- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “f98fba14”, “metadata”: {
- “papermill”: {
“duration”: 0.010142, “end_time”: “2021-08-17T08:10:47.633549”, “exception”: false, “start_time”: “2021-08-17T08:10:47.623407”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Blackbody”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “c76b54ef”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:47.931377Z”, “iopub.status.busy”: “2021-08-17T08:10:47.928163Z”, “iopub.status.idle”: “2021-08-17T08:10:50.791070Z”, “shell.execute_reply”: “2021-08-17T08:10:50.792059Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 3.149619, “end_time”: “2021-08-17T08:10:50.792525”, “exception”: false, “start_time”: “2021-08-17T08:10:47.642906”, “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”: “6fe4f016”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:50.817296Z”, “iopub.status.busy”: “2021-08-17T08:10:50.816350Z”, “iopub.status.idle”: “2021-08-17T08:10:50.823258Z”, “shell.execute_reply”: “2021-08-17T08:10:50.824200Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.022724, “end_time”: “2021-08-17T08:10:50.824494”, “exception”: false, “start_time”: “2021-08-17T08:10:50.801770”, “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”: “401d82fe”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:50.849295Z”, “iopub.status.busy”: “2021-08-17T08:10:50.848325Z”, “iopub.status.idle”: “2021-08-17T08:10:50.855155Z”, “shell.execute_reply”: “2021-08-17T08:10:50.855916Z”
}, “papermill”: {
“duration”: 0.022549, “end_time”: “2021-08-17T08:10:50.856220”, “exception”: false, “start_time”: “2021-08-17T08:10:50.833671”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Blackbody"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “92418267”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:50.883311Z”, “iopub.status.busy”: “2021-08-17T08:10:50.882308Z”, “iopub.status.idle”: “2021-08-17T08:10:50.889164Z”, “shell.execute_reply”: “2021-08-17T08:10:50.890150Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.024874, “end_time”: “2021-08-17T08:10:50.890461”, “exception”: false, “start_time”: “2021-08-17T08:10:50.865587”, “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”: “8f88ca28”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.00915, “end_time”: “2021-08-17T08:10:50.908841”, “exception”: false, “start_time”: “2021-08-17T08:10:50.899691”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “19a0c577”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:50.940334Z”, “iopub.status.busy”: “2021-08-17T08:10:50.939291Z”, “iopub.status.idle”: “2021-08-17T08:10:50.947449Z”, “shell.execute_reply”: “2021-08-17T08:10:50.948669Z”
}, “papermill”: {
