{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to perform PRF photometry?\n", "\n", "In previous tutorials we discussed [how aperture photometry works](1.03-what-are-lightcurves.html) and how you can [create your own custom masks](2.05-making-custom-apertures.html) to extract aperture photometry.\n", "\n", "As a reminder, aperture photometry works by summing up the flux values across all the pixels in a pre-defined mask. It tends to offer the fastest and most robust way to extract photometry for an isolated star, but it suffers from the three limitations:\n", "1. When two or more stars overlap, aperture photometry will return the sum of the flux of all stars. Separating their signals requires additional assumptions.\n", "2. When the telescope experience motion or focus changes, the fraction of the flux of a star captured by the pre-defined aperture mask will change. This imposes systematics on the data which need to be removed (e.g. using the CBV or SFF techniques).\n", "3. It may be difficult to separate a background signal from the lightcurve of a star. In particular, this is a concern when time-varying background signals are present, such as the so-called \"rolling bands\" in Kepler.\n", "\n", "An alternative to aperture photometry which addresses the above limitations is called called *Point Response Function (PRF) model fitting photometry*, also known as *Point Spread Function (PSF) fitting photometry* or *PRF (PSF) photometry* in short. In this method, a parameterized model is fit to the data. This method is significantly more complicated then aperture photometry and is prone to a different set of systematics. However it offers the ability to separate the signals of overlapping stars in very crowded regions or star clusters.\n", "\n", "This tutorial demonstrates how *lightkurve* can be used to extract lightcurves using the model fitting technique. We will start with an easy example and then show how you can tune the parameters of the model to create more careful analyses.\n", "\n", "## Table of contents\n", "\n", "1. Simple example: PRF photometry of Kepler-10\n", "2. The `TPFModel` object\n", "3. Visualizing and fitting a `TPFModel` object\n", "4. Changing `TPFModel` priors\n", "5. Creating lightcurves with a custom `TPFModel` object\n", "\n", "\n", "## Simple example: PRF photometry of Kepler-10\n", "\n", "Let's start with a simple example by obtaining PRF photometry for planet system *Kepler-10*. We start by downloading the pixel data from the archive:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from lightkurve import KeplerTargetPixelFile\n", "tpf = KeplerTargetPixelFile.from_archive(\"Kepler-10\", quarter=3, quality_bitmask='hardest')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The easiest way to get PRF photometry is to simply call the `to_lightcurve` method with parameter `method='prf'`. This will fit the pixel data of all cadences with a default model and return the lightcurve." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning: PRF-fitting photometry is experimental in this version of lightkurve.\n", "Fitting cadences: 100%|██████████| 3634/3634 [00:59<00:00, 60.90it/s]\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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kyZOpUKECY8aMoWbNmgGPM+HXsWNHJk2axNSpU4HTb/ePGjWKOnXqcPPNNxd4jnr16pGQkOCdk94YE5w9Iwnil19+CVjerl07v55CwZpNTOkSkTxzywN07drVO3ukiITUfNW/f3+6d+/uN9TMunXrWLhwIQ8//HDJBW1MlLM7kiBmzpwZsDzaRtI1/grzDCQlJYVffvmF48ePe4bbYeTIkfYcxRgfdkcSxH/+8x/v8pw5c1i5cmVID+BNdJg7dy7XXXddgfsFaqrMycnhwIEDHD582Kb8NeWeJZIgOnfuzLJlywDo3bs3vXv3jmxApkR169bNu/zTTz+RkpIS8rHVq1fn6NGjfsf7NoEZU55Y01YQNWvWpF69et5kYmJDYmIi4D+IZmGTgG8SAbjmmmvsbtWUW5ZIgpgyZQq7d++mU6dOkQ7FlKBVq1Yxc+ZMRIQLLrjAm1AuvfRShg4dWqQ5JL766ivvA/iSmPvFmGgi5eGhYVpamq5cubLgHXPJzMy09m8fsVgfOTk55OTk+M1I+euvv4Y8onBun376Kb/97W/p2LEjn3/+eUmFGTVi8WekOKK9PkRklaqmFbSf3ZGYci0uLi7PtMbx8fE0adKEKVOmeMs8LzQW5Le//S0AK1asYOLEiZw4cYK4uDgGDRpUckEbU8ZYIjEmgG3btvGHP/zBu557WHrPYJxz5szJ9xwjRoygcuXKqCrTpk3jySefpHHjxhw6dCg8QRsTIZZIjAni5MmTfPDBB7z00kv06tXLW3722WcDFKqn1yOPPMKPP/7I3LlzSzxOYyIprIlERKaJyF4R2ZDPdhGRF0Vkq4isE5G2PtvuEJEt7ucOn/KLRGS9e8yLYuO0mzCKj4/nmmuuISEhgb59+wLw0EMPee9IivKM8dSpUyxevLhE4zQmksJ9R/IPINg8r72AZu5nMDAZQERqAaOBDkB7YLQ7fzvuPoN9jovteWRNmdG3b1+eeuopRo8eHXCemYULF4Z0njvuuIMePXqwYMGCkg7RmIgIayJR1eXA/iC79AZed6fdXQHUEJH6QA9giaruV9UDwBKgp7utuqp+rs6fgq8DvwvndzDGo2LFiowcOZLExERatWoFOO+kLFmyhCFDhtCzZ+H+pvnvf//rN4+NMdEq0s9IGgI7fNYz3LJg5RkByo0pVdOnTyc9PZ2GDRty5ZVX5js8Pfi/Qe/rvvvuY+jQodx0003s2LEj4D7GRINID5ES6PmGFqE874lFBuM0gZGamkpmZmahgyvKi2mxzOrDX+vWrfPUyaRJk9iyZQtjxozxjgo9ZcqUfGdgnDx5MgBbtmyJial/7WfEX3mpj0gnkgygkc96KrDLLe+cq3yZW54aYP88VHUKMAWcFxKL+lJQNL9MFA5WH3n51onvEPbTp08nMTGR3/zmNwWeY926dSQlJQWdHjha2M+Iv/JQH5FOJHOBoSLyNs6D9SxV3S0ii4AnfR6wdwdGqep+ETkkIh2BL4ABwN8iErkxBRg4cGCh9ve8c2JMtAl399+ZwOfAuSKSISKDRORuEbnb3WUB8D2wFZgK3AOgqvuBccBX7mesWwYwBPi7e8x/gdC6yhgTYVdccUW+c9x4iAibN28upYiMKRlhvSNR1b4FbFfg3ny2TQOmBShfCbQskQCNKSUHDhwgMTHRO2pwt27dWLJkScB9zz33XLszMVEl0r22jCkXatSoQaVKlahRowYHDhzgww8/5H/+538AZ26T3K688kpOnTpV2mEaUySWSIwpZTVq1CAuLs77UuOwYcPy7JOens6oUaNKOzRjisQSiTER8sc//pGrr76aIUOGcOaZZ+bZPmHCBA4cOBCByIwpHEskxkRIgwYNmDdvHnXq1Mn3PZNatWrZ8xJT5lkiMaYMmDp1KhMnTuTOO+/Ms+3AgQOcPHkyAlEZExpLJMaUAU2bNmXYsGE899xzebbVrl3b+5a8MWWRJRJjypCaNWsGLD948CDgDEH/yy+/lGZIxhTIEokxZcyHH37IQw89lGc04ddee43777+fmjVrcuzYsQhFZ0xekR4ixRiTS48ePejRowd79uyhfv363vK77rrLu3z48GGqVKkSifCMycPuSIwpo+rVq5fvtvzeijcmEgpMJCJSN0DZueEJxxgTittvv539+4PNGWdM6QnljuQTEbnFsyIiw4D3wxeSMSYUtWvXpk2bNjz00EPceOON7N27N9IhFcqaNWuYOnWqzRIZA0J5RtIZmCIiNwMpwLc486gbY0pJTk4OcXF5/+5bu3Yta9euBeC9995j165dNGjQAHCeoyQmJpKenk7Xrl0DHh9JF154IeDE+cADD0Q4GlMcBf5kqepu4EPgYqAJzhzrh8MclzHGh2dcroJ4kghAtWrVuPrqq+nWrRtt2rRh0qRJ4Qqv0LKzs73Ln3/+eQQjMSUhlGckS3AmnWoJXAVMEpGJ4Q7MGAMbNmwo1vwkCxc60/WsX7+eBx98EBEhJSWFDRs28MMPP9C1a1d++OEH7r//fo4fP15SYRdo7Nix3uUPPvig1K5rwiOUpq2XVXWOu/yLiFwC2LCkxpSC888/P0/Z9u3badKkSZHPuXfvXlq1auVdb9myJYcPH+acc87h3nsDTg9UopYvX8748eO96/Hx8WG/pgmvUJq25uRaz1bVcaGcXER6isgmEdkqIiMDbG8sIukisk5ElolIqs+2Z0Rkg/u51ae8q4isdstniIi9C2PKhRkzZrB06dISnwP88GGnpXro0KGIiPeTlZVVotcBZ9ywfv36ceaZZ/Lll18CcNttt5X4dUzpCqVp65CIHHQ/x0XklIgU+BMmIhWAl4FeQAugr4i0yLXbRJxnLq2BscBT7rFXA22BNjjNaiNEpLqIxAEzgD6q2hL4Abgj1C9rTDQbMGAAnTt3JjEx0Vt23nnn5dnv6aefLpHr/fWvf2XSpEk8++yzAOzZs6dYXY5VlcGDB7N7927++c9/eu+qXn311ZII10RQKHckSapa3f1UBm7ESRAFaQ9sVdXvVfUk8DbQO9c+LYB0d3mpz/YWwMfu3c8RYC3QE6gNnFBVT6PxEjceY8oNz4P366+/nhkzZgDQtm1b73bfRFMcjz/+OA8++CAPP/wwIkL9+vWpXbu2X7PU0qVL+eabb9i8eTM//fQT27dvB2DVqlUkJiayZ88e777Tpk1j9uzZjB8/nnbt2vnNDPnTTz+VSMwmMgrdH9Bt6uoawq4NgR0+6xluma+1nE4E1wNJIlLbLe8lIokikgx0ARoBmUC8iKS5x9zklhtTrhw9epR33nnH28zVqFEjbr3VaQH27ea7a9cub+JZs2ZNiVz70Ucf5S9/+Qtvv/02Xbt2pWXLlpx77rnUq1ePdu3aAfDcc89x7NgxPvroIwC+++477rvvPrp27crw4cMBSEhI8J7znXfeKZHYTGQU+HxBRG7wWY0D0oBQZtoJ1F8x93HDgZdEZCCwHNgJZKvqYhFpB3wG7AM+d8tVRPrg9BxLABYD2QQgIoOBwQCpqalkZmaGELK/cLQRRzOrj7wiWSdHjhwhKSmJN954g4svvtjbpHXkyBFatGjBxo0biYuL47LLLmP58uV+Q67s3buXunWdQSseeOCBQncNfuqpp/LdtnfvXo4ePQrAsWPH2LlzJ7fccguVK1fmhRde8Gsee/PNN+nXrx9/+tOfuOqqqwLOXx/Nys3/M6oa9ANM9/lMBR4B6oZw3MXAIp/1UcCoIPtXAzLy2fZP4KoA5d2BWQXFctFFF2lR7Nu3r0jHxSqrj7zKUp38/PPPOnjwYD169KiePHlSMzMzVVX14MGDun79elVVrVixoiYkJKiqKs4fdvr99997l0v68+STT+qDDz6ogP773//OE/OmTZu8+3bu3FnXrVunl1xyie7evbv0Ki6MytLPR1EAK7WA36+qWnAiKeoH527ne6ApUAmnuer8XPskA3Hu8nhgrLtcAajtLrcGNgAV3fW67r8JOM9XuhYUiyWSkmH1kVe01cnBgwf18OHDquokkmbNmunOnTsV0GrVquns2bPDklDuueeefGMKtP+YMWN03759Onfu3NKqmrCItp+P3EJNJPk2bYnI3wjShKWq9+W3zd2eLSJDgUVuYpimqt+IyFg3uLk4w688JSKK07Tl6cQejzPGF8BBoJ+qepqwRojINTjNbJNV9f+CxWGMOS0pKcm7/O2331KvXj3vnPC33HILFSpUAKB3796cffbZPPfcc0yYMIEXXniBjIyMIl934sTCvcM8evRoRo8eDThNZTZDZNkW7BnJyuKeXFUXAAtylT3mszwbmB3guOM4PbcCnXMEMKK4sRlT3jVv3ty7/N1333HWWWfx448/AnDTTTfx9ddfe7d7BlacOHGi92F5qFJSUoo1d0rdunV5+umnefjhh4t8DhNewXptvaWqM/L7lFqExpiwO/fcc6lYsSJnnnkmJ06coF+/flStWhVwXlg8cOAAAH369PEe43mREeCcc84ByDOrI/j3ziqqkSNHkpmZ6b3ml19+ycqVK23k4DIiWCL50rPgNnMZY8qBSpUqAXDFFVcA0KFDB+/Uvr5NTL4JwjPMSXx8PMuXL/c7X0kkEs+1k5KSEBE6dOhAu3btqFChAhdeeCEjRozgjTfeKJHrmMILlkh8u+9eGu5AjDFlS6dOnThw4AC9evViwoQJgJNkxo1zRkiqWPF0y7gnkVSqVInUVGeko8qVKwPOeyfhtGbNGiZOnMiAAQN4++23OXLkSFivZ/IKlkhCeVfEGBPDatSoAcDw4cO9D+UfffRR73J6ejoLFy70uyPxJJBKlSqhqgwYMCCkay1atKjY8fbt25dq1apx+PBhVJVTp04V+5ymYMESSXN3MMX1PsvrRGS9iKwrrQCNMWVX165d6dmzpzeRJCQkeBNJqL/Ex40bx/nnn0/37t05cuQIx44d49dff/Vu37BhQ6Hj6tu3L88++ywVK1bk4MGDhT7eFE6wXlt5R4MzxpgAPImkYsWK3mcioSaSRx991Nv85TtOWJs2bejevTv169cvdDzz5s1j3rx5gNN9ONbemC9r8r0jUdUfgn1KM0hjTNl26NAhAKpUqULlypVp2LBhsUf1/frrr3nmmWeoVq2at6xXr17e5Q4dOoR0ng4dOjB58uRixWKCK1uTOBtjotLq1asBZ5yvuLg4MjIyQn42UhBPLzI4PUT++eefz8UXXwzAGWecEfT4/fv3c88995RILCYwSyTGmBIzffr0sJx31KhRLF26lBYtWjBw4EBmzZrlbQYbNmyY31z1+fEd/t6ULEskxpgS4xlRuKQ9+eSTdO7cmYoVKzJ9+nRatGhBjx49APjtb3/r7UUWzKOPPsq2bdv47rvvwhJjeZZvIvH0zsrvU5pBGmPKtu+//56uXbuycmWxR1YK2eWXX86RI0fo0qWLN4GtWLHCu33gwIF5jjnzzDMDzippiidYr61r3H89Ayl6Xhu9HTgatoiMMVGnadOmpKenF2nen+LwNG/NmzeP+fPn+z2Anz59Ov/4xz9KNZ7yqsBeW8ClqvqQqq53PyOBHqUXojHGBJeamsof//jHkPfv1asXIuLtJGCKJ5RnJFVF5LeeFRG5BKgavpCMMaboHnjgATp16hR0nw8//BCALl268Morr/jOkWSKoMCpdoFBwDQROQNn2JQs4PdhjcoYY4ro+eefz1OWnZ3tNzaYx8GDBxkyZAiqyj333MOiRYvo3r17aYQZUwpMJKq6CrhARKoDoqrlZBJiY0y0e//99zl16pR3wq78eN4z6dGjB/379+eOO+6gZcuWpKSklEaYUa/ARCIiKcCTQANV7SUiLYCLVfW1sEdnjDHF8Lvf/S5P2caNG2nRIuC8eQC88cYb3iHpX3vtNeLi4rjhhhuIj4/3TtCVlZVV4IuQ5Ukoz0j+gTNdrueNn83An0M5uYj0FJFNIrJVREYG2N5YRNLdLsXLRCTVZ9szIrLB/dzqU36FiKwWkTUi8qmInB1KLMYYAxRqtsZBgwZx5513csYZZ5CYmMi0adMYPnw4NWrU4D//+Q8AJ0+e9BtksjwKJZEkq+osIAecudiBAkdjE5EKwMtAL5xpc/u6dzO+JgKvq2prYCzwlHsloP23AAAdRUlEQVTs1UBboA3QAWeeds+oa5OB21W1DfBPILyTHRhjYsKbb77JihUrijXR1qBBg3juuecAZ1759evXk5CQQKVKlfxmjCxvQkkkR0SkNu78JCLSEeeBe0HaA1tV9XtVPQm8DfTOtU8LIN1dXuqzvQXwsapmq+oRYC3gmcNTAU9SOQPYFUIsxphy7vbbb6dDhw7eRFKpUiUWLFhQ5POlp6fTunVr73pSUhKjRo3y9ggrT0LptTUMmAucJSL/AeoAN4dwXENgh896Bs7dha+1wI3AC8D1QJKbtNYCo0XkeSAR6AJsdI+5C1ggIseAg0DHQBcXkcHAYHD6mBflRamsLOtX4MvqIy+rE3/RUB+eO4eqVavSrl07Xn31VWbNmsXx48e9zVVF9fTTT/P000/TuHFjVq5cGRX1URJC6rUlIp2Ac3Gm392kqqE0CEqAstwdtYcDL4nIQGA5sBPIVtXFItIO+AzYB3wOZLvHPABcpapfiMgI4Hmc5JI77inAFIC0tDRNTk4OIeS8inpcrLL6yMvqxF9Zr4/atWvz2GOPcfPNN5OcnMzgwYMZPHgwl112WYld44cffmDWrFnccsstZb4+SkKBTVsi8l/gLlX9RlU3qOqvIjIvhHNnAI181lPJ1QylqrtU9QZVvRB4xC3Lcv8dr6ptVLUbTlLaIiJ1gAtU9Qv3FP8CLgkhFmOMAUBEGDNmDC1btvQrf/jhhwFn2HmPXbtO/8ryfaD+yiuvFHide++9t9hzskSLUJ6R/Ap0EZHpIuKZGKBhCMd9BTQTkabucX1wmsi8RCRZRDwxjAKmueUV3CYuRKQ10BpYDBwAzhCRc9xjugHfhhCLMcYEdc0116Cq1KxZ01uWlJTkXfZ9oTHUuVY8Mz/GulASyVFVvRXnF/YnItKYvE1Uebi9u4bidB3+Fpilqt+IyFgRuc7drTOwSUQ2AymAZ8KAePdaG3Gap/q5D96zgT8A74rIWqA/MCLE72qMMYXimUI4WLnnOUi1atUYNWpUqcRV1oTysF0AVPVZEVmFkxhqhXJyVV0ALMhV9pjP8mxgdoDjjuP03Ap0zveB90O5vjHGFEd8fDwTJkzgo48+8iv3fVM+KSmJYcOG0a9fP157Le972tdcc413/vhYFUoi8f3Fny4iPYA7wheSMcaUDXFxcQwfPpzhw4cDMGHCBGrVqoXI6b5EIsLEiROBwBN7zZ8/nyVLltCtW7fSCToCgk1s1dxd3CkibT0foDYQ2+nVGFOuffHFF4wdOzZP+fDhw/n9750xa3v16sWbb77pt90zlP3UqVP93jGJ9YEgg92RDMN5HvFcgG0KdA1LRMYYE2Ht27enffv2QfcJ9DJj3bp1vcPRz5s3j3XrTk8me+jQIb+H97Ek30Siqn9w/+1SeuEYY0xsyD2m1/Hjx8tfIhGRG4IdqKrvlXw4xhgTm2J5YMdgTVvXBtmmgCUSY4zJR7Vq1fzWBw8eHLO9t4I1bd1ZmoEYY0wsmTBhAtWrV/fO2Dh//vwIRxQ+obyQiIhcLSIPichjnk+4AzPGmGhWo0YNRo0axbZt27xlsdq8FcpYW68AtwJ/wnk58WagcZjjMsaYmNCkSRPvcqVKlfLfMYqFckdyiaoOAA6o6hjgYvwHYzTGGBOinJycSIdQ4kJJJMfcf4+KSAOcQRybhi8kY4yJXbE4rHwoiWSeiNQAJgCrge04sx0aY4wJgW8PrgMHDkQwkvAoMJGo6jhV/UVV38V5NtJcVf8n/KEZY0xs8H1OEotCedheQUSuE5H7gHuBQSLyYPhDM8aY2DBnzhyefvpp7/rBgwcjGE3JC6Vp6wNgIM5gjUk+H2OMMSE466yzvDMwAvz73/+OYDQlL5REkupOhztaVcd4PmGPzBhjYsxjjzmv4IU6w2K0CCWRLBSRIo2BLCI9RWSTiGwVkZEBtjcWkXQRWSciy0Qk1WfbMyKywf3c6lP+iYiscT+7RGROUWIzxpjStnr16kiHEBahJJIVwPsickxEDorIIREpsIFPRCoALwO9cGY77CsiuWc9nAi8rqqtgbHAU+6xVwNtgTZAB2CEiFQHUNXLVLWNqrYBPsfG/DLGRIl27dp5lw8fPhzBSEpWKInkOZyXEBNVtbqqJqlq9RCOaw9sVdXvVfUkTpfh3rn2aQGku8tLfba3AD5252k/AqwFevoeKCJJOHOi2B2JMSYqjBx5umFmzZo1EYykZIUy1e4WYIN6ZmsJXUNgh896Bs7dha+1wI3AC8D1QJKI1