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epassaro/cross_sections_example.ipynb

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cross_sections_example.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "e0c36288",
"metadata": {},
"source": [
"# `CMFGENReader` with cross-sections"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e16567d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ChiantiPy version 0.8.4 \n",
" found PyQt5 widgets\n",
" using PyQt5 widgets\n"
]
}
],
"source": [
"import astropy.units as u\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from carsus.io.cmfgen import CMFGENReader"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "09f08ade",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for H 0. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for He 0. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for He 1. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for C 0. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for C 1. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for C 2. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for C 3. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for N 0. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for Si 0. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for Si 1. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for S 0. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for S 1. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Configuration schema found for Co 2. (\u001b[1mbase.py\u001b[0m:598)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for H 0. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for He 0. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for He 1. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for C 0. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for C 1. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for C 2. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for C 3. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Leibowitz's cross-section type 4 not implemented yet. (\u001b[1mbase.py\u001b[0m:760)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Leibowitz's cross-section type 4 not implemented yet. (\u001b[1mbase.py\u001b[0m:760)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Leibowitz's cross-section type 4 not implemented yet. (\u001b[1mbase.py\u001b[0m:760)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Leibowitz's cross-section type 4 not implemented yet. (\u001b[1mbase.py\u001b[0m:760)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Leibowitz's cross-section type 4 not implemented yet. (\u001b[1mbase.py\u001b[0m:760)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Leibowitz's cross-section type 4 not implemented yet. (\u001b[1mbase.py\u001b[0m:760)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for N 0. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for Si 0. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for Si 1. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for S 0. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for S 1. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][ \u001b[1;37mINFO\u001b[0m] - Loading atomic data for Co 2. (\u001b[1mbase.py\u001b[0m:796)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d4(4G)4s_4p2_q6Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_r4De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_q2Pe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)8p_q4So'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_r4Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)8p_q4Po'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)7s_q2De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q4De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_q2Se'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)8p_q4Do'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d4(4P)4s_4p2_q6Pe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)8s_q4Pe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d4(4G)4s_4p2_r6Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)8s_q4Fe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_r4De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)8p_q2Do'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q4Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q4Fe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q4He'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_q2Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_r2Se'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)8p_q2So'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)8p_q2Po'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q2Fe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q2Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q2He'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)8s_q2Fe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q2De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1S)5g_q2Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d4(4G)4s_4p2_q4Ge'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_q2Fe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d5(2S)4s_4p_r2Po'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3H)8p_q4Ho'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3H)8p_q4Io'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3H)8p_q4Go'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3H)8p_r4Ho'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_q4Pe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3H)8p_q2Go'. (\u001b[1mbase.py\u001b[0m:662)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6d_r4Pe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d4(4G)4s_4p2_q4De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_q4Pe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3F)7d_r4Fe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d4(4P)4s_4p2_q6De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3H)8p_q2Ho'. (\u001b[1mbase.py\u001b[0m:662)\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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},
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1G)6d_q2Le'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1G)6d_t2De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6f_q2Ho'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1G)6d_q2De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6f_q2Po'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1G)6d_q2Pe'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1G)6d_q2Se'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3P)9d_q4De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3D)6f_q2Go'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d4(4G)4s_4p2_q2He'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1I)6f_q2Fo'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1I)6d_q2De'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(3H)8f_q4Lo'. (\u001b[1mbase.py\u001b[0m:662)\n",
"[\u001b[1m carsus.io.cmfgen.base\u001b[0m][\u001b[1;33mWARNING\u001b[0m] - Level not found: '3d6(1I)6d_q2Fe'. (\u001b[1mbase.py\u001b[0m:662)\n"
]
}
],
"source": [
"atomic_path = '/home/epassaro/Desktop/tardis-sn/atomic_data/CMFGEN/atomic/'\n",
"reader = CMFGENReader.from_config('H-Co', atomic_path, priority=20, cross_sections=True)"
]
},
{
"cell_type": "markdown",
"id": "fd733300",
"metadata": {},
"source": [
"The number of cross-sections must match the number of energy levels **without splitting**."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7d978ed6",
"metadata": {},
"outputs": [],
"source": [
"n_levels = len(reader.levels.loc[(6,0)].index.unique())\n",
"n_cross_sections = len(reader.cross_sections.loc[(6, 0)].index.unique())\n",
"\n",
"assert n_levels == n_cross_sections"
]
},
{
"cell_type": "markdown",
"id": "6ee73701",
"metadata": {},
"source": [
"For splitted levels the unique cross-section for that specific level is weighted."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "52032f70",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='energy'>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = reader.cross_sections.loc[(6,0,0)].plot(x='energy', y='sigma', legend=False, xlim=(0.5,3))\n",
"reader.cross_sections.loc[(6,0,1)].plot(x='energy', y='sigma', legend=False, ax=ax)\n",
"reader.cross_sections.loc[(6,0,2)].plot(x='energy', y='sigma', legend=False, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1b4c21d9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>energy</th>\n",
" <th>sigma</th>\n",
" </tr>\n",
" <tr>\n",
" <th>atomic_number</th>\n",
" <th>ion_charge</th>\n",
" <th>level_index</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"11\" valign=\"top\">1</th>\n",
" <th rowspan=\"11\" valign=\"top\">0</th>\n",
" <th>0</th>\n",
" <td>0.999466</td>\n",
" <td>6.310025e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.099413</td>\n",
" <td>4.887967e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.209354</td>\n",
" <td>3.777104e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.330289</td>\n",
" <td>2.911504e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.463318</td>\n",
" <td>2.238792e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>623367.118235</td>\n",
" <td>2.849569e-19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>685703.829809</td>\n",
" <td>2.041667e-19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>754274.212515</td>\n",
" <td>1.462807e-19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>829701.633464</td>\n",
" <td>1.048059e-19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>912671.796478</td>\n",
" <td>7.508979e-20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>145 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" energy sigma\n",
"atomic_number ion_charge level_index \n",
"1 0 0 0.999466 6.310025e+00\n",
" 0 1.099413 4.887967e+00\n",
" 0 1.209354 3.777104e+00\n",
" 0 1.330289 2.911504e+00\n",
" 0 1.463318 2.238792e+00\n",
"... ... ...\n",
" 0 623367.118235 2.849569e-19\n",
" 0 685703.829809 2.041667e-19\n",
" 0 754274.212515 1.462807e-19\n",
" 0 829701.633464 1.048059e-19\n",
" 0 912671.796478 7.508979e-20\n",
"\n",
"[145 rows x 2 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reader.cross_sections.loc[1,0,0]"
]
},
{
"cell_type": "markdown",
"id": "ea393ea4",
"metadata": {},
"source": [
"Energy units are in `Ry` and cross-sections in `Mb` and should be the same for all cross-sections types. This choice was inherited from the `artisatomic` script. The unit conversion to `Hz` and `cm^2` will take place in the output module to keep the comparison against `artisatomic` simple."
