- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “da7e571f”, “metadata”: {
- “papermill”: {
“duration”: 0.010576, “end_time”: “2021-08-17T08:11:10.591400”, “exception”: false, “start_time”: “2021-08-17T08:11:10.580824”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Log parabola”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “6644f88f”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:10.625837Z”, “iopub.status.busy”: “2021-08-17T08:11:10.618603Z”, “iopub.status.idle”: “2021-08-17T08:11:14.218056Z”, “shell.execute_reply”: “2021-08-17T08:11:14.219037Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 3.615442, “end_time”: “2021-08-17T08:11:14.219495”, “exception”: false, “start_time”: “2021-08-17T08:11:10.604053”, “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”: “94196ce3”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:14.247695Z”, “iopub.status.busy”: “2021-08-17T08:11:14.245606Z”, “iopub.status.idle”: “2021-08-17T08:11:14.254751Z”, “shell.execute_reply”: “2021-08-17T08:11:14.255745Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.026603, “end_time”: “2021-08-17T08:11:14.256093”, “exception”: false, “start_time”: “2021-08-17T08:11:14.229490”, “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”: “b62def94”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:14.282175Z”, “iopub.status.busy”: “2021-08-17T08:11:14.280458Z”, “iopub.status.idle”: “2021-08-17T08:11:14.286472Z”, “shell.execute_reply”: “2021-08-17T08:11:14.285355Z”
}, “papermill”: {
“duration”: 0.021298, “end_time”: “2021-08-17T08:11:14.286766”, “exception”: false, “start_time”: “2021-08-17T08:11:14.265468”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Log_parabola"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “ae7a5138”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:14.318208Z”, “iopub.status.busy”: “2021-08-17T08:11:14.314137Z”, “iopub.status.idle”: “2021-08-17T08:11:14.321492Z”, “shell.execute_reply”: “2021-08-17T08:11:14.323241Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.02756, “end_time”: “2021-08-17T08:11:14.323753”, “exception”: false, “start_time”: “2021-08-17T08:11:14.296193”, “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”: “4a05a2b2”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.010047, “end_time”: “2021-08-17T08:11:14.344341”, “exception”: false, “start_time”: “2021-08-17T08:11:14.334294”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “7fd19e34”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:14.377600Z”, “iopub.status.busy”: “2021-08-17T08:11:14.375957Z”, “iopub.status.idle”: “2021-08-17T08:11:14.381108Z”, “shell.execute_reply”: “2021-08-17T08:11:14.382237Z”
}, “papermill”: {
“duration”: 0.028697, “end_time”: “2021-08-17T08:11:14.382574”, “exception”: false, “start_time”: “2021-08-17T08:11:14.353877”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A log-parabolic function. NOTE that we use the high-energy convention of using the natural log in place of the base-10 logarithm. This means that beta is a factor 1 / log10(e) larger than what returned by those software using the other convention.