HbLR4vI80Ai0AXYmOvY64F0VQ14dyQig4HBAKmpqWRmZhYyfMjKyir0MbHM6iMvqxN/Vh/+gtXHX//6V5o3b57v9mgSSiLZDSwTkYXACU+hqj5fwHESoCx3MhoOvCQiA4HlwE4gW1UXi0g74DNgH04TVnauY/sCf8/v4qo6BZgCkJaWpkV9mzQW30ItDquPvKxO/Fl9+MuvPt59911mz55dytGERyhNW9twmp8qUbjuvxn4j8mVCuzy3UFVd7k9wi4EHnHLstx/x7vPQrrhJKUtnuPcu5b2QOyOy2yMiXmB5oWPRkHvSNwH5tVUdUQRzv0V0ExEmuLcafQBbst1/mRgv6rmAKOAaT7XraGqP4tIa6A1sNjn0JuBeap6vAhxGWNMmTB69Ghvl+BoFvSORFVP4fSeKjRVzQaGAouAb4FZqvqNiIwVkevc3ToDm0RkM5ACjHfL44FPRGQjTvNUP/d8Hn2AmUWJyxhjImnx4sUF7xRlQnlGskZE5gLvAEc8haHM2a6qC4AFucoe81meDeRpJHTvNHJ3Ffbd3jmEuI0xpszp1q0b8+fP5+qrrwZAVREJ9Eg5eoTyjKQW8DNOV9tr3c814QzKGGNi2ZVXXuld/vzzzyMYScko8I7E5m43xpiS5TtT4qWXXsrKlSu56KKLIhhR8YQy+m+qiLwvIntF5CcRedd3KBNjjDGF53mHpGrVqkyZMiXC0RRPKE1b04G5QAOclww/cMuMMcYU0SeffMKnn35KSkpK1A+XEsrD9jqq6ps4/iEifw5XQMYYUx4kJyeTnJxM1apV2bdvX6TDKZZQ7kgyRaSfO8FVBRHph/Pw3RhjTDGtX7+eJUuW8Oqrr0Y6lCILJZH8HrgF2IMzXMpNbpkxxpgScvfdd0c6hCILpdfWj8B1Be1njDGmfMo3kYhIsPf2VVXHhSEeY4wpVwYMGMDrr78e6TCKJVjT1pEAH4BBwMP5HWSMMSZ0o0aNinQIxZZvIlHV5zwfnPGuqgB34kxQdWYpxWeMMTHNd06Sd955J4KRFF3Qh+0iUktEngDW4TSDtVXVh1V1b6lEZ4wx5cgtt9zCqVOnIh1GoeWbSERkAs5Q8IeAVqr6uKoeKLXIjDGmnEhLS/Mu/+53v4tgJEUT7I5kGM7b7I8Cu0TkoPs5JCIBp7c1xhhTePXq1fMuz5s3L4KRFE2+vbZUNZR3TIwxxhRTfHx8pEMoFksWxhgTYZMnT450CMUS1kQiIj1FZJOIbBWRkQG2NxaRdBFZJyLLfEcVFpFnRGSD+7nVp1xEZLyIbBaRb0XkvnB+B2OMCbeUlBQaN27sXV+/fn0Eoym8sCUSd971l4FeOLMd9hWR3LMeTgReV9XWwFjgKffYq3Gm+G0DdABGiEh195iBQCOguaqeh9Md2Rhjotr27dupU6cO4D/xVTQI5x1Je2Crqn6vqidxfuH3zrVPCyDdXV7qs70F8LGqZqvqEWAt0NPdNgQYq6o5ANYV2RgTK/r27QvA3r3R9WstnImkIbDDZz3DLfO1FrjRXb4eSBKR2m55LxFJFJFkoAvOXQjAWcCtIrJSRBaKSLOwfQNjjClFl1xySaRDKJJQ5iMpqkCz2Wuu9eHASyIyEFgO7ASyVXWxiLQDPgP2AZ8D2e4xCcBxVU0TkRuAacBleS4uMhgYDJCamkpmZmahv0BWVlahj4llVh95WZ34s/rwV9j66NKli3f52muvZfr06JhDMJyJJIPTdxEAqcAu3x1UdRdwA4CIVANuVNUsd9t4YLy77Z/AFp/zvusuv08+szWq6hScoV1IS0vT5OTkIn2Joh4Xq6w+8rI68Wf14a+o9TFv3ryoqctwNm19BTQTkaYiUgnogzNlr5eIJIuIJ4ZROHcXuBNo1XaXWwOtgcXufnOAru5yJ2BzGL+DMcaUqho1aniXv/766whGErqwJRJVzQaGAouAb4FZqvqNiIwVEc/8Jp2BTSKyGUjBvQMB4oFPRGQjzl1FP/d8AE8DN4rIepxeXneF6zsYY0xpmzNnjnf5L3/5SwQjCV04m7ZQ1QXAglxlj/kszwZmBzjuOE7PrUDn/AW4umQjNcaYsuGss87yLm/atCmCkYTO3mw3xpgypHLlyt7lbdu2RUUHBkskxhhThiQmJvqt9+6d+/W7sscSiTHGlCGJiYksXbrU+5b7xx9/HOGICmaJxBhjypjOnTszY8aMSIcRMkskxhhTBvXs2dO7fOTIkQhGUjBLJMYYUwaJnB4cpFq1ahGMpGCWSIwxxhSLJRJjjCmj7r33Xu/yyZMnIxhJcJZIjDGmjGrXrp13uWXLlhGMJDhLJMYYEwW2bNlS8E4RYonEGGPKqAoVKvit79+/P0KRBGeJxBhjyqibbrqJbt26edfbtm0bwWjyZ4nEGGPKqMqVK7No0SLv+g8//BDBaPJnicQYY8ow3/dJAFRzTzQbeZZIjDGmjNu5c6d3ec+ePRGMJDBLJMYYU8Y1aNDAu9y/f/8IRhJYWBOJiPQUkU0islVERgbY3lhE0kVknYgsE5FUn23PiMgG93OrT/k/RGSbiKxxP23C+R2MMaYsSU9Pj3QIeYQtkYhIBeBloBfObId9RST3rIcTgddVtTUwFmfqXETkaqAt0AboAIwQkeo+x41Q1TbuZ024voMxxpQVDzzwgHc5MzMzgpHkFc47kvbAVlX9XlVPAm8DuWdoaQF40utSn+0tgI9VNVtVjwBrgZ4YY0w55ftOiWeukrIinImkIbDDZz3DLfO1FrjRXb4eSBKR2m55LxFJFJFkoAvQyOe48W5z2CQRSQhP+MYYU3bExZXdR9oVw3huCVCWu9/acOAlERkILAd2AtmqulhE2gGfAfuAz4Fs95hRwB6gEjAFeBinWcz/4iKDgcEAqampRboVjIa5kkuT1UdeVif+rD78lWR9HD9+3G99/vz5dOjQocTOXxzhTCQZ+N9FpAK7fHdQ1V3ADQAiUg24UVWz3G3jgfHutn8CW9zy3e7hJ0RkOk4yykNVp+AkGtLS0jQ5OblIX6Kox8Uqq4+8rE78WX34K6n6yD0nyc8//1xm6jqc90pfAc1EpKmIVAL6AHN9dxCRZBHxxDAKmOaWV3CbuBCR1kBrYLG7Xt/9V4DfARvC+B2MMaZMGDp0KBdffLF3vWnTphGMxl/YEomqZgNDgUXAt8AsVf1GRMaKyHXubp2BTSKyGUjBvQMB4oFPRGQjzl1FP/d8AG+JyHpgPZAMPBGu72CMMWVF/fr1+eyzz1i+fDkAl19+OQsWLIhwVI5wNm2hqguABbnKHvNZng3MDnDccZyeW4HO2bWEwzTGmKjRsWNH73Lfvn3LxHOpstsNwBhjTB7x8fHe5YMHD0YwktMskRhjTBQrCxNeWSIxxpgods4553DixImIxmCJxBhjotyECRMien1LJMYYE+U2btxIrVq1GDduXESub4nEGGOi3MyZMzlw4ACPPfZYwTuHgSUSY4yJMsuWLYt0CH4skRhjTJTp1KkTGzdu5J577smzLRJdgi2RGGNMFDrvvPN4+eWX85Q///zzpR6LJRJjjIkhX375Zalf0xKJMcZEsd///vd+6wsXLiz1GCyRGGNMFHvttdciHYIlEmOMiXa5B25UzT2HYHhZIjHGmChXvXp1tm3b5l1v3rx5qV7fEokxxsSAJk2aeJc3b97Mnj17Su3alkiMMSYG1a9fn2effbZUrhXWRCIiPUVkk4hsFZGRAbY3FpF0EVknIstEJNVn2zMissH93Brg2L+JyOFwxm+MMdHs4YcfLpXnJWFLJCJSAXgZ6IUz22FfEck96+FE4HVVbQ2MBZ5yj70aaAu0AToAI0Skus+504Aa4YrdGGNixa5du8J+jXDekbQHtqrq96p6Engb6J1rnxZAuru