- {
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}, “tags”: []
}, “source”: [
“# Cubic”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “a4599dce”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:36.910623Z”, “iopub.status.busy”: “2021-08-17T08:12:36.909497Z”, “iopub.status.idle”: “2021-08-17T08:12:40.032954Z”, “shell.execute_reply”: “2021-08-17T08:12:40.034088Z”
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}, “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”: “9d006526”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:40.072767Z”, “iopub.status.busy”: “2021-08-17T08:12:40.071653Z”, “iopub.status.idle”: “2021-08-17T08:12:40.081466Z”, “shell.execute_reply”: “2021-08-17T08:12:40.082256Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.035981, “end_time”: “2021-08-17T08:12:40.082649”, “exception”: false, “start_time”: “2021-08-17T08:12:40.046668”, “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”: “5128dafd”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:40.110117Z”, “iopub.status.busy”: “2021-08-17T08:12:40.109095Z”, “iopub.status.idle”: “2021-08-17T08:12:40.116930Z”, “shell.execute_reply”: “2021-08-17T08:12:40.117776Z”
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“duration”: 0.024569, “end_time”: “2021-08-17T08:12:40.118105”, “exception”: false, “start_time”: “2021-08-17T08:12:40.093536”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Cubic"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “594ae7b0”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:40.147960Z”, “iopub.status.busy”: “2021-08-17T08:12:40.146860Z”, “iopub.status.idle”: “2021-08-17T08:12:40.155979Z”, “shell.execute_reply”: “2021-08-17T08:12:40.156674Z”
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}, “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”: “97089a56”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009746, “end_time”: “2021-08-17T08:12:40.177055”, “exception”: false, “start_time”: “2021-08-17T08:12:40.167309”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “023faf44”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:40.210541Z”, “iopub.status.busy”: “2021-08-17T08:12:40.209459Z”, “iopub.status.idle”: “2021-08-17T08:12:40.218415Z”, “shell.execute_reply”: “2021-08-17T08:12:40.219203Z”
}, “papermill”: {
“duration”: 0.032375, “end_time”: “2021-08-17T08:12:40.219519”, “exception”: false, “start_time”: “2021-08-17T08:12:40.187144”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