]
},
{
"cell_type": "markdown",
"id": "8fe5951c",
"metadata": {},
"source": [
"The cross-section obtained for the first energy level of `H I` looks similar to the figure from [this publication](https://www.researchgate.net/figure/The-photoionization-cross-section-of-the-hydrogen-atom-The-exact-results-were-obtained_fig2_335880839)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "14b299b3",
"metadata": {},
"outputs": [],
"source": [
"reader.cross_sections['energy'] = u.Quantity(reader.cross_sections['energy'], 'Ry').to('eV')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "24304609",
"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": [
"reader.cross_sections.loc[1,0,0].plot(x='energy', y='sigma', xlim=(13, 50), ylim=(0, 6.5), legend=False)\n",
"\n",
"plt.xlabel('Photon energy [eV]')\n",
"plt.ylabel('Cross-section [Mb]')\n",
"plt.grid(which='both', linestyle='dotted')"
]
},
{
"cell_type": "markdown",
"id": "5997cb73",
"metadata": {},
"source": [
"The first level of `He I` looks [a bit different](https://www.researchgate.net/figure/Photoionization-cross-section-of-the-helium-atom-The-experimental-results-are-taken-from_fig3_335880839)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "eea4252b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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OUE+7qKdd1NNdui34ReQf3f3eGPMVuzre4saTJrB+y07uWLCGMQPzOHvmSLeVFEVR9plEjerziHbiPgV8CKRmiawYqVyIpSeICL/96nRqtwW5+bmVDO+Xy7D03gkkt6/0VsC7fUU97aKedvGLZ7IkalsZBvwImEZ0Na1TgCZjzDvGmHdsy6Snp9s+5T6TnZHOA5fOYVRhLt+av5TNbf5Yiq1///5uKzhCPe2innbxi2eydFvwG2PajTGvG2MuA44iOpJnoYjckAqZcDicitPuM/3zsnjsm0eQm5XOVU+WUNvc+6ElkqWhocFtBUeop13U0y5+8UyWhL2pIpItIucBjwPXAX8AXkyFTFZWVipOa4VRA/KY/80jaIsI33j4I7Y6jObpFmPG+CPshHraRT3t4hfPZEk0jn8+sIToAiq/NMYcboz5L2NMXSpkvN6eNmVYAT8+rpC65iBXPLqUXW3e/IYCsGZNd9GyvYN62kU97eIXz2RJFJY5AuyMfey4owDGGFNgU8bNIG3J8GbZJq5+fBnHTRzMg5fN1VDOiqK4hvWQDcaYNGNM39iroMOrr+1CH9wJy5wsy5Yt49RDh/Gbc6fzzprN3PL8SiKR1K1i1lP8Ek5WPe2innbxi2eypHTpxWTxS40/zp/+Xcnv31zDlceO4ydnHoJISke7KoqifI5ULL1Y7OCiCfdxil9q/HGuO2ECVxwzlofeXcddb1W6aPV5/FJTUU+7qKdd/OKZLIna+INAdyWaAP2MMVa6vv1W4weIRAy3vVjKM0UbuPWMKVz9xfFuKymKcgCRirDMU4Avd/M6Czg6edXOcXMhFqeUlJTs8TktTfjNedP5yowR/O61Ch57v9odsb3Y29OrqKdd1NMufvFMFk+18bu9EIsTwuFwp+Gjd7dHuPaJYhaUN3D7+YdxwdzRLth9RleeXkM97aKedvGDZyoXYukRIlItIqUiskJEErbhhEKhVOpYYe3azpchyExP40//MYvjJg7i1hdW8s+Sjb1stiddeXoN9bSLetrFL57J0hsD0E8wxsx08kTy8szdOKNGjeryd9G4PnOZO7aQ7z2zgtdXWV+P3jHdeXoJ9bSLetrFL57J4qmZR16N1dORpqambn+fm5XOQ5fN5bBR/bj+yWJeX2V9vRpHJPL0CuppF/W0i188k8VRwS8i54lIpYhsF5EWEdkhIi0ODjXAmyKyTESu6uLcV4lIkYgUNTU10dTURH19PXV1dTQ3N1NVVUUwGKS8vJxIJEJxcXT0aLwvoLi4mEgkQnl5OcFgkKqqKpqbm6mrq6O+vp6mpiaqq6sJBAJUVFQQDoc/7bCJnyP+XlpaSigUorKykpaWFmpqamhsbKSxsZGamhpaWlrYtm0boVCI0tLSTs9RUlJCbobw42MHMG1EX659ophnlqzp9TRt3LjRcZoqKysTpikcDlNRUUEgEKC6utra3ykYDKbk72Q7Tfn5+a7fe07SlJubm5K/k+007dq1y/V7z0ma4v9Hbt57idLUExx17orIWuDLxpiPkzq5yAhjzEYRGQIsAG4wxizqav8ZM2YYr/ei19fXM3z4cEf7BkJhLn/4I5Zv2MYfL57Fl6Y7O84GyXi6iXraRT3t4gfPVHbuNiRb6AMYYzbG3huBl4Ajkj2H10hmsZg+2Rk8+s0jmD2mPzc8tZx/rey9Zh+vLWrTFeppF/W0i188k8VpwV8kIs+IyMWxZp/zYqGau0RE8kWkb/xn4FRgVbcyHlpztyvy8vKS2r9PdgaPXBEt/G98ejmvrOyd0T7JerqFetpFPe3iF89kcVrSFgC7iBbeHSdvdcdQ4F0RKQE+Av5ljHm9uwP80Lm7devWpI/pk53Bo1ccwZwxA7jp6RW8WFybArM96YmnG6inXdTTLn7xTBZPTeCaPXu2iXdieJVgMEhubm6Pjt0ZCnPV34p4b+0W/uvsQ7l03li7ch3YF8/eRD3top528YNnytr4RWSUiLwkIo0i0iAiL4iI9QGubW3eXtUKYN26dT0+Nj87g4cuO5xTpg7lpy+Xce/C1E0O2RfP3kQ97aKedvGLZ7I4HdWzAHgS+Fts09eBS4wxp9iU8UOQtkgkss99EbvbI/zguRJeXrGRa48fz82nTbYe0tmGZ2+gnnZRT7v4wTOVo3oGG2MeMcaEY69HgcFJGybAD2GZV6xYsc/nyExP464LZvIfR47h3oVV/PwfZdYXc7Hh2Ruop13U0y5+8UwWpzX+t4BHgadimy4GrjDGnGRTxg81fpsYY/jdaxXcv+gTzps9ktu/ehgZuoyjoihJkMoa/zeBC4BNQD1wfmybVfxQ47cZPVREuPWMKfzg1Em8WFzHd/62zNoC7l6PchpHPe2innbxi2eyeGpUz4FW4+/I4x+s52cvr+KwUf15+PLDKcz3fsA6RVHcJxVLL94Se/+jiPxh79e+yHaGH2r8qRpu+vWjDuK+r8/h4/oWzr9vCRu27lteeH1YbBz1tIt62sUvnsmSaOnFLxtj/ikil3X2e2PMfJsyfqjxp7qXv6h6K1fOLyIrI41HLj+caSP79eg8fhiNAOppG/W0ix88rdf4jTH/jP24