</li>n”, “n”, “<li>formula: $ K \left( \frac{x}{piv} \right)^{\alpha -\beta \log{\left( \frac{x}{piv} \right)}} $</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>K: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Normalization</li>n”, “n”, “<li>min_value: 1e-30</li>n”, “n”, “<li>max_value: 100000.0</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: True</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>piv: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Pivot (keep this fixed)</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: False</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>alpha: n”, “<ul>n”, “n”, “<li>value: -2.0</li>n”, “n”, “<li>desc: index</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: 0.2</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>beta: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: curvature (positive is concave, negative is convex)</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”
], “text/plain”: [
” * description: A log-parabolic function. NOTE that we use the high-energy conventionn”, ” * of using the natural log in place of the base-10 logarithm. This means that betan”, ” * is a factor 1 / log10(e) larger than what returned by those software using the othern”, ” * convention.n”, ” * formula: $ K \left( \frac{x}{piv} \right)^{\alpha -\beta \log{\left( \frac{x}{piv}n”, ” * \right)}} $n”, ” * parameters:n”, ” * K:n”, ” * value: 1.0n”, ” * desc: Normalizationn”, ” * min_value: 1.0e-30n”, ” * max_value: 100000.0n”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 0.1n”, ” * free: truen”, ” * piv:n”, ” * value: 1.0n”, ” * desc: Pivot (keep this fixed)n”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: falsen”, ” * alpha:n”, ” * value: -2.0n”, ” * desc: indexn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.2n”, ” * free: truen”, ” * beta:n”, ” * value: 1.0n”, ” * desc: curvature (positive is concave, negative is convex)n”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: true”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “cdf06613”, “metadata”: {
- “papermill”: {
“duration”: 0.010723, “end_time”: “2021-08-17T08:11:14.404564”, “exception”: false, “start_time”: “2021-08-17T08:11:14.393841”, “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”: “7d910a26”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:14.493462Z”, “iopub.status.busy”: “2021-08-17T08:11:14.455803Z”, “iopub.status.idle”: “2021-08-17T08:11:15.288334Z”, “shell.execute_reply”: “2021-08-17T08:11:15.288966Z”
}, “papermill”: {
“duration”: 0.874019, “end_time”: “2021-08-17T08:11:15.289302”, “exception”: false, “start_time”: “2021-08-17T08:11:14.415283”, “status”: “completed”
}, “tags”: [
“nbsphinx-thumbnail”
]
}, “outputs”: [
- {
- “data”: {
“image/png”: “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”, “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”: “1b4bff3b”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.010784, “end_time”: “2021-08-17T08:11:15.311789”, “exception”: false, “start_time”: “2021-08-17T08:11:15.301005”, “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”: “b2dbae31”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:15.343597Z”, “iopub.status.busy”: “2021-08-17T08:11:15.342561Z”, “iopub.status.idle”: “2021-08-17T08:11:16.146111Z”, “shell.execute_reply”: “2021-08-17T08:11:16.150167Z”
