diff --git a/lecture_1.ipynb b/lecture_1.ipynb
index 25b8417bfbe8900a7cb8ecb52acfa510fde758da..c5b8e8246c39bd92cc02789ed4f805cbe40e5bfe 100644
--- a/lecture_1.ipynb
+++ b/lecture_1.ipynb
@@ -915,29 +915,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "id": "b44e356e-b829-4879-b5fc-9706fffe873d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[5., 0.],\n",
-       "       [2., 1.],\n",
-       "       [0., 1.],\n",
-       "       [0., 3.],\n",
-       "       [0., 1.],\n",
-       "       [2., 2.],\n",
-       "       [4., 0.],\n",
-       "       [2., 1.],\n",
-       "       [1., 3.]])"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "data = np.loadtxt('./exercises/09_data.txt')\n",
@@ -947,42 +928,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "id": "2aeacb94-518d-464f-a7ab-5282b97bc225",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5., 2., 0., 0., 0., 2., 4., 2., 1.])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data[0:9,0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "1829fa78-7d7b-4bac-9f24-5129dca63629",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.0), np.float64(6.0))"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.min(data), np.max(data)"
    ]
@@ -1001,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "871f915d-09f8-4d14-a79a-8fbd2f16ab75",
    "metadata": {
     "cell_style": "center",
@@ -1011,30 +970,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([74., 96.,  0., 67.,  0., 43., 13.,  0., 10.,  3.]),\n",
-       " array([0. , 0.6, 1.2, 1.8, 2.4, 3. , 3.6, 4.2, 4.8, 5.4, 6. ]),\n",
-       " <BarContainer object of 10 artists>)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -1057,33 +993,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "f8263279-48b5-4421-be05-604ddbfd8d6f",
    "metadata": {
     "cell_style": "center"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'k')"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAGzCAYAAADqhoemAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdCElEQVR4nO3df3DX9X3A8VcKEsGFKFQSMqOmXVqt+KtgOaIddGp2jnp63KodtmXXbgcHWlPmEMY2ozcTpZOxlcou7o7CehT/sEx39Qe5aWMd6xWjrIz2tD2p5KxZbh1NULkw4bM/en5vMXSF+k2/r9jH4+5zx/fz/Xw/eeVzejx5f39VFUVRBABAMu+p9AAAAMcjUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJQmnuwDnn766fjiF78Yvb298eqrr8aOHTvi+uuvL91fFEXceeed0dXVFQcPHoy5c+fGl7/85bjgggtKxwwPD8dtt90WX/va1+Lw4cNx5ZVXxv333x9nnXXWCc1w7Nix+PGPfxw1NTVRVVV1sr8CAFABRVHEoUOHoqGhId7znhNYJylO0qOPPlqsXbu2eOihh4qIKHbs2DHi/nvuuaeoqakpHnrooWLv3r3FjTfeWMycObMYGhoqHbNs2bLiN3/zN4vu7u7iueeeKz72sY8VF198cfHmm2+e0Ax9fX1FRNhsNpvNZhuHW19f3wn9fV9VFL/8FwxWVVWNWEkpiiIaGhqira0tbr/99oj42apJXV1d3HvvvbF06dIYHByMM888M/7xH/8xbrzxxoiI+PGPfxyNjY3x6KOPxu/+7u/+wp87ODgYp59+evT19cXUqVN/2fEBgF+hoaGhaGxsjJ/+9KdRW1v7C48/6ad7/j/79++P/v7+aG1tLe2rrq6O+fPnx65du2Lp0qXR29sb//M//zPimIaGhpg1a1bs2rXruJEyPDwcw8PDpduHDh2KiIipU6eKFAAYZ070pRplfeFsf39/RETU1dWN2F9XV1e6r7+/PyZNmhRnnHHGzz3m7To7O6O2tra0NTY2lnNsACChMXl3z9sLqSiKX1hN/98xa9asicHBwdLW19dXtlkBgJzKGin19fUREaNWRAYGBkqrK/X19XHkyJE4ePDgzz3m7aqrq0tP7XiKBwB+PZQ1UpqamqK+vj66u7tL+44cORI9PT3R0tISERGzZ8+OU045ZcQxr776avzHf/xH6RgAgJN+4exrr70WP/zhD0u39+/fH3v27Ilp06bF2WefHW1tbdHR0RHNzc3R3NwcHR0dMWXKlFi8eHFERNTW1sbnPve5+JM/+ZOYPn16TJs2LW677ba48MIL46qrrirfbwYAjGsnHSnPPvtsfOxjHyvdXrlyZURELFmyJL7yla/EqlWr4vDhw7F8+fLSh7nt3LkzampqSo/5m7/5m5g4cWLccMMNpQ9z+8pXvhITJkwow68EALwbvKPPSamUoaGhqK2tjcHBQa9PAYBx4mT//vbdPQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSOulPnIX/69zV36j0CMf1o3sWVnoEAN4hKykAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKRU9kh5880348///M+jqakpJk+eHO973/virrvuimPHjpWOKYoi2tvbo6GhISZPnhwLFiyIffv2lXsUAGAcK3uk3HvvvfH3f//3sXHjxvj+978f69atiy9+8YvxpS99qXTMunXrYv369bFx48bYvXt31NfXx9VXXx2HDh0q9zgAwDhV9kj5t3/7t7juuuti4cKFce6558bv//7vR2trazz77LMR8bNVlA0bNsTatWtj0aJFMWvWrNiyZUu88cYbsW3btnKPAwCMU2WPlCuuuCL+5V/+JV588cWIiPj3f//3eOaZZ+L3fu/3IiJi//790d/fH62traXHVFdXx/z582PXrl3HPefw8HAMDQ2N2ACAd7eJ5T7h7bffHoODg3HeeefFhAkT4ujRo3H33XfHH/zBH0RERH9/f0RE1NXVjXhcXV1dvPzyy8c9Z2dnZ9x5553lHhUASKzsKykPPvhgfPWrX41t27bFc889F1u2bIm//uu/ji1btow4rqqqasTtoihG7XvLmjVrYnBwsLT19fWVe2wAIJmyr6T86Z/+aaxevTo++clPRkTEhRdeGC+//HJ0dnbGkiVLor6+PiJ+tqIyc+bM0uMGBgZGra68pbq6Oqqrq8s9KgCQWNlXUt544414z3tGnnbChAmltyA3NTVFfX19dHd3l+4/cuRI9PT0REtLS7nHAQDGqbKvpFx77bVx9913x9lnnx0XXHBBPP/887F+/fr47Gc/GxE/e5qnra0tOjo6orm5OZqbm6OjoyOmTJkSixcvLvc4AMA4VfZI+dKXvhR/8Rd/EcuXL4+BgYFoaGiIpUuXxl/+5V+Wjlm1alUcPnw4li9fHgcPHoy5c+fGzp07o6amptzjAADjVFVRFEWlhzhZQ0NDUVtbG4ODgzF16tRKj/Nr7dzV36j0CMf1o3sWVnoEAN7mZP/+9t09AEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkNLESg+Q0bmrv1HpEUb50T0LKz0CAPxKWUkBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFKaWOkB4NfFuau/UekRjutH9yys9AgAx2UlBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICUxiRSXnnllfjUpz4V06dPjylTpsQll1wSvb29pfuLooj29vZoaGiIyZMnx4IFC2Lfvn1jMQoAME6VPVIOHjwYl19+eZxyyinx2GOPxfe+972477774vTTTy8ds27duli/fn1s3Lgxdu/eHfX19XH11VfHoUOHyj0OADBOTSz3Ce+9995obGyMzZs3l/ade+65pT8XRREbNmyItWvXxqJFiyIiYsuWLVFXVxfbtm2LpUuXlnskAGAcKvtKyiOPPBJz5syJT3ziEzFjxoy49NJL44EHHijdv3///ujv74/W1tbSvurq6pg/f37s2rXruOccHh6OoaGhERsA8O5W9kh56aWXYtOmTdHc3BxPPPFELFu2LD7/+c/H1q1bIyKiv78/IiLq6upGPK6urq5039t1dnZGbW1taWtsbCz32ABAMmWPlGPHjsWHP/zh6OjoiEsvvTSWLl0af/zHfxybNm0acVxVVdWI20VRjNr3ljVr1sTg4GBp6+vrK/fYAEAyZY+UmTNnxoc+9KER+84///w4cOBARETU19dHRIxaNRkYGBi1uvKW6urqmDp16ogNAHh3K3ukXH755fHCCy+M2Pfiiy/GOeecExERTU1NUV9fH93d3aX7jxw5Ej09PdHS0lLucQCAcars7+75whe+EC0tLdHR0RE33HBDfOc734murq7o6uqKiJ89zdPW1hYdHR3R3Nwczc3N0dHREVOmTInFixeXexwAYJwqe6RcdtllsWPHjlizZk3cdddd0dTUFBs2bIibbrqpdMyqVavi8OHDsXz58jh48GDMnTs3du7cGTU1NeUeBwAYp8oeKRERH//4x+PjH//4z72/qqoq2tvbo729fSx+PADwLuC7ewCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAIKWJlR4A4O3OXf2NSo9wXD+6Z2GlR4BfK1ZSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmNeaR0dnZGVVVVtLW1lfYVRRHt7e3R0NAQkydPjgULFsS+ffvGehQAYBwZ00jZvXt3dHV1xUUXXTRi/7p162L9+vWxcePG2L17d9TX18fVV18dhw4dGstxAIBxZMwi5bXXXoubbropHnjggTjjjDNK+4uiiA0bNsTatWtj0aJFMWvWrNiyZUu88cYbsW3btrEaBwAYZ8YsUlasWBELFy6Mq666asT+/fv3R39/f7S2tpb2VVdXx/z582PXrl3HPdfw8HAMDQ2N2ACAd7cx+Vj87du3x3PPPRe7d+8edV9/f39ERNTV1Y3YX1dXFy+//PJxz9fZ2Rl33nln+QcFANIq+0pKX19f3HrrrfHVr341Tj311J97XFVV1YjbRVGM2veWNWvWxODgYGnr6+sr68wAQD5lX0np7e2NgYGBmD17dmnf0aNH4+mnn46NGzfGCy+8EBE/W1GZOXNm6ZiBgYFRqytvqa6ujurq6nKPCgAkVvaVlCuvvDL27t0be/bsKW1z5syJm266Kfbs2RPve9/7or6+Prq7u0uPOXLkSPT09ERLS0u5xwEAxqmyr6TU1NTErFmzRuw77bTTYvr06aX9bW1t0dHREc3NzdHc3BwdHR0xZcqUWLx4cbnHAQDGqTF54ewvsmrVqjh8+HAsX748Dh48GHPnzo2dO3dGTU1NJcYBABL6lUTKN7/5zRG3q6qqor29Pdrb238VPx4AGId8dw8AkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAIKWyR0pnZ2dcdtllUVNTEzNmzIjrr78+XnjhhRHHFEUR7e3t0dDQEJMnT44FCxbEvn37yj0KADCOlT1Senp6YsWKFfHtb387uru7480334zW1tZ4/fXXS8esW7cu1q9fHxs3bozdu3dHfX19XH311XHo0KFyjwMAjFMTy33Cxx9/fMTtzZs3x4wZM6K3tzd++7d