diff --git a/doc/HandWrittenNotes/2025/FYSSTKweek34.pdf b/doc/HandWrittenNotes/2025/FYSSTKweek34.pdf new file mode 100644 index 000000000..53867e1ba Binary files /dev/null and b/doc/HandWrittenNotes/2025/FYSSTKweek34.pdf differ diff --git a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png index 26c0df89e..1f8faa57a 100644 Binary files a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png and b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png differ diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb index 899bd8e9f..1bad18d69 100644 --- a/doc/pub/week34/ipynb/week34.ipynb +++ b/doc/pub/week34/ipynb/week34.ipynb @@ -954,7 +954,10 @@ "id": "66625f81", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -977,7 +980,10 @@ "id": "7d44772f", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1003,7 +1009,10 @@ "id": "bd150398", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1029,7 +1038,10 @@ "id": "de9d1b88", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1060,7 +1072,10 @@ "id": "eadd4613", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1089,7 +1104,10 @@ "id": "f1153763", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1114,7 +1132,10 @@ "id": "3417f7b5", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1139,7 +1160,10 @@ "id": "e9055b4f", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1168,7 +1192,10 @@ "id": "7f400b7f", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1193,7 +1220,10 @@ "id": "16b1c27a", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1219,7 +1249,10 @@ "id": "b10affa1", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1245,7 +1278,10 @@ "id": "35f1236f", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1272,7 +1308,10 @@ "id": "821bc62d", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1299,7 +1338,10 @@ "id": "d821d3ff", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1405,7 +1447,10 @@ "id": "74dd353c", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1432,7 +1477,10 @@ "id": "f07645f8", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1478,13 +1526,88 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "id": "e428bcdc", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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866\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _open_file_or_url(fname) \u001b[38;5;28;01mas\u001b[39;00m fd:\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/contextlib.py:119\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/matplotlib/__init__.py:843\u001b[0m, in \u001b[0;36m_open_file_or_url\u001b[0;34m(fname)\u001b[0m\n\u001b[1;32m 842\u001b[0m fname \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexpanduser(fname)\n\u001b[0;32m--> 843\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 844\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m f\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'seaborn'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 10\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mdescribe())\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpylab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plt, mpl\n\u001b[0;32m---> 10\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mseaborn\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfont.family\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mserif\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 13\u001b[0m df\u001b[38;5;241m.\u001b[39mcumsum()\u001b[38;5;241m.\u001b[39mplot(lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.0\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/matplotlib/style/core.py:139\u001b[0m, in \u001b[0;36muse\u001b[0;34m(style)\u001b[0m\n\u001b[1;32m 137\u001b[0m style \u001b[38;5;241m=\u001b[39m _rc_params_in_file(style)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstyle\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m is not a valid package style, path of style \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile, URL of style file, or library style name (library \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyles are listed in `style.available`)\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 143\u001b[0m filtered \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m style: \u001b[38;5;66;03m# don't trigger RcParams.__getitem__('backend')\u001b[39;00m\n", + "\u001b[0;31mOSError\u001b[0m: 'seaborn' is not a valid package style, path of style file, URL of style file, or library style name (library styles are listed in `style.available`)" + ] + } + ], "source": [ "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n", "df.index = np.arange(10)\n", @@ -1668,7 +2264,10 @@ "id": "0438c751", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1783,7 +2382,10 @@ "id": "8c7472f2", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1937,7 +2539,10 @@ "id": "0de5719f", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -1986,7 +2591,10 @@ "id": "cde68f17", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -2407,11 +3015,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "id": "cd57c0cd", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -2472,7 +3083,10 @@ "id": "309536df", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -2503,11 +3117,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "id": "1c60fe5f", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -2552,13 +3169,38 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "id": "85ffeaf7", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N Z A Element Ebinding\n", + "A \n", + "4 0 1 3 4 Li 1.153760\n", + "5 2 3 2 5 He 5.512132\n", + "6 7 3 3 6 Li 5.332331\n", + "7 12 4 3 7 Li 5.606439\n", + "8 17 4 4 8 Be 7.062435\n", + "... ... ... ... ... ...\n", + "264 3297 156 108 264 Hs 7.298375\n", + "265 3303 157 108 265 Hs 7.296247\n", + "266 3310 158 108 266 Hs 7.298273\n", + "269 3331 159 110 269 Ds 7.250154\n", + "270 3337 160 110 270 Ds 7.253775\n", + "\n", + "[264 rows x 5 columns]\n" + ] + } + ], "source": [ "A = Masses['A']\n", "Z = Masses['Z']\n", @@ -2581,11 +3223,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "id": "365fbba9", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -2610,11 +3255,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "id": "60ba302b", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -2635,13 +3283,38 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "id": "a5cba204", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean squared error: 0.02\n", + "Variance score: 0.95\n", + "Mean absolute error: 0.05\n", + "[ 0.00000000e+00 -2.96611194e-02 2.01719003e-01 1.08078025e+01\n", + " -4.03097597e+01] 5.294399745619595\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n", @@ -2684,7 +3357,10 @@ "id": "66c27da6", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3233,7 +3909,10 @@ "id": "297415bf", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3773,7 +4452,10 @@ "id": "ce0d14e1", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3799,7 +4481,10 @@ "id": "d1b21aae", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3823,7 +4508,10 @@ "id": "98630c99", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3860,7 +4548,10 @@ "id": "2d8cd3da", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3884,7 +4575,10 @@ "id": "f8cc2dd0", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3907,7 +4601,10 @@ "id": "acdf8d3d", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -3934,7 +4631,10 @@ "id": "fcf52c3c", "metadata": { "collapsed": false, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -4411,7 +5111,25 @@ ] } ], - "metadata": {}, + "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": "3.9.15" + } + }, "nbformat": 4, "nbformat_minor": 5 }