“duration”: 0.030822, “end_time”: “2021-08-17T08:10:50.948996”, “exception”: false, “start_time”: “2021-08-17T08:10:50.918174”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A blackbody function</li>n”, “n”, “<li>formula: $f(x) = K \frac{x^2}{\exp(\frac{x}{kT}) -1} $</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>K: n”, “<ul>n”, “n”, “<li>value: 0.0001</li>n”, “n”, “<li>desc: None</li>n”, “n”, “<li>min_value: 0.0</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: True</li>n”, “n”, “<li>delta: 1e-05</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>kT: n”, “<ul>n”, “n”, “<li>value: 30.0</li>n”, “n”, “<li>desc: temperature of the blackbody</li>n”, “n”, “<li>min_value: 0.0</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 3.0</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 blackbody functionn”, ” * formula: $f(x) = K \frac{x^2}{\exp(\frac{x}{kT}) -1} $n”, ” * parameters:n”, ” * K:n”, ” * value: 0.0001n”, ” * desc: Nonen”, ” * min_value: 0.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 1.0e-05n”, ” * free: truen”, ” * kT:n”, ” * value: 30.0n”, ” * desc: temperature of the blackbodyn”, ” * min_value: 0.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 3.0n”, ” * free: true”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “328e1218”, “metadata”: {
- “papermill”: {
“duration”: 0.010109, “end_time”: “2021-08-17T08:10:50.969660”, “exception”: false, “start_time”: “2021-08-17T08:10:50.959551”, “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”: “83d6fd96”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:51.022832Z”, “iopub.status.busy”: “2021-08-17T08:10:50.998023Z”, “iopub.status.idle”: “2021-08-17T08:10:52.144994Z”, “shell.execute_reply”: “2021-08-17T08:10:52.145987Z”
}, “papermill”: {
“duration”: 1.166633, “end_time”: “2021-08-17T08:10:52.146301”, “exception”: false, “start_time”: “2021-08-17T08:10:50.979668”, “status”: “completed”
}, “tags”: [
“nbsphinx-thumbnail”
]
}, “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”, “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”: “3ecb8acd”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011175, “end_time”: “2021-08-17T08:10:52.168851”, “exception”: false, “start_time”: “2021-08-17T08:10:52.157676”, “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”: “fc1bec6a”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:52.256631Z”, “iopub.status.busy”: “2021-08-17T08:10:52.200511Z”, “iopub.status.idle”: “2021-08-17T08:10:52.843923Z”, “shell.execute_reply”: “2021-08-17T08:10:52.844722Z”
}, “papermill”: {
“duration”: 0.664738, “end_time”: “2021-08-17T08:10:52.845044”, “exception”: false, “start_time”: “2021-08-17T08:10:52.180306”, “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”: “f186d8ce”, “metadata”: {