/O4qiiA0bNsTatWtj0aJFERGxZcuWqKuri23btsXSpUvLPRIAMA6N+WtSBgcHIyJi2rRpERGxf//+6O/vj9bW1tIx1dXVMX/+/Ni1a9dxzzE8PBxDQ0MjNgDg3W1MI6Uoili5cmVcccUVMWvWrIiI6O/vj4iIurq6EcfW1dWV7nu7zs7OqK2tLW2NjY1jOTYAkMCYRsrNN98c3/3ud+NrX/vaqPuqqqpG3C6KYtS+t6xZsyYGBwdLW19f35jMCwDkUfbXpLzllltuiUceeSSefvrpOOuss0r76+vrI+JnKyozZ84s7R8YGBi1uvKW6urqqK6uHqtRAYCEyr6SUhRF3HzzzfH1r389nnzyyWhqahpxf1NTU9TX10d3d3dp35EjR6KnpydaWlrKPQ4AME6VfSVlxYoVsW3btnj44Yejpqam9DqT2tramDx5clRVVUVbW1t0dHREc3NzNDc3R0dHR0yZMiUWL15c7nEAgHGq7JGyadOmiIhYsGDBiP2bN2+OP/zDP4yIiFWrVsXhw4dj+fLlcfDgwZg7d27s3Lkzampqyj0OADBOlT1SiqL4hcdUVVVFe3t7tLe3l/vHAwDvEr67BwBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACCliZUeAIBf3rmrv1HpEY7rR/csrPQIvAtYSQEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASj4WH4BfGxm/RsBXCPx8VlIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApCRSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAApiRQAICWRAgCkJFIAgJRECgCQkkgBAFISKQBASiIFAEhJpAAAKYkUACAlkQIApFTRSLn//vujqakpTj311Jg9e3Z861vfquQ4AEAiFYuUBx98MNra2mLt2rXx/PPPx0c/+tG45ppr4sCBA5UaCQBIpGKRsn79+vjc5z4Xf/RHfxTnn39+bNiwIRobG2PTpk2VGgkASGRiJX7okSNHore3N1avXj1if2tra+zatWvU8cPDwzE8PFy6PTg4GBERQ0NDYzLfseE3xuS878RY/a7vVMZrFZHzerlWJ861OnGu1cnJeL2yXqux8NbvWhTFiT2gqIBXXnmliIjiX//1X0fsv/vuu4sPfOADo46/4447ioiw2Ww2m832Ltj6+vpOqBcqspLylqqqqhG3i6IYtS8iYs2aNbFy5crS7WPHjsV///d/x/Tp0497fBZDQ0PR2NgYfX19MXXq1EqPk5prdeJcqxPnWp041+rEuVYn5/9er5qamjh06FA0NDSc0GMrEinvfe97Y8KECdHf3z9i/8DAQNTV1Y06vrq6Oqqrq0fsO/3008dyxLKaOnWq/5BPkGt14lyrE+danTjX6sS5VifnretVW1t7wo+pyAtnJ02aFLNnz47u7u4R+7u7u6OlpaUSIwEAyVTs6Z6VK1fGpz/96ZgzZ07Mmzcvurq64sCBA7Fs2bJKjQQAJFKxSLnxxhvjJz/5Sdx1113x6quvxqxZs+LRRx+Nc845p1IjlV11dXXccccdo56qYjTX6sS5VifOtTpxrtWJc61Ozju5XlVFcaLvAwIA+NXx3T0AQEoiBQBISaQAACmJFAAgJZEyRu6///5oamqKU089NWbPnh3f+ta3Kj1SSk8//XRce+210dDQEFVVVfFP//RPlR4prc7OzrjsssuipqYmZsyYEddff3288MILlR4rpU2bNsVFF11U+vCoefPmxWOPPVbpscaFzs7OqKqqira2tkqPkk57e3tUVVWN2Orr6ys9VlqvvPJKfOpTn4rp06fHlClT4pJLLone3t6TOodIGQMPPvhgtLW1xdq1a+P555+Pj370o3HNNdfEgQMHKj1aOq+//npcfPHFsXHjxkqPkl5PT0+sWLEivv3tb0d3d3e8+eab0draGq+//nqlR0vnrLPOinvuuSeeffbZePbZZ+N3fud34rrrrot9+/ZVerTUdu/eHV1dXXHRRRdVepS0Lrjggnj11VdL2969eys9UkoHDx6Myy+/PE455ZR47LHH4nvf+17cd999J/9p8e/86wJ5u4985CPFsmXLRuw777zzitWrV1doovEhIoodO3ZUeoxxY2BgoIiIoqenp9KjjAtnnHFG8Q//8A+VHiOtQ4cOFc3NzUV3d3cxf/784tZbb630SOnccccdxcUXX1zpMcaF22+/vbjiiive8XmspJTZkSNHore3N1pbW0fsb21tjV27dlVoKt6NBgcHIyJi2rRpFZ4kt6NHj8b27dvj9ddfj3nz5lV6nLRWrFgRCxcujKuuuqrSo6T2gx/8IBoaGqKpqSk++clPxksvvVTpkVJ65JFHYs6cOfGJT3wiZsyYEZdeemk88MADJ30ekVJm//Vf/xVHjx4d9UWJdXV1o75QEX5ZRVHEypUr44orrohZs2ZVepyU9u7dG7/xG78R1dXVsWzZstixY0d86EMfqvRYKW3fvj2ee+656OzsrPQoqc2dOze2bt0aTzzxRDzwwAPR398fLS0t8ZOf/KTSo6Xz0ksvxaZNm6K5uTmeeOKJWLZsWXz+85+PrVu3ntR5Kvax+O92VVVVI24XRTFqH/yybr755vjud78bzzzzTKVHSeuDH/xg7NmzJ37605/GQw89FEuWLImenh6h8jZ9fX1x6623xs6dO+PUU0+t9DipXXPNNaU/X3jhhTFv3rx4//vfH1u2bImVK1dWcLJ8jh07FnPmzImOjo6IiLj00ktj3759sWnTpvjMZz5zwuexklJm733ve2PChAmjVk0GBgZGra7AL+OWW26JRx55JJ566qk466yzKj1OWpMmTYrf+q3fijlz5kRnZ2dcfPHF8bd/+7eVHiud3t7eGBgYiNmzZ8fEiRNj4sSJ0dPTE3/3d38XEydOjKNHj1Z6xLROO+20uPDCC+MHP/hBpUdJZ+bMmaP+QXD++eef9BtIREqZTZo0KWbPnh3d3d0j9nd3d0dLS0uFpuLdoCiKuPnmm+PrX/96PPnkk9HU1FTpkcaVoihieHi40mOkc+WVV8bevXtjz549pW3OnDlx0003xZ49e2LChAmVHjGt4eHh+P73vx8zZ86s9CjpXH755aM+IuHFF1886S8R9nTPGFi5cmV8+tOfjjlz5sS8efOiq6srDhw4EMuWLav0aOm89tpr8cMf/rB0e//+/bFnz56YNm1anH322RWcLJ8VK1bEtm3b4uGHH46amprSal1tbW1Mnjy5wtPl8