yxgzv+OL6Exeq7S2tto+pXUqKipSev65Ywt54Zp5ZKWnceH977O4cnOPzpNqT1uop13U0y5+8UwWp4+y2xxu2yf8sBDLuHHjUn6NCUP68sI1RzO6MI8rHlnKS8uTD/HQG542UE+7qKdd/OKZLIna+M8QkT8CI/dq338UsB5YZ/fu3bZPaZ14fO5UM6xfDs98Zx5zxw7ge8+UcOeCNSTTEd9bnvuKetpFPe3iF89kSVTj3wgUAa3Asg6vfwCn2Zbx+qLGAIWFhb12rX65mTz2zSP52pxR/OH/KrnhqeW07m53dGxveu4L6mkX9bSLXzyTpduS1hhTApSIyEvATmNMO4CIpAPZtmX8EPt6165dDBgwoNeul5WRxu3nH8b4IX34n9crqG0O8sA35jCkb063x/W2Z09RT7uop1384pksTtv43wQ6hqjLBd6yr+N93OjhFxGu/uJ47rtkDqs37eDcPy/h4/ruV770+kiEOOppF/W0i188k8VpqnKMMYH4h9jP1lcosB2oLBVkZma6du3Tpw3juavnEY5EOP++Jfy7oqHLfd30TAb1tIt62sUvnsnitODfKSKz4x9EZA4QtC3jh6aeQCCQeKcUMm1kP16+7ljGDsrnW/OL+OuiTzrt9HXb0ynqaRf1tItfPJPFacH/XeA5EVksIouBZ4Drbcv4oXN30KBBbiswrF8Oz109j1OnDuO/X/2Ym55eQbBtz05fL3g6QT3top528Ytnsjgq+I0xS4EpwDXAtcAhxhjr0Yv8sBBLbW3ql010Ql5WBvd9fTY3nzaZf67cyLn3vkfNls/m1HnFMxHqaRf1tItfPJPFaVjmPOD7wEHGmG+LyERgsjHmFZsyc+bMMV6PhhcOhz33zWTh6kZufGo5IsIfL57FFyYN9qRnZ6inXdTTLn7wTGVY5keANmBe7HMt8OtkLuQEP4RsKCsrc1vhcxw/eQj/vOFYhvfL4fJHPuK+hVWsWrXKbS1HeDE/O0M97aKe7uK0xl9kjJkrIsuNMbNi20qMMTNsyvghSJuX2dUW5ubnV/KvlfWcOX04t59/GPnZ3q6tKIqyb6Syxt8mIrmAiV1oPBByKJUuIstFJGGzkB/CMnu5KSovK4M/XTyL286Ywmul9Zz95/dY07DDba1u8XJ+dkQ97aKe7uK0xn8K8BNgKtHJXMcAlxtjFjo49vvAXKDAGHNWd/tqjd8eS9Y2cePTKwiEdvPrc6Zz/pxRbispipICUlbjN8YsAM4DLie67u5ch4X+KOBM4EEn19Eavz2yt6/n1RuPZebo/vzguRJufq7kc0M+vYBf8lM97aKe7uKo4BeRY4BWY8y/gP7Aj0TkIAeH3g3cAnQ5M0tErhKRIhEp2r59O01NTdTX11NXV0dzczNVVVUEg0HKy8uJRCKfrogT/4MUFxcTiUQoLy8nGAxSVVVFc3MzdXV11NfX09TURHV1NYFAgIqKCsLhMCUlJXucI/5eWlpKKBSisrKSlpYWampqaGxspLGxkZqaGlpaWigoKCAUClFaWtrpOUpKSgiHw1RUVBAIBKiurnYlTQBDCnK49ch8rvnCOJ5fVstZf1jEohVrPpemyspK19I0bNiwlPydbKdp1qxZrt97TtI0Y8YM1+89J2kaPHiw6/eekzTFcfPeS5SmHmGMSfgCVgICzABKgJuAdxIccxZwb+zn44FXEl1n6tSpxuusXLnSbQVH7O35zupGM+tXb5pDfvqaeWHZBpesPo9f89OrqKdd/OAJFBkH5XjHl9M2/mJjzGwR+RlQZ4x5KL6tm2N+C1xKNG5/DlAAvGiM+XpXx/hhHH8oFCI723pgUut05rlpeys3PrWcj6q3cvbMEfzXOdMoyHE3Fomf89OLqKdd/OCZylE9O0TkNuDrwL9iYZm7LTGMMbcZY0YZY8YCFwH/7q7QB3/M3K2pqXFbwRGdeQ7rl8OT3z6S7508iVdW1vOlexazbP1WF+w+w8/56UXU0y5+8UwWpwX/hUSHb15pjNkEjAT+17aM12fIAQwdOtRtBUd05ZmRnsZNJ0/k2e9E5+JdcP8H3PNWJeF2dwLk+T0/vYZ62sUvnsnidFTPJmPMncaYxSJyljGmxhjzmNOLGGMWmgRDOQHa27036mRvtm3b5raCIxJ5zjloAK/edBxfPmw4d721hose+IANW3t/VNX+kp9eQT3t4hfPZOnJKgO/sm4Rww+LHuTkdL/ylVdw4lmQk8ndF83i7gtnUrFpB1+6ZzHPFW1Iam3ffWV/yk8voJ528YtnsvSkpPX+ailKUpwzaySv3XQchwwv4ObnV/Ltx5bRuMP7cZMURekZPSn4v2PdIoYfFmLxQyA5SN5zdGEeT191FD858xAWVW7mtLsW8a+V9Smy+4z9NT/dQj3t4hfPZHE6getrItI39vE0EXmx44pctkhPT7d9Suv079/fbQVH9MQzLU341nEH8+qNxzKmMI/rnizmhqeW07wzdaOt9uf8dAP1tItfPJPFaY3/p8aYHSJyLHAKMB+4z7ZMOBy2fUrrNDR0vc6tl9gXzwlD+vLCNUfzg1Mn8fqqek69exELylOT7gMhP3sT9bSLXzyTxWnBHx9ucybwF2PMy0CWbZmsLOuntM6YMWPcVnDEvnpmpKdx/YkT+ft1xzAwP4tvP1bEdU8Ws3mHo6CsjjlQ8rO3UE+7+MUzWZwW/HUicj9wAfCqiGQncaxj/NCetmbNGrcVHGHL89AR/fjH9cfyn6dMYkFZAyff+Q7PL6u1NvLnQMvPVKOedvGLZ7Iks/Ti6UCpMaZSRIYD040xb9qU0bDM3mZt4w5ufaGUovXNHDdxEL85dzqjC/Pc1lKUA5pUhmwYDvwrVugfD3wN+Cg5vcRoWGZ7pMJzwpC+PPudefzX2YdSvL6ZU+9axIOLP9mnWb8Hcn6mAvW0i188k8VpjX8F0cVUxgJvAP8gutj6l2zKaI3fP9RtC/KTl0p5e/VmDh1RwK/PmcasMQPc1lKUA45U1vgjxpgw0cVY7jbGfI/otwCraI3fHqn2HNk/l4cvP5w//ccsmgIhzrtvCT96qZRtu5Ib+qn5aRf1tItfPJPFaY3/Q6KLqvwY+LIxZp2IrDLGTLMpozV+f7KjdTd3v1XJo0uq6ZebyW1nTOH8OaMQ0UneipJqUlnjvwKYB/x3rNAfBzyerGAigsGg7VNaZ++VebxKb3r2zcnkp2dN5Z/XH8vYgXnc/PxKLrj/fVZvSrzQu+anXdTTLn7xTBZHNX4AEckCJsU+rjbG7LYt44eFWMLhsC/CR7vlGYkYnl9Wy29f+5iW1jCXHnUQ3zt5Ev3yOl++QfPTLuppFz94pqzGHxvJUwn8GbgXWCMiX0hWMBGhkN3JQalg7dq1bis4wi3PtDThgsNH8+//PJ4LDx/NY+9Xc/zv3+Zv71d3OvpH89Mu6mkXv3gmi9M2/mXAfxhjVsc+TwKeMsbMsSkze/ZsE19U2KsEAgH69OnjtkZCvOJZvrGFX71SxgefbGXy0L787MtTOWbCoE9/7xXPRKinXdTTHqls48+MF/oAxpg1JFh6sSf4IVZPU1OT2wqO8Irn1BEFPPXto/jL12ezsy3MJQ9+yHf+VkTNlugILq94JkI97aKe7uK08WqZiDwE/C32+RLAemO8HxZi8frTP46XPEWE06cN5/jJQ3jo3XX8+e21nHznO1x29EFcdFih23qO8FJ+dod62sUvnsnitKS9GigDbgRuAspj26zSmys/9ZTdu633aacEL3rmZKZz3QkTePsHx/OVmSN48N11nPPgCh5YVEXrbm8vu+nF/OwM9bSLXzyTJWHBLyJpwLLYmrvnGWPONcbcZYzxfk9sCvDDYjHgbc+hBTn8/mszePXG4zh0WB6/ebWCk+54hxeLa4lEvPnw93J+dkQ97eIXz2RJWPAbYyJAiYikPD6pH5p68vL8EZTMD56HDC/gvoum8+S3jqQwP4vvP1vCWX98l8WVm91W+xx+yE9QT9v4xTNZkgnSViYi/yci/4i/ujtARHJE5CMRKRGRMhH5ZaKL+KFzd+vWrW4rOMJPnkdPGMTL1x3DPRfNZEdoN5c+9BGXPPgBxTXNbut9ip/y0w+op7s4Hc75xc62G2Pe6eYYAfKNMQERyQTeBW4yxnzQ1TF+GM4ZDAbJzc11WyMhfvUMhdt5/IMa7n17LVt2tnHilCF8/5RJTBvZz0VL/+anV1FPe1gfzikiE0TkGGPMOx1fgAFquzvWRAnEPmbGXt0+ZdraUre2qy3WrVvntoIj/OqZnZHOlceOY9EtJ3DL6ZNZtr6Zs/74Ltc8vow1DYlDQKQKv+anV1FPd0nU1HM30Nl/267Y77pFRNJjIZ0bgQXGmA872ecqESkSkaLm5maampqor6+nrq6O5uZmqqqqCAaDlJeXE4lEiH8jiId2KC4uJhKJUF5eTjAYpKqqiubmZurq6qivr6epqYnq6moCgQAVFRWEw+FP42/EzxF/Ly0tJRQKUVlZSUtLCzU1NTQ2NtLY2EhNTQ0tLS1kZGQQCoUoLS3t9BwlJSWEw2EqKioIBAJUV1e7kqZ4pFMnaaqsrHQtTX379u00TdnpcMyAnSz+4QlcMDWfxZVNnHbXIm56ejlvvFfc62maNGmS6/eekzRNmDDB9XvPSZry8/Ndv/ecpCn+f2T772QzTT2h26ae7iJwikipMWa6o4uI9AdeAm4wxqzqar+pU6eaniaktyguLmb27NluayRkf/Ns3tnG/Ys+4dEl62gLRzh75kiuO2E8E4b07QXL/S8/3UY97dGTpp5EBf9aY8yEZH/Xxf4/B3YaY37f1T4alllJxOYdIe5/p4onPqyhNdzOl6YN5/oTJ3DI8AK31RTFFVIRsmGpiHy7kwtdSYKZuyIyOFbTR0RygZOBiu6O0YVY7LG/eg7um81PzprKuz88gWu+OJ531mzmjHsW8635RZRs2JYaSfbf/HQL9XSXRDX+oUSbaNr4rKCfC2QB5xpjNnVz7GHAfCCd6APmWWPMr7qT0Rq/kizbdrXx6JJqHn53HS2tYb4waTDXnzCBI8b5IxSEouwr1mv8xpgGY8zRwC+B6tjrl8aYed0V+rFjVxpjZhljDjPGTEtU6IM/avxeH24a50Dx7J+XxXdPnsR7t57ILadPZlXddi64/33Ou/c9Xl+1ydpM4AMlP3sL9XQXxwux9AZ+qPFHIhFfzDA+UD13tYV5rqiWvy7+hNrmIAcPyudbxx3MebNHkpOZ7hnPVKGedvGDZyrDMvcKra2tbiskpKKi224Kz3CgeuZlZXDZ0WNZ+IPj+ePFs8jPzuBHL5Vy7P+8zZ/+XZn0YvCp8kwV6mkXv3gmi6dq/Dpz1x7qGcUYw/ufbOGBRZ+wcPVm8rLSuWDuaC4/eixjB+V7xtMW6mkXP3j6vsbvhxCoGzdudFvBEeoZRUQ4evwgHr3iCN747hc4Y9pwnvhwPSfcsZArH13K4srNjsKBa37aRT3dxVMFv9cXNQYoLPTHaBH1/DyTh/Xljgtm8N4PT+TGEydSUruNSx/6iFPuWsTjH6xnV1vXQQI1P+2inu7iqYLfD7Gv/TDyCNSzO4YU5PC9U6Ijge68YAa5men85O+rOOo3/8dvXv2YDVs/76T5aRf1dBfvV7E9htd7+OOoZ2KyM9I5b/Yozp01kmXrm3lkSTUPvbuOvy7+hBMmD+GSI8dw/OQhpKeJ5qdl1NNdPFXwRyM5e5vMTOtrzKcE9XSOiDB3bCFzxxaycVuQJz+s4ZmiDVw5v4iR/XO56PDRnDI+n+FuizrAC/npBPV0F089zvzQ1BMIBBLv5AHUs2eM6J/LD06bzJJbT+S+S2YzblA+dyxYw5kPLOeax5fxbmWTZ5eHBO/lZ1eop7t4qsbvh87dQYMGua3gCPXcNzLT0zhj+nDOmD6cdU07mf/uWl5e2cBrqzYxdmAeFx4+hq/OHsmQghy3VffAq/m5N+rpLp6q8fthIZba2m7Xn/EM6mmPcYPyuWhKNu/fdhJ3XziTwX2z+Z/XK5j3u3/zrflLeaNsE7vbvfFt1Q/5CerpNp6awDVnzhzj9Wh44XDYF99M1NMue3t+sjnAc8tqeWFZLY07QgzMz+LcWSO54PDRTBraO2sEOPH0KuppD99P4PJDyIaysjK3FRyhnnbZ2/PgwX344elTWHLriTx8+VyOGFfI/PerOfWuRZz95/d44sP1PQ4PYdPTq6inu3iqxu+HIG2K0hVbAiH+vmIjzy7dwOqGHWSmCydMHsI5s0Zy4pQh+xQkTlG6wvc1fj9MlvB6U1Qc9bSLE8+BfbK58thxvP7d43jlhmO5bN5Ylm/YxrVPFHP4r9/iludLWLK2ifYUjgran/LTC/jFM1m0xq8oKaQ9Yni/agsvLa/jjbJNBEJhhhXk8JWZIzhn5kgOGd7XF/NXFO+iNf5ewC81APW0S08909OEYycO4o4LZrD0xyfzx4tnMW1kAQ+/u44v/WExp929iD/9u5JPNtsZL76/52dv4xfPZNEav6K4wNadbfyrtJ6Xl9dRtL4ZgCnD+nLm9OF86bDhjB/cx2VDxS/4vsYfDAbdVkhIaWmp2wqOUE+72PYszM/i0qMO4vlrjub9207kZ2dNpU92BncsWMNJd7zDaXct4p63KlnbuMNVz1Shnu7iqRq/H8bxh0IhsrOz3dZIiHrapbc8N21v5bVV9bxaWk/R+maMgUlD+/Cl6cM5c/pwJiaYI6D5aRc/ePq+xu+Hmbs1NTVuKzhCPe3SW57D+uVwxTHjeO7qo/ngtpP4xZen0j83i3v+r5JT7lrEiXcs5LevfUxR9dZORwdpftrFL57JkrIav4iMBh4DhgER4AFjzD3dHTNr1iyzfPnylPjYoqWlhYKCArc1EqKednHbs7GlldfLNrGgvIH3q7YQjhgG9cnixClDOGXqMI6dMIjcrHTXPZ2invboSY0/lXORw8B/GmOKRaQvsExEFhhjyrs6oL29PYU6dti2bZvnbwRQT9u47TmkIIdvzBvLN+aNpaV1NwtXb2ZBeQOvlW7i2aJacjLTOHbCYOYMy+BrxxzCoD7ebp5wOz+d4hfPZElZwW+MqQfqYz/vEJGPgZFAlwW/HxY9yMnxVjTGrlBPu3jJsyAnk6/MGMFXZoygLRzho3VbWVAe/Tbw1set3L6wjtljBnDSIUM4ftIQT84V8FJ+dodfPJOlV6IPichYYBbwYW9cT1EOFLIy0jh24iCOnTiIX3zlUN4rW0/RpjYWlDdw++uruf311QwtyOaLkwZz/OQhHDtxEAU5++fiIopzUl7FFpE+wAvAd40xLZ38/ioRKRKRooaGBpqamqivr6euro7m5maqqqoIBoOUl5cTiUQoLi4GPptYUVxcTCQSoby8nGAwSFVVFc3NzdTV1VFfX09TUxPV1dUEAgEqKioIh8OUlJTscY74e2lpKaFQiMrKSlpaWqipqaGxsZHGxkZqampoaWlh/fr1hEKhT4d57X2OkpISwuEwFRUVBAIBqqurXUlTRUWF4zRVVla6lqb6+vqU/J1spykYDLp+7yVK08qVKxmZbzh9VIRnvjmLFy6dxM9OO5hDh+by6sqNXPtEMTN/+SZfvfddfv7MElbVbvvcOXorTXV1da7fe07SFP8/cvPeS5SmnpDS4Zwikgm8ArxhjLkz0f7auWsP9bSL3z3D7RGWb9jGwtWNLFy9mbKN0TrYkL6ffRs46uBCBvZS34Df89NLeKpzV6KNig8BHzsp9CEa+9rrNDQ0eP5GAPW0jd89M9LTOHxsIYePLeTm06bQ2NLKO2s2s3DNZt4o28Rzy6ILjkwe2pd54wdy+NhCJg3tw0ED88nKsN8w4