81Gd7C+BjVc1W1SPAWqAneBPUBOChMMZujDExIScnJ+zXCGciaQjs8FnPcMt8rQVudJevB5JEpLZb3ktEEkUkGegCNHL3GwrMVdXdYYvcGGNiRKNGjQreqZgqhvHcEqAsd2PdcOAlERkILAd2AtmqulhE2gGfAfuAz4FsEWkA3Ax0LvDiIoOBwQCpqalkZmYW+gvk7ptd3ll95GV14s/qw1+k6mPevHlcc801AEX63VdY4UwkGZy+iwBIBfwa61R1F3ADgIhUA25U1Sx323hgvLvtn8AW4ELgbGCriAAkishWVT0798VVdQowBSAtLU2Tk5OL9CWKelyssvrIy+rEn9WHv9KsjzVr1iAitGrVqlSvH85E8hXQTESa4txp9AFu893Bbbbar6o5wChgmlteAaihqj+LSGugNbBYVbOBej7HHw6URIwxpjy64IILvMuTJ0+mdevWpXLdsCUSVc0WkaHAIqACME1VvxGRscBKVZ2L00T1lIgoTtPWve7h8cAn7l3HQaCfm0SMMcaE4O677y61a4XzjgRVXQAsyFX2mM/ybGB2gOOO4/TcKuj81UogTGOMMcVgb7YbY4wpFkskxhhjisUSiTHGmGKxRGKMMaZYLJEYY4wpFkskxhhjisUSiTHGmGKR0p7bNxJEZB/wQxEOTQbCP1BN9LD6yMvqxJ/Vh79or4/GqlqnoJ3KRSIpKhFZqappkY6jrLD6yMvqxJ/Vh7/yUh/WtGWMMaZYLJEYY4wpFkskwU2JdABljNVHXlYn/qw+/JWL+rBnJMYYY4rF7kiMMcYUS7lOJCLSSESWisi3IvKNiNzvs+1PIrLJLX/Wp3yUiGx1t/WITOThkV99iEgbEVkhImtEZKWItHfLRURedOtjnYi0jew3KFkiUllEvhSRtW59jHHLm4rIFyKyRUT+JSKV3PIEd32ru71JJOMvaUHq4y33/4cNIjJNROLd8nL58+Gz/W8icthnPXZ/PlS13H6A+kBbdzkJ2IwzD0oX4CMgwd1W1/23BbAWSACaAv8FKkT6e5RCfSwGernlVwHLfJYXAgJ0BL6I9Hco4foQoJq7HA984X7PWUAft/wVYIi7fA/wirvcB/hXpL9DKdXHVe42AWb61Ee5/Plw19OAN4DDPvvH7M9Hub4jUdXdqrraXT4EfAs0BIYAT6vqCXfbXveQ3sDbqnpCVbcBW4H2pR95eASpDwWqu7udAexyl3sDr6tjBVBDROqXcthh434vz1+U8e5Hga6cnpBtBvA7d7m3u467/Qpxp/mMBfnVh6oucLcp8CWQ6u5TLn8+3KnCJwAP5TokZn8+ynUi8eXeZl6I81fFOcBl7u3nxyLSzt2tIbDD57AMtyzm5KqPPwMTRGQHMBEY5e4W8/UhIhVEZA2wF1iCcxf6i56e+tn3O3vrw92eBdQu3YjDK3d9qOoXPtvigf7Ah25Rufv5cOtjKDBXVXfn2j1mfz4skQAiUg14F/izqh7EmYK4Js7t+AhglvuXQ6C/HmKu21uA+hgCPKCqjYAHgNc8uwY4PKbqQ1VPqWobnL+y2wPnBdrN/bfc1YeItPTZ/L/AclX9xF0vj/VxOXAz8LcAu8dsfZT7ROL+FfUu8JaqvucWZwDvubeuXwI5OGPmZACNfA5P5XQzT0zIpz7uADzL73C6OS/m68NDVX8BluH8cVFDRCq6m3y/s7c+3O1nAPtLN9LS4VMfPQFEZDRQB3jQZ7fy+PPRBTgb2Coi24FEEdnq7hazPx/lOpG4dxmvAd+q6vM+m+bgtIMjIucAlXAGXpsL9HF7XzQFmuG0CceEIPWxC+jkLncFtrjLc4EBbu+cjkBWgNv5qCUidUSkhrtcBbgS57nRUuAmd7c7gH+7y3Pdddzt/+c+N4gJ+dTHdyJyF9AD6KuqOT6HlMefj1WqWk9Vm6hqE+Coqp7tHhKzPx8VC94lpl2K06a73m3nBPgLMA2YJiIbgJPAHe5/8G9EZBawEcgG7lXVUxGIO1zyq48/AC+4f0UdBwa72xbg9MzZChwF7izdcMOuPjDDfXgaB8xS1XkishF4W0SeAL7mdFPfa8Ab7l+g+3F65sSS/OojG2d07c/dZ8fvqepYyunPR5D9Y/bnw95sN8YYUyzlumnLGGNM8VkiMcYYUyyWSIwxxhSLJRJjjDHFYonEGGNMsVgiMWWaiNQWZ9ThNSKyR0R2+qx/VsqxPCgiG92RbNNFpHGu7dXd+F7yKVvmjozriblurmNuEhEVkTR3PV5EZojIenFGYR7lluc70qyIvOaWrxOR2e7IBIHi/52IPJbPtsOByotKRD4SkZoleU5Tdln3XxM1RORxnNFUJ0bo+l1wRrA9KiJDgM6qeqvP9hdw3u7er6pD3bJlwHBVXRngfEnAfJwXXoeq6koRuQ24TlX7iEgizjtLnXHe06iqqofd0Qc+Be5X1RUiUt0dygYReR7Yq6pPB7jeZ+65MwNsO6yqARNQUYjIHUCqqo4vqXOassvuSEzU8vwVLSKdxRlcc5aIbBaRp0Xkdvcv+PUicpa7Xx0ReVdEvnI/lxbmeqq6VFWPuqsrOD3KLSJyEZCCM+R+qMYBz+K85Om9DFDVffmzCs4LsQeDjESMTxIR95g8fx26IzSc8CQRceZU+dyth3E++1Vz77ZWu3XX2y0fJ/7z9YwXkftEpL6ILHfvtjaIyGXuLnOBvoWoCxPFLJGYWHEBcD/QCuft/HNUtT3wd+BP7j4vAJNUtR1wo7utqAbhzLWBiMQBz+EM8BnIdPcX7f+4v+wRkQuBRgHehJ4NHAF2Az8CE1V1v3tMsJF3pwN7gOYEHjDwUmC1z/oLwGS3Lvb4lB8HrlfVtjjjRj3nxvwa7vAe7vftA7wF3AYscgcuvABYA6CqB4AEEYmJ0W1NcJZITKz4yp1P5QTOUO+eO4P1QBN3+UrgJfeX8Vygutu8VCgi0g9n4qIJbtE9wAJV3RFg99tVtRVwmfvp7/4ingQMC7B/e+AU0ABn8rRhInImBB95V1XvdI/5Frg1z1md4Tz2+axfijMJFTgTMHm/HvCkiKzDmdytIZCiqtuBn90E2B34WlV/Br4C7nSbHVu589h47HVjMjHOEomJFSd8lnN81nM4PaZcHHCxqrZxPw1z/eJDRDx3DwsCXURErgQewXnW4LnGxcBQcUZ7nYgzUOHTAKq60/33EPBPnESRBLQElrnHdATmug/cbwM+VNVf1ZlQ7T84Scsr98i7PuWngH/h3G3ldgyonKss0APS23Ge81zkJq2ffI77OzAQZ8ysae41lwOXAztxxpEa4HOuyu51TYyzRGLKk8U4kw4Bzlz0uXdQ1TvdJHNV7m3uX+Ov4iSRvT7H3K6qv3FHex2OMyvgSBGpKCLJ7rHxwDXABlXNUtVknxFiV7jnXInTnNVVHFVxksx3kv/IuyIiZ7vlAlwLfBfgu3+LM7y5x384PWjg7T7lZ+A8rP/V7Vzg2zPtfZzk1Q5Y5F6zsbv/VJzmr7Y+sdQDtgeIxcSY8j76rylf7gNedpttKgLLgbsLcfwEoBrwjvuo40dVvS7I/gnAIjeJVMBpKppawDVeBqYDG3Camaar6joRaU3gkXfj3PLq7v5rcSYiy2057vMOdyTr+4F/ug/Q3/XZ7y3gAxFZifO8w5uUVPWkiCzFmSHSM+p1Z2CEiPwKHAY8dyQXASt8ZpI0Mcy6/xpTTrjdkz9Q1Y+KeHwczgP7m1V1SwH7voAz3Wx6Ua5loos1bRlTfjwJJBblQBFpgTOvSHpBScS1wZJI+WF3JMYYY4rF7kiMMcYUiyUSY4wxxWKJxBhjTLFYIjHGGFMslkiMMcYUiyUSY4wxxfL/nOPU8XoNKCAAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lc = tpf.to_lightcurve(method='prf')\n", "lc.