\n",
"\n",
"**This is a fixed-text formatted version of a Jupyter notebook**\n",
"\n",
"- Try online [](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.13?urlpath=lab/tree/source_population_model.ipynb)\n",
"- You can contribute with your own notebooks in this\n",
"[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n",
"- **Source files:**\n",
"[source_population_model.ipynb](../_static/notebooks/source_population_model.ipynb) |\n",
"[source_population_model.py](../_static/notebooks/source_population_model.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Astrophysical source population modeling with Gammapy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"The [gammapy.astro.population](..\/astro/population/index.rst) package contains some simple Galactic source population models.\n",
"\n",
"Here we provide some Python code to compute observable parameter distributions for Galactic gamma-ray source populations.\n",
"\n",
"* Observables: Flux, GLON, GLAT\n",
"* Source classes: Pulsar (PSR), Supernova remnant (SNR), pulsar wind nebula (PWN)\n",
"\n",
"References:\n",
"\n",
"* Section 6.2 in the Fermi-LAT collaboration paper [\"The First Fermi-LAT Catalog of Sources Above 10 GeV\"](https://ui.adsabs.harvard.edu/abs/2013ApJS..209...34A)\n",
"* Axel Donath's bachelor thesis [\"Modelling Galactic gamma-ray source populations\"](http://pubman.mpdl.mpg.de/pubman/item/escidoc:912132:1/component/escidoc:912131/BScThesis_ddonath.pdf), specifically Chapter 4.\n",
"* Casanova & Dingus (2008), [\"Constraints on the TeV source population and its contribution to the galactic diffuse TeV emission\"](https://ui.adsabs.harvard.edu/abs/2008APh....29...63C)\n",
"* Strong (2007), [\"Source population synthesis and the Galactic diffuse gamma-ray emission\"](https://ui.adsabs.harvard.edu/abs/2007Ap%26SS.309...35S)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import astropy.units as u\n",
"from gammapy.utils.random import sample_powerlaw\n",
"from gammapy.astro import population"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate positions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Spatial distribution using Lorimer (2006) model\n",
"n_sources = int(1e5)\n",
"\n",
"table = population.make_base_catalog_galactic(\n",
" n_sources=n_sources,\n",
" rad_dis=\"L06\",\n",
" vel_dis=\"F06B\",\n",
" max_age=1e6 * u.yr,\n",
" spiralarms=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate luminosities"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several source population models, e.g. the 1FHL paper or Strong (2007), use power-law luminosity functions.\n",
"\n",
"Here we implement the \"reference model\" from the 1FHL catalog paper section 6.2."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Source luminosity (ph s^-1)\n",