- “papermill”: {
“duration”: 0.011958, “end_time”: “2021-08-17T08:10:52.869630”, “exception”: false, “start_time”: “2021-08-17T08:10:52.857672”, “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”: “c92d1f12”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:52.972125Z”, “iopub.status.busy”: “2021-08-17T08:10:52.912133Z”, “iopub.status.idle”: “2021-08-17T08:10:53.561821Z”, “shell.execute_reply”: “2021-08-17T08:10:53.562830Z”
}, “papermill”: {
“duration”: 0.68171, “end_time”: “2021-08-17T08:10:53.563171”, “exception”: false, “start_time”: “2021-08-17T08:10:52.881461”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
“image/png”: “iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEUCAYAAADwYOuyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAr+UlEQVR4nO3deZxU5Z3v8c+prbu6q7uBZpNFEIWfIogJRskicQkmRNGYjKNJHJOZGM3kakyieeXeeBMzNxmNBjJjzDYxmkxwm4lRCRqNiQEURKONooI+CAiKsja9UL1VVVfdP6pgiraBbuiqU939fb9e9ep6Tp2nzq/gwLefc06dx8tkMoiIiBRDwO8CRERk8FDoiIhI0Sh0RESkaBQ6IiJSNCG/CyhVdXV17WT/fOr9rkVEpB+pBVIzZ84s7+5Fhc6BhYBgKBQa6XchIiL9RSqVOujrCp0Dqw+FQiNnzJjhdx0iIv3G6tWrSaVSBzxCpHM6IiJSNAodEREpGoWOiIgUjUJHRESKRqEjIiJFo9AREZGiUeiIiEjR6Hs6BfBqx07+2LquIO8dwCOAR9DzCBDItj2PYG55wMu9TmDf867rhLwAYS9IiOzPyL7nASJesMtrAUJekDDZ1wJ4eJ5XkM8mIgOfQqcA3u5s5pGWwoSO3wJ4lHshyrwQ5V6IaCC0X7vcCxH1QpQF/qdd7oWJeiEqA2EqAxFiXoTKQO7hhSn3QgoykUFCoVMA0yIjuX7Yhwvwzhk6MxnS5B6ZDJ1kSJMmnVveuW95et86+cvTZEhl0iTpJJVJk8h0kiJNMtNJMpP7+a723nWz67dmkrRmktmSOo/8UwXxcgEUoTIQJpZ7XhMspyZQRk0g72ewfF+7wgsrrET6GYVOAYwOVTE6VOV3GQXTmUnTkemkPZOkLZOiI5OiPZ2iPZP6n/Z+y7LrtaQT2UcmmfuZoCWdJJ5O0JzuoJmOXoVYiEA2jILlDAmUUxusoDYQzf7c7xGlzNOuLlIK9C9Rei3oBajwAlQQ7pP3y2QyJOikJZ0gnk7Skknkgqidps4OmtLtNKX3/mynOd1BY2f2eX26jfp02yG3EfMi1AazgTQqGGNUKMbo3M9RwUpGBCsJerquRqTQFDriO8/zKCNEWTDEsGDP+2UyGVoySZrS7TR0trG7s41d6VbqO1uzzztb2d3ZSn26jeZ0B/FUgs2ppm7fK4DH8GDFviA6KljF2FA148LVjAtVUxUo66NPKzK4KXSk3/I8j5gXIRaIMDZUfdB1E5kUuzvb2NnZyo7OONtTLWzr3MP2VAvbO+NsS8XZ0dnCjs4WSGx/V/+aQBnjQjWMC1XnHtnn48M1RLxeJKXIIKfQkUEh4oXyzrWNetfrmUyGxnT7vgDamtrD26lm3ko1syXVTEO6jabEDtYkduzXL4DH+FA1x4SH7vc4KlhFQBc5iLzLgAsdM6sEngfe55yL+12P9A+e5zE0GGVoMMrxkRHver0lncgLoSa2JJt5M9XE5mQjm1NNbE41sbRt0771y70QE0JDODYyjCnhWiwynGPCQzUqkkGvJEPHzKYDNznnzsu1rwG+DqSA651z9x2k+3eAjYWvUgaTykCEKZHhTIkM3295ZybNO6k9vJFq4I1kA28kG3kj2cDbqWZcchcuuYs/5tYNEeCY8FAsUsuU8HCmRGqZFB5GSBcwyCBScqFjZjcDnwZeyrWnAlcCU4EKoM7MHnXOveuMsJl9HniM7o6fHHybtWTn9d5n4cKFwWBQv5XKwQW9AOPDNYwP1zA7OnHf8kQmxeZkE+uT9bhEPS6xi43J3byerOf1ZD2Q/fJwmRfk+PAIppWNZFpkFCeWjSCmixZkACu50AGWA3uAWbn2POBe51wL0GJmy4CzzGwSMCev36XAmUADcCpwFfCDHm7zauCG/AXxeJxoNHrYH0IGt4gXYnKklsmRWuZWZpclM51sSjbiErt4PVnPa4ldbEjuZnViG6sT2wDwgGPCQzkxMpKTykbxnrKjGBas8O+DiPSxkgsd59xiM8sPnYnAyrxVtgBjnHMLgAVdun8OwMx+A/ykF5u9Dbgnf0EsFns6EonUHmB9kV4Le8F9