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A good way to verify that the model-fitting photometry produced a sensible output is by comparing it against standard aperture photometry. You can obtain aperture photometry using the same `to_lightcurve` method and the parameter `method='aperture'` (which is the default value of this parameter)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lc_aper = tpf.to_lightcurve(method='aperture')\n", "lc_aper.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a notable difference in the long-term trend in both lightcurves. This is because PRF-fitting photometry yields a local estimate of the background which is often different from the Kepler pipeline's global background level estimates.\n", "\n", "Let's fold both lightcurves on the period of the known planet *Kepler-10 b* to show that the transit can be recovered equally well: " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "_, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n", "ax[0].set_title('PRF photometry')\n", "lc.flatten().fold(period=0.8376).plot(ax=ax[0])\n", "ax[1].set_title('Aperture photometry')\n", "lc_aper.flatten().fold(period=0.8376).plot(ax=ax[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Success! Note that the outliers between *phase* -0.4 and 0.0 are caused by the transits of another planet with a different period.\n", "\n", "Note that PRF photometry appears to offer a few percent less noise compared to its aperture counterpart:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The CDPP noise metric equals:\n", "32.4 ppm for PRF-fitting photometry;\n", "32.7 ppm for aperture photometry.\n" ] } ], "source": [ "print(\"\"\"The CDPP noise metric equals:\n", "{:.1f} ppm for PRF-fitting photometry;\n", "{:.1f} ppm for aperture photometry.\"\"\".format(lc.cdpp(), lc_aper.cdpp()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is all you need to know to get started with PRF photometry! In what follows, we explain how you can tune the parameters of the model fitting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The `TPFModel` object\n", "\n", "It is often possible to obtain better results by carefully tuning the model that is being fit to the data. This is true in particular when the data contain multiple overlapping stars. Let's have a look at how you can interact and change the model.\n", "\n", "*Lightkurve* uses objects of type `TPFModel` to specify the model. \n", "You can obtain the default model using the `get_model()` method:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "lightkurve.prf.tpfmodel.TPFModel" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = tpf.get_model()\n", "type(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `TPFModel` object encapsulates our beliefs about the stars contained in a pixel file. The key parameters are:\n", "\n", "* `model.star_priors` captures our prior beliefs about the positions and fluxes of the point sources in the data using a list of `StarPrior` objects. Each `StarPrior` object is parameterized by `col` and `row` in pixel coordinates and `flux` in electrons/second;\n", "* `model.background_prior` captures our beliefs about the per-pixel background, parameterized by `flux` in electrons/second;\n", "* `model.focus_prior` captures our beliefs on the focus of the spacecraft, parameterized by the unit-less stretch factors `scale_col` and `scale_row`, and `rotation_angle` in radians;\n", "* `model.motion_prior` captures our beliefs about spacecraft motion, parameterized by `shift_col` and `shift_row` in pixel coordinates; \n", "* `model.prfmodel` an object of type `KeplerPRF` which models the Point Response Function.\n", "* `model.fit_background`, `model.fit_focus`, and `model.fit_motion` are booleans which indicate whether or not the background, focus, and motion parameters should be considered free parameters, i.e. whether or not our beliefs should assumed to be perfect.