print(f'{favorittfag} - interessant!')\n",
+    "print(f'Ha en fin dag, {navn}')\n",
+    "print(f'- og lykke til med {favorittfag}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Har en sirkel med radius 5.4 som er grunnflate i en sylinder med høyde 7.9\n",
+      "Omkrets av sirkelen: 33.929200658769766\n",
+      "Areal av sirkelen: 91.60884177867838\n",
+      "Areal av sylinderen: 451.25836876163794\n"
+     ]
+    }
+   ],
+   "source": [
+    "import math\n",
+    "import numpy as np\n",
+    "   \n",
+    "r = 5.4\n",
+    "h = 7.9\n",
+    "print(\"Har en sirkel med radius\", r, \"som er grunnflate i en sylinder med høyde\", h)\n",
+    "omkrets = math.tau * r\n",
+    "print(\"Omkrets av sirkelen:\", omkrets)  #tau er det samme som 2 pi\n",
+    "areal_sirkel = np.pi * r**2\n",
+    "print(\"Areal av sirkelen:\", areal_sirkel)\n",
+    "areal_sylinder = omkrets * h + 2 * areal_sirkel\n",
+    "print(\"Areal av sylinderen:\", areal_sylinder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Per er 5 år unna idealalderen\n"
+     ]
+    }
+   ],
+   "source": [
+    "fornavn = \"Per\"\n",
+    "ideal_alder = 42\n",
+    "kundensAlder = 37\n",
+    "differanse = ideal_alder - kundensAlder\n",
+    "print(fornavn, \"er\", differanse, \"år unna idealalderen\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jeg heter Bob Bernt, og er 46 år\n"
+     ]
+    }
+   ],
+   "source": [
+    "navn = 'Bob Bernt'\n",
+    "alder = 46\n",
+    "\n",
+    "print(f'Jeg heter {navn}, og er {alder} år')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Losningsforslag/Oving1/Kalkulasjoner lf.ipynb b/Losningsforslag/Oving1/Kalkulasjoner lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4283c65182057541f736f3c2405582a9f5fb5a0b
--- /dev/null
+++ b/Losningsforslag/Oving1/Kalkulasjoner lf.ipynb	
@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 2\n",
+    "b = 3\n",
+    "c = 5*a + b\n",
+    "d = a * b + c\n",
+    "e = (-b + 4) / (a - 4)\n",
+    "f = 5 ** (a * b + 2)\n",
+    "g = (a + b) * c - d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def f(x):\n",
+    "    return 2 * x + 1\n",
+    "\n",
+    "def g(x):\n",
+    "    return (-4 * x + 2) / (5 * x + 3)\n",
+    "\n",
+    "def h(x):\n",
+    "    return x**2 + 2*x + 1\n",
+    "\n",
+    "def i(x):\n",
+    "    return np.sqrt(x)\n",
+    "\n",
+    "def j(x):\n",
+    "    return np.sin(x) + np.cos(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def antall_minutt_sekund(minutter):\n",
+    "    return minutter // 60\n",
+    "\n",
+    "def antall_dogn_timer(timer):\n",
+    "    return timer // 24\n",
+    "    \n",
+    "def antall_timer_minutt_sekund(sek):\n",
+    "    timer = sek // 3600\n",
+    "    minutter = sek % 60\n",
+    "    return timer, minutter"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Losningsforslag/Oving1/Plotting.ipynb b/Losningsforslag/Oving1/Plotting.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6e550e7f73150ee80c99e52e567990ae83cce78e
--- /dev/null
+++ b/Losningsforslag/Oving1/Plotting.ipynb
@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "055b05c9",
+   "metadata": {},
+   "source": [
+    "#### a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e4a6ad7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def f(x):\n",
+    "    return x**2 - 3*x + 2\n",
+    "\n",
+    "def g(x):\n",
+    "    return 3 * np.sin(x)\n",
+    "\n",
+    "def h(x):\n",
+    "    return 42*x / (x**2 + 4)\n",
+    "\n",
+    "x_verdier = np.array([-3, -1, 1, 3, 5])\n",
+    "\n",
+    "plt.plot(x_verdier, f(x_verdier))\n",
+    "plt.plot(x_verdier, g(x_verdier))\n",
+    "plt.plot(x_verdier, h(x_verdier))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba8d951",
+   "metadata": {},
+   "source": [
+    "#### b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80c1f5e5",
+   "metadata": {},
+   "source": [
+    "Dette er eksempel på hvordan grafen i oppgaven kan se ut. Den vil være forskjellig med forskjellige start, stopp og intervaller verdier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "91030506",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def f(x):\n",
+    "    return x**2 - 3*x + 2\n",
+    "\n",
+    "def g(x):\n",
+    "    return 3 * np.sin(x)\n",
+    "\n",
+    "def h(x):\n",
+    "    return 42*x / (x**2 + 4)\n",
+    "\n",
+    "start = -3\n",
+    "stopp = 5\n",
+    "intervaller = 50\n",
+    "\n",
+    "x_verdier = np.linspace(start, stopp, intervaller)\n",
+    "\n",
+    "plt.plot(x_verdier, f(x_verdier))\n",
+    "plt.plot(x_verdier, g(x_verdier))\n",
+    "plt.plot(x_verdier, h(x_verdier))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ade688a7",
+   "metadata": {},
+   "source": [
+    "#### c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3f995bc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def f(x):\n",
+    "    return x**2 - 3*x + 2\n",
+    "\n",
+    "def g(x):\n",
+    "    return 3 * np.sin(x)\n",
