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- “cells”: [
- {
“cell_type”: “markdown”, “id”: “1b45845c”, “metadata”: {
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“duration”: 0.01187, “end_time”: “2021-08-17T08:14:15.807019”, “exception”: false, “start_time”: “2021-08-17T08:14:15.795149”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Band grbm”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “70408216”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:15.845867Z”, “iopub.status.busy”: “2021-08-17T08:14:15.844702Z”, “iopub.status.idle”: “2021-08-17T08:14:18.878844Z”, “shell.execute_reply”: “2021-08-17T08:14:18.880165Z”
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“duration”: 3.061488, “end_time”: “2021-08-17T08:14:18.880620”, “exception”: false, “start_time”: “2021-08-17T08:14:15.819132”, “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”: “c8804072”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:18.906382Z”, “iopub.status.busy”: “2021-08-17T08:14:18.905291Z”, “iopub.status.idle”: “2021-08-17T08:14:18.913359Z”, “shell.execute_reply”: “2021-08-17T08:14:18.914596Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.024811, “end_time”: “2021-08-17T08:14:18.915102”, “exception”: false, “start_time”: “2021-08-17T08:14:18.890291”, “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”: “d6b91f4e”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:18.941253Z”, “iopub.status.busy”: “2021-08-17T08:14:18.938850Z”, “iopub.status.idle”: “2021-08-17T08:14:18.948823Z”, “shell.execute_reply”: “2021-08-17T08:14:18.949789Z”
}, “papermill”: {
“duration”: 0.02481, “end_time”: “2021-08-17T08:14:18.950205”, “exception”: false, “start_time”: “2021-08-17T08:14:18.925395”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Band_grbm"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “508187d4”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:18.983022Z”, “iopub.status.busy”: “2021-08-17T08:14:18.981900Z”, “iopub.status.idle”: “2021-08-17T08:14:18.990678Z”, “shell.execute_reply”: “2021-08-17T08:14:18.991355Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.029236, “end_time”: “2021-08-17T08:14:18.991719”, “exception”: false, “start_time”: “2021-08-17T08:14:18.962483”, “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”: “ea16337f”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011391, “end_time”: “2021-08-17T08:14:19.013583”, “exception”: false, “start_time”: “2021-08-17T08:14:19.002192”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “bbb37be6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:19.051024Z”, “iopub.status.busy”: “2021-08-17T08:14:19.044761Z”, “iopub.status.idle”: “2021-08-17T08:14:19.055639Z”, “shell.execute_reply”: “2021-08-17T08:14:19.056384Z”
}, “papermill”: {
“duration”: 0.032798, “end_time”: “2021-08-17T08:14:19.057383”, “exception”: false, “start_time”: “2021-08-17T08:14:19.024585”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