Pf89DupHM55LLAYKCU6nBPgR8aYV7s6Ridw2UM97bI/e4bbI5TUbueDT7bwftUWitZvpXV39F82I00YMzCPiUP6MCH+GtyX8UPyycvqeb1xf87P3qYnNX5Pzdw99NBDjdcXPigtLWX69OluayREPe1yIHmGwu2s2RRg7eYdrG0MUNkQYO3mAOu37Npj0tjI/rmMH9KHCYP7MHFo/KHQhwH5Wb3i2Rv4wdP3Bb8GaVMU79IWjrB+y07WNgair83Rh8InTYFPvyEADMzPYvzgPhw8OJ+xg/IZNyifgwflM2ZgHtkZuhiNbTzVxt8T/BKWec6cOW5rJEQ97aKe0aGjE4f2/Vy8oEjEULct+NkDoTFA1eYAb33cQFPgszAsaQIjB+QyblAf+kR2cuTUcYyLPRhG9M8lPc1bcw3AP3/3ZNEav6IoKWN7cDfVTTtZ17STT2Lv65oCrNu8k51tn83Uz0pP46CBedEHweDYN4TCfA4amMewghzSPPhQ8Apa4+8F/FIDUE+7qGfP6JebyYzR/Zkxuv8e24uKihgzaVqHh8FnD4e3Vzeyu/2zCmlWehqjCnM5qDCPgwbmM6YwjzGFeRw0MI/RhXkpXcvYa/lpC63xK4riKcLtETZua2X91p2s37KLDVt3sX7LLtZv3UXNlj2/KQAMK8iJPgwG5nFQ7H1M7CExIC/Tc+EqbOP7Gr8fFmIpKSlhxowZbmskRD3top526c4zIz0tWngPzOO4iXv+zhjD1p1tsYdA9IFQs3UXNVt3smjNZhp3hPbYv292BmMG5jFqQC6jBuz5PnJAbsLwFX7Jz2TxVI3fD+P4w+EwGRmeel52inraRT3tkirPYFs7G5pj3xC27Ix+W9i6i9rmILXNu/YYfQRQkJOxx4Pgs4dD9Of8TPF8fvq+xh8KhRLv5DJr165lypQpbmskRD3top52SZVnblY6k4b2ZVInK5XFvy1EHwLRB0HdtujP1Vt28u7aJnbt1YyUl5nGmIH5ez0QchneL5fh/XMYlJ/ty45nTxX8WVmJJ364zahRo9xWcIR62kU97eKGp4gwsE82A/tkf66zGaIPhm27dn/6UKhtDrJucwuNgd3UNu/ig0+2EAjtGVYmM10Y1i8n+iCIvY/o/9nnEf1zPdnP4KmC3w+xepqamujTp4/bGglRT7uop1286CkiDMjPYkB+FtNH9QOgurqasWPHAtEHQ0swzIbmXdRvb6V+e5CN26Lv9dtaWba+mYaW+j1GJAHkZKZ1/mDon8OI2Htvh8r2VMHvh4VYvHazdoV62kU97eJHTxGhX14m/fL6MW1kv073j0QMTYEQG7e3Ur8t+Ol7/fZWNm4P8t7aJhp3tLL3csn5WekMLchhSEE2wwpyYj/nxH7O/vR3tmY+e6rg91JHc1fs3r3bbQVHqKdd1NMu+6tnWpowJFZoz+ykOQmiw1UbdoT2eDA0tIRo2NFKw/ZWltU009ASoi0c+dyxA/IyGRp7MMQfCD3BUwW/H4hEPv/H8CLqaRf1tMuB7JmRnsbI/rmM7J/b5T7x/oaGHa3Rh8L2VhpaWmnY0cqm7SEad7RSsamFzTt6NiDGUwW/H5p68vLy3FZwhHraRT3top7d07G/YcqwrvcLt0fI/F3y5/dUSeuHzt2tW7e6reAI9bSLetpFPe2Qkd6zItxTBX9mpvcXgR4xYoTbCo5QT7uop13U0108VfC3tbUl3sll1q1b57aCI9TTLuppF/V0F0+FbPBDkLZIJOKLvgj1tIt62kU97dGTkA2eSpEfwjKvWLHCbQVHqKdd1NMu6ukuWuNXFEXxMVrj7wW8Hj00jnraRT3top7uojV+RVEUH+OpGr+IPCwijSKyyukxfqjxFxcXu63gCPW0i3raRT3dJWU1fhH5AhAAHjPGTHNyjB9q/H7o5Qf1tI162kU97eGpGr8xZhGQ1LS31tbWFNnYo6Kiwm0FR6inXdTTLurpLq4/ykTkKhEpEpGi5uZmmpqaqK+vp66ujubmZqqqqggGg5SXlxOJRD796hXvdCkuLiYSiVBeXk4wGKSqqorm5mbq6uqor6+nqamJ6upqAoEAFRUVhMNhSkpK9jhH/L20tJRQKERlZSUtLS3U1NTQ2NhIY2MjNTU1tLS0ANGVwkpLSzs9R0lJCeFwmIqKCgKBANXV1a6kKRAIOE5TZWWla2nKzc1Nyd/JdpoOOugg1+89J2kaPXq06/eekzRlZWW5fu85SVP8/8jNey9RmnpCSjt3RWQs8IrTpp7p06ebeGZ5laqqKsaPH++2RkLU0y7qaRf1tIenmnp6gtcXNQYoLCx0W8ER6mkX9bSLerqLpwp+P8To9sPII1BP26inXdTTXVI5nPMp4H1gsojUisiVqbpWb+L1Hv446mkX9bSLerpLytpWjDEXJ3uM11ai7ww/hI4G9bSNetpFPd3FUzN3RWQHsNptjwQMAprclnCAetpFPe2invaYbIzpm8wBXutNXZ1s73RvIyJFXncE9bSNetpFPe0hIknPet0/G7AURVGULtGCX1EU5QDDawX/A24LOMAPjqCetlFPu6inPZJ29FTnrqIoipJ6vFbjVxRFUVKMFvyKoigHGK4U/CIyWkTeFpGPRaRMRG6Kbf+FiNSJyIrY60tu+HXwzBGRj0SkJOb5y9j2QhFZICKVsfcBHvX0VH7GnNJFZLmIvBL77Km8jNOJpxfzslpESmM+RbFtnsvPLjy9mJ/9ReR5EamIlU3zPJqfnXkmlZ+utPGLyHBguDGmWET6AsuAc4ALgIAx5ve9LtUJEp1KnG+MCYhIJvAucBNwHrDVGPM7EbkVGGCM+aEHPU/HQ/kJICLfB+YCBcaYs0TkdjyUl3E68fwF3svLamCuMaapwzbP5WcXnr/Ae/k5H1hsjHlQRLKAPOBHeC8/O/P8Lknkpys1fmNMvTGmOPbzDuBjYKQbLt1hogRiHzNjLwOcDcyPbZ9P9KHlGt14egoRGQWcCTzYYbOn8hK69PQLnstPPyAiBcAXgIcAjDFtxphteCw/u/FMCtfb+CUas38W8GFs0/UislKia/Z64WtVuoisABqBBcaYD4Ghxph6iD7EgCEuKgJdeoK38vNu4BagYxhWz+UlnXuCt/ISog/3N0VkmYhcFdvmxfzszBO8lZ8HA5uBR2JNfA+KSD7ey8+uPCGJ/HS14BeRPsALwHeNMS3AfcB4YCZQD9zhnl0UY0y7MWYmMAo4QkQcLSrT23Th6Zn8FJGzgEZjzDK3HJzQjadn8rIDxxhjZgNnANdJdJ1rL9KZp9fyMwOYDdxnjJkF7ARudVepU7ryTCo/XSv4Y23RLwBPGGNeBDDGNMQKsAjwV+AIt/z2JvZ1aiHRdvOGWD9FvL+i0T2zPeno6bH8PAb4Sqy992ngRBF5HO/lZaeeHstLAIwxG2PvjcBLRJ28lp+denowP2uB2g7flJ8nWsB6LT879Uw2P90a1SNE26g+Nsbc2WH78A67nQus6m23jojIYBHpH/s5FzgZqAD+AVwW2+0y4GVXBGN05eml/DTG3GaMGWWMGQtcBPzbGPN1PJaXXXl6KS8BRCQ/NjCC2Ff9U2NOnsrPrjy9lp/GmE3ABhGZHNt0ElCOx/KzK89k89Ot6JzHAJcCpbF2aYj2nl8sIjOJtglWA99xQ64Dw4H5IpJO9CH5rDHmFRF5H3hWoovL1ABfc1OSrj3/5rH87Izf4a287IrbPZaXQ4GXonUoMoAnjTGvi8hSvJWfXXl68d68AXgiNlLmE+AKYv9PHspP6NzzD8nkp4ZsUBRFOcBwfVSPoiiK0rtowa8oinKAoQW/oijKAYYW/IqiKAcYWvAriqIcYGjBr/QaItIeixy4SkSeE5E8ERkrIkmN4RaRy0VkRKo8vUAsjZtFpNt4QSKyrsOY7vi2u0XkFhE5TkTKk