\n", "\n", "The parameters of each of these components are prior distributions.\n", "\n", "We can inspect the our beliefs by accessing the properties, for example, this is the prior on the column pixel coordinate of the star in our data:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.star_priors[0].col" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And this is the model's prior on the background flux:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.background_prior.flux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can inspect all the parameters at once using the `print` function:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " \n", " row=\n", " flux=\n", " targetid=11904151)>\n", " )>\n", " \n", " scale_row=\n", " rotation_angle=)>\n", " \n", " shift_row=)>\n", " \n", " fit_background=True\n", " fit_focus=False\n", " fit_motion=False\n", "\n" ] } ], "source": [ "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing and fitting a `TPFModel` object\n", "\n", "A `TPFModel` object contains several methods to interact with the model. For example, you can visualize the default prediction using the `plot()` method. This will visualize the model prediction using the prior means. Let's plot the model below (left panel) and compare it against the observed data (right panel):" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "_, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n", "model.plot(ax=ax[0])\n", "tpf.plot(ax=ax[1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can fit the model to a single cadence of pixel data using the `fit()` method, which returns an object of type `TPFModelParameters` which encapulsates the maximum likelihood values of the parameters for that cadence:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " \n", " \n", " \n", " \n", " residual_image:\n", " [ 13.5211913 -24.99054271 -36.49246532 -36.4521516 ...\n", " predicted_image:\n", " [175.23897106 175.23897106 175.23897106 175.23897106...\n", "\n" ] } ], "source": [ "parameters = model.fit(tpf.flux[0] + tpf.flux_bkg[0])\n", "print(parameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, we can access the maximum likelihood value of the stellar flux as follows:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "528781.7188502487" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parameters.stars[0].flux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the background flux was found to be:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "175.2389710561001" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parameters.background.flux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can visualize the model again, this time passing the fitted parameters:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n", "model.plot(parameters, ax=ax[0])\n", "tpf.plot(ax=ax[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Changing `TPFModel` priors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can change a `TPFModel` by manipulating its properties. For example, we can add a star to the lower right corner of the pixel file as follows:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lightkurve.prf import StarPrior\n", "model = tpf.get_model()\n", "model.star_priors.append(StarPrior(col=660, row=243, flux=1e6))\n", "model.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can build a `TPFModel` from scratch. In the example below, we use an arbitrary combination of Gaussian, Uniform, and Fixed priors to initialize a model. Note that the ideal choices for these assumptions depend strongly on your scientific objectives and may require trial-and-error." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lightkurve.prf import TPFModel, StarPrior, BackgroundPrior, FocusPrior, MotionPrior\n", "from lightkurve.prf import GaussianPrior, UniformPrior, FixedValuePrior\n", "model = TPFModel(star_priors=[StarPrior(col=GaussianPrior(mean=656, var=1),\n", " row=GaussianPrior(mean=247, var=1),\n", " flux=UniformPrior(lb=0, ub=1e7)),\n", " StarPrior(col=FixedValuePrior(value=660),\n", " row=FixedValuePrior(value=243),\n", " flux=UniformPrior(lb=0, ub=1e7))],\n", " background_prior=BackgroundPrior(flux=GaussianPrior(mean=100., var=10.)),\n", " focus_prior=FocusPrior(scale_col=GaussianPrior(mean=1, var=0.0001),\n", " scale_row=GaussianPrior(mean=1, var=0.0001),\n", " rotation_angle=UniformPrior(lb=-3.1415, ub=3.1415)),\n", " motion_prior=MotionPrior(shift_col=GaussianPrior(mean=0., var=0.01),\n", " shift_row=GaussianPrior(mean=0., var=0.01)),\n", " prfmodel=tpf.get_prf_model(),\n", " fit_background=True,\n", " fit_focus=False,\n", " fit_motion=False)\n", "model.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating lightcurves with a custom `TPFModel` object" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we demonstrated how you can interact with a `TPFModel` and fit its parameters to a single cadence. Most users will be interested in obtaining a lightcurve by fitting the model to *all* the cadences rather than a single one.\n", "\n", "To make this easy, *lightkurve* provides a helper class called `PRFPhotometry`, which makes it easy to fit the model to all cadences and inspect the results. For example:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fitting cadences: 100%|██████████| 3634/3634 [00:47<00:00, 76.85it/s]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lightkurve import PRFPhotometry\n", "prfphot = PRFPhotometry(model=tpf.get_model())\n", "prfphot.run(tpf.flux + tpf.flux_bkg)\n", "prfphot.plot_results()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can access the details of the fit in each cadence using the `prfphot.results` property, e.g.:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "528781.7188502487" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prfphot.results[0].stars[0].flux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternative, you can access the fitted fluxes as a `LightCurve` object:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prfphot.lightcurves[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the focus and motion parameters in the example above are constant. This is because the `fit_focus` and `fit_motion` parameters were set to `False` in the model. By setting these to `True`, we can potentially improve the quality of the fit. Beware however, this comes at the cost of slowing down the fitting process and potentially introducing more complicated systematics and parameter degeneracies." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fitting cadences: 100%|██████████| 3634/3634 [03:04<00:00, 19.69it/s]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prfphot.model.fit_focus = True\n", "prfphot.model.fit_motion = True\n", "prfphot.run(tpf.flux + tpf.flux_bkg)\n", "prfphot.plot_results()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prfphot.lightcurves[0].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uhoh, the increased model flexibility introduced complicated systematics. These systematics are caused in part by the degeneracy between the star's `col` and `row` position and the `shift_col` and `shift_row` parameters. The degeneracy will disappear when additional stars are present in the data, or when tighter priors are employed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comments?\n", "\n", "At present we are evaluating the design and use of *lightkurve*'s PRF photometry for use in crowded fields. If you encounter issues, or would like to contribute a tutorial or working example, then please open an issue or pull request on GitHub!" ] } ], "metadata": { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }