- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “90ce317a”, “metadata”: {
- “papermill”: {
“duration”: 0.011887, “end_time”: “2021-08-17T08:15:43.743003”, “exception”: false, “start_time”: “2021-08-17T08:15:43.731116”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Cauchy”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “e0ebaa3d”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:43.776091Z”, “iopub.status.busy”: “2021-08-17T08:15:43.774938Z”, “iopub.status.idle”: “2021-08-17T08:15:46.885713Z”, “shell.execute_reply”: “2021-08-17T08:15:46.897652Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 3.144728, “end_time”: “2021-08-17T08:15:46.898130”, “exception”: false, “start_time”: “2021-08-17T08:15:43.753402”, “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”: “05226641”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:46.931556Z”, “iopub.status.busy”: “2021-08-17T08:15:46.927806Z”, “iopub.status.idle”: “2021-08-17T08:15:46.952357Z”, “shell.execute_reply”: “2021-08-17T08:15:46.944269Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.045069, “end_time”: “2021-08-17T08:15:46.952708”, “exception”: false, “start_time”: “2021-08-17T08:15:46.907639”, “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”: “b66bc164”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:46.980537Z”, “iopub.status.busy”: “2021-08-17T08:15:46.979468Z”, “iopub.status.idle”: “2021-08-17T08:15:46.994343Z”, “shell.execute_reply”: “2021-08-17T08:15:46.997965Z”
}, “papermill”: {
“duration”: 0.03624, “end_time”: “2021-08-17T08:15:46.998379”, “exception”: false, “start_time”: “2021-08-17T08:15:46.962139”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Cauchy"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “d57e8fbb”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:47.030271Z”, “iopub.status.busy”: “2021-08-17T08:15:47.029021Z”, “iopub.status.idle”: “2021-08-17T08:15:47.040494Z”, “shell.execute_reply”: “2021-08-17T08:15:47.041344Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.033514, “end_time”: “2021-08-17T08:15:47.041710”, “exception”: false, “start_time”: “2021-08-17T08:15:47.008196”, “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”: “9ab20392”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009819, “end_time”: “2021-08-17T08:15:47.061051”, “exception”: false, “start_time”: “2021-08-17T08:15:47.051232”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “4a9037d2”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:47.093273Z”, “iopub.status.busy”: “2021-08-17T08:15:47.091961Z”, “iopub.status.idle”: “2021-08-17T08:15:47.100876Z”, “shell.execute_reply”: “2021-08-17T08:15:47.101766Z”
}, “papermill”: {
