- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “555578a8”, “metadata”: {
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“duration”: 0.010003, “end_time”: “2021-08-17T08:12:24.679470”, “exception”: false, “start_time”: “2021-08-17T08:12:24.669467”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Line”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “bdb3e633”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:24.710087Z”, “iopub.status.busy”: “2021-08-17T08:12:24.709037Z”, “iopub.status.idle”: “2021-08-17T08:12:27.719757Z”, “shell.execute_reply”: “2021-08-17T08:12:27.721006Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 3.032564, “end_time”: “2021-08-17T08:12:27.721486”, “exception”: false, “start_time”: “2021-08-17T08:12:24.688922”, “status”: “completed”
}, “tags”: []
}, “outputs”: [], “source”: [
“%%capturen”, “n”, “import numpy as npn”, “n”, “import matplotlib.pyplot as pltn”, “n”, “import warningsn”, “warnings.simplefilter("ignore")n”, “n”, “from astromodels.functions.function import _known_functionsn”, “n”, “n”, “from jupyterthemes import jtplotn”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “%matplotlib inline”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “id”: “3be98cfd”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:27.747628Z”, “iopub.status.busy”: “2021-08-17T08:12:27.746496Z”, “iopub.status.idle”: “2021-08-17T08:12:27.754161Z”, “shell.execute_reply”: “2021-08-17T08:12:27.755384Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.024812, “end_time”: “2021-08-17T08:12:27.755746”, “exception”: false, “start_time”: “2021-08-17T08:12:27.730934”, “status”: “completed”
}, “tags”: [
“parameters”
]
}, “outputs”: [], “source”: [
“func_name = "TbAbs"n”, “n”, “x_scale="log"n”, “y_scale="log"n”, “n”, “linear_range = Falsen”, “n”, “wide_energy_range = False”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “id”: “97b15564”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:27.783326Z”, “iopub.status.busy”: “2021-08-17T08:12:27.782252Z”, “iopub.status.idle”: “2021-08-17T08:12:27.789503Z”, “shell.execute_reply”: “2021-08-17T08:12:27.790451Z”
}, “papermill”: {
“duration”: 0.024589, “end_time”: “2021-08-17T08:12:27.790800”, “exception”: false, “start_time”: “2021-08-17T08:12:27.766211”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Line"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “c6c4cd3f”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:27.821439Z”, “iopub.status.busy”: “2021-08-17T08:12:27.820335Z”, “iopub.status.idle”: “2021-08-17T08:12:27.828401Z”, “shell.execute_reply”: “2021-08-17T08:12:27.829231Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.028905, “end_time”: “2021-08-17T08:12:27.829596”, “exception”: false, “start_time”: “2021-08-17T08:12:27.800691”, “status”: “completed”
}, “tags”: []
}, “outputs”: [], “source”: [
“func = _known_functions[func_name]()n”, “n”, “if wide_energy_range:n”, “n”, ” energy_grid = np.geomspace(1e2,1e4,500)n”, ” n”, “else:n”, ” n”, ” energy_grid = np.geomspace(2e-1,1e1,1000)n”, “n”, “if linear_range:n”, “n”, “tenergy_grid = np.linspace(-5,5,1000)n”, “n”, ” n”, “blue = "#4152E3"n”, “red = "#E3414B"n”, “green = "#41E39E"”