QbRXWzrJa4ldvJLYzisdO1ib2MHGZAMbkw0sbnEATAwN4b3lY3hv2VHMKBtNZSDi10cQOWIlFzrdSAPJLu3UwTo45z7fmw045+qB+vxldXV1nbrFihRaNBDmPeVH8Z7yo4DsOaJNyUZeSWxndcc2VnVsZVOqkU3xRh6IryWAx/GR4by3bAynlY/j+MhwfalV+pX+EDpbgHF57bHAkz7VIlJQQS/AsZFhHBsZxgWxE0hnMmxM7mZVx1ZWtb/DS4ntrE3sZG1iJ3ftWU1NoIzTyscxq3w8p5SPJaZRkJS4/hA6i4DfmtmPgeHAacA/+1uSSHEEPI/jIrUcF6nl76umkcx08mpiJ8+1v82z7VtYn9zN460beLx1A0E8TiobzfvLx3N6dAKjQjG/yxd5l5IPHefcWjO7D1hD9tDaV51zh77ZlsgAFPaCnFQ2mpPKRvOFmpnsTLXwTPtbrGzfwqr2d3ihYysvdGzlZ01/44TICD4cncjs6ERGK4CkRHiZTMbvGkpSXV3d9lAoNHLGjBl+lyLSI+3pFC90bGV522aWt21mTyax77Xjw8P5cMVEzogeoxGQFNTq1atJpVI7Zs6c2e1XV0p+pCMiPVMeCPH+6HjeHx3P1zIf4IWOrSxrfYPl7W/yWnIXrzXt4pdNz3Ny2VF8tOI4To9OIBromzuFi/SUQkdkAAp5Ad5XPpb3lY/lq5n0vgBa1rZp3yG4f29cyezoBM6pOI6Ty47SveKkKBQ6IgNcfgBdnZnFirY3ebxlPc93vLPvIoRRwRjzKo25lZMZGtSXoqVwFDoig0iZF+KsikmcVTGJXZ2t/KV1A39qWc/mVCO/aq7jN80vcHp0AufHjuekyChNBy59TqEjMkgND1ZwSdV0Lo5NY01iB39oeY1lrZtY0vYGS9reYGJoCBfEjueciuN07kf6jEJHZJDzPI9pZaOYVjaKL9ecxmOtr7M47tiUauTWxme4s2kV58eO5xOxE6jVfeDkCCl0RGSfIcFyLqmazt/HpvF8x9v8fs9anut4m7v3vMR/73mFsyuO5aKqEzkmPNTvUqWfUuiIyLsEPI9Ty8dxavk4NiZ387s9a3iidSOPtb7OY62v8/7y8VxWfTLWZX4hkUNR6IjIQU0KD+Obw07nCzUzeSj+Kn+Iv8bK9rdY2f4Wp5WP47Kqkzmh7N2zrYp0R6EjIj0yPFjB5TUz+XTVdB6Kv8rv4mt4tn0Lz7Zv4ZSyMVxWfTLTyno1f6IMQgodEemVykCEz1bP4MLYVBbFX+W/46/wfMc7PL/zHT5QPp7La05hYniI32VKiVLoiMhhqQiE+XT1SXwidgJ/aHmNe/e8zNPtb/FM+xY+WnEcn69+DyNClX6XKSVGsz+JyBGJBsJcXDWdu0b/HZ+pOomwF+DR1tf5h22/55eNzxFPd/hdopQQhY6I9IlYIMLlNTP57ehPcW7lFFKkuS/+Cpdte4A/tqwjrTvaCwPk8JqZzQauyjV/BiwHzgTOcc59w7fCRAahEcFKrh36Qf4udiL/0fQcz7RvYX7DChbHHV8ZOosTIrrSbTArqZGOmU03s4fz2teY2WYz22Bmlxyk62zgMuBy4HPAJOAMQDeOEvHJhPAQbhw+hxtrP8LYUBUuuYv/teNhbtm9nN2dmodxsCqZ0DGzm4FH8tpTgSuBqcAs4BYzq+mur3Pu+8AQ4BbgdufcOuD2QtcsIoc2KzqeO0ZdyOXVMyn3QjzW+jqf2/YAj7SsQ5NIDj6ldHhtObCHbMAAzAPudc61AC1mtgw4y8wmAXPy+l0KzMitf71zrr63GzazWqA2f9nChQuDwWCw959CRN4l4gX5TPVJzKk4ll80PceStjdY0LCCJ1o38PUhH2BcuNvfJ2UAKpmRjnNuMdng2WsisDmvvQUY45xb4Jz7WN5jF/AVYCiwwMyuOIzNXw24/Ec8Hq9ta9MhAJG+NCJUybdrz+Cm2jmMDFbyYsc2vrB9Efc0v0Qqk/a7PCmCUhrpdJUGkl3aqe5WdM5d0M2yTcB1PdzWbcA9+QtisdjTkUik9gDri8gROC06jl+XXcgdzXU8GH+VXzXXsbTtDf7PsNm6megAV8qhswUYl9ceCzxZiA3lDsntd1iurq6uUxNYiRRONBDmqiGzOCs6ifkNK1if3M2Xti/m8pr38qnYiZo+e4AqmcNr3VgEXGxmZWY2FjiNAoWOiPhnatlIfjFqHhfFTiRFJz9veo7rdj3GtlTc79KkAEo2dJxza4H7gDXAEuBa55xOsogMQBEvxD8POZUfjZjLqGCMFzu28cXtD/Hnlg1+lyZ9zNMli92rq6vbHgqFRs6YMcPvUkQGlZZ0gp80PsufWtcDMLdiMlcPmUV5oJTPBsheq1evJpVK7Zg5c2a3txwv2ZGOiAxOlYEI3xx2Ot8ZdgaVXphHW1/nyzsWsynZ6Hdp0gcUOiJSks6oOIZfjDqfyeFaNqUa+fKOxTzest7vsuQIKXREpGSNDVVz28hzuTB2Au2ZFD9oeIr5DStIZDr9Lk0Ok0JHREpaxAty9ZBZfHfYmVR6Yf7Yso6v73yU+s5Wv0uTw6DQEZF+YXbFRH46ch7jQzWsTezkS9sX82rHTr/Lkl5S6IhIv3F0uIafjjyPWeXjqE+38tWdf+Sxltf9Lkt6QaEjIv1KLBDhe7Vn89mqk0iS5paG5fy88W+aJK6fUOiISL8T9AJ8oWYm3xl2BmVekN/F1/D/di+lI9Pt7RmlhCh0RKTfOqPiGH40fC5DAuU82baJ63b+iabOdr/LkoNQ6IhIv3ZC2Qh+MvI8xoWqWZPYwdU7H+HtVLPfZckBKHREpN8bE6rithHnMj0yii2pZq7e8QivJXRlWylS6IjIgFATLOeHI87hjOhEGtPtXLvzMV5s3+p3WdKFQkdEBoyIF+L/DjuD8yuPpy2T4n/v+jPPtL3ld1mSR6EjIgNKwPO4ZsgsPlN1Egk6+Xb9EzzRutHvsiRHoSMiA47neVxeM5MvVs+kkww37l7Gw3Hnd1lCaU9XfUhmdjZwBVAB3Ai0AdcDQeA259wSH8sTEZ99uvokYoEI/964kh81Pg3AeTHzuarBrSRCx8ymAzc5587Lta8Bvg6kgOudc/cdoOs04LPAScC5ZEduNwMNwJfIzjgqIoPYvNjxhLwg8xuW86PGp/E8j3Mrp/hd1qDl++E1M7sZeCSvPRW4EpgKzAJuMbOa7vo6524FPgj8ClgOPA78BngYWFrIukWk/5hbOZnrhn4QD1jQsIJHW9b5XdKgVQojneXAHrIBAzAPuNc51wK0mNky4CwzmwTMyet3KXC+c+5OM5sF/CeQBmbmXr+LvDA7GDOrBWrzly1cuDAYDAYP8yOJSKmZmxvd/LBhBfMbVgAecysn+1vUIOR76DjnFptZfuhMBFbmrbIFGOOcWwAsyO9rZp6Z3QWUA78FIsCvyZ7TubsXZVwN3JC/IB6PE41Ge/EWIlLq5lZOIQPMb1jB/IbllHtBzqyY5HdZg4rvodONNJDs0u72Ln7OuTuAO7osvv8wtnkbcE/+glgs9nQkEqk9wPoi0k99vHIKyUyaWxtXctPup6gMRDi1fJzfZQ0apRg6W4D8PWAs8GQhN+icqwfq85fV1dV1ep5XyM2KiE8uiB1PPN3BHc2ruKH+r9wy/KNMLxvld1mDgu8XEnRjEXCxmZWZ2VjgNAocOiIy+Hym6iT+PjaNjkwn39r1F9Yn6g/dSY5YyYWOc24tcB+whuwlz9c659r8rUpEBhrP87iy5hQ+XjGZlkyCb+56XHenLgIvo9n2ulVXV7c9FAqNnDFjht+liEgBdWbSfG/3Up5s28y4UDW3jTiXmmC532X1W6tXryaVSu2YOXNmt8crS26kIyJSTEEvwLeGzWZaZCRbUs18u/4JEpqBtGAUOiIy6EW8EN+rPZtxoWpeSezgB7ufIq2jQAWh0BERITsfz03D51ATKGNp2yZub3re75IGJIWOiEjO2FA1/1r7ESIE+a/4K7ozdQEodERE8kwtG8m3amcDcGvjSl7q2OZzRQOLQkdEpIvZ0Yn8Y/V76CTDd+uXsC0V97ukAUOhIyLSjUurZjA7OpHGdDvfqX+C9rSuaOsLCh0RkW54nsc3h36ISeGhrE/u5ocNy9H3Go+cQkdE5ACigTDfrz2b6kAZS9re4N49L/tdUr+n0BEROYjRoSq+O+xMgnjc2byKVe3v+F1Sv6bQERE5hJPLj+KLNaeQJsO/7l7Grs5Wv0vqtxQ6IiI9cFHsRD5YfjQN6Xa+X7+Uzkza75L6JYWOiEgPeJ7HN4d9iKOCVbyU2M4dzav8LqlfUuiIiPRQLFDGd2vPJEyQ+/a8zMq2t/wuqd9R6IiI9MLkSC1XDzkNgJt2P8kOfXG0V0pxuuoeM7NpwPeAJuAPwLr8tnPuAR/LE5EB6tzKKazu2MYTbRu5afdTzB/xUYKefofviZL4UzKz6Wb2cF77GjPbbGYbzOySg3R9H7D3bN7L3bRFRPqc53lcM/T9jA7GWJ3Yxn/tecXvkvoN30c6ZnYz8GngpVx7KnAlMBWoAOrM7FHnXFM33Z8DFgEe8FPg+13aBwus/Bpqgdr8ZQsXLgwGg8HD+UgiMgjEAhG+NWw2X935KL9uXsV7y4/i+MgIv8sqeb6HDrAc2APMyrXnAfc651qAFjNbBpxlZpOAOXn9LgXeQ/aQGmRHOF3bPXU1cEP+gng8TjQa7c3nEJFBZlrZKC6tmsFv97zIjbuf5D9Gnk80EPa7rJLme+g45xabWX7oTARW5q2yBRjjnFsALMjva2ZbgYVAG/BjINal3VO3AffkL4jFYk9HIpHaA6wvIgLAP1TP4PmOt1mb2MlPG5/lumEf8rukkuZ76HQjDSS7tLu9vatz7i/AX7os7to+JOdcPVCfv6yurq7T87zevpWIDDJBL8C3hn2YK7Yv4o+trzMrOp4PRSf4XVbJKokLCbrYAozLa48FNvlTiojIoY0JVe27jPrfGlbS1Nnuc0WlqxRDZxFwsZmVmdlY4DTgSZ9rEhE5qHMqjmNW+Xga0m3c1vis3+WUrJILHefcWuA+YA2wBLjWOdfmb1UiIgfneR5fH/oBYl6Ev7Zt5Km2zX6XVJI8TUrUvbq6uu2hUGjkjBkz/C5FRPqRx1vW84OGpxgaKOfOURdSEyz3u6SiWr16NalUasfMmTNHdff6YY10zOx0M/snM5tyZOWJiAwscyqOzR1ma9dhtm4c7uG1DwEtwA1mdsOhVhYRGSx0mO3gDit0nHM3Oef+yzn3WWCrmS04ZCcRkUFieLCCq3JXs/24YSXxdMLnikrHYX1Px8x+BRwFjAbCgK4PFBHJM6fiWP7SuoHnO97hzqZVfGXorEN3GgQO98uhPwO2Atudc5o+T0Ski703Bf3CtodY1PIqcyqP5QTdm+2wD6+tcs5tVeCIiBzY2FA1l1WfTAb4UcPTmuKaQ4SOmb3ZpX1+YcsRERlYLqo6kYmhIWxI7ub38bV+l+O7Q410xnVpP1ioQkREBqKwF+TrQz8AwG+aX2DbIJ9p9FCh0/Wbo7oDpohIL00rG8V5lVNoz6T4ceMzfpfjq96e09HtC0REDsMXa05hSKCcZ9rf4pm2t/wuxzeHvHrNzEaQHeF4XdoAOOd2FKw6EZEBoipQxuU1M5nfsIKfNf2NmeVjCHuDb3biQ410PGBb7vFOrr09b9nWglYnIjKAfKxiMhYezpZU86C9qOCgoeOcCzjngkAVcA7Z6aLLc8v3viYiIj0Q8Lx9dypY2Pwi9Z2tPldUfD09p7MJ+A5wE/C2mV1UsIpERAawE8tGMqfiWNoyKX7VVOd3OUXX09A51jn3YefcqcBc4F/M7H8XsC4RkQHrizWnEPVC/Kl1PWs7Btdp8R7dBsc515z3/HkzOxV40MwCzrkbC1bdIZjZ9cD0vEU/A67a+9w5t7ToRYmIHMLwYAX/UH0yv2x6ntsan+WnI88j4A2Ob6T0KHTM7KPA2LzHuNzje8ARh46ZTQducs6dl2tfA3wdSAHXO+fu666fc+5fc+t/GXgROAu4DIgAtwJLj7Q2EZFC+GRsKn9sWYdL7uKJ1o3MqTzW75KKoqc3/Pw52avX3gHeBpYB9+aeHxEzuxn4NPBSrj0VuBKYClQAdWb2qHOu6QD9RwFTnHM/A542s9HAd4Hbe1FDLVCbv2zhwoXBYFDXSYhIYUS8IF+sOYUb6v/KHc11fLhiAhHvcO/B3H/09PDapALWsBzYA+y97/c84F7nXAvQYmbLgLPMbBLZq+f2utQ5twv4GtlQxMzOzvW/3jlX34sargb2m4wuHo8TjUYP5/OIiPTIh8qP5sTISNYkdvBg/FUurpp+6E793OHOHNpnnHOLyQbPXhOB/Kn2tgBjnHMLnHMfy3vsyr1+gnPO5Z5/BRgKLDCzK3pRxm2A5T9isVi9QkdECsnzPK6sOQWAu5tfojnd4XNFhVeKY7k0kOzSTh1oZefcBd09743cqGi/kVFdXV2nN0hO7ImIf6aVjeL06ASeatvM3c2r+echp/pdUkH5PtLpxhb2v7v1WLLfExIRGZAur55JAI+H4q+yLbXH73IKqhRDZxFwsZmVmdlY4DTgSZ9rEhEpmPHhGuZVGknS3NG0yu9yCqrkQsc5txa4D1gDLAGudc61+VuViEhhXVZ9MlEvxBNtG1mX2HXoDv1USZzTyX2Jc2leez4w3696RESKbWgwyiVV0/l18wvc2byKHww/x++SCqLkRjoiIoPVp2InUh0o42/tb7NmgN4eR6EjIlIiKgJhLsl9V+fXzQPz3I5CR0SkhHyi8gSGBqKs6tjKi+0Db8oyhY6ISAkpD4T4zL7RzgtkMhmfK+pbCh0RkRIzL2YMD1bwcmI7z3e843c5fUqhIyJSYiJeiEurZgDw66ZVA2q0o9ARESlBcysnMzoY47XkLla2v+V3OX1GoSMiUoLCXpB/qM6Odn7b/OKAGe0odEREStSciuMYHYyxLlk/YM7tKHREREpUyAvsm2PnrubVPlfTNxQ6IiIlbG7lcdQGoryc2M5LHdv8LueIKXREREpYxAtxUdU0IDvRW3+n0BERKXHzKo3qQBnPdbyN6+d3oFboiIiUuGggzKdiUwG4u5+f21HoiIj0A5+InUCFF2Z5+5tsSjb6Xc5hK4n5dA6XmZ0MXA8EgduAocBngTgw3zn3sn/ViYj0napAGRfEjufePS9zz56X+Naw2X6XdFhKYqRjZtPN7OG89jVmttnMNpjZJQfpegFwM/AN4OPAp4FLgK8B3ypkzSIixfZ3sRMp84L8tXUj21Jxv8s5LL6PdMzsZrJh8VKuPRW4EpgKVAB1Zvaoc66pm+6PA78hO9K5DthFdsSzK9e3pzXUArX5yxYuXBgMBoO9/TgiIgUzNBjloxWT+UPLazwQX8uXh5zqd0m95nvoAMuBPcCsXHsecK9zrgVoMbNlwFlmNgmYk9fvUuAqYGaufRfwGHA1MBm4ohc1XA3ckL8gHo8TjUZ7+VFERArr72JTWdzyGo+0OC6rnkEsUOZ3Sb3ie+g45xabWX7oTARW5q2yBRjjnFsALMjva2YPAr8mO9K5G2jLtaPA13tRxm3APfkLYrHY05FIpPYA64uI+GJcuIYPlB/NivY3ebhl3b6ZRvsL30OnG2kg2aWd6m5F59z9wP1dFv+ptxt0ztUD9fnL6urqOj3P6+1biYgU3N9XTWNF+5s8EF/Lp2JTCXv951RASVxI0MUWYFxeeyywyZ9SRERKz7TISE6IjGBXZytLWzf5XU6vlGLoLAIuNrMyMxsLnAY86XNNIiIlw/M8LoqdCMDv4q/0q2kPSi50nHNrgfuANcAS4FrnXJu/VYmIlJbToxM4KhhjfXI3L3Rs9bucHiuJczrOuaXA0rz2fGC+X/WIiJS6oBfgU7ET+UnTs9wfX8N7y8f4XVKPlNxIR0REeuZjlZOp8MI8276Ft1PNfpfTIwodEZF+qiIQ5mOVx5EBHoq/6nc5PaLQERHpxy6oPAGAx1pepy2dPMTa/lPoiIj0Y+PDNZxaPpaWTJLHWzf4Xc4hKXRERPq5Cyuzc+08FF9b8pdPK3RERPq595WPZVyoms2pJlaV+OXTCh0RkX4u4Hl8Indu58H4Wp+rOTiFjojIAPDRyuOIeiFWtr/F1tQev8s5IIWOiMgAUBmIcE5F9vLph1uc3+UckEJHRGSAmBczIHv5dDLT6XM13VPoiIgMEJPCw5gaGUFDup0VbW/6XU63FDoiIgPI+ZXHA7C4RA+xKXRERAaQD1dMpMqL8ELHVt5KNvldzrsodEREBpAyL8ScyuOA0rygQKEjIjLAzKvMXlDwp9b1JDIpn6vZX0nMp9NTZhYCzgTOcc59w8ymA98HWoB/A3YD3wU6gCXOubv9qlVExC8TwkM4KTKKlxLbeaptM2dXHOt3Sfv4MtIxs+lm9nBe+xoz22xmG8zskoN0nQScAXi59peAf8o9vgpcA2wj+7n+VoDSRUT6hb2XTy+Ol9YhtqKHjpndDDyS154KXAlMBWYBt5hZTXd9nXPrgNvzFtU65+qdc+1ABDge+CXwLbIjnp7WVGtmU/IfnZ2dwVK/cZ6IyIGcHp1IdaCMlxLbS+qCAj8Ory0H9pANGIB5wL3OuRagxcyWAWeZ2SRgTl6/S51zu7q81x4zGwokgHjuZxPQDPTmm1FXAzfkL4jH40Sj0V68hYhI6Yh4QT5SMYkH4q/yp9b1XF4z0++SAB9Cxzm32MzyQ2cisDJvlS3AGOfcAmDBId7u38mObBLAjUAZ8Auglew5np66Dbgnf0EsFns6EonU9uI9RERKytyKKdnQaVnPP1a/h6Dn/7VjpXAhQRpIdmkf8HIL59wm4Lrc8zXARV1W+WRvC3DO1QP1+cvq6uo6Pc87QA8RkdJ3bGQYx4WHsT65m+fb3+G06Di/SyqJS6a3APl/EmOBTf6UIiIysMytnAzAo62v+1xJVimEziLgYjMrM7OxwGnAkz7XJCIyIJxdcSxhAjzd9iZNne1+l+N/6Djn1gL3AWuAJcC1zrk2f6sSERkYqgNlfDB6NCnSPNG60e9y/Dmn45xbCizNa88H5vtRi4jIQDe3cgpL2zbxWOvrfLJqqq+1+D7SERGRwnpv2VGMCFawPrmb1xP1h+5QQAodEZEBLugFOKciexPQx3y+oEChIyIyCHw0FzpLWt8glUn7VodCR0RkEBgXruGEyAga0+083/62b3UodEREBomPVEwC4M+tG3yrQaEjIjJInBmdRBCPFW1v0pJO+FKDQkdEZJAYEiznfeXjSNDJU22bfalBoSMiMojMyR1i+4tPh9gUOiIig8gHokdT4YV5oWMrO1MtRd++QkdEZBAp80LMjk4kAzzRVvzb4ih0REQGmY/4eIhNoSMiMsicnLstzsZkAxsSu4u6bYWOiMggE/A8zoxmRztL294o7raLujURESkJZ1UcA8BfW98gk8kUbbulMF11j5lZCDgTOMc59w0zmw1clXv5Z0AQuAKoAG50zq30p1IRkdI2OVzLmGAV73TuYV2yHosML8p2fRnpmNl0M3s4r32NmW02sw1mdslBuk4CzgC8XHs2cBlwOfA5YBrwWeAG4COFqF1EZCDwPI8zc6OdJa3FO8RW9NAxs5uBR/LaU4ErganALOAWM6vprq9zbh1we177+8AQ4BbgdufcrcAHgV8Bywv0EUREBoQzotnQWdr2BukiHWLz4/DacmAP2YABmAfc65xrAVrMbBlwlplNAubk9bvUObcr/43M7Oxc/+udc/Vm9k/OuTvNbBbwn2Snvz4kM6sFavOXLVy4MBgMBg/j44mI9A+TwkOZEKphc6qJtYkdTCsbVfBtFj10nHOLzSw/dCYC+edetgBjnHMLgAWHeLuvAI3AAjN7Gug0s7uAcuC3vSjrarKH5PaJx+NEo9FevIWISP/ieR5nVBzDfza/yNK2NwZm6HQjDSS7tFMHWtk5twm4Lvf8gm5WueMwargNuCd/QSwWezoSidQeYH0RkQHhzGg2dJa1buKfa04l6BX2rEsphM4WYFxeeyzwZDELcM7VA/tNHF5XV9fped4BeoiIDAxHh4cwKTyUjckGXknsYEbZ6IJurxS+p7MIuNjMysxsLHAaRQ4dEZHB7Mzo3u/sFP5ebL6HjnNuLXAfsIbsif9rnXNt/lYlIjJ4nJG7dPqpts10ZtIF3ZYvh9ecc0uBpXnt+cB8P2oRERnsxoaqOS48jPXJ3bzcsZ2Ty48q2LZ8H+mIiIj/ZkcnAvBkgWcUVeiIiAizoxOA7CG2Qn5RVKEjIiIcHR7ChNAQ6tOtvJrYWbDtKHRERAT4n9HOk22bCrYNhY6IiAD/c15nedvmgk13oNAREREgey+2McEqtnbGWZ8szIyiCh0REQGy92I7vcCH2BQ6IiKyz95DbE8V6NJphY6IiOxjkeGMCFbQkemkqbO9z9+/FG74KSIiJSLgefx05DxqA1EKcdNjhY6IiOxneLCiYO+tw2siIlI0Ch0RESkahY6IiBSNQkdERIpGoSMiIkWj0BERkaLRJdMHVptKpVi9erXfdYiI9BupVAqg9kCvK3QOLAXQ2toaj0QiHQdaKZFIlHX3ejqd9hKJREUkEmkNBAKFmxGpjx3o85T6to7kvXrbt6fr92S9Q60z0PYvKN4+pv3Lt/2rltz/n93KZDJ6HOQxZcqUew7n9SlZmSlTpkzx+zP05ect1W0dyXv1tm9P1+/JeoNt/+rrv/dibUf7V989dE5HRESKRqFzaL8/wtf7m2J+nr7c1pG8V2/79nT9nqw32PYvKN5n0v5VgvuXl8n0q8PB/YaZTQEcYM65dX7XIwOL9i8ppELuXxrpiIhI0Sh