“duration”: 0.031041, “end_time”: “2021-08-17T08:15:47.102122”, “exception”: false, “start_time”: “2021-08-17T08:15:47.071081”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: The Cauchy distribution</li>n”, “n”, “<li>formula: $ K \frac{1}{ \gamma \pi} \left[ \frac{\gamma^2}{(x-x_0)^2 + \gamma^2} \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: Integral between -inf and +inf. Fix this to 1 to obtain a Cauchy distribution</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>x0: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: Central value</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>gamma: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: standard deviation</li>n”, “n”, “<li>min_value: 1e-12</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: The Cauchy distributionn”, ” * formula: $ K \frac{1}{ \gamma \pi} \left[ \frac{\gamma^2}{(x-x_0)^2 + \gamma^2} \right]n”, ” * $n”, ” * parameters:n”, ” * K:n”, ” * value: 1.0n”, ” * desc: Integral between -inf and +inf. Fix this to 1 to obtain a Cauchy distributionn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * x0:n”, ” * value: 0.0n”, ” * desc: Central valuen”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * gamma:n”, ” * value: 1.0n”, ” * desc: standard deviationn”, ” * min_value: 1.0e-12n”, ” * 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”: “e86d02d2”, “metadata”: {
- “papermill”: {
“duration”: 0.011677, “end_time”: “2021-08-17T08:15:47.124965”, “exception”: false, “start_time”: “2021-08-17T08:15:47.113288”, “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”: “288838ba”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:47.220683Z”, “iopub.status.busy”: “2021-08-17T08:15:47.212461Z”, “iopub.status.idle”: “2021-08-17T08:15:47.352321Z”, “shell.execute_reply”: “2021-08-17T08:15:47.353246Z”
}, “papermill”: {
“duration”: 0.215065, “end_time”: “2021-08-17T08:15:47.353579”, “exception”: false, “start_time”: “2021-08-17T08:15:47.138514”, “status”: “completed”
}, “tags”: [
“nbsphinx-thumbnail”
]
}, “outputs”: [
- {
- “data”: {
“image/png”: “iVBORw0KGgoAAAANSUhEUgAAAZAAAAEQCAYAAACeDyIUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA0j0lEQVR4nO3dd3gc9bXw8e82SSvJkmzZcm+4HDe5Yhsw1U6oMUlIoSUhcAklCTGB5N4bIIHcJPAGSCWV0AJJbEKzKQFCcWyKabax5Xbci9yRq6SVtO39Y3btRdjWStYWSefzPPt4Z2ZHcxZ298yvu6LRKMYYY0xzuTMdgDHGmLbJEogxxpgWsQRijDGmRSyBGGOMaRFvpgNIl4ULF9bhvN+qTMdijDFtSCkQmjBhQl7jAx0mgeC8V4/X6y3LdCDGGNNWhEKhox7rSAmkyuv1lo0ZMybTcRhjTJuxZMkSQqHQEWturA3EGGNMi1gCMcYY0yKWQIwxxrSIJRBjjDEtYgnEGGNMi1gCMcYY0yKWQIwxxrRIRxoHYkzWef/DOma/WM3uqjDDh+Rw6ec70aPMvpambbBPqjEZ8sis/Tz6xMFD22s3BJn3doCf/qCUUcNyMxiZMcmxKixjMuDZl6t59ImD+Hzw7auK+csvyjhrip8D1RFuu6uKbTuOPn2EMdnCEogxabZ1e4jfP7wPlwt+dFMpF13QiUEDcrjtu12YeqqTRO75wx5stVCT7SyBGJNm9z24j2AQPndeIVMm+Q/td7lc3HxdZ8q6eliyvIHX3ghkMEpjmmYJxJg0Wq71vLe4juIiN1ddWvSJ436/m29eWQLAo/88QDhspRCTvSyBGJNGj/7zAAAXf7YTBflH/vqdNjmPwQN9VG4P8dobtekMz5hmsQRiTJpsqgzy/of1FBW6+ew5BUd9ncvl4itfdEonTzxXbW0hJmtZAjEmTZ5/pQaAc6bm4/cf+6s3ZWIeZV09rNsYZOWahnSEZ0yzWQIxJg0aGqL8+z9OAvnMp49e+ojzeFxcEHvdsy/XpDQ2Y1oqLQMJRWQGcBMQAm5V1VkJx4qBvwFDgSDwv6r6vIh0Ah4HRgKVwMWqWpmOeI1pbfPfCXCwOsrYkbn07eVL6pzzpxXw18cPMH9BgBnfiODPs/s9k11S/okUkRHAtcAI4CTg7ljSiLsFeFdVBbgYuC+2/zZgoar2Bx4A7k51rMakSrwx/Lxp+UmfU9rZw7jyXOrqo7yzsC5VoRnTYukogUwHZqpqDVAjIvOAqcAzsePPAxp73g3YH3t+IXB27Pks4J5kLygipUBp4r7HHnvM4/F4WvQGjDkeB6sjLFxah8/Hx8Z9JGPqqfksXFLP62/UctaU5JOPMemQjjLxAGBTwnYl0Cu+oapvqOouEXkbmAvcHzvUN/ZaVDUABEWk6cpjxw04SenQo7q6ujQQsIFZJv3eei9AKASTxuWR30TjeWOnTfbj9cJ7i+uoromkKEJjWiYdCSSC07aRuP2JiX5U9RSgHPiJiHQBgqoabeq8o7gPkMRHYWFhld/fvLs/Y1rDf952blzOPKX5JYjCAjeTxuURDMHb79sNkMku6UgglUCfhO3ewMb4hog8JCI9AVR1GbAOKAN2iUi32Gt8QEhV65O5oKpWqerqxIfH4wm7XK7WeUfGJKm6JsKiCqf66uQT81r0N06b7Nz4LPjA2kFMdklHApkDXCwiuSLSG5gMzE847gIuBRCRoUBXYD0wG7gi9pqvAy+kIVZjWtXCpXWEQjC+vPnVV3GTxufhcsEHS+oIBm1QockeKU8gqroCpxF8OU4bx81AuYi8FnvJrcBnRGQ1MBO4UlUbgLuAT4vIOpzeWT9MdazGtLZ3FzmlhpMmtKz0AdC52MPwITnU1EapWJVUIdyYtEjLOBBVvRe4t9HuabFj23B6ZTU+Zx9wTsqDMyZFIpEo78USyKRxLU8g4CSgFasbeOeDOsaXH9/fMqa12MgkY1Jk7cYge/ZF6N/HS8/ux3evFm8/sfEgJptYAjEmReLVV5PHH3+J4YT+Psq6eqjcHmLLtmDTJxiTBpZAjEmR1kwgLpeLE8c6f2fRUmsHMdnBEogxKVBdE2HVmgby8lyMGpbbKn9zwmjn71gCMdnCEogxKVCxsp5IBEYPz8Hna53xR+NGOQnkw+V1tlKhyQqWQIxJgQ+XOaWEsaNar8dUSbGHE/r7OFgdZe0GawcxmWcJxJgUWBxLIPFSQ2sZXx6rxqqw3lgm8yyBGNPK9h8Ms3ZDkIJ8F4MHJrf2R7LGx9tBKqwdxGSeJRBjWtmSePXVyFw8ntadf23MiFw8Hli2soGGBmsHMZllCcSYVnao+qq8dauvAPx+N8OH5FDfEGV5cnOLGpMylkCMaWWLK1q/AT3R2Fi7ytKVDSn5+8YkyxKIMa3ooz1hNm8NUVLkZkDf1Ew1N3pELIGssBKIySxLIMa0oiXLnR/1MSNzcbtTs/7MyKE5uN2wQhtseneTUZZAjGlFy2LTrZePaP32jzi/382QE3zUN0RZs8GqsUzmWAIxphUtW+X8oJcPy0npdUYPj1djWQIxmWMJxJhWUl0TYf2mIP48Fyf0b93xH41ZO4jJBpZAjGklK1c3EI3CCMlp9fEfjY0a7pRwlq2qJxKxdhCTGZZAjGkl8eVmy1tp9t1jKe7kYUBfL9U1UTZstnmxTGZYAjGmlSyLjcsYleL2j7jyWDtIhY0HMRliCcSYVhAKRVm5pgG3G4YPSU8CsXYQk2mWQIxpBWs2BKlviDJkoA+/Pz1fq9EjnERVsbKeaNTaQUz6WQIxphXEx3+MTEP7R1y3Ui89yjxU7Y2wbUc4bdc1Js4SiDGtIN7+UT48PdVXcfF2kHgCMyadUjNZTyMiMgO4CQgBt6rqrIRjXuABYBpwELhNVZ8WkU8DfwHiK+f8VVXvSke8xjRHNBo99APeWuufJ6t8WA6vzKulYlUD55xVkNZrG5PyBCIiI4BrgRFAPrBQRF5U1f2xl1wOdAH6Az2Ad0TkZWAI8H+q+lCqYzTmeGzbEWbv/gg9u3so7exJ67VHxUsgK60EYtIvHSWQ6cBMVa0BakRkHjAVeCZ2vDvwoKpGgG0isgvoCwwAXm7JBUWkFChN3PfYY495PJ70frlNx1AR+/GOVyelU7/eXooK3WzeGmLf/jAlxfYZN+mTjjaQAcCmhO1KoFd8Q1XvVtU5ACIyHSehbIidd6uIrBSRZ0WkXzOueQOgiY/q6urSQCBwPO/DmCM6VH0l6W3/AHC7XYyMjTtZrjYexKRXOhJIBAg22g4lvkBECkTkd8DvgS+qaj3wNnCLqg7HKYk82Ixr3gdI4qOwsLDK7/e3/F0YcxTxCRRHZaAEAokDCq0ay6RXOqqwKoE+Cdu9gfnxDREpAt4A3gHKE9pGHlLVA7HnjwA/TPaCqloFVCXuW7hwYdjlSu38RKbj2bffWUCqU6GLfr3T0iflE+Ij3+OJzJh0SUcJZA5wsYjkikhvYDIJCQT4LvAfVb02njxExAWsEpHy2GsuwimRGJNV4tVGIyV1C0g1ZeigHHw+0HUN1NVHMhKD6ZhSnkBUdQUwC1gOzAVuBspF5LXYSyYCF4nIqvgDGAx8HZgpIitxemp9J9WxGtNchxaQSvP4j0Q5PhfDh+QQDsOqNVYKMemTljK3qt4L3Nto97TYsc8c5bQ1wKhUxmXM8aqIt3+kefxHY+XDclm6ooGKVQ2MHZWX0VhMx2Ej0Y1pofr6KKvXNeDzggzKXAkEEtYHsYZ0k0aWQIxpIV3XQCgEMjiHnJzMdtAYKbm4XE6bTDhsEyua9LAEYkwLHZ6+JLOlD4DCAjcD+/moDdgCUyZ9LIEY00LxbrPpnIH3WOIN+bbAlEkXSyDGtEAkEmW5Zm4E+pHEG/JtZl6TLpZAjGmBTZUhDlZH6dfbS3FRdsw/FS+BLLUFpkyaWAIxpgWWZ1H7R1xZVy9lXT1U7Ymwc7ctMGVSzxKIMS2wTLNj/Edjh9tBrBrLpJ4lEGNaIJt6YCU63A5iDekm9SyBGNNMe/aG2bYjTEmRm949MzOB4tGMHmEz85r0sQRiTDPFSx8jh+WQbTM89+/jpbDAxcYtIQ4ctIkVTWpZAjGmmeLVQ5lYgbApbrfrUDVWvJuxMaliCcSYZlqm2dn+ERePy6qxTKpZAjGmGerqI6xZHyQnB4YMzM4EcniFQmtIN6llCcSYZli1poFwGIYNzsHny672jzgZlIPP60z2WF9vAwpN6lgCMaYZlmXJ+h/HkpPjQgbnEAo5ScSYVLEEYkwzHE4g2Vl9FWfzYpl0sARiTJISJ1AcMTR7SyBgM/Oa9LAEYkySNm4JUVMbZUBfL0WdsvurM1IOl0AiEWsHMamR3d8CY7LI4elLsrv0AVDUyc2Avl5qaqNs3BLKdDimnbIEYkyS2kr7R9zh7rzWDmJSo8kEIiLXHGFfjojcnpqQjMlOS1e0nRIIHE4g1pBuUiWZEsgMEXlLREYBiMingeXA51IZmDHZZMeuELs+CtOt1EPP7tmxgFRTDo9It4Z0kxrJTCU6BvgOMFdEVgIjgduBPyR7ERGZAdwEhIBbVXVWwjEv8AAwDTgI3KaqT4tIJ+Dx2PUqgYtVtTLZaxrTmuKljzEjc7NuAsWj6d7NQ7dSD7s+CrNzd4ju3bJr5mDT9iVTAgkDdYAL6APsAJapalJTfYrICOBaYARwEnC3iBQnvORyoAvQH/gU8GsRKQBuAxaqan+cBHN3Uu/ImBRYstxJIKOHt432DwCXy3WoFGLrg5hUSCaBfAh8F7hMVU8Afg08KSJPJ3mN6cBMVa1R1d3APGBqwvHuwIOqGlHVbcAuoC9wIXB/7DWzgLOTvJ4xrW7pCucHePTIttH+EWftICaVkkkgzwHlqvpvAFX9CzAcOJDkNQYAmxK2K4Fe8Q1VvVtV5wCIyHSchLIBJ4lUxl4TAIKxkkmTRKRURIYmPsLhsCcatf7wpvk+2hNm644QnUvc9O3VtqqBbEChSaVkvg2/BYpEpKjR/v9O8hoRINho+2Md02OJ4ec4pY4vqmq9iARVNXqs847hBpx2mkOqq6vx+/1Jnm7MYYerr9pO+0fcgL4+CvJdbNgcpLomQmGB9dw3rSeZT9MOYHvs3/jz7cDKJK9RidN2Etcb2BjfiCWmtwEfTknn3dihXSLSLfYaHxBSTXqFnPsASXwUFhZWWQIxLZHYgN7WeDwuRkoO0ahVY5nW12QJRFUPJRkRcQE9gKuBZFsT5wCPishvga7AZOD6hOPfBf6jqjManTcbuAK4F/g68EKS10NVq4CqxH0LFy4Mt7W7R5Md2nICAWfcynuL61m2qoGTJthNlGk9zarQjVUpbQd+IiKLgR8mcc4KEZmFM3YkAtwIlIvIXao6DZgIjBGRcxJOmw7cBTwuItfjtIlc3JxYjWkNe/eH2VQZoqjQTf8+bav9I85GpJtUafIbISJljXa5gROBpG9lVPVenJJEommxY585xqnnHOOYMSkXL32Uj8jB7W6bJdhhg3PwemHV2gYaglFysnQhLNP2JHNLtQOI4owDIfZ8C/DNVAVlTLaId99tq9VXALm5LoYOymGFNrB6XUObmYrFZL9mtYEY09EsWRbrgTWibf/olg9zEsjSFfWWQEyrOWoCEZEmu+mqqo0ON+3Wnn1h1m8OUlToZlB/X6bDOS5jR+Xy+JxqFlfUc9lFmY7GtBfHKoEMT1sUxmShxRVO6WPsqFw8nrbdbjB6eC5eL1SsqqehIUpOTtt+PyY7HCuBzFfVh0XkKlV9KG0RGZMlFlfUATB+dNuv8vH73QwfkkPFygaWaT3jy/MyHZJpB46VQH4lIjnAH0TkIIcb0Q9R1X+mLDJjMigajbJwqVMCGV/e9hMIwITReVSsbGDRUksgpnUcK4H8BGfshYsj97iKApZATLu0bWeYnbvDlHX10Ltn2xz/0dj40bk88jgsXFrH1ZcXN32CMU046jdDVX8B/EJEHlbVK9MYkzEZt3jp4eqr9jKDwbDBOfjzXKxZH+RgdYROhdbB0hyfJj9BljxMR3S4+qr9VPV4vS7GjMwlEoEPl9uodHP87BbEmEYikSiLY+M/xrWT9o+4eIeARbESljHHwxKIMY2s3xTkwMEIA/p6Ke3cNtY/T9aE0U6JatFSK4GY45dU62BsOvVujV+vqptTEZQxmdQeq6/iBvT10rnEzZZtIXZXhehW2j46CJjMaLIEIiLX4Kw+uBHQhMeqlEZmTIa8v9ip3pkwpn1VX4GzTnq8W/LCJVYKMccnmduP24FLgOdVNZzieIzJqEAgQsXKenxeZwR6ezRhdB6vvRHg/Q/rOHdqUqtEG3NEybaBzLXkYTqCRcvqCYZg9Mhc/Hnts4lw0jinau6DJXWEw9EmXm3M0SVTArkDuF9EfkyjVf5UdVcqgjImU95b5FRfxX9k26MunT0MOcHHmvVBVq6x6d1NyyVzi/Vn4Ms4Kwru4ONroxvTbkSjUd6LtX9MbscJBGDyeOf9vbvIuvOalrP1QIyJ2bw1xM7dYXqUeejbu333Tpo0Lo+/PXmQdxfV8V+X2bQmpmWS7cY7DfgCUIbTG+tRVV2awriMSbvE6qv2Mn3J0QwfkkNRoZu1G4JU7Q23u/EuJj2S7cb7CFAJvAxEgH+LyDdSG5ox6RWvvmrP7R9xHo+LE8c6bR/x921McyVTAvkfYLqqfhjfISL/BJ4G/pKiuIxJq0AgwtIVTvfdce20+25jk8fn8fqbAd5dVMd51p3XtEAy7RvFwKZG+9YCJa0ejTEZEu++Wz48F7+/YzT7TRybh8sFC5fUEQpZd17TfMl8U54F/iwiAwBEpCfwa+C11IVlTHq99V4AgFMmtv/qq7iSYg8y2EdNbZSKlTYq3TRfMgnkBmAfsEJEgjilkQLg6hTGZUzahMNR3vnAaQc4ZaI/w9Gk1yknOu/3rfesHcQ0XzIJZKSqXoOTNHoD+ar6JaBfshcRkRkisklE1onIJUd5zXQR+V3CtojIFhFZFXv87kjnGXO8VqxuYN+BCIMH+uhR1r677zZ26mQngbz5XoBo1KqxTPMc9dsiIpNiTxeIyEkcXhN9gIh0xWlEb7K8LyIjgGuBEUA+sFBEXlTV/bHjBcDPcebbmpVw6iDgYVX9UfPekjHN82YHrL6K69/HS59eXiq3hVizPsjQQTmZDsm0Ice63Xo89q+LT659HgL+mOQ1pgMzVbUGqBGRecBU4JmEv/UqTvfgxBLRAD7ZeJ8UESkFShP3PfbYYx6Px/q6m4+LRqOH2j9OndSxqq/AmZ331El5zJpdzZvvBSyBmGY51proAwFipYXzjuMaA4AFCduVQK+E69QDs0WkBDix0XmniMjNOFOnzFDViiSveQPOLMKHVFdX4/d3vB8Ic2wbt4TYtiNM924eBg3wZTqcjDh1st9JIO8GuOpSG5VukpfMVCbnicgw4EIOj0R/XFV3J3mNCBBstB1K4rxlwH9U9V8iMh14Chia5DXvA/6RuKOwsPDtnJyc0qO83nRQb7/vlD6mTPS3+9HnRzNscA6lnd1s3BKicluQPr06ZiI1zZfMSPTPAW8Cg4F64HRglYhcmOQ1KoE+Cdu9cZJQU2ar6r8AVPU5oEREkqqkVtUqVV2d+PB4POGO+gNhju6Nd2PtH5M6XvtHnNvtOtT77E3rjWWaIZleWD8DvqSq16jqrar6ZeBS4N4krzEHuFhEckWkNzAZmJ/Eea+KyAUAInI6sEVV7dNtWs3W7SFWrwvSudjNmBEdY/T50ST2xjImWckkkF7Aokb7FuCUJJqkqitwelctB+YCNwPlItLUQMTrgB+LiOIksa8lcz1jkjVvQS0Ap53kx+Pp2KXTsSNzKSxwsUIb2PVRMjXMxiQ3F9Zc4HYRuUVV60TEC/wv8E6yF1HVe/lkiWVao9c8gjNpY3x7ER9vVDemVc19y7nbPmtKfoYjyTyfz8Vpk/28+Hot/3k7wJcv7JTpkEwbkEwJ5FpgFLBHRNYBe4FPAzYbr2mzNm8Nsm5jkC4lbkYNs66rAGed6iTS19+szXAkpq1IphfWbuBsEemBM/p8l6puTHVgxqTSvLed0scZp+R3+OqruHGjculc7Gb1uqD1xjJJaTKBxAblXYszLsMb2weAql6VwtiMSZm5bzl32WeeYmOD4jweF6ef7GfOSzXMfTvAV79oCcQcWzJVWHOAT+F0vdVGD2PanA2bg2zcEqJrFw8jxaqvEk07LVaN9UatzY1lmpRMI/oEoLuqHkh1MMakw8tzawCYdpoft9uqrxKNGJpDWVcPmypDrN8UZNAAS7Dm6JIpgbyB9YYy7UQ4HOXV+U711dln2ip8jbndLs6a4lTrxf87GXM0x5qN9w+xp/uAF0TkZWA7cKhcq6rfTGl0xrSyhUvr2bMvwpATfAzsZ3X8R3L2mQU8PqeaV+bVcvXlxdbJwBzVsaqwdib8uzwNsRiTcvHqq7PPsLEfRzOwnw8Z7EPXBnlvcR0nn2gdDcyRHWs23h8nbovICTiTKW5X1RZNs25MJlXXRHjr/QAez+HGYnNk555VgK7dx0uv11oCMUeVzGSKo0RkBU4pZCbO0raLRWRIyqMzphXNWxCgoQEmj8+jpNjWhjmWqafm4/PBgoUB9h8IZzock6WSaUR/OPYoiK0R0gV4GfhrKgMzprX969VY9ZU1njepU6GbUyf5CYWsMd0cXTIJRIDfqmoEDi0A9X/AmFQGZkxrWrO+gZVrGuhS4uaUEzvu1O3Nce5ZTqJ98TUbE2KOLJkE8hecmXETXc4nl7k1Jms9/4pT+jhvWgFer/UqSsb40bl07+Zh/eYgy7Uh0+GYLJTMQMJpONOv34azOFRfnGqstbG2EQBUdURqQjTm+NQGIrw6vxaXCy74lFVfJcvjcTH97AIe+PsB5rxUzahhHXvNFPNJySSQGSmPwpgUeu2NWgJ1UU6akEePsmQ+8ibu/GkF/PXxA8xbEOCbV4bpbJ0PTIJkZuOdl45AjEmFaDTKc/92qq+mn22lj+YqKfZwxin5vDq/lhdfq+Gyi4oyHZLJIsm0gRjTZi1d0cDaDUHKunqYNM4az1vis+c6iffZl2sIh60x3RxmCcS0a08+fxCAiy4otCk5WmjE0BwGD/Sx66Mwb39Ql+lwTBaxBGLara3bQ7z9fh3+PBfnT7Pqq5ZyuVxcdH4hAP+cczDD0ZhsYgnEtFtPvXCQaBTO/1QBhQX2UT8e007Lp7SLm+XawLJV9ZkOx2QJ+1aZdulgdYSXXq/F7ebQ3bNpOZ8voRTyrJVCjMMSiGmXnn25mrr6KKdO9tOzu3XdbQ3Tzy7En+firffq2LItmOlwTBawBGLanUAgwhPPVQNw8Wc7ZTia9qOwwM0FnyogGoUnnq3OdDgmC6QlgYjIDBHZJCLrROSSo7xmuoj8LmHbKyJ/FZHNIrJERMrTEatp+579dw0HDkaYODaX4UNsSdbW9MXphXi98NLcGnbuDmU6HJNhKU8gIjICuBYYAZwE3C0ixQnHC2KJ4+FGp34DcKlqP+BG4M+pjtW0fXX1kUM9hb76JRv01trKuno5b2oBoRD842lrC+no0lECmQ7MVNUaVd0NzAOmJhwPAa8C/2h03oU4EzmiqnOBPiLSJZkLikipiAxNfITDYY/NKNr+vfBKDXv3RxhXnmtzN6XIZRd1wuuFF1+3UkhHl44EMgBIXMGwEugV31DVelWdDSxq4rytQM8kr3kDoImP6urq0kAg0Jy4TRtTVx9h1uxY6eOL1vaRKt27WSnEONKRQCJAsNF2MrctLT0P4D6cdUwOPQoLC6v8fluasz17+oVqqvZGGDsqlzEjrfSRSpd/4XApZPtOK4V0VOlIIJVAn4Tt3sDGFpxXhlMKaZKqVqnq6sSHx+MJu1w2lUV7tf9gmJnPOHfD13y1GPt/nVplXb185tNOKeSBv+/PdDgmQ9KRQOYAF4tIroj0BiYD85M4bzZwBYCIfBpYo6rWd9Ac0T+ePkhNbZQzTvYzbLD1vEqHr32piIJ8F3PfCrBytY1O74hSnkBUdQUwC1gOzAVuxlmg6rUmTn0A6CIi64CfAd9OaaCmzdq5O8TsF6txu+Gqy6znVbqUFHu49PNOW9OfHt1vy952QGkZoquq9wL3Nto9rdFrHgEeSdgOApelOjbT9v3pr/sJBp31Pvr28mU6nA7lCxd04tmXa6hY2cBb79Vx6mRrZ+xIbCS6adMWVdQxb0GAwgIXV15ipY90y8118V+xUt+fH91PQ4OVQjoSSyCmzQqFotz3wD4ArrykmBJbbjUjpp2Wz7AhPrbuCDFztnXr7UgsgZg2a/ZL1WyqDHFCPx8XnmPrfWSK2+3ixms643bDP546QKVNtNhhWAIxbdKOXSEennkAgBuuLrHVBjNs6Ak5fO7cQoIh+M0D+6xBvYOwBGLanGg0yi//tJdAXZTzp+XboMEsceWlRZR2drNwST2vv2mzPnQElkBMm/Py3Fo+WFJPaRc3111RkulwTExBvptvXVUCwH0P7mPP3nBmAzIpZwnEtClVe8P84ZF9ANx4TWdbqjbLnHGyn9NP8nPgYIRf/GmvVWW1c/btM21GJBLlnt/vpbomyllT/EyZaGMOso3L5eLGa0roXOxmwQd1vDy3NtMhmRSyBGLajKf/Vc17i+so7eLmO1eXZDoccxQlxR5uur4zAL9/eB87dtlki+2VJRDTJqxZ38D9j+3H5YJbvtOF4iIb85HNpkz0c+7UfGpqo/zkl3sIBq0qqz2yBGKyXiAQ4ae/2kMoBJd+vhPjyvMyHZJJwg1XldCvt5eVaxq4/282Y297ZAnEZLVoNMrPf7+XLdtCDB+Sw9cvtulK2gq/383t3yslN8fFU89XM3+BtYe0N5ZATFab+cxB5i8IUFLk5kc3d8HrtQGDbcnAfj5mfKMEgHv+sJdNlTZKvT2xBGKy1ruLAjz4jwN4PHD790rp3i0tk0ebVnbu1ALO/1QBNbVRbr3rI/YftPEh7YUlEJOVNmwO8tNf7SEaheu/XmKjzdu4GVeXMHpEDtt2hLnjniprVG8nLIGYrLO7KsT//OQjamqjnP+pAj5/nk2U2Nb5fC5+/P1Senb3sGR5A7+63wYZtgeWQExWqa6J8L8//YiP9oSZNC6PG79RYuubtxPFRR7uvKUrBfkuXnq9lvsfs1UM2zpLICZrBOoi3HrXR2zYHGLoIB+3W6N5u9O/j487b+lKbo6Lx+dUM/MZWz+kLbMEYrJCXX2EW++somJlA717eLnzlq74/fbxbI/Kh+dy+/e64PHAA38/wJyXqjMdkmkh+4aajKuvj3LbXVV8uLyent09/PL/utKlxEaat2cnTfDzg+90weWC3/xlH8/8y5JIW2QJxGRUbSDCLXd9xKKKenqWefjlj7vRrdS663YEU0/N57+/5axkeN+D+5hly+G2OZZATMbs3R/mptt3s7iinh5lHn7x42421qODOeesAm670anOuv+x/TwyyxrW2xJLICYjduwKMePW3axeF+SEfj5++7MyepRZ8uiIzpySzx3fK8XnhUefOMg9f9hLKGRJpC1IyzdWRGYANwEh4FZVndXo+M+BS4Fa4HpVnSsiArwK1MRe9qqqfjsd8ZrUqlhZzx33VLF3f4Ty4Tn87AddbWGoDm7KJD933daV2++u4qXXa9m1O8wd3y+1z0WWS/n/HREZAVwLjABOAu4WkeKE4+cBk4CBwGeBB0TEDQwCHlbVYbGHJY924IVXa7j5jt3s3R/h9JP83P3DbvYjYQAYX57HfXeW0b2bh0UV9dxwyy4229xZWS0d39zpwExVrVHV3cA8YGrC8QuBh1Q1rKoKbAPGAAOATS25oIiUisjQxEc4HPZY3WrmBINRfvvAXn7xx72EQnDFxUX86OYu5ObaOA9z2IC+Pn5/VxnDhvjYVBni+v/ZxTybxTdrpSOBDODjiaAS6JXE8QHAlSKyQkReF5HyZlzzBkATH9XV1aWBQKDZwZvjt3V7iO/cuovZL9aQl+fiju+XcsWXi3C7LXmYT+rS2cOv/6+Mz3y6gEBdlB/fu4c//nWftYtkoXQkkAgQbLQdSuL4MuBOVR0B/Ap4qhnXvA+QxEdhYWGV329raKfbq/NrueZ7O9F1Qfr38fK7O8s4/ST7/2COLSfHxU3Xdea/v9WZnBx44tlqvn3LLpsOPsukoxG9EuiTsN0bmN/E8Y3AAlU9AKCqz4nIgyKSp6p1TV1QVauAqsR9CxcuDNucSumzd3+Y3z24j7lvOaW+Cz5VwLeuKiYv19o7TPLOnVrA4IE+fvbrPaxeF+Ta7+/kmq8U87nzCq0EmwXS8W2eA1wsIrki0huYzMcTyGzgqyLiFpFRQHGsLeRVEbkAQEROB7YkkzxMZkWjUV6dX8uVM3Yy960AnQpd/OjmLtx8fWdLHqZFBg/M4U93d+eiCwppaIDfPbSfm+/YbQ3sWSDl32hVXQHMApYDc4GbgXIReS12/EVgHbAm9rprYqdeB/xYRBT4GfC1VMdqjs/WHSFuvauKO3+zhwMHnV5WD/+6B2eekp/p0Ewbl5vr4ttXlXDPj7pS1tWZEv7qm3fy0Mz91Ndb20imuDpKz6SFCxfu9Hq9ZWPGjMl0KO1ObSDC3586yJPPHSQYgs4lbmZcXcLpJ1viMK0vEIjwyD8P8NTz1UQi0KuHh+u+VsKUSXk29X8KLFmyhFAotGvChAndGx+zob+mxcLhKP/+Ty0PzdxP1d4IbjdcdEEhV3y5iE6FVl1lUsPvd3P9FSWcfUY+v/zTPlauaeBHd1dRPjyHa79WzIihtnplulgCMc0WiUSZ+1aAvz5+gMrtToe6CWNy+daVJQzo68twdKajGDQgh/vu7Mar82t5aOYBKlY28O0f7Ob0k/1c8eUiBvazz2KqWQIxSQuHo7zxToDHnjzAhs1O4hg0wMdVlxZx0gSrPjDp53a7OPvMAs44OZ9nXqzm708dYP6CAPMXBDj9JD9f+WInBg/MyXSY7ZYlENOkQF2El16v5cnnDrJ9VxiA/n28fP2SIk6b7LfulCbjcnNdXPK5Tpw3NZ8nnqtm9ovVzH8nwPx3Apw0IY8vTS9k7Khcu8lpZZZAzFHt2BXihVdqePbf1RysdjpbDBrg45LPdeLMU/x4PPZlNNmluMjD1ZcX8+ULO/H0vw7y9AvVvLOwjncW1nFCfx8XnV/ItNPybQqdVmK9sMzHhMNRFnxQx/OvVPP+h/XEPx4Tx+by5Qs7MX603cWZtqOmNsK/Xqth9r+qD5Weizq5OefMfM6dWmDtJEk4Vi8sSyCGaDTK+k1BXnsjwCvza6jaEwEg3+9i2mn5XHhOAYMGWD2yabvC4SjvLKrj6ReqWVxRf2i/DPJx7tQCpp6abz0Hj8K68Zoj2r4zxOtv1vLaG7Vs3HJ4ejIZ5OMzZxcydYofv9++VKbt83hcTJnoZ8pEP5srg7w0t4Z//6cWXRdE1+3jDw/vY8KYPM48xc8pE/22xECSrATSgUSjUdZuCPL2B3W8/X6ANesPTwVR1tXDWVP8TDst33qtmA4hHI7y3uI6Xny9hncX1hGM3UP5vBxKJpPG5VFS7MlsoBlmJZAOLFAXoWJlAws+CLDggzp2fRQ+dKyok5szTnaSxqhhOdabynQoHo+Lk0/0c/KJfqprIrz9foB5CwJ88GHdoYZ3lwuGD8lh8vg8Jo/PY/BAn31PElgCaWdCoSir1jawuKKehUvrWLG6gVDC5Pl9eno5ZWIep0z0M1JyrCeVMUBhgZuzzyzg7DMLDiWTt96rO/QdWrG6gYdnHaBziZsJo