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” * description: Band model from Band et al., 1993, parametrized with the cutoff energyn”, ” * formula: $ $n”, ” * parameters:n”, ” * K:n”, ” * value: 0.0001n”, ” * desc: Differential flux at the pivot energyn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 1.0e-05n”, ” * free: truen”, ” * alpha:n”, ” * value: -1.0n”, ” * desc: low-energy photon indexn”, ” * min_value: -1.5n”, ” * max_value: 3.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * xc:n”, ” * value: 500.0n”, ” * desc: cutoff of expn”, ” * min_value: 10.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 50.0n”, ” * free: truen”, ” * beta:n”, ” * value: -2.0n”, ” * desc: high-energy photon indexn”, ” * min_value: -5.0n”, ” * max_value: -1.6n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.2n”, ” * free: truen”, ” * piv:n”, ” * value: 100.0n”, ” * desc: pivot energyn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 10.0n”, ” * free: false”
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“func.display()”
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“cell_type”: “markdown”, “id”: “dfb667e7”, “metadata”: {
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“duration”: 0.017226, “end_time”: “2021-08-17T08:14:19.088745”, “exception”: false, “start_time”: “2021-08-17T08:14:19.071519”, “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”: “ec52f154”, “metadata”: {
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}, “tags”: [
“nbsphinx-thumbnail”
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}, “outputs”: [
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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”, “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”: “f9e83e95”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011785, “end_time”: “2021-08-17T08:14:20.255596”, “exception”: false, “start_time”: “2021-08-17T08:14:20.243811”, “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”: “95a24ed7”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:20.334046Z”, “iopub.status.busy”: “2021-08-17T08:14:20.317498Z”, “iopub.status.idle”: “2021-08-17T08:14:21.026194Z”, “shell.execute_reply”: “2021-08-17T08:14:21.027497Z”
}, “papermill”: {
“duration”: 0.760806, “end_time”: “2021-08-17T08:14:21.027870”, “exception”: false, “start_time”: “2021-08-17T08:14:20.267064”, “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”: “a864e7fe”, “metadata”: {
- “papermill”: {
“duration”: 0.012728, “end_time”: “2021-08-17T08:14:21.054188”, “exception”: false, “start_time”: “2021-08-17T08:14:21.041460”, “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”: “8f198806”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:21.239402Z”, “iopub.status.busy”: “2021-08-17T08:14:21.135033Z”, “iopub.status.idle”: “2021-08-17T08:14:21.744666Z”, “shell.execute_reply”: “2021-08-17T08:14:21.745646Z”
}, “papermill”: {