0Cqce+JfcT5G+pv1LCqlg+5cOr4mISNFopCMiIkWj0BERkaJR6IiISNEodEREpGgUOiIiUjQKHRERKRqFjoiIFI1CR0REikahIyIiRaPQERGRotHMoUViZrOBq3LNnznnlvpYjgwwZnY2cAVQAdzonFvpc0kywJhZJfA88D7nXPxw30cjnT5gZtPN7OG89jVmttnMNpjZJbnFs4HLgMuBz/lRp/RPPdy/pgGfBW4APuJHndI/9XD/AvgOsPFIt6fQOUJmdjPwSF57KnAlMBWYBdxiZjXOue8DQ4BbgNt9KFX6oV7sX7cCHwR+BSz3o1bpf3q6f5nZ54HHgJ1Huk0dXjtyy4E9ZP+CAOYB9zrnWoAWM1sGnGVmzbnXrnfO6Xb00lM93b+GOufuNLNZwH8CS/wpV/qZHu1fwJlAA3Aq2dMEPzjcDWqkc4Scc4vZ/zfLicDmvPYWYAzwFWAosMDMrihagdKv9WL/8szsLuAe4N6iFSj9Wk/3L+fc55xzXwX+BvzkSLapkU7fSwPJLu2Uc+4Cn+qRgeVA+9cdwB3+lCQDSLf7196Gc+7zR7oBjXT63hZgXF57LLDJn1JkANL+JYVU8P1LodP3FgEXm1mZmY0FTgOe9LkmGTi0f0khFXz/Uuj0MefcWuA+YA3Zk7nXOufa/K1KBgrtX1JIxdi/vEwm05fvJyIickAa6YiISNEodEREpGgUOiIiUjQKHRERKRqFjoiIFI1CR0REika3wREZgMzsGmAbsB34hXPu+F70XQ484Zy7IW+ZB7wJfJfs3ay/75w74tvcy+CjkY5IiTCzYB+9TzXZuZvuP8y3uBf4ZJdlpwIjgd8DPyY7RYdIr2mkI9JLuVk6/w04luxcJF9yzu02s98AO8iOBGYAjwOfcc61m9lEsvMovZ/st72vcM6tzs1T8ikgQ/aXwPPM7GvAN4E24G7gQ8D5ZOcymeacez1XxwrgDufcnV1K/CLwkHOu08zy6x4GPAX80jl3q5mdDPwCmA48DVzunNsM/A641cwm790W2RB61DnXCLxoZhPM7ETn3Joj+9OUwUYjHZFeMLPxZKcP+DIwAUgAP8xb5XO51yaR/c/8EjMLAH8AHiA7DcHdZEcMe30c+C1woZmdCVxDNmjOAD4B4JxrBv6aWxczqwVOyb1vV5/KrZtfdwXZgHwoFzixXPsHZG/quAq4M7etHcATuffZ60L2nzJhCaA7p0uvKXREeuezwP3OueXOuV3A9WT/Q95roXNude4/7hVk79h7KhB0zv3cOdfsnPsxEDSzk3J9nnfO3e+cSwKfITsSWZ8bdfwo770fBM7NPf8YsCJXwz65gHsf2dHUXiGyh9oanXPX55adB7zonHsoN3r5NvB+MxuSe33fITYzmw4cxf4Bt5rsqE2kV3R4TaR3JgBfMrMv5y80s/Lc0/wQaCf7b2wCMNXMut7ocEzuZ0PesqOBZXntLXnPFwH/bmaVZEc8D3RTXy3ZXyab8pYdC9QDx5nZMOfc7lxNH++mptFAI9mA+3luZPdJYFGXGz/uJjtCEukVjXREemc78EPnnOec84By4D3OufZD9Hlub59cvxnsHy57tZA9Yb/X0XufOOe2Ay8A5+Qei7rpHwK83GOvLWQP1z0DfC+vpt/l1RMge7huXW5bTcCjZAPnk7x7NtIM2Qm+RHpFIx2R3rkf+KOZLSR7CfGNZEcsFx6kzzPASDP7BPBnsv+J3wwc0826TwBXmdn9ZP99Xkf20ue9HgRuADY6597qpv9OshcgDCE7GgFocc4lzewbwCoz+znZQLnJzD4EvAhcDVzknHtv3nvdm/t8Q8leFJFvCNmLJkR6RSMdkV5wzr1C9sqyh4B3yB1uO0SfdrIXBPwfsqHwNeCTzrmOblb/D+BPZM+ZLMk9T+W9/iDZUdJ/H2BbKeA54KRuXlsL/Bq4NTdqupTs1Ws7gLnAJV26PEz2cNve8035ppENU5Fe0Xw6IiXEzEbBvkNpmNmVwJnOuUty7RDZ8zNTnXNvH+A9vgFUO+e+XcA6V5C9VPzlQm1DBiaNdERKy1zgETMbZWaTyV4+/WcAM4sCnyZ71Vq3gZNzO3BuLqD6nJlNAxoUOHI4FDoipeUushcLrAOeJXt47Te5134M3ET28uYDyl0C/Uvefbisr3yV7CFGkV7T4TURESkajXRERKRoFDoiIlI0Ch0RESkahY6IiBSNQkdERIrm/wMZughT/sitBQAAAABJRU5ErkJggg==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”: 8.236065, “end_time”: “2021-08-17T08:10:54.162981”, “environment_variables”: {}, “exception”: null, “input_path”: “Blackbody.ipynb”, “output_path”: “../docs/notebooks/Blackbody.ipynb”, “parameters”: {
“func_name”: “Blackbody”, “linear_range”: false, “wide_energy_range”: true, “x_scale”: “log”, “y_scale”: “log”
}, “start_time”: “2021-08-17T08:10:45.926916”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}