/MYOzKXMaNy6dXd06E7lVgCaeMCdRFWrXE+4MtWNVCxsp7awOFqSZ8PxpXncuKYXKZM9NOvj/U6MeZYEpNJMBhl2ap63l1Ux7uL6thUGeLV+bW8Ot9ZJbFbqYcxI3MZOzKXUcNz6NPT26FKKJZA2pBIJMr2nWFWrW1gudazXBtYtzFIJHL4NS6X0wg+fnQe48tzGTUs1/q8G9NCPp+LceV5jCvP47ornLFRS5bX8+HyepYsr2fHrvDHEkphgYthg3MYNiSH4bFHe25DsQSSpYLBKJsqg6zZEGTthiBrNzjJIrF0AZCX63xgR0gOIyWHkZJLUSfrQWJMKvQo89KjzMs5ZxUAH08oK7SBLdtCfLCkng+WHO4q3LPMw+CBOQwa4GPQAB8n9PfRo6x9VH1ZAsmwYDBK5fYQm7cG2VwZYlNlkE2xfxPbLsApXfTt5WXICT5GSi4jh+UwqL/P2jGMyZDGCeVgdYRVaxtYtaaBlbHH9l1htu8K8Ma7gUPnFeS7OKG/k0wG9PXRt7eXfr19lHZ2t6nEYgkkDcLhKLurwmzfGWL7zjDbdjoJYnNliK07Qh+rgorzeWHICT6GDPQxeGAOgwf6GNTfZ+MyjMlinQrdTBybx8SxeYDTdX7HrjDrNwVZt9F5rN8UZOuOEBUrG6hY2fCx8/P9Lvr0cpJJ315e+vb20qenk6SycWyKJZBWEIlE2Xcgwu6Pwuw6lChCbNvhJIwdu0OfKE3E5eRA314++vX2MqCvj359nA9Pn55efL62cydijPkkl8tFz+5eenb3MmWS/9D+QCDChi1OQtlUGWLL1iBbtoXYuTvM6nVBVq/75HK9nQpd9Cjz0rPMS/cyDz3LvPQo89Czu5fu3TwZWTLaEkgS9ux1EsPuqjC7Pwo5/yY8PtoTPmqCAPB6ne6zvXp4Dn2Y+sWKrN27eawKypgOxu93M2Jo7icWv6qvj1K53UkmW7aG2LItxNbtIXbsCrF3f4SD1cGPDQBOVFTopmuph26lHrp2cf7tVuqha2y7RzdPq9dgWAJJwrd+sIudu8NHPe71Qo+yw//jesWSRM/uHnr18NK1iyUJY0zTcnNdDBqQc8S55wJ1EXbsCrNjV+jQv9sTnh+ojnCgOsL6TUdOMJdf1In/ury4VeO1BJKEEUNz6N4tnJDRvZSVHs7wJcXuDtX32xiTfv48NwP7uY86g3BdfYSPGtWM7K4KO/v2hOnbu/V/7i2BJOGHN5VmOgRjjDmmvFw3fXq56dMrfYOFs69Z3xhjTJtgCcQYY0yLWAIxxhjTImlpAxGRGcBNQAi4VVVnNTr+c+BSoBa4XlXniogXeBA4C9gLfEVVK9IRrzHGmKalvAQiIiOAa4ERwEnA3SJSnHD8PGASMBD4LPCAiLiBbwAuVe0H3Aj8OdWxGmOMSV46qrCmAzNVtUZVdwPzgKkJxy8EHlLVsKoqsA0YE9v/FwBVnQv0EZEuyVxQREpFZGjiIxwOezrK6ovGGJMO6UggA4BNCduVQK8kjjfevxXomeQ1bwA08VFdXV0aCASOfZYxxpikpaMNJAIEG22Hkjje1HnHch/wj8QdRUVFK1wul2fJkiVJ/gljjDEhZ56mIw6GS0cCqQT6JGz3BuY3cXxjwv7tsf1lOKWQJqlqFVCVuG/hwoUhgFAoVHXEk7JUJBJxNTQ05Ofk5NS63e52XwfX0d4v2Hu295z1SjnKzXs6Esgc4FER+S3QFZgMXJ9wfDZwvYg8hdPQXqyqKiKzgSuA90Xk08AaVa1uaRATJkzIa+m5mSQiQ3Gq4car6upMx5NqHe39gr1ne89tV8rbQFR1BTALWA7MBW4GykXktdjxF4F1wJrY666JnfoA0EVE1gE/A76d6liNMcYkLy3jQFT1XuDeRrunJRz/LvDdRucEgctSH50xxpiWsJHoxhhjWsQSiDHGmBaxBJL9qoAf06hXWTvW0d4v2HvuKNrde3bZ6GxjjDEtYSUQY4wxLWIJxBhjTItYAjHGGNMilkCMMca0iCUQY4wxLWIJxBhjTItYAjHGGNMilkCMMca0SFomUzStQ0R+CeSoarufmVhEZuDM3BwAnlLVWzIcUsrE3utNOGsu3KqqszIcUsqJyM9xJkutBn6nqr/PcEhpISJPA2/HJpht86wE0kaIyKnAVzMdRzqIyECc2ZnHAuXA6SJyekaDShERGQFci7MWzknA3SJSnNmoUktEzsCZjXswMAn4Tuz/ebsmIpcDZ2Q6jtZkCaQNEJF84P/FHh3BAOBvqrpHVRuAd3B+YNuj6cBMVa1R1d3APGBqhmNKtT7Ag6par6oHgQpAMhxTSolIT5wbhT9nOpbWZFVYbcPPgV8CRUC7v1NT1bk4i48hIoOAS4HzMhpU6gwAFiRsVwK9MhNKeqjq3+PPRWQicDrwrcxFlBZ/AL4HnJ/pQFqTlUCynIicCXRT1aczHUu6icj1OD+uP1LVpZmOJ0UiQLDR9hHXn25PRMQrIrcDzwFXqerOTMeUKiJyBbBaVd/LdCytzUogWUREvgN8p9HuTcBAEVkFFAN5IlKvqjenPcAUOMp7Xgdsw6kjP0VV16Y9sPSpxKnSiesNzM9QLGkhIl7gRZwG9LGquiPDIaXaNOBkEfks0BWIiEhEVX+Z4biOm03n3oaIyNeBE9t7L6xYI+svgJNjSxu3W7FG9EeBKTg/Lq8C41U1kNHAUih2R36Rqn4207Gkm4jcAVS3l15YVgIx2WgiTltPhcihttWfJNadtxequkJEZgHLcaqvbmzPySNmIjA5VqqOu05V/5OheEwLWQnEGGNMi1gjujHGmBaxBGKMMaZFLIEYY4xpEUsgxhhjWsQSiDHGmBaxbrzGtAGxGXt3ADuBP6nqsGac+ybwmqrenrDPBWwG7sAZg/JTVV3fqkGbds9KIMakiIh4WunvFAFfA55s4Z+YCVzUaN8koAx4CvgtcHeLAzQdlpVATIcnItOAXwGDgBdwBrXtEZFHgF04d+hjgH8Dl6lqnYgMAP4CnIwzCPAaVV0Smy3gC0AU5wbtMyLyXeB/cNY2+TtwKnAhsBsYpaprYnG8hTNL7UONQvwGMFtVwwkDKxGRLsAbwP2q+hsRGQv8CWcK/LeBq1V1E/AE8BsRGRK/Fk5CeVFV9wEfikh/ERmpqsuP77+m6UisBGI6NBHpC/wD+CbQH2gA7kl4yRWxYyfg/DBfIiJu4FngaZyZc/+Ocycfdz7O9CSfF5GzgBk4SeNM4HMAqnoAeD32WkSkFDgx9ncb+0LstYlx5+Mku9mx5FEY2/5/OPNpLQIeil1rF/Ba7O/EfR6nZBI3F+hwU4uY42MJxHR0lwNPquqbqvoRcCvOj2vcY6q6JPYj/BbOxIeTAI+q/lFVD6jqbwGPiIyOnfOBqj4Zm8frMpwSwtpYaSBxAr1ngAtiz88F3orFcEgsWU3EKeXEeXGqs/ap6q2xfZ8BPlTV2bFSxQ9xJvAriR0/VI0lIuVATz6erJbglKaMSZpVYZmOrj9wnYh8M3GniOTFnib+oNfhfGf6AyNEpPE8QPF1PPYm7OuHs0hUXGXC8znAr0WkAKckcqQp+0txbvT2J+wbBFQBg0Wki6ruicV0/hFi6gHsw0lWf4yVuC4C5jSac2sPTsnFmKRZCcR0dDuBe1TVpaouIA8Yp6p1TZzzfvyc2Hlj+HiiiKvBaayO6xd/ElsDYzFwduwx5wjnewFX7BFXiVMl9g7wk4SYnkiIx41TJbY6dq39OFOoXxR7JFZfgdNmEznGezbmE6wEYjq6J4F/ichjON1a78QpSXz+GOe8A5SJyOeAV3B+kH/OkVeLfA34tog8ifN9+x5Od9y4Z4DbgfWquuUI5+/GaXwvwSklANSoalBEvg8sEpE/4iSHu0TkVOBD4AbgS6o6PuFvzYy9v844HQISleB0GDAmaVYCMR2aqi7D6SE1G2cRq/7AdU2cU4fTGP4DnB/47+Ksb1F/hJf/GXgZp41hbux54oqDz+CUXv55lGuFgPeB0Uc4tgJ4GPhNrDTzFZxeWLtwlgC+pNEpz+NUacXbZxKNwkmMxiTNpnM3JoVEpDscqq5CRK4FzlLVS2LbXpz2jBGquvUof+P7QJGq/jCFcb6F0325IlXXMO2PlUCMSa3zgBdEpLuIDMHp0vsKgIj4gUtxel8dMXnE/AW4IJZsWp2IjAL2WvIwzWUJxJjU+htOQ/lq4F2cKqxHYsd+C9yF0+X2qGLdcu/nk1VSreVGnGo8Y5rFqrCMMca0iJVAjDHGtIglEGOMMS1iCcQYY0yLWAIxxhjTIpZAjDHGtMj/B3drRJKojfudAAAAAElFTkSuQmCCn”, “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”: “8560f260”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.012439, “end_time”: “2021-08-17T08:15:47.378343”, “exception”: false, “start_time”: “2021-08-17T08:15:47.365904”, “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”: “ac6e4071”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:47.444140Z”, “iopub.status.busy”: “2021-08-17T08:15:47.410536Z”, “iopub.status.idle”: “2021-08-17T08:15:47.873546Z”, “shell.execute_reply”: “2021-08-17T08:15:47.872768Z”
}, “papermill”: {
“duration”: 0.482883, “end_time”: “2021-08-17T08:15:47.873903”, “exception”: false, “start_time”: “2021-08-17T08:15:47.391020”, “status”: “completed”
}, “tags”: []
}, “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”, “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”: “1db59269”, “metadata”: {
- “papermill”: {
“duration”: 0.013554, “end_time”: “2021-08-17T08:15:47.899981”, “exception”: false, “start_time”: “2021-08-17T08:15:47.886427”, “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”: “52d63cc4”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:47.967566Z”, “iopub.status.busy”: “2021-08-17T08:15:47.939588Z”, “iopub.status.idle”: “2021-08-17T08:15:48.144645Z”, “shell.execute_reply”: “2021-08-17T08:15:48.138458Z”
}, “papermill”: {
“duration”: 0.231986, “end_time”: “2021-08-17T08:15:48.145026”, “exception”: false, “start_time”: “2021-08-17T08:15:47.913040”, “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”: 6.212941, “end_time”: “2021-08-17T08:15:48.706630”, “environment_variables”: {}, “exception”: null, “input_path”: “Cauchy.ipynb”, “output_path”: “../docs/notebooks/Cauchy.ipynb”, “parameters”: {
“func_name”: “Cauchy”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:15:42.493689”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}