81fZf9HC36lNwkaY2YaY6YBbcDVPTzP5YCnC34RsTFH5hljzLcS7PM00Ylm8eumAefHjl0MuB7uWPEeWvArbrEYmBD7OV1E/irRtQTejM0+RkRmisgHscBTL4nIABE5n2i45Cdi3x5yReSkWMCq0liAquzY8dUi8ksRKY79bsreEhINbve/IrI0dp3vxLYfLyIL5bO450/EZpwjInNE5B2JBh17o8OU/oUi8hsReQe4SUQOj53z/dg1VsX2WxybbBN3eE9EDusus7ryBJ6iQ8FPNHJjtTFmfXJ/DuVAQgt+pdeJ1YbPAEpjmyYCfzbGHApsA74a2/4Y8ENjzGGxfX9ujHkeKAIuiQWlM8CjwIXGmOlEZ4de0+FyTbEAYfcBP+hE50pguzHmcOBw4NsiMi72u1lE45xPJRoV8RiJxpj6I3C+MWYO8DDw3x3O198Y80VjzB3AI8DVxph5QHuHfR4k+q0FEZkEZBtjVibItk49Y8dFRGRGbL+LiD4MFKVLtOBXepPcWIiOIqLT3x+KbV9njFkR+3kZMFZE+hEtRN+JbZ9PtDa7N5Njx6/pYr8XO563k+NPBb4R8/oQGEj0QQTwkTGmNhb4akXs+MnANGBB7JifEI2IGucZiK6SBPQ1xiyJbX+ywz7PAWfFHiLfJPrgSkR3nk8BF8UeqGfHzq8oXeJWrB7lwCQYq6V/Sqz1JNRhUzuQm8Q5JcHv4+dup/P7XYAbjDFv7OV1fCdeGbH9y2K1+M7YmcjLGLNLRBYQLaQvINp0lYhOPWM8BbwJvAOsjEXBVJQu0Rq/4kmMMduBZhE5LrbpUqIFG8AOoG/s5wqi3xAmdLKfE94AronVvhGRSfLZwhadsRoYLCLzYvtnisihnfg3AztE5KjYpov22uVB4A/AUmPM1n3xNMZUAVuIBrzTZh4lIVrjV7zMZcBfRCSPz6IQQrRp5C8iEgTmxbY/F2vqWAr8JYlrPEi0Cac41nm7mW6W1zPGtMU6mP8Qa47KILpiV1knu18J/FVEdhJdI2F7h/MsE5EWov0ANjyfAn5LNN69onSLRudUlBQhIn3iayFLdKHu4caYm2KfRxB9GEyJ9SHsfezlRBcov34fHcYCr8SG0CoKoE09ipJKzoxPWAOOA34NICLfINpB++POCv0YQeCMRBO4uiPWTPZPoKmn51D2T7TGryiKcoChNX5FUZQDDC34FUVRDjC04FcURTnA0IJfURTlAEMLfkVRlAOM/weNXLsXJA+w6AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"reader.cross_sections.loc[2,0,0].plot(x='energy', y='sigma', xlim=(25, 65), ylim=(0.8, 8), legend=False)\n",
"\n",
"plt.xlabel('Photon energy [eV]')\n",
"plt.ylabel('Cross-section [Mb]')\n",
"plt.grid(which='both', linestyle='dotted')"
]
},
{
"cell_type": "markdown",
"id": "d8219453",
"metadata": {},
"source": [
"## Types of cross-sections\n",
"\n",
"- 0: null\n",
"- 20, 21 and 22: Table of points\n",
"- 1 and 7: Seaton\n",
"- 3: Hydrogenic (n)\n",
"- 2 and 8: Hydrogenic (n-l)\n",
"- 5: Opacity Project\n",
"- 6: Hummer\n",
"- 9: Verner & Yakolev\n",
"- 4: Leibowitz (null, not implemented)\n",
"- Other (null, not implemented)"
]
},
{
"cell_type": "markdown",
"id": "deccc165",
"metadata": {},
"source": [
"Types 20, 21, 22 are already covered by the `C` example, while types 3 and 6 are covered by the `H` and `He` examples, respectively.\n",
"\n",
"### Type 1"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "96874f26",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='energy'>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"reader.cross_sections.loc[6,0,34].plot(x='energy', y='sigma', xlim=(0, 20), legend=False)"
]
},
{
"cell_type": "markdown",
"id": "eee5d9e5",
"metadata": {},
"source": [
"### Type 7\n",
"\n",
"_Example required..._"
]
},
{
"cell_type": "markdown",
"id": "463b15a3",
"metadata": {},
"source": [
"### Type 2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4edde74e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='energy'>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"reader.cross_sections.loc[2,0,26].plot(x='energy', y='sigma', xlim=(0, 3), legend=False)"
]
},
{
"cell_type": "markdown",
"id": "7b60876e",
"metadata": {},
"source": [
"### Type 8"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "90d4f9ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='energy'>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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kNP11LBQIUjP6MNeP/pJuoiR0GTWEYwg6DyFaCgSpOXUZJV9x/AASFgjKg0gpEESkPBBi7jLKFZxMWuchxEGBIDWnz3TyFQeUs+kUg4kYQygedipRUiBIzegY8vrRPxSEQEdrNvYWQr7gpFOQMk1uFzUFgtScxhCSr9hCmNPWGP8YgjvpVIp0yshpECFSCgSpOf3IS77iGEJHa2NiWgiZVIpcvhBrXaYaBYLUjIKgfvQPHw+EpIwhZNLGSF5voigpEKTm1GWUfMUuo462LCN5ZyTGX+b5gpMyI5tOxVqPqUiBICL0D+VIp4wZzVkg3kNP8+Fhp5m0kVMLIVIKBKk5dR0l39HBHNOyaZqzaQAGYxxYLrYQGtRCiJwCQWpGOVA/+gZztDc30NwQBEKsLYTwmsoN6RQjOsooUgoEqTmNISRf72COtqaG0RZCXIHg7kELIWU0pE1HGUWs4kAws4Vm9n0z22xmG83s02H5LDN72My2hbczS9a5w8y2m9kWM7t+MnZARKrXNzhCW1PmeAshpi6jfNgiyKSMTEpdRlGrpoWQA/67u78JeAdwq5ldBtwObHD3JcCG8DHhc6uAy4EbgC+ZWbqaykuy6SzT+tE3mKO9KUNTzIFQPMy0IZ2iIZPSYacRqzgQ3H2Puz8b3u8DNgMLgBXA