"\n",
"luminosity = sample_powerlaw(x_min=1e34, x_max=1e37, gamma=1.5, size=n_sources)\n",
"table[\"luminosity\"] = luminosity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute observable parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"
\n",
" name dtype unit description \n",
"---------- ------- --------- --------------------------------------\n",
" age float64 yr Age of the source\n",
" n_ISM float64 1 / cm3 Interstellar medium density\n",
" spiralarm str18 Which spiralarm?\n",
" x_birth float64 kpc Galactocentric x coordinate at birth\n",
" y_birth float64 kpc Galactocentric y coordinate at birth\n",
" z_birth float64 kpc Galactocentric z coordinate at birth\n",
" x float64 kpc Galactocentric x coordinate\n",
" y float64 kpc Galactocentric y coordinate\n",
" z float64 kpc Galactocentric z coordinate\n",
" vx float64 km / s Galactocentric velocity in x direction\n",
" vy float64 km / s Galactocentric velocity in y direction\n",
" vz float64 km / s Galactocentric velocity in z direction\n",
" v_abs float64 km / s Galactocentric velocity (absolute)\n",
"luminosity float64 \n",
" distance float64 pc Distance observer to source center\n",
" GLON float64 deg Galactic longitude\n",
" GLAT float64 deg Galactic latitude\n",
" VGLON float64 deg / Myr Velocity in Galactic longitude\n",
" VGLAT float64 deg / Myr Velocity in Galactic latitude\n",
" RA float64 deg Right ascension\n",
" DEC float64 deg Declination\n",
" flux float64 1 / kpc2 Source flux\n"
]
}
],
"source": [
"table = population.add_observed_parameters(table)\n",
"table.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simulation is done, you could save the simulated catalog to a file.\n",
"\n",
"Here we just plot a few distributions to check if the results look OK."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(table[\"x\"][:1000], table[\"y\"][:1000]);"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(table[\"GLON\"], bins=100);"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(table[\"GLAT\"], bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ZJDpoNAbmN0/Xu8x7x0YJ2srG1DPTxrs+v45D7pOA+3/YZoaUnMabAADWCu52v19p3Y2n/RSQVpwqRJAPGyjTPtheYWYisjV9a1xmalEzOW2adMO/tYTfbE0mtcl665D69u09Qym7plTnnH93PrTX+t4LyvY6GJu0BgnLwuBkafW4TTxdIyLXAvgvAHoB/HdV3WJ7b5Qer0FzXVnsPOXNY9vSKaZyl3oEEMwZudCvU4yJX1ooXxWcFKf+cglnzk37PsX19gjeet48/Kw2GfhmKe6URdAbtqRSJWFuGD83ss/YF2H1Oxfi1Z/WIt10ppauEZFeAH8K4MMAjgJ4XkR2qOrfx7mdMOOy+I29klaQDzJrUdC783bHXjfttzykuygZQZ7gpqZ1zpDOd2zfg9u37/Gt74kzZRHHpCxRzvGwTwdPvzRuXI+3s1sSTxhJp2uuAXDImdAbIvJNANcDiDXIA8F7stkO6kRttrNNGlp1WooacEs9gmnUv5iu+hgq54xjrw+vGOxIByAqjiCBqlXKYmSsiru/fWCmc513aIlGYTr6JZEqCdvR0PYdbrwxi7vHftLt5AcBvO75