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}, “metadata”: {}, “output_type”: “display_data”
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], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “a717115f”, “metadata”: {
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}, “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”
]
}, {
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“nbsphinx-thumbnail”
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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”: “5204417d”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011362, “end_time”: “2021-08-17T08:12:40.490806”, “exception”: false, “start_time”: “2021-08-17T08:12:40.479444”, “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”: “4180f5b6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:40.543521Z”, “iopub.status.busy”: “2021-08-17T08:12:40.522239Z”, “iopub.status.idle”: “2021-08-17T08:12:40.950104Z”, “shell.execute_reply”: “2021-08-17T08:12:40.950739Z”
}, “papermill”: {
“duration”: 0.448907, “end_time”: “2021-08-17T08:12:40.951068”, “exception”: false, “start_time”: “2021-08-17T08:12:40.502161”, “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”: “e6440baa”, “metadata”: {
- “papermill”: {
“duration”: 0.01333, “end_time”: “2021-08-17T08:12:40.977763”, “exception”: false, “start_time”: “2021-08-17T08:12:40.964433”, “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”: “2d19c8b7”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:41.034384Z”, “iopub.status.busy”: “2021-08-17T08:12:41.014204Z”, “iopub.status.idle”: “2021-08-17T08:12:41.197190Z”, “shell.execute_reply”: “2021-08-17T08:12:41.197832Z”
}, “papermill”: {
“duration”: 0.207444, “end_time”: “2021-08-17T08:12:41.198165”, “exception”: false, “start_time”: “2021-08-17T08:12:40.990721”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
“image/png”: 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Mx/p8N5/YeD85ilw44XCOzcyJu6SyqZSezvaKQG675fxO2nPuHgzRPlw3A98tbWhpaXmkvr6+dQfri4jslmwxx8c77mdTMcvxjXO4ZOKRcZdUVpUaOu3A7JLlWcCLJe2vRu0zCHs7681surtvMLM6IO/u/cPdmbt3AB2lbW1tbYXEOL9Dn4hUlkJQ5HObHmBVbhP7103l41NPIpWoiAGnstmtozWzd5jZX5rZQaNdUORu4INmljSzw4BJ7u5R+8VRDacDz7t7d2k7cAklkxJERCpBEAR8uetxHul7hanJDJ9rPY1Msi7usspudyP2BKAH+JSZfWoU6wHA3e8FVgHPE04M+JvopduAqWa2Cvgc8NGo/Trg9Kj9fOATo12TiMie+OaWJ7mr+1kaE2n+ddppzKiRiQPbSwRBsOu1