+    "\n",
+    "def h(x):\n",
+    "    return 42*x / (x**2 + 4)\n",
+    "\n",
+    "\n",
+    "start = -3\n",
+    "stopp = 5\n",
+    "intervaller = 50\n",
+    "\n",
+    "x = np.linspace(start, stopp, intervaller)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "ax.set_title(\"Plot av tre forskjellige grafer\")\n",
+    "\n",
+    "ax.spines['left'].set_position('zero')\n",
+    "ax.spines['bottom'].set_position('zero')\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "\n",
+    "f_graf, = ax.plot(x_verdier, f(x_verdier))\n",
+    "g_graf, = ax.plot(x_verdier, g(x_verdier))\n",
+    "h_graf, = ax.plot(x_verdier, h(x_verdier))\n",
+    "\n",
+    "ax.legend([f_graf, g_graf, h_graf], ['f(x) = x^2 - 3x + 2', 'g(x) = 3sin(x)', 'h(x) = 42x / x^2 + 4'])\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Losningsforslag/Oving1/Priser med og uten moms.ipynb b/Losningsforslag/Oving1/Priser med og uten moms.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..05da05bca924ab3609868503aca89e39d9e916a5
--- /dev/null
+++ b/Losningsforslag/Oving1/Priser med og uten moms.ipynb	
@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dd5b8b22",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e5b575cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pris_m_valgfri_moms(pris_u_moms, moms):\n",
+    "    return pris_u_moms * (1+ moms)\n",
+    "\n",
+    "def pris_u_moms(pris):\n",
+    "    return pris / 1.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "232a8da1",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ceeaa17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "priser_inkl_moms = np.array([25, 18, 42, 75, 5, 34.5])\n",
+    "moms = pris_m_valgfri_moms(priser_inkl_moms, .1)\n",
+    "moms_til_betaling = np.sum(moms)\n",
+    "moms_til_betaling"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.7 ('tdt4195')",
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "63b89d9bfc14eb4486c27c9b239bf0a08c4c63a21c176f83036370f0e204c130"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Losningsforslag/Oving1/Tallkonvertering- lf.ipynb b/Losningsforslag/Oving1/Tallkonvertering- lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..439b5ffa1136a59dc91bdbbb87d359e799035d61
--- /dev/null
+++ b/Losningsforslag/Oving1/Tallkonvertering- lf.ipynb	
@@ -0,0 +1,148 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25\n"
+     ]
+    }
+   ],
+   "source": [
+    "def legg_sammen_to_tall(a, b):\n",
+    "    return int(a) + int(b)\n",
+    "\n",
+    "print(legg_sammen_to_tall(10, 15))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def legg_til_landskode(telefonnummer, landskode):\n",
+    "    return f'+{landskode} {telefonnummer}'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = '1'\n",
+    "b = True\n",
+    "c = False\n",
+    "d = '1.5'\n",
+    "e = '2.45'\n",
+    "\n",
+    "a = int(a)\n",
+    "b = int(b)\n",
+    "c = int(c)\n",
+    "d = int(float(d))\n",
+    "e = int(float(e))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def mult_list_with_x(l, x):\n",
+    "    return list(np.array(l) * x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rund_av(tall, desimaler):\n",
+    "    return round(tall, desimaler)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def negativt_eller_positivt(tall):\n",
+    "    return 2 * bool(tall + abs(tall)) - bool(tall)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Losningsforslag/Oving1/Tetraeder lf.ipynb b/Losningsforslag/Oving1/Tetraeder lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e6a01bd06a551291cdb21ec7468328408c44d2a0
--- /dev/null
+++ b/Losningsforslag/Oving1/Tetraeder lf.ipynb	
@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def areal(h):\n",
+    "    a = (3 / np.sqrt(6)) * h\n",
+    "    return np.sqrt(3) * np.power(a, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def volum(h):\n",
+    "    h = 3\n",
+    "    a = 3/np.sqrt(6)*h\n",
+    "    return 1/12*np.sqrt(2)*np.power(a,3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def areal(h):\n",
+    "    a = calculate_a(h)\n",
+    "    return np.sqrt(3) * np.power(a, 2)\n",
+    "\n",
+    "def volum(h):\n",
+    "    a = calculate_a(h)\n",
+    "    return 1/12*np.sqrt(2)*np.power(a,3)\n",
+    "\n",
+    "def calculate_a(h):\n",
+    "    return (3 / np.sqrt(6)) * h"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Losningsforslag/Oving1/Vitenskapelig notasjon lf.ipynb b/Losningsforslag/Oving1/Vitenskapelig notasjon lf.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bf919ee076dae1a6da70d10604c82caa8146d81d
--- /dev/null
+++ b/Losningsforslag/Oving1/Vitenskapelig notasjon lf.ipynb	
@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def E(v):\n",
+    "    return 6.626e-34 * v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def molekyler_av_stoff(g, molvekt):\n",
+    "    return 6.022e+23 * (g / molvekt)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}