/aizbIaIrBORUREZHR83X+ni5HdQb9++B+/c+F18bmSf9T1JsZ0A7onZTv7TNdhfxi3XXNJ0sKd1bqoHmDuUbVzD/FL3sQ0jbevXUp2oYcUfPoHff2jPnN7T7tASpvbkYe7Ok2hjH/bpIMwFJc6n6KSDvKm735yooqrbVLWiqpWBgeQ7MLU6qFOq+PquIx0P9LYTQFDP5Z06e67ptR4ApV7/HpVuzvHpl8aN6R0T7zAEbscVorBMgarxnPLWIb15ehKmU9QdWqJRmI5+UTph2YTtaGi7wIVZRzuSDvJHAVzi+f1iAMcS3qav4RX+PTNdDz73esv3APH1YFu/dpn1ivjgc6833XEDwAV9JWz9ravnfGG8vHcqYe4MvCfY8IpBPLvhA9aTsb6d3HecpoDc706Q0RrcGbAavx/uOTXYXw5chxTH3bm7XXdSlqjpkHa233ih+eSqocR78Sadk38ewOUishRAFcCtAP5Fwtts6a7rrmzZwSfIBBNxN8uybdFWlonTk3PqIvxq+i8o2zu1eNlOMFtOM2hPVyqGf/jluaa5aE3KpV6suWLA9/vR7o2HK66JcNrVzvZNdYeVSxcm+jckGuRV9ZyIfBbATtSbUH5NVQ8kuc0ggsw2FGSquLu/3dxRqJ1KE/diYWObgLrxxPerfA44892cR9ggE027F4TGi4238oyKw5byW9BXQt/8eS37a3i/H/19pUDnSKlHfO+O4wiI7fadiWP7SQ9/nHhnKFX9LoDvJr2dsNwda2u7uuqyBdamhUD9pLCdoGErTUxfBpc7pot3eFh3eZhHuokAX6bB/rK1Xb470fT583tnhh84b545TeMdDjjOST4ouyZOT86M9++6o0VP2SBddPxa18QlCx2lklSIHq9RfGG4PuOQdx7OVZctwI+O/Mz3oPsNdduq0qTxrsEvxeHeWUd9pGu1ncaLhq1d/jnP+DITtUncsX0PRl87ObMfvdzy3fnQ3lDzq1L+mM75Vs0Wf2ZJH0Ydtymsok881PVBHqgHem+QWr3lqZYH3e9u3e8O23TXYOul6r2zjvpIt37tMqz/1l7j47bpbilom14F8MCuI6hcutBYPlNHL8ovUy/rUo/g9NlzMxOTuDcgrYbsyMrE2nke+yqIQgb5qGPTtDroI2NV65C7/eWS77ZMdw2K5uEI4q5hd8u0eceBmQrYBX0l3HWd+VE4TEcoxeyTjWm/u+tnnj673Jy63zF3bwYAzKmrOeWZGcz21Gv6LoYZtylJWbnYJKUQQw17xTE2jd84F35znAbZjt8QvIP95cyMzGebRNrvbCmXelvud7bCyZ5Sr2Drb12NO7bvaXl8G49nliaZb1cRJggv/HjyXnEMROR30G0BqlcE93z86o4P0BRFq5O78cRdc8VA0+QsLlsLoF4RTKs2nfh+Fzu/9WVNqVfq6YqYR90sl3qw8PzzAs+jG3y9s5OzNAak5Xc/0bKZbeN5mtR8CJ0WJUhnYdDDwo8n7xVHfs3vMdPWYmBaNfAUZnE9oka9e2hV4WSrBzDNwmXLt7uBOsy4OD0CfOK9lzS1KHKfJLwtfJLi9ze53Ce7rTsP4nSL80sA9PTInLGD/LZtegKKOnG5aeAw9xwKuu7GY9bpVEdSd8xR6rxs3yN32O20Z68qXFfFuOY0tfWOi7r+uLpXB5kbtpV2LohfGL4K996yvKn8QYY+CDouzrQC39l7fE6vyv5yCffeshyvbvkYDvzhtbjPKUNQg/1l3Od8/lOrhlq+t9Xf5N7RBunUUy714t5bluOem69Gn6F3cKlX0F8u+Z4PwysG0R9h/KJXDb08vedQuzo572oc53wS/I5/FspYuDv5pCtz4li/311D0DuVOJp9tXsXZit/kBY03nFxgNmnpf6+Ev7hl+dmWv80pg3OnJubDvG2xV//13t9e2A2phgqly7E9ufNQ0W4x9Jdvy0dUZ2ozfSjsFXCu9v2HkNTGizond7m32zdU9tWBhO//hlBdbLXaVabOrZqpJB2GQsX5JM+6ZJcf6fHx47zghikFzHQPC6O+7nVW57yzTnbvijDKwbxrdEjePbwSePnTH/P1p0HrReFm1bOvYDZvsDuyImAedgJv5xsu6mBxn3snWBmzRUDePyF40370O94ttNE0NQTPOkem66sNnX0a4zhSrOMhQvyQPInXVLrD5PbiyMXampW+ZYIg425+8VWuSyw9yEI8iWwvedvXzEHeADGQOu3rYd3V+e0+Td9gW2tjGyVzHHyO/e+MHxVqKeEduYL+MR7L2n9poRktaljkBucNMtYyCCfV0FyewCsHU1snVJa8aZC3jw9GblLty0wfnLVUKSAY/ui+DXCCdsHoDY5hU2P7pvppev2gPZOz2b77LRq6i1KbBcBU/APcgfq9alVQ8aezZ2SlXb1Jn5DeaRdxsJVvOZZq6u9t+KysQK3v1wCpB6kw1RK+eU522WqXL73luW+AaLVBCVx16v4bevU2amZFMyUKp49fBJrrhjAvbcs911v2neUNqYKS3eqvreUeuZU+n7KMvTtfS2OXyfE1WghSVksI+/kMyRsbq8xp91YWRmkwiepPGfYlFZjXccF5RJE6gNftXoq6Sv1GNupm1qyeLcVZkydbzx3pKlJZ6M1VyQ/6U07bL2sgfpNgdv6x90vSQ99G0Wn8v9RZK2MDPIZEiW3126wzlKes90vx3+68d34/Yf2zJlVqEfqy/22BQQfU2da0fJ9toma09bqHGi8GchakKJomK7JGLd9/n23LA/V/rjd9vudbOeclOEVg/jyx+e23f/yx5e3DFSmR+uAw+4bxd2CIq5Zx4JcsNNuoZJXcR2jJPFOPqPCNtVst1Kqk+2ckxSlWaL3c7b5BcqlHtRaDF0Q59NPnGOcB0kDZrU+IcvyMg49g3yGhQlcUYI1H89nmeYX+MR7L0Hl0oW+gTLup584O/40pgGTHvG0W2S1c1YjBvkCKWqw7vQIf43zC3i1UzHcjrgrxL3nRtojJhZFVjtnNUosyIvIZgD/GoBbG/UHzlSAREam4AMgM4/EnbyIJlkhXtSbgU7LUqMFP0lXvN6rqsudfwzwZGUbfGrzDvNk6Xc+tDfTlV1RFaFCvOjycoyYrqFMsOU3ww5hXBRFqRAvsrwco8QmDXHSNb8D4OcARgHcqapvGt63DsA6ABgaGlr52muvJVIeyrZWk4i00u6kK8xPUxEkNmmIiHwfwDsML20C8BUAn0e9Iv/zAO4B8LuNb1TVbQC2AfWZoaKUh/LJb87cBX0l/HJyOvAQxmG3m5V8f1bwolc8kYK8qn4oyPtE5KsAvhNlW1nGL0b73EBrG673ruvmThxtuxi0U9mVlyZwncKLXjEl2bpmkaoed369AcD+pLaVJn4xorFNXNErMmdgJ2/zv7hG+ctLE7hO4UWvmJJsXfPHIrJPRF4AsAbAHQluKzVJjOLYTWwB1TZnbpyj/MU1VWRR8KJXTIndyavqbye17izhFyOadtoax9XOO8vjk6chL+2+KRwOUBYR7wajSbOtcRbH/k5TXtp9UzhsJx8R7wajSbutMXt/zkr7WFAyEmsn345KpaKjo6NpFyM0tq4hojQl1k6e6ng3SERZxSBPucEnJqLwGOQpF9gfgag9bF1DucD+CETtYZCnXGB/BKL2MF1DucCOOsljnUcx8U6ecoEddZJlm7SliBOydBsGecoF9k5NFus8iovpGsoN9kdIDus8iot38kTEMZgKjEGeKCUjY1Ws3vJUJiYkZ51HcTFdQ5SCrHXu4uBkxcUgT5SCLM7CxDqPYmK6higFrOikTokU5EXkZhE5ICLTIlJpeG2jiBwSkYMisjZaMYmKJa6Kzizl9Smbot7J7wdwI4BnvAtF5F0AbgVwJYBrAfyZiPQ2f5yoO8VR0ckOTBREpCCvqi+qqqm3xPUAvqmqZ1T1xwAOAbgmyraIiiSOzl3swERBJFXxOghgl+f3o84yInJErehkXp+CaBnkReT7AN5heGmTqj5m+5hhmXGeQRFZB2AdAAwNDbUqDhE5OGgbBdEyXaOqH1LVXzP8swV4oH7nfonn94sBHLOsf5uqVlS1MjAwEK70RF2MHZgoiKSaUO4AcKuInCciSwFcDuCHCW2LqCtx0DYKIlJOXkRuAPDfAAwAeFxE9qjqWlU9ICIPAfh7AOcA/J6qTvmti4jCYwcmaiVSkFfVRwE8anntiwC+GGX9REQUDYc1oELiLEdEdQzyVDhZG/yLKE0cu4YKh52EiGYxyFPhsJMQ0SwGeSocznJENItBngqHnYSIZrHilQqHsxwRzWKQp0JiJyGiOqZriIgKjEGeiKjAmK6h3GKvVqLWGOQpl9irlSgYpmsol9irlSgYBnnKJfZqJQqG6RpKTJI5c059RxQM7+QpEW7OvDpRg2I2Zz4yVo1l/ezVShQMgzwlIumcOae+IwqG6RpKRCdy5uzVStRapDt5EblZRA6IyLSIVDzLl4hITUT2OP/+PHpRKU84EiRRNkRN1+wHcCOAZwyvHVbV5c6/z0TcDuUMc+ZE2RB1Iu8XAUBE4ikNFQZHgiTKhiRz8ktFZAzAzwF8TlX/j+lNIrIOwDoAGBoaSrA41GnMmROlr2WQF5HvA3iH4aVNqvqY5WPHAQyp6k9FZCWAERG5UlV/3vhGVd0GYBsAVCoVDV50IiJqpWWQV9UPhV2pqp4BcMb5ebeIHAbwqwBGQ5eQiIjalkg7eREZEJFe5+fLAFwO4JUktkVERHZRm1DeICJHAfw6gMdFZKfz0vsAvCAiewH8NYDPqOrJaEUlIqKworaueRTAo4blDwN4OMq6iYgoOg5rQERUYAzyREQFxiBPRFRgDPJERAXGIE9EVGAM8kREBcYgT0RUYAzyREQFxiBPRFRgDPJERAXGOV4pd0bGqpyMhCggBnnKlZGxKjY+sg+1ySkAQHWiho2P7AMABnoiA6ZrKFe27jw4E+BdtckpbN15MKUSEWUbgzzlyrGJWqjlRN2OQZ5yZXF/OdRyom7HIE+5sn7tMpRLvXOWlUu9WL92WUolIso2VrxSrriVq2xdQxRMpCAvIlsBXAfgLIDDAP6Vqk44r20E8GkAUwD+rarutK6IKIThFYMM6kQBRU3XPAng11T13QD+H4CNACAi7wJwK4ArAVwL4M/cib2JiKhzIgV5VX1CVc85v+4CcLHz8/UAvqmqZ1T1xwAOAbgmyraIiCi8OCtefxfA95yfBwG87nntqLOMiIg6qGVOXkS+D+Adhpc2qepjzns2ATgH4AH3Y4b3q2X96wCsA4ChoaEARSYioqBaBnlV/ZDf6yJyG4B/DuCDquoG8qMALvG87WIAxyzr3wZgGwBUKhXjhYCIiNojs3G5jQ+LXAvgywD+maqOe5ZfCeAbqOfhFwP4AYDLVXXKuKLZz40DeK3tAiXrQgA/SbsQAbGsyWBZk5On8maxrJeq