2nCxtcDN4f0VwDfcfcjdXwW2A9dUun0RmTy9gyO0NTXQ2pgByqfDjtJw2CJoSBsNKVMLIWKTMoZgZouAq4AfAvPcfQ8EoQHMDRdbAOwqWa07LBvv9W4xsy4z6+rp6ZmMKorIKRRbCK1NQSAcHRqJpR7FAGjMpGhIp3TYacSqDgQzawX+Gfgtd+891aLjlI3713b3u9x9mbsvmzNnTrVVFJFTcHeODuXKWghHh2JqIeSKLYTgTOVhtRAiVVUgmFkDQRjc6+7fCov3mtn88Pn5wL6wvBtYWLJ6J7C7mu1Lsum3XX04NpwnX3DamjK0FVsIg7nTrFUbI/njgdCQTpErKBCiVM1RRgZ8Fdjs7n9Z8tR6YHV4fzXwQEn5KjNrNLPFwBLg6Uq3LyKTo3cw6B5qa2qgMZMik7LYu4yymRQNaWMkp58VUcpUse51wC8CL5rZ82HZ7wOfBdaZ2SeAncBKAHffaGbrgE0ERyjd6u7xzqIlIvSFrYG2pgxmxrTGTGwthOHc8aOMMukUI2ohRKriQHD3f2f8cQGA5SdZZw2wptJtSn3RUaf1oW+0hRB8HbQ2ZugbiikQRlsImtwuDjpTWWSK6w1bA+3NDUAQDEkYQ8ikNLld1BQIIlNc70DQQmhvCgKhtTEzOh121EbCo4yyoyemqYUQJQWCyBS3r3cICC6fCdDaFF8gDBVbCJlUeGKaWghRUiBIDenDXA/29Q3SmEnRXjKGEFeXUXHKjJZsOjwxTS2EKCkQpGY0qFwf9vUNMbe9EQunpW1rim9QuRgIzQ3p4CgjtRAipUCQmilOZW+a/zrR9vUOMa+tafRxnC2EY+G0283ZNNnwTGVNkhgdBYLUTPGDrDhItn19g8xtbxx93NbUwMBInqFc9KcJDQwHQdSSzTAtnEbjWIxXb5tqFAhSM8XfdSm9yxJtX98Qc0taCOfNagFg54FjkddlYDgYM2huSJdMtBdPa2Uq0kdVaqYw2kJQGyGpBobz9A3myloIF8yZBsDLPf2R1+fYSI5sJkU6ZbSFh8EWT5yT2lMgSO2o6zfx9vYOApS1EC6Y0wrAK/uPRl6fo4M52sKuouJtX0zjGVORAkFqppgHGlNOruKX/uKOltGy1sYM89ob2fJGX+T1OTIwwvTwjOlil5ECIToKBKkZ11FGibd1bxAIF81tKytftmgWDzy/m9VfezrSPvwjAyNlU2iAxhCipECQminoKKPE27q3j3ntjaO/yos+/+Gl3PGBS3l0aw9/+8i2yOrTW9pCaIz32gxTkQJBakZDCMm3be9RLp7XdkJ5czbNf/3pC/m5qxaw9okdHBmIZmD34LFhZrYUWwjBba8GlSOjQJCaKZ6HkFITIZFG8gW27zvKkrknBkLRx69bzOBIgfuf7a55fdydvb1DzG0PBrjbGjM0pI2eo0M137YEFAhSMzrBNB7uzso7n2DtEztOudzzuw4zMJLnmsUzT7rMlZ3TWdo5nf/79M6anzHcO5BjOFdgbjjJXiplzJ/ezJ7DgzXdrhynQJCa8bDTqKBgiFT3oQGe2XGIz6zfSOEU//mPb+0hZXDthR2nfL2Pvv18tu49Stdrhya7qmV2HAjOe+icefyIp/nTm9hzZKCm25XjFAhSM8WrH+pC6dF6ofvw6P0fvnrwpMt9f0sPSxfOOGFAeawbl86nrTHDvU+9NllVHNe2fcERTxfPax0tO3dGM7vVQoiMAkFqJhf+Op3KedDTN8TgSLRz8fy4+wgA6ZTx0KY3xl3mhV2HefH1I9y09NzTvl5LNsPKZQv5lx/vYeve2p2bsG1vH9lManTqDIALOqbx+uEBDSxHRIEgNZMvBsIUHkx425p/45P/8KNIt/nCrsMsXTiDd144mye2Hxh3mTsffZnWxgwfvrrzjF7zU//pIlobM/zRv2ys2VjC87sOs2RuK5n08a+lKzqnA7Dx9d6abFPKKRCkZopdRfkpOohQ3O8fbOmpyev/7jdf4NZ7ny0r6xsc4bldh7n6vJm888IOtuztY19feZfLd1/cw3dfeoNb3n3B6KGdpzNrWpbfef/F/Mf2A3z50ZcnbR+KDvUP88yOg7z3krll5VctnEE6ZTy2rTb/h1JOgSA1MxxeH3dqxgEMTEJX0dOvHjyhy+mux17mkZ/sZV1XN99+cU/Zcw9u3MtwrsCNS+fzriXBYPG9T+0cff47L+7ht/7xeZZ2TueTP33hhOry0befz01Lz+UvvreFux9/ZVJbCn//1GsUHD5w5Tll5TNasvzURR2sf363rq8cgUzcFZCzV3845UA2XV+/O7bu7WP2tCyzWxtPv/ApHBuu7gzb1w708/NfeZKVV3dyyTltvG3RLJYunMGffecn4y6fLzj/+MxOFs5q5qqFMwD40FUL+NtHtrHljT66Dx/jpdd7WbpwBvf88tvIZib2d0mljM+tfDNDuTx/+u3NPLq1h9uWL2Fp54wJv1apx7b28MXvb+eGy8/h8nOnn/D8L117Pp9Y28X/efwVfuM9F1W8ndN57UA/C2e2kJrCJ85EHghmdgPwBSAN3O3un426DhKN4hw0mXT9fMAKBef9f/UY589u4dH/8d6qXmtwuLJftD/uPswL3UdYdn5wfsAL3Yf5px8FJ4bt+OzPjrvOwf5h1nx7M8/sOMSffejK0fmj/vTmK2hqSPMf2/fT0Zrlj1dczkeuOY+GCkO6MZPmzo9dzT1P7OCv/20bK+98kkzKeHPndN5xwWwuOaeNC+e0srhj2ugFbsa6/7luHt+2n4vntfH8zsM8uOkNlsxt5c9/7spxl1/+pnl88Mpz+PyDW+ic2XJGA+ET9frhAX76cz/gN95zIb97w6WT/vr1ItJAMLM08L+BnwG6gWfMbL27b4qyHrW0/+gQDzy/mxvfPJ957U2nX+E03J2hXIHewRF6B0bYvq+f53YeYseBfi6e18Z7L53L0s6gn3U4VyBllA3KRc3dKXgwkLzrYHD8+I79/fT0DTGnrbpf3BMxnCuQKxRoyU7sLb51X3AUzWuTcHGYYyMnbyH85cNbedeSDt62aBYQ9P23ZDOkU8ZNX/wPAP7lUz8FBF/2RSvvfOKE13rXXzzCnsOD5N351Hsv4hfeft7oc9MaMyf9oq2UmfHx6xbz4as7eXRrDy+93stTrxzgzkdfLjvnpDGTYkZLAzOas2TSRjoVXBnjhfAoKIBz2pu45V0X8On3LTnl3+rzK5ey/+gwt933HF959GUWd0xj0expzJqWpb25gWwmxayWLAV3coUCz752mKvPn8lwvkBjJsX2fUf5029vBuBbv/FO3tI5g4GRPP3DOfoGc7x+KHiv3vvDndy2fAnZdIpNe3qZ0dLAghnN5ApO32COmS0NmBn7jw6xfd9RjgyMMJQr8IErzqk4ZJPEorxeqZldC/yhu18fPr4DwN3//GTrtC28xJd9+iunfe0z3Y+J7O1wrkBP3xCtTRmmjXmzFrfno4+D28MDwwyOFGhuSDO7NYt7sKwTfEm6F0/UCr44i1+gHj5Xupzj5At+woXGG9LGwlkt7NjfT8GDLpkZLQ30Do4wnCvQ3txAS0OadDoIib7BHO1NDTQ2HH/DFn+zl85EWnAP/hVK7pfUMV/wkn04/nyhcPz+WCk7fmLa7GlZGjOp0W2ahf+w8DbY/+K+B/93x/+/T3hu9P+99HFQjyMDI5jB3LZG0maYGakUpMxImZUFl5fs4xu9xwdgz53eRCplZX/D4r4Xt1XcroX/l8XehnwhCPJiK2luW+Po+iP5wujcQKMTuA3lyKSMVBjsE/Xr77mQD121YNx5iaIylMuzY/8xXu45ymsHjnHo2DCHjw1zZGCEXN7Je/B+ntfexO0fuJRMypjRkj3j1+8fynHXY6/w3K7D7NjfT/ehY5Gc9Gh2/H3Y3JAmk7Zxp+TOZlKkLQw+C