]
}, {
“cell_type”: “markdown”, “id”: “baa17e80”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009779, “end_time”: “2021-08-17T08:12:27.849175”, “exception”: false, “start_time”: “2021-08-17T08:12:27.839396”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “e06205b3”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:27.881466Z”, “iopub.status.busy”: “2021-08-17T08:12:27.880175Z”, “iopub.status.idle”: “2021-08-17T08:12:27.884772Z”, “shell.execute_reply”: “2021-08-17T08:12:27.885466Z”
}, “papermill”: {
“duration”: 0.02687, “end_time”: “2021-08-17T08:12:27.885819”, “exception”: false, “start_time”: “2021-08-17T08:12:27.858949”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A linear function</li>n”, “n”, “<li>formula: $ b * x + a $</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>a: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: intercept</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>b: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: coeff</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
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” * description: A linear functionn”, ” * formula: $ b * x + a $n”, ” * parameters:n”, ” * a:n”, ” * value: 0.0n”, ” * desc: interceptn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * b:n”, ” * value: 1.0n”, ” * desc: coeffn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: true”
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}, “metadata”: {}, “output_type”: “display_data”
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], “source”: [
“func.display()”
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}, {
“cell_type”: “markdown”, “id”: “fed4a436”, “metadata”: {
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“duration”: 0.010303, “end_time”: “2021-08-17T08:12:27.906367”, “exception”: false, “start_time”: “2021-08-17T08:12:27.896064”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Shape n”, “n”, “The shape of the function. n”, “n”, “If this is not a photon model but a prior or linear function then ignore the units as these docs are auto-generated”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “id”: “3291a72b”, “metadata”: {
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“nbsphinx-thumbnail”
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y3a9KKMjXeSPSObYZENba6cB0Y8wCa+01nTiTSJfW0uJw/6PVLH20hnAY9t4zm8njAhzeT+V60rnacpqrwkGkk3z4STNTZ27my2/CZGbCkIuLGXG5H59PB6Gl8+ndNCJJoKExysIHqnnkabdcr2+fHCaXBzhwP1WeiXcUECIee2dNI9Mrgnz/Q4ScbLjmSj9XXFhMdrZWDeKtNgWEMSYH2H3ry1trv07EUCJdQW1dlNn3VfHMi2653iHGx5TyAL32VrmeJIe2lPWNBu4EsnA/n3oLB/ejSEWknV57u4E75gbZtDlKXl4G1w0r4cKzC8nM1KpBkkdbVhC3AFcCf7bWRnZ0YRHZtmBVhLvvCbHyNbdc79gj3HK9Ht21t1eST1v/Va5MRDgYYyYCNwNh4DfW2gc6+j5EkoHjOLz0agN33xuiuiZKcZFbrnfWKSrXk+TVloD4HTDXGPN7YFPrH1hrN+zsHRtj+gFjgH64u6oqjTErrLVVO3ubIslow8Ywt88J8dY7brnewBPyuWFUKd0CqsmQ5NaWgJgT+/3yrbY7uMcldtb5wDJrbR1QZ4xZBZwGPLa9Kxljytjqs7AXL16clZWl/2ySXKJRh6eer2PekirqGxwCpZlMHFXKwAE6dCepwcvPg+gNvNHq+3XAnm243gTc4yI/qa2tJT9f3TSSPNZ918K0iiD/+KAZgLNPK2Ds8FL8xarJkNTR1tNcTwcuBboDXwL3WWv/sYv3HQVatvo+3Ibr3QUsbb2hqKjodZ/PV7aNy4t0mkjE4aGnaln4YBXNzbDH7lncPDZA/yPzvB5NpN3aeprrb4EK4F3gAOB5Y8xvrbXzduG+1wF7t/p+L+CVHV3JWruJrY6FVFZWRnSgT7z22ZfNTJ0V5OPPWsjIgEsGFXHtUD/5KteTFNWWFcS/Audba9/bssEYsxx4FNiVgHgCuM8YMwPYDTgeGLcLtyfiieYWhyUPV7PssRoiEei1VzaTywMcepDK9SS1tSUgSoCvttr2KVC6K3dsrf3AGPMAsBZ399KN1tqGXblNkc621jYxbVaQr9a55XrDLi3mqstUrifpoS0B8SQwxxjza2vtl8aYnsB/Ay/t6p1ba6cB03b1dkQ6W0NDlHuWVfPYM7U4jluu9+vxAfr2UbmepI+2BMQE4Hbgg1gnk4O7e2hUIgcTSVZ/X93IbbODrN8QIScHrr7Cz+UXFJOVpVWDpJe2BMQh1trRxpgxuIV9m621YWPMUWx1sFgkndXURpm9KMSKl+sBOOxgH5PLA+yzp8r1JD1tMyCMMcfFvnzDGHMC//xM6t7GmN1wD1Lr3D3pEl59q4E75wbZHIqSn5fB6KtKOP8sletJetveCuLB2O8Z/Pyzp8O4p72KpLXNoQh3zQ+x6g33/InjjsrlpjEB9thd5XqS/rb3mdR9AGL9SOd03kgi3nMchxdW1TNzQYiaWgd/USbl15Rw5skq15Ouoy1VG+cYYw4CLuCf76R+0Fr7Y4JnE/HE+g1hbp8T5O33mgA45cR8rr+2lG6l6vuSrmWHb/E0xlwE/BXoCzQBA4GPjDEXJHY0kc4VjTo8vqKWa2/6gbffa6IskMkffl3Gf04qUzhIl9SWHan/FxhsrV25ZYMx5izgbtz3SIikvK+/bWF6RZA1H7rleoNOL2DsiFKKClWTIV1XWwJiT+Cdrba9gdudJJLSwmGH5U/WsGh5NS0t0LN7FpPGBTj6cJ2gJ9KWgFgJ3GKM+XdrbaMxJhv4P8CbiR1NJLE++dwt1/v0C7dc79Lzihg5xE9+nlYNItC2gBgD3A9sNsZ8j3ug+kPcz6kWSTnNzQ6LH6pm2eM1RKOw797ZTBkfoN+BKtcTaa0tZzH9CJxljOkB9AI2WGu/TPRgIonw/kdNTJ0Z5JvvwmRlwfDBxQy91I8vR6euimytLZ8HUYa7iui95fLGGACstSMTOJtIh6lviDL//iqeeLYOxwGzfw6TywPs31vleiLb0pZdTE8AzcCLQCSx44h0vLffbWT67CAbNkbw+eCaK0u47LwileuJ7EBbAuIYYA9rbXWihxHpSNU1USoWhnjuL2653hGH+Jg8rht79VRNhkhbtOV/yqvAscDLCZ5FpMO88kY9d84PEQxFKcjPYMzwEs49Q+V6Iu2xvTbXWbEvQ8DTxpjngO9xPw8CAGtteUKnE2mnTcEIM+aFePUtt1zvhGPyuGlMKbuXadUg0l7b+1/zQ6vf13bCLCI7zXEcnl1ZT8XCELV1Dv7iTCZcW8ppJ+WrXE9kJ22vzfX3rb83xuyH+x6I7621W39GtYhn1m8IM312kMrVbrneaSflc/3IUkpL1J8ksivacprrobifB9EHWA90N8Z8DFxurf0kwfOJbFMk4vDEs7XMX1pNY6PDbt2yuHF0KSf2z/d6NJG00JZOgQWxX4Wxz4joBjwHLErkYCLb89W6Fm787Y/cfW8VjY0O551ZyL137KFwEOlAbTlyZ4AZ1toogLW2yRjzB0CfByGdLhx2eODxGhY/VE1LGPbskcWksQGOOkzleiIdrS0BMQ8YC9zZatswfv4xpCIJ9fFnbrneZ1+2kJkJgy8o4por/eTlqlxPJBHaEhCnA4cZY/4DWAfsg7ub6VNjzAdbLmSt7ZeYEaWra2pyWLS8muVPuuV6fXplM7m8GwcfoJoMkURqS0BMTPgUItuwem0T0yuCrPs+THY2DB/sZ8jFxeSoXE8k4drS5rqqMwYRaa2u/p/legAHHZDDlPJu9OmV4/FkIl2H3l4qSeetdxq4fU6IDRsj5PoyGDnUzyWDVK4n0tkUEJI0qmoizFpQxQur3HK9Iw/NZdK4AHv10D9TES/of554znEcVr3ewIz5IULVUQoLMhg7opRBpxeoJkPEQwoI8dTGzRHunBvktbcbATixfx4Trwuwe5lqMkS85llAGGMmApOABuARa+2/ezWLdD7HcVjxUj0Vi0LU1TuU+jOZMKqUU05UuZ5IsvAkIIwxfYCbgCOBWuBlY8xAa+0rXswjneu79W653rtr3HK9MwYWMP6aEkr8WjWIJBOvVhC9gSXW2s0Axpg3gX6AAiKNRSIOj62o5d6l1TQ2OexelsVNY0o54Rj1J4kkI08Cwlq7ElgJYIzZHxgCnNOW6xpjyoCy1tsWL16clZWlV5/J7IuvW5g2K8iHnzQDcMG/FHLdr0ooLFBNhkiy8vQgtTFmHPB74N+stf9o49UmALe03lBbW0t+vl6FJqOWFodlj9Ww5JFqwmHYu2c2k8YFOOKQXK9HE5EdSHhAGGNuAG7YavNnwHdAX+BEa+2n7bjJu4ClrTcUFRW97vP5yrZxefHIR582M21mkM+/dsv1rryoiBGXl5Cbq4PQIqkg4QFhrZ0BzGi9zRhzMjAdGGCtbWnn7W0CNrXeVllZGdGZL8mjsSnKwgeqefjPtUSjsN++OUwpD2D6qlxPJJV4tYupP+4n1K0xxmzZ9kdr7f0ezSMd5L33G5leEeLb9WFysmHEFX6GXFRMdrYCXCTVeHWQehowzYv7lsSorYsyb0kVTz3vluv1O9DH5PIAvfdRuZ5IqtI7qWWXvVnZwG2zQ2zcHCEvN4Nrh/q56ByV64mkOgWE7LRQVYSZC0K89GoDAMccnsvNYwP03EP/rETSgf4nS7s5jsPLf23g7ntDVFVHKSrMYNzVpZx9qsr1RNKJAkLa5cdNYW6fE+LNSrdc76Tj3XK9soDeqCiSbhQQ0ibRqMMzL9YxZ3EVdfUOgZJMbriulIEnqFxPJF0pIGSHvv0+zPSKIO+tdcv1zjqlgHFXl1BSrFWDSDpTQMg2RSIOjzxdy4Jl1TQ1O3TfLYubxwY47qg8r0cTkU6ggJC4Pv+qhamzNmM/dd/oftE5hYwaVkJBvsr1RLoKBYT8L80tDksfqeb+R2uIRGCfPbOZXB7gsINVrifS1Sgg5CcfftzE