dsLM/gYwd//Y6JRUGdra2tal0+kZCxcujLsUERnn7tj6NF/pepw6klw37XTe2rhP3CWNmeXLl5PP59cvWrRoyHsy7NY5HTO7jXDW2N5AHdC3+yWKiFSv/+52vtL1OEkSXNN6clUHznDs7kSCLxOezF/n7sVRrEdEpGr8aOszfKnrNySAq6e8vWqvHD0SuxU67v7b0S5ERKSafG/L77l1SxtJEvyfKSdwRvMBcZdUEXY6kcDMXt5u+eyxLUdEZHwrBgG3dbVx65Y2UiS4ZupJCpwSu+rpzN5u+S5A14YRERnCQJDn+k0PsTS7mjpSfLL1ZN6emRt3WRVlV6Gz/dQ2fVtSRGQIXYU+PtFxP08NrGdisoF/bT2NwxqGnMBV00Z6TmfP5leLiFQhH9jIpzuWsq7Qzez0RK6bdjqz0hPjLqsi7TJ0zGw6YQ8nsd0yAO6+fsyqExGpYEEQ8NOe57h582PkKHJkw0w+OfVkJqUa4y6tYu0qdBLAa9HPIPq5ruT1AJ3jEZEatLXYz02dj3F/9gUALpxwOJdMPLLmrqU2UjsNHXdPAkS3CRi8182v3X1grAsTEalUy/rW8PnOh9hY6KUlUc8/TX0Hx2vCwLAM95zOi8AzQAaYb2Yfcfc7xqwqEZEK1F3s59auNn7S4wC8rWEfrp56AtNTI7l9V20bbujs7+5bAMzsbcC3zGx/d/+3sStNRKQyBEHAfb2ruKVrGZ3FPhoSKT486SjObj4Y3QJlZIYVOoOBEz1/wsyOBu4ys6S7Xztm1YmIxOzZgQ18dfMyVgyEp7OPbpzF5ZOP1ey03TSs0DGzMwlvpDb4mB09PgsodESk6ryY28zXu9p4qC+8MMteqRY+Ovlojm+cq97NHhju8NpXgLXRYw3wAPC96LmISNV4fqCDH2x9iqXZ1RQJaE7U84EJb+G9LQtoTFbqzZbHj+EOr+031oWIiMSlGAS09a/lB1ufoq1/LQANiRTntSzg/RPewsRkQ8wVVg/FtojUrI5CLz/vWcnPep5jbWErABOTDZzbfAjnthzCZH3Jc9QpdESkpnQX+3kk+wq/yr7I433tFKOre81JT+K8lkP4o6YDNYw2hvQnKyJVb32+myf61/JQ9iWe6FtLnvDekw2JFCdn5nNW80EcVj9DEwTKQKEjIlWnq9DH0wPr+W3/Wp7oW8vL+a5tr9WR4vjGOZyUmc9xmTm0JOtjrLT2KHREZFzLFnO8mN/McwMdPDOwnmcHNtCe3/KGdaYkG1nUuA/HNM7m2MY5NCtoYqPQEZGKVwiKbCz08lqhm9fyW3k538Xq3GZezHXyWqH7TetPTDZwSP10FjbszVENs5hfN4Wkhs4qQlWFjpldCfw9kAc+7u5LYi5JRHagGARkgxy9xRybi310FrNsLvSxqZils5Blc7EvDJr8VtYXeijs4HZezYl65tdNZr+6qSyon84h9dOZnZ6o8zMVqmpCx8wWAB8CFgBNQJuZ3evuXTvfUmpFEAQUCchTJB9ED17/WQjCeUyDs5kCAoIg+skQP4Oh20v3F74Pb3gl2PZzu/UH9xsMZ/2gpL1kKXjj+gyxbbDdv4NghOuXHF+OAgNBgVxQJBcUyAUFBgiXB9sHgjx9QZ7eIEdPcYCeKGh6gxzDVUeSmekJ7J1qYe90C/ukJ7Jf3RTmp6cwLdWkgBlHqiZ0gPcA33P3HqDHzB4ATgXu2tWGZtYKtJa2LV68OJVK6VZBlSAXFOgsZOkq9rOl2M/WYn/0vI8txX56ijn6ghx90YdbtpinL8iRjZYHggKFoEgumrEklaExkaY5UUcmWcekZCNTko1MSWWYkswwJdXIlGSGqakMe6cnMDWZ0fBYlaim0NkXeLRkuR3YZ5jbXg58qrShu7ubTCYzOpXJTg0Eedbkt/JKvos1uS2sL/SwsdDLhkIPGwq9dBaze7yPFAkaE2lSJEknEqRJkk6k3vA8RSL6BxKJ6Cev/4TwLoaDH36lr5X+HLyv7rblbUtse483vhL9TLx5/cSbtn7j+yZKdrir9be1JHZWy+vv9YbtE5S0h/9OkiCdSFJPivpEirpEkrpEirpEinpeX65PpMgk0jQl62hK1NOcrKMpUaebndWoagqdIpDbbjk/zG1vBr5b2tDS0vJIfX196w7Wl91QDALW5LfwfK6D5wY6eCG3iVfyW1hf6N7BaH0ok0jTmmpiUrKRicmGNz2ak/VkEnU0JtJkkmkaB58n0jQm09ST0vCLSIWoptBpJ7zy9aBZwIPD2dDdO4CO0ra2traCPqj2TLaY4+mB9fy+fx0r+tfxXG4j2eDNvwdkEmlmpycxOz2ROelJ7JVuZlqqmempZqanmjS9VaSKVFPo3EN4c7mbgGnAMcBl8ZZUW4Ig4PlcB4/2vcKyvjX4wMY3zDhKAHPTkziwrpUD61s