“duration”: 0.67894, “end_time”: “2021-08-17T08:14:21.745976”, “exception”: false, “start_time”: “2021-08-17T08:14:21.067036”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
“image/png”: 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9zIqPZ3LdyLDLEREpiIKmjD2cXUVzfmJ6yJWIiBROQVOmDvR183T3ZuqJcUFiStjliIgUTEFTppZ2vU6aDG9rOpaWaEPY5YiIFExBU6bUbSYi1UJBU4Y2pvbzSmo3Y6KNnN44OexyRESGRUFThh7OLgcwT+vOiEgVqAu7gOEys9nAjUAn8D3n3DMhlzQsGc9jSbK/20zfnRGRylfWvy6b2WwzeyBn+3oz22Bm68zs2uzdnwL+W/b22RDKLKoXeraxqy/J1PoxTKsfG3Y5IiLDVrYtGjO7BfgA8GJ2eyZwHTATSAArzOwhoNU5tyf7nCFNbWxmrUBr7n133XVXLBaLDf8NFOjh5P8fBBDRlDMiUgXKNmiA5UA7cHZ2+0pgoXOuE+g0s2XARUC7mY0BeoGOIb7GPwM35N7R0dFBU1PTsAovVFcmxeNdG4gSYV5iaig1iIgUW9l2nTnn7scPm35TgA0525uBScD3gZ9mb98Z4sv8H8Byby0tLXvCCponujbQ7aU5vWESrbFEKDWIiBRbObdoBsoAqQHbaefcKuC9hRww2+W2J/e+FStW9IXVZbUkO9rskmZ9d0ZEqkfZtmgGsRk4Jmd7MrA+nFKKb2e6g+d6ttIcqefcpuPCLkdEpGgqKWgWAe83swYzmwycBTweck1F8+fka3jA+U1TaIhUUkNTROTwKiZonHOrgbuBVcBS4AvOua5wqyoOz/PUbSYiVausf3V2zj0GPJaz/R2GfsG/7K1L7WVDej/jY83Mjk8IuxwRkaKqmBZNNftz0l9Fc15iqpZrFpGqo6AJWZ+XObhcs747IyLVSEETshd7drA7O+XMVE05IyJVSEETskf6Z2puUmtGRKqTgiZEvV6aZV3+ZAcXqdtMRKqUgiZET3dvptPr5dT4BCbUtYRdjohIIBQ0IXokO9rsYq07IyJVTEETko5MD093baKOKG9PTAm7HBGRwChoQvJ41wZSZDir8RhGRhvCLkdEJDAKmpD8uX+0mQYBiEiVU9CEYFdfJyt7tpOI1HNO07FhlyMiEigFTQgezc7UfF7T8ZqpWUSqnoImBBptJiK1REFTYutT+1mb2ktrtIm3NEwMuxwRkcApaEqsf8qZCxNTiUX01y8i1U+fdCXkeV5Ot5lGm4lIbaiZK9FmVgdcCMx3zn0pjBpW9e5ke18Hx9aN4sT61jBKEBEpuZIGTfbD/kfA+UAf8D+ccw8M8RizgZucc1fk3Hc98HkgDXzFOXf3ILtOBS4AQltZLHeBs4gWOBORGlHqrrOPAAngJOAS4N/NrDnfnc3sFuDBAffNBK4DZgJnA7ea2aiB+zrn1gC3F1768KS9DI91vQ7oS5oiUltK3XV2GrDYOecBW8xsB3AO8GczGwnMcc49AWBmk4CjnXMrcvZfDrTjB0q/K4GFzrlOoNPMlgEXmdlU/DDr9+HDFWZm1wDXLFiwYGRjY+Pw3uUgVnRvpS3Tw0n145hcN7LoxxcRKVelDprVwKVmthA4ETgFGJ99bDSwwMw+BrwCLAa+DhwMGufc/WY2MGimAE/lbG8GJjnnbgNuy31xMzvkXPzOuXuBe1esWLGjrq6u6EmzNLtcs9adEZFaU+qguR2YBbwErMcPkU4A59xGM3sncD+QBL6b/fA/kgyQGrCdHuyJzrn1wBcLrL1gvV6a5V0biQDna6ZmEakxpb5GcwzwTefcyc65y/BbMy/lPL4R2AMcDTyb5zE3Z4/bbzJ+iJWNv3ZvIemlODU+kaNieV+SEhGpCqUOmvOBWwHM7HIg6Zxbl92uB+4B/gi8F1hkZvnM0bIIeL+ZNZjZZOAs4PEgii/U0uxoswsTJ4RciYhI6ZW66+zXwGVm9jqwF/jHnMemAiucc/8GYGafAS4C1h3ugM651WZ2N7AKv9vss865riCKL0RXJsVT3ZuIEuHtTVPCLkdEpOQinueFXUNZyQ4GGD9nzpyiHO+R5Gt8c+8yzmiYzC1HzS/KMUVEys3KlStJp9M7586dO2HgY5qCJmDqNhORWqegCVBHpodnurdQT5Rzm44LuxwRkVAoaAK0vGsjKTKc2XgMLdGGsMsREQmFgiZAS5P+lDPqNhORWqagCcj+vm5W9GylMVLHOY3Hhl2OiEhoFDQBebxrPRk8zmk8lqZofdjliIiERkETEHWbiYj4FDQB2NXXyYu922mO1HNm4+SwyxERCZWCJgDLkuvxgHObjiceqZlFTEVEBqWgCcDSLnWbiYj0U9AU2bZ0Oy/37mJktIHTGiaFXY6ISOgUNEXWPwjg/KYp1EX01ysiok/CIjvYbdakbjMREVDQFNWG1H7WpfbSGm1idsObJjAVEalJCpoiOthtljiBmLrNREQABU3ReJ53sNvsInWbiYgcpKApkv2ZbtJeHxNiLZwcPyrsckREyoa+TVgkY2JN/Grie9iT6SISiYRdjohI2aiZoDGzOuBCYL5z7ktBvEYkEmFcLBHEoUVEKlbJg8bMvgdcht9td4tz7o4h7j8buMk5d0XOfdcDnwfSwFecc3cPsutU4AJAzQ0RkRIq6TUaM5sHzAZmAucAN5vZiCHsfwvw4ID7ZgLXZY95NnCrmY0auK9zbg1we+HVi4hIIUrdoukDGoE40AL0AikAMxsJzHHOPZHdngQc7ZxbkbP/cqAdP1D6XQksdM51Ap1mtgy4yMymApfkPO/DwbwlERE5nFIHzTIgA+wAEsB3nHPd2cdGAwvM7GPAK8Bi4OvAwaBxzt1vZgODZgrwVM72ZmCSc+424LbcFzezlkMVZmbXANcsWLBgZGNjY0FvTkRE3qzUw5s/DuwHxuMHxAeyXV845zYC7wR+DjwCfNc5d28ex8yQbRXlbKcHe6Jzbr1z7ouHeOxe59wHE4lEWzwez+/diIjIEZU6aE4Dfuec63HObQEeBU7JeXwjsAc4Gng2z2NuBo7J2Z4MrB9+qSIiUgyl7jpbCVxmZr/G7zo7A7gZwMzqgXuAP+J3sS0ys/nOuXVHOOYi4E4z+yEwDjgL+Kdh1NiaTqdZuXLlMA4hIlJb0uk0QOtgj5U6aG7HH3X2Cv7AgB9kR4OBP/x4hXPu3wDM7DPARcBhg8Y5t9rM7gZW4XebfdY51zWMGtOe50XS6fTuQz2ht7e3IR6P9wy8P5PJRHp7exPxeDwZjUa9YdRQcod6T+X+WoUea6j7DeX5R3puoY/r/Cr9aw3nWEGdY/k873DPCfD8auUQly3wPE+3AbcZM2b8ppDHZ/i8GTNmzAj7PRT7PZfraxV6rKHuN5TnF3r+HOlxnV+lf63hHCuocyyf5x3uOWGcX5rrTEREAqWgGdyRRrvlMxqu0pTyPRXztQo91lD3G8rzh3v+6Pwqn9cazrGCOsfyed7hnlPy8yvieRXV1VvWzGwG4ADLufYkUhQ6vyRIQZ5fatGIiEigFDTFtQd/NoM9YRciVUnnlwQpsPNLXWciIhIotWhERCRQChoREQmUgkZERAKloBERkUApaEREJFAKGhERCZSCRkREAqWgERGRQCloREQkUAoaEREJVKlX2KwpZvZ24DPZzR855x4LsRypMmY2D/gk/rLo33LOPRVySVKFzKwZeBY4wznXUcgx1KIpkJnNNrMHcravN7MNZrbOzK7N3v124L8CHwc+EkadUpnyPL9mAR8CbgAuDqNOqVx5nmMAXwVeG85rKWgKYGa3AA/mbM8ErgNmAmcDt5rZKOfcjcBo4Fbg9hBKlQo0hPPrB8DbgAXA8jBqlcqU7zlmZh8FFgO7hvN66jorzHKgHf8fBOBKYKFzrhPoNLNlwEVm1pZ97CvOOU3tLvnK9/wa45z7mZmdDfwSWBpOuVKB8jrHgAuBfcCZ+JcBbi7kxdSiKYBz7n7e+BvkFGBDzvZmYBLw34ExwG1m9smSFSgVbQjnV8TMfgX8BlhYsgKl4uV7jjnnPuKc+yzwN+DfC309tWiKIwOkBmynnXPvDKkeqS6HOr/uAO4IpySpMoOeY/0bzrmPDufgatEUx2bgmJztycD6cEqRKqTzS4IW6DmmoCmORcD7zazBzCYDZwGPh1yTVA+dXxK0QM8xBU0ROOdWA3cDq/AvyH7BOdcVblVSLXR+SdCCPscinucV61giIiJvohaNiIgESkEjIiKBUtCIiEigFDQiIhIoBY2IiARKQSMiIoHSFDQiVcTMrge2AzuAnzjnThrCvsuBR5xzN+TcFwE2Al/Dnyn6RufcsKaMl9qjFo1IyMwsVqTjjMRf/+ieAg+xEHj3gPvOBMYD9wI/xF/yQmRI1KIRyVN2RcvvAdPw1/L4lHNur5n9AtiJ/xv/HOBh4IPOuW4zm4K/FtE5+N+6/qRzbmV2nY9rAA//F74rzOxzwJeBLuDXwLnAVfhrgcxyzr2areMvwB3OuZ8NKPETwH3OuT4zy617LPAE8FPn3A/M7C3AT4DZwJPAx51zG4DfAT8wsxP7Xws/eB5yzu0HXjCz483sFOfcquH9bUotUYtGJA9mdiz+dPyfBo4HeoFv5zzlI9nHpuJ/gF9rZlHgP4Hf40/r/2v8lkG/dwB3Aleb2YXA9fjhcgHwLgDnXBvwaPa5mFkrcHr2uANdk31ubt0J/FC8LxsyLdntm/EnTnwO+Fn2tXYCj2SP0+9q3rgEwVJAs5LLkChoRPLzIeAe59xy59xu4Cv4H8L97nLOrcx+WP8FfybcM4GYc+7Hzrk259wPgZiZnZrd51nn3D3OuRTwQfwWx9ps6+K7Ocf+A3B59udLgb9kazgoG2pn4Lea+tXhd6Ptd859JXvfFcALzrn7sq2UfwXOMbPR2ccPdp+Z2WzgaN4YaivxW2cieVPXmUh+jgc+ZWafzr3TzBqzP+Z+8Hfj/986HphpZgMnFJyU/XNfzn3HActytjfn/LwI+L6ZNeO3bH4/SH2t+L84Hsi5bxqwB5huZmOdc3uzNb1jkJomAvvxQ+3H2Rbcu4FFAyZX3IvfEhLJm1o0IvnZAXzbORdxzkWARuCtzrnuI+zzTP8+2f3m8MZA6deJf9G933H9PzjndgDPA/Ozt0WD7F8HRLK3fpvxu+KeBr6RU9PvcuqJ4nfFrcm+1gHgIfyQeTdvXrnTw18USyRvatGI5Oce4I9mdhf+cN9v4bdMrj7MPk8D483sXcAS/A/uW4ATBnnuI8BnzOwe/P+XX8QfptzvD8ANwGvOuU2D7L8LfxDBaPxWB0Cncy5lZl8CnjOzH+OHyE1mdi7wAvDPwHudc6flHGth9v2NwR/YkGs0/sAHkbypRSOSB+fcS/gjwu4DtpLtSjvCPt34F/X/BT8IPge82znXM8jT/y/wJ/xrIEuzP6dzHv8Dfmvot4d4rTTwDHDqII+tBn4O/CDbOvow/qizncBlwLUDdnkAvyut//pRrln4ASqSN61HI1IGzGwCHOwmw8yuAy50zl2b3a7Dv94y0zm35RDH+BIw0jn3rwHW+Rf8Yd1/D+o1pPqoRSNSHi4DHjSzCWZ2Iv5Q5yUAZtYEfAB/tNmgIZN1O3B5NpSKzsxmAfsUMjJUChqR8vAr/Av+a4C/4ned/SL72A+Bm/CHIh9SdrjyT3lzV1ixfBa/+1BkSNR1JiIigVKLRkREAqWgERGRQCloREQkUAoaEREJlIJGREQC9f8AhDHoYYloBnUAAAAASUVORK5CYII=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”: {
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