1FlBvvwmTGYmDL2kmOGD/fh8Oggt0hUpIISGxigLllXzyNO1OA707ZPD5PIAB+6ncj2RrkwB0cW9s6aR6RVBvv8hQk4OjLjcz+UXqFxPRBQQXVZtXZTZ91XxzItuud6hB/mYPC5Ar71VriciLgVEF/Ta2w3cMTfIps1R8vIyuG5YCReeXUhmplYNIvJPCoguJFgV4e57Qqx8zS3X639kLjeNCdCju/4ZiMjP6ZmhC3AchxdfqWfmvVVU10YpLsqg/OpSzjpF5Xoism0KiDS3YaNbrvfWO2653sAB+dxwbSndVK4nIjuggEhT0ajDU8/XMW9JFfUNDoHSTCZeF2DgCflejyYiKUIBkYbWfdfCtIog//igGYCzTytg3IhSiotUkyEibaeASCORiMNDT9Wy8MEqmpuhR3e3XO/YI1SuJyLtp4BIE5992cytM4N88nkLGRlwyaAirh3qJ1/leiKykxQQKa65xWHJw9Use8wt1+u1l1uud+hBKtcTkV2jgEhha20T02YF+WpdmKwsGHZpMVddpnI9EekYCogU1NAQ5Z5l1Tz2jFuud8B+OUwpD9C3j8r1RKTjKCBSzN9XN3Lb7CDrN0Tw+eDqK0oYfH4RWVlaNYhIx1JApIia2iizF4VY8XI9AIcd7GNyeYB99lS5nogkhgIiBbz6VgN3zg2yORQlPy+D0VeVcP5ZKtcTkcRSQCSxzcEIM+4J8cobbrnecUflcdOYUvbYXX9tIpJ4eqZJQo7j8MKqemYuCFFT6+AvymT8yBLOGKhyPRHpPAqIJLN+Q5jb5wR5+70mAE45MZ8Jo0oJlKhcT0Q6lwIiSUSjDk8+55brNTQ6lAXccr2Tjle5noh4w/OAMMbcBvistdd7PYtXvv62hekVQdZ86JbrDTqjkLHDSygqVE2GiHjH04AwxpwEXAU86OUcXgmHHZY/WcOi5dW0tEDP7llMGhfg6MNVrici3vMsIIwxBcD/xH71acf1yoCy1tsWL16clZWVWvvoP/m8mamzgnz6hVuud9l5RVwzxE9+nlYNIpIcvFxB/Am4DfDTjoAAJgC3tN5QW1tLfn5q7Ktvbna476FqHni8hmgU9t07mynjA/Q7UOV6IpJcPAkIY8wpwO7W2keNMVe38+p3AUtbbygqKnrd5/OVbePySeP9j5qYOjPIN9+55XrDBxcz9FI/vhyduioiySfhAWGMuQG4YavNXwF9jDEfASVAnjGmyVo7aUe3Z63dBGxqva2ysjKSzO8PqG+IMv/+Kp54tg7HAbN/DlPGd2O/fVWTISLJK+EBYa2dAczY1s9jK4hj2xIOqejtdxuZPjvIho1uud7IISVceq7K9UQk+Xl+mmu6qq6JMmthiOf/4pbrHXGIj8njurFXT/2Ri0hq8PzZylq7EFjo8RgdatUb9cyYFyJYFaUgP4Oxw0sYdIbK9UQktXgeEOlkUzDCjHkhXn3LLdc74Ri3XG/3Mv0xi0jq0TNXB3Ach2dX1lOxMERtnUOJP5PrR5Zy2kn5KtcTkZSlgNhF6zeEmT47SOVqt1zv9F/mM/6aUkpVriciKU4BsZMiEYcnnq1l/tJqGhsdduuWxY2jSzmxf2q8YU9EZEcUEDvhq3UtTJ0V5APrluudf1Yh1/1K5Xoikl4UEO0QDjs88HgNix+qpiUMe/bIYtLYAEcdpnI9EUk/Cog2+vizZm6dGeTzr1rIzITLLyji6iv95OVq1SAi6UkBsQNNTQ6Lllez/Em3XG+/XjlMHh/goL4+r0cTEUkoBcR2rF7bxPSKIOu+D5OdDcMH+xlycTE5KtcTkS5AARFHXX2UeUuqePK5OgAOPsDH5PIAfXqpXE9Eug4FxFberGzgjrkhNmyMkOvLYORQP5cMUrmeiHQ9CoiYquoIMxdU8eIrbrneUYflMmlsgD176I9IRLqmLv/s5zgOq15vYMb8EKHqKIUFGYwdUcqg0wtUkyEiXVqXD4jZ91Xx0JO1AJzYP4+J1wXYvUw1GSIiXT4gThmQz0uv1HP9yFJOPlHleiIiW3T5gDj4wFyWVvTE51MwiIi0prcBg8JBRCQOBYSIiMSlgBARkbgUECIiEpcCQkRE4lJAiIhIXAoIERGJK13eB1EWDodZvXq113OIiKSMcDgMULatn6dLQIQBwuHwJq8HaY9oNJrR3Nxc4PP56jMzMx2v5+kMesx6zOkohR9vGbHnz3gyHCeVHkt6McYcCFjAWGs/9nqezqDHrMecjtL18eoYhIiIxKWAEBGRuBQQIiISlwJCRETiUkB4axPw+9jvXYUec9fQ1R5zWj5encUkIiJxaQUhIiJxKSBERCQuBYSIiMSlgBARkbgUECIiEpcCQkRE4lJAiIhIXAoIERGJK10+DyLlGWNuA3zW2uu9niXRjDETgUlAA/CItfbfPR4pYWKP9Wbczv3fWGsf8HikhDPG/AkYCtQCd1trZ3o8UqcxxjwKvG6tneb1LB1BK4gkYIw5CbjK6zk6gzGmD3ATcCRwGDDQGDPQ06ESxBjTDxgD9ANOAG41xpR4O1ViGWNOBk4H+gLHATfE/s7TnjFmGHCy13N0JAWEx4wxBcD/xH51Bb2BJdbazdbaZuBN3CfQdHQ+sMxaW2et/RFYBZzm8UyJtjdwj7W2yVpbA6wBjMczJZwxpifui4E5Xs/SkbSLyXt/Am4D/EDav9Ky1q4EVgIYY/YHhgDneDpU4vQG3mj1/TpgT29G6RzW2vu3fG2M6Q8MBMZ7N1GnmQVMBgZ5PUhH0grCQ8aYU4DdrbWPej1LZzPGjMN98vxPa+0/vJ4nQaJAy1bfb/Pzf9OFMSbbGHML8BQw0lr7g9czJZIxZgTwsbX2b17P0tG0gugkxpgbgBu22vwV0McY8xFQAuQZY5qstZM6fcAE2MZj/gz4Dncf9YnW2k87fbDOsw53l8sWewGveDRLpzDGZAMrcA9QH2mtXe/xSJ3hdGCAMeZCYDcgaoyJWmtv83iuXaa67yRhjLkaODbdz2KKHcScDgyw1rbs6PKpLHaQ+j7gF7hPHC8CR1trGzwdLIFir6YvsdZe6PUsXjDG/A6oTZezmLSCkM7WH/dYyxpjfjp2+cfW+67ThbX2A2PMA8Ba3N1LN6ZzOMT0B46PrYq3GGut/YtH88gu0ApCRETi0kFqERGJSwEhIiJxKSBERCQuBYSIiMSlgBARkbh0mquIx2KNr+uBH4DZ1tqD2nHdvwIvWWtvabUtA/ga+B3uezD+y1r7eYcOLV2CVhAiO8EYk9VBt+MHhgMP7+RNLAMu2WrbcUB34BFgBnDrTg8oXZpWEJLWjDGnA7cD+wNP475pa7MxZiGwAfcV9hHA88BQa22jMaY3MA8YgPsmt9HW2tWxd7tfCji4L67OM8bcBPwr7mdb3A+cBFwA/Agcaq39JDbHa7gtp/duNeJ1wOPW2kirNw5ijOkGvArMtdbeaYw5EpiNW5H+OjDKWvsV8BBwpzHmgC33hRsYK6y1IeA9Y8y+xphDrLVrd+1PU7oarSAkbRlj9gGWAuXAvkAzMLXVRUbEfrYf7hPvlcaYTOBJ4FHc5tX7cV+JbzEItz7jYmPMqcBE3FA4BbgIwFpbDbwcuyzGmDLg2Njtbu3S2GVbz12AG2aPx8KhKPb9/+D2Ob0D3Bu7rw3AS7Hb2eJi3JXFFiuBLll9IbtGASHpbBjwsLX2r9bajcBvcJ88t1hsrV0de5J9DbdY7zggy1pbYa2tttbOALKMMYfHrvN3a+3DsR6pobiv8D+NvZpvXc72GHBu7OuzgddiM/wkFkb9cVcpW2Tj7m4KWWt/E9t2HvCetfbx2Krgt7jlcKWxn/+0m8kYcxjQk/8dRqtxV0Mi7aJdTJLO9gXGGmPKW280xuTFvmz9hN2I+/9hX6CfMWbrDpotn+MQbLWtF+6HAG2xrtXXTwB3GGMKcVcS8Srdy3BfpFW12rY/sAnoa4zpZq3dHJtpUJyZegAh3DCqiK2YLgGe2KrzaTPuykOkXbSCkHT2AzDVWpthrc0A8oCjrLWNO7jO21uuE7veEfzvINiiDvdg8Ba9tnwR+wyEd4GzYr+eiHP9bCAj9muLdbi7rN4E/thqpodazZOJu8vq49h9VeFWbF8S+9V69xK4x0yi23nMInFpBSHp7GHgGWPMYtzTPv8f7krg4u1c502guzHmIuAF3CfcPxH/0/5eAq43xjyM+39pMu7pqls8BtwCfG6t/SbO9X/EPbhdivsqH6DOWttijJkCvGOMqcB98v/v2GeXvwdMAAZba49udVvLYo8vgHvAvbVS3APyIu2iFYSkLWvt+7hnGD2O+yFF+wJjd3CdRtyDzf+G+wR+E+7nGzTFufgc4DncffwrY1+3/sS4x3BXH8u3cV9h4G3g8Dg/+wBYANwZW438Cvcspg24H9F65VZX+TPuLqctx0daOxQ3+ETaRXXfIjvJGLMH/LQ7CWPMGOBUa+2Vse+zcY8n9LPWfruN25gC+K21v03gnK/hnt67JlH3IelJKwiRnXcO8LQxZg9jzAG4p7y+AGCMyQeG4J69FDccYuYB58bCpMMZYw4FggoH2RkKCJGdtwT3QPTHwFu4u5gWxn42A/hv3FNStyl22upcfr7LqKPciLubTaTdtItJRETi0gpCRETiUkCIiEhcCggREYlLASEiInEpIEREJK7/D/iz0/npUzDAAAAAAElFTkSuQmCCn”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“fig, ax = plt.subplots()n”, “n”, “n”, “ax.plot(energy_grid, func(energy_grid), color=blue)n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel("photon flux")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”
]
}, {
“cell_type”: “markdown”, “id”: “dcc65548”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011838, “end_time”: “2021-08-17T08:12:28.151076”, “exception”: false, “start_time”: “2021-08-17T08:12:28.139238”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## F$_{\nu}$n”, “n”, “The F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot”
]
}, {
“cell_type”: “code”, “execution_count”: 7, “id”: “20a53bd8”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:28.247912Z”, “iopub.status.busy”: “2021-08-17T08:12:28.209564Z”, “iopub.status.idle”: “2021-08-17T08:12:28.609275Z”, “shell.execute_reply”: “2021-08-17T08:12:28.610120Z”
}, “papermill”: {
“duration”: 0.447267, “end_time”: “2021-08-17T08:12:28.610439”, “exception”: false, “start_time”: “2021-08-17T08:12:28.163172”, “status”: “completed”
}, “tags”: []
}, “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”: [
“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid * func(energy_grid), red)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"energy flux (F$_{\nu}$)")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”, “n”
]
}, {
“cell_type”: “markdown”, “id”: “a261e126”, “metadata”: {
- “papermill”: {