}, “papermill”: {
“duration”: 0.82743, “end_time”: “2021-08-17T08:11:16.150542”, “exception”: false, “start_time”: “2021-08-17T08:11:15.323112”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
“image/png”: “iVBORw0KGgoAAAANSUhEUgAAAZoAAAEUCAYAAAASvPDLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAzUUlEQVR4nO3deXhU1f3H8fedSSbbQBICAQURUflaCmKLCrXuVmtVtFKtWPcFcGHHhUItblVAEREFBVEEFeqGuLVuxQXRaqPigj1uVUt/QiBAyL7//phgRxokEzLcSfJ5PU8ec+/c5RO95ptz77nneHV1dYiIiMRLwO8AIiLSuqnQiIhIXKnQiIhIXKnQiIhIXCX5HSCR5OXllRP5d1LgdxYRkRYkB6ju379/akMfqtB8XxIQTEpKyvU7iIhIS1FdXf2Dn6vQfF9BUlJSbr9+/fzOISLSYqxatYrq6urt3gnSMxoREYkrFRoREYkrFRoREYkrFRoREYmrFtkZwMySgKOA45xzV2677G86ERGJ5muLxsz6mtkzUcujzexrM/vCzIb8wK49gSMBbzvLIiKSIHwrNGY2FXg2ark3MBzoDQwEpplZZkP7Ouc+BeZtb7mR588xs17RXzU1NUGNZi0i0rz8vHW2AigiUlQABgGLnXMlQImZvQocbWY9gWOj9jvbObehGc4/EpgcvaK4uJi0tLQmHazqX1+zedY9hH97KqkDDsTz1LgSEQEfC41z7mkziy40PYA3ozZZA+zunJsOTI9DhFnAw9ErwuHwylAolNOUgxU/9RwV739AxfsfkNSjO+1+O5j0Y47AS05ulrAiIi1VInUGqAWqtlne7rgGzrmvgCu2t7wjzrkCthnTLC8vr6apLZGsSy4kuWcPih9dSvVX37Bp2u0Uzl9Iu9+cTMZJvyIQzmjScUVEWrpE6t68BugWtdwV+MqfKLHzUlIID/oVne+fQ851Ewn13o/ago0Uzl3At0POZ/Oc+VTnr/c7pojILpdILZplwEIzuwPoCAwALvU3Uuy8YJC0ww4h7bBDqPjoE4r+/DjlK/9O8aNLKX7iKdKPPpzwbwcT2nsvv6OKiOwSCVNonHOrzWwJ8DGR22ZjnHNlPsfaKSl9fkRKnz9Q9c0aih97kpLnX6b0xeWUvriclIN+SrvfDiblp/3UcUBEWjVP3Xn/Ky8vb108R2+u2biJ4iefoeSp56jdUgRA8j570+6MwaQdeSheMBiX84qIxFP96M35/fv379zQ54n0jKbVC3bIJvPCc+iy+H6yRgwn2KUzVZ9/wcY/3cLac4dT/Mxfqaus2vGBRERaEBUaHwTSUgkPHkSXRXPpcM1VJO/Tk5pv17L5tjv59qyLKPrzE9SWlvodU0SkWajQ+MgLBkk/6nBy75lJzs3XEur740hPtXvu49szL6RwwUPUFG7xO6aIyE5JmM4AbZnneaQNOJC0AQdS8eHHFD38KOV//wdFCxdT/MhSMk76Je1OP5Vgp45+RxURiZkKTYJJ6ftjUm7+MZWff0nR4kcpe/UNih9bRvGTz5Jx3NGEh/yG5G5d/Y4pItJounWWoEL79CTnmqvpvGAOGSccB0DJcy+w7vxLKbhhKpWff+lzQhGRxlGhSXDJ3bqSfcUoujx0L+HTTsELJVO2/HXyh41iw6Trqfznp35HFBH5QSo0LURSp45kXTaULovvp925Z+KFMyh/823yLxvHhgmTqVj9T78jiog0SM9oWphgZnsyzz+LdqefSvGTz1D86FLK386j/O08Uvr/hPbnDiGl74/9jiki8h0VmhYqkJFO+7N+S/jUkyh56jmKHllKRd57rM97j5QD9qfduUNI6ddXw9uIiO9UaFq4QHo67YacRsYpJ1HyzF8oWvL4d/PihPr+mPbnnqnx1ETEV3pG00oE0lJpd/qp7PbwfDJHDCOQ04HKDz9mw5V/YP3IKyl/Ow+NayciflChaWW8lBTaDT6Z3R66l6zRlxLs1JHK1f9kw4TJ5F82jrK33lHBEZFdSoWmlfJCIcKnnEiXRfPIGjeCYOdcqtxnFEy8jvUjrqD8nXdVcERkl1ChaeW8UDLhk46ny6K5ZI8fSbBzLpWfODZc/UfWj7ma8vc+8DuiiLRyKjRthJeURMaJv6TLwnvIGnMZwY45VH64mg3jJ7J+3EQqPvzY74gi0kq1uF5nZpYEHAUc55y70sz6ADcAhcBTzrknfA2Y4LzkZMInn0DG8b+g5Jnn2fLwI1S8/wHrR39AyoE/of0FZ5PyI/M7poi0Ir61aMysr5k9E7U82sy+NrMvzGzID+zaEzgS2Npf9yAiUz8DfBiXsK2QFwoRHjyI3R66l8xLLyKQlUnFP95j/eXj2TDxOio//dzviCLSSvhSaMxsKvBs1HJvYDjQGxgITDOzzIb2dc59CsyLWvUOMBQYT6RlIzHwUlJod/qpdHnoXtoPPZ9A+3aUv/UO+ZeMYcM1N1L5hQbvFJGd49etsxVAEZGiAjAIWOycKwFKzOxV4Ggz6wkcG7Xf2c65Ddsc6yfA1pEla2kkM8sBcqLXLVq0KBgMBhv/U7QigbQ02p95GuGTT6D4iacoenQp5W+8Rfkbb5F2xKG0v+Askrvv4XdMEWmBfCk0zrmnzSy60PQA3ozaZA2wu3NuOjB9B4f7FlgElAF3xBBjJDA5ekVxcTFpaWkxHKL1CWSk0/6cIYRPPYmix5ZR/NiTlL26grLXV5L+y2Nof+6ZJHXO9TumiLQgidIZoBao2ma5ensbO+e+Aq6o//4l4KUmnHMW8HD0inA4vDIUCuVsZ/s2JRAOk3n+WYRPHUTRkscoXvoMpX95kdKXlhM+5UTa/e63BLMavLspIvI9iVJo1gDdopa7Aq/F84TOuQKgIHpdXl5ejcYE+75gZnuyhl9Iu8Ens+XBJZQ8+wLFjy2j5NkXCJ/+a9qdfiqBjHS/Y4pIAkuU92iWAWeYWYqZdQUGEOdCI7EJdupI9tgRdF4wh7SjDqeurIyihYtZe9bFFD26lLrKSr8jikiCSohC45xbDSwBPgaWA+Odc2X+ppKGJHfrSs41V5E79w5SBxxI7ZYtFM6Zz9pzhlHy3AvU1dT4HVFEEoyn8a7+Ky8vb11SUlJuv379/I7SYlR88BGF9y6k8qPVACTt0Y32F5xN2uGH4AUS4u8YEYmzVatWUV1dnd+/f//ODX2u3wSyU1L270OnmVPJuWkyyT17UP3vNWy8fgr5l47VwJ0iAiROZwBpwTzPI23gQaQe3J+y5a9TeP+DVH32BRuu/iMp/Q8gc9gFhPbd2++YIuITtWik2XiBAOnHHEGXBXPIGnMZgQ7ZVOS9T/7w0Wy8aTrVa/P9jigiPlChkWbnJSURPvkEuiyaS/vzz8JLTaX0peWsPW8Ym+++j9qiYr8jisgupEIjcRNIS6P9uWfS5cF5ZJx8AtTUUvzIE3x79sUUPfKEukSLtBEqNBJ3wQ7ZZI+5jM73zSb10J9RV1RM4d33sfbc4ZS8uJy62kYPUSciLZAKjewyyd270fH6SXS6Yxqh3vtRk7+eTTdPJ/+SsZTnve93PBGJExUa2eVS+vSm06xbyLl2Iknddqfq8y/YcOUfWH/1HzUtgUgrpO7N4gvP80g7/BBSDzmYkmefZ8sDD1Pxzrvk/+M90o87mvYXnkNSp45+xxSRZqAWjfjKS0oifMqJdHlwHu3OGYKXEqL0+ZdZd+5wChc8RG1Zud8RRWQnqdBIQgikp5N5wdl0WTiX9F8dS11lZWTQznOHUfL8y+owINKCqdBIQgl2zKHDlaPJvft2Ug7oS23BRjZNnUH+ZeOo+OAjv+OJSBOo0EhCCu27Nx2n30TO9ZNI6robVZ9+zvoxEyi49iaq/2+t3/FEJAYqNJKwPM8j7dCf0fm+2WReejFeOIOy11ay9oJLIiMMFJf4HVFEGkGFRhKel5xMu9N/TZdF8wifOui7EQbWnjOU4mXPaQ4ckQSnQiMtRjCzPVkjh9P5vrtIHXgQtYVb2DxzNusuHkn523l+xxOR7VChkRYnufsedLxpMh2n3UDSXntS/fU3bJgwmfUTJlP1zb/9jici22hxL2yaWRJwFHCcc+5KMxsMnAUUA7c65z70NaDsMqkH/oTOc++g5C8vsuX+B6l4O491ee8THjyI9uecSSCc4XdEEcHHFo2Z9TWzZ6KWR5vZ12b2hZkN+YFdewJHAl798pnAEGAsMDFeeSUxecEg4ZOOp8vCuYTPGAyeR/GjT7L2vOGU/OUFvX8jkgB8KTRmNhV4Nmq5NzAc6A0MBKaZWWZD+zrnPgXmRa26FZgFjAPSY8iQY2a9or9qamqCmnq4ZQpkpJM1/EI6z7+L1IP7U7tpM5tuuYP8y8dT8fEnfscTadP8unW2AigiUlQABgGLnXMlQImZvQocbWY9gWOj9jvbObdhm2P1AUYC+wLDYsgwEpgcvaK4uJi0tLQYDiGJJnmPrnScch1lb71D4ex5VLnPWD/yStKPPYrMoecT7Jjjd0SRNseXQuOce9rMogtND+DNqE3WALs756YD03dwuDXA/UAakVZNY80CHo5eEQ6HV4ZCIf0magXSBh5E6k8PoPiJZWxZ9GdKX1xO2Yq3aHf2GbT7zSl4oWS/I4q0GYnSGaAWqNpmuXp7GzvnvgKuqP/+eeD5WE/onCsACqLX5eXl1Xiet509pKXxQsm0G3Ia6cceTeG8BZS+8De2zFtA6XPPk3n5MNIGHuR3RJE2IVG6N68BukUtdwW+8ieKtDbBnA50mDCOTnfeSrLtS/V/vqVg4nVsmDCZqm/W+B1PpNVLlEKzDDjDzFLMrCswAHjN50zSyqT03o/cu6aTfeVoAtlZlL+dx7qLLo8MZ1Na6nc8kVYrIQqNc241sAT4GFgOjHfOlfmbSlojLxAg41fH0uWBewiffipAZDib8y6h9G+vol6HIs3P0/9Y/5WXl7cuKSkpt1+/fn5HkV2k6ps1bJ51DxV57wGQcsD+ZI26hOQe3X1OJtJyrFq1iurq6vz+/ft3bujzhGjRiPgluXs3Ok67ng6TJxDsmEPF+x+wbuhINs+9n9oyNapFmkOTCo2Zhc2s0S9HiiQyz/NIP+JQOj9wN+EhvwGgeMnjrDvvEkpfWaHbaSI7qVHdm80sBbgAOBX4GZBRv74MeAtYCtyn5yrSkgXS0sgadgEZv/wFm2fOoeL9D9h4/RRS+h9A1shLSO7ebccHEZH/scMWjZmdBbxOZIyx24H9iBSadGBv4GZgd2CFmQ2NW1KRXSR5zz3oOP1PdPjDlQRyOlCR9z7rLh5B4b0PUFtW7nc8kRanMS2aNOBQ51xlA5+tq/962cwmA79rznAifvE8j/SjjyB1wEFsWfgwxY8/RdHDj1L60itkXTaU1MN+hl7uFWmcJvc6M7PDiIwvtqJ+oMsWT73OZHuq/vUVm2beTeUHHwGQcnB/skYMI7lbV5+Tifgvnr3ODgVKgMn1rRmRVit5rx50mnEz2b8fTyA7KzL3zUWXU7jgIeoqG2rsi8hWTS40zrmbnXN/ds6dBXxrZjsa/FKkRfM8j4xjj4q87Dl4ENTUUrRwMesuHkH5u6v8jieSsJo8qKaZ3QvsBnQBkgE9JZU2IRDOIGvEcNKPO4ZNM+6iyn3Ghismkf6Lo8i89CKC2Vl+RxRJKDszevNs4FtgnXNO0xhKmxPqtQ+5d95KyVPPUTh/IaUvLafs7++QOfR8Mk44Di+g96FFoHHdm7/ZZvlkAOfcu865b1VkpC3zgkHCpw6iy4K7STviUOqKitl8252sHzOBqn995Xc8kYTQmD+5tn1LbWk8goi0ZMGOOeRMnkDOzdcS7NKZyo9Ws27YaArnLqC2XHeVpW1rTKHZtv+zXh4Q2Y60AQfS+b67aHfmaQAULXmMdRdeTtlb7/icTMQ/TbmJrIGfRH5AIDWVzKHn03nuTEJ9elOzdh0FE6+j4NqbqdlQsOMDiLQyjR3rrBNRLZltl51z+c0fTaRlS96rB51un0LpX15k89z7KXvtDcr/8S6ZF55Lxikn4AWDfkcU2SUa06LxgLVRXx6RYWe2Ln8bt3QiLZwXCJBx4i/psuBu0o89irrSMjbfeQ/5I6+k6suv/I4nskto4rMoGoJG4q383VVsvv0uqtf8HwSDtDvzNNqffQZeKOR3NJEm29EQNDu8dWZmpzvnHm3EdgFgiHPu4SbkbDQzOxwYUb84GwgCw4iMJn2Tc+7NeJ5fZGek/rQfnefNYsuiJRQteZyiB/9M2atvkD1+BCn79/E7nkhcNOYZzW5m9g7wJPA34GPn3BYAM8sC9gd+AZwAzG3sic2sL3Czc+6k+uXRwDigGpjknFuynV0PB84FQsBM4H3grPocJwIqNJLQvJQUMi8+j7SjDmPTrbOocp+xfswEMgYdT+bQCwiEM/yOKNKsGnXrzMzaAUOB3wAHEXlO4wFlwDvAc8A851xhY05qZlOBM4EPnHMnmVlv4LH6Y6cDeUDf7R3PzLoA1wILnXMrzewIYAYw3jm3vJEZcoCc6HWLFi1aGQqFcg444IDGHEJkp9XV1FD8xFNsuf9B6sorCOR0IHvUJaQddojf0UQabadvnQE454qA24DbzMwj8gs6CVjvnKtpQq4VQBEwsH55ELDYOVcClJjZq8DRZtYTODZqv7OBfvXbT3LOFZjZhc65+8xsIPAA0KhCA4wEvjfqdHFxMWlpaU34cUSaxgsGaXf6qaQd+jM2zbiLin+8R8Hkm0g77BCyRg4n2DFnxwcRSXAxj3XmnKsDNuzMSZ1zT5tZdKHpwfdvea0BdnfOTQe+Nyq0mY0CNgPTzWwlUGNmDwKpwMIYYswCvvc8KRwOrwyFQvo/W3a5pN260HHq9ZS+uJzC2fdS9vpKyt9dRebwCzRumrR4OzOoZnOqBaq2Wa5uaEPn3CkNrJ4f6wmdcwXA996ey8vLq9GsieIXz/PIOO5oUg/uT+HseZS+9Aqbb7uT0pdfIXvcSJL30CRr0jIlyp9Ja/j+mGpdga/8iSLir2BWJh0mXhEZNy23E5WrPmLdxSPY8tAj1FU3+PeXSEJLlEKzDDjDzFLMrCswAHjN50wivkobcCCd759NePDJUF3NlvkLyb9sPJWff+l3NJGYNLrQmNmwBtaFmmMaZ+fcamAJ8DGRh/njnXNlO3tckZYukJZG1ohhdLrzVpJ6dKfq8y/Iv3Qshfc/SF1V1Y4PIJIAGj0ygJl9TOQh/HDn3EdmdiyRFyaLnXM/iV/EXUcjA0giq6usYsuDSyh6+FGorSVprz3pcOVoQvv18juatHE76t4cy62zfsDjwHIze41IC2Qm0H/nY4rIjnihZDIvPIfcOTNI3qcn1f/6mvwRV1A4dwF1lZV+xxPZrlgKTQ1QTuRFzW5EBtT8SDNsiuxaoX33Jnf2bbS/4GwIBCJz3gwdRcVHn/gdTaRBsRSa94GxwFnOuZ7A7cBjZvZEHHKJyA/wkpJof84QOt9zO8m2L9X/XsP60Vex+a55mtFTEk4sheZpoI9z7nkA59w8YD+gUcPOiEjzS96rB7l33krmsPMhKYnix5ex7uKRlL//gd/RRL4TywubvwEGm1m8sohIE3jBIO2GnEbqIQPYNG0mlav/yYZxE8k45UQyh55HID3d74jSxsVSaC6J+j4AdAbOA95u1kQi0iTJ3feg08ypFC99hi3zF1Ky7FnK33qH7PEjST2wVXQMlRZqpyY+M7MkYKVz7uDmi+QfdW+W1qL6P9+y8daZVK76CICMk44n85IL1bqRuGjO7s0NyQQ67OQxRKSZJXXdjU7TbyJr9KV4qamUPPNXPbsR3zT61pmZfQJEN38CwJ7An5o7lIjsPC8QIHzKiaQe1J+N026n8oOP2DBuIuHBg2h/8XkEUlP9jihtRFOf0UBkhOU1zrl/NWMeEWlmSbt3odNtN1G89GkK5z1A8RNPU/52HtlXjSWlz4/8jidtQKMLjXPu1XgGEZH48QIB2v3mFFIP6s+maTOoXO1YP+Zqwr89lczzz8ILhfyOKK3YDgtNA7fM/odzrnezJRKRuEnu3o1OM6dR9MhStix4kOIlj1P+5jt0+P04Qr328TuetFKNadHcAnwB7AN8Ht84IhJvXjBI+zNPI23ggWy8eUZkROjLxtHu7DNof9Zv8ZKT/Y4orUxjep3dSWRcs7nAauCTBr5EpIVJ3qsHubOn0/6834HnUbRwMfmXj6fqy6/8jiatTGMKzV+IFBMPWEek6KwFvo36p4i0QF5SEu3P+x25d00naa89qfr8S9ZdMoYtDz9CXU2N3/GklYhlPpp/Oef2inMeX+mFTWnL6iqr2PLAQxT9+QmorSV5v150mDCO5O7ddryztGk7emEzll5nCVFkzOxwYET94mzg50DfrZ8754b4kUukpfNCyWQOPZ/Unw9k05QZVP3zU/KHjybzkgvJOPkEPM/zO6K0UDs7MkCTmVlfM3smanm0mX1tZl+Y2Q8Vi8OBc4GLgfOcc3+qLy6vAXfEN7VI65fSez86z7uD8OBB1FVUsHnmHDZMmEzNhgK/o0kL5UuhMbOpwLNRy72B4UBvYCAwzcwyG9rXOXcjkAVMA+bV798Z6OWcWxnf5CJtg5eSQtaI4XScdgOBnA5UvPMu6y4eQemrK/yOJi1QLCMDNKcVQBGRogIwCFjsnCsBSszsVeBoM+sJHBu139lEppQeBExyzm39E2ssMCeWAGaWA+REr1u0aFEwGAzG+rOItFqpB/6ELvPvYtPM2ZQtf52N102h/NijyBp5CYFwht/xpIWIZayzV4AngKXOuX/vzEmdc0+bWXSh6QG8GbXJGmB359x0YPo2OUYBm4HpZrbSOTcX+JFzzsUYYyQwOXpFcXExaWlpMR5GpHULtG9HzjVXU3rIADbdPofSF5dTseojsieMJfWA/f2OJy1ALC2aB4ETgRvN7J/Ak8DjTfgF35BaoGqb5eqGNnTOndKYdY0wC3g4ekU4HF4ZCoVytrO9SJuWfsyRhPr+mE1TZ1Dx3gdsGD+J8Gm/JvOiczSEjfygWHqd3Qvca2bJwBFEis4zZlYKPAQscs419Z2aNUB0H8quRB7ux039bbfvPd3My8urUc8ake1Lyu1Ex1tupPiJpymct4DiR5dSkfce2b8fT2jvhOiYKgmoKZ0BcoDuRKYI6AgUAj2Bt83smibmWAacYWYpZtYVGECcC42INI0XCNDutFPofPftJO/Tk6ovvyL/srEULXlcL3lKgxpdaMzsRjN7F/gXMBh4jkhPr8Odc5cABwITmhLCObcaWAJ8DCwHxjvnyppyLBHZNZL32pPcu6bT7szToLqGwrn3s378JKrXrvM7miSYWJ7R7A3cDDxX3ztsWwXAcY09mHPuFeCVqOVbgVtjyCMiPvOS61/yHHgQG2++jcoPPmLdxSPIGnUJ6ccerZc8BYit0AwkcktrqplFr68GNgAvAlOaL5qItBQpfX9M53mz2HzXXEr/+hKbpsyg/O95ZI+9jEA47Hc88Vksz2jmAl8BFwG/BIYB/wYeA64FDiMyJIyItEGBjHQ6XDWGDtf+Hq9dmLLlr7Fu6CgqPvzY72jis1gKzUjgdOfccufcZ865l4AzgAuccy8QGRZmcDxCikjLkX74z+l8752kHLA/NevyWT/29xTe/6A6CrRhsRSaSmCPbdZ1iDpGcv02ItLGJXXqSMdbbqD90PMjc90sWsL60VdR/R/NKtIWxfKMZgLwopk9DnwD5BJp0cwws25EeovFNAyMiLReW2fyTO1/ABtvvIXK1Y51w0aRNVodBdqaRrdonHNLgEOIFJnuQA1wkXNuClBMZCTlP8YlpYi0WKFe+5B7z0wyTjiOurIyNk2ZwcYbb6G2uNjvaLKLNGriMzMLAO8CBzvnWu3tMU18JhJfpa+9wabps6grKiaY24kOE8eTsn8fv2PJTtrRxGeNatE452qBfxDpaSYi0iTf6yiQv5714yZSeN8i6qobHNpQWolYntHsD1xgZpOATdEfOOd6N2sqEWm1tnYUKH50KYXzF1H04J+pyHufDhOvIKnrbn7HkziIpdBcGbcUItKmeMEg7YacRspP6zsKfFLfUWDMZWQce5Tf8aSZxTJ686tbvzezUGt+ViMiu8bWjgKFd82l5LkX2HTzdCry3iNr9KUENDdUqxHLoJpZZjbPzAqAUjPb38weN7NOccwnIq1cIC2V7CtG0eGPE/AyMih94W/kDx9D5Wdf+B1NmkksL2zeDaQAP63f7xNgLTA/DrlEpI1JP/JQOs+dSehHRvWa/5A/YjzFTzxNY3rGSmKLpdCcCIxxzn0N1DnnqoCJgG6oikizSNqtC51mTo1MPVBVzeY776HgmhupKdzidzTZCbEUmi8B22bdHoAmnxCRZuMlJZE59Hw6Tr2eQHYW5Sv/Tv6wUVSs+sjvaNJEsRSaccBSM5sFeGY2BXgGmBSXZCLSpqUe9FM6z5tFSv+fULN+A+vHT2TLAw9rcM4WKJYhaF4GDgLWEHleUwac6Jz7c5yyiUgbF+yQTcep15E57HzwPLY88DDrr5hE9foNfkeTGDRqCJpEY2bHEBmlIB24CXiHyLOi45xzTX7fR0PQiCSuik8cG2+YRs3adQTatyf76jGk/exgv2MJOx6CptHv0ZjZgcA0oNu2+znnejYlnJn1BW52zp1UvzyayC26amBS/UCeDekDnEVktIITiUwjfSSg4WBFWqmUHxmd585k0/Q7KXt1BQWTric8+GQyh12AF0r2O578gFie0SwE8oChwJnbfMXMzKYCz0Yt9waGA72JTBs9zcwyG9rXOTcT+DlwL7DCOfcpMC/G8+eYWa/or5qammBLbOGJtBWBcJgOf7yarHEjIBSi+ImnyB9xBVVr/uN3NPkBsQxB0w34o3OurJnOvQIoIlJUAAYBi51zJUCJmb0KHG1mPYFjo/Y7GzjZOXefmQ0EHiAyF06sRgKTo1cUFxeTpreRRRKa53mETzqelB//iIIbplL1+RfkDx9D9rgRpB9zhN/xpAGxFJp5wCXAjOY4sXPuaTOLLjQ9gDejNlkD7O6cmw5Mj97XzDwzexBIJdLSaopZwMPRK8Lh8MpQKJTTxOOJyC6UvNee5M6+jcLZ91LyzF/Z+KdbqPjgQ7IuH4YXCvkdT6LEUmgOBQ40s6uBjfXrPCIvbzbH6M21QNU2yw2OHe6cm882IxI4574CrmjsyZxzBUSe7XwnLy+vRrP+ibQcgdRUsseNIKVfXzbddiclT/+VytWODpMnkNytq9/xpF4sheaqqO9TgfJmzrKGyO25rboCrzXzOUSkFUo/5giS992bjddPoeqLf5F/yRiyrxhF+pGH+R1NiK0zwAfAOcATRB7ibwJGAaubKcsy4AwzSzGzrsAAVGhEpJGSu3cj967pZJz4S+pKy9h4/VQ2zZxDXaUGmvdbLIVmDhDifwfVvLc5gjjnVgNLgI+JPNwf34wdD0SkDfBSUsgeP5IOE8fjpaZSsuxZ8kdcSfV/vvU7WpvW6Bc26x/c7+mc22hmNc65YH334zXOuXZxTbmL6IVNkdaj6pt/U3DdFKr/9TVeRnrkVtoRh/odq1Xa0QubGlRTRFql5O57kHvXdNJ/dSx1JaVsvG4Km+64m7rKqh3vLM1Kg2qKSKsVSE2lw5WjyZ4wFi81hZInnyF/9FVU/99av6O1KbEOqnkgGlRTRFqYjOOOIXf2DJL27E6V+4x1w0dT9vpKv2O1GS1yUM140TMakdattqyczXfMofT5lwEiY6UNvwAvWWOl7YzmfEYjItKiBdJS6XD1WLKvGoOXkhIZK2301VSvy/c7WqumQiMibU7G8b8gd/Z0krp3o+qfn5I/fAzl77zrd6xWS4VGRNqk5L16kDv7NtKOPIzaLVvYMGEyWxYupq621u9orY4KjYi0WYH0dDpccxWZI4ZBIMCWBQ9RMPE6agq3+B2tVVGhEZE2zfM82g0+mU4zphDI6UD523nkDx9N5T8/9Ttaq6FCIyICpPT5EZ3n3kHKT/anJn89+aOvovjpv6CeuTtPhUZEpF4wO4uO026g3Vm/hapqNs+4i01TbqO2vLkHq29bVGhERKJ4wSCZF51Lzo3X4IUzKH1xOes1XfROUaEREWlA2iED6Hz3TJL32ZuqL78i/9KxlL2m0QSaQoVGRGQ7knbvQu6saaSfcBx1JaUUXHsTm+fMp666wcl/ZTtUaEREfoCXkkKHK0aRfeUoCIUofnQp68dPoqZg4453FkCFRkSkUTJ+dRy5s24huFsXKj/8mHXDRlHxwUd+x2oRVGhERBoptO/edL7ndlJ/djC1mzazfvwkip54Sl2gdyDJ7wCxMrNjgGFAOnATUATcABQCTznnnvAxnoi0coFwmJwb/kDRQ4+wZcFDFN45lyr3GVljLyeQmup3vITkW4vGzPqa2TNRy6PN7Gsz+8LMhvzArn2As4DJwC+Ag4CtgxN9GLfAIiL1vECA9ucMIeemyf/tAj3qKqq/1YRqDfGl0JjZVODZqOXewHCgNzAQmGZmmQ3t65ybCfwcuBdYAbwDDAXGE2nZNDZDjpn1iv6qqakJqgksIo2VNuBAOs+5neSePaj6/EvyLx2rUaAb4NetsxVEbnkNrF8eBCx2zpUAJWb2KnC0mfUEjo3a72zgZOfcfWY2EHgAeA7YOihRLMOujiTSKvpOcXExaWlpMf8wItJ2JXXdjU6zbmXT9FmU/e1VNkyYTPuLzqHdmafjeZ7f8RKCL4XGOfe0mUUXmh7Am1GbrAF2d85NB6ZH72tmnpk9CKQCC4FSYBGRqaXviCHGLODh6BXhcHhlKBTKieEYIiKRCdUmXUHxfr0ovHs+W+5dSJX7nOyrxxBIT/c7nu8SpTNALVC1zXKDb0Q55+YD87dZ/VKsJ3TOFQAF0evy8vJq9BeIiDSF53m0O+0UkvfZi43XT6Xs9ZVUff0NOdf/geTu3fyO56tE6d68Boj+L9EV+MqfKCIiTZd6wP7k3jOT5P16Uf3NGvIvG0vZijd3vGMrliiFZhlwhpmlmFlXYADwms+ZRESaJKlTR3Jvn0LGCcdRV1pGwR//ROH8RdTV1PgdzRcJUWicc6uBJcDHwHJgvHOuzN9UIiJN54VCZF8xiqxxIyA5iaKH/syGSddTu6XI72i7nKfuvP+Vl5e3LikpKbdfv35+RxGRVqTiE8fGyTdRs6GA4G5dyLl+IqG9e/odq9msWrWK6urq/P79+3du6POEaNGIiLRmKT8ycu++ndD+faj5di3rR1xJ6Suv+x1rl1GhERHZBYIdsul0642EB59MXUUFG6+fSuH8hdTVxvL6X8ukQiMisot4SUlkjRhG9lVj6p/bPELBH26gtrjE72hxpUIjIrKLZRz/CzrdPpVATgfK33qH/MvHU/XNGr9jxY0KjYiID1J+ZHSeM4NQb6P632vIv3w8ZX//h9+x4kKFRkTEJ8GOOXS6bQrpx/+CupISCiZex5bFj7W6+W1UaEREfOSFksm+cjRZI4aD57Fl3gI23ngLteXlfkdrNio0IiI+8zyP8OBBdJx2A4H27Slb/hrrR11N9dp8v6M1CxUaEZEEkfrTfuTOua1+fpsvyL90DBWrPvI71k5ToRERSSBJu3Wh06xbSDv859QWbmH9FZMoXvZci35uo0IjIpJgAmlpdJg8gfYXngO1tWyeOZvNt91JXVXVjndOQCo0IiIJyPM82p99Bjk3/AEvPY2SZ59n/fhJ1Gzc5He0mKnQiIgksLRDBpB7120kddudyo9Wk3/ZOCo/+8LvWDFRoRERSXDJe+5B7uzbSDnwJ9Tkr2f96Ksoe22l37EaTYVGRKQFCITDdLz5WsKDB1FXXkHBtTexZdGSFtFJQIVGRKSF8IJBskYMj0ymFgyy5f4H2XjjLdRVVPgd7Qcl+R0gVmZ2DDAMSAduAnYDzgKKgVudcx/6GE9EJO7CJx1P8h5dKZh8M2XLXyP//76l4w1/INgxx+9oDfKtRWNmfc3smajl0Wb2tZl9YWZDfmDXPkQKy2TgF8CZwBBgLDAxnplFRBJFSr++5M6+jaQ9u1PlPmPdpWOpdJ/5HatBvhQaM5sKPBu13BsYDvQGBgLTzCyzoX2dczOBnwP3AiuAW4FZwDgirRwRkTYhafcu5N55K6kDDqS2YCPrx0xIyJk7/bp1tgIoIlJUAAYBi51zJUCJmb0KHG1mPYFjo/Y7GzjZOXefmQ0EHgBeAkYC+xK5pdYoZpYDfK+duWjRomAwGGzijyQisusFMtLJufEaCucuoPjRpWy8fipVX31D+3PPxAskxmN4XwqNc+5pM4suND2AN6M2WQPs7pybDkyP3tfMPDN7EEgFFgIVwP1AGpFWTWONJHL77TvFxcWkpaXFcAgREf95wSBZl15Eco/ubJpxF0ULF1P91TdkTxhLIDXV73gJ0xmgFqjaZrm6oQ2dc/OB+dusfr4J55wFPBy9IhwOrwyFQon5NE1EZAcyfnUsSd12p+CPf6LstTeo/nYtOTdeQ1Knjr7mSox2VaQF0y1quSvwVTxP6JwrcM59Gv0VDAZrPM+L52lFROIqpe+PyZ0zIzIC9GdfkH/pWCo+cb5mSpRCsww4w8xSzKwrMAB4zedMIiItUlKXznS6YxqphwygduOmSCeBl1/xLU9CFBrn3GpgCfAxsBwY75wr8zeViEjLFUhPJ+f6SbQ78zSoqmLjn26l8L5F1NXW7vIsXksYvmBXycvLW5eUlJTbr18/v6OIiDSbkhf+xqbpd0BVNWlHHkaHq8fgpaQ02/FXrVpFdXV1fv/+/Ts39HlCtGhERCR+Mo47mk7TbyKQ2Z6yV14nf+zvd+l0Ayo0IiJtQEqf3pHpBvbcg6p/fhqZbuCLf+2Sc6vQiIi0EUm7dyF31i2k9K+fbmDUVZS99U7cz6tCIyLShgTCYTpOuZaMk0+grqyMgj/cQNFjy+I63YAKjYhIG+MFg2SNvpSsEcMBKJw9j82330VddYPvye80FRoRkTbI8zzCgweRc+M1eGlplDz9Vzb8/lpqS0qb/VwqNCIibVjawIPInXULwdxOUFuLlxJq9nMkylhnIiLik+SePcidfRtecjJeUvOXBRUaEREh2CE7bsfWrTMREYkrFRoREYkrFRoREYkrFRoREYkrFRoREYkrFRoREYkrdW/+vpzq6mpWrVrldw4RkRajOjJ0Tc72Pleh+b5qgNLS0uJQKFSxvY0qKytTGvq8trbWq6ysTA+FQqWBQKDFzCi3vZ8n0c+1M8eKdd/Gbt+Y7Xa0TWu7vmDXXWO6vny7vnKo//3ZoLq6On1t89WrV6+Hm/J5r4i6Xr169fL7Z2jOnzdRz7Uzx4p138Zu35jt2tr11dz/3XfVeXR9Nd+XntGIiEhcqdA07PGd/Lyl2ZU/T3Oea2eOFeu+jd2+Mdu1tesLdt3PpOsrAa8vr66uRd3qTWhm1gtwgDnnPvU7j7Quur4knuJ5falFIyIicaVC07wKgOvq/ynS3HR9STzF7frSrTMREYkrtWhERCSuVGhERCSuVGhERCSuVGhERCSuVGhERCSuVGhERCSuVGhERCSuVGhERCSuVGhERCSuVGhERCSuNMNmHJnZ4cCI+sXZzrlXfIwjrYyZHQMMA9KBm5xzb/ocSVoZM8sA/gEc5Jwrbupx1KJpIjPra2bPRC2PNrOvzewLMxtSv/pw4FzgYuA8P3JKy9TI66sPcBYwGfiFHzmlZWrk9QXwR+