md/9mdxzTXXRGNjYxw6dCi2b98e3/zmN+Pxxx+v9Gjp1NTUjHpd02mnnRbTp0/3eqe3ue222+Laa6+Ns88+OwYGBuKv/uqvYmhoKJYsWVLp0dL5whe+EC0tLdHR0RE33HBDfOc734murq7o6uo6uRO94/cHcVxf/vKXi3POOaeYNGlS8eEPf9jbRH+Op556qoiIUduSJUsqPVo6x7tOEVFs3ry50qOl89nPfrb0/9+ZZ55ZXHnllcXOnTsrPda44S3Ix3fjjTcWM2fOLE455ZSioaGhWLRoUbFv375Kj5XWP//zPxezZs0qqquri/POO6/o6uo66XNUFUVRlDGeAADKwmtSAICURAoAkJJIAQBSEikAQEoiBQBISaQAACmJFAAgJZECpLFgwYJoa2ur9BhAEiIFAEhJpAAAKYkUIK3HH388amtrY+vWrZUeBagAkQKktH379rjhhhti69at8ZnPfKbS4wAVIFKAdO6///5YtmxZPPzww3HddddVehygQiZWegCA/+uhhx6K//zP/4xnnnkmPvKRj1R6HKCCrKQAqVxyySVx5plnxubNm6MoikqPA1SQSAFSef/73x9PPfVUPPzww3HLLbdUehyggjzdA6TzgQ98IJ566qlYsGBBTJw4MTZs2FDpkYAKEClASh/84AfjySefjAULFsSECRPivvvuq/RIwK9YVeFJXwAgIa9JAQBSEikAQEoiBQBISaQAACmJFAAgJZECAKQkUgCAlEQKAJCSSAEAUhIpAEBKIgUASEmkAAAp/S+yutai6Y48NAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.hist(data[:, 0], bins=np.arange(-0.25,6.25,0.5))\n",
     "plt.xlabel(\"k\")"
@@ -1103,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "5056a717-6561-41ec-be39-1757984863a9",
    "metadata": {
     "cell_style": "split",
@@ -1112,18 +1027,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "plt.hist(data[:, 0], bins=np.arange(-0.25,6.26,0.5))\n",
@@ -1134,23 +1038,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "3f64d35c",
    "metadata": {
     "cell_style": "split"
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.hist(data[:, 1], bins=np.arange(-0.25,6.26,0.5))\n",
     "plt.xlabel(\"l\")\n",
@@ -1172,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "4ecfb198-c621-4ee4-9d24-9f9a8cb8bec7",
    "metadata": {
     "slideshow": {
@@ -1180,25 +1073,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "median 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGwCAYAAAB7MGXBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdY0lEQVR4nO3dcWzX9Z348VeltnWO1hO0wqzYOXXcmDjbk2uRnNOtSzVk5paDxTthmyZrDmXQcxlIMicxq3c5jXMKjil6JG6SnTpdZEqTeYjD5UYP7oySm3e4lWmxKbtrkV3KKN/7w5/Nr9ei/Vbw1cLjkXyTfd98Pt/v6/vNlj73+Xy/n29JoVAoBABAkpOyBwAATmxiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFSl2QOMxuHDh+ONN96IyZMnR0lJSfY4AMAoFAqF2L9/f0yfPj1OOunIxz8mRIy88cYbUVNTkz0GADAGe/bsibPPPvuI/z4hYmTy5MkR8faLqaysTJ4GABiNvr6+qKmpGfw7fiQTIkbeOTVTWVkpRgBggnmvj1j4ACsAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkKroGHn++edj/vz5MX369CgpKYkf//jH77nPli1boq6uLioqKuKjH/1o3H///WOZFQA4DhUdIwcOHIjZs2fHvffeO6rtX3vttbjqqqti3rx5sWPHjrjlllti6dKl8dhjjxU9LABw/Cn6V3ubm5ujubl51Nvff//9cc4558Tdd98dEREzZ86M7du3x9///d/HF77whWKfHgA4zhzzz4y8+OKL0dTUNGTtc5/7XGzfvj3+8Ic/jLhPf39/9PX1DbkBAMenoo+MFGvv3r1RXV09ZK26ujoOHToUPT09MW3atGH7tLW1xW233XasRwOAE9K5K54ecv/Xd1ydNMnbPpBv05SUlAy5XygURlx/x8qVK6O3t3fwtmfPnmM+IwCQ45gfGTnrrLNi7969Q9a6u7ujtLQ0pkyZMuI+5eXlUV5efqxHAwDGgWMeIw0NDfGTn/xkyNrmzZujvr4+Tj755GP99ACcgP7vaQjGt6JP07z11luxc+fO2LlzZ0S8/dXdnTt3RmdnZ0S8fYpl0aJFg9u3tLTEb37zm2htbY1du3bF+vXr48EHH4ybb7756LwCAGBCK/rIyPbt2+PTn/704P3W1taIiFi8eHE8/PDD0dXVNRgmERG1tbWxadOmWL58edx3330xffr0uOeee3ytFwCIiIiSwjufJh3H+vr6oqqqKnp7e6OysjJ7HADGOadpinOsvk0z2r/ffpsGAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVMf8t2kAeH9cwIvjnSMjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApCrNHgDg3BVPZ48AJHJkBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRiBABIJUYAgFRjipE1a9ZEbW1tVFRURF1dXWzduvVdt3/kkUdi9uzZ8aEPfSimTZsWX/7yl2Pfvn1jGhgAOL4UHSMbN26MZcuWxapVq2LHjh0xb968aG5ujs7OzhG3f+GFF2LRokVx/fXXx8svvxw