“duration”: 0.012639, “end_time”: “2021-08-17T08:12:28.636170”, “exception”: false, “start_time”: “2021-08-17T08:12:28.623531”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## $\nu$F$_{\nu}$n”, “n”, “The $\nu$F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “id”: “029c2ef7”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:28.714755Z”, “iopub.status.busy”: “2021-08-17T08:12:28.693097Z”, “iopub.status.idle”: “2021-08-17T08:12:28.845343Z”, “shell.execute_reply”: “2021-08-17T08:12:28.846306Z”
}, “papermill”: {
“duration”: 0.197829, “end_time”: “2021-08-17T08:12:28.846669”, “exception”: false, “start_time”: “2021-08-17T08:12:28.648840”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
“image/png”: 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eGWOmA5cB04EqYKkx5nFrbYe3lYnkhuM4OIBD9k+HtNNP23vWd++ncUg7DgknRa+TIu6k6HWS7n1SxDP3u9NxutJxIuk4EaeXrnScrnQvkXSctlSUrszZ77vjx8eoQA1jgrUcFKxjclkjB5c1MKmsgUodKjxk7e0rxLhdlv8AFMIhGx8EHrDWdgPdxphngNNx69stY0wYCGe3zZs3LxAIFMJbkkLjOA5RJ0lbOkpbKkpHOkZ3OkG3E6cnnaDbSbg/03F6nARxJ0XCSZEg7f50UsSdNHEnRZIUaccNAnBIZwXE/wfGzoFSKGp8IcKBKpoCVTQGKgkHqhgeqGJMYBhjgrWMCtboSC55j72Fy67/xwvl4PKJwOKs5Y3AvsxQdyVwfXZDJBKhsrIyd5VJ0Yik42xNRtiS6mJLMsLWVDdbkl00p3poS8doT0fpdVJ7f6ED4AN8+PDh9gDAh9+3c1vffZ/Pl1nmPW3ssq7f9//3Q74AIV+Acl+Qcl+A0I6fAcp9Aap8IWr9IWr85dT4Q9T6QtT6y6n2h2gIVOiILdkvA/1fUyhfqNJAYpfl/gd9d/Yj4P7shpqamkWhUCi8m/WlBLSlorydaGNdsp11iQ7WJdpZn2zfca2P3fHjo8FfQUOgknp/BXX+Cmr8Iap9ZVT7Q1T5y6j2uT+rfGWEfAHKfAHKfH7KcO+HfH5CvgBBAgSyPvD9+HQioJS0vYaLMWY4fV+wdl4GwFq7bTdPzaeN7DxkNxZ4dm9Psta2AC3ZbUuXLk3pj7x0dKZ7WRlvxvbdEs1sT/X0u26Dv4JRwVpGBWoYGaxxfwZqGB6sosFfyTB/uQ6LFdlPewsXH7Al89PJ/Nya9biDN/tgHgXuM8bcCTQBxwCXe1CHeKw9FeO13i0s693Ca/EtrE20vWedBn8lU8oamVBW796CdUwoq9chsCJ5tMdwsdb6AYwx1cCxmebnrLV7Pnwkz6y1bxpjHgTewB0S+6K1NuplTTI4Uk6aN+PbWRzbwEuxje8Jk2pfiENDTZisW5O/SkNQIoNsX/e5vAO8CVQCk4wxV1hrf5u3qvaBtfZ7wPe8rEEGRzSd4IXYBhZF3UDJPjS21hfiiPJRzMzcDi5r0FCWSAHY13CZbK3tBDDGzMYdkppsrb01f6XJUBZ3krwU28SCnrdZHNuw00l6U8vCHFdxEMdUjsOUNeFXr0Sk4OxTuPQFS+b+y8aYucAfjDF+a+2381adDCmO42ATzfy5eyVP97y9Y+oQPz7mlI/lpMoJHFt5EE2BKo8rFZG92adwMcacg3tEVt9tXOZ2M6BwkQMSScd5qmcNf+5eyepEK+AeOTIzNIrTqiZxcuVE6gMV3hYpIgOyr8NidwPvZm6bgGeABzL3RfbLpmQnD3e9wV96Vu8Y9hoRqOa86qmcWzWVkcEajysUkf21r8NiB+e7EBk6VvRu5aGuFSyMrcfBHfY6oWI876+expyKsdohL1ICNK+DDArHcXi1dzP/3bmM1+PuqVIVviDvq57Gh2umMzpY63GFIpJLChfJu2Wxzfyq81Vey4RK2F/Fh2un84HqadToREaRkqRwkbxZGW/mno6XebV3MwBhfyUXDzuC91dPI6TJEEVKmv7CJee2p7q5t+MV5vesxsGdfuXi2hl8oMZohl2RIUJ/6ZIzvU6SB7te53+6VhBzklT4glxUO4N/qjlMF40SGWIULpITS2Kb+EHbYjanuvAB51ZN5VN1R+uER5EhSuEiB6Q11cNd7Uv4W3QtANNDw7mq/limhZo8rkxEvKRwkf3iOA5P9KzirvYldDtxqn0hLq2bxfurjeb6EhGFiwxcayrK7W0LWRzbAMBplZP4XP1cGjUEJiIZChcZkGd73uH77YvoSPfS4K/gSw0ncELleK/LEpECo3CRfRJNJ7iz/QX+0rMagJMqJ3B1/fGaUFJE+qVwkb16O9HGTS0LWJfsoNoX4qr6Yzmz6mBd3VFEdkvhInv0RPcqfti+mF4nxSFlTXwjfCqjNA+YiOyFwkX61esk+WHbCzzRswqAC2umc1ndbMp8AY8rE5FioHCR92hO9XBd81PYRDPVvhBfbTyRkyoneF2WiBQRhYvs5K34dr7R/BQt6SgTgvV8s+kMxgaHeV2WiBQZhYvs8GTPGr7bupAEKY6tOIhrG0+m2h/yuiwRKUIKF8FxHP67cxn3dS0D4CO1M/jUsKN1RUgR2W8KlyEu5aT5fvtiHuteSRl+vtJ4ImdWTfa6LBEpcgqXISyWTnJz69Msjm2g2lfGTeEzOKpitNdliUgJULgMUR2pGNe2PMmb8e2E/ZXc2nQ2k0ONXpclIiVC4TIEtaZ6uGb7X1iXbGd8sI5bm85mVLDG67JEpIQoXIaY7clurml+go3JTkxZE7c2nUWd5gcTkRxTuAwhW5IRrtn+BJtTXRweGsEtTWfpUGMRyQuFyxCxKdnJNdufYFuqmyPLR/Gt8Jm6rr2I5E1Bh4sxZj6QfbGQOdbaLmPMbcBHgB7gcmvtAk8KLBKbk11cvf1xmlM9zCofw83hM6jwF/Q/vYgUuUL/hGm01h6S3WCMOQ+YC0wCpgCPGWOmWmvTXhRY6Lanuvny9idoTvUwt2IsN4VPJ+Qr9H92ESl2BXsKtjGmCrdnsqvzgXuttSlrrQXeBWYOanFFoi0V5Svb/8LmVIQjy0dxo4JFRAZJIX/STATGGGOWAFXAT6y1d2XaH8habyMwBnh1by9ojAkD4ey2efPmBQKB0ptGvivdy1eb57M+2cH00HC+FT6TcgWLiAySQv60SQL3AncAw4AFxpjlQBpIZK2Xzqy7L64Ers9uiEQiVFZWHni1BSSaTvDvzX9lTaKVKWWN3Np0lnbei8igKohwMcZcBVy1S/Ma4IPW2jgQM8Y8BszA7amMy1pvLPDOPm7qR8D92Q01NTWLQqFQeDfrF52Uk+bG1gW8Fd/OhGAd32k6hxp/uddlicgQ43Mcx+sa+mWMuQI4BbgIqAaeAz4JjAYuBz4ETAfmWWuP2t/tLF26dGswGBwxc2bx77ZxHIfb2xfxWPdKmgJV/Hj4+xmhM+9FJA+WL19OMpncNmvWrJH9PV4QPZfd+DlwLLAW6AbustYuA5YZY84GVgFR3MARYF7Xch7rXkm1r4xbm85SsIiIZwo2XDLDYf+6m8euBq4e3IoK2+Pdq/hV56sE8XNT+AwOLtMklCLinYI9FFn23ZLYJm5vWwjA1xpP1LT5IuI5hUuR25Do4KaWp0nj8JlhszhDF/oSkQKgcClikXQv17Y8SbcT55yqKVxUO8PrkkREAIVL0Uo5aW5ueYaNyU6mh4ZzdcPx+Hw+r8sSEQEULkXrZx0vs6R3E8MDVZn5wkpvlgERKV4KlyL0RPcqfht5g3JfgJvDZ9AYqPK6JBGRnShciszqeAvfb1sMwNcaTmJaqMnjikRE3kvhUkQi6V6ub1lAghT/UnM4p1ZN8rokEZF+KVyKhOM43Nb6PJtTXcwIjeTTdbO8LklEZLcULkXiocgKFsbW0+Cv5BvhUwn49E8nIoVLn1BFYHnvFv6rYyl+fFzXeAph7cAXkQKncClw7akY38ycgf+pYUdzpKZ2EZEioHApYI7j8N2252lJRzmmYpzOwBeRoqFwKWB/7P47i2MbaPBX8tWGE/HrDHwRKRIKlwL1dqKNu9uXAPDvjSfRECitSzGLSGlTuBSguJPkW63PECfFP9YcxpyKsV6XJCIyIAqXAvSzjpdZm2hjclmjzmcRkaKkcCkwS2Kb+H3kLcp9Af6z8RRNSCkiRUnhUkAi6Tjfa3segM/WzWFCWb23BYmI7CeFSwG5u/0ltqd6mFU+hvOrD/G6HBGR/aZwKRAvRDfweM8qqnxlXNNwgi78JSJFTeFSALrSvdzetgiAy+vnMipY43FFIiIHRuFSAH7S/iIt6R7mlI/lfVVTvS5HROSAKVw8tii6nvk9a6j2hfiyhsNEpEQoXDzUnY7zg3b3qpJX1M9leLDa44pERHJD4eKheztfoTnVw9Hlozm3aorX5YiI5IzCxSNv9W7nkchbhAhwdcPxGg4TkZKicPFA0klze/tCHOCSYTMZGxzmdUkiIjmlcPHAb7tWsDbRxqRgPf9ce7jX5YiI5JzCZZBtSnZyX9cyfMCXGk6gTHOHiUgJUrgMIsdxuLPtBXqdFOdXH8Jh5SO8LklEJC+CXhfQxxgTBuZba2dltX0B+BKQBK611j6Yab8N+AjQA1xurV3gQckD9nxsPUt6N9Hor+TfNJW+iJSwgggXY8xXgE8C6ay26cBlwHSgClhqjHkcOB6YC0wCpgCPGWOmWmvT73nhAtLrJLmr/SUALqubQ40/5HFFIiL5UxDhArwCdABXZbV9EHjAWtsNdBtjngFOB84G7rXWpgBrjHkXmAm8ureNZHpH4ey2efPmBQKB/O/3eKDzdbamIswIjeTMqoPzvj0RES8VxD4Xa+1TwPxdmicC67KWNwJj9tC+L64EbPYtEomEo9HowIsegHeTXTzQ9Tp+fFxZf4zOaRGRklcoPZf+pIHELsvJPbTvix8B92c31NTULAqFQuHdrJ8Td7e/RIIUF1QfwpT8bkpEpCAMargYY65i56EvgDXW2nP6WX0jMC5reSzw7G7a39mX7VtrW4CW7LalS5em8tmTeDG6kYWx9dT5y/lk3dF5246ISCEZ1HCx1t4J3LmPqz8K3GeMuRNoAo4BLscdyrvcGPM73J39ddZam496D1TCSfGTjhcB+HTdbIb5yz2uSERkcBTEPpf+WGvfBB4E3gAWANdYa6PW2seBNcCqzOOXelflnj0S+Tsbk51MKwtznq7TIiJDiM9xHK9r8NTSpUu3BoPBETNnzszp63ame7lk88N0OXF+MPw8jigfldPXFxHx0vLly0kmk9tmzZo1sr/HC7bnUuzmdS6jy4lzYsV4BYuIDDkKlzzYlOzk0cjfCeDj0rrZXpcjIjLoFC558LOOl0mS5oKaQxlXVud1OSIig07hkmPLe7fwXHQdNb4Q/zrsSK/LERHxhMIlh9KOw0/blwDuRcB06LGIDFUKlxxaEH0bm2hmdKCWC2oO9bocERHPKFxyJOmk+WXHKwB8pm4WIV0ETESGMIVLjjzevZJ3U11MLQtzcuVEr8sREfGUwiUHYukk93UuA+DTdbPwa9ZjERniFC458Ej3W7Sko8wMjWJ2+b7O/i8iUroULgcoku7lgc7XALfXomu1iIgoXA7Y/3StoMuJc3zFQRxWPsLrckRECoLC5QC0pqL8LvImPuDf6mZ5XY6ISMFQuByA33QtJ+YkOaNqMpPKGrwuR0SkYChcDsDoQC0N/ko+Mewor0sRESkog3olylLzj7WHcUHNIZTphEkRkZ2o53KAFCwiIu+lcBERkZxTuIiISM4pXEREJOcULiIiknMKFxERyTmFi4iI5JzOc4FwMplk+fLlXtchIlI0kskkQHh3jytcIAmQTCZbvC5kINLptC8ej1eFQqEev9/veF3PYNB7Lv33PNTeLxT1ew6T+fzsj89xium9SB9jzDTAAsZau9LregaD3nPpv+eh9n6hdN+z9rmIiEjOKVxERCTnFC4iIpJzChcREck5hUvxagFuzPwcKvSeS99Qe79Qou9ZR4uJiEjOqeciIiI5p3AREZGcU7iIiEjOKVxERCTnFC4iIpJzChcREck5hYuIiOScwkVERHJO13MpAcaYO4CQtfbzXteSb8aYLwDXAFHgd9ba//C4pLzJvNcv4V4z41pr7YMel5R3xpjbgIuBCPBja+1PPC5p0Bhjfg8sstZ+z+tackE9lyJnjDkRuMTrOgaDMWYScDVwJDADONkYc7KnReWJMWY6cBkwHTgW+I4xps7bqvLLGHMKcAYwBZgLXJX5Ny95xpiPAqd4XUcuKVyKmDGmCrg1cxsKJgK/tta2WmvjwAu4H76l6IPAA9babmvtduAZ4HSPa8q3ccAvrLW91tou4HXAeFxT3hljRuN+kbjH61pyScNixe024A5gGFDy3/CstQuABQDGmMnAR4DzPC0qfyYCi7OWNwJjvCllcFhrf9N33xgzBzgZ+Jx3FQ2au4AvA+/zupBcUs+lSBljTgWGW2t/73Utg80YcznuB+83rLWveV1PnqSBxC7Lu71eeakwxgSNMdcDfwI+Za3d6nVN+WSM+Tiw0lr7kte15Jp6LkXAGHMVcNUuzeuAScaYvwN1QIUxptdae82gF5gHu3nPa4B3ccfkj7fWrh70wgbPRtxhoj5jgWc9qmVQGGOCwOO4O/OPtNZu8bikwXAGcJwx5gKgCUgbY9LW2js8ruuAacr9EmCM+QQwu9SPFsvs8L0dOM5am9jb+sUss0P/PuAE3A+dJ4GjrbVRTwvLo8y3+AuttRd4XYsXjDE3AJFSOVpMPRcpJnNw9y29bsyO/bw3Z4/Vlwpr7ZvGmAeBN3CHxL5YysGSMQc4JtMb7/NZa+3THtUjB0A9FxERyTnt0BcRkZxTuIiISM4pXEREJOcULiIiknMKFxERyTkdiixSxDIzJ28BtgI/tdYeMoDnPg88Za29PqvNB6wHbsA9x+ab1tq1OS1ahgT1XEQGmTEmkKPXGQb8K/Dwfr7EA8CFu7TNBUYAvwPuBL6z3wXKkKaei8huGGPOAL4PTAb+jHtCX6sx5lfANtxv9jOB+cDF1tqYMWYi8F/AcbgnQF5qrV2emUXhw4CD+6XuA8aYq4Gv4V6b5jfAicD5wHbgcGvtqkwdC3FnC753lxI/AzxirU1lnVSKMaYReA74mbX2h8aYI4Gf4l6mYBHwaWvtOuC3wA+NMVP7toUbNo9ba9uBZcaYCcaYw6y1bxzYb1OGGvVcRPphjDkIuB+4ApgAxIHvZq3y8cxjB+N+aF9kjPEDfwR+jzuD8W9wewB93oc7pcs/GGNOA76AGyinAh8CsNZ2An/LrIsxJgzMzrzurj6cWTe77ircIHwkEyw1meVbcecnewW4N7OtbcBTmdfp8w+4PZo+C4AhOR2LHBiFi0j/Pgo8bK193lrbDFyL+8HbZ561dnnmA3oh7iSTc4GAtfZua22ntfZOIGCMOSLznJettQ9n5kW7GLdnsTrTi8ieqPAPwPsz988FFmZq2CETZHNwe0d9grhDZO3W2mszbR8AlllrH8n0Rq7DnSixPvP4jqExY8wMYDQ7B9ly3F6YyIBoWEykfxOAzxpjrshuNMZUZO5mf9jHcP+WJgDTjTG7zqnUdx2Wtqy28bgXAOuzMev+o8APjDHVuD2Y/i6rEMb9ctiR1TYZaAGmGGMarbWtmZre109No4B23CC7O9NTuxB4dJc5zFpxezwiA6Kei0j/tgLftdb6rLU+oAI4ylob28tzlvQ9J/O8mewcIn26cXec9xnfdydzDZNXgbMzt0f7eX4Q8GVufTbiDrO9ANycVdNvs+rx4w6zrcxsqwN3mvsLM7fsITFw9xGl9/CeRfqlnotI/x4GHjPGzMM9NPfbuD2Qf9jDc14ARhhjPgT8FffD+jb6v0roU8DnjTEP4/4dfhn3kOI+fwCuB9Zaazf08/ztuAcC1OP2LgC6rbUJY8xXgFeMMXfjBsctxpgTgWXAlcA/WWuPznqtBzLvrwH34IRs9bgHL4gMiHouIv2w1q7APZLrEdwLlE0APruX58Rwd8x/HffD/2rc65P09rP6PcBfcPdpLMjcz77S5B9wez0P7WZbSWAJcEQ/j70J/BL4YaYX9DHco8W24V4W+qJdnvK/uMNkffuDsh2OG5oiA6Ip90U8YIwZCTuGwDDGXAacZq29KLMcxN1/Mt1au2k3r/EVYJi19ro81rkQ9xDs1/O1DSlN6rmIeOM84M/GmJHGmKm4hyX/FcAYUwl8BPcosX6DJeO/gPdngijnjDGHA20KFtkfChcRb/wad6f9SuBF3GGxX2UeuxO4Bfew4d3KHFr8M947zJUrX8QdGhQZMA2LiYhIzqnnIiIiOadwERGRnFO4iIhIzilcREQk5xQuIiKSc/8HT2lCHea7dDcAAAAASUVORK5CYII=n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid**2 * func(energy_grid), color=green)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"$\nu$F$_{\nu}$")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”
]
}
], “metadata”: {
- “jupytext”: {
“formats”: “ipynb,md”
}, “kernelspec”: {
“display_name”: “Python 3”, “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”: “3.7.11”
}, “papermill”: {
“default_parameters”: {}, “duration”: 5.99407, “end_time”: “2021-08-17T08:12:29.362956”, “environment_variables”: {}, “exception”: null, “input_path”: “Line.ipynb”, “output_path”: “../docs/notebooks/Line.ipynb”, “parameters”: {
“func_name”: “Line”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:12:23.368886”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}