DLnT2fCk0TmNlU4Nmo5d7AcKA3MBCYZmaZzrkbgSxgGjDPh6jSAsVwfc0Efg7cC6zwI6u0PI29vszsfOCvwPqdPadunTXNCqCIyH8UgEHAYudcCVBiZq8CR5vZlvrPJjnnNLS7NFZjr69s59x9ZjYQeABY7k9caWEadX0BRwGbgIOJPAKY0tQTqkXTBM65p/n+X5A9gK+jltcAuwOjgGxgupkN22UBpUWL4fryzOxB4GFg8S4LKC1aY68v59x5zrkxwNvAnTtzTrVomkctULXNcrVz7hSf8kjrsr3raz4w359I0oo0eH1tXXDOnb+zJ1CLpnmsAbpFLXcFvvInirRCur4knuJ+fanQNI9lwBlmlmJmXYEBwGs+Z5LWQ9eXxFPcry8VmmbgnFsNLAE+JvJAdrxzrszfVNJa6PqSeNoV15dXV1fXnMcTERH5HrVoREQkrlRoREQkrlRoREQkrlRoREQkrlRoREQkrlRoREQkrjQEjUgrYWajgbXAOuBu59x+Mey7AnjZOTc5ap0HfANcS2SU6Budczs9ZLy0PWrRiPjIzILNdJz2ROY+eqyJh1gMDN5m3cFALvA4cAeR6S5EYqYWjUgj1M9mOQPYm8hcHpc45zaa2QIgn8hf/P2AF4DfOefKzawHkXmIfkbkrethzrlV9fN8/AaoI/LH3klmNha4GigDHgIOBU4mMhdIH+fcZ/U53gDmO+fu2ybiUOBJ51yNmUXn7gC8Dsx1zs00swOAu4G+wErgYufc18CjwEwz23fruYgUnr845zYD75vZnmb2Y+fcxzv3b1PaGrVoRHbAzPYgMhT/ZcCeQCVwS9Qm59V/1pPIL/AhZhYAngKeIDKk/0NEWgZbnQAsBE41s6OA0USKy5HArwGcc1uAv9Vvi5nlAAfWH3dbv6nfNjp3OpGi+GR9kQnXL08hMnDiu8B99efKB16uP85Wp/L96QeWAxqRXGKmQiOyY2cBjznnVjjnNgCTiPwS3mqRc25V/S/rN4iMhHswEHTOzXHObXHO3QEEzWz/+n3+4Zx7zDlXBfyOSIvj8/rWxW1Rx14KnFj//fHAG/UZvlNf1A4i0mraKonIbbTNzrlJ9etOAt53zj1Z30q5BviZmWXVf/7d7TMz6wvsxveL2ioirTORmOjWmciO7QlcYmaXRa80s9T6b6N/8ZcT+f9qT6C3mW07mODu9f/cFLWuO/Bq1PKaqO+XAbebWQaRls0TDeTLIfJHY2HUur2BAmAfM+vgnNtYn+mEBjJ1ATYTKWpz6ltwg4Fl2wyuuJFIS0gkJmrRiOzYOuAW55znnPOAVOAnzrnyHezzztZ96vfrx/cLylYlRB66b9V96zfOuXXAe8Bx9V/LGtg/CfDqv7ZaQ+RW3FvADVGZHo3KEyByK+7T+nMVAn8hUmQG87+zdtYRmRRLJCZq0Yjs2GPAc2a2iEh335uItExO/YF93gJyzezXwItEfnFPBfZqYNuXgRFm9hiR/yevINJNeaulwGTgS+fcvxvYfz2RTgRZRFodACXOuSozuxJ418zmECkiN5vZocD7wEjgdOfcT6OOtbj+58sm0rEhWhaRjg8iMVGLRmQHnHMfEekR9iTwf9TfStvBPuVEHur/nkghGAsMds5VNLD5PcDzRJ6BLK//vjrq86VEWkOPbOdc1cA7wP4NfLYauB+YWd86OptIr7N84FfAkG12eYbIrbStz4+i9SFSQEViovloRHxmZp3hu9tkmNlw4Cjn3JD65SQiz1t6O+f+s51jXAm0d85dE8ecbxDp1v1hvM4hrZNaNCL++xXwrJl1NrN9iXR1fhHAzNKAM4n0NmuwyNSbB5xYX5SanZn1ATapyEhTqNCI+O9BIg/8PwX+TuTW2YL6z+4AbibSFXm76rsrz+V/b4U1lzFEbh+KxEy3zkREJK7UohERkbhSoRERkbhSoRERkbhSoRERkbhSoRERkbj6f1w15yM9Q+VKAAAAAElFTkSuQmCCn”, “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”: “7a025448”, “metadata”: {
- “papermill”: {
“duration”: 0.012462, “end_time”: “2021-08-17T08:11:16.175536”, “exception”: false, “start_time”: “2021-08-17T08:11:16.163074”, “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”: “cb65af61”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:16.301125Z”, “iopub.status.busy”: “2021-08-17T08:11:16.267911Z”, “iopub.status.idle”: “2021-08-17T08:11:17.138726Z”, “shell.execute_reply”: “2021-08-17T08:11:17.139501Z”
}, “papermill”: {
“duration”: 0.951528, “end_time”: “2021-08-17T08:11:17.139816”, “exception”: false, “start_time”: “2021-08-17T08:11:16.188288”, “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**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”: 9.428462, “end_time”: “2021-08-17T08:11:17.927528”, “environment_variables”: {}, “exception”: null, “input_path”: “Log_parabola.ipynb”, “output_path”: “../docs/notebooks/Log_parabola.ipynb”, “parameters”: {
“func_name”: “Log_parabola”, “linear_range”: false, “wide_energy_range”: true, “x_scale”: “log”, “y_scale”: “log”
}, “start_time”: “2021-08-17T08:11:08.499066”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}