95rxY/X2M9baZv5+EfQyh6PbVePnTR47IWnxj7/1O+/70fuvuyUOzdG1F1GC4BdJY+7gbePXcjMbgFuAWg/9wKuXHBiv+J4znSW5TPtwMikU8xuzdI/lBu9tF/pduyEx0ZzNs3yN83lwY1vcGw4P/pllwq/+ILLSQZfHKNvGoI3Svnj8MsrZbQ2ZmhvytDW1MD5s1u49Jx2mrNpDh8b5tGtPWza08vh/hGaGlK0Nzdw6NhwcB1aD96kLdkMvYMj5PLlg7ylGepAevRNbKRTx++ninWxoB85VVJWfD74EJQv29SQZuXVC3l8ew9rvr2Z6y7qGN2uE3yTjn6xhuXFYLDw/4ExgVEWIOEC4z03a1o2+II/MjAawh6GWN4pqX/4dxn9/4aL57XRfWiAIwMjFApe8rcp3XZ52diQSKeMhnSKjbuPsHBWC9l0qqy++/uGOHdG82gd+gZHmNaYIZtJkc87B/uHWdwxjce37+faC2ZTcKe9qYHndx3mqvNmYGZ0tGY5p72JKzunT7glVAuNmTSXnNPGJefUJpSmNWb47Z+5ePRxvuD0DY5wZCD4IXSgf5hMKvhCbsykw2Dq59wZzVyxYDozmhvYuvcoj27dR3M2w7RsmpbGDA0pY2/vEPlCgXnTm+gdyDE4kufJlw8wuzXLRXNbyaZTDIzkOdg/TFNDmqFcgY27j7C4Yxo7Dx7j5X1H+cjbzwt/HAV1O9mPRi/5WXq8rPzx2J+uY3/vnvB4nOWfmsD/bVHULYSVwPXu/qvh418ErnH33zzZOsuWLfOurq6oqigiclYwswm3EKLu9OoGFpY87gR2R1wHEREZR9SB8AywxMwWm1kWWAWsj7gOIiIyjkg7Ht09Z2afAh4kOOz0a+6+Mco6iIjI+CIfiXL37wDfiXq7IiJyavV/4KyIiEwKBYKIiAAKBBERCSkQREQEiPjEtEqYWR+wJe561EgHsD/uStSQ9q++af/qVxPQ7e43TGSlegiEromebVcvzuZ9A+1fvdP+1a9K901dRiIiAigQREQkVA+BcFfcFaihs3nfQPtX77R/9auifUv8GIKIiESjHloIIiISAQWCiIgACQ4EM5thZt80s5+Y2ebw8ptnBTO7xMyeL/nXa2a/FXe9JpOZ/baZbTSzl8zsPjOr/gLTCWJmnw73bePZ8Lczs6+Z2T4ze6mkbJaZPWxm28LbmXHWsVIn2beV4d+uYGZ1fejpSfbvc+F354/N7H4zm3Emr5XYQAC+AHzP3S8FlgKbY67PpHH3Le7+Fnd/C3A1cAy4P95aTR4zWwDcBixz9ysIpjpfFW+tJo+ZXQH8GnANwXvzRjNbEm+tqnYPMPYkptuBDe6+BNgQPq5H93Divr0E/BzwWOS1mXz3cOL+PQxc4e5vBrYCd5zJCyUyEMysHXg38FUAdx9298OxVqp2lgMvu/trcVdkkmWAZjPLAC2cXVfGexPwlLsfc/cc8CjwoZjrVBV3fww4OKZ4BbA2vL8WuDnKOk2W8fbN3Te7+1kxA8JJ9u+h8L0JweWVO8/ktRIZCMAFQA/wd2b2nJndbWbT4q5UjawC7ou7EpPJ3V8HPg/sBPYAR9z9oXhrNaleAt5tZrPNrAX4IOWXhj1bzHP3PQDh7dyY6yOV+RXgu2eyYFIDIQO8Ffiyu18F9FO/zdWTCi8jehPwT3HXZTKFfc0rgMXAucA0M/tYvLWaPO6+GfhfBM3y7wEvALlTriQSAzP7A4L35r1nsnxSA6GbYGKmH4aPv0kQEGebDwDPuvveuCsyyd4HvOruPe4+AnwLeGfMdZpU7v5Vd3+ru7+boLm+Le461cBeM5sPEN7ui7k+MgFmthq4Efion+EJZ4kMBHd/A9hlZpeERcuBTTFWqVY+wlnWXRTaCbzDzFrMzAj+fmfNQQEAZjY3vD2PYHDybPw7rgdWh/dXAw/EWBeZADO7Afg94CZ3P3bG6yX1TGUzewtwN5AFXgE+7u6HYq3UJAr7nncBF7j7kbjrM9nM7I+A/0LQXH0O+FV3H4q3VpPHzB4HZgMjwH9z9w0xV6kqZnYf8B6CKaH3Ap8B/h+wDjiPIORXuvvYgefEO8m+HQT+FpgDHAaed/frY6piVU6yf3cAjcCBcLGn3P2Tp32tpAaCiIhEK5FdRiIiEj0FgoiIAAoEEREJKRBERARQIIiISEiBICIigAJBZNKEE/mJ1C0FgkxZZvYxM3s6vCbFV8wsbWZHzWyNmb1gZk+Z2bxw2Tlm9s9m9kz477qw/A/N7C4zewj4erjcw2b2bPiar5lZh5n9iZl9umTba8zstph2XWRcCgSZkszsTQRnUl8XXpciD3wUmEZwVudSgrnyfy1c5QvAX7n724D/THAWfdHVwAp3/wWCs0Qfcfe3Elzj4rxwma8STgNhZimCWW7PaMIxkaioiStT1XKCL/JngumWaCaYvG0Y+NdwmR8BPxPefx9wWbgsQLuZtYX317v7QHj/pwivjeDu3zOzQ+H9HWZ2wMyuAuYBz7l7cVoBkURQIMhUZcBady+7kpSZ/U7JzJB5jn9GUsC1JV/8xeUhmJ699HVP5m7gl4FzgK9VXHORGlGXkUxVG4APl8xaOsvMzj/F8g8Bnyo+CCdfHM+/Az8fLvN+oPQ6xPcTXOrwbcCDFddcpEYUCDIlufsm4H8CD5nZjwkudjP/FKvcBiwLL1q+CTjZzJF/BLzfzJ4luN7FHqAv3OYw8H1gnbvnJ2dPRCaPZjsVmURm1gjk3T1nZtcSXPXvLeFzKeBZgmmkz8YL6kid0xiCyOQ6D1gXfvkPEx6lZGaXEQxW368wkKRSC0FERACNIYiISEiBICIigAJBRERCCgQREQEUCCIiEvr/OyhoKQM+q+sAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"reader.cross_sections.loc[14,0,36].plot(x='energy', y='sigma', xlim=(6, 12), legend=False)"
]
},
{
"cell_type": "markdown",
"id": "18aef0be",
"metadata": {},
"source": [
"### Type 5"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9e1634b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='energy'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"reader.cross_sections.loc[6,3,5].plot(x='energy', y='sigma', xlim=(0, 200), legend=False)"
]
},
{
"cell_type": "markdown",
"id": "b7d099ee",
"metadata": {},
"source": [
"### Type 9"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f45f76a8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='energy'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"reader.cross_sections.loc[27,2,0].plot(x='energy', y='sigma', legend=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6 (carsus)",
"language": "python",
"name": "carsus"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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