5oG4qc+sm05rMqCciUiOqJnTc/RkzWwI8TTi0dpW77/nJANmpQlBkef9r/Cq7mkez7XQUe7e91pBIcXj9DA5v2IvD6/fm4PppZJJ1MVYrInGrmtABcPcbgRvjrqMWrBzo4L7eVfyyd/UbgmZeehLHNs7h2MwcFtRPpy6hGYAi8rqqCh0ZWwNBgQd6X+Tunmd5dmDDtvb966ZyWtN+vCMzT7fwFZGdUujILnUV+rir+1l+3PMHNhf7AGhNZjiz+UBOa9qP+XVTYq5QRMYLhY7sUGchyx1bn+Kenj9sm3V2eP1enNtyCCdk5pHW9yxEZIQUOvIm2WKOJVtX8IPup+gPCiSAkzP7csGEwzlAX10SkT2g0JFtCkGRn/eu5Otdv2VTMUsCeGfTflw4YSHz6ibHXZ6IVAGFjgCwamATN3Y+jOc2ArCoYR8+POko9q+fGnNlIlJNFDo1rj/Is3jLcpZsXUGRgNnpiXxk0tEc0zhbX9gUkVGn0Klhzw1s5HObHuSVfBcpEnxwwkIunHg49Qn9ZyEiY0OfLjWoGATc2f00t3W1kafIwXXT+Iepb2e/Og2licjYUujUmC3Ffq7teIDH+9eQAC6asJCLJx6hy8yLSFkodGrI6lwn12y8n1cLW5mWauJfppzIEY0z4y5LRGqIQqdG/Dr7EtdtepC+IM/h9Xvx6dZTmZxqjLssEakxCp0acOfWp/ly1+MAnNN8MH87+RhdTUBEYqHQqWJBEHDrljaWbF1BkgRXTj6W97QcHHdZIlLDFDpVqhAUubHzYX7eu5J6Unyi9WTenpkbd1kiUuMUOlWoEBS5dtODLM2upiVRz7XT3slhDXvFXZaIiEKn2hSCIv+26dcsza5mYrKBf5/2R7qUjYhUDJ1NriKFoMgNnQ9xf/YFBY6IVCSFTpUIgoD/2vw49/WuYkKinhunnanAEZGKo9CpEku2ruDunmdpTKS5YfqZuu+NiFQkhU4V+EXPSm7d0kaKBJ9pPRWrnxZ3SSIiQ6qYiQRm1gr8wt0XlbRdCfw9kAc+7u5LovbrgQ8AvcBl7r7UzNLA7cApQCdwkbuvKPNhlN3v+l7l850PAXD1lBM4qnFWzBWJiOxYRYSOmV0N/AVQLGlbAHwIWAA0AW1mdi9wPHA0MB84APiZmR0IXAok3H2umZ0C3BKtW7Vey2/lM5uWUiDgrya+lTOaD4i7JBGRnaqI0AF+C3QBV5S0vQf4nrv3AD1m9gBwKnAG8HV3LwBuZmuBhcDZwLUAUc9ntplNdfdNu9p51Mt6w0mQxYsXp1Kp1Cgc2tjIFnN8ouOXbCn2c0pmPhdMODzukkREdqkizum4+/3AL7Zr3hd4qWS5HdhnBO1rgOFeQvlywEsf3d3drdlsdpibl1cQBNzY+TCrcps4oG4qV085QXf5FJFxoVJ6OkMpArntlvO70T4cNwPfLW1oaWl5pL6+MqeA/aj7GZZmVzMp2cD/bT2VxmQl/zWKiLyurJ9WZnYFbxxCA1jl7mcOsXo7MLtkeRbw4A7aXyxpfzVqn0HY29kld+8AOkrb2traCpXYe3huYCO3dD1BArhm6snsnZ4Qd0kiIsNW1tBx95uAm4a5+j3At8zsJmAacAxwGeGQ4GVm9kPCSQaT3N3N7G7gYmCZmZ0OPO/u3aN9DHHqLeb4100PkKfIhRMOZ1HjPnGXJCIyIhU7LuPuz5jZEuBpwqGyq9w9C9xrZmcAzwNZwllvALcB3zSzVYS9lvfHUPaY+uLmR2nPb2FB/XQunnhk3OWIiIxYIgiCuGuoSG1tbevS6fSMhQsXxl0KAPf3vsDnNj1Ac6KeW/c6W8NqIlKRli9fTj6fX79o0aIhL21fEbPXZOc2FbLcvPkxAP5+ynEKHBEZtxQ6FS4IAv6z8xG2FPs5MbMvpzTtF3dJIiK7TaFT4X6VXc1DfS8zMdnAlZOPjbscEZE9otCpYJ2FLF+MhtWunHwsU1KZmCsSEdkzCp0K9tWuZWwp9nNC41xOzsyPuxwRkT2m0KlQy/tf477eVTQl6rhyynG6zI2IVAWFTgXKB0Vu6nwUgEsmHklrqinmikRERodCpwLd1f0Mq/Ob2a9uCue1HBJ3OSIio0ahU2E2Fnr5xpbfAXDF5GNJJfRXJCLVQ59oFeb2rjayQZ4zmvbn8Ia94y5HRGRUKXQqyKqBTfyidyWNiTSXTnpb3OWIiIw6hU4F+WrXMgLgz1oO0+QBEalKCp0KsaxvDW39a5mSzHD+hMPiLkdEZEwodCpAIShyS9cyAC6ZeASZZF3MFYmIjA2FTgW4r3cVL+Q6mZeexFnNB8VdjojImFHoxCwfFFm85UkA/nrSIk2RFpGqpk+4mP28dyWvFrqxumkc3zg37nJERMaUQidGuaDAd7YsB+DiiUfo+moiUvUUOjH6ec9KXot6Occ0zo67HBGRMafQiUkuKPDtrerliEhtUejE5Oc9K1lf6OGQ+unq5YhIzVDoxKAQFFmydQUAH5ywUL0cEakZ6bgLADCzK4GPAVngh+7+L1H79cAHgF7gMndfamZp4HbgFKATuMjdV5jZBOD7wKFAO3C+u7eX/2h27aHsy6wtbGW/uinq5YhITYm9p2Nm84G/A44A3gKcaGYnmtm7gKOB+cA5wG1mlgQuBRLuPhe4CrgleqtrgDZ3nwfcBtxQzuMYriAItvVyzm95i3o5IlJTKqGnsy/wbXffBGBmjwELgIXA1929ALiZrY3azgauBYh6PrPNbGrUfkb0nkuAzw+3ADNrBVpL2xYvXpxKpVJ7clxD+v3AOjy3kRmpZk5pmj/q7y8iUsli7+m4+1J3vwbAzPYnHE57hDCMXipZtR3YZ4j2NcBMYE60Du6eBXJm1jzMMi4HvPTR3d3dms1md++gdmKwl/O+lkNJ6+oDIlJjKuZTz8wuAx4FPunuvweKQK5klSKQ30l7zt2DIdqH42bASh8tLS0dmUxmdw5lh17IbeI3fe20JOp5t66xJiI1qKzDa2Z2BXDFds2rgLXAAcDx7r4yam8HSs+yzwJeLGl/NWqfQdjbWW9m0919g5nVAXl37x9OXe7eAXSUtrW1tRVG+3zLHVufBuDsloNp0pWkRaQGlTV03P0m4KbSNjM7Cfh34Dh3L+3B3A1cZmY/JDzHM8nd3czuBi4GlpnZ6cDz7t5d0n4jcAnw07E9mpHpKvTxy97VpEhwbsshcZcjIhKLSphIcBThDLUVZjbY9ll3/46ZnQE8TziV+i+i124Dvmlmqwh7J++P2q8Dvh8N060Gzi9T/cPys57nyFHglMx8pumuoCJSoxJBEOx6rRrU1ta2Lp1Oz1i4cOEev1chKHLha3eyvtDDTdPP4rCGvUahQhGRyrN8+XLy+fz6RYsWDflBVzETCarZY32vsL7QwwF1Uzm0fkbc5YiIxEahUwZ3d/8BgHNbDtGXQUWkpil0xthLuc209a9lQqKeUzP7xV2OiEisFDpj7J6ol/Ou5oNoTFbCvA0RkfgodMZQf5Dnvt5VALynxXaxtohI9VPojKFfZ1+iJxjgyIaZzEpPjLscEZHYKXTG0M96ngPgrOYDY65ERKQyKHTGyJr8Fp7sf42WRD0nZObFXY6ISEVQ6IyR/+l5HoB3Nu1PQ0ITCEREQKEzJgpBcVvoaGhNROR1Cp0x8HjfGjqKWQ6sa+WA+tZdbyAiUiMUOmNAEwhERIam0BllQRAwMz2B1mQTpzXpCgQiIqV0hnuUJRIJPjL5aD406W2kdDtqEZE30KfiGFHgiIi8mT4ZRUSkbBQ6IiJSNgodEREpG4WOiIiUjUJHRETKRqEjIiJlo+/p7FhrPp9n+fLlcdchIjJu5PN5gB1e/0uhs2N5gHw+3xF3ISNRLBYTAwMDTfX19b3JZDKIu55yqLVjrrXjBR3zODvmVqLPz6EkgmA8HYvsipkdBDhg7v5c3PWUQ60dc60dL+iYq+mYdU5HRETKRqEjIiJlo9AREZGyUeiIiEjZKHSqTwfwmehnrai1Y6614wUdc9XQ7DURESkb9XRERKRsFDoiIlI2Ch0RESkbhY6IiJSNQkdERMpGoSMiImWj0BERkbJR6IiISNnofjpVzMy+ANS7+0fjrmWsmdmVwMeALPBDd/+XmEsaM9Gx/j3hPUs+7u5LYi5pzJnZ9cAFQDfwJXf/r5hLKhsz+xHwiLvfGHcto0E9nSplZicAH4y7jnIws/nA3wFHAG8BTjSzE2MtaoyY2QLgQ8AC4FjgBjObFG9VY8vMTgJOAw4AjgauiP7Oq56ZXQicFHcdo0mhU4XMrAn4t+hRC/YFvu3um9x9AHiM8EO5Gr0H+J6797j7BuAB4NSYaxprs4Hb3b3f3bcCKwCLuaYxZ2YzCX/BuCXuWkaThteq0/XAF4CJQNX/RujuS4GlAGa2P/AB4F2xFjV29gUeLVluB/aJp5TycPfvDD43s6OAE4G/ja+isvky8A/AWXEXMprU06kyZnYyMN3dfxR3LeVmZpcRfiB/0t1/H3c9Y6QI5LZb3uH96KuFmaXN7FPAT4C/dPd1cdc0lszsYuA5d3887lpGm3o645iZXQFcsV3zS8B8M/sDMAloNLN+d/9Y2QscAzs45lXAWsIx/+PdfWXZCyufdsLhpkGzgAdjqqUszCwN3Es4ieAId38t5pLK4TTgODM7B5gGFM2s6O5fiLmuPaZbG1QxM7sEeFu1z16LTjT/O3Ccu+d2tf54Fk0k+BbwdsIPo/8F3uru2VgLG0PRb/3vdfdz4q4lDmb2aaC7Wmavqacj1eAownNXK8y2nV/+bOm5gGrh7s+Y2RLgacKhtauqOXAiRwHHRL33QR9291/FVI/sAfV0RESkbDSRQEREykahIyIiZaPQERGRslHoiIhI2Sh0RESkbDRlWqQKRVeifg1YB3zV3Q8ewbYPAfe7+6dK2hLAy8CnCb8j9K/u/sKoFi01QT0dkQphZqlRep+JwJ8Dd+7mW3wPeO92bUcDM4AfAjcBN+x2gVLT1NMRGSEzOw34D2B/4KeEX1TcZGbfANYT9gQWAr8ALnD3PjPbF7gVOI7wi51/4+7Lo6tGvA8ICH8J/GMz+zvgHwnvDfQd4ATgbGADcJi7Px/V8TDh1Ze/vl2JlwJ3u3uh5MuymNlU4NfA19z9i2Z2BPBVwttBPAL8tbu/BNwBfNHMDhzcF2EI3evum4EnzWyemR3q7k/v2Z+m1Br1dERGwMzmAN8FPgLMAwaAz5escnH02n6EH+bvN7Mk8GPgR4RXhP4OYY9h0FmEl7Y5z8xOAa4kDJqTgXMB3H0L8MtoXcysFXhb9L7be1+0bmndTYQBeXcUOC3R8r8RXr/tt8DXo32tB+6P3mfQeYQ9oEFLgZq8LI3sGYWOyMhcCNzp7g+5+0bg44QfyIMWu/vy6IP7YcKLcx4NpNz9K+6+xd1vAlJmdni0zRPufmd03bgLCHsiK6NeR+kFHu8C3h09/yPg4aiGbaKAO4qwNzUoTTjUttndPx61/THwpLvfHfVePkF4gcnJ0evbhtjM7C3ATN4YcMsJe20iI6LhNZGRmQd82Mw+UtpoZo3R09IQ6CP8f2wesMDMtr/m1OB9cDpL2uYS3phtUHvJ83uA/zSzZsIez1C3r2gl/GWyq6Rtf6ADOMDMprr7pqims4aoaW9gM2HAfSXq2b0XuGe7a7xtIuwhiYyIejoiI7MO+Ly7J9w9ATQCR7p73y62WTa4TbTdQt4YLoN6CE/YD5o7+CS6h8zvgDOixz1DbJ8GEtFjUDvhcN1jwGdLarqjpJ4k4XDdc9G+ughvJ/De6FE6tAbhOajiTo5ZZEjq6YiMzJ3Az8xsMeEU4msJeyzn7WSbx4AZZnYucB/hh/j1DH1X1/uBj5rZnYT/f/4D4dTnQXcBnwJecPdXhth+A+EEhMmEvRGAHnfPmdnVwG/N7CuEgXKdmZ0APAlcDvypu7+15L2+Fx3fFMJJEaUmE06aEBkR9XRERsDdnyKcWXY34Y3j5gEf3sU2fYQTAv6ZMBT+jvD+MP1DrH4L8HPCcyZLo+eldwa9i7CX9IMd7CsPLAMOH+K1Z4D/B3wx6jVdRDh7bT3h7b3fv90m/0043DZ4vqnUYYRhKjIiurWBSAUxs71g21AaZvYh4BR3f3+0nCY8P7PA3dfs4D2uBia6+yfGsM6HCaeKrxirfUh1Uk9HpLK8C/ipme1lZgcSTp++D8DMMsAHCGetDRk4kVuBd0cBNerM7DCgU4Eju0OhI1JZvk04WeA54DeEw2vfiF67CbiOcHrzDkVToL/Gm4fLRstVhEOMIiOm4TURESkb9XRERKRsFDoiIlI2Ch0RESkbhY6IiJSNQkdERMrm/wPvZKC/iH+WKQAAAABJRU5ErkJggg==n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid**2 * func(energy_grid), color=green)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"$\nu$F$_{\nu}$")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”
]
}
], “metadata”: {
- “jupytext”: {
“formats”: “ipynb,md”
}, “kernelspec”: {
“display_name”: “Python 3”, “language”: “python”, “name”: “python3”
}, “language_info”: {
- “codemirror_mode”: {
“name”: “ipython”, “version”: 3
}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.7.11”
}, “papermill”: {
“default_parameters”: {}, “duration”: 5.97379, “end_time”: “2021-08-17T08:12:41.723334”, “environment_variables”: {}, “exception”: null, “input_path”: “Cubic.ipynb”, “output_path”: “../docs/notebooks/Cubic.ipynb”, “parameters”: {
“func_name”: “Cubic”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:12:35.749544”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}