6oDphahB/hCA8wD81Fm0S1U/47y2CfU8/TkAt6vq98xryQcRGVXVStrlCIJlTQbLmpw8lTdPZQUitpNX1XZlG4IAAAO3SURBVF/xee2LAL4YZf1ERBQNhzUgIiowBvngtqVdgBBY1mSwrMnJU3nzVNZoOXkiIso23skTERUYg7yBiNwsIgdEZFpEKp7lS0SkJiJ7nH9/7nltpYjsE5FDIvJfRcTUIaxjZXVe2+iU56CIrE27rA1l2ywiVc++/GircqdJRK51ynNIRDakXZ5GIvKqc0z3iMios2yhiDwpIi87/y9IqWxfE5ETIrLfs8xatjSPv6WsuTpXm6gq/zX8A/BPACwD8DcAKp7lSwDst3zmhwB+HfXevt8D8Bspl/VdAPai3sR1KeqjhPamWdaGcm8G8O8Ny63lTvF86HXKcRmA+U753pVmmQxlfBXAhQ3L/hjABufnDQD+KKWyvQ/Ae7zfHVvZ0j7+lrLm5lw1/eOdvIGqvqiqgScNFZFFAN6mqn+n9aP/VwCGEyugh09ZjYPEpVnWgLI4uN01AA6p6iuqehbAN1EvZ9ZdD+B+5+f7kdJxVtVnAJxsWGwrW6rH31JWmyyeq00Y5MNbKiJjIvK/ReSfOssGUR/KwZWFAdlsg8RlqayfFZEXnEdk93E9i4PbZbFMjRTAEyKy2xkPCgDerqrHAcD5/6LUStfMVras7uu8nKtNunZmqCADrxkcBzCkqj8VkZUARpwhHAIPyNaONstqK1OiZZ1TAJ9yA/gKgM872/48gHtQ7yHdsfKFkMUyNVqtqsdE5CIAT4rIS2kXqE1Z3Nd5OlebdG2Q1xYDr1k+cwbAGefn3SJyGMCvon4Fv9jzVuuAbO1op6ywDxKXaFm9gpZbRL4K4DvOr4EHt+ugLJZpDlU95vx/QkQeRT1t8IaILFLV406a7kSqhZzLVrbM7WtVfcP9OQfnahOma0IQkQF3hisRuQzA5QBecR43fyEiq5yWKv8SgO0Ou1N2ALhVRM4TkaWol/WHWSmr88V23QDAbc1gLHeny9fgeQCXi8hSEZmP+qxnO1Iu0wwROV9E3ur+DOAjqO/PHQBuc952G9I/J71sZcvc8c/Zudos7ZrfLP5D/UAeRf2u/Q0AO53lNwE4gHqN+o8AXOf5TAX1g38YwJ/A6WiWVlmd1zY55TkITwuatMraUO7/CWAfgBdQ/7IsalXulM+Jj6I+xeVh1NNkqZfJU7bLnHNyr3N+bnKW/2PUR4B92fl/YUrlexD1VOekc65+2q9saR5/S1lzda42/mOPVyKiAmO6hoiowBjkiYgKjEGeiKjAGOSJiAqMQZ6IqMAY5ImICoxBnoiowBjkiYgK7P8DiqWYYnLH1pUAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(table[\"GLON\"][:1000], table[\"GLAT\"][:1000]);"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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8cDKlAxlXsnq0/Vngk7OuZ0h957N6hPxR4ImTNbI6b/ZPwDPdz3cNvOaTXX+eZmBFDLDI6v/wzwI3M/0DXXey+vX4f1gdQVw7ybqBtwJ/BxxjdbXA+VvYj78Fvg081v0DOqsH/fgFVr+SPwY80v13Zd/2ySn60at9AvwM8K9dvY8Df9y1z+X+8PIDktSgvk3LSJJGYLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBv0fZ4RuNUhf75IAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(table[\"distance\"], bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(np.log10(table[\"luminosity\"]), bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(np.log10(table[\"flux\"]), bins=100, log=True);"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# TODO: plot GLON, GLAT, FLUX distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"TODO"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Start exercises here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What next?\n",
"\n",
"TODO: summarise what was done here briefly.\n",
"\n",
"TODO: add some pointers to other documentation."
]
}
],
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"display_name": "Python 3",
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"name": "python3"
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