/+tGP4pe//GXccMMN73t4AGDiKzpG7rrrrrj++uvjhhtuiJkzZ8bdd98dNTU1sXbt2hG3/8UvfhHnnntuLF26NGpra+Oyyy6Lr371q7F9+/YjPkd/f3/09fUNuQEAx6eiYuTgwYPR0dERTU1NQ9abmppi27ZtI+7T2NgYv/3tb2PTpk1RKBTizTffjH/8x3+Mq6+++ojP09bWFlVVVYO3mpqaYsYEACaQomKkp6cnBgYGorq6esh6dXV17N27d8R9Ghsb45FHHomFCxdGWVlZnHXWWXHaaafFd7/73SM+z8qVK6O3t3fwtmfPnmLGBAAmkDF9gLWkpGTI/UKhMGztHa+88kosXbo0vvnNb0ZHR0c888wz8dprr0VLS8sRH7+8vDwqKyuH3ACA41NpMRtPnTo1Jk2aNOwoSHd397CjJe9oa2uLuXPnxte//vWIiLjooovi1FNPjXnz5sXtt98e06ZNG+PoAMDxoKgjI2VlZVFXVxft7e1D1tvb26OxsXHEfX7/+9/HSScNfZpJkyZFxNtHVACAE1vRp2laW1vjgQceiPXr18euXbti+fLl0dnZOXjaZeXKlbFo0aLB7efPnx+PP/54rF27Nnbv3h0///nPY+nSpXHppZfG9OnTj94rAQAmpKJO00RELFy4MPbt2xerV6+Orq6umDVrVmzatClmzJgRERFdXV1DrjnypS99Kfbv3x/33ntv/M3f/E2cdtppccUVV8Tf/u3fHr1XAQBMWCWFCXCupK+vL6qqqqK3t9eHWeE4dO6Kp7NHgBPar+848uU23o/R/v322zQAQKqiT9MA787/ywcojiMjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApBIjAECqMcXImjVrora2NioqKqKuri62bt36rtv39/fHqlWrYsaMGVFeXh7nnXderF+/fkwDAwDHl9Jid9i4cWMsW7Ys1qxZE3Pnzo3vfe970dzcHK+88kqcc845I+6zYMGCePPNN+PBBx+Mj33sY9Hd3R2HDh1638MDABNfSaFQKBSzw5w5c+KSSy6JtWvXDq7NnDkzrrnmmmhraxu2/TPPPBNf/OIXY/fu3XH66aePaci+vr6oqqqK3t7eqKysHNNjwAfl3BVPZ48AUJRf33H1MXnc0f79Luo0zcGDB6OjoyOampqGrDc1NcW2bdtG3Oepp56K+vr6+Lu/+7v4yEc+EhdccEHcfPPN8T//8z9HfJ7+/v7o6+sbcgMAjk9Fnabp6emJgYGBqK6uHrJeXV0de/fuHXGf3bt3xwsvvBAVFRXxxBNPRE9PT/z1X/91/O53vzvi50ba2tritttuK2Y0AGCCGtMHWEtKSobcLxQKw9becfjw4SgpKYlHHnkkLr300rjqqqvirrvuiocffviIR0dWrlwZvb29g7c9e/aMZUwAYAIo6sjI1KlTY9KkScOOgnR3dw87WvKOadOmxUc+8pGoqqoaXJs5c2YUCoX47W9/G+eff/6wfcrLy6O8vLyY0QCACaqoIyNlZWVRV1cX7e3tQ9bb29ujsbFxxH3mzp0bb7zxRrz11luDa7/61a/ipJNOirPPPnsMIwMAx5OiT9O0trbGAw88EOvXr49du3bF8uXLo7OzM1paWiLi7VMsixYtGtz+2muvjSlTpsSXv/zleOWVV+L555+Pr3/96/GVr3wlTjnllKP3SgCACano64wsXLgw9u3bF6tXr46urq6YNWtWbNq0KWbMmBEREV1dXdHZ2Tm4/Yc//OFob2+Pm266Kerr62PKlCmxYMGCuP3224/eqwAAJqyirzOSwXVGmEhcZwSYaCbUdUYAAI42MQIApBIjAEAqMQIApBIjAEAqMQIApBIjAEAqMQIApCr6CqyceFzEC4BjyZERACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUokRACCVGAEAUo0pRtasWRO1tbVRUVERdXV1sXXr1lHt9/Of/zxKS0vj4osvHsvTAgDHoaJjZOPGjbFs2bJYtWpV7NixI+bNmxfNzc3R2dn5rvv19vbGokWL4sorrxzzsADA8afoGLnrrrvi+uuvjxtuuCFmzpwZd999d9TU1MTatWvfdb+vfvWrce2110ZDQ8OYhwUAjj9FxcjBgwejo6Mjmpqahqw3NTXFtm3bjrjfQw89FP/5n/8Zt95666iep7+/P/r6+obcAIDjU1Ex0tPTEwMDA1FdXT1kvbq6Ovbu3TviPq+++mqsWLEiHnnkkSgtLR3V87S1tUVVVdXgraamppgxAYAJZEwfYC0pKRlyv1AoDFuLiBgYGIhrr702brvttrjgggtG/fgrV66M3t7ewduePXvGMiYAMAGM7lDF/zN16tSYNGnSsKMg3d3dw46WRETs378/tm/fHjt27Igbb7wxIiIOHz4chUIhSktLY/PmzXHFFVcM26+8vDzKy8uLGQ0AmKCKOjJSVlYWdXV10d7ePmS9vb09Ghsbh21fWVkZL730UuzcuXPw1tLSEhdeeGHs3Lkz5syZ8/6mBwAmvKKOjEREtLa2xnXXXRf19fXR0NAQ69ati87OzmhpaYmIt0+xvP7667Fhw4Y46aSTYtasWUP2P/PMM6OiomLYOgBwYio6RhYuXBj79u2L1atXR1dXV8yaNSs2bdoUM2bMiIiIrq6u97zmCADAO0oKhUIhe4j30tfXF1VVVdHb2xuVlZXZ45xwzl3xdPYIABxDv77j6mPyuKP9++23aQCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVGIEAEglRgCAVKXZA2Q7d8XT2SMAwAnNkREAIJUYAQBSiREAIJUYAQBSiREAIJUYAQBSiREAIJUYAQBSiREAIJUYAQBSjSlG1qxZE7W1tVFRURF1dXWxdevWI277+OOPx2c/+9k444wzorKyMhoaGuLZZ58d88AAwPGl6BjZuHFjLFu2LFatWhU7duyIefPmRXNzc3R2do64/fPPPx+f/exnY9OmTdHR0RGf/vSnY/78+bFjx473PTwAMPGVFAqFQjE7zJkzJy655JJYu3bt4NrMmTPjmmuuiba2tlE9xic+8YlYuHBhfPOb3xzx3/v7+6O/v3/wfl9fX9TU1ERvb29UVlYWM+578kN5AJzofn3H1cfkcfv6+qKqquo9/34XdWTk4MGD0dHREU1NTUPWm5qaYtu2baN6jMOHD8f+/fvj9NNPP+I2bW1tUVVVNXirqakpZkwAYAIpKkZ6enpiYGAgqqurh6xXV1fH3r17R/UYd955Zxw4cCAWLFhwxG1WrlwZvb29g7c9e/YUMyYAMIGUjmWnkpKSIfcLhcKwtZH88Ic/jG9961vx5JNPxplnnnnE7crLy6O8vHwsowEAE0xRMTJ16tSYNGnSsKMg3d3dw46W/F8bN26M66+/Pn70ox/FZz7zmeInBQCOS0WdpikrK4u6urpob28fst7e3h6NjY1H3O+HP/xhfOlLX4of/OAHcfXVx+ZDMgDAxFT0aZrW1ta47rrror6+PhoaGmLdunXR2dkZLS0tEfH25z1ef/312LBhQ0S8HSKLFi2K73znO/Gnf/qng0dVTjnllKiqqjqKLwUAmIiKjpGFCxfGvn37YvXq1dHV1RWzZs2KTZs2xYwZMyIioqura8g1R773ve/FoUOHYsmSJbFkyZLB9cWLF8fDDz/8/l8BADChFX2dkQyj/Z7yWLjOCAAnugl1nREAgKNNjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBqTDGyZs2aqK2tjYqKiqirq4utW7e+6/ZbtmyJurq6qKioiI9+9KNx//33j2lYAOD4U3SMbNy4MZYtWxarVq2KHTt2xLx586K5uTk6OztH3P61116Lq666KubNmxc7duyIW265JZYuXRqPPfbY+x4eAJj4SgqFQqGYHebMmROXXHJJrF27dnBt5syZcc0110RbW9uw7b/xjW/EU089Fbt27Rpca2lpiX/913+NF198cVTP2dfXF1VVVdHb2xuVlZXFjPuezl3x9FF9PACYaH59x9XH5HFH+/e7tJgHPXjwYHR0dMSKFSuGrDc1NcW2bdtG3OfFF1+MpqamIWuf+9zn4sEHH4w//OEPcfLJJw/bp7+/P/r7+wfv9/b2RsTbL+poO9z/+6P+mAAwkRyLv6///+O+13GPomKkp6cnBgYGorq6esh6dXV17N27d8R99u7dO+L2hw4dip6enpg2bdqwfdra2uK2224btl5TU1PMuADAKFTdfWwff//+/VFVVXXEfy8qRt5RUlIy5H6hUBi29l7bj7T+jpUrV0Zra+vg/cOHD8fvfve7mDJlyrs+T7H6+vqipqYm9uzZc9RP/xyPvF+j570aPe/V6HmvRs97NXrH8r0qFAqxf//+mD59+rtuV1SMTJ06NSZNmjTsKEh3d/ewox/vOOuss0bcvrS0NKZMmTLiPuXl5VFeXj5k7bTTTitm1KJUVlb6L2sRvF+j570aPe/V6HmvRs97NXrH6r16tyMi7yjq2zRlZWVRV1cX7e3tQ9bb29ujsbFxxH0aGhqGbb958+aor68f8fMiAMCJpeiv9ra2tsYDDzwQ69evj127dsXy5cujs7MzWlpaIuLtUyyLFi0a3L6lpSV+85vfRGtra+zatSvWr18fDz74YNx8881H71UAABNW0Z8ZWbhwYezbty9Wr14dXV1dMWvWrNi0aVPMmDEjIiK6urqGXHOktrY2Nm3aFMuXL4/77rsvpk+fHvfcc0984QtfOHqvYozKy8vj1ltvHXZKiJF5v0bPezV63qvR816Nnvdq9MbDe1X0dUYAAI4mv00DAKQSIwBAKjECAKQSIwBAqhM6RtasWRO1tbVRUVERdXV1sXXr1uyRxqXnn38+5s+fH9OnT4+SkpL48Y9/nD3SuNTW1hZ/8id/EpMnT44zzzwzrrnmmvj3f//37LHGpbVr18ZFF100eJGlhoaG+OlPf5o91oTQ1tYWJSUlsWzZsuxRxqVvfetbUVJSMuR21llnZY81br3++uvxV3/1VzFlypT40Ic+FBdffHF0dHR84HOcsDGycePGWLZsWaxatSp27NgR8+bNi+bm5iFfS+ZtBw4ciNmzZ8e9996bPcq4tmXLlliyZEn84he/iPb29jh06FA0NTXFgQMHskcbd84+++y44447Yvv27bF9+/a44oor4vOf/3y8/PLL2aONa7/85S9j3bp1cdFFF2WPMq594hOfiK6ursHbSy+9lD3SuPRf//VfMXfu3Dj55JPjpz/9abzyyitx5513HtMrnh9R4QR16aWXFlpaWoasffzjHy+sWLEiaaKJISIKTzzxRPYYE0J3d3chIgpbtmzJHmVC+KM/+qPCAw88kD3GuLV///7C+eefX2hvby/82Z/9WeFrX/ta9kjj0q233lqYPXt29hgTwje+8Y3CZZddlj1GoVAoFE7IIyMHDx6Mjo6OaGpqGrLe1NQU27ZtS5qK401vb29ERJx++unJk4xvAwMD8eijj8aBAweioaEhe5xxa8mSJXH11VfHZz7zmexRxr1XX301pk+fHrW1tfHFL34xdu/enT3SuPTUU09FfX19/MVf/EWceeaZ8alPfSq+//3vp8xyQsZIT09PDAwMDPtxv+rq6mE/6gdjUSgUorW1NS677LKYNWtW9jjj0ksvvRQf/vCHo7y8PFpaWuKJJ56IP/7jP84ea1x69NFH41/+5V+ira0te5Rxb86cObFhw4Z49tln4/vf/37s3bs3GhsbY9++fdmjjTu7d++OtWvXxvnnnx/PPvtstLS0xNKlS2PDhg0f+CxFXw7+eFJSUjLkfqFQGLYGY3HjjTfGv/3bv8ULL7yQPcq4deGFF8bOnTvjv//7v+Oxxx6LxYsXx5YtWwTJ/7Fnz5742te+Fps3b46Kiorscca95ubmwf/8yU9+MhoaGuK8886Lf/iHf4jW1tbEycafw4cPR319fXz729+OiIhPfepT8fLLL8fatWuH/MbcB+GEPDIyderUmDRp0rCjIN3d3cOOlkCxbrrppnjqqafiueeei7PPPjt7nHGrrKwsPvaxj0V9fX20tbXF7Nmz4zvf+U72WONOR0dHdHd3R11dXZSWlkZpaWls2bIl7rnnnigtLY2BgYHsEce1U089NT75yU/Gq6++mj3KuDNt2rRh8T9z5syUL3KckDFSVlYWdXV10d7ePmS9vb09Ghsbk6ZioisUCnHjjTfG448/Hj/72c+itrY2e6QJpVAoRH9/f/YY486VV14ZL730UuzcuXPwVl9fH3/5l38ZO3fujEmTJmWPOK719/fHrl27Ytq0admjjDtz584ddvmBX/3qV4M/fPtBOmFP07S2tsZ1110X9fX10dDQEOvWrYvOzs5oaWnJHm3ceeutt+I//uM/Bu+/9tprsXPnzjj99NPjnHPOSZxsfFmyZEn84Ac/iCeffDImT548eOStqqoqTjnllOTpxpdbbrklmpubo6amJvbv3x+PPvpo/NM//VM888wz2aONO5MnTx72uaNTTz01pkyZ4vNII7j55ptj/vz5cc4550R3d3fcfvvt0dfXF4sXL84ebdxZvnx5NDY2xre//e1YsGBB/PM//3OsW7cu1q1b98EPk/tlnlz33XdfYcaMGYWysrLCJZdc4iuYR/Dcc88VImLYbfHixdmjjSsjvUcRUXjooYeyRxt3vvKVrwz+b++MM84oXHnllYXNmzdnjzVh+GrvkS1cuLAwbdq0wsknn1yYPn164c///M8LL7/8cvZY49ZPfvKTwqxZswrl5eWFj3/844V169alzFFSKBQKH3wCAQC87YT8zAgAMH6IEQAglRgBAFKJEQAglRgBAFKJEQAglRgBAFKJEQAglRgBUlx++eWxbNmy7DGAcUCMAACpxAgAkEqMAOPCM888E1VVVbFhw4bsUYAPmBgB0j366KOxYMGC2LBhQyxatCh7HOADJkaAVGvWrImWlpZ48skn4/Of/3z2OECC0uwBgBPXY489Fm+++Wa88MILcemll2aPAyRxZARIc/HFF8cZZ5wRDz30UBQKhexxgCRiBEhz3nnnxXPPPRdPPvlk3HTTTdnjAEmcpgFSXXDBBfHcc8/F5ZdfHqWlpXH33XdnjwR8wMQIkO7CCy+Mn/3sZ3H55ZfHpEmT4s4778weCfgAlRScqAUAEvnMCACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQ6n8BMh3HN5rbMX8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.hist(data[:, 0], bins=100, cumulative=True, density = True, label=\"kumuliert\")\n",
     "plt.xlabel(\"k\")\n",
@@ -1290,69 +1165,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "76d92d18-1d77-40d2-a910-592183635d3b",
    "metadata": {
     "slideshow": {
-     "slide_type": "notes"
+     "slide_type": "skip"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean [1.56535948 1.26470588]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"mean\", np.mean(data, axis=0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "5f0f34b4-5bbe-439b-8b4e-fbb05794b790",
    "metadata": {
     "slideshow": {
-     "slide_type": "notes"
+     "slide_type": "skip"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "variance [1.85357128 1.27306805]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"variance\", np.var(data, axis=0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "70a1920f-beda-4154-ad77-22a9ffe2e39f",
    "metadata": {
     "slideshow": {
-     "slide_type": "notes"
+     "slide_type": "skip"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "standard deviation: [1.36145925 1.12830317]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"standard deviation:\", np.std(data, axis=0))"
    ]
@@ -1371,31 +1222,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "id": "bf609514",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.hist2d(data[:,0], data[:,1], bins=np.arange(-0.5,6.1,1))\n",
     ";"
@@ -1411,46 +1241,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "751f9384",
    "metadata": {
     "slideshow": {
-     "slide_type": "notes"
+     "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 1.85964856 -0.1927676 ]\n",
-      " [-0.1927676   1.27724204]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(np.cov(data, rowvar=False))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "abda1c9f",
    "metadata": {
     "slideshow": {
-     "slide_type": "notes"
+     "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 1.         -0.12507831]\n",
-      " [-0.12507831  1.        ]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(np.corrcoef(data, rowvar=False))"
    ]
@@ -1495,25 +1307,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "id": "f88ff1a1",
    "metadata": {
     "slideshow": {
-     "slide_type": "notes"
+     "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.136890603235832\n",
-      "[[2.75135541]]\n",
-      "2.7423640480157205\n",
-      "3.510914605493613\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "A = np.array([[1, 1]])\n",
     "V = np.cov(data, rowvar=False)\n",
@@ -1567,18 +1368,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "id": "b424b5b0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.22733602246716966\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -1590,42 +1383,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "id": "785734fb",
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.24660218 0.23393333 0.78231662 ... 0.48546766 0.51880662 0.3569082 ]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "u = rng.random(100000)\n",
     "print(u)\n",
@@ -1634,136 +1399,6 @@
     ";"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "fd133d10",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "source": [
-    "# What is meant with error/uncertainty on a measured quantity?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d369a48b",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "source": [
-    "If we quote $a = 1 \\pm 0.5$, we usually mean that the probability for the *true* value of $a$ is Gaussian $G(a, \\mu, \\sigma)$ distributed with $\\mu = 1$ and $\\sigma = 0.5$.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a56aa4e8",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "source": [
-    "# How often can/should the measurement be outside one sigma?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf26407a",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "source": [
-    "Let's use pseudo-experiments/Monte Carlo:\n",
-    "\n",
-    " * generate 10.000 Gaussian distributed measurements\n",
-    " * count how ofter they differ by more than one sigma\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ab4af7a",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "source": [
-    "Relatively easy with *scipy* and *numpy*:\n",
-    " * use [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)\n",
-    " * use [scipy.stats.norm](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html) class\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d0adc358",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.62483765 1.14886134 0.26443731 ... 1.71203625 1.6365167  1.52227868]\n",
-      "[False False  True ...  True  True  True]\n",
-      "fraction outside one sigma: 0.3178\n"
-     ]
-    }
-   ],
-   "source": [
-    "import scipy.stats as stats\n",
-    "import numpy as np\n",
-    "\n",
-    "pseudo_a = stats.norm.rvs(1, 0.5, 10000)\n",
-    "print(pseudo_a)\n",
-    "is_outside = abs(pseudo_a - 1) > 0.5\n",
-    "print(is_outside)\n",
-    "print(\"fraction outside one sigma:\", sum(is_outside)/len(pseudo_a)) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa7c75ae",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "source": [
-    "# Why is it a Gaussian"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf5c16d9",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    },
-    "tags": []
-   },
-   "source": [
-    "Central limit theorem:\n",
-    "\n",
-    "\"let $X_{1},X_{2},\\dots ,X_{n}$ denote a statistical sample of size $n$  from a population with expected value (average) $\\mu$ and finite positive variance $\\sigma ^{2}$, and let $\\bar {X_{n}}$ denote the sample mean (which is itself a random variable). Then the limit as $n\\to \\infty$ of the distribution of $\\frac {({\\bar {X}}_{n}-\\mu )}{\\frac {\\sigma }{\\sqrt {n}}}$, is a normal distribution with mean 0  and variance 1.\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,